pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing the Secrecy Rate with Direction-range Focusing with FDA and RIS
Authors: Chu Li, Stefan Roth, Aydin Sezgin
Date: 2024-01-26 00:00:00

One of the great potentials to improve the confidentiality in mmWave/THz at the physical layer of technical communication, measured by the secrecy rate, lies in the use of reconfigurable intelligent surfaces (RISs). However, an important open problem arises when the eavesdropper is aligned with the legitimate user or in proximity to the RIS or legitimate user. The limitation comes, on one hand, from the high directional gain caused by the dominant line-of-sight (LOS) path in high-frequency transmission, and, on the other hand, from the high energy leakage in the proximity of the RIS and the legitimate user. To address these issues, we employ the concept of frequency diverse arrays (FDA) at the base station (BS) associated with random inverted transmit beamforming and reflective element subset selection (RIBES). More specifically, we consider a passive eavesdropper with unknown location, and design the transmit beamforming and RIS configuration based on the channel information of the legitimate user only. In this context, the secrecy rate with the proposed transmission technique is evaluated in the case of deterministic eavesdropper channel, demonstrating that we can ensure a secure transmission regarding both direction and range. Furthermore, assuming no prior information about the eavesdropper, we describe the wiretap region and derive the worst-case secrecy rate in closed form. The latter is further optimized by determining the optimal subset sizes of the transmit antennas and reflective elements. Simulations verify the correctness of the closed-form expressions and demonstrate that we can effectively improve the secrecy rate, especially when the eavesdropper is close to the RIS or the legitimate user.
Evaluating the consequences: Impact of sex-selective harvesting on fish population and identifying tipping points via life-history parameters
Authors: Joydeb Bhattacharyya, Arnab Chattopadhyay, Anurag Sau, Sabyasachi Bhattacharya
Date: 2024-01-29 00:00:00

Fish harvesting often targets larger individuals, which can be sex-specific due to size dimorphism or differences in behaviors like migration and spawning. Sex-selective harvesting can have dire consequences in the long run, potentially pushing fish populations towards collapse much earlier due to skewed sex ratios and reduced reproduction. To investigate this pressing issue, we used a single-species sex-structured mathematical model with a weak Allee effect on the fish population. Additionally, we incorporate a realistic harvesting mechanism resembling the Michaelis-Menten function. Our analysis illuminates the intricate interplay between life history traits, harvesting intensity, and population stability. The results demonstrate that fish life history traits, such as a higher reproductive rate, early maturation of juveniles, and increased longevity, confer advantages under intensive harvesting. To anticipate potential population collapse, we employ a novel early warning tool (EWT) based on the concept of basin stability to pinpoint tipping points before they occur. Harvesting yield at our proposed early indicator can act as a potential pathway to achieve optimal yield while keeping the population safely away from the brink of collapse, rather than relying solely on the established maximum sustainable yield (MSY), where the population dangerously approaches the point of no return. Furthermore, we show that density-dependent female stocking upon receiving an EWT signal significantly shifts the tipping point, allowing safe harvesting even at MSY levels, thus can act as a potential intervention strategy.
Applications of the multi-sigmoidal deterministic and stochastic logistic models for plant dynamics
Authors: Antonio Di Crescenzo, Paola Paraggio, Patricia Román-Román, Francisco Torres-Ruiz
Date: 2024-01-28 00:00:00

We consider a generalization of the classical logistic growth model introducing more than one inflection point. The growth, called multi-sigmoidal, is firstly analyzed from a deterministic point of view in order to obtain the main properties of the curve, such as the limit behavior, the inflection points and the threshold-crossing-time through a fixed boundary. We also present an application in population dynamics of plants based on real data. Then, we define two different birth-death processes, one with linear birth and death rates and the other with quadratic rates, and we analyze their main features. The conditions under which the processes have a mean of multi-sigmoidal logistic type and the first-passage-time problem are also discussed. Finally, with the aim of obtaining a more manageable stochastic description of the growth, we perform a scaling procedure leading to a lognormal diffusion process with mean of multi-sigmoidal logistic type. We finally conduct a detailed probabilistic analysis of this process
Why evolution needs the old: a theory of ageing as adaptive force
Authors: Alessandro Fontana, Marios Kyriazis
Date: 2024-01-29 00:00:00

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species' design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. On the other hand, a small minority of experiments have positive outcome, offering valuable insight into the direction evolution should pursue. This hypothesis is consistent with the concept of "terminal addition", by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system's evolvability by exerting a regularisation effect on the fitness landscape of evolution.
Transduction of an immortalized olfactory ensheathing glia cell line with the green fluorescent protein (GFP) gene: evaluation of its neuroregenerative capacity as a proof of concept
Authors: N Plaza, D Simón, J Sierra, MT Moreno-Flores
Date: 2024-01-26 00:00:00

