Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling safe walking rehabilitation on the exoskeleton Atalante: experimental results

This paper exposes a control architecture enabling rehabilitation of walking impaired patients with the lower-limb exoskeleton Atalante. Atalante's control system is modified to allow the patient to contribute to the walking motion through their efforts. Only the swing leg degree of freedom along the nominal path is relaxed. An online trajectory optimization checks that the muscle forces do not jeopardize stability. The optimization generates reference trajectories that satisfy several key constraints from the current point to the end of the step. One of the constraints requires that the center or pressure remains inside the support polygon, which ensures that the support leg subsystem successfully tracks the reference trajectory. As a result of the presented works, the robot provides a non-zero force in the direction of motion only when required, helping the patient go fast enough to maintain balance (or preventing him from going too fast). Experimental results are reported. They illustrate that variations of ±50% of the duration of the step can be achieved in response to the patient's efforts and that many steps are achieved without falling.
Utilisation of an Air-conditioning System to Control the Levels of Radon and Radon Progeny in a Workplace Environment

From long-term real-time radon and radon progeny measurements taken in a relatively large retail store, cyclical patterns were evident, which were found to relate to the overriding influence of the timed air-conditioning system. Concentration of radon, radon progeny and the variability of F factor were found to depend significantly on the intermittent operation of this ventilation/air-conditioning system. After pressure equalisation remedial measures proved ineffective, the air-movement system was utilised to reduce the levels of radon and radon progeny to well within established norms applicable during working hours. It is demonstrated that the average levels for radon and radon progeny are reduced in absolute terms. This amounted to less than 12% of the general level, during designated work periods. Where air movement systems are already installed, as well as other circumstances, their regulation provides an economical solution to meeting legal and other standards for radon in the workplace.
Sur le plus grand sous-arbre commun à plusieurs X-arbres

Etant donnes plusieurs X-arbres, ou arbres phylogenetiques, sur le meme ensemble X, nous cherchons a construire un plus grand sous-ensemble Y C X tel que les arbres partiels induits sur Y soient identiques d'un point de vue topologique, c'est-a-dire independamment des longueurs des aretes. Ce probleme, connu sous le nom de MAST (Maximum Agreement SubTree), est NP-Difficile, dans le cas general, des que le nombre de X-arbres est superieur a 2. Nous presentons un algorithme approche qui construit un arbre partiel commun maximal. Il est facilement programmable et suffisamment efficace sur une centaine de X-arbres connectant une centaine d'elements pour evaluer la taille moyenne d'un sous-arbre commun a des X-arbres independants. La distribution observee permet d'estimer la taille critique d'un sous-arbre commun et de mesurer la congruence de plusieurs arbres evolutifs.
Blood eosinophils to direct oral corticosteroid treatment for patients with nasal polyps - an open label, non-inferiority, randomized control trial.

BACKGROUND Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disorder. We aimed to evaluate the value of blood eosinophil count (BEC) for guiding oral corticosteroid therapy for CRSwNP. METHODS Subjects with CRSwNP were entered into a 2:1 randomized biomarker-directed corticosteroid versus standard therapy study base on the principle of potential benefits to patients. Subjects in the standard arm received oral prednisone (30mg/day) alone for 7 days, whereas in the biomarker-directed arm, prednisone (30mg/day), or nasal steroid spray (budesonide 256ug/day) was given according to the BEC which was measured to define eosinophil-high and -low CRSwNP (BEC ≥ and < 0.37×109/L, respectively). The primary outcome was the total nasal symptom scores (TNSS) of the two arms with the non-inferiority margin of 1.8. Secondary outcomes included nasal polyp size scores (NPSS) and SNOT-22. Patients were followed up the day after last dose of treatment. RESULTS A total of 105 subjects with CRSwNP were randomized into the biomarker-directed therapy group or the standard care group. The biomarker therapy demonstrated non-inferiority compared to standard care. There were no between-group differences for TNSS, NPSS and SNOT-22 improvements after treatment. Comparisons of TNSS, SNOT-22 and NPSS revealed no significant difference in terms of the effectiveness ratios of the biomarker-directed therapy and the standard care. CONCLUSION A biomarker-directed strategy using the BEC can be used to direct corticosteroid therapy without increasing treatment failure or worsening of symptoms in patients with CRSwNP.
Exploring Blockchain Technology through a Modular Lens: A Survey

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications.
Transformer-Based Graph Neural Networks for Outfit Generation

Suggesting complementary clothing items to compose an outfit is a process of emerging interest, yet it involves a fine understanding of fashion trends and visual aesthetics. Previous works have mainly focused on recommendation by scoring visual appeal and representing garments as ordered sequences or as collections of pairwise-compatible items. This limits the full usage of relations among clothes. We attempt to bridge the gap between outfit recommendation and generation by leveraging a graph-based representation of items in a collection. The work carried out in this article, tries to build a bridge between outfit recommendation and generation, by discovering new appealing outfits starting from a collection of pre-existing ones. We propose a transformer-based architecture, named TGNN, which exploits multi-headed self attention to capture relations between clothing items in a graph as a message passing step in Convolutional Graph Neural Networks. Specifically, starting from a seed, i.e. one or more garments, outfit generation is performed by iteratively choosing the garment that is most compatible with the previously chosen ones. Extensive experimentations are conducted with two different datasets, demonstrating the capability of the model to perform seeded outfit generation as well as obtaining state of the art results on compatibility estimation tasks.
Accelerated Distributed Aggregative Optimization

In this paper, we investigate a distributed aggregative optimization problem in a network, where each agent has its own local cost function which depends not only on the local state variable but also on an aggregated function of state variables from all agents. To accelerate the optimization process, we combine heavy ball and Nesterov's accelerated methods with distributed aggregative gradient tracking, and propose two novel algorithms named DAGT-HB and DAGT-NES for solving the distributed aggregative optimization problem. We analyse that the DAGT-HB and DAGT-NES algorithms can converge to an optimal solution at a global $\mathbf{R}-$linear convergence rate when the objective function is smooth and strongly convex, and when the parameters (e.g., step size and momentum coefficients) are selected within certain ranges. A numerical experiment on the optimal placement problem is given to verify the effectiveness and superiority of our proposed algorithms.
Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification

Model mis-specification in multivariate econometric models can strongly influence quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be mis-specified along important but unknown dimensions. Coupled with a conjugate prior, this results in a computationally simple model. As opposed to more flexible specifications, our approach avoids overfitting, is simple to implement and estimation is fast. The resulting cBVAR performs well in simulations for several types of mis-specification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs, and lead to milder but more persistent negative effects of uncertainty shocks on output.
Surveillance Face Presentation Attack Detection Challenge

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.
Finding Geometric Models by Clustering in the Consensus Space

We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space.
Attentive Q-Matrix Learning for Knowledge Tracing

As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.
Causal models in string diagrams

The framework of causal models provides a principled approach to causal reasoning, applied today across many scientific domains. Here we present this framework in the language of string diagrams, interpreted formally using category theory. A class of string diagrams, called network diagrams, are in 1-to-1 correspondence with directed acyclic graphs. A causal model is given by such a diagram with its components interpreted as stochastic maps, functions, or general channels in a symmetric monoidal category with a 'copy-discard' structure (cd-category), turning a model into a single mathematical object that can be reasoned with intuitively and yet rigorously. Building on prior works by Fong and Jacobs, Kissinger and Zanasi, as well as Fritz and Klingler, we present diagrammatic definitions of causal models and functional causal models in a cd-category, generalising causal Bayesian networks and structural causal models, respectively. We formalise general interventions on a model, including but beyond do-interventions, and present the natural notion of an open causal model with inputs. We also give an approach to conditioning based on a normalisation box, allowing for causal inference calculations to be done fully diagrammatically. We define counterfactuals in this setup, and treat the problems of the identifiability of causal effects and counterfactuals fully diagrammatically. The benefits of such a presentation of causal models lie in foundational questions in causal reasoning and in their clarificatory role and pedagogical value. This work aims to be accessible to different communities, from causal model practitioners to researchers in applied category theory, and discusses many examples from the literature for illustration. Overall, we argue and demonstrate that causal reasoning according to the causal model framework is most naturally and intuitively done as diagrammatic reasoning.
Playing Congestion Games with Bandit Feedbacks (Extended Abstract)

Almost all convergence results from each player adopting specic \no-regret" learning algorithms such as multiplicative updates or the more general mirror-descent algorithms in repeated games are only known in the more generous information model, in which each player is assumed to have access to the costs of all possible choices, even the unchosen ones, at each time step. This assumption in general may seem too strong, while a more realistic one is captured by the bandit model, in which each player at each time step is restricted to know only the cost of her currently chosen path, but not any of the unchosen ones. Can convergence still be achieved in such a more challenging bandit model? We answer this question positively. While existing bandit algorithms do not seem to work here, we develop a new family of bandit algorithms based on the mirror-descent algorithm with such a guarantee in atomic congestion games.
Implication des vésicules extracellulaires des cellules initiatrices tumorales dans l’augmentation de la perméabilité vasculaire du glioblastome

Les capillaires cerebraux sont caracterises par une structure et une organisation particuliere au sein de l’unite neurovasculaire. Au travers de jonctions endotheliales particulierement selectives, la barriere hemato-encephalique (BHE) orchestre les echanges de cellules, fluides, proteines et metabolites plasmatiques entre le sang et le compartiment cerebral. La VE-cadherine, proteine transmembranaire des jonctions endotheliales, est particulierement importante dans l’integrite vasculaire puisque sa destabilisation entraine un affaiblissement de la BHE et conduit a sa rupture dans certaines pathologies. Le glioblastome est une tumeur cerebrale extremement agressive et associee a un haut degre de vascularisation dont la permeabilite est anormalement elevee. Ceci contribue a la formation d’œdemes vasculaires peri-tumoraux prejudiciables pour la sante du patient. Depuis la derniere decennie, un grand nombre d’etudes ont relie la presence d’une sous-population de cellules souches gliomateuses (CSG) a l’initiation, la recurrence et l’agressivite du glioblastome. De facon importante, ces CSG sont localisees dans un microenvironnement particulier, appele niche vasculaire, dans lequel elles communiquent etroitement et echangent de maniere bidirectionnelle avec l’endothelium cerebral. Sur la base d’un modele de coculture entre CSG issues de patients, et cellules endotheliales cerebrales recapitulant les proprietes de la BHE, notre laboratoire a porte son attention sur la Semaphorine 3A (Sema3A). Cette proteine est en effet secretee par les CSG et exerce, via son corecepteur Neuropiline-1 (Nrp-1), une action positive sur la permeabilite vasculaire par destabilisation de la VE-cadherine. Durant mes travaux de these, nous avons identifie et caracterise la presence de la Sema3A a la membrane de vesicules extracellulaires (EV) produites par les CSG. Un nombre grandissant d’etudes met en exergue l’implication de ces vesicules dans la biologie tumorale. Dans ce sens, nous avons demontre que les EV des CSG peuvent penetrer dans les cellules endotheliales, et moduler leurs proprietes intrinseques. Au travers de modeles in vivo originaux et de la combinaison de strategies genetiques (ARN interferent) et pharmacologiques (anticorps bloquant humanises), nous avons d’une part montre que la Sema3A, portee par les EV, agit specifiquement via la Nrp-1 exprimee par les cellules endotheliales afin d’augmenter leur permeabilite. D’autre part, dans un modele de xenogreffe orthotopique de CSG, nous avons identifie une augmentation significative du taux de Sema3A dans la fraction de EV circulantes. De maniere interessante, des resultats similaires ont ete obtenus a partir de prelevements de patients glioblastome nouvellement diagnostiques. La Sema3A de ces vesicules, apte a augmenter la permeabilite vasculaire a distance, in vitro et in vivo au travers de la Nrp-1, representerait donc un bon candidat en tant que futur marqueur theranostique du glioblastome.
OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization

Transformer-based large language models (LLMs) have achieved great success with the growing model size. LLMs' size grows by 240× every two years, which outpaces the hardware progress and makes model inference increasingly costly. Model quantization is a promising approach to mitigate the widening gap between LLM size and hardware capacity. However, the existence of outliers, values with significant magnitudes, in LLMs makes existing quantization methods less effective. Prior outlier-aware quantization schemes adopt sparsity encoding techniques to separate outliers from normal values where the process requires global coordination (e.g., a global sparsity coordination list). This incurs complex encoding/decoding hardware logics and an extra orchestration controller for the computation between outlier and normal values. As such, it is not hardware-efficient and hence only achieves sub-optimal quantization benefits. We propose OliVe, an algorithm/architecture co-designed solution that adopts an outlier-victim pair (OVP) quantization and handles outlier values locally with low hardware overheads and high performance gains. The key insight of OliVe is that outliers are important while the normal values next to them are not. Thus those normal values (called victims) can be sacrificed to accommodate outliers. This enables a memory-aligned OVP encoding scheme, which can be efficiently integrated to the existing hardware accelerators like systolic array and tensor core. As a result, OliVe-based accelerator surpasses the existing outlier-aware accelerator, GOBO, by 4.5× speedup and 4.0× energy reduction, respectively, with a superior model accuracy.
Classification of news spreading barriers

News media is one of the most effective mechanisms for spreading information internationally, and many events from different areas are internationally relevant. However, news coverage for some news events is limited to a specific geographical region because of information spreading barriers, which can be political, geographical, economic, cultural, or linguistic. In this paper, we propose an approach to barrier classification where we infer the semantics of news articles through Wikipedia concepts. To that end, we collected news articles and annotated them for different kinds of barriers using the metadata of news publishers. Then, we utilize the Wikipedia concepts along with the body text of news articles as features to infer the news-spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using Wikipedia concepts based semantic knowledge offers better performance than the usual for classifying the news-spreading barriers.