pwc-archive/papers-with-abstracts huggingface dataset:
Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by Random Circuit Perturbation
Authors: Bin Huang, Mingyang Lu, Madeline Galbraith, Herbert Levine, Jose N. Onuchic, Dongya Jia
Date: 2020-03-31 00:00:00
Tasks: Decision Making

Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, Random Circuit Perturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified fifteen robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.
Resilient Control of Dynamic Flow Networks Subject to Stochastic Cyber-Physical Disruptions
Authors: Yu Tang, Li Jin
Date: 2020-03-31 00:00:00

Modern network systems, such as transportation and communication systems, are prone to cyber-physical disruptions and thus suffer efficiency loss. This paper studies network resiliency, in terms of throughput, and develops resilient control to improve throughput. We consider single-commodity networks that admit congestion propagation. We also apply a Markov process to model disruption switches. For throughput analysis, we first use insights into congestion spillback to propose novel Lyapunov functions and then exploit monotone network dynamics to reduce computational costs of verifying stability conditions. For control design, we show that (i) for a network with infinite link storage space, there exists an open-loop control that attains the min-expected-cut capacity; (ii) for a network with observable disruptions that restrict maximum sending and/or receiving flows, there exists a mode-dependent control that attains the expected-min-cut capacity; (iii) for general networks, there exists a closed-loop control with throughput guarantees. We also derive lower bounds of resiliency scores for a set of numerical examples and verify resiliency improvement with our method.
Molecular docking studies on Jensenone from eucalyptus essential oil as a potential inhibitor of COVID 19 corona virus infection
Date: 2020-04-17 00:00:00
Tasks: Drug Design, Molecular Docking

COVID-19, a member of corona virus family is spreading its tentacles across
the world due to lack of drugs at present. However, the main viral proteinase
(Mpro/3CLpro) has recently been regarded as a suitable target for drug design
against SARS infection due to its vital role in polyproteins processing
necessary for coronavirus reproduction. The present in silico study was
designed to evaluate the effect of Jensenone, a essential oil component from
eucalyptus oil, on Mpro by docking study. In the present study, molecular
docking studies were conducted by using 1-click dock and swiss dock tools.
Protein interaction mode was calculated by Protein Interactions Calculator.The
calculated parameters such as binding energy, and binding site similarity
indicated effective binding of Jensenone to COVID-19 proteinase. Active site
prediction further validated the role of active site residues in ligand
binding. PIC results indicated that, Mpro/ Jensenone complexes forms
hydrophobic interactions, hydrogen bond interactions and strong ionic
interactions. Therefore, Jensenone may represent potential treatment potential
to act as COVID-19 Mpro inhibitor. However, further research is necessary to
investigate their potential medicinal use.
Stationarity of Time-Series on Graph via Bivariate Translation Invariance
Authors: Amin Jalili, Chong-Yung Chi
Date: 2020-04-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis, Translation

Stationarity is a cornerstone in classical signal processing (CSP) for modeling and characterizing various stochastic signals for the ensuing analysis. However, in many complex real world scenarios, where the stochastic process lies over an irregular graph structure, CSP discards the underlying structure in analyzing such structured data. Then it is essential to establish a new framework to analyze the high-dimensional graph structured stochastic signals by taking the underlying structure into account. To this end, looking through the lens of operator theory, we first propose a new bivariate isometric joint translation operator (JTO) consistent with the structural characteristic of translation operators in other signal domains. Moreover, we characterize time-vertex filtering based on the proposed JTO. Thereupon, we put forth a new definition of joint wide-sense stationary (JWSS) signals in time-vertex domain using the proposed isometric JTO together with its spectral characterization. Then a new joint power spectral density (JPSD) estimator, called generalized Welch method (GWM), is presented. Simulation results are provided to show the efficacy of this JPSD estimator. Furthermore, to show the usefulness of JWSS modeling, we focus on the classification of time-series on graph. To that end, by modeling the brain Electroencephalography (EEG) signals as JWSS processes, we use JPSD as the feature for the Emotion and Alzheimer's disease (AD) recognition. Experimental results demonstrate that JPSD yields superior Emotion and AD recognition accuracy in comparison with the classical power spectral density (PSD) and graph PSD (GPSD) as the feature set for both applications. Eventually, we provide some concluding remarks.
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals
Date: 2020-03-31 00:00:00
Tasks: Time Series, Time Series Analysis

Motivated by analyzing complicated time series, nonlinear-type time-frequency
analysis became an active research topic in the past decades. Those developed
tools have been applied to various problems. In this article, we review those
developed tools and summarize their applications to high-frequency biomedical
signals.
Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
Date: 2020-03-29 00:00:00

The fifth generation and beyond wireless communication will support vastly
heterogeneous services and use demands such as massive connection, low latency
and high transmission rate. Network slicing has been envisaged as an efficient
technology to meet these diverse demands. In this paper, we propose a dynamic
virtual resources allocation scheme based on the radio access network (RAN)
slicing for uplink communications to ensure the quality-of-service (QoS). To
maximum the weighted-sum transmission rate performance under delay constraint,
formulate a joint optimization problem of subchannel allocation and power
control as an infinite-horizon average-reward constrained Markov decision
process (CMDP) problem. Based on the equivalent Bellman equation, the optimal
control policy is first derived by the value iteration algorithm. However, the
optimal policy suffers from the widely known curse-of-dimensionality problem.
To address this problem, the linear value function approximation (approximate
dynamic programming) is adopted. Then, the subchannel allocation Q-factor is
decomposed into the per-slice Q-factor. Furthermore, the Q-factor and
Lagrangian multipliers are updated by the use of an online stochastic learning
algorithm. Finally, simulation results reveal that the proposed algorithm can
meet the delay requirements and improve the user transmission rate compared
with baseline schemes.
Non-Stationary Power System Forced Oscillation Analysis using Synchrosqueezing Transform
Authors: Pablo Gill Estevez, Pablo Marchi, Cecilia Galarza, Marcelo Elizondo
Date: 2020-04-01 00:00:00
Tasks: Retrieval

Non-stationary forced oscillations (FOs) have been observed in power system operations. However, most detection methods assume that the frequency of FOs is stationary. In this paper, we present a methodology for the analysis of non-stationary FOs. Firstly, Fourier synchrosqueezing transform (FSST) is used to provide a concentrated time-frequency representation of the signals that allows identification and retrieval of non-stationary signal components. To continue, the Dissipating Energy Flow (DEF) method is applied to the extracted components to locate the source of forced oscillations. The methodology is tested using simulated as well as real PMU data. The results show that the proposed FSST-based signal decomposition provides a systematic framework for the application of DEF Method to non-stationary FOs.
A Note on the Provision of a Public Service of Different Qualities
Authors: Monica Anna Giovanniello, Simone Tonin
Date: 2020-03-31 00:00:00

We study how the quality dimension affects the social optimum in a model of spatial differentiation where two facilities provide a public service. If quality enters linearly in the individuals' utility function, a symmetric configuration, in which both facilities have the same quality and serve groups of individuals of the same size, does not maximize the social welfare. This is a surprising result as all individuals are symmetrically identical having the same quality valuation. We also show that a symmetric configuration of facilities may maximize the social welfare if the individuals' marginal utility of quality is decreasing.
On finite population games of optimal trading
Authors: David Evangelista, Yuri Thamsten
Date: 2020-04-02 00:00:00

We investigate stochastic differential games of optimal trading comprising a finite population. There are market frictions in the present framework, which take the form of stochastic permanent and temporary price impacts. Moreover, information is asymmetric among the traders, with mild assumptions. For constant market parameters, we provide specialized results. Each player selects her parameters based not only on her informational level but also on her particular preferences. The first part of the work is where we examine the unconstrained problem, in which traders do not necessarily have to reach the end of the horizon with vanishing inventory. In the sequel, we proceed to analyze the constrained situation as an asymptotic limit of the previous one. We prove the existence and uniqueness of a Nash equilibrium in both frameworks, alongside a characterization, under suitable assumptions. We conclude the paper by presenting an extension of the basic model to a hierarchical market, for which we establish the existence, uniqueness, and characterization of a Stackelberg-Nash equilibrium.
Status hierarchy and group cooperation: A generalized model
Date: 2020-08-17 00:00:00

In a refreshing mathematical investigation, Mark (2018) shows that status
hierarchy may facilitate the emergence of cooperation in groups. Despite the
contribution, the present paper notes that there are limitations in Mark's
model that makes it less realistic than it could in explaining real-world
experiences. Consequently, we present a more generalized modified framework in
which his model is a special case, by developing and introducing a new
hierarchy measure into the model to estimate the cooperation level in a set of
hierarchical structures omitted in Mark's work yet common in everyday
life--those with multiple leaders. We derived the conditions under which
cooperation can emerge in these groups, and verified our analytical predictions
in agent-based computer simulations. In so doing, not only does our model
elaborate on its predecessor and support Mark's general prediction. For theory,
our work further reveals two novel phenomena of group cooperation: Both the
relative number of cooperators to defectors in groups and the assortativity
among these different roles can backfire; they are not always the higher, the
better for cooperation to thrive. For methodology, the hierarchy measure
developed and our model using the measure may also be applied in future
research on a wide range of related topics.
A Bayesian approach to recover the theoretical temperature-dependent hatch date distribution from biased samples: the case of the common dolphinfish (Coryphaena hippurus)
Authors: Vicenç Moltó, Andres Ospina-Alvarez, Mark Gatt, Miquel Palmer, Ignacio A. Catalán
Date: 2020-03-30 00:00:00
Tasks: Management

