Semi-supervised medical image segmentation aims to leverage minimal expert annotations, yet remains confronted by challenges in maintaining high-quality consistency learning. Excessive perturbations can degrade alignment and hinder precise decision boundaries, especially in regions with uncertain predictions. In this paper, we introduce Confidence-Aware Adaptive Displacement (CAD), a framework that selectively identifies and replaces the largest low-confidence regions with high-confidence patches. By dynamically adjusting both the maximum allowable replacement size and the confidence threshold throughout training, CAD progressively refines the segmentation quality without overwhelming the learning process. Experimental results on public medical datasets demonstrate that CAD effectively enhances segmentation quality, establishing new state-of-the-art accuracy in this field. The source code will be released after the paper is published.

Warning: This paper contains text examples that are offensive or harmful in nature. Despite careful safety alignment, current large language models (LLMs) remain vulnerable to various attacks. To further unveil the safety risks of LLMs, we introduce a Safety Concept Activation Vector (SCAV) framework, which effectively guides the attacks by accurately interpreting LLMs' safety mechanisms. We then develop an SCAV-guided attack method that can generate both attack prompts and embedding-level attacks with automatically selected perturbation hyperparameters. Both automatic and human evaluations demonstrate that our attack method significantly improves the attack success rate and response quality while requiring less training data. Additionally, we find that our generated attack prompts may be transferable to GPT-4, and the embedding-level attacks may also be transferred to other white-box LLMs whose parameters are known. Our experiments further uncover the safety risks present in current LLMs. For example, we find that six out of seven open-source LLMs that we attack consistently provide relevant answers to more than 85% malicious instructions. Finally, we provide insights into the safety mechanism of LLMs.

Despite the surprisingly high intelligence exhibited by Large Language Models (LLMs), we are somehow intimidated to fully deploy them into real-life applications considering their black-box nature. Concept-based explanations arise as a promising avenue for explaining what the LLMs have learned, making them more transparent to humans. However, current evaluations for concepts tend to be heuristic and non-deterministic, e.g. case study or human evaluation, hindering the development of the field. To bridge the gap, we approach concept-based explanation evaluation via faithfulness and readability. We first introduce a formal definition of concept generalizable to diverse concept-based explanations. Based on this, we quantify faithfulness via the difference in the output upon perturbation. We then provide an automatic measure for readability, by measuring the coherence of patterns that maximally activate a concept. This measure serves as a cost-effective and reliable substitute for human evaluation. Finally, based on measurement theory, we describe a meta-evaluation method for evaluating the above measures via reliability and validity, which can be generalized to other tasks as well. Extensive experimental analysis has been conducted to validate and inform the selection of concept evaluation measures.