pwc-archive/papers-with-abstracts huggingface dataset:
Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering
Authors: Swayambhoo Jain, Seung-Jun Kim, Georgios B. Giannakis
Date: 2014-09-30 00:00:00
Tasks: Clustering, Computational Efficiency, regression

Multi-cell cooperative processing with limited backhaul traffic is studied
for cellular uplinks. Aiming at reduced backhaul overhead, a
sparsity-regularized multi-cell receive-filter design problem is formulated.
Both unstructured distributed cooperation as well as clustered cooperation, in
which base station groups are formed for tight cooperation, are considered.
Dynamic clustered cooperation, where the sparse equalizer and the cooperation
clusters are jointly determined, is solved via alternating minimization based
on spectral clustering and group-sparse regression. Furthermore, decentralized
implementations of both unstructured and clustered cooperation schemes are
developed for scalability, robustness and computational efficiency. Extensive
numerical tests verify the efficacy of the proposed methods.
Language Models for Image Captioning: The Quirks and What Works
Authors: Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell
Date: 2015-05-07 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Image Captioning, Language Modeling, Language Modelling, Sentence

Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent sentence. The second uses the penultimate activation layer of the
CNN as input to a recurrent neural network (RNN) that then generates the
caption sequence. In this paper, we compare the merits of these different
language modeling approaches for the first time by using the same
state-of-the-art CNN as input. We examine issues in the different approaches,
including linguistic irregularities, caption repetition, and data set overlap.
By combining key aspects of the ME and RNN methods, we achieve a new record
performance over previously published results on the benchmark COCO dataset.
However, the gains we see in BLEU do not translate to human judgments.
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
Authors: Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen
Date: 2015-09-10 00:00:00
Tasks: Sign Language Recognition

Sign language recognition is important for natural and convenient
communication between deaf community and hearing majority. We take the highly
efficient initial step of automatic fingerspelling recognition system using
convolutional neural networks (CNNs) from depth maps. In this work, we consider
relatively larger number of classes compared with the previous literature. We
train CNNs for the classification of 31 alphabets and numbers using a subset of
collected depth data from multiple subjects. While using different learning
configurations, such as hyper-parameter selection with and without validation,
we achieve 99.99% accuracy for observed signers and 83.58% to 85.49% accuracy
for new signers. The result shows that accuracy improves as we include more
data from different subjects during training. The processing time is 3 ms for
the prediction of a single image. To the best of our knowledge, the system
achieves the highest accuracy and speed. The trained model and dataset is
available on our repository.
Dynamical spectral unmixing of multitemporal hyperspectral images
Authors: Simon Henrot, Jocelyn Chanussot, Christian Jutten
Date: 2015-10-14 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we consider the problem of unmixing a time series of
hyperspectral images. We propose a dynamical model based on linear mixing
processes at each time instant. The spectral signatures and fractional
abundances of the pure materials in the scene are seen as latent variables, and
assumed to follow a general dynamical structure. Based on a simplified version
of this model, we derive an efficient spectral unmixing algorithm to estimate
the latent variables by performing alternating minimizations. The performance
of the proposed approach is demonstrated on synthetic and real multitemporal
hyperspectral images.
Inheritance in Object-Oriented Knowledge Representation
Authors: Dmytro Terletskyi
Date: 2015-10-14 00:00:00
Tasks: General Classification, Object

This paper contains the consideration of inheritance mechanism in such
knowledge representation models as object-oriented programming, frames and
object-oriented dynamic networks. In addition, inheritance within
representation of vague and imprecise knowledge are also discussed. New types
of inheritance, general classification of all known inheritance types and
approach, which allows avoiding in many cases problems with exceptions,
redundancy and ambiguity within object-oriented dynamic networks and their
fuzzy extension, are introduced in the paper. The proposed approach bases on
conception of homogeneous and inhomogeneous or heterogeneous class of objects,
which allow building of inheritance hierarchy more flexibly and efficiently.
Exploiters-Based Knowledge Extraction in Object-Oriented Knowledge Representation
Authors: Dmytro Terletskyi
Date: 2015-10-14 00:00:00
Tasks: Object

