pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Authors: Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, Ruheena Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera-Paredes, Christopher Kelly, Alan Karthikesalingam, Carlton Chu, Dawn Carnell, Cheng Boon, Derek D'Souza, Syed Ali Moinuddin, Bethany Garie, Yasmin McQuinlan, Sarah Ireland, Kiarna Hampton, Krystle Fuller, Hugh Montgomery, Geraint Rees, Mustafa Suleyman, Trevor Back, Cían Hughes, Joseph R. Ledsam, Olaf Ronneberger
Date: 2018-09-12 00:00:00
Tasks: Anatomy, Computed Tomography (CT)

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing inter-operator variability with resulting downstream radiation dose differences. While auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying and achieving expert performance remain. Adopting a deep learning approach, we demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck OARs commonly segmented in clinical practice. The model was trained on a dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus OAR definitions. We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts. We also introduce surface Dice similarity coefficient (surface DSC), a new metric for the comparison of organ delineation, to quantify deviation between OAR surface contours rather than volumes, better reflecting the clinical task of correcting errors in the automated organ segmentations. The model's generalisability is then demonstrated on two distinct open source datasets, reflecting different centres and countries to model training. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data
Authors: Xiaoliang Sheng, Muhammad Yousefnezhad, Tonglin Xu, Ning Yuan, Daoqiang Zhang
Date: 2018-09-12 00:00:00

Representational Similarity Analysis (RSA) aims to explore similarities
between neural activities of different stimuli. Classical RSA techniques employ
the inverse of the covariance matrix to explore a linear model between the
neural activities and task events. However, calculating the inverse of a
large-scale covariance matrix is time-consuming and can reduce the stability
and robustness of the final analysis. Notably, it becomes severe when the
number of samples is too large. For facing this shortcoming, this paper
proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
proposed method is not restricted to a linear model. In fact, there is a
growing interest in finding more effective ways of using multi-subject and
whole-brain fMRI data. Searchlight technique can extend RSA from the localized
brain regions to the whole-brain regions with smaller memory footprint in each
process. Based on Searchlight, we propose a new method called Spatiotemporal
Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
the whole-brain data. Further, our approach can handle some computational
challenges while dealing with large-scale, multi-subject fMRI data.
Experimental studies on multi-subject datasets confirm that both proposed
approaches achieve superior performance to other state-of-the-art RSA
algorithms.
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Authors: Long Chen, Haizhou Ai, Zijie Zhuang, Chong Shang
Date: 2018-09-12 00:00:00
Tasks: Large-Scale Person Re-Identification, Multi-Object Tracking, Multiple People Tracking, Object Tracking, Online Multi-Object Tracking, Person Re-Identification

Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.
Label Denoising with Large Ensembles of Heterogeneous Neural Networks
Authors: Pavel Ostyakov, Elizaveta Logacheva, Roman Suvorov, Vladimir Aliev, Gleb Sterkin, Oleg Khomenko, Sergey I. Nikolenko
Date: 2018-09-12 00:00:00
Tasks: Data Augmentation, Denoising, General Classification, Knowledge Distillation, Video Classification, Video Understanding

Despite recent advances in computer vision based on various convolutional
architectures, video understanding remains an important challenge. In this
work, we present and discuss a top solution for the large-scale video
classification (labeling) problem introduced as a Kaggle competition based on
the YouTube-8M dataset. We show and compare different approaches to
preprocessing, data augmentation, model architectures, and model combination.
Our final model is based on a large ensemble of video- and frame-level models
but fits into rather limiting hardware constraints. We apply an approach based
on knowledge distillation to deal with noisy labels in the original dataset and
the recently developed mixup technique to improve the basic models.
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Authors: Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf
Date: 2018-09-12 00:00:00
Tasks: Gaussian Processes, Mixture-of-Experts, regression, Time Series Analysis

While Gaussian processes (GPs) are the method of choice for regression tasks,
they also come with practical difficulties, as inference cost scales cubic in
time and quadratic in memory. In this paper, we introduce a natural and
expressive way to tackle these problems, by incorporating GPs in sum-product
networks (SPNs), a recently proposed tractable probabilistic model allowing
exact and efficient inference. In particular, by using GPs as leaves of an SPN
we obtain a novel flexible prior over functions, which implicitly represents an
exponentially large mixture of local GPs. Exact and efficient posterior
inference in this model can be done in a natural interplay of the inference
mechanisms in GPs and SPNs. Thereby, each GP is -- similarly as in a mixture of
experts approach -- responsible only for a subset of data points, which
effectively reduces inference cost in a divide and conquer fashion. We show
that integrating GPs into the SPN framework leads to a promising probabilistic
regression model which is: (1) computational and memory efficient, (2) allows
efficient and exact posterior inference, (3) is flexible enough to mix
different kernel functions, and (4) naturally accounts for non-stationarities
in time series. In a variate of experiments, we show that the SPN-GP model can
learn input dependent parameters and hyper-parameters and is on par with or
outperforms the traditional GPs as well as state of the art approximations on
real-world data.
Artificial Intelligence for the Public Sector: Opportunities and challenges of cross-sector collaboration
Authors: Slava Jankin Mikhaylov, Marc Esteve, Averill Campion
Date: 2018-09-12 00:00:00
Tasks: Management

