pwc-archive/papers-with-abstracts huggingface dataset:
Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process
Authors: Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo
Date: 2015-03-30 00:00:00
Tasks: Information Retrieval, Retrieval

Incorporating the side information of text corpus, i.e., authors, time
stamps, and emotional tags, into the traditional text mining models has gained
significant interests in the area of information retrieval, statistical natural
language processing, and machine learning. One branch of these works is the
so-called Author Topic Model (ATM), which incorporates the authors's interests
as side information into the classical topic model. However, the existing ATM
needs to predefine the number of topics, which is difficult and inappropriate
in many real-world settings. In this paper, we propose an Infinite Author Topic
(IAT) model to resolve this issue. Instead of assigning a discrete probability
on fixed number of topics, we use a stochastic process to determine the number
of topics from the data itself. To be specific, we extend a gamma-negative
binomial process to three levels in order to capture the
author-document-keyword hierarchical structure. Furthermore, each document is
assigned a mixed gamma process that accounts for the multi-author's
contribution towards this document. An efficient Gibbs sampling inference
algorithm with each conditional distribution being closed-form is developed for
the IAT model. Experiments on several real-world datasets show the capabilities
of our IAT model to learn the hidden topics, authors' interests on these topics
and the number of topics simultaneously.
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Authors: Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Caglar Gulcehre, Vincent Michalski, Kishore Konda, Sébastien Jean, Pierre Froumenty, Yann Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron Courville, Pascal Vincent, Roland Memisevic, Christopher Pal, Yoshua Bengio
Date: 2015-03-05 00:00:00
Tasks: Deep Learning, Emotion Recognition, Multimodal Deep Learning

The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset.
Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location
Authors: Lex Fridman, Steven Weber, Rachel Greenstadt, Moshe Kam
Date: 2015-03-29 00:00:00

Active authentication is the problem of continuously verifying the identity
of a person based on behavioral aspects of their interaction with a computing
device. In this study, we collect and analyze behavioral biometrics data from
200subjects, each using their personal Android mobile device for a period of at
least 30 days. This dataset is novel in the context of active authentication
due to its size, duration, number of modalities, and absence of restrictions on
tracked activity. The geographical colocation of the subjects in the study is
representative of a large closed-world environment such as an organization
where the unauthorized user of a device is likely to be an insider threat:
coming from within the organization. We consider four biometric modalities: (1)
text entered via soft keyboard, (2) applications used, (3) websites visited,
and (4) physical location of the device as determined from GPS (when outdoors)
or WiFi (when indoors). We implement and test a classifier for each modality
and organize the classifiers as a parallel binary decision fusion architecture.
We are able to characterize the performance of the system with respect to
intruder detection time and to quantify the contribution of each modality to
the overall performance.
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding
Authors: Hidetoshi Shimodaira
Date: 2014-12-29 00:00:00
Tasks: Dimensionality Reduction, Graph Embedding

Data vectors are obtained from multiple domains. They are feature vectors of
images or vector representations of words. Domains may have different numbers
of data vectors with different dimensions. These data vectors from multiple
domains are projected to a common space by linear transformations in order to
search closely related vectors across domains. We would like to find projection
matrices to minimize distances between closely related data vectors. This
formulation of cross-domain matching is regarded as an extension of the
spectral graph embedding to multi-domain setting, and it includes several
multivariate analysis methods of statistics such as multiset canonical
correlation analysis, correspondence analysis, and principal component
analysis. Similar approaches are very popular recently in pattern recognition
and vision. In this paper, instead of proposing a novel method, we will
introduce an embarrassingly simple idea of coding the data vectors for
explaining all the above mentioned approaches. A data vector is concatenated
with zero vectors from all other domains to make an augmented vector. The
cross-domain matching is solved by applying the single-domain version of
spectral graph embedding to these augmented vectors of all the domains. An
interesting connection to the classical associative memory model of neural
networks is also discussed by noticing a coding for association. A
cross-validation method for choosing the dimension of the common space and a
regularization parameter will be discussed in an illustrative numerical
example.
On computable abstractions (a conceptual introduction)
Authors: Alejandro Sanchez Guinea
Date: 2014-08-29 00:00:00

