Portail HAL des publications du LIRMM
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TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors
No full textInternational audienceCharacterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed
A Data-Driven Model Selection Approach to Spatio-Temporal Prediction
No full textInternational audienceSpatio-temporal Predictive Queries encompass a spatio tem- poral constraint, defining a region, a target variable, and an evaluation metric. The output of such queries presents the future values for the tar- get variable computed by predictive models at each point of the spatio- temporal region. Unfortunately, especially for large spatio-temporal do- mains with millions of points, training temporal models at each spatial domain point is prohibitive. In this work, we propose a data-driven ap- proach for selecting pre-trained temporal models to be applied at each query point. The chosen approach applies a model to a point according to the training and input time series similarity. The approach avoids train- ing a different model for each domain point, saving model training time. Moreover, it provides a technique to decide on the best-trained model to be applied to a point for prediction. In order to assess the applicability of the proposed strategy, we evaluate a case study for temperature fore- casting using historical data and auto-regressive models. Computational experiments show that the proposed approach, compared to the base- line, achieves equivalent predictive performance using a composition of pre-trained models at a fraction of the total computational cost
Investigation of Single-Event Effects for Space Applications: Instrumentation for In-Depth System Monitoring
No full textInternational audienceIonizing radiation induces the degradation of electronic systems. For memory devices, this phenomenon is often observed as the corruption of the stored data and, in some cases, the occurrence of sudden increases in current consumption during the operation. In this work, we propose enhanced experimental instrumentation to perform in-depth Single-Event Effects (SEE) monitoring and analysis of electronic systems. In particular, we focus on the Single-Event Latch-up (SEL) phenomena in memory devices, in which current monitoring and control are required for testing. To expose the features and function of the proposed instrumentation, we present results for a case study of an SRAM memory that has been used on-board PROBA-V ESA satellite. For this study, we performed experimental campaigns in two different irradiation facilities with protons and heavy ions, demonstrating the instrumentation capabilities, such as synchronization, high sampling rate, fast response time, and flexibility. Using this instrumentation, we could report the cross section for the observed SEEs and further investigate their correlation with the observed current behavior. Notably, it allowed us to identify that 95% of Single-Event Functional Interrupts (SEFIs) were triggered during SEL event
PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers
No full textInternational audienceComputer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery
Computing the worst-case due dates violations with budget uncertainty
No full textInternational audienceWe study the problem of maximizing the violation of due dates when considering either the total violation, or the number of jobs that are tardy. We consider classical completion times and a variant useful in heuristics. The four problems arise when solving (exactly or heuristically) robust scheduling problems with release and due dates/deadlines and processing time uncertainty, and also routing problems with (soft) time windows and travel time uncertainty. We provide polynomial dynamic programming algorithms for the four problems
Mapping global orchid assemblages with deep learning provides novel conservation insights
No full textInternational audienceAlthough increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess and map at kilometre resolution the conservation status of the iconic orchid family, and discuss the insights conveyed at multiple scales. We introduce a new Deep Species Distribution Model trained on 1 M occurrences of 14 K orchid species to predict their assemblages at global scale and at kilometre resolution. We propose two main indicators of the conservation status of the assemblages: (i) the proportion of threatened species, and (ii) the status of the most threatened species in the assemblage. We show and analyze the variation of these indicators at World scale and in relation to currently protected areas in Sumatra island. Global and interactive maps available online show the indicators of conservation status of orchid assemblages, with sharp spatial variations at all scales. The highest level of threat is found at Madagascar and the neighbouring islands. In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island. Recent advances in deep learning enable reliable mapping of the conservation status of species assemblages on a global scale. As an umbrella taxon, orchid family provides a reference for identifying vulnerable ecosystems worldwide, and prioritising conservation actions both at international and local levels
Companion Paper: Moderate Exponential-time Quantum Dynamic Programming Across the Subsets for Scheduling Problems
No full textThis work is the companion paper of "Moderate Exponential-time Quantum Dynamic Programming Across the Subsets for Scheduling Problems
The -binding function of -directional segment graphs
No full text11 pages, 3 figuresGiven a positive integer , the class -DIR is defined as all those intersection graphs formed from a finite collection of line segments in having at most slopes. Since each slope induces an interval graph, it easily follows for every in -DIR with clique number at most that the chromatic number of is at most . We show for every even value of how to construct a graph in -DIR that meets this bound exactly. This partially confirms a conjecture of Bhattacharya, Dvo\v{r}\'ak and Noorizadeh. Furthermore, we show that the -binding function of -DIR is for even and for odd. This refutes said conjecture of Bhattacharya, Dvo\v{r}\'ak and Noorizadeh
Investigation on Radiation-Induced Latch-Ups in COTS SRAM Memories On-Board PROBA-V
No full textInternational audienceThis work investigates the flight behavior of Static Random-Access Memories (SRAMs) on board the PROBA-V satellite. During the mission, unexpected error rates were observed for redundant modules that used Commercial Off-The-Shelf (COTS) SRAMs. After undergoing an initial assessment, it was inferred that these errors were caused by Single-Event Latch-ups (SELs). This result led to a broader study on Single-Event Effects (SEEs) in these SRAM devices and their impact on the PROBA-V operation. Therefore, we proposed an experimental approach for evaluating and comparing the phenomena with the available PROBA-V flight data. Three experiments were performed: two irradiation campaigns, using heavy ions and protons, and laser testing. For that, a dedicated experimental setup was developed to enable the evaluation of specific test conditions and the discussion of hypotheses. The main results are reported, compared, and discussed. As an outcome of this study, we proposed an explanation for the observed behavior due to distinct environmental conditions of the redundant modules containing the SRAMs, notably temperature and satellite shielding
A novel intelligent RISE feedback control of autonomous tethered underwater vehicles: Design & real-time experiments
No full textInternational audienceThis paper deals with the tracking control problem of small autonomous tethered underwater vehicles. It proposes a new extended robust integral of the sign of the error (RISE) feedback control. The proposed RISE-based extension benefits from a fuzzy inference system to automatically and online tune the parameters of the RISE controller. The resulting intelligent control scheme is named Fuzzy RISE (FRISE) feedback control. Several real-time experimental scenarios, in different operating conditions, were conducted on Leonard underwater vehicle to demonstrate the efficiency and robustness of the proposed control scheme. It was also compared with some existing controllers from the literature to show its performances