Network Recovery
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Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach
arXiv:2606.07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central challenge is to recover the hidden influence network behind these cascades.
STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing
Announce Type: new Abstract: Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a considerable reduction in the model's accuracy, and thus it is usually followed by a healing process that regains some of the lost accuracy. In this paper, we propose a new healing method, STARFISH, that can recover (most of) the accuracy of any pruned network efficiently.
Generalisation of training-induced recovery in occipital stroke: neurochemical and fMRI correlates
BACKGROUND: Damage to the early visual cortex after an occipital stroke typically results in the loss of conscious vision in the contralateral hemifield. Nonetheless, extensive perceptual training can restore visual motion discrimination in the blind-field. Here, we assessed, in a cohort study, whether improvements transferred to an untrained Gabor detection task and whether awareness within the blind field increased.
Climate network characterization of the AMOC edge state
arXiv:2606.08623v1 Announce Type: new Abstract: The Atlantic Meridional Overturning Circulation (AMOC) has been identified as a tipping element in the Earth system. Under the current climate change scenarios, it is urgent to develop robust methods for determining the probability of future AMOC transitions. Recent studies using an Earth System Model of Intermediate Complexity (EMIC) have revealed the importance of an AMOC edge state, located on the boundary of the attraction basin of the...
Commvault says it's time to rethink resiliency as AI crooks leave victims in a 'dark, dead' state
AI-enabled cybercriminals have better tools and are inflicting more pain on their victims, wiping out virtual machines and hypervisors and leaving infrastructure in a "dark, dead" state after an attack, said Commvault Chief Technology Officer Brian Brockway. "The majority of cyber cases that we've seen in the customer base have moved well beyond the breaking inside, and encrypting and corrupting some of your key files and folders, to taking over control of your entire VM environment, wiping...
MAdam: Metric-Aware Multi-Objective Adam
Announce Type: new Abstract: Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{kingma2015adam}. We show this coupling introduces two systematic gaps between the solver's intent and the optimizer's execution. The first is a \emph{weighting mismatch}: Adam's second-moment denominator entangles the time-varying...
SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
arXiv:2606.08712v1 Announce Type: new Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context. However, these measurements are often noisy, low-resolution, and sparsely sampled, which limits the recovery of fine spatial structure.
Missing Links in Public Email and Covert Networks: A Comparative Evaluation of Link Prediction, Hyperlink Prediction, and ERGM Estimation
arXiv:2605.22606v2 Announce Type: replace Abstract: We study missing-link inference in partially observed networks by systematically comparing dyadic link prediction (LP) with hyperlink prediction (HP) and an estimation-based ERGM comparator. LP serves as the primary baseline, using classical heuristics computed on the observed graph. HP extends this framework by scoring candidate higher-order structures (cliques) via lifted dyadic scores and via the CHEbyshev Spectral HyperlInk pREdictor...
Chronic Stress Exacerbates Long-term Microvascular Network Dysfunction Following Brain Trauma
Background: Preexisting factors are among the strongest predictors of recovery following traumatic brain injury (TBI), with chronic stress closely associated with permanent disability and worse long-term outcomes. While chronic cerebrovascular dysfunction is linked to these poor trajectories, as impaired blood flow regulation drives secondary disease progression, the mechanisms regulating this vascular failure remain incompletely understood. Crucially, how premorbid chronic stress and TBI...