pwc-archive/papers-with-abstracts huggingface dataset:
Monotone one-port circuits
Authors: Thomas Chaffey, Rodolphe Sepulchre
Date: 2021-11-30 00:00:00

Maximal monotonicity is explored as a generalization of the linear theory of passivity, aiming at an algorithmic input/output analysis of physical models. The theory is developed for maximal monotone one-port circuits, formed by the series and parallel interconnection of basic elements. These circuits generalize passive LTI transfer functions. Periodic input signals are shown to be mapped to periodic output signals, and these input-output behaviors can be efficiently computed using a maximal monotone splitting algorithm, which decomposes the computation according to the circuit topology. A new splitting algorithm is presented, which applies to any monotone one-port circuit defined as a port interconnection of monotone elements.
Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation
Authors: Changjun He, Xiuqiang He, Hua Geng, Huadong Sun, Shiyun Xu
Date: 2021-11-30 00:00:00

This study examines the transient stability of low-inertia power systems with inverter-based generation (IBG) and proposes a sufficient stability criterion. In low-inertia grids, transient interactions are induced between the electromagnetic dynamics of the IBG and the electromechanical dynamics of the synchronous generator (SG) under a fault. For this, a hybrid IBG-SG system is established and a delta-power-frequency model is developed. Based on this model, new mechanisms of transient instability different from those of conventional power systems from the energy perspective are discovered. First, two loss-of-synchronization (LOS) types are identified based on the relative power imbalance owing to the mismatch between the inertia of the IBG and SG under a fault. Second, the relative angle and frequency will jump at the moment of a fault, thus affecting the system energy. Third, the cosine damping coefficient induces a positive energy dissipation, thereby contributing to the system stability. A unified criterion for identifying the two LOS types is proposed using the energy function method. This criterion is proved to be a sufficient stability condition for addressing the effects of the jumps and cosine damping coefficient on the system stability. The new mechanisms and effectiveness of the criterion are verified based on simulation results.
Passivity of Electrical Transmission Networks modelled using Rectangular and Polar D-Q variables
Authors: Kaustav Dey, A. M. Kulkarni
Date: 2021-11-30 00:00:00

The increasing penetration of converter-interfaced distributed energy resources has brought out the need to develop decentralized criteria that would ensure the small-signal stability of the inter-connected system. Passivity of the D-Q admittance or impedance is a promising candidate for such an approach. It is facilitated by the inherent passivity of the D-Q impedance of an electrical network. However, the passivity conditions are generally restrictive and cannot be complied with in the low frequency range by the D-Q admittance of devices that follow typical power control strategies. However, this does not imply that the system is unstable. Therefore, alternative formulations that use polar variables (magnitude/phase angle of voltages and real/reactive power injection instead of the D-Q components of voltages and currents) are investigated. Passivity properties of the electrical network using these different formulations are brought out in this paper through analytical results and illustrative examples.
Gaussian Mechanisms Against Statistical Inference: Synthesis Tools
Authors: Haleh Hayati, Carlos Murguia, Nathan van de Wouw
Date: 2021-11-30 00:00:00
Tasks: Privacy Preserving

In this manuscript, we provide a set of tools (in terms of semidefinite programs) to synthesize Gaussian mechanisms to maximize privacy of databases. Information about the database is disclosed through queries requested by (potentially) adversarial users. We aim to keep part of the database private (private sensitive information); however, disclosed data could be used to estimate private information. To avoid an accurate estimation by the adversaries, we pass the requested data through distorting (privacy-preserving) mechanisms before transmission and send the distorted data to the user. These mechanisms consist of a coordinate transformation and an additive dependent Gaussian vector. We formulate the synthesis of distorting mechanisms in terms of semidefinite programs in which we seek to minimize the mutual information (our privacy metric) between private data and the disclosed distorted data given a desired distortion level -- how different actual and distorted data are allowed to be.
Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless
Authors: Chung-Ching Lin, Veljko Boljanovic, Han Yan, Erfan Ghaderi, Mohammad Ali Mokri, Jayce Jeron Gaddis, Aditya Wadaskar, Chase Puglisi, Soumen Mohapatra, Qiuyan Xu, Sreeni Poolakkal, Deukhyoun Heo, Subhanshu Gupta, Danijela Cabric
Date: 2021-11-30 00:00:00

