DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics

Real-Time Crowdsourcing of Detailed Soccer Data

International audienceThis article explores how spectators of a live soccer game can collect detailed data while watching the game. Our motivation arouse from the lack of free detailed sport data, contrasting with the large amount of simple statistics collected for every popular games and available on the web. Assuming many spectators carry a smart phone during a game, we implemented a series of input interfaces for collecting data in real time. In a user study, we asked participants to use those interfaces to perform tracking tasks such as locating players in the field, qualifying ball passes, and naming the player with ball while watching a video clip of a real soccer game. Our two main results are 1) the crowd can collect detailed-and fairly complex-data in real-time with reasonable quality while each participant is assigned a simple task, and 2) a set of design implications for crowd-powered interfaces to collect live sport data. We also discuss the use of such data into a system we developed to visualize soccer phases, and the design implications coming with the visual communication of missing and uncertain detailed data

How Transferable are Reasoning Patterns in VQA?

Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing high-level reasoning. Classical methods address this by removing biases from training data, or adding branches to models to detect and remove biases. In this paper, we argue that uncertainty in vision is a dominating factor preventing the successful learning of reasoning in vision and language problems. We train a visual oracle and in a large scale study provide experimental evidence that it is much less prone to exploiting spurious dataset biases compared to standard models. We propose to study the attention mechanisms at work in the visual oracle and compare them with a SOTA Transformer-based model. We provide an in-depth analysis and visualizations of reasoning patterns obtained with an online visualization tool which we make publicly available (https://reasoningpatterns.github.io). We exploit these insights by transferring reasoning patterns from the oracle to a SOTA Transformer-based VQA model taking standard noisy visual inputs via fine-tuning. In experiments we report higher overall accuracy, as well as accuracy on infrequent answers for each question type, which provides evidence for improved generalization and a decrease of the dependency on dataset biases

Nouvelles cartes isochrones pour améliorer la mobilité urbaine

Journées Visu 201

Generalizing email messages digests

International audienceAn email digest is a message that results from the combination of other messages. Mailing list management systems implement digests to let subscribers reduce their email messages frequency. In this paper we address the issue of generalizing this digest technique for any message (i.e. not only issued from mailing lists). By generalizing we mean creating new message combinations while 1) keeping an email centric approach, and 2) generating a compact visualization to assist a user task. We implemented a preliminary prototype as a webmail and we will describe a series of digests providing users multiple visualizations in the context of a meeting planning by email

Visualisation localisée en mouvement pour la natation

International audienceCompetitive sports coverage increasingly includes information on athlete or team statistics and records. Sports video coverage has traditionally embedded representations of this data in fixed locations on the screen, but more recently also attached representations to athletes or other targets in motion. These publicly used representations so far have been rather simple and systematic investigations of the research space of embedded visualizations in motion are still missing. Here we report on our preliminary research in the domain of professional and amateur swimming. We analyzed how visualizations are currently added to the coverage of Olympics swimming competitions and then plan to derive a design space for embedded data representations for swimming competitions. We are currently conducting a crowdsourced survey to explore which kind of swimming-related data general audiences are interested in, in order to identify opportunities for additional visualizations to be added to swimming competition coverage

Reflections on Visualization in Motion for Fitness Trackers

International audienceIn this paper, we reflect on our past work towards understanding how to design visualizations for fitness trackers that are used in motion. We have coined the term "visualization in motion" for visualizations that are used in the presence of relative motion between a viewer and the visualization. Here, we describe how visualization in motion is relevant to sports scenarios. We also provide new data on current smartwatch visualizations for sports and discuss future challenges for visualizations in motion for fitness trackers

Research response to coronavirus disease 2019 needed better coordination and collaboration: a living mapping of registered trials

Objectives: Researchers worldwide are actively engaging in research activities to search for preventive and therapeutic interventions against coronavirus disease 2019 (COVID-19). Our aim was to describe the planning of randomized controlled trials (RCTs) in terms of timing related to the course of the COVID-19 epidemic and research question evaluated. Study Design and Setting: We performed a living mapping of RCTs registered in the WHO International Clinical Trials Registry Platform. We systematically search the platform every week for all RCTs evaluating preventive interventions and treatments for COVID-19 and created a publicly available interactive mapping tool at https://covid-nma.com to visualize all trials registered. Results: By August 12, 2020, 1,568 trials for COVID-19 were registered worldwide. Overall, the median ([Q1–Q3]; range) delay between the first case recorded in each country and the first RCT registered was 47 days ([33–67]; 15–163). For the 9 countries with the highest number of trials registered, most trials were registered after the peak of the epidemic (from 100% trials in Italy to 38% in the United States). Most trials evaluated treatments (1,333 trials; 85%); only 223 (14%) evaluated preventive strategies and 12 postacute period intervention. A total of 254 trials were planned to assess different regimens of hydroxychloroquine with an expected sample size of 110,883 patients. Conclusion: This living mapping analysis showed that COVID-19 trials have relatively small sample size with certain redundancy in research questions. Most trials were registered when the first peak of the pandemic has passed

Exploration Visuelle de Données Spatio-Temporelles Brutes

National audienceLes données spatio-temporelles sont omniprésentes, et pour les comprendre dès leur collecte, il est nécessaire de développer des outils agnostiquesvis à vis de leur type, volume et distribution. Nous proposons l’utilisation de graphiques standards (histogrammes, scatterplots, etc.) coordonnés pour l’analyse visuelle de ces données à ce stade très en amont sans hypothèse initiale. Ces graphiques ont la particularité de ne pas être dépendant des caractéristiques du jeu de données et passent à l’échelle facilement. Nous montrons un exemple d’implémentation ce type d’interface appliqué à des centaines de milliers de trajectoires aériennes de manière dynamique et dans le navigateur web