Reproductive phenology, growth and mortality rates are key ecological parameters that determine population dynamics and are therefore of vital importance to stock assessment models for fisheries management. In many fish species, the spawning phenology is sensitive to environmental factors that modulate or trigger the spawning event, which differ between regions and seasons. In addition, climate change may also alter patterns of reproductive phenology at the community level. Usually, hatch-date distributions are determined back-calculating the age estimated on calcified structures from the capture date. However, these estimated distributions could be biased due to mortality processes or time spaced samplings derived from fishery. Here, we present a Bayesian approach that functions as a predictive model for the hatching date of individuals from a fishery-dependent sampling with temporal biases. We show that the shape and shift of the observed distribution is corrected. This model can be applied in fisheries with multiple cohorts, for species with a wide geographical distribution and living under contrasting environmental regimes and individuals with different life histories such as thermo-dependent growth, length-dependent mortality rates, etc.
On the Feedback Law in Stochastic Optimal Nonlinear Control
Authors: Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal, Ran Wang
Date: 2020-04-01 00:00:00
Tasks: Model Predictive Control

We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman's "curse of dimensionality". We present a result that shows that repeatedly solving an open-loop deterministic problem from the current state with progressively shorter horizons, similar to Model Predictive Control (MPC), results in a feedback policy that is $O(\epsilon^4)$ near to the true global stochastic optimal policy, where $\epsilon$ is a perturbation parameter modulating the noise. We also show that the optimal deterministic feedback problem has a perturbation structure such that higher-order terms of the feedback law do not affect lower-order terms and that this structure is lost in the optimal stochastic feedback problem. Consequently, solving the Stochastic Dynamic Programming problem is highly susceptible to noise, even in low dimensional problems, and in practice, the MPC-type feedback law offers superior performance even for high noise levels.
Quantum mechanics-based signal and image representation: application to denoising
Authors: Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Date: 2020-04-02 00:00:00
Tasks: Denoising, Image Denoising

Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis. Such a decomposition is commonly obtained using fixed transforms (e.g., Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In this work, we investigate in detail a new approach of constructing such a signal or image-dependent bases inspired by quantum mechanics tools, i.e., by considering the signal or image as a potential in the discretized Schroedinger equation. To illustrate the potential of the proposed decomposition, denoising results are reported in the case of Gaussian, Poisson, and speckle noise and compared to the state of the art algorithms based on wavelet shrinkage, total variation regularization or patch-wise sparse coding in learned dictionaries, non-local means image denoising, and graph signal processing.
Optical Characterization of Ultra-Low latency Visible Light Communication System for Intelligent Transportation Systems
Date: 2020-03-31 00:00:00

This paper reports a detailed experimental characterization of optical
performances of Visible Light Communication (VLC) system using a real traffic
light for ultra-low latency, infrastructure-to-vehicle (I2V) communications for
intelligent transportation systems (ITS) protocols. Despite the implementation
of long sought ITS protocols poses the crucial need to detail how the features
of optical stages influence the overall performances of a VLC system in
realistic configurations, such characterization has rarely been addressed at
present. We carried out an experimental investigation in a realistic
configuration where a regular traffic light (TX), enabled for VLC transmission,
sends digital information towards a receiving stage (RX), composed by an
optical condenser and a dedicated amplified photodiode stage. We performed a
detailed measurements campaign of VLC performances encompassing a broad set of
optical condensers, and for TX-RX distances in the range 3 - 50 m, in terms of
both effective field of view (EFOV) and packet error rate (PER). The results
show several nontrivial behaviors for different lens sets as a function of
position on the measurement grid, highlighting critical aspects as well as
identifying most suitable optical configurations depending on the specific
application and on the required EFOV. In this paper we also provide a
theoretical model for both the signal intensity and the EFOV as a function of
several parameters, such as distance, RX orientation and focal length of the
specific condenser. Our results could be very relevant in the near future to
assess a most suited solution in terms of acceptance angle when designing a VLC
system for real applications, where angle-dependent misalignment effects play a
non-negligible role, and we argue that it could have more general implications
with respect to the pristine I2V case mentioned here.
New vehicle-actuated access algorithms for intersections close to oversaturation
Authors: Rik W. Timmerman, Marko A. A. Boon
Date: 2020-03-09 00:00:00

