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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 ↗5489 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
Bioacoustic raw-audio transformer effectively and efficiently encodes mixed sample rate data ↗
Abstract Deep learning has been widely adopted in the field of bioacoustics, enabling behavioral and ecological research into many sound producing animals through the use of specially designed and trained models. So-called foundational models can generalize to novel species or tasks, but require larger and more diverse training datasets. But the broad range of frequencies that animals produce poses a problem: bioacoustic datasets are recorded at various sample rates to capture different frequency ranges. Combining these data to train generalist models becomes exceedingly costly the wider the f…
- OpenAlex · Published · First archived
Physics-Guided Machine Learning for Joint Fault Diagnosis in Inverter-Fed Induction Motor Drives ↗
In intelligent electric vehicle (EV) traction drives, pulse-width modulation and field-oriented control (FOC) introduce broadband harmonics and non-stationary start-up transients. These dynamics obscure incipient inter-turn stator faults (ITSF), particularly when inverter switch failures and measurement interference co-occur. To meet the low-latency requirements of on-board vehicle control units (VCUs), this paper proposes a data-centric, physics-guided diagnosis pipeline. Relying exclusively on current measurements, the framework jointly detects and localises ITSF (1–3% severity), open-circui…
- OpenAlex · Published · First archived
Weighted Proportional Conflict Redistribution for Heterogeneous LLM Ensembles ↗
Smaller language models often exhibit high confidence in erroneous predictions, undermining traditional ensemble methods. In heterogeneous ensembles, simple averaging can grant disproportionate influence to overconfident but incorrect outputs. This paper introduces a reliability-weighted extension of the Proportional Conflict Redistribution rule (PCR6), where model-specific weights are derived from validation accuracy. We provide a formal treatment of the conflict measure, prove that the weighted rule avoids Zadeh's paradox in the high-conflict regime, and establish a quasi-associativity bound…
- OpenAlex · Published · First archived
Beyond access and algorithms: a curricular model for inquiry-driven student-AI engagement ↗
Purpose This paper proposes a pedagogical framework for cultivating inquiry-driven agency in student engagement with artificial intelligence (AI). It addresses a gap in the AI-in-education literature, in which substantial scholarly attention to material access and algorithmic fairness has been accompanied by comparatively less sustained attention to the learner-side capacities that determine whether AI is engaged with critically or passively. The paper offers three connected contributions: a cyclical model of inquiry-driven learning, a four-phase curricular sequence for student-AI engagement,…
- OpenAlex · Published · First archived
ClusSumm a user centered hierarchical clustering and adaptive transformer framework for efficient summarization of MOOC learner reviews ↗
Massive Open Online Courses (MOOCs) have become a major source of online education, generating large volumes of learner reviews that provide valuable insights into course quality and learning experiences. However, manually analyzing this feedback is time-consuming, while existing transformer-based summarization models often struggle to balance summary conciseness with information preservation. In addition, most evaluation frameworks rely solely on automatic metrics and rarely consider user preferences. To address these limitations, this study proposes ClusSumm, a user-centered hierarchical clu…
- OpenAlex · Published · First archived
AI-based smart energy consumption prediction for sustainable buildings using IoT data ↗
Abstract Two complementary evidence layers are investigated for this study to look at next-hour building electricity forecasting. The first is a commercial building case study showing a 14 month monitoring experience of a 12,400 square metre office building in Bengaluru, India. The statistics revealed in the past include an RMSE of 3.27 kWh, a MAE of 2.41 kWh, a MAPE of 4.83%, a CoD of 0.971, and a median edge inference time of 38 ms. Unfortunately, the original telemetry, feature dictionary, checkpoints and residuals at the time stamp level were not available, and therefore these values are n…
- OpenAlex · Published · First archived
MSMFusion: A Method for Multiscale Dual-Path State Space Modeling for Multimodal Medical Image Fusion ↗
Multimodal medical image fusion aims to integrate information from different imaging techniques to generate a single fused image that is comprehensive and rich in detail. Structural and functional imaging provide high-resolution anatomical details and tissue metabolic information, respectively. Complementary fusion improves quantitative fusion quality and preserves structural and functional information in one view, which aids clinical interpretation. Currently, convolutional neural network-based methods struggle to efficiently capture global features, whereas Transformer-based models excel at…
- OpenAlex · Published · First archived
Enerzyme: An Efficient Reactive Neural Network Potential Training Framework for Enzyme Cluster Models with Application to Methyltransferases ↗
Abstract Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but QM calculations remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce their cost, but enzymes introduce new challenges not faced by NNPs for small molecules, including large system sizes, the need for inclusion of solvent environments, substantial polarization, and charge transfer. Here, we demonstrate an integrated software framework for efficient training of NNPs for mechanistic study of enzymes, with demonstrations o…
- OpenAlex · Published · First archived
A comparative investigation of physics-driven machine learning architectures for consolidation analysis ↗
Physics-driven machine learning has emerged as a powerful framework for forward and inverse modeling of partial differential equations (PDEs), with increasing applications in geotechnical engineering. However, the relative performance of different physics-driven learning strategies remains problem-dependent, and their suitability for typical geotechnical problems has not been systematically established. This study systematically compares four physics-driven machine learning paradigms for consolidation analysis by combining Multi‑Layer Perceptrons (MLPs) and Kolmogorov–Arnold Networks (KANs) wi…
- OpenAlex · Published · First archived
Optimizing FeNiCrAlCu high entropy superalloy design: Integrating thermodynamic calculations, first-principles, and machine learning for enhanced stacking fault energy modeling ↗
High-entropy superalloys (HESA) are promising alternatives to traditional superalloys, offering enhanced performance, greater sustainability, and increased cost-effectiveness for applications involving elevated temperatures. This research centers on Fe-based HESA, emphasizing the critical role of stacking fault energy (SFE) on the mechanisms of deformation and creep. By using Fe as the base alloy instead of Ni, the development becomes more economical. We introduce an innovative approach for predicting SFE through a combination of machine learning and computational thermodynamics. Additionally,…
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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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