pwc-archive/papers-with-abstracts huggingface dataset:
Learning, Generalization, and Functional Entropy in Random Automata Networks
Authors: Alireza Goudarzi, Christof Teuscher, Natali Gulbahce, Thimo Rohlf
Date: 2013-06-25 00:00:00
Tasks: Memorization

It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that
feedforward Boolean networks can learn to perform specific simple tasks and
generalize well if only a subset of the learning examples is provided for
learning. Here, we extend this body of work and show experimentally that random
Boolean networks (RBNs), where both the interconnections and the Boolean
transfer functions are chosen at random initially, can be evolved by using a
state-topology evolution to solve simple tasks. We measure the learning and
generalization performance, investigate the influence of the average node
connectivity $K$, the system size $N$, and introduce a new measure that allows
to better describe the network's learning and generalization behavior. We show
that the connectivity of the maximum entropy networks scales as a power-law of
the system size $N$. Our results show that networks with higher average
connectivity $K$ (supercritical) achieve higher memorization and partial
generalization. However, near critical connectivity, the networks show a higher
perfect generalization on the even-odd task.
The Geometry of Generalized Binary Search
Authors: Robert D. Nowak
Date: 2009-10-22 00:00:00

This paper investigates the problem of determining a binary-valued function
through a sequence of strategically selected queries. The focus is an algorithm
called Generalized Binary Search (GBS). GBS is a well-known greedy algorithm
for determining a binary-valued function through a sequence of strategically
selected queries. At each step, a query is selected that most evenly splits the
hypotheses under consideration into two disjoint subsets, a natural
generalization of the idea underlying classic binary search. This paper
develops novel incoherence and geometric conditions under which GBS achieves
the information-theoretically optimal query complexity; i.e., given a
collection of N hypotheses, GBS terminates with the correct function after no
more than a constant times log N queries. Furthermore, a noise-tolerant version
of GBS is developed that also achieves the optimal query complexity. These
results are applied to learning halfspaces, a problem arising routinely in
image processing and machine learning.
DNA Reservoir Computing: A Novel Molecular Computing Approach
Authors: Alireza Goudarzi, Matthew R. Lakin, Darko Stefanovic
Date: 2013-06-25 00:00:00

We propose a novel molecular computing approach based on reservoir computing.
In reservoir computing, a dynamical core, called a reservoir, is perturbed with
an external input signal while a readout layer maps the reservoir dynamics to a
target output. Computation takes place as a transformation from the input space
to a high-dimensional spatiotemporal feature space created by the transient
dynamics of the reservoir. The readout layer then combines these features to
produce the target output. We show that coupled deoxyribozyme oscillators can
act as the reservoir. We show that despite using only three coupled
oscillators, a molecular reservoir computer could achieve 90% accuracy on a
benchmark temporal problem.
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams
Authors: Menaka Gandhi. J, K. S. Gayathri
Date: 2013-06-25 00:00:00
Tasks: Activity Recognition

Smart home technology is a better choice for the people to care about
security, comfort and power saving as well. It is required to develop
technologies that recognize the Activities of Daily Living (ADLs) of the
residents at home and detect the abnormal behavior in the individual's
patterns. Data mining techniques such as Frequent pattern mining (FPM), High
Utility Pattern (HUP) Mining were used to find those activity patterns from the
collected sensor data. But applying the above technique for Activity
Recognition from the temporal sensor data stream is highly complex and
challenging task. So, a new approach is proposed for activity recognition from
sensor data stream which is achieved by constructing Frequent Pattern Stream
tree (FPS - tree). FPS is a sliding window based approach to discover the
recent activity patterns over time from data streams. The proposed work aims at
identifying the frequent pattern of the user from the sensor data streams which
are later modeled for activity recognition. The proposed FPM algorithm uses a
data structure called Linked Sensor Data Stream (LSDS) for storing the sensor
data stream information which increases the efficiency of frequent pattern
mining algorithm through both space and time. The experimental results show the
efficiency of the proposed algorithm and this FPM is further extended for
applying for power efficiency using HUP to detect the high usage of power
consumption of residents at smart home.
Direct Uncertainty Estimation in Reinforcement Learning
Authors: Sergey Rodionov, Alexey Potapov, Yurii Vinogradov
Date: 2013-06-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimal probabilistic approach in reinforcement learning is computationally
infeasible. Its simplification consisting in neglecting difference between true
environment and its model estimated using limited number of observations causes
exploration vs exploitation problem. Uncertainty can be expressed in terms of a
probability distribution over the space of environment models, and this
uncertainty can be propagated to the action-value function via Bellman
iterations, which are computationally insufficiently efficient though. We
consider possibility of directly measuring uncertainty of the action-value
function, and analyze sufficiency of this facilitated approach.
Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System
Authors: Sri Hartati, Shofwatul 'Uyun
Date: 2013-06-25 00:00:00

Determination of dietary food consumed a day for patients with diseases in
general, greatly affect the health of the body and the healing process, is no
exception for people with kidney disease and urinary tract. This paper presents
the determination of diet composition in the form of food subtance for people
with kidney and urinary tract diseases with a genetic fuzzy approach. This
approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy
logic fuzzy tools and techniques to model the components of the genetic
algorithm and adapting genetic algorithm control parameters, with the aim of
improving system performance. The Mamdani fuzzy inference model and fuzzy rules
based on population parameters and generation are used to determine the
probability of crossover and mutation, and was using In this study, 400 food
survey data along with their substances was used as test material. From the
data, a varying amount of population is established. Each chromosome has 10
genes in which the value of each gene indicates the index number of foodstuffs
in the database. The fuzzy genetic approach produces 10 best food substance and
their compositions. The composition of these foods has nutritional value in
accordance with the number of calories needed by people with kidney and urinary
tract diseases by type of food.
Supersparse Linear Integer Models for Predictive Scoring Systems
Authors: Berk Ustun, Stefano Traca, Cynthia Rudin
Date: 2013-06-25 00:00:00
Tasks: Binary Classification, Classification, General Classification

We introduce Supersparse Linear Integer Models (SLIM) as a tool to create
scoring systems for binary classification. We derive theoretical bounds on the
true risk of SLIM scoring systems, and present experimental results to show
that SLIM scoring systems are accurate, sparse, and interpretable
classification models.
Distributed Heuristic Forward Search for Multi-Agent Systems
Authors: Raz Nissim, Ronen Brafman
Date: 2013-06-25 00:00:00

This paper describes a number of distributed forward search algorithms for
solving multi-agent planning problems. We introduce a distributed formulation
of non-optimal forward search, as well as an optimal version, MAD-A*. Our
algorithms exploit the structure of multi-agent problems to not only distribute
the work efficiently among different agents, but also to remove symmetries and
reduce the overall workload. The algorithms ensure that private information is
not shared among agents, yet computation is still efficient -- outperforming
current state-of-the-art distributed planners, and in some cases even
centralized search -- despite the fact that each agent has access only to
partial information.
Cellular Tree Classifiers
Authors: Gérard Biau, Luc Devroye
Date: 2013-01-20 00:00:00
Tasks: Distributed Computing, General Classification

The cellular tree classifier model addresses a fundamental problem in the
design of classifiers for a parallel or distributed computing world: Given a
data set, is it sufficient to apply a majority rule for classification, or
shall one split the data into two or more parts and send each part to a
potentially different computer (or cell) for further processing? At first
sight, it seems impossible to define with this paradigm a consistent classifier
as no cell knows the "original data size", $n$. However, we show that this is
not so by exhibiting two different consistent classifiers. The consistency is
universal but is only shown for distributions with nonatomic marginals.
Accelerated Canonical Polyadic Decomposition by Using Mode Reduction
Authors: Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Date: 2012-11-15 00:00:00
Tasks: Dimensionality Reduction

Canonical Polyadic (or CANDECOMP/PARAFAC, CP) decompositions (CPD) are widely
applied to analyze high order tensors. Existing CPD methods use alternating
least square (ALS) iterations and hence need to unfold tensors to each of the
$N$ modes frequently, which is one major bottleneck of efficiency for
large-scale data and especially when $N$ is large. To overcome this problem, in
this paper we proposed a new CPD method which converts the original $N$th
($N>3$) order tensor to a 3rd-order tensor first. Then the full CPD is realized
by decomposing this mode reduced tensor followed by a Khatri-Rao product
projection procedure. This way is quite efficient as unfolding to each of the
$N$ modes are avoided, and dimensionality reduction can also be easily
incorporated to further improve the efficiency. We show that, under mild
conditions, any $N$th-order CPD can be converted into a 3rd-order case but
without destroying the essential uniqueness, and theoretically gives the same
results as direct $N$-way CPD methods. Simulations show that, compared with
state-of-the-art CPD methods, the proposed method is more efficient and escape
from local solutions more easily.
Constrained Optimization for a Subset of the Gaussian Parsimonious Clustering Models
Authors: Ryan P. Browne, Sanjeena Subedi, Paul McNicholas
Date: 2013-06-25 00:00:00
Tasks: Clustering, parameter estimation

The expectation-maximization (EM) algorithm is an iterative method for
finding maximum likelihood estimates when data are incomplete or are treated as
being incomplete. The EM algorithm and its variants are commonly used for
parameter estimation in applications of mixture models for clustering and
classification. This despite the fact that even the Gaussian mixture model
likelihood surface contains many local maxima and is singularity riddled.
Previous work has focused on circumventing this problem by constraining the
smallest eigenvalue of the component covariance matrices. In this paper, we
consider constraining the smallest eigenvalue, the largest eigenvalue, and both
the smallest and largest within the family setting. Specifically, a subset of
the GPCM family is considered for model-based clustering, where we use a
re-parameterized version of the famous eigenvalue decomposition of the
component covariance matrices. Our approach is illustrated using various
experiments with simulated and real data.
The AdaBoost Flow
Authors: A. Lykov, S. Muzychka, K. Vaninsky
Date: 2011-10-28 00:00:00
Tasks: ARC

We introduce a dynamical system which we call the AdaBoost flow. The flow is
defined by a system of ODEs with control. We show that three algorithms of the
AdaBoost family (i) the AdaBoost algorithm of Schapire and Freund (ii) the
arc-gv algorithm of Breiman (iii) the confidence rated prediction of Schapire
and Singer can be can be embedded in the AdaBoost flow.
  The nontrivial part of the AdaBoost flow equations coincides with the
equations of dynamics of nonperiodic Toda system written in terms of spectral
variables. We provide a novel invariant geometrical description of the AdaBoost
algorithm as a gradient flow on a foliation defined by level sets of the
potential function.
  We propose a new approach for constructing boosting algorithms as a
continuous time gradient flow on measures defined by various metrics and
potential functions. Finally we explain similarity of the AdaBoost algorithm
with the Perelman's construction for the Ricci flow.
Recovering Block-structured Activations Using Compressive Measurements
Authors: Sivaraman Balakrishnan, Mladen Kolar, Alessandro Rinaldo, Aarti Singh
Date: 2012-09-15 00:00:00
Tasks: compressed sensing

We consider the problems of detection and localization of a contiguous block
of weak activation in a large matrix, from a small number of noisy, possibly
adaptive, compressive (linear) measurements. This is closely related to the
problem of compressed sensing, where the task is to estimate a sparse vector
using a small number of linear measurements. Contrary to results in compressed
sensing, where it has been shown that neither adaptivity nor contiguous
structure help much, we show that for reliable localization the magnitude of
the weakest signals is strongly influenced by both structure and the ability to
choose measurements adaptively while for detection neither adaptivity nor
structure reduce the requirement on the magnitude of the signal. We
characterize the precise tradeoffs between the various problem parameters, the
signal strength and the number of measurements required to reliably detect and
localize the block of activation. The sufficient conditions are complemented
with information theoretic lower bounds.
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
Authors: Michael Kallitsis, Stilian Stoev, George Michailidis
Date: 2013-06-24 00:00:00
Tasks: Stochastic Optimization

