pwc-archive/papers-with-abstracts huggingface dataset:
Vibrational Control of Cluster Synchronization: Connections with Deep Brain Stimulation
Authors: Yuzhen Qin, Danielle S. Bassett, Fabio Pasqualetti
Date: 2022-04-01 00:00:00

Cluster synchronization underlies various functions in the brain. Abnormal patterns of cluster synchronization are often associated with neurological disorders. Deep brain stimulation (DBS) is a neurosurgical technique used to treat several brain diseases, which has been observed to regulate neuronal synchrony patterns. Despite its widespread use, the mechanisms of DBS remain largely unknown. In this paper, we hypothesize that DBS plays a role similar to vibrational control since they both highly rely on high-frequency excitation to function. Under the framework of Kuramoto-oscillator networks, we study how vibrations introduced to network connections can stabilize cluster synchronization. We derive some sufficient conditions and also provide an effective approach to design vibrational control. Also, a numerical example is presented to demonstrate our theoretical findings.
Safe Backstepping with Control Barrier Functions
Authors: Andrew J. Taylor, Pio Ong, Tamas G. Molnar, Aaron D. Ames
Date: 2022-04-01 00:00:00

Complex control systems are often described in a layered fashion, represented as higher-order systems where the inputs appear after a chain of integrators. While Control Barrier Functions (CBFs) have proven to be powerful tools for safety-critical controller design of nonlinear systems, their application to higher-order systems adds complexity to the controller synthesis process -- it necessitates dynamically extending the CBF to include higher order terms, which consequently modifies the safe set in complex ways. We propose an alternative approach for addressing safety of higher-order systems through Control Barrier Function Backstepping. Drawing inspiration from the method of Lyapunov backstepping, we provide a constructive framework for synthesizing safety-critical controllers and CBFs for higher-order systems from a top-level dynamics safety specification and controller design. Furthermore, we integrate the proposed method with Lyapunov backstepping, allowing the tasks of stability and safety to be expressed individually but achieved jointly. We demonstrate the efficacy of this approach in simulation.
Raman Signal Extraction from CARS Spectra Using a Learned-Matrix Representation of the Discrete Hilbert Transform
Authors: Charles H. Camp Jr
Date: 2022-04-01 00:00:00
Tasks: Retrieval

Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra due to interference with the nonresonant background (NRB) is vital for quantitative analysis. Popular computational approaches, the Kramers-Kronig relation and the maximum entropy method, have demonstrated success but may generate significant errors due to peaks that extend in any part beyond the recording window. In this work, we present a learned matrix approach to the discrete Hilbert transform that is easy to implement, fast, and dramatically improves accuracy of Raman retrieval using the Kramers-Kronig approach.
Kernel-weighted specification testing under general distributions
Authors: Sid Kankanala, Victoria Zinde-Walsh
Date: 2022-04-04 00:00:00
Tasks: regression

Kernel-weighted test statistics have been widely used in a variety of settings including non-stationary regression, inference on propensity score and panel data models. We develop the limit theory for a kernel-based specification test of a parametric conditional mean when the law of the regressors may not be absolutely continuous to the Lebesgue measure and is contaminated with singular components. This result is of independent interest and may be useful in other applications that utilize kernel smoothed U-statistics. Simulations illustrate the non-trivial impact of the distribution of the conditioning variables on the power properties of the test statistic.
A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions
Authors: Karsten Reichold, Carsten Jentsch
Date: 2022-04-04 00:00:00

Traditional inference in cointegrating regressions requires tuning parameter choices to estimate a long-run variance parameter. Even in case these choices are "optimal", the tests are severely size distorted. We propose a novel self-normalization approach, which leads to a nuisance parameter free limiting distribution without estimating the long-run variance parameter directly. This makes our self-normalized test tuning parameter free and considerably less prone to size distortions at the cost of only small power losses. In combination with an asymptotically justified vector autoregressive sieve bootstrap to construct critical values, the self-normalization approach shows further improvement in small to medium samples when the level of error serial correlation or regressor endogeneity is large. We illustrate the usefulness of the bootstrap-assisted self-normalized test in empirical applications by analyzing the validity of the Fisher effect in Germany and the United States.
Robust Estimation of Conditional Factor Models
Authors: Qihui Chen
Date: 2022-04-02 00:00:00

This paper develops estimation and inference methods for conditional quantile factor models. We first introduce a simple sieve estimation, and establish asymptotic properties of the estimators under large $N$. We then provide a bootstrap procedure for estimating the distributions of the estimators. We also provide two consistent estimators for the number of factors. The methods allow us not only to estimate conditional factor structures of distributions of asset returns utilizing characteristics, but also to conduct robust inference in conditional factor models, which enables us to analyze the cross section of asset returns with heavy tails. We apply the methods to analyze the cross section of individual US stock returns.
Technology and jobs: A systematic literature review
Authors: Kerstin Hötte, Melline Somers, Angelos Theodorakopoulos
Date: 2022-04-04 00:00:00
Tasks: Systematic Literature Review

Does technological change destroy or create jobs? New technologies may replace human workers, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. Furthermore, technology-driven productivity growth may increase disposable income, stimulating a demand-induced expansion of employment. To synthesize the existing knowledge on this question, we systematically review the empirical literature on the past four decades of technological change and its impact on employment, distinguishing between five broad technology categories (ICT, Robots, Innovation, TFP-style, Other). Overall, we find across studies that the labor-displacing effect of technology appears to be more than offset by compensating mechanisms that create or reinstate labor. This holds for most types of technology, suggesting that previous anxieties over widespread technology-driven unemployment lack an empirical base, at least so far. Nevertheless, low-skill, production, and manufacturing workers have been adversely affected by technological change, and effective up- and reskilling strategies should remain at the forefront of policy making along with targeted social support systems.
Fewer, better pathways for all? Intersectional impacts of rural school consolidation in China's minority regions
Authors: Emily Hannum, Fan Wang
Date: 2022-04-04 00:00:00
Tasks: All

Primary school consolidation--the closure of small community schools or their mergers into larger, better-resourced schools--is emerging as a significant policy response to changing demographics in middle income countries with large rural populations. In China, large-scale consolidation took place in the early 21st century. Because officially-recognized minority populations disproportionately reside in rural and remote areas, minority students were among those at elevated risk of experiencing school consolidation. We analyze heterogeneous effects of consolidation on educational attainment and reported national language ability in China by exploiting variations in closure timing across villages and cohorts captured in a 2011 survey of provinces and autonomous regions with substantial minority populations. We consider heterogeneous treatment effects across groups defined at the intersections of minority status, gender, and community ethnic composition and socioeconomic status. Compared to villages with schools, villages whose schools had closed reported that the schools students now attended were better resourced, less likely to offer minority language of instruction, more likely to have Han teachers, farther away, and more likely to require boarding. Much more than Han youth, ethnic minority youth were negatively affected by closure, in terms of its impact on both educational attainment and written Mandarin facility. However, significant penalties accruing to minority youth occurred only in the poorest villages. Penalties were generally heavier for girls, but in the most ethnically segregated minority villages, boys from minority families were highly vulnerable to closure effects on attainment and written Mandarin facility. Results show that intersections of minority status, gender, and community characteristics can delineate significant heterogeneities in policy impacts.
The Price of COVID-19 Risk in a Public University
Authors: Duha Altindag, Samuel Cole, R. Alan Seals Jr
Date: 2022-04-02 00:00:00

We study the allocation of and compensation for occupational COVID-19 risk at Auburn University, a large public university in the U.S. In Spring 2021, approximately half of the face-to-face classes had enrollments above the legal capacity allowed by a public health order, which followed CDC social distancing guidelines. We find lower-ranked graduate student teaching assistants and adjunct instructors were systematically recruited to deliver riskier classes. Using an IV strategy in which teaching risk is shifted by classroom features (geometry and furniture), we show instructors who taught at least one risky class earned $7,400 more than those who did not.
Rural Pension System and Farmers' Participation in Residents' Social Insurance
Authors: Tao Xu
Date: 2022-04-02 00:00:00
Tasks: Management

As the ageing population and childlessness are increasing in rural China, social pensions will become the mainstream choice for farmers, and the level of social pensions must be supported by better social insurance. The paper compares the history of rural pension insurance system, outlines the current situation and problems, analyses China Family Panel Studies data and explores the key factors influencing farmers' participation through an empirical approach. The paper shows that residents' social pension insurance is facing problems in the rural areas such as low level of protection and weak management capacity, which have contributed to the under-insured rate, and finds that there is a significant impact on farmers' participation in insurance from personal characteristics factors such as gender, age, health and (family) financial factors such as savings, personal income, intergenerational mobility of funds. And use of the Internet can help farmers enroll in pension insurance. The paper argues for the need to continue to implement the rural revitalisation strategy, with the government as the lead and the market as the support, in a concerted effort to improve the protection and popularity of rural pension insurance.
Individual Rationality Conditions of Identifying Matching Costs in Transferable Utility Matching Games
Authors: Suguru Otani
Date: 2022-04-01 00:00:00

