WIRED brought 10 of the most popular artificial Christmas trees into a studio and got three interior designers to pick the best through blind judging. For extra trimming, we checked in on how those trees fared once they were taken home and decorated. Shopping for an artificial Christmas tree can be overwhelming, especially when you're doing it online. You'll find yourself staring at product photos, wondering: How realistic does it look? Will it shed all over my living room? Can you see daylight through the branches? Are the branches strong enough to hold that lopsided homemade macaroni ornament you've hung on your tree since 2004? We got tired of guessing, so we did a little experiment. We brought 10 of the most popular artificial trees from three top brands (Balsam Hill, King of Christmas, and National Tree Company) and hauled them to a photo studio in Kansas.

We brought 10 of the most popular artificial Christmas trees into a studio, had volunteers assemble them, then got three interior designers to pick the best through blind judging. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You can spend hours scrolling through lists of the best artificial Christmas trees and still end up wondering what to buy. How real does it look? Are the branches strong enough to hold that lopsided homemade macaroni ornament you've hung on your tree since 2004? We decided to settle the debate once and for all by bringing the best-selling artificial trees from three leading brands into a studio for a blind-judged contest. We got 10 trees from Balsam Hill, King of Christmas, and National Tree Company, then found 10 assemblers to put the trees together and fluff them.

Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and constraint programming expertise. We introduce Gala, a framework that addresses this challenge with a global agentic approach: multiple specialized large language model (LLM) agents decompose the modeling task by global constraint type. Each agent is dedicated to detecting and generating code for a specific class of global constraint, while a final assembler agent integrates these constraint snippets into a complete MiniZinc model. By dividing the problem into smaller, well-defined sub-tasks, each LLM handles a simpler reasoning challenge, potentially reducing overall complexity. We conduct initial experiments with several LLMs and show better performance against baselines such as one-shot prompting and chain-of-thought prompting. Finally, we outline a comprehensive roadmap for future work, highlighting potential enhancements and directions for improvement.

Factorio is a 2D construction and management simulation video game about building automated factories to produce items of increasing complexity. A core feature of the game is its blueprint system, which allows players to easily save and replicate parts of their designs. Blueprints can reproduce any layout of objects in the game, but are typically used to encapsulate a complex behaviour, such as the production of a non-basic object. Once created, these blueprints are then used as basic building blocks, allowing the player to create a layer of abstraction. The usage of blueprints not only eases the expansion of the factory but also allows the sharing of designs with the game's community. The layout in a blueprint can be optimised using various criteria, such as the total space used or the final production throughput. The design of an optimal blueprint is a hard combinatorial problem, interleaving elements of many well-studied problems such as bin-packing, routing or network design. This work presents a new challenging problem and explores the feasibility of a constraint model to optimise Factorio blueprints, balancing correctness, optimality, and performance.

We provide partial implementations of von Neumann's universal constructor and universal copier, starting out with three types of simple building blocks using minimal assumptions. Using the same principles, we also construct Turing machines. Combining both, we arrive at a proposal for a self-replicating Turing machine. Our construction allows for mutations if desired, and we give a simple description language.

A case study reveals the theoretical analysis of algorithms is not always as helpful as standard dogma might suggest.

Pyspark is an Apache Spark which is an open-source cluster-computing framework for large-scale data processing written in Scala.

Machine learning based compilation has been explored for over a decade [1]. Early work focused on learning profitability heuristics while more recently, deep learning models have been used to build code-to-code models, for translating or decompiling code. However, to the best of our knowledge, there has been no prior work on using machine learning to entirely automate compilation i.e given a high level source code program generate the equivalent assembler code. In this paper, we investigate whether it is possible to learn an end-to-end machine compiler using neural machine translation. In particular, we focus on the translation of small C functions to x86 assembler We use an existing function-level C corpus, Anghabench [2], to build a parallel C-x86 assembler corpus.

Nintendo Co. has added Sharp Corp. as an assembler of its Switch console, according to people directly involved in the matter, as it works to stabilize production and hedge against U.S.-China trade tensions. The video game giant has struggled to produce enough units for most of this year as the hit game Animal Crossing: New Horizons and stuck-at-home consumers fueled demand. While the coronavirus outbreak hurt production early on, Nintendo President Shuntaro Furukawa said this month that output has returned to normal and the Switch is now made in Malaysia, in addition to existing China and Vietnam locations. That Malaysia factory is owned by Sharp, said the people, who asked not to be identified because the information isn't public. Nintendo's main assembly partner Foxconn Technology Co., a key unit of Foxconn Technology Group, owns a Sharp stake and helped connect the two Japanese companies, they added.

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized data structure for electronic health systems -- and several AUTOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and model- auditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability.