Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Restoration Dantzig Selection for Censored Data

Dimension reduction, modeland variable selectionhavebecome ubiquitous concepts in modern statisticalscience. Thispaperis concernedwith simultaneous estimation and variable selectionin thelinear model orleast-squares setup,principlebuildingblocks of complete-data model selection techniques. In contrast to the complete-data setup, we considerthecommonsituationwheretheoutcomesmayberight-censored. Threemost common estimatorsin this settinginclude the rank-based estimator, theBuckley-James estimator, andinverse-probability weighted(IPW) estimator. TheBuckley-James and IPW estimators are popular among applied statisticians because the estimators are based on simple missing data principles and they require no knowledge of linear programming. However, both estimators suffer difficulties. The IPW estimator is particularly inefficient whiletheBuckley-James estimating functionpossesses multiple roots in large and small samples and its iterative estimation procedure may result in infinite oscillation. Recently, authors have rediscovered that infinite oscillation can be avoided with consistent initial value. The goal of this paper is to offer a new Buckley-Jamestype estimator for high dimensional, sparse regression in the accelerated failure time model. Theideaisto start with agoodinitial value and replacelinear regression with Dantzig selection (DS). Compared with competing DS-type estimators for censored survival data, our method is much faster because it performs the DS optimization once, not iteratively. We illustrate the utility of this method through simulation studies and application to three real data sets.
Dynamic Merging of Frontiers for Accelerating the Evaluation of Betweenness Centrality

Betweenness Centrality (BC) is a widely used metric of the relevance of a node in a network. The fastest-known algorithm for the evaluation of BC on unweighted graphs builds a tree representing information about the shortest paths for each vertex to calculate its contribution to the BC score. Actually, for specific vertices, the shortest-path trees of neighboring nodes could be leveraged to reduce the computational burden, but existing BC algorithms do not exploit that information and carry out redundant computations. We propose a new algorithm, called dynamic merging of frontiers, which makes use of such information to derive the BC score of degree-2 vertices by re-using the results of the sub-trees of the neighbors. We implemented our idea in parallel fashion exploiting Graphics Processing Units. Compared to state-of-the-art implementations, our approach achieves a linear improvement in the number of degree-2 vertices and an average improvement of × over a variety of real-world graphs.
Nonlinear theory of geostrophic adjustment. Part 2. Two-layer and continuously stratified primitive equations

This paper continues the work started in Part 1 (Reznik, Zeitlin & Ben Jelloul 2001) and generalizes it to the case of a stratified environment. Geostrophic adjustment of localized disturbances is considered in the context of the two-layer shallow-water and continuously stratified primitive equations in the vertically bounded and horizontally infinite domain on the $f$-plane. Using multiple-time-scale perturbation expansions in Rossby number $\hbox{\it Ro}$ we show that stratification does not substantially change the adjustment scenario established in Part 1 and any disturbance of well-defined scale is split in a unique way into slow and fast components with characteristic time scales $f_0^{-1}$ and $(f_0 \hbox{\it Ro})^{-1}$ respectively, where $f_0$ is the Coriolis parameter. As in Part 1 we distinguish two basic dynamical regimes: quasi-geostrophic (QG) and frontal geostrophic (FG) with small and large deviations of the isopycnal surfaces, respectively. We show that the dynamics of the FG regime in the two-layer model depends strongly on the ratio of the layer depths. The difference between QG and FG scenarios of adjustment is demonstrated. In the QG case the fast component of the flow essentially does not ‘feel’ the slow one and is rapidly dispersed leaving the slow component to evolve according to the standard QG equation (corrections to this equation are found for times $t\,{\gg}\, (f_0 \hbox{\it Ro})^{-1}$). In the FG case the fast component is a packet of inertial oscillations produced by the initial perturbation. The space-time evolution of the envelope of inertial oscillations obeys a Schrödinger-type modulation equation with coefficients depending on the slow component. In both QG and FG cases we show by direct computations that the fast component does not produce any drag terms in the equations for the slow component; the slow component remains close to the geostrophic balance. However, in the continuously stratified FG regime, as well as in the two-layer regime with the layers of comparable thickness, the splitting is incomplete in the sense that the slow vortical component and the inertial oscillations envelope evolve on the same time scale.
Langevin type limiting processes for Adaptive MCMC

Adaptive Markov Chain Monte Carlo (AMCMC) is a class of MCMC algorithms where the proposal distribution changes at every iteration of the chain. In this case it is important to verify that such a Markov Chain indeed has a stationary distribution. In this paper we discuss a diffusion approximation to a discrete time AMCMC. This diffusion approximation is different when compared to the diffusion approximation as in Gelman, Gilks and Roberts (1997) where the state space increases in dimension to infinity. In our approach the time parameter is sped up in such a way that the limiting distribution (as the mesh size goes to 0) is to a non-trivial continuous time diffusion process.
Eigenpolynomials of a 2-D harmonic signal

We consider the characteristic polynomials of two-dimensional (2-D) harmonics. The concept of the eigenpolynomial of a 2-D harmonic signal is presented. A representation formula of the eigenpolynomial is given and proved. An advantage of the eigenpolynomials is that they can uniquely determine the frequency pairs of a 2-D harmonic signal.
Model-based covariable decorrelation in linear regression (CorReg). Application to missing data and to steel industry

This thesis was motivated by correlation issues in real datasets, in particular industrial datasets. The main idea stands in explicit modeling of the correlations between covariates by a structure of sub-regressions, that simply is a system of linear regressions between the covariates. It points out redundant covariates that can be deleted in a pre-selection step to improve matrix conditioning without significant loss of information and with strong explicative potential because this pre-selection is explained by the structure of sub-regressions, itself easy to interpret. An algorithm to find the sub-regressions structure inherent to the dataset is provided, based on a full generative model and using Monte-Carlo Markov Chain (MCMC) method. This pre-treatment does not depend on a response variable and thus can be used in a more general way with any correlated datasets. In a second part, a plug-in estimator is defined to get back the redundant covariates sequentially. Then all the covariates are used but the sequential approach acts as a protection against correlations. Finally, the generative model defined here allows, as a perspective, to manage missing values both during the MCMC and then for imputation. Then we are able to use classical methods that are not compatible with missing datasets. Once again, linear regression is used to illustrate the benefits of this method but it remains a pre-treatment that can be used in other contexts, like clustering and so on. The R package CorReg implements the methods created during this thesis.
Effect of Pulsating Strouhal Number on Heat Transfer around a Heated Cylinder in Pulsating Cross-Flow

An experimental study was conducted to investigate the effect of pulsating Strouhal number, Sγp, on flow and heat transfer around a heated cylinder in a pulsating cross-flow. Averaged Reynolds number was kept at 400 while oscillatory Reynolds number was changed from 40 to 300. Pulsating Strouhal number, Sγp, ranged from 0 to 1.37. Two remarkable peaks of the mean Nusselt number were observed at approximately Sγp=0.2 and 0.6. Flow visualization experiments revealed that twin symmetric vorticies were periodically formed behind the cylinder at Sγp corresponding to the 'lock-on' of the shedding of vortices. From results of velocity field measured by PIV, it was found that the flow reversal approaching to the back face of the cylinder was intensified by twin vortices. This flow reversal caused heat transfer enhancement on the back face of the cylinder.
An Algorithm for the Retrieval of Albedo from Space Using New GO Kernel-Driven BRDF Model

Multi angular remote sensing supplies reflectance of land surface in different directions. To simulate the relationship between bi directional reflectance distribution ( BRDF ) and structure of land surface and optical characteristic of objects,more models were developed. Because they are simple,rapid,grasp the main factors affecting BRDF and have some physical meaning,semi empirical models,especially,kernel driven models are applied broadly in data processing in batches. Thus,kernel driven BRDF model was the core of the AMBRALS,an algorithm for MODIS land surface BRDF and albedo products. In the onboard version of AMBRALS,the LiSparseR Gometrical Optical (GO) kernel was used. But a new derived kernel LiTransit kernel is also good at transition from LiSparse kernel to LiDense kernel when zenith angle is large,and accords more to the basic principle of GO model than LiSparseR kernel. Results of validation show:RossThick LiTransit kernels combination has more stability when extrapolated to large zenith angles with LiSparseR kernel. Therefore,we will use the Litransit kernel instead of LiSparseR kernel in the new version of AMBRALS. We introduce the algorithm based on this new kernel in this paper,including the kernel driven model and its inversion,albedo retrieval based on BRDF model,broad band albedo retrieval and realization of this algorithm. The speed requirement of tremendous data processing can't be met easily,such as MODIS data. Although we can calculate the integration of the kernel beforehand,store up and acquire through look up table method during retrieving albedo,it's inconvenient for an integrated data processing system. Thus we need to get the simple form of the integration of the kernels. Because the integrations of the kernels are approxinately independent on directions than BRDF ,it's sufficient to use a polynome dependent on the solar zenith angle to regress the integration of kernel. In this paper, we study the polynome regression of LiTransit kernel to instead LiSparseR, but not affect the systematic of the algorithm at the same time. Comparing the numerical integration of the kernel and the polynome regression result show the relationship is very well,the polynome can be used in the algorithm directly.
Individual Cost, Benefit and Efficiency of Manipulation

In this paper, we propose three methods to compare the manipulability degrees of two groups of Social Choice Correspondences (SCCs); the Scoring Rules and the Condorcet rules. We pick one rule from the first group, the Borda rule, and three from the other, the Copeland rule, the Topset and the Uncovered set. Therefore, we run three extension rules for our analysis; the Lexicographic extension rule (Leximax), the Max-Min ordering and the Expected ranking. Our findings based on our main methods with their sub-methods support that the Borda rule is more manipulable than the aforementioned Condorcet rules.
Comparison of machine-learning and logistic regression models to predict 30-day unplanned readmission: a development and validation study

We compared the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and logistic regression (LR) with the least absolute shrinkage and selection operator (LASSO) for 30-day unplanned readmission, according to the number of predictor variables and presence/absence of blood-test results. We used electronic health records of patients discharged alive from 38 hospitals in 2015-2017 for derivation (n=339,513) and in 2018 for validation (n=118,074), including basic characteristics (age, sex, admission diagnosis category, number of hospitalizations in the past year, discharge location), diagnosis, surgery, procedure, and drug codes, and blood-test results. We created six patterns of datasets having different numbers of binary variables (that [≥]5% or [≥]1% of patients or [≥]10 patients had) with and without blood-test results. For the dataset with the smallest number of variables (102), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the dataset with the largest number of variables (1543), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720). We found that GBDT generally outperformed LR-LASSO, but the difference became smaller when the number of variables was increased and blood-test results were used.
Modeling opinion polarization under perception bias

Social networks have provided a platform for the effective exchange of ideas or opinions but also served as a hotbed of polarization. While much research attempts to explore different causes of opinion polarization, the effect of perception bias caused by the network structure itself is largely understudied. To this end, we propose a threshold model that simulates the evolution of opinions by taking into account the perception bias, which is the gap between global information and locally available information from the neighborhood within networks. Our findings suggest that polarization occurs when the collective stubbornness of the population exceeds a critical value which is largely affected by the perception bias. In addition, as the level of stubbornness grows, the occurrence of first-order and second-order phase transitions between consensus and polarization becomes more prevalent, and the types of these phase transitions rely on the initial proportion of active opinions. Notably, for regular network structures, a step-wise pattern emerges that corresponds to various levels of polarization and is strongly associated with the formation of echo chambers. Our research presents a valuable framework for investigating the connection between perception bias and opinion polarization and provides valuable insights for mitigating polarization in the context of biased information.
Normalized Benchmarking of Hybrid Switched-Capacitor DC-DC Converters

Hybrid switched-capacitor converters (HSCC) offer great potential for high efficiency and power density compared to purely capacitor- or inductor-based converters. However, the recent proliferation of HSCC topologies has made it difficult to choose the best one for a particular application. This paper presents a benchmarking framework that allows for direct comparison of popular HSCC topologies by analyzing various performance metrics such as passive component volume and bandwidth. By comparing all topologies at the same efficiency, same inductor ripple, and same output voltage ripple, this approach generates guidelines for topology selection and optimization, which can aid in wider industrial adoption and exploration of new topologies.
Challenges in Automatic Software Optimization: the Energy Efficiency Case

With the advent of the Exascale capability allowing supercomputers to perform at least $10^{18}$ IEEE 754 Double Precision (64 bits) operations per second, many concerns have been raised regarding the energy consumption of high-performance computing code. Recently, Frontier operated by the Oak Ridge National Laboratory, has become the first supercomputer to break the exascale barrier. In total, contains 9,408 CPUs, 37,632 GPUs, and 8,730,112 cores. This world-leading supercomputer consumes about 21 megawatts which is truly remarkable as was also ranked first on the Green500 list before being recently replaced. The previous top Green500 machine, MN-3 in Japan, provided 39.38 gigaflops per watt, while the delivered 62.68 gigaflops per watt. All these infrastructure and hardware improvements are just the tip of the Iceberg. Energy-aware code is now required to minimize the energy consumption of distributed and/or multi-threaded software. For example, the data movement bottleneck is responsible for $35-60\%$ of a system's energy consumption during intra-node communication. In an HPC environment, additional energy is consumed through inter-node communication. This position paper aims to introduce future research directions to enter now in the age of energy-aware software. The paper is organized as follows. First, we introduce related works regarding measurement and energy optimization. Then we propose to focus on the two different levels of granularity in energy optimization.
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks

Transformer architectures are complex and their use in NLP, while it has engendered many successes, makes their interpretability or explainability challenging. Recent debates have shown that attention maps and attribution methods are unreliable (Pruthi et al., 2019; Brunner et al., 2019). In this paper, we present some of their limitations and introduce COCKATIEL, which successfully addresses some of them. COCKATIEL is a novel, post-hoc, concept-based, model-agnostic XAI technique that generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task by using Non-Negative Matrix Factorization (NMF) to discover the concepts the model leverages to make predictions and by exploiting a Sensitivity Analysis to estimate accurately the importance of each of these concepts for the model. It does so without compromising the accuracy of the underlying model or requiring a new one to be trained. We conduct experiments in single and multi-aspect sentiment analysis tasks and we show COCKATIEL's superior ability to discover concepts that align with humans' on Transformer models without any supervision, we objectively verify the faithfulness of its explanations through fidelity metrics, and we showcase its ability to provide meaningful explanations in two different datasets.
Quantum gravity in the triangular gauge

Vielbeins are necessary when coupling General Relativity (GR) to fermionic matter. This enhances the gauge group of GR to include local Lorentz transformations. In view of a reduced phase space formulation of quantum gravity, in this work we completely gauge fix that Lorentz gauge symmetry by using a so-called triangular gauge. Having solved the Gauss constraints already classically opens access to new Hilbert space representations which are free of the complications that otherwise arise due to a non Abelian gauge symmetry. In that sense, a connection formulation as being pursued in Loop Quantum Gravity is no longer the only practicable option and other less dimension dependent representations e.g. based on triads and even metrics suggest themselves. These formulations make it easier to identify states representing non-degenerate quantum geometries and thus to investigate the hypersurface deformation algebra which implicitly assumes non-degeneracy.
Matrix Tri-Factorization Over the Tropical Semiring

Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, and decision problems. Previous studies have applied a matrix two-factorization algorithm based on the tropical semiring to investigate bipartite and tripartite networks. Tri-factorization algorithms based on standard linear algebra are used to solve tasks such as data fusion, co-clustering, matrix completion, community detection, and more. However, there is currently no tropical matrix tri-factorization approach that would allow for the analysis of multipartite networks with many parts. To address this, we propose the triFastSTMF algorithm, which performs tri-factorization over the tropical semiring. We applied it to analyze a four-partition network structure and recover the edge lengths of the network. We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network. When trained on a specific subnetwork and used to predict the entire network, triFastSTMF outperforms Fast-NMTF by several orders of magnitude smaller error. The robustness of triFastSTMF is due to tropical operations, which are less prone to predict large values compared to standard operations.
Physics-Informed Neural Networks for Discovering Localised Eigenstates in Disordered Media

The Schr\"{o}dinger equation with random potentials is a fundamental model for understanding the behaviour of particles in disordered systems. Disordered media are characterised by complex potentials that lead to the localisation of wavefunctions, also called Anderson localisation. These wavefunctions may have similar scales of eigenenergies which poses difficulty in their discovery. It has been a longstanding challenge due to the high computational cost and complexity of solving the Schr\"{o}dinger equation. Recently, machine-learning tools have been adopted to tackle these challenges. In this paper, based upon recent advances in machine learning, we present a novel approach for discovering localised eigenstates in disordered media using physics-informed neural networks (PINNs). We focus on the spectral approximation of Hamiltonians in one dimension with potentials that are randomly generated according to the Bernoulli, normal, and uniform distributions. We introduce a novel feature to the loss function that exploits known physical phenomena occurring in these regions to scan across the domain and successfully discover these eigenstates, regardless of the similarity of their eigenenergies. We present various examples to demonstrate the performance of the proposed approach and compare it with isogeometric analysis.
A priori estimates for solutions of $g$-Laplace type problems

In this work we study a priori bounds for weak solution to elliptic problems with nonstandard growth that involves the so-called $g-$Laplace operator. The $g-$Laplacian is a generalization of the $p-$Laplace operator that takes into account different behaviors than pure powers. The method to obtain this a priori estimates is the so called ``blow-up'' argument developed by Gidas and Spruck. Then we applied this a priori bounds to show some existence results for these problems.
Sphere fibrations over highly connected manifolds

We construct sphere fibrations over $(n-1)$-connected $2n$-manifolds such that the total space is a connected sum of sphere products. More precisely, for $n$ even, we construct fibrations $S^{n-1} \to \#^{k-1}(S^n \times S^{2n-1}) \to M_k$, where $M_k$ is a $(n-1)$-connected $2n$-dimensional Poincar\'{e} duality complex which satisfies $H_n(M_k)\cong \mathbb{Z}^k$, in a localized category of spaces. The construction of the fibration is proved for $k\geq 2$, where the prime $2$, and the primes which occur as torsion in $\pi_{2n-1}(S^n)$ are inverted. In specific cases, by either assuming $n$ is small, or assuming $k$ is large we can reduce the number of primes that need to be inverted. Integral results are obtained for $n=2$ or $4$, and if $k$ is bigger than the number of cyclic summands in the stable stem $\pi_{n-1}^s$, we obtain results after inverting $2$. Finally, we prove some applications for fibrations over $N\# M_k$, and for looped configuration spaces.