pwc-archive/papers-with-abstracts huggingface dataset:
Nonlinear functional mapping of the human brain
Authors: Nicholas Allgaier, Tobias Banaschewski, Gareth Barker, Arun L. W. Bokde, Josh C. Bongard, Uli Bromberg, Christian Büchel, Anna Cattrell, Patricia J. Conrod, Christopher M. Danforth, Sylvane Desrivières, Peter S. Dodds, Herta Flor, Vincent Frouin, Jürgen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Scott Mackey, Jean-Luc Martinot, Kevin Murphy, Frauke Nees, Dimitri Papadopoulos-Orfanos, Luise Poustka, Michael N. Smolka, Henrik Walter, Robert Whelan, Gunter Schumann, Hugh Garavan, IMAGEN Consortium
Date: 2015-09-08 00:00:00

The field of neuroimaging has truly become data rich, and novel analytical
methods capable of gleaning meaningful information from large stores of imaging
data are in high demand. Those methods that might also be applicable on the
level of individual subjects, and thus potentially useful clinically, are of
special interest. In the present study, we introduce just such a method, called
nonlinear functional mapping (NFM), and demonstrate its application in the
analysis of resting state fMRI from a 242-subject subset of the IMAGEN project,
a European study of adolescents that includes longitudinal phenotypic,
behavioral, genetic, and neuroimaging data. NFM employs a computational
technique inspired by biological evolution to discover and mathematically
characterize interactions among ROI (regions of interest), without making
linear or univariate assumptions. We show that statistics of the resulting
interaction relationships comport with recent independent work, constituting a
preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in
the models generated by NFM, suggesting that some of the interactions
characterized here are not discoverable by standard linear methods of analysis.
We discuss one such nonlinear interaction in the context of a direct comparison
with a procedure involving pairwise correlation, designed to be an analogous
linear version of functional mapping. We find another such interaction that
suggests a novel distinction in brain function between drinking and
non-drinking adolescents: a tighter coupling of ROI associated with emotion,
reward, and interoceptive processes such as thirst, among drinkers. Finally, we
outline many improvements and extensions of the methodology to reduce
computational expense, complement other analytical tools like graph-theoretic
analysis, and allow for voxel level NFM to eliminate the necessity of ROI
selection.
DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System
Authors: Justice Amoh, Kofi Odame
Date: 2015-09-08 00:00:00
Tasks: General Classification, Sensitivity, Specificity

In this paper, we present a system that employs a wearable acoustic sensor
and a deep convolutional neural network for detecting coughs. We evaluate the
performance of our system on 14 healthy volunteers and compare it to that of
other cough detection systems that have been reported in the literature.
Experimental results show that our system achieves a classification sensitivity
of 95.1% and a specificity of 99.5%.
Nucleosome positioning: resources and tools online
Authors: Vladimir B. Teif
Date: 2015-08-27 00:00:00

Nucleosome positioning is an important process required for proper genome
packing and its accessibility to execute the genetic program in a
cell-specific, timely manner. In the recent years hundreds of papers have been
devoted to the bioinformatics, physics and biology of nucleosome positioning.
The purpose of this review is to cover a practical aspect of this field, namely
to provide a guide to the multitude of nucleosome positioning resources
available online. These include almost 300 experimental datasets of genome-wide
nucleosome occupancy profiles determined in different cell types and more than
40 computational tools for the analysis of experimental nucleosome positioning
data and prediction of intrinsic nucleosome formation probabilities from the
DNA sequence. A manually curated, up to date list of these resources will be
maintained at http://generegulation.info.
Designing A Composite Dictionary Adaptively From Joint Examples
Authors: Zhangyang Wang, Yingzhen Yang, Jianchao Yang, Thomas S. Huang
Date: 2015-03-12 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Super-Resolution

We study the complementary behaviors of external and internal examples in
image restoration, and are motivated to formulate a composite dictionary design
framework. The composite dictionary consists of the global part learned from
external examples, and the sample-specific part learned from internal examples.
The dictionary atoms in both parts are further adaptively weighted to emphasize
their model statistics. Experiments demonstrate that the joint utilization of
external and internal examples leads to substantial improvements, with
successful applications in image denoising and super resolution.
Unsupervised Learning of Spatiotemporally Coherent Metrics
Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann Lecun
Date: 2014-12-18 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification, Metric Learning

Current state-of-the-art classification and detection algorithms rely on
supervised training. In this work we study unsupervised feature learning in the
context of temporally coherent video data. We focus on feature learning from
unlabeled video data, using the assumption that adjacent video frames contain
semantically similar information. This assumption is exploited to train a
convolutional pooling auto-encoder regularized by slowness and sparsity. We
establish a connection between slow feature learning to metric learning and
show that the trained encoder can be used to define a more temporally and
semantically coherent metric.
Edge-enhancing Filters with Negative Weights
Authors: Andrew Knyazev
Date: 2015-09-08 00:00:00
Tasks: Denoising

