pwc-archive/papers-with-abstracts huggingface dataset:
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition
Authors: Ahmed Ali, Peter Bell, James Glass, Yacine Messaoui, Hamdy Mubarak, Steve Renals, Yifan Zhang
Date: 2016-09-19 00:00:00
Tasks: Acoustic Modelling, Diversity, Language Modelling, Word Alignment

This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year's English MGB Challenge, which focused on recognition of diverse TV genres, this year, the challenge has an emphasis on handling the diversity in dialect in Arabic speech. Audio data comes from 19 distinct programmes from the Aljazeera Arabic TV channel between March 2005 and December 2015. Programmes are split into three groups: conversations, interviews, and reports. A total of 1,200 hours have been released with lightly supervised transcriptions for the acoustic modelling. For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera.net for a 10 year duration 2000-2011. Two lexicons have been provided, one phoneme based and one grapheme based. Finally, two tasks were proposed for this year's challenge: standard speech transcription, and word alignment. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
Authors: Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, Weiran He
Date: 2017-05-14 00:00:00
Tasks: Attribute, Conditional Image Generation, Image Generation, Object

Object Transfiguration replaces an object in an image with another object
from a second image. For example it can perform tasks like "putting exactly
those eyeglasses from image A on the nose of the person in image B". Usage of
exemplar images allows more precise specification of desired modifications and
improves the diversity of conditional image generation. However, previous
methods that rely on feature space operations, require paired data and/or
appearance models for training or disentangling objects from background. In
this work, we propose a model that can learn object transfiguration from two
unpaired sets of images: one set containing images that "have" that kind of
object, and the other set being the opposite, with the mild constraint that the
objects be located approximately at the same place. For example, the training
data can be one set of reference face images that have eyeglasses, and another
set of images that have not, both of which spatially aligned by face landmarks.
Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that
contain multiple representatives of different types of glasses. Consequently,
we can perform fine-grained control of generated images, like swapping the
glasses in two images by swapping the projected components in the "eyeglasses"
subspace, to create novel images of people wearing eyeglasses.
  Overall, our deterministic generative model learns disentangled attribute
subspaces from weakly labeled data by adversarial training. Experiments on
CelebA and Multi-PIE datasets validate the effectiveness of the proposed model
on real world data, in generating images with specified eyeglasses, smiling,
hair styles, and lighting conditions etc. The code is available online.
A Closed-Form Model for Image-Based Distant Lighting
Authors: Mais Alnasser, Hassan Foroosh
Date: 2017-05-14 00:00:00
Tasks: Form

In this paper, we present a new mathematical foundation for image-based
lighting. Using a simple manipulation of the local coordinate system, we derive
a closed-form solution to the light integral equation under distant environment
illumination. We derive our solution for different BRDF's such as lambertian
and Phong-like. The method is free of noise, and provides the possibility of
using the full spectrum of frequencies captured by images taken from the
environment. This allows for the color of the rendered object to be toned
according to the color of the light in the environment. Experimental results
also show that one can gain an order of magnitude or higher in rendering time
compared to Monte Carlo quadrature methods and spherical harmonics.
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Authors: Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney
Date: 2017-05-14 00:00:00
Proceeding: ICML 2017 8

In many modern machine learning applications, structures of underlying
mathematical models often yield nonconvex optimization problems. Due to the
intractability of nonconvexity, there is a rising need to develop efficient
methods for solving general nonconvex problems with certain performance
guarantee. In this work, we investigate the accelerated proximal gradient
method for nonconvex programming (APGnc). The method compares between a usual
proximal gradient step and a linear extrapolation step, and accepts the one
that has a lower function value to achieve a monotonic decrease. In specific,
under a general nonsmooth and nonconvex setting, we provide a rigorous argument
to show that the limit points of the sequence generated by APGnc are critical
points of the objective function. Then, by exploiting the
Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we
establish the linear and sub-linear convergence rates of the function value
sequence generated by APGnc. We further propose a stochastic variance reduced
APGnc (SVRG-APGnc), and establish its linear convergence under a special case
of the \KL property. We also extend the analysis to the inexact version of
these methods and develop an adaptive momentum strategy that improves the
numerical performance.
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry
Authors: Shinjini Kundu, Soheil Kolouri, Kirk I Erickson, Arthur F Kramer, Edward McAuley, Gustavo K. Rohde
Date: 2017-05-14 00:00:00
Tasks: blind source separation

