pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure
Authors: Xin Wang, Junich Yamagishi
Date: 2022-03-28 00:00:00
Tasks: Active Learning, Data Augmentation, Self-Supervised Learning

Training a spoofing countermeasure (CM) that generalizes to various unseen data is desired but challenging. While methods such as data augmentation and self-supervised learning are applicable, the imperfect CM performance on diverse test sets still calls for additional strategies. This study took the initiative and investigated CM training using active learning (AL), a framework that iteratively selects useful data from a large pool set and fine-tunes the CM. This study compared a few methods to measure the data usefulness and the impact of using different pool sets collected from various sources. The results showed that the AL-based CMs achieved better generalization than our strong baseline on multiple test tests. Furthermore, compared with a top-line CM that simply used the whole data pool set for training, the AL-based CMs achieved similar performance using less training data. Although no single best configuration was found for AL, the rule of thumb is to include diverse spoof and bona fide data in the pool set and to avoid any AL data selection method that selects the data that the CM feels confident in.
Curriculum learning for self-supervised speaker verification
Authors: Hee-Soo Heo, Jee-weon Jung, Jingu Kang, Youngki Kwon, You Jin Kim, Bong-Jin Lee, Joon Son Chung
Date: 2022-03-28 00:00:00
Tasks: Self-Supervised Learning, Speaker Recognition, Speaker Verification

The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised learning framework. The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset. The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds. A range of experiments conducted using the DINO self-supervised framework on the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our proposed curriculum learning strategies. We report a competitive equal error rate of 4.47% with a single-phase training, and we also demonstrate that the performance further improves to 1.84% by fine-tuning on a small labelled dataset.
Multi-source wideband doa estimation method by frequency focusing and error weighting
Authors: Jing Zhou, Changchun Bao
Date: 2022-03-28 00:00:00

In this paper, a new multi-source wideband direction of arrival (MSW-DOA) estimation method is proposed for the signal with non-uniform distribution using the sub-array of uniform linear array. Different from conventional methods, based on the free far-field model, the proposed method mainly makes two contributions. One is that the sub-array decomposition is adopted to improve the accuracy of MSW-DOA estimation by minimizing the weighted error, and the other one is that the frequency focusing procedure is optimized according to the presence probability of sound sources for reducing the influence of the sub-bands with low signal to noise ratio (SNR). Simulation results show that the proposed method can effectively improve the performance of wideband DOA estimation in the case of multiple sound sources.
RF Low Noise Amplifiers and Power Amplifiers using Tunnelling Barrier modulated Superconductor-Semiconductor-Superconductor Junctions
Authors: Debopam Banerjee
Date: 2022-03-28 00:00:00

In high-performance RF transceivers the thrust of has been on lower noise LNAs and high-power high-speed PAs. The performance trade-off has forced many solutions to have the LNA and PA on a separate die compared to the rest of baseband. Often fabricated in GaAs or InP, they need to be properly interfaced with the rest of signal chain leading to signal integrity issues. Applications for such technologies range from defence, aerospace to scientific instrumentation. In this paper we propose a device using superconductor-semiconductor-superconductor (SC-Sm-SC) junction with a controllable gate terminal to modulate the tunnelling resistance. This arrangement in a typical resonant-tank LNA or an impedance matching PA would reduce the parasitic capacitances leading to higher frequency operation. Also by virtue of Cooper-pair bosons being the bulk carriers, noise due to carrier-carrier and carrier-lattice scattering will be much lower than its conventional CMOS counterparts. Our calculation shows that it can be used in a LNA with 20dB gain till 36GHz and in a PA to deliver -10dBm power till 350GHz.
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning
Authors: Yinan Zou, Zixin Wang, Xu Chen, Haibo Zhou, Yong Zhou
Date: 2022-03-28 00:00:00
Tasks: Federated Learning

In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the optimal transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.
Universal Graph Filter Design based on Butterworth, Chebyshev and Elliptic Functions
Authors: Zirui Ge, Haiyan Guo, Tingting Wang, Zhen Yang
Date: 2022-03-28 00:00:00

Graph filters are crucial tools in processing the spectrum of graph signals. In this paper, we propose to design universal IIR graph filters with low computational complexity by using three kinds of functions, which are Butterworth, Chebyshev, and Elliptic functions, respectively. Specifically, inspired by the classical analog filter design method, we first derive the zeros and poles of graph frequency responses. With these zeros and poles, we construct the conjugate graph filters to design the Butterworth high pass graph filter, Chebyshev high pass graph filter, and Elliptic high pass graph filter, respectively. On this basis, we further propose to construct a desired graph filter of low pass, band pass, and band stop by mapping the parameters of the desired graph filter to those of the equivalent high pass graph filter. Furthermore, we propose to set the graph filter order given the maximum passband attenuation and the minimum stopband attenuation. Our numerical results show that the proposed graph filter design methods realize the desired frequency response more accurately than the autoregressive moving average (ARMA) graph filter design method, the linear least-squares fitting (LLS) based graph filter design method, and the Chebyshev FIR polynomial graph filter design method.
Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features
Authors: Md. Johirul Islam, Shamim Ahmad, Fahmida Haque, Mamun Bin Ibne Reaz, Mohammad A. S. Bhuiyan, Md. Rezaul Islam
Date: 2022-03-28 00:00:00
Tasks: Specificity

