| 1. |
- Ablikim, M., et al.
(författare)
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Observation of the decay psi(3686) -> Lambda(Sigma)over-bar(+/-) pi(-/+) + c.c
- 2013
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Ingår i: Physical Review D. - 1550-7998 .- 1550-2368. ; 88:11, s. 112007-
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Ablikim, M., et al.
(författare)
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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
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Ingår i: Physical Review D. - 1550-7998 .- 1550-2368. ; 88:11, s. 112001-
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Tidskriftsartikel (refereegranskat)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.
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| 3. |
- Kristanl, Matej, et al.
(författare)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 4. |
- An, Junghwa, et al.
(författare)
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Permanent Genetic Resources added to Molecular Ecology Resources Database 1 October 2009-30 November 2009
- 2010
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Ingår i: Molecular Ecology Resources. - : Wiley. - 1755-098X .- 1755-0998. ; 10:2, s. 404-408
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Tidskriftsartikel (refereegranskat)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.
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| 5. |
- Shao, Wen-Ze, et al.
(författare)
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On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces
- 2019
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Ingår i: Neurocomputing. - : ELSEVIER. - 0925-2312 .- 1872-8286. ; 364, s. 1-15
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Tidskriftsartikel (refereegranskat)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.
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| 6. |
- Blanton, Michael R., et al.
(författare)
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Sloan Digital Sky Survey IV : Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
- 2017
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Ingår i: Astronomical Journal. - : IOP Publishing Ltd. - 0004-6256 .- 1538-3881. ; 154:1
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Tidskriftsartikel (refereegranskat)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.
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| 7. |
- Li, Liangxing, et al.
(författare)
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熔融液滴与冷却剂相互作用实验中液滴释放控制机构的研制
- 2008
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Ingår i: Hedongli Gongcheng. - ChengDu, China. - 0258-0926. ; 29, s. 42-45
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Tidskriftsartikel (refereegranskat)abstract
- 在高温熔融液滴与冷却剂相互作用的实验中,熔融液滴的释放控制对整个实验起至关重要的作用。本文在以往实验机械控制装置的基础上,自主设计一个自动控制电路来代替人工机械操作,利用控制电路给电磁铁提供瞬时脉冲来控制液滴瞬时释放下落,简化实验的可持续性操作,提高实验的重复性、精确性和操作方便性。
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| 8. |
- Li, Xin, et al.
(författare)
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Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
- 2023
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Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 230
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Tidskriftsartikel (refereegranskat)abstract
- Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.
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| 9. |
- Yu, Guangsen, et al.
(författare)
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Fabrication of Nickel-YSZ cermet nanofibers via electrospinning
- 2017
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Ingår i: Journal of Alloys and Compounds. - : Elsevier BV. - 0925-8388. ; 693, s. 1214-1219
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Tidskriftsartikel (refereegranskat)abstract
- Abstract The Ni-YSZ cerment as a promising anode for solid oxide fuel cells and its performance strongly depends on its miscrocture. This work focused on fabrication of Ni-8YSZ nanofibers via an electrospinning technique using yttria and zirconia nanoparticles as precussors. The effects of viscosity on the quality of fibers were assessed by adjusting the polymer and solid contents in slurry. The fibers sintered at different temperatures showed varying morphologies and a bead-like chain at temperatures higher than 800 °C was obtained. Crystalization of the YSZ was completed above 1400 °C and NiO was above 800 °C. The porosity of the fiber structure can reach high up to 45% before NiO reducation. The nanocrystallization of an anode for SOFCs was beneficial to enhance the electrochemical reaction sites and also to reduce the difficulties for gas diffusion.
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| 10. |
- Aguado, D. S., et al.
(författare)
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The Fifteenth Data Release of the Sloan Digital Sky Surveys : First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library
- 2019
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Ingår i: Astrophysical Journal Supplement Series. - : Institute of Physics Publishing (IOPP). - 0067-0049 .- 1538-4365. ; 240:2
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Tidskriftsartikel (refereegranskat)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.
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