By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency com...
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Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for...
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Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for harnessing solar energy. These cells utilize perovskite-structured materials, typically hybrid organicinorganic lead halide compounds, as the light-absorbing layer.
Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific...
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Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used ...
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The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, as well as the capability to meet different accuracy demands. However, the compression per...
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Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. ...
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Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. However, the resulting high model performance, measured by a data utility function, may not be preserved when some data owners, enabled by the GDPR's right to erasure, request their data to be deleted from the ML model. This raises an important question for learners who are temporarily unable or unwilling to acquire data again: During the initial data acquisition of a training set of size k, can we proactively maximize the data utility after future unknown deletions? We propose that the learner anticipates/estimates the probability that (i) each data owner in the feasible set will independently delete its data or (ii) a number of deletions occur out of k, and justify our proposal with concrete real-world use cases. Then, instead of directly maximizing the data utility function, the learner can maximize the expected or risk-averse post-deletion utility based on the anticipated probabilities. We further propose how to construct these deletion-anticipative data selection (DADS) maximization objectives to preserve monotone submodularity and near-optimality of greedy solutions, how to optimize the objectives and empirically evaluate DADS' performance on real-world datasets. Copyright 2024 by the author(s)
With the enhanced usage of artificial-intelligence-driven applications, the researchers often face challenges in improving the accuracy of data classification models, while trading off the complexity. In this article,...
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The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as...
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Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting i...
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A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilis...
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A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and O(N−1/2) suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments. Copyright 2024 by the author(s)
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