Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the effici...Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.展开更多
This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-...This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-stage simulation flow to study the deviceto-circuit performance of advanced memory technologies in presence of statistical and process variability.We present a DRAM example to highlight the DTCO enablement for both memory and periphery.Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.展开更多
Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural l...Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural locations in Laborecká vrchovina (Slovakia). Samples were collected by destructive methods in all three locations. Silicon content was determined in dry biomass by AAS. Silicon content in plants ranged from 21.11 ± 3.24 g·kg-1 to 32.80 ± 8.03 g·kg-1. The highest content of silicon exhibited samples of the September collection. We found that the location and the year in terms of silicon content were not statistically significant. The main sources for statistical variability in the accumulation of silicon were during the collections.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.11390371,11303036,11390374,11233004 and 61202315)The Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences+6 种基金Funding for the project has been provided by the National Development and Reform CommissionFunding for SDSS-Ⅲ has been provided by the Alfred P.Sloan Foundationthe Participating Institutionsthe National Science Foundationthe U.S.Department of Energy Office of Sciencefunded by NASANSF
文摘Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spec- tral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum clas- sification plane in the J - H versus/-/- Ks color diagram which can be used to distinguish C-H from C-N stars with their J - H and H - Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and ampli- tudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.
文摘This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization(DTCO)of advanced semiconductor memories.After reviewing the DTCO approach to semiconductor devices scaling,we introduce a multi-stage simulation flow to study the deviceto-circuit performance of advanced memory technologies in presence of statistical and process variability.We present a DRAM example to highlight the DTCO enablement for both memory and periphery.Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.
基金The work was supported by the Agency of Ministry of Education,Science,Research and Sport of the Slovak Republic,the project:00162-0001(MS SR-3634/2010-11).
文摘Horsetail (Equisetum arvense L.) is a perennial herb which creates during the life cycle spring and summer stems. The selected species and populations were monitored in the years 2009-2011 in three different natural locations in Laborecká vrchovina (Slovakia). Samples were collected by destructive methods in all three locations. Silicon content was determined in dry biomass by AAS. Silicon content in plants ranged from 21.11 ± 3.24 g·kg-1 to 32.80 ± 8.03 g·kg-1. The highest content of silicon exhibited samples of the September collection. We found that the location and the year in terms of silicon content were not statistically significant. The main sources for statistical variability in the accumulation of silicon were during the collections.
