While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing su...
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While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing such feasibility assumptions, and in particular address the problem in the linear bandit setting, thus characterising the costs of feasibility testing for an unknown linear program using bandit feedback. Concretely, we test if ∃x : Ax ≥ 0 for an unknown A ∈ m×d, by playing a sequence of actions xt ∈ d, and observing Axt + noise in response. By identifying the hypothesis as determining the sign of the value of a minimax game, we construct a novel test based on low-regret algorithms and a nonasymptotic law of iterated logarithms. We prove that this test is reliable, and adapts to the 'signal level,' Γ, of any instance, with mean sample costs scaling as Õ(d2/Γ2). We complement this by a minimax lower bound of Ω(d/Γ2) for sample costs of reliable tests, dominating prior asymptotic lower bounds by capturing the dependence on d, and thus elucidating a basic insight missing in the extant literature on such problems. Copyright 2024 by the author(s)
Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more i...
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Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.
This research presents the performance of submillimetre airborne radar systems in the presence of various atmospheric propagation impairments. The study aims to assess the effects of environmental factors such as rain...
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Social media has greatly streamlined communication in recent years which uses network, shares information, and keep up with current events. Many posts on social media are questionable and meant to deceive. There is a ...
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With the exponential growth of the Internet of Things through time there has been an enormous increase in the usage of tiny devices and information being exchanged, between low resource devices like sensors, PDA’s an...
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The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue...
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The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.
This study presents an in-depth evaluation of sentiment analysis models applied to a US Twitter dataset. The performance of machine learning models considered in this study are Random Forest, Support Vector Machine (S...
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In recent years, with the rapid development of the Internet of Vehicles (IoVs) and the widespread application of integrated sensing and communication (ISAC) in the IoVs, the integrated sensing and computation offloadi...
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Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the re...
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Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.
A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of maliciou...
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