pwc-archive/papers-with-abstracts huggingface dataset:
Computing extracellular electric potentials from neuronal simulations
Authors: Torbjørn V. Ness, Geir Halnes, Solveig Næss, Klas H. Pettersen, Gaute T. Einevoll
Date: 2020-06-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Measurements of electric potentials from neural activity have played a key role in neuroscience for almost a century, and simulations of neural activity is an important tool for understanding such measurements. Volume conductor (VC) theory is used to compute extracellular electric potentials such as extracellular spikes, MUA, LFP, ECoG and EEG surrounding neurons, and also inversely, to reconstruct neuronal current source distributions from recorded potentials through current source density methods. In this book chapter, we show how VC theory can be derived from a detailed electrodiffusive theory for ion concentration dynamics in the extracellular medium, and show what assumptions that must be introduced to get the VC theory on the simplified form that is commonly used by neuroscientists. Furthermore, we provide examples of how the theory is applied to compute spikes, LFP signals and EEG signals generated by neurons and neuronal populations.
Verification of Initial-State Opacity for Switched Systems: A Compositional Approach
Date: 2020-06-30 00:00:00

The security in information-flow has become a major concern for
cyber-physical systems (CPSs). In this work, we focus on the analysis of an
information-flow security property, called opacity. Opacity characterizes the
plausible deniability of a system's secret in the presence of a malicious
outside intruder. We propose a methodology of checking a notion of opacity,
called approximate initial-state opacity, for networks of discrete-time
switched systems. Our framework relies on compositional constructions of finite
abstractions for networks of switched systems and their so-called approximate
initial-state opacity-preserving simulation functions (InitSOPSFs). Those
functions characterize how close concrete networks and their finite
abstractions are in terms of the satisfaction of approximate initial-state
opacity. We show that such InitSOPSFs can be obtained compositionally by
assuming some small-gain type conditions and composing so-called local
InitSOPSFs constructed for each subsystem separately. Additionally, assuming
certain stability property of switched systems, we also provide a technique on
constructing their finite abstractions together with the corresponding local
InitSOPSFs. Finally, we illustrate the effectiveness of our results through an
example.
Experimental visually-guided investigation of sub-structures in three-dimensional Turing-like patterns
Date: 2020-06-30 00:00:00

In his 1952 paper "The chemical basis of morphogenesis", Alan M. Turing
presented a model for the formation of skin patterns. While it took several
decades, the model has been validated by finding corresponding natural
phenomena, e.g. in the skin pattern formation of zebrafish. More surprising,
seemingly unrelated pattern formations can also be studied via the model, like
e.g. the formation of plant patches around termite hills. In 1984, David A.
Young proposed a discretization of Turing's model, reducing it to an
activator/inhibitor process on a discrete domain. From this model, the concept
of three-dimensional Turing-like patterns was derived.
  In this paper, we consider this generalization to pattern-formation in
three-dimensional space. We are particularly interested in classifying the
different arising sub-structures of the patterns. By providing examples for the
different structures, we prove a conjecture regarding these structures within
the setup of three-dimensional Turing-like pattern. Furthermore, we investigate
- guided by visual experiments - how these sub-structures are distributed in
the parameter space of the discrete model. We found two-fold versions of zero-
and one-dimensional sub-structures as well as two-dimensional sub-structures
and use our experimental findings to formulate several conjectures for
three-dimensional Turing-like patterns and higher-dimensional cases.
Expectation and Price in Incomplete Markets
Authors: Paul McCloud
Date: 2020-06-30 00:00:00

Risk-neutral pricing dictates that the discounted derivative price is a martingale in a measure equivalent to the economic measure. The residual ambiguity for incomplete markets is here resolved by minimising the entropy of the price measure from the economic measure, subject to mark-to-market constraints, following arguments based on the optimisation of portfolio risk. The approach accounts for market and funding convexities and incorporates available price information, interpolating between methodologies based on expectation and replication.
Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors
Authors: Zonglong Bai, Liming Shi, Jinwei Sun, Mads Græsbøll Christensen
Date: 2020-06-30 00:00:00

Due to its self-regularizing nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real-value signal model, with the complex-value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning (SBL) framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state-of-the-art methods.
Deep Learning Based Load Balancing for improved QoS towards 6G
Authors: Vishnu Vardhan Nimmalapudi, Ajith Kumar Mengani, Roopa Vuppula, Rahul Jashvantbhai Pandya
Date: 2020-06-30 00:00:00
Tasks: Deep Learning

