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Zhen Li

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.


Papers
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Proceedings ArticleDOI
23 Jun 2013
TL;DR: The decision function for verification is proposed to be viewed as a joint model of a distance metric and a locally adaptive thresholding rule, and the inference on the decision function is formulated as a second-order large-margin regularization problem, and an efficient algorithm is provided in its dual from.
Abstract: This paper considers the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or human body images is about the same person, even if the person is not seen before. Traditional methods usually look for a distance (or similarity) measure between images (e.g., by metric learning algorithms), and make decisions based on a fixed threshold. We show that this is nevertheless insufficient and sub-optimal for the verification problem. This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule. We further formulate the inference on our decision function as a second-order large-margin regularization problem, and provide an efficient algorithm in its dual from. We evaluate our algorithm on both human body verification and face verification problems. Our method outperforms not only the classical metric learning algorithm including LMNN and ITML, but also the state-of-the-art in the computer vision community.

533 citations

Journal ArticleDOI
TL;DR: An inhomogeneous planar substrate (g-C(3)N(4)) promotes electron-rich and hole-rich regions, i.e., forming a well-defined electron-hole puddle, on the supported graphene layer, which can potentially allow overcoming the graphene's band gap hurdle in constructing field effect transistors.
Abstract: Opening up a band gap and finding a suitable substrate material are two big challenges for building graphene-based nanodevices. Using state-of-the-art hybrid density functional theory incorporating long-range dispersion corrections, we investigate the interface between optically active graphitic carbon nitride (g-C(3)N(4)) and electronically active graphene. We find an inhomogeneous planar substrate (g-C(3)N(4)) promotes electron-rich and hole-rich regions, i.e., forming a well-defined electron-hole puddle, on the supported graphene layer. The composite displays significant charge transfer from graphene to the g-C(3)N(4) substrate, which alters the electronic properties of both components. In particular, the strong electronic coupling at the graphene/g-C(3)N(4) interface opens a 70 meV gap in g-C(3)N(4)-supported graphene, a feature that can potentially allow overcoming the graphene's band gap hurdle in constructing field effect transistors. Additionally, the 2-D planar structure of g-C(3)N(4) is free of dangling bonds, providing an ideal substrate for graphene to sit on. Furthermore, when compared to a pure g-C(3)N(4) monolayer, the hybrid graphene/g-C(3)N(4) complex displays an enhanced optical absorption in the visible region, a promising feature for novel photovoltaic and photocatalytic applications.

532 citations

Journal ArticleDOI
TL;DR: The hollow CH@LDH polyhedra with complex shell structures not only maximize the advantages of hollow nanostructures for encapsulating a high content of sulfur, but also provide sufficient self-functionalized surfaces for chemically bonding with polysulfides to suppress their outward dissolution.
Abstract: Lithium–sulfur (Li-S) batteries have been considered as a promising candidate for next-generation electrochemical energy-storage technologies because of their overwhelming advantages in energy density. Suppression of the polysulfide dissolution while maintaining a high sulfur utilization is the main challenge for Li–S batteries. Here, we have designed and synthesized double-shelled nanocages with two shells of cobalt hydroxide and layered double hydroxides (CH@LDH) as a conceptually new sulfur host for Li–S batteries. Specifically, the hollow CH@LDH polyhedra with complex shell structures not only maximize the advantages of hollow nanostructures for encapsulating a high content of sulfur (75 wt %), but also provide sufficient self-functionalized surfaces for chemically bonding with polysulfides to suppress their outward dissolution. When evaluated as cathode material for Li–S batteries, the CH@LDH/S composite shows a significantly improved electrochemical performance.

508 citations

Journal ArticleDOI
TL;DR: Aggregation in poor solvents and complexation with calf thymus DNA and bovine serum albumin turn "on" the fluorescence of tetraphenylethylene derivatives, due to the restriction of intra-molecular rotations of the dyes in the aggregates and complexes.

490 citations

Journal ArticleDOI
TL;DR: In this article, high fluorescent cysteine-capped CdTe/CdS core-shell nanowires were successfully synthesized by reacting CdCl2 with NaHTe in aqueous solution under refluxing at 100°C for 140min.
Abstract: Highly fluorescent cysteine-capped CdTe/CdS core–shell nanowires were successfully synthesized by reacting CdCl2 with NaHTe in aqueous solution under refluxing at 100 °C for 140 min. On increasing the reaction time from 10 to 140 min, CdTe/CdS nanocrystals gradually grew into nanorods and eventually completely evolved into nanowires. The nanowires have amino and carboxyl functional groups on their surfaces and can be well dispersed in aqueous solution. The as-prepared CdTe/CdS nanowires show a fluorescence quantum yield (QY) of 7.25 % due to the unique nature of cysteine and the formation of a CdS shell on the surface of the CdTe core, they have a narrower diameter distribution (d = ~5 nm) and a length in the range of 175–275 nm, and their aspect ratio is between 1/35 and 1/55.

474 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
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

Journal ArticleDOI
15 Jul 2021-Nature
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