pwc-archive/papers-with-abstracts huggingface dataset:
The Neoplasia as embryological phenomenon and its implication in the animal evolution and the origin of cancer. III. The role of flagellated cell fusion in the formation of the first animal and evolutionary clues to the Warburg effect
Authors: Jaime Cofre
Date: 2022-10-19 00:00:00

Cytasters have been underestimated in terms of their potential relevance to embryonic development and evolution. From the perspective discussed herein, structures such as the multiciliated cells of comb rows and balancers supporting mineralized statoliths and macrocilia in Beroe ovata point to a past event of multiflagellate fusion in the origin of metazoans. These structures, which are unique in evolutionary history, indicate that early animals handled basal bodies and their duplication in a manner consistent with a "developmental program" originated in the Ctenophora. Furthermore, the fact that centrosome amplification leads to spontaneous tumorigenesis suggests that the centrosome regulation process was co-opted into a neoplastic functional module. Multicilia, cilia, and flagella are deeply rooted in the evolution of animals and Neoplasia. The fusion of several flagellated microgametes into a cell with a subsequent phase of zygotic (haplontic) meiosis might have been at the origin of both animal evolution and the neoplastic process. In the Ediacaran ocean, we also encounter evolutionary links between the Warburg effect and Neoplasia.
Granger Causality for Compressively Sensed Sparse Signals
Authors: Aditi Kathpalia, Nithin Nagaraj
Date: 2022-09-23 00:00:00
Tasks: Causal Inference, compressed sensing, Connectivity Estimation, Quantum State Tomography

Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted and stored using far fewer measurements than done by conventional means employing Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a number of applied physics and engineering applications, particularly in designing signal and image acquisition strategies, e.g., magnetic resonance imaging, quantum state tomography, scanning tunneling microscopy, analog to digital conversion technologies. Contemporaneously, causal inference has become an important tool for the analysis and understanding of processes and their interactions in many disciplines of science, especially those dealing with complex systems. Direct causal analysis for compressively sensed data is required to avoid the task of reconstructing the compressed data. Also, for some sparse signals, such as for sparse temporal data, it may be difficult to discover causal relations directly using available data-driven/ model-free causality estimation techniques. In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality. We then verify this theorem on a number of bivariate and multivariate coupled sparse signal simulations which are compressed using these matrices. We also demonstrate a real world application of network causal connectivity estimation from sparse neural spike train recordings from rat prefrontal cortex.
Analysis of Smooth Pursuit Assessment in Virtual Reality and Concussion Detection using BiLSTM
Authors: Prithul Sarker, Khondker Fariha Hossain, Isayas Berhe Adhanom, Philip K Pavilionis, Nicholas G. Murray, Alireza Tavakkoli
Date: 2022-10-12 00:00:00

The sport-related concussion (SRC) battery relies heavily upon subjective symptom reporting in order to determine the diagnosis of a concussion. Unfortunately, athletes with SRC may return-to-play (RTP) too soon if they are untruthful of their symptoms. It is critical to provide accurate assessments that can overcome underreporting to prevent further injury. To lower the risk of injury, a more robust and precise method for detecting concussion is needed to produce reliable and objective results. In this paper, we propose a novel approach to detect SRC using long short-term memory (LSTM) recurrent neural network (RNN) architectures from oculomotor data. In particular, we propose a new error metric that incorporates mean squared error in different proportions. The experimental results on the smooth pursuit test of the VR-VOMS dataset suggest that the proposed approach can predict concussion symptoms with higher accuracy compared to symptom provocation on the vestibular ocular motor screening (VOMS).
Millimeter-level Resolution Photonic Multiband Radar Using a Single MZM and Sub-GHz-Bandwidth Electronics
Authors: Peixuan Li, Wenlin Bai, Xihua Zou, Ningyuan Zhong, Wei Pan, Lianshan Yan
Date: 2022-10-18 00:00:00

