The Analysis of High School Students' Conceptions of Learning in Different Domains

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Sadi, Özlem

2015-01-01

The purpose of this study is to investigate whether or not conceptions of learning diverge in different science domains by identifying high school students' conceptions of learning in physics, chemistry and biology. The Conceptions of Learning Science (COLS) questionnaire was adapted for physics (Conceptions of Learning Physics, COLP), chemistry…

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

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Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Learn every day about numbers 100 best ideas from teachers

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Charner, Kathy

2009-01-01

Classroom-tested and teacher approved, these activities help children ages three to six learn all about numbers. With one hundred engaging and fun activities, Learn Every Day About Numbers offers everything a teacher needs to build a foundation for future math learning. Children will love becoming a Number Detective, a Flashlight Writer, or a Number Hero as they investigate the wonderful world of numbers. Each activity offers learning objectives to meet standards, a materials list, related children's books, and an assessment component to measure children's learning. Learning about numbers has never been so much fun!.

Formative assessment in an online learning environment to support flexible on-the-job learning in complex professional domains

NARCIS (Netherlands)

Tamara van Gog; Desirée Joosten-ten Brinke; F. J. Prins; Dominique Sluijsmans

2010-01-01

This article describes a blueprint for an online learning environment that is based on prominent instructional design and assessment theories for supporting learning in complex domains. The core of this environment consists of formative assessment tasks (i.e., assessment for learning) that center on

Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account

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Renkl, Alexander; Hilbert, Tatjana; Schworm, Silke

2009-01-01

One classical instructional effect of cognitive load theory (CLT) is the worked-example effect. Although the vast majority of studies have focused on well-structured and algorithmic sub-domains of mathematics or physics, more recent studies have also analyzed learning with examples from complex domains in which only heuristic solution strategies…

Developing a Domain Theory Defining and Exemplifying a Learning Theory of Progressive Attainments

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Bunderson, C. Victor

2011-01-01

This article defines the concept of Domain Theory, or, when educational measurement is the goal, one might call it a "Learning Theory of Progressive Attainments in X Domain". The concept of Domain Theory is first shown to be rooted in validity theory, then the concept of domain theory is expanded to amplify its necessary but long neglected…

Exploring Children's Passion for Learning in Six Domains

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Coleman, Laurence J.; Guo, Aige

2013-01-01

Passion for learning (PFL) in children is a phenomenon that is little understood. The experience of PFL was studied with phenomenological and qualitative modes of inquiry. Case studies of six domains (acting, reading, filmmaking, spelling, math, and preaching) describe how the passion developed using the voices of children and parents. Their…

Knowing, Applying, and Reasoning about Arithmetic: Roles of Domain-General and Numerical Skills in Multiple Domains of Arithmetic Learning

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Zhang, Xiao; Räsänen, Pekka; Koponen, Tuire; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Nurmi, Jari-Erik

2017-01-01

The longitudinal relations of domain-general and numerical skills at ages 6-7 years to 3 cognitive domains of arithmetic learning, namely knowing (written computation), applying (arithmetic word problems), and reasoning (arithmetic reasoning) at age 11, were examined for a representative sample of 378 Finnish children. The results showed that…

The effect of observational learning on students' performance, processes, and motivation in two creative domains.

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Groenendijk, Talita; Janssen, Tanja; Rijlaarsdam, Gert; van den Bergh, Huub

2013-03-01

Previous research has shown that observation can be effective for learning in various domains, for example, argumentative writing and mathematics. The question in this paper is whether observational learning can also be beneficial when learning to perform creative tasks in visual and verbal arts. We hypothesized that observation has a positive effect on performance, process, and motivation. We expected similarity in competence between the model and the observer to influence the effectiveness of observation. Sample.  A total of 131 Dutch students (10(th) grade, 15 years old) participated. Two experiments were carried out (one for visual and one for verbal arts). Participants were randomly assigned to one of three conditions; two observational learning conditions and a control condition (learning by practising). The observational learning conditions differed in instructional focus (on the weaker or the more competent model of a pair to be observed). We found positive effects of observation on creative products, creative processes, and motivation in the visual domain. In the verbal domain, observation seemed to affect the creative process, but not the other variables. The model similarity hypothesis was not confirmed. Results suggest that observation may foster learning in creative domains, especially in the visual arts. © 2011 The British Psychological Society.

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

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Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Information Pre-Processing using Domain Meta-Ontology and Rule Learning System

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Ranganathan, Girish R.; Biletskiy, Yevgen

Around the globe, extraordinary amounts of documents are being created by Enterprises and by users outside these Enterprises. The documents created in the Enterprises constitute the main focus of the present chapter. These documents are used to perform numerous amounts of machine processing. While using thesedocuments for machine processing, lack of semantics of the information in these documents may cause misinterpretation of the information, thereby inhibiting the productiveness of computer assisted analytical work. Hence, it would be profitable to the Enterprises if they use well defined domain ontologies which will serve as rich source(s) of semantics for the information in the documents. These domain ontologies can be created manually, semi-automatically or fully automatically. The focus of this chapter is to propose an intermediate solution which will enable relatively easy creation of these domain ontologies. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that are substantially labor intensive; therefore, some intermediate solutions which would assist in capturing domain ontologies must be developed. This chapter proposes a solution in this direction which involves building a meta-ontology that will serve as an intermediate information source for the main domain ontology. This chapter proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a domain of interest from huge amount of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from documents, and then refined in order to form better domain ontology either through automatic ontology learning methods or some other relevant ontology building approach.

Cross-Domain Statistical-Sequential Dependencies Are Difficult To Learn

Directory of Open Access Journals (Sweden)

Anne McClure Walk

2016-02-01

Full Text Available Recent studies have demonstrated participants’ ability to learn cross-modal associations during statistical learning tasks. However, these studies are all similar in that the cross-modal associations to be learned occur simultaneously, rather than sequentially. In addition, the majority of these studies focused on learning across sensory modalities but not across perceptual categories. To test both cross-modal and cross-categorical learning of sequential dependencies, we used an artificial grammar learning task consisting of a serial stream of auditory and/or visual stimuli containing both within- and cross-domain dependencies. Experiment 1 examined within-modal and cross-modal learning across two sensory modalities (audition and vision. Experiment 2 investigated within-categorical and cross-categorical learning across two perceptual categories within the same sensory modality (e.g. shape and color; tones and non-words. Our results indicated that individuals demonstrated learning of the within-modal and within-categorical but not the cross-modal or cross-categorical dependencies. These results stand in contrast to the previous demonstrations of cross-modal statistical learning, and highlight the presence of modality constraints that limit the effectiveness of learning in a multimodal environment.

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

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Kundu, Kousik; Backofen, Rolf

2017-01-01

Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

Quality of the Home Learning Environment during Preschool Age--Domains and Contextual Conditions

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Kluczniok, Katharina; Lehrl, Simone; Kuger, Susanne; Rossbach, Hans-Guenther

2013-01-01

The quality of the home learning environment has been proven to be of major importance for child development, but little is known about the role of domain specificity in promoting early childhood learning at home and its dependence on family background. This article presents a framework of the home learning environment in early childhood that…

Learning from Number Board Games: You Learn What You Encode

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Laski, Elida V.; Siegler, Robert S.

2014-01-01

We tested the hypothesis that encoding the numerical-spatial relations in a number board game is a key process in promoting learning from playing such games. Experiment 1 used a microgenetic design to examine the effects on learning of the type of counting procedure that children use. As predicted, having kindergartners count-on from their current…

Optimizing the number of steps in learning tasks for complex skills.

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Nadolski, Rob J; Kirschner, Paul A; van Merriënboer, Jeroen J G

2005-06-01

Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning. The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps. Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female. Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed. Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task. An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain

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Huang, Lan; Du, Youfu; Chen, Gongyang

2015-03-01

Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.

Learning Potentials in Number Blocks

DEFF Research Database (Denmark)

Majgaard, Gunver; Misfeldt, Morten; Nielsen, Jacob

2012-01-01

This paper describes an initial exploration of how an interactive cubic user-configurable modular robotic system can be used to support learning about numbers and how they are pronounced. The development is done in collaboration with a class of 7-8 year old children and their mathematics teacher....

Conceptualizing the e-Learning Assessment Domain using an Ontology Network

Directory of Open Access Journals (Sweden)

Lucía Romero

2012-09-01

Full Text Available During the last year, approaches that use ontologies, the backbone of the Semantic Web technologies, for different purposes in the assessment domain of e-Learning have emerged. One of these purposes is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. The most of the approaches that deal with this problem have proposed individual ontologies that model only a part of the assessment domain. The main contribution of this paper is an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of it. The main advantage of this network is that it is enriched with rules for considering not only technical aspects of an assessment but also pedagogic

Research Into the Role of Students’ Affective Domain While Learning Geology in Field Environments

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Elkins, J.

2009-12-01

Existing research programs in field-based geocognition include assessment of cognitive, psychomotor, and affective domains. Assessment of the affective domain often involves the use of instruments and techniques uncommon to the geosciences. Research regarding the affective domain also commonly results in the collection and production of qualitative data that is difficult for geoscientists to analyze due to their lack of familiarity with these data sets. However, important information about students’ affective responses to learning in field environments can be obtained by using these methods. My research program focuses on data produced by students’ affective responses to field-based learning environments, primarily among students at the introductory level. For this research I developed a Likert-scale Novelty Space Survey, which presents student ‘novelty space’ (Orion and Hofstien, 1993) as a polygon; the larger the polygons, the more novelty students are experiencing. The axises for these polygons correspond to novelty domains involving geographic, social, cognitive, and psychological factors. In addition to the Novelty Space Survey, data which I have collected/generated includes focus group interviews on the role of recreational experiences in geology field programs. I have also collected data concerning the motivating factors that cause students to take photographs on field trips. The results of these studies give insight to the emotional responses students have to learning in the field and are important considerations for practitioners of teaching in these environments. Collaborative investigations among research programs that cross university departments and include multiple institutions is critical at this point in development of geocognition as a field due to unfamiliarity with cognitive science methodology by practitioners teaching geosciences and the dynamic nature of field work by cognitive scientists. However, combining the efforts of cognitive

Pornographic image recognition and filtering using incremental learning in compressed domain

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Zhang, Jing; Wang, Chao; Zhuo, Li; Geng, Wenhao

2015-11-01

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents' physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

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Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

The Effect of Observational Learning on Students' Performance, Processes, and Motivation in Two Creative Domains

Science.gov (United States)

Groenendijk, Talita; Janssen, Tanja; Rijlaarsdam, Gert; van den Bergh, Huub

2013-01-01

Background. Previous research has shown that observation can be effective for learning in various domains, for example, argumentative writing and mathematics. The question in this paper is whether observational learning can also be beneficial when learning to perform creative tasks in visual and verbal arts. Aims. We hypothesized that observation…

TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

CERN Document Server

Hester, Todd

2013-01-01

This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

Reinforcement Learning in Distributed Domains: Beyond Team Games

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Wolpert, David H.; Sill, Joseph; Turner, Kagan

2000-01-01

Distributed search algorithms are crucial in dealing with large optimization problems, particularly when a centralized approach is not only impractical but infeasible. Many machine learning concepts have been applied to search algorithms in order to improve their effectiveness. In this article we present an algorithm that blends Reinforcement Learning (RL) and hill climbing directly, by using the RL signal to guide the exploration step of a hill climbing algorithm. We apply this algorithm to the domain of a constellations of communication satellites where the goal is to minimize the loss of importance weighted data. We introduce the concept of 'ghost' traffic, where correctly setting this traffic induces the satellites to act to optimize the world utility. Our results indicated that the bi-utility search introduced in this paper outperforms both traditional hill climbing algorithms and distributed RL approaches such as team games.

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

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Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Shifting of wrapped phase maps in the frequency domain using a rational number

International Nuclear Information System (INIS)

Gdeisat, Munther A; Abushakra, Ahmad; Qaddoura, Maen; Burton, David R; Lilley, Francis; Arevalillo-Herráez, Miguel

2016-01-01

The number of phase wraps in an image can be either reduced, or completely eliminated, by transforming the image into the frequency domain using a Fourier transform, and then shifting the spectrum towards the origin. After this, the spectrum is transformed back to the spatial domain using the inverse Fourier transform and finally the phase is extracted using the arctangent function. However, it is a common concern that the spectrum can be shifted only by an integer number, meaning that the phase wrap reduction is often not optimal. In this paper we propose an algorithm than enables the spectrum to be frequency shifted by a rational number. The principle of the proposed method is confirmed both by using an initial computer simulation and is subsequently validated experimentally on real fringe patterns. The technique may offer in some cases the prospects of removing the necessity for a phase unwrapping process altogether and/or speeding up the phase unwrapping process. This may be beneficial in terms of potential increases in signal recovery robustness and also for use in time-critical applications. (paper)

Leveraging Random Number Generation for Mastery of Learning in Teaching Quantitative Research Courses via an E-Learning Method

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Boonsathorn, Wasita; Charoen, Danuvasin; Dryver, Arthur L.

2014-01-01

E-Learning brings access to a powerful but often overlooked teaching tool: random number generation. Using random number generation, a practically infinite number of quantitative problem-solution sets can be created. In addition, within the e-learning context, in the spirit of the mastery of learning, it is possible to assign online quantitative…

The Learning Potentials of Number Blocks

DEFF Research Database (Denmark)

Majgaard, Gunver; Nielsen, Jacob; Misfeldt, Morten

2012-01-01

This paper describes an initial exploration of how an interactive cubic user-configurable modular robotic system can be used to support learning about numbers and how they are pronounced. The development is done in collaboration with a class of 7-8 year old children and their mathematics teacher...

Learning Random Numbers: A Matlab Anomaly

Czech Academy of Sciences Publication Activity Database

Savický, Petr; Robnik-Šikonja, M.

2008-01-01

Roč. 22, č. 3 (2008), s. 254-265 ISSN 0883-9514 R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : random number s * machine learning * classification * attribute evaluation * regression Subject RIV: BA - General Mathematics Impact factor: 0.795, year: 2008

Pedagogically-Driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

Science.gov (United States)

Romero, Lucila; North, Matthew; Gutiérrez, Milagros; Caliusco, Laura

2015-01-01

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to…

Socioeconomic variation, number competence, and mathematics learning difficulties in young children.

Science.gov (United States)

Jordan, Nancy C; Levine, Susan C

2009-01-01

As a group, children from disadvantaged, low-income families perform substantially worse in mathematics than their counterparts from higher-income families. Minority children are disproportionately represented in low-income populations, resulting in significant racial and social-class disparities in mathematics learning linked to diminished learning opportunities. The consequences of poor mathematics achievement are serious for daily functioning and for career advancement. This article provides an overview of children's mathematics difficulties in relation to socioeconomic status (SES). We review foundations for early mathematics learning and key characteristics of mathematics learning difficulties. A particular focus is the delays or deficiencies in number competencies exhibited by low-income children entering school. Weaknesses in number competence can be reliably identified in early childhood, and there is good evidence that most children have the capacity to develop number competence that lays the foundation for later learning.

Numbered head together with scientific approach in geometry learning

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Indarti, Dwi; Mardiyana; Pramudya, Ikrar

2017-12-01

The aim of this research was to find out the influence of learning model implementation toward student’s achievement in mathematics. This research was using quasi-experimental research. The population of the research was all of 7th grade students in Karanganyar. Sample was taken using stratified cluster random sampling technique. The data collection has been conducted based on students’ mathematics achievement test. The results from the data analysis showed that the learning mathematics by using Numbered Head Together (NHT) learning model with scientific approach improved student’s achievement in mathematics rather than direct learning model particularly in learning object of quadrilateral. Implementation of NHT learning model with scientific approach could be used by the teachers in teaching and learning, particularly in learning object of quadrilateral.

An emergentist perspective on the origin of number sense.

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Zorzi, Marco; Testolin, Alberto

2017-02-19

The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a 'nativist' stance on the origin of number sense. Here, we tackle this issue within the 'emergentist' perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense.This article is part of a discussion meeting issue 'The origins of numerical abilities'. © 2017 The Authors.

Evaluation of bispectrum in the wave number domain based on multi-point measurements

Directory of Open Access Journals (Sweden)

Y. Narita

2008-10-01

Full Text Available We present an estimator of the bispectrum, a measure of three-wave couplings. It is evaluated directly in the wave number domain using a limited number of detectors. The ability of the bispectrum estimator is examined numerically and then it is applied to fluctuations of magnetic field and electron density in the terrestrial foreshock region observed by the four Cluster spacecraft, which indicates the presence of a three-wave coupling in space plasma.

Domain-Specific and Domain-General Training to Improve Kindergarten Children’s Mathematics

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Geetha B. Ramani

2017-12-01

Full Text Available Ensuring that kindergarten children have a solid foundation in early numerical knowledge is of critical importance for later mathematical achievement. In this study, we targeted improving the numerical knowledge of kindergarteners (n = 81 from primarily low-income backgrounds using two approaches: one targeting their conceptual knowledge, specifically, their understanding of numerical magnitudes; and the other targeting their underlying cognitive system, specifically, their working memory. Both interventions involved playing game-like activities on tablet computers over the course of several sessions. As predicted, both interventions improved children’s numerical magnitude knowledge as compared to a no-contact control group, suggesting that both domain-specific and domain-general interventions facilitate mathematical learning. Individual differences in effort during the working memory game, but not the number knowledge training game predicted children’s improvements in number line estimation. The results demonstrate the potential of using a rapidly growing technology in early childhood classrooms to promote young children’s numerical knowledge.

The effectivenes of science domain-based science learning integrated with local potency

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Kurniawati, Arifah Putri; Prasetyo, Zuhdan Kun; Wilujeng, Insih; Suryadarma, I. Gusti Putu

2017-08-01

This research aimed to determine the significant effect of science domain-based science learning integrated with local potency toward science process skills. The research method used was a quasi-experimental design with nonequivalent control group design. The population of this research was all students of class VII SMP Negeri 1 Muntilan. The sample of this research was selected through cluster random sampling, namely class VII B as an experiment class (24 students) and class VII C as a control class (24 students). This research used a test instrument that was adapted from Agus Dwianto's research. The aspect of science process skills in this research was observation, classification, interpretation and communication. The analysis of data used the one factor anova at 0,05 significance level and normalized gain score. The significance level result of science process skills with one factor anova is 0,000. It shows that the significance level < alpha (0,05). It means that there was significant effect of science domain-based science learning integrated with local potency toward science learning process skills. The results of analysis show that the normalized gain score are 0,29 (low category) in control class and 0,67 (medium category) in experiment class.

Improvement of learning domains of nursing students with the use of authentic assessment pedagogy in clinical practice.

Science.gov (United States)

Chong, Edmund Jun Meng; Lim, Jessica Shih Wei; Liu, Yuchan; Lau, Yvonne Yen Lin; Wu, Vivien Xi

2016-09-01

With evolving healthcare demands, nursing educators need to constantly review their teaching methodologies in order to enhance learners' knowledge and competency of skills in the clinical settings. Learning is an active process in which meaning is accomplished on the basis of experience and that authentic assessment pedagogy will enable nursing students to play an active part in their learning. The study was conducted with an aim to examine nursing students' learning domains through the introduction of the authentic assessment pedagogy during their clinical practice. A quasi-experimental study (n = 54) was conducted over a period of 10 weeks at a local tertiary hospital. The experimental group was exposed to the authentic assessment pedagogy and were taught to use the assessment rubrics as an instrument to help enhance their learning. Students were assessed and scored according to the assessment rubrics, which were categorized into four domains; cognitive, psychomotor, affective and critical thinking abilities. The findings indicated that an overall score for the four domains between the experimental and control groups were significant, with p value of pedagogy in the clinical setting. Copyright © 2016 Elsevier Ltd. All rights reserved.

LEARNING ONE-DIGIT DECIMAL NUMBERS BY MEASUREMENT AND GAME PREDICTING LENGTH

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Puji Astuti

2014-01-01

Full Text Available This paper aims to describe how students develop understanding of one-digit decimals. To achieve the aim, Local Instruction Theory (LIT about the process of learning decimals and the means designed to support that learning are developed. Along with this idea, the framework of Realistic Mathematics Education (RME is proposed. Based on the aim, design research methodology is used. This paper discusses learning activities of three meetings from teaching experiment of the focus group students of the fourth grade elementary school in Surabaya: SDIT Al Ghilmani. The data indicated that the learning activities promoted the students’ understanding of one-digit decimal numbers.Keyword: measurement, decimal numbers, number line DOI: http://dx.doi.org/10.22342/jme.5.1.1447.35-46

Children's learning of number words in an indigenous farming-foraging group.

Science.gov (United States)

Piantadosi, Steven T; Jara-Ettinger, Julian; Gibson, Edward

2014-07-01

We show that children in the Tsimane', a farming-foraging group in the Bolivian rain-forest, learn number words along a similar developmental trajectory to children from industrialized countries. Tsimane' children successively acquire the first three or four number words before fully learning how counting works. However, their learning is substantially delayed relative to children from the United States, Russia, and Japan. The presence of a similar developmental trajectory likely indicates that the incremental stages of numerical knowledge - but not their timing - reflect a fundamental property of number concept acquisition which is relatively independent of language, culture, age, and early education. © 2014 John Wiley & Sons Ltd.

Generative Learning Objects Instantiated with Random Numbers Based Expressions

Directory of Open Access Journals (Sweden)

Ciprian Bogdan Chirila

2015-12-01

Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

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Raaijmakers, Steven F; Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J G; van Gog, Tamara

2018-01-01

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

Science.gov (United States)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.

Science.gov (United States)

Ma, Jianzhu; Wang, Sheng

2015-01-01

The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms

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Chien-Hung Huang

2015-01-01

Full Text Available Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI data, domain-domain interaction (DDI data, weighted domain frequency score (DFS, and cancer linker degree (CLD data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I using individual algorithm, (II combining algorithms, and (III combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

Which Type of Rational Numbers Should Students Learn First?

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Tian, Jing; Siegler, Robert S.

2017-01-01

Many children and adults have difficulty gaining a comprehensive understanding of rational numbers. Although fractions are taught before decimals and percentages in many countries, including the USA, a number of researchers have argued that decimals are easier to learn than fractions and therefore teaching them first might mitigate children's…

Domain-General Factors Influencing Numerical and Arithmetic Processing

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André Knops

2017-12-01

Full Text Available This special issue contains 18 articles that address the question how numerical processes interact with domain-general factors. We start the editorial with a discussion of how to define domain-general versus domain-specific factors and then discuss the contributions to this special issue grouped into two core numerical domains that are subject to domain-general influences (see Figure 1. The first group of contributions addresses the question how numbers interact with spatial factors. The second group of contributions is concerned with factors that determine and predict arithmetic understanding, performance and development. This special issue shows that domain-general (Table 1a as well as domain-specific (Table 1b abilities influence numerical and arithmetic performance virtually at all levels and make it clear that for the field of numerical cognition a sole focus on one or several domain-specific factors like the approximate number system or spatial-numerical associations is not sufficient. Vice versa, in most studies that included domain-general and domain-specific variables, domain-specific numerical variables predicted arithmetic performance above and beyond domain-general variables. Therefore, a sole focus on domain-general aspects such as, for example, working memory, to explain, predict and foster arithmetic learning is also not sufficient. Based on the articles in this special issue we conclude that both domain-general and domain-specific factors contribute to numerical cognition. But the how, why and when of their contribution still needs to be better understood. We hope that this special issue may be helpful to readers in constraining future theory and model building about the interplay of domain-specific and domain-general factors.

Interaction and Technological Resources to Support Learning of Complex Numbers

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Cassiano Scott Puhl

2016-02-01

Full Text Available This article presents a didactic proposal, a workshop for the introduction of the study of complex numbers. Unlike recurrent practices, the workshop began developing the geometric shape of the complex number, implicitly, through vectors. Eliminating student formal vision and algebraic, enriching the teaching practice. The main objective of the strategy was to build the concept of imaginary unit without causing a feeling of strangeness or insignificance of number. The theory of David Ausubel, meaningful learning, the workshop was based on a strategy developed to analyze the subsumers of students and develop a learning by subject. Combined with dynamic and interactive activities in the workshop, there is the use of a learning object (http://matematicacomplexa.meximas.com/. An environment created and basing on the theory of meaningful learning, making students reflect and interact in developed applications sometimes being challenged and other testing hypotheses and, above all, building knowledge. This proposal provided a rich environment for exchange of information between participants and deepening of ideas and concepts that served as subsumers. The result of the experience was very positive, as evidenced by the comments and data submitted by the participants, thus demonstrating that the objectives of this didactic proposal have been achieved.

A Comparative Analysis of Numbers and Biology Content Domains between Turkey and the USA

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Incikabi, Lutfi; Ozgelen, Sinan; Tjoe, Hartono

2012-01-01

This study aimed to compare Mathematics and Science programs focusing on TIMSS content domains of Numbers and Biology that produced the largest achievement gap among students from Turkey and the USA. Specifically, it utilized the content analysis method within Turkish and New York State (NYS) frameworks. The procedures of study included matching…

Monte Carlo learning/biasing experiment with intelligent random numbers

International Nuclear Information System (INIS)

Booth, T.E.

1985-01-01

A Monte Carlo learning and biasing technique is described that does its learning and biasing in the random number space rather than the physical phase-space. The technique is probably applicable to all linear Monte Carlo problems, but no proof is provided here. Instead, the technique is illustrated with a simple Monte Carlo transport problem. Problems encountered, problems solved, and speculations about future progress are discussed. 12 refs

Warfighter information services: lessons learned in the intelligence domain

Science.gov (United States)

Bray, S. E.

2014-05-01

A vision was presented in a previous paper of how a common set of services within a framework could be used to provide all the information processing needs of Warfighters. Central to that vision was the concept of a "Virtual Knowledge Base". The paper presents an implementation of these ideas in the intelligence domain. Several innovative technologies were employed in the solution, which are presented and their benefits explained. The project was successful, validating many of the design principles for such a system which had been proposed in earlier work. Many of these principles are discussed in detail, explaining lessons learned. The results showed that it is possible to make vast improvements in the ability to exploit available data, making it discoverable and queryable wherever it is from anywhere within a participating network; and to exploit machine reasoning to make faster and better inferences from available data, enabling human analysts to spend more of their time doing more difficult analytical tasks rather than searching for relevant data. It was also demonstrated that a small number of generic Information Processing services can be combined and configured in a variety of ways (without changing any software code) to create "fact-processing" workflows, in this case to create different intelligence analysis capabilities. It is yet to be demonstrated that the same generic services can be reused to create analytical/situational awareness capabilities for logistics, operations, planning or other military functions but this is considered likely.

A self-organizing learning account of number-form synaesthesia.

Science.gov (United States)

Makioka, Shogo

2009-09-01

Some people automatically and involuntarily "see" mental images of numbers in spatial arrays when they think of numbers. This phenomenon, called number forms, shares three key characteristics with the other types of synaesthesia, within-individual consistency, between-individual variety, and mixture of regularity and randomness. A theoretical framework called SOLA (self-organizing learning account of number forms) is proposed, which explains the generation process of number forms and the origin of those three characteristics. The simulations replicated the qualitative properties of the shapes of number forms, the property that numbers are aligned in order of size, that discontinuity usually occurs at the point of carry, and that continuous lines tend to have many bends.

AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model

Directory of Open Access Journals (Sweden)

Jianzhu Ma

2015-01-01

Full Text Available Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

Learning Faster by Discovering and Exploiting Object Similarities

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Tadej Janež

2013-03-01

Full Text Available In this paper we explore the question: “Is it possible to speed up the learning process of an autonomous agent by performing experiments in a more complex environment (i.e., an environment with a greater number of different objects?” To this end, we use a simple robotic domain, where the robot has to learn a qualitative model predicting the change in the robot's distance to an object. To quantify the environment's complexity, we defined cardinal complexity as the number of objects in the robot's world, and behavioural complexity as the number of objects' distinct behaviours. We propose Error reduction merging (ERM, a new learning method that automatically discovers similarities in the structure of the agent's environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent's learning. We performed a series of experiments in worlds of increasing complexity. The results in our simple domain indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.

Thinking Beyond Numbers: Learning Numeracy for the Future Workplace. Support Document

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Marr, Beth; Hagston, Jan

2007-01-01

The use, learning and transfer of workplace numeracy skills, as well as current understandings of the term numeracy, are examined in this study. It also highlights the importance of numeracy as an essential workplace skill. "Thinking Beyond Numbers: Learning Numeracy for the Future Workplace" challenges the training system and training…

Estimation of the applicability domain of kernel-based machine learning models for virtual screening

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Fechner Nikolas

2010-03-01

Full Text Available Abstract Background The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. Results We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening

Estimation of the applicability domain of kernel-based machine learning models for virtual screening.

Science.gov (United States)

Fechner, Nikolas; Jahn, Andreas; Hinselmann, Georg; Zell, Andreas

2010-03-11

The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening. The proposed applicability domain formulations

Enhancing visuospatial performance through video game training to increase learning in visuospatial science domains.

Science.gov (United States)

Sanchez, Christopher A

2012-02-01

Although previous research has demonstrated that performance on visuospatial assessments can be enhanced through relevant experience, an unaddressed question is whether such experience also produces a similar increase in target domains (such as science learning) where visuospatial abilities are directly relevant for performance. In the present study, participants completed either spatial or nonspatial training via interaction with video games and were then asked to read and learn about the geologic topic of plate tectonics. Results replicate the benefit of playing appropriate video games in enhancing visuospatial performance and demonstrate that this facilitation also manifests itself in learning science topics that are visuospatial in nature. This novel result suggests that visuospatial training not only can impact performance on measures of spatial functioning, but also can affect performance in content areas in which these abilities are utilized.

