Diverse super-resolution with pretrained deep hiererarchical VAEs

International audienceImage super-resolution is a one-to-many problem, but most deep-learning based methods only provide one single solution to this problem. In this work, we tackle the problem of diverse super-resolution by reusing VD-VAE, a state-of-the art variational autoencoder (VAE). We find that the hierarchical latent representation learned by VD-VAE naturally separates the image low-frequency information, encoded in the latent groups at the top of the hierarchy, from the image high-frequency details, determined by the latent groups at the bottom of the latent hierarchy. Starting from this observation, we design a super-resolution model exploiting the specific structure of VD-VAE latent space. Specifically, we train an encoder to encode low-resolution images in the subset of VD-VAE latent space encoding the low-frequency information, and we combine this encoder with VD-VAE generative model to sample diverse super-resolved version of a low-resolution input. We demonstrate the ability of our method to generate diverse solutions to the super-resolution problem on face super-resolution with upsampling factors x4, x8, and x16

V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices

International audienceWe present V-Mail, a framework of cross-platform applications, interactive techniques, and communication protocols for improved multi-person correspondence about spatial 3D datasets. Inspired by the daily use of e-mail, V-Mail seeks to enable a similar style of rapid, multi-person communication accessible on any device; however, it aims to do this in the new context of spatial 3D communication, where limited access to 3D graphics hardware typically prevents such communication. The approach integrates visual data storytelling with data exploration, spatial annotations, and animated transitions. V-Mail "data stories" are exported in a standard video file format to establish a common baseline level of access on (almost) any device. The V-Mail framework also includes a series of complementary client applications and plugins that enable different degrees of story co-authoring and data exploration, adjusted automatically to match the capabilities of various devices. A lightweight, phone-based V-Mail app makes it possible to annotate data by adding captions to the video. These spatial annotations are then immediately accessible to team members running high-end 3D graphics visualization systems that also include a V-Mail client, implemented as a plugin. Results and evaluation from applying V-Mail to assist communication within an interdisciplinary science team studying Antarctic ice sheets confirm the utility of the asynchronous, cross-platform collaborative framework while also highlighting some current limitations and opportunities for future work

Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation

International audienceWe present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specific data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from five different scientific communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specific, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/

A new PET for Data Collection via Forms with Data Minimization, Full Accuracy and Informed Consent

International audienceThe advent of privacy laws and principles such as data minimization and informed consent are supposed to protect citizens from over-collection of personal data. Nevertheless, current processes, mainly through filling forms are still based on practices that lead to over-collection. Indeed, any citizen wishing to apply for a benefit (or service) will transmit all their personal data involved in the evaluation of the eligibility criteria. The resulting problem of over-collection affects millions of individuals, with considerable volumes of information collected. If this problem of compliance concerns both public and private organizations (e.g., social services, banks, insurance companies), it is because it faces non-trivial issues, which hinder the implementation of data minimization by developers. In this paper, we propose a new modeling approach that enables data minimization and informed choices for the users, for any decision problem modeled using classical logic, which covers a wide range of practical cases. Our data minimization solution uses game theoretic notions to explain and quantify the privacy payoff for the user. We show how our algorithms can be applied to practical cases study as a new PET for minimal, fully accurate (all due services must be preserved) and informed data collection

Detection of common subtrees with identical label distribution

40 pagesInternational audienceFrequent pattern mining is a relevant method to analyse structured data, like sequences, trees or graphs. It consists in identifying characteristic substructures of a dataset. This paper deals with a new type of patterns for tree data: common subtrees with identical label distribution. Their detection is far from obvious since the underlying isomorphism problem is graph isomorphism complete. An elaborated search algorithm is developed and analysed from both theoretical and numerical perspectives. Based on this, the enumeration of patterns is performed through a new lossless compression scheme for trees, called DAG-RW, whose complexity is investigated as well. The method shows very good properties, both in terms of computation times and analysis of real datasets from the literature. Compared to other substructures like topological subtrees and labelled subtrees for which the isomorphism problem is linear, the patterns found provide a more parsimonious representation of the data

