Determination of impact parameter in high-energy heavy-ion collisions via deep learning

In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about $0.4$ fm with CNN behaving slightly better. Then, we test the neural networks for different beam energies and pseudorapidity ranges in this task. It turns out that these two models work well for both low and high energies. But when making test for a larger pseudorapidity window, we observe that the CNN shows higher prediction accuracy than the DNN. With the method of Grad-CAM, we shed light on the `attention' mechanism of the CNN model

6-Formyl-2-naphthyl cis-1,5,7-trimethyl-2,4-dioxo-3-aza­bicyclo­[3.3.1]nonane-7-carboxyl­ate

In the title compound, C23H23NO5, the C5N ring adopts an envelope conformation with a C atom as the flap, whilst the saturated C6 ring fused to it adopts a chair conformation. In the crystal, inversion dimers linked by pairs of N—H⋯O hydrogen bonds generate R 2 2(8) loops

Detecting Chiral Magnetic Effect via Deep Learning

The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we construct an observable-independent CME-meter based on a deep convolutional neural network. After trained over data set generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In a transfer learning manner, the CME-meter is validated in isobaric collision systems, showing good transferability among different colliding systems. Based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectra that demonstrates the physical contents the machine learns.Comment: 7 pages, 10 figure

Epimedium brevicornu Maxim extract shows protective activity against Alzheimer's disease in mice

Purpose: To investigate the protective effect of Epimedium brevicornu Maxim extract (EBME) against Alzheimer's disease in 3xTg-AD mice. Methods: The cognitive function of 3xTg-AD mice was assessed using Morris water maze test. The levels of amyloid beta deposits and NeuN in the mouse hippocampus were evaluated by immunohistochemistry. Brain neurotrophic-derived factor (BDNF) and tyrosine kinase B (TrkB) expressions were examined by western blot analysis. Results: EBME treatment significantly ameliorated learning and memory deficits in AD mice, as shown by the increased time spent in the target zone during probe tests. Compared with the 3xTg-AD mice (8.4 ± 1.1 s), the escape latency in animals treated with 600 mg/kg EBME (21.5 ± 1.1 s) was significantly increased (p < 0.01). In addition, EBME significantly decreased Aβ deposits, increased NeuN-positive cells, and upregulated the expressions of BDNF (1.5 ± 0.2, p < 0.05) and TrkB (1.6 ± 0.2, p < 0.05) in the 3xTg AD mice. Conclusion: EBME treatment may be a useful therapeutic strategy for managing memory impairment

Experimental investigation of the non-Markovian dynamics of classical and quantum correlations

We experimentally investigate the dynamics of classical and quantum correlations of a Bell diagonal state in a non-Markovian dephasing environment. The sudden transition from classical to quantum decoherence regime is observed during the dynamics of such kind of Bell diagonal state. Due to the refocusing effect of the overall relative phase, the quantum correlation revives from near zero and then decays again in the subsequent evolution. However, the non-Markovian effect is too weak to revive the classical correlation, which remains constant in the same evolution range. With the implementation of an optical $\sigma_{x}$ operation, the sudden transition from quantum to classical revival regime is obtained and correlation echoes are formed. Our method can be used to control the revival time of correlations, which would be important in quantum memory.Comment: extended revision, accepted for publication in Physical Review