Author
Zhen Li
Other affiliations: Tsinghua University, Hong Kong University of Science and Technology, Academia Sinica ...read more
Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.
Topics: Medicine, Computer science, Materials science, Chemistry, Biology
Papers published on a yearly basis
Papers
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TL;DR: In this paper, ordered tungsten oxide WO 3 films have been prepared on the natural mica substrate by a simple hydrothermal method, and the XRD result reveals that the films are hexagonal WO3.
19 citations
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TL;DR: This paper addresses the problem of semantic labeling of RGB-D scenes by developing a novel Long Short-Term Memorized Fusion (LSTM-F) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training.
Abstract: Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by i) developing a novel Long Short-Term Memorized Fusion (LSTM-F) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, global contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., 48.1% average class accuracy over 37 categories 11.8% improvement), on the large-scale SUNRGBD dataset.1
19 citations
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TL;DR: In this article, the authors investigated the influence of column height in the downcomer of a two-phase loop thermosyphon (TPLT) system through an experiment and a numerical simulation.
Abstract: A two-phase loop thermosyphon (TPLT) is a highly efficient heat transfer device. It is a type of gravity-assisted heat pipe system. Therefore, there is a high refrigerant column height in the downcomer that supplies the driving force for the TPLT system. This study focused on the investigation of factors that influence the refrigerant column height in the downcomer of a TPLT system through an experiment and a numerical simulation. These factors were identified as the flow resistance, increased working pressure, and amount of liquid phase flow in the two-phase flow. In addition, the effect of the condenser height was considered. The extreme situation in which the condenser was filled with liquid refrigerant was analyzed through an experiment. The results indicated that a high refrigerant column height would occupy part of the condenser space and decrease the performance of the TPLT. In practical applications, the relationship between the condenser and refrigerant column height should be considered. These influencing factors of the column height in the downcomer should be considered when deciding the geometric parameters and filling ratio of a TPLT system.
19 citations
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TL;DR: Well-defined core-shell nanoparticles (NPs) containing concave cubic Au cores and TiO2 shells (CA@T) were synthesized in colloidal suspension, offering great potential in other applications including light-matter interaction, photocatalytic energy conversion and new-generation solar cells.
19 citations
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TL;DR: In this article, X-ray powder diffraction has been performed to provide an admixture of wurtzite and zinc-blende (ZB) structure units separated by different types of stacking faults.
Abstract: CdSe nanowires (NWs) have been prepared by a solution-liquid-solid (SLS) approach using Bi nanocatalysts. Structural characterization has been performed by X-ray powder diffraction providing an admixture of wurtzite and zinc-blende (ZB) structure units separated by different types of stacking faults. The relative contributions of ZB type stacking units within the NWs were determined to be in the order of 3-6% from a set of ratios of reflection intensities appearing in only wurtzite structure to those appearing in both ZB and wurtzite (W) structure. In addition, the anisotropy of domain size within the NWs was evaluated from the evolution of peak broadening for increasing scattering length. The coherence lengths along the growth direction are found to be changing between 16 and 21 nm, smaller than the results obtained from TEM measurement, while the NW diameters are determined to be between 5 and 8 nm which is in good agreement with TEM inspection.
19 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
29,323 citations
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28,685 citations
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TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
10,601 citations