pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Input Embedding Size Search For Recommender Systems
Authors: Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin
Date: 2023-04-07 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning (RL)

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e-commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.
A Security-Constrained Optimal Power Management Algorithm for Shipboard Microgrids with Battery Energy Storage System
Authors: Fabio D Agostino, Marco Gallo, Matteo Saviozzi, Federico Silvestro
Date: 2023-04-07 00:00:00
Tasks: Management

This work proposes an optimal power management strategy for shipboard microgrids equipped with diesel generators and a battery energy storage system. The optimization provides both the unit commitment and the optimal power dispatch of all the resources, in order to ensure reliable power supply at minimum cost and with minimum environmental impact. The optimization is performed solving a mixed integer linear programming problem, where the constraints are defined according to the operational limits of the resources when a contingency occurs. The algorithm is tested on a notional all-electric ship where the ship's electrical load is generated through a Markov chain, modeled on real measurement data. The results show that the proposed power management strategy successfully maximizes fuel saving while ensuring blackout prevention capability.
Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
Authors: Ali Beikmohammadi, Sindri Magnússon
Date: 2023-03-17 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.
Margin-Mixup: A Method for Robust Speaker Verification in Multi-Speaker Audio
Authors: Jenthe Thienpondt, Nilesh Madhu, Kris Demuynck
Date: 2023-04-07 00:00:00
Tasks: Speaker Verification

This paper is concerned with the task of speaker verification on audio with multiple overlapping speakers. Most speaker verification systems are designed with the assumption of a single speaker being present in a given audio segment. However, in a real-world setting this assumption does not always hold. In this paper, we demonstrate that current speaker verification systems are not robust against audio with noticeable speaker overlap. To alleviate this issue, we propose margin-mixup, a simple training strategy that can easily be adopted by existing speaker verification pipelines to make the resulting speaker embeddings robust against multi-speaker audio. In contrast to other methods, margin-mixup requires no alterations to regular speaker verification architectures, while attaining better results. On our multi-speaker test set based on VoxCeleb1, the proposed margin-mixup strategy improves the EER on average with 44.4% relative to our state-of-the-art speaker verification baseline systems.
Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication
Authors: Nikhil Ravi, Anna Scaglione, Julieta Giraldez, Parth Pradhan, Chuck Moran, Sean Peisert
Date: 2023-04-07 00:00:00
Tasks: Bayesian Optimization

Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations. Still, they are reluctant to share them, as even sharing aggregated or anonymized electric grid data risks the disclosure of sensitive information. This paper highlights how applying differential privacy to distributed energy resource production data can preserve the usefulness of that data for operations, planning, and research purposes without violating privacy constraints. Differentially private mechanisms can be optimized for queries of interest in the energy sector, with provable privacy and accuracy trade-offs, and can help design differentially private databases for further analysis and research. In this paper, we consider the problem of inference and publication of solar photovoltaic systems' metadata. Metadata such as nameplate capacity, surface azimuth and surface tilt may reveal personally identifiable information regarding the installation behind-the-meter. We describe a methodology to infer the metadata and propose a mechanism based on Bayesian optimization to publish the inferred metadata in a differentially private manner. The proposed mechanism is numerically validated using real-world solar power generation data.
Sorta Solving the OPF by Not Solving the OPF: DAE Control Theory and the Price of Realtime Regulation
Authors: Muhammad Nadeem, Ahmad F. Taha
Date: 2023-04-07 00:00:00

This paper presents a new approach to approximate the AC optimal power flow (ACOPF). By eliminating the need to solve the ACOPF every few minutes, the paper showcases how a realtime feedback controller can be utilized in lieu of ACOPF and its variants. By (i) forming the grid dynamics as a system of differential-algebraic equations (DAE) that naturally encode the non-convex OPF power flow constraints, (ii) utilizing DAELyapunov theory, and (iii) designing a feedback controller that captures realtime uncertainty while being uncertainty-unaware, the presented approach demonstrates promises of obtaining solutions that are close to the OPF ones without needing to solve the OPF. The proposed controller responds in realtime to deviations in renewables generation and loads, guaranteeing improvements in system transient stability, while always yielding approximate solutions of the ACOPF with no constraint violations. As the studied approach herein yields slightly more expensive realtime generator controls, the corresponding price of realtime control and regulation is examined. Cost comparisons with the traditional ACOPF are also showcased -- all via case studies on standard power networks.
Parameterization-Free Observer Design for Nonlinear Systems: Application to the State Estimation of Networked SIR Epidemics
Authors: Muhammad Umar B. Niazi, Karl H. Johansson
Date: 2023-04-07 00:00:00
Tasks: State Estimation

Traditional observer design methods rely on certain properties of the system's nonlinearity, such as Lipschitz continuity, one-sided Lipschitzness, a bounded Jacobian, or quadratic boundedness. These properties are described by parameterized inequalities. However, enforcing these inequalities globally can lead to very large parameters, resulting in overly conservative observer design criteria. These criteria become infeasible for highly nonlinear applications, such as networked epidemic processes. In this paper, we present an observer design approach for estimating the state of nonlinear systems, without requiring any parameterization of the system's nonlinearities. The proposed observer design depends only on systems' matrices and applies to systems with any nonlinearity. We establish different design criteria for ensuring both asymptotic and exponential convergence of the estimation error to zero. To demonstrate the efficacy of our approach, we employ it for estimating the state of a networked SIR epidemic model. We show that, even in the presence of measurement noise, the observer can accurately estimate the epidemic state of each node in the network. To the best of our knowledge, the proposed observer is the first that is capable of estimating the state of networked SIR models.
Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model
Authors: Yutong Li, Nan Li, Anouck Girard, Ilya Kolmanovsky
Date: 2023-04-07 00:00:00
Tasks: Scheduling

Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment. In this paper, we develop a constrained optimization based approach for scheduling the PPIs dosage. In particular, we exploit a mathematical prediction model describing the gastric acid secretion, and use it within the optimization algorithm to predict the acid level. The dosage of the PPIs which is used to enforce acid level constraints is computed by solving a constrained optimization problem. Simulation results show that the proposed approach can successfully suppress the gastric acid level with less PPIs intake compared with the conventional fixed PPIs dosage regimen, which may reduce the long-term side effects of the PPIs.
Privacy-Preserving Decentralized Energy Management for Networked Microgrids via Objective-Based ADMM
Authors: Jesus Silva-Rodriguez, Xingpeng Li
Date: 2023-04-07 00:00:00
Tasks: energy management, Management, Privacy Preserving

This paper proposes a decentralized energy management (DEM) strategy for a network of local microgrids, providing economically balanced energy schedules for all participating microgrids. The proposed DEM strategy can preserve the privacy of each microgrid by only requiring them to share network power exchange information. The proposed DEM strategy enhances the traditional alternating direction method of multipliers (ADMM) formulation for networks of microgrids by examining the global objective value as well as the solution quality. A novel stopping decision combining these two metrics is proposed for the enhanced objective-based ADMM (OB-ADMM) method. This paper also presents a centralized energy management (CEM) model as a benchmark, and a post-processing proportional exchange algorithm (PEA) to balance the economic benefit of each microgrid. The resulting proposed OB-ADMM model combined with the PEA delivers the final high-fidelity optimal solution for multiple microgrids in a grid-connected collaborative power exchange network. Moreover, the proposed decentralized operational strategy preserves the economic and privacy interests of individual microgrid participants. Different network cases are simulated to test the algorithm's performance, and the results validate the aforementioned claims.
An investigation of factors affecting Fast-Interaction Converter-driven Stability in Microgrids
Authors: Georgia Saridaki, Alexandros G. Paspatis, Panos Kotsampopoulos, Nikos Hatziargyriou
Date: 2023-04-07 00:00:00

Massive integration of power electronic devices with multiple control schemes in a wide frequency range pose new challenges regarding systems stability and reliability. Interactions between the fast control loops or between the fast control loops and passive elements of the grid, have been reported in literature and have led to introducing a new type of stability: the Fast-Interaction Converter-driven Stability (FICDS). In this paper, factors affecting the FICDS, such as tuning controller parameters, line parameters, number of interconnected inverters, are explored in four microgrid topologies, operating in grid connected and islanded mode. With the use of an impedance based model which tracks the poles of the CDS transfer functions of each system, their stability has been assessed. The obtained results have been verified via time-domain simulations. Simulations from the islanded microgrid of Gaidouromantra in Greece showcase the impact of the control parameters on the operation of the system and indicate the need for further investigation.
Automated Tuning of Nonlinear Kalman Filters for Optimal Trajectory Tracking Performance of AUVs
Authors: Maximilian Nitsch, David Stenger, Dirk Abel
Date: 2023-04-07 00:00:00
Tasks: Bayesian Optimization

The performance of navigation algorithms significantly determines the trajectory tracking accuracy of the guidance, navigation, and control (GNC) system of an autonomous underwater vehicle (AUV). In closed-loop operation, the interaction among path planning, control, and navigation plays a crucial role in the tracking accuracy of the overall GNC system. A Doppler velocity log (DVL) is often used for AUVs to measure velocity over the ground, positively affecting the closed-loop tracking error. However, a DVL may not be installed in miniaturized AUVs due to limited space and energy. In this paper, a navigation filter for an underactuated miniature AUV (nanoAUV) is considered that is mainly based on acoustic localization using a novel highly-miniaturized ultra-short baseline (USBL) system and a depth pressure sensor. The nanoAUV is being developed for subglacial lake exploration. We compare two unscented Kalman filters (UKF) with different prediction models - the classical strapdown inertial navigation systems (SINS) model and a hydrodynamic motion model (HMM). To enable a fair comparison, filter parameters are auto-tuned with Bayesian optimization (BO) for open and closed-loop performance, which is novel in AUV navigation. The results indicate that BO performs similarly to particle swarm optimization (PSO) regarding sample efficiency for the proposed problem. To quantify the GNC tracking performance, we use extensive Monte Carlo simulations. Results suggest that with BO-tuned navigation filter parameters, the median tracking error is reduced by up to 50% compared to default parametrization.
Robust data-driven control for nonlinear systems using the Koopman operator
Authors: Robin Strässer, Julian Berberich, Frank Allgöwer
Date: 2023-04-07 00:00:00

Data-driven analysis and control of dynamical systems have gained a lot of interest in recent years. While the class of linear systems is well studied, theoretical results for nonlinear systems are still rare. In this paper, we present a data-driven controller design method for discrete-time control-affine nonlinear systems. Our approach relies on the Koopman operator, which is a linear but infinite-dimensional operator lifting the nonlinear system to a higher-dimensional space. Particularly, we derive a linear fractional representation of a lifted bilinear system representation based on measured data. Further, we restrict the lifting to finite dimensions, but account for the truncation error using a finite-gain argument. We derive a linear matrix inequality based design procedure to guarantee robust local stability for the resulting bilinear system for all error terms satisfying the finite-gain bound and, thus, also for the underlying nonlinear system. Finally, we apply the developed design method to the nonlinear Van der Pol oscillator.
Deep Reinforcement Learning Based Optimal Infinite-Horizon Control of Probabilistic Boolean Control Networks
Authors: Jingjie Ni, Fangfei Li, Zheng-Guang Wu
Date: 2023-04-07 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs). Compared with the existing literatures, the proposed method is model-free, namely, the system model and the initial states needn't to be known. Meanwhile, it is suitable for large-scale PBCNs. First, we establish the connection between deep reinforcement learning and optimal infinite-horizon control, and structure the problem into the framework of the Markov decision process. Then, PBCNs are defined as large-scale or small-scale, depending on whether the memory of the action-values exceeds the RAM of the computer. Based on the newly introduced definition, Q-learning (QL) and double deep Q-network (DDQN) are applied to the optimal infinite-horizon control of small-scale and large-scale PBCNs, respectively. Meanwhile, the optimal state feedback controllers are designed. Finally, two examples are presented, which are a small-scale PBCN with 3 nodes, and a large-scale one with 28 nodes. To verify the convergence of QL and DDQN, the optimal control policy and the optimal action-values, which are obtained from both the algorithms, are compared with the ones based on a model-based method named policy iteration. Meanwhile, the performance of QL is compared with DDQN in the small-scale PBCN.
An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification
Authors: Rodrigo A. González, Angel L. Cedeño, María Coronel, Juan C. Agüero, Cristian R. Rojas
Date: 2023-04-06 00:00:00
Tasks: parameter estimation

This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the output data is exploited in this work to derive an expectation-maximization (EM) algorithm for parameter estimation of the state-space and noise covariance matrices. For this purpose, we use the incremental discrete-time equivalent of the system, which leads to EM iterations of the continuous-time state-space matrices that can be computed by standard filtering and smoothing procedures. The effectiveness of the identification method is tested via Monte Carlo simulations.
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach
Authors: Rodrigo A. González, Koen Tiels, Tom Oomen
Date: 2023-04-06 00:00:00

Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement devices that sample equidistantly in the amplitude domain. The aim of this paper is to develop a non-parametric estimator of the impulse response of continuous-time systems based on such sampling strategy, known as Lebesgue-sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the output intersample behavior, which ultimately leads to more accurate models and more efficient output sampling compared to the standard approach. The efficacy of this method is demonstrated through a mass-spring damper case study.
Prediction of Protein Aggregation Propensity via Data-driven Approaches
Authors: Seungpyo Kang, Minseon Kim, Jiwon Sun, Myeonghun Lee, Kyoungmin Min
Date: 2023-04-06 00:00:00

Protein aggregation occurs when misfolded or unfolded proteins physically bind together, and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed using this database. The graph-based regression model outperformed the feature-based model, resulting in R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using Curated Protein Aggregation Database 2.0, to predict the aggregated intensity curves. In summary, this study suggests the approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising
Authors: Hyeonsu Kim, Jeheon Woo, SeongHwan Kim, Seokhyun Moon, Jun Hyeong Kim, Woo Youn Kim
Date: 2023-03-28 00:00:00
Tasks: Denoising

As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level quantum mechanical calculations, which are practically infeasible, limiting their applicability to real-world problems. To tackle this, we propose a new training framework, GeoTMI, that employs denoising process to predict properties accurately using easy-to-obtain geometries (corrupted versions of correct geometries, such as those obtained from low-level calculations). Our starting point was the idea that the correct geometry is the best description of the target property. Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property. GeoTMI also explicitly updates the corrupted input to approach the correct geometry as it passes through the GNN layers, contributing to more effective denoising. We investigated the performance of the proposed method using 3D GNNs for three prediction tasks: molecular properties, a chemical reaction property, and relaxed energy in a heterogeneous catalytic system. Our results showed consistent improvements in accuracy across various tasks, demonstrating the effectiveness and robustness of GeoTMI.
Idaho Blacks: Quiet Economic Triumph of Enduring Champions
Authors: Rama K. Malladi, Phillip Thompson
Date: 2023-04-07 00:00:00

As the United States is witnessing elevated racial differences pertaining to economic disparities, we have found a unique example contrary to the traditional narrative. Idaho is the only US state where Blacks earn more than Whites and all other races. In this paper, we examine how Idaho Blacks might have achieved economic success and, more importantly, what factors might have led to this achievement in reducing racial and economic disparities. Preliminary research suggests that fewer barriers to land ownership, smaller populations, well-knit communities, men's involvement in the family, and a relatively less hostile environment have played a significant role. Further research by historians can help the nation uncover the underlying factors to see if some factors are transportable to other parts of the country.