This release contains the source code used to produce the results reported in "Compact temporal-coding neurons for fast and energy-efficient edge computing". Please refer to the README for installation instructions, dependencies, and commands for reproducing the results. Full Changelog: https://github.com/ttttwwww/FNL-...
HTTP/2 (Hypertext Transfer Protocol Version 2) 4.4.5 Comparative Analysis 4.4.6 Future Outlook of Application Protocols 4.5 Standardization, Interoperability, and Performance Evaluation 4.5.1 Standardization in IoT 4.5.2Interoperability in IoT 4.5.3Performance Evaluation of IoT Systems 4.5.4Challenges and Research Oppo...
K. Rajasankari, Dr.S. SILVIA PRISCILA· 0 citations
Vehicular Ad-hoc Networks (VANETs), utilized as a part of Intelligent Transportation Systems (ITS), are crucial for transmitting the information in real time between the vehicles as well as between the vehicles and the infrastructure, thereby improving safety, traffic management, as well as driving efficiency. Despite...
Mr. Hari Krishna Chilakala, Boddepalli Kiran Kumar· International Journal of Sci...· 0 citations
The Cohesion UFT has a property that follows from its construction rather than fromany decision about presentation: it executes. Its constants are derived in closed formfrom a single axiom with no fitted parameters, its causal order is fixed, its vocabularyis finite and every term has a mechanical referent, and the sam...
Dexter Gilbert· Zenodo (CERN European Organi...· 0 citations
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Experiment outputs behind every table and figure of Persistence, Not Consensus: Measured Triage of Unsupervised Anomaly Rankings of TESS Light Curves, produced with lctriage 1.1.0 on TESS sectors 1, 2 and 13. Kept apart from the catalogue so that scores computed on synthetically modified light curves cannot be mistaken...
Théo Chopard-Vilhem· Zenodo (CERN European Organi...· 0 citations
This version corrects two citation errors found by an automated check and confirmed by hand. In the Selection Process, HANDOFF was cited as arXiv:2606.06491 (TempoVLA) and now cites arXiv:2606.06493, and RoboNaldo was cited as arXiv:2606.11091 (QUIET, a network-neuroscience paper) and now cites arXiv:2606.11092. A refe...
Saluca Agentic AI Research Team· Zenodo (CERN European Organi...· 0 citations
Electricity transmission networks are changing from being based on individual assets to becoming systems that can be observed digitally, are driven by data and are becoming more adaptive. This shift is particularly important for Saudi Arabia, since the rapid expansion of renewable energy, the great distances across the...
Muhammad Waseem Raza· International Journal of Adv...· 0 citations
Overview EVRPTW-ARC (Arc-Based Realistic Conditions) is a multi-scenario dataset providing realistic travel information for research on the Electric Vehicle Routing Problem with Time Windows (EVRPTW). The dataset is built from the 92 benchmark instances proposed by Schneider et al. (2014). The original routing informat...
Nada MORGHAM, Mariem Belhor· Zenodo (CERN European Organi...· 0 citations
This dataset contains the corpus spreadsheet (163 papers), the rating evidence trail (156 entries), and the completed PRISMA 2020 checklist for the systematic survey "Simulation Tools for Resource Scheduling in Fog and Edge Computing: A Survey on Machine Learning Integration," submitted to Discover Artificial Intellige...
Manjula Shenoy K, Harry John· Zenodo (CERN European Organi...· 0 citations
Overview EVRPTW-ARC (Arc-Based Realistic Conditions) is a multi-scenario dataset providing realistic travel information for research on the Electric Vehicle Routing Problem with Time Windows (EVRPTW). The dataset is built from the 92 benchmark instances proposed by Schneider et al. (2014). The original routing informat...
Nada MORGHAM, Mariem Belhor· Zenodo (CERN European Organi...· 0 citations
Vehicular Ad-hoc Networks (VANETs), utilized as a part of Intelligent Transportation Systems (ITS), are crucial for transmitting the information in real time between the vehicles as well as between the vehicles and the infrastructure, thereby improving safety, traffic management, as well as driving efficiency. Despite...
Mr. Hari Krishna Chilakala, Boddepalli Kiran Kumar· International Journal of Sci...· 0 citations
We introduce the E8‑Phi Causal Tensor Network (EPC‑TN), which assigns each of the 240 E8 root vectors to a distinct causal edge in a brain‑wide tensor graph. By phi‑modulating a 132 Hz standing wave and coupling it to neuronal microtubule helices, the network self‑organizes into 120 paired harmonic eigenstates that cor...
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
