nils-herrmann/clean_scirepeval huggingface dataset:
Indium Oxide Thin-Film Transistors Fabricated by RF Sputtering at Room Temperature

Thin-film transistors (TFTs) were fabricated using an indium oxide (In<sub>2</sub>O<sub>3</sub>) thin film as the n-channel active layer by RF sputtering at room temperature. The TFTs showed a thickness-dependent performance in the range of 48-8 nm, which is ascribed to the total carrier number in the active layer. Optimum device performance at 8-nm-thick In<sub>2</sub>O<sub>3</sub> TFTs had a field-effect mobility of 15.3 cm<sup>2</sup> · V<sup>-1</sup> · s<sup>-1</sup>, a threshold voltage of 3.1 V, an ON-OFF current ratio of 2.2 × 10<sup>8</sup>, a subthreshold gate voltage swing of 0.25 V · decade<sup>-1</sup>, and, most importantly, a normally OFF characteristic. These results suggest that sputter-deposited In<sub>2</sub>O<sub>3</sub> is a promising candidate for high-performance TFTs for transparent and flexible electronics.
Temporal Superpixels Based on Proximity-Weighted Patch Matching

A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work. We develop the proximity-weighted patch matching (PPM), which estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the target superpixel. In each frame, we initialize superpixels by transferring the superpixel labels of the previous frame using PPM motion vectors. Then, we update the superpixel labels of boundary pixels, based on a cost function, composed of color, spatial, contour, and temporal consistency terms. Finally, we execute superpixel splitting, merging, and relabeling to regularize superpixel sizes and reduce incorrect labels. Experiments show that the proposed algorithm outperforms the state-of-the-art conventional algorithms significantly.
Hybrid representation and rendering of indoor environments using meshes and point clouds

We propose a novel hybrid representation scheme for indoor environments using meshes and point clouds. Points are suitable for rendering detailed objects, while meshes can represent planar shapes more compactly. Based on these properties, we use points or meshes adaptively according to local characteristics of an input scene. More specifically, we first determine building structures, which can be represented by meshes, in indoor environments. Then, we create meshes corresponding to the building structures, while representing the remaining parts with points. Experimental results demonstrate that the proposed hybrid scheme renders indoor environments more efficiently than the point-based representation or the mesh-based representation.
Combining Local Regularity Estimation and Total Variation Optimization for Scale-Free Texture Segmentation

Texture segmentation constitutes a standard image processing task, crucial for many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders; Third, segmentation from local regularity faces a fundamental bias variance tradeoff. In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this tradeoff. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures.
On Wigner-based sparse time-frequency distributions

Signals made of the superimposition of a reduced number of AM-FM components can be characterized by a time-frequency signature which consists of weighted trajectories in the plane, thus ending up with an ideal representation of their energy distribution that is intrinsically sparse. Elaborating on first studies that pioneered a compressed sensing solution to the question of approaching such an ideally localized distribution by selecting samples in the ambiguity domain and imposing sparsity in the time-frequency domain, the present paper discusses new advances aimed at achieving better performance in the construction of “cross-term-free” Wigner-type distributions. Improved optimization schemes are first proposed, that both speed up the computation and prove more versatile to accommodate for side constraints such as positivity. A special attention is then paid to the choice of the necessary measurements in the ambiguity plane (in fixed or adapted geometries), emphasizing the key role played by the Heisenberg minimum area, regardless of the signal complexity.
Local regularity for texture segmentation: Combining wavelet leaders and proximal minimization

Texture segmentation constitutes a classical yet crucial task in image processing. In many applications of very different natures (biomedical, geophysics,...) textures are naturally defined in terms of their local regularity fluctuations, which can be quantified as the variations of local Hölder exponents. Furthermore, such images are often naturally embedded in the class of piece-wise constant local regularity functions. The present contribution aims at proposing and assessing a segmentation procedure for this class of images. Its originality is twofold: First, local regularity is estimated using wavelet leaders, a novel multiresolution quantity recently introduced for multifractal analysis but barely used in local regularity measurement, comparisons against wavelet coefficient based estimation are conducted; Second, the challenging minimal partition problem underlying segmentation is convexified and conducted within a customized proximal framework. The estimation of the number of regions and their target regularity is obtained from a total-variation estimate that enables the actual use of proximal minimization for texture segmentation. Performance is assessed and illustrated on synthetic textures.
Non-Linear Wavelet Regression and Branch & Bound Optimization for the Full Identification of Bivariate Operator Fractional Brownian Motion

Self-similarity is widely considered the reference framework for modeling the scaling properties of real-world data. However, most theoretical studies and their practical use have remained univariate. Operator fractional Brownian motion (OfBm) was recently proposed as a multivariate model for self-similarity. Yet, it has remained seldom used in applications because of serious issues that appear in the joint estimation of its numerous parameters. While the univariate fractional Brownian motion requires the estimation of two parameters only, its mere bivariate extension already involves seven parameters that are very different in nature. The present contribution proposes a method for the full identification of bivariate OfBm (i.e., the joint estimation of all parameters) through an original formulation as a non-linear wavelet regression coupled with a custom-made Branch & Bound numerical scheme. The estimation performance (consistency and asymptotic normality) is mathematically established and numerically assessed by means of Monte Carlo experiments. The impact of the parameters defining OfBm on the estimation performance as well as the associated computational costs are also thoroughly investigated.
Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features (average depth of decelerations <inline-formula><tex-math notation="LaTeX">$\textrm {MAD}_{\rm dtrd}$</tex-math></inline-formula>, baseline level <inline-formula><tex-math notation="LaTeX">$\beta _0$</tex-math> </inline-formula>, and variability <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula>) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-of-the-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.
Multiclass SVM with graph path coding regularization for face classification

We consider the problem of learning graphs in a sparse multiclass support vector machines framework. For such a problem, sparse graph penalty is useful to select the significant features and interpret the results. Classical ℓ1-norm learns a sparse solution without considering the structure between the features. In this paper, a structural knowledge is encoded as directed acyclic graph and a graph path penalty is incorporated to multiclass SVM. The learned classifiers not only improve the performance, but also help in the interpretation of the learned features. The performance of the proposed method highly depends on an initialization graph. Two generic ways to initialize the graph between the features are considered: one is built from similarities while the other one uses Graphical Lasso. The experiments of face classification task on Extended YaleB database verify that: i) graph regularization with multiclass SVM improves the performance and also leads to a more sparse solution compared to ℓ1-norm.
Non-linear regression for bivariate self-similarity identification — application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts

Internet traffic monitoring is a crucial task for network security. Self-similarity, a key property for a relevant description of internet traffic statistics, has already been massively and successfully involved in anomaly detection. Self-similar analysis was however so far applied either to byte or Packet count time series independently, while both signals are jointly collected and technically deeply related. The present contribution elaborates on a recently proposed multivariate self-similar model, Operator fractional Brownian Motion (OfBm), to analyze jointly self-similarity in bytes and packets. A non-linear regression procedure, based on an original Branch & Bound resolution procedure, is devised for the full identification of bivariate OfBm. The estimation performance is assessed by means of Monte Carlo simulations. Further, an Internet traffic anomaly detection procedure is proposed, that makes use of the vector of Hurst exponents underlying the OfBm based Internet data modeling. Applied to a large set of high quality and modern Internet data from the MAWI repository, proof-of-concept results in anomaly detection are detailed and discussed.
2D Prony-Huang Transform: A New Tool for 2D Spectral Analysis

This paper provides an extension of the 1D Hilbert Huang transform for the analysis of images using recent optimization techniques. The proposed method consists of: 1) adaptively decomposing an image into oscillating parts called intrinsic mode functions (IMFs) using a mode decomposition procedure and 2) providing a local spectral analysis of the obtained IMFs in order to get the local amplitudes, frequencies, and orientations. For the decomposition step, we propose two robust 2D mode decompositions based on nonsmooth convex optimization: 1) a genuine 2D approach, which constrains the local extrema of the IMFs and 2) a pseudo-2D approach, which separately constrains the extrema of lines, columns, and diagonals. The spectral analysis step is an optimization strategy based on Prony annihilation property and applied on small square patches of the IMFs. The resulting 2D Prony-Huang transform is validated on simulated and real data.
Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration

A fruitful approach for solving signal deconvolution problems consists of resorting to a frame-based convex variational formulation. In this context, parallel proximal algorithms and related alternating direction methods of multipliers have become popular optimization techniques to approximate iteratively the desired solution. Until now, in most of these methods, either Lipschitz differentiability properties or tight frame representations were assumed. In this paper, it is shown that it is possible to relax these assumptions by considering a class of non-necessarily tight frame representations, thus offering the possibility of addressing a broader class of signal restoration problems. In particular, it is possible to use non-necessarily maximally decimated filter banks with perfect reconstruction, which are common tools in digital signal processing. The proposed approach allows us to solve both frame analysis and frame synthesis problems for various noise distributions. In our simulations, it is applied to the deconvolution of data corrupted with Poisson noise or Laplacian noise by using (non-tight) discrete dual-tree wavelet representations and filter bank structures.
2D Hilbert-Huang Transform

This paper presents a 2D transposition of the Hilbert-Huang Transform (HHT), an empirical data analysis method designed for studying instantaneous amplitudes and phases of non-stationary data. The principle is to adaptively decompose an image into oscillating parts called Intrinsic Mode Functions (IMFs) using an Empirical Mode Decomposition method (EMD), and then to perform Hilbert spectral analysis on the IMFs in order to recover local amplitudes and phases. For the decomposition step, we propose a new 2D mode decomposition method based on non-smooth convex optimization, while for the instantaneous spectral analysis, we use a 2D transposition of Hilbert spectral analysis called monogenic analysis, based on Riesz transform and allowing to extract instantaneous amplitudes, phases, and orientations. The resulting 2D-HHT is validated on simulated data.
Multifractal-based texture segmentation using variational procedure

The present contribution aims at segmenting a scale-free texture into different regions, characterized by an a priori (unknown) multifractal spectrum. The multifractal properties are quantified using multiscale quantities C<sub>1, j</sub> and C<sub>2, j</sub> that quantify the evolution along the analysis scales 2<sup>j</sup> of the empirical mean and variance of a nonlinear transform of wavelet coefficients. The segmentation is performed jointly across all the scales j on the concatenation of both C<sub>1, j</sub> and C<sub>2, j</sub> by an efficient vectorial extension of a convex relaxation of the piecewise constant Potts segmentation problem. We provide comparisons with the scalar segmentation of the Hölder exponent as well as independent vectorial segmentations over C<sub>1</sub> and C<sub>2</sub>.
On-The-Fly Approximation of Multivariate Total Variation Minimization

In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data. In this contribution, an extension of such an on-the-fly strategy to multivariate data is investigated. The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker conditions on the dual problem. Showing that the non-local nature of the multivariate setting precludes to obtain an exact on-the-fly solution, we devise an on-the-fly algorithm delivering an approximate solution, whose quality is controlled by a practitioner-tunable parameter, acting as a trade-off between quality and computational cost. Performance assessment shows that high quality solutions are obtained on-the-fly while benefiting of computational costs several orders of magnitude lower than standard iterative procedures. The proposed algorithm thus provides practitioners with an efficient multivariate change-point detection on-the-fly procedure.
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-destination matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. Taking advantage of probe trajectories, whose capture is made possible by new measurement technologies, the present contribution extends the concept of ODM to that of link-dependent ODM (LODM). LODM also contains the flow distribution on links making specification of assignment models, e.g., by means of routing matrices, unnecessary. An original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimization problem, where the functional to be minimized consists of five convex functions, each modeling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having similar origins and destinations, and positivity of traffic flows. A proximal primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations.
Bayesian-driven criterion to automatically select the regularization parameter in the ℓ1-Potts model

