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2,462 papers
Precision calculations in quantum field theory rely very often on perturbation theory and thus on the computation of Feynman integrals. Feynman integrals are also fascinating objects from a mathematical point of view and show deep connections to algebraic geometry. Cutting-edge Feynman integrals usually have geometries...
Abstract: Modern automated computing systems increasingly deploy Large Language Models (LLMs) to resolve runtime operational triage, incurring prohibitive latency (>100–500 ms), severe memory allocation (>4–8 GB VRAM), and high thermodynamic dissipation. Extending the foundational theory of Mandelbrot Fractal Neural Sy...
In this thesis, we aim to set some important steps towards better understanding the role of the Casimir effect in the fundamental mechanism behind color confinement. We investigate the Casimir effect in several settings using a functional methodology. This method is flexible with regard to the specific boundaries and t...
In the Industrial Internet of Things (IIoT), secure data sharing via edge computing is pivotal for enabling collaborative manufacturing. However, existing bilateral access control (BAC) schemes suffer from a “Transmit-then-Verify” limitation: full ciphertexts must be transmitted before privilege verification, causing s...
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Edge intelligence is revolutionizing the Internet of Things (IoT) by migrating AI training from centralized clouds to distributed end devices. However, enabling continual learning (CL) on edge nodes faces a “rigid triangle constraint” of restricted memory, limited computation, and battery-dependent energy, heavily dict...
The rapid development of uncrewed aerial vehicles (UAVs) has expanded the application scenarios of mobile edge computing (MEC). However, existing multi-UAV collaborative MEC schemes usually rely on fixed cooperation structures or limited collaboration mechanisms, making it difficult to fully utilize distributed UAV com...
Cloud–edge collaborative inference has emerged as a promising paradigm to address the latency, energy, and privacy challenges of large language models (LLMs). However, current offloading mechanisms often struggle to efficiently capture the dynamic semantic dependencies between historical context and ongoing queries. Th...
Due to limited computing resources and severe blockage in dense urban environments, uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) faces significant challenges in serving the Internet of Things (IoT) devices. In this article, by deploying multiple simultaneously transmitting and reflecting reconfigur...
Energy-efficient neuromorphic hardware with ultralow power consumption is increasingly promising for edge–artificial intelligence (AI) applications. Most digital neuromorphic hardware is designed with asynchronous circuits as they naturally match the sparsity and event-driven features of neuromorphic computing. However...
This is the extended, definitive version (v16.2) of the P0_Distilled research programme, with full technical appendices — it supplements the distilled entry-point paper (P0_Distilled_v0.1) with the complete Lakatosian structure, derivations, and experimental protocol. The programme's hard core is a single metaphysical...
Predictive analysis techniques for crop and soil sensors have revolutionized agriculture by enabling data-driven decision-making and optimizing farming practices. This chapter begins with an overview of crop and soil stresses, highlighting their impact on agricultural productivity. It then examines enabling technologie...
This publication establishes the unified Arcstone Executive Epistemic Whitepaper Series (EXEC01–EXEC03), providing the foundational theory and operational mechanics for machine-native execution boundaries and zero-operational-drag computing (C_ops = 0).INCLUDED PAPERS IN THIS BLOCK:1. ARC-PUB-2026-EXEC01: Human-First T...
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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