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Covalent organic frameworks (COFs) have been acknowledged as a potential platform for heterogeneous photoredox cross-coupling due to their excellent chemical stability, admirable controllability, and extremely prominent surface area. However, synthesizing COFs with bidentate ligand units and utilizing active sites remain a grand challenge. Herein, we report a promising new family of 2,6-pyridinedicarboxaldehyde-bis-(p-aminophenylimine)-based two-dimensional (2D) COFs (PP-COF) using an amine monomer and classic tri-aldehydes. On this basis, dispersed Ni single-atom sites were immobilized on three-types imine-based bi-coordinated 2D COFs (Ni SAS-PP-COF) as heterogeneous dual photoredox catalysts for photo/Ni dual-catalyzed C–N cross-coupling between aryl bromides and alkyl/sulfo amines. Under solar energy irradiation, PP-COF could absorb light to generate electrons and holes, then the photogenerated electrons are transferred to Ni sites to reduce divalent nickel to monovalent nickel. Monovalent nickel is necessary to drive the nickel catalytic cycle. Due to the increased charge separation and abundant active sites, the state-of-the-art Ni SAS-PP-COFs catalyst achieves excellent catalytic performance in comparison of pristine PP-COF. The heterogeneous Ni SAS-PP-COF catalytic system not only confirms the prospect of COFs as potential photoredox/transition-metal dual catalysts, but also provides in-depth insights into the synthesis of functional COFs toward practical metallaphotocatalytic application., QC 20240122

Sluggish oxygen evolution kinetics and serious charge recombination restrict the development of photoelectrochemical (PEC) water splitting. The advancement of novel metal–organic frameworks (MOFs) catalysts bears practical significance for improving PEC water splitting performance. Herein, a MOF glass catalyst through melting glass-forming cobalt-based zeolitic imidazolate framework (Co-agZIF-62) was introduced on various metal oxide (MO: Fe2O3, WO3 and BiVO4) semiconductor substrates coupled with NiO hole transport layer, constructing the integrated Co-agZIF-62/NiO/MO photoanodes. Owing to the excellent conductivity, stability and open active sites of MOF glass, Co-agZIF-62/NiO/MO photoanodes exhibit a significantly enhanced photoelectrochemical water oxidation activity and stability in comparison to pristine MO photoanodes. From experimental analyses and density functional theory calculations, Co-agZIF-62 can effectively promote charge transfer and separation, improve carrier mobility, accelerate the kinetics of oxygen evolution reaction (OER), and thus improve PEC performance. This MOF glass not only serves as an excellent OER cocatalyst on tunable photoelectrodes, but also enables promising opportunities for PEC devices for solar energy conversion., QC 20230829

Deep neural networks have been applied in many applications exhibiting extraordinary abilities in the field of computer vision. However, complex network architectures challenge efficient real-time deployment and require significant computation resources and energy costs. These challenges can be overcome through optimizations such as network compression. Network compression can often be realized with little loss of accuracy. In some cases accuracy may even improve. This paper provides a survey on two types of network compression: pruning and quantization. Pruning can be categorized as static if it is performed offline or dynamic if it is performed at run-time. We compare pruning techniques and describe criteria used to remove redundant computations. We discuss trade-offs in element-wise, channel-wise, shape-wise, filter-wise, layer-wise and even network-wise pruning. Quantization reduces computations by reducing the precision of the datatype. Weights, biases, and activations may be quantized typically to 8-bit integers although lower bit width implementations are also discussed including binary neural networks. Both pruning and quantization can be used independently or combined. We compare current techniques, analyze their strengths and weaknesses, present compressed network accuracy results on a number of frameworks, and provide practical guidance for compressing networks.

High bandwidth requirements are an obstacle for accelerating the training and inference of deep neural networks. Most previous research focuses on reducing the size of kernel maps for inference. We analyze parameter sparsity of six popular convolutional neural networks - AlexNet, MobileNet, ResNet-50, SqueezeNet, TinyNet, and VGG16. Of the networks considered, those using ReLU (AlexNet, SqueezeNet, VGG16) contain a high percentage of 0-valued parameters and can be statically pruned. Networks with Non-ReLU activation functions in some cases may not contain any 0-valued parameters (ResNet-50, TinyNet). We also investigate runtime feature map usage and find that input feature maps comprise the majority of bandwidth requirements when depth-wise convolution and point-wise convolutions used. We introduce dynamic runtime pruning of feature maps and show that 10% of dynamic feature map execution can be removed without loss of accuracy. We then extend dynamic pruning to allow for values within an epsilon of zero and show a further 5% reduction of feature map loading with a 1% loss of accuracy in top-1.

Jinjun Liang,1,2 Xiaoran Yuan,1,2 Shaobo Shi,1,2 Fang Wang,1,2 Yingying Chen,1,2 Chuan Qu,1,2 Jingjing Chen,1,2 Dan Hu,1–3 Yang Bo1,2 1Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Cardiovascular Research Institute, Wuhan University, Wuhan, People’s Republic of China; 3Masonic Medical Research Laboratory, Utica, NY, USA Background: Major depression is diagnosed in 18% of patients following myocardial infarction (MI), and the antidepressant fluoxetine is shown to effectively decrease depressive symptoms and improve coronary heart disease prognosis. We observed the effect of fluoxetine on cardiac electrophysiology in vivo in a rat model of post-MI depression and the potential mechanism. Methods and results: Eighty adult male Sprague Dawley rats (200–250 g) were randomly assigned to five groups: normal control (control group), MI (MI group), depression (depression group), post-MI depression (model group), and post-MI depression treated with intragastric administration of 10 mg/kg fluoxetine (fluoxetine group). MI was induced by left anterior descending coronary artery ligation. Depression was developed by 4-week chronic mild stress (CMS). Behavior measurement was done before and during the experiment. Electrophysiology study in vivo and Western blot analysis were carried on after 4 weeks of CMS. After 4 weeks of CMS, depression-like behaviors were observed in the MI, depression, and model groups, and chronic fluoxetine administration could significantly improve those behaviors (P<0.05 vs model group). Fluoxetine significantly increased the ventricular fibrillation threshold compared with the model group (20.20±9.32 V vs 14.67±1.85 V, P<0.05). Expression of Kv4.2 was significantly reduced by 29%±12%, 24%±6%, and 41%±15%, respectively, in the MI group, CMS group, and model group, which could be improved by fluox