pwc-archive/papers-with-abstracts huggingface dataset:
Perceiving and Reasoning About Liquids Using Fully Convolutional Networks
Authors: Conor Schenck, Dieter Fox
Date: 2017-03-05 00:00:00

Liquids are an important part of many common manipulation tasks in human
environments. If we wish to have robots that can accomplish these types of
tasks, they must be able to interact with liquids in an intelligent manner. In
this paper, we investigate ways for robots to perceive and reason about
liquids. That is, a robot asks the questions What in the visual data stream is
liquid? and How can I use that to infer all the potential places where liquid
might be? We collected two datasets to evaluate these questions, one using a
realistic liquid simulator and another on our robot. We used fully
convolutional neural networks to learn to detect and track liquids across
pouring sequences. Our results show that these networks are able to perceive
and reason about liquids, and that integrating temporal information is
important to performing such tasks well.
Compact Environment-Invariant Codes for Robust Visual Place Recognition
Authors: Unnat Jain, Vinay P. Namboodiri, Gaurav Pandey
Date: 2017-09-23 00:00:00
Tasks: Visual Place Recognition

Robust visual place recognition (VPR) requires scene representations that are
invariant to various environmental challenges such as seasonal changes and
variations due to ambient lighting conditions during day and night. Moreover, a
practical VPR system necessitates compact representations of environmental
features. To satisfy these requirements, in this paper we suggest a
modification to the existing pipeline of VPR systems to incorporate supervised
hashing. The modified system learns (in a supervised setting) compact binary
codes from image feature descriptors. These binary codes imbibe robustness to
the visual variations exposed to it during the training phase, thereby, making
the system adaptive to severe environmental changes. Also, incorporating
supervised hashing makes VPR computationally more efficient and easy to
implement on simple hardware. This is because binary embeddings can be learned
over simple-to-compute features and the distance computation is also in the
low-dimensional hamming space of binary codes. We have performed experiments on
several challenging data sets covering seasonal, illumination and viewpoint
variations. We also compare two widely used supervised hashing methods of
CCAITQ and MLH and show that this new pipeline out-performs or closely matches
the state-of-the-art deep learning VPR methods that are based on
high-dimensional features extracted from pre-trained deep convolutional neural
networks.
Optimizing Quantum Models of Classical Channels: The reverse Holevo problem
Authors: S. Loomis, J. R. Mahoney, C. Aghamohammadi, J. P. Crutchfield
Date: 2017-09-23 00:00:00

Given a classical channel---a stochastic map from inputs to outputs---the input can often be transformed to an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate variable is a quantum state. We determine when and how well quantum simulations of classical channels may improve upon the minimal rates of classical simulation. This inverts Holevo's original question of quantifying the capacity of quantum channels with classical resources. We also show that this problem is equivalent to another, involving the local generation of a distribution from common entanglement.
Language Independent Acquisition of Abbreviations
Authors: Michael R. Glass, Md. Faisal Mahbub Chowdhury, Alfio M. Gliozzo
Date: 2017-09-23 00:00:00

This paper addresses automatic extraction of abbreviations (encompassing
acronyms and initialisms) and corresponding long-form expansions from plain
unstructured text. We create and are going to release a multilingual resource
for abbreviations and their corresponding expansions, built automatically by
exploiting Wikipedia redirect and disambiguation pages, that can be used as a
benchmark for evaluation. We address a shortcoming of previous work where only
the redirect pages were used, and so every abbreviation had only a single
expansion, even though multiple different expansions are possible for many of
the abbreviations. We also develop a principled machine learning based approach
to scoring expansion candidates using different techniques such as indicators
of near synonymy, topical relatedness, and surface similarity. We show improved
performance over seven languages, including two with a non-Latin alphabet,
relative to strong baselines.
A Generic Regression Framework for Pose Recognition on Color and Depth Images
Authors: Wenye He
Date: 2017-09-23 00:00:00
Tasks: General Classification, Object Recognition, Pose Estimation, regression

