pwc-archive/papers-with-abstracts huggingface dataset:
Argumentation Semantics for Prioritised Default Logic
Authors: Anthony P. Young, Sanjay Modgil, Odinaldo Rodrigues
Date: 2015-06-26 00:00:00

We endow prioritised default logic (PDL) with argumentation semantics using
the ASPIC+ framework for structured argumentation, and prove that the
conclusions of the justified arguments are exactly the prioritised default
extensions. Argumentation semantics for PDL will allow for the application of
argument game proof theories to the process of inference in PDL, making the
reasons for accepting a conclusion transparent and the inference process more
intuitive. This also opens up the possibility for argumentation-based
distributed reasoning and communication amongst agents with PDL representations
of mental attitudes.
Beyond Semantic Image Segmentation : Exploring Efficient Inference in Video
Authors: Subarna Tripathi, Serge Belongie, Truong Nguyen
Date: 2015-07-01 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Video Semantic Segmentation

We explore the efficiency of the CRF inference module beyond image level
semantic segmentation. The key idea is to combine the best of two worlds of
semantic co-labeling and exploiting more expressive models. Similar to
[Alvarez14] our formulation enables us perform inference over ten thousand
images within seconds. On the other hand, it can handle higher-order clique
potentials similar to [vineet2014] in terms of region-level label consistency
and context in terms of co-occurrences. We follow the mean-field updates for
higher order potentials similar to [vineet2014] and extend the spatial
smoothness and appearance kernels [DenseCRF13] to address video data inspired
by [Alvarez14]; thus making the system amenable to perform video semantic
segmentation most effectively.
A Tool for Computing and Estimating the Volume of the Solution Space of SMT(LA)
Authors: Cunjing Ge, Feifei Ma, Jian Zhang
Date: 2015-07-01 00:00:00

There are already quite a few tools for solving the Satisfiability Modulo
Theories (SMT) problems. In this paper, we present \texttt{VolCE}, a tool for
counting the solutions of SMT constraints, or in other words, for computing the
volume of the solution space. Its input is essentially a set of Boolean
combinations of linear constraints, where the numeric variables are either all
integers or all reals, and each variable is bounded. The tool extends SMT
solving with integer solution counting and volume computation/estimation for
convex polytopes. Effective heuristics are adopted, which enable the tool to
deal with high-dimensional problem instances efficiently and accurately.
Prior Polarity Lexical Resources for the Italian Language
Authors: Valeria Borzì, Simone Faro, Arianna Pavone, Sabrina Sansone
Date: 2015-07-01 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

In this paper we present SABRINA (Sentiment Analysis: a Broad Resource for
Italian Natural language Applications) a manually annotated prior polarity
lexical resource for Italian natural language applications in the field of
opinion mining and sentiment induction. The resource consists in two different
sets, an Italian dictionary of more than 277.000 words tagged with their prior
polarity value, and a set of polarity modifiers, containing more than 200
words, which can be used in combination with non neutral terms of the
dictionary in order to induce the sentiment of Italian compound terms. To the
best of our knowledge this is the first prior polarity manually annotated
resource which has been developed for the Italian natural language.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
Authors: Fang Liu, Junfei Shi, Licheng Jiao, Hongying Liu, Shuyuan Yang, Jie Wu, Hongxia Hao, Jialing Yuan
Date: 2015-07-01 00:00:00
Tasks: General Classification, image-classification, Image Classification

For polarimetric SAR (PolSAR) image classification, it is a challenge to
classify the aggregated terrain types, such as the urban area, into semantic
homogenous regions due to sharp bright-dark variations in intensity. The
aggregated terrain type is formulated by the similar ground objects aggregated
together. In this paper, a polarimetric hierarchical semantic model (PHSM) is
firstly proposed to overcome this disadvantage based on the constructions of a
primal-level and a middle-level semantic. The primal-level semantic is a
polarimetric sketch map which consists of sketch segments as the sparse
representation of a PolSAR image. The middle-level semantic is a region map
which can extract semantic homogenous regions from the sketch map by exploiting
the topological structure of sketch segments. Mapping the region map to the
PolSAR image, a complex PolSAR scene is partitioned into aggregated, structural
and homogenous pixel-level subspaces with the characteristics of relatively
coherent terrain types in each subspace. Then, according to the characteristics
of three subspaces above, three specific methods are adopted, and furthermore
polarimetric information is exploited to improve the segmentation result.
Experimental results on PolSAR data sets with different bands and sensors
demonstrate that the proposed method is superior to the state-of-the-art
methods in region homogeneity and edge preservation for terrain classification.
On the Equivalence of Factorized Information Criterion Regularization and the Chinese Restaurant Process Prior
Authors: Shaohua Li
Date: 2015-06-30 00:00:00
Tasks: Model Selection

Factorized Information Criterion (FIC) is a recently developed information
criterion, based on which a novel model selection methodology, namely
Factorized Asymptotic Bayesian (FAB) Inference, has been developed and
successfully applied to various hierarchical Bayesian models. The Dirichlet
Process (DP) prior, and one of its well known representations, the Chinese
Restaurant Process (CRP), derive another line of model selection methods. FIC
can be viewed as a prior distribution over the latent variable configurations.
Under this view, we prove that when the parameter dimensionality $D_{c}=2$, FIC
is equivalent to CRP. We argue that when $D_{c}>2$, FIC avoids an inherent
problem of DP/CRP, i.e. the data likelihood will dominate the impact of the
prior, and thus the model selection capability will weaken as $D_{c}$
increases. However, FIC overestimates the data likelihood. As a result, FIC may
be overly biased towards models with less components. We propose a natural
generalization of FIC, which finds a middle ground between CRP and FIC, and may
yield more accurate model selection results than FIC.
A Study of Gradient Descent Schemes for General-Sum Stochastic Games
Authors: H. L. Prasad, Shalabh Bhatnagar
Date: 2015-07-01 00:00:00

Zero-sum stochastic games are easy to solve as they can be cast as simple
Markov decision processes. This is however not the case with general-sum
stochastic games. A fairly general optimization problem formulation is
available for general-sum stochastic games by Filar and Vrieze [2004]. However,
the optimization problem there has a non-linear objective and non-linear
constraints with special structure. Since gradients of both the objective as
well as constraints of this optimization problem are well defined, gradient
based schemes seem to be a natural choice. We discuss a gradient scheme tuned
for two-player stochastic games. We show in simulations that this scheme indeed
converges to a Nash equilibrium, for a simple terrain exploration problem
modelled as a general-sum stochastic game. However, it turns out that only
global minima of the optimization problem correspond to Nash equilibria of the
underlying general-sum stochastic game, while gradient schemes only guarantee
convergence to local minima. We then provide important necessary conditions for
gradient schemes to converge to Nash equilibria in general-sum stochastic
games.
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation
Authors: Guillaume Corriveau, Raynald Guilbault, Antoine Tahan, Robert Sabourin
Date: 2015-07-01 00:00:00
Tasks: Diversity

Numerous genotypic diversity measures (GDMs) are available in the literature
to assess the convergence status of an evolutionary algorithm (EA) or describe
its search behavior. In a recent study, the authors of this paper drew
attention to the need for a GDM validation framework. In response, this study
proposes three requirements (monotonicity in individual varieties, twinning,
and monotonicity in distance) that can clearly portray any GDMs. These
diversity requirements are analysed by means of controlled population
arrangements. In this paper four GDMs are evaluated with the proposed
validation framework. The results confirm that properly evaluating population
diversity is a rather difficult task, as none of the analysed GDMs complies
with all the diversity requirements.
Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks
Authors: Jongpil Kim, Vladimir Pavlovic
Date: 2015-06-30 00:00:00
Tasks: General Classification

In this paper, we propose a novel method to find characteristic landmarks on
ancient Roman imperial coins using deep convolutional neural network models
(CNNs). We formulate an optimization problem to discover class-specific regions
while guaranteeing specific controlled loss of accuracy. Analysis on
visualization of the discovered region confirms that not only can the proposed
method successfully find a set of characteristic regions per class, but also
the discovered region is consistent with human expert annotations. We also
propose a new framework to recognize the Roman coins which exploits
hierarchical structure of the ancient Roman coins using the state-of-the-art
classification power of the CNNs adopted to a new task of coin classification.
Experimental results show that the proposed framework is able to effectively
recognize the ancient Roman coins. For this research, we have collected a new
Roman coin dataset where all coins are annotated and consist of observe (head)
and reverse (tail) images.
Fast Cross-Validation for Incremental Learning
Authors: Pooria Joulani, András György, Csaba Szepesvári
Date: 2015-06-30 00:00:00
Tasks: Incremental Learning

Cross-validation (CV) is one of the main tools for performance estimation and
parameter tuning in machine learning. The general recipe for computing CV
estimate is to run a learning algorithm separately for each CV fold, a
computationally expensive process. In this paper, we propose a new approach to
reduce the computational burden of CV-based performance estimation. As opposed
to all previous attempts, which are specific to a particular learning model or
problem domain, we propose a general method applicable to a large class of
incremental learning algorithms, which are uniquely fitted to big data
problems. In particular, our method applies to a wide range of supervised and
unsupervised learning tasks with different performance criteria, as long as the
base learning algorithm is incremental. We show that the running time of the
algorithm scales logarithmically, rather than linearly, in the number of CV
folds. Furthermore, the algorithm has favorable properties for parallel and
distributed implementation. Experiments with state-of-the-art incremental
learning algorithms confirm the practicality of the proposed method.
Selective Inference and Learning Mixed Graphical Models
Authors: Jason D. Lee
Date: 2015-06-30 00:00:00
Tasks: Model Selection, valid

This thesis studies two problems in modern statistics. First, we study
selective inference, or inference for hypothesis that are chosen after looking
at the data. The motiving application is inference for regression coefficients
selected by the lasso. We present the Condition-on-Selection method that allows
for valid selective inference, and study its application to the lasso, and
several other selection algorithms.
  In the second part, we consider the problem of learning the structure of a
pairwise graphical model over continuous and discrete variables. We present a
new pairwise model for graphical models with both continuous and discrete
variables that is amenable to structure learning. In previous work, authors
have considered structure learning of Gaussian graphical models and structure
learning of discrete models. Our approach is a natural generalization of these
two lines of work to the mixed case. The penalization scheme involves a novel
symmetric use of the group-lasso norm and follows naturally from a particular
parametrization of the model. We provide conditions under which our estimator
is model selection consistent in the high-dimensional regime.
Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Authors: Quanquan Gu, Yuan Cao, Yang Ning, Han Liu
Date: 2015-02-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper proposes a unified framework to quantify local and global
inferential uncertainty for high dimensional nonparanormal graphical models. In
particular, we consider the problems of testing the presence of a single edge
and constructing a uniform confidence subgraph. Due to the presence of unknown
marginal transformations, we propose a pseudo likelihood based inferential
approach. In sharp contrast to the existing high dimensional score test method,
our method is free of tuning parameters given an initial estimator, and extends
the scope of the existing likelihood based inferential framework. Furthermore,
we propose a U-statistic multiplier bootstrap method to construct the
confidence subgraph. We show that the constructed subgraph is contained in the
true graph with probability greater than a given nominal level. Compared with
existing methods for constructing confidence subgraphs, our method does not
rely on Gaussian or sub-Gaussian assumptions. The theoretical properties of the
proposed inferential methods are verified by thorough numerical experiments and
real data analysis.
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and POS Tagging for Micro-blog Texts
Authors: Xipeng Qiu, Peng Qian, Liusong Yin, Shiyu Wu, Xuanjing Huang
Date: 2015-05-28 00:00:00
Tasks: Chinese Word Segmentation, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation

In this paper, we give an overview for the shared task at the 4th CCF
Conference on Natural Language Processing \& Chinese Computing (NLPCC 2015):
Chinese word segmentation and part-of-speech (POS) tagging for micro-blog
texts. Different with the popular used newswire datasets, the dataset of this
shared task consists of the relatively informal micro-texts. The shared task
has two sub-tasks: (1) individual Chinese word segmentation and (2) joint
Chinese word segmentation and POS Tagging. Each subtask has three tracks to
distinguish the systems with different resources. We first introduce the
dataset and task, then we characterize the different approaches of the
participating systems, report the test results, and provide a overview analysis
of these results. An online system is available for open registration and
evaluation at http://nlp.fudan.edu.cn/nlpcc2015.
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision
Authors: Ali Madooei, Mark S. Drew, Hossein Hajimirsadeghi
Date: 2015-06-30 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning

We propose a novel approach to identify one of the most significant
dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish
structure. In this paper, we achieve this goal in a Multiple Instance Learning
framework using only image-level labels of whether the feature is present or
not. As the output, we predict the image classification label and as well
localize the feature in the image. Experiments are conducted on a challenging
dataset with results outperforming state-of-the-art. This study provides an
improvement on the scope of modelling for computerized image analysis of skin
lesions, in particular in that it puts forward a framework for identification
of dermoscopic local features from weakly-labelled data.
On anthropomorphic decision making in a model observer
Authors: Ali R. N. Avanaki, Kathryn S. Espig, Tom R. L. Kimpe, Andrew D. A. Maidment
Date: 2015-06-30 00:00:00
Tasks: Decision Making, Human Detection, Lesion Detection

By analyzing human readers' performance in detecting small round lesions in
simulated digital breast tomosynthesis background in a location known exactly
scenario, we have developed a model observer that is a better predictor of
human performance with different levels of background complexity (i.e.,
anatomical and quantum noise). Our analysis indicates that human observers
perform a lesion detection task by combining a number of sub-decisions, each an
indicator of the presence of a lesion in the image stack. This is in contrast
to a channelized Hotelling observer, where the detection task is conducted
holistically by thresholding a single decision variable, made from an optimally
weighted linear combination of channels. However, it seems that the sub-par
performance of human readers compared to the CHO cannot be fully explained by
their reliance on sub-decisions, or perhaps we do not consider a sufficient
number of sub-decisions. To bridge the gap between the performances of human
readers and the model observer based upon sub-decisions, we use an additive
noise model, the power of which is modulated with the level of background
complexity. The proposed model observer better predicts the fast drop in human
detection performance with background complexity.
Aging display's effect on interpretation of digital pathology slides
Authors: Ali R. N. Avanaki, Kathryn S. Espig, Sameer Sawhney, Liron Pantanowitz, Anil V. Parwani, Albert Xthona, Tom R. L. Kimpe
Date: 2015-06-30 00:00:00
Tasks: Diagnostic

