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

Draft Genome of the Leopard Gecko, \u3cem\u3eEublepharis Macularius\u3c/em\u3e

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 compble 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 comptive genomic studies of geckos and other squamate reptiles

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