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Demo : Dynamic Management of Wireless Sensor Networks using Virtual Objects and a Rule Engine
No full textInternational audienceIn this demonstration paper, we present the implementation of a virtualized Wireless Sensor Network (WSN) following a specific software stack called Virtual Object Stack (VOStack). This stack, proposed by the Horizon Europe (HE) NEPHELE project, promotes openness and interoperability in the Cloud-to-Edge-to-IoT continuum. The demonstration focuses on the management of a dynamic WSN through VOStack, the interactions between the physical objects and their virtual counterparts, and a rule engine enabling data processing and alert triggering.</div
Mapler: Assessing assembly quality in taxonomically-rich metagenomes sequenced with HiFi reads
No full textNational audienceEvaluating the quality of metagenome assemblies can be a challenging task, especially when no reference genome is available and when comparing samples at various taxonomic complexity and sequencing depth. A high quality assembly is expected not only to produce high quality bins, but also to be representative of most of the read sequences, especially in complex samples where algorithms struggle reconstructing low-abundance genomes. Recent studies showed a great improvement in number and quality of bins obtained with highly accurate PacBio HiFi long reads. It remains however to be assessed how much of the sample these bins represent, especially in highly complex environmental samples. There is therefore a need to use and compare other evaluation methods. We designed and implemented Mapler, a metagenomic assembly and evaluation pipeline with a primary focus on evaluating the quality of HiFi-based metagenome assemblies. It incorporates state-of-the-art tools for assembly, binning, and assembly evaluation. In addition to classifying assembly bins in classical quality categories according to their marker gene content and taxonomic assignment, Mapler analyzes the alignment of reads on contigs. To do so, it calculates the ratio of mapped reads and bases, and separately analyzes mapped and unmapped reads via their k-mer frequency, read quality, and taxonomic assignment
Statistical Collusion by Collectives on Learning Platforms
No full textCode available at: https://github.com/GauthierE/statistical-collusionAs platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain
Acceptability of Overground Wearable Powered Exoskeletons for People with Spinal Cord Injury: A Multicenter Qualitative Study
No full textInternational audienceBackground: Exoskeletons are used in rehabilitation centers for people with spinal cord injuries (SCI) due to the potential benefits they offer for locomotor rehabilitation. The acceptability of exoskeletons is crucial to promote rehabilitation and to ensure a successful implementation of this technology. The objective was to explore the acceptability of overground wearable powered exoskeleton used in rehabilitation among people with SCI. Methods: Fourteen individuals with SCI (9 men, mean [SD] age 47 years [14.8], a majority with traumatic and thoracic lesion (T6-T12)) who had utilized an exoskeleton in Canada or in France during their rehabilitation participated in a semi-structured interview. A thematic analysis using the theoretical framework of acceptability was carried out. Results: Participants were motivated to use an exoskeleton during their rehabilitation. They reported several perceived benefits to its use, including better walking pattern, increased endurance, and greater muscle mass. They also experienced mild pain, notable concentration demands, and fatigue. Most participants reported that using exoskeletons in their rehabilitation process was appropriate and relevant to them. Conclusions: Exoskeletons are generally well accepted by participants in this study. Adjustments in their use, such as conducting training sessions in obstacle-free environment and technological improvements to address the device’s restrictive characteristics, heaviness, and massiveness are however still needed
A Comprehensive Survey of Benchmarks for Improvement of Software's Non-Functional Properties
No full textInternational audienceDespite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about the representativeness of current benchmarks of real-world software. To address these issues and facilitate further research on improvement of non-functional properties of software, we conducted a comprehensive survey on the benchmarks used in the field thus far. We searched five major online repositories of research work, collecting 5499 publications (4066 unique), and systematically identified relevant papers to construct a rich and diverse corpus of 425 relevant studies. We find that execution time is the most frequently improved property in research work (63%), while multi-objective improvement is rarely considered (7%). Static approaches for improvement of non-functional software properties are prevalent (51%), with exploratory approaches (18% evolutionary and 15% non-evolutionary) increasingly popular in the last 10 years. Only 39% of the 425 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (63 papers). We also provide recommendations for future work, noting, for instance, lack of benchmarks for non-functional improvement that covers Python, JavaScript, or mobile devices. All the details regarding the 425 identified papers are available on our dedicated webpage: https://bloa.github.io/nfunc_survey
The digital labour of artificial intelligence in Latin America: a comparison of Argentina, Brazil, and Venezuela
No full textInternational audienceThe current hype around artificial intelligence (AI) conceals the substantial human intervention underlying its development. This article lifts the veil on the precarious and low-paid ‘data workers’ who prepare data to train, test, check, and otherwise support models in the shadow of globalized AI production. We use original questionnaire and interview data collected from 220 workers in Argentina (2021-22), 477 in Brazil (2023), and 214 in Venezuela (2021-22). We compare them to detect common patterns and reveal the specificities of data work in Latin America, while disclosing its role in AI production.We show that data work is intertwined with economic hardship, inequalities, and informality. Despite workers’ high educational attainment, disadvantage is widespread, though with cross-country disparities. By acknowledging the interconnections between AI development, data work, and globalized production, we provide insights for the regulation of AI and the future of work, aiming to achieve positive outcomes for all stakeholders
