pwc-archive/papers-with-abstracts huggingface dataset:
A bio-inspired algorithm for fuzzy user equilibrium problem by aid of Physarum Polycephalum
Authors: Yang Liu, Xiaoge Zhang, Yong Deng
Date: 2014-06-09 00:00:00

The user equilibrium in traffic assignment problem is based on the fact that
travelers choose the minimum-cost path between every origin-destination pair
and on the assumption that such a behavior will lead to an equilibrium of the
traffic network. In this paper, we consider this problem when the traffic
network links are fuzzy cost. Therefore, a Physarum-type algorithm is developed
to unify the Physarum network and the traffic network for taking full of
advantage of Physarum Polycephalum's adaptivity in network design to solve the
user equilibrium problem. Eventually, some experiments are used to test the
performance of this method. The results demonstrate that our approach is
competitive when compared with other existing algorithms.
Adaptive Stochastic Alternating Direction Method of Multipliers
Authors: Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li
Date: 2013-12-16 00:00:00

The Alternating Direction Method of Multipliers (ADMM) has been studied for
years. The traditional ADMM algorithm needs to compute, at each iteration, an
(empirical) expected loss function on all training examples, resulting in a
computational complexity proportional to the number of training examples. To
reduce the time complexity, stochastic ADMM algorithms were proposed to replace
the expected function with a random loss function associated with one uniformly
drawn example plus a Bregman divergence. The Bregman divergence, however, is
derived from a simple second order proximal function, the half squared norm,
which could be a suboptimal choice.
  In this paper, we present a new family of stochastic ADMM algorithms with
optimal second order proximal functions, which produce a new family of adaptive
subgradient methods. We theoretically prove that their regret bounds are as
good as the bounds which could be achieved by the best proximal function that
can be chosen in hindsight. Encouraging empirical results on a variety of
real-world datasets confirm the effectiveness and efficiency of the proposed
algorithms.
Unsupervised Pretraining Encourages Moderate-Sparseness
Authors: Jun Li, Wei Luo, Jian Yang, Xiao-Tong Yuan
Date: 2013-12-20 00:00:00

It is well known that direct training of deep neural networks will generally
lead to poor results. A major progress in recent years is the invention of
various pretraining methods to initialize network parameters and it was shown
that such methods lead to good prediction performance. However, the reason for
the success of pretraining has not been fully understood, although it was
argued that regularization and better optimization play certain roles. This
paper provides another explanation for the effectiveness of pretraining, where
we show pretraining leads to a sparseness of hidden unit activation in the
resulting neural networks. The main reason is that the pretraining models can
be interpreted as an adaptive sparse coding. Compared to deep neural network
with sigmoid function, our experimental results on MNIST and Birdsong further
support this sparseness observation.
Learning directed acyclic graphs via bootstrap aggregating
Authors: Ru Wang, Jie Peng
Date: 2014-06-09 00:00:00

Probabilistic graphical models are graphical representations of probability
distributions. Graphical models have applications in many fields including
biology, social sciences, linguistic, neuroscience. In this paper, we propose
directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed
procedure is named as DAGBag. Specifically, an ensemble of DAGs is first
learned based on bootstrap resamples of the data and then an aggregated DAG is
derived by minimizing the overall distance to the entire ensemble. A family of
metrics based on the structural hamming distance is defined for the space of
DAGs (of a given node set) and is used for aggregation. Under the
high-dimensional-low-sample size setting, the graph learned on one data set
often has excessive number of false positive edges due to over-fitting of the
noise. Aggregation overcomes over-fitting through variance reduction and thus
greatly reduces false positives. We also develop an efficient implementation of
the hill climbing search algorithm of DAG learning which makes the proposed
method computationally competitive for the high-dimensional regime. The DAGBag
procedure is implemented in the R package dagbag.
RuleCNL: A Controlled Natural Language for Business Rule Specifications
Authors: Paul Brillant Feuto Njonko, Sylviane Cardey, Peter Greenfield, Walid El Abed
Date: 2014-06-09 00:00:00

Business rules represent the primary means by which companies define their
business, perform their actions in order to reach their objectives. Thus, they
need to be expressed unambiguously to avoid inconsistencies between business
stakeholders and formally in order to be machine-processed. A promising
solution is the use of a controlled natural language (CNL) which is a good
mediator between natural and formal languages. This paper presents RuleCNL,
which is a CNL for defining business rules. Its core feature is the alignment
of the business rule definition with the business vocabulary which ensures
traceability and consistency with the business domain. The RuleCNL tool
provides editors that assist end-users in the writing process and automatic
mappings into the Semantics of Business Vocabulary and Business Rules (SBVR)
standard. SBVR is grounded in first order logic and includes constructs called
semantic formulations that structure the meaning of rules.
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved
Authors: Xue Li, Yu-Jin Zhang, Bin Shen, Bao-Di Liu
Date: 2014-06-09 00:00:00
Tasks: Denoising, TAG

A novel tag completion algorithm is proposed in this paper, which is designed
with the following features: 1) Low-rank and error s-parsity: the incomplete
initial tagging matrix D is decomposed into the complete tagging matrix A and a
sparse error matrix E. However, instead of minimizing its nuclear norm, A is
further factor-ized into a basis matrix U and a sparse coefficient matrix V,
i.e. D=UV+E. This low-rank formulation encapsulating sparse coding enables our
algorithm to recover latent structures from noisy initial data and avoid
performing too much denoising; 2) Local reconstruction structure consistency:
to steer the completion of D, the local linear reconstruction structures in
feature space and tag space are obtained and preserved by U and V respectively.
Such a scheme could alleviate the negative effect of distances measured by
low-level features and incomplete tags. Thus, we can seek a balance between
exploiting as much information and not being mislead to suboptimal performance.
Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts
dataset demonstrate the effectiveness and efficiency of the proposed method.
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Authors: Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, Alan Yuille
Date: 2014-06-08 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Semantic Part Detection

Detecting objects becomes difficult when we need to deal with large shape
deformation, occlusion and low resolution. We propose a novel approach to i)
handle large deformations and partial occlusions in animals (as examples of
highly deformable objects), ii) describe them in terms of body parts, and iii)
detect them when their body parts are hard to detect (e.g., animals depicted at
low resolution). We represent the holistic object and body parts separately and
use a fully connected model to arrange templates for the holistic object and
body parts. Our model automatically decouples the holistic object or body parts
from the model when they are hard to detect. This enables us to represent a
large number of holistic object and body part combinations to better deal with
different "detectability" patterns caused by deformations, occlusion and/or low
resolution.
  We apply our method to the six animal categories in the PASCAL VOC dataset
and show that our method significantly improves state-of-the-art (by 4.1% AP)
and provides a richer representation for objects. During training we use
annotations for body parts (e.g., head, torso, etc), making use of a new
dataset of fully annotated object parts for PASCAL VOC 2010, which provides a
mask for each part.
Rational Closure in SHIQ
Authors: Laura Giordano, Valentina Gliozzi, Nicola Olivetti, Gian Luca Pozzato
Date: 2014-06-08 00:00:00

We define a notion of rational closure for the logic SHIQ, which does not
enjoys the finite model property, building on the notion of rational closure
introduced by Lehmann and Magidor in [23]. We provide a semantic
characterization of rational closure in SHIQ in terms of a preferential
semantics, based on a finite rank characterization of minimal models. We show
that the rational closure of a TBox can be computed in EXPTIME using entailment
in SHIQ.
Two-dimensional Sentiment Analysis of text
Authors: Rahul Tejwani
Date: 2014-06-08 00:00:00
Tasks: Sentiment Analysis, Vocal Bursts Valence Prediction

