pwc-archive/papers-with-abstracts huggingface dataset:
Peculiaridades de la Economia islandesa en los albores del siglo XXI
Authors: I. Martin-de-Santos
Date: 2022-08-17 00:00:00

Se repasa brevemente la historia y las finanzas islandesas de manera diacr\'onica. Se presenta a Islandia como basti\'on del estallido de la crisis financiera internacional que comienza a gestarse a principios del siglo XXI y cuyo origen se hace evidente en la fecha simb\'olica del a\~no 2008. Se analizan las razones fundamentales de esta crisis, centrandonos en las particularidades de la estructura econ\'omica islandesa. Se consideran las diferencias y parecidos de esta situaci\'on en relaci\'on a algunos otros pa\'ises en similares circunstancias. Se estudia el caso del banco Icesave. Se considera la repercusi\'on que la crisis experimentada por Islandia tiene en el \'ambito internacional, especialmente en los inversores extranjeros y en los conflictos jur\'idicos surgidos a ra\'iz de las medidas adoptadas por el gobierno island\'es para sacar al pa\'is de la bancarrota. -- Icelandic history and diachronically finances are briefly reviewed. Iceland is presented as a bastion of the outbreak of the global financial crisis begins to take shape in the early twenty-first century and whose origin is evident in the symbolic date of 2008. The main reasons for this crisis are analyzed, focusing on the particularities of Iceland's economic structure. The differences and similarities of this in relation to some other countries in similar circumstances are considered. Bank Icesave case is studied. The impact of the crises experienced by Iceland has in the international arena, especially foreign investors and legal disputes arising out of actions taken by the Icelandic government to pull the country out of bankruptcy is considered.
Marginal stochastic choice
Authors: Yaron Azrieli, John Rehbeck
Date: 2022-08-17 00:00:00

Models of stochastic choice typically use conditional choice probabilities given menus as the primitive for analysis, but in the field these are often hard to observe. Moreover, studying preferences over menus is not possible with this data. We assume that an analyst can observe marginal frequencies of choice and availability, but not conditional choice frequencies, and study the testable implications of some prominent models of stochastic choice for this dataset. We also analyze whether parameters of these models can be identified. Finally, we characterize the marginal distributions that can arise under two-stage models in the spirit of Gul and Pesendorfer [2001] and of kreps [1979] where agents select the menu before choosing an alternative.
An unexpected stochastic dominance: Pareto distributions, dependence, and diversification
Authors: Yuyu Chen, Paul Embrechts, Ruodu Wang
Date: 2022-08-17 00:00:00

We find the perhaps surprising inequality that the weighted average of independent and identically distributed Pareto random variables with infinite mean is larger than one such random variable in the sense of first-order stochastic dominance. This result holds for more general models including super-Pareto distributions, negative dependence, and triggering events, and yields superadditivity of the risk measure Value-at-Risk for these models.
Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment
Authors: Heng Kong, Shuqiang Wang
Date: 2022-08-09 00:00:00

Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD), and the detection of MCI is of great clinical significance. Analyzing the structural brain networks of patients is vital for the recognition of MCI. However, the current studies on structural brain networks are totally dependent on specific toolboxes, which is time-consuming and subjective. Few tools can obtain the structural brain networks from brain diffusion tensor images. In this work, an adversarial learning-based structural brain-network generative model(SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images. By analyzing the differences in structural brain networks across subjects, we found that the structural brain networks of subjects showed a consistent trend from elderly normal controls(NC) to early mild cognitive impairment(EMCI) to late mild cognitive impairment(LMCI): structural connectivity progressed in a progressively weaker direction as the condition worsened. In addition, our proposed model tri-classifies EMCI, LMCI, and NC subjects, achieving a classification accuracy of 83.33\% on the Alzheimer's Disease Neuroimaging Initiative(ADNI) database.
Performance Analysis of RIS-Assisted Large-Scale Wireless Networks Using Stochastic Geometry
Authors: Tianxiong Wang, Gaojie Chen, Mihai-Alin Badiu, Justin P. Coon
Date: 2022-08-18 00:00:00

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.
A Self-Replicating Single-Shape Tiling Technique for the Design of Highly Modular Planar Phased Arrays -- The Case of L-Shaped Rep-Tiles
Authors: Nicola Anselmi, Luca Tosi, Paolo Rocca, Giovanni Toso, Andrea Massa
Date: 2022-08-18 00:00:00

The design of irregular planar phased arrays (PAs) characterized by a highly-modular architecture is addressed. By exploiting the property of self-replicating tile shapes, also known as rep-tiles, the arising array layouts consist of tiles having different sizes, but equal shape, all being generated by assembling a finite number of smaller and congruent copies of a single elementary building-block. Towards this end, a deterministic optimization strategy is used so that the arising rep-tile arrangement of the planar PA is an optimal trade-off between complexity, costs, and fitting of user-defined requirements on the radiated power pattern, while guaranteeing the complete overlay of the array aperture. As a representative instance, such a synthesis method is applied to tile rectangular apertures with L-shaped tromino tiles. A set of representative results, concerned with ideal and real antenna models, as well, is reported for validation purposes, but also to point out the possibility/effectiveness of the proposed approach, unlike state-of-the-art tiling techniques, to reliably handle large-size array apertures.
Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing based MIMO Radars
Authors: Hamza Malik, Jehanzeb Burki, Muhammad Zeeshan Mumtaz
Date: 2022-08-18 00:00:00
Tasks: Autonomous Vehicles, Diversity

Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.
Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems
Authors: Hong Ren, Zhenkun Zhang, Zhangjie Peng, Li Li, Cunhua Pan
Date: 2022-08-18 00:00:00

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This paper proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. Firstly, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV's trajectory, hovering time and the RIS's reflection coefficients. To solve this complex non-convex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV's trajectory and hovering time, in which a minorization-maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV's trajectory, flight time and the RIS's reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.
Precoding for High Throughput Satellite Communication Systems: A Survey
Authors: Malek Khammassi, Abla Kammoun, Mohamed-Slim Alouini
Date: 2022-08-17 00:00:00
Tasks: Survey, Vocal Bursts Intensity Prediction

With the expanding demand for high data rates and extensive coverage, high throughput satellite (HTS) communication systems are emerging as a key technology for future communication generations. However, current frequency bands are increasingly congested. Until the maturity of communication systems to operate on higher bands, the solution is to exploit the already existing frequency bands more efficiently. In this context, precoding emerges as one of the prolific approaches to increasing spectral efficiency. This survey presents an overview and a classification of the recent precoding techniques for HTS communication systems from two main perspectives: 1) a problem formulation perspective and 2) a system design perspective. From a problem formulation point of view, precoding techniques are classified according to the precoding objective, group, and level. From a system design standpoint, precoding is categorized based on the system architecture, the precoding implementation, and the type of the provided service. Further, practical system impairments are discussed, and robust precoding techniques are presented. Finally, future trends in precoding for satellites are addressed to spur further research.
Visible light backscattering with applications to the Internet of Things: State-of-the-art, challenges, and opportunities
Authors: Muhammad Habib Ullah, Giacinto Gelli, Francesco Verde
Date: 2022-08-15 00:00:00

Visible light backscatter (VLB) is an innovative optical transmission paradigm to enable ultra low-power passive communication and localization for the Internet of Things (IoT), by overcoming some of the limitations of conventional (i.e., active) visible light communication (VLC) as well as active/passive radio-frequency (RF) technologies. In this paper, we provide a comprehensive survey of recent research activities in the VLB field. After describing the principles of operation and the main enabling technologies, we classify the existing VLB techniques according to several features, discussing their merits and limitations. Moreover, we introduce the potential applications of VLB techniques in several IoT domains. Finally, we present the main open challenges in this area and delineate a number of future research directions
EEG Machine Learning for Analysis of Mild Traumatic Brain Injury: A survey
Authors: Weiqing Gu, Ryan Chang, Bohan Yang
Date: 2022-08-10 00:00:00
Tasks: Articles, EEG, Electroencephalogram (EEG)

