pwc-archive/papers-with-abstracts huggingface dataset:
Improving spatial cues for hearables using a parameterized binaural CDR estimator
Authors: Reza Ghanavi, Craig Jin
Date: 2022-07-17 00:00:00
Tasks: Speech Enhancement

We investigate a speech enhancement method based on the binaural coherence-to-diffuse power ratio (CDR), which preserves auditory spatial cues for maskers and a broadside target. Conventional CDR estimators typically rely on a mathematical coherence model of the desired signal and/or diffuse noise field in their formulation, which may influence their accuracy in natural environments. This work proposes a new robust and parameterized directional binaural CDR estimator. The estimator is calculated in the time-frequency domain and is based on a geometrical interpretation of the spatial coherence function between the binaural microphone signals. The binaural performance of the new CDR estimator is compared with three state-of-the-art CDR estimators in cocktail-party-like environments and has shown improvements in terms of several objective speech quality metrics such as PESQ and SRMR. We also discuss the benefits of the parameterizable CDR estimator for varying sound environments and briefly reflect on several informal subjective evaluations using a low-latency real-time framework.
Multi-channel target speech enhancement based on ERB-scaled spatial coherence features
Authors: Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Date: 2022-07-17 00:00:00
Tasks: Speech Enhancement

Recently, speech enhancement technologies that are based on deep learning have received considerable research attention. If the spatial information in microphone signals is exploited, microphone arrays can be advantageous under some adverse acoustic conditions compared with single-microphone systems. However, multichannel speech enhancement is often performed in the short-time Fourier transform (STFT) domain, which renders the enhancement approach computationally expensive. To remedy this problem, we propose a novel equivalent rectangular bandwidth (ERB)-scaled spatial coherence feature that is dependent on the target speaker activity between two ERB bands. Experiments conducted using a four-microphone array in a reverberant environment, which involved speech interference, demonstrated the efficacy of the proposed system. This study also demonstrated that a network that was trained with the ERB-scaled spatial feature was robust against variations in the geometry and number of the microphones in the array.
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
Authors: Viet Anh Trinh, Pegah Ghahremani, Brian King, Jasha Droppo, Andreas Stolcke, Roland Maas
Date: 2022-07-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Fairness, speech-recognition, Speech Recognition

We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER). However, when the ASR model is adapted to get better performance on these high-WER regions, its parameters wander from the previous optimal values, which can lead to worse performance in other regions. In our proposed method, we utilize the elastic weight consolidation (EWC) regularization loss to identify directions in parameters space along which the ASR weights can vary to improve for high-error regions, while still maintaining performance on the speaker population overall. Our results demonstrate that EWC can reduce the word error rate (WER) in the region with highest WER by 3.2% relative while reducing the overall WER by 1.3% relative. We also evaluate the role of language and acoustic models in ASR fairness and propose a clustering algorithm to identify WER disparities based on geographic region.
Adversarial Reweighting for Speaker Verification Fairness
Authors: Minho Jin, Chelsea J. -T. Ju, Zeya Chen, Yi-Chieh Liu, Jasha Droppo, Andreas Stolcke
Date: 2022-07-15 00:00:00
Tasks: Fairness, Metric Learning, Speaker Verification

We address performance fairness for speaker verification using the adversarial reweighting (ARW) method. ARW is reformulated for speaker verification with metric learning, and shown to improve results across different subgroups of gender and nationality, without requiring annotation of subgroups in the training data. An adversarial network learns a weight for each training sample in the batch so that the main learner is forced to focus on poorly performing instances. Using a min-max optimization algorithm, this method improves overall speaker verification fairness. We present three different ARWformulations: accumulated pairwise similarity, pseudo-labeling, and pairwise weighting, and measure their performance in terms of equal error rate (EER) on the VoxCeleb corpus. Results show that the pairwise weighting method can achieve 1.08% overall EER, 1.25% for male and 0.67% for female speakers, with relative EER reductions of 7.7%, 10.1% and 3.0%, respectively. For nationality subgroups, the proposed algorithm showed 1.04% EER for US speakers, 0.76% for UK speakers, and 1.22% for all others. The absolute EER gap between gender groups was reduced from 0.70% to 0.58%, while the standard deviation over nationality groups decreased from 0.21 to 0.19.
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting
Authors: Yuliang Li
Date: 2022-06-22 00:00:00
Tasks: Position, Super-Resolution, Translation

