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CAD modeling, despite being the industry-standard, remains restricted to usage by skilled practitioners due to two key barriers. First, the user must be able to mentally parse a final shape into a valid sequence of supported CAD commands; and second, the user must be sufficiently conversant with CAD software packages to be able to execute the corresponding CAD commands. As a step towards addressing both these challenges, we present Free2CAD wherein the user can simply sketch the final shape and our system parses the input strokes into a sequence of commands expressed in a simplified CAD language. When executed, these commands reproduce the sketched object. Technically, we cast sketch-based CAD modeling as a sequence-to-sequence translation problem, for which we leverage the powerful Transformers neural network architecture. Given the sequence of pen strokes as input, we introduce the new task of grouping strokes that correspond to individual CAD operations. We combine stroke grouping with geometric fitting of the operation parameters, such that intermediate groups are geometrically corrected before being reused, as context, for subsequent steps in the sequence inference. Although trained on synthetically-generated data, we demonstrate that Free2CAD generalizes to sketches created from real-world CAD models as well as to sketches drawn by novice users. Code and data are at https://github.com/Enigma-li/Free2CAD.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available., This work was supported by the National Natural Science Foundation of China (41877506), the Fudan’s Wangdao Undergraduate Research Opportunities Program (18107), the Chinese Thousand Youth Talents Program, the PolEASIA-ANR project (ANR15-CE04-0005), and the Australia-China Centre for Air Quality Science and Management. J.P. and J.S. acknowledge financial support from the Catalan Government grant AGAUR-2020PANDE00117. P.C. acknowledges support from the European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project (ESRIN/4000123002/18/I-NB) and the Observation-based system for monitoring and verification of greenhouse gases project (VERIFY, grant 725546).

We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities between the steps industrial designers follow to draw 3D shapes, and the operations CAD modeling systems offer to create similar shapes. To overcome the strong ambiguity with parsing 2D sketches, we observe that in a sketching sequence, each step makes sense and can be interpreted in the context of what has been drawn before. In our system, this context corresponds to a partial CAD model, inferred in the previous steps, which we feed along with the input sketch to a deep neural network in charge of interpreting how the model should be modified by that sketch. Our deep network architecture then recognizes the intended CAD operation and segments the sketch accordingly, such that a subsequent optimization estimates the parameters of the operation that best fit the segmented sketch strokes. Since there exists no datasets of paired sketching and CAD modeling sequences, we train our system by generating synthetic sequences of CAD operations that we render as line drawings. We present a proof of concept realization of our algorithm supporting four frequently used CAD operations. Using our system, participants are able to quickly model a large and diverse set of objects, demonstrating Sketch2CAD to be an alternate way of interacting with current CAD modeling systems.

We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method., Comment: Accepted at IEEE VIS 2020 (Info VIS)

International audience; Incentive is crucial to the success of mobile crowd sensing (MCS) systems. Over the different manners of incentives, providing monetary rewards has been proved quite useful. However, existing monetary-based incentive studies (e.g., the reverse auction based methods) mainly encourage user participation, whereas sensing quality is often neglected. First, the budget setting is static and may not meet the sensing contexts or user anticipation. Second, they do not measure the quality of data contributed. Third, the design of most incentive schemes is quantity- or cost-focused and not quality-oriented. To address these issues, we propose a novel MCS incentive mechanism called TaskMe. An LBSN (location-based social network)-powered model is leveraged for dynamic budgeting and proper worker selection, and a combination of multi-facet quality measurements and a multi-payment-enhanced reverse auction scheme are used to improve sensing quality. Experiments on several user studies and the crawled dataset validate TaskMe's effectiveness

International audience; Self-supporting surfaces are widely used in contemporary architecture, but their design remains a challenging problem. This paper aims to provide a heuristic strategy for the design of complex self-supporting surfaces. In our method, non-uniform rational B-spline (NURBS) surfaces are used to describe the smooth geometry of the self-supporting surface. The equilibrium state of the surface is derived with membrane shell theory and Airy stresses within the surfaces are used as tunable variables for the proposed heuristic design strategy. The corresponding self-supporting shapes to the given stress states are calculated by the nonlinear isogeometric analysis (IGA) method. Our validation using analytic catenary surfaces shows that the proposed method finds the correct self-supporting shape with a convergence rate one order higher than the degree of the applied NURBS basis function. Tests on boundary conditions show that the boundary's influence propagates along the main stress directions in the surface. Various self-supporting masonry structures, including models with complex topology, are constructed using the presented method. Compared with existing methods such as thrust network analysis and dynamic relaxation, the proposed method benefits from the advantages of NURBS-based IGA, featuring smooth geometric description, good adaption to complex shapes and increased efficiency of computation.

