pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems
Authors: Julien Deantoni, Paula Muñoz, Cláudio Gomes, Clark Verbrugge, Rakshit Mittal, Robert Heinrich, Stijn Bellis, Antonio Vallecillo
Date: 2024-02-16 00:00:00

Uncertainty is an inherent property of any complex system, especially those that integrate physical parts or operate in real environments. In this paper, we focus on the Digital Twins of adaptive systems, which are particularly complex to design, verify, and optimize. One of the problems of having two systems (the physical one and its digital replica) is that their behavior may not always be consistent. In addition, both twins are normally subject to different types of uncertainties, which complicates their comparison. In this paper we propose the explicit representation and treatment of the uncertainty of both twins, and show how this enables a more accurate comparison of their behaviors. Furthermore, this allows us to reduce the overall system uncertainty and improve its behavior by properly averaging the individual uncertainties of the two twins. An exemplary incubator system is used to illustrate and validate our proposal.
Understanding team collapse via probabilistic graphical models
Authors: Iasonas Nikolaou, Konstantinos Pelechrinis, Evimaria Terzi
Date: 2024-02-14 00:00:00

In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest.
Signed Diverse Multiplex Networks: Clustering and Inference
Authors: Marianna Pensky
Date: 2024-02-14 00:00:00
Tasks: Clustering

The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model, which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in addition, edges can be positive or negative. The setting is extended to a multiplex version, where all layers have the same collection of nodes and follow the SGRDPG. The only common feature of the layers of the network is that they can be partitioned into groups with common subspace structures, while otherwise matrices of connection probabilities can be all different. The setting above is extremely flexible and includes a variety of existing multiplex network models as its particular cases. The paper fulfills two objectives. First, it shows that keeping signs of the edges in the process of network construction leads to a better precision of estimation and clustering and, hence, is beneficial for tackling real world problems such as, for example, analysis of brain networks. Second, by employing novel algorithms, our paper ensures strongly consistent clustering of layers and high accuracy of subspace estimation. In addition to theoretical guarantees, both of those features are demonstrated using numerical simulations and a real data example.
A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying Detection
Authors: Naveed Ejaz, Fakhra Kashif, Salimur Choudhury
Date: 2024-02-09 00:00:00

In recent years, the rising use of social media has propelled automated cyberbullying detection into a prominent research domain. However, challenges persist due to the absence of a standardized definition and universally accepted datasets. Many researchers now view cyberbullying as a facet of cyberaggression, encompassing factors like repetition, peer relationships, and harmful intent in addition to online aggression. Acquiring comprehensive data reflective of all cyberbullying components from social media networks proves to be a complex task. This paper provides a description of an extensive semi-synthetic cyberbullying dataset that incorporates all of the essential aspects of cyberbullying, including aggression, repetition, peer relationships, and intent to harm. The method of creating the dataset is succinctly outlined, and a detailed overview of the publicly accessible dataset is additionally presented. This accompanying data article provides an in-depth look at the dataset, increasing transparency and enabling replication. It also aids in a deeper understanding of the data, supporting broader research use.
Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application
Authors: Cristiano M. Garcia, Alceu de Souza Britto Jr, Jean Paul Barddal
Date: 2024-02-08 00:00:00
Tasks: TAG

Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on similar topics. In this paper, based on a hashtag stream regarding the hashtag #mybodymychoice, we analyze hashtag drifts over time using concepts from graph analysis and textual data streams using the Girvan-Newman method to uncover hashtag communities in annual snapshots between 2018 and 2022. In addition, we offer insights about some correlated hashtags found in the study. Our approach can be useful for monitoring changes over time in opinions and sentiment patterns about an entity on social media. Even though the hashtag #mybodymychoice was initially coupled with women's rights, abortion, and bodily autonomy, we observe that it suffered drifts during the studied period across topics such as drug legalization, vaccination, political protests, war, and civil rights. The year 2021 was the most significant drifting year, in which the communities detected and their respective sizes suggest that #mybodymychoice had a significant drift to vaccination and Covid-19-related topics.
Electronic Conductivity Measurements in Solid Electrolytes Using an Ion Blocking Microelectrode: Noise Rejection Based on a Median Filter
Authors: Veyis Gunes, Jean-Yves Botquelen, Odile Bohnke
Date: 2024-02-16 00:00:00
Tasks: Blocking

