pwc-archive/papers-with-abstracts huggingface dataset:
Development of a multi-timescale method for classifying hybrid energy storage systems in grid applications
Authors: C. Zugschwert, S. Göschl, F. Martin Ibanez, K. Pettinger
Date: 2022-03-15 00:00:00
Tasks: Clustering, energy management, Management

An extended use of renewable energies and a trend towards increasing energy consumption lead to challenges such as temporal and spatial decoupling of energy generation and consumption. This work evaluates the possible applications and advantages of hybrid energy storage systems compared to conventional, single energy storage applications. In a mathematical approach, evaluation criteria such as frequency, probability of power transients, as well as absolute power peaks are combined to identify suitable thresholds for energy management systems on a multi-timescale basis. With experimental load profiles from a municipal application, an airport, and an industrial application, four categories, clustering similar roles of the VRFB and the SC, are developed.
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant
Authors: Zhong Guo, Austin R. Coffman, Prabir Barooah
Date: 2022-03-14 00:00:00
Tasks: Model Predictive Control, Q-Learning, reinforcement-learning, Reinforcement Learning (RL), Scheduling

District cooling energy plants (DCEPs) consisting of chillers, cooling towers, and thermal energy storage (TES) systems consume a considerable amount of electricity. Optimizing the scheduling of the TES and chillers to take advantage of time-varying electricity price is a challenging optimal control problem. The classical method, model predictive control (MPC), requires solving a high dimensional mixed-integer nonlinear program (MINLP) because of the on/off actuation of the chillers and charging/discharging of TES, which are computationally challenging. RL is an attractive alternative to MPC: the real time control computation is a low-dimensional optimization problem that can be easily solved. However, the performance of an RL controller depends on many design choices. In this paper, we propose a Q-learning based reinforcement learning (RL) controller for this problem. Numerical simulation results show that the proposed RL controller is able to reduce energy cost over a rule-based baseline controller by approximately 8%, comparable to savings reported in the literature with MPC for similar DCEPs. We describe the design choices in the RL controller, including basis functions, reward function shaping, and learning algorithm parameters. Compared to existing work on RL for DCEPs, the proposed controller is designed for continuous state and actions spaces.
Bilinear Systems Induced by Proper Lie Group Actions
Authors: Gong Cheng, Wei zhang, Jr-Shin Li
Date: 2022-03-14 00:00:00

In the study of induced bilinear systems, the classical Lie algebra rank condition (LARC) is known to be impractical since it requires computing the rank everywhere. On the other hand, the transitive Lie algebra condition, while more commonly used, relies on the classification of transitive Lie algebras, which is elusive except for few simple geometric objects such as spheres. We prove in this note that for bilinear systems induced by proper Lie group actions, the underlying Lie algebra is closely related to the orbits of the group action. Knowing the pattern of the Lie algebra rank over the manifold, we show that the LARC can be relaxed so that it suffices to check the rank at an arbitrary single point. Moreover, it removes the necessity for classifying transitive Lie algebras. Finally, this relaxed rank condition also leads to a characterization of controllable submanifolds by orbits.
$\mathcal{H}_{\infty}$-optimal Interval Observer Synthesis for Uncertain Nonlinear Dynamical Systems via Mixed-Monotone Decompositions
Authors: Mohammad Khajenejad, Sze Zheng Yong
Date: 2022-03-14 00:00:00

This paper introduces a novel $\mathcal{H}_{\infty}$-optimal interval observer synthesis for bounded-error/uncertain locally Lipschitz nonlinear continuous-time (CT) and discrete-time (DT) systems with noisy nonlinear observations. Specifically, using mixed-monotone decompositions, the proposed observer is correct by construction, i.e., the interval estimates readily frame the true states without additional constraints or procedures. In addition, we provide sufficient conditions for input-to-state (ISS) stability of the proposed observer and for minimizing the $\mathcal{H}_{\infty}$ gain of the framer error system in the form of semi-definite programs (SDPs) with Linear Matrix Inequalities (LMIs) constraints. Finally, we compare the performance of the proposed $\mathcal{H}_{\infty}$-optimal interval observers with some benchmark CT and DT interval observers.
Phases of MANES: Multi-Asset Non-Equilibrium Skew Model of a Strongly Non-Linear Market with Phase Transitions
Authors: Igor Halperin
Date: 2022-03-14 00:00:00

This paper presents an analytically tractable and practically-oriented model of non-linear dynamics of a multi-asset market in the limit of a large number of assets. The asset price dynamics are driven by money flows into the market from external investors, and their price impact. This leads to a model of a market as an ensemble of interacting non-linear oscillators with the Langevin dynamics. In a homogeneous portfolio approximation, the mean field treatment of the resulting Langevin dynamics produces the McKean-Vlasov equation as a dynamic equation for market returns. Due to the strong non-linearity of the McKean-Vlasov equation, the resulting dynamics give rise to ergodicity breaking and first- or second-order phase transitions under variations of model parameters. Using a tractable potential of the Non-Equilibrium Skew (NES) model previously suggested by the author for a single-stock case, the new Multi-Asset NES (MANES) model enables an analytically tractable framework for a multi-asset market. The equilibrium expected market log-return is obtained as a self-consistent mean field of the McKean-Vlasov equation, and derived in closed form in terms of parameters that are inferred from market prices of S&P 500 index options. The model is able to accurately fit the market data for either a benign or distressed market environments, while using only a single volatility parameter.
Quantum effects in an expanded Black-Scholes model
Authors: Anantya Bhatnagar, Dimitri D. Vvedensky
Date: 2022-03-15 00:00:00

The limitations of the classical Black-Scholes model are examined by comparing calculated and actual historical prices of European call options on stocks from several sectors of the S&P 500. Persistent differences between the two prices point to an expanded model proposed by Segal and Segal (1998) in which information not simultaneously observable or actionable with public information can be represented by an additional pseudo-Wiener process. A real linear combination of the original and added processes leads to a commutation relation analogous to that between a boson field and its canonical momentum in quantum field theory. The resulting pricing formula for a European call option replaces the classical volatility with the norm of a complex quantity, whose imaginary part is shown to compensate for the disparity between prices obtained from the classical Black-Scholes model and actual prices of the test call options. This provides market evidence for the influence of a non-classical process on the price of a security based on non-commuting operators.
Pairwise Valid Instruments
Authors: Zhenting Sun, Kaspar Wüthrich
Date: 2022-03-15 00:00:00
Tasks: valid

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and provides estimates of the LATEs for all remaining pairs, which can be aggregated into a single parameter of interest using researcher-specified weights. We show that the proposed VSIV estimators are asymptotically normal under weak conditions and remove or reduce the asymptotic bias relative to standard LATE estimators (that is, LATE estimators that do not use testable implications to remove invalid variation). We evaluate the finite sample properties of VSIV estimation in application-based simulations and apply our method to estimate the returns to college education using parental education as an instrument.
Non-Existent Moments of Earnings Growth
Authors: Silvia Sarpietro, Yuya Sasaki, Yulong Wang
Date: 2022-03-15 00:00:00

The literature often employs moment-based earnings risk measures like variance, skewness, and kurtosis. However, under heavy-tailed distributions, these moments may not exist in the population. Our empirical analysis reveals that population kurtosis, skewness, and variance often do not exist for the conditional distribution of earnings growth. This challenges moment-based analyses. We propose robust conditional Pareto exponents as novel earnings risk measures, developing estimation and inference methods. Using the UK New Earnings Survey Panel Dataset (NESPD) and US Panel Study of Income Dynamics (PSID), we find: 1) Moments often fail to exist; 2) Earnings risk increases over the life cycle; 3) Job stayers face higher earnings risk; 4) These patterns persist during the 2007--2008 recession and the 2015--2016 positive growth period.
Gender differences of the effect of vaccination on perceptions of COVID-19 and mental health in Japan
Authors: Eiji Yamamura, Youki Kosaka, Yoshiro Tsutsui, Fumio Ohtake
Date: 2022-03-15 00:00:00

Vaccination has been promoted to mitigate the spread of the coronavirus disease 2019 (COVID-19). Vaccination is expected to reduce the probability of and alleviate the seriousness of COVID-19 infection. Accordingly, this might significantly change an individuals subjective well-being and mental health. However, it is unknown how vaccinated people perceive the effectiveness of COVID-19 and how their subjective well-being and mental health change after vaccination. We thus observed the same individuals on a monthly basis from March 2020 to September 2021 in all parts of Japan. Then, large sample panel data (N=54,007) were independently constructed. Using the data, we compared the individuals perceptions of COVID-19, subjective well-being, and mental health before and after vaccination. Furthermore, we compared the effect of vaccination on the perceptions of COVID-19 and mental health for females and males. We used the fixed-effects model to control for individual time-invariant characteristics. The major findings were as follows: First, the vaccinated people perceived the probability of getting infected and the seriousness of COVID-19 to be lower than before vaccination. This was observed not only when we used the whole sample, but also when we used sub-samples. Second, using the whole sample, subjective well-being and mental health improved. The same results were also observed using the sub-sample of females, whereas the improvements were not observed using a sub-sample of males.
Effect of the COVID-19 vaccine on preventive behaviors: Evidence from Japan
Authors: Eiji Yamamura, Youki Koska, Yoshiro Tsutsui, Fumio Ohtake
Date: 2022-03-15 00:00:00
Tasks: Decision Making

Vaccination against the coronavirus disease 2019 (COVID-19) is a key measure to reduce the probability of getting infected with the disease. Accordingly, this might significantly change an individuals perception and decision-making in daily life. For instance, it is predicted that with widespread vaccination, individuals will exhibit less rigid preventive behaviors, such as staying at home, frequently washing hands, and wearing a mask. We observed the same individuals on a monthly basis for 18 months, from March 2020 (the early stage of the COVID-19 pandemic) to September 2021, in Japan to independently construct large sample panel data (N=54,007). Using the data, we compare the individuals preventive behaviors before and after they got vaccinated; additionally, we compare their behaviors with those individuals who did not get vaccinated. Furthermore, we compare the effect of vaccination on the individuals less than or equal to 40 years of age with those greater than 40 years old. The major findings determined after controlling for individual characteristics using the fixed effects model and various factors are as follows. First, as opposed to the prediction, based on the whole sample, the vaccinated people were observed to stay at home and did not change their habits of frequently washing hands and wearing a mask. Second, using the sub-sample of individuals aged equal to or below 40, we find that the vaccinated people are more likely to go out. Third, the results obtained using a sample comprising people aged over 40 are similar to those obtained using the whole sample. Preventive behaviors are affecting oneself and generating externalities on others during this pandemic. Informal social norms motivate people to increase or maintain preventive behaviors even after being vaccinated in societies where such behaviors are not enforced.
Investigating self-supervised learning for speech enhancement and separation
Authors: Zili Huang, Shinji Watanabe, Shu-wen Yang, Paola Garcia, Sanjeev Khudanpur
Date: 2022-03-15 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement, Speech Separation

Speech enhancement and separation are two fundamental tasks for robust speech processing. Speech enhancement suppresses background noise while speech separation extracts target speech from interfering speakers. Despite a great number of supervised learning-based enhancement and separation methods having been proposed and achieving good performance, studies on applying self-supervised learning (SSL) to enhancement and separation are limited. In this paper, we evaluate 13 SSL upstream methods on speech enhancement and separation downstream tasks. Our experimental results on Voicebank-DEMAND and Libri2Mix show that some SSL representations consistently outperform baseline features including the short-time Fourier transform (STFT) magnitude and log Mel filterbank (FBANK). Furthermore, we analyze the factors that make existing SSL frameworks difficult to apply to speech enhancement and separation and discuss the representation properties desired for both tasks. Our study is included as the official speech enhancement and separation downstreams for SUPERB.
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network
Authors: Zehua Zhang, Lu Zhang, Xuyi Zhuang, Yukun Qian, Heng Li, Mingjiang Wang
Date: 2022-03-15 00:00:00
Tasks: Denoising, Speech Denoising, Speech Enhancement

In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data and computational complexity, real-time enhancement of full-band (48 kHz) speech signals is still very challenging. Because of the low energy of spectral information in the high-frequency part, it is more difficult to directly model and enhance the full-band spectrum using neural networks. To solve this problem, this paper proposes a two-stage real-time speech enhancement model with extraction-interpolation mechanism for a full-band signal. The 48 kHz full-band time-domain signal is divided into three sub-channels by extracting, and a two-stage processing scheme of `masking + compensation' is proposed to enhance the signal in the complex domain. After the two-stage enhancement, the enhanced full-band speech signal is restored by interval interpolation. In the subjective listening and word accuracy test, our proposed model achieves superior performance and outperforms the baseline model overall by 0.59 MOS and 4.0% WAcc for the non-personalized speech denoising task.
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting
Authors: Viktoria Heimann, Nils Genser, André Kaup
Date: 2022-03-15 00:00:00
Tasks: Super-Resolution

Many applications in image processing require resampling of arbitrarily located samples onto regular grid positions. This is important in frame-rate up-conversion, super-resolution, and image warping among others. A state-of-the-art high quality model-based resampling technique is frequency-selective mesh-to-grid resampling which requires pre-estimation of key points. In this paper, we propose a new key point agnostic frequency-selective mesh-to-grid resampling that does not depend on pre-estimated key points. Hence, the number of data points that are included is reduced drastically and the run time decreases significantly. To compensate for the key points, a spectral weighting function is introduced that models the optical transfer function in order to favor low frequencies more than high ones. Thereby, resampling artefacts like ringing are supressed reliably and the resampling quality increases. On average, the new AFSMR is conceptually simpler and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid resampling while being approximately 14.5 times faster.
Training Generative Adversarial Networks for Optical Property Mapping using Synthetic Image Data
Authors: Ahmed Osman, Jane Crowley, George Gordon
Date: 2022-03-15 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network

