AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,497 papers
Overall, when evaluating both Chapel and MPI+X implementations of partitioned Jaccard similarity on up to 16 A100-80GB GPUs across four nodes, Chapel achieves comparable performance to MPI+X while also delivering significantly better programmer productivity and agility.
The Dual-Stage Spiking Swin Transformer (D2S-SwinT), a neuromorphic architecture that integrates the feature representation capability of Swin Transformers with the event-driven efficiency of brain-inspired computation, is proposed, a neuromorphic architecture that integrates the feature representation capability of Sw...
Vadar adopts a non-intrusive dynamic interception mechanism to monitor parallel applications with state transition detection to cope with workload change at runtime, and provides comprehensive monitoring for both regular and irregular computational workloads.
TFS is presented, a tile-aware fusion framework that eliminates this intermediate materialization by fusing SpMM and GeMM directly inside the tile registers of Intel’s Advanced Matrix Extensions (AMX).
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Passenger flow in rapidly developing high-speed railway networks varies with temporal fluctuations, spatial interactions among stations, and regional travel dependencies. Accurate forecasts support the management of station operations, resource allocation, and service optimization, yet methods that impose fixed spatial...
The rapid evolution of 6G networks is expected to transform immersive media broadcasting by enabling ultra-low-latency communication, intelligent edge computing, and high-capacity transmission for extended reality, holographic content, and interactive multimedia. However, conventional edge resource management remains l...
The reliance on single-point metric estimates (such as a solitary mAP score) to benchmark object detection models frequently masks underlying sample variances, leading to overconfident deployment decisions. This paper presents an automated post-training validation pipeline for YOLO models designed to enforce statistica...
LUCYD-PISCO training-set sources (v5) Source data needed to regenerate the synthetic training set used to retrain the LUCYD deconvolution network for PISCO (Plankton In Situ Camera Observation) extended-depth-of-field (TAG-lens) images. Code: https://git.geomar.de/team-optics/plankton/underwater-lucyd (sim/gen_trainset...
Mirror, Mirror, in XR: Motion-Capture Dataset, Processing Pipeline, and Analysis Code Description This deposit contains the full data-processing and analysis pipeline underlying a within-subjects VR study of imposed social mirroring. Twelve dyads (24 participants) held structured conversations under three counterbalanc...
Changes in v1.0.7. Two faults in the cavity figure, both found on the page. The near lower corner of the box at the far end was missing, and with it about a sixth of the front lower edge. The two panels of that figure are each wider and taller than the drawing they hold, which is what lets a long flat cavity and a cone...
Abstract The Climate and Economic Justice Screening Tool (CEJST) was developed by the U.S. federal government in 2021 to identify and support marginalized communities disproportionately impacted by climate change. This tool identifies disadvantaged communities based on the presence of low-income residents and one of ei...
Technical White Paper v1.5 | Document ID: QN-RP-2026-V1.5 Affiliation: QuantNature Global (https://www.quantnature.com)Release Date: September 11, 2026 Executive Summary This white paper presents the Resonance Plexus (RP v1.5), a novel non-symbolic physical intelligence framework designed to overcome the structural bri...
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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