This paper contains the consideration of knowledge extraction mechanisms of
such object-oriented knowledge representation models as frames, object-oriented
programming and object-oriented dynamic networks. In addition, conception of
universal exploiters within object-oriented dynamic networks is also discussed.
The main result of the paper is introduction of new exploiters-based knowledge
extraction approach, which provides generation of a finite set of new classes
of objects, based on the basic set of classes. The methods for calculation of
quantity of new classes, which can be obtained using proposed approach, and of
quantity of types, which each of them describes, are proposed. Proof that basic
set of classes, extended according to proposed approach, together with union
exploiter create upper semilattice is given. The approach always allows
generating of finitely defined set of new classes of objects for any
object-oriented dynamic network. A quantity of these classes can be precisely
calculated before the generation. It allows saving of only basic set of classes
in the knowledge base.
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
Authors: Paris Giampouras, Konstantinos Themelis, Athanasios Rontogiannis, Konstantinos Koutroumbas
Date: 2015-04-07 00:00:00
Tasks: Compressive Sensing

In a plethora of applications dealing with inverse problems, e.g. in image
processing, social networks, compressive sensing, biological data processing
etc., the signal of interest is known to be structured in several ways at the
same time. This premise has recently guided the research to the innovative and
meaningful idea of imposing multiple constraints on the parameters involved in
the problem under study. For instance, when dealing with problems whose
parameters form sparse and low-rank matrices, the adoption of suitably combined
constraints imposing sparsity and low-rankness, is expected to yield
substantially enhanced estimation results. In this paper, we address the
spectral unmixing problem in hyperspectral images. Specifically, two novel
unmixing algorithms are introduced, in an attempt to exploit both spatial
correlation and sparse representation of pixels lying in homogeneous regions of
hyperspectral images. To this end, a novel convex mixed penalty term is first
defined consisting of the sum of the weighted $\ell_1$ and the weighted nuclear
norm of the abundance matrix corresponding to a small area of the image
determined by a sliding square window. This penalty term is then used to
regularize a conventional quadratic cost function and impose simultaneously
sparsity and row-rankness on the abundance matrix. The resulting regularized
cost function is minimized by a) an incremental proximal sparse and low-rank
unmixing algorithm and b) an algorithm based on the alternating minimization
method of multipliers (ADMM). The effectiveness of the proposed algorithms is
illustrated in experiments conducted both on simulated and real data.
Object-Oriented Dynamic Networks
Authors: Dmytro Terletskyi, Alexandr Provotar
Date: 2015-10-14 00:00:00
Tasks: Logical Reasoning, Object

This paper contains description of such knowledge representation model as
Object-Oriented Dynamic Network (OODN), which gives us an opportunity to
represent knowledge, which can be modified in time, to build new relations
between objects and classes of objects and to represent results of their
modifications. The model is based on representation of objects via their
properties and methods. It gives us a possibility to classify the objects and,
in a sense, to build hierarchy of their types. Furthermore, it enables to
represent relation of modification between concepts, to build new classes of
objects based on existing classes and to create sets and multisets of concepts.
OODN can be represented as a connected and directed graph, where nodes are
concepts and edges are relations between them. Using such model of knowledge
representation, we can consider modifications of knowledge and movement through
the graph of network as a process of logical reasoning or finding the right
solutions or creativity, etc. The proposed approach gives us an opportunity to
model some aspects of human knowledge system and main mechanisms of human
thought, in particular getting a new experience and knowledge.
Universal and Determined Constructors of Multisets of Objects
Authors: Dmytro Terletskyi
Date: 2015-10-14 00:00:00
Tasks: Object

