pwc-archive/papers-with-abstracts huggingface dataset:
{did2s}: Two-Stage Difference-in-Differences
Authors: Kyle Butts, John Gardner
Date: 2021-09-10 00:00:00
Tasks: Vocal Bursts Valence Prediction

Recent work has highlighted the difficulties of estimating difference-in-differences models when treatment timing occurs at different times for different units. This article introduces the R package did2s which implements the estimator introduced in Gardner (2021). The article provides an approachable review of the underlying econometric theory and introduces the syntax for the function did2s. Further, the package introduces a function, event_study, that provides a common syntax for all the modern event-study estimators and plot_event_study to plot the results of each estimator.
The state of health of the Russian population during the pandemic (according to sample surveys)
Authors: Leysan Anvarovna Davletshina, Natalia Alekseevna Sadovnikova, Alexander Valeryevich Bezrukov, Olga Guryevna Lebedinskaya
Date: 2021-09-13 00:00:00

The article analyzes the population's assessment of their own health and attitude to a healthy lifestyle in the context of distribution by age groups. Of particular interest is the presence of transformations taking into account the complex epidemiological situation, the increase in the incidence of coronavirus infection in the population (the peak of the incidence came during the period of selective observation in 2020). The article assesses the closeness of the relationship between the respondents ' belonging to a particular socio-demographic group and their social well-being during the period of self-isolation, quarantine or other restrictions imposed during the coronavirus pandemic in 2020. To solve this problem, the demographic and socio-economic characteristics of respondents are presented, the distribution of responses according to the survey results is estimated and the most significant factor characteristics are selected. The distributions of respondents ' responses are presented for the selected questions. To determine the closeness of the relationship between the respondents ' answers to the question and their gender or age distribution, the coefficients of mutual conjugacy and rank correlation coefficients were calculated and analyzed. The ultimate goal of the analytical component of this study is to determine the social well-being of the Russian population during the pandemic on the basis of sample survey data. As a result of the analysis of changes for the period 2019-2020, the assessment of the closeness of communication revealed the parameters that form differences (gender, wealth, territory of residence).
Willingness to Pay to Prevent Water and Sanitation-Related Diseases Suffered by Slum Dwellers and Beneficiary Households: Evidence from Chittagong, Bangladesh
Authors: Mohammad Nur Nobi
Date: 2021-09-12 00:00:00
Tasks: regression

A significant proportion of slum residents offer vital services that are relied upon by wealthier urban residents. However, the lack of access to clean drinking water and adequate sanitation facilities causes considerable health risks for slum residents, leading to interruption in services and potential transmission of diseases to the beneficiaries. This study explores the willingness of the households benefitting from these services to contribute financially towards the measures that can mitigate the negative externalities of the diseases resulting from poor water and sanitation in slums. This study adopts the Contingent Valuation Method using face-to-face interviews with 260 service-receiving households in Chittagong City Corporation of Bangladesh. Estimating the logistic regression model, the findings indicate that 74 percent of respondents express their willingness to contribute financially towards an improvement of water and sanitation facilities in the slums. Within this group, 16 percent are willing to pay 1.88 USD/month, 18 percent prefer 3.86 USD/year, and 40 percent are willing to contribute a lump sum of 3.92 USD. The empirical findings suggest a significant influence of gender, college, and housemaids working hours in the households on respondents willingness to pay. For example, female respondents with a college degree and households with longer working hours of housemaids are more likely to contribute towards the improvement of the water and sanitation facilities in slums. Though the findings are statistically significant at a 5% level across different estimated models, the regression model exhibits a low goodness of fit.
Cost-Benefit Analysis of Kaptai Dam in Rangamati District, Chittagong, Bangladesh
Authors: Mohammad Nur Nobi
Date: 2021-09-12 00:00:00

