nils-herrmann/clean_scirepeval huggingface dataset:
FROM GIGABYTES TO BYTES: AUTOMATED DENOISING AND FEATURE IDENTIFICATION IN ELECTRON TOMOGRAMS OF INTACT BACTERIAL CELLS

Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medically relevant objects with resolutions in the "nano" range of 10-1000 nm. The development of methods to automatically analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically very noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment relevant 3D features in cellular tomograms. Procedures for segmentation must work robustly and rapidly in spite of the low signal to noise ratios inherent to biological electron microscopy. This work first evaluates various non-linear denoising techniques on tomograms recorded at cryogenic temperatures. Using datasets of bacterial tomograms as an example, we demonstrate that non-linear diffusion techniques significantly improve the fidelity of automated feature extraction. Our approach represents an important step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitates the overall goal of speeding up the process of reducing gigabyte-sized tomograms to relevant byte-sized data.
Universal Regularizers for Robust Sparse Coding and Modeling

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding theory, we propose a framework for designing sparsity regularization terms which have theoretical and practical advantages when compared with the more standard <formula formulatype="inline"><tex Notation="TeX">${\ell_{0}}$</tex></formula> or <formula formulatype="inline"><tex Notation="TeX">${\ell_{1}}$</tex></formula> ones. The presentation of the framework and theoretical foundations is complemented with examples that show its practical advantages in image denoising, zooming and classification.
Statistical analysis of RNA backbone

Local conformation is an important determinant of RNA catalysis and binding. The analysis of RNA conformation is particularly difficult due to the large number of degrees of freedom (torsion angles) per residue. Proteins, by comparison, have many fewer degrees of freedom per residue. In this work, we use and extend classical tools from statistics and signal processing to search for clusters in RNA conformational space. Results are reported both for scalar analysis, where each torsion angle is separately studied, and for vectorial analysis, where several angles are simultaneously clustered. Adapting techniques from vector quantization and clustering to the RNA structure, we find torsion angle clusters and RNA conformational motifs. We validate the technique using well-known conformational motifs, showing that the simultaneous study of the total torsion angle space leads to results consistent with known motifs reported in the literature and also to the finding of new ones
Sex differences in the human connectome: 4-Tesla high angular resolution diffusion imaging (HARDI) tractography in 234 young adult twins

Cortical connectivity is associated with cognitive and behavioral traits that are thought to vary between sexes. Using high-angular resolution diffusion imaging at 4 Tesla, we scanned 234 young adult twins and siblings (mean age: 23.4 ± 2.0 SD years) with 94 diffusion-encoding directions. We applied a novel Hough transform method to extract fiber tracts throughout the entire brain, based on fields of constant solid angle orientation distribution functions (ODFs). Cortical surfaces were generated from each subject's 3D T1-weighted structural MRI scan, and tracts were aligned to the anatomy. Network analysis revealed the proportions of fibers interconnecting 5 key subregions of the frontal cortex, including connections between hemispheres. We found significant sex differences (147 women/87 men) in the proportions of fibers connecting contralateral superior frontal cortices. Interhemispheric connectivity was greater in women, in line with long-standing theories of hemispheric specialization. These findings may be relevant for ongoing studies of the human connectome.
Gaussian mixture models for score-informed instrument separation

A new framework for representing quasi-harmonic signals, and its application to score-informed single channel musical instruments separation, is introduced in this paper. In the proposed approach, the signal's pitch and spectral envelope are modeled separately. The model combines parametric filters enforcing an harmonic structure in the representation, with Gaussian modeling for representing the spectral envelope. The estimation of the signal's model is cast as an inverse problem efficiently solved via a maximum a posteriori expectation-maximization algorithm. The relation of the proposed framework with common non-negative factorization methods is also discussed. The algorithm is evaluated with both real and synthetic instruments mixtures, and comparisons with recently proposed techniques are presented.
Shape preserving local contrast enhancement

A novel approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based on the image grey-values and spatial relations between its pixels. Following mathematical morphology, these constitute the basic objects in the scene. We give examples for both grey-valued and color images.
Robust anisotropic diffusion and sharpening of scalar and vector images

Relations between anisotropic diffusion and robust statistics are described. We show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new "edge-stopping" function based on Tukey's biweight robust estimator, that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in the image. We extend the framework to vector-valued images and show applications to robust image sharpening.
A variational model for denoising high angular resolution diffusion imaging

The presence of noise in High Angular Resolution Diffusion Imaging (HARDI) data of the brain can limit the accuracy with which fiber pathways of the brain can be extracted. In this work, we present a variational model to denoise HARDI data corrupted by Rician noise. Numerical experiments are performed on three types of data: 2D synthetic data, 3D diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data of a hardware phantom containing synthetic fibers, and 3D real HARDI brain data. Experiments show that our model is effective for denoising HARDI-type data while preserving important aspects of the fiber pathways such as fractional anisotropy and the orientation distribution functions.
Reducing structural variation to determine the genetics of white matter integrity across hemispheres - A DTI study of 100 twins

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.
Robust Foreground Detection In Video Using Pixel Layers

A framework for robust foreground detection that works under difficult conditions such as dynamic background and moderately moving camera is presented in this paper. The proposed method includes two main components: coarse scene representation as the union of pixel layers, and foreground detection in video by propagating these layers using a maximum-likelihood assignment. We first cluster into "layers" those pixels that share similar statistics. The entire scene is then modeled as the union of such nonparametric layer-models. An incoming pixel is detected as foreground if it does not adhere to these adaptive models of the background. A principled way of computing thresholds is used to achieve robust detection performance with a prespecified number of false alarms. Correlation between pixels in the spatial vicinity is exploited to deal with camera motion without precise registration or optical flow. The proposed technique adapts to changes in the scene, and allows to automatically convert persistent foreground objects to background and reconvert them to foreground when they become interesting. This simple framework addresses the important problem of robust foreground and unusual region detection, at about 10 frames per second on a standard laptop computer. The presentation of the proposed approach is complemented by results on challenging real data and comparisons with other standard techniques.
Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems

Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the allowed error without increasing the computational cost of each iteration considerably. Relying on recent recovery techniques developed for settings in which the desired signal belongs to some low-dimensional set, we show that using a coarse estimate of this set may lead to faster convergence at the cost of an additional reconstruction error related to the accuracy of the set approximation. Our theory ties to recent advances in sparse recovery, compressed sensing, and deep learning. Particularly, it may provide a possible explanation to the successful approximation of the $\ell _1$ -minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm.
Differential information content in staggered multiple shell hardi measured by the tensor distribution function

Diffusion tensor imaging has accelerated the study of brain connectivity, but single-tensor diffusion models are too simplistic to model fiber crossing and mixing. Hybrid diffusion imaging (HYDI) samples the radial and angular structure of local diffusion on multiple spherical shells in q-space, combining the high SNR and CNR achievable at low and high b-values, respectively. We acquired and analyzed human multi-shell HARDI at ultra-high field-strength (7 Tesla; b=1000, 2000, 3000 s/mm2). In experiments with the tensor distribution function (TDF), the b-value affected the intrinsic uncertainty for estimating component fiber orientations and their diffusion eigenvalues. We computed orientation density functions by least-squares fitting in multiple HARDI shells simultaneously. Within the range examined, higher b-values gave improved orientation estimates but poorer eigenvalue estimates; lower b-values showed opposite strengths and weaknesses. Combining these strengths, multiple-shell HARDI, especially with staggered angular sampling, outperformed single-shell scanning protocols, even when overall scanning time was held constant.
Learning to Identify While Failing to Discriminate

Privacy and fairness are critical in computer vision applications, in particular when dealing with human identification. Achieving a universally secure, private, and fair systems is practically impossible as the exploitation of additional data can reveal private information in the original one. Faced with this challenge, we propose a new line of research, where the privacy is learned and used in a closed environment. The goal is to ensure that a given entity, trusted to infer certain information with our data, is blocked from inferring protected information from it. We design a system that learns to succeed on the positive task while simultaneously fail at the negative one, and illustrate this with challenging cases where the positive task (face verification) is harder than the negative one (gender classification). The framework opens the door to privacy and fairness in very important closed scenarios, ranging from private data accumulation companies to law-enforcement and hospitals.
Multiscale Representation and Segmentation of Hyperspectral Imagery Using Geometric Partial Differential Equations and Algebraic Multigrid Methods

A fast algorithm for multiscale representation and segmentation of hyperspectral imagery is introduced in this paper. The multiscale/scale-space representation is obtained by solving a nonlinear diffusion partial differential equation (PDE) for vector-valued images. We use algebraic multigrid techniques to obtain a fast and scalable solution of the PDE and to segment the hyperspectral image following the intrinsic multigrid structure. We test our algorithm on four standard hyperspectral images that represent different environments commonly found in remote sensing applications: agricultural, urban, mining, and marine. The experimental results show that the segmented images lead to better classification than using the original data directly, in spite of the use of simple similarity metrics and piecewise constant approximations obtained from the segmentation maps.
Wavelet-domain reconstruction of lost blocks in wireless image transmission and packet-switched networks

A fast scheme for wavelet-domain interpolation of lost image blocks in wireless image transmission is presented in this paper. In the transmission of block-coded images, fading in wireless channels and congestion in packet-switched networks can cause entire blocks to be lost. Instead of using retransmission query protocols, we reconstruct the lost block in the wavelet-domain using the correlation between the lost block and its neighbors. The algorithm first uses simple thresholding to determine the presence or absence of edges in the lost block. This is followed by an interpolation scheme, designed to minimize the blockiness effect, while preserving the edges or texture in the interior of the block. The interpolation scheme minimizes the square of the error between the border coefficients of the lost block and those of its neighbors, at each transform scale. The performance of the algorithm on standard test images, its low computational overhead at the decoder, and its performance vis-a-vis other reconstruction schemes, is discussed.
Covariate-dependent dictionary learning and sparse coding

A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. As an application, we consider the simultaneous sparse modeling of multiple images, with the covariate of a given image linked to its similarity to all other images (as applied in manifold learning). Efficient inference is performed using hybrid Gibbs, Metropolis-Hastings and slice sampling.
Segmenting skin lesions with partial differential equations based image processing algorithms

A PDE-based system for detecting the boundary of skin lesions in digital clinical skin images is presented. The image is first pre-processed via contrast-enhancement and anisotropic diffusion. If the lesion is covered by hairs, a PDE-based continuous morphological filter that removes them is used as an additional pre-processing step. Following these steps, the skin lesion is segmented either by the geodesic active contours model or the geodesic edge tracing approach. These techniques are based on computing, again via PDE's, a geodesic curve in a space defined by the image content. Examples showing the performance of the algorithm are given.
A new approach for 3D segmentation of cellular tomograms obtained using three-dimensional electron microscopy

Electron tomography allows determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for interpretation of features in tomograms, is an important problem, but is a challenging prospect because of the low signal-to-noise ratios that are inherent to biological electron microscopic images. As a first step in this direction, we report methods for the automated statistical analysis of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel, robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. Our expectation is that such methods will provide tools for semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells, and present opportunities for correlation with biochemical markers of HIV infection.
Area and Length Preserving Geometric Invariant Scale-Spaces

In this paper, area preserving multi-scale representations of planar curves are described. This allows smoothing without shrinkage at the same time preserving all the scale-space properties. The representations are obtained deforming the curve via geometric heat flows while simultaneously magnifying the plane by a homethety which keeps the enclosed area constant. When the Euclidean geometric heat flow is used, the resulting representation is Euclidean invariant, and similarly it is affine invariant when the affine one is used. The flows are geometrically intrinsic to the curve, and exactly satisfy all the basic requirements of scale-space representations. In the case of the Euclidean heat flow, it is completely local as well. The same approach is used to define length preserving geometric flows. A similarity (scale) invariant geometric heat flow is studied as well in this work.
From LOGO-I to the JPEG-LS standard

LOGO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. The algorithm was conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit based on Golomb codes. The JPEG-LS standard evolved after successive refinements of the core algorithm, and a description of its design principles and main algorithmic components is presented in this paper. LS attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level.
Is image steganography natural?

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. We experimentally investigate if stego-images, bearing a secret message, are statistically "natural". For this purpose, we use recent results on the statistics of natural images and investigate the effect of some popular steganography techniques. We found that these fundamental statistics of natural images are, in fact, generally altered by the hidden "nonnatural" information. Frequently, the change is consistently biased in a given direction. However, for the class of natural images considered, the change generally falls within the intrinsic variability of the statistics, and, thus, does not allow for reliable detection, unless knowledge of the data hiding process is taken into account. In the latter case, significant levels of detection are demonstrated.
Tell me where you are and I tell you where you are going: Estimation of dynamic mobility graphs

The interest in problems related to graph inference has been increasing significantly during the last decade. However, the vast majority of the problems addressed are either static, or systems where changes in one node are immediately reflected in other nodes. In this paper we address the problem of mobility graph estimation, when the available dataset has an asynchronous and time-variant nature. We present a formulation for this problem consisting on an optimization of a cost function having a fitting term to explain the observations with the dynamics of the system, and a sparsity promoting penalty term, in order to select the paths actually used. The formulation is tested on two publicly available real datasets on US aviation and NY taxi traffic, showing the importance of the problem and the applicability of the proposed framework.
Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery

A method is presented for subpixel modeling, mapping, and classification in hyperspectral imagery using learned block-structured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms, allowing for linear mixture analysis. This model provides flexibility in source representation and selection, thus accounting for spectral variability, small-magnitude errors, and noise. A spatial-spectral coherence regularizer in the optimization allows pixel classification to be influenced by similar neighbors. We extend the proposed approach for cases for which there is no knowledge of the materials in the scene, unsupervised classification, and provide experiments and comparisons with simulated and real data. We also present results when the data have been significantly undersampled and then reconstructed, still retaining high-performance classification, showing the potential role of compressive sensing and sparse modeling techniques in efficient acquisition/transmission missions for hyperspectral imagery.