In this work, three novel imidazolium-based ionic liquids containing aromatic group in the cation combined with bis(trifluoromethylsulfonyl)imide anion were synthesized and characterized. Their thermophysical properties namely, density, viscosity, thermal decomposition, glass transition, and heat capacity were measured at various temperatures and atmospheric pressure. In addition, physicochemical properties such as molar volume (Vm) and crystal energy (UPOT) were also determined from the obtained density data. Novel group contribution parameters were obtained using Gardas and Coutinho model aiming at providing simple method to predict density and viscosity of ionic liquids.

Impaired hippocampal synaptic plasticity contributes to cognitive impairment in Huntington’s disease (HD). However, the molecular basis of such synaptic plasticity defects is not fully understood. Combining live-cell nanoparticle tracking and super-resolution imaging, we show that AMPAR surface diffusion, a key player in synaptic plasticity, is disturbed in various rodent models of HD. We demonstrate that defects in the brain-derived neurotrophic factor (BDNF)–tyrosine receptor kinase B (TrkB) signaling pathway contribute to the deregulated AMPAR trafficking by reducing the interaction between transmembrane AMPA receptor regulatory proteins (TARPs) and the PDZ-domain scaffold protein PSD95. The disturbed AMPAR surface diffusion is rescued by the antidepressant drug tianeptine via the BDNF signaling pathway. Tianeptine also restores the impaired LTP and hippocampus-dependent memory in different HD mouse models. These findings unravel a mechanism underlying hippocampal synaptic and memory dysfunction in HD, and highlight AMPAR surface diffusion as a promising therapeutic target., Cognitive decline in Huntington’s disease (HD) may be due to impaired hippocampal synaptic plasticity. In this study the authors show that AMPA receptor surface diffusion, a key player in synaptic plasticity, is deregulated in multiple HD mouse models as a result of impaired BDNF signalling that underlies the memory deficits, and can be pharmacologically rescued.

International audience; We are delighted to present this special issue of World Wide Web on Mobile Crowdsourcing. Recently, with the rapid development of mobile Internet and mobile social networking techniques, the scope of crowd problem-solving systems using mobile devices has been broadened and the traditional Internet Crowdsourcing is evolving into a new paradigm, i.e., Mobile Crowdsourcing (MCS), which facilitates the increasing number of mobile device users to participate crowdsourcing tasks. As a result, quite a number of crowdsourcing tasks that are difficult to complete based on Internet crowdsourcing has now become feasible, e.g., monitoring pollution level or noise level at the city-scale, predicting the arrival time of buses, collecting the truth happenings after a disaster, etc.

International audience; Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd-powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging

International audience

One-step absolute solvent-free acid-catalyzed mechanical depolymerization of pine sawdust (PSD) and commercially available α-cellulose to water-soluble sugars was carried out using ball milling. For comparison purposes, the commonly reported “solvent-free” mechanocatalytic depolymerization of lignocellulose method, which normally involves three steps (acid impregnation in solvent, vacuum drying, and mechanical depolymerization of lignocellulose), was performed. The 3,5-dinitrosalicylic acid (DNS) method was used to measure the total reducing sugar (TRS) of the obtained sugar solution, and major monosaccharides in the solution were analyzed by capillary electrophoresis (CE). More than 90% of the PSD became water-soluble through milling. Furthermore, most of the PSD was converted into TRS in approximately 30 min, and the highest TRS yield obtained was 31%, based on the dry mass. Interestingly, the TRS solutions obtained from the processed PSD were much darker than those obtained from α-cellulose due to the chromophores that formed during the depolymerization of lignin.

This study performed a direct solvent-free acid-catalysed mechanical depolymerisation of fibre sludge to reducing sugars which involves one step of acid milling in a planetary mill. The common reported ‘solvent-free’ mechanocatalytic depolymerisation of lignocellulose which includes 1) acid impregnation, 2) vacuum evaporation and 3) mechanocatalytic depolymerisation was also performed as a reference. The major converted monosaccharides were determined by capillary electrophoresis and the results of total reducing sugar (TRS) yields were carried out based on the 3,5-dinitrosalicylic acid (DNS) method. The results showed that the TRS conversion of direct acid-catalysed mechanical depolymerisation of biomass is 35%, whereas, the commonly reported method yields 31% under the same milling conditions. In addition, the direct acid-catalysed mechanical depolymerisation simplifies the reaction procedures to one step and shortens the total reaction time. Moreover, the present study indicates that the frequency of the transferred energy which is caused by the collisions, acid concentration, milling time and moisture content of the sample are the major factors in influencing yield of converted TRS in acid-catalysed mechanical depolymerisation of fibre sludge.

