pwc-archive/papers-with-abstracts huggingface dataset:
Agent based simulators for epidemic modelling: Simulating larger models using smaller ones
Authors: Daksh Mittal, Sandeep Juneja, Shubhada Agrawal
Date: 2022-09-07 00:00:00
Tasks: Epidemiology

Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interactions as well as detailed governmental mobility restrictions. Typically, for accuracy, each person is modelled separately. This however may make computational time prohibitive when the city population and the simulated time is large. In this paper, we dig deeper into the underlying probabilistic structure of a generic, locally detailed ABS for epidemiology to arrive at modifications that allow smaller models (models with less number of agents) to give accurate statistics for larger ones, thus substantially speeding up the simulation. We observe that simply considering a smaller aggregate model and scaling up the output leads to inaccuracies. We exploit the observation that in the initial disease spread phase, the starting infections create a family tree of infected individuals more-or-less independent of the other trees and are modelled well as a multi-type super-critical branching process. Further, although this branching process grows exponentially, the relative proportions amongst the population types stabilise quickly. Once enough people have been infected, the future evolution of the epidemic is closely approximated by its mean field limit with a random starting state. We build upon these insights to develop a shifted, scaled and restart-based algorithm that accurately evaluates the ABS's performance using a much smaller model while carefully reducing the bias that may otherwise arise. We apply our algorithm for Covid-19 epidemic in a city and theoretically support the proposed algorithm through an asymptotic analysis where the population size increases to infinity.
A Ridge-Regularised Jackknifed Anderson-Rubin Test
Authors: Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis
Date: 2022-09-07 00:00:00

We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments -- possibly more than the number of observations. We show that a ridge-regularised version of the jackknifed Anderson Rubin (1949, henceforth AR) test controls asymptotic size in the presence of heteroskedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularisation extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte-Carlo simulations indicate that our method has favourable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the US illustrates the usefulness of the proposed method for practitioners.
Local Projection Inference in High Dimensions
Authors: Robert Adamek, Stephan Smeekes, Ines Wilms
Date: 2022-09-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.
Estimating the Effects of Syrian Civil War
Authors: Aleksandar Keseljevic, Rok Spruk
Date: 2022-09-07 00:00:00
Tasks: counterfactual, Humanitarian

We examine the effect of civil war in Syria on economic growth, human development and institutional quality. Building on the synthetic control method, we estimate the missing counterfactual scenario in the hypothetical absence of the armed conflict that led to unprecedented humanitarian crisis and population displacement in modern history. By matching Syrian growth and development trajectories with the characteristics of the donor pool of 66 countries with no armed internal conflict in the period 1996-2021, we estimate a series of growth and development gaps attributed to civil war. Syrian civil war appears to have had a temporary negative effect on the trajectory of economic growth that almost disappeared before the onset of COVID19 pandemic. By contrast, the civil war led to unprecedented losses in human development, rising infant mortality and rampantly deteriorating institutional quality. Down to the present day, each year of the conflict led to 5,700 additional under-five child deaths with permanently derailed negative effect on longevity. The civil war led to unprecedent and permanent deterioration in institutional quality indicated by pervasive weakening of the rule of law and deleterious impacts on government effectiveness, civil liberties and widespread escalation of corruption. The estimated effects survive a battery of placebo checks.
Clarifying Theoretical Intricacies through the Use of Conceptual Visualization: Case of Production Theory in Advanced Microeconomics
Authors: Alexandra Naumenko, Seyyed Ali Zeytoon Nejad Moosavian
Date: 2022-09-06 00:00:00
Tasks: LEMMA

Production theory, defined as the study of the economic process of transforming inputs into outputs, consists of two simultaneous economic forces: cost minimization and profit maximization. The cost minimization problem involves deriving conditional factor demand functions and the cost function. The profit maximization problem involves deriving the output supply function, the profit function, and unconditional factor demand functions. Nested within the process are Shephard's lemma, Hotelling's lemmas, direct and indirect mathematical relations, and other elements contributing to the dynamics of the process. The intricacies and hidden underlying influences pose difficulties in presenting the material for an instructor, and inhibit learning by students. Simply put, the primary aim of this paper is to facilitate the teaching and learning of the production theory realm of Economics through the use of a conceptual visual model. This paper proposes a pedagogical tool in the form of a detailed graphic illustrating t he relationship between profit maximization and cost minimization under technical constraints, with an emphasis on the similarities and differences between the perfect competition and monopoly cases. The potential that such a visual has to enhance learning when supplementing traditional context is discussed under the context of contemporary learning literature. Embedded in the discussion is an example of how we believe our model could be conceptualized and utilized in a real-world setting to evaluate an industrial project with an economic point of view.
The Visual Decoding of the Wheel of Duality in Consumer Theory in Modern Microeconomics
Authors: Seyyed Ali Zeytoon Nejad Moosavian
Date: 2022-09-06 00:00:00

