Who cares about kids when adult collectors are willing to pay top dollar? From the moment a pixelated Gengar and Nidorino faced off in the opening animation of the first games on the original Game Boy back in 1996, the franchise has been a perennial favorite of kids and adults alike. With 2026 marking 30th anniversary, Lego's first-ever collaboration with the enduringly popular monster-catching megahit is perfectly timed--a crossover of pop culture titans with just one problem: Anyone who isn't an ultra-fan with cavernously deep pockets isn't invited. The recent announcement of a line of Lego Pokémon wasn't a surprise--the Danish brick brand first revealed it had entered into a "multi-year partnership" with The Pokémon Company back in March 2025 --but the makeup of the range itself was. Despite the mass appeal, Lego is launching with just three sets, and every single one is age-rated 18+.

Artist Tom Jung's 1977 painting introduced the world to the look and feel of George Lucas' blockbuster adventure. Breakthroughs, discoveries, and DIY tips sent every weekday. Darth Vader's reign has ended. For a brief time, he owned the mantle of "Most Expensive Piece of Star Wars Memorabilia," but before you could say "more wealth than you can imagine" he fell once again, with a new challenger rising to take his place. It was only this past September that a verified screen-used lightsaber hilt wielded by the Dark Lord of the Sith in and set a sales record by fetching $3.65 million.

The'King of American Coins' remained hidden in a late collector's archive for decades. Breakthroughs, discoveries, and DIY tips sent every weekday. One of the country's rarest coins is rarer than even expert coin collectors believed. After the surprise discovery of a long-lost 1804 dollar (aka the " King of American Coins "), the rarity's total known count now stands at 16. Regardless of its ranking, the silver coin is expected to fetch significantly more than its original worth when it hits the auction block on December 9. According to auctioneers at Stack's Bowers Galleries, the story begins with former President Andrew Jackson.

We analyze how our MeT A and DA techniques work for temporal graph learning. Next, we provide therotical analysis to show that our methods meet this expectation. Based on Alg. 1, because edges This theorem guarantees that our DA techiniques do not break the original edge time distribution. Otherwise, if the original distribution is broken, e.g., all the edge time decrease to 0, the augmented edges cannot reflect the practical condition and may degrade the models' generalization. DA technique does not change the number of edges.

Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR and CPRA. Violations of this principle have substantial real-world consequences, with regulatory actions resulting in fines reaching hundreds of millions of dollars. Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large datasets, resulting in an emerging research area known as Data Minimization in Machine Learning (DMML). At the same time, existing work on other ML privacy and security topics often addresses concerns relevant to DMML without explicitly acknowledging the connection. This disconnect leads to confusion among practitioners, complicating their efforts to implement DM principles and interpret the terminology, metrics, and evaluation criteria used across different research communities. To address this gap, our work introduces a comprehensive framework for DMML, including a unified data pipeline, adversaries, and points of minimization. This framework allows us to systematically review the literature on data minimization and \emph{DM-adjacent} methodologies, for the first time presenting a structured overview designed to help practitioners and researchers effectively apply DM principles. Our work facilitates a unified DM-centric understanding and broader adoption of data minimization strategies in AI/ML.

Respiratory insufficiency is a medic symptom in which a person gets a reduced amount of oxygen in the blood. This paper reports the experience of building SPIRA: an intelligent system for detecting respiratory insufficiency from voice. It compiles challenges faced in two succeeding implementations of the same architecture, summarizing lessons learned on data collection, training, and inference for future projects in similar systems.

--The Internet comprises of interconnected, independently managed Autonomous Systems (AS) that rely on the Border Gateway Protocol (BGP) for inter-domain routing. BGP anomalies--such as route leaks and hijacks--can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security. Although existing rule-based and machine learning methods can detect these anomalies using structured metrics, they still require experts with in-depth BGP knowledge of, for example, AS relationships and historical incidents, to interpret events and propose remediation. In this paper, we introduce BEAR (BGP Event Analysis and Reporting), a novel framework that leverages large language models (LLMs) to automatically generate comprehensive reports explaining detected BGP anomaly events. BEAR employs a multi-step reasoning process that translates tabular BGP data into detailed textual narratives, enhancing interpretability and analytical precision. T o address the limited availability of publicly documented BGP anomalies, we also present a synthetic data generation framework powered by LLMs. Evaluations on both real and synthetic datasets demonstrate that BEAR achieves 100% accuracy, outperforming Chain-of-Thought and in-context learning baselines. This work pioneers an automated approach for explaining BGP anomaly events, offering valuable operational insights for network management. The Border Gateway Protocol (BGP) is the principal inter-domain routing protocol that facilitates data exchange across the Internet by enabling autonomous systems (ASes) to disseminate network reachability information [1]. As the backbone of Internet connectivity, BGP's proper functioning is critical for maintaining global network stability and performance [2].

Over the last couple of weeks I have been tidying our attic, and while the general aim has been to prevent its contents from collapsing through the ceiling, I have a side-mission. My most valued possession when I was twelve was a Casio GD-8 Car Race watch – a digital timepiece that included a built-in racing game on its tiny monochrome LCD display. Two big buttons on the front let you steer left and right to avoid incoming vehicles and your aim was to stay alive as long as possible. I lost count of the number of times it was confiscated by teachers at my school. I used to lend it to the hardest boys in the year, thereby guaranteeing me protection against bullies.

In this demo work we develop a method to plan and coordinate a multi-agent team to gather information on demand. The data is periodically requested by a static Operation Center (OC) from changeable goals locations. The mission of the team is to reach these locations, taking measurements and delivering the data to the OC. Due to the limited communication range as well as signal attenuation because of the obstacles, the agents must travel to the OC, to upload the data. The agents can play two roles: ones as workers gathering data, the others as collectors traveling invariant paths for collecting the data of the workers to re-transmit it to the OC. The refreshing time of the delivered information depends on the number of available agents as well as of the scenario. The proposed algorithm finds out the best balance between the number of collectors-workers and the partition of the scenario into working areas in the planning phase, which provides the minimum refreshing time and will be the one executed by the agents.