Kehua Guo

Ximeng Zhai, Bohan Xu, Yaohong Chen et al.

Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as mixing spots in the infrared image. In this paper, we propose a novel task, Sequential CSIST Unmixing, namely detecting all targets in the form of sub-pixel localization from a highly dense CSIST group. However, achieving such precise detection is an extremely difficult challenge. In addition, the lack of high-quality public datasets has also restricted the research progress. To this end, firstly, we contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task, along with the implementation of 23 relevant methods. Furthermore, we propose the Deformable Refinement Network (DeRefNet), a model-driven deep learning framework that introduces a Temporal Deformable Feature Alignment (TDFA) module enabling adaptive inter-frame information aggregation. To the best of our knowledge, this work is the first endeavor to address the CSIST Unmixing task within a multi-frame paradigm. Experiments on the SeqCSIST dataset demonstrate that our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3\%. Our dataset and toolkit are available from https://github.com/GrokCV/SeqCSIST.

Yuhan Cai, Huijun Wu, Zhuo Tang et al.

Burst buffers (BBs) are essential for mitigating I/O bottlenecks in modern HPC systems. However, existing BB file systems often suffer from structural performance degradation due to fixed data layouts that fail to align with diverse application behaviors. While current machine-learning-based optimizations focus primarily on tuning storage stack parameters for a given layout, they offer diminishing returns when a fundamental mismatch exists between I/O patterns and the underlying data organization. Furthermore, these approaches typically incur prohibitive costs due to extensive training or intrusive profiling. To bridge this gap, we present Proteus, a semantic-aware BB system that treats data layout as a first-class optimization dimension. The core insight of Proteus is that application I/O intent can be reconstructed by synergetically combining static code structures with lightweight runtime signals. Through a hybrid pipeline and a single execution probe, Proteus extracts latent semantic cues to determine the optimal layout prior to production runs-eliminating the need for prior training or exhaustive profiling. Evaluation with representative HPC workloads shows that Proteus achieves 91.30\% decision accuracy, delivering up to 3.24$\times$ and 2.9$\times$ speedups for write-intensive and metadata-intensive workloads, respectively.

Sheng Ren, Yan He, Neal N. Xiong et al.

Incremental learning is a form of online learning. Incremental learning can modify the parameters and structure of the deep learning model so that the model does not forget the old knowledge while learning new knowledge. Preventing catastrophic forgetting is the most important task of incremental learning. However, the current incremental learning is often only for one type of input. For example, if the input images are of the same type, the current incremental model can learn new knowledge while not forgetting old knowledge. However, if several categories are added to the input graphics, the current model will not be able to deal with it correctly, and the accuracy will drop significantly. Therefore, this paper proposes a kind of incremental method, which adjusts the parameters of the model by identifying the prototype vector and increasing the distance of the vector, so that the model can learn new knowledge without catastrophic forgetting. Experiments show the effectiveness of our proposed method.