pwc-archive/papers-with-abstracts huggingface dataset:
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Authors: Mana Jalali, Manish K. Singh, Vassilis Kekatos, Georgios B. Giannakis, Chen-Ching Liu
Date: 2022-02-15 00:00:00
Tasks: Computational Efficiency, Gaussian Processes, Sensitivity

Fast inverter control is a desideratum towards the smoother integration of renewables. Adjusting inverter injection setpoints for distributed energy resources can be an effective grid control mechanism. However, finding such setpoints optimally requires solving an optimal power flow (OPF), which can be computationally taxing in real time. This work proposes learning the mapping from grid conditions to OPF minimizers using Gaussian processes (GPs). This GP-OPF model predicts inverter setpoints when presented with a new instance of grid conditions. Training enjoys closed-form expressions, and GP-OPF predictions come with confidence intervals. To improve upon data efficiency, we uniquely incorporate the sensitivities (partial derivatives) of the OPF mapping into GP-OPF. This expedites the process of generating a training dataset as fewer OPF instances need to be solved to attain the same accuracy. To further reduce computational efficiency, we approximate the kernel function of GP-OPF leveraging the concept of random features, which is neatly extended to sensitivity data. We perform sensitivity analysis for the second-order cone program (SOCP) relaxation of the OPF, whose sensitivities can be computed by merely solving a system of linear equations. Extensive numerical tests using real-world data on the IEEE 13- and 123-bus benchmark feeders corroborate the merits of GP-OPF.
Theoretical Analysis of SIRVVD Model to Provide Insight on the Target Rate of COVID-19/SARS-CoV-2 Vaccination in Japan
Authors: Yuto Omae, Makoto Sasaki, Jun Toyotani, Kazuyuki Hara, Hirotaka Takahashi
Date: 2022-02-13 00:00:00

The effectiveness of the first and second dose vaccinations are different for COVID-19; therefore, a susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model that can represent the states of the first and second vaccination doses has been proposed. By the previous study, we can carry out simulating the spread of infectious disease considering the effects of the first and second doses of the vaccination based on the SIRVVD model. However, theoretical analysis of the SIRVVD Model is insufficient. Therefore, we obtained an analytical expression of the infectious number, by treating the numbers of susceptible persons and vaccinated persons as parameters. We used the solution to determine the target rate of the vaccination for decreasing the infection numbers of the COVID-19 Delta variant (B.1.617) in Japan. Further, we investigated the target vaccination rates for cases with strong or weak variants by comparison with the COVID-19 Delta variant (B.1.617). This study contributes to the mathematical development of the SIRVVD model and provides insight into the target rate of the vaccination to decrease the number of infections.
Pressure and temperature dependence of fluorescence anisotropy of Green Fluorescent Protein
Authors: Harpreet Kaur, Khanh Nguyen, Pradeep Kumar
Date: 2022-02-15 00:00:00

We have studied the effect of high hydrostatic pressure and temperature on the steady state fluorescence anisotropy of Green Fluorescent Protein (GFP). We find that the fluorescence anisotropy of GFP at a constant temperature decreases with increasing pressure. At atmospheric pressure, anisotropy decreases with increasing temperature but exhibits a maximum with temperature for pressure larger than 20 MPa. The temperature corresponding to the maximum of anisotropy increases with increasing pressure. By taking into account of the rotational correlation time changes of GFP with the pressure-temperature dependent viscosity of the solvent, we argue that viscosity increase with pressure is not a major contributing factor to the decrease in anisotropy with pressure. Our results suggest that the decrease of fluorescence anisotropy with pressure may result from changes in H-bonding environment around the chromophore.
Situation-based memory in spiking neuron-astrocyte network
Authors: Susanna Gordleeva, Yuliya A. Tsybina, Mikhail I. Krivonosov, Ivan Y. Tyukin, Victor B. Kazantsev, Alexey A. Zaikin, Alexander N. Gorban
Date: 2022-02-15 00:00:00
Tasks: Retrieval

