The practical deployment of Federated Learning (FL) is hampered by a trilemma of security, privacy, and efficiency. Existing solutions address these challenges in isolation, often creating conflicting, suboptimal trade-offs. We propose FedTrim, a unified framework that synergistically resolves this trilemma. The core innovation is an in-TEE model debloating mechanism, where model updates are atomically pruned for efficiency and then perturbed with local differential privacy for formal guarantees, all within a client-side Trusted Execution Environment (TEE). The entire process is remotely attested to ensure training integrity and thwart attacks such as freeriding. FedTrim transforms these conflicting requirements into a synergistic, verifiable solution, providing a robust foundation for practical and trustworthy FL applications.

Closed Set Domain Adaptation (CSDA) assumes identical class sets between source and target domains and is an important solution for reducing domain bias. Compared to CSDA, Open Set Domain Adaptation (OSDA) is closer to realworld applications by allowing unknown class samples in the target domain. In addition, previous OSDA methods mainly rely on similarity detection between the target and source domains to identify unknown classes, which does not fully capture the characteristics of the target domain. To address these limitations, this letter proposes an open set domain adaptation method integrating entropy-guided weighted adversarial networks and contrastive self-supervised learning for hyperspectral image (HSI) classification. The approach introduces an entropy-guided weighted adversarial network to distinguish between known and unknown classes in the target domain, while weighing their importance for aligning the feature distributions. Contrastive self-supervised learning is introduced to learn the intrinsic structure and discriminative features of the target domain from unlabeled target domain data. Experimental validation on two HSI cross-domain datasets demonstrates significant performance improvements over existing methods.

With the rapid advancement of hyperspectral imaging technology, there has been a dramatic surge in the volume of hyperspectral data, presenting unprecedented challenges for the incremental classification of hyperspectral images (HSI). In cross-scene HSI incremental classification, two critical challenges emerge: (1) the effectiveness of traditional sample replay methods is limited by high-dimensional data storage and privacy protection constraints; and (2) diverse scenes lead to catastrophic forgetting during new task learning. To address these challenges, this paper proposes an Incremental Classification algorithm based on Dual Constraints and Knowledge Transfer (IC-DCKT) for cross-scene hyperspectral images. First, to overcome the limitations of traditional replay methods in data storage and privacy protection, IC-DCKT innovatively introduces a sample-free storage approach. By combining regularization with knowledge distillation techniques, it achieves efficient knowledge transfer and retention. Second, to effectively mitigate catastrophic forgetting, the algorithm implements a dual-constraint mechanism: the approximate Null Space Projection Constraint (NSPC) restricts gradient update directions to preserve historical task feature distributions, while the Cosine Similarity Distillation Constraint (CSDC) enforces feature alignment between old and new models, significantly enhancing the model’s ability to retain knowledge of old tasks. Finally, this paper pioneers the integration of the pretrained hyperspectral large model HyperSIGMA into an incremental learning framework. The Feature Alignment Loss (FAL) not only improves the speed of new task learning but also compensates for the loss of critical information in new tasks caused by gradient update direction constraints imposed by NSPC. Experimental results on three hyperspectral datasets demonstrate that the proposed IC-DCKT method outperforms existing state-of-the-art incremental learning approaches.

Meta-learning has demonstrated significant potential in addressing the limited annotation data challenge in hyperspectral target detection. However, existing meta-learningbased methods face two major challenges: 1) weak inter-task correlation leading to unstable optimization directions, and 2) meta-knowledge adaptation based on single prior spectral information fails to effectively characterize spectral variation properties of targets in new scenarios. To overcome these challenges, this letter proposes a cooperative meta-learning framework with spectral diversity adaptation for hyperspectral target detection. The framework introduces a co-learner through cooperative learning to dynamically capture cross-task knowledge and stabilize optimization directions, while designing a spectral diversity-based meta-knowledge adaptation strategy to enhance the model’s ability to understand the spectral variation characteristics of targets in new scenarios and precisely distinguish the spectral features between targets and backgrounds. Experimental results on two public datasets demonstrate that the proposed method outperforms state-of-the-art hyperspectral target detection algorithms. The code is available at https://github.com/Li-ZK/CMLSDA.

Various remote sensing applications have widely used domain adaptation (DA) methods. Since it does not need to add human interpretation in the target domain, it can be used in cross-region, multi-temporal, and multi-sensor application scenarios. In order to further optimize the design of the loss function and better address the challenges of DA in remote sensing, in this paper, we propose a new universal DA method named C3DA for scene recognition of remote sensing images. It has a comprehensive C3criterion for recognizing the "unknown" classes by innovatively fusing confidence, consistency, and certainty of samples to make our network training more efficient. We evaluate the performance of our proposed method based on six transfer tasks on three remote sensing datasets. The evaluation results show that our proposed method achieves an average H-score of 58.44%, significantly higher than other SOTA universal DA methods with an average improvement of 2.32~29.43%. Compared to the baseline ResNet-50, it achieves up to 19.92% improvement, demonstrating that the proposed method outperforms in the universal DA scenario. In the future, we also plan to expand the application of this method to more scenarios.

Molecular dynamics (MD) is one of the most crucial computer simulation methods for understanding real-world processes at the atomic level. Reactive potentials based on the bond order concept have the ability to model dynamic bond breaking and formation with close to quantum mechanical (QM) precision without actually requiring expensive QM calculations. In this article, we focus on the adaptive intermolecular reactive empirical bond-order (AIREBO) potential in LAMMPS for the simulation of carbon and hydrocarbon systems on the new Sunway supercomputer. To achieve scalable performance, we propose a parallel two-level building scheme and periodic buffering strategy for the tailored data design to explore data locality and data reuse. Furthermore, we design two optimized nearest-neighbor access algorithms: the redistribution of accumulated coefficients algorithm and the double-end search connectivity algorithm. Finally, we implement parallel force computation with an AoS data layout and hardware/software co-cache. In addition, we have designed a low-overhead atomic operation-based load balancing method and vectorization. The overall performance of AIREBO achieves a speedup of nearly$20\times$on a single core group (CG), and more than$5\times$and$4\times$over an Intel Xeon E5 2680 v3 core and an Intel Xeon Gold 6138 core, respectively. Compared with the Intel accelerator package in LAMMPS, our performance further achieves$3.0\times$of an Intel Xeon E5 2680 v3 core and is better than that of an Intel Xeon Gold 6138 core. We complete the validation of the results in no more than 20.5 hours on a single node with 2,000,000 running steps (i.e., 1 ns). Our experiments show that the simulation of 2,139,095,040 atoms on 798,720 ((1MPE+64CPEs) × 12,288 processes) cores exhibits a parallel efficiency of 88% under weak scaling.

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, 2X performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly 200--330X compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to 231 atoms. And nanosecond simulations in one day can be realized.