Olfactory ensheathing glia (OEG) cells are known to foster axonal regeneration of central nervous system (CNS) neurons. Several lines of reversibly immortalized human OEG (ihOEG) have been previously established that enabled to develop models for their validation in vitro and in vivo. In this work, a constitutively GFP-expressing ihOEG cell line was obtained, and named Ts14-GFP. Ts14-GFP neuroregenerative ability was similar to that found for the parental line Ts14 and it can be assayed using in vivo transplantation experimental paradigms, after spinal cord or optic nerve damage. Additionally, we have engineered a low-regenerative ihOEG line, hTL2, using lentiviral transduction of the large T antigen from SV40 virus, denominated from now on Ts12. Ts12 can be used as a low regeneration control in these experiments.
Deciphering regulatory architectures from synthetic single-cell expression patterns
Authors: Rosalind Wenshan Pan, Tom Roeschinger, Kian Faizi, Hernan Garcia, Rob Phillips
Date: 2024-01-29 00:00:00
Tasks: Specificity

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
Resting-State fingerprints of Acceptance and Reappraisal. The role of Sensorimotor, Executive and Affective networks
Authors: Parisa Ahmadi Ghomroudi, Roma Siugzdaite, Irene Messina, Alessandro Grecucci
Date: 2024-01-29 00:00:00
Tasks: Functional Connectivity

Acceptance and Reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks contributions to these abilities, especially for what concerns Acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the Affective network and increased activity inside the Executive and Sensorimotor networks to be predicted of acceptance. We also expect that these networks may be associated at least in part with Reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased Affective, and increased Executive, and Sensorimotor networks, while reappraisal was predicted by an increase in the Sensorimotor network. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal, with the Sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, demonstrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.
EM and XRM Connectomics Imaging and Experimental Metadata Standards
Authors: Miguel E. Wimbish, Nicole K. Guittari, Victoria A. Rose, Jorge L. Rivera Jr, Patricia K. Rivlin, Mark A. Hinton, Jordan K. Matelsky, Nicole E. Stock, Brock A. Wester, Erik C. Johnson, William R. Gray-Roncal
Date: 2024-01-27 00:00:00

High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are derived from an increasing number of species, in an increasing number of brain regions, and with an increasing number of techniques. Each of these large-scale datasets, often surpassing petascale levels, is typically accompanied by a unique and varied set of metadata. These datasets can be used to derive connectomes, or neuron-synapse level connectivity diagrams, to investigate the fundamental organization of neural circuitry, neuronal development, and neurodegenerative disease. Standardization is essential to facilitate comparative connectomics analysis and enhance data utilization. Although the neuroinformatics community has successfully established and adopted data standards for many modalities, this effort has not yet encompassed EM and XRM/ XHN connectomics data. This lack of standardization isolates these datasets, hindering their integration and comparison with other research performed in the field. Towards this end, our team formed a working group consisting of community stakeholders to develop Image and Experimental Metadata Standards for EM and XRM/XHN data to ensure the scientific impact and further motivate the generation and sharing of these data. This document addresses version 1.1 of these standards, aiming to support metadata services and future software designs for community collaboration. Standards for derived annotations are described in a companion document. Standards definitions are available on a community github page. We hope these standards will enable comparative analysis, improve interoperability between connectomics software tools, and continue to be refined and improved by the neuroinformatics community.
Estimation of domain truncation error for a system of PDEs arising in option pricing
Authors: Anindya Goswami, Kuldip Singh Patel
Date: 2024-01-28 00:00:00

In this paper, a multidimensional system of parabolic partial differential equations arising in European option pricing under a regime-switching market model is studied in details. For solving that numerically, one must truncate the domain and impose an artificial boundary data. By deriving an estimate of the domain truncation error at all the points in the truncated domain, we extend some results in the literature those deal with option pricing equation under constant regime case only. We differ from the existing approach to obtain the error estimate that is sharper in certain region of the domain. Hence, the minimum of proposed and existing gives a strictly sharper estimate. A comprehensive comparison with the existing literature is carried out by considering some numerical examples. Those examples confirm that the improvement in the error estimates is significant.
Fast and General Simulation of Lévy-driven OU processes for Energy Derivatives
Authors: Roberto Baviera, Pietro Manzoni
Date: 2024-01-27 00:00:00

L\'evy-driven Ornstein-Uhlenbeck (OU) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capture typical features of market dynamics. However, in the current state of play, Monte Carlo simulations of these processes are not straightforward for two main reasons: i) algorithms are available only for some specific processes within this class; ii) they are often computationally expensive. In this paper, we introduce a new simulation technique designed to address both challenges. It relies on the numerical inversion of the characteristic function, offering a general methodology applicable to all L\'evy-driven OU processes. Moreover, leveraging FFT, the proposed methodology ensures fast and accurate simulations, providing a solid basis for the widespread adoption of these processes in the energy sector. Lastly, the algorithm allows an optimal control of the numerical error. We apply the technique to the pricing of energy derivatives, comparing the results with the existing benchmarks. Our findings indicate that the proposed methodology is at least one order of magnitude faster than the existing algorithms, while maintaining an equivalent level of accuracy.
Analytic Pricing of SOFR Futures Contracts with Smile and Skew
Authors: Aurelio Romero-Bermúdez, Colin Turfus
Date: 2024-01-28 00:00:00

We introduce a perturbative formalism to solve the backward-looking futures pricing problem. The formalism is based on a time-ordered exponential series which allows to derive the functional form of the integral kernel associated to the backward-Kolmogorov diffusion PDE. We present an analytic pricing formula for SOFR futures contracts under an extension of the Hull-White model which incorporates not only the intrinsic convexity adjustments captured by Mercurio [2018], but also the skew and smile observed in options markets as done in Turfus and Romero-Berm\'udez [2023].
The Carbon Premium: Correlation or Causation? Evidence from S&P 500 Companies
Authors: Namasi G. Sankar, Suryadeepto Nag, Siddhartha P. Chakrabarty, Sankarshan Basu
Date: 2024-01-29 00:00:00
Tasks: Selection bias

In the context of whether investors are aware of carbon-related risks, it is often hypothesized that there may be a carbon premium in the value of stocks of firms, conferring an abnormal excess value to firms' shares as a form of compensation to investors for their transition risk exposure through the ownership of carbon instensive stocks. However, there is little consensus in the literature regarding the existence of such a premium. Moreover few studies have examined whether the correlation that is often observed is actually causal. The pertinent question is whether more polluting firms give higher returns or do firms with high returns have less incentive to decarbonize? In this study, we investigate whether firms' emissions is causally linked to the presence of a carbon premium in a panel of 141 firms listed in the S\&P500 index using fixed-effects analysis, with propensity score weighting to control for selection bias in which firms increase their emissions. We find that there is a statistically significant positive carbon premium associated with Scope 1 emissions, while there is no significant premium associated with Scope 2 emissions, implying that risks associated with direct emissions by the firm are priced, while bought emissions are not.
Partial Law Invariance and Risk Measures
Authors: Yi Shen, Zachary Van Oosten, Ruodu Wang
Date: 2024-01-30 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Management

We introduce the concept of partial law invariance, generalizing the concepts of law invariance and probabilistic sophistication widely used in decision theory, as well as statistical and financial applications. This new concept is motivated by practical considerations of decision making under uncertainty, thus connecting the literature on decision theory and that on financial risk management. We fully characterize partially law-invariant coherent risk measures via a novel representation formula. Strong partial law invariance is defined to bridge the gap between the above characterization and the classic representation formula of Kusuoka. We propose a few classes of new risk measures, including partially law-invariant versions of the Expected Shortfall and the entropic risk measures, and illustrate their applications in risk assessment under different types of uncertainty. We provide a tractable optimization formula for computing a class of partially law-invariant coherent risk measures and give a numerical example.
Improving Business Insurance Loss Models by Leveraging InsurTech Innovation
Authors: Zhiyu Quan, Changyue Hu, Panyi Dong, Emiliano A. Valdez
Date: 2024-01-30 00:00:00
Tasks: Management

Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies' risk management. Our study further utilizes various machine learning techniques to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house. The quantification process provides a deeper understanding of the value of the InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic-industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations
Authors: Luke Marks
Date: 2023-11-29 00:00:00
Tasks: Language Modeling, Language Modelling, Object, Self-Supervised Learning

Self-supervised learning is the backbone of state of the art language modeling. It has been argued that training with predictive loss on a self-supervised dataset causes simulators: entities that internally represent possible configurations of real-world systems. Under this assumption, a mathematical model for simulators is built based in the Cartesian frames model of embedded agents, which is extended to multi-agent worlds through scaling a two-dimensional frame to arbitrary dimensions, where literature prior chooses to instead use operations on frames. This variant leveraging scaling dimensionality is named the Cartesian object, and is used to represent simulations (where individual simulacra are the agents and devices in that object). Around the Cartesian object, functions like token selection and simulation complexity are accounted for in formalizing the behavior of a simulator, and used to show (through the L\"obian obstacle) that a proof of alignment between simulacra by inspection of design is impossible in the simulator context. Following this, a scheme is proposed and termed Partial Simulation Extrapolation aimed at circumventing the L\"obian obstacle through the evaluation of low-complexity simulations.
AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities
Authors: Lal Verda Cakir, Kubra Duran, Craig Thomson, Matthew Broadbent, Berk Canberk
Date: 2024-01-28 00:00:00

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data capturing in traditional approaches, resulting in inaccurate modelling and high resource and energy consumption challenges. To fill this gap, we explore spatiotemporal graphs and propose the Reinforcement Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN). By doing so, our study contributes to advancing Green Cities and showcases tangible benefits in accuracy, synchronisation, resource optimization, and energy efficiency. As a result, we note the spatiotemporal graphs are able to offer a consistent accuracy and 55% higher querying performance when implemented using graph databases. In addition, our model demonstrates right-time data capturing with 20% lower overhead and 25% lower energy consumption.