pwc-archive/papers-with-abstracts huggingface dataset:
Single-scatter channel impulse response model of non-line-of-sight ultraviolet communications
Authors: Tian Cao, Shihan Chen, Tianfeng Wu, Changyong Pan, Jian Song
Date: 2022-08-28 00:00:00

Previous studies on the temporal characteristics of single-scatter transmission in non-line-of-sight (NLOS) ultraviolet communications (UVC) were based on the prolate-spheroidal coordinate system. In this work, a novel single-scatter channel impulse response (CIR) model is proposed in the spherical coordinate system, which is more natural and comprehensible than the prolate-spheroidal coordinate system in practical applications. Additionally, the results of the widely accepted Monte-Carlo (MC)-based channel model of NLOS UVC are provided to verify the proposed single-scatter CIR model. Results indicate that the computational time costed by the proposed single-scatter CIR model is decreased to less than 0.7% of the MC-based one with comparable accuracy in assessing the temporal characteristics of NLOS UVC channels.
Information overload and environmental degradation: learning from H.A. Simon and W. Wenders
Authors: Tommaso Luzzati, Ilaria Tucci, Pietro Guarnieri
Date: 2022-09-02 00:00:00

This paper discusses the relevance of information overload for explaining environmental degradation. Our argument goes that information overload and detachment from nature, caused by energy abundance, have made individuals unaware of the unsustainable effects of their choices and lifestyles.
Costs and Benefits of the Paris Climate Targets
Authors: Richard S. J. Tol
Date: 2022-09-02 00:00:00

The temperature targets in the Paris Agreement cannot be met without very rapid reduction of greenhouse gas emissions and removal of carbon dioxide from the atmosphere. The latter requires large, perhaps prohibitively large subsidies. The central estimate of the costs of climate policy, unrealistically assuming least-cost implementation, is 3.8-5.6\% of GDP in 2100. The central estimate of the benefits of climate policy, unrealistically assuming constant vulnerability, is 2.8-3.2\% of GDP. The uncertainty about the benefits is larger than the uncertainty about the costs. The Paris targets do not pass the cost-benefit test unless risk aversion is high and discount rate low.
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering
Authors: Taishi Nakashima, Nobutaka Ono
Date: 2022-09-02 00:00:00
Tasks: blind source separation

In this paper, we propose a new online independent vector analysis (IVA) algorithm for real-time blind source separation (BSS). In many BSS algorithms, the iterative projection (IP) has been used for updating the demixing matrix, a parameter to be estimated in BSS. However, it requires matrix inversion, which can be costly, particularly in online processing. To improve this situation, we introduce iterative source steering (ISS) to online IVA. ISS does not require any matrix inversions, and thus its computational complexity is less than that of IP. Furthermore, when only part of the sources are moving, ISS enables us to update the demixing matrix flexibly and effectively so that the steering vectors of only the moving sources are updated. Numerical experiments under a dynamic condition confirm the efficacy of the proposed method.
Multi-scale temporal-frequency attention for music source separation
Authors: LianWu Chen, Xiguang Zheng, Chen Zhang, Liang Guo, Bing Yu
Date: 2022-09-02 00:00:00
Tasks: Music Source Separation

In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS). Although previous methods have addressed the large receptive field modeling using various methods, the temporal and frequency correlations of the music spectrogram with repeated patterns have not been explicitly explored for the MSS task. In this paper, a temporal-frequency attention module is proposed to model the spectrogram correlations along both temporal and frequency dimensions. Moreover, a multi-scale attention is proposed to effectively capture the correlations for music signal. The experimental results on MUSDB18 dataset show that the proposed method outperforms the existing state-of-the-art systems with 9.51 dB signal-to-distortion ratio (SDR) on separating the vocal stems, which is the primary practical application of MSS.
Edge-Aware Extended Star-Tetrix Transforms for CFA-Sampled Raw Camera Image Compression
Authors: Taizo Suzuki, Liping Huang
Date: 2022-09-02 00:00:00
Tasks: Image Compression

