Efficient Numerical Algorithm for Large-Scale Damped Natural Gradient Descent

We propose a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available samples. This problem is fundamental for natural gradient descent and stochastic reconfiguration. Our algorithm is based on Cholesky decomposition and is generally applicable. Benchmark results show that the algorithm is significantly faster than existing methods

From stateflow simulation to verified implementation: A verification approach and a real-time train controller design

Simulink is widely used for model driven development (MDD) of industrial software systems. Typically, the Simulink based development is initiated from Stateflow modeling, followed by simulation, validation and code generation mapped to physical execution platforms. However, recent industrial trends have raised the demands of rigorous verification on safety-critical applications, which is unfortunately challenging for Simulink. In this paper, we present an approach to bridge the Stateflow based model driven development and a well- defined rigorous verification. First, we develop a self- contained toolkit to translate Stateflow model into timed automata, where major advanced modeling features in Stateflow are supported. Taking advantage of the strong verification capability of Uppaal, we can not only find bugs in Stateflow models which are missed by Simulink Design Verifier, but also check more important temporal properties. Next, we customize a runtime verifier for the generated nonintrusive VHDL and C code of Stateflow model for monitoring. The major strength of the customization is the flexibility to collect and analyze runtime properties with a pure software monitor, which opens more opportunities for engineers to achieve high reliability of the target system compared with the traditional act that only relies on Simulink Polyspace. We incorporate these two parts into original Stateflow based MDD seamlessly. In this way, safety-critical properties are both verified at the model level, and at the consistent system implementation level with physical execution environment in consideration. We apply our approach on a train controller design, and the verified implementation is tested and deployed on a real hardware platform

Evaluation of β2-microglobulin in the condition and prognosis of psoriasis patients.

BACKGROUND Numerous studies have linked the inflammatory pathway in psoriasis and metabolic disease, while no specific marker defined it. It is worth exploring the association of β2-microglobulin (β2M) in psoriasis severity and comorbidities. OBJECTIVES To investigate the correlation between blood β2M level and psoriasis severity, to explore the inflammatory factors influencing the occurrence of psoriasis comorbidities such as arthritis, diabetes, and hypertension. METHODS Ninety-seven psoriasis patients were analyzed in the cohort retrospective study during 12 weeks. RESULTS Significantly higher levels of blood β2M and ESR were observed in the group that patients' PASI ≥10 than in the group that PASI <10. Blood β2M level had strong significantly positive correlations with the PASI in Pearson's correlation analysis. In the model that systemic inflammatory factors to find psoriasis comorbidity risk factors, logistic regression analysis showed that blood β2M level was the significant risk factor associated with diabetes and hypertension. High-sensitivity C-reactive protein (hsCRP) was the significant risk factor associated with arthritis. CONCLUSIONS Patients with a severer psoriasis tended to have higher blood β2M levels and severer inflammatory state. In the systemic inflammation indexes, the level of blood β2M affected the risk of hypertension and diabetes, and hsCRP affected the risk of arthritis in patients with psoriasis

Advances in novel biomaterials combined with traditional Chinese medicine rehabilitation technology in treatment of peripheral nerve injury

Peripheral nerve injuries (PNI) represent one of the primary neuropathies leading to lifelong disability. Nerve regeneration and targeted muscle atrophy stand as the two most crucial factors influencing functional rehabilitation post peripheral nerve injury. Over time, traditional Chinese medicine (TCM) rehabilitation approaches such as acupuncture, Tuina, and microneedles serve as pivot means to activate the regeneration of injured nerve Schwann cells. By promoting axon regeneration, these approaches can accomplish nerve repair, reconstruction, and functional rehabilitation. Although TCM rehabilitation approaches have clinically demonstrated effectiveness in promoting the repair and regeneration of PNI, the related molecular mechanisms remain unclear. This significantly hampers the application and promotion of TCM rehabilitation in PNI recovery. Therefore, deeply delving into the cellular and molecular mechanisms of TCM rehabilitation technologies to foster nerve regeneration stands as the most pressing issue. On the other hand, in recent years, novel biomaterials represented by hydrogels, microfluidic platforms, and new chitosan scaffolds have showed their unique roles in treating various degrees of nerve injury. These methods exhibit immense potential in conducting high-throughput cell and organoid culture in vitro and synthesizing diverse tissue engineering scaffolds and drug carriers. We believe that the combination of TCM rehabilitation technology and novel biomaterials can more effectively address precise treatment issues such as identification of treatment target and dosage control. Therefore, this paper not only summarizes the molecular mechanisms of TCM rehabilitation technology and novel biomaterials in treating peripheral nerve injury individually, but also explores the research direction of precise treatment by integrating the two at both macro and micro levels. Such integration may facilitate the exploration of cellular and molecular mechanisms related to neurodegeneration and regeneration, providing a scientific and theoretical foundation for the precise functional rehabilitation of PNI in the future

Ground Calibration Result of the Lobster Eye Imager for Astronomy

We report on results of the on-ground X-ray calibration of the Lobster Eye Imager for Astronomy (LEIA), an experimental space wide-field (18.6*18.6 square degrees) X-ray telescope built from novel lobster eye mirco-pore optics. LEIA was successfully launched on July 27, 2022 onboard the SATech-01 satellite. To achieve full characterisation of its performance before launch, a series of tests and calibrations have been carried out at different levels of devices, assemblies and the complete module. In this paper, we present the results of the end-to-end calibration campaign of the complete module carried out at the 100-m X-ray Test Facility at IHEP. The PSF, effective area and energy response of the detectors were measured in a wide range of incident directions at several X-ray line energies. The distributions of the PSF and effective areas are roughly uniform across the FoV, in large agreement with the prediction of lobster-eye optics. The mild variations and deviations from the prediction of idealized lobster-eye optics can be understood to be caused by the imperfect shapes and alignment of the micro-pores as well as the obscuration by the supporting frames, which can be well reproduced by MC simulations. The spatial resolution of LEIA defined by the FWHM of the focal spot ranges from 4-8 arcmin with a median of 5.7. The measured effective areas are in range of 2-3 $cm^2$ at ~1.25 keV across the entire FoV, and its dependence on photon energy is in large agreement with simulations. The gains of the CMOS sensors are in range of 6.5-6.9 eV/DN, and the energy resolutions in the range of ~120-140 eV at 1.25 keV and ~170-190 eV at 4.5 keV. These results have been ingested into the calibration database and applied to the analysis of the scientific data acquired by LEIA. This work paves the way for the calibration of the Wide-field X-Ray Telescope modules of the Einstein Probe mission.Comment: 24 pages, 13 figures. Submitted to Experimental Astronom

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure