Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
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Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation ***,existing knowledge-aware recommendation methods face challenges such as weak user-it...
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Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation ***,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge *** tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge ***,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and ***,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view *** paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge ***,this paper introduces multi-task learning to mitigate the problem of weak supervisory *** validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM *** results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.
Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these proce...
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Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.
Removing noise in the real-world scenario has been a daunting task in the field of natural language processing. Research has shown that Deep Neural Networks (DNN) have proven to be very useful in terms of noise genera...
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Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless ...
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In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized *** nested structure of polar codes ensures that the MMI quantization can be implemented stage by *** results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization ***,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of in...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much atten...
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Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational ***,crossover studies of the two fields seem to be missing in the *** paper initiates the study of quantum algorithms for matroid property *** is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits(bases,flats,hyperplanes)of a matroid,and for the decision problem of deciding whether a matroid is uniform or Eulerian,by giving a uniform lower boundΩ■on the query complexity of all these *** the other hand,for the uniform matroid decision problem,an asymptotically optimal quantum algorithm is proposed which achieves the lower bound,and for the girth problem,an almost optimal quantum algorithm is given with query complexityO■.In addition,for the paving matroid decision problem,a lower boundΩ■on the query complexity is obtained,and an O■ quantum algorithm is presented.
The advancement of computer vision and surveillance devices has underscored the significance of safeguarding privacy in facial images and videos, necessitating the development of effective face de-identification metho...
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