pwc-archive/papers-with-abstracts huggingface dataset:
Stabilization control of networked mobile robot using past observation-based preditive filter
Date: 2020-05-13 00:00:00
Tasks: State Estimation

This paper addresses the stabilization control problem for networked mobile
robot subject to communication delay. A new state estimation filter namely past
observation-based predictive filter is developed. This filter enables the
prediction of system state from delayed measurement. The state estimator
combined with developed control laws ensures the asymptotic stability of the
networked system. Simulations with parameters extracted from a real robot
system were conducted and results confirmed the correctness as well as
applicability of proposed approach.
AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems
Date: 2020-05-13 00:00:00
Tasks: Cloud Computing

In this paper, we propose a software tool, called AMYTISS, implemented in
C++/OpenCL, for designing correct-by-construction controllers for large-scale
discrete-time stochastic systems. This tool is employed to (i) build finite
Markov decision processes (MDPs) as finite abstractions of given original
systems, and (ii) synthesize controllers for the constructed finite MDPs
satisfying bounded-time high-level properties including safety, reachability
and reach-avoid specifications. In AMYTISS, scalable parallel algorithms are
designed such that they support the parallel execution within CPUs, GPUs and
hardware accelerators (HWAs). Unlike all existing tools for stochastic systems,
AMYTISS can utilize high-performance computing (HPC) platforms and
cloud-computing services to mitigate the effects of the state-explosion
problem, which is always present in analyzing large-scale stochastic systems.
We benchmark AMYTISS against the most recent tools in the literature using
several physical case studies including robot examples, room temperature and
road traffic networks. We also apply our algorithms to a 3-dimensional
autonomous vehicle and 7-dimensional nonlinear model of a BMW 320i car by
synthesizing an autonomous parking controller.
In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus
Authors: Sakshi Piplani, Puneet Kumar Singh, David A. Winkler, Nikolai Petrovsky
Date: 2020-05-13 00:00:00

The devastating impact of the COVID-19 pandemic caused by SARS coronavirus 2 (SARS CoV 2) has raised important questions about viral origin, mechanisms of zoonotic transfer to humans, whether companion or commercial animals can act as reservoirs for infection, and why there are large variations in SARS-CoV-2 susceptibilities across animal species. Powerful in silico modelling methods can rapidly generate information on newly emerged pathogens to aid countermeasure development and predict future behaviours. Here we report an in silico structural homology modelling, protein-protein docking, and molecular dynamics simulation study of the key infection initiating interaction between the spike protein of SARS-Cov-2 and its target, angiotensin converting enzyme 2 (ACE2) from multiple species. Human ACE2 has the strongest binding interaction, significantly greater than for any species proposed as source of the virus. Binding to pangolin ACE2 was the second strongest, possibly due to the SARS-CoV-2 spike receptor binding domain (RBD) being identical to pangolin CoV spike RDB. Except for snake, pangolin and bat for which permissiveness has not been tested, all those species in the upper half of the affinity range (human, monkey, hamster, dog, ferret) have been shown to be at least moderately permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and permissiveness. Our data indicates that the earliest isolates of SARS-CoV-2 were surprisingly well adapted to human ACE2, potentially explaining its rapid transmission.
On Kalman-Bucy filters, linear quadratic control and active inference
Date: 2020-05-13 00:00:00
Tasks: Bayesian Inference

Linear Quadratic Gaussian (LQG) control is a framework first introduced in
control theory that provides an optimal solution to linear problems of
regulation in the presence of uncertainty. This framework combines Kalman-Bucy
filters for the estimation of hidden states with Linear Quadratic Regulators
for the control of their dynamics. Nowadays, LQG is also a common paradigm in
neuroscience, where it is used to characterise different approaches to
sensorimotor control based on state estimators, forward and inverse models.
According to this paradigm, perception can be seen as a process of Bayesian
inference and action as a process of optimal control. Recently, active
inference has been introduced as a process theory derived from a variational
approximation of Bayesian inference problems that describes, among others,
perception and action in terms of (variational and expected) free energy
minimisation. Active inference relies on a mathematical formalism similar to
LQG, but offers a rather different perspective on problems of sensorimotor
control in biological systems based on a process of biased perception. In this
note we compare the mathematical treatments of these two frameworks for linear
systems, focusing on their respective assumptions and highlighting their
commonalities and technical differences.
Degradation of 2-mercaptobenzothizaole in microbial electrolysis cells: intermediates, toxicity, and microbial communities
Authors: M. Isabel San-Martin, Adrian Escapaa, Raul M. Alonso, Moises Canle, Antonio Moran
Date: 2020-05-13 00:00:00