This study aims to assess the net benefit of the kaptai dam on the Karnafuli river in Kaptai, Chittagong, Bangladesh. Kaptai Dam, the only hydroelectricity power source in Bangladesh, provides only 5% electricity demand of Bangladesh. The Dam is located on the Karnafuli River at Kaptai in Rangamati District, 65 km upstream from Chittagong. It is an earth-fill or embankment dam with a reservoir with a water storage capacity of 11,000 skm. Though the Dam's primary purpose is to generate electricity, it became a reservoir of water used for fishing and tourism. To find the net benefit value and estimate the environmental costs and benefits, we considered the environmental net benefit from 1962 to 1997. We identify the costs of Kaptai Dam, including its establishment cost, operational costs, the costs of lives that have been lost due to conflicts, and environmental costs, including loss of biodiversity, loss of land uses, and loss of human displacements. Also, we assess the benefits of electricity production, earnings from fisheries production, and gain from tourism to Kaptai Lake. The findings show that the Dam contributes tremendous value to Bangladesh. As a source of hydroelectricity, the Kaptai Dam is a source of clean energy, and its value might have been worthy of this Dam produced a significant portion of the electricity. However, providing less than 5% of the national demand for electricity followed by various external and sensitive costs, the Dam hardly contributes to the Bangladesh economy. This study thus recommends that Bangladesh should look for other sources of clean energy that have no chances of eco-political conflicts.
Estimating the Environmental Cost of Shrimp Farming in Coastal Areas of Chittagong and Coxs bazaar in Bangladesh
Authors: Mohammad Nur Nobi, Dr. A N M Moinul Islam
Date: 2021-09-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

During the last three decades, shrimp has remained one of the major export items in Bangladesh. It contributes to the development of this country by enhancing export earnings and promoting employment. However, coastal wetlands and agricultural lands are used for shrimp culture, which reduces agricultural opportunity and peasants income, and destroys the mangroves and coastal eco-system. These are the external environmental costs that are not reflected in farmers price and output decisions. This study has aimed to estimate those external environmental costs through the contingent valuation method. The calculated environmental cost of shrimp farming is USD 13.66 per acre per year. Findings suggest that current shrimp production and shrimp price will no longer be optimal once the external costs are internalized. Thus alternative policy recommendations have been proposed so that shrimp farming becomes a sustainable and equitable means of aquaculture.
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
Authors: Felix Meissen, Georgios Kaissis, Daniel Rueckert
Date: 2021-09-13 00:00:00
Tasks: Anomaly Detection, Anomaly Segmentation, Segmentation

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions.
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
Authors: Hong Wang, Yuexiang Li, Haimiao Zhang, Jiawei Chen, Kai Ma, Deyu Meng, Yefeng Zheng
Date: 2021-09-11 00:00:00
Tasks: Metal Artifact Reduction

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in \url{https://github.com/hongwang01/InDuDoNet}.%method on the tasks of MAR and downstream multi-class pelvic fracture segmentation.
Beamformer Design and Optimization for Full-Duplex Joint Communication and Sensing at mm-Waves
Authors: Carlos Baquero Barneto, Taneli Riihonen, Sahan Damith Liyanaarachchi, Mikko Heino, Nuria González-Prelcic, Mikko Valkama
Date: 2021-09-13 00:00:00

In this article, we study the joint communication and sensing (JCAS) paradigm in the context of millimeter-wave (mm-wave) mobile communication networks. We specifically address the JCAS challenges stemming from the full-duplex operation and from the co-existence of multiple simultaneous beams for communications and sensing purposes. To this end, we first formulate and solve beamforming optimization problems for hybrid beamforming based multiuser multiple-input and multiple-output JCAS systems. The cost function to be maximized is the beamformed power at the sensing direction while constraining the beamformed power at the communications directions, suppressing interuser interference and cancelling full-duplexing related self-interference (SI). We then also propose new transmitter and receiver beamforming solutions for purely analog beamforming based JCAS systems that maximize the beamforming gain at the sensing direction while controlling the beamformed power at the communications direction(s), cancelling the SI as well as eliminating the potential reflection from the communication direction and optimizing the combined radar pattern (CRP). Both closed-form and numerical optimization based formulations are provided. We analyze and evaluate the performance through extensive simulations, and show that substantial gains and benefits in terms of radar transmit gain, CRP, and SI suppression can be achieved with the proposed beamforming methods.
List-encoding CCDM: A Nonlinearity-tolerant Shaper Aided by Energy Dispersion Index
Authors: Kaiquan Wu, Gabriele Liga, Alireza Sheikh, Yunus Can Gültekin, Frans M. J. Willems, Alex Alvarado
Date: 2021-09-13 00:00:00

