pwc-archive/papers-with-abstracts huggingface dataset:
INS/DVL Fusion with DVL Based Acceleration Measurements
Authors: Orzion Levy, Itzik Klein
Date: 2023-08-22 00:00:00

Autonomous underwater vehicles (AUVs) are increasingly used in many applications such as oceanographic surveys, mapping, and inspection of underwater structures. To successfully complete those tasks, a Doppler velocity log (DVL) and an inertial navigation system (INS) are utilized to determine the AUV navigation solution. In such fusion, DVL velocity measurement is used to update the navigation states. In this paper, we propose calculating the AUV acceleration vector based on past DVL measurements and using it as an additional update to increase the system's accuracy. Simulations and sea experiments were conducted to demonstrate the efficiency of our approach. The results indicate that the proposed method exhibits rapid convergence and significantly improves the overall performance compared to the baseline INS and DVL fusion approach.
Flying Capacitor Cell Equalization for Li-ion Automotive Battery Stacks
Authors: Manish Ramaswamy
Date: 2023-08-22 00:00:00

The automotive industry is fast evolving to Li-ion chemistries, which have more favorable power, energy density, and efficiency. To meet the demands of greater electric ranges, parallel strings of batteries are required to increase the overall system capacity. Differences in chemical characteristics, internal resistance, and operating conditions can cause variations in remaining cell capacity, decreasing the total battery storage capacity over time, shortening the battery lifetime and eventually damaging the cells. Cell equalization tries to restore all the cells in the pack to an equal state of charge in order to prolong the battery lifetime and to ensure safe battery operations. This work presents an active charge equalization scheme with a flying capacitor to shuttle charge between the unbalanced cells in a parallel battery pack. The theoretical framework is accompanied by MATLAB simulations on a twelve cell pack in series/parallel configuration supporting the validity of the chosen approach.
Optically-Transparent EM Skins for Outdoor-to-Indoor mm-Wave Wireless Communications
Authors: Giacomo Oliveri, Francesco Zardi, Andrea Massa
Date: 2023-08-17 00:00:00

Optically-transparent opportunistic electromagnetic skins (OTO-EMSs) are proposed to enable outdoor-to-indoor (O2I) millimiter-wave (mmW) wireless communications with existing windows/glass-panels. More in detail, static passive EMSs consisting of optically-transparent conducting patterned layers attached to standard glass-panels are designed. Towards this end, both the phase coverage and the optical transparency of a meshed copper-based meta-atom printed on a non-dedicated insulated glass substrate are optimized. Successively, the feasibility of OTO-EMSs able to support mmW high-efficiency O2I transmissions along non-Snell refraction directions is numerically demonstrated.
General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold
Authors: Won Kyu Kim, Kiri Choi, Changbong Hyeon, Seogjoo J. Jang
Date: 2023-08-23 00:00:00

This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory is applicable to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the presence and the absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated.
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
Authors: Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
Date: 2023-08-10 00:00:00
Tasks: Transfer Learning

We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pre-training the model using synthetic data generated from a less accurate analytical model and fine-tuning with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model, or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve < 1 dB root-mean-square error on the matrix weights implemented by a 3x3 photonic chip while using only 25% of the available data.
Discrimination and Constraints: Evidence from The Voice
Authors: Anuar Assamidanov
Date: 2023-08-23 00:00:00

Gender discrimination in the hiring process is one significant factor contributing to labor market disparities. However, there is little evidence on the extent to which gender bias by hiring managers is responsible for these disparities. In this paper, I exploit a unique dataset of blind auditions of The Voice television show as an experiment to identify own gender bias in the selection process. The first televised stage audition, in which four noteworthy recording artists are coaches, listens to the contestants blindly (chairs facing away from the stage) to avoid seeing the contestant. Using a difference-in-differences estimation strategy, a coach (hiring person) is demonstrably exogenous with respect to the artist's gender, I find that artists are 4.5 percentage points (11 percent) more likely to be selected when they are the recipients of an opposite-gender coach. I also utilize the machine-learning approach in Athey et al. (2018) to include heterogeneity from team gender composition, order of performance, and failure rates of the coaches. The findings offer a new perspective to enrich past research on gender discrimination, shedding light on the instances of gender bias variation by the gender of the decision maker and team gender composition.
The Impact of Stocks on Correlations between Crop Yields and Prices and on Revenue Insurance Premiums using Semiparametric Quantile Regression
Authors: Matthew Stuart, Cindy Yu, David A. Hennessy
Date: 2023-08-22 00:00:00
Tasks: quantile regression