Disease in the brain is often associated with subtle, spatially diffuse, or
complex tissue changes that may lie beneath the level of gross visual
inspection, even on magnetic resonance imaging (MRI). Unfortunately, current
computer-assisted approaches that examine pre-specified features, whether
anatomically-defined (i.e. thalamic volume, cortical thickness) or based on
pixelwise comparison (i.e. deformation-based methods), are prone to missing a
vast array of physical changes that are not well-encapsulated by these metrics.
In this paper, we have developed a technique for automated pattern analysis
that can fully determine the relationship between brain structure and
observable phenotype without requiring any a priori features. Our technique,
called transport-based morphometry (TBM), is an image transformation that maps
brain images losslessly to a domain where they become much more separable. The
new approach is validated on structural brain images of healthy older adult
subjects where even linear models for discrimination, regression, and blind
source separation enable TBM to independently discover the characteristic
changes of aging and highlight potential mechanisms by which aerobic fitness
may mediate brain health later in life. TBM is a generative approach that can
provide visualization of physically meaningful shifts in tissue distribution
through inverse transformation. The proposed framework is a powerful technique
that can potentially elucidate genotype-structural-behavioral associations in
myriad diseases.
Spatial-Temporal Union of Subspaces for Multi-body Non-rigid Structure-from-Motion
Authors: Suryansh Kumar, Yuchao Dai, Hongdong Li
Date: 2017-05-14 00:00:00
Tasks: 3D Reconstruction

Non-rigid structure-from-motion (NRSfM) has so far been mostly studied for
recovering 3D structure of a single non-rigid/deforming object. To handle the
real world challenging multiple deforming objects scenarios, existing methods
either pre-segment different objects in the scene or treat multiple non-rigid
objects as a whole to obtain the 3D non-rigid reconstruction. However, these
methods fail to exploit the inherent structure in the problem as the solution
of segmentation and the solution of reconstruction could not benefit each
other. In this paper, we propose a unified framework to jointly segment and
reconstruct multiple non-rigid objects. To compactly represent complex
multi-body non-rigid scenes, we propose to exploit the structure of the scenes
along both temporal direction and spatial direction, thus achieving a
spatio-temporal representation. Specifically, we represent the 3D non-rigid
deformations as lying in a union of subspaces along the temporal direction and
represent the 3D trajectories as lying in the union of subspaces along the
spatial direction. This spatio-temporal representation not only provides
competitive 3D reconstruction but also outputs robust segmentation of multiple
non-rigid objects. The resultant optimization problem is solved efficiently
using the Alternating Direction Method of Multipliers (ADMM). Extensive
experimental results on both synthetic and real multi-body NRSfM datasets
demonstrate the superior performance of our proposed framework compared with
the state-of-the-art methods.
Volumetric Super-Resolution of Multispectral Data
Authors: Vildan Atalay Aydin, Hassan Foroosh
Date: 2017-05-14 00:00:00
Tasks: Pansharpening, Super-Resolution

Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Authors: Mandar Joshi, Eunsol Choi, Daniel S. Weld, Luke Zettlemoyer
Date: 2017-05-09 00:00:00
Proceeding: ACL 2017 7
Tasks: Reading Comprehension, Sentence, TriviaQA

We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for answering the questions. We show that, in comparison to
other recently introduced large-scale datasets, TriviaQA (1) has relatively
complex, compositional questions, (2) has considerable syntactic and lexical
variability between questions and corresponding answer-evidence sentences, and
(3) requires more cross sentence reasoning to find answers. We also present two
baseline algorithms: a feature-based classifier and a state-of-the-art neural
network, that performs well on SQuAD reading comprehension. Neither approach
comes close to human performance (23% and 40% vs. 80%), suggesting that
TriviaQA is a challenging testbed that is worth significant future study. Data
and code available at -- http://nlp.cs.washington.edu/triviaqa/
Distributed Adaptive Learning of Graph Signals
Authors: P. Di Lorenzo, P. Banelli, S. Barbarossa, S. Sardellitti
Date: 2016-09-20 00:00:00
Tasks: Action Localization

The aim of this paper is to propose distributed strategies for adaptive
learning of signals defined over graphs. Assuming the graph signal to be
bandlimited, the method enables distributed reconstruction, with guaranteed
performance in terms of mean-square error, and tracking from a limited number
of sampled observations taken from a subset of vertices. A detailed mean square
analysis is carried out and illustrates the role played by the sampling
strategy on the performance of the proposed method. Finally, some useful
strategies for distributed selection of the sampling set are provided. Several
numerical results validate our theoretical findings, and illustrate the
performance of the proposed method for distributed adaptive learning of signals
defined over graphs.
Awareness improves problem-solving performance
Authors: José F. Fontanari
Date: 2017-05-13 00:00:00

The brain's self-monitoring of activities, including internal activities -- a
functionality that we refer to as awareness -- has been suggested as a key
element of consciousness. Here we investigate whether the presence of an
inner-eye-like process (monitor) that supervises the activities of a number of
subsystems (operative agents) engaged in the solution of a problem can improve
the problem-solving efficiency of the system. The problem is to find the global
maximum of a NK fitness landscape and the performance is measured by the time
required to find that maximum. The operative agents explore blindly the fitness
landscape and the monitor provides them with feedback on the quality (fitness)
of the proposed solutions. This feedback is then used by the operative agents
to bias their searches towards the fittest regions of the landscape. We find
that a weak feedback between the monitor and the operative agents improves the
performance of the system, regardless of the difficulty of the problem, which
is gauged by the number of local maxima in the landscape. For easy problems
(i.e., landscapes without local maxima), the performance improves monotonically
as the feedback strength increases, but for difficult problems, there is an
optimal value of the feedback strength beyond which the system performance
degrades very rapidly.
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response
Authors: Shre Kumar Chatterjee, Saptarshi Das, Koushik Maharatna, Elisa Masi, Luisa Santopolo, Ilaria Colzi, Stefano Mancuso, Andrea Vitaletti
Date: 2017-05-13 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

Plants monitor their surrounding environment and control their physiological
functions by producing an electrical response. We recorded electrical signals
from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3)
and Sulfuric Acid (H2SO4) under laboratory conditions. After applying
pre-processing techniques such as filtering and drift removal, we extracted few
statistical features from the acquired plant electrical signals. Using these
features, combined with different classification algorithms, we used a decision
tree based multi-class classification strategy to identify the three different
external chemical stimuli. We here present our exploration to obtain the
optimum set of ranked feature and classifier combination that can separate a
particular chemical stimulus from the incoming stream of plant electrical
signals. The paper also reports an exhaustive comparison of similar feature
based classification using the filtered and the raw plant signals, containing
the high frequency stochastic part and also the low frequency trends present in
it, as two different cases for feature extraction. The work, presented in this
paper opens up new possibilities for using plant electrical signals to monitor
and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
Authors: Ahmed ElSayed, Ausif Mahmood, Tarek Sobh
Date: 2017-03-23 00:00:00
Tasks: 3D Face Alignment, Face Alignment, Face Recognition, Image Super-Resolution, Super-Resolution, Unsupervised face recognition

Majority of the face recognition algorithms use query faces captured from
uncontrolled, in the wild, environment. Often caused by the cameras limited
capabilities, it is common for these captured facial images to be blurred or
low resolution. Super resolution algorithms are therefore crucial in improving
the resolution of such images especially when the image size is small requiring
enlargement. This paper aims to demonstrate the effect of one of the
state-of-the-art algorithms in the field of image super resolution. To
demonstrate the functionality of the algorithm, various before and after 3D
face alignment cases are provided using the images from the Labeled Faces in
the Wild (lfw). Resulting images are subject to testing on a closed set face
recognition protocol using unsupervised algorithms with high dimension
extracted features. The inclusion of super resolution algorithm resulted in
significant improved recognition rate over recently reported results obtained
from unsupervised algorithms.
No, This is not a Circle
Authors: Zoltán Kovács
Date: 2017-04-27 00:00:00