Mammalian brains operate in a very special surrounding: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes. This set-up with reduced "ontology" is referred to as a "situation". Situations are usually local in space and time. In this work, we propose that neuron-astrocyte networks provide a network topology that is effectively adapted to accommodate situation-based memory. In order to illustrate this, we numerically simulate and analyze a well-established model of a neuron-astrocyte network, which is subjected to stimuli conforming to the situation-driven environment. Three pools of stimuli patterns are considered: external patterns, patterns from the situation associative pool regularly presented to the network and learned by the network, and patterns already learned and remembered by astrocytes. Patterns from the external world are added to and removed from the associative pool. Then we show that astrocytes are structurally necessary for an effective function in such a learning and testing set-up. To demonstrate this we present a novel neuromorphic model for short-term memory implemented by a two-net spiking neural-astrocytic network. Our results show that such a system tested on synthesized data with selective astrocyte-induced modulation of neuronal activity provides an enhancement of retrieval quality in comparison to standard spiking neural networks trained via Hebbian plasticity only. We argue that the proposed set-up may offer a new way to analyze, model, and understand neuromorphic artificial intelligence systems.
Investigating the genomic background of CRISPR-Cas genomes for CRISPR-based antimicrobials
Authors: Hyunjin Shim
Date: 2022-02-15 00:00:00
Tasks: Specificity, Time Series Analysis

CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that the diversity and frequency of these pathways differ by the CRISPR-Cas type. The interplay between CRISPR-Cas systems and DNA repair pathways is essential for the acquisition of new spacers in CRISPR arrays. We conduct simulation studies to demonstrate that the efficiency of these DNA repair pathways may be inferred from the time-series patterns in the RNA structure of CRISPR repeats. This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to consider multifaceted interactions between different genes and systems to design effective CRISPR-based antimicrobials.
Client applications and Server Side docker for management of RNASeq and/or VariantSeq workflows and pipelines of the GPRO Suite
Authors: Ahmed Hafez, Beatriz Soriano, Aya A. Elsayed, Ricardo Futami, Raquel Ceprian, Ricardo Ramos-Ruiz, Genis Martinez, Francisco J. Roig, Miguel A. Torres-Font, Fernando Naya-Català, Josep Alvar Calduch-Giner, Lucia Trilla-Fuertes, Angelo Gamez-Pozo, Vicente Arnau, Jose M. Sempere, Jaume Perez-Sánchez, Toni Gabaldón, Carlos Llorens
Date: 2022-02-14 00:00:00
Tasks: Chatbot, Management

The GPRO suite is an in-progress bioinformatic project for -omic data analyses. As part of the continued growth of this project, we introduce a client side & server side solution for comparative transcriptomics and analysis of variants. The client side consists of two Java applications called "RNASeq" and "VariantSeq" to manage workflows for RNA-seq and Variant-seq analysis, respectively, based on the most common command line interface tools for each topic. Both applications are coupled with a Linux server infrastructure (named GPRO Server Side) that hosts all dependencies of each application (scripts, databases, and command line interface tools). Implementation of the server side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server Side can be deployed via a Docker container that can be installed in the user's PC using any operating system or on remote servers as a cloud solution. The two applications are available as desktop and cloud applications and provide two execution modes: a Step-by-Step mode enables each step of a workflow to be executed independently and a Pipeline mode allows all steps to be run sequentially. The two applications also feature an experimental support system called GENIE that consists of a virtual chatbot/assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline job panel provides information about the status of each task executed in the GPRO Server Side, and the expert provides the user with a potential recommendation to identify or fix failed analyses. The two applications and the GPRO Server Side combine the user-friendliness and security of client software with the efficiency of front-end & back-end solutions to manage command line interface software for RNA-seq and variant-seq analysis via interface environments.
Using transcription-based detectors to emulate the behaviour of sequential probability ratio-based concentration detectors
Authors: Chun Tung Chou
Date: 2022-02-15 00:00:00
Tasks: Decision Making

The sequential probability ratio test (SPRT) from statistics is known to have the least mean decision time compared to other sequential or fixed-time tests for given error rates. In some circumstances, cells need to make decisions accurately and quickly, therefore it has been suggested the SPRT may be used to understand the speed-accuracy tradeoff in cellular decision making. It is generally thought that in order for cells to make use of the SPRT, it is necessary to find biochemical circuits that can compute the log-likelihood ratio needed for the SPRT. However, this paper takes a different approach. We recognise that the high-level behaviour of the SPRT is defined by its positive detection or hit rate, and the computation of the log-likelihood ratio is just one way to realise this behaviour. In this paper, we will present a method which uses a transcription-based detector to emulate the hit rate of the SPRT without computing the exact log-likelihood ratio. We consider the problem of using a promoter with multiple binding sites to accurately and quickly detect whether the concentration of a transcription factor is above a target level. We show that it is possible to find binding and unbinding rates of the transcription factor to the promoter's binding sites so that the probability that the amount of mRNA produced will be higher than a threshold is approximately equal to the hit rate of the SPRT detector. Moreover, we show that the average time that this transcription-based detector needs to make a positive detection is less than or equal to that of the SPRT for a wide range of concentrations. We remark that the last statement does not contradict Wald's optimality result because our transcription-based detector uses an open-ended test.
Novel methods for estimating the instantaneous and overall COVID-19 case fatality risk among care home residents in England
Authors: Christopher E. Overton, Luke Webb, Uma Datta, Mike Fursman, Jo Hardstaff, Iina Hiironen, Karthik Paranthaman, Heather Riley, James Sedgwick, Julia Verne, Steve Willner, Lorenzo Pellis, Ian Hall
Date: 2022-02-15 00:00:00

