nils-herrmann/clean_scirepeval huggingface dataset:
A novel unsupervised initial pattern recognition algorithm based on wavelet transform and window selection

This paper presents an unsupervised initial pattern recognition approach to determine spectral regions contributing most to intrinsic clustering patterns in nuclear magnetic resonance (NMR) spectra associated with a given pathological stimulus using a small number of realizations based on wavelet transform coupled with a window selection algorithm. Results of different choices of window size are given. The pattern recognition algorithm proposed in this paper can be used as the first step in metabolomic data analysis followed by future supervised algorithms to refine potential biomarkers characterizing tissue-specific lesions further within these regions.
Multiscale signal enhancement: beyond the normality and independence assumption

Current approaches to denoising or signal enhancement in a wavelet-based framework have generally relied on the assumption of normally distributed perturbations. In practice, this assumption is often violated and sometimes prior information of the probability distribution of a noise process is not even available. To relax this assumption, we propose a novel nonlinear filtering technique in this paper. The key idea is to project a noisy signal onto a wavelet domain and to suppress wavelet coefficients by a mask derived from curvature extrema in its scale space representation. For a piecewise smooth signal, it can be shown that filtering by this curvature mask is equivalent to preserving the signal pointwise Hölder exponents at the singular points and lifting its smoothness elsewhere.
Non-parametric bounds on the nearest neighbor classification accuracy based on the Henze-Penrose metric

Analysis procedures for higher-dimensional data are generally computationally costly; thereby justifying the high research interest in the area. Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are very difficult to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose test statistic, to estimate the divergence between different classes of vehicles. In this regard, we apply some common feature extraction techniques to further characterize the distributional separation relative to the original data. Moreover, we employ the Henze-Penrose metric to obtain bounds for the Nearest Neighbor (NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds are exploited for selecting the least number of features that would retain sufficient discriminative information.
A data-based enumeration technique for fully correlated signals

Presents a novel method for estimating the number of signals impinging on a uniform linear array using observed sensor data. Unlike other algorithms that apply Rissanen's minimum description length (MDL) principle to the observed data for source enumeration, this method applies it to the prediction errors of a linear model that has been fitted to an appropriate data matrix. It is a 1D method that achieves improved performance even for fully correlated signals over contemporary approaches, particularly with short data records and closely spaced signals. Asymptotic consistency is shown and substantiating simulation examples are included.
A distributed collapse of a network's dimensionality

Algebraic topology has been successfully applied to detect and localize sensor network coverage holes with minimal assumptions on sensor locations. These methods all use a computation of topological invariants called homology spaces. We develop a distributed algorithm for collapsing a sensor network, hence simplifying its analysis. We prove that the collapse is equivalent to a previously developed strong collapse in that it preserves coverage hole locations. In this way, the collapse simplifies the network without losing crucial information about the coverage region. We show that the algorithm requires only one-hop information in a communication network, making it faster than clique-finding algorithms that increase the number of computations necessary for hole localization. This makes it an effective pre-processing step to finding network coverage holes.
Information diffusion in interconnected heterogeneous networks

In this paper, we are interested in modeling the diffusion of information in a multilayer network of agents using a thermodynamic diffusion approach. The state of each agent is viewed as a topic mixture, to describe his/her resources, and represented by a distribution over multiple topics. We observe and learn diffusion-related thermodynamical patterns in the training data set, and we use the estimated diffusion structure to predict the future states of the agents. With a priori knowledge of a fraction of the state of all agents, the problem is shown to turn into a Kalman predictor problem that refines the predicted system states using the estimation error of the agents' states. A real world Twitter data set is then used to evaluate and validate our information diffusion model.
Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis

This paper presents a family of algorithms based on sparse decompositions that detect anomalies in video sequences obtained from slow moving cameras. These algorithms start by computing the union of subspaces that best represents all the frames from a reference (anomaly free) video as a low-rank projection plus a sparse residue. Then, they perform a low-rank representation of a target (possibly anomalous) video by taking advantage of both the union of subspaces and the sparse residue computed from the reference video. Such algorithms provide good detection results while at the same time obviating the need for previous video synchronization. However, this is obtained at the cost of a large computational complexity, which hinders their applicability. Another contribution of this paper approaches this problem by using intrinsic properties of the obtained data representation in order to restrict the search space to the most relevant subspaces, providing computational complexity gains of up to two orders of magnitude. The developed algorithms are shown to cope well with videos acquired in challenging scenarios, as verified by the analysis of 59 videos from the VDAO database that comprises videos with abandoned objects in a cluttered industrial scenario.
Analysis of changes in body surface potentials during PTCA-induced ischemia using the temporal wavelet transform

Potential maps recorded from the body surface during PTCA-induced inflation provide a controlled study of the electrocardiographic changes caused by severe ischemia. The authors apply the temporal wavelet transform to this data to separate smooth and rapidly varying parts of the waveforms. They hope to reduce alignment sensitivity, retain temporal resolution and dynamic information, and extract features which reflect the ischemia-induced changes.<<ETX>>
Detection of abandoned objects using robust subspace recovery with intrinsic video alignment

The detection of abandoned objects in videos from moving cameras is of great importance to automatic surveillance systems that monitor large and visually complex areas. This paper proposes a new method based on sparse decompositions to identify video anomalies associated with abandoned objects. The proposed scheme inherently incorporates synchronization between the reference (anomaly-free) and target (under analysis) sequences thus reducing the implementation complexity of the overall surveillance system. Results indicate that the proposed video-processing scheme can lead to 95% complexity reduction while maintaining excellent detection capability of foreground objects.
Abandoned object detection using operator-space pursuit

This work presents a framework to be used in the detection of abandoned objects and other video events in a cluttered environment with a moving camera. In the proposed method a target video, that may have features we would like to detect, is compared with a pre-acquired reference video, which is assumed to have no objects nor video events of interest. The comparison is carried out by way of the achieved optimized operators, generated from the reference video, that produce Gaussian outputs when applied to it. Any anomaly of interest in the target video leads to a non-Gaussian output. The method dispenses with the target and reference videos being either synchronized or precisely registered, being robust to rotations and translations between the frames. Experiments show its good performance in the proposed environment.
Analysis dictionary learning for scene classification

This paper proposes a new framework for scene classification based on an analysis dictionary learning approach. Despite their tremendous success in various image processing tasks, synthesis-based and analysis-based sparse models fall short in classification tasks. It was hypothesized that this is partly due to the linear dependence of the dictionary atoms. In this work, we aim at improving classification performances by compensating for such dependence. The proposed methodology consists in grouping the atoms of the dictionary using clustering methods. This allows to sparsely model images from various scene classes and use such a model for classification. Experimental evidence shows the benefit of such an approach. Finally, we propose a supervised way to train the baseline representation for each class-specific dictionary, and achieve multiple classification by finding the minimum distance between the learned baseline representation and the data's sub-dictionary representation. Experiments seem to indicate that such approach achieves scene-classification performances that are comparable to the state of the art.
Flexible Skew-Symmetric Shape Model for Shape Representation, Classification, and Sampling

Skewness of shape data often arises in applications (e.g., medical image analysis) and is usually overlooked in statistical shape models. In such cases, a Gaussian assumption is unrealistic and a formulation of a general shape model which accounts for skewness is in order. In this paper, we present a novel statistical method for shape modeling, which we refer to as the flexible skew-symmetric shape model (FSSM). The model is sufficiently flexible to accommodate a departure from Gaussianity of the data and is fairly general to learn a "mean shape" (template), with a potential for classification and random generation of new realizations of a given shape. Robustness to skewness results from deriving the FSSM from an extended class of flexible skew-symmetric distributions. In addition, we demonstrate that the model allows us to extract principal curves in a point cloud. The idea is to view a shape as a realization of a spatial random process and to subsequently learn a shape distribution which captures the inherent variability of realizations, provided they remain, with high probability, within a certain neighborhood range around a mean. Specifically, given shape realizations, FSSM is formulated as a joint bimodal distribution of angle and distance from the centroid of an aggregate of random points. Mean shape is recovered from the modes of the distribution, while the maximum likelihood criterion is employed for classification
Bayesian wavelet denoising using Besov priors

