The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

Deep neural networks (DNNs) have immense potential for precise clinical decision-making in the field of biomedical imaging. However, accessing high-quality data is crucial for ensuring the high-performance of DNNs. Obtaining medical imaging data is often challenging in terms of both quantity and quality. To address these issues, we propose a score-based counterfactual generation (SCG) framework to create counterfactual images from latent space, to compensate for scarcity and imbalance of data. In addition, some uncertainties in external physical factors may introduce unnatural features and further affect the estimation of the true data distribution. Therefore, we integrated a learnable FuzzyBlock into the classifier of the proposed framework to manage these uncertainties. The proposed SCG framework can be applied to both classification and lesion localization tasks. The experimental results revealed a remarkable performance boost in classification tasks, achieving an average performance enhancement of 3-5% compared to previous state-of-the-art (SOTA) methods in interpretable lesion localization.

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.

Artificial intelligence-driven automation has gradually become the technical trend of the new automation era. At present, many artificial intelligence technologies have been applied to improve the intelligence level in the field of automation. Among them, convolutional neural network (CNN) technology is one of the most representative, which is used in the detection of defective products in industrial automation, robot human tracking has been widely used in the field of machine vision driven automation. However, the high dependence of the current neural network application leads to the potential failure of the defective product detection system. In this article, we model the learning and decision-making process of CNN with a statistical physical percolation model. Based on the differentiation degree and vulnerability of percolation, we propose the concept of CNN differentiation degree and summarize the empirical formula to quantify it. The relationship between the differentiation degree and vulnerability is analyzed from both adversarial attack and adversarial training perspectives to explain the decision-making mechanism of CNN and classification reliability. The physical model can approach the essence of things and finally guide the reliable CNN for industrial automation.

Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like “walk past the chair”. When starting navigation, the instruction-related action prompt set is retrieved from a prebuilt action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multilayer transformer for action prediction. To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CLIP) model which has powerful cross-modality alignment ability. A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially. Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods.

This article investigates a fuzzy adaptive two-bit-triggered control for uncertain nonlinear systems with actuator failures and dead-zone constraint. Actuator failures and dead-zone constraint exist frequently in practical systems, which will affect the system performance greatly. Based on the improved fuzzy-logic systems (FLSs), a fuzzy adaptive compensation control is established to address these issues. The approximation error is introduced to the control design as a time-varying function. In addition, for the limited transmission resources of the practical system, a two-bit-triggered control mechanism is proposed to further save system transmission resources. It is proved that the proposed method can guarantee the system tracking performance and all the signals are bounded. Its effectiveness is verified by the simulation examples.

This paper considers that CSCW theory is still in immature phase. Effective theory is badly required in order to overcome the chasm in the developmental process. Moreover, CSCW systems consist of many specifications different with most traditional systems. These requirements pose new challenges to the formal description tools. This paper argues that a series of specifications in CSCW systems can be sufficiently expressed based on temporal logic. Then an extended temporal logic CWTL is brought forward on this principle. At the end of the paper, an example is presented showing how to specify a simplified cooperative authoring system with CWTL