pwc-archive/papers-with-abstracts huggingface dataset:
Heat and Economic Preferences
Authors: Michelle Escobar Carias, David Johnston, Rachel Knott, Rohan Sweeney
Date: 2021-10-05 00:00:00
Tasks: Decision Making, Survey

The empirical evidence suggests that key accumulation decisions and risky choices associated with economic development depend, at least in part, on economic preferences such as willingness to take risk and patience. This paper studies whether temperature could be one of the potential channels that influences such economic preferences. Using data from the Indonesia Family Life Survey and NASAs Modern Era Retrospective Analysis for Research and Applications data we exploit quasi exogenous variations in outdoor temperatures caused by the random allocation of survey dates. This approach allows us to estimate the effects of temperature on elicited measures of risk aversion, rational choice violations, and impatience. We then explore three possible mechanisms behind this relationship, cognition, sleep, and mood. Our findings show that higher temperatures lead to significantly increased rational choice violations and impatience, but do not significantly increase risk aversion. These effects are mainly driven by night time temperatures on the day prior to the survey and less so by temperatures on the day of the survey. This impact is quasi linear and increasing when midnight outdoor temperatures are above 22C. The evidence shows that night time temperatures significantly deplete cognitive functioning, mathematical skills in particular. Based on these findings we posit that heat induced night time disturbances cause stress on critical parts of the brain, which then manifest in significantly lower cognitive functions that are critical for individuals to perform economically rational decision making.
Uncertainty and Robustness of Surplus Extraction
Authors: Giuseppe Lopomo, Luca Rigotti, Chris Shannon
Date: 2018-11-04 00:00:00

This paper studies a robust version of the classic surplus extraction problem, in which the designer knows only that the beliefs of each type belong to some set, and designs mechanisms that are suitable for all possible beliefs in that set. We derive necessary and sufficient conditions for full extraction in this setting, and show that these are natural set-valued analogues of the classic convex independence condition identified by Cremer and McLean (1985, 1988). We show that full extraction is neither generically possible nor generically impossible, in contrast to the standard setting in which full extraction is generic. When full extraction fails, we show that natural additional conditions can restrict both the nature of the contracts a designer can offer and the surplus the designer can obtain.
Group network effects in price competition
Authors: Renato Soeiro, Alberto Pinto
Date: 2021-10-12 00:00:00

The partition of society into groups, polarization, and social networks are part of most conversations today. How do they influence price competition? We discuss Bertrand duopoly equilibria with demand subject to network effects. Contrary to models where network effects depend on one aggregate variable (demand for each choice), partitioning the dependence into groups creates a wealth of pure price equilibria with profit for both price setters, even if positive network effects are the dominant element of the game. If there is some asymmetry in how groups interact, two groups are sufficient. If network effects are based on undirected and unweighted graphs, at least five groups are required but, without other differentiation, outcomes are symmetric.
Motivating Effort with Information about Future Rewards
Authors: Chang Liu
Date: 2021-10-11 00:00:00

This paper studies the optimal mechanism to motivate effort in a dynamic principal-agent model without transfers. An agent is engaged in a task with uncertain future rewards and can shirk irreversibly at any time. The principal knows the reward of the task and provides information to the agent over time in order to motivate effort. We derive the optimal information policy in closed form and thus identify two conditions, each of which guarantees that delayed disclosure is valuable. First, if the principal is impatient compared to the agent, she prefers the front-loaded effort schedule induced by delayed disclosure. In a stationary environment, delayed disclosure is beneficial if and only if the principal is less patient than the agent. Second, if the environment makes the agent become pessimistic over time in absence of any information disclosure, then providing delayed news can counteract this downward trend in the agent's belief and encourage the agent to work longer. Notably, the level of patience remains a crucial determinant of the optimal policy structure.
Ambient Backscatter Communications in Mobile Networks: Crowd-Detectable Zero-Energy-Devices
Authors: D. -T. Phan-Huy, D. Barthel, P. Ratajczak, R. Fara, M. Di Renzo, J. de Rosny
Date: 2021-10-12 00:00:00

In this paper, we introduce the new concept of Crowd-Detectable Zero-Energy-Devices. Such devices harvest solar or ambient light energy to power themselves, backscatter ambient waves to communicate, and are detected simultaneously by a smartphone connected to the network, and the network itself, as long as the device is close to the considered smartphone. Such device is a promising sustainable solution for the future of the Internet of Things. We describe an example of use case: asset tracking with zero added energy, zero new signals and zero new infrastructure in the network, thanks to the anonymous and transparent participation of smartphones connected to the wireless network. We then present our first prototypes of such devices that backscatter TV, 4G and 5G signals, and show our first experiments in challenging conditions and environments.
Joint Minimum DL-UL Rate Maximization for Cell-Free Massive MIMO
Authors: Bikshapathi Gouda, Italo Atzeni, Antti Tölli
Date: 2021-10-12 00:00:00
Tasks: Scheduling

In cell-free massive multiple-input multiple-output (MIMO) systems, the beamforming strategies at the base stations (BSs) and user equipments (UEs) can be computed building on bi-directional training. However, the precoding/decoding optimization in the downlink (DL) and in the uplink (UL) generally requires two separate bi-directional training phases, which can be wasteful in the case of short scheduling blocks. This paper proposes a framework to reduce the bi-directional training overhead by considering a common beamforming training strategy for both DL and UL when the UEs to be served in the two directions are the same. In doing so, we consider the problem of maximizing the (weighted) minimum DL-UL rate among all the UEs. Numerical results show that, in scenarios with short scheduling blocks, the proposed framework outperforms the case where the DL and UL beamforming strategies are computed individually via two separate bi-directional training phases thanks to the reduced training overhead. Even more substantial gains are observed with respect to the case with a single bi-directional training phase, where the DL (resp. UL) beamforming strategies are reused in the UL (resp. DL).
Explicit CSI Feedback Compression via Learned Approximate Message Passing
Authors: Benedikt Groß, Rana Ahmed Salem, Thorsten Wild, Gerhard Wunder
Date: 2021-10-12 00:00:00
Tasks: compressed sensing

Explicit channel state information at the transmitter side is helpful to improve downlink precoding performance for multi-user MIMO systems. In order to reduce feedback signalling overhead, compression of Channel State Information (CSI) is essential. In this work different low complexity compressed sensing algorithms are compared in the context of an explicit CSI feedback scheme for 5G new radio. A neural network approach, based on learned approximate message passing for the computation of row-sparse solutions to matrix-valued compressed sensing problems is introduced. Due to extensive weight sharing, it shares the low memory footprint and fast evaluation of the forward pass with few iterations of a first order iterative algorithm. Furthermore it can be trained on purely synthetic data prior to deployment. Its performance in the explicit CSI feedback application is evaluated, and its key benefits in terms of computational complexity savings are discussed.
Swish-Driven GoogleNet for Intelligent Analog Beam Selection in Terahertz Beamspace MIMO
Authors: Hosein Zarini, Mohammad Robat Mili, Mehdi Rasti, Sergey Andreev, Pedro H. J. Nardelli
Date: 2021-10-12 00:00:00
Tasks: Transfer Learning

In this paper, we propose an intelligent analog beam selection strategy in a terahertz (THz) band beamspace multiple-input multiple-output (MIMO) system. First inspired by transfer learning, we fine-tune the pre-trained off-the-shelf GoogleNet classifier, to learn analog beam selection as a multi-class mapping problem. Simulation results show 83% accuracy for the analog beam selection, which subsequently results in 12% spectral efficiency (SE) gain, upon the existing counterparts. Towards a more accurate classifier, we replace the conventional rectified linear unit (ReLU) activation function of the GoogleNet with the recently proposed Swish and retrain the fine-tuned GoogleNet to learn analog beam selection. It is numerically indicated that the fine-tuned Swish-driven GoogleNet achieves 86% accuracy, as well as 18% improvement in achievable SE, upon the similar schemes. Eventually, a strong ensembled classifier is developed to learn analog beam selection by sequentially training multiple fine-tuned Swish-driven GoogleNet classifiers. According to the simulations, the strong ensembled model is 90% accurate and yields 27% gain in achievable SE, in comparison with prior methods.
Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information
Authors: Pengfei Yi, Liang Zhu, Lipeng Zhu, Zhenyu Xiao, Zhu Han, Xiang-Gen Xia
Date: 2021-10-12 00:00:00

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms two benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.
Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification
Authors: Abu Hasnat Mohammad Rubaiyat, Mohammad Shifat-E-Rabbi, Yan Zhuang, Shiying Li, Gustavo K. Rohde
Date: 2021-10-11 00:00:00
Tasks: ECG Classification