The compound 2-mercaptobenzothizaole (MBT) has been frequently detected in wastewater and surface water and is a potential threat to both aquatic organisms and human health (its mutagenic potential has been demonstrated). This study investigated the degradation routes of MBT in the anode of a microbial electrolysis cell (MEC) and the involved microbial communities. The results indicated that graphene-modified anodes promoted the presence of more enriched, developed, and specific communities compared to bare anodes. Moreover, consecutive additions of the OH substituent to the benzene ring of MBT were only detected in the reactor equipped with the graphene-treated electrode. Both phenomena, together with the application of an external voltage, may be related to the larger reduction of biotoxicity observed in the MEC equipped with graphene-modified anodes (46.2 eqtox/m3 to 27.9 eqtox/m3).
Multivariate non-Gaussian models for financial applications
Authors: Michele Leonardo Bianchi, Asmerilda Hitaj, Gian Luca Tassinari
Date: 2020-05-13 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we consider several continuous-time multivariate non-Gaussian models applied to finance and proposed in the literature in the last years. We study the models focusing on the parsimony of the number of parameters, the properties of the dependence structure, and the computational tractability. For each model we analyze the main features, we provide the characteristic function, the marginal moments up to order four, the covariances and the correlations. Thus, we describe how to calibrate them on the time-series of log-returns with a view toward practical applications and possible numerical issues. To empirically compare these models, we conduct an analysis on a five-dimensional series of stock index log-returns.
Control Barrier Functions for Sampled-Data Systems with Input Delays
Date: 2020-05-13 00:00:00

This paper considers the general problem of transitioning theoretically safe
controllers to hardware. Concretely, we explore the application of control
barrier functions (CBFs) to sampled-data systems: systems that evolve
continuously but whose control actions are computed in discrete time-steps.
While this model formulation is less commonly used than its continuous
counterpart, it more accurately models the reality of most control systems in
practice, making the safety guarantees more impactful. In this context, we
prove robust set invariance with respect to zero-order hold controllers as well
as state uncertainty, without the need to explicitly compute any control
invariant sets. It is then shown that this formulation can be exploited to
address input delays in this system, with the result being CBF constraints that
are affine in the input. The results are demonstrated in a high-fidelity
simulation of an unstable Segway robotic system in real-time.
A Smart Meter Data-driven Distribution Utility Rate Model for Networks with Prosumers
Authors: Athindra Venkatraman, Anupam Thatte, Le Xie
Date: 2020-05-13 00:00:00

Distribution grids across the world are undergoing profound changes due to advances in energy technologies. Electrification of the transportation sector and the integration of Distributed Energy Resources (DERs), such as photo-voltaic panels and energy storage devices, have gained substantial momentum, especially at the grid edge. Transformation in the technological aspects of the grid could directly conflict with existing distribution utility retail tariff structures. We propose a smart meter data-driven rate model to recover distribution network-related charges, where the implementation of these grid-edge technologies is aligned with the interest of the various stakeholders in the electricity ecosystem. The model envisions a shift from charging end-users based on their KWh volumetric consumption, towards charging them a "grid access fee" that approximates the impact of end-users' time-varying demand on their local distribution network. The proposed rate incorporates two cost metrics affecting distribution utilities (DUs), namely 'magnitude' and 'variability' of customer demand. The proposed rate can be applied to prosumers and conventional consumers without DERs.
COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
Date: 2020-05-22 00:00:00

We consider here an extended SIR model, including several features of the
recent COVID-19 outbreak: in particular the infected and recovered individuals
can either be detected (+) or undetected (-) and we also integrate an intensive
care unit (ICU) capacity. Our model enables a tractable quantitative analysis
of the optimal policy for the control of the epidemic dynamics using both
lockdown and detection intervention levers. With parametric specification based
on literature on COVID-19, we investigate the sensitivities of various
quantities on the optimal strategies, taking into account the subtle trade-off
between the sanitary and the socio-economic cost of the pandemic, together with
the limited capacity level of ICU. We identify the optimal lockdown policy as
an intervention structured in 4 successive phases: First a quick and strong
lockdown intervention to stop the exponential growth of the contagion; second a
short transition phase to reduce the prevalence of the virus; third a long
period with full ICU capacity and stable virus prevalence; finally a return to
normal social interactions with disappearance of the virus. The optimal
scenario hereby avoids the second wave of infection, provided the lockdown is
released sufficiently slowly. We also provide optimal intervention measures
with increasing ICU capacity, as well as optimization over the effort on
detection of infectious and immune individuals. Whenever massive resources are
introduced to detect infected individuals, the pressure on social distancing
can be released, whereas the impact of detection of immune individuals reveals
to be more moderate.
High fidelity epigenetic inheritance: Information theoretic model predicts $k$-threshold filling of histone modifications post replication
Authors: Nithya Ramakrishnan, Sibi Raj B Pillai, Ranjith Padinhateeri
Date: 2020-05-13 00:00:00

Beyond the genetic code, there is another layer of information encoded as chemical modifications on histone proteins positioned along the DNA. Maintaining these modifications is crucial for survival and identity of cells. How the information encoded in the histone marks gets inherited, given that only half the parental nucleosomes are transferred to each daughter chromatin, is a puzzle. We address this problem using ideas from Information theory and understanding from recent biological experiments. Mapping the replication and reconstruction of modifications to equivalent problems in communication, we ask how well an enzyme-machinery can recover information, if they were ideal computing machines. Studying a parameter regime where realistic enzymes can function, our analysis predicts that, pragmatically, enzymes may implement a threshold$-k$ filling algorithm which derives from maximum \`a posteriori probability decoding. Simulations using our method produce modification patterns similar to what is observed in recent experiments.
Immunological determinants of clinical outcomes in COVID-19: A quantitative perspective
Authors: Elizabeth Krieger, Nicole Vissichelli, Stefan Leichtle, Markos Kashioris, Roy Sabo, Don Brophy, Xiang-Yang Wang, Pamela Kimbal, Michael Neale, Myrna G. Serrano, Gregory A. Buck, Catherine Roberts, Rehan Qayyum, Daniel Nixon, Steven Grossman, Amir A. Toor
Date: 2020-05-13 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a variable clinical presentation that ranges from asymptomatic, to severe disease with cytokine storm. The mortality rates also differ across the globe, ranging from 0.5-13%. This variation is likely due to both pathogen and host factors. Host factors may include genetic differences in the immune response genes as well as variation in HLA and KIR allotypes. To better understand what impact these genetic variants in immune response genes may have in the differences observed in the immune response to SARS-CoV-2, a quantitative analysis of a dynamical systems model that considers both, the magnitude of viral growth, and the subsequent innate and adaptive response required to achieve control of infection is considered. Based on this broad quantitative framework it may be posited that the spectrum of symptomatic to severely symptomatic presentations of COVID19 represents the balance between innate and adaptive immune responses. In asymptomatic patients, prompt and adequate adaptive immune response quells infection, whereas in those with severe symptoms a slower inadequate adaptive response leads to a runaway cytokine cascade fueled by ongoing viral replication. Polymorphisms in the various components of the innate and adaptive immune response may cause altered immune response kinetics that would result in variable severity of illness. Understanding how this genetic variation may alter the response to SARS-CoV-2 infection is critical to develop successful treatment strategies.
Regional analysis of COVID-19 in France from fit of hospital data with different evolutionary models
Date: 2020-06-16 00:00:00
Tasks: Unity

The SIR evolutionary model predicts too sharp a decrease of the fractions of
people infected with COVID-19 in France after the start of the national
lockdown, compared to what is observed. I fit the daily hospital data: arrivals
in regular and critical care units, releases and deaths, using extended SEIR
models. These involve ratios of evolutionary timescales to branching fractions,
assumed uniform throughout a country, and the basic reproduction number, $R_0$,
before and during the national lockdown, for each region of France. The
joint-region Bayesian analysis allows precise evaluations of the time/fraction
ratios and pre-hospitalized fractions. The hospital data are well fit by the
models, except the arrivals in critical care, which decrease faster than
predicted, indicating better treatment over time. Averaged over France, the
analysis yields $R_0$= 3.4$\pm$0.1 before the lockdown and 0.65$\pm$0.04 (90%
c.l.) during the lockdown, with small regional variations. On 11 May 2020, the
Infection Fatality Rate in France was 4 $\pm$1% (90% c.l.), while the Feverish
vastly outnumber the Asymptomatic, contrary to the early phases. Without the
lockdown nor social distancing, over 2 million deaths from COVID-19 would have
occurred throughout France, while a lockdown that would have been enforced 10
days earlier would have led to less than 1000 deaths. The fraction of immunized
people reached a plateau below 1% throughout France (3% in Paris) by late April
2020 (95% c.l.), suggesting a lack of herd immunity. The widespread
availability of face masks on 11 May, when the lockdown was partially lifted,
should keep $R_0$ below unity if at least 46% of the population wear them
outside their home. Otherwise, without enhanced other social distancing, a
second wave is inevitable and cause the number of deaths to triple between
early May and October (if $R_0$=1.2) or even late June (if $R_0$=2).
Short-Term Investments and Indices of Risk
Authors: Yuval Heller, Amnon Schreiber
Date: 2020-05-13 00:00:00