Online transfer learning with extreme learning machine

Science.gov (United States)

Yin, Haibo; Yang, Yun-an

2017-05-01

In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

Domain General Mediators of the Relation between Kindergarten Number Sense and First-Grade Mathematics Achievement

Science.gov (United States)

Hassinger-Das, Brenna; Jordan, Nancy C.; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2013-01-01

Domain general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that attention problems and executive functioning both were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance while executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. PMID:24237789

Ketidakseimbangan Instrumen Penilaian Pada Domain Pembelajaran

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Yuberti Yuberti

2015-04-01

Full Text Available Generally, the result of teaching and learning process pointed to three basic aspects, they are; cognitive, affective, and psycomotoric that must be achieved by the students. These three aspects can not be divided because they are a unity. Teaching and learning hold one important aspect in education, that is to develop and empower cognitive, affective, and psycomotoric to create students effectively. The three domains should be underwritten in teaching learning process they cover lesson planning, lesson implementation, the result of evaluation and supervision of teaching and learning process. Based on the concept result teaching and learning throughly, the teacher are obligated to make instruments for three domains in teaching and learning process and it’s application. Various kind of evaluation are made to get the responsibly result of students’ teaching and learning can describe students ability comprehensively. Secara umum, hasil pembelajaran mengarah pada tiga hal pokok yang harus mampu dicapai peserta didik, yaitu Afektif, Kognitif dan Psikomotorik. Ketiga hal ini tidak boleh dipisahkan karena merupakan satu kesatuan. Pembelajaran sebagai salah satu aspek penting dalam pendidikan memegang peranan mengembangkan dan memberdayakan domain kognitif, afektif, dan psikomotor bagi peserta didik secara seimbang. Keseimbangan pengembangan dan pemberdayaan ketiga domain tersebut harus tertuang dengan jelas dalam proses pembelajaran, meliputi perencanaan pembelajaran, pelaksanaan pembelajaran, penilaian hasil pembelajaran, dan pengawasan proses pembelajaran. Berdasarkan konsep hasil belajar yang bersifat menyeluruh, sudah menjadi keharusan bahwa guru harus membuat instrumen pada ketiga ranah dalam pembelajaran tersebut dan melakukan penerapan penilaiannya. Berbagai bentuk penilaian dibuat untuk memperoleh hasil belajar peserta didik yang dapat dipertanggungjawabkan serta benar-benar dapat menggambarkan kemampuan peserta didik secara komprehensif

Two memory associated genes regulated by amyloid precursor protein intracellular domain ovel insights into the pathogenesis of learning and memory impairment in Alzheimer's disease

Institute of Scientific and Technical Information of China (English)

Chuandong Zheng; Xi Gu; Zhimei Zhong; Rui Zhu; Tianming Gao; Fang Wang

2012-01-01

In this study, we employed chromatin immunoprecipitation, a useful method for studying the locations of transcription factors bound to specific DNA regions in specific cells, to investigate amyloid precursor protein intracellular domain binding sites in chromatin DNA from hippocampal neurons of rats, and to screen out five putative genes associated with the learning and memory functions. The promoter regions of the calcium/calmodulin-dependent protein kinase II alpha and glutamate receptor-2 genes were amplified by PCR from DNA products immunoprecipitated by amyloid precursor protein intracellular domain. An electrophoretic mobility shift assay and western blot analysis suggested that the promoter regions of these two genes associated with learning and memory were bound by amyloid precursor protein intracellular domain (in complex form). Our experimental findings indicate that the amyloid precursor protein intracellular domain is involved in the transcriptional regulation of learning- and memory-associated genes in hippocampal neurons. These data may provide new insights into the molecular mechanism underlying the symptoms of progressive memory loss in Alzheimer's disease.

When increasing distraction helps learning: Distractor number and content interact in their effects on memory.

Science.gov (United States)

Nussenbaum, Kate; Amso, Dima; Markant, Julie

2017-11-01

Previous work has demonstrated that increasing the number of distractors in a search array can reduce interference from distractor content during target processing. However, it is unclear how this reduced interference influences learning of target information. Here, we investigated how varying the amount and content of distraction present in a learning environment affects visual search and subsequent memory for target items. In two experiments, we demonstrate that the number and content of competing distractors interact in their influence on target selection and memory. Specifically, while increasing the number of distractors present in a search array made target detection more effortful, it did not impair learning and memory for target content. Instead, when the distractors contained category information that conflicted with the target, increasing the number of distractors from one to three actually benefitted learning and memory. These data suggest that increasing numbers of distractors may reduce interference from conflicting conceptual information during encoding.

Domain-general mediators of the relation between kindergarten number sense and first-grade mathematics achievement.

Science.gov (United States)

Hassinger-Das, Brenna; Jordan, Nancy C; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2014-02-01

Domain-general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that both attention problems and executive functioning were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance, whereas executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. Copyright © 2013 Elsevier Inc. All rights reserved.

SOCAP: Lessons learned in applying SIPE-2 to the military operations crisis action planning domain

Science.gov (United States)

Desimone, Roberto

1992-01-01

This report describes work funded under the DARPA Planning and Scheduling Initiative that led to the development of SOCAP (System for Operations Crisis Action Planning). In particular, it describes lessons learned in applying SIPE-2, the underlying AI planning technology within SOCAP, to the domain of military operations deliberate and crisis action planning. SOCAP was demonstrated at the U.S. Central Command and at the Pentagon in early 1992. A more detailed report about the lessons learned is currently being prepared. This report was presented during one of the panel discussions on 'The Relevance of Scheduling to AI Planning Systems.'

Effects of the Badge Mechanism on Self-Efficacy and Learning Performance in a Game-Based English Learning Environment

Science.gov (United States)

Yang, Jie Chi; Quadir, Benazir; Chen, Nian-Shing

2016-01-01

A growing number of studies have been conducted on digital game-based learning (DGBL). However, there has been a lack of attention paid to individuals' self-efficacy and learning performance in the implementation of DGBL. This study therefore investigated how the badge mechanism in DGBL enhanced users' self-efficacy in the subject domain of…

Optimizing the number of steps in learning tasks for complex skills.

NARCIS (Netherlands)

Nadolski, Rob; Kirschner, Paul A.; Van Merriënboer, Jeroen

2007-01-01

Background. Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimised for efficient and effective learning. Aim. The aim of the study is

Does Grammatical Structure Accelerate Number Word Learning? Evidence from Learners of Dual and Non-Dual Dialects of Slovenian.

Directory of Open Access Journals (Sweden)

Franc Marušič

Full Text Available How does linguistic structure affect children's acquisition of early number word meanings? Previous studies have tested this question by comparing how children learning languages with different grammatical representations of number learn the meanings of labels for small numbers, like 1, 2, and 3. For example, children who acquire a language with singular-plural marking, like English, are faster to learn the word for 1 than children learning a language that lacks the singular-plural distinction, perhaps because the word for 1 is always used in singular contexts, highlighting its meaning. These studies are problematic, however, because reported differences in number word learning may be due to unmeasured cross-cultural differences rather than specific linguistic differences. To address this problem, we investigated number word learning in four groups of children from a single culture who spoke different dialects of the same language that differed chiefly with respect to how they grammatically mark number. We found that learning a dialect which features "dual" morphology (marking of pairs accelerated children's acquisition of the number word two relative to learning a "non-dual" dialect of the same language.

Does Grammatical Structure Accelerate Number Word Learning? Evidence from Learners of Dual and Non-Dual Dialects of Slovenian

Science.gov (United States)

Plesničar, Vesna; Razboršek, Tina; Sullivan, Jessica; Barner, David

2016-01-01

How does linguistic structure affect children’s acquisition of early number word meanings? Previous studies have tested this question by comparing how children learning languages with different grammatical representations of number learn the meanings of labels for small numbers, like 1, 2, and 3. For example, children who acquire a language with singular-plural marking, like English, are faster to learn the word for 1 than children learning a language that lacks the singular-plural distinction, perhaps because the word for 1 is always used in singular contexts, highlighting its meaning. These studies are problematic, however, because reported differences in number word learning may be due to unmeasured cross-cultural differences rather than specific linguistic differences. To address this problem, we investigated number word learning in four groups of children from a single culture who spoke different dialects of the same language that differed chiefly with respect to how they grammatically mark number. We found that learning a dialect which features “dual” morphology (marking of pairs) accelerated children’s acquisition of the number word two relative to learning a “non-dual” dialect of the same language. PMID:27486802

Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

Science.gov (United States)

Engelbrecht, Jeffrey C.

2003-01-01

Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

DEFF Research Database (Denmark)

Pan, Xiaoyong; Shen, Hong Bin

2017-01-01

, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains...... space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can...... be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in i...

Contribution of Content Knowledge and Learning Ability to the Learning of Facts.

Science.gov (United States)

Kuhara-Kojima, Keiko; Hatano, Giyoo

1991-01-01

In 3 experiments, 1,598 Japanese college students were examined concerning the learning of facts in 2 content domains, baseball and music. Content knowledge facilitated fact learning only in the relevant domain; learning ability facilitated fact learning in both domains. Effects of content knowledge and learning ability were additive. (SLD)

The Contributions of Domain-General and Numerical Factors to Third-Grade Arithmetic Skills and Mathematical Learning Disability

Science.gov (United States)

Cowan, Richard; Powell, Daisy

2014-01-01

Explanations of the marked individual differences in elementary school mathematical achievement and mathematical learning disability (MLD or dyscalculia) have involved domain-general factors (working memory, reasoning, processing speed, and oral language) and numerical factors that include single-digit processing efficiency and multidigit skills…

Mindfulness, Adult Learning and Therapeutic Education: Integrating the Cognitive and Affective Domains of Learning

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Hyland, Terry

2010-01-01

Although it has been given qualified approval by a number of philosophers of education, the so-called "therapeutic turn" in education has been the subject of criticism by several commentators on post-compulsory and adult learning over the last few years. A key feature of this alleged development in recent educational policy is said to be the…

Engaging Students to Learn through the Affective Domain: A New Framework for Teaching in the Geosciences

Science.gov (United States)

van der Hoeven Kraft, Katrien J.; Srogi, LeeAnn; Husman, Jenefer; Semken, Steven; Fuhrman, Miriam

2011-01-01

To motivate student learning, the affective domain--emotion, attitude, and motivation--must be engaged. We propose a model that is specific to the geosciences with theoretical components of motivation and emotion from the field of educational psychology, and a term we are proposing, "connections with Earth" based on research in the…

Teachers' Obstacles in Implementing Numbered Head Together in Social Science Learning

Science.gov (United States)

Widyaningtyas, Harini; Winarni, Retno; Murwaningsih, Tri

2018-01-01

This study is aimed at describing teachers' obstacles in applying Numbered Head Together learning model in social science learning. The type of research is qualitative descriptive. The subject of the research is the third-grade teacher of elementary school in Sukoharjo Sub-district. The findings of the research were analyzed using interactive…

Human mate-choice copying is domain-general social learning.

Science.gov (United States)

Street, Sally E; Morgan, Thomas J H; Thornton, Alex; Brown, Gillian R; Laland, Kevin N; Cross, Catharine P

2018-01-29

Women appear to copy other women's preferences for men's faces. This 'mate-choice copying' is often taken as evidence of psychological adaptations for processing social information related to mate choice, for which facial information is assumed to be particularly salient. No experiment, however, has directly investigated whether women preferentially copy each other's face preferences more than other preferences. Further, because prior experimental studies used artificial social information, the effect of real social information on attractiveness preferences is unknown. We collected attractiveness ratings of pictures of men's faces, men's hands, and abstract art given by heterosexual women, before and after they saw genuine social information gathered in real time from their peers. Ratings of faces were influenced by social information, but no more or less than were images of hands and abstract art. Our results suggest that evidence for domain-specific social learning mechanisms in humans is weaker than previously suggested.

Re-Thinking ‘Normal’ Development in the Early Learning of Number

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Alf Coles

2018-06-01

Full Text Available In this article we suggest that, notwithstanding noted differences, one unmarked similarity across psychology and mathematics education is the continued dominance of the view that there is a ‘normal’ path of development. We focus particularly on the case of the early learning of number and point to evidence that puts into question the dominant narrative of how number sense develops through the concrete and the cardinal. Recent neuroscience findings have raised the potential significance of ordinal approaches to learning number, which in privileging the symbolic—and hence the abstract—reverse one aspect of the ‘normal’ development order. We draw on empirical evidence to suggest that what children can do, and in what order, is sensitive to, among other things, the curriculum approach—and also the tools they have at their disposition. We draw out implications from our work for curriculum organisation in the early years of schooling, to disrupt taken-for-granted paths.

On the relation between grammatical number and cardinal numbers in development.

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Sarnecka, Barbara W

2014-01-01

This mini-review focuses on the question of how the grammatical number system of a child's language may help the child learn the meanings of cardinal number words (e.g., "one" and "two"). Evidence from young children learning English, Russian, Japanese, Mandarin, Slovenian, or Saudi Arabic suggests that trajectories of number-word learning differ for children learning different languages. Children learning English, which distinguishes between singular and plural, seem to learn the meaning of the cardinal number "one" earlier than children learning Japanese or Mandarin, which have very little singular/plural marking. Similarly, children whose languages have a singular/dual/plural system (Slovenian and Saudi Arabic) learn the meaning of "two" earlier than English-speaking children. This relation between grammatical and cardinal number may shed light on how humans acquire cardinal-number concepts. There is an ongoing debate about whether mental symbols for small cardinalities (concepts for "oneness," "twoness," etc.) are innate or learned. Although an effect of grammatical number on number-word learning does not rule out nativist accounts, it seems more consistent with constructivist accounts, which portray the number-learning process as one that requires significant conceptual change.

On the Relation Between Grammatical Number and Cardinal Numbers in Development

Directory of Open Access Journals (Sweden)

Barbara W Sarnecka

2014-10-01

Full Text Available This mini-review focuses on the question of how the grammatical number system of a child’s language may help the child learn the meanings of cardinal number words (e.g., ‘one’ and ‘two’. Evidence from young children learning English, Russian, Japanese, Mandarin, Slovenian or Saudi Arabic suggests that trajectories of number-word learning differ for children learning different languages. Children learning English, which distinguishes between singular and plural, seem to learn the meaning of the cardinal number ‘one’ earlier than children learning Japanese or Mandarin, which have very little singular/plural marking. Similarly, children whose languages have a singular/dual/plural system (Slovenian and Saudi Arabic learn the meaning of ‘two’ earlier than English-speaking children. This relation between grammatical and cardinal number may shed light on how humans acquire cardinal-number concepts. There is an ongoing debate about whether mental symbols for small cardinalities (concepts for ‘oneness,’ ‘twoness,’ etc. are innate or learned. Although an effect of grammatical number on number-word learning does not rule out nativist accounts, it seems more consistent with constructivist accounts, which portray the number-learning process as one that requires significant conceptual change.

Evaluation of magnetic helicity density in the wave number domain using multi-point measurements in space

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Y. Narita

2009-10-01

Full Text Available We develop an estimator for the magnetic helicity density, a measure of the spiral geometry of magnetic field lines, in the wave number domain as a wave diagnostic tool based on multi-point measurements in space. The estimator is numerically tested with a synthetic data set and then applied to an observation of magnetic field fluctuations in the Earth foreshock region provided by the four-point measurements of the Cluster spacecraft. The energy and the magnetic helicity density are determined in the frequency and the wave number domain, which allows us to identify the wave properties in the plasma rest frame correcting for the Doppler shift. In the analyzed time interval, dominant wave components have parallel propagation to the mean magnetic field, away from the shock at about Alfvén speed and a left-hand spatial rotation sense of helicity with respect to the propagation direction, which means a right-hand temporal rotation sense of polarization. These wave properties are well explained by the right-hand resonant beam instability as the driving mechanism in the foreshock. Cluster observations allow therefore detailed comparisons with various theories of waves and instabilities.

Imbalanced Class Learning in Epigenetics

OpenAIRE

Haque, M. Muksitul; Skinner, Michael K.; Holder, Lawrence B.

2014-01-01

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the mino...

Feature-level domain adaptation

DEFF Research Database (Denmark)

Kouw, Wouter M.; Van Der Maaten, Laurens J P; Krijthe, Jesse H.

2016-01-01

-level domain adaptation (flda), that models the dependence between the two domains by means of a feature-level transfer model that is trained to describe the transfer from source to target domain. Subsequently, we train a domain-adapted classifier by minimizing the expected loss under the resulting transfer...... modeled via a dropout distribution, which allows the classiffier to adapt to differences in the marginal probability of features in the source and the target domain. Our experiments on several real-world problems show that flda performs on par with state-of-the-art domainadaptation techniques.......Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature...

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

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Erdt, Mojisola; Fernandez, Alejandro; Rensing, Christoph

2015-01-01

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like…

International comparisons of Foundation Phase number domain ...

African Journals Online (AJOL)

Hennie

wide variety of factors interact to impact on the quality of the ... people/learners in a social setting, where culture and context are .... The research design adopted for this study can be described ..... involve learners in active learning and to plan.

PRGPred: A platform for prediction of domains of resistance gene analogue (RGA in Arecaceae developed using machine learning algorithms

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MATHODIYIL S. MANJULA

2015-12-01

Full Text Available Plant disease resistance genes (R-genes are responsible for initiation of defense mechanism against various phytopathogens. The majority of plant R-genes are members of very large multi-gene families, which encode structurally related proteins containing nucleotide binding site domains (NBS and C-terminal leucine rich repeats (LRR. Other classes possess' an extracellular LRR domain, a transmembrane domain and sometimes, an intracellular serine/threonine kinase domain. R-proteins work in pathogen perception and/or the activation of conserved defense signaling networks. In the present study, sequences representing resistance gene analogues (RGAs of coconut, arecanut, oil palm and date palm were collected from NCBI, sorted based on domains and assembled into a database. The sequences were analyzed in PRINTS database to find out the conserved domains and their motifs present in the RGAs. Based on these domains, we have also developed a tool to predict the domains of palm R-genes using various machine learning algorithms. The model files were selected based on the performance of the best classifier in training and testing. All these information is stored and made available in the online ‘PRGpred' database and prediction tool.

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels.

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Kaneko, Hiromasa

2018-02-26

To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.

Relations between the development of future time perspective in three life domains, investment in learning, and academic achievement

NARCIS (Netherlands)

Peetsma, T.; van der Veen, I.

2011-01-01

Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students’ investment in learning and academic achievement were examined in this study. Participants were 584 students in the

Natural Alternatives to Natural Number: The Case of Ratio

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Percival G. Matthews

2018-06-01

Full Text Available The overwhelming majority of efforts to cultivate early mathematical thinking rely primarily on counting and associated natural number concepts. Unfortunately, natural numbers and discretized thinking do not align well with a large swath of the mathematical concepts we wish for children to learn. This misalignment presents an important impediment to teaching and learning. We suggest that one way to circumvent these pitfalls is to leverage students’ non-numerical experiences that can provide intuitive access to foundational mathematical concepts. Specifically, we advocate for explicitly leveraging a students’ perceptually based intuitions about quantity and b students’ reasoning about change and variation, and we address the affordances offered by this approach. We argue that it can support ways of thinking that may at times align better with to-be-learned mathematical ideas, and thus may serve as a productive alternative for particular mathematical concepts when compared to number. We illustrate this argument using the domain of ratio, and we do so from the distinct disciplinary lenses we employ respectively as a cognitive psychologist and as a mathematics education researcher. Finally, we discuss the potential for productive synthesis given the substantial differences in our preferred methods and general epistemologies.

Ecological information systems and support of learning: Coupling work domain information to user characteristics

DEFF Research Database (Denmark)

Pejtersen, Annelise Mark; Rasmussen, Jens

1997-01-01

This chapter presents a framework for design of work support systems for a modern, dynamic work environment in which stable work procedures are replaced with discretionary tasks and the request of continuous learning and adaptation to change. In this situation, classic task analysis is less effec...... in a dynamic environment is therefore a human-work interface directed towards a transparent presentation of the action possibilities and functional/intentional boundaries and constraints of the work domain relevant for typical task situations and user categories....

Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.

2006-01-01

Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large

Pharmacological interventions for the MATRICS cognitive domains in schizophrenia: what's the evidence?

Directory of Open Access Journals (Sweden)

Wilhelmina A.M. Vingerhoets

2013-12-01

Full Text Available Schizophrenia is a disabling, chronic psychiatric disorder with a prevalence rate of 0.5-1% in the general population. Symptoms include positive (e.g. delusions, hallucinations, negative (e.g. blunted affect, social withdrawal, as well as cognitive symptoms (e.g. memory and attention problems. Although 75-85% of patients with schizophrenia report cognitive impairments, the underlying neuropharmacological mechanisms are not well understood and currently no effective treatment is available for these impairments. This has led to the MATRICS initiative (Measurement and Treatment Research to Improve Cognition in Schizophrenia, which established seven cognitive domains that are fundamentally impaired in schizophrenia. These domains include verbal learning and memory, visual learning and memory, working memory, attention and vigilance, processing speed, reasoning and problem solving, and social cognition. Recently, a growing number of studies have been conducted trying to identify the underlying neuropharmacological mechanisms of cognitive impairments in schizophrenia patients. Specific cognitive impairments seem to arise from different underlying neuropharmacological mechanisms. However, most review articles describe cognition in general and an overview of the mechanisms involved in these seven separate cognitive domains is currently lacking. Therefore, we reviewed the underlying neuropharmacological mechanisms focussing on the domains as established by the MATRICS initiative which are considered most crucial in schizophrenia.

Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels

Directory of Open Access Journals (Sweden)

McDermott Drew

2009-08-01

Full Text Available Abstract Background Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and residues. Each level offers a distinct and complementary set of features for computationally predicting interactions, including functional genomic features of whole proteins, evolutionary features of domain families and physical-chemical features of individual residues. The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity. Results To link up the predictions at the three levels, we propose a multi-level machine-learning framework that allows for explicit information flow between the levels. We demonstrate, using representative yeast interaction networks, that our algorithm is able to utilize complementary feature sets to make more accurate predictions at the three levels than when the three problems are approached independently. To facilitate application of our multi-level learning framework, we discuss three key aspects of multi-level learning and the corresponding design choices that we have made in the implementation of a concrete learning algorithm. 1 Architecture of information flow: we show the greater flexibility of bidirectional flow over independent levels and unidirectional flow; 2 Coupling mechanism of the different levels: We show how this can be accomplished via augmenting the training sets at each level, and discuss the prevention of error propagation between different levels by means of soft coupling; 3 Sparseness of data: We show that the multi-level framework compounds data sparsity issues, and discuss how this can be dealt with by building local models in information-rich parts of the data. Our proof-of-concept learning algorithm demonstrates the advantage of combining levels, and opens up

Time-domain modeling of electromagnetic diffusion with a frequency-domain code

NARCIS (Netherlands)

Mulder, W.A.; Wirianto, M.; Slob, E.C.

2007-01-01

We modeled time-domain EM measurements of induction currents for marine and land applications with a frequency-domain code. An analysis of the computational complexity of a number of numerical methods shows that frequency-domain modeling followed by a Fourier transform is an attractive choice if a

Taking It to the Classroom: Number Board Games as a Small Group Learning Activity

Science.gov (United States)

Ramani, Geetha B.; Siegler, Robert S.; Hitti, Aline

2012-01-01

We examined whether a theoretically based number board game could be translated into a practical classroom activity that improves Head Start children's numerical knowledge. Playing the number board game as a small group learning activity promoted low-income children's number line estimation, magnitude comparison, numeral identification, and…

Numerical capacities as domain-specific predictors beyond early mathematics learning: a longitudinal study.

Science.gov (United States)

Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

2013-01-01

The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3(rd) and 4(th) grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively "start-up" tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school.

The YARHG domain: an extracellular domain in search of a function.

Directory of Open Access Journals (Sweden)

Penny Coggill

Full Text Available We have identified a new bacterial protein domain that we hypothesise binds to peptidoglycan. This domain is called the YARHG domain after the most highly conserved sequence-segment. The domain is found in the extracellular space and is likely to be composed of four alpha-helices. The domain is found associated with protein kinase domains, suggesting it is associated with signalling in some bacteria. The domain is also found associated with three different families of peptidases. The large number of different domains that are found associated with YARHG suggests that it is a useful functional module that nature has recombined multiple times.

Numerical morphology supports early number word learning: Evidence from a comparison of young Mandarin and English learners

Science.gov (United States)

Corre, Mathieu Le; Li, Peggy; Huang, Becky H.; Jia, Gisela; Carey, Susan

2016-01-01

Previous studies showed that children learning a language with an obligatory singular/plural distinction (Russian and English) learn the meaning of the number word for one earlier than children learning Japanese, a language without obligatory number morphology (Barner, Libenson, Cheung, & Takasaki, 2009; Sarnecka, Kamenskaya, Yamana, Ogura, & Yudovina, 2007). This can be explained by differences in number morphology, but it can also be explained by many other differences between the languages and the environments of the children who were compared. The present study tests the hypothesis that the morphological singular/plural distinction supports the early acquisition of the meaning of the number word for one by comparing young English learners to age and SES matched young Mandarin Chinese learners. Mandarin does not have obligatory number morphology but is more similar to English than Japanese in many crucial respects. Corpus analyses show that, compared to English learners, Mandarin learners hear number words more frequently, are more likely to hear number words followed by a noun, and are more likely to hear number words in contexts where they denote a cardinal value. Two tasks show that, despite these advantages, Mandarin learners learn the meaning of the number word for one three to six months later than do English learners. These results provide the strongest evidence to date that prior knowledge of the numerical meaning of the distinction between singular and plural supports the acquisition of the meaning of the number word for one. PMID:27423486

Numerical morphology supports early number word learning: Evidence from a comparison of young Mandarin and English learners.

Science.gov (United States)

Le Corre, Mathieu; Li, Peggy; Huang, Becky H; Jia, Gisela; Carey, Susan

2016-08-01

Previous studies showed that children learning a language with an obligatory singular/plural distinction (Russian and English) learn the meaning of the number word for one earlier than children learning Japanese, a language without obligatory number morphology (Barner, Libenson, Cheung, & Takasaki, 2009; Sarnecka, Kamenskaya, Yamana, Ogura, & Yudovina, 2007). This can be explained by differences in number morphology, but it can also be explained by many other differences between the languages and the environments of the children who were compared. The present study tests the hypothesis that the morphological singular/plural distinction supports the early acquisition of the meaning of the number word for one by comparing young English learners to age and SES matched young Mandarin Chinese learners. Mandarin does not have obligatory number morphology but is more similar to English than Japanese in many crucial respects. Corpus analyses show that, compared to English learners, Mandarin learners hear number words more frequently, are more likely to hear number words followed by a noun, and are more likely to hear number words in contexts where they denote a cardinal value. Two tasks show that, despite these advantages, Mandarin learners learn the meaning of the number word for one three to six months later than do English learners. These results provide the strongest evidence to date that prior knowledge of the numerical meaning of the distinction between singular and plural supports the acquisition of the meaning of the number word for one. Copyright © 2016. Published by Elsevier Inc.

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

Science.gov (United States)

Rzhetsky, A; Gomez, S M

2001-10-01

Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. ar345@columbia.edu.

Pharmacological Interventions for the MATRICS Cognitive Domains in Schizophrenia: What’s the Evidence?

Science.gov (United States)

Vingerhoets, Wilhelmina A. M.; Bloemen, Oswald J. N.; Bakker, Geor; van Amelsvoort, Therese A. M. J.

2013-01-01

Schizophrenia is a disabling, chronic psychiatric disorder with a prevalence rate of 0.5–1% in the general population. Symptoms include positive (e.g., delusions, hallucinations), negative (e.g., blunted affect, social withdrawal), as well as cognitive symptoms (e.g., memory and attention problems). Although 75–85% of patients with schizophrenia report cognitive impairments, the underlying neuropharmacological mechanisms are not well understood and currently no effective treatment is available for these impairments. This has led to the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative, which established seven cognitive domains that are fundamentally impaired in schizophrenia. These domains include verbal learning and memory, visual learning and memory, working memory, attention and vigilance, processing speed, reasoning and problem solving, and social cognition. Recently, a growing number of studies have been conducted trying to identify the underlying neuropharmacological mechanisms of cognitive impairments in schizophrenia patients. Specific cognitive impairments seem to arise from different underlying neuropharmacological mechanisms. However, most review articles describe cognition in general and an overview of the mechanisms involved in these seven separate cognitive domains is currently lacking. Therefore, we reviewed the underlying neuropharmacological mechanisms focusing on the domains as established by the MATRICS initiative which are considered most crucial in schizophrenia. PMID:24363646

Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

Science.gov (United States)

Dunjko, Vedran; Briegel, Hans J

2018-03-05

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and

An Examination of Multiple Intelligence Domains and Learning Styles of Pre-Service Mathematics Teachers: Their Reflections on Mathematics Education

Science.gov (United States)

Ozgen, Kemal; Tataroglu, Berna; Alkan, Huseyin

2011-01-01

The present study aims to identify pre-service mathematics teachers' multiple intelligence domains and learning style profiles, and to establish relationships between them. Employing the survey model, the study was conducted with the participation of 243 pre-service mathematics teachers. The study used the "multiple intelligence domains…

The Effect of Number and Presentation Order of High-Constraint Sentences on Second Language Word Learning.

Science.gov (United States)

Ma, Tengfei; Chen, Ran; Dunlap, Susan; Chen, Baoguo

2016-01-01

This paper presents the results of an experiment that investigated the effects of number and presentation order of high-constraint sentences on semantic processing of unknown second language (L2) words (pseudowords) through reading. All participants were Chinese native speakers who learned English as a foreign language. In the experiment, sentence constraint and order of different constraint sentences were manipulated in English sentences, as well as L2 proficiency level of participants. We found that the number of high-constraint sentences was supportive for L2 word learning except in the condition in which high-constraint exposure was presented first. Moreover, when the number of high-constraint sentences was the same, learning was significantly better when the first exposure was a high-constraint exposure. And no proficiency level effects were found. Our results provided direct evidence that L2 word learning benefited from high quality language input and first presentations of high quality language input.

Relations between the Development of Future Time Perspective in Three Life Domains, Investment in Learning, and Academic Achievement

Science.gov (United States)

Peetsma, Thea; van der Veen, Ineke

2011-01-01

Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students' investment in learning and academic achievement were examined in this study. Participants were 584 students in the first and 584 in the second year of the lower…

Extracting meronomy relations from domain-specific, textual corporate databases

NARCIS (Netherlands)

Ittoo, R.A.; Bouma, G.; Maruster, L.; Wortmann, J.C.; Hopfe, C.J.; Rezgui, Y.; Métais, E.; Preece, A.; Li, H.