In [DOI:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by
projecting the noisy image to a lower dimensional Krylov subspace of the graph
Laplacian, constructed using nonnegative weights determined by distances
between image data corresponding to image pixels. We~extend the construction of
the graph Laplacian to the case, where some graph weights can be negative.
Removing the positivity constraint provides a more accurate inference of a
graph model behind the data, and thus can improve quality of filters for
graph-based signal processing, e.g., denoising, compared to the standard
construction, without affecting the costs.
Deep Attributes from Context-Aware Regional Neural Codes
Authors: Jianwei Luo, Jianguo Li, Jun Wang, Zhiguo Jiang, Yurong Chen
Date: 2015-09-08 00:00:00
Tasks: Attribute, General Classification, image-classification, Image Classification, Retrieval

Recently, many researches employ middle-layer output of convolutional neural
network models (CNN) as features for different visual recognition tasks.
Although promising results have been achieved in some empirical studies, such
type of representations still suffer from the well-known issue of semantic gap.
This paper proposes so-called deep attribute framework to alleviate this issue
from three aspects. First, we introduce object region proposals as intermedia
to represent target images, and extract features from region proposals. Second,
we study aggregating features from different CNN layers for all region
proposals. The aggregation yields a holistic yet compact representation of
input images. Results show that cross-region max-pooling of soft-max layer
output outperform all other layers. As soft-max layer directly corresponds to
semantic concepts, this representation is named "deep attributes". Third, we
observe that only a small portion of generated regions by object proposals
algorithm are correlated to classification target. Therefore, we introduce
context-aware region refining algorithm to pick out contextual regions and
build context-aware classifiers.
  We apply the proposed deep attributes framework for various vision tasks.
Extensive experiments are conducted on standard benchmarks for three visual
recognition tasks, i.e., image classification, fine-grained recognition and
visual instance retrieval. Results show that deep attribute approaches achieve
state-of-the-art results, and outperforms existing peer methods with a
significant margin, even though some benchmarks have little overlap of concepts
with the pre-trained CNN models.
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Authors: Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman
Date: 2015-09-07 00:00:00
Tasks: Probabilistic Programming

Lightweight, source-to-source transformation approaches to implementing MCMC
for probabilistic programming languages are popular for their simplicity,
support of existing deterministic code, and ability to execute on existing fast
runtimes. However, they are also slow, requiring a complete re-execution of the
program on every Metropolis Hastings proposal. We present a new extension to
the lightweight approach, C3, which enables efficient, incrementalized
re-execution of MH proposals. C3 is based on two core ideas: transforming
probabilistic programs into continuation passing style (CPS), and caching the
results of function calls. We show that on several common models, C3 reduces
proposal runtime by 20-100x, in some cases reducing runtime complexity from
linear in model size to constant. We also demonstrate nearly an order of
magnitude speedup on a complex inverse procedural modeling application.
Accelerated graph-based spectral polynomial filters
Authors: Andrew Knyazev, Alexander Malyshev
Date: 2015-09-08 00:00:00
Tasks: Denoising

Graph-based spectral denoising is a low-pass filtering using the
eigendecomposition of the graph Laplacian matrix of a noisy signal. Polynomial
filtering avoids costly computation of the eigendecomposition by projections
onto suitable Krylov subspaces. Polynomial filters can be based, e.g., on the
bilateral and guided filters. We propose constructing accelerated polynomial
filters by running flexible Krylov subspace based linear and eigenvalue solvers
such as the Block Locally Optimal Preconditioned Conjugate Gradient (LOBPCG)
method.
Evolving TSP heuristics using Multi Expression Programming
Authors: Mihai Oltean, D. Dumitrescu
Date: 2015-09-08 00:00:00
Tasks: Traveling Salesman Problem

Multi Expression Programming (MEP) is an evolutionary technique that may be
used for solving computationally difficult problems. MEP uses a linear solution
representation. Each MEP individual is a string encoding complex expressions
(computer programs). A MEP individual may encode multiple solutions of the
current problem. In this paper MEP is used for evolving a Traveling Salesman
Problem (TSP) heuristic for graphs satisfying triangle inequality. Evolved MEP
heuristic is compared with Nearest Neighbor Heuristic (NN) and Minimum Spanning
Tree Heuristic (MST) on some difficult problems in TSPLIB. For most of the
considered problems the evolved MEP heuristic outperforms NN and MST. The
obtained algorithm was tested against some problems in TSPLIB. The results
emphasizes that evolved MEP heuristic is a powerful tool for solving difficult
TSP instances.
A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles
Authors: Yeounoh Chung, Chang-yong Park, Noo-ri Kim, Hana Cho, Taebok Yoon, Hunjoo Lee, Jee-Hyong Lee
Date: 2015-09-08 00:00:00

