pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotically stable polarization of multi-agent gradient flows over manifolds
Authors: La Mi, Jorge Gonçalves, Johan Markdahl
Date: 2023-01-12 00:00:00

Multi-agent systems are known to exhibit stable emergent behaviors, including polarization, over $\mathbb{R}^n$ or highly symmetric nonlinear spaces. In this article, we eschew linearity and symmetry of the underlying spaces, and study the stability of polarized equilibria of multi-agent gradient flows evolving on general hypermanifolds. The agents attract or repel each other according to the partition of the communication graph that is connected but otherwise arbitrary. The manifolds are outfitted with geometric features styled ``dimples'' and ``pimples'' that characterize the absence of flatness. The signs of inter-agent couplings together with these geometric features give rise to stable polarization under various sufficient conditions. We propose tangible interpretation of the system in the context of opinion dynamics, and highlight throughout the text its versatility in modeling various aspects of the polarization phenomenon.
Safety-Critical Traffic Control by Connected Automated Vehicles
Authors: Huan Yu, Chenguang Zhao, Tamas G. Molnar
Date: 2023-01-12 00:00:00

Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability. Although various longitudinal control strategies have been developed for CAVs to achieve string stability in mixed-autonomy traffic systems, the potential impact of these controllers on safety has not yet been fully addressed. This paper proposes safety-critical traffic control (STC) by CAVs -- a strategy that allows a CAV to stabilize the traffic behind it, while maintaining safety relative to both the preceding vehicle and the following connected human-driven vehicles (HDVs). Specifically, we utilize control barrier functions (CBFs) to impart collision-free behavior with formal safety guarantees to the closed-loop system. The safety of both the CAV and HDVs is incorporated into the framework through a quadratic program-based controller, that minimizes deviation from a nominal stabilizing traffic controller subject to CBF-based safety constraints. Considering that some state information of the following HDVs may be unavailable to the CAV, we employ state observer-based CBFs for STC. Finally, we conduct extensive numerical simulations -- that include vehicle trajectories from real data -- to demonstrate the efficacy of the proposed approach in achieving string stable and, at the same time, provably safe traffic.
On Phase Change Rate Maximization with Practical Applications
Authors: Chung-Yao Kao, Shinji Hara, Yutaka Hori, Tetsuya Iwasaki, Sei Zhen Khong
Date: 2023-01-12 00:00:00

We recapitulate the notion of phase change rate maximization and demonstrate the usefulness of its solution on analyzing the robust instability of a cyclic network of multi-agent systems subject to a homogenous multiplicative perturbation. Subsequently, we apply the phase change rate maximization result to two practical applications. The first is a magnetic levitation system, while the second is a repressilator with time-delay in synthetic biology.
The role of hosting capacity study in power system advancements: a review
Authors: Utkarsh Singh
Date: 2023-01-11 00:00:00

The fast depletion of conventional energy sources due to increased energy demands and environmental concern has motivated power utilities to integrate more renewable energy sources into their power systems. Due to the intermittent nature and low or non-existent inertial response of these sources, a high penetration of RESs can lead to various issues in the operation of power systems such as oscillations in power system voltage and frequency, increased harmonic distortion, failure of protective equipment, overloading of transformers and feeders, and increased line losses. Such problems arise when the hosting capacity, defined as the maximum RES capacity that can be installed without having any technical and operational problems, of the network exceeds its limit due to the increased integration of RESs to the existing network. This paper reviews the progress made in HC assessment and enhancement of electrical networks research and development since its inception. Attempts are also made to highlight the current and future issues involved in HC technology for the development of an affordable, in-exhaustive, clean and reliable power supply for longer term benefits
QoS Based Contract Design for Profit Maximization in IoT-Enabled Data Markets
Authors: Juntao Chen, Junaid Farooq, Quanyan Zhu
Date: 2023-01-11 00:00:00

