Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work, we explore learning (structured) spa...
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Multi-Objective optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objecti...
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ISBN:
(纸本)9781424492695
Multi-Objective optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
Feasibility of the novel utilization of a pressure switch mechanism for re-calibrating drifted implanted pressure sensors in-situ is demonstrated. We have designed and characterized the pressure response of a system, ...
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ISBN:
(纸本)9781509010134
Feasibility of the novel utilization of a pressure switch mechanism for re-calibrating drifted implanted pressure sensors in-situ is demonstrated. We have designed and characterized the pressure response of a system, which can quantify the offset of a sensor after it has been implanted. The benchtop device is constructed of a 25 pm thick titanium diaphragm with 10 mm working diameter. An optimization algorithm detected a characteristic change in the pressure response produced by the activation of a pressure switch. The repeatability of detection across three sensors is within ±0.23 mmHg over 8 pressurization cycles.
Orthogonal binary sequences play an important role in Multiple-input multiple-output (MIMO) radar. However, mainly due to mathematical difficulties, the synthesis problem of multiple binary sequences with low auto and...
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ISBN:
(纸本)9781510822023
Orthogonal binary sequences play an important role in Multiple-input multiple-output (MIMO) radar. However, mainly due to mathematical difficulties, the synthesis problem of multiple binary sequences with low auto and cross correlation sidelobes is still an open problem. In this paper, mismatched filter, which basically aims at reducing the sidelobe levels at the pulse compression output, is exploited and multiple binary sequences and mismatched filters are optimized by joint synthesis algorithms (JSA), based on an alternating direction method. JSA for minimizing the integrated sidelobe level (ISL) and peak sidelobe level (PSL) metrics are proposed, and the corresponding computation complexities are analyzed. Simulation results show the effectiveness of our proposed algorithms.
Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regre...
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This paper presents a linear FPT algorithm to find a tree decomposition with a 2-approximation of the treewidth with a significantly smaller exponential dependence on the treewidth in the running time than previously ...
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In the construction industry, a specialist subcontractor manages a taskforce of single-skilled laborers to work on multiple construction sites, aiming to minimize the total cost and stay profitable and competitive. Th...
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ISBN:
(纸本)9781424427086
In the construction industry, a specialist subcontractor manages a taskforce of single-skilled laborers to work on multiple construction sites, aiming to minimize the total cost and stay profitable and competitive. This paper presents a simulation-based approach to assist the subcontractor in scheduling the application of limited laborer resources to handle jobs over multiple concurring sites. Factoring in technological constraints, repetitive building cycles, alternative method options, and the limited quantity of skilled laborers, we resort to computer power (including simulation and optimization algorithms resulting from recent research) in search of the best combination of construction methods at individual sites along with the optimum size of labor force, aimed to find the least cost for completing the jobs at all sites. A case study of bar-bender scheduling over three sites by use of an in-house computer tool results in the optimum method combinations, the optimum crew size, and the optimum resource schedule.
Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to autom...
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ISBN:
(纸本)9781538646595
Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.
In this paper, we consider the runtime monitoring of norms with imperfect monitors. A monitor is imperfect for a norm if it has insufficient observational capabilities to determine if a given execution trace of a mult...
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ISBN:
(纸本)9781634391313
In this paper, we consider the runtime monitoring of norms with imperfect monitors. A monitor is imperfect for a norm if it has insufficient observational capabilities to determine if a given execution trace of a multi-agent system complies with or violates the norm. One approach to the problem of imperfect monitors is to enhance the observational capabilities of the normative organisation. However this may be costly or in some cases impossible. Instead we show how to synthesise an approximation of an 'ideal' norm that can be perfectly monitored given a monitor, and which is optimal in the sense that any other approximation would fail to detect at least as many violations of the ideal norm. We give a logical analysis of (im)perfect monitors. We state the computational complexity of the norm approximation problem, and give an optimal algorithm for generating optimal approximations of norms given a monitor.
Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is ge...
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ISBN:
(纸本)9781509041183
Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally non-convex, the solution it obtains is not guaranteed to be globally optimal. In this paper, we describe linear and nonlinear tomographic reconstruction methods and compare them numerically. We present a review of the LT and, in addition, we investigate the influence of the initialization and exemplify the effect of regularization on the convergence of the algorithm. In particular, we show that both are essential for high-quality imaging in strongly scattering scenarios.
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