263 research outputs found
Design optimization and application of bolt-shotcrete support for East Tianshan tunnel project in China
Bolt-shotcrete support is a form of support with low cost, convenient for construction, uniform structural stress, which is widely used in international tunnel engineering. In this paper, the 2# inclined shaft of East Tianshan tunnel in China is taken as the research object. The stress characteristics of composite lining support and bolt-shotcrete support are analyzed and compared by FLAC3D software, and the bolt-shotcrete support scheme suitable for this project is put forward. Based on the principle of orthogonal experiment, the most reasonable shotcrete material proportion is selected, and structural stress and displacement monitoring is carried out during the construction stage of typical sections. The results show that: (1) in FLAC3D simulation calculation, the interface element is applied between different layers, which can simulate the interaction between different layers of lining structure and reflect the mechanical characteristics and displacement characteristics of the interface between layers; (2) from the aspect of mechanical performance, single layer lining which can meet the requirements of tunnel support with thinner structural thickness and has higher economic efficiency, is better than composite lining; (3) the field monitoring results show that the deformation of bolt-shotcrete support structure is small, the structural stress meets the material performance requirements, and there is no structural damage during the construction of the test section; (4) during the implementation of bolt-shotcrete support, the cost of support per meter is reduced by 36.78%, and the average excavation efficiency is increased by 38.9%, which verifies the applicability and advantages of the optimization scheme. The research results in this paper can provide reference for the follow-up construction of tunnels and similar projects.
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LOT: Layer-wise Orthogonal Training on Improving Certified Robustness
Recent studies show that training deep neural networks (DNNs) with Lipschitz
constraints are able to enhance adversarial robustness and other model
properties such as stability. In this paper, we propose a layer-wise orthogonal
training method (LOT) to effectively train 1-Lipschitz convolution layers via
parametrizing an orthogonal matrix with an unconstrained matrix. We then
efficiently compute the inverse square root of a convolution kernel by
transforming the input domain to the Fourier frequency domain. On the other
hand, as existing works show that semi-supervised training helps improve
empirical robustness, we aim to bridge the gap and prove that semi-supervised
learning also improves the certified robustness of Lipschitz-bounded models. We
conduct comprehensive evaluations for LOT under different settings. We show
that LOT significantly outperforms baselines regarding deterministic l2
certified robustness, and scales to deeper neural networks. Under the
supervised scenario, we improve the state-of-the-art certified robustness for
all architectures (e.g. from 59.04% to 63.50% on CIFAR-10 and from 32.57% to
34.59% on CIFAR-100 at radius rho = 36/255 for 40-layer networks). With
semi-supervised learning over unlabelled data, we are able to improve
state-of-the-art certified robustness on CIFAR-10 at rho = 108/255 from 36.04%
to 42.39%. In addition, LOT consistently outperforms baselines on different
model architectures with only 1/3 evaluation time.Comment: NeurIPS 202
SoK: Certified Robustness for Deep Neural Networks
Great advances in deep neural networks (DNNs) have led to state-of-the-art
performance on a wide range of tasks. However, recent studies have shown that
DNNs are vulnerable to adversarial attacks, which have brought great concerns
when deploying these models to safety-critical applications such as autonomous
driving. Different defense approaches have been proposed against adversarial
attacks, including: a) empirical defenses, which can usually be adaptively
attacked again without providing robustness certification; and b) certifiably
robust approaches, which consist of robustness verification providing the lower
bound of robust accuracy against any attacks under certain conditions and
corresponding robust training approaches. In this paper, we systematize
certifiably robust approaches and related practical and theoretical
implications and findings. We also provide the first comprehensive benchmark on
existing robustness verification and training approaches on different datasets.
In particular, we 1) provide a taxonomy for the robustness verification and
training approaches, as well as summarize the methodologies for representative
algorithms, 2) reveal the characteristics, strengths, limitations, and
fundamental connections among these approaches, 3) discuss current research
progresses, theoretical barriers, main challenges, and future directions for
certifiably robust approaches for DNNs, and 4) provide an open-sourced unified
platform to evaluate 20+ representative certifiably robust approaches.Comment: To appear at 2023 IEEE Symposium on Security and Privacy (SP); 14
pages for the main text; benchmark & tool website:
http://sokcertifiedrobustness.github.io
Fairness in Federated Learning via Core-Stability
Federated learning provides an effective paradigm to jointly optimize a model
benefited from rich distributed data while protecting data privacy.
Nonetheless, the heterogeneity nature of distributed data makes it challenging
to define and ensure fairness among local agents. For instance, it is
intuitively "unfair" for agents with data of high quality to sacrifice their
performance due to other agents with low quality data. Currently popular
egalitarian and weighted equity-based fairness measures suffer from the
aforementioned pitfall. In this work, we aim to formally represent this problem
and address these fairness issues using concepts from co-operative game theory
and social choice theory. We model the task of learning a shared predictor in
the federated setting as a fair public decision making problem, and then define
the notion of core-stable fairness: Given agents, there is no subset of
agents that can benefit significantly by forming a coalition among
themselves based on their utilities and (i.e., ). Core-stable predictors are robust to low quality local data from
some agents, and additionally they satisfy Proportionality and
Pareto-optimality, two well sought-after fairness and efficiency notions within
social choice. We then propose an efficient federated learning protocol CoreFed
to optimize a core stable predictor. CoreFed determines a core-stable predictor
when the loss functions of the agents are convex. CoreFed also determines
approximate core-stable predictors when the loss functions are not convex, like
smooth neural networks. We further show the existence of core-stable predictors
in more general settings using Kakutani's fixed point theorem. Finally, we
empirically validate our analysis on two real-world datasets, and we show that
CoreFed achieves higher core-stability fairness than FedAvg while having
similar accuracy.Comment: NeurIPS 2022; code:
https://openreview.net/attachment?id=lKULHf7oFDo&name=supplementary_materia
Certifying Out-of-Domain Generalization for Blackbox Functions
Certifying the robustness of model performance under bounded data
distribution drifts has recently attracted intensive interest under the
umbrella of distributional robustness. However, existing techniques either make
strong assumptions on the model class and loss functions that can be certified,
such as smoothness expressed via Lipschitz continuity of gradients, or require
to solve complex optimization problems. As a result, the wider application of
these techniques is currently limited by its scalability and flexibility --
these techniques often do not scale to large-scale datasets with modern deep
neural networks or cannot handle loss functions which may be non-smooth such as
the 0-1 loss. In this paper, we focus on the problem of certifying
distributional robustness for blackbox models and bounded loss functions, and
propose a novel certification framework based on the Hellinger distance. Our
certification technique scales to ImageNet-scale datasets, complex models, and
a diverse set of loss functions. We then focus on one specific application
enabled by such scalability and flexibility, i.e., certifying out-of-domain
generalization for large neural networks and loss functions such as accuracy
and AUC. We experimentally validate our certification method on a number of
datasets, ranging from ImageNet, where we provide the first non-vacuous
certified out-of-domain generalization, to smaller classification tasks where
we are able to compare with the state-of-the-art and show that our method
performs considerably better.Comment: 39th International Conference on Machine Learning (ICML) 202
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