Duality is the heart of advanced microeconomics. It exists everywhere throughout advanced microeconomics, from the beginning of consumer theory to the end of production theory. The complex, circular relationships among various theoretical microeconomic concepts involved in the setting of duality theory have led it to be called the "wheel of pain" by many graduate economics students . Put simply, the main aim of this paper is to turn this "wheel of pain" into a "wheel of joy". To be more specific, the primary purpose of this paper is to graphically decode the logical, complex relationships among a quartet of dual functions which present preferences as well as a quartet of demand-related functions in a visual manner.
Identifying the Effect of Parenthood on Labor Force Participation: A Gender Comparison
Authors: Seyyed Ali Zeytoon Nejad Moosavian
Date: 2022-09-06 00:00:00

Identifying the factors that influence labor force participation could elucidate how individuals arrive at their labor supply decisions, whose understanding is, in turn, of crucial importance in analyzing how the supply side of the labor market functions. This paper investigates the effect of parenthood status on Labor Force Participation (LFP) decisions using an individual-level fixed-effects identification strategy. The differences across individuals and over time in having or not having children as well as being or not being in the labor force provide the variation needed to assess the association between individuals' LFP behavior and parenthood. Parenthood could have different impacts on mothers than it would on fathers. In order to look at the causal effect of maternity and paternity on LFP separately, the data is disaggregated by gender. To this end, the effect of a change in the parenthood status can be measured using individual-level fixed-effects to account for time-invariant characteristics of individuals becoming a parent. The primary data source used is the National Longitudinal Surveys (NLS). Considering the nature of LFP variable, this paper employs Binary Response Models (BRMs) to estimate LFP equations using individual-level micro data. The findings of the study show that parenthood has a negative overall effect on LFP. However, paternity has a significant positive effect on the likelihood of being in the labor force, whilst maternity has a significant negative impact of LFP. In addition, the results imply that the effect of parenthood on LFP has been fading away over time, regardless of the gender of parents. These two pieces of evidence precisely map onto the theoretical predictions made by the related mainstream economic theories (the traditional neoclassical theory of labor supply as well as Becker's household production model). These results are ...
Market Design for Tradable Mobility Credits
Authors: Siyu Chen, Ravi Seshadri, Carlos Lima Azevedo, Arun P. Akkinepally, Renming Liu, Andrea Araldo, Yu Jiang, Moshe E. Ben-Akiva
Date: 2021-01-03 00:00:00
Tasks: Management

Tradable mobility credit (TMC) schemes are an approach to travel demand management that have received significant attention in recent years. This paper proposes and analyzes alternative market models for a TMC system -- focusing on market design aspects such as allocation/expiration of tokens, rules governing trading, transaction fees, and regulator intervention -- and develops a methodology to explicitly model the dis-aggregate behavior of individuals within the market. Extensive simulation experiments are conducted within a combined mode and departure time context for the morning commute problem to compare the performance of the alternative designs relative to congestion pricing and a no-control scenario. The simulation experiments employ a day-to-day assignment framework wherein transportation demand is modeled using a logit-mixture model with income effects and supply is modeled using a standard bottleneck model. The results indicate that small fixed transaction fees can effectively mitigate undesirable behavior in the market without a significant loss in efficiency (total welfare) whereas proportional transaction fees are less effective both in terms of efficiency and in avoiding undesirable market behavior. Further, an allocation of tokens in continuous time can be beneficial in dealing with non-recurrent events and avoiding concentrated trading activity. In the presence of income effects, despite small fixed transaction fees, the TMC system yields a marginally higher social welfare than congestion pricing while attaining revenue neutrality. Further, it is more robust in the presence of forecasting errors and non-recurrent events due to the adaptiveness of the market. Finally, as expected, the TMC scheme is more equitable (when revenues from congestion pricing are not redistributed) although it is not guaranteed to be Pareto-improving when tokens are distributed equally.
Quaternion Tensor Train Rank Minimization with Sparse Regularization in a Transformed Domain for Quaternion Tensor Completion
Authors: Jifei Miao, Kit Ian Kou, Liqiao Yang, Dong Cheng
Date: 2022-09-07 00:00:00

The tensor train rank (TT-rank) has achieved promising results in tensor completion due to its ability to capture the global low-rankness of higher-order (>3) tensors. On the other hand, recently, quaternions have proven to be a very suitable framework for encoding color pixels, and have obtained outstanding performance in various color image processing tasks. In this paper, the quaternion tensor train (QTT) decomposition is presented, and based on that the quaternion TT-rank (QTT-rank) is naturally defined, which are the generalizations of their counterparts in the real number field. In addition, to utilize the local sparse prior of the quaternion tensor, a general and flexible transform framework is defined. Combining both the global low-rank and local sparse priors of the quaternion tensor, we propose a novel quaternion tensor completion model, i.e., QTT-rank minimization with sparse regularization in a transformed domain. Specifically, we use the quaternion weighted nuclear norm (QWNN) of mode-n canonical unfolding quaternion matrices to characterize the global low-QTT-rankness, and the l1-norm of the quaternion tensor in a transformed domain to characterize the local sparse property. Moreover, to enable the QTT-rank minimization to handle color images and better handle color videos, we generalize KA, a tensor augmentation method, to quaternion tensors and define quaternion KA (QKA), which is a helpful pretreatment step for QTT-rank based optimization problems. The numerical experiments on color images and color videos inpainting tasks indicate the advantages of the proposed method over the state-of-the-art ones.
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation
Authors: Zhenya Zang, Dong Xiao, Quan Wang, Ziao Jiao, Chen Yu, David Day-Uei Li
Date: 2022-09-07 00:00:00
Tasks: Quantization

This paper reported a bespoke adder-based deep learning network for time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN) without multiplication-based convolutions to reduce the computational complexity. Further, we compressed fluorescence decays in temporal dimension using a log-scale merging technique to discard redundant temporal information derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23 compression ratios compared with FLAN and a conventional 1-D convolutional neural network (1-D CNN) while maintaining high accuracy in retrieving lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real data. A traditional fitting method and other non-fitting, high-accuracy algorithms were compared with our networks for synthetic data. Our networks attained a minor reconstruction error in different photon-count scenarios. For real data, we used fluorescent beads' data acquired by a confocal microscope to validate the effectiveness of real fluorophores, and our networks can differentiate beads with different lifetimes. Additionally, we implemented the network architecture on a field-programmable gate array (FPGA) with a post-quantization technique to shorten the bit-width, thereby improving computing efficiency. FLAN+LS on hardware achieves the highest computing efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of our network and hardware architecture for other time-resolved biomedical applications using photon-efficient, time-resolved sensors.
THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication
Authors: Chong Han, Yongzhi Wu, Zhi Chen, Yi Chen, Guangjian Wang
Date: 2022-09-07 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management

The Terahertz (0.1-10 THz) band holds enormous potential for supporting unprecedented data rates and millimeter-level accurate sensing thanks to its ultra-broad bandwidth. Terahertz integrated sensing and communication (ISAC) is viewed as a game-changing technology to realize connected intelligence in 6G and beyond systems. In this article, challenges from THz channel and transceiver perspectives, as well as difficulties of ISAC are elaborated. Motivated by these challenges, THz ISAC channels are studied in terms of channel types, measurement and models. Moreover, four key signal processing techniques to unleash the full potential of THz ISAC are investigated, namely, waveform design, receiver processing, narrowbeam management, and localization. Quantitative studies demonstrate the benefits and performance of the state-of-the-art signal processing methods. Finally, open problems and potential solutions are discussed.
Multi-Armed Bandit Dynamic Beam Zooming for mmWave Alignment and Tracking
Authors: Nathan Blinn, Matthieu Bloch
Date: 2022-09-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