Over the past few years, there has been a great research interest in thresholding methods for nonlinear wavelet regression over spaces of smooth functions. Near-minimax convergence rates were in particular established for simple hard and soft thresholding rules over Besov and Triebel bodies. In this paper, we propose a Bayesian approach where the functional properties of the underlying signal in noise are directly modeled using Besov norm priors on its wavelet decomposition coefficients. A Gibbs sampler is subsequently presented to estimate the model parameters and the posterior mean of the signal in the case of possibly non-Gaussian noise.
Nonlinear diffusion: A probabilistic view

A probabilistic view of diffusion is presented. A discrete symmetric random walk is shown to be equivalent to a heat equation evolution, and an extension to nonlinear evolutions including Perona-Malik equation is shown to be of utmost importance for analysis. 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 outperform existing approaches, and to lift the long-standing problem of when to stop the evolution. Substantiating examples of image enhancement and segmentation are provided.
Invariant object recognition by shape space analysis

This paper describes a new approach to invariant object recognition. In this approach, an object is represented by a set of key points called landmarks. All possible translation, scaling, and rotation of the object are placed into an equivalent class and associated to a single point in a complex projective space called the shape space. Object recognition is then achieved by distance calculations in this shape space. This approach is invariant to object translation, scaling, and rotation, and is computationally simple. Our experimental results also indicate that it is insensitive to noise and moderate occlusions.
Operator approach to performance analysis of root-MUSIC and root-min-norm

The authors carry out a performance analysis of two eigenstructure-based direction-of-arrival estimation algorithms, using a series expansion of projection operators (or projectors) on the signal and noise subspaces. In the interest of algebraic simplicity, an operator formalism is utilized. A perturbation analysis is performed on the projectors, the results of which are used to determine the effect on the estimated parameters. The approach makes it possible to carry out the analysis to any chosen order of expansion of the projectors by using an original recurrence formula developed for the higher-order terms in the series expansion of the projectors. This method is used to study the root-MUSIC and root-min-norm algorithms and establish the superiority of root-MUSIC in all cases. The analysis has also resulted in insightful asymptotic expressions that describe the statistical behavior of the estimated angles and radii of the signal zeros.
Hierarchical stochastic modeling of SAR imagery for segmentation/compression

There has been a growing interest in synthetic aperture radar (SAR) imaging on account of its importance in a variety of applications. One attribute leading to its gain in popularity is its ability to image terrain at extraordinary rates. Acquiring data at such rates, however, has drawbacks in the form of exorbitant costs in data storage and transmission over relatively slow channels; thus, addressing these problems is clearly important. To abate these and related costs, we propose a segmentation-driven compression technique using hierarchical stochastic modeling within a multiscale framework. Our approach to SAR image compression is unique in that we exploit the multiscale stochastic structure inherent in SAR imagery. This structure is well captured by a set of scale auto-regressive (AR) models that accurately characterize the evolution in scale of homogeneous regions of different classes of terrain. We thus use them to generate a multiresolution segmentation of the image. The segmentation is subsequently used in tandem with the corresponding models in a pyramid encoder to provide a robust, hierarchical compression technique that, in addition to coding the segmentation, achieves high compression ratios with impressive image quality.
Parsimony and wavelet methods for denoising

Some wavelet-based methods for signal estimation in the presence of noise are reviewed in the context of the parsimonious representation of the underlying signal. Three approaches are considered. The first is based on the application of the minimum description length (MDL) principle. The robustness of this method is improved in the second approach, by relaxing the assumption of known noise distribution following Huber's (1967) work. In the third approach, a Bayesian strategy is adopted in order to incorporate prior information pertaining to the signal of interest; this method is especially useful at low signal-to-noise ratios.
Wheeze Detection and Location using Spectro-temporal Analysis of Lung Sounds

Wheezes are abnormal lung sounds, which usually imply obstructive airways diseases. The objective of this study is to design an automatic wheeze detector for a wearable health monitoring system, which is able to locate the wheezes inside the respiratory cycle with high accuracy, and low computational complexity. We compute important features of wheezes, which we classify as temporal and spectral characteristics and employed to analyze recorded lung sounds including wheezes from patients with asthma. Time-frequency (TF) technique as well as wavelet packet decomposition (WPD) is used for this purpose. Experimental results verify the promising performance of described methods.
Object Recognition Through Topo-Geometric Shape Models Using Error-Tolerant Subgraph Isomorphisms

