pwc-archive/papers-with-abstracts huggingface dataset:
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking
Authors: Han Shen, Lichao Huang, Chang Huang, Wei Xu
Date: 2018-08-05 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object Tracking, Unity

Traditional multiple object tracking methods divide the task into two parts:
affinity learning and data association. The separation of the task requires to
define a hand-crafted training goal in affinity learning stage and a
hand-crafted cost function of data association stage, which prevents the
tracking goals from learning directly from the feature. In this paper, we
present a new multiple object tracking (MOT) framework with data-driven
association method, named as Tracklet Association Tracker (TAT). The framework
aims at gluing feature learning and data association into a unity by a bi-level
optimization formulation so that the association results can be directly
learned from features. To boost the performance, we also adopt the popular
hierarchical association and perform the necessary alignment and selection of
raw detection responses. Our model trains over 20X faster than a similar
approach, and achieves the state-of-the-art performance on both MOT2016 and
MOT2017 benchmarks.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Authors: Hyeong Kyu Choi
Date: 2018-08-05 00:00:00
Tasks: Portfolio Optimization, Stock Market Prediction

Predicting the price correlation of two assets for future time periods is
important in portfolio optimization. We apply LSTM recurrent neural networks
(RNN) in predicting the stock price correlation coefficient of two individual
stocks. RNNs are competent in understanding temporal dependencies. The use of
LSTM cells further enhances its long term predictive properties. To encompass
both linearity and nonlinearity in the model, we adopt the ARIMA model as well.
The ARIMA model filters linear tendencies in the data and passes on the
residual value to the LSTM model. The ARIMA LSTM hybrid model is tested against
other traditional predictive financial models such as the full historical
model, constant correlation model, single index model and the multi group
model. In our empirical study, the predictive ability of the ARIMA-LSTM model
turned out superior to all other financial models by a significant scale. Our
work implies that it is worth considering the ARIMA LSTM model to forecast
correlation coefficient for portfolio optimization.
Deep Multi-Center Learning for Face Alignment
Authors: Zhiwen Shao, Hengliang Zhu, Xin Tan, Yangyang Hao, Lizhuang Ma
Date: 2018-08-05 00:00:00
Tasks: Deep Learning, Face Alignment, Prediction

Facial landmarks are highly correlated with each other since a certain
landmark can be estimated by its neighboring landmarks. Most of the existing
deep learning methods only use one fully-connected layer called shape
prediction layer to estimate the locations of facial landmarks. In this paper,
we propose a novel deep learning framework named Multi-Center Learning with
multiple shape prediction layers for face alignment. In particular, each shape
prediction layer emphasizes on the detection of a certain cluster of
semantically relevant landmarks respectively. Challenging landmarks are focused
firstly, and each cluster of landmarks is further optimized respectively.
Moreover, to reduce the model complexity, we propose a model assembling method
to integrate multiple shape prediction layers into one shape prediction layer.
Extensive experiments demonstrate that our method is effective for handling
complex occlusions and appearance variations with real-time performance. The
code for our method is available at
https://github.com/ZhiwenShao/MCNet-Extension.
3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks
Authors: Rongtian Ye, Fangyu Liu, Liqiang Zhang
Date: 2018-08-05 00:00:00
Tasks: General Classification

Standard 3D convolution operations require much larger amounts of memory and
computation cost than 2D convolution operations. The fact has hindered the
development of deep neural nets in many 3D vision tasks. In this paper, we
investigate the possibility of applying depthwise separable convolutions in 3D
scenario and introduce the use of 3D depthwise convolution. A 3D depthwise
convolution splits a single standard 3D convolution into two separate steps,
which would drastically reduce the number of parameters in 3D convolutions with
more than one order of magnitude. We experiment with 3D depthwise convolution
on popular CNN architectures and also compare it with a similar structure
called pseudo-3D convolution. The results demonstrate that, with 3D depthwise
convolutions, 3D vision tasks like classification and reconstruction can be
carried out with more light-weighted neural networks while still delivering
comparable performances.
Smart City Development with Urban Transfer Learning
Authors: Leye Wang, Bin Guo, Qiang Yang
Date: 2018-08-05 00:00:00
Tasks: Management, Transfer Learning

Nowadays, the smart city development levels of different cities are still
unbalanced. For a large number of cities which just started development, the
governments will face a critical cold-start problem: 'how to develop a new
smart city service with limited data?'. To address this problem, transfer
learning can be leveraged to accelerate the smart city development, which we
term the urban transfer learning paradigm. This article investigates the common
process of urban transfer learning, aiming to provide city planners and
relevant practitioners with guidelines on how to apply this novel learning
paradigm. Our guidelines include common transfer strategies to take, general
steps to follow, and case studies in public safety, transportation management,
etc. We also summarize a few research opportunities and expect this article can
attract more researchers to study urban transfer learning.
Triplet Network with Attention for Speaker Diarization
Authors: Huan Song, Megan Willi, Jayaraman J. Thiagarajan, Visar Berisha, Andreas Spanias
Date: 2018-08-04 00:00:00
Tasks: Metric Learning, speaker-diarization, Speaker Diarization, Triplet

In automatic speech processing systems, speaker diarization is a crucial
front-end component to separate segments from different speakers. Inspired by
the recent success of deep neural networks (DNNs) in semantic inferencing,
triplet loss-based architectures have been successfully used for this problem.
However, existing work utilizes conventional i-vectors as the input
representation and builds simple fully connected networks for metric learning,
thus not fully leveraging the modeling power of DNN architectures. This paper
investigates the importance of learning effective representations from the
sequences directly in metric learning pipelines for speaker diarization. More
specifically, we propose to employ attention models to learn embeddings and the
metric jointly in an end-to-end fashion. Experiments are conducted on the
CALLHOME conversational speech corpus. The diarization results demonstrate
that, besides providing a unified model, the proposed approach achieves
improved performance when compared against existing approaches.
Global Convergence to the Equilibrium of GANs using Variational Inequalities
Authors: Ian Gemp, Sridhar Mahadevan
Date: 2018-08-04 00:00:00
Tasks: Generative Adversarial Network

In optimization, the negative gradient of a function denotes the direction of steepest descent. Furthermore, traveling in any direction orthogonal to the gradient maintains the value of the function. In this work, we show that these orthogonal directions that are ignored by gradient descent can be critical in equilibrium problems. Equilibrium problems have drawn heightened attention in machine learning due to the emergence of the Generative Adversarial Network (GAN). We use the framework of Variational Inequalities to analyze popular training algorithms for a fundamental GAN variant: the Wasserstein Linear-Quadratic GAN. We show that the steepest descent direction causes divergence from the equilibrium, and convergence to the equilibrium is achieved through following a particular orthogonal direction. We call this successful technique Crossing-the-Curl, named for its mathematical derivation as well as its intuition: identify the game's axis of rotation and move "across" space in the direction towards smaller "curling".
Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture
Authors: Denis Tome, Matteo Toso, Lourdes Agapito, Chris Russell
Date: 2018-08-04 00:00:00
Tasks: 3D Human Pose Estimation, Markerless Motion Capture, Pose Estimation

We propose a CNN-based approach for multi-camera markerless motion capture of
the human body. Unlike existing methods that first perform pose estimation on
individual cameras and generate 3D models as post-processing, our approach
makes use of 3D reasoning throughout a multi-stage approach. This novelty
allows us to use provisional 3D models of human pose to rethink where the
joints should be located in the image and to recover from past mistakes. Our
principled refinement of 3D human poses lets us make use of image cues, even
from images where we previously misdetected joints, to refine our estimates as
part of an end-to-end approach. Finally, we demonstrate how the high-quality
output of our multi-camera setup can be used as an additional training source
to improve the accuracy of existing single camera models.
Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia
Authors: Prashnna K Gyawali, B. Milan Horacek, John L. Sapp, Linwei Wang
Date: 2018-08-04 00:00:00

The increasing availability of electrocardiogram (ECG) data has motivated the
use of data-driven models for automating various clinical tasks based on ECG
data. The development of subject-specific models are limited by the cost and
difficulty of obtaining sufficient training data for each individual. The
alternative of population model, however, faces challenges caused by the
significant inter-subject variations within the ECG data. We address this
challenge by investigating for the first time the problem of learning
representations for clinically-informative variables while disentangling other
factors of variations within the ECG data. In this work, we present a
conditional variational autoencoder (VAE) to extract the subject-specific
adjustment to the ECG data, conditioned on task-specific representations
learned from a deterministic encoder. To encourage the representation for
inter-subject variations to be independent from the task-specific
representation, maximum mean discrepancy is used to match all the moments
between the distributions learned by the VAE conditioning on the code from the
deterministic encoder. The learning of the task-specific representation is
regularized by a weak supervision in the form of contrastive regularization. We
apply the proposed method to a novel yet important clinical task of classifying
the origin of ventricular tachycardia (VT) into pre-defined segments,
demonstrating the efficacy of the proposed method against the standard VAE.
DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging
Authors: Simon Koppers, Dorit Merhof
Date: 2018-08-04 00:00:00
Tasks: Deep Learning