This paper introduces abstractions that are meaningful for computers and that
can be built and used according to computers' own criteria, i.e., computable
abstractions. It is analyzed how abstractions can be seen to serve as the
building blocks for the creation of one own's understanding of things, which is
essential in performing intellectual tasks. Thus, abstractional machines are
defined, which following a mechanical process can, based on computable
abstractions, build and use their own understanding of things. Abstractional
machines are illustrated through an example that outlines their application to
the task of natural language processing.
Founding Digital Currency on Imprecise Commodity
Authors: Zimu Yuan, Zhiwei Xu
Date: 2015-03-29 00:00:00

Current digital currency schemes provide instantaneous exchange on precise
commodity, in which "precise" means a buyer can possibly verify the function of
the commodity without error. However, imprecise commodities, e.g. statistical
data, with error existing are abundant in digital world. Existing digital
currency schemes do not offer a mechanism to help the buyer for payment
decision on precision of commodity, which may lead the buyer to a dilemma
between having to buy and being unconfident. In this paper, we design a
currency schemes IDCS for imprecise digital commodity. IDCS completes a trade
in three stages of handshake between a buyer and providers. We present an IDCS
prototype implementation that assigns weights on the trustworthy of the
providers, and calculates a confidence level for the buyer to decide the
quality of a imprecise commodity. In experiment, we characterize the
performance of IDCS prototype under varying impact factors.
Maximum Entropy, Word-Frequency, Chinese Characters, and Multiple Meanings
Authors: Xiao-Yong Yan, Petter Minnhagen
Date: 2014-02-09 00:00:00
Tasks: Prediction

The word-frequency distribution of a text written by an author is well
accounted for by a maximum entropy distribution, the RGF (random group
formation)-prediction. The RGF-distribution is completely determined by the a
priori values of the total number of words in the text (M), the number of
distinct words (N) and the number of repetitions of the most common word
(k_max). It is here shown that this maximum entropy prediction also describes a
text written in Chinese characters. In particular it is shown that although the
same Chinese text written in words and Chinese characters have quite
differently shaped distributions, they are nevertheless both well predicted by
their respective three a priori characteristic values. It is pointed out that
this is analogous to the change in the shape of the distribution when
translating a given text to another language. Another consequence of the
RGF-prediction is that taking a part of a long text will change the input
parameters (M, N, k_max) and consequently also the shape of the frequency
distribution. This is explicitly confirmed for texts written in Chinese
characters. Since the RGF-prediction has no system-specific information beyond
the three a priori values (M, N, k_max), any specific language characteristic
has to be sought in systematic deviations from the RGF-prediction and the
measured frequencies. One such systematic deviation is identified and, through
a statistical information theoretical argument and an extended RGF-model, it is
proposed that this deviation is caused by multiple meanings of Chinese
characters. The effect is stronger for Chinese characters than for Chinese
words. The relation between Zipf's law, the Simon-model for texts and the
present results are discussed.
Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection
Authors: Min Wei, Tommy W. S. Chow, Rosa H. M. Chan
Date: 2014-11-24 00:00:00
Tasks: feature selection

Conventional mutual information (MI) based feature selection (FS) methods are
unable to handle heterogeneous feature subset selection properly because of
data format differences or estimation methods of MI between feature subset and
class label. A way to solve this problem is feature transformation (FT). In
this study, a novel unsupervised feature transformation (UFT) which can
transform non-numerical features into numerical features is developed and
tested. The UFT process is MI-based and independent of class label. MI-based FS
algorithms, such as Parzen window feature selector (PWFS), minimum redundancy
maximum relevance feature selection (mRMR), and normalized MI feature selection
(NMIFS), can all adopt UFT for pre-processing of non-numerical features. Unlike
traditional FT methods, the proposed UFT is unbiased while PWFS is utilized to
its full advantage. Simulations and analyses of large-scale datasets showed
that feature subset selected by the integrated method, UFT-PWFS, outperformed
other FT-FS integrated methods in classification accuracy.
Active Model Aggregation via Stochastic Mirror Descent
Authors: Ravi Ganti
Date: 2015-03-28 00:00:00
Tasks: Active Learning, Binary Classification, General Classification, model

