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Now showing items 1 - 16 of 179

  • Development of a microstructural grand potential-based sintering model

    Greenquist, Ian   Tonks, Michael R.   Aagesen, Larry K.   Zhang, Yongfeng  

    Microstructure is a controlling factor in the behavior of sintered materials. This work presents a quantitative phase field model of thermal sintering that predicts the evolution of the microstructure by capturing the sintering stress, GB/vacancy interactions, non-uniform diffusion, and grain coarsening without introducing a separate rigid body motion term. The model provides a mechanistic description of sintering using the grand potential phase field approach. Small test simulations are used to verify the new model against sintering theory, and they show that 3D simulations predict faster densification and coarsening than 2D simulations. 3D simulations are compared against experimental data available in the literature. The results of this comparison show that the model provides a reasonable estimate of the sintering behavior, though it overpredicts the sintering rate. This may be due to uncertainty in the material parameters and the relatively small scale of the simulation.
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  • Explainable Recommendation: A Survey and New Perspectives

    Zhang, Yongfeng   Chen, Xu  

    Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches - especially model-based methods - have been proposed and applied in real-world systems. In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation. We also devote a section to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.
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  • Visual Semantic Image Recommendation

    Guo, Guibing   Meng, Yuan   Zhang, Yongfeng   Han, Chunyan   Li, Yanjie  

    Image recommendation is an essential component of the modern online image sharing applications (e.g., Flickr), aiming to provide users with interesting images for further exploration. However, most existing approaches tend to treat the image in question as a single object, ignoring the important semantics of the sub-objects within the image. The loss of these semantic objects may lead to the misunderstanding of the user preference toward an image. In this paper, we propose a novel pairwise preference model, called Visual Semantic Model (VSM), to address this issue for a better recommendation. Specifically, we model the image representation by combining the feature embeddings of the fine-grained image objects, the weights of which may be distinct for different users. Then, we enhance the user modeling by taking into account the interacted images along with their relative importance. Two attention networks on both object and image levels are adapted to compute the weights of objects and images, respectively. The experimental results on the Flickr dataset show that our VSM model achieves significant improvements (around 9.18% on average in terms of Precision @5) over the state-of-the-art approaches in terms of the recommendation accuracy.
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  • Explainable Product Search with a Dynamic Relation Embedding Model

    Ai, Qingyao   Zhang, Yongfeng   Bi, Keping   Croft, W. Bruce  

    Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. However, they ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.
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  • CRUCIBLE, EVAPORATION DEPOSITION DEVICE AND EVAPORATION DEPOSITION SYSTEM

    A crucible, an evaporation deposition device and an evaporation deposition system, wherein the crucible includes a receiving chamber (1) for containing materials to be heated, the crucible further includes a collector (2) located in the receiving chamber, and the opening (21) of the collector (2) faces the top of the receiving chamber (1).
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  • Thermal behavior and kinetic analysis of halloysite-stearic acid complex

    Zhang, Yinmin   Li, Yaqiong   Zhang, Yongfeng   Ding, Daqian   Wang, Lu   Liu, Meng   Zhang, Fengchu  

    The thermal behavior and kinetic parameters of halloysite-stearic acid (SA) intercalation complex were investigated by thermogravimetry and derivative thermogravimetry (TG-DTG), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopic analysis. The XRD data indicated that the intercalation of stearic acid into halloysite caused an increase in the basal spacing from 0.734 to 4.001nm. However, the intensity of the 4.001nm gradually decreased with the increase in temperature and disappeared around 200-300 degrees C. In the infrared spectra, the appearance of two significant bands at 2924 and 2851cm(-1) attributed to methyl and methylene indicated that SA molecules have been successfully inserted into halloysite interlayer. Nevertheless, the intensities of these bands gradually decreased with the temperature rising and remained until around 300 degrees C. The TG-DTG results indicated that the mass loss of the halloysite-SA complex contained two main stages, which correspond to (a) deintercalation of the SA molecules and (b) dehydroxylation of halloysite. The multi-heating rate TG data reveal that the halloysite-SA complex is stable below 300 degrees C, which is in agreement with the XRD and FT-IR data. The kinetics results showed that the deintercalation reaction and dehydroxylation reaction occurred with an average activation energy (E) of 88.3 and 212.4kJmol(-1), respectively. Meanwhile, the deintercalation reaction of halloysite-SA intercalation complex was single mechanism.
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  • Thermal and transport properties of U3Si2

