pwc-archive/papers-with-abstracts huggingface dataset:
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Authors: Jenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard
Date: 2016-07-21 00:00:00

Big longitudinal observational medical data potentially hold a wealth of
information and have been recognised as potential sources for gaining new drug
safety knowledge. Unfortunately there are many complexities and underlying
issues when analysing longitudinal observational data. Due to these
complexities, existing methods for large-scale detection of negative side
effects using observational data all tend to have issues distinguishing between
association and causality. New methods that can better discriminate causal and
non-causal relationships need to be developed to fully utilise the data. In
this paper we propose using a set of causality considerations developed by the
epidemiologist Bradford Hill as a basis for engineering features that enable
the application of supervised learning for the problem of detecting negative
side effects. The Bradford Hill considerations look at various perspectives of
a drug and outcome relationship to determine whether it shows causal traits. We
taught a classifier to find patterns within these perspectives and it learned
to discriminate between association and causality. The novelty of this research
is the combination of supervised learning and Bradford Hill's causality
considerations to automate the Bradford Hill's causality assessment. We
evaluated the framework on a drug safety gold standard know as the
observational medical outcomes partnership's nonspecified association reference
set. The methodology obtained excellent discriminate ability with area under
the curves ranging between 0.792-0.940 (existing method optimal: 0.73) and a
mean average precision of 0.640 (existing method optimal: 0.141). The proposed
features can be calculated efficiently and be readily updated, making the
framework suitable for big observational data.
An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
Authors: Christopher Roadknight, Durga Suryanarayanan, Uwe Aickelin, John Scholefield, Lindy Durrant
Date: 2016-07-21 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, General Classification, Prognosis, Tumour Classification

This paper primarily addresses a dataset relating to cellular, chemical and
physical conditions of patients gathered at the time they are operated upon to
remove colorectal tumours. This data provides a unique insight into the
biochemical and immunological status of patients at the point of tumour removal
along with information about tumour classification and post-operative survival.
The relationship between severity of tumour, based on TNM staging, and survival
is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it
is possible to predict survival rate more accurately using a selection of
machine learning techniques applied to subsets of data to gain a deeper
understanding of the relationships between a patient's biochemical markers and
survival. We use a range of feature selection and single classification
techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients
which initially produces less than ideal results. The performance of each model
individually is then compared with subsets of the data where agreement is
reached for multiple models. This novel method of selective ensembling
demonstrates that significant improvements in model accuracy on an unseen test
set can be achieved for patients where agreement between models is achieved.
Finally we point at a possible method to identify whether a patients prognosis
can be accurately predicted or not.
Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication
Authors: Javier Navarro, Christian Wagner, Uwe Aickelin, Lynsey Green, Robert Ashford
Date: 2016-07-21 00:00:00

In the context of cancer treatment and surgery, quality of life assessment is
a crucial part of determining treatment success and viability. In order to
assess it, patients completed questionnaires which employ words to capture
aspects of patients well-being are the norm. As the results of these
questionnaires are often used to assess patient progress and to determine
future treatment options, it is important to establish that the words used are
interpreted in the same way by both patients and medical professionals. In this
paper, we capture and model patients perceptions and associated uncertainty
about the words used to describe the level of their physical function used in
the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS)
questionnaire. The paper provides detail about the interval-valued data capture
as well as the subsequent modelling of the data using fuzzy sets. Based on an
initial sample of participants, we use Jaccard similarity on the resulting
words models to show that there may be considerable differences in the
interpretation of commonly used questionnaire terms, thus presenting a very
real risk of miscommunication between patients and medical professionals as
well as within the group of medical professionals.
Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation
Authors: Javier Navarro, Christian Wagner, Uwe Aickelin
Date: 2016-07-21 00:00:00
Tasks: Classification, Clustering, General Classification

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to
provide so-called interpretable classifiers, i.e. classifiers which can be
introspective, understood, validated and augmented by human experts by relying
on fuzzy-set based rules. This paper builds on prior work for interval type-2
fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are
generated using an initial clustering stage. By introducing Subtractive
Clustering in order to identify multiple cluster prototypes, the proposed
approach has the potential to deliver improved classification performance while
maintaining good interpretability, i.e. without resulting in an excessive
number of rules. The paper provides a detailed overview of the proposed FRBC
framework, followed by a series of exploratory experiments on both linearly and
non-linearly separable datasets, comparing results to existing rule-based and
SVM approaches. Overall, initial results indicate that the approach enables
comparable classification performance to non rule-based classifiers such as
SVM, while often achieving this with a very small number of rules.
Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics
Authors: Yan Shang, David B. Dunson, Jing-Sheng Song
Date: 2015-01-21 00:00:00

