Legal professionals are essential in analyzing data breach incident reports and guiding the response to comply with data privacy laws and regulations. Their expertise helps mitigate privacy and security risks and prevents failures in privacy compliance. However, little research has been done to understand how legal professionals perceive, react to, and face challenges within the data breach incident reporting procedure. In this study, we conducted a simulated incident report assessment experiment and semi-structured interviews with 33 legal professionals who varied in age, gender, and legal background. We reported the criteria used by legal professionals to identify privacy-related items and also uncovered that the agreement among legal professionals on the concepts of privacy-related items is low. Furthermore, we presented findings regarding the perceptions and strategies of legal professionals concerning legal and regulatory compliance, as well as the key features of incident responses that facilitate efficient analysis of data privacy and security law compliance. After taking into account the challenges and suggestions provided by legal professionals, we concluded this study with recommendations for enhancing the effectiveness of legal compliance analysis for incident responses.

Extensive previous research has focused on post-training knowledge editing (KE) for language models (LMs) to ensure that knowledge remains accurate and up-to-date.One desired property and open question in KE is to let edited LMs correctly handle ripple effects, where LM is expected to answer its logically related knowledge accurately.In this paper, we answer the question of why most KE methods still create messy ripple effects.We conduct extensive analysis and identify a salient indicator, GradSim, that effectively reveals when and why updated knowledge ripples in LMs.GradSim is computed by the cosine similarity between gradients of the original fact and its related knowledge.We observe a strong positive correlation between ripple effect performance and GradSim across different LMs, KE methods, and evaluation metrics.Further investigations into three counter-intuitive failure cases (Negation, Over-Ripple, Multi-Lingual) of ripple effects demonstrate that these failures are often associated with very low GradSim.This finding validates that GradSim is an effective indicator of when knowledge ripples in LMs.The code is available.2

Ultrasound-guided percutaneous interventions have numerous advantages over traditional techniques. Accurate needle placement in the target anatomy is crucial for successful intervention, and reliable visual information is essential to achieve this. However, previous studies have revealed several challenges, such as the variability in needle echogenicity and the common misalignment of the ultrasound beam and the needle. Advanced techniques have been developed to optimize needle visualization, including hardware-based and image-processing-based methods. This paper proposes a novel strategy of integrating ultrasound-based deep learning approaches into an optical navigation system to enhance needle visualization and improve tip positioning accuracy. Both the tracking and detection algorithms are optimized utilizing optical tracking information. The information is introduced into the tracking network to define the search patch update strategy and form a trajectory reference to correct tracking results. In the detection network, the original image is processed according to the needle insertion position and current position given by the optical localization system to locate a coarse region, and the depth-score criterion is adopted to optimize detection results. Extensive experiments demonstrate that our approach achieves promising tip tracking and detection performance with tip localization errors of 1.11 ± 0.59 mm and 1.17 ± 0.70 mm, respectively. Moreover, we establish a paired dataset consisting of ultrasound images and their corresponding spatial tip coordinates acquired from the optical tracking system and conduct real puncture experiments to verify the effectiveness of the proposed methods. Our approach significantly improves needle visualization and provides physicians with visual guidance for posture adjustment.

Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human $\textbf{H}$and-$\textbf{In}$formed visual representation learning framework to solve difficult $\textbf{Dex}$terous manipulation tasks ($\textbf{H-InDex}$) with reinforcement learning. Our framework consists of three stages: $\textit{(i)}$ pre-training representations with 3D human hand pose estimation, $\textit{(ii)}$ offline adapting representations with self-supervised keypoint detection, and $\textit{(iii)}$ reinforcement learning with exponential moving average BatchNorm. The last two stages only modify $0.36$% parameters of the pre-trained representation in total, ensuring the knowledge from pre-training is maintained to the full extent. We empirically study $\textbf{12}$ challenging dexterous manipulation tasks and find that $\textbf{H-InDex}$ largely surpasses strong baseline methods and the recent visual foundation models for motor control. Code and videos are available at https://yanjieze.com/H-InDex .