5,064 research outputs found
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network
http://ieeexplore.ieee.org/Advances in wireless sensor networks and positioning technologies enable traffic management (e.g. routing traffic) that uses real-time data monitored by GPS-enabled cars. Location management has become an enabling technology in such application. The location modeling and trajectory prediction of moving objects are the fundamental components of location management in mobile locationaware applications. In this paper, we model the road network and moving objects in a graph of cellular automata (GCA), which makes full use of the constraints of the network and the stochastic behavior of the traffic. A simulation-based method based on graphs of cellular automata is proposed to predict future trajectories. Our technique strongly differs from the linear prediction method, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changes. The experiments, carried on two different datasets, show that the simulation-based prediction method provides higher accuracy than the linear prediction method
THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE
Durant les quatre derniĂšres dĂ©cennies, la miniaturisation a permis la diffusion Ă large Ă©chelle des ordinateurs, les rendant omniprĂ©sents. Aujourdâhui, le nombre dâobjets connectĂ©s Ă Internet ne cesse de croitre et cette tendance nâa pas lâair de ralentir. Ces objets, qui peuvent ĂȘtre des tĂ©lĂ©phones mobiles, des vĂ©hicules ou des senseurs, gĂ©nĂšrent de trĂšs grands volumes de donnĂ©es qui sont presque toujours associĂ©s Ă un contexte spatiotemporel. Le volume de ces donnĂ©es est souvent si grand que leur traitement requiert la crĂ©ation de systĂšme distribuĂ©s qui impliquent la coopĂ©ration de plusieurs ordinateurs. La capacitĂ© de traiter ces donnĂ©es revĂȘt une importance sociĂ©tale. Par exemple: les donnĂ©es collectĂ©es lors de trajets en voiture permettent aujourdâhui dâĂ©viter les em-bouteillages ou de partager son vĂ©hicule. Un autre exemple: dans un avenir proche, les donnĂ©es collectĂ©es Ă lâaide de gyroscopes capables de dĂ©tecter les trous dans la chaussĂ©e permettront de mieux planifier les interventions de maintenance Ă effectuer sur le rĂ©seau routier. Les domaines dâapplications sont par consĂ©quent nombreux, de mĂȘme que les problĂšmes qui y sont associĂ©s. Les articles qui composent cette thĂšse traitent de systĂšmes qui partagent deux caractĂ©ristiques clĂ©s: un contexte spatiotemporel et une architecture dĂ©centralisĂ©e. De plus, les systĂšmes dĂ©crits dans ces articles sâarticulent autours de trois axes temporels: le prĂ©sent, le passĂ©, et le futur. Les systĂšmes axĂ©s sur le prĂ©sent permettent Ă un trĂšs grand nombre dâobjets connectĂ©s de communiquer en fonction dâun contexte spatial avec des temps de rĂ©ponses proche du temps rĂ©el. Nos contributions dans ce domaine permettent Ă ce type de systĂšme dĂ©centralisĂ© de sâadapter au volume de donnĂ©e Ă traiter en sâĂ©tendant sur du matĂ©riel bon marchĂ©. Les systĂšmes axĂ©s sur le passĂ© ont pour but de faciliter lâaccĂšs a de trĂšs grands volumes donnĂ©es spatiotemporelles collectĂ©es par des objets connectĂ©s. En dâautres termes, il sâagit dâindexer des trajectoires et dâexploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particuliĂšrement denses, ce qui nâavait pas Ă©tĂ© fait auparavant. Enfin, les systĂšmes axĂ©s sur le futur utilisent les trajectoires passĂ©es pour prĂ©dire les trajectoires que des objets connectĂ©s suivront dans lâavenir. Nos contributions permettent de prĂ©dire les trajectoires suivies par des objets connectĂ©s avec une granularitĂ© jusque lĂ inĂ©galĂ©e. Bien quâimpliquant des domaines diffĂ©rents, ces contributions sâarticulent autour de dĂ©nominateurs communs des systĂšmes sous-jacents, ouvrant la possibilitĂ© de pouvoir traiter ces problĂšmes avec plus de gĂ©nĂ©ricitĂ© dans un avenir proche.
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During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is
ideally suited to learn representations for structured data and speed up the
exploration of chemical space. While convolutional neural networks have proven
to be the first choice for images, audio and video data, the atoms in molecules
are not restricted to a grid. Instead, their precise locations contain
essential physical information, that would get lost if discretized. Thus, we
propose to use continuous-filter convolutional layers to be able to model local
correlations without requiring the data to lie on a grid. We apply those layers
in SchNet: a novel deep learning architecture modeling quantum interactions in
molecules. We obtain a joint model for the total energy and interatomic forces
that follows fundamental quantum-chemical principles. This includes
rotationally invariant energy predictions and a smooth, differentiable
potential energy surface. Our architecture achieves state-of-the-art
performance for benchmarks of equilibrium molecules and molecular dynamics
trajectories. Finally, we introduce a more challenging benchmark with chemical
and structural variations that suggests the path for further work
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