The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly in healthcare, energy, transportation, etc. However, data collection may have to be outsourced to external service providers (SPs) due to cost considerations or lack of specialized equipment. Hence, the data market plays a critical role in such scenarios where SPs have different quality levels of available data, and IoT users have different application-specific data needs. The pairing between data available to the SP and users in the data market requires an effective mechanism design that considers the SPs' profitability and the quality-of-service (QoS) needs of the users. We develop a generic framework to analyze and enable such interactions efficiently, leveraging tools from contract theory and mechanism design theory. It can enable and empower emerging data sharing paradigms such as Sensing-as-a-Service (SaaS). The contract design creates a pricing structure for on-demand sensing data for IoT users. By considering a continuum of user types, we capture a diverse range of application requirements and propose optimal pricing and allocation rules that ensure QoS provisioning and maximum profitability for the SP. Furthermore, we provide analytical solutions for fixed distributions of user types to analyze the developed approach. For comparison, we consider the benchmark case assuming complete information of the user types and obtain optimal contract solutions. Finally, a case study is presented to demonstrate the efficacy of the proposed contract design framework.
Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea
Authors: Chen Shen, Zhao Song, Lei Shi, Jun Tanimoto, Zhen Wang
Date: 2023-01-12 00:00:00
Tasks: Open-Ended Question Answering

Altruistic punishment, where individuals incur personal costs to punish others who have harmed third parties, presents an evolutionary conundrum as it undermines individual fitness. Resolving this puzzle is crucial for understanding the emergence and maintenance of human cooperation. This study investigates the role of an alternative strategy, the exit option, in explaining altruistic punishment. We analyze a two-stage prisoner's dilemma game in well-mixed and networked populations, considering both finite and infinite scenarios. Our findings reveal that the exit option does not significantly enhance altruistic punishment in well-mixed populations. However, in networked populations, the exit option enables the existence of altruistic punishment and gives rise to complex dynamics, including cyclic dominance and bi-stable states. This research contributes to our understanding of costly punishment and sheds light on the effectiveness of different voluntary participation strategies in addressing the conundrum of punishment.
Interacting Treatments with Endogenous Takeup
Authors: Mate Kormos, Robert P. Lieli, Martin Huber
Date: 2023-01-12 00:00:00
Tasks: Causal Inference

We study causal inference in randomized experiments (or quasi-experiments) following a $2\times 2$ factorial design. There are two treatments, denoted $A$ and $B$, and units are randomly assigned to one of four categories: treatment $A$ alone, treatment $B$ alone, joint treatment, or none. Allowing for endogenous non-compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal interpretation of various instrumental variable estimands under more general compliance conditions than in the literature. In general, if treatment takeup is driven by both instruments for some units, it becomes difficult to separate treatment interaction from treatment effect heterogeneity. We provide auxiliary conditions and various bounding strategies that may help zero in on causally interesting parameters. As an empirical illustration, we apply our results to a program randomly offering two different treatments, namely tutoring and financial incentives, to first year college students, in order to assess the treatments' effects on academic performance.
Inference on quantile processes with a finite number of clusters
Authors: Andreas Hagemann
Date: 2023-01-11 00:00:00

I introduce a generic method for inference on entire quantile and regression quantile processes in the presence of a finite number of large and arbitrarily heterogeneous clusters. The method asymptotically controls size by generating statistics that exhibit enough distributional symmetry such that randomization tests can be applied. The randomization test does not require ex-ante matching of clusters, is free of user-chosen parameters, and performs well at conventional significance levels with as few as five clusters. The method tests standard (non-sharp) hypotheses and can even be asymptotically similar in empirically relevant situations. The main focus of the paper is inference on quantile treatment effects but the method applies more broadly. Numerical and empirical examples are provided.
Measuring Corporate Digital Divide with web scraping: Evidence from Italy
Authors: Mazzoni Leonardo, Pinelli Fabio, Riccaboni Massimo
Date: 2023-01-12 00:00:00

With the increasing pervasiveness of ICTs in the fabric of economic activities, the corporate digital divide has emerged as a new crucial topic to evaluate the IT competencies and the digital gap between firms and territories. Given the scarcity of available granular data to measure the phenomenon, most studies have used survey data. To bridge the empirical gap, we scrape the website homepage of 182 705 Italian firms, extracting ten features related to their digital footprint characteristics to develop a new corporate digital assessment index. Our results highlight a significant digital divide across dimensions, sectors and geographical locations of Italian firms, opening up new perspectives on monitoring and near-real-time data-driven analysis.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Authors: Siteng Chen, Xiyue Wang, Jun Zhang, Liren Jiang, Ning Zhang, Feng Gao, Wei Yang, Jinxi Xiang, Sen yang, Junhua Zheng, Xiao Han
Date: 2023-01-12 00:00:00
Tasks: Diagnostic, Prognosis, whole slide images

Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderisk achieved aera the curve (AUC) of 0.840 (95% confidence interval: 0.805-0.871) in the TCGA cohort, 0.840 (0.805-0.871) in the General cohort, and 0.840 (0.805-0.871) in the CPTAC cohort for the recognition of high-grade tumor. The OSrisk for the prediction of 5-year survival status achieved AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Cox regression analysis indicated that graderisk, OSrisk, tumor grade, and tumor stage were found to be independent prognostic factors, which were further incorporated into the competing-risk nomogram (CRN). Kaplan-Meier survival analyses further illustrated that our CRN could significantly distinguish patients with high survival risk, with hazard ratio of 5.664 (3.893-8.239, p < 0.0001) in the TCGA cohort, 35.740 (5.889-216.900, p < 0.0001) in the General cohort and 6.107 (1.815 to 20.540, p < 0.0001) in the CPTAC cohort. Comparison analyses conformed that our CRN outperformed current prognosis indicators in the prediction of survival status, with higher concordance index for clinical prognosis.
Centroid adapted frequency selective extrapolation for reconstruction of lost image areas
Authors: Wolfgang Schnurrer, Markus Jonscher, Jürgen Seiler, Thomas Richter, Michel Bätz, André Kaup
Date: 2023-01-12 00:00:00
Tasks: Image Reconstruction

Lost image areas with different size and arbitrary shape can occur in many scenarios such as error-prone communication, depth-based image rendering or motion compensated wavelet lifting. The goal of image reconstruction is to restore these lost image areas as close to the original as possible. Frequency selective extrapolation is a block-based method for efficiently reconstructing lost areas in images. So far, the actual shape of the lost area is not considered directly. We propose a centroid adaption to enhance the existing frequency selective extrapolation algorithm that takes the shape of lost areas into account. To enlarge the test set for evaluation we further propose a method to generate arbitrarily shaped lost areas. On our large test set, we obtain an average reconstruction gain of 1.29 dB.
Graph-based compensated wavelet lifting for 3-D+t medical CT data
Authors: Daniela Lanz, André Kaup
Date: 2023-01-12 00:00:00
Tasks: Computed Tomography (CT), Data Compression, graph construction, Motion Compensation

An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the original signal is needed. Image and video coders based on wavelet transforms provide an adequate way to naturally achieve scalability. This paper presents a new approach for improving the visual quality of the lowpass band by using a novel graph-based method for motion compensation, which is an important step considering data compression. We compare different kinds of neighborhoods for graph construction and demonstrate that a higher amount of referenced nodes increases the quality of the lowpass band while the mean energy of the highpass band decreases. We show that for cardiac CT data the proposed method outperforms a traditional mesh-based approach of motion compensation by approximately 11 dB in terms of PSNR of the lowpass band. Also the mean energy of the highpass band decreases by around 30%.
Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets
Authors: Daniela Lanz, André Kaup
Date: 2023-01-12 00:00:00
Tasks: Computed Tomography (CT), Motion Compensation

Medical applications like Computed Tomography (CT) or Magnetic Resonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wavelet transforms. The visual quality of the resulting lowpass band, which shall be used as a representative, can be improved by applying motion compensation methods during the transform. This paper presents a new approach of using the distorted edge lengths of a mesh-based compensated grid instead of the approximated intensity values of the underlying frame to perform a motion compensation. We will show that an edge adaptive graph-based compensation and its usage for compensated wavelet lifting improves the visual quality of the lowpass band by approximately 2.5 dB compared to the traditional mesh-based compensation, while the additional filesize required for coding the motion information doesn't change.
Physical Layer Security Techniques Applied to Vehicle-to-Everything Networks
Authors: Leonardo Barbosa da Silva, Evelio Martín Garcia Fernández, Ândrei Camponogara
Date: 2023-01-12 00:00:00

Physical Layer Security (PLS) is an emerging concept in the field of secrecy for wireless communications that can be used alongside cryptography to prevent unauthorized devices from eavesdropping a legitimate transmission. It offers low computational cost and overhead by injecting an interfering signal in the wiretap channels of potential eavesdroppers. This paper discusses the benefits of the Artificial Noise and Cooperative Jamming techniques in the context of Vehicle-to-everything (V2X) networks, which require secure data exchange with small latency. The simulations indicate that messages can be safely delivered even with devices that have low available power.
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Authors: Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love, Christopher G. Brinton
Date: 2023-01-12 00:00:00
Tasks: Deep Reinforcement Learning

