Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects the rights of users requesting unlearning, it also introduces new privacy risks. Prior works have primarily focused on the privacy of data that has been unlearned, while the risks to retained data remain largely unexplored. To address this gap, we focus on the privacy risks of retained data and, for the first time, reveal the vulnerabilities introduced by machine unlearning under the dual-view setting, where an adversary can query both the original and the unlearned models. From an information-theoretic perspective, we introduce the concept of privacy knowledge gain and demonstrate that the dual-view setting allows adversaries to obtain more information than querying either model alone, thereby amplifying privacy leakage. To effectively demonstrate this threat, we propose DVIA, a Dual-View Inference Attack, which extracts membership information on retained data using black-box queries to both models. DVIA eliminates the need to train an attack model and employs a lightweight likelihood ratio inference module for efficient inference. Experiments across different datasets and model architectures validate the effectiveness of DVIA and highlight the privacy risks inherent in the dual-view setting.

Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D structures, they still overlook simultaneous modeling of global sequence semantic features and local topological structural features within drugs and proteins, and represent drugs as flat sequences without atomic-level, substructural-level, and molecular-level multi-scale features. We propose HiF-DTA, a hierarchical network that adopts a dual-pathway strategy to extract both global sequence semantic and local topological features from drug and protein sequences, and models drugs multi-scale to learn atomic, substructural, and molecular representations fused via a multiscale bilinear attention module. Experiments on Davis, KIBA, and Metz datasets show HiF-DTA outperforms state-of-the-art baselines, with ablations confirming the importance of globallocal extraction and multi-scale fusion.

Positron emission tomography (PET) is a nuclear medical imaging technique where image quality depends on the dose of radionuclides administered to the patient. While standard-dose PET (SPET) offers high-quality imaging, it also poses radiation risks. If reconstructing low-dose PET (LPET) images can guarantee the same level of imaging quality, LPET could serve as a safer alternative by reducing patient radiation exposure. Recent approaches using convolutional neural networks (CNNs) struggle to capture complex features of LPET images due to the fixed convolutional kernels and local receptive fields. Generative adversarial network (GAN)-based methods may suffer from mode collapse and unstable training. Diffusion model-based works can ensure stability and high-quality sample generation but come with high computational costs. Furthermore, all these methods focus solely on spatial domain information, neglecting crucial frequency domain information.To address these issues, we propose an efficient residual-based LPET reconstruction framework combining CNN and diffusion model. Our framework includes a multi-scale dynamic convolution network with a wavelet reconstruction module (MN-WR) to leverage multi-scale spatial and frequency domain information. We also introduce a residual-based diffusion model (ReD) to enhance local detail reconstruction while reducing computational costs. Finally, we design a wavelet-based frequency domain fusion (WFF) module to combine low-frequency edge information from MN-WR with high-frequency details from ReD. Experimental results on public LPET datasets show that our method outperforms state-of-the-art techniques and significantly reduces computational costs. Code is available at https://github.com/TAI-Medical-Lab/LPET-Reconstruction.

Embodied AI represents systems where AI is integrated into physical entities. Multimodal Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BadRobot, the first attack paradigm designed to jailbreak robotic manipulation, making embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge's flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BadRobot's attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BadRobot. We emphasize that addressing this emerging vulnerability is crucial for the secure deployment of LLMs in robotics. Warning: This paper contains harmful AI-generated language and aggressive actions.

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.

Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous driving and robotic manipulation. Recent research has increasingly explored adversarial attacks on VLMs to reveal their vulnerabilities. However, these attacks either rely on overly strong assumptions, requiring full knowledge of the victim VLM, which is impractical for attacking VLM-based agents, or exhibit limited effectiveness. The latter stems from disrupting most semantic information in the image, which leads to a misalignment between the perception and the task context defined by system prompts. This inconsistency interrupts the VLM's reasoning process, resulting in invalid outputs that fail to affect interactions in the physical world. To this end, we propose a fine-grained adversarial attack framework, AdvEDM, which modifies the VLM's perception of only a few key objects while preserving the semantics of the remaining regions. This attack effectively reduces conflicts with the task context, making VLMs output valid but incorrect decisions and affecting the actions of agents, thus posing a more substantial safety threat in the physical world. We design two variants of based on this framework, AdvEDM-R and AdvEDM-A, which respectively remove the semantics of a specific object from the image and add the semantics of a new object into the image. The experimental results in both general scenarios and EDM tasks demonstrate fine-grained control and excellent attack performance.

MOTIVATION: Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids' structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody-antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody-antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody-antigen Binding affinity prediction. RESULTS: MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody-antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody-antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies. AVAILABILITY AND IMPLEMENTATION: Datasets we used and source code are available on our public GitHub repository https://github.com/TAI-Medical-Lab/MVSF-AB.

Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is vital for drug discovery. Recently, deep learning methods have emerged as a significant approach for estimating the binding strength between drugs and target proteins. However, existing methods simply utilize the drug’s local information from molecular topology rather than global information. Additionally, the features of drugs and proteins are usually fused with a simple concatenation operation, limiting their effectiveness. To address these challenges, we proposed ViDTA, an enhanced DTA prediction framework. We introduce virtual nodes into the Graph Neural Network (GNN)-based drug feature extraction network, which acts as a global memory to exchange messages more efficiently. By incorporating virtual graph nodes, we seamlessly integrate local and global features of drug molecular structures, expanding the GNN’s receptive field. Additionally, we propose an attention-based linear feature fusion network for better capturing the interaction information between drugs and proteins. Experimental results evaluated on various benchmarks including Davis, Metz, and KIBA demonstrate that our proposed ViDTA outperforms the state-of-the-art baselines.