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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 ↗6056 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
Q-Conditioning and Q-Gating: Metadata-Driven Attention Mechanisms for Medical Vision Transformers ↗
Background: Vision Transformers (ViTs) have demonstrated efficacy in visual recognition tasks through global dependency modeling via self-attention. However, standard ViT architectures lack native mechanisms for integrating external clinical metadata (e.g., patient demographics, lesion characteristics, image quality indicators) into the visual processing pipeline, limiting their utility in multi-modal diagnostic scenarios. Methods: We propose two novel attention mechanisms: Q-Conditioning and Q-Gating, designed for efficient metadata fusion within the transformer framework. In Q-Conditioning,…
- OpenAlex · Published · First archived
AI-accelerated scalable synthesis of nanocrystals for cost-effective photovoltaics ↗
Metal halide perovskite nanocrystals (PNCs) are promising candidates for next-generation optoelectronics. However, their commercial application necessitates low-cost synthesis and processing methods that surpass conventional lab-scale techniques. To address this, we report a ligand triggered room-temperature injection (LTRI) method that initiates nanocrystal nucleation by tuning precursor reactivity with addition of short-chain octanoic acid (OcA). Subsequently, integrating automation synthesis and machine learning, we further develop an efficient AI-accelerated framework towards fast and targ…
- OpenAlex · Published · First archived
Development and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text ↗
Abstract Systematized Nomenclature of Medicine–Clinical Terminology (SNOMED CT) is the principal international standard for semantic interoperability of clinical information, but mapping free-text clinical narratives to SNOMED CT concepts remains labor-intensive. We developed a large language model agent system for mapping bilingual clinical text to SNOMED CT concepts and evaluated its effect on mapping accuracy and efficiency within a human-AI collaborative workflow. We designed a three-module agent system comprising translation, abbreviation expansion, and vector-based retrieval components,…
- OpenAlex · Published · First archived
Advertising with HAART: Introducing Human–AI Asymmetric Relationship Theory ↗
Generative artificial intelligence (GenAI)is rapidly reconfiguring advertising from message delivery and optimization to relationship-like exchanges between consumers and brands. While existing work has explained these interactions through social response accounts and interpersonal relationship theories, this lens risks overlooking a defining structural mismatch: AI agents convincingly enact relational characteristics but cannot experience relational benefits, costs, or commitment. Perceived mutuality can be high even when the underlying relational capacity for reciprocity is absent. We introd…
- OpenAlex · Published · First archived
A Neuro-Symbolic Framework for LLM-Driven Task Planning and Execution in Industrial Assembly ↗
This paper proposes a neuro-symbolic framework that combines large language model (LLM)-driven task planning with formal symbolic verification and reactive Behavior Tree execution to support high-precision operations in industrial assembly. While Large Language Models (LLMs) demonstrate expertise in interpreting natural language instructions, they often struggle with processing structured manufacturing data and lack long-horizon planning capabilities and deterministic physical awareness required for complex robotic execution. Furthermore, their susceptibility to generating physically unfeasibl…
- OpenAlex · Published · First archived
Machine Learning for Screening Bioactive Compounds in Traditional Chinese Medicine: Computational Prediction, Multi-Omics Integration, and Clinical Translation Potential ↗
Identifying bioactive compounds in traditional Chinese medicine (TCM) requires computational prioritization to be connected with chemical and pharmacological evidence. This structured narrative and critical review examines machine learning applications in compound prediction, formula-compatibility analysis, multi-omics integration, and drug development and repurposing. Molecular fingerprints, graph and sequence representations, knowledge graphs, and generative models are discussed in relation to their intended endpoints and validation conditions. Studies are classified along two independent di…
- OpenAlex · Published · First archived
Few-Shot Remote Sensing Scene Classification Leveraging Collaboration of Foundation Models ↗
Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language model and seldom exploit the complementary strengths of different foundation models. Moreover, the inconsistency between visual and textual representations limits the effectiveness of cross-modal learning under limited supervision. To address these issues, we propose a framework for few-shot remote sensing scene classification leveraging collaboration of fo…
- OpenAlex · Published · First archived
Prediction of Ultra-High-Performance Concrete (UHPC) Compressive Strength in Structural Applications Using Machine Learning: A Comprehensive Evaluation via Multiple Random Seeds and Hyperparameter Optimization ↗
Ultra-high-performance concrete (UHPC) is increasingly being used in academic and commercial applications due to its superior mechanical properties, high durability, and long service life. To ensure that UHPC can be reliably used in structural applications, it is important to predict its compressive strength in advance. The complex and nonlinear effects of different mix components and their proportions on compressive strength make strength prediction a significant engineering challenge. Reliable compressive strength prediction is crucial for supporting trial mix selection during construction p…
- OpenAlex · Published · First archived
Forced-Response Analysis and Machine-Learning-Based Prediction of Aero-Engine Fan Blades Using the Harmonic Balance Method ↗
This study combines harmonic-balance simulations, modal response analysis, strain-gauge measurements, and machine-learning regression to assess the forced response of a three-row axial fan. At 10,500 rpm, Campbell-diagram analysis identifies a near-resonant third-mode rotor response to fourth-engine-order (4EO) excitation and a second-mode stator response to 13EO rotor-passing excitation. Separate fixed-blade and prescribed-motion calculations reveal the aerodynamic excitation and motion-induced aerodynamic contributions. The predicted peak alternating-stress amplitudes are 14.76 MPa for the r…
- OpenAlex · Published · First archived
Optimization of chimney design for an extruded radial heat sink using experiments and neural network models ↗
This study investigates chimney-assisted natural-convection cooling for an Extruded Radial Heat Sink (ERHS) in high-power Light Emitting Diode (LED) applications. Although chimneys have been applied to Forged Radial Heat Sinks (FRHSs), their application to ERHSs remains insufficiently studied. Direct attachment of a chimney to the flat top of the ERHS obstructs the internal airflow path, requiring a geometry tailored to the ERHS. Accordingly, the effects of chimney geometry and inlet shape were investigated through experiments and steady-state CFD simulations. The sharp-edged inlet induces flo…
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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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