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We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the "blank" areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset-specific adaptations., Comment: CVPR 2023. Project page: https://jeff-liangf.github.io/projects/ovseg

We report discoveries of 165 new quasar Ca II absorbers from the Sloan Digital Sky Survey (SDSS) Data Release 7 and 12. Our Ca II rest frame equivalent width distribution supports the weak and strong subpopulations, split at ${W}^{\lambda3934}_{0}=0.7${\AA}. Comparison of both populations' dust depletion shows clear consistency for weak absorber association with halo-type gas in the Milky Way (MW) while strong absorbers have environments consistent with halo and disc-type gas. We probed our high redshift Ca II absorbers for 2175{\AA} dust bumps, discovering 12 2175{\AA} dust absorbers (2DAs). This clearly shows that some Ca II absorbers follow the Large Magellanic Cloud (LMC) extinction law rather than the Small Magellanic Cloud extinction law. About 33% of our strong Ca II absorbers exhibit the 2175{\AA} dust bump while only 6% of weak Ca II absorbers show this bump. 2DA detection further supports the theory that strong Ca II absorbers are associated with disk components and are dustier than the weak population. Comparing average Ca II absorber dust depletion patterns to that of Damped Ly{\alpha} Absorbers (DLAs), Mg II absorbers, and 2DAs shows that Ca II absorbers generally have environments with more dust than DLAs and Mg II absorbers, but less dust than 2DAs. Comparing 2175{\AA} dust bump strengths from different samples and also the MW and LMC, the bump strength appears to grow stronger as the redshift decreases, indicating dust growth and the global chemical enrichment of galaxies in the universe over time., Comment: 17 pages, 24 figures, 5 tables; Acccepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS)

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Ca II absorbers, which are one of the dustiest absorbers and are located at lower redshifts than most other absorbers, are especially valuable when studying physical processes and conditions in recent galaxies. However, the number of known quasar Ca II absorbers is relatively low due to the difficulty of detecting them with traditional methods. In this work, we developed an accurate and quick approach to search for Ca II absorption lines using deep learning. In our deep learning model, a convolutional neural network, tuned using simulated data, is used for the classification task. The simulated training data are generated by inserting artificial Ca II absorption lines into original quasar spectra from the Sloan Digital Sky Survey (SDSS) whilst an existing Ca II catalog is adopted as the test set. The resulting model achieves an accuracy of 96% on the real data in the test set. Our solution runs thousands of times faster than traditional methods, taking a fraction of a second to analyze thousands of quasars while traditional methods may take days to weeks. The trained neural network is applied to quasar spectra from SDSS's DR7 and DR12 and discovered 399 new quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II absorbers identified previously by other research groups through traditional methods., Comment: 15 pages, 15 figures, 5 tables; To be published in the Monthly Notices of the Royal Astronomical Society (MNRAS)

In recent years, the millimeter-wave radar to identify human behavior has been widely used in medical,security, and other fields. When multiple radars are performing detection tasks, the validity of the features contained in each radar is difficult to guarantee. In addition, processing multiple radar data also requires a lot of time and computational cost. The Complementary Ensemble Empirical Mode Decomposition-Energy Slice (CEEMD-ES) multistatic radar selection method is proposed to solve these problems. First, this method decomposes and reconstructs the radar signal according to the difference in the reflected echo frequency between the limbs and the trunk of the human body. Then, the radar is selected according to the difference between the ratio of echo energy of limbs and trunk and the theoretical value. The time domain, frequency domain and various entropy features of the selected radar are extracted. Finally, the Extreme Learning Machine (ELM) recognition model of the ReLu core is established. Experiments show that this method can effectively select the radar, and the recognition rate of three kinds of human actions is 98.53%., Comment: 4 pages, 5 figures

Haizhe Jin,1 Meng Xiao,2 Zibo Gong,3 Yinan Zhao4 1Department of Industrial Engineering, School of Business Administration, Northeastern University, Shenyang, People’s Republic of China; 2School of Business Administration, Northeastern University, Shenyang, People’s Republic of China; 3Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 4Department of Neurology, The First Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorrespondence: Zibo Gong, Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, People’s Republic of China, Email gongzibomed@163.com Yinan Zhao, Department of Neurology, The First Hospital of China Medical University, Shenyang, People’s Republic of China, Email zhaoyinan1616@yahoo.co.jpBackground: COVID-19 has brought greater workload pressures to the medical field, such as medical staff being required to wear personal protective equipment (PPE). While PPE can protect the safety of staff during the pandemic, it can also accelerate the accumulation of fatigue among operators.Objective: This study explores the influence of different protection states on the mental fatigue of nurses.Methods: In this study, 10 participants (5 males and 5 females) were randomly selected among applicants to monitor mental fatigue during the nurses’ daily work in four different PPE states (low temperature and low protection; low temperature and high protection; high temperature and low protection; high temperature and high protection). The NASA subjective mental fatigue scale was used for subjective evaluation. Reaction time, attention concentration, attention distribution, memory, and main task completion time were used for objective evaluation.Results: The results demonstrated a significant difference in the effects of different protection states on mental fatigue. The state of high temperature and high pro