In cargo logistics, a key performance measure is transport risk, defined as
the deviation of the actual arrival time from the planned arrival time. Neither
earliness nor tardiness is desirable for customer and freight forwarders. In
this paper, we investigate ways to assess and forecast transport risks using a
half-year of air cargo data, provided by a leading forwarder on 1336 routes
served by 20 airlines. Interestingly, our preliminary data analysis shows a
strong multimodal feature in the transport risks, driven by unobserved events,
such as cargo missing flights. To accommodate this feature, we introduce a
Bayesian nonparametric model -- the probit stick-breaking process (PSBP)
mixture model -- for flexible estimation of the conditional (i.e.,
state-dependent) density function of transport risk. We demonstrate that using
simpler methods, such as OLS linear regression, can lead to misleading
inferences. Our model provides a tool for the forwarder to offer customized
price and service quotes. It can also generate baseline airline performance to
enable fair supplier evaluation. Furthermore, the method allows us to separate
recurrent risks from disruption risks. This is important, because hedging
strategies for these two kinds of risks are often drastically different.
Streaming Recommender Systems
Authors: Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang
Date: 2016-07-21 00:00:00
Tasks: Recommendation Systems

The increasing popularity of real-world recommender systems produces data
continuously and rapidly, and it becomes more realistic to study recommender
systems under streaming scenarios. Data streams present distinct properties
such as temporally ordered, continuous and high-velocity, which poses
tremendous challenges to traditional recommender systems. In this paper, we
investigate the problem of recommendation with stream inputs. In particular, we
provide a principled framework termed sRec, which provides explicit
continuous-time random process models of the creation of users and topics, and
of the evolution of their interests. A variational Bayesian approach called
recursive meanfield approximation is proposed, which permits computationally
efficient instantaneous on-line inference. Experimental results on several
real-world datasets demonstrate the advantages of our sRec over other
state-of-the-arts.
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
Authors: Stephen Gould, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, Edison Guo
Date: 2016-07-19 00:00:00
Tasks: BIG-bench Machine Learning

Some recent works in machine learning and computer vision involve the
solution of a bi-level optimization problem. Here the solution of a
parameterized lower-level problem binds variables that appear in the objective
of an upper-level problem. The lower-level problem typically appears as an
argmin or argmax optimization problem. Many techniques have been proposed to
solve bi-level optimization problems, including gradient descent, which is
popular with current end-to-end learning approaches. In this technical report
we collect some results on differentiating argmin and argmax optimization
problems with and without constraints and provide some insightful motivating
examples.
Feature Descriptors for Tracking by Detection: a Benchmark
Authors: Alessandro Pieropan, Mårten Björkman, Niklas Bergström, Danica Kragic
Date: 2016-07-21 00:00:00
Tasks: 3D Reconstruction, Object Recognition

In this paper, we provide an extensive evaluation of the performance of local
descriptors for tracking applications. Many different descriptors have been
proposed in the literature for a wide range of application in computer vision
such as object recognition and 3D reconstruction. More recently, due to fast
key-point detectors, local image features can be used in online tracking
frameworks. However, while much effort has been spent on evaluating their
performance in terms of distinctiveness and robustness to image
transformations, very little has been done in the contest of tracking. Our
evaluation is performed in terms of distinctiveness, tracking precision and
tracking speed. Our results show that binary descriptors like ORB or BRISK have
comparable results to SIFT or AKAZE due to a higher number of key-points.
On the Prior Sensitivity of Thompson Sampling
Authors: Che-Yu Liu, Lihong Li
Date: 2015-06-10 00:00:00
Tasks: Sensitivity, Thompson Sampling

The empirically successful Thompson Sampling algorithm for stochastic bandits
has drawn much interest in understanding its theoretical properties. One
important benefit of the algorithm is that it allows domain knowledge to be
conveniently encoded as a prior distribution to balance exploration and
exploitation more effectively. While it is generally believed that the
algorithm's regret is low (high) when the prior is good (bad), little is known
about the exact dependence. In this paper, we fully characterize the
algorithm's worst-case dependence of regret on the choice of prior, focusing on
a special yet representative case. These results also provide insights into the
general sensitivity of the algorithm to the choice of priors. In particular,
with $p$ being the prior probability mass of the true reward-generating model,
we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the
bad- and good-prior cases, respectively, as well as \emph{matching} lower
bounds. Our proofs rely on the discovery of a fundamental property of Thompson
Sampling and make heavy use of martingale theory, both of which appear novel in
the literature, to the best of our knowledge.
Local Multiple Directional Pattern of Palmprint Image
Authors: Lunke Fei, Jie Wen, Zheng Zhang, Ke Yan, Zuofeng Zhong
Date: 2016-07-21 00:00:00

Lines are the most essential and discriminative features of palmprint images,
which motivate researches to propose various line direction based methods for
palmprint recognition. Conventional methods usually capture the only one of the
most dominant direction of palmprint images. However, a number of points in
palmprint images have double or even more than two dominant directions because
of a plenty of crossing lines of palmprint images. In this paper, we propose a
local multiple directional pattern (LMDP) to effectively characterize the
multiple direction features of palmprint images. LMDP can not only exactly
denote the number and positions of dominant directions but also effectively
reflect the confidence of each dominant direction. Then, a simple and effective
coding scheme is designed to represent the LMDP and a block-wise LMDP
descriptor is used as the feature space of palmprint images in palmprint
recognition. Extensive experimental results demonstrate the superiority of the
LMDP over the conventional powerful descriptors and the state-of-the-art
direction based methods in palmprint recognition.
Compositional Sequence Labeling Models for Error Detection in Learner Writing
Authors: Marek Rei, Helen Yannakoudakis
Date: 2016-07-20 00:00:00
Proceeding: ACL 2016 8
Tasks: Grammatical Error Detection

In this paper, we present the first experiments using neural network models
for the task of error detection in learner writing. We perform a systematic
comparison of alternative compositional architectures and propose a framework
for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14
shared task dataset show the model is able to outperform other participants on
detecting errors in learner writing. Finally, the model is integrated with a
publicly deployed self-assessment system, leading to performance comparable to
human annotators.
Supervised quantum gate "teaching" for quantum hardware design
Authors: Leonardo Banchi, Nicola Pancotti, Sougato Bose
Date: 2016-07-20 00:00:00

We show how to train a quantum network of pairwise interacting qubits such
that its evolution implements a target quantum algorithm into a given network
subset. Our strategy is inspired by supervised learning and is designed to help
the physical construction of a quantum computer which operates with minimal
external classical control.
Learning the Roots of Visual Domain Shift
Authors: Tatiana Tommasi, Martina Lanzi, Paolo Russo, Barbara Caputo
Date: 2016-07-20 00:00:00
Tasks: Domain Adaptation, General Classification, Specificity

In this paper we focus on the spatial nature of visual domain shift,
attempting to learn where domain adaptation originates in each given image of
the source and target set. We borrow concepts and techniques from the CNN
visualization literature, and learn domainnes maps able to localize the degree
of domain specificity in images. We derive from these maps features related to
different domainnes levels, and we show that by considering them as a
preprocessing step for a domain adaptation algorithm, the final classification
performance is strongly improved. Combined with the whole image representation,
these features provide state of the art results on the Office dataset.
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
Authors: Amirhossein Tavanaei, Anthony S. Maida
Date: 2016-06-02 00:00:00

It is of some interest to understand how statistically based mechanisms for
signal processing might be integrated with biologically motivated mechanisms
such as neural networks. This paper explores a novel hybrid approach for
classifying segments of sequential data, such as individual spoken works. The
approach combines a hidden Markov model (HMM) with a spiking neural network
(SNN). The HMM, consisting of states and transitions, forms a fixed backbone
with nonadaptive transition probabilities. The SNN, however, implements a
biologically based Bayesian computation that derives from the spike
timing-dependent plasticity (STDP) learning rule. The emission (observation)
probabilities of the HMM are represented in the SNN and trained with the STDP
rule. A separate SNN, each with the same architecture, is associated with each
of the states of the HMM. Because of the STDP training, each SNN implements an
expectation maximization algorithm to learn the emission probabilities for one
HMM state. The model was studied on synthesized spike-train data and also on
spoken word data. Preliminary results suggest its performance compares
favorably with other biologically motivated approaches. Because of the model's
uniqueness and initial promise, it warrants further study. It provides some new
ideas on how the brain might implement the equivalent of an HMM in a neural
circuit.
Sequence to sequence learning for unconstrained scene text recognition
Authors: Ahmed Mamdouh A. Hassanien
Date: 2016-07-20 00:00:00
Tasks: Scene Text Recognition

