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The steel rib and the lattice girder are two typical steel arch frames used in the primary lining of the New Austrian Tunnelling Method (NATM) highway tunnel. In the process of tunnel construction, it is necessary to choose the support method according to the mechanical properties, durability, construction difficulty, and economic benefits. In order to analyze the mechanical characteristics of steel arch frames, a loading and measurement test system for steel arch was designed, and destructive tests were carried out on a three-bar lattice girder, four-bar lattice girder, and I-beam steel rib. The steel utilization coefficient was introduced to compare the mechanical properties of the different types of steel frames. The results show that the I-beam steel rib has the largest steel utilization coefficient, but it is not well combined with concrete. Among the lattice girders, the three-bar W-shaped lattice girder uses the least amount of steel under the same load, which reduces the construction cost and is worth applying and popularizing. Additionally, the steel utilization coefficient of different types of lattice girders can be optimized through the ratio of steel bar diameters. It is proved that the steel utilization coefficient can provide a reference for the evaluation and selection of steel frames for the primary lining of highway tunnel engineering.

Due to the excellent results achieved by transformers in computer vision, more and more scholars have introduced transformers into the field of medical image segmentation. However, the use of transformers will make the model’s parameters very large, which occupies a large amount of the computer’s resources, making them very time-consuming during training. In order to alleviate this disadvantage, this paper explores a flexible and efficient search strategy that can find the best subnet from a continuous transformer network. The method is based on a learnable and uniform L1 sparsity constraint, which contains factors that reflect the global importance of the continuous search space in different dimensions, while the search process is simple and efficient, containing a single round of training. At the same time, in order to compensate for the loss of accuracy caused by the search, a pixel classification module is introduced into the model to compensate for the loss of accuracy in the model search process. Our experiments show that the model in this paper compresses 30% of the parameters and FLOPs used, while also showing a slight increase in the accuracy of the model on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset.

AI models of code have made significant progress over the past few years. However, many models are actually not learning task-relevant source code features. Instead, they often fit non-relevant but correlated data, leading to a lack of robustness and generalizability, and limiting the subsequent practical use of such models. In this work, we focus on improving the model quality through signal awareness, i.e., learning the relevant signals in the input for making predictions. We do so by leveraging the heterogeneity of code samples in terms of their signal-to-noise content. We perform an end-to-end exploration of model signal awareness, comprising: (i) uncovering the reliance of AI models of code on task-irrelevant signals, via predictionpreserving input minimization; (ii) improving models’ signal awareness by incorporating the notion of code complexity during model training, via curriculum learning; (iii) improving models’ signal awareness by generating simplified signal-preserving programs and augmenting them to the training dataset; and (iv) presenting a novel interpretation of the model learning behavior from the perspective of the dataset, using its code complexity distribution. We propose a new metric to measure model signal awareness, Signal-aware Recall, which captures how much of the model’s performance is attributable to task-relevant signal learning. Using a software vulnerability detection use-case, our model probing approach uncovers a significant lack of signal awareness in the models, across three different neural network architectures and three datasets. Signal-aware Recall is observed to be in the sub-50s for models with traditional Recall in the high 90s, suggesting that the models are presumably picking up a lot of noise or dataset nuances while learning their logic. With our code-complexity-aware model learning enhancement techniques, we are able to assist the models toward more task-relevant learning, recording up-to 4.8× improvement in model signal awareness. Finally, we employ our model learning introspection approach to uncover the aspects of source code where the model is facing difficulty, and we analyze how our learning enhancement techniques alleviate it. [ABSTRACT FROM AUTHOR]