A comparative study on the effect of VUV radiation in plasma SiOx-coated polyimide and polypropylene films

Wettability of polyimide (PI) and polypropylene (PP) films have been improved using SiO_x-like thin layers deposited from a mixture of hexamethyldisiloxane (HMDSO) and oxygen in a microwave distributed electron cyclotron resonance plasma reactor. The films wettability evolution behaviors were evaluated through the results of contact angle measurements, atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS). The plasma depositions of SiO_x thin layers in presence of VUV radiation induce a contact angle decrease to about 7° and 35° for PI and PP films, respectively. XPS data showed that such difference in wettability is attributed to the increase of hydrophilic group's proportion at the surface of coated PI films due to VUV irradiation. AFM images showed that the PI surface topography remains relatively smooth when coated in presence of VUV radiation. However, in the case of PP films, AFM images revealed the growth of irregular structure due to a substrate etching effect supported by VUV radiation. For polymers coated without VUV irradiation, the deconvolution of the C1s peaks showed a significant decrease of CO bonds for both PI and PP substrates.     

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.     

Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning

State-of-the-art distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation. Others try to minimize inter-node communication, which requires an expensive data preprocessing phase, leading to a high startup cost. Apriori knowledge of the query workload has also been used to create partitions, which, however, are static and do not adapt to workload changes. In this paper, we propose AdPart, a distributed RDF system, which addresses the shortcomings of previous work. First, AdPart applies lightweight partitioning on the initial data, which distributes triples by hashing on their subjects; this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdPart takes full advantage of the partitioning to (1) support the fully parallel processing of join patterns on subjects and (2) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdPart implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdPart: (1) starts faster than all existing systems; (2) processes thousands of queries before other systems become online; and (3) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds.     

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.     

Spatial and seasonal variations and ecotoxicological significance of sediment trace metal concentrations in Kebir-Rhumel basin (Northeast of Algeria)

This study sought to assess sediment contamination by trace metals (cadmium, chromium, cobalt, copper, manganese, nickel, lead and zinc), to localize contaminated sites and to identify environmental risk for aquatic organisms in Wadis of Kebir Rhumel basin in the Northeast of Algeria. Water and surficial sediments (0-5 cm) were sampled in winter, spring, summer and autumn from 37 sites along permanent Wadis of the Kebir Rhumel basin. Sediment trace metal contents were measured by Flame Atomic Absorption Spectroscopy. Trace metals median concentrations in sediments followed a decreasing order: Mn > Zn > Pb > Cr > Cu > Ni > Co > Cd. Extreme values (dry weights) of the trace metals are as follows: 0.6-3.4 microg/g for Cd, 10-216 microg/g for Cr, 9-446 microg/g for Cu, 3-20 microg/g for Co, 105-576 microg/g for Mn, 10-46 microg/g for Ni, 11-167 microg/g for Pb, and 38-641 microg/g for Zn. According to world natural concentrations, all sediments collected were considered as contaminated by one or more elements. Comparing measured concentrations with American guidelines (Threshold Effect Level: TEL and Probable Effect Level: PEL) showed that biological effects could be occasionally observed for cadmium, chromium, lead and nickel levels but frequently observed for copper and zinc levels. Sediment quality was shown to be excellent for cobalt and manganese but medium to bad for cadmium, chromium, copper, lead, nickel and zinc regardless of sites.     

Modelling of the filling of micro-cavities of finite geometry by amorphous polymers using hot-embossing

The possibility to fill cavities of finite geometry could be described using an analytical model of the hot-embossing process of viscoelastic polymers. This model is based on the volume conservation during the forming process which allows to predict data concerning the geometrical evolution of the material on one hand, and on the other hand the filling time of cavities in the mould. A particular attention was drawn on the necessary time to fill the cavities depending on their shape or a scale factor for a given cavity shape.     

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.