pwc-archive/papers-with-abstracts huggingface dataset:
Capacitated Beam Placement for Multi-beam Non-Geostationary Satellite Systems
Authors: Nariman Torkzaban, Asim Zoulkarni, Anousheh Gholami, John S. Baras
Date: 2023-01-18 00:00:00

Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention both from industry and academia, over the past several years. Beam placement is among the major resource allocation problems in multi-beam NGSO systems. In this paper, we formulate the beam placement problem as a Euclidean disk cover optimization model. We aim at minimizing the number of placed beams while satisfying the total downlink traffic demand of targeted ground terminals without exceeding the capacity of the placed beams. We present a low-complexity deterministic annealing (DA)-based algorithm to solve the NP-hard optimization model for near-optimal solutions. We further propose an extended variant of the previous model to ensure the traffic assigned to the beams is balanced. We verify the effectiveness of our proposed methods by means of numerical experiments and show that our scheme is superior to the state-of-the-art methods in that it covers the ground users by fewer number of beams on average.
Correct Approximation of Stationary Distributions
Authors: Tobias Meggendorfer
Date: 2023-01-18 00:00:00

A classical problem for Markov chains is determining their stationary (or steady-state) distribution. This problem has an equally classical solution based on eigenvectors and linear equation systems. However, this approach does not scale to large instances, and iterative solutions are desirable. It turns out that a naive approach, as used by current model checkers, may yield completely wrong results. We present a new approach, which utilizes recent advances in partial exploration and mean payoff computation to obtain a correct, converging approximation.
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
Authors: Erik Buhmann, Gregor Kasieczka, Jesse Thaler
Date: 2023-01-17 00:00:00
Tasks: Generative Adversarial Network, Point Cloud Generation

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
Agent-based Integrated Assessment Models: Alternative Foundations to the Environment-Energy-Economics Nexus
Authors: Karl Naumann-Woleske
Date: 2023-01-17 00:00:00

Climate change is a major global challenge today. To assess how policies may lead to mitigation, economists have developed Integrated Assessment Models, however, most of the equilibrium based models have faced heavy critiques. Agent-based models have recently come to the fore as an alternative macroeconomic modeling framework. In this paper, four Agent-based Integrated Assessment Models linking environment, energy and economy are reviewed. These models have several advantages over existing models in terms of their heterogeneous agents, the allocation of damages amongst the individual agents, representation of the financial system, and policy mixes. While Agent-based Integrated Assessment Models have made strong advances, there are several avenues into which research should be continued, including incorporation of natural resources and spatial dynamics, closer analysis of distributional effects and feedbacks, and multi-sectoral firm network structures.
Digital Divide: Empirical Study of CIUS 2020
Authors: Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
Date: 2023-01-19 00:00:00
Tasks: Survey

As Canada and other major economies consider implementing "digital money" or Central Bank Digital Currencies, understanding how demographic and geographic factors influence public engagement with digital technologies becomes increasingly important. This paper uses data from the 2020 Canadian Internet Use Survey and employs survey-adapted Lasso inference methods to identify individual socio-economic and demographic characteristics determining the digital divide in Canada. We also introduce a score to measure and compare the digital literacy of various segments of Canadian population. Our findings reveal that disparities in the use of e.g. online banking, emailing, and digital payments exist across different demographic and socio-economic groups. In addition, we document the effects of COVID-19 pandemic on internet use in Canada and describe changes in the characteristics of Canadian internet users over the last decade.
An MCMC Approach to Classical Estimation
Authors: Victor Chernozhukov, Han Hong
Date: 2023-01-18 00:00:00

This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile, nonlinear GMM and value-at-risk models. The LTE's are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained for regular cases.
Optimal Transport for Counterfactual Estimation: A Method for Causal Inference
Authors: Arthur Charpentier, Emmanuel Flachaire, Ewen Gallic
Date: 2023-01-18 00:00:00
Tasks: Causal Inference, counterfactual

Many problems ask a question that can be formulated as a causal question: "what would have happened if...?" For example, "would the person have had surgery if he or she had been Black?" To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
Algorithmic Writing Assistance on Jobseekers' Resumes Increases Hires
Authors: Emma van Inwegen, Zanele Munyikwa, John J. Horton
Date: 2023-01-19 00:00:00

There is a strong association between the quality of the writing in a resume for new labor market entrants and whether those entrants are ultimately hired. We show that this relationship is, at least partially, causal: a field experiment in an online labor market was conducted with nearly half a million jobseekers in which a treated group received algorithmic writing assistance. Treated jobseekers experienced an 8% increase in the probability of getting hired. Contrary to concerns that the assistance is taking away a valuable signal, we find no evidence that employers were less satisfied. We present a model in which better writing is not a signal of ability but helps employers ascertain ability, which rationalizes our findings.
Endogenous Labour Flow Networks
Authors: Kathyrn R. Fair, Omar A. Guerrero
Date: 2023-01-19 00:00:00
Tasks: Navigate

In the last decade, the study of labour dynamics has led to the introduction of labour flow networks (LFNs) as a way to conceptualise job-to-job transitions, and to the development of mathematical models to explore the dynamics of these networked flows. To date, LFN models have relied upon an assumption of static network structure. However, as recent events (increasing automation in the workplace, the COVID-19 pandemic, a surge in the demand for programming skills, etc.) have shown, we are experiencing drastic shifts to the job landscape that are altering the ways individuals navigate the labour market. Here we develop a novel model that emerges LFNs from agent-level behaviour, removing the necessity of assuming that future job-to-job flows will be along the same paths where they have been historically observed. This model, informed by microdata for the United Kingdom, generates empirical LFNs with a high level of accuracy. We use the model to explore how shocks impacting the underlying distributions of jobs and wages alter the topology of the LFN. This framework represents a crucial step towards the development of models that can answer questions about the future of work in an ever-changing world.
Input-Output Analysis: New Results From Markov Chain Theory
Authors: Nizar Riane, Claire David
Date: 2023-01-13 00:00:00
Tasks: Sensitivity

In this work, we propose a new lecture of input-output model reconciliation Markov chain and the dominance theory, in the field of interindustrial poles interactions. A deeper lecture of Leontieff table in term of Markov chain is given, exploiting spectral properties and time to absorption to characterize production processes, then the dualities local-global/dominance- Sensitivity analysis are established, allowing a better understanding of economic poles arrangement. An application to the Moroccan economy is given.
Machine Learning-Based Secret Key Generation for IRS-assisted Multi-antenna Systems
Authors: Chen Chen, Junqing Zhang, Tianyu Lu, Magnus Sandell, Liquan Chen
Date: 2023-01-19 00:00:00

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.
Relative Entropy-Based Constant-Envelope Beamforming for Target Detection in Large-Scale MIMO Radar With Low-Resoultion ADCs
Authors: Ziyang Cheng, Linlong Wu, Bowen Wang, Julan Xie, Huiyong Li
Date: 2023-01-19 00:00:00

Hybrid digital/analog architecture and low-resolution analog-to-digital/digital-to-analog converters (ADCs /DACs) are two low-cost implementations for large-scale millimeter wave (mmWave) systems. In this paper, we investigate the problem of constant-envelope transmit beamforming for large-scale multiple-input multiple-output (MIMO) radar system, where the transmit array adopts a hybrid digital/analog architecture with a small number of RF chains and the receive array adopts a fully digital architecture with low-resolution ADCs. We derive the relative entropy between the probability density functions associated with the two test hypotheses under low-resolution ADCs. We formulate our optimization problem by maximizing the relative entropy, subject to the constant envelope and orthogonality constraints. To suboptimally solve the resultant problem, a two-stage framework is developed. In the first stage, we optimize the transmit power at the directions of the target and clutter. In the second stage, an efficient iterative algorithm based on majorization-minimization is presented to obtain the constant-envelope beamformer according to the attained transmit power. Specifically, we apply a quadratic function as the minorizer, leading to a low-complexity solution at each iteration. In addition, to further facilitate low-cost implementation of the constant-envelope beamformer, we consider the problem of one-bit beamforming design and propose an efficient iterative method based on the Nesterov-like gradient method to solve it. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.
Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework
Authors: Chengjie Zhao, Jun Wang, Wei Huang, Xiaonan Chen, Tianfu Qi
Date: 2023-01-19 00:00:00

In many communication scenarios, the communication signals simultaneously suffer from white Gaussian noise (WGN) and non-Gaussian impulsive noise (IN), i.e., mixed Gaussian-impulsive noise (MGIN). Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN. Moreover, as the widely adopted IN model has no analytical and general closed-form expression of probability density function (PDF), it is extremely hard to obtain optimal communication signal and corresponding detection schemes based on classical stochastic signal processing theory. To circumvent these difficulties, we propose a data-driven end-to-end framework to address the communication signal design and detection under MGIN channel in this paper. In this proposed framework, a channel noise simulator (CNS) is elaborately designed based on an improved generative adversarial net (GAN) to simulate the MGIN without requirement of any analytical PDF. Meanwhile, a multi-level wavelet convolutional neural network (MWCNN) based preprocessing network is used to mitigate the negative effect of outliers due to the IN. Compared with conventional approaches and existing end-to-end systems, extensive simulation results verify that our proposed novel end-to-end communication system can achieve better performance in terms of bit-error rate (BER) under MGIN environments.
Loss Attitude Aware Energy Management for Signal Detection
Authors: Baocheng Geng, Chen Quan, Tianyun Zhang, Makan Fardad, Pramod K. Varshney
Date: 2023-01-18 00:00:00
Tasks: energy management, Management

This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approach to solve energy management problems for signal detection while balancing multiple objectives. In particular, the optimal amount of resource consumption is determined so as to maximize a profit-loss based expected utility function. Next, we study the human behavior of resource consumption while taking individuals' behavioral disparity into account. Unlike rational decision makers who consume the amount of resource to maximize the expected utility function, human decision makers act to maximize their subjective utilities. We employ prospect theory to model humans' loss aversion towards a risky event. The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans. It is shown that loss attitudes may lead the human to behave quite differently from a rational decision maker.
Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling
Authors: Luis Almeida, Jérôme Denis, Nathalie Ferrand, Tommaso Lorenzi, Antonin Prunet, Michéle Sabbah, Chiara Villa
Date: 2023-01-19 00:00:00

Glucose is a primary energy source for cancer cells. Several lines of evidence support the idea that monocarboxylate transporters, such as MCT1, elicit metabolic reprogramming of cancer cells in glucose-poor environments, allowing them to reuse lactate, a byproduct of glucose metabolism, as an alternative energy source with serious consequences for disease progression. We employ a synergistic experimental and mathematical modelling approach to explore the evolutionary processes at the root of cancer cell adaptation to glucose deprivation, with particular focus on the mechanisms underlying the increase in MCT1 expression observed in glucose-deprived aggressive cancer cells. Data from in vitro experiments on breast cancer cells are used to inform and calibrate a mathematical model that comprises a partial integro-differential equation for the dynamics of a population of cancer cells structured by the level of MCT1 expression. Analytical and numerical results of this model suggest that environment-induced changes in MCT1 expression mediated by lactate-associated signalling pathways enable a prompt adaptive response of glucose-deprived cancer cells, whilst fluctuations in MCT1 expression due to epigenetic changes create the substrate for environmental selection to act upon, speeding up the selective sweep underlying cancer cell adaptation to glucose deprivation, and may constitute a long-term bet-hedging mechanism.
Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech
Authors: Yuran Zhang, Jiajie Zou, Nai Ding
Date: 2023-01-19 00:00:00

When listening to connected speech, human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural encoding of different levels of linguistic units is captured by the neural response tracking speech envelope in different frequency bands (e.g., around 1 Hz for phrases, around 2 Hz for words, and around 4 Hz for syllables). Here, we analyze the coherence between speech envelope and idealized responses, each of which tracks a single level of linguistic unit. Four units, i.e., phones, syllables, words, and sentences, are separately considered. It is shown that the idealized phone-, syllable-, and word-tracking responses all correlate with the speech envelope both around 3-6 Hz and below ~1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain decomposition of envelope-tracking activity cannot separate the neural responses to different linguistic units in natural speech.
Modeling sustained transmission of Wolbachia among Anopheles mosquitoes: Implications for malaria control in Haiti
Authors: Daniela Florez, Alyssa Young, Kerlly J. Bernabe, James M. Hyman, Zhuolin Qu
Date: 2023-01-19 00:00:00

Wolbachia infection in Anopheles albimanus mosquitoes can render mosquitoes less capable of spreading malaria. We develop and analyze an ordinary differential equation model to evaluate the effectiveness of Wolbachia-based vector control strategies among wild Anopheles mosquitoes in Haiti. The model tracks the mosquito life stages, including egg, larva, and adult (male and female). It also accounts for critical biological effects, such as the maternal transmission of Wolbachia through infected females and cytoplasmic incompatibility, which effectively sterilizes uninfected females when they mate with infected males. We derived and interpreted dimensionless numbers, including the basic reproductive number and next-generation numbers. The proposed system presents backward bifurcation, which indicates a threshold infection that needs to be exceeded to establish a stable Wolbachia infection. The sensitivity analysis ranks the relative importance of the epidemiological parameters at the baseline. We simulate different intervention scenarios, including pre-release mitigation using larviciding and thermal fogging before the release, multiple releases of infected populations, and different release timing. Our simulations show that the most efficient approach to establishing Wolbachia is to release all the infected mosquitoes immediately after the pre-release mitigation process. Also, the model predicts that it is more efficient to release during the dry season than the wet season.
Finding analytical approximations for discrete, stochastic, individual-based models of ecology
Authors: Linnéa Gyllingberg, David J. T. Sumpter, Åke Brännström
Date: 2023-01-19 00:00:00

Discrete time, spatially extended models play an important role in ecology, modelling population dynamics of species ranging from micro-organisms to birds. An important question is how 'bottom up', individual-based models can be approximated by 'top down' models of dynamics. Here, we study a class of spatially explicit individual-based models with contest competition: where species compete for space in local cells and then disperse to nearby cells. We start by describing simulations of the model, which exhibit large-scale discrete oscillations and characterise these oscillations by measuring spatial correlations. We then develop two new approximate descriptions of the resulting spatial population dynamics. The first is based on local interactions of the individuals and allows us to give a difference equation approximation of the system over small dispersal distances. The second approximates the long-range interactions of the individual-based model. These approximations capture demographic stochasticity from the individual-based model and show that dispersal stabilizes population dynamics. We calculate extinction probability for the individual-based model and show convergence between the local approximation and the non-spatial global approximation of the individual-based model as dispersal distance and population size simultaneously tend to infinity. Our results provide new approximate analytical descriptions of a complex bottom-up model and deepen understanding of spatial population dynamics.