We here propose a novel cost-effective millimeter-level resolution photonic multiband radar system using a single MZM driven by a 1-GHz-bandwidth LFM signal. It experimentally shows an ~8.5-mm range resolution through coherence-processing-free multiband data fusion.
On SIR-type epidemiological models and population heterogeneity effects
Authors: Silke Klemm, Lucrezia Ravera
Date: 2022-10-20 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper we elaborate on homogeneous and heterogeneous SIR-type epidemiological models. We find an unexpected correspondence between the epidemic trajectory of a transmissible disease in a homogeneous SIR-type model and radial null geodesics in the Schwarzschild spacetime. We also discuss modeling of population heterogeneity effects by considering both a one- and two-parameter gamma-distributed function for the initial susceptibility distribution, and deriving the associated herd immunity threshold. We furthermore describe how mitigation measures can be taken into account by model fitting.
Allowing for weak identification when testing GARCH-X type models
Authors: Philipp Ketz
Date: 2022-10-20 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we use the results in Andrews and Cheng (2012), extended to allow for parameters to be near or at the boundary of the parameter space, to derive the asymptotic distributions of the two test statistics that are used in the two-step (testing) procedure proposed by Pedersen and Rahbek (2019). The latter aims at testing the null hypothesis that a GARCH-X type model, with exogenous covariates (X), reduces to a standard GARCH type model, while allowing the "GARCH parameter" to be unidentified. We then provide a characterization result for the asymptotic size of any test for testing this null hypothesis before numerically establishing a lower bound on the asymptotic size of the two-step procedure at the 5% nominal level. This lower bound exceeds the nominal level, revealing that the two-step procedure does not control asymptotic size. In a simulation study, we show that this finding is relevant for finite samples, in that the two-step procedure can suffer from overrejection in finite samples. We also propose a new test that, by construction, controls asymptotic size and is found to be more powerful than the two-step procedure when the "ARCH parameter" is "very small" (in which case the two-step procedure underrejects).
Low-rank Panel Quantile Regression: Estimation and Inference
Authors: Yiren Wang, Liangjun Su, Yichong Zhang
Date: 2022-10-20 00:00:00
Tasks: quantile regression, regression

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope matrix equals one, and the other for the null hypothesis that the slope coefficient exhibits an additive structure under the case that the true rank of slope matrix equals two. We illustrate the finite sample performance of estimation and inference via Monte Carlo simulations and real datasets.
Efficient variational approximations for state space models
Authors: Rubén Loaiza-Maya, Didier Nibbering
Date: 2022-10-20 00:00:00
Tasks: State Space Models

Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This paper proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distribution within the exponential family of distributions. We show that our method can accurately and quickly estimate a multivariate Skellam stochastic volatility model with high-frequency tick-by-tick discrete price changes of four stocks, and a time-varying parameter vector autoregression with a stochastic volatility model using eight macroeconomic variables.
Exploring Causes, Effects, and Solutions to Financial Illiteracy and Exclusion among Minority Demographic Groups
Authors: Abhinav Shanbhag
Date: 2022-10-20 00:00:00

Americans across demographic groups tend to have low financial literacy, with low-income people and minorities at highest risk. This opens the door to the exploitation of unbanked low-income families through high-interest alternative financial services. This paper studies the causes and effects of financial illiteracy and exclusion in the most at-risk demographic groups, and solutions proven to bring them into the financial mainstream. This paper finds that immigrants, ethnic minorities, and low-income families are most likely to be unbanked. Furthermore, the causes for being unbanked include the high fees of bank accounts, the inability of Americans to maintain bank accounts due to low financial assets or time, banking needs being met by alternative financial services, and being provided minimal help while transitioning from welfare to the workforce. The most effective solutions to financial illiteracy and exclusion include partnerships between nonprofits, banks, and businesses that use existing alternative financial service platforms to transition the unbanked into using products that meet their needs, educating the unbanked in the use of mobile banking, and providing profitable consumer credit products targeting unbanked families with features that support their needs in addition to targeted and properly implemented financial literacy programs.
How a Brand's Social Activism Impacts Consumers' Brand Evaluations: The Role of Brand Relationship Norms
Authors: Jingjing Li, Nicole Montgomery, Reza Mousavi
Date: 2022-10-19 00:00:00

