This chapter explores how Artificial Intelligence (AI) will support the future of wireless communication systems through advances in 6G networks, IoT ecosystems, and edge computing technologies. Technologies that enhance machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms to improve ne...
Abdulmumini Yakubu Musah, Farida Shehu Garki, Muhammad Aliyu Suleiman et al.· Handbook of Artificial Intel...· 0 citations
In this study, the geometric function that governs the hydraulic conductance of the liquid phase in grooved heat pipes is reformulated as a function of the edge angle. In the H-PAT (Heat pipe analysis toolbox) model, the geometric function is assumed to be constant within each axial slice, which limits the physical acc...
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems—graph construction, representation learning, and real-time serving—yet existing work addresses each in isolation. We present RankGraph-2, a framework deployed at Meta that co-designs all three lifecycle stages for simila...
Renzhi Wu, Zikun Cui, Jie Yang et al.· Proceedings of the 20th ACM...· 0 citations
This is the extended, definitive version (v16.2) of the P0_Distilled research programme, with full technical appendices — it supplements the distilled entry-point paper (P0_Distilled_v0.1) with the complete Lakatosian structure, derivations, and experimental protocol. The programme's hard core is a single metaphysical...
Francesco Iavarone· Zenodo (CERN European Organi...· 0 citations
Most phylogenetic comparative methods use a species-level phylogeny, ignoring the effect of incomplete lineage sorting (ILS) and hemiplasy on the traits of interest. We consider here a trait controlled additively by one or more unknown loci. Their gene trees may differ from the species phylogeny due to ILS. The species...
Cécile Ané, Paul Bastide· Peer Community Journal· 0 citations
Based on the provided documentation, here is a comprehensive summary of the automated interceptor system's architecture, divided into its core subsystems and physical requirements. Core Functional Disciplines An automated short-range or counter-unmanned aerial system (C-UAS) interceptor is split into three foundational...
francis lee· Zenodo (CERN European Organi...· 0 citations
A three-dimensional fractional-order discrete memristive Chialvo neuron map is proposed and investigated using the Grunwald–Letnikov difference scheme. The system couples the classical Chialvo neuron with a flux-controlled memristor under incommensurate fractional orders. Through twin-trajectory Lyapunov exponent compu...
J. Praveena, Karthikeyan Rajagopal, Dianavinnarasi Joseph et al.· International Journal of Bif...· 0 citations
Abstract: Modern automated computing systems increasingly deploy Large Language Models (LLMs) to resolve runtime operational triage, incurring prohibitive latency (>100–500 ms), severe memory allocation (>4–8 GB VRAM), and high thermodynamic dissipation. Extending the foundational theory of Mandelbrot Fractal Neural Sy...
This paper proposes an augmented reality real-time registration system for e-commerce live streaming environments and applies it to an outcome-oriented education (OBE) practical teaching framework. The system employs a vision-inertial tightly coupled state estimation, integrating local bundle adjustment and semantic ma...
Wan-Lan Xie· International Conference on...· 0 citations
Based on the provided documentation, here is a comprehensive summary of the automated interceptor system's architecture, divided into its core subsystems and physical requirements. Core Functional Disciplines An automated short-range or counter-unmanned aerial system (C-UAS) interceptor is split into three foundational...
francis lee· Zenodo (CERN European Organi...· 0 citations
To address image quality degradation, difficulty in recognizing small defect targets, difficulty in quantifying external-damage risks, and limited computing resources of embedded devices in edge inspection of the power Internet of Things (Power IoT), this paper proposes an image enhancement and lightweight object detec...
Zikang Wei· ICST Transactions on Scalabl...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026