Breast cancer diagnosis using histopathological images is a challenging task due to the scarcity of annotated medical data, particularly for rare cancer stages. Traditional deep learning models struggle to generalize effectively in such low-data scenarios. To address this problem, we propose a few-shot classification framework for breast histopathological images based on metric-based learning. Our approach leverages Vision Transformers (ViTs) for feature extraction, capturing global contextual information better than conventional Convolutional Neural Networks (CNNs). Additionally, we integrate BLIP-2, a Vision Language Model (VLM), to incorporate manual text prompts and contextual textual descriptions, enhancing the model's interpretability and adaptability. The extracted visual and textual features are fused using a novel feature fusion module, and classified the samples based on cosine distance. We evaluated our approach on BreakHis and BACH datasets, showing its effectiveness in few-shot learning (FSL). Our model achieves 57.12% and around 89% in 5-shot setting, respectively, on the BACH and BreakHis datasets. As the number of support samples increases, the performance of the model improves. Our findings suggest that combining transformer-based architectures with VLMs enhances the performance of FSL based medical image classification systems. The code implementation of the methodology is available at MultiModal-FewShot

Erasure coding is an effective technique for guaranteeing data reliability for storage systems, yet it incurs a high repair penalty with amplified repair traffic. The repair becomes more intricate in clustered storage systems with the bandwidth diversity property. We present TPRepair , a T ree-based P ipelined Repair approach, aiming to expedite the overall repair process with the tailored pipelined repair procedure. TPRepair first prioritizes selecting racks with the current minimum load to participate in the repair process. It subsequently formulates tree-based links, tailored to align seamlessly with the pipelined repair procedure. TPRepair further designs an optimization algorithm to reduce the bottleneck load when repairing multiple chunks. Large-scale simulations demonstrate that TPRepair can increase 13.8%–41.3% of the balance ratio without amplifying cross-rack traffic. Meanwhile, Alibaba Cloud ECS experiments indicate that TPRepair can increase repair throughput by 11.3% to 72.9%.

Erasure coding has been demonstrated as a storage-efficient means against failures, yet its tunability remains a challenging issue in data centers, which is prone to induce substantial cross-cluster traffic. In this article, we presentClusterRT, a cluster-aware redundancy transitioning approach that can dynamically tailor the redundancy degree of erasure coding in data centers.ClusterRTformulates the data relocation as the maximum flow problem to reduce cross-cluster data transfers. It then designs a parity-coordinated update algorithm, which gathers the parity chunks within the same cluster and leverages encoding dependency to further decrease the cross-cluster update traffic.ClusterRTfinally rotates the parity chunks to balance the cross-cluster transitioning traffic across the data center. Large-scale simulation and Alibaba Cloud ECS experiments show thatClusterRTreduces 94.0% to 96.2% of transitioning traffic and reduces 70.4% to 88.4% of transitioning time.

Nowadays the practice of researching pseudorandom number generators (PRNG) becomes more scalable because of its spreading in many spheres of computer science and, especially, cybersecurity.The problem is that existing generators are still have many disadvantages in terms of velocity, complexity or flexibility.Thus, the area of researching new algorithms of generating pseudorandom sequences is more than just applicable method, but the target for multiplying cybersecurity from the hardware to application level.This leads to make the set of available and useful PRNG larger and better by their features, like velocity, performance, simplicity in realization.These features match PRNG, based on cellular automata (CA), but not all rules, used in CA are appropriate for their transition functions.Bent functions are perfectly complement statistical weakness of some rules because of their non-linearity without loss of other features.