Du dialecte à la langue internationale : Etude sociolinguistique du français

Ce texte vise à étudier le statut du français en évolution dans le temps et dans l’espace. Il s’agit d’analyser sa fonction à titre de langue vernaculaire et de langue véhiculaire, son rapport au latin et à d’autres dialectes territoriaux ainsi que son état de défense surtout face à l’anglais et son avenir passant par le combat pour la diversité culturelle

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202

Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. {\blue Specifically, the detection success rate is improved by up to 65\%, 55\%, and 51\% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.Comment: accepted by IEEE TIFS in Dec. 202

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Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.info:eu-repo/semantics/publishedVersio

OFDMA-F2^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface

Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-access (OFDMA) air interface, termed as ``OFDMA-F$^2$L'', allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulations, adapting to channel conditions and computing powers. Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L capturing the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a ``winner-takes-all'' policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives.Comment: Accepted by IEEE TWC in Nov. 202

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention

Transformation of worst weed into N-, S-, and P-tridoped carbon nanorings as metal-free electrocatalysts for the oxygen reduction reaction

Substituting sustainable/cost-effective catalysts for scarce and costly metal ones is currently among the major targets of sustainable chemistry. Herein we report the synthesis of N-, S-, and P-tridoped, worst-weed-derived carbon nanorings (WWCNRs) that can serve as metal-free and selective electrocatalyst for the oxygen reduction reaction (ORR). The WWCNRs are synthesized via activation-free polymerization of worst weed, Eclipta prostrate, and then removal of the metallic residues by HCl. The WWCNRs exhibit good catalytic activity towards the 4 electron-transfer ORR with low onset potential and high kinetic limiting current density, along with high selectivity (introducing CO, the sample loses onl

Extracellular Matrix Protein Tenascin C Increases Phagocytosis Mediated by CD47 Loss of Function in Glioblastoma.

Glioblastomas (GBM) are highly infiltrated by myeloid-derived innate immune cells that contribute to the immunosuppressive nature of the brain tumor microenvironment (TME). CD47 has been shown to mediate immune evasion, as the CD47-SIRPα axis prevents phagocytosis of tumor cells by macrophages and other myeloid cells. In this study, we established CD47 homozygous deletion (CD47-/-) in human and mouse GBM cells and investigated the impact of eliminating the "don't eat me" signal on tumor growth and tumor-TME interactions. CD47 knockout (KO) did not significantly alter tumor cell proliferation in vitro but significantly increased phagocytosis of tumor cells by macrophages in cocultures. Compared with CD47 wild-type xenografts, orthotopic xenografts derived from CD47-/- tumor cells grew significantly slower with enhanced tumor cell phagocytosis and increased recruitment of M2-like tumor-associated microglia/macrophages (TAM). CD47 KO increased tumor-associated extracellular matrix protein tenascin C (TNC) in xenografts, which was further examined in vitro. CD47 loss of function upregulated TNC expression in tumor cells via a Notch pathway-mediated mechanism. Depletion of TNC in tumor cells enhanced the growth of CD47-/- xenografts in vivo and decreased the number of TAM. TNC knockdown also inhibited phagocytosis of CD47-/- tumor cells in cocultures. Furthermore, TNC stimulated release of proinflammatory factors including TNFα via a Toll-like receptor 4 and STAT3-dependent mechanism in human macrophage cells. These results reveal a vital role for TNC in immunomodulation in brain tumor biology and demonstrate the prominence of the TME extracellular matrix in affecting the antitumor function of brain innate immune cells. SIGNIFICANCE: These findings link TNC to CD47-driven phagocytosis and demonstrate that TNC affects the antitumor function of brain TAM, facilitating the development of novel innate immune system-based therapies for brain tumors

Pipeline for precise insoluble matrisome coverage in tissue extracellular matrices

The extracellular matrix (ECM) is assembled by hundreds of proteins orchestrating tissue patterning and surrounding cell fates via the mechanical–biochemical feedback loop. Aberrant ECM protein production or assembly usually creates pathological niches eliciting lesions that mainly involve fibrogenesis and carcinogenesis. Yet, our current knowledge about the pathophysiological ECM compositions and alterations in healthy or diseased tissues is limited since the methodology for precise insoluble matrisome coverage in the ECM is a “bottleneck.” Our current study proposes an enhanced sodium dodecyl sulfonate (E-SDS) workflow for thorough tissue decellularization and an intact pipeline for the accurate identification and quantification of highly insoluble ECM matrisome proteins. We tested this pipeline in nine mouse organs and highlighted the full landscape of insoluble matrisome proteins in the decellularized ECM (dECM) scaffolds. Typical experimental validations and mass spectrometry (MS) analysis confirmed very little contamination of cellular debris remaining in the dECM scaffolds. Our current study will provide a low-cost, simple, reliable, and effective pipeline for tissue insoluble matrisome analysis in the quest to comprehend ECM discovery proteomic studies

Incidence of Readmission Following Pediatric Hand Surgery: An Analysis of 6600 Patients

BACKGROUND: Quality in surgical outcomes is frequently assessed by the 30-day readmission rate. There are limited data available in the published literature regarding readmission rates following pediatric hand surgery. This study aims to identify factors associated with an increased risk of readmission following hand surgery in a pediatric population. METHODS: The 2012-2017 National Surgical Quality Improvement Project - Pediatric (NSQIP-P) databases were queried for pediatric patients who underwent procedures with hand-specific current procedural terminology (CPT) codes. The primary outcome was readmission. RESULTS: A total of 6600 pediatric patients were identified and included in the analysis. There were 45 patients who were readmitted in the study cohort, giving an overall readmission rate of 0.68%. The median time to readmission was 12 (IQR 5-20) days. On univariate analysis, factors associated with readmission included younger age, smaller size, prematurity, higher American Society of Anesthesiologists (ASA) class, inpatient admission at index operation, and longer anesthesia and operative times. Complex syndactyly repair was also associated with higher readmission rates. On multivariate analysis, ASA class 3 or 4 and inpatient surgery remained significant predictors of readmission. CONCLUSIONS: Overall, pediatric hand surgery is associated with a very low risk of 30-day readmission. Higher ASA class and inpatient surgery increase patients\u27 risk for readmission. In particular, complex syndactyly repair is associated with a higher risk of readmission than other hand procedures. This information is useful in surgical planning and preoperative counseling of parents