Route Selection in Low-cost Participatory Mobile Sensing of Air Quality

International audienceMobile crowdsensing is a powerful paradigm that takes advantage of low-cost sensors and population density. It allows for large-scale deployments and collection of extensive data, offering a great advantage in multiple fields such as air pollution monitoring, which is a major concern worldwide. Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route selection/planning solutions to better guide the crowd through the area of interest and maximize the quality of monitoring. In this paper, we propose two route selection algorithms that take into consideration the low accuracy of low-cost sensors in order to find the most informative routes. The similarity-based route selection algorithm aims to maximize spatial coverage by reducing overlaps between participant routes. The cluster-based route selection takes advantage of hierarchical clustering to build groups of similar points of the map according to explanatory variables. We compare the proposed solutions to baseline route selection algorithms, and the results show that our solutions allow for a better estimation while being efficient in terms of travel distance

Second order perturbation theory of two-scale systems in fluid dynamics

In the present paper we study slow-fast systems of coupled equations from fluid dynamics, where the fast component is perturbed by additive noise. We prove that, under a suitable limit of infinite separation of scales, the slow component of the system converges in law to a solution of the initial equation perturbed with transport noise, and subject to the influence of an additional Itō-Stokes drift. The obtained limit equation is very similar to turbulent models derived heuristically. Our results apply to the Navier-Stokes equations in dimension $d=2,3$; the Surface Quasi-Geostrophic equations in dimension $d=2$; and the Primitive equations in dimension $d=2,3$

Polyglot Software Development: Wait, What?

International audienceThe notion of polyglot software development refers to the fact that most software projects nowadays rely on multiple languages to deal with widely different concerns, from core business concerns to user interface, security, and deployment concerns among many others. Many different wordings around this notion have been proposed in the literature, with little understanding of their differences. In this article, we propose a concise and unambiguous definition of polyglot software development including a conceptual model and its illustration on a well-known, open-source project. We further characterize the techniques used for the specification and operationalization of polyglot software development with a feature model, concentrating on polyglot programming. We conclude the article outlining the many challenges and perspectives raised by polyglot software development

The effect of light intensity on microalgae biofilm structures and physiology under continuous illumination

Abstract Biofilm-based microalgae technology improves productivity, reduces energy consumption and facilitates harvesting. However, the effect of light received less attention than for planktonic cultures. This work assessed the effect of Photon Flux Density (PFD) on Chlorella vulgaris biofilm dynamics (structure, physiology, activity). Microalgae biofilms were cultivated in a flow-cell system with PFD from 100 to 500 μmol·m-2·s-1. In the first stage of biofilm development, uniform cell distribution was observed on the substratum exposed to 100 μmol·m-2·s-1 while cell clusters were formed under 500 μmol·m-2·s-1. Though similar specific growth rate in exponential phase (ca. 0.3 d-1) was obtained under all light intensities, biofilm cells at 500 μmol·m-2 ·s-1 seem to be ultimately photoinhibited (lower final cell density). Chlorella vulgaris showed a remarkable capability to cope with high light. This was marked for sessile cells at 300 μmol·m-2·s-1, which reduce very rapidly (in two days) their chlorophyll-a content, most probably to reduce photodamage, while maintaining a high final cell density. Besides cellular physiological adjustments, our data demonstrate that cellular spatial organization is light-dependent

Efficient and robust estimation of many-qubit Hamiltonians

29 pages, 3 figuresInternational audienceCharacterizing the interactions and dynamics of quantum mechanical systems is an essential task in the development of quantum technologies. We propose a novel protocol for estimating the underlying Hamiltonian dynamics and Markovian noise of a multi-qubit device. It is based on the efficient estimation of the time-derivatives of few qubit observables using polynomial interpolation. For finite range dynamics, our protocol exponentially improves the necessary time-resolution of the measurements and quadratically reduces the overall sample complexity compared to previous approaches. Furthermore, we show that our protocol can characterize the dynamics of systems with algebraically decaying interactions. The implementation of the protocol requires only the preparation of product states and single-qubit measurements, rendering it applicable for the characterization of both current and future quantum devices