Classification in medical images using adaptive metric k-NN
Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.
2010-03-01
The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.
Classification in medical image analysis using adaptive metric k-NN
Chen, Chen; Chernoff, Konstantin; Karemore, Gopal
2010-01-01
with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based...... on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure...
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
Goutte, Cyril; Larsen, Jan
1998-01-01
by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.
Mei, Gang; Xu, Nengxiong; Xu, Liangliang
2016-01-01
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization
Lei La
2012-01-01
Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.
H-Metric: Characterizing Image Datasets via Homogenization Based on KNN-Queries
Welington M da Silva
2012-01-01
Full Text Available Precision-Recall is one of the main metrics for evaluating content-based image retrieval techniques. However, it does not provide an ample perception of the properties of an image dataset immersed in a metric space. In this work, we describe an alternative metric named H-Metric, which is determined along a sequence of controlled modifications in the image dataset. The process is named homogenization and works by altering the homogeneity characteristics of the classes of the images. The result is a process that measures how hard it is to deal with a set of images in respect to content-based retrieval, offering support in the task of analyzing configurations of distance functions and of features extractors.
Cluster-based adaptive metric classification
Giotis, Ioannis; Petkov, Nicolai
2012-01-01
Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM) c
Cluster-based adaptive metric classification
Giotis, Ioannis; Petkov, Nicolai
2012-01-01
Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM)
Learning adaptive metric for robust visual tracking.
Jiang, Nan; Liu, Wenyu; Wu, Ying
2011-08-01
Matching the visual appearances of the target over consecutive image frames is the most critical issue in video-based object tracking. Choosing an appropriate distance metric for matching determines its accuracy and robustness, and thus significantly influences the tracking performance. Most existing tracking methods employ fixed pre-specified distance metrics. However, this simple treatment is problematic and limited in practice, because a pre-specified metric does not likely to guarantee the closest match to be the true target of interest. This paper presents a new tracking approach that incorporates adaptive metric learning into the framework of visual object tracking. Collecting a set of supervised training samples on-the-fly in the observed video, this new approach automatically learns the optimal distance metric for more accurate matching. The design of the learned metric ensures that the closest match is very likely to be the true target of interest based on the supervised training. Such a learned metric is discriminative and adaptive. This paper substantializes this new approach in a solid case study of adaptive-metric differential tracking, and obtains a closed-form analytical solution to motion estimation and visual tracking. Moreover, this paper extends the basic linear distance metric learning method to a more powerful nonlinear kernel metric learning method. Extensive experiments validate the effectiveness of the proposed approach, and demonstrate the improved performance of the proposed new tracking method.
J. Sofia Priya Dharshini
2014-09-01
Full Text Available In MIMO Technology, a cross layer design enhances the spectral efficiency, reliability and throughput of the network. In this paper, a cross-layer approach using k-NN based Adaptive Modulation Coding (AMC and Incremental Redundancy Hybrid Automatic Repeat Request (IR-HARQ is proposed for MIMO Systems. The proposed cross layer approach connects physical layer and data link layer to enhance the performance of MIMO network. By means of MIMO fading channels, the coded symbols are forwarded in the physical layer on a frame by frame fashion subsequently using Space Time Block Coding (STBC. The receiver computes the signal to noise ratio (SNR and forwards back to the AMC controller. The controller selects a suitable MCS for the next transmission through k-NN classifier supervised learning algorithm. IR-HARQ is utilized at the data link layer to regulate packet retransmissions. The obtained results prove that the proposed technique has better performance in terms of throughput, BER and spectral efficiency
Adapting the M3 Surveillance Metrics for an Unknown Baseline
Hamada, Michael Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Abes, Jeff I. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Jaramillo, Brandon Michael Lee [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-11-30
The original M_{3} surveillance metrics assume that the baseline is known. In this article, adapted M_{3} metrics are presented when the baseline is not known and estimated by available data. Deciding on how much available data is enough is also discussed.
Applications of image metrics in dynamic scene adaptation
Sadjadi, Firooz A.
1992-08-01
One of the major problems in dealing with the changes in the information contented a scene which is one of the characteristics of any dynamic scene is how adapt to these variations such that the performance of any automatic scene analyzer such as object recognizer be at its optimum. In this paper we examine the use of image and signal metrics for characterizing any scene variations and then we describe an automated system for the extraction of these quality measures and finally we will show how these metrics can be used for the automatic adaptation of an object recognition system and the resulting jump in the performance of this system.
Adaptive distance metric learning for diffusion tensor image segmentation.
Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W
2014-01-01
High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
Adaptive distance metric learning for diffusion tensor image segmentation.
Youyong Kong
Full Text Available High quality segmentation of diffusion tensor images (DTI is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.
Case-Based Behavior Adaptation Using an Inverse Trust Metric
2014-06-01
Case-Based Behavior Adaptation Using an Inverse Trust Metric Michael W. Floyd and Michael Drinkwater Knexus Research Corporation Springfield...Laboratory (Code 5514) Washington, DC , USA david.aha@nrl.navy.mil Abstract Robots are added to human teams to increase the team’s skills or...could result in the humans under- utilizing the it, unnecessarily monitoring the robot’s ac - tions, or possibly not using it at all. A robot could be
Application of Bounded Linear Stability Analysis Method for Metrics-Driven Adaptive Control
Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje
2009-01-01
This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.
Applicability of Existing Objective Metrics of Perceptual Quality for Adaptive Video Streaming
Søgaard, Jacob; Krasula, Lukás; Shahid, Muhammad
2016-01-01
Objective video quality metrics are designed to estimate the quality of experience of the end user. However, these objective metrics are usually validated with video streams degraded under common distortion types. In the presented work, we analyze the performance of published and known full......-reference and noreference quality metrics in estimating the perceived quality of adaptive bit-rate video streams knowingly out of scope. Experimental results indicate not surprisingly that state of the art objective quality metrics overlook the perceived degradations in the adaptive video streams and perform poorly...
Data-driven spatially-adaptive metric adjustment for visual tracking.
Jiang, Nan; Liu, Wenyu
2014-04-01
Matching visual appearances of the target over consecutive video frames is a fundamental yet challenging task in visual tracking. Its performance largely depends on the distance metric that determines the quality of visual matching. Rather than using fixed and predefined metric, recent attempts of integrating metric learning-based trackers have shown more robust and promising results, as the learned metric can be more discriminative. In general, these global metric adjustment methods are computationally demanding in real-time visual tracking tasks, and they tend to underfit the data when the target exhibits dynamic appearance variation. This paper presents a nonparametric data-driven local metric adjustment method. The proposed method finds a spatially adaptive metric that exhibits different properties at different locations in the feature space, due to the differences of the data distribution in a local neighborhood. It minimizes the deviation of the empirical misclassification probability to obtain the optimal metric such that the asymptotic error as if using an infinite set of training samples can be approximated. Moreover, by taking the data local distribution into consideration, it is spatially adaptive. Integrating this new local metric learning method into target tracking leads to efficient and robust tracking performance. Extensive experiments have demonstrated the superiority and effectiveness of the proposed tracking method in various tracking scenarios.
Quality Assessment of Adaptive Bitrate Videos using Image Metrics and Machine Learning
Søgaard, Jacob; Forchhammer, Søren; Brunnström, Kjell
2015-01-01
Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method...
Hyperspectral Imagery Super-Resolution by Adaptive POCS and Blur Metric
Hu, Shaoxing; Zhang, Shuyu; Zhang, Aiwu; Chai, Shatuo
2017-01-01
The spatial resolution of a hyperspectral image is often coarse as the limitations on the imaging hardware. A novel super-resolution reconstruction algorithm for hyperspectral imagery (HSI) via adaptive projection onto convex sets and image blur metric (APOCS-BM) is proposed in this paper to solve these problems. Firstly, a no-reference image blur metric assessment method based on Gabor wavelet transform is utilized to obtain the blur metric of the low-resolution (LR) image. Then, the bound used in the APOCS is automatically calculated via LR image blur metric. Finally, the high-resolution (HR) image is reconstructed by the APOCS method. With the contribution of APOCS and image blur metric, the fixed bound problem in POCS is solved, and the image blur information is utilized during the reconstruction of HR image, which effectively enhances the spatial-spectral information and improves the reconstruction accuracy. The experimental results for the PaviaU, PaviaC and Jinyin Tan datasets indicate that the proposed method not only enhances the spatial resolution, but also preserves HSI spectral information well. PMID:28054947
Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics
Samuel, Paul D.; Pines, Darryll J.
2009-01-01
This paper presents a methodology for detecting and diagnosing gear faults in the planetary stage of a helicopter transmission. This diagnostic technique is based on the constrained adaptive lifting (CAL) algorithm, an adaptive manifestation of the lifting scheme. Lifting is a time domain, prediction-error realization of the wavelet transform that allows for greater flexibility in the construction of wavelet bases. Adaptivity is desirable for gear diagnostics as it allows the technique to tailor itself to a specific transmission by selecting a set of wavelets that best represent vibration signals obtained while the gearbox is operating under healthy-state conditions. However, constraints on certain basis characteristics are necessary to enhance the detection of local wave-form changes caused by certain types of gear damage. The proposed methodology analyzes individual tooth-mesh waveforms from a healthy-state gearbox vibration signal that was generated using the vibration separation synchronous signal-averaging algorithm. Each waveform is separated into analysis domains using zeros of its slope and curvature. The bases selected in each analysis domain are chosen to minimize the prediction error, and constrained to have approximately the same-sign local slope and curvature as the original signal. The resulting set of bases is used to analyze future-state vibration signals and the lifting prediction error is inspected. The constraints allow the transform to effectively adapt to global amplitude changes, yielding small prediction errors. However, local waveform changes associated with certain types of gear damage are poorly adapted, causing a significant change in the prediction error. A diagnostic metric based on the lifting prediction error vector termed CAL4 is developed. The CAL diagnostic algorithm is validated using data collected from the University of Maryland Transmission Test Rig and the CAL4 metric is compared with the classic metric FM4.
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.
Saini, Indu; Singh, Dilbag; Khosla, Arun
2013-07-01
The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.
Guo, Shiping; Zhang, Rongzhi; Yang, Yikang; Xu, Rong; Liu, Changhai; Li, Jisheng
2016-04-01
Adaptive optics (AO) in conjunction with subsequent postprocessing techniques have obviously improved the resolution of turbulence-degraded images in ground-based astronomical observations or artificial space objects detection and identification. However, important tasks involved in AO image postprocessing, such as frame selection, stopping iterative deconvolution, and algorithm comparison, commonly need manual intervention and cannot be performed automatically due to a lack of widely agreed on image quality metrics. In this work, based on the Laplacian of Gaussian (LoG) local contrast feature detection operator, we propose a LoG domain matching operation to perceive effective and universal image quality statistics. Further, we extract two no-reference quality assessment indices in the matched LoG domain that can be used for a variety of postprocessing tasks. Three typical space object images with distinct structural features are tested to verify the consistency of the proposed metric with perceptual image quality through subjective evaluation.
Vorontsov, Mikhail; Weyrauch, Thomas; Lachinova, Svetlana; Gatz, Micah; Carhart, Gary
2012-07-15
Maximization of a projected laser beam's power density at a remotely located extended object (speckle target) can be achieved by using an adaptive optics (AO) technique based on sensing and optimization of the target-return speckle field's statistical characteristics, referred to here as speckle metrics (SM). SM AO was demonstrated in a target-in-the-loop coherent beam combining experiment using a bistatic laser beam projection system composed of a coherent fiber-array transmitter and a power-in-the-bucket receiver. SM sensing utilized a 50 MHz rate dithering of the projected beam that provided a stair-mode approximation of the outgoing combined beam's wavefront tip and tilt with subaperture piston phases. Fiber-integrated phase shifters were used for both the dithering and SM optimization with stochastic parallel gradient descent control.
An Adaptive Steganographic Method in Frequency Domain Based on Statistical Metrics of Image
Seyyed Amin Seyyedi
2015-05-01
Full Text Available Steganography is a branch of information hiding. A tradeoff between the hiding payload and quality of digital image steganographic schemes is major challenge of the steganographic methods. An adaptive steganographic method for embedding secret message into gray scale images is proposed. Before embedding the secret message, the cover image is transformed into frequency domain by integer wavelet. The middle frequency band of cover image is partitioned into 4×4 non overlapping blocks. The blocks by deviation and entropy metrics are classified into three categories: smooth, edge, and texture regions. Number of bits which can be embedded in a block is defined by block features. Moreover, RC4 encryption method is used to increase secrecy protection. Experimental results denote the feasibility of the proposed method. Statistical tests were conducted to collect related data to verify the security of method.
Optimal stellar photometry for multi-conjugate adaptive optics systems using science-based metrics
Turri, P; Stetson, P B; Fiorentino, G; Andersen, D R; Bono, G; Massari, D; Veran, J -P
2016-01-01
We present a detailed discussion of how to obtain precise stellar photometry in crowded fields using images obtained with multi-conjugate adaptive optics (MCAO), with the intent of informing the scientific development of this key technology for the Extremely Large Telescopes. We use deep J and K$_\\mathrm{s}$ exposures of NGC 1851 obtained using the Gemini Multi-Conjugate Adaptive Optics System (GeMS) on Gemini South to quantify the performance of the system and to develop an optimal strategy for extracting precise stellar photometry from the images using well-known PSF-fitting techniques. We judge the success of the various techniques we employ by using science-based metrics, particularly the width of the main sequence turn-off region. We also compare the GeMS photometry with the exquisite HST data of the same target in the visible. We show that the PSF produced by GeMS possesses significant spatial and temporal variability that must be accounted for during the photometric analysis by allowing the PSF model a...
Potter, Margaret A; Schuh, Russell G; Pomer, Bruce; Stebbins, Samuel
2013-01-01
Local health departments are organized, resourced, and operated primarily for routine public health services. For them, responding to emergencies and disasters requires adaptation to meet the demands of an emergency, and they must reallocate or augment resources, adjust work schedules, and, depending on severity and duration of the event, even compromise routine service outputs. These adaptations occur to varying degrees regardless of the type of emergency or disaster. The Adaptive Response Metric was developed through collaboration between a number of California health departments and university-based preparedness researchers. It measures the degree of "stress" from an emergency response as experienced by local health departments at the level of functional units (eg, nursing, administration, environmental services). Pilot testing of the Adaptive Response Metric indicates its utility for emergency planning, real-time decision making, and after-action analytics.
ELM-KNN for photometric redshift estimation of quasars
Zhang, Yanxia; Tu, Yang; Zhao, Yongheng; Tian, Haijun
2017-06-01
We explore photometric redshift estimation of quasars with the SDSS DR12 quasar sample. Firstly the quasar sample is separated into three parts according to different redshift ranges. Then three classifiers based on Extreme Learning Machine (ELM) are created in the three redshift ranges. Finally k-Nearest Neighbor (kNN) approach is applied on the three samples to predict photometric redshifts of quasars with multiwavelength photometric data. We compare the performance with different input patterns by ELM-KNN with that only by kNN. The experimental results show that ELM-KNN is feasible and superior to kNN (e.g. rms is 0.0751 vs. 0.2626 for SDSS sample), in other words, the ensemble method has the potential to increase regressor performance beyond the level reached by an individual regressor alone and will be a good choice when facing much more complex data.
Karsten Schulz
2009-11-01
Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.
Self-organized manifold learning and heuristic charting via adaptive metrics
Horvath, Denis; Brutovsky, Branislav
2014-01-01
Classical metric and non-metric multidimensional scaling (MDS) variants are widely known manifold learning (ML) methods which enable construction of low dimensional representation (projections) of high dimensional data inputs. However, their use is crucially limited to the cases when data are inherently reducible to low dimensionality. In general, drawbacks and limitations of these, as well as pure, MDS variants become more apparent when the exploration (learning) is exposed to the structured data of high intrinsic dimension. As we demonstrate on artificial and real-world datasets, the over-determination problem can be solved by means of the hybrid and multi-component discrete-continuous multi-modal optimization heuristics. Its remarkable feature is, that projections onto 2D are constructed simultaneously with the data categorization (classification) compensating in part for the loss of original input information. We observed, that the optimization module integrated with ML modeling, metric learning and categ...
Self-organised manifold learning and heuristic charting via adaptive metrics
Horvath, Denis; Ulicny, Jozef; Brutovsky, Branislav
2016-01-01
Classical metric and non-metric multidimensional scaling (MDS) variants represent the well-known manifold learning (ML) methods which enable construction of low-dimensional representation (projections) of high-dimensional data inputs. However, their use is limited to the cases when data are inherently reducible to low dimensionality. In general, drawbacks and limitations of these, as well as pure, MDS variants become more apparent when the exploration (learning) is exposed to the structured data of high intrinsic dimension. As we demonstrate on artificial as well as real-world datasets, the over-determination problem can be solved by means of the hybrid and multi-component discrete-continuous multi-modal optimisation heuristics. A remarkable feature of the approach is that projections onto 2D are constructed simultaneously with the data categorisation compensating in part for the loss of original input information. We observed that the optimisation module integrated with ML modelling, metric learning and categorisation leads to a nontrivial mechanism resulting in heuristic charting of data.
Spectral-Spatial Hyperspectral Image Classification Based on KNN
Huang, Kunshan; Li, Shutao; Kang, Xudong; Fang, Leyuan
2016-12-01
Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral-spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.
Cogntive Consistency Analysis in Adaptive Bio-Metric Authentication System Design
Gahangir Hossain
2015-07-01
Full Text Available Cognitive consistency analysis aims to continuously monitor one's perception equilibrium towards successful accomplishment of cognitive task. Opposite to cognitive flexibility analysis – cognitive consistency analysis identifies monotone of perception towards successful interaction process (e.g., biometric authentication and useful in generation of decision support to assist one in need. This study consider fingertip dynamics (e.g., keystroke, tapping, clicking etc. to have insights on instantaneous cognitive states and its effects in monotonic advancement towards successful authentication process. Keystroke dynamics and tapping dynamics are analyzed based on response time data. Finally, cognitive consistency and confusion (inconsistency are computed with Maximal Information Coefficient (MIC and Maximal Asymmetry Score (MAS, respectively. Our preliminary study indicates that a balance between cognitive consistency and flexibility are needed in successful authentication process. Moreover, adaptive and cognitive interaction system requires in depth analysis of user’s cognitive consistency to provide a robust and useful assistance.
A Novel Approach for the Diagnosis of Diabetes and Liver Cancer using ANFIS and Improved KNN
C. Kalaiselvi
2014-07-01
Full Text Available The multi-factorial, chronicle, severe diseases are cancer and diabetes. As a result of abnormal level of glucose in body leads to heart attack, kidney disease, renal failure and cancer. Many studies have been proved that several types of cancer are possible in diabetes patients having a high blood sugar. Many approaches are proposed in the past to diagnose both cancer and diabetes. Even though the existing approaches are efficient one, the classification accuracy is poor. An Enhanced approach is proposed to achieve a higher efficiency and lower complexity. Adaptive neuro fuzzy inference system is used to classify the dataset with the help of adaptive group based KNN. The Pima Indian diabetes dataset are used as input dataset and classified based on the attribute information. The experimental result shows the classification accuracy is better than the existing approaches such FLANN, ANN with FUZZYKNN.
Adaptive ant-based routing in wireless sensor networks using Energy Delay metrics
Yao-feng WEN; Yu-quan CHEN; Min PAN
2008-01-01
To find the optimal routing is always an important topic in wireless sensor networks (WSNs). Considering a WSN where the nodes have limited energy, we propose a novel Energy*Delay model based on ant algorithms ("E&D ANTS" for short)to minimize the time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. Our goal is not only to maximize the lifetime of the network but also to provide real-time data transmission services. However, because of the tradeoff of energy and delay in wireless network systems, the reinforcement learning (RL) algorithm is introduced to train the model. In this survey, the paradigm of E&D ANTS is explicated and compared to other ant-based routing algorithms like AntNet and AntChain about the issues of routing information, routing overhead and adaptation. Simulation results show that our method performs about seven times better than AntNet and also outperforms AntChain by more than 150% in terms of energy cost and delay per round.
Effects of poling process on KNN-modified piezoceramic properties
Marcos Rubio, F.; Romero, J. J.; Ochoa Guerrero, Diego A.; García García, José Eduardo; Pérez Pérez, Rafael; Fernández, José Francisco
2010-01-01
The influence of the orthorhombic to tetragonal phase transition near room temperature in the poling process of KNN-modified piezoceramics was studied. Poling temperatures of 25° and 120°C were used. The percentage of 90° domains reorientation induced by poling was evaluated trough X-ray diffraction analysis. The improvement of the piezoelectric properties when the poling temperature was 25°C could not be explained by the reorientation of 90° domains alone. Raman spectroscopy evidenced that t...
Metric Education Evaluation Package.
Kansky, Bob; And Others
This document was developed out of a need for a complete, carefully designed set of evaluation instruments and procedures that might be applied in metric inservice programs across the nation. Components of this package were prepared in such a way as to permit local adaptation to the evaluation of a broad spectrum of metric education activities.…
Kroon, Cindy D.
2007-01-01
Created for a Metric Day activity, Metric Madness is a board game for two to four players. Students review and practice metric vocabulary, measurement, and calculations by playing the game. Playing time is approximately twenty to thirty minutes.
Efficient and Flexible KNN Query Processing in Real-Life Road Networks
Lu, Yang; Bui, Bin; Zhao, Jiakui;
2008-01-01
Along with the developments of mobile services, effectively modeling road networks and efficiently indexing and querying network constrained objects has become a challenging problem. In this paper, we first introduce a road network model which captures real-life road networks better than previous...... models. Then, based on the proposed model, we propose a novel index named the RNG (Road Network Grid) index for accelerating KNN queries and continuous KNN queries over road network constrained data points. In contrast to conventional methods, speed limitations and blocking information of roads...... are included into the RNG index, which enables the index to support both distance-based and time-based KNN queries and continuous KNN queries. Our work extends previous ones by taking into account more practical scenarios, such as complexities in real-life road networks and time-based KNN queries. Extensive...
Synthesis and characterizations of BNT-BT-KNN ceramics for energy storage applications
Chandrasekhar, M.; Kumar, P.
2016-09-01
Dielectric, ferroelectric and piezoelectric properties of the (0.94-x) Bi0.5Na0.5TiO3-0.06BaTiO3-xK0.5Na0.5NbO3/BNT-BT-KNN ceramics with x = 0.02 and 0.05 (2KNN and 5KNN) were studied in detail. Dielectric study and temperature-dependent polarization hysteresis loops indicated a ferroelectric-to-antiferroelectric transition at depolarization temperature (Td). The low Td in both the ceramic samples suggested the dominant antiferroelectric ordering at room temperature (RT), which was also confirmed by RT polarization and strain hysteresis loops studies. Antiferroelectric-to-paraelectric phase transition temperature (Tm) was nearly same for both systems. The 5KNN ceramic samples showed the relaxor behaviour. The values of the dielectric constant, Td, and maximum strain percentage increased, whereas the coercive field and remnant polarization decreased with the increase of the KNN percentage in the BNT-BT-KNN system. High-energy storage density ∼0.5 J/cm3 at RT hinted about the suitability of the 5KNN system for energy storage applications.
The measurement of KNN and KLL in at 800 MeV
Glass, G.; Bhatia, T. S.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.; Verwest, B. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Riley, P. J.; Pepin, G. P.; Bonner, B. E.; Simmons, J. E.
1983-09-01
The spin-transfer parameters KNN and KLL have been measured for ? at 0° and 800 MeV for neutron momenta between 700 and {1200 MeV}/{c}. Peak values of KNN and KLL are -0.30 ± 0.05 and -0.5 ± 0.1 respectively. These results are in substantial disagreement with VerWest's field theoretic model.
Fabrication of transparent lead-free KNN glass ceramics by incorporation method.
Yongsiri, Ploypailin; Eitssayeam, Sukum; Rujijanagul, Gobwut; Sirisoonthorn, Somnuk; Tunkasiri, Tawee; Pengpat, Kamonpan
2012-02-16
The incorporation method was employed to produce potassium sodium niobate [KNN] (K0.5Na0.5NbO3) glass ceramics from the KNN-SiO2 system. This incorporation method combines a simple mixed-oxide technique for producing KNN powder and a conventional melt-quenching technique to form the resulting glass. KNN was calcined at 800°C and subsequently mixed with SiO2 in the KNN:SiO2 ratio of 75:25 (mol%). The successfully produced optically transparent glass was then subjected to a heat treatment schedule at temperatures ranging from 525°C -575°C for crystallization. All glass ceramics of more than 40% transmittance crystallized into KNN nanocrystals that were rectangular in shape and dispersed well throughout the glass matrix. The crystal size and crystallinity were found to increase with increasing heat treatment temperature, which in turn plays an important role in controlling the properties of the glass ceramics, including physical, optical, and dielectric properties. The transparency of the glass samples decreased with increasing crystal size. The maximum room temperature dielectric constant (εr) was as high as 474 at 10 kHz with an acceptable low loss (tanδ) around 0.02 at 10 kHz.
Scalable Large-Margin Mahalanobis Distance Metric Learning
Shen, Chunhua; Wang, Lei
2010-01-01
For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. By employing the principle of margin maximization to achieve better generalization performances, this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite (psd) matrix is the unknown variable. a specialized gradient descent method is proposed. our algorithm is much more efficient and has a better performance in scalability compared with existing methods. Experiments on benchmark data sets suggest that, compared with state-of-the-art metric learning algorithms, our algorithm can achieve a comparable classification accuracy with reduced computation...
Klauder, J R
1998-01-01
Canonical quantization may be approached from several different starting points. The usual approaches involve promotion of c-numbers to q-numbers, or path integral constructs, each of which generally succeeds only in Cartesian coordinates. All quantization schemes that lead to Hilbert space vectors and Weyl operators---even those that eschew Cartesian coordinates---implicitly contain a metric on a flat phase space. This feature is demonstrated by studying the classical and quantum ``aggregations'', namely, the set of all facts and properties resident in all classical and quantum theories, respectively. Metrical quantization is an approach that elevates the flat phase space metric inherent in any canonical quantization to the level of a postulate. Far from being an unwanted structure, the flat phase space metric carries essential physical information. It is shown how the metric, when employed within a continuous-time regularization scheme, gives rise to an unambiguous quantization procedure that automatically ...
López-Hoffman, Laura; Breshears, David D.; Allen, Craig D.; Miller, Marc L.
2013-01-01
Under a changing climate, devising strategies to help stakeholders adapt to alterations to ecosystems and their services is of utmost importance. In western North America, diminished snowpack and river flows are causing relatively gradual, homogeneous (system-wide) changes in ecosystems and services. In addition, increased climate variability is also accelerating the incidence of abrupt and patchy disturbances such as fires, floods and droughts. This paper posits that two key variables often considered in landscape ecology—the rate of change and the degree of patchiness of change—can aid in developing climate change adaptation strategies. We use two examples from the “borderland” region of the southwestern United States and northwestern Mexico. In piñon-juniper woodland die-offs that occurred in the southwestern United States during the 2000s, ecosystem services suddenly crashed in some parts of the system while remaining unaffected in other locations. The precise timing and location of die-offs was uncertain. On the other hand, slower, homogeneous change, such as the expected declines in water supply to the Colorado River delta, will likely impact the entire ecosystem, with ecosystem services everywhere in the delta subject to alteration, and all users likely exposed. The rapidity and spatial heterogeneity of faster, patchy climate change exemplified by tree die-off suggests that decision-makers and local stakeholders would be wise to operate under a Rawlsian “veil of ignorance,” and implement adaptation strategies that allow ecosystem service users to equitably share the risk of sudden loss of ecosystem services before actual ecosystem changes occur. On the other hand, in the case of slower, homogeneous, system-wide impacts to ecosystem services as exemplified by the Colorado River delta, adaptation strategies can be implemented after the changes begin, but will require a fundamental rethinking of how ecosystems and services are used and valued. In
GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.
Ahmed Shamsul Arefin
Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.
Baldwin, Carryl L; Penaranda, B N
2012-01-02
Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms.
Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM
Lin, Jian; Hamidouche, Khaled; Zheng, Jie; Lu, Xiaoyi; Vishnu, Abhinav; Panda, Dhabaleswar
2015-08-05
Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemic evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.
2012-11-01
As the old 'publish or perish' adage is brought into question, additional research-impact indices, known as altmetrics, are offering new evaluation alternatives. But such metrics may need to adjust to the evolution of science publishing.
Efficient kNN Classification With Different Numbers of Nearest Neighbors.
Zhang, Shichao; Li, Xuelong; Zong, Ming; Zhu, Xiaofeng; Wang, Ruili
2017-04-12
k nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed k value (even though set by experts) to all test samples. Previous solutions assign different k values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal k values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal k values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal k values. In the test stage, the kTree fast outputs the optimal k value for each test sample, and then, the kNN classification can be conducted using the learned optimal k value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed k value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different k values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of
A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition
Pao, Tsang-Long; Liao, Wen-Yuan; Chen, Yu-Te
2008-01-01
In this chapter, we present a speech emotion recognition system to compare several classifiers on the clean speech and noisy speech. Our proposed WD-KNN classifier outperforms the other three KNN-based classifiers at every SNR level and achieves highest accuracy from clean speech to 20dB noisy speech when compared with all other classifiers. Similar to (Neiberg et al, 2006), GMM is a feasible technique for emotion classification on the frame level and the results of GMM are better than perfor...
Hybrid collaborative filtering algorithm based on KNN-SVM%基于KNN-SVM的混合协同过滤推荐算法
吕成戍; 王维国; 丁永健
2012-01-01
数据稀疏性问题对协同过滤推荐系统的推荐精度有很大影响,为此,融合缺失数据平衡方法,提出了一个基于KNN-SVM的混合协同过滤推荐算法.利用K-最近邻法对训练集中的缺失数据进行填补,然后通过支持向量机交叉验证进行分类,综合两者优点,从而克服数据质量对推荐算法的影响.在标杆数据集上进行了仿真实验,数值结果证明了方法的有效性.%The problem of data sparsenees has great influence on collaborative filtering recommendation system' s accuracy, balance for this missing data fusion method, this paper proposed a hybrid collaborative filtering algorithms based on KNN-SVM. K-nearest neighbor method used the training set to fill the missing data, and then cross-validated by SVM classification. Comprehend advantages both KNN and SVM in order to overcome impact of data quality on the recommended algorithm. The proposed approach was applied to benchmark problems, and the simulation results show it is valid.
The measurement of KNN and KLL in p↘p → n↘X at 800 MeV
Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Riley, P. J.; Pepin, G. P.; Bonner, B. E.; Simmons, J. E.
1981-03-01
The spin transfer parameters, KNN and KLL have been measured in p↘p → n↘X at 0° and 800 MeV for neutron momenta between 700 and 1200 MeV/c. Peak values of KNN and KLL are -.3±.05 and -.5±.1 respectively.
Bellet, Aurelien; Sebban, Marc
2015-01-01
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learnin
Maximum-entropy parameter estimation for the k-NN modified value-difference kernel
Hendrickx, I.H.E.; van den Bosch, A.; Verbruggen, R.; Taatgen, N.; Schomaker, L.
2004-01-01
We introduce an extension of the modified value-difference kernel of $k$-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybri
Maximum-Entropy Parameter Estimation for the k-nn Modified Value-Difference Kernel
Hendrickx, Iris; Bosch, Antal van den
2005-01-01
We introduce an extension of the modified value-difference kernel of k-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid
Photometric redshift estimation for quasars by integration of KNN and SVM
Han, Bo; Ding, Hong-Peng; Zhang, Yan-Xia; Zhao, Yong-Heng
2016-05-01
The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding z phot. Our analysis has found two dense regions with catastrophic failure, one in the range of z phot ɛ [0.3, 1.2] and the other in the range of zphot ɛ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).
Quevedo, Hernando
2016-01-01
We review the problem of describing the gravitational field of compact stars in general relativity. We focus on the deviations from spherical symmetry which are expected to be due to rotation and to the natural deformations of mass distributions. We assume that the relativistic quadrupole moment takes into account these deviations, and consider the class of axisymmetric static and stationary quadrupolar metrics which satisfy Einstein's equations in empty space and in the presence of matter represented by a perfect fluid. We formulate the physical conditions that must be satisfied for a particular spacetime metric to describe the gravitational field of compact stars. We present a brief review of the main static and axisymmetric exact solutions of Einstein's vacuum equations, satisfying all the physical conditions. We discuss how to derive particular stationary and axisymmetric solutions with quadrupolar properties by using the solution generating techniques which correspond either to Lie symmetries and B\\"acku...
Schiaffino, L.; Rosado Muñoz, A.; Guerrero Martínez, J.; Francés Villora, J.; Gutiérrez, A.; Martínez Torres, I.; Kohan, y. D. R.
2016-04-01
Deep Brain Stimulation (DBS) applies electric pulses into the subthalamic nucleus (STN) improving tremor and other symptoms associated to Parkinson’s disease. Accurate STN detection for proper location and implant of the stimulating electrodes is a complex task and surgeons are not always certain about final location. Signals from the STN acquired during DBS surgery are obtained with microelectrodes, having specific characteristics differing from other brain areas. Using supervised learning, a trained model based on previous microelectrode recordings (MER) can be obtained, being able to successfully classify the STN area for new MER signals. The K Nearest Neighbours (K-NN) algorithm has been successfully applied to STN detection. However, the use of the fuzzy form of the K-NN algorithm (KNN-F) has not been reported. This work compares the STN detection algorithm of K-NN and KNN-F. Real MER recordings from eight patients where previously classified by neurophysiologists, defining 15 features. Sensitivity and specificity for the classifiers are obtained, Wilcoxon signed rank non-parametric test is used as statistical hypothesis validation. We conclude that the performance of KNN-F classifier is higher than K-NN with p<0.01 in STN specificity.
基于相似度衡量的决策树自适应迁移%Self-adaptive Transfer for Decision Trees Based on Similarity Metric
王雪松; 潘杰; 程玉虎; 曹戈
2013-01-01
如何解决迁移学习中的负迁移问题并合理把握迁移的时机与方法,是影响迁移学习广泛应用的关键点.针对这个问题,提出一种基于相似度衡量机制的决策树自适应迁移方法(Self-adaptive transfer for decision trees based on a similarity metric,STDT).首先,根据源任务数据集是否允许访问,自适应地采用成分预测概率或路径预测概率对决策树间的相似性进行判定,其亲和系数作为量化衡量关联任务相似程度的依据.然后,根据多源判定条件确定是否采用多源集成迁移,并将相似度归一化后依次分配给待迁移源决策树作为迁移权值.最后,对源决策树进行集成迁移以辅助目标任务实现决策.基于UCI机器学习库的仿真结果说明,与多源迁移加权求和算法(Weighted sum rule,WSR)和MS-TrAdaBoost相比,STDT能够在保证决策精度的前提下实现更为快速的迁移.
Nawrocki, J; Chino, J; Light, K; Vergalasova, I; Craciunescu, O [Duke University Medical Center, Durham, NC (United States)
2014-06-01
Purpose: To compare PET extracted metrics and investigate the role of a gradient-based PET segmentation tool, PET Edge (MIM Software Inc., Cleveland, OH), in the context of an adaptive PET protocol for node positive gynecologic cancer patients. Methods: An IRB approved protocol enrolled women with gynecological, PET visible malignancies. A PET-CT was obtained for treatment planning prescribed to 45–50.4Gy with a 55– 70Gy boost to the PET positive nodes. An intra-treatment PET-CT was obtained between 30–36Gy, and all volumes re-contoured. Standard uptake values (SUVmax, SUVmean, SUVmedian) and GTV volumes were extracted from the clinician contoured GTVs on the pre- and intra-treament PET-CT for primaries and nodes and compared with a two tailed Wilcoxon signed-rank test. The differences between primary and node GTV volumes contoured in the treatment planning system and those volumes generated using PET Edge were also investigated. Bland-Altman plots were used to describe significant differences between the two contouring methods. Results: Thirteen women were enrolled in this study. The median baseline/intra-treatment primary (SUVmax, mean, median) were (30.5, 9.09, 7.83)/( 16.6, 4.35, 3.74), and nodes were (20.1, 4.64, 3.93)/( 6.78, 3.13, 3.26). The p values were all < 0.001. The clinical contours were all larger than the PET Edge generated ones, with mean difference of +20.6 ml for primary, and +23.5 ml for nodes. The Bland-Altman revealed changes between clinician/PET Edge contours to be mostly within the margins of the coefficient of variability. However, there was a proportional trend, i.e. the larger the GTV, the larger the clinical contours as compared to PET Edge contours. Conclusion: Primary and node SUV values taken from the intratreament PET-CT can be used to assess the disease response and to design an adaptive plan. The PET Edge tool can streamline the contouring process and lead to smaller, less user-dependent contours.
Processing and characterizations of BNT-KNN ceramics for actuator applications
Mallam Chandrasekhar
2016-06-01
Full Text Available BNT-KNN powder (with composition 0.93Bi0.5Na0.5TiO3–0.07K0.5Na0.5NbO3 was synthesized as a single perovskite phase by conventional solid state reaction route and dense ceramics were obtained by sintering of powder compacts at 1100 °C for 4 h. Dielectric study confirmed relaxor behaviour, whereas the microstructure study showed sharp cornered cubic like grains with an average grain size ∼1.15 µm. The saturated polarization vs. electric field (P-E hysteresis loops confirmed the ferroelectric (FE nature while the butterfly shaped strain vs. electric field (S-E loops suggested the piezoelectric nature of the BNT-KNN ceramic samples. Maximum electric field induced strain of ∼0.62% suggested the usefulness of this system for actuator applications.
Continually Answering Constraint k-NN Queries in Unstructured P2P Systems
Bin Wang; Xiao-Chun Yang; Guo-Ren Wang; Ge Yu; Lei Chen; X. Sean Wang,; Xue-Min Lin
2008-01-01
We consider the problem of efficiently computing distributed geographical k-NN queries in an unstructured peer-to-peer (P2P) system, in which each peer is managed by an individual organization and can only communicate with its logical neighboring peers. Such queries are based on local filter query statistics, and require as less communication cost as possible, which makes it more difficult than the existing distributed k-NN queries. Especially, we hope to reduce candidate peers and degrade communication cost. In this paper, we propose an efficient pruning technique to minimize the number of candidate peers to be processed to answer the k-Nnqueries. Our approach is especially suitable for continuous k-Nnqueries when updating peers, including changing ranges of peers, dynamically leaving or joining peers, and updating data in a peer.In addition, simulation results show that the proposed approach outperforms the existing Minimum Bounding Rectangle (MBR)-based query approaches, especially for continuous queries.
The First-Principle Calculation of La-doping Effect on Piezoelectricity in Tetragonal KNN Crystal
Zhang, Qiaoli; Zhu, Jiliang; Yuan, Daqing; Zhu, Bo; Wang, Mingsong; Zhu, Xiaohong; Fan, Ping; Zuo, Yi; Zheng, Yongnan; Zhu, Shengyun
2012-05-01
The La-dopping effect on the piezoelectricity in the K0.5Na0.5NbO3 (KNN) crystal with a tetragonal phase is investigated for the first time using the first-principle calculation based on density functional theory. The full potentiallinearized augumented plane wave plus local orbitals (APW-LO) method and the supercell method are used in the calculation for the KNN crystal with and without the La doping. The results show that the piezoelectricity originates from the strong hybridization between the Nb atom and the O atom, and the substitution of the K or Na atom by the La impurity atom introduces the anisotropic relaxation and enhances the piezoelectricity at first and then restrains the hybridization of the Nb-O atoms when the La doping content further increases.
KNN/BNT Composite Lead-Free Films for High-Frequency Ultrasonic Transducer Applications
Lau, Sien Ting; Ji, Hong Fen; Li, Xiang; Ren, Wei; Zhou, Qifa; Shung, K. Kirk
2011-01-01
Lead-free K0.5Na0.5NbO3/Bi0.5Na0.5TiO3 (KNN/BNT) films have been fabricated by a composite sol-gel technique. Crystalline KNN fine powder was dispersed in the BNT precursor solution to form a composite slurry which was then spin-coated onto a platinum-buffered Si substrate. Repeated layering and vacuum infiltration were applied to produce 5-μm-thick dense composite film. By optimizing the sintering temperature, the films exhibited good dielectric and ferroelectric properties comparable to PZT films. A 193-MHz high-frequency ultrasonic transducer fabricated from this composite film showed a −6-dB bandwidth of approximately 34%. A tungsten wire phantom was imaged to demonstrate the capability of the transducer. PMID:21244994
Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers
Sanal Kumar, K. P.; Bhavani, R., Dr.
2017-08-01
Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.
A record flexible piezoelectric KNN ultrafine-grained nanopowder-based nanogenerator
Qing-tang Xue
2015-01-01
Full Text Available We explore a type piezoelectric material 0.9525(K0.5Na0.5NbO3-0.0475LiTaO3 (KNN-LTS which can be used to fabricate nanogenerator with high output voltage and current due to its high piezoelectric constant (d33. Because of its unique structure mixed with multi-wall carbon nanotube and polydimethylsiloxane, the output voltage is up to 53 V and the output current is up to 15 uA (current density of 12.5 uA/cm2 respectively. The value of the output voltage and output current represent the highest level in the piezoelectric field reported to date. The KNN-LTS nanopowder-based nanogenerator can also be used as a sensitive motion detection sensor.
Akmal Mat Harttat Maziati
2015-06-01
Full Text Available Alkaline niobate mainly potassium sodium niobate, (KxNa1-x NbO3 (abreviated as KNN has long attracted attention as piezoelectric materials as its high Curie temperature (Tc and piezoelectric properties. The volatility of alkaline element (K, Na is, however detrimental to the stoichiometry of KNN, contributing to the failure to achieve high-density structure and lead to the formation of intrinsic defects. By partially doping of several rare-earth elements, the inherent defects could be improved significantly. Therefore, considerable attempts have been made to develop doped-KNN based ceramic materials with high electrical properties. In this paper, these research activities are reviewed, including dopants type and doping role in KNN perovskite structure.
A multiple-point spatially weighted k-NN method for object-based classification
Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.
2016-10-01
Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.
Improving Estimation Accuracy of Quasars’ Photometric Redshifts by Integration of KNN and SVM
Han, Bo; Ding, Hongpeng; Zhang, Yanxia; Zhao, Yongheng
2015-08-01
The massive photometric data collected from multiple large-scale sky surveys offers significant opportunities for measuring distances of many celestial objects by photometric redshifts zphot in a wide coverage of the sky. However, catastrophic failure, an unsolved problem for a long time, exists in the current photometric redshift estimation approaches (such as k-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding zphot. By analysis, we observe two dense regions with catastrophic failure, one in the range of zphot [0.1,1.1], the other in the range of zphot [1.5,2.5]. In the second stage, we map the photometric multiband input pattern of points falling into the two ranges from original attribute space into high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space, many bad estimation points resulted from catastrophic failure by using simple Euclidean distance computation in KNN can be identified by classification hyperplane SVM and further be applied correction. Experimental results based on SDSS data for quasars showed that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshift.
Lead-free piezoelectric KNN-BZ-BNT films with a vertical morphotropic phase boundary
Wen Chen
2015-07-01
Full Text Available The lead-free piezoelectric 0.915K0.5Na0.5NbO3-0.075BaZrO3-0.01Bi0.5Na0.5TiO3 (0.915KNN-0.075BZ-0.01BNT films were prepared by a chemical solution deposition method. The films possess a pure rhomobohedral perovskite phase and a dense surface without crack. The temperature-dependent dielectric properties of the specimens manifest that only phase transition from ferroelectric to paraelectric phase occurred and the Curie temperature is 217 oC. The temperature stability of ferroelectric phase was also supported by the stable piezoelectric properties of the films. These results suggest that the slope of the morphotropic phase boundary (MPB for the solid solution formed with the KNN and BZ in the films should be vertical. The voltage-induced polarization switching, and a distinct piezo-response suggested that the 0.915 KNN-0.075BZ-0.01BNT films show good piezoelectric properties.
Sharp metric obstructions for quasi-Einstein metrics
Case, Jeffrey S
2011-01-01
Using the tractor calculus to study conformally warped manifolds, we adapt results of Gover and Nurowski to give sharp metric obstructions to the existence of quasi-Einstein metrics on suitably generic manifolds. We do this by introducing an analogue of the curvature tractor, itself the tractor analogue of the curvature of the Fefferman-Graham ambient metric. We then use these obstructions to produce a tensorial invariant which is polynomial in the Riemann curvature and its divergence, and which gives the desired obstruction. In particular, this leads to a generalization to arbitrary dimensions of an algorithm due to Bartnik and Tod for finding static metrics. We also explore the consequences of this work for gradient Ricci solitons, finding an obstruction to their existence on suitably generic manifolds, and observing an interesting similarity between the nonnegativity of the curvature tractor and Hamilton's matrix Harnack inequality.
GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.
Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim
2016-08-01
In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.
Ver Hoef, Jay M; Temesgen, Hailemariam
2013-01-01
Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages.
Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance
Ruan, Yue; Xue, Xiling; Liu, Heng; Tan, Jianing; Li, Xi
2017-08-01
K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QKNN achieves O(n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).
Rubio-Marcos, F., E-mail: frmarcos@icv.csic.es [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain); Marchet, P.; Merle-Mejean, T. [SPCTS, UMR 6638 CNRS, Universite de Limoges, 123, Av. A. Thomas, 87060 Limoges (France); Fernandez, J.F. [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain)
2010-09-01
Lead-free KNN-modified piezoceramics of the system (Li,Na,K)(Nb,Ta,Sb)O{sub 3} were prepared by conventional solid-state sintering. The X-ray diffraction patterns revealed a perovskite phase, together with some minor secondary phase, which was assigned to K{sub 3}LiNb{sub 6}O{sub 17}, tetragonal tungsten-bronze (TTB). A structural evolution toward a pure tetragonal structure with the increasing sintering time was observed, associated with the decrease of TTB phase. A correlation between higher tetragonality and higher piezoelectric response was clearly evidenced. Contrary to the case of the LiTaO{sub 3} modified KNN, very large abnormal grains with TTB structure were not detected. As a consequence, the simultaneous modification by tantalum and antimony seems to induce during sintering a different behaviour from the one of LiTaO{sub 3} modified KNN.
Photometric Redshift Estimation for Quasars by Integration of KNN and SVM
Han, Bo; Zhang, Yanxia; Zhao, Yongheng
2016-01-01
The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding z$_{\\rm phot}$. By analysis, we find two dense regions with catastrophic failure, one in the range of z$_{\\rm phot}\\in[0.3,1.2]$, the other in the range of z$_{\\rm phot}\\in [1.2,2.1]$. In the second stage, we map the photometric input pattern of points falling into the two ranges from original attribute space into a high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space,...
Could k-NN Classifier be Useful in Tree Leaves Recognition?
Horaisová Kateřina
2014-06-01
Full Text Available This paper presents a method for affine invariant recognition of two-dimensional binary objects based on 2D Fourier power spectrum. Such function is translation invariant and their moments of second order enable construction of affine invariant spectrum except of the rotation effect. Harmonic analysis of samples on circular paths generates Fourier coefficients whose absolute values are affine invariant descriptors. Affine invariancy is approximately saved also for large digital binary images as demonstrated in the experimental part. The proposed method is tested on artificial data set first and consequently on a large set of 2D binary digital images of tree leaves. High dimensionality of feature vectors is reduced via the kernel PCA technique with Gaussian kernel and the k-NN classifier is used for image classification. The results are summarized as k-NN classifier sensitivity after dimensionality reduction. The resulting descriptors after dimensionality reduction are able to distinguish real contours of tree leaves with acceptable classification error. The general methodology is directly applicable to any set of large binary images. All calculations were performed in the MATLAB environment
BC-iDistance: an optimized high-dimensional index for KNN processing
LIANG Jun-jie; FENG Yu-cai
2008-01-01
To facilitate high-dimensional KNN queries, based on techniques of approximate vector presentation and one-dimensional transformation, an optimal index is proposed, namely Bit-Code based iDistance ( BC-iDis-tance). To overcome the defect of much information loss for iDistance in one-dimensional transformation, the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector, and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively. By employing the classical B + tree, this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing. Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.
A New Method to Improve the Electrical Properties of KNN-based Ceramics: Tailoring Phase Fraction
Lv, Xiang
2017-08-18
Although both the phase type and fraction of multi-phase coexistence can affect the electrical properties of (K,Na)NbO3 (KNN)-based ceramics, effects of phase fraction on their electrical properties were few concerned. In this work, through changing the calcination temperature of CaZrO3 powders, we successfully developed the 0.96K0.5Na0.5Nb0.96Sb0.04O3-0.01CaZrO3-0.03Bi0.5Na0.5HfO3 ceramics containing a wide rhombohedral-tetragonal (R-T) phase coexistence with the variations of T (or R) phase fractions. It was found that higher T phase fraction can warrant a larger piezoelectric constant (d33) and d33 also showed a linear variation with respect to tetragonality ratio (c/a). More importantly, a number of domain patterns were observed due to high T phase fraction and large c/a ratio, greatly benefiting the piezoelectricity. In addition, the improved ferroelectric fatigue behavior and thermal stability were also shown in the ceramics containing high T phase fraction. Therefore, this work can bring a new viewpoint into the physical mechanism of KNN-based ceramics behind R-T phase coexistence.
Improving Shape Retrieval by Integrating AIR and Modified Mutual kNN Graph
Nouman Qadeer
2015-01-01
Full Text Available In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutual kNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutual kNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.
Complexity Metrics for Spreadsheet Models
Bregar, Andrej
2008-01-01
Several complexity metrics are described which are related to logic structure, data structure and size of spreadsheet models. They primarily concentrate on the dispersion of cell references and cell paths. Most metrics are newly defined, while some are adapted from traditional software engineering. Their purpose is the identification of cells which are liable to errors. In addition, they can be used to estimate the values of dependent process metrics, such as the development duration and effort, and especially to adjust the cell error rate in accordance with the contents of each individual cell, in order to accurately asses the reliability of a model. Finally, two conceptual constructs - the reference branching condition cell and the condition block - are discussed, aiming at improving the reliability, modifiability, auditability and comprehensibility of logical tests.
Yasno, J. P.; Tirado-Mejia, L.; Kiminami, R.; Gaona, J.; Raigoza, C. F. V.
2013-09-01
Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi{sub 1}/2Na{sub 1}/2)1-x(Bi{sub 1}/2K{sub 1}/2)xTiO{sub 3}]-0,03[(Na{sub 1}/2K{sub 1}/2)NbO{sub 3}] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27). This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 degree centigrade. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.
Yasno, J. P.; Tirado-Mejia, L.; Kiminamp, R. H. G. A.; Gaona, J. S.; Raigoza, C. E. V.
2013-10-01
Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi{sub 1}/2Na{sub 1}/2){sub 1}-x(Bi{sub 1}/2K{sub 1}/2)xTiO{sub 3}]-0,03[(Na{sub 1}/2K{sub 1}/2)NbO{sub 3}] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27). This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 degree centigrade. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy. (Author)
Ferrari, Frank, E-mail: frank.ferrari@ulb.ac.be [Service de Physique Theorique et Mathematique, Universite Libre de Bruxelles and International Solvay Institutes, Campus de la Plaine, CP 231, 1050 Bruxelles (Belgium); Klevtsov, Semyon, E-mail: semyon.klevtsov@ulb.ac.be [Service de Physique Theorique et Mathematique, Universite Libre de Bruxelles and International Solvay Institutes, Campus de la Plaine, CP 231, 1050 Bruxelles (Belgium); ITEP, B. Cheremushkinskaya 25, Moscow 117218 (Russian Federation); Zelditch, Steve, E-mail: zelditch@math.northwestern.edu [Department of Mathematics, Northwestern University, Evanston, IL 60208 (United States)
2013-04-01
The purpose of this article is to propose a new method to define and calculate path integrals over metrics on a Kaehler manifold. The main idea is to use finite dimensional spaces of Bergman metrics, as an approximation to the full space of Kaehler metrics. We use the theory of large deviations to decide when a sequence of probability measures on the spaces of Bergman metrics tends to a limit measure on the space of all Kaehler metrics. Several examples are considered.
Quality Metric Development Framework (qMDF
K. Mustafa
2005-01-01
Full Text Available Several object-oriented metrics have been developed and used in conjunction with the quality models to predict the overall quality of software. However, it may not be enough to propose metrics. The fundamental question may be of their validity, utility and reliability. It may be much significant to be sure that these metrics are really useful and for that their construct validity must be assured. Thereby, good quality metrics must be developed using a foolproof and sound framework / model. A critical review of literature on the attempts in this regard reveals that there is no standard framework or model available for such an important activity. This study presents a framework for the quality metric development called Metric Development Framework (qMDF, which is prescriptive in nature. qMDF is a general framework but it has been established specially with ideas of object-oriented metrics. qMDF has been implemented to develop a good quality design metric, as a validation of proposed framework. Finally, it is defended that adaptation of qMDF by metric developers would yield good quality metrics, while ensuring their construct validity, utility, reliability and reduced developmental effort.
Measurement of KNN, KSS, KSL, and KLL in n↘p → p↘n at 800 MeV in the CEX region
Ransome, R. D.; Hollas, C. L.; Riley, P. J.; Bonner, B. E.; Gibbs, W. R.; McNaughton, M. W.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.
1981-03-01
The spin transfer parameters1 KNN, KSS, and KLL have been measured for np elastic scattering at 800 MeV between 165° and 180° c.m. The parameters KNN and KLL are in good agreement with the quasi-free reaction p↘d → n↘pp at 180°.2
Park, Soo-Byung; Yang, Yu-Mi; Kim, Yong-Il; Cho, Bong-Hae; Jung, Yun-Hoa; Hwang, Dae-Seok
2012-08-01
The aim of the present study was to use cone-beam computed tomography volume superimposition to investigate the effect of bimaxillary orthognathic surgery on condylar head remodeling. Using a retrospective study design, 2 investigators evaluated the cone-beam computed tomography data of subjects who had undergone Le Fort I osteotomy and mandibular setback surgery. The predictor variable was time, grouped as preoperative versus postoperative. The outcome variables were the measurement changes of the condylar heads and the distribution of the condylar head remodeling signs. Paired t and χ(2) tests were performed for the purposes of the 2-dimensional metric analysis and the condylar head remodeling distribution. P Bimaxillary orthognathic surgery caused a decrease in the condylar heights and condylar head remodeling. The cone-beam computed tomography volume superimposition method showed that the condylar head had undergone remodeling after bimaxillary surgery. Copyright © 2012 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.
Evaluation of normalization methods for cDNA microarray data by k-NN classification
Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J
2004-12-17
Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double
NASA science publications have used the metric system of measurement since 1970. Although NASA has maintained a metric use policy since 1979, practical constraints have restricted actual use of metric units. In 1988, an amendment to the Metric Conversion Act of 1975 required the Federal Government to adopt the metric system except where impractical. In response to Public Law 100-418 and Executive Order 12770, NASA revised its metric use policy and developed this Metric Transition Plan. NASA's goal is to use the metric system for program development and functional support activities to the greatest practical extent by the end of 1995. The introduction of the metric system into new flight programs will determine the pace of the metric transition. Transition of institutional capabilities and support functions will be phased to enable use of the metric system in flight program development and operations. Externally oriented elements of this plan will introduce and actively support use of the metric system in education, public information, and small business programs. The plan also establishes a procedure for evaluating and approving waivers and exceptions to the required use of the metric system for new programs. Coordination with other Federal agencies and departments (through the Interagency Council on Metric Policy) and industry (directly and through professional societies and interest groups) will identify sources of external support and minimize duplication of effort.
Kamath, Sudha D; Mahato, Krishna K
2009-08-01
The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325 nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.
Tang, Y.; Jing, L.; Li, H.; Liu, Q.; Ding, H.
2016-04-01
In this paper, the potential of multiple-point statistics (MPS) for object-based classification is explored using a modified k-nearest neighbour (k-NN) classification method (MPk-NN). The method first utilises a training image derived from a classified map to characterise the spatial correlation between multiple points of land cover classes, overcoming the limitations of two-point geostatistical methods, and then the spatial information in the form of multiple-point probability is incorporated into the k-NN classifier. The remotely sensed image of an IKONOS subscene of the Beijing urban area was selected to evaluate the method. The image was object-based classified using the MPk-NN method and several alternatives, including the traditional k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the MPk-NN approach can achieve greater classification accuracy relative to the alternatives, which are 82.05% and 89.12% based on pixel and object testing data, respectively. Thus, the proposed method is appropriate for object-based classification.
A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION
Nima Khairdoost
2015-03-01
Full Text Available Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA and Linear Discriminant Analysis (LDA are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA and cosine similarity-based K Nearest Neighbor (KNN classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.
Estimation of the activation energy of sintering in KNN ceramics using master sintering theory
Singh, Rajan; Patro, P. K.; Kulkarni, Ajit R.; Harendranath, C. S.
2014-04-01
The master sintering curve (MSC) of K0.5Na0.5NbO3 (KNN) ceramics was constructed using constant heating rate dilatometry data based on the combined stage sintering model. The linear shrinkage was recorded using three heating rates 5 °C, 7 °C and 11 °C/ min. The obtained results suggest that in MSC, the sintered density is a unique function of the integral of a temperature function over time and it is independent of the sintering history. The MSC theory can be applied to predict shrinkage and final density. Also, it can be used to design a reproducible process to fabricate ceramics with required density.
The First-Principle Calculation of La-doping Effect on Piezoelectricity in Tetragonal KNN Crystal
张乔丽; 朱基亮; 袁大庆; 朱波; 王明松; 朱小红; 范平; 左翼; 郑永男; 朱升云
2012-01-01
The La-dopping effect on the piezoelectricity in the K0.5Na0.5NbO3 （KNN） crystal with a tetragonal phase is investigated for the first time using the first-principle calculation based on density functional theory. The full potentiallinearized augumented plane wave plus local orbitals （APW-LO） method and the supercell method are used in the calculation for the KNN crystal with and without the La doping. The results show that the piezoelectricity originates from the strong hybridization between the Nb atom and the O atom, and the substitution of the K or Na atom by the La impurity atom introduces the anisotropic relaxation and enhances the piezoelectricity at first and then restrains the hybridization of the Nb-O atoms when the La doping content further increases.
The high density phase of the k-NN hard core lattice gas model
Nath, Trisha; Rajesh, R.
2016-07-01
The k-NN hard core lattice gas model on a square lattice, in which the first k next nearest neighbor sites of a particle are excluded from being occupied by another particle, is the lattice version of the hard disc model in two dimensional continuum. It has been conjectured that the lattice model, like its continuum counterpart, will show multiple entropy-driven transitions with increasing density if the high density phase has columnar or striped order. Here, we determine the nature of the phase at full packing for k up to 820 302 . We show that there are only eighteen values of k, all less than k = 4134, that show columnar order, while the others show solid-like sublattice order.
Zhu, Benpeng; Zhang, Zhiqiang; Ma, Teng; Yang, Xiaofei; Li, Yongxiang; Shung, K. Kirk; Zhou, Qifa
2015-04-01
Using tape-casting technology, 35 μm free-standing (100)-textured Li doped KNN (KNLN) thick film was prepared by employing NaNbO3 (NN) as template. It exhibited similar piezoelectric behavior to lead containing materials: a longitudinal piezoelectric coefficient (d33) of ˜150 pm/V and an electromechanical coupling coefficient (kt) of 0.44. Based on this thick film, a 52 MHz side-looking miniature transducer with a bandwidth of 61.5% at -6 dB was built for Intravascular ultrasound (IVUS) imaging. In comparison with 40 MHz PMN-PT single crystal transducer, the rabbit aorta image had better resolution and higher noise-to-signal ratio, indicating that lead-free (100)-textured KNLN thick film may be suitable for IVUS (>50 MHz) imaging.
Daza, Maicol A Ochoa
2011-01-01
We introduce and develop the theory of metric sheaves. A metric sheaf $\\A$ is defined on a topological space $X$ such that each fiber is a metric model. We describe the construction of the generic model as the quotient space of the sheaf through an appropriate filter. Semantics in this model is completely controlled and understood by the forcing rules in the sheaf.
Metrics for Polyphonic Sound Event Detection
Annamaria Mesaros
2016-05-01
Full Text Available This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.
李华兵; 杨昆
2016-01-01
针对现有差异甲基化区域 DMRs 识别方法中过度删除显著性弱的甲基化位点、DMRs 长度受限以及不能直接处理多类的问题，提出了一种利用滑动窗口和 KNN 算法识别不同类别间DMRs 的算法.算法先通过滑动窗口结合 KNN 分类器筛选候选区域，再根据误差率合并候选区域得到 DMRs.真实数据上的实验表明，算法的分类性能、聚类指数明显优于对照算法，扩展了对照的 Ong 算法识别的 DMRs 长度，并能发现 Ong 算法未发现的 DMRs.%In view of the shortcomings of the existing methods for identifying differentially methylated regions(DMRs),such as over deletion of sites that significance are weaker,region length limitation and can’t be directly processed by the multi-class.An algorithm of identifying DMRs based on sliding window and k-nearest neighbor(KNN)is proposed.In this method,candidate regions are obtained using sliding windows and KNN,and it merges candidate regions to get DMRs.Through real data simulation results demonstrate the method is superior to control method, such as classification performance,cluster index,the DMRs length of the control methods of Ong is extended and find some DMRs that can’t be found in control algorithm of Ong.
Optical and Piezoelectric Study of KNN Solid Solutions Co-Doped with La-Mn and Eu-Fe
Jesús-Alejandro Peña-Jiménez
2016-09-01
Full Text Available The solid-state method was used to synthesize single phase potassium-sodium niobate (KNN co-doped with the La3+–Mn4+ and Eu3+–Fe3+ ion pairs. Structural determination of all studied solid solutions was accomplished by XRD and Rietveld refinement method. Electron paramagnetic resonance (EPR studies were performed to determine the oxidation state of paramagnetic centers. Optical spectroscopy measurements, excitation, emission and decay lifetime were carried out for each solid solution. The present study reveals that doping KNN with La3+–Mn4+ and Eu3+–Fe3+ at concentrations of 0.5 mol % and 1 mol %, respectively, improves the ferroelectric and piezoelectric behavior and induce the generation of optical properties in the material for potential applications.
Chistyakov, Vyacheslav
2015-01-01
Aimed toward researchers and graduate students familiar with elements of functional analysis, linear algebra, and general topology; this book contains a general study of modulars, modular spaces, and metric modular spaces. Modulars may be thought of as generalized velocity fields and serve two important purposes: generate metric spaces in a unified manner and provide a weaker convergence, the modular convergence, whose topology is non-metrizable in general. Metric modular spaces are extensions of metric spaces, metric linear spaces, and classical modular linear spaces. The topics covered include the classification of modulars, metrizability of modular spaces, modular transforms and duality between modular spaces, metric and modular topologies. Applications illustrated in this book include: the description of superposition operators acting in modular spaces, the existence of regular selections of set-valued mappings, new interpretations of spaces of Lipschitzian and absolutely continuous mappings, the existe...
Metric diffusion along foliations
Walczak, Szymon M
2017-01-01
Up-to-date research in metric diffusion along compact foliations is presented in this book. Beginning with fundamentals from the optimal transportation theory and the theory of foliations; this book moves on to cover Wasserstein distance, Kantorovich Duality Theorem, and the metrization of the weak topology by the Wasserstein distance. Metric diffusion is defined, the topology of the metric space is studied and the limits of diffused metrics along compact foliations are discussed. Essentials on foliations, holonomy, heat diffusion, and compact foliations are detailed and vital technical lemmas are proved to aide understanding. Graduate students and researchers in geometry, topology and dynamics of foliations and laminations will find this supplement useful as it presents facts about the metric diffusion along non-compact foliation and provides a full description of the limit for metrics diffused along foliation with at least one compact leaf on the two dimensions.
刘翔; 侯志强; 余旺盛; 黄安奇
2015-01-01
This paper researched the problem that the traditional Mean-Shift algorithm could not track a size-changing object effectively,and proposed a new algorithm based on assistant decision-making of object similarity metric,to estimate the scale and orientation of a tracking window with adaptive bandwidth.Firstly,it adopted the saliency of object and background to im-prove tracking accuracy for orientation,and then employed the local exhaustive search to compute the similarity metric between object model and the certain region where was around the tracking center in each frame.Finally,it determined the object scale variation by similar pixel amounts.What’s more,it defined a novel bandwidth criterion for improving adaptability in tracking bandwidth.The experimental results prove that the present method can improve the tracking accuracy effectively in orientation between space and scale.%针对传统窗宽固定不变的Mean-Shift跟踪算法不能实时地适应目标尺寸大小变化这一问题，提出了一种基于目标相似度辅助决策的带宽自适应跟踪算法。首先利用目标与背景的特征显著性，提高跟踪算法空间定位准确性；然后利用局部穷搜索的方法，计算目标模型与每一帧目标跟踪中心点附近一定区域的相似性；最后通过统计分析前后帧相似像素点数目变化，确定目标尺度变化情况，从而建立一种自适应更新带宽准则，提高算法对目标尺度变化的自适应性。实验结果表明，改进的算法可以有效地提高Mean-Shift跟踪算法空间和尺度定位准确性。
V. Myroniuk
2015-01-01
This paper deals with modern experience of statistical inventory of forests using ground-based inventory and remote sensing data (RSD). A detailed analysis of the k-NN method of classification of satellite images is given and features of its applying for thematic mapping of forest fund under the statistical forest inventory defined. The algorithm for calculating the stock of plantings for the statistical software with R open source is shown on the example of local research material.
Enterprise Sustainment Metrics
The Air Force sustainment enterprise does not have metrics that . . . adequately measure key sustainment parameters, according to the 2011 National...standardized and do not contribute to the overall assessment of the sustainment enterprise . This paper explores the development of a single metric...is not feasible. To answer the question does the sustainment enterprise provide cost-effective readiness for a weapon system, a suite of metrics is
-Metric Space: A Generalization
Farshid Khojasteh
2013-01-01
Full Text Available We introduce the notion of -metric as a generalization of a metric by replacing the triangle inequality with a more generalized inequality. We investigate the topology of the spaces induced by a -metric and present some essential properties of it. Further, we give characterization of well-known fixed point theorems, such as the Banach and Caristi types in the context of such spaces.
D.A. Adeniyi
2016-01-01
Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.
QSAR analysis of furanone derivatives as potential COX-2 inhibitors: kNN MFA approach
Ruchi Bhatiya
2014-12-01
Full Text Available A series of thirty-two furanone derivatives with their cyclooxygenase-2 inhibitory activity were subjected to quantitative structural–activity relationship analysis to derive a correlation between biological activity as a dependent variable and various descriptors as independent variables by using V-LIFE MDS3.5 software. The significant 2D QSAR model showed correlation coefficient (r2 = 0.840, standard error of estimation (SEE = 0.195, and a cross-validated squared correlation coefficient (q2 = 0.773. The descriptors involved in the building of 2D QSAR model are retention index for six membered rings, total number of oxygen connected with two single bonds, polar surface area excluding P and S plays a significant role in COX-2 inhibition. 3D-QSAR performed via Step Wise K Nearest Neighbor Molecular Field Analysis [(SW kNN MFA] with partial least-square (PLS technique showed high predictive ability (r2 = 0.7622, q2 = 0.7031 and standard error = 0.3660 explaining the majority of the variance in the data with two principle components. The results of the present study may be useful in the design of more potent furanone derivatives as COX-2 inhibitors.
Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier
Md. Kamrul Hasan
2017-01-01
Full Text Available Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn, standard deviation (SD, standard error (SE, modified mean absolute value (MMAV, roll-off (R, and zero crossing (ZC from the epileptic signal. The k-nearest neighbours (k-NN algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.
The measurement of KNN,KLLINpd -->nX and p9Be-->nX at 800 MeV
Riley, P. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Bonner, B. E.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.
1981-07-01
The polarization transfer parameters,KNN and KLL, have been measured in pd-->nX and p9Be-->nX at 0° and 800 MeV. The quasifree pd-->nX values for KNN and KLL are close to the free n-p measured values. The rather large values of KLL demonstrate that this transfer mechanism will provide a useful source of polarized neutrons at medium energies.
Mattioli W
2012-02-01
Full Text Available Estimation and mapping of forest attributes are a fundamental support for forest management planning. This study describes a practical experimentation concerning the use of design-based k-Nearest Neighbors (k-NN approach to estimate and map selected attributes in the framework of inventories at forest management level. The study area was the Chiarino forest within the Gran Sasso and Monti della Laga National Park (central Italy. Aboveground biomass and current annual increment of tree volume were selected as the attributes of interest for the test. Field data were acquired within 28 sample plots selected by stratified random sampling. Satellite data were acquired by a Landsat 5 TM multispectral image. Attributes from field surveys and Landsat image processing were coupled by k-NN to predict the attributes of interest for each pixel of the Landsat image. Achieved results demonstrate the effectiveness of the k-NN approach for statistical estimation, that is compatible with the produced forest attribute raster maps and also proves to be characterized, in the considered study case, by a precision double than that obtained by conventional inventory based on field sample plots only.
Prognostic Performance Metrics
National Aeronautics and Space Administration — This chapter presents several performance metrics for offline evaluation of prognostics algorithms. A brief overview of different methods employed for performance...
Topics in Metric Approximation
Leeb, William Edward
This thesis develops effective approximations of certain metrics that occur frequently in pure and applied mathematics. We show that distances that often arise in applications, such as the Earth Mover's Distance between two probability measures, can be approximated by easily computed formulas for a wide variety of ground distances. We develop simple and easily computed characterizations both of norms measuring a function's regularity -- such as the Lipschitz norm -- and of their duals. We are particularly concerned with the tensor product of metric spaces, where the natural notion of regularity is not the Lipschitz condition but the mixed Lipschitz condition. A theme that runs throughout this thesis is that snowflake metrics (metrics raised to a power less than 1) are often better-behaved than ordinary metrics. For example, we show that snowflake metrics on finite spaces can be approximated by the average of tree metrics with a distortion bounded by intrinsic geometric characteristics of the space and not the number of points. Many of the metrics for which we characterize the Lipschitz space and its dual are snowflake metrics. We also present applications of the characterization of certain regularity norms to the problem of recovering a matrix that has been corrupted by noise. We are able to achieve an optimal rate of recovery for certain families of matrices by exploiting the relationship between mixed-variable regularity conditions and the decay of a function's coefficients in a certain orthonormal basis.
Nakasho Kazuhisa
2016-09-01
Full Text Available In this article, we mainly formalize in Mizar [2] the equivalence among a few compactness definitions of metric spaces, norm spaces, and the real line. In the first section, we formalized general topological properties of metric spaces. We discussed openness and closedness of subsets in metric spaces in terms of convergence of element sequences. In the second section, we firstly formalize the definition of sequentially compact, and then discuss the equivalence of compactness, countable compactness, sequential compactness, and totally boundedness with completeness in metric spaces.
Robust Transfer Metric Learning for Image Classification.
Ding, Zhengming; Fu, Yun
2017-02-01
Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes that the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice, because of the expensive labeling cost. In this paper, we develop a robust transfer metric learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain. Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, i.e., sample space and feature space. In the sample space, domain-wise and class-wise adaption schemes are adopted to bridge the gap of marginal and conditional distribution disparities across two domains. In the feature space, our metric is built in a marginalized denoising fashion and low-rank constraint, which make it more robust to tackle noisy data in reality. Furthermore, we design an explicit rank constraint regularizer to replace the rank minimization NP-hard problem to guide the low-rank metric learning. Experimental results on several standard benchmarks demonstrate the effectiveness of our proposed RTML by comparing it with the state-of-the-art transfer learning and metric learning algorithms.
Regina Lopes Schimitt
2011-01-01
Full Text Available INTRODUÇÃO: O ritmo social é um conceito que integra a relação entre Zeitgebers (sincronizadores sociais e os marcadores de tempo endógenos, e pode ser avaliado com a Escala de Ritmo Social (Social Rhythm Metric-17, SRM-17. O objetivo deste estudo foi realizar a adaptação da versão brasileira da SRM-17 para o português angolano, comparando as duas escalas em populações que utilizam o mesmo idioma mas apresentam diferenças culturais. MÉTODOS: A versão brasileira da SRM-17 foi submetida à avaliação de 10 estudantes universitários angolanos, que analisaram o grau de clareza de cada um dos 15 itens do instrumento usando uma escala visual analógica de 10 cm e propuseram modificações ao texto. Foi realizada revisão dos resultados para a elaboração da versão final, bem como prova de leitura e relatório final. RESULTADOS: A versão final angolana manteve uma equivalência de itens com relação à versão em português brasileiro. A versão avaliada demonstrou um grau satisfatório de clareza e equivalência semântica na maioria dos itens. Porém, alguns itens apresentaram um escore na clareza inferior à média aritmética de compreensão global do instrumento (8,38±1,0. CONCLUSÃO: Apesar de o português ser o idioma oficial nos dois países, há diferenças culturais significativas nas duas populações. Este trabalho apresenta uma versão adaptada à realidade angolana de um instrumento específico para aferir ritmo social. O processo de adaptação transcultural deve efetivar-se com estudos de validação do instrumento final em uma amostra maior da população, onde também poderão ser avaliadas as equivalências operacional, de medida e funcional.INTRODUCTION: Social rhythm is a concept that correlates social Zeitgebers (synchronizers with endogenous markers of time, and can be assessed with the Social Rhythm Metric-17 (SRM-17. The aim of this study was to adapt the Brazilian version of the SRM-17 to Angolan
Metrics, Media and Advertisers: Discussing Relationship
Marco Aurelio de Souza Rodrigues
2014-11-01
Full Text Available This study investigates how Brazilian advertisers are adapting to new media and its attention metrics. In-depth interviews were conducted with advertisers in 2009 and 2011. In 2009, new media and its metrics were celebrated as innovations that would increase advertising campaigns overall efficiency. In 2011, this perception has changed: New media’s profusion of metrics, once seen as an advantage, started to compromise its ease of use and adoption. Among its findings, this study argues that there is an opportunity for media groups willing to shift from a product-focused strategy towards a customer-centric one, through the creation of new, simple and integrative metrics.
Surveillance Metrics Sensitivity Study
Bierbaum, R; Hamada, M; Robertson, A
2011-11-01
In September of 2009, a Tri-Lab team was formed to develop a set of metrics relating to the NNSA nuclear weapon surveillance program. The purpose of the metrics was to develop a more quantitative and/or qualitative metric(s) describing the results of realized or non-realized surveillance activities on our confidence in reporting reliability and assessing the stockpile. As a part of this effort, a statistical sub-team investigated various techniques and developed a complementary set of statistical metrics that could serve as a foundation for characterizing aspects of meeting the surveillance program objectives. The metrics are a combination of tolerance limit calculations and power calculations, intending to answer level-of-confidence type questions with respect to the ability to detect certain undesirable behaviors (catastrophic defects, margin insufficiency defects, and deviations from a model). Note that the metrics are not intended to gauge product performance but instead the adequacy of surveillance. This report gives a short description of four metrics types that were explored and the results of a sensitivity study conducted to investigate their behavior for various inputs. The results of the sensitivity study can be used to set the risk parameters that specify the level of stockpile problem that the surveillance program should be addressing.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in transportation, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational terminology,…
Metrics for Food Distribution.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in food distribution, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Computational visual distinctness metric
Martínez-Baena, J.; Toet, A.; Fdez-Vidal, X.R.; Garrido, A.; Rodríguez-Sánchez, R.
1998-01-01
A new computational visual distinctness metric based on principles of the early human visual system is presented. The metric is applied to quantify (1) the visual distinctness of targets in complex natural scenes and (2) the perceptual differences between compressed and uncompressed images. The new
ZHAOZhen-gang
2005-01-01
We have constructed the positive definite metric matrixes for the bounded domains of Rn and proved an inequality which is about the Jacobi matrix of a harmonic mapping on a bounded domain of Rn and the metric matrix of the same bounded domain.
Privacy Metrics and Boundaries
L-F. Pau (Louis-François)
2005-01-01
textabstractThis paper aims at defining a set of privacy metrics (quantitative and qualitative) in the case of the relation between a privacy protector ,and an information gatherer .The aims with such metrics are: -to allow to assess and compare different user scenarios and their differences; for ex
Prevention of Spammers and Promoters in Video Social Networks using SVM-KNN
Indira K
2014-10-01
Full Text Available As online social networks acquire larger user bases, they also become more interesting targets for spammers and promoters. Spam can take very different forms on social websites, especially in the form of videos and cannot always be detected by analyzing textual content. There are online video sharing systems that allow the users to post videos s response to any type of discussion topic. This feature encourages some of the users to post polluted content illegally as responses and there may be content promoters who try to promote them in the top listed search. Content pollution like spread advertise to generate sales, disseminate pornography, and compromise system reputation may threaten the trust of users on the system, thus weaken its success in promoting social interactions. As a solution for this problem, we classify the users as spammers, content promoters and legitimate users by building a test collection of real YouTube users using which we can provide a classification we use of content, individual and social attributes that help in characterizing each user class. For effective classification we use SVMKNN which is an active learning approach. Our proposed approach poses a promising alternative to simply considering all users as legitimate or to randomly selecting users for manual inspection. In simple SVM training is very slow on whole dataset and not works very well on multiple classes. To overcome this problem and to provide efficient classification in fast manner we proposed new approach is SVM-KNN. Train a Support Vector Machine on K no of collections of nearest neighbours.
Holographic Spherically Symmetric Metrics
Petri, Michael
The holographic principle (HP) conjectures, that the maximum number of degrees of freedom of any realistic physical system is proportional to the system's boundary area. The HP has its roots in the study of black holes. It has recently been applied to cosmological solutions. In this article we apply the HP to spherically symmetric static space-times. We find that any regular spherically symmetric object saturating the HP is subject to tight constraints on the (interior) metric, energy-density, temperature and entropy-density. Whenever gravity can be described by a metric theory, gravity is macroscopically scale invariant and the laws of thermodynamics hold locally and globally, the (interior) metric of a regular holographic object is uniquely determined up to a constant factor and the interior matter-state must follow well defined scaling relations. When the metric theory of gravity is general relativity, the interior matter has an overall string equation of state (EOS) and a unique total energy-density. Thus the holographic metric derived in this article can serve as simple interior 4D realization of Mathur's string fuzzball proposal. Some properties of the holographic metric and its possible experimental verification are discussed. The geodesics of the holographic metric describe an isotropically expanding (or contracting) universe with a nearly homogeneous matter-distribution within the local Hubble volume. Due to the overall string EOS the active gravitational mass-density is zero, resulting in a coasting expansion with Ht = 1, which is compatible with the recent GRB-data.