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic.
Hatchery-induced transition of the effective size in a Pareto population
Authors: Hiro-Sato Niwa
Date: 2022-02-14 00:00:00

It seems paradoxical to have observed the absence of reduced effective population sizes $N_{\mathrm{e}}$ under marine hatchery practices. This paper studies the Ryman-Laikre, or two-demographic-component, model of the hatchery impact related to inbreeding in a population with power-law family-size distribution, where hatchery inputs are represented by a Dirac delta function. By examining the asymptotic (i.e. large-population limit) behavior of the normalized sizes (or weights) of families of the mixture population, I derive the distribution properties of the average weight of families (i.e. the sum of the squared weights, $Y$) over the population existing at any given time. The reciprocal of the average weight $Y$ gives the effective number of families (or reproducing lineages) in the population, $N_{\mathrm{e}}=1/Y$. When the specific production in the hatchery (i.e. the number of offspring per broodstock) is low, the most probable value of $N_{\mathrm{e}}$ is close to the lower bound of the $N_{\mathrm{e}}$-distribution. When the specific hatchery-production is increased to a critical value with fixed mixing proportion of hatchery fish, a discontinuous transition takes place, so that the most probable $N_{\mathrm{e}}$ jumps to the upper extreme of the distribution. This hatchery-induced transition is attributed to the breaking of reciprocal symmetry, i.e. the fact that the typical value of $Y$ and its reciprocal (the typical $N_{\mathrm{e}}$) do not vary with the population size in opposite ways. At a high specific hatchery-production, the symmetry breaking disappears.
A Queueing Model of Patient Flow for Stroke Networks to Estimate Acute Stroke Transfer Capacity
Authors: Hojun Choi, Ohad Perry, Jane Holl, Shyam Prabhakaran
Date: 2022-02-14 00:00:00

Background: Most acute stroke (AS) patients in the United States are initially evaluated at a primary stroke center (PSC) and a significant proportion requires transfer to a comprehensive stroke center (CSC) for advanced treatment. A CSC typically accepts patients from multiple PSCs in its network, leading to capacity limits. This study uses a queueing model to estimate impacts on CSC capacity due to transfers from PSCs. Methods: The model assumes that the number of AS patients arriving at each PSC, proportion of AS patients transferred, and length of stay in the CSC Neurologic Intensive Care Unit (Neuro-ICU) by type of AS are random, while the transfer rates of ischemic and hemorrhagic AS patients are control variables. The main outcome measure is the "overflow" probability, namely, the probability of a CSC not having capacity (unavailability of a Neuro-ICU bed) to accept a transfer. Data simulations of the model, using a base case and an expanded case, were performed to illustrate the effects of changing key parameters, such as transfer rates from PSCs and CSC Neuro-ICU capacity on overflow capacity. Results: Data simulations of the model using a base case show that an increase of a PSC's ischemic stroke transfer rate from 15% to 55% raises the overflow probability from 30.62% to 36.13%. Further simulations of the expanded case show that to maintain an a priori CSC overflow probability of 30.62% when adding a PSC with a AS transfer rate of 15% to the network, other PSCs would need to decrease their transfer rate by 12.5% or the CSC Neuro-ICU would need to add 2 beds. Discussion: A queuing model can be used to estimate the effects of change in the size of a PSC-CSC network, change in AS transfer rates, or change in number of CSC Neuro-ICU beds of a CSC on its capacity on the overflow probability in the CSC.
Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach
Authors: Kathrin Hellmuth, Christian Klingenberg
Date: 2022-02-15 00:00:00
Tasks: BIG-bench Machine Learning

In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete nature of market data. We thus model the pricing process of financial derivatives by the Black Scholes equation, where the volatility is a function of a finite number of random variables. This reflects an influence of uncertain factors when determining volatility. The aim is to quantify the effect of this uncertainty when computing the price of derivatives. Our underlying method is the generalized Polynomial Chaos (gPC) method in order to numerically compute the uncertainty of the solution by the stochastic Galerkin approach and a finite difference method. We present an efficient numerical variation of this method, which is based on a machine learning technique, the so-called Bi-Fidelity approach. This is illustrated with numerical examples.
$ρ$-arbitrage and $ρ$-consistent pricing for star-shaped risk measures
Authors: Martin Herdegen, Nazem Khan
Date: 2022-02-15 00:00:00
Tasks: Sensitivity