2010-01-01

Various techniques for learning meronymy relationships from open-domain corpora exist. However, extracting meronymy relationships from domain-specific, textual corporate databases has been overlooked, despite numerous application opportunities particularly in domains like product development and/or

Learning linear spatial-numeric associations improves accuracy of memory for numbers

Directory of Open Access Journals (Sweden)

Clarissa Ann Thompson

2016-01-01

Full Text Available Memory for numbers improves with age and experience. One potential source of improvement is a logarithmic-to-linear shift in children’s representations of magnitude. To test this, Kindergartners and second graders estimated the location of numbers on number lines and recalled numbers presented in vignettes (Study 1. Accuracy at number-line estimation predicted memory accuracy on a numerical recall task after controlling for the effect of age and ability to approximately order magnitudes (mapper status. To test more directly whether linear numeric magnitude representations caused improvements in memory, half of children were given feedback on their number-line estimates (Study 2. As expected, learning linear representations was again linked to memory for numerical information even after controlling for age and mapper status. These results suggest that linear representations of numerical magnitude may be a causal factor in development of numeric recall accuracy.

The enigma of number: why children find the meanings of even small number words hard to learn and how we can help them do better.

Directory of Open Access Journals (Sweden)

Michael Ramscar

Full Text Available Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions ("there are three balls" structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions ("balls, there are three" will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early

Number comparison and number ordering as predictors of arithmetic performance in adults: Exploring the link between the two skills, and investigating the question of domain-specificity.

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Morsanyi, Kinga; O'Mahony, Eileen; McCormack, Teresa

2017-12-01

Recent evidence has highlighted the important role that number-ordering skills play in arithmetic abilities, both in children and adults. In the current study, we demonstrated that number comparison and ordering skills were both significantly related to arithmetic performance in adults, and the effect size was greater in the case of ordering skills. Additionally, we found that the effect of number comparison skills on arithmetic performance was mediated by number-ordering skills. Moreover, performance on comparison and ordering tasks involving the months of the year was also strongly correlated with arithmetic skills, and participants displayed similar (canonical or reverse) distance effects on the comparison and ordering tasks involving months as when the tasks included numbers. This suggests that the processes responsible for the link between comparison and ordering skills and arithmetic performance are not specific to the domain of numbers. Finally, a factor analysis indicated that performance on comparison and ordering tasks loaded on a factor that included performance on a number line task and self-reported spatial thinking styles. These results substantially extend previous research on the role of order processing abilities in mental arithmetic.

De novo identification of replication-timing domains in the human genome by deep learning.

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Liu, Feng; Ren, Chao; Li, Hao; Zhou, Pingkun; Bo, Xiaochen; Shu, Wenjie

2016-03-01

The de novo identification of the initiation and termination zones-regions that replicate earlier or later than their upstream and downstream neighbours, respectively-remains a key challenge in DNA replication. Building on advances in deep learning, we developed a novel hybrid architecture combining a pre-trained, deep neural network and a hidden Markov model (DNN-HMM) for the de novo identification of replication domains using replication timing profiles. Our results demonstrate that DNN-HMM can significantly outperform strong, discriminatively trained Gaussian mixture model-HMM (GMM-HMM) systems and other six reported methods that can be applied to this challenge. We applied our trained DNN-HMM to identify distinct replication domain types, namely the early replication domain (ERD), the down transition zone (DTZ), the late replication domain (LRD) and the up transition zone (UTZ), using newly replicated DNA sequencing (Repli-Seq) data across 15 human cells. A subsequent integrative analysis revealed that these replication domains harbour unique genomic and epigenetic patterns, transcriptional activity and higher-order chromosomal structure. Our findings support the 'replication-domain' model, which states (1) that ERDs and LRDs, connected by UTZs and DTZs, are spatially compartmentalized structural and functional units of higher-order chromosomal structure, (2) that the adjacent DTZ-UTZ pairs form chromatin loops and (3) that intra-interactions within ERDs and LRDs tend to be short-range and long-range, respectively. Our model reveals an important chromatin organizational principle of the human genome and represents a critical step towards understanding the mechanisms regulating replication timing. Our DNN-HMM method and three additional algorithms can be freely accessed at https://github.com/wenjiegroup/DNN-HMM The replication domain regions identified in this study are available in GEO under the accession ID GSE53984. shuwj@bmi.ac.cn or boxc

Numerical Capacities as Domain-Specific Predictors beyond Early Mathematics Learning: A Longitudinal Study

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Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

2013-01-01

The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3rd and 4th grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively “start-up” tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school. PMID:24255710

The BRCT domain is a phospho-protein binding domain.

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Yu, Xiaochun; Chini, Claudia Christiano Silva; He, Miao; Mer, Georges; Chen, Junjie

2003-10-24

The carboxyl-terminal domain (BRCT) of the Breast Cancer Gene 1 (BRCA1) protein is an evolutionarily conserved module that exists in a large number of proteins from prokaryotes to eukaryotes. Although most BRCT domain-containing proteins participate in DNA-damage checkpoint or DNA-repair pathways, or both, the function of the BRCT domain is not fully understood. We show that the BRCA1 BRCT domain directly interacts with phosphorylated BRCA1-Associated Carboxyl-terminal Helicase (BACH1). This specific interaction between BRCA1 and phosphorylated BACH1 is cell cycle regulated and is required for DNA damage-induced checkpoint control during the transition from G2 to M phase of the cell cycle. Further, we show that two other BRCT domains interact with their respective physiological partners in a phosphorylation-dependent manner. Thirteen additional BRCT domains also preferentially bind phospho-peptides rather than nonphosphorylated control peptides. These data imply that the BRCT domain is a phospho-protein binding domain involved in cell cycle control.

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

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Pan, Xiaoyong; Shen, Hong-Bin

2017-02-28

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6

Computational domain length and Reynolds number effects on large-scale coherent motions in turbulent pipe flow

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Feldmann, Daniel; Bauer, Christian; Wagner, Claus

2018-03-01

We present results from direct numerical simulations (DNS) of turbulent pipe flow at shear Reynolds numbers up to Reτ = 1500 using different computational domains with lengths up to ?. The objectives are to analyse the effect of the finite size of the periodic pipe domain on large flow structures in dependency of Reτ and to assess a minimum ? required for relevant turbulent scales to be captured and a minimum Reτ for very large-scale motions (VLSM) to be analysed. Analysing one-point statistics revealed that the mean velocity profile is invariant for ?. The wall-normal location at which deviations occur in shorter domains changes strongly with increasing Reτ from the near-wall region to the outer layer, where VLSM are believed to live. The root mean square velocity profiles exhibit domain length dependencies for pipes shorter than 14R and 7R depending on Reτ. For all Reτ, the higher-order statistical moments show only weak dependencies and only for the shortest domain considered here. However, the analysis of one- and two-dimensional pre-multiplied energy spectra revealed that even for larger ?, not all physically relevant scales are fully captured, even though the aforementioned statistics are in good agreement with the literature. We found ? to be sufficiently large to capture VLSM-relevant turbulent scales in the considered range of Reτ based on our definition of an integral energy threshold of 10%. The requirement to capture at least 1/10 of the global maximum energy level is justified by a 14% increase of the streamwise turbulence intensity in the outer region between Reτ = 720 and 1500, which can be related to VLSM-relevant length scales. Based on this scaling anomaly, we found Reτ⪆1500 to be a necessary minimum requirement to investigate VLSM-related effects in pipe flow, even though the streamwise energy spectra does not yet indicate sufficient scale separation between the most energetic and the very long motions.

Impact evaluation of domains of learning on Universal Work Precautions (UWP amongst nursing staff in a Tertiary Care Hospital, Western India

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Rashmi Sharma

2016-01-01

Full Text Available Introduction: Second key strategy of National AIDS Control Program (NACP IV is comprehensive care and support by providing quality services through zero stigma and discrimination. Quality of services can be improved by eliminating stigma and discrimination and making health care provider aware of associated occupational hazards. Nursing staff play crucial role and are more at risk therefore their understanding, perception and skill must be assessed in different domains of learning to improve the contents and methodology of trainings. Material and Methods: Total 85 nursing staff underwent 1 day training in 3 batches focusing on Universal Work Precautions (UWP, Post Exposure Prophylaxis (PEP and sensitization of the participants towards PLHA (People living with HIV/AIDS. Their learning was evaluated under different domains (cognitive, psychomotor and affective using structured questionnaire. Results: In pretest evaluation scores showed minor and statistically not significant variations in terms of participant′s gender, age, designation work experience and status of having received any similar training in the past. Impact of the training was visible as overall mean scores increased from 10.6 ± 2.7 to 13.8 ± 5.8; gain being statistically highly significant (P value < 0.001. Gain was highest in cognitive (from 58% to 77% followed by psychomotor (from 48% to 62% and minimal in affective domain (from 75% to 76%. Conclusions: After undergoing the training, participants were benefitted more in cognitive domain than psychomotor and affective domain. Acquired knowledge, skill and communication skill if evaluated as done in this study will improve the methodology of such trainings making them more effective.

Friendship Predictors of Global Self-Worth and Domain-Specific Self-Concepts in University Students with and without Learning Disability

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Shany, Michal; Wiener, Judith; Assido, Michal

2013-01-01

This study investigated the association among friendship, global self-worth, and domain-specific self-concepts in 102 university students with and without learning disabilities (LD). Students with LD reported lower global self-worth and academic self-concept than students without LD, and this difference was greater for women. Students with LD also…

System Quality Characteristics for Selecting Mobile Learning Applications

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Mohamed SARRAB

2015-10-01

Full Text Available The majority of M-learning (Mobile learning applications available today are developed for the formal learning and education environment. These applications are characterized by the improvement in the interaction between learners and instructors to provide high interaction and flexibility to the learning process. M-learning is gaining increased recognition and adoption by different organizations. With the high number of M-learning applications available today, making the right decision about which, application to choose can be quite challenging. To date there is no complete and well defined set of system characteristics for such M-learning applications. This paper presents system quality characteristics for selecting M-learning applications based on the result of a systematic review conducted in this domain.

Learning Activity Package, Physical Science. LAP Numbers 1, 2, 3, and 4.

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Williams, G. J.

These four units of the Learning Activity Packages (LAPs) for individualized instruction in physical science cover measuring techniques, operations of instruments, metric system heat, matter, energy, elements, atomic numbers, isotopes, molecules, mixtures, compounds, physical and chemical properties, liquids, solids, and gases. Each unit contains…

Domain Adaptation of Translation Models for Multilingual Applications

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2009-04-01

employed. In the past two years, domain adaptation for NLP tasks has become an active research area [3, 38, 25, 23]. New domain adaptation tasks have...and unlabeled data in the target domain and learn a mixture model to adapt from the source domain. Other NLP tasks where domain adaptation has been...evaluation forum, http://www.clef-campaign.org. [13] K. Darwish and D. Oard, CLIR experiments at maryland for TREC-2002: Evidence combination for arabic

Low Working Memory Capacity Impedes both Efficiency and Learning of Number Transcoding in Children

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Camos, Valerie

2008-01-01

This study aimed to evaluate the impact of individual differences in working memory capacity on number transcoding. A recently proposed model, ADAPT (a developmental asemantic procedural transcoding model), accounts for the development of number transcoding from verbal form to Arabic form by two mechanisms: the learning of new production rules…

Impaired Acuity of the Approximate Number System Underlies Mathematical Learning Disability (Dyscalculia)

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Mazzocco, Michele M. M.; Feigenson, Lisa; Halberda, Justin

2011-01-01

Many children have significant mathematical learning disabilities (MLD, or dyscalculia) despite adequate schooling. The current study hypothesizes that MLD partly results from a deficiency in the Approximate Number System (ANS) that supports nonverbal numerical representations across species and throughout development. In this study of 71 ninth…

Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

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Liu, Wuying; Wang, Lin

2018-03-01

The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning.

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Chein, Jason M; Schneider, Walter

2005-12-01

Functional magnetic resonance imaging and a meta-analysis of prior neuroimaging studies were used to characterize cortical changes resulting from extensive practice and to evaluate a dual-processing account of the neural mechanisms underlying human learning. Three core predictions of the dual processing theory are evaluated: 1) that practice elicits generalized reductions in regional activity by reducing the load on the cognitive control mechanisms that scaffold early learning; 2) that these control mechanisms are domain-general; and 3) that no separate processing pathway emerges as skill develops. To evaluate these predictions, a meta-analysis of prior neuroimaging studies and a within-subjects fMRI experiment contrasting unpracticed to practiced performance in a paired-associate task were conducted. The principal effect of practice was found to be a reduction in the extent and magnitude of activity in a cortical network spanning bilateral dorsal prefrontal, left ventral prefrontal, medial frontal (anterior cingulate), left insular, bilateral parietal, and occipito-temporal (fusiform) areas. These activity reductions are shown to occur in common regions across prior neuroimaging studies and for both verbal and nonverbal paired-associate learning in the present fMRI experiment. The implicated network of brain regions is interpreted as a domain-general system engaged specifically to support novice, but not practiced, performance.

Number Meaning and Number Grammar in English and Spanish

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Bock, Kathryn; Carreiras, Manuel; Meseguer, Enrique

2012-01-01

Grammatical agreement makes different demands on speakers of different languages. Being widespread in the languages of the world, the features of agreement systems offer valuable tests of how language affects deep-seated domains of human cognition and categorization. Number agreement is one such domain, with intriguing evidence that typological…

Action priors for learning domain invariances

CSIR Research Space (South Africa)

Rosman, Benjamin S

2015-04-01

Full Text Available behavioural invariances in the domain, by identifying actions to be prioritised in local contexts, invariant to task details. This information has the effect of greatly increasing the speed of solving new problems. We formalise this notion as action priors...

Turkish secondary education students' perceptions of their classroom learning environment and their attitude towards Biology

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Telli, S.; Cakiroglu, J.; den Brok, P.

2006-01-01

The domain of learning environments research has produced many promising findings, leading to an enhancement of the teaching and learning process in many countries. However, there have been a limited number of studies in this field in Turkey. For that reason, the purpose of the present study was to

Turkish secondary education students' perceptions of their classroom learning environment and their attitude towards biology

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Telli, S.; Cakiroglu, J.; Brok, den P.J.; Fisher, D. L.; Khine, M. S.

2006-01-01

The domain of learning environments research has produced many promising findings, leading to an enhancement of the teaching and learning process in many countries. However, there have been a limited number of studies in this field in Turkey. For that reason, the purpose of the present study was to

Nonverbal learning disabilities and developmental dyscalculia: Differential diagnosis of two Brazilian children

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Magda Solange Vanzo Pestun

Full Text Available Nonverbal learning disabilities (NVLD, a clinical condition still little reported in Brazil, are characterized by damages in the visual spatial domains, visual motor integration, fine motor skills, math skills and social and emotional difficulties. Developmental Dyscalculia (DD is a neurodevelopmental disorder that affects basic arithmetic skills acquisition, including storage and recovery of arithmetic facts, calculation fluency and precision and number sense domain. Although both are persistent Math learning disorder/disability, they cause different damages. The objective of this case report is to describe, compare and analyze the neuropsychological profile of two Brazilian children with similar complaints but distinct diagnosis.

Influence of Discussion Rating in Cooperative Learning Type Numbered Head Together on Learning Results Students VII MTSN Model Padang

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Sasmita, E.; Edriati, S.; Yunita, A.

2018-04-01

Related to the math score of the first semester in class at seventh grade of MTSN Model Padang which much the score that low (less than KKM). It because of the students who feel less involved in learning process because the teacher don't do assessment the discussions. The solution of the problem is discussion assessment in Cooperative Learning Model type Numbered Head Together. This study aims to determine whether the discussion assessment in NHT effect on student learning outcomes of class VII MTsN Model Padang. The instrument used in this study is discussion assessment and final tests. The data analysis technique used is the simple linear regression analysis. Hypothesis test results Fcount greater than the value of Ftable then the hypothesis in this study received. So it concluded that the assessment of the discussion in NHT effect on student learning outcomes of class VII MTsN Model Padang.

Expanding the landscape of chromatin modification (CM-related functional domains and genes in human.

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Shuye Pu

2010-11-01

Full Text Available Chromatin modification (CM plays a key role in regulating transcription, DNA replication, repair and recombination. However, our knowledge of these processes in humans remains very limited. Here we use computational approaches to study proteins and functional domains involved in CM in humans. We analyze the abundance and the pair-wise domain-domain co-occurrences of 25 well-documented CM domains in 5 model organisms: yeast, worm, fly, mouse and human. Results show that domains involved in histone methylation, DNA methylation, and histone variants are remarkably expanded in metazoan, reflecting the increased demand for cell type-specific gene regulation. We find that CM domains tend to co-occur with a limited number of partner domains and are hence not promiscuous. This property is exploited to identify 47 potentially novel CM domains, including 24 DNA-binding domains, whose role in CM has received little attention so far. Lastly, we use a consensus Machine Learning approach to predict 379 novel CM genes (coding for 329 proteins in humans based on domain compositions. Several of these predictions are supported by very recent experimental studies and others are slated for experimental verification. Identification of novel CM genes and domains in humans will aid our understanding of fundamental epigenetic processes that are important for stem cell differentiation and cancer biology. Information on all the candidate CM domains and genes reported here is publicly available.

Detecting atypical examples of known domain types by sequence similarity searching: the SBASE domain library approach.

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Dhir, Somdutta; Pacurar, Mircea; Franklin, Dino; Gáspári, Zoltán; Kertész-Farkas, Attila; Kocsor, András; Eisenhaber, Frank; Pongor, Sándor

2010-11-01

SBASE is a project initiated to detect known domain types and predicting domain architectures using sequence similarity searching (Simon et al., Protein Seq Data Anal, 5: 39-42, 1992, Pongor et al, Nucl. Acids. Res. 21:3111-3115, 1992). The current approach uses a curated collection of domain sequences - the SBASE domain library - and standard similarity search algorithms, followed by postprocessing which is based on a simple statistics of the domain similarity network (http://hydra.icgeb.trieste.it/sbase/). It is especially useful in detecting rare, atypical examples of known domain types which are sometimes missed even by more sophisticated methodologies. This approach does not require multiple alignment or machine learning techniques, and can be a useful complement to other domain detection methodologies. This article gives an overview of the project history as well as of the concepts and principles developed within this the project.

Eliciting explanations: Constraints on when self-explanation aids learning.

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Rittle-Johnson, Bethany; Loehr, Abbey M

2017-10-01

Generating explanations for oneself in an attempt to make sense of new information (i.e., self-explanation) is often a powerful learning technique. Despite its general effectiveness, in a growing number of studies, prompting for self-explanation improved some aspects of learning, but reduced learning of other aspects. Drawing on this recent research, as well as on research comparing self-explanation under different conditions, we propose four constraints on the effectiveness of self-explanation. First, self-explanation promotes attention to particular types of information, so it is better suited to promote particular learning outcomes in particular types of domains, such as transfer in domains guided by general principles or heuristics. Second, self-explaining a variety of types of information can improve learning, but explaining one's own solution methods or choices may reduce learning under certain conditions. Third, explanation prompts focus effort on particular aspects of the to-be-learned material, potentially drawing effort away from other important information. Explanation prompts must be carefully designed to align with target learning outcomes. Fourth, prompted self-explanation often promotes learning better than unguided studying, but alternative instructional techniques may be more effective under some conditions. Attention to these constraints should optimize the effectiveness of self-explanation as an instructional technique in future research and practice.

Creativity as Predictor of Mathematical Abilities in Fourth Graders in Addition to Number Sense and Working Memory

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Evelyn H. Kroesbergen

2017-12-01

Full Text Available In this study, it was investigated how domain-specific (number sense and domain-general (working memory, creativity factors explain the variance in mathematical abilities in primary school children. A total of 166 children aged 8 to 10 years old participated. Several tests to measure math ability, mathematical creativity, number sense, verbal and visual spatial working memory and creativity were administered. Data were analyzed with a series of correlation and regression analyses. Number sense, working memory and creativity were all found to be important predictors of academic and creative mathematical ability. Furthermore, groups with math learning disabilities (MLD and mathematical giftedness (MG were compared to a typically developing (TD group. The results show that the MLD group scored lower on number line estimation and visual spatial working memory than the TD group, while the MG group differed from the TD group on visual spatial working memory and creativity. It is concluded that creativity plays a significant role in mathematics, above working memory and number sense.

[Family of ribosomal proteins S1 contains unique conservative domain].

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Deriusheva, E I; Machulin, A V; Selivanova, O M; Serdiuk, I N

2010-01-01

Different representatives of bacteria have different number of amino acid residues in the ribosomal proteins S1. This number varies from 111 (Spiroplasma kunkelii) to 863 a.a. (Treponema pallidum). Traditionally and for lack of this protein three-dimensional structure, its architecture is represented as repeating S1 domains. Number of these domains depends on the protein's length. Domain's quantity and its boundaries data are contained in the specialized databases, such as SMART, Pfam and PROSITE. However, for the same object these data may be very different. For search of domain's quantity and its boundaries, new approach, based on the analysis of dicted secondary structure (PsiPred), was used. This approach allowed us to reveal structural domains in amino acid sequences of S1 proteins and at that number varied from one to six. Alignment of S1 proteins, containing different domain's number, with the S1 RNAbinding domain of Escherichia coli PNPase elicited a fact that in family of ribosomal proteins SI one domain has maximal homology with S1 domain from PNPase. This conservative domain migrates along polypeptide chain and locates in proteins, containing different domain's number, according to specified pattern. In this domain as well in the S1 domain from PNPase, residues Phe-19, Phe-22, His-34, Asp-64 and Arg-68 are clustered on the surface and formed RNA binding site.

Design Research on Mathematics Education: Investigating The Progress of Indonesian Fifth Grade Students’ Learning on Multiplication of Fractions With Natural Numbers

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Nenden Octavarulia Shanty

2011-07-01

Full Text Available This study aimed at investigating the progress of students’ learning onmultiplication fractions with natural numbers through the five activitylevels based on Realistic Mathematics Education (RME approachproposed by Streefland. Design research was chosen to achieve thisresearch goal. In design research, the Hypothetical Learning Trajectory(HLT plays important role as a design and research instrument. ThisHLT tested to thirty-seven students of grade five primary school (i.e.SDN 179 Palembang.The result of the classroom practices showed that measurement (lengthactivity could stimulate students’ to produce fractions as the first levelin learning multiplication of fractions with natural numbers.Furthermore, strategies and tools used by the students in partitioninggradually be developed into a more formal mathematics in whichnumber line be used as the model of measuring situation and the modelfor more formal reasoning. The number line then could bring thestudents to the last activity level, namely on the way to rules formultiplying fractions with natural numbers. Based on this findings, it is suggested that Streefland’s five activity levels can be used as aguideline in learning multiplication of fractions with natural numbers in which the learning process become a more progressive learning.

Inferring domain-domain interactions from protein-protein interactions with formal concept analysis.

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Susan Khor

Full Text Available Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains.

Inferring Domain-Domain Interactions from Protein-Protein Interactions with Formal Concept Analysis

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Khor, Susan

2014-01-01

Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. PMID:24586450

Learning domain abstractions for long lived robots

CSIR Research Space (South Africa)

Rosman, Benjamin S

2014-06-01

Full Text Available the ability to continually learn from a lifetime of experience. Key to this is the ability to generalise from experiences and form representations which facilitate faster learning of new tasks, as well as the transfer of knowledge between different situations...

Translating Learning into Numbers: A Generic Framework for Learning Analytics

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Greller, Wolfgang; Drachsler, Hendrik

2012-01-01

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning…

Simplicity and Specificity in Language: Domain-General Biases Have Domain-Specific Effects

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Culbertson, Jennifer; Kirby, Simon

2016-01-01

The extent to which the linguistic system—its architecture, the representations it operates on, the constraints it is subject to—is specific to language has broad implications for cognitive science and its relation to evolutionary biology. Importantly, a given property of the linguistic system can be “specific” to the domain of language in several ways. For example, if the property evolved by natural selection under the pressure of the linguistic function it serves then the property is domain-specific in the sense that its design is tailored for language. Equally though, if that property evolved to serve a different function or if that property is domain-general, it may nevertheless interact with the linguistic system in a way that is unique. This gives a second sense in which a property can be thought of as specific to language. An evolutionary approach to the language faculty might at first blush appear to favor domain-specificity in the first sense, with individual properties of the language faculty being specifically linguistic adaptations. However, we argue that interactions between learning, culture, and biological evolution mean any domain-specific adaptations that evolve will take the form of weak biases rather than hard constraints. Turning to the latter sense of domain-specificity, we highlight a very general bias, simplicity, which operates widely in cognition and yet interacts with linguistic representations in domain-specific ways. PMID:26793132

Deep Transfer Metric Learning.

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Junlin Hu; Jiwen Lu; Yap-Peng Tan; Jie Zhou

2016-12-01

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.

Principles underlying the design of "The Number Race", an adaptive computer game for remediation of dyscalculia

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Cohen Laurent

2006-05-01

Full Text Available Abstract Background Adaptive game software has been successful in remediation of dyslexia. Here we describe the cognitive and algorithmic principles underlying the development of similar software for dyscalculia. Our software is based on current understanding of the cerebral representation of number and the hypotheses that dyscalculia is due to a "core deficit" in number sense or in the link between number sense and symbolic number representations. Methods "The Number Race" software trains children on an entertaining numerical comparison task, by presenting problems adapted to the performance level of the individual child. We report full mathematical specifications of the algorithm used, which relies on an internal model of the child's knowledge in a multidimensional "learning space" consisting of three difficulty dimensions: numerical distance, response deadline, and conceptual complexity (from non-symbolic numerosity processing to increasingly complex symbolic operations. Results The performance of the software was evaluated both by mathematical simulations and by five weeks of use by nine children with mathematical learning difficulties. The results indicate that the software adapts well to varying levels of initial knowledge and learning speeds. Feedback from children, parents and teachers was positive. A companion article 1 describes the evolution of number sense and arithmetic scores before and after training. Conclusion The software, open-source and freely available online, is designed for learning disabled children aged 5–8, and may also be useful for general instruction of normal preschool children. The learning algorithm reported is highly general, and may be applied in other domains.

The Moderating Role of Non-Controlling Supervision and Organizational Learning Culture on Employee Creativity: The Influences of Domain Expertise and Creative Personality

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Jeong, Shinhee; McLean, Gary N.; McLean, Laird D.; Yoo, Sangok; Bartlett, Kenneth

2017-01-01

Purpose: By adopting a multilevel approach, this paper aims to examine the relationships among employee creativity and creative personality, domain expertise (i.e. individual-level factors), non-controlling supervision style and organizational learning culture (i.e. team-level factors). It also investigates the cross-level interactions between…

‘Living' theory: a pedagogical framework for process support in networked learning

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Philipa Levy

2006-12-01

Full Text Available This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case-study analysis of learners' experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case-study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes–orientation, communication, socialisation and organisation–that were associated with ‘learning to learn' in the course's networked environment, and offers a flavour of participants' experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process-focused (as well as domain-focused learning tasks.

Imbalanced class learning in epigenetics.

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Haque, M Muksitul; Skinner, Michael K; Holder, Lawrence B

2014-07-01

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

Multi-domain training in healthy old age: Hotel Plastisse as an iPad-based serious game to systematically compare multi-domain and single-domain training

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Binder, Julia C.; Zöllig, Jacqueline; Eschen, Anne; Mérillat, Susan; Röcke, Christina; Schoch, Sarah F.; Jäncke, Lutz; Martin, Mike

2015-01-01

Finding effective training interventions for declining cognitive abilities in healthy aging is of great relevance, especially in view of the demographic development. Since it is assumed that transfer from the trained to untrained domains is more likely to occur when training conditions and transfer measures share a common underlying process, multi-domain training of several cognitive functions should increase the likelihood of such an overlap. In the first part, we give an overview of the literature showing that cognitive training using complex tasks, such as video games, leisure activities, or practicing a series of cognitive tasks, has shown promising results regarding transfer to a number of cognitive functions. These studies, however, do not allow direct inference about the underlying functions targeted by these training regimes. Custom-designed serious games allow to design training regimes according to specific cognitive functions and a target population's need. In the second part, we introduce the serious game Hotel Plastisse as an iPad-based training tool for older adults that allows the comparison of the simultaneous training of spatial navigation, visuomotor function, and inhibition to the training of each of these functions separately. Hotel Plastisse not only defines the cognitive functions of the multi-domain training clearly, but also implements training in an interesting learning environment including adaptive difficulty and feedback. We propose this novel training tool with the goal of furthering our understanding of how training regimes should be designed in order to affect cognitive functioning of older adults most broadly. PMID:26257643

Multi-domain training in healthy old age: Hotel Plastisse as an iPad-based serious game to systematically compare multi-domain and single-domain training.

Science.gov (United States)

Binder, Julia C; Zöllig, Jacqueline; Eschen, Anne; Mérillat, Susan; Röcke, Christina; Schoch, Sarah F; Jäncke, Lutz; Martin, Mike

2015-01-01

Finding effective training interventions for declining cognitive abilities in healthy aging is of great relevance, especially in view of the demographic development. Since it is assumed that transfer from the trained to untrained domains is more likely to occur when training conditions and transfer measures share a common underlying process, multi-domain training of several cognitive functions should increase the likelihood of such an overlap. In the first part, we give an overview of the literature showing that cognitive training using complex tasks, such as video games, leisure activities, or practicing a series of cognitive tasks, has shown promising results regarding transfer to a number of cognitive functions. These studies, however, do not allow direct inference about the underlying functions targeted by these training regimes. Custom-designed serious games allow to design training regimes according to specific cognitive functions and a target population's need. In the second part, we introduce the serious game Hotel Plastisse as an iPad-based training tool for older adults that allows the comparison of the simultaneous training of spatial navigation, visuomotor function, and inhibition to the training of each of these functions separately. Hotel Plastisse not only defines the cognitive functions of the multi-domain training clearly, but also implements training in an interesting learning environment including adaptive difficulty and feedback. We propose this novel training tool with the goal of furthering our understanding of how training regimes should be designed in order to affect cognitive functioning of older adults most broadly.

Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

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Vicente García-Díaz

2015-12-01

Full Text Available Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.

Patterns, principles, and practices of domain-driven design

CERN Document Server

Millett, Scott

2015-01-01

Methods for managing complex software construction following the practices, principles and patterns of Domain-Driven Design with code examples in C# This book presents the philosophy of Domain-Driven Design (DDD) in a down-to-earth and practical manner for experienced developers building applications for complex domains. A focus is placed on the principles and practices of decomposing a complex problem space as well as the implementation patterns and best practices for shaping a maintainable solution space. You will learn how to build effective domain models through the use of tactical pat

Domain Adaptation for Machine Translation with Instance Selection

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Biçici Ergun

2015-04-01

Full Text Available Domain adaptation for machine translation (MT can be achieved by selecting training instances close to the test set from a larger set of instances. We consider 7 different domain adaptation strategies and answer 7 research questions, which give us a recipe for domain adaptation in MT. We perform English to German statistical MT (SMT experiments in a setting where test and training sentences can come from different corpora and one of our goals is to learn the parameters of the sampling process. Domain adaptation with training instance selection can obtain 22% increase in target 2-gram recall and can gain up to 3:55 BLEU points compared with random selection. Domain adaptation with feature decay algorithm (FDA not only achieves the highest target 2-gram recall and BLEU performance but also perfectly learns the test sample distribution parameter with correlation 0:99. Moses SMT systems built with FDA selected 10K training sentences is able to obtain F1 results as good as the baselines that use up to 2M sentences. Moses SMT systems built with FDA selected 50K training sentences is able to obtain F1 point better results than the baselines.

Investigating the Variability in Cumulus Cloud Number as a Function of Subdomain Size and Organization using large-domain LES

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Neggers, R.

2017-12-01

Recent advances in supercomputing have introduced a "grey zone" in the representation of cumulus convection in general circulation models, in which this process is partially resolved. Cumulus parameterizations need to be made scale-aware and scale-adaptive to be able to conceptually and practically deal with this situation. A potential way forward are schemes formulated in terms of discretized Cloud Size Densities, or CSDs. Advantages include i) the introduction of scale-awareness at the foundation of the scheme, and ii) the possibility to apply size-filtering of parameterized convective transport and clouds. The CSD is a new variable that requires closure; this concerns its shape, its range, but also variability in cloud number that can appear due to i) subsampling effects and ii) organization in a cloud field. The goal of this study is to gain insight by means of sub-domain analyses of various large-domain LES realizations of cumulus cloud populations. For a series of three-dimensional snapshots, each with a different degree of organization, the cloud size distribution is calculated in all subdomains, for a range of subdomain sizes. The standard deviation of the number of clouds of a certain size is found to decrease with the subdomain size, following a powerlaw scaling corresponding to an inverse-linear dependence. Cloud number variability also increases with cloud size; this reflects that subsampling affects the largest clouds first, due to their typically larger neighbor spacing. Rewriting this dependence in terms of two dimensionless groups, by dividing by cloud number and cloud size respectively, yields a data collapse. Organization in the cloud field is found to act on top of this primary dependence, by enhancing the cloud number variability at the smaller sizes. This behavior reflects that small clouds start to "live" on top of larger structures such as cold pools, favoring or inhibiting their formation (as illustrated by the attached figure of cloud mask

A pre-registered naturalistic observation of within domain mental fatigue and domain-general depletion of self-control.

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Daniel Randles

Full Text Available Self-control is often believed to operate as if it were a finite, domain-general resource. However, recent attempts to demonstrate this under transparent conditions have failed to yield positive results. In the current study, we monitor two groups of students (N1 = 8,867, N2 = 8,754 over separate 17-week intervals with 24-hour coverage, as they engage in voluntary learning and self-testing using an online program. We use daily behavior to assess whether time-of-day effects support domain-general theories of self-control. Additionally, we assess whether mental fatigue emerges within task during prolonged persistent effort. Results reveal within-task fatigue emerges within an hour on-task. However, there is a negligible effect on ability throughout the day. Additionally, time-of-day has no detrimental effect on motivation; rather there is a strong tendency to increase learning time at night. Results are consistent with theories indicating people lose motivation within a specific task, but at odds with theories that argue for a domain-general self-control resource.

Applying Technology to Inquiry-Based Learning in Early Childhood Education

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Wang, Feng; Kinzie, Mable B.; McGuire, Patrick; Pan, Edward

2010-01-01

Children naturally explore and learn about their environments through inquiry, and computer technologies offer an accessible vehicle for extending the domain and range of this inquiry. Over the past decade, a growing number of interactive games and educational software packages have been implemented in early childhood education and addressed a…

Visual Input Enhancement and Grammar Learning: A Meta-Analytic Review

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Lee, Sang-Ki; Huang, Hung-Tzu

2008-01-01

Effects of pedagogical interventions with visual input enhancement on grammar learning have been investigated by a number of researchers during the past decade and a half. The present review delineates this research domain via a systematic synthesis of 16 primary studies (comprising 20 unique study samples) retrieved through an exhaustive…

Multi-domain training in healthy old age – Hotel Plastisse as an iPad-based serious game to systematically compare multi-domain and single-domain training

Directory of Open Access Journals (Sweden)

Julia Claudia Binder

2015-07-01

Full Text Available Finding effective training interventions for declining cognitive abilities in healthy aging is of great relevance, especially in view of the demographic development. Since it is assumed that transfer from the trained to untrained domains is more likely to occur when training conditions and transfer measures share a common underlying process, multi-domain training of several cognitive functions should increase the likelihood of such an overlap. In the first part, we give an overview of the literature showing that cognitive training using complex tasks such as video games, leisure activities, or practicing a series of cognitive tasks has shown promising results regarding transfer to a number of cognitive functions. These studies, however, do not allow direct inference about the underlying functions targeted by these training regimes. Custom-designed serious games allow to design training regimes according to specific cognitive functions and a target population’s need. In the second part, we introduce the serious game Hotel Plastisse as an iPad-based training tool for older adults that allows the comparison of the simultaneous training of spatial navigation, visuomotor function and inhibition to the training of each of these functions separately. Hotel Plastisse not only defines the cognitive functions of the multi-domain training clearly, but also implements training in an interesting learning environment including adaptive difficulty and feedback. We propose this novel training tool with the goal of furthering our understanding of how training regimes should be designed in order to affect cognitive functioning of older adults most broadly.

The adventure of numbers

CERN Document Server

Godefroy, Gilles

2004-01-01

Numbers are fascinating. The fascination begins in childhood, when we first learn to count. It continues as we learn arithmetic, algebra, geometry, and so on. Eventually, we learn that numbers not only help us to measure the world, but also to understand it and, to some extent, to control it. In The Adventure of Numbers, Gilles Godefroy follows the thread of our expanding understanding of numbers to lead us through the history of mathematics. His goal is to share the joy of discovering and understanding this great adventure of the mind. The development of mathematics has been punctuated by a n

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

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Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

THE USE OF NUMBERED HEADS TOGETHER (NHT LEARNING MODEL WITH SCIENCE, ENVIRONMENT, TECHNOLOGY, SOCIETY (SETS APPROACH TO IMPROVE STUDENT LEARNING MOTIVATION OF SENIOR HIGH SCHOOL

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B. Sutipnyo

2018-01-01

Full Text Available This research was aimed to determine the increasing of students' motivation that has been applied by Numbered Heads Together (NHT learning model with Science, Environment, Technology, Society (SETS approach. The design of this study was quasi experiment with One Group Pretest-Posttest Design. The data of students’ learning motivation obtained through questionnaire administered before and after NHT learning model with SETS approach. In this research, the indicators of learning-motivation were facing tasks diligently, showing interest in variety of problems, prefering to work independently, keeping students’ opinions, and feeling happy to find and solve problems. Increasing of the students’ learning motivation was analyzed by using a gain test. The results showed that applying NHT learning model with SETS approach could increase the students’ learning motivation in medium categories.

Domain similarity based orthology detection.

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Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

2015-05-13

Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

Attention deficits predict phenotypic outcomes in syndrome-specific and domain-specific ways

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Kim eCornish

2012-07-01

Full Text Available Attentional difficulties, both at home and in the classroom, are reported across a number of neurodevelopmental disorders. However, exactly how attention influences early socio-cognitive learning remains unclear. We addressed this question both concurrently and longitudinally in a cross-syndrome design, with respect to the communicative domain of vocabulary and to the cognitive domain of early literacy, and then extended the analysis to social behavior. Participants were young children (aged 4 to 9 years at Time 1 with either Williams syndrome (WS, N=26 or Down syndrome (DS, N=26 and typically developing controls (N=103. Children with WS displayed significantly greater attentional deficits (as indexed by teacher report of behavior typical of attention deficit hyperactivity disorder, ADHD than children with DS, but both groups had greater attentional problems than the controls. Despite their attention differences, children with DS and those with WS were equivalent in their cognitive abilities of reading single words, both at Time 1 and 12 months later, at Time 2, although they differed in their early communicative abilities in terms of vocabulary. Greater ADHD-like behaviors predicted poorer subsequent literacy for children with DS, but not for children with WS, pointing to syndrome-specific attentional constraints on specific aspects of early development. Overall, our findings highlight the need to investigate more precisely whether and, if so, how, syndrome-specific profiles of behavioral difficulties constrain learning and socio-cognitive outcomes across different domains.

Domain Approach: An Alternative Approach in Moral Education

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Vengadasalam, Chander; Mamat, Wan Hasmah Wan; Mail, Fauziah; Sudramanian, Munimah

2014-01-01

This paper discusses the use of the domain approach in moral education in an upper secondary school in Malaysia. Moral Education needs a creative and an innovative approach. Therefore, a few forms of approaches are used in the teaching-learning of Moral Education. This research describes the use of domain approach which comprises the moral domain…

Rapid response learning of brand logo priming: Evidence that brand priming is not dominated by rapid response learning.

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Boehm, Stephan G; Smith, Ciaran; Muench, Niklas; Noble, Kirsty; Atherton, Catherine

2017-08-31

Repetition priming increases the accuracy and speed of responses to repeatedly processed stimuli. Repetition priming can result from two complementary sources: rapid response learning and facilitation within perceptual and conceptual networks. In conceptual classification tasks, rapid response learning dominates priming of object recognition, but it does not dominate priming of person recognition. This suggests that the relative engagement of network facilitation and rapid response learning depends on the stimulus domain. Here, we addressed the importance of the stimulus domain for rapid response learning by investigating priming in another domain, brands. In three experiments, participants performed conceptual decisions for brand logos. Strong priming was present, but it was not dominated by rapid response learning. These findings add further support to the importance of the stimulus domain for the relative importance of network facilitation and rapid response learning, and they indicate that brand priming is more similar to person recognition priming than object recognition priming, perhaps because priming of both brands and persons requires individuation.

Java problem-based learning

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Goran P, Šimić

2012-01-01

Full Text Available The paper describes the self-directed problem-based learning system (PBL named Java PBL. The expert module is the kernel of Java PBL. It involves a specific domain model, a problem generator and a solution generator. The overall system architecture is represented in the paper. Java PBL can act as the stand-alone system, but it is also designed to provide support to learning management systems (LMSs. This is provided by a modular design of the system. An LMS can offer the declarative knowledge only. Java PBL offers the procedural knowledge and the progress of the learner programming skills. The free navigation, unlimited numbers of problems and recommendations represent the main pedagogical strategies and tactics implemented into the system.

Alternative to domain wall fermions

International Nuclear Information System (INIS)

Neuberger, H.

2002-01-01

An alternative to commonly used domain wall fermions is presented. Some rigorous bounds on the condition number of the associated linear problem are derived. On the basis of these bounds and some experimentation it is argued that domain wall fermions will in general be associated with a condition number that is of the same order of magnitude as the product of the condition number of the linear problem in the physical dimensions by the inverse bare quark mass. Thus, the computational cost of implementing true domain wall fermions using a single conjugate gradient algorithm is of the same order of magnitude as that of implementing the overlap Dirac operator directly using two nested conjugate gradient algorithms. At a cost of about a factor of two in operation count it is possible to make the memory usage of direct implementations of the overlap Dirac operator independent of the accuracy of the approximation to the sign function and of the same order as that of standard Wilson fermions

Middle Level Learning Number 47

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Lapham, Steven S.; Hanes, Peter; Turner, Thomas N.; Clabough, Jeremiah C.; Cole, William

2013-01-01

This issue's "Middle Level Learning" section presents two articles. The first is "Harriet Tubman: Emancipate Yourself!" (by Steven S. Lapham and Peter Hanes). "Argo," which won the 2012 Oscar for best picture, was about a daring escape of six U.S. diplomats from Iran during the 1979 hostage crisis. Now imagine the…

Classification of domains of closed operators

International Nuclear Information System (INIS)

Lassner, G.; Timmermann, W.

1975-01-01

The structure of domains of determining closed operators in the Hilbert space by means of sequence spaces is investigated. The final classification provides three classes of these domains. Necessary and sufficient conditions of equivalence of these domains are obtained in the form of equivalency of corresponding sequences of natural numbers. Connection with the perturbation theory is mentioned [ru

Targeting Discoidin Domain Receptors in Prostate Cancer

Science.gov (United States)

2017-08-01

AWARD NUMBER: W81XWH-15-1-0226 TITLE: Targeting Discoidin Domain Receptors in Prostate Cancer PRINCIPAL INVESTIGATOR: Dr. Rafael Fridman...AND SUBTITLE 5a. CONTRACT NUMBER Targeting Discoidin Domain Receptors in Prostate Cancer 5b. GRANT NUMBER W81XWH-15-1-0226 5c. PROGRAM ELEMENT...response to collagen in prostate cancer. The project’s goal is to define the expression and therapeutic potential of DDRs in prostate cancer. During

Recommender Systems for Learning

CERN Document Server

Manouselis, Nikos; Verbert, Katrien; Duval, Erik

2013-01-01

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Science.gov (United States)

Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

2018-01-01

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

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Zhiyuan Ma

2018-03-01

Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

L'apprentissage des langues médiatisé par les technologies (ALMT – Étude d'un domaine de recherche émergent à travers les publications de la revue Alsic Technology-mediated language learning: an emergent research domain under study through the review of a French scientific journal's publications

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Nicolas Guichon

2012-11-01

Full Text Available Dans cette étude, il est postulé que l'apprentissage des langues médiatisé par les technologies (ALMT est un domaine de recherche qui s'intéresse au développement et à l'intégration des technologies dans l'enseignement-apprentissage d'une langue. Ce domaine étant émergent, la présente recherche vise tout d'abord à comprendre comment s'est formée la communauté de chercheurs autour de cet objet. Puis, à travers l'analyse critique de 79 articles publiés dans la revue en ligne francophone Alsic entre 1998 et 2010, la présente contribution s'emploie à définir les contours épistémologiques de ce domaine en étudiant les moyens de production de connaissance.In this study, it is postulated that technology mediated language learning is a research domain that focuses on the design and integration of technologies for language learning and teaching. Because this domain is emergent, the present study first aims at understanding how a community of researchers has developed around this object. Then, thanks to the critical analysis of 79 articles published in Alsic, a French-speaking online journal, the present article endeavours to define the epistemological contours of this research domain by studying the means employed to produce knowledge.

The approximate number system and domain-general abilities as predictors of math ability in children with normal hearing and hearing loss.

Science.gov (United States)

Bull, Rebecca; Marschark, Marc; Nordmann, Emily; Sapere, Patricia; Skene, Wendy A

2018-06-01

Many children with hearing loss (CHL) show a delay in mathematical achievement compared to children with normal hearing (CNH). This study examined whether there are differences in acuity of the approximate number system (ANS) between CHL and CNH, and whether ANS acuity is related to math achievement. Working memory (WM), short-term memory (STM), and inhibition were considered as mediators of any relationship between ANS acuity and math achievement. Seventy-five CHL were compared with 75 age- and gender-matched CNH. ANS acuity, mathematical reasoning, WM, and STM of CHL were significantly poorer compared to CNH. Group differences in math ability were no longer significant when ANS acuity, WM, or STM was controlled. For CNH, WM and STM fully mediated the relationship of ANS acuity to math ability; for CHL, WM and STM only partially mediated this relationship. ANS acuity, WM, and STM are significant contributors to hearing status differences in math achievement, and to individual differences within the group of CHL. Statement of contribution What is already known on this subject? Children with hearing loss often perform poorly on measures of math achievement, although there have been few studies focusing on basic numerical cognition in these children. In typically developing children, the approximate number system predicts math skills concurrently and longitudinally, although there have been some contradictory findings. Recent studies suggest that domain-general skills, such as inhibition, may account for the relationship found between the approximate number system and math achievement. What does this study adds? This is the first robust examination of the approximate number system in children with hearing loss, and the findings suggest poorer acuity of the approximate number system in these children compared to hearing children. The study addresses recent issues regarding the contradictory findings of the relationship of the approximate number system to math ability

"I know your name, but not your number"--Patients with verbal short-term memory deficits are impaired in learning sequences of digits.

Science.gov (United States)

Bormann, Tobias; Seyboth, Margret; Umarova, Roza; Weiller, Cornelius

2015-06-01

Studies on verbal learning in patients with impaired verbal short-term memory (vSTM) have revealed dissociations among types of verbal information. Patients with impaired vSTM are able to learn lists of known words but fail to acquire new word forms. This suggests that vSTM is involved in new word learning. The present study assessed both new word learning and the learning of digit sequences in two patients with impaired vSTM. In two experiments, participants were required to learn people's names, ages and professions, or their four digit 'phone numbers'. The STM patients were impaired on learning unknown family names and phone numbers, but managed to acquire other verbal information. In contrast, a patient with a severe verbal episodic memory impairment was impaired across information types. These results indicate verbal STM involvement in the learning of digit sequences. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

Science.gov (United States)

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

2016-01-01

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

Children with Learning Disabilities. Facts for Families. Number 16

Science.gov (United States)

American Academy of Child & Adolescent Psychiatry (NJ1), 2011

2011-01-01

Parents are often worried when their child has learning problems in school. There are many reasons for school failure, but a common one is a specific learning disability. Children with learning disabilities can have intelligence in the normal range but the specific learning disability may make teachers and parents concerned about their general…

Validating Domain Ontologies: A Methodology Exemplified for Concept Maps

Science.gov (United States)

Steiner, Christina M.; Albert, Dietrich

2017-01-01

Ontologies play an important role as knowledge domain representations in technology-enhanced learning and instruction. Represented in form of concept maps they are commonly used as teaching and learning material and have the potential to enhance positive educational outcomes. To ensure the effective use of an ontology representing a knowledge…

Multiple graph regularized protein domain ranking.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-11-19

Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Geometrical Modification of Learning Vector Quantization Method for Solving Classification Problems

Directory of Open Access Journals (Sweden)

Korhan GÜNEL

2016-09-01

Full Text Available In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.

[Problem based learning: achievement of educational goals in the information and comprehension sub-categories of Bloom cognitive domain].

Science.gov (United States)

Montecinos, P; Rodewald, A M

1994-06-01

The aim this work was to assess and compare the achievements of medical students, subjected to problem based learning methodology. The information and comprehension categories of Bloom were tested in 17 medical students in four different occasions during the physiopathology course, using a multiple choice knowledge test. There was a significant improvement in the number of correct answers towards the end of the course. It is concluded that these medical students obtained adequate learning achievements in the information subcategory of Bloom using problem based learning methodology, during the physiopathology course.

Exploring the Deep-Level Reasoning Questions Effect during Vicarious Learning among Eighth to Eleventh Graders in the Domains of Computer Literacy and Newtonian Physics

Science.gov (United States)

Gholson, Barry; Witherspoon, Amy; Morgan, Brent; Brittingham, Joshua K.; Coles, Robert; Graesser, Arthur C.; Sullins, Jeremiah; Craig, Scotty D.

2009-01-01

This paper tested the deep-level reasoning questions effect in the domains of computer literacy between eighth and tenth graders and Newtonian physics for ninth and eleventh graders. This effect claims that learning is facilitated when the materials are organized around questions that invite deep-reasoning. The literature indicates that vicarious…

Cheminoes: A Didactic Game to Learn Chemical Relationships between Valence, Atomic Number, and Symbol

Science.gov (United States)

Moreno, Luis F.; Hincapié, Gina; Alzate, María Victoria

2014-01-01

Cheminoes is a didactic game that enables the meaningful learning of some relations between concepts such as chemical element, valence, atomic number, and chemical symbol for the first 36 chemical elements of the periodic system. Among the students who have played the game, their opinions of the activity were positive, considering the game to be a…

Computer Mathematics Games and Conditions for Enhancing Young Children's Learning of Number Sense

Science.gov (United States)

Kermani, Hengameh

2017-01-01

Purpose: The present study was designed to examine whether mathematics computer games improved young children's learning of number sense under three different conditions: when used individually, with a peer, and with teacher facilitation. Methodology: This study utilized a mixed methodology, collecting both quantitative and qualitative data. A…

Domain Adaptation for Opinion Classification: A Self-Training Approach

Directory of Open Access Journals (Sweden)

Yu, Ning

2013-03-01

Full Text Available Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

Students Engaged in Learning

Science.gov (United States)

Ismail, Emad A.; Groccia, James E.

2018-01-01

Engaging students in learning is a basic principle of effective undergraduate education. Outcomes of engaging students include meaningful learning experiences and enhanced skills in all learning domains. This chapter reviews the influence of engaging students in different forms of active learning on cognitive, psychomotor, and affective skill…

Effects of sub-domain structure on initial magnetization curve and domain size distribution of stacked media

International Nuclear Information System (INIS)

Sato, S.; Kumagai, S.; Sugita, R.

2015-01-01

In this paper, in order to confirm the sub-domain structure in stacked media demagnetized with in-plane field, initial magnetization curves and magnetic domain size distribution were investigated. Both experimental and simulation results showed that an initial magnetization curve for the medium demagnetized with in-plane field (MDI) initially rose faster than that for the medium demagnetized with perpendicular field (MDP). It is inferred that this is because the MDI has a larger number of domain walls than the MDP due to the existence of the sub-domains, resulting in an increase in the probability of domain wall motion. Dispersion of domain size for the MDI was larger than that for the MDP. This is because sub-domains are formed not only inside the domain but also at the domain boundary region, and they change the position of the domain boundary to affect the domain size. - Highlights: • An initial magnetization curve for MDI initially rose faster than that for MDP. • Dispersion of domain size for the MDI was larger than that for the MDP. • Experimental and simulation results can be explained by existence of sub-domains

Multiple graph regularized protein domain ranking

KAUST Repository

Wang, Jim Jing-Yan

2012-11-19

Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

Multiple graph regularized protein domain ranking

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-01-01

Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

Multiple graph regularized protein domain ranking

Directory of Open Access Journals (Sweden)

Wang Jim

2012-11-01

Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

Science.gov (United States)

Treder, Maximilian; Lauermann, Jost Lennart; Eter, Nicole

2018-02-01

Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT). A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD. After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.

Learning Skills; Review and Domain Chart.

Science.gov (United States)

Clark, N. Cecil; Thompson, Faith E.

A major goal of the elementary and secondary schools is to help each person become an efficient and autonomous learner. Outlined in this report are skills abstracted from the literature on such topics as verbal learning, problem solving, study habits, and behavior modification. The learner-oriented skills are presented so that they may be…

Polar Domain Discovery with Sparkler

Science.gov (United States)

Duerr, R.; Khalsa, S. J. S.; Mattmann, C. A.; Ottilingam, N. K.; Singh, K.; Lopez, L. A.

2017-12-01

The scientific web is vast and ever growing. It encompasses millions of textual, scientific and multimedia documents describing research in a multitude of scientific streams. Most of these documents are hidden behind forms which require user action to retrieve and thus can't be directly accessed by content crawlers. These documents are hosted on web servers across the world, most often on outdated hardware and network infrastructure. Hence it is difficult and time-consuming to aggregate documents from the scientific web, especially those relevant to a specific domain. Thus generating meaningful domain-specific insights is currently difficult. We present an automated discovery system (Figure 1) using Sparkler, an open-source, extensible, horizontally scalable crawler which facilitates high throughput and focused crawling of documents pertinent to a particular domain such as information about polar regions. With this set of highly domain relevant documents, we show that it is possible to answer analytical questions about that domain. Our domain discovery algorithm leverages prior domain knowledge to reach out to commercial/scientific search engines to generate seed URLs. Subject matter experts then annotate these seed URLs manually on a scale from highly relevant to irrelevant. We leverage this annotated dataset to train a machine learning model which predicts the `domain relevance' of a given document. We extend Sparkler with this model to focus crawling on documents relevant to that domain. Sparkler avoids disruption of service by 1) partitioning URLs by hostname such that every node gets a different host to crawl and by 2) inserting delays between subsequent requests. With an NSF-funded supercomputer Wrangler, we scaled our domain discovery pipeline to crawl about 200k polar specific documents from the scientific web, within a day.

STEPP: A Grounded Model to Assure the Quality of Instructional Activities in e-Learning Environments

Directory of Open Access Journals (Sweden)

Hamdy AHMED ABDELAZIZ

2013-07-01

Full Text Available The present theoretical paper aims to develop a grounded model for designing instructional activities appropriate to e-learning and online learning environments. The suggested model is guided by learning principles of cognitivism, constructivism, and connectivism learning principles to help online learners constructing meaningful experiences and moving from knowledge acquisition to knowledge creation process. The proposed model consists of five dynamic and grounded domains that assure the quality of designing and using e-learning activities: Ø Social Domain; Ø Technological Domain; Ø Epistemological Domain; Ø Psychological domain; and Ø Pedagogical Domain. Each of these domains needs four types of presences to reflect the design and the application process of e-learning activities. These four presences are: Ø cognitive presence, Ø human presence, Ø psychological presence and Ø mental presence. Applying the proposed model (STEPP throughout all online and adaptive e-learning environments may improve the process of designing and developing e-learning activities to be used as mindtools for current and future learners.

Training Peer-Feedback Skills on Geometric Construction Tasks: Role of Domain Knowledge and Peer-Feedback Levels

Science.gov (United States)

Alqassab, Maryam; Strijbos, Jan-Willem; Ufer, Stefan

2018-01-01

Peer feedback is widely used to train assessment skills and to support collaborative learning of various learning tasks, but research on peer feedback in the domain of mathematics is limited. Although domain knowledge seems to be a prerequisite for peer-feedback provision, it only recently received attention in the peer-feedback literature. In…

Machine learning \\& artificial intelligence in the quantum domain

OpenAIRE

Dunjko, Vedran; Briegel, Hans J.

2017-01-01

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent th...

Recent developments in learning control and system identification for robots and structures

Science.gov (United States)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

Domain Adaptation for Pedestrian Detection Based on Prediction Consistency

Directory of Open Access Journals (Sweden)

Yu Li-ping

2014-01-01

Full Text Available Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.

Exploring Effectiveness and Moderators of Language Learning Strategy Instruction on Second Language and Self-Regulated Learning Outcomes

Science.gov (United States)

Ardasheva, Yuliya; Wang, Zhe; Adesope, Olusola O.; Valentine, Jeffrey C.

2017-01-01

This meta-analysis synthesized recent research on strategy instruction (SI) effectiveness to estimate SI effects and their moderators for two domains: second/foreign language and self-regulated learning. A total of 37 studies (47 independent samples) for language domain and 16 studies (17 independent samples) for self-regulated learning domain…

Technically Speaking: Transforming Language Learning through Virtual Learning Environments (MOOs).

Science.gov (United States)

von der Emde, Silke; Schneider, Jeffrey; Kotter, Markus

2001-01-01

Draws on experiences from a 7-week exchange between students learning German at an American college and advanced students of English at a German university. Maps out the benefits to using a MOO (multiple user domains object-oriented) for language learning: a student-centered learning environment structured by such objectives as peer teaching,…

"Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

Science.gov (United States)

Coya, Liliam de Barbosa; Perez-Coffie, Jorge

1982-01-01

"Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

Science.gov (United States)

Oluwadare, Oluwatosin; Cheng, Jianlin

2017-11-14

With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function. Here, we formulate the TAD identification problem as an unsupervised machine learning (clustering) problem, and develop a new TAD identification method called ClusterTAD. We introduce a novel method to represent chromosomal contacts as features to be used by the clustering algorithm. Our results show that ClusterTAD can accurately predict the TADs on a simulated Hi-C data. Our method is also largely complementary and consistent with existing methods on the real Hi-C datasets of two mouse cells. The validation with the chromatin immunoprecipitation (ChIP) sequencing (ChIP-Seq) data shows that the domain boundaries identified by ClusterTAD have a high enrichment of CTCF binding sites, promoter-related marks, and enhancer-related histone modifications. As ClusterTAD is based on a proven clustering approach, it opens a new avenue to apply a large array of clustering methods developed in the machine learning field to the TAD identification problem. The source code, the results, and the TADs generated for the simulated and real Hi-C datasets are available here: https://github.com/BDM-Lab/ClusterTAD .

Number Worlds: Visual and Experimental Access to Elementary Number Theory Concepts

Science.gov (United States)

Sinclair, Nathalie; Zazkis, Rina; Liljedahl, Peter

2004-01-01

Recent research demonstrates that many issues related to the structure of natural numbers and the relationship among numbers are not well grasped by students. In this article, we describe a computer-based learning environment called "Number Worlds" that was designed to support the exploration of elementary number theory concepts by…

Learning about the past with new technologies : Fostering historical reasoning in computer-supported collaborative learning

NARCIS (Netherlands)

Drie, J.P. van

2005-01-01

Recent technological developments have provided new environments for learning, giving rise to the question of how characteristics of such new learning environments can facilitate the process of learning in specific domains. The focus of this thesis is on computer-supported collaborative learning

How Well can We Learn Interpretable Entity Types from Text?