The decadal research in integrated true-time-delay arrays have seen organic growth enabling realization of wideband beamformers for large arrays with wide aperture widths. This article introduces highly reconfigurable delay elements implementable at analog or digital baseband that enables multiple SSP functions including wideband beamforming, wideband interference cancellation, and fast beam training. Details of the beam-training algorithm, system design considerations, system architecture and circuits with large delay range-to-resolution ratios are presented leveraging integrated delay compensation techniques. The article lays out the framework for true-time-delay based arrays in next-generation network infrastructure supporting 3D beam training in planar arrays, low latency massive multiple access, and emerging wireless communications standards.
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Authors: Yize Chen, Yuanyuan Shi, Daniel Arnold, Sean Peisert
Date: 2021-11-30 00:00:00
Tasks: Deep Reinforcement Learning

Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast-changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe VoltagE Regulator, which is composed of an RL learner and a specifically designed, computational efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages to be within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.
State-of-Charge Aware EV Charging
Authors: Yize Chen, Baosen Zhang
Date: 2021-11-30 00:00:00
Tasks: Scheduling

Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids. In particular, due to the physical constraints on charging stations' capacity and uncertainty in charging demand, it becomes an emerging challenge to design high performance scheduling algorithms to better serve charging sessions. In this paper, we design a predictive charging controller by actively incorporating each EV's state-of-charge (SOC) information, which has strong effects on the utilization of dispatchable power during peak hours. Simulation results on both synthetic and real-world EV session and charging demand data demonstrate the proposed algorithm's benefits on maximizing charging throughput and achieving higher rate of feasible charging sessions while satisfying battery and station physical constraints at the same time.
Forecasting battery capacity and power degradation with multi-task learning
Authors: Weihan Li, Haotian Zhang, Bruis van Vlijmen, Philipp Dechent, Dirk Uwe Sauer
Date: 2021-11-29 00:00:00
Tasks: Multi-Task Learning

Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.
Unique steady annual cycle in marine ecosystem model simulations
Authors: Markus Pfeil, Thomas Slawig
Date: 2021-11-30 00:00:00

Marine ecosystem models are an important tool to assess the role of the ocean biota in climate change and to identify relevant biogeochemical processes by validating the model outputs against observational data. For the assessment of the marine ecosystem models, the existence and uniqueness of an annual periodic solution (i.e., a steady annual cycle) is desirable. To analyze the uniqueness of a steady annual cycle, we performed a larger number of simulations starting from different initial concentrations for a hierarchy of biogeochemical models with an increasing complexity. The numerical results suggested that the simulations finished always with the same steady annual cycle regardless of the initial concentration. Due to numerical instabilities, some inadmissible approximations of the steady annual cycle, however, occurred in some cases for the three most complex biogeochemical models. Our numerical results indicate a unique steady annual cycle for practical applications.
GEOSCAN: Global Earth Observation using Swarm of Coordinated Autonomous Nanosats
Authors: Changrak Choi, Anthony B. Davis
Date: 2021-11-27 00:00:00
Tasks: Earth Observation

The climate crisis we are facing calls for significant improvements in our understanding of natural phenomena, with clouds being identified as a dominant source of uncertainty. To this end, the emerging field of 3D computed cloud tomography (CCT) aims to more precisely characterize clouds by utilizing multi-dimensional imaging to reconstruct their outer and inner structure. In this paper, we propose a future Earth observation mission concept, driven by the needs of CCT, that operates constellation of NanoSats to provide multi-angular, spectrally-resolved, spatial and temporal scientific measurements of natural atmospheric phenomena. Our proposed mission, GEOSCAN, will on-board active steering capability to rapidly reconfigure networked swarm of autonomous Nanosats to track evolving phenomena of interest, on-demand, in real-time. We present the structure of the GEOSCAN constellation and discuss details of the mission concept from both science and engineering perspectives. On the science side, we outline the types of remote Earth observation measurements that GEOSCAN enables beyond the state-of-the-art, and how such measurements translate to improvements in CCT that can lead to reduction in uncertainty of the global climate models (GCMs). From the engineering side, we investigate feasibility of the concept starting from hardware components of the NanoSat that form the basis of the constellation. In particular, we focus on the active steering capability of the GEOSCAN with algorithmic approaches that enable coordination from new software. We identify technology gaps that need to be bridged and discuss other aspects of the mission that require in-depth analysis to further mature the concept.
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry
Authors: Weiliang Tao, Yan Liu, Zhimin Ma, Wenbin Hu
Date: 2021-11-29 00:00:00
Tasks: Optical Flow Estimation

This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging. In this paper, an ultrasonic PIV (UPIV) system is constructed by integrating a medical ultrasound instrument and an ultrasonic particle image velocimetry algorithm. The medical ultrasound instrument with a phased sensor array is used to acquire acoustic echo signals and generate two-dimensional underwater ultrasound images. Based on the optical flow field, the instantaneous velocity of the particles in water corresponding to the pixels in the ultrasonic particle images is derived from the grayscale change between adjacent images under the L-K local constraint, and finally, the two-dimensional flow field is obtained. Through multiple sets of experiments, the proposed algorithm is verified. The experimental results are compared with those of the conventional cross-correlation algorithms. The results show that the L-K optical flow method can not only obtain the underwater velocity field accurately but also has the advantages of good smoothness and extensive suitability, especially for the flow field measurement in sediment-laden fluid than conventional algorithms.
Analise Demografica e Socioeconomica do Uso e do Acesso a Medicamentos Antidepressivos no Brasil
Authors: Karinna Moura Boaviagem, José Ricardo Bezerra Nogueira
Date: 2021-11-30 00:00:00

Depressive disorders, in addition to causing direct negative impacts on health, are also responsible for imposing substantial costs on society. In relation to the treatment of depression, antidepressants have proven effective, and, to the World Health Organization, access to psychotropic drugs for people with mental illnesses offers a chance of improved health and an opportunity for reengagement in society. The aim of this study is to analyze the use of and access to antidepressants in Brazil, according to macro-regions and to demographic, social and economic conditions of the population, using the National Survey on Access, Use and Promotion of Rational Use of Medicines (PNAUM 2013/2014). The results show that there is a high prevalence of antidepressant use in individuals with depression in Brazil. The main profile of use of these drugs is: female individuals, between 20 and 59 years old, white, from the Southeast region, of the economic class D/E, with a high schooling level, in a marital situation, without health insurance coverage, without limitations derived from depression, and who self-evaluated health as regular.
Inefficient Peace or Preventive War?
Authors: Liqun Liu, Tusi, Wen
Date: 2021-11-30 00:00:00

We study a model of two-player bargaining game in the shadow of a preventive trade war that examines why states deliberately maintain trade barriers in the age of globalization. Globalization can induce substantial power shifts between states, which makes the threat of a preventive trade war salient. In this situation, there may exist "healthy" levels of trade barriers that dampen the war incentives by reducing states' expected payoffs from such a war. Thus, we demonstrate that trade barriers can sometimes serve as breaks and cushions necessary to sustain inefficient yet peaceful economic cooperation between states. We assess the theoretical implications by examining the US-China trade relations since 1972.
Product recalls, market size and innovation in the pharmaceutical industry
Authors: Federico Nutarelli, Massimo Riccaboni, Andrea Morescalchi
Date: 2021-11-30 00:00:00

The idea that research investments respond to market rewards is well established in the literature on markets for innovation (Schmookler, 1966; Acemoglu and Linn, 2004; Bryan and Williams, 2021). Empirical evidence tells us that a change in market size, such as the one measured by demographical shifts, is associated with an increase in the number of new drugs available (Acemoglu and Linn, 2004; Dubois et al., 2015). However, the debate about potential reverse causality is still open (Cerda et al., 2007). In this paper we analyze market size's effect on innovation as measured by active clinical trials. The idea is to exploit product recalls an innovative instrument tested to be sharp, strong, and unexpected. The work analyses the relationship between US market size and innovation at ATC-3 level through an original dataset and the two-step IV methodology proposed by Wooldridge et al. (2019). The results reveal a robust and significantly positive response of number of active trials to market size.
China's Easily Overlooked Monetary Transmission Mechanism: Monetary Reservoir
Authors: Shuguang Xiao, Xinglin Lai, Jiamin Peng
Date: 2021-11-30 00:00:00
Tasks: parameter estimation

