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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 ↗5377 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
Hybrid transformer and CNN for hyperspectral image classification with spectral band selection ↗
Abstract Hyperspectral images (HSI) play a vital role in remote sensing applications such as surveillance and precision agriculture, yet they face challenges including limited labeled samples and class imbalance. This paper presents a new method for HSI classification by integrating spectral band selection with advanced deep learning techniques. First, we propose an optimized band selection method alongside data augmentation using crop-and-resize transformations to enhance feature diversity. Second, we introduce a hybrid convolution-transformer framework that combines Vision Transformer (ViT)…
- OpenAlex · Published · First archived
From Sensing to Transforming: An AI-Enabled Capability Framework for Circular Construction and Demolition Waste Management ↗
Artificial intelligence (AI) and AI-enabled digital technologies are increasingly proposed as enablers of circularity in construction and demolition (C&D) waste management, yet existing reviews are organised around technology and lifecycle stage and rarely examine the organisational conditions under which technically capable applications move from pilot to sustained practice. This review addresses that gap through a PRISMA 2020-guided systematic review of 217 recent Scopus records (2022–2026), interpreted through dynamic capabilities theory, diffusion of innovation theory, and the circular eco…
- OpenAlex · Published · First archived
Decoupled Multiplicative Physics-Guided Neural Network for Extrapolative Modeling of GaN HEMT ↗
Rapid direct-current (DC) surrogate modeling of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) requires accurate prediction of the current-capability, conduction-loss, and self-heating indicators used in geometry screening. This work presents an electrothermal decoupled multiplicative physics-guided neural network (ET-DM-PGNN) for this task. It combines a gray-box backbone, separate high- and low-field polarization channels, a latent DC self-heating update, and bounded target-specific multiplicative residuals. A two-stage strategy first calibrates the complete surrogate and t…
- OpenAlex · Published · First archived
Energy Consumer Model Estimation Using Machine Learning ↗
A novel utility model of the daily electricity consumption patterns of any residential unit is proposed. The model is semi-parametric—its internal parameters are tractable to straightforward interpretations from the standpoints of microeconomics as well as game theory. The model considers fixed and shiftable loads. A modified Cobb–Douglas preference function is applied to incorporate utility tradeoffs in operating shiftable loads at different hours of the day based on the consumer’s intrinsic preference and time-of-use pricing. Model parameter estimation is formulated as a quadratic optimizati…
- OpenAlex · Published · First archived
Rapid Response Strategy for Transformer Gas Relay Fault Diagnosis Assisted by the IDBO-VMD-SVM Combined Model ↗
Power transformers are core components of power grids, and their accurate fault diagnosis is critical for ensuring secure and stable grid operation. To address the limitations of conventional gas relays: inadequate extraction of dissolved gas analysis (DGA) features in transformer oil, poor model precision, and susceptibility to imbalanced datasets, this paper presents an IDBO-VMD-SVM combined model-assisted rapid response strategy for transformer gas relays. A multi-dimensional feature space consisting of gas concentrations, proportions and ratios is established and the dataset is divided. A…
- OpenAlex · Published · First archived
Physics-Informed Machine Learning Framework for Predicting Joule Heating Activated Shape Recovery in Carbon Fiber Reinforced Shape Memory Epoxy Composites ↗
Shape memory epoxy composites recover a programmed shape when Joule heating drives the matrix through its glass transition, and a key challenge for these smart materials is predicting the recovery angle at drive currents outside the tested range. In this work, three models were compared for this task: a Kohlrausch–Williams–Watts (KWW) theory based Physics-KWW constitutive model, a gradient-boosted tree model (XGBoost) using thermomechanical state features, and a support vector regression (SVR) model using raw experimental variables together with thermomechanical features. All models were asses…
- OpenAlex · Published · First archived
Efficient Spiking Neural Network Prototype on FPGA for Classifying Gases ↗
Spiking neural networks (SNNs) are numerical models whose computational properties come from interconnected biological neurons and synapses, leading to the development of neuromorphic systems in either software or hardware. These models in digital hardware operate in the discrete time domain, computing mainly the membrane potential of a neuron, where the arithmetic processes can be reduced to additions with minimal loss in accuracy. This advantage comes from approximating the solution of the original differential membrane equation by a recurrent discrete-time algorithm. In this work, we presen…
- OpenAlex · Published · First archived
Energy constraints in large scale artificial intelligence and insights from human cognition for efficient large language models ↗
Abstract Large-scale artificial intelligence (AI) systems, especially large language models (LLMs), face growing constraints related to energy use, power delivery, memory traffic, experimentation, and infrastructure capacity. These pressures are often studied separately, which makes it difficult to understand how they interact across the full AI lifecycle. This review develops a cross-layer framework that brings these constraints together and examines whether resource-allocation strategies observed in human cognition can inform more sustainable AI design. We compare human and artificial system…
- OpenAlex · Published · First archived
Machine learning-enabled parameter space exploration for drilling fluid filtration ↗
Abstract Drilling-fluid filtration is governed by coupled effects of particle size distribution (PSD), solids concentration, and particle-retention conditions, making systematic exploration by direct particle-scale simulation computationally demanding. This study integrates computational fluid dynamics–discrete element method simulations with a machine-learning surrogate to enable rapid interrogation of filtration behaviour while restricting interpretation to data-supported regions. The surrogate predicts stabilised filtrate flux from numerical retention cutoff, volume-weighted particle-size-d…
- OpenAlex · Published · First archived
Verifier-guided knowledge distillation of large language models for auditable thermal comfort reasoning ↗
Occupant-centric building operation requires models that can not only predict thermal sensation, but also justify predictions through auditable reasoning. Traditional mechanistic models, such as Fanger’s predicted mean vote (PMV), often show limited field accuracy, whereas advanced machine learning models can improve prediction but typically remain opaque to facility managers and building operators. This study proposes a verifier-guided knowledge distillation framework for auditable thermal comfort reasoning using a lightweight LLaMA-3.2-3B student model. The framework consists of three sequen…
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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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