Recently, a metric called energy dispersion index (EDI) was proposed to indicate the nonlinear interference (NLI) induced by correlated symbols during optical transmission. In this paper, we propose a new shaper architecture to decrease the EDI of transmitted symbols and thus, increase the signal-to-noise ratio (SNR). We call this shaper the list-encoding constant-composition distribution matcher (L-CCDM). L-CCDM consists of an additional EDI selecting module, which is compatible with standard probabilistic amplitude shaping (PAS) architecture. Numerical results obtained from a multi-span multi-channel system show that when compared to standard CCDM with 256-ary quadrature amplitude modulation (256QAM), the proposed architecture offers an effective SNR gain of 0.35 dB, an achievable information rate gain of 0.22 bit/4D-symbol, or equivalently an 8% reach extension.
Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection
Authors: Bingzhao Zhu, Uisub Shin, Mahsa Shoaran
Date: 2021-09-13 00:00:00

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures.
Error performance analysis of different modulations over TWDP fading channel
Authors: Almir Maric, Pamela Njemcevic
Date: 2021-09-13 00:00:00
Tasks: Diversity

Two-wave with diffuse power (TWDP) is one of the most promising distribution for description of a small-scale fading in the emerging mmWave band. However, traditional error performance analysis in these channels faces two fundamental issues. It is mostly based on conventional TWDP parameterization which is not in accordance with the model's underlying physical mechanisms and which hinders accurate observation of the impact of a model parameters on a system's performance metrics. In addition, the existing average bit/symbol error probability (ABEP/ASEP) expressions for most modulations and diversity schemes are available as approximations, which are accurate only for specific combinations of TWDP parameters. Accordingly, in this paper, the exact ASEP expressions are derived for M-ary rectangular quadrature amplitude modulation (RQAM) with coherent detection and for M-ary DPSK modulation, and are given in terms of physically justified parameters. Besides, in order to relax computational complexity of proposed exact ASEPs in high signal-to-noise ratio (SNR) region, their asymptotic counterparts are derived as the simple closed-form expressions, matching the exact ones for SNR>30dB. Results are verified by Monte-Carlo simulation.
Performance of UAV assisted Multiuser Terrestrial-Satellite Communication System over Mixed FSO/RF Channels
Authors: Praveen Kumar Singya, Mohamed-Slim Alouini
Date: 2021-09-13 00:00:00
Tasks: Scheduling

In this work, performance of a multi-antenna multiuser unmanned aerial vehicle (UAV) assisted terrestrial-satellite communication system over mixed free space optics (FSO)/ radio frequency (RF) channels is analyzed. Downlink transmission from the satellite to the UAV is completed through FSO link which follows Gamma-Gamma distribution with pointing error impairments. Both the heterodyne detection and intensity modulation direct detection techniques are considered at the FSO receiver. To avail the antenna diversity, multiple transmit antennas are considered at the UAV. Selective decode-and-forward scheme is assumed at the UAV and opportunistic user scheduling is performed while considering the practical constraints of outdated channel state information (CSI) during the user selection and transmission phase. The RF links are assumed to follow Nakagami-m distribution due to its versatile nature. In this context, for the performance analysis, analytical expressions of outage probability, asymptotic outage probability, ergodic capacity, effective capacity, and generalized average symbol-error-rate expressions of various quadrature amplitude modulation (QAM) schemes such as hexagonal-QAM, cross-QAM, and rectangular QAM are derived. A comparison of various modulation schemes is presented. Further, the impact of pointing error, number of antennas, delay constraint, fading severity, and imperfect CSI are highlighted on the system performance. Finally, all the analytical results are verified through the Monte-Carlo simulations.
Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning
Authors: Tianyi Liu, Andreas M. Tillmann, Yang Yang, Yonina C. Eldar, Marius Pesavento
Date: 2021-09-13 00:00:00
Tasks: Dictionary Learning, Retrieval