Blecher, David P
2012-01-01
The present paper is a sequel to our paper "Metric characterization of isometries and of unital operator spaces and systems". We characterize certain common objects in the theory of operator spaces (unitaries, unital operator spaces, operator systems, operator algebras, and so on), in terms which are purely linear-metric, by which we mean that they only use the vector space structure of the space and its matrix norms. In the last part we give some characterizations of operator algebras (which are not linear-metric in our strict sense described in the paper).
Characterization of Multiplicative Metric Completeness
Badshshah e Romer
2016-03-01
Full Text Available We established fixed point theorems in multiplicative metric spaces. The obtained results generalize Banach contraction principle in multiplicative metric spaces and also characterize completeness of the underlying multiplicative metric space.
Webb, Ted
1976-01-01
Describes the program to convert to the metric system all of General Motors Corporation products. Steps include establishing policy regarding employee-owned tools, setting up training plans, and making arrangements with suppliers. (MF)
Schweizer, B
2005-01-01
Topics include special classes of probabilistic metric spaces, topologies, and several related structures, such as probabilistic normed and inner-product spaces. 1983 edition, updated with 3 new appendixes. Includes 17 illustrations.
Carver, Gary P.
1994-05-01
The federal agencies are working with industry to ease adoption of the metric system. The goal is to help U.S. industry compete more successfully in the global marketplace, increase exports, and create new jobs. The strategy is to use federal procurement, financial assistance, and other business-related activities to encourage voluntary conversion. Based upon the positive experiences of firms and industries that have converted, federal agencies have concluded that metric use will yield long-term benefits that are beyond any one-time costs or inconveniences. It may be time for additional steps to move the Nation out of its dual-system comfort zone and continue to progress toward metrication. This report includes 'Metric Highlights in U.S. History'.
Mass Customization Measurements Metrics
Nielsen, Kjeld; Brunø, Thomas Ditlev; Jørgensen, Kaj Asbjørn
2014-01-01
A recent survey has indicated that 17 % of companies have ceased mass customizing less than 1 year after initiating the effort. This paper presents measurement for a company’s mass customization performance, utilizing metrics within the three fundamental capabilities: robust process design, choice...... navigation, and solution space development. A mass customizer when assessing performance with these metrics can identify within which areas improvement would increase competitiveness the most and enable more efficient transition to mass customization....
Balvín, Radek
2013-01-01
With growing amount of data produced by users on social media the need of extraction of relevant data for marketing, research and other uses grows as well. The bachelor thesis named "Social media metrics" presents the issues of monitoring, measurement and metrics of social media. In the research part it also maps and captures the present Czech practice in measurement and monitoring of social media. I also rate the use of social media monitoring tools and usual methods of social media measurem...
METRICS DEVELOPMENT FOR PATENTS.
Veiga, Daniela Francescato; Ferreira, Lydia Masako
2015-01-01
To develop a proposal for metrics for patents to be applied in assessing the postgraduate programs of Medicine III - Capes. From the reading and analysis of the 2013 area documents of all the 48 areas of Capes, a proposal for metrics for patents was developed to be applied in Medicine III programs. Except for the areas Biotechnology, Food Science, Biological Sciences III, Physical Education, Engineering I, III and IV and Interdisciplinary, most areas do not adopt a scoring system for patents. The proposal developed was based on the criteria of Biotechnology, with adaptations. In general, it will be valued, in ascending order, the deposit, the granting and licensing/production. It will also be assigned higher scores to patents registered abroad and whenever there is a participation of students. This proposal can be applied to the item Intellectual Production of the evaluation form, in subsection Technical Production/Patents. The percentage of 10% for academic programs and 40% for Masters Professionals should be maintained. The program will be scored as Very Good when it reaches 400 points or over; Good, between 200 and 399 points; Regular, between 71 and 199 points; Weak up to 70 points; Insufficient, no punctuation. Desenvolver uma proposta de métricas para patentes a serem aplicadas na avaliação dos Programas de Pós-Graduação da Área Medicina III - Capes. A partir da leitura e análise dos documentos de área de 2013 de todas as 48 Áreas da Capes, desenvolveu-se uma proposta de métricas para patentes, a ser aplicada na avaliação dos programas da área. Constatou-se que, com exceção das áreas Biotecnologia, Ciência de Alimentos, Ciências Biológicas III, Educação Física, Engenharias I, III e IV e Interdisciplinar, a maioria não adota sistema de pontuação para patentes. A proposta desenvolvida baseou-se nos critérios da Biotecnologia, com adaptações. De uma forma geral, foi valorizado, em ordem crescente, o depósito, a concessão e o
M. Punithavalli
2012-01-01
Full Text Available The high usage of software system poses high quality demand from users, which results in increased software complexity. To address these complexities, software quality engineering methods should be updated accordingly and enhance their quality assuring methods. Fault prediction, a sub-task of SQE, is designed to solve this issue and provide a strategy to identify faulty parts of a program, so that the testing process can concentrate only on those regions. This will improve the testing process and indirectly help to reduce development life cycle, project risks, resource and infrastructure costs. Measuring quality using software metrics for fault identification is gaining wide interest in software industry as they help to reduce time and cost. Existing system use either traditional simple metrics or object oriented metrics during fault detection combined with single classifier prediction system. This study combines the use of simple and object oriented metrics and uses a multiple classifier prediction system to identify module faults. In this study, a total of 20 metrics combining both traditional and OO metrics are used for fault detection. To analyze the performance of these metrics on fault module detection, the study proposes the use of ensemble classifiers that uses three frequently used classifiers, Back Propagation Neural Network (BPNN, Support Vector Machine (SVM and K-Nearest Neighbour (KNN. A novel classifier aggregation method is proposed to combine the classification results. Four methods, Sequential Selection, Random Selection with No Replacement, Selection with Bagging and Selection with Boosting, are used to generate different variants of input dataset. The three classifiers were grouped together as 2-classifier and 3-classifier prediction ensemble models. A total of 16 ensemble models were proposed for fault prediction. The performance of the proposed prediciton models was analyzed using accuracy, precision, recall and F
结合SVM和KNN的Web日志挖掘技术研究方法%Research method of Web log mining technology with combination of SVM and KNN
曾俊
2012-01-01
将SVM和KNN算法结合在一起,组成一种新的Web文本分类算法-SVM-KNN算法.当Web文本和SVM最优超平面的距离大于预选设定的阈值,则采用SVM进行分类,反之采用SVM作为代表点的KNN算法对样本分类.实证结果表明,SVM-KNN分类算法的分类精度比单纯SVM或KNN分类算法有不同程度的提高,为Web数据挖掘提供了一种有效的分类方法.%This paper used SVM and KNN algorithm together to form a new classification algorithm for Web text-SVM-KNN algorithm. When optimal super plane distance of Web text and SVM was greater than the preselected threshold, used SVM to classify, otherwise it adopted KNN algorithm to classify the samples of SVM as the representative point. The experimental results show that the accuracy of SVM-KNN classification algorithm are better than pure SVM or KNN classification algorithm, and the Web text classification provides an effective classification method.
S Wiegand; S Flege; O Baake; W Ensinger
2012-10-01
Thin films of (Na0.5K0.5)NbO3 (KNN) were synthesized on Pt/Ti/SiO2/Si substrates with repeated spin-coating after fabrication of the precursor solution by a sol–gel process. The KNN precursor solution was prepared from K- and Na-acetate, Nb-pentaethoxide and 1,3-propanediol. Based on three characteristic temperatures derived from thermal analysis (TG–DTA) experiments, five heat treatment programs were developed. All programs lead to single phase perovskite KNN films with random crystal orientation, but only the programs that included a treatment after each single spin-coating step provided pore free surfaces with grains of about 100 nm size. The lowest leakage current at 150 kV cm-1 was obtained for the temperature program that included pyrolysis and calcination steps after each deposited layer.
Einstein Metrics on Complex Surfaces
Lebrun, C
1995-01-01
We consider compact complex surfaces with Hermitian metrics which are Einstein but not Kaehler. It is shown that the manifold must be CP2 blown up at 1,2, or 3 points, and the isometry group of the metric must contain a 2-torus. Thus the Page metric on CP2#(-CP2) is almost the only metric of this type.
Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin
2015-01-01
Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.
Study on Topic Tracking Based on KNN%基于KNN的话题跟踪研究
李树平; 夏春艳; 李胜东; 亓智斌; 赵杰
2012-01-01
The key technology of topic tracking task is text classification algorithm, its difficulty is topic / reports representation mod- el. According to the definition of topic tracking, contrast to commonly used text classification algorithms and text representation meth- ods, this paper selects KNN text classification algorithm as key technology of topic tracking, uses Topic vector space model to design topic / reports representation model, combines topic detection and tracking evaluation method to achieve the topic tracking system. Experimental results prove that the system has stable topic tracking performance when key technology of topic tracking is KNN.%话题跟踪任务的关键技术是文本分类算法，难点在于话题服道表示模型。根据话题跟踪的定义，对比常用的文本分类算法和文本表示方法，选择KNN文本分类算法作为话题跟踪关键技术，利用向量空间模型设计话题/报道表示模型，结合话题检测与跟踪评测方法实现了话题跟踪系统，试验结果证明KNN作为话题跟踪关键技术，系统具有较稳定的话题跟踪性能。
An Approach to Indexing and Retrieval of Spatial Data with Reduced R+ Tree and K-NN Query Algorithm
S. Palaniappan
2015-05-01
Full Text Available Recently, “spatial data bases have been extensively adopted in the recent decade and various methods have been presented to store, browse, search and retrieve spatial objects”. In this study, a method is plotted for retrieving nearest neighbors from spatial data indexed by R+ tree. The approach uses a reduced R+tree for the purpose of representing the spatial data. Initially the spatial data is selected and R+tree is constructed accordingly. Then a function called joining nodes is applied to reduce the number of nodes by combining the half-filled nodes to form completely filled. The idea behind reducing the nodes is to perform search and retrieval quickly and efficiently. The reduced R+ tree is then processed with KNN query algorithm to fetch the nearest neighbors to a point query. The basic procedures of KNN algorithm are used in the proposed approach for retrieving the nearest neighbors. The proposed approach is evaluated for its performance withspatial data and results are plotted in the experimental analysis section. The experimental results showed that the proposed approach is remarkably up a head than the conventional methods. The maximum time required to index the 1000 data points by the R+ tree is 10324 ms. The number of nodes possessed by reduced R+ tree is also less for 1000 data points as compared to the conventional R+ tree algorithm.
Huang, Jian; Liu, Gui-xiong
2016-09-01
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm ( k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z ' if T z ' was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.
Dissertation: Geodesics of Random Riemannian Metrics
LaGatta, Tom
2011-01-01
We introduce Riemannian First-Passage Percolation (Riemannian FPP) as a new model of random differential geometry, by considering a random, smooth Riemannian metric on $\\mathbb R^d$. We are motivated in our study by the random geometry of first-passage percolation (FPP), a lattice model which was developed to model fluid flow through porous media. By adapting techniques from standard FPP, we prove a shape theorem for our model, which says that large balls under this metric converge to a deterministic shape under rescaling. As a consequence, we show that smooth random Riemannian metrics are geodesically complete with probability one. In differential geometry, geodesics are curves which locally minimize length. They need not do so globally: consider great circles on a sphere. For lattice models of FPP, there are many open questions related to minimizing geodesics; similarly, it is interesting from a geometric perspective when geodesics are globally minimizing. In the present study, we show that for any fixed st...
Frye, Jason Neal; Veitch, Cynthia K.; Mateski, Mark Elliot; Michalski, John T.; Harris, James Mark; Trevino, Cassandra M.; Maruoka, Scott
2012-03-01
Threats are generally much easier to list than to describe, and much easier to describe than to measure. As a result, many organizations list threats. Fewer describe them in useful terms, and still fewer measure them in meaningful ways. This is particularly true in the dynamic and nebulous domain of cyber threats - a domain that tends to resist easy measurement and, in some cases, appears to defy any measurement. We believe the problem is tractable. In this report we describe threat metrics and models for characterizing threats consistently and unambiguously. The purpose of this report is to support the Operational Threat Assessment (OTA) phase of risk and vulnerability assessment. To this end, we focus on the task of characterizing cyber threats using consistent threat metrics and models. In particular, we address threat metrics and models for describing malicious cyber threats to US FCEB agencies and systems.
Isospectral Metrics on Projective Spaces
Rueckriemen, Ralf
2011-01-01
We construct isospectral non isometric metrics on real and complex projective space. We recall the construction using isometric torus actions by Carolyn Gordon in chapter 2. In chapter 3 we will recall some facts about complex projective space. In chapter 4 we build the isospectral metrics. Chapter 5 is devoted to the non isometry proof of the metrics built in chapter 4. In chapter 6 isospectral metrics on real projective space are derived from metrics on the sphere.
Vortices as degenerate metrics
Baptista, J M
2012-01-01
We note that the Bogomolny equation for abelian vortices is precisely the condition for invariance of the Hermitian-Einstein equation under a degenerate conformal transformation. This leads to a natural interpretation of vortices as degenerate hermitian metrics that satisfy a certain curvature equation. Using this viewpoint, we rephrase standard results about vortices and make some new observations. We note the existence of a conceptually simple, non-linear rule for superposing vortex solutions, and we describe the natural behaviour of the L^2-metric on the moduli space upon certain restrictions.
Uniformly Convex Metric Spaces
Kell Martin
2014-01-01
In this paper the theory of uniformly convex metric spaces is developed. These spaces exhibit a generalized convexity of the metric from a fixed point. Using a (nearly) uniform convexity property a simple proof of reflexivity is presented and a weak topology of such spaces is analyzed. This topology called co-convex topology agrees with the usualy weak topology in Banach spaces. An example of a $CAT(0)$-spaces with weak topology which is not Hausdorff is given. This answers questions raised b...
Sarkar, Sarben
2010-01-01
The role of Finsler-like metrics in situations where Lorentz symmetry breaking and also CPT violation are discussed. Various physical instances of such metrics both in quantum gravity and analogue systems are discussed. Both differences and similarities between the cases will be emphasised. In particular the medium of D-particles that arise in string theory will be examined. In this case the breaking of Lorentz invariance, at the level of quantum fluctuations, together with concomitant CPT in certain situations will be analysed. In particular it will be shown correlations for neutral meson pairs will be modified and a new contribution to baryogenesis will appear.
徐宇明; 陈诚; 熊赟; 朱扬勇
2011-01-01
分类是一种常见的数据挖掘方法,而属性值缺失是分类过程中常见的一类数据质量问题,缺失值填充可以减少属性值缺失造成的分类错误.缺失值填充首先要求准确率高,在许多实际应用当中,缺失值填充还必须保证较高的计算效率.提出了一种填充缺失属性值算法APT-KNN,APT-KNN算法利用属性与属性之间的相互关系,根据与目标最相似的几个实例属性值来估计缺失值,以保证填充结果具有更高的准确性,同时设计了一种优化的AntiPole树索引结构,提高了缺失属性值的填充效率.实验表明,APT-KNN方法与现有的几种缺失属性填充方法相比,具有更高的准确率和填充效率.%Classification is one of the common data mining methods. However,one common data quality problem in classification process is attribute value missing,and missing data imputation can reduce the effect on the classification errors caused by the attribute value missing.Missing data imputation requires high accuracy first, and it shall ensure higher computation efficiency in many practical applications as well. In this paper,we present a new imputation method for missed attribute value - APT-KNN ,it makes use of the relations among the attributes and estimates the missing value according to a couple of instance attribute values which are most similar to the object,so as to guarantee higher accuracy of the imputed results. At the same time,an optimised AntiPole-Tree index structure is designed, which improves the efficiency of missed attribute values imputation. Experiments show that APT-KNN outperforms several current methods of missed attribute imputation on efficiency and accuracy.
Tice, Bradley S.
Metrical phonology, a linguistic process of phonological stress assessment and diagrammatic simplification of sentence and word stress, is discussed as it is found in the English language with the intention that it may be used in second language instruction. Stress is defined by its physical and acoustical correlates, and the principles of…
1991-07-01
March 1979, pp. 121-128. Gorla, Narasimhaiah, Alan C. Benander, and Barbara A. Benander, "Debugging Effort Estimation Using Software Metrics", IEEE...Society, IEEE Guide for the Use of IEEE Standard Dictionary of Measures to Produce Reliable Software, IEEE Std 982.2-1988, June 1989. Jones, Capers
MA Zhi-Hao
2008-01-01
Metric of quantum states plays an important role in quantum information theory. In this letter, we find the deep connection between quantum logic theory and quantum information theory. Using the method of quantum logic, we can get a famous inequality in quantum information theory, and we answer a question raised by S. Gudder.
Engineering performance metrics
Delozier, R.; Snyder, N.
1993-03-01
Implementation of a Total Quality Management (TQM) approach to engineering work required the development of a system of metrics which would serve as a meaningful management tool for evaluating effectiveness in accomplishing project objectives and in achieving improved customer satisfaction. A team effort was chartered with the goal of developing a system of engineering performance metrics which would measure customer satisfaction, quality, cost effectiveness, and timeliness. The approach to developing this system involved normal systems design phases including, conceptual design, detailed design, implementation, and integration. The lessons teamed from this effort will be explored in this paper. These lessons learned may provide a starting point for other large engineering organizations seeking to institute a performance measurement system accomplishing project objectives and in achieving improved customer satisfaction. To facilitate this effort, a team was chartered to assist in the development of the metrics system. This team, consisting of customers and Engineering staff members, was utilized to ensure that the needs and views of the customers were considered in the development of performance measurements. The development of a system of metrics is no different than the development of any type of system. It includes the steps of defining performance measurement requirements, measurement process conceptual design, performance measurement and reporting system detailed design, and system implementation and integration.
The measurement of KNN, KLL in p¯d→n¯X and p¯9Be→n¯X at 800 MeV
Riley, P. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Bonner, B. E.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.
1981-03-01
The spin transfer parameters, KNN and KLL have been measured in pd→nX and p9Be→nX at 0° and 800 MeV. The rather large values of KLL demonstrate that this transfer mechanism will provide a useful source of polarized neutrons at LAMPF energies.
A monitoring and advisory system for diabetes patient management using a rule-based method and KNN.
Lee, Malrey; Gatton, Thomas M; Lee, Keun-Kwang
2010-01-01
Diabetes is difficult to control and it is important to manage the diabetic's blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN) classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA) programming.
A Monitoring and Advisory System for Diabetes Patient Management Using a Rule-Based Method and KNN
Malrey Lee
2010-04-01
Full Text Available Diabetes is difficult to control and it is important to manage the diabetic’s blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA programming.
Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm
Joon Heo
2009-06-01
Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.
NPScape Metric GIS Data - Housing
National Park Service, Department of the Interior — NPScape housing metrics are calculated using outputs from the Spatially Explicit Regional Growth Model. Metric GIS datasets are produced seamlessly for the United...
Teukolsky, Saul A
2014-01-01
This review describes the events leading up to the discovery of the Kerr metric in 1963 and the enormous impact the discovery has had in the subsequent 50 years. The review discusses the Penrose process, the four laws of black hole mechanics, uniqueness of the solution, and the no-hair theorems. It also includes Kerr perturbation theory and its application to black hole stability and quasi-normal modes. The Kerr metric's importance in the astrophysics of quasars and accreting stellar-mass black hole systems is detailed. A theme of the review is the "miraculous" nature of the solution, both in describing in a simple analytic formula the most general rotating black hole, and in having unexpected mathematical properties that make many calculations tractable. Also included is a pedagogical derivation of the solution suitable for a first course in general relativity.
Metric adjusted skew information
Hansen, Frank
2008-01-01
establish a connection between the geometrical formulation of quantum statistics as proposed by Chentsov and Morozova and measures of quantum information as introduced by Wigner and Yanase and extended in this article. We show that the set of normalized Morozova-Chentsov functions describing the possible...... quantum statistics is a Bauer simplex and determine its extreme points. We determine a particularly simple skew information, the "¿-skew information," parametrized by a ¿ ¿ (0, 1], and show that the convex cone this family generates coincides with the set of all metric adjusted skew informations.......We extend the concept of Wigner-Yanase-Dyson skew information to something we call "metric adjusted skew information" (of a state with respect to a conserved observable). This "skew information" is intended to be a non-negative quantity bounded by the variance (of an observable in a state...
Linda Bennett
2013-07-01
Full Text Available Continuing purchase of AHSS resources is threatened more by library budget squeezes than that of STM resources. Librarians must justify all expenditure, but quantitative metrical analysis to assess the value to the institution of journals and specialized research databases for AHSS subjects can be inconclusive; often the number of recorded transactions is lower than for STM, as the resource may be relevant to a smaller number of users. This paper draws on a literature review and extensive primary research, including a survey of 570 librarians and academics across the Anglophone countries, findings from focus group meetings and the analysis of user behaviour at a UK university before and after the installation of the Summon discovery system. It concludes that providing a new approach to metrics can help to develop resources strategies that meet changing user needs; and that usage statistics can be complemented with supplementary ROI measures to make them more meaningful.
Learning Sequence Neighbourhood Metrics
Bayer, Justin; van der Smagt, Patrick
2011-01-01
Recurrent neural networks (RNNs) in combination with a pooling operator and the neighbourhood components analysis (NCA) objective function are able to detect the characterizing dynamics of sequences and embed them into a fixed-length vector space of arbitrary dimensionality. Subsequently, the resulting features are meaningful and can be used for visualization or nearest neighbour classification in linear time. This kind of metric learning for sequential data enables the use of algorithms tailored towards fixed length vector spaces such as R^n.
Todd Carpenter
2015-07-01
Full Text Available An important and timely plenary session at the 2015 UKSG Conference and Exhibition focused on the role of metrics in research assessment. The two excellent speakers had slightly divergent views.Todd Carpenter from NISO (National Information Standards Organization argued that altmetrics aren’t alt anymore and that downloads and other forms of digital interaction, including social media reference, reference tracking, personal library saving, and secondary linking activity now provide mainstream approaches to the assessment of scholarly impact. James Wilsdon is professor of science and democracy in the Science Policy Research Unit at the University of Sussex and is chair of the Independent Review of the Role of Metrics in Research Assessment commissioned by the Higher Education Funding Council in England (HEFCE. The outcome of this review will inform the work of HEFCE and the other UK higher education funding bodies as they prepare for the future of the Research Excellence Framework. He is more circumspect arguing that metrics cannot and should not be used as a substitute for informed judgement. This article provides a summary of both presentations.
Depperschmidt, Andrej; Pfaffelhuber, Peter
2011-01-01
A marked metric measure space (mmm-space) is a triple (X,r,mu), where (X,r) is a complete and separable metric space and mu is a probability measure on XxI for some Polish space I of possible marks. We study the space of all (equivalence classes of) marked metric measure spaces for some fixed I. It arises as state space in the construction of Markov processes which take values in random graphs, e.g. tree-valued dynamics describing randomly evolving genealogical structures in population models. We derive here the topological properties of the space of mmm-spaces needed to study convergence in distribution of random mmm-spaces. Extending the notion of the Gromov-weak topology introduced in (Greven, Pfaffelhuber and Winter, 2009), we define the marked Gromov-weak topology, which turns the set of mmm-spaces into a Polish space. We give a characterization of tightness for families of distributions of random mmm- spaces and identify a convergence determining algebra of functions, called polynomials.
Geometry of manifolds with area metric: Multi-metric backgrounds
Schuller, Frederic P. [Perimeter Institute for Theoretical Physics, 31 Caroline Street N, Waterloo N2L 2Y5 (Canada) and Instituto de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico, A. Postal 70-543, Mexico D.F. 04510 (Mexico)]. E-mail: fschuller@perimeterinstitute.ca; Wohlfarth, Mattias N.R. [II. Institut fuer Theoretische Physik, Universitaet Hamburg, Luruper Chaussee 149, 22761 Hamburg (Germany)]. E-mail: mattias.wohlfarth@desy.de
2006-07-24
We construct the differential geometry of smooth manifolds equipped with an algebraic curvature map acting as an area measure. Area metric geometry provides a spacetime structure suitable for the discussion of gauge theories and strings, and is considerably more general than Lorentzian geometry. Our construction of geometrically relevant objects, such as an area metric compatible connection and derived tensors, makes essential use of a decomposition theorem due to Gilkey, whereby we generate the area metric from a finite collection of metrics. Employing curvature invariants for multi-metric backgrounds we devise a class of gravity theories with inherently stringy character, and discuss gauge matter actions.
Some References on Metric Information.
National Bureau of Standards (DOC), Washington, DC.
This resource work lists metric information published by the U.S. Government and the American National Standards Institute. Also organizations marketing metric materials for education are given. A short table of conversions is included as is a listing of basic metric facts for everyday living. (LS)
Projectively related complex Finsler metrics
Aldea, Nicoleta
2011-01-01
In this paper we introduce in study the projectively related complex Finsler metrics. We prove the complex versions of the Rapcs\\'{a}k's theorem and characterize the weakly K\\"{a}hler and generalized Berwald projectively related complex Finsler metrics. The complex version of Hilbert's Fourth Problem is also pointed out. As an application, the projectiveness of a complex Randers metric is described.
Generative local metric learning in k nearest neighbors%kNN中局部生成模型测度学习
赵传钢
2011-01-01
已有的关于k近邻测度学习算法的工作主要集中于纯区分模型.在假定隐含的生成模型已知的情况下,提出了一种通过分析样本的k个近邻点的概率密度学习测度的方法.实验表明,这种基于类的生成模型假设学习到的局部测度可以有效改善kNN区分模型的性能.%Previous work on metric learning for k Nearest Neighbor(Knn) has focused on purely discriminative approach.A approach is proposed to learn a metric by analyzing the probability distribution on nearest neighbors provided that the underlying generative model is known.Experiments show that this learned local metric can improve the performance of the discriminative Knn approach using simple class conditional generative model.
A Unification of G-Metric, Partial Metric, and b-Metric Spaces
Nawab Hussain
2014-01-01
Full Text Available Using the concepts of G-metric, partial metric, and b-metric spaces, we define a new concept of generalized partial b-metric space. Topological and structural properties of the new space are investigated and certain fixed point theorems for contractive mappings in such spaces are obtained. Some examples are provided here to illustrate the usability of the obtained results.
Degenerate pseudo-Riemannian metrics
Hervik, Sigbjorn; Yamamoto, Kei
2014-01-01
In this paper we study pseudo-Riemannian spaces with a degenerate curvature structure i.e. there exists a continuous family of metrics having identical polynomial curvature invariants. We approach this problem by utilising an idea coming from invariant theory. This involves the existence of a boost, the existence of this boost is assumed to extend to a neighbourhood. This approach proves to be very fruitful: It produces a class of metrics containing all known examples of degenerate metrics. To date, only Kundt and Walker metrics have been given, however, our study gives a plethora of examples showing that degenerate metrics extend beyond the Kundt and Walker examples. The approach also gives a useful criterion for a metric to be degenerate. Specifically, we use this to study the subclass of VSI and CSI metrics (i.e., spaces where polynomial curvature invariants are all vanishing or constants, respectively).
2016-03-02
520, 2004. 16 [12] E.C. Hall and R.M. Willett. Online convex optimization in dynamic environ- ments. Selected Topics in Signal Processing, IEEE Journal...Conference on Machine Learning, pages 1160–1167. ACM, 2008. [25] Eric P Xing, Michael I Jordan, Stuart Russell, and Andrew Y Ng. Distance metric...whereBψ is any Bregman divergence and ηt is the learning rate parameter. From ( Hall & Willett, 2015) we have: Theorem 1. G` = max θ∈Θ,`∈L ‖∇f(θ)‖ φmax = 1
Metrics for Multiagent Systems
Lass, Robert N.; Sultanik, Evan A.; Regli, William C.
A Multiagent System (MAS) is a software paradigm for building large scale intelligent distributed systems. Increasingly these systems are being deployed on handheld computing devices that rely on non-traditional communications mediums such as mobile ad hoc networks and satellite links. These systems present new challenges for computer scientists in describing system performance and analyzing competing systems. This chapter surveys existing metrics that can be used to describe MASs and related components. A framework for analyzing MASs is provided and an example of how this framework might be employed is given for the domain of distributed constraint reasoning.
Sustainable chemistry metrics.
Calvo-Flores, Francisco García
2009-01-01
Green chemistry has developed mathematical parameters to describe the sustainability of chemical reactions and processes, in order to quantify their environmental impact. These parameters are related to mass and energy magnitudes, and enable analyses and numerical diagnoses of chemical reactions. The environmental impact factor (E factor), atom economy, and reaction mass efficiency have been the most influential metrics, and they are interconnected by mathematical equations. The ecodesign concept must also be considered for complex industrial syntheses, as a part of the sustainability of manufacturing processes. The aim of this Concept article is to identify the main parameters for evaluating undesirable environmental consequences.
傅德胜; 经正俊
2015-01-01
在计算机取证领域，数据碎片的取证分析已成为获取数字证据的一种重要手段。本文针对取证中数据碎片的取证问题提出了一种新的基于内容特征的数据碎片类型识别算法，该方法首先对数据碎片进行分块主成分分析PCA 后，对 PCA 特征向量进行线性鉴别分析 LDA 获取组合特征向量，然后利用 K 最邻近 KNN 算法和序列最小优化SMO 算法组成融合分类器，运用获取的组合特征向量对数据碎片进行分类识别。实验表明，该算法与其他相关算法相比，具有较高的识别准确率和识别速率，取得了良好的识别效果。%In the computer forensics field, the forensic analysis of data fragment has become an important means to obtain digital evidence. Aiming at the problem of data fragment forensics, this paper proposes a novel algorithm of data classification identification based on the content feature. Firstly, it makes principal component analysis (PCA) of each blocks in the data fragment; secondly, it makes linear discriminant analysis (LDA) of each PCA feature vector so as to get the combinational feature vector; finally, the author identifies the type of data fragment with the combinational fea-ture vector by using the fusion classifier of k nearest neighbor (KNN) algorithm and sequential minimal optimization algorithm (SMO). Experimental results have shown that compared with the related algorithms the proposed algorithm has better identification accuracy and identification rate which achieves better identification results.
3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM
P. S. Hiremath
2014-06-01
Full Text Available Biometrics (or biometric authentication refers to the identification of humans by their characteristics or traits. Bio-metrics is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Three dimensional (3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D face recognition. In the previous research works, there are several methods for face recognition using range images that are limited to the data acquisition and pre-processing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA. The Radon transform (RT is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face databases. The experimental results are shown that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT+PCA. It is observed that 40 Eigen faces of PCA and 5 LDA components lead to an average recognition rate of 99.20% using SVM classifier.
Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis
David Cárdenas-Peña
2017-07-01
Full Text Available Alzheimer's disease (AD is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (k-nn, SVM and NNs for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi
Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis.
Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German
2017-01-01
Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (k-nn, SVM and NNs) for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi-class scenario
张俊丽; 张帆
2007-01-01
目前,大多数搜索引擎都是用相关度或page-rank或HITS(Hyperlink-Induced Topic Search)算法对匹配的结果进行排序,然后以列表的方式呈现给用户.事实表明:其索引质量不高,对所收集的信息缺乏有效的分类处理,用户面对成千上万的搜索结果无法--查看,而真正符合需要的搜索结果常常因为排在后面而被漏检,返回的结果只有极少部分得到了用户的有效利用.文章提出运用基于K近邻的模糊C均值算法(以下简称KNN-FCM)对搜索引擎的初始结果进行自动聚类,系统再针对用户作出的适时反馈进行相应的输出调整,从而方便用户查找信息.
Metric-adjusted skew information
Liang, Cai; Hansen, Frank
2010-01-01
We give a truly elementary proof of the convexity of metric-adjusted skew information following an idea of Effros. We extend earlier results of weak forms of superadditivity to general metric-adjusted skew information. Recently, Luo and Zhang introduced the notion of semi-quantum states on a bipa......We give a truly elementary proof of the convexity of metric-adjusted skew information following an idea of Effros. We extend earlier results of weak forms of superadditivity to general metric-adjusted skew information. Recently, Luo and Zhang introduced the notion of semi-quantum states...
Canonical metrics on complex manifold
YAU Shing-Tung
2008-01-01
@@ Complex manifolds are topological spaces that are covered by coordinate charts where the Coordinate changes are given by holomorphic transformations. For example, Riemann surfaces are one dimensional complex manifolds. In order to understand complex manifolds, it is useful to introduce metrics that are compatible with the complex structure. In general, we should have a pair (M, ds2M) where ds2M is the metric. The metric is said to be canonical if any biholomorphisms of the complex manifolds are automatically isometries. Such metrics can naturally be used to describe invariants of the complex structures of the manifold.
Canonical metrics on complex manifold
YAU; Shing-Tung(Yau; S.-T.)
2008-01-01
Complex manifolds are topological spaces that are covered by coordinate charts where the coordinate changes are given by holomorphic transformations.For example,Riemann surfaces are one dimensional complex manifolds.In order to understand complex manifolds,it is useful to introduce metrics that are compatible with the complex structure.In general,we should have a pair(M,ds~2_M)where ds~2_M is the metric.The metric is said to be canonical if any biholomorphisms of the complex manifolds are automatically isometries.Such metrics can naturally be used to describe invariants of the complex structures of the manifold.
The metric system: An introduction
Lumley, Susan M.
On 13 Jul. 1992, Deputy Director Duane Sewell restated the Laboratory's policy on conversion to the metric system which was established in 1974. Sewell's memo announced the Laboratory's intention to continue metric conversion on a reasonable and cost effective basis. Copies of the 1974 and 1992 Administrative Memos are contained in the Appendix. There are three primary reasons behind the Laboratory's conversion to the metric system. First, Public Law 100-418, passed in 1988, states that by the end of fiscal year 1992 the Federal Government must begin using metric units in grants, procurements, and other business transactions. Second, on 25 Jul. 1991, President George Bush signed Executive Order 12770 which urged Federal agencies to expedite conversion to metric units. Third, the contract between the University of California and the Department of Energy calls for the Laboratory to convert to the metric system. Thus, conversion to the metric system is a legal requirement and a contractual mandate with the University of California. Public Law 100-418 and Executive Order 12770 are discussed in more detail later in this section, but first they examine the reasons behind the nation's conversion to the metric system. The second part of this report is on applying the metric system.
Isabel Garrido
2016-04-01
Full Text Available The class of metric spaces (X,d known as small-determined spaces, introduced by Garrido and Jaramillo, are properly defined by means of some type of real-valued Lipschitz functions on X. On the other hand, B-simple metric spaces introduced by Hejcman are defined in terms of some kind of bornologies of bounded subsets of X. In this note we present a common framework where both classes of metric spaces can be studied which allows us to see not only the relationships between them but also to obtain new internal characterizations of these metric properties.
Remarks on Vertex-Distinguishing IE-Total Coloring of Complete Bipartite Graphs K4,n and Kn,n
Xiang'en CHEN; Xiaoqing XIN; Wenyu HE
2012-01-01
Let G be a simple graph.An IE-total coloring f of G refers to a coloring of the vertices and edges of G so that no two adjacent vertices receive the same color.Let C(u) be the set of colors of vertex u and edges incident to u under f.For an IE-total coloring f of G using k colors,if C(u) ≠ C(v) for any two different vertices u and v of V(G),then f is called a k-vertex-distinguishing IE-total-coloring of G,or a k-VDIET coloring of G for short.The minimum number of colors required for a VDIET coloring of G is denoted by xievt(G),and it is called the VDIET chromatic number of G.We will give VDIET chromatic numbers for complete bipartite graph K4,n (n ≥ 4),Kn,n (5 ≤ n ≤ 21) in this article.
M. Umemura
2016-06-01
Full Text Available We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS using K-Nearest Neighbor (KNN of feature obtained by Convolutional Neural Network (CNN. Image labeling assigns labels (e.g., road, cross-walk and road shoulder to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images and test sets (8 images. We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.
Umemura, Masaki; Hotta, Kazuhiro; Nonaka, Hideki; Oda, Kazuo
2016-06-01
We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image labeling assigns labels (e.g., road, cross-walk and road shoulder) to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet) pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.
Muhammad Bilal
2016-07-01
Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.
Software metrics: Software quality metrics for distributed systems. [reliability engineering
Post, J. V.
1981-01-01
Software quality metrics was extended to cover distributed computer systems. Emphasis is placed on studying embedded computer systems and on viewing them within a system life cycle. The hierarchy of quality factors, criteria, and metrics was maintained. New software quality factors were added, including survivability, expandability, and evolvability.
Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
2014-06-27
Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.
Metrical Phonology: German Sound System.
Tice, Bradley S.
Metrical phonology, a linguistic process of phonological stress assessment and diagrammatic simplification of sentence and word stress, is discussed as it is found in the English and German languages. The objective is to promote use of metrical phonology as a tool for enhancing instruction in stress patterns in words and sentences, particularly in…
Metrics for Hard Goods Merchandising.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in hard goods merchandising, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Metrics for Soft Goods Merchandising.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students interested in soft goods merchandising, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Conversion to the Metric System
Crunkilton, John C.; Lee, Jasper S.
1974-01-01
The authors discuss background information about the metric system and explore the effect of metrication of agriculture in areas such as equipment calibration, chemical measurement, and marketing of agricultural products. Suggestions are given for possible leadership roles and approaches that agricultural education might take in converting to the…
Douglas, M R; Lukic, S; Reinbacher, R; Douglas, Michael R.; Karp, Robert L.; Lukic, Sergio; Reinbacher, Rene
2006-01-01
We develop numerical methods for approximating Ricci flat metrics on Calabi-Yau hypersurfaces in projective spaces. Our approach is based on finding balanced metrics, and builds on recent theoretical work by Donaldson. We illustrate our methods in detail for a one parameter family of quintics. We also suggest several ways to extend our results.
Metric Supplement to Technical Drawing.
Henschel, Mark
This manual is intended for use in training persons whose vocations involve technical drawing to use the metric system of measurement. It could be used in a short course designed for that purpose or for individual study. The manual begins with a brief discussion of the rationale for conversion to the metric system. It then provides a…
Aksoy, Asuman Guven
2010-01-01
Using isometric embedding of metric trees into Banach spaces, this paper will investigate barycenters, type and cotype, and various measures of compactness of metric trees. A metric tree ($T$, $d$) is a metric space such that between any two of its points there is an unique arc that is isometric to an interval in $\\mathbb{R}$. We begin our investigation by examining isometric embeddings of metric trees into Banach spaces. We then investigate the possible images $x_0=\\pi ((x_1+\\ldots+x_n)/n)$, where $\\pi$ is a contractive retraction from the ambient Banach space $X$ onto $T$ (such a $\\pi$ always exists) in order to understand the "metric" barycenter of a family of points $ x_1, \\ldots,x_n$ in a tree $T$. Further, we consider the metric properties of trees such as their type and cotype. We identify various measures of compactness of metric trees (their covering numbers, $\\epsilon$-entropy and Kolmogorov widths) and the connections between them. Additionally, we prove that the limit of the sequence of Kolmogorov...
