This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library “MaStar”). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

Because interior design is subject to inefficiency, more creativity is imperative. Due to the development of artificial intelligence diffusion models, the utilization of text descriptions for the generation of creative designs has become a novel method for solving the aforementioned problem. Herein, we build a unique interior decoration style dataset. Thus, we solve the problem pertaining to the need for datasets, propose a new loss function that considers the decoration style, and retrain the diffusion model using this dataset. The trained model learns interior design knowledge and can generate an interior design through text. The proposed method replaces the designer’s drawing with computer-generated creative design, thereby enhancing the design efficiency and creative generation. Specifically, the proposed diffusion model can generate interior design images of specific decoration styles and spatial functions end to end from text descriptions, and the generated designs are easy to modify. This novel and creative design method can efficiently generate various interior designs, promote the generation of creative designs, and enhance the design and decision-making efficiency.

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We use the Herschel SPIRE color-color diagram to study the spectral energy distribution (SED) and the redshift estimation of high-z galaxies. We compiled a sample of 57 galaxies with spectroscopically confirmed redshifts and SPIRE detections in all three bands at z = 2.5-6.4, and compared their average SPIRE colors with SED templates from local and high-z libraries. We find that local SEDs are inconsistent with high-z observations. The local calibrations of the parameters need to be adjusted to describe the average colors of high-z galaxies. For high-z libraries, the templates with an evolution from z = 0 to 3 can well describe the average colors of the observations at high redshift. Using these templates, we defined color cuts to divide the SPIRE color-color diagram into different regions with different mean redshifts. We tested this method and two other color cut methods using a large sample of 783 Herschelselected galaxies, and find that although these methods can separate the sample into populations with different mean redshifts, the dispersion of redshifts in each population is considerably large. Additional information is needed for better sampling.

We aim to investigate the propriety of stellar parameter errors of the official data re- lease of the LAMOST low-resolution spectroscopy (LRS) survey. We diagnose the errors of radial velocity (RV), atmospheric parameters ([Fe/H], T eff , log g) and α-enhancement ([α/M]) for the latest data release version of DR7, including 6,079,235 effective spectra of 4,546,803 stars. Based on the duplicate observational sample and comparing the deviation of multiple measurements to their given errors, we find that, in general, the error of [α/M] is largely un- derestimated, and the error of radial velocity is slightly overestimated. We define a correction factor k to quantify these misestimations and correct the errors to be expressed as proper inter- nal uncertainties. Using this self-calibration technique, we find that the k-factors significantly vary with the stellar spectral types and the spectral signal-to-noise ratio (SNR). Particularly, we reveal a strange but evident trend between k-factors and error themselves for all five stellar parameters. Larger errors tend to have smaller k-factor values, i.e., they were more overesti- mated. After the correction, we recreate and quantify the tight correlations between SNR and errors, for all five parameters, while these correlations have dependence on spectral types. It also suggests that the parameter errors from each spectrum should be corrected individually. Finally, we provide the error correction factors of each derived parameter of each spectrum for the entire LAMOST-LRS DR7.