Cascaded regression method is a fast and accurate method on finding 2D pose
of objects in RGB images. It is able to find the accurate pose of objects in an
image by a great number of corrections on the good initial guess of the pose of
objects. This paper explains the algorithm and shows the result of two
experiments carried by the researchers. The presented new method to quickly and
accurately predict 3D positions of body joints from a single depth image, using
no temporal information. We take an object recognition approach, designing an
intermediate body parts representation that maps the difficult pose estimation
problem into a simpler per-pixel classification problem. Our large and highly
varied training dataset allows the classifier to estimate body parts invariant
to pose, body shape, clothing. Finally, we generate confidence-scored 3D
proposals of several body parts by re-projecting the classification result and
finding local modes.
Adaptive Measurement Network for CS Image Reconstruction
Authors: Xuemei Xie, Yu-Xiang Wang, Guangming Shi, Chenye Wang, Jiang Du, Zhifu Zhao
Date: 2017-09-23 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

Conventional compressive sensing (CS) reconstruction is very slow for its
characteristic of solving an optimization problem. Convolu- tional neural
network can realize fast processing while achieving compa- rable results. While
CS image recovery with high quality not only de- pends on good reconstruction
algorithms, but also good measurements. In this paper, we propose an adaptive
measurement network in which measurement is obtained by learning. The new
network consists of a fully-connected layer and ReconNet. The fully-connected
layer which has low-dimension output acts as measurement. We train the
fully-connected layer and ReconNet simultaneously and obtain adaptive
measurement. Because the adaptive measurement fits dataset better, in contrast
with random Gaussian measurement matrix, under the same measuremen- t rate, it
can extract the information of scene more efficiently and get better
reconstruction results. Experiments show that the new network outperforms the
original one.
Piecewise Latent Variables for Neural Variational Text Processing
Authors: Iulian V. Serban, Alexander G. Ororbia II, Joelle Pineau, Aaron Courville
Date: 2016-12-01 00:00:00
Proceeding: EMNLP (ACL) 2017 9
Tasks: Text Generation, Variational Inference

Advances in neural variational inference have facilitated the learning of
powerful directed graphical models with continuous latent variables, such as
variational autoencoders. The hope is that such models will learn to represent
rich, multi-modal latent factors in real-world data, such as natural language
text. However, current models often assume simplistic priors on the latent
variables - such as the uni-modal Gaussian distribution - which are incapable
of representing complex latent factors efficiently. To overcome this
restriction, we propose the simple, but highly flexible, piecewise constant
distribution. This distribution has the capacity to represent an exponential
number of modes of a latent target distribution, while remaining mathematically
tractable. Our results demonstrate that incorporating this new latent
distribution into different models yields substantial improvements in natural
language processing tasks such as document modeling and natural language
generation for dialogue.
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
Authors: Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
Date: 2017-09-23 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Voice Conversion

A method for statistical parametric speech synthesis incorporating generative
adversarial networks (GANs) is proposed. Although powerful deep neural networks
(DNNs) techniques can be applied to artificially synthesize speech waveform,
the synthetic speech quality is low compared with that of natural speech. One
of the issues causing the quality degradation is an over-smoothing effect often
observed in the generated speech parameters. A GAN introduced in this paper
consists of two neural networks: a discriminator to distinguish natural and
generated samples, and a generator to deceive the discriminator. In the
proposed framework incorporating the GANs, the discriminator is trained to
distinguish natural and generated speech parameters, while the acoustic models
are trained to minimize the weighted sum of the conventional minimum generation
loss and an adversarial loss for deceiving the discriminator. Since the
objective of the GANs is to minimize the divergence (i.e., distribution
difference) between the natural and generated speech parameters, the proposed
method effectively alleviates the over-smoothing effect on the generated speech
parameters. We evaluated the effectiveness for text-to-speech and voice
conversion, and found that the proposed method can generate more natural
spectral parameters and $F_0$ than conventional minimum generation error
training algorithm regardless its hyper-parameter settings. Furthermore, we
investigated the effect of the divergence of various GANs, and found that a
Wasserstein GAN minimizing the Earth-Mover's distance works the best in terms
of improving synthetic speech quality.
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
Authors: Steven McDonagh, Benjamin Hou, Konstantinos Kamnitsas, Ozan Oktay, Amir Alansary, Mary Rutherford, Jo V. Hajnal, Bernhard Kainz
Date: 2017-02-28 00:00:00
Tasks: Super-Resolution

