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.

Nearly monodisperse graphene quantum dots fabricated by amine-assisted cutting and ultrafiltration

Abstract: We report a novel procedure involving polyethylenimine-assisted hydrothermal cutting and subsequent ultrafiltration for fabricating nearly monodisperse graphene quantum dots with a uniform lateral size and confined layer number. The isolated monolayer quantum dots exhibit a sharp band-edge absorption feature and strong photoluminescence (quantum yield of 21%) independent of excitation wavelength and pH. Their preliminary application in bioimaging has been demonstrated.

Multi-compound Transformer for Accurate Biomedical Image Segmentation.

Abstract: The recent vision transformer (i.e. for image classification) learns non-local attentive interaction of different patch tokens. However, prior arts miss learning the cross-scale dependencies of different pixels, the semantic correspondence of different labels, and the consistency of the feature representations and semantic embeddings, which are critical for biomedical segmentation. In this paper, we tackle the above issues by proposing a unified transformer network, termed Multi-Compound Transformer (MCTrans), which incorporates rich feature learning and semantic structure mining into a unified framework. Specifically, MCTrans embeds the multi-scale convolutional features as a sequence of tokens, and performs intra- and inter-scale self-attention, rather than single-scale attention in previous works. In addition, a learnable proxy embedding is also introduced to model semantic relationship and feature enhancement by using self-attention and cross-attention, respectively. MCTrans can be easily plugged into a UNet-like network, and attains a significant improvement over the state-of-the-art methods in biomedical image segmentation in six standard benchmarks. For example, MCTrans outperforms UNet by 3.64%, 3.71%, 4.34%, 2.8%, 1.88%, 1.57% in Pannuke, CVC-Clinic, CVC-Colon, Etis, Kavirs, ISIC2018 dataset, respectively. Code is available at https://github.com/JiYuanFeng/MCTrans.

C-axis preferentially oriented and fully activated TiO2 nanotube arrays for lithium ion batteries and supercapacitors

Abstract: We report the fabrication of long titanium dioxide nanotube arrays with highly c-axis preferentially oriented crystallization and a high concentration of oxygen vacancies by second anodization in ethylene glycol and annealing under poor-oxygen conditions. By optimizing the growth and annealing conditions, the [001] oriented crystallization is maximized, and 31.7% of the total Ti ions exists as Ti3+ ions. The carrier density of the [001]-oriented TiO2 nanotube arrays is two orders of magnitude higher than that of the randomly oriented TiO2 nanotube arrays. The unusual c-textured crystallization confined within nanotubes may involve the formation of TiO62− octahedra with a gradient distribution along the tube axis, preferential nucleation at the top, and preferential growth downwards along the c axis. Because of the c-axis preferential orientation and a high-concentration of oxygen vacancies, long TiO2 nanotube arrays can serve as superior electrodes for both lithium ion batteries and supercapacitors without the addition of any conductive agents. Long c-oriented TiO2 nanotube arrays deliver reversible capacities of 293 mA h g−1 at 0.5 C and 174 mA h g−1 at 5 C with Coulombic efficiencies of over 99%, and hold an areal capacitance of 8.21 mF cm−2 with an 85% capacitance retention after 5000 cycles. Double roles played by oxygen vacancies are identified in increasing electrical conductivity and activating the rich-Li phase.

Feature-based image registration using the shape context

Abstract: Image registration is a basic and important process for multi-sensor or multi-temporal remote sensing. In this article, a new feature-based method named shape context is proposed for airborne multi-sensor image matching. This method has been found to be robust in hand-written digit and object recognition, and it is now introduced into remote-sensing image matching after some adjustments. In the proposed method, control points (CPs) are extracted on the reference image, and edge features are extracted on the reference and the sensed image, respectively. The shape context exploits feature similarity between circular regions of the two images to find corresponding CPs on the sensed image. Finally, the sensed image is warped according to the CPs using thin-plate spline interpolation. This method is successfully applied to register airborne optical and multi-band synthetic aperture radar (SAR) images in two experiments, and the results demonstrate its robustness and accuracy.

I and i

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 …

Fast parallel algorithms for short-range molecular dynamics

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.

Highly accurate protein structure prediction with AlphaFold

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.