Optimizing doping parameters of target to enhance direct-drive implosion

Direct-drive is an important approach to achieving the ignition of inertial confinement fusion. To enhance implosion performance while keeping the risk of hydrodynamic instability at a low level, we have designed a procedure to optimize the parameters of the target doped with mid- or high-$Z$ atoms. In the procedure, a one-dimensional implosion can be automatically simulated, while its implosion performance and high-dimensional instability are integrally evaluated at the same time. To find the optimal doping parameters, the procedure is performed in the framework of global optimization algorithm, where we have used the particle swarm optimization in the current work. In the optimization, the opacity of mixture materials is quickly obtained by using an interpolation method, showing only a slight difference from the data of TOPS, which is an online doping program of Los Alamos National Laboratory. To test the procedure, optimization has been carried out for the CH ablator in the double cone ignition scheme [Phil. Trans. R. Soc. A. 378.2184 (2020)] by doping with Si and Cl. Both one- and two-dimensional simulations show that doping with either Si or Cl can efficiently mitigate the instability during the acceleration phase and does not result in significant degradation of the peak areal density. The results from one- and two-dimensional simulations qualitatively match with each other, demonstrating the validity of our optimization procedure

Arctigenin-induced reversal of drug resistance in cisplastin-resistant cell line A549/DDP, and the mechanism involved

Purpose: To investigate the drug resistance reversal effect of arctigenin (ARG) on cisplatin-insensitive A549/DDP cancer cells, and to elucidate the underlying mechanism(s). Methods: Four groups of cells: control, DDP, ARG and ADP were used. The degrees of inhibition of proliferation, drug resistance and apoptotic changes were measured using MTT assay, CCK-8 assay and flow cytometry, respectively. Expressions of PTEN and STAT3 proteins were determined by Western blotting. Results: At ARG concentration of 5 μmol/L, A549/DDP cells were significantly inhibited (p < 0.05). The combination therapy was more effective in reversing A549/DDP cells resistance than the single therapy. The expression level of PTEN protein increased with increase in ARG concentration, while STAT3 protein expression decreased with increase in ARG concentration. ADP group up-regulated PTEN but decreased STAT3 expression levels. Conclusion: ARG regulates drug resistance in A549/DDP cells, possibly via a mechanism involving reduction of A549/DDP cell sensitivity to DDP, thereby regulating the stress pathways associated with PTEN and STAT3. The combination of ARG and DDP effectively reduces A549/DDP cells resistance

Laser pulse shape designer for direct-drive inertial confinement fusion

A pulse shape designer for direct drive inertial confinement fusion has been developed, it aims at high compression of the fusion fuel while keeping hydrodynamics instability within tolerable level. Fast linear analysis on implosion instability enables the designer to fully scan the vast pulse configuration space at a practical computational cost, machine learning helps to summarize pulse performance into an implicit scaling metric that promotes the pulse shape evolution. The designer improves its credibility by incorporating various datasets including extra high-precision simulations or experiments. When tested on the double-cone ignition scheme [J. Zhang et al, Phil. Trans. R. Soc. A. 378.2184 (2020)], optimized pulses reach the assembly requirements, show significant imprint mitigation and adiabatic shaping capability, and have the potential to achieve better implosion performance in real experiments. This designer serves as an efficient alternative to traditional empirical pulse shape tuning procedure, reduces workload and time consumption. The designer can be used to quickly explore the unknown parameter space for new direct-drive schemes, assists design iteration and reduces experiment risk

DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Targeting to understand the underlying explainable factors behind observations and modeling the conditional generation process on these factors, we connect disentangled representation learning to Diffusion Probabilistic Models (DPMs) to take advantage of the remarkable modeling ability of DPMs. We propose a new task, disentanglement of (DPMs): given a pre-trained DPM, without any annotations of the factors, the task is to automatically discover the inherent factors behind the observations and disentangle the gradient fields of DPM into sub-gradient fields, each conditioned on the representation of each discovered factor. With disentangled DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of DisDiff.Comment: Accepted by NeurIPS 202