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future 6G intelligent wireless networks. Load balancing presents an efficient, cost-effective method to improve the data process capability, throughput, and expand the bandwidth, thus enhancing the adaptability and availability of networks. Hence a load balancing algorithm based on Long Short Term Memory(LSTM) deep neural network is proposed through which the coverage area of base station changes according to geographic traffic distribution, catering the requirement for future generation 6G heterogeneous network. The LSTM model performance is evaluated by considering three different scenarios, and the results were presented. Load variance coefficient(LVC) and load factor(LF) are introduced and validated over two wireless network layouts(WNL) to study the Quality of Service(QoS) and load distribution. The proposed method shows a decrease of LVC by 98.311% and 99.21% for WNL1, WNL2 respectively.
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
Authors: Hong-Li Zeng, Eugenio Mauri, Vito Dichio, Simona Cocco, Remi Monasson, Erik Aurell
Date: 2020-06-30 00:00:00
Tasks: valid

We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness). We further consider the problem of inferring fitness in the evolutionary dynamics from one or several snap-shots of the distribution of genotypes in the population. In the recent literature this has been done by applying the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura in the limit of high recombination. Here we show that the approach also works in the interesting regime where the effects of mutations are comparable to or larger than recombination. This leads to a modified main epistatic fitness inference formula where the rates of mutation and recombination occur together. We also derive this formula using by a previously developed Gaussian closure that formally remains valid when recombination is absent. The findings are validated through numerical simulations.
Siting thousands of radio transmitter towers on terrains with billions of points
Authors: W. Randolph Franklin, Salles Viana Gomes de Magalhães, Wenli Li
Date: 2020-06-27 00:00:00
Tasks: CPU

This paper presents a system that sites (finds optimal locations for) thousands of radio transmitter towers on terrains of up to two billion elevation posts. Applications include cellphone towers, camera systems, or even mitigating environmental visual nuisances. The transmitters and receivers may be situated above the terrain. The system has been parallelized with OpenMP to run on a multicore CPU.
Optimal surface topography for cell adhesion is driven by cell membrane mechanics
Authors: Matej Daniel, Kristina Eleršič Filipič, Eva Filová, Jaroslav Fojt
Date: 2020-06-30 00:00:00

Titanium surface treated with titanium oxide nanotubes was used in many studies to quantify the effect of surface topography on cell fate. However, the predicted optimal diameter of nanotubes considerably differs among studies. We propose a model that explain cell adhesion to nanostructured surface by considering deformation energy of cell protrusions into titanium nanotubes and adhesion to surface. The optimal surface topology is defined as a geometry that gives membrane a minimum energy shape. A dimensionless parameter, the cell interaction index, was proposed to describe interplay between the cell membrane bending, intrinsic curvature and strength of cell adhesion. Model simulation show that optimal nanotube diameter ranging from 20 nm to 100 nm (cell interaction index between 0.2 and 1, respectively) is feasible within certain range of parameters describing adhesion and bending energy. The results indicates a possibility to tune the topology of nanostructural surface in order to enhance proliferation and differentation of cells mechanically compatible with given surface geometry while suppress the growth of other mechanically incompatible cells.
Application of statistical analysis to working memory problem
Authors: A. V. Erofeeva, T. V. Zakharova
Date: 2020-06-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This article is devoted to EEG studying of connectivity cortical areas involved in keeping vision information in working memory. VAR-modeling was used for describing signals got from connected with working memory brain zones. Brain connections were estimated by based in Granger Causality Partial Directed Coherence (PDC) and then compared by Wilcoxon signed-rank test. In paper connection intensity dependence on executing task was found.
User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks
Authors: Salman Khalid, Waqas bin Abbas, Farhan Khalid, Michele Zorzi
Date: 2020-06-30 00:00:00

A hybrid architecture for millimeter wave (mmW) massive MIMO systems is considered practically implementable due to low power consumption and high energy efficiency. However, due to the limited number of RF chains, user selection becomes necessary for such architecture. Traditional user selection algorithms suffer from high computational complexity and, therefore, may not be scalable in 5G and beyond wireless mobile communications. To address this issue, in this letter we propose a low complexity CNN framework for user selection. The proposed CNN accepts as input the channel matrix and gives as output the selected users. Simulation results show that the proposed CNN performs close to optimal exhaustive search in terms of achievable rate, with negligible computational complexity. In addition, CNN based user selection outperforms the evolutionary algorithm and the greedy algorithm in terms of both achievable rate and computational complexity. Finally, simulation results also show that the proposed CNN based user selection scheme is robust to channel imperfections.
Primary Control Effort under Fluctuating Power Generation in Realistic High-Voltage Power Networks
Authors: Melvyn Tyloo, Philippe Jacquod
Date: 2020-06-30 00:00:00

