pwc-archive/papers-with-abstracts huggingface dataset:
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
Authors: Francesca Ronchini, Samuele Cornell, Romain Serizel, Nicolas Turpault, Eduardo Fonseca, Daniel P. W. Ellis
Date: 2022-10-14 00:00:00
Tasks: Event Segmentation

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation.
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge
Authors: Yan Jia, Mi Hong, Jingyu Hou, Kailong Ren, Sifan Ma, Jin Wang, Fangzhen Peng, Yinglin Ji, Lin Yang, Junjie Wang
Date: 2022-10-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Decoder, Language Modeling, Language Modelling, Speech Enhancement, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.
A real-time GP based MPC for quadcopters with unknown disturbances
Authors: Niklas Schmid, Jonas Gruner, Hossam S. Abbas, Philipp Rostalski
Date: 2022-10-14 00:00:00
Tasks: Model Predictive Control, Raspberry Pi 4

Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications. Thus GPs are commonly trained offline, which is not suited for learning disturbances as their dynamics may vary with time. Recently, state-space formulation of GPs has been introduced, allowing inference and learning with linear computational complexity. This paper presents a framework that enables online learning of disturbance dynamics on quadcopters, which can be executed within milliseconds using a state-space formulation of GPs. The obtained disturbance predictions are combined with MPC leading to a significant performance increase in simulations with jMAVSim. The computational burden is evaluated on a Raspberry Pi 4 B to prove the real-time applicability.
National-scale bi-directional EV fleet control for ancillary service provision
Authors: Lorenzo Nespoli, Nina Wiedemann, Esra Suel, Yanan Xin, Martin Raubal, Vasco Medici
Date: 2022-10-14 00:00:00

Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.
Distributed Emergency Frequency Control Considering Transient Stability Constraints in Multi-Infeed Hybrid AC-DC System
Authors: Ye Liu, Chen Shen, Zhaojian Wang
Date: 2022-10-14 00:00:00

Due to possible emergency faults and frequency regulation reserve shortage in the multi-infeed hybrid AC-DC (MIDC) system, the emergency frequency control (EFC) with LCC-HVDC systems participating is important for system frequency stability. Nevertheless, the existing decentralized EFC strategies cannot guarantee the transient stability constraints of lines and cannot meet the frequency restoration requirement. To fill this gap, this paper proposes a complementary distributed EFC strategy. By selecting the semi-distributed or fully-distributed control law according to the state of AC system control center, the proposed distributed EFC can improve the control performance with a normal control center and enhance the control reliability in case of control center failure. Then, to derive the semi-distributed and fully-distributed control laws, a general design method is proposed by formulating an optimal EFC problem. Both of these two control laws can guarantee transient stability constraints, restore system frequency and achieve the defined optimal control objective. Moreover, the optimality of the closed-loop equilibrium is proven, and a Lyapunov analysis shows the asymptotic stability of the closed-loop system with the discontinuous control dynamics. A case study verifies the effectiveness of the proposed distributed EFC strategy.
Real-time computational powered landing guidance using convex optimization and neural networks
Authors: Zhipeng Shen, Shiyu Zhou, Jianglong Yu
Date: 2022-10-14 00:00:00

Computational guidance is an emerging and accelerating trend in aerospace guidance and control. Combining machine learning and convex optimization, this paper presents a real-time computational guidance method for the 6-degrees-of-freedom powered landing guidance problem. The powered landing guidance problem is formulated as an optimal control problem, which is then transformed into a convex optimization problem. Instead of brutally using the neural networks as the controller, we use neural networks to improve the state-of-the-art sequential convex programming (SCP) algorithm. Based on the deep neural network, an initial trajectory generator is designed to provide a satisfactory initial guess for the SCP algorithm. Benefitting from designing the initial trajectory generator as a sequence model predictor, the proposed data-driven SCP architecture is capable of improving the performance of any state-of-the-art SCP algorithm in various applications, not just powered landing guidance. The simulation results show that the proposed method can precisely guide the vehicle to the landing site. Moreover, through Monte Carlo tests, the proposed method can averagely save 40.8% of the computation time compared with the SCP method, while ensuring higher terminal states accuracy. The proposed computational guidance scheme is suitable for real-time applications.
Human Behavioral Models Using Utility Theory and Prospect Theory
Authors: Anuradha M. Annaswamy, Vineet Jagadeesan Nair
Date: 2022-10-13 00:00:00
Tasks: Decision Making

Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system. Many of these decision-making problems necessitate an appropriate model of human behavior. Tools from Utility Theory have been used successfully in several problems in transportation for resource allocation and balance of supply and demand \citep{ben1985discrete}. More recently, Prospect Theory has been demonstrated as a useful tool in behavioral economics and cognitive psychology for deriving human behavioral models that characterize their subjective decision-making in the presence of stochastic uncertainties and risks, as an alternative to conventional Utility Theory \citep{kahneman_prospect_2012}. These models will be described in this article. Theoretical implications of Prospect Theory are also discussed. Examples will be drawn from transportation use cases such as shared mobility to illustrate these models as well as the distinctions between Utility Theory and Prospect Theory.
Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control
Authors: Qiong Liu, Ye Guo, Lirong Deng, Haotian Liu, Dongyu Li, Hongbin Sun
Date: 2022-10-10 00:00:00
Tasks: Deep Reinforcement Learning

Reference-model-assisted deep reinforcement learning (DRL) for inverter-based Volt-Var Control (IB-VVC) in active distribution networks is proposed. We investigate that a large action space increases the learning difficulties of DRL and degrades the optimization performance in the process of generating data and training neural networks. To reduce the action space of DRL, we design a reference-model-assisted DRL approach. We introduce definitions of the reference model, reference-model-based optimization, and reference actions. The reference-model-assisted DRL learns the residual actions between the reference actions and optimal actions, rather than learning the optimal actions directly. Since the residual actions are considerably smaller than the optimal actions for a reference model, we can design a smaller action space for the reference-model-assisted DRL. It reduces the learning difficulties of DRL and optimises the performance of the reference-model-assisted DRL approach. It is noteworthy that the reference-model-assisted DRL approach is compatible with any policy gradient DRL algorithms for continuous action problems. This work takes the soft actor-critic algorithm as an example and designs a reference-model-assisted soft actor-critic algorithm. Simulations show that 1) large action space degrades the performance of DRL in the whole training stage, and 2) reference-model-assisted DRL requires fewer iteration times and returns a better optimization performance.
Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model
Authors: M. Ahumada, A. Ledesma-Araujo, Leonardo Gordillo, Juan F. Marín
Date: 2022-10-13 00:00:00

The crisis caused by the COVID-19 outbreak around the globe raised an increasing concern about the ongoing emergence of variants of SARS-CoV-2 that may evade the immune response provided by vaccines. New variants appear due to mutation, and as the cases accumulate, the probability of the emergence of a variant of concern increases. In this article, we propose a modified SIR model with waning immunity that captures the competition of two strain classes of an infectious disease under the effect of vaccination with a highly contagious and deadly strain class emerging from a prior strain due to mutation. When these strains compete for a limited supply of susceptible individuals, changes in the efficiency of vaccines may affect the behaviour of the disease in a non-trivial way, resulting in complex outcomes. We characterise the parameter space including intrinsic parameters of the disease, and using the vaccine efficiencies as control variables. We find different types of transcritical bifurcations between endemic fixed points and a disease-free equilibrium and identify a region of strain competition where the two strain classes coexist during a transient period. We show that a strain can be extinguished either due to strain competition or vaccination, and we obtain the critical values of the efficiency of vaccines to eradicate the disease. Numerical studies using parameters estimated from publicly reported data agree with our theoretical results. Our mathematical model could be a tool to assess quantitatively the vaccination policies of competing and emerging strains using the dynamics in epidemics of infectious diseases.
Machine learning frontier orbital energies of nanodiamonds
Authors: Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé
Date: 2022-09-30 00:00:00
Tasks: Graph Neural Network

