Design of cycloidal rays in optical waveguides in analogy to the fastest descending problem

In this work, we present the design of cycloidal waveguides from a gradient refractive index (GRIN) medium in analogy to the fastest descending problem in classical mechanics. Light rays propagate along cycloids in this medium, of which the refractive index can be determined from relating to the descending speed under gravity force. It can be used as GRIN lenses or waveguides, and the frequency specific focusing and imaging properties have been discussed. The results suggest that the waveguide can be viewed as an optical filter. Its frequency response characteristics change with the refractive index profile and the device geometries.Comment: 10 pages, 6 figure

Visual memory benefits from prolonged encoding time regardless of stimulus type

It is generally assumed that the storage capacity of visual working memory (VWM) is limited, holding about 3-4 items. Recent work with real-world objects, however, has challenged this view by providing evidence that the VWM capacity for real-world objects is not fixed but instead increases with prolonged encoding time (Brady, Stormer, & Alvarez, 2016). Critically, in this study, no increase with prolonged encoding time was observed for storing simple colors. Brady et al. (2016) argued that the larger capacity for real-world objects relative to colors is due to the additional conceptual information of real-world objects. With basically the same methods of Brady et al., in Experiments 1-3, we were unable to replicate their basic findings. Instead, we found that visual memory for simple colors also benefited from prolonged encoding time. Experiment 4 showed that the scale of the encoding time benefit was the same for familiar and unfamiliar objects, suggesting that the added conceptual information does not contribute to this benefit. We conclude that visual memory benefits from prolonged encoding time regardless of stimulus type

Quantitative Implementation of Artificial Intelligence Based on Task Completion Analysis

With the further development of the new generation of artificial intelligence science and technology, the new generation of artificial intelligence science and technology has been applied in many fields. AlphaGo program uses high technology of quantitative analysis to realize qualitative research and development of artificial intelligence, which has important reference significance for the research and development of a new generation of artificial intelligence in the future. From the perspective of task accessibility, this paper analyzes the defects of the disturbance, so as to achieve the quantitative implementation of the new generation of artificial intelligence task accessibility analysis method

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

Draft genome of the leopard gecko, <i>Eublepharis macularius</i>

BACKGROUND: Geckos are among the most species-rich reptile groups and the sister clade to all other lizards and snakes. Geckos possess a suite of distinctive characteristics, including adhesive digits, nocturnal activity, hard, calcareous eggshells, and a lack of eyelids. However, one gecko clade, the Eublepharidae, appears to be the exception to most of these ‘rules’ and lacks adhesive toe pads, has eyelids, and lays eggs with soft, leathery eggshells. These differences make eublepharids an important component of any investigation into the underlying genomic innovations contributing to the distinctive phenotypes in ‘typical’ geckos. FINDINGS: We report high-depth genome sequencing, assembly, and annotation for a male leopard gecko, Eublepharis macularius (Eublepharidae). Illumina sequence data were generated from seven insert libraries (ranging from 170 to 20 kb), representing a raw sequencing depth of 136X from 303 Gb of data, reduced to 84X and 187 Gb after filtering. The assembled genome of 2.02 Gb was close to the 2.23 Gb estimated by k-mer analysis. Scaffold and contig N50 sizes of 664 and 20 kb, respectively, were comparable to the previously published Gekko japonicus genome. Repetitive elements accounted for 42 % of the genome. Gene annotation yielded 24,755 protein-coding genes, of which 93 % were functionally annotated. CEGMA and BUSCO assessment showed that our assembly captured 91 % (225 of 248) of the core eukaryotic genes, and 76 % of vertebrate universal single-copy orthologs. CONCLUSIONS: Assembly of the leopard gecko genome provides a valuable resource for future comparative genomic studies of geckos and other squamate reptiles. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13742-016-0151-4) contains supplementary material, which is available to authorized users

Tong-Xie-Yao-Fang Regulates 5-HT Level in Diarrhea Predominant Irritable Bowel Syndrome Through Gut Microbiota Modulation

Tong-Xie-Yao-Fang (TXYF) has been widely used for the treatment of diarrhea-predominant irritable bowel syndrome (IBS-D) in traditional Chinese medicine. However, its mechanism of action in the treatment of IBS-D remains to be fully understood. Recent reports have shown that Clostridium species in the gut can induce 5-HT production in the colon, which then contributes to IBS-D. Due to the wide use of TXYF in the clinical treatment of IBS-D and the close relationship between gut microbiota and IBS-D, we hypothesize that TXYF treats IBS-D by modulating gut microbiota and regulating colonic 5-HT levels. In this study, variation analysis of 16S rRNA was conducted to evaluate changes in the distribution of gut microbiota in IBS-D model rats after TXYF treatment. Moreover, we investigated whether TXYF could affect colonic 5-HT levels in IBS-D model rats. We then performed fecal transplantation experiments to confirm the effects of TXYF on gut microbiota and 5-HT levels. We found that TXYF treatment can ameliorate IBS-D and regulate 5-HT levels in colon tissue homogenates. TXYF treatment also affected the diversity of gut microbiota and altered the relative abundance of Akkermansia and Clostridium sensu stricto 1 in gut flora populations. Finally, we showed that fecal transplantation from TXYF-treated rats could relieve IBS-D and regulate 5-HT levels in colon tissue homogenates. In conclusion, the present study demonstrates that TXYF treatment diminishes colonic 5-HT levels and alleviates the symptoms of IBS-D by favorably affecting microbiota levels in gut flora communities

Construction of Red Fox Chromosomal Fragments from the Short-Read Genome Assembly

The genome of a red fox (Vulpes vulpes) was recently sequenced and assembled using next-generation sequencing (NGS). The assembly is of high quality, with 94X coverage and a scaffold N50 of 11.8 Mbp, but is split into 676,878 scaffolds, some of which are likely to contain assembly errors. Fragmentation and misassembly hinder accurate gene prediction and downstream analysis such as the identification of loci under selection. Therefore, assembly of the genome into chromosome-scale fragments was an important step towards developing this genomic model. Scaffolds from the assembly were aligned to the dog reference genome and compared to the alignment of an outgroup genome (cat) against the dog to identify syntenic sequences among species. The program Reference-Assisted Chromosome Assembly (RACA) then integrated the comparative alignment with the mapping of the raw sequencing reads generated during assembly against the fox scaffolds. The 128 sequence fragments RACA assembled were compared to the fox meiotic linkage map to guide the construction of 40 chromosomal fragments. This computational approach to assembly was facilitated by prior research in comparative mammalian genomics, and the continued improvement of the red fox genome can in turn offer insight into canid and carnivore chromosome evolution. This assembly is also necessary for advancing genetic research in foxes and other canids