Codecs using spectral-spatial transforms efficiently compress raw camera images captured with a color filter array (CFA-sampled raw images) by changing their RGB color space into a decorrelated color space. This study describes two types of spectral-spatial transform, called extended Star-Tetrix transforms (XSTTs), and their edge-aware versions, called edge-aware XSTTs (EXSTTs), with no extra bits (side information) and little extra complexity. They are obtained by (i) extending the Star-Tetrix transform (STT), which is one of the latest spectral-spatial transforms, to a new version of our previously proposed wavelet-based spectral-spatial transform and a simpler version, (ii) considering that each 2-D predict step of the wavelet transform is a combination of two 1-D diagonal or horizontal-vertical transforms, and (iii) weighting the transforms along the edge directions in the images. Compared with XSTTs, the EXSTTs can decorrelate CFA-sampled raw images well: they reduce the difference in energy between the two green components by about $3.38$--$30.08$ \% for high-quality camera images and $8.97$--$14.47$ \% for mobile phone images. The experiments on JPEG 2000-based lossless and lossy compression of CFA-sampled raw images show better performance than conventional methods. For high-quality camera images, the XSTTs/EXSTTs produce results equal to or better than the conventional methods: especially for images with many edges, the type-I EXSTT improves them by about $0.03$--$0.19$ bpp in average lossless bitrate and the XSTTs improve them by about $0.16$--$0.96$ dB in average Bj\o ntegaard delta peak signal-to-noise ratio. For mobile phone images, our previous work perform the best, whereas the XSTTs/EXSTTs show similar trends to the case of high-quality camera images.
Ambient backscatter communications using LTE cell specific reference signals
Authors: Kalle Ruttik, Xiyu Wang, Jingyi Liao, Riku Jantti, Phan-Huy Dinh-Thuy
Date: 2022-09-02 00:00:00

Long Term Evolution (LTE) systems provide ubiquitous coverage for mobile communications, which makes it a promising candidate to be used as a signal source in the ambient backscatter communications. In this paper, we propose a system in which a backscatter device modulates the ambient LTE signal by changing its reflection coefficient and the receiver uses the LTE Cell Specific Reference Signals (CRS) to estimate the channel and demodulates the backscattered signal from the obtained channel impulse response estimates. We first outline the overall system, discuss the receiver operation, and then provide experimental evidence on the practicality of the proposed system.
TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations
Authors: Dandan Jiang, Han Hao, Lu Yang, Rui Wang
Date: 2022-09-02 00:00:00
Tasks: Capacity Estimation

Capacity is one of the most important performance metrics for wireless communication networks. It describes the maximum rate at which the information can be transmitted of a wireless communication system. To support the growing demand for wireless traffic, wireless networks are becoming more dense and complicated, leading to a higher difficulty to derive the capacity. Unfortunately, most existing methods for the capacity calculation take a polynomial time complexity. This will become unaffordable for future ultra-dense networks, where both the number of base stations (BSs) and the number of users are extremely large. In this paper, we propose a fast algorithm TOSE to estimate the capacity for ultra-dense wireless networks. Based on the spiked model of random matrix theory (RMT), our algorithm can avoid the exact eigenvalue derivations of large dimensional matrices, which are complicated and inevitable in conventional capacity calculation methods. Instead, fast eigenvalue estimations can be realized based on the spike approximations in our TOSE algorithm. Our simulation results show that TOSE is an accurate and fast capacity approximation algorithm. Its estimation error is below 5%, and it runs in linear time, which is much lower than the polynomial time complexity of existing methods. In addition, TOSE has superior generality, since it is independent of the distributions of BSs and users, and the shape of network areas.
Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments
Authors: Yue Cao, Shaoshi Yang, Zhiyong Feng
Date: 2022-09-02 00:00:00

