pwc-archive/papers-with-abstracts huggingface dataset:
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic Programming
Authors: Zhu Yiheng, Yao Yichen, Wu Zili, Chen Yujie, Li Guozheng, Hu Haoyuan, Xu Yinghui
Date: 2018-11-26 00:00:00
Tasks: Diversity, Neural Architecture Search

Convolutional neural networks (CNNs) are effective at solving difficult
problems like visual recognition, speech recognition and natural language
processing. However, performance gain comes at the cost of laborious
trial-and-error in designing deeper CNN architectures. In this paper, a genetic
programming (GP) framework for convolutional neural network architecture
search, abbreviated as GP-CNAS, is proposed to automatically search for optimal
CNN architectures. GP-CNAS encodes CNNs as trees where leaf nodes (GP
terminals) are selected residual blocks and non-leaf nodes (GP functions)
specify the block assembling procedure. Our tree-based representation enables
easy design and flexible implementation of genetic operators. Specifically, we
design a dynamic crossover operator that strikes a balance between exploration
and exploitation, which emphasizes CNN complexity at early stage and CNN
diversity at later stage. Therefore, the desired CNN architecture with balanced
depth and width can be found within limited trials. Moreover, our GP-CNAS
framework is highly compatible with other manually-designed and NAS-generated
block types as well. Experimental results on the CIFAR-10 dataset show that
GP-CNAS is competitive among the state-of-the-art automatic and semi-automatic
NAS algorithms.
Assessing the accuracy of direct-coupling analysis for RNA contact prediction
Date: 2019-11-29 00:00:00

Many non-coding RNAs are known to play a role in the cell directly linked to
their structure. Structure prediction based on the sole sequence is however a
challenging task. On the other hand, thanks to the low cost of sequencing
technologies, a very large number of homologous sequences are becoming
available for many RNA families. In the protein community, it has emerged in
the last decade the idea of exploiting the covariance of mutations within a
family to predict the protein structure using the direct-coupling-analysis
(DCA) method. The application of DCA to RNA systems has been limited so far. We
here perform an assessment of the DCA method on 17 riboswitch families,
comparing it with the commonly used mutual information analysis and with
state-of-the-art R-scape covariance method. We also compare different flavors
of DCA, including mean-field, pseudo-likelihood, and a proposed stochastic
procedure (Boltzmann learning) for solving exactly the DCA inverse problem.
Boltzmann learning outperforms the other methods in predicting contacts
observed in high resolution crystal structures.
Portfolio Rebalancing under Uncertainty Using Meta-heuristic Algorithm
Date: 2018-12-18 00:00:00

In this paper, we solve portfolio rebalancing problem when security returns
are represented by uncertain variables considering transaction costs. The
performance of the proposed model is studied using constant-proportion
portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed
that uncertain parameters and different belief degrees will produce different
efficient frontiers, and affect the performance of the proposed model.
Moreover, CPPI strategy performs as an insurance mechanism and limits downside
risk in bear markets while it allows potential benefit in bull markets.
Finally, using a globally optimization solver and genetic algorithm (GA) for
solving the model, we concluded that the problem size is an important factor in
solving portfolio rebalancing problem with uncertain parameters and to gain
better results, it is recommended to use a meta-heuristic algorithm rather than
a global solver.
Impact of the purposeful kinesis on running waves
Date: 2018-11-22 00:00:00

The basic model of purposeful kinesis was developed recently (Gorban and
Cabukoglu, Ecol. Complex. 2018, 33, 75 83) on the basis of the let well enough
alone idea. According to this model the diffusion drops while the reproduction
coefficient is increasing. That is, species prefer to stay in a good condition
and the population gives birth; otherwise, in the bad situation individuals
want to run away because of the fatal conditions. In this study, we analyse the
impact of the purposeful kinesis model on running waves. The running waves in
the population with kinesis are studied using numerical experiments. Both
monotonic and non monotonic (Allee effect) dependence of the reproduction
coefficient on the population density are studied. The possible benefits of the
purposeful kinesis are demonstrated: with the higher diffusion, while the
population without kinesis ends up with extinction, the population with kinesis
stays alive and has the running wave behavior. While the kinesis of the prey
population is decreasing, the wave amplitude gets smaller. On the other hand,
for the lower kinesis of predators they have a sharp increase.
GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly
Date: 2018-12-18 00:00:00

