pwc-archive/papers-with-abstracts huggingface dataset:
Risk-sensitive Markov control processes
Authors: Yun Shen, Wilhelm Stannat, Klaus Obermayer
Date: 2011-10-28 00:00:00

We introduce a general framework for measuring risk in the context of Markov
control processes with risk maps on general Borel spaces that generalize known
concepts of risk measures in mathematical finance, operations research and
behavioral economics. Within the framework, applying weighted norm spaces to
incorporate also unbounded costs, we study two types of infinite-horizon
risk-sensitive criteria, discounted total risk and average risk, and solve the
associated optimization problems by dynamic programming. For the discounted
case, we propose a new discount scheme, which is different from the
conventional form but consistent with the existing literature, while for the
average risk criterion, we state Lyapunov-like stability conditions that
generalize known conditions for Markov chains to ensure the existence of
solutions to the optimality equation.
Spatially regularized reconstruction of fibre orientation distributions in the presence of isotropic diffusion
Authors: Q. Zhou, O. Michailovich, Y. Rathi
Date: 2014-01-23 00:00:00

The connectivity and structural integrity of the white matter of the brain is
nowadays known to be implicated into a wide range of brain-related disorders.
However, it was not before the advent of diffusion Magnetic Resonance Imaging
(dMRI) that researches have been able to examine the properties of white matter
in vivo. Presently, among a range of various methods of dMRI, high angular
resolution diffusion imaging (HARDI) is known to excel in its ability to
provide reliable information about the local orientations of neural fasciculi
(aka fibre tracts). Moreover, as opposed to the more traditional diffusion
tensor imaging (DTI), HARDI is capable of distinguishing the orientations of
multiple fibres passing through a given spatial voxel. Unfortunately, the
ability of HARDI to discriminate between neural fibres that cross each other at
acute angles is always limited, which is the main reason behind the development
of numerous post-processing tools, aiming at the improvement of the directional
resolution of HARDI. Among such tools is spherical deconvolution (SD). Due to
its ill-posed nature, however, SD standardly relies on a number of a priori
assumptions which are to render its results unique and stable. In this paper,
we propose a different approach to the problem of SD in HARDI, which accounts
for the spatial continuity of neural fibres as well as the presence of
isotropic diffusion. Subsequently, we demonstrate how the proposed solution can
be used to successfully overcome the effect of partial voluming, while
preserving the spatial coherency of cerebral diffusion at moderate-to-severe
noise levels. In a series of both in silico and in vivo experiments, the
performance of the proposed method is compared with that of several available
alternatives, with the comparative results clearly supporting the viability and
usefulness of our approach.
Risk-sensitive Reinforcement Learning
Authors: Yun Shen, Michael J. Tobia, Tobias Sommer, Klaus Obermayer
Date: 2013-11-08 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We derive a family of risk-sensitive reinforcement learning methods for
agents, who face sequential decision-making tasks in uncertain environments. By
applying a utility function to the temporal difference (TD) error, nonlinear
transformations are effectively applied not only to the received rewards but
also to the true transition probabilities of the underlying Markov decision
process. When appropriate utility functions are chosen, the agents' behaviors
express key features of human behavior as predicted by prospect theory
(Kahneman and Tversky, 1979), for example different risk-preferences for gains
and losses as well as the shape of subjective probability curves. We derive a
risk-sensitive Q-learning algorithm, which is necessary for modeling human
behavior when transition probabilities are unknown, and prove its convergence.
As a proof of principle for the applicability of the new framework we apply it
to quantify human behavior in a sequential investment task. We find, that the
risk-sensitive variant provides a significantly better fit to the behavioral
data and that it leads to an interpretation of the subject's responses which is
indeed consistent with prospect theory. The analysis of simultaneously measured
fMRI signals show a significant correlation of the risk-sensitive TD error with
BOLD signal change in the ventral striatum. In addition we find a significant
correlation of the risk-sensitive Q-values with neural activity in the
striatum, cingulate cortex and insula, which is not present if standard
Q-values are used.
Iterative Universal Hash Function Generator for Minhashing
Authors: Fabricio Olivetti de Franca
Date: 2014-01-23 00:00:00

Minhashing is a technique used to estimate the Jaccard Index between two sets
by exploiting the probability of collision in a random permutation. In order to
speed up the computation, a random permutation can be approximated by using an
universal hash function such as the $h_{a,b}$ function proposed by Carter and
Wegman. A better estimate of the Jaccard Index can be achieved by using many of
these hash functions, created at random. In this paper a new iterative
procedure to generate a set of $h_{a,b}$ functions is devised that eliminates
the need for a list of random values and avoid the multiplication operation
during the calculation. The properties of the generated hash functions remains
that of an universal hash function family. This is possible due to the random
nature of features occurrence on sparse datasets. Results show that the
uniformity of hashing the features is maintaned while obtaining a speed up of
up to $1.38$ compared to the traditional approach.
Identifying Bengali Multiword Expressions using Semantic Clustering
Authors: Tanmoy Chakraborty, Dipankar Das, Sivaji Bandyopadhyay
Date: 2014-01-23 00:00:00
Tasks: Clustering, Natural Language Understanding

One of the key issues in both natural language understanding and generation
is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge
problem to the precise language processing due to their idiosyncratic nature
and diversity in lexical, syntactical and semantic properties. The semantics of
a MWE cannot be expressed after combining the semantics of its constituents.
Therefore, the formalism of semantic clustering is often viewed as an
instrument for extracting MWEs especially for resource constraint languages
like Bengali. The present semantic clustering approach contributes to locate
clusters of the synonymous noun tokens present in the document. These clusters
in turn help measure the similarity between the constituent words of a
potentially candidate phrase using a vector space model and judge the
suitability of this phrase to be a MWE. In this experiment, we apply the
semantic clustering approach for noun-noun bigram MWEs, though it can be
extended to any types of MWEs. In parallel, the well known statistical models,
namely Point-wise Mutual Information (PMI), Log Likelihood Ratio (LLR),
Significance function are also employed to extract MWEs from the Bengali
corpus. The comparative evaluation shows that the semantic clustering approach
outperforms all other competing statistical models. As a by-product of this
experiment, we have started developing a standard lexicon in Bengali that
serves as a productive Bengali linguistic thesaurus.
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection
Authors: Hai Zhao, Xiaotian Zhang, Chunyu Kit
Date: 2014-01-23 00:00:00
Tasks: Dependency Parsing, feature selection, Semantic Dependency Parsing, Semantic Parsing, Sentence

Semantic parsing, i.e., the automatic derivation of meaning representation
such as an instantiated predicate-argument structure for a sentence, plays a
critical role in deep processing of natural language. Unlike all other top
systems of semantic dependency parsing that have to rely on a pipeline
framework to chain up a series of submodels each specialized for a specific
subtask, the one presented in this article integrates everything into one
model, in hopes of achieving desirable integrity and practicality for real
applications while maintaining a competitive performance. This integrative
approach tackles semantic parsing as a word pair classification problem using a
maximum entropy classifier. We leverage adaptive pruning of argument candidates
and large-scale feature selection engineering to allow the largest feature
space ever in use so far in this field, it achieves a state-of-the-art
performance on the evaluation data set for CoNLL-2008 shared task, on top of
all but one top pipeline system, confirming its feasibility and effectiveness.
Generating Approximate Solutions to the TTP using a Linear Distance Relaxation
Authors: Richard Hoshino, Ken-ichi Kawarabayashi
Date: 2014-01-23 00:00:00
Tasks: Scheduling

In some domestic professional sports leagues, the home stadiums are located
in cities connected by a common train line running in one direction. For these
instances, we can incorporate this geographical information to determine
optimal or nearly-optimal solutions to the n-team Traveling Tournament Problem
(TTP), an NP-hard sports scheduling problem whose solution is a double
round-robin tournament schedule that minimizes the sum total of distances
traveled by all n teams. We introduce the Linear Distance Traveling Tournament
Problem (LD-TTP), and solve it for n=4 and n=6, generating the complete set of
possible solutions through elementary combinatorial techniques. For larger n,
we propose a novel "expander construction" that generates an approximate
solution to the LD-TTP. For n congruent to 4 modulo 6, we show that our
expander construction produces a feasible double round-robin tournament
schedule whose total distance is guaranteed to be no worse than 4/3 times the
optimal solution, regardless of where the n teams are located. This
4/3-approximation for the LD-TTP is stronger than the currently best-known
ratio of 5/3 + epsilon for the general TTP. We conclude the paper by applying
this linear distance relaxation to general (non-linear) n-team TTP instances,
where we develop fast approximate solutions by simply "assuming" the n teams
lie on a straight line and solving the modified problem. We show that this
technique surprisingly generates the distance-optimal tournament on all
benchmark sets on 6 teams, as well as close-to-optimal schedules for larger n,
even when the teams are located around a circle or positioned in
three-dimensional space.
Replanning in Domains with Partial Information and Sensing Actions
Authors: Ronen I. Brafman, Guy Shani
Date: 2014-01-23 00:00:00

Replanning via determinization is a recent, popular approach for online
planning in MDPs. In this paper we adapt this idea to classical, non-stochastic
domains with partial information and sensing actions, presenting a new planner:
SDR (Sample, Determinize, Replan). At each step we generate a solution plan to
a classical planning problem induced by the original problem. We execute this
plan as long as it is safe to do so. When this is no longer the case, we
replan. The classical planning problem we generate is based on the
translation-based approach for conformant planning introduced by Palacios and
Geffner. The state of the classical planning problem generated in this approach
captures the belief state of the agent in the original problem. Unfortunately,
when this method is applied to planning problems with sensing, it yields a
non-deterministic planning problem that is typically very large. Our main
contribution is the introduction of state sampling techniques for overcoming
these two problems. In addition, we introduce a novel, lazy, regression-based
method for querying the agents belief state during run-time. We provide a
comprehensive experimental evaluation of the planner, showing that it scales
better than the state-of-the-art CLG planner on existing benchmark problems,
but also highlighting its weaknesses with new domains. We also discuss its
theoretical guarantees.
Matrix factorization with Binary Components
Authors: Martin Slawski, Matthias Hein, Pavlo Lutsik
Date: 2014-01-23 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: LEMMA

Motivated by an application in computational biology, we consider low-rank
matrix factorization with $\{0,1\}$-constraints on one of the factors and
optionally convex constraints on the second one. In addition to the
non-convexity shared with other matrix factorization schemes, our problem is
further complicated by a combinatorial constraint set of size $2^{m \cdot r}$,
where $m$ is the dimension of the data points and $r$ the rank of the
factorization. Despite apparent intractability, we provide - in the line of
recent work on non-negative matrix factorization by Arora et al. (2012) - an
algorithm that provably recovers the underlying factorization in the exact case
with $O(m r 2^r + mnr + r^2 n)$ operations for $n$ datapoints. To obtain this
result, we use theory around the Littlewood-Offord lemma from combinatorics.
Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras
Authors: Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Stephen Pulman, Bob Coecke
Date: 2014-01-23 00:00:00

Compact closed categories have found applications in modeling quantum
information protocols by Abramsky-Coecke. They also provide semantics for
Lambek's pregroup algebras, applied to formalizing the grammatical structure of
natural language, and are implicit in a distributional model of word meaning
based on vector spaces. Specifically, in previous work Coecke-Clark-Sadrzadeh
used the product category of pregroups with vector spaces and provided a
distributional model of meaning for sentences. We recast this theory in terms
of strongly monoidal functors and advance it via Frobenius algebras over vector
spaces. The former are used to formalize topological quantum field theories by
Atiyah and Baez-Dolan, and the latter are used to model classical data in
quantum protocols by Coecke-Pavlovic-Vicary. The Frobenius algebras enable us
to work in a single space in which meanings of words, phrases, and sentences of
any structure live. Hence we can compare meanings of different language
constructs and enhance the applicability of the theory. We report on
experimental results on a number of language tasks and verify the theoretical
predictions.
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics
Authors: Nan Wang, Jan Melchior, Laurenz Wiskott
Date: 2014-01-23 00:00:00
Tasks: blind source separation

We present a theoretical analysis of Gaussian-binary restricted Boltzmann
machines (GRBMs) from the perspective of density models. The key aspect of this
analysis is to show that GRBMs can be formulated as a constrained mixture of
Gaussians, which gives a much better insight into the model's capabilities and
limitations. We show that GRBMs are capable of learning meaningful features
both in a two-dimensional blind source separation task and in modeling natural
images. Further, we show that reported difficulties in training GRBMs are due
to the failure of the training algorithm rather than the model itself. Based on
our analysis we are able to propose several training recipes, which allowed
successful and fast training in our experiments. Finally, we discuss the
relationship of GRBMs to several modifications that have been proposed to
improve the model.
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification
Authors: Shu Kong, Zhuolin Jiang, Qiang Yang
Date: 2014-01-22 00:00:00
Tasks: Age Estimation, Clustering, Face Recognition, Gender Classification, General Classification, image-classification, Image Classification, Object Categorization, Open-Ended Question Answering, Quantization

We now know that mid-level features can greatly enhance the performance of
image learning, but how to automatically learn the image features efficiently
and in an unsupervised manner is still an open question. In this paper, we
present a very efficient mid-level feature learning approach (MidFea), which
only involves simple operations such as $k$-means clustering, convolution,
pooling, vector quantization and random projection. We explain why this simple
method generates the desired features, and argue that there is no need to spend
much time in learning low-level feature extractors. Furthermore, to boost the
performance, we propose to model the neuron selectivity (NS) principle by
building an additional layer over the mid-level features before feeding the
features into the classifier. We show that the NS-layer learns
category-specific neurons with both bottom-up inference and top-down analysis,
and thus supports fast inference for a query image. We run extensive
experiments on several public databases to demonstrate that our approach can
achieve state-of-the-art performances for face recognition, gender
classification, age estimation and object categorization. In particular, we
demonstrate that our approach is more than an order of magnitude faster than
some recently proposed sparse coding based methods.
Hierarchical pixel clustering for image segmentation
Authors: M. Kharinov
Date: 2014-01-23 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

