pwc-archive/papers-with-abstracts huggingface dataset:
Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
Authors: Santosh S. Vempala, Ying Xiao
Date: 2014-12-09 00:00:00
Tasks: Tensor Decomposition

We present a simple, general technique for reducing the sample complexity of
matrix and tensor decomposition algorithms applied to distributions. We use the
technique to give a polynomial-time algorithm for standard ICA with sample
complexity nearly linear in the dimension, thereby improving substantially on
previous bounds. The analysis is based on properties of random polynomials,
namely the spacings of an ensemble of polynomials. Our technique also applies
to other applications of tensor decompositions, including spherical Gaussian
mixture models.
Learning language through pictures
Authors: Grzegorz Chrupała, Ákos Kádár, Afra Alishahi
Date: 2015-06-11 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Sentence, Word Embeddings

We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently predict its visual representation and the next word in the
sentence. Mimicking an important aspect of human language learning, it acquires
meaning representations for individual words from descriptions of visual
scenes. Moreover, it learns to effectively use sequential structure in semantic
interpretation of multi-word phrases.
Approximate Inference with the Variational Holder Bound
Authors: Guillaume Bouchard, Balaji Lakshminarayanan
Date: 2015-06-19 00:00:00
Tasks: Bayesian Inference, Numerical Integration

We introduce the Variational Holder (VH) bound as an alternative to
Variational Bayes (VB) for approximate Bayesian inference. Unlike VB which
typically involves maximization of a non-convex lower bound with respect to the
variational parameters, the VH bound involves minimization of a convex upper
bound to the intractable integral with respect to the variational parameters.
Minimization of the VH bound is a convex optimization problem; hence the VH
method can be applied using off-the-shelf convex optimization algorithms and
the approximation error of the VH bound can also be analyzed using tools from
convex optimization literature. We present experiments on the task of
integrating a truncated multivariate Gaussian distribution and compare our
method to VB, EP and a state-of-the-art numerical integration method for this
problem.
Graph-based compression of dynamic 3D point cloud sequences
Authors: Dorina Thanou, Philip A. Chou, Pascal Frossard
Date: 2015-06-19 00:00:00
Tasks: Motion Estimation

This paper addresses the problem of compression of 3D point cloud sequences
that are characterized by moving 3D positions and color attributes. As
temporally successive point cloud frames are similar, motion estimation is key
to effective compression of these sequences. It however remains a challenging
problem as the point cloud frames have varying numbers of points without
explicit correspondence information. We represent the time-varying geometry of
these sequences with a set of graphs, and consider 3D positions and color
attributes of the points clouds as signals on the vertices of the graphs. We
then cast motion estimation as a feature matching problem between successive
graphs. The motion is estimated on a sparse set of representative vertices
using new spectral graph wavelet descriptors. A dense motion field is
eventually interpolated by solving a graph-based regularization problem. The
estimated motion is finally used for removing the temporal redundancy in the
predictive coding of the 3D positions and the color characteristics of the
point cloud sequences. Experimental results demonstrate that our method is able
to accurately estimate the motion between consecutive frames. Moreover, motion
estimation is shown to bring significant improvement in terms of the overall
compression performance of the sequence. To the best of our knowledge, this is
the first paper that exploits both the spatial correlation inside each frame
(through the graph) and the temporal correlation between the frames (through
the motion estimation) to compress the color and the geometry of 3D point cloud
sequences in an efficient way.
Quantifying the Effect of Sentiment on Information Diffusion in Social Media
Authors: Emilio Ferrara, Zeyao Yang
Date: 2015-06-19 00:00:00

Social media have become the main vehicle of information production and
consumption online. Millions of users every day log on their Facebook or
Twitter accounts to get updates and news, read about their topics of interest,
and become exposed to new opportunities and interactions. Although recent
studies suggest that the contents users produce will affect the emotions of
their readers, we still lack a rigorous understanding of the role and effects
of contents sentiment on the dynamics of information diffusion. This work aims
at quantifying the effect of sentiment on information diffusion, to understand:
(i) whether positive conversations spread faster and/or broader than negative
ones (or vice-versa); (ii) what kind of emotions are more typical of popular
conversations on social media; and, (iii) what type of sentiment is expressed
in conversations characterized by different temporal dynamics. Our findings
show that, at the level of contents, negative messages spread faster than
positive ones, but positive ones reach larger audiences, suggesting that people
are more inclined to share and favorite positive contents, the so-called
positive bias. As for the entire conversations, we highlight how different
temporal dynamics exhibit different sentiment patterns: for example, positive
sentiment builds up for highly-anticipated events, while unexpected events are
mainly characterized by negative sentiment. Our contribution is a milestone to
understand how the emotions expressed in short texts affect their spreading in
online social ecosystems, and may help to craft effective policies and
strategies for content generation and diffusion.
A general framework for the IT-based clustering methods
Authors: Teng Qiu, Yong-Jie Li
Date: 2015-06-19 00:00:00
Tasks: Clustering

Previously, we proposed a physically inspired rule to organize the data
points in a sparse yet effective structure, called the in-tree (IT) graph,
which is able to capture a wide class of underlying cluster structures in the
datasets, especially for the density-based datasets. Although there are some
redundant edges or lines between clusters requiring to be removed by computer,
this IT graph has a big advantage compared with the k-nearest-neighborhood
(k-NN) or the minimal spanning tree (MST) graph, in that the redundant edges in
the IT graph are much more distinguishable and thus can be easily determined by
several methods previously proposed by us.
  In this paper, we propose a general framework to re-construct the IT graph,
based on an initial neighborhood graph, such as the k-NN or MST, etc, and the
corresponding graph distances. For this general framework, our previous way of
constructing the IT graph turns out to be a special case of it. This general
framework 1) can make the IT graph capture a wider class of underlying cluster
structures in the datasets, especially for the manifolds, and 2) should be more
effective to cluster the sparse or graph-based datasets.
Tensor Analysis and Fusion of Multimodal Brain Images
Authors: Esin Karahan, Pedro A. Rojas-Lopez, Maria L. Bringas-Vega, Pedro A. Valdes-Hernandez, Pedro A. Valdes-Sosa
Date: 2015-06-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.
moco: Fast Motion Correction for Calcium Imaging
Authors: Alexander Dubbs, James Guevara, Darcy S. Peterka, Rafael Yuste
Date: 2015-06-19 00:00:00

Motion correction is the first in a pipeline of algorithms to analyze calcium
imaging videos and extract biologically relevant information, for example the
network structure of the neurons therein. Fast motion correction would be
especially critical for closed-loop activity triggered stimulation experiments,
where accurate detection and targeting of specific cells in necessary. Our
algorithm uses a Fourier-transform approach, and its efficiency derives from a
combination of judicious downsampling and the accelerated computation of many
$L_2$ norms using dynamic programming and two-dimensional, fft-accelerated
convolutions. Its accuracy is comparable to that of established community-used
algorithms, and it is more stable to large translational motions. It is
programmed in Java and is compatible with ImageJ.
Measuring Emotional Contagion in Social Media
Authors: Emilio Ferrara, Zeyao Yang
Date: 2015-06-19 00:00:00

Social media are used as main discussion channels by millions of individuals
every day. The content individuals produce in daily social-media-based
micro-communications, and the emotions therein expressed, may impact the
emotional states of others. A recent experiment performed on Facebook
hypothesized that emotions spread online, even in absence of non-verbal cues
typical of in-person interactions, and that individuals are more likely to
adopt positive or negative emotions if these are over-expressed in their social
network. Experiments of this type, however, raise ethical concerns, as they
require massive-scale content manipulation with unknown consequences for the
individuals therein involved. Here, we study the dynamics of emotional
contagion using Twitter. Rather than manipulating content, we devise a null
model that discounts some confounding factors (including the effect of
emotional contagion). We measure the emotional valence of content the users are
exposed to before posting their own tweets. We determine that on average a
negative post follows an over-exposure to 4.34% more negative content than
baseline, while positive posts occur after an average over-exposure to 4.50%
more positive contents. We highlight the presence of a linear relationship
between the average emotional valence of the stimuli users are exposed to, and
that of the responses they produce. We also identify two different classes of
individuals: highly and scarcely susceptible to emotional contagion. Highly
susceptible users are significantly less inclined to adopt negative emotions
than the scarcely susceptible ones, but equally likely to adopt positive
emotions. In general, the likelihood of adopting positive emotions is much
greater than that of negative emotions.
Crowd Flow Segmentation in Compressed Domain using CRF
Authors: Srinivas S. S. Kruthiventi, R. Venkatesh Babu
Date: 2015-06-19 00:00:00
Tasks: Optical Flow Estimation

Crowd flow segmentation is an important step in many video surveillance
tasks. In this work, we propose an algorithm for segmenting flows in H.264
compressed videos in a completely unsupervised manner. Our algorithm works on
motion vectors which can be obtained by partially decoding the compressed video
without extracting any additional features. Our approach is based on modelling
the motion vector field as a Conditional Random Field (CRF) and obtaining
oriented motion segments by finding the optimal labelling which minimises the
global energy of CRF. These oriented motion segments are recursively merged
based on gradient across their boundaries to obtain the final flow segments.
This work in compressed domain can be easily extended to pixel domain by
substituting motion vectors with motion based features like optical flow. The
proposed algorithm is experimentally evaluated on a standard crowd flow dataset
and its superior performance in both accuracy and computational time are
demonstrated through quantitative results.
Design of OFDM radar pulses using genetic algorithm based techniques
Authors: Gabriel Lellouch, Amit Kumar Mishra, Michael Inggs
Date: 2015-06-19 00:00:00
Tasks: Evolutionary Algorithms