As the widely applied method for measuring matching assortativeness in a transferable utility matching game, a matching maximum score estimation is proposed by \cite{fox2010qe}. This article reveals that combining unmatched agents, transfers, and individual rationality conditions with sufficiently large penalty terms makes it possible to identify the coefficient parameter of a single common constant, i.e., matching costs in the market.
The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge
Authors: Juan M. Martín-Doñas, Iván G. Torre, Aitor Álvarez, Joaquin Arellano
Date: 2022-04-04 00:00:00
Tasks: Self-Supervised Learning, Speaker Verification

This paper describes our proposed integration system for the spoofing-aware speaker verification challenge. It consists of a robust spoofing-aware verification system that use the speaker verification and antispoofing embeddings extracted from specialized neural networks. First, an integration network, fed with the test utterance's speaker verification and spoofing embeddings, is used to compute a spoof-based score. This score is then linearly combined with the cosine similarity between the speaker verification embeddings from the enrollment and test utterances, thus obtaining the final scoring decision. Moreover, the integration network is trained using a one-class loss function to discriminate between target trials and unauthorized accesses. Our proposed system is evaluated in the ASVspoof19 database, exhibiting competitive performance compared to other integration approaches. In addition, we test, along with our integration approach, state of the art speaker verification and antispoofing systems based on self-supervised learning, yielding high-performance speech biometric systems.
Sparse Tensor-based Point Cloud Attribute Compression
Authors: Jianqiang Wang, Zhan Ma
Date: 2022-04-03 00:00:00
Tasks: Attribute

Recently, numerous learning-based compression methods have been developed with outstanding performance for the coding of the geometry information of point clouds. On the contrary, limited explorations have been devoted to point cloud attribute compression (PCAC). Thus, this study focuses on the PCAC by applying sparse convolution because of its superior efficiency for representing the geometry of unorganized points. The proposed method simply stacks sparse convolutions to construct the variational autoencoder (VAE) framework to compress the color attributes of a given point cloud. To better encode latent elements at the bottleneck, we apply the adaptive entropy model with the joint utilization of hyper prior and autoregressive neighbors to accurately estimate the bit rate. The qualitative measurement of the proposed method already rivals the latest G-PCC (or TMC13) version 14 at a similar bit rate. And, our method shows clear quantitative improvements to G-PCC version 6, and largely outperforms existing learning-based methods, which promises encouraging potentials for learnt PCAC.
Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach
Authors: Yun Chen, Joan Palacios, Nuria González-Prelcic, Takayuki Shimizu, HongSheng Lu
Date: 2022-04-04 00:00:00

High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is required. An additional challenge is the need of path order information to apply the appropriate geometric relationships between the channel path parameters and the vehicle, RSU and scatterers position. In this paper, we propose a low complexity channel estimation strategy of the angle of departure and time difference of arrival based on multidimensional orthogonal matching pursuit. We also design a deep neural network that predicts the order of the channel paths so only the LoS and first order reflections are used for localization. Simulation results obtained with realistic vehicular channels generated by ray tracing show that sub-meter accuracy can be achieved for 50% of the users, without resorting to perfect synchronization assumptions or unfeasible all-digital high resolution MIMO architectures.
Localizing the Vehicle's Antenna: an Open Problem in 6G Network Sensing
Authors: Francesco Linsalata, Dario Tagliaferri, Luca Rinaldi, Lorenzo Bezzetto, Marouan Mizmizi, Davide Scazzoli, Damiano Badini, Christian Mazzucco, Maurizio Magarini, Umberto Spagnolini
Date: 2022-04-03 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management, Position

Millimeter Waves (mmW) and sub-THz frequencies are the candidate bands for the upcoming Sixth Generation (6G) of communication systems. The use of collimated beams at mmW/sub-THz to compensate for the increased path and penetration loss arises the need for a seamless Beam Management (BM), especially for high mobility scenarios such as the Vehicle-to-Infrastructure (V2I) one. Recent research advances in Integrated Sensing and Communication (ISAC) indicate that equipping the network infrastructure, e.g., the Base Station (BS), with either a stand-alone radar or sensing capabilities using optimized waveforms, represents the killer technology to facilitate the BM. However, radio sensing should accurately localize the Vehicular Equipment (VE)'s antenna, which is not guaranteed in general. Differently, employing side information from VE's onboard positioning sensors might overcome this limitation at the price of an increased control signaling between VE and BS. This paper provides a pragmatic comparison between radar-assisted and position-assisted BM for mmW V2I systems in a typical urban scenario in terms of BM training time and beamforming gain loss due to a wrong BM decision. Simulation results, supported by experimental evidence, show that the point target approximation of a traveling VE does not hold in practical V2I scenarios with radar-equipped BS. Therefore, the true antenna position has a residual uncertainty that is independent of radar's resolution and implies 50\,\% more BM training time on average. Moreover, there is not a winning technology for BM between BS-mounted radar and VE's onboard positioning systems. They provide complementary performance, depending on position, although outperforming blind BM techniques compared to conventional blind methods. Thus, we propose to optimally combine radar and positioning information in a multi-technology integrated BM solution.
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Authors: Tae-Kyoung Kim, Yo-Seb Jeon, Jun Li, Nima Tavangaran, H. Vincent Poor
Date: 2022-04-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate. In this paper, we propose a semi-data-aided channel estimator for multiple-input multiple-output communication systems. Our strategy is to leverage reinforcement learning (RL) for selecting reliable detected symbols among the symbols in the first part of transmitted data block. This strategy facilitates an update of the channel estimate before the end of data block transmission and therefore achieves a significant reduction in communication latency compared to conventional data-aided channel estimation approaches. Towards this end, we first define a Markov decision process (MDP) which sequentially decides whether to use each detected symbol as an additional pilot signal. We then develop an RL algorithm to efficiently find the best policy of the MDP based on a Monte Carlo tree search approach. In this algorithm, we exploit the a-posteriori probability for approximating both the optimal future actions and the corresponding state transitions of the MDP and derive a closed-form expression for the best policy. Simulation results demonstrate that the proposed channel estimator effectively mitigates both channel estimation error and detection performance loss caused by insufficient pilot signals.
Waveform Design for Wireless Power Transfer with Power Amplifier and Energy Harvester Non-Linearities
Authors: Yumeng Zhang, Bruno Clerckx
Date: 2022-04-02 00:00:00

Waveform optimization has shown its great potential to boost the performance of far-field wireless power transfer (WPT). Current research has optimized transmit waveform, adaptive to channel state information (CSI), to maximize the harvested power in WPT while accounting for energy harvester (EH)'s non-linearity. However, the existing transmit waveform design disregards the non-linear high power amplifiers (HPA) at the transmitter. Driven by this, this paper optimizes the multi-carrier waveform at the input of HPA to maximize the harvested DC power considering both HPA's and EH's non-linearities. Two optimization models are formulated based on whether the frequencies of the multi-carrier waveform are concentrated within the transmit pass band or not. Analysis and simulations show that, while EH's non-linearity boosts the power harvesting performance, HPA's non-linearity degrades the harvested power. Hence, the optimal waveform shifts from multi-carrier that exploits EH's non-linearity to single-carrier that reduces HPA's detrimental non-linear distortion as the operational regime of WPT becomes more sensitive to HPA's non-linearity and less sensitive to EH's non-linearity (and inversely). Simultaneously, operating towards HPA's non-linear regime by increasing the input signal power benefits the harvested power since HPA's DC power supply is better exploited, whereas the end-to-end power transfer efficiency (PTE) might decrease because of the increasing non-linear degradation. Throughout the simulations, the proposed waveforms show significant gain over those not accounting for HPA's non-linearity, especially in frequency-flat channels. We also compare the two proposed waveforms and show that the severity of HPA's non-linearity dictates which of the two proposed waveforms is more beneficial.
Artificial Intelligence for 6G Networks: Technology Advancement and Standardization
Authors: Muhammad K. Shehzad, Luca Rose, M. Majid Butt, Istvan Z. Kovacs, Mohamad Assaad, Mohsen Guizani
Date: 2022-04-02 00:00:00
Tasks: Management

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.