Variational Bayesian learning (VBL)-based sparse channel state information (CSI) estimation is conceived for multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) and for orthogonal time sequenc...
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In this paper, an incident-angle-insensitive, dual-polarization metacell is proposed using a machine-Iearning-based optimization method. The metacell is based on a three-layer dual-polarized metacell with two substrat...
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The increased intensity and frequency of heat waves are impacting communities and power grid operations worldwide. Resilience hubs can provide communities with several essential services and resources, including commu...
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This paper presents a comparative analysis of renewable energy power output using forecast weather with different margins and historical weather data as benchmarks for selected days. The analysis evaluates the accurac...
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The increasing reliance on web services has led to a rise in cybersecurity threats, particularly Cross-Site Scripting (XSS) attacks, which target client-side layers of web applications by injecting malicious scripts. ...
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Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized. Current experimental methods remain ti...
Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized. Current experimental methods remain time, cost and labor intensive, limiting the number of enzymes they can reasonably screen. In this work, we propose a computational framework for in silico enzyme screening. Through a contrastive objective, we train CLIPZyme to encode and align representations of enzyme structures and reaction pairs. With no standard computational baseline, we compare CLIPZyme to existing EC (enzyme commission) predictors applied to virtual enzyme screening and show improved performance in scenarios where limited information on the reaction is available (BEDROC85 of 44.69%). Additionally, we evaluate combining EC predictors with CLIPZyme and show its generalization capacity on both unseen reactions and protein clusters. Copyright 2024 by the author(s)
This study introduces a comprehensive framework for investigating the impacts of depot charging on distribution grids. The framework is applied to a synthetic electric bus fleet modeled after real-world transit routes...
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The self-attention mechanism distinguishes transformer-based large language models (LLMs) apart from convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference ...
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In modern manufacturing, technology such as Virtual Reality and Augmented Reality is leveraged to facilitate efficient and targeted production. Projection Mapping (PM) integrated with RFID is utilised in this research...
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In computational electromagnetics (CEM) and engineering, uncertainty quantification (UQ) plays a critical role in enhancing the accuracy and reliability of analyses and designs, particularly when dealing with complex ...
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