| 1. |
- An, Junghwa, et al.
(author)
-
Permanent Genetic Resources added to Molecular Ecology Resources Database 1 October 2009-30 November 2009
- 2010
-
In: Molecular Ecology Resources. - : Wiley. - 1755-098X .- 1755-0998. ; 10:2, s. 404-408
-
Journal article (peer-reviewed)abstract
- This article documents the addition of 411 microsatellite marker loci and 15 pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Acanthopagrus schlegeli, Anopheles lesteri, Aspergillus clavatus, Aspergillus flavus, Aspergillus fumigatus, Aspergillus oryzae, Aspergillus terreus, Branchiostoma japonicum, Branchiostoma belcheri, Colias behrii, Coryphopterus personatus, Cynogolssus semilaevis, Cynoglossus semilaevis, Dendrobium officinale, Dendrobium officinale, Dysoxylum malabaricum, Metrioptera roeselii, Myrmeciza exsul, Ochotona thibetana, Neosartorya fischeri, Nothofagus pumilio, Onychodactylus fischeri, Phoenicopterus roseus, Salvia officinalis L., Scylla paramamosain, Silene latifo, Sula sula, and Vulpes vulpes. These loci were cross-tested on the following species: Aspergillus giganteus, Colias pelidne, Colias interior, Colias meadii, Colias eurytheme, Coryphopterus lipernes, Coryphopterus glaucofrenum, Coryphopterus eidolon, Gnatholepis thompsoni, Elacatinus evelynae, Dendrobium loddigesii Dendrobium devonianum, Dysoxylum binectariferum, Nothofagus antarctica, Nothofagus dombeyii, Nothofagus nervosa, Nothofagus obliqua, Sula nebouxii, and Sula variegata. This article also documents the addition of 39 sequencing primer pairs and 15 allele specific primers or probes for Paralithodes camtschaticus.
|
|
| 2. |
- Shao, Wen-Ze, et al.
(author)
-
On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces
- 2019
-
In: Neurocomputing. - : ELSEVIER. - 0925-2312 .- 1872-8286. ; 364, s. 1-15
-
Journal article (peer-reviewed)abstract
- Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 x 16 pixels, hallucinating them to their 8 x upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.
|
|
| 3. |
- Ablikim, M., et al.
(author)
-
Observation of the decay psi(3686) -> Lambda(Sigma)over-bar(+/-) pi(-/+) + c.c
- 2013
-
In: Physical Review D. - 1550-7998 .- 1550-2368. ; 88:11, s. 112007-
-
Journal article (peer-reviewed)abstract
- Using a sample of 1:06 X 10(8) psi(3686) events collected with the BESIII detector, we present the first observation of the decays of psi(3686) -> Lambda(Sigma) over bar (+) pi(-) + c.c. and psi(3686) -> Lambda(Sigma) over bar (-) pi(+) + c.c. The branching fractions are measured to be B(psi(3686) -> Lambda(Sigma) over bar (+) pi(-) + c.c.) = (1.40 +/- 0.03 +/- 0.13) X 10(-4) and B(psi(3686) -> Lambda (Sigma) over bar (-) pi(+) + c.c.) = (1.54 +/- 0.04 +/- 0.13) X 10(-4) where the first errors are statistical and the second ones systematic.
|
|
| 4. |
- Ablikim, M., et al.
(author)
-
Search for eta(c)(2S)h(c) -> p(p)over-bar decays and measurements of the chi(cJ) -> p(p)over-bar branching fractions
- 2013
-
In: Physical Review D. - 1550-7998 .- 1550-2368. ; 88:11, s. 112001-
-
Journal article (peer-reviewed)abstract
- Using a sample of 1.06 x 10(8)psi(3686) events collected with the BESIII detector at BEPCII, the decays eta(c)(2S) -> p (p) over bar and h(c) -> p (p) over bar are searched for, where eta(c)(2S) and h(c) are reconstructed in the decay chains psi(3686) -> gamma eta(c)(2S), eta(c)(2S) -> p (p) over bar and psi(3686) -> pi(0)h(c), h(c) -> p (p) over bar, respectively. No significant signals are observed. The upper limits of the product branching fractions are determined to be B(psi(3686) -> gamma eta(c)(2S)) x B(eta(c)(2S) -> p (p) over bar) < 1.4 x 10(-6) and B(psi(3686) -> pi(0)h(c)) x B(h(c) -> p<(p)over bar>) < 1.3 x 10(-7) at the 90% C.L.. The branching fractions for chi(cJ) -> p<(p)over bar> (J = 0, 1, 2) are also measured to be (24.5 +/- 0.8 +/- 1.3, 8.6 +/- 0.5 +/- 0.5, 8.4 +/- 0.5 +/- 0.5) x 10(-5), which are the world's most precise measurements.
|
|
| 5. |
- Aguado, D. S., et al.
(author)
-
The Fifteenth Data Release of the Sloan Digital Sky Surveys : First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library
- 2019
-
In: Astrophysical Journal Supplement Series. - : Institute of Physics Publishing (IOPP). - 0067-0049 .- 1538-4365. ; 240:2
-
Journal article (peer-reviewed)abstract
- Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July-2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA-we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020-2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.
|
|
| 6. |
|
|
| 7. |
- Blanton, Michael R., et al.
(author)
-
Sloan Digital Sky Survey IV : Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
- 2017
-
In: Astronomical Journal. - : IOP Publishing Ltd. - 0004-6256 .- 1538-3881. ; 154:1
-
Journal article (peer-reviewed)abstract
- We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and. high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median z similar to 0.03). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between z similar to 0.6 and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs. and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the. Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.
|
|
| 8. |
- Chen, Xiao-Jia, et al.
(author)
-
Effect of ion adsorption on CEC separation of small molecules using hypercrosslinked porous polymer monolithic capillary columns
- 2012
-
In: Journal of Separation Science. - : Wiley-VCH Verlagsgesellschaft. - 1615-9306 .- 1615-9314. ; 35:12, s. 1502-1505
-
Journal article (peer-reviewed)abstract
- Both poly(styrene-co-vinylbenzyl chloride-co-divinylbenzene) and poly(4-methylstyrene-co-vinylbenzyl chloride-co-divinylbenzene) monolithic columns have been hypercrosslinked and for the first time used to achieve capillary electrochromatographic separations. Although these columns do not contain ionizable functionalities, electroosmotic flow was observed due to adsorption of ions from a buffer solution contained in the mobile phase on the surface of the hydrophobic polymer. An increase of more than one order of magnitude was observed with the use of both monolithic polymers. The hypercrosslinking reaction creates a large surface area thus enabling adsorption of a much larger number of ions. Alkylbenzenes were successfully separated using the hypercrosslinked monolithic columns.
|
|
| 9. |
- Ge, Qi, et al.
(author)
-
Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal
- 2017
-
In: IEEE Transactions on Image Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1057-7149 .- 1941-0042. ; 26:7, s. 3098-3112
-
Journal article (peer-reviewed)abstract
- Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.
|
|
| 10. |
- He, Zhiyi, et al.
(author)
-
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
- 2020
-
In: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 191
-
Journal article (peer-reviewed)abstract
- Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.
|
|
