Due to the efficiency of computation and storage, the dual-encoder model has been usually utilized for retrieval tasks involving a large volume of multimodal data. The dualencoder model trains visual tokens and text t...
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The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-bi...
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The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-binding model of the Chern insulator with a Chern number C=±2 by coupling two layers of the half Bernevig–Hughes–Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological *** determine the Chern number of our Floquet-version model,we extend the energy pumping method proposed by Martin et al.[2017 ***.X 7041008]and the topological oscillation method introduced by Boyers et al.[2020 ***.125160505],followed by numerical simulations for both *** simulation results demonstrate the successful extraction of the Chern number using either of these methods,providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig–Hughes–Zhang ***,we briefly discuss a potential experimental implementation for our *** work demonstrates significant potential for simulating complex topological matter using quantum computing platforms,thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena.
The research on 'Gesture and Emotion Detection using Quantum Computing' is driven by an increasing need to overcome the linguistic and emotional barriers encountered by the Deaf community. With a deep understa...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neut...
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The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data *** utilizes on-demand resource provisioni...
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The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data *** utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization *** this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud *** capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource *** is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into *** addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS *** further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM *** results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for *** statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
Cancer was found to be a leading cause of human mortality in the year 2020, accounting for one in six deaths worldwide, as per data published by the World Health Organization. Early detection and treatment can play a ...
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Cancer was found to be a leading cause of human mortality in the year 2020, accounting for one in six deaths worldwide, as per data published by the World Health Organization. Early detection and treatment can play a major role in averting these deaths. Delayed cancer care often leads to lower chances of survival, greater complications associated with treatment and higher costs. Histopathology image analysis is a technology that plays a vital role in the early detection and diagnosis of cancer. The segmentation of regions of interest (RoIs) from whole slide images (WSIs) provides useful information for differentiating diseased tissues from normal ones. A strong segmentation framework is required in this case due to the rich and irregular mix of visual patterns of the RoIs. In this work, we present an atrous inception-resnet based UNet model with dense skip connections (AIR-UNet++) for the effective segmentation and detection of various RoIs from histopathology images stained with Hematoxylin and Eosin (H &E). To test the performance of the proposed method, experiments are carried out on five different datasets, including nuclei segmentation, TNBC, MoNuSeg, lymphocyte detection and MoNuSAC (Lymphocyte, Neutrophils, Macrophages, Epithelial). Experimental results show that the proposed AIR-UNet++ method outperforms other UNet variants, pre-trained models. Specifically, for the nuclei segmentation dataset, we achieved a Dice coefficient (DC) of 0.74 and a Jaccard Index (JI) of 0.64. For the TNBC dataset, our method achieved a DC of 0.88 and a JI of 0.79, while on the MoNuSeg dataset, we obtained a DC of 0.79 and a JI of 0.67. For the Lymphocyte detection dataset, we achieved an accuracy of 0.98 and an F1 score of 0.88. Notably, in the MoNuSAC-Lymphocyte dataset, our model achieved a DC of 0.85 and a JI of 0.75. Similarly, for the MoNuSAC-Neutrophils dataset, the DC was 0.83 with a JI of 0.72, for MoNuSAC-Macrophages, the DC was 0.82 with a JI of 0.72, and for MoNuSAC-Ep
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)
Accurate and reliable wind power prediction is the key to realize the smooth grid connection of wind power generation. However, the influence of wind speed, wind direction, and air density and other factors leads to t...
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Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. Ho...
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