pwc-archive/papers-with-abstracts huggingface dataset:
Morphogen-regulated contact-mediated signaling between cells can drive the transitions underlying body segmentation in vertebrates
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Sitabhra Sinha
Date: 2020-03-27 00:00:00
Tasks: Segmentation

We propose a unified mechanism that reproduces the sequence of dynamical transitions observed during somitogenesis, the process of body segmentation during embryonic development, that is invariant across all vertebrate species. This is achieved by combining inter-cellular interactions mediated via receptor-ligand coupling with global spatial heterogeneity introduced through a morphogen gradient known to occur along the anteroposterior axis. Our model reproduces synchronized oscillations in the gene expression in cells at the anterior of the presomitic mesoderm (PSM) as it grows by adding new cells at its posterior, followed by traveling waves and subsequent arrest of activity, with the eventual appearance of somite-like patterns. This framework integrates a boundary-organized pattern formation mechanism, which uses positional information provided by a morphogen gradient, with the coupling-mediated self-organized emergence of collective dynamics, to explain the processes that lead to segmentation.
On the utilization of Macroscopic Information for String Stability of a Vehicular Platoon
Authors: Marco Mirabilio, Alessio Iovine, Elena De Santis, Maria Domenica Di Benedetto, Giordano Pola
Date: 2020-03-27 00:00:00
Tasks: Autonomous Vehicles

The use of macroscopic information for the control of a vehicular platoon composed of autonomous vehicles is investigated. A mesoscopic control law is provided, and String Stability is proved by Lyapunov functions and Input-to-State Stability (ISS) concepts. Simulations are implemented in order to validate the controller and to show the efficacy of the proposed approach for mitigating traffic oscillations.
Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach
Authors: Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler
Date: 2020-03-27 00:00:00

We consider a network of agents that locate themselves in an environment through sensor measurements and aim to transmit a message signal to a base station via collaborative beamforming. The agents' sensor measurements result in localization errors, which degrade the quality of service at the base station due to unknown phase offsets that arise in the agents' communication channels. Assuming that each agent's localization error follows a Gaussian distribution, we study the problem of forming a reliable communication link between the agents and the base station despite the localization errors. In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold. When the variances of the localization errors are below a certain threshold characterized in terms of the carrier frequency, we show that greedy algorithms can be used to globally minimize the variance of the received SNR. On the other hand, when some agents have localization errors with large variances, we show that the variance of the received SNR can be locally minimized by exploiting the supermodularity of the mean and variance of the received SNR. In numerical simulations, we demonstrate that the proposed algorithms have the potential to synthesize beamformers orders of magnitude faster than convex optimization-based approaches while achieving comparable performances using less number of agents.
Distributed and time-varying primal-dual dynamics via contraction analysis
Authors: Pedro Cisneros-Velarde, Saber Jafarpour, Francesco Bullo
Date: 2020-03-27 00:00:00
Tasks: Distributed Optimization

In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis. For the well-known standard version of the problem: we establish convergence under convexity and the contracting rate under strong convexity. Then, for a canonical distributed optimization problem, we use partial contractivity to establish global exponential convergence of its primal-dual dynamics. As an application, we propose a new distributed solver for the least-squares problem with the same convergence guarantees. Finally, for time-varying versions of both centralized and distributed primal-dual dynamics, we exploit their contractive nature to establish bounds on their tracking error. To support our analyses, we introduce novel results on contraction theory.
On the Evolution of Covid-19 in Italy: a Follow up Note
Date: 2020-03-27 00:00:00

In a previous note we made an analysis of the spreading of the COVID disease
in Italy. We used a model based on the logistic and Hubbert functions, the
analysis we exploited has shown limited usefulness in terms of predictions and
failed in fixing fundamental indications like the point of inflection of the
disease growth. In this note we elaborate on the previous model, using
multi-logistic models and attempt a more realistic analysis.
Objective Multi-variable Classification and Inference of Biological Neuronal Networks
Authors: Michael Taynnan Barros, Harun Siljak, Peter Mullen, Constantinos Papadias, Jari Hyttinen, Nicola Marchetti
Date: 2020-03-28 00:00:00
Tasks: Classification, General Classification

