pwc-archive/papers-with-abstracts huggingface dataset:
Animation of 3D Human Model Using Markerless Motion Capture Applied To Sports
Authors: Ashish Shingade, Archana Ghotkar
Date: 2014-02-11 00:00:00
Tasks: Image Animation, Markerless Motion Capture

Markerless motion capture is an active research in 3D virtualization. In
proposed work we presented a system for markerless motion capture for 3D human
character animation, paper presents a survey on motion and skeleton tracking
techniques which are developed or are under development. The paper proposed a
method to transform the motion of a performer to a 3D human character (model),
the 3D human character performs similar movements as that of a performer in
real time. In the proposed work, human model data will be captured by Kinect
camera, processed data will be applied on 3D human model for animation. 3D
human model is created using open source software (MakeHuman). Anticipated
dataset for sport activity is considered as input which can be applied to any
HCI application.
Learning-assisted Theorem Proving with Millions of Lemmas
Authors: Cezary Kaliszyk, Josef Urban
Date: 2014-02-11 00:00:00
Tasks: Automated Theorem Proving

Large formal mathematical libraries consist of millions of atomic inference
steps that give rise to a corresponding number of proved statements (lemmas).
Analogously to the informal mathematical practice, only a tiny fraction of such
statements is named and re-used in later proofs by formal mathematicians. In
this work, we suggest and implement criteria defining the estimated usefulness
of the HOL Light lemmas for proving further theorems. We use these criteria to
mine the large inference graph of the lemmas in the HOL Light and Flyspeck
libraries, adding up to millions of the best lemmas to the pool of statements
that can be re-used in later proofs. We show that in combination with
learning-based relevance filtering, such methods significantly strengthen
automated theorem proving of new conjectures over large formal mathematical
libraries such as Flyspeck.
Sparsity averaging for radio-interferometric imaging
Authors: Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux
Date: 2014-02-11 00:00:00
Tasks: compressed sensing

We propose a novel regularization method for compressive imaging in the
context of the compressed sensing (CS) theory with coherent and redundant
dictionaries. Natural images are often complicated and several types of
structures can be present at once. It is well known that piecewise smooth
images exhibit gradient sparsity, and that images with extended structures are
better encapsulated in wavelet frames. Therefore, we here conjecture that
promoting average sparsity or compressibility over multiple frames rather than
single frames is an extremely powerful regularization prior.
Modeling sequential data using higher-order relational features and predictive training
Authors: Vincent Michalski, Roland Memisevic, Kishore Konda
Date: 2014-02-10 00:00:00
Tasks: Prediction

Bi-linear feature learning models, like the gated autoencoder, were proposed
as a way to model relationships between frames in a video. By minimizing
reconstruction error of one frame, given the previous frame, these models learn
"mapping units" that encode the transformations inherent in a sequence, and
thereby learn to encode motion. In this work we extend bi-linear models by
introducing "higher-order mapping units" that allow us to encode
transformations between frames and transformations between transformations.
  We show that this makes it possible to encode temporal structure that is more
complex and longer-range than the structure captured within standard bi-linear
models. We also show that a natural way to train the model is by replacing the
commonly used reconstruction objective with a prediction objective which forces
the model to correctly predict the evolution of the input multiple steps into
the future. Learning can be achieved by back-propagating the multi-step
prediction through time. We test the model on various temporal prediction
tasks, and show that higher-order mappings and predictive training both yield a
significant improvement over bi-linear models in terms of prediction accuracy.
Feature and Variable Selection in Classification
Authors: Aaron Karper
Date: 2014-02-10 00:00:00
Tasks: BIG-bench Machine Learning, Classification, CPU, feature selection, General Classification, Variable Selection