Sentiment Analysis aims to get the underlying viewpoint of the text, which
could be anything that holds a subjective opinion, such as an online review,
Movie rating, Comments on Blog posts etc. This paper presents a novel approach
that classify text in two-dimensional Emotional space, based on the sentiments
of the author. The approach uses existing lexical resources to extract feature
set, which is trained using Supervised Learning techniques.
Introduction to Neutrosophic Statistics
Authors: Florentin Smarandache
Date: 2014-06-08 00:00:00

Neutrosophic Statistics means statistical analysis of population or sample
that has indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data.
For example, the population or sample size might not be exactly determinate
because of some individuals that partially belong to the population or sample,
and partially they do not belong, or individuals whose appurtenance is
completely unknown. Also, there are population or sample individuals whose data
could be indeterminate. In this book, we develop the 1995 notion of
neutrosophic statistics. We present various practical examples. It is possible
to define the neutrosophic statistics in many ways, because there are various
types of indeterminacies, depending on the problem to solve.
A Semantic Enhanced Model for effective Spatial Information Retrieval
Authors: Adeyinka K. Akanbi, Olusanya Y. Agunbiade, Sadiq Kuti, Olumuyiwa J. Dehinbo
Date: 2014-06-08 00:00:00
Tasks: Information Retrieval, Retrieval

A lot of information on the web is geographically referenced. Discovering and
retrieving this geographic information to satisfy various users needs across
both open and distributed Spatial Data Infrastructures (SDI) poses eminent
research challenges. However, this is mostly caused by semantic heterogeneity
in users query and lack of semantic referencing of the Geographic Information
(GI) metadata. To addressing these challenges, this paper discusses ontology
based semantic enhanced model, which explicitly represents GI metadata, and
provides linked RDF instances of each entity. The system focuses on semantic
search, ontology, and efficient spatial information retrieval. In particular,
an integrated model that uses specific domain information extraction to improve
the searching and retrieval of ranked spatial search results.
Structured Dictionary Learning for Classification
Authors: Yuanming Suo, Minh Dao, Umamahesh Srinivas, Vishal Monga, Trac. D. Tran
Date: 2014-06-08 00:00:00
Tasks: Classification, compressed sensing, Dictionary Learning, Face Recognition, General Classification

Sparsity driven signal processing has gained tremendous popularity in the
last decade. At its core, the assumption is that the signal of interest is
sparse with respect to either a fixed transformation or a signal dependent
dictionary. To better capture the data characteristics, various dictionary
learning methods have been proposed for both reconstruction and classification
tasks. For classification particularly, most approaches proposed so far have
focused on designing explicit constraints on the sparse code to improve
classification accuracy while simply adopting $l_0$-norm or $l_1$-norm for
sparsity regularization. Motivated by the success of structured sparsity in the
area of Compressed Sensing, we propose a structured dictionary learning
framework (StructDL) that incorporates the structure information on both group
and task levels in the learning process. Its benefits are two-fold: (i) the
label consistency between dictionary atoms and training data are implicitly
enforced; and (ii) the classification performance is more robust in the cases
of a small dictionary size or limited training data than other techniques.
Using the subspace model, we derive the conditions for StructDL to guarantee
the performance and show theoretically that StructDL is superior to $l_0$-norm
or $l_1$-norm regularized dictionary learning for classification. Extensive
experiments have been performed on both synthetic simulations and real world
applications, such as face recognition and object classification, to
demonstrate the validity of the proposed DL framework.
An exact and two heuristic strategies for truthful bidding in combinatorial transport auctions
Authors: Tobias Buer
Date: 2014-06-07 00:00:00