3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but
low-resolution image acquisition and highly detailed but slow image
acquisition. Fast imaging is required for targets that move to avoid motion
artefacts. This is in particular difficult for fetal MRI. Spatially independent
upsampling techniques, which are the state-of-the-art to address this problem,
are error prone and disregard contextual information. In this paper we propose
a context-sensitive upsampling method based on a residual convolutional neural
network model that learns organ specific appearance and adopts semantically to
input data allowing for the generation of high resolution images with sharp
edges and fine scale detail. By making contextual decisions about appearance
and shape, present in different parts of an image, we gain a maximum of
structural detail at a similar contrast as provided by high-resolution data. We
experiment on $145$ fetal scans and show that our approach yields an increased
PSNR of $1.25$ $dB$ when applied to under-sampled fetal data \emph{cf.}
baseline upsampling. Furthermore, our method yields an increased PSNR of $1.73$
$dB$ when utilizing under-sampled fetal data to perform brain volume
reconstruction on motion corrupted captured data.
Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation
Authors: Noreddine Gherabi, Redouane Nejjahi, Abderrahim Marzouk
Date: 2017-09-23 00:00:00
Tasks: General Classification

The new era of the Web is known as the semantic Web or the Web of data. The
semantic Web depends on ontologies that are seen as one of its pillars. The
bigger these ontologies, the greater their exploitation. However, when these
ontologies become too big other problems may appear, such as the complexity to
charge big files in memory, the time it needs to download such files and
especially the time it needs to make reasoning on them. We discuss in this
paper approaches for segmenting such big Web ontologies as well as its
usefulness. The segmentation method extracts from an existing ontology a
segment that represents a layer or a generation in the existing ontology; i.e.
a horizontally extraction. The extracted segment should be itself an ontology.
Object-Oriented Knowledge Representation and Data Storage Using Inhomogeneous Classes
Authors: Dmytro Terletskyi
Date: 2017-09-23 00:00:00

This paper contains analysis of concept of a class within different
object-oriented knowledge representation models. The main attention is paid to
structure of the class and its efficiency in the context of data storage, using
object-relational mapping. The main achievement of the paper is extension of
concept of homogeneous class of objects by introducing concepts of single-core
and multi-core inhomogeneous classes of objects, which allow simultaneous
defining of a few different types within one class of objects, avoiding
duplication of properties and methods in representation of types, decreasing
sizes of program codes and providing more efficient information storage in the
databases. In addition, the paper contains results of experiment, which show
that data storage in relational database, using proposed extensions of the
class, in some cases is more efficient in contrast to usage of homogeneous
classes of objects.
Deep Learning for Secure Mobile Edge Computing
Authors: Yuanfang Chen, Yan Zhang, Sabita Maharjan
Date: 2017-09-23 00:00:00
Tasks: Cloud Computing, Deep Learning, Edge-computing

Mobile edge computing (MEC) is a promising approach for enabling
cloud-computing capabilities at the edge of cellular networks. Nonetheless,
security is becoming an increasingly important issue in MEC-based applications.
In this paper, we propose a deep-learning-based model to detect security
threats. The model uses unsupervised learning to automate the detection
process, and uses location information as an important feature to improve the
performance of detection. Our proposed model can be used to detect malicious
applications at the edge of a cellular network, which is a serious security
threat. Extensive experiments are carried out with 10 different datasets, the
results of which illustrate that our deep-learning-based model achieves an
average gain of 6% accuracy compared with state-of-the-art machine learning
algorithms.
When Traffic Flow Prediction Meets Wireless Big Data Analytics
Authors: Yuanfang Chen, Mohsen Guizani, Yan Zhang, Lei Wang, Noel Crespi, Gyu Myoung Lee
Date: 2017-09-23 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Traffic flow prediction is an important research issue for solving the
traffic congestion problem in an Intelligent Transportation System (ITS).
Traffic congestion is one of the most serious problems in a city, which can be
predicted in advance by analyzing traffic flow patterns. Such prediction is
possible by analyzing the real-time transportation data from correlative roads
and vehicles. This article first gives a brief introduction to the
transportation data, and surveys the state-of-the-art prediction methods. Then,
we verify whether or not the prediction performance is able to be improved by
fitting actual data to optimize the parameters of the prediction model which is
used to predict the traffic flow. Such verification is conducted by comparing
the optimized time series prediction model with the normal time series
prediction model. This means that in the era of big data, accurate use of the
data becomes the focus of studying the traffic flow prediction to solve the
congestion problem. Finally, experimental results of a case study are provided
to verify the existence of such performance improvement, while the research
challenges of this data-analytics-based prediction are presented and discussed.
Combining Machine Learning and Physics to Understand Glassy Systems
Authors: Samuel S. Schoenholz
Date: 2017-09-23 00:00:00
Tasks: BIG-bench Machine Learning, Physical Intuition

Our understanding of supercooled liquids and glasses has lagged significantly
behind that of simple liquids and crystalline solids. This is in part due to
the many possibly relevant degrees of freedom that are present due to the
disorder inherent to these systems and in part to non-equilibrium effects which
are difficult to treat in the standard context of statistical physics. Together
these issues have resulted in a field whose theories are under-constrained by
experiment and where fundamental questions are still unresolved. Mean field
results have been successful in infinite dimensions but it is unclear to what
extent they apply to realistic systems and assume uniform local structure. At
odds with this are theories premised on the existence of structural defects.
However, until recently it has been impossible to find structural signatures
that are predictive of dynamics. Here we summarize and recast the results from
several recent papers offering a data driven approach to building a
phenomenological theory of disordered materials by combining machine learning
with physical intuition.
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
Authors: David Golub, Po-Sen Huang, Xiaodong He, Li Deng
Date: 2017-06-29 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Reading Comprehension, Transfer Learning, Vocal Bursts Valence Prediction

We develop a technique for transfer learning in machine comprehension (MC)
using a novel two-stage synthesis network (SynNet). Given a high-performing MC
model in one domain, our technique aims to answer questions about documents in
another domain, where we use no labeled data of question-answer pairs. Using
the proposed SynNet with a pretrained model from the SQuAD dataset on the
challenging NewsQA dataset, we achieve an F1 measure of 44.3% with a single
model and 46.6% with an ensemble, approaching performance of in-domain models
(F1 measure of 50.0%) and outperforming the out-of-domain baseline of 7.6%,
without use of provided annotations.
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Authors: Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu
Date: 2017-08-01 00:00:00
Tasks: Matrix Completion

Low-rank modeling has many important applications in computer vision and
machine learning. While the matrix rank is often approximated by the convex
nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
better empirical performance. However, the resulting optimization problem is
much more challenging. Recent state-of-the-art requires an expensive full SVD
in each iteration. In this paper, we show that for many commonly-used nonconvex
low-rank regularizers, a cutoff can be derived to automatically threshold the
singular values obtained from the proximal operator. This allows such operator
being efficiently approximated by power method. Based on it, we develop a
proximal gradient algorithm (and its accelerated variant) with inexact proximal
splitting and prove that a convergence rate of O(1/T) where T is the number of
iterations is guaranteed. Furthermore, we show the proposed algorithm can be
well parallelized, which achieves nearly linear speedup w.r.t the number of
threads. Extensive experiments are performed on matrix completion and robust
principal component analysis, which shows a significant speedup over the
state-of-the-art. Moreover, the matrix solution obtained is more accurate and
has a lower rank than that of the nuclear norm regularizer.
A semi-automated segmentation method for detection of pulmonary embolism in True-FISP MRI sequences
Authors: Luis R Soenksen, Luis Jiménez-Angeles, Gabriela Melendez, Aloha Meave
Date: 2017-09-23 00:00:00
Tasks: Diagnostic

Pulmonary embolism (PE) is a highly mortal disease, currently assessed by
pulmonary CT angiography. True-FISP MRI has emerged as an innocuous alternative
that does not hold many of the limitations of x-ray imaging. However, True-FISP
MRI is very sensitive to turbulent blood flow, generating artifacts that may
resemble fake clots in the pulmonary vasculature. These misinterpretations
reduce its overall diagnostic accuracy to 94%, limiting a wider use in clinical
environments. A new segmentation algorithm is proposed to confirm the presence
of real pulmonary clots in True-FISP MR images by quantitative means, measuring
the shape, intensity, and solidity of the formation. The algorithm was
evaluated in 37 patients. The developed method increased the diagnostic
accuracy of expert observers assessing Pulmonary True-FISP MRI sequences by 6%
without the use of ionizing radiation, achieving a diagnostic accuracy
comparable to standard CT angiography.
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection
Authors: Jorge Rivero, Bernardete Ribeiro, Ning Chen, Fátima Silva Leite
Date: 2017-09-23 00:00:00
Tasks: Attribute, Intrusion Detection, Network Intrusion Detection, Zero-Shot Learning

One of the main problems in Network Intrusion Detection comes from constant
rise of new attacks, so that not enough labeled examples are available for the
new classes of attacks. Traditional Machine Learning approaches hardly address
such problem. This can be overcome with Zero-Shot Learning, a new approach in
the field of Computer Vision, which can be described in two stages: the
Attribute Learning and the Inference Stage. The goal of this paper is to
propose a new Inference Stage algorithm for Network Intrusion Detection. In
order to attain this objective, we firstly put forward an experimental setup
for the evaluation of the Zero-Shot Learning in Network Intrusion Detection
related tasks. Secondly, a decision tree based algorithm is applied to extract
rules for generating the attributes in the AL stage. Finally, using a
representation of a Zero-Shot Class as a point in the Grassmann manifold, an
explicit formula for the shortest distance between points in that manifold can
be used to compute the geodesic distance between the Zero-Shot Classes which
represent the new attacks and the Known Classes corresponding to the attack
categories. The experimental results in the datasets KDD Cup 99 and NSL-KDD
show that our approach with Zero-Shot Learning successfully addresses the
Network Intrusion Detection problem.
Learning Invariant Riemannian Geometric Representations Using Deep Nets
Authors: Suhas Lohit, Pavan Turaga
Date: 2017-08-30 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification

Non-Euclidean constraints are inherent in many kinds of data in computer
vision and machine learning, typically as a result of specific invariance
requirements that need to be respected during high-level inference. Often,
these geometric constraints can be expressed in the language of Riemannian
geometry, where conventional vector space machine learning does not apply
directly. The central question this paper deals with is: How does one train
deep neural nets whose final outputs are elements on a Riemannian manifold? To
answer this, we propose a general framework for manifold-aware training of deep
neural networks -- we utilize tangent spaces and exponential maps in order to
convert the proposed problem into a form that allows us to bring current
advances in deep learning to bear upon this problem. We describe two specific
applications to demonstrate this approach: prediction of probability
distributions for multi-class image classification, and prediction of
illumination-invariant subspaces from a single face-image via regression on the
Grassmannian. These applications show the generality of the proposed framework,
and result in improved performance over baselines that ignore the geometry of
the output space. In addition to solving this specific problem, we believe this
paper opens new lines of enquiry centered on the implications of Riemannian
geometry on deep architectures.