Results 101 to 110 of about 27,184 (230)

Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy

Small Methods, EarlyView.
A generative AI model for super‐resolution microscopy images is presented. Super‐resolution microscopy provides high spatial detail at the expense of lower time resolution. Using it for live samples requires computational image reconstruction. It is unclear what good priors and metrics for AI‐generated super‐resolution images are.
Meri Abgaryan, Xinning Cui, Nandu Gopan, Gabriel della Maggiora, Artur Yakimovich, Ivo F. Sbalzarini   +5 more
wiley   +1 more source

Event-Related Potentials in Parkinson's Disease Patients with Visual Hallucination. [PDF]

Parkinsons Dis, 2016
Chang YP, Yang YH, Lai CL, Liou LM.
europepmc   +1 more source

Erving Goffman at 100: A Chameleon Seen as a Rorschach Test within a Kaleidoscope

Symbolic Interaction, EarlyView.
The 100th anniversary of Erving Goffman's birth was in 2022. Drawing on his work, the Goffman archives, the secondary literature, and personal experiences with him and those in his university of Chicago cohort, I reflect on some implications of his work and life, and the inseparable issues of understanding society.
Gary T. Marx
wiley   +1 more source

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert‐Like Systems Engineering Artifacts and a Characterization of Failure Modes

Systems Engineering, EarlyView.
ABSTRACT Multi‐purpose large language models (LLMs), a subset of generative artificial intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep‐domain knowledge and ...
Taylan G. Topcu, Mohammed Husain, Max Ofsa, Paul Wach   +3 more
wiley   +1 more source

An ethics module on academic integrity and generative AI

New Directions for Teaching and Learning, EarlyView.
Abstract This article explores the intersection between academic integrity and generative AI (GenAI). It presents a tested framework for a versatile 3‐h module applicable to various disciplines. Since ChatGPT's emergence, GenAI's impact on academic integrity has raised concerns, challenged established norms, and blurred lines of authorship.
Christopher Hill, Jace Hargis
wiley   +1 more source

The Pareidolia Test: A Simple Neuropsychological Test Measuring Visual Hallucination-Like Illusions. [PDF]

PLoS One, 2016
Mamiya Y, Nishio Y, Watanabe H, Yokoi K, Uchiyama M, Baba T, Iizuka O, Kanno S, Kamimura N, Kazui H, Hashimoto M, Ikeda M, Takeshita C, Shimomura T, Mori E.   +14 more
europepmc   +1 more source

Unveiling large multimodal models in pulmonary CT: A comparative assessment of generative AI performance in lung cancer diagnostics

VIEW, EarlyView.
1. The emergence of generative artificial intelligence (Gen‐AI) requires rigorous validation to assess its diagnostic reliability and limitations. 2. Three Gen‐AI models (GPT‐4‐turbo, Gemini‐pro‐vision, and Claude‐3‐opus) performed inconsistently across different diagnostic environments, demonstrating significant internal variability and overall ...
Lihaoyun Huang, Anqi Lin, Haitao Li, Qun Wang, Junyi Shen, Aimin Jiang, Chang Qi, Wenyi Gan, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Bufu Tang, Mingjia Xiao, Guangdi Chu, Jian Zhang, Quan Cheng, Peng Luo, Ting Wei   +17 more
wiley   +1 more source

A machine‐learning‐driven data labeling pipeline for scientific analysis in MLExchange

Journal of Applied Crystallography, EarlyView.
A web‐based labeling pipeline is introduced that accelerates the annotation of large scientific data sets with artificial‐intelligence‐guided tagging techniques.This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)‐guided tagging techniques.
Tanny Chavez, Zhuowen Zhao, Runbo Jiang, Wiebke Koepp, Dylan McReynolds, Petrus H. Zwart, Daniel B. Allan, Eliot H. Gann, Nicholas Schwarz, Daniela Ushizima, Edward S. Barnard, Apurva Mehta, Subramanian Sankaranarayanan, Alexander Hexemer   +13 more
wiley   +1 more source

From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment

Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. This generative approach to image caption enrichment further makes textual captions more descriptive, improving alignment with the visual context ...
Hachiuma, Ryo, Hirota, Yusuke, Nakashima, Yuta, Yang, Chao-Han Huck   +3 more
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