APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

Solution combustion synthesis of a nanometer-scale Co3O4 anode material for Li-ion batteries

A novel solution combustion synthesis of nanoscale spinel-structured Co3O4 powder was proposed in this work. The obtained material was composed of loosely arranged nanoparticles whose average diameter was about 36 nm. The as-prepared cobalt oxide powder was also tested as the anode material for Li-ion batteries and revealed specific capacities of 1060 and 533 mAh.g(-1) after 100 cycles at charge-discharge current densities of 100 and 500 mA.g(-1), respectively. Moreover, electrochemical measurements indicate that even though the synthesized nanomaterial possesses a low active surface area, it exhibits a relatively high specific capacity measured at 100 mA.g(-1) after 100 cycles and a quite good rate capability at current densities between 50 and 5000 mA.g(-1).Web of Science1243142

Obstructive sleep apnea affects lacrimal gland function

Purpose: To determine the effect of obstructive sleep apnea syndrome (OSA) on lacrimal gland function and its mechanism. Methods: Male mice aged seven to eight weeks were housed in cages with cyclic intermittent hypoxia to mimic OSA, and the control group was kept in a normal environment. Slit-lamp observation, fluorescein staining, and corneal sensitivity detection are used to assess cornea changes. Tear secretion was detected by phenol red cotton thread, and the pathological changes of lacrimal gland were observed by hematoxylin and eosin staining, oil red O staining, cholesterol and triglyceride kits, immunofluorescence staining, immunohistochemical staining, real-time polymerase chain reaction, transmission electron microscopy, and Western blot. Results: Studies revealed a decreased tear secretion, corneal epithelial defects and corneal hypersensitivity. Myoepithelial cell damage, abnormal lipid accumulation, reduced cell proliferation, increased apoptosis and inflammatory cell infiltration in the lacrimal gland were also seen. Hifα and NF-κB signaling pathways, moreover, were activated, while Pparα was downregulated, in the lacrimal glands of OSA mice. Fenofibrate treatment significantly alleviated pathological changes of the lacrimal gland induced by OSA. Conclusion: OSA disturbs the Hifα/Pparα/NF-κB signaling axis, which affects lacrimal gland structure and function and induces dry eye

Nipocalimab, an anti-FcRn monoclonal antibody, in participants with moderate to severe active rheumatoid arthritis and inadequate response or intolerance to anti-TNF therapy: results from the phase 2a IRIS-RA study

Objectives: To investigate the efficacy, safety, pharmacokinetics and pharmacodynamics of nipocalimab in participants with moderate to severe active rheumatoid arthritis (RA) and inadequate response or intolerance to ≥1 antitumour necrosis factor agent. Methods: In this phase 2a study, participants with RA seropositive for anticitrullinated protein antibodies (ACPA) or rheumatoid factors were randomised 3:2 to nipocalimab (15 mg/kg intravenously every 2 weeks) or placebo from Weeks 0 to 10. Efficacy endpoints (primary endpoint: change from baseline in Disease Activity Score 28 using C reactive protein (DAS28-CRP) at Week 12) and patient-reported outcomes (PROs) were assessed through Week 12. Safety, pharmacokinetics and pharmacodynamics were assessed through Week 18. Results: 53 participants were enrolled (nipocalimab/placebo, n=33/20). Although the primary endpoint did not reach statistical significance for nipocalimab versus placebo, a numerically higher change from baseline in DAS28-CRP at Week 12 was observed (least squares mean (95% CI): –1.03 (–1.66 to –0.40) vs –0.58 (–1.24 to 0.07)), with numerically higher improvements in all secondary efficacy outcomes and PROs. Serious adverse events were reported in three participants (burn infection, infusion-related reaction and deep vein thrombosis). Nipocalimab significantly and reversibly reduced serum immunoglobulin G, ACPA and circulating immune complex levels but not serum inflammatory markers, including CRP. ACPA reduction was associated with DAS28-CRP remission and 50% response rate in American College of Rheumatology (ACR) criteria; participants with a higher baseline ACPA had greater clinical improvement. Conclusions: Despite not achieving statistical significance in the primary endpoint, nipocalimab showed consistent, numerical efficacy benefits in participants with moderate to severe active RA, with greater benefit observed for participants with a higher baseline ACPA. Trial registration number: NCT04991753

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