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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 ↗5938 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
SET-HOI: Skeleton-Enhanced Transformer with Parallel Fusion for HOI Detection ↗
Human–Object Interaction (HOI) detection seeks to identify the relationships between humans and objects in images that play a pivotal role in high-level vision tasks and biomimetic systems, such as biomimetic robotics, intelligent prosthetics, and embodied AI for intent understanding. However, existing approaches that rely solely on visual appearance features are susceptible to background interference, which adversely affects detection accuracy. Inspired by the biomimetic dual-stream mechanism of biological visual perception, we propose a Transformer-based HOI detection model with a dual-branc…
- OpenAlex · Published · First archived
Explainable machine learning for path loss modeling in mmWave and Sub-THz communication systems ↗
Machine learning (ML) has emerged as a promising approach for large-scale path loss modeling in millimeter-wave (mmWave) and sub-terahertz (sub-THz) wireless communication systems because of its ability to capture complex propagation behavior beyond conventional empirical models. However, most existing ML-based path loss predictors operate as black-box models, limiting their interpretability, physical reliability, and practical adoption in deployment-critical wireless system design and standardization. To address these limitations, this paper proposes an explainable machine learning (XML) fram…
- OpenAlex · Published · First archived
Effective Sequence-to-Expression Prediction for a Model Membrane Protein Using Machine Learning and Computational Protein Design ↗
Abstract The recombinant expression of integral membrane proteins is notoriously challenging. One way to address this challenge is via computational genotype-to-phenotype models that determine how particular sequence features correlate with protein expression levels. However, the potential of such approaches is yet to be fully realized, at least partly because so few expression datasets are available. Here, we study the sequence-to-expression relationships of a variant library originally derived from combinatorial computational design. The controlled sequence diversity of this library makes th…
- OpenAlex · Published · First archived
Mission-Profile-Aware Physics-of-Failure Reliability Assessment and Quantitative FMEA of a 10 kW Solid-State Transformer ↗
Solid-state transformers (SSTs) are pivotal for next-generation power systems, yet their widespread adoption is hindered by reliability uncertainties under diverse operational conditions. This paper introduces a mission-profile-aware, physics-of-failure (PoF) reliability assessment framework that explicitly links operating conditions, degradation mechanisms, and statistical uncertainty. The approach combines PoF models for stress-sensitive components with handbook-based reliability models for passive elements, employing Joint Electron Device Engineering Council (JEDEC)-aligned statistical meth…
- OpenAlex · Published · First archived
From card catalogs to chatbots: envisioning academic library services in the era of AI and LLMs ↗
Purpose This article introduces the CARDLG framework: Collections, Access, Retrieval, Description, Literacy and Governance. The framework analyzes how generative artificial intelligence (AI) and large language models reconfigure recurring library infrastructural dimensions rather than representing wholly unprecedented disruption. Design/methodology/approach The analysis draws on library history from manuscript repositories through digital discovery systems, tracing how each CARDLG dimension evolved across technological eras. It identifies continuities, accelerations and ruptures in AI/large la…
- OpenAlex · Published · First archived
A Scalable Large Language Model‐Aided Approach for Mining Plant‐Compound Pairs From Scientific Literature ↗
Societal Impact Statement Understanding plant chemicals is vital for medicine and agriculture, but global data remain fragmented and biased. We developed an AI‐powered tool to automatically extract plant‐compound information from scientific literature at scale and low cost. Our analysis revealed significant knowledge gaps, particularly in the Southern Hemisphere. By efficiently uncovering thousands of potential new plant‐chemical links, this work provides a crucial resource for bioprospecting, taxonomy and conservation efforts, helping to ensure the benefits of nature's chemical diversity are…
- OpenAlex · Published · First archived
Glucuronyl Glucosyloleanolate: A Safe and Potent AI ‐Discovered Depigmenting Saponin ↗
ABSTRACT Purpose Saponins are amphiphilic glycosides found in plants. They have long been used in traditional Asian remedies for their anti‐inflammatory, protective, and rejuvenating properties. One representative, Glucuronyl Glucosyloleanolate (GGO), was recently discovered using a combination of artificial intelligence and experimental testing as a depigmenting agent. Its safety has been verified in humans during a patch test on 12 volunteers, followed by an HRIPT study on 100 volunteers. Finally, its depigmenting efficacy has been demonstrated after a clinical trial on 60 volunteers. Method…
- OpenAlex · Published · First archived
Generative AI as a Mediator in Creator Collaboration: Challenges, Design Opportunities, and Concerns ↗
As Generative AI (GenAI) expands beyond individual workflows and becomes increasingly integrated into collaborative creative work, its potential to address the fundamental challenge of building common ground among creators with diverse expertise remains underexplored. Specifically, we lack empirical understanding of how GenAI can mediate collaboration asymmetries among heterogeneous creators. To address this gap, we conducted a multi-method study with narrative writers (N = 10) and visual designers (N = 10) in the context of game development. Our findings demonstrate that GenAI goes beyond ind…
- OpenAlex · Published · First archived
AI-assisted speaking practice for EFL learners: effects on anxiety, enjoyment, and engagement compared to traditional teacher-led practice ↗
Abstract Although recent studies have demonstrated the potential of AI chatbots in reducing foreign language speaking anxiety (FLSA) and enhancing positive emotions, significant gaps remain in the literature. Most previous research has examined anxiety, enjoyment, and engagement in isolation, with few studies investigating their dynamic interplay within a single intervention. Moreover, speaking, the skill most susceptible to anxiety, has received comparatively less attention in AI intervention research compared to writing. To address these gaps, this quasi-experimental study compares AI-assist…
- OpenAlex · Published · First archived
Large language models for pneumonia detection in radiology reports via text analysis ↗
Pneumonia is a common infection in critically ill patients with poor outcomes, and its identification relies heavily on radiological evidence. Most MIMIC-based pneumonia studies rely on structured codes rather than radiological text, which may limit the precision of pneumonia-related case identification. This study evaluated large language models (LLMs) and retrieval-augmented generation (RAG) frameworks for automated detection of radiographic evidence of pneumonia from radiology reports. Radiological reports from the MIMIC-IV database were analyzed using pre-trained models in an inference-onl…
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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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