The computational problem of inferring the full haplotype of a cell starting
from read sequencing data is known as haplotype assembly, and consists in
assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly
one of the two chromosomes. Indeed, the knowledge of complete haplotypes is
generally more informative than analyzing single SNPs and plays a fundamental
role in many medical applications. To reconstruct the two haplotypes, we
addressed the weighted Minimum Error Correction (wMEC) problem, which is a
successful approach for haplotype assembly. This NP-hard problem consists in
computing the two haplotypes that partition the sequencing reads into two
disjoint sub-sets, with the least number of corrections to the SNP values. To
this aim, we propose here GenHap, a novel computational method for haplotype
assembly based on Genetic Algorithms, yielding optimal solutions by means of a
global search process. In order to evaluate the effectiveness of our approach,
we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454
and PacBio RS II sequencing technologies. We compared the performance of GenHap
against HapCol, an efficient state-of-the-art algorithm for haplotype phasing.
Our results show that GenHap always obtains high accuracy solutions (in terms
of haplotype error rate), and is up to 4x faster than HapCol in the case of
Roche/454 instances and up to 20x faster when compared on the PacBio RS II
dataset. Finally, we assessed the performance of GenHap on two different real
datasets. Future-generation sequencing technologies, producing longer reads
with higher coverage, can highly benefit from GenHap, thanks to its capability
of efficiently solving large instances of the haplotype assembly problem.
3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian
Date: 2018-12-19 00:00:00
Tasks: Denoising

Point cloud is a collection of 3D coordinates that are discrete geometric
samples of an object's 2D surfaces. Imperfection in the acquisition process
means that point clouds are often corrupted with noise. Building on recent
advances in graph signal processing, we design local algorithms for 3D point
cloud denoising. Specifically, we design a reweighted graph Laplacian
regularizer (RGLR) for surface normals and demonstrate its merits in rotation
invariance, promotion of piecewise smoothness, and ease of optimization. Using
RGLR as a signal prior, we formulate an optimization problem with a general
lp-norm fidelity term that can explicitly model two types of independent noise:
small but non-sparse noise (using l2 fidelity term) and large but sparser noise
(using l1 fidelity term).
  To establish a linear relationship between normals and 3D point coordinates,
we first perform bipartite graph approximation to divide the point cloud into
two disjoint node sets (red and blue). We then optimize the red and blue nodes'
coordinates alternately. For l2-norm fidelity term, we iteratively solve an
unconstrained quadratic programming (QP) problem, efficiently computed using
conjugate gradient with a bounded condition number to ensure numerical
stability. For l1-norm fidelity term, we iteratively minimize an l1-l2 cost
function sing accelerated proximal gradient (APG), where a good step size is
chosen via Lipschitz continuity analysis. Finally, we propose simple mean and
median filters for flat patches of a given point cloud to estimate the noise
variance given the noise type, which in turn is used to compute a weight
parameter trading off the fidelity term and signal prior in the problem
formulation. Extensive experiments show state-of-the-art denoising performance
among local methods using our proposed algorithms.
Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber
Date: 2018-12-19 00:00:00
Tasks: Computational Efficiency, Retrieval

Artificial neural networks (ANNs) can be used to replace traditional methods
in various fields, making signal processing more efficient and meeting the
real-time processing requirements of the Internet of Things (IoT). As a special
type of ANN, recently the feedforward neural network (FNN) has been used to
replace the time-consuming Lorentzian curve fitting (LCF) method in Brillouin
optical time-domain analysis (BOTDA) to retrieve the Brillouin frequency shift
(BFS), which could be used as the indicator in temperature/strain sensing, etc.
However, FNN needs to be re-trained if the generalization ability is not
satisfactory, or the frequency scanning step is changing in the experiment.
This is a cumbersome and inefficient process. In this paper, FNN only needs to
be trained once with the proposed method. 150.62 km BOTDA is built to verify
the performance of the trained FNN. Simulation and experimental results show
that the proposed method is promising in BOTDA because of its high
computational efficiency and wide adaptability.
Constrained Sampling: Optimum Reconstruction in Subspace with Minimax Regret Constraint
Date: 2019-10-17 00:00:00