A method of electronic conductivity measurement is presented. It combines two well known methods of electrochemistry: cyclic voltammetry and chronoamperometry. This DC technique uses the Hebb/Wagner approach to block ionic conduction when steady state conditions are reached and allows electronic conduction of solid electrolytes to be determined. In order to get short diffusion times, a micro contact is used as an ion blocking electrode. However, as the electronic conduction in electrolytes is and should be very low, the current is also very low, typically some tens of nanoamps. Thus, the heating system inevitably generates noise problems that are solved using a median filter. Our system allows the determination of the conductivities without any preliminary smoothing or fitting of the curves. Some results with oxygen ion conductors are also give
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning
Authors: Benjamin Sullivan, Jingjing Jiang, Georgios Mavros, Wen-Hua Chen
Date: 2024-02-16 00:00:00
Tasks: Active Learning, Friction, parameter estimation

Autonomous Emergency Braking (AEB) potentially brings significant improvements in automotive safety due to its ability to autonomously prevent collisions in situations where the driver may not be able to do so. Driven by the poor performance of the state of the art in recent testing, this work provides an online solution to identify critical parameters such as the current and maximum friction coefficients. The method introduced here, namely Torque Vectoring for Active Learning (TVAL), can perform state and parameter estimation whilst following the driver's input. Importantly with less power requirements than normal driving. Our method is designed with a crucial focus on ensuring minimal disruption to the driver, allowing them to maintain full control of the vehicle. Additionally, we exploit a rain/light sensor to drive the observer resampling to maintain estimation certainty across prolonged operation. Then a scheme to modulate TVAL is introduced that considers powertrain efficiency, safety, and availability in an online fashion. Using a high-fidelity vehicle model and drive cycle we demonstrate the functionality of TVAL controller across changing road surfaces where we successfully identify the road surface whenever possible.
Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems
Authors: Michael Fink, Tim Brüdigam, Dirk Wollherr, Marion Leibold
Date: 2024-02-16 00:00:00
Tasks: Model Predictive Control

Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing a small probability of constraint violation. In this work, we propose a linear model predictive control approach that minimizes the probability that linear state constraints are violated in the presence of additive uncertainty. This is achieved by first determining a set of inputs that minimize the probability of constraint violation. Then, this resulting set is used to define admissible inputs for the optimal control problem. Recursive feasibility is guaranteed and input-to-state stability is proved under assumptions. Numerical results illustrate the benefits of the proposed model predictive control approach.
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Authors: Zirui Liao, Jian Shi, Yuwei Zhang, Shaoping Wang, Zhiyong Sun
Date: 2024-02-16 00:00:00
Tasks: Distributed Optimization, Miscellaneous, Survey

Cyber-physical systems (CPSs) facilitate the integration of physical entities and cyber infrastructures through the utilization of pervasive computational resources and communication units, leading to improved efficiency, automation, and practical viability in both academia and industry. Due to its openness and distributed characteristics, a critical issue prevalent in CPSs is to guarantee resilience in presence of malicious attacks. This paper conducts a comprehensive survey of recent advances on resilient coordination for CPSs. Different from existing survey papers, we focus on the node injection attack and propose a novel taxonomy according to the multi-layered framework of CPS. Furthermore, miscellaneous resilient coordination problems are discussed in this survey. Specifically, some preliminaries and the fundamental problem settings are given at the beginning. Subsequently, based on a multi-layered framework of CPSs, promising results of resilient consensus are classified and reviewed from three perspectives: physical structure, communication mechanism, and network topology. Next, two typical application scenarios, i.e., multi-robot systems and smart grids are exemplified to extend resilient consensus to other coordination tasks. Particularly, we examine resilient containment and resilient distributed optimization problems, both of which demonstrate the applicability of resilient coordination approaches. Finally, potential avenues are highlighted for future research.
A Language Model for Particle Tracking
Authors: Andris Huang, Yash Melkani, Paolo Calafiura, Alina Lazar, Daniel Thomas Murnane, Minh-Tuan Pham, Xiangyang Ju
Date: 2024-02-14 00:00:00
Tasks: Deep Learning, Language Modeling, Language Modelling, model

Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning model for one task with supervised learning techniques. The trained models work well for tasks they are trained on but show no or little generalization capabilities. We propose to unify these models with a language model. In this paper, we present a tokenized detector representation that allows us to train a BERT model for particle tracking. The trained BERT model, namely TrackingBERT, offers latent detector module embedding that can be used for other tasks. This work represents the first step towards developing a foundational model for particle detector understanding.
Manipulation Test for Multidimensional RDD
Authors: Federico Crippa
Date: 2024-02-16 00:00:00
Tasks: Causal Inference