We propose a geographic and spatio-temporal information based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.
A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling
Authors: Duc-Phuc Nguyen, Yoshifumi Shiraki, Jun Muramatsu, Takehiro Moriya
Date: 2022-09-02 00:00:00

We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-aware values for frozen bit indices in systematic polar code to minimize the run-length without using run-length-limited code. Our compact system can support soft-decision forward-error-correction decoding with excellent spectral efficiency compared with related work based on hybrid modulation schemes.
Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks
Authors: Jianyuan Yu, William W. Howard, Yue Xu, R. Michael Buehrer
Date: 2022-09-01 00:00:00

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance.
Deep Learning-Based Position Detection for Hydraulic Cylinders Using Scattering Parameters
Authors: Chen Xin, Thomas Motz, Wolfgang Fuhl, Andreas Hartel, Enkelejda Kasneci
Date: 2022-09-01 00:00:00
Tasks: Deep Learning, Position

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that outlines the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To address these limitations, we propose machine learning and deep learning-based methods to learn the relationship directly in a data-driven manner. As a result, all deep learning models in this paper consistently outperform the physical one by a large margin. We further deliberate on the choice of models based on domain knowledge and provide in-depth analyses combining model performance with real-world physical characteristics. Specifically, we use Convolutional Neural Network (CNN) to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. The combination of these techniques results in our best-performing model, a complex-valued CNN with Frequency Encoding, which exhibits substantial improvement in accuracy with an error reduction of 1/12 compared to the traditional physical model.
A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection
Authors: Negin Alamatsaz, Leyla s Tabatabaei, Mohammadreza Yazdchi, Hamidreza Payan, Nima Alamatsaz, Fahimeh Nasimi
Date: 2022-08-29 00:00:00
Tasks: Arrhythmia Detection, Classification, Diagnostic, Rhythm

Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper introduces a light deep learning approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythm. To leverage deep learning method, resampling and baseline wander removal techniques are applied to ECG signals. In this study, 500 sample ECG segments were used as model inputs. The rhythm classification was done by an 11-layer network in an end-to-end manner without the need for hand-crafted manual feature extraction. Results: In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed deep learning framework based on the combination of Convolutional Neural Network(CNN) and Long Short Term Memory (LSTM) showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance: Since the present work uses a light classification technique with high diagnostic accuracy compared to other notable methods, it could successfully be implemented in holter monitor devices for arrhythmia detection.
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning
Authors: Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
Date: 2022-08-26 00:00:00
Tasks: Activity Recognition, Classification, Contrastive Learning, Human Activity Recognition, Representation Learning, Self-Supervised Learning, Transfer Learning

In this paper, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels. Specifically, we intend to enable cross-dataset transfer learning such that our network pre-trained on a particular dataset can perform effective activity classification on other datasets (successive to a small amount of fine-tuning). To tackle this problem, we design our solution with the intention of learning as much information from the accelerometer signals as possible. As a result, we design two separate pipelines, one that learns the data in time-frequency domain, and the other in time-domain alone. In order to address the issues mentioned above in regards to cross-dataset transfer learning, we use self-supervised contrastive learning to train each of these streams. Next, each stream is fine-tuned for final classification, and eventually the two are fused to provide the final results. We evaluate the performance of the proposed solution on three datasets, namely MotionSense, HAPT, and HHAR, and demonstrate that our solution outperforms prior works in this field. We further evaluate the performance of the method in learning generalized features, by using MobiAct dataset for pre-training and the remaining three datasets for the downstream classification task, and show that the proposed solution achieves better performance in comparison with other self-supervised methods in cross-dataset transfer learning.
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset
Authors: Kushagra Sharma, Rasit Eskicioglu
Date: 2022-08-25 00:00:00
Tasks: Anomaly Detection, ECG Classification

