pwc-archive/papers-with-abstracts huggingface dataset:
Monitoring Germany's Core Energy System Dataset: A Data Quality Analysis of the Marktstammdatenregister
Authors: Florian Kotthoff, Christoph Muschner, Deniz Tepe, Esther Vogt, Ludwig Hülk
Date: 2023-04-20 00:00:00

The energy system in Germany consists of a large number of distributed facilities, including millions of PV plants, wind turbines, and biomass plants. To understand and manage this system efficiently, accurate and reliable information about all facilities is essential. In Germany, the Marktstammdatenregister (MaStR) serves as a central registry for units of the energy system. The reliability of this data is critical for the registry's usefulness, but few validation studies have been published. In this work we provide a review of existing literature that relies on data from the MaStR and thereby show the registry's importance. We then build a data and testing pipeline for relevant data of the registry, with a focus on the two aspects of facility's location and size. All test results are published online in a reproducible workflow. Hence, this work contributes to a reliable data foundation for the German energy system and starts an open validation process of the Marktstammdatenregister from an academic perspective.
Evolutionary Shaping of Low-Dimensional Path Facilitates Robust and Plastic Switching Between Phenotypes
Authors: Ayaka Sakata, Kunihiko Kaneko
Date: 2023-04-22 00:00:00

Biological systems must be robust for stable function against perturbations, but robustness alone is not sufficient. The ability to switch between appropriate states (phenotypes) in response to different conditions is essential for biological functions. How are robustness and plasticity simultaneously acquired through evolution? We examine the evolution of genotypes that realize plastic switching between two phenotypes upon external inputs, as well as stationary expressions of phenotypes. We introduce a statistical physics model consisting of spins, with active and regulatory sites, which are distinct from each other. We represent the phenotype and genotype as spin configurations and the spin-spin interactions, respectively. The fitness for selection is given so that it takes a higher value as more of the active sites take two requested spin configurations depending on the regulation. We numerically evolve the interaction matrix by changing them with mutations and selection of those with higher fitness. Our numerical simulations show that characteristic genotypes evolve slightly above the transition temperature between replica symmetric and replica symmetry breaking phase. These genotypes shape two spin configurations separately depending on the regulation, where the two phenotypes are dominantly represented by the genotypes' first and second eigenmodes, and smooth switching of two phenotypes are achieved by following a one-dimensional path connecting the two phenotypes. Upon changes in regulations, spin configurations are attracted to this path, which allows for robust and plastic switching between the two phenotypes. The statistical-physics analysis show that the free energy landscape has a valley along the switching path. Our finding indicates that the compatibility of the robustness and plasticity is acquired by the evolution of the low-dimensionality in the phenotype space.
Geometry of ecological coexistence and niche differentiation
Authors: Emmy Blumenthal, Pankaj Mehta
Date: 2023-04-21 00:00:00

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's $R^*$ and species coexistence cones. Here, we extend these arguments by constructing a novel geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically-stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.
Laser Doppler vibrometer and accelerometer for vibrational analysis of the automotive components during Simulink simulation for validation
Authors: Fatemeh Rezaei
Date: 2023-04-16 00:00:00
Tasks: Diagnostic

In current research, laser Doppler vibrometer (LDV) as a new diagnostic tool is utilized for non-destructive testing of the automotive equipment. LDV technique is working based on measurement of the Doppler shift of a moving object in an interference set-up. The effects of different noises are considered and eliminated from data analysis. Here, the performance of LDV technique is compared with a reference accelerometer device. Furthermore, a simulation by Matlab Simulink is added to the analysis which confirms the results of the experimental data. Results demonstrated that the laser Doppler vibrometer can measure excellently the frequencies of different automotive components for employing in industry. Therefore, it is proposed that LDV technique can be substituted with other traditional non-destructive testing methods.
Auditory distraction in open-plan office environments: The effect of multi-talker acoustics
Authors: Manuj Yadav, Jungsoo Kim, Densil Cabrera, Richard de Dear
Date: 2023-04-14 00:00:00
Tasks: Experimental Design

