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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 ↗5974 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
Machine Learning and Deep Learning Techniques for Fault Location in Transmission Lines ↗
Fault location in a power system is a sensitive task because of the grid’s complex environment. Traditional fault-location methods that rely only on impedance estimates or traveling waves are less accurate than before because modern transmission networks are more complex due to the integration of wind resources, power electronic interfaces, and changing operating conditions. This study provides an artificial intelligence-based comparative framework for transmission line fault locating based on models such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), convol…
- OpenAlex · Published · First archived
A noise-aware physics-informed neural network method for nonlinear consolidation with non-Darcian flow and continuous drainage boundary ↗
Accurate characterization of soft soil nonlinear consolidation with non-Darcian flow and complex boundary conditions is fundamental to assessing foundation stability. This study presents a Noise-Aware Physics-Informed Neural Network (NA-PINN) framework for one-dimensional nonlinear consolidation problems involving a continuous drainage boundary and non-Darcian flow. Multiple nonlinear couplings can cause gradient imbalance in standard PINNs. Furthermore, when measurement noise and sparse data are present, the inversion of the flow index is susceptible to ill-posedness. To address this problem,…
- OpenAlex · Published · First archived
Drugging the Undruggable in Oncology: Structural Computation, Machine Learning, and Generative AI for Historically Intractable Targets ↗
For four decades the central oncogenic drivers of human cancer the RAS GTPases, the transcription-factor MYC, mutant p53, and the protein-tyrosine phosphatases, fusion oncoproteins and intrinsically disordered regulators that surround them were regarded as undruggable: lacking deep, well-defined small-molecule binding pockets, governed by femtomolar substrate affinities, or driven through flat protein–protein interfaces that classical medicinal chemistry could not engage. The last decade has overturned that verdict. This narrative review argues that the convergence of three advances has made t…
- OpenAlex · Published · First archived
Continuous Active Correction of Deployable Space Telescopes Using Machine Learning ↗
Achieving high-resolution imaging for a deployable space telescope requires segments co-phased to nanometer precision. Typical alignment uses point sources (distant stars) or extended ground scenes. An on-board active correction system has been developed to provide continuous alignment by using a fibre source instead. IMPACT (Image-based Mirror Phasing and Alignment using Convolutional neTworks) measures point spread functions (PSFs) on a secondary mirror detector separated from the main science camera. By applying a deep machine learning algorithm, piston and tip/tilt aberrations can be retri…
- OpenAlex · Published · First archived
Artificial intelligence approaches to resistome profiling and predictive antimicrobial resistance surveillance ↗
Abstract Antimicrobial resistance (AMR) is a complex One Health challenge driven by the interconnected emergence, selection, persistence, and dissemination of resistance across clinical, animal, agricultural, food, and environmental microbiomes. Resistome profiling through whole-genome and metagenomic sequencing has transformed surveillance by enabling comprehensive characterization of antimicrobial resistance genes (ARGs); however, conventional homology-based approaches remain constrained by incomplete reference databases and may overlook divergent or previously uncharacterized resistance det…
- OpenAlex · Published · First archived
Domain-adaptive Bengali text summarization: Harnessing transformer-based large language models in closed-set contexts ↗
This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation. In general-domain summarization on the XLSum Bengali dataset, mT5 achieved stronger overall performance than Flan-T5, with ROUGE-1 and BERTScore values of 0.21 and 0.72, respectively. The Flan-T5-XLSUM model out…
- OpenAlex · Published · First archived
A Cache-Aware Compression Framework: Deriving the Compression Target and Stopping Rule for Transformers on CPU-Class Hardware ↗
In post-training compression of Transformer models, the compression ratio or pruning rate is often predetermined and does not directly take into account the memory capabilities of the target hardware. This work introduces a cache-aware post-training compression framework for CPU-class hardware. The framework derives the minimum required compression ratio from the effective weight working set of the operator or layer and the effective level-3 (L3) cache budget. The cache budget is treated as a hardware resource shared with concurrent modules rather than as a latency heuristic. If functional red…
- OpenAlex · Published · First archived
In silico AI-guided de novo design of S1PR1-targeting ligands from a siponimod scaffold for angiogenesis and fat-cartilage graft survival ↗
Fat cartilage graft survival is frequently limited by insufficient early vascularization, resulting in ischemia and partial tissue loss. Sphingosine-1-phosphate receptor 1 (S1PR1) is a key regulator of endothelial barrier integrity, vascular maturation, and controlled angiogenic signaling. Modulation of S1PR1 has been associated with stabilization of vascular networks and regulation of endothelial sprouting, making it a potential therapeutic target for structured angiogenesis. Targeted modulation of endogenous vascular regulatory pathways may provide a strategy to enhance structured angiogenes…
- OpenAlex · Published · First archived
ATAN: A hybrid graph neural network framework for accelerating aging-aware timing analysis ↗
CMOS device aging critically challenges digital circuit design, where conventional timing analysis struggles with the large number of input conditions, standard cells, and process corners. This work proposes ATAN, a novel framework for accelerating aging-aware timing analysis, employing a hybrid graph neural network. Synergistically combining a relational graph convolutional network and a graph attention network, the unique dual-branch framework captures complementary structural timing information and aging-sensitive variations for accurate aging-aware delay prediction. Integrated few-shot lea…
- OpenAlex · Published · First archived
Integrated Robotic System for Autonomous Inspection of Power Transmission Lines Using Multimodal Perception and Reconfigurable Topologies ↗
This paper presents an integrated robotic system for autonomous power transmission line inspection, evaluating three mechanical topologies–Line Walker, ModuClimber, and FlexRover–and a multimodal perception suite combining depth sensors. The comparative analysis reveals a distinct trade-off between mechanical simplicity and obstacle negotiation capabilities, with complex articulated designs proving essential for traversing obstacles. Crucially, the study uncovers significant challenges in algorithm portability; machine learning models trained on a specific robot topology suffered severe perfor…
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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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