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Compressive sensing provides a promising sampling paradigm for video acquisition for resource‐limited sensor applications. However, the reconstruction of original video signals from sub‐sampled measurements is still a great challenge. To exploit the temporal redundancies within videos during the recovery, previous works tend to perform alignment on initial reconstructions, which are too coarse to provide accurate motion estimations. To solve this problem, the authors propose a novel reconstruction network, named TSRN, for compressive video sensing. Specifically, the authors utilise a number of stacked temporal shift reconstruction blocks (TSRBs) to enhance the initial reconstruction progressively. Each TSRB could learn the temporal structures by exchanging information with last and next time step, and no additional computations is imposed on the network compared to regular 2D convolutions due to the high efficiency of temporal shift operations. After the enhancement, a bidirectional alignment module to build accurate temporal dependencies directly with the help of optical flows is employed. Different from previous methods that only extract supplementary information from the key frames, the proposed alignment module can receive temporal information from the whole video sequence via bidirectional propagations, thus yielding better performance. Experimental results verify the superiority of the proposed method over other state‐of‐the‐art approaches quantitatively and qualitatively. To exploit the temporal redundancies within videos during the compressive sensing recovery, the authors utilise a number of stacked temporal shift reconstruction blocks (TSRBs) to enhance the initial reconstruction progressively. After that, the authors employ a bidirectional alignment module to build accurate temporal dependencies directly with the help of optical flows, and reconstruct the final frames from the aligned results. Experimental results verify the superiority of the proposed method over other state‐of‐the‐art approaches quantitatively and qualitatively.

Channel competition and further photochemical control of relaxation pathways in excited molecules are of primary importance in photochemistry and related areas. Acrolein, as the simplest and most typical α,β-enone, is suitable to provide a model for understanding the photochemistry and photophysics of α,β-enones. Here, the ultrafast dynamics in acrolein following S1(nπ*) excitation has been studied by time-resolved photoelectron imaging (TRPEI) and mass spectroscopy. The competition between intersystem crossing (ISC) and internal conversion (IC) is investigated. The key factor influencing the decay pathways and the relative contributions are revealed to be the position of the excitation relative to the energy of the S1/S0conical intersection (CI), which is obtained to be 3.65–3.76 eV experimentally. If the excitation is above the CI, IC is superior to ISC and most excited molecules go back to the ground. Otherwise, ISC will dominate the relaxation and lead the triplet products formation. These results show the potential of affecting the dynamics and governing the fate of excited molecules by adjusting the excitation conditions from the point of view of chemical control.