This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique in the SCDT domain to provide a non-iterative, effective, and simple to implement classification algorithm. Experiments show that the proposed technique outperforms the state-of-the-art neural networks using a very low number of training samples and is also robust to out-of-distribution examples on simulated data. We also demonstrate the efficacy of the proposed technique in real-world applications by applying it to an ECG classification problem. The python code implementing the proposed classifier can be found in PyTransKit (https://github.com/rohdelab/PyTransKit).
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
Authors: Xiang Lin, Shenghang Luo, Sunish Kumar Orappanpara Soman, Octavia A. Dobre, Lutz Lampe, Deyuan Chang, Chuandong Li
Date: 2021-10-11 00:00:00

Derived from the regular perturbation treatment of the nonlinear Schrodinger equation, a machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is proposed. Referred to as the perturbation theory-aided (PA) learned digital back-propagation (LDBP), the proposed scheme constructs a deep neural network (DNN) in a way similar to the split-step Fourier method: linear and nonlinear operations alternate. Inspired by the perturbation analysis, the intra-channel cross-phase modulation term is conveniently represented by matrix operations in the DNN. The introduction of this term in each nonlinear operation considerably improves the performance, as well as enables the flexibility of PA-LDBP by adjusting the numbers of spans per step. The proposed scheme is evaluated by numerical simulations of a single carrier optical fiber communication system operating at 32 Gbaud with 64-quadrature amplitude modulation and 20*80 km transmission distance. The results show that the proposed scheme achieves approximately 3.5 dB, 1.8 dB, 1.4 dB, and 0.5 dB performance gain in terms of Q2 factor over the linear compensation, when the numbers of spans per step are 1, 2, 4, and 10, respectively. Two methods are proposed to reduce the complexity of PALDBP, i.e., pruning the number of perturbation coefficients and chromatic dispersion compensation in the frequency domain for multi-span per step cases. Investigation of the performance and complexity suggests that PA-LDBP attains improved performance gains with reduced complexity when compared to LDBP in the cases of 4 and 10 spans per step.
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease
Authors: Mahdieh Kazemimoghadam, Nicholas P. Fey
Date: 2021-10-08 00:00:00
Tasks: Activity Recognition

Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson's disease (PD) has been primarily limited to detection of steady-state/static tasks (sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson's disease.
Subjective Difficulty in a Verbal Recognition-based Memory Task: Exploring Brain-behaviour Relationships at the Individual Level in Healthy Young Adults
Authors: Jason Steffener, Chris Habeck, Dylan Franklin, Meghan Lau, Yara Yakoub, Maryse Gad
Date: 2021-10-12 00:00:00

The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data.
Diurnal variations of resting-state fMRI data: A graph-based analysis
Authors: Farzad V. Farahani, Waldemar Karwowski, Mark D Esposito, Richard F. Betzel, Magdalena Fafrowicz, Bartosz Bohaterewicz, Tadeusz Marek, Pamela K. Douglas
Date: 2021-10-12 00:00:00
Tasks: Functional Connectivity

Circadian rhythms synchronize a variety of physiological processes ranging from neural activity and hormone secretion to sleep cycles and feeding habits. Despite significant diurnal variation, time-of-day (TOD) is rarely recorded or analyzed in human brain research. Moreover, sleep-wake patterns, diurnal preferences, and daytime alertness vary across individuals, known as sleep chronotypes. Here, we performed graph-theory network analysis on resting-state functional MRI (rs-fMRI) data to explore topological differences in whole-brain functional networks between morning and evening sessions (TOD effect), and between extreme morning-type and evening-type chronotypes. To that end, 62 individuals (31 extreme morning, 31 evening-type) underwent two fMRI sessions: about 1 hour after the wake-up time (morning), and 10 hours thereafter, scheduled in accord with their declared habitual sleep-wake pattern. TOD significantly altered functional connectivity (FC) patterns, but there was no significant difference in chronotypic categories. Compared to the morning session, we found relatively increased small-worldness, modularity, assortativity, and synchronization in the evening session, indicating more efficient functional topology. Local measures were changed during the day predominantly across the areas involved in somatomotor, ventral attention, as well as default mode networks. Also, connectivity and hub analyses showed that the somatomotor, ventral attention, and visual networks are the most densely-connected brain areas in both sessions, respectively, with the first being more active in the evening session and the two latter in the morning session. Collectively, these findings suggest TOD can impact classic analyses used in human neuroimaging such as functional connectivity, and should be recorded and included in the analysis of functional neuroimaging data.
Study of Drug Assimilation in Human System using Physics Informed Neural Networks
Authors: Kanupriya Goswami, Arpana Sharma, Madhu Pruthi, Richa Gupta
Date: 2021-10-08 00:00:00