DEFF Research Database (Denmark)

Hovy, Dirk

2014-01-01

We investigate a largely unsupervised approach to learning interpretable, domain-specific entity types from unlabeled text. It assumes that any common noun in a domain can function as potential entity type, and uses those nouns as hidden variables in a HMM. To constrain training, it extracts co......-occurrence dictionaries of entities and common nouns from the data. We evaluate the learned types by measuring their prediction accuracy for verb arguments in several domains. The results suggest that it is possible to learn domain-specific entity types from unlabeled data. We show significant improvements over...

Postnatal Loss of Mef2c Results in Dissociation of Effects on Synapse Number and Learning and Memory.

Science.gov (United States)

Adachi, Megumi; Lin, Pei-Yi; Pranav, Heena; Monteggia, Lisa M

2016-07-15

Myocyte enhancer factor 2 (MEF2) transcription factors play critical roles in diverse cellular processes during central nervous system development. Studies attempting to address the role of MEF2 in brain have largely relied on overexpression of a constitutive MEF2 construct that impairs memory formation or knockdown of MEF2 function that increases spine numbers and enhances memory formation. Genetic deletion of individual MEF2 isoforms in brain during embryogenesis demonstrated that Mef2c loss negatively regulates spine numbers resulting in learning and memory deficits, possibly as a result of its essential role in development. To investigate MEF2C function in brain further, we genetically deleted Mef2c during postnatal development in mice. We characterized these conditional Mef2c knockout mice in an array of behavioral paradigms and examined the impact of postnatal loss of Mef2c on long-term potentiation. We observed increased spine numbers in hippocampus of the conditional Mef2c knockout mice. However, the postnatal loss of Mef2c did not impact learning and memory, long-term potentiation, or social and repetitive behaviors. Our findings demonstrate a critical role for MEF2C in the regulation of spine numbers with a dissociation of learning and memory, synaptic plasticity, and measures of autism-related behaviors in postnatal brain. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Basic number processing in children with specific learning disorders: Comorbidity of reading and mathematics disorders.

Science.gov (United States)

Moll, Kristina; Göbel, Silke M; Snowling, Margaret J

2015-01-01

As well as being the hallmark of mathematics disorders, deficits in number processing have also been reported for individuals with reading disorders. The aim of the present study was to investigate separately the components of numerical processing affected in reading and mathematical disorders within the framework of the Triple Code Model. Children with reading disorders (RD), mathematics disorders (MD), comorbid deficits (RD + MD), and typically developing children (TD) were tested on verbal, visual-verbal, and nonverbal number tasks. As expected, children with MD were impaired across a broad range of numerical tasks. In contrast, children with RD were impaired in (visual-)verbal number tasks but showed age-appropriate performance in nonverbal number skills, suggesting their impairments were domain specific and related to their reading difficulties. The comorbid group showed an additive profile of the impairments of the two single-deficit groups. Performance in speeded verbal number tasks was related to rapid automatized naming, a measure of visual-verbal access in the RD but not in the MD group. The results indicate that deficits in number skills are due to different underlying cognitive deficits in children with RD compared to children with MD: a phonological deficit in RD and a deficit in processing numerosities in MD.

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Microresonator-Based Optical Frequency Combs: A Time Domain Perspective

Science.gov (United States)

2016-04-19

AFRL-AFOSR-VA-TR-2016-0165 (BRI) Microresonator-Based Optical Frequency Combs: A Time Domain Perspective Andrew Weiner PURDUE UNIVERSITY 401 SOUTH...Optical Frequency Combs: A Time Domain Perspective 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-12-1-0236 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data

Hidden physics models: Machine learning of nonlinear partial differential equations

Science.gov (United States)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

Phylogeny of the TRAF/MATH domain.

Science.gov (United States)

Zapata, Juan M; Martínez-García, Vanesa; Lefebvre, Sophie

2007-01-01

The TNF-receptor associated factor (TRAF) domain (TD), also known as the meprin and TRAF-C homology (MATH) domain is a fold of seven anti-parallel p-helices that participates in protein-protein interactions. This fold is broadly represented among eukaryotes, where it is found associated with a discrete set of protein-domains. Virtually all protein families encompassing a TRAF/MATH domain seem to be involved in the regulation of protein processing and ubiquitination, strongly suggesting a parallel evolution of the TRAF/MATH domain and certain proteolysis pathways in eukaryotes. The restricted number of living organisms for which we have information of their genetic and protein make-up limits the scope and analysis of the MATH domain in evolution. However, the available information allows us to get a glimpse on the origins, distribution and evolution of the TRAF/MATH domain, which will be overviewed in this chapter.

CREATING CRITICAL THINKING FROM AFFECTIVE DOMAIN IN SUCCESSFUL LEARNING OF MATHEMATICS.

OpenAIRE

Kholidah Sitanggang; Herman Mawengkang; Tulus.

2018-01-01

The success of the learning process can be seen from the results of learning that is visible from the change in behavior on students, both the attitude and skills which are better than before. Mathematics learning success is not only determined by cognitive abilities but also affective abilities. Successful learning in terms of cognitive and psychomotor is affected by the affective condition of the students. Students who have interest in learning and a positive attitude toward learning will b...

A Telescopic Binary Learning Machine for Training Neural Networks.

Science.gov (United States)

Brunato, Mauro; Battiti, Roberto

2017-03-01

This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

Blended learning versus traditional teaching-learning-setting: Evaluation of cognitive and affective learning outcomes for the inter-professional field of occupational medicine and prevention / Blended Learning versus traditionelles Lehr-Lernsetting: Evaluierung von kognitiven und affektiven Lernergebnissen für das interprofessionelle Arbeitsfeld Arbeitsmedizin und Prävention

Directory of Open Access Journals (Sweden)

Eckler Ursula

2017-11-01

Full Text Available Blended learning is characterised as a combination of face-to-face teaching and e-learning in terms of knowledge transfer, students’ learning activities and reduced presence at the teaching facility. The present cohort study investigated long-term effects of blended learning regarding cognitive outcomes as well as self-indicated estimates of immediate learning effects on the affective domain in the inter-professional field of occupational medicine. Physiotherapy students (bachelor degree at FH Campus Wien – University of Applied Sciences completed the course Occupational Medicine/Prevention either in a traditional teaching-learning setting entirely taught face-to-face (control-group, n=94, or with a blended learning model (intervention-group, n=93. Long-term effects (1.5 year follow-up on the cognitive learning outcomes were assessed according to four levels of Bloom’s learning objectives. In addition, students estimated potential benefits resulting from blended learning based on four Krathwohl’s learning objectives for the affective domain by means of a six-option Likert scale (n=282. Concerning cognitive outcomes, significant results favouring both groups were found with effect sizes from small to medium. The traditional teaching-learning setting resulted in significantly better results in the upmost aspired learning objective (analysis at the long-term (p<0,01; r=-0,33. In contrast, the intervention group resulted in significantly better long-term results on learning objective levels 1 (knowledge and 2 (understanding (p=0,01; r=-0,20 and, p=0,02; r=-0,17, respectively. Hence, no general recommendation favouring either the classical setting or blending learning can be drawn regarding the cognitive domain. However, students’ self-indications on the affective domain give preference to blended learning, particularly if inter-professional teamwork is a course objective.

Cross-lingual and cross-domain discourse segmentation of entire documents

DEFF Research Database (Denmark)

Braud, Chloé; Lacroix, Ophélie; Søgaard, Anders

2017-01-01

-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when...... no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total....

Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

Science.gov (United States)

Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

2012-12-01

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

Named Entity Recognition for Novel Types by Transfer Learning

OpenAIRE

Qu, Lizhen; Ferraro, Gabriela; Zhou, Liyuan; Hou, Weiwei; Baldwin, Timothy

2016-01-01

In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also labe...

Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging.

Science.gov (United States)

Jiang, Shaowei; Liao, Jun; Bian, Zichao; Guo, Kaikai; Zhang, Yongbing; Zheng, Guoan

2018-04-01

A whole slide imaging (WSI) system has recently been approved for primary diagnostic use in the US. The image quality and system throughput of WSI is largely determined by the autofocusing process. Traditional approaches acquire multiple images along the optical axis and maximize a figure of merit for autofocusing. Here we explore the use of deep convolution neural networks (CNNs) to predict the focal position of the acquired image without axial scanning. We investigate the autofocusing performance with three illumination settings: incoherent Kohler illumination, partially coherent illumination with two plane waves, and one-plane-wave illumination. We acquire ~130,000 images with different defocus distances as the training data set. Different defocus distances lead to different spatial features of the captured images. However, solely relying on the spatial information leads to a relatively bad performance of the autofocusing process. It is better to extract defocus features from transform domains of the acquired image. For incoherent illumination, the Fourier cutoff frequency is directly related to the defocus distance. Similarly, autocorrelation peaks are directly related to the defocus distance for two-plane-wave illumination. In our implementation, we use the spatial image, the Fourier spectrum, the autocorrelation of the spatial image, and combinations thereof as the inputs for the CNNs. We show that the information from the transform domains can improve the performance and robustness of the autofocusing process. The resulting focusing error is ~0.5 µm, which is within the 0.8-µm depth-of-field range. The reported approach requires little hardware modification for conventional WSI systems and the images can be captured on the fly without focus map surveying. It may find applications in WSI and time-lapse microscopy. The transform- and multi-domain approaches may also provide new insights for developing microscopy-related deep-learning networks. We have made

Semantic e-Learning: Next Generation of e-Learning?

Science.gov (United States)

Konstantinos, Markellos; Penelope, Markellou; Giannis, Koutsonikos; Aglaia, Liopa-Tsakalidi

Semantic e-learning aspires to be the next generation of e-learning, since the understanding of learning materials and knowledge semantics allows their advanced representation, manipulation, sharing, exchange and reuse and ultimately promote efficient online experiences for users. In this context, the paper firstly explores some fundamental Semantic Web technologies and then discusses current and potential applications of these technologies in e-learning domain, namely, Semantic portals, Semantic search, personalization, recommendation systems, social software and Web 2.0 tools. Finally, it highlights future research directions and open issues of the field.

It's Greek to me: Domain specific relationships between intellectual helplessness and academic performance.

Science.gov (United States)

Krejtz, Izabela; Nezlek, John B

2016-01-01

In a study of the domain specificity of intellectual learned helplessness, we collected data from 376 students in 14 classrooms. We measured feelings of intellectual helplessness for mathematics and language skills, anxiety about performance in each of these domains, and general working memory. Multilevel modeling analyses found that feelings of helplessness in language skills were negatively related to grades in language but were unrelated to grades in mathematics. Similarly, feelings of helplessness in mathematics were negatively related to grades in mathematics but were unrelated to grades in language. Controlling for anxiety or working memory did not change these relationships, nor did they vary across the age of students. The results support conceptualizations in which learned helplessness has a domain specific component.

Structure Mapping for Social Learning.

Science.gov (United States)

Christie, Stella

2017-07-01

Analogical reasoning is a foundational tool for human learning, allowing learners to recognize relational structures in new events and domains. Here I sketch some grounds for understanding and applying analogical reasoning in social learning. The social world is fundamentally characterized by relations between people, with common relational structures-such as kinships and social hierarchies-forming social units that dictate social behaviors. Just as young learners use analogical reasoning for learning relational structures in other domains-spatial relations, verbs, relational categories-analogical reasoning ought to be a useful cognitive tool for acquiring social relations and structures. Copyright © 2017 Cognitive Science Society, Inc.

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

Science.gov (United States)

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Domain-specific knowledge as playful interaction

DEFF Research Database (Denmark)

Valente, Andrea; Marchetti, Emanuela

2015-01-01

Starting from reflections on designing games for learning, aimed at providing a tangible grounding to abstract knowledge, we designed Prime Slaughter, a game to support learning of factorisation and prime numbers, targeted to primary and early secondary school children. This new study draws upon ...... on activity theory, aimed at facilitating the transposition of abstract knowledge into playful interactions, so to develop new learning games of this kind, also keeping into account children’s individual needs regarding play.......Starting from reflections on designing games for learning, aimed at providing a tangible grounding to abstract knowledge, we designed Prime Slaughter, a game to support learning of factorisation and prime numbers, targeted to primary and early secondary school children. This new study draws upon...

Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives

Science.gov (United States)

Ku, David Tawei; Huang, Yung-Hsin

2012-01-01

This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…

The neuroscience of learning: beyond the Hebbian synapse.

Science.gov (United States)

Gallistel, C R; Matzel, Louis D

2013-01-01

From the traditional perspective of associative learning theory, the hypothesis linking modifications of synaptic transmission to learning and memory is plausible. It is less so from an information-processing perspective, in which learning is mediated by computations that make implicit commitments to physical and mathematical principles governing the domains where domain-specific cognitive mechanisms operate. We compare the properties of associative learning and memory to the properties of long-term potentiation, concluding that the properties of the latter do not explain the fundamental properties of the former. We briefly review the neuroscience of reinforcement learning, emphasizing the representational implications of the neuroscientific findings. We then review more extensively findings that confirm the existence of complex computations in three information-processing domains: probabilistic inference, the representation of uncertainty, and the representation of space. We argue for a change in the conceptual framework within which neuroscientists approach the study of learning mechanisms in the brain.

Expansion of protein domain repeats.

Directory of Open Access Journals (Sweden)

Asa K Björklund

2006-08-01

Full Text Available Many proteins, especially in eukaryotes, contain tandem repeats of several domains from the same family. These repeats have a variety of binding properties and are involved in protein-protein interactions as well as binding to other ligands such as DNA and RNA. The rapid expansion of protein domain repeats is assumed to have evolved through internal tandem duplications. However, the exact mechanisms behind these tandem duplications are not well-understood. Here, we have studied the evolution, function, protein structure, gene structure, and phylogenetic distribution of domain repeats. For this purpose we have assigned Pfam-A domain families to 24 proteomes with more sensitive domain assignments in the repeat regions. These assignments confirmed previous findings that eukaryotes, and in particular vertebrates, contain a much higher fraction of proteins with repeats compared with prokaryotes. The internal sequence similarity in each protein revealed that the domain repeats are often expanded through duplications of several domains at a time, while the duplication of one domain is less common. Many of the repeats appear to have been duplicated in the middle of the repeat region. This is in strong contrast to the evolution of other proteins that mainly works through additions of single domains at either terminus. Further, we found that some domain families show distinct duplication patterns, e.g., nebulin domains have mainly been expanded with a unit of seven domains at a time, while duplications of other domain families involve varying numbers of domains. Finally, no common mechanism for the expansion of all repeats could be detected. We found that the duplication patterns show no dependence on the size of the domains. Further, repeat expansion in some families can possibly be explained by shuffling of exons. However, exon shuffling could not have created all repeats.

PACE: Probabilistic Assessment for Contributor Estimation- A machine learning-based assessment of the number of contributors in DNA mixtures.

Science.gov (United States)

Marciano, Michael A; Adelman, Jonathan D

2017-03-01

The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely considered the most important, and, if incorrectly chosen, the most likely to negatively influence the mixture interpretation of a DNA profile. Unfortunately, most current approaches to mixture deconvolution require the assumption that the number of contributors is known by the analyst, an assumption that can prove to be especially faulty when faced with increasingly complex mixtures of 3 or more contributors. In this study, we propose a probabilistic approach for estimating the number of contributors in a DNA mixture that leverages the strengths of machine learning. To assess this approach, we compare classification performances of six machine learning algorithms and evaluate the model from the top-performing algorithm against the current state of the art in the field of contributor number classification. Overall results show over 98% accuracy in identifying the number of contributors in a DNA mixture of up to 4 contributors. Comparative results showed 3-person mixtures had a classification accuracy improvement of over 6% compared to the current best-in-field methodology, and that 4-person mixtures had a classification accuracy improvement of over 20%. The Probabilistic Assessment for Contributor Estimation (PACE) also accomplishes classification of mixtures of up to 4 contributors in less than 1s using a standard laptop or desktop computer. Considering the high classification accuracy rates, as well as the significant time commitment required by the current state of the art model versus seconds required by a machine learning-derived model, the approach described herein provides a promising means of estimating the number of contributors and, subsequently, will lead to improved DNA mixture interpretation. Copyright © 2016

Considerations upon the Machine Learning Technologies

OpenAIRE

Alin Munteanu; Cristina Ofelia Sofran

2006-01-01

Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

Towards a semantic learning model fostering learning object reusability

OpenAIRE

Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami

2005-01-01

We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Transfer of learning in binary decision making problems.

OpenAIRE

Robotti, O. P.

2007-01-01

Transfer, the use of acquired knowledge, skills and abilities across tasks and contexts, is a key and elusive goal of learning. Most evidence available in literature is based on a limited number of tasks, predominantly open-ended problems, game-like problems and taught school subjects (e.g. maths, physics, algebra). It is not obvious that findings from this work can be extended to the domain of decision making problems. This thesis, which aims to broaden the understanding of enhancing and lim...

An improved multi-domain convolution tracking algorithm

Science.gov (United States)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Deep Learning in the Automotive Industry: Applications and Tools

OpenAIRE

Luckow, Andre; Cook, Matthew; Ashcraft, Nathan; Weill, Edwin; Djerekarov, Emil; Vorster, Bennie

2017-01-01

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, ...

Classical theory of algebraic numbers

CERN Document Server

Ribenboim, Paulo

2001-01-01

Gauss created the theory of binary quadratic forms in "Disquisitiones Arithmeticae" and Kummer invented ideals and the theory of cyclotomic fields in his attempt to prove Fermat's Last Theorem These were the starting points for the theory of algebraic numbers, developed in the classical papers of Dedekind, Dirichlet, Eisenstein, Hermite and many others This theory, enriched with more recent contributions, is of basic importance in the study of diophantine equations and arithmetic algebraic geometry, including methods in cryptography This book has a clear and thorough exposition of the classical theory of algebraic numbers, and contains a large number of exercises as well as worked out numerical examples The Introduction is a recapitulation of results about principal ideal domains, unique factorization domains and commutative fields Part One is devoted to residue classes and quadratic residues In Part Two one finds the study of algebraic integers, ideals, units, class numbers, the theory of decomposition, iner...

Learning Physical Domains: Toward a Theoretical Framework.

Science.gov (United States)

1986-12-01

advanced ids o the iaime doinain in containing more information, especially perceptual " ’It. iho lI b1 rwt... tI hat. psychboigists by no means...Acquisitions Dr Kenneth D Forbus 4833 Rugby Avenue University of Illinois Dr Robert Glaser Bethesda, MD 20014 Department of Computer Science Learning

affective variables of language learning

Institute of Scientific and Technical Information of China (English)

李文敬

2011-01-01

why people enjoy different degrees of success in second language learning,given similar opportunities.in the presence of overly negative emotions such as anxiety,fear,stress,anger or depression,our optimal learning potential maybe compromised.the affective domain refers to the emotional domain that has to do with the emotional behavior of human beings.it includes such factors as self-confidence,extroversion,anxiety,attitudes and motivation.three major factors are introduced here:self-confidence,anxiety and motivation.

[Intel random number generator-based true random number generator].

Science.gov (United States)

Huang, Feng; Shen, Hong

2004-09-01

To establish a true random number generator on the basis of certain Intel chips. The random numbers were acquired by programming using Microsoft Visual C++ 6.0 via register reading from the random number generator (RNG) unit of an Intel 815 chipset-based computer with Intel Security Driver (ISD). We tested the generator with 500 random numbers in NIST FIPS 140-1 and X(2) R-Squared test, and the result showed that the random number it generated satisfied the demand of independence and uniform distribution. We also compared the random numbers generated by Intel RNG-based true random number generator and those from the random number table statistically, by using the same amount of 7500 random numbers in the same value domain, which showed that the SD, SE and CV of Intel RNG-based random number generator were less than those of the random number table. The result of u test of two CVs revealed no significant difference between the two methods. Intel RNG-based random number generator can produce high-quality random numbers with good independence and uniform distribution, and solves some problems with random number table in acquisition of the random numbers.

Assessment of first-year medical students' perceptions of teaching and learning through team-based learning sessions.

Science.gov (United States)

Obad, Adam S; Peeran, Ahmed A; Shareef, Mohammad Abrar; Alsheikh, Wissal J; Kalagi, Dana A; AlAmodi, Abdulhadi A; Khan, Tehreem A; Shaikh, Abdul Ahad; Ganguly, Paul; Yaqinuddin, Ahmed

2016-12-01

Team-based learning (TBL) is an emerging teaching and learning strategy being employed in medical schools. The College of Medicine at Alfaisal University has adopted a TBL approach as an instructional method for first-year medical students. The aim of the present study was to describe the TBL method employed at Alfaisal University College of Medicine and to assess first-year medical students' perceptions of this learning modality for the anatomy- and physiology-based blocks/courses in organ systems form of curriculum. A five-point Likert scale questionnaire was structured based on Kirkpatrick's theory and assessed three major domains: reaction, learning, and behavior. Confirmatory factor analysis (CFA) and Cronbach's α-coefficient tests were used to assess the validity and reliability of the construct, respectively. CFA showed an adequate validity of the survey and Cronbach's α revealed an acceptable internal uniformity (0.69). A total of 185 respondents rated reaction, learning, and behavior toward introduction of TBL as 3.53 ± 1.01, 3.59 ± 1.12, and 3.57 ± 1.12, respectively. Excellent students rated TBL highly in all major domains compared with borderline students (reaction, behavior, and learning domains with P values of teaching and learning strategy for functional anatomy, and prior involvement in teamwork and academic performance correlates with higher ratings of TBL. Copyright © 2016 the American Physiological Society.

Deposition and growth of domains in one dimension

Science.gov (United States)

Rodgers, G. J.; Tavassoli, Z.

1998-09-01

A model of deposition and growth in one dimension is studied in which finite sized domains are deposited by the random sequential adsorption process. The domains then grow with a time dependent growth rate. When the initial deposited domains are monomers and dimers the coverage is found exactly for a number of different growth rates. A continuum version of this model is also considered.

Identification of structural domains in proteins by a graph heuristic

NARCIS (Netherlands)

Wernisch, Lorenz; Hunting, M.M.G.; Wodak, Shoshana J.

1999-01-01

A novel automatic procedure for identifying domains from protein atomic coordinates is presented. The procedure, termed STRUDL (STRUctural Domain Limits), does not take into account information on secondary structures and handles any number of domains made up of contiguous or non-contiguous chain

Learning Science: Some Insights from Cognitive Science

Science.gov (United States)

Matthews, P. S. C.

Theories of teaching and learning, including those associated with constructivism, often make no overt reference to an underlying assumption that they make; that is, human cognition depends on domain-free, general-purpose processing by the brain. This assumption is shown to be incompatible with evidence from studies of children's early learning. Rather, cognition is modular in nature, and often domain-specific. Recognition of modularity requires a re-evaluation of some aspects of current accounts of learning science. Especially, children's ideas in science are sometimes triggered rather than learned. It is in the nature of triggered conceptual structures that they are not necessarily expressible in language, and that they may not be susceptible to change by later learning.

Blocking-resistant communication through domain fronting

Directory of Open Access Journals (Sweden)

Fifield David

2015-06-01

Full Text Available We describe “domain fronting,” a versatile censorship circumvention technique that hides the remote endpoint of a communication. Domain fronting works at the application layer, using HTTPS, to communicate with a forbidden host while appearing to communicate with some other host, permitted by the censor. The key idea is the use of different domain names at different layers of communication. One domain appears on the “outside” of an HTTPS request—in the DNS request and TLS Server Name Indication—while another domain appears on the “inside”—in the HTTP Host header, invisible to the censor under HTTPS encryption. A censor, unable to distinguish fronted and nonfronted traffic to a domain, must choose between allowing circumvention traffic and blocking the domain entirely, which results in expensive collateral damage. Domain fronting is easy to deploy and use and does not require special cooperation by network intermediaries. We identify a number of hard-to-block web services, such as content delivery networks, that support domain-fronted connections and are useful for censorship circumvention. Domain fronting, in various forms, is now a circumvention workhorse. We describe several months of deployment experience in the Tor, Lantern, and Psiphon circumvention systems, whose domain-fronting transports now connect thousands of users daily and transfer many terabytes per month.

Considerations upon the Machine Learning Technologies

Directory of Open Access Journals (Sweden)

Alin Munteanu

2006-01-01

Full Text Available Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

Procedural-Based Category Learning in Patients with Parkinson's Disease: Impact of Category Number and Category Continuity

Directory of Open Access Journals (Sweden)

J. Vincent eFiloteo

2014-02-01

Full Text Available Previously we found that Parkinson's disease (PD patients are impaired in procedural-based category learning when category membership is defined by a nonlinear relationship between stimulus dimensions, but these same patients are normal when the rule is defined by a linear relationship (Filoteo et al., 2005; Maddox & Filoteo, 2001. We suggested that PD patients' impairment was due to a deficit in recruiting ‘striatal units' to represent complex nonlinear rules. In the present study, we further examined the nature of PD patients' procedural-based deficit in two experiments designed to examine the impact of (1 the number of categories, and (2 category discontinuity on learning. Results indicated that PD patients were impaired only under discontinuous category conditions but were normal when the number of categories was increased from two to four. The lack of impairment in the four-category condition suggests normal integrity of striatal medium spiny cells involved in procedural-based category learning. In contrast, and consistent with our previous observation of a nonlinear deficit, the finding that PD patients were impaired in the discontinuous condition suggests that these patients are impaired when they have to associate perceptually distinct exemplars with the same category. Theoretically, this deficit might be related to dysfunctional communication among medium spiny neurons within the striatum, particularly given that these are cholinergic neurons and a cholinergic deficiency could underlie some of PD patients’ cognitive impairment.

Improving pairwise comparison of protein sequences with domain co-occurrence

Science.gov (United States)

Gascuel, Olivier

2018-01-01

Comparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure. Here, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 14% of the number of significant BLAST hits and an increase of 25% of the proteome area that can be covered with a domain. Our method identified 2240 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains. Source code of the proposed approach and supplementary data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence PMID:29293498

Integrative curriculum reform, domain dependent knowing, and teachers` epistemological theories: Implications for middle-level teaching

Energy Technology Data Exchange (ETDEWEB)

Powell, R.R. [Texas Tech Univ., Lubbock, TX (United States). College of Education

1998-12-01

Integrative curriculum as both a theoretical construct and a practical reality, and as a theme-based, problem-centered, democratic way of schooling, is becoming more widely considered as a feasible alternative to traditional middle-level curricula. Importantly for teaching and learning, domain dependence requires teachers to view one area of knowledge as fully interdependent with other areas of knowledge during the learning process. This requires teachers to adopt personal epistemological theories that reflect integrative, domain dependent knowing. This study explored what happened when teachers from highly traditional domain independent school settings encountered an ambitious college-level curriculum project that was designed to help the teachers understand the potential that integrative, domain dependent teaching holds for precollege settings. This study asked: What influence does an integrative, domain dependent curriculum project have on teachers` domain independent, epistemological theories for teaching and learning? Finding an answer to this question is essential if we, as an educational community, are to understand how integrative curriculum theory is transformed by teachers into systemic curriculum reform. The results suggest that the integrative curriculum project that teachers participated in did not explicitly alter their classroom practices in a wholesale manner. Personal epistemological theories of teachers collectively precluded teachers from making any wholesale changes in their individual classroom teaching. However, teachers became aware of integrative curriculum as an alternative, and they expressed interest in infusing integrative practices into their classrooms as opportunities arise.

Data Mining in the E-Learning Domain

Science.gov (United States)

Hanna, Margo

2004-01-01

Higher education (HE) is becoming a big business, with huge investments in IT technology supporting online learning. With the awareness of the knowledge economy has come a growing consciousness that HE constitutes a large industry or economic sector in its own right. In a marketing fashion, we understand that some customers present much greater…

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale

Directory of Open Access Journals (Sweden)

Sean Tackett

2015-07-01

Full Text Available Purpose: While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. Methods: First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM, the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. Results: The overall response rate was 369/429 (86%. After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%, with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%. The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92 and the seven domains (α, 0.56-0.85. Conclusion: The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention.

Hydrology Domain Cyberinfrastructures: Successes, Challenges, and Opportunities

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Horsburgh, J. S.

2015-12-01

Anticipated changes to climate, human population, land use, and urban form will alter the hydrology and availability of water within the water systems on which the world's population relies. Understanding the effects of these changes will be paramount in sustainably managing water resources, as well as maintaining associated capacity to provide ecosystem services (e.g., regulating flooding, maintaining instream flow during dry periods, cycling nutrients, and maintaining water quality). It will require better information characterizing both natural and human mediated hydrologic systems and enhanced ability to generate, manage, store, analyze, and share growing volumes of observational data. Over the past several years, a number of hydrology domain cyberinfrastructures have emerged or are currently under development that are focused on providing integrated access to and analysis of data for cross-domain synthesis studies. These include the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), the Critical Zone Observatory Information System (CZOData), HyroShare, the BiG CZ software system, and others. These systems have focused on sharing, integrating, and analyzing hydrologic observations data. This presentation will describe commonalities and differences in the cyberinfrastructure approaches used by these projects and will highlight successes and lessons learned in addressing the challenges of big and complex data. It will also identify new challenges and opportunities for next generation cyberinfrastructure and a next generation of cyber-savvy scientists and engineers as developers and users.

Learning Potential and Cognitive Modifiability

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Kozulin, Alex

2011-01-01

The relationship between thinking and learning constitutes one of the fundamental problems of cognitive psychology. Though there is an obvious overlap between the domains of thinking and learning, it seems more productive to consider learning as being predominantly acquisition while considering thinking as the application of the existent concepts…

Relevance as a metric for evaluating machine learning algorithms

NARCIS (Netherlands)

Kota Gopalakrishna, A.; Ozcelebi, T.; Liotta, A.; Lukkien, J.J.

2013-01-01

In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel

A new pseudorandom number generator based on a complex number chaotic equation

International Nuclear Information System (INIS)

Liu Yang; Tong Xiao-Jun

2012-01-01

In recent years, various chaotic equation based pseudorandom number generators have been proposed. However, the chaotic equations are all defined in the real number field. In this paper, an equation is proposed and proved to be chaotic in the imaginary axis. And a pseudorandom number generator is constructed based on the chaotic equation. The alteration of the definitional domain of the chaotic equation from the real number field to the complex one provides a new approach to the construction of chaotic equations, and a new method to generate pseudorandom number sequences accordingly. Both theoretical analysis and experimental results show that the sequences generated by the proposed pseudorandom number generator possess many good properties

Designing Teaching Materials for Learning Problem Solving in Technology Education

NARCIS (Netherlands)

Doornekamp, B.G.

