The super-resolution (SR) of Magnetic Resonance Imaging (MRI) is essential for utilizing clinical scans with limited resolution, noise, and anisotropic sampling, such as multi-modal brain tumor imaging. In this work, we propose a Tri-Representation hybrid framework for MRI SR, TRT-GLA, that redefines the MRI SR task as a joint spatial–spectral–structural high-resolution image generation problem. TRT-GLA utilizes (i) spatial global–local attentions for modeling the spatial anatomy, (ii) a Fourier spectral transfer mechanism for upholding spectral consistency, and (iii) multi-scale hierarchical spectral decomposition for improved edge details. To adapt the learning framework to medical imaging characteristics, we introduce a tri-representation consistent loss function that explicitly combines pixel-wise, spectral, and edge structure priors from the high-resolution ground-truth, as well as a progressive resolution learning strategy. Our large-scale brain tumor experiments, on the IXI, BraTS 2019, 2020, and 2023 datasets, show that TRT-GLA achieves state-of-the-art results at upsampling factors of ×2, ×4, and ×8, respectively, achieving substantial improvements across CNN, GAN, and transformer-based methods in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Multi-scale Structural Similarity Index (MS-SSIM). We further demonstrate how SR benefits brain tumor segmentation through the downstream task evaluation of a dual-branch segmentation framework. TRT-GLA produces highly accurate tumor segmentation results from low-resolution inputs, improving over native high-resolution inputs at ×8 in critical tumor boundary regions and in small tumor regions. There remains a small gap between native, high-resolution imaging and SR-enhanced performance, which TRT-GLA nearly closes under realistic scenarios. Our results highlight the importance of synthesizing unified priors over spatial, spectral, and structural domains within a transformer for anatomically faithful reconstructions. Importantly, we also establish the utility of TRT-GLA in supporting quantitative analysis through a downstream tumor segmentation experiment that is clinically relevant.

Intrusion detectors for Internet of Things (IoT)-enabled substations must run on gateways with limited memory, yet the strongest published detectors are heavy tree ensembles. This study compresses a five-member soft-voting ensemble into compact multilayer perceptron (MLP) and gradient-boosted decision tree (GBDT) students on Distributed Network Protocol 3 (DNP3) flows (six attack families) and on phasor measurement unit (PMU) records of the MSU/ORNL dataset (attack versus natural events). Students were trained with class-weighted cross-entropy, weighting-only controls, and standard, minority-aware (MA-KD) and balanced distillation, and evaluated under stratified, chronological, and capture-held-out protocols. A class-weighted GBDT matched the teacher on both layers (macro-F1 0.986 versus 0.987 on DNP3; 0.908 versus 0.903 on PMU data) with 2.4% and 1.1% of its resident memory and 43.8 and 72.8 times lower single-thread CPU time per record, and stayed within 0.007 macro-F1 of the teacher under the stricter protocols. Distillation did not improve GBDT students. For MLP students, MA-KD was best on the stratified PMU split (0.734 versus 0.718 for standard distillation), whereas on DNP3 square-root class weighting without a teacher was best. Under the stricter protocols, PMU macro-F1 fell to about 0.60–0.63 for the teacher and all compact students (ExtraTrees: 0.53–0.55), identifying generalisation, not model size, as the main open problem.

Network intrusion detection systems (NIDS) increasingly rely on deep learning to cope with evolving cyber attacks, yet they typically require large amounts of labeled data per attack type and exhibit poor generalization to novel threats. Recent advances in meta-learning and few-shot learning have shown promise for alleviating data scarcity, while depthwise separable convolutions (DSC) offer a computationally efficient means of extracting fine-grained spatial–temporal features from network traffic. In this paper, we propose a meta-learning framework for few-shot network intrusion detection that combines a model-agnostic meta-learning (MAML) objective with a one-dimensional DSC backbone. Tasks are defined episodically across attack families and datasets, enabling the meta-learner to acquire an initialization that rapidly adapts to unseen attack types using only a handful of labeled flows. We derive the corresponding bilevel optimization problem, analyze the computational benefits of DSC compared with standard convolutions, and present an end-to-end architecture tailored for flow-based NIDS. Experiments on CICIDS2017, NSL-KDD and UNSW-NB15 under challenging 5-shot and 10-shot settings demonstrate consistent improvements in accuracy and macro-\(F_1\) over strong convolutional, metric-based and attention-enhanced baselines inspired by recent literature. The proposed approach reduces parameters by up to 65% relative to conventional convolutions while maintaining or improving detection performance. We conclude with a discussion of limitations and directions for integrating cross-domain adaptation and self-attention into meta-learned DSC architectures.

Super-resolution (SR) facilitates high-fidelity mapping beyond the spatial resolution capacity of clinical neuro-oncology MRI. While state-of-the-art deep learning algorithms have augmented this domain, such methods exclusively learn from spatial or spectral domains and are often compounded by tumor boundary over-smoothing and geometrically inconsistent pathologies. In this paper, we introduce Hy-DTGL, a hybrid-domain transformer with a global–local attention modal super-resolution (SR) framework that learns a joint reconstruction across four inter-dependent knowledge domains: local spatial, global spatial, spectral and wavelet. Using windowed self-attention, Hy-DTGL learns highly localized pathologic textures; pooled global attention constrains anatomical plausibility; Fourier-learnable modulation provides spectral energy per semblance; and multi-scale discrete wavelet tiling preserves multi-scale tumor morphology. Optimization occurs via a hybrid objective, combining Charbonnier loss, gradient edge-loss, and spectral domain loss. The proposed framework was trained on BraTS 2019 (BT_19) and evaluated on BT_19, BraTS 2020 (BT_20), BraTS 2023 (BT_23), and IXI under both in-domain and strict zero-shot settings, and achieves competitive performance relative to the evaluated baselines under the adopted degradation and evaluation protocol at ×2, ×4, and ×8 SR factors. In the BraTS 2020 ×4 test resolution, 37.46 dB PSNR and 0.986 SSIM are attained, outperforming deep learning CNN-based SR (ECFNet) by 2.47 dB and transformer-based (WavTrans) by 4.05 dB in PSNR. Ablative studies reveal the spatial, spectral and multi-scale constraints notably converge to a faithfully mapped image space with restored tumor boundary delineation and global-structure consistency. We also show the Hy-DTGL generalizability by how it improves downstream tumor segmentation metrics: the super-resolved scans improve the segmentation Dice coefficient by 0.063 and significant reductions are observed in the Hausdorff Distance from 19.05 mm to 13.01 mm relative to the original low-resolution images, suggesting potential utility for downstream neuro-oncological image analysis.