Polarization-division multiplexing (PDM) in coherent optical communications enhances system capacity but is vulnerable to various channel distortions including transmitter and receiver in-phase/quadrature (IQ) mismatch, rotation of state of polarization (RSOP), frequency offset (FO) and phase noise (PN), which significantly degrade the system performance. To address these challenges, we propose a novel approach using sequential number-theoretic optimization (SNTO) for the joint estimation of these distortions. We further introduce a decision-aided scheme with a window-split structure to accurately track and compensate for time-varying RSOP and PN, thereby implementing signal detection. Through comprehensive mean squared error (MSE) and bit error rate (BER) analysis under different signal-to-noise ratio (SNR) conditions and varying RSOP speeds, our method demonstrates high precision and effectiveness. The SNTO-based algorithm maintains robust performance with superior estimation accuracy and resilience against ultra-fast RSOP. This work introduces an innovative solution for high-dimensional channel equalization in coherent optical PDM systems with not only effectiveness but also robustness.

Computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally employ attention mechanisms to aggregate instance-level features. However, the weakly supervised signal and the imbalanced instance distribution often lead to inaccurate attention localization, compromising the performance and generalization capability of the MIL framework. To address these problems, this paper presents a novel MIL framework called FAMIL that focuses on inaccurate attention and refines them. FAMIL adopts a dual-branch structure and incorporates two innovative online data augmentation strategies: attention-based Mixup (ABMix) and attention-based Masking (ABMask). ABMix emphasizes the significance of positive instances, generalizing Mixup in the MIL scenarios, while ABMask flexibly identifies challenging positive instances to optimize the feature representation. Moreover, these two methods are plug-and-play and can be easily embedded into attention-based MIL methods. Extensive experiments on three public benchmarks demonstrate the superiority of our FAMIL, outperforming current state-of-the-art methods. The test AUC for the binary tumor classification can be up to 92.61% over CAMELYON16. And the AUC over the cancer subtype classification can be up to 93.81% and 98.41% on TCGA-NSCLC and TCGA-RCC datasets, respectively.

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.

Recent research has shown that deep learning networks are vulnerable to adversarial samples. Although there has been great progress in the study of adversarial attacks on images, there is relatively little research on adversarial attacks in the video domain, especially on intrinsic factors of videos, such as motion blur. In this paper, we devise a novel Grad-Weighted based One-step Motion Blur Attack (GWO-MBA) and a Discrete-Fusion based Progressive Motion Blur Attack (DFP-MBA) for video recognition, starting from the idea of integrating global adversarial attacks and adversarial patch attacks. Concretely, we use gradient maps to filter and weighted fusion motion blur (termed GWO-MBA) to achieve the attack that matches the motion information in the context of the video. In order to make the generated motion blur attack perturbations more natural and improve the attack success rate, we further introduce a progressive decomposition motion blur strategy (termed DFP-MBA) to progressively fuse more realistic discrete motion blurs. Besides, we propose an Aggressive Motion Blur Generation (AMBG), which generates natural motion blur based on the video context and has a better attack effect. The extensive experiments, on the HMDB-51 and UCF-101 datasets, demonstrate the effectiveness and superiority of our proposed attack method. In addition, the attack effectiveness of the mainstream denoising defense model and the deblur model further validates the robustness of our attack method.

In recent years, the application of video Internet of Things (IoT) in various cities and public places has brought unprecedented opportunities to the security field and achieved great success. However, the latest research shows that video recognition models are also vulnerable to adversarial examples, but adversarial examples based on physical attacks are easily detected by humans, making it difficult to pass human review. To address this problem, in this article, we propose to introduce a novel multigranular spatiotemporal attention network (MSANet), which can attack the video action recognition models imperceptibly. Specifically, to exploit video motion information more effectively and to reduce the detectability of attack perturbations, we design a multiplexed spatiotemporal attention module to select and enhance spatial regions and temporal frames at coarse-grained and fine-grained levels, respectively, thus maintaining a certain degree of smoothness while reducing the perturbation size and avoiding attacking overfitting. In addition, our proposed MSANet achieves imperceptible perturbations to video sequences through alternate iterative optimization combined with the PGD attack mechanism. extended experimental results on two different models (e.g., TDN and TSM) and two widely used data sets [HMDB-51 (Kuehne et al., 2011) and UCF-101 (Soomro et al., 2012)], compared to the state-of-the-art model, demonstrate the effectiveness of our devised video action recognition attack approach.

2D convolutional neural network, due to its low computational complexity and fast recognition speed, has attracted more and more attention from researchers in the field of video action recognition. Temporal shift and temporal differential, have made tremendous progress, but the lack of crucial spatiotemporal attention mechanism has led to huge performance loss. To address this issue, we propose a Temporal-guided Multiattention Network (TMN), which fully excavate and fuse spatio-temporal attention information for effective video action recognition. Concretely, the multi-attention module squeezes and expands spatio-temporal features to achieve weighting of corresponding regions for video in spatio-temporal dimensions, while the adaptive temporal guidance module imports temporal guiding signal to the spatial attention and re-weight the global temporal attention to accomplish the accurate temporal modeling. Extensive experiments and analyses show that our proposed temporal-guided multiattention network can achieve state-of-the-art promising video action recognition performance on the widely used benchmarks (HMDB51, UCF101 and Something-Something V1).