We present a data structure for answering approximate shortest path queries in a planar subdivision from a fixed source. Let $\rho\geqslant1$ be a real number. Distances in each face of this subdivision are measured by a possibly asymmetric convex distance function whose unit disk is contained in a concentric unit Euclidean disk and contains a concentric Euclidean disk with radius $1/\rho$. Different convex distance functions may be used for different faces, and obstacles are allowed. Let $\varepsilon$ be any number strictly between 0 and 1. Our data structure returns a $(1+\varepsilon)$ approximation of the shortest path cost from the fixed source to a query destination in $O(\log\frac{\rho n}{\varepsilon})$ time. Afterwards, a $(1+\varepsilon)$-approximate shortest path can be reported in $O(\log n)$ time plus the complexity of the path. The data structure uses $O(\frac{\rho^2n^3}{\varepsilon^2}\log\frac{\rho n}{\varepsilon})$ space and can be built in $O(\frac{\rho^2n^3}{\varepsilon^2}(\log\frac{\rho n}{\varepsilon})^2)$ time. Our time and space bounds do not depend on any other parameter; in particular, they do not depend on any geometric parameter of the subdivision such as the minimum angle.

International audience

International audience; With the development of mobile sensing and mobile social networking techniques, mobile crowd sensing and computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characteristic features: it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and it presents the fusion of human and machine intelligence in both the sensing and computing processes. This article characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data

International audience; For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.

This thesis addresses the problem of modeling pronunciation variations in non-native English speech. In particular, it develops a computer assisted pronunciation learning (CAPL) system to assist speakers in Singapore to speak standard English. The dictionary of the CAPL system, also known as the lexicon, contains the sequences of sub-word units (usually phonemes) to describe how words are pronounced. However, it is often difficult to cover all the possible pronunciations. This work presents a method to improve a given initial lexicon to include new pronunciations that can explain the pronunciation variants of regional English accents in Singapore. The method learns pronunciation rules from an orthographically transcribed speech corpus to generate common pronunciation variants. All variants are then compiled into a compact pronunciation dictionary. The upgraded dictionary are then integrated into the CAPL system, where they are used to score the user's pronunciations. The work has three novel contributions. Firstly it constructs a Singapore English corpus, which is one of the few standard corpora for speech research on the regional accent. The corpus consists of sentences used in the standard LDC TIMIT corpus. Secondly, it learns pronunciation rules from the speech data using a combination of data-driven and knowledge-based approaches in pronunciation modeling. Thirdly, it designs a prototype pronunciation scoring algorithm to evaluate and score the goodness of pronunciation in the CAPL system. The simulation shows satisfactory performance in the proposed pronunciation scoring system. Master of Engineering (SCE)

Many natural phenomena consist of geometric elements with dynamic motions characterized by small scale repetitions over large scale structures, such as particles, herds, threads, and sheets. Due to their ubiquity, controlling the appearance and behavior of such phenomena is important for a variety of graphics applications. However, such control is often challenging; the repetitive elements are often too numerous for manual edit, while their overall structures are often too versatile for fully automatic computation. We propose a method that facilitates easy and intuitive controls at both scales: high-level structures through spatial-temporal output constraints (e.g. overall shape and motion of the output domain), and low-level details through small input exemplars (e.g. element arrangements and movements). These controls are suitable for manual specification, while the corresponding geometric and dynamic repetitions are suitable for automatic computation. Our system takes such user controls as inputs, and generates as outputs the corresponding repetitions satisfying the controls. Our method, which we call dynamic element textures , aims to produce such controllable repetitions through a combination of constrained optimization (satisfying controls) and data driven computation (synthesizing details). We use spatial-temporal samples as the core representation for dynamic geometric elements. We propose analysis algorithms for decomposing small scale repetitions from large scale themes, as well as synthesis algorithms for generating outputs satisfying user controls. Our method is general, producing a range of artistic effects that previously required disparate and specialized techniques.

International audience; The potential for using stroke gestures to enter, retrieve and select commands and text has been recently unleashed by the popularity of touchscreen devices. This monograph provides a state-of-the-art inte- grative review of a body of human-computer interaction research on stroke gestures. It begins with an analysis of the design dimensions of stroke gestures as an interaction medium. The analysis classifies gestures into analogue versus abstract gestures, gestures for com- mands versus for symbols, gestures with different orders of complexity, visual-spatial dependent and independent gestures, and finger versus stylus drawn gestures. Gesture interfaces such as the iOS interface, the Graffiti text entry method for Palm devices, marking menus, and the SHARK/ShapeWriter word-gesture keyboard, make different choices in this multi-dimensional design space. The main body of this work consists of reviewing and synthe- sizing some of the foundational studies in the literature on stroke gesture interaction, particularly those done by the authors in the last decade. The human performance factors covered include motor control complexity, visual and auditory feedback, and human memory capabil- ities in dealing with gestures. Based on these foundational studies this review presents a set of design principles for creating stroke gesture interfaces. These include making gestures analogous to physical effects or cultural conventions, keeping gestures simple and distinct, defining stroke gestures systematically, making them self-revealing, supporting appropriate levels of chunking, and facilitating progress from visually guided performance to recall-driven performance. The overall theme is on making learning gestures easier while designing for long-term efficiency. Important system implementation issues of stroke gesture interfaces such as gesture recognition algorithms and gesture design toolkits are also covered in this review. The monograph ends with a few call-to-action research topics.

International audience; The BioNLP Shared Task 2011, an information extraction task held over 6 months up to March 2011, met with community-wide participation, receiving 46 final submissions from 24 teams. Five main tasks and three supporting tasks were arranged, and their results show advances in the state of the art in fine-grained biomedical domain information extraction and demonstrate that extraction methods successfully generalize in various aspects.

International audience; Anisotropic triangle meshes are used for efficient approximation of surfaces and flow data in finite element analysis, and in these applications it is desirable to have as few obtuse triangles as possible to reduce the discretization error. We present a variational approach to suppressing obtuse triangles in anisotropic meshes. Specifically, we introduce a hexagonal Minkowski metric, which is sensitive to triangle orientation, to give a new formulation of the centroidal Voronoi tessellation (CVT) method. Furthermore, we prove several relevant properties of the CVT method with the newly introduced metric. Experiments show that our algorithm produces anisotropic meshes with much fewer obtuse triangles than using existing methods while maintaining mesh anisotropy

EUROGRAPHICS 2010 (full paper) - Session Rendering I; International audience; We present a real-time algorithm for rendering translucent objects of arbitrary shapes. We approximate the scattering of light inside the objects using the diffusion equation, which we solve on-the-fly using the GPU. Our algorithm is general enough to handle arbitrary geometry, heterogeneous materials, deformable objects and modifications of lighting, all in real-time. In a pre-processing step, we discretize the object into a regular 4-connected structure (quadgraph). Due to its regular connectivity, this structure is easily packed into a texture and stored on the GPU. At runtime, we use the quadgraph stored on the GPU to solve the diffusion equation, in real-time, taking into account the varying input conditions: incoming light, object material and geometry. We handle deformable objects, provided the deformation does not change the topological structure of the objects.

International audience; Existing solid texture synthesis algorithms generate a full volume of color content from a set of 2D example images. We introduce a new algorithm with the unique ability to restrict synthesis to a subset of the voxels, while enforcing spatial determinism. This is especially useful when texturing objects, since only a thick layer around the surface needs to be synthesized. A major difficulty lies in reducing the dependency chain of neighborhood matching, so that each voxel only depends on a small number of other voxels. Our key idea is to synthesize a volume from a set of pre-computed 3D candidates, each being a triple of interleaved 2D neighborhoods. We present an efficient algorithm to carefully select in a pre-process only those candidates forming consistent triples. This significantly reduces the search space during subsequent synthesis. The result is a new parallel, spatially deterministic solid texture synthesis algorithm which runs efficiently on the GPU. Our approach generates high resolution solid textures on surfaces within seconds. Memory usage and synthesis time only depend on the output textured surface area. The GPU implementation of our method rapidly synthesizes new textures for the surfaces appearing when interactively breaking or cutting objects.

ISBN 2-7261-1297 8; International audience; We present a novel active imaging approach that uses optimized wide band filtered illumination to obtain multispectral reflectance information. Our optimization algorithm utilizes light source and camera spectral information in order to maximize the signal strength and the robustness to noise. Through the use of active wide band illumination, our system can obtain material reflectance information in the presence of moderate (indoor) unknown ambient illumination. Our method is very simple and does not require special equipment. It can be used by professional and amateur photographers alike to obtain material properties and to synthesize captured scenes under arbitrary illumination.

In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.

International audience; Relief mosaics are collections of registered images that extend traditional mosaics by supporting motion parallax. A simple parallax interpolation algorithm based on computed correspondence information allows high quality blurfree and ghost-free mosaics to be created using images from moving hand-held cameras that would not be suitable for traditional mosaicing. The renderer can also display local parallax changes, giving a local but visually convincing illusion of depth. Moreover, relief mosaics can be used for approximate plenoptic modeling from hand-held cameras at lower spatial sampling rates than existing light-field methods. We present a fully automatic correspondence based construction system for relief mosaics, and show how they can be used in applications.

International audience; General SFM methods give poor results for images captured by constrained motions such as planar motion of concentric mosaics (CM). In this paper, we propose new SFM algorithms for both images captured by CM and composite mosaic images from CM. We first introduce 1D affine camera model for completing 1D camera models. Then we show that a 2D image captured by CM can be decoupled into two 1D images: one 1D projective and one 1D affine; a composite mosaic image can by rebinned into a calibrated 1D panorama projective camera. Finally we describe subspace reconstruction methods and demonstrate both in theory and experiments the advantage of the decomposition method over the general SFM methods by incorporating the constrained motion into the earliest stage of motion analysis.