pwc-archive/papers-with-abstracts huggingface dataset:
Describing Textures in the Wild
Authors: Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi
Date: 2013-11-14 00:00:00
Proceeding: CVPR 2014 6
Tasks: Material Recognition, Object Recognition

Patterns and textures are defining characteristics of many natural objects: a
shirt can be striped, the wings of a butterfly can be veined, and the skin of
an animal can be scaly. Aiming at supporting this analytical dimension in image
understanding, we address the challenging problem of describing textures with
semantic attributes. We identify a rich vocabulary of forty-seven texture terms
and use them to describe a large dataset of patterns collected in the wild.The
resulting Describable Textures Dataset (DTD) is the basis to seek for the best
texture representation for recognizing describable texture attributes in
images. We port from object recognition to texture recognition the Improved
Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized
texture descriptors not only on our problem, but also in established material
recognition datasets. We also show that the describable attributes are
excellent texture descriptors, transferring between datasets and tasks; in
particular, combined with IFV, they significantly outperform the
state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks.
We also demonstrate that they produce intuitive descriptions of materials and
Internet images.
Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction
Authors: Mahmood A. Rashid, M. A. Hakim Newton, Md. Tamjidul Hoque, Abdul Sattar
Date: 2013-11-15 00:00:00
Tasks: Protein Structure Prediction

Protein structure prediction (PSP) is computationally a very challenging
problem. The challenge largely comes from the fact that the energy function
that needs to be minimised in order to obtain the native structure of a given
protein is not clearly known. A high resolution 20x20 energy model could better
capture the behaviour of the actual energy function than a low resolution
energy model such as hydrophobic polar. However, the fine grained details of
the high resolution interaction energy matrix are often not very informative
for guiding the search. In contrast, a low resolution energy model could
effectively bias the search towards certain promising directions. In this
paper, we develop a genetic algorithm that mainly uses a high resolution energy
model for protein structure evaluation but uses a low resolution HP energy
model in focussing the search towards exploring structures that have
hydrophobic cores. We experimentally show that this mixing of energy models
leads to significant lower energy structures compared to the state-of-the-art
results.
Planning based on classification by induction graph
Authors: Sofia Benbelkacem, Baghdad Atmani, Mohamed Benamina
Date: 2013-11-15 00:00:00
Tasks: Classification, Decision Making, General Classification, Scheduling

In Artificial Intelligence, planning refers to an area of research that
proposes to develop systems that can automatically generate a result set, in
the form of an integrated decision-making system through a formal procedure,
known as plan. Instead of resorting to the scheduling algorithms to generate
plans, it is proposed to operate the automatic learning by decision tree to
optimize time. In this paper, we propose to build a classification model by
induction graph from a learning sample containing plans that have an associated
set of descriptors whose values change depending on each plan. This model will
then operate for classifying new cases by assigning the appropriate plan.
Video Text Localization using Wavelet and Shearlet Transforms
Authors: Purnendu Banerjee, B. B. Chaudhuri
Date: 2013-07-18 00:00:00
Tasks: Clustering, Text Detection

Text in video is useful and important in indexing and retrieving the video
documents efficiently and accurately. In this paper, we present a new method of
text detection using a combined dictionary consisting of wavelets and a
recently introduced transform called shearlets. Wavelets provide optimally
sparse expansion for point-like structures and shearlets provide optimally
sparse expansions for curve-like structures. By combining these two features we
have computed a high frequency sub-band to brighten the text part. Then K-means
clustering is used for obtaining text pixels from the Standard Deviation (SD)
of combined coefficient of wavelets and shearlets as well as the union of
wavelets and shearlets features. Text parts are obtained by grouping
neighboring regions based on geometric properties of the classified output
frame of unsupervised K-means classification. The proposed method tested on a
standard as well as newly collected database shows to be superior to some
existing methods.
Periodicity Extraction using Superposition of Distance Matching Function and One-dimensional Haar Wavelet Transform
Authors: V. Asha, N. U. Bhajantri, P. Nagabhushan
Date: 2013-11-15 00:00:00
Tasks: Texture Synthesis

Periodicity of a texture is one of the important visual characteristics and
is often used as a measure for textural discrimination at the structural level.
Knowledge about periodicity of a texture is very essential in the field of
texture synthesis and texture compression and also in the design of frieze and
wall papers. In this paper, we propose a method of periodicity extraction from
noisy images based on superposition of distance matching function (DMF) and
wavelet decomposition without de-noising the test images. Overall DMFs are
subjected to single-level Haar wavelet decomposition to obtain approximate and
detailed coefficients. Extracted coefficients help in determination of
periodicities in row and column directions. We illustrate the usefulness and
the effectiveness of the proposed method in a texture synthesis application.
Structural Weights in Ontology Matching
Authors: Mohammad Mehdi Keikha, Mohammad Ali Nematbakhsh, Behrouz Tork Ladani
Date: 2013-11-15 00:00:00
Tasks: Ontology Matching

Ontology matching finds correspondences between similar entities of different
ontologies. Two ontologies may be similar in some aspects such as structure,
semantic etc. Most ontology matching systems integrate multiple matchers to
extract all the similarities that two ontologies may have. Thus, we face a
major problem to aggregate different similarities. Some matching systems use
experimental weights for aggregation of similarities among different matchers
while others use machine learning approaches and optimization algorithms to
find optimal weights to assign to different matchers. However, both approaches
have their own deficiencies. In this paper, we will point out the problems and
shortcomings of current similarity aggregation strategies. Then, we propose a
new strategy, which enables us to utilize the structural information of
ontologies to get weights of matchers, for the similarity aggregation task. For
achieving this goal, we create a new Ontology Matching system which it uses
three available matchers, namely GMO, ISub and VDoc. We have tested our
similarity aggregation strategy on the OAEI 2012 data set. Experimental results
show significant improvements in accuracies of several cases, especially in
matching the classes of ontologies. We will compare the performance of our
similarity aggregation strategy with other well-known strategies
Ensemble Relational Learning based on Selective Propositionalization
Authors: Nicola Di Mauro, Floriana Esposito
Date: 2013-11-15 00:00:00
Tasks: Relational Reasoning

Dealing with structured data needs the use of expressive representation
formalisms that, however, puts the problem to deal with the computational
complexity of the machine learning process. Furthermore, real world domains
require tools able to manage their typical uncertainty. Many statistical
relational learning approaches try to deal with these problems by combining the
construction of relevant relational features with a probabilistic tool. When
the combination is static (static propositionalization), the constructed
features are considered as boolean features and used offline as input to a
statistical learner; while, when the combination is dynamic (dynamic
propositionalization), the feature construction and probabilistic tool are
combined into a single process. In this paper we propose a selective
propositionalization method that search the optimal set of relational features
to be used by a probabilistic learner in order to minimize a loss function. The
new propositionalization approach has been combined with the random subspace
ensemble method. Experiments on real-world datasets shows the validity of the
proposed method.
On Estimating Many Means, Selection Bias, and the Bootstrap
Authors: Noah Simon, Richard Simon
Date: 2013-11-15 00:00:00
Tasks: Selection bias

With recent advances in high throughput technology, researchers often find
themselves running a large number of hypothesis tests (thousands+) and esti-
mating a large number of effect-sizes. Generally there is particular interest
in those effects estimated to be most extreme. Unfortunately naive estimates of
these effect-sizes (even after potentially accounting for multiplicity in a
testing procedure) can be severely biased. In this manuscript we explore this
bias from a frequentist perspective: we give a formal definition, and show that
an oracle estimator using this bias dominates the naive maximum likelihood
estimate. We give a resampling estimator to approximate this oracle, and show
that it works well on simulated data. We also connect this to ideas in
empirical Bayes.
Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations
Authors: Shelley D. Dionne, Hiroki Sayama, Francis J. Yammarino
Date: 2013-11-14 00:00:00
Tasks: Clustering, Decision Making, Diversity

Collective, especially group-based, managerial decision making is crucial in
organizations. Using an evolutionary theoretic approach to collective decision
making, agent-based simulations were conducted to investigate how human
collective decision making would be affected by the agents' diversity in
problem understanding and/or behavior in discussion, as well as by their social
network structure. Simulation results indicated that groups with consistent
problem understanding tended to produce higher utility values of ideas and
displayed better decision convergence, but only if there was no group-level
bias in collective problem understanding. Simulation results also indicated the
importance of balance between selection-oriented (i.e., exploitative) and
variation-oriented (i.e., explorative) behaviors in discussion to achieve
quality final decisions. Expanding the group size and introducing non-trivial
social network structure generally improved the quality of ideas at the cost of
decision convergence. Simulations with different social network topologies
revealed collective decision making on small-world networks with high local
clustering tended to achieve highest decision quality more often than on random
or scale-free networks. Implications of this evolutionary theory and simulation
approach for future managerial research on collective, group, and multi-level
decision making are discussed.
Scalable Influence Estimation in Continuous-Time Diffusion Networks
Authors: Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha
Date: 2013-11-14 00:00:00
Proceeding: NeurIPS 2013 12

If a piece of information is released from a media site, can it spread, in 1
month, to a million web pages? This influence estimation problem is very
challenging since both the time-sensitive nature of the problem and the issue
of scalability need to be addressed simultaneously. In this paper, we propose a
randomized algorithm for influence estimation in continuous-time diffusion
networks. Our algorithm can estimate the influence of every node in a network
with |V| nodes and |E| edges to an accuracy of $\varepsilon$ using
$n=O(1/\varepsilon^2)$ randomizations and up to logarithmic factors
O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence
maximization algorithm, our proposed method is guaranteed to find a set of
nodes with an influence of at least (1-1/e)OPT-2$\varepsilon$, where OPT is the
optimal value. Experiments on both synthetic and real-world data show that the
proposed method can easily scale up to networks of millions of nodes while
significantly improves over previous state-of-the-arts in terms of the accuracy
of the estimated influence and the quality of the selected nodes in maximizing
the influence.
Uniform random generation of large acyclic digraphs
Authors: Jack Kuipers, Giusi Moffa
Date: 2012-02-29 00:00:00

Directed acyclic graphs are the basic representation of the structure
underlying Bayesian networks, which represent multivariate probability
distributions. In many practical applications, such as the reverse engineering
of gene regulatory networks, not only the estimation of model parameters but
the reconstruction of the structure itself is of great interest. As well as for
the assessment of different structure learning algorithms in simulation
studies, a uniform sample from the space of directed acyclic graphs is required
to evaluate the prevalence of certain structural features. Here we analyse how
to sample acyclic digraphs uniformly at random through recursive enumeration,
an approach previously thought too computationally involved. Based on
complexity considerations, we discuss in particular how the enumeration
directly provides an exact method, which avoids the convergence issues of the
alternative Markov chain methods and is actually computationally much faster.
The limiting behaviour of the distribution of acyclic digraphs then allows us
to sample arbitrarily large graphs. Building on the ideas of recursive
enumeration based sampling we also introduce a novel hybrid Markov chain with
much faster convergence than current alternatives while still being easy to
adapt to various restrictions. Finally we discuss how to include such
restrictions in the combinatorial enumeration and the new hybrid Markov chain
method for efficient uniform sampling of the corresponding graphs.
Flexible sampling of discrete data correlations without the marginal distributions
Authors: Alfredo Kalaitzis, Ricardo Silva
Date: 2013-06-12 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering, Dimensionality Reduction, parameter estimation

Learning the joint dependence of discrete variables is a fundamental problem
in machine learning, with many applications including prediction, clustering
and dimensionality reduction. More recently, the framework of copula modeling
has gained popularity due to its modular parametrization of joint
distributions. Among other properties, copulas provide a recipe for combining
flexible models for univariate marginal distributions with parametric families
suitable for potentially high dimensional dependence structures. More
radically, the extended rank likelihood approach of Hoff (2007) bypasses
learning marginal models completely when such information is ancillary to the
learning task at hand as in, e.g., standard dimensionality reduction problems
or copula parameter estimation. The main idea is to represent data by their
observable rank statistics, ignoring any other information from the marginals.
Inference is typically done in a Bayesian framework with Gaussian copulas, and
it is complicated by the fact this implies sampling within a space where the
number of constraints increases quadratically with the number of data points.
The result is slow mixing when using off-the-shelf Gibbs sampling. We present
an efficient algorithm based on recent advances on constrained Hamiltonian
Markov chain Monte Carlo that is simple to implement and does not require
paying for a quadratic cost in sample size.
Stratified Graphical Models - Context-Specific Independence in Graphical Models
Authors: Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander
Date: 2013-09-25 00:00:00
Tasks: Computer Security

Theory of graphical models has matured over more than three decades to
provide the backbone for several classes of models that are used in a myriad of
applications such as genetic mapping of diseases, credit risk evaluation,
reliability and computer security, etc. Despite of their generic applicability
and wide adoptance, the constraints imposed by undirected graphical models and
Bayesian networks have also been recognized to be unnecessarily stringent under
certain circumstances. This observation has led to the proposal of several
generalizations that aim at more relaxed constraints by which the models can
impose local or context-specific dependence structures. Here we consider an
additional class of such models, termed as stratified graphical models. We
develop a method for Bayesian learning of these models by deriving an
analytical expression for the marginal likelihood of data under a specific
subclass of decomposable stratified models. A non-reversible Markov chain Monte
Carlo approach is further used to identify models that are highly supported by
the posterior distribution over the model space. Our method is illustrated and
compared with ordinary graphical models through application to several real and
synthetic datasets.
High-dimensional learning of linear causal networks via inverse covariance estimation
Authors: Po-Ling Loh, Peter Bühlmann
Date: 2013-11-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We establish a new framework for statistical estimation of directed acyclic
graphs (DAGs) when data are generated from a linear, possibly non-Gaussian
structural equation model. Our framework consists of two parts: (1) inferring
the moralized graph from the support of the inverse covariance matrix; and (2)
selecting the best-scoring graph amongst DAGs that are consistent with the
moralized graph. We show that when the error variances are known or estimated
to close enough precision, the true DAG is the unique minimizer of the score
computed using the reweighted squared l_2-loss. Our population-level results
have implications for the identifiability of linear SEMs when the error
covariances are specified up to a constant multiple. On the statistical side,
we establish rigorous conditions for high-dimensional consistency of our
two-part algorithm, defined in terms of a "gap" between the true DAG and the
next best candidate. Finally, we demonstrate that dynamic programming may be
used to select the optimal DAG in linear time when the treewidth of the
moralized graph is bounded.
Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities
Authors: Seyed-Mehdi-Reza Beheshti, Srikumar Venugopal, Seung Hwan Ryu, Boualem Benatallah, Wei Wang
Date: 2013-11-14 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Cross Document Coreference Resolution

Information Extraction (IE) is the task of automatically extracting
structured information from unstructured/semi-structured machine-readable
documents. Among various IE tasks, extracting actionable intelligence from
ever-increasing amount of data depends critically upon Cross-Document
Coreference Resolution (CDCR) - the task of identifying entity mentions across
multiple documents that refer to the same underlying entity. Recently, document
datasets of the order of peta-/tera-bytes has raised many challenges for
performing effective CDCR such as scaling to large numbers of mentions and
limited representational power. The problem of analysing such datasets is
called "big data". The aim of this paper is to provide readers with an
understanding of the central concepts, subtasks, and the current
state-of-the-art in CDCR process. We provide assessment of existing
tools/techniques for CDCR subtasks and highlight big data challenges in each of
them to help readers identify important and outstanding issues for further
investigation. Finally, we provide concluding remarks and discuss possible
directions for future work.
Anytime Belief Propagation Using Sparse Domains
Authors: Sameer Singh, Sebastian Riedel, Andrew McCallum
Date: 2013-11-14 00:00:00
Tasks: Scheduling

Belief Propagation has been widely used for marginal inference, however it is
slow on problems with large-domain variables and high-order factors. Previous
work provides useful approximations to facilitate inference on such models, but
lacks important anytime properties such as: 1) providing accurate and
consistent marginals when stopped early, 2) improving the approximation when
run longer, and 3) converging to the fixed point of BP. To this end, we propose
a message passing algorithm that works on sparse (partially instantiated)
domains, and converges to consistent marginals using dynamic message
scheduling. The algorithm grows the sparse domains incrementally, selecting the
next value to add using prioritization schemes based on the gradients of the
marginal inference objective. Our experiments demonstrate local anytime
consistency and fast convergence, providing significant speedups over BP to
obtain low-error marginals: up to 25 times on grid models, and up to 6 times on
a real-world natural language processing task.
HINO: a BFO-aligned ontology representing human molecular interactions and pathways
Authors: Yongqun He, Zoushuang Xiang
Date: 2013-11-14 00:00:00
Tasks: Data Integration

Many database resources, such as Reactome, collect manually annotated
reactions, interactions, and pathways from peer-reviewed publications. The
interactors (e.g., a protein), interactions, and pathways in these data
resources are often represented as instances in using BioPAX, a standard
pathway data exchange format. However, these interactions are better
represented as classes (or universals) since they always occur given
appropriate conditions. This study aims to represent various human interaction
pathways and networks as classes via a formal ontology aligned with the Basic
Formal Ontology (BFO). Towards this goal, the Human Interaction Network
Ontology (HINO) was generated by extending the BFO-aligned Interaction Network
Ontology (INO). All human pathways and associated processes and interactors
listed in Reactome and represented in BioPAX were first converted to ontology
classes by aligning them under INO. Related terms and associated relations and
hierarchies from external ontologies (e.g., CHEBI and GO) were also retrieved
and imported into HINO. HINO ontology terms were resolved in the linked
ontology data server Ontobee. The RDF triples stored in the RDF triple store
are queryable through a SPARQL program. Such an ontology system supports
advanced pathway data integration and applications.