The possibility of leptonic CP-violation measurement with JUNO

The existence of CP-violation in the leptonic sector is one of the most important issues for modern science. Neutrino physics is a key to the solution of this problem. JUNO (under construction) is the near future of neutrino physics. However CP-violation is not a priority for the current scientific program. We estimate the capability of $\delta_{\rm CP}$ measurement, assuming a combination of the JUNO detector and a superconductive cyclotron as the antineutrino source. This method of measuring CP-violation is an alternative to conventional beam experiments. A significance level of 3$\sigma$ can be reached for 22% of the $\delta_{\rm CP}$ range. The accuracy of measurement lies between 8$^{\rm o}$ and 22$^{\rm o}$. It is shown that the dominant influence on the result is the uncertainty in the mixing angle $\Theta_{23}$

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure

The preoperative plasma fibrinogen level is an independent prognostic factor for overall survival of breast cancer patients who underwent surgical treatment

AbstractBackgroundPrevious studies have suggested that plasma fibrinogen contributes to tumor cell proliferation, progression and metastasis. The current study was performed to evaluate the prognostic relevance of preoperative plasma fibrinogen in breast cancer patients.MethodData of 2073 consecutive breast cancer patients, who underwent surgery between January 2002 and December 2008 at the Sun Yat-sen University Cancer Center, were retrospectively evaluated. Plasma fibrinogen levels were routinely measured before surgeries. Participants were grouped by the cutoff value estimated by the receiver operating characteristic (ROC) curve analysis. Overall survival (OS) was assessed using Kaplan–Meier analysis, and multivariate Cox proportional hazards regression model was performed to evaluate the independent prognostic value of plasma fibrinogen level.ResultsThe optimal cutoff value of preoperative plasma fibrinogen was determined to be 2.83 g/L. The Kaplan–Meier analysis showed that patients with high fibrinogen levels had shorter OS than patients with low fibrinogen levels (p < 0.001). Multivariate analysis suggested preoperative plasma fibrinogen as an independent prognostic factor for OS in breast cancer patients (HR = 1.475, 95% confidence interval (CI): 1.177–1.848, p = 0.001). Subgroup analyses revealed that plasma fibrinogen level was an unfavorable prognostic parameter in stage II–III, Luminal subtypes and triple-negative breast cancer patients.ConclusionElevated preoperative plasma fibrinogen was independently associated with poor prognosis in breast cancer patients and may serve as a valuable parameter for risk assessment in breast cancer patients

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$