Optimal traffic-light settings are generally hard to obtain, certainly for actuated access control of an intersection. Typically, computationally expensive (microscopic) simulations or complicated optimization schemes are required to find those optimal settings. Based on recent developments regarding the Fixed-Cycle Traffic-Light (FCTL) queue, we propose a new allocation scheme for actuated access control at isolated intersections. Our scheme allows for very easy computations and an intuitive explanation, which are both based on the Central Limit Theorem, a key tool in probability theory. The desirable properties observed for the FCTL queue can also be achieved in case of actuated access control of an intersection. We show this by means of simulation of the underlying queueing model, because the actuated control defies an exact analysis so far. We also observe the same type of results employing the microscopic traffic simulator SUMO. The intuition and insights obtained might also open up new possibilities for obtaining optimal actuated access control for a network of intersections.
Safe Feedback Motion Planning: A Contraction Theory and $\mathcal{L}_1$-Adaptive Control Based Approach
Date: 2020-05-25 00:00:00
Tasks: Motion Planning

Autonomous robots that are capable of operating safely in the presence of
imperfect model knowledge or external disturbances are vital in safety-critical
applications. In this paper, we present a planner-agnostic framework to design
and certify safe tubes around desired trajectories that the robot is always
guaranteed to remain inside of. By leveraging recent results in contraction
analysis and $\mathcal{L}_1$-adaptive control we synthesize an architecture
that induces safe tubes for nonlinear systems with state and time-varying
uncertainties. We demonstrate with a few illustrative examples how contraction
theory-based $\mathcal{L}_1$-adaptive control can be used in conjunction with
traditional motion planning algorithms to obtain provably safe trajectories.
Predicting Skill Shortages in Labor Markets: A Machine Learning Approach
Date: 2020-08-26 00:00:00
Tasks: BIG-bench Machine Learning

Skill shortages are a drain on society. They hamper economic opportunities
for individuals, slow growth for firms, and impede labor productivity in
aggregate. Therefore, the ability to understand and predict skill shortages in
advance is critical for policy-makers and educators to help alleviate their
adverse effects. This research implements a high-performing Machine Learning
approach to predict occupational skill shortages. In addition, we demonstrate
methods to analyze the underlying skill demands of occupations in shortage and
the most important features for predicting skill shortages. For this work, we
compile a unique dataset of both Labor Demand and Labor Supply occupational
data in Australia from 2012 to 2018. This includes data from 7.7 million job
advertisements (ads) and 20 official labor force measures. We use these data as
explanatory variables and leverage the XGBoost classifier to predict yearly
skills shortage classifications for 132 standardized occupations. The models we
construct achieve macro-F1 average performance scores of up to 83 per cent. Our
results show that job ads data and employment statistics were the highest
performing feature sets for predicting year-to-year skills shortage changes for
occupations. We also find that features such as 'Hours Worked', years of
'Education', years of 'Experience', and median 'Salary' are highly important
features for predicting occupational skill shortages. This research provides a
robust data-driven approach for predicting and analyzing skill shortages, which
can assist policy-makers, educators, and businesses to prepare for the future
of work.
Alternating quarantine for sustainable epidemic mitigation
Date: 2020-09-22 00:00:00

Absent a drug or vaccine, containing epidemic outbreaks is achieved by means
of social distancing, specifically mobility restrictions and lock-downs. Such
measures impose a hurtful toll on the economy, and are difficult to sustain for
extended periods. As an alternative, we propose here an alternating quarantine
strategy, in which at every instance, half of the population remains under
lock-down while the other half continues to be active, maintaining a routine of
weekly succession between activity and quarantine. This regime affords a dual
partition:\ half of the population interacts for only half of the time,
resulting in a dramatic reduction in transmission, comparable to that achieved
by a population-wide lock-down. All the while, it enables socioeconomic
continuity at $50\%$ capacity. The proposed weekly alternations also address an
additional challenge, with specific relevance to COVID-19. Indeed, SARS-CoV-2
exhibits a relatively long incubation period, in which individuals experience
no symptoms, but may already contribute to the spread. Unable to selectively
isolate these invisible spreaders, we resort to population-wide restrictions.
However, under the alternating quarantine routine, if an individual was exposed
during their active week, by the time they complete their quarantine they will,
in most cases, begin to exhibit symptoms. Hence this strategy isolates the
majority of pre-symptomatic individuals during their infectious phase, leading
to a rapid decline in the viral spread, thus addressing one of the main
challenges in COVID-19 mitigation.