Tootfinder

The US military has always had a massive global advantage against enemies by having bases all over the world. There are bases in every NATO country. This would appear to be a powerful threat to anyone willing to oppose American hegemon, and under normal conditions it would be.
But a lot of those kids serving on those bases joined, not because they love America but, because they needed a ticket out of poverty. They joined for the education, for the money, maybe a bit for the adventure, but, more than anything, to escape the ghetto or podunk backwater that trapped them. Under normal times, this is the best deal they could expect. Maybe they risk their lives, usually they sit around being bored for a few years, and they get to come out with respect and paid college.
But what they are being offered is normal in most of the countries they're stationed in. Free healthcare, cheap or free education, is just what citizens in a lot of countries have come to expect. If the US attacked a NATO country, how many would snap up citizenship if they were given a chance to defect? Bonus points for taking some hardware with you, I'm sure.
But there are some who love their country. There are some patriotic Americans on those bases. Some of them joined specifically to protect the US from all enemies, foreign *and* domestic. Given a chance to fulfill that oath or violate international law, what happens?
There are a good number of former military folks too who now are unsafe in the countries they served, who would do just about anything for citizenship in any EU country and almost any NATO ally. Some of those folks know things they swore an oath to never share, but the country they swore an oath to has betrayed them. Today there's no value in leaking those secrets, but in a war between the US and NATO allies things would be different. Some of those former military folks still believe in their oath, and know exactly who the real enemy is. What happens when there's a real threat of war, when they can use their knowledge to fulfill that oath to protect the US against those domestic threats?
There are a bunch of civilian tech workers who have become targets of the regime. Some of them had clearance, or know about the skeletons in the closet. They know about critical infrastructure, classified systems, all sorts of things that would be extremely valuable to an opponent. But the opponents of the US have always been a frightening *other*, never familiar societies these folks look up to, have visited, have thought about moving to, are trying to escape to.
All I'm saying here is that invading Venezuela and kidnapping the president has a very different calculus than does attacking Greenland. I don't know if Trump or his people are able to understand that, but if he and his folks aren't then I hope European leaders are. But more than that, I hope it never comes down to finding out.
But perhaps we should all think about what we would do to make sure things ended quickly if American leadership ever made such an incredible mistake.

»Introduction to CSS if() Statements and Conditional Logic«
CSS will probably become logically structurable after a long time. It's not a programming language and that's why it's all the more exciting.
🖌️ https://markodenic.com/introduction-to

Introduction to CSS if Statements and Conditional Logic
Conditional logic is a familiar concept to anyone who has written a programming language. Languages like JavaScript or Python use if/else statements to evaluate expressions and execute different blocks of code depending on whether the condition is true or false.

How did the Council approve the #Mercosur agreement, exactly?
«EU capitals now have until 5 p.m. on Friday to lodge any objections and formalize the vote. This so-called written procedure»
https://www.

Selling Privacy in Blockchain Transactions
Georgios Chionas, Olga Gorelkina, Piotr Krysta, Rida Laraki
https://arxiv.org/abs/2512.08096 https://arxiv.org/pdf/2512.08096 https://arxiv.org/html/2512.08096
arXiv:2512.08096v1 Announce Type: new
Abstract: We study methods to enhance privacy in blockchain transactions from an economic angle. We consider mechanisms for privacy-aware users whose utility depends not only on the outcome of the mechanism but also negatively on the exposure of their economic preferences. Specifically, we study two auction-theoretic settings with privacy-aware users. First, we analyze an order flow auction, where a user auctions off to specialized agents, called searchers, the right to execute her transaction while maintaining a degree of privacy. We examine how the degree of privacy affects the revenue of the auction and, broadly, the net utility of the privacy-aware user. In this new setting, we describe the optimal auction, which is a sealed-bid auction. Subsequently, we analyze a variant of a Dutch auction in which the user gradually decreases the price and the degree of privacy until the transaction is sold. We compare the revenue of this auction to that of the optimal one as a function of the number of communication rounds. Then, we introduce a two-sided market - a privacy marketplace - with multiple users selling their transactions under their privacy preferences to multiple searchers. We propose a posted-price mechanism for the two-sided market that guarantees constant approximation of the optimal social welfare while maintaining incentive compatibility (from both sides of the market) and budget balance. This work builds on the emerging line of research that attempts to improve the performance of economic mechanisms by appending cryptographic primitives to them.
toXiv_bot_toot