We study various decision problems regarding short-term investments in risky assets whose returns evolve continuously in time. We show that in each problem, all risk-averse decision makers have the same (problem-dependent) ranking over short-term risky assets. Moreover, in each problem, the ranking is represented by the same risk index as in the case of CARA utility agents and normally distributed risky assets.
Infinite-Duration All-Pay Bidding Games
Authors: Guy Avni, Ismaël Jecker, Đorđe Žikelić
Date: 2020-05-12 00:00:00
Tasks: All

In a two-player zero-sum graph game the players move a token throughout a graph to produce an infinite path, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In {\em bidding games}, however, the players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token: both players simultaneously submit bids and the higher bidder moves the token. The bidding mechanisms differ in their payment schemes. Bidding games were largely studied with variants of {\em first-price} bidding in which only the higher bidder pays his bid. We focus on {\em all-pay} bidding, where both players pay their bids. Finite-duration all-pay bidding games were studied and shown to be technically more challenging than their first-price counterparts. We study for the first time, infinite-duration all-pay bidding games. Our most interesting results are for {\em mean-payoff} objectives: we portray a complete picture for games played on strongly-connected graphs. We study both pure (deterministic) and mixed (probabilistic) strategies and completely characterize the optimal sure and almost-sure (with probability $1$) payoffs that the players can respectively guarantee. We show that mean-payoff games under all-pay bidding exhibit the intriguing mathematical properties of their first-price counterparts; namely, an equivalence with {\em random-turn games} in which in each turn, the player who moves is selected according to a (biased) coin toss. The equivalences for all-pay bidding are more intricate and unexpected than for first-price bidding.
Millimeter Wave Remove UAV Control and Communications for Public Safety Scenarios
Authors: William Xia, Michele Polese, Marco Mezzavilla, Giuseppe Loianno, Sundeep Rangan, Michele Zorzi
Date: 2020-05-13 00:00:00

Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings. In such uses, millimeter wave (mmWave) wireless links can provide high throughput and low latency connectivity between the UAV and a remote command center. However, maintaining reliable aerial communication in the mmWave bands is challenging due to the need to support high speed beam tracking and overcome blockage. This paper provides a simulation study aimed at assessing the feasibility of public safety UAV connectivity through a 5G link at 28 GHz. Real flight motion traces are captured during maneuvers similar to those expected in public safety settings. The motions traces are then incorporated into a detailed mmWave network simulator that models the channel, blockage, beamforming and full 3GPP protocol stack. We show that 5G mmWave communications can deliver throughput up to 1 Gbps with consistent sub ms latency when the base station is located near the mission area, enabling remote offloading of the UAV control and perception algorithms.
Turing's Children: Representation of Sexual Minorities in STEM
Date: 2020-05-13 00:00:00

We provide the first nationally representative estimates of sexual minority
representation in STEM fields by studying 142,641 men and women in same-sex
couples from the 2009-2018 American Community Surveys. These data indicate that
men in same-sex couples are 12 percentage points less likely to have completed
a bachelor's degree in a STEM field compared to men in different-sex couples;
there is no gap observed for women in same-sex couples compared to women in
different-sex couples. The STEM gap between men in same-sex and different-sex
couples is larger than the STEM gap between white and black men but is smaller
than the gender STEM gap. We also document a gap in STEM occupations between
men in same-sex and different-sex couples, and we replicate this finding using
independently drawn data from the 2013-2018 National Health Interview Surveys.
These differences persist after controlling for demographic characteristics,
location, and fertility. Our findings further the call for interventions
designed at increasing representation of sexual minorities in STEM.
Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty
Authors: Yongchang Deng, Yuze Zou, Shimin Gong, Bin Lyu, Dinh Thai Hoang, Dusit Niyato
Date: 2020-05-14 00:00:00

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the user's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization method, we firstly propose a heuristic scheme to decompose the IRS's {phase shift} optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.
Merging 1D and 3D genomic information: Challenges in modelling and validation
Authors: Alessandra Merlotti, Angelo Rosa, Daniel Remondini
Date: 2020-05-14 00:00:00

Genome organization in eukaryotes during interphase stems from the delicate balance between non-random correlations present in the DNA polynucleotide linear sequence and the physico/chemical reactions which shape continuously the form and structure of DNA and chromatin inside the nucleus of the cell. It is now clear that these mechanisms have a key role in important processes like gene regulation, yet the detailed ways they act simultaneously and, eventually, come to influence each other even across very different length-scales remain largely unexplored. In this paper, we recapitulate some of the main results concerning gene regulatory and physical mechanisms, in relation to the information encoded in the 1D sequence and the 3D folding structure of DNA. In particular, we stress how reciprocal crossfeeding between 1D and 3D models may provide original insight into how these complex processes work and influence each other.