An Improved KNN Algorithm Based on Multi-attribute Classification%基于多属性分类的KNN改进算法
张炯辉; 许尧舜
2013-01-01
To improve the classification accuracy of the conventional Euclidean KNN algorithm and the im-proved KNN algorithm based on information entropy,this paper proposes an improved KNN algorithm based on multi-attribute classification. The procedures of the new algorithm comprise:i) classify the attributes according to the percentage of their attribute values in an entire attribute of sample set into those discrete attributes suit-able for entropy-based KNN algorithm and those continuous attributes suitable for conventional Euclidean KNN similarity-based algorithm;ii) process the two types of attributes separately and then sum up the two series of results with weighing and put the sum as the distance between samples;iii) select k samples those are closest to the test sample to determine the decision attribute type of the test sample.%提出了一种基于多属性分类的KNN改进算法，可有效提高传统的欧几里德KNN算法和基于信息熵的KNN改进算法的分类准确度。首先，按照单个属性不同属性值的个数占整个属性包含样本的比例进行属性的分类，分为基于信息熵的KNN算法处理的离散属性和基于传统欧几里德KNN相似度处理的连续属性两类，然后分别对不同属性进行区别处理；其次，将两类不同处理后得到的结果按比例求和作为样本之间的距离；最后，选取与待测样本的距离最小的k个样本判断测试样本的决策属性类别。
Clustering of Rotavirus Based on KNN-kernel Function%基于KNN核函数聚类的轮状病毒统计分析
许华萍
2015-01-01
Objective] To discuss application of KNN-kernel clustering methods for diarrhea patients serum immune indexes detection data classification and diagnosis of applicability and clinical significance. [Methods] To reveal the applicability and clinical signnificance of KNN-kernel function clustering method in the diagnosis of serun immune index. In this research, the KNNCLUST algorithm is used to program the serum immune index data of 74 patients with diarrhea by Matlab software. [Results] 74 patients were divided into 5 categories by cluster analysis. The patients with diarrhea were divided into rotavirus negative and positive class, and the patients were further subdivided, especially the three early rotavirus tests were negative but later confirmed positive and were clustered into one group. [Conclusions] This can be seen that the KNN-kernel clustering method is helpful for early screening of rotavirus infection, practical clinical significance on the early treatment of disease.%[目的]探讨K最近邻（K-Nearest Neighbors, KNN）核函数聚类方法在腹泻患者血清免疫指标分类诊断中的适用性和临床意义。[方法]利用KNNCLUST算法的原理和步骤用Matlab软件进行编程，对74例腹泻患者的血清免疫指标数据进行聚类分析，揭示KNN-核函数聚类方法在腹泻患者血清免疫指标分类诊断中的适用性和临床意义。[结果]74例患者经聚类分析分成了5类。该分类不仅把腹泻患者分成轮状病毒阴性和阳性两类，而且把患者进一步进行细分，尤其是把3个初期轮状病毒检测阴性但后期证实是阳性的患者聚成一类。[结论]应用基于KNN-核函数的非参数聚类方法，有助于筛选前期轮状病毒感染者，对疾病的早期诊断治疗具有一定临床意义。
Generalized metric spaces and mappings
Lin, Shou
2016-01-01
The idea of mutual classification of spaces and mappings is one of the main research directions of point set topology. In a systematical way, this book discusses the basic theory of generalized metric spaces by using the mapping method, and summarizes the most important research achievements, particularly those from Chinese scholars, in the theory of spaces and mappings since the 1960s. This book has three chapters, two appendices and a list of more than 400 references. The chapters are "The origin of generalized metric spaces", "Mappings on metric spaces" and "Classes of generalized metric spaces". Graduates or senior undergraduates in mathematics major can use this book as their text to study the theory of generalized metric spaces. Researchers in this field can also use this book as a valuable reference.
A quasiperiodic Gibbons-Hawking metric and spacetime foam
Nergiz, S; Nergiz, Serdar; Saclioglu, Cihan
1995-01-01
We present a quasiperiodic self-dual metric of the Gibbons--Hawking type with one gravitational instanton per spacetime cell. The solution, based on an adaptation of Weierstrassian \\zeta and \\sigma functions to three dimensions, conforms to a definition of spacetime foam given by Hawking.
Hawking's singularity theorem for $C^{1,1}$-metrics
Kunzinger, Michael; Stojkovic, Milena; Vickers, James A
2014-01-01
We provide a detailed proof of Hawking's singularity theorem in the regularity class $C^{1,1}$, i.e., for spacetime metrics possessing locally Lipschitz continuous first derivatives. The proof uses recent results in $C^{1,1}$-causality theory and is based on regularisation techniques adapted to the causal structure.
2008-01-01
As Global Positioning Satellite (GPS) applications become more prevalent for land- and air-based vehicles, GPS applications for space vehicles will also increase. The Applied Technology Directorate of Kennedy Space Center (KSC) has developed a lightweight, low-cost GPS Metric Tracking Unit (GMTU), the first of two steps in developing a lightweight, low-cost Space-Based Tracking and Command Subsystem (STACS) designed to meet Range Safety's link margin and latency requirements for vehicle command and telemetry data. The goals of STACS are to improve Range Safety operations and expand tracking capabilities for space vehicles. STACS will track the vehicle, receive commands, and send telemetry data through the space-based asset, which will dramatically reduce dependence on ground-based assets. The other step was the Low-Cost Tracking and Data Relay Satellite System (TDRSS) Transceiver (LCT2), developed by the Wallops Flight Facility (WFF), which allows the vehicle to communicate with a geosynchronous relay satellite. Although the GMTU and LCT2 were independently implemented and tested, the design collaboration of KSC and WFF engineers allowed GMTU and LCT2 to be integrated into one enclosure, leading to the final STACS. In operation, GMTU needs only a radio frequency (RF) input from a GPS antenna and outputs position and velocity data to the vehicle through a serial or pulse code modulation (PCM) interface. GMTU includes one commercial GPS receiver board and a custom board, the Command and Telemetry Processor (CTP) developed by KSC. The CTP design is based on a field-programmable gate array (FPGA) with embedded processors to support GPS functions.
More on effective composite metrics
Heisenberg, Lavinia
2015-07-01
In this work we study different classes of effective composite metrics proposed in the context of one-loop quantum corrections in bimetric gravity. For this purpose we consider contributions of the matter loops in the form of cosmological constants and potential terms yielding two types of effective composite metrics. This guarantees a nice behavior at the quantum level. However, the theoretical consistency at the classical level needs to be ensured additionally. It turns out that among all these possible couplings, only one unique effective metric survives these criteria at the classical level.
More on effective composite metrics
Heisenberg, Lavinia
2015-01-01
In this work we study different classes of effective composite metrics proposed in the context of one-loop quantum corrections in bimetric gravity. For this purpose we consider contributions of the matter loops in form of cosmological constants and potential terms yielding two types of effective composite metrics. This guarantees a nice behaviour at the quantum level. However, the theoretical consistency at the classical level needs to be ensured additionally. It turns out that among all these possible couplings only one unique effective metric survives this criteria at the classical level.
Generalized Painleve-Gullstrand metrics
Lin Chunyu [Department of Physics, National Cheng Kung University, Tainan 70101, Taiwan (China)], E-mail: l2891112@mail.ncku.edu.tw; Soo Chopin [Department of Physics, National Cheng Kung University, Tainan 70101, Taiwan (China)], E-mail: cpsoo@mail.ncku.edu.tw
2009-02-02
An obstruction to the implementation of spatially flat Painleve-Gullstrand (PG) slicings is demonstrated, and explicitly discussed for Reissner-Nordstroem and Schwarzschild-anti-deSitter spacetimes. Generalizations of PG slicings which are not spatially flat but which remain regular at the horizons are introduced. These metrics can be obtained from standard spherically symmetric metrics by physical Lorentz boosts. With these generalized PG metrics, problematic contributions to the imaginary part of the action in the Parikh-Wilczek derivation of Hawking radiation due to the obstruction can be avoided.
Daylight metrics and energy savings
Mardaljevic, John; Heschong, Lisa; Lee, Eleanor
2009-12-31
The drive towards sustainable, low-energy buildings has increased the need for simple, yet accurate methods to evaluate whether a daylit building meets minimum standards for energy and human comfort performance. Current metrics do not account for the temporal and spatial aspects of daylight, nor of occupants comfort or interventions. This paper reviews the historical basis of current compliance methods for achieving daylit buildings, proposes a technical basis for development of better metrics, and provides two case study examples to stimulate dialogue on how metrics can be applied in a practical, real-world context.
Conformal Patterson-Walker metrics
Hammerl, Matthias; Šilhan, Josef; Taghavi-Chabert, Arman; Žádník, Vojtěch
2016-01-01
The classical Patterson-Walker construction of a split-signature (pseudo-)Riemannian structure from a given torsion-free affine connection is generalized to a construction of a split-signature conformal structure from a given projective class of connections. A characterization of the induced structures is obtained. We achieve a complete description of Einstein metrics in the conformal class formed by the Patterson-Walker metric. Finally, we describe all symmetries of the conformal Patterson-Walker metric. In both cases we obtain descriptions in terms of geometric data on the original structure.
Zimmerman, Marianna
1975-01-01
Describes a classroom activity which involved sixth grade students in a learning situation including making ice cream, safety procedures in a science laboratory, calibrating a thermometer, using metric units of volume and mass. (EB)
A unifying process capability metric
John Jay Flaig
2009-07-01
Full Text Available A new economic approach to process capability assessment is presented, which differs from the commonly used engineering metrics. The proposed metric consists of two economic capability measures – the expected profit and the variation in profit of the process. This dual economic metric offers a number of significant advantages over other engineering or economic metrics used in process capability analysis. First, it is easy to understand and communicate. Second, it is based on a measure of total system performance. Third, it unifies the fraction nonconforming approach and the expected loss approach. Fourth, it reflects the underlying interest of management in knowing the expected financial performance of a process and its potential variation.
Zimmerman, Marianna
1975-01-01
Describes a classroom activity which involved sixth grade students in a learning situation including making ice cream, safety procedures in a science laboratory, calibrating a thermometer, using metric units of volume and mass. (EB)
SiMPSON: Efficient Similarity Search in Metric Spaces over P2P Structured Overlay Networks
Vu, Quang Hieu; Lupu, Mihai; Wu, Sai
Similarity search in metric spaces over centralized systems has been significantly studied in the database research community. However, not so much work has been done in the context of P2P networks. This paper introduces SiMPSON: a P2P system supporting similarity search in metric spaces. The aim is to answer queries faster and using less resources than existing systems. For this, each peer first clusters its own data using any off-the-shelf clustering algorithms. Then, the resulting clusters are mapped to one-dimensional values. Finally, these one-dimensional values are indexed into a structured P2P overlay. Our method slightly increases the indexing overhead, but allows us to greatly reduce the number of peers and messages involved in query processing: we trade a small amount of overhead in the data publishing process for a substantial reduction of costs in the querying phase. Based on this architecture, we propose algorithms for processing range and kNN queries. Extensive experimental results validate the claims of efficiency and effectiveness of SiMPSON.
Phantom metrics with Killing spinors
W.A. Sabra
2015-11-01
Full Text Available We study metric solutions of Einstein–anti-Maxwell theory admitting Killing spinors. The analogue of the IWP metric which admits a space-like Killing vector is found and is expressed in terms of a complex function satisfying the wave equation in flat (2+1-dimensional space–time. As examples, electric and magnetic Kasner spaces are constructed by allowing the solution to depend only on the time coordinate. Euclidean solutions are also presented.
Coverage Metrics for Model Checking
Penix, John; Visser, Willem; Norvig, Peter (Technical Monitor)
2001-01-01
When using model checking to verify programs in practice, it is not usually possible to achieve complete coverage of the system. In this position paper we describe ongoing research within the Automated Software Engineering group at NASA Ames on the use of test coverage metrics to measure partial coverage and provide heuristic guidance for program model checking. We are specifically interested in applying and developing coverage metrics for concurrent programs that might be used to support certification of next generation avionics software.
Candelas, Philip; McOrist, Jock
2016-01-01
Heterotic vacua of string theory are realised, at large radius, by a compact threefold with vanishing first Chern class together with a choice of stable holomorphic vector bundle. These form a wide class of potentially realistic four-dimensional vacua of string theory. Despite all their phenomenological promise, there is little understanding of the metric on the moduli space of these. What is sought is the analogue of special geometry for these vacua. The metric on the moduli space is important in phenomenology as it normalises D-terms and Yukawa couplings. It is also of interest in mathematics, since it generalises the metric, first found by Kobayashi, on the space of gauge field connections, to a more general context. Here we construct this metric, correct to first order in alpha', in two ways: first by postulating a metric that is invariant under background gauge transformations of the gauge field, and also by dimensionally reducing heterotic supergravity. These methods agree and the resulting metric is Ka...
基于KD-Tree的KNN文本分类算法%KNN Algorithm for Text Classification Based on KD-Tree
刘忠; 刘洋; 建晓
2012-01-01
This paper apply KD-Tree to KNN text classification algorithm,firstly put a training text set into a KD-Tree,then search KD-Tree for the all parents nodes of the tested text node,the set including these parents text nodes is the most nearest text set,the type of the tested text is the same as the type of the most nearest text which has the most similarity with the test text,this algorithm decreases the number of the compared texts,and the time complexity is o（log2N）.Experiments show that the improved KNN text classification algorithm is better than the traditional KNN text classification in classification efficiency.%本文将KD-Tree应用到KNN文本分类算法中,先对训练文本集建立一个KD-Tree,然后在KD-Tree中搜索测试文本的所有祖先节点文本,这些祖先节点文本集合就是待测文本的最邻近文本集合,与测试文本有最大相似度的祖先的文本类型就是待测试文本的类型,这种算法大大减少了参与比较的向量文本数目,时间复杂度仅为O（log2N）。实验表明,改进后的KNN文本分类算法具有比传统KNN文本分类法更高的分类效率。
Method Points: towards a metric for method complexity
Graham McLeod
1998-11-01
Full Text Available A metric for method complexity is proposed as an aid to choosing between competing methods, as well as in validating the effects of method integration or the products of method engineering work. It is based upon a generic method representation model previously developed by the author and adaptation of concepts used in the popular Function Point metric for system size. The proposed technique is illustrated by comparing two popular I.E. deliverables with counterparts in the object oriented Unified Modeling Language (UML. The paper recommends ways to improve the practical adoption of new methods.
SYSTEMATIC REVIEW OF METRICS IN SOFTWARE AGILE PROJECTS
Amrita Raj Mukker
2015-11-01
Full Text Available This is a review paper in which things discussed would be about the various software metrics and about agile methodology. Nowadays Agile practices are increasing popularity in software development communities. This paper is a summary of the various metrics, agile and agile methodology used in software industries. Further this papers shows how Extreme Programming practices (XP could enhance the development and implementation of a large -scale and geographically distributed systems .Adaptation of Extreme Programming practices in the project has increased the human factor output and its has helped in bringing up promising idea to enhance the conceptualization and implementation as well as future extensions of large scale projects.
Systematic Review of Metrics in Software Agile Projects
Amrita Raj Mukker
2014-02-01
Full Text Available This is a review paper in which things discussed would be about the various software metrics and about agile methodology. Nowadays Agile practices are increasing popularity in software development communities. This paper is a summary of the various metrics, agile and agile methodology used in software industries. Further this papers shows how Extreme Programming practices (XP could enhance the development and imp lementation of a large -scale and geographically distributed systems .Adaptation of Extreme Programming practices in the project has increased the human factor output and its has helped in bringing up promising idea to enhance the conceptualization and implementation as well as future extensions of large scale projects.
Projectively related metrics, Weyl nullity, and metric projectively invariant equations
Gover, A Rod
2015-01-01
A metric projective structure is a manifold equipped with the unparametrised geodesics of some pseudo-Riemannian metric. We make acomprehensive treatment of such structures in the case that there is a projective Weyl curvature nullity condition. The analysis is simplified by a fundamental and canonical 2-tensor invariant that we discover. It leads to a new canonical tractor connection for these geometries which is defined on a rank $(n+1)$-bundle. We show this connection is linked to the metrisability equations that govern the existence of metrics compatible with the structure. The fundamental 2-tensor also leads to a new class of invariant linear differential operators that are canonically associated to these geometries; included is a third equation studied by Gallot et al. We apply the results to study the metrisability equation, in the nullity setting described. We obtain strong local and global results on the nature of solutions and also on the nature of the geometries admitting such solutions, obtaining ...
Metrics for phylogenetic networks II: nodal and triplets metrics.
Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel
2009-01-01
The assessment of phylogenetic network reconstruction methods requires the ability to compare phylogenetic networks. This is the second in a series of papers devoted to the analysis and comparison of metrics for tree-child time consistent phylogenetic networks on the same set of taxa. In this paper, we generalize to phylogenetic networks two metrics that have already been introduced in the literature for phylogenetic trees: the nodal distance and the triplets distance. We prove that they are metrics on any class of tree-child time consistent phylogenetic networks on the same set of taxa, as well as some basic properties for them. To prove these results, we introduce a reduction/expansion procedure that can be used not only to establish properties of tree-child time consistent phylogenetic networks by induction, but also to generate all tree-child time consistent phylogenetic networks with a given number of leaves.
田鑫鑫; 陈珉; 王会; 裴恩乐; 袁晓; 沈国平; 蔡锋; 徐桂林
2012-01-01
为了掌握獐(Hydropotes inermis)的警戒行为特征并为重引入项目提供管理依据,以人为干扰源观察獐的警戒反应,发现其警戒模式包括听(hear)或扫视(scan)、盯视(stare)、走开(walk away)、跑开(run away)、吼叫(bark)和压脖(stretch).利用逃跑起始距离对上海松江野化圈养(自主采食)獐、上海华夏圈养(人工饲喂)獐和江苏盐城野生獐警戒性进行比较,得出人工饲喂獐警戒性最小,野生獐警戒性最大.野化獐警戒性提高,表明可通过降低人类活动和种群密度、扩大区域面积等途径野化提高獐警戒性.%From September, 2010 to August, 2011 , we tested the vigilance pattern of the semi-captive Chinese Water Deer ( Hydropotes inermis) with human simulated predator in Songjing, Shanghai, and results suggest that Chinese water deer's vigilance pattern includes hearing and scanning, staring and walking away or running away, and sometimes they bark or stretch their necks while staring. Barking in Chinese water deer mainly functions as an anti-predator behavior against predators instead of sending signals to other deer. Stretching may function as a trial to tell the level of threats from a predator or function as a ritualized behavior which indicates the health status of the water deer. We didn't observe aggressive behavior in Chinese water deer. We used flight initiation distance ( FID) as a metric to compare the vigilance level of water deer populations of different captive status, including captive, human supplementary water deer in Huaxia, captive, free grazing water deer in Songjiang, and wild water deer in Yancheng Natural Reserve. The results suggest that the vigilance level differs significantly, which means captive water deer decrease their vigilance level compared to their wild counterparts, however human raised water deer could be trained to increased vigilance level. Experiences with human, size of space, population density and the existence of
Enqvist, Kari [Physics Department, University of Helsinki, and Helsinki Institute of Physics, FIN-00014 Helsinki (Finland); Koivisto, Tomi [Institute for Theoretical Physics and Spinoza Institute, Leuvenlaan 4, 3584 CE Utrecht (Netherlands); Rigopoulos, Gerasimos, E-mail: kari.enqvist@helsinki.fi, E-mail: T.S.Koivisto@astro.uio.no, E-mail: rigopoulos@physik.rwth-aachen.de [Institut für Theoretische Teilchenphysik und Kosmologie, RWTH Aachen University, D-52056 Aachen (Germany)
2012-05-01
We consider inflation within the context of what is arguably the simplest non-metric extension of Einstein gravity. There non-metricity is described by a single graviscalar field with a non-minimal kinetic coupling to the inflaton field Ψ, parameterized by a single parameter γ. There is a simple equivalent description in terms of a massless field and an inflaton with a modified potential. We discuss the implications of non-metricity for chaotic inflation and find that it significantly alters the inflaton dynamics for field values Ψ∼>M{sub P}/γ, dramatically changing the qualitative behaviour in this regime. In the equivalent single-field description this is described as a cuspy potential that forms of barrier beyond which the inflation becomes a ghost field. This imposes an upper bound on the possible number of e-folds. For the simplest chaotic inflation models, the spectral index and the tensor-to-scalar ratio receive small corrections dependent on the non-metricity parameter. We also argue that significant post-inflationary non-metricity may be generated.
Lagrange Spaces with (γ,β-Metric
Suresh K. Shukla
2013-01-01
Full Text Available We study Lagrange spaces with (γ,β-metric, where γ is a cubic metric and β is a 1-form. We obtain fundamental metric tensor, its inverse, Euler-Lagrange equations, semispray coefficients, and canonical nonlinear connection for a Lagrange space endowed with a (γ,β-metric. Several other properties of such space are also discussed.
METRICS FOR DYNAMIC SCALING OF DATABASE IN CLOUDS
Alexander V. Boichenko
2013-01-01
Full Text Available This article analyzes the main methods of scaling databases (replication, sharding and their support at the popular relational databases and NoSQL solutions with different data models: a document-oriented, key-value, column-oriented, graph. The article provides an assessment of the capabilities of modern cloud-based solution and gives a model for the organization of dynamic scaling in the cloud infrastructure. In the article are analyzed different types of metrics and are included the basic metrics that characterize the functioning parameters and database technology, as well as sets the goals of the integral metrics, necessary for the implementation of adaptive algorithms for dynamic scaling databases in the cloud infrastructure. This article was prepared with the support of RFBR grant № 13-07-00749.
Eye Tracking Metrics for Workload Estimation in Flight Deck Operation
Ellis, Kyle; Schnell, Thomas
2010-01-01
Flight decks of the future are being enhanced through improved avionics that adapt to both aircraft and operator state. Eye tracking allows for non-invasive analysis of pilot eye movements, from which a set of metrics can be derived to effectively and reliably characterize workload. This research identifies eye tracking metrics that correlate to aircraft automation conditions, and identifies the correlation of pilot workload to the same automation conditions. Saccade length was used as an indirect index of pilot workload: Pilots in the fully automated condition were observed to have on average, larger saccadic movements in contrast to the guidance and manual flight conditions. The data set itself also provides a general model of human eye movement behavior and so ostensibly visual attention distribution in the cockpit for approach to land tasks with various levels of automation, by means of the same metrics used for workload algorithm development.
Learning Objects Reusability Effectiveness Metric (LOREM
Torky Ibrahim Sultan
2014-03-01
Full Text Available In this research we aim to propose an advanced metric to evaluate the effectiveness of learning objects in order to be reused in new contexts. By the way learning objects reusability is achieving economic benefits from educational technology as it saving time and improving quality, but in case of choosing unsuitable learning object it may be less benefit than creating the learning object from scratch. Actually learning objects reusability can facilitate systems development and adaptation. By surveying the current evaluation metrics, we found that while they cover essential aspects, they enables all reviewers of learning objects to evaluate all criteria without paying attention to their roles in creating the learning object which affect their capability to evaluate specific criteria. Our proposed Approach (LOREM is evaluating learning objects based on a group of Aspects which measure their level of effectiveness in order to be reused in other contexts. LOREM classifies reviewers into 3 categories; 1. Academic Group: (Subject Expert Matter “SME” and Instructor. 2. Technical Group: (Instructional Designer “ID”, LO Developer and LO Designer. 3. Students group. The authorization of reviewers in these several categories are differentiated according to reviewer's type, e.g., (Instructor, LO Developer and their area of expert (their expertise subjects for academic and students reviewers.
Synthesis Array Topology Metrics in Location Characterization
Shanmugha Sundaram, GA
2015-08-01
Towards addressing some of the fundamental mysteries in physics at the micro- and macro-cosm level, that form the Key Science Projects (KSPs) for the Square Kilometer Array (SKA; such as Probing the Dark Ages and the Epoch of Reionization in the course of an Evolving Universe; Galaxy Evolution, Cosmology, and Dark Energy; and the Origin and evolution of Cosmic Magnetism) a suitable interfacing of these goals has to be achieved with its optimally designed array configuration, by means of a critical evaluation of the radio imagingcapabilities and metrics. Of the two forerunner sites, viz. Australia and South Africa, where pioneering advancements to state-of-the-art in synthesis array radio astronomy instrumentation are being attempted in the form of pathfinders to the SKA, for its eventual deployment, a diversity of site-dependent topology and design metrics exists. Here, the particular discussion involves those KSPs that relate to galactic morphology and evolution, and explores their suitability as a scientific research goal from the prespective of the location-driven instrument design specification. Relative merits and adaptability with regard to either site shall be presented from invoking well-founded and established array-design and optimization principles designed into a customized software tool.
Robertson, Stanley L
2016-01-01
Magnetic Eternally Collapsing Objects (MECO) have been proposed as the central engines of galactic black hole candidates (GBHC) and supermassive active galactic nuclei (AGN). Previous work has shown that their luminosities and spectral and timing characteristics are in good agreement with observations. These features and the formation of jets are generated primarily by the interactions of accretion disks with an intrinsically magnetic central MECO. The interaction of accretion disks with the anchored magnetic fields of the central objects permits a unified description of properties for GBHC, AGN, neutron stars in low mass x-ray binaries and dwarf novae systems. The previously published MECO models have been based on a quasistatic Schwarzschild metric of General Relativity; however, the only essential feature of this metric is its ability to produce extreme gravitational redshifts. For reasons discussed in this article, an alternative development based on a quasistatic exponential metric is considered here.
Complexity Metrics for Workflow Nets
Lassen, Kristian Bisgaard; van der Aalst, Wil M.P.
2009-01-01
, etc. It seems obvious that the complexity of the model contributes to design errors and a lack of understanding. It is not easy to measure complexity, however. This paper presents three complexity metrics that have been implemented in the process analysis tool ProM. The metrics are defined...... analysts have difficulties grasping the dynamics implied by a process model. Recent empirical studies show that people make numerous errors when modeling complex business processes, e.g., about 20 percent of the EPCs in the SAP reference model have design flaws resulting in potential deadlocks, livelocks...... for a subclass of Petri nets named Workflow nets, but the results can easily be applied to other languages. To demonstrate the applicability of these metrics, we have applied our approach and tool to 262 relatively complex Protos models made in the context of various student projects. This allows us to validate...
Moduli spaces of riemannian metrics
Tuschmann, Wilderich
2015-01-01
This book studies certain spaces of Riemannian metrics on both compact and non-compact manifolds. These spaces are defined by various sign-based curvature conditions, with special attention paid to positive scalar curvature and non-negative sectional curvature, though we also consider positive Ricci and non-positive sectional curvature. If we form the quotient of such a space of metrics under the action of the diffeomorphism group (or possibly a subgroup) we obtain a moduli space. Understanding the topology of both the original space of metrics and the corresponding moduli space form the central theme of this book. For example, what can be said about the connectedness or the various homotopy groups of such spaces? We explore the major results in the area, but provide sufficient background so that a non-expert with a grounding in Riemannian geometry can access this growing area of research.
Rainbow metric from quantum gravity
Assaniousssi, Mehdi; Lewandowski, Jerzy
2014-01-01
In this letter, we describe a general mechanism for emergence of a rainbow metric from a quantum cosmological model. This idea is based on QFT on a quantum space-time. Under general assumptions, we discover that the quantum space-time on which the field propagates can be replaced by a classical space-time, whose metric depends explicitly on the energy of the field: as shown by an analysis of dispersion relations, quanta of different energy propagate on different metrics, similar to photons in a refractive material (hence the name "rainbow" used in the literature). In deriving this result, we do not consider any specific theory of quantum gravity: the qualitative behavior of high-energy particles on quantum space-time relies only on the assumption that the quantum space-time is described by a wave-function $\\Psi_o$ in a Hilbert space $\\mathcal{H}_G$.
Bessem Samet
2013-01-01
Full Text Available In 2005, Mustafa and Sims (2006 introduced and studied a new class of generalized metric spaces, which are called G-metric spaces, as a generalization of metric spaces. We establish some useful propositions to show that many fixed point theorems on (nonsymmetric G-metric spaces given recently by many authors follow directly from well-known theorems on metric spaces. Our technique can be easily extended to other results as shown in application.
S-curvature of isotropic Berwald metrics
Akbar TAYEBI; Mehdi RAFIE-RAD
2008-01-01
Isotropic Berwald metrics are as a generalization of Berwald metrics. Shen proved that every Berwald metric is of vanishing S-curvature. In this paper, we generalize this fact and prove that every isotropic Berwald metric is of isotropic S-curvature. Let F = α + β be a Randers metric of isotropic Berwald curvature. Then it corresponds to a conformal vector field through navigation representation.
DLA Energy Biofuel Feedstock Metrics Study
2012-12-11
moderately/highly in- vasive Metric 2: Genetically modified organism ( GMO ) hazard, Yes/No and Hazard Category Metric 3: Species hybridization...4– biofuel distribution Stage # 5– biofuel use Metric 1: State inva- siveness ranking Yes Minimal Minimal No No Metric 2: GMO hazard Yes...may utilize GMO microbial or microalgae species across the applicable biofuel life cycles (stages 1–3). The following consequence Metrics 4–6 then
Sengur, Abdulkadir
2008-03-01
In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.
Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio
2016-02-01
The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.
基于KNN的Android智能手机微信取证方法%A KNN based forensic method of Android smartphone WeChat
吴熙曦; 李炳龙; 张天琪
2014-01-01
To solve the problem that data of WeChat is so much that data related to the case can’t be found quickly,a Android smart phone WeChat forensic method based KNN algorithm was presented.Word similarity was introduced to calculate the distance of conversations.The conversations would be represented as a vector of feature words and catego-rized with KNN algorithm to quickly find the crime-related data.The experiments verify the feasibility and accuracy of the method.%针对微信数据多，无法从中快速找到与案件相关数据的问题，提出了一种基于KNN（k-nearest neighbor）算法的Android智能手机微信取证方法。引入词语相似度计算会话间的距离，将微信会话表示成特征词的向量，用KNN算法对会话进行分类，迅速找到与犯罪有关的聊天内容，并通过实验验证了该方法的可行性与准确性。
26KNN approach to denoising f rom ALS point clouds%基于26KNN的机载点云去噪方法
李峰海
2013-01-01
Data of point clouds from ALS have huge noises w hich infence the accuracy of process-ing and occupy mounts of computer memory .Therefore denoising is highly enssential before huge point cloud processing .According to the traditional KNN method ,this paper proposed an algo-rithm called 26KNN which provides a reliable preprocessing method of huge point cloud .%机载扫描系统（ALS）点云数据中含有数量巨大的噪声点，影响数据处理精度，同时也占用了大量的内存。因此在处理点云数据前必须对超大点云进行去噪。根据传统的KNN算法，结合分块读取、存贮技术，提出基于26KNN的机载点云去噪方法，成功实现了超大点云的预处理。
Metrics correlation and analysis service (MCAS)
Baranovski, Andrew; Dykstra, Dave; Garzoglio, Gabriele; Hesselroth, Ted; Mhashilkar, Parag; Levshina, Tanya; /Fermilab
2009-05-01
The complexity of Grid workflow activities and their associated software stacks inevitably involves multiple organizations, ownership, and deployment domains. In this setting, important and common tasks such as the correlation and display of metrics and debugging information (fundamental ingredients of troubleshooting) are challenged by the informational entropy inherent to independently maintained and operated software components. Because such an information 'pond' is disorganized, it a difficult environment for business intelligence analysis i.e. troubleshooting, incident investigation and trend spotting. The mission of the MCAS project is to deliver a software solution to help with adaptation, retrieval, correlation, and display of workflow-driven data and of type-agnostic events, generated by disjoint middleware.
Thermodynamic Metrics and Optimal Paths
Sivak, David; Crooks, Gavin
2012-05-08
A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.
Separable metrics and radiating stars
G Z ABEBE; S D MAHARAJ
2017-01-01
We study the junction condition relating the pressure to heat flux at the boundary of an accelerating and expanding spherically symmetric radiating star. We transform the junction condition to an ordinary differential equation by making a separability assumption on the metric functions in the space–time variables. The condition of separability on the metric functions yields several new exact solutions. A class of shear-free models is found which contains a linear equation of state and generalizes a previously obtained model. Four new shearing models are obtained; all the gravitational potentials can be written explicitly. A brief physical analysis indicates that the matter variables are well behaved.
Einstein metrics in projective geometry
Cap, A; Macbeth, H R
2012-01-01
It is well known that pseudo-Riemannian metrics in the projective class of a given torsion free affine connection can be obtained from (and are equivalent to) the solutions of a certain overdetermined projectively invariant differential equation. This equation is a special case of a so-called first BGG equation. The general theory of such equations singles out a subclass of so-called normal solutions. We prove that non-degerate normal solutions are equivalent to pseudo-Riemannian Einstein metrics in the projective class and observe that this connects to natural projective extensions of the Einstein condition.
Complexity Metrics for Workflow Nets
Lassen, Kristian Bisgaard; van der Aalst, Wil M.P.
2009-01-01
Process modeling languages such as EPCs, BPMN, flow charts, UML activity diagrams, Petri nets, etc.\\ are used to model business processes and to configure process-aware information systems. It is known that users have problems understanding these diagrams. In fact, even process engineers and system......, etc. It seems obvious that the complexity of the model contributes to design errors and a lack of understanding. It is not easy to measure complexity, however. This paper presents three complexity metrics that have been implemented in the process analysis tool ProM. The metrics are defined...
The flexibility of optical metrics
Bittencourt, Eduardo; Smolyaninov, Igor; Smolyaninova, Vera N
2015-01-01
We firstly revisit the importance, naturalness and limitations of the so-called optical metrics for describing the propagation of light rays in the limit of geometric optics. We then exemplify their flexibility and nontriviality in some nonlinear material media and in the context of nonlinear theories of the electromagnetism, both underlain by curved backgrounds, where optical metrics could be flat and impermeable membranes only to photons could be conceived, respectively. Finally, we underline and discuss the relevance and potential applications of our analyses in a broad sense, ranging from material media to compact astrophysical systems.
The Extended Edit Distance Metric
Fuad, Muhammad Marwan Muhammad
2007-01-01
Similarity search is an important problem in information retrieval. This similarity is based on a distance. Symbolic representation of time series has attracted many researchers recently, since it reduces the dimensionality of these high dimensional data objects. We propose a new distance metric that is applied to symbolic data objects and we test it on time series data bases in a classification task. We compare it to other distances that are well known in the literature for symbolic data objects. We also prove, mathematically, that our distance is metric.
Performance Metrics for Haptic Interfaces
Samur, Evren
2012-01-01
Haptics technology is being used more and more in different applications, such as in computer games for increased immersion, in surgical simulators to create a realistic environment for training of surgeons, in surgical robotics due to safety issues and in mobile phones to provide feedback from user action. The existence of these applications highlights a clear need to understand performance metrics for haptic interfaces and their implications on device design, use and application. Performance Metrics for Haptic Interfaces aims at meeting this need by establishing standard practices for the ev
徐山; 杜卫锋
2013-01-01
The overrunning of the unwanted short messages seriously impacts the social ethos and disrupts the normal life order of people .It has considerable practical value to research and develop the filtering technology of harmful short messages .In this paper, ICTCLAS segmentation system developed by the Institute of Computing Technology of CAS is applied to realise the transition of short message text to the eigenvectors in combination with keywords extraction using TFIDF word right metrics , then the kNN method is adopted to realise the discriminant of short messagescategories, thus the filtration of bad short messages is realised .In addition, according to the unbalanced distribution of training set, we apply the density-based improved method to solve the case of original classification results which are prone to the categories of big sample quite efficiently.Experiments show that the accuracy rate of the improved method reaches about 79.18%, a 1.23% increase compared with the originalmethod.This method is able to more effectively filter the unwanted short messages , and has certain practical value .%不良短信的泛滥，严重影响了社会风气，干扰了人们正常的生活秩序，研发不良短信过滤技术具有相当的实用价值。应用中科院计算所研制开发的ICTCLAS分词系统，结合TFIDF词权度量指标提取关键词，实现短信文本到特征向量的转换，然后采用kNN方法实现短信的类别判断，从而实现不良短信的过滤。另外，针对训练集分布不均衡的情况，应用基于密度的改进方法，较为有效地处理了原来分类结果倾向于大类别样本的情况。实验表明，改进后的方法的准确率约79．18％，比原方法提升了约1．23％。该方法能够比较有效地过滤不良短信，具有一定的实用价值。
Danila, Bogdan; Lobo, Francisco S N; Mak, M K
2016-01-01
We consider the internal structure and the physical properties of specific classes of neutron, quark and Bose-Einstein Condensate stars in the hybrid metric-Palatini gravity theory, which is a combination of the metric and Palatini $f(R)$ formalisms. The theory is very successful in accounting for the observed phenomenology, since it unifies local constraints at the Solar System level and the late-time cosmic acceleration, even if the scalar field is very light. We derive the equilibrium equations for a spherically symmetric configuration (mass continuity and Tolman-Oppenheimer-Volkoff) in the framework of hybrid metric-Palatini theory, and we investigate their solutions numerically for different equations of state of neutron and quark matter, by adopting for the scalar field potential a Higgs-type form. Stellar models, described by the stiff fluid, radiation-like, the bag model and the Bose-Einstein Condensate equations of state are explicitly constructed in both General Relativity and hybrid metric-Palatini...
Ulicny, Brian; Baclawski, Ken; Magnus, Amy
2007-04-01
Blogs represent an important new arena for knowledge discovery in open source intelligence gathering. Bloggers are a vast network of human (and sometimes non-human) information sources monitoring important local and global events, and other blogs, for items of interest upon which they comment. Increasingly, issues erupt from the blog world and into the real world. In order to monitor blogging about important events, we must develop models and metrics that represent blogs correctly. The structure of blogs requires new techniques for evaluating such metrics as the relevance, specificity, credibility and timeliness of blog entries. Techniques that have been developed for standard information retrieval purposes (e.g. Google's PageRank) are suboptimal when applied to blogs because of their high degree of exophoricity, quotation, brevity, and rapidity of update. In this paper, we offer new metrics related for blog entry relevance, specificity, timeliness and credibility that we are implementing in a blog search and analysis tool for international blogs. This tools utilizes new blog-specific metrics and techniques for extracting the necessary information from blog entries automatically, using some shallow natural language processing techniques supported by background knowledge captured in domain-specific ontologies.
Socio-technical security metrics
Gollmann, D.; Herley, C.; Koenig, V.; Pieters, W.; Sasse, M.A.
2015-01-01
Report from Dagstuhl seminar 14491. This report documents the program and the outcomes of Dagstuhl Seminar 14491 “Socio-Technical Security Metrics”. In the domain of safety, metrics inform many decisions, from the height of new dikes to the design of nuclear plants. We can state, for example, that t
Leading Gainful Employment Metric Reporting
Powers, Kristina; MacPherson, Derek
2016-01-01
This chapter will address the importance of intercampus involvement in reporting of gainful employment student-level data that will be used in the calculation of gainful employment metrics by the U.S. Department of Education. The authors will discuss why building relationships within the institution is critical for effective gainful employment…
Strong metric dimension: A survey
Kratica Jozef
2014-01-01
Full Text Available The strong metric dimension has been a subject of considerable amount of research in recent years. This survey describes the related development by bringing together theoretical results and computational approaches, and places the recent results within their historical and scientific framework. [Projekat Ministarstva nauke Republike Srbije, br. 174010 i br. 174033
On a Schwarzschild like metric
Anastasiei, M
2011-01-01
In this short Note we would like to bring into the attention of people working in General Relativity a Schwarzschild like metric found by Professor Cleopatra Mociu\\c{t}chi in sixties. It was obtained by the A. Sommerfeld reasoning from his treatise "Elektrodynamik" but using instead of the energy conserving law from the classical Physics, the relativistic energy conserving law.
Area metric gravity and accelerating cosmology
Punzi, R; Wohlfarth, M N R; Punzi, Raffaele; Schuller, Frederic P.; Wohlfarth, Mattias N.R.
2007-01-01
Area metric manifolds emerge as effective classical backgrounds in quantum string theory and quantum gauge theory, and present a true generalization of metric geometry. Here, we consider area metric manifolds in their own right, and develop in detail the foundations of area metric differential geometry. Based on the construction of an area metric curvature scalar, which reduces in the metric-induced case to the Ricci scalar, we re-interpret the Einstein-Hilbert action as dynamics for an area metric spacetime. In contrast to modifications of general relativity based on metric geometry, no continuous deformation scale needs to be introduced; the extension to area geometry is purely structural and thus rigid. We present an intriguing prediction of area metric gravity: without dark energy or fine-tuning, the late universe exhibits a small acceleration.
Adaptive edge histogram descriptor for landmine detection using GPR
Frigui, Hichem; Fadeev, Aleksey; Karem, Andrew; Gader, Paul
2009-05-01
The Edge Histogram Detector (EHD) is a landmine detection algorithm for sensor data generated by ground penetrating radar (GPR). It uses edge histograms for feature extraction and a possibilistic K-Nearest Neighbors (K-NN) rule for confidence assignment. To reduce the computational complexity of the EHD and improve its generalization, the K-NN classifier uses few prototypes that can capture the variations of the signatures within each class. Each of these prototypes is assigned a label in the class of mines and a label in the class of clutter to capture its degree of sharing among these classes. The EHD has been tested extensively. It has demonstrated excellent performance on large real world data sets, and has been implemented in real time versions in hand-held and vehicle mounted GPR. In this paper, we propose two modifications to the EHD to improve its performance and adaptability. First, instead of using a fixed threshold to decide if the edge at a certain location is strong enough, we use an adaptive threshold that is learned from the background surrounding the target. This modification makes the EHD more adaptive to different terrains and to mines buried at different depths. Second, we introduce an additional training component that tunes the prototype features and labels to different environments. Results on large and diverse GPR data collections show that the proposed adaptive EHD outperforms the baseline EHD. We also show that the edge threshold can vary significantly according to the edge type, alarm depth, and soil conditions.
Length spectra and degeneration of flat metrics
Duchin, Moon; Rafi, Kasra
2009-01-01
In this paper we consider flat metrics (semi-translation structures) on surfaces of finite type. There are two main results. The first is a complete description of when a set of simple closed curves is spectrally rigid, that is, when the length vector determines a metric among the class of flat metrics. Secondly, we give an embedding into the space of geodesic currents and use this to get a boundary for the space of flat metrics. The geometric interpretation is that flat metrics degenerate to "mixed structures" on the surface: part flat metric and part measured foliation.
The escape velocity and Schwarzschild metric
Murzagalieva, A G; Murzagaliev, G Z
2002-01-01
The escape velocity value in the terms of general relativity by means Schwarzschild metric is provided to make of the motion equation with Friedman cosmological model behavior build in the terms of Robertson-Worker metric. (author)
Security Metrics in Industrial Control Systems
Collier, Zachary A; Ganin, Alexander A; Kott, Alex; Linkov, Igor
2015-01-01
Risk is the best known and perhaps the best studied example within a much broader class of cyber security metrics. However, risk is not the only possible cyber security metric. Other metrics such as resilience can exist and could be potentially very valuable to defenders of ICS systems. Often, metrics are defined as measurable properties of a system that quantify the degree to which objectives of the system are achieved. Metrics can provide cyber defenders of an ICS with critical insights regarding the system. Metrics are generally acquired by analyzing relevant attributes of that system. In terms of cyber security metrics, ICSs tend to have unique features: in many cases, these systems are older technologies that were designed for functionality rather than security. They are also extremely diverse systems that have different requirements and objectives. Therefore, metrics for ICSs must be tailored to a diverse group of systems with many features and perform many different functions. In this chapter, we first...
Dimension of the boundary in different metrics
Klén, Riku
2010-01-01
On domains $\\Omega\\subset\\R^n$, we consider metrics induced by continuous densities $\\rho\\colon\\Omega\\rightarrow(0,\\infty)$ and study the Hausdorff and packing dimensions of the boundary of $\\Omega$ with respect to these metrics.
Hybrid metric-Palatini gravity
Capozziello, Salvatore; Koivisto, Tomi S; Lobo, Francisco S N; Olmo, Gonzalo J
2015-01-01
Recently, the phenomenology of f(R) gravity has been scrutinized motivated by the possibility to account for the self-accelerated cosmic expansion without invoking dark energy sources. Besides, this kind of modified gravity is capable of addressing the dynamics of several self-gravitating systems alternatively to the presence of dark matter. It has been established that both metric and Palatini versions of these theories have interesting features but also manifest severe and different downsides. A hybrid combination of theories, containing elements from both these two formalisms, turns out to be also very successful accounting for the observed phenomenology and is able to avoid some drawbacks of the original approaches. This article reviews the formulation of this hybrid metric-Palatini approach and its main achievements in passing the local tests and in applications to astrophysical and cosmological scenarios, where it provides a unified approach to the problems of dark energy and dark matter.
Hofer's metrics and boundary depth
Usher, Michael
2011-01-01
We show that if (M,\\omega) is a closed symplectic manifold which admits a nontrivial Hamiltonian vector field all of whose contractible closed orbits are constant, then Hofer's metric on the group of Hamiltonian diffeomorphisms of (M,\\omega) has infinite diameter, and indeed admits infinite-dimensional quasi-isometrically embedded normed vector spaces. A similar conclusion applies to Hofer's metric on various spaces of Lagrangian submanifolds, including those Hamiltonian-isotopic to the diagonal in M x M when M satisfies the above dynamical condition. To prove this, we use the properties of a Floer-theoretic quantity called the boundary depth, which measures the nontriviality of the boundary operator on the Floer complex in a way that encodes robust symplectic-topological information.
Projective Compactifications and Einstein metrics
Cap, Andreas
2013-01-01
For complete affine manifolds we introduce a definition of compactification based on the projective differential geometry (i.e.\\ geodesic path data) of the given connection. The definition of projective compactness involves a real parameter $\\alpha$ called the order of projective compactness. For volume preserving connections, this order is captured by a notion of volume asymptotics that we define. These ideas apply to complete pseudo-Riemannian spaces, via the Levi-Civita connection, and thus provide a notion of compactification alternative to conformal compactification. For each order $\\alpha$, we provide an asymptotic form of a metric which is sufficient for projective compactness of the given order, thus also providing many local examples. Distinguished classes of projectively compactified geometries of orders one and two are associated with Ricci-flat connections and non--Ricci--flat Einstein metrics, respectively. Conversely, these geometric conditions are shown to force the indicated order of projectiv...
Quality Metrics in Inpatient Neurology.
Dhand, Amar
2015-12-01
Quality of care in the context of inpatient neurology is the standard of performance by neurologists and the hospital system as measured against ideal models of care. There are growing regulatory pressures to define health care value through concrete quantifiable metrics linked to reimbursement. Theoretical models of quality acknowledge its multimodal character with quantitative and qualitative dimensions. For example, the Donabedian model distils quality as a phenomenon of three interconnected domains, structure-process-outcome, with each domain mutually influential. The actual measurement of quality may be implicit, as in peer review in morbidity and mortality rounds, or explicit, in which criteria are prespecified and systemized before assessment. As a practical contribution, in this article a set of candidate quality indicators for inpatient neurology based on an updated review of treatment guidelines is proposed. These quality indicators may serve as an initial blueprint for explicit quality metrics long overdue for inpatient neurology.
Marketing metrics for medical practices.
Zahaluk, David; Baum, Neil
2012-01-01
There's a saying by John Wanamaker who pontificated, "Half the money I spend on advertising is wasted; the trouble is, I don't know which half". Today you have opportunities to determine which parts of your marketing efforts are effective and what is wasted. However, you have to measure your marketing results. This article will discuss marketing metrics and how to use them to get the best bang for your marketing buck.
Multi-Metric Sustainability Analysis
Cowlin, Shannon [National Renewable Energy Lab. (NREL), Golden, CO (United States); Heimiller, Donna [National Renewable Energy Lab. (NREL), Golden, CO (United States); Macknick, Jordan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mann, Margaret [National Renewable Energy Lab. (NREL), Golden, CO (United States); Pless, Jacquelyn [National Renewable Energy Lab. (NREL), Golden, CO (United States); Munoz, David [Colorado School of Mines, Golden, CO (United States)
2014-12-01
A readily accessible framework that allows for evaluating impacts and comparing tradeoffs among factors in energy policy, expansion planning, and investment decision making is lacking. Recognizing this, the Joint Institute for Strategic Energy Analysis (JISEA) funded an exploration of multi-metric sustainability analysis (MMSA) to provide energy decision makers with a means to make more comprehensive comparisons of energy technologies. The resulting MMSA tool lets decision makers simultaneously compare technologies and potential deployment locations.
Toktarbay, Saken
2015-01-01
We present a stationary generalization of the static $q-$metric, the simplest generalization of the Schwarzschild solution that contains a quadrupole parameter. It possesses three independent parameters that are related to the mass, quadrupole moment and angular momentum. We investigate the geometric and physical properties of this exact solution of Einstein's vacuum equations, and show that it can be used to describe the exterior gravitational field of rotating, axially symmetric, compact objects.
Balanced metrics on Hartogs domains
Loi, Andrea
2010-01-01
An n-dimensional strictly pseudoconvex Hartogs domain $D_F$ can be equipped with a natural Kaehler metric g_F. In this paper we prove that if m_0g_F is balanced for a given positive integer m_0 then m_0>n and (D_F, g_F) is holomorphically isometric to an open subset of the n-dimensional complex hyperbolic space.
Extremal almost-Kahler metrics
Lejmi, Mehdi
2009-01-01
We generalize the notion of the Futaki invariant and extremal vector field to the general almost-Kahler case and we prove the periodicity of the extremal vector field when the symplectic form represents an integral cohomology class modulo torsion. We give also an explicit formula of the hermitian scalar curvature which allows us to obtain examples of non-integrable extremal almost-Kahler metrics saturating LeBrun's estimates.
Sensory Metrics of Neuromechanical Trust.
Softky, William; Benford, Criscillia
2017-09-01
Today digital sources supply a historically unprecedented component of human sensorimotor data, the consumption of which is correlated with poorly understood maladies such as Internet addiction disorder and Internet gaming disorder. Because both natural and digital sensorimotor data share common mathematical descriptions, one can quantify our informational sensorimotor needs using the signal processing metrics of entropy, noise, dimensionality, continuity, latency, and bandwidth. Such metrics describe in neutral terms the informational diet human brains require to self-calibrate, allowing individuals to maintain trusting relationships. With these metrics, we define the trust humans experience using the mathematical language of computational models, that is, as a primitive statistical algorithm processing finely grained sensorimotor data from neuromechanical interaction. This definition of neuromechanical trust implies that artificial sensorimotor inputs and interactions that attract low-level attention through frequent discontinuities and enhanced coherence will decalibrate a brain's representation of its world over the long term by violating the implicit statistical contract for which self-calibration evolved. Our hypersimplified mathematical understanding of human sensorimotor processing as multiscale, continuous-time vibratory interaction allows equally broad-brush descriptions of failure modes and solutions. For example, we model addiction in general as the result of homeostatic regulation gone awry in novel environments (sign reversal) and digital dependency as a sub-case in which the decalibration caused by digital sensorimotor data spurs yet more consumption of them. We predict that institutions can use these sensorimotor metrics to quantify media richness to improve employee well-being; that dyads and family-size groups will bond and heal best through low-latency, high-resolution multisensory interaction such as shared meals and reciprocated touch; and
Toktarbay, S.; Quevedo, H.
2014-10-01
We present a stationary generalization of the static $q-$metric, the simplest generalization of the Schwarzschild solution that contains a quadrupole parameter. It possesses three independent parameters that are related to the mass, quadrupole moment and angular momentum. We investigate the geometric and physical properties of this exact solution of Einstein's vacuum equations, and show that it can be used to describe the exterior gravitational field of rotating, axially symmetric, compact objects.
Du, Pufeng; Cao, Shengjiao; Li, Yanda
2009-11-21
The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.
GENERAL RELATIVITY AND METRIC OF LOCAL SUPERCLUSTER
Trunev A. P.
2013-12-01
Full Text Available It is shown that the metric of clusters of galaxies should be universal, depending only on the fundamental constants and compatible with the metric of the universe. There are examples of universal metrics obtained in Einstein's theory of gravitation. On the basis of axisymmetric solutions of Einstein’s equation proposed universal metric describing the properties of galaxies, groups and clusters of galaxies
Metrics for Finite Markov Decision Processes
Ferns, Norman; Panangaden, Prakash; Precup, Doina
2012-01-01
We present metrics for measuring the similarity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion of bisimulation for MDPs, with an aim towards solving discounted infinite horizon reinforcement learning tasks. Such metrics can be used to aggregate states, as well as to better structure other value function approximators (e.g., memory-based or nearest-neighbor approximators). We provide bounds that relate our metric distances to the opti...
Almost contact metric 3-submersions
Bill Watson
1984-01-01
Full Text Available An almost contact metric 3-submersion is a Riemannian submersion, π from an almost contact metric manifold (M4m+3,(φi,ξi,ηii=13,g onto an almost quaternionic manifold (N4n,(Jii=13,h which commutes with the structure tensors of type (1,1;i.e., π*φi=Jiπ*, for i=1,2,3. For various restrictions on ∇φi, (e.g., M is 3-Sasakian, we show corresponding limitations on the second fundamental form of the fibres and on the complete integrability of the horizontal distribution. Concommitantly, relations are derived between the Betti numbers of a compact total space and the base space. For instance, if M is 3-quasi-Saskian (dΦ=0, then b1(N≤b1(M. The respective φi-holomorphic sectional and bisectional curvature tensors are studied and several unexpected results are obtained. As an example, if X and Y are orthogonal horizontal vector fields on the 3-contact (a relatively weak structure total space of such a submersion, then the respective holomorphic bisectional curvatures satisfy: Bφi(X,Y=B′J′i(X*,Y*−2. Applications to the real differential geometry of Yarg-Milis field equations are indicated based on the fact that a principal SU(2-bundle over a compactified realized space-time can be given the structure of an almost contact metric 3-submersion.
Metrics for Event Driven Software
Neha Chaudhary
2016-01-01
Full Text Available The evaluation of Graphical User Interface has significant role to improve its quality. Very few metrics exists for the evaluation of Graphical User Interface. The purpose of metrics is to obtain better measurements in terms of risk management, reliability forecast, project scheduling, and cost repression. In this paper structural complexity metrics is proposed for the evaluation of Graphical User Interface. Structural complexity of Graphical User Interface is considered as an indicator of complexity. The goal of identifying structural complexity is to measure the GUI testability. In this testability evaluation the process of measuring the complexity of the user interface from testing perspective is proposed. For the GUI evaluation and calculating structural complexity an assessment process is designed which is based on types of events. A fuzzy model is developed to evaluate the structural complexity of GUI. This model takes five types of events as input and return structural complexity of GUI as output. Further a relationship is established between structural complexity and testability of event driven software. Proposed model is evaluated with four different applications. It is evident from the results that higher the complexities lower the testability of application.
Game Refinement Relations and Metrics
de Alfaro, Luca; Raman, Vishwanath; Stoelinga, Mariëlle
2008-01-01
We consider two-player games played over finite state spaces for an infinite number of rounds. At each state, the players simultaneously choose moves; the moves determine a successor state. It is often advantageous for players to choose probability distributions over moves, rather than single moves. Given a goal, for example, reach a target state, the question of winning is thus a probabilistic one: what is the maximal probability of winning from a given state? On these game structures, two fundamental notions are those of equivalences and metrics. Given a set of winning conditions, two states are equivalent if the players can win the same games with the same probability from both states. Metrics provide a bound on the difference in the probabilities of winning across states, capturing a quantitative notion of state similarity. We introduce equivalences and metrics for two-player game structures, and we show that they characterize the difference in probability of winning games whose goals are expressed in the...
一种新的中文文本分类算法——One Class SVM-KNN算法%A New Text Classification Algorithm-One Class SVM-KNN
刘文; 吴陈
2012-01-01
中文文本分类在数据库及搜索引擎中得到广泛的应用,K-近邻(KNN)算法是常用于中文文本分类中的分类方法,但K-近邻在分类过程中需要存储所有的训练样本,并且直到待测样本需要分类时才建立分类,而且还存在类倾斜现象以及存储和计算的开销大等缺陷.单类SVM对只有一类的分类问题具有很好的效果,但不适用于多类分类问题,因此针对KNN存在的缺陷及单类SVM的特点提出One Class SVM-KNN算法,并给出了算法的定义及详细分析.通过实验证明此方法很好地克服了KNN算法的缺陷,并且查全率、查准率明显优于K-近邻算法.%Text classification is widely used in database and search engine. KNN is widely used in Chinese text categorization,however, KNN has many defects in the application of text classification. The deficiency of KNN classification algorithm is that all the training samples are kept until the samples are classified. When the size of samples is very large, the storage and computation will be costly, which will result in classification deviation. One class SVM is a simple and effective classification algorithm in one class. To solve KNN problems, a new algorithm based on harmonic one-class-SVM and KNN was proposed, which will achieve better classification effect. The experiment result is shown that the recall computed using the proposed method is obviously more highly than the KNN method.
李丽双; 党延忠; 李丹
2011-01-01
Extracting Chinese proper names is a key step in the fields of text mining, information retrieval and machine translation.This paper presents a method of extracting proper names from Chinese texts based on the fusion of support vector machine (SVM) and modified K nearest neighbors (KNN).Different classifiers are used for classifying the different test samples in spatial distributions.In the class phase, the algorithm computes the distance from the test sample to the hyperplane of SVM.If the distance is greater than the given threshold, the test sample would be classified on SVM;otherwise, the KNN algorithm will be used.In the practical training corpora, the negative class is represented by a large number of examples while the positive one is represented by only a few.To fit the unbalanced data, a normalized KNN classifier is proposed to modify classic KNN.The experimental results show that this model is more efficient than sole SVM and classic SVM-KNN in extracting Chinese proper names.The modified SVM-KNN model can be generalized to other fields of machine learning with unbalanced class distribution.%专有名词的自动抽取是文本挖掘、信息检索和机器翻译等领域的关键技术.本文研究了组合SVM和KNN两种分类器进行汉语专有名词自动抽取的方法.对样本在空间的不同分布使用不同的分类方法,当测试样本与SVM最优超平面的距离大于给定的阈值时使用SVM分类,否则使用KNN;在实际训练语料中,常常是负类样本数远多于正类样本数,而传统KNN方法对不平衡训练集存在敏感性,所以提出了用归一化的思想对传统的KNN方法进行修正.实验表明,用SVM与修正的KNN组合算法进行汉语专有名词抽取比单一的SVM方法以及原始的SVM-KNN方法更具优越性,而且这种方法可以推广到其他非平衡分布样本的分类问题.
Metric Education. Interpretive Report No. 1.
George Washington Univ., Washington, DC. Inst. for Educational Leadership.
This report reviews the findings of two projects funded by the National Institute of Education (NIE) ano conducted by the American Institutes for Research (AIR). The project reports, "Going Metric" and "Metric Inservice Teacher Training," document the impact of metric conversion on the educational systems of Great Britain, New Zeland, Australia,…
Metrics for Evaluation of Student Models
Pelanek, Radek
2015-01-01
Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…
Metrics Made Easy: A Classroom Guide - 1978.
Blau, Sharon; And Others
This classroom guide for metric education included a brief rationale and history of metrics, a preliminary metric quiz, a symbol summary, and a list of recommended instructional materials. The guide is comprised primarily of four sections covering the topics of: weight, length, volume, and temperature. Each of these sections contains goals and…
Load Balancing Metric with Diversity for Energy Efficient Routing in Wireless Sensor Networks
Moad, Sofiane; Hansen, Morten Tranberg; Jurdak, Raja
2011-01-01
The expected number of transmission (ETX) represents a routing metric that considers the highly variable link qualities for a specific radio in Wireless Sensor Networks (WSNs). To adapt to these differences, radio diversity is a recently explored solution for WSNs. In this paper, we propose...... an energy balancing metric which explores the diversity in link qualities present at different radios. The goal is to effectively use the energy of the network and therefore extend the network lifetime. The proposed metric takes into account the transmission and reception costs for a specific radio in order...... to choose an energy efficient radio. In addition, the metric uses the remaining energy of nodes in order to regulate the traffic so that critical nodes are avoided. We show by simulations that our metric can improve the network lifetime up to 20%....
A possible molecular metric for biological evolvability
Aditya Mittal; B Jayaram
2012-07-01
Proteins manifest themselves as phenotypic traits, retained or lost in living systems via evolutionary pressures. Simply put, survival is essentially the ability of a living system to synthesize a functional protein that allows for a response to environmental perturbations (adaptation). Loss of functional proteins leads to extinction. Currently there are no universally applicable quantitative metrics at the molecular level for either measuring ‘evolvability’ of life or for assessing the conditions under which a living system would go extinct and why. In this work, we show emergence of the first such metric by utilizing the recently discovered stoichiometric margin of life for all known naturally occurring (and functional) proteins. The constraint of having well-defined stoichiometries of the 20 amino acids in naturally occurring protein sequences requires utilization of the full scope of degeneracy in the genetic code, i.e. usage of all codons coding for an amino acid, by only 11 of the 20 amino acids. This shows that the non-availability of individual codons for these 11 amino acids would disturb the fine stoichiometric balance resulting in non-functional proteins and hence extinction. Remarkably, these amino acids are found in close proximity of any given amino acid in the backbones of thousands of known crystal structures of folded proteins. On the other hand, stoichiometry of the remaining 9 amino acids, found to be farther/distal from any given amino acid in backbones of folded proteins, is maintained independent of the number of codons available to synthesize them, thereby providing some robustness and hence survivability.
Danilǎ, Bogdan; Harko, Tiberiu; Lobo, Francisco S. N.; Mak, M. K.
2017-02-01
We consider the internal structure and the physical properties of specific classes of neutron, quark and Bose-Einstein condensate stars in the recently proposed hybrid metric-Palatini gravity theory, which is a combination of the metric and Palatini f (R ) formalisms. It turns out that the theory is very successful in accounting for the observed phenomenology, since it unifies local constraints at the Solar System level and the late-time cosmic acceleration, even if the scalar field is very light. In this paper, we derive the equilibrium equations for a spherically symmetric configuration (mass continuity and Tolman-Oppenheimer-Volkoff) in the framework of the scalar-tensor representation of the hybrid metric-Palatini theory, and we investigate their solutions numerically for different equations of state of neutron and quark matter, by adopting for the scalar field potential a Higgs-type form. It turns out that the scalar-tensor definition of the potential can be represented as an Clairaut differential equation, and provides an explicit form for f (R ) given by f (R )˜R +Λeff, where Λeff is an effective cosmological constant. Furthermore, stellar models, described by the stiff fluid, radiation-like, bag model and the Bose-Einstein condensate equations of state are explicitly constructed in both general relativity and hybrid metric-Palatini gravity, thus allowing an in-depth comparison between the predictions of these two gravitational theories. As a general result it turns out that for all the considered equations of state, hybrid gravity stars are more massive than their general relativistic counterparts. Furthermore, two classes of stellar models corresponding to two particular choices of the functional form of the scalar field (constant value, and logarithmic form, respectively) are also investigated. Interestingly enough, in the case of a constant scalar field the equation of state of the matter takes the form of the bag model equation of state describing
Crowdsourcing metrics of digital collections
Tuula Pääkkönen
2015-12-01
Full Text Available In the National Library of Finland (NLF there are millions of digitized newspaper and journal pages, which are openly available via the public website http://digi.kansalliskirjasto.fi. To serve users better, last year the front end was completely overhauled with its main aim in crowdsourcing features, e.g., by giving end-users the opportunity to create digital clippings and a personal scrapbook from the digital collections. But how can you know whether crowdsourcing has had an impact? How much crowdsourcing functionalities have been used so far? Did crowdsourcing work? In this paper the statistics and metrics of a recent crowdsourcing effort are analysed across the different digitized material types (newspapers, journals, ephemera. The subjects, categories and keywords given by the users are analysed to see which topics are the most appealing. Some notable public uses of the crowdsourced article clippings are highlighted. These metrics give us indications on how the end-users, based on their own interests, are investigating and using the digital collections. Therefore, the suggested metrics illustrate the versatility of the information needs of the users, varying from citizen science to research purposes. By analysing the user patterns, we can respond to the new needs of the users by making minor changes to accommodate the most active participants, while still making the service more approachable for those who are trying out the functionalities for the first time. Participation in the clippings and annotations can enrich the materials in unexpected ways and can possibly pave the way for opportunities of using crowdsourcing more also in research contexts. This creates more opportunities for the goals of open science since source data becomes available, making it possible for researchers to reach out to the general public for help. In the long term, utilizing, for example, text mining methods can allow these different end-user segments to
A nonextension result on the spectral metric
Han, Zhigang
2008-01-01
The spectral metric, defined by Schwarz and Oh using Floer-theoretical method, is a bi-invariant metric on the Hamiltonian diffeomorphism group. We show in this note that for certain symplectic manifolds, this metric can not be extended to a bi-invariant metric on the full group of symplectomorphisms. We also study the bounded isometry conjecture of Lalonde and Polterovich in the context of the spectral metric. In particular, we show that the conjecture holds for the torus with all linear symplectic forms.
Angles between Curves in Metric Measure Spaces
Han Bang-Xian
2017-08-01
Full Text Available The goal of the paper is to study the angle between two curves in the framework of metric (and metric measure spaces. More precisely, we give a new notion of angle between two curves in a metric space. Such a notion has a natural interplay with optimal transportation and is particularly well suited for metric measure spaces satisfying the curvature-dimension condition. Indeed one of the main results is the validity of the cosine formula on RCD*(K, N metric measure spaces. As a consequence, the new introduced notions are compatible with the corresponding classical ones for Riemannian manifolds, Ricci limit spaces and Alexandrov spaces.
Statistical Structures on Metric Path Spaces
Mircea CRASMAREANU; Cristina-Elena HRETCANU
2012-01-01
The authors extend the notion of statistical structure from Riemannian geometry to the general framework of path spaces endowed with a nonlinear connection and a generalized metric.Two particular cases of statistical data are defined.The existence and uniqueness of a nonlinear connection corresponding to these classes is proved.Two Koszul tensors are introduced in accordance with the Riemannian approach.As applications,the authors treat the Finslerian (α,β)-metrics and the Beil metrics used in relativity and field theories while the support Riemannian metric is the Fisher-Rao metric of a statistical model.
Forged seal detection based on the seal overlay metric.
Lee, Joong; Kong, Seong G; Lee, Young-Soo; Moon, Ki-Woong; Jeon, Oc-Yeub; Han, Jong Hyun; Lee, Bong-Woo; Seo, Joong-Suk
2012-01-10
This paper describes a method for verifying the authenticity of a seal impression imprinted on a document based on the seal overlay metric, which refers to the ratio of an effective seal impression pattern and the noise in the neighborhood of the reference impression region. A reference seal pattern is obtained by taking the average of a number of high-quality impressions of a genuine seal. A target seal impression to be examined, often on paper with some background texts and lines, is segmented out from the background by an adaptive threshold applied to the histogram of color components. The segmented target seal impression is then spatially aligned with the reference by maximizing the count of matching pixels. Then the seal overlay metric is computed for the reference and the target. If the overlay metric of a target seal is below a predetermined limit for the similarity to the genuine, then the target is classified as a forged seal. To further reduce the misclassification rate, the seal overlay metric is adjusted by the filling rate, which reflects the quality of inked pattern of the target seal. Experiment results demonstrate that the proposed method can detect elaborate seal impressions created by advanced forgery techniques such as lithography and computer-aided manufacturing. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Web metrics for library and information professionals
Stuart, David
2014-01-01
This is a practical guide to using web metrics to measure impact and demonstrate value. The web provides an opportunity to collect a host of different metrics, from those associated with social media accounts and websites to more traditional research outputs. This book is a clear guide for library and information professionals as to what web metrics are available and how to assess and use them to make informed decisions and demonstrate value. As individuals and organizations increasingly use the web in addition to traditional publishing avenues and formats, this book provides the tools to unlock web metrics and evaluate the impact of this content. The key topics covered include: bibliometrics, webometrics and web metrics; data collection tools; evaluating impact on the web; evaluating social media impact; investigating relationships between actors; exploring traditional publications in a new environment; web metrics and the web of data; the future of web metrics and the library and information professional.Th...
Adapting Dyna-METRIC to Assess Non-Aircraft Systems.
1986-05-01
geographically separated TACS units deployed throughout Germany would improve the supply support and 17 capability of the units. To accomplish this they...value in the "ordered" segement of the pipeline, which is an incorrect and undesireable result. Once again, AFLC/XRS investigated the problem and could
ADAPTIVE ASYNCHRONOUS SIMULATED AND METRICAL SOFTWARE PACKAGE IN AIRCRAFT
Mr. Vladimir A. Tikhomirov
2016-09-01
Full Text Available The article is devoted to the peculiarities and problems of development of applied measuring software packages at the stage of mass production for aircraft avionics testing and measuring.
Anabalon, Andres
2016-01-01
In four dimensions, the most general metric admitting two Killing vectors and a rank-two Killing tensor can be parameterized by ten arbitrary functions of a single variable. We show that picking a special vierbien, reducing the system to eight functions, implies the existence of two geodesic and share-free, null congruences, generated by two principal null directions of the Weyl tensor. Thus, if the spacetime is an Einstein manifold, the Goldberg-Sachs theorem implies it is Petrov type D, and by explicit construction, is in the Carter class. Hence, our analysis provide an straightforward connection between the most general integrable structure and the Carter family of spacetimes.
Comparing Resource Adequacy Metrics: Preprint
Ibanez, E.; Milligan, M.
2014-09-01
As the penetration of variable generation (wind and solar) increases around the world, there is an accompanying growing interest and importance in accurately assessing the contribution that these resources can make toward planning reserve. This contribution, also known as the capacity credit or capacity value of the resource, is best quantified by using a probabilistic measure of overall resource adequacy. In recognizing the variable nature of these renewable resources, there has been interest in exploring the use of reliability metrics other than loss of load expectation. In this paper, we undertake some comparisons using data from the Western Electricity Coordinating Council in the western United States.
Metric scales for emotion measurement
Martin Junge
2016-09-01
Full Text Available The scale quality of indirect and direct scalings of the intensity of emotional experiences was investigated from the perspective of representational measurement theory. Study 1 focused on sensory pleasantness and disgust, Study 2 on surprise and amusement, and Study 3 on relief and disappointment. In each study, the emotion intensities elicited by a set of stimuli were estimated using Ordinal Difference Scaling, an indirect probabilistic scaling method based on graded pair comparisons. The obtained scale values were used to select test cases for the quadruple axiom, a central axiom of difference measurement. A parametric bootstrap test was used to decide whether the participants’ difference judgments systematically violated the axiom. Most participants passed this test. The indirect scalings of these participants were then linearly correlated with their direct emotion intensity ratings to determine whether they agreed with them up to measurement error, and hence might be metric as well. The majority of the participants did not pass this test. The findings suggest that Ordinal Difference Scaling allows to measure emotion intensity on a metric scale level for most participants. As a consequence, quantitative emotion theories become amenable to empirical test on the individual level using indirect measurements of emotional experience.
Nutku, Y.; Sheftel, M. B.
2014-02-01
This is a corrected and essentially extended version of the unpublished manuscript by Y Nutku and M Sheftel which contains new results. It is proposed to be published in honour of Y Nutku’s memory. All corrections and new results in sections 1, 2 and 4 are due to M Sheftel. We present new anti-self-dual exact solutions of the Einstein field equations with Euclidean and neutral (ultra-hyperbolic) signatures that admit only one rotational Killing vector. Such solutions of the Einstein field equations are determined by non-invariant solutions of Boyer-Finley (BF) equation. For the case of Euclidean signature such a solution of the BF equation was first constructed by Calderbank and Tod. Two years later, Martina, Sheftel and Winternitz applied the method of group foliation to the BF equation and reproduced the Calderbank-Tod solution together with new solutions for the neutral signature. In the case of Euclidean signature we obtain new metrics which asymptotically locally look like a flat space and have a non-removable singular point at the origin. In the case of ultra-hyperbolic signature there exist three inequivalent forms of metric. Only one of these can be obtained by analytic continuation from the Calderbank-Tod solution whereas the other two are new.
Metrics for building performance assurance
Koles, G.; Hitchcock, R.; Sherman, M.
1996-07-01
This report documents part of the work performed in phase I of a Laboratory Directors Research and Development (LDRD) funded project entitled Building Performance Assurances (BPA). The focus of the BPA effort is to transform the way buildings are built and operated in order to improve building performance by facilitating or providing tools, infrastructure, and information. The efforts described herein focus on the development of metrics with which to evaluate building performance and for which information and optimization tools need to be developed. The classes of building performance metrics reviewed are (1) Building Services (2) First Costs, (3) Operating Costs, (4) Maintenance Costs, and (5) Energy and Environmental Factors. The first category defines the direct benefits associated with buildings; the next three are different kinds of costs associated with providing those benefits; the last category includes concerns that are broader than direct costs and benefits to the building owner and building occupants. The level of detail of the various issues reflect the current state of knowledge in those scientific areas and the ability of the to determine that state of knowledge, rather than directly reflecting the importance of these issues; it intentionally does not specifically focus on energy issues. The report describes work in progress and is intended as a resource and can be used to indicate the areas needing more investigation. Other reports on BPA activities are also available.
Statistical 2D and 3D shape analysis using Non-Euclidean Metrics
Larsen, Rasmus; Hilger, Klaus Baggesen; Wrobel, Mark Christoph
2002-01-01
We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furtherm......We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition...
From Smooth Curves to Universal Metrics
Gurses, Metin; Tekin, Bayram
2016-01-01
A special class of metrics, called universal metrics, solve all gravity theories defined by covariant field equations purely based on the metric tensor. Since we currently lack the knowledge of what the full of quantum corrected field equations of gravity are at a given microscopic length scale, these metrics are particularly important in understanding quantum fields in curved backgrounds in a consistent way. But, finding explicit universal metrics has been a hard problem as there does not seem to be a procedure for it. In this work, we overcome this difficulty and give a construction of universal metrics of d dimensional spacetime from curves constrained to live in a d-1 dimensional Minkowski spacetime or a Euclidean space.
Affine and Projective Tree Metric Theorems
Harel, Matan; Pachter, Lior
2011-01-01
The tree metric theorem provides a combinatorial four point condition that characterizes dissimilarity maps derived from pairwise compatible split systems. A similar (but weaker) four point condition characterizes dissimilarity maps derived from circular split systems (Kalmanson metrics). The tree metric theorem was first discovered in the context of phylogenetics and forms the basis of many tree reconstruction algorithms, whereas Kalmanson metrics were first considered by computer scientists, and are notable in that they are a non-trivial class of metrics for which the traveling salesman problem is tractable. We present a unifying framework for these theorems based on combinatorial structures that are used for graph planarity testing. These are (projective) PC-trees, and their affine analogs, PQ-trees. In the projective case, we generalize a number of concepts from clustering theory, including hierarchies, pyramids, ultrametrics and Robinsonian matrices, and the theorems that relate them. As with tree metric...
From smooth curves to universal metrics
Gürses, Metin; Şişman, Tahsin ćaǧrı; Tekin, Bayram
2016-08-01
A special class of metrics, called universal metrics, solves all gravity theories defined by covariant field equations purely based on the metric tensor. Since we currently lack the knowledge of what the full quantum-corrected field equations of gravity are at a given microscopic length scale, these metrics are particularly important in understanding quantum fields in curved backgrounds in a consistent way. However, finding explicit universal metrics has been a difficult problem as there does not seem to be a procedure for it. In this work, we overcome this difficulty and give a construction of universal metrics of d -dimensional spacetime from curves constrained to live in a (d -1 )-dimensional Minkowski spacetime or a Euclidean space.
Ramified optimal transportation in geodesic metric spaces
Xia, Qinglan
2009-01-01
An optimal transport path may be viewed as a geodesic in the space of probability measures under a suitable family of metrics. This geodesic may exhibit a tree-shaped branching structure in many applications such as trees, blood vessels, draining and irrigation systems. Here, we extend the study of ramified optimal transportation between probability measures from Euclidean spaces to a geodesic metric space. We investigate the existence as well as the behavior of optimal transport paths under various properties of the metric such as completeness, doubling, or curvature upper boundedness. We also introduce the transport dimension of a probability measure on a complete geodesic metric space, and show that the transport dimension of a probability measure is bounded above by the Minkowski dimension and below by the Hausdorff dimension of the measure. Moreover, we introduce a metric, called "the dimensional distance", on the space of probability measures. This metric gives a geometric meaning to the transport dimen...
Obtención de polvos cerámicos de BNKT-KNN por el método Pechini
Yasnó, J. P.
2013-10-01
Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3]or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.
Obtención de polvos cerámicos de BNKT-KNN por el método Pechini
Yasnó, J. P.
2013-08-01
Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.
Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander
2008-09-01
The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement
The Kerr-Newman metric: A Review
Adamo, Tim
2014-01-01
The Kerr-Newman metric describes a very special rotating, charged mass and is the most general of the asymptotically flat stationary 'black hole' solutions to the Einstein-Maxwell equations of general relativity. We review the derivation of this metric from the Reissner-Nordstrom solution by means of a complex transformation algorithm and provide a brief overview of its basic geometric properties. We also include some discussion of interpretive issues, related metrics, and higher-dimensional analogues.
Common Metrics for Human-Robot Interaction
Steinfeld, Aaron; Lewis, Michael; Fong, Terrence; Scholtz, Jean; Schultz, Alan; Kaber, David; Goodrich, Michael
2006-01-01
This paper describes an effort to identify common metrics for task-oriented human-robot interaction (HRI). We begin by discussing the need for a toolkit of HRI metrics. We then describe the framework of our work and identify important biasing factors that must be taken into consideration. Finally, we present suggested common metrics for standardization and a case study. Preparation of a larger, more detailed toolkit is in progress.
A Note on Discrete Einstein Metric
Ge, Huabin
2015-01-01
In this short note, we prove that the space of all admissible piecewise linear metrics parameterized by length square on a triangulated manifolds is a convex cone. We further study Regge's Einstein-Hilbert action and give a much more reasonable definition of discrete Einstein metric than our former version in \\cite{G}. Finally, we introduce a discrete Ricci flow for three dimensional triangulated manifolds, which is closely related to the existence of discrete Einstein metrics.
The definitive guide to IT service metrics
McWhirter, Kurt
2012-01-01
Used just as they are, the metrics in this book will bring many benefits to both the IT department and the business as a whole. Details of the attributes of each metric are given, enabling you to make the right choices for your business. You may prefer and are encouraged to design and create your own metrics to bring even more value to your business - this book will show you how to do this, too.
A Metric Observer for Induction Motors Control
Mohamed Benbouzid
2016-01-01
Full Text Available This paper deals with metric observer application for induction motors. Firstly, assuming that stator currents and speed are measured, a metric observer is designed to estimate the rotor fluxes. Secondly, assuming that only stator currents are measured, another metric observer is derived to estimate rotor fluxes and speed. The proposed observer validity is checked throughout simulations on a 4 kW induction motor drive.
On Nakhleh's metric for reduced phylogenetic networks.
Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel
2009-01-01
We prove that Nakhleh's metric for reduced phylogenetic networks is also a metric on the classes of tree-child phylogenetic networks, semibinary tree-sibling time consistent phylogenetic networks, and multilabeled phylogenetic trees. We also prove that it separates distinguishable phylogenetic networks. In this way, it becomes the strongest dissimilarity measure for phylogenetic networks available so far. Furthermore, we propose a generalization of that metric that separates arbitrary phylogenetic networks.
Metrics for antibody therapeutics development.
Reichert, Janice M
2010-01-01
A wide variety of full-size monoclonal antibodies (mAbs) and therapeutics derived from alternative antibody formats can be produced through genetic and biological engineering techniques. These molecules are now filling the preclinical and clinical pipelines of every major pharmaceutical company and many biotechnology firms. Metrics for the development of antibody therapeutics, including averages for the number of candidates entering clinical study and development phase lengths for mAbs approved in the United States, were derived from analysis of a dataset of over 600 therapeutic mAbs that entered clinical study sponsored, at least in part, by commercial firms. The results presented provide an overview of the field and context for the evaluation of on-going and prospective mAb development programs. The expansion of therapeutic antibody use through supplemental marketing approvals and the increase in the study of therapeutics derived from alternative antibody formats are discussed.
A Metric Conceptual Space Algebra
Adams, Benjamin; Raubal, Martin
The modeling of concepts from a cognitive perspective is important for designing spatial information systems that interoperate with human users. Concept representations that are built using geometric and topological conceptual space structures are well suited for semantic similarity and concept combination operations. In addition, concepts that are more closely grounded in the physical world, such as many spatial concepts, have a natural fit with the geometric structure of conceptual spaces. Despite these apparent advantages, conceptual spaces are underutilized because existing formalizations of conceptual space theory have focused on individual aspects of the theory rather than the creation of a comprehensive algebra. In this paper we present a metric conceptual space algebra that is designed to facilitate the creation of conceptual space knowledge bases and inferencing systems. Conceptual regions are represented as convex polytopes and context is built in as a fundamental element. We demonstrate the applicability of the algebra to spatial information systems with a proof-of-concept application.
THE QUALITY METRICS OF INFORMATION SYSTEMS
Zora Arsovski
2008-06-01
Full Text Available Information system is a special kind of products which is depend upon great number variables related to nature, conditions during implementation and organizational clime and culture. Because that quality metrics of information system (QMIS has to reflect all previous aspects of information systems. In this paper are presented basic elements of QMIS, characteristics of implementation and operation metrics for IS, team - management quality metrics for IS and organizational aspects of quality metrics. In second part of this paper are presented results of study of QMIS in area of MIS (Management IS.
Einstein Manifolds and Extremal Kahler Metrics
LeBrun, Claude
2010-01-01
In joint work with Chen and Weber, the author has elsewhere shown that CP2#2(-CP2) admits an Einstein metric. The present paper presents a new and rather different proof of the existence of such an Einstein metric, using a variational approach which simultaneously casts new light on the related uniqueness problem. Our results include new existence theorems for extremal Kahler metrics, and these allow one to prove the above existence statement by deforming the Kahler-Einstein metric on CP2#3(-CP2) until bubbling-off occurs.
Reconstructing propagation networks with temporal similarity metrics
Liao, Hao
2014-01-01
Node similarity is a significant property driving the growth of real networks. In this paper, based on the observed spreading results we apply the node similarity metrics to reconstruct propagation networks. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops to nearly zero. In order to improve the similarity-based reconstruction method, we finally propose a temporal similarity metric to take into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.
Radiation-dominated area metric cosmology
Schuller, Frederic P
2007-01-01
We provide further crucial support for a refined, area metric structure of spacetime. Based on the solution of conceptual issues, such as the consistent coupling of fermions and the covariant identification of radiation fields on area metric backgrounds, we show that the radiation-dominated epoch of area metric cosmology is equivalent to that epoch in standard Einstein cosmology. This ensures, in particular, successful nucleosynthesis. This surprising result complements the previously derived prediction of a small late-time acceleration of an area metric universe.
Clark, H. Clifford; Richmond, Alan
1983-01-01
Sixth-grade students and teachers were tested to determine students' metric achievement and their teachers' attitudes toward metric instruction after seven years of regular classroom instruction. Results were somewhat disappointing. (MNS)
Using Genetic Algorithms for Building Metrics of Collaborative Systems
Cristian CIUREA
2011-01-01
Full Text Available he paper objective is to reveal the importance of genetic algorithms in building robust metrics of collaborative systems. The main types of collaborative systems in economy are presented and some characteristics of genetic algorithms are described. A genetic algorithm was implemented in order to determine the local maximum and minimum points of the relative complexity function associated to a collaborative banking system. The intelligent collaborative systems based on genetic algorithms, representing the new generation of collaborative systems, are analyzed and the implementation of auto-adaptive interfaces in a banking application is described.
基于K-均值聚类的小样本集KNN分类算法%KNN CLASSIFICATION ALGORITHM FOR SMALL SAMPLE SETS BASED ON K-MEANS CLUSTERING
刘应东; 牛惠民
2011-01-01
When KNN and its improved algorithms are performing classification, it always influences the final classification accuracy because of either too dense or too few the samples or too large the density differences among various kinds of samples. The paper proposes a small sample set KNN classification algorithm based on clustering technology. A new sample set is generated through clustering and editing which contains various kinds of samples with close densities. That new sample set is used to classify and label data objects whose classification and label numbers are unknown. Tests by standard data sets reveal that the algorithm can improve KNN classification accuracy and obtain satisfactory results.%KNN及其改进算法进行分类时,如样本集中、样本过少或各类样本的密度差异较大,都将会影响最后的分类精度.提出一种基于聚类技术的小样本集KNN分类算法.通过聚类和剪理,形成各类的样本密度接近的新的样本集,并利用该新样本集对类标号未知数据对象进行类别标识.通过使用标准数据集的测试,发现该算法能够提高KNN的分类精度,取得了较满意的结果.
Information metrics (iMetrics): A research specialty with a socio-cognitive identity?
Milojević, S.; Leydesdorff, L.
2013-01-01
"Bibliometrics", "scientometrics", "informetrics", and "webometrics" can all be considered as manifestations of a single research area with similar objectives and methods, which we call "information metrics" or iMetrics. This study explores the cognitive and social distinctness of iMetrics with resp
Metric of a Slow Rotating Body with Quadrupole Moment from the Erez-Rosen Metric
Frutos-Alfaro, Francisco; Cordero-García, Iván; Ulloa-Esquivel, Oscar
2012-01-01
A metric representing a slow rotating object with quadrupole moment is obtained using the Newman-Janis formalism to include rotation into the weak limit of the Erez-Rosen metric. This metric is intended to tackle relativistic astrometry and gravitational lensing problems in which a quadrupole moment has to be taken into account.
Fuzzy Set Field and Fuzzy Metric
Gebru Gebray; B. Krishna Reddy
2014-01-01
The notation of fuzzy set field is introduced. A fuzzy metric is redefined on fuzzy set field and on arbitrary fuzzy set in a field. The metric redefined is between fuzzy points and constitutes both fuzziness and crisp property of vector. In addition, a fuzzy magnitude of a fuzzy point in a field is defined.
Metrics for Automotive Merchandising, Petroleum Marketing.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of students in automotive merchandising and petroleum marketing classes, this instructional package is one of five for the marketing and distribution cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know…
Invariant metric for nonlinear symplectic maps
Govindan Rangarajan; Minita Sachidanand
2002-03-01
In this paper, we construct an invariant metric in the space of homogeneous polynomials of a given degree (≥ 3). The homogeneous polynomials specify a nonlinear symplectic map which in turn represents a Hamiltonian system. By minimizing the norm constructed out of this metric as a function of system parameters, we demonstrate that the performance of a nonlinear Hamiltonian system is enhanced.
Einstein Hermitian Metrics of Positive Sectional Curvature
Koca, Caner
2011-01-01
In this paper we will prove that the only compact 4-manifold M with an Einstein metric of positive sectional curvature which is also hermitian with respect to some complex structure on M, is the complex projective plane CP^2, with its Fubini-Study metric.
Finite Metric Spaces of Strictly Negative Type
Hjorth, Poul; Lisonek, P.; Markvorsen, Steen
1998-01-01
We prove that, if a finite metric space is of strictly negative type, then its transfinite diameter is uniquely realized by the infinite extender (load vector). Finite metric spaces that have this property include all spaces on two, three, or four points, all trees, and all finite subspaces of Eu...
Slowly rotating Curzon-Chazy Metric
Montero-Camacho, Paulo; Gutierrez-Chaves, Carlos
2014-01-01
A new rotation version of the Curzon-Chazy metric is found. This new metric was obtained by means of a perturbation method, in order to include slow rotation. The solution is then proved to fulfill the Einstein field equations using a REDUCE program. Furthermore, the applications of this new solution are discussed.
Metrics for Offset Printing Press Operation.
Cooper, Gloria S., Ed.; Magisos, Joel H., Ed.
Designed to meet the job-related metric measurement needs of offset printing press operation students, this instructional package is one of six for the communication media occupations cluster, part of a set of 55 packages for metric instruction in different occupations. The package is intended for students who already know the occupational…
Fixed point theory in metric type spaces
Agarwal, Ravi P; O’Regan, Donal; Roldán-López-de-Hierro, Antonio Francisco
2015-01-01
Written by a team of leading experts in the field, this volume presents a self-contained account of the theory, techniques and results in metric type spaces (in particular in G-metric spaces); that is, the text approaches this important area of fixed point analysis beginning from the basic ideas of metric space topology. The text is structured so that it leads the reader from preliminaries and historical notes on metric spaces (in particular G-metric spaces) and on mappings, to Banach type contraction theorems in metric type spaces, fixed point theory in partially ordered G-metric spaces, fixed point theory for expansive mappings in metric type spaces, generalizations, present results and techniques in a very general abstract setting and framework. Fixed point theory is one of the major research areas in nonlinear analysis. This is partly due to the fact that in many real world problems fixed point theory is the basic mathematical tool used to establish the existence of solutions to problems which arise natur...
Discrete homology theory for metric spaces
H. Barcelo (Hélène); V. Capraro (Valerio); J. A. White; H. Barcelo (Hélène)
2014-01-01
htmlabstractWe define and study a notion of discrete homology theory for metric spaces. Instead of working with simplicial homology, our chain complexes are given by Lipschitz maps from an n n -dimensional cube to a fixed metric space. We prove that the resulting homology theory satisfies a
Incidental learning of temporal structures conforming to a metrical framework.
Brandon, Melissa; Terry, Josephine; Stevens, Catherine J; Tillmann, Barbara
2012-01-01
Implicit learning of sequential structures has been investigated mostly for visual, spatial, or motor learning, but rarely for temporal structure learning. The few experiments investigating temporal structure learning have concluded that temporal structures can be learned only when coupled with another structural dimension, such as musical pitch or spatial location. In these studies, the temporal structures were without metrical organization and were dependent upon participants' response times (Response-to-Stimulus Intervals). In our study, two experiments investigated temporal structure learning based on Inter-Onset-Intervals in the presence of an uncorrelated second dimension (ordinal structure) with metrically organized temporal structures. Our task was an adaptation of the classical Serial Reaction Time paradigm, using an implicit task in the auditory domain (syllable identification). Reaction times (RT) revealed that participants learned the temporal structures over the exposure blocks (decrease in RT) without a correlated ordinal dimension. The introduction of a test block with a novel temporal structure slowed RT and exemplified the typical implicit learning profile. Post-test results suggested that participants did not have explicit knowledge of the metrical temporal structures. These findings provide the first evidence of the learning of temporal structure with an uncorrelated ordinal structure, and set a foundation for further investigation of temporal cognition.
Trust Metric based Soft Security in Mobile Pervasive Environment
Madhu Sharma Gaur
2014-09-01
Full Text Available In the decentralized and highly dynamic environment like Mobile Pervasive Environments (MPE trust and security measurement are two major challenging issues for community researchers. So far primarily many of architectural frameworks and models developed and being used. In the vision of pervasive computing where mobile applications are growing immensely with the potential of low cost, high performance, and user centric solutions. This paradigm is highly dynamic and heterogeneous and brings along trust and security challenges regarding vulnerabilities and threats due to inherent open connectivity. Despite advances in the technology, there is still a lack of methods to measure the security and level of trust and framework for the assessment and calculation of the degree of the trustworthiness. In this paper, we explore security and trust metrics concerns requirement and challenges to decide the trust computations metric parameters for a self-adaptive self-monitoring trust based security assurance in mobile pervasive environment. The objective is to identify the trust parameters while routing and determine the node behavior for soft security trust metric. In winding up, we put our efforts to set up security assurance model to deal with attacks and vulnerabilities requirements of system under exploration.
Program for implementing software quality metrics
Yule, H.P.; Riemer, C.A.
1992-04-01
This report describes a program by which the Veterans Benefit Administration (VBA) can implement metrics to measure the performance of automated data systems and demonstrate that they are improving over time. It provides a definition of quality, particularly with regard to software. Requirements for management and staff to achieve a successful metrics program are discussed. It lists the attributes of high-quality software, then describes the metrics or calculations that can be used to measure these attributes in a particular system. Case studies of some successful metrics programs used by business are presented. The report ends with suggestions on which metrics the VBA should use and the order in which they should be implemented.
Deformations of three-dimensional metrics
Pugliese, Daniela; Stornaiolo, Cosimo
2015-03-01
We examine three-dimensional metric deformations based on a tetrad transformation through the action the matrices of scalar field. We describe by this approach to deformation the results obtained by Coll et al. (Gen. Relativ. Gravit. 34:269, 2002), where it is stated that any three-dimensional metric was locally obtained as a deformation of a constant curvature metric parameterized by a 2-form. To this aim, we construct the corresponding deforming matrices and provide their classification according to the properties of the scalar and of the vector used in Coll et al. (Gen Relativ Gravit 34:269, 2002) to deform the initial metric. The resulting causal structure of the deformed geometries is examined, too. Finally we apply our results to a spherically symmetric three geometry and to a space sector of Kerr metric.
FABASOFT BEST PRACTICES AND TEST METRICS MODEL
Nadica Hrgarek
2007-06-01
Full Text Available Software companies have to face serious problems about how to measure the progress of test activities and quality of software products in order to estimate test completion criteria, and if the shipment milestone will be reached on time. Measurement is a key activity in testing life cycle and requires established, managed and well documented test process, defined software quality attributes, quantitative measures, and using of test management and bug tracking tools. Test metrics are a subset of software metrics (product metrics, process metrics and enable the measurement and quality improvement of test process and/or software product. The goal of this paper is to briefly present Fabasoft best practices and lessons learned during functional and system testing of big complex software products, and to describe a simple test metrics model applied to the software test process with the purpose to better control software projects, measure and increase software quality.
Smart Grid Status and Metrics Report Appendices
Balducci, Patrick J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Antonopoulos, Chrissi A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Clements, Samuel L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gorrissen, Willy J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Kirkham, Harold [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ruiz, Kathleen A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Smith, David L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Weimar, Mark R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gardner, Chris [APQC, Houston, TX (United States); Varney, Jeff [APQC, Houston, TX (United States)
2014-07-01
A smart grid uses digital power control and communication technology to improve the reliability, security, flexibility, and efficiency of the electric system, from large generation through the delivery systems to electricity consumers and a growing number of distributed generation and storage resources. To convey progress made in achieving the vision of a smart grid, this report uses a set of six characteristics derived from the National Energy Technology Laboratory Modern Grid Strategy. The Smart Grid Status and Metrics Report defines and examines 21 metrics that collectively provide insight into the grid’s capacity to embody these characteristics. This appendix presents papers covering each of the 21 metrics identified in Section 2.1 of the Smart Grid Status and Metrics Report. These metric papers were prepared in advance of the main body of the report and collectively form its informational backbone.
Topology on locally finite metric spaces
Capraro, Valerio
2011-01-01
The necessity of a theory of General Topology and, most of all, of Algebraic Topology on locally finite metric spaces comes from many areas of research in both Applied and Pure Mathematics: Molecular Biology, Mathematical Chemistry, Computer Science, Topological Graph Theory and Metric Geometry. In this paper we propose the basic notions of such a theory and some applications: we replace the classical notions of continuous function, homeomorphism and homotopic equivalence with the notions of NPP-function, NPP-local-isomorphism and NPP-homotopy (NPP stands for Nearest Point Preserving); we also introduce the notion of NPP-isomorphism. We construct three invariants under NPP-isomorphisms and, in particular, we define the fundamental group of a locally finite metric space. As first applications, we propose the following: motivated by the longstanding question whether there is a purely metric condition which extends the notion of amenability of a group to any metric space, we propose the property SN (Small Neighb...
The metrics of science and technology
Geisler, Eliezer
2000-01-01
Dr. Geisler's far-reaching, unique book provides an encyclopedic compilation of the key metrics to measure and evaluate the impact of science and technology on academia, industry, and government. Focusing on such items as economic measures, patents, peer review, and other criteria, and supported by an extensive review of the literature, Dr. Geisler gives a thorough analysis of the strengths and weaknesses inherent in metric design, and in the use of the specific metrics he cites. His book has already received prepublication attention, and will prove especially valuable for academics in technology management, engineering, and science policy; industrial R&D executives and policymakers; government science and technology policymakers; and scientists and managers in government research and technology institutions. Geisler maintains that the application of metrics to evaluate science and technology at all levels illustrates the variety of tools we currently possess. Each metric has its own unique strengths and...
Algorithms for Game Metrics (Full Version)
Chatterjee, Krishnendu; Majumdar, Rupak; Raman, Vishwanath
2008-01-01
Simulation and bisimulation metrics for stochastic systems provide a quantitative generalization of the classical simulation and bisimulation relations. These metrics capture the similarity of states with respect to quantitative specifications written in the quantitative mu-calculus and related probabilistic logics. We show that game metrics, besides being logically characterized by the quantitative mu-calculus, also provide a bound for discounted and long-run average values of games. We then present algorithms for computing the metrics on Markov decision processes (MDPs), turn-based stochastic games, and concurrent games. For turn-based games and MDPs, we provide a polynomial-time algorithm for the computation of the one-step metric distance between states. The algorithm is based on linear programming. For concurrent games, we show that computing the exact distance between states is at least as hard as computing the value of concurrent reachability games and the square-root-sum problem in computational geome...
Geometry of manifolds with area metric
Schuller, F P
2005-01-01
We construct the differential geometry of smooth manifolds equipped with an algebraic curvature map acting as an area measure. Area metric geometry provides a spacetime structure suitable for the discussion of gauge theories and strings, and is considerably more general than Lorentzian geometry. Our construction of geometrically relevant objects, such as an area metric compatible connection and derived tensors, makes essential use of a decomposition theorem due to Gilkey, showing that a general area metric is generated by a finite collection of metrics rather than by a single one. Employing curvature invariants for area metric manifolds we devise an entirely new class of gravity theories with inherently stringy character, and discuss gauge matter actions.
String Rearrangement Metrics: A Survey
Amir, Amihood; Levy, Avivit
A basic assumption in traditional pattern matching is that the order of the elements in the given input strings is correct, while the description of the content, i.e. the description of the elements, may be erroneous. Motivated by questions that arise in Text Editing, Computational Biology, Bit Torrent and Video on Demand, and Computer Architecture, a new pattern matching paradigm was recently proposed by [2]. In this model, the pattern content remains intact, but the relative positions may change. Several papers followed the initial definition of the new paradigm. Each paper revealed new aspects in the world of string rearrangement metrics. This new unified view has already proven itself by enabling the solution of an open problem of the mathematician Cayley from 1849. It also gave better insight to problems that were already studied in different and limited situations, such as the behavior of different cost functions, and enabled deriving results for cost functions that were not yet sufficiently analyzed by previous research. At this stage, a general understanding of this new model is beginning to coalesce. The aim of this survey is to present an overview of this recent new direction of research, the problems, the methodologies, and the state-of-the-art.
Metrics for border management systems.
Duggan, Ruth Ann
2009-07-01
There are as many unique and disparate manifestations of border systems as there are borders to protect. Border Security is a highly complex system analysis problem with global, regional, national, sector, and border element dimensions for land, water, and air domains. The complexity increases with the multiple, and sometimes conflicting, missions for regulating the flow of people and goods across borders, while securing them for national security. These systems include frontier border surveillance, immigration management and customs functions that must operate in a variety of weather, terrain, operational conditions, cultural constraints, and geopolitical contexts. As part of a Laboratory Directed Research and Development Project 08-684 (Year 1), the team developed a reference framework to decompose this complex system into international/regional, national, and border elements levels covering customs, immigration, and border policing functions. This generalized architecture is relevant to both domestic and international borders. As part of year two of this project (09-1204), the team determined relevant relative measures to better understand border management performance. This paper describes those relative metrics and how they can be used to improve border management systems.
Entropy of continuous maps on quasi-metric spaces
Sayyari, Y.; Molaei, M.; Moghayer, S.M.
2015-01-01
The category of metric spaces is a subcategory of quasi-metric spaces. In this paper the notion of entropy for the continuous maps of a quasi-metric space is extended via spanning and separated sets. Moreover, two metric spaces that are associated to a given quasi-metric space are introduced and the
A New Metrics for Hierarchical Clustering
YANGGuangwen; SHIShuming; WANGDingxing
2003-01-01
Hierarchical clustering is a popular method of performing unsupervised learning. Some metric must be used to determine the similarity between pairs of clusters in hierarchical clustering. Traditional similarity metrics either can deal with simple shapes (i.e. spherical shapes) only or are very sensitive to outliers (the chaining effect). The main contribution of this paper is to propose some potential-based similarity metrics (APES and AMAPES) between clusters in hierarchical clustering, inspired by the concepts of the electric potential and the gravitational potential in electromagnetics and astronomy. The main features of these metrics are: the first, they have strong antijamming capability; the second, they are capable of finding clusters of different shapes such as spherical, spiral, chain, circle, sigmoid, U shape or other complex irregular shapes; the third, existing algorithms and research fruits for classical metrics can be adopted to deal with these new potential-based metrics with no or little modification. Experiments showed that the new metrics are more superior to traditional ones. Different potential functions are compared, and the sensitivity to parameters is also analyzed in this paper.
Altmetrics - a complement to conventional metrics.
Melero, Remedios
2015-01-01
Emerging metrics based on article-level does not exclude traditional metrics based on citations to the journal, but complements them. Both can be employed in conjunction to offer a richer picture of an article use from immediate to long terms. Article-level metrics (ALM) is the result of the aggregation of different data sources and the collection of content from multiple social network services. Sources used for the aggregation can be broken down into five categories: usage, captures, mentions, social media and citations. Data sources depend on the tool, but they include classic metrics indicators based on citations, academic social networks (Mendeley, CiteULike, Delicious) and social media (Facebook, Twitter, blogs, or Youtube, among others). Altmetrics is not synonymous with alternative metrics. Altmetrics are normally early available and allow to assess the social impact of scholarly outputs, almost at the real time. This paper overviews briefly the meaning of altmetrics and describes some of the existing tools used to apply this new metrics: Public Library of Science--Article-Level Metrics, Altmetric, Impactstory and Plum.
Altmetrics – a complement to conventional metrics
Melero, Remedios
2015-01-01
Emerging metrics based on article-level does not exclude traditional metrics based on citations to the journal, but complements them. Both can be employed in conjunction to offer a richer picture of an article use from immediate to long terms. Article-level metrics (ALM) is the result of the aggregation of different data sources and the collection of content from multiple social network services. Sources used for the aggregation can be broken down into five categories: usage, captures, mentions, social media and citations. Data sources depend on the tool, but they include classic metrics indicators based on citations, academic social networks (Mendeley, CiteULike, Delicious) and social media (Facebook, Twitter, blogs, or Youtube, among others). Altmetrics is not synonymous with alternative metrics. Altmetrics are normally early available and allow to assess the social impact of scholarly outputs, almost at the real time. This paper overviews briefly the meaning of altmetrics and describes some of the existing tools used to apply this new metrics: Public Library of Science - Article-Level Metrics, Altmetric, Impactstory and Plum. PMID:26110028
Metrics required for Power System Resilient Operations and Protection
Eshghi, K.; Johnson, B. K.; Rieger, C. G.
2016-08-01
Today’s complex grid involves many interdependent systems. Various layers of hierarchical control and communication systems are coordinated, both spatially and temporally to achieve gird reliability. As new communication network based control system technologies are being deployed, the interconnected nature of these systems is becoming more complex. Deployment of smart grid concepts promises effective integration of renewable resources, especially if combined with energy storage. However, without a philosophical focus on resilience, a smart grid will potentially lead to higher magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure depends upon its ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially catastrophic event. Future system operations can be enhanced with a resilient philosophy through architecting the complexity with state awareness metrics that recognize changing system conditions and provide for an agile and adaptive response. The starting point for metrics lies in first understanding the attributes of performance that will be qualified. In this paper, we will overview those attributes and describe how they will be characterized by designing a distributed agent that can be applied to the power grid.
Metric Entropy of Nonautonomous Dynamical Systems
Kawan Christoph
2014-01-01
Full Text Available We introduce the notion of metric entropy for a nonautonomous dynamical system given by a sequence (Xn; μn of probability spaces and a sequence of measurable maps fn : Xn → Xn+1 with fnμn = μn+1. This notion generalizes the classical concept of metric entropy established by Kolmogorov and Sinai, and is related via a variational inequality to the topological entropy of nonautonomous systems as defined by Kolyada, Misiurewicz, and Snoha. Moreover, it shares several properties with the classical notion of metric entropy. In particular, invariance with respect to appropriately defined isomorphisms, a power rule, and a Rokhlin-type inequality are proved
Finite Metric Spaces of Strictly negative Type
Hjorth, Poul G.
If a finite metric space is of strictly negative type then its transfinite diameter is uniquely realized by an infinite extent (“load vector''). Finite metric spaces that have this property include all trees, and all finite subspaces of Euclidean and Hyperbolic spaces. We prove that if the distan...... matrix of a finite metric space is both hypermetric and regular, then it is of strictly negative type. We show that the strictly negative type finite subspaces of spheres are precisely those which do not contain two pairs of antipodal points....
Software metrics a rigorous and practical approach
Fenton, Norman
2014-01-01
A Framework for Managing, Measuring, and Predicting Attributes of Software Development Products and ProcessesReflecting the immense progress in the development and use of software metrics in the past decades, Software Metrics: A Rigorous and Practical Approach, Third Edition provides an up-to-date, accessible, and comprehensive introduction to software metrics. Like its popular predecessors, this third edition discusses important issues, explains essential concepts, and offers new approaches for tackling long-standing problems.New to the Third EditionThis edition contains new material relevant
Holographic computations of the Quantum Information Metric
Trivella, Andrea
2016-01-01
In this note we show how the Quantum Information Metric can be computed holographically using a perturbative approach. In particular when the deformation of the conformal field theory state is induced by a scalar operator the corresponding bulk configuration reduces to a scalar field perturbatively probing the unperturbed background. We study two concrete examples: a CFT ground state deformed by a primary operator and thermofield double state in $d=2$ deformed by a marginal operator. Finally, we generalize the bulk construction to the case of a multi dimensional parameter space and show that the Quantum Information Metric coincides with the metric of the non-linear sigma model for the corresponding scalar fields.
Generalized Painlev\\'e-Gullstrand metrics
Lin, Chun-Yu
2008-01-01
An obstruction to the implementation of spatially flat Painleve-Gullstrand(PG) slicings is demonstrated, and explicitly discussed for Reissner-Nordstrom and Schwarzschild-anti-deSitter spacetimes. Generalizations of PG slicings which are not spatially flat but which remain regular at the horizons are introduced. These metrics can be obtained from standard spherically symmetric metrics by physical Lorentz boosts. With these generalized PG metrics, problematic contributions to the imaginary part of the action in the Parikh-Wilczek derivation of Hawking radiation due to the obstruction can be avoided.
Applying Sigma Metrics to Reduce Outliers.
Litten, Joseph
2017-03-01
Sigma metrics can be used to predict assay quality, allowing easy comparison of instrument quality and predicting which tests will require minimal quality control (QC) rules to monitor the performance of the method. A Six Sigma QC program can result in fewer controls and fewer QC failures for methods with a sigma metric of 5 or better. The higher the number of methods with a sigma metric of 5 or better, the lower the costs for reagents, supplies, and control material required to monitor the performance of the methods.
p-Hausdorff度量%p-Hausdorff metric
何日高
2011-01-01
According to the properties of Firey combination,we first introduce the p-Hausdorff metric,which coincides with the well-known Hausdorff metric in the case p = 1.Then we give two important results on the p-Hausdorff metric.%根据Firey组合的属性,引入p-Hausdorff度量,特别地,当p=1时,p-Hausdorff度量就是著名的Hausdorff度量.进一步运用凸几何分析理论证明关于p-Hausdorff度量的2个重要结论.
Propagation of light in area metric backgrounds
Punzi, Raffaele; Wohlfarth, Mattias N R [Zentrum fuer Mathematische Physik und II. Institut fuer Theoretische Physik, Universitaet Hamburg, Luruper Chaussee 149, 22761 Hamburg (Germany); Schuller, Frederic P, E-mail: raffaele.punzi@desy.d, E-mail: fps@aei.mpg.d, E-mail: mattias.wohlfarth@desy.d [Max Planck Institut fuer Gravitationsphysik, Albert Einstein Institut, Am Muehlenberg 1, 14467 Potsdam (Germany)
2009-02-07
The propagation of light in area metric spacetimes, which naturally emerge as refined backgrounds in quantum electrodynamics and quantum gravity, is studied from first principles. In the geometric-optical limit, light rays are found to follow geodesics in a Finslerian geometry, with the Finsler norm being determined by the area metric tensor. Based on this result, and an understanding of the nonlinear relation between ray vectors and wave covectors in such refined backgrounds, we study light deflection in spherically symmetric situations and obtain experimental bounds on the non-metricity of spacetime in the solar system.
Azizi E.
2016-06-01
Full Text Available Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method: In this algorithm, L-sec epochs of signals are displayed in form of a thirdorder tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN is used to classify the selected features. Results: The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion: Today, the lack of an automated system to detect or predict the seizure onset is strongly felt.
Dakhlaoui, H.; Bargaoui, Z.
2007-12-01
The Calibration of Rainfall-Runoff models can be viewed as an optimisation problem involving an objective function that measures the model performance expressed as a distance between observed and calculated discharges. Effectiveness (ability to find the optimum) and efficiency (cost expressed in number of objective function evaluations to reach the optimum) are the main criteria of choose of the optimisation method. SCE-UA is known as one of the most effective and efficient optimisation method. In this work we tried to improve the SCE-UA efficiency, in the case of the calibration of HBV model by using KNN technique to estimate the objective function. In fact after a number of iterations by SCE-UA, when objective function is evaluated by model simulation, a data base of parameter explored and respective objective function values is constituted. Within this data base it is proposed to estimate the objective function in further iterations, by an interpolation using nearest neighbours in a normalised parameter space with weighted Euclidean distance. Weights are chosen proportional to the sensitivity of parameter to objective function that gives more importance to sensitive parameter. Evaluation of model output is done through the objective function RV=R2- w |RD| where R2 is Nash Sutcliffe coefficient related to discharges, w : a weight and RD the relative bias. Applied to theoretical and practical cases in several catchments under different climatic conditions : Rottweil (Germany) and Tessa, Barbra, and Sejnane (Tunisia), the hybrid SCE-UA presents efficiency better then that of initial SCE-UA by about 20 to 30 %. By using other techniques as parameter space transformation and SCE-UA modification (2), we may obtain an algorithm two to three times faster. (1) Avi Ostfeld, Shani Salomons, "A hybrid genetic-instance learning algorithm for CE*QAL-W2 calibration", Journal of Hydrology 310 (2005) 122-125 (2) Nitin Mutil and Shie-Yui Liong, "Improved robustness and Efficiency
Rezaee, Kh.; Azizi, E.; Haddadnia, J.
2016-01-01
Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628
Shi, Y.; Gorban, A. N.; Y Yang, T.
2014-03-01
This case study tests the possibility of prediction for 'success' (or 'winner') components of four stock & shares market indices in a time period of three years from 02-Jul-2009 to 29-Jun-2012.We compare their performance ain two time frames: initial frame three months at the beginning (02/06/2009-30/09/2009) and the final three month frame (02/04/2012-29/06/2012).To label the components, average price ratio between two time frames in descending order is computed. The average price ratio is defined as the ratio between the mean prices of the beginning and final time period. The 'winner' components are referred to the top one third of total components in the same order as average price ratio it means the mean price of final time period is relatively higher than the beginning time period. The 'loser' components are referred to the last one third of total components in the same order as they have higher mean prices of beginning time period. We analyse, is there any information about the winner-looser separation in the initial fragments of the daily closing prices log-returns time series.The Leave-One-Out Cross-Validation with k-NN algorithm is applied on the daily log-return of components using a distance and proximity in the experiment. By looking at the error analysis, it shows that for HANGSENG and DAX index, there are clear signs of possibility to evaluate the probability of long-term success. The correlation distance matrix histograms and 2-D/3-D elastic maps generated from ViDaExpert show that the 'winner' components are closer to each other and 'winner'/'loser' components are separable on elastic maps for HANGSENG and DAX index while for the negative possibility indices, there is no sign of separation.
Metrics of Risk Associated with Defects Rediscovery
Miranskyy, Andriy V; Reesor, Mark
2011-01-01
Software defects rediscovered by a large number of customers affect various stakeholders and may: 1) hint at gaps in a software manufacturer's Quality Assurance (QA) processes, 2) lead to an over-load of a software manufacturer's support and maintenance teams, and 3) consume customers' resources, leading to a loss of reputation and a decrease in sales. Quantifying risk associated with the rediscovery of defects can help all of these stake-holders. In this chapter we present a set of metrics needed to quantify the risks. The metrics are designed to help: 1) the QA team to assess their processes; 2) the support and maintenance teams to allocate their resources; and 3) the customers to assess the risk associated with using the software product. The paper includes a validation case study which applies the risk metrics to industrial data. To calculate the metrics we use mathematical instruments like the heavy-tailed Kappa distribution and the G/M/k queuing model.
Classroom reconstruction of the Schwarzschild metric
Kassner, Klaus
2015-01-01
A promising way to introduce general relativity in the classroom is to study the physical predictions that follow from certain given metrics, such as the Schwarzschild one. This involves lower mathematical expenditure than an approach focusing on differential geometry in its full glory and permits to emphasize physical aspects before attacking the field equations. Even so, in terms of motivation, lacking justification of the metric employed may pose an obstacle. The paper discusses how to establish the weak-field limit of the Schwarzschild metric with a minimum of relatively simple physical assumptions. Since this does not appear sufficient to arrive at a form of the metric useful for more than the most basic predictions (gravitational redshift), the determination of a single additional parameter from experiment is admitted. An attractive experimental candidate is the measurement of the perihelion precession of Mercury, because the result was already known before the completion of general relativity. It is sh...
Flight Crew State Monitoring Metrics Project
National Aeronautics and Space Administration — eSky will develop specific crew state metrics based on the timeliness, tempo and accuracy of pilot inputs required by the H-mode Flight Control System (HFCS)....
Software Metrics Evaluation Based on Entropy
Selvarani, R; Ramachandran, Muthu; Prasad, Kamakshi
2010-01-01
Software engineering activities in the Industry has come a long way with various improve- ments brought in various stages of the software development life cycle. The complexity of modern software, the commercial constraints and the expectation for high quality products demand the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this paper the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. We have also proposed the design complexity of object-oriented software with Weighted Methods per Class m...
Medicare Contracting - Redacted Benchmark Metric Reports
U.S. Department of Health & Human Services — The Centers for Medicare and Medicaid Services has compiled aggregate national benchmark cost and workload metrics using data submitted to CMS by the AB MACs and the...
NPScape Metric GIS Data - Conservation Status
National Park Service, Department of the Interior — NPScape conservation status metrics are calculated using data from the USGS Gap Analysis Program (PAD-US), World Protected Areas Database (WDPA), and National Marine...
MPLS/VPN traffic engineering: SLA metrics
Cherkaoui, Omar; MacGibbon, Brenda; Blais, Michel; Serhrouchni, Ahmed
2001-07-01
Traffic engineering must be concerned with a broad definition of service that includes network availability, reliability and stability, as well as traditional traffic data on loss, throughput, delay and jitter. MPLS and Virtual Private Networks (VPNs) significantly contribute to security and Quality of Service (QoS) within communication networks, but there remains a need for metric measurement and evaluation. The purpose of this paper is to propose a methodology which gives a measure for LSP ( Lfew abel Switching Paths) metrics in VPN MPLS networks. We propose here a statistical method for the evaluation of those metrics. Statistical methodology is very important in this type of study since there is a large amount of data to consider. We use the notions of sample surveys, self-similar processes, linear regression, additive models and bootstrapping. The results obtained allows us to estimate the different metrics for such SLAs.
Metrics and Energy Landscapes in Irreversible Thermodynamics
Bjarne Andresen
2015-09-01
Full Text Available We describe how several metrics are possible in thermodynamic state space but that only one, Weinhold’s, has achieved widespread use. Lengths calculated based on this metric have been used to bound dissipation in finite-time (irreversible processes be they continuous or discrete, and described in the energy picture or the entropy picture. Examples are provided from thermodynamics of heat conversion processes as well as chemical reactions. Even losses in economics can be bounded using a thermodynamic type metric. An essential foundation for the metric is a complete equation of state including all extensive variables of the system; examples are given. Finally, the second law of thermodynamics imposes convexity on any equation of state, be it analytical or empirical.
Lorentzian Einstein metrics with prescribed conformal infinity
Enciso, Alberto
2014-01-01
We prove that there are asymptotically anti-de Sitter Einstein metrics with prescribed conformal infinity. More precisely we show that, given any suitably small perturbation $\\hat g$ of the conformal metric of the $(n+1)$-dimensional anti-de Sitter space at timelike infinity, which is given by the canonical Lorentzian metric on the $n$-dimensional cylinder, there is a Lorentzian Einstein metric on $(-T,T)\\times \\mathbb{B}^n$ whose conformal geometry is given by $\\hat g$. This is a Lorentzian counterpart of the Graham-Lee theorem in Riemannian geometry and is motivated by the holographic prescription problem in the context of the AdS/CFT correspondence in string theory.
Thermodynamic motivations of spherically symmetric static metrics
Moradpour, H
2015-01-01
Bearing the thermodynamic arguments together with the two definitions of mass in mind, we try to find metrics with spherical symmetry. We consider the adiabatic condition along with the Gong-Wang mass, and evaluate the $g_{rr}$ element which points to a null hypersurface. In addition, we generalize the thermodynamics laws to this hypersurface to find its temperature and thus the corresponding surface gravity which enables us to get a relation for the $g_{tt}$ element. Finally, we investigate the mathematical and physical properties of the discovered metric in the Einstein relativity framework which shows that the primary mentioned null hypersurface is an event horizon. We also show that if one considers the Misner-Sharp mass in the calculations, the Schwarzschild metric will be got. The relationship between the two mass definitions in each metric is studied. The results of considering the geometrical surface gravity are also addressed.
Clean Cities Annual Metrics Report 2009 (Revised)
Johnson, C.
2011-08-01
Document provides Clean Cities coalition metrics about the use of alternative fuels; the deployment of alternative fuel vehicles, hybrid electric vehicles (HEVs), and idle reduction initiatives; fuel economy activities; and programs to reduce vehicle miles driven.
Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability
Wesley Ingwersen
2014-03-01
Full Text Available Life cycle approaches are critical for identifying and reducing environmental burdens of products. While these methods can indicate potential environmental impacts of a product, current Life Cycle Assessment (LCA methods fail to integrate the multiple impacts of a system into unified measures of social, economic or environmental performance related to sustainability. Integrated metrics that combine multiple aspects of system performance based on a common scientific or economic principle have proven to be valuable for sustainability evaluation. In this work, we propose methods of adapting four integrated metrics for use with LCAs of product systems: ecological footprint, emergy, green net value added, and Fisher information. These metrics provide information on the full product system in land, energy, monetary equivalents, and as a unitless information index; each bundled with one or more indicators for reporting. When used together and for relative comparison, integrated metrics provide a broader coverage of sustainability aspects from multiple theoretical perspectives that is more likely to illuminate potential issues than individual impact indicators. These integrated metrics are recommended for use in combination with traditional indicators used in LCA. Future work will test and demonstrate the value of using these integrated metrics and combinations to assess product system sustainability.
Business model metrics: an open repository
Heikkila, M.; Bouwman, W.A.G.A.; Heikkila, J; Solaimani, S.; Janssen, W
2015-01-01
Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and performance measurement literature, we develop a generic open repository of metrics related to core business model concepts. We validate and assess the practical value of the repository based on four ...
Multipole solutions in metric-affine gravity
Socorro, J; Macías, A; Mielke, E W; Socorro, José; Lämmerzahl, Claus; Macías, Alfredo; Mielke, Eckehard W.
1998-01-01
Above Planck energies, the spacetime might become non--Riemannian, as it is known fron string theory and inflation. Then geometries arise in which nonmetricity and torsion appear as field strengths, side by side with curvature. By gauging the affine group, a metric affine gauge theory emerges as dynamical framework. Here, by using the harmonic map ansatz, a new class of multipole like solutions in the metric affine gravity theory (MAG) is obtained.
Metric Diophantine approximation on homogeneous varieties
Ghosh, Anish; Nevo, Amos
2012-01-01
We develop the metric theory of Diophantine approximation on homogeneous varieties of semisimple algebraic groups and prove results analogous to the classical Khinchin and Jarnik theorems. In full generality our results establish simultaneous Diophantine approximation with respect to several completions, and Diophantine approximation over general number fields using S-algebraic integers. In several important examples, the metric results we obtain are optimal. The proof uses quantitative equidistribution properties of suitable averaging operators, which are derived from spectral bounds in automorphic representations.
Canonical metrics on Cartan--Hartogs domains
Zedda, Michela
2011-01-01
In this paper we address two problems concerning a family of domains $M_{\\Omega}(\\mu) \\subset \\C^n$, called Cartan-Hartogs domains, endowed with a natural Kaehler metric $g(\\mu)$. The first one is determining when the metric $g(\\mu)$ is extremal (in the sense of Calabi), while the second one studies when the coefficient $a_2$ in the Engli\\v{s} expansion of Rawnsley $\\epsilon$-function associated to $g(\\mu)$ is constant.
METRICS IN ORGANIZATIONAL CENTRALIZATION AND DECENTRALIZATION
Vladimir Modrak; Sorin Mihai Radu; Jan Modrak
2014-01-01
Continual improvement of business processes requires, apart from other efforts, to develop effective metrics, by which managers and/or process engineers will be able to manage the organization's growth. Obviously, there are plenty measures that can be taken to optimize processes. Once effective metrics are identified, the assessment team should do what works best for them. In this paper, an organizational “centralization” or “decentralization” is a matter of interest. The dichotomous term “ce...
Modeling Languages: metrics and assessing tools
Fonte, Daniela; Boas, Ismael Vilas; Azevedo, José; Peixoto, José João; Faria, Pedro; Silva, Pedro; Sá, Tiago de, 1990-; Costa, Ulisses; da Cruz, Daniela; Henriques, Pedro Rangel
2012-01-01
Any traditional engineering field has metrics to rigorously assess the quality of their products. Engineers know that the output must satisfy the requirements, must comply with the production and market rules, and must be competitive. Professionals in the new field of software engineering started a few years ago to define metrics to appraise their product: individual programs and software systems. This concern motivates the need to assess not only the outcome but also the process and tools em...
GRC GSFC TDRSS Waveform Metrics Report
Mortensen, Dale J.
2013-01-01
The report presents software metrics and porting metrics for the GGT Waveform. The porting was from a ground-based COTS SDR, the SDR-3000, to the CoNNeCT JPL SDR. The report does not address any of the Operating Environment (OE) software development, nor the original TDRSS waveform development at GSFC for the COTS SDR. With regard to STRS, the report presents compliance data and lessons learned.
Application-adaptive resource scheduling in a computational grid
LUAN Cui-ju; SONG Guang-hua; ZHENG Yao
2006-01-01
Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid.Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly.This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same.Experimental results are presented to demonstrate the adaptability of the HGSA.
Constrained Metric Learning by Permutation Inducing Isometries.
Bosveld, Joel; Mahmood, Arif; Huynh, Du Q; Noakes, Lyle
2016-01-01
The choice of metric critically affects the performance of classification and clustering algorithms. Metric learning algorithms attempt to improve performance, by learning a more appropriate metric. Unfortunately, most of the current algorithms learn a distance function which is not invariant to rigid transformations of images. Therefore, the distances between two images and their rigidly transformed pair may differ, leading to inconsistent classification or clustering results. We propose to constrain the learned metric to be invariant to the geometry preserving transformations of images that induce permutations in the feature space. The constraint that these transformations are isometries of the metric ensures consistent results and improves accuracy. Our second contribution is a dimension reduction technique that is consistent with the isometry constraints. Our third contribution is the formulation of the isometry constrained logistic discriminant metric learning (IC-LDML) algorithm, by incorporating the isometry constraints within the objective function of the LDML algorithm. The proposed algorithm is compared with the existing techniques on the publicly available labeled faces in the wild, viewpoint-invariant pedestrian recognition, and Toy Cars data sets. The IC-LDML algorithm has outperformed existing techniques for the tasks of face recognition, person identification, and object classification by a significant margin.
Dynamical Systems on Spectral Metric Spaces
Bellissard, Jean V; Reihani, Kamran
2010-01-01
Let (A,H,D) be a spectral triple, namely: A is a C*-algebra, H is a Hilbert space on which A acts and D is a selfadjoint operator with compact resolvent such that the set of elements of A having a bounded commutator with D is dense. A spectral metric space, the noncommutative analog of a complete metric space, is a spectral triple (A,H,D) with additional properties which guaranty that the Connes metric induces the weak*-topology on the state space of A. A *-automorphism respecting the metric defined a dynamical system. This article gives various answers to the question: is there a canonical spectral triple based upon the crossed product algebra AxZ, characterizing the metric properties of the dynamical system ? If $\\alpha$ is the noncommutative analog of an isometry the answer is yes. Otherwise, the metric bundle construction of Connes and Moscovici is used to replace (A,$\\alpha$) by an equivalent dynamical system acting isometrically. The difficulties relating to the non compactness of this new system are di...
Positive Semidefinite Metric Learning with Boosting
Shen, Chunhua; Wang, Lei; Hengel, Anton van den
2009-01-01
The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \\BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \\BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. \\BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classi...
Isometry groups of proper metric spaces
Niemiec, Piotr
2012-01-01
Given a locally compact Polish space X, a necessary and sufficient condition for a group G of homeomorphisms of X to be the full isometry group of (X,d) for some proper metric d on X is given. It is shown that every locally compact Polish group G acts freely on GxY as the full isometry group of GxY with respect to a certain proper metric on GxY, where Y is an arbitrary locally compact Polish space with (card(G),card(Y)) different from (1,2). Locally compact Polish groups which act effectively and almost transitively on complete metric spaces as full isometry groups are characterized. Locally compact Polish non-Abelian groups on which every left invariant metric is automatically right invariant are characterized and fully classified. It is demonstrated that for every locally compact Polish space X having more than two points the set of proper metrics d such that Iso(X,d) = {id} is dense in the space of all proper metrics on X.
Bi-metric pseudo-Finslerian spacetimes
Skakala, Jozef; Visser, Matt
2011-08-01
Finsler spacetimes have become increasingly popular within the theoretical physics community over the last two decades. However, because physicists need to use pseudo-Finsler structures to describe propagation of signals, there will be nonzero null vectors in both the tangent and cotangent spaces — this causes significant problems in that many of the mathematical results normally obtained for "usual" (Euclidean signature) Finsler structures either do not apply, or require significant modifications to their formulation and/or proof. We shall first provide a few basic definitions, explicitly demonstrating the interpretation of bi-metric theories in terms of pseudo-Finsler norms. We shall then discuss the tricky issues that arise when trying to construct an appropriate pseudo-Finsler metric appropriate to bi-metric spacetimes. Whereas in Euclidian signature the construction of the Finsler metric typically fails only at the zero vector, in Lorentzian signature the Finsler metric is typically ill-defined on the entire null cone. Consequently it is not a good idea to try to encode bi-metricity into pseudo-Finsler geometry. One has to be very careful when applying the concept of pseudo-Finsler geometry in physics.
Wang, Wei
2014-06-22
In this work, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-managing: self-labeling, self-updating and self-adapting. Our framework employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies. Two large real HTTP traffic streams collected in our institute as well as a set of benchmark KDD’99 data are used to validate the framework and the method. The test results show that the autonomic model achieves better results in terms of effectiveness and efficiency compared to adaptive Sequential Karhunen–Loeve method and static AP as well as three other static anomaly detection methods, namely, k-NN, PCA and SVM.
Panin, S. V.; Titkov, V. V.; Lyubutin, P. S.; Chemezov, V. O.; Eremin, A. V.
2016-11-01
Application of weight coefficients of the bilateral filter used to determine weighted similarity metrics of image ranges in optical flow computation algorithm that employs 3-dimension recursive search (3DRS) was investigated. By testing the algorithm applying images taken from the public test database Middlebury benchmark, the effectiveness of this weighted similarity metrics for solving the image processing problem was demonstrated. The necessity of matching the equation parameter values when calculating the weight coefficients aimed at taking into account image texture features was proved for reaching the higher noise resistance under the vector field construction. The adaptation technique which allows excluding manual determination of parameter values was proposed and its efficiency was demonstrated.
徐辉
2012-01-01
中文文本分类的主要问题是特征空间的高维性.提出了基于混沌二进制粒子群的KNN文本分类算法,利用混沌二进制粒子群算法遍历训练集的特征空间,选择特征子空间,然后在特征子空间中使用KNN算法进行文本分类.在粒子群的迭代优化过程中,利用混沌映射,指导群体进行混沌搜索,使算法摆脱局部最优,扩大寻找全局最优解的能力.实验结果表明,提出的新分类算法对中文文本分类是有效的,其分类准确率、召回率都优于KNN算法.%The main problem of Chinese text classification is the high dimenmonat teature space particle swarm optimization, KNN text classification algorithm is proposed. It uses chaotic particle swarm algorithm to traverse feature space of the training set, selects the feature subspace, and then it uses KNN algorithm to classify text in feature subspace. In particle swarm＇ s iterative process, It uses chaotic map to guide swarms for chaotic search,it makes the algorithm out of local optimum, and expands the ability of finding global optimal solution. Experimental results show that the proposed new classification algorithm for Chinese text classification is effective, the classification accuracy and recall are better than KNN algorithm.
Quantitative Adaptation Analytics for Assessing Dynamic Systems of Systems.
Gauthier, John H.; Miner, Nadine E.; Wilson, Michael L.; Le, Hai D.; Kao, Gio K; Melander, Darryl J.; Longsine, Dennis Earl [Sandia National Laboratories, Unknown, Unknown; Vander Meer, Robert Charles,
2015-01-01
Our society is increasingly reliant on systems and interoperating collections of systems, known as systems of systems (SoS). These SoS are often subject to changing missions (e.g., nation- building, arms-control treaties), threats (e.g., asymmetric warfare, terrorism), natural environments (e.g., climate, weather, natural disasters) and budgets. How well can SoS adapt to these types of dynamic conditions? This report details the results of a three year Laboratory Directed Research and Development (LDRD) project aimed at developing metrics and methodologies for quantifying the adaptability of systems and SoS. Work products include: derivation of a set of adaptability metrics, a method for combining the metrics into a system of systems adaptability index (SoSAI) used to compare adaptability of SoS designs, development of a prototype dynamic SoS (proto-dSoS) simulation environment which provides the ability to investigate the validity of the adaptability metric set, and two test cases that evaluate the usefulness of a subset of the adaptability metrics and SoSAI for distinguishing good from poor adaptability in a SoS. Intellectual property results include three patents pending: A Method For Quantifying Relative System Adaptability, Method for Evaluating System Performance, and A Method for Determining Systems Re-Tasking.
空间数据库中的线段k近邻查询研究%Research on line segment kNN query in spatial database
周屹; 杨泽雪
2015-01-01
K-nearest neighbor query is one of the most important queries in spatial database. K-nearest neighbor query has important applications in the content similarity search, pattern recognition and geographic information systems. Exist-ing k-nearest neighbor query is the query based on the point. The line segment k-nearest neighbor queries are put forward. That is finding k line segments whose distances to query point are the nearest. The algorithm of line segment kNN query based on Voronoi diagram is proposed and the relevant theorem and proof are given. The algorithm finds a candidate set with the adjacent properties of the segment Voronoi diagram, then finds the final results. Experiments on synthetic data sets show that the proposed algorithm outperforms brute-force method and the algorithm based on R-tree.%K近邻查询是空间数据库中的重要查询之一，k近邻查询在内容的相似性检索、模式识别、地理信息系统中有重要应用。针对现有k近邻查询都是基于点查询的情况，提出基于平面线段的k近邻查询，查找线段集中给定查询点的k个最近线段。给出基于Voronoi图的线段k近邻查询算法及给出相关定理和证明。该算法通过线段Voronoi图的邻接特性找到一个候选集，然后从中找到最终结果。通过随机数据的实验证明，所提算法明显优于线性扫描算法和基于R树的k近邻查询算法。
Privacy Preserving Moving KNN Queries
Hashem, Tanzima; Zhang, Rui
2011-01-01
We present a novel approach that protects trajectory privacy of users who access location-based services through a moving k nearest neighbor (MkNN) query. An MkNN query continuously returns the k nearest data objects for a moving user (query point). Simply updating a user's imprecise location such as a region instead of the exact position to a location-based service provider (LSP) cannot ensure privacy of the user for an MkNN query: continuous disclosure of regions enables the LSP to follow a user's trajectory. We identify the problem of trajectory privacy that arises from the overlap of consecutive regions while requesting an MkNN query and provide the first solution to this problem. Our approach allows a user to specify the confidence level that represents a bound of how much more the user may need to travel than the actual kth nearest data object. By hiding a user's required confidence level and the required number of nearest data objects from an LSP, we develop a technique to prevent the LSP from tracking...
Graphlet Based Metrics for the Comparison of Gene Regulatory Networks
Martin, Alberto J. M.; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S.; Perez-Acle, Tomas
2016-01-01
Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). PMID:27695050
Flowing liquid crystal simulating the Schwarzschild metric
Pereira, Erms R.; Moraes, Fernando [Universidade Federal da Paraiba (UFPB), Joao Pessoa, PB (Brazil)
2009-07-01
Full text. We show how to simulate the equatorial section of the Schwarzschild metric through a flowing liquid crystal in its nematic phase. Inside a liquid crystal in the nematic phase, a traveling light ray feels an effective metric, whose properties are linked to perpendicular and parallel refractive indexes, no e ne respectively, of the rod-like molecule of the liquid crystal. As these indexes depend on the scalar order parameter of the liquid crystal, the Beris-Edwards hydrodynamic theory is used to connect the order parameter with the velocity of a liquid crystal flow at each point. This way we calculate a radial velocity profile that simulates the equatorial section of the Schwarzschild metric in the nematic phase of the liquid crystal. This work will be presented in the following way. First, we show the effective metric that describes the light propagation around a (k = 1; c = 0) disclination defect of the nematic phase of a liquid crystalline sample and how this light propagation can be described by the order parameter q of the liquid crystalline material. Afterwards, we consider the liquid crystal flowing radially and we use the Beris-Edwards theory to analyze the dependence of the order parameter of the material with the flowing velocity module. In these two cases we consider the more general situation of three space dimensions. Finally, we employ the result from the second part in the first and we compare with the Schwarzschild metric written in isotropic coordinates. (author)
Future of the PCI Readmission Metric.
Wasfy, Jason H; Yeh, Robert W
2016-03-01
Between 2013 and 2014, the Centers for Medicare and Medicaid Services and the National Cardiovascular Data Registry publically reported risk-adjusted 30-day readmission rates after percutaneous coronary intervention (PCI) as a pilot project. A key strength of this public reporting effort included risk adjustment with clinical rather than administrative data. Furthermore, because readmission after PCI is common, expensive, and preventable, this metric has substantial potential to improve quality and value in American cardiology care. Despite this, concerns about the metric exist. For example, few PCI readmissions are caused by procedural complications, limiting the extent to which improved procedural technique can reduce readmissions. Also, similar to other readmission measures, PCI readmission is associated with socioeconomic status and race. Accordingly, the metric may unfairly penalize hospitals that care for underserved patients. Perhaps in the context of these limitations, Centers for Medicare and Medicaid Services has not yet included PCI readmission among metrics that determine Medicare financial penalties. Nevertheless, provider organizations may still wish to focus on this metric to improve value for cardiology patients. PCI readmission is associated with low-risk chest discomfort and patient anxiety. Therefore, patient education, improved triage mechanisms, and improved care coordination offer opportunities to minimize PCI readmissions. Because PCI readmission is common and costly, reducing PCI readmission offers provider organizations a compelling target to improve the quality of care, and also performance in contracts involve shared financial risk.
Implementing the Data Center Energy Productivity Metric
Sego, Landon H.; Marquez, Andres; Rawson, Andrew; Cader, Tahir; Fox, Kevin M.; Gustafson, William I.; Mundy, Christopher J.
2012-10-01
As data centers proliferate in both size and number, their energy efficiency is becoming increasingly important. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented, high performance computing data center. We found that DCeP was successful in clearly distinguishing between different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve (or even maximize) energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and among data centers.
Cleanroom Energy Efficiency: Metrics and Benchmarks
International SEMATECH Manufacturing Initiative; Mathew, Paul A.; Tschudi, William; Sartor, Dale; Beasley, James
2010-07-07
Cleanrooms are among the most energy-intensive types of facilities. This is primarily due to the cleanliness requirements that result in high airflow rates and system static pressures, as well as process requirements that result in high cooling loads. Various studies have shown that there is a wide range of cleanroom energy efficiencies and that facility managers may not be aware of how energy efficient their cleanroom facility can be relative to other cleanroom facilities with the same cleanliness requirements. Metrics and benchmarks are an effective way to compare one facility to another and to track the performance of a given facility over time. This article presents the key metrics and benchmarks that facility managers can use to assess, track, and manage their cleanroom energy efficiency or to set energy efficiency targets for new construction. These include system-level metrics such as air change rates, air handling W/cfm, and filter pressure drops. Operational data are presented from over 20 different cleanrooms that were benchmarked with these metrics and that are part of the cleanroom benchmark dataset maintained by Lawrence Berkeley National Laboratory (LBNL). Overall production efficiency metrics for cleanrooms in 28 semiconductor manufacturing facilities in the United States and recorded in the Fabs21 database are also presented.
SOFTWARE METRICS VALIDATION METHODOLOGIES IN SOFTWARE ENGINEERING
K.P. Srinivasan
2014-12-01
Full Text Available In the software measurement validations, assessing the validation of software metrics in software engineering is a very difficult task due to lack of theoretical methodology and empirical methodology [41, 44, 45]. During recent years, there have been a number of researchers addressing the issue of validating software metrics. At present, software metrics are validated theoretically using properties of measures. Further, software measurement plays an important role in understanding and controlling software development practices and products. The major requirement in software measurement is that the measures must represent accurately those attributes they purport to quantify and validation is critical to the success of software measurement. Normally, validation is a collection of analysis and testing activities across the full life cycle and complements the efforts of other quality engineering functions and validation is a critical task in any engineering project. Further, validation objective is to discover defects in a system and assess whether or not the system is useful and usable in operational situation. In the case of software engineering, validation is one of the software engineering disciplines that help build quality into software. The major objective of software validation process is to determine that the software performs its intended functions correctly and provides information about its quality and reliability. This paper discusses the validation methodology, techniques and different properties of measures that are used for software metrics validation. In most cases, theoretical and empirical validations are conducted for software metrics validations in software engineering [1-50].
Metric for Estimating Congruity between Quantum Images
Abdullah M. Iliyasu
2016-10-01
Full Text Available An enhanced quantum-based image fidelity metric, the QIFM metric, is proposed as a tool to assess the “congruity” between two or more quantum images. The often confounding contrariety that distinguishes between classical and quantum information processing makes the widely accepted peak-signal-to-noise-ratio (PSNR ill-suited for use in the quantum computing framework, whereas the prohibitive cost of the probability-based similarity score makes it imprudent for use as an effective image quality metric. Unlike the aforementioned image quality measures, the proposed QIFM metric is calibrated as a pixel difference-based image quality measure that is sensitive to the intricacies inherent to quantum image processing (QIP. As proposed, the QIFM is configured with in-built non-destructive measurement units that preserve the coherence necessary for quantum computation. This design moderates the cost of executing the QIFM in order to estimate congruity between two or more quantum images. A statistical analysis also shows that our proposed QIFM metric has a better correlation with digital expectation of likeness between images than other available quantum image quality measures. Therefore, the QIFM offers a competent substitute for the PSNR as an image quality measure in the quantum computing framework thereby providing a tool to effectively assess fidelity between images in quantum watermarking, quantum movie aggregation and other applications in QIP.
Homology-independent metrics for comparative genomics.
Coutinho, Tarcisio José Domingos; Franco, Glória Regina; Lobo, Francisco Pereira
2015-01-01
A mainstream procedure to analyze the wealth of genomic data available nowadays is the detection of homologous regions shared across genomes, followed by the extraction of biological information from the patterns of conservation and variation observed in such regions. Although of pivotal importance, comparative genomic procedures that rely on homology inference are obviously not applicable if no homologous regions are detectable. This fact excludes a considerable portion of "genomic dark matter" with no significant similarity - and, consequently, no inferred homology to any other known sequence - from several downstream comparative genomic methods. In this review we compile several sequence metrics that do not rely on homology inference and can be used to compare nucleotide sequences and extract biologically meaningful information from them. These metrics comprise several compositional parameters calculated from sequence data alone, such as GC content, dinucleotide odds ratio, and several codon bias metrics. They also share other interesting properties, such as pervasiveness (patterns persist on smaller scales) and phylogenetic signal. We also cite examples where these homology-independent metrics have been successfully applied to support several bioinformatics challenges, such as taxonomic classification of biological sequences without homology inference. They where also used to detect higher-order patterns of interactions in biological systems, ranging from detecting coevolutionary trends between the genomes of viruses and their hosts to characterization of gene pools of entire microbial communities. We argue that, if correctly understood and applied, homology-independent metrics can add important layers of biological information in comparative genomic studies without prior homology inference.
Codes in W*-Metric Spaces: Theory and Examples
Bumgardner, Christopher J.
2011-01-01
We introduce a "W*"-metric space, which is a particular approach to non-commutative metric spaces where a "quantum metric" is defined on a von Neumann algebra. We generalize the notion of a quantum code and quantum error correction to the setting of finite dimensional "W*"-metric spaces, which includes codes and error correction for classical…
g-Weak Contraction in Ordered Cone Rectangular Metric Spaces
S. K. Malhotra
2013-01-01
Full Text Available We prove some common fixed-point theorems for the ordered g-weak contractions in cone rectangular metric spaces without assuming the normality of cone. Our results generalize some recent results from cone metric and cone rectangular metric spaces into ordered cone rectangular metric spaces. Examples are provided which illustrate the results.
Demonstration of the Symmetry Properties of Gravitational Metric Fields
邵亮; H.NODA; 邵丹; 邵常贵
2002-01-01
We calculate some Wilson loop functionals in a static sphere-symmetrical diagonal metric field and a gravitational metric field established by a cosmic string. Using the direction change of vector when it is parallel transported in the metric field of cosmic string, the cone symmetry of the metric field is shown.
34 CFR 74.15 - Metric system of measurement.
2010-07-01
... 34 Education 1 2010-07-01 2010-07-01 false Metric system of measurement. 74.15 Section 74.15... Metric system of measurement. The Metric Conversion Act, as amended by the Omnibus Trade and Competitiveness Act (15 U.S.C. 205) declares that the metric system is the preferred measurement system for...
When is a metric not a metric? Remarks on direct curve comparison in bioequivalence studies.
Jawień, Wojciech
2009-06-01
The majority of measures proposed to date for direct curve comparison in bioequivalence studies were investigated. These measures have often been called metrics, but in most cases this was incorrect in the mathematical sense. It was demonstrated, with a set of counter-examples, that the axioms of a metric are fulfilled only for the integral p-metric and some of its transforms. The Rescigno index and two other measures devised by Polli and McLean are the semi-metrics, lacking the triangle inequality, while others also lack symmetry. The use of the p-metric is therefore recommended, and statistical analysis is suggested as a point at which the scaling of differences might be carried out.
Single Kerr-Schild metrics: a double view
McIntosh, C.B.G.; Hickman, M.S.
1988-08-01
Real-vacuum single Kerr-Schild (ISKS) metrics are discussed and new results proved. It is shown that if they Weyl tensor of such a metric has a twist-free expanding principal null direction, then it belongs to the Schwarzchild family of metrics-there are no Petrov type-II Robinson-Trautman metrics of Kerr-Schild type. If such a metric has twist then it belongs either to the Kerr family or else its Weyl tensor is of Petrov type II. The main part of the paper is concerned with complexified versions of Kerr-Schild metrics. The general real ISKS metric is written in double Kerr-Schild (IDKS) form. The H and l potentials which generate IDKS metrics are determined for the general vacuum ISKS metric and given explicitly for the Schwarzchild and Kerr families of metrics.
Pragmatic quality metrics for evolutionary software development models
Royce, Walker
1990-01-01
Due to the large number of product, project, and people parameters which impact large custom software development efforts, measurement of software product quality is a complex undertaking. Furthermore, the absolute perspective from which quality is measured (customer satisfaction) is intangible. While we probably can't say what the absolute quality of a software product is, we can determine the relative quality, the adequacy of this quality with respect to pragmatic considerations, and identify good and bad trends during development. While no two software engineers will ever agree on an optimum definition of software quality, they will agree that the most important perspective of software quality is its ease of change. We can call this flexibility, adaptability, or some other vague term, but the critical characteristic of software is that it is soft. The easier the product is to modify, the easier it is to achieve any other software quality perspective. This paper presents objective quality metrics derived from consistent lifecycle perspectives of rework which, when used in concert with an evolutionary development approach, can provide useful insight to produce better quality per unit cost/schedule or to achieve adequate quality more efficiently. The usefulness of these metrics is evaluated by applying them to a large, real world, Ada project.
Evaluating Search Engine Relevance with Click-Based Metrics
Radlinski, Filip; Kurup, Madhu; Joachims, Thorsten
Automatically judging the quality of retrieval functions based on observable user behavior holds promise for making retrieval evaluation faster, cheaper, and more user centered. However, the relationship between observable user behavior and retrieval quality is not yet fully understood. In this chapter, we expand upon, Radlinski et al. (How does clickthrough data reflect retrieval quality, In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), 43-52, 2008), presenting a sequence of studies investigating this relationship for an operational search engine on the arXiv.org e-print archive. We find that none of the eight absolute usage metrics we explore (including the number of clicks observed, the frequency with which users reformulate their queries, and how often result sets are abandoned) reliably reflect retrieval quality for the sample sizes we consider. However, we find that paired experiment designs adapted from sensory analysis produce accurate and reliable statements about the relative quality of two retrieval functions. In particular, we investigate two paired comparison tests that analyze clickthrough data from an interleaved presentation of ranking pairs, and find that both give accurate and consistent results. We conclude that both paired comparison tests give substantially more accurate and sensitive evaluation results than the absolute usage metrics in our domain.
Extraretinal signal metrics in multiple-saccade sequences.
Collins, Thérèse
2010-12-06
Executing sequences of memory-guided movements requires combining sensory information with information about previously made movements. In the oculomotor system, extraretinal information must be combined with stored visual information about target location. The use of extraretinal signals in oculomotor planning can be probed in the double-step task. Using this task and a multiple-step version, the present study examined whether an extraretinal signal was used on every trial, whether its metrics represented desired or actual eye displacement, and whether it was best characterized as a direct estimate of orbital eye position or a vector representation of eye displacement. The results show that accurate information, including saccadic adaptation, about the first saccade is used to plan the second saccade. Furthermore, with multiple saccades, endpoint variability increases with the number of saccades. Controls ruled out that this was due to the perceptual or memory requirements of storing several target locations. Instead, each memory-guided movement depends on an internal copy of an executed movement, which may present a small discrepancy with the actual movement. Increasing the number of estimates increases the variability because this small discrepancy accumulates over several saccades. Such accumulation is compatible with a corollary discharge signal carrying metric information about saccade vectors.
Evaluating and Estimating the WCET Criticality Metric
Jordan, Alexander
2014-01-01
Static analysis tools that are used for worst-case execution time (WCET) analysis of real-time software just provide partial information on an analyzed program. Only the longest-executing path, which currently determines the WCET bound is indicated to the programmer. This limited view can prevent...... a programmer (or compiler) from targeting optimizations the right way. A possible resort is to use a metric that targets WCET and which can be efficiently computed for all code parts of a program. Similar to dynamic profiling techniques, which execute code with input that is typically expected...... to estimate the Criticality metric, by relaxing the precision of WCET analysis. Through this, we can reduce analysis time by orders of magnitude, while only introducing minor error. To evaluate our estimation approach and share our garnered experience using the metric, we evaluate real-time programs, which...
SOCIAL METRICS APPLIED TO SMART TOURISM
O. Cervantes
2016-09-01
Full Text Available We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.
Social Metrics Applied to Smart Tourism
Cervantes, O.; Gutiérrez, E.; Gutiérrez, F.; Sánchez, J. A.
2016-09-01
We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services) to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.
Metric learning for automatic sleep stage classification.
Phan, Huy; Do, Quan; Do, The-Luan; Vu, Duc-Lung
2013-01-01
We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.
Robust Metric Learning by Smooth Optimization
Huang, Kaizhu; Xu, Zenglin; Liu, Cheng-Lin
2012-01-01
Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many mistakes. In this work, we aim to learn a distance metric from noisy constraints by robust optimization in a worst-case scenario, to which we refer as robust metric learning. We formulate the learning task initially as a combinatorial optimization problem, and show that it can be elegantly transformed to a convex programming problem. We present an efficient learning algorithm based on smooth optimization [7]. It has a worst-case convergence rate of O(1/{\\surd}{\\varepsilon}) for smooth optimization problems, where {\\varepsilon} is the desired error of the approximate solution. Finally, our empirical study with UCI data sets demonstrate the effectiveness of ...
Rainbow Rindler metric and Unruh effect
Yadav, Gaurav; Majhi, Bibhas Ranjan
2016-01-01
The energy of a particle moving on a spacetime, in principle, can affect the background metric. The modifications to it depend on the ratio of energy of the particle and the Planck energy, known as rainbow gravity. Here we find the explicit expressions for the coordinate transformations from rainbow Minkowski spacetime to accelerated frame. The corresponding metric is also obtained which we call as rainbow-Rindler metric. So far we are aware of, no body has done it in a concrete manner. Here this is found from the first principle and hence all the parameters are properly identified. The advantage of this is that the calculated Unruh temperature is compatible with the Hawking temperature of the rainbow black hole horizon, obtained earlier. Since the accelerated frame has several importance in revealing various properties of gravity, we believe that the present result will not only fill that gap, but also help to explore different aspects of rainbow gravity paradigm.
Enhanced Accident Tolerant LWR Fuels: Metrics Development
Shannon Bragg-Sitton; Lori Braase; Rose Montgomery; Chris Stanek; Robert Montgomery; Lance Snead; Larry Ott; Mike Billone
2013-09-01
The Department of Energy (DOE) Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC) is conducting research and development on enhanced Accident Tolerant Fuels (ATF) for light water reactors (LWRs). This mission emphasizes the development of novel fuel and cladding concepts to replace the current zirconium alloy-uranium dioxide (UO2) fuel system. The overall mission of the ATF research is to develop advanced fuels/cladding with improved performance, reliability and safety characteristics during normal operations and accident conditions, while minimizing waste generation. The initial effort will focus on implementation in operating reactors or reactors with design certifications. To initiate the development of quantitative metrics for ATR, a LWR Enhanced Accident Tolerant Fuels Metrics Development Workshop was held in October 2012 in Germantown, MD. This paper summarizes the outcome of that workshop and the current status of metrics development for LWR ATF.
Development of testing metrics for military robotics
Resendes, Raymond J.
1993-05-01
The use of robotics or unmanned systems offers significant benefits to the military user by enhancing mobility, logistics, material handling, command and control, reconnaissance, and protection. The evaluation and selection process for the procurement of an unmanned robotic system involves comparison of performance and physical characteristics such as operating environment, application, payloads and performance criteria. Testing an unmanned system for operation in an unstructured environment using emerging technologies, which have not yet been fully tested, presents unique challenges for the testing community. Standard metrics, test procedures, terminologies, and methodologies simplify comparison of different systems. A procedure was developed to standardize the test and evaluation process for UGVs. This procedure breaks the UGV into three components: the platform, the payload, and the command and control link. Standardized metrics were developed for these components which permit unbiased comparison of different systems. The development of these metrics and their application will be presented.
Metric Learning for Hyperspectral Image Segmentation
Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca
2011-01-01
We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.
Metric perturbations in Einstein-Cartan Cosmology
Garcia de Andrade, L C
2002-01-01
Metric perturbations the stability of solution of Einstein-Cartan cosmology (ECC) are given. The first addresses the stability of solutions of Einstein-Cartan (EC) cosmological model against Einstein static universe background. In this solution we show that the metric is stable against first-order perturbations and correspond to acoustic oscillations. The second example deals with the stability of de Sitter metric also against first-order perturbations. Torsion and shear are also computed in these cases. The resultant perturbed anisotropic spacetime with torsion is only de Sitter along one direction or is unperturbed along one direction and perturbed against the other two. Cartan torsion contributes to the frequency of oscillations in the model. Therefore gravitational waves could be triggered by the spin-torsion scalar density .
Steiner trees for fixed orientation metrics
Brazil, Marcus; Zachariasen, Martin
2009-01-01
We consider the problem of constructing Steiner minimum trees for a metric defined by a polygonal unit circle (corresponding to s = 2 weighted legal orientations in the plane). A linear-time algorithm to enumerate all angle configurations for degree three Steiner points is given. We provide...... a simple proof that the angle configuration for a Steiner point extends to all Steiner points in a full Steiner minimum tree, such that at most six orientations suffice for edges in a full Steiner minimum tree. We show that the concept of canonical forms originally introduced for the uniform orientation...... metric generalises to the fixed orientation metric. Finally, we give an O(s n) time algorithm to compute a Steiner minimum tree for a given full Steiner topology with n terminal leaves....
On 2-dimensional Kaehler metrics with one holomorphic isometry
Chimento, Samuele
2016-01-01
We show how to write any Kaehler metric of complex dimension 2 admitting a holomorphic isometry as a simple 1-real-function deformation of a Gibbons-Hawking metric. Hyper-Kaehler metrics with a tri-holomorphic isometry (Gibbons-Hawking metrics) or with a mono-holomorphic isometry are recovered for particular values of the additional function. The new general metric can be used as an Ansatz in several interesting physical problems.
Enhancing the quality metric of protein microarray image
王立强; 倪旭翔; 陆祖康; 郑旭峰; 李映笙
2004-01-01
The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.
Boolean metric spaces and Boolean algebraic varieties
Avilés, Antonio
2009-01-01
The concepts of Boolean metric space and convex combination are used to characterize polynomial maps in a class of commutative Von Neumann regular rings including Boolean rings and p-rings, that we have called CFG-rings. In those rings, the study of the category of algebraic varieties (i.e. sets of solutions to a finite number of polynomial equations with polynomial maps as morphisms) is equivalent to the study of a class of Boolean metric spaces, that we call here CFG-spaces.
Geons and the quantum information metric
Sinamuli, Musema; Mann, Robert B.
2017-07-01
We investigate the proposed duality between a quantum information metric in a CFTd +1 and the volume of a maximum time slice in the dual AdSd +2 for topological geons. Examining the specific cases of Banados-Teitelboim-Zannelli (BTZ) black holes and planar Schwarzschild-anti-de Sitter black holes, along with their geon counterparts, we find that the proposed duality relation for geons is the same apart from a factor of 4. The information metric therefore provides a probe of the topology of the bulk spacetime.
Metric-affine gravitation theory and superpotentials
Giachetta, G.; Mangiarotti, L.; Saltarelli, A. [Camerino, Univ. (Italy). Dipt. di Matematica e Fisica
1997-05-01
They consider a metric-affine theory of gravity in which the dynamical fields are the Lorentzian metrics and the non-symmetric linear connections on the worked manifold X. Working with a Lagrangian density which is invariant under general covariant transformations and using standard tools of the calculus of variations, they study the corresponding currents. They find that the superpotential takes a nice form involving the torsion of the linear connection in a simple way and generalizing the well-known Komar superpotential. A feature of our approach is the use of the Poincare`-Cartan form in relation to the first variational formula of the calculus of variations.
Cohesion Metrics for Ontology Design and Application
Haining Yao
2005-01-01
Full Text Available Recently, domain specific ontology development has been driven by research on the Semantic Web. Ontologies have been suggested for use in many application areas targeted by the Semantic Web, such as dynamic web service composition and general web service matching. Fundamental characteristics of these ontologies must be determined in order to effectively make use of them: for example, Sirin, Hendler and Parsia have suggested that determining fundamental characteristics of ontologies is important for dynamic web service composition. Our research examines cohesion metrics for ontologies. The cohesion metrics examine the fundamental quality of cohesion as it relates to ontologies.
Fast Link Adaptation for MIMO-OFDM
Jensen, Tobias Lindstrøm; Kant, Shashi; Wehinger, Joachim;
2010-01-01
We investigate link-quality metrics (LQMs) based on raw bit-error-rate, effective signal-to-interference-plus-noise ratio, and mutual information (MI) for the purpose of fast link adaptation (LA) in communication systems employing orthogonal frequency-division multiplexing and multiple-input–mult......We investigate link-quality metrics (LQMs) based on raw bit-error-rate, effective signal-to-interference-plus-noise ratio, and mutual information (MI) for the purpose of fast link adaptation (LA) in communication systems employing orthogonal frequency-division multiplexing and multiple...
Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm
Koromyslova, A.; Semenkina, M.; Sergienko, R.
2017-02-01
The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms.
Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set
Jinna Li
2012-01-01
Full Text Available A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT detection method and k-nearest neighbor (KNN rule-based statistical process control (SPC approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.
