pwc-archive/papers-with-abstracts huggingface dataset:
Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'
Authors: Ebba Mark, Ryan Rafaty, Moritz Schwarz
Date: 2022-11-22 00:00:00

The United States, much like other countries around the world, faces significant obstacles to achieving a rapid decarbonization of its economy. Crucially, decarbonization disproportionately affects the communities that have been historically, politically, and socially embedded in the nation's fossil fuel production. However, this effect has rarely been quantified in the literature. Using econometric estimation methods that control for unobserved heterogeneity via two-way fixed effects, spatial effects, heterogeneous time trends, and grouped fixed effects, we demonstrate that mine closures induce a significant and consistent contemporaneous rise in the unemployment rate across US counties. A single mine closure can raise a county's unemployment rate by 0.056 percentage points in a given year; this effect is amplified by a factor of four when spatial econometric dynamics are considered. Although this response in the unemployment rate fades within 2-3 years, it has far-reaching effects in its immediate vicinity. Furthermore, we use cluster analysis to build a novel typology of coal counties based on qualities that are thought to facilitate a successful recovery in the face of local industrial decline. The combined findings of the econometric analysis and typology point to the importance of investing in alternative sectors in places with promising levels of economic diversity, retraining job seekers in places with lower levels of educational attainment, providing relocation (or telecommuting) support in rural areas, and subsidizing childcare and after school programs in places with low female labor force participation due to the gendered division of domestic work.
Financing Urban Infrastructure through Land Leasing: Evidence from Bahir Dar City, Ethiopia
Authors: Wudu Muluneh, Tadesse Amsalu
Date: 2022-11-22 00:00:00
Tasks: Descriptive

The provision of essential urban infrastructure and services for the expanding population is a persistent financial challenge for many of the rapidly expanding cities in developing nations like Ethiopia. The land lease system has received little academic attention as a means of financing urban infrastructure in developing countries. Therefore, the main objective of this study is to assess the contribution of land leasing in financing urban infrastructure and services using evidence from Bahir Dar city, Ethiopia. Primary and secondary data-gathering techniques have been used. Descriptive statistics and qualitative analysis have been adopted. The results show land lease revenue is a dominant source of extra-budgetary revenue for Bahir Dar city. As evidenced by Bahir Dar city, a significant portion of urban infrastructure expenditure is financed by revenues from land leasing. However, despite the critical importance of land lease revenue to investments in urban infrastructure, there is inefficiency in the collection of potential lease revenue due to weak information exchange, inadequate land provision for various uses, lack of transparency in tender committees, and the existence of poor documentation. Our findings suggest that Bahir Dar City needs to manage lease revenue more effectively to increase investment in urban infrastructure while giving due consideration to availing more land for leasing. Keywords: urban, land, revenue, inefficiency, lease, financing, Bahir Dar City
Birth Order and Son Preference to Determine the Children of Shandong Province So Tall
Authors: Zhu Xiaoxu, Fan kecai, He hai, Zhang Ziyu
Date: 2022-11-22 00:00:00

More children in Shandong Province are stunted than any other province in China. Data on more than 122,000 children show a dramatic increase in height advantage with birth order in Shandong relative to the average of other provinces. We suggest that the steep birth order gradient in Shandong is due to a preference for the eldest child, which influences parental fertility decisions and resource allocation to children. We show that within Shandong province, the gradient is steeper for regions and cultures with a high preference for the eldest child. As predicted, this gradient also varies with the sex of the sibling. By back-calculating, the steeper birth order gradient in Shandong Province explains more than half of the average height gap between Shandong Province and the rest of China.
The Impact of US Medical Product Regulatory Complexity on Innovation: Preliminary Evidence of Interdependence, Early Acceleration, and Subsequent Inversion
Authors: Iraj Daizadeh
Date: 2022-11-21 00:00:00

