SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations

Due to the prevalence of scale variance in nature images, we propose to use image scale as a self-supervised signal for Masked Image Modeling (MIM). Our method involves selecting random patches from the input image and downsampling them to a low-resolution format. Our framework utilizes the latest advances in super-resolution (SR) to design the prediction head, which reconstructs the input from low-resolution clues and other patches. After 400 epochs of pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to capture scale invariance representation. For the very low resolution (VLR) recognition task, our model achieves the best performance, surpassing DeriveNet by 1.3%. Our method also achieves an accuracy of 74.84% on the task of recognizing low-resolution facial expressions, surpassing the current state-of-the-art FMD by 9.48%

Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature

Recent research has shown that Deep Neural Networks (DNNs) are highly vulnerable to adversarial samples, which are highly transferable and can be used to attack other unknown black-box models. To improve the transferability of adversarial samples, several feature-based adversarial attack methods have been proposed to disrupt neuron activation in middle layers. However, current state-of-the-art feature-based attack methods typically require additional computation costs for estimating the importance of neurons. To address this challenge, we propose a Singular Value Decomposition (SVD)-based feature-level attack method. Our approach is inspired by the discovery that eigenvectors associated with the larger singular values decomposed from the middle layer features exhibit superior generalization and attention properties. Specifically, we conduct the attack by retaining the decomposed Top-1 singular value-associated feature for computing the output logits, which are then combined with the original logits to optimize adversarial perturbations. Our extensive experimental results verify the effectiveness of our proposed method, which significantly enhances the transferability of adversarial samples against various baseline models and defense strategies.The source code of this study is available at \href{https://anonymous.4open.science/r/SVD-SSA-13BF/README.md}

Effectiveness of triple inhalation therapy and non-invasive ventilation in the treatment of acute exacerbated chronic obstructive pulmonary disease

Purpose: To determine the clinical effectiveness of combining triple inhalation therapy with noninvasive ventilation in treating acute exacerbated chronic obstructive pulmonary disease (AECOPD).Methods: A total of 128 AECOPD patients admitted in the Department of Respiratory Medicine of our Hospital were involved in the study. Two groups of patients were used (64 patients per group). The study group was given triple inhalation therapy and non-invasive ventilation, while only non-invasive ventilation was given to the control group. The curative effects of the two treatments and their effects on arterial PaCO2 (partial pressure of carbon dioxide), pH and PaO2 (partial pressure of oxygen) were determined.Results: The study group showed significantly higher treatment effectiveness than the control group (p < 0.05). Post-treatment PaCO2, pH, PaO2, respiratory rate and heart rate differed significantly between the two groups (p < 0.05). Improvements in the five indices were more in the study group than in the control group (p < 0.05).Conclusion: Combining triple inhalation therapy with non-invasive ventilation in the treatment of AECOPD enhances therapeutic effect, improves pulmonary ventilation, and reduces side effects.Keywords: Chronic obstructive pulmonary disease, Acute exacerbation, Triple inhalation, Non-invasive ventilatio