Differential equations play a pivotal role in modern world ranging from science, engineering, ecology, economics and finance where these can be used to model many physical systems and processes. In this paper, we study two mathematical models of a drug assimilation in the human system using Physics Informed Neural Networks (PINNs). In the first model, we consider the case of single dose of drug in the human system and in the second case, we consider the course of this drug taken at regular intervals. We have used the compartment diagram to model these cases. The resulting differential equations are solved using PINN, where we employ a feed forward multilayer perceptron as function approximator and the network parameters are tuned for minimum error. Further, the network is trained by finding the gradient of the error function with respect to the network parameters. We have employed DeepXDE, a python library for PINNs, to solve the simultaneous first order differential equations describing the two models of drug assimilation. The results show high degree of accuracy between the exact solution and the predicted solution as much as the resulting error reaches10^(-11) for the first model and 10^(-8) for the second model. This validates the use of PINN in solving any dynamical system.
EpiBeds: Data informed modelling of the COVID-19 hospital burden in England
Authors: Christopher E. Overton, Lorenzo Pellis, Helena B. Stage, Francesca Scarabel, Joshua Burton, Christophe Fraser, Ian Hall, Thomas A. House, Chris Jewell, Anel Nurtay, Filippo Pagani, Katrina A. Lythgoe
Date: 2021-10-12 00:00:00

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. Data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.
Getting Genetic Ancestry Right for Science and Society
Authors: Anna C. F. Lewis, Santiago J. Molina, Paul S Appelbaum, Bege Dauda, Anna Di Rienzo, Agustin Fuentes, Stephanie M. Fullerton, Nanibaa' A. Garrison, Nayanika Ghosh, Evelynn M. Hammonds, David S. Jones, Eimear E. Kenny, Peter Kraft, Sandra S. -J. Lee, Madelyn Mauro, John Novembre, Aaron Panofsky, Mashaal Sohail, Benjamin M. Neale, Danielle S. Allen
Date: 2021-10-12 00:00:00

There is a scientific and ethical imperative to embrace a multidimensional, continuous view of ancestry and move away from continental ancestry categories
Genetic Regulation of Cytokine Response in Patients with Acute Community-acquired Pneumonia
Authors: Andreas Kühnapfel, Katrin Horn, Ulrike Klotz, Michael Kiehntopf, Maciej Rosolowski, Markus Loeffler, Peter Ahnert, Norbert Suttorp, Martin Witzenrath, Markus Scholz
Date: 2021-10-11 00:00:00

Background: Community-acquired pneumonia (CAP) is an acute disease condition with a high risk of rapid deteriorations. We analysed the influence of genetics on cytokine regulation to obtain a better understanding of patient's heterogeneity. Methods: For up to N=389 genotyped participants of the PROGRESS study of hospitalised CAP patients, we performed a genome-wide association study of ten cytokines IL-1b, IL-6, IL-8, IL-10, IL-12, MCP-1 (MCAF), MIP-1a (CCL3), VEGF, VCAM-1, and ICAM-1. Consecutive secondary analyses were performed to identify independent hits and corresponding causal variants. Results: 102 SNPs from 14 loci showed genome-wide significant associations with five of the cytokines. The most interesting associations were found at 6p21.1 for VEGF (p=1.58x10E-20), at 17q21.32 (p=1.51x10E-9) and at 10p12.1 (p=2.76x10E-9) for IL-1b, at 10p13 for MIP-1a (CCL3) (p=2.28x10E-9), and at 9q34.12 for IL-10 (p=4.52x10E-8). Functionally plausible genes could be assigned to the majority of loci including genes involved in cytokine secretion, granulocyte function, and cilial kinetics. Conclusions: This is the first context-specific genetic association study of blood cytokine concentrations in CAP patients revealing numerous biologically plausible candidate genes. Two of the loci were also associated with atherosclerosis with probable common or consecutive pathomechanisms.