pwc-archive/papers-with-abstracts huggingface dataset:
Prosody: The Rhythms and Melodies of Speech
Authors: Dafydd Gibbon
Date: 2017-04-09 00:00:00
Tasks: Rhythm

The present contribution is a tutorial on selected aspects of prosody, the
rhythms and melodies of speech, based on a course of the same name at the
Summer School on Contemporary Phonetics and Phonology at Tongji University,
Shanghai, China in July 2016. The tutorial is not intended as an introduction
to experimental methodology or as an overview of the literature on the topic,
but as an outline of observationally accessible aspects of fundamental
frequency and timing patterns with the aid of computational visualisation,
situated in a semiotic framework of sign ranks and interpretations. After an
informal introduction to the basic concepts of prosody in the introduction and
a discussion of the place of prosody in the architecture of language, a
selection of acoustic phonetic topics in phonemic tone and accent prosody, word
prosody, phrasal prosody and discourse prosody are discussed, and a stylisation
method for visualising aspects of prosody is introduced. Examples are taken
from a number of typologically different languages: Anyi/Agni (Niger-Congo>Kwa,
Ivory Coast), English, Kuki-Thadou (Sino-Tibetan, North-East India and
Myanmar), Mandarin Chinese, Tem (Niger-Congo>Gur, Togo) and Farsi. The main
focus is on fundamental frequency patterns, but issues of timing and rhythm are
also discussed. In the final section, further reading and possible future
research directions are outlined.
Learning Diverse Image Colorization
Authors: Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh, Min Jin Chong, David Forsyth
Date: 2016-12-06 00:00:00
Proceeding: CVPR 2017 7
Tasks: Colorization, Decoder, Diversity, Generative Adversarial Network, Image Colorization

Colorization is an ambiguous problem, with multiple viable colorizations for
a single grey-level image. However, previous methods only produce the single
most probable colorization. Our goal is to model the diversity intrinsic to the
problem of colorization and produce multiple colorizations that display
long-scale spatial co-ordination. We learn a low dimensional embedding of color
fields using a variational autoencoder (VAE). We construct loss terms for the
VAE decoder that avoid blurry outputs and take into account the uneven
distribution of pixel colors. Finally, we build a conditional model for the
multi-modal distribution between grey-level image and the color field
embeddings. Samples from this conditional model result in diverse colorization.
We demonstrate that our method obtains better diverse colorizations than a
standard conditional variational autoencoder (CVAE) model, as well as a
recently proposed conditional generative adversarial network (cGAN).
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Authors: Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia
Date: 2017-04-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Dichotomous Image Segmentation, GPU, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation, Vocal Bursts Intensity Prediction

We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branches under proper label guidance to address this
challenge. We provide in-depth analysis of our framework and introduce the
cascade feature fusion unit to quickly achieve high-quality segmentation. Our
system yields real-time inference on a single GPU card with decent quality
results evaluated on challenging datasets like Cityscapes, CamVid and
COCO-Stuff.
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Authors: Dongrui Wu, Brent J. Lance, Vernon J. Lawhern, Stephen Gordon, Tzyy-Ping Jung, Chin-Teng Lin
Date: 2017-04-27 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), regression

Riemannian geometry has been successfully used in many brain-computer
interface (BCI) classification problems and demonstrated superior performance.
In this paper, for the first time, it is applied to BCI regression problems, an
important category of BCI applications. More specifically, we propose a new
feature extraction approach for Electroencephalogram (EEG) based BCI regression
problems: a spatial filter is first used to increase the signal quality of the
EEG trials and also to reduce the dimensionality of the covariance matrices,
and then Riemannian tangent space features are extracted. We validate the
performance of the proposed approach in reaction time estimation from EEG
signals measured in a large-scale sustained-attention psychomotor vigilance
task, and show that compared with the traditional powerband features, the
tangent space features can reduce the root mean square estimation error by
4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.
A Survey of Neural Network Techniques for Feature Extraction from Text
Authors: Vineet John
Date: 2017-04-27 00:00:00
Tasks: General Classification, text-classification, Text Classification, Text Generation

This paper aims to catalyze the discussions about text feature extraction
techniques using neural network architectures. The research questions discussed
in the paper focus on the state-of-the-art neural network techniques that have
proven to be useful tools for language processing, language generation, text
classification and other computational linguistics tasks.
Pyramid Scene Parsing Network
Authors: Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia
Date: 2016-12-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dichotomous Image Segmentation, Image Classification, Lesion Segmentation, Real-Time Semantic Segmentation, Scene Parsing, Semantic Segmentation, Thermal Image Segmentation, Video Semantic Segmentation

Scene parsing is challenging for unrestricted open vocabulary and diverse
scenes. In this paper, we exploit the capability of global context information
by different-region-based context aggregation through our pyramid pooling
module together with the proposed pyramid scene parsing network (PSPNet). Our
global prior representation is effective to produce good quality results on the
scene parsing task, while PSPNet provides a superior framework for pixel-level
prediction tasks. The proposed approach achieves state-of-the-art performance
on various datasets. It came first in ImageNet scene parsing challenge 2016,
PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new
record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on
Cityscapes.
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Authors: Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, Min Sun
Date: 2017-04-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation, Semantic Segmentation

Despite the recent success of deep-learning based semantic segmentation,
deploying a pre-trained road scene segmenter to a city whose images are not
presented in the training set would not achieve satisfactory performance due to
dataset biases. Instead of collecting a large number of annotated images of
each city of interest to train or refine the segmenter, we propose an
unsupervised learning approach to adapt road scene segmenters across different
cities. By utilizing Google Street View and its time-machine feature, we can
collect unannotated images for each road scene at different times, so that the
associated static-object priors can be extracted accordingly. By advancing a
joint global and class-specific domain adversarial learning framework,
adaptation of pre-trained segmenters to that city can be achieved without the
need of any user annotation or interaction. We show that our method improves
the performance of semantic segmentation in multiple cities across continents,
while it performs favorably against state-of-the-art approaches requiring
annotated training data.
Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes
Authors: Raphaël Berthon, Mickael Randour, Jean-François Raskin
Date: 2017-02-17 00:00:00

The beyond worst-case synthesis problem was introduced recently by Bruy\`ere
et al. [BFRR14]: it aims at building system controllers that provide strict
worst-case performance guarantees against an antagonistic environment while
ensuring higher expected performance against a stochastic model of the
environment. Our work extends the framework of [BFRR14] and follow-up papers,
which focused on quantitative objectives, by addressing the case of
$\omega$-regular conditions encoded as parity objectives, a natural way to
represent functional requirements of systems.
  We build strategies that satisfy a main parity objective on all plays, while
ensuring a secondary one with sufficient probability. This setting raises new
challenges in comparison to quantitative objectives, as one cannot easily mix
different strategies without endangering the functional properties of the
system. We establish that, for all variants of this problem, deciding the
existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity
class as classical parity games. Hence, our framework provides additional
modeling power while staying in the same complexity class.
  [BFRR14] V\'eronique Bruy\`ere, Emmanuel Filiot, Mickael Randour, and
Jean-Fran\c{c}ois Raskin. Meet your expectations with guarantees: Beyond
worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha
Portier, editors, 31st International Symposium on Theoretical Aspects of
Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of
LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik,
2014.
Real-Time Visual Place Recognition for Personal Localization on a Mobile Device
Authors: Michał Nowicki, Jan Wietrzykowski, Piotr Skrzypczyński
Date: 2016-11-07 00:00:00
Tasks: Visual Place Recognition

The paper presents an approach to indoor personal localization on a mobile
device based on visual place recognition. We implemented on a smartphone two
state-of-the-art algorithms that are representative to two different approaches
to visual place recognition: FAB-MAP that recognizes places using individual
images, and ABLE-M that utilizes sequences of images. These algorithms are
evaluated in environments of different structure, focusing on problems commonly
encountered when a mobile device camera is used. The conclusions drawn from
this evaluation are guidelines to design the FastABLE system, which is based on
the ABLE-M algorithm, but introduces major modifications to the concept of
image matching. The improvements radically cut down the processing time and
improve scalability, making it possible to localize the user in long image
sequences with the limited computing power of a mobile device. The resulting
place recognition system compares favorably to both the ABLE-M and the FAB-MAP
solutions in the context of real-time personal localization.
Optimal client recommendation for market makers in illiquid financial products
Authors: Dieter Hendricks, Stephen J. Roberts
Date: 2017-04-27 00:00:00
Tasks: Position

The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation
Authors: Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
Date: 2017-02-21 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

The empirical fact that classifiers, trained on given data collections,
perform poorly when tested on data acquired in different settings is
theoretically explained in domain adaptation through a shift among
distributions of the source and target domains. Alleviating the domain shift
problem, especially in the challenging setting where no labeled data are
available for the target domain, is paramount for having visual recognition
systems working in the wild. As the problem stems from a shift among
distributions, intuitively one should try to align them. In the literature,
this has resulted in a stream of works attempting to align the feature
representations learned from the source and target domains. Here we take a
different route. Rather than introducing regularization terms aiming to promote
the alignment of the two representations, we act at the distribution level
through the introduction of \emph{DomaIn Alignment Layers} (\DIAL), able to
match the observed source and target data distributions to a reference one.
Thorough experiments on three different public benchmarks we confirm the power
of our approach.
Locality Preserving Projections for Grassmann manifold
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, Bao-Cai Yin
Date: 2017-04-27 00:00:00
Tasks: Clustering, Dimensionality Reduction

Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
Authors: Bo Li, Yuchao Dai, Huahui Chen, Mingyi He
Date: 2017-04-27 00:00:00
Tasks: Depth Estimation

This paper proposes a new residual convolutional neural network (CNN)
architecture for single image depth estimation. Compared with existing deep CNN
based methods, our method achieves much better results with fewer training
examples and model parameters. The advantages of our method come from the usage
of dilated convolution, skip connection architecture and soft-weight-sum
inference. Experimental evaluation on the NYU Depth V2 dataset shows that our
method outperforms other state-of-the-art methods by a margin.
Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting
Authors: Mohamed Abuella, Badrul Chowdhury
Date: 2017-04-27 00:00:00
Tasks: Ensemble Learning, regression

To mitigate the uncertainty of variable renewable resources, two
off-the-shelf machine learning tools are deployed to forecast the solar power
output of a solar photovoltaic system. The support vector machines generate the
forecasts and the random forest acts as an ensemble learning method to combine
the forecasts. The common ensemble technique in wind and solar power
forecasting is the blending of meteorological data from several sources. In
this study though, the present and the past solar power forecasts from several
models, as well as the associated meteorological data, are incorporated into
the random forest to combine and improve the accuracy of the day-ahead solar
power forecasts. The performance of the combined model is evaluated over the
entire year and compared with other combining techniques.
A GRU-Gated Attention Model for Neural Machine Translation
Authors: Biao Zhang, Deyi Xiong, Jinsong Su
Date: 2017-04-27 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

Neural machine translation (NMT) heavily relies on an attention network to produce a context vector for each target word prediction. In practice, we find that context vectors for different target words are quite similar to one another and therefore are insufficient in discriminatively predicting target words. The reason for this might be that context vectors produced by the vanilla attention network are just a weighted sum of source representations that are invariant to decoder states. In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder. GAtt uses a gated recurrent unit (GRU) to combine two types of information: treating a source annotation vector originally produced by the bidirectional encoder as the history state while the corresponding previous decoder state as the input to the GRU. The GRU-combined information forms a new source annotation vector. In this way, we can obtain translation-sensitive source representations which are then feed into the attention network to generate discriminative context vectors. We further propose a variant that regards a source annotation vector as the current input while the previous decoder state as the history. Experiments on NIST Chinese-English translation tasks show that both GAtt-based models achieve significant improvements over the vanilla attentionbased NMT. Further analyses on attention weights and context vectors demonstrate the effectiveness of GAtt in improving the discrimination power of representations and handling the challenging issue of over-translation.
Multimodal Word Distributions
Authors: Ben Athiwaratkun, Andrew Gordon Wilson
Date: 2017-04-27 00:00:00
Proceeding: ACL 2017 7
Tasks: Word Embeddings, Word Similarity

Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning
Authors: Honglun Zhang, Haiyang Wang, Xiaming Chen, Yongkun Wang, Yaohui Jin
Date: 2017-04-27 00:00:00
Tasks: Deep Learning, Reading Comprehension, Sentiment Analysis

P2P lending presents as an innovative and flexible alternative for
conventional lending institutions like banks, where lenders and borrowers
directly make transactions and benefit each other without complicated
verifications. However, due to lack of specialized laws, delegated monitoring
and effective managements, P2P platforms may spawn potential risks, such as
withdraw failures, investigation involvements and even runaway bosses, which
cause great losses to lenders and are especially serious and notorious in
China. Although there are abundant public information and data available on the
Internet related to P2P platforms, challenges of multi-sourcing and
heterogeneity matter. In this paper, we promote a novel deep learning model,
OMNIRank, which comprehends multi-dimensional features of P2P platforms for
risk quantification and produces scores for ranking. We first construct a
large-scale flexible crawling framework and obtain great amounts of
multi-source heterogeneous data of domestic P2P platforms since 2007 from the
Internet. Purifications like duplication and noise removal, null handing,
format unification and fusion are applied to improve data qualities. Then we
extract deep features of P2P platforms via text comprehension, topic modeling,
knowledge graph and sentiment analysis, which are delivered as inputs to
OMNIRank, a deep learning model for risk quantification of P2P platforms.
Finally, according to rankings generated by OMNIRank, we conduct flourish data
visualizations and interactions, providing lenders with comprehensive
information supports, decision suggestions and safety guarantees.
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
Authors: Phi Vu Tran
Date: 2016-04-02 00:00:00
Tasks: Cardiac Segmentation, Management, Right Ventricle Segmentation, Segmentation

Automated cardiac segmentation from magnetic resonance imaging datasets is an
essential step in the timely diagnosis and management of cardiac pathologies.
We propose to tackle the problem of automated left and right ventricle
segmentation through the application of a deep fully convolutional neural
network architecture. Our model is efficiently trained end-to-end in a single
learning stage from whole-image inputs and ground truths to make inference at
every pixel. To our knowledge, this is the first application of a fully
convolutional neural network architecture for pixel-wise labeling in cardiac
magnetic resonance imaging. Numerical experiments demonstrate that our model is
robust to outperform previous fully automated methods across multiple
evaluation measures on a range of cardiac datasets. Moreover, our model is fast
and can leverage commodity compute resources such as the graphics processing
unit to enable state-of-the-art cardiac segmentation at massive scales. The
models and code are available at
https://github.com/vuptran/cardiac-segmentation
A Neural Model for User Geolocation and Lexical Dialectology
Authors: Afshin Rahimi, Trevor Cohn, Timothy Baldwin
Date: 2017-04-13 00:00:00
Proceeding: ACL 2017 7

We propose a simple yet effective text- based user geolocation model based on
a neural network with one hidden layer, which achieves state of the art
performance over three Twitter benchmark geolocation datasets, in addition to
producing word and phrase embeddings in the hidden layer that we show to be
useful for detecting dialectal terms. As part of our analysis of dialectal
terms, we release DAREDS, a dataset for evaluating dialect term detection
methods.
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection
Authors: Xinru Yan, Ted Pedersen
Date: 2017-04-27 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Humor Detection

This paper describes the Duluth system that participated in SemEval-2017 Task
6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks
A and B using N-gram language models, ranking highly in the task evaluation.
This paper discusses the results of our system in the development and
evaluation stages and from two post-evaluation runs.