DELIMIT is a framework extension for deep learning in diffusion imaging,
which extends the basic framework PyTorch towards spherical signals. Based on
several novel layers, deep learning can be applied to spherical diffusion
imaging data in a very convenient way. First, two spherical harmonic
interpolation layers are added to the extension, which allow to transform the
signal from spherical surface space into the spherical harmonic space, and vice
versa. In addition, a local spherical convolution layer is introduced that adds
the possibility to include gradient neighborhood information within the
network. Furthermore, these extensions can also be utilized for the
preprocessing of diffusion signals.
Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
Authors: Daniele Ramazzotti, Peter Clardy, Leo Anthony Celi, David J. Stone, Robert S. Rudin
Date: 2018-08-04 00:00:00
Tasks: ICU Admission

We considered observational data available from the MIMIC-III open-access ICU
database and collected within a study period between year 2002 up to 2011. If a
patient had multiple admissions to the ICU during the 30 days before death,
only the first stay was analyzed, leading to a final set of 6,436 unique ICU
admissions during the study period. We tested two hypotheses: (i)
administration of invasive intervention during the ICU stay immediately
preceding end-of-life would decrease over the study time period and (ii)
time-to-death from ICU admission would also decrease, due to the decrease in
invasive intervention administration. To investigate the latter hypothesis, we
performed a subgroups analysis by considering patients with lowest and highest
severity. To do so, we stratified the patients based on their SAPS I scores,
and we considered patients within the first and the third tertiles of the
score. We then assessed differences in trends within these groups between years
2002-05 vs. 2008-11.
  Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in
endotracheal ventilation among patients who died within 30 days of ICU
admission (120.8 vs. 68.5 hours for the lowest severity patients, p<0.001; 47.7
vs. 46.0 hours for the highest severity patients, p=0.004). This is explained
in part by an increase in the use of non-invasive ventilation. Comparing the
period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors
and inotropes among patients with the lowest severity who died within 30 days
of ICU admission (41.8 vs. 36.2 hours, p<0.001) but not among those with the
highest severity. Despite a reduction in the use of invasive interventions, we
did not find a reduction in the time to death between 2002-2005 vs. 2008-2011
(7.8 days vs. 8.2 days for the lowest severity patients, p=0.32; 2.1 days vs.
2.0 days for the highest severity patients, p=0.74).
MCRM: Mother Compact Recurrent Memory
Authors: Abduallah A. Mohamed, Christian Claudel
Date: 2018-08-04 00:00:00

LSTMs and GRUs are the most common recurrent neural network architectures used to solve temporal sequence problems. The two architectures have differing data flows dealing with a common component called the cell state (also referred to as the memory). We attempt to enhance the memory by presenting a modification that we call the Mother Compact Recurrent Memory (MCRM). MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU hidden state. The concatenation of the forget gate and input gate interactions from the LSTM are considered an input to the GRU cell. Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that acts explicitly in both long-term and short-term fashions. For some specific tasks, empirical results show that MCRMs outperform previously used architectures.
Spatial Deep Learning for Wireless Scheduling
Authors: Wei Cui, Kaiming Shen, Wei Yu
Date: 2018-08-04 00:00:00
Tasks: Deep Learning, Fairness, Few-Shot Camera-Adaptive Color Constancy, Scheduling

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers, due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.
Troy: Give Attention to Saliency and for Saliency
Authors: Pingping Zhang, Huchuan Lu, Chunhua Shen
Date: 2018-08-04 00:00:00

In addition, our work has text overlap with arXiv:1804.06242,
arXiv:1705.00938 by other authors. We want to rewrite this paper for avoiding
this fact.
A survey on Deep Learning Advances on Different 3D Data Representations
Authors: Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev, Djamila Aouada, Bjorn Ottersten
Date: 2018-08-04 00:00:00
Tasks: Deep Learning

3D data is a valuable asset the computer vision filed as it provides rich
information about the full geometry of sensed objects and scenes. Recently,
with the availability of both large 3D datasets and computational power, it is
today possible to consider applying deep learning to learn specific tasks on 3D
data such as segmentation, recognition and correspondence. Depending on the
considered 3D data representation, different challenges may be foreseen in
using existent deep learning architectures. In this work, we provide a
comprehensive overview about various 3D data representations highlighting the
difference between Euclidean and non-Euclidean ones. We also discuss how Deep
Learning methods are applied on each representation, analyzing the challenges
to overcome.
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Authors: Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
Date: 2018-08-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Depth Prediction, Translation, Unsupervised Domain Adaptation

Current methods for single-image depth estimation use training datasets with
real image-depth pairs or stereo pairs, which are not easy to acquire. We
propose a framework, trained on synthetic image-depth pairs and unpaired real
images, that comprises an image translation network for enhancing realism of
input images, followed by a depth prediction network. A key idea is having the
first network act as a wide-spectrum input translator, taking in either
synthetic or real images, and ideally producing minimally modified realistic
images. This is done via a reconstruction loss when the training input is real,
and GAN loss when synthetic, removing the need for heuristic
self-regularization. The second network is trained on a task loss for synthetic
image-depth pairs, with extra GAN loss to unify real and synthetic feature
distributions. Importantly, the framework can be trained end-to-end, leading to
good results, even surpassing early deep-learning methods that use real paired
data.
Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection
Authors: Raghav Gurbaxani, Shivank Mishra
Date: 2018-08-04 00:00:00
Tasks: General Classification, image-classification, Image Classification, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Despite the recent advancements in deploying neural networks for image
classification, it has been found that adversarial examples are able to fool
these models leading them to misclassify the images. Since these models are now
being widely deployed, we provide an insight on the threat of these adversarial
examples by evaluating their characteristics and transferability to more
complex models that utilize Image Classification as a subtask.
  We demonstrate the ineffectiveness of adversarial examples when applied to
Instance Segmentation & Object Detection models. We show that this
ineffectiveness arises from the inability of adversarial examples to withstand
transformations such as scaling or a change in lighting conditions. Moreover,
we show that there exists a small threshold below which the adversarial
property is retained while applying these input transformations.
  Additionally, these attacks demonstrate weak cross-network transferability
across neural network architectures, e.g. VGG16 and ResNet50, however, the
attack may fool both the networks if passed sequentially through networks
during its formation.
  The lack of scalability and transferability challenges the question of how
adversarial images would be effective in the real world.
Structure-Aware Shape Synthesis
Authors: Elena Balashova, Vivek Singh, Jiangping Wang, Brian Teixeira, Terrence Chen, Thomas Funkhouser
Date: 2018-08-04 00:00:00

We propose a new procedure to guide training of a data-driven shape
generative model using a structure-aware loss function. Complex 3D shapes often
can be summarized using a coarsely defined structure which is consistent and
robust across variety of observations. However, existing synthesis techniques
do not account for structure during training, and thus often generate
implausible and structurally unrealistic shapes. During training, we enforce
structural constraints in order to enforce consistency and structure across the
entire manifold. We propose a novel methodology for training 3D generative
models that incorporates structural information into an end-to-end training
pipeline.
Abstractive Summarization Improved by WordNet-based Extractive Sentences
Authors: Niantao Xie, Sujian Li, Huiling Ren, Qibin Zhai
Date: 2018-08-04 00:00:00
Tasks: Abstractive Text Summarization, Sentence

Recently, the seq2seq abstractive summarization models have achieved good
results on the CNN/Daily Mail dataset. Still, how to improve abstractive
methods with extractive methods is a good research direction, since extractive
methods have their potentials of exploiting various efficient features for
extracting important sentences in one text. In this paper, in order to improve
the semantic relevance of abstractive summaries, we adopt the WordNet based
sentence ranking algorithm to extract the sentences which are most semantically
to one text. Then, we design a dual attentional seq2seq framework to generate
summaries with consideration of the extracted information. At the same time, we
combine pointer-generator and coverage mechanisms to solve the problems of
out-of-vocabulary (OOV) words and duplicate words which exist in the
abstractive models. Experiments on the CNN/Daily Mail dataset show that our
models achieve competitive performance with the state-of-the-art ROUGE scores.
Human evaluations also show that the summaries generated by our models have
high semantic relevance to the original text.