We consider the problem of learning convex aggregation of models, that is as
good as the best convex aggregation, for the binary classification problem.
Working in the stream based active learning setting, where the active learner
has to make a decision on-the-fly, if it wants to query for the label of the
point currently seen in the stream, we propose a stochastic-mirror descent
algorithm, called SMD-AMA, with entropy regularization. We establish an excess
risk bounds for the loss of the convex aggregate returned by SMD-AMA to be of
the order of $O\left(\sqrt{\frac{\log(M)}{{T^{1-\mu}}}}\right)$, where $\mu\in
[0,1)$ is an algorithm dependent parameter, that trades-off the number of
labels queried, and excess risk.
Sparse Linear Regression With Missing Data
Authors: Ravi Ganti, Rebecca M. Willett
Date: 2015-03-28 00:00:00
Tasks: regression, Stochastic Optimization

This paper proposes a fast and accurate method for sparse regression in the
presence of missing data. The underlying statistical model encapsulates the
low-dimensional structure of the incomplete data matrix and the sparsity of the
regression coefficients, and the proposed algorithm jointly learns the
low-dimensional structure of the data and a linear regressor with sparse
coefficients. The proposed stochastic optimization method, Sparse Linear
Regression with Missing Data (SLRM), performs an alternating minimization
procedure and scales well with the problem size. Large deviation inequalities
shed light on the impact of the various problem-dependent parameters on the
expected squared loss of the learned regressor. Extensive simulations on both
synthetic and real datasets show that SLRM performs better than competing
algorithms in a variety of contexts.
Selection Bias Correction and Effect Size Estimation under Dependence
Authors: Kean Ming Tan, Noah Simon, Daniela Witten
Date: 2014-05-16 00:00:00
Tasks: Selection bias

We consider large-scale studies in which it is of interest to test a very
large number of hypotheses, and then to estimate the effect sizes corresponding
to the rejected hypotheses. For instance, this setting arises in the analysis
of gene expression or DNA sequencing data. However, naive estimates of the
effect sizes suffer from selection bias, i.e., some of the largest naive
estimates are large due to chance alone. Many authors have proposed methods to
reduce the effects of selection bias under the assumption that the naive
estimates of the effect sizes are independent. Unfortunately, when the effect
size estimates are dependent, these existing techniques can have very poor
performance, and in practice there will often be dependence. We propose an
estimator that adjusts for selection bias under a recently-proposed frequentist
framework, without the independence assumption. We study some properties of the
proposed estimator, and illustrate that it outperforms past proposals in a
simulation study and on two gene expression data sets.
Some Further Evidence about Magnification and Shape in Neural Gas
Authors: Giacomo Parigi, Andrea Pedrini, Marco Piastra
Date: 2015-03-28 00:00:00
Tasks: Quantization

Neural gas (NG) is a robust vector quantization algorithm with a well-known
mathematical model. According to this, the neural gas samples the underlying
data distribution following a power law with a magnification exponent that
depends on data dimensionality only. The effects of shape in the input data
distribution, however, are not entirely covered by the NG model above, due to
the technical difficulties involved. The experimental work described here shows
that shape is indeed relevant in determining the overall NG behavior; in
particular, some experiments reveal richer and complex behaviors induced by
shape that cannot be explained by the power law alone. Although a more
comprehensive analytical model remains to be defined, the evidence collected in
these experiments suggests that the NG algorithm has an interesting potential
for detecting complex shapes in noisy datasets.
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Authors: Danesh Tarapore, Jean-Baptiste Mouret
Date: 2014-10-18 00:00:00

Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Authors: Giacomo Parigi, Angelo Stramieri, Danilo Pau, Marco Piastra
Date: 2015-03-28 00:00:00
Tasks: GPU

Among the many possible approaches for the parallelization of self-organizing
networks, and in particular of growing self-organizing networks, perhaps the
most common one is producing an optimized, parallel implementation of the
standard sequential algorithms reported in the literature. In this paper we
explore an alternative approach, based on a new algorithm variant specifically
designed to match the features of the large-scale, fine-grained parallelism of
GPUs, in which multiple input signals are processed at once. Comparative tests
have been performed, using both parallel and sequential implementations of the
new algorithm variant, in particular for a growing self-organizing network that
reconstructs surfaces from point clouds. The experimental results show that
this approach allows harnessing in a more effective way the intrinsic
parallelism that the self-organizing networks algorithms seem intuitively to
suggest, obtaining better performances even with networks of smaller size.
Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization
Authors: Rosemarie Velik, Pascal Nicolay
Date: 2015-03-28 00:00:00
Tasks: Benchmarking, energy management, Management

This article introduces a modified simulated annealing optimization approach
for automatically determining optimal energy management strategies in
grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings
and neighbourhoods based on user-specific goals. For evaluating the modified
simulated annealing optimizer, a number of test scenarios in the field of
energy self-consumption maximization are defined and results are compared to a
gradient descent and a total state space search approach. The benchmarking
against these two reference methods demonstrates that the modified simulated
annealing approach is able to find significantly better solutions than the
gradient descent algorithm - being equal or very close to the global optimum -
with significantly less computational effort and processing time than the total
state space search approach.
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals
Authors: Yong Huang, James L. Beck, Stephen Wu, Hui Li
Date: 2015-03-28 00:00:00
Tasks: Compressive Sensing, Structural Health Monitoring, Uncertainty Quantification

The application of compressive sensing (CS) to structural health monitoring
is an emerging research topic. The basic idea in CS is to use a
specially-designed wireless sensor to sample signals that are sparse in some
basis (e.g. wavelet basis) directly in a compressed form, and then to
reconstruct (decompress) these signals accurately using some inversion
algorithm after transmission to a central processing unit. However, most
signals in structural health monitoring are only approximately sparse, i.e.
only a relatively small number of the signal coefficients in some basis are
significant, but the other coefficients are usually not exactly zero. In this
case, perfect reconstruction from compressed measurements is not expected. A
new Bayesian CS algorithm is proposed in which robust treatment of the
uncertain parameters is explored, including integration over the
prediction-error precision parameter to remove it as a "nuisance" parameter.
The performance of the new CS algorithm is investigated using compressed data
from accelerometers installed on a space-frame structure and on a cable-stayed
bridge. Compared with other state-of-the-art CS methods including our
previously-published Bayesian method which uses MAP (maximum a posteriori)
estimation of the prediction-error precision parameter, the new algorithm shows
superior performance in reconstruction robustness and posterior uncertainty
quantification. Furthermore, our method can be utilized for recovery of lost
data during wireless transmission, regardless of the level of sparseness in the
signal.
CRF Learning with CNN Features for Image Segmentation
Authors: Fayao Liu, Guosheng Lin, Chunhua Shen
Date: 2015-03-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Conditional Random Rields (CRF) have been widely applied in image
segmentations. While most studies rely on hand-crafted features, we here
propose to exploit a pre-trained large convolutional neural network (CNN) to
generate deep features for CRF learning. The deep CNN is trained on the
ImageNet dataset and transferred to image segmentations here for constructing
potentials of superpixels. Then the CRF parameters are learnt using a
structured support vector machine (SSVM). To fully exploit context information
in inference, we construct spatially related co-occurrence pairwise potentials
and incorporate them into the energy function. This prefers labelling of object
pairs that frequently co-occur in a certain spatial layout and at the same time
avoids implausible labellings during the inference. Extensive experiments on
binary and multi-class segmentation benchmarks demonstrate the promise of the
proposed method. We thus provide new baselines for the segmentation performance
on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC
2011 datasets.