pwc-archive/papers-with-abstracts huggingface dataset:
Contextual Abductive Reasoning with Side-Effects
Authors: Luís Moniz Pereira, Emmanuelle-Anna Dietz, Steffen Hölldobler
Date: 2014-05-14 00:00:00
Tasks: valid

The belief bias effect is a phenomenon which occurs when we think that we
judge an argument based on our reasoning, but are actually influenced by our
beliefs and prior knowledge. Evans, Barston and Pollard carried out a
psychological syllogistic reasoning task to prove this effect. Participants
were asked whether they would accept or reject a given syllogism. We discuss
one specific case which is commonly assumed to be believable but which is
actually not logically valid. By introducing abnormalities, abduction and
background knowledge, we adequately model this case under the weak completion
semantics. Our formalization reveals new questions about possible extensions in
abductive reasoning. For instance, observations and their explanations might
include some relevant prior abductive contextual information concerning some
side-effect or leading to a contestable or refutable side-effect. A weaker
notion indicates the support of some relevant consequences by a prior abductive
context. Yet another definition describes jointly supported relevant
consequences, which captures the idea of two observations containing mutually
supportive side-effects. Though motivated with and exemplified by the running
psychology application, the various new general abductive context definitions
are introduced here and given a declarative semantics for the first time, and
have a much wider scope of application. Inspection points, a concept introduced
by Pereira and Pinto, allows us to express these definitions syntactically and
intertwine them into an operational semantics.
Iterative Non-Local Shrinkage Algorithm for MR Image Reconstruction
Authors: Yasir Q. Moshin, Greg Ongie, Mathews Jacob
Date: 2014-05-15 00:00:00
Tasks: Image Reconstruction

We introduce a fast iterative non-local shrinkage algorithm to recover MRI
data from undersampled Fourier measurements. This approach is enabled by the
reformulation of current non-local schemes as an alternating algorithm to
minimize a global criterion. The proposed algorithm alternates between a
non-local shrinkage step and a quadratic subproblem. We derive analytical
shrinkage rules for several penalties that are relevant in non-local
regularization. The redundancy in the searches used to evaluate the shrinkage
steps are exploited using filtering operations. The resulting algorithm is
observed to be considerably faster than current alternating non-local
algorithms. The comparisons of the proposed scheme with state-of-the-art
regularization schemes show a considerable reduction in alias artifacts and
preservation of edges.
On learning to localize objects with minimal supervision
Authors: Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell
Date: 2014-03-05 00:00:00
Tasks: Weakly Supervised Object Detection

Learning to localize objects with minimal supervision is an important problem
in computer vision, since large fully annotated datasets are extremely costly
to obtain. In this paper, we propose a new method that achieves this goal with
only image-level labels of whether the objects are present or not. Our approach
combines a discriminative submodular cover problem for automatically
discovering a set of positive object windows with a smoothed latent SVM
formulation. The latter allows us to leverage efficient quasi-Newton
optimization techniques. Our experiments demonstrate that the proposed approach
provides a 50% relative improvement in mean average precision over the current
state-of-the-art on PASCAL VOC 2007 detection.
CDF-Intervals: A Reliable Framework to Reason about Data with Uncertainty
Authors: Aya Saad
Date: 2014-05-15 00:00:00

This research introduces a new constraint domain for reasoning about data
with uncertainty. It extends convex modeling with the notion of p-box to gain
additional quantifiable information on the data whereabouts. Unlike existing
approaches, the p-box envelops an unknown probability instead of approximating
its representation. The p-box bounds are uniform cumulative distribution
functions (cdf) in order to employ linear computations in the probabilistic
domain. The reasoning by means of p-box cdf-intervals is an interval
computation which is exerted on the real domain then it is projected onto the
cdf domain. This operation conveys additional knowledge represented by the
obtained probabilistic bounds. Empirical evaluation shows that, with minimal
overhead, the output solution set realizes a full enclosure of the data along
with tighter bounds on its probabilistic distributions.
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
Authors: Yichao Lu, Dean P. Foster
Date: 2014-05-15 00:00:00
Tasks: Computational Efficiency, regression