Ghost imaging radar is a new system of gaze imaging radar with high detection sensitivity, super-resolution and better anti-interference performance, but the relative motion between the radar system and the target will make the target imaging deteriorate. This paper proposes to perform absolute position localisation of a single target in the field of view by weighting the correlation strength of a single frame image of rough target, and to compensate translation of the reference arm speckle according to the localisation and tracking trajectory to accumulate the rough image into a high quality image. The proposed correlation intensity weighted localization and tracking imaging method has been verified by simulation to be able to locate and image targets in the field of view well.
Realistic mask generation for matter-wave lithography via machine learning
Authors: Johannes Fiedler, Adrià Salvador Palau, Eivind Kristen Osestad, Pekka Parviainen, Bodil Holst
Date: 2022-07-15 00:00:00
Tasks: BIG-bench Machine Learning

Fast production of large area patterns with nanometre resolution is crucial for the established semiconductor industry and for enabling industrial-scale production of next-generation quantum devices. Metastable atom lithography with binary holography masks has been suggested as a higher resolution/low-cost alternative to the current state of the art: extreme ultraviolet (EUV) lithography. However, it was recently shown that the interaction of the metastable atoms with the mask material (SiN) leads to a strong perturbation of the wavefront, not included in existing mask generation theory, which is based on classical scalar waves. This means that the inverse problem (creating a mask based on the desired pattern) cannot be solved analytically even in 1D. Here we present a machine learning approach to mask generation targeted for metastable atoms. Our algorithm uses a combination of genetic optimisation and deep learning to obtain the mask. A novel deep neural architecture is trained to produce an initial approximation of the mask. This approximation is then used to generate the initial population of the genetic optimisation algorithm that can converge to arbitrary precision. We demonstrate the generation of arbitrary 1D patterns for system dimensions within the Fraunhofer approximation limit.
A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions
Authors: Tala Talaei Khoei, Hadjar Ould Slimane, Naima Kaabouch
Date: 2022-06-22 00:00:00

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In addition, some of these surveys focused on the Transmission Control Protocol/Internet Protocol (TCP/IP) model, which does not differentiate between the application, session, and presentation and the data link and physical layers of the Open System Interconnection (OSI) model. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.
The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave
Authors: Miguel E. P. Silva, Martyn Fyles, Li Pi, Jasmina Panovska-Griffiths, Caroline Jay, Thomas House, Elizabeth Fearon
Date: 2022-07-18 00:00:00

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with `lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Authors: Xiaoyu Dong, Naoto Yokoya, Longguang Wang, Tatsumi Uezato
Date: 2022-07-19 00:00:00
Tasks: Super-Resolution

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-to-guide modulation and a guide-to-source modulation. In these modulations, we develop cross-domain adaptive filters to fully exploit cross-modal spatial dependency and help induce the source to emulate the resolution of the guide and induce the guide to mimic the modality characteristics of the source. Moreover, we adopt a cycle consistency constraint to train MMSR in a fully self-supervised manner. Experiments on various tasks demonstrate the state-of-the-art performance of our MMSR.
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond
Authors: Yuzheng Hu, Tianle Cai, Jinyong Shan, Shange Tang, Chaochao Cai, Ethan Song, Bo Li, Dawn Song
Date: 2022-07-19 00:00:00
Tasks: Federated Learning, Philosophy, Privacy Preserving, regression, Vertical Federated Learning

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques.
Implementation of Machine Learning-based DER Local Control Schemes on Measurement Devices for Counteracting Communication Failures
Authors: Rajkumar Palaniappan, Jan-Niklas Ceschlaw, Stavros Karagiannopoulos, Christian Rehtanz
Date: 2022-07-18 00:00:00
Tasks: regression

One of the significant challenges linked with the massive integration of distributed energy resources (DER) in the active distribution grids is the uncertainty it brings along. The grid operation becomes more arduous to avoid voltage or thermal violations. While the Optimal Power Flow (OPF) algorithm is vastly discussed in the literature, little attention has been given to the robustness of such centralised implementation, such as the provision of redundant control solutions during a communication failure. This paper aims to implement a machine learning-based algorithm at each Intelligent Electronic Device (IED) that mimics the centralised OPF used during communication failures using IEC 61850 data models. Under normal circumstances, the IEDs communicate for centralised OPF. In addition, the system is trained offline for all operational conditions and the individual look-up tables linking the actual voltages to the DER setpoints are sent to the respective controllers. The regression models allow for the local reconstruction of the DER setpoints, emulating the overall OPF, in case of a communication failure. In addition to the regression control, the paper also explains an offline learning approach for periodic re-training of the regression models. The implementation is experimentally verified using a Hardware-in-the-loop test setup. The tests showed promising results compared to conventional control strategies during communication failures. When properly trained and coordinated, such an intuitive local control approach for each DER could be very beneficial for the bulk power system. This machine learning-based approach could also replace the existing Q(V) control strategies, to better support the bulk power system.
Rapid and robust synchronization via weak synaptic coupling Extended arXiv version
Authors: Jin Gyu Lee, Rodolphe Sepulchre
Date: 2022-07-18 00:00:00

