pwc-archive/papers-with-abstracts huggingface dataset:
HARQ Retransmissions in C-V2X: A BSM Latency Analysis
Authors: Abdurrahman Fouda, Randall Berry, Ivan Vukovic
Date: 2023-11-28 00:00:00
Tasks: Scheduling

Cellular vehicular-to-everything (C-V2X) systems offer the potential for improving road safety, in part through the exchange of periodic basic safety messages (BSMs) between nearby vehicles. The reliability and latency of these messages is a key metric. Hybrid automatic repeat request (HARQ) retransmissions are one technique used to this end. However, HARQ may come at the expense of consuming the limited available wireless resources, especially in highly congested scenarios. This paper studies BSM transmission latency and reliability when HARQ retransmissions are used with the semi-persistent scheduling (SPS) in C-V2X transmission mode 4. We do so through extensive system-level simulations that closely follow the SPS process. Furthermore, we provide an analytical model for the tail behavior of the BSM latency distribution with HARQ retransmissions that is a good approximation to the simulation results. Our study reveals the impact of several deployment settings (e.g., bandwidth configurations and vehicle density).
Study of BSM Inter-Packet Gap Tails in C-V2X Networks
Authors: Abdurrahman Fouda, Randall Berry, Ivan Vukovic
Date: 2023-11-28 00:00:00
Tasks: Scheduling

Cellular vehicle-to-everything (C-V2X) enables safety-critical connected vehicular service by exchanging basic safety messages (BSMs) among nearby vehicular users (VUEs). Timely transmission of BSMs is crucial to avoid stale information at VUEs. However, successive packet losses can lead to large inter-packet gaps (IPGs), reducing the BSMs' reliability. This paper investigates the tail behavior of IPG and information age (IA) distributions in C-V2X mode 4, a decentralized resource allocation method based on semi-persistent scheduling (SPS). We study the improvements and trade-offs introduced by SAE one-shot transmission to decrease the number of successive BSM losses at destination VUEs. The study employs high-fidelity system-level simulations that closely follow the SPS process of CV2X mode 4 to evaluate the performance of interleaved one-shot SPS transmissions. The numerical results demonstrate significant improvement in the IPG and IA tail distributions in various simulation scenarios. Additionally, we propose an accurate analytical model to characterize the IPG tail behavior of C-V2X BSM transmissions. The proposed model is validated by comparing its results with those obtained using the system-level simulations. Our validation shows that the proposed model generates analytical results that coincide with the asymptotic slopes of IPG distribution in different BSM transmission modes.
A Short Overview of 6G V2X Communication Standards
Authors: Donglin Wang, Yann Nana Nganso, Hans D. Schotten
Date: 2023-11-28 00:00:00

We are on the verge of a new age of linked autonomous cars with unheard-of user experiences, dramatically improved air quality and road safety, extremely varied transportation settings, and a plethora of cutting-edge apps. A substantially improved Vehicle-to-Everything (V2X) communication network that can simultaneously support massive hyper-fast, ultra-reliable, and low-latency information exchange is necessary to achieve this ambitious goal. These needs of the upcoming V2X are expected to be satisfied by the Sixth Generation (6G) communication system. In this article, we start by introducing the history of V2X communications by giving details on the current, developing, and future developments. We compare the applications of communication technologies such as Wi-Fi, LTE, 5G, and 6G. we focus on the new technologies for 6G V2X which are brain-vehicle interface, blocked-based V2X, and Machine Learning (ML). To achieve this, we provide a summary of the most recent ML developments in 6G vehicle networks. we discuss the security challenges of 6G V2X. We address the strengths, open challenges, development, and improving areas of further study in this field.
Emulators in JINSP
Authors: Lei Zhao, Miaomiao Zhang, Lv Zhe
Date: 2023-11-07 00:00:00

JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of basic emulators and their combinations, such as the simulation of the protocol stack for dynamic users in a real environment, which is composed of user behavior simulation, base station simulation, and terminal simulation. It is applied in specific business scenarios, such as multi-target antenna optimization, compression feedback, and so on. This paper provides detailed descriptions of each emulator and its combination based on this foundation, including the implementation process of the emulator, integration with the platform, experimental results, and other aspects.
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Authors: Fabian Zaiser, Andrzej S. Murawski, Luke Ong
Date: 2023-05-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Inference, Probabilistic Programming

We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine functions, (stochastic) branching, and conditioning on discrete events. Our key tool is probability generating functions: they provide a compact closed-form representation of distributions that are definable by programs, thus enabling the exact computation of posterior probabilities, expectation, variance, and higher moments. Our inference method is provably correct and fully automated in a tool called Genfer, which uses automatic differentiation (specifically, Taylor polynomials), but does not require computer algebra. Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy. On a range of real-world inference problems that none of these exact tools can solve, Genfer's performance is competitive with approximate Monte Carlo methods, while avoiding approximation errors.
iMagLS: Interaural Level Difference with Magnitude Least-Squares Loss for Optimized First-Order Head-Related Transfer Function
Authors: Or Berebi, Zamir Ben-Hur, David Lou Alon, Boaz Rafaely
Date: 2023-11-28 00:00:00

Binaural reproduction for headphone-based listening is an active research area due to its widespread use in evolving technologies such as augmented and virtual reality (AR and VR). On the one hand, these applications demand high quality spatial audio perception to preserve the sense of immersion. On the other hand, recording devices may only have a few microphones, leading to low-order representations such as first-order Ambisonics (FOA). However, first-order Ambisonics leads to limited externalization and spatial resolution. In this paper, a novel head-related transfer function (HRTF) preprocessing optimization loss is proposed, and is minimized using nonlinear programming. The new method, denoted iMagLS, involves the introduction of an interaural level difference (ILD) error term to the now widely used MagLS optimization loss for the lateral plane angles. Results indicate that the ILD error could be substantially reduced, while the HRTF magnitude error remains similar to that obtained with MagLS. These results could prove beneficial to the overall spatial quality of first-order Ambisonics, while other reproduction methods could also benefit from considering this modified loss.
Compression-based inference of network motif sets
Authors: Alexis Bénichou, Jean-Baptiste Masson, Christian L. Vestergaard
Date: 2023-11-27 00:00:00

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred as "building blocks of complex networks". Their well-defined structures and small sizes also enables the testing of their functions in synthetic and natural biological experiments. The statistical inference of network motifs is however fraught with difficulties, from defining and sampling the right null model to accounting for the large number of possible motifs and their potential correlations in statistical testing. Here we develop a framework for motif mining based on lossless network compression using subgraph contractions. The minimum description length principle allows us to select the most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. The approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. This provides an alternative definition of motif significance which guarantees more robust statistical inference. Our approach overcomes the common problems in classic testing-based motif analysis. We apply our methodology to perform comparative connectomics by evaluating the compressibility and the circuit motifs of a range of synaptic-resolution neural connectomes.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Authors: Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang
Date: 2023-06-17 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph structure learning, Representation Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. We also find that there is no significant correlation between the homophily of the learned structure and task performance, challenging the common belief. Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in this field. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.
Learning Large Graph Property Prediction via Graph Segment Training
Authors: Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi
Date: 2023-05-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Property Prediction, Prediction, Property Prediction

Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during training is bounded. Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint. GST first divides a large graph into segments and then backpropagates through only a few segments sampled per training iteration. We refine the GST paradigm by introducing a historical embedding table to efficiently obtain embeddings for segments not sampled for backpropagation. To mitigate the staleness of historical embeddings, we design two novel techniques. First, we finetune the prediction head to fix the input distribution shift. Second, we introduce Stale Embedding Dropout to drop some stale embeddings during training to reduce bias. We evaluate our complete method GST-EFD (with all the techniques together) on two large graph property prediction benchmarks: MalNet and TpuGraphs. Our experiments show that GST-EFD is both memory-efficient and fast, while offering a slight boost on test accuracy over a typical full graph training regime.
CAT-Walk: Inductive Hypergraph Learning via Set Walks
Authors: Ali Behrouz, Farnoosh Hashemi, Sadaf Sadeghian, Margo Seltzer
Date: 2023-06-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Hyperedge Prediction, Node Classification, Representation Learning

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-order interactions in complex systems. Representation learning for hypergraphs is essential for extracting patterns of the higher-order interactions that are critically important in real-world problems in social network analysis, neuroscience, finance, etc. However, existing methods are typically designed only for specific tasks or static hypergraphs. We present CAT-Walk, an inductive method that learns the underlying dynamic laws that govern the temporal and structural processes underlying a temporal hypergraph. CAT-Walk introduces a temporal, higher-order walk on hypergraphs, SetWalk, that extracts higher-order causal patterns. CAT-Walk uses a novel adaptive and permutation invariant pooling strategy, SetMixer, along with a set-based anonymization process that hides the identity of hyperedges. Finally, we present a simple yet effective neural network model to encode hyperedges. Our evaluation on 10 hypergraph benchmark datasets shows that CAT-Walk attains outstanding performance on temporal hyperedge prediction benchmarks in both inductive and transductive settings. It also shows competitive performance with state-of-the-art methods for node classification. (https://github.com/ubc-systopia/CATWalk)
Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning
Authors: Jasper Knox, Mark Blyth, Alastair Hales
Date: 2023-11-28 00:00:00
Tasks: State Estimation

Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be directly obtained, and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate SOC estimators. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18-hour automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.
Advancements in Arc Fault Detection for Electrical Distribution Systems: A Comprehensive Review from Artificial Intelligence Perspective
Authors: Kriti Thakur, Divyanshi Dwivedi, K. Victor Sam Moses Babu, Alivelu Manga Parimi, Pradeep Kumar Yemula, Pratyush Chakraborty, Mayukha Pal
Date: 2023-11-28 00:00:00
Tasks: ARC, Fault Detection

This comprehensive review paper provides a thorough examination of current advancements and research in the field of arc fault detection for electrical distribution systems. The increasing demand for electricity, coupled with the increasing utilization of renewable energy sources, has necessitated vigilance in safeguarding electrical distribution systems against arc faults. Such faults could lead to catastrophic accidents, including fires, equipment damage, loss of human life, and other critical issues. To mitigate these risks, this review article focuses on the identification and early detection of arc faults, with a particular emphasis on the vital role of artificial intelligence (AI) in the detection and prediction of arc faults. The paper explores a wide range of methodologies for arc fault detection and highlights the superior performance of AI-based methods in accurately identifying arc faults when compared to other approaches. A thorough evaluation of existing methodologies is conducted by categorizing them into distinct groups, which provides a structured framework for understanding the current state of arc fault detection techniques. This categorization serves as a foundation for identifying the existing constraints and future research avenues in the domain of arc fault detection for electrical distribution systems. This review paper provides the state of the art in arc fault detection, aiming to enhance safety and reliability in electrical distribution systems and guide future research efforts.
A Cooperative Statistical Approach for Abnormal Node Detection with Adversary Resistance
Authors: Yingying Huangfu, Tian Bai
Date: 2023-11-28 00:00:00

Distinguishing abnormal nodes from those with normal packet loss in clusters helps reduce the loss of clustered network resources. The detection performance of existing detection schemes is limited by the techniques to quantify node behaviors, and most schemes cannot avoid being misled by the falsified information. This paper presents a novel probabilistic abnormal node detection scheme CSD -- Cooperative Statistical Detection -- for accurate and efficient detection in the existence of falsified detection data in clustered networks. Specifically, employing the likelihood ratio test (LRT) based detection method to measure node forwarding behaviors, we propose a modified Z-score based falsification-resistant mechanism to filter out falsifications. We show that both the false alarm and missed detection probabilities can decrease exponentially if and only if the transmissions from the nodes falsifying the data are less than half of the total. Furthermore, the optimal threshold of the modified Z-score method is derived, which guarantees perfect detection of our CSD under any falsification strategy in the proposed detection model. Evaluation results validate the effectiveness, robustness, and superiority of our scheme compared to the state-of-the-art.
Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience
Authors: Jiaqi Ruan, Xiangrui Meng, Yifan Zhu, Gaoqi Liang, Xianzhuo Sun, Huayi Wu, Huijuan Xiao, Mengqian Lu, Pin Gao, Jiapeng Li, Wai-kin Wong, Zhao Xu, Junhua Zhao
Date: 2023-11-28 00:00:00

Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change. Yet, failure to identify the intricate relationship between wind-related climate risks and power systems could lead to serious short- and long-term issues, including partial or complete blackouts. Here, we develop a comprehensive framework to assess China's power system resilience across various climate change scenarios, enabling a holistic evaluation of the repercussions induced by wind-related climate change. Our findings indicate that China's current wind projects and planning strategies could be jeopardized by wind-related climate change, with up to a 12\% decline in regional wind power availability. Moreover, our results underscore a pronounced vulnerability of power system resilience amidst the rigors of hastened climate change, unveiling a potential amplification of resilience deterioration, even approaching fourfold by 2060 under the most severe scenario, relative to the 2020 benchmark. This work advocates for strategic financial deployment within the power sector aimed at climate adaptation, enhancing power system resilience to avert profound losses from long-term, wind-influenced climatic fluctuations.
Motor State Prediction and Friction Compensation for Brushless DC Motor Drives Using Data-Driven Techniques
Authors: Nimantha Dasanayake, Shehara Perera
Date: 2023-11-28 00:00:00
Tasks: Friction, Position

In order to provide robust, reliable, and accurate position and velocity control of motor drives, friction compensation has emerged as a key difficulty. Non-characterised friction could give rise to large position errors and vibrations which could be intensified by stick-slip motion and limit cycles. This paper presents an application of two data-driven nonlinear model identification techniques to discover the governing equations of motor dynamics that also characterise friction. Namely, the extraction of low-power data from time-delayed coordinates of motor velocity and sparse regression on nonlinear terms was applied to data acquired from a Brushless DC (BLDC) motor, to identify the underlying dynamics. The latter can be considered an extension of the conventional linear motor model commonly used in many model-based controllers. The identified nonlinear model was then contrasted with a nonlinear model that included the LuGre friction model and a linear model without friction. A nonlinear grey box model estimation method was used to calculate the optimum friction parameters for the LuGre model. The resulting nonlinear motor model with friction characteristics was then validated using a feedback friction compensation algorithm. The novel model showed more than 90% accuracy in predicting the motor states in all considered input excitation signals. In addition, the model-based friction compensation scheme showed a relative increase in performance when compared with a system without friction compensation.
Phase Preservation of N-Port Networks under General Connections
Authors: Jianqi Chen, Wei Chen, Chao Chen, Li Qiu
Date: 2023-11-28 00:00:00

This study first introduces the frequency-wise phases of n-port linear time-invariant networks based on recently defined phases of complex matrices. Such a phase characterization can be used to quantify the well-known notion of passivity for networks. Further, a class of matrix operations induced by fairly common n-port network connections is examined. The intrinsic phase properties of networks under such connections are preserved. Concretely, a scalable phase-preserving criterion is proposed, which involves only the phase properties of individual subnetworks, under the matrix operations featured by connections. This criterion ensures that the phase range of the integrated network can be verified effectively and that the scalability of the analyses can be maintained. In addition, the inverse operations of the considered connections, that is, network subtractions with correspondences are examined. With the known phase ranges of the integrated network and one of its subnetworks, the maximal allowable phase range of the remaining subnetwork can also be determined explicitly in a unified form for all types of subtractions. Finally, we extend the phase-preserving properties from the aforementioned connections to more general matrix operations defined using a certain indefinite inner product.