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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 ↗5519 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 deep hybrid feature extraction model based on MobileNet and vision transformer for acute lymphoblastic leukemia prediction ↗
Abstract Early and accurate identification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images can support microscopic screening, but reliable evaluation requires both discriminative feature learning and transparent reporting of the experimental protocol. This study presents MoViTHAFX-Net, a hybrid MobileNet–Vision Transformer (ViT) architecture with Hierarchical Attention Fusion (HAF) and Grad-CAM++ visualization for binary classification of ALL blasts and healthy hematogones (HEM) using the 10,661 labelled cell images in the training component of the C-NMC 2019 dataset.…
- OpenAlex · Published · First archived
DSFormer: An Enhanced Dual-Selective Fusion Transformer Framework for Hyperspectral Image Classification++ ↗
Hyperspectral image classification requires joint modeling of spectral signatures and spatial structure. In patch-based classification, each input patch is assigned the label of its center pixel, although neighboring pixels may belong to different land-cover classes. This setting motivates coordinating shallow spatial features, contextual refinement within the patch, and supervision of the resulting representation. We propose DSFormer++, an enhanced framework based on the original Dual Selective Fusion Transformer Network (DSFormer). Shallow Feature Compensation (SFC) combines shallow and patc…
- OpenAlex · Published · First archived
Intelligent Systems for Space Conditioning and Shading in Buildings: A Systematic Review of Artificial Intelligence Techniques and Their Energy Impact ↗
Artificial intelligence (AI) is increasingly applied to heating, ventilation, and air-conditioning (HVAC) control, whereas solar-radiation management still relies mainly on reactive strategies; whether AI capabilities validated in HVAC could be transferred to solar control has not been systematically assessed. This review compares both domains in terms of experimental maturity, climatic coverage, and input variables. Web of Science studies (2015–2026) reporting quantitative energy-demand variations were systematically reviewed following PRISMA 2020 and synthesized descriptively, with a sensiti…
- OpenAlex · Published · First archived
AI-enhanced ultrasound video analysis for BI-RADS 4–5 breast lesions: a multicenter study of diagnostic performance ↗
Abstract Purpose This study aims to develop a computer-aided diagnosis model for aiding ultrasonographers in classification of benign or malignant breast lesions in BI-RADS categories 4–5 using ultrasound videos. Materials and methods This prospective multicenter study enrolled 532 breast lesions categorized as breast imaging reporting and data system (BI-RADS) 4–5 by ultrasound. Of them, 334 breast lesions were included as training and validation sets. The remaining 198 lesions were assigned to a test set. We acquired both static and dynamic video ultrasound images of lesions for each patient…
- OpenAlex · Published · First archived
Detection of HI Holes in NGC 6946 Using Artificial Intelligence: New Candidates from moment-0 Maps ↗
The automated detection of HI holes in galaxy disks has been a long-standing challenge in extragalactic radio astronomy and has become increasingly urgent due to the large data volumes generated by current and forthcoming large-area HI surveys. Conventional approaches based on visual inspection are subjective, difficult to reproduce, and fundamentally incompatible with the scale of the data delivered by instruments such as MeerKAT and the Square Kilometer Array. We present a detection pipeline based on a convolutional neural network, which was specifically designed for the identification of HI…
- OpenAlex · Published · First archived
FlexENN: A Graph Neural Network for Binding Energy Prediction of Globular and Intrinsically Disordered Proteins ↗
Abstract Intrinsically disordered proteins (IDPs) drive a large fraction of cellular signaling, transcription, and assembly through interfaces that often lack a defined geometry, making the prediction of their binding energies challenging for methods developed primarily for folded complexes. Here, we introduce FlexENN, an interface-focused message-passing graph neural network framework comprising two independently trained models: FlexENN-F for folded protein complexes and FlexENN-I for complexes containing a disordered partner. The FlexENN architecture constructs a local interface graph in whi…
- OpenAlex · Published · First archived
Real-time hydrogen gas explosion prediction from fuel cell vehicles by physics-informed graph neural network ↗
Hydrogen fuel cell vehicles (HFCVs) generate electricity from hydrogen via electrochemical reactions and offer an efficient, environmentally friendly pathway to the dual‑carbon targets. Nevertheless, hydrogen leakage can rapidly mix with air to form large flammable vapor clouds. In congested urban areas, the resulting vapor cloud explosions may cause severe structural damage and significant economic losses. Machine learning techniques have been developed to predict the evolution of gas explosions. Nevertheless, accurately capturing the spatial relationships among obstacles and predicting explo…
- OpenAlex · Published · First archived
Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem ↗
We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM), and apply both approaches to the parametrized one-dimensional shallow-water dam-break problem. Both reduced models do not require time integration and learn a direct solution map from space, time, and dam-break parameters to the physical state. We present a detailed comparison for out-of-sample and extrapolated parameter values. In addition, we demonstrate that it is essential to introduce shock-aware collocation to improve the robustness of t…
- OpenAlex · Published · First archived
Validation of diagnostic artificial intelligence models for prostate pathology in a Middle Eastern cohort ↗
Artificial intelligence (AI) for pathology is accelerating cancer diagnostics. For global adoption, studies in currently under-represented populations - where the potential gains from AI may also be greatest - are needed. We present the first external validation study in a Middle Eastern setting, focusing on diagnosis and Gleason grading of prostate cancer. We digitised 339 prostate glass slides from Kurdistan, Iraq, representing a consecutive series of 185 patients from 2013 to 2024. Each slide was scanned on three digital pathology scanners, generating 1,017 whole-slide images. Task-specific…
- Hugging Face · Model / dataset date · First archived
mertkayacs/Wahler-4B-GGUF — model update ↗
# Wähler-4B GGUF: small German language model for local decisions GGUF builds of Wähler-4B, a small German language model for text classification and decisions on a CPU. **Full-precision Wähler-4B: 92.0% accuracy; Kev-4B: 81.1%** on held-out German decisions from the training data pipeline. This is a full-precision comparison; GGUF agreement is measured separately below. ## Run with JevAlt and llama.cpp Q4_K_M is the default, with **3.03 GB peak RAM** at a 4k context. ```sh pip install "jevalt[serve,gguf] @ git+https://github.com/mertkayacs/jevalt" && jevalt serve --model mertkayacs/Wahler-4B-
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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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