pwc-archive/papers-with-abstracts huggingface dataset:
The Distribution of Occupational Tasks in the United States: Implications for a Diverse and Aging Population
Authors: Samuel Cole, Zachary Cowell, John M. Nunley, R. Alan Seals Jr
Date: 2022-05-01 00:00:00

We document the age-race-gender intersectionality in the distribution of occupational tasks in the United States. We also investigate how the task content of work changed from the early-2000s to the late-2010s for different age-race/ethnicity-gender groups. Using the Occupation Information Network (O*NET) and pooled cross-sectional data from the American Community Survey (ACS) we examine how the tasks that workers perform vary with age and over time. We find that White men transition to occupations high in non-routine cognitive tasks early in their careers, whereas Hispanic and Black men work mostly in physically demanding jobs over their entire working lives. Routine manual tasks increased dramatically for 55-67 year-old workers, except for Asian men and women. Policymakers will soon be challenged by financial stress on entitlement programs, reforms could have disproportionate effects on gender and racial/ethnic groups due to inequality in the distribution of occupational tasks.
Evaluating the Impact of Bitcoin on International Asset Allocation using Mean-Variance, Conditional Value-at-Risk (CVaR), and Markov Regime Switching Approaches
Authors: Mohammadreza Mahmoudi
Date: 2022-04-30 00:00:00
Tasks: Portfolio Optimization

This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-variance and CVaR frameworks indicate that Bitcoin improves the diversification of a well-diversified international portfolio, they assume that assets' returns are developed linearly and normally distributed. However, the Bitcoin return does not have both of these characteristics. Due to this, I developed a Markov regime switching approach to analyze the effect of Bitcoin on an international portfolio performance. The results show that there are two regimes based on the assets' returns: 1- bear state, where returns have low means and high volatility, 2- bull state, where returns have high means and low volatility.
From Service Quality to E-Service Quality: Measurement, Dimensions and Model
Authors: Salome O. Ighomereho, Afolabi A. Ojo, Samuel O. Omoyele, Samuel O. Olabode
Date: 2022-04-29 00:00:00

With the global increase in online services, there is a paradigm shift from service quality to e-service quality. In order to sustain this strategic change, there is need to measure and evaluate the quality of e-services. Consequently, the paper seeks to determine the relevant e-service quality dimensions for e-channels. The aim is to generate a concise set of dimensions that managers can use to measure e-service quality. The paper proposed an e-service quality model comprising seven e-service quality dimensions (website appearance, ease of use, reliability, security, personalisation, fulfilment and responsiveness) and overall e-service quality. The study employed a cross-sectional research design and quantitative research approach. The data were collected via a questionnaire from 400 e-channel users in Lagos State, Nigeria. However, 318 copies of the questionnaire were found useful. The data were analysed using mean, frequency, percentages, correlation and multiple regression analysis. The results revealed that the relevant e-service quality dimensions influencing overall e-service quality are reliability, security, fulfilment, ease of use and responsiveness. These e-service quality dimensions are expected to provide information for managers to evaluate and improve their e-channel service delivery.
Strategic Behavior under Context Misalignment
Authors: Pierfrancesco Guarino, Gabriel Ziegler
Date: 2022-05-01 00:00:00

We study the behavioral implications of Rationality and Common Strong Belief in Rationality (RCSBR) with contextual assumptions allowing players to entertain misaligned beliefs, i.e., players can hold beliefs concerning their opponents' beliefs where there is no opponent holding those very beliefs. Taking the analysts' perspective, we distinguish the infinite hierarchies of beliefs actually held by players ("real types") from those that are a byproduct of players' hierarchies ("imaginary types") by introducing the notion of separating type structure. We characterize the behavioral implications of RCSBR for the real types across all separating type structures via a family of subsets of Full Strong Best-Reply Sets of Battigalli & Friedenberg (2012). By allowing misalignment, in dynamic games we can obtain behavioral predictions inconsistent with RCSBR (in the standard framework), contrary to the case of belief-based analyses for static games--a difference due to the dichotomy "non-monotonic vs. monotonic" reasoning.
Mentors and Recombinators: Multi-Dimensional Social Learning
Authors: Srinivas Arigapudi, Omer Edhan, Yuval Heller, Ziv Hellman
Date: 2022-04-30 00:00:00

We study games in which the set of strategies is multi-dimensional, and new agents might learn various strategic dimensions from different mentors. We introduce a new family of dynamics, the recombinator dynamics, which is characterised by a single parameter, the recombination rate r in [0,1]. The case of r = 0 coincides with the standard replicator dynamics. The opposite case of r = 1 corresponds to a setup in which each new agent learns each new strategic dimension from a different mentor, and combines these dimensions into her adopted strategy. We fully characterise stationary states and stable states under these dynamics, and we show that they predict novel behaviour in various applications.
Improving the Deferred Acceptance with Minimal Compromise
Authors: Mustafa Oguz Afacan, Umut Dur, A. Arda Gitmez, Özgür Yılmaz
Date: 2022-04-29 00:00:00
Tasks: Fairness

In school choice problems, the motivation for students' welfare (efficiency) is restrained by concerns to respect schools' priorities (fairness). Among the fair matchings, even the best one in terms of welfare (SOSM) is inefficient. Moreover, any mechanism that improves welfare over the SOSM is manipulable by the students. First, we characterize the "least manipulable" mechanisms in this class: monotonically-promoting transformation proofness ensures that no student is better off by promoting their assigned school under the true preferences. Second, we use the notion that a matching is less unfair if it yields a smaller set of students whose priorities are violated, and define minimal unfairness accordingly. We then show that the Efficiency Adjusted Deferred Acceptance (EADA) mechanism is minimally unfair in the class of efficient and monotonically-promoting transformation proof mechanisms. When the objective is to improve students' welfare over the SOSM, this characterization implies an important insight into the frontier of the main axioms in school choice.
Terahertz Spatio-Temporal Deep Learning Computed Tomography
Authors: Yi-Chun Hung, Ta-Hsuan Chao, Pojen Yu, Shang-Hua Yang
Date: 2022-04-30 00:00:00
Tasks: Deep Learning, SSIM

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields' practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information. THz DL-CT restores superior THz tomographic images of 3D objects by extracting features from spatio-temporal THz signals without any prior material information. Compared with conventional and machine learning based methods, THz DL-CT delivers at least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural similarity index (SSIM), respectively. Additionally, we have experimentally demonstrated that the pretrained THz DL-CT model can generalize to reconstruct multi-material systems with no prerequisite information. THz CT through the DL data fusion approach provides a new pathway for non-invasive functional imaging in object investigation.
Fast and Scalable Human Pose Estimation using mmWave Point Cloud
Authors: Sizhe An, Umit Y. Ogras
Date: 2022-04-29 00:00:00
Tasks: Meta-Learning, Pose Estimation

Millimeter-Wave (mmWave) radar can enable high-resolution human pose estimation with low cost and computational requirements. However, mmWave data point cloud, the primary input to processing algorithms, is highly sparse and carries significantly less information than other alternatives such as video frames. Furthermore, the scarce labeled mmWave data impedes the development of machine learning (ML) models that can generalize to unseen scenarios. We propose a fast and scalable human pose estimation (FUSE) framework that combines multi-frame representation and meta-learning to address these challenges. Experimental evaluations show that FUSE adapts to the unseen scenarios 4$\times$ faster than current supervised learning approaches and estimates human joint coordinates with about 7 cm mean absolute error.
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task
Authors: Anant Jain, Lalan Kumar
Date: 2022-05-02 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Kinematics decoding from brain activity helps in developing rehabilitation or power-augmenting brain-computer interface devices. Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associated with the neural motor correlation utilised for motor trajectory decoding (MTD). In this communication, the ability to decode motor kinematics trajectory from pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy participants. In particular, two deep learning-based neural decoders called PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related neural information existing in the pre-movement EEG data. EEG data segments with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the movement onset are utilised for the same. The MTD is presented for grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken as input to the neural decoders. The performance of the proposed decoders are compared with the state-of-the-art multi-variable linear regression (mLR) model. Pearson correlation coefficient and hand trajectory are utilised as performance metric. The results demonstrate the viability of decoding 3D hand kinematics using pre-movement EEG data, enabling better control of BCI-based external devices such as exoskeleton/exosuit.
Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems
Authors: Eduardo Noboro Tominaga, Onel Luiz Alcaraz López, Richard Demo Souza, Hirley Alves
Date: 2022-05-02 00:00:00
Tasks: Diversity

The Fifth Generation (5G) of wireless networks introduced native support for Machine-Type Communication (MTC), which is a key enabler for the Internet of Things (IoT) revolution. Current 5G standards are not yet capable of fully satisfying the requirements of critical MTC (cMTC) and massive MTC (mMTC) use cases. This is the main reason why industry and academia have already started working on technical solutions for beyond-5G and Sixth Generation (6G) networks. One technological solution that has been extensively studied is the combination of network densification, massive Multiple-Input Multiple-Output (mMIMO) systems and user-centric design, which is known as distributed mMIMO or Cell-Free (CF) mMIMO. Under this new paradigm, there are no longer cell boundaries: all the Access Points (APs) on the network cooperate to jointly serve all the devices. In this paper, we compare the performance of traditional mMIMO and different distributed mMIMO setups, and quantify the macro diversity and signal spatial diversity performance they provide. Aiming at the uplink in industrial indoor scenarios, we adopt a path loss model based on real measurement campaigns. Monte Carlo simulation results show that the grid deployment of APs provide higher average channel gains, but radio stripes deployments provide lower variability of the received signal strength.
Balanced Performance Between Energy-Delay and Bit Error Rate in UAV Relay Networks
Authors: Muhammad Khalil
Date: 2022-05-02 00:00:00

This paper presents a new strategy for simultaneously reducing energy consumption, transmission delays, and bit error rate in Unmanned Aerial Vehicle UAV networks. A UAV is fitted with a wireless Bidirectional Relay BR to enable coverage network extension and increase transmission throughput. The downside of the BR advantages is the delay in data transmission caused by the UAV movement. A consequence of this delay is increased total energy consumption, causing further degradation in bit error rate performance, especially at high SNR levels. In wireless communication, the trade-off between delay and energy consumption is, fortunately, possible to improve performance. Therefore, this study aims to enhance UAV network performance by reducing energy consumption, data transmission delay, and bit error rate. A multi-objective algorithm is employed to generate an adaptive optimal energy allocation strategy based on the balance between energy consumption and transmission delays. The results of theoretical analysis are illustrated with several examples. As herein demonstrated, the proposed solution effectively balances delay and energy efficiency in a customised system design and improves the bit error rate in UAV networks
Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems
Authors: Bohan Li, Lie-Liang Yang, Robert G Maunder, Songlin Sun, Pei Xiao
Date: 2022-05-01 00:00:00
Tasks: Graph Neural Network

In-band full duplex cell-free (CF) systems suffer from severe self-interference and cross-link interference, especially when CF systems are operated in distributed way. To this end, we propose the multicarrier-division duplex as an enabler for achieving full-duplex operation in the distributed CF massive MIMO systems, where downlink and uplink transmissions occur simultaneously in the same frequency band but on the mutually orthogonal subcarriers. To maximize the spectral-efficiency (SE), we introduce a heterogeneous graph neural network (HGNN) specific for CF systems, referred to as CF-HGNN, to optimize the power-allocation (PA). We design the adaptive node embedding layer for CF-HGNN to be scalable to the various numbers of access points (APs), mobile stations (MSs) and subcarriers. The attention mechanism of CF-HGNN enables individual AP/MS nodes to aggregate information from the interfering and communication paths with different priorities. For comparison, we propose a quadratic transform and successive convex approximation (QT-SCA) algorithm to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99\% of the SE achievable by QT-SCA but using only $10^{-4}$ times of its operation time. CF-HGNN significantly outperforms the traditional greedy unfair method in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user clustering.
On Dynamic Ray Tracing and Anticipative Channel Prediction for Dynamic Environments
Authors: Denis Bilibashi, Enrico M. Vitucci, Vittorio Degli-Esposti
Date: 2022-04-30 00:00:00
Tasks: Cloud Computing

Ray tracing algorithms, that can simulate multipath radio propagation in presence of geometric obstacles such as buildings, objects or vehicles, are becoming quite popular, due to the increasing availability of digital environment databases and high-performance computation platforms, such as multicore computers and cloud computing services. When objects or vehicles are moving, which is the case of industrial or vehicular environments, multiple successive representations of the environment ("snapshots") and multiple ray tracing runs are often necessary, which require a great human effort and a great deal of computation resources. Recently, the Dynamic Ray Tracing (DRT) approach has been proposed to predict the multipath evolution within a given time lapse on the base of the current multipath geometry, assuming constant speeds and/or accelerations for moving objects, using analytical extrapolation formulas. This is done without re-running a full ray tracing for every "snapshot" of the environment, therefore with a great computation time saving. When DRT is embedded in a mobile radio system and used in real-time, ahead-of-time (or anticipative) field prediction is possible that opens the way to interesting applications. In the present work, a full-3D DRT algorithm is presented that allows to account for multiple reflections, edge diffraction and diffuse scattering for the general case where moving objects can translate and rotate. For the purpose of validation, the model is first applied to some ideal cases and then to realistic cases where results are compared with conventional ray tracing simulation and measurements available in the literature.
Research on Sparsity Measures for Rotating Machinery Health Monitoring
Authors: Yudong Cao
Date: 2022-04-30 00:00:00
Tasks: Management

