Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: Planning Capability, which involves decomposing complex manipulation instructions into manageable sub-tasks; Affordance Perception, the ability to recognize and interpret the affordances of interactive objects; and Trajectory Prediction, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain’s core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot’s diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities. Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various robotic tasks, highlighting its potential to advance robotic brain capabilities. Project website: RoboBrain.

ABSTRACT Identity‐based fully homomorphic signature (IBFHS) allows untrusted servers to conduct homomorphic evaluations on outsourced data to obtain a new valid signature, ensuring the evaluated output's correctness while significantly simplifying key management. However, the public composability of IBFHS limits its applicability in scenarios requiring restricted verification rights. For example, in cloud storage data audit systems, introducing a designated verifier mechanism ensures that only authorized third‐party auditors (TPAs) can verify data integrity, preventing malicious auditors from tampering with or forging results. To address this limitation, we propose an identity‐based strong designated verifier fully homomorphic signature (IBSDVFHS) scheme, where only an authorized entity can verify the validity of homomorphically evaluated signatures. We establish formal security definitions for IBSDVFHS, including unforgeability, nontransferability, privacy of the signer's identity, and robustness. Furthermore, we propose a specific design of IBSDVFHS with provable security under the small integer solution (SIS) assumption and the learning with errors (LWE) assumption in the random oracle model.

Fine-grained land cover classification from high-resolution remote sensing imagery plays a vital role in urban and environmental monitoring. While existing spatial-domain based approaches achieve notable progress, their performance in complex scenarios remains constrained by insufficient modeling of characteristics. This letter proposes the continuous low-frequency decomposition architecture (CLFDA) to address insufficient cross-domain modeling of multi-scale frequency characteristics in current methods. The architecture introduces frequency domain features through continuous low-frequency decomposition, where each frequency decomposition and enhancement module employ discrete wavelet transform to separate spatial and low-frequency features into low-frequency and high-frequency subbands. Low-frequency features feed back into the encoder for global context, while high-frequency features are routed to the decoder via attention mechanisms for detail refinement, enabling bidirectional spatial-frequency fusion. By integrating convolutional neural networks, vision transformer, and mamba backbones, our CLFDA achieves 2.0% and 3.46% averagemIoUimprovements on the GID-15 and the FUSU datasets, respectively. These consistent performance gains across heterogeneous backbones demonstrate the effectiveness and generalizability of our CLFDA in modeling frequency domain features. The code is at https://github.com/GeoRSAI/CLFDA.

BACKGROUND: Aging is characterized by a gradual breakdown of cellular structures. Nuclear abnormality is a hallmark of progeria in human. Analysis of age-dependent nuclear morphological changes in Caenorhabditis elegans is of great value to aging research, and this calls for an automatic image processing method that is suitable for both normal and abnormal structures. RESULTS: Our image processing method consists of nuclear segmentation, feature extraction and classification. First, taking up the challenges of defining individual nuclei with fuzzy boundaries or in a clump, we developed an accurate nuclear segmentation method using fused two-channel images with seed-based cluster splitting and k-means algorithm, and achieved a high precision against the manual segmentation results. Next, we extracted three groups of nuclear features, among which five features were selected by minimum Redundancy Maximum Relevance (mRMR) for classifiers. After comparing the classification performances of several popular techniques, we identified that Random Forest, which achieved a mean class accuracy (MCA) of 98.69%, was the best classifier for our data set. Lastly, we demonstrated the method with two quantitative analyses of C. elegans nuclei, which led to the discovery of two possible longevity indicators. CONCLUSIONS: We produced an automatic image processing method for two-channel C. elegans nucleus-labeled fluorescence images. It frees biologists from segmenting and classifying the nuclei manually.