Ischemic axonal injury up-regulates MARK4 in cortical neurons and primes tau phosphorylation and aggregation.

Ischemic injury to white matter tracts is increasingly recognized to play a key role in age-related cognitive decline, vascular dementia, and Alzheimer's disease. Knowledge of the effects of ischemic axonal injury on cortical neurons is limited yet critical to identifying molecular pathways that link neurodegeneration and ischemia. Using a mouse model of subcortical white matter ischemic injury coupled with retrograde neuronal tracing, we employed magnetic affinity cell sorting with fluorescence-activated cell sorting to capture layer-specific cortical neurons and performed RNA-sequencing. With this approach, we identified a role for microtubule reorganization within stroke-injured neurons acting through the regulation of tau. We find that subcortical stroke-injured Layer 5 cortical neurons up-regulate the microtubule affinity-regulating kinase, Mark4, in response to axonal injury. Stroke-induced up-regulation of Mark4 is associated with selective remodeling of the apical dendrite after stroke and the phosphorylation of tau in vivo. In a cell-based tau biosensor assay, Mark4 promotes the aggregation of human tau in vitro. Increased expression of Mark4 after ischemic axonal injury in deep layer cortical neurons provides new evidence for synergism between axonal and neurodegenerative pathologies by priming of tau phosphorylation and aggregation

Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning

Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handling system heterogeneity, we propose a training scheme that can extend their capabilities to cope with this challenge. In this paper, we commence our study with a detailed exploration of homogeneous and heterogeneous FL settings and discover three key observations: (1) a positive correlation between client performance and layer similarities, (2) higher similarities in the shallow layers in contrast to the deep layers, and (3) the smoother gradients distributions indicate the higher layer similarities. Building upon these observations, we propose InCo Aggregation that leverags internal cross-layer gradients, a mixture of gradients from shallow and deep layers within a server model, to augment the similarity in the deep layers without requiring additional communication between clients. Furthermore, our methods can be tailored to accommodate model-homogeneous FL methods such as FedAvg, FedProx, FedNova, Scaffold, and MOON, to expand their capabilities to handle the system heterogeneity. Copious experimental results validate the effectiveness of InCo Aggregation, spotlighting internal cross-layer gradients as a promising avenue to enhance the performance in heterogenous FL.Comment: Preprint. Under revie

Set Aggregation Network as a Trainable Pooling Layer

Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data. Based on the recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an alternative global pooling layer. In contrast to typical pooling operators, SAN allows to embed a given set of features to a vector representation of arbitrary size. We show that by adjusting the size of embedding, SAN is capable of preserving the whole information from the input. In experiments, we demonstrate that replacing global pooling layer by SAN leads to the improvement of classification accuracy. Moreover, it is less prone to overfitting and can be used as a regularizer.Comment: ICONIP 201

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System