This paper examines how weak synaptic coupling can achieve rapid synchronization in heterogeneous networks. The assumptions aim at capturing the key mathematical properties that make this possible for biophysical networks. In particular, the combination of nodal excitability and synaptic coupling are shown to be essential to the phenomenon.
Data-driven Self-triggered Control via Trajectory Prediction
Authors: Wenjie Liu, Jian Sun, Gang Wang, Francesco Bullo, Jie Chen
Date: 2022-07-18 00:00:00
Tasks: Model Predictive Control, Prediction, Trajectory Prediction

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples.
Generalized Analysis and Unified Design of EM Skins
Authors: Giacomo Oliveri, Marco Salucci, Andrea Massa
Date: 2022-07-18 00:00:00
Tasks: Computational Efficiency

A generalized formulation is derived for the analysis of the field manipulation properties of electromagnetic skins (EMSs) in the working regimes of interest for wireless communications. Based on such a theoretical framework, a unified method for the design of anomalous-reflecting and focusing EMSs is presented. Representative results, from a wide set of numerical experiments, are reported and validated with full-wave HFSS simulations to give the interested readers some insights on the accuracy, the effectiveness, and the computational efficiency of the proposed analysis/synthesis tools.
Modeling and Control of Multi-Energy Dynamical Systems: Hidden Paths to Decarbonization
Authors: Marija Ilic, Rupamathi Jaddivada
Date: 2022-07-18 00:00:00

This paper points out some key drawbacks of today's modeling and control underlying hierarchical electric power system operations and planning as the hidden roadblocks on the way to decarbonization. We suggest that these can be overcome by enhancing today's information exchange and control. This can be done by revealing and utilising inherent structure-preserving features of complex physical systems, and, based on this, by establishing multi-layered energy modeling. Each module (component, control area, non-utility-owned entities) can be characterized in terms of its interaction variable, and higher level models can be used to understand the interaction dynamics between different modules. Once the structure is understood, we propose nonlinear energy control for these modules which supports feed-forward self-adaptation to ensure feasible interconnected system. Based on these technology agnostic structures it becomes possible to expand today's Balancing Authorities (BA) to multi-layered interactive intelligent Balancing Authorities (iBAs) and to introduce protocols for flexible utilization of diverse technologies over broad ranges of temporal and spatial conditions.
Optimization of stochastic switching buffer network via DC programming
Authors: Chengyan Zhao, Kazunori Sakurama, Masaki Ogura
Date: 2022-07-18 00:00:00

This letter deals with the optimization problems of stochastic switching buffer networks, where the switching law is governed by Markov process. The dynamical buffer network is introduced, and its application in modeling the car-sharing network is also presented. To address the nonconvexity for getting a solution as close-to-the-global-optimal as possible of the optimization problem, we adopt a succinct but effective nonconvex optimization method called \emph{ DC (difference of convex functions) programming}. By resorting to the log-log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to DC programming problems. Finally, we verify the effectiveness of our results by simulation experiments.
Quantized Consensus under Data-Rate Constraints and DoS Attacks: A Zooming-In and Holding Approach
Authors: Maopeng Ran, Shuai Feng, Juncheng Li, Lihua Xie
Date: 2022-07-18 00:00:00
Tasks: Quantization

This paper is concerned with the quantized consensus problem for uncertain nonlinear multi-agent systems under data-rate constraints and Denial-of-Service (DoS) attacks. The agents are modeled in strict-feedback form with unknown nonlinear dynamics and external disturbance. Extended state observers (ESOs) are leveraged to estimate agents' total uncertainties along with their states. To mitigate the effects of DoS attacks, a novel dynamic quantization with zooming-in and holding capabilities is proposed. The idea is to zoom-in and hold the variable to be quantized if the system is in the absence and presence of DoS attacks, respectively. The control protocol is given in terms of the outputs of the ESOs and the dynamic-quantization-based encoders and decoders. We show that, for a connected undirected network, the developed control protocol is capable of handling any DoS attacks inducing bounded consecutive packet losses with merely 3-level quantization. The application of the zooming-in and holding approach to known linear multi-agent systems is also discussed.