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

Phase Relations in MAFSH System up to 21 GPa: Implications for Water Cycles in Martian Interior

To elucidate the water cycles in iron-rich Mars, we investigated the phase relation of a water-undersaturated (2 wt.%) analog of Martian mantle in simplified MgO-Al2O3-FeO-SiO2-H2O (MAFSH) system between 15 and 21 GPa at 900–1500 °C using a multi-anvil apparatus. Results showed that phase E coexisting with wadsleyite or ringwoodite was at least stable at 15–16.5 GPa and below 1050 °C. Phase D coexisted with ringwoodite at pressures higher than 16.5 GPa and temperatures below 1100 °C. The transition pressure of the loop at the wadsleyite-ringwoodite boundary shifted towards lower pressure in an iron-rich system compared with a hydrous pyrolite model of the Earth. Some evidence indicates that water once existed on the Martian surface on ancient Mars. The water present in the hydrous crust might have been brought into the deep interior by the convecting mantle. Therefore, water might have been transported to the deep Martian interior by hydrous minerals, such as phase E and phase D, in cold subduction plates. Moreover, it might have been stored in wadsleyite or ringwoodite after those hydrous materials decomposed when the plates equilibrated thermally with the surrounding Martian mantle

Dynamic network quantile regression model

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016

Dynamic network quantile regression model

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016

Dynamic network quantile regression model

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016

Estimating Stochastic Linear Combination of Non-Linear Regressions

In this paper we study the problem of estimating stochastic linear combination of non-linear regressions, which has a close connection with many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks. Specifically, we first show that with some mild assumptions, if the variate vector x is multivariate Gaussian, then there is an algorithm whose output vectors have ℓ2-norm estimation errors of O(√p/n) with high probability, where p is the dimension of x and n is the number of samples. Then we extend our result to the case where x is sub-Gaussian using the zero-bias transformation, which could be seen as a generalization of the classic Stein's lemma. We also show that with some additional assumptions there is an algorithm whose output vectors have ℓ∞-norm estimation errors of O(1/√p + √p/n) with high probability. Finally, for both Gaussian and sub-Gaussian cases we propose a faster sub-sampling based algorithm and show that when the sub-sample sizes are large enough then the estimation errors will not be sacrificed by too much. Experiments for both cases support our theoretical results. To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model

Intensified aridity in the Qaidam Basin during the middle Miocene: constraints from ostracod, stable isotope and weathering records

The thick and continuous Cenozoic successions in the Qaidam Basin provide an excellent paleoclimate archive. Here, we focus on the ostracod fauna, stable isotope records, and paleoweathering indices from a well-dated Cenozoic sedimentary section in the Qaidam Basin, to develop an understanding of the middle Miocene aridification in central Asia. Microfossil analyses suggest that the ostracod species diversity suddenly decreased after 13.3 Ma, and that the dominant ostracod genus shifted from Ilyocypris to Cyprideis. Stable isotope data from ostracod valves display abrupt positive shifts of 3.75‰ in δ18O values and 5.28‰ in δ13C values since 13.3 Ma. The Chemical Index of Weathering (CIW) and K/Na ratios decrease markedly after 13.3 Ma, reflecting a significant decrease in chemical weathering intensity. These combined and consistent observations suggest that the Qaidam Basin has experienced increased aridification since 13.3 Ma. The dating was obtained direct from previous magnetostratigraphic studies and can be accurately correlated with global climate evolution and regional tectonic events. A comparing of these results with global paleoclimatic records and previous geologic studies of the Tibetan Plateau, revealed that global cooling rather than uplift of the Tibetan Plateau played a key role in the drying of the Qaidam Basin at approximately 13 Ma.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios