Stream-based active learning for sliding windows under the influence of verification latency

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency

Characterization of Turing diffusion-driven instability on evolving domains

In this paper we establish a general theoretical framework for Turing diffusion-driven instability for reaction-diffusion systems on time-dependent evolving domains. The main result is that Turing diffusion-driven instability for reaction-diffusion systems on evolving domains is characterised by Lyapunov exponents of the evolution family associated with the linearised system (obtained by linearising the original system along a spatially independent solution). This framework allows for the inclusion of the analysis of the long-time behavior of the solutions of reaction-diffusion systems. Applications to two special types of evolving domains are considered: (i) time-dependent domains which evolve to a final limiting fixed domain and (ii) time-dependent domains which are eventually time periodic. Reaction-diffusion systems have been widely proposed as plausible mechanisms for pattern formation in morphogenesis

Techno-Economic Assessment & Life Cycle Assessment Guidelines for CO2 Utilization (Version 1.1)

Climate change is one of the greatest challenges of our time. Under the auspices of the UN Framework Convention on Climate Change and through the Paris Agreement, there is a commitment to keep global temperature rise this century to well below two degrees Celsius compared with pre-industrial levels. This will require a variety of strategies, including increased renewable power generation, broad-scale electrification, greater energy efficiency, and carbon-negative technologies. With increasing support worldwide, innovations in carbon capture and utilization (CCU) technologies are now widely acknowledged to contribute to achieving climate mitigation targets while creating economic opportunities. To assess the environmental impacts and commercial competitiveness of these innovations, Life Cycle Assessment (LCA) and Techno-Economic Assessment (TEA) are needed. Against this background, guidelines (Version 1.0) on LCA and TEA were published in 2018 as a valuable toolkit for evaluating CCU technology development. Ever since, an open community of practitioners, commissioners, and users of such assessments has been involved in gathering feedback on the initial document. That feedback has informed the improvements incorporated in this updated Version 1.1 of the Guidelines. The revisions take into account recent publications in this evolving field of research; correct minor inconsistencies and errors; and provide better alignment of TEA with LCA. Compared to Version 1.0, some sections have been restructured to be more reader-friendly, and the specific guideline recommendations are renamed ‘provisions.’ Based on the feedback, these provisions have been revised and expanded to be more instructive.Global CO2 Initiative at the University of MichiganEIT Climate-KIChttp://deepblue.lib.umich.edu/bitstream/2027.42/162573/5/TEA&LCA Guidelines for CO2 Utilization v1.1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162573/7/ESI reference scenario data_Corrected.xlsxSEL

A' juhimlőről és ennek béoltásáról, melly által ezen dögleletes nyavalyának pusztító ereje elvétethetik, sőt a' juhimlő az egész országból is kiirtathatik

Hozzájárul egy rövid értekezés, a' gyapju javításának hasznos voltáró

Stream-based active learning for sliding windows under the influence of verification latency

Gefördert im Rahmen des Projekts DEA

Toward optimal probabilistic active learning using a Bayesian approach

Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims

Toward optimal probabilistic active learning using a Bayesian approach

Gefördert im Rahmen des Projekts DEA

Toward optimal probabilistic active learning using a Bayesian approach

Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims

Stream-based active learning for sliding windows under the influence of verification latency

Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier’s performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label’s utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label’s arrival date a priori and the classifier’s training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency