pwc-archive/papers-with-abstracts huggingface dataset:
Trading USDCHF filtered by Gold dynamics via HMM coupling
Authors: Donny Lee
Date: 2013-08-05 00:00:00

We devise a USDCHF trading strategy using the dynamics of gold as a filter.
Our strategy involves modelling both USDCHF and gold using a coupled hidden
Markov model (CHMM). The observations will be indicators, RSI and CCI, which
will be used as triggers for our trading signals. Upon decoding the model in
each iteration, we can get the next most probable state and the next most
probable observation. Hopefully by taking advantage of intermarket analysis and
the Markov property implicit in the model, trading with these most probable
values will produce profitable results.
Online Ensemble Learning for Imbalanced Data Streams
Authors: Boyu Wang, Joelle Pineau
Date: 2013-10-30 00:00:00
Tasks: Ensemble Learning

While both cost-sensitive learning and online learning have been studied
extensively, the effort in simultaneously dealing with these two issues is
limited. Aiming at this challenge task, a novel learning framework is proposed
in this paper. The key idea is based on the fusion of online ensemble
algorithms and the state of the art batch mode cost-sensitive bagging/boosting
algorithms. Within this framework, two separately developed research areas are
bridged together, and a batch of theoretically sound online cost-sensitive
bagging and online cost-sensitive boosting algorithms are first proposed.
Unlike other online cost-sensitive learning algorithms lacking theoretical
analysis of asymptotic properties, the convergence of the proposed algorithms
is guaranteed under certain conditions, and the experimental evidence with
benchmark data sets also validates the effectiveness and efficiency of the
proposed methods.
Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models
Authors: Weicong Ding, Prakash Ishwar, Mohammad H. Rohban, Venkatesh Saligrama
Date: 2013-10-30 00:00:00
Tasks: Topic Models

The simplicial condition and other stronger conditions that imply it have
recently played a central role in developing polynomial time algorithms with
provable asymptotic consistency and sample complexity guarantees for topic
estimation in separable topic models. Of these algorithms, those that rely
solely on the simplicial condition are impractical while the practical ones
need stronger conditions. In this paper, we demonstrate, for the first time,
that the simplicial condition is a fundamental, algorithm-independent,
information-theoretic necessary condition for consistent separable topic
estimation. Furthermore, under solely the simplicial condition, we present a
practical quadratic-complexity algorithm based on random projections which
consistently detects all novel words of all topics using only up to
second-order empirical word moments. This algorithm is amenable to distributed
implementation making it attractive for 'big-data' scenarios involving a
network of large distributed databases.
Toward a Formal Model of the Shifting Relationship between Concepts and Contexts during Associative Thought
Authors: Tomas Veloz, Liane Gabora, Mark Eyjolfson, Diederik Aerts
Date: 2013-10-29 00:00:00

The quantum inspired State Context Property (SCOP) theory of concepts is
unique amongst theories of concepts in offering a means of incorporating that
for each concept in each different context there are an unlimited number of
exemplars, or states, of varying degrees of typicality. Working with data from
a study in which participants were asked to rate the typicality of exemplars of
a concept for different contexts, and introducing an exemplar typicality
threshold, we built a SCOP model of how states of a concept arise differently
in associative versus analytic (or divergent and convergent) modes of thought.
Introducing measures of state robustness and context relevance, we show that by
varying the threshold, the relevance of different contexts changes, and
seemingly atypical states can become typical. The formalism provides a pivotal
step toward a formal explanation of creative thought proesses.
Automatic Classification of Variable Stars in Catalogs with missing data
Authors: Karim Pichara, Pavlos Protopapas
Date: 2013-10-29 00:00:00
Tasks: Classification, Classification Of Variable Stars, General Classification, Missing Values

We present an automatic classification method for astronomical catalogs with
missing data. We use Bayesian networks, a probabilistic graphical model, that
allows us to perform inference to pre- dict missing values given observed data
and dependency relationships between variables. To learn a Bayesian network
from incomplete data, we use an iterative algorithm that utilises sampling
methods and expectation maximization to estimate the distributions and
probabilistic dependencies of variables from data with missing values. To test
our model we use three catalogs with missing data (SAGE, 2MASS and UBVI) and
one complete catalog (MACHO). We examine how classification accuracy changes
when information from missing data catalogs is included, how our method
compares to traditional missing data approaches and at what computational cost.
Integrating these catalogs with missing data we find that classification of
variable objects improves by few percent and by 15% for quasar detection while
keeping the computational cost the same.
A comparison of bandwidth selectors for mean shift clustering
Authors: José E. Chacón, Pablo Monfort
Date: 2013-10-29 00:00:00
Tasks: Clustering

We explore the performance of several automatic bandwidth selectors,
originally designed for density gradient estimation, as data-based procedures
for nonparametric, modal clustering. The key tool to obtain a clustering from
density gradient estimators is the mean shift algorithm, which allows to obtain
a partition not only of the data sample, but also of the whole space. The
results of our simulation study suggest that most of the methods considered
here, like cross validation and plug in bandwidth selectors, are useful for
cluster analysis via the mean shift algorithm.
A Complete Parameterized Complexity Analysis of Bounded Planning
Authors: Christer Baeckstroem, Peter Jonsson, Sebastian Ordyniak, Stefan Szeider
Date: 2013-10-29 00:00:00
Tasks: General Classification

The propositional planning problem is a notoriously difficult computational
problem, which remains hard even under strong syntactical and structural
restrictions. Given its difficulty it becomes natural to study planning in the
context of parameterized complexity. In this paper we continue the work
initiated by Downey, Fellows and Stege on the parameterized complexity of
planning with respect to the parameter "length of the solution plan." We
provide a complete classification of the parameterized complexity of the
planning problem under two of the most prominent syntactical restrictions,
i.e., the so called PUBS restrictions introduced by Baeckstroem and Nebel and
restrictions on the number of preconditions and effects as introduced by
Bylander. We also determine which of the considered fixed-parameter tractable
problems admit a polynomial kernel and which don't.
An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection
Authors: Jimmy SJ. Ren, Wei Wang, Jiawei Wang, Stephen Liao
Date: 2013-10-29 00:00:00
Tasks: General Classification

Sophisticated automatic incident detection (AID) technology plays a key role
in contemporary transportation systems. Though many papers were devoted to
study incident classification algorithms, few study investigated how to enhance
feature representation of incidents to improve AID performance. In this paper,
we propose to use an unsupervised feature learning algorithm to generate higher
level features to represent incidents. We used real incident data in the
experiments and found that effective feature mapping function can be learnt
from the data crosses the test sites. With the enhanced features, detection
rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are
significantly improved in all of the three representative cases. This approach
also provides an alternative way to reduce the amount of labeled data, which is
expensive to obtain, required in training better incident classifiers since the
feature learning is unsupervised.
Structured Optimal Transmission Control in Network-coded Two-way Relay Channels
Authors: Ni Ding, Parastoo Sadeghi, Rodney A. Kennedy
Date: 2013-10-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper considers a transmission control problem in network-coded two-way
relay channels (NC-TWRC), where the relay buffers random symbol arrivals from
two users, and the channels are assumed to be fading. The problem is modeled by
a discounted infinite horizon Markov decision process (MDP). The objective is
to find a transmission control policy that minimizes the symbol delay, buffer
overflow and transmission power consumption and error rate simultaneously and
in the long run. By using the concepts of submodularity, multimodularity and
L-natural convexity, we study the structure of the optimal policy searched by
dynamic programming (DP) algorithm. We show that the optimal transmission
policy is nondecreasing in queue occupancies or/and channel states under
certain conditions such as the chosen values of parameters in the MDP model,
channel modeling method, modulation scheme and the preservation of stochastic
dominance in the transitions of system states. The results derived in this
paper can be used to relieve the high complexity of DP and facilitate real-time
control.
Distributed Reinforcement Learning via Gossip
Authors: Adwaitvedant S. Mathkar, Vivek S. Borkar
Date: 2013-10-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the classical TD(0) algorithm implemented on a network of agents
wherein the agents also incorporate the updates received from neighboring
agents using a gossip-like mechanism. The combined scheme is shown to converge
for both discounted and average cost problems.
Optimization of Clustering for Clustering-based Image Denoising
Authors: Mohsen Joneidi, Mostafa Sadeghi
Date: 2013-06-12 00:00:00
Tasks: Clustering, Denoising, Dictionary Learning, Image Denoising

In this paper, the problem of de-noising of an image contaminated with
additive white Gaussian noise (AWGN) is studied. This subject has been
continued to be an open problem in signal processing for more than 50 years. In
the present paper, we suggest a method based on global clustering of image
constructing blocks. Noting that the type of clustering plays an important role
in clustering-based de-noising methods, we address two questions about the
clustering. First, which parts of data should be considered for clustering?
Second, what data clustering method is suitable for de-noising? Clustering is
exploited to learn an over complete dictionary. By obtaining sparse
decomposition of the noisy image blocks in terms of the dictionary atoms, the
de-noised version is achieved. Experimental results show that our dictionary
learning framework outperforms traditional dictionary learning methods such as
K-SVD.
Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures
Authors: David M. Bryson, Charles Ofria
Date: 2013-09-03 00:00:00
Tasks: CPU

We investigate fundamental decisions in the design of instruction set
architectures for linear genetic programs that are used as both model systems
in evolutionary biology and underlying solution representations in evolutionary
computation. We subjected digital organisms with each tested architecture to
seven different computational environments designed to present a range of
evolutionary challenges. Our goal was to engineer a general purpose
architecture that would be effective under a broad range of evolutionary
conditions. We evaluated six different types of architectural features for the
virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more
precisely modify the function of genetic instructions, (2) memory: we provided
an increased number of registers in the virtual CPUs, (3) decoupled sensors and
actuators: we separated input and output operations to enable greater control
over data flow. We also tested a variety of methods to regulate expression: (4)
explicit labels that allow programs to dynamically refer to specific genome
positions, (5) position-relative search instructions, and (6) multiple new flow
control instructions, including conditionals and jumps. Each of these features
also adds complication to the instruction set and risks slowing evolution due
to epistatic interactions. Two features (multiple argument specification and
separated I/O) demonstrated substantial improvements int the majority of test
environments. Some of the remaining tested modifications were detrimental,
thought most exhibit no systematic effects on evolutionary potential,
highlighting the robustness of digital evolution. Combined, these observations
enhance our understanding of how instruction architecture impacts evolutionary
potential, enabling the creation of architectures that support more rapid
evolution of complex solutions to a broad range of challenges.
Ranking basic belief assignments in decision making under uncertain environment
Authors: Yuxian Du, Shi-Yu Chen, Yong Hu, Felix T. S. Chan, Sankaran Mahadevan, Yong Deng
Date: 2013-10-28 00:00:00
Tasks: Decision Making

Dempster-Shafer theory (D-S theory) is widely used in decision making under
the uncertain environment. Ranking basic belief assignments (BBAs) now is an
open issue. Existing evidence distance measures cannot rank the BBAs in the
situations when the propositions have their own ranking order or their inherent
measure of closeness. To address this issue, a new ranking evidence distance
(RED) measure is proposed. Compared with the existing evidence distance
measures including the Jousselme's distance and the distance between betting
commitments, the proposed RED measure is much more general due to the fact that
the order of the propositions in the systems is taken into consideration. If
there is no order or no inherent measure of closeness in the propositions, our
proposed RED measure is reduced to the existing evidence distance. Numerical
examples show that the proposed RED measure is an efficient alternative to rank
BBAs in decision making under uncertain environment.
Semantic Description of Web Services
Authors: Thabet Slimani
Date: 2013-10-28 00:00:00
Tasks: General Classification

The tasks of semantic web service (discovery, selection, composition, and
execution) are supposed to enable seamless interoperation between systems,
whereby human intervention is kept at a minimum. In the field of Web service
description research, the exploitation of descriptions of services through
semantics is a better support for the life-cycle of Web services. The large
number of developed ontologies, languages of representations, and integrated
frameworks supporting the discovery, composition and invocation of services is
a good indicator that research in the field of Semantic Web Services (SWS) has
been considerably active. We provide in this paper a detailed classification of
the approaches and solutions, indicating their core characteristics and
objectives required and provide indicators for the interested reader to follow
up further insights and details about these solutions and related software.
Distributed Matrix Completion and Robust Factorization
Authors: Lester Mackey, Ameet Talwalkar, Michael. I. Jordan
Date: 2011-07-05 00:00:00
Tasks: Collaborative Filtering, Distributed Computing, Matrix Completion

If learning methods are to scale to the massive sizes of modern datasets, it
is essential for the field of machine learning to embrace parallel and
distributed computing. Inspired by the recent development of matrix
factorization methods with rich theory but poor computational complexity and by
the relative ease of mapping matrices onto distributed architectures, we
introduce a scalable divide-and-conquer framework for noisy matrix
factorization. We present a thorough theoretical analysis of this framework in
which we characterize the statistical errors introduced by the "divide" step
and control their magnitude in the "conquer" step, so that the overall
algorithm enjoys high-probability estimation guarantees comparable to those of
its base algorithm. We also present experiments in collaborative filtering and
video background modeling that demonstrate the near-linear to superlinear
speed-ups attainable with this approach.
Compressed Sensing SAR Imaging with Multilook Processing
Authors: Jian Fang, Zongben Xu, Bingchen Zhang, Wen Hong, Yirong Wu
Date: 2013-10-27 00:00:00
Tasks: compressed sensing, Compressive Sensing, Image Reconstruction

