The technical feasibility of the proposed UAV-assisted recognition framework under the evaluated experimental conditions is demonstrated and its potential to support communication in scenarios where fixed-camera systems may be affected by occlusions, limited fields of view, or environmental variability is indicated.
Hafsa Waheed, Ghulam E. Mustafa Abro, S. Memon et al.· Scientific Reports· 0 citations
Ramsey’s theorem says that if it is large enough, order must appear. This paper asks how large “large enough” is──the answer is the theorem does not say. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──Ramsey’s theorem, the...
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
The doubling strategy──stake twice as much after every loss──can push the win rate arbitrarily close to 1. This paper asks whether the expectation moves with it──the answer is it stays exactly 0. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no...
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
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A companion paper (doi:10.5281/zenodo.22168191, "What must a theory of perturbational complexity explain?") consolidated nine constraints and challenged any theory to pass them — including C3: debiased perturbational complexity (R-dim, the reproducible dimensionality of the evoked response) is an inverted-U in network...
Nicolás Federico Galindez· Zenodo (CERN European Organi...· 0 citations
Between the plates of a charging capacitor, not one charge crosses. And still a magnetic field stands there. This paper asks what, then, a current is──the answer is not the motion of charge. What closes Ampere's law is what has been called a current. No new mathematical theorem and no new law is claimed. Scope of this...
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
Publication status. This manuscript is a critical narrative review and translational framework, not a registered systematic review or meta-analysis. It synthesizes targeted peer-reviewed biomedical and engineering literature, methodological guidance, and the supplied PDAD dossier. The evidence is used to separate estab...
M. A. N. Makeen· Zenodo (CERN European Organi...· 0 citations
Exact house edge and complete basic-strategy decision tables for 24 common blackjack rule sets (1/2/6 decks × S17/H17 × DAS/NDAS × LS/NLS), computed with a combinatorial total-dependent expected-value engine. Validated against established references: 6-deck S17 DAS LS = 0.485% (Wizard of Odds). Version 2.0 — published...
Damir Agonyan· Zenodo (CERN European Organi...· 0 citations
Publication status. This manuscript is a critical narrative review and translational framework, not a registered systematic review or meta-analysis. It synthesizes targeted peer-reviewed biomedical and engineering literature, methodological guidance, and the supplied PDAD dossier. The evidence is used to separate estab...
M. A. N. Makeen· Zenodo (CERN European Organi...· 0 citations
The quarter in the Bekenstein–Hawking entropy is one of the most quoted numbers in physics, and one that no closed framework derives from first principles. This paper reports what happens to that number inside a framework whose gravity is induced rather than fundamental — manufactured at one loop by an underlying E8 fi...
E.U.O.· Zenodo (CERN European Organi...· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 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.
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