Mild Traumatic Brain Injury (mTBI) is a common brain injury and affects a diverse group of people: soldiers, constructors, athletes, drivers, children, elders, and nearly everyone. Thus, having a well-established, fast, cheap, and accurate classification method is crucial for the well-being of people around the globe. Luckily, using Machine Learning (ML) on electroencephalography (EEG) data shows promising results. This survey analyzed the most cutting-edge articles from 2017 to the present. The articles were searched from the Google Scholar database and went through an elimination process based on our criteria. We reviewed, summarized, and compared the fourteen most cutting-edge machine learning research papers for predicting and classifying mTBI in terms of 1) EEG data types, 2) data preprocessing methods, 3) machine learning feature representations, 4) feature extraction methods, and 5) machine learning classifiers and predictions. The most common EEG data type was human resting-state EEG, with most studies using filters to clean the data. The power spectral, especially alpha and theta power, was the most prevalent feature. The other non-power spectral features, such as entropy, also show their great potential. The Fourier transform is the most common feature extraction method while using neural networks as automatic feature extraction generally returns a high accuracy result. Lastly, Support Vector Machine (SVM) was our survey's most common ML classifier due to its lower computational complexity and solid mathematical theoretical basis. The purpose of this study was to collect and explore a sparsely populated sector of ML, and we hope that our survey has shined some light on the inherent trends, advantages, disadvantages, and preferences of the current state of machine learning-based EEG analysis for mTBI.
Adaptive Partially-Observed Sequential Change Detection and Isolation
Authors: Xinyu Zhao, Jiuyun Hu, Yajun Mei, Hao Yan
Date: 2022-08-09 00:00:00
Tasks: Change Detection, Change Point Detection

High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev-Roberts procedure on the failure mode level and utilize the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.
Estimating Sunlight Using GNSS Signal Strength from Smartphone
Authors: Yuuki Nishiyama, Kosuke Hatai, Kota Tsubouchi, Kaoru Sezaki
Date: 2022-08-09 00:00:00
Tasks: Feature Importance, Time Series Analysis

Excessive or inadequate exposure to ultraviolet light (UV) is harmful to health and causes osteoporosis, colon cancer, and skin cancer. The UV Index, a standard scale of UV light, tends to increase in sunny places and sharply decrease in the shade. A method for distinguishing shady and sunny places would help us to prevent and cure diseases caused by UV. However, the existing methods, such as carrying UV sensors, impose a load on the user, whereas city-level UV forecasts do not have enough granularity for monitoring an individual's UV exposure. This paper proposes a method to detect sunny and shady places by using an off-the-shelf mobile device. The method detects these places by using a characteristic of the GNSS signal strength that is attenuated by objects around the device. As a dataset, we collected GNSS signal data, such as C/N0, satellite ID, satellite angle, and sun angle, together with reference data (i.e., sunny and shady place information every minute) for four days from five locations. Using the dataset, we created twelve classification models by using supervised machine learning methods and evaluated their performance by 4-fold cross-validation. In addition, we investigated the feature importance and the effect of combining features. The performance evaluation showed that our classification model could classify sunny and shady places with more than 97% accuracy in the best case. Moreover, our investigation revealed that the value of C/N0 at a moment and its time series (i.e., C/N0 value before and after the moment) are more important features.
Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
Authors: Radhika Dua, Jiyoung Lee, Joon-Myoung Kwon, Edward Choi
Date: 2022-08-08 00:00:00
Tasks: Deep Learning

Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.
Study of General Robust Subband Adaptive Filtering
Authors: Yi Yu, Hongsen He, Rodrigo C. de Lamare, Badong Chen
Date: 2022-08-04 00:00:00

In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.
Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach
Authors: Harshit Sharma, Yi Xiao, Victoria Tumanova, Asif Salekin
Date: 2022-08-03 00:00:00
Tasks: Multiple Instance Learning

The presented first-of-its-kind study effectively identifies and visualizes the second-by-second pattern differences in the physiological arousal of preschool-age children who do stutter (CWS) and who do not stutter (CWNS) while speaking perceptually fluently in two challenging conditions i.e speaking in stressful situations and narration. The first condition may affect children's speech due to high arousal; the latter introduces linguistic, cognitive, and communicative demands on speakers. We collected physiological parameters data from 70 children in the two target conditions. First, we adopt a novel modality-wise multiple-instance-learning (MI-MIL) approach to classify CWS vs. CWNS in different conditions effectively. The evaluation of this classifier addresses four critical research questions that align with state-of-the-art speech science studies' interests. Later, we leverage SHAP classifier interpretations to visualize the salient, fine-grain, and temporal physiological parameters unique to CWS at the population/group-level and personalized-level. While group-level identification of distinct patterns would enhance our understanding of stuttering etiology and development, the personalized-level identification would enable remote, continuous, and real-time assessment of stuttering children's physiological arousal, which may lead to personalized, just-in-time interventions, resulting in an improvement in speech fluency. The presented MI-MIL approach is novel, generalizable to different domains, and real-time executable. Finally, comprehensive evaluations are done on multiple datasets, presented framework, and several baselines that identified notable insights on CWSs' physiological arousal during speech production.