An approach for game bot detection in MMORPGs is proposed based on the
analysis of game playing behavior. Since MMORPGs are large scale games, users
can play in various ways. This variety in playing behavior makes it hard to
detect game bots based on play behaviors. In order to cope with this problem,
the proposed approach observes game playing behaviors of users and groups them
by their behavioral similarities. Then, it develops a local bot detection model
for each player group. Since the locally optimized models can more accurately
detect game bots within each player group, the combination of those models
brings about overall improvement. For a practical purpose of reducing the
workloads of the game servers in service, the game data is collected at a low
resolution in time. Behavioral features are selected and developed to
accurately detect game bots with the low resolution data, considering common
aspects of MMORPG playing. Through the experiment with the real data from a
game currently in service, it is shown that the proposed local model approach
yields more accurate results.
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression
Authors: Arnold Salas, Stephen J. Roberts, Michael A. Osborne
Date: 2015-09-08 00:00:00
Tasks: General Classification, regression, Variational Inference

Online Passive-Aggressive (PA) learning is a class of online margin-based
algorithms suitable for a wide range of real-time prediction tasks, including
classification and regression. PA algorithms are formulated in terms of
deterministic point-estimation problems governed by a set of user-defined
hyperparameters: the approach fails to capture model/prediction uncertainty and
makes their performance highly sensitive to hyperparameter configurations. In
this paper, we introduce a novel PA learning framework for regression that
overcomes the above limitations. We contribute a Bayesian state-space
interpretation of PA regression, along with a novel online variational
inference scheme, that not only produces probabilistic predictions, but also
offers the benefit of automatic hyperparameter tuning. Experiments with various
real-world data sets show that our approach performs significantly better than
a more standard, linear Gaussian state-space model.
Improved Twitter Sentiment Prediction through Cluster-then-Predict Model
Authors: Rishabh Soni, K. James Mathai
Date: 2015-09-08 00:00:00
Tasks: Clustering, General Classification, Marketing, Sentiment Analysis

Over the past decade humans have experienced exponential growth in the use of
online resources, in particular social media and microblogging websites such as
Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line,
etc. Many companies have identified these resources as a rich mine of marketing
knowledge. This knowledge provides valuable feedback which allows them to
further develop the next generation of their product. In this paper, sentiment
analysis of a product is performed by extracting tweets about that product and
classifying the tweets showing it as positive and negative sentiment. The
authors propose a hybrid approach which combines unsupervised learning in the
form of K-means clustering to cluster the tweets and then performing supervised
learning methods such as Decision Trees and Support Vector Machines for
classification.
Central Pattern Generators for the control of robotic systems
Authors: Carlos Garcia-Saura
Date: 2015-09-08 00:00:00

Bio-inspired control of motion is an active field of research with many
applications in real world tasks. In the case of robotic systems that need to
exhibit oscillatory behaviour (i.e. locomotion of snake-type or legged robots),
Central Pattern Generators (CPGs) are among the most versatile solutions. These
controllers are often based on loosely-coupled oscillators similar to those
found in the neural circuits of many animal species, and can be more robust to
uncertainty (i.e. external perturbations) than traditional control approaches.
This project provides an overview of the state-of-the-art in the field of CPGs,
and in particular their applications within robotic systems. The project also
tackles the implementation of a CPG-based controller in a small 3D-printed
hexapod.
Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition
Authors: Mortaza Doulaty, Oscar Saz, Thomas Hain
Date: 2015-09-08 00:00:00
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Speech recognition systems are often highly domain dependent, a fact widely
reported in the literature. However the concept of domain is complex and not
bound to clear criteria. Hence it is often not evident if data should be
considered to be out-of-domain. While both acoustic and language models can be
domain specific, work in this paper concentrates on acoustic modelling. We
present a novel method to perform unsupervised discovery of domains using
Latent Dirichlet Allocation (LDA) modelling. Here a set of hidden domains is
assumed to exist in the data, whereby each audio segment can be considered to
be a weighted mixture of domain properties. The classification of audio
segments into domains allows the creation of domain specific acoustic models
for automatic speech recognition. Experiments are conducted on a dataset of
diverse speech data covering speech from radio and TV broadcasts, telephone
conversations, meetings, lectures and read speech, with a joint training set of
60 hours and a test set of 6 hours. Maximum A Posteriori (MAP) adaptation to
LDA based domains was shown to yield relative Word Error Rate (WER)
improvements of up to 16% relative, compared to pooled training, and up to 10%,
compared with models adapted with human-labelled prior domain knowledge.
Data-selective Transfer Learning for Multi-Domain Speech Recognition
Authors: Mortaza Doulaty, Oscar Saz, Thomas Hain
Date: 2015-09-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Transfer Learning

Negative transfer in training of acoustic models for automatic speech
recognition has been reported in several contexts such as domain change or
speaker characteristics. This paper proposes a novel technique to overcome
negative transfer by efficient selection of speech data for acoustic model
training. Here data is chosen on relevance for a specific target. A submodular
function based on likelihood ratios is used to determine how acoustically
similar each training utterance is to a target test set. The approach is
evaluated on a wide-domain data set, covering speech from radio and TV
broadcasts, telephone conversations, meetings, lectures and read speech.
Experiments demonstrate that the proposed technique both finds relevant data
and limits negative transfer. Results on a 6--hour test set show a relative
improvement of 4% with data selection over using all data in PLP based models,
and 2% with DNN features.
Weakly Supervised Learning for Salient Object Detection
Authors: Huaizu Jiang
Date: 2015-01-29 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Superpixels, Weakly-supervised Learning

Recent advances in supervised salient object detection has resulted in
significant performance on benchmark datasets. Training such models, however,
requires expensive pixel-wise annotations of salient objects. Moreover, many
existing salient object detection models assume that at least one salient
object exists in the input image. Such an assumption often leads to less
appealing saliency maps on the background images, which contain no salient
object at all. To avoid the requirement of expensive pixel-wise salient region
annotations, in this paper, we study weakly supervised learning approaches for
salient object detection. Given a set of background images and salient object
images, we propose a solution toward jointly addressing the salient object
existence and detection tasks. We adopt the latent SVM framework and formulate
the two problems together in a single integrated objective function: saliency
labels of superpixels are modeled as hidden variables and involved in a
classification term conditioned to the salient object existence variable, which
in turn depends on both global image and regional saliency features and
saliency label assignment. Experimental results on benchmark datasets validate
the effectiveness of our proposed approach.
Empirical risk minimization is consistent with the mean absolute percentage error
Authors: Arnaud De Myttenaere, Bénédicte Le Grand, Fabrice Rossi
Date: 2015-09-08 00:00:00
Tasks: regression

We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We also show that, under some asumptions,
universal consistency of Empirical Risk Minimization remains possible using the
MAPE.
On the complexity of piecewise affine system identification
Authors: Fabien Lauer
Date: 2015-09-08 00:00:00

The paper provides results regarding the computational complexity of hybrid
system identification. More precisely, we focus on the estimation of piecewise
affine (PWA) maps from input-output data and analyze the complexity of
computing a global minimizer of the error. Previous work showed that a global
solution could be obtained for continuous PWA maps with a worst-case complexity
exponential in the number of data. In this paper, we show how global optimality
can be reached for a slightly more general class of possibly discontinuous PWA
maps with a complexity only polynomial in the number of data, however with an
exponential complexity with respect to the data dimension. This result is
obtained via an analysis of the intrinsic classification subproblem of
associating the data points to the different modes. In addition, we prove that
the problem is NP-hard, and thus that the exponential complexity in the
dimension is a natural expectation for any exact algorithm.
Modelling time evolving interactions in networks through a non stationary extension of stochastic block models
Authors: Marco Corneli, Pierre Latouche, Fabrice Rossi
Date: 2015-09-08 00:00:00
Tasks: Clustering, Stochastic Block Model

In this paper, we focus on the stochastic block model (SBM),a probabilistic
tool describing interactions between nodes of a network using latent clusters.
The SBM assumes that the networkhas a stationary structure, in which
connections of time varying intensity are not taken into account. In other
words, interactions between two groups are forced to have the same features
during the whole observation time. To overcome this limitation,we propose a
partition of the whole time horizon, in which interactions are observed, and
develop a non stationary extension of the SBM,allowing to simultaneously
cluster the nodes in a network along with fixed time intervals in which the
interactions take place. The number of clusters (K for nodes, D for time
intervals) as well as the class memberships are finallyobtained through
maximizing the complete-data integrated likelihood by means of a greedy search
approach. After showing that the model works properly with simulated data, we
focus on a real data set. We thus consider the three days ACM Hypertext
conference held in Turin,June 29th - July 1st 2009. Proximity interactions
between attendees during the first day are modelled and an
interestingclustering of the daily hours is finally obtained, with times of
social gathering (e.g. coffee breaks) recovered by the approach. Applications
to large networks are limited due to the computational complexity of the greedy
search which is dominated bythe number $K\_{max}$ and $D\_{max}$ of clusters
used in the initialization. Therefore,advanced clustering tools are considered
to reduce the number of clusters expected in the data, making the greedy search
applicable to large networks.