Public sector organisations are increasingly interested in using data science
and artificial intelligence capabilities to deliver policy and generate
efficiencies in high uncertainty environments. The long-term success of data
science and AI in the public sector relies on effectively embedding it into
delivery solutions for policy implementation. However, governments cannot do
this integration of AI into public service delivery on their own. The UK
Government Industrial Strategy is clear that delivering on the AI grand
challenge requires collaboration between universities and public and private
sectors. This cross-sectoral collaborative approach is the norm in applied AI
centres of excellence around the world. Despite their popularity, cross-sector
collaborations entail serious management challenges that hinder their success.
In this article we discuss the opportunities and challenges from AI for public
sector. Finally, we propose a series of strategies to successfully manage these
cross-sectoral collaborations.
A Framework for Approval-based Budgeting Methods
Authors: Piotr Faliszewski, Nimrod Talmon
Date: 2018-09-12 00:00:00

We define and study a general framework for approval-based budgeting methods
and compare certain methods within this framework by their axiomatic and
computational properties. Furthermore, we visualize their behavior on certain
Euclidean distributions and analyze them experimentally.
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Authors: Yin Cheng Ng, Nicolo Colombo, Ricardo Silva
Date: 2018-09-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Active Learning, Gaussian Processes, Variational Inference

We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.
Predicting citation counts based on deep neural network learning techniques
Authors: Ali Abrishami, Sadegh Aliakbary
Date: 2018-09-12 00:00:00

With the growing number of published scientific papers world-wide, the need
to evaluation and quality assessment methods for research papers is increasing.
Scientific fields such as scientometrics, informetrics and bibliometrics
establish quantified analysis methods and measurements for scientific papers.
In this area, an important problem is to predict the future influence of a
published paper. Particularly, early discrimination between influential papers
and insignificant papers may find important applications. In this regard, one
of the most important metrics is the number of citations to the paper, since
this metric is widely utilized in the evaluation of scientific publications and
moreover, it serves as the basis for many other metrics such as h-index. In
this paper, we propose a novel method for predicting long-term citations of a
paper based on the number of its citations in the first few years after
publication. In order to train a citations prediction model, we employed
artificial neural networks which is a powerful machine learning tool with
recently growing applications in many domains including image and text
processing. The empirical experiments show that our proposed method
out-performs state-of-the-art methods with respect to the prediction accuracy
in both yearly and total prediction of the number of citations.
Thermal Features for Presentation Attack Detection in Hand Biometrics
Authors: Ewelina Bartuzi, Mateusz Trokielewicz
Date: 2018-09-12 00:00:00
Tasks: Classification, General Classification

This paper proposes a method for utilizing thermal features of the hand for
the purpose of presentation attack detection (PAD) that can be employed in a
hand biometrics system's pipeline. By envisaging two different operational
modes of our system, and by employing a DCNN-based classifiers fine-tuned with
a dataset of real and fake hand representations captured in both visible and
ther- mal spectrum, we were able to bring two important deliverables. First, a
PAD method operating in an open-set mode, capable of correctly discerning 100%
of fake thermal samples, achieving Attack Presentation Classification Error
Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal
to 0%, which can be easily implemented into any existing system as a separate
component. Second, a hand biometrics system operating in a closed-set mode,
that has PAD built right into the recognition pipeline, and operating
simultaneously with the user-wise classification, achieving rank-1 recognition
accuracy of up to 99.75%. We also show that thermal images of the human hand,
in addition to liveness features they carry, can also improve classification
accuracy of a biometric system, when coupled with visible light images. To
follow the reproducibility guidelines and to stimulate further research in this
area, we share the trained model weights, source codes, and a newly created
dataset of fake hand representations with interested researchers.
Iterative Delegations in Liquid Democracy with Restricted Preferences
Authors: Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau
Date: 2018-09-12 00:00:00
Tasks: Decision Making

In this paper, we study liquid democracy, a collective decision making paradigm which lies between direct and representative democracy. One main feature of liquid democracy is that voters can delegate their votes in a transitive manner so that: A delegates to B and B delegates to C leads to A delegates to C. Unfortunately, this process may not converge as there may not even exist a stable state (also called equilibrium). In this paper, we investigate the stability of the delegation process in liquid democracy when voters have restricted types of preference on the agent representing them (e.g., single-peaked preferences). We show that various natural structures of preferences guarantee the existence of an equilibrium and we obtain both tractability and hardness results for the problem of computing several equilibria with some desirable properties.
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm
Authors: Dimitrios Kollias, Stefanos Zafeiriou
Date: 2018-09-12 00:00:00
Tasks: Emotion Recognition

A novel procedure is presented in this paper, for training a deep
convolutional and recurrent neural network, taking into account both the
available training data set and some information extracted from similar
networks trained with other relevant data sets. This information is included in
an extended loss function used for the network training, so that the network
can have an improved performance when applied to the other data sets, without
forgetting the learned knowledge from the original data set. Facial expression
and emotion recognition in-the-wild is the test bed application that is used to
demonstrate the improved performance achieved using the proposed approach. In
this framework, we provide an experimental study on categorical emotion
recognition using datasets from a very recent related emotion recognition
challenge.
Deep learning for time series classification: a review
Authors: Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller
Date: 2018-09-12 00:00:00
Tasks: Deep Learning, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
Authors: Shailza Jolly, Sandro Pezzelle, Tassilo Klein, Andreas Dengel, Moin Nabi
Date: 2018-09-12 00:00:00
Tasks: Question Answering, Semantic Similarity, Semantic Textual Similarity, Visual Question Answering, Visual Question Answering (VQA)

We introduce MASSES, a simple evaluation metric for the task of Visual
Question Answering (VQA). In its standard form, the VQA task is operationalized
as follows: Given an image and an open-ended question in natural language,
systems are required to provide a suitable answer. Currently, model performance
is evaluated by means of a somehow simplistic metric: If the predicted answer
is chosen by at least 3 human annotators out of 10, then it is 100% correct.
Though intuitively valuable, this metric has some important limitations. First,
it ignores whether the predicted answer is the one selected by the Majority
(MA) of annotators. Second, it does not account for the quantitative
Subjectivity (S) of the answers in the sample (and dataset). Third, information
about the Semantic Similarity (SES) of the responses is completely neglected.
Based on such limitations, we propose a multi-component metric that accounts
for all these issues. We show that our metric is effective in providing a more
fine-grained evaluation both on the quantitative and qualitative level.
Compact Optimization Algorithms with Re-sampled Inheritance
Authors: Giovanni Iacca, Fabio Caraffini
Date: 2018-09-12 00:00:00

Compact optimization algorithms are a class of Estimation of Distribution
Algorithms (EDAs) characterized by extremely limited memory requirements (hence
they are called "compact"). As all EDAs, compact algorithms build and update a
probabilistic model of the distribution of solutions within the search space,
as opposed to population-based algorithms that instead make use of an explicit
population of solutions. In addition to that, to keep their memory consumption
low, compact algorithms purposely employ simple probabilistic models that can
be described with a small number of parameters. Despite their simplicity,
compact algorithms have shown good performances on a broad range of benchmark
functions and real-world problems. However, compact algorithms also come with
some drawbacks, i.e. they tend to premature convergence and show poorer
performance on non-separable problems. To overcome these limitations, here we
investigate a possible algorithmic scheme obtained by combining compact
algorithms with a non-disruptive restart mechanism taken from the literature,
named Re-Sampled Inheritance (RI). The resulting compact algorithms with RI are
tested on the CEC 2014 benchmark functions. The numerical results show on the
one hand that the use of RI consistently enhances the performances of compact
algorithms, still keeping a limited usage of memory. On the other hand, our
experiments show that among the tested algorithms, the best performance is
obtained by compact Differential Evolution with RI.
Deep Learning in Information Security
Authors: Stefan Thaler, Vlado Menkovski, Milan Petkovic
Date: 2018-09-12 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Machine learning has a long tradition of helping to solve complex information
security problems that are difficult to solve manually. Machine learning
techniques learn models from data representations to solve a task. These data
representations are hand-crafted by domain experts. Deep Learning is a
sub-field of machine learning, which uses models that are composed of multiple
layers. Consequently, representations that are used to solve a task are learned
from the data instead of being manually designed.
  In this survey, we study the use of DL techniques within the domain of
information security. We systematically reviewed 77 papers and presented them
from a data-centric perspective. This data-centric perspective reflects one of
the most crucial advantages of DL techniques -- domain independence. If
DL-methods succeed to solve problems on a data type in one domain, they most
likely will also succeed on similar data from another domain. Other advantages
of DL methods are unrivaled scalability and efficiency, both regarding the
number of examples that can be analyzed as well as with respect of
dimensionality of the input data. DL methods generally are capable of achieving
high-performance and generalize well.
  However, information security is a domain with unique requirements and
challenges. Based on an analysis of our reviewed papers, we point out
shortcomings of DL-methods to those requirements and discuss further research
opportunities.
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Authors: Weihao Yuan, Kaiyu Hang, Haoran Song, Danica Kragic, Michael Y. Wang, Johannes A. Stork
Date: 2018-09-12 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Moving a human body or a large and bulky object can require the strength of
whole arm manipulation (WAM). This type of manipulation places the load on the
robot's arms and relies on global properties of the interaction to
succeed---rather than local contacts such as grasping or non-prehensile
pushing. In this paper, we learn to generate motions that enable WAM for
holding and transporting of humans in certain rescue or patient care scenarios.
We model the task as a reinforcement learning problem in order to provide a
behavior that can directly respond to external perturbation and human motion.
For this, we represent global properties of the robot-human interaction with
topology-based coordinates that are computed from arm and torso positions.
These coordinates also allow transferring the learned policy to other body
shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue
scenario and show in quantitative experiments that the policy can solve unseen
scenarios with differently-shaped humans, floating humans, or with perception
noise. Our qualitative experiments show the subsequent transporting after
holding is achieved and we demonstrate that the policy can be directly
transferred to a real world setting.