The causal inference model proposed by Lee (2008) for the regression discontinuity design (RDD) relies on assumptions that imply the continuity of the density of the assignment (running) variable. The test for this implication is commonly referred to as the manipulation test and is regularly reported in applied research to strengthen the design's validity. The multidimensional RDD (MRDD) extends the RDD to contexts where treatment assignment depends on several running variables. This paper introduces a manipulation test for the MRDD. First, it develops a theoretical model for causal inference with the MRDD, used to derive a testable implication on the conditional marginal densities of the running variables. Then, it constructs the test for the implication based on a quadratic form of a vector of statistics separately computed for each marginal density. Finally, the proposed test is compared with alternative procedures commonly employed in applied research.
The Famous American Economist H. Markowitz and Mathematical Overview of his Portfolio Selection Theory
Authors: Ignas Gasparavičius, Andrius Grigutis
Date: 2024-02-15 00:00:00
Tasks: Survey

This survey article is dedicated to the life of the famous American economist H. Markowitz (1927--2023). We do revisit the main statements of the portfolio selection theory in terms of mathematical completeness including all the necessary auxiliary details.
Analysis of Neural Video Compression Networks for 360-Degree Video Coding
Authors: Andy Regensky, Fabian Brand, André Kaup
Date: 2024-02-15 00:00:00
Tasks: ERP, Video Compression

With the increasing efforts of bringing high-quality virtual reality technologies into the market, efficient 360-degree video compression gains in importance. As such, the state-of-the-art H.266/VVC video coding standard integrates dedicated tools for 360-degree video, and considerable efforts have been put into designing 360-degree projection formats with improved compression efficiency. For the fast-evolving field of neural video compression networks (NVCs), the effects of different 360-degree projection formats on the overall compression performance have not yet been investigated. It is thus unclear, whether a resampling from the conventional equirectangular projection (ERP) to other projection formats yields similar gains for NVCs as for hybrid video codecs, and which formats perform best. In this paper, we analyze several generations of NVCs and an extensive set of 360-degree projection formats with respect to their compression performance for 360-degree video. Based on our analysis, we find that projection format resampling yields significant improvements in compression performance also for NVCs. The adjusted cubemap projection (ACP) and equatorial cylindrical projection (ECP) show to perform best and achieve rate savings of more than 55% compared to ERP based on WS-PSNR for the most recent NVC. Remarkably, the observed rate savings are higher than for H.266/VVC, emphasizing the importance of projection format resampling for NVCs.
Power Allocation Scheme for Device-Free Localization in 6G ISAC Networks
Authors: Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova
Date: 2024-02-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) is considered one of the crucial technologies in the upcoming sixth-generation (6G) mobile communication systems that could facilitate ultra-precise positioning of passive and active targets and extremely high data rates through spectrum coexistence and hardware sharing. Such an ISAC network offers a lot of benefits, but comes with the challenge of managing the mutual interference between the sensing and communication services. In this paper, we investigate the problem of localization accuracy in a monostatic ISAC network under consideration of inter-BS interference due to communication signal and sensing echoes, and self-interference at the respective BS. We propose a power allocation algorithm that minimizes BS's maximum range estimate error while considering minimum communication signal-to-interference-plus-noise ratio (SINR) and total power constraint. Our numerical results demonstrate the effectiveness of the proposed algorithm and indicate that it can enhance the sensing performance when the self-interference is effectively suppressed.
Fast Data-driven Greedy Sensor Selection for Ridge Regression
Authors: Yasuo Sasaki, Keigo Yamada, Takayuki Nagata, Yuji Saito, Taku Nonomura
Date: 2024-02-16 00:00:00
Tasks: Dimensionality Reduction, regression

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection which prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets which are generated from linear systems and a real-wold dataset which are aimed for selection of pressure-sensor locations for estimating yaw angle of a ground vehicle. The demonstration for the datasets reveal that the proposed algorithm computes a sensor set resulting in more accurate estimation than existing data-drive selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
Advanced Receiver Autonomous Integrity Monitoring: Impact of Time-Correlated Pseudorange Measurement Noise
Authors: Jindrich Dunik, Martin Orejas
Date: 2024-02-16 00:00:00