In the process of designing teaching materials for learning problem solving in technology education, domain-specific design specifications are considered important elements to raise learning outcomes with these materials. Two domain-specific design specifications were drawn up using a four-step

Lifelong learning in an age of measurement

DEFF Research Database (Denmark)

Kauffmann, Oliver

2013-01-01

There has been a shift in interest from ‘lifelong education’ to ‘lifelong learning’ in the Western world since the 1990s. This shift is closely related to strategies for securing the competitiveness of national economies. For this purpose one of the tools applied by educational policy makers has...... been to invoke ‘the golden standard(s)’ of evidence based research into the domain of learning. A number of problems with this approach are that the very conception of learning is broad, vague, ambiguous and does not in itself give us a normative handle which can help us with education. There might...... be one particular area, however, where evidence based learning research might be thought to have a strong foothold: in the brain sciences. And certainly a rapidly growing interest in ‘educational neuroscience’ has emerged within the last 10 years. But is it possible to bridge the gap between ‘studying...

The Case for Case-Based Transfer Learning

Science.gov (United States)

2011-01-01

Thorndike and Woodworth 1901; Perkins and Salomon 1994; Bransford, Brown, and Cocking 2000), among other disciplines. Transfer learning uses knowledge...Transfer Learning for Rein- forcement Learning Domains: A Survey. Journal of Machine Learning Research 10(1): 1633–1685. Thorndike , E. L., and

Roles and Domains to Teach in Online Learning Environments: Educational ICT Competency Framework for University Teachers

Science.gov (United States)

Guasch, Teresa; Alvarez, Ibis; Espasa, Anna

This chapter is aimed at presenting an integrated framework of the educational information and communications technology (ICT) competencies that university teachers should have to teach in an online learning environment. Teaching through ICT in higher education involves performing three main roles - pedagogical, socialist, and design/planning - and also two cross-cutting domains that arise from the online environment: technological and managerial. This framework as well as the competencies for university teachers associated with it were validated at a European level by a dual process of net-based focus groups of teachers and teacher trainers in each of the participating countries in a European Project (Elene-TLC) and an online Delphi method involving 78 experts from 14 universities of ten European countries. The competency framework and the examples provided in the chapter are the basis for designing innovative professional development activities in online university environments.

The Dynamic Interdependence of Developmental Domains across Emerging Adulthood

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Sneed, Joel R.; Hamagami, Fumiaki; McArdle, John J.; Cohen, Patricia; Chen, Henian

2007-01-01

Emerging adulthood is a period in which profound role changes take place across a number of life domains including finance, romance, and residence. On the basis of dynamic systems theory, change in one domain should be related to change in another domain, because the concept of development according to this approach is a relational one. To…

Human-Guided Learning for Probabilistic Logic Models

Directory of Open Access Journals (Sweden)

Phillip Odom

2018-06-01

Full Text Available Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final structure of the model.

Templates, Numbers & Watercolors.

Science.gov (United States)

Clemesha, David J.

1990-01-01

Describes how a second-grade class used large templates to draw and paint five-digit numbers. The lesson integrated artistic knowledge and vocabulary with their mathematics lesson in place value. Students learned how draftspeople use templates, and they studied number paintings by Charles Demuth and Jasper Johns. (KM)

Differential splicing and glycosylation of Apoer2 alters synaptic plasticity and fear learning.

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Wasser, Catherine R; Masiulis, Irene; Durakoglugil, Murat S; Lane-Donovan, Courtney; Xian, Xunde; Beffert, Uwe; Agarwala, Anandita; Hammer, Robert E; Herz, Joachim

2014-11-25

Apoer2 is an essential receptor in the central nervous system that binds to the apolipoprotein ApoE. Various splice variants of Apoer2 are produced. We showed that Apoer2 lacking exon 16, which encodes the O-linked sugar (OLS) domain, altered the proteolytic processing and abundance of Apoer2 in cells and synapse number and function in mice. In cultured cells expressing this splice variant, extracellular cleavage of OLS-deficient Apoer2 was reduced, consequently preventing γ-secretase-dependent release of the intracellular domain of Apoer2. Mice expressing Apoer2 lacking the OLS domain had increased Apoer2 abundance in the brain, hippocampal spine density, and glutamate receptor abundance, but decreased synaptic efficacy. Mice expressing a form of Apoer2 lacking the OLS domain and containing an alternatively spliced cytoplasmic tail region that promotes glutamate receptor signaling showed enhanced hippocampal long-term potentiation (LTP), a phenomenon associated with learning and memory. However, these mice did not display enhanced spatial learning in the Morris water maze, and cued fear conditioning was reduced. Reducing the expression of the mutant Apoer2 allele so that the abundance of the protein was similar to that of Apoer2 in wild-type mice normalized spine density, hippocampal LTP, and cued fear learning. These findings demonstrated a role for ApoE receptors as regulators of synaptic glutamate receptor activity and established differential receptor glycosylation as a potential regulator of synaptic function and memory. Copyright © 2014, American Association for the Advancement of Science.

An Ontology for Learning Services on the Shop Floor

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Ullrich, Carsten

2016-01-01

An ontology expresses a common understanding of a domain that serves as a basis of communication between people or systems, and enables knowledge sharing, reuse of domain knowledge, reasoning and thus problem solving. In Technology-Enhanced Learning, especially in Intelligent Tutoring Systems and Adaptive Learning Environments, ontologies serve as…

Observation on the uses of Mobile Phones to Support Informal Learning

Directory of Open Access Journals (Sweden)

Mohd Azlishah Othman

2012-10-01

Full Text Available This paper explores how a group of undergraduate students in one of the university in South of Malaysian use their mobile phones to perform informal learning activities related to the content of their courses outside the classroom. The paper also addresses the usefulness of informal learning activities to support students’ learning. The study adopts an exploratory case study design and uses two methods of data collection including questionnaires and interviews. Main findings suggest that students performed informal learning activities mostly from office, home, interacting mainly with classmates. It also shows that students were in control of their informal learning activities without tutor or SMEs’ input. However, it was found that students used only a limited number of applications but these were considered useful to their learning. The paper contributes to a discussion of the implications of training and instructional support to help students to take more advantage of mobile phone applications to support informal learning. The conclusion is discussed about the further research in this domain.

Posthuman learning

DEFF Research Database (Denmark)

Hasse, Cathrine

This book shall explore the concept of learning from the new perspective of the posthuman. The vast majority of cognitive, behavioral and part of the constructionist learning theories operate with an autonomous individual who learn in a world of separate objects. Technology is (if mentioned at all......) understood as separate from the individual learner and perceived as tools. Learning theory has in general not been acknowledging materiality in their theorizing about what learning is. A new posthuman learning theory is needed to keep up with the transformations of human learning resulting from new...... technological experiences. One definition of learning is that it is a relatively permanent change in behavior as the result of experience. During the first half of the twentieth century, two theoretical approaches dominated the domain of learning theory: the schools of thought commonly known as behaviorism...

Physics education students’ cognitive and affective domains toward ecological phenomena

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Napitupulu, N. D.; Munandar, A.; Redjeki, S.; Tjasyono, B.

2018-05-01

Environmental education is become prominent in dealing with natural phenomena that occur nowadays. Studying environmental physics will lead students to have conceptual understanding which are importent in enhancing attitudes toward ecological phenomena that link directry to cognitive and affective domains. This research focused on the the relationship of cognitive and affective domains toward ecological phenomena. Thirty-seven Physics Education students participated in this study and validated sources of data were collected to eksplore students’ conceptual understanding as cognitive domain and to investigate students’ attitudes as affective domain. The percentage of cognitive outcome and affective outcome are explore. The features of such approaches to environmental learning are discussion through analysis of contribution of cognitive to develop the attitude ecological as affective outcome. The result shows that cognitive domains do not contribute significantly to affective domain toward ecological henomena as an issue trend in Central Sulawesi although students had passed Environmental Physics instruction for two semester. In fact, inferior knowledge in a way actually contributes to the attitude domain caused by the prior knowledge that students have as ombo as a Kaili local wisdom.

The effect of numbered heads together (NHT) cooperative learning model on the cognitive achievement of students with different academic ability

Science.gov (United States)

Leasa, Marleny; Duran Corebima, Aloysius

2017-01-01

Learning models and academic ability may affect students’ achievement in science. This study, thus aimed to investigate the effect of numbered heads together (NHT) cooperative learning model on elementary students’ cognitive achievement in natural science. This study employed a quasi-experimental design with pretest-posttest non-equivalent control group with 2 x 2 factorial. There were two learning models compared NHT and the conventional, and two academic ability high and low. The results of ana Cova test confirmed the difference in the students’ cognitive achievement based on learning models and general academic ability. However, the interaction between learning models and academic ability did not affect the students’ cognitive achievement. In conclusion, teachers are strongly recommended to be more creative in designing learning using other types of cooperative learning models. Also, schools are required to create a better learning environment which is more cooperative to avoid unfair competition among students in the classroom and as a result improve the students’ academic ability. Further research needs to be conducted to explore the contribution of other aspects in cooperative learning toward cognitive achievement of students with different academic ability.

The number of genes encoding repeat domain-containing proteins positively correlates with genome size in amoebal giant viruses

Science.gov (United States)

Shukla, Avi; Chatterjee, Anirvan

2018-01-01

Abstract Curiously, in viruses, the virion volume appears to be predominantly driven by genome length rather than the number of proteins it encodes or geometric constraints. With their large genome and giant particle size, amoebal viruses (AVs) are ideally suited to study the relationship between genome and virion size and explore the role of genome plasticity in their evolutionary success. Different genomic regions of AVs exhibit distinct genealogies. Although the vertically transferred core genes and their functions are universally conserved across the nucleocytoplasmic large DNA virus (NCLDV) families and are essential for their replication, the horizontally acquired genes are variable across families and are lineage-specific. When compared with other giant virus families, we observed a near–linear increase in the number of genes encoding repeat domain-containing proteins (RDCPs) with the increase in the genome size of AVs. From what is known about the functions of RDCPs in bacteria and eukaryotes and their prevalence in the AV genomes, we envisage important roles for RDCPs in the life cycle of AVs, their genome expansion, and plasticity. This observation also supports the evolution of AVs from a smaller viral ancestor by the acquisition of diverse gene families from the environment including RDCPs that might have helped in host adaption. PMID:29308275

On the low Mach number limit of compressible flows in exterior moving domains

Czech Academy of Sciences Publication Activity Database

Feireisl, Eduard; Kreml, Ondřej; Mácha, Václav; Nečasová, Šárka

2016-01-01

Roč. 16, č. 3 (2016), s. 705-722 ISSN 1424-3199 R&D Projects: GA ČR GA13-00522S Institutional support: RVO:67985840 Keywords : compressible Navier-Stokes system * incompressible limit * moving domain Subject RIV: BA - General Mathematics Impact factor: 1.038, year: 2016 http://link.springer.com/article/10.1007%2Fs00028-016-0338-2

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…

Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.

Science.gov (United States)

Huang, Yue; Zheng, Han; Liu, Chi; Ding, Xinghao; Rohde, Gustavo K

2017-11-01

Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our paper assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.

Towards Self-Learning Based Hypotheses Generation in Biomedical Text Domain.

Science.gov (United States)

Gopalakrishnan, Vishrawas; Jha, Kishlay; Xun, Guangxu; Ngo, Hung Q; Zhang, Aidong

2017-12-26

The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top-K results. This level of efficiency enables the discovery algorithm to look for higher-order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to performboth open and closed discovery.We also experimentally validate that the core data-structures upon which the system bases its decision has a high concordance with the opinion of the experts.This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. The relevant JAVA codes are available at: https://github.com/vishrawas/Medline-Code_v2. vishrawa@buffalo.edukishlayj@buffalo.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email

Structural and functional analysis of multi-interface domains.

Directory of Open Access Journals (Sweden)

Liang Zhao

Full Text Available A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8% of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

Science.gov (United States)

Jauregi Unanue, Iñigo; Zare Borzeshi, Ehsan; Piccardi, Massimo

2017-12-01

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary. Copyright © 2017 Elsevier Inc. All rights reserved.

Enhancing a Multi-body Mechanism with Learning-Aided Cues in an Augmented Reality Environment

International Nuclear Information System (INIS)

Sidhu, Manjit Singh

2013-01-01

Augmented Reality (AR) is a potential area of research for education, covering issues such as tracking and calibration, and realistic rendering of virtual objects. The ability to augment real world with virtual information has opened the possibility of using AR technology in areas such as education and training as well. In the domain of Computer Aided Learning (CAL), researchers have long been looking into enhancing the effectiveness of the teaching and learning process by providing cues that could assist learners to better comprehend the materials presented. Although a number of works were done looking into the effectiveness of learning-aided cues, but none has really addressed this issue for AR-based learning solutions. This paper discusses the design and model of an AR based software that uses visual cues to enhance the learning process and the outcome perception results of the cues.

Enhancing a Multi-body Mechanism with Learning-Aided Cues in an Augmented Reality Environment

Science.gov (United States)

Singh Sidhu, Manjit

2013-06-01

Augmented Reality (AR) is a potential area of research for education, covering issues such as tracking and calibration, and realistic rendering of virtual objects. The ability to augment real world with virtual information has opened the possibility of using AR technology in areas such as education and training as well. In the domain of Computer Aided Learning (CAL), researchers have long been looking into enhancing the effectiveness of the teaching and learning process by providing cues that could assist learners to better comprehend the materials presented. Although a number of works were done looking into the effectiveness of learning-aided cues, but none has really addressed this issue for AR-based learning solutions. This paper discusses the design and model of an AR based software that uses visual cues to enhance the learning process and the outcome perception results of the cues.

Primetime for Learning Genes.

Science.gov (United States)

Keifer, Joyce

2017-02-11

Learning genes in mature neurons are uniquely suited to respond rapidly to specific environmental stimuli. Expression of individual learning genes, therefore, requires regulatory mechanisms that have the flexibility to respond with transcriptional activation or repression to select appropriate physiological and behavioral responses. Among the mechanisms that equip genes to respond adaptively are bivalent domains. These are specific histone modifications localized to gene promoters that are characteristic of both gene activation and repression, and have been studied primarily for developmental genes in embryonic stem cells. In this review, studies of the epigenetic regulation of learning genes in neurons, particularly the brain-derived neurotrophic factor gene ( BDNF ), by methylation/demethylation and chromatin modifications in the context of learning and memory will be highlighted. Because of the unique function of learning genes in the mature brain, it is proposed that bivalent domains are a characteristic feature of the chromatin landscape surrounding their promoters. This allows them to be "poised" for rapid response to activate or repress gene expression depending on environmental stimuli.

Development of an Assessment Tool to Measure Students' Meaningful Learning in the Undergraduate Chemistry Laboratory

Science.gov (United States)

Galloway, Kelli R.; Bretz, Stacey Lowery

2015-01-01

Research on learning in the undergraduate chemistry laboratory necessitates an understanding of students' perspectives of learning. Novak's Theory of Meaningful Learning states that the cognitive (thinking), affective (feeling), and psychomotor (doing) domains must be integrated for meaningful learning to occur. The psychomotor domain is the…

Statistical learning across development: Flexible yet constrained

Directory of Open Access Journals (Sweden)

Lauren eKrogh

2013-01-01

Full Text Available Much research in the past two decades has documented infants’ and adults' ability to extract statistical regularities from auditory input. Importantly, recent research has extended these findings to the visual domain, demonstrating learners' sensitivity to statistical patterns within visual arrays and sequences of shapes. In this review we discuss both auditory and visual statistical learning to elucidate both the generality of and constraints on statistical learning. The review first outlines the major findings of the statistical learning literature with infants, followed by discussion of statistical learning across domains, modalities, and development. The second part of this review considers constraints on statistical learning. The discussion focuses on two categories of constraint: constraints on the types of input over which statistical learning operates and constraints based on the state of the learner. The review concludes with a discussion of possible mechanisms underlying statistical learning.

Multi-domain comparison of safety standards

International Nuclear Information System (INIS)

Baufreton, Ph.; Derrien, J.C.; Ricque, B.; Blanquart, J.P.; Boulanger, J.L.; Delseny, H.; Gassino, J.; Ladier, G.; Ledinot, E.; Leeman, M.; Quere, Ph.

2011-01-01

This paper presents an analysis of safety standards and their implementation in certification strategies from different domains such as aeronautics, automation, automotive, nuclear, railway and space. This work, performed in the context of the CG2E ('Club des Grandes Entreprises de l'Embarque'), aims at identifying the main similarities and dissimilarities, for potential cross-domain harmonization. We strive to find the most comprehensive 'trans-sectorial' approach, within a large number of industrial domains. Exhibiting the 'true goals' of their numerous applicable standards, related to the safety of system and software, is a first important step towards harmonization, sharing common approaches, methods and tools whenever possible. (authors)

The TENCompetence Infrastructure: A Learning Network Implementation

Science.gov (United States)

Vogten, Hubert; Martens, Harrie; Lemmers, Ruud

The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.

The planning illusion: Does active planning of a learning route support learning as well as learners think it does?

NARCIS (Netherlands)

Bonestroo, W.J.; de Jong, Anthonius J.M.

2012-01-01

Is actively planning one’s learning route through a learning domain beneficial for learning? Moreover, can learners accurately judge the extent to which planning has been beneficial for them? This study examined the effects of active planning on learning. Participants received a tool in which they

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Science.gov (United States)

Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

2017-02-01

Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

Human-level control through deep reinforcement learning

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning.

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model.

Science.gov (United States)

De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F

2016-07-08

E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.

New e-learning method using databases

Directory of Open Access Journals (Sweden)

Andreea IONESCU

2012-10-01

Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

ALARA notes, Number 8

International Nuclear Information System (INIS)

Khan, T.A.; Baum, J.W.; Beckman, M.C.

1993-10-01

This document contains information dealing with the lessons learned from the experience of nuclear plants. In this issue the authors tried to avoid the 'tyranny' of numbers and concentrated on the main lessons learned. Topics include: filtration devices for air pollution abatement, crack repair and inspection, and remote handling equipment

Multi-domain comparison of safety standards; Comparaison de normes de securite-innocuite de plusieurs domaines industriels

Energy Technology Data Exchange (ETDEWEB)

Baufreton, Ph.; Derrien, J.C.; Ricque, B. [Sagem Defense Securite, 75 - Paris (France); Blanquart, J.P. [Astrium Satellites, France (France); Boulanger, J.L. [CERTIFER, 75 - Paris (France); Delseny, H. [Airbus, 31 - Toulouse (France); Gassino, J. [Institut de Radioprotection et de Surete Nucleaire, IRSN, 92 - Fontenay aux Roses (France); Ladier, G. [Airbus / Aerospace Valley, 31 - Toulouse (France); Ledinot, E. [Dassault Aviation, 92 - Saint Cloud (France); Leeman, M. [Valeo, 75 - Paris (France); Quere, Ph. [Renault, 75 - Paris (France)

2011-07-01

This paper presents an analysis of safety standards and their implementation in certification strategies from different domains such as aeronautics, automation, automotive, nuclear, railway and space. This work, performed in the context of the CG2E ('Club des Grandes Entreprises de l'Embarque'), aims at identifying the main similarities and dissimilarities, for potential cross-domain harmonization. We strive to find the most comprehensive 'trans-sectorial' approach, within a large number of industrial domains. Exhibiting the 'true goals' of their numerous applicable standards, related to the safety of system and software, is a first important step towards harmonization, sharing common approaches, methods and tools whenever possible. (authors)

Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain

Science.gov (United States)

Botlani, Mohsen; Siddiqui, Ahnaf; Varma, Sameer

2018-06-01

Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E's PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulator-bound and regulator-free states. The employed protocol reproduces methyl deuterium order parameters from NMR. Results from unsupervised clustering of Cij combined with flow analyses of weighted graphs of Cij show that regulator binding significantly alters the global signaling network in the protein; however, not by altering the spatial arrangement of strongly interacting amino acid clusters but by modifying the connectivity between clusters. Additionally, we find that regulator-induced shifts in conformational ensembles, which we evaluate by repartitioning ensembles using supervised learning, are, in fact, correlated. This correlation Δij is less extensive compared to Cij, but in contrast to Cij, Δij depends inversely on the distance from the regulator binding site. Assuming that Δij is an indicator of the transduction of the regulatory signal leads to the conclusion that the regulatory signal weakens with distance from the regulatory site. Overall, this

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

Science.gov (United States)

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain

Finding the Secret of Image Saliency in the Frequency Domain.

Science.gov (United States)

Li, Jia; Duan, Ling-Yu; Chen, Xiaowu; Huang, Tiejun; Tian, Yonghong

2015-12-01

There are two sides to every story of visual saliency modeling in the frequency domain. On the one hand, image saliency can be effectively estimated by applying simple operations to the frequency spectrum. On the other hand, it is still unclear which part of the frequency spectrum contributes the most to popping-out targets and suppressing distractors. Toward this end, this paper tentatively explores the secret of image saliency in the frequency domain. From the results obtained in several qualitative and quantitative experiments, we find that the secret of visual saliency may mainly hide in the phases of intermediate frequencies. To explain this finding, we reinterpret the concept of discrete Fourier transform from the perspective of template-based contrast computation and thus develop several principles for designing the saliency detector in the frequency domain. Following these principles, we propose a novel approach to design the saliency detector under the assistance of prior knowledge obtained through both unsupervised and supervised learning processes. Experimental results on a public image benchmark show that the learned saliency detector outperforms 18 state-of-the-art approaches in predicting human fixations.

Fundamentals of number theory

CERN Document Server

LeVeque, William J

1996-01-01

This excellent textbook introduces the basics of number theory, incorporating the language of abstract algebra. A knowledge of such algebraic concepts as group, ring, field, and domain is not assumed, however; all terms are defined and examples are given - making the book self-contained in this respect.The author begins with an introductory chapter on number theory and its early history. Subsequent chapters deal with unique factorization and the GCD, quadratic residues, number-theoretic functions and the distribution of primes, sums of squares, quadratic equations and quadratic fields, diopha

Supporting students' learning in the domain of computer science

Science.gov (United States)

Gasparinatou, Alexandra; Grigoriadou, Maria

2011-03-01

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas high-knowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n = 65) read one of four versions of a text concerning Local Network Topologies, orthogonally varying local and global cohesion. Participants' comprehension was examined through free-recall measure, text-based, bridging-inference, elaborative-inference, problem-solving questions and a sorting task. The results indicated that high-knowledge readers benefited from the low-cohesion text. The interaction of text cohesion and knowledge was reliable for the sorting activity, for elaborative-inference and for problem-solving questions. Although high-knowledge readers performed better in text-based and in bridging-inference questions with the low-cohesion text, the interaction of text cohesion and knowledge was not reliable. The results suggest a more complex view of when and for whom textual cohesion affects comprehension and consequently learning in computer science.

Not-so-social learning strategies.

Science.gov (United States)

Heyes, Cecilia; Pearce, John M

2015-03-07

Social learning strategies (SLSs) are rules specifying the conditions in which it would be adaptive for animals to copy the behaviour of others rather than to persist with a previously established behaviour or to acquire a new behaviour through asocial learning. In behavioural ecology, cultural evolutionary theory and economics, SLSs are studied using a 'phenotypic gambit'-from a purely functional perspective, without reference to their underlying psychological mechanisms. However, SLSs are described in these fields as if they were implemented by complex, domain-specific, genetically inherited mechanisms of decision-making. In this article, we suggest that it is time to begin investigating the psychology of SLSs, and we initiate this process by examining recent experimental work relating to three groups of strategies: copy when alternative unsuccessful, copy when model successful and copy the majority. In each case, we argue that the reported behaviour could have been mediated by domain-general and taxonomically general psychological mechanisms; specifically, by mechanisms, identified through conditioning experiments, that make associative learning selective. We also suggest experimental manipulations that could be used in future research to resolve more fully the question whether, in non-human animals, SLSs are mediated by domain-general or domain-specific psychological mechanisms. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Framework for Creating Semantically Adaptive Collaborative E-learning Environments

Directory of Open Access Journals (Sweden)

Marija Cubric

2009-09-01

Full Text Available In this paper we present a framework that can be used to generate web-based, semantically adaptive, e-learning and computer-assisted assessment (CAA tools for any given knowledge domain, based upon dynamic ontological modeling. We accomplish this by generating “learning ontologies” for a given knowledge domain. The generated learning ontologies are built upon our previous work on a domain “Glossary” ontology and augmented with additional conceptual relations from the WordNet 3.0 lexical database, using Text2Onto, an open source ontology extraction tool. The main novelty of this work is in “on the fly” generation of computer assisted assessments based on the underlying ontology and pre-defined question templates that are founded on the Bloom’s taxonomy of educational objectives. The main deployment scenario for the framework is a web-service providing collaborative e- learning and knowledge management capabilities to various learning communities. The framework can be extended to provide collection and exploitation of the users’ learning behaviour metrics, in order to further adapt the generated e-learning environment to the learners’ needs.

Equivalence of linear canonical transform domains to fractional Fourier domains and the bicanonical width product: a generalization of the space-bandwidth product.

Science.gov (United States)

Oktem, Figen S; Ozaktas, Haldun M

2010-08-01

Linear canonical transforms (LCTs) form a three-parameter family of integral transforms with wide application in optics. We show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and ordered by the corresponding fractional order parameter and provides insight into the evolution of light through an optical system modeled by LCTs. If a set of signals is highly confined to finite intervals in two arbitrary LCT domains, the space-frequency (phase space) support is a parallelogram. The number of degrees of freedom of this set of signals is given by the area of this parallelogram, which is equal to the bicanonical width product but usually smaller than the conventional space-bandwidth product. The bicanonical width product, which is a generalization of the space-bandwidth product, can provide a tighter measure of the actual number of degrees of freedom, and allows us to represent and process signals with fewer samples.

Ontology Learning for Chinese Information Organization and Knowledge Discovery in Ethnology and Anthropology

Directory of Open Access Journals (Sweden)

Jing Kong

2007-09-01

Full Text Available This paper presents an ontology learning architecture that reflects the interaction between ontology learning and other applications such as ontology-engineering tools and information systems. Based on this architecture, we have developed a prototype system CHOL: a Chinese ontology learning tool. CHOL learns domain ontology from Chinese domain specific texts. On the one hand, it supports a semi-automatic domain ontology acquisition and dynamic maintenance, and on the other hand, it supports an auto-indexing and auto-classification of Chinese scholarly literature. CHOL has been applied in ethnology and anthropology for Chinese information organization and knowledge discovery.

The Domains of Organizational Learning Practices: An Agency-Structure Perspective

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Nancy Beauregard

2015-10-01

Full Text Available Background: Organizational learning theory has retained considerable attention in the past decades from a wide array of academic disciplines in social sciences. Yet few integrative efforts have satisfactorily offered a comprehensive and systematic articulation of the concept of organizational learning with regards to: (a its core constitutive dimensions and associated mechanisms; (b the analytical levels from such mechanisms operate (e.g., workers, teams, organizations; as well as (c their interplay. Methods: This article builds on a critical synthesis of predominant approaches in organizational learning theory (i.e., structural functionalist, social constructivist and middle range approaches, highlighting the contributions of each approach on the key analytical elements guiding our inquiry (i.e., core dimensions and associated mechanisms, analytical levels, interplay. Drawing from the work of sociologists Anthony Giddens and Margaret Archer on agency-structure theory, we develop a series of theoretical propositions supporting the Organizational Learning Practices (OLP concept as a unifying heuristic tool. Results: OLP are defined as a set of collectively shared practices held by members of a given organization embedded in normative, political, and semantic dynamics. At the heart of such dynamics lies organizational knowledge as a power resource pivotal to the sustainable development of organizations, as well as that of their members. Conclusion: OLP offer promising answers to on-going debates in organizational learning theory, and we conclude by discussing concrete guidelines to advance research and practice on OLP.

Beyond the Didactic Classroom: Educational Models to Encourage Active Student Involvement in Learning

OpenAIRE

Shreeve, Michael W.

2008-01-01

In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpos...

An Algebro-Topological Description of Protein Domain Structure

Science.gov (United States)

Penner, Robert Clark; Knudsen, Michael; Wiuf, Carsten; Andersen, Jørgen Ellegaard

2011-01-01

The space of possible protein structures appears vast and continuous, and the relationship between primary, secondary and tertiary structure levels is complex. Protein structure comparison and classification is therefore a difficult but important task since structure is a determinant for molecular interaction and function. We introduce a novel mathematical abstraction based on geometric topology to describe protein domain structure. Using the locations of the backbone atoms and the hydrogen bonds, we build a combinatorial object – a so-called fatgraph. The description is discrete yet gives rise to a 2-dimensional mathematical surface. Thus, each protein domain corresponds to a particular mathematical surface with characteristic topological invariants, such as the genus (number of holes) and the number of boundary components. Both invariants are global fatgraph features reflecting the interconnectivity of the domain by hydrogen bonds. We introduce the notion of robust variables, that is variables that are robust towards minor changes in the structure/fatgraph, and show that the genus and the number of boundary components are robust. Further, we invesigate the distribution of different fatgraph variables and show how only four variables are capable of distinguishing different folds. We use local (secondary) and global (tertiary) fatgraph features to describe domain structures and illustrate that they are useful for classification of domains in CATH. In addition, we combine our method with two other methods thereby using primary, secondary, and tertiary structure information, and show that we can identify a large percentage of new and unclassified structures in CATH. PMID:21629687

Universal features in the genome-level evolution of protein domains.

Science.gov (United States)

Cosentino Lagomarsino, Marco; Sellerio, Alessandro L; Heijning, Philip D; Bassetti, Bruno

2009-01-01

Protein domains can be used to study proteome evolution at a coarse scale. In particular, they are found on genomes with notable statistical distributions. It is known that the distribution of domains with a given topology follows a power law. We focus on a further aspect: these distributions, and the number of distinct topologies, follow collective trends, or scaling laws, depending on the total number of domains only, and not on genome-specific features. We present a stochastic duplication/innovation model, in the class of the so-called 'Chinese restaurant processes', that explains this observation with two universal parameters, representing a minimal number of domains and the relative weight of innovation to duplication. Furthermore, we study a model variant where new topologies are related to occurrence in genomic data, accounting for fold specificity. Both models have general quantitative agreement with data from hundreds of genomes, which indicates that the domains of a genome are built with a combination of specificity and robust self-organizing phenomena. The latter are related to the basic evolutionary 'moves' of duplication and innovation, and give rise to the observed scaling laws, a priori of the specific evolutionary history of a genome. We interpret this as the concurrent effect of neutral and selective drives, which increase duplication and decrease innovation in larger and more complex genomes. The validity of our model would imply that the empirical observation of a small number of folds in nature may be a consequence of their evolution.

The e-Learning Effectiveness Versus Traditional Learning on a Health Informatics Laboratory Course.

Science.gov (United States)

Zogas, Spyros; Kolokathi, Aikaterini; Birbas, Konstantinos; Chondrocoukis, Gregory; Mantas, John

2016-01-01

This paper presents a comparison between e-Learning and traditional learning methods of a University course on Health Informatics domain. A pilot research took place among University students who divided on two learning groups, the e-learners and the traditional learners. A comparison of the examinations' marks for the two groups of students was conducted in order to find differences on students' performance. The study results reveal that the students scored almost the same marks independently of the learning procedure. Based on that, it can be assumed that the e-learning courses have the same effectiveness as the in-classroom learning sessions.

Stable measures of number sense accuracy in math learning disability: Is it time to proceed from basic science to clinical application?

Science.gov (United States)

Júlio-Costa, Annelise; Starling-Alves, Isabella; Lopes-Silva, Júlia Beatriz; Wood, Guilherme; Haase, Vitor Geraldi

2015-12-01

Math learning disability (MLD) or developmental dyscalculia is a highly prevalent and persistent difficulty in learning arithmetic that may be explained by different cognitive mechanisms. The accuracy of the number sense has been implicated by some evidence as a core deficit in MLD. However, research on this topic has been mainly conducted in demographically selected samples, using arbitrary cut-off scores to characterize MLD. The clinical relevance of the association between number sense and MLD remains to be investigated. In this study, we aimed at assessing the stability of a number sense accuracy measure (w) across five experimental sessions, in two clinically defined cases of MLD. Stable measures of number sense accuracy estimate are required to clinically characterize subtypes of MLD and to make theoretical inferences regarding the underlying cognitive mechanisms. G. A. was a 10-year-old boy with MLD in the context of dyslexia and phonological processing impairment and his performance remained steadily in the typical scores range. The performance of H. V., a 9-year-old girl with MLD associated with number sense inaccuracy, remained consistently impaired across measurements, with a nonsignificant tendency to worsen. Qualitatively, H. V.'s performance was also characterized by greater variability across sessions. Concomitant clinical observations suggested that H. V.'s difficulties could be aggravated by developing symptoms of mathematics anxiety. Results in these two cases are in line with the hypotheses that at least two reliable patterns of cognitive impairment may underlie math learning difficulties in MLD, one related to number sense inaccuracy and the other to phonological processing impairment. Additionally, it indicates the need for more translational research in order to examine the usefulness and validity of theoretical advances in numerical cognition to the clinical neuropsychological practice with MLD. © 2015 The Institute of Psychology, Chinese

A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

Directory of Open Access Journals (Sweden)

Hsiu-Wen Wang

2015-04-01

Full Text Available As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures. Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

A Model for Discussing the Quality of Technology-Enhanced Learning in Blended Learning Programmes

Science.gov (United States)

Casanova, Diogo; Moreira, António

2017-01-01

This paper presents a comprehensive model for supporting informed and critical discussions concerning the quality of Technology-Enhanced Learning in Blended Learning programmes. The model aims to support discussions around domains such as how institutions are prepared, the participants' background and expectations, the course design, and the…

Science of Learning Is Learning of Science: Why We Need a Dialectical Approach to Science Education Research

Science.gov (United States)

Roth, Wolff-Michael

2012-01-01

Research on learning science in informal settings and the formal (sometimes experimental) study of learning in classrooms or psychological laboratories tend to be separate domains, even drawing on different theories and methods. These differences make it difficult to compare knowing and learning observed in one paradigm/context with those observed…

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Transfer Learning for Collaborative Filtering Using a Psychometrics Model

Directory of Open Access Journals (Sweden)

Haijun Zhang

2016-01-01

Full Text Available In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain to help improve the recommendation performance of another website (target domain is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.

Entity recognition in the biomedical domain using a hybrid approach.

Science.gov (United States)

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

An N-terminal nuclear localization sequence but not the calmodulin-binding domain mediates nuclear localization of nucleomorphin, a protein that regulates nuclear number in Dictyostelium

International Nuclear Information System (INIS)

Myre, Michael A.; O'Day, Danton H.

2005-01-01

Nucleomorphin is a novel nuclear calmodulin (CaM)-binding protein (CaMBP) containing an extensive DEED (glu/asp repeat) domain that regulates nuclear number. GFP-constructs of the 38 kDa NumA1 isoform localize as intranuclear patches adjacent to the inner nuclear membrane. The translocation of CaMBPs into nuclei has previously been shown by others to be mediated by both classic nuclear localization sequences (NLSs) and CaM-binding domains (CaMBDs). Here we show that NumA1 possesses a CaMBD ( 171 EDVSRFIKGKLLQKQQKIYKDLERF 195 ) containing both calcium-dependent-binding motifs and an IQ-like motif for calcium-independent binding. GFP-constructs containing only NumA1 residues 1-129, lacking the DEED and CaMBDs, still localized as patches at the internal periphery of nuclei thus ruling out a direct role for the CaMBD in nuclear import. These constructs contained the amino acid residues 48 KKSYQDPEIIAHSRPRK 64 that include both a putative bipartite and classical NLS. GFP-bipartite NLS constructs localized uniformly within nuclei but not as patches. As with previous work, removal of the DEED domain resulted in highly multinucleate cells. However as shown here, multinuclearity only occurred when the NLS was present allowing the protein to enter nuclei. Site-directed mutation analysis in which the NLS was changed to 48 EF 49 abolished the stability of the GFP fusion at the protein but not RNA level preventing subcellular analyses. Cells transfected with the 48 EF 49 construct exhibited slowed growth when compared to parental AX3 cells and other GFP-NumA1 deletion mutants. In addition to identifying an NLS that is sufficient for nuclear translocation of nucleomorphin and ruling out CaM-binding in this event, this work shows that the nuclear localization of NumA1 is crucial to its ability to regulate nuclear number in Dictyostelium

Measuring meaningful learning in the undergraduate chemistry laboratory

Science.gov (United States)

Galloway, Kelli R.

The undergraduate chemistry laboratory has been an essential component in chemistry education for over a century. The literature includes reports on investigations of singular aspects laboratory learning and attempts to measure the efficacy of reformed laboratory curriculum as well as faculty goals for laboratory learning which found common goals among instructors for students to learn laboratory skills, techniques, experimental design, and to develop critical thinking skills. These findings are important for improving teaching and learning in the undergraduate chemistry laboratory, but research is needed to connect the faculty goals to student perceptions. This study was designed to explore students' ideas about learning in the undergraduate chemistry laboratory. Novak's Theory of Meaningful Learning was used as a guide for the data collection and analysis choices for this research. Novak's theory states that in order for meaningful learning to occur the cognitive, affective, and psychomotor domains must be integrated. The psychomotor domain is inherent in the chemistry laboratory, but the extent to which the cognitive and affective domains are integrated is unknown. For meaningful learning to occur in the laboratory, students must actively integrate both the cognitive domain and the affective domains into the "doing" of their laboratory work. The Meaningful Learning in the Laboratory Instrument (MLLI) was designed to measure students' cognitive and affective expectations and experiences within the context of conducting experiments in the undergraduate chemistry laboratory. Evidence for the validity and reliability of the data generated by the MLLI were collected from multiple quantitative studies: a one semester study at one university, a one semester study at 15 colleges and universities across the United States, and a longitudinal study where the MLLI was administered 6 times during two years of general and organic chemistry laboratory courses. Results from

The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

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Astiti Kade kAyu

2018-01-01

Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.

Meanings for Fraction as Number-Measure by Exploring the Number Line

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Psycharis, Giorgos; Latsi, Maria; Kynigos, Chronis

2009-01-01

This paper reports on a case-study design experiment in the domain of fraction as number-measure. We designed and implemented a set of exploratory tasks concerning comparison and ordering of fractions as well as operations with fractions. Two groups of 12-year-old students worked collaboratively using paper and pencil as well as a specially…

FILE: a tool for the study of inquiry learning.

NARCIS (Netherlands)

Hulshof, C.D.; Wilhelm, P.; Beishuizen, J.J.; Beishuizen, J.J.; van Rijn, H.

2005-01-01

A computerized learning environment (Flexible Inquiry Learning Environment; FILE) is discussed. FILE allows researchers in inquiry learning to design, administer, and analyze learning tasks in which content domain and task complexity can be configured independently, while other factors (e.g., the

National Guard Forces in the Cyber Domain

Science.gov (United States)

2015-05-22

TITLE AND SUBTITLE National Guard Forces in the Cyber Domain 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...Soldiers. Army Cyber Command (ARCYBER) commander, Lieutenant General Edward Cardon stated that Guard will begin to build combat power with...90 2014 Quadrennial Defense Review, 15. 91 Ibid. 92 Edward C. Cardon , "ARMY.MIL, The Official Homepage of the United

Adaptation of interoperability standards for cross domain usage

Science.gov (United States)

Essendorfer, B.; Kerth, Christian; Zaschke, Christian

2017-05-01

As globalization affects most aspects of modern life, challenges of quick and flexible data sharing apply to many different domains. To protect a nation's security for example, one has to look well beyond borders and understand economical, ecological, cultural as well as historical influences. Most of the time information is produced and stored digitally and one of the biggest challenges is to receive relevant readable information applicable to a specific problem out of a large data stock at the right time. These challenges to enable data sharing across national, organizational and systems borders are known to other domains (e.g., ecology or medicine) as well. Solutions like specific standards have been worked on for the specific problems. The question is: what can the different domains learn from each other and do we have solutions when we need to interlink the information produced in these domains? A known problem is to make civil security data available to the military domain and vice versa in collaborative operations. But what happens if an environmental crisis leads to the need to quickly cooperate with civil or military security in order to save lives? How can we achieve interoperability in such complex scenarios? The paper introduces an approach to adapt standards from one domain to another and lines out problems that have to be overcome and limitations that may apply.

Quantifying information transfer by protein domains: Analysis of the Fyn SH2 domain structure

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Serrano Luis

2008-10-01

Full Text Available Abstract Background Efficient communication between distant sites within a protein is essential for cooperative biological response. Although often associated with large allosteric movements, more subtle changes in protein dynamics can also induce long-range correlations. However, an appropriate formalism that directly relates protein structural dynamics to information exchange between functional sites is still lacking. Results Here we introduce a method to analyze protein dynamics within the framework of information theory and show that signal transduction within proteins can be considered as a particular instance of communication over a noisy channel. In particular, we analyze the conformational correlations between protein residues and apply the concept of mutual information to quantify information exchange. Mapping out changes of mutual information on the protein structure then allows visualizing how distal communication is achieved. We illustrate the approach by analyzing information transfer by the SH2 domain of Fyn tyrosine kinase, obtained from Monte Carlo dynamics simulations. Our analysis reveals that the Fyn SH2 domain forms a noisy communication channel that couples residues located in the phosphopeptide and specificity binding sites and a number of residues at the other side of the domain near the linkers that connect the SH2 domain to the SH3 and kinase domains. We find that for this particular domain, communication is affected by a series of contiguous residues that connect distal sites by crossing the core of the SH2 domain. Conclusion As a result, our method provides a means to directly map the exchange of biological information on the structure of protein domains, making it clear how binding triggers conformational changes in the protein structure. As such it provides a structural road, next to the existing attempts at sequence level, to predict long-range interactions within protein structures.

Automatic Earthquake Detection by Active Learning

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Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Predictive Variable Gain Iterative Learning Control for PMSM

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Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

Generalized query-based active learning to identify differentially methylated regions in DNA.

Science.gov (United States)

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

A machine learning approach to create blocking criteria for record linkage.

Science.gov (United States)

Giang, Phan H

2015-03-01

Record linkage, a part of data cleaning, is recognized as one of most expensive steps in data warehousing. Most record linkage (RL) systems employ a strategy of using blocking filters to reduce the number of pairs to be matched. A blocking filter consists of a number of blocking criteria. Until recently, blocking criteria are selected manually by domain experts. This paper proposes a new method to automatically learn efficient blocking criteria for record linkage. Our method addresses the lack of sufficient labeled data for training. Unlike previous works, we do not consider a blocking filter in isolation but in the context of an accompanying matcher which is employed after the blocking filter. We show that given such a matcher, the labels (assigned to record pairs) that are relevant for learning are the labels assigned by the matcher (link/nonlink), not the labels assigned objectively (match/unmatch). This conclusion allows us to generate an unlimited amount of labeled data for training. We formulate the problem of learning a blocking filter as a Disjunctive Normal Form (DNF) learning problem and use the Probably Approximately Correct (PAC) learning theory to guide the development of algorithm to search for blocking filters. We test the algorithm on a real patient master file of 2.18 million records. The experimental results show that compared with filters obtained by educated guess, the optimal learned filters have comparable recall but reduce throughput (runtime) by an order-of-magnitude factor.

Evaluation of Damping Using Time Domain OMA Techniques

DEFF Research Database (Denmark)

Bajric, Anela; Brincker, Rune; Georgakis, Christos T.

2014-01-01

. In this paper a comparison is made of the effectiveness of three existing OMA techniques in providing accurate damping estimates for varying loadings, levels of noise, number of added measurement channels and structural damping. The evaluated techniques are derived in the time domain and are namely the Ibrahim...... Time Domain (ITD), Eigenvalue Realization Algorithm (ERA) and the Polyreference Time Domain (PTD). The response of a two degree-of-freedom (2DOF) system is numerically established from specified modal parameters with well separated and closely spaced modes. Two types of response are considered, free...

Informal learning.

Science.gov (United States)

Callanan, Maureen; Cervantes, Christi; Loomis, Molly

2011-11-01

We consider research and theory relevant to the notion of informal learning. Beginning with historical and definitional issues, we argue that learning happens not just in schools or in school-aged children. Many theorists have contrasted informal learning with formal learning. Moving beyond this dichotomy, and away from a focus on where learning occurs, we discuss five dimensions of informal learning that are drawn from the literature: (1) non-didactive, (2) highly socially collaborative, (3) embedded in meaningful activity, (4) initiated by learner's interest or choice, and (5) removed from external assessment. We consider these dimensions in the context of four sample domains: learning a first language, learning about the mind and emotions within families and communities, learning about science in family conversations and museum settings, and workplace learning. Finally, we conclude by considering convergences and divergences across the different literatures and suggesting areas for future research. WIREs Cogni Sci 2011 2 646-655 DOI: 10.1002/wcs.143 For further resources related to this article, please visit the WIREs website. Copyright © 2011 John Wiley & Sons, Ltd.

Applications of Deep Learning and Reinforcement Learning to Biological Data.

Science.gov (United States)

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Design of digital learning material for bioprocess-engineering-education

NARCIS (Netherlands)

Schaaf, van der H.

2007-01-01

With the advance of computers and the internet, new types of learning material can be developed: web-based digital learning material. Because many complex learning objectives in the food- and bioprocess technology domain are difficult to achieve in a traditional learning environment, a project was

Hebb learning, verbal short-term memory, and the acquisition of phonological forms in children.

Science.gov (United States)

Mosse, Emma K; Jarrold, Christopher

2008-04-01

Recent work using the Hebb effect as a marker for implicit long-term acquisition of serial order has demonstrated a functional equivalence across verbal and visuospatial short-term memory. The current study extends this observation to a sample of five- to six-year-olds using verbal and spatial immediate serial recall and also correlates the magnitude of Hebb learning with explicit measures of word and nonword paired-associate learning. Comparable Hebb effects were observed in both domains, but only nonword learning was significantly related to the magnitude of Hebb learning. Nonword learning was also independently related to individuals' general level of verbal serial recall. This suggests that vocabulary acquisition depends on both a domain-specific short-term memory system and a domain-general process of learning through repetition.

Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge.

Science.gov (United States)

Guyon, Isabelle; Saffari, Amir; Dror, Gideon; Cawley, Gavin

2008-01-01

We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design, or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants in the "prior knowledge" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants in the "agnostic learning" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performance. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: http://www.agnostic.inf.ethz.ch/.

Creating a Context for Learning: Activating Children’s Whole Number Knowledge Prepares Them to Understand Fraction Division

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Pooja Gupta Sidney

2017-07-01

Full Text Available When children learn about fractions, their prior knowledge of whole numbers often interferes, resulting in a whole number bias. However, many fraction concepts are generalizations of analogous whole number concepts; for example, fraction division and whole number division share a similar conceptual structure. Drawing on past studies of analogical transfer, we hypothesize that children’s whole number division knowledge will support their understanding of fraction division when their relevant prior knowledge is activated immediately before engaging with fraction division. Children in 5th and 6th grade modeled fraction division with physical objects after modeling a series of addition, subtraction, multiplication, and division problems with whole number operands and fraction operands. In one condition, problems were blocked by operation, such that children modeled fraction problems immediately after analogous whole number problems (e.g., fraction division problems followed whole number division problems. In another condition, problems were blocked by number type, such that children modeled all four arithmetic operations with whole numbers in the first block, and then operations with fractions in the second block. Children who solved whole number division problems immediately before fraction division problems were significantly better at modeling the conceptual structure of fraction division than those who solved all of the fraction problems together. Thus, implicit analogies across shared concepts can affect children’s mathematical thinking. Moreover, specific analogies between whole number and fraction concepts can yield a positive, rather than a negative, whole number bias.

Computational Investigations of Multiword Chunks in Language Learning.

Science.gov (United States)

McCauley, Stewart M; Christiansen, Morten H

2017-07-01

Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language. Copyright © 2017 Cognitive Science Society, Inc.

Immersive Learning Technologies

Science.gov (United States)

2009-08-20

Immersive Learning Technologies Mr. Peter Smith Lead, ADL Immersive Learning Team 08/20/2009 Report Documentation Page Form ApprovedOMB No. 0704...to 00-00-2009 4. TITLE AND SUBTITLE Immersive Learning Technologies 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Why Immersive Learning Technologies

Blended Learning: The Student Viewpoint.

Science.gov (United States)

Shantakumari, N; Sajith, P

2015-01-01

Blended learning (BL) is defined as "a way of meeting the challenges of tailoring learning and development to the needs of individuals by integrating the innovative and technological advances offered by online learning with the interaction and participation offered in the best of traditional learning." The Gulf Medical University (GMU), Ajman, UAE, offers a number of courses which incorporate BL with contact classes and online component on an E-learning platform. Insufficient learning satisfaction has been stated as an obstacle to its implementation and efficacy. To determine the students' perceptions toward BL which in turn will determine their satisfaction and the efficacy of the courses offered. This was a cross-sectional study conducted at the GMU, Ajman between January and December 2013. Perceptions of BL process, content, and ease of use were collected from 75 students enrolled in the certificate courses offered by the university using a questionnaire. Student perceptions were assessed using Mann-Whitney U-test and Kruskal-Wallis test on the basis of gender, age, and course enrollment. The median scores of all the questions in the three domains were above three suggesting positive perceptions on BL. The distribution of perceptions was similar between gender and age. However, significant differences were observed in the course enrollment (P = 0.02). Students hold a positive perception of the BL courses being offered in this university. The difference in perceptions among students of different courses suggest that the BL format offered needs modification according to course content to improve its perception.

Statistical learning: a powerful mechanism that operates by mere exposure.

Science.gov (United States)

Aslin, Richard N

2017-01-01

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Development of magnitude processing in children with developmental dyscalculia: Space, time and number

Directory of Open Access Journals (Sweden)

Kenny eSkagerlund

2014-06-01

Full Text Available Developmental dyscalculia (DD is a learning disorder associated with impairments in a preverbal non-symbolic approximate number system (ANS pertaining to areas in and around the intraparietal sulcus (IPS. The current study sought to enhance our understanding of the developmental trajectory of the ANS and symbolic number processing skills, thereby getting insight into whether a deficit in the ANS precedes or is preceded by impaired symbolic and exact number processing. Recent work has also suggested that humans are endowed with a shared magnitude system (beyond the number domain in the brain. We therefore investigated whether children with DD demonstrated a general magnitude deficit, stemming from the proposed magnitude system, rather than a specific one limited to numerical quantity. Fourth graders with DD were compared to age-matched controls and a group of ability-matched second graders, on a range of magnitude processing tasks pertaining to space, time, and number. Children with DD displayed difficulties across all magnitude dimensions compared to age-matched peers and showed impaired ANS acuity compared to the younger, ability-matched control group, while exhibiting intact symbolic number processing. We conclude that (1 children with DD suffer from a general magnitude-processing deficit, (2 a shared magnitude system likely exists, and (3 a symbolic number-processing deficit in DD tends to be preceded by an ANS deficit.

Development of magnitude processing in children with developmental dyscalculia: space, time, and number.

Science.gov (United States)

Skagerlund, Kenny; Träff, Ulf

2014-01-01

Developmental dyscalculia (DD) is a learning disorder associated with impairments in a preverbal non-symbolic approximate number system (ANS) pertaining to areas in and around the intraparietal sulcus (IPS). The current study sought to enhance our understanding of the developmental trajectory of the ANS and symbolic number processing skills, thereby getting insight into whether a deficit in the ANS precedes or is preceded by impaired symbolic and exact number processing. Recent work has also suggested that humans are endowed with a shared magnitude system (beyond the number domain) in the brain. We therefore investigated whether children with DD demonstrated a general magnitude deficit, stemming from the proposed magnitude system, rather than a specific one limited to numerical quantity. Fourth graders with DD were compared to age-matched controls and a group of ability-matched second graders, on a range of magnitude processing tasks pertaining to space, time, and number. Children with DD displayed difficulties across all magnitude dimensions compared to age-matched peers and showed impaired ANS acuity compared to the younger, ability-matched control group, while exhibiting intact symbolic number processing. We conclude that (1) children with DD suffer from a general magnitude-processing deficit, (2) a shared magnitude system likely exists, and (3) a symbolic number-processing deficit in DD tends to be preceded by an ANS deficit.

Kernel-Based Learning for Domain-Specific Relation Extraction

Science.gov (United States)

Basili, Roberto; Giannone, Cristina; Del Vescovo, Chiara; Moschitti, Alessandro; Naggar, Paolo

In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.

Powerful Feelings: Exploring the Affective Domain of Informal and Arts-Based Learning

Science.gov (United States)

Lawrence, Randee Lipson

2008-01-01

This article looks at the ways in which people learn informally through artistic expression such as dance, drama, poetry, music, literature, film, and all of the visual arts and how people access this learning through their emotions. The author begins with a look at the limitations of relying primarily on technical-rational learning processes.…

Robots Learn Writing

Directory of Open Access Journals (Sweden)

Huan Tan

2012-01-01

Full Text Available This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.

The Value of Significant Learning Strategies in Undergraduate Education

Science.gov (United States)

Coco, Charles M.

2012-01-01

Learning taxonomies can assist faculty in developing course structures that promote enhanced student learning in the cognitive and affective domains. Significant Learning is one approach to course design that allows for development in six key areas: Foundational Knowledge, Application, Integration, Human Dimension, Caring, and Learning How to…

Distributional Language Learning: Mechanisms and Models of ategory Formation.

Science.gov (United States)

Aslin, Richard N; Newport, Elissa L

2014-09-01

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

The Impact of Robot Tutor Nonverbal Social Behavior on Child Learning

Directory of Open Access Journals (Sweden)

James Kennedy

2017-04-01

Full Text Available Several studies have indicated that interacting with social robots in educational contexts may lead to a greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human–robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behavior a robot should employ in such interactions. Inspiration can be taken from human–human studies; this often leads to an assumption that the more social behavior an agent utilizes, the better the learning outcome will be. We apply a nonverbal behavior metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioral manipulations. We find a trend, which generally agrees with the pedagogy literature, but also that overt nonverbal behavior does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behavior and learning. We suggest that the combination of nonverbal behavior and social cue congruency is necessary to facilitate learning.

A Parallel Non-Overlapping Domain-Decomposition Algorithm for Compressible Fluid Flow Problems on Triangulated Domains

Science.gov (United States)

Barth, Timothy J.; Chan, Tony F.; Tang, Wei-Pai

1998-01-01

This paper considers an algebraic preconditioning algorithm for hyperbolic-elliptic fluid flow problems. The algorithm is based on a parallel non-overlapping Schur complement domain-decomposition technique for triangulated domains. In the Schur complement technique, the triangulation is first partitioned into a number of non-overlapping subdomains and interfaces. This suggests a reordering of triangulation vertices which separates subdomain and interface solution unknowns. The reordering induces a natural 2 x 2 block partitioning of the discretization matrix. Exact LU factorization of this block system yields a Schur complement matrix which couples subdomains and the interface together. The remaining sections of this paper present a family of approximate techniques for both constructing and applying the Schur complement as a domain-decomposition preconditioner. The approximate Schur complement serves as an algebraic coarse space operator, thus avoiding the known difficulties associated with the direct formation of a coarse space discretization. In developing Schur complement approximations, particular attention has been given to improving sequential and parallel efficiency of implementations without significantly degrading the quality of the preconditioner. A computer code based on these developments has been tested on the IBM SP2 using MPI message passing protocol. A number of 2-D calculations are presented for both scalar advection-diffusion equations as well as the Euler equations governing compressible fluid flow to demonstrate performance of the preconditioning algorithm.

Chasing probabilities — Signaling negative and positive prediction errors across domains

DEFF Research Database (Denmark)

Meder, David; Madsen, Kristoffer H; Hulme, Oliver

2016-01-01

of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum...

Learning and the transformative potential of citizen science.

Science.gov (United States)

Bela, Györgyi; Peltola, Taru; Young, Juliette C; Balázs, Bálint; Arpin, Isabelle; Pataki, György; Hauck, Jennifer; Kelemen, Eszter; Kopperoinen, Leena; Van Herzele, Ann; Keune, Hans; Hecker, Susanne; Suškevičs, Monika; Roy, Helen E; Itkonen, Pekka; Külvik, Mart; László, Miklós; Basnou, Corina; Pino, Joan; Bonn, Aletta

2016-10-01

The number of collaborative initiatives between scientists and volunteers (i.e., citizen science) is increasing across many research fields. The promise of societal transformation together with scientific breakthroughs contributes to the current popularity of citizen science (CS) in the policy domain. We examined the transformative capacity of citizen science in particular learning through environmental CS as conservation tool. We reviewed the CS and social-learning literature and examined 14 conservation projects across Europe that involved collaborative CS. We also developed a template that can be used to explore learning arrangements (i.e., learning events and materials) in CS projects and to explain how the desired outcomes can be achieved through CS learning. We found that recent studies aiming to define CS for analytical purposes often fail to improve the conceptual clarity of CS; CS programs may have transformative potential, especially for the development of individual skills, but such transformation is not necessarily occurring at the organizational and institutional levels; empirical evidence on simple learning outcomes, but the assertion of transformative effects of CS learning is often based on assumptions rather than empirical observation; and it is unanimous that learning in CS is considered important, but in practice it often goes unreported or unevaluated. In conclusion, we point to the need for reliable and transparent measurement of transformative effects for democratization of knowledge production. © 2016 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Elementary number theory

CERN Document Server

Dudley, Underwood

2008-01-01

Ideal for a first course in number theory, this lively, engaging text requires only a familiarity with elementary algebra and the properties of real numbers. Author Underwood Dudley, who has written a series of popular mathematics books, maintains that the best way to learn mathematics is by solving problems. In keeping with this philosophy, the text includes nearly 1,000 exercises and problems-some computational and some classical, many original, and some with complete solutions. The opening chapters offer sound explanations of the basics of elementary number theory and develop the fundamenta

Informal Learning in a Formal Educational System

DEFF Research Database (Denmark)

Busch, Henrik

2001-01-01

This paper presents findings related to a research study which aims to describe and understand some of the essential learning processes involved in changing a first-year physics student into a research scientist. One part of this study explores a common feature of most undergraduate studies in sc....... The settings for students' learning bear much resemblance to informal learning settings reported in earlier studies related to e.g. science center visits....... in science - the pronounced border between the domains of production and acquisition of knowledge. Based on ongoing ethnographic fieldwork, certain aspects of this division between the two domains are investigated. A case study representing students' border-crossing activities is described and discussed...

Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

Science.gov (United States)

Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

2017-01-01

In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

Polarization of concave domains by traveling wave pinning.

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Slawomir Bialecki

Full Text Available Pattern formation is one of the most fundamental yet puzzling phenomena in physics and biology. We propose that traveling front pinning into concave portions of the boundary of 3-dimensional domains can serve as a generic gradient-maintaining mechanism. Such a mechanism of domain polarization arises even for scalar bistable reaction-diffusion equations, and, depending on geometry, a number of stationary fronts may be formed leading to complex spatial patterns. The main advantage of the pinning mechanism, with respect to the Turing bifurcation, is that it allows for maintaining gradients in the specific regions of the domain. By linking the instant domain shape with the spatial pattern, the mechanism can be responsible for cellular polarization and differentiation.

Learning Analytics

Directory of Open Access Journals (Sweden)

Erik Duval

2012-06-01

Full Text Available This paper provides a brief introduction to the domain of ‘learning analytics’. We first explain the background and idea behind the concept. Then we give a brief overview of current research issues. We briefly list some more controversial issues before concluding.

A Bayesian Approach for Structural Learning with Hidden Markov Models

Directory of Open Access Journals (Sweden)

Cen Li

2002-01-01

Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

Data Mining and Machine Learning in Time-Domain Discovery and Classification

Science.gov (United States)

Bloom, Joshua S.; Richards, Joseph W.