It is our conjecture that the variability of colors in a pathology image
effects the interpretation of pathology cases, whether it is diagnostic
accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital
pathology images are analyzed to quantify the perceived difference in color
that occurs due to display aging, in particular a change in the maximum
luminance, white point, and color gamut. The digital pathology images studied
include diagnostically important features, such as the conspicuity of nuclei.
Three different display aging models are applied to images: aging of luminance
& chrominance, aging of chrominance only, and a stabilized luminance &
chrominance (i.e., no aging). These display models and images are then used to
compare conspicuity of nuclei using CIE deltaE2000, a perceptual color
difference metric. The effect of display aging using these display models and
images is further analyzed through a human reader study designed to quantify
the effects from a clinical perspective. Results from our reader study indicate
significant impact of aged displays on workflow as well as diagnosis as follow.
As compared to the originals (no-aging), slides with the effect of aging
simulated were significantly more difficult to read (p-value of 0.0005) and
took longer to score (p-value of 0.02). Moreover, luminance+chrominance aging
significantly reduced inter-session percent agreement of diagnostic scores
(p-value of 0.0418).
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis
Authors: Christian Widmer, Marius Kloft, Vipin T Sreedharan, Gunnar Rätsch
Date: 2015-06-30 00:00:00
Tasks: Multi-Task Learning

We present a general regularization-based framework for Multi-task learning
(MTL), in which the similarity between tasks can be learned or refined using
$\ell_p$-norm Multiple Kernel learning (MKL). Based on this very general
formulation (including a general loss function), we derive the corresponding
dual formulation using Fenchel duality applied to Hermitian matrices. We show
that numerous established MTL methods can be derived as special cases from
both, the primal and dual of our formulation. Furthermore, we derive a modern
dual-coordinate descend optimization strategy for the hinge-loss variant of our
formulation and provide convergence bounds for our algorithm. As a special
case, we implement in C++ a fast LibLinear-style solver for $\ell_p$-norm MKL.
In the experimental section, we analyze various aspects of our algorithm such
as predictive performance and ability to reconstruct task relationships on
biologically inspired synthetic data, where we have full control over the
underlying ground truth. We also experiment on a new dataset from the domain of
computational biology that we collected for the purpose of this paper. It
concerns the prediction of transcription start sites (TSS) over nine organisms,
which is a crucial task in gene finding. Our solvers including all discussed
special cases are made available as open-source software as part of the SHOGUN
machine learning toolbox (available at \url{http://shogun.ml}).
Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation
Authors: Saehoon Yi, Vladimir Pavlovic
Date: 2015-06-30 00:00:00
Proceeding: ICCV 2015 12
Tasks: Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

Video segmentation is a stepping stone to understanding video context. Video
segmentation enables one to represent a video by decomposing it into coherent
regions which comprise whole or parts of objects. However, the challenge
originates from the fact that most of the video segmentation algorithms are
based on unsupervised learning due to expensive cost of pixelwise video
annotation and intra-class variability within similar unconstrained video
classes. We propose a Markov Random Field model for unconstrained video
segmentation that relies on tight integration of multiple cues: vertices are
defined from contour based superpixels, unary potentials from temporal smooth
label likelihood and pairwise potentials from global structure of a video.
Multi-cue structure is a breakthrough to extracting coherent object regions for
unconstrained videos in absence of supervision. Our experiments on VSB100
dataset show that the proposed model significantly outperforms competing
state-of-the-art algorithms. Qualitative analysis illustrates that video
segmentation result of the proposed model is consistent with human perception
of objects.
Algorithms for Lipschitz Learning on Graphs
Authors: Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman
Date: 2015-05-01 00:00:00

We develop fast algorithms for solving regression problems on graphs where
one is given the value of a function at some vertices, and must find its
smoothest possible extension to all vertices. The extension we compute is the
absolutely minimal Lipschitz extension, and is the limit for large $p$ of
$p$-Laplacian regularization. We present an algorithm that computes a minimal
Lipschitz extension in expected linear time, and an algorithm that computes an
absolutely minimal Lipschitz extension in expected time $\widetilde{O} (m n)$.
The latter algorithm has variants that seem to run much faster in practice.
These extensions are particularly amenable to regularization: we can perform
$l_{0}$-regularization on the given values in polynomial time and
$l_{1}$-regularization on the initial function values and on graph edge weights
in time $\widetilde{O} (m^{3/2})$.