Within the soundscapes of open-plan offices, irrelevant speech has consistently been reported as the most distracting, and causing performance decrements for workers. Notwithstanding this generalization, the 'babble' created by multiple simultaneously active talkers can sometimes provide beneficial sound masking, but due to spatial release from masking (SRM), speech may still be sufficiently intelligible up to a certain number of talkers (estimated to be about four). This was explored within a highly-realistic office simulation, where the cognitive performance, and subjective distraction of participants were tested. The experimental design was a 4x2 factorial (4 talker numbers, 2 levels of broadband sound masking, as the factors). The results indicated that within lower sound pressure level (SPL) of broadband sound masking, multi-talker sound environments degraded cognitive tasks performance more than those with a single talker, suggesting SRM effects. For higher SPL broadband sound masking, the cognitive test scores were similar within the different talker numbers. The subjective distraction increased monotonically with the number of talkers, with higher distraction within lower SPL broadband sound masking. Overall, the results call into question the single talker assumption (being the most distracting) within the international standard for measuring open-plan office acoustic environments (ISO 3382-3:2012). Soundscapes with 4 simultaneous talkers were still not adequately providing beneficial 'babble' masking, and were more distracting than 1 active talker. In conclusion, it is suggested that the acoustics environment of open-plan offices needs better characterization by incorporating some of the complexity and psychoacoustics of multi-talker scenarios.
Signal in the noise: temporal variation in exponentially growing populations
Authors: Eric W. Jones, Joshua Derrick, Roger M. Nisbet, Will Ludington, David A. Sivak
Date: 2023-04-22 00:00:00

In exponential population growth, variability in the timing of individual division events and environmental factors (including stochastic inoculation) compound to produce variable growth trajectories. In several stochastic models of exponential growth we show power-law relationships that relate variability in the time required to reach a threshold population size to growth rate and inoculum size. Population-growth experiments in E. coli and S. aureus with inoculum sizes ranging between 1 and 100 are consistent with these relationships. We quantify how noise accumulates over time, finding that it encodes -- and can be used to deduce -- information about the early growth rate of a population.
Automated Solubility Analysis System and Method Using Computer Vision and Machine Learning
Authors: Gahee Kim, Minwoo Jeon, Hyun Do Choi, Jun Ki Cho, Youn-Suk Choi, Hyoseok Hwang
Date: 2023-04-07 00:00:00

In this study, a novel active solubility sensing device using computer vision is proposed to improve separation purification performance and prevent malfunctions of separation equipment such as preparative liquid chromatographers and evaporators. The proposed device actively measures the solubility by transmitting a solution using a background image. The proposed system is a combination of a device that uses a background image and a method for estimating the dissolution and particle presence by changing the background image. The proposed device consists of four parts: camera, display, adjustment, and server units. The camera unit is made up of a rear image sensor on a mobile phone. The display unit is comprised of a tablet screen. The adjustment unit is composed of rotating and height-adjustment jigs. Finally, the server unit consists of a socket server for communication between the units and a PC, including an automated solubility analysis system implemented in Python. The dissolution status of the solution was divided into four categories and a case study was conducted. The algorithms were trained based on these results. Six organic materials and four organic solvents were combined with 202 tests to train the developed algorithm. As a result, the evaluation rate for the dissolution state exhibited an accuracy of 95 %. In addition, the device and method must develop a feedback function that can add a solvent or solute after dissolution detection using solubility results for use in autonomous systems, such as a synthetic automation system. Finally, the diversification of the sensing method is expected to extend not only to the solution but also to the solubility and homogeneity analysis of the film.
A Framework for Combustion Chemistry Acceleration with DeepONets
Authors: Anuj Kumar, Tarek Echekki
Date: 2023-04-06 00:00:00
Tasks: Computational Efficiency

A combustion chemistry acceleration scheme is developed based on deep operator networks (DeepONets). The scheme is based on the identification of combustion reaction dynamics through a modified DeepOnet architecture such that the solutions of thermochemical scalars are projected to new solutions in small and flexible time increments. The approach is designed to efficiently implement chemistry acceleration without the need for computationally expensive integration of stiff chemistry. An additional framework of latent-space dynamics identification with modified DeepOnet is also proposed which enhances the computational efficiency and widens the applicability of the proposed scheme. The scheme is demonstrated on simple chemical kinetics of hydrogen oxidation to more complex chemical kinetics of n-dodecane high- and low-temperature oxidations. The proposed framework accurately learns the chemical kinetics and efficiently reproduces species and temperature temporal profiles corresponding to each application. In addition, a very large speed-up with a great extrapolation capability is also observed with the proposed scheme.
Time Reversal Enabled Fiber-Optic Time Synchronization
Authors: Yufeng Chen, Hongfei Dai, Wenlin Li, Fangmin Wang, Bo wang, Lijun Wang
Date: 2023-04-14 00:00:00

