Learning Compositional Visual Concepts with Mutual Consistency

Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201

Progressive Multi-view Human Mesh Recovery with Self-Supervision

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.Comment: Accepted by AAAI202

PREF: Predictability Regularized Neural Motion Fields

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.Comment: Accepted at ECCV 2022 (oral). Paper + supplementary materia

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Ecosystem Services Research in Green Sustainable Science and Technology Field: Trends, Issues, and Future Directions

Ecosystem services (ES) has an important place in sustainability science research as a powerful bridge between society and nature. Based on 513 papers correlated with ES in the field of green sustainable science and technology (GSST) indexed in ISI Web of Science database, we employ the bibliometric methods to analyze the disciplinary co-occurrence, keyword co-occurrence, partnerships, publication characteristics, co-citation, research themes, and transformative potential of these papers. The results show that innovation in research themes of the ES research in the GSST field is increasing rapidly in 2015–2018, while innovation in research themes is decreasing in 2018–2021. Moreover, keyword co-occurrence analysis indicates that the hot topics of previous research with respect to “environmental service”, “capacity”, “perception”, “landscape”, “forest management”, “carbon sequestration”, “contingent valuation”, and “sustainable development”. Recent hotspots include “blue carbon”, “environmental impact”, “coastal”, “ecosystem services mapping”, and “use/land cover change”. Finally, the cluster analysis of co-cited references abstract thirteen largest clusters. The top six clusters are “mapping ecosystem service”, “spatial gradient difference”, “ecosystem service value”, “water-related ecosystem service”, “linking forest landscape model”, and “culture ecosystem service”. Moreover, the integration of spatial, value, environmental, and sociocultural dimensions may help to develop supportive policies, which is a future direction of ES research in the GSST field

Study of a Nano-SiO₂ Microsphere-Modified Basalt Flake Epoxy Resin Coating

Basalt flakes (BFs) have been widely used in recent years as a novel anticorrosion material in the marine industry to prevent the corrosion of metal substrates. In this study, BFs were modified with 1⁻7‰ nano-SiO2 microspheres, and a modified BF epoxy coating was successfully prepared. Experimental results showed that the BF epoxy resin coating modified with 3‰ nano-SiO2 microspheres exhibited excellent chemical durability (surface weight loss rate of 2.2% in the alkali solution and only 1.1% in the acid solution at room temperature after 480 h), low water infiltration (water absorption of 0.72% after 480 h), and good mechanical performance (tensile strength of approximately 33.4 MPa). This study proves the feasibility of using nano-SiO2 microspheres to modify BF epoxy resin coating and enhance the chemical durability and mechanical properties provided by the coating

Prognostic value of fibrosis-5 index combined with C-reactive protein in patients with acute decompensated heart failure

Abstract Background Fibrosis-5 (FIB-5) index is a marker of liver fibrosis and has been shown to have a good prognostic value for patients with acute heart failure (AHF), and C-reactive protein (CRP) has inflammatory properties and predicts adverse prognosis in patients with HF. However, the long-term prognostic value of FIB-5 index combined with CRP in patients with acute decompensated HF (ADHF) is yet unclear. Methods This retrospective study included 1153 patients with ADHF hospitalized from January 2018 to May 2022.The FIB-5 index was calculated as (albumin [g/L]×0.3 + PLT count [109/L]×0.05)−(ALP [U/L]×0.014 + AST to ALT ratio×6 + 14). Patients were stratified into the following four groups according to the median value of FIB-5 index (=-2.11) and CRP (= 4.5): Group 1 had a high FIB-5 index (FIB-5 index >-2.11) and a low CRP (CRP ≤ 4.5); Group 2 had both low FIB-5 index and low CRP; Group 3 had both high FIB-5 index and high CRP; Group 4 had a low FIB-5 index (FIB-5 index ≤-2.11) and a high CRP (CRP > 4.5). The endpoint was major adverse cardiac and cerebral events (MACCEs). Multivariate Cox analysis was used to evaluate the association of the combination with the development of MACCEs. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analysis were used to compare the accuracy of the combination with a single prognostic factor for predicting the risk of MACCEs. Results During the mean follow-up period of 584 ± 12 days, 488 (42.3%) patients had MACCEs. Kaplan–Meier analysis revealed that the incidence of MACCEs was different in the four groups (P < 0.001). After adjusting for the confounding factors, the hazard ratio (HR) for MACCEs in Group 4 (low FIB-5 index + high CRP) was the highest (Model 1, HR = 2.04, 95%CI 1.58–2.65, P < 0.001; Model 2, HR = 1.67, 95%CI 1.28–2.18, P < 0.001; Model 3, HR = 1.66, 95%CI: 1.27–2.17, P < 0.001). Additionally, the combination of FIB-5 index and CRP enabled more accurate prediction of MACCEs than FIB-5 index alone (NRI, 0.314,95%CI 0.199–0.429; P < 0.001; IDI, 0.023; 95% CI 0.015–0.032; P < 0.001). Conclusions In patients with ADHF, the combination of the FIB-5 index and CRP may be useful in risk stratification in the future