This paper contains analysis of creation of sets and multisets as an approach
for modeling of some aspects of human thinking. The creation of sets is
considered within constructive object-oriented version of set theory (COOST),
from different sides, in particular classical set theory, object-oriented
programming (OOP) and development of intelligent information systems (IIS). The
main feature of COOST in contrast to other versions of set theory is an
opportunity to describe essences of objects more precisely, using their
properties and methods, which can be applied to them. That is why this version
of set theory is object-oriented and close to OOP. Within COOST, the author
proposes universal constructor of multisets of objects that gives us a
possibility to create arbitrary multisets of objects. In addition, a few
determined constructors of multisets of objects, which allow creating
multisets, using strictly defined schemas, also are proposed in the paper. Such
constructors are very useful in cases of very big cardinalities of multisets,
because they give us an opportunity to calculate a multiplicity of each object
and cardinality of multiset before its creation. The proposed constructors of
multisets of objects allow us to model in a sense corresponding processes of
human thought, that in turn give us an opportunity to develop IIS, using these
tools.
VB calibration to improve the interface between phone recognizer and i-vector extractor
Authors: Niko Brümmer
Date: 2015-10-12 00:00:00

The EM training algorithm of the classical i-vector extractor is often
incorrectly described as a maximum-likelihood method. The i-vector model is
however intractable: the likelihood itself and the hidden-variable posteriors
needed for the EM algorithm cannot be computed in closed form. We show here
that the classical i-vector extractor recipe is actually a mean-field
variational Bayes (VB) recipe.
  This theoretical VB interpretation turns out to be of further use, because it
also offers an interpretation of the newer phonetic i-vector extractor recipe,
thereby unifying the two flavours of extractor.
  More importantly, the VB interpretation is also practically useful: it
suggests ways of modifying existing i-vector extractors to make them more
accurate. In particular, in existing methods, the approximate VB posterior for
the GMM states is fixed, while only the parameters of the generative model are
adapted. Here we explore the possibility of also mildly adjusting (calibrating)
those posteriors, so that they better fit the generative model.
Mathematical Foundations for Designing and Development of Intelligent Systems of Information Analysis
Authors: D. O. Terletskyi, O. I. Provotar
Date: 2015-10-14 00:00:00
Tasks: General Classification, Object

This article is an attempt to combine different ways of working with sets of objects and their classes for designing and development of artificial intelligent systems (AIS) of analysis information, using object-oriented programming (OOP). This paper contains analysis of basic concepts of OOP and their relation with set theory and artificial intelligence (AI). Process of sets and multisets creation from different sides, in particular mathematical set theory, OOP and AI is considered. Definition of object and its properties, homogeneous and inhomogeneous classes of objects, set of objects, multiset of objects and constructive methods of their creation and classification are proposed. In addition, necessity of some extension of existing OOP tools for the purpose of practical implementation AIS of analysis information, using proposed approach, is shown.
Embarrassingly Parallel Variational Inference in Nonconjugate Models
Authors: Willie Neiswanger, Chong Wang, Eric Xing
Date: 2015-10-14 00:00:00
Tasks: Variational Inference

We develop a parallel variational inference (VI) procedure for use in
data-distributed settings, where each machine only has access to a subset of
data and runs VI independently, without communicating with other machines. This
type of "embarrassingly parallel" procedure has recently been developed for
MCMC inference algorithms; however, in many cases it is not possible to
directly extend this procedure to VI methods without requiring certain
restrictive exponential family conditions on the form of the model.
Furthermore, most existing (nonparallel) VI methods are restricted to use on
conditionally conjugate models, which limits their applicability. To combat
these issues, we make use of the recently proposed nonparametric VI to
facilitate an embarrassingly parallel VI procedure that can be applied to a
wider scope of models, including to nonconjugate models. We derive our
embarrassingly parallel VI algorithm, analyze our method theoretically, and
demonstrate our method empirically on a few nonconjugate models.
Artificial Intelligence and Asymmetric Information Theory
Authors: Tshilidzi Marwala, Evan Hurwitz
Date: 2015-10-10 00:00:00

When human agents come together to make decisions, it is often the case that
one human agent has more information than the other. This phenomenon is called
information asymmetry and this distorts the market. Often if one human agent
intends to manipulate a decision in its favor the human agent can signal wrong
or right information. Alternatively, one human agent can screen for information
to reduce the impact of asymmetric information on decisions. With the advent of
artificial intelligence, signaling and screening have been made easier. This
paper studies the impact of artificial intelligence on the theory of asymmetric
information. It is surmised that artificial intelligent agents reduce the
degree of information asymmetry and thus the market where these agents are
deployed become more efficient. It is also postulated that the more artificial
intelligent agents there are deployed in the market the less is the volume of
trades in the market. This is because for many trades to happen the asymmetry
of information on goods and services to be traded should exist, creating a
sense of arbitrage.
Distributional Sentence Entailment Using Density Matrices
Authors: Esma Balkir, Mehrnoosh Sadrzadeh, Bob Coecke
Date: 2015-06-22 00:00:00
Tasks: Lexical Entailment, Sentence

Categorical compositional distributional model of Coecke et al. (2010)
suggests a way to combine grammatical composition of the formal, type logical
models with the corpus based, empirical word representations of distributional
semantics. This paper contributes to the project by expanding the model to also
capture entailment relations. This is achieved by extending the representations
of words from points in meaning space to density operators, which are
probability distributions on the subspaces of the space. A symmetric measure of
similarity and an asymmetric measure of entailment is defined, where lexical
entailment is measured using von Neumann entropy, the quantum variant of
Kullback-Leibler divergence. Lexical entailment, combined with the composition
map on word representations, provides a method to obtain entailment relations
on the level of sentences. Truth theoretic and corpus-based examples are
provided.
Fine-Grained Product Class Recognition for Assisted Shopping
Authors: Marian George, Dejan Mircic, Gábor Sörös, Christian Floerkemeier, Friedemann Mattern
Date: 2015-10-14 00:00:00
Tasks: Active Learning

Assistive solutions for a better shopping experience can improve the quality
of life of people, in particular also of visually impaired shoppers. We present
a system that visually recognizes the fine-grained product classes of items on
a shopping list, in shelves images taken with a smartphone in a grocery store.
Our system consists of three components: (a) We automatically recognize useful
text on product packaging, e.g., product name and brand, and build a mapping of
words to product classes based on the large-scale GroceryProducts dataset. When
the user populates the shopping list, we automatically infer the product class
of each entered word. (b) We perform fine-grained product class recognition
when the user is facing a shelf. We discover discriminative patches on product
packaging to differentiate between visually similar product classes and to
increase the robustness against continuous changes in product design. (c) We
continuously improve the recognition accuracy through active learning. Our
experiments show the robustness of the proposed method against cross-domain
challenges, and the scalability to an increasing number of products with
minimal re-training.
UAVs using Bayesian Optimization to Locate WiFi Devices
Authors: Mattia Carpin, Stefano Rosati, Mohammad Emtiyaz Khan, Bixio Rimoldi
Date: 2015-10-13 00:00:00
Tasks: Bayesian Optimization

We address the problem of localizing non-collaborative WiFi devices in a
large region. Our main motive is to localize humans by localizing their WiFi
devices, e.g. during search-and-rescue operations after a natural disaster. We
use an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs)
to collect signal-strength measurements at informative locations. The problem
is challenging since the measurement is received at arbitrary times and they
are received only when the UAV is in close proximity to the device. For these
reasons, it is extremely important to make prudent decision with very few
measurements. We use the Bayesian optimization approach based on Gaussian
process (GP) regression. This approach works well for our application since GPs
give reliable predictions with very few measurements while Bayesian
optimization makes a judicious trade-off between exploration and exploitation.
In field experiments conducted over a region of 1000 $\times$ 1000 $m^2$, we
show that our approach reduces the search area to less than 100 meters around
the WiFi device within 5 minutes only. Overall, our approach localizes the
device in less than 15 minutes with an error of less than 20 meters.
Quantifying origin and character of long-range correlations in narrative texts
Authors: Stanisław Drożdż, Paweł Oświęcimka, Andrzej Kulig, Jarosław Kwapień, Katarzyna Bazarnik, Iwona Grabska-Gradzińska, Jan Rybicki, Marek Stanuszek
Date: 2014-12-29 00:00:00
Tasks: Sentence

