Multi-level Gated Bayesian Recurrent Neural Network for State Estimation

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely a multi-level gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian multi-level gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods

MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of state-action pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework named Multi-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.Comment: 4 pages, 2 figures, accepted by SIGIR 202

Laminated Ti-Al composites: Processing, structure and strength

Laminated Ti-Al composite sheets with different layer thickness ratios have been fabricated through hot pressing followed by multi-pass hot rolling at 500 °C.The laminated sheets show strong bonding with intermetallic interface layers of nanoscale thickness between the layers of Ti and Al. The mechanical properties of the composites with different volume fractions of Al from 10% to 67% show a good combination of strength and ductility. A constraint strain in the hot-rolled laminated structure between the hard and soft phases introduces an elastic-plastic deformation stage, which becomes more pronounced as the volume fraction of Al decreases. Moreover, the thin intermetallic interface layer may also contribute to the strength of the composites, and this effect increases with increasing volume fraction of the interface layer

The performance of large-pitch AC-LGAD with different N+ dose

AC-Coupled LGAD (AC-LGAD) is a new 4D detector developed based on the Low Gain Avalanche Diode (LGAD) technology, which can accurately measure the time and spatial information of particles. Institute of High Energy Physics (IHEP) designed a large-size AC-LGAD with a pitch of 2000 {\mu}m and AC pad of 1000 {\mu}m, and explored the effect of N+ layer dose on the spatial resolution and time resolution. The spatial resolution varied from 32.7 {\mu}m to 15.1 {\mu}m depending on N+ dose. The time resolution does not change significantly at different N+ doses, which is about 15-17 ps. AC-LGAD with a low N+ dose has a large attenuation factor and better spatial resolution. Large signal attenuation factor and low noise level are beneficial to improve the spatial resolution of the AC-LGAD sensor

Use of Praziquantel as an Adjuvant Enhances Protection and Tc-17 Responses to Killed H5N1 Virus Vaccine in Mice

BACKGROUND: H5N1 is a highly pathogenic influenza A virus, which can cause severe illness or even death in humans. Although the widely used killed vaccines are able to provide some protection against infection via neutralizing antibodies, cytotoxic T-lymphocyte responses that are thought to eradicate viral infections are lacking. METHODOLOGY/PRINCIPAL FINDINGS: Aiming to promote cytotoxic responses against H5N1 infection, we extended our previous finding that praziquantel (PZQ) can act as an adjuvant to induce IL-17-producing CD8(+) T cells (Tc17). We found that a single immunization of 57BL/6 mice with killed viral vaccine plus PZQ induced antigen-specific Tc17 cells, some of which also secreted IFN-γ. The induced Tc17 had cytolytic activities. Induction of these cells was impaired in CD8 knockout (KO) or IFN-γ KO mice, and was even lower in IL-17 KO mice. Importantly, the inoculation of killed vaccine with PZQ significantly reduced virus loads in the lung tissues and prolonged survival. Protection against H5N1 virus infection was obtained by adoptively transferring PZQ-primed wild type CD8(+) T cells and this was more effective than transfer of activated IFN-γ KO or IL-17 KO CD8(+) T cells. CONCLUSIONS/SIGNIFICANCE: Our results demonstrated that adding PZQ to killed H5N1 vaccine could promote broad Tc17-mediated cytotoxic T lymphocyte activity, resulting in improved control of highly pathogenic avian influenza virus infection

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