The merit of evolutionary algorithms (EA) to solve convex optimization
problems is widely acknowledged. In this paper, a genetic algorithm (GA)
optimization based waveform design framework is used to improve the features of
radar pulses relying on the orthogonal frequency division multiplexing (OFDM)
structure. Our optimization techniques focus on finding optimal phase code
sequences for the OFDM signal. Several optimality criteria are used since we
consider two different radar processing solutions which call either for single
or multiple-objective optimizations. When minimization of the so-called
peak-to-mean envelope power ratio (PMEPR) single-objective is tackled, we
compare our findings with existing methods and emphasize on the merit of our
approach. In the scope of the two-objective optimization, we first address
PMEPR and peak-to-sidelobe level ratio (PSLR) and show that our approach based
on the non-dominated sorting genetic algorithm-II (NSGA-II) provides design
solutions with noticeable improvements as opposed to random sets of phase
codes. We then look at another case of interest where the objective functions
are two measures of the sidelobe level, namely PSLR and the integrated-sidelobe
level ratio (ISLR) and propose to modify the NSGA-II to include a constrain on
the PMEPR instead. In the last part, we illustrate via a case study how our
encoding solution makes it possible to minimize the single objective PMEPR
while enabling a target detection enhancement strategy, when the SNR metric
would be chosen for the detection framework.
Stereoscopic Cinema
Authors: Frédéric Devernay, Paul Beardsley
Date: 2015-06-19 00:00:00

Stereoscopic cinema has seen a surge of activity in recent years, and for the
first time all of the major Hollywood studios released 3-D movies in 2009. This
is happening alongside the adoption of 3-D technology for sports broadcasting,
and the arrival of 3-D TVs for the home. Two previous attempts to introduce 3-D
cinema in the 1950s and the 1980s failed because the contemporary technology
was immature and resulted in viewer discomfort. But current technologies --
such as accurately-adjustable 3-D camera rigs with onboard computers to
automatically inform a camera operator of inappropriate stereoscopic shots,
digital processing for post-shooting rectification of the 3-D imagery, digital
projectors for accurate positioning of the two stereo projections on the cinema
screen, and polarized silver screens to reduce cross-talk between the viewers
left- and right-eyes -- mean that the viewer experience is at a much higher
level of quality than in the past. Even so, creation of stereoscopic cinema is
an open, active research area, and there are many challenges from acquisition
to post-production to automatic adaptation for different-sized display. This
chapter describes the current state-of-the-art in stereoscopic cinema, and
directions of future work.
Strategy Synthesis for General Deductive Games Based on SAT Solving
Authors: Miroslav Klimos, Antonin Kucera
Date: 2014-07-15 00:00:00

We propose a general framework for modelling and solving deductive games,
where one player selects a secret code and the other player strives to discover
this code using a minimal number of allowed experiments that reveal some
partial information about the code. The framework is implemented in a software
tool Cobra, and its functionality is demonstrated by producing new results
about existing deductive games.
Enhanced Lasso Recovery on Graph
Authors: Xavier Bresson, Thomas Laurent, James Von Brecht
Date: 2015-06-19 00:00:00
Tasks: compressed sensing

This work aims at recovering signals that are sparse on graphs. Compressed
sensing offers techniques for signal recovery from a few linear measurements
and graph Fourier analysis provides a signal representation on graph. In this
paper, we leverage these two frameworks to introduce a new Lasso recovery
algorithm on graphs. More precisely, we present a non-convex, non-smooth
algorithm that outperforms the standard convex Lasso technique. We carry out
numerical experiments on three benchmark graph datasets.
Corpus sp{é}cialis{é} et ressource de sp{é}cialit{é}
Authors: Bernard Jacquemin, Sabine Ploux
Date: 2008-01-08 00:00:00
Tasks: Navigate

"Semantic Atlas" is a mathematic and statistic model to visualise word senses
according to relations between words. The model, that has been applied to
proximity relations from a corpus, has shown its ability to distinguish word
senses as the corpus' contributors comprehend them. We propose to use the model
and a specialised corpus in order to create automatically a specialised
dictionary relative to the corpus' domain. A morpho-syntactic analysis
performed on the corpus makes it possible to create the dictionary from
syntactic relations between lexical units. The semantic resource can be used to
navigate semantically - and not only lexically - through the corpus, to create
classical dictionaries or for diachronic studies of the language.
HuTO: an Human Time Ontology for Semantic Web Applications
Authors: Papa Fary Diallo, Olivier Corby, Isabelle Mirbel, Moussa Lo, Seydina M. Ndiaye
Date: 2015-06-19 00:00:00
Tasks: Information Retrieval, Retrieval

