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Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may…

Machine Learning · Computer Science 2023-02-24 Jose Pablo Folch , Robert M Lee , Behrang Shafei , David Walz , Calvin Tsay , Mark van der Wilk , Ruth Misener

We investigate the dynamics of the phase variable of an ideal underdamped Josephson junction in switching current experiments. These experiments have provided the first evidence for macroscopic quantum tunneling in large Josephson junctions…

Superconductivity · Physics 2009-11-11 G. S. Paraoanu

Likelihood methods for measuring statistical evidence obey the likelihood principle while maintaining bounded and well-controlled frequency properties. These methods lend themselves to sequential study designs because they measure the…

Methodology · Statistics 2017-11-07 Jeffrey D Blume , Leena Choi

Given n experiment subjects with potentially heterogeneous covariates and two possible treatments, namely active treatment and control, this paper addresses the fundamental question of determining the optimal accuracy in estimating the…

Machine Learning · Statistics 2024-11-13 Jiachun Li , David Simchi-Levi , Yunxiao Zhao

The problem of detecting a single anomalous process among multiple independent processes is considered. Under a constraint on the number of processes that can be probed simultaneously, the decision maker should decide which processes to…

Signal Processing · Electrical Eng. & Systems 2021-01-15 Fengfan Qin , Da Chen , Hui Feng , Qing Zhao , Tao Yang , Bo Hu

In this paper we consider two-stage adaptive dose-response study designs, where the study design is changed at an interim analysis based on the information collected so far. In a simulation study, two approaches will be compared for these…

Methodology · Statistics 2016-02-08 Emma McCallum , Björn Bornkamp

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions by itself or even for discovering new materials with minimal human intervention.…

Machine Learning · Computer Science 2024-10-03 Imtiaz Ahmed , Satish Bukkapatnam , Bhaskar Botcha , Yu Ding

Measurement of signals generated by superconducting Josephson junction (JJ) circuits require ultra-fast components located in close proximity to the generating circuitry. We report a detailed study of optimal design criteria for a JJ-based…

Instrumentation and Detectors · Physics 2025-03-27 Logan Howe , Bart van Zeghbroeck , David Olaya , John Biesecker , Charles J. Burroughs , Samuel P. Benz , Peter F. Hopkins

This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing…

Methodology · Statistics 2025-03-04 Elvis Han Cui , Michael Collins , Jessica Munson , Weng Kee Wong

We show that two capacitively-coupled Josephson junctions, in the quantum limit, form a simple coupled qubit system with effective coupling controlled by the junction bias currents. We compute numerically the energy levels and wave…

Josephson junctions and superconducting wires when probed with current pulses exhibit stochastic switching from superconducting to a stable non-zero voltage state. Electrical current dependence of the switching probability (so called…

Mesoscale and Nanoscale Physics · Physics 2019-05-29 Marek Foltyn , Konrad Norowski , Alexander Savin , Maciej Zgirski

This paper provides a statistical method to test whether a system that performs a binary sequential hypothesis test is optimal in the sense of minimizing the average decision times while taking decisions with given reliabilities. The…

Information Theory · Computer Science 2018-01-08 Meik Dörpinghaus , Izaak Neri , Édgar Roldán , Heinrich Meyr , Frank Jülicher

We consider the sequential experimental design problem in the predict-then-optimize paradigm. In this paradigm, the outputs of the prediction model are used as coefficient vectors in a downstream linear optimization problem. Traditional…

Machine Learning · Statistics 2026-02-06 Beichen Wan , Mo Liu , Paul Grigas , Zuo-Jun Max Shen

Bayesian persuasion and its derived information design problem has been one of the main research agendas in the economics and computation literature over the past decade. However, when attempting to apply its model and theory, one is often…

Computer Science and Game Theory · Computer Science 2023-03-21 Bonan Ni , Weiran Shen , Pingzhong Tang

In this article we consider a bosonic Josephson junction, a model system composed by two coupled nonlinear quantum oscillators which can be implemented in various physical contexts, initially prepared in a product of weakly populated…

Quantum Physics · Physics 2018-06-05 Dionisis Stefanatos , Emmanuel Paspalakis

Data collection costs can vary widely across variables in data science tasks. Two-phase designs can be employed to save data collection costs. This paper considers the two-phase studies where inexpensive variables are collected for all…

Methodology · Statistics 2025-12-04 Ruoyu Wang , Qihua Wang , Wang Miao

In recent years, more attention has been paid prominently to accelerated degradation testing in order to characterize accurate estimation of reliability properties for systems that are designed to work properly for years of even decades.…

Applications · Statistics 2021-09-23 Helmi Shat

Systems with both quantitative and qualitative responses are widely encountered in many applications. Design of experiment methods are needed when experiments are conducted to study such systems. Classic experimental design methods are…

Methodology · Statistics 2023-04-24 Lulu Kang , Xinwei Deng , Ran Jin

We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where…

Machine Learning · Computer Science 2024-05-16 Stefan Pricopie , Richard Allmendinger , Manuel Lopez-Ibanez , Clyde Fare , Matt Benatan , Joshua Knowles

By coupling a harmonic oscillator to a quantum system it is possible to perform a dispersive measurement that is quantum non-demolition (QND), with minimal backaction. A non-linear oscillator has the advantage of measurement gain, but what…

Mesoscale and Nanoscale Physics · Physics 2008-09-12 O. Naaman , J. Aumentado , L. Friedland , J. S. Wurtele , I. Siddiqi