We prove a conjecture of T. Piesk stated in 2013 in the OEIS entries A002487 (Stern's diatomic sequence) and A223541 (the array of nim-products 2^m ⊗ 2^n): for every n >= 0, the number of distinct values of 2^x ⊗ 2^y with x + y = n equals A002487(n+1), the number of hyperbinary representations of n. Here ⊗ is Conway's...
Roberto Blanco Gómez· Zenodo (CERN European Organi...· 0 citations
We prove a conjecture of T. Piesk stated in 2013 in the OEIS entries A002487 (Stern's diatomic sequence) and A223541 (the array of nim-products 2^m ⊗ 2^n): for every n >= 0, the number of distinct values of 2^x ⊗ 2^y with x + y = n equals A002487(n+1), the number of hyperbinary representations of n. Here ⊗ is Conway's...
Roberto Blanco Gómez· Zenodo (CERN European Organi...· 0 citations
The evolution of embedded systems is more rapid and is bringing about a transformation across all systems of healthcare, automotive, industrial automation, and consumer electronics where intelligent, energy efficient and application specific solutions are becoming more important. The paper seeks to describe the complet...
S.Poornimadarshini, T M Sathish Kumar· SCCTS Journal of Embedded Sy...· 0 citations
Renewable Energy Explained: Power Systems, Storage & Sustainable Technologies is a publication-grade Open Educational Resource (OER) module covering the thermodynamic principles, electrochemical modeling, conversion technologies, and grid integration of sustainable power systems. Serving as an integral core module with...
Prep4Uni.Online· Zenodo (CERN European Organi...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Abstract High penetration of inverter-interfaced generation, faults, and sudden load changes in power systems makes them more vulnerable to voltage and frequency deviations. Traditional controllers often struggle to adapt to varying disturbance types and severities in real time. Adaptive, data-driven control methods ar...
Thirteen executed Colab lab notebooks on interpreting, evaluating, and trusting large language models: feature attribution, attention analysis, probing, chain-of-thought faithfulness, mechanistic interpretability, behavioral auditing, explanation-aware training, and production explainability. Each lab runs on a free-ti...
Hadi Mohammadi· Zenodo (CERN European Organi...· 3 citations
Explainable Artificial Intelligence (AI) enables humans to understand and interpret decisions of AI models. Instead of having a black box, explainability supports humans in understanding AI models'behavior. Existing explainable AI approaches often present explanations on 2D displays using pre-recorded data, requiring u...
Ana Stanescu, Lucchas Ribeiro Skreinig, T. Langlotz et al.· 0 citations
We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and Multi-Author Writing Style Analysis), unified by a shared analytical framework: feature robustness under distribution shift is governed by support overlap between trai...
The CPI-XAI Governance Framework is introduced, a theory-informed model for operationalizing algorithmic transparency as an organizational information-systems capability in clinical process intelligence environments and addresses explainability as an organizational capability rather than a model-level technical feature...
Albert Adusei Brobbey, Narayan P. Bhosale, James Ackom· Frontiers in Artificial Inte...· 0 citations
The paper provides a comprehensive review of the research conducted across skill extraction, large language model (LLM)-based career guidance, knowledge graph construction, learning path recommendation, prerequisite modelling, and knowledge tracing, fairness and algorithmic bias in career AI, and country-based salary i...
S. H. Puwakgolla, W. Gunathilake, D. C. T. Disanayaka· Journal of Multidisciplinary...· 0 citations
This systematic review synthesizes 19 peer-reviewed articles published from 2015-2025 to map and classify ML and AI techniques used in higher education adaptive learning systems, assess evidence of impact on student academic performance, engagement and retention, and explore ethical issues such as algorithmic bias, dat...
M. I. H. Madurapperuma, G. A. H. J. Perera· Journal of Multidisciplinary...· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
