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.

This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical workflows, highlights central limitations, i.e. data and annotation availability and explainability, that need to be addressed, and emphasises the importance of interdisciplinary approaches for fully harnessing the potential of Machine Learning in gait and sports biomechanics.

Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs' annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating.

CloudMoyo is the partner of choice for solutions at the intersection of cloud and analytics. We help modern enterprises define their path to the Cloud and leverage the power of data driven insights. Headquartered in Bellevue, WA, with a presence in Overland Park, Kansas and an innovation center in Pune, India, CloudMoyo is set apart by the company's relentless focus on delighting customers, the strength of our smart technology accelerators, our strong business domain experience, and a deep pool of technical talent with experience in the Microsoft Cloud & Advanced Analytics.

Health care information technology company Netsmart has purchased Remarkable Health, a Chandler, Arizona-based provider of artificial intelligence (AI) technology and software solutions for organizations focused on behavioral health and individuals with intellectual and developmental disabilities (I/DD). Terms of deal, which was announced Thursday, were not disclosed. Remarkable Health's products include CT One -- a management platform for behavioral health claims and records -- and Bells, a notetaking documentation solution for behavioral health clinical staff. Remarkable Health's products will complement that of Netsmart's CareFabric platform, an operating system that includes resources such as electronic health records and management tools, and which are utilized by providers like those in behavioral health, addiction treatment and autism care. Remarkable Health claims that the Bells platform helps reduce time spent on clinical documentation by over 50%, enabling organizations to serve six more clients per month.

WellSky is looking for a Principal Engineer (Principal Data Architect) to power and guide our Enterprise Data Platform and Enterprise Data Engineering operations. The Principal Engineer is a thought leader responsible for executing the technical aspects of WellSky's Enterprise & Platform Data. This is absolutely an individual contributor role with no team or people management. You will have direct impact on the users of our solutions, mainly doctors, nurses, and others on the front lines of healthcare and community services. Your hard work will touch the lives of real people and families navigating life and death issues with the support of our solutions.

OVERLAND PARK, Kan.--(BUSINESS WIRE)--Ready, set, INNOVATE! T-Mobile US, Inc. (NASDAQ: TMUS) today unveiled six exciting companies handpicked to participate in this year's T-Mobile Accelerator. These companies will work directly with T-Mobile leaders and other industry experts and mentors to develop and commercialize the next disruptive emerging products, applications and solutions made possible by T-Mobile's nationwide 5G network today and in the future. Formerly the Sprint Accelerator, the immersive program runs through July 30 and will culminate in Demo Day where participants showcase their accomplishments. "We are committed to using our broad and deep nationwide 5G network to accelerate innovation and spur the development of new, transformative applications. Mentoring, collaborating with, and providing resources to these six promising companies is an important part of that mission," said Neville Ray, President of Technology at T-Mobile.

Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare. AI, machine learning, and deep learning are already increasing profits in the healthcare industry. For example, according to research firm Frost & Sullivan by 2021, AI systems will generate $6.7 billion in global healthcare industry revenue.

Deep Convolutional Neural Networks (DCNNs) are used extensively in biomedical image segmentation. However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to semantic image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, different atrous blocks, shortcut connections and normalization methods are explored to select the optimal network structure setting. These lead to a new and full-scale DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Fine Group Normalization (FGN). Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve comparable segmentation Dice Similarity Coefficients (DSCs) to U-Net, optimized U-Net and hybrid network, but with significantly reduced trainable parameters due to the use of full-scale feature maps and therefore computationally is much more efficient for both the training and inference.