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun adding enormous flexibility to RAN implementations. O-RAN is a natural architectural fit for cell-free massive multiple-input multiple-output (CFmMIMO) systems, where many geographically-distributed access points (APs) are employed to achieve ubiquitous coverage and enhanced user performance. In this paper, we address the decentralized pilot assignment (PA) problem for scalable O-RAN-based CFmMIMO systems. We propose a low-complexity PA scheme using a multi-agent deep reinforcement learning (MA-DRL) framework in which multiple learning agents perform distributed learning over the O-RAN communication architecture to suppress pilot contamination. Our approach does not require prior channel knowledge but instead relies on real-time interactions made with the environment during the learning procedure. In addition, we design a codebook search (CS) scheme that exploits the decentralization of our O-RAN CFmMIMO architecture, where different codebook sets can be utilized to further improve PA performance without any significant additional complexities. Numerical evaluations verify that our proposed scheme provides substantial computational scalability advantages and improvements in channel estimation performance compared to the state-of-the-art.
Machine-learning Analysis of Opioid Use Disorder Informed by MOR, DOR, KOR, NOR and ZOR-Based Interactome Networks
Authors: Hongsong Feng, Rana Elladki, Jian Jiang, Guo-Wei Wei
Date: 2023-01-12 00:00:00

Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments. Preferable therapeutic treatments of OUD rely on the advances in understanding the neurobiological mechanism of opioid dependence. Proteins including mu, delta, kappa, nociceptin, and zeta opioid receptors are the direct targets of opioids. Each receptor has a large protein-protein interaction (PPI) network, that behaves differently when subjected to various treatments, thus increasing the complexity in the drug development process for an effective opioid addiction treatment. The report below analyzes the work by presenting a PPI-network informed machine-learning study of OUD. We have examined more than 500 proteins in the five opioid receptor networks and subsequently collected 74 inhibitor datasets. Machine learning models were constructed by pairing gradient boosting decision tree (GBDT) algorithm with two advanced natural language processing (NLP)-based molecular fingerprints. With these models, we systematically carried out evaluations of screening and repurposing potential of drug candidates for four opioid receptors. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were also considered in the screening of potential drug candidates. Our study can be a valuable and promising tool of pharmacological development for OUD treatments.
Making sense of noise: introducing students to stochastic processes in order to better understand biological behaviors
Authors: Michael W. Klymkowsky
Date: 2023-01-11 00:00:00

Biological systems are characterized by the ubiquitous roles of weak, that is, non-covalent molecular interactions, small, often very small, numbers of specific molecules per cell, and Brownian motion. These combine to produce stochastic behaviors at all levels from the molecular and cellular to the behavioral. That said, students are rarely introduced to the ubiquitous role of stochastic processes in biological systems, and how they produce unpredictable behaviors. Here I present the case that they need to be and provide some suggestions as to how it might be approached.
mRNA active transport in oocyte-early embryo: 3D agent-based modeling
Authors: Marat A. Sabirov, Ekaterina M. Myasnikova, Alexander V. Spirov
Date: 2023-01-10 00:00:00

Axes of polarity (and primary morphogenetic gradients) are established in the oocyte - early embryo through active transport and localization of maternal factors. It is the oocyte - syncytial embryo of Drosophila (D. melanogaster) that is a model object for studying the molecular machinery of such transport systems. The attention of researchers is focused on the processes of formation, maintenance, and functioning of active transport systems of maternal mRNAs and proteins that are key for early Drosophila embryogenesis. Here we develop an approach for agent-based 3D modeling of the key components of transport by molecular motors (by elements of the cytoskeleton) of the Drosophila oocyte-syncytial embryo. The models were developed using Skeledyne software developed by Odell and Foe [Odell and Foe, 2008]. We start with the results of modeling transport along oriented microtubule (MT) bundles in the oocyte. This is a model of transport systems in the Drosophila oocyte, where three maternal mRNAs (bicoid (bcd), oskar, and gurken) that are key to embryonic polarity are transported along their oriented MT bundles. Then we consider models of oriented MT networks in the volume of a cell (oocyte) generated by a single microtubule organization center (or a pair of the centers). This model reproduces the formation of bcd mRNA intrusions deep into the cytoplasm in the head half of the early syncytial embryo. Finally, we consider models for the active transport of bcd mRNA in a syncytial embryo along a randomized network of many short MT strands. In conclusion, we consider the prospects for the implementation of cytoplasmic fountain flows in the active transport model.