nils-herrmann/clean_scirepeval huggingface dataset:
Design considerations for high-power converters interfacing 10 MW superconducting wind power generators

The design of power electronic converters for the integration of wind generated power into the grid is more and more important due to a new class of superconducting generators (SG) with power ratings of up to 20 MW. High efficiency of power converters for high-power applications is mandatory in order to reduce the overall cost of the system. This study proposes a design method to minimise the cost of the system by finding the optimal number of power devices and capacitors for different high-power converter topologies. The investigation focuses on determining the optimal number of voltage levels for a back-to-back (BTB) neutral point clamped (NPC) converter. The design method is demonstrated by estimating the cost of different BTB NPC power converter topologies for the integration of a 10 MW SG to the grid.
Efficient Reduction of Model-Based Generated Test Suites through Test Case Pair Prioritization

During the development and maintenance of software, test suites often reach a size that exceeds the costs allocated for test suite execution. In such a case, the test suite needs to be reduced. Many papers are dedicated to the problem of test suite reduction. Most of them consider the removal or merging of test cases. However, less attention has been paid to the identification of test case pairs, which are eminently suitable for merging. In this paper, we fill this gap by presenting a novel approach that helps identifying those test case pairs within a given set of systematically generated test cases which, when merged, have potential for high test suite reduction. As a result, test suites reduced by our approach are considerably smaller in size than those, whose pairs are selected randomly.
Characterization of memristor based on non-linear ion drift model

In the age of information and communication, information storage with the capability of non-volatile low power has attracted considerable interest. Memristive devices tend to pose these attributes and were achieved using nanotechnology. However, to design circuits with these memristive devices, the characteristion needs to be described by a mathematical model. Many models such as linear ion-drift model, non-linear ion-drift model, ThrEshold Adaptive Memristor(TEAM) model have been proposed. These models should mimic the physical devices and also be computationally efficient. In the present paper, the characterization of memristor based on SPICE non-linear ion drift model focusing on various parametric results to achieve better i-v characteristics compared to linear ion-drift model are presented.
Determining articulated motion from perspective views: a decomposition approach

The problem is to estimate the 3D motion of an articulated object, such as a robot arm or a human body, from a monocular sequence of 2D perspective views. We advocate an approach of decomposition. The object is decomposed into simpler parts, each containing a small number of links. We estimate the motion of the simplest part(s) and then propagate the analysis to the remaining parts of the object. Human gait is used as an example; however, the approach is general. To use this approach, we need results for the motion of simple articulated objects, motion estimation algorithms, and the number of solutions (especially, how many views are needed for uniqueness). With the help of algebraic geometry, we have results for a number of cases which are particularly useful for human gait analysis.<<ETX>>
Correlation Metric for Generalized Feature Extraction

Beyond linear and kernel-based feature extraction, we propose in this paper the generalized feature extraction formulation based on the so-called graph embedding framework. Two novel correlation metric based algorithms are presented based on this formulation. correlation embedding analysis (CEA), which incorporates both correlational mapping and discriminating analysis, boosts the discriminating power by mapping data from a high-dimensional hypersphere onto another low-dimensional hypersphere and preserving the intrinsic neighbor relations with local graph modeling. correlational principal component analysis (CPCA) generalizes the conventional Principal Component Analysis (PCA) algorithm to the case with data distributed on a high-dimensional hypersphere. Their advantages stem from two facts: 1) tailored to normalized data, which are often the outputs from the data preprocessing step, and 2) directly designed with correlation metric, which shows to be generally better than Euclidean distance for classification purpose. Extensive comparisons with existing algorithms on visual classification experiments demonstrate the effectiveness of the proposed algorithms.
Calibrating Head Pose Estimation in Videos for Meeting Room Event Analysis

In this paper, we study the calibration of head pose estimation in stereo camera setting for meeting room video event analysis. Head pose information infers the direction of attention of the subjects in video, therefore is valuable for video event analysis/indexing, especially in meeting room scenario. We are developing a multi-modal meeting room data analyzing system for studying meeting room interaction dynamics, in which head pose estimation is one of the key components. As each subject in the meeting room can be observed by a pair of stereo cameras, we do 2D head tracking for the subject in each camera, and the 3D coordinate of the head can be obtained by triangulation. The 3D head pose is estimated in one of the camera coordinate system, we develop a procedure to accurately convert the estimated 3D pose in the camera coordinate system to that in the world coordinate system. In the experiment, visualization of the estimated head pose and location in world coordinate system verifies the soundness of our design. The estimated head pose and 3D location of the subjects in the meeting room allows further analysis of meeting room interaction dynamics, such as F-formation, floor-control, etc.
Applied topology in static and dynamic sensor networks

In the study of sensor networks, many applications require topological analysis, and for some problems topological information is even sufficient. Here, we review how algebraic topology (and specifically simplicial homology theory) can be used as a general framework for detection of coverage holes in a coordinate-free sensor network. Extensions to distributed processing and localization algorithms are also reviewed, before progressing into discussion of a new way to apply algebraic topological methods to the analysis of coverage properties in dynamic sensor networks. Zigzag persistent homology is a recently developed method to track homological features (such as holes) over a sequence of spaces. This paper demonstrates the promise of this method for the identification of coverage holes in a time-varying coordinate-free sensor network, as well as the designation of coverage holes as significant or not, based on the length of time they are present in the sequence.
Real time detection of harmonic structure: A case for topological signal analysis

The goal of this study is to find evidence of cyclicity or periodicity in data with low computational complexity and high accuracy. Using delay embeddings, we transform the timedomain signal into a point cloud, whose topology reflects the periodic behavior of the signal. Persistent homology is employed to determine the underlying manifold of the point cloud, and the Euler characteristic provides for a fast computation of topology of the resulting manifold. We apply the introduced approach to breathing sound signals for wheeze detection. Our experiments substantiate the capabilities of the proposed method.
Feature-based segmentation of ECG signals

Automatic segmentation of ECG signals is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. Here, the authors propose a segmentation technique based on the best local trigonometric basis. They show by means of real data examples that the entropy criterion which achieves the most parsimonious representation of a signal results in an overly-fine segmentation of the ECG signal, and thus establish the need for a more comprehensive criterion. The authors introduce a novel best basis search criterion which is based on a linear combination of the entropy measure and a local measure of smoothness and curvature. They tested the algorithm on the MIT-BIH arrythmia database.
Computing persistent features in big data: A distributed dimension reduction approach

Persistent homology has become one of the most popular tools used in topological data analysis for analyzing big data sets. In an effort to minimize the computational complexity of finding the persistent homology of a data set, we develop a simplicial collapse algorithm called the selective collapse. This algorithm works by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. Finally, we demonstrate the savings in computational complexity using geometric random graphs.
Pansharpening via coupled triple factorization dictionary learning

Data fusion is the operation of integrating data from different modalities to construct a single consistent representation. This paper proposes variations of coupled dictionary learning through an additional factorization. One variation of this model is applicable to the pansharpening data fusion problem. Real world pansharpening data was applied to train and test our proposed formulation. The results demonstrate that the data fusion model can successfully be applied to the pan-sharpening problem.
Analysis and Control of Beliefs in Social Networks

In this paper, we investigate the problem of how beliefs diffuse among members of social networks. We propose an information flow model (IFM) of belief that captures how interactions among members affect the diffusion and eventual convergence of a belief. The IFM model includes a generalized Markov Graph (GMG) model as a social network model, which reveals that the diffusion of beliefs depends heavily on two characteristics of the social network characteristics, namely degree centralities and clustering coefficients. We apply the IFM to both converged belief estimation and belief control strategy optimization. The model is compared with an IFM including the Barabási-Albert model, and is evaluated via experiments with published real social network data.
A behavior-based evaluation of product quality

In the pharmaceutical industry, quality is often measured by the impact of a product on a population. Knowledge about the behaviour of mosquitos responding to a repellent is a case in point in helping to improve the effect of insect repellent. It is ideally carried out using 3D videos which require a stereoscopic apparatus. To do so using 2D video and effectively evaluate the repellent is an difficult problem as is known in the biotechnology research field. In this paper, we propose a general framework for the swarm motion analysis of multiple mosquitos based on 2D videos. The effectiveness and robustness of our algorithm are verified by multiple 2D videos capturing mosquitos behavior in different experimental conditions.
Best basis segmentation of ECG signals using novel optimality criteria