With the proliferation of social activism online, brands face heightened pressure from consumers to publicly address these issues. Yet, the optimal brand response strategy (i.e., whether and how to respond) in these contexts remains unclear. This research investigates consumers' reactions to brand response strategies (e.g., engage vs. not) during social activism and offers potentially effective responses that brands can employ to engage in these issues. By analyzing real-world data collected from Twitter and conducting four randomized experiments, this research discovers that brand relationship type (exchange, communal) affects consumers' brand evaluations in the wake of social activism. Communal (vs. exchange) brands are evaluated less favorably when they do not respond or utilize a low-empathy response. This difference is attenuated when brands employ a high-empathy response. These findings are attributable to consumers' perceptions of whether the brand's response strategy complies with relationship norms during social activism. The effects persist across activism events that vary in their political polarization. This research contributes to the literatures on brand engagement in social activism, brand relationships, and crisis communication. The findings also offer guidance to practitioners on crafting response strategies during social activism and aid activists in securing brand support for societal benefits.
Never Say Never: Optimal Exclusion and Reserve Prices with Expectations-Based Loss-Averse Buyers
Authors: Benjamin Balzer, Antonio Rosato
Date: 2022-10-20 00:00:00

We study reserve prices in auctions with independent private values when bidders are expectations-based loss averse. We find that the optimal public reserve price excludes fewer bidder types than under risk neutrality. Moreover, we show that public reserve prices are not optimal as the seller can earn a higher revenue with mechanisms that better leverage the ``attachment effect''. We discuss two such mechanisms: i) an auction with a secrete and random reserve price, and ii) a two-stage mechanism where an auction with a public reserve price is followed by a negotiation if the reserve price is not met. Both of these mechanisms expose more bidders to the attachment effect, thereby increasing bids and ultimately revenue.
Routine Usage of AI-based Chest X-ray Reading Support in a Multi-site Medical Supply Center
Authors: Karsten Ridder, Alexander Preuhs, Axel Mertins, Clemens Joerger
Date: 2022-10-17 00:00:00

Research question: How can we establish an AI support for reading of chest X-rays in clinical routine and which benefits emerge for the clinicians and radiologists. Can it perform 24/7 support for practicing clinicians? 2. Findings: We installed an AI solution for Chest X-ray in a given structure (MVZ Uhlenbrock & Partner, Germany). We could demonstrate the practicability, performance, and benefits in 10 connected clinical sites. 3. Meaning: A commercially available AI solution for the evaluation of Chest X-ray images is able to help radiologists and clinical colleagues 24/7 in a complex environment. The system performs in a robust manner, supporting radiologists and clinical colleagues in their important decisions, in practises and hospitals regardless of the user and X-ray system type producing the image-data.
Compressive Image Classification using Deterministic Sensing Matrices
Authors: Sheel Shah, Kushal Kejriwal
Date: 2022-10-15 00:00:00
Tasks: Classification, compressed sensing, image-classification, Image Classification

We look at the use of deterministic sensing matrices for compressed sensing and provide worst-case bounds on the classification accuracy of SVMs on compressively sensed data.
Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
Authors: Yanbin He, Geethu Joseph
Date: 2022-10-20 00:00:00

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works.
Robust Multitask Diffusion Normalized M-estimate Subband Adaptive Filtering Algorithm Over Adaptive Networks
Authors: Wenjing Xu, Haiquan Zhao, Shaohui Lv
Date: 2022-10-20 00:00:00
Tasks: parameter estimation

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in the distributed parameter estimation and target tracking of multitask network. However, its performance is mainly limited by two aspects, i.e, the correlated input signal and impulsive noise interference. To overcome these two limitations simultaneously, this paper firstly introduces the subband adaptive filter (SAF) into the multitask network. Then, a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed by solving the modified Huber function based global network optimization problem in a distributed manner, which endows the multitask network strong decorrelation ability for correlated inputs and robustness to impulsive noise interference, and accelerates the convergence of the algorithm significantly. Compared with the robust multitask diffusion affine projection M-estimate (MD-APM) algorithm, the computational complexity of the proposed MD-NMSAF is greatly reduced. In addition, the stability condition, the analytical expressions of the theoretical transient and steady-state network mean square deviation (MSD) of the MD-NMSAF are also provided and verified through computer simulations. Simulation results under different input signals and impulsive noise environment fully demonstrate the performance advantages of the MD-NMSAF algorithm over some other competitors in terms of steady-state accuracy and tracking speed.
Federated Learning via Unmanned Aerial Vehicle
Authors: Min Fu, Yuanming Shi, Yong Zhou
Date: 2022-10-20 00:00:00
Tasks: Federated Learning, Scheduling

To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devices for a shared model training. Specifically, by exploiting the UAV's high altitude and mobility, the UAV can proactively establish short-distance line-of-sight links with devices and prevent any device from being a communication straggler. Thus, the model aggregation process can be accelerated while the cumulative model loss caused by device scheduling can be reduced, resulting in a decreased completion time. We first present the convergence analysis of FL without the assumption of convexity, demonstrating the effect of device scheduling on the global gradients. Based on the derived convergence bound, we further formulate the completion time minimization problem by jointly optimizing device scheduling, UAV trajectory, and time allocation. This problem explicitly incorporates the devices' energy budgets, dynamic channel conditions, and convergence accuracy of FL constraints. Despite the non-convexity of the formulated problem, we exploit its structure to decompose it into two sub-problems and further derive the optimal solutions via the Lagrange dual ascent method. Simulation results show that the proposed design significantly improves the tradeoff between completion time and prediction accuracy in practical FL settings compared to existing benchmarks.
Joint Transmit and Receive Beamforming Design in Full-Duplex Integrated Sensing and Communications
Authors: Ziang Liu, Sundar Aditya, Hongyu Li, Bruno Clerckx
Date: 2022-10-19 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Integrated sensing and communication (ISAC) has been envisioned as a solution to realize the sensing capability required for emerging applications in wireless networks, while efficiently utilizing the available spectral, hardware and energy resources. A commonly studied scenario in the ISAC literature involves a transceiver (e.g., a base station) serving a communication user, while simultaneously sensing targets from the reflected echo signals, by acting as a mono-static radar. However, as signal transmission durations are typically much longer than the radar echo round-trip times, the radar returns are drowned by the strong self interference (SI) from the transmitter - a phenomenon termed the echo-miss problem. A promising approach involves the ISAC transceiver to be full-duplex (FD), and in this paper we jointly design the transmit and receive beamformers at the transceiver, transmit precoder at the uplink user, and receive combiner at the downlink user to simultaneously (a) maximize the uplink and downlink rate, (b) maximize the transmit and receive radar beampattern power at the target, and (c) suppress the residual SI. Numerical results illustrate that the proposed design can effectively achieve up to 60 dB digital-domain SI cancellation, a higher average sum-rate, and more accurate radar parameter estimation compared with previous ISAC FD studies.
ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G
Authors: Ahmed Benfaid, Nadia Adem, Abdurrahman Elmaghbub
Date: 2022-10-13 00:00:00
Tasks: Deep Reinforcement Learning

Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both promise high spectral efficiency (SE). As managing UAVs efficiently may not be possible using model-based techniques, another key innovative technology that UAVs will inevitably need to leverage is artificial intelligence (AI). Designing an AI-based technique that adaptively allocates radio resources and places UAVs in 3D space to meet certain communication objectives, however, is a tough row to hoe. In this paper, we propose a neuroevolution of augmenting topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV networks. ProSky exhibits a remarkable performance improvement over a model-based method. Moreover, ProSky learns 5.3 times faster than and outperforms, in both SE and energy efficiency EE while being reasonably fair, a deep reinforcement learning DRL based scheme. The ProSky source code is accessible to use here: https://github.com/Fouzibenfaid/ProSky
Multi-Mode High Altitude Platform Stations (HAPS) for Next Generation Wireless Networks
Authors: Safwan Alfattani, Wael Jaafar, Halim Yanikomeroglu, Abbas Yongaçoglu
Date: 2022-10-05 00:00:00

The high altitude platform station (HAPS) concept has recently received notable attention from both industry and academia to support future wireless networks. A HAPS can be equipped with 5th generation (5G) and beyond technologies such as massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS). Hence, it is expected that HAPS will support numerous applications in both rural and urban areas. However, this comes at the expense of high energy consumption and thus shorter loitering time. To tackle this issue, we envision the use of a multi-mode HAPS that can adaptively switch between different modes so as to reduce energy consumption and extend the HAPS loitering time. These modes comprise a HAPS super macro base station (HAPS-SMBS) mode for enhanced computing, caching, and communication services, a HAPS relay station (HAPS-RS) mode for active communication, and a HAPS-RIS mode for passive communication. This multimode HAPS ensures that operations rely mostly on the passive communication payload, while switching to an energy-greedy active mode only when necessary. In this article, we begin with a brief review of HAPS features compared to other non-terrestrial systems, followed by an exposition of the different HAPS modes proposed. Subsequently, we illustrate the envisioned multi-mode HAPS, and discuss its benefits and challenges. Finally, we validate the multi-mode efficiency through a case study.