Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the syste...
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The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and soc...
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Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thun...
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The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitativ...
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In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. ...
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Multidimensional data, such as color images and videos, often exhibit inherent low-rank and local smoothness properties, with temporal and spatial correlations playing a crucial role in data recovery. While most exist...
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Multidimensional data, such as color images and videos, often exhibit inherent low-rank and local smoothness properties, with temporal and spatial correlations playing a crucial role in data recovery. While most existing methods focus on modeling these properties independently, they often overlook their coupled correlation in the factor space derived during tensor decomposition. In this study, we propose a novel Matrix Correlated Total Variation (MCTV) regularizer to explicitly model the coupled correlation between low-rankness and smoothness within Tucker decomposition. Unlike traditional methods, MCTV propagates the low-rankness of factor matrices to the Tucker rank, eliminating the need to predefine the core tensor size. It also preserves temporal and spatial smoothness correlations across all tensor modes through operations on smooth factor matrices. By integrating MCTV into a Tucker-based tensor completion model, we remove the dependence on hyperparameters like the Tucker rank and tradeoff parameters, creating a unified framework for capturing these coupled correlations. To optimize the proposed model, we design an efficient Alternating Direction Method of Multipliers (ADMM) algorithm. Experimental results on benchmark datasets demonstrate the superiority of our method in recovering multidimensional data by effectively modeling the synergy between low-rankness and smoothness.
The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods,and thus it has attracted much attention *** retrieval based on such a...
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The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods,and thus it has attracted much attention *** retrieval based on such a combination is usually called the content-and-text based image retrieval(CTBIR).Nevertheless,existing studies in CTBIR mainly make efforts on improving the retrieval *** the best of our knowledge,little attention has been focused on how to enhance the retrieval ***,image data is widespread and expanding rapidly in our daily ***,it is important and interesting to investigate the retrieval *** this end,this paper presents an efficient image retrieval method named CATIRI(content-and-text based image retrieval using indexing).CATIRI follows a three-phase solution framework that develops a new indexing structure called *** MHIM-tree seamlessly integrates several elements including Manhattan Hashing,Inverted index,and *** use our MHIM-tree wisely in the query,we present a set of important metrics and reveal their inherent *** on them,we develop a top-k query algorithm for *** results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.
Light carrying time-varying orbital angular momentum (OAM) is a recently discovered type of structured electromagnetic field compared with a typical vortex field whose OAM is static. Such so-called self-torqued light ...
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Light carrying time-varying orbital angular momentum (OAM) is a recently discovered type of structured electromagnetic field compared with a typical vortex field whose OAM is static. Such so-called self-torqued light is employed for manipulating the fast magnetic, topological, and quantum excitations and increasing its intensity and having access to shorter pulse durations would be of great benefit. Here we theoretically and numerically demonstrate the generation of intense self-torqued harmonics and attosecond pulses in the relativistic regime, driven by two time-delayed relativistic vortex lasers with different OAMs l1 and l2. The OAM of the nth harmonic spans nl1 to nl2, and the OAM of the attosecond pulses changes from l1 to l2. Such intense self-torqued harmonics and attosecond pulses may offer alternative possibilities in ultrafast spectroscopy.
The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may...
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ISBN:
(纸本)9781665425889
The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.
Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, inclu...
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