pwc-archive/papers-with-abstracts huggingface dataset:
A Python-based Mixed Discrete-Continuous Simulation Framework for Digital Twins
Authors: Neha Karanjkar, Subodh M. Joshi
Date: 2022-07-31 00:00:00
Tasks: Decision Making

The use of Digital Twins is set to transform the manufacturing sector by aiding monitoring and real-time decision making. For several applications in this sector, the system to be modeled consists of a mix of discrete-event and continuous processes interacting with each other. Building simulation-based Digital Twins of such systems necessitates an open, flexible simulation framework which can support easy modeling and fast simulation of both continuous and discrete-event components, and their interactions. In this paper, we present an outline and key design aspects of a Python-based framework for performing mixed discrete-continuous simulations. The continuous processes in the system are assumed to be loosely coupled to other components via pre-defined events. For example, a continuous state variable crossing a threshold may trigger an external event. Similarly, external events may lead to a sudden change in the trajectory, state value or boundary conditions in a continuous process. We first present a systematic events-based interface using which such interactions can be modeled and simulated. We then discuss implementation details of the framework along with a detailed example. In our implementation, the advancement of time is controlled and performed using the event-stepped engine of SimPy (a popular discrete-event simulation library in Python). The continuous processes are modelled using existing frameworks with a Python wrapper providing the events interface. We discuss possible improvements to the time advancement scheme, a roadmap and use cases for the framework.
Generalised Lotka-Volterra model with hierarchical interactions
Authors: Lyle Poley, Joseph W. Baron, Tobias Galla
Date: 2022-08-02 00:00:00
Tasks: model

In the analysis of complex ecosystems it is common to use random interaction coefficients, often assumed to be such that all species are statistically equivalent. In this work we relax this assumption by choosing interactions according to the cascade model, which we incorporate into a generalised Lotka-Volterra dynamical system. These interactions impose a hierarchy in the community. Species benefit more, on average, from interactions with species further below them in the hierarchy than from interactions with those above. Using dynamic mean-field theory, we demonstrate that a strong hierarchical structure is stabilising, but that it reduces the number of species in the surviving community, as well as their abundances. Additionally, we show that increased heterogeneity in the variances of the interaction coefficients across positions in the hierarchy is destabilising. We also comment on the structure of the surviving community and demonstrate that the abundance and probability of survival of a species is dependent on its position in the hierarchy.
Tunable high-resolution synthetic aperture radar imaging
Authors: Arnold D. Kim, Chrysoula Tsogka
Date: 2022-08-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We have recently introduced a modification of the multiple signal classification (MUSIC) method for synthetic aperture radar. This method depends on a tunable, user-defined parameter, $\epsilon$, that allows for quantitative high-resolution imaging. It requires however, relative large single-to-noise ratios (SNR) to work effectively. Here, we first identify the fundamental mechanism in that method that produces high-resolution images. Then we introduce a modification to Kirchhoff Migration (KM) that uses the same mechanism to produces tunable, high-resolution images. This modified KM method can be applied to low SNR measurements. We show simulation results that demonstrate the features of this method.
Mixed Anhydrides at the Intersection Between Peptide and RNA Autocatalytic Sets: Evolution of Biological Coding
Authors: Stuart Kauffman, Niles Lehman
Date: 2022-05-25 00:00:00

We present a scenario for the origin of biological coding. In this context, coding is a semiotic relationship between chemical information stored in one location that links to chemical information stored in a separate location. Coding originated by the cooperative interaction of two, originally separate collectively autocatalytic sets, one for nucleic acids and one for peptides. When these two sets interacted, a series of RNA-folding-directed processes led to their joint cooperativity. The amino acyl adenylate, today amino acid-AMP, was the first covalent association made by these two collectively autocatalytic sets and solidified their interdependence. This molecule is a palimpsest of this era, and is a relic of the original semiotic, and thus coding, relationship between RNA and proteins. More defined coding was driven by selection pressure to eliminate waste in the collective autocatalytic sets. Eventually a 1:1 relationship between single amino acids and short RNA pieces (e.g., three nucleotides) was established, leading to what is today known as the genetic code. Transfer RNA aminoacylating enzymes, or aaRSs, arose concomitantly with the advent of specific coding. The two classes of aaRS enzymes are remnants of the duality of complementary information in two nucleic acid strands, as originally postulated by Rodin and Ohno. Every stage in the evolution of coding was driven by the downward selection on the components of a system to satisfy the Kantian whole. Coding was ultimately forced because there were at least two chemically distinct classes of polymers needed for open-ended evolution; systems with only one polymer cannot exhibit this characteristic. Coding is thus synonymous with life as we know it, and can be thought of as a phase transition in the history of the universe.
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
Authors: Akshay Iyer, Clifford Lindsay, Hendrik Pretorius, Michael King
Date: 2022-07-20 00:00:00

In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to translate noisy internal motion to external surrogate: 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of cardiac perfusion SPECT acquisitions for which cardiac motion was estimated (input: center-of-count-mass - COM signals) in conjunction with a respiratory surrogate motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT signals). We obtained an average R-score of 0.76 between the predicted surrogate and the EMT signal. Our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.
Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment
Authors: Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge
Date: 2022-08-02 00:00:00
Tasks: regression

We revisit the problem of estimating the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) when control variables are available, either to render the instrumental variable (IV) suitably exogenous or to improve precision. Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so-called inverse probability weighted regression adjustment (IPWRA) estimators. By properly choosing models for the propensity score and outcome models, fitted values are ensured to be in the logical range determined by the response variable, producing DR estimators of LATE and LATT with appealing small sample properties. Inference is relatively straightforward both analytically and using the nonparametric bootstrap. Our DR LATE and DR LATT estimators work well in simulations. We also propose a DR version of the Hausman test that can be used to assess the unconfoundedness assumption through a comparison of different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance. Unlike the usual test that compares OLS and IV estimates, this procedure is robust to treatment effect heterogeneity.
The Impact of Retail Investors Sentiment on Conditional Volatility of Stocks and Bonds
Authors: Elroi Hadad, Haim Kedar-Levy
Date: 2022-08-02 00:00:00

We measure bond and stock conditional return volatility as a function of changes in sentiment, proxied by six indicators from the Tel Aviv Stock Exchange. We find that changes in sentiment affect conditional volatilities at different magnitudes and often in an opposite manner in the two markets, subject to market states. We are the first to measure bonds conditional volatility of retail investors sentiment thanks to a unique dataset of corporate bond returns from a limit-order-book with highly active retail traders. This market structure differs from the prevalent OTC platforms, where institutional investors are active yet less prone to sentiment.
Risk in Network Economies
Authors: Victor Sellemi
Date: 2022-08-02 00:00:00

Economic models with input-output networks assume that firm or sector (unit) growth is driven by a weighted sum of trade partners' growth and an independently-drawn idiosyncratic shock. I show that the idiosyncratic risk assumption in a broad class of network models implicitly generates restrictions on the network weights which are unrealistic. When allowing for correlated shocks, units are exposed to an additional risk term which captures the ability to substitute away from supply and demand shocks propagating through the network. I provide empirical evidence that changes in substitutability between trade partners are inversely related to changes in the panel of realized industry variance. Moreover, I find that supply-side (demand-side) substitutability is closely related to technological (product) dispersion of a unit's suppliers (customers). To synthesize these results, I propose a production-based asset pricing model in which supply chain substitutability is a function of dispersion in product/technology space and correlation in supply and demand shocks is driven by shared customers and suppliers between firms. The model predicts that assets which are positively exposed to average propagation of upstream and downstream shocks are useful hedges and thus earn lower average risk premia. Consistently, I find that estimated upstream (downstream) propagation factors earn return spreads of -11.4% (-4.2%) and are negatively associated with aggregate consumption, output, and dividend growth.
Review of Energy Transition Policies in Singapore, London, and California
Authors: Chunmeng Yang, Siqi Bu, Yi Fan, Wayne Xinwei Wan, Ruoheng Wang, Aoife Foley
Date: 2022-08-02 00:00:00

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively.
Time Instability of the Fama-French Multifactor Models: An International Evidence
Authors: Koichiro Moriya, Akihiko Noda
Date: 2022-08-02 00:00:00

This paper investigates the time-varying structure of Fama and French's (1993; 2015) multi-factor models using Fama and MacBeth's (1973) two-step estimation based on the rolling window method. In particular, we employ the generalized GRS statistics proposed by Kamstra and Shi (2024) to examine whether the validity of the risk factors (or factor redundancy) in the FF3 and FF5 models remains stable over time, and investigate whether the manner of portfolio sorting affects the time stability of the validity of the risk factors. In addition, we examine whether the similar results are obtained even when we use different datasets by country and region. First, we find that the effectiveness of factors in the FF3 and FF5 models is not stable over time in all countries. Second, the effectiveness of factors is also affected by the manner of portfolio sorting. Third, the validity of the FF3, FF5, and their nested models do not remain stable over time except for Japan. This suggests that the efficient market hypothesis is supported in the Japanese stock market. Finally, the factor redundancy varies over time and is affected by the manner of portfolio sorting mainly in the U.S. and Europe.
Simple models predict behavior at least as well as behavioral scientists
Authors: Dillon Bowen
Date: 2022-08-01 00:00:00

How accurately can behavioral scientists predict behavior? To answer this question, we analyzed data from five studies in which 640 professional behavioral scientists predicted the results of one or more behavioral science experiments. We compared the behavioral scientists' predictions to random chance, linear models, and simple heuristics like "behavioral interventions have no effect" and "all published psychology research is false." We find that behavioral scientists are consistently no better than - and often worse than - these simple heuristics and models. Behavioral scientists' predictions are not only noisy but also biased. They systematically overestimate how well behavioral science "works": overestimating the effectiveness of behavioral interventions, the impact of psychological phenomena like time discounting, and the replicability of published psychology research.
The character of non-manipulable collective choices between two alternatives
Authors: Achille Basile, K. P. S. Bhaskara Rao, Surekha Rao
Date: 2022-08-02 00:00:00
Tasks: Form, Vocal Bursts Valence Prediction

We consider classes of non-manipulable social choice functions with range of cardinality at most two within a set of at least two alternatives. We provide the functional form for each of the classes we consider. This functional form is a characterization that explicitly describes how a social choice function of that particular class selects the collective choice corresponding to a profile. We provide a unified formulation of these characterizations using the new concept of "character". The choice of the character, depending on the class of social choice functions, gives the functional form of all social choice functions of the class.
The design and optimization of synchronization sequence for Ultraviolet communication
Authors: Shihui Yu, Chen Gong, Zhengyuan Xu
Date: 2022-08-02 00:00:00

In the ultraviolet (UV) scattering communication, the received signals exhibit the characteristics of discrete photoelectrons due to path loss. The synchronization is based on maximum Pulse Number-Sequence correlation problem. First of all, the accuracy of synchronization is vital to channel estimation and decoding. This article focuses on improving synchronization accuracy by designing and optimizing synchronization sequences. As for the maximum Pulse Number-Sequence correlation problem, it is assumed that the correlation values satisfy the Gaussian distribution and their mathematical expectation, variance and covariance are derived to express the upper bound of synchronization offset. The synchronization sequence we designed has two equilong RANDOM parts (Symbols meet Bernoulli distribution with equal probability.) and a $\{1,0,1,0,1,0,...,1,0,1,0\}$ part between them with $ \alpha $ as its proportion of entire sequence. On the premise of ensuring the synchronization reliability, the synchronization deviation can be reduced by optimizing $ \alpha $. There are simulation experiments to verify correctness of the derivation, reasonableness of the hypothesis and reliability of optimization. Compared with equilong random sequence, the synchronization accuracy of the optimized synchronization sequence is significantly improved.
Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities
Authors: Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas, Mehdi Bennis
Date: 2022-08-02 00:00:00
Tasks: ISAC

The sixth generation (6G) wireless networks aim to achieve ultra-high data transmission rates, very low latency and enhanced energy-efficiency. To this end, terahertz (THz) band is one of the key enablers of 6G to meet such requirements. The THz-band systems are also quickly emerging as high-resolution sensing devices because of their ultra-wide bandwidth and very narrow beamwidth. As a means to efficiently utilize spectrum and thereby save cost and power, THz integrated sensing and communications (ISAC) paradigm envisages a single integrated hardware platform with a common signaling mechanism. However, ISAC at THz-band entails several design challenges such as beam split, range-dependent bandwidth, near-field beamforming, and distinct channel model. This article examines the technologies that have the potential to bring forth ISAC and THz transmission together. In particular, it provides an overview of antenna and array design, hybrid beamforming, integration with reflecting surfaces and data-driven techniques such as machine learning. These systems also provide research opportunities in developing novel methodologies for channel estimation, near-field beam split, waveform design, and beam misalignment.
Optimal Measurement of Drone Swarm in RSS-based Passive Localization with Region Constraints
Authors: Xin Cheng, Feng Shu, YiFan Li, Zhihong Zhuang, Di wu, Jiangzhou Wang
Date: 2022-08-02 00:00:00

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing sensor placement strategies mainly work on the cases without any constraints on the sensors locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., minimizing the determinate of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.
Time-Dependent Performance Modeling for Platooning Communications at Intersection
Authors: Qiong Wu, Yu Zhao, Qiang Fan
Date: 2022-08-02 00:00:00
Tasks: Autonomous Driving

With the development of internet of vehicles, platooning strategy has been widely studied as the potential approach to ensure the safety of autonomous driving. Vehicles in the form of platoon adopt 802.11p to exchange messages through vehicle to vehicle (V2V) communications. When multiple platoons arrive at an intersection, the leader vehicle of each platoon adjusts its movement characteristics to ensure that it can cross the intersection and thus the following vehicles have to adjust their movement characteristics accordingly. In this case, the time-varying connectivity among vehicles leads to the significant non-stationary performance change in platooning communications, which may incur safety issues. In this paper, we construct the time-dependent model to evaluate the platooning communication performance at the intersection based on the initial movement characteristics. We first consider the movement behaviors of vehicles at the intersection including turning, accelerating, decelerating and stopping as well as the periodic change of traffic lights to construct movement model, and then establish a hearing network to reflect the time-varying connectivity among vehicles. Afterwards, we adopt the pointwise stationary fluid flow approximation (PSFFA) to model the non-stationary behavior of transmission queue. Then, we consider four access categories (ACs) and continuous backoff freezing of 802.11p to construct the models to describe the time-dependent access process of 802.11p. Finally, based on the time-dependent model, the packet transmission delay and packet delivery ratio are derived. The accuracy of our proposed model is verified by comparing the simulation results with analytical results.
Multi-user Downlink Beamforming using Uplink Downlink Duality with CEQs for Frequency Selective Channels
Authors: Khurram Usman Mazher, Amine Mezghani, Robert W Heath Jr
Date: 2022-08-01 00:00:00
Tasks: Quantization

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.
The Effects of Land-use Change on Brucellosis Transmission in the Greater Yellowstone Ecosystem: A Three Species Mathematical-Epidemiological Model
Authors: Dustin G. Padilla
Date: 2022-08-01 00:00:00
Tasks: Management

This paper models brucellosis transmission between elk, cattle, and bison, of high conservation value, in the Greater Yellowstone Ecosystem (GYE). It aims to show how landscape changes in the GYE concomitantly impact brucellosis prevalence in the three species. The approach allows us to see how landscape changes in one location influence disease prevalence in populations elsewhere. The model uses the fact that the populations are configured in such a way that elk are an intermediary for disease transmission between cattle and bison. Using landscape ecology metrics applied to the habitat overlaps between elk and cattle and between elk and bison, the landscape parameters are varied to determine how disease propagates throughout the ecosystem as land-use change occurs. Result aim to provide insights into how land management used for the control of disease spread between cattle and elk, may impact (and be impacted by) disease prevalence in bison.