Machine health management is one of the main research contents of PHM technology, which aims to monitor the health states of machines online and evaluate degradation stages through real-time sensor data. In recent years, classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness index, negative entropy, and Gini index have been widely used to characterize the impulsivity of repetitive transients. Since smoothness index and negative entropy were proposed, the sparse properties have not been fully analyzed. The first work of this paper is to analyze six properties of smoothness index and negative entropy. In addition, this paper conducts a thorough investigation on multivariate power average function and finds that existing classical sparsity measures can be respectively reformulated as the ratio of multivariate power mean functions (MPMFs). Finally, a general paradigm of index design are proposed for the expansion of sparsity measures family, and several newly designed dimensionless health indexes are given as examples. Two different run-to-failure bearing datasets were used to analyze and validate the capabilities and advantages of the newly designed health indexes. Experimental results prove that the newly designed health indexes show good performance in terms of monotonic degradation description, first fault occurrence time determination and degradation state assessment.
Antigenic cooperation in Viral Populations: Transformation of Functions of Intra-Host Viral Variants
Authors: Leonid Bunimovich, Athulya Ram, Pavel Skums
Date: 2022-05-02 00:00:00

In this paper we study intra-host viral adaptation by antigenic cooperation - a mechanism of immune escape that serves as an alternative to the standard mechanism of escape by continuous genomic diversification and allows to explain a number of experimental observations associated with the establishment of chronic infections by highly mutable viruses. Within this mechanism, the topology of a cross-immunoreactivity network forces intra-host viral variants to specialize for complementary roles and adapt to host's immune response as a quasi-social ecosystem. Here we study dynamical changes in immune adaptation caused by evolutionary and epidemiological events. First, we show that the emergence of a viral variant with altered antigenic features may result in a rapid re-arrangement of the viral ecosystem and a change in the roles played by existing viral variants. In particular, it may push the population under immune escape by genomic diversification towards the stable state of adaptation by antigenic cooperation. Next, we study the effect of a viral transmission between two chronically infected hosts, which results in merging of two intra-host viral populations in the state of stable immune-adapted equilibrium. In this case, we also describe how the newly formed viral population adapts to the host's environment by changing the functions of its members. The results are obtained analytically for minimal cross-immunoreactivity networks and numerically for larger populations.
Fitting Generalized Tempered Stable distribution: Fractional Fourier Transform (FRFT) Approach
Authors: A. H. Nzokem, V. T. Montshiwa
Date: 2022-05-02 00:00:00

The paper investigates the rich class of Generalized Tempered Stable distribution, an alternative to Normal distribution and the $\alpha$-Stable distribution for modelling asset return and many physical and economic systems. Firstly, we explore some important properties of the Generalized Tempered Stable (GTS) distribution. The theoretical tools developed are used to perform empirical analysis. The GTS distribution is fitted using S&P 500, SPY ETF and Bitcoin BTC. The Fractional Fourier Transform (FRFT) technique evaluates the probability density function and its derivatives in the maximum likelihood procedure. Based on the results from the statistical inference and the Kolmogorov-Smirnov (K-S) goodness-of-fit, the GTS distribution fits the underlying distribution of the SPY ETF return. The right side of the Bitcoin BTC return, and the left side of the S&P 500 return underlying distributions fit the Tempered Stable distribution; while the left side of the Bitcoin BTC return and the right side of the S&P 500 return underlying distributions are modelled by the compound Poisson process
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
Authors: Jiahui Chen, Yuchi Qiu, Rui Wang, Guo-Wei Wei
Date: 2022-05-01 00:00:00

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape variations in virus-human protein-protein binding, which are crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topology and shape of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36\% more infectious than BA.2 and are projected to become new dominating variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes.