pwc-archive/papers-with-abstracts huggingface dataset:
Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
Authors: Naoki Kanda, Chihaya Abe, Shintaro Goto, Keigo Yamada, Kumi Nakai, Yuji Saito, Keisuke Asai, Taku Nonomura
Date: 2022-07-14 00:00:00

In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image velocimetry (PIV), whereas SPPIV was previously proposed with its feasibility study and its real-time demonstration is conducted for the first time in this study. In the wind tunnel test, the PIV measurement and real-time measurement using SPPIV were conducted for the flow velocity field around the NACA0015 airfoil model. The off-line analysis results of the test show that the flow velocity field can be estimated from a small number of processing points by applying SPPIV, and also illustrates the following characteristics of SPPIV. The estimation accuracy improves as the number of processing points increases, whereas the processing time per step increases in proportion to the number of processing points. Therefore, it is necessary to set an optimal number of processing points. In addition, the application of the Kalman filter significantly improves the estimation accuracy with a small number of processing points while suppressing the processing time. When the flow velocity fields with different angles of attack are used as the training data with that of test data, the estimation using SPPIV is found to be reasonable if the difference in angle of attack between the training and test data is equal to or less than 2 deg and the flow phenomena of the training data are similar to that of the test data. For this reason, training data should be prepared at least every 4 deg. Finally, the demonstration of SPPIV as a real-time flow observation was conducted for the first time. In this demonstration, the real-time measurement is found to be possible at a sampling rate of 2000 Hz at 20 or less processing points in the top 10 modes estimation as expected by the off-line analyses.
High Dimensional Generalised Penalised Least Squares
Authors: Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios
Date: 2022-07-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we develop inference for high dimensional linear models, with serially correlated errors. We examine Lasso under the assumption of strong mixing in the covariates and error process, allowing for fatter tails in their distribution. While the Lasso estimator performs poorly under such circumstances, we estimate via GLS Lasso the parameters of interest and extend the asymptotic properties of the Lasso under more general conditions. Our theoretical results indicate that the non-asymptotic bounds for stationary dependent processes are sharper, while the rate of Lasso under general conditions appears slower as $T,p\to \infty$. Further we employ the debiased Lasso to perform inference uniformly on the parameters of interest. Monte Carlo results support the proposed estimator, as it has significant efficiency gains over traditional methods.
Parallel Trends and Dynamic Choices
Authors: Philip Marx, Elie Tamer, Xun Tang
Date: 2022-07-14 00:00:00

Difference-in-differences is a common method for estimating treatment effects, and the parallel trends condition is its main identifying assumption: the trend in mean untreated outcomes is independent of the observed treatment status. In observational settings, treatment is often a dynamic choice made or influenced by rational actors, such as policy-makers, firms, or individual agents. This paper relates parallel trends to economic models of dynamic choice. We clarify the implications of parallel trends on agent behavior and study when dynamic selection motives lead to violations of parallel trends. Finally, we consider identification under alternative assumptions that accommodate features of dynamic choice.
Monotone Comparative Statics for Equilibrium Problems
Authors: Alfred Galichon, Larry Samuelson, Lucas Vernet
Date: 2022-07-14 00:00:00

We introduce a notion of substitutability for correspondences and establish a monotone comparative static result, unifying results such as the inverse isotonicity of M-matrices, Berry, Gandhi and Haile's identification of demand systems, monotone comparative statics, and results on the structure of the core of matching games without transfers (Gale and Shapley) and with transfers (Demange and Gale). More specifically, we introduce the notions of 'unified gross substitutes' and 'nonreversingness' and show that if Q is a supply correspondence defined on a set of prices P which is a sublattice of R^N, and Q satisfies these two properties, then the set of prices yielding supply vector q is increasing (in the strong set order) in q; and it is a sublattice of P.
A Neural-Network Framework for the Design of Individualised Hearing-Loss Compensation
Authors: Fotios Drakopoulos, Sarah Verhulst
Date: 2022-07-14 00:00:00

Sound processing in the human auditory system is complex and highly non-linear, whereas hearing aids (HAs) still rely on simplified descriptions of auditory processing or hearing loss to restore hearing. Even though standard HA amplification strategies succeed in restoring audibility of faint sounds, they still fall short of providing targeted treatments for complex sensorineural deficits and adverse listening conditions. These shortcomings of current HA devices demonstrate the need for advanced hearing-loss compensation strategies that can effectively leverage the non-linear character of the auditory system. Here, we propose a differentiable deep-neural-network (DNN) framework that can be used to train DNN-based HA models based on biophysical auditory-processing differences between normal-hearing and hearing-impaired systems. We investigate different loss functions to accurately compensate for impairments that include outer-hair-cell (OHC) loss and cochlear synaptopathy (CS), and evaluate the benefits of our trained DNN-based HA models for speech processing in quiet and in noise. Our results show that auditory-processing enhancement was possible for all considered hearing-loss cases, with OHC loss proving easier to compensate than CS. Several objective metrics were considered to estimate the expected speech intelligibility after processing, and these simulations hold promise in yielding improved understanding of speech-in-noise for hearing-impaired listeners who use our DNN-based HA processing. Since our framework can be tuned to the hearing-loss profiles of individual listeners, we enter an era where truly individualised and DNN-based hearing-restoration strategies can be developed and be tested experimentally.
Adaptive frequency prior for frequency selective reconstruction of images from non-regular subsampling
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-14 00:00:00

Image signals typically are defined on a rectangular two-dimensional grid. However, there exist scenarios where this is not fulfilled and where the image information only is available for a non-regular subset of pixel position. For processing, transmitting or displaying such an image signal, a re-sampling to a regular grid is required. Recently, Frequency Selective Reconstruction (FSR) has been proposed as a very effective sparsity-based algorithm for solving this under-determined problem. For this, FSR iteratively generates a model of the signal in the Fourier-domain. In this context, a fixed frequency prior inspired by the optical transfer function is used for favoring low-frequency content. However, this fixed prior is often too strict and may lead to a reduced reconstruction quality. To resolve this weakness, this paper proposes an adaptive frequency prior which takes the local density of the available samples into account. The proposed adaptive prior allows for a very high reconstruction quality, yielding gains of up to 0.6 dB PSNR over the fixed prior, independently of the density of the available samples. Compared to other state-of-the-art algorithms, visually noticeable gains of several dB are possible.
Multiple Selection Extrapolation for Improved Spatial Error Concealment
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-14 00:00:00

This contribution introduces a novel signal extrapolation algorithm and its application to image error concealment. The signal extrapolation is carried out by iteratively generating a model of the signal suffering from distortion. Thereby, the model results from a weighted superposition of two-dimensional basis functions whereas in every iteration step a set of these is selected and the approximation residual is projected onto the subspace they span. The algorithm is an improvement to the Frequency Selective Extrapolation that has proven to be an effective method for concealing lost or distorted image regions. Compared to this algorithm, the novel algorithm is able to reduce the processing time by a factor larger than three, by still preserving the very high extrapolation quality.
Adaptive joint spatio-temporal error concealment for video communication
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-14 00:00:00

In the past years, video communication has found its application in an increasing number of environments. Unfortunately, some of them are error-prone and the risk of block losses caused by transmission errors is ubiquitous. To reduce the effects of these block losses, a new spatio-temporal error concealment algorithm is presented. The algorithm uses spatial as well as temporal information for extrapolating the signal into the lost areas. The extrapolation is carried out in two steps, first a preliminary temporal extrapolation is performed which then is used to generate a model of the original signal, using the spatial neighborhood of the lost block. By applying the spatial refinement a significantly higher concealment quality can be achieved resulting in a gain of up to 5.2 dB in PSNR compared to the unrefined underlying pure temporal extrapolation.
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects
Authors: Qiqi Dai, Yee Hui Lee, Hai-Han Sun, Jiwei Qian, Genevieve Ow, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel
Date: 2022-07-13 00:00:00
Tasks: Decoder, GPR, Transfer Learning

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver.
Joint UAV Placement and IRS Phase Shift Optimization in Downlink Networks
Authors: Hung Nguyen-Kha, Hieu V. Nguyen, Mai T. P. Le, Oh-Soon Shin
Date: 2022-07-14 00:00:00

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS-UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.
Downlink Analysis and Evaluation of Multi-Beam LEO Satellite Communication in Shadowed Rician Channels
Authors: Eunsun Kim, Ian P. Roberts, Jeffrey G. Andrews
Date: 2022-07-14 00:00:00

The extension of wide area wireless connectivity to low-earth orbit (LEO) satellite communication systems demands a fresh look at the effects of in-orbit base stations, sky-to-ground propagation, and cell planning. A multi-beam LEO satellite delivers widespread coverage by forming multiple spot beams that tessellate cells over a given region on the surface of the Earth. In doing so, overlapping spot beams introduce interference when delivering downlink concurrently in the same area using the same frequency spectrum. To permit forecasting of communication system performance, we characterize desired and interference signal powers, along with SNR, INR, SIR, and SINR, under the measurement-backed Shadowed Rician (SR) sky-to-ground channel model. We introduce a minor approximation to the fading order of SR channels that greatly simplifies the PDF and CDF of these quantities and facilitates statistical analyses of LEO satellite systems such as probability of outage. We conclude this paper with an evaluation of multi-beam LEO satellite communication in SR channels of varying intensity fitted from existing measurements. Our numerical results highlight the effects satellite elevation angle has on SNR, INR, and SINR, which brings attention to the variability in system state and potential performance as a satellite traverses across the sky along its orbit.
Reconstruction of Time-varying Graph Signals via Sobolev Smoothness
Authors: Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans
Date: 2022-07-13 00:00:00

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals.
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
Authors: Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana, Fu-Chun Zheng, Branka Vucetic
Date: 2022-07-11 00:00:00
Tasks: Diversity, Graph Neural Network

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks. To obtain time diversity within the channel coherence time, we first put forward a random repetition scheme that randomizes the interference power. Then, we optimize the number of reserved slots and the number of repetitions for each packet to minimize the QoS violation probability, defined as the percentage of users that cannot achieve URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to represent the repetition scheme and develop a model-free unsupervised learning method to train it. We analyze the QoS violation probability using stochastic geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES) method to find the optimal solution. Simulation results show that in the symmetric scenario, the QoS violation probabilities achieved by the model-free learning method and the model-based ES method are nearly the same. In more general scenarios, the cascaded REGNN generalizes very well in wireless networks with different scales, network topologies, cell densities, and frequency reuse factors. It outperforms the model-based ES method in the presence of the model mismatch.
Attention mechanisms for physiological signal deep learning: which attention should we take?
Authors: Seong-A Park, Hyung-Chul Lee, Chul-Woo Jung, Hyun-Lim Yang
Date: 2022-07-04 00:00:00
Tasks: Deep Learning, regression

Attention mechanisms are widely used to dramatically improve deep learning model performance in various fields. However, their general ability to improve the performance of physiological signal deep learning model is immature. In this study, we experimentally analyze four attention mechanisms (e.g., squeeze-and-excitation, non-local, convolutional block attention module, and multi-head self-attention) and three convolutional neural network (CNN) architectures (e.g., VGG, ResNet, and Inception) for two representative physiological signal prediction tasks: the classification for predicting hypotension and the regression for predicting cardiac output (CO). We evaluated multiple combinations for performance and convergence of physiological signal deep learning model. Accordingly, the CNN models with the spatial attention mechanism showed the best performance in the classification problem, whereas the channel attention mechanism achieved the lowest error in the regression problem. Moreover, the performance and convergence of the CNN models with attention mechanisms were better than stand-alone self-attention models in both problems. Hence, we verified that convolutional operation and attention mechanisms are complementary and provide faster convergence time, despite the stand-alone self-attention models requiring fewer parameters.
Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG
Authors: Harlin Lee, Aaqib Saeed
Date: 2022-06-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Sleep is particularly important to the health of infants, children, and adolescents, and sleep scoring is the first step to accurate diagnosis and treatment of potentially life-threatening conditions. But pediatric sleep is severely under-researched compared to adult sleep in the context of machine learning for health, and sleep scoring algorithms developed for adults usually perform poorly on infants. Here, we present the first automated sleep scoring results on a recent large-scale pediatric sleep study dataset that was collected during standard clinical care. We develop a transformer-based model that learns to classify five sleep stages from millions of multi-channel electroencephalogram (EEG) sleep epochs with 78% overall accuracy. Further, we conduct an in-depth analysis of the model performance based on patient demographics and EEG channels. The results point to the growing need for machine learning research on pediatric sleep.
Improving self-supervised pretraining models for epileptic seizure detection from EEG data
Authors: Sudip Das, Pankaj Pandey, Krishna Prasad Miyapuram
Date: 2022-06-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection, Time Series, Time Series Analysis

There is abundant medical data on the internet, most of which are unlabeled. Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them. They are also prone to human error and biased as a select few expert annotators label them. These issues are mitigated by Self-supervision, where we generate pseudo-labels from unlabelled data by seeing the data itself. This paper presents various self-supervision strategies to enhance the performance of a time-series based Diffusion convolution recurrent neural network (DCRNN) model. The learned weights in the self-supervision pretraining phase can be transferred to the supervised training phase to boost the model's prediction capability. Our techniques are tested on an extension of a Diffusion Convolutional Recurrent Neural network (DCRNN) model, an RNN with graph diffusion convolutions, which models the spatiotemporal dependencies present in EEG signals. When the learned weights from the pretraining stage are transferred to a DCRNN model to determine whether an EEG time window has a characteristic seizure signal associated with it, our method yields an AUROC score $1.56\%$ than the current state-of-the-art models on the TUH EEG seizure corpus.
Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach
Authors: Eran Vertzberger, Itzik Klein
Date: 2022-06-22 00:00:00

Attitude determination using the smartphone's inertial sensors poses a major challenge due to the sensor low-performance grade and variate nature of the walking pedestrian. In this paper, data-driven techniques are employed to address that challenge. To that end, a hybrid deep learning and model based solution for attitude estimation is proposed. Here, classical model based equations are applied to form an adaptive complementary filter structure. Instead of using constant or model based adaptive weights, the accelerometer weights in each axis are determined by a unique neural network. The performance of the proposed hybrid approach is evaluated relative to popular model based approaches using experimental data.
Experimental emulation for OTFS waveform RF-impairments
Authors: Abdelrahman Abushattal, Salah Eddine Zegrar, Ayhan Yazgan, Huseyin Arslan
Date: 2022-06-17 00:00:00

Orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in the high-mobility environment with high time-frequency dispersive channels. Since practical pulse shaping design and RF-impairments effects have a direct impact on waveform behavior, this paper investigates experimental implementation for practical pulse shaping design and RF-impairments that affect OTFS waveform performance and compares them to OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, then an experimental framework is established for investigating the RF-impairments include non-linearity, Carrier frequency offset, I/Q-imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR)based on the Keysight EXG X-Serie devices. The experimental results validate the accuracy of the theoretical results.