pwc-archive/papers-with-abstracts huggingface dataset:
Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law
Authors: Fritz Braeuer, Max Kleinebrahm, Elias Naber, Fabian Scheller, Russell McKenna
Date: 2021-05-24 00:00:00

Involving residential actors in the energy transition is crucial for its success. Local energy generation, consumption and trading are identified as desirable forms of involvement, especially in energy communities. The potentials for energy communities in the residential building stock are high but are largely untapped in multi-family buildings. In many countries, rapidly evolving legal frameworks aim at overcoming related barriers, e.g. ownership structures, principal-agent problems and system complexity. But academic literature is scarce regarding the techno-economic and environmental implications of such complex frameworks. This paper develops a mixed-integer linear program (MILP) optimisation model for assessing the implementation of multi-energy systems in an energy community in multi-family buildings with a special distinction between investor and user. The model is applied to the German Tenant Electricity Law. Based on hourly demands from appliances, heating and electric vehicles, the optimal energy system layout and dispatch are determined. The results contain a rich set of performance indicators that demonstrate how the legal framework affects the technologies' interdependencies and economic viability of multi-energy system energy communities. Certain economic technology combinations may fail to support national emissions mitigation goals and lead to lock-ins in Europe's largest residential building stock. The subsidies do not lead to the utilisation of a battery storage. Despite this, self-sufficiency ratios of more than 90% are observable for systems with combined heat and power plants and heat pumps. Public CO2 mitigation costs range between 147.5-272.8 EUR/tCO2. Finally, the results show the strong influence of the heat demand on the system layout.
Does energy efficiency affect ambient PM2.5? The moderating role of energy investment
Authors: Cunyi Yang, Tinghui Li, Khaldoon Albitar
Date: 2021-05-24 00:00:00
Tasks: Attribute

The difficulty of balance between environment and energy consumption makes countries and enterprises face a dilemma, and improving energy efficiency has become one of the ways to solve this dilemma. Based on data of 158 countries from 1980 to 2018, the dynamic TFP of different countries is calculated by means of the Super-SBM-GML model. The TFP is decomposed into indexes of EC (Technical Efficiency Change), TC (Technological Change) and EC has been extended to PEC (Pure Efficiency Change) and SEC (Scale Efficiency Change). Then the fixed effect model and fixed effect panel quantile model are used to analyze the moderating effect and exogenous effect of energy efficiency on PM2.5 concentration on the basis of verifying that energy efficiency can reduce PM2.5 concentration. We conclude, first, the global energy efficiency has been continuously improved during the sample period, and both of technological progress and technical efficiency have been improved. Second, the impact of energy efficiency on PM2.5 is heterogeneous which is reflected in the various elements of energy efficiency decomposition. The increase of energy efficiency can inhibit PM2.5 concentration and the inhibition effect mainly comes from TC and PEC but SEC promotes PM2.5 emission. Third, energy investment plays a moderating role in the environmental protection effect of energy efficiency. Fourth, the impact of energy efficiency on PM2.5 concentration is heterogeneous in terms of national attribute, which is embodied in the differences of national development, science & technology development level, new energy utilization ratio and the role of international energy trade.
Does Geopolitics Have an Impact on Energy Trade? Empirical Research on Emerging Countries
Authors: Fen Li, Cunyi Yang, Zhenghui Li, Pierre Failler
Date: 2021-05-24 00:00:00
Tasks: Attribute

The energy trade is an important pillar of each country's development, making up for the imbalance in the production and consumption of fossil fuels. Geopolitical risks affect the energy trade of various countries to a certain extent, but the causes of geopolitical risks are complex, and energy trade also involves many aspects, so the impact of geopolitics on energy trade is also complex. Based on the monthly data from 2000 to 2020 of 17 emerging economies, this paper employs the fixed-effect model and the regression-discontinuity (RD) model to verify the negative impact of geopolitics on energy trade first and then analyze the mechanism and heterogeneity of the impact. The following conclusions are drawn: First, geopolitics has a significant negative impact on the import and export of the energy trade, and the inhibition on the export is greater than that on the import. Second, the impact mechanism of geopolitics on the energy trade is reflected in the lagging effect and mediating effect on the imports and exports; that is, the negative impact of geopolitics on energy trade continued to be significant 10 months later. Coal and crude oil prices, as mediating variables, decreased to reduce the imports and exports, whereas natural gas prices showed an increase. Third, the impact of geopolitics on energy trade is heterogeneous in terms of national attribute characteristics and geo-event types.
Blessing or Curse of Democracy?: Current Evidence from the Covid-19 Pandemic
Authors: Ryan P. Badman, Yunxin Wu, Keigo Inukai, Rei Akaishi
Date: 2021-05-23 00:00:00

Background: A major question in Covid-19 research is whether democracies handled the Covid-19 pandemic crisis better or worse than authoritarian countries. However, it is important to consider the issues of democracy versus authoritarianism, and state fragility, when examining official Covid-19 death counts in research, because these factors can influence the accurate reporting of pandemic deaths by governments. In contrast, excess deaths are less prone to variability in differences in definitions of Covid-19 deaths and testing capacities across countries. Here we use excess pandemic deaths to explore potential relationships between political systems and public health outcomes. Methods: We address these issues by comparing the official government Covid-19 death counts in a well-established John Hopkins database to the generally more reliable excess mortality measure of Covid-19 deaths, taken from the recently released World Mortality Dataset. We put the comparison in the context of the political and fragile state dimensions. Findings: We find (1) significant potential underreporting of Covid-19 deaths by authoritarian governments and governments with high state fragility and (2) substantial geographic variation among countries and regions with regard to standard democracy indices. Additionally, we find that more authoritarian governments are (weakly) associated with more excess deaths during the pandemic than democratic governments. Interpretations: The inhibition and censorship of information flows, inherent to authoritarian states, likely results in major inaccuracies in pandemic statistics that confound global public health analyses. Thus, both excess pandemic deaths and official Covid-19 death counts should be examined in studies using death as an outcome variable.
Deprivation, Crime, and Abandonment: Do Other Midwestern Cities Have 'Little Detroits'?
Authors: Scott W. Hegerty
Date: 2021-05-21 00:00:00

Both within the United States and worldwide, the city of Detroit has become synonymous with economic decline, depopulation, and crime. Is Detroit's situation unique, or can similar neighborhoods be found elsewhere? This study examines Census block group data, as well as local crime statistics for 2014, for a set of five Midwestern cities. Roughly three percent of Chicago's and Milwaukee's block groups--all of which are in majority nonwhite areas--exceed Detroit's median values for certain crimes, vacancies, and a poverty measure. This figure rises to 11 percent for St. Louis, while Minneapolis has only a single "Detroit-like" block group. Detroit's selected areas are more likely to be similar to the entire city itself, both spatially and statistically, while these types of neighborhoods for highly concentrated "pockets" of poverty elsewhere. Development programs that are targeted in one city, therefore, must take these differences into account and should be targeted to appropriate neighborhoods.
The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries
Authors: C. Seri, A. de Juan Fernandez
Date: 2021-05-18 00:00:00
Tasks: Time Series, Time Series Analysis

We employ an ARDL bounds testing approach to cointegration and Unrestricted Error Correction Models (UECMs) to estimate the relationship between income and CO2 emissions per capita in 21 Latin American Countries (LACs) over 1960-2017. Using time series we estimate six different specifications of the model to take into account the independent effect on CO2 emissions per capita of different factors considered as drivers of different dynamics of CO2 emissions along the development path. This approach allows to address two concerns. First, the estimation of the model controlling for different variables serves to assess if the EKC hypothesis is supported by evidence in any of the LACs considered and to evaluate if this evidence is robust to different model specifications. Second, the inclusion of control variables accounting for the effect on CO2 emissions is directed at increasing our understanding of CO2 emissions drivers in different countries. The EKC hypothesis effectively describes the long term income-emissions relationship only in a minority of LACs and, in many cases, the effect on CO2 emissions of different factors depends on the individual country experience and on the type and quantity of environmental policies adopted. Overall, these results call for increased environmental action in the region.
A Random Attention and Utility Model
Authors: Nail Kashaev, Victor H. Aguiar
Date: 2021-05-24 00:00:00
Tasks: model

We generalize the stochastic revealed preference methodology of McFadden and Richter (1990) for finite choice sets to settings with limited consideration. Our approach is nonparametric and requires partial choice set variation. We impose a monotonicity condition on attention first proposed by Cattaneo et al. (2020) and a stability condition on the marginal distribution of preferences. Our framework is amenable to statistical testing. These new restrictions extend widely known parametric models of consideration with heterogeneous preferences.
Information Cascades and Social Learning
Authors: Sushil Bikhchandani, David Hirshleifer, Omer Tamuz, Ivo Welch
Date: 2021-05-23 00:00:00

We review the theory of information cascades and social learning. Our goal is to describe in a relatively integrated and accessible way the more important themes, insights and applications of the literature as it has developed over the last thirty years. We also highlight open questions and promising directions for further theoretical and empirical exploration.
Dominance Solvability in Random Games
Authors: Noga Alon, Kirill Rudov, Leeat Yariv
Date: 2021-05-22 00:00:00

We study the effectiveness of iterated elimination of strictly-dominated actions in random games. We show that dominance solvability of games is vanishingly small as the number of at least one player's actions grows. Furthermore, conditional on dominance solvability, the number of iterations required to converge to Nash equilibrium grows rapidly as action sets grow. Nonetheless, when games are highly imbalanced, iterated elimination simplifies the game substantially by ruling out a sizable fraction of actions. Technically, we illustrate the usefulness of recent combinatorial methods for the analysis of general games.
Probabilistic Fixed Ballot Rules and Hybrid Domains
Authors: Shurojit Chatterji, Souvik Roy, Soumyarup Sadhukhan, Arunava Sen, Huaxia Zeng
Date: 2021-05-22 00:00:00
Tasks: Diversity

We study a class of preference domains that satisfies the familiar properties of minimal richness, diversity and no-restoration. We show that a specific preference restriction, hybridness, has been embedded in these domains so that the preferences are single-peaked at the "extremes" and unrestricted in the "middle". We also study the structure of strategy-proof and unanimous Random Social Choice Functions on these domains. We show them to be special cases of probabilistic fixed ballot rules (introduced by Ehlers, Peters, and Storcken (2002)).
Fast Crack Detection Using Convolutional Neural Network
Authors: Jiesheng Yang, Fangzheng Lin, Yusheng Xiang, Peter Katranuschkov, Raimar J. Scherer
Date: 2021-05-23 00:00:00
Tasks: Transfer Learning

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use.
Multi-Feature Fusion-based Scene Classification Framework for HSR Images
Authors: Zhengrui Huang
Date: 2021-05-22 00:00:00
Tasks: Classification, Scene Classification

To realize high-accuracy classification of high spatial resolution (HSR) images, this letter proposes a new multi-feature fusion-based scene classification framework (MF2SCF) by fusing local, global, and color features of HSR images. Specifically, we first extract the local features with the help of image slicing and densely connected convolutional networks (DenseNet), where the outputs of dense blocks in the fine-tuned DenseNet-121 model are jointly averaged and concatenated to describe local features. Second, from the perspective of complex networks (CN), we model a HSR image as an undirected graph based on pixel distance, intensity, and gradient, and obtain a gray-scale image (GSI), a gradient of image (GoI), and three CN-based feature images to delineate global features. To make the global feature descriptor resist to the impact of rotation and illumination, we apply uniform local binary patterns (LBP) on GSI, GoI, and feature images, respectively, and generate the final global feature representation by concatenating spatial histograms. Third, the color features are determined based on the normalized HSV histogram, where HSV stands for hue, saturation, and value, respectively. Finally, three feature vectors are jointly concatenated for scene classification. Experiment results show that MF2SCF significantly improves the classification accuracy compared with state-of-the-art LBP-based methods and deep learning-based methods.
Counter-Epidemiological Projections of e-Coaching
Authors: Kenneth Lai, Svetlana Yanushkevich, Vlad Shmerko
Date: 2021-05-24 00:00:00
Tasks: Management

This paper considers e-coaching at times of pandemic. It utilizes the Emergency Management Cycle (EMC), a core doctrine for managing disasters. The EMC dimensions provide a useful taxonomical view for the development and application of e-coaching systems, emphasizing technological and societal issues. Typical pandemic symptoms such as anxiety, panic, avoidance, and stress, if properly detected, can be mitigated using the e-coaching tactic and strategy. In this work, we focus on a stress monitoring assistant developed upon machine learning techniques. We provide the results of an experimental study of a prototype of such an assistant. Our study leads to the conclusion that stress monitoring shall become a valuable component of e-coaching at all EMC phases.
Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-clouds
Authors: Jacopo Pegoraro, Michele Rossi
Date: 2021-05-24 00:00:00
Tasks: Edge-computing, Object Tracking

Mm-wave radars have recently gathered significant attention as a means to track human movement and identify subjects from their gait characteristics. A widely adopted method to perform the identification is the extraction of the micro-Doppler signature of the targets, which is computationally demanding in case of co-existing multiple targets within the monitored physical space. Such computational complexity is the main problem of state-of-the-art approaches, and makes them inapt for real-time use. In this work, we present an end-to-end, low-complexity but highly accurate method to track and identify multiple subjects in real-time using the sparse point-cloud sequences obtained from a low-cost mm-wave radar. Our proposed system features an extended object tracking Kalman filter, used to estimate the position, shape and extension of the subjects, which is integrated with a novel deep learning classifier, specifically tailored for effective feature extraction and fast inference on radar point-clouds. The proposed method is thoroughly evaluated on an edge-computing platform from NVIDIA (Jetson series), obtaining greatly reduced execution times (reduced complexity) against the best approaches from the literature. Specifically, it achieves accuracies as high as 91.62%, operating at 15 frames per seconds, in identifying three subjects that concurrently and freely move in an unseen indoor environment, among a group of eight.
Phase Noise and Frequency Accuracy in Crystal-less Wireless Edge Nodes
Authors: Xing Chen, David D Wentzloff
Date: 2021-05-23 00:00:00

This paper presents a fundamental analysis connecting phase noise and long-term frequency accuracy of oscillators and explores the possibilities and limitations in crystal-less frequency calibration for wireless edge nodes from a noise-impact perspective. N-period-average jitter (NPAJ) is introduced as a link between the spectral characterization of phase noise and long-term frequency accuracy. It is found that flicker noise or other colored noise profiles coming from the reference in a frequency synthesizer is the dominant noise source affecting long-term frequency accuracy. An average processing unit embedded in an ADPLL is proposed based on the N-period-average jitter concept to enhance frequency accuracy in a Calibrate and Open-loop scenario commonly used in low power radios. With this low-cost block, the frequency calibration accuracy can be directly associated with the reference noise performance. Thus, the feasibility of XO-less design with certain communication standards can be easily evaluated with the proposed theory.
Analysis and Calibration of SAR ADC
Authors: Harshit Roy
Date: 2021-05-23 00:00:00

This paper presents the effect of capacitor mismatch on the weights of binary and split SAR ADC. It proposes a matrix formulation to calculate the nodal voltages for N-section split SAR ADC. The second part of the paper proposes a new weight estimation methodology.
Decentralized Baseband Processing with Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems
Authors: Zhenyu Zhang, Yuanyuan Dong, Keping Long, Xiyuan Wang, Xiaoming Dai
Date: 2021-05-22 00:00:00

Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we develop a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. By projecting the discrete probability distribution into a complex Gaussian function, the local means and variances iteratively calculated in each antenna cluster are fused to generate the global symbol beliefs based on the proposed message fusion rule in the centralized processing unit. We present the framework and analysis of the convergence of the decentralized GMP detection based on state evolution under the assumptions of large-system limit and Gaussian sources. Analytical results corroborated by simulations demonstrate that nonuniform antenna cluster partition scheme exhibits higher convergence rate than the uniform counterpart. Simulation results illustrate that the proposed decentralized GMP detection outperforms the recently proposed decentralized algorithms.
Optimal Consumption with Reference to Past Spending Maximum
Authors: Shuoqing Deng, Xun Li, Huyen Pham, Xiang Yu
Date: 2020-06-12 00:00:00

This paper studies the infinite-horizon optimal consumption with a path-dependent reference under exponential utility. The performance is measured by the difference between the nonnegative consumption rate and a fraction of the historical consumption maximum. The consumption running maximum process is chosen as an auxiliary state process, and hence the value function depends on two state variables. The Hamilton-Jacobi-Bellman (HJB) equation can be heuristically expressed in a piecewise manner across different regions to take into account all constraints. By employing the dual transform and smooth-fit principle, some thresholds of the wealth variable are derived such that a classical solution to the HJB equation and the feedback optimal investment and consumption strategies can be obtained in closed form in each region. A complete proof of the verification theorem is provided, and numerical examples are presented to illustrate some financial implications.
Model-Free Finance and Non-Lattice Integration
Authors: Christian Bender, Sebastian Ferrando, Alfredo Gonzalez
Date: 2021-05-22 00:00:00
Tasks: model

Starting solely with a set of possible prices for a traded asset $S$ (in infinite discrete time) expressed in units of a numeraire, we explain how to construct a Daniell type of integral representing prices of integrable functions depending on the asset. Such functions include the values of simple dynamic portfolios obtained by trading with $S$ and the numeraire. The space of elementary integrable functions, i.e. the said portfolio values, is not a vector lattice. It then follows that the integral is not classical, i.e. it is not associated to a measure. The essential ingredient in constructing the integral is a weak version of the no-arbitrage condition but here expressed in terms of properties of the trajectory space. We also discuss the continuity conditions imposed by Leinert (Archiv der Mathematik, 1982) and K\"onig (Mathematische Annalen, 1982) in the abstract theory of non-lattice integration from a financial point of view and establish some connections between these continuity conditions and the existence of martingale measures