Automatic segmentation of the electrocardiogram (ECG) is important in both clinical and research settings. Past algorithms have relied on incorporation of detailed heuristics. We avoid heuristics by employing a best-basis algorithm. As large variability of the local SNR causes the standard entropy criterion to produce an overly-fine segmentation, we introduce a novel optimality criterion which is based on a linear combination of the entropy measure and a function of a smoothness measure, and is quite general in form. We tested the algorithm on the MIT-BIH arrythmia database and body surface potential maps.
Global Optimization by Adapted Diffusion

In this paper, we study a diffusion stochastic dynamics with a general diffusion coefficient. The main result is that adapting the diffusion coefficient to the Hamiltonian allows to escape local wide minima and to speed up the convergence of the dynamics to the global minima. We prove the convergence of the invariant measure of the modified dynamics to a measure concentrated on the set of global minima and show how to choose a diffusion coefficient for a certain class of Hamiltonians.
A Novel Framework for Pulse Pressure Wave Analysis Using Persistent Homology

Four characteristic points of pulse pressure waves-the systolic peak, the anacrotic notch, the dicrotic notch, and the diastolic foot-are used to estimate various aspects of cardiovascular function, such as heart rate and augmentation index. We propose a novel approach to extracting these characteristic points using a topological signal processing framework. We characterize the topology of the signals using a collection of persistence intervals, which are encapsulated in a persistence diagram. The characteristic points are identified based on their time of occurrence and their distance from the identity line in the persistence diagram. We validate this approach by collecting radial pulse pressure data from twenty-eight participants using a wearable tonometer, and computing the peripheral augmentation index using a traditional derivative-based method and our novel persistence-based method. The augmentation index values computed using the two methods are statistically indistinguishable, suggesting that this representation merits further exploration as a tool for analyzing pulse pressure waves.
Divide and Conquer: Localizing Coverage Holes in Sensor Networks

Sensor Networks are inherently complex networks, and associated problems where analysis of some global features becomes more important than local ones, often arise. Localizing the holes in the overall coverage is one such problem. We present here, a distributed algorithm in a generalized combinatorial setting to localize holes in the coverage, with no a priori localization information for the nodes. We follow a divide and conquer approach, strategically dissecting the network so that the overall topology is preserved, while simultaneously minimizing the computational complexity. The detection of holes is enabled by first attributing a combinatorial object called a "Rips Complex" to each network segment, and by then checking for the triviality of the first homology class of this complex. Our estimate approaches the location of the holes exponentially with each iteration leading to a very fast convergence coupled with optimal usage of valuable resources such as power and memory. We demonstrate the effectiveness of the presented algorithm with simulations
Smart nonlinear diffusion: a probabilistic approach

In this paper, a stochastic interpretation of nonlinear diffusion equations used for image filtering is proposed. This is achieved by relating the problem of evolving/smoothing images to that of tracking the transition probability density functions of an underlying random process. We show that such an interpretation of, e.g., Perona-Malik equation, in turn allows additional insight and sufficient flexibility to further investigate some outstanding problems of nonlinear diffusion techniques. In particular, upon unraveling the limitations as well as the advantages of such an equation, we are able to propose a new approach which is demonstrated to improve performance over existing approaches and, more importantly, to lift the longstanding problem of a stopping criterion for a nonlinear evolution equation with no data term constraint. Substantiating examples in image enhancement and segmentation are provided.
Renyi entropy based divergence measures for ICA

Information measures based on Renyi entropy provide a distance measure among a group of probability densities with tunable and flexible parameters to allow differentially granular differences in data. We interpret a recently developed measure, a /spl alpha/-JR divergence, as an alternative to mutual information (MI). We also present in this paper, its potential as an improved ICA criterion, and demonstrate its performance. We also propose a computationally efficient technique to approximate Renyi mutual divergence and apply it to analyze dependent data.
Facial feature extraction using topological methods

Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recognition, facial image compression and low-bit video coding. The methodology presented in this paper, considers the facial image as a surface. Topological properties of the facial surface, such as principal curvatures are used to extract the eyes and mouth, which form deep valleys on the surface. Ravines are points on the surface where the maximum curvature is a local maximum in the corresponding principal direction. The basic idea of the proposed method is to model the facial features as ravines on the facial surface. Experimental results have shown accurate extraction of the eye boundaries and the mouth opening in a very small computational time.
On identification of discrete symmetric planar shapes from a single view

