pwc-archive/papers-with-abstracts huggingface dataset:
Oscillations-Aware Frequency Security Assessment via Efficient Worst-Case Frequency Nadir Computation
Authors: Yan Jiang, Hancheng Min, Baosen Zhang
Date: 2024-02-26 00:00:00

Frequency security assessment following major disturbances has long been one of the central tasks in power system operations. The standard approach is to study the center of inertia frequency, an aggregate signal for an entire system, to avoid analyzing the frequency signal at individual buses. However, as the amount of low-inertia renewable resources in a grid increases, the center of inertia frequency is becoming too coarse to provide reliable frequency security assessment. In this paper, we propose an efficient algorithm to determine the worst-case frequency nadir across all buses for bounded power disturbances, as well as identify the power disturbances leading to that severest scenario. The proposed algorithm allows oscillations-aware frequency security assessment without conducting exhaustive simulations and intractable analysis.
Physics-Informed LSTM-Based Delay Compensation Framework for Teleoperated UGVs
Authors: Ahmad Abubakar, Yahya Zweiri, AbdelGafoor Haddad, Mubarak Yakubu, Ruqayya Alhammadi, Lakmal Seneviratne
Date: 2024-02-26 00:00:00

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from transmission delays over a network presents a challenge in maintaining high-fidelity closed-loop integration, potentially hindering UGV controls and leading to poor command-tracking performance. To address this challenge, this paper proposes a novel predictor framework that employs a Physics-informed Long Short-Term Memory (PiLSTM) network for designing bilateral teleoperator controls that effectively compensate for large delays. Contrasting with conventional model-free predictor frameworks, which are limited by their linear nature in capturing nonlinear and temporal dynamic behaviors, our approach integrates the LSTM structure with physical constraints for enhanced performance and better generalization across varied scenarios. Specifically, four distinct predictors were employed in the framework: two compensate for forward delays, while the other two compensate for backward delays. Due to their effectiveness in learning from temporal data, the proposed PiLSTM framework demonstrates a 26.1\ improvement in delay compensation over the conventional model-free predictors for large delays in open-loop case studies. Subsequently, experiments were conducted to validate the efficacy of the framework in close-loop scenarios, particularly to compensate for the real-network delays experienced by teleoperated UGVs coupled with longitudinal slippage. The results confirm the proposed framework is effective in restoring the fidelity of the closed-loop integration. This improvement is showcased through improved performance and transparency, which leads to excellent command-tracking performance.
An Execution-time-certified Riccati-based IPM Algorithm for RTI-based Input-constrained NMPC
Authors: Liang Wu, Krystian Ganko, Shimin Wang, Richard D. Braatz
Date: 2024-02-25 00:00:00
Tasks: Model Predictive Control

Establishing an execution time certificate in deploying model predictive control (MPC) is a pressing and challenging requirement. As nonlinear MPC (NMPC) results in nonlinear programs, differing from quadratic programs encountered in linear MPC, deriving an execution time certificate for NMPC seems an impossible task. Our prior work \cite{wu2023direct} introduced an input-constrained MPC algorithm with the exact and only \textit{dimension-dependent} (\textit{data-independent}) number of floating-point operations ([flops]). This paper extends it to input-constrained NMPC problems via the real-time iteration (RTI) scheme, which results in \textit{data-varying} (but \textit{dimension-invariant}) input-constrained MPC problems. Therefore, applying our previous algorithm can certify the execution time based on the assumption that processors perform fixed [flops] in constant time. As the RTI-based scheme generally results in MPC with a long prediction horizon, this paper employs the efficient factorized Riccati recursion, whose computational cost scales linearly with the prediction horizon, to solve the Newton system at each iteration. The execution-time certified capability of the algorithm is theoretically and numerically validated through a case study involving nonlinear control of the chaotic Lorenz system.
Nonparametric Steady-State Learning for Nonlinear Output Feedback Regulation
Authors: Shimin Wang, Martin Guay, Richard D. Braatz
Date: 2024-02-25 00:00:00

This article addresses the nonadaptive and robust output regulation problem of the general nonlinear output feedback system with error output. The global robust output regulation problem for a class of general output feedback nonlinear systems with an uncertain exosystem and high relative degree can be tackled by constructing a linear generic internal model, provided that a continuous nonlinear mapping exists. Leveraging the proposed nonadaptive framework facilitates the conversion of the nonlinear robust output regulation problem into a robust nonadaptive stabilization formulation for the augmented system endowed with Input-to-State Stable dynamics. This approach removes the need for constructing a specific Lyapunov function with positive semi-definite derivatives and avoids the common assumption of linear parameterization of the nonlinear system. The nonadaptive approach is extended by incorporating the nonparametric learning framework to ensure the feasibility of the nonlinear mapping, which can be tackled using a data-driven method. Moreover, the introduced nonparametric learning framework allows the controlled system to learn the dynamics of the steady-state input behaviour from the signal generated from the internal model with the output error as the feedback. As a result, the nonadaptive/nonparametric approach can be advantageous to guarantee the convergence of the estimation and tracking error even when the underlying controlled system dynamics are complex or poorly understood. The effectiveness of the theoretical results is illustrated for a benchmark example: a controlled duffing system and two practical examples: a continuously stirred tank reactor and a continuous bioreactor.
100 Gbps Indoor Access and 4.8 Gbps Outdoor Point-to-Point LiFi Transmission Systems using Laser-based Light Sources
Authors: Cheng Cheng, Sovan Das, Stefan Videv, Adrian Spark, Sina Babadi, Aravindh Krishnamoorthy, Changmin Lee, Daniel Grieder, Kathleen Hartnett, Paul Rudy, James Raring, Marzieh Najafi, Vasilis K. Papanikolaou, Robert Schober, Harald Haas
Date: 2024-02-25 00:00:00

In this paper, we demonstrate the communication capabilities of light-fidelity (LiFi) systems based on highbrightness and high-bandwidth integrated laser-based sources in a surface mount device (SMD) packaging platform. The laserbased source is able to deliver 450 lumens of white light illumination and the resultant light brightness is over 1000 cd mm2. It is demonstrated that a wavelength division multiplexing (WDM) LiFi system with ten parallel channels is able to deliver over 100 Gbps data rate with the assistance of Volterra filter-based nonlinear equalisers. In addition, an aggregated transmission data rate of 4.8 Gbps has been achieved over a link distance of 500 m with the same type of SMD light source. This work demonstrates the scalability of LiFi systems that employ laserbased light sources, particularly in their capacity to enable highspeed short range, as well as long-range data transmission.
Positioning Error Impact Compensation through Data-Driven Optimization in User-Centric Networks
Authors: Waseem Raza, Fahd Ahmed Khan, Muhammad Umar Bin Farooq, Sabit Ekin, Ali Imran
Date: 2024-02-24 00:00:00

The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network. Accurately determining the Szone radius requires the precise location of the user equipment (UE) and data base stations (DBSs). Even a slight error in reported positions of DBSs or UE will lead to an incorrect determination of Szone radius and UE-DBS pairing, leading to degradation of the UE-DBS communication link. To compensate for the positioning error impact and improve the ASE and EE of the UCUDN, this work proposes a data-driven optimization and error compensation (DD-OEC) framework. The framework comprises an additional machine learning model that assesses the impact of residual errors and regulates the erroneous datadriven optimization to output Szone radius, transmit power, and DBS density values which improve network ASE and EE. The performance of the framework is compared to a baseline scheme, which does not employ the residual, and results demonstrate that the DD-OEC framework outperforms the baseline, achieving up to a 23% improvement in performance.
Design and Implementation of Low-Cost Electric Vehicles (Evs) Supercharger: A Comprehensive Review
Authors: Md Khaledur Rahman, Faysal Amin Tanvir, Md Saiful Islam, Md Shameem Ahsan, Manam Ahmed
Date: 2024-02-24 00:00:00

This article presents a probabilistic modeling method utilizing smart meter data and an innovative agent-based simulator for electric vehicles (EVs). The aim is to assess the effects of different cost-driven EV charging strategies on the power distribution network (PDN). We investigate the effects of a 40% EV adoption on three parts of Frederiksberg's low voltage distribution network (LVDN), a densely urbanized municipality in Denmark. Our findings indicate that cable and transformer overloading especially pose a challenge. However, the impact of EVs varies significantly between each LVDN area and charging scenario. Across scenarios and LVDNs, the share of cables facing congestion ranges between 5% and 60%. It is also revealed that time-of-use (ToU)-based and single-day cost-minimized charging could be beneficial for LVDNs with moderate EV adoption rates. In contrast, multiple-day optimization will likely lead to severe congestion, as such strategies concentrate demand on a single day that would otherwise be distributed over several days, thus raising concerns about how to prevent it. The broader implications of our research suggest that, despite initial worries primarily centered on congestion due to unregulated charging during peak hours, a transition to cost-based smart charging, propelled by an increasing awareness of time-dependent electricity prices, may lead to a significant rise in charging synchronization, bringing about undesirable consequences for the power distribution network (PDN).
Error Bounds for Compositions of Piecewise Affine Approximations
Authors: Jonah J. Glunt, Jacob A. Siefert, Andrew F. Thompson, Herschel C. Pangborn
Date: 2024-02-23 00:00:00

Nonlinear expressions are often approximated by piecewise affine (PWA) functions to simplify analysis or reduce computational costs. To reduce computational complexity, multivariate functions can be represented as compositions of functions with one or two inputs, which can be approximated individually. This paper provides efficient methods to generate PWA approximations of nonlinear functions via functional decomposition. The key contributions focus on intelligent placement of breakpoints for PWA approximations without requiring optimization, and on bounding the error of PWA compositions as a function of the error tolerance for each component of that composition. The proposed methods are used to systematically construct a PWA approximation for a complicated function, either to within a desired error tolerance or to a given level of complexity.
Continuous Power Beaming to Lunar Far Side from EMLP-2 Halo Orbit
Authors: Baris Donmez, Gunes Karabulut Kurt
Date: 2024-02-26 00:00:00

This paper focuses on FSO-based wireless power transmission (WPT) from Earth-Moon Lagrangian Point-2 (EMLP-2) to a receiver optical antenna equipped with solar cells that can be located anywhere on the lunar far side (LFS). Different solar-powered satellite (SPS) configurations which are EMLP-2 located single stable satellite and EMLP-2 halo orbit revolving single, double, and triple satellites are evaluated in terms of 100% LFS surface coverage percentage (SCP) and continuous Earth visibility. It is found that an equidistant triple satellite scheme on EMLP-2 halo orbit with a semi-major axis length of 15,000 km provides full SCP for LFS and it is essential for the continuous LFS wireless power transmission. In our proposed dynamic cislunar space model, geometric and temporal parameters of the Earth-Moon systems are used in affine transformations. Our dynamic model enables us to determine the full coverage time rate of a specific region such as the LFS southern pole. The outcomes show that the equidistant double satellite scheme provides SCP=100% during 88.60% time of these satellites' single revolution around the EMLP-2 halo orbit. Finally, the probability density function (PDF) of the random harvested power $P_H$ is determined and it validates the simulation data extracted from the stable EMLP-2 satellite and revolving satellite around EMLP-2 halo orbit for minimum and maximum LoS distances. Although the pointing devices to mitigate random misalignment errors are considered for the stable and revolving SPSs, better pointing accuracy is considered for the stable satellite. Our simulations show that the probability of $P_H\le$41.6 W is around 0.5 for the stable satellite whereas the CDF=0.99 for the revolving satellite case for a transmit power of 1 kW.
Renormalisation Group Methods for Effective Epidemiological Models
Authors: Stefan Hohenegger, Francesco Sannino
Date: 2024-02-26 00:00:00

Epidemiological models describe the spread of an infectious disease within a population. They capture microscopic details on how the disease is passed on among individuals in various different ways, while making predictions about the state of the entirety of the population. However, the type and structure of the specific model considered typically depend on the size of the population under consideration. To analyse this effect, we study a family of effective epidemiological models in space and time that are related to each other through scaling transformations. Inspired by a similar treatment of diffusion processes, we interpret the latter as renormalisation group transformations, both at the level of the underlying differential equations and their solutions. We show that in the large scale limit, the microscopic details of the infection process become irrelevant, safe for a simple real number, which plays the role of the infection rate in a basic compartmental model.
Information Theory Unification of Epidemiological and Population Dynamics
Authors: Baptiste Filoche, Stefan Hohenegger, Francesco Sannino
Date: 2024-02-26 00:00:00
Tasks: Epidemiology

We reformulate models in epidemiology and population dynamics in terms of probability distributions. This allows us to construct the Fisher information, which we interpret as the metric of a one-dimensional differentiable manifold. For systems that can be effectively described by a single degree of freedom, we show that their time evolution is fully captured by this metric. In this way, we discover universal features across seemingly very different models. This further motivates a reorganisation of the dynamics around zeroes of the Fisher metric, corresponding to extrema of the probability distribution. Concretely, we propose a simple form of the metric for which we can analytically solve the dynamics of the system that well approximates the time evolution of various established models in epidemiology and population dynamics, thus providing a unifying framework.
Deep Learning-based Kinetic Analysis in Paper-based Analytical Cartridges Integrated with Field-effect Transistors
Authors: Hyun-June Jang, Hyou-Arm Joung, Artem Goncharov, Anastasia Gant Kanegusuku, Clarence W. Chan, Kiang-Teck Jerry Yeo, Wen Zhuang, Aydogan Ozcan, Junhong Chen
Date: 2024-02-27 00:00:00

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of < 6.46% and a decent concentration measurement correlation with an r2 value of > 0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies can create a new generation of FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.
tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling
Authors: Thomas-Otavio Peulen, Katherina Hemmen, Annemarie Greife, Benjamin M. Webb, Suren Felekyan, Andrej Sali, Claus A. M. Seidel, Hugo Sanabria, Katrin G. Heinze
Date: 2024-02-27 00:00:00