This contribution focuses, within the ℓ1-Potts model, on the automated estimation of the regularization parameter balancing the ℓ1 data fidelity term and the TVℓ0 penalization. Variational approaches based on total variation gained considerable interest to solve piecewise constant denoising problems thanks to their deterministic setting and low computational cost. However, the quality of the achieved solution strongly depends on the tuning of the regularization parameter. While recent works have tailored various hierarchical Bayesian procedures to additionally estimate the regularization parameter for Gaussian noise, less attention has been granted to Laplacian noise, of interested in numerous applications. This contribution promotes a fast and parameter-free denoising procedure for piecewise constant signals corrupted by Laplacian noise, that includes automated selection of the regularization parameter. It relies on the minimization of a Bayesian-driven criterion whose similarities with the ℓ1-Potts model permit to derive a computationally efficient algorithm.
Parallel algorithm and hybrid regularization for dynamic PET reconstruction

To improve the estimation at the voxel level in dynamic Positron Emission Tomography (PET) imaging, we propose to develop a convex optimization approach based on a recently proposed parallel proximal method (PPXA). This class of algorithms was successfully employed for 2D deconvolution in the presence of Poisson noise and it is extended here to (dynamic) space + time PET image reconstruction. Hybrid regularization defined as a sum of a total variation and a sparsity measure is considered in this paper. The total variation is applied to each temporal-frame and a wavelet regularization is considered for the space+time data. Total variation allows us to smooth the wavelet artifacts introduced when the wavelet regularization is used alone. The proposed algorithm was evaluated on simulated dynamic fluorodeoxyglucose (FDG) brain data and compared with a regularized Expectation Maximization (EM) reconstruction. From the reconstructed dynamic images, parametric maps of the cerebral metabolic rate of glucose (CMRglu) were computed. Our approach shows a better reconstruction at the voxel level.
Proximity Operator of a Sum of Functions; Application to Depth Map Estimation

Proximal splitting algorithms for convex optimization are largely used in signal and image processing. They make possible to call the individual proximity operators of an arbitrary number of functions, whose sum is to be minimized. But the larger this number, the slower the convergence. In this letter, we show how to compute the proximity operator of a sum of two functions, for a certain type of functions operating on objects having a graph structure. The gain provided by avoiding unnecessary splitting is illustrated by an application to depth map estimation.
Inverse problem formulation for regularity estimation in images

The identification of texture changes is a challenging problem that can be addressed by considering local regularity fluctuations in an image. This work develops a procedure for local regularity estimation that combines a convex optimization strategy with wavelet leaders, specific wavelet coefficients recently introduced in the context of multifractal analysis. The proposed procedure is formulated as an inverse problem that combines the joint estimation of both local regularity exponent and of the optimal weights underlying regularity measurement. Numerical experiments using synthetic texture indicate that the performance of the proposed approach compares favorably against other wavelet based local regularity estimation formulations. The method is also illustrated with an example involving real-world texture.
A 2-D spectral analysis method to estimate the modulation parameters in structured illumination microscopy

Structured illumination microscopy is a recent imaging technique that aims at going beyond the classical optical resolution limits by reconstructing a high-resolution image from several low-resolution images acquired through modulation of the transfer function of the microscope. A precise knowledge of the sinusoidal modulation parameters is necessary to enable the super-resolution effect expected after reconstruction. In this work, we investigate the retrieval of these parameters directly from the acquired data, using a novel 2D spectral estimation method.
A wavelet-based quadratic extension method for image deconvolution in the presence of poisson noise

Iterative optimization algorithms such as the forward-backward and Douglas-Rachford algorithms have recently gained much popularity since they provide efficient solutions to a wide class of non-smooth convex minimization problems arising in signal/image recovery. However, when images are degraded by a convolution operator and a Poisson noise, a particular attention must be paid to the associated minimization problem. To solve it, we propose a new optimization method which consists of two nested iterative steps. The effectiveness of the proposed method is demonstrated via numerical comparisons.