Cyclic functional causal models beyond unique solvability with a graph separation theorem
No full text33+16 pages. A companion paper by the same authors, focussing on cyclic quantum causal models has been submitted to the arXiv concurrently with primary class [quant-ph]. Comments are welcomeFunctional causal models (fCMs) specify functional dependencies between random variables associated to the vertices of a graph. In directed acyclic graphs (DAGs), fCMs are well-understood: a unique probability distribution on the random variables can be easily specified, and a crucial graph-separation result called the d-separation theorem allows one to characterize conditional independences between the variables. However, fCMs on cyclic graphs pose challenges due to the absence of a systematic way to assign a unique probability distribution to the fCM's variables, the failure of the d-separation theorem, and lack of a generalization of this theorem that is applicable to all consistent cyclic fCMs. In this work, we develop a causal modeling framework applicable to all cyclic fCMs involving finite-cardinality variables, except inconsistent ones admitting no solutions. Our probability rule assigns a unique distribution even to non-uniquely solvable cyclic fCMs and reduces to the known rule for uniquely solvable fCMs. We identify a class of fCMs, called averagely uniquely solvable, that we show to be the largest class where the probabilities admit a Markov factorization. Furthermore, we introduce a new graph-separation property, p-separation, and prove this to be sound and complete for all consistent finite-cardinality cyclic fCMs while recovering the d-separation theorem for DAGs. These results are obtained by considering classical post-selected teleportation protocols inspired by analogous protocols in quantum information theory. We discuss further avenues for exploration, linking in particular problems in cyclic fCMs and in quantum causality
Cyclic quantum causal modelling with a graph separation theorem
No full text41+41 pages. A companion paper by the same authors, focussing on cyclic classical (functional) causal models has been submitted to the arXiv concurrently with primary class [math.ST]. Comments are welcomeCausal modelling frameworks link observable correlations to causal explanations, which is a crucial aspect of science. These models represent causal relationships through directed graphs, with vertices and edges denoting systems and transformations within a theory. Most studies focus on acyclic causal graphs, where well-defined probability rules and powerful graph-theoretic properties like the d-separation theorem apply. However, understanding complex feedback processes and exotic fundamental scenarios with causal loops requires cyclic causal models, where such results do not generally hold. While progress has been made in classical cyclic causal models, challenges remain in uniquely fixing probability distributions and identifying graphseparation properties applicable in general cyclic models. In cyclic quantum scenarios, existing frameworks have focussed on a subset of possible cyclic causal scenarios, with graph-separation properties yet unexplored. This work proposes a framework applicable to all consistent quantum and classical cyclic causal models on finite-dimensional systems. We address these challenges by introducing a robust probability rule and a novel graph-separation property, p-separation, which we prove to be sound and complete for all such models. Our approach maps cyclic causal models to acyclic ones with post-selection, leveraging the post-selected quantum teleportation protocol. We characterize these protocols and their success probabilities along the way. We also establish connections between this formalism and other classical and quantum frameworks to inform a more unified perspective on causality. This provides a foundation for more general cyclic causal discovery algorithms and to systematically extend open problems and techniques from acyclic informational networks (e.g., certification of non-classicality) to cyclic causal structures and networks
Variations on a theme of Apéry
No full text14 pagesApéry's remarkable discovery of rapidly converging continued fractions with small coefficients for and has led to a flurry of important activity in an incredible variety of different directions. Our purpose is to show that modifications of Apéry's continued fractions can give interesting results including new rapidly convergent continued fractions for certain interesting constants
EMT-driven plasticity prospectively increases cell–cell variability to promote therapeutic adaptation in breast cancer
No full textInternational audienceCellular plasticity enables cancer cells to adapt non-genetically, thereby preventing therapeutic success. The epithelial-mesenchymal transition (EMT) is a type of plasticity linked to resistance and metastasis. However, its exact impact on population diversity and its dynamics under chemotherapy is unknown. We used single-cell transcriptomics to investigate phenotypic diversity dynamics upon treatment in two in vitro models of triple negative breast cancer (TNBC), where EMT-driven plasticity is either induced or spontaneously occurring. We report that EMT-driven plasticity confers higher phenotypic cell-cell variability (p < 0.001) while enriching for stem-like cells. Genetic and phenotypic cell-cell variability were not consistently correlated. High-plasticity populations displayed more pre-adapted cells before treatment (p = 0.03). In a population displaying spontaneous EMT and phenotypic variation, pre-adapted cells were a rare minority of high-scoring outliers whose expression patterns correlated with survival in TNBC patients subjected to chemotherapy (p = 0.03). Higher plasticity was not associated with a partial EMT status. Our results provide novel insights on how EMT-driven plasticity promotes a prospective diversification process increasing population phenotypic diversity, which can yield rare pre-adapted states before treatment. This highlights the need to tackle phenotypic diversity prior to treatment in high-plasticity tumours