Classification of biological neuron types and networks poses challenges to the full understanding of the brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal types and networks based on the communication metrics of neurons. This presents advantages against the existing approaches since the mutual information or the delay between neurons obtained from spike trains are more abundant data compare to conventional morphological data. We firstly designed two open-access supporting computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then we investigate how the concept of network tomography could be achieved with cortical neuronal circuits for morphological, topological and electrical classification of neurons. We extract the simulated data to many different classifiers (including SVM, Decision Trees, Random Forest, and Artificial Neuron Networks) classifying the specific cell type (and sub-group types) achieving accuracies of up to 70\%. Inference of biological network structures using network tomography reached up to 65\% of accuracy. We also analysed recall, precision and F1score of the classification of five layers, 25 cell m-types, and 14 cell e-types. Our research not only contributes to existing classification efforts but sets the road-map for future usage of cellular-scaled brain-machine interfaces for in-vivo objective classification of neurons as a sensing mechanism of the brain's structure.
EEG Signal Classification using Variational Mode Decomposition
Date: 2020-03-28 00:00:00
Tasks: Classification, EEG, EEG Signal Classification, Electroencephalogram (EEG)

Epilepsy affects about 1% of the population every year, and is characterized
by abnormal and sudden hyper-synchronous excitation of the neurons in the
brain. The electroencephalogram(EEG) is the most widely used method to record
brain signals and diagnose epilepsy and seizure cases. In this paper we use the
method of Variational Mode Decomposition (VMD) in our analysis to classify
seizure/seizure free signals. This technique uses variational non recursive
mode decomposition, in comparison to other methods like Empirical Mode (EMD)
and Hilbert-Huang transform which recursively decompose the signals, making
them more susceptible to noise and sampling rate. VMD decomposes a signal into
its components which are called principal modes. In our analysis, 4 features of
the decomposed signals namely Renyi Entropy, second order difference plot
(SODP), fourth order difference plot(FODP) and average amplitude are
investigated, both individually and using a ranking methodology considering all
4 features at the same time. The SODP of decomposed signal modes is an
elliptical structure. The 95% confidence ellipse area measured from the SODP of
the decomposed signal modes has been used as a feature in order to discriminate
seizure-free EEG signals from the epileptic seizure EEG signal. For the
classification, a Multilayer Perceptron(MLP) with back propagation algorithm as
the training method was used. A high percentage of accuracy was obtained when
the features were used individually for classification and an even higher
degree of accuracy was obtained when the ranking methodology was used.
speckle-tracking: a Software Suite for Ptychographic X-ray Speckle Tracking
Date: 2020-03-28 00:00:00
Tasks: CPU, GPU

In recent years, x-ray speckle tracking techniques have emerged as viable
tools for wavefront metrology and sample imaging applications. These methods
are based on the measurement of near-field images. Thanks to the simple
experimental set-up, high angular sensitivity and compatibility with low
coherence sources these methods have been actively developed for use with
synchrotron and laboratory light sources. Not only do speckle-tracking
techniques give the potential for high resolution imaging, but they also
provide rapid and robust characterisation of aberrations of x-ray optical
elements, focal spot profiles and the sample position and transmission
properties. In order to realise these capabilities, we require software
implementations that are equally rapid and robust. To address this need, a
software suite has been developed for the "ptychographic x-ray speckle tracking
technique" -- an x-ray speckle based method suitable for highly divergent
wavefields. The software suite is written in Python 3, with an OpenCL back end
for GPU and multi-CPU core processing. It is accessible as a Python module,
through the command line or through a graphical user interface and is available
as source code under version 3 or later of the GNU General Public License.
Submodular Input Selection for Synchronization in Kuramoto Networks
Date: 2020-03-31 00:00:00

Synchronization is an essential property of engineered and natural networked
dynamical systems. The Kuramoto model of nonlinear synchronization has been
widely studied in applications including entrainment of clock cells in brain
networks and power system stability. Synchronization of Kuramoto networks has
been found to be challenging in the presence of signed couplings between
oscillators and when the network includes oscillators with heterogeneous
natural frequencies. In this paper, we study the problem of minimum-set control
input selection for synchronizing signed Kuramoto networks. We first derive
sufficient conditions for synchronization in homogeneous as well as
heterogeneous Kuramoto networks using a passivity-based framework. We then
develop a submodular algorithm for selecting a minimum set of control inputs
for a given Kuramoto network. We evaluate our approach through a numerical
study on multiple classes of graphs, including undirected, directed, and cycle
graphs.
A phenomenological approach to COVID-19 spread in a population
Date: 2020-03-28 00:00:00

A phenomenological model to describe the Corona Virus(covid-19) Pandemic
spread in a given population is developed. It enables the identification of the
key quantities required to form adequate policies for control and mitigation in
terms of observable parameters using the Landau-Stuart equation. It is intended
to be complementary to detailed simulations and methods published recently by
Ferguson and collaborators, March 16, (2020). The results suggest that the
initial growth/spreading rate gamma-c of the disease, and the fraction of
infected persons in the population p-i can be used to define a
`retardation/inhibition coefficient' k-star , which is a measure of the
effectiveness of the control policies adopted.
  The results are obtained analytically and numerically using a simple Python
code. The solutions provide both qualitative and quantitative information. They
substantiate and justify two basic control policies enunciated by WHO and
adopted in many countries: a) Systematic and early intensive testing
individuals for covid-19 and b) Sequestration policies such as `social/physical
distancing' and population density reduction by strict quarantining are
essential for making k-star greater than 1, necessary for suppressing the
pandemic. The model indicates that relaxing such measures when the infection
rate starts to decrease as a result of earlier policies could simply restart
the infection rate in the non-infected population. Presently available
available statistical data in WHO and other reports can be readily used to
determine the the key parameters of the model. Possible extensions to the basic
model to make it more realistic are indicated.
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction
Date: 2020-03-28 00:00:00
Tasks: Time Series, Time Series Analysis

Sometimes, it is of interest to single out the fluctuations associated to a
given frequency. We propose a new variant of SSA, Circulant SSA (CiSSA), that
allows to extract the signal associated to any frequency specified beforehand.
This is a novelty when compared with other procedures that need to identify
ex-post the frequencies associated to extracted signals. We prove that CiSSA is
asymptotically equivalent to these alternative procedures although with the
advantage of avoiding the need of the subsequent frequency identification. We
check its good performance and compare it to alternative SSA methods through
several simulations for linear and nonlinear time series. We also prove its
validity in the nonstationary case. To show how it works with real data, we
apply CiSSA to extract the business cycle and deseasonalize the Industrial
Production Index of six countries. Economists follow this indicator in order to
assess the state of the economy in real time. We find that the estimated cycles
match the dated recessions from the OECD showing its reliability for business
cycle analysis. Finally, we analyze the strong separability of the estimated
components. In particular, we check that the deseasonalized time series do not
show any evidence of residual seasonality.
SARS-CoV-2 (COVID-19) by the numbers
Authors: Yinon M. Bar-On, Avi I. Flamholz, Rob Phillips, Ron Milo
Date: 2020-03-28 00:00:00

The current SARS-CoV-2 pandemic is a harsh reminder of the fact that, whether in a single human host or a wave of infection across continents, viral dynamics is often a story about the numbers. In this snapshot, our aim is to provide a one-stop, curated graphical source for the key numbers that help us understand the virus driving our current global crisis. The discussion is framed around two broad themes: 1) the biology of the virus itself and 2) the characteristics of the infection of a single human host. Our one-page summary provides the key numbers pertaining to SARS-CoV-2, based mostly on peer-reviewed literature. The numbers reported in summary format are substantiated by the annotated references below. Readers are urged to remember that much uncertainty remains and knowledge of this pandemic and the virus driving it is rapidly evolving. In the paragraphs below we provide "back of the envelope" calculations that exemplify the insights that can be gained from knowing some key numbers and using quantitative logic. These calculations serve to improve our intuition through sanity checks, but do not replace detailed epidemiological analysis.
tACS Facilitates Flickering Driving by Boosting Steady-State Visual Evoked Potentials
Date: 2020-03-29 00:00:00
Tasks: SSVEP

There has become of increasing interest in transcranial alternating current
stimulation (tACS) since its inception nearly a decade ago. tACS in modulating
brain state is an active area of research and has been demonstrated effective
in various neuropsychological and clinical domains. In the visual domain, much
effort has been dedicated to brain rhythms and rhythmic stimulation, i.e.,
tACS. However, little is known about the interplay between the rhythmic
stimulation and visual stimulation. Here, we used steady-state visual evoked
potential (SSVEP), induced by flickering driving as a widely used technique for
frequency-tagging, to investigate the aftereffect of tACS in healthy human
subjects. Seven blocks of 64-channel electroencephalogram were recorded before
and after the administration of 20-min 10-Hz tACS, while subjects performed
several blocks of SSVEP tasks. We characterized the physiological properties of
tACS aftereffect by comparing and validating the temporal, spatial,
spatiotemporal and signal-to-noise ratio (SNR) patterns between and within
blocks in real tACS and sham tACS. Our result revealed that tACS boosted the
10-Hz SSVEP significantly. Besides, the aftereffect on SSVEP was mitigated with
time and lasted up to 5 min. Our results demonstrate the feasibility of
facilitating the flickering driving by external rhythmic stimulation and open a
new possibility to alter the brain state in a direction by noninvasive
transcranial brain stimulation.
The C-SHIFT algorithm for normalizing covariances
Authors: Evgenia Chunikhina, Paul Logan, Yevgeniy Kovchegov, Anatoly Yambartsev, Debashis Mondal, Andrey Morgun
Date: 2020-03-29 00:00:00
Tasks: Philosophy

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.
Secure Platooning of Autonomous Vehicles Under Attacked GPS Data
Date: 2020-03-29 00:00:00
Tasks: Autonomous Vehicles

In this paper, we study how to secure the platooning of autonomous vehicles
when an unknown vehicle is under attack and bounded system uncertainties exist.
For the attacked vehicle, its position and speed measurements from GPS can be
manipulated arbitrarily by a malicious attacker. First, to find out which
vehicle is under attack, two detectors are proposed by using the relative
measurements (by camera or radar) and the local innovation obtained through
measurements from neighboring vehicles. Then, based on the results of the
detectors, we design a local state observer for each vehicle by applying a
saturation method to the measurement innovation. Moreover, based on the
neighbor state estimates provided by the observer, a distributed controller is
proposed to achieve the consensus in vehicle speed and keep fixed desired
distance between two neighboring vehicles. The estimation error by the observer
and the platooning error by the controller are shown to be asymptotically upper
bounded under certain conditions. The effectiveness of the proposed methods is
also evaluated in numerical simulations.
Divergence Conditions for Investigation and Control of Nonautonomous Dynamical Systems
Date: 2020-03-29 00:00:00

The paper describes a novel method for studying the stability of
nonautonomous dynamical systems. This method based on the flow and divergence
of the vector field with coupling to the method of Lyapunov functions. The
necessary and sufficient stability conditions are formulated. It is shown that
the necessary stability conditions are related to the integral and differential
forms of continuity equations with the sources (the flux is directed inward)
located in the equilibrium points of the dynamical system. The sufficient
stability conditions are applied to design the state feedback control laws. The
proposed control law is found as a solution of partial differential inequality,
whereas the control law based on Lyapunov technique is found from the solution
of algebraic inequality. The examples illustrate the effectiveness of the
proposed method compared with some existing ones.
Mechanical classification of voice quality
Date: 2020-03-29 00:00:00
Tasks: Classification

While there is no a priori definition of good singing voices, we tend to make
consistent evaluations of the quality of singing almost instantaneously. Such
an instantaneous evaluation might be based on the sound spectrum that can be
perceived in a short time. Here we devise a Bayesian algorithm that learns to
evaluate the choral proficiency, musical scale, and gender of individual
singers using the sound spectra of singing voices. In particular, the
classification is performed on a set of sound spectral intensities, whose
frequencies are selected by minimizing the Bayes risk. This optimization allows
the algorithm to capture sound frequencies that are essential for each
discrimination task, resulting in a good assessment performance. Experimental
results revealed that a sound duration of about 0.1 sec is sufficient for
determining the choral proficiency and gender of a singer. With a program
constructed on this algorithm, everyone can evaluate choral voices of others
and perform private vocal exercises.
Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data
Authors: Vladimír Holý, Petra Tomanová
Date: 2020-03-29 00:00:00

We investigate the computational issues related to the memory size in the estimation of quadratic covariation, taking into account the specifics of financial ultra-high-frequency data. In multivariate price processes, we consider both contamination by the market microstructure noise and the non-synchronicity of the observations. We formulate a multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate this estimation as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without this constraint.
SOS: Online probability estimation and generation of T and B cell receptors
Authors: Giulio Isacchini, Carlos Olivares, Armita Nourmohammad, Aleksandra M. Walczak, Thierry Mora
Date: 2020-03-29 00:00:00

Recent advances in modelling VDJ recombination and subsequent selection of T and B cell receptors provide useful tools to analyze and compare immune repertoires across time, individuals, and tissues. A suite of tools--IGoR [1], OLGA [2] and SONIA [3]--have been publicly released to the community that allow for the inference of generative and selection models from high-throughput sequencing data. However using these tools requires some scripting or command-line skills and familiarity with complex datasets. As a result the application of the above models has not been available to a broad audience. In this application note we fill this gap by presenting Simple OLGA & SONIA (SOS), a web-based interface where users with no coding skills can compute the generation and post-selection probabilities of their sequences, as well as generate batches of synthetic sequences. The application also functions on mobile phones.