The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not account for the unpredictable nature of sewer decline, whereas probabilistic methods depend on extensive historical data, which is frequently lacking or incomplete. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models artificial neural networks (ANNs) and multiple linear regression (MLR) by integrating factors such as pipe age, material, diameter, environmental influences, and PACP ratings. ANNs utilized ReLU activation functions and Adam optimization, whereas MLR applied regularization to address multicollinearity, with both models assessed through metrics like RMSE, MAE, and R2. The findings indicated that ANNs surpassed MLR, attaining an R2 of 0.9066 compared to MLRs 0.8474, successfully modeling nonlinear relationships while preserving generalization. MLR, on the other hand, offered enhanced interpretability by pinpointing significant predictors such as residual buildup. As a result, pipeline degradation is driven by pipe length, age, and pipe diameter as key predictors, while depth, soil type, and segment show minimal influence in this analysis. Future studies ought to prioritize hybrid models that merge the accuracy of ANNs with the interpretability of MLR, incorporating advanced methods such as SHAP analysis and transfer learning to improve scalability in managing infrastructure and promoting environmental sustainability.

Group LASSO is widely used to enforce the structural sparsity, which achieves the sparsity at the inter-group level. In this paper, we propose a new formulation called "exclusive group LASSO", which brings out sparsity at intra-group level in the context of feature selection. The proposed exclusive group LASSO is applicable on any feature structures, regardless of their overlapping or non-overlapping structures. We provide analysis on the properties of exclusive group LASSO, and propose an effective iteratively re-weighted algorithm to solve the corresponding optimization problem with rigorous convergence analysis. We show applications of exclusive group LASSO for uncorrelated feature selection. Extensive experiments on both synthetic and real-world datasets validate the proposed method.

In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions. However, aligning WSIs with diagnostic captions presents a significant challenge. This difficulty arises from two main factors: 1) Gigapixel WSIs are unsuitable for direct input into deep learning models, and the redundancy and correlation among the patches demand more attention; and 2) Authentic WSI diagnostic captions are extremely limited, making it difficult to train an effective model. To overcome these obstacles, we present PathM3, a multimodal, multi-task, multiple instance learning (MIL) framework for WSI classification and captioning. PathM3 adapts a query-based transformer to effectively align WSIs with diagnostic captions. Given that histopathology visual patterns are redundantly distributed across WSIs, we aggregate each patch feature with MIL method that considers the correlations among instances. Furthermore, our PathM3 overcomes data scarcity in WSI-level captions by leveraging limited WSI diagnostic caption data in the manner of multi-task joint learning. Extensive experiments with improved classification accuracy and caption generation demonstrate the effectiveness of our method on both WSI classification and captioning task.

Walmart is expanding its drone delivery program from one pocket of the Dallas-Fort Worth area to millions of people in 30 municipalities in the area, Chief Executive Doug McMillon announced Tuesday at CES 2024. The retailer will use drone delivery systems operated by startup Zipline and by Alphabet subsidiary Wing, companies that have made hundreds of thousands of deliveries in recent years. They each recently obtained FAA clearance to fly their drones beyond visual line of sight (BVLOS) -- in other words, out of the eyesight of a human operator -- which makes large-scale drone delivery operations more practical and economical. Delivery drones offer fast service, with Walmart packages arriving between 10 and 30 minutes after an order is placed from stores up to 10 miles away. Walmart touts the technology for people who need missing cooking ingredients, last-minute birthday gifts, over-the-counter medications or movie night snacks.

Two years ago, Walmart began making its first delivery by drone. Since then, that program has expanded to seven states and 36 stores, with more than 10,000 deliveries completed. Now, it's looking to expand its delivery footprint. Also: The best drones: Which flying camera is for you? Walmart has partnered with Wing, a drone delivery service owned by Google's parent company, Alphabet, to bring drone delivery to two more stores -- both in the Dallas-Fort Worth area.

Wing, Alphabet's aviation subsidiary, is partnering with Walmart to kick off drone deliveries from the retail chain in the Dallas-Fort Worth (DFW) metro area. The flights will begin taking off "in the coming weeks" from a Walmart Supercenter in Frisco, TX, and the companies plan to expand to a second DFW location before the end of the year. The companies say the coverage area from both stores will cover 60,000 homes. The service will be available to homes within about six miles of the supported stores. Residents in those areas can order things like quick meals, groceries, essentials and over-the-counter medicines. The drones can fly up to 65 mph, and Wing says you'll get your items in under 30 minutes.

Sustainability and generative AI are two topics that are currently taking the world by storm. While they may seem unrelated, there is a significant intersection between the two. A recent study called "Making AI Less Thirsty" reveals just how much water is consumed when training large AI models like OpenAI's ChatGPT and Google's Bard. The study was conducted by researchers from the University of Colorado Riverside and the University of Texas Arlington. It compares and measures the environmental impact of AI training that requires massive amounts of constant electricity and water. The water is used to cool data centers that are essential to keep them running.

Americans shared whether or not they believe the government should regulate Artificial Intelligence amid the technology's rapid, and ongoing, advancement. AUSTIN, Texas – The majority of Americans who spoke with Fox News said the government should stay out of regulating artificial intelligence technologies. "Keep the government out of regulating things," a Fort Worth resident told Fox News. "They regulate too many things already." Brian similarly opposed state regulation of the technology.

Linear transformation of the inputs alters the training performance of feed-forward networks that are otherwise equivalent. However, most linear transforms are viewed as a pre-processing operation separate from the actual training. Starting from equivalent networks, it is shown that pre-processing inputs using linear transformation are equivalent to multiplying the negative gradient matrix with an autocorrelation matrix per training iteration. Second order method is proposed to find the autocorrelation matrix that maximizes learning in a given iteration. When the autocorrelation matrix is diagonal, the method optimizes input gains. This optimal input gain (OIG) approach is used to improve two first-order two-stage training algorithms, namely back-propagation (BP) and hidden weight optimization (HWO), which alternately update the input weights and solve linear equations for output weights. Results show that the proposed OIG approach greatly enhances the performance of the first-order algorithms, often allowing them to rival the popular Levenberg-Marquardt approach with far less computation. It is shown that HWO is equivalent to BP with Whitening transformation applied to the inputs. HWO effectively combines Whitening transformation with learning. Thus, OIG improved HWO could be a significant building block to more complex deep learning architectures.

There's an experimental McDonald's franchise in Fort Worth, Texas, different from any other you've been to. Moreover, while there are human employees, they aren't necessarily tired-looking teens running registers. A human restaurant team preps food orders and places them onto food and beverage conveyors, but automation is key in ensuring customers get their orders. Success or failure may hint at fast food's near future. In an email to Entrepreneur, a McDonald's spokesperson stressed that there aren't actual robots serving the food.