The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, existing four feature extraction methods, variable window size and various signal to noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection
Authors: Xiaying Wang, Michael Hersche, Michele Magno, Luca Benini
Date: 2022-03-28 00:00:00
Tasks: channel selection, EEG, Electroencephalogram (EEG), Motor Imagery

A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays a vital role in prosthesis control and motor rehabilitation. To improve user comfort, preserve data privacy, and reduce the system's latency, a new trend in wearable BMIs is to execute algorithms on low-power microcontroller units (MCUs) embedded on edge devices to process the electroencephalographic (EEG) data in real-time close to the sensors. However, most of the classification models present in the literature are too resource-demanding, making them unfit for low-power MCUs. This paper proposes an efficient convolutional neural network (CNN) for EEG-based MI classification that achieves comparable accuracy while being orders of magnitude less resource-demanding and significantly more energy-efficient than state-of-the-art (SoA) models for a long-lifetime battery operation. To further reduce the model complexity, we propose an automatic channel selection method based on spatial filters and quantize both weights and activations to 8-bit precision with negligible accuracy loss. Finally, we implement and evaluate the proposed models on leading-edge parallel ultra-low-power (PULP) MCUs. The final 2-class solution consumes as little as 30 uJ/inference with a runtime of 2.95 ms/inference and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming the new SoA for embedded MI-BMI and defining a new Pareto frontier in the three-way trade-off among accuracy, resource cost, and power usage.
Passive Motion Detection via mmWave Communication System
Authors: Jie Li, Chao Yu, Yan Luo, Yifei Sun, Rui Wang
Date: 2022-03-28 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Motion Detection

In this paper, an integrated passive sensing and communication system working in 60 GHz band is elaborated, and the sensing performance is investigated in an application of hand gesture recognition. Specifically, in this integrated system, there are two radio frequency (RF) chains at the receiver and one at the transmitter. Each RF chain is connected with one phased array for analog beamforming. To facilitate simultaneous sensing and communication, the transmitter delivers one stream of information-bearing signals via two beam lobes, one is aligned with the main signal propagation path and the other is directed to the sensing target. Signals from the two lobes are received by the two RF chains at the receiver, respectively. By cross ambiguity coherent processing, the time-Doppler spectrograms of hand gestures can be obtained. Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively. Then a neural network is trained by the dataset for motion detection. It is shown that the classification accuracy rate is high as long as sufficient sensing time is assured. Finally, an empirical model characterizing the relation between the classification accuracy and sensing duration is derived analytically.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Authors: Chen Qian, Zi Wang, Xinlin Zhang, Boxuan Shi, Boyu Jiang, Ran Tao, Jing Li, Yuwei Ge, Taishan Kang, Jianzhong Lin, Di Guo, Xiaobo Qu
Date: 2022-03-28 00:00:00

Objective: Multi-shot interleaved echo planer imaging can obtain diffusion-weighted images (DWI) with high spatial resolution and low distortion, but suffers from ghost artifacts introduced by phase variations between shots. In this work, we aim at solving the challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Methods: An explicit phase model with paired phase and magnitude priors is proposed to regularize the reconstruction (PAIR). The former prior is derived from the smoothness of the shot phase and enforced with low-rankness in the k-space domain. The latter explores similar edges among multi-b-value and multi-direction DWI with weighted total variation in the image domain. Results: Extensive simulation and in vivo results show that PAIR can remove ghost artifacts very well under a high number of shots (8 shots) and significantly suppress the noise under the ultra-high b-value (4000 s/mm2). Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio. Significance: PAIR has great potential in advanced clinical DWI applications and brain function research.
3D Uplink Channel Modeling of UAV-based mmWave Fronthaul Links for Future Small Cell Networks
Authors: Mohammad Taghi Dabiri, Mazen Omar Hasna
Date: 2022-03-27 00:00:00

In this study, we consider an unmanned aerial vehicle (UAV)-assisted heterogeneous network that is offered as a cost-effective and easy to deploy solution to solve the problem related to transferring traffic of distributed small cells to the core network. For any given distribution of small cell base stations (SBSs), we first characterize an accurate millimeter wave (mmWave) channel model for SBS to networked flying platform (NFP) by taking into consideration real parameters such as UAV's vibrations, distribution of SBSs, position of UAVs in the sky, real three-dimensional (3D) antenna pattern model provided by 3GPP along with interference caused by antenna side lobes and frequency reuse. Then, for the characterized channel, we derive an analytical closed-form expression for the end-to-end signal-to-noise plus interference ratio (SINR). Based on that, we derive the closed-form expressions for the outage probability and channel capacity of the considered UAV-based mmWave uplinks. The accuracy of the derived analytical expressions is verified by Monte Carlo simulations. Finally, we investigate the effects of different channel parameters such as antenna pattern gain, strength of UAV's vibrations, UAVs' positions in the sky, distribution of SBSs, and frequency reuse on the performance of the considered UAV-based uplink channel in terms of average capacity and outage probability.
Distributed Detection Fusion in Clustered Sensor Networks over Multiple Access Fading Channels
Authors: Sami Aldalahmeh, Domenico Ciuonzo
Date: 2022-03-26 00:00:00

In this paper, we tackle decision fusion for distributed detection in a randomly-deployed clustered wireless sensor networks (WSNs) operating over a non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading, pathloss and additive noise. To mitigate fading, we propose the distributed equal gain transmit combining (dEGTC) and distributed maximum ratio transit combining (dMRTC). The first and second order statistics of the received signals were analytically computed via stochastic geometry tools. Then the distribution of the received signal over the MAC are approximated by Gaussian and log-normal distributions via moment matching. This enabled the derivation of moment matching optimal fusion rules (MOR)for both distributions. Moreover, suboptimal simpler fusion rules were also proposed, in which all the CHs data are equally weighed, which is termed moment matching equal gain fusion rule (MER). It is shown by simulations that increasing the number of clusters improve the performance. Moreover, MOR-Gaussian based algorithms are better under free-space propagation whereas their lognormal counterparts are more suited in the ground-reflection case. Also, the latter algorithms show better results in low SNR and SN numbers conditions. We have proved that the received power at the CH in MAC is proportional O(2R2) and to O(2ln2R) in the free-space propagation and the ground-reflection cases respectively, whereis SN deployment intensity and R is the cluster radius. This implies that having more clusters decreases the required transmission power for a given SNR at the receiver.
Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques
Authors: Khouloud Abdelli, Danish Rafique, Helmut Griesser, Stephan Pachnicke
Date: 2022-03-19 00:00:00
Tasks: BIG-bench Machine Learning

A novel approach based on an artificial neural network (ANN) for lifetime prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms the conventional lifetime projection using accelerated aging tests.
Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals
Authors: Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke
Date: 2022-03-19 00:00:00
Tasks: Event Detection, Fault Detection

Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a crucial need to develop an automatic and reliable algorithm for real time optical fiber fault detection and diagnosis leveraging the telemetry data obtained by an optical time domain reflectometry (OTDR) instrument. In this paper, we propose a novel data driven approach based on convolutional neural networks (CNNs) to detect and characterize the fiber reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns. In our simulations, we achieved a higher detection capability with low false alarm rate and greater localization accuracy even for low SNR values compared to conventionally employed techniques.
Rough volatility: fact or artefact?
Authors: Rama Cont, Purba Das
Date: 2022-03-24 00:00:00
Tasks: Time Series Analysis

We investigate the statistical evidence for the use of `rough' fractional processes with Hurst exponent $H< 0.5$ for the modeling of volatility of financial assets, using a model-free approach. We introduce a non-parametric method for estimating the roughness of a function based on discrete sample, using the concept of normalized $p$-th variation along a sequence of partitions. We investigate the finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes using detailed numerical experiments based on sample paths of fractional Brownian motion and other fractional processes. We then apply this method to estimate the roughness of realized volatility signals based on high-frequency observations. Detailed numerical experiments based on stochastic volatility models show that, even when the instantaneous volatility has diffusive dynamics with the same roughness as Brownian motion, the realized volatility exhibits rough behaviour corresponding to a Hurst exponent significantly smaller than $0.5$. Comparison of roughness estimates for realized and instantaneous volatility in fractional volatility models with different values of Hurst exponent shows that, irrespective of the roughness of the spot volatility process, realized volatility always exhibits `rough' behaviour with an apparent Hurst index $\hat{H}<0.5$. These results suggest that the origin of the roughness observed in realized volatility time-series lies in the microstructure noise rather than the volatility process itself.
Biophysical models of intrinsic homeostasis: Firing rates and beyond
Authors: Nelson Niemeyer, Jan-Hendrik Schleimer, Susanne Schreiber
Date: 2022-03-28 00:00:00

In view of ever-changing conditions both in the external world and in intrinsic brain states, maintaining the robustness of computations poses a challenge, adequate solutions to which we are only beginning to understand. At the level of cell-intrinsic properties, biophysical models of neurons permit one to identify relevant physiological substrates that can serve as regulators of neuronal excitability and to test how feedback loops can stabilize crucial variables such as long-term calcium levels and firing rates. Mathematical theory has also revealed a rich set of complementary computational properties arising from distinct cellular dynamics and even shaping processing at the network level. Here, we provide an overview over recently explored homeostatic mechanisms derived from biophysical models and hypothesize how multiple dynamical characteristics of cells, including their intrinsic neuronal excitability classes, can be stably controlled.
Free energy and inference in living systems
Authors: Chang Sub Kim
Date: 2022-03-27 00:00:00
Tasks: Bayesian Inference

Organisms are nonequilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical free-energy cost. In contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational free energy. As an integrated approach to living systems, this study presents a free-energy minimization theory overarching the essential features of both the thermodynamic and neuroscientific free-energy principles. Our results reveal that the perception and action of animals result from active inference entailed by free-energy minimization in the brain, and the brain operates as Schr{\"o}dinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.