Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process ...because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.
Tourist behaviour Pearce, Philip L
2005., 2005, 2005-09-27, Letnik:
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eBook
Tourism is an inherently social phenomenon. Tourists travel with others and experience places and cultures through interacting with both familiar and unfamiliar others. This volume presents a ...thorough tour of the social psychological processes which underpin contemporary travel. The fascinating phenomenon of tourist behaviour deals with topics such as motivation, destination choice, travellers' on site experiences, satisfaction and learning. This book uses an array of developing and recently constructed conceptual frameworks to both synthesise what is established, and to create new insights and directions for further analysis and, ultimately, management action.
The dominant paradigm for inference in psychology is a null-hypothesis significance testing one. Recently, the foundations of this paradigm have been shaken by several notable replication failures. ...One recommendation to remedy the replication crisis is to collect larger samples of participants. We argue that this recommendation misses a critical point, which is that increasing sample size will not remedy psychology’s lack of strong measurement, lack of strong theories and models, and lack of effective experimental control over error variance. In contrast, there is a long history of research in psychology employing small-
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designs that treats the individual participant as the replication unit, which addresses each of these failings, and which produces results that are robust and readily replicated. We illustrate the properties of small-
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and large-
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designs using a simulated paradigm investigating the stage structure of response times. Our simulations highlight the high power and inferential validity of the small-
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design, in contrast to the lower power and inferential indeterminacy of the large-
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design. We argue that, if psychology is to be a mature quantitative science, then its primary theoretical aim should be to investigate systematic, functional relationships as they are manifested at the individual participant level and that, wherever possible, it should use methods that are optimized to identify relationships of this kind.
The selective deuteration of organic molecules through electrochemistry is proving to be an effective alternative to conventional
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H labelling strategies, which traditionally require high ...temperatures, high pressures of deuterium gas in hydrothermal autoclave reactors, or require reagents capable of generating highly reactive species which are then quenched by a deuterium source. Such harsh conditions or reagents can consequently lower chemo- or regioselectivity in many deuteration processes. Transition metal catalysis and more recently photocatalysis have emerged as methods to access selectively deuterated compounds under significantly more mild conditions. Now, electrochemistry, which is increasingly becoming a mainstream synthetic tool, is primed to enter this space. Accordingly, this highlight will feature a selection of electrochemical deuteration methods developed in recent years, and propose where the use of electrosynthesis could access novel reactivity in the context of deuteration.
A highlight of recent synthetic methods for selective deuteration of organic molecules using electrochemistry.
This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitrarily switchings, and the ...unknown functions which are constructed with the whole states. Neural network is used to simulate the uncertain systems and a filter-based state observer is designed to estimate the immeasurable states in this paper, respectively. Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented. Due to the developed finite-time control strategy, the closed-loop signals can be ensured to be bounded under arbitrarily switchings, and the outputs of systems can quickly track the desired reference signals in finite time. The effectiveness of the proposed method is given through its application to a mass-spring-damper system.
A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of ...TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The k-means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neurofuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent.
The intensive use of antibiotics results in their continuous release into the environment and the subsequent widespread occurrence of antibiotic resistant bacteria (ARB), antibiotic resistance genes ...(ARGs) and mobile genetic elements (MGEs). This study used Illumina high-throughput sequencing to investigate the broad-spectrum profiles of both ARGs and MGEs in activated sludge and anaerobically digested sludge from a full-scale wastewater treatment plant. A pipeline for identifying antibiotic resistance determinants was developed that consisted of four categories: gene transfer potential, ARG potential, ARGs pathway and ARGs phylogenetic origin. The metagenomic analysis showed that the activated sludge and the digested sludge exhibited different microbial communities and changes in the types and occurrence of ARGs and MGEs. In total, 42 ARGs subtypes were identified in the activated sludge, while 51 ARG subtypes were detected in the digested sludge. Additionally, MGEs including plasmids, transposons, integrons (intI1) and insertion sequences (e.g. ISSsp4, ISMsa21 and ISMba16) were abundant in the two sludge samples. The co-occurrence pattern between ARGs and microbial taxa revealed by network analysis indicated that some environmental bacteria (e.g. Clostridium and Nitrosomonas) might be potential hosts of multiple ARGs. The findings increase our understanding of WWTPs as hotspots of ARGs and MGEs, and contribute towards preventing their release into the downstream environment.
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•Metagenomic sequencing was used to investigate profiles of ARGs and MGEs.•Wastewater treatment plants might be hotspots of ARGs and MGEs.•Some environmental bacteria might be potential hosts of multiple ARGs.
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