Coordinate denoising has emerged as a promising method for 3D molecular pretraining due to its theoretical connection to learning molecular force field. However, existing denoising methods rely on oversimplied molecular dynamics that assume atomic motions to be isotropic and homoscedastic. To address these limitations, we propose a novel denoising framework AniDS: Anisotropic Variational Autoencoder for 3D Molecular Denoising. AniDS introduces a structure-aware anisotropic noise generator that can produce atom-specific, full covariance matrices for Gaussian noise distributions to better reflect directional and structural variability in molecular systems. These covariances are derived from pairwise atomic interactions as anisotropic corrections to an isotropic base. Our design ensures that the resulting covariance matrices are symmetric, positive semi-definite, and SO(3)-equivariant, while providing greater capacity to model complex molecular dynamics. Extensive experiments show that AniDS outperforms prior isotropic and homoscedastic denoising models and other leading methods on the MD17 and OC22 benchmarks, achieving average relative improvements of 8.9% and 6.2% in force prediction accuracy. Our case study on a crystal and molecule structure shows that AniDS adaptively suppresses noise along the bonding direction, consistent with physicochemical principles. Our code is available at https://github.com/ZeroKnighting/AniDS.

The representation form of multimodal motion prediction results is critical to the efficiency of the prediction-planning workflow. However, most existing methods generate multiple sets of trajectories for each independent agent to cover diverse modalities, overlooking interactions among agents during the prediction period, making it challenging to achieve scene-level consistency in multi-agent predictions. This paper introduces MliG, a scene-level multimodal conditional motion prediction framework based on Multi-Layer Interaction Graph. The interaction graph designed explicitly represent the interaction behaviors of multi-agents over future periods, guiding conditional trajectory prediction to achieve planning-friendly prediction result representation. First, an Interaction-Mask-based determination strategy is introduced to obtain ground truth interaction labels, enabling data-driven implicit relationship learning, and the generated training labels can support the accurate construction of the interaction graph. Second, scene simplification and modality decoupling using the interaction graph make it easier to fuse information and represent driving scenarios efficiently. Based on the multi-layer design, the complex interactions between agent pairs are decomposed into different group and modality layers, ensuring the diversity of interactions while improving the efficiency of conditional predictions. Extensive experiments on the Argoverse 2, Argoverse 1, and INTERACTION Datasets demonstrate that the proposed MliG achieves high prediction accuracy and significantly improves the scene-level consistency of trajectory prediction modalities for multi-agents in the same driving scenario.

The accuracy and stability of motion prediction are crucial for the safe planning of autonomous driving systems. The widely used attention mechanisms effectively improve prediction accuracy. However, their computational cost grows quadratically with sequence length, presenting challenges for handling complex, large-scale scenarios. The attention patterns of motion prediction tasks exhibit significant data-related sparsity, indicating that not all scene elements are worth interaction. Therefore, this paper proposes a novel motion prediction framework that enhances the efficiency and stability of interaction fusion while achieving promising prediction accuracy. Firstly, an adaptive self-distillation attention module based on sparse interaction graph is designed. This module adaptively filters high-value sequences for each target to achieve sparse attention calculation and balance scene scale. Due to the lack of interaction labels in the original dataset, a self-distillation strategy is employed for model training. Secondly, a multi-stage dynamic anchor decoder is introduced that leverages the information filtering and aggregation capabilities of the sparse attention mechanism to improve prediction accuracy. The decoder dynamically updates the target anchor representations as the prediction progresses, ensuring consistency between interaction states and the prediction process in long-term forecasting. This approach effectively focuses attention calculation on the most relevant scene context fusion and trajectory decoding at each prediction stage. Validation results on Argoverse 1 and Argoverse 2 demonstrate that the proposed method achieves competitive accuracy while effectively reducing computational resource consumption. The proposed method can also serve as a plug-in that can be seamlessly incorporated into standard motion prediction pipelines to optimize scene interaction, making it more friendly to low-cost devices.

Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degradation modeling, inclusive of data formats and evaluative benchmarks, is conspicuously absent. Recognizing these impediments, we present BatteryML—a one-step, all-encompass, and open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering an environment where experts from diverse specializations can collaboratively contribute, thus elevating the collective understanding and advancement of battery research.