Hierarchical data are common in many domains like life sciences and e-commerce, and their embeddings often play a critical role. Although hyperbolic embeddings offer a grounded approach to representing hierarchical structures in low-dimensional spaces, their utility is hindered by optimization difficulties in hyperbolic space and dependence on handcrafted structural constraints. We propose RegD, a novel Euclidean framework that addresses these limitations by representing hierarchical data as geometric regions with two new metrics: (1) depth distance, which preserves the representational power of hyperbolic spaces for hierarchical data, and (2) boundary distance, which explicitly encodes set-inclusion relationships between regions in a general way. Our empirical evaluation on diverse real-world datasets shows consistent performance gains over state-of-the-art methods and demonstrates RegD's potential for broader applications beyond hierarchy alone tasks.

Mike Johns boarded a driverless Waymo taxi to an airport in Scottsdale, Arizona, but it began spinning in circles in a parking lot. He filmed the moment he was trapped in the vehicle, unable to stop the car or get help. Johns said he almost missed his flight.

Embedding models are crucial for various natural language processing tasks but can be limited by factors such as limited vocabulary, lack of context, and grammatical errors. This paper proposes a novel approach to improve embedding performance by leveraging large language models (LLMs) to enrich and rewrite input text before the embedding process. By utilizing ChatGPT 3.5 to provide additional context, correct inaccuracies, and incorporate metadata, the proposed method aims to enhance the utility and accuracy of embedding models. The effectiveness of this approach is evaluated on three datasets: Banking77Classification, TwitterSemEval 2015, and Amazon Counter-factual Classification. Results demonstrate significant improvements over the baseline model on the TwitterSemEval 2015 dataset, with the best-performing prompt achieving a score of 85.34 compared to the previous best of 81.52 on the Massive Text Embedding Benchmark (MTEB) Leaderboard. However, performance on the other two datasets was less impressive, highlighting the importance of considering domain-specific characteristics. The findings suggest that LLM-based text enrichment has shown promising results to improve embedding performance, particularly in certain domains. Hence, numerous limitations in the process of embedding can be avoided.

Deep neural networks, including Convolutional Neural Networks (CNNs) and Visual Transformers (ViT), have achieved stunning success in medical image domain. We study thorax disease classification in this paper. Effective extraction of features for the disease areas is crucial for disease classification on radiographic images. While various neural architectures and training techniques, such as self-supervised learning with contrastive/restorative learning, have been employed for disease classification on radiographic images, there are no principled methods which can effectively reduce the adverse effect of noise and background, or non-disease areas, on the radiographic images for disease classification. To address this challenge, we propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is universally applicable to the training of all neural networks. The LRFL method is both empirically motivated by the low frequency property observed on all the medical datasets in this paper, and theoretically motivated by our sharp generalization bound for neural networks with low-rank features. In the empirical study, using a neural network such as a ViT or a CNN pre-trained on unlabeled chest X-rays by Masked Autoencoders (MAE), our novel LRFL method is applied on the pre-trained neural network and demonstrate better classification results in terms of both multiclass area under the receiver operating curve (mAUC) and classification accuracy.

Charger logistics Inc. is a world- class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger logistics has transformed into a world-class transport provider and continue to grow. Charger logistics invests time and support into its employees to provide them with the room to learn and grow their expertise and work their way up. We are entrepreneurial-minded organization that welcomes and support individual idea and strategies. We are currently expanding and looking to add a motivated individual to our team based in our Scottsdale, AZ office.

We are currently seeking a highly innovative, passionate, and talented individual to drive innovation through Global Identity & Fraud, technology and analytics with an emphasis on accelerating new product development. This role is part of a small visionary solutions-oriented group developing and executing projects at the point where emerging data science and technology trends meet consumer and client needs, products, and services. We are looking for a versatile and experienced individual who can work across clients, business units, and regional teams. In this role, you will be a critical member of a fast-paced cross-functional development team focused on execution, so you must thrive in a time-constrained, multitasking environment. This position is part of Experian's Global Identity & Fraud Product team, concentrating on research and development of novel analytical solutions, product prototyping, as well as new digital data asset evaluations, implementation, and modeling in support of our Device Intelligence portfolio.

Back in 1969 I finally caught 2001: A Space Odyssey in a Cinerama theater in Scottsdale, Arizona. At that point, the film had been running in that theater for over a year. I had longed to see it since its release in 1968 (I remember seeing it on the marquee of a theater in downtown Indianapolis), but when we visited relatives in Phoenix the following summer the opportunity finally presented itself. After the crescendo of its end, and the credits that ran to the tune of Johann Strauss' "The Blue Danube," I stepped out of the theater in a fog, completely stunned. From the hype I had heard about the film I was expecting something of an ambitious, up-to-date Destination Moon.

Walmart is signaling its commitment to autonomous deliveries with a new investment in self-driving company Cruise. The two already have a cozy relationship, having recently worked together on a delivery pilot in Scottsdale, Arizona. Walmart was so impressed with Cruise's "differentiated business, unique tech and unmatched driverless testing" that it decided to take part in the GM subsidiary's $2.75 billion funding round. The investment will see Cruise become an important part of the retailer's "last mile delivery ecosystem" -- industry parlance for the final journey from warehouse to customer. Walmart has struck additional partnerships on driverless deliveries with companies including Google's Waymo, Ford and Udelv.

Autonomous vehicle startup Cruise has partnered with Walmart to deliver orders from a Scottsdale, AZ, Walmart store to local customers' homes, starting early next year. General Motors-backed autonomous vehicle startup Cruise has announced a partnership with Walmart to deliver orders from a Scottsdale, AZ, Walmart store to local customers' homes, starting early next year. Customers will be able to place orders to the store and have them delivered in one of Cruise's electric self-driving Chevy Bolts. If the pilot goes well, a Cruise spokesperson said, the company will mull launching on-demand delivery with other retailers in the future. Walmart has forged driverless vehicle delivery partnerships with other automakers and startups.

Walmart is teaming up with autonomous vehicle startup Cruise for a pilot delivery program that uses self-driving cars to bring orders from stores to homes around Scottsdale, Arizona. Customers place an order from their local Walmart and it will be delivered, contact-free by one of Cruise's all-electric self-driving Chevy Bolts. The program is set to start in early 2021 and will use a number of vehicles that have have at least one safety driver behind the wheel during every trip. Walmart has recently made its way into delivery programs with self-driving vehicles in several US locations and has teamed up with a number of firms including Ford and Waymo. Little details has been provided about the pilot program, but Walmart shared that the GM-owned Cruise will help with finding new ways to'use technology to serve customers in the future.'