VisorGPT: Learning Visual Prior via Generative Pre-Training

Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can result in visually inaccurate synthetic results. This work aims to explicitly learn the visual prior and enable the customization of sampling. Inspired by advances in language modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes, human pose, and instance masks, into sequences, VisorGPT can model visual prior through likelihood maximization. Besides, prompt engineering is investigated to unify various visual locations and enable customized sampling of sequential outputs from the learned prior. Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet. Code will be released at https://github.com/Sierkinhane/VisorGPT.Comment: Project web-page: https://sierkinhane.github.io/visor-gpt

Toward Waveform Nonlinear Optics Using Multimillijoule Sub-Cycle Waveform Synthesizers

Waveform nonlinear optics aims to study and control the nonlinear interactions of matter with extremely short optical waveforms custom-tailored within a single cycle of light. Different technological routes to generate such multimillijoule sub-optical-cycle waveforms are currently pursued, opening up unprecedented opportunities in attoscience and strong-field physics. Here, we discuss the experimental schemes, introduce the technological challenges, and present our experimental results on high-energy sub-cycle optical waveform synthesis based on (1) parametric amplification and (2) induced-phase modulation in a two-color-driven gas-filled hollow-core fiber compressor. More specifically, for (1), we demonstrate a carrier-envelope-phase (CEP)-stable, multimillijoule three-channel parametric waveform synthesizer generating a >2-octave-wide spectrum (0.52-2.4 μm). After two amplification stages, the combined 125-μJ output supports 1.9-fs FWHM waveforms; energy scaling to >2 mJ is achieved after three amplification stages. FROG pulse characterization of all three second-stage outputs demonstrates the feasibility to recompress all three channels simultaneously close to the Fourier limit and shows the flexibility of our intricate dispersion management scheme for different experimental situations. For (2), we generate CEP-stable 1.7-mJ waveforms covering 365-930 nm (measured at 1% of the peak intensity) obtained from induced-phase modulation in a two-color-driven gas-filled hollow-core fiber. Using custom-designed double-chirped mirrors and a UV spatial light modulator will permit compression close to the 0.9-fs FWHM transform limit. These novel sources will become versatile tools for controlling strong-field interactions in matter and for attosecond pump-probe spectroscopy using VIS/IR and XUV/soft-X-ray pulses

Associations of COVID-19 lockdown with birth weight in China

BackgroundDuring the special period of the global spread of COVID-19, pregnant women are sensitive groups to the impacts of COVID-19 epidemic. However, the effects of lockdown measures implemented in response to the COVID-19 on fetal birthweight remain unclear.ObjectivesThis study investigated the associations of COVID-19 lockdown with birth weight in Chinese population.MethodsWe collected 730,153 data of participants from hospitals of five cities in the south of China, we defined the time period of level I response (1/23-2/24/2020) as level I lockdown, and women who were pregnant during level I lockdown as the exposure group. Women who were pregnant during the same calendar month from 2015 to 2019 were defined as the unexposed group. We quantitatively estimate the individual cumulative exposure dose by giving different weights to days with different emergency response levels. Generalized linear regression models were used to estimate the association between COVID-19 lockdown exposure with birth weight and risk of low birth weight (<2,500 g) and macrosomia (>4,000 g).ResultsThe birth weight of the exposed group is heavier than the unexposed group (3,238.52 vs. 3,224.11 g: adjusted β = 24.39 g [95% CI: 21.88, 26.91 g]). The exposed group had a higher risk of macrosomia (2.8% vs. 2.6%; adjusted OR = 1.17 [95% CI: 1.12, 1.22]). More obvious associations were found between COVID-19 lockdown and macrosomia in women who experienced the lockdown in their early pregnancy. Women who experienced the lockdown at their 4–7 weeks of pregnancy showed statistically significant heavier birth weight than unexposed group (after adjustment): β = 1.28 (95% CI: 1.11, 1.46) g. We also observed a positive association between cumulative exposure dose of COVID-19 lockdown in all pregnant women and birth weight, after divided into four groups, Q1: β = 32.95 (95% CI: 28.16, 37.75) g; Q2: β = 18.88 (95% CI: 14.12, 23.64) g; Q3: β = 19.50 (95% CI: 14.73, 24.28) g; Q4: β = 21.82 (95% CI: 17.08, 26.56) g. However, there was no statistically significant difference in the risk of low birth weight between exposed and unexposed groups.ConclusionsThe COVID-19 lockdown measures were associated with a heavier birth weight and a higher risk of macrosomia. Early pregnancy periods may be a more susceptible exposure window for a heavier birth weight and a higher risk of macrosomia. We also observed a positive association between cumulative exposure dose of COVID-19 lockdown and birth weight. The government and health institutions should pay attention to the long-term health of the infants born during the COVID-19 lockdown period, and follow up these mothers and infants is necessary

The Structural Characterization and Antigenicity of the S Protein of SARS-CoV

The corona-like spikes or peplomers on the surface of the virion under electronic microscope are the most striking features of coronaviruses. The S (spike) protein is the largest structural protein, with 1,255 amino acids, in the viral genome. Its structure can be divided into three regions: a long N-terminal region in the exterior, a characteristic transmembrane (TM) region, and a short C-terminus in the interior of a virion. We detected fifteen substitutions of nucleotides by comparisons with the seventeen published SARS-CoV genome sequences, eight (53.3%) of which are non-synonymous mutations leading to amino acid alternations with predicted physiochemical changes. The possible antigenic determinants of the S protein are predicted, and the result is confirmed by ELISA (enzyme-linked immunosorbent assay) with synthesized peptides. Another profound finding is that three disulfide bonds are defined at the C-terminus with the N-terminus of the E (envelope) protein, based on the typical sequence and positions, thus establishing the structural connection with these two important structural proteins, if confirmed. Phylogenetic analysis reveals several conserved regions that might be potent drug targets

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA