AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,908 papers
We study proportional fairness in graphical resource allocation, where agents are vertices, indivisible items are edges, and each item must be allocated to one of its two endpoints. It has been proved that PROP1 orientations always exist and PROPx orientations may not, but it has remained open whether the intermediate...
A deterministic polynomial-time approximation for Maximum Weighted $3-Set Packing, breaking the $\sqrt3$ locality-gap barrier of squared-weight local search and proving that $\sqrt3$ is a locality-gap lower bound for the squared-weight objective even with exchanges of arbitrary size.
This work proposes a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC and employs multi-agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions.
Two-dimensional (2D) non-layered oxides, such as MoO2, are emerging candidates for next-generation electronics, optoelectronics and sensors, especially novel in-sensor computing. However, there is a lack of effective method to grow large-area 2D MoO2 single crystals due to multiple preferred orientations induced by unc...
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DECISION: REJECT ELEMENT: No element survives — the pool-density ≥90 filter is a statistical artifact, not an E8-geometry-compatible edge. EXPECTED IMPACT: The +1.5pp backtest gain (n=1800) is within noise for a 32.5%→34.0% shift (σ≈1.1pp). Merlin's own live-window projection shows only +8.6pp on 7 trades (n=7, meaning...
This study designed an edge computing detection system for maize pests and diseases based on an improved YOLOv11 model and designed a C2PSLCA module based on linear self-attention to replace the C2PSA module in YOLOv11, enhancing the model’s ability to extract features of maize pests and diseases in complex environment...
Decision Support Systems (DSS) in precision agriculture have evolved from static rule-based engines into data-driven frameworks powered by Internet of Things (IoT) telemetry and supervised machine learning. While recent literature increasingly focuses on deep learning and volatile commodity forecasting, empirical farm-...
Deliverable D3.5 Production Planning & Reconfiguration v1 provides an overall status update of two core components developed within the CIRCMAN5.0 project’s Work Package 3 (WP3). They are aligned with WP3’s purpose that is to deliver novel simulation and modelling software to improve process and product manufacturing....
Connectomics datasets now provide synapse-resolution wiring diagrams for entire insect nervous systems, raisinga question that is easy to pose but rarely tested directly: does the specific wiring these datasets reveal confer afunctional advantage over a structurally-matched random control, when deployed as an actual re...
The Music Perception Toolbox is an open‑source package—available in both MATLAB and Python—for computing perceptually and cognitively motivated measures of pitch similarity, consonance, and scale and rhythmic structure. Inputs may be symbolic pitches or spectral peaks extracted from audio. The toolbox covers three fami...
This dataset contains the 25,000-sample training dataset used in "Deep Learning-Accelerated Inverse Design of a Nonlinear Duffing-Type Metamaterial: Amplitude-Programmable, Bifurcation-Aware Bandgap Prediction." Each row corresponds to a sampled design point (nonlinear stiffness k3, mass ratio m2/m1, excitation amplitu...
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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