Approximate Cartesian Tree Matching with Substitutions
Panagiotis Charalampopoulos, Jonas Ellert, Manal Mohamed
https://arxiv.org/abs/2602.08570 https://arxiv.org/pdf/2602.08570 https://arxiv.org/html/2602.08570
arXiv:2602.08570v1 Announce Type: new
Abstract: The Cartesian tree of a sequence captures the relative order of the sequence's elements. In recent years, Cartesian tree matching has attracted considerable attention, particularly due to its applications in time series analysis. Consider a text $T$ of length $n$ and a pattern $P$ of length $m$. In the exact Cartesian tree matching problem, the task is to find all length-$m$ fragments of $T$ whose Cartesian tree coincides with the Cartesian tree $CT(P)$ of the pattern. Although the exact version of the problem can be solved in linear time [Park et al., TCS 2020], it remains rather restrictive; for example, it is not robust to outliers in the pattern.
To overcome this limitation, we consider the approximate setting, where the goal is to identify all fragments of $T$ that are close to some string whose Cartesian tree matches $CT(P)$. In this work, we quantify closeness via the widely used Hamming distance metric. For a given integer parameter $k>0$, we present an algorithm that computes all fragments of $T$ that are at Hamming distance at most $k$ from a string whose Cartesian tree matches $CT(P)$. Our algorithm runs in time $\mathcal O(n \sqrt{m} \cdot k^{2.5})$ for $k \leq m^{1/5}$ and in time $\mathcal O(nk^5)$ for $k \geq m^{1/5}$, thereby improving upon the state-of-the-art $\mathcal O(nmk)$-time algorithm of Kim and Han [TCS 2025] in the regime $k = o(m^{1/4})$.
On the way to our solution, we develop a toolbox of independent interest. First, we introduce a new notion of periodicity in Cartesian trees. Then, we lift multiple well-known combinatorial and algorithmic results for string matching and periodicity in strings to Cartesian tree matching and periodicity in Cartesian trees.
toXiv_bot_toot

Interesting how Poetiq (company) can improve on the performance of the standard Gemini 3.0 Pro model by adding refinements and tricks. It leads to a 9% improvement on the ARC-AGI-2 Benchmark.
https://poetiq.ai/posts/arcagi_verified/

Poetiq Shatters ARC-AGI-2 State of the Art at Half the Cost
We are proud to confirm that our system has officially outperformed existing methods, establishing a new state-of-the-art by a significant margin.

The US withdrawing from many organisations including GFCE (Global Forum on Cyber Expertise) doesn’t look promising about their commitment to cybersecurity.
#us #cybersecurity

Withdrawing the United States from International Organizations, Conventions, and Treaties that Are Contrary to the Interests of the United States
MEMORANDUM FOR THE HEADS OF EXECUTIVE DEPARTMENTS AND AGENCIES By the authority vested in me as President by the Constitution and the laws of the United

A profile of Anthropic and its key executives like Chris Olah, and a look at Project Vend, an internal "Claudius" experiment to run the office vending machine (Gideon Lewis-Kraus/New Yorker)
https://www.newyorker.com/magazine/2026/02

What Is Claude? Anthropic Doesn’t Know, Either
Researchers at the company are trying to understand their A.I. system’s mind—examining its neurons, running it through psychology experiments, and putting it on the therapy couch.

us_agencies: U.S. government agency websites (2018)
50 networks, one for each U.S. state, representing the web-based links between their associated government agencies websites. A node is an entire agency website and a directed edge (i,j) represents the existence of a hyperlink from any webpage in website i to some webpage in website j. Data was collected with a crawler. Nodes are annotated with the number of webpages per website, website name (related to its government function) and U…

Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
toXiv_bot_toot