In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.

Deep learning has become an essential tool for classifying medical images. However, label noise remains pervasive in medical datasets and can severely compromise model robustness and generalization, To address this challenge, we propose Dual-channel Generalized Cross Entropy (DC-GCE), a robust optimization strategy that integrates the standard Generalized Cross Entropy (GCE), which prioritizes the target class, with a newly formulated Reverse Generalized Cross Entropy (RGCE) emphasizing non-target classes. This dual-channel design enables the model to suppress predictions on low-probability incorrect classes (non-target suppression) while concurrently mitigating overfitting to mislabeled samples, thereby enhancing robustness to label noise in medical image classification. We comprehensively evaluate DC-GCE on challenging biomedical microscopy benchmarks (PathMNIST and TissueMNIST from MedMNIST v2) across a wide range of symmetric and asymmetric label noise conditions, simulating both random annotation errors and systematic biases. Experimental results demonstrate that DC-GCE consistently outperforms state-of-theart robust learning methods, achieving superior classification accuracy even under extreme noise (e.g., a 2.89 % improvement over the best baseline at 80 % noise), thereby demonstrating state-of-the-art performance in robust medical image classification.

Non-convex optimization poses significant challenges in training deep neural networks, often leading to suboptimal solutions by converging to local rather than global minima. To address this, we propose the Normalized Risk-Averse Cross-Entropy (NRACE) loss function, specifically designed to improve generalization and convergence behavior. NRACE extends the Cross-Entropy loss by introducing a normalized risk-averse term, enhancing the model’s robustness against suboptimal solutions. This term encourages the model to prioritize regions of the parameter space associated with lower risk, thereby guiding optimization toward improved approximations of global optima. Our theoretical analysis demonstrates that NRACE’s convexity region expands monotonically with its risk sensitivity index, providing stronger optimization guarantees than traditional loss functions. Empirical experiments across various benchmark datasets and neural network architectures validate NRACE’s effectiveness. Comparative analysis with the traditional Cross-Entropy loss demonstrates that NRACE improves generalization performance and accelerates convergence, leading to higher-quality solutions.

Embedded and real-time devices in many domains are increasingly dependent on network connectivity. The ability to offload computations encourages Cost, Size, Weight and Power (C-SWaP) optimizations, while coordination over the network effectively enables systems to sense the environment beyond their own local sensors, and to collaborate globally. The promise is significant: Autonomous Vehicles (AVs) coordinating with each other through infrastructure, factories aggregating data for global optimization, and power-constrained devices leveraging offloaded inference tasks. Low-latency wireless (e.g., 5G) technologies paired with the edge cloud, are further enabling these trends. Unfortunately, computation at the edge poses significant challenges due to the challenging combination of limited resources, required high performance, security due to multi-tenancy, and real-time latency. This paper introduces Edge-RT, a set of OS extensions for the edge designed to meet the end-to-end (packet reception to transmission) deadlines across chains of computations. It supports strong security by executing a chain per-client device, thus isolating tenant and device computations. Despite a practical focus on deadlines and strong isolation, it maintains high system efficiency. To do so, Edge-RT focuses on per-packet deadlines inherited by the computations that operate on it. It introduces mechanisms to avoid per-packet system overheads, while trading only bounded impacts on predictable scheduling. Results show that compared to Linux and EdgeOS, Edge-RT can both maintain higher throughput and meet significantly more deadlines both for systems with bimodal workloads with utilization above 60%, in the presence of malicious tasks, and as the system scales up in clients.