新型加成型硅橡胶灌封胶的研究

本文合成了两种新型加成型硅橡胶：ＴＡＳ硅橡胶和ＭＤＡＳ硅橡胶。与通常使用的乙烯基加成型硅橡胶相比，ＴＡＳ或ＭＤＡＳ硅橡胶的固化条件与之相似，但ＴＡＳ－ｐｔ和ＭＤＡＳ－ｐｔ固化体系对许多能阻滞乙烯基加成型硅橡胶固化的物质不敏感。另一方面，ＴＡＳ－ｐｔ和ＭＤＡＳ－ｐｔ固化体系对许多材料（尤其对金属）呈现良好的粘接性能，且当样品浸于水中１５天后，仍保持其粘性。上述性能，使之具有许多用途。本文还讨论了ＴＡＳ和ＭＤＡＳ生胶的合成方法，以及影响ＴＡＳ、ＭＤＡＳ固化的若干因素。     

Formation of Polymeric Lewis Acid–Lewis Base Complexes with Well-defined Organoboron Polymers

The binding of Lewis bases to organoboron polymeric Lewis acids has been studied and the parameters that determine the complexation equilibrium have been investigated, which include (i) the strength of the individual Lewis acids and Lewis bases, (ii) concentration, and (iii) temperature. While the strongly Lewis acidic borane polymers poly(4-bis(pentafluorophenyl)borylstyrene) (PS-BPf) and poly(4-(di-2-thienylboryl)styrene) (PS-BTh) form isolable complexes with strong Lewis bases such as 4-t-butylpyridine (^tPy), a temperature dependent equilibrium is established with weaker bases such as THF. Similarly, the weakly Lewis acidic boronate polymer poly(4-diethoxyborylstyrene) (PS-BOEt) undergoes a temperature dependent equilibrium with the strong Lewis base 4-dimethylaminopyridine (DMAP), while poly(4-pinacolatoborylstyrene) (PS-BPin) does not significantly bind to pyridine bases. Decomplexation of PS-BTh· ^t Py is achieved by treatment with the stronger Lewis acid, B(C₆F₅)₃, thereby confirming the reversible nature of the polymeric Lewis acid–base adducts. This paper is dedicated to Professor Ian Manners in gratitude of his guidance throughout the years and recognition of his scientific accomplishments     

Holographic algorithms on bases of rank 2

An essential problem in the design of holographic algorithms is to decide whether the required signatures can be realized under a suitable basis transformation (SRP). For holographic algorithms with matchgates on domain size 2, , ,  and  have built a systematical theory. In this paper, we reduce SRP on domain size k≥3$k\ge 3$ to SRP on domain size 2 for holographic algorithms with matchgates on bases of rank 2. Furthermore, we generalize the collapse theorem of [3] to domain size k≥3$k\ge 3$.     

Oil supply between OPEC and non-OPEC based on game theory

The competing strategies between OPEC (Organization of the Petroleum Exporting Countries) and non-OPEC producers make the oil supply market a complex system, and thus, it is very difficult to model and to make predictions. In this paper, we combine the macro-model based on game theory and micro-model to propose a new approach for forecasting oil supply. We take into account the microscopic behaviour in the clearing market and also use the game relationships to adjust oil supplies in our approach. For the supply model, we analyse and consider the different behaviour of non-OPEC and OPEC producers. According to our analysis, limiting the oil supply, and thus maintaining oil price, is the best strategy for OPEC in the low-price scenario, while the rising supply is the best strategy in the high-price scenario. No matter what the oil price is, the dominant strategy for non-OPEC producers is to increase their oil supply. In the high-price scenario, OPEC will try to deplete non-OPEC’s share in the oil supply market, which is to OPEC’s advantage.     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

Multi-label core vector machine with a zero label

Multi-label core vector machine (Rank-CVM) is an efficient and effective algorithm for multi-label classification. But there still exist two aspects to be improved: reducing training and testing computational costs further, and detecting relevant labels effectively. In this paper, we extend Rank-CVM via adding a zero label to construct its variant with a zero label, i.e., Rank-CVMz, which is formulated as the same quadratic programming form with a unit simplex constraint and non-negative ones as Rank-CVM, and then is solved by Frank–Wolfe method efficiently. Attractively, our Rank-CVMz has fewer variables to be solved than Rank-CVM, which speeds up training procedure dramatically. Further, the relevant labels are effectively detected by the zero label. Experimental results on 12 benchmark data sets demonstrate that our method achieves a competitive performance, compared with six existing multi-label algorithms according to six indicative instance-based measures. Moreover, on the average, our Rank-CVMz runs 83 times faster and has slightly fewer support vectors than its origin Rank-CVM.     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model

Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci^®${\mathrm{daVinci}}^{\circledR }$ surgical robotic platform and a synthesized data set with known ground truth.     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.