The amount of information in the form of features and variables avail- able
to machine learning algorithms is ever increasing. This can lead to classifiers
that are prone to overfitting in high dimensions, high di- mensional models do
not lend themselves to interpretable results, and the CPU and memory resources
necessary to run on high-dimensional datasets severly limit the applications of
the approaches. Variable and feature selection aim to remedy this by finding a
subset of features that in some way captures the information provided best. In
this paper we present the general methodology and highlight some specific
approaches.
Étude cognitive des processus de construction d'une requête dans un système de gestion de connaissances médicales
Authors: Nathalie Chaignaud, Valérie Delavigne, Maryvonne Holzem, Jean-Philippe Kotowicz, Alain Loisel
Date: 2014-02-10 00:00:00

This article presents the Cogni-CISMeF project, which aims at improving
medical information search in the CISMeF system (Catalog and Index of
French-language health resources) by including a conversational agent to
interact with the user in natural language. To study the cognitive processes
involved during the information search, a bottom-up methodology was adopted.
Experimentation has been set up to obtain human dialogs between a user (playing
the role of patient) dealing with medical information search and a CISMeF
expert refining the request. The analysis of these dialogs underlined the use
of discursive evidence: vocabulary, reformulation, implicit or explicit
expression of user intentions, conversational sequences, etc. A model of
artificial agent is proposed. It leads the user in its information search by
proposing to him examples, assistance and choices. This model was implemented
and integrated in the CISMeF system. ---- Cet article d\'ecrit le projet
Cogni-CISMeF qui propose un module de dialogue Homme-Machine \`a int\'egrer
dans le syst\`eme d'indexation de connaissances m\'edicales CISMeF (Catalogue
et Index des Sites M\'edicaux Francophones). Nous avons adopt\'e une d\'emarche
de mod\'elisation cognitive en proc\'edant \`a un recueil de corpus de
dialogues entre un utilisateur (jouant le r\^ole d'un patient) d\'esirant une
information m\'edicale et un expert CISMeF af inant cette demande pour
construire la requ\^ete. Nous avons analys\'e la structure des dialogues ainsi
obtenus et avons \'etudi\'e un certain nombre d'indices discursifs :
vocabulaire employ\'e, marques de reformulation, commentaires m\'eta et
\'epilinguistiques, expression implicite ou explicite des intentions de
l'utilisateur, encha\^inement conversationnel, etc. De cette analyse, nous
avons construit un mod\`ele d'agent artificiel dot\'e de capacit\'es cognitives
capables d'aider l'utilisateur dans sa t\^ache de recherche d'information. Ce
mod\`ele a \'et\'e impl\'ement\'e et int\'egr\'e dans le syst\`eme CISMeF.
B-tests: Low Variance Kernel Two-Sample Tests
Authors: Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
Date: 2013-07-08 00:00:00
Tasks: Two-sample testing, Vocal Bursts Valence Prediction

A family of maximum mean discrepancy (MMD) kernel two-sample tests is
introduced. Members of the test family are called Block-tests or B-tests, since
the test statistic is an average over MMDs computed on subsets of the samples.
The choice of block size allows control over the tradeoff between test power
and computation time. In this respect, the $B$-test family combines favorable
properties of previously proposed MMD two-sample tests: B-tests are more
powerful than a linear time test where blocks are just pairs of samples, yet
they are more computationally efficient than a quadratic time test where a
single large block incorporating all the samples is used to compute a
U-statistic. A further important advantage of the B-tests is their
asymptotically Normal null distribution: this is by contrast with the
U-statistic, which is degenerate under the null hypothesis, and for which
estimates of the null distribution are computationally demanding. Recent
results on kernel selection for hypothesis testing transfer seamlessly to the
B-tests, yielding a means to optimize test power via kernel choice.
Unsupervised edge map scoring: a statistical complexity approach
Authors: Javier Gimenez, Jorge Martinez, Ana Georgina Flesia
Date: 2013-02-21 00:00:00

