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Deep learning has made significant advancements in medical data analysis, which has made it possible to advance the diagnostics process, prognostication, and therapeutic care through new methods. However, the usability of artificial neural networks in common clinical practice is still hindered by their excessive comput...
Bruhat–Tits buildings are the canonical combinatorial-geometric objects attached to reductive groups over non-Archimedean local fields, and the recent QNFO constructions of p-adic braid groups [12], p-adic anyon models [13], and a p-adic Temperley–Lieb parameter [14] anchor their discrete braiding data at the *vertex s...
The Visibility Problem Hypershape Shadow Hypothesis II Chloe J. Tully https://orcid.org/0009-0007-5661-7332 HSH Paper 1 https://doi.org/10.5281/zenodo.23010822 Project UAP Studies v0.1 3 October 2026 Preprint. Not peer-reviewed. Companion to HSH I (28 September 2026). Tier-2 material is not in this paper. DISCIPLINE HS...
The Visibility Problem Hypershape Shadow Hypothesis II Chloe J. Tully https://orcid.org/0009-0007-5661-7332 HSH Paper 1 https://doi.org/10.5281/zenodo.23010822 Project UAP Studies v0.1 3 October 2026 Preprint. Not peer-reviewed. Companion to HSH I (28 September 2026). Tier-2 material is not in this paper. DISCIPLINE HS...
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This monograph is the third volume of "Algorithmic Non-commutative Class Field Theory". It studies spectral, dynamical and arithmetic structures on finite quotients of Ã₂ (and, where possible, Ã_d) buildings, in particular on the complexes X₃, Y₂₁, …, Y₁₆₈ of Volumes I–II and on the canonical 2-tower of Cartwright–Steg...
This monograph is the third volume of "Algorithmic Non-commutative Class Field Theory". It studies spectral, dynamical and arithmetic structures on finite quotients of Ã₂ (and, where possible, Ã_d) buildings, in particular on the complexes X₃, Y₂₁, …, Y₁₆₈ of Volumes I–II and on the canonical 2-tower of Cartwright–Steg...
Lacan wrote that a signifier represents the subject for another signifier — S1 → S2 — and the subject is often pictured as a loop between S1 and S2. Read as a labelling of a closed chain, that picture has a flaw two crayons can expose: around a chain with an odd number of links, two colours cannot alternate. This note...
The bulk-boundary correspondence asserts that topological data of a (2+1)-dimensional phase determine the physics of its boundary, but in the setting of anyon condensation the correspondence is usually stated structurally rather than quantitatively. We propose a set of computable indices that quantify the correspondenc...
Photonic spiking neural networks (PSNNs) leverage inherently discrete and event‐driven operations, thereby serving as a promising architecture for optical digital logic. However, limited physical degrees of freedom in current schemes often lead to non‐standardized encoding methods, thereby preventing the realization...
Bruhat–Tits buildings are the canonical combinatorial-geometric objects attached to reductive groups over non-Archimedean local fields, and the recent QNFO constructions of p-adic braid groups [12], p-adic anyon models [13], and a p-adic Temperley–Lieb parameter [14] anchor their discrete braiding data at the *vertex s...
Executive Abstract (English) Unmanned commercial facilities (e.g., 24-hour laundromats, automated storage units, and self-service currency exchange kiosks) suffer from an acute systemic vulnerability: conventional passive video surveillance (CCTV) records crime retroactively but fails to prevent active financial or phy...
As businesses strive to improve efficiency, reduce latency, and leverage the power of data, understanding the strengths and limitations of these two computing paradigms is crucial. Full article: https://davidohnstad.com/edge-computing-vs-cloud-computing-which-is-the-future/
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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