pwc-archive/papers-with-abstracts huggingface dataset:
AttentiveMOS: A Lightweight Attention-Only Model for Speech Quality Prediction
Authors: Imran E Kibria, Donald S. Williamson
Date: 2024-10-16 00:00:00

Research in modeling subjective metrics for quality assessment has led to the development of no-reference speech models that directly operate on utterance waveforms to predict the Mean Opinion Score (MOS). These models often rely on convolutional layers for local feature extraction and embeddings from impractically large pretrained networks to enhance generalization. We propose an attention-only model based on Swin transformer and standard transformer layers to extract local context features and global utterance features, respectively. The self-attention operator excels at processing sequences, and our lightweight design enhances generalization on limited MOS datasets while improving real-world applicability. We train our network using a sequential self-teaching strategy to improve generalization on MOS labels affected by noise in listener ratings. Experiments on three datasets confirm the effectiveness of our design and demonstrate improvement over baseline models.
Contrasting results of surface metrology techniques for three-dimensional human fingerprints
Authors: Brian Lee Beatty, Shani Kahan, Burcak Bas, Bettina Zou, Nicole Werpachowski
Date: 2024-10-16 00:00:00

Fingerprints, otherwise known as dermatoglyphs, are most commonly thought of in the context of identification, but have myriad other roles in human biology. They are formed by the restricted ability of ridges and furrows of the epidermis to flatten. The patterns these ridges and furrows make can be represented as 2D fingerprints, but also as 3D structures with cross-sectional shapes that may add new levels of detail to identification, forensic, and behavioral uses/studies. Surface metrology techniques better allow for the quantification of these features, though it is unclear what tool and what scale is most appropriate. A Sensofar S Neox white light reflectance confocal microscope and a Gelsight Mobile 2 were used to independently measure the surface roughness of the fingerprints of four individuals from preserved cadaveric remains. Scale-sensitive fractal analyses (SSFA) were performed on the data from the S Neox (a small area), Gelsight (a larger area), and the same Gelsight datasets cropped down to the size of the S Neox scan size. Though fewer SSFA parameters identified differences between individuals from the smaller, extracted Gelsight area, all three forms of measurement found significant differences between some individuals from the study. No significant differences were found that differ between fingers themselves. Though only an initial step, these data suggest that a variety of surface metrology techniques may be useful in differentiating individuals.
Counterfactual Analysis in Empirical Games
Authors: Brendan Kline, Elie Tamer
Date: 2024-10-16 00:00:00
Tasks: counterfactual

We address counterfactual analysis in empirical models of games with partially identified parameters, and multiple equilibria and/or randomized strategies, by constructing and analyzing the counterfactual predictive distribution set (CPDS). This framework accommodates various outcomes of interest, including behavioral and welfare outcomes. It allows a variety of changes to the environment to generate the counterfactual, including modifications of the utility functions, the distribution of utility determinants, the number of decision makers, and the solution concept. We use a Bayesian approach to summarize statistical uncertainty. We establish conditions under which the population CPDS is sharp from the point of view of identification. We also establish conditions under which the posterior CPDS is consistent if the posterior distribution for the underlying model parameter is consistent. Consequently, our results can be employed to conduct counterfactual analysis after a preliminary step of identifying and estimating the underlying model parameter based on the existing literature. Our consistency results involve the development of a new general theory for Bayesian consistency of posterior distributions for mappings of sets. Although we primarily focus on a model of a strategic game, our approach is applicable to other structural models with similar features.
The Pond Dilemma with Heterogeneous Relative Concerns
Authors: Paweł Gola
Date: 2024-10-16 00:00:00

This paper explores team formation when workers differ in skills and their desire to out-earn co-workers. I cast this question as a two-dimensional assignment problem with imperfectly transferable utility and show that equilibrium sorting optimally trades off output maximisation with the need to match high-skill workers to co-workers with weak relative concerns. This can lead to positive (negative) assortative matching in skill even with submodular (supermodular) production functions. Under supermodular production, this heterogeneity in preferences benefits all workers and reduces wage inequality. With submodular production, the distributional consequences are ambiguous, and some workers become worse off. The model reveals that skill-biased technological change (SBTC) incentivises domestic outsourcing, as firms seek to avoid detrimental social comparisons between high- and low-skill workers, thus providing a compelling explanation for the long-term increase in outsourcing. Finally, the benefits of SBTC can trickle down to low-skill workers-but only those whose relative concerns are weak.
Circulating Currents in Windings: Fundamental Property
Authors: Taha El Hajji, Antti Lehikoinen, Anouar Belahcen
Date: 2024-10-16 00:00:00

Circulating currents in windings refer to unwanted electrical currents flowing between the parallel conductors of a winding. These currents arise due to several phenomena such as asymmetries, imperfections in the winding layout, and differences in electric potential between the parallel conductors. This effect is visible typically in windings of transformers, motors, or generators. At on-load condition, this is equivalent to having a current unevenly distributed between parallel conductors. Circulating currents have two main drawbacks: increased losses in windings and potential degradation of insulation over time. The former is an intuitive property that is widely acknowledged in the literature. This paper presents a formal proof of this fundamental property, building upon the authors' previous work and embedding it within a rigorous mathematical framework. The mathematical definition of circulating currents is provided, along with a case application in an electric machine.
DRIP: A Versatile Family of Space-Time ISAC Waveforms
Authors: Dexin Wang, Ahmad Bazzi, Marwa Chafii
Date: 2024-10-16 00:00:00
Tasks: ISAC

The following paper introduces Dual beam-similarity awaRe Integrated sensing and communications (ISAC) with controlled Peak-to-average power ratio (DRIP) waveforms. DRIP is a novel family of space-time ISAC waveforms designed for dynamic peak-to-average power ratio (PAPR) adjustment. The proposed DRIP waveforms are designed to conform to specified PAPR levels while exhibiting beampattern properties, effectively targeting multiple desired directions and suppressing interference for multi-target sensing applications, while closely resembling radar chirps. For communication purposes, the proposed DRIP waveforms aim to minimize multi-user interference across various constellations. Addressing the non-convexity of the optimization framework required for generating DRIP waveforms, we introduce a block cyclic coordinate descent algorithm. This iterative approach ensures convergence to an optimal ISAC waveform solution. Simulation results validate the DRIP waveforms' superior performance, versatility, and favorable ISAC trade-offs, highlighting their potential in advanced multi-target sensing and communication systems.
Low Complexity Rate Splitting Approach in RIS-Aided Systems Based on Channel Statistics
Authors: Sadaf Syed, Michael Joham, Wolfgang Utschick
Date: 2024-10-16 00:00:00

Rate splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) are two prospective technologies for improving the spectral and energy efficiency in future wireless communication systems. In this work, we investigate a rate splitting (RS) technique for an RIS-aided system in the presence of only statistical channel knowledge. We propose an algorithm with a quasi closed-form solution based only on the second-order channel statistics, which reduces the design complexity of the system as it does not require estimation of the channel state information (CSI) and optimisation of the precoding filters and phase shifts of the RIS in every channel coherence interval.
Mean Field-based Dynamic Backoff Optimization for MIMO-enabled Grant-Free NOMA in Massive IoT Networks
Authors: Haibo Wang, Hongwei Gao, Pai Jiang, Matthieu De Mari, Panzer Gu, Yinsheng Liu
Date: 2024-10-16 00:00:00

In the 6G Internet of Things (IoT) paradigm, unprecedented challenges will be raised to provide massive connectivity, ultra-low latency, and energy efficiency for ultra-dense IoT devices. To address these challenges, we explore the non-orthogonal multiple access (NOMA) based grant-free random access (GFRA) schemes in the cellular uplink to support massive IoT devices with high spectrum efficiency and low access latency. In particular, we focus on optimizing the backoff strategy of each device when transmitting time-sensitive data samples to a multiple-input multiple-output (MIMO)-enabled base station subject to energy constraints. To cope with the dynamic varied channel and the severe uplink interference due to the uncoordinated grant-free access, we formulate the optimization problem as a multi-user non-cooperative dynamic stochastic game (MUN-DSG). To avoid dimensional disaster as the device number grows large, the optimization problem is transformed into a mean field game (MFG), and its Nash equilibrium can be achieved by solving the corresponding Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Thus, a Mean Field-based Dynamic Backoff (MFDB) scheme is proposed as the optimal GFRA solution for each device. Extensive simulation has been fulfilled to compare the proposed MFDB with contemporary random access approaches like access class barring (ACB), slotted-Additive Links On-line Hawaii Area (ALOHA), and minimum backoff (MB) under both static and dynamic channels, and the results proved that MFDB can achieve the least access delay and cumulated cost during multiple transmission frames. Keywords: 6G; Internet of Things; grant-free random access; NOMA; dynamic backoff
Optimizing Radio Access Technology Selection and Precoding in CV-Aided ISAC Systems
Authors: Yulan Gao, Ziqiang Ye, Ming Xiao, Yue Xiao
Date: 2024-10-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) systems promise to revolutionize wireless networks by concurrently supporting high-resolution sensing and high-performance communication. This paper presents a novel radio access technology (RAT) selection framework that capitalizes on vision sensing from base station (BS) cameras to optimize both communication and perception capabilities within the ISAC system. Our framework strategically employs two distinct RATs, LTE and millimeter wave (mmWave), to enhance system performance. We propose a vision-based user localization method that employs a 3D detection technique to capture the spatial distribution of users within the surrounding environment. This is followed by geometric calculations to accurately determine the state of mmWave communication links between the BS and individual users. Additionally, we integrate the SlowFast model to recognize user activities, facilitating adaptive transmission rate allocation based on observed behaviors. We develop a Deep Deterministic Policy Gradient (DDPG)-based algorithm, utilizing the joint distribution of users and their activities, designed to maximize the total transmission rate for all users through joint RAT selection and precoding optimization, while adhering to constraints on sensing mutual information and minimum transmission rates. Numerical simulation results demonstrate the effectiveness of the proposed framework in dynamically adjusting resource allocation, ensuring high-quality communication under challenging conditions.
AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning
Authors: Jingqing Wang, Wenchi Cheng, Wei zhang
Date: 2024-10-16 00:00:00
Tasks: Deep Reinforcement Learning

