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  • Association for Computing Machinery (ACM)  (2)
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  • Association for Computing Machinery (ACM)  (2)
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  • 1
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2016
    In:  ACM Transactions on Graphics Vol. 35, No. 4 ( 2016-07-11), p. 1-11
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 35, No. 4 ( 2016-07-11), p. 1-11
    Abstract: This paper presents CofiFab, a coarse-to-fine 3D fabrication solution, combining 3D printing and 2D laser cutting for cost-effective fabrication of large objects at lower cost and higher speed. Our key approach is to first build coarse internal base structures within the given 3D object using laser cutting, and then attach thin 3D-printed parts, as an external shell, onto the base to recover the fine surface details. CofiFab achieves this with three novel algorithmic components. First, we formulate an optimization model to compute fabricatable polyhedrons of maximized volume, as the geometry of the internal base. Second, we devise a new interlocking scheme to tightly connect the laser-cut parts into a strong internal base, by iteratively building a network of nonorthogonal joints and interlocking parts around polyhedral corners. Lastly, we optimize the partitioning of the external object shell into 3D-printable parts, while saving support material and avoiding overhangs. Besides cost saving, these components also consider aesthetics, stability and balancing. Hence, CofiFab can efficiently produce large objects by assembly. To evaluate CofiFab, we fabricate objects of varying shapes and sizes, and show that CofiFab can significantly outperform previous methods.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2016
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 2
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2024
    In:  ACM Transactions on Information Systems Vol. 42, No. 1 ( 2024-01-31), p. 1-27
    In: ACM Transactions on Information Systems, Association for Computing Machinery (ACM), Vol. 42, No. 1 ( 2024-01-31), p. 1-27
    Abstract: Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC) , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.
    Type of Medium: Online Resource
    ISSN: 1046-8188 , 1558-2868
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2024
    detail.hit.zdb_id: 602352-6
    detail.hit.zdb_id: 2006337-4
    SSG: 24,1
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