Other affiliations: Chinese Academy of Sciences, City University of Hong Kong, National Institute for Nanotechnology ...read more
Bio: Jie Chen is an academic researcher from Peking University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 71, co-authored 751 publications receiving 20210 citations. Previous affiliations of Jie Chen include Chinese Academy of Sciences & City University of Hong Kong.
Papers published on a yearly basis
•15 Jun 2000
TL;DR: The results obtained imply that the current Univariate Normal Model is a good fit for the Hazard Function and the Multivariate Normal and Regression models are good candidates for the Regression Model.
Abstract: Preface.- Preliminaries.- Introduction.- Univariate Normal Model.- Multivariate Normal Model.- Regression Model.- Gamma Model.- Exponential Model.- Change Point Model for the Hazard Function.- Discrete Models.- Other Models.- Bibliography.- Author Index.- Subject Index.
••01 Oct 2019
TL;DR: AoANet as mentioned in this paper proposes an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries and achieves state-of-the-art performance.
Abstract: Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to guide the caption decoding process. However, the decoder has little idea of whether or how well the attended vector and the given attention query are related, which could make the decoder give misled results. In this paper, we propose an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries. AoA first generates an information vector and an attention gate using the attention result and the current context, then adds another attention by applying element-wise multiplication to them and finally obtains the attended information, the expected useful knowledge. We apply AoA to both the encoder and the decoder of our image captioning model, which we name as AoA Network (AoANet). Experiments show that AoANet outperforms all previously published methods and achieves a new state-of-the-art performance of 129.8 CIDEr-D score on MS COCO Karpathy offline test split and 129.6 CIDEr-D (C40) score on the official online testing server. Code is available at https://github.com/husthuaan/AoANet.
TL;DR: The state-of-the-art positioning designs are surveyed, focusing specifically on signal processing techniques in network-aided positioning, to provide new directions for future research.
Abstract: Wireless positioning has attracted much research attention and has become increasingly important in recent years. Wireless positioning has been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Although many positioning devices and services are currently available, it is necessary to develop an integrated and seamless positioning platform to provide a uniform solution for different network configurations. This article surveys the state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years.
25 Jul 2018
TL;DR: A hearing aid with the TENG technique, which can simplify the signal processing circuit and reduce the power consuming is proposed, which expresses notable advantages of using TENG technology to build a new generation of auditory systems for meeting the challenges in social robotics.
Abstract: The auditory system is the most efficient and straightforward communication strategy for connecting human beings and robots. Here, we designed a self-powered triboelectric auditory sensor (TAS) for constructing an electronic auditory system and an architecture for an external hearing aid in intelligent robotic applications. Based on newly developed triboelectric nanogenerator (TENG) technology, the TAS showed ultrahigh sensitivity (110 millivolts/decibel). A TAS with the broadband response from 100 to 5000 hertz was achieved by designing the annular or sectorial inner boundary architecture with systematic optimization. When incorporated with intelligent robotic devices, TAS demonstrated high-quality music recording and accurate voice recognition for realizing intelligent human-robot interaction. Furthermore, the tunable resonant frequency of TAS was achieved by adjusting the geometric design of inner boundary architecture, which could be used to amplify a specific sound wave naturally. On the basis of this unique property, we propose a hearing aid with the TENG technique, which can simplify the signal processing circuit and reduce the power consuming. This work expresses notable advantages of using TENG technology to build a new generation of auditory systems for meeting the challenges in social robotics.
TL;DR: The differences between G QDs and other nanomaterials, including their nanocarbon cousins, are emphasized, and the unique advantages of GQDs for specific applications are highlighted.
Abstract: Graphene quantum dots (GQDs) that are flat 0D nanomaterials have attracted increasing interest because of their exceptional chemicophysical properties and novel applications in energy conversion and storage, electro/photo/chemical catalysis, flexible devices, sensing, display, imaging, and theranostics. The significant advances in the recent years are summarized with comparative and balanced discussion. The differences between GQDs and other nanomaterials, including their nanocarbon cousins, are emphasized, and the unique advantages of GQDs for specific applications are highlighted. The current challenges and outlook of this growing field are also discussed.
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.
28 Jul 2005
01 Jan 2009