    Antonio, Daniel J.   Shrestha, Keshav   Harp, Jason M.   Adkins, Cynthia A.   Zhang, Yongfeng   Carmack, Jon   Gofryk, Krzysztof  

    We have studied U3Si2 by means of the heat capacity, electrical resistivity, Seebeck and Hall effects, and thermal conductivity in the temperature range 2-300 K and in magnetic fields up to 9 T. All the results obtained point to delocalized nature of 5f-electrons in this material. The low temperature heat capacity is enhanced (gamma(el) similar to 150 mJ/mol-K-2) and shows an upturn in C-p/T (T), characteristic of spin fluctuations. The thermal conductivity of U3Si2 is similar to 8.5 W/m-K at room temperature and we show that the electronic part dominates heat transport above 300 K as expected for a metallic system, although the lattice contribution cannot be completely neglected. (C) 2018 Elsevier B.V. All rights reserved.
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  • Breast panniculitis with liquefactive fat necrosis: A case report

    Zhang, Yongfeng   Shi, Xuhua   Lu, Yuewu  

    Panniculitis is a group of heterogeneous disorders characterized by inflammation of the subcutaneous adipose tissue. Panniculitis of breast tissue as the initial manifestation has rarely been reported and is often misdiagnosed. Breast panniculitis may cause substantial morbidity and early diagnosis and treatment are important for the prognosis of the disease. The present study has reported a case of panniculitis with inflammation of the mammary glands as the initial presentation and provided a detailed description of ultrasonography, X-ray, computed tomography, magnetic resonance imaging and other imaging features of breast panniculitis. The treatment and follow-up were also described. Following treatment with systemic corticosteroids combined with methotrexate and thalidomide for 2 months, the breast appeared to be normal without scar formation. The present case report provides a good reference for the future diagnosis and treatment of breast panniculitis.
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  • Attentive Aspect Modeling for Review-Aware Recommendation

    Guan, Xinyu   Cheng, Zhiyong   He, Xiangnan   Zhang, Yongfeng   Zhu, Zhibo   Peng, Qinke   Chua, Tat-Seng  

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  • Visual Semantic Image Recommendation

    Guo, Guibing   Meng, Yuan   Zhang, YongFeng   Han, ChunYan   Li, YanJie  

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  • Ab initio theory of noble gas atoms in bcc transition metals

    Jiang, Chao   Zhang, Yongfeng   Gao, Yipeng   Gan, Jian  

    Systematic ab initio calculations based on density functional theory have been performed to gain fundamental understanding of the interactions between noble gas atoms (He, Ne, Ar and Kr) and bcc transition metals in groups 5B (V, Nb and Ta), 6B (Cr, Mo and W) and 8B (Fe). Our charge density analysis indicates that the strong polarization of nearest-neighbor metal atoms by noble gas interstitials is the electronic origin of their high formation energies. Such polarization becomes more significant with an increasing gas atom size and interstitial charge density in the host bcc metal, which explains the similar trend followed by the unrelaxed formation energies of noble gas interstitials. Upon allowing for local relaxation, nearby metal atoms move farther away from gas interstitials in order to decrease polarization, albeit at the expense of increasing the elastic strain energy. Such atomic relaxation is found to play an important role in governing both the energetics and site preference of noble gas atoms in bcc metals. Our most notable finding is that the fully relaxed formation energies of noble gas interstitials are strongly correlated with the elastic shear modulus of the bcc metal, and the physical origin of this unexpected correlation has been elucidated by our theoretical analysis based on the effective-medium theory. The kinetic behavior of noble gas atoms and their interaction with pre-existing vacancies in bcc transition metals have also been discussed in this work.
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  • Thermal behavior and kinetic analysis of halloysite–stearic acid complex

    Zhang, Yinmin   Li, Yaqiong   Zhang, Yongfeng   Ding, Daqian   Wang, Lu   Liu, Meng   Zhang, Fengchu  

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  • Molecular dynamics investigation of grain boundaries and surfaces in U3Si2

    Beeler, Benjamin   Baskes, Michael   Andersson, David   Cooper, Michael W. D.   Zhang, Yongfeng  

    Uranium-silicide (U-Si) fuels are being pursued as a possible accident tolerant fuel (ATF). This uranium alloy benefits from higher thermal conductivity and higher fissile density compared to uranium dioxide (UO2). In order to perform engineering scale nuclear fuel performance simulations, the material properties of the fuel must be known. Currently, the experimental data available for U-Si fuels is rather limited. Thus, multi-scale modeling efforts are underway to address this gap in knowledge. Interfaces play a critical role in the microstructural evolution of nuclear fuel under irradiation, acting both as sinks for point defects and as preferential nucleation sites for fission gas bubbles. In this study, a semiempirical modified Embedded-Atom Method (REAM) potential is utilized to investigate grain boundaries and free surfaces in U3Si2. The interfacial energy as a function of temperature is investigated for ten symmetric tilt grain boundaries, eight unique free surfaces and voids of radius up to 35 angstrom. The point defect segregation energy for both U and Si interstitials and vacancies is also determined for two grain boundary orientations. Finally, the entropy change and free energy change for grain boundaries is calculated as a function of temperature. This is the first study into grain boundary properties of U-Si nuclear fuel. Published by Elsevier B.V.
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  • Molecular dynamics investigation of grain boundaries and surfaces in U3Si2

    Beeler, Benjamin   Baskes, Michael   Andersson, David   Cooper, Michael WD.   Zhang, Yongfeng  

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  • A molecular dynamics study of the behavior of Xe in U3Si2

    Beeler, Benjamin   Andersson, David   Cooper, Michael W. D.   Zhang, Yongfeng  

    Uranium-silicide (U-Si) fuels are being pursued as a possible accident tolerant fuel (ATF). This uranium alloy fuel benefits from higher thermal conductivity and higher fissile density compared to uranium dioxide (UO2). The role of fission gas swelling on the operational performance of U-Si fuels remains an open question, however, fission gas swelling is a critical phenomenon in UO2, U-Zr and U-Mo nuclear fuels. Given the lack of experimental data, in order to study the fundamentals of bubble formation and evolution in U-Si, it is critical that there be an atomistic description of Xe within the U-Si system. In this work, a recently developed U-Si MEAM interatomic potential is leveraged to generate a description of the U-Si-Xe system fit to density functional theory data. The point defect energies of Xe in U3Si2 are determined, in addition to the point defect segregation energy for Xe with respect to two grain boundary orientations. Finally, the properties of small Xe bubbles are analyzed and an equation of state is developed. Published by Elsevier B.V.
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  • Adversarial Distillation for Efficient Recommendation with External Knowledge

    Chen, Xu   Zhang, Yongfeng   Xu, Hongteng   Qin, Zheng   Zha, Hongyuan  

    Integrating external knowledge into the recommendation system has attracted increasing attention in both industry and academic communities. Recent methods mostly take the power of neural network for effective knowledge representation to improve the recommendation performance. However, the heavy deep architectures in existing models are usually incorporated in an embedded manner, which may greatly increase the model complexity and lower the runtime efficiency. To simultaneously take the power of deep learning for external knowledge modeling as well as maintaining the model efficiency at test time, we reformulate the problem of recommendation with external knowledge into a generalized distillation framework. The general idea is to free the complex deep architecture into a separate model, which is only used in the training phrase, while abandoned at test time. In particular, in the training phrase, the external knowledge is processed by a comprehensive teacher model to produce valuable information to teach a simple and efficient student model. Once the framework is learned, the teacher model is abandoned, and only the succinct yet enhanced student model is used to make fast predictions at test time. In this article, we specify the external knowledge as user review, and to leverage it in an effective manner, we further extend the traditional generalized distillation framework by designing a Selective Distillation Network (SDNet) with adversarial adaption and orthogonality constraint strategies to make it more robust to noise information. Extensive experiments verify that our model can not only improve the performance of rating prediction, but also can significantly reduce time consumption when making predictions as compared with several stateof-the-art methods.
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