OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks

Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks. In our investigation, we identified that the SEW-ResNet, a prominent representative of deep residual spiking neural networks, incorporates non-event-driven operations. To rectify this, we introduce the OR Residual connection (ORRC) to the architecture. Additionally, we propose the Synergistic Attention (SynA) module, an amalgamation of the Inhibitory Attention (IA) module and the Multi-dimensional Attention (MA) module, to offset energy loss stemming from high quantization. When integrating SynA into the network, we observed the phenomenon of "natural pruning", where after training, some or all of the shortcuts in the network naturally drop out without affecting the model's classification accuracy. This significantly reduces computational overhead and makes it more suitable for deployment on edge devices. Experimental results on various public datasets confirmed that the SynA enhanced OR-Spiking ResNet achieved single-sample classification with as little as 0.8 spikes per neuron. Moreover, when compared to other spike residual models, it exhibited higher accuracy and lower power consumption. Codes are available at https://github.com/Ym-Shan/ORRC-SynA-natural-pruning.Comment: 16 pages, 8 figures and 11table

Ships Detection in SAR Images Based on Anchor-Free Model With Mask Guidance Features

Ship targets in synthetic aperture radar (SAR) images have various scales. The detection model based on anchor boxes requires manual design of candidate boxes, which are fixed and cannot completely match all kinds of targets. Instead, large of anchor boxes with different sizes also result in large amounts of computing resources being consumed. Another potential issue comes from complex background information of near-coast scenes, which leads to ship targets being unrecognized because the background contains similar appearing objects. Therefore, this article proposes an anchor-free detection model based on mask guidance features, which achieves detection mainly through three modifications. First, feature maps of multiple scales are fused to obtain high-resolution feature maps containing rich semantic information. Second, a transformer encoder module is introduced to focus on the context relationship between the target object and the global image and to enhance the dependence between ship targets. Third, the mask guide feature is used to highlight the positions of the target in the feature map, and a loss function in the mask guide mechanism is designed to optimize the mask feature map to reduce false detections and missed detections. Testing the model on the public dataset SAR ship detection dataset, the model's detection accuracy reached 96.17%, with its accuracy on small-size ships reaching 96.11% and 97.84% on large ships

Determination of the Hydration Damage Instability Period in a Shale Borehole Wall and Its Application to a Fuling Shale Gas Reservoir in China

In reviewing Chinese shale gas reserves and national policies regarding shale gas exploitation, shale gas will be of critical importance in providing clean natural gas to China. However, compared to those in the United States, the cost of shale gas extraction and the complex problems encountered in more complex and deep drilling in China are key technologies that need to be overcome. Shale wellbore wall instability is a complex problem that often occurs during drilling. During the process of drilling in shale, the complex stress and fluid-structure interactions result in the wall rock generating a strong hydration diffusion and swelling effect, which alters the stress distribution in the rock wall and deteriorates the mechanical parameters of the rock. This results in instability damage of the shale wellbore wall. In this study, the stratigraphic stress characteristics of the Fuling Shale Gas Field were initially predicted, and the shale sample phase composition and development of bedding and microcracks were analyzed using X-ray diffraction and scanning electronic microscopy. The main driving potential difference function between the drilling fluid and shale was analyzed, and a radial adsorption diffusion model of the shale plane was established. Through a laboratory study, the space time change law of the water diffusion of the shale rock was assessed as well as the rock damage evolutionary law of the elastic modulus and compressive strength with water content. Then, combined with the shale hydration stress and strength deformation theory, a damage evolutionary equation for shale with water was derived, and the shale damage evolutionary limit equation and the method of determining the collapse cycle were established. Finally, the method was applied to the Fuling Shale Gas Field, the largest shale gas field in China, and a shale wellbore collapse cycle of approximately seven days in the field was obtained. The severity of economic loss resulting from wellbore wall instability was also determined. This study provides insight and guidance for reducing the costs of shale gas reservoir well drilling and efficient development

In situ full-field measurement of surface oxidation on Ni-based alloy using high temperature scanning probe microscopy

Abstract We use in situ scanning probe microscopy (SPM) to investigate the high temperature oxidation of Ni-based single crystal alloys at the micro-/nanoscale. SiO2 micro-pillar arrays were pre-fabricated on the alloy surface as markers before the oxidation experiment. The SPM measurement of the oxidized surface in the vicinity of SiO2 micro-pillars was conducted real time at temperatures from 300 °C to 800 °C. The full-field evolution of oxide film thickness is quantitatively characterized by using the height of SiO2 micro-pillars as reference. The results reveal the non-uniform oxide growth featuring the nucleation and coalescence of oxide islands on the alloy surface. The outward diffusion of Ni and Co is responsible for the formation and coalescence of first-stage single-grain oxide islands. The second-stage of oxidation involves the formation and coalescence of poly-grain oxide islands

Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma

PurposeThis study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs).Materials and methodsThis cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful.ResultsThe independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808–0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21).ConclusionThe radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions