Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Semantic context based refinement for news video annotation

Automatic video annotation is to bridge the semantic gap and facilitate concept based video retrieval by detecting high level concepts from video data. Recently, utilizing context information has emerged as an important direction in such domain. In this paper, we present a novel video annotation refinement approach by utilizing extrinsic semantic context extracted from video subtitles and intrinsic context among candidate annotation concepts. The extrinsic semantic context is formed by identifying a set of key terms from video subtitles. The semantic similarity between those key terms and the candidate annotation concepts is then exploited to refine initial annotation results, while most existing approaches utilize textual information heuristically. Similarity measurements including Google distance and WordNet distance have been investigated for such a refinement purpose, which is different with approaches deriving semantic relationship among concepts from given training datasets. Visualness is also utilized to discriminate individual terms for further refinement. In addition, Random Walk with Restarts (RWR) technique is employed to perform final refinement of the annotation results by exploring the inter-relationship among annotation concepts. Comprehensive experiments on TRECVID 2005 dataset have been conducted to demonstrate the effectiveness of the proposed annotation approach and to investigate the impact of various factors.     

Linearized method: A new approach for kinetic analysis of central dopamine D(2) receptor specific binding

We proposed a new method (;linearized method') to analyze neuroleptic ligand-receptor specific binding in a human brain using positron emission tomography (PET). We derived the linear equation to solve four rate constants, k(3), k(4), k(5), k(6) from PET data. This method does not demand a radioactivity curve in plasma as an input function to the brain, and can perform fast calculations in order to determine rate constants. We also tested the nonlinearized method including nonlinear equations which is a conventional analysis using plasma radioactivity corrected for ligand metabolites as an input function. We applied these methods to evaluate dopamine D(2) receptor specific binding of [(11)C] YM-09151-2. The value of B(max)/K(d)=k(3)/k (4) obtained by the linearized method was 5.72+/-3.1 which was consistent with the value of 5.78+/-3.4 obtained by the nonlinearized method.     

Fast parametric imaging algorithm for dual-input biomedical system parameter estimation

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.     

2D representation of facial surfaces for multi-pose 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the intrinsic difficulties faced by conventional face recognition using 2D images. Instead of extending 2D recognition algorithms for 3D purpose, this letter proposes a novel strategy for 3D face recognition from the perspective of representing each 3D facial surface with a 2D attribute image and taking the advantage of the advances in 2D face recognition. In our approach, each 3D facial surface is mapped homeomorphically onto a 2D lattice, where the value at each site is an attribute that represents the local 3D geometrical or textural properties on the surface, therefore invariant to pose changes. This lattice is then interpolated to generate a 2D attribute image. 3D face recognition can be achieved by applying the traditional 2D face recognition techniques to obtained attribute images. In this study, we chose the pose invariant local mean curvature calculated at each vertex on the 3D facial surface to construct the 2D attribute image and adopted the eigenface algorithm for attribute image recognition. We compared our approach to state-of-the-art 3D face recognition algorithms in the FRGC (Version 2.0), GavabDB and NPU3D database. Our results show that the proposed approach has improved the robustness to head pose variation and can produce more accurate 3D multi-pose face recognition.     

Efficient Incorporation of Clickable Unnatural Amino Acids Enables Rapid and Biocompatible Labeling of Proteins in Vitro and in Bacteria

Site-specific incorporation of unnatural amino acids (uAAs) bearing a bioorthogonal group has enabled the attachment – typically at a single site or at a few sites per protein – of chemical groups at precise locations for protein and biomaterial labeling, conjugation, and functionalization. Herein, we report the evolution of chromosomal Methanocaldococcus jannaschii tyrosyl-tRNA synthetase (aaRS) for the alkyne-bearing uAA, 4-propargyloxy-l -phenylalanine (pPR), with ∼30-fold increased production of green fluorescent protein containing three instances of pPR compared with a previously described M. jannaschii-derived aaRS for pPR, when expressed from a single chromosomal copy. We show that when expressed from multicopy plasmids, the evolved aaRSs enable the production – using a genomically recoded Escherichia coli and the non-recoded BL21 E. coli strain – of elastin-like polypeptides (ELPs) containing multiple pPR residues in high yields. We further show that the multisite incorporation of pPR in ELPs facilitates the rapid, robust, and nontoxic fluorescent labeling of these proteins in bacteria. The evolved variants described in this work can be used to produce a variety of protein and biomaterial conjugates and to create efficient minimal tags for protein labeling.     

DNA‐Templated Synthesis of Perylenediimide Stacks Utilizing Abasic Sites as Binding Pockets and Reactive Sites

DNA is considered to be a promising biomolecule as a template and scaffold for arranging and organizing functional molecules on the nanoscale. The construction and evaluation of DNAs containing multiple functional molecules that are useful for optoelectronic devices and sensors has been studied. In this paper we report the efficient incorporation of perylenediimide (PDI) units into DNA by using abasic sites both as binding sites and as reactive sites and the construction of PDI stacks within the DNA structure, accomplished through the preorganization of the PDI units in the hydrophobic pocket within the DNA. Our approach could become a valuable method for construction of DNA/chromophore hybrid structures potentially useful for the design of DNA‐based devices and biosensors.     

基于神经网络的气体渗碳碳势测定

本文作者提出一种基于人工神经网络原理的碳势测定新方法－碳势软件传感器,其特点是精度及可靠性高,利于在线检测与实时控制,成本低且使用方便,并具有一定的抗噪声能力。     

纵波AVO,转换横波信息在浅层气检测中的应用

东部地区,浅层气藏一般呈亮点反射特征。由于叠加对振幅的影响,给使用亮点技术检测油气带来困难。本文提出用AVO纵波信息代替常规叠加剖面,并结合转换横波信息来检测浅层亮点气。文中给出了从常规地震资料中提取自激自收纵波、转换横波的方法及其相对波阻抗反演方法,并以一浅层气藏的检测为例,说明该方法有良好的应用效果。