Evolutionary Many-objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aiming at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker’s preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems

Biological aspects of radiation and drug-eluting stents for the prevention of restenosis

Based on recent advances, this article aims to review the biological basis for the use of either radiation or drug-eluting stents for the prevention of restenosis, and to elucidate the complementary role that they may play in the future. Vascular restenosis is a multifactorial process primarily driven by the remodeling of the arterial wall, as well as by the hyperproliferation of smooth muscle cells (SMC). These pathophysiological features are the target of therapeutic strategies aimed at inhibiting constrictive remodeling as well as inhibiting SMC proliferation. The success of radiation as well as anti-proliferative drugs such as paclitaxel and sirolimus lies in the primary and/or multifactorial inhibition of cell proliferation. Radiation has the additional feature of preventing constrictive remodeling while sirolimus has the potential property of being anti-inflammatory, which may be a desirable feature. The effects of radiation are not reliant on any uptake and "metabolism” by the target cells, as in the case with drugs, and thus radiation potentially may be more effective as a result of its more-direct action. However, radiation does have some significant drawbacks compared to drug-eluting stents, including a much delayed re-endothelialization resulting in the need for prolonged anti-platelet therapy. Based on recent clinical data, drug-eluting stents have been shown to markedly reduce the likelihood of restenosis, which actually favors this approach for the prevention of restenosis. From a biological perspective, drug-eluting stents and radiation have certain differences, which are reviewed in this articl

Pseudo Label Selection is a Decision Problem

Pseudo-Labeling is a simple and effective approach to semi-supervised learning. It requires criteria that guide the selection of pseudo-labeled data. The latter have been shown to crucially affect pseudo-labeling's generalization performance. Several such criteria exist and were proven to work reasonably well in practice. However, their performance often depends on the initial model fit on labeled data. Early overfitting can be propagated to the final model by choosing instances with overconfident but wrong predictions, often called confirmation bias. In two recent works, we demonstrate that pseudo-label selection (PLS) can be naturally embedded into decision theory. This paves the way for BPLS, a Bayesian framework for PLS that mitigates the issue of confirmation bias. At its heart is a novel selection criterion: an analytical approximation of the posterior predictive of pseudo-samples and labeled data. We derive this selection criterion by proving Bayes-optimality of this "pseudo posterior predictive". We empirically assess BPLS for generalized linear, non-parametric generalized additive models and Bayesian neural networks on simulated and real-world data. When faced with data prone to overfitting and thus a high chance of confirmation bias, BPLS outperforms traditional PLS methods. The decision-theoretic embedding further allows us to render PLS more robust towards the involved modeling assumptions. To achieve this goal, we introduce a multi-objective utility function. We demonstrate that the latter can be constructed to account for different sources of uncertainty and explore three examples: model selection, accumulation of errors and covariate shift.Comment: Accepted for presentation at the 46th German Conference on Artificial Intelligenc

Towards Bayesian Data Selection

A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of data addition into decision theory by framing data selection as a decision problem. This paves the way for finding Bayes-optimal selections of data. For the illustrative case of self-training in semi-supervised learning, we derive the respective Bayes criterion. We further show that deploying this criterion mitigates the issue of confirmation bias by empirically assessing our method for generalized linear models, semi-parametric generalized additive models, and Bayesian neural networks on simulated and real-world data.Comment: 5th Workshop on Data-Centric Machine Learning Research (DMLR) at ICML 202

Characterisation of feline renal cortical fibroblast cultures and their transcriptional response to transforming growth factor beta 1

Chronic kidney disease (CKD) is common in geriatric cats, and the most prevalent pathology is chronic tubulointerstitial inflammation and fibrosis. The cell type predominantly responsible for the production of extra-cellular matrix in renal fibrosis is the myofibroblast, and fibroblast to myofibroblast differentiation is probably a crucial event. The cytokine TGF-β1 is reportedly the most important regulator of myofibroblastic differentiation in other species. The aim of this study was to isolate and characterise renal fibroblasts from cadaverous kidney tissue of cats with and without CKD, and to investigate the transcriptional response to TGF-β1

Imprecise Bayesian optimization

Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we propose a robust version of BO grounded in the theory of imprecise probabilities: Prior-mean-RObust Bayesian Optimization (PROBO). Our method is motivated by an empirical and theoretical analysis of the GP prior specifications’ effect on BO’s convergence. A thorough simulation study finds the prior’s mean parameters to have the highest influence on BO’s convergence among all prior components. We thus turn to this part of the prior GP in more detail. In particular, we prove regret bounds for BO under misspecification of GP prior’s mean parameters. We show that sublinear regret bounds become linear under GP misspecification but stay sublinear if the misspecification-induced error is bounded by the variance of the GP. In response to these empirical and theoretical findings, we introduce PROBO as a univariate generalization of BO that avoids prior mean parameter misspecification. This is achieved by explicitly accounting for prior GP mean imprecision via a prior near-ignorance model. We deploy our approach on graphene production, a real-world optimization problem in materials science, and observe PROBO to converge faster than classical BO

Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision

We provide a theoretical and computational investigation of the Gamma-Maximin method with soft revision, which was recently proposed as a robust criterion for pseudo-label selection (PLS) in semi-supervised learning. Opposed to traditional methods for PLS we use credal sets of priors ("generalized Bayes") to represent the epistemic modeling uncertainty. These latter are then updated by the Gamma-Maximin method with soft revision. We eventually select pseudo-labeled data that are most likely in light of the least favorable distribution from the so updated credal set. We formalize the task of finding optimal pseudo-labeled data w.r.t. the Gamma-Maximin method with soft revision as an optimization problem. A concrete implementation for the class of logistic models then allows us to compare the predictive power of the method with competing approaches. It is observed that the Gamma-Maximin method with soft revision can achieve very promising results, especially when the proportion of labeled data is low.Comment: Accepted at the 11th International Conference on Soft Methods in Probability and Statistics (SMPS) 202

Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System

One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approachComment: 8 pages, 4 figures. To be published in 2023 IEEE Conference on Control Technology and Applications (CCTA) proceeding