Is the complexity of medical product (medicines and medical devices) regulation impacting innovation in the US? If so, how? Here, this question is investigated as follows: Various novel proxy metrics of regulation (FDA-issued guidelines) and innovation (corresponding FDA-registrations) from 1976-2020 are used to determine interdependence, a concept relying on strong correlation and reciprocal causality (estimated via variable lag transfer entropy and wavelet coherence). Based on this interdependence, a mapping of regulation onto innovation is conducted and finds that regulation seems to accelerate then supports innovation until on or around 2015; at which time, an inverted U-curve emerged. If empirically evidentiary, an important innovation-regulation nexus in the US has been reached; and, as such, stakeholders should (re)consider the complexity of the regulatory landscape to enhance US medical product innovation. Study limitations, extensions, and further thoughts complete this investigation.
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles
Authors: Tomohiro Hirano, Ryo Jinnai, Alexis Akira Toda
Date: 2022-11-23 00:00:00
Tasks: Time Series Analysis, Vocal Bursts Valence Prediction

We present a general equilibrium macro-finance model with a positive feedback loop between capital investment and land price. As leverage is relaxed beyond a critical value, through the financial accelerator, a phase transition occurs from balanced growth where land prices reflect fundamentals (present value of rents) to unbalanced growth where land prices grow faster than rents, generating land price bubbles. Unbalanced growth dynamics and bubbles are associated with financial loosening and technological progress. In an analytically tractable two-sector large open economy model with unique equilibria, financial loosening simultaneously leads to low interest rates, asset overvaluation, and top-end wealth concentration.
Revenue Comparisons of Auctions with Ambiguity Averse Sellers
Authors: Sosung Baik, Sung-Ha Hwang
Date: 2022-11-23 00:00:00

We study the revenue comparison problem of auctions when the seller has a maxmin expected utility preference. The seller holds a set of priors around some reference belief, interpreted as an approximating model of the true probability law or the focal point distribution. We develop a methodology for comparing the revenue performances of auctions: the seller prefers auction X to auction Y if their transfer functions satisfy a weak form of the single-crossing condition. Intuitively, this condition means that a bidder's payment is more negatively associated with the competitor's type in X than in Y. Applying this methodology, we show that when the reference belief is independent and identically distributed (IID) and the bidders are ambiguity neutral, (i) the first-price auction outperforms the second-price and all-pay auctions, and (ii) the second-price and all-pay auctions outperform the war of attrition. Our methodology yields results opposite to those of the Linkage Principle.
Dynamic Acoustic Compensation and Adaptive Focal Training for Personalized Speech Enhancement
Authors: Xiaofeng Ge, Jiangyu Han, Haixin Guan, Yanhua Long
Date: 2022-11-22 00:00:00
Tasks: Speech Enhancement

Recently, more and more personalized speech enhancement systems (PSE) with excellent performance have been proposed. However, two critical issues still limit the performance and generalization ability of the model: 1) Acoustic environment mismatch between the test noisy speech and target speaker enrollment speech; 2) Hard sample mining and learning. In this paper, dynamic acoustic compensation (DAC) is proposed to alleviate the environment mismatch, by intercepting the noise or environmental acoustic segments from noisy speech and mixing it with the clean enrollment speech. To well exploit the hard samples in training data, we propose an adaptive focal training (AFT) strategy by assigning adaptive loss weights to hard and non-hard samples during training. A time-frequency multi-loss training is further introduced to improve and generalize our previous work sDPCCN for PSE. The effectiveness of proposed methods are examined on the DNS4 Challenge dataset. Results show that, the DAC brings large improvements in terms of multiple evaluation metrics, and AFT reduces the hard sample rate significantly and produces obvious MOS score improvement.
Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer
Authors: Juan Han, Liqiao Yang, Kit Ian Kou, Jifei Miao, LiZhi Liu
Date: 2022-11-23 00:00:00
Tasks: Matrix Completion

Matrix completion is one of the most challenging problems in computer vision. Recently, quaternion representations of color images have achieved competitive performance in many fields. Because it treats the color image as a whole, the coupling information between the three channels of the color image is better utilized. Due to this, low-rank quaternion matrix completion (LRQMC) algorithms have gained considerable attention from researchers. In contrast to the traditional quaternion matrix completion algorithms based on quaternion singular value decomposition (QSVD), we propose a novel method based on quaternion Qatar Riyal decomposition (QQR). In the first part of the paper, a novel method for calculating an approximate QSVD based on iterative QQR is proposed (CQSVD-QQR), whose computational complexity is lower than that of QSVD. The largest $r \ (r>0)$ singular values of a given quaternion matrix can be computed by using CQSVD-QQR. Then, we propose a new quaternion matrix completion method based on CQSVD-QQR which combines low-rank and sparse priors of color images. Experimental results on color images and color medical images demonstrate that our model outperforms those state-of-the-art methods.
A Classification Model Utilizing Facial Landmark Tracking to Determine Sentence Types for American Sign Language Recognition
Authors: Janice Nguyen, Y. Curtis Wang
Date: 2022-11-23 00:00:00
Tasks: Landmark Tracking, Sentence, Sign Language Recognition

The deaf and hard of hearing community relies on American Sign Language (ASL) as their primary mode of communication, but communication with others who do not know ASL can be difficult, especially during emergencies where no interpreter is available. As an effort to alleviate this problem, research in computer vision based real time ASL interpreting models is ongoing. However, most of these models are hand shape (gesture) based and lack the integration of facial cues, which are crucial in ASL to convey tone and distinguish sentence types. Thus, the integration of facial cues in computer vision based ASL interpreting models has the potential to improve performance and reliability. In this paper, we introduce a simple, computationally efficient facial expression based classification model that can be used to improve ASL interpreting models. This model utilizes the relative angles of facial landmarks with principal component analysis and a Random Forest Classification tree model to classify frames taken from videos of ASL users signing a complete sentence. The model classifies the frames as statements or assertions. The model was able to achieve an accuracy of 86.5%.
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Authors: Paul Goyes, Edwin Vargas, Claudia Correa, Yu Sun, Ulugbek Kamilov, Brendt Wohlberg, Henry Arguello
Date: 2022-11-21 00:00:00

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation(CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Experimental results on real and synthetic 3D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.
Super-resolution Reconstruction of Single Image for Latent features
Authors: Xin Wang, Jing-Ke Yan, Jing-Ye Cai, Jian-Hua Deng, Qin Qin, Yao Cheng
Date: 2022-11-16 00:00:00
Tasks: Denoising, Diversity, Image Reconstruction, Image Super-Resolution, SSIM, Super-Resolution

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge
Authors: Mohammadali Sharifshazileh, Giacomo Indiveri
Date: 2022-11-23 00:00:00
Tasks: Anomaly Detection

Typical bio-signal processing front-ends are designed to maximize the quality of the recorded data, to allow faithful reproduction of the signal for monitoring and off-line processing. This leads to designs that have relatively large area and power consumption figures. However, wearable devices for always-on biomedical applications do not necessarily require to reproduce highly accurate recordings of bio-signals, provided their end-to-end classification or anomaly detection performance is not compromised. Within this context, we propose an adaptive Asynchronous Delta Modulator (ADM) circuit designed to encode signals with an event-based representation optimally suited for low-power on-line spiking neural network processors. The novel aspect of this work is the adaptive thresholding feature of the ADM, which allows the circuit to modulate and minimize the rate of events produced with the amplitude and noise characteristics of the signal. We describe the circuit's basic mode of operation, we validate it with experimental results, and characterize the new circuits that endow it with its adaptive thresholding properties.
IoT application using a unique rectangular 2.4GHz microstrip patch antenna
Authors: Oahidul Islam, Sumaya Akter Shikha
Date: 2022-11-23 00:00:00

Internet of Things (IoT)-based applications must be integrated with wireless communication technologies for this make application data easily accessible. The wireless communication system is a crucial component of IoT infrastructure, serving as a bridge for bidirectional connection for data gathering and control message delivery. In this article, a modified meander form microstrip patch antenna for IoT applications in 2.4 GHz ISM (Industrial, Scientific, and Medical) band is suggested.The suggested antenna has a gain of up to 4.01 dBi and an efficiency of up to 90% directivity up to 7.14. Theperformance after simulation of this antenna that is combined with a 2.4GHz radio frequency module and IoT sensors. Having small size and high fractional bandwidth which gives a good performance in IoT application. The patch's length width is 30.2 mm, and its length 47mm at a resonance frequency of 2.4 GHz is 38 mm, with a feeding offset position of 6 mm. The substrate has a height of 1.5 mm that appropriate for short-range IoT applications.
Efficient Rigid Body Localization based on Euclidean Distance Matrix Completion for AGV Positioning under Harsh Environment
Authors: Xinyuan An, Xiaowei Cui, Sihao Zhao, Gang Liu, Mingquan Lu
Date: 2022-11-23 00:00:00
Tasks: Matrix Completion, Position, TAG