Phase retrieval aims at reconstructing unknown signals from magnitude measurements of linear mixtures. In this paper, we consider the phase retrieval with dictionary learning problem, which includes an additional prior information that the measured signal admits a sparse representation over an unknown dictionary. The task is to jointly estimate the dictionary and the sparse representation from magnitude-only measurements. To this end, we study two complementary formulations and develop efficient parallel algorithms by extending the successive convex approximation framework using a smooth majorization. The first algorithm is termed compact-SCAphase and is preferable in the case of less diverse mixture models. It employs a compact formulation that avoids the use of auxiliary variables. The proposed algorithm is highly scalable and has reduced parameter tuning cost. The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models. It also renders simple incorporation of additional side constraints. The performance of both methods is evaluated when applied to blind sparse channel estimation from subband magnitude measurements in a multi-antenna random access network. Simulation results demonstrate the efficiency of the proposed techniques compared to state-of-the-art methods.
Multi-Antenna Systems by Transmissive Reconfigurable Meta-Surface
Authors: Zhendong Li, Wen Chen, Chong He, Xudong Bai, Jianmin Lu
Date: 2021-09-12 00:00:00

Reconfigurable meta-surface (RMS) is proposed as a very promising and novel technology, which is composed of a large number of low-cost passive elements, and can achieve passive beamforming by controlling the amplitude and phase of incident electromagnetic (EM) waves. Therefore, in order to solve the challenges of high power consumption and high cost of existing base stations (BSs), we propose a low-cost and low-power consumption transmissive RMS multi-antenna system in this paper. Specifically, we first provide an overview of the transmissive RMS multi-antenna system, including its advantages, network architecture, transmission mechanism, modulation principle, channel model and channel estimation technique. Then, we address transceiver design and optimization for downlink (DL) and uplink (UL), and some numerical results are also given to verify the effectiveness of the proposed algorithm. Finally, several potential research directions of the transmissive RMS multi-antenna system are given to inspire further investigation in future work.
HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation
Authors: Chia-Hsiang Lin, Yi-Chun Hung, Feng-Yu Wang, Shang-Hua Yang
Date: 2021-09-12 00:00:00
Tasks: blind source separation, Hyperspectral Unmixing, Vocal Bursts Type Prediction

Terahertz (THz) technology has been a great candidate for applications, including pharmaceutic analysis, chemical identification, and remote sensing and imaging due to its non-invasive and non-destructive properties. Among those applications, penetrating-type hyperspectral THz signals, which provide crucial material information, normally involve a noisy, complex mixture system. Additionally, the measured THz signals could be ill-conditioned due to the overlap of the material absorption peak in the measured bands. To address those issues, we consider penetrating-type signal mixtures and aim to develop a blind hyperspectral unmixing (HU) method without requiring any information from a prebuilt database. The proposed HYperspectral Penetrating-type Ellipsoidal ReconstructION (HYPERION) algorithm is unsupervised, not relying on collecting extensive data or sophisticated model training. Instead, it is developed based on elegant ellipsoidal geometry under a very mild requirement on data purity, whose excellent efficacy is experimentally demonstrated.
Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors
Authors: Asim Ihsan, Wen Chen, Wali Ullah Khan, Qingqing Wu, Kunlun Wang
Date: 2021-09-11 00:00:00

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano's formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.
An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking
Authors: Thomas Kropfreiter, Florian Meyer, Franz Hlawatsch
Date: 2021-09-11 00:00:00
Tasks: Object

We propose an efficient random finite set (RFS) based algorithm for multiobject tracking in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.