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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 ↗5971 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
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images ↗
Understanding how cardiomyocyte structure governs contractile function is fundamental to cardiac biology and disease modeling, yet current approaches rely on time‐resolved imaging and computationally intensive analysis. Here, we present a generative artificial intelligence (AI) framework that directly predicts contractile behavior of human‐induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs) from single static images. Our approach integrates a U‐Net‐based generator with a patch‐based generative adversarial network (GAN) discriminator to translate morphological and sarcomere structu…
- OpenAlex · Published · First archived
Causal-Convolutional Architectures for IoT Intrusion Detection: A Systematic Comparison Against Classical Machine Learning Baselines and a Bidirectionality Ablation ↗
Intrusion detection for the Internet of Things (IoT) increasingly runs on IoT gateways and enterprise sensors that need architectures accurate enough to trust without being computationally heavy. Causal convolution offers gated, dilated, and multi-scale receptive-field mechanisms at low cost compared with recurrent or attention-based models. Whether such mechanisms need a bidirectional wrapper to approach classical machine-learning classifiers on tabular network-flow data remains unexamined. This study compares three causal-convolution mechanisms, dilated gated convolution, depthwise-separable…
- OpenAlex · Published · First archived
A Binary Hybrid Particle Whale Optimization Framework for Feature Selection in Industrial Transformer Partial Discharge Classification ↗
Partial discharge (PD) testing based on IEC 60270 is widely employed for assessing the insulation integrity of power transformers. Although numerous statistical descriptors can be extracted from PD measurements, redundant and irrelevant features can degrade classification performance, particularly in small datasets. This study proposes a binary hybrid particle whale optimization algorithm (BHPWOA) for multi-objective wrapper-based feature selection in transformer insulation severity classification. The analysis comprises 64 IEC 60270 partial-discharge test sessions collected from 30 power tran…
- OpenAlex · Published · First archived
Deep Neural Network-Driven Reliability Assessment Methodology for Total Ionizing Dose Effects in FDSOI MOSFETs ↗
This work investigates the effects of total ionizing dose (TID) on 22 nm FDSOI MOSFET devices through combined technology computer-aided design (TCAD) simulations and actual TID irradiation experiments, establishing a comprehensive dataset of radiation-induced degradation characteristics. By utilizing both simulated and experimental radiation data as training foundations, this research developed and optimized a deep neural network (DNN) capable of conducting efficient and accurate reliability analysis of TID effects under various radiation conditions, with demonstrated effectiveness in predict…
- OpenAlex · Published · First archived
Optical Fiber Parameters Determination Using Neural Network with Noise Analysis ↗
The objective of this work is to develop and analyze a method based on the use of a convolutional neural network (CNN), which would be capable of inferring the most relevant physical parameters of an optical fiber—specifically, the second-order group velocity dispersion (GVD) coefficient β2 and the nonlinearity coefficient γ—from the observation of temporal profiles of propagated optical pulses. We analyze the model robustness in the presence of noise, a critical aspect for any potential experimental application. In particular, as the main result, we explore how different training schemes affe…
- OpenAlex · Published · First archived
Protodetect: Prototype Compactness and Inference Calibration for Few-Shot Out-of-Distribution Detection ↗
Detecting out-of-distribution (OOD) samples from limited labeled data is important for reliable recognition under semantic novelty. Existing few-shot approaches use synthetic or auxiliary unknowns, model only in-distribution (ID) data, or rely on large pretrained vision–language models. This paper presents Protodetect, a metric-learning framework for the conventional episodic, prototype-based setting. Model weights are optimized using ID episodes only, while held-out OOD samples during meta-validation are used to select inference hyperparameters. Protodetect combines the prototypical classific…
- OpenAlex · Published · First archived
Open-ended Human Activity Understanding via LLM-assisted Motion Decomposition and Semantic Fusion ↗
Human activity is a fundamental component of intelligent interactive systems. Existing human activity recognition (HAR) follows a closed-ended assumption—defining a fixed set of activity classes and classifying each sensor signal into one of them—yet real-world human behavior is complex and ever-changing and thus cannot be fully represented by any fixed set. We present a new paradigm for open-ended human activity understanding (HAU), shifting the goal from closed-set classification to understanding activities via their meta-motions. To achieve this, we design MOSAIC, a motion decomposition and…
- OpenAlex · Published · First archived
A pilot pre–post study of a scaffolded AI chatbot for financial literacy education among university students ↗
This pilot study examines pre- to post-test changes in university students’ financial literacy knowledge following a one-week student-directed learning period using a pedagogically scaffolded GPT‑4o Application Programming Interface (API) chatbot. Guided by constructivist learning theory [ 29 ], the immediate feedback model [ 10 ], and self-determination theory [ 24 ], the study employed a single-group pre-test–post-test quantitative design with 32 undergraduate students at a Turkish public university. The primary outcome was financial literacy knowledge, measured with 32 factual statements ra…
- OpenAlex · Published · First archived
Automated Health Care Thematic Analysis Using a Multiagent Large Language Model: Algorithm Development and Evaluation Study ↗
Background Understanding patients’ experiences is essential for advancing patient-centered care, especially in chronic diseases that require ongoing communication. Qualitative thematic analysis is widely used to explore these experiences; however, the process remains labor-intensive, subjective, and difficult to scale. Objective This study aimed to develop and evaluate Collaborative Theme Identification Agents (CoTI), a multiagent large language model framework designed to support manual thematic analysis by rapidly generating supporting excerpts, initial codes, and themes. Methods CoTI consis…
- OpenAlex · Published · First archived
Evaluating Large Language Models for Biomedical Text Summarization: A Study of Cardiovascular Research ↗
In this study, presented a comprehensive evaluation of abstractive and extractive summarization performance across three prominent large language models (LLMs): ChatGPT, DeepSeek, and Gemini. A total of 8,000 cardiovascular-related research abstracts were collected from PubMed and summarized using two distinct prompting strategies: abstractive and extractive. This process yielded a dataset of 48,000 summaries. To assess summarization quality, applied a multi-metric evaluation framework including semantic similarity (SBERT cosine), BLEU, GLEU, ROUGE-1 F1, ROUGE-2 F1, ROUGE-L F1, and METEOR. The…
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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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