The robustness and integrity of IP networks require efficient tools for
traffic monitoring and analysis, which scale well with traffic volume and
network size. We address the problem of optimal large-scale flow monitoring of
computer networks under resource constraints. We propose a stochastic
optimization framework where traffic measurements are done by exploiting the
spatial (across network links) and temporal relationship of traffic flows.
Specifically, given the network topology, the state-space characterization of
network flows and sampling constraints at each monitoring station, we seek an
optimal packet sampling strategy that yields the best traffic volume estimation
for all flows of the network. The optimal sampling design is the result of a
concave minimization problem; then, Kalman filtering is employed to yield a
sequence of traffic estimates for each network flow. We evaluate our algorithm
using real-world Internet2 data.
Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning
Authors: Vijay Manikandan Janakiraman, XuanLong Nguyen, Jeff Sterniak, Dennis Assanis
Date: 2013-06-24 00:00:00

Advanced combustion technologies such as homogeneous charge compression
ignition (HCCI) engines have a narrow stable operating region defined by
complex control strategies such as exhaust gas recirculation (EGR) and variable
valve timing among others. For such systems, it is important to identify the
operating envelope or the boundary of stable operation for diagnostics and
control purposes. Obtaining a good model of the operating envelope using
physics becomes intractable owing to engine transient effects. In this paper, a
machine learning based approach is employed to identify the stable operating
boundary of HCCI combustion directly from experimental data. Owing to imbalance
in class proportions in the data, two approaches are considered. A re-sampling
(under-sampling, over-sampling) based approach is used to develop models using
existing algorithms while a cost-sensitive approach is used to modify the
learning algorithm without modifying the data set. Support vector machines and
recently developed extreme learning machines are used for model development and
results compared against linear classification methods show that cost-sensitive
versions of ELM and SVM algorithms are well suited to model the HCCI operating
envelope. The prediction results indicate that the models have the potential to
be used for predicting HCCI instability based on sensor measurement history.
Encoding Higher Level Extensions of Petri Nets in Answer Set Programming
Authors: Saadat Anwar, Chitta Baral, Katsumi Inoue
Date: 2013-06-15 00:00:00

Answering realistic questions about biological systems and pathways similar
to the ones used by text books to test understanding of students about
biological systems is one of our long term research goals. Often these
questions require simulation based reasoning. To answer such questions, we need
formalisms to build pathway models, add extensions, simulate, and reason with
them. We chose Petri Nets and Answer Set Programming (ASP) as suitable
formalisms, since Petri Net models are similar to biological pathway diagrams;
and ASP provides easy extension and strong reasoning abilities. We found that
certain aspects of biological pathways, such as locations and substance types,
cannot be represented succinctly using regular Petri Nets. As a result, we need
higher level constructs like colored tokens. In this paper, we show how Petri
Nets with colored tokens can be encoded in ASP in an intuitive manner, how
additional Petri Net extensions can be added by making small code changes, and
how this work furthers our long term research goals. Our approach can be
adapted to other domains with similar modeling needs.
Encoding Petri Nets in Answer Set Programming for Simulation Based Reasoning
Authors: Saadat Anwar, Chitta Baral, Katsumi Inoue
Date: 2013-06-15 00:00:00

One of our long term research goals is to develop systems to answer realistic
questions (e.g., some mentioned in textbooks) about biological pathways that a
biologist may ask. To answer such questions we need formalisms that can model
pathways, simulate their execution, model intervention to those pathways, and
compare simulations under different circumstances. We found Petri Nets to be
the starting point of a suitable formalism for the modeling and simulation
needs. However, we need to make extensions to the Petri Net model and also
reason with multiple simulation runs and parallel state evolutions. Towards
that end Answer Set Programming (ASP) implementation of Petri Nets would allow
us to do both. In this paper we show how ASP can be used to encode basic Petri
Nets in an intuitive manner. We then show how we can modify this encoding to
model several Petri Net extensions by making small changes. We then highlight
some of the reasoning capabilities that we will use to accomplish our ultimate
research goal.
Using Genetic Programming to Model Software
Authors: W. B. Langdon, M. Harman
Date: 2013-06-24 00:00:00
Tasks: model

We study a generic program to investigate the scope for automatically
customising it for a vital current task, which was not considered when it was
first written. In detail, we show genetic programming (GP) can evolve models of
aspects of BLAST's output when it is used to map Solexa Next-Gen DNA sequences
to the human genome.
Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
Authors: Sebastian Hitziger, Maureen Clerc, Alexandre Gramfort, Sandrine Saillet, Christian Bénar, Théodore Papadopoulo
Date: 2013-01-16 00:00:00
Tasks: Dictionary Learning, EEG, Electroencephalogram (EEG), Position

Dictionary Learning has proven to be a powerful tool for many image
processing tasks, where atoms are typically defined on small image patches. As
a drawback, the dictionary only encodes basic structures. In addition, this
approach treats patches of different locations in one single set, which means a
loss of information when features are well-aligned across signals. This is the
case, for instance, in multi-trial magneto- or electroencephalography (M/EEG).
Learning the dictionary on the entire signals could make use of the alignement
and reveal higher-level features. In this case, however, small missalignements
or phase variations of features would not be compensated for. In this paper, we
propose an extension to the common dictionary learning framework to overcome
these limitations by allowing atoms to adapt their position across signals. The
method is validated on simulated and real neuroelectric data.
Model Reframing by Feature Context Change
Authors: Celestine-Periale Maguedong-Djoumessi
Date: 2013-06-23 00:00:00
Tasks: Attribute, Missing Values, model, regression

The feature space (including both input and output variables) characterises a
data mining problem. In predictive (supervised) problems, the quality and
availability of features determines the predictability of the dependent
variable, and the performance of data mining models in terms of
misclassification or regression error. Good features, however, are usually
difficult to obtain. It is usual that many instances come with missing values,
either because the actual value for a given attribute was not available or
because it was too expensive. This is usually interpreted as a utility or
cost-sensitive learning dilemma, in this case between misclassification (or
regression error) costs and attribute tests costs. Both misclassification cost
(MC) and test cost (TC) can be integrated into a single measure, known as joint
cost (JC). We introduce methods and plots (such as the so-called JROC plots)
that can work with any of-the-shelf predictive technique, including ensembles,
such that we re-frame the model to use the appropriate subset of attributes
(the feature configuration) during deployment time. In other words, models are
trained with the available attributes (once and for all) and then deployed by
setting missing values on the attributes that are deemed ineffective for
reducing the joint cost. As the number of feature configuration combinations
grows exponentially with the number of features we introduce quadratic methods
that are able to approximate the optimal configuration and model choices, as
shown by the experimental results.
Commonsense Reasoning and Large Network Analysis: A Computational Study of ConceptNet 4
Authors: Dimitrios I. Diochnos
Date: 2013-04-22 00:00:00
Tasks: Clustering

In this report a computational study of ConceptNet 4 is performed using tools
from the field of network analysis. Part I describes the process of extracting
the data from the SQL database that is available online, as well as how the
closure of the input among the assertions in the English language is computed.
This part also performs a validation of the input as well as checks for the
consistency of the entire database. Part II investigates the structural
properties of ConceptNet 4. Different graphs are induced from the knowledge
base by fixing different parameters. The degrees and the degree distributions
are examined, the number and sizes of connected components, the transitivity
and clustering coefficient, the cores, information related to shortest paths in
the graphs, and cliques. Part III investigates non-overlapping, as well as
overlapping communities that are found in ConceptNet 4. Finally, Part IV
describes an investigation on rules.