Gaining the Sparse Rewards by Exploring Binary Lottery Tickets in Spiking Neural Network

Spiking Neural Network (SNN) as a brain-inspired strategy receives lots of attention because of the high-sparsity and low-power properties derived from its inherent spiking information state. To further improve the efficiency of SNN, some works declare that the Lottery Tickets (LTs) Hypothesis, which indicates that the Artificial Neural Network (ANN) contains a subnetwork without sacrificing the performance of the original network, also exists in SNN. However, the spiking information handled by SNN has a natural similarity and affinity with binarization in sparsification. Therefore, to further explore SNN efficiency, this paper focuses on (1) the presence or absence of LTs in the binary SNN, and (2) whether the spiking mechanism is a superior strategy in terms of handling binary information compared to simple model binarization. To certify these consumptions, a sparse training method is proposed to find Binary Weights Spiking Lottery Tickets (BinW-SLT) under different network structures. Through comprehensive evaluations, we show that BinW-SLT could attain up to +5.86% and +3.17% improvement on CIFAR-10 and CIFAR-100 compared with binary LTs, as well as achieve 1.86x and 8.92x energy saving compared with full-precision SNN and ANN.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multiple enzymatic approaches to hydrolysis of fungal beta-glucans by the soil bacterium Chitinophaga pinensis

The genome of the soil Bacteroidota Chitinophaga pinensis encodes a large number of glycoside hydrolases (GHs) with noteworthy features and potentially novel functions. Several are predicted to be active on polysaccharide components of fungal and oomycete cell walls, such as chitin, beta-1,3-glucan and beta-1,6-glucan. While several fungal beta-1,6-glucanase enzymes are known, relatively few bacterial examples have been characterised to date. We have previously demonstrated that C. pinensis shows strong growth using beta-1,6-glucan as the sole carbon source, with the efficient release of oligosaccharides from the polymer. We here characterise the capacity of the C. pinensis secretome to hydrolyse the beta-1,6-glucan pustulan and describe three distinct enzymes encoded by its genome, all of which show different levels of beta-1,6-glucanase activity and which are classified into different GH families. Our data show that C. pinensis has multiple tools to deconstruct pustulan, allowing the species' broad utility of this substrate, with potential implications for bacterial biocontrol of pathogens via cell wall disruption. Oligosaccharides derived from fungal beta-1,6-glucans are valuable in biomedical research and drug synthesis, and these enzymes could be useful tools for releasing such molecules from microbial biomass, an underexploited source of complex carbohydrates

Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training

Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization. Previous compression algorithms usually start from the pre-trained dense models and only focus on efficient inference, while time-consuming training is still unavoidable. In contrast, this paper points out that the million-scale training data is redundant, which is the fundamental reason for the tedious training. To address the issue, this paper aims to introduce sparsity into data and proposes an end-to-end efficient training framework from three sparse perspectives, dubbed Tri-Level E-ViT. Specifically, we leverage a hierarchical data redundancy reduction scheme, by exploring the sparsity under three levels: number of training examples in the dataset, number of patches (tokens) in each example, and number of connections between tokens that lie in attention weights. With extensive experiments, we demonstrate that our proposed technique can noticeably accelerate training for various ViT architectures while maintaining accuracy. Remarkably, under certain ratios, we are able to improve the ViT accuracy rather than compromising it. For example, we can achieve 15.2% speedup with 72.6% (+0.4) Top-1 accuracy on Deit-T, and 15.7% speedup with 79.9% (+0.1) Top-1 accuracy on Deit-S. This proves the existence of data redundancy in ViT.Comment: AAAI 202

Plant type II NAD(P)H dehydrogenases : Structure, regulation and evolution of NDB proteins

In living organisms, respiration is a biological process degrading different carbon substrates, consuming O2, and releasing the carbon as CO2. Plants have several alternative enzymes that are involved in the respiratory processes, as compared to animals. These alternative respiratory enzymes allow electrons to be transferred to oxygen in the mitochondrial inner membrane, but bypassing ATP synthesis. The alternative enzymes, e.g., type II NAD(P)H dehydrogenases (NDH-2), affect cellular NAD(P)H redox status, which is of vital importance for energy metabolism, ROS production and removal, anti-oxidation and reductive biosynthesis. Plant NDB-type proteins are NDH-2 enzymes located at the external mitochondrial inner membrane. It was earlier found that NDB1 oxidise cytosolic NADPH, and NDB2 oxidise cytosolic NADH. In this study, the regulatory mechanisms of Arabidopsis thaliana and Solanum tuberosum NDB1 by cytosolic Ca2+ and pH were studied and compared to NDB2, using purified mitochondria and E. coli-produced proteins in a membrane-bound and a purified soluble state. Membrane bound NDB1 and NDB2 oxidised NADPH and NADH, respectively. Soluble forms of NDB1 oxidise both NADH and NADPH, with higher NADPH activity. Soluble forms of NDB2 oxidised only NADH like the membrane-bound enzyme. In solution, the active StNDB1 resided as oligomers of dimeric units, mainly hexamers, and recombinant AtNDB2 was highly oligomeric. Within a physiological pH range, an acidic pH was found to lower the Ca2+ demand for activation of the mitochondrial and E. coli-produced NADPH oxidation via NDB1, as compared to a more alkaline pH. Depending on pH, 3-82 µM Ca2+ was needed. In contrast, the sub-µM Ca2+ demand for activation of NADH oxidation was not linked to pH. Both soluble and mitochondrial StNDB1 (NADPH oxidation) could respond quickly to increased and decreased Ca2+, whereas mitochondrial NADH oxidation responded quickly to Ca2+ increase but slowly to Ca2+ decrease. Overall, the results suggest that in vivo, the activity of NDB1 is rapidly controlled by pH-shift-associated Ca2+ spikes in the cytosol whereas NDB2 may be more continuously active. Based on modelling of NDB1, the core catalytic parts and dimerization surface showed distinct similarities to the structures of yeast ScNDI1 and Plasmodium falciparum PfNDH-2. This analysis highlighted motifs that correlate with NAD(P)H substrate specificity, and which were followed by evolutionary analysis. Most eukaryotic species have NDB proteins that contain a non-acidic motif for NADPH binding. Angiosperms and liverworts contain NDB proteins of NDB1- and NDB2- type, i.e. they contain acidic and non-acidic motifs for NADH and NADPH binding, respectively. This indicates that plants have more flexibility for external NAD(P)H oxidation as compared to other eukaryotes. Based on the evolutionary analysis, Ca2+-dependent external NADPH oxidation appears to be an ancient process as compared to NADH oxidation, and thus possibly has a more fundamental function in cellular redox metabolism

Effects of the anti-hedging policy on income smoothing and managerial wealth diversification

To enhance the alignment of shareholders and managers, companies have been increasingly adopting the anti-hedging policy that prevents managers from hedging their equity incentives. Using the propensity score-matched sample, we find that managers mitigate the effect of the anti-hedging policy on the risk of their equity incentive by smoothing reported earnings and selling their equity for diversification. We find insignificant changes in earnings informativeness, corporate risk-taking, financial misreporting, real activity manipulation, and CEO annual pay components following anti-hedging policy adoptions. Our findings provide implications on the incentive alignment effects of stock-based compensation contracts with anti-hedging provisions

Integrity Assessment of Isolated Plant Mitochondria

The integrity of isolated mitochondria can be estimated functionally using enzymatic activities or the permeability of mitochondrial membranes to molecules of different sizes. Thus, the permeability of the outer membrane to the protein cytochrome c, the permeability of the inner membrane to protons, and the permeability of the inner membrane to NAD+, NADH and organic acids using soluble matrix dehydrogenases as markers have all been used. These assays all have limitations to how the data can be converted into a measure of integrity, are differently sensitive to artifacts and require widely variable amounts of material. Therefore, each method has a restricted utility for estimating integrity, depending on the type of mitochondria analysed. Here, we review the advantages and disadvantages of different integrity assays and present protocols for integrity assays that require relatively small amounts of mitochondria. They are based on the permeability of the outer membrane to cytochrome c, and the inner membrane to protons or NAD(H). The latter has the advantage of utilizing a membrane-bound activity (complex I) and the pore-forming peptide alamethicin to gain access to the matrix space. These methods together provide a toolbox for the determination of functionality and quality of isolated mitochondria

Mitochondrial NAD(P)H oxidation pathways and nitrate/ammonium redox balancing in plants

Plant mitochondrial oxidative phosphorylation is characterised by alternative electron transport pathways with different energetic efficiencies, allowing turnover of cellular redox compounds like NAD(P)H. These electron transport chain pathways are profoundly affected by soil nitrogen availability, most commonly as oxidized nitrate (NO3 −) and/or reduced ammonium (NH4 +). The bioenergetic strategies involved in assimilating different N sources can alter redox homeostasis and antioxidant systems in different cellular compartments, including the mitochondria and the cell wall. Conversely, changes in mitochondrial redox systems can affect plant responses to N. This review explores the integration between N assimilation, mitochondrial redox metabolism, and apoplast metabolism

Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices