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Sample records for based action representation

  1. Primitive Based Action Representation and Recognition

    DEFF Research Database (Denmark)

    Baby, Sanmohan; Krüger, Volker

    2009-01-01

    There has been a recent interest in segmenting action sequences into   meaningful parts (action primitives) and to model actions on a   higher level based on these action primitives. Unlike previous works where action primitives are defined    a-priori and search is made for them later, we present...

  2. Electrophysiology of action representation.

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    Fadiga, Luciano; Craighero, Laila

    2004-01-01

    We continuously act on objects, on other individuals, and on ourselves, and actions represent the only way we have to manifest our own desires and goals. In the last two decades, electrophysiological experiments have demonstrated that actions are stored in the brain according to a goal-related organization. The authors review a series of experimental data showing that this "vocabulary of motor schemata" could also be used for non-strictly motor purposes. In the first section, they present data from monkey experiments describing the functional properties of inferior premotor cortex and, in more detail, the properties of visuomotor neurons responding to objects and others' actions observation (mirror neurons). In the second section, human data are reviewed, with particular regard to electrophysiological experiments aiming to investigate how action representations are stored and addressed. The specific facilitatory effect of motor imagery, action/object observation, and speech listening on motor excitability shown by these experiments provides strong evidence that the motor system is constantly involved whenever the idea of an action is evoked.

  3. Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

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    Shirui Huo

    2017-01-01

    Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

  4. A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

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    Allah Bux Sargano

    2017-01-01

    Full Text Available Human activity recognition (HAR is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

  5. Action simulation: time course and representational mechanisms

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    Springer, Anne; Parkinson, Jim; Prinz, Wolfgang

    2013-01-01

    The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others' actions implies a mirroring of their activities, based on one's own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the future course of actions that are temporarily occluded from view. We address transitions between perceptual mechanisms (referring to action representation before and after occlusion) and simulation mechanisms (referring to action representation during occlusion). Findings suggest that action simulation runs in real-time; acting on newly created action representations rather than relying on continuous visual extrapolations. A further focus of our discussion pertains to the functional characteristics of the mechanisms involved in predicting other people's actions. We propose that two processes are engaged, dynamic updating and static matching, which may draw on both semantic and motor information. In a concluding section, we discuss these findings in the context of broader theoretical issues related to action and event representation, arguing that a detailed functional analysis of action simulation in cognitive, neural, and computational terms may help to further advance our understanding of action cognition and motor control. PMID:23847563

  6. Learning Semantic-Aligned Action Representation.

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    Ni, Bingbing; Li, Teng; Yang, Xiaokang

    2017-08-31

    A fundamental bottleneck for achieving highly discriminative action representation is that local motion/appearance features are usually not semantic aligned. Namely, a local feature, such as a motion vector or motion trajectory, does not possess any attribute that indicates which moving body part or operated object it is associated with. This mostly leads to global feature pooling/representation learning methods that are often too coarse. Inspired by the recent success of end-to-end (pixel-to-pixel) deep convolutional neural networks (DCNNs), in this paper, we first propose a DCNN architecture, which maps a human centric image region onto human body part response maps. Based on these response maps, we propose a second DCNN, which achieves semantic-aligned feature representation learning. Prior knowledge that only a few parts are responsible for a certain action is also utilized by introducing a group (part) sparseness prior during feature learning. The learned semantic-aligned feature not only boosts the discriminative capability of action representation, but also possesses the good nature of robustness to pose variations and occlusions. Finally, an iterative mining method is employed for learning discriminative action primitive detectors. Extensive experiments on action recognition benchmarks demonstrate a superior recognition performance of the proposed framework.

  7. Action representation: crosstalk between semantics and pragmatics.

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    Prinz, Wolfgang

    2014-03-01

    Marc Jeannerod pioneered a representational approach to movement and action. In his approach, motor representations provide both, declarative knowledge about action and procedural knowledge for action (action semantics and action pragmatics, respectively). Recent evidence from language comprehension and action simulation supports the claim that action pragmatics and action semantics draw on common representational resources, thus challenging the traditional divide between declarative and procedural action knowledge. To account for these observations, three kinds of theoretical frameworks are discussed: (i) semantics is grounded in pragmatics, (ii) pragmatics is anchored in semantics, and (iii) pragmatics is part and parcel of semantics. © 2013 Elsevier Ltd. All rights reserved.

  8. Efficient Interaction Recognition through Positive Action Representation

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    Tao Hu

    2013-01-01

    Full Text Available This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI, including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

  9. XML-BASED REPRESENTATION

    Energy Technology Data Exchange (ETDEWEB)

    R. KELSEY

    2001-02-01

    For focused applications with limited user and use application communities, XML can be the right choice for representation. It is easy to use, maintain, and extend and enjoys wide support in commercial and research sectors. When the knowledge and information to be represented is object-based and use of that knowledge and information is a high priority, then XML-based representation should be considered. This paper discusses some of the issues involved in using XML-based representation and presents an example application that successfully uses an XML-based representation.

  10. Action co-representation and social exclusion.

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    Costantini, Marcello; Ferri, Francesca

    2013-05-01

    Humans are thought to be able to form shared representations, considered a keystone of social cognition. However, whether and to what extent such representations are social in nature is still open for debate. In the present study, we address the question of whether action co-representation can be modulated by social attitudes, such as judgments about one's own social status. Two groups of participants performed an Interactive Simon task after the experimental induction of a feeling of social inclusion or exclusion (ostracism) by means of a virtual ball tossing game. Results showed a compatibility effect in included, but not in excluded participants. This indicates that judgments about one's own social status modulate action co-representation. We suggest that this modulation may occur by way of a redirection of one's attentional focus away from others when one experiences social exclusion. This is a far-reaching issue given the increasing need for integration in modern society. Indeed, if integration fails, modern society fails also.

  11. Learning a Mid-Level Representation for Multiview Action Recognition

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    Cuiwei Liu

    2018-01-01

    Full Text Available Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognition methods are supposed to have the same camera view during both training and testing. And thus performances of these single-view approaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the above problem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step, subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboids sampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests are built upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiview subvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learned representation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectively recognize human actions depicted in multiview videos.

  12. Deformation Based Curved Shape Representation.

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    Demisse, Girum G; Aouada, Djamila; Ottersten, Bjorn

    2017-06-02

    In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g. local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets.

  13. Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

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    Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

    2014-02-01

    In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

  14. Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations.

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    Brayanov, Jordan B; Press, Daniel Z; Smith, Maurice A

    2012-10-24

    Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices.

  15. Invariant recognition drives neural representations of action sequences.

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    Andrea Tacchetti

    2017-12-01

    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  16. Invariant recognition drives neural representations of action sequences.

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    Tacchetti, Andrea; Isik, Leyla; Poggio, Tomaso

    2017-12-01

    Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  17. A Common Representation of Spatial Features Drives Action and Perception

    DEFF Research Database (Denmark)

    Christiansen, Jens H; Christensen, Jeppe Høy; Grünbaum, Thor

    2014-01-01

    Spatial features of an object can be specified using two different response types: either by use of symbols or motorically by directly acting upon the object. Is this response dichotomy reflected in a dual representation of the visual world: one for perception and one for action? Previously......, symbolic and motoric responses, specifying location, has been shown to rely on a common representation. What about more elaborate features such as length and orientation? Here we show that when motoric and symbolic responses are made within the same trial, the probability of making the same symbolic...... and motoric response is well above chance for both length and orientation. This suggests that motoric and symbolic responses to length and orientation are driven by a common representation. We also show that, for both response types, the spatial features of an object are processed independently. This finding...

  18. Learning Visual Representations for Perception-Action Systems

    DEFF Research Database (Denmark)

    Piater, Justus; Jodogne, Sebastien; Detry, Renaud

    2011-01-01

    and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a nonparametric representation of grasp success......We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily complex, we argue that in many cases it is advantageous to link them rather directly to action via learned mappings....... These arguments are illustrated by two examples of our own work. First, our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split...

  19. Cognitive representation of human action: theory, applications, and perspectives

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    Christian eSeegelke

    2016-02-01

    Full Text Available In this perspective article, we propose a cognitive architecture model of human action that stresses the importance of cognitive representations stored in long-term memory (LTM as reference structures underlying and guiding voluntary motor performance. We introduce an experimental approach to ascertain cognitive representation structures, and provide evidence from a variety of different studies, ranging from basic research in manual action to application-oriented research such as athlete performance and rehabilitation. As results from these studies strongly support the presence of functional links between cognitive and motor processes, we regard this approach as a suitable and valuable tool for a variety of different disciplines related to cognition and movement. We conclude this article by highlighting current advances in ongoing research projects aimed at improving interaction capabilities in technical systems, particularly for rehabilitation and everyday support of the elderly, and outline future research directions.

  20. Young children's agent-neutral representations of action roles.

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    Rakoczy, Hannes; Gräfenhain, Maria; Clüver, Annette; Dalhoff, Ann Christin Schulze; Sternkopf, Anika

    2014-12-01

    Recent developmental research has shown that young children coordinate complementary action roles with others. But what do they understand about the logical structure of such roles? Do they have an agent-neutral conception of complementary action roles, grasping that such roles can be variably filled by any two agents or even by one agent over time? Accordingly, can they make use of such representations for planning both their own and others' actions? To address these questions, 3- and 4-year-olds were introduced to an activity comprising two action roles, A and B, by seeing either two agents performing A and B collaboratively or one agent performing A and B individually. Children's flexible inferences from these demonstrations were then tested by asking them later on to plan ahead for the fulfillment of one of the roles either by themselves or by someone else. The 4-year-olds competently drew inferences in all directions, from past individual and collaborative demonstrations, when planning how they or someone else would need to fulfill the roles in the future. The 3-year-olds, in contrast, showed more restricted competence; they were capable of such inferences only when planning in the immediate present. Taken together, these results suggest that children form and use agent-neutral representations of action roles by 3 years of age and flexibly use such representations for episodic memory and future deliberation in planning their own and others' actions by 4 years of age. The findings are discussed in the broader context of the development of understanding self-other equivalence and agent-neutral frames of references. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. Representation of action in occipito-temporal cortex.

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    Wiggett, Alison J; Downing, Paul E

    2011-07-01

    A fundamental question for social cognitive neuroscience is how and where in the brain the identities and actions of others are represented. Here we present a replication and extension of a study by Kable and Chatterjee [Kable, J. W., & Chatterjee, A. Specificity of action representations in the lateral occipito-temporal cortex. Journal of Cognitive Neuroscience, 18, 1498-1517, 2006] examining the role of occipito-temporal cortex in these processes. We presented full-cue movies of actors performing whole-body actions and used fMRI to test for action- and identity-specific adaptation effects. We examined a series of functionally defined regions, including the extrastriate and fusiform body areas, the fusiform face area, the parahippocampal place area, the lateral occipital complex, the right posterior superior temporal sulcus, and motion-selective area hMT+. These regions were analyzed with both standard univariate measures as well as multivoxel pattern analyses. Additionally, we performed whole-brain tests for significant adaptation effects. We found significant action-specific adaptation in many areas, but no evidence for identity-specific adaptation. We argue that this finding could be explained by differences in the familiarity of the stimuli presented: The actions shown were familiar but the actors performing the actions were unfamiliar. However, in contrast to previous findings, we found that the action adaptation effect could not be conclusively tied to specific functionally defined regions. Instead, our results suggest that the adaptation to previously seen actions across identities is a widespread effect, evident across lateral and ventral occipito-temporal cortex.

  2. Marketing actions can modulate neural representations of experienced pleasantness.

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    Plassmann, Hilke; O'Doherty, John; Shiv, Baba; Rangel, Antonio

    2008-01-22

    Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.

  3. Is social inhibition of return due to action co-representation?

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    Atkinson, Mark A; Simpson, Andrew; Skarratt, Paul A; Cole, Geoff G

    2014-07-01

    When two individuals alternate reaching responses to visual targets presented on a shared workspace, one individual is slower to respond to targets occupying the same position as their partner's previous response. This phenomenon is thought to be due to processes that inhibit the initiation of a movement to a location recently acted upon. However, two distinct forms of the inhibition account have been posited, one based on inhibition of an action, the other based on inhibition of an action and location. Furthermore, an additional recent explanation suggests the phenomenon is due to mechanisms that give rise to action congruency effects. Thus the three different theories differ in the degree to which action co-representation plays a role in the effect. The aim of the present work was to examine these competing accounts. Three experiments demonstrated that when identical actions are made, the effect is modulated by the configuration of the visual stimuli acted upon and the perceptual demands of the task. In addition, when the co-actors perform different actions to the same target, the effect is still observed. These findings support the hypothesis that this particular joint action phenomenon is generated via social cues that induce location-based inhibition of return rather than being due to shared motor co-representations. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Typical neural representations of action verbs develop without vision.

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    Bedny, M; Caramazza, A; Pascual-Leone, A; Saxe, R

    2012-02-01

    Many empiricist theories hold that concepts are composed of sensory-motor primitives. For example, the meaning of the word "run" is in part a visual image of running. If action concepts are partly visual, then the concepts of congenitally blind individuals should be altered in that they lack these visual features. We compared semantic judgments and neural activity during action verb comprehension in congenitally blind and sighted individuals. Participants made similarity judgments about pairs of nouns and verbs that varied in the visual motion they conveyed. Blind adults showed the same pattern of similarity judgments as sighted adults. We identified the left middle temporal gyrus (lMTG) brain region that putatively stores visual-motion features relevant to action verbs. The functional profile and location of this region was identical in sighted and congenitally blind individuals. Furthermore, the lMTG was more active for all verbs than nouns, irrespective of visual-motion features. We conclude that the lMTG contains abstract representations of verb meanings rather than visual-motion images. Our data suggest that conceptual brain regions are not altered by the sensory modality of learning.

  5. Contribution of motor representations to action verb processing.

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    Andres, Michael; Finocchiaro, Chiara; Buiatti, Marco; Piazza, Manuela

    2015-01-01

    Electrophysiological and brain imaging studies show a somatotopic activation of the premotor cortex while subjects process action verbs. This somatotopic motor activation has been taken as an indication that the meaning of action verbs is embedded in motor representations. However, discrepancies in the literature led to the alternative hypothesis that motor representations are activated during the course of a mental imagery process emerging only after the meaning of the action has been accessed. In order to address this issue, we asked participants to decide whether a visually presented verb was concrete or abstract by pressing a button or a pedal (primary task) and then to provide a distinct vocal response to low and high sounds played soon after the verb display (secondary task). Manipulations of the visual display (lower vs. uppercase), verb imageability (concrete vs. abstract), verb meaning (hand vs. foot-related), and response effector (hand vs. foot) allowed us to trace the perceptual, semantic and response stages of verb processing. We capitalized on the psychological refractory period (PRP), which implies that the initiation of the secondary task should be delayed only by those factors that slow down the central decision process in the primary task. In line with this prediction, our results showed that the time cost resulting from the processing of abstract verbs, when compared to concrete verbs, was still observed in the subsequent response to the sounds, whereas the overall advantage of hand over foot responses did not influence sound judgments. Crucially, we also observed a verb-effector compatibility effect (i.e., foot-related verbs are responded faster with the foot and hand-related verbs with the hand) that contaminated the performance of the secondary task, providing clear evidence that motor interference from verb meaning occurred during the central decision stage. These results cannot be explained by a mental imagery process that would deploy only

  6. Exploration of solids based on representation systems

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    Publio Suárez Sotomonte

    2011-01-01

    Full Text Available This article refers to some of the findings of a research project implemented as a teaching strategy to generate environments for the learning of platonic and archimedean solids, with a group of eighth grade students. This strategy was based on the meaningful learning approach and on the use of representation systems using the ontosemiotic approach in mathematical education, as a framework for the construction of mathematical concepts. This geometry teaching strategy adopts the stages of exploration, representation-modeling, formal construction and study of applications. It uses concrete, physical and tangible materials for origami, die making, and structures for the construction of threedimensional solids considered external tangible solid representation systems, as well as computer based educational tools to design dynamic geometry environments as intangible external representation systems.These strategies support both the imagination and internal systems of representation, fundamental to the comprehension of geometry concepts.

  7. The representation of knowledge within model-based control systems

    International Nuclear Information System (INIS)

    Weygand, D.P.; Koul, R.

    1987-01-01

    Representation of knowledge in artificially intelligent systems is discussed. Types of knowledge that might need to be represented in AI systems are listed, and include knowledge about objects, events, knowledge about how to do things, and knowledge about what human beings know (meta-knowledge). The use of knowledge in AI systems is discussed in terms of acquiring and retrieving knowledge and reasoning about known facts. Different kinds of reasonings or representations are ghen described with some examples given. These include formal reasoning or logical representation, which is related to mathematical logic, production systems, which are based on the idea of condition-action pairs (production), procedural reasoning, which uses pre-formed plans to solve problems, frames, which provide a structure for representing knowledge in an organized manner, direct analogical representations, which represent knowledge in such a manner that permits some observation without deduction

  8. ''How To Do Things with Words'': Role of Motor Cortex in Semantic Representation of Action Words

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    Kana, Rajesh K.; Blum, Elizabeth R.; Ladden, Stacy Levin; Ver Hoef, Lawrence W.

    2012-01-01

    Language, believed to have originated from actions, not only functions as a medium to access other minds, but it also helps us commit actions and enriches our social life. This fMRI study investigated the semantic and neural representations of actions and mental states. We focused mainly on language semantics (comprehending sentences with "action"…

  9. Spontaneous Action Representation in Smokers when Watching Movie Characters Smoke

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    Wagner, Dylan D.; Cin, Sonya Dal; Sargent, James D.; Kelley, William M.; Heatherton, Todd F.

    2013-01-01

    Do smokers simulate smoking when they see someone else smoke? For regular smokers, smoking is such a highly practiced motor skill that it often occurs automatically, without conscious awareness. Research on the brain basis of action observation has delineated a frontopareital network that is commonly recruited when people observe, plan or imitate actions. Here, we investigated whether this action observation network would be preferentially recruited in smokers when viewing complex smoking cues, such as those occurring in motion pictures. Seventeen right-handed smokers and seventeen non-smokers watched a popular movie while undergoing functional magnetic resonance imaging. Using a natural stimulus, such as a movie, allowd us to keep both smoking and non-smoking participants naïve to the goals of the experiment. Brain activity evoked by scenes of movie smoking was contrasted with non-smoking control scenes which were matched for frequency and duration. Compared to non-smokers, smokers showed greater activity in left anterior intraparietal sulcus and inferior frontal gyrus, both regions involved in the simulation of contralateral hand-based gestures, when viewing smoking vs. control scenes. These results demonstrate that smokers spontaneously represent the action of smoking when viewing others smoke, the consequence of which may make it more difficult to abstain from smoking. PMID:21248113

  10. Knowledge representation and knowledge base design for operator advisor system

    International Nuclear Information System (INIS)

    Hangos, K.M.; Sziano, T.; Tapolcai, L.

    1990-01-01

    The problems of knowledge representation, knowledge base handling and design has been described for an Operator Advisor System in the Paks Nuclear Power Plant. The Operator Advisor System is to be implemented as a part of the 5th and 6th unit. The knowledge of the Operator Advisor system is described by a few elementary knowledge items (diagnostic event functions, fault graph, action trees), weighted directed graphs have been found as their common structure. List-type and relational representation of these graphs have been used for the on-line and off-line part of the knowledge base respectively. A uniform data base design and handling has been proposed which consists of a design system, a knowledge base editor and a knowledge base compiler

  11. The structure of affective action representations: temporal binding of affective response codes.

    Science.gov (United States)

    Eder, Andreas B; Müsseler, Jochen; Hommel, Bernhard

    2012-01-01

    Two experiments examined the hypothesis that preparing an action with a specific affective connotation involves the binding of this action to an affective code reflecting this connotation. This integration into an action plan should lead to a temporary occupation of the affective code, which should impair the concurrent representation of affectively congruent events, such as the planning of another action with the same valence. This hypothesis was tested with a dual-task setup that required a speeded choice between approach- and avoidance-type lever movements after having planned and before having executed an evaluative button press. In line with the code-occupation hypothesis, slower lever movements were observed when the lever movement was affectively compatible with the prepared evaluative button press than when the two actions were affectively incompatible. Lever movements related to approach and avoidance and evaluative button presses thus seem to share a code that represents affective meaning. A model of affective action control that is based on the theory of event coding is discussed.

  12. Primitive Based Action Representation and recognition

    DEFF Research Database (Denmark)

    Baby, Sanmohan

    from the way the body parts are moving, but as well from how their eect on the involved object. While human movements can look vastly dierent even under minor changes in location, orientation and scale, the use of the object can provide a strong invariant for the detection of motion primitives...

  13. Marginalised Stacked Denoising Autoencoders for Robust Representation of Real-Time Multi-View Action Recognition

    Directory of Open Access Journals (Sweden)

    Feng Gu

    2015-07-01

    Full Text Available Multi-view action recognition has gained a great interest in video surveillance, human computer interaction, and multimedia retrieval, where multiple cameras of different types are deployed to provide a complementary field of views. Fusion of multiple camera views evidently leads to more robust decisions on both tracking multiple targets and analysing complex human activities, especially where there are occlusions. In this paper, we incorporate the marginalised stacked denoising autoencoders (mSDA algorithm to further improve the bag of words (BoWs representation in terms of robustness and usefulness for multi-view action recognition. The resulting representations are fed into three simple fusion strategies as well as a multiple kernel learning algorithm at the classification stage. Based on the internal evaluation, the codebook size of BoWs and the number of layers of mSDA may not significantly affect recognition performance. According to results on three multi-view benchmark datasets, the proposed framework improves recognition performance across all three datasets and outputs record recognition performance, beating the state-of-art algorithms in the literature. It is also capable of performing real-time action recognition at a frame rate ranging from 33 to 45, which could be further improved by using more powerful machines in future applications.

  14. Dissociable somatotopic representations of Chinese action verbs in the motor and premotor cortex.

    Science.gov (United States)

    Wu, Haiyan; Mai, Xiaoqin; Tang, Honghong; Ge, Yue; Luo, Yue-Jia; Liu, Chao

    2013-01-01

    The embodied view of language processing holds that language comprehension involves the recruitment of sensorimotor information, as evidenced by the somatotopic representation of action verbs in the motor system. However, this review has not yet been examined in logographic scripts such as Chinese, in which action verbs can provide explicit linguistic cues to the effectors (arm, leg, mouth) that conduct the action (hit, jump, drink). We compared the somatotopic representation of Chinese verbs that contain such effector cues and those that do not. The results showed that uncued verbs elicited similar somatotopic representation in the motor and premotor cortex as found in alphabetic scripts. However, effector-cued verbs demonstrated an inverse somatotopic pattern by showing reduced activation in corresponding motor areas, despite that effector-cued verbs actually are rated higher in imageability than uncued verbs. Our results support the universality of somatotopic representation of action verbs in the motor system.

  15. Toward a brain-based componential semantic representation.

    Science.gov (United States)

    Binder, Jeffrey R; Conant, Lisa L; Humphries, Colin J; Fernandino, Leonardo; Simons, Stephen B; Aguilar, Mario; Desai, Rutvik H

    2016-01-01

    Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and

  16. Body-specific representations of action word meanings in right and left handers

    OpenAIRE

    Daniel Casasanto

    2007-01-01

    If understanding action words involves mentally simulating our own actions, then the neurocognitive representation of word meanings must differ for people with different kinds of bodies, who perform actions in systematically different ways. In a test of the _Body-Specificity Hypothesis_, right- and left-handers were compared on two motor-meaning congruity tasks. Double dissociations in both action execution and recognition memory results showed that right and left handers form body-specific r...

  17. Fingerprint Compression Based on Sparse Representation.

    Science.gov (United States)

    Shao, Guangqi; Wu, Yanping; A, Yong; Liu, Xiao; Guo, Tiande

    2014-02-01

    A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.

  18. Spatial knowledge during skilled action sequencing: Hierarchical versus nonhierarchical representations.

    Science.gov (United States)

    Behmer, Lawrence P; Crump, Matthew J C

    2017-11-01

    Typists can type 4 to 5 keystrokes per second at around 95% accuracy, yet they appear to have poor declarative knowledge of key locations. Logan and Crump (2011, Psychology of Learning and Motivation, Vol. 54, pp. 1-27) accounted for this paradox by proposing that typing is hierarchically organized into two loops, with an outer loop that transforms sentences into words and passes each word, one at a time, to an inner loop that transforms each word into its constituent keystrokes; however, the nature of the inner loop's spatial knowledge is not well understood. Key locations may be learned through the experiences of locating and traversing between keys. In daily life, people tend to type structured language, and, as a consequence, certain keys and key-to-key transitions are experienced more frequently than others. Here, we asked whether or not this knowledge is structured hierarchically. For example, knowledge of key locations may be nested within representations of words, or the inner loop may rely on knowledge that is independent from higher level structures. To test this, we had people type English, English-like, and random strings during normal, partially occluded, and occluded typing. In both partially occluded and occluded typing, error rates were higher while typing random strings compared to English and English-like strings, whereas there was no difference in error rates between English and English-like strings. This suggests that typists' spatial knowledge of the keyboard is not driven by hierarchical word-level representations, but instead is likely driven by a collection of individual processes, such as knowledge of the sequential structure of language acquired by typing more frequently occurring letters.

  19. Body-specific representations of action verbs: neural evidence from right- and left-handers.

    Science.gov (United States)

    Willems, Roel M; Hagoort, Peter; Casasanto, Daniel

    2010-01-01

    According to theories of embodied cognition, understanding a verb like throw involves unconsciously simulating the action of throwing, using areas of the brain that support motor planning. If understanding action words involves mentally simulating one's own actions, then the neurocognitive representation of word meanings should differ for people with different kinds of bodies, who perform actions in systematically different ways. In a test of the body-specificity hypothesis, we used functional magnetic resonance imaging to compare premotor activity correlated with action verb understanding in right- and left-handers. Right-handers preferentially activated the left premotor cortex during lexical decisions on manual-action verbs (compared with nonmanual-action verbs), whereas left-handers preferentially activated right premotor areas. This finding helps refine theories of embodied semantics, suggesting that implicit mental simulation during language processing is body specific: Right- and left-handers, who perform actions differently, use correspondingly different areas of the brain for representing action verb meanings.

  20. Representation of action semantics in the motor cortex and Broca's area.

    Science.gov (United States)

    Zhang, Zuo; Sun, Yaoru; Wang, Zijian

    2018-04-01

    Previous studies have shown that both reading action words and observing actions engage the motor cortex and Broca's area, but it is still controversial whether a somatotopic representation exists for action verbs within the motor cortex and whether Broca's area encodes action-specific semantics for verbs. Here we examined these two issues using a set of functional MRI experiments, including word reading, action observation and a movement localiser task. Results from multi-voxel pattern analysis (MVPA) showed a somatotopic organisation within the motor areas and action-specific activation in Broca's area for observed actions, suggesting the representation of action semantics for observed actions in these neural regions. For action verbs, however, a lack of finding for the somatotopic activation argues against semantic somatotopy within the motor cortex. Furthermore, activation patterns in Broca's area were not separable between action verbs and unrelated verbs, suggesting that Broca's area does not encode action-specific semantics for verbs. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Analysis-Driven Design of Representations For Sensing-Action Systems

    Science.gov (United States)

    2017-10-01

    the data. While for traditional signal processing such nuisances were limited to (typically white, zero-mean, additive) noise , for the case of images...tasks. At the heart of this was the definition of a notion of information (named Actionable Information) that discounted nuisance variability in...can be ascribed to nuisance factors. 2.2 Optimal Representations In 2015, we formalized the defining characteristics of a representation, which

  2. Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

    DEFF Research Database (Denmark)

    Petrick, Ronald; Kraft, Dirk; Mourao, Kira

    We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledgelevel planning, and connectionist machine......-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level lepresentation from low-level world state information....... learning, and focuses on the representational needs of these components.We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high...

  3. From Action Representation to Action Execution: Exploring the Links Between Cognitive and Biomechanical Levels of Motor Control

    Directory of Open Access Journals (Sweden)

    William eLand

    2013-09-01

    Full Text Available Along with superior performance, research indicates that expertise is associated with a number of mediating cognitive adaptations. To this extent, extensive practice is associated with the development of general and task-specific mental representations, which play an important role in the organization and control of action. Recently, new experimental methods have been developed, which allow for investigating the organization and structure of these representations, along with the functional structure of the movement kinematics. In the current article, we present a new approach for examining the overlap between skill representations and motor output. In doing so, we first present an architecture model, which addresses links between biomechanical and cognitive levels of motor control. Next, we review the state of the art in assessing memory structures underlying complex action. Following we present a new spatio-temporal decomposition method for illuminating the functional structure of movement kinematics, and finally, we apply these methods to investigate the overlap between the structure of motor representations in memory and their corresponding kinematic structures. Our aim is to understand the extent to which the output at a kinematic level is governed by representations at a cognitive level of motor control.

  4. Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

    Science.gov (United States)

    Shahnazian, Danesh; Holroyd, Clay B

    2018-02-01

    Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.

  5. Fleeing or not: Responsivity of a chased target influences the cognitive representation of the chasing action.

    Science.gov (United States)

    Yin, Jun; Chen, Man; Wang, Xiaoting; Ding, Xiaowei

    2018-03-19

    The chasing action, in which an actor chases a target, is a fundamental activity for the evolutionary shaping of social abilities. Where previous research has emphasized the chaser's role, the current study explored whether the fleeing responsivity of a chased target influences the cognitive representation of the chasing action. We investigated this with a change-detection task, in which a set of chasing actions, either exhibiting or not exhibiting fleeing behavior, were memorized in sequence, and it was tested whether a memorized action reappeared after altering an object's appearance. The results suggest that the target's fleeing responsivity influenced the detection of representation-related changes in the appearance of the involved agents, especially when the appearance of one target was replaced with another (i.e., a new pair, but with the same functional role), showing impaired sensitivity to change for the chasing action (Experiment 1). This effect disappeared, however, when the perceived chasing from presented movements was impaired, while displaying largely the same low-level differences as those present in earlier trials through the use of mirrored chasing (Experiment 2) and setting the faced direction opposite of the moving direction (Experiment 3). These findings suggest that the fleeing responsivity of the chased target can influence the stored representation of the action. This differentiation may be attributed to the stronger construction of social interaction structure for chasing action with fleeing than without, since the fleeing behavior can be deemed a contingency cue for social interaction interpretation.

  6. Embodied representation of tool-use action verbs and hand action verbs: evidence from a tone judgment task.

    Science.gov (United States)

    Yang, Jie; Shu, Hua

    2011-04-15

    Many studies have found that language comprehension involves sensory-motor system. However, the relationship between word form and embodied semantic representation still lacks evidence. The current fMRI study used Chinese tool-use action verbs, hand action verbs and a Mandarin lexical tone task to explore the issue. In the tone task, all verbs showed strong effects in hand motor areas. However, the contrasts between the hand action verbs and the tool-use action verbs yielded differences mainly in tone processing areas, and the hand action verbs had stronger effects. The ROI analyses indicated consistent result pattern with the contrast analyses. In short, these results revealed that word processing involves basic sensory-motor information automatically, whereas the fine grained information which distinguishes among different semantics can be hindered by the processing of word form. Published by Elsevier Ireland Ltd.

  7. Neural networks for action representation underlying automatic mimicry: A functional magnetic-resonance imaging and dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Akihiro T Sasaki

    2012-08-01

    Full Text Available Automatic mimicry is based on the tight linkage between motor and perception action representations in which internal models play a key role. Based on the anatomical connection, we hypothesized that the direct effective connectivity from the posterior superior temporal sulcus (pSTS to the ventral premotor area (PMv formed an inverse internal model, converting visual representation into a motor plan, and that reverse connectivity formed a forward internal model, converting the motor plan into a sensory outcome of action. To test this hypothesis, we employed dynamic causal-modeling analysis with functional magnetic-resonance imaging. Twenty-four normal participants underwent a change-detection task involving two visually-presented balls that were either manually rotated by the investigator’s right hand (‘Hand’ or automatically rotated. The effective connectivity from the pSTS to the PMv was enhanced by hand observation and suppressed by execution, corresponding to the inverse model. Opposite effects were observed from the PMv to the pSTS, suggesting the forward model. Additionally, both execution and hand observation commonly enhanced the effective connectivity from the pSTS to the inferior parietal lobule (IPL, the IPL to the primary sensorimotor cortex (S/M1, the PMv to the IPL, and the PMv to the S/M1. Representation of the hand action therefore was implemented in the motor system including the S/M1. During hand observation, effective connectivity toward the pSTS was suppressed whereas that toward the PMv and S/M1 was enhanced. Thus the action-representation network acted as a dynamic feedback-control system during action observation.

  8. Neural representation of the sensorimotor speech-action-repository

    Directory of Open Access Journals (Sweden)

    Cornelia eEckers

    2013-04-01

    Full Text Available A speech-action-repository (SAR or mental syllabary has been proposed as a central module for sensorimotor processing of syllables. In this approach, syllables occurring frequently within language are assumed to be stored as holistic sensorimotor patterns, while non-frequent syllables need to be assembled from sub-syllabic units. Thus, frequent syllables are processed efficiently and quickly during production or perception by a direct activation of their sensorimotor patterns. Whereas several behavioral psycholinguistic studies provided evidence in support of the existence of a syllabary, fMRI studies have failed to demonstrate its neural reality. In the present fMRI study a reaction paradigm using homogeneous vs. heterogeneous syllable blocks are used during overt vs. covert speech production and auditory vs. visual presentation modes. Two complementary data analyses were performed: (1 in a logical conjunction, activation for syllable processing independent of input modality and response mode was assessed, in order to support the assumption of existence of a supramodal hub within a SAR. (2 In addition priming effects in the BOLD response in homogeneous vs. heterogeneous blocks were measured in order to identify brain regions, which indicate reduced activity during multiple production/perception repetitions of a specific syllable in order to determine state maps. Auditory-visual conjunction analysis revealed an activation network comprising bilateral precentral gyrus and left inferior frontal gyrus (area 44. These results are compatible with the notion of a supramodal hub within the SAR. The main effect of homogeneity priming revealed an activation pattern of areas within frontal, temporal, and parietal lobe. These findings are taken to represent sensorimotor state maps of the SAR. In conclusion, the present study provided preliminary evidence for a SAR.

  9. Palmprint Recognition Based on Complete Direction Representation.

    Science.gov (United States)

    Jia, Wei; Zhang, Bob; Lu, Jingting; Zhu, Yihai; Zhao, Yang; Zuo, Wangmeng; Ling, Haibin

    2017-05-18

    Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilized all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain. In this paper, we propose a general framework for DR-based method named Complete Direction Representation (CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance. Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. Firstly, we extract CDR using multi-scale modified finite radon transformation (MFRAT). Then, an effective correlation filter, namely Band-Limited Phase-Only Correlation (BLPOC), is explored for pattern matching. To remove feature redundancy, the Sequential Forward Selection (SFS) method is used to select a small number of CDR images. Finally, the matching scores obtained from different selected features are integrated using score-level-fusion. Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications.

  10. Dissociating the Representation of Action- and Sound-Related Concepts in Middle Temporal Cortex

    Science.gov (United States)

    Kiefer, Markus; Trumpp, Natalie; Herrnberger, Barbel; Sim, Eun-Jin; Hoenig, Klaus; Pulvermuller, Friedemann

    2012-01-01

    Modality-specific models of conceptual memory propose close links between concepts and the sensory-motor systems. Neuroimaging studies found, in different subject groups, that action-related and sound-related concepts activated different parts of posterior middle temporal gyrus (pMTG), suggesting a modality-specific representation of conceptual…

  11. Unifying Class-Based Representation Formalisms

    OpenAIRE

    Calvanese, D.; Lenzerini, M.; Nardi, D.

    2011-01-01

    The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representatio...

  12. Unaware Processing of Tools in the Neural System for Object-Directed Action Representation.

    Science.gov (United States)

    Tettamanti, Marco; Conca, Francesca; Falini, Andrea; Perani, Daniela

    2017-11-01

    The hypothesis that the brain constitutively encodes observed manipulable objects for the actions they afford is still debated. Yet, crucial evidence demonstrating that, even in the absence of perceptual awareness, the mere visual appearance of a manipulable object triggers a visuomotor coding in the action representation system including the premotor cortex, has hitherto not been provided. In this fMRI study, we instantiated reliable unaware visual perception conditions by means of continuous flash suppression, and we tested in 24 healthy human participants (13 females) whether the visuomotor object-directed action representation system that includes left-hemispheric premotor, parietal, and posterior temporal cortices is activated even under subliminal perceptual conditions. We found consistent activation in the target visuomotor cortices, both with and without perceptual awareness, specifically for pictures of manipulable versus non-manipulable objects. By means of a multivariate searchlight analysis, we also found that the brain activation patterns in this visuomotor network enabled the decoding of manipulable versus non-manipulable object picture processing, both with and without awareness. These findings demonstrate the intimate neural coupling between visual perception and motor representation that underlies manipulable object processing: manipulable object stimuli specifically engage the visuomotor object-directed action representation system, in a constitutive manner that is independent from perceptual awareness. This perceptuo-motor coupling endows the brain with an efficient mechanism for monitoring and planning reactions to external stimuli in the absence of awareness. SIGNIFICANCE STATEMENT Our brain constantly encodes the visual information that hits the retina, leading to a stimulus-specific activation of sensory and semantic representations, even for objects that we do not consciously perceive. Do these unconscious representations encompass the motor

  13. Weighted Discriminative Dictionary Learning based on Low-rank Representation

    International Nuclear Information System (INIS)

    Chang, Heyou; Zheng, Hao

    2017-01-01

    Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods. (paper)

  14. Representation of Behavioral Tactics and Tactics-Action Transformation in the Primate Medial Prefrontal Cortex.

    Science.gov (United States)

    Matsuzaka, Yoshiya; Tanji, Jun; Mushiake, Hajime

    2016-06-01

    To expedite the selection of action under a structured behavioral context, we develop an expedient to promote its efficiency: tactics for action selection. Setting up a behavioral condition for subhuman primates (Macaca fuscata) that induced the development of a behavioral tactics, we explored neuronal representation of tactics in the medial frontal cortex. Here we show that neurons in the posterior medial prefrontal cortex, but not much in the medial premotor cortex, exhibit activity representing the behavioral tactics, in advance of action-selective activity. Such activity appeared during behavioral epochs of its retrieval from instruction cues, maintenance in short-term memory, and its implementation for the achievement of action selection. At a population level, posterior medial prefrontal cortex neurons take part in transforming the tactics information into the information representing action selection. The tactics representation revealed an aspect of neural mechanisms for an adaptive behavioral control, taking place in the medial prefrontal cortex. We studied behavioral significance of neuronal activity in the posterior medial prefrontal cortex (pmPFC) and found the representation of behavioral tactics defined as specific and efficient ways to achieve objectives of actions. Neuronal activity appeared during behavioral epochs of its retrieval from instruction cues, maintenance in short-term memory, and its use preceding the achievement of action selection. We found further that pmPFC neurons take part in transforming the tactics information into the information representing action selection. A majority of individual neurons was recruited during a limited period in each behavioral epoch, constituting, as a whole, a temporal cascade of activity. Such dynamics found in behavioral-tactics specific activity characterize the participation of pmPFC neurons in executive control of purposeful behavior. Copyright © 2016 the authors 0270-6474/16/365974-14$15.00/0.

  15. Far from action-blind: Representation of others' actions in individuals with autism

    NARCIS (Netherlands)

    Sebanz, N.; Knoblich, G.K.; Stumpf, L.; Prinz, W.G.

    2005-01-01

    It has been suggested that theory of mind may rely on several precursors including gaze processing, joint attention, the ability to distinguish between actions of oneself and others, and the ability to represent goal-directed actions. Some of these processes have been shown to be impaired in

  16. A word in the hand: action, gesture and mental representation in humans and non-human primates

    Science.gov (United States)

    Cartmill, Erica A.; Beilock, Sian; Goldin-Meadow, Susan

    2012-01-01

    The movements we make with our hands both reflect our mental processes and help to shape them. Our actions and gestures can affect our mental representations of actions and objects. In this paper, we explore the relationship between action, gesture and thought in both humans and non-human primates and discuss its role in the evolution of language. Human gesture (specifically representational gesture) may provide a unique link between action and mental representation. It is kinaesthetically close to action and is, at the same time, symbolic. Non-human primates use gesture frequently to communicate, and do so flexibly. However, their gestures mainly resemble incomplete actions and lack the representational elements that characterize much of human gesture. Differences in the mirror neuron system provide a potential explanation for non-human primates' lack of representational gestures; the monkey mirror system does not respond to representational gestures, while the human system does. In humans, gesture grounds mental representation in action, but there is no evidence for this link in other primates. We argue that gesture played an important role in the transition to symbolic thought and language in human evolution, following a cognitive leap that allowed gesture to incorporate representational elements. PMID:22106432

  17. Value representations: a value based dialogue tool

    DEFF Research Database (Denmark)

    Petersen, Marianne Graves; Rasmussen, Majken Kirkegaard

    2011-01-01

    , as the perspective brings valuable insights on different approaches to technology, but instead to view gender through a value lens. Contributing to this perspective, we have developed Value Representations as a design-oriented instrument for staging a reflective dialogue with users. Value Representations...... are fictional, value-driven concepts developed to promote dialogue with users about their values and how they may materialize with respect to interaction design in their everyday lives....

  18. Taking action: a cross-modal investigation of discourse-level representations.

    Science.gov (United States)

    Kaiser, Elsi

    2012-01-01

    Segmenting stimuli into events and understanding the relations between those events is crucial for understanding the world. For example, on the linguistic level, successful language use requires the ability to recognize semantic coherence relations between events (e.g., causality, similarity). However, relatively little is known about the mental representation of discourse structure. We report two experiments that used a cross-modal priming paradigm to investigate how humans represent the relations between events. Participants repeated a motor action modeled by the experimenter (e.g., rolled a ball toward mini bowling pins to knock them over), and then completed an unrelated sentence-continuation task (e.g., provided a continuation for "Peter scratched John.…"). In two experiments, we tested whether and how the coherence relations represented by the motor actions (e.g., causal events vs. non-causal events) influence participants' performance in the linguistic task. (A production study was also conducted to explore potential syntactic priming effects.) Our analyses focused on the coherence relations between the prompt sentences and participants' continuations, as well as the referential shifts in the continuations. As a whole, the results suggest that the mental representations activated by motor actions overlap with the mental representations used during linguistic discourse-level processing, but nevertheless contain fine-grained information about sub-types of causality (reaction vs. consequence). In addition, the findings point to parallels between shifting one's attention from one-event to another and shifting one's attention from one referent to another, and indicate that the event structure of causal sequences is conceptualized more like single events than like two distinct events. As a whole, the results point toward common representations activated by motor sequences and discourse-semantic relations, and further our understanding of the mental

  19. Taking Action: A Cross-Modal Investigation of Discourse-Level Representations

    Science.gov (United States)

    Kaiser, Elsi

    2012-01-01

    Segmenting stimuli into events and understanding the relations between those events is crucial for understanding the world. For example, on the linguistic level, successful language use requires the ability to recognize semantic coherence relations between events (e.g., causality, similarity). However, relatively little is known about the mental representation of discourse structure. We report two experiments that used a cross-modal priming paradigm to investigate how humans represent the relations between events. Participants repeated a motor action modeled by the experimenter (e.g., rolled a ball toward mini bowling pins to knock them over), and then completed an unrelated sentence-continuation task (e.g., provided a continuation for “Peter scratched John.…”). In two experiments, we tested whether and how the coherence relations represented by the motor actions (e.g., causal events vs. non-causal events) influence participants’ performance in the linguistic task. (A production study was also conducted to explore potential syntactic priming effects.) Our analyses focused on the coherence relations between the prompt sentences and participants’ continuations, as well as the referential shifts in the continuations. As a whole, the results suggest that the mental representations activated by motor actions overlap with the mental representations used during linguistic discourse-level processing, but nevertheless contain fine-grained information about sub-types of causality (reaction vs. consequence). In addition, the findings point to parallels between shifting one’s attention from one-event to another and shifting one’s attention from one referent to another, and indicate that the event structure of causal sequences is conceptualized more like single events than like two distinct events. As a whole, the results point toward common representations activated by motor sequences and discourse-semantic relations, and further our understanding of the mental

  20. Signal- and Symbol-based Representations in Computer Vision

    DEFF Research Database (Denmark)

    Krüger, Norbert; Felsberg, Michael

    We discuss problems of signal-- and symbol based representations in terms of three dilemmas which are faced in the design of each vision system. Signal- and symbol-based representations are opposite ends of a spectrum of conceivable design decisions caught at opposite sides of the dilemmas. We make...

  1. Mu rhythm suppression demonstrates action representation in pianists during passive listening of piano melodies.

    Science.gov (United States)

    Wu, C Carolyn; Hamm, Jeff P; Lim, Vanessa K; Kirk, Ian J

    2016-08-01

    Musicians undergo extensive training which enhances established neural links between auditory and motor areas of the brain. Long-term training develops, strengthens and enables flexibility in these connections allowing proficiency in performance. Previous research has indicated that passive listening of trained music results in the recruitment of premotor areas. It has been argued that this sound-action representation may rely on activity in mirror neuron systems and that these systems are heavily dependent on actual sensorimotor experience. Action observation studies using electroencephalography have associated changes in mu rhythm activity with the mirror neuron system in the visuomotor domain. We aimed to investigate similar effects in the audiomotor domain. We utilised a mu suppression method in our action-listening study to detect involuntary motor coactivation when pianists passively listened to piano melodies. Wavelet analysis revealed sensorimotor mu rhythm suppression while pianists listened passively to piano melodies. Thus, we show that this spectral analysis method can also be used to demonstrate that auditory stimuli can activate the human mirror neuron system when sounds are linked to actions. Mu suppression could be a useful index for further research on action representation and training-induced plasticity.

  2. Dissociation between Semantic Representations for Motion and Action Verbs: Evidence from Patients with Left Hemisphere Lesions.

    Science.gov (United States)

    Taylor, Lawrence J; Evans, Carys; Greer, Joanna; Senior, Carl; Coventry, Kenny R; Ietswaart, Magdalena

    2017-01-01

    This multiple single case study contrasted left hemisphere stroke patients ( N = 6) to healthy age-matched control participants ( N = 15) on their understanding of action (e.g., holding, clenching) and motion verbs (e.g., crumbling, flowing). The tasks required participants to correctly identify the matching verb or associated picture. Dissociations on action and motion verb content depending on lesion site were expected. As predicted for verbs containing an action and/or motion content, modified t -tests confirmed selective deficits in processing motion verbs in patients with lesions involving posterior parietal and lateral occipitotemporal cortex. In contrast, deficits in verbs describing motionless actions were found in patients with more anterior lesions sparing posterior parietal and lateral occipitotemporal cortex. These findings support the hypotheses that semantic representations for action and motion are behaviorally and neuro-anatomically dissociable. The findings clarify the differential and critical role of perceptual and motor regions in processing modality-specific semantic knowledge as opposed to a supportive but not necessary role. We contextualize these results within theories from both cognitive psychology and cognitive neuroscience that make claims over the role of sensory and motor information in semantic representation.

  3. Integrating Globality and Locality for Robust Representation Based Classification

    Directory of Open Access Journals (Sweden)

    Zheng Zhang

    2014-01-01

    Full Text Available The representation based classification method (RBCM has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC method and collaborative representation classification (CRC method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy.

  4. Brain extraction based on locally linear representation-based classification.

    Science.gov (United States)

    Huang, Meiyan; Yang, Wei; Jiang, Jun; Wu, Yao; Zhang, Yu; Chen, Wufan; Feng, Qianjin

    2014-05-15

    Brain extraction is an important procedure in brain image analysis. Although numerous brain extraction methods have been presented, enhancing brain extraction methods remains challenging because brain MRI images exhibit complex characteristics, such as anatomical variability and intensity differences across different sequences and scanners. To address this problem, we present a Locally Linear Representation-based Classification (LLRC) method for brain extraction. A novel classification framework is derived by introducing the locally linear representation to the classical classification model. Under this classification framework, a common label fusion approach can be considered as a special case and thoroughly interpreted. Locality is important to calculate fusion weights for LLRC; this factor is also considered to determine that Local Anchor Embedding is more applicable in solving locally linear coefficients compared with other linear representation approaches. Moreover, LLRC supplies a way to learn the optimal classification scores of the training samples in the dictionary to obtain accurate classification. The International Consortium for Brain Mapping and the Alzheimer's Disease Neuroimaging Initiative databases were used to build a training dataset containing 70 scans. To evaluate the proposed method, we used four publicly available datasets (IBSR1, IBSR2, LPBA40, and ADNI3T, with a total of 241 scans). Experimental results demonstrate that the proposed method outperforms the four common brain extraction methods (BET, BSE, GCUT, and ROBEX), and is comparable to the performance of BEaST, while being more accurate on some datasets compared with BEaST. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. The Representation of Objects in Apraxia: From Action Execution to Error Awareness

    Science.gov (United States)

    Canzano, Loredana; Scandola, Michele; Gobbetto, Valeria; Moretto, Giuseppe; D’Imperio, Daniela; Moro, Valentina

    2016-01-01

    Apraxia is a well-known syndrome characterized by the sufferer’s inability to perform routine gestures. In an attempt to understand the syndrome better, various different theories have been developed and a number of classifications of different subtypes have been proposed. In this article review, we will address these theories with a specific focus on how the use of objects helps us to better understand upper limb apraxia. With this aim, we will consider transitive vs. intransitive action dissociation as well as less frequent types of apraxia involving objects, i.e., constructive apraxia and magnetic apraxia. Pantomime and the imitation of objects in use are also considered with a view to dissociating the various different components involved in upper limb apraxia. Finally, we discuss the evidence relating to action recognition and awareness of errors in the execution of actions. Various different components concerning the use of objects emerge from our analysis and the results show that knowledge of an object and sensory-motor representations are supported by other functions such as spatial and body representations, executive functions and monitoring systems. PMID:26903843

  6. The Representation of Motor (Interaction, States of Action, and Learning: Three Perspectives on Motor Learning by Way of Imagery and Execution

    Directory of Open Access Journals (Sweden)

    Cornelia Frank

    2017-05-01

    Full Text Available Learning in intelligent systems is a result of direct and indirect interaction with the environment. While humans can learn by way of different states of (interaction such as the execution or the imagery of an action, their unique potential to induce brain- and mind-related changes in the motor action system is still being debated. The systematic repetition of different states of action (e.g., physical and/or mental practice and their contribution to the learning of complex motor actions has traditionally been approached by way of performance improvements. More recently, approaches highlighting the role of action representation in the learning of complex motor actions have evolved and may provide additional insight into the learning process. In the present perspective paper, we build on brain-related findings and sketch recent research on learning by way of imagery and execution from a hierarchical, perceptual-cognitive approach to motor control and learning. These findings provide insights into the learning of intelligent systems from a perceptual-cognitive, representation-based perspective and as such add to our current understanding of action representation in memory and its changes with practice. Future research should build bridges between approaches in order to more thoroughly understand functional changes throughout the learning process and to facilitate motor learning, which may have particular importance for cognitive systems research in robotics, rehabilitation, and sports.

  7. Action-based flood forecasting for triggering humanitarian action

    Science.gov (United States)

    Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

    2016-09-01

    Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

  8. Neural representations of kinematic laws of motion: evidence for action-perception coupling.

    Science.gov (United States)

    Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar

    2007-12-18

    Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.

  9. Mental representation for action in the elderly: implications for movement efficiency and injury risk.

    Science.gov (United States)

    Gabbard, Carl

    2015-04-01

    Recent research findings indicate that with older adulthood, there are functional decrements in spatial cognition and more specially, in the ability to mentally represent and effectively plan motor actions. A typical finding is a significant over- or underestimation of one's actual physical abilities with movement planning-planning that has implications for movement efficiency and physical safety. A practical, daily life example is estimation of reachability--a situation that for the elderly may be linked with fall incidence. A strategy used to mentally represent action is the use of motor imagery--an ability that also declines with advancing older age. This brief review highlights research findings on mental representation and motor imagery in the elderly and addresses the implications for improving movement efficiency and lowering the risk of movement-related injury. © The Author(s) 2013.

  10. The representation of risk in routine medical experience: what actions for contemporary health policy?

    Directory of Open Access Journals (Sweden)

    Silvia Riva

    Full Text Available The comprehension of appropriate information about illnesses and treatments, can have beneficial effects on patients' satisfaction and on important health outcomes. However, it is questionable whether people are able to understand risk properly.To describe patients' representation of risk in common medical experiences by linking such a representation to the concept of trust. A further goal was to test whether the representation of risk in the medical domain is associated to the level of expertise. The third goal was to verify whether socio-demographic differences influence the representation of risk.Eighty voluntary participants from 6 health-centers in northern Italy were enrolled to conduct a semi-structured interview which included demographic questions, term-associations about risk representation, closed and open questions about attitudes and perception of risk in the medical context, as well as about medical expertise and trust.The results showed that people do not have in mind a scientific definition of risk in medicine. Risk is seen as a synonym for surgery and disease and it is often confused with fear. However, general knowledge of medical matters helps people to have a better health management through risk identification and risk information, adoption of careful behaviors and tendency to have a critical view about safety and medical news. Finally, trust proved to be an important variable in risk representation and risk and trust were correlated positively.People must receive appropriate information about the risks and benefits of treatment, in a form that they can understand and apply to their own circumstances. Moreover, contemporary health policy should empower patients to adopt an active self-care attitude. Methodologies to enhance people's decision-making outcomes based on better risk communication should be improved in order to enable low literacy population as well elderly people to better understand their treatment and

  11. Independent representations of verbs and actions in left lateral temporal cortex.

    Science.gov (United States)

    Peelen, Marius V; Romagno, Domenica; Caramazza, Alfonso

    2012-10-01

    Verbs and nouns differ not only on formal linguistic grounds but also in what they typically refer to: Verbs typically refer to actions, whereas nouns typically refer to objects. Prior neuroimaging studies have revealed that regions in the left lateral temporal cortex (LTC), including the left posterior middle temporal gyrus (pMTG), respond selectively to action verbs relative to object nouns. Other studies have implicated the left pMTG in action knowledge, raising the possibility that verb selectivity in LTC may primarily reflect action-specific semantic features. Here, using functional neuroimaging, we test this hypothesis. Participants performed a simple memory task on visually presented verbs and nouns that described either events (e.g., "he eats" and "the conversation") or states (e.g., "he exists" and "the value"). Verb-selective regions in the left pMTG and the left STS were defined in individual participants by an independent localizer contrast between action verbs and object nouns. Both regions showed equally strong selectivity for event and state verbs relative to semantically matched nouns. The left STS responded more to states than events, whereas there was no difference between states and events in the left pMTG. Finally, whole-brain group analysis revealed that action verbs, relative to state verbs, activated a cluster in pMTG that was located posterior to the verb-selective pMTG clusters. Together, these results indicate that verb selectivity in LTC is independent of action representations. We consider other differences between verbs and nouns that may underlie verb selectivity in LTC, including the verb property of predication.

  12. Supervised Filter Learning for Representation Based Face Recognition.

    Directory of Open Access Journals (Sweden)

    Chao Bi

    Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  13. Supervised Filter Learning for Representation Based Face Recognition.

    Science.gov (United States)

    Bi, Chao; Zhang, Lei; Qi, Miao; Zheng, Caixia; Yi, Yugen; Wang, Jianzhong; Zhang, Baoxue

    2016-01-01

    Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  14. Representation Learning Based Speech Assistive System for Persons With Dysarthria.

    Science.gov (United States)

    Chandrakala, S; Rajeswari, Natarajan

    2017-09-01

    An assistive system for persons with vocal impairment due to dysarthria converts dysarthric speech to normal speech or text. Because of the articulatory deficits, dysarthric speech recognition needs a robust learning technique. Representation learning is significant for complex tasks such as dysarthric speech recognition. We focus on robust representation for dysarthric speech recognition that involves recognizing sequential patterns of varying length utterances. We propose a hybrid framework that uses a generative learning based data representation with a discriminative learning based classifier. In this hybrid framework, we propose to use Example Specific Hidden Markov Models (ESHMMs) to obtain log-likelihood scores for a dysarthric speech utterance to form fixed dimensional score vector representation. This representation is used as an input to discriminative classifier such as support vector machine.The performance of the proposed approach is evaluatedusingUA-Speechdatabase.The recognitionaccuracy is much better than the conventional hidden Markov model based approach and Deep Neural Network-Hidden Markov Model (DNN-HMM). The efficiency of the discriminative nature of score vector representation is proved for "very low" intelligibility words.

  15. Evidence for the representation of movement kinematics in the discharge of F5 mirror neurons during the observation of transitive and intransitive actions.

    Science.gov (United States)

    Papadourakis, Vassilis; Raos, Vassilis

    2017-12-01

    Mirror neurons (MirNs) are sensorimotor neurons that fire both when an animal performs a goal-directed action and when the same animal observes another agent performing the same or a similar transitive action. It has been claimed that the observation of intransitive actions does not activate MirNs in a monkey's brain. Prompted by recent evidence indicating that the discharge of MirNs is modulated also by non-object-directed actions, we investigated thoroughly the efficacy of intransitive actions to trigger MirNs' discharge. Using representational similarity analysis, we also studied whether the elements constituting the visual scene presented to the monkey during the observation of actions (both transitive and intransitive) are represented in the discharge of MirNs. For this purpose, the moving hand was modeled by its kinematics and the object by features of its geometry. We found that MirNs respond to the observation of both transitive and intransitive actions and that the discharge differences evoked by the observation of object- and non-object-directed actions are correlated more with the kinematic differences of these actions than with the differences of the objects' features. These findings support the view that observed action kinematics contribute to action mirroring. NEW & NOTEWORTHY Mirror neurons in the monkey brain are thought to respond exclusively to the observation of object-directed actions. Here, we show that mirror neurons also respond to the observation of intransitive actions and that the kinematics of the observed movements are represented in their discharge. This finding supports the view that mirror neurons provide also a kinematics-based representation of actions. Copyright © 2017 the American Physiological Society.

  16. The representation of knowledge within model-based control systems

    International Nuclear Information System (INIS)

    Weygand, D.P.; Koul, R.

    1987-01-01

    The ability to represent knowledge is often considered essential to build systems with reasoning capabilities. In computer science, a good solution often depends on a good representation. The first step in development of most computer applications is selection of a representation for the input, output, and intermediate results that the program will operate upon. For applications in artificial intelligence, this initial choice of representation is especially important. This is because the possible representational paradigms are diverse and the forcing criteria for the choice are usually not clear in the beginning. Yet, the consequences of an inadequate choice can be devastating in the later state of a project if it is discovered that critical information cannot be encoded within the chosen representational paradigm. Problems arise when designing representational systems to support any kind of Knowledge-Base System, that is a computer system that uses knowledge to perform some task. The general case of knowledge-based systems can be thought of as reasoning agents applying knowledge to achieve goals. Artificial Intelligence (AI) research involves building computer systems to perform tasks of perception and reasoning, as well as storage and retrieval of data. The problem of automatically perceiving large patterns in data is a perceptual task that begins to be important for many expert systems applications. Most of AI research assumes that what needs to be represented is known a priori; an AI researcher's job is just figuring out how to encode the information in the system's data structure and procedures. 10 refs

  17. Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing

    2014-01-01

    Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

  18. Medical Image Fusion Based on Feature Extraction and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Yin Fei

    2017-01-01

    Full Text Available As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM and energy information map (EM as well as structure and energy map (SEM to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

  19. Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions

    Directory of Open Access Journals (Sweden)

    Tatsuro Yamada

    2017-12-01

    Full Text Available An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as “not,” “and,” and “or” simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human–robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as “true,” “false,” and “not” work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word “and,” which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word “or,” which required action generation that looked apparently random, was represented as an

  20. Conformal-Based Surface Morphing and Multi-Scale Representation

    Directory of Open Access Journals (Sweden)

    Ka Chun Lam

    2014-05-01

    Full Text Available This paper presents two algorithms, based on conformal geometry, for the multi-scale representations of geometric shapes and surface morphing. A multi-scale surface representation aims to describe a 3D shape at different levels of geometric detail, which allows analyzing or editing surfaces at the global or local scales effectively. Surface morphing refers to the process of interpolating between two geometric shapes, which has been widely applied to estimate or analyze deformations in computer graphics, computer vision and medical imaging. In this work, we propose two geometric models for surface morphing and multi-scale representation for 3D surfaces. The basic idea is to represent a 3D surface by its mean curvature function, H, and conformal factor function λ, which uniquely determine the geometry of the surface according to Riemann surface theory. Once we have the (λ, H parameterization of the surface, post-processing of the surface can be done directly on the conformal parameter domain. In particular, the problem of multi-scale representations of shapes can be reduced to the signal filtering on the λ and H parameters. On the other hand, the surface morphing problem can be transformed to an interpolation process of two sets of (λ, H parameters. We test the proposed algorithms on 3D human face data and MRI-derived brain surfaces. Experimental results show that our proposed methods can effectively obtain multi-scale surface representations and give natural surface morphing results.

  1. Visual tracking based on extreme learning machine and sparse representation.

    Science.gov (United States)

    Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

    2015-10-22

    The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.

  2. Explicit representation of confidence informs future value-based decisions

    DEFF Research Database (Denmark)

    Folke, Tomas; Jacobsen, Catrine; Fleming, Stephen M.

    2016-01-01

    Humans can reflect on decisions and report variable levels of confidence. But why maintain an explicit representation of confidence for choices that have already been made and therefore cannot be undone? Here we show that an explicit representation of confidence is harnessed for subsequent changes...... of mind. Specifically, when confidence is low, participants are more likely to change their minds when the same choice is presented again, an effect that is most pronounced in participants with greater fidelity in their confidence reports. Furthermore, we show that choices reported with high confidence...... of confidence has a positive impact on the quality of future value-based decisions....

  3. Time-frequency representation based on time-varying ...

    Indian Academy of Sciences (India)

    A parametric time-frequency representation is presented based on timevarying autoregressive model (TVAR), followed by applications to non-stationary vibration signal processing. The identification of time-varying model coefficients and the determination of model order, are addressed by means of neural networks and ...

  4. Converting biomolecular modelling data based on an XML representation.

    Science.gov (United States)

    Sun, Yudong; McKeever, Steve

    2008-08-25

    Biomolecular modelling has provided computational simulation based methods for investigating biological processes from quantum chemical to cellular levels. Modelling such microscopic processes requires atomic description of a biological system and conducts in fine timesteps. Consequently the simulations are extremely computationally demanding. To tackle this limitation, different biomolecular models have to be integrated in order to achieve high-performance simulations. The integration of diverse biomolecular models needs to convert molecular data between different data representations of different models. This data conversion is often non-trivial, requires extensive human input and is inevitably error prone. In this paper we present an automated data conversion method for biomolecular simulations between molecular dynamics and quantum mechanics/molecular mechanics models. Our approach is developed around an XML data representation called BioSimML (Biomolecular Simulation Markup Language). BioSimML provides a domain specific data representation for biomolecular modelling which can effciently support data interoperability between different biomolecular simulation models and data formats.

  5. A Representation System User Interface for Knowledge Base Designers

    OpenAIRE

    Fikes, Richard E.

    1982-01-01

    A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effe...

  6. Smooth Particle Hydrodynamics-based Wind Representation

    Energy Technology Data Exchange (ETDEWEB)

    Prescott, Steven [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Curtis [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hess, Stephen [Idaho National Lab. (INL), Idaho Falls, ID (United States); Lin, Linyu [Idaho National Lab. (INL), Idaho Falls, ID (United States); Sampath, Ram [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-12-01

    As a result of the 2011 accident at the Fukushima Dai-ichi NPP and other operational NPP experience, there is an identified need to better characterize and evaluate the potential impacts of externally generated hazards on NPP safety. Due to the ubiquitous occurrence of high winds around the world and the possible extreme magnitude of the hazard that has been observed, the assessment of the impact of the high-winds hazard has been identified as an important activity by both NPP owner-operators and regulatory authorities. However, recent experience obtained from the conduct of high-winds risk assessments indicates that such activities have been both labor-intensive and expensive to perform. Additionally, the existing suite of methods and tools to conduct such assessments (which were developed decades ago) do not make use of modern computational architectures (e.g., parallel processing, object-oriented programming techniques, or simple user interfaces) or methods (e.g., efficient and robust numerical-solution schemes). As a result, the current suite of methods and tools will rapidly become obsolete. Physics-based 3D simulation methods can provide information to assist in the RISMC PRA methodology. This research is intended to determine what benefits SPH methods could bring to high-winds simulations for the purposes of assessing their potential impact on NPP safety. The initial investigation has determined that SPH can simulate key areas of high-wind events with reasonable accuracy, compared to other methods. Some problems, such as simulation voids, need to be addressed, but possible solutions have been identified and will be tested with continued work. This work also demonstrated that SPH simulations can provide a means for simulating debris movement; however, further investigations into the capability to determine the impact of high winds and the impacts of wind-driven debris that lead to SSC failures need to be done. SPH simulations alone would be limited in size

  7. Action-based effects on music perception.

    Science.gov (United States)

    Maes, Pieter-Jan; Leman, Marc; Palmer, Caroline; Wanderley, Marcelo M

    2014-01-03

    The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral processes. In contrast, embodied accounts of music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework that captures the ways in which the human motor system and its actions can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory, postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modeling), and vice versa, to predict the sensory outcomes of planned actions (forward modeling). Embodied accounts typically refer to inverse modeling to explain action effects on music perception (Leman, 2007). We extend this account by pinpointing forward modeling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system and its actions suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music) cognition in the sense that it needs to be considered as a dynamical process, in which aspects of action, perception, introspection, and social interaction are of crucial importance.

  8. Action-based effects on music perception

    Directory of Open Access Journals (Sweden)

    Pieter-Jan eMaes

    2014-01-01

    Full Text Available The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral phenomena. In contrast, embodied accounts to music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework capturing the ways that the human motor system, and the actions it produces, can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modelling, and vice versa, to predict the sensory outcomes of planned actions (forward modelling. Embodied accounts typically adhere to inverse modelling to explain action effects on music perception (Leman, 2007. We extent this account by pinpointing forward modelling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system, and the action it produces, suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music cognition in the sense that it needs to be considered as a dynamic process, in which aspects of action, perception, introspection, and social interaction are of crucial

  9. Graph-based representation for multiview image geometry.

    Science.gov (United States)

    Maugey, Thomas; Ortega, Antonio; Frossard, Pascal

    2015-05-01

    In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.

  10. Action-Research and Food and Nutrition Security: A School Experience Mediated by Conceptual Graphic Representation Tool

    Science.gov (United States)

    Graebner, Ivete Teresinha; de Souza, Elizabeth Maria Tala; Saito, Carlos Hiroo

    2009-01-01

    This study deals with the development of a graphic representation tool as a way to support educational planning in an elementary school in the rural area of Brasilia (Brazil's capital), aiming at the implementation of an integrated action-research project focusing on hunger and nutrition. The graphic tool made it possible to promote…

  11. A Subdivision-Based Representation for Vector Image Editing.

    Science.gov (United States)

    Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

    2012-11-01

    Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

  12. Improved quantum circuit modelling based on Heisenberg representation

    Science.gov (United States)

    Lee, Y. H.; Khalil-Hani, M.; Marsono, M. N.

    2018-02-01

    Heisenberg model allows a more compact representation of certain quantum states and enables efficient modelling of stabilizer gates operation and single-qubit measurement in computational basis on classical computers. Since generic quantum circuit modelling appears intractable on classical computers, the Heisenberg representation that makes the modelling process at least practical for certain circuits is crucial. This paper proposes efficient algorithms to facilitate accurate global phase maintenance for both stabilizer and non-stabilizer gates application that play a vital role in the stabilizer frames data structure, which is based on the Heisenberg representation. The proposed algorithms are critical as maintaining global phase involves compute-intensive operations that are necessary for the modelling of each quantum gate. In addition, the proposed work overcomes the limitations of prior work where the phase factors due to non-stabilizer gates application was not taken into consideration. The verification of the proposed algorithms is made against the golden reference model that is constructed based on the conventional state vector approach.

  13. An Improved Information Hiding Method Based on Sparse Representation

    Directory of Open Access Journals (Sweden)

    Minghai Yao

    2015-01-01

    Full Text Available A novel biometric authentication information hiding method based on the sparse representation is proposed for enhancing the security of biometric information transmitted in the network. In order to make good use of abundant information of the cover image, the sparse representation method is adopted to exploit the correlation between the cover and biometric images. Thus, the biometric image is divided into two parts. The first part is the reconstructed image, and the other part is the residual image. The biometric authentication image cannot be restored by any one part. The residual image and sparse representation coefficients are embedded into the cover image. Then, for the sake of causing much less attention of attackers, the visual attention mechanism is employed to select embedding location and embedding sequence of secret information. Finally, the reversible watermarking algorithm based on histogram is utilized for embedding the secret information. For verifying the validity of the algorithm, the PolyU multispectral palmprint and the CASIA iris databases are used as biometric information. The experimental results show that the proposed method exhibits good security, invisibility, and high capacity.

  14. Teaching object concepts for XML-based representations.

    Energy Technology Data Exchange (ETDEWEB)

    Kelsey, R. L. (Robert L.)

    2002-01-01

    Students learned about object-oriented design concepts and knowledge representation through the use of a set of toy blocks. The blocks represented a limited and focused domain of knowledge and one that was physical and tangible. The blocks helped the students to better visualize, communicate, and understand the domain of knowledge as well as how to perform object decomposition. The blocks were further abstracted to an engineering design kit for water park design. This helped the students to work on techniques for abstraction and conceptualization. It also led the project from tangible exercises into software and programming exercises. Students employed XML to create object-based knowledge representations and Java to use the represented knowledge. The students developed and implemented software allowing a lay user to design and create their own water slide and then to take a simulated ride on their slide.

  15. Teachers' Classroom-Based Action Research

    Science.gov (United States)

    Cain, Tim

    2011-01-01

    Teachers' classroom-based action research is sometimes misunderstood by those who undertake it and support it, in three respects. First, it is wrongly assumed to fall into either positivist or interpretive paradigms (or perhaps a mixture of both) or to be critical. Second, there is little understanding as to why action research is necessarily…

  16. Human action recognition based on estimated weak poses

    Science.gov (United States)

    Gong, Wenjuan; Gonzàlez, Jordi; Roca, Francesc Xavier

    2012-12-01

    We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method.

  17. An analysis of primary school students’ representational ability in mathematics based on gender perspective

    Science.gov (United States)

    Kowiyah; Mulyawati, I.

    2018-01-01

    Mathematic representation is one of the basic mathematic skills that allows students to communicate their mathematic ideas through visual realities such as pictures, tables, mathematic expressions and mathematic equities. The present research aims at: 1) analysing students’ mathematic representation ability in solving mathematic problems and 2) examining the difference of students’ mathematic ability based on their gender. A total of sixty primary school students participated in this study comprising of thirty males and thirty females. Data required in this study were collected through mathematic representation tests, interviews and test evaluation rubric. Findings of this study showed that students’ mathematic representation of visual realities (image and tables) was reported higher at 62.3% than at in the form of description (or statement) at 8.6%. From gender perspective, male students performed better than the females at action planning stage. The percentage of males was reported at 68% (the highest), 33% (medium) and 21.3% (the lowest) while the females were at 36% (the highest), 37.7% (medium) and 32.6% (the lowest).

  18. Converting Biomolecular Modelling Data Based on an XML Representation

    Directory of Open Access Journals (Sweden)

    Sun Yudong

    2008-06-01

    Full Text Available Biomolecular modelling has provided computational simulation based methods for investigating biological processes from quantum chemical to cellular levels. Modelling such microscopic processes requires atomic description of a biological system and conducts in fine timesteps. Consequently the simulations are extremely computationally demanding. To tackle this limitation, different biomolecular models have to be integrated in order to achieve high-performance simulations. The integration of diverse biomolecular models needs to convert molecular data between different data representations of different models. This data conversion is often non-trivial, requires extensive human input and is inevitably error prone. In this paper we present an automated data conversion method for biomolecular simulations between molecular dynamics and quantum mechanics/molecular mechanics models. Our approach is developed around an XML data representation called BioSimML (Biomolecular Simulation Markup Language. BioSimML provides a domain specific data representation for biomolecular modelling which can effciently support data interoperability between different biomolecular simulation models and data formats.

  19. 3D ear identification based on sparse representation.

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    Full Text Available Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person's identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm.

  20. Body-Specific Representations of Action Verbs: Neural Evidence From Right- and Left-Handers

    NARCIS (Netherlands)

    Willems, R.M.; Hagoort, P.; Casasanto, D.

    2010-01-01

    According to theories of embodied cognition, understanding a verb like throw involves unconsciously simulating the action of throwing, using areas of the brain that support motor planning. If understanding action words involves mentally simulating one's own actions, then the neurocognitive

  1. Ubiquitous log odds: a common representation of probability and frequency distortion in perception, action and cognition

    Directory of Open Access Journals (Sweden)

    Hang eZhang

    2012-01-01

    Full Text Available In decision from experience, the source of probability information affects how probability is distorted in the decision task. Understanding how and why probability is distorted is a key issue in understanding the peculiar character of experience-based decision. We consider how probability information is used not just in decision making but also in a wide variety of cognitive, perceptual and motor tasks. Very similar patterns of distortion of probability/frequency information have been found in visual frequency estimation, frequency estimation based on memory, signal detection theory, and in the use of probability information in decision-making under risk and uncertainty. We show that distortion of probability in all cases is well captured as linear transformations of the log odds of frequency and/or probability, a model with a slope parameter and an intercept parameter. We then consider how task and experience influence these two parameters and the resulting distortion of probability. We review how the probability distortions change in systematic ways with task and report three experiments on frequency distortion where the distortions change systematically in the same task. We found that the slope of frequency distortions decreases with the sample size, which is echoed by findings in decision from experience. We review previous models of the representation of uncertainty and find that none can account for the empirical findings.

  2. Airborne LIDAR Points Classification Based on Tensor Sparse Representation

    Science.gov (United States)

    Li, N.; Pfeifer, N.; Liu, C.

    2017-09-01

    The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

  3. USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION

    Directory of Open Access Journals (Sweden)

    V. S. Gorbatsevich

    2016-06-01

    Full Text Available In this paper, we propose an original method for objects detection based on a special tree-structured image representation – the trees of morphlets. The method provides robust detection of various types of objects in an image without employing a machine learning procedure. Along with a bounding box creation on a detection step, the method makes pre-segmentation, which can be further used for recognition purposes. Another important feature of the proposed approach is that there are no needs to use a running window as well as a features pyramid in order to detect the objects of different sizes.

  4. An object-based methodology for knowledge representation

    Energy Technology Data Exchange (ETDEWEB)

    Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)

    1997-11-01

    An object based methodology for knowledge representation is presented. The constructs and notation to the methodology are described and illustrated with examples. The ``blocks world,`` a classic artificial intelligence problem, is used to illustrate some of the features of the methodology including perspectives and events. Representing knowledge with perspectives can enrich the detail of the knowledge and facilitate potential lines of reasoning. Events allow example uses of the knowledge to be represented along with the contained knowledge. Other features include the extensibility and maintainability of knowledge represented in the methodology.

  5. Sparse Representation Based Classification with Structure Preserving Dimension Reduction

    Science.gov (United States)

    2014-03-13

    established from the original data Y. A case study of this algorithm is shown in Fig. 1. The algorithm is run over the ‘‘ Libras Movement’’ data set...Residual on different categories (COR) (b) (c) Fig. 1 Sparse-representation- based classifier is applied to the ‘‘ Libras Movement’’ data set with 3...UCI data sets Name Feature number Total size Test size Class Wine 13 178 89 3 Glass 10 214 107 7 Libras Movement 90 360 180 15 Wine Quality 11 4,898

  6. Modeling the Maturation of Grip Selection Planning and Action Representation: Insights from Typical and Atypical Motor Development.

    Directory of Open Access Journals (Sweden)

    Ian eFuelscher

    2016-02-01

    Full Text Available We investigated the purported association between developmental changes in grip selection planning and improvements in an individual’s capacity to represent action at an internal level (i.e., motor imagery. Participants were groups of healthy children aged 6-7 years and 8-12 years respectively, while a group of adolescents (13-17 years and adults (18-34 years allowed for consideration of childhood development in the broader context of motor maturation. A group of children aged 8-12 years with probable DCD (pDCD was included as a reference group for atypical motor development. Participants’ proficiency to generate and/or engage internal action representations was inferred from performance on the hand rotation task, a well-validated measure of motor imagery. A grip selection task designed to elicit the end-state comfort (ESC effect provided a window into the integrity of grip selection planning. Consistent with earlier accounts, the efficiency of grip selection planning followed a non-linear developmental progression in neurotypical individuals. As expected, analysis confirmed that these developmental improvements were predicted by an increased capacity to generate and/or engage internal action representations. The profile of this association remained stable throughout the (typical developmental spectrum. These findings are consistent with computational accounts of action planning that argue that internal action representations are associated with the expression and development of grip selection planning across typical development. However, no such association was found for our sample of children with pDCD, suggesting that individuals with atypical motor skill may adopt an alternative, sub-optimal strategy to plan their grip selection compared to their same-age control peers.

  7. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang

    2016-01-01

    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  8. Teachers’ classroom-based action research

    OpenAIRE

    Cain, T.

    2011-01-01

    Teachers’ classroom-based action research is sometimes misunderstood by those who undertake it and support it, in three respects. First, it is wrongly assumed to fall into either positivist or interpretive paradigms (or perhaps a mixture of both) or to be critical. Second, there is little understanding as to why action research is necessarily self-reflexive, collaborative and political. Third, there is a view that classroom research studies are not suitable for dissemination because they are ...

  9. Effects of Computer-Based Visual Representation on Mathematics Learning and Cognitive Load

    Science.gov (United States)

    Yung, Hsin I.; Paas, Fred

    2015-01-01

    Visual representation has been recognized as a powerful learning tool in many learning domains. Based on the assumption that visual representations can support deeper understanding, we examined the effects of visual representations on learning performance and cognitive load in the domain of mathematics. An experimental condition with visual…

  10. Context and vision effects on real and imagined actions: support for the common representation hypothesis of motor imagery.

    Science.gov (United States)

    Glover, Scott; Dixon, Peter

    2013-10-01

    Motor imagery may use the same mental representations as overt actions, or it may be performed using a nonmotoric cognitive estimation strategy. These competing hypotheses were tested by having participants perform either simple overt pointing tasks or analogous motor imagery tasks while manipulating the visual context and the availability of visual information. In three pairs of experiments, visual illusions, word labels, and numeric labels were all found to have comparable effects on overt pointing and motor imagery. In each case, effects of the contextual variables on overt performance and imagery were larger when vision was removed and a delay imposed before movement initiation. These findings support the hypothesis that a common mental representation is used in both motor imagery and overt actions. In contrast, the results were inconsistent with the view that motor imagery is performed using a cognitive estimation strategy. Limitations in the ability of motor imagery to faithfully simulate overt actions are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  11. Children's Representation and Imitation of Events: How Goal Organization Influences 3-Year-Old Children's Memory for Action Sequences.

    Science.gov (United States)

    Loucks, Jeff; Mutschler, Christina; Meltzoff, Andrew N

    2017-09-01

    Children's imitation of adults plays a prominent role in human cognitive development. However, few studies have investigated how children represent the complex structure of observed actions which underlies their imitation. We integrate theories of action segmentation, memory, and imitation to investigate whether children's event representation is organized according to veridical serial order or a higher level goal structure. Children were randomly assigned to learn novel event sequences either through interactive hands-on experience (Study 1) or via storybook (Study 2). Results demonstrate that children's representation of observed actions is organized according to higher level goals, even at the cost of representing the veridical temporal ordering of the sequence. We argue that prioritizing goal structure enhances event memory, and that this mental organization is a key mechanism of social-cognitive development in real-world, dynamic environments. It supports cultural learning and imitation in ecologically valid settings when social agents are multitasking and not demonstrating one isolated goal at a time. Copyright © 2016 Cognitive Science Society, Inc.

  12. Representation of body identity and body actions in extrastriate body area and ventral premotor cortex.

    Science.gov (United States)

    Urgesi, Cosimo; Candidi, Matteo; Ionta, Silvio; Aglioti, Salvatore M

    2007-01-01

    Although inherently linked, body form and body action may be represented in separate neural substrates. Using repetitive transcranial magnetic stimulation in healthy individuals, we show that interference with the extrastriate body area impairs the discrimination of bodily forms, and interference with the ventral premotor cortex impairs the discrimination of bodily actions. This double dissociation suggests that whereas extrastriate body area mainly processes actors' body identity, premotor cortex is crucial for visual discriminations of actions.

  13. AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION

    Directory of Open Access Journals (Sweden)

    N. Li

    2017-09-01

    Full Text Available The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

  14. A Patch-based Sparse Representation for Sketch Recognition

    DEFF Research Database (Denmark)

    Qi, Yonggong; Zhang, Honggang; Song, Yi-Zhe

    2014-01-01

    , such as Symmetric-aware Flip Invariant Sketch Histogram (SYM-FISH). We present a novel patch-based sparse representation (PSR) for describing sketch image and it is evaluated under a sketch recognition framework. Extensive experiments on a large scale human drawn sketch dataset demonstrate the effectiveness......Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. One...... of the most fundamental problem is how to effectively describe a sketch image. Many existing descriptors, such as Histogram of Oriented Gradients (HOG) and Shape Context (SC), have achieved great success. Moreover, some works have attempted to design features specifically engineered for sketches...

  15. Group-based sparse representation for Fourier ptychography microscopy

    Science.gov (United States)

    Zhang, Yongbing; Cui, Ze; Zhang, Jian; Song, Pengming; Dai, Qionghai

    2017-12-01

    Fourier ptychography microscopy (FPM), which employs alternative projection between spatial and Fourier domains to stitch low-resolution images captured under angularly varying illumination, reconstructs one image with high-resolution and wide field of view (FOV) to bypass the limitation of the space-bandwidth product (SBP) of the traditional optical system. However, system noises such as pupil aberrations, LEDs misalignment and so on, are inevitably introduced in the process of capturing low-resolution images. To address this problem, we propose a new method to insert the Group-based sparse representation (GSR) into the convergence-related metric of FPM as the regularization term in this paper. We have carried out the experiments over both synthetic and real captured images, and the results demonstrate that the proposed method is able to have promising performance while inhibiting the noises efficiently.

  16. Atomic Action Refinement in Model Based Testing

    NARCIS (Netherlands)

    van der Bijl, H.M.; Rensink, Arend; Tretmans, G.J.

    2007-01-01

    In model based testing (MBT) test cases are derived from a specification of the system that we want to test. In general the specification is more abstract than the implementation. This may result in 1) test cases that are not executable, because their actions are too abstract (the implementation

  17. Pose Sentences: A new representation for action recognition using sequence of pose words

    NARCIS (Netherlands)

    Hatun, Kardelen; Duygulu, Pinar

    2008-01-01

    We propose a method for recognizing human actions in videos. Inspired from the recent bag-of-words approaches, we represent actions as documents consisting of words, where a word refers to the pose in a frame. Histogram of oriented gradients (HOG) features are used to describe poses, which are then

  18. A Systematic Investigation of the Effect of Action Observation Training and Motor Imagery Training on the Development of Mental Representation Structure and Skill Performance.

    Science.gov (United States)

    Kim, Taeho; Frank, Cornelia; Schack, Thomas

    2017-01-01

    Action observation training and motor imagery training have independently been studied and considered as an effective training strategy for improving motor skill learning. However, comparative studies of the two training strategies are relatively few. The purpose of this study was to investigate the effects of action observation training and motor imagery training on the development of mental representation structure and golf putting performance as well as the relation between the changes in mental representation structure and skill performance during the early learning stage. Forty novices were randomly assigned to one of four groups: action observation training, motor imagery training, physical practice and no practice. The mental representation structure and putting performance were measured before and after 3 days of training, then after a 2-day retention period. The results showed that mental representation structure and the accuracy of the putting performance were improved over time through the two types of cognitive training (i.e., action observation training and motor imagery training). In addition, we found a significant positive correlation between changes in mental representation structure and skill performance for the action observation training group only. Taken together, these results suggest that both cognitive adaptations and skill improvement occur through the training of the two simulation states of action, and that perceptual-cognitive changes are associated with the change of skill performance for action observation training.

  19. Grip force is part of the semantic representation of manual action verbs.

    Science.gov (United States)

    Frak, Victor; Nazir, Tatjana; Goyette, Michel; Cohen, Henri; Jeannerod, Marc

    2010-03-16

    Motor actions and action verbs activate similar cortical brain regions. A functional interference can be taken as evidence that there is a parallel treatment of these two types of information and would argue for the biological grounding of language in action. A novel approach examining the relationship between language and grip force is presented. With eyes closed and arm extended, subjects listened to words relating (verbs) or not relating (nouns) to a manual action while holding a cylinder with an integrated force sensor. There was a change in grip force when subjects heard verbs that related to manual action. Grip force increased from about 100 ms following the verb presentation, peaked at 380 ms and fell abruptly after 400 ms, signalling a possible inhibition of the motor simulation evoked by these words. These observations reveal the intimate relationship that exists between language and grasp and show that it is possible to elucidate online new aspects of sensorimotor interaction.

  20. Grip force is part of the semantic representation of manual action verbs.

    Directory of Open Access Journals (Sweden)

    Victor Frak

    Full Text Available Motor actions and action verbs activate similar cortical brain regions. A functional interference can be taken as evidence that there is a parallel treatment of these two types of information and would argue for the biological grounding of language in action. A novel approach examining the relationship between language and grip force is presented. With eyes closed and arm extended, subjects listened to words relating (verbs or not relating (nouns to a manual action while holding a cylinder with an integrated force sensor. There was a change in grip force when subjects heard verbs that related to manual action. Grip force increased from about 100 ms following the verb presentation, peaked at 380 ms and fell abruptly after 400 ms, signalling a possible inhibition of the motor simulation evoked by these words. These observations reveal the intimate relationship that exists between language and grasp and show that it is possible to elucidate online new aspects of sensorimotor interaction.

  1. Knowledge representation to support reasoning based on multiple models

    Science.gov (United States)

    Gillam, April; Seidel, Jorge P.; Parker, Alice C.

    1990-01-01

    Model Based Reasoning is a powerful tool used to design and analyze systems, which are often composed of numerous interactive, interrelated subsystems. Models of the subsystems are written independently and may be used together while they are still under development. Thus the models are not static. They evolve as information becomes obsolete, as improved artifact descriptions are developed, and as system capabilities change. Researchers are using three methods to support knowledge/data base growth, to track the model evolution, and to handle knowledge from diverse domains. First, the representation methodology is based on having pools, or types, of knowledge from which each model is constructed. In addition information is explicit. This includes the interactions between components, the description of the artifact structure, and the constraints and limitations of the models. The third principle we have followed is the separation of the data and knowledge from the inferencing and equation solving mechanisms. This methodology is used in two distinct knowledge-based systems: one for the design of space systems and another for the synthesis of VLSI circuits. It has facilitated the growth and evolution of our models, made accountability of results explicit, and provided credibility for the user community. These capabilities have been implemented and are being used in actual design projects.

  2. Children's Representation and Imitation of Events: How Goal Organization Influences 3-Year-Old Children's Memory for Action Sequences

    Science.gov (United States)

    Loucks, Jeff; Mutschler, Christina; Meltzoff, Andrew N.

    2017-01-01

    Children's imitation of adults plays a prominent role in human cognitive development. However, few studies have investigated how children represent the complex structure of observed actions which underlies their imitation. We integrate theories of action segmentation, memory, and imitation to investigate whether children's event representation is…

  3. Face antispoofing based on frame difference and multilevel representation

    Science.gov (United States)

    Benlamoudi, Azeddine; Aiadi, Kamal Eddine; Ouafi, Abdelkrim; Samai, Djamel; Oussalah, Mourad

    2017-07-01

    Due to advances in technology, today's biometric systems become vulnerable to spoof attacks made by fake faces. These attacks occur when an intruder attempts to fool an established face-based recognition system by presenting a fake face (e.g., print photo or replay attacks) in front of the camera instead of the intruder's genuine face. For this purpose, face antispoofing has become a hot topic in face analysis literature, where several applications with antispoofing task have emerged recently. We propose a solution for distinguishing between real faces and fake ones. Our approach is based on extracting features from the difference between successive frames instead of individual frames. We also used a multilevel representation that divides the frame difference into multiple multiblocks. Different texture descriptors (local binary patterns, local phase quantization, and binarized statistical image features) have then been applied to each block. After the feature extraction step, a Fisher score is applied to sort the features in ascending order according to the associated weights. Finally, a support vector machine is used to differentiate between real and fake faces. We tested our approach on three publicly available databases: CASIA Face Antispoofing database, Replay-Attack database, and MSU Mobile Face Spoofing database. The proposed approach outperforms the other state-of-the-art methods in different media and quality metrics.

  4. Pedestrian detection from thermal images: A sparse representation based approach

    Science.gov (United States)

    Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

    2016-05-01

    Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

  5. Feature representation and compression for content-based retrieval

    Science.gov (United States)

    Xie, Hua; Ortega, Antonio

    2000-12-01

    In semantic content-based image/video browsing and navigation systems, efficient mechanisms to represent and manage a large collection of digital images/videos are needed. Traditional keyword-based indexing describes the content of multimedia data through annotations such as text or keywords extracted manually by the user from a controlled vocabulary. This textual indexing technique lacks the flexibility of satisfying various kinds of queries requested by database users and also requires huge amount of work for updating the information. Current content-based retrieval systems often extract a set of features such as color, texture, shape motion, speed, and position from the raw multimedia data automatically and store them as content descriptors. This content-based metadata differs from text-based metadata in that it supports wider varieties of queries and can be extracted automatically, thus providing a promising approach for efficient database access and management. When the raw data volume grows very large, explicitly extracting the content-information and storing it as metadata along with the images will improve querying performance since metadata requires much less storage than the raw image data and thus will be easier to manipulate. In this paper we maintain that storing metadata together with images will enable effective information management and efficient remote query. We also show, using a texture classification example, that this side information can be compressed while guaranteeing that the desired query accuracy is satisfied. We argue that the compact representation of the image contents not only reduces significantly the storage and transmission rate requirement, but also facilitates certain types of queries. Algorithms are developed for optimized compression of this texture feature metadata given that the goal is to maximize the classification performance for a given rate budget.

  6. The colourless canvas: representation, therapeutic action and the creation of mind.

    Science.gov (United States)

    Levine, Howard B

    2012-06-01

    Freud's initial formulations viewed psychoanalysis as working towards the rediscovery of psychic elements - thoughts, feelings, memories, wishes, etc. - that were once known - represented in the mind, articulatable, thinkable - but then disguised and/or barred from consciousness. His subsequent revisions implicated a second, more extensive category of inchoate forces that either lost or never attained psychic representation and, although motivationally active, were not fixed in meaning, symbolically embodied, attached to associational chains, etc. Following Freud's theory of representation, the author conceptualizes these latter forces as "unrepresented" or "weakly represented" mental states that make a demand upon the mind for work and require transformation into something that is represented in the psyche, if they are to be thought about or used to think with. This paper describes, discusses and presents illustrations of this transformational process (figurability),that moves intersubjectively from unrepresented or weakly represented mental states to represented mental states, from force to meaning, from the inchoate to mental order. Copyright © 2012 Institute of Psychoanalysis.

  7. Familiarity and Voice Representation: From Acoustic-Based Representation to Voice Averages

    Directory of Open Access Journals (Sweden)

    Maureen Fontaine

    2017-07-01

    Full Text Available The ability to recognize an individual from their voice is a widespread ability with a long evolutionary history. Yet, the perceptual representation of familiar voices is ill-defined. In two experiments, we explored the neuropsychological processes involved in the perception of voice identity. We specifically explored the hypothesis that familiar voices (trained-to-familiar (Experiment 1, and famous voices (Experiment 2 are represented as a whole complex pattern, well approximated by the average of multiple utterances produced by a single speaker. In experiment 1, participants learned three voices over several sessions, and performed a three-alternative forced-choice identification task on original voice samples and several “speaker averages,” created by morphing across varying numbers of different vowels (e.g., [a] and [i] produced by the same speaker. In experiment 2, the same participants performed the same task on voice samples produced by familiar speakers. The two experiments showed that for famous voices, but not for trained-to-familiar voices, identification performance increased and response times decreased as a function of the number of utterances in the averages. This study sheds light on the perceptual representation of familiar voices, and demonstrates the power of average in recognizing familiar voices. The speaker average captures the unique characteristics of a speaker, and thus retains the information essential for recognition; it acts as a prototype of the speaker.

  8. Familiarity and Voice Representation: From Acoustic-Based Representation to Voice Averages

    Science.gov (United States)

    Fontaine, Maureen; Love, Scott A.; Latinus, Marianne

    2017-01-01

    The ability to recognize an individual from their voice is a widespread ability with a long evolutionary history. Yet, the perceptual representation of familiar voices is ill-defined. In two experiments, we explored the neuropsychological processes involved in the perception of voice identity. We specifically explored the hypothesis that familiar voices (trained-to-familiar (Experiment 1), and famous voices (Experiment 2)) are represented as a whole complex pattern, well approximated by the average of multiple utterances produced by a single speaker. In experiment 1, participants learned three voices over several sessions, and performed a three-alternative forced-choice identification task on original voice samples and several “speaker averages,” created by morphing across varying numbers of different vowels (e.g., [a] and [i]) produced by the same speaker. In experiment 2, the same participants performed the same task on voice samples produced by familiar speakers. The two experiments showed that for famous voices, but not for trained-to-familiar voices, identification performance increased and response times decreased as a function of the number of utterances in the averages. This study sheds light on the perceptual representation of familiar voices, and demonstrates the power of average in recognizing familiar voices. The speaker average captures the unique characteristics of a speaker, and thus retains the information essential for recognition; it acts as a prototype of the speaker. PMID:28769836

  9. Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

    Science.gov (United States)

    Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

  10. A communication-channel-based representation system for software

    NARCIS (Netherlands)

    Demirezen, Zekai; Tanik, Murat M.; Aksit, Mehmet; Skjellum, Anthony

    We observed that before initiating software development the objectives are minimally organized and developers introduce comparatively higher organization throughout the design process. To be able to formally capture this observation, a new communication channel representation system for software is

  11. Supramodal and modality-sensitive representations of perceived action categories in the human brain

    NARCIS (Netherlands)

    Ramsey, R.; Cross, E.S.; Hamilton, A.F.D.C.

    2013-01-01

    Seeing Suzie bite an apple or reading the sentence ‘Suzie munched the apple’ both convey a similar idea. But is there a common neural basis for action comprehension when generated through video or text? The current study used functional magnetic resonance imaging to address this question.

  12. How writing system and age influence spatial representations of actions: a developmental, cross-linguistic study.

    Science.gov (United States)

    Dobel, Christian; Diesendruck, Gil; Bölte, Jens

    2007-06-01

    Recently, researchers reported a bias for placing agents predominantly on the left side of pictures. Both hemispheric specialization and cultural preferences have been hypothesized to be the origin of this bias. To evaluate these hypotheses, we conducted a study with participants exposed to different reading and writing systems: Germans, who use a left-to-right system, and Israelis, who use a right-to-left system. In addition, we manipulated the degree of exposure to the writing systems by testing preschoolers and adults. Participants heard agent-first or recipient-first sentences and were asked to draw the content of the sentences or to arrange transparencies of protagonists and objects such that their arrangement depicted the sentences. Although preschool-age children in both countries showed no directional bias, adults manifested a bias that was consistent with the writing system of their language. These results support the cultural hypothesis regarding the origin of spatial-representational biases.

  13. Frontal and Parietal Cortical Interactions with Distributed Visual Representations during Selective Attention and Action Selection

    Science.gov (United States)

    Stokes, Mark; Nobre, Anna C.; Rushworth, Matthew F. S.

    2013-01-01

    Using multivoxel pattern analysis (MVPA), we studied how distributed visual representations in human occipitotemporal cortex are modulated by attention and link their modulation to concurrent activity in frontal and parietal cortex. We detected similar occipitotemporal patterns during a simple visuoperceptual task and an attention-to-working-memory task in which one or two stimuli were cued before being presented among other pictures. Pattern strength varied from highest to lowest when the stimulus was the exclusive focus of attention, a conjoint focus, and when it was potentially distracting. Although qualitatively similar effects were seen inside regions relatively specialized for the stimulus category and outside, the former were quantitatively stronger. By regressing occipitotemporal pattern strength against activity elsewhere in the brain, we identified frontal and parietal areas exerting top-down control over, or reading information out from, distributed patterns in occipitotemporal cortex. Their interactions with patterns inside regions relatively specialized for that stimulus category were higher than those with patterns outside those regions and varied in strength as a function of the attentional condition. One area, the frontal operculum, was distinguished by selectively interacting with occipitotemporal patterns only when they were the focus of attention. There was no evidence that any frontal or parietal area actively inhibited occipitotemporal representations even when they should be ignored and were suppressed. Using MVPA to decode information within these frontal and parietal areas showed that they contained information about attentional context and/or readout information from occipitotemporal cortex to guide behavior but that frontal regions lacked information about category identity. PMID:24133250

  14. Analytic representations based on SU(1,1) coherent states and their applications

    OpenAIRE

    Brif, C.; Vourdas, A.; Mann, A.

    1996-01-01

    We consider two analytic representations of the SU(1,1) Lie group: the representation in the unit disk based on the SU(1,1) Perelomov coherent states and the Barut-Girardello representation based on the eigenstates of the SU(1,1) lowering generator. We show that these representations are related through a Laplace transform. A ``weak'' resolution of the identity in terms of the Perelomov SU(1,1) coherent states is presented which is valid even when the Bargmann index $k$ is smaller than one ha...

  15. A simulation of T-wave alternans vectocardiographic representation performed by changing the ventricular heart cells action potential duration.

    Science.gov (United States)

    Janusek, D; Kania, M; Zaczek, R; Zavala-Fernandez, H; Maniewski, R

    2014-04-01

    The presence of T wave alternans (TWA) in the surface ECG signals has been recognized as a marker of electrical instability, and is hypothesized to be related to patients at increased risk for ventricular arrhythmias. In this paper we present a TWA simulation study. The TWA phenomenon was simulated by changing the duration of the ventricular heart cells action potential. The magnitude was calculated in the surface ECG with the use of the time domain method. The spatially concordant TWA, where during one heart beat all ventricular cells display a short-duration action potential and during the next beat they exhibit a long-duration action potential, as well as the discordant TWA, where at least one region is out of phase, was simulated. The vectocardiographic representation was employed. The obtained results showed a high level of T-loop pattern and location disturbances connected to the discordant TWA simulation in contrast to the concordant one. This result may be explained by the spatial heterogeneity of the ventricular repolarization process, which could be higher for the discordant TWA than for the concordant TWA. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  16. Hands on the future: facilitation of cortico-spinal hand-representation when reading the future tense of hand-related action verbs.

    Science.gov (United States)

    Candidi, Matteo; Leone-Fernandez, Barbara; Barber, Horacio A; Carreiras, Manuel; Aglioti, Salvatore Maria

    2010-08-01

    Reading action-related verbs brings about sensorimotor neural activity, suggesting that the linguistic representation of actions impinges upon neural structures largely overlapping with those involved in actual action execution. While studies of direct action observation indicate that motor mirroring is inherently anticipatory, no information is currently available on whether deriving action-related knowledge from language also takes into account the temporal deployment of actions. Using transcranial magnetic stimulation, here we sought to determine whether reading action verbs conjugated in the future induced higher cortico-spinal activity with respect to when the same verbs were conjugated in the past tense. We recorded motor-evoked potentials (MEPs) from relaxed hand and leg muscles of healthy subjects who were reading silently hand- or leg-related action, sensorial (non-somatic) and abstract verbs conjugated either in future or past tense. The amplitude of MEPs recorded from the hand was higher during reading hand-related action verbs conjugated in the future than in the past. No future-related modulation of leg muscles activity was found during reading leg-related action verbs. In a similar vein, no future-related change of hand or leg muscles reactivity was found for abstract or sensorial verbs. These results indicate that the anticipatory mirroring of hand actions may be triggered by linguistic representations and not only by direct action observation.

  17. Arithmetic word problem solving: evidence for a magnitude-based mental representation.

    Science.gov (United States)

    Orrantia, Josetxu; Múñez, David

    2013-01-01

    Previous findings have suggested that number processing involves a mental representation of numerical magnitude. Other research has shown that sensory experiences are part and parcel of the mental representation (or "simulation") that individuals construct during reading. We aimed at exploring whether arithmetic word-problem solving entails the construction of a mental simulation based on a representation of numerical magnitude. Participants were required to solve word problems and to perform an intermediate figure discrimination task that matched or mismatched, in terms of magnitude comparison, the mental representations that individuals constructed during problem solving. Our results showed that participants were faster in the discrimination task and performed better in the solving task when the figures matched the mental representations. These findings provide evidence that an analog magnitude-based mental representation is routinely activated during word-problem solving, and they add to a growing body of literature that emphasizes the experiential view of language comprehension.

  18. Mathematical Representation Ability by Using Project Based Learning on the Topic of Statistics

    Science.gov (United States)

    Widakdo, W. A.

    2017-09-01

    Seeing the importance of the role of mathematics in everyday life, mastery of the subject areas of mathematics is a must. Representation ability is one of the fundamental ability that used in mathematics to make connection between abstract idea with logical thinking to understanding mathematics. Researcher see the lack of mathematical representation and try to find alternative solution to dolve it by using project based learning. This research use literature study from some books and articles in journals to see the importance of mathematical representation abiliy in mathemtics learning and how project based learning able to increase this mathematical representation ability on the topic of Statistics. The indicators for mathematical representation ability in this research classifies namely visual representation (picture, diagram, graph, or table); symbolize representation (mathematical statement. Mathematical notation, numerical/algebra symbol) and verbal representation (written text). This article explain about why project based learning able to influence student’s mathematical representation by using some theories in cognitive psychology, also showing the example of project based learning that able to use in teaching statistics, one of mathematics topic that very useful to analyze data.

  19. Collective Memories of Portuguese Colonial Action in Africa: Representations of the Colonial Past among Mozambicans and Portuguese Youths

    Directory of Open Access Journals (Sweden)

    João Feijó

    2010-05-01

    Full Text Available Social representations of the colonization and decolonization processes among young people from a former European colonial power (Portugal and from an African ex-colony (Mozambique were investigated through surveys using open- and closed-ended questions about national history, focusing on the identity functions of collective memories. Hegemonic and contested representations were found of the most prominent events related to Portuguese colonization of Mozambique, arousing a range of collective emotions. A central place is occupied by memories of the Colonial War, which ended with the Carnation Revolution in Portugal and the subsequent independence of the Portuguese African colonies. Overall, the depiction of colonialism was more negative for Mozambican than for Portuguese participants. The violent effects of colonial action were very salient in Mozambican memories, which stressed the most oppressive aspects of the colonial period, associated with slave trade and brutal repression. On the Portuguese side, the idealization of the voyages of discovery persisted, obscuring the most violent effects of colonial expansion. However, collective memories of colonization of former colonizer and former colonized do not simply stand opposed. Both Mozambican and Portuguese participants reported ambivalent feelings towards the colonization process.

  20. Convolution-based classification of audio and symbolic representations of music

    DEFF Research Database (Denmark)

    Velarde, Gissel; Cancino Chacón, Carlos; Meredith, David

    2018-01-01

    We present a novel convolution-based method for classification of audio and symbolic representations of music, which we apply to classification of music by style. Pieces of music are first sampled to pitch–time representations (piano-rolls or spectrograms) and then convolved with a Gaussian filter......-class composer identification, methods specialised for classifying symbolic representations of music are more effective. We also performed experiments on symbolic representations, synthetic audio and two different recordings of The Well-Tempered Clavier by J. S. Bach to study the method’s capacity to distinguish...

  1. Illusory body-ownership entails automatic compensative movement: for the unified representation between body and action.

    Science.gov (United States)

    Asai, Tomohisa

    2015-03-01

    The sense of body-ownership involves the integration of vision and somatosensation. In the rubber hand illusion (RHI), watching a rubber hand being stroked for a short time synchronously as one's own unseen hand is also stroked causes the observers to attribute the rubber hand to their own body. The RHI may elicit proprioceptive drift: The observers' sense of their own hand's location drifts toward the external proxy hand. The current experiments examined the possibility of observing, not the proprioceptive drift, but the actual drift "movement" during RHI induction. The participants' hand, located on horizontally movable board, tended to move toward the rubber hand only while they observed synchronous visuo-tactile stimulation. Furthermore, even when the participants' hand was located on a fixed, unmovable board (that is, the conventional RHI paradigm), participants automatically administered the force toward the rubber hand. These findings suggest that since awareness of our own body and action are fundamental to self-consciousness, these components of "minimal self" are closely related and integrated into "one agent" with a unified awareness of the body and action.

  2. A Description Logic Based Knowledge Representation Model for Concept Understanding

    DEFF Research Database (Denmark)

    Badie, Farshad

    2018-01-01

    This research employs Description Logics in order to focus on logical description and analysis of the phenomenon of ‘concept understanding’. The article will deal with a formal-semantic model for figuring out the underlying logical assumptions of ‘concept understanding’ in knowledge representation...

  3. Time-Frequency Distribution of Music based on Sparse Wavelet Packet Representations

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft

    We introduce a new method for generating time-frequency distributions, which is particularly useful for the analysis of music signals. The method presented here is based on $\\ell1$ sparse representations of music signals in a redundant wavelet packet dictionary. The representations are found using...

  4. A Walk-based Semantically Enriched Tree Kernel Over Distributed Word Representations

    DEFF Research Database (Denmark)

    Srivastava, Shashank; Hovy, Dirk

    2013-01-01

    We propose a walk-based graph kernel that generalizes the notion of tree-kernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed representations. Using vector representations, such ...... diverse NLP tasks, showing state-of-the-art results....

  5. Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

    Directory of Open Access Journals (Sweden)

    Ting Shu

    2017-01-01

    Full Text Available At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

  6. A Constrained Algorithm Based NMFα for Image Representation

    Directory of Open Access Journals (Sweden)

    Chenxue Yang

    2014-01-01

    Full Text Available Nonnegative matrix factorization (NMF is a useful tool in learning a basic representation of image data. However, its performance and applicability in real scenarios are limited because of the lack of image information. In this paper, we propose a constrained matrix decomposition algorithm for image representation which contains parameters associated with the characteristics of image data sets. Particularly, we impose label information as additional hard constraints to the α-divergence-NMF unsupervised learning algorithm. The resulted algorithm is derived by using Karush-Kuhn-Tucker (KKT conditions as well as the projected gradient and its monotonic local convergence is proved by using auxiliary functions. In addition, we provide a method to select the parameters to our semisupervised matrix decomposition algorithm in the experiment. Compared with the state-of-the-art approaches, our method with the parameters has the best classification accuracy on three image data sets.

  7. An evolutionary approach to realism-based adverse event representations.

    Science.gov (United States)

    Ceusters, W; Capolupo, M; de Moor, G; Devlies, J; Smith, B

    2011-01-01

    Part of the ReMINE project involved the creation of an ontology enabling computer-assisted decision support for optimal adverse event management. The ontology was required to satisfy the following requirements: 1) to be able to account for the distinct and context-dependent ways in which authoritative sources define the term 'adverse event', 2) to allow the identification of relevant risks against patient safety (RAPS) on the basis of the disease history of a patient as documented in electronic health records, and 3) to be compatible with present and future ontologies developed under the Open Biomedical Ontology (OBO) Foundry framework. We used as feeder ontologies the Basic Formal Ontology, the Foundational Model of Anatomy, the Ontology for General Medical Science, the Information Artifact Ontology and the Ontology of Mental Health. We further used relations defined according to the pattern set forth in the OBO Relation Ontology. In light of the intended use of the ontology for the representation of adverse events that have actually occurred and therefore are registered in a database, we also applied the principles of referent tracking. We merged the upper portions of the mentioned feeder ontologies and introduced 22 additional representational units of which 13 are generally applicable in biomedicine and nine in the adverse event context. We provided for each representational unit a textual definition that can be translated into equivalent formal definitions. The resulting ontology satisfies all of the requirements set forth. Merging the feeder ontologies, although all designed under the OBO Foundry principles, brought new insight into what the representational units of such ontologies actually denote.

  8. Sparse representation-based color visualization method for hyperspectral imaging

    Science.gov (United States)

    Wang, Li-Guo; Liu, Dan-Feng; Zhao, Liang

    2013-06-01

    In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.

  9. Individual differences in action co-representation: not personal distress or subclinical psychotic experiences but sex composition modulates joint action performance.

    Science.gov (United States)

    van der Weiden, Anouk; Aarts, Henk; Prikken, Merel; van Haren, Neeltje E M

    2016-02-01

    Successful social interaction requires the ability to integrate as well as distinguish own and others' actions. Normally, the integration and distinction of self and other are a well-balanced process, occurring without much effort or conscious attention. However, not everyone is blessed with the ability to balance self-other distinction and integration, resulting in personal distress in reaction to other people's emotions or even a loss of self [e.g., in (subclinical) psychosis]. Previous research has demonstrated that the integration and distinction of others' actions cause interference with one's own action performance (commonly assessed with a social Simon task). The present study had two goals. First, as previous studies on the social Simon effect employed relatively small samples (N action interference effect. Second, we tested to what extent action interference reflects individual differences in traits related to self-other distinction (i.e., personal distress in reaction to other people's emotions and subclinical psychotic symptoms). Based on a questionnaire study among a large sample (N = 745), we selected a subsample (N = 130) of participants scoring low, average, or high on subclinical psychotic symptoms, or on personal distress. The selected participants performed a social Simon task. Results showed a robust social Simon effect, regardless of individual differences in personal distress or subclinical psychotic symptoms. However, exploratory analyses revealed that the sex composition of interaction pairs modulated social Simon effects. Possible explanations for these findings are discussed.

  10. Action-based mechanisms of attention.

    OpenAIRE

    Tipper, S P; Howard, L A; Houghton, G

    1998-01-01

    Actions, which have effects in the external world, must be spatiotopically represented in the brain. The brain is capable of representing space in many different forms (e.g. retinotopic-, environment-, head- or shoulder-centred), but we maintain that actions are represented in action-centred space, meaning that, at the cellular level, the direction of movement is defined by the activity of cells. In reaching, for example, object location is defined as the direction and distance between the or...

  11. Action Based Object Separation with Situated Agents

    OpenAIRE

    Flores, Christoph

    2007-01-01

    Visual scene analysis can be augmented by proper manipulative actions like changing perspective or interacting with elements of the environment. They reveal some of the non-obvious (e.g. non-visual or visually ambiguous) intrinsic qualities of the scene's setting. A framing for actions is formalizable using situation semantics notification. This supports a non-classic view of perception: the perception of the situation is the action taken to inspect it, or is at least inseparably connected to...

  12. Project transition to risk based corrective action

    International Nuclear Information System (INIS)

    Judge, J.M.; Cormier, S.L.

    1996-01-01

    Many states have adopted or are considering the adoption of the America Society for Testing and Materials (ASTM) Standard E 1739, Standard for Risk Based Corrective Action (RBCA) Applied to Petroleum Release Sites. This standard is being adopted to regulate leaking underground storage tank (LUST) sites. The case studies of two LUST sites in Michigan will be presented to demonstrate the decision making process and limiting factors involved in transitioning sites to the RBCA program. Both of these case studies had been previously investigated and one was actively remediated. The first case study involves a private petroleum facility where soil and ground water have been impacted. Remediation involved a ground water pump and treat system. Subsequent monitoring during system operation indicated that analytical data were still above the Tier 1 RBSLs but below the Tier 2 SSTLs. The closure strategy that was developed was based on the compounds of concern that were below the SSTLs. A deed restriction was also developed for the site as an institutional control. The second LUST site exhibited BTEX concentrations in soil and ground water above the Tier 1 RBSLs. Due to the exceedence of the Tier 1 RBSLs, the second site required a Tier 2 assessment to develop SSTLs as remedial objectives and remove hot spots in the soil and treat the ground water to achieve closure. Again, a deed restriction was instituted along with a performance monitoring plan

  13. Robust Face Recognition using Voting by Bit-plane Images based on Sparse Representation

    Directory of Open Access Journals (Sweden)

    Dongmei Wei

    2015-08-01

    Full Text Available Plurality voting is widely employed as combination strategies in pattern recognition. As a technology proposed recently, sparse representation based classification codes the query image as a sparse linear combination of entire training images and classifies the query sample class by class exploiting the class representation error. In this paper, an improvement face recognition approach using sparse representation and plurality voting based on the binary bit-plane images is proposed. After being equalized, gray images are decomposed into eight bit-plane images, sparse representation based classification is exploited respectively on the five bit-plane images that have more discrimination information. Finally, the true identity of query image is voted by these five identities obtained. Experiment results shown that this proposed approach is preferable both in recognition accuracy and in recognition speed.

  14. Infants Generate Goal-Based Action Predictions

    Science.gov (United States)

    Cannon, Erin N.; Woodward, Amanda L.

    2012-01-01

    Predicting the actions of others is critical to smooth social interactions. Prior work suggests that both understanding and anticipation of goal-directed actions appears early in development. In this study, on-line goal prediction was tested explicitly using an adaptation of Woodward's (1998) paradigm for an eye-tracking task. Twenty 11-month-olds…

  15. Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Zhi He

    2016-08-01

    Full Text Available Recently, sparse representation has yielded successful results in hyperspectral image (HSI classification. In the sparse representation-based classifiers (SRCs, a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

  16. Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification.

    Science.gov (United States)

    Min, Rui; Cheng, Jian; Price, True; Wu, Guorong; Shen, Dinggang

    2014-01-01

    In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.

  17. Visual Vehicle Tracking Based on Deep Representation and Semisupervised Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2017-01-01

    Full Text Available Discriminative tracking methods use binary classification to discriminate between the foreground and background and have achieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking. Hence, discriminative classifiers must use their own classification results to update themselves, which may lead to feedback-induced tracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and transfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear mapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer train and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the background images. Finally, a particle filter is used to estimate vehicle position. We demonstrate experimentally that our proposed vehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with similar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.

  18. Learning connective-based word representations for implicit discourse relation identification

    DEFF Research Database (Denmark)

    Braud, Chloé Elodie; Denis, Pascal

    2016-01-01

    We introduce a simple semi-supervised ap-proach to improve implicit discourse relation identification. This approach harnesses large amounts of automatically extracted discourse connectives along with their arguments to con-struct new distributional word representations. Specifically, we represen...... their simplicity, these connective-based rep-resentations outperform various off-the-shelf word embeddings, and achieve state-of-the-art performance on this problem.......We introduce a simple semi-supervised ap-proach to improve implicit discourse relation identification. This approach harnesses large amounts of automatically extracted discourse connectives along with their arguments to con-struct new distributional word representations. Specifically, we represent...... words in the space of discourse connectives as a way to directly encode their rhetorical function. Experiments on the Penn Discourse Treebank demonstrate the effectiveness of these task-tailored repre-sentations in predicting implicit discourse re-lations. Our results indeed show that, despite...

  19. Developing Action Plans Based on Strategy

    DEFF Research Database (Denmark)

    Vinter, Otto; Carstensen, Peter H.

    2018-01-01

    The authors have performed a thorough study of the change strategy literature that is the foundation for the 10 overall change strategies defined in ISO/IEC 33014. Then the authors identified eight aspects that should be considered when developing the concrete actions for executing the strategy....

  20. Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

    Directory of Open Access Journals (Sweden)

    Fan Yang

    2015-07-01

    Full Text Available Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC. Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR sensor of the Technical University of Denmark (DTU. Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

  1. SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

    Directory of Open Access Journals (Sweden)

    Zhang Xinzheng

    2017-10-01

    Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

  2. Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Qi Jia

    2015-03-01

    Full Text Available In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

  3. Action Based Teaching in Nigeria: Issues and Reflections

    African Journals Online (AJOL)

    Toshiba

    terms of intrinsic motivation and autonomy, motivation and autonomy being two sides of the same coin. Action based teaching stimulates motivation and autonomy in learning. This paper regards action based ... structures that enhance rather than impede the processes that lead to meaningful and lasting learning.

  4. Semantic Representation of Evidence-based Clinical Guidelines

    NARCIS (Netherlands)

    Huang, Zhisheng; Harmelen, Frank Van

    2014-01-01

    Evidence-based Clinical Guidelines (EbCGs) are that the document or recommendation has been created using the best clinical research findings of the highest value to aid in the delivery of optimum clinical care to patients. In this paper, we propose a lightweight formal-ism of evidence-based

  5. Silhouette analysis for human action recognition based on supervised temporal t-SNE and incremental learning.

    Science.gov (United States)

    Cheng, Jian; Liu, Haijun; Wang, Feng; Li, Hongsheng; Zhu, Ce

    2015-10-01

    This paper develops a human action recognition method for human silhouette sequences based on supervised temporal t-stochastic neighbor embedding (ST-tSNE) and incremental learning. Inspired by the SNE and its variants, ST-tSNE is proposed to learn the underlying relationship between action frames in a manifold, where the class label information and temporal information are introduced to well represent those frames from the same action class. As to the incremental learning, an important step for action recognition, we introduce three methods to perform the low-dimensional embedding of new data. Two of them are motivated by local methods, locally linear embedding and locality preserving projection. Those two techniques are proposed to learn explicit linear representations following the local neighbor relationship, and their effectiveness is investigated for preserving the intrinsic action structure. The rest one is based on manifold-oriented stochastic neighbor projection to find a linear projection from high-dimensional to low-dimensional space capturing the underlying pattern manifold. Extensive experimental results and comparisons with the state-of-the-art methods demonstrate the effectiveness and robustness of the proposed ST-tSNE and incremental learning methods in the human action silhouette analysis.

  6. Reaching the goal: Active experience facilitates 8-month-old infants' prospective analysis of goal-based actions.

    Science.gov (United States)

    Krogh-Jespersen, Sheila; Woodward, Amanda L

    2018-02-27

    From early in development, infants view others' actions as structured by intentions, and this action knowledge may be supported by shared action production/perception systems. Because the motor system is inherently prospective, infants' understanding of goal-directed actions should support predictions of others' future actions, yet little is known about the nature and developmental origins of this ability, specifically whether young infants use the goal-directed nature of an action to rapidly predict future social behaviors and whether their action experience influences this ability. Across three conditions, we varied the level of action experience infants engaged in to determine whether motor priming influenced infants' ability to generate rapid social predictions. Results revealed that young infants accurately generated goal-based visual predictions when they had previously been reaching for objects; however, infants who passively observed a demonstration were less successful. Further analyses showed that engaging the cognitively based prediction system to generate goal-based predictions following motor engagement resulted in slower latencies to predict, suggesting that these smart predictions take more time to deploy. Thus, 8-month-old infants may have motor representations of goal-directed actions, yet this is not sufficient for them to predict others' actions; rather, their own action experience supports the ability to rapidly implement knowledge to predict future behavior. Copyright © 2018 Elsevier Inc. All rights reserved.

  7. Skeleton-based human action recognition using multiple sequence alignment

    Science.gov (United States)

    Ding, Wenwen; Liu, Kai; Cheng, Fei; Zhang, Jin; Li, YunSong

    2015-05-01

    Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

  8. Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations.

    Science.gov (United States)

    Peng, Xi; Lu, Canyi; Yi, Zhang; Tang, Huajin

    2018-01-01

    A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to sparse representation and nuclear-norm-based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism. In this brief, we fill this gap by building the theoretical connections between FNR and NNR. More specially, we prove that: 1) when the dictionary can provide enough representative capacity, FNR is exactly NNR even though the data set contains the Gaussian noise, Laplacian noise, or sample-specified corruption and 2) otherwise, FNR and NNR are two solutions on the column space of the dictionary.

  9. Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

    Science.gov (United States)

    Bing, Lu; Wang, Wei

    2017-01-01

    We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

  10. A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model

    Directory of Open Access Journals (Sweden)

    Tao Chen

    2015-08-01

    Full Text Available In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

  11. Improving students’ mathematical representational ability through RME-based progressive mathematization

    Science.gov (United States)

    Warsito; Darhim; Herman, T.

    2018-01-01

    This study aims to determine the differences in the improving of mathematical representation ability based on progressive mathematization with realistic mathematics education (PMR-MP) with conventional learning approach (PB). The method of research is quasi-experiments with non-equivalent control group designs. The study population is all students of class VIII SMPN 2 Tangerang consisting of 6 classes, while the sample was taken two classes with purposive sampling technique. The experimental class is treated with PMR-MP while the control class is treated with PB. The instruments used are test of mathematical representation ability. Data analysis was done by t-test, ANOVA test, post hoc test, and descriptive analysis. The result of analysis can be concluded that: 1) there are differences of mathematical representation ability improvement between students treated by PMR-MP and PB, 2) no interaction between learning approach (PMR-MP, PB) and prior mathematics knowledge (PAM) to improve students’ mathematical representation; 3) Students’ mathematical representation improvement in the level of higher PAM is better than medium, and low PAM students. Thus, based on the process of mathematization, it is very important when the learning direction of PMR-MP emphasizes on the process of building mathematics through a mathematical model.

  12. Image decomposition fusion method based on sparse representation and neural network.

    Science.gov (United States)

    Chang, Lihong; Feng, Xiangchu; Zhang, Rui; Huang, Hua; Wang, Weiwei; Xu, Chen

    2017-10-01

    For noisy images, in most existing sparse representation-based models, fusion and denoising proceed simultaneously using the coefficients of a universal dictionary. This paper proposes an image fusion method based on a cartoon + texture dictionary pair combined with a deep neural network combination (DNNC). In our model, denoising and fusion are carried out alternately. The proposed method is divided into three main steps: denoising + fusion + network denoising. More specifically, (1) denoise the source images using external/internal methods separately; (2) fuse these preliminary denoised results with external/internal cartoon and texture dictionary pair to obtain the external cartoon + texture sparse representation result (E-CTSR) and internal cartoon + texture sparse representation result (I-CTSR); and (3) combine E-CTSR and I-CTSR using DNNC (EI-CTSR) to obtain the final result. Experimental results demonstrate that EI-CTSR outperforms not only the stand-alone E-CTSR and I-CTSR methods but also state-of-the-art methods such as sparse representation (SR) and adaptive sparse representation (ASR) for isomorphic images, and E-CTSR outperforms SR and ASR for heterogeneous multi-mode images.

  13. Generalized Hough transform based time invariant action recognition with 3D pose information

    Science.gov (United States)

    Muench, David; Huebner, Wolfgang; Arens, Michael

    2014-10-01

    Human action recognition has emerged as an important field in the computer vision community due to its large number of applications such as automatic video surveillance, content based video-search and human robot interaction. In order to cope with the challenges that this large variety of applications present, recent research has focused more on developing classifiers able to detect several actions in more natural and unconstrained video sequences. The invariance discrimination tradeoff in action recognition has been addressed by utilizing a Generalized Hough Transform. As a basis for action representation we transform 3D poses into a robust feature space, referred to as pose descriptors. For each action class a one-dimensional temporal voting space is constructed. Votes are generated from associating pose descriptors with their position in time relative to the end of an action sequence. Training data consists of manually segmented action sequences. In the detection phase valid human 3D poses are assumed as input, e.g. originating from 3D sensors or monocular pose reconstruction methods. The human 3D poses are normalized to gain view-independence and transformed into (i) relative limb-angle space to ensure independence of non-adjacent joints or (ii) geometric features. In (i) an action descriptor consists of the relative angles between limbs and their temporal derivatives. In (ii) the action descriptor consists of different geometric features. In order to circumvent the problem of time-warping we propose to use a codebook of prototypical 3D poses which is generated from sample sequences of 3D motion capture data. This idea is in accordance with the concept of equivalence classes in action space. Results of the codebook method are presented using the Kinect sensor and the CMU Motion Capture Database.

  14. Modeling Representation Uncertainty in Concept-Based Multimedia Retrieval

    NARCIS (Netherlands)

    Aly, Robin

    2010-01-01

    This thesis considers concept-based multimedia retrieval, where documents are represented by the occurrence of concepts (also referred to as semantic concepts or high-level features). A concept can be thought of as a kind of label, which is attached to (parts of) the multimedia documents in which it

  15. Modeling representation uncertainty in concept-based multimedia retrieval

    NARCIS (Netherlands)

    Aly, Robin

    2010-01-01

    This thesis considers concept-based multimedia retrieval, where documents are represented by the occurrence of concepts (also referred to as semantic concepts or high-level features). A concept can be thought of as a kind of label, which is attached to (parts of) the multimedia documents in which it

  16. Extracting bimodal representations for language-based image text retrieval

    NARCIS (Netherlands)

    Westerveld, T.H.W.; Hiemstra, Djoerd; de Jong, Franciska M.G.; Correia, N.; Chambel, T.; Davenport, G.

    2000-01-01

    This paper explores two approaches to multimedia indexing that might contribute to the advancement of text-based conceptual search for pictorial information. Insights from relatively mature retrieval areas (spoken document retrieval and cross-language retrieval) are taken as a starting point for an

  17. Polarity classification using structure-based vector representations of text

    NARCIS (Netherlands)

    Hogenboom, Alexander; Frasincar, Flavius; de Jong, Franciska M.G.; Kaymak, Uzay

    The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep

  18. Towards Web-based representation and processing of health information

    DEFF Research Database (Denmark)

    Gao, S.; Mioc, Darka; Yi, X.L.

    2009-01-01

    Background: There is great concern within health surveillance, on how to grapple with environmental degradation, rapid urbanization, population mobility and growth. The Internet has emerged as an efficient way to share health information, enabling users to access and understand data...... at their fingertips. Increasingly complex problems in the health field require increasingly sophisticated computer software, distributed computing power, and standardized data sharing. To address this need, Web-based mapping is now emerging as an important tool to enable health practitioners, policy makers......, and the public to understand spatial health risks, population health trends and vulnerabilities. Today several web-based health applications generate dynamic maps; however, for people to fully interpret the maps they need data source description and the method used in the data analysis or statistical modeling...

  19. Representation of Students in Solving Simultaneous Linear Equation Problems Based on Multiple Intelligence

    Science.gov (United States)

    Yanti, Y. R.; Amin, S. M.; Sulaiman, R.

    2018-01-01

    This study described representation of students who have musical, logical-mathematic and naturalist intelligence in solving a problem. Subjects were selected on the basis of multiple intelligence tests (TPM) consists of 108 statements, with 102 statements adopted from Chislet and Chapman and 6 statements equal to eksistensial intelligences. Data were analyzed based on problem-solving tests (TPM) and interviewing. See the validity of the data then problem-solving tests (TPM) and interviewing is given twice with an analyzed using the representation indikator and the problem solving step. The results showed that: the stage of presenting information known, stage of devising a plan, and stage of carrying out the plan those three subjects were using same form of representation. While he stage of presenting information asked and stage of looking back, subject of logical-mathematic was using different forms of representation with subjects of musical and naturalist intelligence. From this research is expected to provide input to the teacher in determining the learning strategy that will be used by considering the representation of students with the basis of multiple intelligences.

  20. Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations

    Science.gov (United States)

    Koops, H. V.; de Haas, W. B.; Bransen, J.; Volk, A.

    2017-05-01

    The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.

  1. A Knowledge Representation Language for Large Knowledge Bases and "Intelligent" Information Retrieval Systems.

    Science.gov (United States)

    Zarri, Gian Piero

    1990-01-01

    Describes a conceptual Knowledge Representation Language (KRL) developed at the French National Center for Scientific Research, that is used for the construction and use of Large Knowledge Bases (LKBs) and/or Intelligent Information Retrieval Systems (IIRSs). Semantic factors are discussed, and the specialization hierarchies used are explained.…

  2. Representation of Coordination Mechanisms in IMS Learning Design to Support Group-based Learning

    NARCIS (Netherlands)

    Miao, Yongwu; Burgos, Daniel; Griffiths, David; Koper, Rob

    2007-01-01

    Miao, Y., Burgos, D., Griffiths, D., & Koper, R. (2008). Representation of Coordination Mechanisms in IMS Learning Design to Support Group-based Learning. In L. Lockyer, S. Bennet, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design and Learning Objects: Issues, Applications and

  3. Training data representation in a neural based robot position estimation system

    International Nuclear Information System (INIS)

    Taraglio, S.; Di Fonzo, F.; Burrascano, P.

    1997-03-01

    The vision subsystem of an autonomous vehicle is studies. It is based on a multi layer perceptron that uses TV images to estimate the position of the vehicle. A comparative study of the effects of output data representation and input data processing is presented and discussed

  4. Ontology and modeling patterns for state-based behavior representation

    Science.gov (United States)

    Castet, Jean-Francois; Rozek, Matthew L.; Ingham, Michel D.; Rouquette, Nicolas F.; Chung, Seung H.; Kerzhner, Aleksandr A.; Donahue, Kenneth M.; Jenkins, J. Steven; Wagner, David A.; Dvorak, Daniel L.; hide

    2015-01-01

    This paper provides an approach to capture state-based behavior of elements, that is, the specification of their state evolution in time, and the interactions amongst them. Elements can be components (e.g., sensors, actuators) or environments, and are characterized by state variables that vary with time. The behaviors of these elements, as well as interactions among them are represented through constraints on state variables. This paper discusses the concepts and relationships introduced in this behavior ontology, and the modeling patterns associated with it. Two example cases are provided to illustrate their usage, as well as to demonstrate the flexibility and scalability of the behavior ontology: a simple flashlight electrical model and a more complex spacecraft model involving instruments, power and data behaviors. Finally, an implementation in a SysML profile is provided.

  5. A survey on vision-based human action recognition

    NARCIS (Netherlands)

    Poppe, Ronald Walter

    Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion

  6. Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

    Science.gov (United States)

    2016-05-01

    classification performance of sparsity-based and deep-network-based techniques with conventional classifiers such as Markov switching vector auto -regression...is unlimited. Contents List of Figures v List of Tables vi 1. Introduction 1 1.1 Motivations 1 1.2 Sparsity-Based Representation for Transient Acoustic...data set were presented as parts of our paper, which will soon be published in IEEE Transactions on Signal Processing.10 • We report comprehensive

  7. Ontology-Based Vaccine Adverse Event Representation and Analysis.

    Science.gov (United States)

    Xie, Jiangan; He, Yongqun

    2017-01-01

    Vaccine is the one of the greatest inventions of modern medicine that has contributed most to the relief of human misery and the exciting increase in life expectancy. In 1796, an English country physician, Edward Jenner, discovered that inoculating mankind with cowpox can protect them from smallpox (Riedel S, Edward Jenner and the history of smallpox and vaccination. Proceedings (Baylor University. Medical Center) 18(1):21, 2005). Based on the vaccination worldwide, we finally succeeded in the eradication of smallpox in 1977 (Henderson, Vaccine 29:D7-D9, 2011). Other disabling and lethal diseases, like poliomyelitis and measles, are targeted for eradication (Bonanni, Vaccine 17:S120-S125, 1999).Although vaccine development and administration are tremendously successful and cost-effective practices to human health, no vaccine is 100% safe for everyone because each person reacts to vaccinations differently given different genetic background and health conditions. Although all licensed vaccines are generally safe for the majority of people, vaccinees may still suffer adverse events (AEs) in reaction to various vaccines, some of which can be serious or even fatal (Haber et al., Drug Saf 32(4):309-323, 2009). Hence, the double-edged sword of vaccination remains a concern.To support integrative AE data collection and analysis, it is critical to adopt an AE normalization strategy. In the past decades, different controlled terminologies, including the Medical Dictionary for Regulatory Activities (MedDRA) (Brown EG, Wood L, Wood S, et al., Drug Saf 20(2):109-117, 1999), the Common Terminology Criteria for Adverse Events (CTCAE) (NCI, The Common Terminology Criteria for Adverse Events (CTCAE). Available from: http://evs.nci.nih.gov/ftp1/CTCAE/About.html . Access on 7 Oct 2015), and the World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) (WHO, The WHO Adverse Reaction Terminology - WHO-ART. Available from: https://www.umc-products.com/graphics/28010.pdf

  8. A novel video recommendation system based on efficient retrieval of human actions

    Science.gov (United States)

    Ramezani, Mohsen; Yaghmaee, Farzin

    2016-09-01

    In recent years, fast growth of online video sharing eventuated new issues such as helping users to find their requirements in an efficient way. Hence, Recommender Systems (RSs) are used to find the users' most favorite items. Finding these items relies on items or users similarities. Though, many factors like sparsity and cold start user impress the recommendation quality. In some systems, attached tags are used for searching items (e.g. videos) as personalized recommendation. Different views, incomplete and inaccurate tags etc. can weaken the performance of these systems. Considering the advancement of computer vision techniques can help improving RSs. To this end, content based search can be used for finding items (here, videos are considered). In such systems, a video is taken from the user to find and recommend a list of most similar videos to the query one. Due to relating most videos to humans, we present a novel low complex scalable method to recommend videos based on the model of included action. This method has recourse to human action retrieval approaches. For modeling human actions, some interest points are extracted from each action and their motion information are used to compute the action representation. Moreover, a fuzzy dissimilarity measure is presented to compare videos for ranking them. The experimental results on HMDB, UCFYT, UCF sport and KTH datasets illustrated that, in most cases, the proposed method can reach better results than most used methods.

  9. Image Super-Resolution Based on Structure-Modulated Sparse Representation.

    Science.gov (United States)

    Zhang, Yongqin; Liu, Jiaying; Yang, Wenhan; Guo, Zongming

    2015-09-01

    Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.

  10. Locality constrained joint dynamic sparse representation for local matching based face recognition.

    Directory of Open Access Journals (Sweden)

    Jianzhong Wang

    Full Text Available Recently, Sparse Representation-based Classification (SRC has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW demonstrate the effectiveness of LCJDSRC.

  11. SAR and Infrared Image Fusion in Complex Contourlet Domain Based on Joint Sparse Representation

    Directory of Open Access Journals (Sweden)

    Wu Yiquan

    2017-08-01

    Full Text Available To investigate the problems of the large grayscale difference between infrared and Synthetic Aperture Radar (SAR images and their fusion image not being fit for human visual perception, we propose a fusion method for SAR and infrared images in the complex contourlet domain based on joint sparse representation. First, we perform complex contourlet decomposition of the infrared and SAR images. Then, we employ the KSingular Value Decomposition (K-SVD method to obtain an over-complete dictionary of the low-frequency components of the two source images. Using a joint sparse representation model, we then generate a joint dictionary. We obtain the sparse representation coefficients of the low-frequency components of the source images in the joint dictionary by the Orthogonal Matching Pursuit (OMP method and select them using the selection maximization strategy. We then reconstruct these components to obtain the fused low-frequency components and fuse the high-frequency components using two criteria——the coefficient of visual sensitivity and the degree of energy matching. Finally, we obtain the fusion image by the inverse complex contourlet transform. Compared with the three classical fusion methods and recently presented fusion methods, e.g., that based on the Non-Subsampled Contourlet Transform (NSCT and another based on sparse representation, the method we propose in this paper can effectively highlight the salient features of the two source images and inherit their information to the greatest extent.

  12. Locality constrained joint dynamic sparse representation for local matching based face recognition.

    Science.gov (United States)

    Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

    2014-01-01

    Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

  13. Perceptual-cognitive changes during motor learning: The influence of mental and physical practice on mental representation, gaze behavior, and performance of a complex action

    Directory of Open Access Journals (Sweden)

    Cornelia eFrank

    2016-01-01

    Full Text Available Despite the wealth of research on differences between experts and novices with respect to their perceptual-cognitive background (e.g., mental representations, gaze behavior, little is known about the change of these perceptual-cognitive components over the course of motor learning. In the present study, changes in one’s mental representation, quiet eye behavior, and outcome performance were examined over the course of skill acquisition as it related to physical and mental practice. Novices (N = 45 were assigned to one of three conditions: physical practice, physical practice plus mental practice, and no practice. Participants in the practice groups trained on a golf putting task over the course of three days, either by repeatedly executing the putt, or by both executing and imaging the putt. Findings revealed improvements in putting performance across both practice conditions. Regarding the perceptual-cognitive changes, participants practicing mentally and physically revealed longer quiet eye durations as well as more elaborate representation structures in comparison to the control group, while this was not the case for participants who underwent physical practice only. Thus, in the present study, combined mental and physical practice led to both formation of mental representations in long-term memory and longer quiet eye durations. Interestingly, the length of the quiet eye directly related to the degree of elaborateness of the underlying mental representation, supporting the notion that the quiet eye reflects cognitive processing. This study is the first to show that the quiet eye becomes longer in novices practicing a motor action. Moreover, the findings of the present study suggest that perceptual and cognitive adaptations co-occur over the course of motor learning.

  14. Perceptual-Cognitive Changes During Motor Learning: The Influence of Mental and Physical Practice on Mental Representation, Gaze Behavior, and Performance of a Complex Action.

    Science.gov (United States)

    Frank, Cornelia; Land, William M; Schack, Thomas

    2015-01-01

    Despite the wealth of research on differences between experts and novices with respect to their perceptual-cognitive background (e.g., mental representations, gaze behavior), little is known about the change of these perceptual-cognitive components over the course of motor learning. In the present study, changes in one's mental representation, quiet eye behavior, and outcome performance were examined over the course of skill acquisition as it related to physical and mental practice. Novices (N = 45) were assigned to one of three conditions: physical practice, combined physical plus mental practice, and no practice. Participants in the practice groups trained on a golf putting task over the course of 3 days, either by repeatedly executing the putt, or by both executing and imaging the putt. Findings revealed improvements in putting performance across both practice conditions. Regarding the perceptual-cognitive changes, participants practicing mentally and physically revealed longer quiet eye durations as well as more elaborate representation structures in comparison to the control group, while this was not the case for participants who underwent physical practice only. Thus, in the present study, combined mental and physical practice led to both formation of mental representations in long-term memory and longer quiet eye durations. Interestingly, the length of the quiet eye directly related to the degree of elaborateness of the underlying mental representation, supporting the notion that the quiet eye reflects cognitive processing. This study is the first to show that the quiet eye becomes longer in novices practicing a motor action. Moreover, the findings of the present study suggest that perceptual and cognitive adaptations co-occur over the course of motor learning.

  15. Fast and accurate grid representations for atom-based docking with partner flexibility.

    Science.gov (United States)

    de Vries, Sjoerd J; Zacharias, Martin

    2017-06-30

    Macromolecular docking methods can broadly be divided into geometric and atom-based methods. Geometric methods use fast algorithms that operate on simplified, grid-like molecular representations, while atom-based methods are more realistic and flexible, but far less efficient. Here, a hybrid approach of grid-based and atom-based docking is presented, combining precalculated grid potentials with neighbor lists for fast and accurate calculation of atom-based intermolecular energies and forces. The grid representation is compatible with simultaneous multibody docking and can tolerate considerable protein flexibility. When implemented in our docking method ATTRACT, grid-based docking was found to be ∼35x faster. With the OPLSX forcefield instead of the ATTRACT coarse-grained forcefield, the average speed improvement was >100x. Grid-based representations may allow atom-based docking methods to explore large conformational spaces with many degrees of freedom, such as multiple macromolecules including flexibility. This increases the domain of biological problems to which docking methods can be applied. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  16. Improving the learning of clinical reasoning through computer-based cognitive representation

    Directory of Open Access Journals (Sweden)

    Bian Wu

    2014-12-01

    Full Text Available Objective: Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods: Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results: A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions: The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge

  17. Improving the learning of clinical reasoning through computer-based cognitive representation.

    Science.gov (United States)

    Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

    2014-01-01

    Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

  18. A neurosemantic theory of concrete noun representation based on the underlying brain codes.

    Directory of Open Access Journals (Sweden)

    Marcel Adam Just

    Full Text Available This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.

  19. GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain.

    Science.gov (United States)

    Martin, Alex

    2016-08-01

    In this article, I discuss some of the latest functional neuroimaging findings on the organization of object concepts in the human brain. I argue that these data provide strong support for viewing concepts as the products of highly interactive neural circuits grounded in the action, perception, and emotion systems. The nodes of these circuits are defined by regions representing specific object properties (e.g., form, color, and motion) and thus are property-specific, rather than strictly modality-specific. How these circuits are modified by external and internal environmental demands, the distinction between representational content and format, and the grounding of abstract social concepts are also discussed.

  20. GRAPES—Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain

    Science.gov (United States)

    Martin, Alex

    2016-01-01

    In this article, I discuss some of the latest functional neuroimaging findings on the organization of object concepts in the human brain. I argue that these data provide strong support for viewing concepts as the products of highly interactive neural circuits grounded in the action, perception, and emotion systems. The nodes of these circuits are defined by regions representing specific object properties (e.g., form, color, and motion) and thus are property-specific, rather than strictly modality-specific. How these circuits are modified by external and internal environmental demands, the distinction between representational content and format, and the grounding of abstract social concepts are also discussed. PMID:25968087

  1. Reexamining the language account of cross-national differences in base-10 number representations.

    Science.gov (United States)

    Vasilyeva, Marina; Laski, Elida V; Ermakova, Anna; Lai, Weng-Feng; Jeong, Yoonkyung; Hachigian, Amy

    2015-01-01

    East Asian students consistently outperform students from other nations in mathematics. One explanation for this advantage is a language account; East Asian languages, unlike most Western languages, provide cues about the base-10 structure of multi-digit numbers, facilitating the development of base-10 number representations. To test this view, the current study examined how kindergartners represented two-digit numbers using single unit-blocks and ten-blocks. The participants (N=272) were from four language groups (Korean, Mandarin, English, and Russian) that vary in the extent of "transparency" of the base-10 structure. In contrast to previous findings with older children, kindergartners showed no cross-language variability in the frequency of producing base-10 representations. Furthermore, they showed a pattern of within-language variability that was not consistent with the language account and was likely attributable to experiential factors. These findings suggest that language might not play as critical a role in the development of base-10 representations as suggested in earlier research. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Mental representation and motor imagery training.

    Science.gov (United States)

    Schack, Thomas; Essig, Kai; Frank, Cornelia; Koester, Dirk

    2014-01-01

    Research in sports, dance and rehabilitation has shown that basic action concepts (BACs) are fundamental building blocks of mental action representations. BACs are based on chunked body postures related to common functions for realizing action goals. In this paper, we outline issues in research methodology and an experimental method, the structural dimensional analysis of mental representation (SDA-M), to assess action-relevant representational structures that reflect the organization of BACs. The SDA-M reveals a strong relationship between cognitive representation and performance if complex actions are performed. We show how the SDA-M can improve motor imagery training and how it contributes to our understanding of coaching processes. The SDA-M capitalizes on the objective measurement of individual mental movement representations before training and the integration of these results into the motor imagery training. Such motor imagery training based on mental representations (MTMR) has been applied successfully in professional sports such as golf, volleyball, gymnastics, windsurfing, and recently in the rehabilitation of patients who have suffered a stroke.

  3. Mental Representation and Motor Imagery Training

    Directory of Open Access Journals (Sweden)

    Thomas eSchack

    2014-05-01

    Full Text Available Research in sports, dance and rehabilitation has shown that Basic Action Concepts (BACs are fundamental building blocks of mental action representations. BACs are based on chunked body postures related to common functions for realizing action goals. In this paper, we outline issues in research methodology and an experimental method, SDA-M (structural dimensional analysis of mental representation, to assess action-relevant representational structures that reflect the organization of BACs. The SDA-M reveals a strong relationship between cognitive representation and performance if complex actions are performed. We show how the SDA-M can improve motor imagery training and how it contributes to our understanding of coaching processes. The SDA-M capitalizes on the objective measurement of individual mental movement representations before training and the integration of these results into the motor imagery training. Such motor imagery training based on mental representations has been applied successfully in professional sports such as golf, volleyball, gymnastics, windsurfing, and recently in the rehabilitation of patients who have suffered a stroke.

  4. Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

    Science.gov (United States)

    Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

    2017-11-01

    The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

  5. A computational shape-based model of anger and sadness justifies a configural representation of faces.

    Science.gov (United States)

    Neth, Donald; Martinez, Aleix M

    2010-08-06

    Research suggests that configural cues (second-order relations) play a major role in the representation and classification of face images; making faces a "special" class of objects, since object recognition seems to use different encoding mechanisms. It is less clear, however, how this representation emerges and whether this representation is also used in the recognition of facial expressions of emotion. In this paper, we show how configural cues emerge naturally from a classical analysis of shape in the recognition of anger and sadness. In particular our results suggest that at least two of the dimensions of the computational (cognitive) space of facial expressions of emotion correspond to pure configural changes. The first of these dimensions measures the distance between the eyebrows and the mouth, while the second is concerned with the height-width ratio of the face. Under this proposed model, becoming a face "expert" would mean to move from the generic shape representation to that based on configural cues. These results suggest that the recognition of facial expressions of emotion shares this expertise property with the other processes of face processing. Copyright 2010 Elsevier Ltd. All rights reserved.

  6. Multi-Label Classification Based on Low Rank Representation for Image Annotation

    Directory of Open Access Journals (Sweden)

    Qiaoyu Tan

    2017-01-01

    Full Text Available Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels. To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR. MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover. We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

  7. Simulation modifies prehension: evidence for a conjoined representation of the graspable features of an object and the action of grasping it.

    Directory of Open Access Journals (Sweden)

    Victor Frak

    Full Text Available Movement formulas, engrams, kinesthetic images and internal models of the body in action are notions derived mostly from clinical observations of brain-damaged subjects. They also suggest that the prehensile geometry of an object is integrated in the neural circuits and includes the object's graspable characteristics as well as its semantic properties. In order to determine whether there is a conjoined representation of the graspable characteristics of an object in relation to the actual grasping, it is necessary to separate the graspable (low-level from the semantic (high-level properties of the object. Right-handed subjects were asked to grasp and lift a smooth 300-g cylinder with one hand, before and after judging the level of difficulty of a "grasping for pouring" action, involving a smaller cylinder and using the opposite hand. The results showed that simulated grasps with the right hand exert a direct influence on actual motor acts with the left hand. These observations add to the evidence that there is a conjoined representation of the graspable characteristics of the object and the biomechanical constraints of the arm.

  8. Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition

    Directory of Open Access Journals (Sweden)

    Chuang Lin

    2013-01-01

    Full Text Available Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The proposed method first adopts a so-called data-dependent kernel to generate an efficient kernel optimization algorithm. Then, a constrained optimization function using general gradient descent method is created to find combination coefficients varied with the input data. After that, optimized kernel PCA (KOPCA is obtained via combination coefficients to extract features. Finally, the sparse representation-based classifier is used to perform pattern classification task. Experimental results on MSTAR SAR images show the effectiveness of the proposed method.

  9. Features based approach for indexation and representation of unstructured Arabic documents

    Directory of Open Access Journals (Sweden)

    Mohamed Salim El Bazzi

    2017-06-01

    Full Text Available The increase of textual information published in Arabic language on the internet, public libraries and administrations requires implementing effective techniques for the extraction of relevant information contained in large corpus of texts. The purpose of indexing is to create a document representation that easily find and identify the relevant information in a set of documents. However, mining textual data is becoming a complicated task, especially when taking semantic into consideration. In this paper, we will present an indexation system based on contextual representation that will take the advantage of semantic links given in a document. Our approach is based on the extraction of keyphrases. Then, each document is represented by its relevant keyphrases instead of its simple keywords. The experimental results confirms the effectiveness of our approach.

  10. Separated representations and PGD-based model reduction fundamentals and applications

    CERN Document Server

    Ladevèze, Pierre

    2014-01-01

    The papers in this volume start with a description of  the construction of reduced models through a review of Proper Orthogonal Decomposition (POD) and reduced basis models, including their mathematical foundations and some challenging applications, then followed by a description of a  new generation of simulation strategies based on the use of separated representations (space-parameters, space-time, space-time-parameters, space-space,…), which have led to what is known as Proper Generalized Decomposition (PGD) techniques. The models can be enriched by treating parameters as additional coordinates, leading to fast and inexpensive online calculations based on richer offline parametric solutions. Separated representations are analyzed in detail in the course, from their mathematical foundations to their most spectacular applications. It is also shown how such an approximation could evolve into a new paradigm in computational science, enabling one to circumvent various computational issues in a vast array of...

  11. Top-down attention based on object representation and incremental memory for knowledge building and inference.

    Science.gov (United States)

    Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

    2013-10-01

    Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Adaboost-based algorithm for human action recognition

    KAUST Repository

    Zerrouki, Nabil

    2017-11-28

    This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

  13. A multiple distributed representation method based on neural network for biomedical event extraction.

    Science.gov (United States)

    Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

    2017-12-20

    Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

  14. Individual differences in action co-representation : not personal distress or subclinical psychotic experiences but sex composition modulates joint action performance

    NARCIS (Netherlands)

    van der Weiden, Anouk; Aarts, Henk; Prikken, Merel; van Haren, Neeltje E M

    Successful social interaction requires the ability to integrate as well as distinguish own and others’ actions. Normally, the integration and distinction of self and other are a well-balanced process, occurring without much effort or conscious attention. However, not everyone is blessed with the

  15. Individual differences in action co-representation : not personal distress or subclinical psychotic experiences but sex composition modulates joint action performance

    NARCIS (Netherlands)

    van der Weiden, Anouk; Aarts, Henk; Prikken, Merel; van Haren, Neeltje E M

    2015-01-01

    Successful social interaction requires the ability to integrate as well as distinguish own and others' actions. Normally, the integration and distinction of self and other are a well-balanced process, occurring without much effort or conscious attention. However, not everyone is blessed with the

  16. Infrared small target tracking based on sample constrained particle filtering and sparse representation

    Science.gov (United States)

    Zhang, Xiaomin; Ren, Kan; Wan, Minjie; Gu, Guohua; Chen, Qian

    2017-12-01

    Infrared search and track technology for small target plays an important role in infrared warning and guidance. In view of the tacking randomness and uncertainty caused by background clutter and noise interference, a robust tracking method for infrared small target based on sample constrained particle filtering and sparse representation is proposed in this paper. Firstly, to distinguish the normal region and interference region in target sub-blocks, we introduce a binary support vector, and combine it with the target sparse representation model, after which a particle filtering observation model based on sparse reconstruction error differences between sample targets is developed. Secondly, we utilize saliency extraction to obtain the high frequency area in infrared image, and make it as a priori knowledge of the transition probability model to limit the particle filtering sampling process. Lastly, the tracking result is brought about via target state estimation and the Bayesian posteriori probability calculation. Theoretical analyses and experimental results show that our method can enhance the state estimation ability of stochastic particles, improve the sparse representation adaptabilities for infrared small targets, and optimize the tracking accuracy for infrared small moving targets.

  17. Representation-based user interfaces for the audiovisual library of the year 2000

    Science.gov (United States)

    Aigrain, Philippe; Joly, Philippe; Lepain, Philippe; Longueville, Veronique

    1995-03-01

    The audiovisual library of the future will be based on computerized access to digitized documents. In this communication, we address the user interface issues which will arise from this new situation. One cannot simply transfer a user interface designed for the piece by piece production of some audiovisual presentation and make it a tool for accessing full-length movies in an electronic library. One cannot take a digital sound editing tool and propose it as a means to listen to a musical recording. In our opinion, when computers are used as mediations to existing contents, document representation-based user interfaces are needed. With such user interfaces, a structured visual representation of the document contents is presented to the user, who can then manipulate it to control perception and analysis of these contents. In order to build such manipulable visual representations of audiovisual documents, one needs to automatically extract structural information from the documents contents. In this communication, we describe possible visual interfaces for various temporal media, and we propose methods for the economically feasible large scale processing of documents. The work presented is sponsored by the Bibliotheque Nationale de France: it is part of the program aiming at developing for image and sound documents an experimental counterpart to the digitized text reading workstation of this library.

  18. Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

    Directory of Open Access Journals (Sweden)

    Lu Bing

    2017-01-01

    Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

  19. A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem

    Directory of Open Access Journals (Sweden)

    Zhenbing Liu

    2016-01-01

    Full Text Available Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use F-measure, G-mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.

  20. Ontology-based time information representation of vaccine adverse events in VAERS for temporal analysis.

    Science.gov (United States)

    Tao, Cui; He, Yongqun; Yang, Hannah; Poland, Gregory A; Chute, Christopher G

    2012-12-20

    The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) provides a valuable data source for post-vaccination adverse event analyses. The structured data in the system has been widely used, but the information in the write-up narratives is rarely included in these kinds of analyses. In fact, the unstructured nature of the narratives makes the data embedded in them difficult to be used for any further studies. We developed an ontology-based approach to represent the data in the narratives in a "machine-understandable" way, so that it can be easily queried and further analyzed. Our focus is the time aspect in the data for time trending analysis. The Time Event Ontology (TEO), Ontology of Adverse Events (OAE), and Vaccine Ontology (VO) are leveraged for the semantic representation of this purpose. A VAERS case report is presented as a use case for the ontological representations. The advantages of using our ontology-based Semantic web representation and data analysis are emphasized. We believe that representing both the structured data and the data from write-up narratives in an integrated, unified, and "machine-understandable" way can improve research for vaccine safety analyses, causality assessments, and retrospective studies.

  1. Ontology-based time information representation of vaccine adverse events in VAERS for temporal analysis

    Directory of Open Access Journals (Sweden)

    Tao Cui

    2012-12-01

    Full Text Available Abstract Background The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS provides a valuable data source for post-vaccination adverse event analyses. The structured data in the system has been widely used, but the information in the write-up narratives is rarely included in these kinds of analyses. In fact, the unstructured nature of the narratives makes the data embedded in them difficult to be used for any further studies. Results We developed an ontology-based approach to represent the data in the narratives in a “machine-understandable” way, so that it can be easily queried and further analyzed. Our focus is the time aspect in the data for time trending analysis. The Time Event Ontology (TEO, Ontology of Adverse Events (OAE, and Vaccine Ontology (VO are leveraged for the semantic representation of this purpose. A VAERS case report is presented as a use case for the ontological representations. The advantages of using our ontology-based Semantic web representation and data analysis are emphasized. Conclusions We believe that representing both the structured data and the data from write-up narratives in an integrated, unified, and “machine-understandable” way can improve research for vaccine safety analyses, causality assessments, and retrospective studies.

  2. DEVELOPMENT OF INTERACTIVE E-BOOK BASED ON CHEMICAL REPRESENTATION REFER TO CURRICULUM 2013

    Directory of Open Access Journals (Sweden)

    L. Tania

    2015-11-01

    Full Text Available This research aimed to develop an interactive e-book based representations of chemistry; describes the characteristics of the interactive e-book developed; the teachers responses in content suitability with curriculum and graphics aspects; and student responses in readibility aspects. The method used was research and development. The characteristics of interactive e-book: it was developed referring to the core competencies (KI and basic competence (KD in the curriculum 2013, allowed active interaction between students and e-book, completed with pictures, animations or videos in three levels of the chemical representation. Teachers’ responses to the content suitability and graphic aspects were very good with the percentage of each 98.46% and 97.5%. The students’ responses in readibility aspects was very good with percentage of 88.5%.

  3. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  4. Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution

    Science.gov (United States)

    Buettner, Florian; Gulliford, Sarah L.; Webb, Steve; Partridge, Mike

    2011-04-01

    Many models exist for predicting toxicities based on dose-volume histograms (DVHs) or dose-surface histograms (DSHs). This approach has several drawbacks as firstly the reduction of the dose distribution to a histogram results in the loss of spatial information and secondly the bins of the histograms are highly correlated with each other. Furthermore, some of the complex nonlinear models proposed in the past lack a direct physical interpretation and the ability to predict probabilities rather than binary outcomes. We propose a parameterized representation of the 3D distribution of the dose to the rectal wall which explicitly includes geometrical information in the form of the eccentricity of the dose distribution as well as its lateral and longitudinal extent. We use a nonlinear kernel-based probabilistic model to predict late rectal toxicity based on the parameterized dose distribution and assessed its predictive power using data from the MRC RT01 trial (ISCTRN 47772397). The endpoints under consideration were rectal bleeding, loose stools, and a global toxicity score. We extract simple rules identifying 3D dose patterns related to a specifically low risk of complication. Normal tissue complication probability (NTCP) models based on parameterized representations of geometrical and volumetric measures resulted in areas under the curve (AUCs) of 0.66, 0.63 and 0.67 for predicting rectal bleeding, loose stools and global toxicity, respectively. In comparison, NTCP models based on standard DVHs performed worse and resulted in AUCs of 0.59 for all three endpoints. In conclusion, we have presented low-dimensional, interpretable and nonlinear NTCP models based on the parameterized representation of the dose to the rectal wall. These models had a higher predictive power than models based on standard DVHs and their low dimensionality allowed for the identification of 3D dose patterns related to a low risk of complication.

  5. Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution

    Energy Technology Data Exchange (ETDEWEB)

    Buettner, Florian; Gulliford, Sarah L; Webb, Steve; Partridge, Mike, E-mail: florian.buttner@icr.ac.uk [Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom)

    2011-04-07

    Many models exist for predicting toxicities based on dose-volume histograms (DVHs) or dose-surface histograms (DSHs). This approach has several drawbacks as firstly the reduction of the dose distribution to a histogram results in the loss of spatial information and secondly the bins of the histograms are highly correlated with each other. Furthermore, some of the complex nonlinear models proposed in the past lack a direct physical interpretation and the ability to predict probabilities rather than binary outcomes. We propose a parameterized representation of the 3D distribution of the dose to the rectal wall which explicitly includes geometrical information in the form of the eccentricity of the dose distribution as well as its lateral and longitudinal extent. We use a nonlinear kernel-based probabilistic model to predict late rectal toxicity based on the parameterized dose distribution and assessed its predictive power using data from the MRC RT01 trial (ISCTRN 47772397). The endpoints under consideration were rectal bleeding, loose stools, and a global toxicity score. We extract simple rules identifying 3D dose patterns related to a specifically low risk of complication. Normal tissue complication probability (NTCP) models based on parameterized representations of geometrical and volumetric measures resulted in areas under the curve (AUCs) of 0.66, 0.63 and 0.67 for predicting rectal bleeding, loose stools and global toxicity, respectively. In comparison, NTCP models based on standard DVHs performed worse and resulted in AUCs of 0.59 for all three endpoints. In conclusion, we have presented low-dimensional, interpretable and nonlinear NTCP models based on the parameterized representation of the dose to the rectal wall. These models had a higher predictive power than models based on standard DVHs and their low dimensionality allowed for the identification of 3D dose patterns related to a low risk of complication.

  6. Individual differences in action co-representation: not personal distress or subclinical psychotic experiences but sex composition modulates joint action performance

    OpenAIRE

    van der Weiden, Anouk; Aarts, Henk; Prikken, Merel; van Haren, Neeltje E. M.

    2015-01-01

    Successful social interaction requires the ability to integrate as well as distinguish own and others' actions. Normally, the integration and distinction of self and other are a well-balanced process, occurring without much effort or conscious attention. However, not everyone is blessed with the ability to balance self-other distinction and integration, resulting in personal distress in reaction to other people's emotions or even a loss of self [e.g., in (subclinical) psychosis]. Previous res...

  7. The influence of implicit hand-based representations on mental arithmetic.

    Science.gov (United States)

    Klein, Elise; Moeller, Korbinian; Willmes, Klaus; Nuerk, Hans-Christoph; Domahs, Frank

    2011-01-01

    Recently, a strong functional relationship between finger counting and number processing has been suggested. It has been argued that bodily experiences such as finger counting may influence the structure of the basic mental representations of numbers even in adults. However, to date it remains unclear whether the structure of finger counting systems also influences educated adults' performance in mental arithmetic. In the present study, we pursued this question by examining finger-based sub-base-five effects in an addition production task. With the standard effect of a carry operation (i.e., base-10 crossing) being replicated, we observed an additional sub-base-five effect such that crossing a sub-base-five boundary led to a relative response time increase. For the case of mental arithmetic sub-base-five effects have previously been reported only in children. However, it remains unclear whether finger-based numerical effects in mental arithmetic reflect an important but transitory step in the development of arithmetical skills. The current findings suggest that even in adults embodied representations such as finger counting patterns modulate arithmetic performance. Thus, they support the general idea that even seemingly abstract cognition in adults may at least partly be rooted in our bodily experiences.

  8. The influence of implicit hand-based representations on mental arithmetic

    Directory of Open Access Journals (Sweden)

    Elise eKlein

    2011-09-01

    Full Text Available Recently, a strong functional relationship between finger counting and number processing has been suggested. It has been argued that bodily experiences such as finger counting may influence the structure of the basic mental representations of numbers even in adults. However, to date it remains unclear whether the structure of finger counting systems also influences educated adults’ performance in mental arithmetic.In the present study, we pursued this question by examining finger-based sub-base-five effects in an addition production task. With the standard effect of a carry operation (i.e., base 10 crossing being replicated, we observed an additional sub-base-five effect such that crossing a sub-base 5 boundary led to a relative response time increase. For the case of mental arithmetic sub-base-five effects have previously been reported only in children. However, it remains unclear whether finger-based numerical effects in mental arithmetic reflect an important but transitory step in the development of arithmetical skills. The current findings suggest that even in adults embodied representations such as finger counting patterns modulate arithmetic performance. Thus, they support the general idea that even seemingly abstract cognition in adults may at least partly be rooted in our bodily experiences.

  9. Representações sociais e culturas de ação The social and cultural representations of action

    Directory of Open Access Journals (Sweden)

    Jean-Marie Barvier

    2010-08-01

    Full Text Available Sustentado por constatações realizadas no mundo profissional e, em particular, no mundo da educação e da formação, o autor propõe o conceito de cultura da ação como um modo compartilhado de organização de construção de sentido sobre as atividades. Este conceito transversal às diferentes formas de ação busca unir os aspectos individuais e coletivos, as permanências e as mudanças, os aspectos mentais e os aspectos conativos na abordagem do envolvimento com a ação. O conceito se inscreve no quadro mais amplo da criação de ferramentas capazes de descrever a construção simultânea de ações de sujeitos individuais e coletivos.Sustained by findings in the professional world and, in particular, in the world of education and training the author proposes the concept of action culture as a shared way of organizing the construction of the meaning of activities. This transversal concept of the different forms of action seeks to unite individual and collective aspects, what remains and what changes and the mental and conative aspects in the approach to involvement with action. The concept is inscribed within the broader frame of the creation of tools that are capable of describing the simultaneous construction of the actions of individuals and groups.

  10. Differential Effects of Age-of-Acquisition for Concrete Nouns and Action Verbs: Evidence for Partly Distinct Representations?

    Science.gov (United States)

    Boulenger, Veronique; Decoppet, Nathalie; Roy, Alice C.; Paulignan, Yves; Nazir, Tatjana A.

    2007-01-01

    There is growing evidence that words that are acquired early in life are processed faster and more accurately than words acquired later, even by adults. As neuropsychological and neuroimaging studies have implicated different brain networks in the processing of action verbs and concrete nouns, the present study was aimed at contrasting reaction…

  11. Shared Representation of SAR Target and Shadow Based on Multilayer Auto-encoder

    Directory of Open Access Journals (Sweden)

    Sun Zhi-jun

    2013-06-01

    Full Text Available Automatic Target Recognition (ATR of Synthetic Aperture Radar (SAR image is investigated. A SAR feature extraction algorithm based on multilayer auto-encoder is proposed. The method makes use of a probabilistic neural network, Restricted Boltzmann Machine (RBM modeling probability distribution of environment. Through the formation of more expressive multilayer neural network, the deep learning model learns shared representation of the target and its shadow outline reflecting the target shape characteristics. Targets are classified automatically through two recognition models. The experiment results based on the MSTAR verify the effectiveness of proposed algorithm.

  12. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J Carlos Moreno

    2010-09-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  13. A knowledge representation meta-model for rule-based modelling of signalling networks

    Directory of Open Access Journals (Sweden)

    Adrien Basso-Blandin

    2016-03-01

    Full Text Available The study of cellular signalling pathways and their deregulation in disease states, such as cancer, is a large and extremely complex task. Indeed, these systems involve many parts and processes but are studied piecewise and their literatures and data are consequently fragmented, distributed and sometimes—at least apparently—inconsistent. This makes it extremely difficult to build significant explanatory models with the result that effects in these systems that are brought about by many interacting factors are poorly understood. The rule-based approach to modelling has shown some promise for the representation of the highly combinatorial systems typically found in signalling where many of the proteins are composed of multiple binding domains, capable of simultaneous interactions, and/or peptide motifs controlled by post-translational modifications. However, the rule-based approach requires highly detailed information about the precise conditions for each and every interaction which is rarely available from any one single source. Rather, these conditions must be painstakingly inferred and curated, by hand, from information contained in many papers—each of which contains only part of the story. In this paper, we introduce a graph-based meta-model, attuned to the representation of cellular signalling networks, which aims to ease this massive cognitive burden on the rule-based curation process. This meta-model is a generalization of that used by Kappa and BNGL which allows for the flexible representation of knowledge at various levels of granularity. In particular, it allows us to deal with information which has either too little, or too much, detail with respect to the strict rule-based meta-model. Our approach provides a basis for the gradual aggregation of fragmented biological knowledge extracted from the literature into an instance of the meta-model from which we can define an automated translation into executable Kappa programs.

  14. Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

    Science.gov (United States)

    Wang, Jiqiang; Ye, Zhifeng; Hu, Zhongzhi; Wu, Xin; Dimirovsky, Georgi; Yue, Hong

    2017-05-01

    Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.

  15. Predicate Structures, Gesture, and Simultaneity in the Representation of Action in British Sign Language: Evidence From Deaf Children and Adults

    Science.gov (United States)

    Cormier, Kearsy

    2013-01-01

    British Sign Language (BSL) signers use a variety of structures, such as constructed action (CA), depicting constructions (DCs), or lexical verbs, to represent action and other verbal meanings. This study examines the use of these verbal predicate structures and their gestural counterparts, both separately and simultaneously, in narratives by deaf children with various levels of exposure to BSL (ages 5;1 to 7;5) and deaf adult native BSL signers. Results reveal that all groups used the same types of predicative structures, including children with minimal BSL exposure. However, adults used CA, DCs, and/or lexical signs simultaneously more frequently than children. These results suggest that simultaneous use of CA with lexical and depicting predicates is more complex than the use of these predicate structures alone and thus may take deaf children more time to master. PMID:23670881

  16. Action Based Teaching in Nigeria: Issues and Reflections | Adirika ...

    African Journals Online (AJOL)

    Action-based teaching is a pedagogical approach that emphasizes meaning and internalization of learning in ways that solidify flow and generate confidence and autonomy in learners. There are principles that are related to its utilization for the achievement of educational goals. The explanations of these principles, their ...

  17. Rule-based emergency action level monitor prototype

    International Nuclear Information System (INIS)

    Touchton, R.A.; Gunter, A.D.; Cain, D.

    1985-01-01

    In late 1983, the Electric Power Research Institute (EPRI) began a program to encourage and stimulate the development of artificial intelligence (AI) applications for the nuclear industry. Development of a rule-based emergency action level classification system prototype is discussed. The paper describes both the full prototype currently under development and the completed, simplified prototype

  18. Gain Scheduling of Observer-Based Controllers with Integral Action

    DEFF Research Database (Denmark)

    Trangbæk, Klaus; Stoustrup, Jakob; Bendtsen, Jan Dimon

    2006-01-01

     This paper presents a method for continuous gain scheduling of  observer-based controllers with integral action. Given two stabilising controllers for a given system, explicit state space formulae are presented, allowing to change gradually from one  controller to the other while preserving...

  19. Rubber airplane: Constraint-based component-modeling for knowledge representation in computer-aided conceptual design

    Science.gov (United States)

    Kolb, Mark A.

    1990-01-01

    Viewgraphs on Rubber Airplane: Constraint-based Component-Modeling for Knowledge Representation in Computer Aided Conceptual Design are presented. Topics covered include: computer aided design; object oriented programming; airfoil design; surveillance aircraft; commercial aircraft; aircraft design; and launch vehicles.

  20. Experience-driven formation of parts-based representations in a model of layered visual memory

    Directory of Open Access Journals (Sweden)

    Jenia Jitsev

    2009-09-01

    Full Text Available Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.

  1. A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations.

    Science.gov (United States)

    Chen, Tao; Wu, Huanxin; Guo, Limin; Liu, Lutao

    2015-11-24

    In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.

  2. Knowledge Representation and Inference for Analysis and Design of Database and Tabular Rule-Based Systems

    Directory of Open Access Journals (Sweden)

    Antoni Ligeza

    2001-01-01

    Full Text Available Rulebased systems constitute a powerful tool for specification of knowledge in design and implementation of knowledge based systems. They provide also a universal programming paradigm for domains such as intelligent control, decision support, situation classification and operational knowledge encoding. In order to assure safe and reliable performance, such system should satisfy certain formal requirements, including completeness and consistency. This paper addresses the issue of analysis and verification of selected properties of a class of such system in a systematic way. A uniform, tabular scheme of single-level rule-based systems is considered. Such systems can be applied as a generalized form of databases for specification of data pattern (unconditional knowledge, or can be used for defining attributive decision tables (conditional knowledge in form of rules. They can also serve as lower-level components of a hierarchical multi-level control and decision support knowledge-based systems. An algebraic knowledge representation paradigm using extended tabular representation, similar to relational database tables is presented and algebraic bases for system analysis, verification and design support are outlined.

  3. The Role of Familiarity for Representations in Norm-Based Face Space.

    Science.gov (United States)

    Faerber, Stella J; Kaufmann, Jürgen M; Leder, Helmut; Martin, Eva Maria; Schweinberger, Stefan R

    2016-01-01

    According to the norm-based version of the multidimensional face space model (nMDFS, Valentine, 1991), any given face and its corresponding anti-face (which deviates from the norm in exactly opposite direction as the original face) should be equidistant to a hypothetical prototype face (norm), such that by definition face and anti-face should bear the same level of perceived typicality. However, it has been argued that familiarity affects perceived typicality and that representations of familiar faces are qualitatively different (e.g., more robust and image-independent) from those for unfamiliar faces. Here we investigated the role of face familiarity for rated typicality, using two frequently used operationalisations of typicality (deviation-based: DEV), and distinctiveness (face in the crowd: FITC) for faces of celebrities and their corresponding anti-faces. We further assessed attractiveness, likeability and trustworthiness ratings of the stimuli, which are potentially related to typicality. For unfamiliar faces and their corresponding anti-faces, in line with the predictions of the nMDFS, our results demonstrate comparable levels of perceived typicality (DEV). In contrast, familiar faces were perceived much less typical than their anti-faces. Furthermore, familiar faces were rated higher than their anti-faces in distinctiveness, attractiveness, likability and trustworthiness. These findings suggest that familiarity strongly affects the distribution of facial representations in norm-based face space. Overall, our study suggests (1) that familiarity needs to be considered in studies of mental representations of faces, and (2) that familiarity, general distance-to-norm and more specific vector directions in face space make different and interactive contributions to different types of facial evaluations.

  4. The brain MRI image sparse representation based on the gradient information and the non-symmetry and anti-packing model.

    Science.gov (United States)

    Liang, Hu; Zhao, Shengrong; Dong, Xiangjun

    2017-12-01

    Nowadays, sparse representation has been widely used in Magnetic Resonance Imaging (MRI). The commonly used sparse representation methods are based on symmetrical partition, which have not considered the complex structure of MRI image. In this paper, we proposed a sparse representation method for the brain MRI image, called GNAMlet transform, which is based on the gradient information and the non-symmetry and anti-packing model. The proposed sparse representation method can reduce the lost detail information, improving the reconstruction accuracy. The experiment results show the superiority of the proposed transform for the brain MRI image representation in comparison with some state-of-the-art sparse representation methods.

  5. Towards an action-based perspective on firm competitiveness

    Directory of Open Access Journals (Sweden)

    Anoop Madhok

    2014-04-01

    Full Text Available Existing theoretical frameworks typically revolve around sustainability of competitive advantage and attribute superior firm performance to its position in the industry structure and/or the possession of critical resources. However, the equilibrium-oriented logic implicit in these perspectives is not consonant with today's environment, characterized by more dynamic and complex behavior of markets and firms, which renders competitive advantages obsolete faster than ever. We propose an alternative action-based perspective on firm competitiveness one that revolves around the logic of action and emphasizes an entrepreneurial orientation and firm agility as the basis of firm competitiveness. This logic of action shifts the focus away from just industry position or resource possession and provides more scope for less advantaged firms to compete with the incumbents.

  6. Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

    Science.gov (United States)

    Gómez-Adorno, Helena; Markov, Ilia; Sidorov, Grigori; Posadas-Durán, Juan-Pablo; Sanchez-Perez, Miguel A; Chanona-Hernandez, Liliana

    2016-01-01

    We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.

  7. Sparse representation of higher-order functional interaction patterns in task-based FMRI data.

    Science.gov (United States)

    Zhang, Shu; Li, Xiang; Lv, Jinglei; Jiang, Xi; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Guo, Lei; Liu, Tianming

    2013-01-01

    Traditional task-based fMRI activation detection methods, e.g., the widely used general linear model (GLM), assume that the brain's hemodynamic responses follow the block-based or event-related stimulus paradigm. Typically, these activation detections are performed voxel-wise independently, and then are usually followed by statistical corrections. Despite remarkable successes and wide adoption of these methods, it remains largely unknown how functional brain regions interact with each other within specific networks during task performance blocks and in the baseline. In this paper, we present a novel algorithmic pipeline to statistically infer and sparsely represent higher-order functional interaction patterns within the working memory network during task performance and in the baseline. Specifically, a collection of higher-order interactions are inferred via the greedy equivalence search (GES) algorithm for both task and baseline blocks. In the next stage, an effective online dictionary learning algorithm is utilized for sparse representation of the inferred higher-order interaction patterns. Application of this framework on a working memory task-based fMRI data reveals interesting and meaningful distributions of the learned sparse dictionary atoms in task and baseline blocks. In comparison with traditional voxel-wise activation detection and recent pair-wise functional connectivity analysis, our framework offers a new methodology for representation and exploration of higher-order functional activities in the brain.

  8. Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2016-08-01

    Full Text Available Cost estimation is one of the most critical tasks for building construction project management. The existing building construction cost estimation methods of many countries, including China, require information from several sources, including material, labor, and equipment, and tend to be manual, time-consuming, and error-prone. To solve these problems, a building construction cost estimation model based on ontology representation and reasoning is established, which includes three major components, i.e., concept model ontology, work item ontology, and construction condition ontology. Using this model, the cost estimation information is modeled into OWL axioms and SWRL rules that leverage the semantically rich ontology representation to reason about cost estimation. Based on OWL axioms and SWRL rules, the cost estimation information can be translated into a set of concept models, work items, and construction conditions associated with the specific construction conditions. The proposed method is demonstrated in Protégé 3.4.8 through case studies based on the Measurement Specifications of Building Construction and Decoration Engineering taken from GB 50500-2013 (the Chinese national mandatory specifications. Finally, this research discusses the limitations of the proposed method and future research directions. The proposed method can help a building construction cost estimator extract information more easily and quickly.

  9. Facial expression recognition based on weber local descriptor and sparse representation

    Science.gov (United States)

    Ouyang, Yan

    2018-03-01

    Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

  10. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed

    2017-02-07

    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals\\'s mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users\\' check-ins and their network of friends, without impairing the model\\'s complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

  11. On the Representation of Subgrid Microtopography Effects in Process-based Hydrologic Models

    Science.gov (United States)

    Jan, A.; Painter, S. L.; Coon, E. T.

    2017-12-01

    Increased availability of high-resolution digital elevation are enabling process-based hydrologic modeling on finer and finer scales. However, spatial variability in surface elevation (microtopography) exists below the scale of a typical hyper-resolution grid cell and has the potential to play a significant role in water retention, runoff, and surface/subsurface interactions. Though the concept of microtopographic features (depressions, obstructions) and the associated implications on flow and discharge are well established, representing those effects in watershed-scale integrated surface/subsurface hydrology models remains a challenge. Using the complex and coupled hydrologic environment of the Arctic polygonal tundra as an example, we study the effects of submeter topography and present a subgrid model parameterized by small-scale spatial heterogeneities for use in hyper-resolution models with polygons at a scale of 15-20 meters forming the surface cells. The subgrid model alters the flow and storage terms in the diffusion wave equation for surface flow. We compare our results against sub-meter scale simulations (acts as a benchmark for our simulations) and hyper-resolution models without the subgrid representation. The initiation of runoff in the fine-scale simulations is delayed and the recession curve is slowed relative to simulated runoff using the hyper-resolution model with no subgrid representation. Our subgrid modeling approach improves the representation of runoff and water retention relative to models that ignore subgrid topography. We evaluate different strategies for parameterizing subgrid model and present a classification-based method to efficiently move forward to larger landscapes. This work was supported by the Interoperable Design of Extreme-scale Application Software (IDEAS) project and the Next-Generation Ecosystem Experiments-Arctic (NGEE Arctic) project. NGEE-Arctic is supported by the Office of Biological and Environmental Research in the

  12. Flutter signal extracting technique based on FOG and self-adaptive sparse representation algorithm

    Science.gov (United States)

    Lei, Jian; Meng, Xiangtao; Xiang, Zheng

    2016-10-01

    Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L0 norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".

  13. Predicting durations of online collective actions based on Peaks' heights

    Science.gov (United States)

    Lu, Peng; Nie, Shizhao; Wang, Zheng; Jing, Ziwei; Yang, Jianwu; Qi, Zhongxiang; Pujia, Wangmo

    2018-02-01

    Capturing the whole process of collective actions, the peak model contains four stages, including Prepare, Outbreak, Peak, and Vanish. Based on the peak model, one of the key variables, factors and parameters are further investigated in this paper, which is the rate between peaks and spans. Although the durations or spans and peaks' heights are highly diversified, it seems that the ratio between them is quite stable. If the rate's regularity is discovered, we can predict how long the collective action lasts and when it ends based on the peak's height. In this work, we combined mathematical simulations and empirical big data of 148 cases to explore the regularity of ratio's distribution. It is indicated by results of simulations that the rate has some regularities of distribution, which is not normal distribution. The big data has been collected from the 148 online collective actions and the whole processes of participation are recorded. The outcomes of empirical big data indicate that the rate seems to be closer to being log-normally distributed. This rule holds true for both the total cases and subgroups of 148 online collective actions. The Q-Q plot is applied to check the normal distribution of the rate's logarithm, and the rate's logarithm does follow the normal distribution.

  14. A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

    2006-10-01

    Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

  15. Model's sparse representation based on reduced mixed GMsFE basis methods

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn [Institute of Mathematics, Hunan University, Changsha 410082 (China); Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn [College of Mathematics and Econometrics, Hunan University, Changsha 410082 (China)

    2017-06-01

    In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in

  16. Model's sparse representation based on reduced mixed GMsFE basis methods

    International Nuclear Information System (INIS)

    Jiang, Lijian; Li, Qiuqi

    2017-01-01

    In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in

  17. A statistical prediction model based on sparse representations for single image super-resolution.

    Science.gov (United States)

    Peleg, Tomer; Elad, Michael

    2014-06-01

    We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.

  18. A fast image encryption system based on chaotic maps with finite precision representation

    International Nuclear Information System (INIS)

    Kwok, H.S.; Tang, Wallace K.S.

    2007-01-01

    In this paper, a fast chaos-based image encryption system with stream cipher structure is proposed. In order to achieve a fast throughput and facilitate hardware realization, 32-bit precision representation with fixed point arithmetic is assumed. The major core of the encryption system is a pseudo-random keystream generator based on a cascade of chaotic maps, serving the purpose of sequence generation and random mixing. Unlike the other existing chaos-based pseudo-random number generators, the proposed keystream generator not only achieves a very fast throughput, but also passes the statistical tests of up-to-date test suite even under quantization. The overall design of the image encryption system is to be explained while detail cryptanalysis is given and compared with some existing schemes

  19. Force Concept Inventory-Based Multiple-Choice Test for Investigating Students' Representational Consistency

    Science.gov (United States)

    Nieminen, Pasi; Savinainen, Antti; Viiri, Jouni

    2010-01-01

    This study investigates students' ability to interpret multiple representations consistently (i.e., representational consistency) in the context of the force concept. For this purpose we developed the Representational Variant of the Force Concept Inventory (R-FCI), which makes use of nine items from the 1995 version of the Force Concept Inventory…

  20. SROT: Sparse representation-based over-sampling technique for classification of imbalanced dataset

    Science.gov (United States)

    Zou, Xionggao; Feng, Yueping; Li, Huiying; Jiang, Shuyu

    2017-08-01

    As one of the most popular research fields in machine learning, the research on imbalanced dataset receives more and more attentions in recent years. The imbalanced problem usually occurs in when minority classes have extremely fewer samples than the others. Traditional classification algorithms have not taken the distribution of dataset into consideration, thus they fail to deal with the problem of class-imbalanced learning, and the performance of classification tends to be dominated by the majority class. SMOTE is one of the most effective over-sampling methods processing this problem, which changes the distribution of training sets by increasing the size of minority class. However, SMOTE would easily result in over-fitting on account of too many repetitive data samples. According to this issue, this paper proposes an improved method based on sparse representation theory and over-sampling technique, named SROT (Sparse Representation-based Over-sampling Technique). The SROT uses a sparse dictionary to create synthetic samples directly for solving the imbalanced problem. The experiments are performed on 10 UCI datasets using C4.5 as the learning algorithm. The experimental results show that compared our algorithm with Random Over-sampling techniques, SMOTE and other methods, SROT can achieve better performance on AUC value.

  1. Rigid Body Attitude Control Based on a Manifold Representation of Direction Cosine Matrices

    International Nuclear Information System (INIS)

    Nakath, David; Clemens, Joachim; Rachuy, Carsten

    2017-01-01

    Autonomous systems typically actively observe certain aspects of their surroundings, which makes them dependent on a suitable controller. However, building an attitude controller for three degrees of freedom is a challenging task, mainly due to singularities in the different parametrizations of the three dimensional rotation group SO (3). Thus, we propose an attitude controller based on a manifold representation of direction cosine matrices: In state space, the attitude is globally and uniquely represented as a direction cosine matrix R ∈ SO (3). However, differences in the state space, i.e., the attitude errors, are exposed to the controller in the vector space ℝ 3 . This is achieved by an operator, which integrates the matrix logarithm mapping from SO (3) to so(3) and the map from so(3) to ℝ 3 . Based on this representation, we derive a proportional and derivative feedback controller, whose output has an upper bound to prevent actuator saturation. Additionally, the feedback is preprocessed by a particle filter to account for measurement and state transition noise. We evaluate our approach in a simulator in three different spacecraft maneuver scenarios: (i) stabilizing, (ii) rest-to-rest, and (iii) nadir-pointing. The controller exhibits stable behavior from initial attitudes near and far from the setpoint. Furthermore, it is able to stabilize a spacecraft and can be used for nadir-pointing maneuvers. (paper)

  2. Improving Mobility Performance in Low Vision With a Distance-Based Representation of the Visual Scene.

    Science.gov (United States)

    van Rheede, Joram J; Wilson, Iain R; Qian, Rose I; Downes, Susan M; Kennard, Christopher; Hicks, Stephen L

    2015-07-01

    Severe visual impairment can have a profound impact on personal independence through its effect on mobility. We investigated whether the mobility of people with vision low enough to be registered as blind could be improved by presenting the visual environment in a distance-based manner for easier detection of obstacles. We accomplished this by developing a pair of "residual vision glasses" (RVGs) that use a head-mounted depth camera and displays to present information about the distance of obstacles to the wearer as brightness, such that obstacles closer to the wearer are represented more brightly. We assessed the impact of the RVGs on the mobility performance of visually impaired participants during the completion of a set of obstacle courses. Participant position was monitored continuously, which enabled us to capture the temporal dynamics of mobility performance. This allowed us to find correlates of obstacle detection and hesitations in walking behavior, in addition to the more commonly used measures of trial completion time and number of collisions. All participants were able to use the smart glasses to navigate the course, and mobility performance improved for those visually impaired participants with the worst prior mobility performance. However, walking speed was slower and hesitations increased with the altered visual representation. A depth-based representation of the visual environment may offer low vision patients improvements in independent mobility. It is important for further work to explore whether practice can overcome the reductions in speed and increased hesitation that were observed in our trial.

  3. The effect of project-based learning on students' statistical literacy levels for data representation

    Science.gov (United States)

    Koparan, Timur; Güven, Bülent

    2015-07-01

    The point of this study is to define the effect of project-based learning approach on 8th Grade secondary-school students' statistical literacy levels for data representation. To achieve this goal, a test which consists of 12 open-ended questions in accordance with the views of experts was developed. Seventy 8th grade secondary-school students, 35 in the experimental group and 35 in the control group, took this test twice, one before the application and one after the application. All the raw scores were turned into linear points by using the Winsteps 3.72 modelling program that makes the Rasch analysis and t-tests, and an ANCOVA analysis was carried out with the linear points. Depending on the findings, it was concluded that the project-based learning approach increases students' level of statistical literacy for data representation. Students' levels of statistical literacy before and after the application were shown through the obtained person-item maps.

  4. Rigid Body Attitude Control Based on a Manifold Representation of Direction Cosine Matrices

    Science.gov (United States)

    Nakath, David; Clemens, Joachim; Rachuy, Carsten

    2017-01-01

    Autonomous systems typically actively observe certain aspects of their surroundings, which makes them dependent on a suitable controller. However, building an attitude controller for three degrees of freedom is a challenging task, mainly due to singularities in the different parametrizations of the three dimensional rotation group SO(3). Thus, we propose an attitude controller based on a manifold representation of direction cosine matrices: In state space, the attitude is globally and uniquely represented as a direction cosine matrix R ∈ SO(3). However, differences in the state space, i.e., the attitude errors, are exposed to the controller in the vector space ℝ3. This is achieved by an operator, which integrates the matrix logarithm mapping from SO(3) to so(3) and the map from so(3) to ℝ3. Based on this representation, we derive a proportional and derivative feedback controller, whose output has an upper bound to prevent actuator saturation. Additionally, the feedback is preprocessed by a particle filter to account for measurement and state transition noise. We evaluate our approach in a simulator in three different spacecraft maneuver scenarios: (i) stabilizing, (ii) rest-to-rest, and (iii) nadir-pointing. The controller exhibits stable behavior from initial attitudes near and far from the setpoint. Furthermore, it is able to stabilize a spacecraft and can be used for nadir-pointing maneuvers.

  5. Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

    Science.gov (United States)

    Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

    2017-08-01

    Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7MMA) for 3MMA) for 0 ≤ x ≤ 3 score. The result shows that problem solving based learning model with multiple representations approach can be an alternative to be applied in improving students' MMA.

  6. Improving Conceptual Understanding and Representation Skills Through Excel-Based Modeling

    Science.gov (United States)

    Malone, Kathy L.; Schunn, Christian D.; Schuchardt, Anita M.

    2018-02-01

    The National Research Council framework for science education and the Next Generation Science Standards have developed a need for additional research and development of curricula that is both technologically model-based and includes engineering practices. This is especially the case for biology education. This paper describes a quasi-experimental design study to test the effectiveness of a model-based curriculum focused on the concepts of natural selection and population ecology that makes use of Excel modeling tools (Modeling Instruction in Biology with Excel, MBI-E). The curriculum revolves around the bio-engineering practice of controlling an invasive species. The study takes place in the Midwest within ten high schools teaching a regular-level introductory biology class. A post-test was designed that targeted a number of common misconceptions in both concept areas as well as representational usage. The results of a post-test demonstrate that the MBI-E students significantly outperformed the traditional classes in both natural selection and population ecology concepts, thus overcoming a number of misconceptions. In addition, implementing students made use of more multiple representations as well as demonstrating greater fascination for science.

  7. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  8. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  9. Psoriasis image representation using patch-based dictionary learning for erythema severity scoring.

    Science.gov (United States)

    George, Yasmeen; Aldeen, Mohammad; Garnavi, Rahil

    2018-02-23

    Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

  10. Establishing action levels for EPID‐based QA for IMRT

    Science.gov (United States)

    Smith, Iris P. N.; Jarrio, Christie S.

    2008-01-01

    Although portal dosimetry is used to provide quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) treatment plans, trends in agreement between the portal dose prediction (PDP) and the measured dose have not been clarified. In this work, we evaluated three scalar parameters of agreement for 152 treatment plans (1152 treatment fields): maximum gamma (γmax), average gamma (γavg), and percentage of the field area with a gamma value greater than 1.0 (γ%>1). These data were then used to set clinical action levels based on the institutional mean and standard deviations. We found that agreement between measured dose and PDP was improved by recalculating the fields at lower dose rates. We conclude that action levels are a useful tool for standardizing the evaluation of EPID‐based IMRT QA. PACS numbers: 87.53.Oq, 87.53.Mr, 87.53.Xd

  11. A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Data.gov (United States)

    National Aeronautics and Space Administration — This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework...

  12. Promoting health equity in cities through evidence-based action.

    Science.gov (United States)

    Kumaresan, Jacob; Prasad, Amit; Alwan, Ala; Ishikawa, Nobukatsu

    2010-09-01

    The impact of the urban setting on health and, in particular, health inequities has been widely documented. However, only a few countries have examined their inter- or intra-city health inequalities, and few do so regularly. Information that shows the gaps between cities or within the same city is a crucial requirement to trigger appropriate local actions to promote health equity. To generate relevant evidence and take appropriate actions to tackle health inequities, local authorities need a variety of tools. In order to facilitate a comprehensive understanding of health systems performance, these tools should: (1) adopt a multi-sectorial approach; (2) link evidence to actions; (3) be simple and user-friendly; and (4) be operationally feasible and sustainable. In this paper we have illustrated the use of one such tool, The World Health Organization's Urban HEART, which guides users through a process to identify health inequities, focusing on health determinants and then developing actions based on the evidence generated. In a time of increasing financial constraints, there is a pressing need to allocate scarce resources more efficiently. Tools are needed to guide policy makers in their planning process to identify best-practice interventions that promote health equity in their cities.

  13. Belief-based action prediction in preverbal infants.

    Science.gov (United States)

    Southgate, Victoria; Vernetti, Angelina

    2014-01-01

    Successful mindreading entails both the ability to think about what others know or believe, and to use this knowledge to generate predictions about how mental states will influence behavior. While previous studies have demonstrated that young infants are sensitive to others' mental states, there continues to be much debate concerning how to characterize early theory of mind abilities. In the current study, we asked whether 6-month-old infants appreciate the causal role that beliefs play in action. Specifically, we tested whether infants generate action predictions that are appropriate given an agent's current belief. We exploited a novel, neural indication of action prediction: motor cortex activation as measured by sensorimotor alpha suppression, to ask whether infants would generate differential predictions depending on an agent's belief. After first verifying our paradigm and measure with a group of adult participants, we found that when an agent had a false belief that a ball was in the box, motor activity indicated that infants predicted she would reach for the box, but when the agent had a false belief that a ball was not in the box, infants did not predict that she would act. In both cases, infants based their predictions on what the agent, rather than the infant, believed to be the case, suggesting that by 6months of age, infants can exploit their sensitivity to other minds for action prediction. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Introducing a Method for Transformation of Paper-Based Research Data into Concept-Based Representation with openEHR.

    Science.gov (United States)

    Saalfeld, Birgit; Tute, Erik; Wolf, Klaus-Hendrik; Marschollek, Michael

    2017-01-01

    Combining research data and clinical routine data is a chance for medical research. We present our method for the transformation of paper-based research data into a concept-based representation. With this representation the study data from research projects can be combined with data from clinical tools with less integration effort. We applied and verified our method using data from a current research study. In this paper we also show our main challenges and lessons learned. Clinical assessment data and study diaries from a long term study (n=24, 3 months observation time each, 17 different clinical assessments) stored on paper were used as the data set. An openEHR-based electronical health record platform was adapted for acquisition and representation of the research data. To avoid transcription errors, the data was entered twice by different student assistants. A third compared and corrected both data sets. Content models (17 archetypes and five templates from openEHR concept) based on clinical assessments were created manually. Web forms for data entry were created automatically on the basis of this concept-based content models. Additionally, form functionalities to support data entry and comparison were implemented. In total, 829 compositions were entered by the student assistants. With our developed method, we are able to represent the study data in a clinical concept-based platform, which means less integration effort for access and processing of research and clinical data. Some minor difficulties occurred during the process. All in all, adapting routine tools, like the EHR platform, seems to be convenient to deal with research data.

  15. Fast L1-based sparse representation of EEG for motor imagery signal classification.

    Science.gov (United States)

    Younghak Shin; Heung-No Lee; Balasingham, Ilangko

    2016-08-01

    Improvement of classification performance is one of the key challenges in electroencephalogram (EEG) based motor imagery brain-computer interface (BCI). Recently, sparse representation based classification (SRC) method has been shown to provide satisfactory classification accuracy in motor imagery classification. In this paper, we aim to evaluate the performance of the SRC method in terms of not only its classification accuracy but also of its computation time. For this purpose, we investigate the performance of recently developed fast L1 minimization methods for their use in SRC, such as homotopy and fast iterative soft-thresholding algorithm (FISTA). From experimental analysis, we note that the SRC method with the fast L1 minimization algorithms is shown to provide robust classification performance, compared to support vector machine (SVM), both in time and accuracy.

  16. Generating Multiple Base-Resolution DNA Methylomes Using Reduced Representation Bisulfite Sequencing.

    Science.gov (United States)

    Chatterjee, Aniruddha; Rodger, Euan J; Stockwell, Peter A; Le Mée, Gwenn; Morison, Ian M

    2017-01-01

    Reduced representation bisulfite sequencing (RRBS) is an effective technique for profiling genome-wide DNA methylation patterns in eukaryotes. RRBS couples size selection, bisulfite conversion, and second-generation sequencing to enrich for CpG-dense regions of the genome. The progressive improvement of second-generation sequencing technologies and reduction in cost provided an opportunity to examine the DNA methylation patterns of multiple genomes. Here, we describe a protocol for sequencing multiple RRBS libraries in a single sequencing reaction to generate base-resolution methylomes. Furthermore, we provide a brief guideline for base-calling and data analysis of multiplexed RRBS libraries. These strategies will be useful to perform large-scale, genome-wide DNA methylation analysis.

  17. Tensor-based cortical surface morphometry via weighted spherical harmonic representation.

    Science.gov (United States)

    Chung, Moo K; Dalton, Kim M; Davidson, Richard J

    2008-08-01

    We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.

  18. Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

    Science.gov (United States)

    Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

    2015-10-30

    Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

    Directory of Open Access Journals (Sweden)

    Xinzheng Zhang

    2016-09-01

    Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

  20. Gender in Facial Representations: A Contrast-Based Study of Adaptation within and between the Sexes

    Science.gov (United States)

    Oruç, Ipek; Guo, Xiaoyue M.; Barton, Jason J. S.

    2011-01-01

    Face aftereffects are proving to be an effective means of examining the properties of face-specific processes in the human visual system. We examined the role of gender in the neural representation of faces using a contrast-based adaptation method. If faces of different genders share the same representational face space, then adaptation to a face of one gender should affect both same- and different-gender faces. Further, if these aftereffects differ in magnitude, this may indicate distinct gender-related factors in the organization of this face space. To control for a potential confound between physical similarity and gender, we used a Bayesian ideal observer and human discrimination data to construct a stimulus set in which pairs of different-gender faces were equally dissimilar as same-gender pairs. We found that the recognition of both same-gender and different-gender faces was suppressed following a brief exposure of 100ms. Moreover, recognition was more suppressed for test faces of a different-gender than those of the same-gender as the adaptor, despite the equivalence in physical and psychophysical similarity. Our results suggest that male and female faces likely occupy the same face space, allowing transfer of aftereffects between the genders, but that there are special properties that emerge along gender-defining dimensions of this space. PMID:21267414

  1. Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery.

    Science.gov (United States)

    Xie, Qi; Zhao, Qian; Meng, Deyu; Xu, Zongben

    2017-08-02

    It is well known that the sparsity/low-rank of a vector/matrix can be rationally measured by nonzero-entries-number ($l_0$ norm)/nonzero- singular-values-number (rank), respectively. However, data from real applications are often generated by the interaction of multiple factors, which obviously cannot be sufficiently represented by a vector/matrix, while a high order tensor is expected to provide more faithful representation to deliver the intrinsic structure underlying such data ensembles. Unlike the vector/matrix case, constructing a rational high order sparsity measure for tensor is a relatively harder task. To this aim, in this paper we propose a measure for tensor sparsity, called Kronecker-basis-representation based tensor sparsity measure (KBR briefly), which encodes both sparsity insights delivered by Tucker and CANDECOMP/PARAFAC (CP) low-rank decompositions for a general tensor. Then we study the KBR regularization minimization (KBRM) problem, and design an effective ADMM algorithm for solving it, where each involved parameter can be updated with closed-form equations. Such an efficient solver makes it possible to extend KBR to various tasks like tensor completion and tensor robust principal component analysis. A series of experiments, including multispectral image (MSI) denoising, MSI completion and background subtraction, substantiate the superiority of the proposed methods beyond state-of-the-arts.

  2. Translating the representation of the tourist landscape: A corpus-based study

    Directory of Open Access Journals (Sweden)

    Gandin Stefania

    2015-06-01

    Full Text Available This paper will present a corpus-based study on the translated language of tourism, focusing in particular on the stylistics of tourist landscapes. Through a comparative analysis of a specifically designed corpus of travel articles originally written in English (namely the TourEC-Tourism English Corpus and a corpus of tourist texts translated from a variety of languages into English (namely the T-TourEC – Translational Tourism English Corpus, the study will investigate a selection of collocates, concordances and keywords related to the description and representation of tourist settings in both corpora. The aim will be that of identifying differences, aspects or practices to be potentially improved that characterize the translated language of tourism with respect to tourist texts originally written in English. Results will show that the discursive patterns of translated texts differ from the stylistic strategies typically employed in native English for the linguistic representation of landscape and settings due to phenomena of translation universals, and that these differences may affect the relating communicative functions, properties and persuasive effects of tourist promotional discourse.

  3. Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

    Directory of Open Access Journals (Sweden)

    Xiuquan Du

    2015-01-01

    Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

  4. Structure-reactivity modeling using mixture-based representation of chemical reactions.

    Science.gov (United States)

    Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre

    2017-09-01

    We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

  5. An iterative representer-based scheme for data inversion in reservoir modeling

    International Nuclear Information System (INIS)

    Iglesias, Marco A; Dawson, Clint

    2009-01-01

    In this paper, we develop a mathematical framework for data inversion in reservoir models. A general formulation is presented for the identification of uncertain parameters in an abstract reservoir model described by a set of nonlinear equations. Given a finite number of measurements of the state and prior knowledge of the uncertain parameters, an iterative representer-based scheme (IRBS) is proposed to find improved parameters. In this approach, the representer method is used to solve a linear data assimilation problem at each iteration of the algorithm. We apply the theory of iterative regularization to establish conditions for which the IRBS will converge to a stable approximation of a solution to the parameter identification problem. These theoretical results are applied to the identification of the second-order coefficient of a forward model described by a parabolic boundary value problem. Numerical results are presented to show the capabilities of the IRBS for the reconstruction of hydraulic conductivity from the steady-state of groundwater flow, as well as the absolute permeability in the single-phase Darcy flow through porous media

  6. A simplified representation of biochemical principles based on the dynamics of chemical flow systems.

    Science.gov (United States)

    Tienari, J

    1995-06-01

    This work suggests that the amount of information included in biochemistry texts is artificially increased, because the knowledge presented contains tautologies that are obscured by the use of inappropriate methods of representation. The work then proposes alternative methods of representation for describing biochemical systems that are based on the dynamics of an idealized chemical open-flow system. They would clarify the fact, in an open system, the concentrations of the reactants and reaction products depend not only on the equilibrium constants but on the absolute velocities of the reactions as well. Similar rules apply to phenomena involving other processes, such as diffusion of ions. Biochemical systems are considered as a set of chemical flow systems in which individual processes have the potential for interactions if their end products influence the rate of other processes. These interactions are used to explain how biochemical systems maintain themselves in states of high order. By use of these formulations, some of the logical sequences by which biochemical principles can be deduced from the principles of chemistry can be given simple and illustrative expression.

  7. Sparse representation based on local time-frequency template matching for bearing transient fault feature extraction

    Science.gov (United States)

    He, Qingbo; Ding, Xiaoxi

    2016-05-01

    The transients caused by the localized fault are important measurement information for bearing fault diagnosis. Thus it is crucial to extract the transients from the bearing vibration or acoustic signals that are always corrupted by a large amount of background noise. In this paper, an iterative transient feature extraction approach is proposed based on time-frequency (TF) domain sparse representation. The approach is realized by presenting a new method, called local TF template matching. In this method, the TF atoms are constructed based on the TF distribution (TFD) of the Morlet wavelet bases and local TF templates are formulated from the TF atoms for the matching process. The instantaneous frequency (IF) ridge calculated from the TFD of an analyzed signal provides the frequency parameter values for the TF atoms as well as an effective template matching path on the TF plane. In each iteration, local TF templates are employed to do correlation with the TFD of the analyzed signal along the IF ridge tube for identifying the optimum parameters of transient wavelet model. With this iterative procedure, transients can be extracted in the TF domain from measured signals one by one. The final signal can be synthesized by combining the extracted TF atoms and the phase of the raw signal. The local TF template matching builds an effective TF matching-based sparse representation approach with the merit of satisfying the native pulse waveform structure of transients. The effectiveness of the proposed method is verified by practical defective bearing signals. Comparison results also show that the proposed method is superior to traditional methods in transient feature extraction.

  8. Motor consciousness during intention-based and stimulus-based actions: modulating attention resources through Mindfulness meditation

    Directory of Open Access Journals (Sweden)

    Yvonne Nathalie Delevoye-Turrell

    2012-09-01

    Full Text Available Mindfulness-Based Stress Reduction meditation (MBSR may offer optimal performance through heightened attention for increased body consciousness. To test this hypothesis, MBSR effects were assessed on the simple task of lifting an object. A dual task paradigm was included to assess the opposite effect of a limited amount of attention on motor consciousness. In a stimulus-based condition, the subjects’ task was to lift an object that was hefted with weights. In an intentional-based condition, subjects were required to lift a light object while imagining that the object was virtually heavier and thus, adjust their grip voluntarily. The degree of motor consciousness was evaluated by calculating correlation factors for each participant between the grip force level used during the lift trial (lift the object and that used during its associated reproduce trial (without lifting, indicate the force you think you used in the previous trial. Under dual task condition, motor consciousness decreased for intention- and stimulus-based actions, revealing the importance of top-down attention for building the motor representation that guides action planning. For MBSR-experts, heightened attention provided stronger levels of motor consciousness; this was true for both intention and stimulus-based actions. For controls, heightened attention decreased the capacity to reproduce force levels, suggesting that voluntary top-down attention interfered with the automatic bottom-up emergence of body sensations.Our results provide strong arguments for involvement of two types of attention for the emergence of motor consciousness. Bottom-up attention would serve as an amplifier of motor-sensory afferences; Top down attention would help transfer the motor-sensory content from a pre-conscious to a conscious state of processing. MBSR would be a specific state for which both types of attention are optimally combined to provide experts with total experiences of their body in

  9. Formal TCA cycle description based on elementary actions

    Indian Academy of Sciences (India)

    Prakash

    2006-12-20

    Dec 20, 2006 ... to the key sets of atom of a molecule that are involved in the described process, whereas the .... sulfur atom; The SMIRKS representation of this transformation is given between parenthesis. (B) Representation of 3 BA ..... specific localization of the molecule that will perform it. Since BioΨ descriptions are ...

  10. Subject-based discriminative sparse representation model for detection of concealed information.

    Science.gov (United States)

    Akhavan, Amir; Moradi, Mohammad Hassan; Vand, Safa Rafiei

    2017-05-01

    The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

    Science.gov (United States)

    Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

    2014-12-01

    Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

  12. Comparison of representational spaces based on structural information in the development of QSAR models for benzylamino enaminone derivatives.

    Science.gov (United States)

    García, G Cerruela; Palacios-Bejarano, B; Ruiz, I Luque; Gómez-Nieto, M Á

    2012-10-01

    In this paper we study different representational spaces of molecule data sets based on 2D representation models for the building of QSAR models for the prediction of the activity of 37 benzylamino enaminone derivatives. Approximations based on classical similarity calculated from fingerprint representation of molecules and isomorphism obtained using sub-graph matching algorithms are compared to fragmentation-based approximations using partial least squares and genetic algorithms. The influence of the anchored position of a non-common moiety and the kind of substituents in the common core structure of the data set are analysed, demonstrating the anomalous behaviour of some molecules and therefore the difficulty in building prediction models. These problems are solved by considering approximate similarity models. These models tune the prediction equations based on the size of the substituent and the anchored position, by adjusting the contribution of each substituent in similarity measurements calculated between the molecule data sets.

  13. Sparse Representation Based Range-Doppler Processing for Integrated OFDM Radar-Communication Networks

    Directory of Open Access Journals (Sweden)

    Bo Kong

    2017-01-01

    Full Text Available In an integrated radar-communication network, multiuser access techniques with minimal performance degradation and without range-Doppler ambiguities are required, especially in a dense user environment. In this paper, a multiuser access scheme with random subcarrier allocation mechanism is proposed for orthogonal frequency division multiplexing (OFDM based integrated radar-communication networks. The expression of modulation Symbol-Domain method combined with sparse representation (SR for range-Doppler estimation is introduced and a parallel reconstruction algorithm is employed. The radar target detection performance is improved with less spectrum occupation. Additionally, a Doppler frequency detector is exploited to decrease the computational complexity. Numerical simulations show that the proposed method outperforms the traditional modulation Symbol-Domain method under ideal and realistic nonideal scenarios.

  14. Intrinsic resonance representation of quantum mechanics

    DEFF Research Database (Denmark)

    Carioli, M.; Heller, E.J.; Møller, Klaus Braagaard

    1997-01-01

    an optimal representation, based purely on classical mechanics. ''Hidden'' constants of the motion and good actions already known to the classical mechanics are thus incorporated into the basis, leaving the quantum effects to be isolated and included by small matrix diagonalizations. This simplifies...

  15. Hologram representation of design data in an expert system knowledge base

    Science.gov (United States)

    Shiva, S. G.; Klon, Peter F.

    1988-01-01

    A novel representational scheme for design object descriptions is presented. An abstract notion of modules and signals is developed as a conceptual foundation for the scheme. This abstraction relates the objects to the meaning of system descriptions. Anchored on this abstraction, a representational model which incorporates dynamic semantics for these objects is presented. This representational model is called a hologram scheme since it represents dual level information, namely, structural and semantic. The benefits of this scheme are presented.

  16. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Directory of Open Access Journals (Sweden)

    Su Yang

    Full Text Available Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1 Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2 The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3 The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  17. Efficient Online Aggregates in Dense-Region-Based Data Cube Representations

    Science.gov (United States)

    Haddadin, Kais; Lauer, Tobias

    In-memory OLAP systems require a space-efficient representation of sparse data cubes in order to accommodate large data sets. On the other hand, most efficient online aggregation techniques, such as prefix sums, are built on dense array-based representations. These are often not applicable to real-world data due to the size of the arrays which usually cannot be compressed well, as most sparsity is removed during pre-processing. A possible solution is to identify dense regions in a sparse cube and only represent those using arrays, while storing sparse data separately, e.g. in a spatial index structure. Previous dense-region-based approaches have concentrated mainly on the effectiveness of the dense-region detection (i.e. on the space-efficiency of the result). However, especially in higher-dimensional cubes, data is usually more cluttered, resulting in a potentially large number of small dense regions, which negatively affects query performance on such a structure. In this paper, our focus is not only on space-efficiency but also on time-efficiency, both for the initial dense-region extraction and for queries carried out in the resulting hybrid data structure. We describe two methods to trade available memory for increased aggregate query performance. In addition, optimizations in our approach significantly reduce the time to build the initial data structure compared to former systems. Also, we present a straightforward adaptation of our approach to support multi-core or multi-processor architectures, which can further enhance query performance. Experiments with different real-world data sets show how various parameter settings can be used to adjust the efficiency and effectiveness of our algorithms.

  18. Transient Variable Caching in Java’s Stack-Based Intermediate Representation

    Directory of Open Access Journals (Sweden)

    Paul Týma

    1999-01-01

    Full Text Available Java’s stack‐based intermediate representation (IR is typically coerced to execute on register‐based architectures. Unoptimized compiled code dutifully replicates transient variable usage designated by the programmer and common optimization practices tend to introduce further usage (i.e., CSE, Loop‐invariant Code Motion, etc.. On register based machines, often transient variables are cached within registers (when available saving the expense of actually accessing memory. Unfortunately, in stack‐based environments because of the need to push and pop the transient values, further performance improvement is possible. This paper presents Transient Variable Caching (TVC, a technique for eliminating transient variable overhead whenever possible. This optimization would find a likely home in optimizers attached to the back of popular Java compilers. Side effects of the algorithm include significant instruction reordering and introduction of many stack‐manipulation operations. This combination has proven to greatly impede the ability to decompile stack‐based IR code sequences. The code that results from the transform is faster, smaller, and greatly impedes decompilation.

  19. Commitment-based action: Rational choice theory and contrapreferential choice

    Directory of Open Access Journals (Sweden)

    Radovanović Bojana

    2014-01-01

    Full Text Available This paper focuses on Sen’s concept of contrapreferential choice. Sen has developed this concept in order to overcome weaknesses of the rational choice theory. According to rational choice theory a decision-maker can be always seen as someone who maximises utility, and each choice he makes as the one that brings to him the highest level of personal wellbeing. Sen argues that in some situations we chose alternatives that bring us lower level of wellbeing than we could achieve if we had chosen some other alternative available to us. This happens when we base our decisions on moral principles, when we act out of duty. Sen calls such action a commitment-based action. When we act out of commitment we actually neglect our preferences and thus we make a contrapreferential choice, as Sen argues. This paper shows that, contrary to Sen, a commitment-based action can be explained within the framework of rational choice theory. However, when each choice we make can be explained within the framework of rational choice theory, when in everything we do maximisation principle can be loaded, then the variety of our motives and traits is lost, and the explanatory power of the rational choice theory is questionable. [Projekat Ministarstva nauke Republike Srbije, br. 47009: Evropske integracije i društveno-ekonomske promene privrede Srbije na putu ka EU i br. 179015: Izazovi i perspektive strukturnih promena u Srbiji: Strateški pravci ekonomskog razvoja i usklađivanje sa zahtevima EU

  20. When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

    Directory of Open Access Journals (Sweden)

    Cong Wang

    2018-02-01

    Full Text Available When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc. among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

  1. Deep Learning based Super-Resolution for Improved Action Recognition

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Guerrero, Sergio Escalera; Rasti, Pejman

    2015-01-01

    and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation...... with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate...

  2. Building the knowledge base for environmental action and sustainability

    DEFF Research Database (Denmark)

    2015-01-01

    was “Building the knowledge base for environmental action and sustainability”. The joint conference was designed to facilitate ‘within‐the‐domain’, as well as to create a space for developing synergies between the two communities. Altogether 125 research and applied papers (including extended abstracts) from 42......‐Packard Joseph Kava, Vice President ‐ Data Centers, Google, California   Special thanks to Tania Nielsen who has acted as key person for all organisational matters, including contact point for speakers and participants and for the publication of the Conference Proceedings and Adjunct Proceedings. Thanks...

  3. Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification

    Directory of Open Access Journals (Sweden)

    Jie Hu

    2018-02-01

    Full Text Available Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems. We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF, TextRank and Rapid Automatic Keyword Extraction (RAKE. The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.

  4. Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum.

    Directory of Open Access Journals (Sweden)

    Makoto Ito

    2015-11-01

    Full Text Available Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the "win-stay, lose-switch" strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS, the dorsomedial striatum (DMS, and the ventral striatum (VS identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.

  5. Parallel Representation of Value-Based and Finite State-Based Strategies in the Ventral and Dorsal Striatum.

    Science.gov (United States)

    Ito, Makoto; Doya, Kenji

    2015-11-01

    Previous theoretical studies of animal and human behavioral learning have focused on the dichotomy of the value-based strategy using action value functions to predict rewards and the model-based strategy using internal models to predict environmental states. However, animals and humans often take simple procedural behaviors, such as the "win-stay, lose-switch" strategy without explicit prediction of rewards or states. Here we consider another strategy, the finite state-based strategy, in which a subject selects an action depending on its discrete internal state and updates the state depending on the action chosen and the reward outcome. By analyzing choice behavior of rats in a free-choice task, we found that the finite state-based strategy fitted their behavioral choices more accurately than value-based and model-based strategies did. When fitted models were run autonomously with the same task, only the finite state-based strategy could reproduce the key feature of choice sequences. Analyses of neural activity recorded from the dorsolateral striatum (DLS), the dorsomedial striatum (DMS), and the ventral striatum (VS) identified significant fractions of neurons in all three subareas for which activities were correlated with individual states of the finite state-based strategy. The signal of internal states at the time of choice was found in DMS, and for clusters of states was found in VS. In addition, action values and state values of the value-based strategy were encoded in DMS and VS, respectively. These results suggest that both the value-based strategy and the finite state-based strategy are implemented in the striatum.

  6. Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

    Directory of Open Access Journals (Sweden)

    Shunfang Wang

    2015-12-01

    Full Text Available An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC, pseudo-amino acid composition (PseAAC and position specific scoring matrix (PSSM, are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

  7. Gender Difference in the Use of Thought Representation--A Corpus-Based Study

    Science.gov (United States)

    Riissanen, Anne; Watson, Greg

    2014-01-01

    This study (Note 1) investigates potential differences in language use between genders, by applying a modified model of thought representation. Our hypothesis is that women use more direct forms of thought representation than men in modern spoken British English. Women are said to favour "private speech" that creates intimacy and…

  8. Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

    Science.gov (United States)

    Wang, Shunfang; Liu, Shuhui

    2015-12-19

    An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

  9. Different Brains Process Numbers Differently: Structural Bases of Individual Differences in Spatial and Nonspatial Number Representations

    NARCIS (Netherlands)

    Krause, F.; Lindemann, O.; Toni, I.; Bekkering, H.

    2014-01-01

    A dominant hypothesis on how the brain processes numerical size proposes a spatial representation of numbers as positions on a "mental number line." An alternative hypothesis considers numbers as elements of a generalized representation of sensorimotor-related magnitude, which is not obligatorily

  10. Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points

    Directory of Open Access Journals (Sweden)

    Nan Li

    2017-11-01

    Full Text Available The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC method for airborne LiDAR (Light Detection and Ranging points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers.

  11. Object-based representation and analysis of light and electron microscopic volume data using Blender.

    Science.gov (United States)

    Asadulina, Albina; Conzelmann, Markus; Williams, Elizabeth A; Panzera, Aurora; Jékely, Gáspár

    2015-07-25

    Rapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze. Here we exploit the advanced visualization capabilities and flexibility of the open-source platform Blender to visualize and analyze anatomical atlases. We use light-microscopy-based gene expression atlases and electron microscopy connectome volume data from larval stages of the marine annelid Platynereis dumerilii. We build object-based larval gene expression atlases in Blender and develop tools for annotation and coexpression analysis. We also represent and analyze connectome data including neuronal reconstructions and underlying synaptic connectivity. We demonstrate the power and flexibility of Blender for visualizing and exploring complex anatomical atlases. The resources we have developed for Platynereis will facilitate data sharing and the standardization of anatomical atlases for this species. The flexibility of Blender, particularly its embedded Python application programming interface, means that our methods can be easily extended to other organisms.

  12. Partition network into communities based on group action on sets

    Science.gov (United States)

    Lin, Shu-Cheng; Tuan, Han-Wen; Tung, Cheng-Tan; Julian, Peterson

    2018-02-01

    In this paper an improved algorithm is provided to detect communities within a network based on group action on sets (GAS). Modularity has been used as the criterion to revise the results of three previous papers, deriving a better method of partition for the network of Karate club. We developed a new method to replace the complicated GAS to achieve the same effect as GAS. Through four examples, we demonstrated that our revised approach reduced the computation amount of modularity values. Based on a branch marked example, a detailed example is provided by us to illustrate that there is too many cores in the initial stage of GAS approach to induce too many communities in the final partition. The findings shown here, will allow scholars to understand using GAS algorithm to partition a network into communities is an unreliable method.

  13. Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman...

  14. A dynamic texture-based approach to recognition of facial actions and their temporal models.

    Science.gov (United States)

    Koelstra, Sander; Pantic, Maja; Patras, Ioannis

    2010-11-01

    In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set.

  15. Wavelet-based joint estimation and encoding of depth-image-based representations for free-viewpoint rendering.

    Science.gov (United States)

    Maitre, Matthieu; Shinagawa, Yoshihisa; Do, Minh N

    2008-06-01

    We propose a wavelet-based codec for the static depth-image-based representation, which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel rate-distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piecewise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec.

  16. Force Concept Inventory-based multiple-choice test for investigating students’ representational consistency

    Directory of Open Access Journals (Sweden)

    Pasi Nieminen

    2010-08-01

    Full Text Available This study investigates students’ ability to interpret multiple representations consistently (i.e., representational consistency in the context of the force concept. For this purpose we developed the Representational Variant of the Force Concept Inventory (R-FCI, which makes use of nine items from the 1995 version of the Force Concept Inventory (FCI. These original FCI items were redesigned using various representations (such as motion map, vectorial and graphical, yielding 27 multiple-choice items concerning four central concepts underpinning the force concept: Newton’s first, second, and third laws, and gravitation. We provide some evidence for the validity and reliability of the R-FCI; this analysis is limited to the student population of one Finnish high school. The students took the R-FCI at the beginning and at the end of their first high school physics course. We found that students’ (n=168 representational consistency (whether scientifically correct or not varied considerably depending on the concept. On average, representational consistency and scientifically correct understanding increased during the instruction, although in the post-test only a few students performed consistently both in terms of representations and scientifically correct understanding. We also compared students’ (n=87 results of the R-FCI and the FCI, and found that they correlated quite well.

  17. Remote Sensing Image Fusion Method Based on Nonsubsampled Shearlet Transform and Sparse Representation

    Science.gov (United States)

    Moonon, Altan-Ulzii; Hu, Jianwen; Li, Shutao

    2015-12-01

    The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with sparse representation (SR) is proposed. Firstly, the low resolution multispectral (MS) image is upsampled and color space is transformed from Red-Green-Blue (RGB) to Intensity-Hue-Saturation (IHS). Then, the high resolution panchromatic (PAN) image and intensity component of MS image are decomposed by NSST to high and low frequency coefficients. The low frequency coefficients of PAN and the intensity component are fused by the SR with the learned dictionary. The high frequency coefficients of intensity component and PAN image are fused by local energy based fusion rule. Finally, the fused result is obtained by performing inverse NSST and inverse IHS transform. The experimental results on IKONOS and QuickBird satellites demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than other remote sensing image fusion methods both in visual effect and object evaluation.

  18. Students Mental Representation of Biology Diagrams/Pictures Conventions Based on Formation of Causal Network

    Science.gov (United States)

    Sampurno, A. W.; Rahmat, A.; Diana, S.

    2017-09-01

    Diagrams/pictures conventions is one form of visual media that often used to assist students in understanding the biological concepts. The effectiveness of use diagrams/pictures in biology learning at school level has also been mostly reported. This study examines the ability of high school students in reading diagrams/pictures biological convention which is described by Mental Representation based on formation of causal networks. The study involved 30 students 11th grade MIA senior high school Banten Indonesia who are studying the excretory system. MR data obtained by Instrument worksheet, developed based on CNET-protocol, in which there are diagrams/drawings of nephron structure and urinary mechanism. Three patterns formed MR, namely Markov chain, feedback control with a single measurement, and repeated feedback control with multiple measurement. The third pattern is the most dominating pattern, differences in the pattern of MR reveal the difference in how and from which point the students begin to uncover important information contained in the diagram to establish a causal networks. Further analysis shows that a difference in the pattern of MR relate to how complex the students process the information contained in the diagrams/pictures.

  19. Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary

    Directory of Open Access Journals (Sweden)

    Yubin Niu

    2016-03-01

    Full Text Available In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR and learned dictionary (LD has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result.

  20. Media Representations of Breech Birth: A Prospective Analysis of Web-Based News Reports.

    Science.gov (United States)

    Petrovska, Karolina; Sheehan, Athena; Homer, Caroline S E

    2017-07-01

    Recent research has demonstrated that the media presentation of childbirth is highly medicalized, often portraying birth as risky and dramatic. Media representation of breech presentation and birth is unexplored in this context. This study aimed to explore the content and tone of news media reports relating to breech presentation and breech birth. Google alerts were created using the terms breech and breech birth in online English-language news sites over a 3-year period from January 1, 2013, to December 31, 2015. Alerts were received daily and filed for analysis, and data were analyzed to generate themes. A total of 138 web-based news reports were gathered from 9 countries. Five themes that arose from the data included the problem of breech presentation, the high drama of vaginal breech birth, the safe option of cesarean birth versus dangers of vaginal breech birth, the defiant mother versus the saintly mother, and vaginal breech birth and medical misadventure. Media reports in this study predominantly demonstrated negative views toward breech presentation and vaginal breech birth. Cesarean birth was portrayed as the safe option for breech birth, while vaginal breech birth was associated with poor outcomes. Media presentations may impact decision making about mode of birth for pregnant women with a breech fetus. Health care providers can play an important role in balancing the media depiction of planned vaginal breech birth by providing nonjudgmental, evidence-based information to such women to facilitate informed decision making for birth. © 2017 by the American College of Nurse-Midwives.

  1. An Address Event Representation-Based Processing System for a Biped Robot

    Directory of Open Access Journals (Sweden)

    Uziel Jaramillo-Avila

    2016-02-01

    Full Text Available In recent years, several important advances have been made in the fields of both biologically inspired sensorial processing and locomotion systems, such as Address Event Representation-based cameras (or Dynamic Vision Sensors and in human-like robot locomotion, e.g., the walking of a biped robot. However, making these fields merge properly is not an easy task. In this regard, Neuromorphic Engineering is a fast-growing research field, the main goal of which is the biologically inspired design of hybrid hardware systems in order to mimic neural architectures and to process information in the manner of the brain. However, few robotic applications exist to illustrate them. The main goal of this work is to demonstrate, by creating a closed-loop system using only bio-inspired techniques, how such applications can work properly. We present an algorithm using Spiking Neural Networks (SNN for a biped robot equipped with a Dynamic Vision Sensor, which is designed to follow a line drawn on the floor. This is a commonly used method for demonstrating control techniques. Most of them are fairly simple to implement without very sophisticated components; however, it can still serve as a good test in more elaborate circumstances. In addition, the locomotion system proposed is able to coordinately control the six DOFs of a biped robot in switching between basic forms of movement. The latter has been implemented as a FPGA-based neuromorphic system. Numerical tests and hardware validation are presented.

  2. Friends reunited? Evolutionary robotics and representational explanation.

    Science.gov (United States)

    Wheeler, Michael

    2005-01-01

    Robotics as practiced within the artificial life community is no longer the bitter enemy of representational explanation in the way that it sometimes seemed to be in the heady, revolutionary days of the 1990s. This rapprochement is, however, fragile, because the field of evolutionary robotics continues to pose two important challenges to the idea that real-time intelligent action must or should be explained by appeal to inner representations. The first of these challenges, the threat from nontrivial causal spread, occurs when extra-neural factors account for the kind of adaptive richness and flexibility normally associated with representation-based control. The second, the threat from continuous reciprocal causation, occurs when the causal contributions made by the systemic components collectively responsible for behavior generation are massively context-sensitive and variable over time. I argue that while the threat from nontrivial causal spread can be resisted, the threat from continuous reciprocal causation provides a stern test for our representational intuitions.

  3. Ontology-based systematic representation and analysis of traditional Chinese drugs against rheumatism.

    Science.gov (United States)

    Liu, Qingping; Wang, Jiahao; Zhu, Yan; He, Yongqun

    2017-12-21

    Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs. In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e

  4. Effects of Ventral Striatum Lesions on Stimulus-Based versus Action-Based Reinforcement Learning.

    Science.gov (United States)

    Rothenhoefer, Kathryn M; Costa, Vincent D; Bartolo, Ramón; Vicario-Feliciano, Raquel; Murray, Elisabeth A; Averbeck, Bruno B

    2017-07-19

    Learning the values of actions versus stimuli may depend on separable neural circuits. In the current study, we evaluated the performance of rhesus macaques with ventral striatum (VS) lesions on a two-arm bandit task that had randomly interleaved blocks of stimulus-based and action-based reinforcement learning (RL). Compared with controls, monkeys with VS lesions had deficits in learning to select rewarding images but not rewarding actions. We used a RL model to quantify learning and choice consistency and found that, in stimulus-based RL, the VS lesion monkeys were more influenced by negative feedback and had lower choice consistency than controls. Using a Bayesian model to parse the groups' learning strategies, we also found that VS lesion monkeys defaulted to an action-based choice strategy. Therefore, the VS is involved specifically in learning the value of stimuli, not actions. SIGNIFICANCE STATEMENT Reinforcement learning models of the ventral striatum (VS) often assume that it maintains an estimate of state value. This suggests that it plays a general role in learning whether rewards are assigned based on a chosen action or stimulus. In the present experiment, we examined the effects of VS lesions on monkeys' ability to learn that choosing a particular action or stimulus was more likely to lead to reward. We found that VS lesions caused a specific deficit in the monkeys' ability to discriminate between images with different values, whereas their ability to discriminate between actions with different values remained intact. Our results therefore suggest that the VS plays a specific role in learning to select rewarded stimuli. Copyright © 2017 the authors 0270-6474/17/376902-13$15.00/0.

  5. Comprehensive evaluation of SNP identification with the Restriction Enzyme-based Reduced Representation Library (RRL method

    Directory of Open Access Journals (Sweden)

    Du Ye

    2012-02-01

    Full Text Available Abstract Background Restriction Enzyme-based Reduced Representation Library (RRL method represents a relatively feasible and flexible strategy used for Single Nucleotide Polymorphism (SNP identification in different species. It has remarkable advantage of reducing the complexity of the genome by orders of magnitude. However, comprehensive evaluation for actual efficacy of SNP identification by this method is still unavailable. Results In order to evaluate the efficacy of Restriction Enzyme-based RRL method, we selected Tsp 45I enzyme which covers 266 Mb flanking region of the enzyme recognition site according to in silico simulation on human reference genome, then we sequenced YH RRL after Tsp 45I treatment and obtained reads of which 80.8% were mapped to target region with an 20-fold average coverage, about 96.8% of target region was covered by at least one read and 257 K SNPs were identified in the region using SOAPsnp software. Compared with whole genome resequencing data, we observed false discovery rate (FDR of 13.95% and false negative rate (FNR of 25.90%. The concordance rate of homozygote loci was over 99.8%, but that of heterozygote were only 92.56%. Repeat sequences and bases quality were proved to have a great effect on the accuracy of SNP calling, SNPs in recognition sites contributed evidently to the high FNR and the low concordance rate of heterozygote. Our results indicated that repeat masking and high stringent filter criteria could significantly decrease both FDR and FNR. Conclusions This study demonstrates that Restriction Enzyme-based RRL method was effective for SNP identification. The results highlight the important role of bias and the method-derived defects represented in this method and emphasize the special attentions noteworthy.

  6. Ontology-based data integration from heterogeneous urban systems : A knowledge representation framework for smart cities

    NARCIS (Netherlands)

    Psyllidis, A.

    2015-01-01

    This paper presents a novel knowledge representation framework for smart city planning and management that enables the semantic integration of heterogeneous urban data from diverse sources. Currently, the combination of information across city agencies is cumbersome, as the increasingly available

  7. Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation.

    Science.gov (United States)

    Tzoumas, Stratis; Rosenthal, Amir; Lutzweiler, Christian; Razansky, Daniel; Ntziachristos, Vasilis

    2014-11-01

    One of the major challenges in dynamic multispectral optoacoustic imaging is its relatively low signal-to-noise ratio which often requires repetitive signal acquisition and averaging, thus limiting imaging rate. The development of denoising methods which prevent the need for signal averaging in time presents an important goal for advancing the dynamic capabilities of the technology. In this paper, a denoising method is developed for multispectral optoacoustic imaging which exploits the implicit sparsity of multispectral optoacoustic signals both in space and in spectrum. Noise suppression is achieved by applying thresholding on a combined wavelet-Karhunen-Loève representation in which multispectral optoacoustic signals appear particularly sparse. The method is based on inherent characteristics of multispectral optoacoustic signals of tissues, offering promise for general application in different incarnations of multispectral optoacoustic systems. The performance of the proposed method is demonstrated on mouse images acquired in vivo for two common additive noise sources: time-varying parasitic signals and white noise. In both cases, the proposed method shows considerable improvement in image quality in comparison to previously published denoising strategies that do not consider multispectral information. The suggested denoising methodology can achieve noise suppression with minimal signal loss and considerably outperforms previously proposed denoising strategies, holding promise for advancing the dynamic capabilities of multispectral optoacoustic imaging while retaining image quality.

  8. Locally linear representation Fisher criterion based tumor gene expressive data classification.

    Science.gov (United States)

    Li, Bo; Tian, Bei-Bei; Zhang, Xiao-Long; Zhang, Xiao-Ping

    2014-10-01

    Tumor gene expressive data are characterized by a large amount of genes with only a small amount of observations, which always appear with high dimensionality. So it is necessary to reduce the dimensionality before identifying their genre. In this paper, a discriminant manifold learning method, named locally linear representation Fisher criterion (LLRFC), is applied to extract features from tumor gene expressive data. In LLRFC, an inter-class graph and an intra-class graph are constructed based on their genre information, where any tumor gene expressive data in the inter-class graph should select k nearest neighbors with different class labels and in the intra-class graph the k nearest neighbors for any tumor gene expressive data must be sampled from those with the same class. And then the locally least linear reconstruction is introduced to optimize the corresponding weights in both graphs. Moreover, a Fisher criterion is modeled to explore a low dimensional subspace where the reconstruction errors in the inter-class graph can be maximized and the reconstruction errors in the intra-class graph can be minimized, simultaneously. Experiments on some benchmark tumor gene expressive data have been conducted with some related algorithms, by which the proposed LLRFC has been validated to be efficient. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

    Directory of Open Access Journals (Sweden)

    Xuan Zhu

    2017-01-01

    Full Text Available Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.

  10. 3D face recognition based on multiple keypoint descriptors and sparse representation.

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    Full Text Available Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD and the sparse representation-based classification (SRC. We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

  11. A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

    Directory of Open Access Journals (Sweden)

    Lianru Gao

    2017-11-01

    Full Text Available Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs. The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1 signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA; and (2 denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS, which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance.

  12. Information dimension analysis of bacterial essential and nonessential genes based on chaos game representation

    International Nuclear Information System (INIS)

    Zhou, Qian; Yu, Yong-ming

    2014-01-01

    Essential genes are indispensable for the survival of an organism. Investigating features associated with gene essentiality is fundamental to the prediction and identification of the essential genes. Selecting features associated with gene essentiality is fundamental to predict essential genes with computational techniques. We use fractal theory to make comparative analysis of essential and nonessential genes in bacteria. The information dimensions of essential genes and nonessential genes available in the DEG database for 27 bacteria are calculated based on their gene chaos game representations (CGRs). It is found that weak positive linear correlation exists between information dimension and gene length. Moreover, for genes of similar length, the average information dimension of essential genes is larger than that of nonessential genes. This indicates that essential genes show less regularity and higher complexity than nonessential genes. Our results show that for bacterium with a similar number of essential genes and nonessential genes, the CGR information dimension is helpful for the classification of essential genes and nonessential genes. Therefore, the gene CGR information dimension is very probably a useful gene feature for a genetic algorithm predicting essential genes. (paper)

  13. Foundations for Reasoning in Cognition-Based Computational Representations of Human Decision Making; TOPICAL

    International Nuclear Information System (INIS)

    SENGLAUB, MICHAEL E.; HARRIS, DAVID L.; RAYBOURN, ELAINE M.

    2001-01-01

    In exploring the question of how humans reason in ambiguous situations or in the absence of complete information, we stumbled onto a body of knowledge that addresses issues beyond the original scope of our effort. We have begun to understand the importance that philosophy, in particular the work of C. S. Peirce, plays in developing models of human cognition and of information theory in general. We have a foundation that can serve as a basis for further studies in cognition and decision making. Peircean philosophy provides a foundation for understanding human reasoning and capturing behavioral characteristics of decision makers due to cultural, physiological, and psychological effects. The present paper describes this philosophical approach to understanding the underpinnings of human reasoning. We present the work of C. S. Peirce, and define sets of fundamental reasoning behavior that would be captured in the mathematical constructs of these newer technologies and would be able to interact in an agent type framework. Further, we propose the adoption of a hybrid reasoning model based on his work for future computational representations or emulations of human cognition

  14. Information dimension analysis of bacterial essential and nonessential genes based on chaos game representation

    Science.gov (United States)

    Zhou, Qian; Yu, Yong-ming

    2014-11-01

    Essential genes are indispensable for the survival of an organism. Investigating features associated with gene essentiality is fundamental to the prediction and identification of the essential genes. Selecting features associated with gene essentiality is fundamental to predict essential genes with computational techniques. We use fractal theory to make comparative analysis of essential and nonessential genes in bacteria. The information dimensions of essential genes and nonessential genes available in the DEG database for 27 bacteria are calculated based on their gene chaos game representations (CGRs). It is found that weak positive linear correlation exists between information dimension and gene length. Moreover, for genes of similar length, the average information dimension of essential genes is larger than that of nonessential genes. This indicates that essential genes show less regularity and higher complexity than nonessential genes. Our results show that for bacterium with a similar number of essential genes and nonessential genes, the CGR information dimension is helpful for the classification of essential genes and nonessential genes. Therefore, the gene CGR information dimension is very probably a useful gene feature for a genetic algorithm predicting essential genes.

  15. Local curvature entropy-based 3D terrain representation using a comprehensive Quadtree

    Science.gov (United States)

    Chen, Qiyu; Liu, Gang; Ma, Xiaogang; Mariethoz, Gregoire; He, Zhenwen; Tian, Yiping; Weng, Zhengping

    2018-05-01

    Large scale 3D digital terrain modeling is a crucial part of many real-time applications in geoinformatics. In recent years, the improved speed and precision in spatial data collection make the original terrain data more complex and bigger, which poses challenges for data management, visualization and analysis. In this work, we presented an effective and comprehensive 3D terrain representation based on local curvature entropy and a dynamic Quadtree. The Level-of-detail (LOD) models of significant terrain features were employed to generate hierarchical terrain surfaces. In order to reduce the radical changes of grid density between adjacent LODs, local entropy of terrain curvature was regarded as a measure of subdividing terrain grid cells. Then, an efficient approach was presented to eliminate the cracks among the different LODs by directly updating the Quadtree due to an edge-based structure proposed in this work. Furthermore, we utilized a threshold of local entropy stored in each parent node of this Quadtree to flexibly control the depth of the Quadtree and dynamically schedule large-scale LOD terrain. Several experiments were implemented to test the performance of the proposed method. The results demonstrate that our method can be applied to construct LOD 3D terrain models with good performance in terms of computational cost and the maintenance of terrain features. Our method has already been deployed in a geographic information system (GIS) for practical uses, and it is able to support the real-time dynamic scheduling of large scale terrain models more easily and efficiently.

  16. Ability-Based View in Action: A Software Corporation Study

    Directory of Open Access Journals (Sweden)

    Farley Simon Nobre

    2014-04-01

    Full Text Available This research investigates antecedents, developments and consequences of dynamic capabilities in an organization. It contributes by searching theoretical and empirical answers to the questions: (a What are the antecedents which can provide an organization with dynamic and ordinary capabilities?; (b How do these antecedents contribute to create capabilities in an organization?; (c How do they affect an organization’s competitive advantage?; (d Can we assess and measure the antecedents and consequences to an organization? From a first (theoretical perspective, this paper searches answers to the first, second and third questions by reviewing concepts of an ability-based view of organizations that involves the abilities of cognition, intelligence, autonomy, learning and knowledge management, and which contributes to explain the dynamic behavior of the firm in the pursuit of competitive advantage. From a second (empirical perspective, this paper reinforces and delivers findings to the second, third and fourth questions by presenting a case study that evidences the ability-based view in action in a software corporation, where it contributes by investigating: (a the development of organizational capabilities; (b the effects of the new capabilities on the organization; and (c the assessment and measurement of the abilities and consequences.

  17. Representation Discovery using Harmonic Analysis

    CERN Document Server

    Mahadevan, Sridhar

    2008-01-01

    Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particu

  18. Embedded data representations

    DEFF Research Database (Denmark)

    Willett, Wesley; Jansen, Yvonne; Dragicevic, Pierre

    2017-01-01

    We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles ......-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications....

  19. Visual tracking based on the sparse representation of the PCA subspace

    Science.gov (United States)

    Chen, Dian-bing; Zhu, Ming; Wang, Hui-li

    2017-09-01

    We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

  20. The application of brain-based learning principles aided by GeoGebra to improve mathematical representation ability

    Science.gov (United States)

    Priatna, Nanang

    2017-08-01

    The use of Information and Communication Technology (ICT) in mathematics instruction will help students in building conceptual understanding. One of the software products used in mathematics instruction is GeoGebra. The program enables simple visualization of complex geometric concepts and helps improve students' understanding of geometric concepts. Instruction applying brain-based learning principles is one oriented at the efforts of naturally empowering the brain potentials which enable students to build their own knowledge. One of the goals of mathematics instruction in school is to develop mathematical communication ability. Mathematical representation is regarded as a part of mathematical communication. It is a description, expression, symbolization, or modeling of mathematical ideas/concepts as an attempt of clarifying meanings or seeking for solutions to the problems encountered by students. The research aims to develop a learning model and teaching materials by applying the principles of brain-based learning aided by GeoGebra to improve junior high school students' mathematical representation ability. It adopted a quasi-experimental method with the non-randomized control group pretest-posttest design and the 2x3 factorial model. Based on analysis of the data, it is found that the increase in the mathematical representation ability of students who were treated with mathematics instruction applying the brain-based learning principles aided by GeoGebra was greater than the increase of the students given conventional instruction, both as a whole and based on the categories of students' initial mathematical ability.

  1. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

    Science.gov (United States)

    He, Fei; Han, Ye; Gong, Jianting; Song, Jiazhi; Wang, Han; Li, Yanwen

    2017-03-01

    Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.

  2. Visualization of large influenza virus sequence datasets using adaptively aggregated trees with sampling-based subscale representation

    Directory of Open Access Journals (Sweden)

    Tatusova Tatiana A

    2008-05-01

    Full Text Available Abstract Background With the amount of influenza genome sequence data growing rapidly, researchers need machine assistance in selecting datasets and exploring the data. Enhanced visualization tools are required to represent results of the exploratory analysis on the web in an easy-to-comprehend form and to facilitate convenient information retrieval. Results We developed an approach to visualize large phylogenetic trees in an aggregated form with a special representation of subscale details. The initial aggregated tree representation is built with a level of resolution automatically selected to fit into the available screen space, with terminal groups selected based on sequence similarity. The default aggregated representation can be refined by users interactively. Structure and data variability within terminal groups are displayed using small trees that have the same vertical size as the text annotation of the group. These subscale representations are calculated using systematic sampling from the corresponding terminal group. The aggregated tree containing terminal groups can be annotated using aggregation of structured metadata, such as seasonal distribution, geographic locations, etc. Availability The algorithms are implemented in JavaScript within the NCBI Influenza Virus Resource 1.

  3. Multi-scale Gaussian representation and outline-learning based cell image segmentation.

    Science.gov (United States)

    Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Dehio, Christoph; Yli-Harja, Olli

    2013-01-01

    High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

  4. The construction of semantic memory: grammar based representations learned from relational episodic information

    Directory of Open Access Journals (Sweden)

    Francesco P Battaglia

    2011-08-01

    Full Text Available After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation, collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside-outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of ``sleep replay'' of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata.

  5. Block-diagonal representations for covariance-based anomalous change detectors

    Energy Technology Data Exchange (ETDEWEB)

    Matsekh, Anna M [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

    2010-01-01

    We use singular vectors of the whitened cross-covariance matrix of two hyper-spectral images and the Golub-Kahan permutations in order to obtain equivalent tridiagonal representations of the coefficient matrices for a family of covariance-based quadratic Anomalous Change Detection (ACD) algorithms. Due to the nature of the problem these tridiagonal matrices have block-diagonal structure, which we exploit to derive analytical expressions for the eigenvalues of the coefficient matrices in terms of the singular values of the whitened cross-covariance matrix. The block-diagonal structure of the matrices of the RX, Chronochrome, symmetrized Chronochrome, Whitened Total Least Squares, Hyperbolic and Subpixel Hyperbolic Anomalous Change Detectors are revealed by the white singular value decomposition and Golub-Kahan transformations. Similarities and differences in the properties of these change detectors are illuminated by their eigenvalue spectra. We presented a methodology that provides the eigenvalue spectrum for a wide range of quadratic anomalous change detectors. Table I summarizes these results, and Fig. I illustrates them. Although their eigenvalues differ, we find that RX, HACD, Subpixel HACD, symmetrized Chronochrome, and WTLSQ share the same eigenvectors. The eigen vectors for the two variants of Chronochrome defined in (18) are different, and are different from each other, even though they share many (but not all, unless d{sub x} = d{sub y}) eigenvalues. We demonstrated that it is sufficient to compute SVD of the whitened cross covariance matrix of the data in order to almost immediately obtain highly structured sparse matrices (and their eigenvalue spectra) of the coefficient matrices of these ACD algorithms in the white SVD-transformed coordinates. Converting to the original non-white coordinates, these eigenvalues will be modified in magnitude but not in sign. That is, the number of positive, zero-valued, and negative eigenvalues will be conserved.

  6. The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information

    Science.gov (United States)

    Battaglia, Francesco P.; Pennartz, Cyriel M. A.

    2011-01-01

    After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation), collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside–outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of “sleep replay” of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata. PMID:21887143

  7. A New Representation in PSO for Discretization-Based Feature Selection.

    Science.gov (United States)

    Tran, Binh; Xue, Bing; Zhang, Mengjie

    2017-06-23

    In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity.

  8. Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

    Science.gov (United States)

    Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

    2018-02-01

    Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

  9. Multi-scale Gaussian representation and outline-learning based cell image segmentation

    Science.gov (United States)

    2013-01-01

    Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

  10. A flexible code based on a scalar representation of toroidal MHD

    International Nuclear Information System (INIS)

    Denton, R.E.; Maschke, E.K.; Urquijo, G.

    1994-01-01

    An exact representation of magneto-hydrodynamics in terms of stream functions and potentials allows to write systems of reduced MHD equations of various complexity, depending on the problem to be investigated. Using this feature of the scalar representation, we develop a code for the nonlinear evolution of solutions of arbitrary reduced systems or of the complete MHD equations. The equations are written in general toroidal flux coordinates r, θ, C. In the present phase of development the code solves three evolutions equations for the vorticity density w, the flux Ψ and the pressure p in toroidal or cylindrical geometry. First results on tearing modes will be presented. (authors). 8 refs., 2 figs

  11. Evaluating a problem based learning course: an action research study.

    Science.gov (United States)

    Walker, J; Bailey, S; Brasell-Brian, R; Gould, S

    2001-03-01

    Problem based learning (PBL) has been widely used in the United States, United Kingdom and Australasia in undergraduate nursing education to develop critical thinking and problem solving skills. PBL has been used since 1996 in a Bachelor of Nursing course at a New Zealand tertiary institution, and several modifications have been made to foster effective learning. The 'pure' PBL process has been adapted to move students gradually from teacher direction to taking responsibility for their learning. This has provided the opportunity for students to develop critical thinking, problem solving, information retrieval and evaluation skills, and group process skills over an 18-week period. Because rigorous evaluation of these changes had not been formally undertaken, the purpose of this study was to evaluate how the current format was developing students' understanding and integration of knowledge. Two cycles of the action research method (Cardno and Piggot-Irvine, 1994) were used, involving 4 lecturers and 17 students. Data was collected both quantitatively and qualitatively over a 16-week period. Findings indicated the importance of: explaining the purpose and process of PBL; communicating in detail the role of both students and lecturers; keeping communication lines open; addressing timetabling issues and valuing this method of learning for nursing practice. Implications for nursing education are addressed.

  12. 3D base: a geometrical data base system for the analysis and visualisation of 3D-shapes obtained from parallel serial sections including three different geometrical representations

    NARCIS (Netherlands)

    Verbeek, F. J.; de Groot, M. M.; Huijsmans, D. P.; Lamers, W. H.; Young, I. T.

    1993-01-01

    In this paper we discuss a geometrical data base that includes three different geometrical representations of one and the same reconstructed 3D shape: the contour-pile, the voxel enumeration, and the triangulation of a surface. The data base is tailored for 3D shapes obtained from plan-parallel

  13. A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Science.gov (United States)

    Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

  14. Concise and Accessible Representations for Multidimensional Datasets: Introducing a Framework Based on the nD-EVM and Kohonen Networks

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2015-01-01

    Full Text Available A new framework intended for representing and segmenting multidimensional datasets resulting in low spatial complexity requirements and with appropriate access to their contained information is described. Two steps are going to be taken in account. The first step is to specify (n-1D hypervoxelizations, n≥2, as Orthogonal Polytopes whose nth dimension corresponds to color intensity. Then, the nD representation is concisely expressed via the Extreme Vertices Model in the n-Dimensional Space (nD-EVM. Some examples are presented, which, under our methodology, have storing requirements minor than those demanded by their original hypervoxelizations. In the second step, 1-Dimensional Kohonen Networks (1D-KNs are applied in order to segment datasets taking in account their geometrical and topological properties providing a non-supervised way to compact even more the proposed n-Dimensional representations. The application of our framework shares compression ratios, for our set of study cases, in the range 5.6496 to 32.4311. Summarizing, the contribution combines the power of the nD-EVM and 1D-KNs by producing very concise datasets’ representations. We argue that the new representations also provide appropriate segmentations by introducing some error functions such that our 1D-KNs classifications are compared against classifications based only in color intensities. Along the work, main properties and algorithms behind the nD-EVM are introduced for the purpose of interrogating the final representations in such a way that it efficiently obtains useful geometrical and topological information.

  15. MAP-Motivated Carrier Synchronization of GMSK Based on the Laurent AMP Representation

    Science.gov (United States)

    Simon, M. K.

    1998-01-01

    Using the MAP estimation approach to carrier synchronization of digital modulations containing ISI together with a two pulse stream AMP representation of GMSK, it is possible to obtain an optimum closed loop configuration in the same manner as has been previously proposed for other conventional modulations with ISI.

  16. Eighth Grade Students' Representations of Linear Equations Based on a Cups and Tiles Model

    Science.gov (United States)

    Caglayan, Gunhan; Olive, John

    2010-01-01

    This study examines eighth grade students' use of a representational metaphor (cups and tiles) for writing and solving equations in one unknown. Within this study, we focused on the obstacles and difficulties that students experienced when using this metaphor, with particular emphasis on the operations that can be meaningfully represented through…

  17. Embodied Numerosity: Implicit Hand-Based Representations Influence Symbolic Number Processing across Cultures

    Science.gov (United States)

    Domahs, Frank; Moeller, Korbinian; Huber, Stefan; Willmes, Klaus; Nuerk, Hans-Christoph

    2010-01-01

    In recent years, a strong functional relationship between finger counting and number processing has been suggested. Developmental studies have shown specific effects of the structure of the individual finger counting system on arithmetic abilities. Moreover, the orientation of the mental quantity representation ("number line") seems to be…

  18. Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

    NARCIS (Netherlands)

    van Noord, Rik; Bos, Johannes

    2017-01-01

    We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-to-sequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1

  19. Developing Instruction Materials Based on Joyful PBL to Improve Students Mathematical Representation Ability

    Science.gov (United States)

    Minarni, Ani; Napitupulu, E. Elvis

    2017-01-01

    Solving problem either within mathematics or beyond is one of the ultimate goal students learn mathematics. It is since mathematics takes role tool as well as vehicle to develop problem solving ability. One of the supporting components to problem solving is mathematical representation ability (MRA). Nowadays, many teachers and researchers find out…

  20. Quantum Computation-Based Image Representation, Processing Operations and Their Applications

    Directory of Open Access Journals (Sweden)

    Fei Yan

    2014-10-01

    Full Text Available A flexible representation of quantum images (FRQI was proposed to facilitate the extension of classical (non-quantum-like image processing applications to the quantum computing domain. The representation encodes a quantum image in the form of a normalized state, which captures information about colors and their corresponding positions in the images. Since its conception, a handful of processing transformations have been formulated, among which are the geometric transformations on quantum images (GTQI and the CTQI that are focused on the color information of the images. In addition, extensions and applications of FRQI representation, such as multi-channel representation for quantum images (MCQI, quantum image data searching, watermarking strategies for quantum images, a framework to produce movies on quantum computers and a blueprint for quantum video encryption and decryption have also been suggested. These proposals extend classical-like image and video processing applications to the quantum computing domain and offer a significant speed-up with low computational resources in comparison to performing the same tasks on traditional computing devices. Each of the algorithms and the mathematical foundations for their execution were simulated using classical computing resources, and their results were analyzed alongside other classical computing equivalents. The work presented in this review is intended to serve as the epitome of advances made in FRQI quantum image processing over the past five years and to simulate further interest geared towards the realization of some secure and efficient image and video processing applications on quantum computers.

  1. Mis/Representations in School-Based Digital Media Production: An Ethnographic Exploration with Muslim Girls

    Science.gov (United States)

    Dahya, Negin; Jenson, Jennifer

    2015-01-01

    In this article, the authors discuss findings from a digital media production club with racialized girls in a low-income school in Toronto, Ontario. Specifically, the authors consider how student-produced media is impacted by ongoing postcolonial structures relating to power and representation in the school and in the media production work of…

  2. Visualising mental representations: a primer on noise-based reverse correlation in social psychology

    NARCIS (Netherlands)

    Brinkman, L|info:eu-repo/dai/nl/400417464; Todorov, Alexander; Dotsch, R|info:eu-repo/dai/nl/328554197

    2017-01-01

    In recent years, social psychologists have adopted the psychophysical method of reverse correlation. Reverse correlation is a data-driven method in which judgments of randomly varying stimuli are used to reconstruct participants' internal representation subtending those judgments. Here, we review

  3. Developing Young Adults' Representational Competence through Infographic-Based Science News Reporting

    Science.gov (United States)

    Gebre, Engida H.; Polman, Joseph L.

    2016-01-01

    This study presents descriptive analysis of young adults' use of multiple representations in the context of science news reporting. Across one semester, 71 high school students, in a socioeconomically diverse suburban secondary school in Midwestern United States, participated in activities of researching science topics of their choice and…

  4. Heart rate variability analysis based on time–frequency representation and entropies in hypertrophic cardiomyopathy patients

    International Nuclear Information System (INIS)

    Clariá, F; Vallverdú, M; Caminal, P; Baranowski, R; Chojnowska, L

    2008-01-01

    In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time–frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0–0.04 Hz), low frequency band (LF, 0.04–0.15 Hz) and high frequency band (HF, 0.15–0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of

  5. Episodic Reasoning for Vision-Based Human Action Recognition

    Directory of Open Access Journals (Sweden)

    Maria J. Santofimia

    2014-01-01

    Full Text Available Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

  6. Sharing Control: Developing Research Literacy through Community-Based Action Research

    Science.gov (United States)

    Juergensmeyer, Erik

    2011-01-01

    This article suggests that the methodology of community-based action research provides concrete strategies for fostering effective community problem solving. To argue for a community research pedagogy, the author draws upon past and present scholarship in action research and participatory action research, experiences teaching an undergraduate…

  7. Representational Thickness

    DEFF Research Database (Denmark)

    Mullins, Michael

    Contemporary communicational and informational processes contribute to the shaping of our physical environment by having a powerful influence on the process of design. Applications of virtual reality (VR) are transforming the way architecture is conceived and produced by introducing dynamic...... elements into the process of design. Through its immersive properties, virtual reality allows access to a spatial experience of a computer model very different to both screen based simulations as well as traditional forms of architectural representation. The dissertation focuses on processes of the current...... by ‘professionals’ to ‘laypeople’. The thesis articulates problems in VR’s current application, specifically the CAVE and Panorama theatres, and seeks an understanding of how these problems may be addressed. The central questions that have motivated this research project are thus: What is architectural VR...

  8. Categorification and higher representation theory

    CERN Document Server

    Beliakova, Anna

    2017-01-01

    The emergent mathematical philosophy of categorification is reshaping our view of modern mathematics by uncovering a hidden layer of structure in mathematics, revealing richer and more robust structures capable of describing more complex phenomena. Categorified representation theory, or higher representation theory, aims to understand a new level of structure present in representation theory. Rather than studying actions of algebras on vector spaces where algebra elements act by linear endomorphisms of the vector space, higher representation theory describes the structure present when algebras act on categories, with algebra elements acting by functors. The new level of structure in higher representation theory arises by studying the natural transformations between functors. This enhanced perspective brings into play a powerful new set of tools that deepens our understanding of traditional representation theory. This volume exhibits some of the current trends in higher representation theory and the diverse te...

  9. Regional Densification of a Global VTEC Model Based on B-Spline Representations

    Science.gov (United States)

    Erdogan, Eren; Schmidt, Michael; Dettmering, Denise; Goss, Andreas; Seitz, Florian; Börger, Klaus; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm F.; Bothmer, Volker; Hinrichs, Johannes; Mrotzek, Niclas

    2017-04-01

    The project OPTIMAP is a joint initiative of the Bundeswehr GeoInformation Centre (BGIC), the German Space Situational Awareness Centre (GSSAC), the German Geodetic Research Institute of the Technical University Munich (DGFI-TUM) and the Institute for Astrophysics at the University of Göttingen (IAG). The main goal of the project is the development of an operational tool for ionospheric mapping and prediction (OPTIMAP). Two key features of the project are the combination of different satellite observation techniques (GNSS, satellite altimetry, radio occultations and DORIS) and the regional densification as a remedy against problems encountered with the inhomogeneous data distribution. Since the data from space-geoscientific mission which can be used for modeling ionospheric parameters, such as the Vertical Total Electron Content (VTEC) or the electron density, are distributed rather unevenly over the globe at different altitudes, appropriate modeling approaches have to be developed to handle this inhomogeneity. Our approach is based on a two-level strategy. To be more specific, in the first level we compute a global VTEC model with a moderate regional and spectral resolution which will be complemented in the second level by a regional model in a densification area. The latter is a region characterized by a dense data distribution to obtain a high spatial and spectral resolution VTEC product. Additionally, the global representation means a background model for the regional one to avoid edge effects at the boundaries of the densification area. The presented approach based on a global and a regional model part, i.e. the consideration of a regional densification is called the Two-Level VTEC Model (TLVM). The global VTEC model part is based on a series expansion in terms of polynomial B-Splines in latitude direction and trigonometric B-Splines in longitude direction. The additional regional model part is set up by a series expansion in terms of polynomial B-splines for

  10. Human action perspectives based on individual plant examination results

    International Nuclear Information System (INIS)

    Forester, J.; Thompson, C.; Drouin, M.; Lois, E.

    1996-01-01

    This paper provides perspectives on human actions gained from reviewing 76 individual plant examination (IPE) submittals. Human actions found to be important in boiling water reactors (BWRs) and in pressurized water reactors (PWRs) are presented and the events most frequently found important are discussed. Since there are numerous factors that can influence the quantification of human error probabilities (HEPs) and introduce significant variability in the resulting HEPs (which in turn can influence which events are found to be important), the variability in HEPs for similar events across IPEs is examined to assess the extent to which variability in results is due to real versus artifactual differences. Finally, similarities and differences in human action observations across BWRs and PWRs are examined

  11. Function and Form of Action-Based Teaching in Higher Education

    DEFF Research Database (Denmark)

    Keiding, Tina Bering

    The aim of the research is to subject progressive, critical and entrepreneurial pedagogy to a didactic inquiry based on the specific application of action-based teaching in order to answer two fundamental didactic questions: What educational purpose does the use of action-based teaching serve? How...

  12. Function and Form of Action-Based Teaching in Higher Education

    DEFF Research Database (Denmark)

    Keiding, Tina Bering

    The aim of the research is to subject progressive, critical and entrepreneurial pedagogy to a didactic inquiry based on the specific application of action-based teaching in order to answer two fundamental didactic questions: What educational purpose does the use of action-based teaching serve? How...... does the educational purpose affect the specific form of the constituting elements of the method?...

  13. A Review of Computer-Based Human Behavior Representations and Their Relation to Military Simulations

    Science.gov (United States)

    2003-08-01

    Analyses of these models suggested the following generalizations concerning the current state of the art in human behavior modeling: (1) decision making is...One of the goals of the Defense Modeling and Simulation Office (DMSO) has been to promote the development and assessment of computational human ... behavior representations (HBRs) that potentially provide synthetic forces -- both Red and Blue -- for live, virtual, and constructive military simulations

  14. Visualising mental representations: a primer on noise-based reverse correlation in social psychology

    OpenAIRE

    Brinkman, L; Todorov, Alexander; Dotsch, R

    2017-01-01

    In recent years, social psychologists have adopted the psychophysical method of reverse correlation. Reverse correlation is a data-driven method in which judgments of randomly varying stimuli are used to reconstruct participants' internal representation subtending those judgments. Here, we review the method and its findings within the context of social psychology, discussing its promises, achievements, and validity. Our review suggests that, even in these early stages, the technique has prove...

  15. Multiple domains of parental secure base support during childhood and adolescence contribute to adolescents' representations of attachment as a secure base script.

    Science.gov (United States)

    Vaughn, Brian E; Waters, Theodore E A; Steele, Ryan D; Roisman, Glenn I; Bost, Kelly K; Truitt, Warren; Waters, Harriet S; Booth-Laforce, Cathryn

    2016-08-01

    Although attachment theory claims that early attachment representations reflecting the quality of the child's "lived experiences" are maintained across developmental transitions, evidence that has emerged over the last decade suggests that the association between early relationship quality and adolescents' attachment representations is fairly modest in magnitude. We used aspects of parenting beyond sensitivity over childhood and adolescence and early security to predict adolescents' scripted attachment representations. At age 18 years, 673 participants from the NICHD Study of Early Child Care and Youth Development completed the Attachment Script Assessment from which we derived an assessment of secure base script knowledge. Measures of secure base support from childhood through age 15 years (e.g., parental monitoring of child activity, father presence in the home) were selected as predictors and accounted for an additional 8% of the variance in secure base script knowledge scores above and beyond direct observations of sensitivity and early attachment status alone, suggesting that adolescents' scripted attachment representations reflect multiple domains of parenting. Cognitive and demographic variables also significantly increased predicted variance in secure base script knowledge by 2% each.

  16. A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering

    Directory of Open Access Journals (Sweden)

    Yubao Sun

    2015-01-01

    Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.

  17. Neural bases of selective attention in action video game players.

    Science.gov (United States)

    Bavelier, D; Achtman, R L; Mani, M; Föcker, J

    2012-05-15

    Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention, yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander, Green, & Bavelier, 2010). The present study used brain imaging to test this hypothesis by comparing attentional network recruitment and distractor processing in action gamers versus non-gamers as attentional demands increased. Moving distractors were found to elicit lesser activation of the visual motion-sensitive area (MT/MST) in gamers as compared to non-gamers, suggestive of a better early filtering of irrelevant information in gamers. As expected, a fronto-parietal network of areas showed greater recruitment as attentional demands increased in non-gamers. In contrast, gamers barely engaged this network as attentional demands increased. This reduced activity in the fronto-parietal network that is hypothesized to control the flexible allocation of top-down attention is compatible with the proposal that action game players may allocate attentional resources more automatically, possibly allowing more efficient early filtering of irrelevant information. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Poetic representation

    DEFF Research Database (Denmark)

    Wulf-Andersen, Trine Østergaard

    2012-01-01

    , and dialogue, of situated participants. The article includes a lengthy example of a poetic representation of one participant’s story, and the author comments on the potentials of ‘doing’ poetic representations as an example of writing in ways that challenges what sometimes goes unasked in participative social...

  19. Community-based knowledge transfer and exchange: Helping community-based organizations link research to action

    Directory of Open Access Journals (Sweden)

    Lavis John N

    2010-04-01

    Full Text Available Abstract Background Community-based organizations (CBOs are important stakeholders in health systems and are increasingly called upon to use research evidence to inform their advocacy, program planning, and service delivery efforts. CBOs increasingly turn to community-based research (CBR given its participatory focus and emphasis on linking research to action. In order to further facilitate the use of research evidence by CBOs, we have developed a strategy for community-based knowledge transfer and exchange (KTE that helps CBOs more effectively link research evidence to action. We developed the strategy by: outlining the primary characteristics of CBOs and why they are important stakeholders in health systems; describing the concepts and methods for CBR and for KTE; comparing the efforts of CBR to link research evidence to action to those discussed in the KTE literature; and using the comparison to develop a framework for community-based KTE that builds on both the strengths of CBR and existing KTE frameworks. Discussion We find that CBR is particularly effective at fostering a climate for using research evidence and producing research evidence relevant to CBOs through community participation. However, CBOs are not always as engaged in activities to link research evidence to action on a larger scale or to evaluate these efforts. Therefore, our strategy for community-based KTE focuses on: an expanded model of 'linkage and exchange' (i.e., producers and users of researchers engaging in a process of asking and answering questions together; a greater emphasis on both producing and disseminating systematic reviews that address topics of interest to CBOs; developing a large-scale evidence service consisting of both 'push' efforts and efforts to facilitate 'pull' that highlight actionable messages from community relevant systematic reviews in a user-friendly way; and rigorous evaluations of efforts for linking research evidence to action. Summary

  20. A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

    Science.gov (United States)

    Basaruddin, T.

    2016-01-01

    One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645. PMID:27843447

  1. Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

    Science.gov (United States)

    Yang, Ying; Wang, Jing; Bailer, Cyntia; Cherkassky, Vladimir; Just, Marcel Adam

    2017-02-01

    The aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang et al., submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations. The model used two types of information gained from the English-based fMRI data to predict the activation for individual sentences in Portuguese. First, it used the mapping weights from NPSFs to voxel activation levels derived from the model for English reading. Second, the brain locations for which the activation levels were predicted were derived from a factor analysis of the brain activation patterns during English reading. These meta-language locations were defined by the clusters of voxels with high loadings on each of the four main dimensions (factors), namely people, places, actions and feelings, underlying the neural representations of the stimulus sentences. This cross-language model succeeded in predicting the brain activation patterns associated with the reading of 60 individual Portuguese sentences that were entirely new to the model, attaining accuracies reliably above chance level. The prediction accuracy was not affected by whether the Portuguese speaker was monolingual or Portuguese-English bilingual. The model's confusion errors indicated an accurate capture of the events or states described in the sentence at a conceptual level. Overall, the cross-language predictive capability of the model demonstrates the neural commonality between speakers of different languages in the representations of everyday events and states, and provides an initial characterization of the common meta

  2. Scenarios, personas and user stories: user-centered evidence-based design representations of communicable disease investigations.

    Science.gov (United States)

    Turner, Anne M; Reeder, Blaine; Ramey, Judith

    2013-08-01

    Despite years of effort and millions of dollars spent to create unified electronic communicable disease reporting systems, the goal remains elusive. A major barrier has been a lack of understanding by system designers of communicable disease (CD) work and the public health workers who perform this work. This study reports on the application of user-centered design representations, traditionally used for improving interface design, to translate the complex CD work identified through ethnographic studies to guide designers and developers of CD systems. The purpose of this work is to: (1) better understand public health practitioners and their information workflow with respect to CD monitoring and control at a local health agency, and (2) to develop evidence-based design representations that model this CD work to inform the design of future disease surveillance systems. We performed extensive onsite semi-structured interviews, targeted work shadowing and a focus group to characterize local health agency CD workflow. Informed by principles of design ethnography and user-centered design we created persona, scenarios and user stories to accurately represent the user to system designers. We sought to convey to designers the key findings from ethnographic studies: (1) public health CD work is mobile and episodic, in contrast to current CD reporting systems, which are stationary and fixed, (2) health agency efforts are focused on CD investigation and response rather than reporting and (3) current CD information systems must conform to public health workflow to ensure their usefulness. In an effort to illustrate our findings to designers, we developed three contemporary design-support representations: persona, scenario, and user story. Through application of user-centered design principles, we were able to create design representations that illustrate complex public health communicable disease workflow and key user characteristics to inform the design of CD information

  3. Scenarios, personas and user stories from design ethnography: Evidence-based design representations of communicable disease investigations

    Science.gov (United States)

    Turner, Anne M; Reeder, Blaine; Ramey, Judith

    2014-01-01

    Purpose Despite years of effort and millions of dollars spent to create a unified electronic communicable disease reporting systems, the goal remains elusive. A major barrier has been a lack of understanding by system designers of communicable disease (CD) work and the public health workers who perform this work. This study reports on the application of User Center Design representations, traditionally used for improving interface design, to translate the complex CD work identified through ethnographic studies to guide designers and developers of CD systems. The purpose of this work is to: (1) better understand public health practitioners and their information workflow with respect to communicable disease (CD) monitoring and control at a local health department, and (2) to develop evidence-based design representations that model this CD work to inform the design of future disease surveillance systems. Methods We performed extensive onsite semi-structured interviews, targeted work shadowing and a focus group to characterize local health department communicable disease workflow. Informed by principles of design ethnography and user-centered design (UCD) we created persona, scenarios and user stories to accurately represent the user to system designers. Results We sought to convey to designers the key findings from ethnographic studies: 1) that public health CD work is mobile and episodic, in contrast to current CD reporting systems, which are stationary and fixed 2) health department efforts are focused on CD investigation and response rather than reporting and 3) current CD information systems must conform to PH workflow to ensure their usefulness. In an effort to illustrate our findings to designers, we developed three contemporary design-support representations: persona, scenario, and user story. Conclusions Through application of user centered design principles, we were able to create design representations that illustrate complex public health communicable

  4. A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition.

    Science.gov (United States)

    Miao, Minmin; Wang, Aimin; Liu, Feixiang

    2017-09-01

    Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.

  5. Quantum cognition based on an ambiguous representation derived from a rough set approximation.

    Science.gov (United States)

    Gunji, Yukio-Pegio; Sonoda, Kohei; Basios, Vasileios

    2016-03-01

    Over the last years, in a series papers by Arecchi and others, a model for the cognitive processes involved in decision making has been proposed and investigated. The key element of this model is the expression of apprehension and judgment, basic cognitive process of decision making, as an inverse Bayes inference classifying the information content of neuron spike trains. It has been shown that for successive plural stimuli this inference, equipped with basic non-algorithmic jumps, is affected by quantum-like characteristics. We show here that such a decision making process is related consistently with an ambiguous representation by an observer within a universe of discourse. In our work the ambiguous representation of an object or a stimuli is defined as a pair of maps from objects of a set to their representations, where these two maps are interrelated in a particular structure. The a priori and a posteriori hypotheses in Bayes inference are replaced by the upper and lower approximations, correspondingly, for the initial data sets that are derived with respect to each map. Upper and lower approximations herein are defined in the context of "rough set" analysis. The inverse Bayes inference is implemented by the lower approximations with respect to the one map and for the upper approximation with respect to the other map for a given data set. We show further that, due to the particular structural relation between the two maps, the logical structure of such combined approximations can only be expressed as an orthomodular lattice and therefore can be represented by a quantum rather than a Boolean logic. To our knowledge, this is the first investigation aiming to reveal the concrete logic structure of inverse Bayes inference in cognitive processes. Copyright © 2016. Published by Elsevier Ireland Ltd.

  6. Virtual terrain: a security-based representation of a computer network

    Science.gov (United States)

    Holsopple, Jared; Yang, Shanchieh; Argauer, Brian

    2008-03-01

    Much research has been put forth towards detection, correlating, and prediction of cyber attacks in recent years. As this set of research progresses, there is an increasing need for contextual information of a computer network to provide an accurate situational assessment. Typical approaches adopt contextual information as needed; yet such ad hoc effort may lead to unnecessary or even conflicting features. The concept of virtual terrain is, therefore, developed and investigated in this work. Virtual terrain is a common representation of crucial information about network vulnerabilities, accessibilities, and criticalities. A virtual terrain model encompasses operating systems, firewall rules, running services, missions, user accounts, and network connectivity. It is defined as connected graphs with arc attributes defining dynamic relationships among vertices modeling network entities, such as services, users, and machines. The virtual terrain representation is designed to allow feasible development and maintenance of the model, as well as efficacy in terms of the use of the model. This paper will describe the considerations in developing the virtual terrain schema, exemplary virtual terrain models, and algorithms utilizing the virtual terrain model for situation and threat assessment.

  7. Spectrum recovery method based on sparse representation for segmented multi-Gaussian model

    Science.gov (United States)

    Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan

    2016-09-01

    Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

  8. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

    Directory of Open Access Journals (Sweden)

    Rami Alazrai

    2017-03-01

    Full Text Available This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD. Furthermore, we have developed a histogram-based representation (HBR based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.

  9. Facilitating Mathematical Practices through Visual Representations

    Science.gov (United States)

    Murata, Aki; Stewart, Chana

    2017-01-01

    Effective use of mathematical representation is key to supporting student learning. In "Principles to Actions: Ensuring Mathematical Success for All" (NCTM 2014), "use and connect mathematical representations" is one of the effective Mathematics Teaching Practices. By using different representations, students examine concepts…

  10. 76 FR 72368 - Representation Case Procedures

    Science.gov (United States)

    2011-11-23

    ...-AAO8 Representation Case Procedures AGENCY: National Labor Relations Board. ACTION: Proposed rule... of petitions relating to the representation of employees for purposes of collective bargaining with... to the Board's rules governing representation-case procedures. A copy of the NPRM may also be...

  11. Simple Ontology of Manipulation Actions based on Hand-Object Relations

    DEFF Research Database (Denmark)

    Wörgötter, Florentin; Aksoy, E. E.; Krüger, Norbert

    2013-01-01

    and time. For this we use as temporal anchor points those moments where two objects (or hand and object) touch or un-touch each other during a manipulation. We show that by this one can define a relatively small tree-like manipulation ontology. We find less than 30 fundamental manipulations. The temporal......Humans can perform a multitude of different actions with their hands (manipulations). In spite of this, so far there have been only a few attempts to represent manipulation types trying to understand the underlying principles. Here we first discuss how manipulation actions are structured in space...... anchors also provide us with information about when to pay attention to additional important information, for example when to consider trajectory shapes and relative poses between objects. As a consequence a highly condensed representation emerges by which different manipulations can be recognized...

  12. Mechanism of action of mRNA-based vaccines.

    Science.gov (United States)

    Iavarone, Carlo; O'hagan, Derek T; Yu, Dong; Delahaye, Nicolas F; Ulmer, Jeffrey B

    2017-09-01

    The present review summarizes the growing body of work defining the mechanisms of action of this exciting new vaccine technology that should allow rational approaches in the design of next generation mRNA vaccines. Areas covered: Bio-distribution of mRNA, localization of antigen production, role of the innate immunity, priming of the adaptive immune response, route of administration and effects of mRNA delivery systems. Expert commentary: In the last few years, the development of RNA vaccines had a fast growth, the rising number of proof will enable rational approaches to improving the effectiveness and safety of this modern class of medicine.

  13. Perception of oyster-based products by French consumers. The effect of processing and role of social representations.

    Science.gov (United States)

    Debucquet, Gervaise; Cornet, Josiane; Adam, Isabelle; Cardinal, Mireille

    2012-12-01

    The search for new markets in the seafood sector, associated with the question of the continuity of raw oyster consumption over generations can be an opportunity for processors to extend their ranges with oyster-based products. The twofold aim of this study was to evaluate the impact of processing and social representation on perception of oyster-based products by French consumers and to identify the best means of development in order to avoid possible failure in the market. Five products with different degrees of processing (cooked oysters in a half-shell, hot preparation for toast, potted oyster, oyster butter and oyster-based soup) were presented within focus groups and consumer tests, at home and in canteens with the staff of several companies in order to reach consumers with different ages and professional activities. The results showed that social representation had a strong impact and that behaviours were contrasted according to the initial profile of the consumer (traditional raw oyster consumers or non-consumers) and their age distribution (younger and older people). The degree of processing has to be adapted to each segment. It is suggested to develop early exposure to influence the food choices and preferences of the youngest consumers on a long-term basis. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Multiscale sample entropy and cross-sample entropy based on symbolic representation and similarity of stock markets

    Science.gov (United States)

    Wu, Yue; Shang, Pengjian; Li, Yilong

    2018-03-01

    A modified multiscale sample entropy measure based on symbolic representation and similarity (MSEBSS) is proposed in this paper to research the complexity of stock markets. The modified algorithm reduces the probability of inducing undefined entropies and is confirmed to be robust to strong noise. Considering the validity and accuracy, MSEBSS is more reliable than Multiscale entropy (MSE) for time series mingled with much noise like financial time series. We apply MSEBSS to financial markets and results show American stock markets have the lowest complexity compared with European and Asian markets. There are exceptions to the regularity that stock markets show a decreasing complexity over the time scale, indicating a periodicity at certain scales. Based on MSEBSS, we introduce the modified multiscale cross-sample entropy measure based on symbolic representation and similarity (MCSEBSS) to consider the degree of the asynchrony between distinct time series. Stock markets from the same area have higher synchrony than those from different areas. And for stock markets having relative high synchrony, the entropy values will decrease with the increasing scale factor. While for stock markets having high asynchrony, the entropy values will not decrease with the increasing scale factor sometimes they tend to increase. So both MSEBSS and MCSEBSS are able to distinguish stock markets of different areas, and they are more helpful if used together for studying other features of financial time series.

  15. Dynamics of random Boolean networks under fully asynchronous stochastic update based on linear representation.

    Directory of Open Access Journals (Sweden)

    Chao Luo

    Full Text Available A novel algebraic approach is proposed to study dynamics of asynchronous random Boolean networks where a random number of nodes can be updated at each time step (ARBNs. In this article, the logical equations of ARBNs are converted into the discrete-time linear representation and dynamical behaviors of systems are investigated. We provide a general formula of network transition matrices of ARBNs as well as a necessary and sufficient algebraic criterion to determine whether a group of given states compose an attractor of length[Formula: see text] in ARBNs. Consequently, algorithms are achieved to find all of the attractors and basins in ARBNs. Examples are showed to demonstrate the feasibility of the proposed scheme.

  16. Nonsymmorphic Weyl superconductivity in UPt3 based on E2 u representation

    Science.gov (United States)

    Yanase, Youichi

    2016-11-01

    We show that a heavy fermion superconductor UPt3 is a topological Weyl superconductor with tunable Weyl nodes. Adopting a generic order parameter in the E2 u representation allowed by nonsymmorphic crystal symmetry, we clarify unusual gap structure and associated topological properties. The pair creation, pair annihilation, and coalescence of Weyl nodes are demonstrated in the time-reversal symmetry broken B-phase. At most 98 point nodes compatible with Blount's theorem give rise to line-node-like behaviors in low-energy excitations, consistent with experimental results. We also show an arc node protected by the nonsymmorphic crystal symmetry on the Brillouin zone face, which is a counterexample of Blount's theorem.

  17. Social Representations of the Development of Intelligence, Parental Values and Parenting Styles: A Theoretical Model for Analysis

    Science.gov (United States)

    Miguel, Isabel; Valentim, Joaquim Pires; Carugati, Felice

    2013-01-01

    Within the theoretical framework of social representations theory, a substantial body of literature has advocated and shown that, as interpretative systems and forms of knowledge concurring in the construction of a social reality, social representations are guides for action, influencing behaviours and social relations. Based on this assumption,…

  18. Mechanisms of Action of Probiotics based on Bacillus subtilis

    Directory of Open Access Journals (Sweden)

    A.V. Savustyanenko

    2016-04-01

    Full Text Available The bacterium B.subtilis is one of the most promi­sing probiotics studied in recent decades. Mechanisms of its probiotic action are associated with the synthesis of antimicrobial agents, increasing of non-specific and specific immunity, stimulation of growth of normal microflora of the intestine and the releasing of digestive enzymes. B.subtilis releases ribosomally synthesized peptides, non-ribosomally synthesized peptides and non-peptide substances with a broad spectrum of antimicrobial activity covering Gram-positive, Gram-negative bacteria, viruses and fungi. Resistance to these antimicrobial agents is rare. Enhancement of non-specific immunity is associated with macrophage activation and the release of pro-inflammatory cytokines from them, increasing of barrier function of the intestinal mucosa, releasing of vitamins and amino acids (including essential ones. Enhancement of specific immunity manifests by activation of T- and B-lymphocytes and the release from the latter of immunoglobulins — IgG and IgA. B.subtilis stimulates the growth of normal intestinal flora, in particular, bacteria of the genus Lactobacillus and Bifidobacterium. Furthermore, probiotic increases the diversity of intestinal microflora. Probiotic secretes all major digestive enzymes to the intestinal lumen: amylases, lipases, proteases, pectinases and cellulases. In addition to digestion, these enzymes destroy antinutritional factors and allergenic substances contained in the food. These mechanisms of action make reasonable the use of B.subtilis in the combination therapy to treat intestinal infections; prevention of respiratory infections during the cold season; prevention of antibiotic-associated diarrhea; for the correction of food digestion and movement impairments of various origin (errors in the diet, changes in the diet, diseases of the gastrointestinal tract, disorders of the autonomic nervous system, etc.. B.subtilis does not usually cause side effects. This

  19. A novel collaborative representation and SCAD based classification method for fibrosis and inflammatory activity analysis of chronic hepatitis C

    Science.gov (United States)

    Cai, Jiaxin; Chen, Tingting; Li, Yan; Zhu, Nenghui; Qiu, Xuan

    2018-03-01

    In order to analysis the fibrosis stage and inflammatory activity grade of chronic hepatitis C, a novel classification method based on collaborative representation (CR) with smoothly clipped absolute deviation penalty (SCAD) penalty term, called CR-SCAD classifier, is proposed for pattern recognition. After that, an auto-grading system based on CR-SCAD classifier is introduced for the prediction of fibrosis stage and inflammatory activity grade of chronic hepatitis C. The proposed method has been tested on 123 clinical cases of chronic hepatitis C based on serological indexes. Experimental results show that the performance of the proposed method outperforms the state-of-the-art baselines for the classification of fibrosis stage and inflammatory activity grade of chronic hepatitis C.

  20. Trait-based representation of hydrological functional properties of plants in weather and ecosystem models

    Directory of Open Access Journals (Sweden)

    Ashley M. Matheny

    2017-02-01

    Full Text Available Land surface models and dynamic global vegetation models typically represent vegetation through coarse plant functional type groupings based on leaf form, phenology, and bioclimatic limits. Although these groupings were both feasible and functional for early model generations, in light of the pace at which our knowledge of functional ecology, ecosystem demographics, and vegetation-climate feedbacks has advanced and the ever growing demand for enhanced model performance, these groupings have become antiquated and are identified as a key source of model uncertainty. The newest wave of model development is centered on shifting the vegetation paradigm away from plant functional types (PFTs and towards flexible trait-based representations. These models seek to improve errors in ecosystem fluxes that result from information loss due to over-aggregation of dissimilar species into the same functional class. We advocate the importance of the inclusion of plant hydraulic trait representation within the new paradigm through a framework of the whole-plant hydraulic strategy. Plant hydraulic strategy is known to play a critical role in the regulation of stomatal conductance and thus transpiration and latent heat flux. It is typical that coexisting plants employ opposing hydraulic strategies, and therefore have disparate patterns of water acquisition and use. Hydraulic traits are deterministic of drought resilience, response to disturbance, and other demographic processes. The addition of plant hydraulic properties in models may not only improve the simulation of carbon and water fluxes but also vegetation population distributions.

  1. Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces.

    Science.gov (United States)

    Unke, Oliver T; Meuwly, Markus

    2017-08-28

    In the early days of computation, slow processor speeds limited the amount of data that could be generated and used for scientific purposes. In the age of big data, the limiting factor usually is the method with which large amounts of data are analyzed and useful information is extracted. A typical example from chemistry are high-level ab initio calculations for small systems, which have nowadays become feasible even if energies at many different geometries are required. Molecular dynamics simulations often require several thousand distinct trajectories to be run. Under such circumstances suitable analytical representations of potential energy surfaces (PESs) based on ab initio calculations are required to propagate the dynamics at an acceptable cost. In this work we introduce a toolkit which allows the automatic construction of multidimensional PESs from gridded ab initio data based on reproducing kernel Hilbert space (RKHS) theory. The resulting representations require no tuning of parameters and allow energy and force evaluations at ab initio quality at the same cost as empirical force fields. Although the toolkit is primarily intended for constructing multidimensional potential energy surfaces for molecular systems, it can also be used for general machine learning purposes. The software is published under the MIT license and can be downloaded, modified, and used in other projects for free.

  2. New solutions of the Yang-Baxter equation based on root of 1 representations of the Para-Bose superalgebra Uq[osp(1/2)

    International Nuclear Information System (INIS)

    Palev, T.D.; Stoilova, N.I.

    1995-07-01

    New solutions of the quantum Yang-Baxter equation, depending in general on three arbitrary parameters, are written down. They are based on the root of unity representations of the quantum orthosymplectic superalgebra U q [osp(1/2)], which were found recently. Representations of the braid group B N are defined within any N th tensorial power of root of 1 U q [osp(1/2)] modules. (author). 40 refs

  3. Semi-analytical Karhunen-Loeve representation of irregular waves based on the prolate spheroidal wave functions

    Science.gov (United States)

    Lee, Gibbeum; Cho, Yeunwoo

    2018-01-01

    A new semi-analytical approach is presented to solving the matrix eigenvalue problem or the integral equation in Karhunen-Loeve (K-L) representation of random data such as irregular ocean waves. Instead of direct numerical approach to this matrix eigenvalue problem, which may suffer from the computational inaccuracy for big data, a pair of integral and differential equations are considered, which are related to the so-called prolate spheroidal wave functions (PSWF). First, the PSWF is expressed as a summation of a small number of the analytical Legendre functions. After substituting them into the PSWF differential equation, a much smaller size matrix eigenvalue problem is obtained than the direct numerical K-L matrix eigenvalue problem. By solving this with a minimal numerical effort, the PSWF and the associated eigenvalue of the PSWF differential equation are obtained. Then, the eigenvalue of the PSWF integral equation is analytically expressed by the functional values of the PSWF and the eigenvalues obtained in the PSWF differential equation. Finally, the analytically expressed PSWFs and the eigenvalues in the PWSF integral equation are used to form the kernel matrix in the K-L integral equation for the representation of exemplary wave data such as ordinary irregular waves. It is found that, with the same accuracy, the required memory size of the present method is smaller than that of the direct numerical K-L representation and the computation time of the present method is shorter than that of the semi-analytical method based on the sinusoidal functions.

  4. Major depression and dimensional representations of DSM-IV personality disorders: a population-based twin study.

    Science.gov (United States)

    Reichborn-Kjennerud, T; Czajkowski, N; Røysamb, E; Ørstavik, R E; Neale, M C; Torgersen, S; Kendler, K S

    2010-09-01

    Major depressive disorder (MDD) co-occurs frequently with personality disorders (PDs). The extent to which this results from shared genetic or environmental risk factors remains uncertain. Young adult twins (n=2801) from the population-based Norwegian Institute of Public Health Twin Panel were assessed at personal interview for DSM-IV lifetime MDD and the 10 Axis II PDs. The relationship between MDD and dimensional representations of all PDs was explored by stepwise logistic regression. Multivariate Cholesky twin models were fitted using the Mx program, and genetic and environmental correlations were estimated. Dimensional representations of borderline (BPD), avoidant (AVPD) and paranoid personality disorder (PPD) were independently and significantly associated with increased risk for MDD. Multivariate twin modeling indicated that one latent factor accounted for the genetic covariance between MDD and the three PDs. The genetic correlations between MDD and dimensional representations of BPD, AVPD and PPD were +0.56, +0.22 and +0.40 respectively. No sex differences or shared environmental effects were found. The structure of the individual-specific environmental factors influencing MDD and the three PDs were similar to the genetic factors but the environmental correlations were lower: +0.39, +0.23 and +0.27 respectively. There is substantial overlap between liability factors for MDD and BPD from cluster B, PPD from cluster A and AVPD from cluster C. The vulnerability to general PD pathology and MDD seem to be closely related. The patterns of co-morbidity observed between diverse psychiatric disorders might result from just a few liability factors.

  5. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

    Science.gov (United States)

    Mu, Lin

    2018-01-01

    This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination. PMID:29309403

  6. Dynamic Mental Representations of Habitual Behaviours: Food Choice on a Web-Based Environment

    Directory of Open Access Journals (Sweden)

    Rui Gaspar

    2016-08-01

    Full Text Available AimRather than being rigid, habitual behaviours may be determined by dynamic mental representations that can adapt to context changes. This adaptive potential may result from particular conditions dependent on the interaction between two sources of mental constructs activation: perceived context applicability and cognitive accessibility.MethodTwo web-shopping simulations offering the choice between habitually chosen and non-habitually chosen food products were presented to participants. This considered two choice contexts differing in the habitual behaviour perceived applicability (low vs. high and a measure of habitual behaviour chronicity.ResultsStudy 1 demonstrated a perceived applicability effect, with more habitual (non-organic than non-habitual (organic food products chosen in a high perceived applicability (familiar than in a low perceived applicability (new context. The adaptive potential of habitual behaviour was evident in the habitual products choice consistency across three successive choices, despite the decrease in perceived applicability. Study 2 evidenced the adaptive potential in strong habitual behaviour participants – high chronic accessibility – who chose a habitual product (milk more than a non-habitual product (orange juice, even when perceived applicability was reduced (new context.ConclusionResults portray consumers as adaptive decision makers that can flexibly cope with changes in their (inner and outer choice contexts.

  7. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

    Science.gov (United States)

    Cang, Zixuan; Mu, Lin; Wei, Guo-Wei

    2018-01-01

    This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.

  8. Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications.

    Science.gov (United States)

    Nalla, Pattabhi Ramaiah; Chalavadi, Krishna Mohan

    2015-01-01

    De-duplication of biometrics is not scalable when the number of people to be enrolled into the biometric system runs into billions, while creating a unique identity for every person. In this paper, we propose an iris classification based on sparse representation of log-gabor wavelet features using on-line dictionary learning (ODL) for large-scale de-duplication applications. Three different iris classes based on iris fiber structures, namely, stream, flower, jewel and shaker, are used for faster retrieval of identities. Also, an iris adjudication process is illustrated by comparing the matched iris-pair images side-by-side to make the decision on the identification score using color coding. Iris classification and adjudication are included in iris de-duplication architecture to speed-up the identification process and to reduce the identification errors. The efficacy of the proposed classification approach is demonstrated on the standard iris database, UPOL.

  9. A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions

    International Nuclear Information System (INIS)

    Do, Phuc; Voisin, Alexandre; Levrat, Eric; Iung, Benoit

    2015-01-01

    This paper deals with a proactive condition-based maintenance (CBM) considering both perfect and imperfect maintenance actions for a deteriorating system. Perfect maintenance actions restore completely the system to the ‘as good as new’ state. Their related cost are however often high. The first objective of the paper is to investigate the impacts of imperfect maintenance actions. In fact, both positive and negative impacts are considered. Positive impact means that the imperfect maintenance cost is usually low. Negative impact implies that (i) the imperfect maintenance restores a system to a state between good-as-new and bad-as-old and (ii) each imperfect preventive action may accelerate the speed of the system's deterioration process. The second objective of the paper is to propose an adaptive maintenance policy which can help to select optimally maintenance actions (perfect or imperfect actions), if needed, at each inspection time. Moreover, the time interval between two successive inspection points is determined according to a remaining useful life (RUL) based-inspection policy. To illustrate the use of the proposed maintenance policy, a numerical example finally is introduced. - Highlights: • A new imperfect maintenance model for deterioration system is proposed. • Both positive and negative impacts of an imperfect maintenance action are investigated. • An adaptive condition-based maintenance policy is introduced. • The optimal number of useful imperfect maintenance actions for each life cycle is optimally provided

  10. A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

    Science.gov (United States)

    Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

    2016-02-19

    In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

  11. Graph-based representation of behavior in detection and prediction of daily living activities.

    Science.gov (United States)

    Augustyniak, Piotr; Ślusarczyk, Grażyna

    2018-04-01

    Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a surveillance system designed for use in elderly smart homes. The description of a subject's activity is first decomposed into elementary poses - easily detectable by dedicated intelligent sensors - and represented by the share coefficients. Then, the activity is represented in the form of an attributed graph, where nodes correspond to elementary poses. As share coefficients of poses are expressed as attributes assigned to graph nodes, their change corresponding to a subject's action is represented by flow in graph edges. The behavioral record is thus a time series of graphs, which tiny size facilitates storage and management of long-term monitoring results. At the system learning stage, the contribution of elementary poses is accumulated, discretized and probability-ordered leading to a finite list representing the possible transitions between states. Such a list is independently built for each room in the supervised residence, and employed for assessment of the current action in the context of subject's habits and a room purpose. The proposed format of a behavioral record, applied to an adaptive surveillance system, is particularly advantageous for representing new activities not known at the setup stage, for providing a quantitative measure of transitions between poses and for expressing the difference between a predicted and actual action in a numerical way. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. Teachers’ individual action theories about competence-based education: the value of the cognitive apprenticeship model

    OpenAIRE

    Seezink, Audrey; Poell, Rob; Kirschner, Paul A.

    2009-01-01

    Seezink, A., Poell, R. F., & Kirschner, P. A. (2009). Teachers' individual action theories about competence-based education: The value of the cognitive apprenticeship model. Journal of Vocational Education & Training, 61, 203-215.

  13. Initial Remedial Action Plan for Expanded Bioventing System BX Service Station, Patrick Air Force Base, Florida

    National Research Council Canada - National Science Library

    1995-01-01

    This initial remedial action plan presents the scope for an expanded bioventing system for in situ treatment of fuel-contaminated soils at the BX Service Station at Patrick Air Force Base (AFB), Florida...

  14. Lambert W-function based exact representation for double diode model of solar cells: Comparison on fitness and parameter extraction

    International Nuclear Information System (INIS)

    Gao, Xiankun; Cui, Yan; Hu, Jianjun; Xu, Guangyin; Yu, Yongchang

    2016-01-01

    Highlights: • Lambert W-function based exact representation (LBER) is presented for double diode model (DDM). • Fitness difference between LBER and DDM is verified by reported parameter values. • The proposed LBER can better represent the I–V and P–V characteristics of solar cells. • Parameter extraction difference between LBER and DDM is validated by two algorithms. • The parameter values extracted from LBER are more accurate than those from DDM. - Abstract: Accurate modeling and parameter extraction of solar cells play an important role in the simulation and optimization of PV systems. This paper presents a Lambert W-function based exact representation (LBER) for traditional double diode model (DDM) of solar cells, and then compares their fitness and parameter extraction performance. Unlike existing works, the proposed LBER is rigorously derived from DDM, and in LBER the coefficients of Lambert W-function are not extra parameters to be extracted or arbitrary scalars but the vectors of terminal voltage and current of solar cells. The fitness difference between LBER and DDM is objectively validated by the reported parameter values and experimental I–V data of a solar cell and four solar modules from different technologies. The comparison results indicate that under the same parameter values, the proposed LBER can better represent the I–V and P–V characteristics of solar cells and provide a closer representation to actual maximum power points of all module types. Two different algorithms are used to compare the parameter extraction performance of LBER and DDM. One is our restart-based bound constrained Nelder-Mead (rbcNM) algorithm implemented in Matlab, and the other is the reported R cr -IJADE algorithm executed in Visual Studio. The comparison results reveal that, the parameter values extracted from LBER using two algorithms are always more accurate and robust than those from DDM despite more time consuming. As an improved version of DDM, the

  15. New Developments in the Moment-Based Representation of Atmospheric Aerosols

    Science.gov (United States)

    McGraw, R.

    2002-05-01

    The Method of Moments (MOM) provides an accurate and efficient representation of aerosol evolution and microphysical properties suitable for incorporation in chemical transport models. The MOM does not provide the particle size distribution (PSD), but it tells you (almost) everything else you want to know about the aerosol through the moments of the PSD. Recent development of the quadrature method of moments (QMOM) has enabled closure of the moment evolution equations under general conditions for nucleation, particle growth through condensation and coagulation, and activation in clouds. Furthermore, the quadrature points obtained by the QMOM enable accurate estimation of aerosol physical and optical properties directly from the moments. This method is becoming sufficiently mature that it can be used in regional-scale simulations. Here we (1) compare the QMOM with benchmark discrete and analytic particle population balance models, (2) summarize application of the QMOM in a regional scale chemical transport model and comparison with observations (Yu et al., 2002), and (3) present new extensions of the QMOM to the simulation of internally-mixed and generally-mixed, multivariate, aerosol populations. Regional-scale comparisons with observations for sulfate are favorable, with the model capturing much of the synoptic-scale and diurnal variability of the observations. Improvements are needed in representing other key aerosol species. ----- S. Yu, P.S. Kasibhatla, D.L. Wright, S.E. Schwartz, R. McGraw, and A. Deng. Simulation of Microphysical Properties of Sulfate Aerosols in the Eastern United States: Model description, evaluation and regional analysis. In preparation for J. Geophys. Res., 2002

  16. Scale coding bag of deep features for human attribute and action recognition

    OpenAIRE

    Khan, Fahad Shahbaz; van de Weijer, Joost; Anwer, Rao Muhammad; Bagdanov, Andrew D.; Felsberg, Michael; Laaksonen, Jorma

    2017-01-01

    Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-in...

  17. Performance-Based Measurement: Action for Organizations and HPT Accountability

    Science.gov (United States)

    Larbi-Apau, Josephine A.; Moseley, James L.

    2010-01-01

    Basic measurements and applications of six selected general but critical operational performance-based indicators--effectiveness, efficiency, productivity, profitability, return on investment, and benefit-cost ratio--are presented. With each measurement, goals and potential impact are explored. Errors, risks, limitations to measurements, and a…

  18. Design and development of a linked open data-based health information representation and visualization system: potentials and preliminary evaluation.

    Science.gov (United States)

    Tilahun, Binyam; Kauppinen, Tomi; Keßler, Carsten; Fritz, Fleur

    2014-10-25

    Healthcare organizations around the world are challenged by pressures to reduce cost, improve coordination and outcome, and provide more with less. This requires effective planning and evidence-based practice by generating important information from available data. Thus, flexible and user-friendly ways to represent, query, and visualize health data becomes increasingly important. International organizations such as the World Health Organization (WHO) regularly publish vital data on priority health topics that can be utilized for public health policy and health service development. However, the data in most portals is displayed in either Excel or PDF formats, which makes information discovery and reuse difficult. Linked Open Data (LOD)-a new Semantic Web set of best practice of standards to publish and link heterogeneous data-can be applied to the representation and management of public level health data to alleviate such challenges. However, the technologies behind building LOD systems and their effectiveness for health data are yet to be assessed. The objective of this study is to evaluate whether Linked Data technologies are potential options for health information representation, visualization, and retrieval systems development and to identify the available tools and methodologies to build Linked Data-based health information systems. We used the Resource Description Framework (RDF) for data representation, Fuseki triple store for data storage, and Sgvizler for information visualization. Additionally, we integrated SPARQL query interface for interacting with the data. We primarily use the WHO health observatory dataset to test the system. All the data were represented using RDF and interlinked with other related datasets on the Web of Data using Silk-a link discovery framework for Web of Data. A preliminary usability assessment was conducted following the System Usability Scale (SUS) method. We developed an LOD-based health information representation, querying

  19. Representational Machines

    DEFF Research Database (Denmark)

    to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological...

  20. Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

    Directory of Open Access Journals (Sweden)

    Zhongwen Hu

    2016-02-01

    Full Text Available The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions.

  1. Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection

    International Nuclear Information System (INIS)

    Kim, Dae Hoe; Kim, Seong Tae; Ro, Yong Man

    2015-01-01

    In digital breast tomosynthesis (DBT), image characteristics of projection views and reconstructed volume are different and both have the advantage of detecting breast masses, e.g. reconstructed volume mitigates a tissue overlap, while projection views have less reconstruction blur artifacts. In this paper, an improved mass detection is proposed by using combined feature representations from projection views and reconstructed volume in the DBT. To take advantage of complementary effects on different image characteristics of both data, combined feature representations are extracted from both projection views and reconstructed volume concurrently. An indirect region-of-interest segmentation in projection views, which projects volume-of-interest in reconstructed volume into the corresponding projection views, is proposed to extract combined feature representations. In addition, a boosting based classification with feature selection has been employed for selecting effective feature representations among a large number of combined feature representations, and for reducing false positives. Experiments have been conducted on a clinical data set that contains malignant masses. Experimental results demonstrate that the proposed mass detection can achieve high sensitivity with a small number of false positives. In addition, the experimental results demonstrate that the selected feature representations for classifying masses complementarily come from both projection views and reconstructed volume. (paper)

  2. The ERP Effects of Combined Cognitive Training on Intention-based and Stimulus-based Actions in Older Chinese Adults

    Directory of Open Access Journals (Sweden)

    Ya-Nan Niu

    2016-10-01

    Full Text Available Age-related decreases in action are caused by neuromuscular weakness and cognitive decline. Although physical interventions have been reported to have beneficial effects on cognitive function in older adults, whether cognitive training improves action-related function remains unclear. In this study, we investigated the effects of combined cognitive training on intention-based and stimulus-based actions in older adults using event-related potentials (ERPs. A total of 26 healthy older adults (16 in the training group and 10 in the control group participated in the study. The training group received 16 sessions of cognitive training, including 8 sessions of executive function training and 8 sessions of memory strategy training. Before and after training, both groups of participants underwent cognitive assessments and ERP recordings during both the acquisition and test phases with a motor cognitive paradigm. During the acquisition phase, subjects were asked to press one of two keys, either using a self-selected (intention-based method or based on the preceding stimulus (stimulus-based. During the test phase, subjects were asked to respond to the pre-cues with either congruent or incongruent tasks. Using ERP indices—including readiness potential, P3 and contingent negative variation to identify motor preparation, stimulus processing and interference effect, respectively—we revealed the effects of training on both intention-based and stimulus-based actions. The correlations were also computed between the improved cognitive performance and the ERP amplitudes. It was shown that the improved executive function might extend substantial benefits to both actions, whereas associative memory may be specifically related to the bidirectional action-effect association of intention-based action, although the training effect of memory was absent during the insufficient training hours. In sum, the present study provided empirical evidence demonstrating that

  3. Caregiving Antecedents of Secure Base Script Knowledge: A Comparative Analysis of Young Adult Attachment Representations

    Science.gov (United States)

    Steele, Ryan D.; Waters, Theodore E. A.; Bost, Kelly K.; Vaughn, Brian E.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn; Roisman, Glenn I.

    2014-01-01

    Based on a subsample (N = 673) of the NICHD Study of Early Child Care and Youth Development (SECCYD) cohort, this article reports data from a follow-up assessment at age 18 years on the antecedents of "secure base script knowledge", as reflected in the ability to generate narratives in which attachment-related difficulties are…

  4. Discovery Learning, Representation, and Explanation within a Computer-Based Simulation: Finding the Right Mix

    Science.gov (United States)

    Rieber, Lloyd P.; Tzeng, Shyh-Chii; Tribble, Kelly

    2004-01-01

    The purpose of this research was to explore how adult users interact and learn during an interactive computer-based simulation supplemented with brief multimedia explanations of the content. A total of 52 college students interacted with a computer-based simulation of Newton's laws of motion in which they had control over the motion of a simple…

  5. Social representations of climate change

    International Nuclear Information System (INIS)

    BOY, D.

    2013-01-01

    Each year since 2000, the French 'ADEME' (Agency for Environment and Energy Management) conducts a survey on the social representations of greenhouse effect and global warming. This survey is administered by telephone to a representative sample of the French population. The information gathered in the database can answer a series of basic questions concerning public perception in this area. What do the concepts of 'greenhouse effect' and 'global warming' mean for the public? To what extent do people think there is a consensus among scientists to explain these phenomena? Is responsibility for human action clearly established? What kind of solutions, based on public regulation or private initiative can help to remedy this situation? Finally, what were the major changes in public opinion over this 12 years period? (author)

  6. Live-action Virtual Reality Games

    OpenAIRE

    Valente, Luis; Clua, Esteban; Silva, Alexandre Ribeiro; Feijó, Bruno

    2016-01-01

    This paper proposes the concept of "live-action virtual reality games" as a new genre of digital games based on an innovative combination of live-action, mixed-reality, context-awareness, and interaction paradigms that comprise tangible objects, context-aware input devices, and embedded/embodied interactions. Live-action virtual reality games are "live-action games" because a player physically acts out (using his/her real body and senses) his/her "avatar" (his/her virtual representation) in t...

  7. Sinusoidal Representation of Acoustic Signals

    Science.gov (United States)

    Honda, Masaaki

    Sinusoidal representation of acoustic signals has been an important tool in speech and music processing like signal analysis, synthesis and time scale or pitch modifications. It can be applicable to arbitrary signals, which is an important advantage over other signal representations like physical modeling of acoustic signals. In sinusoidal representation, acoustic signals are composed as sums of sinusoid (sine wave) with different amplitudes, frequencies and phases, which is based on the timedependent short-time Fourier transform (STFT). This article describes the principles of acoustic signal analysis/synthesis based on a sinusoid representation with focus on sine waves with rapidly varying frequency.

  8. Evaluation of the Evidence Base for the Alcohol Industry's Actions to Reduce Drink Driving Globally.

    Science.gov (United States)

    Esser, Marissa B; Bao, James; Jernigan, David H; Hyder, Adnan A

    2016-04-01

    To evaluate the evidence base for the content of initiatives that the alcohol industry implemented to reduce drink driving from 1982 to May 2015. We systematically analyzed the content of 266 global initiatives that the alcohol industry has categorized as actions to reduce drink driving. Social aspects public relations organizations (i.e., organizations funded by the alcohol industry to handle issues that may be damaging to the business) sponsored the greatest proportion of the actions. Only 0.8% (n = 2) of the sampled industry actions were consistent with public health evidence of effectiveness for reducing drink driving. The vast majority of the alcohol industry's actions to reduce drink driving does not reflect public health evidenced-based recommendations, even though effective drink-driving countermeasures exist, such as a maximum blood alcohol concentration limit of 0.05 grams per deciliter for drivers and widespread use of sobriety checkpoints.

  9. Representational Machines

    DEFF Research Database (Denmark)

    Petersson, Dag; Dahlgren, Anna; Vestberg, Nina Lager

    Photography not only represents space. Space is produced photographically. Since its inception in the 19th century, photography has brought to light a vast array of represented subjects. Always situated in some spatial order, photographic representations have been operatively underpinned by social...... to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological......, technical, and institutional mechanisms. Geographically, bodily, and geometrically, the camera has positioned its subjects in social structures and hierarchies, in recognizable localities, and in iconic depth constructions which, although they show remarkable variation, nevertheless belong specifically...

  10. Representation learning for cross-modality classification

    NARCIS (Netherlands)

    G. van Tulder (Gijs); M. de Bruijne (Marleen)

    2017-01-01

    textabstractDifferences in scanning parameters or modalities can complicate image analysis based on supervised classification. This paper presents two representation learning approaches, based on autoencoders, that address this problem by learning representations that are similar across domains.

  11. A Novel Relevance Feedback Approach Based on Similarity Measure Modification in an X-Ray Image Retrieval System Based on Fuzzy Representation Using Fuzzy Attributed Relational Graph

    Directory of Open Access Journals (Sweden)

    Hossien Pourghassem

    2011-04-01

    Full Text Available Relevance feedback approaches is used to improve the performance of content-based image retrieval systems. In this paper, a novel relevance feedback approach based on similarity measure modification in an X-ray image retrieval system based on fuzzy representation using fuzzy attributed relational graph (FARG is presented. In this approach, optimum weight of each feature in feature vector is calculated using similarity rate between query image and relevant and irrelevant images in user feedback. The calculated weight is used to tune fuzzy graph matching algorithm as a modifier parameter in similarity measure. The standard deviation of the retrieved image features is applied to calculate the optimum weight. The proposed image retrieval system uses a FARG for representation of images, a fuzzy matching graph algorithm as similarity measure and a semantic classifier based on merging scheme for determination of the search space in image database. To evaluate relevance feedback approach in the proposed system, a standard X-ray image database consisting of 10000 images in 57 classes is used. The improvement of the evaluation parameters shows proficiency and efficiency of the proposed system.

  12. Symbolic and Verbal Representation Process of Student in Solving Mathematics Problem Based Polya's Stages

    Science.gov (United States)

    Anwar, Rahmad Bustanul; Rahmawati, Dwi

    2017-01-01

    The purpose of this research was to reveal how the construction process of symbolic representation and verbal representation made by students in problem solving. The construction process in this study referred to the problem-solving stage by Polya covering; 1) understanding the problem, 2) devising a plan, 3) carrying out the plan, and 4) looking…

  13. The Age Effects on the Cognitive Processes of Intention-Based and Stimulus-Based Actions: An ERP Study.

    Science.gov (United States)

    Niu, Ya-Nan; Zhu, Xinyi; Li, Juan

    2017-01-01

    The functional decline in action among older adults is caused not only by physical weakness but also by cognitive decline. In this study, we aimed to compare the cognitive effects of age between intention-based and stimulus-based action modes electrophysiologically. Because age-related declines in cognitive function might proceed distinctly according to specific action modes and processes, four specific cognitive processes, action-effect binding, stimulus-response linkage, action-effect feedback control , and effect-action retrieval , were investigated. We recorded event-related potentials (ERPs) during a modified acquisition-test paradigm in young (mean age = 21, SD = 2) and old (mean age = 69, SD = 5) groups. A temporal bisection task and a movement pre-cuing task were used during the acquisition and test phases, respectively. Using ERP indices including readiness potential (RP), P3, N2 and contingent negative variation (CNV) to identify these four specific processes for the two action modes, we revealed the effects of age on each ERP index. The results showed similar patterns of waveforms but consistently decreasing amplitudes of all four ERP indices in the old age group compared with the young age group, which indicates not only generally declining functions of action preparation in older adults but also age effects specific to the action modes and processes that might otherwise be mixed together under confounding experimental conditions. Particularly, an interference effect indexed by the differences in the amplitudes of CNV between congruent and incongruent tasks was observed in the young age group, which is consistent with previous behavioral reports. However, this effect was absent in the old age group, indicating a specific age-related deficit in the effect-action retrieval process of intention-based action, which might be caused by an age-related deficit in associative memory. In sum, this study investigated the cognitive processes of two action modes

  14. Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

    Science.gov (United States)

    Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

    2014-03-01

    We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

  15. Implementation of evidence-based health care using action research: An emancipatory approach.

    Science.gov (United States)

    Cordeiro, Luciana; Soares, Cassia Baldini

    2016-08-01

    The aim of the study is to discuss the emancipatory approach to action research as an appropriate methodology for workers' meaningful implementation of evidence-based health care. Implementation of evidence-based health care using action research is well supported by the literature. There are various approaches to action research, and they are coherent with the objectives and methods elected to develop the investigation. It is not clear which approach of action research is responsible for meaningful worker engagement in changing praxis. This is a discussion paper based on our experiences and supported by literature on collective health. Health care is defined as a social praxis, dependent upon the capitalist mode of production in which health workers engage themselves in a labour process that has negative (as alienation) as well as positive (as creativity) meanings. Emancipatory changes of social praxis through implementation of evidence-based health care require that participants understand the positive and negative meanings of their work and engage health workers in a conscious and intentional collaborative educational process. Implementation of evidence-based health care through emancipatory action research is capable of overcoming alienation and changing social practice through a participatory meaningful process of knowledge translation. © 2016 John Wiley & Sons Australia, Ltd.

  16. Tensor Based Representation and Analysis of Diffusion-Weighted Magnetic Resonance Images

    Science.gov (United States)

    Barmpoutis, Angelos

    2009-01-01

    Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each voxel of a diffusion-weighted MRI data set. This approximation produces tensor-valued datasets that contain information about the underlying local structure of the scanned tissue.…

  17. CogSkillnet: An Ontology-Based Representation of Cognitive Skills

    Science.gov (United States)

    Askar, Petek; Altun, Arif

    2009-01-01

    A number of studies emphasized the need to capture learners' interaction patterns in order to personalize their learning process as they study through learning objects. In education context, learning materials are designed based on pre-determined expectations and learners are evaluated to what extent they master these expectations. Representation…

  18. Representation of uncertainty and integration of PGIS-based grazing intensity maps using evidential belief functions

    NARCIS (Netherlands)

    Bemigisha, J.; Carranza, J.; Skidmore, A.K.; McCall, M.; Polce, C.; Prins, H.H.T.

    2009-01-01

    In a project to classify livestock grazing intensity using participatory geographic information systems (PGIS), we encountered the problem of how to synthesize PGIS-based maps of livestock grazing intensity that were prepared separately by local experts. We investigated the utility of evidential

  19. Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning

    NARCIS (Netherlands)

    Petkov, Nikolay

    1995-01-01

    A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input

  20. Improving Conceptual Understanding and Representation Skills through Excel-Based Modeling

    Science.gov (United States)

    Malone, Kathy L.; Schunn, Christian D.; Schuchardt, Anita M.

    2018-01-01

    The National Research Council framework for science education and the Next Generation Science Standards have developed a need for additional research and development of curricula that is both technologically model-based and includes engineering practices. This is especially the case for biology education. This paper describes a quasi-experimental…

  1. Action Categories in Lateral Occipitotemporal Cortex Are Organized Along Sociality and Transitivity.

    Science.gov (United States)

    Wurm, Moritz F; Caramazza, Alfonso; Lingnau, Angelika

    2017-01-18

    How neural specificity for distinct conceptual knowledge categories arises is central for understanding the organization of semantic memory in the human brain. Although there is a large body of research on the neural processing of distinct object categories, the organization of action categories remains largely unknown. In particular, it is unknown whether different action categories follow a specific topographical organization on the cortical surface analogously to the category-specific organization of object knowledge. Here, we tested whether the neural representation of action knowledge is organized in terms of nonsocial versus social and object-unrelated versus object-related actions (sociality and transitivity, respectively, hereafter). We hypothesized a major distinction of sociality and transitivity along dorsal and ventral lateral occipitotemporal cortex (LOTC), respectively. Using fMRI-based multivoxel pattern analysis, we identified neural representations of action information associated with sociality and transitivity in bilateral LOTC. Representational similarity analysis revealed a dissociation between dorsal and ventral LOTC. We found that action representations in dorsal LOTC are segregated along features of sociality, whereas action representations in ventral LOTC are segregated along features of transitivity. In addition, representations of sociality and transitivity features were found more anteriorly in LOTC than representations of specific subtypes of actions, suggesting a posterior-anterior gradient from concrete to abstract action features. These findings elucidate how the neural representations of perceptually and conceptually diverse actions are organized in distinct subsystems in the LOTC. The lateral occipitotemporal cortex (LOTC) is critically involved in the recognition of objects and actions, but our knowledge about the underlying organizing principles is limited. Here, we discovered a dorsal-ventral distinction of actions in LOTC

  2. Representation and Imperialism in Edward Said

    Directory of Open Access Journals (Sweden)

    Bruno Sciberras de Carvalho

    2010-12-01

    Full Text Available Based on the idea that culture and identities involve a politically built process, the importance of the concepts of representation and imperialism in the work of Edward Said is discussed. The article analyses two of the author’s general proposals. Firstly, the thesis of the centrality of cultural dimension in political relations, which leads to the review of postulates that consider culture as a mere reflection of a phenomena seen as essential or as a sphere isolated from the power practices. Secondly, the study examines how the author’s conception of imperialism is used as a means of identification that keep structures of authority and hegemony, which, in turn, may be defied by processes of resistance. Finally, the validity of the open character of Said’s political theory is debated, which encompasses both the power of representations and an action of political questioning.

