Interactive holography offers unmatched levels of immersion and user engagement in the field of future display. Despite of the substantial progress has been made in dynamic meta-holography, the realization of real-time, highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations. In this study, we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates. To our knowledge, this is the first reported practical dynamic interactive metasurface holographic system. We spatially divided the metasurface device into multiple distinct channels, each projecting a reconstructed sub-pattern. The switching states of these channels were mapped to bitwise operations on a set of bit values, which avoids complex hologram computations, enabling an ultra-high computational frame rate. Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform. According to this methodology, we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay, color display, and on-the-fly hologram creation. Our technology presents an inspiration for advanced dynamic meta-holography, which is promising for a broad range of applications including advanced human-computer interaction, real-time 3D visualization, and next-generation virtual and augmented reality systems.

针对红外图像序列中复杂背景干扰下容易出现的高虚警问题，提出一种基于 ${L_{1 - 2}}$时空域总变分正则项的红外弱小目标检测算法。首先，将红外图像序列转化为时空域红外张量块，该步骤可利用张量的高维数据结构优势关联图像序列中的时空域信息。然后，利用加权Schatten $p$范数和 ${L_{1 - 2}}$时空域总变分正则项对低秩背景成分进行重构，以保留背景中起伏剧烈的边缘和角点，提高稀疏目标的重构精度。最后，将目标张量恢复为图像序列，利用自适应阈值分割方法得到最终的目标图像。与另外5种检测算法进行对比实验，结果显示，该方法的虚警率较Maxemeidan算法、Tophat算法、LIRDNet算法、DNANet算法以及WSNMSTIPT算法平均分别下降了71.4%、71.1%、68.5%、74.3%和20.47%；而在检测实时性方面，该算法耗时为Maxemeidan算法、DNANet算法以及WSNMSTIPT算法的42.4%、82.9%和28.7%。实验结果验证了该方法在检测性能上的优越性，表明该算法能够显著提高复杂背景干扰下的目标检测精度和效率。

云检测对于遥感图像的应用具有重要意义。目前已有的云检测方法关于遥感图像的偏振信息研究较少，性能和泛化能力有限。为有效利用遥感图像偏振信息，提出了一种基于深度学习的多模态融合遥感图像云检测方法并进行了初步实验评价。该网络是一种三参数输入流架构，具有编码器-解码器结构，利用通道空间注意模块对遥感图像中的反射率特征和偏振特征进行多模态融合。在解码器上采样阶段，利用迭代注意特征融合方法融合高、低级特征映射。评价实验数据集来源于多角度偏振成像仪 (DPC) 云产品和云掩码产品。评价实验结果表明，所提出的网络模型实现了良好的云检测性能，识别准确率达到93.91%。

To obtain cold atom samples with temperatures lower than 100 pK in the cold atom physics rack experiment of the Chinese Space Station, we propose to use the momentum filtering method for deep cooling of atoms. This paper introduces the experimental results of the momentum filtering method verified by our ground testing system. In the experiment, we designed a specific experimental sequence of standing-wave light pulses to control the temperature, atomic number, and size of the atomic cloud. The results show that the momentum filter can effectively and conveniently reduce the temperature of the atomic cloud and the energy of Bose–Einstein condensation, and can be flexibly combined with other cooling methods to enhance the cooling effect. This work provides a method for the atomic cooling scheme of the ultra-cold atomic system on the ground and on the space station, and shows a way of deep cooling atoms.

悬浮光力传感技术利用真空环境的光阱实现对微纳尺度机械振子的悬浮和囚禁，将待测物理量转换为光悬浮机械振子运动参数的变化，理论上该振子与外部环境热噪声和振动完全隔绝，具有极高的测量分辨率潜力和易于小型化的独特优势。该技术在精密测量、微观热力学研究、暗物质观测、宏观量子态操控等领域具有广阔的应用前景。首先，阐述了悬浮光力系统中光力与光阱的基础概念和力学测量等基本理论；其次，介绍了其中初始起支、光力增强、位移测量、输出信号标定和等效反馈冷却等关键技术的研究进展，对比分析各子技术的特点，随后列举了悬浮光力传感技术在极弱力、加速度、微观质量、电学量、力矩等物理量测量中的典型应用；最后，总结了该技术的发展趋势，并提出相关建议。