Humans have longed for computers to take over and make the monotonous and tedious tasks obsolete. Artificial Intelligence(AI) is just the right answers to this problem. There are not one but many sectors that are bein...
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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)
Virtual Power Plants (VPPs) are a key factor in smart grids, and they use cloud computing to integrate and manage Distributed Energy Resources (DERs). VPPs use Machine Learning (ML) methods to optimize various tasks. ...
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Literature on power quality compensators (PQC) was shown to increase the reliability of the power system. While finite control set model predictive control (MPC) achieves high fidelity tracking for multi-objective cos...
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Machine learning (ML), or artificial intelligence (AI), is one of the emerging technologies for implementing the next-generation smart grid. In recent years, significant efforts have been devoted to exploring the pote...
Machine learning (ML), or artificial intelligence (AI), is one of the emerging technologies for implementing the next-generation smart grid. In recent years, significant efforts have been devoted to exploring the potentials of ML and AI for solving complex power system problems. The IEEE WG on Machine Learning for Power Systems has recently organized several events in this heatedly studied area. These events include the following:
Intermittent deep neural network (DNN) inference is a promising technique to enable intelligent applications on tiny devices powered by ambient energy sources. Nonetheless, intermittent execution presents inherent cha...
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This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies stru...
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This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging *** envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these *** enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image ***,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent *** utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic *** computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition *** validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced *** refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original *** Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX *** envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of lumi
Vehicle suspension systems play a pivotal role in ensuring both comfort and safety during driving by maintaining stability and handling. Recent advancements in reinforcement learning (RL) for control design offer adap...
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This research systematically evaluates the performance of diverse Convolutional Neural Network (CNN) architectures in enhancing the accuracy of bone fracture detection in medical imaging. The study aims to understand ...
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Offline planning has recently emerged as a promising reinforcement learning (RL) paradigm for locomotion and control tasks. In particular, model-based offline planning learns an approximate dynamics model from the off...
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