pwc-archive/papers-with-abstracts huggingface dataset:
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
Authors: Audrey G. Chung, Mohammad Javad Shafiee, Devinder Kumar, Farzad Khalvati, Masoom A. Haider, Alexander Wong
Date: 2015-09-01 00:00:00
Tasks: Diagnostic, Prognosis

Prostate cancer is the most diagnosed form of cancer in Canadian men, and is
the third leading cause of cancer death. Despite these statistics, prognosis is
relatively good with a sufficiently early diagnosis, making fast and reliable
prostate cancer detection crucial. As imaging-based prostate cancer screening,
such as magnetic resonance imaging (MRI), requires an experienced medical
professional to extensively review the data and perform a diagnosis,
radiomics-driven methods help streamline the process and has the potential to
significantly improve diagnostic accuracy and efficiency, and thus improving
patient survival rates. These radiomics-driven methods currently rely on
hand-crafted sets of quantitative imaging-based features, which are selected
manually and can limit their ability to fully characterize unique prostate
cancer tumour phenotype. In this study, we propose a novel \textit{discovery
radiomics} framework for generating custom radiomic sequences tailored for
prostate cancer detection. Discovery radiomics aims to uncover abstract
imaging-based features that capture highly unique tumour traits and
characteristics beyond what can be captured using predefined feature models. In
this paper, we discover new custom radiomic sequencers for generating new
prostate radiomic sequences using multi-parametric MRI data. We evaluated the
performance of the discovered radiomic sequencer against a state-of-the-art
hand-crafted radiomic sequencer for computer-aided prostate cancer detection
with a feedforward neural network using real clinical prostate multi-parametric
MRI data. Results for the discovered radiomic sequencer demonstrate good
performance in prostate cancer detection and clinical decision support relative
to the hand-crafted radiomic sequencer. The use of discovery radiomics shows
potential for more efficient and reliable automatic prostate cancer detection.
Robust Semi-Supervised Classification for Multi-Relational Graphs
Authors: Junting Ye, Leman Akoglu
Date: 2015-10-19 00:00:00
Tasks: Classification, General Classification, Informativeness

Graph-regularized semi-supervised learning has been used effectively for
classification when (i) instances are connected through a graph, and (ii)
labeled data is scarce. If available, using multiple relations (or graphs)
between the instances can improve the prediction performance. On the other
hand, when these relations have varying levels of veracity and exhibit varying
relevance for the task, very noisy and/or irrelevant relations may deteriorate
the performance. As a result, an effective weighing scheme needs to be put in
place. In this work, we propose a robust and scalable approach for
multi-relational graph-regularized semi-supervised classification. Under a
convex optimization scheme, we simultaneously infer weights for the multiple
graphs as well as a solution. We provide a careful analysis of the inferred
weights, based on which we devise an algorithm that filters out irrelevant and
noisy graphs and produces weights proportional to the informativeness of the
remaining graphs. Moreover, the proposed method is linearly scalable w.r.t. the
number of edges in the union of the multiple graphs. Through extensive
experiments we show that our method yields superior results under different
noise models, and under increasing number of noisy graphs and intensity of
noise, as compared to a list of baselines and state-of-the-art approaches.
Single Memristor Logic Gates: From NOT to a Full Adder
Authors: Ella Gale
Date: 2015-10-19 00:00:00

Memristors have been suggested as a novel route to neuromorphic computing
based on the similarity between them and neurons (specifically synapses and ion
pumps). The d.c. action of the memristor is a current spike which imparts a
short-term memory to the device. Here it is demonstrated that this short-term
memory works exactly like habituation (e.g. in \emph{Aplysia}). We elucidate
the physical rules, based on energy conservation, governing the interaction of
these current spikes: summation, `bounce-back', directionality and `diminishing
returns'. Using these rules, we introduce 4 different logical systems to
implement sequential logic in the memristor and demonstrate how sequential
logic works by instantiating a NOT gate, an AND gate, an XOR gate and a Full
Adder with a single memristor. The Full Adder makes use of the memristor's
short-term memory to add together three binary values and outputs the sum, the
carry digit and even the order they were input in. A memristor full adder also
outputs the arithmetical sum of bits, allowing for a logically (but not
physically) reversible system. Essentially, we can replace an input/output port
with an extra time-step, allowing a single memristor to do a hither-to
unexpectedly large amount of computation. This makes up for the memristor's
slow operation speed and may relate to how neurons do a similarly-large
computation with such slow operations speeds. We propose that using spiking
logic, either in gates or as neuron-analogues, with plastic rewritable
connections between them, would allow the building of a neuromorphic computer.
When slower is faster
Authors: Carlos Gershenson, Dirk Helbing
Date: 2015-06-22 00:00:00

The slower is faster (SIF) effect occurs when a system performs worse as its
components try to do better. Thus, a moderate individual efficiency actually
leads to a better systemic performance. The SIF effect takes place in a variety
of phenomena. We review studies and examples of the SIF effect in pedestrian
dynamics, vehicle traffic, traffic light control, logistics, public transport,
social dynamics, ecological systems, and adaptation. Drawing on these examples,
we generalize common features of the SIF effect and suggest possible future
lines of research.
Protein Structure Prediction by Protein Alignments
Authors: Jianzhu Ma
Date: 2015-10-19 00:00:00
Tasks: Prediction, Protein Structure Prediction

Proteins are the basic building blocks of life. They usually perform
functions by folding to a particular structure. Understanding the folding
process could help the researchers to understand the functions of proteins and
could also help to develop supplemental proteins for people with deficiencies
and gain more insight into diseases associated with troublesome folding
proteins. Experimental methods are both expensive and time consuming. In this
thesis I introduce a new machine learning based method to predict the protein
structure. The new method improves the performance from two directions:
creating accurate protein alignments and predicting accurate protein contacts.
First, I present an alignment framework MRFalign which goes beyond
state-of-the-art methods and uses Markov Random Fields to model a protein
family and align two proteins by aligning two MRFs together. Compared to other
methods, that can only model local-range residue correlation, MRFs can model
long-range residue interactions and thus, encodes global information in a
protein. Secondly, I present a Group Graphical Lasso method for contact
prediction that integrates joint multi-family Evolutionary Coupling analysis
and supervised learning to improve accuracy on proteins without many sequence
homologs. Different from single-family EC analysis that uses residue
co-evolution information in only the target protein family, our joint EC
analysis uses residue co-evolution in both the target family and its related
families, which may have divergent sequences but similar folds. Our method can
also integrate supervised learning methods to further improve accuracy. We
evaluate the performance of both methods including each of its components on
large public benchmarks. Experiments show that our methods can achieve better
accuracy than existing state-of-the-art methods under all the measurements on
most of the protein classes.
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
Authors: Nan Zhou, Yangyang Xu, Hong Cheng, Jun Fang, Witold Pedrycz
Date: 2015-06-02 00:00:00
Tasks: feature selection

As we aim at alleviating the curse of high-dimensionality, subspace learning
is becoming more popular. Existing approaches use either information about
global or local structure of the data, and few studies simultaneously focus on
global and local structures as the both of them contain important information.
In this paper, we propose a global and local structure preserving sparse
subspace learning (GLoSS) model for unsupervised feature selection. The model
can simultaneously realize feature selection and subspace learning. In
addition, we develop a greedy algorithm to establish a generic combinatorial
model, and an iterative strategy based on an accelerated block coordinate
descent is used to solve the GLoSS problem. We also provide whole iterate
sequence convergence analysis of the proposed iterative algorithm. Extensive
experiments are conducted on real-world datasets to show the superiority of the
proposed approach over several state-of-the-art unsupervised feature selection
approaches.
Sequence to Sequence -- Video to Text
Authors: Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko
Date: 2015-05-03 00:00:00
Tasks: Caption Generation, Language Modeling, Language Modelling, Sentence

Real-world videos often have complex dynamics; and methods for generating
open-domain video descriptions should be sensitive to temporal structure and
allow both input (sequence of frames) and output (sequence of words) of
variable length. To approach this problem, we propose a novel end-to-end
sequence-to-sequence model to generate captions for videos. For this we exploit
recurrent neural networks, specifically LSTMs, which have demonstrated
state-of-the-art performance in image caption generation. Our LSTM model is
trained on video-sentence pairs and learns to associate a sequence of video
frames to a sequence of words in order to generate a description of the event
in the video clip. Our model naturally is able to learn the temporal structure
of the sequence of frames as well as the sequence model of the generated
sentences, i.e. a language model. We evaluate several variants of our model
that exploit different visual features on a standard set of YouTube videos and
two movie description datasets (M-VAD and MPII-MD).
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
Authors: Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
Date: 2015-04-27 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Multi-Armed Bandits

We study contextual bandits with budget and time constraints, referred to as
constrained contextual bandits.The time and budget constraints significantly
complicate the exploration and exploitation tradeoff because they introduce
complex coupling among contexts over time.Such coupling effects make it
difficult to obtain oracle solutions that assume known statistics of bandits.
To gain insight, we first study unit-cost systems with known context
distribution. When the expected rewards are known, we develop an approximation
of the oracle, referred to Adaptive-Linear-Programming (ALP), which achieves
near-optimality and only requires the ordering of expected rewards. With these
highly desirable features, we then combine ALP with the upper-confidence-bound
(UCB) method in the general case where the expected rewards are unknown {\it a
priori}. We show that the proposed UCB-ALP algorithm achieves logarithmic
regret except for certain boundary cases. Further, we design algorithms and
obtain similar regret analysis results for more general systems with unknown
context distribution and heterogeneous costs. To the best of our knowledge,
this is the first work that shows how to achieve logarithmic regret in
constrained contextual bandits. Moreover, this work also sheds light on the
study of computationally efficient algorithms for general constrained
contextual bandits.
A Sparse Gaussian Process Framework for Photometric Redshift Estimation
Authors: Ibrahim A. Almosallam, Sam N. Lindsay, Matt J. Jarvis, Stephen J. Roberts
Date: 2015-05-20 00:00:00
Tasks: Gaussian Processes, Photometric Redshift Estimation, regression

Accurate photometric redshifts are a lynchpin for many future experiments to
pin down the cosmological model and for studies of galaxy evolution. In this
study, a novel sparse regression framework for photometric redshift estimation
is presented. Simulated and real data from SDSS DR12 were used to train and
test the proposed models. We show that approaches which include careful data
preparation and model design offer a significant improvement in comparison with
several competing machine learning algorithms. Standard implementations of most
regression algorithms have as the objective the minimization of the sum of
squared errors. For redshift inference, however, this induces a bias in the
posterior mean of the output distribution, which can be problematic. In this
paper we directly target minimizing $\Delta z = (z_\textrm{s} -
z_\textrm{p})/(1+z_\textrm{s})$ and address the bias problem via a
distribution-based weighting scheme, incorporated as part of the optimization
objective. The results are compared with other machine learning algorithms in
the field such as Artificial Neural Networks (ANN), Gaussian Processes (GPs)
and sparse GPs. The proposed framework reaches a mean absolute $\Delta z =
0.0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$
on the simulated data, and $\Delta z = 0.0178(1+z_\textrm{s})$ over the entire
redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in
the literature. We also investigate how the relative size of the training set
affects the photometric redshift accuracy. We find that a training set of
\textgreater 30 per cent of total sample size, provides little additional
constraint on the photometric redshifts, and note that our GP formalism
strongly outperforms ANNz in the sparse data regime for the simulated data set.
Application of Machine Learning Techniques in Human Activity Recognition
Authors: Jitenkumar Babubhai Rana, Rashmi Shetty, Tanya Jha
Date: 2015-10-19 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, BIG-bench Machine Learning, Human Activity Recognition

Human activity detection has seen a tremendous growth in the last decade
playing a major role in the field of pervasive computing. This emerging
popularity can be attributed to its myriad of real-life applications primarily
dealing with human-centric problems like healthcare and elder care. Many
research attempts with data mining and machine learning techniques have been
undergoing to accurately detect human activities for e-health systems. This
paper reviews some of the predictive data mining algorithms and compares the
accuracy and performances of these models. A discussion on the future research
directions is subsequently offered.
Optimization for Gaussian Processes via Chaining
Authors: Emile Contal, Cédric Malherbe, Nicolas Vayatis
Date: 2015-10-19 00:00:00
Tasks: Gaussian Processes, Stochastic Optimization

In this paper, we consider the problem of stochastic optimization under a
bandit feedback model. We generalize the GP-UCB algorithm [Srinivas and al.,
2012] to arbitrary kernels and search spaces. To do so, we use a notion of
localized chaining to control the supremum of a Gaussian process, and provide a
novel optimization scheme based on the computation of covering numbers. The
theoretical bounds we obtain on the cumulative regret are more generic and
present the same convergence rates as the GP-UCB algorithm. Finally, the
algorithm is shown to be empirically more efficient than its natural
competitors on simple and complex input spaces.
On the Computability of AIXI
Authors: Jan Leike, Marcus Hutter
Date: 2015-10-19 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

How could we solve the machine learning and the artificial intelligence
problem if we had infinite computation? Solomonoff induction and the
reinforcement learning agent AIXI are proposed answers to this question. Both
are known to be incomputable. In this paper, we quantify this using the
arithmetical hierarchy, and prove upper and corresponding lower bounds for
incomputability. We show that AIXI is not limit computable, thus it cannot be
approximated using finite computation. Our main result is a limit-computable
{\epsilon}-optimal version of AIXI with infinite horizon that maximizes
expected rewards.
Across neighbourhood search for numerical optimization
Authors: Guohua Wu
Date: 2014-01-14 00:00:00
Tasks: Evolutionary Algorithms

Population-based search algorithms (PBSAs), including swarm intelligence
algorithms (SIAs) and evolutionary algorithms (EAs), are competitive
alternatives for solving complex optimization problems and they have been
widely applied to real-world optimization problems in different fields. In this
study, a novel population-based across neighbourhood search (ANS) is proposed
for numerical optimization. ANS is motivated by two straightforward assumptions
and three important issues raised in improving and designing efficient PBSAs.
In ANS, a group of individuals collaboratively search the solution space for an
optimal solution of the optimization problem considered. A collection of
superior solutions found by individuals so far is maintained and updated
dynamically. At each generation, an individual directly searches across the
neighbourhoods of multiple superior solutions with the guidance of a Gaussian
distribution. This search manner is referred to as across neighbourhood search.
The characteristics of ANS are discussed and the concept comparisons with other
PBSAs are given. The principle behind ANS is simple. Moreover, ANS is easy for
implementation and application with three parameters being required to tune.
Extensive experiments on 18 benchmark optimization functions of different types
show that ANS has well balanced exploration and exploitation capabilities and
performs competitively compared with many efficient PBSAs (Related Matlab codes
used in the experiments are available from
http://guohuawunudt.gotoip2.com/publications.html).
Sparse + Low Rank Decomposition of Annihilating Filter-based Hankel Matrix for Impulse Noise Removal
Authors: Kyong Hwan Jin, Jong Chul Ye
Date: 2015-10-19 00:00:00
Tasks: Image Inpainting

Recently, so called annihilating filer-based low rank Hankel matrix (ALOHA)
approach was proposed as a powerful image inpainting method. Based on the
observation that smoothness or textures within an image patch corresponds to
sparse spectral components in the frequency domain, ALOHA exploits the
existence of annihilating filters and the associated rank-deficient Hankel
matrices in the image domain to estimate the missing pixels. By extending this
idea, here we propose a novel impulse noise removal algorithm using sparse +
low rank decomposition of an annihilating filter-based Hankel matrix. The new
approach, what we call the robust ALOHA, is motivated by the observation that
an image corrupted with impulse noises has intact pixels; so the impulse noises
can be modeled as sparse components, whereas the underlying image can be still
modeled using a low-rank Hankel structured matrix. To solve the sparse + low
rank decomposition problem, we propose an alternating direction method of
multiplier (ADMM) method with initial factorized matrices coming from low rank
matrix fitting (LMaFit) algorithm. To adapt the local image statistics that
have distinct spectral distributions, the robust ALOHA is applied patch by
patch. Experimental results from two types of impulse noises - random valued
impulse noises and salt/pepper noises - for both single channel and
multi-channel color images demonstrate that the robust ALOHA outperforms the
existing algorithms up to 8dB in terms of the peak signal to noise ratio
(PSNR).
Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS
Authors: Mehmet Emin Basbug, Koray Ozcan, Senem Velipasalar
Date: 2015-10-19 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Variational Inference

As part of daily monitoring of human activities, wearable sensors and devices
are becoming increasingly popular sources of data. With the advent of
smartphones equipped with acceloremeter, gyroscope and camera; it is now
possible to develop activity classification platforms everyone can use
conveniently. In this paper, we propose a fast inference method for an
unsupervised non-parametric time series model namely variational inference for
sticky HDP-SLDS(Hierarchical Dirichlet Process Switching Linear Dynamical
System). We show that the proposed algorithm can differentiate various indoor
activities such as sitting, walking, turning, going up/down the stairs and
taking the elevator using only the acceloremeter of an Android smartphone
Samsung Galaxy S4. We used the front camera of the smartphone to annotate
activity types precisely. We compared the proposed method with Hidden Markov
Models with Gaussian emission probabilities on a dataset of 10 subjects. We
showed that the efficacy of the stickiness property. We further compared the
variational inference to the Gibbs sampler on the same model and show that
variational inference is faster in one order of magnitude.
Confidence Sets for the Source of a Diffusion in Regular Trees
Authors: Justin Khim, Po-Ling Loh
Date: 2015-10-19 00:00:00

We study the problem of identifying the source of a diffusion spreading over
a regular tree. When the degree of each node is at least three, we show that it
is possible to construct confidence sets for the diffusion source with size
independent of the number of infected nodes. Our estimators are motivated by
analogous results in the literature concerning identification of the root node
in preferential attachment and uniform attachment trees. At the core of our
proofs is a probabilistic analysis of P\'{o}lya urns corresponding to the
number of uninfected neighbors in specific subtrees of the infection tree. We
also provide an example illustrating the shortcomings of source estimation
techniques in settings where the underlying graph is asymmetric.
Color graph based wavelet transform with perceptual information
Authors: Mohamed Malek, David Helbert, Philippe Carre
Date: 2015-10-19 00:00:00
Tasks: Denoising, Image Restoration

In this paper, we propose a numerical strategy to define a multiscale
analysis for color and multicomponent images based on the representation of
data on a graph. Our approach consists in computing the graph of an image using
the psychovisual information and analysing it by using the spectral graph
wavelet transform. We suggest introducing color dimension into the computation
of the weights of the graph and using the geodesic distance as a means of
distance measurement. We thus have defined a wavelet transform based on a graph
with perceptual information by using the CIELab color distance. This new
representation is illustrated with denoising and inpainting applications.
Overall, by introducing psychovisual information in the graph computation for
the graph wavelet transform we obtain very promising results. Therefore results
in image restoration highlight the interest of the appropriate use of color
information.