pwc-archive/papers-with-abstracts huggingface dataset:
Mobility Offer Allocations in Corporate Settings
Authors: Sebastian Knopp, Benjamin Biesinger, Matthias Prandtstetter
Date: 2018-10-11 00:00:00
Tasks: Scheduling

Corporate mobility is often based on a fixed assignment of vehicles to employees. Relaxing this fixation and including alternatives such as public transportation or taxis for business and private trips could increase fleet utilization and foster the use of battery electric vehicles. We introduce the mobility offer allocation problem as the core concept of a flexible booking system for corporate mobility. The problem is equivalent to interval scheduling on dedicated unrelated parallel machines. We show that the problem is NP-hard to approximate within any factor. We describe problem specific conflict graphs for representing and exploring the structure of feasible solutions. A characterization of all maximum cliques in these conflict graphs reveals symmetries which allow to formulate stronger integer linear programming models. We also present an adaptive large neighborhood search based approach which makes use of conflict graphs as well. In a computational study, the approaches are evaluated. It was found that greedy heuristics perform best if very tight run-time requirements are given, a solver for the integer linear programming model performs best on small and medium instances, and the adaptive large neighborhood search performs best on large instances.
A Novel Domain Adaptation Framework for Medical Image Segmentation
Authors: Amir Gholami, Shashank Subramanian, Varun Shenoy, Naveen Himthani, Xiangyu Yue, Sicheng Zhao, Peter Jin, George Biros, Kurt Keutzer
Date: 2018-10-11 00:00:00
Tasks: Domain Adaptation, Image Registration, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

We propose a segmentation framework that uses deep neural networks and
introduce two innovations. First, we describe a biophysics-based domain
adaptation method. Second, we propose an automatic method to segment white and
gray matter, and cerebrospinal fluid, in addition to tumorous tissue. Regarding
our first innovation, we use a domain adaptation framework that combines a
novel multispecies biophysical tumor growth model with a generative adversarial
model to create realistic looking synthetic multimodal MR images with known
segmentation. Regarding our second innovation, we propose an automatic approach
to enrich available segmentation data by computing the segmentation for healthy
tissues. This segmentation, which is done using diffeomorphic image
registration between the BraTS training data and a set of prelabeled atlases,
provides more information for training and reduces the class imbalance problem.
Our overall approach is not specific to any particular neural network and can
be used in conjunction with existing solutions. We demonstrate the performance
improvement using a 2D U-Net for the BraTS'18 segmentation challenge. Our
biophysics based domain adaptation achieves better results, as compared to the
existing state-of-the-art GAN model used to create synthetic data for training.
Computational ghost imaging using a field-programmable gate array
Authors: Ikuo Hoshi, Tomoyoshi Shimobaba, Takashi Kakue, Tomoyoshi Ito
Date: 2018-10-10 00:00:00
Tasks: CPU, Image Reconstruction

Computational ghost imaging is a promising technique for single-pixel imaging
because it is robust to disturbance and can be operated over broad wavelength
bands, unlike common cameras. However, one disadvantage of this method is that
it has a long calculation time for image reconstruction. In this paper, we have
designed a dedicated calculation circuit that accelerated the process of
computational ghost imaging. We implemented this circuit by using a
field-programmable gate array, which reduced the calculation time for the
circuit compared to a CPU. The dedicated circuit reconstructs images at a frame
rate of 300 Hz.
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN
Authors: Saifuddin Hitawala, Yao Li, Xian Wang, Dongyang Yang
Date: 2018-10-10 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Over the past decade, many Super Resolution techniques have been developed
using deep learning. Among those, generative adversarial networks (GAN) and
very deep convolutional networks (VDSR) have shown promising results in terms
of HR image quality and computational speed. In this paper, we propose two
approaches based on these two algorithms: VDSR-ResNeXt, which is a deep
multi-branch convolutional network inspired by VDSR and ResNeXt; and SRCGAN,
which is a conditional GAN that explicitly passes class labels as input to the
GAN. The two methods were implemented on common SR benchmark datasets for both
quantitative and qualitative assessment.
CRH: A Simple Benchmark Approach to Continuous Hashing
Authors: Miao Cheng, Ah Chung Tsoi
Date: 2018-10-10 00:00:00

In recent years, the distinctive advancement of handling huge data promotes
the evolution of ubiquitous computing and analysis technologies. With the
constantly upward system burden and computational complexity, adaptive coding
has been a fascinating topic for pattern analysis, with outstanding
performance. In this work, a continuous hashing method, termed continuous
random hashing (CRH), is proposed to encode sequential data stream, while
ignorance of previously hashing knowledge is possible. Instead, a random
selection idea is adopted to adaptively approximate the differential encoding
patterns of data stream, e.g., streaming media, and iteration is avoided for
stepwise learning. Experimental results demonstrate our method is able to
provide outstanding performance, as a benchmark approach to continuous hashing.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Authors: Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Lei Han, Yang Zheng, Haobo Fu, Tong Zhang, Ji Liu, Han Liu
Date: 2018-10-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most existing deep reinforcement learning (DRL) frameworks consider either
discrete action space or continuous action space solely. Motivated by
applications in computer games, we consider the scenario with
discrete-continuous hybrid action space. To handle hybrid action space,
previous works either approximate the hybrid space by discretization, or relax
it into a continuous set. In this paper, we propose a parametrized deep
Q-network (P- DQN) framework for the hybrid action space without approximation
or relaxation. Our algorithm combines the spirits of both DQN (dealing with
discrete action space) and DDPG (dealing with continuous action space) by
seamlessly integrating them. Empirical results on a simulation example, scoring
a goal in simulated RoboCup soccer and the solo mode in game King of Glory
(KOG) validate the efficiency and effectiveness of our method.
Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks
Authors: Andrej Junginger, Markus Hanselmann, Thilo Strauss, Sebastian Boblest, Jens Buchner, Holger Ulmer
Date: 2018-10-10 00:00:00
Tasks: Style Transfer, Vocal Bursts Intensity Prediction

Neural networks have proven their capabilities by outperforming many other
approaches on regression or classification tasks on various kinds of data.
Other astonishing results have been achieved using neural nets as data
generators, especially in settings of generative adversarial networks (GANs).
One special application is the field of image domain translations. Here, the
goal is to take an image with a certain style (e.g. a photography) and
transform it into another one (e.g. a painting). If such a task is performed
for unpaired training examples, the corresponding GAN setting is complex, the
neural networks are large, and this leads to a high peak memory consumption
during, both, training and evaluation phase. This sets a limit to the highest
processable image size. We address this issue by the idea of not processing the
whole image at once, but to train and evaluate the domain translation on the
level of overlapping image subsamples. This new approach not only enables us to
translate high-resolution images that otherwise cannot be processed by the
neural network at once, but also allows us to work with comparably small neural
networks and with limited hardware resources. Additionally, the number of
images required for the training process is significantly reduced. We present
high-quality results on images with a total resolution of up to over 50
megapixels and emonstrate that our method helps to preserve local image details
while it also keeps global consistency.
DeFind: A Protege Plugin for Computing Concept Definitions in EL Ontologies
Authors: Denis Ponomaryov, Stepan Yakovenko
Date: 2018-10-10 00:00:00

We introduce an extension to the Protege ontology editor, which allows for
discovering concept definitions, which are not explicitly present in axioms,
but are logically implied by an ontology. The plugin supports ontologies
formulated in the Description Logic EL, which underpins the OWL 2 EL profile of
the Web Ontology Language and despite its limited expressiveness captures most
of the biomedical ontologies published on the Web. The developed tool allows to
verify whether a concept can be defined using a vocabulary of interest
specified by a user. In particular, it allows to decide whether some vocabulary
items can be omitted in a formulation of a complex concept. The corresponding
definitions are presented to the user and are provided with explanations
generated by an ontology reasoner.
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only
Authors: Tianhang Zheng, Changyou Chen, Kui Ren
Date: 2018-10-10 00:00:00
Tasks: Adversarial Attack, Quantization

Recent work on adversarial attack and defense suggests that PGD is a
universal $l_\infty$ first-order attack, and PGD adversarial training can
significantly improve network robustness against a wide range of first-order
$l_\infty$-bounded attacks, represented as the state-of-the-art defense method.
However, an obvious weakness of PGD adversarial training is its
highly-computational cost in generating adversarial samples, making it
computationally infeasible for large and high-resolution real datasets such as
the ImageNet dataset. In addition, recent work also has suggested a simple
"close-form" solution to a robust model on MNIST. Therefore, a natural question
raised is that is PGD adversarial training really necessary for robust defense?
In this paper, we give a negative answer by proposing a training paradigm that
is comparable to PGD adversarial training on several standard datasets, while
only using noisy-natural samples. Specifically, we reformulate the min-max
objective in PGD adversarial training by a problem to minimize the original
network loss plus $l_1$ norms of its gradients w.r.t. the inputs. For the
$l_1$-norm loss, we propose a computationally-feasible solution by embedding a
differentiable soft-quantization layer after the network input layer. We show
formally that the soft-quantization layer trained with noisy-natural samples is
an alternative approach to minimizing the $l_1$-gradient norms as in PGD
adversarial training. Extensive empirical evaluations on standard datasets show
that our proposed models are comparable to PGD-adversarially-trained models
under PGD and BPDA attacks. Remarkably, our method achieves a 24X speed-up on
MNIST while maintaining a comparable defensive ability, and for the first time
fine-tunes a robust Imagenet model within only two days. Code is provided on
\url{https://github.com/tianzheng4/Noisy-Training-Soft-Quantization}
Rate Distortion For Model Compression: From Theory To Practice
Authors: Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh
Date: 2018-10-09 00:00:00
Tasks: Data Compression, Model Compression, Quantization

The enormous size of modern deep neural networks makes it challenging to
deploy those models in memory and communication limited scenarios. Thus,
compressing a trained model without a significant loss in performance has
become an increasingly important task. Tremendous advances has been made
recently, where the main technical building blocks are parameter pruning,
parameter sharing (quantization), and low-rank factorization. In this paper, we
propose principled approaches to improve upon the common heuristics used in
those building blocks, namely pruning and quantization.
  We first study the fundamental limit for model compression via the rate
distortion theory. We bring the rate distortion function from data compression
to model compression to quantify this fundamental limit. We prove a lower bound
for the rate distortion function and prove its achievability for linear models.
Although this achievable compression scheme is intractable in practice, this
analysis motivates a novel model compression framework. This framework provides
a new objective function in model compression, which can be applied together
with other classes of model compressor such as pruning or quantization.
Theoretically, we prove that the proposed scheme is optimal for compressing
one-hidden-layer ReLU neural networks. Empirically, we show that the proposed
scheme improves upon the baseline in the compression-accuracy tradeoff.
UOLO - automatic object detection and segmentation in biomedical images
Authors: Teresa Araújo, Guilherme Aresta, Adrian Galdran, Pedro Costa, Ana Maria Mendonça, Aurélio Campilho
Date: 2018-10-09 00:00:00
Tasks: Fovea Detection, Medical Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

We propose UOLO, a novel framework for the simultaneous detection and
segmentation of structures of interest in medical images. UOLO consists of an
object segmentation module which intermediate abstract representations are
processed and used as input for object detection. The resulting system is
optimized simultaneously for detecting a class of objects and segmenting an
optionally different class of structures. UOLO is trained on a set of bounding
boxes enclosing the objects to detect, as well as pixel-wise segmentation
information, when available. A new loss function is devised, taking into
account whether a reference segmentation is accessible for each training image,
in order to suitably backpropagate the error. We validate UOLO on the task of
simultaneous optic disc (OD) detection, fovea detection, and OD segmentation
from retinal images, achieving state-of-the-art performance on public datasets.
Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT
Authors: Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink, Ivana Isgum
Date: 2018-10-09 00:00:00
Tasks: Morphological Analysis

Morphological analysis and identification of pathologies in the aorta are
important for cardiovascular diagnosis and risk assessment in patients. Manual
annotation is time-consuming and cumbersome in CT scans acquired without
contrast enhancement and with low radiation dose. Hence, we propose an
automatic method to segment the ascending aorta, the aortic arch and the
thoracic descending aorta in low-dose chest CT without contrast enhancement.
Segmentation was performed using a dilated convolutional neural network (CNN),
with a receptive field of 131X131 voxels, that classified voxels in axial,
coronal and sagittal image slices. To obtain a final segmentation, the obtained
probabilities of the three planes were averaged per class, and voxels were
subsequently assigned to the class with the highest class probability. Two-fold
cross-validation experiments were performed where ten scans were used to train
the network and another ten to evaluate the performance. Dice coefficients of
0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44,
1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and
the descending aorta, respectively. The results indicate that the proposed
method could be used in large-scale studies analyzing the anatomical location
of pathology and morphology of the thoracic aorta.
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
Authors: Alex Olsen, Dmitry A. Konovalov, Bronson Philippa, Peter Ridd, Jake C. Wood, Jamie Johns, Wesley Banks, Benjamin Girgenti, Owen Kenny, James Whinney, Brendan Calvert, Mostafa Rahimi Azghadi, Ronald D. White
Date: 2018-10-09 00:00:00
Tasks: Classification, Deep Learning, General Classification, Management, Robust classification

Robotic weed control has seen increased research of late with its potential
for boosting productivity in agriculture. Majority of works focus on developing
robotics for croplands, ignoring the weed management problems facing rangeland
stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic
weed control is the robust classification of weed species in their natural
environment. The unparalleled successes of deep learning make it an ideal
candidate for recognising various weed species in the complex rangeland
environment. This work contributes the first large, public, multiclass image
dataset of weed species from the Australian rangelands; allowing for the
development of robust classification methods to make robotic weed control
viable. The DeepWeeds dataset consists of 17,509 labelled images of eight
nationally significant weed species native to eight locations across northern
Australia. This paper presents a baseline for classification performance on the
dataset using the benchmark deep learning models, Inception-v3 and ResNet-50.
These models achieved an average classification accuracy of 95.1% and 95.7%,
respectively. We also demonstrate real time performance of the ResNet-50
architecture, with an average inference time of 53.4 ms per image. These strong
results bode well for future field implementation of robotic weed control
methods in the Australian rangelands.
Neural Network based classification of bone metastasis by primary cacinoma
Authors: Marija Prokopijević, Aleksandar Stančić, Jelena Vasiljević, Željko Stojković, Goran Dimić, Jelena Sopta, Dalibor Ristić, Dhinaharan Nagamalai
Date: 2018-10-08 00:00:00
Tasks: Classification, Diagnostic, General Classification

Neural networks have been known for a long time as a tool for different types
of classification, but only just in the last decade they have showed their
entire power. Along with appearing of hardware that is capable to support
demanding matrix operations and parallel algorithms, the neural network, as a
universal function approximation framework, turns out to be the most successful
classification method widely used in all fields of science. On the other side,
multifractal (MF) approach is an efficient way for quantitative description of
complex structures [1] such as metastatic carcinoma, which recommends this
method as an accurate tool for medical diagnostics. The only part that is
missing is classification method. The goal of this research is to describe and
apply a feed-forward neural network as an auxiliary diagnostic method for
classification of multifractal parameters in order to determine primary cancer.
Ineffectiveness of Dictionary Coding to Infer Predictability Limits of Human Mobility
Authors: Yunheng Han, Weiwei Sun, Baihua Zheng
Date: 2018-10-08 00:00:00

