3,538 research outputs found
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
Multipurpose S-shaped solvable profiles of the refractive index: application to modeling of antireflection layers and quasi-crystals
A class of four-parameter solvable profiles of the electromagnetic admittance
has recently been discovered by applying the newly developed Property & Field
Darboux Transformation method (PROFIDT). These profiles are highly flexible. In
addition, the related electromagnetic-field solutions are exact, in closed-form
and involve only elementary functions. In this paper, we focus on those who are
S-shaped and we provide all the tools needed for easy implementation. These
analytical bricks can be used for high-level modeling of lightwave propagation
in photonic devices presenting a piecewise-sigmoidal refractive-index profile
such as, for example, antireflection layers, rugate filters, chirped filters
and photonic crystals. For small amplitude of the index modulation, these
elementary profiles are very close to a cosine profile. They can therefore be
considered as valuable surrogates for computing the scattering properties of
components like Bragg filters and reflectors as well. In this paper we present
an application for antireflection layers and another for 1D quasicrystals (QC).
The proposed S-shaped profiles can be easily manipulated for exploring the
optical properties of smooth QC, a class of photonic devices that adds to the
classical binary-level QC.Comment: 14 pages, 18 fi
A Formal Framework for Speedup Learning from Problems and Solutions
Speedup learning seeks to improve the computational efficiency of problem
solving with experience. In this paper, we develop a formal framework for
learning efficient problem solving from random problems and their solutions. We
apply this framework to two different representations of learned knowledge,
namely control rules and macro-operators, and prove theorems that identify
sufficient conditions for learning in each representation. Our proofs are
constructive in that they are accompanied with learning algorithms. Our
framework captures both empirical and explanation-based speedup learning in a
unified fashion. We illustrate our framework with implementations in two
domains: symbolic integration and Eight Puzzle. This work integrates many
strands of experimental and theoretical work in machine learning, including
empirical learning of control rules, macro-operator learning, Explanation-Based
Learning (EBL), and Probably Approximately Correct (PAC) Learning.Comment: See http://www.jair.org/ for any accompanying file
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It enables the discovery and
acquisition of large repertoires of skills through self-generation,
self-selection, self-ordering and self-experimentation of learning goals. We
present an algorithmic approach called Intrinsically Motivated Goal Exploration
Processes (IMGEP) to enable similar properties of autonomous or self-supervised
learning in machines. The IMGEP algorithmic architecture relies on several
principles: 1) self-generation of goals, generalized as fitness functions; 2)
selection of goals based on intrinsic rewards; 3) exploration with incremental
goal-parameterized policy search and exploitation of the gathered data with a
batch learning algorithm; 4) systematic reuse of information acquired when
targeting a goal for improving towards other goals. We present a particularly
efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a
population-based policy and an object-centered modularity in goals and
mutations. We provide several implementations of this architecture and
demonstrate their ability to automatically generate a learning curriculum
within several experimental setups including a real humanoid robot that can
explore multiple spaces of goals with several hundred continuous dimensions.
While no particular target goal is provided to the system, this curriculum
allows the discovery of skills that act as stepping stone for learning more
complex skills, e.g. nested tool use. We show that learning diverse spaces of
goals with intrinsic motivations is more efficient for learning complex skills
than only trying to directly learn these complex skills
Maximizing Algebraic Connectivity of Constrained Graphs in Adversarial Environments
This paper aims to maximize algebraic connectivity of networks via topology
design under the presence of constraints and an adversary. We are concerned
with three problems. First, we formulate the concave maximization topology
design problem of adding edges to an initial graph, which introduces a
nonconvex binary decision variable, in addition to subjugation to general
convex constraints on the feasible edge set. Unlike previous methods, our
method is justifiably not greedy and capable of accommodating these additional
constraints. We also study a scenario in which a coordinator must selectively
protect edges of the network from a chance of failure due to a physical
disturbance or adversarial attack. The coordinator needs to strategically
respond to the adversary's action without presupposed knowledge of the
adversary's feasible attack actions. We propose three heuristic algorithms for
the coordinator to accomplish the objective and identify worst-case preventive
solutions. Each algorithm is shown to be effective in simulation and we provide
some discussion on their compared performance.Comment: 8 pages, submitted to European Control Conference 201
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