The Age of Information (AoI) has recently gained recognition as a critical quality-of-service (QoS) metric for quantifying the freshness of status updates, playing a crucial role in supporting massive ultra-reliable and low-latency communications (mURLLC) services. In mURLLC scenarios, due to the inherent system dynamics and varying environmental conditions, optimizing AoI under such multi-QoS constraints considering both delay and reliability often results in non-convex and computationally intractable problems. Motivated by the demonstrated efficacy of deep reinforcement learning (DRL) in addressing large-scale networking challenges, this work aims to apply DRL techniques to derive optimal resource allocation solutions in real time. Despite its potential, the effective integration of FBC in DRL-based AoI optimization remains underexplored, especially in addressing the challenge of simultaneously upper-bounding both delay and error-rate. To address these challenges, we propose a DRL-based framework for AoI-aware optimal resource allocation in mURLLC-driven multi-QoS schemes, leveraging AoI as a core metric within the finite blocklength regime. First, we design a wireless communication architecture and AoI-based modeling framework that incorporates FBC. Second, we proceed by deriving upper-bounded peak AoI and delay violation probabilities using stochastic network calculus (SNC). Subsequently, we formulate an optimization problem aimed at minimizing the peak AoI violation probability through FBC. Third, we develop DRL algorithms to determine optimal resource allocation policies that meet statistical delay and error-rate requirements for mURLLC. Finally, to validate the effectiveness of the developed schemes, we have executed a series of simulations.
A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks
Authors: Haocheng Zhang, Wei Wang, Hao Zhou, Zhiping Lu, Ming Li
Date: 2024-10-16 00:00:00
Tasks: Deep Reinforcement Learning, Management

As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, their deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance. A promising strategy is to treat RIS as a public resource managed by an RIS provider (RP), which can enhance resource allocation efficiency by allowing dynamic access for multiple OPs. However, the intricate nature of coordinating management and optimizing RIS configurations significantly complicates the implementation process. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) approach that decomposes the complicated RIS resource optimization problem into several subtasks. Specifically, a top-level RP-agent is responsible for RIS allocation, while low-level OP-agents control their assigned RISs and handle beamforming, RIS phase-shifts, and user association. By utilizing the semi-Markov decision process (SMDP) theory, we establish a sophisticated interaction mechanism between the RP and OPs, and introduce an advanced hierarchical proximal policy optimization (HPPO) algorithm. Furthermore, we propose an improved sequential-HPPO (S-HPPO) algorithm to address the curse of dimensionality encountered with a single RP-agent. Experimental results validate the stability of the HPPO algorithm across various environmental parameters, demonstrating its superiority over other benchmarks for joint resource optimization. Finally, we conduct a detailed comparative analysis between the proposed S-HPPO and HPPO algorithms, showcasing that the S-HPPO algorithm achieves faster convergence and improved performance in large-scale RIS allocation scenarios.
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving
Authors: Kui Wang, Kazuma Nonomura, Zongdian Li, Tao Yu, Kei Sakaguchi, Omar Hashash, Walid Saad, Changyang She, Yonghui Li
Date: 2024-10-16 00:00:00
Tasks: Autonomous Driving, Reinforcement Learning (RL)

Vehicle-road collaboration is a promising approach for enhancing the safety and efficiency of autonomous driving by extending the intelligence of onboard systems to smart roadside infrastructures. The introduction of digital twins (DTs), particularly local DTs (LDTs) at the edge, in smart mobility presents a new embodiment of augmented intelligence, which could enhance information exchange and extract human driving expertise to improve onboard intelligence. This paper presents a novel LDT-assisted hybrid autonomous driving system for improving safety and efficiency in traffic intersections. By leveraging roadside units (RSUs) equipped with sensory and computing capabilities, the proposed system continuously monitors traffic, extracts human driving knowledge, and generates intersection-specific local driving agents through an offline reinforcement learning (RL) framework. When connected and automated vehicles (CAVs) pass through RSU-equipped intersections, RSUs can provide local agents to support safe and efficient driving in local areas. Meanwhile, they provide real-time cooperative perception (CP) to broaden onboard sensory horizons. The proposed LDT-assisted hybrid system is implemented with state-of-the-art products, e.g., CAVs and RSUs, and technologies, e.g., millimeter-wave (mmWave) communications. Hardware-in-the-loop (HiL) simulations and proof-of-concept (PoC) tests validate system performance from two standpoints: (i) The peak latency for CP and local agent downloading are 8.51 ms and 146 ms, respectively, aligning with 3GPP requirements for vehicle-to-everything (V2X) and model transfer use cases. Moreover, (ii) local driving agents can improve safety measures by 10% and reduce travel time by 15% compared with conventional onboard systems. The implemented prototype also demonstrates reliable real-time performance, fulfilling the targets of the proposed system design.
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
Authors: Yang Li, Jiankai Gao, Yuanzheng Li, Chen Chen, Sen Li, Mohammad Shahidehpour, Zhe Chen
Date: 2024-10-15 00:00:00
Tasks: AutoML, Computational Efficiency, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Scheduling

To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming. To overcome the non-convex limitations of karush-kuhn-tucker (KKT)-based methods, a novel optimization solution method based on DRL theory is proposed to handle the bi-level programming through alternate iterations between levels. Specifically, by combining a DRL algorithm named asynchronous advantage actor-critic (A3C) and automated machine learning-prioritized experience replay (AutoML-PER) strategy to improve the generalization performance of A3C to address the above problems, an improved A3C algorithm, called AutoML-PER-A3C, is designed to solve the upper-level problem; while the DOCPLEX optimizer is adopted to address the lower-level problem. In this solution process, AutoML is used to automatically optimize hyperparameters and PER improves learning efficiency and quality by extracting the most valuable samples. The test results demonstrate that the presented approach manages to reconcile the interests between multiple stakeholders in MG by fully exploiting various flexibility resources. Furthermore, in terms of economic viability and computational efficiency, the proposal vastly exceeds other advanced reinforcement learning methods.
Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach
Authors: Bohan Zhang, Zhongkai Yi, Ying Xu, Zhenghong Tu
Date: 2024-10-17 00:00:00
Tasks: Reinforcement Learning (RL)

The complexity and nonlinearity of active distribution network (ADN), coupled with the fast-changing renewable energy (RE), necessitate advanced real-time and safe dispatch approach. This paper proposes a complementary reinforcement learning (RL) and optimization approach, namely SA2CO, to address the coordinated dispatch of the energy storage systems (ESSs) in the ADN. The proposed approach leverages RL's capability to make fast decision and address the model inaccuracies, while optimization methods ensure the ADN security. Furthermore, a hybrid data-driven and expert-experience auxiliary neural network is formulated as a rapid security assessment component in the SA2CO algorithm, enabling dynamic switching between RL and optimization methodologies. Simulation results demonstrate the proposed method's effectiveness and scalability in achieving real-time, safe, and economical dispatch of multiple ESSs in the ADN, surpassing the performance of the state-of-the-art RL and optimization methods.
Floor Plan Image Segmentation Via Scribble-Based Semi-Weakly Supervised Learning: A Style and Category-Agnostic Approach
Authors: Jielin CHEN;Rudi STOUFFS
Date: 2024-02-15 00:00:00
Proceeding: N.A. 2024 2
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Weakly-supervised Learning, Weakly supervised segmentation

The field of architectural design is experiencing a transformative shift towards the integration of advanced computational methodologies, aiming to revolutionize traditional practices through automation. A pivotal aspect is the automation of floor plan recognition. This task faces challenges due to varied floor plan styles and the need for large-scale annotated datasets for learning-based methods, hindered by the lack of standardized visualization rules and specialized annotation knowledge. Our study introduces a novel scribble-based semi-weakly-supervised framework, merging weakly annotated and unlabeled images to boost model robustness and generalizability. This framework benefits from a simplified annotation process while retaining detailed information. Accordingly, we provide a new benchmark dataset for floor plan image parsing covering a wide range of architectural styles and categories. Experiments with our proposed framework demonstrate marked improvements in parsing accuracy and model adaptability, significantly surpassing current state-of-the-art solutions.