pwc-archive/papers-with-abstracts huggingface dataset:
Nano-scale reservoir computing
Authors: Oliver Obst, Adrian Trinchi, Simon G. Hardin, Matthew Chadwick, Ivan Cole, Tim H. Muster, Nigel Hoschke, Diet Ostry, Don Price, Khoa N. Pham, Tim Wark
Date: 2013-09-06 00:00:00

This work describes preliminary steps towards nano-scale reservoir computing
using quantum dots. Our research has focused on the development of an
accumulator-based sensing system that reacts to changes in the environment, as
well as the development of a software simulation. The investigated systems
generate nonlinear responses to inputs that make them suitable for a physical
implementation of a neural network. This development will enable
miniaturisation of the neurons to the molecular level, leading to a range of
applications including monitoring of changes in materials or structures. The
system is based around the optical properties of quantum dots. The paper will
report on experimental work on systems using Cadmium Selenide (CdSe) quantum
dots and on the various methods to render the systems sensitive to pH, redox
potential or specific ion concentration. Once the quantum dot-based systems are
rendered sensitive to these triggers they can provide a distributed array that
can monitor and transmit information on changes within the material.
Entanglement Zoo II: Examples in Physics and Cognition
Authors: Diederik Aerts, Sandro Sozzo
Date: 2013-03-30 00:00:00
Tasks: General Classification

We have recently presented a general scheme enabling quantum modeling of
different types of situations that violate Bell's inequalities. In this paper,
we specify this scheme for a combination of two concepts. We work out a quantum
Hilbert space model where 'entangled measurements' occur in addition to the
expected 'entanglement between the component concepts', or 'state
entanglement'. We extend this result to a macroscopic physical entity, the
'connected vessels of water', which maximally violates Bell's inequalities. We
enlighten the structural and conceptual analogies between the cognitive and
physical situations which are both examples of a nonlocal non-marginal box
modeling in our classification.
Entanglement Zoo I: Foundational and Structural Aspects
Authors: Diederik Aerts, Sandro Sozzo
Date: 2013-03-30 00:00:00
Tasks: General Classification

We put forward a general classification for a structural description of the
entanglement present in compound entities experimentally violating Bell's
inequalities, making use of a new entanglement scheme that we developed
recently. Our scheme, although different from the traditional one, is
completely compatible with standard quantum theory, and enables quantum
modeling in complex Hilbert space for different types of situations. Namely,
situations where entangled states and product measurements appear ('customary
quantum modeling'), and situations where states and measurements and evolutions
between measurements are entangled ('nonlocal box modeling', 'nonlocal
non-marginal box modeling'). The role played by Tsirelson's bound and marginal
distribution law is emphasized. Specific quantum models are worked out in
detail in complex Hilbert space within this new entanglement scheme.
A Small Universal Petri Net
Authors: Dmitry A. Zaitsev
Date: 2013-09-05 00:00:00
Tasks: TAG, Translation

A universal deterministic inhibitor Petri net with 14 places, 29 transitions
and 138 arcs was constructed via simulation of Neary and Woods' weakly
universal Turing machine with 2 states and 4 symbols; the total time complexity
is exponential in the running time of their weak machine. To simulate the blank
words of the weakly universal Turing machine, a couple of dedicated transitions
insert their codes when reaching edges of the working zone. To complete a chain
of a given Petri net encoding to be executed by the universal Petri net, a
translation of a bi-tag system into a Turing machine was constructed. The
constructed Petri net is universal in the standard sense; a weaker form of
universality for Petri nets was not introduced in this work.
Information fusion in multi-task Gaussian processes
Authors: Shrihari Vasudevan, Arman Melkumyan, Steven Scheding
Date: 2012-10-06 00:00:00
Tasks: Gaussian Processes

This paper evaluates heterogeneous information fusion using multi-task
Gaussian processes in the context of geological resource modeling.
Specifically, it empirically demonstrates that information integration across
heterogeneous information sources leads to superior estimates of all the
quantities being modeled, compared to modeling them individually. Multi-task
Gaussian processes provide a powerful approach for simultaneous modeling of
multiple quantities of interest while taking correlations between these
quantities into consideration. Experiments are performed on large scale real
sensor data.
Weighted regret-based likelihood: a new approach to describing uncertainty
Authors: Joseph Y. Halpern
Date: 2013-09-05 00:00:00

Recently, Halpern and Leung suggested representing uncertainty by a weighted
set of probability measures, and suggested a way of making decisions based on
this representation of uncertainty: maximizing weighted regret. Their paper
does not answer an apparently simpler question: what it means, according to
this representation of uncertainty, for an event E to be more likely than an
event E'. In this paper, a notion of comparative likelihood when uncertainty is
represented by a weighted set of probability measures is defined. It
generalizes the ordering defined by probability (and by lower probability) in a
natural way; a generalization of upper probability can also be defined. A
complete axiomatic characterization of this notion of regret-based likelihood
is given.
Compact Representations of Extended Causal Models
Authors: Joseph Y. Halpern, Christopher Hitchcock
Date: 2013-09-05 00:00:00

Judea Pearl was the first to propose a definition of actual causation using
causal models. A number of authors have suggested that an adequate account of
actual causation must appeal not only to causal structure, but also to
considerations of normality. In earlier work, we provided a definition of
actual causation using extended causal models, which include information about
both causal structure and normality. Extended causal models are potentially
very complex. In this paper, we show how it is possible to achieve a compact
representation of extended causal models.
Graded Causation and Defaults
Authors: Joseph Y. Halpern, Christopher Hitchcock
Date: 2013-09-05 00:00:00
Tasks: Philosophy

Recent work in psychology and experimental philosophy has shown that
judgments of actual causation are often influenced by consideration of
defaults, typicality, and normality. A number of philosophers and computer
scientists have also suggested that an appeal to such factors can help deal
with problems facing existing accounts of actual causation. This paper develops
a flexible formal framework for incorporating defaults, typicality, and
normality into an account of actual causation. The resulting account takes
actual causation to be both graded and comparative. We then show how our
account would handle a number of standard cases.
Some Options for L1-Subspace Signal Processing
Authors: Panos P. Markopoulos, George N. Karystinos, Dimitris A. Pados
Date: 2013-09-04 00:00:00
Tasks: Dimensionality Reduction, Direction of Arrival Estimation

We describe ways to define and calculate $L_1$-norm signal subspaces which
are less sensitive to outlying data than $L_2$-calculated subspaces. We focus
on the computation of the $L_1$ maximum-projection principal component of a
data matrix containing N signal samples of dimension D and conclude that the
general problem is formally NP-hard in asymptotically large N, D. We prove,
however, that the case of engineering interest of fixed dimension D and
asymptotically large sample support N is not and we present an optimal
algorithm of complexity $O(N^D)$. We generalize to multiple
$L_1$-max-projection components and present an explicit optimal $L_1$ subspace
calculation algorithm in the form of matrix nuclear-norm evaluations. We
conclude with illustrations of $L_1$-subspace signal processing in the fields
of data dimensionality reduction and direction-of-arrival estimation.
Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification
Authors: Rashmi Gupta, Nisheeth Joshi, Iti Mathur
Date: 2013-09-04 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, General Classification, Machine Translation, Sentence, Translation

This paper considers the problem for estimating the quality of machine
translation outputs which are independent of human intervention and are
generally addressed using machine learning techniques.There are various
measures through which a machine learns translations quality. Automatic
Evaluation metrics produce good co-relation at corpus level but cannot produce
the same results at the same segment or sentence level. In this paper 16
features are extracted from the input sentences and their translations and a
quality score is obtained based on Bayesian inference produced from training
data.
Learning to answer questions
Authors: Ana Cristina Mendes, Luísa Coheur, Sérgio Curto
Date: 2013-09-04 00:00:00
Tasks: Open-Domain Question Answering, Question Answering

We present an open-domain Question-Answering system that learns to answer
questions based on successful past interactions. We follow a pattern-based
approach to Answer-Extraction, where (lexico-syntactic) patterns that relate a
question to its answer are automatically learned and used to answer future
questions. Results show that our approach contributes to the system's best
performance when it is conjugated with typical Answer-Extraction strategies.
Moreover, it allows the system to learn with the answered questions and to
rectify wrong or unsolved past questions.
Boosting in Location Space
Authors: Damian Eads, David Helmbold, Ed Rosten
Date: 2013-09-04 00:00:00
Tasks: Object, object-detection, Object Detection

The goal of object detection is to find objects in an image. An object
detector accepts an image and produces a list of locations as $(x,y)$ pairs.
Here we introduce a new concept: {\bf location-based boosting}. Location-based
boosting differs from previous boosting algorithms because it optimizes a new
spatial loss function to combine object detectors, each of which may have
marginal performance, into a single, more accurate object detector. A
structured representation of object locations as a list of $(x,y)$ pairs is a
more natural domain for object detection than the spatially unstructured
representation produced by classifiers. Furthermore, this formulation allows us
to take advantage of the intuition that large areas of the background are
uninteresting and it is not worth expending computational effort on them. This
results in a more scalable algorithm because it does not need to take measures
to prevent the background data from swamping the foreground data such as
subsampling or applying an ad-hoc weighting to the pixels. We first present the
theory of location-based boosting, and then motivate it with empirical results
on a challenging data set.
Thermal Human face recognition based on Haar wavelet transform and series matching technique
Authors: Ayan Seal, Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri, Dipak kr. Basu
Date: 2013-09-04 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, image-classification, Image Classification

