35,259 research outputs found
Scene extraction in motion pictures
This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method
Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective
In this paper we present and analyze a queueing-theoretical model for
autonomous mobility-on-demand (MOD) systems where robotic, self-driving
vehicles transport customers within an urban environment and rebalance
themselves to ensure acceptable quality of service throughout the entire
network. We cast an autonomous MOD system within a closed Jackson network model
with passenger loss. It is shown that an optimal rebalancing algorithm
minimizing the number of (autonomously) rebalancing vehicles and keeping
vehicles availabilities balanced throughout the network can be found by solving
a linear program. The theoretical insights are used to design a robust,
real-time rebalancing algorithm, which is applied to a case study of New York
City. The case study shows that the current taxi demand in Manhattan can be met
with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi
fleet). Finally, we extend our queueing-theoretical setup to include congestion
effects, and we study the impact of autonomously rebalancing vehicles on
overall congestion. Collectively, this paper provides a rigorous approach to
the problem of system-wide coordination of autonomously driving vehicles, and
provides one of the first characterizations of the sustainability benefits of
robotic transportation networks.Comment: 10 pages, To appear at RSS 201
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
Reverse Engineering Approach to Quantum Electrodynamics
The S matrix of e--e scattering has the structure of a projection operator
that projects incoming separable product states onto entangled two-electron
states. In this projection operator the empirical value of the fine-structure
constant alpha acts as a normalization factor. When the structure of the
two-particle state space is known, a theoretical value of the normalization
factor can be calculated. For an irreducible two-particle representation of the
Poincare group, the calculated normalization factor matches Wyler's
semi-empirical formula for the fine-structure constant alpha. The empirical
value of alpha, therefore, provides experimental evidence that the state space
of two interacting electrons belongs to an irreducible two-particle
representation of the Poincare group.Comment: 12 pages, minor change
Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays
Microarrays have been developed that tile the entire nonrepetitive genomes of
many different organisms, allowing for the unbiased mapping of active
transcription regions or protein binding sites across the entire genome. These
tiling array experiments produce massive correlated data sets that have many
experimental artifacts, presenting many challenges to researchers that require
innovative analysis methods and efficient computational algorithms. This paper
presents a doubly stochastic latent variable analysis method for transcript
discovery and protein binding region localization using tiling array data. This
model is unique in that it considers actual genomic distance between probes.
Additionally, the model is designed to be robust to cross-hybridized and
nonresponsive probes, which can often lead to false-positive results in
microarray experiments. We apply our model to a transcript finding data set to
illustrate the consistency of our method. Additionally, we apply our method to
a spike-in experiment that can be used as a benchmark data set for researchers
interested in developing and comparing future tiling array methods. The results
indicate that our method is very powerful, accurate and can be used on a single
sample and without control experiments, thus defraying some of the overhead
cost of conducting experiments on tiling arrays.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS248 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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