Over the past few decades, fiber-optic time synchronization (FOTS) has provided fundamental support for the efficient operation of modern society. Looking toward the future beyond fifth-generation/sixth-generation (B5G/6G) scenarios and very large radio telescope arrays, developing high-precision, low-complexity and scalable FOTS technology is crucial for building a large-scale time synchronization network. However, the traditional two-way FOTS method needs a data layer to exchange time delay information. This increases the complexity of system and makes it impossible to realize multiple-access time synchronization. In this paper, a time reversal enabled FOTS method is proposed. It measures the clock difference between two locations without involving a data layer, which can reduce the complexity of the system. Moreover, it can also achieve multiple-access time synchronization along the fiber link. Tests over a 230 km fiber link have been carried out to demonstrate the high performance of the proposed method.
Enhanced multilayer perceptron with feature selection and grid search for travel mode choice prediction
Authors: Li Tang, Chuanli Tang, Qi Fu
Date: 2023-04-25 00:00:00
Tasks: Discrete Choice Models, feature selection, Management, Prediction

Accurate and reliable prediction of individual travel mode choices is crucial for developing multi-mode urban transportation systems, conducting transportation planning and formulating traffic demand management strategies. Traditional discrete choice models have dominated the modelling methods for decades yet suffer from strict model assumptions and low prediction accuracy. In recent years, machine learning (ML) models, such as neural networks and boosting models, are widely used by researchers for travel mode choice prediction and have yielded promising results. However, despite the superior prediction performance, a large body of ML methods, especially the branch of neural network models, is also limited by overfitting and tedious model structure determination process. To bridge this gap, this study proposes an enhanced multilayer perceptron (MLP; a neural network) with two hidden layers for travel mode choice prediction; this MLP is enhanced by XGBoost (a boosting method) for feature selection and a grid search method for optimal hidden neurone determination of each hidden layer. The proposed method was trained and tested on a real resident travel diary dataset collected in Chengdu, China.
Determination of the effective cointegration rank in high-dimensional time-series predictive regressions
Authors: Puyi Fang, Zhaoxing Gao, Ruey S. Tsay
Date: 2023-04-24 00:00:00
Tasks: Time Series

This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process $\mathbf{x}_t\in \mathbb{R}^N$ and a stationary series $\mathbf{y}_t\in \mathbb{R}^p$ of interest, our goal is to predict future values of $\mathbf{y}_t$ using $\mathbf{x}_t$ and lagged values of $\mathbf{y}_t$. The proposed framework consists of a two-step estimation procedure. First, the Principal Component Analysis is used to identify all cointegrating vectors of $\mathbf{x}_t$. Second, the co-integrated stationary series are used as regressors, together with some lagged variables of $\mathbf{y}_t$, to predict $\mathbf{y}_t$. The estimated reduced rank is then defined as the effective cointegration rank of $\mathbf{x}_t$. Under the scenario that the autoregressive coefficient matrices are sparse (or of low-rank), we apply the Least Absolute Shrinkage and Selection Operator (or the reduced-rank techniques) to estimate the autoregressive coefficients when the dimension involved is high. Theoretical properties of the estimators are established under the assumptions that the dimensions $p$ and $N$ and the sample size $T \to \infty$. Both simulated and real examples are used to illustrate the proposed framework, and the empirical application suggests that the proposed procedure fares well in predicting stock returns.
The Ordinary Least Eigenvalues Estimator
Authors: Yassine Sbai Sassi
Date: 2023-04-25 00:00:00
Tasks: valid

We propose a rate optimal estimator for the linear regression model on network data with interacted (unobservable) individual effects. The estimator achieves a faster rate of convergence $N$ compared to the standard estimators' $\sqrt{N}$ rate and is efficient in cases that we discuss. We observe that the individual effects alter the eigenvalue distribution of the data's matrix representation in significant and distinctive ways. We subsequently offer a correction for the \textit{ordinary least squares}' objective function to attenuate the statistical noise that arises due to the individual effects, and in some cases, completely eliminate it. The new estimator is asymptotically normal and we provide a valid estimator for its asymptotic covariance matrix. While this paper only considers models accounting for first-order interactions between individual effects, our estimation procedure is naturally extendable to higher-order interactions and more general specifications of the error terms.
Policy Learning under Biased Sample Selection
Authors: Lihua Lei, Roshni Sahoo, Stefan Wager
Date: 2023-04-23 00:00:00

Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population. A recurring concern in doing so is that, even if the randomized trial was well-executed (i.e., internal validity holds), the study participants may not represent a random sample of the target population (i.e., external validity fails)--and this may lead to policies that perform suboptimally on the target population. We consider a model where observable attributes can impact sample selection probabilities arbitrarily but the effect of unobservable attributes is bounded by a constant, and we aim to learn policies with the best possible performance guarantees that hold under any sampling bias of this type. In particular, we derive the partial identification result for the worst-case welfare in the presence of sampling bias and show that the optimal max-min, max-min gain, and minimax regret policies depend on both the conditional average treatment effect (CATE) and the conditional value-at-risk (CVaR) of potential outcomes given covariates. To avoid finite-sample inefficiencies of plug-in estimates, we further provide an end-to-end procedure for learning the optimal max-min and max-min gain policies that does not require the separate estimation of nuisance parameters.
From Misalignment to Synergy: Analysis of Patents from Indian Universities & Research Institutions
Authors: Shoyeb Khan, Satyendra Kumar Sharma, Arnab Kumar Laha
Date: 2023-04-24 00:00:00

Indian Universities and Research Institutions have been the cornerstone of human resource development in the country, nurturing bright minds and shaping the leaders of tomorrow. Their unwavering commitment to excellence in education and research has not only empowered individuals but has also made significant contributions to the overall growth and progress of the nation. Despite the significant strides made by Indian universities and research institutions, the country still lags behind many developed nations in terms of the number of patents filed as well as in the commercialization of the granted patents. With 34 percent1 of students choosing STEM fields in India, and over 750 Universities and nearly 40,000 colleges, the concentration of patent applications in only a few top 10 institutions raises concerns. Innovation and technological advancement have become key drivers of economic growth and development in modern times. Therefore, our study aims to unravel the patent landscape of Indian Universities and Research Institutions, examining it through the lens of supply and demand for innovations and ideas. Delving into the dynamics of patent filing and innovation trends, this study seeks to shed light on the current state of intellectual property generation in the country's academic and research ecosystem.
On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement
Authors: Hannes Wallimann, Silvio Sticher
Date: 2023-04-24 00:00:00

In railway infrastructure, construction and maintenance is typically procured using competitive procedures such as auctions. However, these procedures only fulfill their purpose - using (taxpayers') money efficiently - if bidders do not collude. Employing a unique dataset of the Swiss Federal Railways, we present two methods in order to detect potential collusion: First, we apply machine learning to screen tender databases for suspicious patterns. Second, we establish a novel category-managers' tool, which allows for sequential and decentralized screening. To the best of our knowledge, we pioneer illustrating the adaption and application of machine-learning based price screens to a railway-infrastructure market.
Generative AI at Work
Authors: Erik Brynjolfsson, Danielle Li, Lindsey Raymond
Date: 2023-04-23 00:00:00

We study the staggered introduction of a generative AI-based conversational assistant using data from 5,172 customer support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15\% on average, with substantial heterogeneity across workers. Less experienced and lower-skilled workers improve both the speed and quality of their output while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for relatively rare problems, where human agents have less baseline training and experience. Finally, we provide evidence that AI assistance improves the experience of work along two key dimensions: customers are more polite and less likely to ask to speak to a manager.
Present-Biased Lobbyists in Linear Quadratic Stochastic Differential Games
Authors: Ali Lazrak, Hanxiao Wang, Jiongmin Yong
Date: 2023-04-23 00:00:00

We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm. Players are time-inconsistent because they discount performance with a non-constant rate. Our objective is to identify a consistent planning equilibrium in which the players are aware of their inconsistency and cannot commit to a lobbying policy. We analyze the equilibrium behavior in both single player and two-player cases, and compare the behavior of the game under constant and non-constant discount rates. The equilibrium behavior is provided in closed-loop form, either analytically or via numerical approximation. Our numerical analysis of the equilibrium reveals that strategic behavior leads to more intense lobbying without resulting in overshooting.
Child Care, Time Allocation, and the Life Cycle
Authors: Hirokuni Iiboshi, Daikuke Ozaki, Yui Yoshii
Date: 2023-04-23 00:00:00

This study explores the impact of gender differences in preferences and productivity in home production on the time allocation in married couples, particularly in relation to childcare responsibilities. Using aggregated data from Japan, we estimate a life-cycle model that tracks the development of a child from infancy to adulthood by extending the work of Blundell et al. (2018). Our findings reveal a decrease in maternal earnings following childbirth, aligning with the empirical evidence of the child penalty. However, the model's projections and the actual data diverge significantly during the phase of maternal earnings recovery, showing a discrepancy of approximately 50%, the size of which implies an involuntary reduction in the wife's market work earnings.