Recently, a series of models have been proposed to predict future movements
of people. Meanwhile, dictionary coding algorithms are used to estimate the
predictability limit of human mobility. Although dictionary coding is optimal,
it takes long time to converge. Consequently, it is ineffective to infer
predictability through dictionary coding algorithms. In this report, we
illustrate this ineffectiveness on the basis of human movements in urban space.
Stemmer for Serbian language
Authors: Nikola Milošević
Date: 2012-09-20 00:00:00
Tasks: Form, Information Retrieval, Retrieval

In linguistic morphology and information retrieval, stemming is the process
for reducing inflected (or sometimes derived) words to their stem, base or root
form; generally a written word form. In this work is presented suffix stripping
stemmer for Serbian language, one of the highly inflectional languages.
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
Authors: Jacques Wainer
Date: 2018-10-16 00:00:00

This paper evaluates six strategies for mitigating imbalanced data: oversampling, undersampling, ensemble methods, specialized algorithms, class weight adjustments, and a no-mitigation approach referred to as the baseline. These strategies were tested on 58 real-life binary imbalanced datasets with imbalance rates ranging from 3 to 120. We conducted a comparative analysis of 10 under-sampling algorithms, 5 over-sampling algorithms, 2 ensemble methods, and 3 specialized algorithms across eight different performance metrics: accuracy, area under the ROC curve (AUC), balanced accuracy, F1-measure, G-mean, Matthew's correlation coefficient, precision, and recall. Additionally, we assessed the six strategies on altered datasets, derived from real-life data, with both low (3) and high (100 or 300) imbalance ratios (IR). The principal finding indicates that the effectiveness of each strategy significantly varies depending on the metric used. The paper also examines a selection of newer algorithms within the categories of specialized algorithms, oversampling, and ensemble methods. The findings suggest that the current hierarchy of best-performing strategies for each metric is unlikely to change with the introduction of newer algorithms.
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
Authors: Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine
Date: 2018-10-16 00:00:00
Tasks: Reinforcement Learning, Robot Navigation

A general-purpose intelligent robot must be able to learn autonomously and be
able to accomplish multiple tasks in order to be deployed in the real world.
However, standard reinforcement learning approaches learn separate
task-specific policies and assume the reward function for each task is known a
priori. We propose a framework that learns event cues from off-policy data, and
can flexibly combine these event cues at test time to accomplish different
tasks. These event cue labels are not assumed to be known a priori, but are
instead labeled using learned models, such as computer vision detectors, and
then `backed up' in time using an action-conditioned predictive model. We show
that a simulated robotic car and a real-world RC car can gather data and train
fully autonomously without any human-provided labels beyond those needed to
train the detectors, and then at test-time be able to accomplish a variety of
different tasks. Videos of the experiments and code can be found at
https://github.com/gkahn13/CAPs
Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform
Authors: Shuai Zhao, Manoop Talasila, Guy Jacobson, Cristian Borcea, Syed Anwar Aftab, John F Murray
Date: 2018-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

Applying Machine Learning (ML) to business applications for automation
usually faces difficulties when integrating diverse ML dependencies and
services, mainly because of the lack of a common ML framework. In most cases,
the ML models are developed for applications which are targeted for specific
business domain use cases, leading to duplicated effort, and making reuse
impossible. This paper presents Acumos, an open platform capable of packaging
ML models into portable containerized microservices which can be easily shared
via the platform's catalog, and can be integrated into various business
applications. We present a case study of packaging sentiment analysis and
classification ML models via the Acumos platform, permitting easy sharing with
others. We demonstrate that the Acumos platform reduces the technical burden on
application developers when applying machine learning models to their business
applications. Furthermore, the platform allows the reuse of readily available
ML microservices in various business domains.