Multilook processing is a widely used speckle reduction approach in synthetic
aperture radar (SAR) imaging. Conventionally, it is achieved by incoherently
summing of some independent low-resolution images formulated from overlapping
subbands of the SAR signal. However, in the context of compressive sensing (CS)
SAR imaging, where the samples are collected at sub-Nyquist rate, the data
spectrum is highly aliased that hinders the direct application of the existing
multilook techniques. In this letter, we propose a new CS-SAR imaging method
that can realize multilook processing simultaneously during image
reconstruction. The main idea is to replace the SAR observation matrix by the
inverse of multilook procedures, which is then combined with random sampling
matrix to yield a multilook CS-SAR observation model. Then a joint sparse
regularization model, considering pixel dependency of subimages, is derived to
form multilook images. The suggested SAR imaging method can not only
reconstruct sparse scene efficiently below Nyquist rate, but is also able to
achieve a comparable reduction of speckles during reconstruction. Simulation
results are finally provided to demonstrate the effectiveness of the proposed
method.
Generalized Thompson Sampling for Contextual Bandits
Authors: Lihong Li
Date: 2013-10-27 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

Thompson Sampling, one of the oldest heuristics for solving multi-armed
bandits, has recently been shown to demonstrate state-of-the-art performance.
The empirical success has led to great interests in theoretical understanding
of this heuristic. In this paper, we approach this problem in a way very
different from existing efforts. In particular, motivated by the connection
between Thompson Sampling and exponentiated updates, we propose a new family of
algorithms called Generalized Thompson Sampling in the expert-learning
framework, which includes Thompson Sampling as a special case. Similar to most
expert-learning algorithms, Generalized Thompson Sampling uses a loss function
to adjust the experts' weights. General regret bounds are derived, which are
also instantiated to two important loss functions: square loss and logarithmic
loss. In contrast to existing bounds, our results apply to quite general
contextual bandits. More importantly, they quantify the effect of the "prior"
distribution on the regret bounds.
A Misanthropic Reinterpretation of the Chinese Room Problem
Authors: Michael S. Laufer
Date: 2013-10-26 00:00:00

The chinese room problem asks if computers can think; I ask here if most
humans can.
Studying a Chaotic Spiking Neural Model
Authors: Mohammad Alhawarat, Waleed Nazih, Mohammad Eldesouki
Date: 2013-10-26 00:00:00
Tasks: model

Dynamics of a chaotic spiking neuron model are being studied mathematically
and experimentally. The Nonlinear Dynamic State neuron (NDS) is analysed to
further understand the model and improve it. Chaos has many interesting
properties such as sensitivity to initial conditions, space filling, control
and synchronization. As suggested by biologists, these properties may be
exploited and play vital role in carrying out computational tasks in human
brain. The NDS model has some limitations; in thus paper the model is
investigated to overcome some of these limitations in order to enhance the
model. Therefore, the models parameters are tuned and the resulted dynamics are
studied. Also, the discretization method of the model is considered. Moreover,
a mathematical analysis is carried out to reveal the underlying dynamics of the
model after tuning of its parameters. The results of the aforementioned methods
revealed some facts regarding the NDS attractor and suggest the stabilization
of a large number of unstable periodic orbits (UPOs) which might correspond to
memories in phase space.
Efficient Information Theoretic Clustering on Discrete Lattices
Authors: Christian Bauckhage, Kristian Kersting
Date: 2013-10-26 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Image Segmentation, Semantic Segmentation

We consider the problem of clustering data that reside on discrete, low
dimensional lattices. Canonical examples for this setting are found in image
segmentation and key point extraction. Our solution is based on a recent
approach to information theoretic clustering where clusters result from an
iterative procedure that minimizes a divergence measure. We replace costly
processing steps in the original algorithm by means of convolutions. These
allow for highly efficient implementations and thus significantly reduce
runtime. This paper therefore bridges a gap between machine learning and signal
processing.
Algorithm Runtime Prediction: Methods & Evaluation
Authors: Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown
Date: 2012-11-05 00:00:00
Tasks: Prediction

Perhaps surprisingly, it is possible to predict how long an algorithm will
take to run on a previously unseen input, using machine learning techniques to
build a model of the algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to algorithm
analysis, portfolio-based algorithm selection, and the automatic configuration
of parameterized algorithms. Over the past decade, a wide variety of techniques
have been studied for building such models. Here, we describe extensions and
improvements of existing models, new families of models, and -- perhaps most
importantly -- a much more thorough treatment of algorithm parameters as model
inputs. We also comprehensively describe new and existing features for
predicting algorithm runtime for propositional satisfiability (SAT), travelling
salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate
these innovations through the largest empirical analysis of its kind, comparing
to a wide range of runtime modelling techniques from the literature. Our
experiments consider 11 algorithms and 35 instance distributions; they also
span a very wide range of SAT, MIP, and TSP instances, with the least
structured having been generated uniformly at random and the most structured
having emerged from real industrial applications. Overall, we demonstrate that
our new models yield substantially better runtime predictions than previous
approaches in terms of their generalization to new problem instances, to new
algorithms from a parameterized space, and to both simultaneously.