The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission of raw data to a remote server, which is not efficient for resource-constrained IoT devices and embedded systems. Additionally, it is challenging to develop a machine learning model for ECG classification due to the lack of an extensive open public database. To an extent, to overcome this challenge PTB-XL dataset has been used. In this work, we have developed machine learning models to be deployed on Raspberry Pi. We present an evaluation of our TensorFlow Model with two classification classes. We also present the evaluation of the corresponding TensorFlow Lite FlatBuffers to demonstrate their minimal run-time requirements while maintaining acceptable accuracy.
E-backtesting
Authors: Qiuqi Wang, Ruodu Wang, Johanna Ziegel
Date: 2022-08-27 00:00:00

In the recent Basel Accords, the Expected Shortfall (ES) replaces the Value-at-Risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most important risk measure in financial regulation. One of the most challenging tasks in risk modeling practice is to backtest ES forecasts provided by financial institutions. To design a model-free backtesting procedure for ES, we make use of the recently developed techniques of e-values and e-processes. Backtest e-statistics are introduced to formulate e-processes for risk measure forecasts, and unique forms of backtest e-statistics for VaR and ES are characterized using recent results on identification functions. For a given backtest e-statistic, a few criteria for optimally constructing the e-processes are studied. The proposed method can be naturally applied to many other risk measures and statistical quantities. We conduct extensive simulation studies and data analysis to illustrate the advantages of the model-free backtesting method, and compare it with the ones in the literature.
In Situ 3D Spatiotemporal Measurement of Soluble Biomarkers in Organoid Culture
Authors: Alexander J McGhee, Eric O McGhee, Jack E Famiglietti, W. Gregory Sawyer
Date: 2022-09-02 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Time Series Analysis

Advanced cell culture techniques such as 3D bio-printing and hydrogel-based cell embedding techniques harbor many new and exciting opportunities to study cells in environments that closely recapitulate in-vivo conditions. Researchers often study these environments using fluorescence microscopy to visualize the protein association with objects such as cells within the 3D environment, yet quantification of concentration profiles in the microenvironment has remained elusive. Here, we present a method to continuously measure the time-dependent concentration gradient of various biomarkers within a 3D cell culture assay using bead-based immunoassays to sequester and concentrate the fluorescence intensity of these tagged proteins. This assay allows for near real-time in situ biomarker detection and enables spatiotemporal quantification of biomarker concentration. Snapshots of concentration profiles can be taken, or time series analysis can be performed enabling time-varying biomarker production estimation. Example assays utilize an osteosacroma tumoroid as a case study for a quantitative single-plexed gel encapsulated assay, and a qualitative multi-plexed 3D bioprinted assay. In both cases, a time-varying cytokine concentration gradient is measured. An estimation for the production rate of the IL-8 cytokine per second per osteosarcoma cell results from fitting an analytical function for continuous point source diffusion to the measured concentration gradient and reveals that each cell produces approximately two IL-8 cytokines per second. Proper calibration and use of this assay is exhaustively explored for the case of diffusion-limited Langmuir kinetics of a spherical adsorber.
genomepy: genes and genomes at your fingertips
Authors: Siebren Frölich, Maarten van der Sande, Tilman Schäfers, Simon J. van Heeringen
Date: 2022-09-02 00:00:00

Analyzing a functional genomics experiment, such as ATAC-, ChIP- or RNA-sequencing, requires reference data including a genome assembly and gene annotation. These resources can generally be retrieved from different organizations and in different versions. Most bioinformatic workflows require the user to supply this genomic data manually, which can be a tedious and error-prone process. Here we present genomepy, which can search, download, and preprocess the right genomic data for your analysis. Genomepy can search genomic data on NCBI, Ensembl, UCSC and GENCODE, and compare available gene annotations to enable an informed decision. The selected genome and gene annotation can be downloaded and preprocessed with sensible, yet controllable, defaults. Additional supporting data can be automatically generated or downloaded, such as aligner indexes, genome metadata and blacklists. Genomepy is freely available at https://github.com/vanheeringen-lab/genomepy under the MIT license and can be installed through pip or bioconda.