pwc-archive/papers-with-abstracts huggingface dataset:
Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
Authors: Zhibin Wang, Yapeng Zhao, Yong Zhou, Yuanming Shi, Chunxiao Jiang, Khaled B. Letaief
Date: 2022-10-19 00:00:00

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from "compute-after-communicate" to "compute-when-communicate". By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.
Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders
Authors: Xin Wang, Junichi Yamagishi
Date: 2022-10-19 00:00:00

A good training set for speech spoofing countermeasures requires diverse TTS and VC spoofing attacks, but generating TTS and VC spoofed trials for a target speaker may be technically demanding. Instead of using full-fledged TTS and VC systems, this study uses neural-network-based vocoders to do copy-synthesis on bona fide utterances. The output data can be used as spoofed data. To make better use of pairs of bona fide and spoofed data, this study introduces a contrastive feature loss that can be plugged into the standard training criterion. On the basis of the bona fide trials from the ASVspoof 2019 logical access training set, this study empirically compared a few training sets created in the proposed manner using a few neural non-autoregressive vocoders. Results on multiple test sets suggest good practices such as fine-tuning neural vocoders using bona fide data from the target domain. The results also demonstrated the effectiveness of the contrastive feature loss. Combining the best practices, the trained CM achieved overall competitive performance. Its EERs on the ASVspoof 2021 hidden subsets also outperformed the top-1 challenge submission.
Deep Learning Based Stage-wise Two-dimensional Speaker Localization with Large Ad-hoc Microphone Arrays
Authors: Shupei Liu, Linfeng Feng, Yijun Gong, Chengdong Liang, Chen Zhang, Xiao-Lei Zhang, Xuelong Li
Date: 2022-10-19 00:00:00

While deep-learning-based speaker localization has shown advantages in challenging acoustic environments, it often yields only direction-of-arrival (DOA) cues rather than precise two-dimensional (2D) coordinates. To address this, we propose a novel deep-learning-based 2D speaker localization method leveraging ad-hoc microphone arrays, where an ad-hoc microphone array is composed of randomly distributed microphone nodes, each of which is equipped with a traditional array. Specifically, we first employ convolutional neural networks at each node to estimate speaker directions. Then, we integrate these DOA estimates using triangulation and clustering techniques to get 2D speaker locations. To further boost the estimation accuracy, we introduce a node selection algorithm that strategically filters the most reliable nodes. Extensive experiments on both simulated and real-world data demonstrate that our approach significantly outperforms conventional methods. The proposed node selection further refines performance. The real-world dataset in the experiment, named Libri-adhoc-node10 which is a newly recorded data described for the first time in this paper, is online available at https://github.com/Liu-sp/Libri-adhoc-nodes10.
Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection
Authors: Wim Boes, Hugo Van hamme
Date: 2022-10-18 00:00:00
Tasks: Event Detection, Sound Event Detection, Temporal Localization

In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher setting to deal with the heterogeneous annotation of the supplied data. However, the time resolution of the predictions was adapted to deal with the fact that these systems are evaluated using two intersection-based metrics involving different needs in terms of temporal localization. This was done by optimizing the pooling operations. For the first of the defined evaluation scenarios, imposing relatively strict requirements on the temporal localization accuracy, our best model achieved a PSDS score of 0.3609 on the validation data. This is only marginally better than the performance obtained by the baseline system (0.342): The amount of pooling in the baseline network already turned out to be optimal, and thus, no substantial changes were made, explaining this result. For the second evaluation scenario, imposing relatively lax restrictions on the localization accuracy, our best-performing system achieved a PSDS score of 0.7312 on the validation data. This is significantly better than the performance obtained by the baseline model (0.527), which can effectively be attributed to the changes that were applied to the pooling operations of the network.
Design and Modeling of a PVDF-TrFe Flexible Wind Energy Harvester
Authors: Berkay Kullukcu, Levent Beker
Date: 2022-10-19 00:00:00

This study presents the simulation, experimentation, and design considerations of a Poly(vinylidene fluoride co-trifluoroethylene)/ Polyethylene Terephthalate (PVDF-TrFe / PET), laser-cut, flexible piezoelectric energy harvester. It is possible to obtain energy from the environment around autonomous sensor systems, which can then be used to power various equipment. This article investigates the actuation means of ambient vibration, which is a good candidate for using piezoelectric energy harvester (PEH) devices. The output voltage characteristics were analyzed in a wind test apparatus. Finite element modeling (FEM) was done for von Mises stress and modal analysis. Resonance frequency sweeps, quality factors, and damping ratios of the circular plate were given numerically. For a PVDF-TrFe piezoelectric layer thickness of 18 $\mu$m and 1.5 mm radius, a damping ratio of 0.117 and a quality factor of 4.284 was calculated. $V_{max}$ was calculated as 984 mV from the wind setup and compared with the FEM outputs.
Potentials of Electric Vehicles for the Provision of Active and Reactive Power Flexibilities as Ancillary Services at Vertical Power System Interconnections
Authors: Manuel Wingenfelder, Marcel Sarstedt, Lutz Hofmann
Date: 2022-10-19 00:00:00