Crop yields and harvest prices are often considered to be negatively correlated, thus acting as a natural risk management hedge through stabilizing revenues. Storage theory gives reason to believe that the correlation is an increasing function of stocks carried over from previous years. Stock-conditioned second moments have implications for price movements during shortages and for hedging needs, while spatially varying yield-price correlation structures have implications for who benefits from commodity support policies. In this paper, we propose to use semi-parametric quantile regression (SQR) with penalized B-splines to estimate a stock-conditioned joint distribution of yield and price. The proposed method, validated through a comprehensive simulation study, enables sampling from the true joint distribution using SQR. Then it is applied to approximate stock-conditioned correlation and revenue insurance premium for both corn and soybeans in the United States. For both crops, Cornbelt core regions have more negative correlations than do peripheral regions. We find strong evidence that correlation becomes less negative as stocks increase. We also show that conditioning on stocks is important when calculating actuarially fair revenue insurance premiums. In particular, revenue insurance premiums in the Cornbelt core will be biased upward if the model for calculating premiums does not allow correlation to vary with stocks available. The stock-dependent correlation can be viewed as a form of tail dependence that, if unacknowledged, leads to mispricing of revenue insurance products.
Studying the Impact of Augmentations on Medical Confidence Calibration
Authors: Adrit Rao, Joon-Young Lee, Oliver Aalami
Date: 2023-08-23 00:00:00
Tasks: Decision Making, Diagnostic

The clinical explainability of convolutional neural networks (CNN) heavily relies on the joint interpretation of a model's predicted diagnostic label and associated confidence. A highly certain or uncertain model can significantly impact clinical decision-making. Thus, ensuring that confidence estimates reflect the true correctness likelihood for a prediction is essential. CNNs are often poorly calibrated and prone to overconfidence leading to improper measures of uncertainty. This creates the need for confidence calibration. However, accuracy and performance-based evaluations of CNNs are commonly used as the sole benchmark for medical tasks. Taking into consideration the risks associated with miscalibration is of high importance. In recent years, modern augmentation techniques, which cut, mix, and combine images, have been introduced. Such augmentations have benefited CNNs through regularization, robustness to adversarial samples, and calibration. Standard augmentations based on image scaling, rotating, and zooming, are widely leveraged in the medical domain to combat the scarcity of data. In this paper, we evaluate the effects of three modern augmentation techniques, CutMix, MixUp, and CutOut on the calibration and performance of CNNs for medical tasks. CutMix improved calibration the most while CutOut often lowered the level of calibration.
Enhanced Residual SwinV2 Transformer for Learned Image Compression
Authors: Yongqiang Wang, Feng Liang, Haisheng Fu, Jie Liang, Haipeng Qin, Junzhe Liang
Date: 2023-08-23 00:00:00
Tasks: Image Compression

Recently, the deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. However, a challenge of many learning-based approaches is that they often achieve better performance via sacrificing complexity, which making practical deployment difficult. To alleviate this issue, in this paper, we propose an effective and efficient learned image compression framework based on an enhanced residual Swinv2 transformer. To enhance the nonlinear representation of images in our framework, we use a feature enhancement module that consists of three consecutive convolutional layers. In the subsequent coding and hyper coding steps, we utilize a SwinV2 transformer-based attention mechanism to process the input image. The SwinV2 model can help to reduce model complexity while maintaining high performance. Experimental results show that the proposed method achieves comparable performance compared to some recent learned image compression methods on Kodak and Tecnick datasets, and outperforms some traditional codecs including VVC. In particular, our method achieves comparable results while reducing model complexity by 56% compared to these recent methods.
Robust RF Data Normalization for Deep Learning
Authors: Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Date: 2023-08-22 00:00:00
Tasks: Deep Learning

Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted growing interest in ultrasound image processing. However, RF data is highly fluctuating and additionally has a high dynamic range. Most previous studies in the literature have relied on conventional data normalization, which has been adopted within the computer vision community. We demonstrate the inadequacy of those techniques for normalizing RF data and propose that individual standardization of each image substantially enhances the performance of deep neural networks by utilizing the data more efficiently. We compare conventional and proposed normalizations in a phase aberration correction task and illustrate how the former enhances the generality of trained models.
Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound
Authors: Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Date: 2023-08-22 00:00:00
Tasks: Image Reconstruction

Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultrasound imaging. Frequency-space prediction filtering or FXPF is a technique that has been applied within focused ultrasound imaging to alleviate the phase aberration effect. It presupposes the existence of an autoregressive (AR) model across the signals received at the transducer elements and removes any components that do not conform to the established model. In this study, we illustrate the challenge of applying this technique to plane-wave imaging, where, at shallower depths, signals from more distant elements lose relevance, and a fewer number of elements contribute to image reconstruction. While the number of contributing signals varies, adopting a fixed-order AR model across all depths, results in suboptimal performance. To address this challenge, we propose an AR model with an adaptive order and quantify its effectiveness using contrast and generalized contrast-to-noise ratio metrics.
Validation of apparent intra-and extra-myocellular lipid content indicator using spiral spectroscopic imaging at 3T
Authors: Antoine Naëgel, Magalie Viallon, Jabrane Karkouri, Thomas Troalen, Pierre Croisille, Hélène Ratiney
Date: 2023-08-22 00:00:00

This work presents a fast and simple method based on spiral MRSI for mapping the IMCL and EMCL apparent content, which is a challenging task and it compares this indicator to classical quantification results in muscles of interest.
Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield
Authors: Dominik Werner Wolf, Markus Ulrich, Nikhil Kapoor
Date: 2023-08-19 00:00:00
Tasks: Autonomous Driving, Sensitivity

Autonomous driving perception techniques are typically based on supervised machine learning models that are trained on real-world street data. A typical training process involves capturing images with a single car model and windshield configuration. However, deploying these trained models on different car types can lead to a domain shift, which can potentially hurt the neural networks performance and violate working ADAS requirements. To address this issue, this paper investigates the domain shift problem further by evaluating the sensitivity of two perception models to different windshield configurations. This is done by evaluating the dependencies between neural network benchmark metrics and optical merit functions by applying a Fourier optics based threat model. Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient.
Transformer-based Nonlinear Equalization for DP-16QAM Coherent Optical Communication Systems
Authors: Naveenta Gautam, Sai Vikranth Pendem, Brejesh lall, Amol Choudhary
Date: 2023-08-23 00:00:00

Compensating for nonlinear effects using digital signal processing (DSP) is complex and computationally expensive in long-haul optical communication systems due to intractable interactions between Kerr nonlinearity, chromatic dispersion (CD), and amplified spontaneous emission (ASE) noise from inline amplifiers. The application of machine learning architectures has demonstrated promising advancements in enhancing transmission performance through the mitigation of fiber nonlinear effects. In this paper, we apply a Transformer-based model to dual-polarisation (DP)-16QAM coherent optical communication systems. We test the performance of the proposed model for different values of fiber lengths and launched optical powers and show improved performance compared to the state-of-the-art digital backpropagation (DBP) algorithm, fully connected neural network (FCNN) and bidirectional long short term memory (BiLSTM) architecture.
Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis
Authors: Josef A. Nossek, Dominik Semmler, Michael Joham, Wolfgang Utschick
Date: 2023-08-23 00:00:00