  3. Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

    Science.gov (United States)

    Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

    2017-12-01

    Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

  4. Atypical brainstem representation of onset and formant structure of speech sounds in children with language-based learning problems.

    Science.gov (United States)

    Wible, Brad; Nicol, Trent; Kraus, Nina

    2004-11-01

    This study investigated how the human auditory brainstem represents constituent elements of speech sounds differently in children with language-based learning problems (LP, n = 9) compared to normal children (NL, n = 11), especially under stress of rapid stimulation. Children were chosen for this study based on performance on measures of reading and spelling and measures of syllable discrimination. In response to the onset of the speech sound /da/, wave V-V(n) of the auditory brainstem response (ABR) had a significantly shallower slope in LP children, suggesting longer duration and/or smaller amplitude. The amplitude of the frequency following response (FFR) was diminished in LP subjects over the 229-686 Hz range, which corresponds to the first formant of the/da/ stimulus, while activity at 114 Hz, representing the fundamental frequency of /da/, was no different between groups. Normal indicators of auditory peripheral integrity suggest a central, neural origin of these differences. These data suggest that poor representation of crucial components of speech sounds could contribute to difficulties with higher-level language processes.

  5. Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Wei Fan

    2014-01-01

    Full Text Available Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.

  6. Application of Passivity-Based Control and Time-Frequency Representation in a Doubly Fed Induction Generator System

    Directory of Open Access Journals (Sweden)

    Yingpei Liu

    2015-01-01

    Full Text Available In order to improve the performance of a doubly fed induction generator (DFIG system, we put forward a high performance nonlinear passivity-based control (PBC method on DFIG. Firstly, we build a PBC mathematical model for DFIG. We design the passive controller for the inner loop in the control system based on passivity theory. Then we calculate the rotor’s control voltages which are modulated afterwards to pulse to control the rotor side converter. The maximal wind energy capture is effectively realized. The rotor speed and DFIG currents fast track their expected values. The independent regulation of the stator active power and reactive power is achieved. Finally we perform simulations to verify the effectiveness of the proposed method. Furthermore, we employ the Wigner-Ville distribution (WVD and continuous wavelet transform (CWT as two time-frequency representation methods to indicate that the proposed method in the paper performs well from the perspective of energy distribution in time and frequency domain.

  7. Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

    Science.gov (United States)

    Zakeri, Fahimeh Sadat; Setarehdan, Seyed Kamaledin; Norouzi, Somayye

    2017-10-01

    Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. View-based encoding of actions in mirror neurons of area f5 in macaque premotor cortex.

    Science.gov (United States)

    Caggiano, Vittorio; Fogassi, Leonardo; Rizzolatti, Giacomo; Pomper, Joern K; Thier, Peter; Giese, Martin A; Casile, Antonino

    2011-01-25

    Converging experimental evidence indicates that mirror neurons in the monkey premotor area F5 encode the goals of observed motor acts [1-3]. However, it is unknown whether they also contribute to encoding the perspective from which the motor acts of others are seen. In order to address this issue, we recorded the visual responses of mirror neurons of monkey area F5 by using a novel experimental paradigm based on the presentation of movies showing grasping motor acts from different visual perspectives. We found that the majority of the tested mirror neurons (74%) exhibited view-dependent activity with responses tuned to specific points of view. A minority of the tested mirror neurons (26%) exhibited view-independent responses. We conclude that view-independent mirror neurons encode action goals irrespective of the details of the observed motor acts, whereas the view-dependent ones might either form an intermediate step in the formation of view independence or contribute to a modulation of view-dependent representations in higher-level visual areas, potentially linking the goals of observed motor acts with their pictorial aspects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  9. A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Qikai Lu

    2014-06-01

    Full Text Available In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original data, can be extracted more effectively. Specifically, in the proposed algorithm, a two-step process is adopted to make use of the clustering-based information. A clustering approach is first used to produce the initial clustering map, and, subsequently, a multiscale cluster histogram (MCH is proposed to represent the spatial information around each pixel. In order to evaluate the robustness of the proposed MCH, four clustering techniques are employed to analyze the influence of the clustering methods. Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM, the 3D wavelet texture, and differential morphological profiles (DMPs. The experiments conducted on four well-known hyperspectral datasets verify that the proposed MCH can significantly improve the classification accuracy, and it outperforms other commonly used spatial features.

  10. Machinery vibration signal denoising based on learned dictionary and sparse representation

    International Nuclear Information System (INIS)

    Guo, Liang; Gao, Hongli; Li, Jun; Huang, Haifeng; Zhang, Xiaochen

    2015-01-01

    Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation. (paper)

  11. XML representation and management of temporal information for web-based cultural heritage applications

    Directory of Open Access Journals (Sweden)

    Fabio Grandi

    2006-01-01

    Full Text Available In this paper we survey the recent activities and achievements of our research group in the deployment of XMLrelated technologies in Cultural Heritage applications concerning the encoding of temporal semantics in Web documents. In particular we will review "The Valid Web", which is an XML/XSL infrastructure we defined and implemented for the definition and management of historical information within multimedia documents available on the Web, and its further extension to the effective encoding of advanced temporal features like indeterminacy, multiple granularities and calendars, enabling an efficient processing in a user-friendly Web-based environment. Potential uses of the developed infrastructures include a broad range of applications in the cultural heritage domain, where the historical perspective is relevant, with potentially positive impacts on E-Education and E-Science.

  12. Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community

    DEFF Research Database (Denmark)

    Olsen, Jesper V; Mann, Matthias

    2011-01-01

    Mass spectrometry-based proteomics has emerged as a technology of choice for global analysis of cell signaling networks. However, reporting and sharing of MS data are often haphazard, limiting the usefulness of proteomics to the signaling community. We argue that raw data should always be provided...... with proteomics studies together with detailed peptide and protein identification and quantification information. Statistical criteria for peptide identification and their posttranslational modifications have largely been established for individual projects. However, the current practice of indiscriminately...... incorporating these individual results into databases such as UniProt is problematic. Because of the vast differences in underlying data quality, we advocate a differentiated annotation of data by level of reliability. Requirements for the reporting of quantitative data are being developed, but there are few...

  13. Status of the phenomena representation, 3D modeling, and cloud-based software architecture development

    International Nuclear Information System (INIS)

    Smith, Curtis L.; Prescott, Steven; Kvarfordt, Kellie; Sampath, Ram; Larson, Katie

    2015-01-01

    Early in 2013, researchers at the Idaho National Laboratory outlined a technical framework to support the implementation of state-of-the-art probabilistic risk assessment to predict the safety performance of advanced small modular reactors. From that vision of the advanced framework for risk analysis, specific tasks have been underway in order to implement the framework. This report discusses the current development of a several tasks related to the framework implementation, including a discussion of a 3D physics engine that represents the motion of objects (including collision and debris modeling), cloud-based analysis tools such as a Bayesian-inference engine, and scenario simulations. These tasks were performed during 2015 as part of the technical work associated with the Advanced Reactor Technologies Program.

  14. Status of the phenomena representation, 3D modeling, and cloud-based software architecture development

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Curtis L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Prescott, Steven [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kvarfordt, Kellie [Idaho National Lab. (INL), Idaho Falls, ID (United States); Sampath, Ram [Idaho National Lab. (INL), Idaho Falls, ID (United States); Larson, Katie [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2015-09-01

    Early in 2013, researchers at the Idaho National Laboratory outlined a technical framework to support the implementation of state-of-the-art probabilistic risk assessment to predict the safety performance of advanced small modular reactors. From that vision of the advanced framework for risk analysis, specific tasks have been underway in order to implement the framework. This report discusses the current development of a several tasks related to the framework implementation, including a discussion of a 3D physics engine that represents the motion of objects (including collision and debris modeling), cloud-based analysis tools such as a Bayesian-inference engine, and scenario simulations. These tasks were performed during 2015 as part of the technical work associated with the Advanced Reactor Technologies Program.

  15. A sparse representation-based approach for copy-move image forgery detection in smooth regions

    Science.gov (United States)

    Abdessamad, Jalila; ElAdel, Asma; Zaied, Mourad

    2017-03-01

    Copy-move image forgery is the act of cloning a restricted region in the image and pasting it once or multiple times within that same image. This procedure intends to cover a certain feature, probably a person or an object, in the processed image or emphasize it through duplication. Consequences of this malicious operation can be unexpectedly harmful. Hence, the present paper proposes a new approach that automatically detects Copy-move Forgery (CMF). In particular, this work broaches a widely common open issue in CMF research literature that is detecting CMF within smooth areas. Indeed, the proposed approach represents the image blocks as a sparse linear combination of pre-learned bases (a mixture of texture and color-wise small patches) which allows a robust description of smooth patches. The reported experimental results demonstrate the effectiveness of the proposed approach in identifying the forged regions in CM attacks.

  16. Trait-based representation of biological nitrification: Model development, testing, and predicted community composition

    Directory of Open Access Journals (Sweden)

    Nick eBouskill

    2012-10-01

    Full Text Available Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an ‘organism’ in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait focused on nitrification (MicroTrait-N that represents the ammonia-oxidizing bacteria (AOB and ammonia-oxidizing archaea (AOA and nitrite oxidizing bacteria (NOB using traits related to enzyme kinetics and physiological properties. We used this model to predict nitrifier diversity, ammonia (NH3 oxidation rates and nitrous oxide (N2O production across pH, temperature and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over six month simulations is maximized in a system where the relationship between AOB and NOB is maintained. When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models.

  17. Design based action research in the world of robot technology and learning

    DEFF Research Database (Denmark)

    Majgaard, Gunver

    2010-01-01

    Why is design based action research method important in the world of robot technology and learning? The article explores how action research and interaction-driven design can be used in development of educational robot technological tools. The actual case is the development of “Fraction Battle......” which is about learning fractions in primary school. The technology is based on robot technology. An outdoor digital playground is taken into to the classroom and then redesigned. The article argues for interaction design takes precedence to technology or goal driven design for development...

  18. Representations of distance

    DEFF Research Database (Denmark)

    Larsen, Gunvor Riber

    2017-01-01

    This paper explores how Danish tourists represent distance in relation to their holiday mobility and how these representations of distance are a result of being aero-mobile as opposed to being land-mobile. Based on interviews with Danish tourists, whose holiday mobility ranges from the European...... continent to global destinations, the first part of this qualitative study identifies three categories of representations of distance that show how distance is being ‘translated’ by the tourists into non-geometric forms: distance as resources, distance as accessibility, and distance as knowledge....... The representations of distance articulated by the Danish tourists show that distance is often not viewed in ‘just’ kilometres. Rather, it is understood in forms that express how transcending the physical distance through holiday mobility is dependent on individual social and economic contexts, and on whether...

  19. Representation of physiological drought at ecosystem level based on model and eddy covariance measurements

    Science.gov (United States)

    Zhang, Y.; Novick, K. A.; Song, C.; Zhang, Q.; Hwang, T.

    2017-12-01

    Drought and heat waves are expected to increase both in frequency and amplitude, exhibiting a major disturbance to global carbon and water cycles under future climate change. However, how these climate anomalies translate into physiological drought, or ecosystem moisture stress are still not clear, especially under the co-limitations from soil moisture supply and atmospheric demand for water. In this study, we characterized the ecosystem-level moisture stress in a deciduous forest in the southeastern United States using the Coupled Carbon and Water (CCW) model and in-situ eddy covariance measurements. Physiologically, vapor pressure deficit (VPD) as an atmospheric water demand indicator largely controls the openness of leaf stomata, and regulates atmospheric carbon and water exchanges during periods of hydrological stress. Here, we tested three forms of VPD-related moisture scalars, i.e. exponent (K2), hyperbola (K3), and logarithm (K4) to quantify the sensitivity of light-use efficiency to VPD along different soil moisture conditions. The sensitivity indicators of K values were calibrated based on the framework of CCW using Monte Carlo simulations on the hourly scale, in which VPD and soil water content (SWC) are largely decoupled and the full carbon and water exchanging information are held. We found that three K values show similar performances in the predictions of ecosystem-level photosynthesis and transpiration after calibration. However, all K values show consistent gradient changes along SWC, indicating that this deciduous forest is less responsive to VPD as soil moisture decreases, a phenomena of isohydricity in which plants tend to close stomata to keep the leaf water potential constant and reduce the risk of hydraulic failure. Our study suggests that accounting for such isohydric information, or spectrum of moisture stress along different soil moisture conditions in models can significantly improve our ability to predict ecosystem responses to future

  20. Posture-based processing in visual short-term memory for actions.

    Science.gov (United States)

    Vicary, Staci A; Stevens, Catherine J

    2014-01-01

    Visual perception of human action involves both form and motion processing, which may rely on partially dissociable neural networks. If form and motion are dissociable during visual perception, then they may also be dissociable during their retention in visual short-term memory (VSTM). To elicit form-plus-motion and form-only processing of dance-like actions, individual action frames can be presented in the correct or incorrect order. The former appears coherent and should elicit action perception, engaging both form and motion pathways, whereas the latter appears incoherent and should elicit posture perception, engaging form pathways alone. It was hypothesized that, if form and motion are dissociable in VSTM, then recognition of static body posture should be better after viewing incoherent than after viewing coherent actions. However, as VSTM is capacity limited, posture-based encoding of actions may be ineffective with increased number of items or frames. Using a behavioural change detection task, recognition of a single test posture was significantly more likely after studying incoherent than after studying coherent stimuli. However, this effect only occurred for spans of two (but not three) items and for stimuli with five (but not nine) frames. As in perception, posture and motion are dissociable in VSTM.

  1. Strength of object representation: its key role in object-based attention for determining the competition result between Gestalt and top-down objects.

    Science.gov (United States)

    Zhao, Jingjing; Wang, Yonghui; Liu, Donglai; Zhao, Liang; Liu, Peng

    2015-10-01

    It was found in previous studies that two types of objects (rectangles formed according to the Gestalt principle and Chinese words formed in a top-down fashion) can both induce an object-based effect. The aim of the present study was to investigate how the strength of an object representation affects the result of the competition between these two types of objects based on research carried out by Liu, Wang and Zhou [(2011) Acta Psychologica, 138(3), 397-404]. In Experiment 1, the rectangles were filled with two different colors to increase the strength of Gestalt object representation, and we found that the object effect changed significantly for the different stimulus types. Experiment 2 used Chinese words with various familiarities to manipulate the strength of the top-down object representation. As a result, the object-based effect induced by rectangles was observed only when the Chinese word familiarity was low. These results suggest that the strength of object representation determines the result of competition between different types of objects.

  2. INTERNET BANKING ACCEPTANCE IN MALAYSIA BASED ON THE THEORY OF REASONED ACTION

    Directory of Open Access Journals (Sweden)

    J Michael Pearson

    2008-09-01

    Full Text Available ABSTRACT The theory of reasoned action originally introduced in the field of Social Psychology has been widely used to explain individuals’ behaviour. The theory postulates that individuals’ behaviour is influenced by their attitude and subjective norm. The purpose of this study was to determine factors that influence an individual’s intention to use a technology based on the theory of reasoned action. We used Internet banking as the target technology and Malaysian subjects as the sampling frame. A principal component analysis was used to validate the constructs and multiple regressions were used to analyze the data. As expected, the results supported the theory’s proposition as that an individuals’ behavioural intention to use Internet banking is influenced by their attitude and subjective norm. Based on the findings, theoretical and practical implications were offered. Keywords: theory of reasoned action, Internet banking, technology acceptance

  3. Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation

    Directory of Open Access Journals (Sweden)

    An Gary

    2008-05-01

    Full Text Available Abstract Background One of the greatest challenges facing biomedical research is the integration and sharing of vast amounts of information, not only for individual researchers, but also for the community at large. Agent Based Modeling (ABM can provide a means of addressing this challenge via a unifying translational architecture for dynamic knowledge representation. This paper presents a series of linked ABMs representing multiple levels of biological organization. They are intended to translate the knowledge derived from in vitro models of acute inflammation to clinically relevant phenomenon such as multiple organ failure. Results and Discussion ABM development followed a sequence starting with relatively direct translation from in-vitro derived rules into a cell-as-agent level ABM, leading on to concatenated ABMs into multi-tissue models, eventually resulting in topologically linked aggregate multi-tissue ABMs modeling organ-organ crosstalk. As an underlying design principle organs were considered to be functionally composed of an epithelial surface, which determined organ integrity, and an endothelial/blood interface, representing the reaction surface for the initiation and propagation of inflammation. The development of the epithelial ABM derived from an in-vitro model of gut epithelial permeability is described. Next, the epithelial ABM was concatenated with the endothelial/inflammatory cell ABM to produce an organ model of the gut. This model was validated against in-vivo models of the inflammatory response of the gut to ischemia. Finally, the gut ABM was linked to a similarly constructed pulmonary ABM to simulate the gut-pulmonary axis in the pathogenesis of multiple organ failure. The behavior of this model was validated against in-vivo and clinical observations on the cross-talk between these two organ systems Conclusion A series of ABMs are presented extending from the level of intracellular mechanism to clinically observed behavior

  4. A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation

    Science.gov (United States)

    Hu, Xiaohua; Lang, Wenhui; Liu, Wei; Xu, Xue; Yang, Jianbo; Zheng, Lei

    2017-08-01

    Terahertz (THz) spectroscopy technique has been researched and developed for rapid and non-destructive detection of food safety and quality due to its low-energy and non-ionizing characteristics. The objective of this study was to develop a flexible identification model to discriminate transgenic and non-transgenic rice seeds based on terahertz (THz) spectroscopy. To extract THz spectral features and reduce the feature dimension, sparse representation (SR) is employed in this work. A sufficient sparsity level is selected to train the sparse coding of the THz data, and the random forest (RF) method is then applied to obtain a discrimination model. The results show that there exist differences between transgenic and non-transgenic rice seeds in THz spectral band and, comparing with Least squares support vector machines (LS-SVM) method, SR-RF is a better model for discrimination (accuracy is 95% in prediction set, 100% in calibration set, respectively). The conclusion is that SR may be more useful in the application of THz spectroscopy to reduce dimension and the SR-RF provides a new, effective, and flexible method for detection and identification of transgenic and non-transgenic rice seeds with THz spectral system.

  5. Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

    Science.gov (United States)

    Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

    2017-03-19

    The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

  6. Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

    Directory of Open Access Journals (Sweden)

    Jian Ou

    2017-03-01

    Full Text Available The extensive applications of multi-function radars (MFRs have presented a great challenge to the technologies of radar countermeasures (RCMs and electronic intelligence (ELINT. The recently proposed cognitive electronic warfare (CEW provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR. With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

  7. Multilevel X-Pol: a fragment-based method with mixed quantum mechanical representations of different fragments.

    Science.gov (United States)

    Wang, Yingjie; Sosa, Carlos P; Cembran, Alessandro; Truhlar, Donald G; Gao, Jiali

    2012-06-14

    The explicit polarization (X-Pol) method is a fragment-based quantum mechanical model, in which a macromolecular system or other large or complex system in solution is partitioned into monomeric fragments. The present study extends the original X-Pol method, where all fragments are treated using the same electronic structure theory, to multilevel representations, called multilevel X-Pol, in which different electronic structure methods are used to describe different fragments. The multilevel X-Pol method has been implemented into a locally modified version of Gaussian 09. A key ingredient that is used to couple interfragment electrostatic interactions at different levels of theory is the use of the response density for the post-self-consistent-field energy. (The response density is also called the generalized density.) The method is useful for treating fragments in a small region of the system such as a solute molecule or the substrate and amino acids in the active site of an enzyme with a high-level theory, and the fragments in the rest of the system by a lower-level and computationally more efficient method. Multilevel X-Pol is illustrated here by applications to hydrogen bonding complexes in which one fragment is treated with the hybrid M06 density functional, Møller-Plesset perturbation theory, or coupled cluster theory, and the other fragments are treated by Hartree-Fock theory or the B3LYP or M06 hybrid density functionals.

  8. A Slicing Tree Representation and QCP-Model-Based Heuristic Algorithm for the Unequal-Area Block Facility Layout Problem

    Directory of Open Access Journals (Sweden)

    Mei-Shiang Chang

    2013-01-01

    Full Text Available The facility layout problem is a typical combinational optimization problem. In this research, a slicing tree representation and a quadratically constrained program model are combined with harmony search to develop a heuristic method for solving the unequal-area block layout problem. Because of characteristics of slicing tree structure, we propose a regional structure of harmony memory to memorize facility layout solutions and two kinds of harmony improvisation to enhance global search ability of the proposed heuristic method. The proposed harmony search based heuristic is tested on 10 well-known unequal-area facility layout problems from the literature. The results are compared with the previously best-known solutions obtained by genetic algorithm, tabu search, and ant system as well as exact methods. For problems O7, O9, vC10Ra, M11*, and Nug12, new best solutions are found. For other problems, the proposed approach can find solutions that are very similar to previous best-known solutions.

  9. Antifouling Action of Polyisoprene-Based Coatings by Inhibition of Photosynthesis in Microalgae

    NARCIS (Netherlands)

    Jellali, R.; Kromkamp, J.C.; Campistron, I.; Laguerre, A.; Lefebvre, S.; Perkins, R.G.; Pilard, J.F.; Mouget, J.L.

    2013-01-01

    Previous studies have demonstrated that ionic and non-ionic natural rubber-based coatings inhibit adhesion and growth of marine bacteria, fungi, microalgae, and spores of macroalgae. Nevertheless, the mechanism of action of these coatings on the different micro-organisms is not known. In the current

  10. Climate Masters of Nebraska: An Innovative Action-Based Approach for Climate Change Education

    Science.gov (United States)

    Pathak, Tapan B.; Bernadt, Tonya; Umphlett, Natalie

    2014-01-01

    Climate Masters of Nebraska is an innovative educational program that strategically trains community volunteers about climate change science and corresponding ways to reduce greenhouse gas emissions in an interactive and action-based teaching environment. As a result of the program, 91% of participants indicated that they made informed changes in…

  11. A Graph-Based Approach to Action Scheduling in a Parallel Database System

    NARCIS (Netherlands)

    Grefen, P.W.P.J.; Apers, Peter M.G.

    Parallel database machines are meant to obtain high performance in transaction processing, both in terms of response time adn throughput. To obtain high performance, a good scheduling of the execution of the various actions in transactions is crucial. This paper describes a graph-based technique for

  12. Innovative mode of action based in vitro assays for detection of marine neurotoxins

    NARCIS (Netherlands)

    Nicolas, J.A.Y.

    2015-01-01

    Innovative mode of action based in vitro assays for detection of marine neurotoxins

    J. Nicolas, P.J.M. Hendriksen, T.F.H. Bovee, I.M.C.M. Rietjens

    Marine biotoxins are naturally occurring compounds produced by particular phytoplankton species. These toxins often accumulate in

  13. Action Research: A Personal Epiphany and Journey with Evidence-Based Practice

    Science.gov (United States)

    Ballard, Susan D.

    2015-01-01

    The author reveals in this article that her action research journey in the land of evidence-based practice was not her own idea. She writes that she was lured by the profession's finest scholars who advocated for reflective dispositions for practitioners to improve their practice and demonstrate the school librarian's critical role in teaching and…

  14. Scaffolding Strategies for Wiki-Based Collaboration: Action Research in a Multicultural Japanese Language Program

    Science.gov (United States)

    Jung, Insung; Suzuki, Yoko

    2015-01-01

    Wikis can be used to encourage and support collaborative constructivist learning. However, their effectiveness depends upon the use of scaffolding strategies to guide the students in their use. This action research investigated three scaffolding strategies for wiki-based multicultural Japanese language learning: worked examples, grouping and peer…

  15. Activity-Based Teaching in Social Studies Education: An Action Research

    Science.gov (United States)

    Akkus, Zekerya

    2015-01-01

    The aim of this study was to determine pre-service social studies teachers' skills to plan and apply the activity-based teaching and contribute to their development of these skills. In the study, the action research design of qualitative research was used. The sample of the study consisted of 6 pre-service teachers who were 4th year students at…

  16. Action Research as Empowering Professional Development: Examining a District-Based Teacher Research Course

    Science.gov (United States)

    Martell, Christopher C.

    2014-01-01

    Using critical constructivism as the theoretical lens, the researcher used action research to systematically examine the experience of PreK-12 teachers in his district-based teacher research professional development course, while also examining his development as a teacher educator. The results of this study showed that the teachers made progress…

  17. Object Similarity Bootstraps Young Children to Action-Based Verb Extension

    Science.gov (United States)

    Haryu, Etsuko; Imai, Mutsumi; Okada, Hiroyuki

    2011-01-01

    Young children often fail to generalize a novel verb based on sameness of action since they have difficulty focusing on the relational similarity across events while at the same time ignoring the objects that are involved. Study 1, with Japanese-speaking 3- and 4-year-olds (N = 28 in each group), found that similarity of objects involved in action…

  18. A fast Na+/Ca2+-based action potential in a marine diatom.

    Directory of Open Access Journals (Sweden)

    Alison R Taylor

    Full Text Available BACKGROUND: Electrical impulses in animals play essential roles in co-ordinating an array of physiological functions including movement, secretion, environmental sensing and development. Underpinning many of these electrical signals is a fast Na+-based action potential that has been fully characterised only in cells associated with the neuromuscular systems of multicellular animals. Such rapid action potentials are thought to have evolved with the first metazoans, with cnidarians being the earliest representatives. The present study demonstrates that a unicellular protist, the marine diatom Odontella sinensis, can also generate a fast Na+/Ca2+ based action potential that has remarkably similar biophysical and pharmacological properties to invertebrates and vertebrate cardiac and skeletal muscle cells. METHODOLOGY/PRINCIPAL FINDINGS: The kinetic, ionic and pharmacological properties of the rapid diatom action potential were examined using single electrode current and voltage clamp techniques. Overall, the characteristics of the fast diatom currents most closely resemble those of vertebrate and invertebrate muscle Na+/Ca2+ currents. CONCLUSIONS/SIGNIFICANCE: This is the first demonstration of voltage-activated Na+ channels and the capacity to generate fast Na+-based action potentials in a unicellular photosynthetic organism. The biophysical and pharmacological characteristics together with the presence of a voltage activated Na+/Ca2+ channel homologue in the recently sequenced genome of the diatom Thalassiosira pseudonana, provides direct evidence supporting the hypothesis that this rapid signalling mechanism arose in ancestral unicellular eukaryotes and has been retained in at least two phylogenetically distant lineages of eukaryotes; opisthokonts and the stramenopiles. The functional role of the fast animal-like action potential in diatoms remains to be elucidated but is likely involved in rapid environmental sensing of these widespread and

  19. Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

    KAUST Repository

    Smaili, Fatima Zohra

    2018-01-31

    We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.

  20. Updating action domain descriptions.

    Science.gov (United States)

    Eiter, Thomas; Erdem, Esra; Fink, Michael; Senko, Ján

    2010-10-01

    Incorporating new information into a knowledge base is an important problem which has been widely investigated. In this paper, we study this problem in a formal framework for reasoning about actions and change. In this framework, action domains are described in an action language whose semantics is based on the notion of causality. Unlike the formalisms considered in the related work, this language allows straightforward representation of non-deterministic effects and indirect effects of (possibly concurrent) actions, as well as state constraints; therefore, the updates can be more general than elementary statements. The expressivity of this formalism allows us to study the update of an action domain description with a more general approach compared to related work. First of all, we consider the update of an action description with respect to further criteria, for instance, by ensuring that the updated description entails some observations, assertions, or general domain properties that constitute further constraints that are not expressible in an action description in general. Moreover, our framework allows us to discriminate amongst alternative updates of action domain descriptions and to single out a most preferable one, based on a given preference relation possibly dependent on the specified criteria. We study semantic and computational aspects of the update problem, and establish basic properties of updates as well as a decomposition theorem that gives rise to a divide and conquer approach to updating action descriptions under certain conditions. Furthermore, we study the computational complexity of decision problems around computing solutions, both for the generic setting and for two particular preference relations, viz. set-inclusion and weight-based preference. While deciding the existence of solutions and recognizing solutions are PSPACE-complete problems in general, the problems fall back into the polynomial hierarchy under restrictions on the additional

  1. The semantic representation of event information depends on the cue modality: an instance of meaning-based retrieval.

    Science.gov (United States)

    Karlsson, Kristina; Sikström, Sverker; Willander, Johan

    2013-01-01

    The semantic content, or the meaning, is the essence of autobiographical memories. In comparison to previous research, which has mainly focused on the phenomenological experience and the age distribution of retrieved events, the present study provides a novel view on the retrieval of event information by quantifying the information as semantic representations. We investigated the semantic representation of sensory cued autobiographical events and studied the modality hierarchy within the multimodal retrieval cues. The experiment comprised a cued recall task, where the participants were presented with visual, auditory, olfactory or multimodal retrieval cues and asked to recall autobiographical events. The results indicated that the three different unimodal retrieval cues generate significantly different semantic representations. Further, the auditory and the visual modalities contributed the most to the semantic representation of the multimodally retrieved events. Finally, the semantic representation of the multimodal condition could be described as a combination of the three unimodal conditions. In conclusion, these results suggest that the meaning of the retrieved event information depends on the modality of the retrieval cues.

  2. The modeling and solution of course of action generation based on IN-DEA

    Science.gov (United States)

    Wan, Lujun; Li, Wen; Dai, Jiangbin; Huang, Aqian

    2017-08-01

    The course of action (COA) generation is the course of designing the task planning flow, which is one of the key supporting techniques while the military organization executing aerial operation. Due to the outstanding advantage of influence nets (IN), describing the causal logic between random variable, this proposal will introduce IN into the fast building mechanism of COA model. The COA generation model utilize influence nets is established. The probability propagation method is designed, which is based on influence intensity. An improved differential evolution algorithm (IDEA) is designed to solve the action policy optimized selected model. At last, the simulated result of aerial arrack campaign case show the action policy optimized selected in variety influence constants can improve the capability of cause-effect modeling, and the improved differential evolution algorithm has fine convergent and optimized capability.

  3. Design and analysis of direct action solenoid valve based on computational intelligence

    International Nuclear Information System (INIS)

    Liu Qianfeng; Bo Hanliang; Qin Benke

    2010-01-01

    Control Rod Hydraulic Drive Mechanism (CRHDM) is a newly invented patent of the Institute of Nuclear and New Energy Technology Tsinghua University which owns CRHDM's independent intellectual property rights while the integrated valve made up of three direct action solenoid valves is the key part of this mechanism. Therefore, the performance of the solenoid valve affects the integrated valve and the CRHDM directly. In this paper, we present a method to design the parameters of the direct action solenoid valve based on orthogonal experiment design, back propagation (BP) neural network and particle swarm optimization (PSO). The result proves that the method is feasible and accurate to design the parameters in order to obtain the biggest electromagnetic force. Besides, the result also shows that it is the current which influences the electromagnetic force of the direct action solenoid valve most.

  4. The Approach for Action Recognition Based on the Reconstructed Phase Spaces

    Directory of Open Access Journals (Sweden)

    Hong-bin Tu

    2014-01-01

    Full Text Available This paper presents a novel method of human action recognition, which is based on the reconstructed phase space. Firstly, the human body is divided into 15 key points, whose trajectory represents the human body behavior, and the modified particle filter is used to track these key points for self-occlusion. Secondly, we reconstruct the phase spaces for extracting more useful information from human action trajectories. Finally, we apply the semisupervised probability model and Bayes classified method for classification. Experiments are performed on the Weizmann, KTH, UCF sports, and our action dataset to test and evaluate the proposed method. The compare experiment results showed that the proposed method can achieve was more effective than compare methods.

  5. Institutional actions based on nursing diagnoses for preventing falls in the elderly

    Directory of Open Access Journals (Sweden)

    Rafaela Vivian Valcarenghi

    2014-06-01

    Full Text Available This study aimed to propose institutional actions based on nursing diagnoses for the prevention of falls in the elderly. Qualitative, exploratory and descriptive research, with 30 institutionalized senior citizens from Rio Grande, RS, Brazil. During data collection five instruments were applied from March to July 2009. One presents the elderly’s profile; aspects that favored the falls; nursing diagnoses; proposals for institutional actions to prevent falls. The nursing diagnoses were identified: impaired physical mobility, decreased ability to transfer, shower self-care deficit, dressing self-care deficit, impaired environmental interpretation syndrome, chronic confusion, impaired memory; syndrome of stress due to changes; risk of falls, risk of trauma. Through the identification of nursing diagnoses it was possible to make a proposal for institutional actions aimed at preventing falls in the elderly who reside in long-stay institutions.

  6. Project management in mine actions using Multi-Criteria-Analysis-based decision support system

    Directory of Open Access Journals (Sweden)

    Marko Mladineo

    2014-12-01

    Full Text Available In this paper, a Web-based Decision Support System (Web DSS, that supports humanitarian demining operations and restoration of mine-contaminated areas, is presented. The financial shortage usually triggers a need for priority setting in Project Management in Mine actions. As part of the FP7 Project TIRAMISU, a specialized Web DSS has been developed to achieve a fully transparent priority setting process. It allows stakeholders and donors to actively join the decision making process using a user-friendly and intuitive Web application. The main advantage of this Web DSS is its unique way of managing a mine action project using Multi-Criteria Analysis (MCA, namely the PROMETHEE method, in order to select priorities for demining actions. The developed Web DSS allows decision makers to use several predefined scenarios (different criteria weights or to develop their own, so it allows project managers to compare different demining possibilities with ease.

  7. Action video games and improved attentional control: Disentangling selection- and response-based processes.

    Science.gov (United States)

    Chisholm, Joseph D; Kingstone, Alan

    2015-10-01

    Research has demonstrated that experience with action video games is associated with improvements in a host of cognitive tasks. Evidence from paradigms that assess aspects of attention has suggested that action video game players (AVGPs) possess greater control over the allocation of attentional resources than do non-video-game players (NVGPs). Using a compound search task that teased apart selection- and response-based processes (Duncan, 1985), we required participants to perform an oculomotor capture task in which they made saccades to a uniquely colored target (selection-based process) and then produced a manual directional response based on information within the target (response-based process). We replicated the finding that AVGPs are less susceptible to attentional distraction and, critically, revealed that AVGPs outperform NVGPs on both selection-based and response-based processes. These results not only are consistent with the improved-attentional-control account of AVGP benefits, but they suggest that the benefit of action video game playing extends across the full breadth of attention-mediated stimulus-response processes that impact human performance.

  8. Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

    Science.gov (United States)

    Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

    2018-01-01

    The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

  9. Grasping actions and social interaction: neural bases and anatomical circuitry in the monkey.

    Directory of Open Access Journals (Sweden)

    Stefano eRozzi

    2015-07-01

    Full Text Available The study of the neural mechanisms underlying grasping actions showed that cognitive functions are deeply embedded in motor organization. In the first part of this review, we describe the anatomical structure of the motor cortex in the monkey and the cortical and sub-cortical connections of the different motor areas. In the second part, we review the neurophysiological literature showing that motor neurons are not only involved in movement execution, but also in the transformation of object physical features into motor programs appropriate to grasp them (through visuo-motor transformations. We also discuss evidence indicating that motor neurons can encode the goal of motor acts and the intention behind action execution. Then, we describe one of the mechanisms – the mirror mechanism – considered to be at the basis of action understanding and intention reading, and describe the anatomo-functional pathways through which information about the social context can reach the areas containing mirror neurons. Finally, we briefly show that a clear similarity exists between monkey and human in the organization of the motor and mirror systems. Based on monkey and human literature, we conclude that the mirror mechanism relies on a more extended network than previously thought, and possibly subserves basic social functions. We propose that this mechanism is also involved in preparing appropriate complementary response to observed actions, allowing two individuals to become attuned and cooperate in joint actions.

  10. Grasping actions and social interaction: neural bases and anatomical circuitry in the monkey

    Science.gov (United States)

    Rozzi, Stefano; Coudé, Gino

    2015-01-01

    The study of the neural mechanisms underlying grasping actions showed that cognitive functions are deeply embedded in motor organization. In the first part of this review, we describe the anatomical structure of the motor cortex in the monkey and the cortical and sub-cortical connections of the different motor areas. In the second part, we review the neurophysiological literature showing that motor neurons are not only involved in movement execution, but also in the transformation of object physical features into motor programs appropriate to grasp them (through visuo-motor transformations). We also discuss evidence indicating that motor neurons can encode the goal of motor acts and the intention behind action execution. Then, we describe one of the mechanisms—the mirror mechanism—considered to be at the basis of action understanding and intention reading, and describe the anatomo-functional pathways through which information about the social context can reach the areas containing mirror neurons. Finally, we briefly show that a clear similarity exists between monkey and human in the organization of the motor and mirror systems. Based on monkey and human literature, we conclude that the mirror mechanism relies on a more extended network than previously thought, and possibly subserves basic social functions. We propose that this mechanism is also involved in preparing appropriate complementary response to observed actions, allowing two individuals to become attuned and cooperate in joint actions. PMID:26236258

  11. A critical Action Research approach to curriculum development in a laboratory-based chemical engineering course

    Science.gov (United States)

    White, Scott R.

    This dissertation is a report of an attempt to critically evaluate a novel laboratory course from within the context of a chemical engineering curriculum. The research was done in a college classroom-laboratory setting, entrenched in the everydayness of classroom activities. All of the students, instructors, and educational researchers were knowing participants in this Action Research study. The students, a mixture of juniors, seniors, & graduate students, worked together on semester-long projects in groups that were mixed by age, gender and academic level. Qualitative techniques were used to gather different forms of representations of the students and instructors' experiences. Emergent patterns from the data gave strength to emergent knowledge claims that informed the instructors and the researcher about what the students were learning about performing experimental work and communicating results with their peers and instructor. The course challenged and in some cases changed the conceptions of instruction previously held by the students and the instructors. The course did not proceed without problems, yet the majority of these problems were overcome by the design of the course. Assertions and recommendations for improvement and application to other educational contexts are suggested.

  12. Place-based and data-rich citizen science as a precursor for conservation action.

    Science.gov (United States)

    Haywood, Benjamin K; Parrish, Julia K; Dolliver, Jane

    2016-06-01

    Environmental education strategies have customarily placed substantial focus on enhancing ecological knowledge and literacy with the hope that, upon discovering relevant facts and concepts, participants will be better equipped to process and dissect environmental issues and, therefore, make more informed decisions. The assumption is that informed citizens will become active citizens--enthusiastically lobbying for, and participating in, conservation-oriented action. We surveyed and interviewed and used performance data from 432 participants in the Coastal Observation and Seabird Survey Team (COASST), a scientifically rigorous citizen science program, to explore measurable change in and links between understanding and action. We found that participation in rigorous citizen science was associated with significant increases in participant knowledge and skills; a greater connection to place and, secondarily, to community; and an increasing awareness of the relative impact of anthropogenic activities on local ecosystems specifically through increasing scientific understanding of the ecosystem and factors affecting it. Our results suggest that a place-based, data-rich experience linked explicitly to local, regional, and global issues can lead to measurable change in individual and collective action, expressed in our case study principally through participation in citizen science and community action and communication of program results to personal acquaintances and elected officials. We propose the following tenets of conservation literacy based on emergent themes and the connections between them explicit in our data: place-based learning creates personal meaning making; individual experience nested within collective (i.e., program-wide) experience facilitates an understanding of the ecosystem process and function at local and regional scales; and science-based meaning making creates informed concern (i.e., the ability to discern both natural and anthropogenic forcing

  13. Acquiring Knowledge in Learning Concepts from Electrical Circuits: The Use of Multiple Representations in Technology-Based Learning Environments

    Directory of Open Access Journals (Sweden)

    Abdeljalil Métioui

    2012-04-01

    Full Text Available The constructivists approach on the conception of relative software of modelling to training and teaching of the concepts of current and voltage requires appraisal of several disciplinary fields in order to provide to the learners a training adapted to their representations. Thus, this approach requires the researchers to have adequate knowledge or skills in data processing, didactics and science content. In this regard, several researches underline that the acquisition of basic concepts that span a field of a given knowledge, must take into account the student and the scientific representations. The present research appears in this perspective, and aims to present the interactive computer environments that take into account the students (secondary and college and scientific representations related to simple electric circuits. These computer environments will help the students to analyze the functions of the electric circuits adequately.

  14. Integration of laboratory bioassays into the risk-based corrective action process

    International Nuclear Information System (INIS)

    Edwards, D.; Messina, F.; Clark, J.

    1995-01-01

    Recent data generated by the Gas Research Institute (GRI) and others indicate that residual hydrocarbon may be bound/sequestered in soil such that it is unavailable for microbial degradation, and thus possibly not bioavailable to human/ecological receptors. A reduction in bioavailability would directly equate to reduced exposure and, therefore, potentially less-conservative risk-based cleanup soil goals. Laboratory bioassays which measure bioavailability/toxicity can be cost-effectively integrated into the risk-based corrective action process. However, in order to maximize the cost-effective application of bioassays several site-specific parameters should be addressed up front. This paper discusses (1) the evaluation of parameters impacting the application of bioassays to soils contaminated with metals and/or petroleum hydrocarbons and (2) the cost-effective integration of bioassays into a tiered ASTM type framework for risk-based corrective action

  15. Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

    Science.gov (United States)

    Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

    2018-04-01

    Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

  16. A generalized wavelet extrema representation

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Jian; Lades, M.

    1995-10-01

    The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.

  17. Alternative approach to nuclear data representation

    International Nuclear Information System (INIS)

    Pruet, J.; Brown, D.; Beck, B.; McNabb, D.P.

    2006-01-01

    This paper considers an approach for representing nuclear data that is qualitatively different from the approach currently adopted by the nuclear science community. Specifically, we examine a representation in which complicated data is described through collections of distinct and self-contained simple data structures. This structure-based representation is compared with the ENDF and ENDL formats, which can be roughly characterized as dictionary-based representations. A pilot data representation for replacing the format currently used at LLNL is presented. Examples are given as is a discussion of promises and shortcomings associated with moving from traditional dictionary-based formats to a structure-rich or class-like representation

  18. On knowledge representation for high energy physics control systems

    International Nuclear Information System (INIS)

    Huuskonen, P.; Kaarela, K.; Meri, M.; Le Goff, J.M.

    1994-01-01

    A framework for knowledge representation in the domain of high energy physics control systems is presented. Models of process equipment, controls, documents, information systems, functional dependencies, physical interconnections, and design decisions are necessary to allow for automated reasoning about such systems. A number of support systems can use these models: alarm processing, fault diagnosis, sensor validation, preventive maintenance, action analysis, information abstraction, intelligent help systems, and on-line documentation. Our aim is to achieve representations that would be understood by end users, could be constructed by domain experts, and would be powerful enough to function as a basis for these support systems. It is proposed to base these models on means-end-analysis, implemented through an entity-relationship type of representation and extended with the notion of contribution. The paper outlines class hierarchies and relation types to form a vocabulary for talking about this specific domain. A number of implementation concerns are raised and some examples of how these representations can be used in real cases are offered. The representations are likely to prove most useful for support systems that function in the user assisting mode, as opposed to fully autonomous systems. Intelligent help and information abstraction applications, in particular, are expected to benefit. The main focus of the work is that of the control information system concepts based on encapsulated real- time objects (CICERO) project at CERN, experiment controls, but the results are usable for accelerator control systems and for industrial control systems in general. (author). 37 refs., 7 figs

  19. An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

    Directory of Open Access Journals (Sweden)

    Ahmed R. Sadik

    2017-11-01

    accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot. Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS and a Business Rule Management System (BRMS is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.

  20. Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

    Science.gov (United States)

    Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

    2016-09-15

    Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.