2026 Shipboard Immersion - Waterfronts Past & Present: Learning How Engineers Design with Nature
"Opportunity to learn about the #GreatLakes and coastal engineering in Milwaukee."
For "formal or nonformal educators who teach at the middle or high-school level that are experienced or new to the Great Lakes Literacy Principles"
STIPENDS Will be provided to o…
For #footpathfriday I'm sharing this one again. It's really one of our most liked trails.
Even though it doesn't gain a lot of elevation, the path along the shore of the lake is all I like:
narrow, pretty nature like, absolutely great views, bends along the shores so that you don't see too far, and mountains all around ... what else would you need 🙂
Customs and Border Protection (CBP) is seeking permission from the California city of San Clemente
to install an Anduril Industries surveillance tower on a cliff that would allow for constant monitoring of entire coastal neighborhoods.
The proposed tower is Anduril's Sentry, part of the "Autonomous Surveillance Tower" (AST) program.
While CBP says it will primarily monitor the coastline for boats carrying migrants,
it will actually be installed 1.5 mile…
Something for my #TTRPG bubble - but not only the TTRPG bubble.
I'm the kind of person who really likes to have transcripts or summaries of TTRPG sessions, but also struggles with participating and taking notes at the same time.
LLM apps for creating automatic meeting transcripts looked really promising, but:
1. Are usually costly …
2. … create privacy concerns …
Solving Problems of Unknown Difficulty
Nicholas Wu
https://arxiv.org/abs/2604.00156 https://arxiv.org/pdf/2604.00156 https://arxiv.org/html/2604.00156
arXiv:2604.00156v1 Announce Type: new
Abstract: This paper studies how uncertainty about problem difficulty shapes problem-solving strategies. I develop a dynamic model where an agent solves a problem by brainstorming approaches of unknown quality and allocating a fixed effort budget among them. Success arrives from spending effort pursuing good approaches, at a rate determined by the unknown problem difficulty. The agent balances costly exploration (expanding the set of approaches) with exploitation (pursuing existing approaches). Failures could signal either a bad idea or a hard problem, and this uncertainty generates novel dynamics: optimal search alternates between trying new approaches and revisiting previously abandoned ones. I then examine a principal-agent environment, where moral hazard arises on the intensive margin: how the agent explores. Dynamic commitment leads contracts to frontload incentives, which can be counteracted by the presence of learning. The framework reflects scientific discovery, product development, and other creative work, providing insights into innovation and organizational design.
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Half of the world's coral reefs suffered major bleaching during the 2014–2017 global heat wave, estimates suggest https://phys.org/news/2026-02-world-coral-reefs-major-global.html
🔊 #NowPlaying on #BBCRadio3:
#Radio3InConcert
- Bluebeard's Castle
The BBC Philharmonic Orchestra's Principal Guest Conductor, Anja Bihlmaier, is joined by Jennifer Johnston and Christopher Purves to unlock the secrets of Duke Bluebeard's Castle.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002qqs4
Towards Efficient Data Structures for Approximate Search with Range Queries
Ladan Kian, Dariusz R. Kowalski
https://arxiv.org/abs/2602.06860 https://arxiv.org/pdf/2602.06860 https://arxiv.org/html/2602.06860
arXiv:2602.06860v1 Announce Type: new
Abstract: Range queries are simple and popular types of queries used in data retrieval. However, extracting exact and complete information using range queries is costly. As a remedy, some previous work proposed a faster principle, {\em approximate} search with range queries, also called single range cover (SRC) search. It can, however, produce some false positives. In this work we introduce a new SRC search structure, a $c$-DAG (Directed Acyclic Graph), which provably decreases the average number of false positives by logarithmic factor while keeping asymptotically same time and memory complexities as a classic tree structure. A $c$-DAG is a tunable augmentation of the 1D-Tree with denser overlapping branches ($c \geq 3$ children per node). We perform a competitive analysis of a $c$-DAG with respect to 1D-Tree and derive an additive constant time overhead and a multiplicative logarithmic improvement of the false positives ratio, on average. We also provide a generic framework to extend our results to empirical distributions of queries, and demonstrate its effectiveness for Gowalla dataset. Finally, we quantify and discuss security and privacy aspects of SRC search on $c$-DAG vs 1D-Tree, mainly mitigation of structural leakage, which makes $c$-DAG a good data structure candidate for deployment in privacy-preserving systems (e.g., searchable encryption) and multimedia retrieval.
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