RISs are an emerging technology for engineering the channels of future wireless communication systems. The vast majority of research publications on RIS are focussing on system-level optimization and are based on very simplistic models ignoring basic physical laws. There are only a few publications with a focus on physical modeling. Nevertheless, the widely employed model is still inconsistent with basic physical laws. We will show that even with a very simple abstract model based on isotropic radiators, ignoring any mismatch, mutual coupling, and losses, each RIS element cannot be modeled to simply reflect the incident signal by manipulating its phase only and letting the amplitude unchanged. We will demonstrate the inconsistencies with the aid of very simple toy examples, even with only one or two RIS elements. Based on impedance parameters, the problems associated with scattering parameters can be identified enabling a correct interpretation of the derived solutions.
A Robust ADMM-Based Optimization Algorithm For Underwater Acoustic Channel Estimation
Authors: Tian Tian, Agastya Raj, Bruno Missi Xavier, Ying Zhang, Feiyun Wu, Kunde Yang
Date: 2023-08-23 00:00:00
Tasks: compressed sensing

Accurate estimation of the Underwater acoustic (UWA) is a key part of underwater communications, especially for coherent systems. The severe multipath effects and large delay spreads make the estimation problem large-scale. The non-stationary, non-Gaussian, and impulsive nature of ocean ambient noise poses further obstacles to the design of estimation algorithms. Under the framework of compressed sensing (CS), this work addresses the issue of robust channel estimation when measurements are contaminated by impulsive noise. A first-order algorithm based on alternating direction method of multipliers (ADMM) is proposed. Numerical simulations of time-varying channel estimation are performed to show its improved performance in highly impulsive noise environments.
Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing Risks
Authors: Xiaobin Shen, Jonathan Elmer, George H. Chen
Date: 2023-08-17 00:00:00
Tasks: Benchmarking, EEG, Survival Analysis

Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (the task of neurological prognostication) could help with treatment decisions. In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients' available EEG time series could vary in length. Predictions are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient's probability of awakening or of dying across multiple time horizons. Our framework uses any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient's static features and summary statistics from the patient's latest hour's worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient's EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks).
Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration
Authors: Yang Yu, Han Chen
Date: 2023-08-17 00:00:00
Tasks: Denoising, Structural Health Monitoring, Time Series

Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure. With the rapid growth of sensor technology, the volume of data generated from various structures has seen an unprecedented surge, bringing forth challenges in efficient analysis and interpretation. This paper introduces a novel deep learning algorithm tailored for the complexities inherent in multimodal vibration signals prevalent in SHM. By amalgamating convolutional and recurrent architectures, the algorithm adeptly captures both localized and prolonged structural behaviors. The pivotal integration of attention mechanisms further enhances the model's capability, allowing it to discern and prioritize salient structural responses from extraneous noise. Our results showcase significant improvements in predictive accuracy, early damage detection, and adaptability across multiple SHM scenarios. In light of the critical nature of SHM, the proposed approach not only offers a robust analytical tool but also paves the way for more transparent and interpretable AI-driven SHM solutions. Future prospects include real-time processing, integration with external environmental factors, and a deeper emphasis on model interpretability.
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns
Authors: Tae Yeob Kang, Haebom Lee, Sungho Suh
Date: 2023-08-16 00:00:00
Tasks: Diagnostic, Dimensionality Reduction, Fault Detection, Fault Diagnosis

In the field of optoelectronics, indium tin oxide (ITO) electrodes play a crucial role in various applications, such as displays, sensors, and solar cells. Effective fault diagnosis and root cause analysis of the ITO electrodes are essential to ensure the performance and reliability of the devices. However, traditional visual inspection is challenging with transparent ITO electrodes, and existing fault diagnosis methods have limitations in determining the root causes of the defects, often requiring destructive evaluations and secondary material characterization techniques. In this study, a fault diagnosis method with root cause analysis is proposed using scattering parameter (S-parameter) patterns, offering early detection, high diagnostic accuracy, and noise robustness. A comprehensive S-parameter pattern database is obtained according to various defect states of the ITO electrodes. Deep learning (DL) approaches, including multilayer perceptron (MLP), convolutional neural network (CNN), and transformer, are then used to simultaneously analyze the cause and severity of defects. Notably, it is demonstrated that the diagnostic performance under additive noise levels can be significantly enhanced by combining different channels of the S-parameters as input to the learning algorithms, as confirmed through the t-distributed stochastic neighbor embedding (t-SNE) dimension reduction visualization of the S-parameter patterns.