HEp-2 cell pattern classification with discriminative dictionary learning

The paper presents a supervised discriminative dictionary learning algorithm specially designed for classifying HEp-2 cell patterns. The proposed algorithm is an extension of the popular K-SVD algorithm: at the training phase, it takes into account the discriminative power of the dictionary atoms and reduces their intra-class reconstruction error during each update. Meanwhile, their inter-class reconstruction effect is also considered. Compared to the existing extension of K-SVD, the proposed algorithm is more robust to parameters and has better discriminative power for classifying HEp-2 cell patterns. Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark¹ and also achieves competitive performance on standard natural image classification benchmark.     

Towards subject independent continuous sign language recognition: A segment and merge approach

This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers.     

HLD-001TM甲苯歧化与烷基转移催化剂的工业试验

对中国石油化工股份有限公司上海石油化工研究院研发的高空速、低氢烃比甲苯歧化与烷基转移催化剂(HLD-001~(TM))进行了工业试验。结果表明,该催化剂具有优良的综合催化性能。在反应温度340～365℃,反应压力2.8～3.0 MPa,质量空速2.0～3.0 h~(-1),氢烃分于比3.0～3.7条件下,转化率46%～48%(质量),选择性90%～93%(质量),产物中二甲苯/苯为2.5～3.0(mol),歧化苯冰点不低于5.45℃,达到优级品的指标。     

气相色谱法测定3-氯-1-丙醇

以填充柱气相色谱对 3 氯 1 丙醇进行了分析研究 ,方法变异系数 0 .5 % ,回收率99.5 0 %～ 10 0 .6 7%     

大掺量粉煤灰高性能混凝土的研究

本文从高分散性表面活性剂显著影响粉煤灰的形态效应而大幅降低混凝土用水量的角度出发,利用复合激发剂激发粉煤灰的火山灰效应,结合粉煤灰自身的微集料效应提高混凝土强度的技术手段,探讨了大掺量粉煤灰在混凝土中的应用,分析了高分散性表面活性剂与复合激发剂的组合对大掺量粉煤灰混凝土的作用机理。     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Know by a handful the whole sack: efficient sampling for top-k influential user identification in large graphs

Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most existing greedy algorithms mainly focus on computing the exact influence spread, leading to low computational efficiency and limiting their application to real-world social networks. While in this paper we show that through supervised sampling, we can efficiently estimate the influence spread at only negligible cost of precision, thus significantly reducing the execution time. Motivated by this, we propose ESMCE, a power-law exponent supervised Monte Carlo estimation method. In particular, ESMCE exploits the power-law exponent of the social network to guide the sampling, and employs multiple iterative steps to guarantee the estimation accuracy. Moreover, ESMCE shows excellent scalability and well suits large-scale social networks. Extensive experiments on six real-world social networks demonstrate that, compared with state-of-the-art greedy algorithms, ESMCE is able to achieve almost two orders of magnitude speedup in execution time with only negligible error (2.21 % on average) in influence spread.     

管材超声波分层缺陷检测中盲区的构成探析

管材超声波分层检测作为控制产品质量的关键步骤,必须引起相关从业人员的重视,特别是要重点分析分层缺陷检测中盲区的构成原因和分布特征。只有这样,才能切实保证检测结果的准确性,更好地控制管材质量。通过对超声波分层缺陷检测的重要性进行阐释,分析了其中盲区的构成和分布等问题。     

壳聚糖固定化脲酶生物传感器选择性测定铜离子

基于重金属对脲酶的抑制作用,研制了用于测定铜离子的生物传感器。该生物传感器的制备以壳聚糖为载体,将脲酶固定于pH电极表面。由于壳聚糖对Cu2+的富集,该生物传感器展现出高灵敏度。在样品溶液中加入5 mmol/L NaI,可以消除Hg2+和Ag+的干扰,从而实现Cu2+的选择性检测。在0.005～0.5μg/mL的浓度范围内,脲酶活性的抑制率与Cu2+浓度的对数呈良好的线性响应关系,其检出限为0.002μg/mL。将使用后的生物传感器浸泡于0.5 mmol/L的EDTA溶液再生5 min,被Cu2+抑制的脲酶的活性可以得到恢复。     

无患子表面活性物及其复配体系的性质研究

对天然植物原料无患子提取物中的表面活性物的性质（临界胶束浓度、表面张力和起泡性能）及其影响因素（温度、pH、水硬度和盐度等）进行了系统的研究，结果表明无患子表面活性物具有很低的cmc和γcmc，且在一定范围内受一些因素的影响也不大，是一种理想的香波原料；同时也研究了无患子活性物与其他温和型的阴离子表面活性剂的复配体系，表明具有明显的协同增效效果。