We propose a method for 3-D shape recognition based on inexact subgraph isomorphisms, by extracting topological and geometric properties of a shape in the form of a shape model, referred to as topo-geometric shape model (TGSM). In a nutshell, TGSM captures topological information through a rigid transformation invariant skeletal graph that is constructed in a Morse theoretic framework with distance function as the Morse function. Geometric information is then retained by analyzing the geometric profile as viewed through the distance function. Modeling the geometric profile through elastic yields a weighted skeletal representation, which leads to a complete shape signature. Shape recognition is carried out through inexact subgraph isomorphisms by determining a sequence of graph edit operations on model graphs to establish subgraph isomorphisms with a test graph. Test graph is recognized as a shape that yields the largest subgraph isomorphism with minimal cost of edit operations. In this paper, we propose various cost assignments for graph edit operations for error correction that takes into account any shape variations arising from noise and measurement errors.
Segmentation and compression of SAR imagery via hierarchical stochastic modeling

To abate the enormous costs incurred in the transmission and storage of SAR data, the authors present a segmentation driven compression technique using hierarchical stochastic modeling within a multiscale framework. Their approach to SAR image compression is unique in that they exploit the multiscale stochastic structure inherent in SAR imagery. This structure is well captured by a set of scale auto-regressive models that accurately characterize the evolution in scale. They thus use the local evolution in scale of SAR imagery to generate a segmentation map which is then used in tandem with the corresponding models to provide a robust, hierarchical compression technique.
The decentralized estimation of the sample covariance

In this paper we consider the problem of estimating the eigenvectors of the sample covariance matrix of decentralized measurements in a distributed fashion. The need for a distributed scheme is motivated by the many moment based methods that resort to the covariance of the data to extract information from the measurements. For large sensor network, gathering the data at a central processor generates a communication bottleneck. Our algorithm is based on a combination of the so called power method, that is used to compute the eigenvectors, and the average consensus protocol, that is utilized to structure the information exchange into a gossiping protocol. Our work shows how a completely distributed scheme based on near neighbors communications is feasible, and applies the proposed method to the estimation of the direction of arrival of a signal source.
Robustness and expression independence in 3D face recognition

We describe a robust method for 3D face recognition under variance of facial expressions. The method utilizes the identical areas on two facial images as the measurement of distance. To segment the identical area, partial shape matching is performed using closest point registration and level set method. The segmentation problem is formalized into an Eikonal equation, which can be efficiently solved by fast marching method. The presented 3D face recognition method shows a very promising recognition rate.
Multiresolution analysis of a class of nonstationary processes

Processing nonstationary signals is an important and challenging problem. We focus on the class of nonstationary processes with stationary increments of an arbitrary order, and place them in a multiscale framework. Unlike other related studies, we concentrate on the discrete-time analysis and derive a number of new results in addition to placing the related existing ones in the same framework. We extend the study to various parametric models for which we derive the resulting multiresolution description. We show that wide-sense stationarity may be achieved by adequately selecting the analysis wavelet. After generalizing the study to wavelet packet analysis, we show that the latter possesses additional properties which are useful in the presence of other types of nonstationarities.
Detection and tracking of systematic time-evolving failures in sensor networks

Sensor networks are often deployed to monitor hazardous environments. Several events/phenomenon in such environments may cause spatially and temporally correlated failures in the network. We present here, a low complexity distributed algorithm for detecting and tracking such failures. We assume that nodes inside the failure region are either destroyed or unable to communicate with any other node. The algorithm presented here does not assume any co-ordinate information for the nodes. We evaluate the algorithm using simulations.
A Shearlet Approach to Edge Analysis and Detection

It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges. Indeed, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and, in particular, have the ability to fully capture directional and other geometrical features. Numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges, and outperforms methods based on wavelets as well as other standard methods. Furthermore, the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions.
A stochastic diffusion approach to signal denoising

We present a stochastic formulation of a linear diffusion equation (or heat equation), and in light of the potential applications ranging from signal denoising to image enhancement/segmentation of its nonlinear extensions, we propose a more general nonlinear stochastic diffusion. The constructed stochastic framework, in contrast to traditional deterministic approaches, unveils the sources of existing limitations and allows us to further significantly improve the performance by addressing the key problem. Substantiating examples are provided.