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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 ↗5465 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
Dynamically perturbed Ruppell Fox optimization with a context aware transformer for coherent document level English to Chinese translation ↗
Document-level English–Chinese neural machine translation requires consistent handling of lexical choices, entities, anaphora, and discourse relations across sentence boundaries. Existing context-aware models partially improve cross-sentence consistency, but many rely on fixed context windows, manually selected configurations, or single-path document representations, limiting their adaptability to domains with different discourse structures and terminology densities. This study proposes a unified framework that integrates a Context-Aware Transformer (CAT) with dynamically perturbed Rüppell’s F…
- OpenAlex · Published · First archived
Governing Large Language Model Services for Confidential Microdata: A Decision Framework for National Statistical Offices ↗
National statistical offices increasingly face requests to provide large language model (LLM) capabilities in secure research environments. The governance problem is to determine which service configurations, if any, can be supported safely on confidential microdata. These services challenge traditional statistical disclosure control because interaction is adaptive, systems may retrieve documents or call analytical tools, and outputs may appear plausible without being grounded in approved data. This paper proposes a governance framework that organizes each request as a sequence of decisions: w…
- OpenAlex · Published · First archived
Long-term multi-section traffic flow forecasting using the CNN–RNLSTM–MLR neural network ↗
Nowadays, traffic congestion significantly contributes to severe economic and environmental problems in cities. Anticipating traffic flow is a highly effective strategy for mitigating congestion, and accurate predictions are vital for practical traffic guidance, management, and control. However, many existing approaches either treat spatial correlations and temporal dependencies separately or rely on single-architecture models, which limits their ability to capture complex spatiotemporal patterns in real-world networks. Even in cases where these factors have been considered in a combined manne…
- OpenAlex · Published · First archived
A Domain-Adapted Multilingual Large Language Model for Mining Safety in African Languages ↗
The African mining industry continues to face a serious communication gap between technical safety documentation and the language used by the workforce. Safety manuals, operating procedures, and regulatory guidance are often produced in English or French, while many workers communicate in isiZulu, chiShona, and other local languages. This mismatch is not just inconvenient; it can be dangerous in safety-critical settings where misunderstandings may contribute to preventable incidents. This paper reports on Talk MINING, a domain-adapted multilingual large language model built on InkubaLM and tra…
- OpenAlex · Published · First archived
Machine learning and multi-omics analyses reveal CLDN1 as a candidate marker associated with colorectal cancer invasion and nutlin-3a (−) response ↗
OBJECTIVE: Colorectal cancer (CRC) is molecularly, cellularly and spatially heterogeneous, and current staging systems and biomarkers incompletely explain prognosis and treatment response. This study integrated transcriptomics, machine learning, single-cell RNA sequencing, spatial transcriptomics and in vitro experiments to prioritize candidate CRC-associated markers and characterize their potential biological and clinical relevance. METHODS: GSE44861 and GSE44076 from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) were used for differential expr…
- OpenAlex · Published · First archived
Fractal-Inspired Biorefineries for Sustainable Polymer Production: A Conceptual Framework Integrating Artificial Intelligence and Industry 5.0 ↗
The transition from petroleum-derived polymers to renewable materials requires biorefineries capable of managing the compositional variability and multiscale structure of lignocellulosic biomass. This conceptual narrative review examines fractal-inspired principles—including hierarchical organization, branching, modularity, and finite-range statistical self-similarity—as a design perspective for integrated biorefineries. Deterministic mathematical fractals are distinguished from statistical fractals and hierarchical engineered systems, and methods for characterizing scale-dependent surfaces, p…
- OpenAlex · Published · First archived
Governing Generative AI in Higher Education: A Cross-National Analysis of Policy Framing and Student Maneuvering ↗
Universities promote generative artificial intelligence (GenAI) as a pedagogical resource while regulating it as a risk to academic integrity and fair assessment. This study examines how differentiated policy framing varies across substantive policy dimensions and national contexts, and how students interpret and navigate such guidance in practice. Using a convergent mixed-methods design with quantitative priority, we analyzed GenAI policy documents from 36 universities in Denmark, the United Kingdom, the United States, and the Netherlands, complemented by eight semi-structured interviews with…
- OpenAlex · Published · First archived
EgoTokens: Egocentric 3D human reconstruction and language-guided shape editing via quantized position tokens in vision-language models ↗
3D human body reconstruction from egocentric images is a challenging task because of environmental and self-occlusion. Downward viewpoints and scale ambiguity can also hinder precise 3D estimation. Current methods mainly rely on specialized regression networks to reconstruct 3D meshes that often lack structural guidance from high-dimensional SMPL-X parameters. In the proposed work, we introduce EgoTokens, a framework that estimate egocentric SMPL-X parameters as a discrete sequence-generation task. Our end-to-end system employs vision-language models (VLMs) to predict SMPL-X body parameters di…
- OpenAlex · Published · First archived
DPCformer: Dual-Prior Calibration Transformer for low-light image enhancement ↗
Low-light image enhancement (LLIE) remains challenging because low-light degradation usually exhibits complex and coupled characteristics, including uneven illumination, weakened structures, and amplified noise. The aim of this paper is to develop an effective and efficient LLIE framework for coordinated exposure correction, structure preservation, and noise-aware detail recovery under complex low-light conditions. To this end, this paper proposes a Dual-Prior Calibration Transformer (DPCformer), which jointly exploits illumination-aware and content-aware priors in a unified restoration framew…
- OpenAlex · Published · First archived
From Numerical Schemes to Neural Network Architectures: A Physics-Informed Framework for Differential Equations ↗
Physics-informed neural networks usually incorporate differential equations primarily through the loss function, while the neural architecture itself is selected independently of the underlying numerical structure. This work develops a scheme-induced approach in which an analytically modified numerical method defines the computational architecture, while selected elements of the numerical scheme are made trainable and refined using physics-informed loss functions. The specific contribution is the formulation and systematic investigation of a class of scheme-parameterized low-fidelity physics-i…
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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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