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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5934 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Modeling of random telegraph noise decay constant in 3D NAND flash memory using machine learning-based Monte Carlo simulation ↗
Abstract Random telegraph noise (RTN) has emerged as a major reliability concern in high-density 3D NAND flash memory, causing significant threshold voltage ( V t ) distribution distortion. While technology computer-aided design (TCAD) simulations provide physical insights, they face fundamental limitations in capturing the statistical randomness of grain boundaries and traps due to high computational costs. In this study, we design the RTN decay constant ( λ ) model in 3D NAND using artificial neural networks (ANN) and Monte Carlo simulation. Our analysis reveals that unlike 2D NAND or single…
- OpenAlex · Published · First archived
Evaluating intent-driven color generation: a framework for assessing LLM-assisted cartographic design ↗
Large language models (LLMs) offer new possibilities for adaptive cartographic design, yet how they interpret user intent remains unclear. This study evaluates how stated analytical goals influence LLM-generated color schemes for thematic maps, assessing three perceptual dimensions: whether colors form a clear sequence matching the data hierarchy (order), whether categories are visually distinct from one another (differentiation), and whether related values share similar or thematically cohesive colors (association). Using a synthetic dataset of 20 lakes classified by pollution level (Low, Med…
- OpenAlex · Published · First archived
Artificial intelligence, augmented reality, and virtual reality in science education: scientific mapping and future research ↗
Abstract This study examines the trends, thematic development, and research collaboration patterns related to Artificial Intelligence (AI), Augmented Reality (AR), and Virtual Reality (VR) in science education through a comprehensive bibliometric analysis. The rapid integration of these emerging technologies has increased the demand for innovative learning approaches capable of enhancing student engagement, conceptual understanding, and immersive learning experiences in science education. Bibliometric analysis was conducted using Scopus-indexed publications from 2014 to 2024, comprising 196 ar…
- OpenAlex · Published · First archived
Toward Intelligent Fatigue Management of Welded Metal Sheet Structures by Integrating Simulation, Sensing, and Machine Learning ↗
Welded metal sheets are used across automotive, aerospace, marine, and civil infrastructure applications for their high strength and light weight, yet fatigue failure remains a persistent concern in relation to their long-term structural stability. Fatigue crack growth is strongly influenced by the interaction of weld geometry and manufacturing-related defects. This review presents a comprehensive overview of fatigue-life prediction approaches, outlining the welding processes, fatigue mechanisms, computational tools, and progress made in predictive approaches central to this field. Fatigue per…
- OpenAlex · Published · First archived
AI-Based Control of Differential Vacuum in the MuVacAS Prototype for IFMIF–DONES Particle Accelerator ↗
Developing materials capable of withstanding extreme irradiation is a fundamental challenge for realizing nuclear fusion as a sustainable energy source. The IFMIF–DONES facility, currently under construction, will address this need by producing a fusion-relevant neutron flux using a high-power deuteron beam striking a flowing lithium target, enabling accelerated qualification of candidate structural materials. Validating the accelerator and target technologies requires precise control of the ultra-high-vacuum region near the lithium surface, yet direct pressure sensing in this irradiated zone…
- OpenAlex · Published · First archived
Machine learning-accelerated time integration of plasticity models ↗
Finite element simulations of structures with nonlinear material behavior require advanced material models to provide accurate predictions. However, the computational costs of these models can be high, as they solve coupled differential algebraic equations at each integration point, in each equilibrium iteration, in every time step. In this study, we propose a machine learning-based framework to accelerate these computations by explicitly calculating the state variable updates with neural networks, enabling large time steps with low computational costs. The neural networks operate on invariant…
- OpenAlex · Published · First archived
A Multi-Agent Framework for SQL Injection Auditing with LLM-Driven Post-Exploitation and Privilege Escalation ↗
Web security audits of SQL injection (SQLi) vulnerabilities still depend primarily on the analyst’s skill and knowledge to select the right tools, interpret results, and determine whether there is any vulnerability. This work presents SQLiAgent, an open-source multi-agent framework that automates the complete cycle of an SQLi audit—from initial recognition to post-exploitation assisted by artificial intelligence (AI)—in order to facilitate the detection and subsequent patching of vulnerabilities. SQLiAgent integrates well-established tools from the pentesting ecosystem within a decoupled and t…
- OpenAlex · Published · First archived
Frequency control of a two-area RES-integrated power system incorporating a smart transformer using a fractional-order controller ↗
The growing demand for electricity, together with high penetration of renewable energy sources (RES), poses serious operational issues, such as power generation fluctuation, reduced system inertia, and unstable control performance. This article investigates a two-area automatic generation control (AGC) system comprising a thermal system (TS) along with renewable energy sources (RES) that include a solar thermal power plant (STPP) and wind turbine system (WTS), utilizing smart transformers (STs). The article proposed a new fractional order proportional integral minus tilt integral derivative (F…
- OpenAlex · Published · First archived
Hierarchy-Aware AI Nuclear Phenotyping in Human iPSC-Derived Cardiomyocytes: A Preclinical Framework for Imaging Biomarker Development ↗
Background/Objectives: Nuclear morphology and chromatin organisation provide quantifiable imaging features that may serve as candidate biomarkers of cellular state. However, many cellular imaging studies analyse multiple cells from the same microscopy field without explicitly accounting for their hierarchical dependence, which may lead to optimistic estimates of model performance. This study developed a hierarchy-aware AI framework to investigate DAPI-derived nuclear phenotypes in human iPSC-derived cardiomyocytes (iPSC-CMs). Methods: A six-day fluorescence microscopy dataset was processed usi…
- OpenAlex · Published · First archived
Artificial Intelligence (AI) assurance: challenges, gaps, and the path forward ↗
Abstract The integration of Artificial Intelligence (AI)/Machine Learning (ML) into high-impact domains such as finance and autonomous systems offers significant benefits, but also introduces complex risks and regulatory challenges. These systems exhibit properties including non-determinism, data dependence, and evolving vulnerabilities that are difficult to address through largely static assurance approaches. This paper provides a critical review of AI assurance initiatives, examining established frameworks such as the NIST AI Risk Management Framework (NIST AI RMF), global guidance including…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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