Miracle-dz/UniFAR-FTUs huggingface dataset:
Triboligie des matériaux carbonés. Etude des mécanismes contrôlant la formation et la stabilité du 3ème corps en frottement sec

L'objectif de ce travail est d'ameliorer nos connaissances concernant les mecanismes fondamentaux du frottement sec et principalement la formation et le maintien des films de transfert (3eme corps) obtenu lors du frottement de graphite sur acier. Dans cette configuration, le 3eme corps prend la forme d'ilots discrets constitues par l'agglomeration de particules nanometriques dont la taille depend des conditions de frottement. L'etude simultanee, par profilometrie a l'echelle micro et nanometrique des caracteristiques morphologiques des agglomerats et de leurs constituants a conduit d'une part a etudier la stabilite des agglomerats et d'autre part a formuler un modele de detachement base sur la fragmentation du graphite qui tient compte de tous les parametres mecaniques, chimiques, physico-chimiques et materiaux intervenant dans la formation des particules d'usure. L'integration de ce modele, formule a l'echelle nanometrique - a l'ensemble du tribo-contact montre comment le frottement et l'usure peuvent etre controles par la quantite d'especes chimiques adsorbees a l'entree du contact et progressivement consommee dans le contact par reactions chimiques avec les surfaces fraichement formees. Ces resultats mettent en evidence que l'usure et le frottement dans le cas des materiaux carbones - resultent d'un meme phenomene controle par environnement.
Is First Person Vision Challenging for Object Tracking?

Understanding human-object interactions is fundamental in First Person Vision (FPV). Tracking algorithms which follow the objects manipulated by the camera wearer can provide useful cues to effectively model such interactions. Visual tracking solutions available in the computer vision literature have significantly improved their performance in the last years for a large variety of target objects and tracking scenarios. However, despite a few previous attempts to exploit trackers in FPV applications, a methodical analysis of the performance of state-of-the-art trackers in this domain is still missing. In this paper, we fill the gap by presenting the first systematic study of object tracking in FPV. Our study extensively analyses the performance of recent visual trackers and baseline FPV trackers with respect to different aspects and considering a new performance measure. This is achieved through TREK-150, a novel benchmark dataset composed of 150 densely annotated video sequences. Our results show that object tracking in FPV is challenging, which suggests that more research efforts should be devoted to this problem so that tracking could benefit FPV tasks.
On the evolution of compact binary black holes

Based on the consideration of potential energy of the di-black-hole as a function of mass asymmetry (transfer) collective coordinate, the possibility of matter transfer between the black holes in a binary system is investigated. The sensitivity of the calculated results is studied to the value of the total mass of binary system. The conditions for the merger of two black holes are analyzed in the context of gravitational wave emission.
Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets

In this work, a novel approach for the construction and training of time series models is presented that deals with the problem of learning on large time series with non-equispaced observations, which at the same time may possess features of interest that span multiple scales. The proposed method is appropriate for constructing predictive models for non-stationary stochastic time series.The efficacy of the method is demonstrated on a simulated stochastic degradation dataset and on a real-world accelerated life testing dataset for ball-bearings. The proposed method, which is based on GraphNets, implicitly learns a model that describes the evolution of the system at the level of a state-vector rather than of a raw observation. The proposed approach is compared to a recurrent network with a temporal convolutional feature extractor head (RNN-tCNN) which forms a known viable alternative for the problem context considered. Finally, by taking advantage of recent advances in the computation of reparametrization gradients for learning probability distributions, a simple yet effective technique for representing prediction uncertainty as a Gamma distribution over remaining useful life predictions is employed.
An optimized material removal process.

We conduct boundary element simulations of a contact problem consisting of an elastic medium subject to tangential load. Using a particle swarm optimization algorithm, we find the optimal shape and location of the micro-contacts to maximize for a given load the stored elastic energy contributing to the removal of a spherical particle contained in between the micro-contacts. We propose an ice scream scoop as an application of this optimization process.
Min-Sum Clustering (with Outliers)

We give a constant factor polynomial time pseudo-approximation algorithm for min-sum clustering with or without outliers. The algorithm is allowed to exclude an arbitrarily small constant fraction of the points. For instance, we show how to compute a solution that clusters 98\% of the input data points and pays no more than a constant factor times the optimal solution that clusters 99\% of the input data points. More generally, we give the following bicriteria approximation: For any $\eps > 0$, for any instance with $n$ input points and for any positive integer $n'\le n$, we compute in polynomial time a clustering of at least $(1-\eps) n'$ points of cost at most a constant factor greater than the optimal cost of clustering $n'$ points. The approximation guarantee grows with $\frac{1}{\eps}$. Our results apply to instances of points in real space endowed with squared Euclidean distance, as well as to points in a metric space, where the number of clusters, and also the dimension if relevant, is arbitrary (part of the input, not an absolute constant).
Data-Aided Sensing for Gaussian Process Regression in IoT Systems

In this article, for efficient data collection with limited bandwidth, data-aided sensing (DAS) is applied to the Gaussian process regression (GPR) that is used to learn data sets collected from sensors in the Internet-of-Things systems. We focus on the interpolation of sensors’ measurements from a small number of measurements uploaded by a fraction of sensors using GPR with DAS. Thanks to active sensor selection, it is shown that GPR with DAS can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, DAS is generalized for distributed selective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one. Numerical results show that modified multichannel ALOHA with predictions can help improve the performance of GPR with DAS compared to the conventional multichannel ALOHA with equal uploading probability.
Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention

Accurate and efficient product classification is significant for E-commerce applications, as it enables various downstream tasks such as recommendation, retrieval, and pricing. Items often contain textual and visual information, and utilizing both modalities usually outperforms classification utilizing either mode alone. In this paper we describe our methodology and results for the SIGIR eCom Rakuten Data Challenge. We employ a dual attention technique to model image-text relationships using pretrained language and image embeddings. While dual attention has been widely used for Visual Question Answering(VQA) tasks, ours is the first attempt to apply the concept for multimodal classification.
AI Discovering a Coordinate System of Chemical Elements: Dual Representation by Variational Autoencoders

The periodic table is a fundamental representation of chemical elements that plays essential theoretical and practical roles. The research article discusses the experiences of unsupervised training of neural networks to represent elements on the 2D latent space based on their electron configurations while forcing disentanglement. To emphasize chemical properties of the elements, the original data of electron configurations has been realigned towards the outermost valence orbitals. Recognizing seven shells and four subshells, the input data has been arranged as (7x4) images. Latent space representation has been performed using a convolutional beta variational autoencoder (beta-VAE). Despite discrete and sparse input data, the beta-VAE disentangles elements of different periods, blocks, groups, and types, while retaining the order along atomic numbers. In addition, it isolates outliers on the latent space that turned out to be known cases of Madelung's rule violations for lanthanide and actinide elements. Considering the generative capabilities of beta-VAE and discrete input data, the supervised machine learning has been set to find out if there are insightful patterns distinguishing electron configurations between real elements and decoded artificial ones. Also, the article addresses the capability of dual representation by autoencoders. Conventionally, autoencoders represent observations of input data on the latent space. However, by transposing and duplicating original input data, it is possible to represent variables on the latent space as well. The latest can lead to the discovery of meaningful patterns among input variables. Applying that unsupervised learning for transposed data of electron configurations, the order of input variables that has been arranged by the encoder on the latent space has turned out to exactly match the sequence of Madelung's rule.
Model Elicitation through Direct Questioning

The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about getting useful information about the human's (teammate's) model. There are many challenges before a robot can interact, such as incorporating the structural differences in the human's model, ensuring simpler responses, etc. In this paper, we investigate how a robot can interact to localize the human model from a set of models. We show how to generate questions to refine the robot's understanding of the teammate's model. We evaluate the method in various planning domains. The evaluation shows that these questions can be generated offline, and can help refine the model through simple answers.
Homotopies and transcendental extensions in colouring problems

We develop the technique of geometric realizations with algebraically independent (over the field of real algebraic numbers) coordinates of vertices and combine it with the oriented volume method inspired by work of McLennan and Tourky on the Sperner's lemma. This enables us to prove new results: the non-draw property of the generalized Y~game, the theorem about triangulation of the product of two simplices, the generalized Atanassov conjecture, and give new proofs of known results: the multilabeled versions of Sperner's and Ky Fan's lemmas.
Learning to Sample the Most Useful Training Patches from Images

Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manually collecting a new training dataset that contains adequate hard samples, however, there are still hard and simple areas even within one single image. In this paper, we present a data-driven approach called PatchNet that learns to select the most useful patches from an image to construct a new training set instead of manual or random selection. We show that our simple idea automatically selects informative samples out from a large-scale dataset, leading to a surprising 2.35dB generalisation gain in terms of PSNR. In addition to its remarkable effectiveness, PatchNet is also resource-friendly as it is applied only during training and therefore does not require any additional computational cost during inference.
Densely connected multidilated convolutional networks for dense prediction tasks

Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)-based approaches interchange representations in different resolutions only a few times. In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net). D3Net involves a novel multidilated convolution that has different dilation factors in a single layer to model different resolutions simultaneously. By combining the multidilated convolution with the DenseNet architecture, D3Net incorporates multiresolution learning with an exponentially growing receptive field in almost all layers, while avoiding the aliasing problem that occurs when we naively incorporate the dilated convolution in DenseNet. Experiments on the image semantic segmentation task using Cityscapes and the audio source separation task using MUSDB18 show that the proposed method has superior performance over stateof-the-art methods.
Code Search Intent Classification Using Weak Supervision

Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding process. Recently, natural language based code search has been an active area of research. However, the lack of real-world large-scale datasets is a significant bottleneck. In this work, we propose a weak supervision based approach for detecting code search intent in search queries for C# and Java programming languages. We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively. Furthermore, we are also releasing the first large-scale real-world dataset of code search queries mined from Bing web search engine. We hope that the dataset will aid future research on code search.
The Chunks and Tasks Matrix Library 2.0

We present a C++ header-only parallel sparse matrix library, based on sparse quadtree representation of matrices using the Chunks and Tasks programming model. The library implements a number of sparse matrix algorithms for distributed memory parallelization that are able to dynamically exploit data locality to avoid movement of data. This is demonstrated for the example of block-sparse matrix-matrix multiplication applied to three sequences of matrices with different nonzero structure, using the CHT-MPI 2.0 runtime library implementation of the Chunks and Tasks model. The runtime library succeeds to dynamically load balance the calculation regardless of the sparsity structure.
Spatiotemporal imaging with diffeomorphic optimal transportation

Motivated by the image reconstruction in spatiotemporal imaging, we introduce a concept named diffeomorphic optimal transportation (DOT), which combines the Wasserstein distance with Benamou–Brenier formula in optimal transportation and the flow of diffeomorphisms in large deformation diffeomorphic metric mapping. Using DOT, we propose a new variational model for joint image reconstruction and motion estimation, which is suitable for spatiotemporal imaging involving mass-preserving large diffeomorphic deformations. We further get its equivalent PDE-constrained optimal control formulation. The proposed model is easy-to-implement and solved by an alternating gradient descent algorithm, which is compared against existing alternatives theoretically and numerically. The performance is validated by several numerical experiments in spatiotemporal tomography, where the projection data is time-dependent sparse and/or high-noise. Moreover, we present several extensions based on DOT. Under appropriate conditions, the proposed algorithm can be adapted as a new algorithm to solve the models using quadratic Wasserstein distance.
High ecological resilience of the sea fan Gorgonia ventalina during two severe hurricanes

Since about the turn of the millennium, octocorals have been increasing in abundance on Caribbean reefs. The mechanisms underlying this trend have not been resolved, but the emergent species assemblage appears to be more resilient than the scleractinians they are replacing. The sea fan Gorgonia ventalina is an iconic species in the contemporary octocoral fauna, and here its population dynamics are described from St. John, US Virgin Islands, from 2013 to 2019. Mean densities of G. ventalina at Yawzi Point (9-m depth) varied from 1.4–1.5 colonies m−2, and their mean heights from 24–30 cm; nearby at Tektite (14-m depth), they varied from 0.6–0.8 colonies m−2 and from 25–33 cm. These reefs were impacted by two Category 5 hurricanes in 2017, but neither the density of G. ventalina, the density of their recruits (< 5-cm tall), nor the height of colonies, differed among years, although growth was depressed after the hurricanes. Nevertheless, at Tektite, colony height trended upwards over time, in part because colonies 10.1–20 cm tall were reduced in abundance after the hurricanes. These trends were sustained without density-associated effects mediating recruitment or self-thinning of adults. The dynamics of G. ventalina over seven years reveals the high resilience of this species that will contribute to the persistence of octocorals as a dominant state on Caribbean reefs.
vcfView: An Extensible Data Visualization and Quality Assurance Platform for Integrated Somatic Variant Analysis

Motivation: Somatic mutations can have critical prognostic and therapeutic implications for cancer patients. Although targeted methods are often used to assay specific cancer driver mutations, high throughput sequencing is frequently applied to discover novel driver mutations and to determine the status of less-frequent driver mutations. The task of recovering somatic mutations from these data is nontrivial as somatic mutations must be distinguished from germline variants, sequencing errors, and other artefacts. Consequently, bioinformatics pipelines for recovery of somatic mutations from high throughput sequencing typically involve a large number of analytical choices in the form of quality filters. Results: We present vcfView, an interactive tool designed to support the evaluation of somatic mutation calls from cancer sequencing data. The tool takes as input a single variant call format (VCF) file and enables researchers to explore the impacts of analytical choices on the mutant allele frequency spectrum, on mutational signatures and on annotated somatic variants in genes of interest. It allows variants that have failed variant caller filters to be re-examined to improve sensitivity or guide the design of future experiments. It is extensible, allowing other algorithms to be incorporated easily. Availability: The shiny application can be downloaded from GitHub (https://github.com/BrianOSullivanGit/vcfView). All data processing is performed within R to ensure platform independence. The app has been tested on RStudio, version 1.1.456, with base R 3.6. 2 and Shiny 1.4. 0. A vignette based on a publicly available data set is also available on GitHub.
Low Self-Perception of Malnutrition in Older Hospitalized Patients

Background Studies focusing on self-perception of nutritional status in older hospitalized patients are lacking. We aimed to examine the self-perception of body weight and nutritional status among older hospitalized patients compared to their actual body weight and nutritional status based on medical assessment. Materials and Methods This observational cross-sectional study investigated 197 older participants (mean age 82.2±6.8 years, 61% women) who were consecutively admitted to the geriatric acute care ward. Body weight status and nutritional status were assessed using WHO-BMI classification and Mini Nutritional Assessment-Short Form (MNA-SF), respectively. Self-perceived body weight status and nutritional status were assessed with a standardized questionnaire. A follow-up was performed with a short telephone interview after three months. Results According to MNA-SF, 49% and 35% were at risk of malnutrition and malnourished, respectively. There was no agreement between self-perceived nutritional status and objective nutritional status according to MNA-SF (Kappa: 0.06). A slight agreement was found between subjective body weight status and objective body weight status according to WHO-BMI classification (Kappa: 0.19). A total of 184 patients completed the 3 months follow-up and additional 9 patients died during this time, of which 7 and 2 were malnourished and at risk of malnutrition according to MNA-SF, respectively. Of those who were malnourished and at risk of malnutrition based on MNA-SF and died during follow-up, 67.7% did not realize their malnutrition. Compared to the patients with normal nutritional status during hospitalization, malnourished patients based on MNA-SF had higher rates of unplanned hospital readmission and further weight loss and more often reported health deterioration and experienced death within three months after discharge. Conclusion No agreement between self-perceived nutritional status and objective nutritional status among older hospitalized patients was found. Our study highlights the need to raise knowledge about the issue of malnutrition and increase awareness of health risks associated with malnutrition among older hospitalized patients.
Identification of hub genes associated with the pathogenesis of diffuse large B-cell lymphoma subtype one characterized by host response via integrated bioinformatic analyses

Background Host response diffuse large B-cell lymphoma (HR DLBCL) shares features of histologically defined T-cell/histiocyte-rich B-cell lymphoma, including fewer genetic abnormalities, frequent splenic and bone marrow involvement, and younger age at presentation. HR DLBCL is inherently less responsive to the standard treatment for DLBCL. Moreover, the mechanism of infiltration of HR DLBCL with preexisting abundant T-cells and dendritic cells is unknown, and their associated underlying immune responses incompletely defined. Here, hub genes and pathogenesis associated with HR DLBCL were explored to reveal molecular mechanisms and treatment targets. Methods Differentially expressed genes were identified in three datasets (GSE25638, GSE44337, GSE56315). The expression profile of the genes in the GSE53786 dataset was used to constructed a co-expression network. Protein-protein interactions analysis in the modules of interest identified candidate hub genes. Then screening of real hub genes was carried out by survival analysis within the GSE53786 and GSE10846 datasets. Expression of hub genes was validated in the Gene expression profiling interactive analysis, Oncomine databases and human tissue specimens. Functional enrichment analysis and Gene set enrichment analysis were utilized to investigate the potential mechanisms. Tumor Immune Estimation Resource and The Cancer Genome Atlas were used to mine the association of the hub gene with tumor immunity, potential upstream regulators were predicted using bioinformatics tools. Results A total of 274 common differentially expressed genes were identified. Within the key module, we identified CXCL10 as a real hub gene. The validation of upregulated expression level of CXCL10 was consistent with our study. CXCL10 might have a regulatory effect on tumor immunity. The predicted miRNA (hsa-mir-6849-3p) and transcription factor (IRF9) might regulate gene expression in the hub module.