pwc-archive/papers-with-abstracts huggingface dataset:
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems
Authors: Hao Wang
Date: 2022-12-06 00:00:00
Tasks: Fairness, Learning-To-Rank, Recommendation Systems

Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Authors: Hao Wang
Date: 2022-12-06 00:00:00
Tasks: Recommendation Systems

Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization is modeled using normal distributions. To find better probabilistic models, algorithms such as RankMat, ZeroMat and DotMat have been invented in recent years. In this paper, we model the user rating behavior in recommender system as a Poisson process, and design an algorithm that relies on no input data to solve the recommendation problem and the cold start issue at the same time. We prove the superiority of our algorithm in comparison with matrix factorization, random placement, Zipf placement, ZeroMat, DotMat, etc.
Evolutionary Dynamics in a Varying Environment: Continuous versus Discrete Noise
Authors: Ami Taitelbaum, Robert West, Mauro Mobilia, Michael Assaf
Date: 2022-12-19 00:00:00

Environmental variations can significantly influence how populations compete for resources, and hence shape their evolution. Here, we study population dynamics subject to a fluctuating environment modeled by a varying carrying capacity changing continuously in time according to either binary random switches, or by being driven by a noise of continuous range. We consider a prototypical example of two competing strains, one growing slightly slower than the other, and consider also the scenario where the slow strain is a public goods producer. By systematically comparing the effect of binary- versus continuously-varying environment, we study how different noise statistics (mean, variance) influence the population size and fixation properties. We show that the slow strain fixation probability can be greatly enhanced for a continuously-varying environment compared to binary switches, even when the first two moments of the carrying capacity coincide.
Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions
Authors: Yao Xuan, Kris T. Delaney, Hector D. Ceniceros, Glenn H. Fredrickson
Date: 2022-12-16 00:00:00
Tasks: Generative Adversarial Network

A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional extension of the framework introduced in [1]. Several innovations and improvements are proposed. (1) A Sobolev space-trained, convolutional neural network (CNN) is employed to handle the exponential dimension increase of the discretized, local average monomer density fields and to strongly enforce both spatial translation and rotation invariance of the predicted, field-theoretic intensive Hamiltonian. (2) A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields without resorting to gradient descent methods that employ the training set. This GAN approach yields important savings of both memory and computational cost. (3) The proposed machine learning framework is successfully applied to 2D cell size optimization as a clear illustration of its broad potential to accelerate the exploration of parameter space for discovering polymer nanostructures. Extensions to three-dimensional phase discovery appear to be feasible.
DeFi Risk Transfer: Towards A Fully Decentralized Insurance Protocol
Authors: Matthias Nadler, Felix Bekemeier, Fabian Schär
Date: 2022-12-20 00:00:00

In this paper, we propose a fully decentralized and smart contract-based insurance protocol. We identify various issues in the Decentralized Finance (DeFi) insurance context and propose a solution to overcome these shortcomings. We introduce an economic model that allows for risk transfer without any external dependencies or centralized intermediaries. In particular, our proposal does not need any sort of subjective claim assessment, community voting or external data providers (oracles). Moreover, it solves the problem of over-insurance and proposes various ways to mitigate the capital inefficiencies usually seen with DeFi collateral. The work takes inspiration from peer-to-peer (P2P) insurance and collateralized debt obligations (CDO). We formally describe the protocol, assess its efficiency and key properties and present a reference implementation. Finally, we address limitations, extensions and ideas for further research.
Reduced Order Model of a Generic Submarine for Maneuvering Near the Surface
Authors: J. Ezequiel Martin, Maxwell Hammond, Nicholas Rober, Yakin Kim, Venanzio Cichella, Pablo Carrica
Date: 2022-12-19 00:00:00

A reduced order model of a generic submarine is presented. Computational fluid dynamics (CFD) results are used to create and validate a model that includes depth dependence and the effect of waves on the craft. The model and the procedure to obtain its coefficients are discussed, and examples of the data used to obtain the model coefficients are presented. An example of operation following a complex path is presented and results from the reduced order model are compared to those from an equivalent CFD calculation. The controller implemented to complete these maneuvers is also presented.
Modeling and optimal control of growth, energy, and resource dynamics of Hermetia illucens in mass production environment
Authors: Murali Padmanabha, Alexander Kobelski, Arne-Jens Hempel, Stefan Streif
Date: 2022-12-20 00:00:00

Mass production of Hermetia illucens insect larvae is now being adopted in many countries and is taking an industrial production approach. Despite abundant literature on factors that affect larvae growth and the optimal static parameters identified in laboratory setup, for an industrial production process it is necessary to identify the trajectories such that the growth as well as the production process is optimal. To achieve this in this work, some of the important requirements and challenges involved thereof are identified and objectives of the automation process are formulated within a model based optimal control setup. Mechanistic models necessary for the optimization framework are derived as differential equations that describe the dynamic variation of resources (feed, water, O2 etc.), energy, and larval biomass. In addition, the elevated metabolic activity of larvae corresponding to the final instar development is identified and also modelled based on the observation from experiments. The mass and energy balance approach used in modelling enables the quantification and distinction of the mass and energy flux components in various levels (e.g. larvae body, growing medium, production environment, and external environment) while holding its applicability for both open and closed/reactor based production setups. Finally, the trajectories generated using the synthesized optimal controller are tested under different scenarios showcasing significant reduction in resource consumption compared to a fixed set-point operation of the production setup. Results presented in this work not only showcase the potential of the mechanistic models and their application in identifying the relevant process parameters (e.g. reactor properties such as volume, thermal conductivity, actuator capacities), but most importantly in optimizing the process dynamically and tuning the process objectives as desired.
Implicit function theorem for nonlinear time-delay systems with algebraic constraints
Authors: Yahao Chen, Malek Ghanes, Jean-Pierre Barbot
Date: 2022-12-20 00:00:00

In this note, we discuss a generalization of the well-known implicit function theorem to the time-delay case. We show that the latter problem is closely related to the bicausal changes of coordinates of time-delay systems. An iterative algorithm is proposed to check the conditions and to construct the desired bicausal change of coordinates for the proposed implicit function theorem. Moreover, we show that our results can be applied to delayed differential-algebraic equations (DDAEs) to reduce their indices and to get their solutions. Some numerical examples are given to illustrate our results.
Auction designs to increase incentive compatibility and reduce self-scheduling in electricity markets
Authors: Conleigh Byers, Brent Eldridge
Date: 2022-12-20 00:00:00
Tasks: Scheduling

The system operator's scheduling problem in electricity markets, called unit commitment, is a non-convex mixed-integer program. The optimal value function is non-convex, preventing the application of traditional marginal pricing theory to find prices that clear the market and incentivize market participants to follow the dispatch schedule. Units that perceive the opportunity to make a profit may be incentivized to self-commit (submitting an offer with zero fixed operating costs) or self-schedule their production (submitting an offer with zero total cost). We simulate bidder behavior to show that market power can be exercised by self-committing/scheduling. Agents can learn to increase their profits via a reinforcement learning algorithm without explicit knowledge of the costs or strategies of other agents. We investigate different non-convex pricing models over a multi-period commitment window simulating the day-ahead market and show that convex hull pricing can reduce producer incentives to deviate from the central dispatch decision. In a realistic test system with approximately 1000 generators, we find strategic bidding under the restricted convex model can increase total producer profits by 4.4\% and decrease lost opportunity costs by 2/3. While the cost to consumers with convex hull pricing is higher at the competitive solution, the cost to consumers is higher with the restricted convex model after strategic bidding.
Modeling and Predictive Control for the Treatment of Hyperthyroidism
Authors: Tobias M. Wolff, Maylin Menzel, Johannes W. Dietrich, Matthias A. Müller
Date: 2022-12-20 00:00:00
Tasks: Model Predictive Control

In this work, we propose an approach to determine the dosages of antithyroid agents to treat hyperthyroid patients. Instead of relying on a trial-and-error approach as it is commonly done in clinical practice, we suggest to determine the dosages by means of a model predictive control (MPC) scheme. To this end, we first extend a mathematical model of the pituitary-thyroid feedback loop such that the intake of methimazole, a common antithyroid agent, can be considered. Second, based on the extended model, we develop an MPC scheme to determine suitable dosages. In numerical simulations, we consider scenarios in which (i) patients are affected by Graves' disease and take the medication orally and (ii) patients suffering from a life-threatening thyrotoxicosis, in which the medication is usually given intravenously. Our conceptual study suggests that determining the medication dosages by means of an MPC scheme could be a promising alternative to the currently applied trial-and-error approach.
Dissipativity of nonlinear ODE model of distribution voltage profile
Authors: Chiaki Kojima, Yuya Muto, Yoshihiko Susuki
Date: 2022-12-20 00:00:00

In this paper, we consider a power distribution system consisting of a straight feeder line. A nonlinear ordinary differential equation (ODE) model is used to describe the voltage distribution profile over the feeder line. At first, we show the dissipativity of the subsystems corresponding to active and reactive powers. We also show that the dissipation rates of these subsystem coincide with the distribution loss given by a square of current amplitudes. Moreover, the entire distribution system is decomposed into two subsystems corresponding to voltage amplitude and phase. As a main result, we prove the dissipativity of these subsystems based on the decomposition. As a physical interpretation of these results, we clarify that the phenomena related to the gradients of the voltage amplitude and phase are induced in a typical power distribution system from the dissipation equalities. Finally, we discuss a reduction of distribution losses by injecting a linear combination of the active and reactive powers as a control input based on the dissipation rate of the subsystem corresponding to voltage amplitude.
Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks
Authors: Dennis Agnew, Sharon Boamah, Reynold Mathieu, Austin Cooper, Janise McNair, Arturo Bretas
Date: 2022-12-20 00:00:00
Tasks: Benchmarking

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of $>20\%$; and an increase in throughput of approximately $11\%$ during the DoS attack scenarios.
High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach
Authors: Austin Cooper, Arturo Bretas, Sean Meyn, Newton G. Bretas
Date: 2022-12-20 00:00:00
Tasks: Fault Detection, State Estimation

This paper presents a model for detecting high-impedance faults (HIFs) using parameter error modeling and a two-step per-phase weighted least squares state estimation (SE) process. The proposed scheme leverages the use of phasor measurement units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through $\chi^2$ Hypothesis Testing applied to the composed measurement error (CME). Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide-area power flow and injection measurements, which are already inherent to the SE process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to HIF identification. Further, this error spread related to the HIF will be clearly discerned from measurement error. Case studies are performed on the 33-Bus Distribution System in Simulink.
Uncertainty Error Modeling for Non-Linear State Estimation With Unsynchronized SCADA and $μ$PMU Measurements
Authors: Austin Cooper, Arturo Bretas, Sean Meyn, Newton G. Bretas
Date: 2022-12-20 00:00:00
Tasks: State Estimation

Distribution systems of the future smart grid require enhancements to the reliability of distribution system state estimation (DSSE) in the face of low measurement redundancy, unsynchronized measurements, and dynamic load profiles. Micro phasor measurement units ($\mu$PMUs) facilitate co-synchronized measurements with high granularity, albeit at an often prohibitively expensive installation cost. Supervisory control and data acquisition (SCADA) measurements can supplement $\mu$PMU data, although they are received at a slower sampling rate. Further complicating matters is the uncertainty associated with load dynamics and unsynchronized measurements-not only are the SCADA and $\mu$PMU measurements not synchronized with each other, but the SCADA measurements themselves are received at different time intervals with respect to one another. This paper proposes a non-linear state estimation framework which models dynamic load uncertainty error by updating the variances of the unsynchronized measurements, leading to a time-varying system of weights in the weighted least squares state estimator. Case studies are performed on the 33-Bus Distribution System in MATPOWER, using Ornstein-Uhlenbeck stochastic processes to simulate dynamic load conditions.
Frequency-limited H$_2$ Model Order Reduction Based on Relative Error
Authors: Umair Zulfiqar, Xin Du, Qiuyan Song, Zhi-Hua Xiao, Victor Sreeram
Date: 2022-12-20 00:00:00

Frequency-limited model order reduction aims to approximate a high-order model with a reduced-order model that maintains high fidelity within a specific frequency range. Beyond this range, a decrease in accuracy is acceptable due to the nature of the problem. The quality of the reduced-order model is typically evaluated using absolute or relative measures of approximation error. Relative error, which represents the percentage error, becomes particularly relevant when reducing a plant model for the purpose of designing a reduced-order controller. This paper derives the necessary conditions for achieving a local optimum of the frequency-limited H2 norm for the relative error system. Based on these optimality conditions, an oblique projection algorithm is proposed to ensure a small relative error within the desired frequency interval. Unlike existing algorithms, the proposed approach does not necessitate solving large-scale Lyapunov and Ricatti equations. Instead, the proposed algorithm relies on solving sparse-dense Sylvester equations, which typically emerge in the majority of H2 model order reduction algorithms, but can be efficiently solved. To evaluate the performance of the proposed algorithm, a comparison is conducted with three existing techniques: frequency-limited balanced truncation, frequency-limited balanced stochastic truncation, and frequency-limited iterative Rational Krylov algorithm. The comparative analysis focuses on designing reduced-order controllers for high-order plants. Numerical results confirm that the reduced-order controllers obtained using the proposed algorithm ensure superior robust closed-loop stability.
H2 Model Order Reduction: A Relative Error Setting
Authors: Umair Zulfiqar, Xin Dua, Qiuyan Song, Muwahida Liaquat, Victor Sreeram
Date: 2022-12-20 00:00:00
Tasks: model

In dynamical system theory, the process of obtaining a reduced-order approximation of the high-order model is called model order reduction. The closeness of the reduced-order model to the original model is generally gauged by using system norms of additive or relative error system. The relative error is a superior criterion to the additive error in assessing accuracy in many applications like reduced-order controller and filter designs. In this paper, we propose an oblique projection algorithm that minimizes the H2 norm of the relative error transfer function. The selection of reduction matrices in the algorithm is motivated by the necessary conditions for local optima of the (squared) H2 norm of the relative error transfer function. Numerical simulation confirms that the proposed algorithm compares well in accuracy with balanced stochastic truncation while avoiding the solution of large-scale Riccati and Lyapunov equations.
Model Predictive Control for Neuromimetic Quantized Systems
Authors: Zexin Sun, John Baillieul
Date: 2022-12-19 00:00:00
Tasks: model, Model Predictive Control, Quantization

Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved with model predictive control (MPC) by deriving the conditions under which the quantized system can guarantee (asymptotic) stability during emulation by optimizing a Lyapunov-like objective function. The neuromimetic model features large numbers of discrete inputs, and the optimization involves integer variables. The approach in the paper begins by solving an optimization using model predictive control (MPC) and then using a neural network to train the data generated in this process and applying Fincke and Pohst's sphere decoding algorithm to narrow down the search for the optimal solution.
Experimental Low-speed Positioning System with VecTwin Rudder for Automatic Docking (Berthing)
Authors: Dimas M. Rachman, Yusuke Aoki, Yoshiki Miyauchi, Naoya Umeda, Atsuo Maki
Date: 2022-12-19 00:00:00

A VecTwin rudder system comprises twin fishtail rudders with reaction fins to increase its performance. With a constant propeller revolution number, the vessel can execute special low-speed maneuvers like hover, crabbing, reverse, and rotation. Such low-speed maneuvers are termed dynamic positioning (DP), and a DP vessel should be fully/overly actuated with several thrusters. This article introduces a novel and experimental VecTwin positioning system (VTPS) without making the ship fully/overly actuated. Unlike the usual dynamic positioning system (DPS), the VTPS is developed for low-speed operations in a calm harbor area. It is designed upon an assumption that the forces due to the interaction between the rudders, the propeller, and the hull are linear with the rudder angles within a range around the hover rudder angle. The linear relationship is obtained through linear regression of the results from several CFD simulations. The VTPS implements a PID controller that regulates the actuator forces to achieve the given low-speed positioning objective. It was tested in combined automatic docking and position-keeping experiments where disturbances from the environment exist. It shows promising potential for a practical application but with further improvements.