Machine Unlearning by Suppressing Sample Contribution

Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten". In this paper, we start from the fundamental distinction between training data and unseen data on their contribution to the model: the training data contributes to the final model while the unseen data does not. We theoretically discover that the input sensitivity can approximately measure the contribution and practically design an algorithm, called MU-Mis (machine unlearning via minimizing input sensitivity), to suppress the contribution of the forgetting data. Experimental results demonstrate that MU-Mis outperforms state-of-the-art MU methods significantly. Additionally, MU-Mis aligns more closely with the application of MU as it does not require the use of remaining data

Learning boosted asymmetric classifiers for object detection

http://ieeexplore.ieee.orgObject detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods

Low-Dimensional Gradient Helps Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominantly investigated the disparity between in-distribution (ID) and OOD data through forward information analysis, the discrepancy in parameter gradients during the backward process of DNNs has received insufficient attention. Existing studies on gradient disparities mainly focus on the utilization of gradient norms, neglecting the wealth of information embedded in gradient directions. To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection. The primary challenge arises from the high dimensionality of gradients due to the large number of network parameters. To solve this problem, we propose performing linear dimension reduction on the gradient using a designated subspace that comprises principal components. This innovative technique enables us to obtain a low-dimensional representation of the gradient with minimal information loss. Subsequently, by integrating the reduced gradient with various existing detection score functions, our approach demonstrates superior performance across a wide range of detection tasks. For instance, on the ImageNet benchmark, our method achieves an average reduction of 11.15% in the false positive rate at 95% recall (FPR95) compared to the current state-of-the-art approach. The code would be released

Traffic sign detection using a cascade method with fast feature extraction and saliency test

Automatic traffic sign detection is challenging due to the complexity of scene images, and fast detection is required in real applications such as driver assistance systems. In this paper, we propose a fast traffic sign detection method based on a cascade method with saliency test and neighboring scale awareness. In the cascade method, feature maps of several channels are extracted efficiently using approximation techniques. Sliding windows are pruned hierarchically using coarse-to-fine classifiers and the correlation between neighboring scales. The cascade system has only one free parameter, while the multiple thresholds are selected by a data-driven approach. To further increase speed, we also use a novel saliency test based on mid-level features to pre-prune background windows. Experiments on two public traffic sign data sets show that the proposed method achieves competing performance and runs 27 times as fast as most of the state-of-the-art methods

Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

As data become increasingly vital for deep learning, a company would be very cautious about releasing data, because the competitors could use the released data to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, imperceptible perturbations could be added to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek adversarial examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by modeling checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures in conventional ensemble. That is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small $\ell_\infty=2/255$ perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56\% to 14.68\%, compared to 41.35\% by the best-known method.Code is available at https://github.com/Sizhe-Chen/SEP

Distinct lipid membrane interaction and uptake of differentially charged nanoplastics in bacteria

Background Nanoplastics have been recently found widely distributed in our natural environment where ubiquitously bacteria are major participants in various material cycles. Understanding how nanoplastics interact with bacterial cell membrane is critical to grasp their uptake processes as well as to analyze their associated risks in ecosystems and human microflora. However, little is known about the detailed interaction of differentially charged nanoplastics with bacteria. The present work experimentally and theoretically demonstrated that nanoplastics enter into bacteria depending on the surface charges and cell envelope structural features, and proved the shielding role of membrane lipids against nanoplastics. Results Positively charged polystyrene nanoplastics (PS-NH2, 80 nm) can efficiently translocate across cell membranes, while negatively charged PS (PS-COOH) and neutral PS show almost no or much less efficacy in translocation. Molecular dynamics simulations revealed that the PS-NH2 displayed more favourable electrostatic interactions with bacterial membranes and was subjected to internalisation through membrane penetration. The positively charged nanoplastics destroy cell envelope of Gram-positive B. subtilis by forming membrane pore, while enter into the Gram-negative E. coli with a relatively intact envelope. The accumulated positively charged nanoplastics conveyed more cell stress by inducing a higher level of reactive oxygen species (ROS). However, the subsequently released membrane lipid-coated nanoplastics were nearly nontoxic to cells, and like wise, stealthy bacteria wrapped up with artifical lipid layers became less sensitive to the positively charged nanoplastics, thereby illustrating that the membrane lipid can shield the strong interaction between the positively charged nanoplastics and cells. Conclusions Our findings elucidated the molecular mechanism of nanoplastics’ interaction and accumulation within bacteria, and implied the shielding and internalization effect of membrane lipid on toxic nanoplastics could promote bacteria for potential plastic bioremediation. Graphical Abstrac

Interferon-Inducible Cholesterol-25-Hydroxylase Inhibits Hepatitis C Virus Replication via Distinct Mechanisms

Cholesterol 25-hydroxylase (CH25H) as an interferon-stimulated gene (ISG) has recently been shown to exert broad antiviral activity through the production of 25-hydroxycholesterol (25HC), which is believed to inhibit the virus-cell membrane fusion during viral entry. However, little is known about the function of CH25H on HCV infection and replication and whether antiviral function of CH25H is exclusively mediated by 25HC. In the present study, we have found that although 25HC produced by CH25H can inhibit HCV replication, CH25H mutants lacking the hydroxylase activity still carry the antiviral activity against HCV but not other viruses such as MHV-68. Further studies have revealed that CH25H can interact with the NS5A protein of HCV and inhibit its dimer formation, which is essential for HCV replication. Thus, our work has uncovered a novel mechanism by which CH25H restricts HCV replication, suggesting that CH25H inhibits viral infection through both 25HC-dependent and independent events

A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery

Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF; support vector machine, SVM; artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM); FS showed statistically significant improvements except for ANN-based MSMAL; SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN; for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN; based on the feature subsets, significant differences were observed for MSMAL and CSML using any pair of MLAs. In general, the proposed approach can contribute to LCM in complex surface-mined and agricultural landscapes

J. Catal.

We compared the efficacies of urea + hydrogen peroxide (U+HP) and hydrogen peroxide (HP) as an oxidizing agent in the epoxidation of propylene catalyzed by a titanium silicate-1 (TS-1) molecular sieve. The TS-1 catalyst exhibited good performance and stability in the TS-1/(U+HP) system. EPR results showed that more active Ti-superoxo species led to better performance of TS-1 in the TS-1/(U+HP) system than in the TS-1/HP system. (c) 2008 Elsevier Inc. All rights reserved.We compared the efficacies of urea + hydrogen peroxide (U+HP) and hydrogen peroxide (HP) as an oxidizing agent in the epoxidation of propylene catalyzed by a titanium silicate-1 (TS-1) molecular sieve. The TS-1 catalyst exhibited good performance and stability in the TS-1/(U+HP) system. EPR results showed that more active Ti-superoxo species led to better performance of TS-1 in the TS-1/(U+HP) system than in the TS-1/HP system. (c) 2008 Elsevier Inc. All rights reserved