Self-Supervision Can Be a Good Few-Shot Learner

Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic perspective, we propose an effective unsupervised FSL method, learning representations with self-supervision. Following the InfoMax principle, our method learns comprehensive representations by capturing the intrinsic structure of the data. Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training. Rather than supervised pre-training focusing on the discriminable features of the seen classes, our self-supervised model has less bias toward the seen classes, resulting in better generalization for unseen classes. We explain that supervised pre-training and self-supervised pre-training are actually maximizing different MI objectives. Extensive experiments are further conducted to analyze their FSL performance with various training settings. Surprisingly, the results show that self-supervised pre-training can outperform supervised pre-training under the appropriate conditions. Compared with state-of-the-art FSL methods, our approach achieves comparable performance on widely used FSL benchmarks without any labels of the base classes.Comment: ECCV 2022, code: https://github.com/bbbdylan/unisia

Stabilisation in distribution by delay feedback control for hybrid stochastic differential equations

This paper is concerned with the design of a feedback control based on past states in order to make a given unstable hybrid stochastic differential equation (SDE) to be stable in distribution (stabilisation in distribution). This is the first paper in this direction. Under the global Lipschitz condition on the coefficients of the given unstable hybrid SDE, we will show that the stabilisation in distribution can be achieved by linear delay feedback controls. In particular, we discuss how to design the feedback controls in two structure cases: state feedback and output injection

Stabilization of hybrid systems by intermittent feedback controls based on discrete-time observations with a time delay

This paper mainly investigates stabilization of hybrid stochastic differential equations (SDEs) via periodically intermittent feedback controls based on discrete-time state observations with a time delay. First, by using the theory of M-matrix and intermittent control strategy, we establish sufficient conditions for the stability of hybrid SDEs. Then, we prove the intermittent stabilization for a given unstable nonlinear hybrid SDE by comparison theorem. Two numerical examples are discussed to support our results of theoretical analysis

Cardiovascular autonomic dysfunction predicts increasing albumin excretion in type 1 diabetes.

OBJECTIVE: To determine the potential role of cardiovascular autonomic dysfunction in the development of renal complications in young people with type 1 diabetes (T1D). METHODS: In this prospective study, 199 children and adolescents recruited to the Oxford Regional Prospective Study underwent assessment of autonomic function ~5 years after diagnosis, and were subsequently followed with longitudinal assessments of HbA1c and urine albumin-creatinine ratio (ACR) over 8.6 ± 3.4 years. Autonomic function was assessed with 4 standardized tests of cardiovascular reflexes: heart rate (HR) response to (1) Valsalva Maneuver, (2) deep breathing, (3) standing, and (4) blood pressure (BP) response to standing. Linear mixed models were used to assess the association between autonomic parameters and future changes in ACR. RESULTS: Independent of HbA1c , each SD increase in HR response to Valsalva Maneuver predicted an ACR increase of 2.16% [95% CI: 0.08; 4.28] per year (P = .04), while each SD increase in diastolic BP response to standing predicted an ACR increase of 2.55% [95% CI: 0.37; 4.77] per year (P = .02). The effect of HR response to standing on ACR reached borderline significance (-2.07% [95% CI: -4.11; 0.01] per year per SD increase, P = .051). CONCLUSIONS: In this cohort of young people with T1D, enhanced cardiovascular reflexes at baseline predicted future increases in ACR. These results support a potential role for autonomic dysfunction in the pathogenesis of diabetic nephropathy

An Unbiased Lipidomics Approach Identifies Early Second Trimester Lipids Predictive of Maternal Glycemic Traits and Gestational Diabetes Mellitus.

OBJECTIVE: To investigate the relationship between early second trimester serum lipidomic variation and maternal glycemic traits at 28 weeks and to identify predictive lipid biomarkers for gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS: Prospective study of 817 pregnant women (discovery cohort, n = 200; validation cohort, n = 617) who provided an early second trimester serum sample and underwent an oral glucose tolerance test (OGTT) at 28 weeks. In the discovery cohort, lipids were measured using direct infusion mass spectrometry and correlated with OGTT results. Variable importance in projection (VIP) scores were used to identify candidate lipid biomarkers. Candidate biomarkers were measured in the validation cohort using liquid chromatography-mass spectrometry and tested for associations with OGTT results and GDM status. RESULTS: Early second trimester lipidomic variation was associated with 1-h postload glucose levels but not with fasting plasma glucose levels. Of the 13 lipid species identified by VIP scores, 10 had nominally significant associations with postload glucose levels. In the validation cohort, 5 of these 10 lipids had significant associations with postload glucose levels that were independent of maternal age and BMI, i.e., TG(51.1), TG(48:1), PC(32:1), PCae(40:3), and PCae(40:4). All except the last were also associated with maternal GDM status. Together, these four lipid biomarkers had moderate ability to predict GDM (area under curve [AUC] = 0.71 ± 0.04, P = 4.85 × 10-7) and improved the prediction of GDM by age and BMI alone from AUC 0.69 to AUC 0.74. CONCLUSIONS: Specific early second trimester lipid biomarkers can predict maternal GDM status independent of maternal age and BMI, potentially enhancing risk factor-based screening.This part of the Cambridge Baby Growth Study was funded by grants from the Wellbeing of Women (RG1644) and Diabetes UK (11/0004241). The lipidomics assays were supported by the Medical Research Council (UD99999906) and Cambridge Lipidomics Biomarker Research Initiative (G0800783). Core funding was also obtained through the Medical Research Council, European Union Framework 5 World Cancer Research Fund, Mothercare Foundation and the Newlife Foundation for Disabled Children. There has also been support from National Institute for Health Research Cambridge Biomedical Research Centre.This is the author accepted manuscript. The final version is available from the American Diabetes Association via https://doi.org/10.2337/dc16-086

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

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

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