This paper revisits mean-risk portfolio selection in a one-period financial market, where risk is quantified by a star-shaped risk measure $\rho$. We make three contributions. First, we introduce the new axiom of sensitivity to large expected losses and show that it is key to ensure the existence of optimal portfolios. Second, we give primal and dual characterisations of (strong) $\rho$-arbitrage. Finally, we use our conditions for the absence of (strong) $\rho$-arbitrage to explicitly derive the (strong) $\rho$-consistent price interval for an external financial contract.
Solvability of Differential Riccati Equations and Applications to Algorithmic Trading with Signals
Authors: Fayçal Drissi
Date: 2022-02-15 00:00:00
Tasks: Algorithmic Trading

We study a differential Riccati equation (DRE) with indefinite matrix coefficients, which arises in a wide class of practical problems. We show that the DRE solves an associated control problem, which is key to provide existence and uniqueness of a solution. As an application, we solve two algorithmic trading problems in which the agent adopts a constant absolute risk-aversion (CARA) utility function, and where the optimal strategies use signals and past observations of prices to improve their performance. First, we derive a multi-asset market making strategy in over-the-counter markets, where the market maker uses an external trading venue to hedge risk. Second, we derive an optimal trading strategy that uses prices and signals to learn the drift in the asset prices.
The ATM implied volatility slope, the (dual) volatility swap, and the (dual) zero vanna implied volatility
Authors: Frido Rolloos
Date: 2022-02-15 00:00:00
Tasks: 1 Image, 2*2 Stitchi

Exact relationships between the short time-to-maturity ATM implied volatility slope, the (dual) volatility swap, and the (dual) zero vanna implied volatility are given.
The Zero-Coupon Rate Model for Derivatives Pricing
Authors: Xiao Lin
Date: 2016-06-04 00:00:00
Tasks: model

The aim of this paper is to present a dual-term structure model of interest rate derivatives in order to solve the two hardest problems in financial modeling: the exact volatility calibration of the entire swaption matrix, and the calculation of bucket vegas for structured products. The model takes a series of long-term zero-coupon rates as basic state variables that are driven directly by one or more Brownian motion. The model volatility is assigned in a matrix form with two terms. A numerical scheme for implementing the model has been developed in the paper. At the end, several examples have been given for the model calibration, the structured products pricing and the calculation of bucket vegas.
Forecasting Stock Options Prices via the Solution of an Ill-Posed Problem for the Black-Scholes Equation
Authors: Michael V. Klibanov, Aleksander A. Shananin, Kirill V. Golubnichiy, Sergey M. Kravchenko
Date: 2022-02-15 00:00:00

In the previous paper (Inverse Problems, 32, 015010, 2016), a new heuristic mathematical model was proposed for accurate forecasting of prices of stock options for 1-2 trading days ahead of the present one. This new technique uses the Black-Scholes equation supplied by new intervals for the underlying stock and new initial and boundary conditions for option prices. The Black-Scholes equation was solved in the positive direction of the time variable, This ill-posed initial boundary value problem was solved by the so-called Quasi-Reversibility Method (QRM). This approach with an added trading strategy was tested on the market data for 368 stock options and good forecasting results were demonstrated. In the current paper, we use the geometric Brownian motion to provide an explanation of that effectivity using computationally simulated data for European call options. We also provide a convergence analysis for QRM. The key tool of that analysis is a Carleman estimate.
International Co-Branding and Firms Finance Performance
Authors: Hengameh Fakhravar, Hesamoddin Tahami
Date: 2022-02-15 00:00:00
Tasks: Marketing

Co-branding has become a widely used marketing strategy, yet little attention has been paid to its impact on a firm's stock value. Prior literature has shown that using a co-branding strategy properly helps firms leverage the brand value and equity. We discussed the theoretical foundations and main accomplishments of prior studies. We developed a conceptual framework and hypothesis to close the existing research gap in the topic of interest. We argued that co-branding event announcement generates positive abnormal returns in the stock market. Furthermore, we investigated the moderating impact of co-branding structure on the relation between co-branding event announcements and abnormal returns. We claimed that higher co-branding integration, greater co-branding exclusivity, and longer co-branding duration generate a greater positive abnormal return for the partnering firms.