In natural language using short sentences is considered efficient for
communication. However, a text composed exclusively of such sentences looks
technical and reads boring. A text composed of long ones, on the other hand,
demands significantly more effort for comprehension. Studying characteristics
of the sentence length variability (SLV) in a large corpus of world-famous
literary texts shows that an appealing and aesthetic optimum appears somewhere
in between and involves selfsimilar, cascade-like alternation of various
lengths sentences. A related quantitative observation is that the power spectra
S(f) of thus characterized SLV universally develop a convincing `1/f^beta'
scaling with the average exponent beta =~ 1/2, close to what has been
identified before in musical compositions or in the brain waves. An
overwhelming majority of the studied texts simply obeys such fractal attributes
but especially spectacular in this respect are hypertext-like, "stream of
consciousness" novels. In addition, they appear to develop structures
characteristic of irreducibly interwoven sets of fractals called multifractals.
Scaling of S(f) in the present context implies existence of the long-range
correlations in texts and appearance of multifractality indicates that they
carry even a nonlinear component. A distinct role of the full stops in inducing
the long-range correlations in texts is evidenced by the fact that the above
quantitative characteristics on the long-range correlations manifest themselves
in variation of the full stops recurrence times along texts, thus in SLV, but
to a much lesser degree in the recurrence times of the most frequent words. In
this latter case the nonlinear correlations, thus multifractality, disappear
even completely for all the texts considered. Treated as one extra word, the
full stops at the same time appear to obey the Zipfian rank-frequency
distribution, however.
Answering Fuzzy Conjunctive Queries over Finitely Valued Fuzzy Ontologies
Authors: Stefan Borgwardt, Theofilos Mailis, Rafael Peñaloza, Anni-Yasmin Turhan
Date: 2015-08-11 00:00:00

Fuzzy Description Logics (DLs) provide a means for representing vague
knowledge about an application domain. In this paper, we study fuzzy extensions
of conjunctive queries (CQs) over the DL $\mathcal{SROIQ}$ based on finite
chains of degrees of truth. To answer such queries, we extend a well-known
technique that reduces the fuzzy ontology to a classical one, and use classical
DL reasoners as a black box. We improve the complexity of previous reduction
techniques for finitely valued fuzzy DLs, which allows us to prove tight
complexity results for answering certain kinds of fuzzy CQs. We conclude with
an experimental evaluation of a prototype implementation, showing the
feasibility of our approach.
Multiresolution Search of the Rigid Motion Space for Intensity Based Registration
Authors: Behrooz Nasihatkon, Fredrik Kahl
Date: 2015-10-14 00:00:00
Tasks: global-optimization, Image Registration

We study the relation between the target functions of low-resolution and
high-resolution intensity-based registration for the class of rigid
transformations. Our results show that low resolution target values can tightly
bound the high-resolution target function in natural images. This can help with
analyzing and better understanding the process of multiresolution image
registration. It also gives a guideline for designing multiresolution
algorithms in which the search space in higher resolution registration is
restricted given the fitness values for lower resolution image pairs. To
demonstrate this, we incorporate our multiresolution technique into a Lipschitz
global optimization framework. We show that using the multiresolution scheme
can result in large gains in the efficiency of such algorithms. The method is
evaluated by applying to 2D and 3D registration problems as well as the
detection of reflective symmetry in 2D and 3D images.