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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 ↗5384 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
Uncertainty-aware digital twin-driven fault-tolerant control for autonomous underwater vehicles using temporal fusion transformer-based fault diagnosis ↗
Autonomous underwater vehicles (AUVs) operate in challenging marine environments. The actuator faults, propulsion degradation and environmental disturbances can significantly compromise mission safety and tracking performance. Existing studies have primarily focused on either fault diagnosis or fault-tolerant control as separate tasks. A limited attention has been given to integrating fault diagnosis, predictive digital twins and uncertainty-aware control within a unified framework. To address this challenge, this paper proposes a novel uncertainty-aware digital twin-driven fault-tolerant cont…
- OpenAlex · Published · First archived
Joint detection and state recognition of traffic lights and signs through a lightweight hybrid CNN–transformer framework ↗
Abstract Accurate detection of traffic lights and signs is a critical task for safe autonomous vehicle systems (AVS). Despite advances in deep learning, most methods treat traffic light detection, traffic sign detection, and state recognition as separate tasks, while CNN-based models lack global context modeling and transformer-based approaches incur high computational cost. To address these limitations, this study presents a unified and lightweight YOLOv11–EfficientViT framework that jointly performs traffic light detection, traffic sign detection, and traffic light state recognition within a…
- OpenAlex · Published · First archived
Physics-guided interpretable machine learning for explicit resistance equation discovery in concrete penetration ↗
Classical Forrestal penetration calculations are attractive because they are explicit, but their empirical strength-only resistance term is too restrictive for heterogeneous concrete-penetration data. This study derives a closed-form resistance equation from a governed literature dataset, reducing 497 raw records from 41 source blocks to 443 useable records from 37 sources through duplicate removal, governed density completion, deep-penetration filtering, and descriptor-only anomaly screening. Forrestal inversion maps each measured penetration depth to an operator-consistent effective resistan…
- OpenAlex · Published · First archived
HideAndSeg: an AI-driven approach to monitoring benthic octopuses in the wild ↗
Abstract Analyzing the behavior and body patterns of octopuses in nature is challenging due to their camouflage capabilities, changes in skin texture and color, body deformations, and frequent occlusions. Underwater lighting and turbidity further complicate these challenges. Addressing the lack of large-scale annotated datasets, we introduce HideAndSeg, a minimally supervised, AI-based tool for segmenting octopus videos. HideAndSeg integrates SAM2 with a custom-trained YOLOv11 object detector. First, the user provides point coordinates to generate the initial segmentation masks with SAM2. Thes…
- OpenAlex · Published · First archived
Learning from the unseen: synthetic data for scoliosis AI segmentation ↗
Abstract Purpose Severe scoliosis remains challenging for 3D vertebral segmentation because annotated pathological computed tomography (CT) data are scarce. Methods We synthesize scoliosis-like CT volumes by jointly warping public healthy spine scans and labels and then train nnU-Net CNN and PRIMUS models under matched compute. We evaluate on in-house out-of-distribution (OOD) adolescent scoliosis CTs and the public Spine1K dataset. Results On 18 in-house adolescent scoliosis CTs, the best CNN setting increased mean Dice from 0.765 to 0.842 and vertebra success rate from 73.2 to 81.6%. In 55 s…
- OpenAlex · Published · First archived
Generative artificial intelligence use for academic writing in English among Kuwaiti nursing and health sciences students in a mixed methods study ↗
Abstract Research on generative AI use in EMI nursing and health sciences contexts in the Gulf region remains limited. This study investigated the uses, experiences, and perceptions of generative AI among nursing and health science students at the Public Authority for Applied Education and Training (PAAET) in Kuwait. Using a single-institution convergent mixed methods survey design, quantitative and qualitative data were collected from 241 undergraduate students. Quantitative data were analyzed using descriptive statistics, while qualitative responses were analyzed through reflexive thematic a…
- OpenAlex · Published · First archived
Unveiling Hidden Patterns in Electrode‐Electrolyte Interphase Chemistry and Architecture Through Machine Learning‐Assisted Mass Spectrometry Analysis ↗
Electrode-electrolyte interphases (EEIs) govern battery performance, yet the interplay between their chemical composition and spatial organization remains challenging to resolve. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) provides rich 3D chemical depth profiles of these interfaces, yet conventional analyses simplify the complexity by collapsing the data into 1D depth profiles or 2D maps, obscuring critical spatial features. Here, we apply a machine-learning-based approach directly to 3D TOF-SIMS depth profiles, leveraging their full spatial and chemical richness to investigate…
- OpenAlex · Published · First archived
AI-generated language remains systematically distinguishable from human communication ↗
Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior and communication, yet validation methods for these applications remain limited. Existing evaluations often rely on human judgments of plausibility, a coarse, difficult-to-scale, and often unreliable benchmark for distinguishing AI-generated from human text. As a result, researchers lack robust tools for assessing the realism of LLM-generated language and for calibrating models to real-world data. We address this gap in two ways. First, we introduce the Conversational Human Authenticity Test (C…
- OpenAlex · Published · First archived
Self-Perceived Knowledge, Self-Efficacy, and Reported Integration of Generative Artificial Intelligence Among University Faculty: An Exploratory Profile Analysis ↗
Generative artificial intelligence (GAI) adoption in higher education raises questions about the relationships among perceived knowledge, confidence, attitudes, and reported teaching practices. A cross-sectional study of 149 faculty members at an Ecuadorian public university examines four self-reported constructs, with Cronbach’s α ranging from 0.841 to 0.981. An exploratory three-group partition describes higher-scoring (44.3%), intermediate-scoring (39.6%), and lower-scoring (16.1%) respondents, although the silhouette criterion favors two groups (0.383 versus 0.299). Self-efficacy correlate…
- OpenAlex · Published · First archived
Calibration-assisted stress-optic phase programming for a 3D-printed optical neural-network frontend ↗
We report a calibration-assisted stress-optic phase-programming method for a 3D-printed optical neural-network (ONN) frontend. Full-vector stress-optic modeling gives a full-stress effective-index shift of Δ 𝑛 e f f = − 2 . 1 2 8 4 × 1 0 − 4 at 850 nm, setting the phase-length rule for Mach–Zehnder interferometer (MZI) phase shifters. A matched MPB modal proxy reproduces the modeled phase sensitivity with a ratio of 0.999,396, giving phase errors below 0 . 0 0 1 2 𝜋 for the evaluated 4- and 8-node phase sets. MZI measurements across three annealing conditions show a monotonic reduction i…
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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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