We propose a new Statistical Complexity Measure (SCM) to qualify edge maps
without Ground Truth (GT) knowledge. The measure is the product of two indices,
an \emph{Equilibrium} index $\mathcal{E}$ obtained by projecting the edge map
into a family of edge patterns, and an \emph{Entropy} index $\mathcal{H}$,
defined as a function of the Kolmogorov Smirnov (KS) statistic.
  This new measure can be used for performance characterization which includes:
(i)~the specific evaluation of an algorithm (intra-technique process) in order
to identify its best parameters, and (ii)~the comparison of different
algorithms (inter-technique process) in order to classify them according to
their quality.
  Results made over images of the South Florida and Berkeley databases show
that our approach significantly improves over Pratt's Figure of Merit (PFoM)
which is the objective reference-based edge map evaluation standard, as it
takes into account more features in its evaluation.
Image Search Reranking
Authors: V Rajakumar, Vipeen V Bopche
Date: 2014-02-10 00:00:00
Tasks: Image Retrieval, Reranking, Re-Ranking

The existing methods for image search reranking suffer from the
unfaithfulness of the assumptions under which the text-based images search
result. The resulting images contain more irrelevant images. Hence the re
ranking concept arises to re rank the retrieved images based on the text around
the image and data of data of image and visual feature of image. A number of
methods are differentiated for this re-ranking. The high ranked images are used
as noisy data and a k means algorithm for classification is learned to rectify
the ranking further. We are study the affect ability of the cross validation
method to this training data. The pre eminent originality of the overall method
is in collecting text/metadata of image and visual features in order to achieve
an automatic ranking of the images. Supervision is initiated to learn the model
weights offline, previous to reranking process. While model learning needs
manual labeling of the results for a some limited queries, the resulting model
is query autonomous and therefore applicable to any other query .Examples are
given for a selection of other classes like vehicles, animals and other
classes.
Characterizing the Sample Complexity of Private Learners
Authors: Amos Beimel, Kobbi Nissim, Uri Stemmer
Date: 2014-02-10 00:00:00
Tasks: PAC learning

In 2008, Kasiviswanathan et al. defined private learning as a combination of
PAC learning and differential privacy. Informally, a private learner is applied
to a collection of labeled individual information and outputs a hypothesis
while preserving the privacy of each individual. Kasiviswanathan et al. gave a
generic construction of private learners for (finite) concept classes, with
sample complexity logarithmic in the size of the concept class. This sample
complexity is higher than what is needed for non-private learners, hence
leaving open the possibility that the sample complexity of private learning may
be sometimes significantly higher than that of non-private learning.
  We give a combinatorial characterization of the sample size sufficient and
necessary to privately learn a class of concepts. This characterization is
analogous to the well known characterization of the sample complexity of
non-private learning in terms of the VC dimension of the concept class. We
introduce the notion of probabilistic representation of a concept class, and
our new complexity measure RepDim corresponds to the size of the smallest
probabilistic representation of the concept class.
  We show that any private learning algorithm for a concept class C with sample
complexity m implies RepDim(C)=O(m), and that there exists a private learning
algorithm with sample complexity m=O(RepDim(C)). We further demonstrate that a
similar characterization holds for the database size needed for privately
computing a large class of optimization problems and also for the well studied
problem of private data release.
Handwritten Character Recognition In Malayalam Scripts- A Review
Authors: Anitha Mary M. O. Chacko, P. M Dhanya
Date: 2014-02-10 00:00:00

Handwritten character recognition is one of the most challenging and ongoing
areas of research in the field of pattern recognition. HCR research is matured
for foreign languages like Chinese and Japanese but the problem is much more
complex for Indian languages. The problem becomes even more complicated for
South Indian languages due to its large character set and the presence of
vowels modifiers and compound characters. This paper provides an overview of
important contributions and advances in offline as well as online handwritten
character recognition of Malayalam scripts.
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models
Authors: Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi
Date: 2014-02-10 00:00:00
Tasks: Model Selection

Practical model building processes are often time-consuming because many
different models must be trained and validated. In this paper, we introduce a
novel algorithm that can be used for computing the lower and the upper bounds
of model validation errors without actually training the model itself. A key
idea behind our algorithm is using a side information available from a
suboptimal model. If a reasonably good suboptimal model is available, our
algorithm can compute lower and upper bounds of many useful quantities for
making inferences on the unknown target model. We demonstrate the advantage of
our algorithm in the context of model selection for regularized learning
problems.
Fuzzy inference system for integrated VVC in isolated power systems
Authors: Eduardo Vega-Fuentes, Juan Manuel Cerezo-Sanchez, Sonia Leon-del Rosario, Aurelio Vega-Martinez
Date: 2014-02-10 00:00:00

This paper presents a fuzzy inference system for integrated volt/var control
(VVC) in distribution substations. The purpose is go forward to automation
distribution applying conservation voltage reduction (CVR) in isolated power
systems where control capabilities are limited. A fuzzy controller has been
designed. Working as an on-line tool, it has been tested under real conditions
and it has managed the operation during a whole day in a distribution
substation. Within the limits of control capabilities of the system, the
controller maintained successfully an acceptable voltage profile, power factor
values over 0,98 and it has ostensibly improved the performance given by an
optimal power flow based automation system. CVR savings during the test are
evaluated and the aim to integrate it in the VVC is presented.
Learning to encode motion using spatio-temporal synchrony
Authors: Kishore Reddy Konda, Roland Memisevic, Vincent Michalski
Date: 2013-06-13 00:00:00
Tasks: Motion Estimation, parameter estimation

We consider the task of learning to extract motion from videos. To this end,
we show that the detection of spatial transformations can be viewed as the
detection of synchrony between the image sequence and a sequence of features
undergoing the motion we wish to detect. We show that learning about synchrony
is possible using very fast, local learning rules, by introducing
multiplicative "gating" interactions between hidden units across frames. This
makes it possible to achieve competitive performance in a wide variety of
motion estimation tasks, using a small fraction of the time required to learn
features, and to outperform hand-crafted spatio-temporal features by a large
margin. We also show how learning about synchrony can be viewed as performing
greedy parameter estimation in the well-known motion energy model.
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC)
Authors: Mohammad Tofighi, Kivanc Kose, A. Enis Cetin
Date: 2014-02-10 00:00:00
Tasks: Compressive Sensing, Denoising

A new signal processing framework based on making orthogonal Projections onto
the Epigraph Set of a Convex cost function (PESC) is developed. In this way it
is possible to solve convex optimization problems using the well-known
Projections onto Convex Set (POCS) approach. In this algorithm, the dimension
of the minimization problem is lifted by one and a convex set corresponding to
the epigraph of the cost function is defined. If the cost function is a convex
function in $R^N$, the corresponding epigraph set is also a convex set in
R^{N+1}. The PESC method provides globally optimal solutions for
total-variation (TV), filtered variation (FV), L_1, L_2, and entropic cost
function based convex optimization problems. In this article, the PESC based
denoising and compressive sensing algorithms are developed. Simulation examples
are presented.
From Small-World Networks to Comparison-Based Search
Authors: Amin Karbasi, Stratis Ioannidis, Laurent Massoulie
Date: 2011-07-15 00:00:00

The problem of content search through comparisons has recently received
considerable attention. In short, a user searching for a target object
navigates through a database in the following manner: the user is asked to
select the object most similar to her target from a small list of objects. A
new object list is then presented to the user based on her earlier selection.
This process is repeated until the target is included in the list presented, at
which point the search terminates. This problem is known to be strongly related
to the small-world network design problem.
  However, contrary to prior work, which focuses on cases where objects in the
database are equally popular, we consider here the case where the demand for
objects may be heterogeneous. We show that, under heterogeneous demand, the
small-world network design problem is NP-hard. Given the above negative result,
we propose a novel mechanism for small-world design and provide an upper bound
on its performance under heterogeneous demand. The above mechanism has a
natural equivalent in the context of content search through comparisons, and we
establish both an upper bound and a lower bound for the performance of this
mechanism. These bounds are intuitively appealing, as they depend on the
entropy of the demand as well as its doubling constant, a quantity capturing
the topology of the set of target objects. They also illustrate interesting
connections between comparison-based search to classic results from information
theory. Finally, we propose an adaptive learning algorithm for content search
that meets the performance guarantees achieved by the above mechanisms.
A Second-order Bound with Excess Losses
Authors: Pierre Gaillard, Gilles Stoltz, Tim van Erven
Date: 2014-02-10 00:00:00

