pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective
Authors: Stanley H. Chan
Date: 2018-08-31 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

The Plug-and-Play (PnP) ADMM algorithm is a powerful image restoration framework that allows advanced image denoising priors to be integrated into physical forward models to generate high quality image restoration results. However, despite the enormous number of applications and several theoretical studies trying to prove the convergence by leveraging tools in convex analysis, very little is known about why the algorithm is doing so well. The goal of this paper is to fill the gap by discussing the performance of PnP ADMM. By restricting the denoisers to the class of graph filters under a linearity assumption, or more specifically the symmetric smoothing filters, we offer three contributions: (1) We show conditions under which an equivalent maximum-a-posteriori (MAP) optimization exists, (2) we present a geometric interpretation and show that the performance gain is due to an intrinsic pre-denoising characteristic of the PnP prior, (3) we introduce a new analysis technique via the concept of consensus equilibrium, and provide interpretations to problems involving multiple priors.
Gromov-Wasserstein Alignment of Word Embedding Spaces
Authors: David Alvarez-Melis, Tommi S. Jaakkola
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Transfer Learning, Translation, Word Embeddings, Word Translation

Cross-lingual or cross-domain correspondences play key roles in tasks ranging
from machine translation to transfer learning. Recently, purely unsupervised
methods operating on monolingual embeddings have become effective alignment
tools. Current state-of-the-art methods, however, involve multiple steps,
including heuristic post-hoc refinement strategies. In this paper, we cast the
correspondence problem directly as an optimal transport (OT) problem, building
on the idea that word embeddings arise from metric recovery algorithms. Indeed,
we exploit the Gromov-Wasserstein distance that measures how similarities
between pairs of words relate across languages. We show that our OT objective
can be estimated efficiently, requires little or no tuning, and results in
performance comparable with the state-of-the-art in various unsupervised word
translation tasks.
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
Authors: Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana
Date: 2018-08-31 00:00:00
Tasks: LV Segmentation, Segmentation

The segmentation of the left ventricle (LV) from CINE MRI images is essential
to infer important clinical parameters. Typically, machine learning algorithms
for automated LV segmentation use annotated contours from only two cardiac
phases, diastole, and systole. In this work, we present an analysis work-flow
for fully-automated LV segmentation that learns from images acquired through
the cardiac cycle. The workflow consists of three components: first, for each
image in the sequence, we perform an automated localization and subsequent
cropping of the bounding box containing the cardiac silhouette. Second, we
identify the LV contours using a Temporal Fully Convolutional Neural Network
(T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a
recurrent mechanism enforcing temporal coherence across consecutive frames.
Finally, we further defined the boundaries using either one of two components:
fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials
and Semantic Flow. Our initial experiments suggest that significant improvement
in performance can potentially be achieved by using a recurrent neural network
component that explicitly learns cardiac motion patterns whilst performing LV
segmentation.
A Deep Neural Network Sentence Level Classification Method with Context Information
Authors: Xingyi Song, Johann Petrak, Angus Roberts
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Sentence, Sentence Classification

In the sentence classification task, context formed from sentences adjacent
to the sentence being classified can provide important information for
classification. This context is, however, often ignored. Where methods do make
use of context, only small amounts are considered, making it difficult to
scale. We present a new method for sentence classification, Context-LSTM-CNN,
that makes use of potentially large contexts. The method also utilizes
long-range dependencies within the sentence being classified, using an LSTM,
and short-span features, using a stacked CNN. Our experiments demonstrate that
this approach consistently improves over previous methods on two different
datasets.
JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles
Authors: Saumya Kumaar, Navaneethkrishnan B, Sumedh Mannar, S. N. Omkar
Date: 2018-08-31 00:00:00
Tasks: Autonomous Vehicles, Junction Detection, Self-Driving Cars

