Historical document binarization struggles with faded ink, bleed-through, and stains that behave differently across visible and near-infrared wavelengths. This paper proposes a conditional GAN with adaptive cross-spectral feature fusion at encoder depth $L=3$, gated by edge maps derived from a targeted 600 nm visible + 1000 nm NIR pair. On 30 SMADI pages under strict five-fold document-level cross-validation, the method achieves statistically significant gains in $F_1$, pseudo-FM, and IoU over fixed averaging and generic attention, showing that physics-informed structural guidance can regulate multispectral restoration without discarding any spectral band.

Multibeam imaging sonar is widely used for underwater perception, but its intensity images are degraded by coupled coherent speckle and system-induced blur, and clean reflectivity ground truth is almost never available. This paper derives the conditional-mean forward operator $A x = x * |h|^2$ from a stochastic coherent image-formation model and uses it to train a self-supervised reconstruction network from registered multi-frame observations. By projecting one frame's latent reconstruction through $A$ and matching it to a second registered frame, then regularizing with an adapted robust equivariant imaging loss, the framework targets joint despeckling and deblurring without any clean targets. The work matters because it replaces ad hoc intensity-domain speckle models with a physics-consistent inverse problem for real sonar platforms.

Single-channel RF receivers cannot use spatial filtering when a desired signal overlaps with a structured co-channel interferer in both time and frequency, a regime where linear filters fail because the impairment is non-Gaussian. FlowSep-RF addresses this by learning a clean-endpoint rectified flow that maps a raw IQ mixture directly to the target waveform, enabling separation with a single neural function evaluation instead of iterative diffusion sampling. The core idea is to treat the received mixture as the source and the clean signal as the target of a near-straight flow, combining time-domain and frequency-domain features to improve reconstruction fidelity and downstream demodulation accuracy. If the gains hold under broader channel conditions, this could provide a low-latency replacement for score-based separators in practical baseband receivers.

Underwater vessel recognition is limited by scarce training data and small public benchmarks. This paper introduces ONCship, a 4521-hour corpus curated from Ocean Networks Canada hydrophones and Automatic Identification System vessel metadata. It evaluates domain-specific self-supervised pre-training, supplementary supervised fine-tuning, and MMSI-Level individual vessel classification, showing that scale and metadata can improve representations even though downstream gains are architecture-dependent and uneven.

Partial-volume (PV) effects blur tissue boundaries in Magnetic Resonance Fingerprinting (MRF), and conventional pipelines rely on multi-stage dictionary matching or statistical segmentation that propagate errors. DeepPV-MRF proposes an end-to-end deep network that directly maps raw, aliased MRF time-series signals to voxel-wise CSF, GM, and WM fraction maps, bypassing intermediate $T_1$/$T_2$ reconstructions. On simulated data the method reports large accuracy gains over dictionary-matching and pseudoinverse baselines, but its real-world evidence rests on synthetic training labels and a small in vivo cohort without ground truth.

The paper solves zero-shot stitching of autoencoders: enabling an encoder from one independently trained model to work with a decoder from another. The core idea is a training-time topological regularizer based on 0-dimensional persistent homology (MST edges) that preserves the input data's geometric skeleton in each latent space, so that after training only a lightweight affine map is needed. This matters for distributed and privacy-sensitive settings where encoders and decoders cannot be jointly optimized or share weights.

Wrist electrodermal activity differs in amplitude from person to person, so affect-recognition models normalize the signal before classification. A hidden protocol choice is whether the normalization statistics come from training subjects only or from the held-out user's own complete recording, which retroactively gives the model a calibration that a newly worn device lacks. The paper introduces SAFE-EDA, a compact convolutional network pretrained on expert artifact annotations, and shows that its measured benefit over training from scratch ranges from 0.020 to 0.227 depending on this normalization choice, with the larger gains occurring in the realistic first-use setting. The authors also survey fifty published WESAD studies and find only ten percent report the source of their normalization statistics, making published scores difficult to compare.

EEG foundation models are typically pretrained on fixed channel layouts, so they struggle when electrode montages differ at test time. This paper proposes CortexBridge, a lightweight adapter that routes arbitrary scalp montages into a fixed cortical atlas space using electrode coordinates and sparse attention. By injecting this anatomically grounded representation into frozen backbones, the authors show consistent accuracy gains across three models, five datasets, and three BCI paradigms.

This paper tackles the challenge of deploying traffic forecasting models in resource-constrained Wi-Fi controllers that manage thousands of access points (APs). The core idea is to use feature-based clustering (k-means on PCA-reduced features) to group APs by traffic behavior, then deploy cluster-specific LSTM models only to high-activity clusters while using a lightweight global model for low-activity clusters. The approach reduces memory footprint by approximately 40% compared to deploying complex models for all clusters, while preserving prediction accuracy through selective specialization.

The paper addresses adaptive broadcast of data-intensive sensory streams (e.g., camera/LiDAR) to heterogeneous edge devices with diverse channel conditions and computational budgets. It proposes Nonlinear Transform Rateless Source-Channel Coding (NTRSCC), integrating learned nonlinear transforms with physical-layer Luby Transform (LT) codes to enable receivers to adaptively adjust the number of received symbols and belief propagation iterations. This achieves an explicit, controllable tradeoff between distortion, transmission rate, and decoding complexity—addressing key limitations of fixed-rate DeepJSCC schemes that either underserve capable devices or require costly retransmissions.

This paper establishes a comprehensive benchmark for photoplethysmography (PPG)-based clinical prediction using the large-scale MIMIC-III-Ext-PPG dataset, evaluating multi-task learning across arrhythmia classification (13 classes) and physiological regression (blood pressure, heart rate, respiratory rate). The core contribution is demonstrating robust atrial fibrillation detection (AUROC 0.96) with strong cross-dataset generalizability, alongside the first systematic assessment of fine-grained arrhythmia classification from PPG alone. It matters because PPG sensors are ubiquitous in wearables and ICUs, yet standardized, large-scale, multi-task benchmarks have been lacking, hindering meaningful algorithm comparison and clinical deployment.

The paper proposes AV-LR, a lightweight amortized variational inference framework for logistic regression with missing covariates that eliminates latent variables entirely. Unlike VAE-based competitors, it directly models the posterior over missing values using a single neural network coupled with a linear classification layer, enabling joint optimization of imputation and prediction. The approach extends naturally to MNAR settings and claims substantial computational speedups over EM-based methods while maintaining comparable statistical accuracy.

This paper tackles the challenging problem of b-jet tagging at the LHC, particularly the difficult discrimination between bottom-quark jets (b-jets) and charm-quark jets (c-jets). The authors propose ECT (Edge Convolution Transformer), a hybrid deep learning architecture that combines local feature extraction via EdgeConv blocks with global context modeling through transformer self-attention. The work is motivated by the need for real-time flavor tagging in high-level trigger systems, where both accuracy and inference latency are critical.