Monte Carlo convex hull model for classification of traditional Chinese paintings

While artists demonstrate their individual styles through paintings and drawings, how to describe such artistic styles well selected visual features towards computerized analysis of the arts remains to be a challenging research problem. In this paper, we propose an integrated feature-based artistic descriptor with Monte Carlo Convex Hull (MCCH) feature selection model and support vector machine (SVM) for characterizing the traditional Chinese paintings and validate its effectiveness via automated classification of Chinese paintings authored by well-known Chinese artists. The integrated artistic style descriptor essentially contains a number of visual features including a novel feature of painting composition and object feature, each of which describes one element of the artistic style. In order to ensure an integrated discriminating power and certain level of adaptability to the variety of artistic styles among different artists, we introduce a novel feature selection method to process the correlations and the synergy across all elements inside the integrated feature and hence complete the proposed style-based descriptor design. Experiments on classification of Chinese paintings via a parallel MCCH model illustrate that the proposed descriptor outperforms the existing representative technique in terms of precision and recall rates

Ranking highlight level of movie clips : a template based adaptive kernel SVM method

This paper looks into a new direction in movie clips analysis – model based ranking of highlight level. A movie clip, containing a short story, is composed of several continuous shots, which is much simpler than the whole movie. As a result, clip based analysis provides a feasible way for movie analysis and interpretation. In this paper, clip-based ranking of highlight level is proposed, where the challenging problem in detecting and recognizing events within clips is not required. Due to the lack of publicly available datasets, we firstly construct a database of movie clips, where each clip is associated with manually derived highlight level as ground truth. From each clip a number of effective visual cues are then extracted. To bridge the gap between low-level features and highlight level semantics, a holistic method of highlight ranking model is introduced. According to the distance between testing clips and selected templates, appropriate kernel function of support vector machine (SVM) is adaptively selected. Promising results are reported in automatic ranking of movie highlight levels

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent

Design and Development of an E-Learning Management System

The trend of e-learning technologies is expanding fast. Web-based learning environments are becoming very common in the higher education institutions. Nowadays e-learning management systems are very popular. Many universities throughout the world deliver educational programs via the Internet. Developments of e-learning systems are generating great impact in the field of education services to improve the teaching and learning process, and overcome geographical displace. In recent years, various kinds of Internet technologies have become available for developers to implement such e-learning system that provide an e-learning gateway on the Internet. The rapid advancements in information and communication technologies, especially the networking and multimedia, have led to the development of many advanced e-learning systems these days. A user-friendly interface and a sophisticated data model are the essential design consideration to make the e-learning system easy-to-use for the instructors and learners. The need for such architecture is critical for designing the system and standards development. The system is developed under Computer Supported Cooperative Work framework and web portal technology. The system integrates all the critical and valuable communication tools that effectively improve the collaboration in an e-learning environment

Generic Supply Chain Management System

Supply chain management refers to all the management functions related to the flow of materials from the company’s direct suppliers to its direct customers. In this paper, we will propose a generic supply chain management system, and describe how the system works in terms of information exchange, workflow coordination and flexible logistic route. We will introduce two data models, which are called PDM and WfMS, and explain how to classify the proposed system into them. And then, we will further describe how the back-end three-layered architecture stores the dynamic data type into the database

RNA sequencing from neural ensembles activated during fear conditioning in the mouse temporal association cortex

The stable formation of remote fear memories is thought to require neuronal gene induction in cortical ensembles that are activated during learning. However, the set of genes expressed specifically in these activated ensembles is not known; knowledge of such transcriptional profiles may offer insights into the molecular program underlying stable memory formation. Here we use RNA-Seq to identify genes whose expression is enriched in activated cortical ensembles labeled during associative fear learning. We first establish that mouse temporal association cortex (TeA) is required for remote recall of auditory fear memories. We then perform RNA-Seq in TeA neurons that are labeled by the activity reporter Arc-dVenus during learning. We identify 944 genes with enriched expression in Arc-dVenus+ neurons. These genes include markers of L2/3, L5b, and L6 excitatory neurons but not glial or inhibitory markers, confirming Arc-dVenus to be an excitatory neuron-specific but non-layer-specific activity reporter. Cross comparisons to other transcriptional profiles show that 125 of the enriched genes are also activity-regulated in vitro or induced by visual stimulus in the visual cortex, suggesting that they may be induced generally in the cortex in an experience-dependent fashion. Prominent among the enriched genes are those encoding potassium channels that down-regulate neuronal activity, suggesting the possibility that part of the molecular program induced by fear conditioning may initiate homeostatic plasticity

Predictive coupled-cluster isomer orderings for some Sin{}_nCm{}_m (m,n≤12m, n\le 12) clusters; A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks

The accurate determination of the preferred ${\rm Si}_{12}{\rm C}_{12}$ isomer is important to guide experimental efforts directed towards synthesizing SiC nano-wires and related polymer structures which are anticipated to be highly efficient exciton materials for opto-electronic devices. In order to definitively identify preferred isomeric structures for silicon carbon nano-clusters, highly accurate geometries, energies and harmonic zero point energies have been computed using coupled-cluster theory with systematic extrapolation to the complete basis limit for set of silicon carbon clusters ranging in size from SiC$_3$ to ${\rm Si}_{12}{\rm C}_{12}$. It is found that post-MBPT(2) correlation energy plays a significant role in obtaining converged relative isomer energies, suggesting that predictions using low rung density functional methods will not have adequate accuracy. Utilizing the best composite coupled-cluster energy that is still computationally feasible, entailing a 3-4 SCF and CCSD extrapolation with triple-$\zeta$ (T) correlation, the {\it closo} ${\rm Si}_{12}{\rm C}_{12}$ isomer is identified to be the preferred isomer in support of previous calculations [J. Chem. Phys. 2015, 142, 034303]. Additionally we have investigated more pragmatic approaches to obtaining accurate silicon carbide isomer energies, including the use of frozen natural orbital coupled-cluster theory and several rungs of standard and double-hybrid density functional theory. Frozen natural orbitals as a way to compute post MBPT(2) correlation energy is found to be an excellent balance between efficiency and accuracy

Genome-Wide Association Study for Identification and Validation of Novel SNP Markers for \u3ci\u3eSr6\u3c/i\u3e Stem Rust Resistance Gene in Bread Wheat

Stem rust (caused by Puccinia graminis f. sp. tritici Erikss. & E. Henn.), is a major disease in wheat (Triticum aestivium L.). However, in recent years it occurs rarely in Nebraska due to weather and the effective selection and gene pyramiding of resistance genes. To understand the genetic basis of stem rust resistance in Nebraska winter wheat, we applied genome-wide association study (GWAS) on a set of 270 winter wheat genotypes (A-set). Genotyping was carried out using genotyping-by-sequencing and ~35,000 high-quality SNPs were identified. The tested genotypes were evaluated for their resistance to the common stem rust race in Nebraska (QFCSC) in two replications. Marker-trait association identified 32 SNP markers, which were significantly (Bonferroni corrected P \u3c 0.05) associated with the resistance on chromosome 2D. The chromosomal location of the significant SNPs (chromosome 2D) matched the location of Sr6 gene which was expected in these genotypes based on pedigree information. A highly significant linkage disequilibrium (LD, r2) was found between the significant SNPs and the specific SSR marker for the Sr6 gene (Xcfd43). This suggests the significant SNP markers are tagging Sr6 gene. Out of the 32 significant SNPs, eight SNPs were in six genes that are annotated as being linked to disease resistance in the IWGSC RefSeq v1.0. The 32 significant SNP markers were located in nine haplotype blocks. All the 32 significant SNPs were validated in a set of 60 different genotypes (V-set) using single marker analysis. SNP markers identified in this study can be used in marker-assisted selection, genomic selection, and to develop KASP (Kompetitive Allele Specific PCR) marker for the Sr6 gene

Valence and Charge-transfer Optical Properties for Some Si\u3csub\u3en\u3c/sub\u3eC\u3csub\u3em\u3c/sub\u3e (m, n ≤ 12) Clusters: Comparing TD-DFT, Complete-basis-limit EOMCC, and Benchmarks from Spectroscopy

Accurate optical characterization of the closo-Si12C12 molecule is important to guide experimental efforts toward the synthesis of nano-wires, cyclic nano-arrays, and related array structures, which are anticipated to be robust and efficient exciton materials for opto-electronic devices. Working toward calibrated methods for the description of closo-Si12C12 oligomers, various electronic structure approaches are evaluated for their ability to reproduce measured optical transitions of the SiC2, Si2Cn (n = 1–3), and Si3Cn (n = 1, 2) clusters reported earlier by Steglich and Maier [Astrophys. J. 801, 119 (2015)]. Complete-basis-limit equation-of-motion coupled-cluster (EOMCC) results are presented and a comparison is made between perturbative and renormalized non-iterative triples corrections. The effect of adding a renormalized correction for quadruples is also tested. Benchmark test sets derived from both measurement and high-level EOMCC calculations are then used to evaluate the performance of a variety of density functionals within the time-dependent density functional theory (TD-DFT) framework. The best-performing functionals are subsequently applied to predict valence TD-DFT excitation energies for the lowest-energy isomers of SinC and Sin−1C7−n (n = 4–6). TD-DFT approaches are then applied to the SinCn (n = 4–12) clusters and unique spectroscopic signatures of closo-Si12C12 are discussed. Finally, various long-range corrected density functionals, including those from the CAM-QTP family, are applied to a charge-transfer excitation in a cyclic (Si4C4)4 oligomer. Approaches for gauging the extent of charge-transfer character are also tested and EOMCC results are used to benchmark functionals and make recommendations