Thermal infrared (IR) images represent the heat patterns emitted from hot
object and they do not consider the energies reflected from an object. Objects
living or non-living emit different amounts of IR energy according to their
body temperature and characteristics. Humans are homoeothermic and hence
capable of maintaining constant temperature under different surrounding
temperature. Face recognition from thermal (IR) images should focus on changes
of temperature on facial blood vessels. These temperature changes can be
regarded as texture features of images and wavelet transform is a very good
tool to analyze multi-scale and multi-directional texture. Wavelet transform is
also used for image dimensionality reduction, by removing redundancies and
preserving original features of the image. The sizes of the facial images are
normally large. So, the wavelet transform is used before image similarity is
measured. Therefore this paper describes an efficient approach of human face
recognition based on wavelet transform from thermal IR images. The system
consists of three steps. At the very first step, human thermal IR face image is
preprocessed and the face region is only cropped from the entire image.
Secondly, Haar wavelet is used to extract low frequency band from the cropped
face region. Lastly, the image classification between the training images and
the test images is done, which is based on low-frequency components. The
proposed approach is tested on a number of human thermal infrared face images
created at our own laboratory and Terravic Facial IR Database. Experimental
results indicated that the thermal infra red face images can be recognized by
the proposed system effectively. The maximum success of 95% recognition has
been achieved.
Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
Authors: Ayan Seal, Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-04 00:00:00
Tasks: Face Recognition, General Classification

In this paper, a thermal infra red face recognition system for human
identification and verification using blood perfusion data and back propagation
feed forward neural network is proposed. The system consists of three steps. At
the very first step face region is cropped from the colour 24-bit input images.
Secondly face features are extracted from the croped region, which will be
taken as the input of the back propagation feed forward neural network in the
third step and classification and recognition is carried out. The proposed
approaches are tested on a number of human thermal infra red face images
created at our own laboratory. Experimental results reveal the higher degree
performance
Advances in the Logical Representation of Lexical Semantics
Authors: Bruno Mery, Christian Retoré
Date: 2013-09-04 00:00:00

The integration of lexical semantics and pragmatics in the analysis of the
meaning of natural lan- guage has prompted changes to the global framework
derived from Montague. In those works, the original lexicon, in which words
were assigned an atomic type of a single-sorted logic, has been re- placed by a
set of many-facetted lexical items that can compose their meaning with salient
contextual properties using a rich typing system as a guide. Having related our
proposal for such an expanded framework \LambdaTYn, we present some recent
advances in the logical formalisms associated, including constraints on lexical
transformations and polymorphic quantifiers, and ongoing discussions and
research on the granularity of the type system and the limits of transitivity.
A Comparative Study of Human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP)
Authors: Ayan Seal, Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-04 00:00:00
Tasks: Face Recognition

Thermal infra-red (IR) images focus on changes of temperature distribution on
facial muscles and blood vessels. These temperature changes can be regarded as
texture features of images. A comparative study of face recognition methods
working in thermal spectrum is carried out in this paper. In these study two
local-matching methods based on Haar wavelet transform and Local Binary Pattern
(LBP) are analyzed. Wavelet transform is a good tool to analyze multi-scale,
multi-direction changes of texture. Local binary patterns (LBP) are a type of
feature used for classification in computer vision. Firstly, human thermal IR
face image is preprocessed and cropped the face region only from the entire
image. Secondly, two different approaches are used to extract the features from
the cropped face region. In the first approach, the training images and the
test images are processed with Haar wavelet transform and the LL band and the
average of LH/HL/HH bands sub-images are created for each face image. Then a
total confidence matrix is formed for each face image by taking a weighted sum
of the corresponding pixel values of the LL band and average band. For LBP
feature extraction, each of the face images in training and test datasets is
divided into 161 numbers of sub images, each of size 8X8 pixels. For each such
sub images, LBP features are extracted which are concatenated in row wise
manner. PCA is performed separately on the individual feature set for
dimensionality reeducation. Finally two different classifiers are used to
classify face images. One such classifier multi-layer feed forward neural
network and another classifier is minimum distance classifier. The Experiments
have been performed on the database created at our own laboratory and Terravic
Facial IR Database.
Automated Thermal Face recognition based on Minutiae Extraction
Authors: Ayan Seal, Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri, Dipak kr. Basu
Date: 2013-09-04 00:00:00
Tasks: Face Recognition

