Thermal storage/management system with phase change materials for building

Peer reviewe

Estimating Photometric Redshifts of Quasars via K-nearest Neighbor Approach Based on Large Survey Databases

We apply one of lazy learning methods named k-nearest neighbor algorithm (kNN) to estimate the photometric redshifts of quasars, based on various datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN arrives at the best performance when k is different with a special input pattern for a special dataset. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results show that generally the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. By comparing the performance of various methods for photometric redshift estimation of quasars, kNN based on KD-Tree shows its superiority with the best accuracy for our case.Comment: 28 pages, 4 figures, 3 tables, accepted for publication in A

{2,2′-[(Benzyl­aza­nedi­yl)dimethylene]diphenolato}(methano­lato)boron

The title compound, C22H22BNO3, was unintentionally obtained from salicyl­aldehyde benzyl­amine and sodium borohydride. The B—O bond lengths lie in the range 1.425 (2)–1.463 (2) Å, and B—N = 1.641 (2) Å. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into chains in the [010] direction

Spatial analysis of ethnic migration behavior: A case study of Chinese immigrants in the New York-Newark-Jersey City metropolitan area

The population of Chinese immigrants in the United States has undergone progressive growth in the past 50 years and has reached an epidemic number. As minorities, the Chinese immigrants move into receiving places to adapt and succeed in a new social structure while not losing their own identity. Previous studies highlight the role of local contexts that lead to an internal moving decision. Most of these studies view local contexts as global factors assumed to apply equally over a study area. However, the contextual factors do not disperse evenly across space, nor their relationships with migration behavior. Understanding the spatial variability of factors related to Chinese people's migration in the study area is necessary. Therefore, this dissertation aims to explore the role in which neighborhood context may predict migration behavior, with particular attention to how migration factors and their effects vary across space. This research presents novel applications of two methods: clustering analysis (followed by regression models) and multiscale geographically weighted regression (MGWR) to the Chinese population in the New York-Newark-Jersey City metropolitan statistical area as a case study. Besides regression analysis, this research also provides a detailed examination of relationships between micro-level factors using decision tree analysis. Wages, education, English proficiency, and self-employment status are crucial variables in differentiating movers from non-movers. Having naturalized citizenship has a dual effect on migration behavior. Among the movers, stratifications exist in the immigrant Chinese population. Each subgroup has its particular migration pattern and significant indicators. Spatial variations exist in the study area. Neighborhood type 2 (low in socioeconomic and stable status) is the residential place for immigrants from other states. And neighborhood type 1 (high in socioeconomic and stable status) has more within-state immigrants. Regression models accounting for the population stratification and spatial variations have a vast improvement over the OLS model. Approaches considering data associations in both geographic dimension and non-geographic dimensions could be promising

Early identification of potential brain death organ donors based on prediction of spontaneous respiratory arrest

Background: This study was designed to build a Nomogarm prediction model of spontaneous respiratory arrest (SRA) in nerocritical patients within 72 hours after brain injury for early identification of potential brain death organ donors.Methods: From October 2017 to May 2019, the neurocritical patients admitted to the First Affiliated Hospital of Sun Yat-sen University, were enrolled. The occurrence of SRA within 72 hours after brain injury was regarded as the time interest point and grouping factor, factors associated with SRA were screened by univariate and multivariate analysis, and then the Nomogarm prediction model was developed. Finally, the Nomogarm prediction model was tested in the validation set.Results: In training set, univariate and multivariate analysis showed that the midline shift (OR=4.56, 95% 1.87-19.21), absent of ambient cistern (OR=4.83, 95% 1.35-16.34), cough reflex absence (OR=3.82, 95% 1.15-12.42), intraventricular hemorrhage (OR=3.16, 95% 1.53-14.52) and serum Na+<125mmol/L (OR=3.06, 95% 1.53-13.44) were associated with SRA within 72 hours. In the training set and validation set, the predicted C index of SRA rate within 72 hours was 0.81 (95% CI 0.76-0.85) and 0.80 (95% CI 0.75-0.83), respectively. Further statistical analysis showed that 140 points, 160 points and 170 points were dangerous cut-off points, of which 140 points, 160 points and 170 points were 30.1%, 65.6% and 93.4% associated with SRA within 72 hours, respectively.Conclusions: Nomogram prediction model based on brain injury assessment parameters can predict the time of SRA in neurocritical patients, and can be used for early identification of potential brain death organ donors

Research and Design of Indoor Parking Guidance System for Urban Traffic

In view of the existing drawbacks of indoor parking guidance system in commercial areas, this paper designs an indoor parking guidance system suitable for urban traffic. The owner first selects the appropriate parking lot through the Mini Program, and reserves a detailed parking space on the Mini Program, and after arriving at the parking lot, the Mini Program performs optimal path planning according algorithm to guide the owner to find the parking space. After arriving at the reserved parking space according to the prompts, the smart parking lock is unlocked by "one-key unlock", and the video detection system observes the parking behavior in real time to avoid the occurrence of illegal parking. At the same time, voice assistants and blind spot guidance facilities are also provided during the induction process to optimize the urban parking guidance system

Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via a transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks, including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin

SDSS quasars in the WISE preliminary data release and quasar candidate selection with optical/infrared colors

We present a catalog of 37,842 quasars in the SDSS Data Release 7, which have counterparts within 6" in the WISE Preliminary Data Release. The overall WISE detection rate of the SDSS quasars is 86.7%, and it decreases to less than 50.0% when the quasar magnitude is fainter than $i=20.5$. We derive the median color-redshift relations based on this SDSS-WISE quasar sample and apply them to estimate the photometric redshifts of the SDSS-WISE quasars. We find that by adding the WISE W1- and W2-band data to the SDSS photometry we can increase the photometric redshift reliability, defined as the percentage of sources with the photometric and spectroscopic redshift difference less than 0.2, from 70.3% to 77.2%. We also obtain the samples of WISE-detected normal and late-type stars with SDSS spectroscopy, and present a criterion in the $z-W1$ versus $g-z$ color-color diagram, $z-W1>0.66(g-z)+2.01$, to separate quasars from stars. With this criterion we can recover 98.6% of 3089 radio-detected SDSS-WISE quasars with redshifts less than four and overcome the difficulty in selecting quasars with redshifts between 2.2 and 3 from SDSS photometric data alone. We also suggest another criterion involving the WISE color only, $W1-W2>0.57$, to efficiently separate quasars with redshifts less than 3.2 from stars. In addition, we compile a catalog of 5614 SDSS quasars detected by both WISE and UKIDSS surveys and present their color-redshift relations in the optical and infrared bands. By using the SDSS $ugriz$, UKIDSS YJHK and WISE W1- and W2-band photometric data, we can efficiently select quasar candidates and increase the photometric redshift reliability up to 87.0%. We discuss the implications of our results on the future quasar surveys. An updated SDSS-WISE quasar catalog consisting of 101,853 quasars with the recently released WISE all-sky data is also provided.Comment: 27 pages, 9 figures and 5 tables. Revised to match the published version in the Astronomical Journal. 5 tables are available electronically at (http://vega.bac.pku.edu.cn/~wuxb/sdsswiseqso.htm). A new SDSS-WISE quasar catalog consisting of 101,853 quasars with the WISE all-sky data is available as Table