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2,123 papers
This chapter explores the convergence of Blockchain and Artificial Intelligence as foundational technologies for secure, transparent, and intelligent smart-contract ecosystems in Construction 6.0. It highlights how blockchain&s;s immutability, decentralized trust, and auditable ledgers address persistent industry chall...
The rapid development of artificial intelligence (AI), machine learning, process mining, and low-code development platforms is transforming the methods used to analyze and optimize organizational processes. Traditional process analysis generally depends on manually constructed process models, expert knowledge, and retr...
What's Changed Publish local launch tasks through the existing publisher by @scvance in https://github.com/harbor-framework/harbor/pull/3184 fix(environments): name the actual build-context dir in the missing-definition error by @hellno in https://github.com/harbor-framework/harbor/pull/2854 fix(codex): preserve web-se...
Abstract Convolutional neural networks (CNNs) have achieved state-of-the-art performance across computer vision tasks, including image classification, object detection, and medical image analysis. However, the opaque, black-box nature of these models limits their adoption in high-stakes domains such as healthcare, auto...
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To explore why organizations increasingly generate knowledge that supports immediate action but fails to contribute to sustained organizational learning. The article introduces the concept of single-use knowledge and examines how digitalization and artificial intelligence (AI) amplify the gap between knowledge crea...
Abstract Money is conventionally understood to depreciate when its purchasing power declines, its exchange value falls, or inflation reduces the quantity of goods and services obtainable from a given monetary unit. This paper proposes an additional and distinct concept: monetary-system relevance decay. Under this hypot...
This treatise is composed of the author's previously unpublished position papers and material on the vibrational physics:This work formulates a fundamental alternative to classical evolutionary biology and astrophysics by consistently subordinating biological, geological, and cosmological phenomena to the laws of vibra...
We prove a conjecture recorded by J. M. Campbell in 2018 in the OEIS entry A001405 (the central binomial coefficients binomial(n, floor(n/2))). Let NSym be the algebra of noncommutative symmetric functions of Gelfand, Krob, Lascoux, Leclerc, Retakh and Thibon, and let C_n(R, H) be the transition matrix from the ribbon...
We prove a statement recorded by H. Gruber in 2012 in the OEIS entry A000079 (the powers of 2), verified by him up to n = 17: for n >= 1, the number of distinct finite languages over a one-letter alphabet whose minimum regular expression has alphabetic width n is 2^n. The alphabetic width of a regular expression is the...
Alzheimer’s Disease (AD) is a progressive neurological disorder that impairs cognitive functions, severely affecting patients’ quality of life. Thus, early and accurate diagnosis is essential to provide a chance for treatment. This paper proposes two deep learning (DL) models to enable automated diagnosis of medical im...
Purpose: Diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD) remains challenging, and existing AI-based neuroimaging tools are typically applied in isolation for diagnosis, multimodal analysis, or progression tracking. This study aims to develop an integrated, three-stage deep learning framework that uni...
This treatise is composed of the author's previously unpublished position papers and material on the vibrational physics:This work formulates a fundamental alternative to classical evolutionary biology and astrophysics by consistently subordinating biological, geological, and cosmological phenomena to the laws of vibra...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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.
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