Covalent triazine framework with efficient photocatalytic activity in aqueous and solid media

Covalent triazine frameworks (CTFs) have been recently employed for visible light-driven photocatalysis due to their unique optical and electronic properties. However, the usually highly hydrophobic nature of CTFs, which originates from their overall aromatic backbone, leads to limitations of CTFs for applications in aqueous media. In this study, we aim to extend the range of the application media of CTFs and design hybrid material of a CTF and mesoporous silica (SBA-15) for efficient photocatalysis in aqueous medium. A thiophene-containing CTF was directly synthesized in mesopores of SBA-15. Due to the high surface area and the added hydrophilic properties by silica, the hybrid material demonstrated excellent adsorption of organic molecules in water. This leads not only to high photocatalytic performance of the hybrid material for the degradation of organic dyes in water, but also for efficient photocatalysis in solvent-free and solid state. Furthermore, the reusability, stability and easy recovery of the hybrid material offers promising metal-free heterogeneous photocatalyst for broader applications in different reaction media.     

基于动态贝叶斯网络的语音识别及音素切分研究*

研究了一种基于动态贝叶斯网络(dynamic bayesian networks, DBN)的语音识别建模方法,利用GMTK(graphical model tool kits)工具构建音素级音频流DBN语音训练和识别模型,同时与传统的基于隐马尔可夫的语音识别结果进行比较,并给出词与音素的切分结果.实验表明,在各种信噪比测试条件下,基于DBN的语音识别结果与基于HMM的语音识别结果相当,并表现出一定的抗噪性,音素的切分结果也比较准确.     

Speech driven photo realistic facial animation based on an articulatory DBN model and AAM features

This paper presents a photo realistic facial animation synthesis approach based on an audio visual articulatory dynamic Bayesian network model (AF_AVDBN), in which the maximum asynchronies between the articulatory features, such as lips, tongue and glottis/velum, can be controlled. Perceptual Linear Prediction (PLP) features from audio speech, as well as active appearance model (AAM) features from face images of an audio visual continuous speech database, are adopted to train the AF_AVDBN model parameters. Based on the trained model, given an input audio speech, the optimal AAM visual features are estimated via a maximum likelihood estimation (MLE) criterion, which are then used to construct face images for the animation. In our experiments, facial animations are synthesized for 20 continuous audio speech sentences, using the proposed AF_AVDBN model, as well as the state-of-art methods, being the audio visual state synchronous DBN model (SS_DBN) implementing a multi-stream Hidden Markov Model, and the state asynchronous DBN model (SA_DBN). Objective evaluations on the learned AAM features show that much more accurate visual features can be learned from the AF_AVDBN model. Subjective evaluations show that the synthesized facial animations using AF_AVDBN are better than those using the state based SA_DBN and SS_DBN models, in the overall naturalness and matching accuracy of the mouth movements to the speech content.     

Fuzzy clustering with learnable cluster-dependent kernels

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster-dependent Kernels (FLeCK), that learns the underlying cluster-dependent dissimilarity measure while seeking compact clusters. The learned dissimilarity is based on a Gaussian kernel function with cluster-dependent parameters. Each cluster’s parameter learned by FLeCK reflects the relative intra-cluster and inter-cluster characteristics. These parameters are learned by optimizing both the intra-cluster and the inter-cluster distances. This optimization is achieved iteratively by dynamically updating the partition and the local kernel. This makes the kernel learning task takes advantages of the available unlabeled data and reciprocally, the categorization task takes advantages of the learned local kernels. Another key advantage of FLeCK is that it is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. Using synthetic and real data sets, we show that FLeCK learns meaningful parameters and outperforms several other algorithms. In particular, we show that when data include clusters with various inter- and intra-cluster distances, learning cluster-dependent parameters is crucial in obtaining a good partition.     

A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.     

Local feature selection for multiple instance learning

Journal of Intelligent Information Systems - We propose a local feature selection method for the Multiple Instance Learning (MIL) framework. Unlike conventional feature selection algorithms that...     

A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality

Managing service quality is vital to retain customer satisfaction and augment revenues for any business organization. Often it is difficult to assess service quality due to lack of quantifiable measures and limited data. In this paper, we present a hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating service quality of urban transportation systems. The proposed approach consists of three steps. The first step involves development of a SERVQUAL based questionnaire to collect data for measuring transportation service quality. The participants provide linguistic assessments to rate the service quality criteria and the alternatives. In step 2, the linguistic ratings are combined through fuzzy TOPSIS to generate an overall performance score for each alternative. The alternative with the highest score is finally chosen. In step 3, sensitivity analysis is conducted to evaluate the influence of criteria weights on the decision making process.The strength of the proposed approach is its practical applicability and ability to provide solution under partial or lack of quantitative information. An application of the proposed approach for evaluation of service quality of metro in Montreal is provided.     

Real-time quality authentication of honey using atmospheric pressure chemical ionisation mass spectrometry (APCI-MS)

The aim of this study was to use gas chromatography-mass spectrometry (GC-MS) and APCI-MS techniques to detect adulteration in honey. The key volatile compounds in the headspace of the adulterated honey were marked by GC-MS and their representative fragment ions were utilised in scanning honey samples using the real-time APCI-MS system. The PLS models validated using independent data sets resulted in coefficient of the determination () of 0.97 and 0.96 and root mean square error in prediction (RMSEP) of 2.62 and 2.45 for the GC-MS and APCI-MS data sets respectively. The most efficient volatiles from GC-MS analysis and their corresponding fragment ions m/z from APCI-MS data analysis were then identified and used to develop new PLS models to predict the level of adulteration. The best PLS model gave of 0.95 and RMEP of 2.60% in the independent validation set indicating that the model was very accurate in predicting the level of adulteration.     

Synthesis and characterization of radiation cross-linked PVP hydrogels and investigation of its potential as an antileishmanial drug carrier

Polymer Bulletin - The aim of this study was to elaborate a suitable hydrogel to be used as drug carrier for antileishmanial treatment. Therefore, a PVP hydrogel was synthesized using gamma...