pwc-archive/papers-with-abstracts huggingface dataset:
Why the Mansfield Rule can't work: a supply demand analysis
Authors: Paola Cecchi Dimeglio
Date: 2023-02-08 00:00:00
Tasks: Diversity, Position

Across the legal profession, statistics related to the numbers of women and other underrepresented groups in leadership roles continue to paint a bleak picture of diversity and inclusion. Some approaches to closing this gap have focused on the cause; some have devised and applied solutions. Questions about the efficacy of many of these solutions remain essentially unanswered. This empirical study represents one of the first of its kind. Studies of the legal profession have not focused on the dynamics of supply and demand in the context of leadership positions (counsel and partner (equity and non-equity)). Neither have they examined the interrelationships of these dynamics to race and gender demographic factors (white female and minorities (male and female)). This research seeks to determine the supply-demand position of leadership in the legal profession and establish market equilibrium for these counsel and partner roles.
Dynamic and Stochastic Rational Behavior
Authors: Nail Kashaev, Victor H. Aguiar, Martin Plávala, Charles Gauthier
Date: 2023-02-09 00:00:00
Tasks: Time Series Analysis

The (static) utility maximization model of Afriat (1967), which is the standard in analysing choice behavior, is under scrutiny. We propose the Dynamic Random Utility Model (DRUM) that is more flexible than the framework of Afriat (1967) and more informative than the static Random Utility Model (RUM) framework of McFadden and Richter (1990). Under DRUM, each decision-maker randomly draws a utility function in each period and maximizes it subject to a menu. DRUM allows for unrestricted time correlation and cross-section heterogeneity in preferences. We characterize DRUM for situations when panel data on choices and menus are available. DRUM is linked to a finite mixture of deterministic behaviors that can be represented as a product of static rationalizable behaviors. This link allows us to convert the characterizations of the static RUM to its dynamic form. In an application, we find that although the static utility maximization model fails to explain population behavior, DRUM can explain it.
New starting point registration method for tagged MRI tongue motion estimation
Authors: Jinglun Yu, Muhan Shao, Zhangxing Bian, Xiao Liang, Jiachen Zhuo, Maureen Stone, Jerry L. Prince
Date: 2023-02-08 00:00:00
Tasks: Motion Estimation, TAG

Accurate tongue motion estimation is essential for tongue function evaluation. The harmonic phase processing (HARP) method and the phase vector incompressible registration algorithm (PVIRA) based on HARP can generate motion estimates from tagged MRI images, but they suffer from tag jumping due to large motions. This paper proposes a new registration method by combining the stationary velocity fields produced by PVIRA between successive time frames as a new initialization of the final registration stage to avoid tag jumping. The experiment results demonstrate the proposed method can avoid tag jumping and outperform the existing methods on tongue motion estimates.
Optical communication-based identification for multi-UAV systems: theory and practice
Authors: Daniel Bonilla Licea, Viktor Walter, Mounir Ghogho, Martin Saska
Date: 2023-02-09 00:00:00

Mutual relative localization and identification is an important feature for the stabilization and navigation of multi-Unmanned Aerial Vehicle (UAV) systems. Camera-based communications technology, also referred to as Optical Camera Communications (OCC) in the literature, is a novel approach that could bring a valuable solution to such a complex task. In such system, the UAVs are equipped with LEDs that act as beacons and with cameras allowing them to locate the LEDs of other UAVs. Specific blinking sequences are assigned to the LEDs of each of the UAVs in order to uniquely identify them. This camera-based relative localization and identification system is immune to Radio Frequency (RF) electromagnetic interference and operates in Global Navigation satellite (GNSS) denied environments. In addition, since many UAVs are already equipped with cameras, the implementation of this system is inexpensive. In this article, we study in detail the capacity of this system and its limitations. Furthermore, we show how to construct blinking sequences for UAV LEDs in order to improve system performance. Finally, experimental results are presented to corroborate the analytical derivations.
Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks
Authors: Ali Nouruzi, Atefeh Rezaei, Ata Khalili, Nader Mokari, Mohammad Reza Javan, Eduard A. Jorswieck, Halim Yanikomeroglu
Date: 2023-02-09 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e., throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.
Joint Uplink and Downlink EMF Exposure: Performance Analysis and Design Insights
Authors: Lin Chen, Ahmed Elzanaty, Mustafa A. Kishk, Luca Chiaraviglio, Mohamed-Slim Alouini
Date: 2023-02-09 00:00:00

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rate in the 5th-generation cellular networks (5G). However, a non-negligible amount of population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population's exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than 20 BSs/km2.
RIS-Position and Orientation Estimation in MIMO-OFDM Systems with Practical Scatterers
Authors: Sheng Hong, Minghui Li, Cunhua Pan, Marco Di Renzo, Wei zhang, Lajos Hanzo
Date: 2023-02-09 00:00:00
Tasks: compressed sensing, Position

In this paper, we investigate the problem of estimating the position and the angle of rotation of a mobile station (MS) in a millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS). The virtual line-of-sight (VLoS) link created by the RIS and the non-line-of-sight (NLoS) links that originate from scatterers in the considered environment are utilized to facilitate the estimation. A two-step positioning scheme is exploited, where the channel parameters are first acquired, and the position-related parameters are then estimated. The channel parameters are obtained through a coarser and a subsequent finer estimation processes. As for the coarse estimation, the distributed compressed sensing orthogonal simultaneous matching pursuit (DCS-SOMP) algorithm, the maximum likelihood (ML) algorithm, and the discrete Fourier transform (DFT) are utilized to separately estimate the channel parameters. The obtained channel parameters are then jointly refined by using the space-alternating generalized expectation maximization (SAGE) algorithm, which circumvents the high-dimensional optimization issue of ML estimation. Departing from the estimated channel parameters, the positioning-related parameters are estimated. The performance of estimating the channel-related and position-related parameters is theoretically quantified by using the Cramer-Rao lower bound (CRLB). Simulation results demonstrate the superior performance of the proposed positioning algorithms.
RIS-aided Localization under Pixel Failures
Authors: Cuneyd Ozturk, Musa Furkan Keskin, Vincenzo Sciancalepore, Henk Wymeersch, Sinan Gezici
Date: 2023-02-09 00:00:00

Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key technological enablers for beyond-5G wireless networks, improving localization and communication performance under line-of-sight (LoS) blockage conditions. However, hardware imperfections might cause RIS elements to become faulty, a problem referred to as pixel failures, which can constitute a major showstopper especially for localization. In this paper, we investigate the problem of RIS-aided localization of a user equipment (UE) under LoS blockage in the presence of RIS pixel failures, considering the challenging single-input single-output (SISO) scenario. We first explore the impact of such failures on accuracy through misspecified Cramer-Rao bound (MCRB) analysis, which reveals severe performance loss with even a small percentage of pixel failures. To remedy this issue, we develop two strategies for joint localization and failure diagnosis (JLFD) to detect failing pixels while simultaneously locating the UE with high accuracy. The first strategy relies on l_1-regularization through exploitation of failure sparsity. The second strategy detects the failures one-by-one by solving a multiple hypothesis testing problem at each iteration, successively enhancing localization and diagnosis accuracy. Simulation results show significant performance improvements of the proposed JLFD algorithms over the conventional failure-agnostic benchmark, enabling successful recovery of failure-induced performance degradations.
Active Simultaneously Transmitting and Reflecting (STAR)-RISs: Modelling and Analysis
Authors: Jiaqi Xu, Jiakuo Zuo, Joey Tianyi Zhou, Yuanwei Liu
Date: 2023-02-09 00:00:00
Tasks: Diversity

A hardware model for active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is proposed consisting of reflection-type amplifiers. The amplitude gains of the STAR element are derived for both coupled and independent phase-shift scenarios. Based on the proposed hardware model, an active STAR-RIS-aided two-user downlink communication system is investigated. Closed-form expressions are obtained for the outage probabilities of both the coupled and independent phase-shift scenarios. To obtain further insights, scaling laws and diversity orders are derived for both users. Analytical results confirm that active STAR-RIS achieves the same diversity orders as passive ones while their scaling laws are different. It is proved that average received SNRs scale with M and M^2 for active and passive STAR-RISs, respectively. Numerical results show that active STAR-RISs outperform passive STAR-RISs in terms of outage probability especially when the number of elements is small.
Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis
Authors: Jiadi Bao, Yunjie Li, Mengtao Zhu, Shafei Wang
Date: 2023-02-09 00:00:00
Tasks: Bayesian Inference, Change Detection, Change Point Detection, parameter estimation, Scheduling

Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.
When does humoral memory enhance infection?
Authors: Ariel Nikas, Hasan Ahmed, Mie R. Moore, Veronika I. Zarnitsyna, Rustom Antia
Date: 2023-02-08 00:00:00

Antibodies and humoral memory are key components of the adaptive immune system. We consider and computationally model mechanisms by which humoral memory present at baseline might instead increase infection load; we refer to this effect as EI-HM (enhancement of infection by humoral memory). We first consider antibody dependent enhancement (ADE) in which antibody enhances the growth of the pathogen, typically a virus, and typically at intermediate "Goldilocks" levels of antibody. Our ADE model reproduces ADE in vitro and enhancement of infection in vivo from passive antibody transfer. But notably the simplest implementation of our ADE model never results in EI-HM. Adding complexity, by making the cross-reactive antibody much less neutralizing than the de novo generated antibody or by including a sufficiently strong non-antibody immune response, allows for ADE-mediated EI-HM. We next consider the possibility that cross-reactive memory causes EI-HM by crowding out a possibly superior de novo immune response. We show that, even without ADE, EI-HM can occur when the cross-reactive response is both less potent and "directly" (i.e. independently of infection load) suppressive with regard to the de novo response. In this case adding a non-antibody immune response to our computational model greatly reduces or completely eliminates EI-HM, which suggests that "crowding out" is unlikely to cause substantial EI-HM. Hence, our results provide examples in which simple models give qualitatively opposite results compared to models with plausible complexity. Our results may be helpful in interpreting and reconciling disparate experimental findings, especially from dengue, and for vaccination.
Simulating the Evolution of Signaling Signatures during CART-Cell -- Tumor Cell Interactions
Authors: Viren Shah, Justin Womack, Anthony E. Zamora, Scott S. Terhune, Ranjan K. Dash
Date: 2023-02-08 00:00:00

Immunotherapies have been proven to have significant therapeutic efficacy in the treatment of cancer. The last decade has seen adoptive cell therapies, such as chimeric antigen receptor T-cell (CART-cell) therapy, gain FDA approval against specific cancers. Additionally, there are numerous clinical trials ongoing investigating additional designs and targets. Nevertheless, despite the excitement and promising potential of CART-cell therapy, response rates to therapy vary greatly between studies, patients, and cancers. There remains an unmet need to develop computational frameworks that more accurately predict CART-cell function and clinical efficacy. Here we present a coarse-grained model simulated with logical rules that demonstrates the evolution of signaling signatures following the inter-action between CART-cells and tumor cells and allows for in silico based prediction of CART-cell functionality prior to experimentation.
Ecological system with fear induced group defence and prey refuge
Authors: Shivam Yadav, Jai Prakash Tripathi, Shrichand Bhuria, Satish Kumar Tiwari, Deepak Tripathi, Vandana Tiwari, Ranjit Kumar Upadhyay, Yun Kang
Date: 2023-02-09 00:00:00

In this study, we investigate the dynamics of a spatial and non spatial prey-predator interaction model that includes the following: (i) fear effect incorporated in prey birth rate; (ii) group defence of prey against predators; and (iii) prey refuge. We provide comprehensive mathematical analysis of extinction and persistence scenarios for both prey and predator species. To better explore the dynamics of the system, a thorough investigation of bifurcation analysis has been performed using fear level, prey birth rate, and prey death rate caused by intra-prey competition as bifurcation parameter. All potential occurrences of bi-stability dynamics have also been investigated for some relevant sets of parametric values. Our numerical evaluations show that high levels of fear can stabilize the prey-predator system by ruling out the possibility of periodic solutions. Also, our model Hopf bifurcation is subcritical in contrast to traditional prey-predator models, which ignore the cost of fear and have supercritical Hopf bifurcations in general. In contrast to the general trend, predator species go extinct at higher values of prey birth rates. We have also found that, contrary to the typical tendency for prey species to go extinct, both prey and predator populations may coexist in the system as intra-prey competition level grows noticeably. The stability and Turing instability of associated spatial model have also been investigated analytically. We also perform the numerical simulation to observe the effect of different parameters on the density distribution of species. Different types of spatiotemporal patterns like spot, mixture of spots and stripes have been observed via variation of time evolution, diffusion coefficient of predator population, level of fear factor and prey refuge. The fear level parameter (k) has a great impact on the spatial dynamics of model system.
COVID-19 Susceptibility, Mortality and Length of Hospitalization based on Age-Sex Composition: Insights for Intervention and Stratification
Authors: Roel F. Ceballos
Date: 2023-02-09 00:00:00

The coronavirus disease (COVID-19) has spread worldwide with an unprecedented impact on society. In the Philippines, several interventions such as mobility restrictions for different age groups and vaccination prioritization programs have been implemented to reduce the risks of infections and mortality. This study aimed to identify age-sex composition with greater susceptibility, longer hospitalization and higher fatality. The COVID-19 cases from March 2020 to April 2021 provided by the Department of Health Davao Region in the Philippines were analyzed. A Chi-square test was used to determine the difference in proportions of COVID-19 cases among age-sex compositions. A correlation plot of \c{hi}2 test residual was employed to investigate the differences in susceptibility. Boxplots and Kruskal-Wallis tests were utilized to compare the length of hospitalizations. The study found a significant difference in the COVID-19 susceptibility among age-sex compositions (p < 0.01). Male children and female senior citizens were the most susceptible age-sex compositions. Furthermore, senior citizens had the longest hospital days wherein the median and IQR days were 19 (15-27) for men and 18 (16-29) for women. Male senior citizen was the subgroup with the highest case fatality (21.4%, p < 0.01). It is recommended that the number of cases among senior citizens be used as an input in the planning and allocation of medical resources at the provincial and regional levels. The local government unit executives in the region can also take advantage of the availability of age-sex composition data in stratifying localities, planning, allocating COVID-19-related resources and imposing mobility restrictions.
Inefficiency of CFMs: hedging perspective and agent-based simulations
Authors: samuel cohen, Marc Sabaté Vidales, David Šiška, Łukasz Szpruch
Date: 2023-02-08 00:00:00

We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk. We first analyse this problem through the lens of continuous-time financial mathematics and derive an upper bound for not-arbitrage fee income that would make CFM efficient and liquidity provision fair. We then evaluate our findings by performing multi-agent simulations by varying CFM fees, market volatility, and rate of arrival of liquidity takers. We observe that, on average, fee income generated from liquidity provision is insufficient to compensate for market risk.
Enhanced dynamic sign language recognition using slowfast networks
Authors: Ahmed Hassan, Ahmed Elgabry, Elsayed Hemayed
Date: 2021-12-30 00:00:00
Proceeding: IEEE 2021 12
Tasks: Sign Language Recognition, Transfer Learning

In this paper, we use the SlowFast Networks developed by the Facebook research team to enhance the accuracy of dynamic sign language recognition. Firstly, we prepared the Word-Level American Sign Language (WLASL) dataset so each sign can be considered an action. We used the pre-trained SLOWFAST_8×8_R50 model provided on the official PySlowFast Github repository to initialize the weights of our model and fine-tune using the WLASL dataset and performed a parameter sweeping to fit the Dynamic Sign Language Recognition task. Through this transfer learning approach, we introduced a new state-of-the-art accuracy on the WLASL300 (300 words e.g., 300 classes) dataset with an improvement of 23.2 % top-1 accuracy compared to the previous state-of-the-art introduced in the WLASL paper using an I3D model. The top-1 accuracy was improved from 56.14% to 79.34% and the top-5 accuracy from 79.94% to 90.31%.