We propose a new two stage algorithm LING for large scale regression
problems. LING has the same risk as the well known Ridge Regression under the
fixed design setting and can be computed much faster. Our experiments have
shown that LING performs well in terms of both prediction accuracy and
computational efficiency compared with other large scale regression algorithms
like Gradient Descent, Stochastic Gradient Descent and Principal Component
Regression on both simulated and real datasets.
Méthodes pour la représentation informatisée de données lexicales / Methoden der Speicherung lexikalischer Daten
Authors: Laurent Romary, Andreas Witt
Date: 2014-05-15 00:00:00
Tasks: Management, Translation

In recent years, new developments in the area of lexicography have altered
not only the management, processing and publishing of lexicographical data, but
also created new types of products such as electronic dictionaries and
thesauri. These expand the range of possible uses of lexical data and support
users with more flexibility, for instance in assisting human translation. In
this article, we give a short and easy-to-understand introduction to the
problematic nature of the storage, display and interpretation of lexical data.
We then describe the main methods and specifications used to build and
represent lexical data. This paper is targeted for the following groups of
people: linguists, lexicographers, IT specialists, computer linguists and all
others who wish to learn more about the modelling, representation and
visualization of lexical knowledge. This paper is written in two languages:
French and German.
Randomized Approximation of the Gram Matrix: Exact Computation and Probabilistic Bounds
Authors: John T. Holodnak, Ilse C. F. Ipsen
Date: 2013-10-05 00:00:00

Given a real matrix A with n columns, the problem is to approximate the Gram
product AA^T by c << n weighted outer products of columns of A. Necessary and
sufficient conditions for the exact computation of AA^T (in exact arithmetic)
from c >= rank(A) columns depend on the right singular vector matrix of A. For
a Monte-Carlo matrix multiplication algorithm by Drineas et al. that samples
outer products, we present probabilistic bounds for the 2-norm relative error
due to randomization. The bounds depend on the stable rank or the rank of A,
but not on the matrix dimensions. Numerical experiments illustrate that the
bounds are informative, even for stringent success probabilities and matrices
of small dimension. We also derive bounds for the smallest singular value and
the condition number of matrices obtained by sampling rows from orthonormal
matrices.
Properties of Stable Model Semantics Extensions
Authors: Mário Abrantes, Luís Moniz Pereira
Date: 2014-05-15 00:00:00
Tasks: model

The stable model (SM) semantics lacks the properties of existence, relevance
and cumulativity. If we prospectively consider the class of conservative
extensions of SM semantics (i.e., semantics that for each normal logic program
P retrieve a superset of the set of stable models of P), one may wander how do
the semantics of this class behave in what concerns the aforementioned
properties. That is the type of issue dealt with in this paper. We define a
large class of conservative extensions of the SM semantics, dubbed affix stable
model semantics, ASM, and study the above referred properties into two
non-disjoint subfamilies of the class ASM, here dubbed ASMh and ASMm. From this
study a number of results stem which facilitate the assessment of semantics in
the class ASMh U ASMm with respect to the properties of existence, relevance
and cumulativity, whilst unveiling relations among these properties. As a
result of the approach taken in our work, light is shed on the characterization
of the SM semantics, as we show that the properties of (lack of) existence and
(lack of) cautious monotony are equivalent, which opposes statements on this
issue that may be found in the literature; we also characterize the relevance
failure of SM semantics in a more clear way than usually stated in the
literature.
Speeding up Convolutional Neural Networks with Low Rank Expansions
Authors: Max Jaderberg, Andrea Vedaldi, Andrew Zisserman
Date: 2014-05-15 00:00:00
Tasks: CPU, GPU, Model Compression

The focus of this paper is speeding up the evaluation of convolutional neural
networks. While delivering impressive results across a range of computer vision
and machine learning tasks, these networks are computationally demanding,
limiting their deployability. Convolutional layers generally consume the bulk
of the processing time, and so in this work we present two simple schemes for
drastically speeding up these layers. This is achieved by exploiting
cross-channel or filter redundancy to construct a low rank basis of filters
that are rank-1 in the spatial domain. Our methods are architecture agnostic,
and can be easily applied to existing CPU and GPU convolutional frameworks for
tuneable speedup performance. We demonstrate this with a real world network
designed for scene text character recognition, showing a possible 2.5x speedup
with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy,
still achieving state-of-the-art on standard benchmarks.
Logistic Regression: Tight Bounds for Stochastic and Online Optimization
Authors: Elad Hazan, Tomer Koren, Kfir. Y. Levy
Date: 2014-05-15 00:00:00
Tasks: regression

The logistic loss function is often advocated in machine learning and
statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this
paper we investigate the question of whether these smoothness and convexity
properties make the logistic loss preferable to other widely considered options
such as the hinge loss. We show that in contrast to known asymptotic bounds, as
long as the number of prediction/optimization iterations is sub exponential,
the logistic loss provides no improvement over a generic non-smooth loss
function such as the hinge loss. In particular we show that the convergence
rate of stochastic logistic optimization is bounded from below by a polynomial
in the diameter of the decision set and the number of prediction iterations,
and provide a matching tight upper bound. This resolves the COLT open problem
of McMahan and Streeter (2012).
Application of Methods for Syntax Analysis of Context-Free Languages to Query Evaluation of Logic Programs
Authors: Heike Stephan
Date: 2014-05-15 00:00:00

My research goal is to employ a parser generation algorithm based on the
Earley parsing algorithm to the evaluation and compilation of queries to logic
programs, especially to deductive databases. By means of partial deduction,
from a query to a logic program a parameterized automaton is to be generated
that models the evaluation of this query. This automaton can be compiled to
executable code; thus we expect a speedup in runtime of query evaluation. An
extended abstract/ full version of a paper accepted to be presented at the
Doctoral Consortium of the 30th International Conference on Logic Programming
(ICLP 2014), July 19-22, Vienna, Austria
Multi-Criteria Optimal Planning for Energy Policies in CLP
Authors: Marco Gavanelli, Stefano Bragaglia, Michela Milano, Federico Chesani, Elisa Marengo, Paolo Cagnoli
Date: 2014-05-15 00:00:00

In the policy making process a number of disparate and diverse issues such as
economic development, environmental aspects, as well as the social acceptance
of the policy, need to be considered. A single person might not have all the
required expertises, and decision support systems featuring optimization
components can help to assess policies. Leveraging on previous work on
Strategic Environmental Assessment, we developed a fully-fledged system that is
able to provide optimal plans with respect to a given objective, to perform
multi-objective optimization and provide sets of Pareto optimal plans, and to
visually compare them. Each plan is environmentally assessed and its footprint
is evaluated. The heart of the system is an application developed in a popular
Constraint Logic Programming system on the Reals sort. It has been equipped
with a web service module that can be queried through standard interfaces, and
an intuitive graphic user interface.
Co-clustering of Fuzzy Lagged Data
Authors: Eran Shaham, David Sarne, Boaz Ben-Moshe
Date: 2014-02-06 00:00:00
Tasks: Clustering, Missing Values

The paper focuses on mining patterns that are characterized by a fuzzy lagged
relationship between the data objects forming them. Such a regulatory mechanism
is quite common in real life settings. It appears in a variety of fields:
finance, gene expression, neuroscience, crowds and collective movements are but
a limited list of examples. Mining such patterns not only helps in
understanding the relationship between objects in the domain, but assists in
forecasting their future behavior. For most interesting variants of this
problem, finding an optimal fuzzy lagged co-cluster is an NP-complete problem.
We thus present a polynomial-time Monte-Carlo approximation algorithm for
mining fuzzy lagged co-clusters. We prove that for any data matrix, the
algorithm mines a fuzzy lagged co-cluster with fixed probability, which
encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns
overhead and completely no rows overhead. Moreover, the algorithm handles
noise, anti-correlations, missing values and overlapping patterns. The
algorithm was extensively evaluated using both artificial and real datasets.
The results not only corroborate the ability of the algorithm to efficiently
mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the
importance of including the fuzziness in the lagged-pattern model.
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Authors: Jinseok Nam, Jungi Kim, Eneldo Loza Mencía, Iryna Gurevych, Johannes Fürnkranz
Date: 2013-12-19 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Neural networks have recently been proposed for multi-label classification
because they are able to capture and model label dependencies in the output
layer. In this work, we investigate limitations of BP-MLL, a neural network
(NN) architecture that aims at minimizing pairwise ranking error. Instead, we
propose to use a comparably simple NN approach with recently proposed learning
techniques for large-scale multi-label text classification tasks. In
particular, we show that BP-MLL's ranking loss minimization can be efficiently
and effectively replaced with the commonly used cross entropy error function,
and demonstrate that several advances in neural network training that have been
developed in the realm of deep learning can be effectively employed in this
setting. Our experimental results show that simple NN models equipped with
advanced techniques such as rectified linear units, dropout, and AdaGrad
perform as well as or even outperform state-of-the-art approaches on six
large-scale textual datasets with diverse characteristics.
Minimum Model Semantics for Extensional Higher-order Logic Programming with Negation
Authors: Angelos Charalambidis, Zoltán Ésik, Panos Rondogiannis
Date: 2014-05-15 00:00:00
Tasks: Negation

Extensional higher-order logic programming has been introduced as a
generalization of classical logic programming. An important characteristic of
this paradigm is that it preserves all the well-known properties of traditional
logic programming. In this paper we consider the semantics of negation in the
context of the new paradigm. Using some recent results from non-monotonic
fixed-point theory, we demonstrate that every higher-order logic program with
negation has a unique minimum infinite-valued model. In this way we obtain the
first purely model-theoretic semantics for negation in extensional higher-order
logic programming. Using our approach, we resolve an old paradox that was
introduced by W. W. Wadge in order to demonstrate the semantic difficulties of
higher-order logic programming.
Transaction Logic with (Complex) Events
Authors: Ana Sofia Gomes, José Júlio Alferes
Date: 2014-05-15 00:00:00
Tasks: General Knowledge

This work deals with the problem of combining reactive features, such as the
ability to respond to events and define complex events, with the execution of
transactions over general Knowledge Bases (KBs).
  With this as goal, we build on Transaction Logic (TR), a logic precisely
designed to model and execute transactions in KBs defined by arbitrary logic
theories. In it, transactions are written in a logic-programming style, by
combining primitive update operations over a general KB, with the usual logic
programming connectives and some additional connectives e.g. to express
sequence of actions. While TR is a natural choice to deal with transactions, it
remains the question whether TR can be used to express complex events, but also
to deal simultaneously with the detection of complex events and the execution
of transactions. In this paper we show that the former is possible while the
latter is not. For that, we start by illustrating how TR can express complex
events, and in particular, how SNOOP event expressions can be translated in the
logic. Afterwards, we show why TR fails to deal with the two issues together,
and to solve the intended problem propose Transaction Logic with Events, its
syntax, model theory and executional semantics. The achieved solution is a
non-monotonic extension of TR, which guarantees that every complex event
detected in a transaction is necessarily responded.
Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts
Authors: Domagoj Margan, Ana Meštrović, Sanda Martinčić-Ipšić
Date: 2014-05-15 00:00:00
Tasks: Sentence

This paper studies the properties of the Croatian texts via complex networks.
We present network properties of normal and shuffled Croatian texts for
different shuffling principles: on the sentence level and on the text level. In
both experiments we preserved the vocabulary size, word and sentence frequency
distributions. Additionally, in the first shuffling approach we preserved the
sentence structure of the text and the number of words per sentence. Obtained
results showed that degree rank distributions exhibit no substantial deviation
in shuffled networks, and strength rank distributions are preserved due to the
same word frequencies. Therefore, standard approach to study the structure of
linguistic co-occurrence networks showed no clear difference among the
topologies of normal and shuffled texts. Finally, we showed that the in- and
out- selectivity values from shuffled texts are constantly below selectivity
values calculated from normal texts. Our results corroborate that the node
selectivity measure can capture structural differences between original and
shuffled Croatian texts.
Oracle Inequalities for High Dimensional Vector Autoregressions
Authors: Anders Bredahl Kock, Laurent A. F. Callot
Date: 2013-11-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper establishes non-asymptotic oracle inequalities for the prediction
error and estimation accuracy of the LASSO in stationary vector autoregressive
models. These inequalities are used to establish consistency of the LASSO even
when the number of parameters is of a much larger order of magnitude than the
sample size. We also give conditions under which no relevant variables are
excluded.
  Next, non-asymptotic probabilities are given for the Adaptive LASSO to select
the correct sparsity pattern. We then give conditions under which the Adaptive
LASSO reveals the correct sparsity pattern asymptotically. We establish that
the estimates of the non-zero coefficients are asymptotically equivalent to the
oracle assisted least squares estimator. This is used to show that the rate of
convergence of the estimates of the non-zero coefficients is identical to the
one of least squares only including the relevant covariates.
INAUT, a Controlled Language for the French Coast Pilot Books Instructions nautiques
Authors: Yannis Haralambous, Julie Sauvage-Vincent, John Puentes
Date: 2014-05-15 00:00:00

We describe INAUT, a controlled natural language dedicated to collaborative
update of a knowledge base on maritime navigation and to automatic generation
of coast pilot books (Instructions nautiques) of the French National
Hydrographic and Oceanographic Service SHOM. INAUT is based on French language
and abundantly uses georeferenced entities. After describing the structure of
the overall system, giving details on the language and on its generation, and
discussing the three major applications of INAUT (document production,
interaction with ENCs and collaborative updates of the knowledge base), we
conclude with future extensions and open problems.
Effective Bayesian Modeling of Groups of Related Count Time Series
Authors: Nicolas Chapados
Date: 2014-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

Time series of counts arise in a variety of forecasting applications, for
which traditional models are generally inappropriate. This paper introduces a
hierarchical Bayesian formulation applicable to count time series that can
easily account for explanatory variables and share statistical strength across
groups of related time series. We derive an efficient approximate inference
technique, and illustrate its performance on a number of datasets from supply
chain planning.
Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques
Authors: Priyanka Saini
Date: 2014-05-15 00:00:00
Tasks: General Classification, Management

Data Mining is the process of extracting useful patterns from the huge amount
of database and many data mining techniques are used for mining these patterns.
Recently, one of the remarkable facts in higher educational institute is the
rapid growth data and this educational data is expanding quickly without any
advantage to the educational management. The main aim of the management is to
refine the education standard; therefore by applying the various data mining
techniques on this data one can get valuable information. This research study
proposed the "classification model for the student's enrollment process in
higher educational courses using data mining techniques". Additionally, this
study contributes to finding some patterns that are meaningful to management.
Vicious Circle Principle and Logic Programs with Aggregates
Authors: Michael Gelfond, Yuanlin Zhang
Date: 2014-05-14 00:00:00

The paper presents a knowledge representation language $\mathcal{A}log$ which
extends ASP with aggregates. The goal is to have a language based on simple
syntax and clear intuitive and mathematical semantics. We give some properties
of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison
with other approaches.