Many recent works in control of electric power systems have investigated their synchronization through global performance metrics under external disturbances. The approach is motivated by fundamental changes in the operation of power grids, in particular by the substitution of conventional power plants with new renewable sources of electrical energy. This substitution will simultaneously increase fluctuations in power generation and reduce the available mechanical inertia. It is crucial to understand how strongly these two evolutions will impact grid stability. With very few, mostly numerical exceptions, earlier works on performance metrics had to rely on unrealistic assumptions of grid homogeneity. Here we show that a modified spectral decomposition can tackle that issue in inhomogeneous power grids in cases where disturbances occur on time scales that are long compared to the intrinsic time scales of the grid. We find in particular that the magnitude of the transient excursion generated by disturbances with long characteristic times does not depend on inertia. For continental-size, high-voltage power grids, this corresponds to power fluctuations that are correlated on time scales of few seconds or more. We conclude that power fluctuations arising from new renewables will not require per se the deployment of additional rotational inertia. We numerically illustrate our results on the IEEE 118-Bus test case and a model of the synchronous grid of continental Europe.
Using A Partial Differential Equation with Google Mobility Data to Predict COVID-19 in Arizona
Date: 2020-07-26 00:00:00

The outbreak of COVID-19 disrupts the life of many people in the world. The
state of Arizona in the U.S. emerges as one of the country's newest COVID-19
hot spots. Accurate forecasting for COVID-19 cases will help governments to
implement necessary measures and convince more people to take personal
precautions to combat the virus. It is difficult to accurately predict the
COVID-19 cases due to many human factors involved. This paper aims to provide a
forecasting model for COVID-19 cases with the help of human activity data from
the Google Community Mobility Reports. To achieve this goal, a specific partial
differential equation (PDE) is developed and validated with the COVID-19 data
from the New York Times at the county level in the state of Arizona in the U.S.
The proposed model describes the combined effects of transboundary spread among
county clusters in Arizona and human actives on the transmission of COVID-19.
The results show that the prediction accuracy of this model is well acceptable
(above 94\%). Furthermore, we study the effectiveness of personal precautions
such as wearing face masks and practicing social distancing on COVID-19 cases
at the local level. The localized analytical results can be used to help to
slow the spread of COVID-19 in Arizona. To the best of our knowledge, this work
is the first attempt to apply PDE models on COVID-19 prediction with the Google
Community Mobility Reports.
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
Authors: Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich, David Brie, José Carlos Moreira Bermudez, Cédric Richard
Date: 2020-06-30 00:00:00
Tasks: Tensor Decomposition

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previously proposed assume that the different observed images are acquired under exactly the same conditions. A recent work proposed to accommodate inter-image spectral variability in the image fusion problem using a matrix factorization-based formulation, but did not account for spatially-localized variations. Moreover, it lacks theoretical guarantees and has a high associated computational complexity. In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model. We first study how the general identifiability of the model is impacted by the presence of such changes. Then, assuming that the high-resolution image and the variation factors admit a Tucker decomposition, two new algorithms are proposed -- one purely algebraic, and another based on an optimization procedure. Theoretical guarantees for the exact recovery of the high-resolution image are provided for both algorithms. Experimental results show that the proposed method outperforms state-of-the-art methods in the presence of spectral and spatial variations between the images, at a smaller computational cost.
Conscious Intelligence Requires Lifelong Autonomous Programming For General Purposes
Date: 2020-06-30 00:00:00
Tasks: Natural Language Understanding

Universal Turing Machines [29, 10, 18] are well known in computer science but
they are about manual programming for general purposes. Although human children
perform conscious learning (i.e., learning while being conscious) from infancy
[24, 23, 14, 4], it is unknown that Universal Turing Machiness can facilitate
not only our understanding of Autonomous Programming For General Purposes
(APFGP) by machines, but also enable early-age conscious learning. This work
reports a new kind of AI---conscious learning AI from a machine's "baby" time.
Instead of arguing what static tasks a conscious machine should be able to do
during its "adulthood", this work suggests that APFGP is a computationally
clearer and necessary criterion for us to judge whether a machine is capable of
conscious learning so that it can autonomously acquire skills along its "career
path". The results here report new concepts and experimental studies for early
vision, audition, natural language understanding, and emotion, with conscious
learning capabilities that are absent from traditional AI systems.
Bringing Anatomical Information into Neuronal Network Models
Date: 2020-08-11 00:00:00
Tasks: Missing Values, Navigate

For constructing neuronal network models computational neuroscientists have
access to wide-ranging anatomical data that nevertheless tend to cover only a
fraction of the parameters to be determined. Finding and interpreting the most
relevant data, estimating missing values, and combining the data and estimates
from various sources into a coherent whole is a daunting task. With this
chapter we aim to provide guidance to modelers by describing the main types of
anatomical data that may be useful for informing neuronal network models. We
further discuss aspects of the underlying experimental techniques relevant to
the interpretation of the data, list particularly comprehensive data sets, and
describe methods for filling in the gaps in the experimental data. Such methods
of `predictive connectomics' estimate connectivity where the data are lacking
based on statistical relationships with known quantities. It is instructive,
and in certain cases necessary, to use organizational principles that link the
plethora of data within a unifying framework where regularities of brain
structure can be exploited to inform computational models. In addition, we
touch upon the most prominent features of brain organization that are likely to
influence predicted neuronal network dynamics, with a focus on the mammalian
cerebral cortex. Given the still existing need for modelers to navigate a
complex data landscape full of holes and stumbling blocks, it is vital that the
field of neuroanatomy is moving toward increasingly systematic data collection,
representation, and publication.
Coverage Analysis and Scaling Laws of Ultra-Dense Networks
Authors: Imene Trigui, Sofiene Affes, Marco Di Renzo, Dushantha Nalin K. Jayakody
Date: 2020-06-30 00:00:00

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments. The proposed framework has the potential of significantly simplifying the cumbersome procedure of analyzing the coverage probability and allowing the remarkable unification of single- and multi-antenna networks through compact representations. By harnessing this key feature, we develop a novel statistical machinery to study the scaling laws of wireless network densification considering general channel power distributions including the entire space of multipath and shadowing models as well as associated beamforming gain due to the use of multiple antenna. We further formulate the relationship between network density, antenna height, antenna array seize and carrier frequency showing how the coverage probability can be maintained with ultra-densification. From a system design perspective, we present a new innovative theoretical discovery stipulating that if multiple antenna BS are deployed and moved to higher frequencies, then monotonically increasing the coverage probability by means of ultra-densification is possible, and this without lowering the antenna height. Such findings are completely different from the conclusions in existing works, who suggest to lower the BS height as to leverage the potential of network densification. Simulation results substantiate performance trends leveraging network densification and antenna deployment and configuration against path loss models and signal-to-noise plus interference (SINR) thresholds.
Functional MRI applications for psychiatric disease subtyping: a review
Date: 2020-06-30 00:00:00
Tasks: Active Learning, Diagnostic, Prognosis

Psychiatric disorders have historically been classified using symptom
information alone. With the advent of new technologies that allowed researchers
to investigate brain mechanisms more directly, interest in the mechanistic
rationale behind defined pathologies and aetiology redefinition has greatly
increased. This is particularly appealing for the field of personalised
medicine, which searches for data-driven approaches to improve individual
diagnosis, prognosis and treatment selection. Here we intend to systematically
analyse the usage of functional MRI on both the elucidation of psychiatric
disease biotypes and the interpretation of subtypes obtained via unsupervised
learning applied to symptom or biomarker data. We searched the existing
literature for functional MRI applications to the obtention or interpretation
of psychiatric disease subtypes. The PRISMA guidelines were applied to filter
the retrieved studies, and the active learning framework ASReviews was applied
for article prioritization. From the 20 studies that met the inclusion
criteria, 5 used functional MRI data to interpret symptom-derived disease
clusters, 4 used it for the interpretation of clusters derived from biomarker
data other than fMRI itself, and 11 applied clustering to fMRI directly. Major
depression disorder and schizophrenia were the two most studied pathologies,
followed by ADHD, psychosis, autism disorder, and early violence. No
trans-diagnostic studies were retrieved. While interest in personalised
medicine and data-driven disease subtyping is on the rise and psychiatry is not
the exception, unsupervised analyses of functional MRI data are inconsistent to
date, and much remains to be done in terms of gathering and centralising data,
standardising pipelines and model validation, and method refinement. The usage
of fMRI in the field of trans-diagnostic psychiatry remains vastly unexplored.