AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,908 papers
Added Power-law noise model (#25): ntpstats noise and ntpstats.noisefit fit h₂…h₋₂ and, optionally, a linear drift to OADEV/MDEV/HDEV curves and the spectrum. The expected estimator values are exact for the discrete Kasdin–Walter model. Model selection uses BIC. Intervals come from a parametric bootstrap. Monte Carlo c...
Added Power-law noise model (#25): ntpstats noise and ntpstats.noisefit fit h₂…h₋₂ and, optionally, a linear drift to OADEV/MDEV/HDEV curves and the spectrum. The expected estimator values are exact for the discrete Kasdin–Walter model. Model selection uses BIC. Intervals come from a parametric bootstrap. Monte Carlo c...
Intelligent decision-making for autonomous flying drones and vehicles requires real-time processing capabilities, while at the same time protecting the privacy of collected data, reducing latency, and guaranteeing effective communication. Centralized machine learning frameworks not only leak sensitive data but also res...
Codes for the semiclassical simulations (solid lines in Figs. 2 and 3): 1) SemiClassicalSimulator.py2) SCSim_header.pyCode to evaluate the quantum diffusion constant (colormap in Fig 4d): DiffusionFromReturnProb.nbCodes for calculation of the mobility edge (white points in Fig. 4d): 1. grscalar.f: Computes eigenvalues...
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The refractive index is a fundamental optical property characteristic of every substance, serving as a distinct identifier that varies predictably as a function of the operational wavelength of light. In traditional experimental physics, measuring this parameter typically relies on manual optical instruments and mechan...
The refractive index is a fundamental optical property characteristic of every substance, serving as a distinct identifier that varies predictably as a function of the operational wavelength of light. In traditional experimental physics, measuring this parameter typically relies on manual optical instruments and mechan...
Bearings are critical components in rotating machinery, and their condition directly determines the operational efficiency and safety of industrial equipment. Traditional bearing fault diagnosis methods rely heavily on manual feature extraction, making it difficult to deal with complex industrial scenarios such as stro...
Purpose: The rapid expansion of research in the field of Knowledge Graphs (KGs) over the past decade has positioned The field as a dynamic area at the intersection of computer science, artificial intelligence. Following Google’s introduction of the KG in 2012, scholars and industry stakeholders have increasingly explor...
The rapid expansion of Internet of things (IoT) devices generates increasing service demands that require reliable computation in regions with limited fixed infrastructure. Uncrewed aerial vehicle (UAV)-based mobile edge computing (MEC) systems provide a flexible solution by relocating computational resources close to...
MORA (Multi-Objective Resource Allocation), a stability-oriented deep reinforcement learning scheduler based on Twin Delayed Deep Deterministic Policy Gradient (TD3), with a normalized multi-objective reward that jointly optimizes energy, latency, SLA adherence, and proactively minimizes container migrations is propose...
Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research...
Education Innovation
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
A Blog post by Liquid AI on Hugging Face
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