In this work we present a state-of-the-art approach for unconstrained natural
scene text recognition. We propose a cascade approach that incorporates a
convolutional neural network (CNN) architecture followed by a long short term
memory model (LSTM). The CNN learns visual features for the characters and uses
them with a softmax layer to detect sequence of characters. While the CNN gives
very good recognition results, it does not model relation between characters,
hence gives rise to false positive and false negative cases (confusing
characters due to visual similarities like "g" and "9", or confusing background
patches with characters; either removing existing characters or adding
non-existing ones) To alleviate these problems we leverage recent developments
in LSTM architectures to encode contextual information. We show that the LSTM
can dramatically reduce such errors and achieve state-of-the-art accuracy in
the task of unconstrained natural scene text recognition. Moreover we manually
remove all occurrences of the words that exist in the test set from our
training set to test whether our approach will generalize to unseen data. We
use the ICDAR 13 test set for evaluation and compare the results with the state
of the art approaches [11, 18]. We finally present an application of the work
in the domain of for traffic monitoring.
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off
Authors: Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda
Date: 2016-07-19 00:00:00

We present a novel dynamic configuration technique for deep neural networks
that permits step-wise energy-accuracy trade-offs during runtime. Our
configuration technique adjusts the number of channels in the network
dynamically depending on response time, power, and accuracy targets. To enable
this dynamic configuration technique, we co-design a new training algorithm,
where the network is incrementally trained such that the weights in channels
trained in earlier steps are fixed. Our technique provides the flexibility of
multiple networks while storing and utilizing one set of weights. We evaluate
our techniques using both an ASIC-based hardware accelerator as well as a
low-power embedded GPGPU and show that our approach leads to only a small or
negligible loss in the final network accuracy. We analyze the performance of
our proposed methodology using three well-known networks for MNIST, CIFAR-10,
and SVHN datasets, and we show that we are able to achieve up to 95% energy
reduction with less than 1% accuracy loss across the three benchmarks. In
addition, compared to prior work on dynamic network reconfiguration, we show
that our approach leads to approximately 50% savings in storage requirements,
while achieving similar accuracy.
Hashmod: A Hashing Method for Scalable 3D Object Detection
Authors: Wadim Kehl, Federico Tombari, Nassir Navab, Slobodan Ilic, Vincent Lepetit
Date: 2016-07-20 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Pose Estimation

We present a scalable method for detecting objects and estimating their 3D
poses in RGB-D data. To this end, we rely on an efficient representation of
object views and employ hashing techniques to match these views against the
input frame in a scalable way. While a similar approach already exists for 2D
detection, we show how to extend it to estimate the 3D pose of the detected
objects. In particular, we explore different hashing strategies and identify
the one which is more suitable to our problem. We show empirically that the
complexity of our method is sublinear with the number of objects and we enable
detection and pose estimation of many 3D objects with high accuracy while
outperforming the state-of-the-art in terms of runtime.
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
Authors: Wadim Kehl, Fausto Milletari, Federico Tombari, Slobodan Ilic, Nassir Navab
Date: 2016-07-20 00:00:00
Tasks: 3D Object Detection, 6D Pose Estimation, 6D Pose Estimation using RGB, Object, object-detection, Object Detection, Pose Estimation, regression

We present a 3D object detection method that uses regressed descriptors of
locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a
convolutional auto-encoder that has been trained on a large collection of
random local patches. During testing, scene patch descriptors are matched
against a database of synthetic model view patches and cast 6D object votes
which are subsequently filtered to refined hypotheses. We evaluate on three
datasets to show that our method generalizes well to previously unseen input
data, delivers robust detection results that compete with and surpass the
state-of-the-art while being scalable in the number of objects.
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery
Authors: Jordan M. Malof, Kyle Bradbury, Leslie M. Collins, Richard G. Newell
Date: 2016-07-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The quantity of small scale solar photovoltaic (PV) arrays in the United
States has grown rapidly in recent years. As a result, there is substantial
interest in high quality information about the quantity, power capacity, and
energy generated by such arrays, including at a high spatial resolution (e.g.,
counties, cities, or even smaller regions). Unfortunately, existing methods for
obtaining this information, such as surveys and utility interconnection
filings, are limited in their completeness and spatial resolution. This work
presents a computer algorithm that automatically detects PV panels using very
high resolution color satellite imagery. The approach potentially offers a
fast, scalable method for obtaining accurate information on PV array location
and size, and at much higher spatial resolutions than are currently available.
The method is validated using a very large (135 km^2) collection of publicly
available [1] aerial imagery, with over 2,700 human annotated PV array
locations. The results demonstrate the algorithm is highly effective on a
per-pixel basis. It is likewise effective at object-level PV array detection,
but with significant potential for improvement in estimating the precise
shape/size of the PV arrays. These results are the first of their kind for the
detection of solar PV in aerial imagery, demonstrating the feasibility of the
approach and establishing a baseline performance for future investigations.