The Oracle Inequalities on Simultaneous Lasso and Dantzig Selector in High-Dimensional Nonparametric Regression

During the last few years, a great deal of attention has been focused on Lasso and Dantzig selector in high-dimensional linear regression when the number of variables can be much larger than the sample size. Under a sparsity scenario, the authors (see, e.g., Bickel et al., 2009, Bunea et al., 2007, Candes and Tao, 2007, Candès and Tao, 2007, Donoho et al., 2006, Koltchinskii, 2009, Koltchinskii, 2009, Meinshausen and Yu, 2009, Rosenbaum and Tsybakov, 2010, Tsybakov, 2006, van de Geer, 2008, and Zhang and Huang, 2008) discussed the relations between Lasso and Dantzig selector and derived sparsity oracle inequalities for the prediction risk and bounds on the estimation loss. In this paper, we point out that some of the authors overemphasize the role of some sparsity conditions, and the assumptions based on this sparsity condition may cause bad results. We give better assumptions and the methods that avoid using the sparsity condition. As a comparison with the results by Bickel et al., 2009, more precise oracle inequalities for the prediction risk and bounds on the estimation loss are derived when the number of variables can be much larger than the sample size

Training with More Confidence: Mitigating Injected and Natural Backdoors During Training

The backdoor or Trojan attack is a severe threat to deep neural networks (DNNs). Researchers find that DNNs trained on benign data and settings can also learn backdoor behaviors, which is known as the natural backdoor. Existing works on anti-backdoor learning are based on weak observations that the backdoor and benign behaviors can differentiate during training. An adaptive attack with slow poisoning can bypass such defenses. Moreover, these methods cannot defend natural backdoors. We found the fundamental differences between backdoor-related neurons and benign neurons: backdoor-related neurons form a hyperplane as the classification surface across input domains of all affected labels. By further analyzing the training process and model architectures, we found that piece-wise linear functions cause this hyperplane surface. In this paper, we design a novel training method that forces the training to avoid generating such hyperplanes and thus remove the injected backdoors. Our extensive experiments on five datasets against five state-of-the-art attacks and also benign training show that our method can outperform existing state-of-the-art defenses. On average, the ASR (attack success rate) of the models trained with NONE is 54.83 times lower than undefended models under standard poisoning backdoor attack and 1.75 times lower under the natural backdoor attack. Our code is available at https://github.com/RU-System-Software-and-Security/NONE

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Naturally fractured gas reservoirs have contributed significantly to global gas reserves and production. The classical gas-well decline analysis relies largely on Arps’ empirical decline models, or modern production decline analysis associating with pseudo-variables. The explicit original gas in place determination methodology is extended from homogeneous reservoir to naturally fractured reservoir under constant or variable bottom-hole pressure conditions in gas-well rate decline analysis. Then, the relationship between gas flow rate and average reservoir pseudo-pressure in the boundary-dominated flow period is re-derived. This formula is in the same format with the equation for homogeneous reservoir by due to the introduction of a new productivity index parameter that captures the inter-porosity flow between fracture and matrix in the natural fractured reservoir. The proposed step-by-step procedures are applied here, which enable the estimation of decline exponent and the explicit and straightforward determination of the original gas in place without any iterative calculations. Four simulated cases prove that our methodology can be successfully used in heterogeneous naturally fractured reservoirs with irregular boundary under constant or variable bottom-hole pressure conditions.Document Type: Original articleCited as: Wang, Y., Wang, J., Zhao, W., Ji, P., Cheng, S., Yu, H. Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis. Advances in Geo-Energy Research, 2023, 9(2): 117-124. https://doi.org/10.46690/ager.2023.08.0

NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models

Prompt-based learning is vulnerable to backdoor attacks. Existing backdoor attacks against prompt-based models consider injecting backdoors into the entire embedding layers or word embedding vectors. Such attacks can be easily affected by retraining on downstream tasks and with different prompting strategies, limiting the transferability of backdoor attacks. In this work, we propose transferable backdoor attacks against prompt-based models, called NOTABLE, which is independent of downstream tasks and prompting strategies. Specifically, NOTABLE injects backdoors into the encoders of PLMs by utilizing an adaptive verbalizer to bind triggers to specific words (i.e., anchors). It activates the backdoor by pasting input with triggers to reach adversary-desired anchors, achieving independence from downstream tasks and prompting strategies. We conduct experiments on six NLP tasks, three popular models, and three prompting strategies. Empirical results show that NOTABLE achieves superior attack performance (i.e., attack success rate over 90% on all the datasets), and outperforms two state-of-the-art baselines. Evaluations on three defenses show the robustness of NOTABLE. Our code can be found at https://github.com/RU-System-Software-and-Security/Notable

Recovery of High-Dimensional Sparse Signals via -Minimization

We consider the recovery of high-dimensional sparse signals via -minimization under mutual incoherence condition, which is shown to be sufficient for sparse signals recovery in the noiseless and noise cases. We study both -minimization under the constraint and the Dantzig selector. Using the two -minimization methods and a technical inequality, some results are obtained. They improve the results of the error bounds in the literature and are extended to the general case of reconstructing an arbitrary signal

Alteration-free and Model-agnostic Origin Attribution of Generated Images

Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement associated with these models. Therefore, it is necessary to analyze the origin of images by inferring if a specific image was generated by a particular model, i.e., origin attribution. Existing methods are limited in their applicability to specific types of generative models and require additional steps during training or generation. This restricts their use with pre-trained models that lack these specific operations and may compromise the quality of image generation. To overcome this problem, we first develop an alteration-free and model-agnostic origin attribution method via input reverse-engineering on image generation models, i.e., inverting the input of a particular model for a specific image. Given a particular model, we first analyze the differences in the hardness of reverse-engineering tasks for the generated images of the given model and other images. Based on our analysis, we propose a method that utilizes the reconstruction loss of reverse-engineering to infer the origin. Our proposed method effectively distinguishes between generated images from a specific generative model and other images, including those generated by different models and real images

Application of Mine Micro-Seismic Monitoring System on Preventing Against Illegal Mining

AbstractThe establishment and application of the Mine Micro-seismic Monitoring System (MMS), provides a powerful method not only to monitor and predict the mine geological hazard, but also to monitor and prevent against the illegal mining. While there was illegal mining, the system can give you the information including the 3-dimensional spatial coordinates in real time.. The monitoring against the illegal mining was aimed at the blast events, so the blast events should be collected and analyzed specially. On the other hand, the energy of the blast events are larger enough to be easily recognized and be 3-d located and then be analyzed by the MMS, and then the satisfied information can be given by the MMS. This kind of usage of the system has been analyzed and confirmed by a practical example