In this paper an efficient approach for human face recognition based on the
use of minutiae points in thermal face image is proposed. The thermogram of
human face is captured by thermal infra-red camera. Image processing methods
are used to pre-process the captured thermogram, from which different
physiological features based on blood perfusion data are extracted. Blood
perfusion data are related to distribution of blood vessels under the face
skin. In the present work, three different methods have been used to get the
blood perfusion image, namely bit-plane slicing and medial axis transform,
morphological erosion and medial axis transform, sobel edge operators.
Distribution of blood vessels is unique for each person and a set of extracted
minutiae points from a blood perfusion data of a human face should be unique
for that face. Two different methods are discussed for extracting minutiae
points from blood perfusion data. For extraction of features entire face image
is partitioned into equal size blocks and the total number of minutiae points
from each block is computed to construct final feature vector. Therefore, the
size of the feature vectors is found to be same as total number of blocks
considered. A five layer feed-forward back propagation neural network is used
as the classification tool. A number of experiments were conducted to evaluate
the performance of the proposed face recognition methodologies with varying
block size on the database created at our own laboratory. It has been found
that the first method supercedes the other two producing an accuracy of 97.62%
with block size 16X16 for bit-plane 4.
Minutiae Based Thermal Face Recognition using Blood Perfusion Data
Authors: Ayan Seal, Mita Nasipuri, Debotosh Bhattacharjee, Dipak Kumar Basu
Date: 2013-09-04 00:00:00
Tasks: Face Recognition, General Classification

This paper describes an efficient approach for human face recognition based
on blood perfusion data from infra-red face images. Blood perfusion data are
characterized by the regional blood flow in human tissue and therefore do not
depend entirely on surrounding temperature. These data bear a great potential
for deriving discriminating facial thermogram for better classification and
recognition of face images in comparison to optical image data. Blood perfusion
data are related to distribution of blood vessels under the face skin. A
distribution of blood vessels are unique for each person and as a set of
extracted minutiae points from a blood perfusion data of a human face should be
unique for that face. There may be several such minutiae point sets for a
single face but all of these correspond to that particular face only. Entire
face image is partitioned into equal blocks and the total number of minutiae
points from each block is computed to construct final vector. Therefore, the
size of the feature vectors is found to be same as total number of blocks
considered. For classification, a five layer feed-forward backpropagation
neural network has been used. A number of experiments were conducted to
evaluate the performance of the proposed face recognition system with varying
block sizes. Experiments have been performed on the database created at our own
laboratory. The maximum success of 91.47% recognition has been achieved with
block size 8X8.
Efficient binary tomographic reconstruction
Authors: Stephane Roux, Hugo Leclerc, François Hild
Date: 2013-09-04 00:00:00

Tomographic reconstruction of a binary image from few projections is
considered. A novel {\em heuristic} algorithm is proposed, the central element
of which is a nonlinear transformation $\psi(p)=\log(p/(1-p))$ of the
probability $p$ that a pixel of the sought image be 1-valued. It consists of
backprojections based on $\psi(p)$ and iterative corrections. Application of
this algorithm to a series of artificial test cases leads to exact binary
reconstructions, (i.e recovery of the binary image for each single pixel) from
the knowledge of projection data over a few directions. Images up to $10^6$
pixels are reconstructed in a few seconds. A series of test cases is performed
for comparison with previous methods, showing a better efficiency and reduced
computation times.
On the Robustness of Temporal Properties for Stochastic Models
Authors: Ezio Bartocci, Luca Bortolussi, Laura Nenzi, Guido Sanguinetti
Date: 2013-09-03 00:00:00

Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic
Hybrid Automata (SHA) are powerful formalisms to model and to reason about the
dynamics of biological systems, due to their ability to capture the
stochasticity inherent in biological processes. A classical question in formal
modelling with clear relevance to biological modelling is the model checking
problem. i.e. calculate the probability that a behaviour, expressed for
instance in terms of a certain temporal logic formula, may occur in a given
stochastic process. However, one may not only be interested in the notion of
satisfiability, but also in the capacity of a system to mantain a particular
emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic
noise, or by possible small changes in the model parameters. To address this
issue, researchers from the verification community have recently proposed
several notions of robustness for temporal logic providing suitable definitions
of distance between a trajectory of a (deterministic) dynamical system and the
boundaries of the set of trajectories satisfying the property of interest. The
contributions of this paper are twofold. First, we extend the notion of
robustness to stochastic systems, showing that this naturally leads to a
distribution of robustness scores. By discussing two examples, we show how to
approximate the distribution of the robustness score and its key indicators:
the average robustness and the conditional average robustness. Secondly, we
show how to combine these indicators with the satisfaction probability to
address the system design problem, where the goal is to optimize some control
parameters of a stochastic model in order to best maximize robustness of the
desired specifications.