In this paper, we consider a problem of identifying a discrete symmetric shape from a single view. For the proposed class of objects, we derive their representation in terms of their "skeletons", which in turn yield invariants readily computable from any single image. In addition, the representation is almost optimal in the sense that it captures virtually all geometric information contained in the image. Further, we consider the case of a noisy image, i.e. when the points defining a shape are known up to an additive Gaussian noise. We derive the distribution for the noisy "skeleton" points and propose an optimal technique to estimate the true "skeleton" and thus identify the true shape.
Robust multiscale representation of processes and optimal signal reconstruction

We propose a statistical approach to obtain a "best basis" representation of an observed random process. We derive statistical properties of a criterion first proposed to determine the best wavelet packet basis, and, proceed to use it in constructing a statistically sound algorithm. For signal enhancement, this best basis algorithm is followed by a nonlinear filter based on the minimum description length (MDL) criterion. We show that it is equivalent to a min-max based algorithm proposed by Donoho and Johnstone (1992).<<ETX>>
Statistical classification of social networks

This paper proposes a new social network classification method by comparing statistics of their centralities and clustering coefficients. Specifically, the proposed method uses the statistics of Degree Centralities and clustering coefficients of networks as a classification criterion. A theoretical justification to this method is also given. In relation to the widely held belief that a social network graph is solely defined by its degree distribution, the novelty of this paper consists in revealing the strong dependence of social networks on Degree Centralities and clustering coefficients, and in using them as minimal information to classify social networks. In addition, experimental classification demonstrates a very good performance of the proposed method on real social network data, and validates the hypothesis that Degree Centralities and clustering coefficients are the only two viable independent properties of a social network.
Structural risk minimization using nearest neighbor rule

We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional support vector machines, and achieves a nearly comparable test error performance.
Minimax description length for signal denoising

Approaches to wavelet-based denoising (or signal enhancement) have generally relied on the assumption of normally distributed perturbations. To relax this assumption, which is often violated in practice, we derive a robust wavelet thresholding technique based on the minimax description length (MMDL) principle. The MMDL approach results in a thresholding scheme that is resistant to heavy-tailed noise.
Probabilistic graph matching by canonical decomposition

We present in this paper a solution to the graph isomorphism problem, by a unique decomposition of a graph into a set of atoms via its clique minimal separators. The resulting set of atoms is then used to collapse the original graph into a bipartite attributed relational graph, having a fewer number of vertices. Finally a probabilistic matching algorithm operates on the reduced graphs, simulating a belief propagation run, and decides whether the original graphs are isomorphic, in which case their corresponding atoms match. The complete approach yields a good suboptimal solution, while retaining a polynomial time complexity.
Multi-level scene understanding via hierarchical classification

In applications where the use of video surveillance is necessary and/or beneficial, it is a common goal to identify the contents of the video automatically. Of particular interest in such applications is the ability to recognize locations in the environment, where events occur, and describe the events common to those locations. This is one of the goals of scene understanding. Scene understanding is traditionally addressed from one of two separate points-of-view: the description of the underlying environment or the action taking-place throughout the scene. Each of these facets is required to address the overarching goal but, is insufficient independently to address the problem entirely. These facets are, in fact, dependent and by considering both, a more complete description becomes available. In this paper, we describe a novel, data-driven scene understanding and classification technique that captures and utilizes information about both the environment and activity within a scene.
A Generalized Markov Graph Model: Application to Social Network Analysis

In this paper we propose a generalized Markov Graph model for social networks and evaluate its application in social network synthesis, and in social network classification. The model reveals that the degree distribution, the clustering coefficient distribution as well as a newly discovered feature, a crowding coefficient distribution, are fundamental to characterizing a social network. The application of this model to social network synthesis leads to a capacity to generate networks dominated by the degree distribution and the clustering coefficient distribution. Another application is a new social network classification method based on comparing the statistics of their degree distributions and clustering coefficient distributions as well as their crowding coefficient distributions. In contrast to the widely held belief that a social network graph is solely defined by its degree distribution, the novelty of this paper consists in establishing the strong dependence of social networks on the degree distribution, the clustering coefficient distribution and the crowding coefficient distribution, and in demonstrating that they form minimal information to classify social networks as well as to design a new social network synthesis tool. We provide numerous experiments with published data and demonstrate very good performance on both counts.