Business model metrics: an open repository
Heikkila, M.; Bouwman, W.A.G.A.; Heikkila, J.; Solaimani, S.; Janssen, W.
2015-01-01
Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and
Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report
Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar
2013-01-01
This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.
Rainbow metric formalism and Relative Locality
Loret, Niccoló
2015-01-01
This proceeding is based on a talk prepared for the XIII Marcell Grossmann meeting. We summarise some results of work in progress in collaboration with Giovanni Amelino-Camelia about momentum dependent (Rainbow) metrics in a Relative Locality framework and we show that this formalism is equivalent to the Hamiltonian formalization of Relative Locality obtained in arXiv:1102.4637.
Colliding waves in metric-affine gravity
García, A; Macías, A; Mielke, E W; Socorro, J; García, Alberto; Lämmerzahl, Claus; Macías, Alfredo; Mielke, Eckehard W.; Socorro, José
1998-01-01
We generalize the formulation of the colliding gravitational waves to metric-affine theories and present an example of such kind of exact solutions. The plane waves are equipped with five symmetries and the resulting geometry after the collision possesses two spacelike Killing vectors.
Thermodynamical properties of metric fluctuations during inflation
Bellini, M
2001-01-01
I study a thermodynamical approach to scalar metric perturbations during the inflationary stage. In the power-law expanding universe here studied, I find a negative heat capacity as a manifestation of superexponential growing for the number of states in super Hubble scales. The power spectrum depends on the Gibbons-Hawking and Hagedorn temperatures.
Validation metrics for turbulent plasma transport
Holland, C.
2016-06-01
Developing accurate models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. In modern computer science and engineering, formal verification and validation processes are used to assess model accuracy and establish confidence in the predictive capabilities of a given model. This paper provides an overview of the key guiding principles and best practices for the development of validation metrics, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. Particular emphasis is given to the importance of uncertainty quantification and its inclusion within the metrics, and the need for utilizing synthetic diagnostics to enable quantitatively meaningful comparisons between simulation and experiment. As a starting point, the structure of commonly used global transport model metrics and their limitations is reviewed. An alternate approach is then presented, which focuses upon comparisons of predicted local fluxes, fluctuations, and equilibrium gradients against observation. The utility of metrics based upon these comparisons is demonstrated by applying them to gyrokinetic predictions of turbulent transport in a variety of discharges performed on the DIII-D tokamak [J. L. Luxon, Nucl. Fusion 42, 614 (2002)], as part of a multi-year transport model validation activity.
Business model metrics: an open repository
Heikkila, M.; Bouwman, W.A.G.A.; Heikkila, J.; Solaimani, S.; Janssen, W.
2015-01-01
Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and per
Metric Conversion and the School Shop
Jackman, Arthur A.
1976-01-01
Cost of metric conversion in school shops is examined, and the author categories all the shops in the school and gives useful information on which shops are the easiest to convert, which are most complicated, where resistance is most likely to be met, and where conversion is most urgent. The math department is seen as catalyst. (Editor/HD)
Outsourced Similarity Search on Metric Data Assets
Yiu, Man Lung; Assent, Ira; Jensen, Christian S.
2012-01-01
This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example...
Calabi–Yau metrics and string compactification
Michael R. Douglas
2015-09-01
Full Text Available Yau proved an existence theorem for Ricci-flat Kähler metrics in the 1970s, but we still have no closed form expressions for them. Nevertheless there are several ways to get approximate expressions, both numerical and analytical. We survey some of this work and explain how it can be used to obtain physical predictions from superstring theory.
Metrical musings on Littlewood and friends
Haynes, A.; Jensen, Jonas Lindstrøm; Kristensen, Simon
We prove a metrical result on a family of conjectures related to the Littlewood conjecture, namely the original Littlewood conjecture, the mixed Littlewood conjecture of de Mathan and Teulié and a hybrid between a conjecture of Cassels and the Littlewood conjecture. It is shown that the set of nu...
Metrical categories in infancy and adulthood.
Hannon, Erin E; Trehub, Sandra E
2005-01-01
Intrinsic perceptual biases for simple duration ratios are thought to constrain the organization of rhythmic patterns in music. We tested that hypothesis by exposing listeners to folk melodies differing in metrical structure (simple or complex duration ratios), then testing them on alterations that preserved or violated the original metrical structure. Simple meters predominate in North American music, but complex meters are common in many other musical cultures. In Experiment 1, North American adults rated structure-violating alterations as less similar to the original version than structure-preserving alterations for simple-meter patterns but not for complex-meter patterns. In Experiment 2, adults of Bulgarian or Macedonian origin provided differential ratings to structure-violating and structure-preserving alterations in complex- as well as simple-meter contexts. In Experiment 3, 6-month-old infants responded differentially to structure-violating and structure-preserving alterations in both metrical contexts. These findings imply that the metrical biases of North American adults reflect enculturation processes rather than processing predispositions for simple meters.
Gravity Dual of Quantum Information Metric
Miyaji, Masamichi; Shiba, Noburo; Takayanagi, Tadashi; Watanabe, Kento
2015-01-01
We study a quantum information metric (or fidelity susceptibility) in conformal field theories with respect to a small perturbation by a primary operator. We argue that its gravity dual is approximately given by a volume of maximal time slice in an AdS spacetime when the perturbation is exactly marginal. We confirm our claim in several examples.
Strong Ideal Convergence in Probabilistic Metric Spaces
Celaleddin Şençimen; Serpil Pehlivan
2009-06-01
In the present paper we introduce the concepts of strongly ideal convergent sequence and strong ideal Cauchy sequence in a probabilistic metric (PM) space endowed with the strong topology, and establish some basic facts. Next, we define the strong ideal limit points and the strong ideal cluster points of a sequence in this space and investigate some properties of these concepts.
Performance Metrics Research Project - Final Report
Deru, M.; Torcellini, P.
2005-10-01
NREL began work for DOE on this project to standardize the measurement and characterization of building energy performance. NREL's primary research objectives were to determine which performance metrics have greatest value for determining energy performance and to develop standard definitions and methods of measuring and reporting that performance.
On Decomposable Measures Induced by Metrics
Dong Qiu
2012-01-01
Full Text Available We prove that for a given normalized compact metric space it can induce a σ-max-superdecomposable measure, by constructing a Hausdorff pseudometric on its power set. We also prove that the restriction of this set function to the algebra of all measurable sets is a σ-max-decomposable measure. Finally we conclude this paper with two open problems.
Business model metrics: an open repository
Heikkila, M.; Bouwman, W.A.G.A.; Heikkila, J.; Solaimani, S.; Janssen, W.
2015-01-01
Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and per
The LSST metrics analysis framework (MAF)
Jones, R. L.; Yoachim, Peter; Chandrasekharan, Srinivasan; Connolly, Andrew J.; Cook, Kem H.; Ivezic, Željko; Krughoff, K. S.; Petry, Catherine; Ridgway, Stephen T.
2014-07-01
We describe the Metrics Analysis Framework (MAF), an open-source python framework developed to provide a user-friendly, customizable, easily-extensible set of tools for analyzing data sets. MAF is part of the Large Synoptic Survey Telescope (LSST) Simulations effort. Its initial goal is to provide a tool to evaluate LSST Operations Simulation (OpSim) simulated surveys to help understand the effects of telescope scheduling on survey performance, however MAF can be applied to a much wider range of datasets. The building blocks of the framework are Metrics (algorithms to analyze a given quantity of data), Slicers (subdividing the overall data set into smaller data slices as relevant for each Metric), and Database classes (to access the dataset and read data into memory). We describe how these building blocks work together, and provide an example of using MAF to evaluate different dithering strategies. We also outline how users can write their own custom Metrics and use these within the framework.
A new universal colour image fidelity metric
Toet, A.; Lucassen, M.P.
2003-01-01
We extend a recently introduced universal grayscale image quality index to a newly developed perceptually decorrelated colour space. The resulting colour image fidelity metric quantifies the distortion of a processed colour image relative to its original version. We evaluated the new colour image fi
Assessing Software Quality Through Visualised Cohesion Metrics
Timothy Shih
2001-05-01
Full Text Available Cohesion is one of the most important factors for software quality as well as maintainability, reliability and reusability. Module cohesion is defined as a quality attribute that seeks for measuring the singleness of the purpose of a module. The module of poor quality can be a serious obstacle to the system quality. In order to design a good software quality, software managers and engineers need to introduce cohesion metrics to measure and produce desirable software. A highly cohesion software is thought to be a desirable constructing. In this paper, we propose a function-oriented cohesion metrics based on the analysis of live variables, live span and the visualization of processing element dependency graph. We give six typical cohesion examples to be measured as our experiments and justification. Therefore, a well-defined, well-normalized, well-visualized and well-experimented cohesion metrics is proposed to indicate and thus enhance software cohesion strength. Furthermore, this cohesion metrics can be easily incorporated with software CASE tool to help software engineers to improve software quality.
Validation metrics for turbulent plasma transport
Holland, C., E-mail: chholland@ucsd.edu [Center for Energy Research, University of California, San Diego, La Jolla, California 92093-0417 (United States)
2016-06-15
Developing accurate models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. In modern computer science and engineering, formal verification and validation processes are used to assess model accuracy and establish confidence in the predictive capabilities of a given model. This paper provides an overview of the key guiding principles and best practices for the development of validation metrics, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. Particular emphasis is given to the importance of uncertainty quantification and its inclusion within the metrics, and the need for utilizing synthetic diagnostics to enable quantitatively meaningful comparisons between simulation and experiment. As a starting point, the structure of commonly used global transport model metrics and their limitations is reviewed. An alternate approach is then presented, which focuses upon comparisons of predicted local fluxes, fluctuations, and equilibrium gradients against observation. The utility of metrics based upon these comparisons is demonstrated by applying them to gyrokinetic predictions of turbulent transport in a variety of discharges performed on the DIII-D tokamak [J. L. Luxon, Nucl. Fusion 42, 614 (2002)], as part of a multi-year transport model validation activity.
On A Schwarszchild-Like Metric
Anastasiei, Mihai; Gottlieb, Ioan
2012-12-01
In this short Note we would like to bring into the attention of people working in General Relativity a Schwarzschild like metric found by Professor Cleopatra Mociuţchi in sixties. It was obtained by the A. Sommerfeld reasoning from his treatise "Elektrodynamik" but using instead of the energy conserving law from the classical Physics, the relativistic energy conserving law.
DIGITAL MARKETING: SUCCESS METRICS, FUTURE TRENDS
Preeti Kaushik
2017-01-01
Abstract – Business Marketing is one of the prospective which has been tremendously affected by digital world in last few years. Digital marketing refers to doing advertising through digital channels. This paper provides detailed study of metrics to measure success of digital marketing platform and glimpse of future of technologies by 2020.
Clean Cities 2011 Annual Metrics Report
Johnson, C.
2012-12-01
This report details the petroleum savings and vehicle emissions reductions achieved by the U.S. Department of Energy's Clean Cities program in 2011. The report also details other performance metrics, including the number of stakeholders in Clean Cities coalitions, outreach activities by coalitions and national laboratories, and alternative fuel vehicles deployed.
Clean Cities 2010 Annual Metrics Report
Johnson, C.
2012-10-01
This report details the petroleum savings and vehicle emissions reductions achieved by the U.S. Department of Energy's Clean Cities program in 2010. The report also details other performance metrics, including the number of stakeholders in Clean Cities coalitions, outreach activities by coalitions and national laboratories, and alternative fuel vehicles deployed.
Nonunital Spectral Triples Associated to Degenerate Metrics
Rennie, A.
We show that one can define (p,∞)-summable spectral triples using degenerate metrics on smooth manifolds. Furthermore, these triples satisfy Connes-Moscovici's discrete and finite dimension spectrum hypothesis, allowing one to use the Local Index Theorem [1] to compute the pairing with K-theory. We demonstrate this with a concrete example.
Calabi-Yau metrics and string compactification
Douglas, Michael R
2015-01-01
Yau proved an existence theorem for Ricci-flat K\\"ahler metrics in the 1970's, but we still have no closed form expressions for them. Nevertheless there are several ways to get approximate expressions, both numerical and analytical. We survey some of this work and explain how it can be used to obtain physical predictions from superstring theory.
Calabi-Yau metrics and string compactification
Douglas, Michael R.
2015-09-01
Yau proved an existence theorem for Ricci-flat Kähler metrics in the 1970s, but we still have no closed form expressions for them. Nevertheless there are several ways to get approximate expressions, both numerical and analytical. We survey some of this work and explain how it can be used to obtain physical predictions from superstring theory.
Santosh Kumar S
2011-10-01
Full Text Available A mobile ad hoc network is collection of self configuring and adaption of wireless link between communicating devices (mobile devices to form an arbitrary topology and multihop wireless connectivity without the use of existing infrastructure. It requires efficient dynamic routing protocol to determine the routes subsequent to a set of rules that enables two or more devices to communicate with each others. This paper basically classifies and evaluates the mobility metrics into two categories- direct mobility metrics and derived mobility metrics. These two mobility metrics has been used to measure different mobility models, this paper considers some of mobility models i.e Random Waypoint Model, Reference Point Group Mobility Model, Random Direction Mobility Model, Random Walk Mobility Model, Probabilistic Random Walk, Gauss Markov, Column Mobility Model, Nomadic Community Mobility Model and Manhattan Grid Model.
Measuring success : metrics that link supply chain management to aircraft readiness
McDoniel, Patrick S.; Balestreri, William G.
2002-01-01
Approved for public release; distribution is unlimited. This thesis evaluates and analyzes current strategic management planning methods that develop performance metrics linking supply chain management to aircraft readiness. Our primary focus is the Marine Aviation Logistics Squadron. Utilizing the Logistics Management Institute's DoD Supply Chain Implementation Guide and adapted SCOR model, we applied the six step process for developing a strategic logistics management plan for implementi...
Computing strong metric dimension of some special classes of graphs by genetic algorithms
Kratica Jozef
2008-01-01
Full Text Available In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.
A Novel Performance Metric for Building an Optimized Classifier
Mohammad Hossin
2011-01-01
Full Text Available Problem statement: Typically, the accuracy metric is often applied for optimizing the heuristic or stochastic classification models. However, the use of accuracy metric might lead the searching process to the sub-optimal solutions due to its less discriminating values and it is also not robust to the changes of class distribution. Approach: To solve these detrimental effects, we propose a novel performance metric which combines the beneficial properties of accuracy metric with the extended recall and precision metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP. Results: In this study, we demonstrate that the OARP metric is theoretically better than the accuracy metric using four generated examples. We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the conventional accuracy metric. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the accuracy metric alone for all binary data sets. Conclusion: The experiments have proved that the OARP metric leads stochastic classifiers such as the MCS towards a better training model, which in turn will improve the predictive results of any heuristic or stochastic classification models.
何晨阳; 周孟然; 闫鹏程
2016-01-01
Rapid identification and classification of mine water inrush is important for flood prevention work underground .This paper proposed a method of KNN combined with PCA identification of water inrush in mine with the laser induced fluorescence spectrum with an immersion probe laser into water samples .The fluorescence spectra of 4 kinds of water samples were obtained . For each set of data preprocessing ,the processed data in each sample from 15 sets of data as the training setwith a total of 60 groups .The other 20 groups were used as the prediction set .The data were processed by principal component analysis (PCA) , and then the KNN algorithm was used to classify and identify the principal component analysis .During the experiment ,the pre‐treatment method in the principal component number is 2 while the correct rate has reached 100% by KNN classification algo‐rithm .%矿井突水的迅速识别与分类对于井下水灾防治工作有着重要的意义。提出一种KNN结合PCA运用在激光诱导荧光光谱快速识别矿井突水水源中的新方法。利用激光器发射激光通过可浸入式探头射入水样，得到四种突水水样共80组荧光光谱数据，再分别对每组数据进行预处理，处理后的数据中每种水样取15组数据作为训练集，共60组，其余20组作为预测集。利用主成分分析（PCA）对数据进行处理，之后在主成分分析的基础上利用KNN算法进行分类识别。实验过程中，各预处理方法在主成分个数为2的情况下，进行KNN算法分类的正确率都达到100％。
How to evaluate objective video quality metrics reliably
Korhonen, Jari; Burini, Nino; You, Junyong
2012-01-01
The typical procedure for evaluating the performance of different objective quality metrics and indices involves comparisons between subjective quality ratings and the quality indices obtained using the objective metrics in question on the known video sequences. Several correlation indicators can...
Regular black hole metrics and the weak energy condition
Balart, Leonardo, E-mail: leonardo.balart@ufrontera.cl [I.C.B. – Institut Carnot de Bourgogne, UMR 5209, CNRS, Faculté des Sciences Mirande, Université de Bourgogne, 9 Avenue Alain Savary, BP 47870, 21078 Dijon Cedex (France); Departamento de Ciencias Físicas, Facultad de Ingeniería y Ciencias, Universidad de La Frontera, Casilla 54-D, Temuco (Chile); Vagenas, Elias C., E-mail: elias.vagenas@ku.edu.kw [Theoretical Physics Group, Department of Physics, Kuwait University, P.O. Box 5969, Safat 13060 (Kuwait)
2014-03-07
In this work we construct a family of spherically symmetric, static, charged regular black hole metrics in the context of Einstein-nonlinear electrodynamics theory. The construction of the charged regular black hole metrics is based on three requirements: (a) the weak energy condition should be satisfied, (b) the energy–momentum tensor should have the symmetry T{sub 0}{sup 0}=T{sub 1}{sup 1}, and (c) these metrics have to asymptotically behave as the Reissner–Nordström black hole metric. In addition, these charged regular black hole metrics depend on two parameters which for specific values yield regular black hole metrics that already exist in the literature. Furthermore, by relaxing the third requirement, we construct more general regular black hole metrics which do not behave asymptotically as a Reissner–Nordström black hole metric.
Structural Properties of Hard Metric TSP Inputs
Mömke, Tobias
The metric traveling salesman problem is one of the most prominent APX-complete optimization problems. An important particularity of this problem is that there is a large gap between the known upper bound and lower bound on the approximability (assuming P ≠ NP). In fact, despite more than 30 years of research, no one could find a better approximation algorithm than the 1.5-approximation provided by Christofides. The situation is similar for a related problem, the metric Hamiltonian path problem, where the start and the end of the path are prespecified: the best approximation ratio up to date is 5/3 by an algorithm presented by Hoogeveen almost 20 years ago.
Metric-Aware Secure Service Orchestration
Gabriele Costa
2012-12-01
Full Text Available Secure orchestration is an important concern in the internet of service. Next to providing the required functionality the composite services must also provide a reasonable level of security in order to protect sensitive data. Thus, the orchestrator has a need to check whether the complex service is able to satisfy certain properties. Some properties are expressed with metrics for precise definition of requirements. Thus, the problem is to analyse the values of metrics for a complex business process. In this paper we extend our previous work on analysis of secure orchestration with quantifiable properties. We show how to define, verify and enforce quantitative security requirements in one framework with other security properties. The proposed approach should help to select the most suitable service architecture and guarantee fulfilment of the declared security requirements.
Machine Learning for ATLAS DDM Network Metrics
Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf
2016-01-01
The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.
Metric for Early Measurement of Software Complexity
Ghazal Keshavarz,
2011-06-01
Full Text Available Software quality depends on several factors such as on time delivery; within budget and fulfilling user's needs. Complexity is one of the most important factors that may affect the quality. Therefore, measuring and controlling the complexity result in improving the quality. So far, most of the researches have tried to identify and measure the complexity in design and code phase. However, whenwe have the code or design for software, it is too late to control complexity. In this article, with emphasis on Requirement Engineering process, we analyze the causes of software complexity, particularly in the first phase of software development, and propose a requirement based metric. This metric enables a software engineer to measure the complexity before actual design and implementation and choosestrategies that are appropriate to the software complexity degree, thus saving on cost and human resource wastage and, more importantly, leading to lower maintenance costs.
A Taxonomy of Metrics for Hosted Databases
Jordan Shropshire
2006-04-01
Full Text Available The past three years has seen exponential growth in the number of organizations who have elected to entrust core information technology functions to application service providers. Of particular interest is the outsourcing of critical systems such as corporate databases. Major banks and financial service firms are contracting with third party organizations, sometimes overseas, for their database needs. These sophisticated contracts require careful supervision by both parties. Due to the complexities of web- based applications and the complicated nature of databases, an entire class of software suites has been developed to measure the quality of service the database is providing. This article investigates the performance metrics which have evolved to satisfy this need and describes a taxonomy of performance metrics for hosted databases.
A universal metric for ferroic energy materials.
Brück, Ekkes; Yibole, Hargen; Zhang, Lian
2016-08-13
After almost 20 years of intensive research on magnetocaloric effects near room temperature, magnetic refrigeration with first-order magnetocaloric materials has come close to real-life applications. Many materials have been discussed as potential candidates to be used in multicaloric devices. However, phase transitions in ferroic materials are often hysteretic and a metric is needed to estimate the detrimental effects of this hysteresis. We propose the coefficient of refrigerant performance, which compares the net work in a reversible cycle with the positive work on the refrigerant, as a universal metric for ferroic materials. Here, we concentrate on examples from magnetocaloric materials and only consider one barocaloric experiment. This is mainly due to lack of data on electrocaloric materials. It appears that adjusting the field-induced transitions and the hysteresis effects can minimize the losses in first-order materials.This article is part of the themed issue 'Taking the temperature of phase transitions in cool materials'.
Infinitesimally Lipschitz functions on metric spaces
Durand, E
2009-01-01
For a metric space $X$, we study the space $D^{\\infty}(X)$ of bounded functions on $X$ whose infinitesimal Lipschitz constant is uniformly bounded. $D^{\\infty}(X)$ is compared with the space $\\LIP^{\\infty}(X)$ of bounded Lipschitz functions on $X$, in terms of different properties regarding the geometry of $X$. We also obtain a Banach-Stone theorem in this context. In the case of a metric measure space, we also compare $D^{\\infty}(X)$ with the Newtonian-Sobolev space $N^{1, \\infty}(X)$. In particular, if $X$ supports a doubling measure and satisfies a local Poincar{\\'e} inequality, we obtain that $D^{\\infty}(X)=N^{1, \\infty}(X)$.
Restoring the sting to metric preheating
Bassett, B A; Maartens, R; Kaiser, D I; Bassett, Bruce A.; Gordon, Chris; Maartens, Roy; Kaiser, David I.
2000-01-01
The relative growth of field and metric perturbations during preheating is sensitive to initial conditions set in the preceding inflationary phase. Recent work suggests this may protect super-Hubble metric perturbations from resonant amplification during preheating. We show that this possibility is fragile and extremely sensitive to the specific form of the interactions between the inflaton and other fields. The suppression is naturally absent in two classes of preheating in which either (1) the critical points (hence the vacua) of the effective potential during inflation are deformed away from the origin, or (2) the effective masses of fields during inflation are small but during preheating are large. Unlike the simple toy model of a g^2 \\phi^2 \\chi^2 coupling, most realistic particle physics models contain these other features. Moreover, they generically lead to both adiabatic and isocurvature modes and non-Gaussian scars on super-Hubble scales. Large-scale coherent magnetic fields may also appear naturally...
Derived Metric Tensors for Flow Surface Visualization.
Obermaier, H; Joy, K I
2012-12-01
Integral flow surfaces constitute a widely used flow visualization tool due to their capability to convey important flow information such as fluid transport, mixing, and domain segmentation. Current flow surface rendering techniques limit their expressiveness, however, by focusing virtually exclusively on displacement visualization, visually neglecting the more complex notion of deformation such as shearing and stretching that is central to the field of continuum mechanics. To incorporate this information into the flow surface visualization and analysis process, we derive a metric tensor field that encodes local surface deformations as induced by the velocity gradient of the underlying flow field. We demonstrate how properties of the resulting metric tensor field are capable of enhancing present surface visualization and generation methods and develop novel surface querying, sampling, and visualization techniques. The provided results show how this step towards unifying classic flow visualization and more advanced concepts from continuum mechanics enables more detailed and improved flow analysis.
Metrics and causality on Moyal planes
Franco, Nicolas
2015-01-01
Metrics structures stemming from the Connes distance promote Moyal planes to the status of quantum metric spaces. We discuss this aspect in the light of recent developments, emphasizing the role of Moyal planes as representative examples of a recently introduced notion of quantum (noncommutative) locally compact space. We move then to the framework of Lorentzian noncommutative geometry and we examine the possibility of defining a notion of causality on Moyal plane, which is somewhat controversial in the area of mathematical physics. We show the actual existence of causal relations between the elements of a particular class of pure (coherent) states on Moyal plane with related causal structure similar to the one of the usual Minkowski space, up to the notion of locality.
Creane, Arthur
2012-07-01
Many soft biological tissues contain collagen fibres, which act as major load bearing constituents. The orientation and the dispersion of these fibres influence the macroscopic mechanical properties of the tissue and are therefore of importance in several areas of research including constitutive model development, tissue engineering and mechanobiology. Qualitative comparisons between these fibre architectures can be made using vector plots of mean orientations and contour plots of fibre dispersion but quantitative comparison cannot be achieved using these methods. We propose a \\'remodelling metric\\' between two angular fibre distributions, which represents the mean rotational effort required to transform one into the other. It is an adaptation of the earth mover\\'s distance, a similarity measure between two histograms\\/signatures used in image analysis, which represents the minimal cost of transforming one distribution into the other by moving distribution mass around. In this paper, its utility is demonstrated by considering the change in fibre architecture during a period of plaque growth in finite element models of the carotid bifurcation. The fibre architecture is predicted using a strain-based remodelling algorithm. We investigate the remodelling metric\\'s potential as a clinical indicator of plaque vulnerability by comparing results between symptomatic and asymptomatic carotid bifurcations. Fibre remodelling was found to occur at regions of plaque burden. As plaque thickness increased, so did the remodelling metric. A measure of the total predicted fibre remodelling during plaque growth, TRM, was found to be higher in the symptomatic group than in the asymptomatic group. Furthermore, a measure of the total fibre remodelling per plaque size, TRM\\/TPB, was found to be significantly higher in the symptomatic vessels. The remodelling metric may prove to be a useful tool in other soft tissues and engineered scaffolds where fibre adaptation is also present.
Enhancing U.S. Coast Guard Metrics
2015-01-01
Enhancing U.S. Coast Guard Metrics Scott Savitz, Henry H. Willis , Aaron C. Davenport, Martina Melliand, William Sasser, Elizabeth Tencza, Dulani...evaluate their utility in other contexts. 6 See Stephanie Young, Henry H. Willis , Melinda Moore, and Jeffrey Engstrom, Measuring Cooperative...front cost of the USS Gerald R. Ford aircraft carrier . U.S. Air Force, “United States Air Force Fiscal Year 2015 Budget Overview,” Washington, D.C
The Planck Vacuum and the Schwarzschild Metrics
Daywitt W. C.
2009-07-01
Full Text Available The Planck vacuum (PV is assumed to be the source of the visible universe. So under conditions of sufficient stress, there must exist a pathway through which energy from the PV can travel into this universe. Conversely, the passage of energy from the visible universe to the PV must also exist under the same stressful conditions. The following examines two versions of the Schwarzschild metric equation for compatability with this open-pathway idea.
Metrics and Its Function in Poetry
XIAO Zhong-qiong; CHEN Min-jie
2013-01-01
Poetry is a special combination of musical and linguistic qualities-of sounds both regarded as pure sound and as mean-ingful speech. Part of the pleasure of poetry lies in its relationship with music. Metrics, including rhythm and meter, is an impor-tant method for poetry to express poetic sentiment. Through the introduction of poetic language and typical examples, the writer of this paper tries to discuss the relationship between sound and meaning.
Autonomous Exploration Using an Information Gain Metric
2016-03-01
quantified by computing the entropy of the robot’s a posteriori pose estimate. The robot’s pose history along its trajectory is captured by the mapping...man-portable robot system. The robot was equipped with additional computing hardware to increase the capabilities of the platform. Similarly, the...Laboratory Autonomous Exploration Using an Information Gain Metric by Nicholas C Fung, Jason M Gregory, and John G Rogers Computational and
Asymptotic properties of the C-Metric
Sladek, Pavel
2010-01-01
The aim of this article is to analyze the asymptotic properties of the C-metric, using a general method specified in work of Tafel and coworkers, [1], [2], [3]. By finding an appropriate conformal factor $\\Omega$, it allows the investigation of the asymptotic properties of a given asymptotically flat spacetime. The news function and Bondi mass aspect are computed, their general properties are analyzed, as well as the small mass, small acceleration, small and large Bondi time limits.
Ocean Model Assessment with Lagrangian Metrics
2016-06-07
Ocean Model Assessment With Lagrangian Metrics” Pearn P. Niiler Scripps Institution of Oceanography 9500 Gilman Drive MC 0213 La Jolla, CA...project are to aid in the development of accurate modeling of upper ocean circulation by using data on circulation observations to test models . These tests...or metrics, will be statistical measures of model and data comparisons. It is believed that having accurate models of upper ocean currents will
Teleparallel Gravitational Energy in the Gamma Metric
Salti, M
2006-01-01
The Moller energy(due to matter and fields including gravity) distribution of the gamma metric is studied in tele-parallel gravity. The result is the same as those obtained in general relativity by Virbhadra in the Weinberg complex and Yang-Radincshi in the Moller definition. Our result is also independent of the three teleparallel dimensionless coupling constants, which means that it is valid not only in the teleparallel equivalent of general relativity, but also in any teleparallel model.
Menger curvature and rectifiability in metric spaces
2012-01-01
We show that for any metric space $X$ the condition \\[ \\int_X\\int_X\\int_X c(z_1,z_2,z_3)^2\\, d\\Hm z_1\\, d\\Hm z_2\\, d\\Hm z_3 < \\infty, \\] where $c(z_1,z_2,z_3)$ is the Menger curvature of the triple $(z_1,z_2,z_3)$, guarantees that $X$ is rectifiable.
A Metrics Approach for Collaborative Systems
Cristian CIUREA
2009-01-01
Full Text Available This article presents different types of collaborative systems, their structure and classification. This paper defines the concept of virtual campus as a collaborative system. It builds architecture for virtual campus oriented on collaborative training processes. It analyses the quality characteristics of collaborative systems and propose techniques for metrics construction and validation in order to evaluate them. The article analyzes different ways to increase the efficiency and the performance level in collaborative banking systems.
Anisotropic rectangular metric for polygonal surface remeshing
Pellenard, Bertrand
2013-06-18
We propose a new method for anisotropic polygonal surface remeshing. Our algorithm takes as input a surface triangle mesh. An anisotropic rectangular metric, defined at each triangle facet of the input mesh, is derived from both a user-specified normal-based tolerance error and the requirement to favor rectangle-shaped polygons. Our algorithm uses a greedy optimization procedure that adds, deletes and relocates generators so as to match two criteria related to partitioning and conformity.
Smart Grid Status and Metrics Report
Balducci, Patrick J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Weimar, Mark R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Kirkham, Harold [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2014-07-01
To convey progress made in achieving the vision of a smart grid, this report uses a set of six characteristics derived from the National Energy Technology Laboratory Modern Grid Strategy. It measures 21 metrics to provide insight into the grid’s capacity to embody these characteristics. This report looks across a spectrum of smart grid concerns to measure the status of smart grid deployment and impacts.
Metric Development for Continuous Process Improvement
2011-03-01
improve the bottom line of an organization. The first step of this process is to solicit the key performance indicators ( KPIs ) that best reflect the...organization’s mission. The second step is to use and/or develop metrics based on those KPIs to measure the organization’s mission performance today...The third step is to capture the trends of those KPIs over time to see if the organization is getting better or worse. The final step is to
What can article-level metrics do for you?
Fenner, Martin
2013-10-01
Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.
Decomposition-based transfer distance metric learning for image classification.
Luo, Yong; Liu, Tongliang; Tao, Dacheng; Xu, Chao
2014-09-01
Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.
Ghost free massive gravity with singular reference metrics
Zhang, Hongsheng; Li, Xin-Zhou
2016-06-01
An auxiliary metric (reference metric) is inevitable in massive gravity theory. In the scenario of the gauge/gravity duality, massive gravity with a singular reference metric is used to study momentum dissipation, which describes the electric and heat conductivity for normal conductors. We demonstrate in detail that the de Rham-Gabadadze-Tolley (dRGT) massive gravity with a singular reference metric is ghost free.
The Development and Demonstration of The Metric Assessment Tool
1993-09-01
motivate continuous improvement and likewise quality. Attributen of MNaninafui Metrica Section Overview. The importance of metrics cannot be overstated...some of the attributes of meaningful measures discussed earlier in this chapter. The Metrica Handbook. This guide is utilized by a variety of Air...Metric Assessment Tool. 3-8 Metrica Belaction The metric assessment tool was designed to apply to any type of metric. Two criteria were established for
Semantic maps as metrics on meanings
Michael Cysouw
2010-01-01
Full Text Available By using the world?s linguistic diversity, the study of meaning can be transformed from an introspective inquiry into a subject of empirical investigation. For this to be possible, the notion of meaning has to be operationalized by defining the meaning of an expression as the collection of all contexts in which the expression can be used. Under this definition, meaning can be empirically investigated by sampling contexts. A semantic map is a technique to show the relations between such sampled contextual occurrences. Or, formulated more technically, a semantic map is a visualization of a metric on contexts sampled to represent a domain of meaning. Or, put more succinctly, a semantic map is a metric on meaning. To establish such a metric, a notion of (dissimilarity is needed. The similarity between two meanings can be empirically investigated by looking at their encoding in many different languages. The more similar these encodings, in language after language, the more similar the contexts. So, to investigate the similarity between two contextualized meanings, only judgments about the similarity between expressions within the structure of individual languages are needed. As an example of this approach, data on cross-linguistic variation in inchoative/causative alternations from Haspelmath (1993 is reanalyzed.
An information theoretic approach for privacy metrics
Michele Bezzi
2010-12-01
Full Text Available Organizations often need to release microdata without revealing sensitive information. To this scope, data are anonymized and, to assess the quality of the process, various privacy metrics have been proposed, such as k-anonymity, l-diversity, and t-closeness. These metrics are able to capture different aspects of the disclosure risk, imposing minimal requirements on the association of an individual with the sensitive attributes. If we want to combine them in a optimization problem, we need a common framework able to express all these privacy conditions. Previous studies proposed the notion of mutual information to measure the different kinds of disclosure risks and the utility, but, since mutual information is an average quantity, it is not able to completely express these conditions on single records. We introduce here the notion of one-symbol information (i.e., the contribution to mutual information by a single record that allows to express and compare the disclosure risk metrics. In addition, we obtain a relation between the risk values t and l, which can be used for parameter setting. We also show, by numerical experiments, how l-diversity and t-closeness can be represented in terms of two different, but equally acceptable, conditions on the information gain..
A computational imaging target specific detectivity metric
Preece, Bradley L.; Nehmetallah, George
2017-05-01
Due to the large quantity of low-cost, high-speed computational processing available today, computational imaging (CI) systems are expected to have a major role for next generation multifunctional cameras. The purpose of this work is to quantify the performance of theses CI systems in a standardized manner. Due to the diversity of CI system designs that are available today or proposed in the near future, significant challenges in modeling and calculating a standardized detection signal-to-noise ratio (SNR) to measure the performance of these systems. In this paper, we developed a path forward for a standardized detectivity metric for CI systems. The detectivity metric is designed to evaluate the performance of a CI system searching for a specific known target or signal of interest, and is defined as the optimal linear matched filter SNR, similar to the Hotelling SNR, calculated in computational space with special considerations for standardization. Therefore, the detectivity metric is designed to be flexible, in order to handle various types of CI systems and specific targets, while keeping the complexity and assumptions of the systems to a minimum.
Statistical estimation of ozone exposure metrics
Blankenship, Erin E.; Stefanski, L. A.
Data from recent experiments at North Carolina State University and other locations provide a unique opportunity to study the effect of ambient ozone on the growth of clover. The data consist of hourly ozone measurements over a 140 day growing season at eight sites in the US, coupled with clover growth response data measured every 28 days. The objective is to model an indicator of clover growth as a function of ozone exposure. A common strategy for dealing with the numerous hourly ozone measurements is to reduce these to a single summary measurement, a so-called exposure metric, for the growth period of interest. However, the mean ozone value is not necessarily the best summarization, as it is widely believed that low levels of ozone have a negligible effect on growth, whereas peak ozone values are deleterious to plant growth. There are also suspected interactions with available sunlight, temperature and humidity. A number of exposure metrics have been proposed that reflect these beliefs by assigning different weights to ozone values according to magnitude, time of day, temperature and humidity. These weighting schemes generally depend on parameters that have, to date, been subjectively determined. We propose a statistical approach based on profile likelihoods to estimate the parameters in these exposure metrics.
Stability of extremal metrics under complex deformations
Rollin, Yann; Tipler, Carl
2011-01-01
Let $(\\mathcal {X},\\Omega)$ be a closed polarized complex manifold, $g$ be an extremal metric on $\\mathcal X$ that represents the K\\"ahler class $\\Omega$, and $G$ be a compact connected subgroup of the isometry group $Isom(\\mathcal{X},g)$. Assume that the Futaki invariant relative to $G$ is nondegenerate at $g$. Consider a smooth family $(\\mathcal{M}\\to B)$ of polarized complex deformations of $(\\mathcal{X},\\Omega)\\simeq (\\mathcal{M}_0,\\Theta_0)$ provided with a holomorphic action of $G$. Then for every $t\\in B$ sufficiently small, there exists an $h^{1,1}(\\cX)$-dimensional family of extremal K\\"ahler metrics on $\\mathcal{M}_t$ whose K\\"ahler classes are arbitrarily close to $\\Theta_t$. We apply this deformation theory to analyze the Mukai-Umemura 3-fold and its complex deformations. In particular, we prove that there are certain complex deformation of the Mukai-Umemura 3-folds which have extremal metric of non constant scalar curvature with Kaehler class $c_1$.
Metric-torsion preheating: cosmic dynamo mechanism?
de Andrade, L C Garcia
2014-01-01
Earlier Bassett et al [Phys Rev D 63 (2001) 023506] investigated the amplification of large scale magnetic fields during preheating and inflation in several different models. They argued that in the presence of conductivity resonance effect is weakened. From a dynamo equation in spacetimes endowed with torsion recently derived by Garcia de Andrade [Phys Lett B 711: 143 (2012)] it is shown that a in a universe with pure torsion in Minkowski spacetime the cosmological magnetic field is enhanced by ohmic or non-conductivity effect, which shows that the metric-torsion effects is worth while of being studied. In this paper we investigated the metric-torsion preheating perturbation, which leads to the seed cosmological magnetic field in the universe with torsion is of the order of $B_{seed}\\sim{10^{-37}Gauss}$ which is several orders of magnitude weaker than the decoupling value obtained from pure metric preheating of $10^{-15}Gauss$. Despite of the weakness of the magnetic field this seed field may seed the galact...
Multi-Armed Bandits in Metric Spaces
Kleinberg, Robert; Upfal, Eli
2008-01-01
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L,X) we define an isometry invariant which bounds f...
Security Metrics: A Solution in Search of a Problem
Rosenblatt, Joel
2008-01-01
Computer security is one of the most complicated and challenging fields in technology today. A security metrics program provides a major benefit: looking at the metrics on a regular basis offers early clues to changes in attack patterns or environmental factors that may require changes in security strategy. The term "security metrics" loosely…