This paper considers the problem of optimum reconstruction in generalized
sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel
framework that minimizes the reconstruction error for inputs in a subspace,
subject to a constraint on the maximum regret-error for any other signal in the
entire signal space. This framework addresses the primary limitation of
existing GSRPs (consistent, subspace and minimax regret), namely, the
assumption that the \emph{a priori} subspace is either fully known or fully
ignored. We formulate constrained GSRP as a constrained optimization problem,
the solution to which turns out to be a convex combination of the subspace and
the minimax regret samplings. Detailed theoretical analysis on the
reconstruction error shows that constrained sampling achieves a reconstruction
that is 1) (sub)optimal for signals in the input subspace, 2) robust for
signals around the input subspace, and 3) reasonably bounded for any other
signals with a simple choice of the constraint parameter. Experimental results
on sampling-reconstruction of a Gaussian input and a speech signal demonstrate
the effectiveness of the proposed scheme.
Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications
Date: 2018-12-19 00:00:00

A critical requirement for automated driving systems is enabling situational
awareness in dynamically changing environments. To that end vehicles will be
equipped with diverse sensors, e.g., LIDAR, cameras, mmWave radar, etc.
Unfortunately the sensing `coverage' is limited by environmental obstructions,
e.g., other vehicles, buildings, people, objects etc. A possible solution is to
adopt collaborative sensing amongst vehicles possibly assisted by
infrastructure. This paper introduces new models and performance analysis for
vehicular collaborative sensing and networking. In particular, coverage gains
are quantified, as are their dependence on the penetration of vehicles
participating in collaborative sensing. We also evaluate the associated
communication loads in terms of the Vehicle-to-Vehicle (V2V) and
Vehicle-to-Infrastructure (V2I) capacity requirements and how these depend on
penetration. We further explore how collaboration with sensing capable
infrastructure improves sensing performance, as well as the benefits in
utilizing spatio-temporal dynamics, e.g., collaborating with vehicles moving in
the opposite direction. Collaborative sensing is shown to greatly improve
sensing performance, e.g., improves coverage from 20% to 80% with a 20%
penetration. In scenarios with limited penetration and high coverage
requirements, infrastructure can be used to both help sense the environment and
relay data. Once penetration is high enough, sensing vehicles provide good
coverage and data traffic can be effectively `offloaded' to V2V connectivity,
making V2I resources available to support other in-car services.
Closed-form approximations with respect to the mixing solution for option pricing under stochastic volatility
Authors: Kaustav Das, Nicolas Langrené
Date: 2018-12-19 00:00:00
Tasks: Form

We consider closed-form approximations for European put option prices within the Heston and GARCH diffusion stochastic volatility models with time-dependent parameters. Our methodology involves writing the put option price as an expectation of a Black-Scholes formula and performing a second-order Taylor expansion around the mean of its argument. The difficulties then faced are simplifying a number of expectations induced by the Taylor expansion. Under the assumption of piecewise-constant parameters, we derive closed-form pricing formulas and devise a fast calibration scheme. Furthermore, we perform a numerical error and sensitivity analysis to investigate the quality of our approximation and show that the errors are well within the acceptable range for application purposes. Lastly, we derive bounds on the remainder term generated by the Taylor expansion.
Spreading of an infectious disease between different locations
Date: 2018-12-19 00:00:00

The endogenous adaptation of agents, that may adjust their local contact
network in response to the risk of being infected, can have the perverse effect
of increasing the overall systemic infectiveness of a disease. We study a
dynamical model over two geographically distinct but interacting locations, to
better understand theoretically the mechanism at play. Moreover, we provide
empirical motivation from the Italian National Bovine Database, for the period
2006-2013.
Machine Learning For In-Region Location Verification In Wireless Networks
Date: 2019-06-12 00:00:00
Tasks: BIG-bench Machine Learning

In-region location verification (IRLV) aims at verifying whether a user is
inside a region of interest (ROI). In wireless networks, IRLV can exploit the
features of the channel between the user and a set of trusted access points. In
practice, the channel feature statistics is not available and we resort to
machine learning (ML) solutions for IRLV. We first show that solutions based on
either neural networks (NNs) or support vector machines (SVMs) and typical loss
functions are Neyman-Pearson (N-P)-optimal at learning convergence for
sufficiently complex learning machines and large training datasets . Indeed,
for finite training, ML solutions are more accurate than the N-P test based on
estimated channel statistics. Then, as estimating channel features outside the
ROI may be difficult, we consider one-class classifiers, namely auto-encoders
NNs and one-class SVMs, which however are not equivalent to the generalized
likelihood ratio test (GLRT), typically replacing the N-P test in the one-class
problem. Numerical results support the results in realistic wireless networks,
with channel models including path-loss, shadowing, and fading.
Coexistence of MIMO Radar and FD MIMO Cellular Systems with QoS Considerations
Date: 2019-04-24 00:00:00

In this work, the feasibility of spectrum sharing between a multiple-input
multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS),
comprising of a full duplex (FD) base station (BS) serving multiple downlink
and uplink users at the same time and frequency is investigated. While a joint
transceiver design technique at the CS's BS and users is proposed to maximise
the probability of detection (PoD) of the MIMO RS, subject to constraints of
quality of service (QoS) of users and transmit power at the CS, null-space
based waveform projection is used to mitigate the interference from RS towards
CS. In particular, the proposed technique optimises the performance of PoD of
RS by maximising its lower bound, which is obtained by exploiting the
monotonically increasing relationship of PoD and its non-centrality parameter.
Numerical results show the utility of the proposed spectrum sharing framework,
but with certain trade-offs in performance corresponding to RS's transmit
power, RS's PoD, CS's residual self interference power at the FD BS and QoS of
users.
A Reinforcement Learning Hyper-Heuristic in Multi-Objective Single Point Search with Application to Structural Fault Identification
Authors: Cao Pei, Tang Jiong
Date: 2018-10-29 00:00:00

Multi-objective optimizations are frequently encountered in engineering
practices. The solution techniques and parametric selections however are
usually problem-specific. In this study we formulate a reinforcement learning
hyper-heuristic scheme, and propose four low-level heuristics which can work
coherently with the single point search algorithm MOSA/R (Multi-Objective
Simulated Annealing Algorithm based on Re-seed) towards multi-objective
optimization problems of general applications. Making use of the domination
amount, crowding distance and hypervolume calculations, the proposed
hyper-heuristic scheme can meet various optimization requirements adaptively
and autonomously. The approach developed not only exhibits improved and more
robust performance compared to AMOSA, NSGA-II and MOEA/D when applied to
benchmark test cases, but also shows promising results when applied to a
generic structural fault identification problem. The outcome of this research
can be extended to a variety of design and manufacturing optimization
applications.
Neural correlates of episodic memory in the Memento cohort
Date: 2018-12-19 00:00:00
Tasks: Retrieval

IntroductionThe free and cued selective reminding test is used to identify
memory deficits in mild cognitive impairment and demented patients. It allows
assessing three processes: encoding, storage, and recollection of verbal
episodic memory.MethodsWe investigated the neural correlates of these three
memory processes in a large cohort study. The Memento cohort enrolled 2323
outpatients presenting either with subjective cognitive decline or mild
cognitive impairment who underwent cognitive, structural MRI and, for a subset,
fluorodeoxyglucose--positron emission tomography evaluations.ResultsEncoding
was associated with a network including parietal and temporal cortices; storage
was mainly associated with entorhinal and parahippocampal regions, bilaterally;
retrieval was associated with a widespread network encompassing frontal
regions.DiscussionThe neural correlates of episodic memory processes can be
assessed in large and standardized cohorts of patients at risk for Alzheimer's
disease. Their relation to pathophysiological markers of Alzheimer's disease
remains to be studied.
Social security and labor absenteeism in a regional health service
Date: 2018-12-19 00:00:00

Background: Absenteism can generate important economic costs. Aim: To analyze
the determinants of the time off work for sick leaves granted to workers of a
regional health service. Material and Methods: Information about 2033
individuals, working at a health service, that were granted at least one sick
leave during 2012, was analyzed. Personal identification was censored. Special
emphasis was given to the type of health insurance system of the workers
(public or private). Results: Workers ascribed to the Chilean public health
insurance system (FONASA) had 11 days more off work than their counterparts
ascribed to private health insurance systems. A higher amount of time off work
was observed among older subjects and women. Conclusions: Age, gender and the
type of health insurance system influence the number of day off work due to
sick leaves.
Estimating biomass migration parameters by analyzing the spatial behavior of the fishing fleet
Date: 2018-12-19 00:00:00
Tasks: regression

In this study, a method will be developed and applied for estimating
biological migration parameters of the biomass of a fishery resource by means
of a decision analysis of the spatial behavior of the fleet. First, a model of
discrete selection is estimated, together with patch capture function. This
will allow estimating the biomass availability on each patch. In the second
regression, values of biomass are used in order to estimate a model of
biological migration between patches. This method is proven in the Chilean jack
mackerel fishery. This will allow estimating statistically significant
migration parameters, identifying migration patterns.
Factorization Machines for Data with Implicit Feedback
Authors: Loni Babak, Larson Martha, Hanjalic Alan
Date: 2018-12-19 00:00:00
Tasks: Collaborative Filtering, Learning-To-Rank

In this work, we propose FM-Pair, an adaptation of Factorization Machines
with a pairwise loss function, making them effective for datasets with implicit
feedback. The optimization model in FM-Pair is based on the BPR (Bayesian
Personalized Ranking) criterion, which is a well-established pairwise
optimization model. FM-Pair retains the advantages of FMs on generality,
expressiveness and performance and yet it can be used for datasets with
implicit feedback. We also propose how to apply FM-Pair effectively on two
collaborative filtering problems, namely, context-aware recommendation and
cross-domain collaborative filtering. By performing experiments on different
datasets with explicit or implicit feedback we empirically show that in most of
the tested datasets, FM-Pair beats state-of-the-art learning-to-rank methods
such as BPR-MF (BPR with Matrix Factorization model). We also show that FM-Pair
is significantly more effective for ranking, compared to the standard FMs
model. Moreover, we show that FM-Pair can utilize context or cross-domain
information effectively as the accuracy of recommendations would always improve
with the right auxiliary features. Finally we show that FM-Pair has a linear
time complexity and scales linearly by exploiting additional features.
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests
Date: 2019-05-30 00:00:00
Tasks: Combinatorial Optimization, Vocal Bursts Valence Prediction

Phase unwrapping is the process of recovering a continuous phase signal from
an original signal wrapped in the ($-\pi$,$\pi$] interval. It is a critical
step of coherent signal processing, with applications such as synthetic
aperture radar, acoustic imaging, magnetic resonance, X-ray crystallography,
and seismic processing. In the field of computational optics, this problem is
classically treated as a norm-minimization problem, in which one seeks to
minimize the differences between the gradients of the original wrapped signal
and those of the continuous unwrapped signal. When the L0-norm is considered,
the number of differences should be minimized, leading to a difficult
combinatorial optimization problem. We propose an approximate model for the
L0-norm phase unwrapping problem in 2D, in which the singularities of the
wrapped phase image are associated with a graph where the vertices have $-1$ or
$+1$ polarities. The objective is to find a minimum-cost balanced spanning
forest where the sum of the polarities is equal to zero in each tree. We
introduce a set of primal and dual heuristics, a branch-and-cut algorithm, and
a hybrid metaheuristic to efficiently find exact or heuristic solutions. These
approaches move us one step closer to optimal solutions for 2D L0-norm phase
unwrapping; such solutions were previously viewed, in the signal processing
literature, as highly desirable but not achievable.