pwc-archive/papers-with-abstracts huggingface dataset:
Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection
Authors: Mehdi Faraji, Irene Cheng, Iris Naudin, Anup Basu
Date: 2018-06-10 00:00:00

Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that
facilitates observing and appraising the vessel wall structure of the human
coronary arteries. Segmentation of arterial wall boundaries from the IVUS
images is not only crucial for quantitative analysis of the vessel walls and
plaque characteristics, but is also necessary for generating 3D reconstructed
models of the artery. The aim of this study is twofold. Firstly, we investigate
the feasibility of using a recently proposed region detector, namely Extremal
Region of Extremum Level (EREL) to delineate the luminal and media-adventitia
borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region
selection strategy to label two ERELs as lumen and media based on the stability
of their textural information. We extensively evaluated our selection strategy
on the test set of a standard publicly available dataset containing 326 IVUS
B-mode images. We showed that in the best case, the average Hausdorff Distances
(HD) between the extracted ERELs and the actual lumen and media were $0.22$ mm
and $0.45$ mm, respectively. The results of our experiments revealed that our
selection strategy was able to segment the lumen with $\le 0.3$ mm HD to the
gold standard even though the images contained major artifacts such as
bifurcations, shadows, and side branches. Moreover, when there was no artifact,
our proposed method was able to delineate media-adventitia boundaries with
$0.31$ mm HD to the gold standard. Furthermore, our proposed segmentation
method runs in time that is linear in the number of pixels in each frame. Based
on the results of this work, by using a 20 MHz IVUS probe with controlled
pullback, not only can we now analyze the internal structure of human arteries
more accurately, but also segment each frame during the pullback procedure
because of the low run time of our proposed segmentation method.
Deconvolution-Based Global Decoding for Neural Machine Translation
Authors: Junyang Lin, Xu sun, Xuancheng Ren, Shuming Ma, Jinsong Su, Qi Su
Date: 2018-06-10 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, NMT, Translation

A great proportion of sequence-to-sequence (Seq2Seq) models for Neural
Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate
translation word by word following a sequential order. As the studies of
linguistics have proved that language is not linear word sequence but sequence
of complex structure, translation at each step should be conditioned on the
whole target-side context. To tackle the problem, we propose a new NMT model
that decodes the sequence with the guidance of its structural prediction of the
context of the target sequence. Our model generates translation based on the
structural prediction of the target-side context so that the translation can be
freed from the bind of sequential order. Experimental results demonstrate that
our model is more competitive compared with the state-of-the-art methods, and
the analysis reflects that our model is also robust to translating sentences of
different lengths and it also reduces repetition with the instruction from the
target-side context for decoding.
LexNLP: Natural language processing and information extraction for legal and regulatory texts
Authors: Michael J Bommarito II, Daniel Martin Katz, Eric M Detterman
Date: 2018-06-10 00:00:00

LexNLP is an open source Python package focused on natural language
processing and machine learning for legal and regulatory text. The package
includes functionality to (i) segment documents, (ii) identify key text such as
titles and section headings, (iii) extract over eighteen types of structured
information like distances and dates, (iv) extract named entities such as
companies and geopolitical entities, (v) transform text into features for model
training, and (vi) build unsupervised and supervised models such as word
embedding or tagging models. LexNLP includes pre-trained models based on
thousands of unit tests drawn from real documents available from the SEC EDGAR
database as well as various judicial and regulatory proceedings. LexNLP is
designed for use in both academic research and industrial applications, and is
distributed at https://github.com/LexPredict/lexpredict-lexnlp.
Embedding Words as Distributions with a Bayesian Skip-gram Model
Authors: Arthur Bražinskas, Serhii Havrylov, Ivan Titov
Date: 2017-11-29 00:00:00
Proceeding: COLING 2018 8

We introduce a method for embedding words as probability densities in a
low-dimensional space. Rather than assuming that a word embedding is fixed
across the entire text collection, as in standard word embedding methods, in
our Bayesian model we generate it from a word-specific prior density for each
occurrence of a given word. Intuitively, for each word, the prior density
encodes the distribution of its potential 'meanings'. These prior densities are
conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian
embeddings, we can also obtain context-specific densities: they encode
uncertainty about the sense of a word given its context and correspond to
posterior distributions within our model. The context-dependent densities have
many potential applications: for example, we show that they can be directly
used in the lexical substitution task. We describe an effective estimation
method based on the variational autoencoding framework. We also demonstrate
that our embeddings achieve competitive results on standard benchmarks.
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Authors: Bin Hu, Stephen Wright, Laurent Lessard
Date: 2018-06-10 00:00:00
Proceeding: ICML 2018 7

Techniques for reducing the variance of gradient estimates used in stochastic
programming algorithms for convex finite-sum problems have received a great
deal of attention in recent years. By leveraging dissipativity theory from
control, we provide a new perspective on two important variance-reduction
algorithms: SVRG and its direct accelerated variant Katyusha. Our perspective
provides a physically intuitive understanding of the behavior of SVRG-like
methods via a principle of energy conservation. The tools discussed here allow
us to automate the convergence analysis of SVRG-like methods by capturing their
essential properties in small semidefinite programs amenable to standard
analysis and computational techniques. Our approach recovers existing
convergence results for SVRG and Katyusha and generalizes the theory to
alternative parameter choices. We also discuss how our approach complements the
linear coupling technique. Our combination of perspectives leads to a better
understanding of accelerated variance-reduced stochastic methods for finite-sum
problems.
On the Covariance-Hessian Relation in Evolution Strategies
Authors: Ofer M. Shir, Amir Yehudayoff
Date: 2018-06-10 00:00:00
Tasks: Relation

We consider Evolution Strategies operating only with isotropic Gaussian mutations on positive quadratic objective functions, and investigate the covariance matrix when constructed out of selected individuals by truncation. We prove that the covariance matrix over $(1,\lambda)$-selected decision vectors becomes proportional to the inverse of the landscape Hessian as the population-size $\lambda$ increases. This generalizes a previous result that proved an equivalent phenomenon when sampling was assumed to take place in the vicinity of the optimum. It further confirms the classical hypothesis that statistical learning of the landscape is an inherent characteristic of standard Evolution Strategies, and that this distinguishing capability stems only from the usage of isotropic Gaussian mutations and rank-based selection. We provide broad numerical validation for the proven results, and present empirical evidence for its generalization to $(\mu,\lambda)$-selection.
Global Encoding for Abstractive Summarization
Authors: Junyang Lin, Xu sun, Shuming Ma, Qi Su
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Decoder

In neural abstractive summarization, the conventional sequence-to-sequence
(seq2seq) model often suffers from repetition and semantic irrelevance. To
tackle the problem, we propose a global encoding framework, which controls the
information flow from the encoder to the decoder based on the global
information of the source context. It consists of a convolutional gated unit to
perform global encoding to improve the representations of the source-side
information. Evaluations on the LCSTS and the English Gigaword both demonstrate
that our model outperforms the baseline models, and the analysis shows that our
model is capable of reducing repetition.
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting
Authors: Aaron M. Roth, Umang Bhatt, Tamara Amin, Afsaneh Doryab, Fei Fang, Manuela Veloso
Date: 2018-06-10 00:00:00
Tasks: Decision Making

With the rapid development of robot and other intelligent and autonomous
agents, how a human could be influenced by a robot's expressed mood when making
decisions becomes a crucial question in human-robot interaction. In this pilot
study, we investigate (1) in what way a robot can express a certain mood to
influence a human's decision making behavioral model; (2) how and to what
extent the human will be influenced in a game theoretic setting. More
specifically, we create an NLP model to generate sentences that adhere to a
specific affective expression profile. We use these sentences for a humanoid
robot as it plays a Stackelberg security game against a human. We investigate
the behavioral model of the human player.
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Authors: Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing
Date: 2018-02-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Optimisation, BIG-bench Machine Learning, Model Selection, Neural Architecture Search

Bayesian Optimisation (BO) refers to a class of methods for global
optimisation of a function $f$ which is only accessible via point evaluations.
It is typically used in settings where $f$ is expensive to evaluate. A common
use case for BO in machine learning is model selection, where it is not
possible to analytically model the generalisation performance of a statistical
model, and we resort to noisy and expensive training and validation procedures
to choose the best model. Conventional BO methods have focused on Euclidean and
categorical domains, which, in the context of model selection, only permits
tuning scalar hyper-parameters of machine learning algorithms. However, with
the surge of interest in deep learning, there is an increasing demand to tune
neural network \emph{architectures}. In this work, we develop NASBOT, a
Gaussian process based BO framework for neural architecture search. To
accomplish this, we develop a distance metric in the space of neural network
architectures which can be computed efficiently via an optimal transport
program. This distance might be of independent interest to the deep learning
community as it may find applications outside of BO. We demonstrate that NASBOT
outperforms other alternatives for architecture search in several cross
validation based model selection tasks on multi-layer perceptrons and
convolutional neural networks.
Centrality measures for graphons: Accounting for uncertainty in networks
Authors: Marco Avella-Medina, Francesca Parise, Michael T. Schaub, Santiago Segarra
Date: 2017-07-28 00:00:00

