A graph invariant is a number that can be easily and uniquely calculated through a ***,part of mathematical graph invariants has been portrayed and utilized for relationship ***,no reliable appraisal has been embraced...
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A graph invariant is a number that can be easily and uniquely calculated through a ***,part of mathematical graph invariants has been portrayed and utilized for relationship ***,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computerscience,physics,and *** this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computerscience like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical *** networks are generally utilized in disorder,fractals,recursive groupings,and complex *** outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced *** study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in *** calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.
Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention...
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Cancer is one of the fatal threats to human beings. However, early detection and diagnosis can significantly reduce death risk, in which cytology classification is indispensable. Researchers have proposed many deep le...
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Traditional serial motif mining methods struggle to quickly identify motif information in large-scale time series data. A CUDA-based multidimensional motif mining algorithm is proposed to discover motifs in multidimen...
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In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with ...
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To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the...
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To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.
Multimodal sentiment analysis integrates various modalities of information to collectively inform decision-making processes. Previous studies often treat different modal features equally or emphasize textual informati...
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How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form...
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How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing *** example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long *** data,accumulated over time,is redundant and consumes a lot ofmemory in its raw *** need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time *** this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal *** that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save *** modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker *** factor matrices and core tensor of the decomposed results can then be used for further data *** compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker *** show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction *** applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to *** were able to model the spatial and temporal air quality *** were also able to verify unusual events,such as chronic ozone alerts and large fire events.
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitat...
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Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.
In this paper we provided an insightful exploration into the critical role of feature matching in enhancing the efficacy of e-commerce recommendation systems. By meticulously analyzing user data and product characteri...
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