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NVS Reports Positive Late-Stage Data on Kidney Disease Drug

Novartis NVS recently announced positive final results from the late-stage, APPLAUSE-IgAN, study on Fabhalta (iptacopan) in ...

The work AI should really be doing, according to these pros

One of them has to do with the notion of AI that functions as a kind of automated creative partner -- as opposed to, say, merely running in the background and assisting with super-specific and mundane ...
eLife

Bilateral equalization of synaptic output in olfactory glomeruli of Xenopus tadpoles

Laboratory of Neurobiology, Department of Pathology and Experimental Therapy, Institute of Neurosciences, University of Barcelona, Barcelona, Spain ...
The Cornell Daily Sun

Shoes that Survive the Slope: Weighing Fashion With Function

I never leave my dorm without being equipped with bandaids –– a lesson I quickly learned after my feet bore the brunt of my poor shoe choices. Some of my most beloved pairs of shoes have left me with ...
eLife

Time-adaptive modulation of evidence evaluation in rat posterior parietal cortex

This valuable study examined the roles of the posterior parietal cortex in rats performing an auditory change-detection decision task. It provided solid evidence for two subpopulations with opposing ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ...
The Georgia Straight

Top 20 Best AI Newsletters

It’s important for anyone wishing to remain competitive in the rapidly advancing areas of machine learning and artificial intelligence (AI) to keep pace with the latest industry news and trends.
IEEE

AlphaGrad: Normalized Gradient Descent for Adaptive Multi-Loss Functions in EEG-Based Motor Imagery Classification

Abstract: In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) ...
IEEE

A Fixed-Time Proximal Gradient Neurodynamic Network With Time-Varying Coefficients for Composite Optimization Problems and Sparse Optimization Problems With Log-Sum Function

Abstract: This article presents a novel proximal gradient neurodynamic network (PGNN) for solving composite optimization problems (COPs). The proposed PGNN with time-varying coefficients can be ...