This paper extends the research regarding the determination of the feasible operation region under the impact of electric vehicles. Thereby, the active and reactive power flexibility potentials of EV for alternating current and direct current bidirectional charging are limited in first instance by regulatory guidelines. In this paper German regulatory guidelines are applied to depicture the current state of the art for EV charging components. Under assumption of bidirectional charging for EV, the corresponding flexibility polygon in the PQ plane is derived. The impact on the FOR is then evaluated within the Cigr\'e European medium voltage benchmark system. In a first step, the flexibility potential of the system is determined for a load case as basis for the comparison with different, realistic penetration levels of EV. DER are considered to depict additional existing power flexibilities. In a second step, the FOR and the corresponding limitations within the system are analyzed. Under variation of the rated line current, the limiting utility constraints are pointed out. The obtained results are the basis for more detailed investigations regarding the potentials of bidirectional charging for an intensified system operators cooperation. The used method presents a promising approach to be used by the lower system operator as tool in the grid planning process to estimate the possible power flexibilities that can be provided to a higher system. Continuing, the potential for ancillary services provided by EV is discussed, resulting in the derivation of future research aspects. Because the accumulation of EV leads to higher grid utilization, which results in quadratically increasing grid losses the closer the grid is operating to the grid constraints, a cost curve for the EVs active power flexibility is introduced, whereas probability zones are indicated.
Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control
Authors: Dimitrios Tsitsokas, Anastasios Kouvelas, Nikolas Geroliminis
Date: 2022-10-19 00:00:00
Tasks: Management

Traffic-responsive signal control is a cost-effective and easy-to-implement network management strategy with high potential in improving performance in congested networks with dynamic characteristics. Max Pressure (MP) distributed controller gained significant popularity due to its theoretically proven ability of queue stabilization and throughput maximization under specific assumptions. However, its effectiveness under saturated conditions is questionable, while network-wide application is limited due to high instrumentation cost. Perimeter control (PC) based on the concept of the Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated strategy that regulates exchange flows between regions, in order to maintain maximum regional travel production and prevent over-saturation. Yet, homogeneity assumption is hardly realistic in congested states, thus compromising PC efficiency. In this paper, the effectiveness of network-wide, parallel application of PC and MP embedded in a two-layer control framework is assessed with mesoscopic simulation. Aiming at reducing implementation cost of MP without significant performance loss, we propose a method to identify critical nodes for partial MP deployment. A modified version of Store-and-forward paradigm incorporating finite queue and spill-back consideration is used to test different configurations of the proposed framework, for a real large-scale network, in moderately and highly congested scenarios. Results show that: (i) combined control of MP and PC outperforms separate MP and PC applications in both demand scenarios; (ii) MP control in reduced critical node sets leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under demand fluctuations of up to 20% of mean.
Model Predictive Vehicle Yaw Stability Control via Integrated Active Front Wheel Steering and Individual Braking
Authors: Mumin Tolga Emirler, Bilin Aksun Guvenc
Date: 2022-10-19 00:00:00
Tasks: Model Predictive Control

Vehicle stability control systems are important components of active safety systems for road transport. The problem of vehicle lateral stability control is addressed in this paper using active front wheel steering and individual braking. Vehicle lateral stability control means keeping the vehicle yaw rate and the vehicle side slip angle in desired values. For this reason, a model-based controller is designed. The desired yaw rate is obtained from the single track vehicle model and the desired side slip angle is chosen as zero. Controller design consists of two parts, lower and upper controller parts. Upper controller is designed based on Model Predictive Control (MPC) method. This upper controller changes front wheel steering angles utilizing steer-by-wire system and also it generates the required control moment for stabilizing the yaw motion of the vehicle. Lower controller is an individual braking algorithm. It determines the individual braking wheel. In this way, the control moment can be applied to the vehicle. The designed controller is tested using the nonlinear single track vehicle model and the higher fidelity CarMaker vehicle model.
From Model-Based to Model-Free: Learning Building Control for Demand Response
Authors: David Biagioni, Xiangyu Zhang, Christiane Adcock, Michael Sinner, Peter Graf, Jennifer King
Date: 2022-10-18 00:00:00
Tasks: model

