9,622 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Modeling networks of spiking neurons as interacting processes with memory of variable length
We consider a new class of non Markovian processes with a countable number of
interacting components, both in discrete and continuous time. Each component is
represented by a point process indicating if it has a spike or not at a given
time. The system evolves as follows. For each component, the rate (in
continuous time) or the probability (in discrete time) of having a spike
depends on the entire time evolution of the system since the last spike time of
the component. In discrete time this class of systems extends in a non trivial
way both Spitzer's interacting particle systems, which are Markovian, and
Rissanen's stochastic chains with memory of variable length which have finite
state space. In continuous time they can be seen as a kind of Rissanen's
variable length memory version of the class of self-exciting point processes
which are also called "Hawkes processes", however with infinitely many
components. These features make this class a good candidate to describe the
time evolution of networks of spiking neurons. In this article we present a
critical reader's guide to recent papers dealing with this class of models,
both in discrete and in continuous time. We briefly sketch results concerning
perfect simulation and existence issues, de-correlation between successive
interspike intervals, the longtime behavior of finite non-excited systems and
propagation of chaos in mean field systems
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