A popular curve shown in introductory maths textbooks, seems like a circle.
But it is actually a different curve. This paper discusses some elementary
approaches to identify the geometric object, including novel technological
means by using GeoGebra. We demonstrate two ways to refute the false
impression, two suggestions to find a correct conjecture, and four ways to
confirm the result by proving it rigorously.
  All of the discussed approaches can be introduced in classrooms at various
levels from middle school to high school.
Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval
Authors: Zihan Zhou, Farshid Farhat, James Z. Wang
Date: 2016-08-15 00:00:00
Tasks: Contour Detection, Image Retrieval, Retrieval

Linear perspective is widely used in landscape photography to create the
impression of depth on a 2D photo. Automated understanding of linear
perspective in landscape photography has several real-world applications,
including aesthetics assessment, image retrieval, and on-site feedback for
photo composition, yet adequate automated understanding has been elusive. We
address this problem by detecting the dominant vanishing point and the
associated line structures in a photo. However, natural landscape scenes pose
great technical challenges because often the inadequate number of strong edges
converging to the dominant vanishing point is inadequate. To overcome this
difficulty, we propose a novel vanishing point detection method that exploits
global structures in the scene via contour detection. We show that our method
significantly outperforms state-of-the-art methods on a public ground truth
landscape image dataset that we have created. Based on the detection results,
we further demonstrate how our approach to linear perspective understanding
provides on-site guidance to amateur photographers on their work through a
novel viewpoint-specific image retrieval system.
Revisiting IM2GPS in the Deep Learning Era
Authors: Nam Vo, Nathan Jacobs, James Hays
Date: 2017-05-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Classification, Deep Learning, Density Estimation, General Classification, image-classification, Image Classification, Photo geolocation estimation, Retrieval, Triplet

Image geolocalization, inferring the geographic location of an image, is a
challenging computer vision problem with many potential applications. The
recent state-of-the-art approach to this problem is a deep image classification
approach in which the world is spatially divided into cells and a deep network
is trained to predict the correct cell for a given image. We propose to combine
this approach with the original Im2GPS approach in which a query image is
matched against a database of geotagged images and the location is inferred
from the retrieved set. We estimate the geographic location of a query image by
applying kernel density estimation to the locations of its nearest neighbors in
the reference database. Interestingly, we find that the best features for our
retrieval task are derived from networks trained with classification loss even
though we do not use a classification approach at test time. Training with
classification loss outperforms several deep feature learning methods (e.g.
Siamese networks with contrastive of triplet loss) more typical for retrieval
applications. Our simple approach achieves state-of-the-art geolocalization
accuracy while also requiring significantly less training data.
Asynchronous Announcements
Authors: Philippe Balbiani, Hans van Ditmarsch, Saúl Fernández González
Date: 2017-05-08 00:00:00
Tasks: Distributed Computing

We propose a multi-agent epistemic logic of asynchronous announcements, where truthful announcements are publicly sent but individually received by agents, and in the order in which they were sent. Additional to epistemic modalities the logic contains dynamic modalities for making announcements and for receiving them. What an agent believes is a function of her initial uncertainty and of the announcements she has received. Beliefs need not be truthful, because announcements already made may not yet have been received. As announcements are true when sent, certain message sequences can be ruled out, just like inconsistent cuts in distributed computing. We provide a complete axiomatization for this \emph{asynchronous announcement logic} (AA). It is a reduction system that also demonstrates that any formula in $AA$ is equivalent to one without dynamic modalities, just as for public announcement logic. A detailed example modelling message exchanging processes in distributed computing in $AA$ closes our investigation.
Deep neural networks on graph signals for brain imaging analysis
Authors: Yiluan Guo, Hossein Nejati, Ngai-Man Cheung
Date: 2017-05-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal
data often degraded by complex, non-Gaussian noise. For reliable analysis of
brain imaging data, it is important to extract discriminative, low-dimensional
intrinsic representation of the recorded data. This work proposes a new method
to learn the low-dimensional representations from the noise-degraded
measurements. In particular, our work proposes a new deep neural network design
that integrates graph information such as brain connectivity with
fully-connected layers. Our work leverages efficient graph filter design using
Chebyshev polynomial and recent work on convolutional nets on graph-structured
data. Our approach exploits graph structure as the prior side information,
localized graph filter for feature extraction and neural networks for high
capacity learning. Experiments on real MEG datasets show that our approach can
extract more discriminative representations, leading to improved accuracy in a
supervised classification task.