Soft OR in China: A critical report

As China's reform steps into the 'deep water zone' where value complexity becomes paramount, general-purpose decision-making aids such as Operational Research (OR) are increasingly confronted with the challenge of dealing with interest conflicts. However, due to historical events and institutional circumstances, OR in China to date is largely constrained by a technocratic approach which is not fit for purpose. Encouragingly, recent OR innovations inside China signify a conscious move to embrace value plurality and tackle social conflicts. OR is not merely a neutral tool for solving technical problems, but a world-building discourse that shapes society. The future of OR, particularly Soft OR, in China will be determined by whether OR workers are willing and capable to act as institutional entrepreneurs promoting scientific and democratic decision-making that deepens the reform toward an open, just and prosperous society. The implications go beyond the OR community and China's borders. © 2013 Elsevier B.V. All rights reserved

A Review of Adversarial Attacks in Computer Vision

Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples. Such adversarial attacks can be invisible to human eyes, but can lead to DNN misclassification, and often exhibits transferability between deep learning and machine learning models and real-world achievability. Adversarial attacks can be divided into white-box attacks, for which the attacker knows the parameters and gradient of the model, and black-box attacks, for the latter, the attacker can only obtain the input and output of the model. In terms of the attacker's purpose, it can be divided into targeted attacks and non-targeted attacks, which means that the attacker wants the model to misclassify the original sample into the specified class, which is more practical, while the non-targeted attack just needs to make the model misclassify the sample. The black box setting is a scenario we will encounter in practice

Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack

Deep neural networks (DNNs) have a wide range of applications in the field of image denoising, and they are superior to traditional image denoising. However, DNNs inevitably show vulnerability, which is the weak robustness in the face of adversarial attacks. In this paper, we find some similitudes between existing deep image denoising methods, as they are consistently fooled by adversarial attacks. First, denoising-PGD is proposed which is a denoising model full adversarial method. The current mainstream non-blind denoising models (DnCNN, FFDNet, ECNDNet, BRDNet), blind denoising models (DnCNN-B, Noise2Noise, RDDCNN-B, FAN), and plug-and-play (DPIR, CurvPnP) and unfolding denoising models (DeamNet) applied to grayscale and color images can be attacked by the same set of methods. Second, since the transferability of denoising-PGD is prominent in the image denoising task, we design experiments to explore the characteristic of the latent under the transferability. We correlate transferability with similitude and conclude that the deep image denoising models have high similitude. Third, we investigate the characteristic of the adversarial space and use adversarial training to complement the vulnerability of deep image denoising to adversarial attacks on image denoising. Finally, we constrain this adversarial attack method and propose the L2-denoising-PGD image denoising adversarial attack method that maintains the Gaussian distribution. Moreover, the model-driven image denoising BM3D shows some resistance in the face of adversarial attacks.Comment: 12 pages, 15 figure

Finite element analysis of mechanical behavior of concrete-filled square steel tube short columns with inner I-shaped CFRP profiles subjected to bi-axial eccentric load

[EN] The concrete-filed square steel tube with inner I-shaped CFRP profiles short columns under bi-axial eccentric load were investigated by the finite element analysis software ABAQUS. The working mechanism of the composite columns which is under bi-axial eccentric load are investigated by using the stress distribution diagram of steel tube concrete and the I-shaped CFRP profiles. In this paper, the main parameters; eccentric ratio, steel ratio, steel yield strength, concrete compressive strength and CFRP distribution rate of the specimens were investigated to know the mechanical behavior of them. The interaction between the steel tube and the concrete interface at different characteristic points of the composite columns were analyzed. The results showed that the ultimate bearing capacity of the concrete-filed square steel tube with inner I-shaped CFRP profiles short columns under bi-axial eccentric load decrease with the increase of eccentric ratio, the ultimate bearing capacity of the composite columns increase with the increase of steel ratio, steel yield strength, concrete compressive strength and CFRP distribution rate. The contact pressure between the steel tube and the concrete decreased from the corner zone to the flat zone, and the contact pressure decreased from the mid-height cross section to other sections.Li, G.; Zhan, Z.; Yang, Z.; Yang, Y. (2018). Finite element analysis of mechanical behavior of concrete-filled square steel tube short columns with inner I-shaped CFRP profiles subjected to bi-axial eccentric load. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 259-266. https://doi.org/10.4995/ASCCS2018.2018.6996OCS25926

Adversarial Training for Physics-Informed Neural Networks

Physics-informed neural networks have shown great promise in solving partial differential equations. However, due to insufficient robustness, vanilla PINNs often face challenges when solving complex PDEs, especially those involving multi-scale behaviors or solutions with sharp or oscillatory characteristics. To address these issues, based on the projected gradient descent adversarial attack, we proposed an adversarial training strategy for PINNs termed by AT-PINNs. AT-PINNs enhance the robustness of PINNs by fine-tuning the model with adversarial samples, which can accurately identify model failure locations and drive the model to focus on those regions during training. AT-PINNs can also perform inference with temporal causality by selecting the initial collocation points around temporal initial values. We implement AT-PINNs to the elliptic equation with multi-scale coefficients, Poisson equation with multi-peak solutions, Burgers equation with sharp solutions and the Allen-Cahn equation. The results demonstrate that AT-PINNs can effectively locate and reduce failure regions. Moreover, AT-PINNs are suitable for solving complex PDEs, since locating failure regions through adversarial attacks is independent of the size of failure regions or the complexity of the distribution

Cooperative work behavior of high strength concrete-filled square high strength tubular stub columns with inner I-shaped CFRP under axial compression

[EN] The finite element software ABAQUS was used to analyze 22 high strength concrete-filled square high strength tubular short columns with inner I-shaped CFRP, all analysis results based on the finite element analysis data, six characteristic points were defined in the load-longitudinal strain curve of composite columns. The shared load of core concrete, square steel tube and inner I-shaped CFRP at different height sections of typical specimen corresponding to each characteristic point were analyzed and the cooperative work behavior of inner I-shaped CFRP, square steel tube and core concrete was analyzed. The results show that the existence of the inner I-shaped CFRP can effectively improve the ultimate bearing capacity composite columns, the middle region I-shaped CFRP sharing more longitudinal load than the end region CFRP and the shared load of concrete at the end region section is bigger than that of middle region section, before the CFRP brittle failure. The longitudinal load of square steel tube does not change with the change of the cross-section height.Li, G.; Yang, Y.; Yang, Z.; Zhan, Z. (2018). Cooperative work behavior of high strength concrete-filled square high strength tubular stub columns with inner I-shaped CFRP under axial compression. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 281-288. https://doi.org/10.4995/ASCCS2018.2018.6999OCS28128

SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification

Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc. Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset partition approach, which has significant overlap between samples in training and test datasets. Furthermore, our experimental observations indicates that regions with larger overlap often exhibit higher classification accuracy. Consequently, the pixel-wise random sampling approach poses a risk of data leakage. Thus, we propose a block-wise sampling method to minimize the potential for data leakage. Our experimental findings also confirm the presence of data leakage in models such as 2DCNN. Further, We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, to address the challenges associated with hyperspectral image classifier that considers HSI as long sequential three-dimensional images. The model comprises two primary components: axial aggregation attention and multi-level spectral-spatial extraction. The axial aggregation attention mechanism effectively exploits the continuity and correlation among spectral bands at each pixel position in hyperspectral images, while aggregating spatial dimension features. This enables SaaFormer to maintain high precision even under block-wise sampling. The multi-level spectral-spatial extraction structure is designed to capture the sensitivity of different material components to specific spectral bands, allowing the model to focus on a broader range of spectral details. The results on six publicly available datasets demonstrate that our model exhibits comparable performance when using random sampling, while significantly outperforming other methods when employing block-wise sampling partition.Comment: arXiv admin note: text overlap with arXiv:2107.02988 by other author

Nine-Lump Kinetic Study of Catalytic Pyrolysis of Gas Oils Derived from Canadian Synthetic Crude Oil

Catalytic pyrolysis of gas oils derived from Canadian synthetic crude oil on a kind of zeolite catalyst was conducted in a confined fluidized bed reactor for the production of light olefins. The overall reactants and products were classified into nine species, and a nine-lump kinetic model was proposed to describe the reactions based on appropriate assumptions. This kinetic model had 24 rate constants and a catalyst deactivation constant. The kinetic constants at 620°C, 640°C, 660°C, and 680°C were estimated by means of nonlinear least-square regression method. Preexponential factors and apparent activation energies were then calculated according to the Arrhenius equation. The apparent activation energies of the three feed lumps were lower than those of the intermediate product lumps. The nine-lump kinetic model showed good calculation precision and the calculated yields were close to the experimental ones