As relational datasets modeled as graphs keep increasing in size and their
data-acquisition is permeated by uncertainty, graph-based analysis techniques
can become computationally and conceptually challenging. In particular, node
centrality measures rely on the assumption that the graph is perfectly known --
a premise not necessarily fulfilled for large, uncertain networks. Accordingly,
centrality measures may fail to faithfully extract the importance of nodes in
the presence of uncertainty. To mitigate these problems, we suggest a
statistical approach based on graphon theory: we introduce formal definitions
of centrality measures for graphons and establish their connections to
classical graph centrality measures. A key advantage of this approach is that
centrality measures defined at the modeling level of graphons are inherently
robust to stochastic variations of specific graph realizations. Using the
theory of linear integral operators, we define degree, eigenvector, Katz and
PageRank centrality functions for graphons and establish concentration
inequalities demonstrating that graphon centrality functions arise naturally as
limits of their counterparts defined on sequences of graphs of increasing size.
The same concentration inequalities also provide high-probability bounds
between the graphon centrality functions and the centrality measures on any
sampled graph, thereby establishing a measure of uncertainty of the measured
centrality score. The same concentration inequalities also provide
high-probability bounds between the graphon centrality functions and the
centrality measures on any sampled graph, thereby establishing a measure of
uncertainty of the measured centrality score.
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Authors: Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao
Date: 2018-06-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Stochastic Optimization

Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning. However, its convergence properties for these complicated nonconvex problems is still largely unknown, because of the current technical limit. Therefore, in this paper, we propose to analyze the algorithm through a simpler but nontrivial nonconvex problem - streaming PCA, which helps us to understand Aync-MSGD better even for more general problems. Specifically, we establish the asymptotic rate of convergence of Async-MSGD for streaming PCA by diffusion approximation. Our results indicate a fundamental tradeoff between asynchrony and momentum: To ensure convergence and acceleration through asynchrony, we have to reduce the momentum (compared with Sync-MSGD). To the best of our knowledge, this is the first theoretical attempt on understanding Async-MSGD for distributed nonconvex stochastic optimization. Numerical experiments on both streaming PCA and training deep neural networks are provided to support our findings for Async-MSGD.
Conditional Noise-Contrastive Estimation of Unnormalised Models
Authors: Ciwan Ceylan, Michael U. Gutmann
Date: 2018-06-10 00:00:00
Proceeding: ICML 2018 7
Tasks: Density Estimation, Open-Ended Question Answering, parameter estimation

Many parametric statistical models are not properly normalised and only
specified up to an intractable partition function, which renders parameter
estimation difficult. Examples of unnormalised models are Gibbs distributions,
Markov random fields, and neural network models in unsupervised deep learning.
In previous work, the estimation principle called noise-contrastive estimation
(NCE) was introduced where unnormalised models are estimated by learning to
distinguish between data and auxiliary noise. An open question is how to best
choose the auxiliary noise distribution. We here propose a new method that
addresses this issue. The proposed method shares with NCE the idea of
formulating density estimation as a supervised learning problem but in contrast
to NCE, the proposed method leverages the observed data when generating noise
samples. The noise can thus be generated in a semi-automated manner. We first
present the underlying theory of the new method, show that score matching
emerges as a limiting case, validate the method on continuous and discrete
valued synthetic data, and show that we can expect an improved performance
compared to NCE when the data lie in a lower-dimensional manifold. Then we
demonstrate its applicability in unsupervised deep learning by estimating a
four-layer neural image model.
Smart Novel Computer-based Analytical Tool for Image Forgery Authentication
Authors: Rozita Teymourzadeh, Amirrize Alpha, VH Mok
Date: 2018-06-10 00:00:00
Tasks: Image Forgery Detection

This paper presents an integration of image forgery detection with image
facial recognition using black propagation neural network (BPNN). We observed
that facial image recognition by itself will always give a matching output or
closest possible output image for every input image irrespective of the
authenticity or otherwise not of the testing input image. Based on this, we are
proposing the combination of the blind but powerful automation image forgery
detection for entire input images for the BPNN recognition program. Hence, an
input image must first be authenticated before being fed into the recognition
program. Thus, an image security identification and authentication requirement,
any image that fails the authentication/verification stage are not to be used
as an input/test image. In addition, the universal smart GUI tool is proposed
and designed to perform image forgery detection with the high accuracy of 2%
error rate.
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation
Authors: Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Stephan Vogel
Date: 2018-06-10 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Machine Translation, Translation

We address the problem of simultaneous translation by modifying the Neural MT
decoder to operate with dynamically built encoder and attention. We propose a
tunable agent which decides the best segmentation strategy for a user-defined
BLEU loss and Average Proportion (AP) constraint. Our agent outperforms
previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova,
2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to
Neural MT training to better match the incremental decoding framework.
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
Authors: Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf
Date: 2017-08-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: image smoothing, Reflection Removal, Synthetic Data Generation

This paper proposes a deep neural network structure that exploits edge
information in addressing representative low-level vision tasks such as layer
separation and image filtering. Unlike most other deep learning strategies
applied in this context, our approach tackles these challenging problems by
estimating edges and reconstructing images using only cascaded convolutional
layers arranged such that no handcrafted or application-specific
image-processing components are required. We apply the resulting transferrable
pipeline to two different problem domains that are both sensitive to edges,
namely, single image reflection removal and image smoothing. For the former,
using a mild reflection smoothness assumption and a novel synthetic data
generation method that acts as a type of weak supervision, our network is able
to solve much more difficult reflection cases that cannot be handled by
previous methods. For the latter, we also exceed the state-of-the-art
quantitative and qualitative results by wide margins. In all cases, the
proposed framework is simple, fast, and easy to transfer across disparate
domains.
SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
Authors: An Yang, Sujian Li
Date: 2018-06-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Machine Translation, Question Answering, Translation

Annotation corpus for discourse relations benefits NLP tasks such as machine
translation and question answering. In this paper, we present SciDTB, a
domain-specific discourse treebank annotated on scientific articles. Different
from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent
discourse structure, which is flexible and simplified to some extent but do not
sacrifice structural integrity. We discuss the labeling framework, annotation
workflow and some statistics about SciDTB. Furthermore, our treebank is made as
a benchmark for evaluating discourse dependency parsers, on which we provide
several baselines as fundamental work.
Deep Learning Estimation of Absorbed Dose for Nuclear Medicine Diagnostics
Authors: Luciano Melodia
Date: 2018-05-23 00:00:00
Tasks: Deep Learning

The distribution of energy dose from Lu$^{177}$ radiotherapy can be estimated by convolving an image of a time-integrated activity distribution with a dose voxel kernel (DVK) consisting of different types of tissues. This fast and inacurate approximation is inappropriate for personalized dosimetry as it neglects tissue heterogenity. The latter can be calculated using different imaging techniques such as CT and SPECT combined with a time consuming monte-carlo simulation. The aim of this study is, for the first time, an estimation of DVKs from CT-derived density kernels (DK) via deep learning in convolutional neural networks (CNNs). The proposed CNN achieved, on the test set, a mean intersection over union (IOU) of $= 0.86$ after $308$ epochs and a corresponding mean squared error (MSE) $= 1.24 \cdot 10^{-4}$. This generalization ability shows that the trained CNN can indeed learn the difficult transfer function from DK to DVK. Future work will evaluate DVKs estimated by CNNs with full monte-carlo simulations of a whole body CT to predict patient specific voxel dose maps.
Neural Disease Named Entity Extraction with Character-based BiLSTM+CRF in Japanese Medical Text
Authors: Ken Yano
Date: 2018-06-10 00:00:00
Tasks: Entity Extraction using GAN, General Classification

We propose an 'end-to-end' character-based recurrent neural network that
extracts disease named entities from a Japanese medical text and simultaneously
judges its modality as either positive or negative; i.e., the mentioned disease
or symptom is affirmed or negated. The motivation to adopt neural networks is
to learn effective lexical and structural representation features for Entity
Recognition and also for Positive/Negative classification from an annotated
corpora without explicitly providing any rule-based or manual feature sets. We
confirmed the superiority of our method over previous char-based CRF or SVM
methods in the results.
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
Authors: Manon Kok, Arno Solin
Date: 2018-04-05 00:00:00
Tasks: Position

We present a method for scalable and fully 3D magnetic field simultaneous
localisation and mapping (SLAM) using local anomalies in the magnetic field as
a source of position information. These anomalies are due to the presence of
ferromagnetic material in the structure of buildings and in objects such as
furniture. We represent the magnetic field map using a Gaussian process model
and take well-known physical properties of the magnetic field into account. We
build local maps using three-dimensional hexagonal block tiling. To make our
approach computationally tractable we use reduced-rank Gaussian process
regression in combination with a Rao-Blackwellised particle filter. We show
that it is possible to obtain accurate position and orientation estimates using
measurements from a smartphone, and that our approach provides a scalable
magnetic field SLAM algorithm in terms of both computational complexity and map
storage.