We propose an Integrated Sensing and Communication (ISAC) algorithm that exploits the structure of a hierarchical codebook of beamforming vectors using a best-arm identification Multi-Armed Bandit (MAB) approach for initial alignment and tracking of a Mobile Entity (ME). The algorithm, called Dynamic Beam Zooming (DBZ), performs beam adjustments that mitigate the severe outages associated with wireless mmWave systems and allow for adaptive control of the parameters governing communications. We analyze the sample complexity of DBZ and use it to inform how the algorithm adapts to the nonstationary MAB statistics based on ME motion and Signal-to-Noise Ratio (SNR). We perform extensive simulations to validate the approach and demonstrate that DBZ is competitive against existing Bayesian algorithms, without requiring channel multipath or fading knowledge. In particular, DBZ outperforms other low-complexity algorithms in the low SNR regime. We also illustrate the efficacy of DBZ in standardized rural and urban scenarios using NYU Sim.
Two-Timescale Transmission Design and RIS Optimization for Integrated Localization and Communications
Authors: Fan Jiang, Andrea Abrardo, Kamran Keykhoshravi, Henk Wymeersch, Davide Dardari, Marco Di Renzo
Date: 2022-09-06 00:00:00

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.
Geometric Signatures of Switching Behavior in Mechanobiology
Authors: Casey O. Barkan, Robijn F. Bruinsma
Date: 2022-09-07 00:00:00
Tasks: Protein Folding

The proteins involved in cells' mechanobiological processes have evolved specialized and surprising responses to applied forces. Biochemical transformations that show catch-to-slip switching and force-induced pathway switching serve important functions in cell adhesion, mechano-sensing and signaling, and protein folding. We show that these switching behaviors are generated by singularities in the flow field that describes force-induced deformation of bound and transition states. These singularities allow for a complete characterization of switching mechanisms in 2-dimensional (2D) free energy landscapes, and provide a path toward elucidating novel forms of switching in higher dimensional models. Remarkably, the singularity that generates a catch-slip switch occurs in almost every 2D free energy landscape, implying that almost any bond admitting a 2D model will exhibit catch-slip behavior under appropriate force. We apply our analysis to models of P-selectin and antigen extraction to illustrate how these singularities provide an intuitive framework for explaining known behaviors and predicting new behaviors.
A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
Authors: Edoardo Balzani, Jean Paul Noel, Pedro Herrero-Vidal, Dora E. Angelaki, Cristina Savin
Date: 2022-09-06 00:00:00

Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas. Nonetheless, existing statistical tools for extracting neural manifolds face limitations in terms of interpretability of latents with respect to task variables, and can be hard to apply to datasets with no trial repeats. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) extends a probabilistic variant of demixed PCA by (1) explicitly partitioning variability into private and shared sources, (2) using a Poisson noise model, and (3) introducing temporal smoothing of latent trajectories in the form of a Gaussian Process prior. This TAME-GP graphical model allows for robust estimation of task-relevant variability in local population responses, and of shared co-variability between brain areas. We demonstrate the efficiency of our estimator on within model and biologically motivated simulated data. We also apply it to neural recordings in a closed-loop virtual navigation task in monkeys, demonstrating the capacity of TAME-GP to capture meaningful intra- and inter-area neural variability with single trial resolution.
Topological AI forecasting of future dominating viral variants
Authors: Guo-Wei Wei
Date: 2022-09-07 00:00:00

The understanding of the mechanisms of SARS-CoV-2 evolution and transmission is one of the greatest challenges of our time. By integrating artificial intelligence (AI), viral genomes isolated from patients, tens of thousands of mutational data, biophysics, bioinformatics, and algebraic topology, the SARS-CoV-2 evolution was revealed to be governed by infectivity-based natural selection. Two key mutation sites, L452 and N501 on the viral spike protein receptor-binding domain (RBD), were predicted in summer 2020, long before they occur in prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda, Mu, and Omicron. Recent studies identified a new mechanism of natural selection: antibody resistance. AI-based forecasting of Omicron's infectivity, vaccine breakthrough, and antibody resistance was later nearly perfectly confirmed by experiments. The replacement of dominant BA.1 by BA.2 in later March was predicted in early February. On May 1, 2022, persistent Laplacian-based AI projected Omicron BA.4 and BA.5 to become the new dominating COVID-19 variants. This prediction became reality in late June. Topological AI models offer accurate prediction of mutational impacts on the efficacy of monoclonal antibodies (mAbs).