In the paper a piecewise constant image approximations of sequential number
of pixel clusters or segments are treated. A majorizing of optimal
approximation sequence by hierarchical sequence of image approximations is
studied. Transition from pixel clustering to image segmentation by reducing of
segment numbers in clusters is provided. Algorithms are proved by elementary
formulas.
Asymptotic Accuracy of Bayes Estimation for Latent Variables with Redundancy
Authors: Keisuke Yamazaki
Date: 2012-05-15 00:00:00
Tasks: Clustering

Hierarchical parametric models consisting of observable and latent variables
are widely used for unsupervised learning tasks. For example, a mixture model
is a representative hierarchical model for clustering. From the statistical
point of view, the models can be regular or singular due to the distribution of
data. In the regular case, the models have the identifiability; there is
one-to-one relation between a probability density function for the model
expression and the parameter. The Fisher information matrix is positive
definite, and the estimation accuracy of both observable and latent variables
has been studied. In the singular case, on the other hand, the models are not
identifiable and the Fisher matrix is not positive definite. Conventional
statistical analysis based on the inverse Fisher matrix is not applicable.
Recently, an algebraic geometrical analysis has been developed and is used to
elucidate the Bayes estimation of observable variables. The present paper
applies this analysis to latent-variable estimation and determines its
theoretical performance. Our results clarify behavior of the convergence of the
posterior distribution. It is found that the posterior of the
observable-variable estimation can be different from the one in the
latent-variable estimation. Because of the difference, the Markov chain Monte
Carlo method based on the parameter and the latent variable cannot construct
the desired posterior distribution.
An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem
Authors: Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim
Date: 2014-01-23 00:00:00
Tasks: Scheduling

The talent scheduling problem is a simplified version of the real-world film
shooting problem, which aims to determine a shooting sequence so as to minimize
the total cost of the actors involved. In this article, we first formulate the
problem as an integer linear programming model. Next, we devise a
branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm
is enhanced by several accelerating techniques, including preprocessing,
dominance rules and caching search states. Extensive experiments over two sets
of benchmark instances suggest that our algorithm is superior to the current
best exact algorithm. Finally, the impacts of different parameter settings are
disclosed by some additional experiments.
Ontology - Based Dynamic Business Process Customization
Authors: V. Karthikeyan, V. J. Vijayalakshmi, P. Jeyakumar
Date: 2014-01-09 00:00:00

The interaction between business models is used in consumer centric manner
instead of using a producer centric approach for customizing the business
process in cloud environment. The knowledge based human semantic web is used
for customizing the business process It introduces the Human Semantic Web as a
conceptual interface, providing human-understandable semantics on top of the
ordinary Semantic Web, which provides machine-readable semantics based on RDF
in this mismatching is a major problem. To overcome this following technique
automatic customization detection is an automated process of detecting possible
elements or variables of a business process that needto be especially treated
in order to suit the requirement of the other process. To the business
processto be customized as the primary business process and those that it
collaborates with as secondary business process or SBP Automatic customization
enactment is an automated process of taking actions to perform the
customization on the PBP according to the detected customization spots and the
automatic reasoning on the customization conceptualization knowledge framework.
The process of customizing businessprocesses by composite the web pages by
using web service.
Towards Unsupervised Learning of Temporal Relations between Events
Authors: Seyed Abolghasem Mirroshandel, Gholamreza Ghassem-Sani
Date: 2014-01-23 00:00:00
Tasks: Question Answering, Relation, Relation Extraction, Temporal Relation Extraction

Automatic extraction of temporal relations between event pairs is an
important task for several natural language processing applications such as
Question Answering, Information Extraction, and Summarization. Since most
existing methods are supervised and require large corpora, which for many
languages do not exist, we have concentrated our efforts to reduce the need for
annotated data as much as possible. This paper presents two different
algorithms towards this goal. The first algorithm is a weakly supervised
machine learning approach for classification of temporal relations between
events. In the first stage, the algorithm learns a general classifier from an
annotated corpus. Then, inspired by the hypothesis of "one type of temporal
relation per discourse, it extracts useful information from a cluster of
topically related documents. We show that by combining the global information
of such a cluster with local decisions of a general classifier, a bootstrapping
cross-document classifier can be built to extract temporal relations between
events. Our experiments show that without any additional annotated data, the
accuracy of the proposed algorithm is higher than that of several previous
successful systems. The second proposed method for temporal relation extraction
is based on the expectation maximization (EM) algorithm. Within EM, we used
different techniques such as a greedy best-first search and integer linear
programming for temporal inconsistency removal. We think that the experimental
results of our EM based algorithm, as a first step toward a fully unsupervised
temporal relation extraction method, is encouraging.