Yang Jian

Xinchi Han, Weihao Jiang, Yingming Mao et al.

Recent years have witnessed the growing deployment of optical circuit switches (OCS) in commercial GPU clusters (e.g., Google A3 GPU cluster) optimized for machine learning (ML) workloads. Such clusters adopt a three-tier leaf-spine-OCS topology, servers attach to leaf-layer electronic packet switches (EPSes); these leaf switches aggregate into spine-layer EPSes to form a Pod; and multiple Pods are interconnected via core-layer OCSes. Unlike EPSes, OCSes only support circuit-based paths between directly connected spine switches, potentially inducing a phenomenon termed routing polarization, which refers to the scenario where the bandwidth requirements between specific pairs of Pods are unevenly fulfilled through links among different spine switches. The resulting imbalance induces traffic contention and bottlenecks on specific leaf-to-spine links, ultimately reducing ML training throughput. To mitigate this issue, we introduce a leaf-centric paradigm to ensure traffic originating from the same leaf switch is evenly distributed across multiple spine switches with balanced loads. Through rigorous theoretical analysis, we establish a sufficient condition for avoiding routing polarization and propose a corresponding logical topology design algorithm with polynomial-time complexity. Large-scale simulations validate up to 19.27% throughput improvement and a 99.16% reduction in logical topology computation overhead compared to Mixed Integer Programming (MIP)-based methods.

Li Jinfu, Song Hong, Xia Jianghan et al.

While illumination changes inevitably affect the quality of infrared and visible image fusion, many outstanding methods still ignore this factor and directly merge the information from source images, leading to modality bias in the fused results. To this end, we propose a dynamic multi-level image fusion network called MoCTEFuse, which applies an illumination-gated Mixture of Chiral Transformer Experts (MoCTE) to adaptively preserve texture details and object contrasts in balance. MoCTE consists of high- and low-illumination expert subnetworks, each built upon the Chiral Transformer Fusion Block (CTFB). Guided by the illumination gating signals, CTFB dynamically switches between the primary and auxiliary modalities as well as assigning them corresponding weights with its asymmetric cross-attention mechanism. Meanwhile, it is stacked at multiple stages to progressively aggregate and refine modality-specific and cross-modality information. To facilitate robust training, we propose a competitive loss function that integrates illumination distributions with three levels of sub-loss terms. Extensive experiments conducted on the DroneVehicle, MSRS, TNO and RoadScene datasets show MoCTEFuse's superior fusion performance. Finally, it achieves the best detection mean Average Precision (mAP) of 70.93% on the MFNet dataset and 45.14% on the DroneVehicle dataset. The code and model are released at https://github.com/Bitlijinfu/MoCTEFuse.

Li Xiang, Chen Shuo, Xia Yan et al.

The merits of fast convergence and potentially better performance of the weight normalization family have drawn increasing attention in recent years. These methods use standardization or normalization that changes the weight $\boldsymbol{W}$ to $\boldsymbol{W}'$, which makes $\boldsymbol{W}'$ independent to the magnitude of $\boldsymbol{W}$. Surprisingly, $\boldsymbol{W}$ must be decayed during gradient descent, otherwise we will observe a severe under-fitting problem, which is very counter-intuitive since weight decay is widely known to prevent deep networks from over-fitting. In this paper, we \emph{theoretically} prove that the weight decay term $\frac{1}{2}λ||{\boldsymbol{W}}||^2$ merely modulates the effective learning rate for improving objective optimization, and has no influence on generalization when the weight normalization family is compositely employed. Furthermore, we also expose several critical problems when introducing weight decay term to weight normalization family, including the missing of global minimum and training instability. To address these problems, we propose an $ε-$shifted $L_2$ regularizer, which shifts the $L_2$ objective by a positive constant $ε$. Such a simple operation can theoretically guarantee the existence of global minimum, while preventing the network weights from being too small and thus avoiding gradient float overflow. It significantly improves the training stability and can achieve slightly better performance in our practice. The effectiveness of $ε-$shifted $L_2$ regularizer is comprehensively validated on the ImageNet, CIFAR-100, and COCO datasets. Our codes and pretrained models will be released in https://github.com/implus/PytorchInsight.

Haoming Luo, Xiaotian Yu, Shengxuming Zhang et al.

Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To address these limitations, we propose a self-calibration enhanced framework for whole slide pathology image analysis, comprising three components: a global branch, a focus predictor, and a detailed branch. The global branch initially classifies using the pathological thumbnail, while the focus predictor identifies relevant regions for classification based on the last layer features of the global branch. The detailed extraction branch then assesses whether the magnified regions correspond to the lesion area. Finally, a feature consistency constraint between the global and detail branches ensures that the global branch focuses on the appropriate region and extracts sufficient discriminative features for final identification. These focused discriminative features prove invaluable for uncovering novel prognostic tumor markers from the perspective of feature cluster uniqueness and tissue spatial distribution. Extensive experiment results demonstrate that the proposed framework can rapidly deliver accurate and explainable results for pathological grading and prognosis tasks.