In real-world applications for automatic guided vehicle (AGV) navigation, the positioning system based on the time-of-flight (TOF) measurements between anchors and tags is confronted with the problem of insufficient measurements caused by blockages to radio signals or lasers, etc. Mounting multiple tags at different positions of the AGV to collect more TOFs is a feasible solution to tackle this difficulty. Vehicle localization by exploiting the measurements between multiple tags and anchors is a rigid body localization (RBL) problem, which estimates both the position and attitude of the vehicle. However, the state-of-the-art solutions to the RBL problem do not deal with missing measurements, and thus will result in degraded localization availability and accuracy in harsh environments. In this paper, different from these existing solutions for RBL, we model this problem as a sensor network localization problem with missing TOFs. To solve this problem, we propose a new efficient RBL solution based on Euclidean distance matrix (EDM) completion, abbreviated as ERBL-EDMC. Firstly, we develop a method to determine the upper and lower bounds of the missing measurements to complete the EDM reliably, using the known relative positions between tags and the statistics of the TOF measurements. Then, based on the completed EDM, the global tag positions are obtained from a coarse estimation followed by a refinement step assisted with inter-tag distances. Finally, the optimal vehicle position and attitude are obtained iteratively based on the estimated tag positions from the previous step. Theoretical analysis and simulation results show that the proposed ERBL-EDMC method effectively solves the RBL problem with incomplete measurements. It obtains the optimal positioning results while maintaining low computational complexity compared with the existing RBL methods based on semi-definite relaxation.
Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios
Authors: Ali Krayani
Date: 2022-11-23 00:00:00
Tasks: Q-Learning

Cognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self-Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum to make the CR learn wrong behaviours and take mistaken actions. The SA module consists of several functionalities that allow the radio to learn a hierarchical representation of the environment and grow its long-term memory incrementally. Therefore, this novel SA module is a way forward towards realizing the original vision of CR (i.e. Mitola's Radio) and AI-enabled radios. Experimental results show that introducing the novel SA functionalities provides the high accuracy of characterizing, detecting, classifying and predicting the jammer's activities and outperforms conventional detection methods such as Energy detectors and advanced classification methods such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE). It also verifies that the proposed approach achieves a higher degree of explainability than deep learning techniques and verifies the capability to learn an efficient strategy to avoid future attacks with higher convergence speed compared to conventional Frequency Hopping and Q-learning.
Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach
Authors: Jiacheng Wang, Hongyang Du, Zengshan Tian, Dusit Niyato, Jiawen Kang, Xuemin, Shen
Date: 2022-11-23 00:00:00
Tasks: Semantic Communication

With the advancement of network and computer technologies, virtual cyberspace keeps evolving, and Metaverse is the main representative. As an irreplaceable technology that supports Metaverse, the sensing information transmission from the physical world to Metaverse is vital. Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse. Leveraging the in-depth understanding of sensing information, we define the semantic bases, through which the semantic encoding of sensing data is achieved for the first time. Consequently, the amount of sensing data that needs to be transmitted is dramatically reduced. Unlike conventional methods that undergo data degradation and require data recovery, our approach achieves the sensing goal without data recovery while maintaining performance. To further improve Metaverse service quality, we introduce contest theory to create an incentive mechanism that motivates users to upload data more frequently. Experimental results show that the average data amount after semantic encoding is reduced to about 27.87% of that before encoding, while ensuring the sensing performance. Additionally, the proposed contest theoretic based incentive mechanism increases the sum of data uploading frequency by 27.47% compared to the uniform award scheme.