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new dataset ND5k, consisting of 5,089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find best performance using the equivariant graph neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here.
Conditional Likelihood Ratio Test with Many Weak Instruments
Authors: Sreevidya Ayyar, Yukitoshi Matsushita, Taisuke Otsu
Date: 2022-10-14 00:00:00
Tasks: regression

This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and many weak instruments. In this setting, we argue that the conventional CLR test with estimated error variance loses exact similarity and is asymptotically invalid. We propose a modified critical value function for the likelihood ratio (LR) statistic with estimated error variance, and prove that this modified test achieves asymptotic validity under many weak instrument asymptotics. Our critical value function is constructed by representing the LR using four statistics, instead of two as in Moreira (2003). A simulation study illustrates the desirable properties of our test.
Spline Sketches: An Efficient Approach for Photon Counting Lidar
Authors: Michael Patrick Sheehan, Julian Tachella, Mike E. Davies
Date: 2022-10-13 00:00:00

Photon counting lidar has become an invaluable tool for 3D depth imaging due to the fine-precision it can achieve over long ranges. However, high frame rate, high resolution lidar devices produce an enormous amount of time-of-flight (ToF) data which can cause a severe data processing bottleneck hindering the deployment of real-time systems. In this paper, an efficient photon counting approach is proposed that exploits the simplicity of piecewise polynomial splines to form a hardware-friendly compressed statistic, or a so-called spline sketch, of the ToF data without sacrificing the quality of the recovered image. As each piecewise polynomial spline is a simple function with limited support over the timing depth window, the spline sketch can be computed efficiently on-chip with minimal computational overhead. We show that a piecewise linear or quadratic spline sketch, requiring minimal on-chip arithmetic computation per photon detection, can reconstruct real-world depth images with negligible loss of resolution whilst achieving $95\%$ compression compared to the full ToF data, as well as offering multi-peak detection performance. These contrast with previously proposed coarse binning histograms that suffer from a highly nonuniform accuracy across depth and can fail catastrophically when associated with bright reflectors. Further, by building range-walk correction into the proposed estimation algorithms, it is demonstrated that the spline sketches can be made robust to photon pile-up effects. The computational complexity of both the reconstruction and range walk correction algorithms scale only with the size of the spline sketch which is independent to both the photon count and temporal resolution of the lidar device.
Receiver Bandwidth Extension Beyond Nyquist Using Channel Bonding
Authors: Sebastian Giehl, Carsten Andrich, Michael Schubert, Maximilian Engelhardt, Alexander Ihlow
Date: 2022-10-14 00:00:00
Tasks: Bandwidth Extension

Current and upcoming communication and sensing technologies require ever larger bandwidths. Channel bonding can be utilized to extend a receiver's instantaneous bandwidth beyond a single converter's Nyquist limit. Two potential joint front-end and converter design approaches are theoretically introduced, realized and evaluated in this paper. The Xilinx RFSoC platform with its 5 GSa/s analog to digital converters (ADCs) is used to implement both a hybrid coupler based in-phase/quadrature (I/Q) sampling and a time-interleaved sampling approach along with channel bonding. Both realizations are demonstrated to be able to reconstruct instantaneous bandwidths of 5 GHz with up to 49 dB image rejection ratio (IRR) typically within 4 to 8 dB the front-ends' theoretical limits.
Performance Evaluation of Wireless Multi-Carrier (MC) Communication Systems
Authors: Mohamed S. Elbakry
Date: 2022-10-14 00:00:00

This thesis is concerned with data-aided (DA) scheme and CFO tracking for OFDM system. OFDM system model is developed first without CFO and then with CFO. The system performance is evaluated via simulation. The bit error rate (BER), constellation diagram, the phase output and phase error were taken as performance measures. The performance is evaluated for different types of modulation over different channel conditions. In this thesis, a dual bandwidth for CFO tracking based on type-2 control loop to reduce the acquisition time of PLL is proposed. It is proved that the proposed scheme is significantly faster than, and improves the system BER. Also, further refinement to the dual bandwidth scheme is improved by using clustered pilot tones. The improved dual bandwidth scheme is better than the dual bandwidth scheme.
Toward Secure and Private Over-the-Air Federated Learning
Authors: Na Yan, Kezhi Wang, Kangda Zhi, Cunhua Pan, Kok Keong Chai, H. Vincent Poor
Date: 2022-10-14 00:00:00
Tasks: Federated Learning, Scheduling, Single Particle Analysis

In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL) framework is studied where noise is employed to protect data privacy and system security. Specifically, the privacy leakage of user data and the security level of the system are measured by differential privacy (DP) and mean square error security (MSE-security), respectively. To mitigate the impact of noise on learning accuracy, we propose a channel-weighted post-processing (CWPP) mechanism, which assigns a smaller weight to the gradient of the device with poor channel conditions. Furthermore, employing CWPP can avoid the issue that the signal-to-noise ratio (SNR) of the overall system is limited by the device with the worst channel condition in aligned over-the-air federated learning (OTA-FL). We theoretically analyze the effect of noise on privacy and security protection and also illustrate the adverse impact of noise on learning performance by conducting convergence analysis. Based on these analytical results, we propose device scheduling policies considering privacy and security protection in different cases of channel noise. In particular, we formulate an integer nonlinear fractional programming problem aiming to minimize the negative impact of noise on the learning process. We obtain the closed-form solution to the optimization problem when the model is with high dimension. For the general case, we propose a secure and private algorithm (SPA) based on the branch-and-bound (BnB) method, which can obtain an optimal solution with low complexity. The effectiveness of the proposed CWPP mechanism and the policies for device selection are validated through simulations.
Variational Message Passing-Based Respiratory Motion Estimation and Detection Using Radar Signals
Authors: Jakob Möderl, Erik Leitinger, Franz Pernkopf, Klaus Witrisal
Date: 2022-10-14 00:00:00
Tasks: Motion Estimation

We present a variational message passing (VMP) approach to detect the presence of a person based on their respiratory chest motion using multistatic ultra-wideband (UWB) radar. In the process, the respiratory motion is estimated for contact-free vital sign monitoring. The received signal is modeled by a backscatter channel and the respiratory motion and propagation channels are estimated using VMP. We use the evidence lower bound (ELBO) to approximate the model evidence for the detection. Numerical analyses and measurements demonstrate that the proposed method leads to a significant improvement in the detection performance compared to a fast Fourier transform (FFT)-based detector or an estimator-correlator, since the multipath components (MPCs) are better incorporated into the detection procedure. Specifically, the proposed method has a detection probability of 0.95 at -20 dB signal-to-noise ratio (SNR), while the estimator-correlator and FFT-based detector have 0.32 and 0.05, respectively.
Measurement of 2x2 LoS MIMO Terahertz Channel
Authors: Suresh Singh, Thanh Le, Ha Tran
Date: 2022-10-14 00:00:00

This paper examines the performance of a 2x2 Line of Sight (LoS) Multiple Input Multiple Output (MIMO) channel at three terahertz frequencies-340 GHz, 410 Ghz, and 460 GHz. While theoretical models predict very high channel capacities, we observe lower capacity which is explained by asymmetric transmit-to-receive signal strengths as well as due to signal attenuation over longer distances. Overall, however, we note that at 460 Ghz, channel capacity of higher than 12 bps/hz is possible even at sub-optimal inter-antenna spacings (for different distances). An important observation is also that we need to maintain appropriate receive signal levels at receive antennas in order to improve capacity.