With a great amount of research going on in the field of autonomous vehicles
or self-driving cars, there has been considerable progress in road detection
and tracking algorithms. Most of these algorithms use GPS to handle road
junctions and its subsequent decisions. However, there are places in the urban
environment where it becomes difficult to get GPS fixes which render the
junction decision handling erroneous or possibly risky. Vision-based junction
detection, however, does not have such problems. This paper proposes a novel
deep convolutional neural network architecture for disambiguation of junctions
from roads with a high degree of accuracy. This network is benchmarked against
other well known classifying network architectures like AlexNet and VGGnet.
Further, we discuss a potential road navigation methodology which uses the
proposed network model. We conclude by performing an experimental validation of
the trained network and the navigational method on the roads of the Indian
Institute of Science (IISc).
Neural DrugNet
Authors: Nishant Nikhil, Shivansh Mundra
Date: 2018-08-31 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification, Language Modeling, Language Modelling

In this paper, we describe the system submitted for the shared task on Social
Media Mining for Health Applications by the team Light. Previous works
demonstrate that LSTMs have achieved remarkable performance in natural language
processing tasks. We deploy an ensemble of two LSTM models. The first one is a
pretrained language model appended with a classifier and takes words as input,
while the second one is a LSTM model with an attention unit over it which takes
character tri-gram as input. We call the ensemble of these two models:
Neural-DrugNet. Our system ranks 2nd in the second shared task: Automatic
classification of posts describing medication intake.
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Authors: Thanh Tran, Kyumin Lee, Yiming Liao, Dongwon Lee
Date: 2018-08-31 00:00:00

Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.
Towards Large Scale Training Of Autoencoders For Collaborative Filtering
Authors: Abdallah Moussawi
Date: 2018-08-30 00:00:00
Tasks: Collaborative Filtering

In this paper, we apply a mini-batch based negative sampling method to
efficiently train a latent factor autoencoder model on large scale and sparse
data for implicit feedback collaborative filtering. We compare our work against
a state-of-the-art baseline model on different experimental datasets and show
that this method can lead to a good and fast approximation of the baseline
model performance. The source code is available in
https://github.com/amoussawi/recoder .
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Authors: Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
Date: 2018-08-30 00:00:00
Tasks: Anomaly Detection

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations it is also difficult to extract domain specific features to identify falls. In this paper, we present a novel framework, \textit{DeepFall}, which formulates the fall detection problem as an anomaly detection problem. The \textit{DeepFall} framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a temporal window to detect unseen falls. We tested the \textit{DeepFall} framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras and show superior results in comparison to traditional autoencoder methods to identify unseen falls.
Skip-gram word embeddings in hyperbolic space
Authors: Matthias Leimeister, Benjamin J. Wilson
Date: 2018-08-30 00:00:00
Tasks: Learning Word Embeddings, Word Embeddings, Word Similarity

Recent work has demonstrated that embeddings of tree-like graphs in hyperbolic space surpass their Euclidean counterparts in performance by a large margin. Inspired by these results and scale-free structure in the word co-occurrence graph, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram negative-sampling architecture of word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss subtleties in the formulation of the analogy task in curved spaces.
Bayesian Outdoor Defect Detection
Authors: Fei Jiang, Guosheng Yin
Date: 2018-08-30 00:00:00
Tasks: Defect Detection

We introduce a Bayesian defect detector to facilitate the defect detection on
the motion blurred images on rough texture surfaces. To enhance the accuracy of
Bayesian detection on removing non-defect pixels, we develop a class of
reflected non-local prior distributions, which is constructed by using the mode
of a distribution to subtract its density. The reflected non-local priors
forces the Bayesian detector to approach 0 at the non-defect locations. We
conduct experiments studies to demonstrate the superior performance of the
Bayesian detector in eliminating the non-defect points. We implement the
Bayesian detector in the motion blurred drone images, in which the detector
successfully identifies the hail damages on the rough surface and substantially
enhances the accuracy of the entire defect detection pipeline.
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Authors: Kleomenis Katevas, Katrin Hänsel, Richard Clegg, Ilias Leontiadis, Hamed Haddadi, Laurissa Tokarchuk
Date: 2018-08-30 00:00:00
Tasks: Community Detection

Remembering our day-to-day social interactions is challenging even if you
aren't a blue memory challenged fish. The ability to automatically detect and
remember these types of interactions is not only beneficial for individuals
interested in their behavior in crowded situations, but also of interest to
those who analyze crowd behavior. Currently, detecting social interactions is
often performed using a variety of methods including ethnographic studies,
computer vision techniques and manual annotation-based data analysis. However,
mobile phones offer easier means for data collection that is easy to analyze
and can preserve the user's privacy. In this work, we present a system for
detecting stationary social interactions inside crowds, leveraging multi-modal
mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope.
To inform the development of such system, we conducted a study with 24
participants, where we asked them to socialize with each other for 45 minutes.
We built a machine learning system based on gradient-boosted trees that
predicts both 1:1 and group interactions with 77.8% precision and 86.5% recall,
a 30.2% performance increase compared to a proximity-based approach. By
utilizing a community detection-based method, we further detected the various
group formation that exist within the crowd. Using mobile phone sensors already
carried by the majority of people in a crowd makes our approach particularly
well suited to real-life analysis of crowd behavior and influence strategies.
An integrated rolling horizon approach to increase operating theatre efficiency
Authors: Belinda Spratt, Erhan Kozan
Date: 2018-08-30 00:00:00

Demand for healthcare is increasing rapidly. To meet demand, we must improve
the efficiency of our public health services. We present a mixed integer
programming (MIP) formulation that simultaneously tackles the integrated Master
Surgical Schedule (MSS) and Surgical Case Assignment (SCA) problems. We
consider volatile surgical durations and non-elective arrivals whilst applying
a rolling horizon approach to adjust the schedule after cancellations,
equipment failure, or new arrivals on the waiting list. A case study of an
Australian public hospital with a large surgical department is the basis for
the model. The formulation includes significant detail and provides
practitioners with a globally implementable model. We produce good feasible
solutions in short amounts of computational time with a constructive heuristic
and two hyper metaheuristics. Using a rolling horizon schedule increases
patient throughput and can help reduce waiting lists.
A real-time reactive framework for the surgical case sequencing problem
Authors: Belinda Spratt, Erhan Kozan
Date: 2018-08-30 00:00:00
Tasks: Scheduling

In this paper, we address the multiple operating room (OR) surgical case sequencing problem (SCSP). The objective is to maximise total OR utilisation during standard opening hours. This work uses a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. We present constructive heuristics based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that this approach maintains schedule feasibility in real-time, whilst increasing operating theatre (OT) utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and we provide recommendations regarding implementation of the approach.
End-to-end Speech Recognition with Adaptive Computation Steps
Authors: Mohan Li, Min Liu, Masanori Hattori
Date: 2018-08-30 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

In this paper, we present Adaptive Computation Steps (ACS) algo-rithm, which
enables end-to-end speech recognition models to dy-namically decide how many
frames should be processed to predict a linguistic output. The model that
applies ACS algorithm follows the encoder-decoder framework, while unlike the
attention-based mod-els, it produces alignments independently at the encoder
side using the correlation between adjacent frames. Thus, predictions can be
made as soon as sufficient acoustic information is received, which makes the
model applicable in online cases. Besides, a small change is made to the
decoding stage of the encoder-decoder framework, which allows the prediction to
exploit bidirectional contexts. We verify the ACS algorithm on a Mandarin
speech corpus AIShell-1, and it achieves a 31.2% CER in the online occasion,
compared to the 32.4% CER of the attention-based model. To fully demonstrate
the advantage of ACS algorithm, offline experiments are conducted, in which our
ACS model achieves an 18.7% CER, outperforming the attention-based counterpart
with the CER of 22.0%.
Chinese Discourse Segmentation Using Bilingual Discourse Commonality
Authors: Jingfeng Yang, Sujian Li
Date: 2018-08-30 00:00:00
Tasks: Discourse Segmentation, Segmentation

Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and
is a fundamental task in discourse analysis. For Chinese, previous researches
identify EDUs just through discriminating the functions of punctuations. In
this paper, we argue that Chinese EDUs may not end at the punctuation positions
and should follow the definition of EDU in RST-DT. With this definition, we
conduct Chinese discourse segmentation with the help of English labeled
data.Using discourse commonality between English and Chinese, we design an
adversarial neural network framework to extract common language-independent
features and language-specific features which are useful for discourse
segmentation, when there is no or only a small scale of Chinese labeled data
available. Experiments on discourse segmentation demonstrate that our models
can leverage common features from bilingual data, and learn efficient
Chinese-specific features from a small amount of Chinese labeled data,
outperforming the baseline models.
Learning Gender-Neutral Word Embeddings
Authors: Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, Kai-Wei Chang
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Word Embeddings

Word embedding models have become a fundamental component in a wide range of
Natural Language Processing (NLP) applications. However, embeddings trained on
human-generated corpora have been demonstrated to inherit strong gender
stereotypes that reflect social constructs. To address this concern, in this
paper, we propose a novel training procedure for learning gender-neutral word
embeddings. Our approach aims to preserve gender information in certain
dimensions of word vectors while compelling other dimensions to be free of
gender influence. Based on the proposed method, we generate a Gender-Neutral
variant of GloVe (GN-GloVe). Quantitative and qualitative experiments
demonstrate that GN-GloVe successfully isolates gender information without
sacrificing the functionality of the embedding model.
Group-Representative Functional Network Estimation from Multi-Subject fMRI Data via MRF-based Image Segmentation
Authors: Aditi Iyer, Bingjing Tang, Vinayak Rao, Nan Kong
Date: 2018-08-29 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation

We propose a novel two-phase approach to functional network estimation of
multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies
model-based image segmentation to determine a group-representative connectivity
map. In our approach, we first improve clustering-based Independent Component
Analysis (ICA) to generate maps of components occurring consistently across
subjects, and then estimate the group-representative map through MAP-MRF
(Maximum a priori - Markov random field) labeling. For the latter, we provide a
novel and efficient variational Bayes algorithm. We study the performance of
the proposed method using synthesized data following a theoretical model, and
demonstrate its viability in blind extraction of group-representative
functional networks using simulated fMRI data. We anticipate the proposed
method will be applied in identifying common neuronal characteristics in a
population, and could be further extended to real-world clinical diagnosis.
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Authors: Wafa Aissa, Laure Soulier, Ludovic Denoyer
Date: 2018-08-29 00:00:00
Proceeding: WS 2018 10
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Search-oriented conversational systems rely on information needs expressed in
natural language (NL). We focus here on the understanding of NL expressions for
building keyword-based queries. We propose a reinforcement-learning-driven
translation model framework able to 1) learn the translation from NL
expressions to queries in a supervised way, and, 2) to overcome the lack of
large-scale dataset by framing the translation model as a word selection
approach and injecting relevance feedback in the learning process. Experiments
are carried out on two TREC datasets and outline the effectiveness of our
approach.
Chest X-ray Inpainting with Deep Generative Models
Authors: Ecem Sogancioglu, Shi Hu, Davide Belli, Bram van Ginneken
Date: 2018-08-29 00:00:00
Tasks: Image Inpainting

Generative adversarial networks have been successfully applied to inpainting
in natural images. However, the current state-of-the-art models have not yet
been widely adopted in the medical imaging domain. In this paper, we
investigate the performance of three recently published deep learning based
inpainting models: context encoders, semantic image inpainting, and the
contextual attention model, applied to chest x-rays, as the chest exam is the
most commonly performed radiological procedure. We train these generative
models on 1.2M 128 $\times$ 128 patches from 60K healthy x-rays, and learn to
predict the center 64 $\times$ 64 region in each patch. We test the models on
both the healthy and abnormal radiographs. We evaluate the results by visual
inspection and comparing the PSNR scores. The outputs of the models are in most
cases highly realistic. We show that the methods have potential to enhance and
detect abnormalities. In addition, we perform a 2AFC observer study and show
that an experienced human observer performs poorly in detecting inpainted
regions, particularly those generated by the contextual attention model.