836,606 research outputs found
Perspectives on Bayesian Optimization for HCI
In this position paper we discuss optimization in the HCI
domain based on our experiences with Bayesian methods
for modeling and optimization of audio systems, including
challenges related to evaluating, designing, and optimizing
such interfaces. We outline and demonstrate how a
combined Bayesian modeling and optimization approach
provides a flexible framework for integrating various user
and content attributes, while also supporting model-based
optimization of HCI systems. Finally, we discuss current
and future research direction and applications, such as
inferring user needs and optimizing interfaces for
computer assisted teaching
Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation and Experimental Validation
Due to the increasing heterogeneity and deployment density of emerging
cellular networks, new flexible and scalable approaches for their modeling,
simulation, analysis and optimization are needed. Recently, a new approach has
been proposed: it is based on the theory of point processes and it leverages
tools from stochastic geometry for tractable system-level modeling, performance
evaluation and optimization. In this paper, we investigate the accuracy of this
emerging abstraction for modeling cellular networks, by explicitly taking
realistic base station locations, building footprints, spatial blockages and
antenna radiation patterns into account. More specifically, the base station
locations and the building footprints are taken from two publicly available
databases from the United Kingdom. Our study confirms that the abstraction
model based on stochastic geometry is capable of accurately modeling the
communication performance of cellular networks in dense urban environments.Comment: submitted for publicatio
An Optimal Control Theory for the Traveling Salesman Problem and Its Variants
We show that the traveling salesman problem (TSP) and its many variants may
be modeled as functional optimization problems over a graph. In this
formulation, all vertices and arcs of the graph are functionals; i.e., a
mapping from a space of measurable functions to the field of real numbers. Many
variants of the TSP, such as those with neighborhoods, with forbidden
neighborhoods, with time-windows and with profits, can all be framed under this
construct. In sharp contrast to their discrete-optimization counterparts, the
modeling constructs presented in this paper represent a fundamentally new
domain of analysis and computation for TSPs and their variants. Beyond its
apparent mathematical unification of a class of problems in graph theory, the
main advantage of the new approach is that it facilitates the modeling of
certain application-specific problems in their home space of measurable
functions. Consequently, certain elements of economic system theory such as
dynamical models and continuous-time cost/profit functionals can be directly
incorporated in the new optimization problem formulation. Furthermore, subtour
elimination constraints, prevalent in discrete optimization formulations, are
naturally enforced through continuity requirements. The price for the new
modeling framework is nonsmooth functionals. Although a number of theoretical
issues remain open in the proposed mathematical framework, we demonstrate the
computational viability of the new modeling constructs over a sample set of
problems to illustrate the rapid production of end-to-end TSP solutions to
extensively-constrained practical problems.Comment: 24 pages, 8 figure
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