With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of ...
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Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model compl...
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Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.
Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests...
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Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. Using an information theoretic point of view, Aldridge 2012 showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required is essentially the same as for non-adaptive GT. Secure GT considers a scenario where there is an eavesdropper who may observe on average a fraction δ of the tests results, yet should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is 1/(1 − δ) times the number of tests without the secrecy constraint. In this paper, we consider Secure Adaptive GT. Specifically, when during the makeup of the pools one has access to a private feedback link from the lab, of rate R f . We prove that the number of tests required for both correct reconstruction at the legitimate lab, with high probability, and negligible mutual information at the eavesdropper is 1/min{1, 1 − δ + R f } times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard the actual test results and simply send keys, these keys should be enhanced through a "secret sharing" scheme before usage. We derive sufficiency and necessity bounds that completely characterizes the Secure Adaptive GT capacity. Moreover, we consider additional models of Secure Adaptive GT, where we make a clear distinction between the lab performing the tests, and the doctor analyzing the results. Specifically, we consider curious but non-malicious, non-cooperating labs. Each lab gets a fraction δ of pool-tests to perform. Yet, we want to keep each lab ignor
Previous articles on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. Howe...
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This paper investigates the impact of integrating large-scale photovoltaic (PV) power plants (LSPVPPs) on the transient stability of power systems. As renewable energy sources, including PV and wind generation systems...
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This work reports a millimeter wave (mmWave) thin-film bulk acoustic resonator (FBAR) in sputtered scandium aluminum nitride (ScAlN). This paper identifies challenges of frequency scaling sputtered ScAlN into mmWave a...
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Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, es...
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The proposed work presents a hybrid signal processing - three level deep learning framework leveraging transfer learning to detect incipient stator inter turn faults in induction motor drives. An experimental test rig...
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Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource langu...
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Fast and accurate methods for spectrum sensing (SS) are the key elements in cognitive radio networks (CRNs) that achieve high SS. This paper proposes a reinforcement learning (RL) scheme for secondary users (SUs) in a...
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