Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data con...
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Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)*** this work,referring to the time-varying propagation circumstances,with semi-superv...
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Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)*** this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming ***,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid *** with the existing network structure,the proposed network structure can achieve better transmission performance and lower ***,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data *** the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed *** the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel *** results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single ***,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.
This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains,including scientific writing,mathematics,education,programming,and *** explore the potential of ChatGPT to enhance produ...
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This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains,including scientific writing,mathematics,education,programming,and *** explore the potential of ChatGPT to enhance productivity,streamline problem-solving processes,and improve writing ***,we highlight the potential risks associated with excessive reliance on ChatGPT in these *** limitations encompass factors like incorrect and fictitious responses,inaccuracies in code,limited logical reasoning abilities,overconfidence,and critical ethical concerns of copyright and privacy *** outline areas and objectives where ChatGPT proves beneficial,applications where it should be used judiciously,and scenarios where its reliability may be *** light of observed limitations,and given that the tool's fundamental errors may pose a special challenge for non-experts,ChatGPT should be used with a strategic *** drawing from comprehensive experimental studies,we offer methods and flowcharts for effectively using *** recommendations emphasize iterative interaction with ChatGPT and independent verification of its *** the importance of utilizing ChatGPT judiciously and with expertise,we recommend its usage for experts who are well-versed in the respective domains.
Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, t...
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Unified programming models can effectively improve program portability on various heterogeneous high-performance *** unified programming models put a lot of effort to code portability but are still far from achieving ...
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Unified programming models can effectively improve program portability on various heterogeneous high-performance *** unified programming models put a lot of effort to code portability but are still far from achieving good performance *** this paper,we present a preliminary design of a performance-portable unified programming model including four aspects:programming language,programming abstraction,compilation optimization,and scheduling ***,domain-specific languages introduce domain knowledge to decouple the optimizations for different applications and *** unified programming abstraction unifies the common features of different architectures to support common ***-level compilation optimization enables comprehensive performance optimization based on multi-level intermediate ***-aware lightweight runtime scheduling system improves the resource utilization of heterogeneous *** is a perspective paper to show our viewpoints on programming models for emerging heterogeneous systems.
In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large langua...
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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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In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further...
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In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp edges could be obtained directly from deep convolutional neural networks. We propose to learn edge maps as the representation of local contrast with a novel local contrast loss. The local contrast is optimized in a stochastic way to focus on specific edge directions. Experiments show that the edge detection network trained with local contrast loss achieves a high accuracy comparable to previous methods and dramatically improves the crispness. We also present several applications of the crisp edges, including image completion, image retrieval, sketch generation, and video stylization.
Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships i...
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In this article, we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the ...
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