AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,612 papers
Climate tipping points—abrupt, potentially irreversible transitions in Earth subsystems such as the Atlantic Meridional Overturning Circulation (AMOC)— represent one of the most consequential risks in climate science. Existing early-warning indicators based on critical slowing down (rising variance and autocorrelation)...
This article presents the design of a vision-based line-following control strategy for an omnidirectional mobile robot operating on surfaces marked by lines. The proposed approach combines computer vision techniques with a Mamdani-type fuzzy controller to generate motion commands directly from visual feedback. Image ac...
AERF is a modular research framework for governed evaluation of predictive-maintenance models across classification, regression, remaining-useful-life, and physical-edge execution routes. The capsule provides the base framework, campaign executor, model adapters, dataset registry, physical-edge validation utilities, re...
Artificial intelligence is dismantling the long‑standing computational bottlenecks of Earth system modeling. Traditional numerical weather prediction (NWP)—rooted in Navier‑Stokes discretization and dependent on exascale supercomputers—faces scaling limits that make sub‑kilometer global resolution prohibitively expensi...
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The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph...
Short takeaway: Mnemorphics is a mega‑document—a merged research codex—combining acoustic metamaterials, shockwave diodes, energy‑harvesting physics, AI cognitive‑warfare systems, particle‑capture engineering, and large‑scale defensive architecture. It reads like a fusion of hard engineering, corrected physical models,...
TOPO-2026 Validation Framework: Complete Summary Document Overview Title: TOPO-2026 Validation Framework: A Dual-Approach Evaluation Suite for Certified Vision-Language Models Author: Frank Morales Aguilera, BEng, MEng, SMIEEE Affiliation: Sovereign Machine Laboratory (SOMALA), Montreal, Canada Date: August 2026 Core P...
Short takeaway: Mnemorphics is a mega‑document—a merged research codex—combining acoustic metamaterials, shockwave diodes, energy‑harvesting physics, AI cognitive‑warfare systems, particle‑capture engineering, and large‑scale defensive architecture. It reads like a fusion of hard engineering, corrected physical models,...
A simple recursive algorithm is presented that computes, for every temporal clique on $n$ vertices, a temporal $3-spanner of size $n^{1+2/\sqrt{\ln n}}=n^{1+o(1)}$, thereby improving the previous best upper bound of $\widetilde{\mathcal{O}}(n^{3/2})$.
AceSpec, an asymmetric edge-cloud collaborative framework that employs an asymmetric communication protocol that transmits minimal main-chain indices uplink and compact sparse distributions downlink and introduces a network-aware, Lagrangian-optimized resource allocation strategy that dynamically maximizes the local ca...
Photon-number-resolving (PNR) detectors are essential for photonic quantum computing, where a single measurement outcome must reliably herald a specific quantum state. However, detector fidelity is conventionally evaluated using ensemble-averaged statistics obtained from many measurements, which can remain high even wh...
This paper analyzes the DePIN technology stack from six layers: physical infrastructure, blockchain, interaction, trust, incentive, and application, with special attention to their cross-layer feedback loops, implementation readiness, and deployment limitations.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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