To support a freight carrier in a combinatorial transport auction, we
proposes an exact and two heuristic strategies for bidding on subsets of
requests. The exact bidding strategy is based on the concept of elementary
request combinations. We show that it is sufficient and necessary for a carrier
to bid on each elementary request combination in order to guarantee the same
result as bidding on each element of the powerset of the set of tendered
requests. Both heuristic bidding strategies identify promising request
combinations. For this, pairwise synergies based on saving values as well as
the capacitated p-median problem are used. The bidding strategies are evaluated
by a computational study that simulates an auction. It is based on 174
benchmark instances and therefore easily extendable by other researchers. On
average, the two heuristic strategies achieve 91 percent and 81 percent of the
available sales potential while generating 36 and only 4 percent of the bundle
bids of the exact strategy. Therefore, the proposed bidding strategies help a
carrier to increase her chance to win and at the same time reduce the
computational burden to participate in a combinatorial transport auction.
Simulation based Hardness Evaluation of a Multi-Objective Genetic Algorithm
Authors: Shahab U. Ansari, Sameen Mansha
Date: 2014-06-07 00:00:00

Studies have shown that multi-objective optimization problems are hard
problems. Such problems either require longer time to converge to an optimum
solution, or may not converge at all. Recently some researchers have claimed
that real culprit for increasing the hardness of multi-objective problems are
not the number of objectives themselves rather it is the increased size of
solution set, incompatibility of solutions, and high probability of finding
suboptimal solution due to increased number of local maxima. In this work, we
have setup a simple framework for the evaluation of hardness of multi-objective
genetic algorithms (MOGA). The algorithm is designed for a pray-predator game
where a player is to improve its lifespan, challenging level and usability of
the game arena through number of generations. A rigorous set of experiments are
performed for quantifying the hardness in terms of evolution for increasing
number of objective functions. In genetic algorithm, crossover and mutation
with equal probability are applied to create offspring in each generation.
First, each objective function is maximized individually by ranking the
competing players on the basis of the fitness (cost) function, and then a
multi-objective cost function (sum of individual cost functions) is maximized
with ranking, and also without ranking where dominated solutions are also
allowed to evolve.
On the Number of Linear Regions of Deep Neural Networks
Authors: Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Date: 2014-02-08 00:00:00
Proceeding: NeurIPS 2014 12

We study the complexity of functions computable by deep feedforward neural
networks with piecewise linear activations in terms of the symmetries and the
number of linear regions that they have. Deep networks are able to sequentially
map portions of each layer's input-space to the same output. In this way, deep
models compute functions that react equally to complicated patterns of
different inputs. The compositional structure of these functions enables them
to re-use pieces of computation exponentially often in terms of the network's
depth. This paper investigates the complexity of such compositional maps and
contributes new theoretical results regarding the advantage of depth for neural
networks with piecewise linear activation functions. In particular, our
analysis is not specific to a single family of models, and as an example, we
employ it for rectifier and maxout networks. We improve complexity bounds from
pre-existing work and investigate the behavior of units in higher layers.
Shape-from-intrinsic operator
Authors: Davide Boscaini, Davide Eynard, Michael M. Bronstein
Date: 2014-06-07 00:00:00

Shape-from-X is an important class of problems in the fields of geometry
processing, computer graphics, and vision, attempting to recover the structure
of a shape from some observations. In this paper, we formulate the problem of
shape-from-operator (SfO), recovering an embedding of a mesh from intrinsic
differential operators defined on the mesh. Particularly interesting instances
of our SfO problem include synthesis of shape analogies, shape-from-Laplacian
reconstruction, and shape exaggeration. Numerically, we approach the SfO
problem by splitting it into two optimization sub-problems that are applied in
an alternating scheme: metric-from-operator (reconstruction of the discrete
metric from the intrinsic operator) and embedding-from-metric (finding a shape
embedding that would realize a given metric, a setting of the multidimensional
scaling problem).
Compressed Gaussian Process
Authors: Rajarshi Guhaniyogi, David B. Dunson
Date: 2014-06-07 00:00:00
Tasks: regression

Nonparametric regression for massive numbers of samples (n) and features (p)
is an increasingly important problem. In big n settings, a common strategy is
to partition the feature space, and then separately apply simple models to each
partition set. We propose an alternative approach, which avoids such
partitioning and the associated sensitivity to neighborhood choice and distance
metrics, by using random compression combined with Gaussian process regression.
The proposed approach is particularly motivated by the setting in which the
response is conditionally independent of the features given the projection to a
low dimensional manifold. Conditionally on the random compression matrix and a
smoothness parameter, the posterior distribution for the regression surface and
posterior predictive distributions are available analytically. Running the
analysis in parallel for many random compression matrices and smoothness
parameters, model averaging is used to combine the results. The algorithm can
be implemented rapidly even in very big n and p problems, has strong
theoretical justification, and is found to yield state of the art predictive
performance.
Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science
Authors: Jan Egger
Date: 2014-06-07 00:00:00
Tasks: Medical Image Analysis, Segmentation

In this contribution, a semi-automatic segmentation algorithm for (medical)
image analysis is presented. More precise, the approach belongs to the category
of interactive contouring algorithms, which provide real-time feedback of the
segmentation result. However, even with interactive real-time contouring
approaches there are always cases where the user cannot find a satisfying
segmentation, e.g. due to homogeneous appearances between the object and the
background, or noise inside the object. For these difficult cases the algorithm
still needs additional user support. However, this additional user support
should be intuitive and rapid integrated into the segmentation process, without
breaking the interactive real-time segmentation feedback. I propose a solution
where the user can support the algorithm by an easy and fast placement of one
or more seed points to guide the algorithm to a satisfying segmentation result
also in difficult cases. These additional seed(s) restrict(s) the calculation
of the segmentation for the algorithm, but at the same time, still enable to
continue with the interactive real-time feedback segmentation. For a practical
and genuine application in translational science, the approach has been tested
on medical data from the clinical routine in 2D and 3D.
Box Drawings for Learning with Imbalanced Data
Authors: Siong Thye Goh, Cynthia Rudin
Date: 2014-03-13 00:00:00
Tasks: General Classification, imbalanced classification

The vast majority of real world classification problems are imbalanced,
meaning there are far fewer data from the class of interest (the positive
class) than from other classes. We propose two machine learning algorithms to
handle highly imbalanced classification problems. The classifiers constructed
by both methods are created as unions of parallel axis rectangles around the
positive examples, and thus have the benefit of being interpretable. The first
algorithm uses mixed integer programming to optimize a weighted balance between
positive and negative class accuracies. Regularization is introduced to improve
generalization performance. The second method uses an approximation in order to
assist with scalability. Specifically, it follows a \textit{characterize then
discriminate} approach, where the positive class is characterized first by
boxes, and then each box boundary becomes a separate discriminative classifier.
This method has the computational advantages that it can be easily
parallelized, and considers only the relevant regions of feature space.
Toward verbalizing ontologies in isiZulu
Authors: C. Maria Keet, Langa Khumalo
Date: 2014-06-07 00:00:00
Tasks: Machine Translation, Sentence, Text Generation, Translation

IsiZulu is one of the eleven official languages of South Africa and roughly
half the population can speak it. It is the first (home) language for over 10
million people in South Africa. Only a few computational resources exist for
isiZulu and its related Nguni languages, yet the imperative for tool
development exists. We focus on natural language generation, and the grammar
options and preferences in particular, which will inform verbalization of
knowledge representation languages and could contribute to machine translation.
The verbalization pattern specification shows that the grammar rules are
elaborate and there are several options of which one may have preference. We
devised verbalization patterns for subsumption, basic disjointness, existential
and universal quantification, and conjunction. This was evaluated in a survey
among linguists and non-linguists. Some differences between linguists and
non-linguists can be observed, with the former much more in agreement, and
preferences depend on the overall structure of the sentence, such as singular
for subsumption and plural in other cases.
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning
Authors: Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
Date: 2014-02-08 00:00:00
Tasks: Dictionary Learning

From concentration inequalities for the suprema of Gaussian or Rademacher
processes an inequality is derived. It is applied to sharpen existing and to
derive novel bounds on the empirical Rademacher complexities of unit balls in
various norms appearing in the context of structured sparsity and multitask
dictionary learning or matrix factorization. A key role is played by the
largest eigenvalue of the data covariance matrix.