6,498 research outputs found
Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations
We demonstrate the utility of an unsupervised machine learning tool for the
detection of phase transitions in off-lattice systems. We focus on the
application of principal component analysis (PCA) to detect the freezing
transitions of two-dimensional hard-disk and three-dimensional hard-sphere
systems as well as liquid-gas phase separation in a patchy colloid model. As we
demonstrate, PCA autonomously discovers order-parameter-like quantities that
report on phase transitions, mitigating the need for a priori construction or
identification of a suitable order parameter--thus streamlining the routine
analysis of phase behavior. In a companion paper, we further develop the method
established here to explore the detection of phase transitions in various model
systems controlled by compositional demixing, liquid crystalline ordering, and
non-equilibrium active forces
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems
Multiagent Learning Through Indirect Encoding
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding
Direct Observation of Early-stage Quantum Dot Growth Mechanisms with High-temperature Ab Initio Molecular Dynamics
Colloidal quantum dots (QDs) exhibit highly desirable size- and
shape-dependent properties for applications from electronic devices to imaging.
Indium phosphide QDs have emerged as a primary candidate to replace the more
toxic CdSe QDs, but production of InP QDs with the desired properties lags
behind other QD materials due to a poor understanding of how to tune the growth
process. Using high-temperature ab initio molecular dynamics (AIMD)
simulations, we report the first direct observation of the early stage
intermediates and subsequent formation of an InP cluster from separated indium
and phosphorus precursors. In our simulations, indium agglomeration precedes
formation of In-P bonds. We observe a predominantly intercomplex pathway in
which In-P bonds form between one set of precursor copies while the carboxylate
ligand of a second indium precursor in the agglomerated indium abstracts a
ligand from the phosphorus precursor. This process produces an indium-rich
cluster with structural properties comparable to those in bulk zinc-blende InP
crystals. Minimum energy pathway characterization of the AIMD-sampled reaction
events confirms these observations and identifies that In-carboxylate
dissociation energetics solely determine the barrier along the In-P bond
formation pathway, which is lower for intercomplex (13 kcal/mol) than
intracomplex (21 kcal/mol) mechanisms. The phosphorus precursor chemistry, on
the other hand, controls the thermodynamics of the reaction. Our observations
of the differing roles of precursors in controlling QD formation strongly
suggests that the challenges thus far encountered in InP QD synthesis
optimization may be attributed to an overlooked need for a cooperative tuning
strategy that simultaneously addresses the chemistry of both indium and
phosphorus precursors.Comment: 40 pages, 9 figures, submitted for publicatio
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Software repositories contain large amounts of textual data, ranging from
source code comments and issue descriptions to questions, answers, and comments
on Stack Overflow. To make sense of this textual data, topic modelling is
frequently used as a text-mining tool for the discovery of hidden semantic
structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used
topic model that aims to explain the structure of a corpus by grouping texts.
LDA requires multiple parameters to work well, and there are only rough and
sometimes conflicting guidelines available on how these parameters should be
set. In this paper, we contribute (i) a broad study of parameters to arrive at
good local optima for GitHub and Stack Overflow text corpora, (ii) an
a-posteriori characterisation of text corpora related to eight programming
languages, and (iii) an analysis of corpus feature importance via per-corpus
LDA configuration. We find that (1) popular rules of thumb for topic modelling
parameter configuration are not applicable to the corpora used in our
experiments, (2) corpora sampled from GitHub and Stack Overflow have different
characteristics and require different configurations to achieve good model fit,
and (3) we can predict good configurations for unseen corpora reliably. These
findings support researchers and practitioners in efficiently determining
suitable configurations for topic modelling when analysing textual data
contained in software repositories.Comment: to appear as full paper at MSR 2019, the 16th International
Conference on Mining Software Repositorie
Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications
We outline how principal component analysis (PCA) can be applied to particle
configuration data to detect a variety of phase transitions in off-lattice
systems, both in and out of equilibrium. Specifically, we discuss its
application to study 1) the nonequilibrium random organization (RandOrg) model
that exhibits a phase transition from quiescent to steady-state behavior as a
function of density, 2) orientationally and positionally driven equilibrium
phase transitions for hard ellipses, and 3) compositionally driven demixing
transitions in the non-additive binary Widom-Rowlinson mixture
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