Grid-interactive building control is a challenging and important problem for reducing carbon emissions, increasing energy efficiency, and supporting the electric power grid. Currently researchers and practitioners are confronted with a choice of control strategies ranging from model-free (purely data-driven) to model-based (directly incorporating physical knowledge) to hybrid methods that combine data and models. In this work, we identify state-of-the-art methods that span this methodological spectrum and evaluate their performance for multi-zone building HVAC control in the context of three demand response programs. We demonstrate, in this context, that hybrid methods offer many benefits over both purely model-free and model-based methods as long as certain requirements are met. In particular, hybrid controllers are relatively sample efficient, fast online, and high accuracy so long as the test case falls within the distribution of training data. Like all data-driven methods, hybrid controllers are still subject to generalization errors when applied to out-of-sample scenarios. Key takeaways for control strategies are summarized and the developed software framework is open-sourced.
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version
Authors: Matthias Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2022-10-18 00:00:00
Tasks: Distributed Optimization, Model Predictive Control

We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Authors: Haitao Huang, Qinqin Yang, Jiechao Wang, Pujie Zhang, Shuhui Cai, Congbo Cai
Date: 2022-10-19 00:00:00
Tasks: Deep Learning, GPU, Position

Objective: Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach: The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main Results: Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T2 mapping and comparable results to conventional methods were obtained in the human brain. Significance: As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 4 Third wave results
Authors: Allister Loder, Fabienne Cantner, Andrea Cadavid, Markus B. Siewert, Stefan Wurster, Sebastian Goerg, Klaus Bogenberger
Date: 2022-10-19 00:00:00
Tasks: Survey

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9EUR per month and a fuel tax cut that reduced fuel prices by more than 15%. In transportation policy and travel behavior research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample 2'263 individuals; for the Munich Study, 919 participants in the survey-and-app group and 425 in the survey-only group have been successfully recruited, while 919 participants have been recruited through a professional panel provider to obtain a representative nation-wide reference group for the three-wave survey. In this fourth report we present the results of the third wave. At the end of the study, all three surveys have been completed by 1'484 participants and 642 participants completed all three surveys and used the travel diary throughout the entire study. Based on our results we conclude that when offering a 49EUR-Ticket as a successor to the 9~EUR-Ticket and a local travel pass for 30EUR/month more than 60% of all 9~EUR-Ticket owners would buy one of the two new travel passes. In other words, a substantial increase in travel pass ownership in Germany can be expected, with our modest estimate being around 20%. With the announcement of the introduction of a successor ticket in 2023 as well as with the prevailing high inflation, this study will continue into the year 2023 to monitor the impact on mobility and daily activities.
Informed Neutrality in Minimalist Market Design: A Case Study on a Constitutional Crisis in India
Authors: Tayfun Sönmez, Utku Ünver
Date: 2022-10-18 00:00:00

In a 3-2 split verdict, the Supreme Court approved the exclusion of India's socially and economically backward classes from its affirmative action measures to address economic deprivation. Dissenting justices, including the Chief Justice of India, protested the Majority Opinion for sanctioning "an avowedly exclusionary and discriminatory principle." To justify their controversial decision, the majority justices relied on technical arguments that are categorically false. The confusion of the justices is due to a subtle technical aspect of the affirmative action system in India: the significance of overlaps between members of protected groups. Conventionally, protected classes were determined by the caste system, which meant they did not overlap. The addition of a new protected class defined by economic criteria alters this structure, unless it is artificially enforced. The majority justices failed to appreciate the significance of this critical change and inaccurately argued that the controversial exclusion is a technical necessity to provide benefits to previously unprotected members of a new class. We show that this case could have been resolved with three competing policies that each avoid the controversial exclusion. One of these policies aligns with the core arguments in the Majority Opinion, whereas a second aligns with those in the Dissenting Opinion.
Housing Forecasts via Stock Market Indicators
Authors: Varun Mittal, Laura P. Schaposnik
Date: 2022-10-18 00:00:00

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.
Public Good Provision with a Distributor
Authors: Chowdhury Mohammad Sakib Anwar, Alexander Matros, Sonali SenGupta
Date: 2022-10-19 00:00:00
Tasks: All

We present a model of public good provision with a distributor. Our main result describes a symmetric mixed-strategy equilibrium, where all agents contribute to a common fund with probability $p$ and the distributor provides either a particular amount of public goods or nothing. A corollary of this finding is the efficient public good provision equilibrium where all agents contribute to the common fund, all agents are expected to contribute, and the distributor spends the entire common fund for the public good provision.