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2,177 papers
Social media platforms are vital spaces for emotional interaction in the age of digital communication, greatly impacting public opinion, behavioral patterns, and group responses to events. It presents EmoFusionNet, a complete and state-of-the-art computational framework created especially to simulate, examine, and fore...
This chapter addresses the challenge of extracting representative scenarios from multiple photovoltaic (PV) stations, where strong uncertainty and spatiotemporal correlations complicate power system optimization. A novel adaptive weighted two-layer graph clustering model is presented. At the local level, an adaptive ne...
This chapter tackles core challenges in multi-site photovoltaic power forecasting, where coarse-resolution numerical weather prediction (NWP) data and decoupled modeling of spatiotemporal dependencies limit performance. While leveraging spatial correlations across sites improves regional forecast reliability, existing...
This work proposes a unified mathematical framework for cultural dynamics that integrates state-space modeling, noncommutative transition operators, correlation structures, and generational modes. The model distinguishes between weakly correlated cultural fields—typical of ancient and pre-modern societies—and strongly...
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Code and raw results reproducing all tables and figures of the manuscript “Federated Graph Neural Networks for Distributed Fraud Detection in Small Cross-Silo Consortia” (submitted to Electronics, MDPI). Includes the FedGNN implementation, experiment runner, figure scripts, results, and README with dataset download ins...
Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes that affects the retina. This study presents a hybrid graph neural network (GNN) architecture that combines a graph isomorphism network (GIN) with GraphSAGE to improve performance and interpretability in diabetic retinopathy (DR) classifi...
The rapid proliferation of high-resolution remote sensing video (RSV) has made object segmentation an increasingly critical yet challenging task. Existing methods face two fundamental limitations in this domain: (1) the intrinsic complexity of RSVs arising from diminutive object sizes, dense distributions, and drastic...
Modern healthcare delivery continues to be con-strained by fragmented digital tools, delayed diagnosis, unmoni-tored drug-drug interactions (DDIs), and insufficient emergency response mechanisms, particularly for users without direct spe-cialist access. This paper presents an AI-Powered Healthcare Assistant—an integrat...
The widespread use of social media has led to a rise in online bullying and abusive language. Huge amounts of content are created on social media daily, creating a challenge for platforms to manually detect and remove offensive posts, making automated detection systems essential. This challenge is even greater in multi...
Settlement prediction for high-fill railway embankments is challenged by monitoring noise and stage-dependent deformation across construction and operation. This paper presents a physics-guided framework integrating multivariate construction information with settlement monitoring data. Monitoring sequences are denoised...
This project develops a unified mathematical and computational framework for cultural dynamics based on noncommutative operators, state-space modeling, and multi-scale correlation structures. Cultural change is represented as the evolution of latent cultural fields that may be weakly correlated (ancient and pre-modern...
# CD-STHGN: Causally Disentangled Supply-chain Temporal Heterogeneous Graph Network A reference implementation and dataset release for green innovation driven digital transformation in Chinese A-share manufacturing supply chains, 2011-2023. [](LICENSE)[!...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
Climate & Sustainability
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