pwc-archive/papers-with-abstracts huggingface dataset:
Criticality in stochastic SIR model for infectious diseases
Authors: Shigehiro Yasui, Yutaka Hatakeyama, Yoshiyasu Okuhara
Date: 2022-02-11 00:00:00
Tasks: model

We discuss the criticality in the stochastic SIR model for infectious diseases. We adopt the path-integral formalism for the propagation of infections among susceptible, infectious, and removed individuals, and perform the perturbative and nonperturbative analyses to evaluate the critical value of the basic reproduction number ${\cal R}$. In the perturbation theory, we calculate the mean values and the variances of the number of infectious individuals near the initial time, and find that the critical value ${\cal R}_{\text{c}}=1/3$-$2/3$ should be adopted in order to suppress the stochastic spread of infections sufficiently. In the nonperturbative approach, we derive the effective potential by integrating out the stochastic fluctuations, and obtain the effective Euler-Lagrange equations for the time-evolution of the numbers of susceptible, infectious, and removed individuals. From the asymptotic behaviors for a long time, we find that the critical value ${\cal R}_{\text{c}}=2/3$ should be adopted for the sufficient convergence of infections. We also find that the endemic state can be generated dynamically by the stochastic fluctuation which is absent in the conventional SIR model. Those analyses show that the critical value of the basic reproduction number should be less than one, against the usually known critical value ${\cal R}_{\text{c}}=1$, when the stochastic fluctuations are taken into account in the SIR model.
Pattern formation and coarsening dynamics in apparent competition models
Authors: J. Menezes, B. Moura
Date: 2022-02-10 00:00:00

Apparent competition is an indirect interaction between species that share natural resources without any mutual aggression but negatively affect each other if there is a common enemy. The negative results of the apparent competition are reflected in the species spatial segregation, which impacts the dynamics of their populations. Performing a series of stochastic simulations, we study a model where organisms of two prey species do not compete for space but share a common predator. Our outcomes elucidate the central role played by the predator in the pattern formation and coarsening dynamics in apparent competition models. Investigating the effects of predator mortality on the persistence of the species, we find a crossover between a curvature driven scaling regime and a coexistence scenario. For low predator mortality, spatial domains mainly inhabited by one type of prey arise, surrounded by interfaces that mostly contain predators. We demonstrate that the dynamics of the interface network are curvature driven whose coarsening follows a scaling law common to other nonlinear systems. The effects of the apparent competition decrease for high predator mortality, allowing organisms of two prey species to share a more significant fraction of lattice. Finally, our results reveal that predation capacity in single-prey domains influences the scaling power law that characterises the coarsening dynamics. Our findings may be helpful to biologists to understand the pattern formation and dynamics of biodiversity in systems with apparent competition.
Flags, Landscapes and Signaling: Contact-mediated inter-cellular interactions enable plasticity in fate determination driven by positional information
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Sitabhra Sinha
Date: 2022-02-11 00:00:00

Multicellular organisms exhibit a high degree of structural organization with specific cell types always occurring in characteristic locations. The conventional framework for describing the emergence of such consistent spatial patterns is provided by Wolpert's "French flag" paradigm. According to this view, intra-cellular genetic regulatory mechanisms use positional information provided by morphogen concentration gradients to differentially express distinct fates, resulting in a characteristic pattern of differentiated cells. However, recent experiments have shown that suppression of inter-cellular interactions can alter these spatial patterns, suggesting that cell fates are not exclusively determined by the regulation of gene expression by local morphogen concentration. Using an explicit model where adjacent cells communicate by Notch signaling, we provide a mechanistic description of how contact-mediated interactions allow information from the cellular environment to be incorporated into cell fate decisions. Viewing cellular differentiation in terms of trajectories along an epigenetic landscape (as first enunciated by Waddington), our results suggest that the contours of the landscape are moulded differently in a cell position-dependent manner, not only by the global signal provided by the morphogen but also by the local environment via cell-cell interactions. We show that our results are robust with respect to different choices of coupling between the inter-cellular signaling apparatus and the intra-cellular gene regulatory dynamics. Indeed, we show that the broad features can be observed even in abstract spin models. Our work reconciles interaction-mediated self-organized pattern formation with boundary-organized mechanisms involving signals that break symmetry.
Vector-borne diseases with non-stationary vector populations: the case of growing and decaying populations
Authors: Àlex Giménez-Romero, Rosa Flaquer-Galmés, Manuel A. Matias
Date: 2022-02-11 00:00:00

Since the last century, deterministic compartmental models have emerged as powerful tools to predict and control epidemic outbreaks, in many cases helping to mitigate their impacts. A key quantity for these models is the so-called Basic Reproduction Number, that measures the number of secondary infections produced by an initial infected individual in a fully susceptible population. Some methods have been developed to allow the direct computation of this quantity provided that some conditions are fulfilled, such that the model has a pre-pandemic disease-free equilibrium state. This condition is only fulfilled when the populations are stationary. In the case of vector-borne diseases, this implies that the vector birth and death rates need to be balanced, what is not fulfilled in many realistic cases in which the vector population grow or decrease. Here we develop a vector-borne epidemic model with growing and decaying vector populations and study the conditions under which the standard methods to compute $R_0$ work and discuss an alternative when they fail. We also show that growing vector populations produce a delay in the epidemic dynamics when compared to the case of the stationary vector population. Finally, we discuss the conditions under which the model can be reduced to the SIR model with fewer compartments and parameters, which helps in solving the problem of parameter unidentifiability of many vector-borne epidemic models.
Structured light dark-field microscope
Authors: Shaobai Li, Bofan Song, Rongguang Liang
Date: 2022-02-10 00:00:00
Tasks: Defect Detection

A resolution-enhanced dark-field microscope by structured light illumination is proposed to improve resolution and contrast. A set of phase-shifted fringes are projected to the sample plane at large angle to capture modulated dark-field images, from which resolution- and contrast-enhanced dark-field image, as well as sectioned dark-field image, can be obtained. Human tissue samples are tested to demonstrate the resolution and contrast enhancement. The system can be implemented in transmission-mode and reflectance-mode, with potential applications ranging from defect detection to biomedical imaging.
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Authors: Patrice Abry, Gersende Fort, Barbara Pascal, Nelly Pustelnik
Date: 2022-02-11 00:00:00
Tasks: Denoising

Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation. Recently, the estimation of the reproduction number, a measure of the pandemic intensity, was formulated as an inverse problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that formulation lacks robustness against the limited quality of the Covid19 data and confidence assessment. The present work aims to address both limitations: First, it discusses solutions to produce a robust assessment of the pandemic intensity by accounting for the low quality of the data directly within the inverse problem formulation. Second, exploiting a Bayesian interpretation of the inverse problem formulation, it devises a Monte Carlo sampling strategy, tailored to a nonsmooth log-concave a posteriori distribution, to produce relevant credibility intervalbased estimates for the Covid19 reproduction number. Clinical relevance Applied to daily counts of new infections made publicly available by the Health Authorities for around 200 countries, the proposed procedures permit robust assessments of the time evolution of the Covid19 pandemic intensity, updated automatically and on a daily basis.
Inflation and income inequality: Does the level of income inequality matter?
Authors: Edmond Berisha, Ram Sewak Dubey, Orkideh Gharehgozli
Date: 2022-02-11 00:00:00

In the recent times of global Covid pandemic, the Federal Reserve has raised the concerns of upsurges in prices. Given the complexity of interaction between inflation and inequality, we examine whether the impact of inflation on inequality differs among distinct levels of income inequality across the US states. Results reveal that there is a negative contemporaneous effect of inflation on the inequality which becomes stronger with higher levels of income inequality. However, over a one year period, we find higher inflation rate to further increase income inequality only when income inequality is initially relatively low.
Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces
Authors: Haiyang Zhang, Nir Shlezinger, Idban Alamzadeh, George C. Alexandropoulos, Mohammadreza F. Imani, Yonina C. Eldar
Date: 2022-02-11 00:00:00

Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role in future wireless communications, enabling programmable radio propagation environments. They are usually considered as nearly passive planar structures that operate as adjustable reflectors, giving rise to a multitude of implementation challenges, including an inherent difficulty in estimating the underlying wireless channels. In this paper, we propose the concept of Hybrid RISs (HRISs), which do not solely reflect the impinging waveform in a controllable fashion, but are also capable of sensing and processing a portion of it via some active reception elements. We first present implementation details for this novel metasurface architecture and propose a simple model for its operation, when considered for wireless communications. As an indicative application of HRISs, we formulate and solve the individual channels identification problem for the uplink of multi-user HRIS-empowered systems. Our numerical results showcase that, in the high signal-to-noise regime, HRISs enable individual channel estimation with notably reduced amounts of pilots, compared to those needed when using a purely reflective RIS that can only estimate the cascaded channel.
Infrastructure-enabled GPS Spoofing Detection and Correction
Authors: Feilong Wang, Yuan Hong, Jeff Ban
Date: 2022-02-11 00:00:00
Tasks: Autonomous Driving

Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. With transportation infrastructure becoming increasingly important in supporting emerging vehicle technologies and systems, this study explores the potential of applying infrastructure data for defending against GPS spoofing. We propose an infrastructure-enabled framework using roadside units as an independent, secured data source. A real-time detector, based on the Isolation Forest, is constructed to detect GPS spoofing. Once spoofing is detected, GPS measurements are isolated, and the potentially compromised location estimator is corrected using secure infrastructure data. We test the proposed method using both simulation and real-world data and show its effectiveness in defending against various GPS spoofing attacks, including stealthy attacks that are proposed to fail the production-grade autonomous driving systems.
High Fidelity RF Clutter Modeling and Simulation
Authors: Sandeep Gogineni, Joseph R. Guerci, Hoan K. Nguyen, Jameson S. Bergin, David R. Kirk, Brian C. Watson, Muralidhar Rangaswamy
Date: 2022-02-10 00:00:00
Tasks: Benchmarking, Vocal Bursts Intensity Prediction

In this paper, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation (M&S) techniques. Traditional statistical approximation based methods will be reviewed followed by more accurate physics-based stochastic transfer function clutter models that facilitate site-specific simulations anywhere on earth. The various factors that go into the computation of these transfer functions will be presented, followed by several examples across multiple RF applications. Finally, we introduce a radar challenge dataset generated using these tools that can enable testing and benchmarking of all cognitive radar algorithms and techniques.
Molecule Generation from Input-Attributions over Graph Convolutional Networks
Authors: Dylan Savoia, Alessio Ragno, Roberto Capobianco
Date: 2022-01-25 00:00:00
Tasks: Drug Design

It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools.
Semi-Supervised GCN for learning Molecular Structure-Activity Relationships
Authors: Alessio Ragno, Dylan Savoia, Roberto Capobianco
Date: 2022-01-25 00:00:00
Tasks: Drug Design, Graph Neural Network

Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design.
Stochastic dynamics of social patch foraging decisions
Authors: Subekshya Bidari, Ahmed El Hady, Jacob Davidson, Zachary P Kilpatrick
Date: 2022-02-11 00:00:00

Animals typically forage in groups. Social foraging can help animals avoid predation and decrease their uncertainty about the richness of food resources. Despite this, theoretical mechanistic models of patch foraging have overwhelmingly focused on the behavior of single foragers. In this study, we develop a mechanistic model describing the behavior of individuals foraging together and departing food patches following an evidence accumulation process. Each individual's belief about patch quality is represented by a stochastically accumulating variable coupled to others' belief, representing the transfer of information. We consider a cohesive group, and model information sharing as either intermittent pulsatile coupling (communicate decision to leave) or continuous diffusive coupling (communicate online belief). Foraging efficiency under pulsatile coupling has a stronger dependence on the coupling strength parameter compared to diffusive. Despite employing minimal information transfer, pulsatile coupling can still provide similar or higher foraging efficiency compared to diffusive coupling. Conversely, diffusive coupling is more robust to parameter detuning and performs better when individuals have heterogeneous departure criteria and social information weighting. Efficiency is measured by a reward rate function that balances the amount of energy accumulated against the time spent in a patch, computed by solving an ordered first passage time problem for the patch departures of each individual. Using synthetic data we show that we can distinguish between the two modes of communication and identify the model parameters. Our model establishes a social patch foraging framework to parse and identify deliberative decision strategies, to distinguish different forms of social communication, and to allow model fitting to real world animal behavior data.
Investigating cognitive ability using action-based models of structural brain networks
Authors: Viplove Arora, Enrico Amico, Joaquín Goñi, Mario Ventresca
Date: 2022-01-19 00:00:00
Tasks: Descriptive

Recent developments in network neuroscience have highlighted the importance of developing techniques for analyzing and modeling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual's tendency to form long-range or non-local connections.
The Evolution of Blockchain: from Lit to Dark
Authors: Agostino Capponi, Ruizhe Jia, Ye Wang
Date: 2022-02-11 00:00:00

Transactions submitted through the blockchain peer-to-peer (P2P) network may leak out exploitable information. We study the economic incentives behind the adoption of blockchain dark venues, where users' transactions are observable only by miners on these venues. We show that miners may not fully adopt dark venues to preserve rents extracted from arbitrageurs, hence creating execution risk for users. The dark venue neither eliminates frontrunning risk nor reduces transaction costs. It strictly increases the payoff of miners, weakly increases the payoff of users, and weakly reduces arbitrageurs' profits. We provide empirical support for our main implications, and show that they are economically significant. A 1% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0.6%. Arbitrageurs' cost-to-revenue ratio increases by a third with a dark venue.
Cashing Out: Assessing the risk of localised financial exclusion as the UK moves towards a cashless society
Authors: George Sullivan, Luke Burns
Date: 2022-01-07 00:00:00

Whilst academic, commercial and policy literature on financial exclusion is extensive and wide-ranging, there have been very few attempts to quantify and measure localised financial exclusion anywhere in the world. This is a subject of growing importance in modern UK society with the withdrawal of cash infrastructure and a shift towards online banking. This research develops a composite indicator using a wide-range of input variables, including the locations of existing cash infrastructure, various demographic factors (such as income and housing tenure) and other freely available lifestyle data to identify areas at greatest risk of financial exclusion, thereby aiding organisations to develop intervention strategies to tackle the problem. The indicator illustrates that whilst there is no apparent correlation between financial exclusion and deprivation, pockets of extreme financial exclusion are generally found in deprived communities, and affluent, suburban areas tend to score consistently more favourably and consequently carry less risk. The attributing causes vary, from a lack of infrastructure, to low car availability, but income levels have a pronounced influence. Three policy proposals are put forward, including offering banking services at PayPoint outlets, and converting cash machines to cash recyclers, but improving digital adoption was found to be the most effective intervention, provided that it is implemented by community organisations. Policies purely targeting infrastructure provision or addressing social exclusion are unlikely to be effective, as community-based initiatives coupled with wider reforms to the financial system are needed.
Black-Scholes-Merton Option Pricing Revisited: Did we Find a Fatal Flaw?
Authors: Mark Mink, Frans J. de Weert
Date: 2022-01-09 00:00:00
Tasks: Mathematical Proofs

The option pricing formula of Black and Scholes (1973) hinges on the continuous-time self-financing condition, which is a special case of the continuous-time budget equation of Merton (1971). The self-financing condition is believed to formalize the economic concept of portfolio rebalancing without inflows or outflows of external funds, but was never formally derived in continuous time. Moreover, and even more problematically, we discover a timing mistake in the model of Merton (1971) and show that his self-financing condition is misspecified both in discrete and continuous time. Our results invalidate seminal contributions to the literature, including the budget equation of Merton (1971), the option pricing formula of Black and Scholes (1973), the continuous trading model of Harrison and Pliska (1981), and the binomial option pricing model of Cox, Ross and Rubinstein (1979). We also show that Black and Scholes (1973) and alternative derivations of their formula implicitly assumed the replication result.