2012-03-01

The changing heavens have played a central role in the scientific effort of astronomers for centuries. Galileo's synoptic observations of the moons of Jupiter and the phases of Venus starting in 1610, provided strong refutation of Ptolemaic cosmology. These observations came soon after the discovery of Kepler's supernova had challenged the notion of an unchanging firmament. In more modern times, the discovery of a relationship between period and luminosity in some pulsational variable stars [41] led to the inference of the size of the Milky way, the distance scale to the nearest galaxies, and the expansion of the Universe (see Ref. [30] for review). Distant explosions of supernovae were used to uncover the existence of dark energy and provide a precise numerical account of dark matter (e.g., [3]). Repeat observations of pulsars [71] and nearby main-sequence stars revealed the presence of the first extrasolar planets [17,35,44,45]. Indeed, time-domain observations of transient events and variable stars, as a technique, influences a broad diversity of pursuits in the entire astronomy endeavor [68]. While, at a fundamental level, the nature of the scientific pursuit remains unchanged, the advent of astronomy as a data-driven discipline presents fundamental challenges to the way in which the scientific process must now be conducted. Digital images (and data cubes) are not only getting larger, there are more of them. On logistical grounds, this taxes storage and transport systems. But it also implies that the intimate connection that astronomers have always enjoyed with their data - from collection to processing to analysis to inference - necessarily must evolve. Figure 6.1 highlights some of the ways that the pathway to scientific inference is now influenced (if not driven by) modern automation processes, computing, data-mining, and machine-learning (ML). The emerging reliance on computation and ML is a general one - a central theme of this book - but the time-domain

Semi-supervised learning and domain adaptation in natural language processing

CERN Document Server

Søgaard, Anders

2013-01-01

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both

Progress towards the development of SH2 domain inhibitors.

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Kraskouskaya, Dziyana; Duodu, Eugenia; Arpin, Carolynn C; Gunning, Patrick T

2013-04-21

Src homology 2 (SH2) domains are 100 amino acid modular units, which recognize and bind to tyrosyl-phosphorylated peptide sequences on their target proteins, and thereby mediate intracellular protein-protein interactions. This review summarizes the progress towards the development of synthetic agents that disrupt the function of the SH2 domains in different proteins as well as the clinical relevance of targeting a specific SH2 domain. Since 1986, SH2 domains have been identified in over 110 human proteins, including kinases, transcription factors, and adaptor proteins. A number of these proteins are over-activated in many diseases, including cancer, and their function is highly dependent on their SH2 domain. Thus, inhibition of a protein's function through disrupting that of its SH2 domain has emerged as a promising approach towards the development of novel therapeutic modalities. Although targeting the SH2 domain is a challenging task in molecular recognition, the progress reported here demonstrates the feasibility of such an approach.

General Information about Learning Disabilities (Fact Sheet Number 7) = Informacion General sobre Impedimentos en el Aprendizaje (Fact Sheet Number 19).

Science.gov (United States)

Interstate Research Associates, Inc., Washington, DC.

This fact sheet providing general information about learning disabilities is presented in both English and Spanish versions. It begins with the federal definition of learning disabilities and a discussion of its implications followed by estimates of incidence. Typical characteristics of students with learning disabilities are then summarized as…

Searching for the Hebb effect in Down syndrome: evidence for a dissociation between verbal short-term memory and domain-general learning of serial order.

Science.gov (United States)

Mosse, E K; Jarrold, C

2010-04-01

The Hebb effect is a form of repetition-driven long-term learning that is thought to provide an analogue for the processes involved in new word learning. Other evidence suggests that verbal short-term memory also constrains now vocabulary acquisition, but if the Hebb effect is independent of short-term memory, then it may be possible to demonstrate its preservation in a sample of individuals with Down syndrome, who typically show a verbal short-term memory deficit alongside surprising relative strengths in vocabulary. In two experiments, individuals both with and without Down syndrome (matched for receptive vocabulary) completed immediate serial recall tasks incorporating a Hebb repetition paradigm in either verbal or visuospatial conditions. Both groups demonstrated equivalent benefit from Hebb repetition, despite individuals with Down syndrome showing significantly lower verbal short-term memory spans. The resultant Hebb effect was equivalent across verbal and visuospatial domains. These studies suggest that the Hebb effect is essentially preserved within Down syndrome, implying that explicit verbal short-term memory is dissociable from potentially more implicit Hebb learning. The relative strength in receptive vocabulary observed in Down syndrome may therefore be supported by largely intact long-term as opposed to short-term serial order learning. This in turn may have implications for teaching methods and interventions that present new phonological material to individuals with Down syndrome.

Rosette Assay: Highly Customizable Dot-Blot for SH2 Domain Screening.

Science.gov (United States)

Ng, Khong Y; Machida, Kazuya

2017-01-01

With a growing number of high-throughput studies, structural analyses, and availability of protein-protein interaction databases, it is now possible to apply web-based prediction tools to SH2 domain-interactions. However, in silico prediction is not always reliable and requires experimental validation. Rosette assay is a dot blot-based reverse-phase assay developed for the assessment of binding between SH2 domains and their ligands. It is conveniently customizable, allowing for low- to high-throughput analysis of interactions between various numbers of SH2 domains and their ligands, e.g., short peptides, purified proteins, and cell lysates. The binding assay is performed in a 96-well plate (MBA or MWA apparatus) in which a sample spotted membrane is incubated with up to 96 labeled SH2 domains. Bound domains are detected and quantified using a chemiluminescence or near-infrared fluorescence (IR) imaging system. In this chapter, we describe a practical protocol for rosette assay to assess interactions between synthesized tyrosine phosphorylated peptides and a library of GST-tagged SH2 domains. Since the methodology is not confined to assessment of SH2-pTyr interactions, rosette assay can be broadly utilized for ligand and drug screening using different protein interaction domains or antibodies.

A Cross-Domain Explanation of the Metaphor "Teaching as Persuasion."

Science.gov (United States)

Woods, Bradford S.; Demerath, Peter

2001-01-01

Examines what the metaphor "teaching as persuasion" would mean in the domains of philosophy, anthropology, and teacher education, asserting that if such a metaphor is to be widely accepted by the educational community and the public, then this discussion is necessary. The metaphor suggests that in teacher education, learning to teach…

Decision support methods for finding phenotype--disorder associations in the bone dysplasia domain.

Directory of Open Access Journals (Sweden)

Razan Paul

Full Text Available A lack of mature domain knowledge and well established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis. We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician.

Assisted Learning Systems in e-Education

Directory of Open Access Journals (Sweden)

Gabriel ZAMFIR

2014-01-01

Full Text Available Human society, analyzed as a learning environment, presumes different languages in order to know, to understand or to develop it. This statement results as a default application of the cog-nitive domain in the educational scientific research, and it highlights a key feature: each essen-tial discovery was available for the entire language compatible society. E-Society is constructed as an application of E-Science in social services, and it is going to reveal a learning system for each application of the information technology developed for a compatible society. This article is proposed as a conceptual one focused on scientific research and the interrelationship be-tween the building blocks of research, defined as an engine for any designed learning system applied in the cognitive domain. In this approach, educational research become a learning sys-tem in e-Education. The purpose of this analysis is to configure the teacher assisted learning system and to expose its main principles which could be integrated in standard assisted instruc-tion applications, available in e-Classroom, supporting the design of specific didactic activities.

Michigan Journal of Community Service Learning. Volume 13, Number 1, Fall 2006

Science.gov (United States)

Howard, Jeffrey, Ed.

2006-01-01

The "Michigan Journal of Community Service Learning" ("MJCSL") is a national, peer-reviewed journal consisting of articles written by faculty and service-learning educators on research, theory, pedagogy, and issues pertinent to the service-learning community. The "MJCSL" aims to: (1) widen the community of…

Individual differences in non-verbal number acuity correlate with maths achievement.

Science.gov (United States)

Halberda, Justin; Mazzocco, Michèle M M; Feigenson, Lisa

2008-10-02

Human mathematical competence emerges from two representational systems. Competence in some domains of mathematics, such as calculus, relies on symbolic representations that are unique to humans who have undergone explicit teaching. More basic numerical intuitions are supported by an evolutionarily ancient approximate number system that is shared by adults, infants and non-human animals-these groups can all represent the approximate number of items in visual or auditory arrays without verbally counting, and use this capacity to guide everyday behaviour such as foraging. Despite the widespread nature of the approximate number system both across species and across development, it is not known whether some individuals have a more precise non-verbal 'number sense' than others. Furthermore, the extent to which this system interfaces with the formal, symbolic maths abilities that humans acquire by explicit instruction remains unknown. Here we show that there are large individual differences in the non-verbal approximation abilities of 14-year-old children, and that these individual differences in the present correlate with children's past scores on standardized maths achievement tests, extending all the way back to kindergarten. Moreover, this correlation remains significant when controlling for individual differences in other cognitive and performance factors. Our results show that individual differences in achievement in school mathematics are related to individual differences in the acuity of an evolutionarily ancient, unlearned approximate number sense. Further research will determine whether early differences in number sense acuity affect later maths learning, whether maths education enhances number sense acuity, and the extent to which tertiary factors can affect both.

Implicit and Explicit Learning in Individuals with Agrammatic Aphasia

Science.gov (United States)

Schuchard, Julia; Thompson, Cynthia K.

2014-01-01

Implicit learning is a process of acquiring knowledge that occurs without conscious awareness of learning, whereas explicit learning involves the use of overt strategies. To date, research related to implicit learning following stroke has been largely restricted to the motor domain and has rarely addressed implications for language. The present…

Simulation of two-phase flows by domain decomposition

International Nuclear Information System (INIS)

Dao, T.H.

2013-01-01

This thesis deals with numerical simulations of compressible fluid flows by implicit finite volume methods. Firstly, we studied and implemented an implicit version of the Roe scheme for compressible single-phase and two-phase flows. Thanks to Newton method for solving nonlinear systems, our schemes are conservative. Unfortunately, the resolution of nonlinear systems is very expensive. It is therefore essential to use an efficient algorithm to solve these systems. For large size matrices, we often use iterative methods whose convergence depends on the spectrum. We have studied the spectrum of the linear system and proposed a strategy, called Scaling, to improve the condition number of the matrix. Combined with the classical ILU pre-conditioner, our strategy has reduced significantly the GMRES iterations for local systems and the computation time. We also show some satisfactory results for low Mach-number flows using the implicit centered scheme. We then studied and implemented a domain decomposition method for compressible fluid flows. We have proposed a new interface variable which makes the Schur complement method easy to build and allows us to treat diffusion terms. Using GMRES iterative solver rather than Richardson for the interface system also provides a better performance compared to other methods. We can also decompose the computational domain into any number of sub-domains. Moreover, the Scaling strategy for the interface system has improved the condition number of the matrix and reduced the number of GMRES iterations. In comparison with the classical distributed computing, we have shown that our method is more robust and efficient. (author) [fr

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

Science.gov (United States)

Molloy, Katharine; Moore, David R; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

Embedding Number-Combinations Practice Within Word-Problem Tutoring

Science.gov (United States)

Powell, Sarah R.; Fuchs, Lynn S.; Fuchs, Douglas

2012-01-01

Two aspects of mathematics with which students with mathematics learning difficulty (MLD) often struggle are word problems and number-combination skills. This article describes a math program in which students receive instruction on using algebraic equations to represent the underlying problem structure for three word-problem types. Students also learn counting strategies for answering number combinations that they cannot retrieve from memory. Results from randomized-control trials indicated that embedding the counting strategies for number combinations produces superior word-problem and number-combination outcomes for students with MLD beyond tutoring programs that focus exclusively on number combinations or word problems. PMID:22661880

Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning.

Science.gov (United States)

Sun, Shichang; Liu, Hongbo; Meng, Jiana; Chen, C L Philip; Yang, Yu

2018-06-01

Sequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy. STLM can achieve the competing goals of preserving the target domain substructure and utilizing the observations from both the target and source domains simultaneously. The estimation of STLM is very efficient since an analytical solution can be derived as a necessary and sufficient condition. The relative usability of substructures to act as regularization parameters and the time complexity of STLM are also analyzed and discussed. Comprehensive experiments of part-of-speech tagging with both Brown and Twitter corpora fully justify that our model can make improvements on all the combinations of source and target domains.

Domains and domain loss

DEFF Research Database (Denmark)

Haberland, Hartmut

2005-01-01

politicians and in the media, especially in the discussion whether some languages undergo ‘domain loss’ vis-à-vis powerful international languages like English. An objection that has been raised here is that domains, as originally conceived, are parameters of language choice and not properties of languages...

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

Directory of Open Access Journals (Sweden)

Zomaya Albert Y

2006-12-01

Full Text Available Abstract Background Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix, secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. Conclusion Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset.

Evolutionary dynamics of protein domain architecture in plants

Directory of Open Access Journals (Sweden)

Zhang Xue-Cheng

2012-01-01

Full Text Available Abstract Background Protein domains are the structural, functional and evolutionary units of the protein. Protein domain architectures are the linear arrangements of domain(s in individual proteins. Although the evolutionary history of protein domain architecture has been extensively studied in microorganisms, the evolutionary dynamics of domain architecture in the plant kingdom remains largely undefined. To address this question, we analyzed the lineage-based protein domain architecture content in 14 completed green plant genomes. Results Our analyses show that all 14 plant genomes maintain similar distributions of species-specific, single-domain, and multi-domain architectures. Approximately 65% of plant domain architectures are universally present in all plant lineages, while the remaining architectures are lineage-specific. Clear examples are seen of both the loss and gain of specific protein architectures in higher plants. There has been a dynamic, lineage-wise expansion of domain architectures during plant evolution. The data suggest that this expansion can be largely explained by changes in nuclear ploidy resulting from rounds of whole genome duplications. Indeed, there has been a decrease in the number of unique domain architectures when the genomes were normalized into a presumed ancestral genome that has not undergone whole genome duplications. Conclusions Our data show the conservation of universal domain architectures in all available plant genomes, indicating the presence of an evolutionarily conserved, core set of protein components. However, the occurrence of lineage-specific domain architectures indicates that domain architecture diversity has been maintained beyond these core components in plant genomes. Although several features of genome-wide domain architecture content are conserved in plants, the data clearly demonstrate lineage-wise, progressive changes and expansions of individual protein domain architectures, reinforcing

Multiscale analysis of damage using dual and primal domain decomposition techniques

NARCIS (Netherlands)

Lloberas-Valls, O.; Everdij, F.P.X.; Rixen, D.J.; Simone, A.; Sluys, L.J.

2014-01-01

In this contribution, dual and primal domain decomposition techniques are studied for the multiscale analysis of failure in quasi-brittle materials. The multiscale strategy essentially consists in decomposing the structure into a number of nonoverlapping domains and considering a refined spatial

Experience during early adulthood shapes the learning capacities and the number of synaptic boutons in the mushroom bodies of honey bees (Apis mellifera).

Science.gov (United States)

Cabirol, Amélie; Brooks, Rufus; Groh, Claudia; Barron, Andrew B; Devaud, Jean-Marc

2017-10-01

The honey bee mushroom bodies (MBs) are brain centers required for specific learning tasks. Here, we show that environmental conditions experienced as young adults affect the maturation of MB neuropil and performance in a MB-dependent learning task. Specifically, olfactory reversal learning was selectively impaired following early exposure to an impoverished environment lacking some of the sensory and social interactions present in the hive. In parallel, the overall number of synaptic boutons increased within the MB olfactory neuropil, whose volume remained unaffected. This suggests that experience of the rich in-hive environment promotes MB maturation and the development of MB-dependent learning capacities. © 2017 Cabirol et al.; Published by Cold Spring Harbor Laboratory Press.

Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.

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Shah, M; Marchand, M; Corbeil, J

2012-01-01

One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.

Facilitation of learning: part 1.

Science.gov (United States)

Warburton, Tyler; Trish, Houghton; Barry, Debbie

2016-04-06

This article, the fourth in a series of 11, discusses the context for the facilitation of learning. It outlines the main principles and theories for understanding the process of learning, including examples which link these concepts to practice. The practical aspects of using these theories in a practice setting will be discussed in the fifth article of this series. Together, these two articles will provide mentors and practice teachers with knowledge of the learning process, which will enable them to meet the second domain of the Nursing and Midwifery Council's Standards to Support Learning and Assessment in Practice on facilitation of learning.

Investigation into the efficacy of generating synthetic pathological oscillations for domain adaptation

Science.gov (United States)

Lewis, Rory; Ellenberger, James; Williams, Colton; White, Andrew M.

2013-11-01

In the ongoing investigation of integrating Knowledge Discovery in Databases (KDD) into neuroscience, we present a paper that facilitates overcoming the two challenges preventing this integration. Pathological oscillations found in the human brain are difficult to evaluate because 1) there is often no time to learn and train off of the same distribution in the fatally sick, and 2) sinusoidal signals found in the human brain are complex and transient in nature requiring large data sets to work with which are costly and often very expensive or impossible to acquire. Overcoming these challenges in today's neuro-intensive-care unit (ICU) requires insurmountable resources. For these reasons, optimizing KDD for pathological oscillations so machine learning systems can predict neuropathological states would be of immense value. Domain adaptation, which allows a way of predicting on a separate set of data than the training data, can theoretically overcome the first challenge. However, the challenge of acquiring large data sets that show whether domain adaptation is a good candidate to test in a live neuro ICU remains a challenge. To solve this conundrum, we present a methodology for generating synthesized neuropathological oscillations for domain adaptation.

Blended learning versus traditional teaching-learning-setting: Evaluation of cognitive and affective learning outcomes for the inter-professional field of occupational medicine and prevention / Blended Learning versus traditionelles Lehr-Lernsetting: Evaluierung von kognitiven und affektiven Lernergebnissen für das interprofessionelle Arbeitsfeld Arbeitsmedizin und Prävention

OpenAIRE

Eckler Ursula; Greisberger Andrea; Höhne Franziska; Putz Peter

2017-01-01

Blended learning is characterised as a combination of face-to-face teaching and e-learning in terms of knowledge transfer, students’ learning activities and reduced presence at the teaching facility. The present cohort study investigated long-term effects of blended learning regarding cognitive outcomes as well as self-indicated estimates of immediate learning effects on the affective domain in the inter-professional field of occupational medicine. Physiotherapy students (bachelor degree) at ...

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Cross-Cultural Design of Mobile Mathematics Learning Service for South African Schools

Science.gov (United States)

Walsh, Tanja; Vainio, Teija; Varsaluoma, Jari

2014-01-01

In the era of mobile devices and services, researchers in the educational domain have been interested in how to support learning with mobile technology in both local and global contexts. Recent human-computer interaction (HCI) research in the educational domain has particularly focused on how to develop mobile learning services and how to evaluate…

U-CrAc Flexible Interior Doctrine, Agile Learning Environments

DEFF Research Database (Denmark)

Poulsen, Søren Bolvig; Rosenstand, Claus Andreas Foss

2012-01-01

The research domain of this article is flexible learning environment for immediate use. The research question is: How can the learning environment support an agile learning process? The research contribution of this article is a flexible interior doctrine. The research method is action research...

Learning models of activities involving interacting objects

DEFF Research Database (Denmark)

Manfredotti, Cristina; Pedersen, Kim Steenstrup; Hamilton, Howard J.

2013-01-01

We propose the LEMAIO multi-layer framework, which makes use of hierarchical abstraction to learn models for activities involving multiple interacting objects from time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were t...

Structural domain walls in polar hexagonal manganites

Science.gov (United States)

Kumagai, Yu

2014-03-01

The domain structure in the multiferroic hexagonal manganites is currently intensely investigated, motivated by the observation of intriguing sixfold topological defects at their meeting points [Choi, T. et al,. Nature Mater. 9, 253 (2010).] and nanoscale electrical conductivity at the domain walls [Wu, W. et al., Phys. Rev. Lett. 108, 077203 (2012).; Meier, D. et al., Nature Mater. 11, 284 (2012).], as well as reports of coupling between ferroelectricity, magnetism and structural antiphase domains [Geng, Y. et al., Nano Lett. 12, 6055 (2012).]. The detailed structure of the domain walls, as well as the origin of such couplings, however, was previously not fully understood. In the present study, we have used first-principles density functional theory to calculate the structure and properties of the low-energy structural domain walls in the hexagonal manganites [Kumagai, Y. and Spaldin, N. A., Nature Commun. 4, 1540 (2013).]. We find that the lowest energy domain walls are atomically sharp, with {210}orientation, explaining the orientation of recently observed stripe domains and suggesting their topological protection [Chae, S. C. et al., Phys. Rev. Lett. 108, 167603 (2012).]. We also explain why ferroelectric domain walls are always simultaneously antiphase walls, propose a mechanism for ferroelectric switching through domain-wall motion, and suggest an atomistic structure for the cores of the sixfold topological defects. This work was supported by ETH Zurich, the European Research Council FP7 Advanced Grants program me (grant number 291151), the JSPS Postdoctoral Fellowships for Research Abroad, and the MEXT Elements Strategy Initiative to Form Core Research Center TIES.

Incremental learning of skill collections based on intrinsic motivation

Science.gov (United States)

Metzen, Jan H.; Kirchner, Frank

2013-01-01

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

Transformative Learning: Patterns of Psychophysiologic Response and Technology-Enabled Learning and Intervention Systems

Science.gov (United States)

2008-09-01

Psychophysiologic Response and Technology -Enabled Learning and Intervention Systems PRINCIPAL INVESTIGATOR: Leigh W. Jerome, Ph.D...NUMBER Transformative Learning : Patterns of Psychophysiologic Response and Technology - Enabled Learning and Intervention Systems 5b. GRANT NUMBER...project entitled “Transformative Learning : Patterns of Psychophysiologic Response in Technology Enabled Learning and Intervention Systems.” The

Impaired Value Learning for Faces in Preschoolers With Autism Spectrum Disorder.

Science.gov (United States)

Wang, Quan; DiNicola, Lauren; Heymann, Perrine; Hampson, Michelle; Chawarska, Katarzyna

2018-01-01

One of the common findings in autism spectrum disorder (ASD) is limited selective attention toward social objects, such as faces. Evidence from both human and nonhuman primate studies suggests that selection of objects for processing is guided by the appraisal of object values. We hypothesized that impairments in selective attention in ASD may reflect a disruption of a system supporting learning about object values in the social domain. We examined value learning in social (faces) and nonsocial (fractals) domains in preschoolers with ASD (n = 25) and typically developing (TD) controls (n = 28), using a novel value learning task implemented on a gaze-contingent eye-tracking platform consisting of value learning and a selective attention choice test. Children with ASD performed more poorly than TD controls on the social value learning task, but both groups performed similarly on the nonsocial task. Within-group comparisons indicated that value learning in TD children was enhanced on the social compared to the nonsocial task, but no such enhancement was seen in children with ASD. Performance in the social and nonsocial conditions was correlated in the ASD but not in the TD group. The study provides support for a domain-specific impairment in value learning for faces in ASD, and suggests that, in ASD, value learning in social and nonsocial domains may rely on a shared mechanism. These findings have implications both for models of selective social attention deficits in autism and for identification of novel treatment targets. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Transnational Learning Processes

DEFF Research Database (Denmark)

Nedergaard, Peter

This paper analyses and compares the transnational learning processes in the employment field in the European Union and among the Nordic countries. Based theoretically on a social constructivist model of learning and methodologically on a questionnaire distributed to the relevant participants......, a number of hypotheses concerning transnational learning processes are tested. The paper closes with a number of suggestions regarding an optimal institutional setting for facilitating transnational learning processes.Key words: Transnational learning, Open Method of Coordination, Learning, Employment......, European Employment Strategy, European Union, Nordic countries....

Understanding the Advising Learning Process Using Learning Taxonomies

Science.gov (United States)

Muehleck, Jeanette K.; Smith, Cathleen L.; Allen, Janine M.

2014-01-01

To better understand the learning that transpires in advising, we used Anderson et al.'s (2001) revision of Bloom's (1956) taxonomy and Krathwohl, Bloom, and Masia's (1964) affective taxonomy to analyze eight student-reported advising outcomes from Smith and Allen (2014). Using the cognitive processes and knowledge domains of Anderson et al.'s…

Understanding the Problems of Learning Mathematics.

Science.gov (United States)

Semilla-Dube, Lilia

1983-01-01

A model is being developed to categorize problems in teaching and learning mathematics. Categories include problems due to language difficulties, lack of prerequisite knowledge, and those related to the affective domain. This paper calls on individuals to share teaching and learning episodes; those submitted will then be compiled and categorized.…

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

Science.gov (United States)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

Why do Team-Based Learning educators use TBL?

Directory of Open Access Journals (Sweden)

Sean Wu

2018-01-01

Full Text Available Aim: Evidence suggests that Team Based Learning (TBL is an effective teaching method for promoting student learning. Many people have also suggested that TBL supports other complex curriculum objectives, such as teamwork and communication skills. However, there is limited rigorous, substantive data to support these claims. Therefore, the purpose of this study was to assess medical educators’ perceptions of the outcomes affected by TBL, thereby highlighting the specific areas of TBL in need of research. Methods: We reviewed the published research on TBL in medical education, and identified 21 unique claims from authors regarding the outcomes of TBL. The claims centred on 4 domains: learning, behaviours, skills, and wellbeing. We created a questionnaire that asked medical educators to rate their support for each claim. The survey was distributed to the medical educators with experience teaching via TBL and who were active users of the Team Based Learning Collaborative listserv. Results: Fifty responses were received. Respondents strongly supported claims that TBL positively impacts behaviours and skills over traditional, lecture based teaching methods, including the promotion of self-directed learning, active learning, peer-to-peer learning, and teaching. In addition, respondents strongly supported claims that TBL promotes teamwork, collaboration, communication and problem solving. Most participants reported that TBL is more effective in promoting interpersonal, accountability, leadership and teaching skills. Conclusion: Medical educators that use TBL have favourable perceptions of the practice across a variety of domains. Future research should examine the actual effects of TBL on these domains.

How to be Brilliant at Numbers

CERN Document Server

Webber, Beryl

2010-01-01

How to be Brilliant at Numbers will help students to develop an understanding of numbers, place value, fractions and decimals. They will develop the language of number, and of the relationships between numbers. They will also use mathematics to solve problems and will develop mathematical reasoning. Using the worksheets in this book, pupils will learn about: ancient Greek numbers; coins; digits; consecutive numbers; magic ladders; fractions; matching pairs; multiples of 10; rounding; decimal un

Fastest learning in small-world neural networks

International Nuclear Information System (INIS)

Simard, D.; Nadeau, L.; Kroeger, H.

2005-01-01

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

Learning e-Learning

Directory of Open Access Journals (Sweden)

Gabriel ZAMFIR

2009-01-01

Full Text Available What You Understand Is What Your Cognitive Integrates. Scientific research develops, as a native environment, knowledge. This environment consists of two interdependent divisions: theory and technology. First division occurs as a recursive research, while the second one becomes an application of the research activity. Over time, theories integrate methodologies and technology extends as infrastructure. The engine of this environment is learning, as the human activity of knowledge work. The threshold term of this model is the concepts map; it is based on Bloom’ taxonomy for the cognitive domain and highlights the notion of software scaffolding which is grounded in Vygotsky’s Social Development Theory with its major theme, Zone of Proximal Development. This article is designed as a conceptual paper, which analyzes specific structures of this type of educational research: the model reflects a foundation for a theory and finally, the theory evolves as groundwork for a system. The outcomes of this kind of approach are the examples, which are, theoretically, learning outcomes, and practically exist as educational objects, so-called e-learning.

Does expert perceptual anticipation transfer to a dissimilar domain?

Science.gov (United States)

Müller, Sean; McLaren, Michelle; Appleby, Brendyn; Rosalie, Simon M

2015-06-01

The purpose of this experiment was to extend theoretical understanding of transfer of learning by investigating whether expert perceptual anticipation skill transfers to a dissimilar domain. The capability of expert and near-expert rugby players as well as novices to anticipate skill type within rugby (learning sport) was first examined using a temporal occlusion paradigm. Participants watched video footage of an opponent performing rugby skill types that were temporally occluded at different points in the opponent's action and then made a written prediction. Thereafter, the capability of participants to transfer their anticipation skill to predict pitch type in baseball (transfer sport) was examined. Participants watched video footage of a pitcher throwing different pitch types that were temporally occluded and made a written prediction. Results indicated that expert and near-expert rugby players anticipated significantly better than novices across all occlusion conditions. However, none of the skill groups were able to transfer anticipation skill to predict pitch type in baseball. The findings of this paper, along with existing literature, support the theoretical prediction that transfer of perceptual anticipation is expertise dependent and restricted to similar domains. (c) 2015 APA, all rights reserved).

Deep learning for healthcare: review, opportunities and challenges.

Science.gov (United States)

Miotto, Riccardo; Wang, Fei; Wang, Shuang; Jiang, Xiaoqian; Dudley, Joel T

2017-05-06

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Autonomous Inter-Task Transfer in Reinforcement Learning Domains

Science.gov (United States)

2008-08-01

Mountain Car. However, because the source task uses a car with a motor more than twice as powerful as in the 3D task, the tran- sition function learned in...powerful car motor or changing the surface friction of the hill • s: changing the range of the state variables • si: changing where the car starts...Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches, 1994. Mazda Ahmadi, Matthew E

Learning during processing Word learning doesn’t wait for word recognition to finish

Science.gov (United States)

Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. PMID:27471082

A Meta-Analysis of Working Memory Deficits in Children with Learning Difficulties: Is There a Difference between Verbal Domain and Numerical Domain?

Science.gov (United States)

Peng, Peng; Fuchs, Douglas

2016-01-01

Children with learning difficulties suffer from working memory (WM) deficits. Yet the specificity of deficits associated with different types of learning difficulties remains unclear. Further research can contribute to our understanding of the nature of WM and the relationship between it and learning difficulties. The current meta-analysis…

Neural Behavior Chain Learning of Mobile Robot Actions

Directory of Open Access Journals (Sweden)

Lejla Banjanovic-Mehmedovic

2012-01-01

Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

Stability with respect to domain of the low Mach number limit of compressible viscous fluids

Czech Academy of Sciences Publication Activity Database