We introduce software for reading, writing and processing fluorescence single-molecule and image spectroscopy data and developing analysis pipelines that unifies various spectroscopic analysis tools. Our software can be used for processing multiple experiment types, e.g., for time-resolved single-molecule (sm) spectroscopy, laser scanning microscopy, fluorescence correlation spectroscopy, and image correlation spectroscopy. The software is file format agnostic, processes and outputs multiple time-resolved data formats. Thereby our software eliminates the need for data conversion and mitigates data archiving issues.
Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning
Authors: Wenhao Gao, Priyanka Raghavan, Ron Shprints, Connor W. Coley
Date: 2024-02-19 00:00:00
Tasks: Contrastive Learning, molecular representation

A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful experiments or low-yielding results are rarely reported. In this work, we explore more deeply the relationship between such publication bias and chemical reactivity beyond the simple analysis of yield distributions using a novel neural network training strategy, substrate scope contrastive learning. By treating reported substrates as positive samples and non-reported substrates as negative samples, our contrastive learning strategy teaches a model to group molecules within a numerical embedding space, based on historical trends in published substrate scope tables. Training on 20,798 aryl halides in the CAS Content Collection$^{\text{TM}}$, spanning thousands of publications from 2010-2015, we demonstrate that the learned embeddings exhibit a correlation with physical organic reactivity descriptors through both intuitive visualizations and quantitative regression analyses. Additionally, these embeddings are applicable to various reaction modeling tasks like yield prediction and regioselectivity prediction, underscoring the potential to use historical reaction data as a pre-training task. This work not only presents a chemistry-specific machine learning training strategy to learn from literature data in a new way, but also represents a unique approach to uncover trends in chemical reactivity reflected by trends in substrate selection in publications.
A Quick Guide to Quantum Communication
Authors: Rohit Singh, Roshan M. Bodile
Date: 2024-02-24 00:00:00

This article provides a quick overview of quantum communication, bringing together several innovative aspects of quantum enabled transmission. We first take a neutral look at the role of quantum communication, presenting its importance for the forthcoming wireless. Then, we summarise the principles and basic mechanisms involved in quantum communication, including quantum entanglement, quantum superposition, and quantum teleportation. Further, we highlight its groundbreaking features, opportunities, challenges and future prospects.
Quasi-Bayesian Estimation and Inference with Control Functions
Authors: Ruixuan Liu, Zhengfei Yu
Date: 2024-02-27 00:00:00
Tasks: Bayesian Inference, Computational Efficiency, Discrete Choice Models, valid

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parameters. This combination leverages the advantages of both frequentist tractability for nonparametric estimation and Bayesian computational efficiency for complicated structural models. We analyze the asymptotic properties of the resulting quasi-posterior distribution, finding that its mean provides a consistent estimator for the parameters of interest, although its quantiles do not yield valid confidence intervals. However, bootstrapping the quasi-posterior mean accounts for the estimation uncertainty from the first stage, thereby producing asymptotically valid confidence intervals.
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inference
Authors: Robin M. Cross, Steven T. Buccola
Date: 2024-02-27 00:00:00
Tasks: Causal Inference, Reading Comprehension

This paper incorporates information about the temporal order of regressors to estimate orthogonal and economically interpretable regression coefficients. We establish new finite sample properties for the Gram-Schmidt orthogonalization process. Coefficients are unbiased and stable with lower standard errors than those from Ordinary Least Squares. We provide conditions under which coefficients represent average total treatment effects on the treated and extend the model to groups of ordered and simultaneous regressors. Finally, we reanalyze two studies that controlled for temporally ordered and collinear characteristics, including race, education, and income. The new approach expands Bohren et al.'s decomposition of systemic discrimination into channel-specific effects and improves significance levels.
Fast Algorithms for Quantile Regression with Selection
Authors: Santiago Pereda-Fernández
Date: 2024-02-26 00:00:00
Tasks: Computational Efficiency, quantile regression, regression

This paper addresses computational challenges in estimating Quantile Regression with Selection (QRS). The estimation of the parameters that model self-selection requires the estimation of the entire quantile process several times. Moreover, closed-form expressions of the asymptotic variance are too cumbersome, making the bootstrap more convenient to perform inference. Taking advantage of recent advancements in the estimation of quantile regression, along with some specific characteristics of the QRS estimation problem, I propose streamlined algorithms for the QRS estimator. These algorithms significantly reduce computation time through preprocessing techniques and quantile grid reduction for the estimation of the copula and slope parameters. I show the optimization enhancements with some simulations. Lastly, I show how preprocessing methods can improve the precision of the estimates without sacrificing computational efficiency. Hence, they constitute a practical solutions for estimators with non-differentiable and non-convex criterion functions such as those based on copulas.