143,901 research outputs found
Pedestrian Trajectory Prediction with Structured Memory Hierarchies
This paper presents a novel framework for human trajectory prediction based
on multimodal data (video and radar). Motivated by recent neuroscience
discoveries, we propose incorporating a structured memory component in the
human trajectory prediction pipeline to capture historical information to
improve performance. We introduce structured LSTM cells for modelling the
memory content hierarchically, preserving the spatiotemporal structure of the
information and enabling us to capture both short-term and long-term context.
We demonstrate how this architecture can be extended to integrate salient
information from multiple modalities to automatically store and retrieve
important information for decision making without any supervision. We evaluate
the effectiveness of the proposed models on a novel multimodal dataset that we
introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from
a radar system and a CCTV camera system installed in a public place. The
performance is also evaluated on the publicly available New York Grand Central
pedestrian database. In both settings, the proposed models demonstrate their
capability to better anticipate future pedestrian motion compared to existing
state of the art.Comment: To appear in ECML-PKDD 201
Entrograms and coarse graining of dynamics on complex networks
Using an information theoretic point of view, we investigate how a dynamics
acting on a network can be coarse grained through the use of graph partitions.
Specifically, we are interested in how aggregating the state space of a Markov
process according to a partition impacts on the thus obtained lower-dimensional
dynamics. We highlight that for a dynamics on a particular graph there may be
multiple coarse grained descriptions that capture different, incomparable
features of the original process. For instance, a coarse graining induced by
one partition may be commensurate with a time-scale separation in the dynamics,
while another coarse graining may correspond to a different lower-dimensional
dynamics that preserves the Markov property of the original process. Taking
inspiration from the literature of Computational Mechanics, we find that a
convenient tool to summarise and visualise such dynamical properties of a
coarse grained model (partition) is the entrogram. The entrogram gathers
certain information-theoretic measures, which quantify how information flows
across time steps. These information theoretic quantities include the entropy
rate, as well as a measure for the memory contained in the process, i.e., how
well the dynamics can be approximated by a first order Markov process. We use
the entrogram to investigate how specific macro-scale connection patterns in
the state-space transition graph of the original dynamics result in desirable
properties of coarse grained descriptions. We thereby provide a fresh
perspective on the interplay between structure and dynamics in networks, and
the process of partitioning from an information theoretic perspective. We focus
on networks that may be approximated by both a core-periphery or a clustered
organization, and highlight that each of these coarse grained descriptions can
capture different aspects of a Markov process acting on the network.Comment: 17 pages, 6 figue
Applied type system
We present a type system that can effectively facilitate the use of types in capturing invariants in stateful programs that may involve (sophisticated) pointer manipulation. With its root in a recently developed framework Applied Type System (ATS), the type system imposes a level of abstraction on program states by introducing a novel notion of recursive stateful views and then relies on a form of linear logic to reason about such views. We consider the design and then the formalization of the type system to constitute the primary contribution of the paper. In addition, we mention a prototype implementation of the type system and then give a variety of examples that attests to the practicality of programming with recursive stateful views.National Science Foundation (CCR-0224244, CCR-0229480
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