We study online aggregation of the predictions of experts, and first show new
second-order regret bounds in the standard setting, which are obtained via a
version of the Prod algorithm (and also a version of the polynomially weighted
average algorithm) with multiple learning rates. These bounds are in terms of
excess losses, the differences between the instantaneous losses suffered by the
algorithm and the ones of a given expert. We then demonstrate the interest of
these bounds in the context of experts that report their confidences as a
number in the interval [0,1] using a generic reduction to the standard setting.
We conclude by two other applications in the standard setting, which improve
the known bounds in case of small excess losses and show a bounded regret
against i.i.d. sequences of losses.
Deeply Coupled Auto-encoder Networks for Cross-view Classification
Authors: Wen Wang, Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
Date: 2014-02-10 00:00:00
Tasks: Classification, Denoising, General Classification, image-classification, Image Classification

The comparison of heterogeneous samples extensively exists in many
applications, especially in the task of image classification. In this paper, we
propose a simple but effective coupled neural network, called Deeply Coupled
Autoencoder Networks (DCAN), which seeks to build two deep neural networks,
coupled with each other in every corresponding layers. In DCAN, each deep
structure is developed via stacking multiple discriminative coupled
auto-encoders, a denoising auto-encoder trained with maximum margin criterion
consisting of intra-class compactness and inter-class penalty. This single
layer component makes our model simultaneously preserve the local consistency
and enhance its discriminative capability. With increasing number of layers,
the coupled networks can gradually narrow the gap between the two views.
Extensive experiments on cross-view image classification tasks demonstrate the
superiority of our method over state-of-the-art methods.
Genomic Prediction of Quantitative Traits using Sparse and Locally Epistatic Models
Authors: Deniz Akdemir
Date: 2014-02-10 00:00:00

In plant and animal breeding studies a distinction is made between the
genetic value (additive + epistatic genetic effects) and the breeding value
(additive genetic effects) of an individual since it is expected that some of
the epistatic genetic effects will be lost due to recombination. In this paper,
we argue that the breeder can take advantage of some of the epistatic marker
effects in regions of low recombination. The models introduced here aim to
estimate local epistatic line heritability by using the genetic map information
and combine the local additive and epistatic effects. To this end, we have used
semi-parametric mixed models with multiple local genomic relationship matrices
with hierarchical designs and lasso post-processing for sparsity in the final
model. Our models produce good predictive performance along with good
explanatory information.
Binary Stereo Matching
Authors: Kang Zhang, Jiyang Li, Yijing Li, Weidong Hu, Lifeng Sun, Shiqiang Yang
Date: 2014-02-10 00:00:00
Tasks: Computational Efficiency, Stereo Matching, Stereo Matching Hand

In this paper, we propose a novel binary-based cost computation and
aggregation approach for stereo matching problem. The cost volume is
constructed through bitwise operations on a series of binary strings. Then this
approach is combined with traditional winner-take-all strategy, resulting in a
new local stereo matching algorithm called binary stereo matching (BSM). Since
core algorithm of BSM is based on binary and integer computations, it has a
higher computational efficiency than previous methods. Experimental results on
Middlebury benchmark show that BSM has comparable performance with
state-of-the-art local stereo methods in terms of both quality and speed.
Furthermore, experiments on images with radiometric differences demonstrate
that BSM is more robust than previous methods under these changes, which is
common under real illumination.