The traditional monetary transmission mechanism usually views the equity markets as the monetary reservoir that absorbs over-issued money, but due to China's unique fiscal and financial system, the real estate sector has become an "invisible" non-traditional monetary reservoir in China for many years. First, using data from Chinese housing market and central bank for parameter estimation, we constructs a dynamic general equilibrium model that includes fiscal expansion and financial accelerator to reveal the mechanism of monetary reservoir. An asset can be called a loan product, which worked as financed asset for local fiscal expansion, as long as it satisfies the following three conditions: leveraged trading system, balance commitment payment, and the existence of the utility of local governments. This paper refers to this mechanism as the monetary reservoir that will push up the premium of loan product, form asset bubbles and has a significant impact on the effectiveness of monetary policy. Local governments leverage the sector of the loan product to obtain short-term growth by influencing the balance sheets of financial intermediaries through fiscal financing, expenditure and also investment, but this mechanism undermines the foundations of long-term growth by crowding out human capital and technological accumulation.
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
Authors: Chen Feng, Duolikun Danier, Charlie Tan, Fan Zhang, David Bull
Date: 2021-11-30 00:00:00
Tasks: Decoder, Video Compression

This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the decoder to restore their original format and enhance reconstruction quality. ViSTRA3 has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under the Joint Video Exploration Team Common Test Conditions. Bj{\o}negaard Delta (BD) measurement results show that the proposed framework consistently outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7% based on the assessment of PSNR and VMAF.
A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios
Authors: Christian Padilla, Ramin Hashemi, Nurul Huda Mahmood, Matti Latva-aho
Date: 2021-11-28 00:00:00

Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability $(1-10^{-5})$ and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm.
Mean Square Performance of a family of Adaptive Algorithms for colored noise
Authors: R Sankara Prasad
Date: 2021-11-22 00:00:00

In real-time applications the characteristics and properties of a signal vary inconsistently. So, to maintain the integrity of such signals there is a need for effective adaptive filters. The conventional Least Mean Squared(LMS) algorithm is widely used because of its computational simplicity and ease of implementation. But, its convergence speed rapidly reduces when colored noise is present in the signal. Affine projection(AP) algorithms are used to speed up the convergence but have high computational costs. In this paper, the mean square performance of LMS and AP algorithms is analyzed when subject to white noise and colored noise.
Brauer and partition diagram models for phylogenetic trees and forests
Authors: Andrew Francis, Peter D Jarvis
Date: 2021-11-30 00:00:00

We introduce a correspondence between phylogenetic trees and Brauer diagrams, inspired by links between binary trees and matchings described by Diaconis and Holmes (1998). This correspondence gives rise to a range of semigroup structures on the set of phylogenetic trees, and opens the prospect of many applications. We furthermore extend the Diaconis-Holmes correspondence from binary trees to non-binary trees and to forests, showing for instance that the set of all forests is in bijection with the set of partitions of finite sets.
A Novel Heart Disease Classification Algorithm based on Fourier Transform and Persistent Homology
Authors: Yin Ni, Fupeng Sun, Yihao Luo, Zhengrui Xiang, Huafei Sun
Date: 2021-11-30 00:00:00
Tasks: Classification

Classification and prediction of heart disease is a significant problem to realize medical treatment and life protection. In this paper, persistent homology is involved to analyze electrocardiograms and a novel heart disease classification method is proposed. Each electrocardiogram becomes a point cloud by sliding windows and fast Fourier transform embedding. The obtained point cloud reveals periodicity and stability characteristics of electrocardiograms. By persistent homology, three topological features including normalized persistent entropy, maximum life of time and maximum life of Betty number are extracted. These topological features show the structural differences between different types of electrocardiograms and display encouraging potentiality in classification of heart disease.