We demonstrate training of a Generative Adversarial Network (GAN) for prediction of optical property maps (scattering and absorption) using spatial frequency domain imaging (SFDI) image data sets generated synthetically with free open-source 3D modelling and rendering software, Blender. The flexibility of Blender is exploited to simulate 3 models with real-life relevance to clinical SFDI of diseased tissue: flat samples, flat samples with spheroidal tumours and cylindrical samples with spheroidal tumours representing imaging inside a tubular organ e.g. the gastro-intestinal tract. In all 3 scenarios we show the GAN provides accurate reconstruction of optical properties from single SFDI images with mean normalised error ranging from 1-1.2% for absorption and 0.7-1.2% for scattering, resulting in visually improved contrast for tumour spheroid structures. This compares favourably with 25% absorption error and 10% scattering error achieved using GANs on experimental SFDI data. However, some of this improvement is due to lower noise and availability of perfect ground truths so we therefore cross-validate our synthetically-trained GAN with a GAN trained on experimental data and observe visually accurate results with error of <40% for absorption and <25% for scattering, due largely to the presence of spatial frequency mismatch artefacts. Our synthetically trained GAN is therefore highly relevant to real experimental samples, but provides significant added benefits of large training datasets, perfect ground-truths and the ability to test realistic imaging geometries, e.g. inside cylinders, for which no conventional single-shot demodulation algorithms exist. In future we expect that application of techniques such as domain adaptation or training on hybrid real-synthetic datasets will create a powerful tool for fast, accurate production of optical property maps from real clinical imaging systems.
Indoor Propagation Measurements with Sekisui Transparent Reflectors at 28/39/120/144 GHz
Authors: Chethan K. Anjinappa, Ashwini P. Ganesh, Ozgur Ozdemir, Kris Ridenour, Wahab Khawaja, Ismail Guvenc, Hiroyuki Nomoto, Yasuaki Ide
Date: 2022-03-15 00:00:00

One of the critical challenges of operating with the terahertz or millimeter-wave wireless networks is the necessity of at least a strong non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies have shown that an economical way of enhancing/improving these NLoS links is by using passive metallic reflectors that provide strong reflections. However, despite its inherent radio advantage, metals can dramatically influence the landscape's appearance - especially the indoor environment. A conceptual view of escaping this is by using transparent reflectors. In this work, for the very first time, we evaluate the wireless propagation characteristics of passive transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and 144 GHz bands. In particular, we investigate the penetration loss and the reflection characteristics at different frequencies and compare them against the other common indoor materials such as ceiling tile, clear glass, drywall, plywood, and metal. The measurement results suggest that the transparent reflector, apart from an obvious advantage of transparency, has a higher penetration loss than the common indoor materials (excluding metal) and performs similarly to metal in terms of reflection. Our experimental results directly translate to better reflection performance and preserving the radio waves within the environment than common indoor materials, with potential applications in controlled wireless communication.
Smoothed Phase-Coded FMCW: Waveform Properties and Transceiver Architecture
Authors: Utku Kumbul, Nikita Petrov, Cicero S. Vaucher, Alexander Yarovoy
Date: 2022-03-14 00:00:00

Smoothed phase-coded frequency modulated continuous waveform (SPC-FMCW), which is aimed to improve the coexistence of multiple radars operating within the same frequency bandwidth, is studied, and the receiving strategy with a low ADC sampling requirement is investigated. The Gaussian filter is applied to obtain smooth waveform phase transitions, and then quadratic phase lag compensation is performed before waveform transmission to enhance decoding. The proposed waveform is examined in different domains, and its waveform properties are analysed theoretically and demonstrated experimentally. Both simulation and experimental results show that the introduced waveform with the investigated processing steps helps combine all advantages of the FMCW waveform, including hardware simplicity and small operational bandwidth of the receiver, with the advantages of phase coding.
Explainability and Graph Learning from Social Interactions
Authors: Valentina Shumovskaia, Konstantinos Ntemos, Stefan Vlaski, Ali H. Sayed
Date: 2022-03-14 00:00:00
Tasks: Graph Learning

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information among the agents. To account for drifting conditions in the environment, this work adopts an adaptive social learning strategy, which is able to track variations in the underlying signal statistics. Among other results, we propose a technique that addresses questions of explainability and interpretability of the results when the graph is hidden. Given observations of the evolution of the beliefs over time, we aim to infer the underlying graph topology, discover pairwise influences between the agents, and identify significant trajectories in the network. The proposed framework is online in nature and can adapt dynamically to changes in the graph topology or the true hypothesis.