Le « cadastre du risque avéré »

Le « cadastre des maladies environnementales » pointait, sur Google Maps, les micro-milieux ayant été cause directe d’au moins un cas avéré de maladie évitable : qui veut assainir le territoire doit savoir où se situe le risque. Produit d’un système ergonomique intégré à la pratique curative quotidienne d’un petit réseau de médecins volontaires, il restituait les informations collectées jour après jour selon une procédure rigoureuse mais non figée, dotée d’un langage et d’outils spécifiques, définie comme « parcours du soupçon à la connaissance des situations à risque. » Cas après cas, reliant le savoir scientifique universel aux connaissances « terroirisées » des personnes prises en charge, cette expérience a produit, entre 1994 et 2016, des résultats intéressants tant du point de vue de la connaissance et de l’assainissement des situations nocives que du développement d’une médecine moins consumériste.The “land register of environmental disease” pointed out, on Google Maps, the micro-environments having been the direct cause of at least one proven case of preventable disease: whoever wants to clean up the territory must know where the risk lies. The product of a system integrated into the daily curative practice of a small network of volunteer general physicians, it restituted the information collected day after day according to a rigorous but not fixed procedure, equipped with a specific language and tools, defined as “a path from suspicion to knowledge of risk situations.” Case after case, linking universal scientific knowledge to the “terroirized” knowledge of the people under care, this experiment produced, between 1994 and 2016, interesting results both from the point of view of the knowledge and remediation of harmful situations and the development of a less consumerist medicine

Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling

Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential of the conformal prediction framework to evaluate model performance and provide practical guidance for achieving formally guaranteed uncertainty bounds

Gradient strikes back: How filtering out high frequencies improves explanations

Recent years have witnessed an explosion in the development of novel prediction-based attribution methods, which have slowly been supplanting older gradient-based methods to explain the decisions of deep neural networks. However, it is still not clear why prediction-based methods outperform gradient-based ones. Here, we start with an empirical observation: these two approaches yield attribution maps with very different power spectra, with gradient-based methods revealing more high-frequency content than prediction-based methods. This observation raises multiple questions: What is the source of this high-frequency information, and does it truly reflect decisions made by the system? Lastly, why would the absence of high-frequency information in prediction-based methods yield better explainability scores along multiple metrics? We analyze the gradient of three representative visual classification models and observe that it contains noisy information emanating from high-frequencies. Furthermore, our analysis reveals that the operations used in Convolutional Neural Networks (CNNs) for downsampling appear to be a significant source of this high-frequency content -- suggesting aliasing as a possible underlying basis. We then apply an optimal low-pass filter for attribution maps and demonstrate that it improves gradient-based attribution methods. We show that (i) removing high-frequency noise yields significant improvements in the explainability scores obtained with gradient-based methods across multiple models -- leading to (ii) a novel ranking of state-of-the-art methods with gradient-based methods at the top. We believe that our results will spur renewed interest in simpler and computationally more efficient gradient-based methods for explainability

Evidence for an RNA Polymerization Activity in Axolotl and Xenopus Egg Extracts

We have previously reported a post-transcriptional RNA amplification observed in vivo following injection of in vitro synthesized transcripts into axolotl oocytes, unfertilized (UFE) or fertilized eggs. To further characterize this phenomenon, low speed extracts (LSE) from axolotl and Xenopus UFE were prepared and tested in an RNA polymerization assay. The major conclusions are: i) the amphibian extracts catalyze the incorporation of radioactive ribonucleotide in RNase but not DNase sensitive products showing that these products correspond to RNA; ii) the phenomenon is resistant to α-amanitin, an inhibitor of RNA polymerases II and III and to cordycepin (3′dAMP), but sensitive to cordycepin 5′-triphosphate, an RNA elongation inhibitor, which supports the existence of an RNA polymerase activity different from polymerases II and III; the detection of radiolabelled RNA comigrating at the same length as the exogenous transcript added to the extracts allowed us to show that iii) the RNA polymerization is not a 3′ end labelling and that iv) the radiolabelled RNA is single rather than double stranded. In vitro cell-free systems derived from amphibian UFE therefore validate our previous in vivo results hypothesizing the existence of an evolutionary conserved enzymatic activity with the properties of an RNA dependent RNA polymerase (RdRp)

Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis

Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the clusters of the learned representations is still limited. In this paper, we aim to elucidate the characterization from theoretical perspectives. To this end, we consider a kernel-based contrastive learning framework termed Kernel Contrastive Learning (KCL), where kernel functions play an important role when applying our theoretical results to other frameworks. We introduce a formulation of the similarity structure of learned representations by utilizing a statistical dependency viewpoint. We investigate the theoretical properties of the kernel-based contrastive loss via this formulation. We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework. We show a new upper bound of the classification error of a downstream task, which explains that our theory is consistent with the empirical success of contrastive learning. We also establish a generalization error bound of KCL. Finally, we show a guarantee for the generalization ability of KCL to the downstream classification task via a surrogate bound

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. Moreover, privacy concerns regarding the source also require a domain-invariant representation. In this work, we provide theoretical results that link domain invariant representations -- measured by the Wasserstein distance on the joint distributions -- to a practical semi-supervised learning objective based on a cross-entropy classifier and a novel domain critic. Quantitative experiments demonstrate that the proposed approach is indeed able to practically learn such an invariant representation (between two domains), and the latter also supports models with higher predictive accuracy on both domains, comparing favorably to existing techniques.Comment: 20 pages including appendix. Under Revie

The Spatial Release of Cognitive Load in Cocktail Party Is Determined by the Relative Levels of the Talkers

In a multi-talker situation, spatial separation between talkers reduces cognitive processing load: this is the “spatial release of cognitive load”. The present study investigated the role played by the relative levels of the talkers on this spatial release of cognitive load. During the experiment, participants had to report the speech emitted by a target talker in the presence of a concurrent masker talker. The spatial separation (0° and 120° angular distance in azimuth) and the relative levels of the talkers (adverse, intermediate, and favorable target-to-masker ratio) were manipulated. The cognitive load was assessed with a prefrontal functional near-infrared spectroscopy. Data from 14 young normal- hearing listeners revealed that the target-to-masker ratio had a direct impact on the spatial release of cognitive load. Spatial separation significantly reduced the prefrontal activity only for the intermediate target-to-masker ratio and had no effect on prefrontal activity for the favorable and the adverse target-to-masker ratios. Therefore, the relative levels of the talkers might be a key point to determine the spatial release of cognitive load and more specifically the prefrontal activity induced by spatial cues in multi- talker environments

A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation

In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs). These methods seek to discover intelligible visual 'concepts' buried within the complex patterns of ANN activations in two key steps: (1) concept extraction followed by (2) importance estimation. While these two steps are shared across methods, they all differ in their specific implementations. Here, we introduce a unifying theoretical framework that comprehensively defines and clarifies these two steps. This framework offers several advantages as it allows us: (i) to propose new evaluation metrics for comparing different concept extraction approaches; (ii) to leverage modern attribution methods and evaluation metrics to extend and systematically evaluate state-of-the-art concept-based approaches and importance estimation techniques; (iii) to derive theoretical guarantees regarding the optimality of such methods. We further leverage our framework to try to tackle a crucial question in explainability: how to efficiently identify clusters of data points that are classified based on a similar shared strategy. To illustrate these findings and to highlight the main strategies of a model, we introduce a visual representation called the strategic cluster graph. Finally, we present https://serre-lab.github.io/Lens, a dedicated website that offers a complete compilation of these visualizations for all classes of the ImageNet dataset