Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most ...previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (multiple-instance learning via embedded instance selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty
WirelessHART is a new standard specifically designed for real-time and reliable communication between sensor and actuator devices for industrial process monitoring and control applications. ...End-to-end communication delay analysis for WirelessHART networks is required to determine the schedulability of real-time data flows from sensors to actuators for the purpose of acceptance test or workload adjustment in response to network dynamics. In this paper, we consider a network model based on WirelessHART, and map the scheduling of real-time periodic data flows in the network to real-time multiprocessor scheduling. We then exploit the response time analysis for multiprocessor scheduling and propose a novel method for the delay analysis that establishes an upper bound of the end-to-end communication delay of each real-time flow in the network. Simulation studies based on both random topologies and real network topologies of a 74-node physical wireless sensor network testbed demonstrate that our analysis provides safe and reasonably tight upper bounds of the end-to-end delays of real-time flows, and hence enables effective schedulability tests for WirelessHART networks.
•A solid acid catalyst was prepared from pyrolyzed rice husk.•The effective acid sites of the prepared catalyst have favorable thermal stability.•The catalyst contains both sulfonic acid groups and ...phenolic hydroxyl groups.•The catalyst is potential to catalyze waste cooking oil to produce biodiesel.
A solid acid catalyst was prepared by sulfonating pyrolyzed rice husk with concentrated sulfuric acid, and the physical and chemical properties of the catalyst were characterized in detail. The catalyst was then used to simultaneously catalyze esterification and transesterification to produce biodiesel from waste cooking oil (WCO). In the presence of the as-prepared catalyst, the free fatty acid (FFA) conversion reached 98.17% after 3h, and the fatty acid methyl ester (FAME) yield reached 87.57% after 15h. By contrast, the typical solid acid catalyst Amberlyst-15 obtained only 95.25% and 45.17% FFA conversion and FAME yield, respectively. Thus, the prepared catalyst had a high catalytic activity for simultaneous esterification and transesterification. In addition, the catalyst had excellent stability, thereby having potential use as a heterogeneous catalyst for biodiesel production from WCO with a high FFA content.
Feature selection (FS) is an important task which can significantly affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ...ant colony optimization (ACO). For n features, existing ACO-based feature selection methods need to traverse a complete graph with O(n2) edges. However, we propose a novel algorithm in which the artificial ants traverse on a directed graph with only O(2n) arcs. The algorithm incorporates the classification performance and feature set size into the heuristic guidance, and selects a feature set with small size and high classification accuracy. We perform extensive experiments on two large image databases and 15 non-image datasets to show that our proposed algorithm can obtain higher processing speed as well as better classification accuracy using a smaller feature set than other existing methods.
► A feature selection algorithm based on ant colony optimization is presented. ► The algorithm can obtain higher processing speed than other existing methods. ► The algorithm can select a smaller feature set than other existing methods. ► Higher quality classification results are obtained using such smaller feature set. ► The advantages of the algorithm are proved empirically.
Epstein‐Barr virus (EBV) BamHI A rightward transcripts (BART) encoded microRNAs (EBV‐miR‐BARTs) are abnormally highly expressed in nasopharyngeal carcinoma (NPC). This study aims to investigate the ...diagnostic and prognostic performance of miR‐BART7‐3p and miR‐BART13‐3p. Plasma levels of EBV DNA, miR‐BART7‐3p, and miR‐BART13‐3p were examined by quantitative PCR in 483 treatment‐naïve NPC patients and 243 controls without NPC. The prognostic performance was examined by comparing plasma levels with rates of distant metastasis during follow‐up. The area under the receiver operating characteristic curve for diagnosing NPC was 0.926 for EBV DNA, 0.964 for plasma miR‐BART7‐3p, 0.973 for miR‐BART13‐3p, and 0.997 for all three indices. Among 465 NPC patients without distant metastasis, the above‐median miR‐BART7‐3p and EBV DNA were independent risk for shorter distant metastasis‐free survival (DMFS) (hazard ratio HR = 2.94, 95% confidence interval CI, 1.44‐5.97, P = .003; HR = 2.27, 95% CI, 1.26‐4.10, P = .006) in multivariate Cox regression. Epstein‐Barr virus DNA, miR‐BART7‐3p, and miR‐BART13‐3p after radiotherapy were detectable in 28.6%, 17.6%, and 54.7% of patients, respectively. In multivariate Cox regression, detectable miR‐BART7‐3p and EBV DNA were independent risks for shorter DMFS (HR = 4.13, 95% CI, 1.89‐9.01, P < .001; HR = 2.14, 95% CI, 1.04‐4.42, P = .039). The 4‐year DMFS rate was 92.0% in subjects (n = 156) with neither detectable miR‐BART7‐3p nor EBV DNA, 80.0% in subjects (n = 65) with either detectable miR‐BART7‐3p or EBV DNA, and 52.9% in subjects (n = 24) with both detectable miR‐BART7‐3p and EBV DNA after radiotherapy (P < .001). Circulating levels of miR‐BART7‐3p and miR‐BART13‐3p show excellent diagnostic performance for NPC. The combination of plasma levels of miR‐BART7‐3p and EBV DNA at diagnosis and after radiotherapy could help stratify patients by risk of poor DMFS.
This is so far the most extensive retrospective study to report the diagnostic and prognostic value of circulating levels of the Epstein‐Barr virus BamHI A rightward transcripts encoded microRNAs (EBV miR‐BARTs) in nasopharyngeal carcinoma. Compared with circulating EBV DNA, this study found that circulating EBV miR‐BARTs is not inferior to EBV DNA in diagnosis and prognosis. Furthermore, the combination of circulating levels of EBV DNA and miR‐BARTs at diagnosis can improve the potential for diagnostic and prognostic evaluation.
Breastfeeding is a classic aspect of health, which has benefits for both children and mothers in the long and short terms. In Europe, the obesity epidemic for children becomes an urgent problem ...needed to be solved. Recent research indicates that prolonging breastfeeding time would reduce the proportion of obesity and overweight. Breastfeeding has been seen as a protective factor. In this article, we make a secondary analysis of the data from several European countries in the WHO European Childhood Obesity Surveillance Initiative (COSI) to study the correlation between breastfeeding and obesity. This survey is the fourth round. The result shows that there is a positive correlation between breastfeeding and obesity.
Severe smog, a form of air pollution, has become a threat to public health in Beijing, China. To examine Beijing residents' protective behavioral intentions against smog, we proposed a conceptual ...model, which applies the health belief model (HBM) and specifies the roles of three distal predictors: exposure to news, discussion, and worry. The proposed model was tested in the context of protective behavioral intentions (i.e., intention to wear facemask & intention to use air purifier). Data were collected from Beijing residents during the period from 2/27 to 3/7 in 2017. Structural-equation-modeling (SEM) analyses of valid cases (N = 523) found support for the health belief model regarding the positive effects of perceived threat, perceived benefit, and perceived self-efficacy on intention to wear facemask or intention to use air purifier. Perceived barrier has a negative effect on intention to use air purifier, but is not related to intention to wear facemask. Neither exposure nor discussion is related to perceived threat. The effect of worry on intention to wear facemaskor intention to use air purifier is mediated by perceived threat. This proposed mediating mechanism is superior to the reverse mechanism (that worry mediates perceived threat). Implications of findings were discussed.
In the present study, I investigated the influence of stimulus types on bilingual control in the language switching process. The commonly employed stimuli in language switching studies - Arabic ...digits and objects - were compared to further investigate the way in which inhibitory control could be modulated by semantic and repetition priming effects. The digit stimuli have two unique characteristics in the language switching paradigm, for example, they are present repeatedly and are semantically related to each other, compared with pictural stimuli. Thus, these unique characteristics might influence the operation of inhibitory control in bilingual language production, modulating the size and asymmetry of switching costs.
Two picture control sets were set up to match those characteristics: (1) a semantic control set, in which picture stimuli belong to the same category group, such as, animals, occupations or transportation and specific semantic categories were presented in a blocked condition; and (2) a repeated control set, in which nine different picture stimuli were repeatedly presented like the Arabic digits from 1 to 9.
When comparing the digit condition and the standard picture condition, analyses of naming latencies and accuracy rates revealed that switching costs were reliably smaller for digit naming than for picture naming and the L1 elicited more switching costs for picture naming than for digit naming. On the other hand, when comparing the digit condition and the two picture control sets, it was found that the magnitude of switching costs became identical and the asymmetry in switching costs became much smaller between the two languages.
Statistical depth functions provide from the deepest point a center-outward ordering of multidimensional data. In this sense, depth functions can measure the extremeness or outlyingness of a data ...point with respect to a given data set. Hence, they can detect outliers observations that appear extreme relative to the rest of the observations. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. In this article, we propose a novel statistical depth, the kernelized spatial depth (KSD), which generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. We demonstrate this by the half-moon data and the ring-shaped data. Based on the KSD, we propose a novel outlier detection algorithm, by which an observation with a depth value less than a threshold is declared as an outlier. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. It applies to a one-class learning setting, in which normal observations are given as the training data, as well as to a missing label scenario, where the training set consists of a mixture of normal observations and outliers with unknown labels. We give upper bounds on the false alarm probability of a depth-based detector. These upper bounds can be used to determine the threshold. We perform extensive experiments on synthetic data and data sets from real applications. The proposed outlier detector is compared with existing methods. The KSD outlier detector demonstrates a competitive performance.
Optical metamaterials consist of artificially engineered structures exhibiting unprecedented optical properties beyond natural materials. Optical metamaterials offer many novel functionalities, such ...as super-resolution imaging, negative refraction and invisibility cloaking. However, most optical metamaterials are comprised of rigid materials that lack tunability and flexibility, which hinder their practical applications. This limitation can be overcome by integrating soft matters within the metamaterials or designing responsive metamaterial structures. In addition, soft metamaterials can be reconfigured via optical, electrical, thermal and mechanical stimuli, thus enabling new optical properties and functionalities. This paper reviews different types of soft and reconfigurable optical metamaterials and their fabrication methods, highlighting their exotic properties. Future directions to employ soft optical metamaterials in next-generation metamaterial devices are identified.
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