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2,612 papers
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, primarily located at edge devices. However, the inherent distributed nature of FL introduces significant challenges, particularly regarding privacy vulnerabilities and substantial communication overhea...
Human Activity Recognition (HAR) systems deployed on wearable and edge devices continuously collect sensitive user data, raising critical privacy concerns and communication bottlenecks. While Federated Learning (FL) mitigates these issues by decentralizing model training and keeping raw data on-device, deploying highly...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, primarily located at edge devices. However, the inherent distributed nature of FL introduces significant challenges, particularly regarding privacy vulnerabilities and substantial communication overhea...
Human Activity Recognition (HAR) systems deployed on wearable and edge devices continuously collect sensitive user data, raising critical privacy concerns and communication bottlenecks. While Federated Learning (FL) mitigates these issues by decentralizing model training and keeping raw data on-device, deploying highly...
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This paper investigates the challenges and proposes a novel approach to distributed edge computing resource scheduling and optimization. The core claim is to design an efficient algorithm that dynamically allocates resources based on task requirements and edge computing node capabilities, ultimately enhancing computati...
When the exact test is least exact, and the χ² default most wrong: achieved size, borrowed power, and a routing rule for R×C tables William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell, Lowell, MA, USA. ORCID 0009-0004-0855-7222. License: MIT for all code; CC BY...
🔗 This paper is accompanied by open-source software and a fully reproducible pipeline. Code and data-generation scripts are available at:https://github.com/ChristosTsirkas/corpus-validation-for-undeciphered-scripts-linear-aLicense: https://github.com/ChristosTsirkas/corpus-validation-for-undeciphered-scripts-linear-a/...
This paper presents an ADL recommendation system based on a Vision Language Model (VLM), which runs locally, describes the objects in the scene, and recommends different ADLs based on the objects in the scene and the capabilities of the robotic system.
A geometrically informed conditional Generative Adversarial Network (cGAN) is implemented through the Pix2Pix framework, to predict layer-wise geometric deviations in LPBF-printed parts with overhang geometries, using paired two-dimensional Computer-Aided Design (2D CAD) slices and corresponding X-ray Computed Tomograp...
Experiments demonstrate that CAFDIM effectively suppresses sparse-view artifacts, preserves anatomical structures, and achieves superior reconstruction accuracy, visual quality, and generalization ability compared with state-of-the-art methods.
sys is presented, a content-aware hierarchical scheduler that screens constellation-wide options to retain a bounded candidate set, then refines each candidate into a complete token compression and layer offloading trajectory using quality prediction and joint planning and balances per-task latency, energy, and quality...
CHARTER — Certification and History Architecture for Robust Transport, Evidence, and Retention — studies exact Eisenstein norm shells and the extra structure required to orient, compare, certify, carry, and intentionally release them without confusing mathematical equality with evidentiary or physical equivalence. The...
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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