The temporal phenomena have many facets that are studied by different
communities. In Semantic Web, large heterogeneous data are handled and
produced. These data often have informal, semi-formal or formal temporal
information which must be interpreted by software agents. In this paper we
present Human Time Ontology (HuTO) an RDFS ontology to annotate and represent
temporal data. A major contribution of HuTO is the modeling of non-convex
intervals giving the ability to write queries for this kind of interval. HuTO
also incorporates normalization and reasoning rules to explicit certain
information. HuTO also proposes an approach which associates a temporal
dimension to the knowledge base content. This facilitates information retrieval
by considering or not the temporal aspect.
Montblanc: GPU accelerated Radio Interferometer Measurement Equations in support of Bayesian Inference for Radio Observations
Authors: Simon Perkins, Patrick Marais, Jonathan Zwart, Iniyan Natarajan, Cyril Tasse, Oleg Smirnov
Date: 2015-01-30 00:00:00
Tasks: Bayesian Inference, CPU, GPU

We present Montblanc, a GPU implementation of the Radio interferometer
measurement equation (RIME) in support of the Bayesian inference for radio
observations (BIRO) technique. BIRO uses Bayesian inference to select sky
models that best match the visibilities observed by a radio interferometer. To
accomplish this, BIRO evaluates the RIME multiple times, varying sky model
parameters to produce multiple model visibilities. Chi-squared values computed
from the model and observed visibilities are used as likelihood values to drive
the Bayesian sampling process and select the best sky model.
  As most of the elements of the RIME and chi-squared calculation are
independent of one another, they are highly amenable to parallel computation.
Additionally, Montblanc caters for iterative RIME evaluation to produce
multiple chi-squared values. Modified model parameters are transferred to the
GPU between each iteration.
  We implemented Montblanc as a Python package based upon NVIDIA's CUDA
architecture. As such, it is easy to extend and implement different pipelines.
At present, Montblanc supports point and Gaussian morphologies, but is designed
for easy addition of new source profiles.
  Montblanc's RIME implementation is performant: On an NVIDIA K40, it is
approximately 250 times faster than MeqTrees on a dual hexacore Intel E5-2620v2
CPU. Compared to the OSKAR simulator's GPU-implemented RIME components it is
7.7 and 12 times faster on the same K40 for single and double-precision
floating point respectively. However, OSKAR's RIME implementation is more
general than Montblanc's BIRO-tailored RIME.
  Theoretical analysis of Montblanc's dominant CUDA kernel suggests that it is
memory bound. In practice, profiling shows that is balanced between compute and
memory, as much of the data required by the problem is retained in L1 and L2
cache.
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Authors: Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
Date: 2014-06-26 00:00:00
Tasks: General Classification, Geometric Matching

Deep convolutional networks have proven to be very successful in learning
task specific features that allow for unprecedented performance on various
computer vision tasks. Training of such networks follows mostly the supervised
learning paradigm, where sufficiently many input-output pairs are required for
training. Acquisition of large training sets is one of the key challenges, when
approaching a new task. In this paper, we aim for generic feature learning and
present an approach for training a convolutional network using only unlabeled
data. To this end, we train the network to discriminate between a set of
surrogate classes. Each surrogate class is formed by applying a variety of
transformations to a randomly sampled 'seed' image patch. In contrast to
supervised network training, the resulting feature representation is not class
specific. It rather provides robustness to the transformations that have been
applied during training. This generic feature representation allows for
classification results that outperform the state of the art for unsupervised
learning on several popular datasets (STL-10, CIFAR-10, Caltech-101,
Caltech-256). While such generic features cannot compete with class specific
features from supervised training on a classification task, we show that they
are advantageous on geometric matching problems, where they also outperform the
SIFT descriptor.
A new Initial Centroid finding Method based on Dissimilarity Tree for K-means Algorithm
Authors: Abhishek Kumar, Suresh Chandra Gupta
Date: 2015-06-19 00:00:00
Tasks: Clustering

Cluster analysis is one of the primary data analysis technique in data mining
and K-means is one of the commonly used partitioning clustering algorithm. In
K-means algorithm, resulting set of clusters depend on the choice of initial
centroids. If we can find initial centroids which are coherent with the
arrangement of data, the better set of clusters can be obtained. This paper
proposes a method based on the Dissimilarity Tree to find, the better initial
centroid as well as every bit more accurate cluster with less computational
time. Theory analysis and experimental results indicate that the proposed
method can effectively improve the accuracy of clusters and reduce the
computational complexity of the K-means algorithm.