The paper deals with the allocation of the probability of false alert within the advanced receiver integrity monitoring method. Namely, the stress is laid on the correct computation of the probability of false alert per sample under assumption of time-correlated pseudorange noise. Detailed analysis of the dependence of the probability of false alert per sample on the measurement noise time constant is given and a numerical algorithm for the correct computation of the probability is proposed. The algorithm is illustrated using a numerical example.
Online Signed Sampling of Bandlimited Graph Signals
Authors: Wenwei Liu, Hui Feng, Feng Ji, Bo Hu
Date: 2024-02-16 00:00:00

The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as "like" or "dislike" for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated. The online signed sampling problem of a graph signal can be formulated as a Markov decision process in a finite horizon. Unfortunately, it is intractable for large size graphs. We propose a low-complexity greedy signed sampling algorithm (GSS) as well as a stopping criterion. Meanwhile, we prove that the objective function is adaptive monotonic and adaptive submodular, so that the performance is close enough to the global optimum with a lower bound. Finally, we demonstrate the effectiveness of the GSS algorithm by both synthesis and realworld data.
Path Loss Modeling for RIS-Assisted Wireless System with Direct Link and Elevation Factors
Authors: vinay kumar chapala, Pratham Sharma, Sameer Sharma, S. M. Zafaruddin
Date: 2024-02-16 00:00:00

The present path loss models for wireless systems employing reconfigurable intelligent surfaces (RIS) do not account for the elevation of the transmitter, receiver, and RIS module. In this paper, we develop an analytical model for path loss of a wireless system utilizing an NxM-element RIS module positioned above the ground surface with elevated transmitter and receiver configurations. Furthermore, we integrate the direct link into the path loss model to enhance its applicability, a crucial aspect often neglected in previous research. We also present simplified analytical expressions for path loss under various configurations, including near-field and far-field scenarios. These expressions elucidate the impact of elevation factors on path loss, facilitating more accurate signal quality estimation at the receiver. Simulation results corroborate that accounting for elevated RIS modules and transceiver units can yield improved deployment strategies for RIS-based wireless systems.
Exploring RIS Coverage Enhancement in Factories: From Ray-Based Modeling to Use-Case Analysis
Authors: Gurjot Singh Bhatia, Yoann Corre, Thierry Tenoux, M. Di Renzo
Date: 2024-02-16 00:00:00

Reconfigurable Intelligent Surfaces (RISs) have risen to the forefront of wireless communications research due to their proactive ability to alter the wireless environment intelligently, promising improved wireless network capacity and coverage. Thus, RISs are a pivotal technology in evolving next-generation communication networks. This paper demonstrates a system-level modeling approach for RIS. The RIS model, integrated with the Volcano ray-tracing (RT) tool, is used to analyze the far-field (FF) RIS channel properties in a typical factory environment and explore coverage enhancement at sub-6 GHz and mmWave frequencies. The results obtained in non-line-of-sight (NLoS) scenarios confirm that RIS application is relevant for 5G industrial networks.
Beam Squinting Compensation: An NCR-Assisted Scenario
Authors: Diego A. Sousa, Fco. Rafael M. Lima, Victor F. Monteiro, Tarcisio F. Maciel, Behrooz Makki
Date: 2024-02-15 00:00:00

Millimeter wave (mmWave) and sub-THz communications, foreseen for sixth generation (6G), suffer from high propagation losses which affect the network coverage. To address this point, smart entities such as network-controlled repeaters (NCRs) have been considered as cost-efficient solutions for coverage extension. NCRs, which have been standardized in 3rd generation partnership project Release 18, are radio frequency repeaters with beamforming capability controlled by the network through side control information. Another challenge raised by the adoption of high frequency bands is the use of large bandwidths. Here, a common configuration is to divide a large frequency band into multiple smaller subbands. In this context, we consider a scenario with NCRs where signaling related to measurements used for radio resource management is transmitted in one subband centered at frequency $f_c$ and data transmission is performed at a different frequency $f_c + \Delta f$ based on the measurements taken at $f_c$. Here, a challenge is that the array radiation pattern can be frequency dependent and, therefore, lead to beam misalignment, called beam squinting. We characterize beam squinting in the context of subband operation and propose a solution where the beam patterns to be employed at a given subband can be adjusted/compensated to mitigate beam squinting. Our results show that, without compensation, the perceived signal to interference-plus-noise ratio (SINR) and so the throughput can be substantially decreased due to beam squinting. However, with our proposed compensation method, the system is able to support NCR subband signaling operation with similar performance as if signaling and data were transmitted at the same frequency.