Breakthroughs, discoveries, and DIY tips sent every weekday. Subway commuters in Shenzhen, China, may soon need to make room for a fleet of chunky, snack-carrying delivery robots. Earlier this week, more than three dozen autonomous, four-wheeled delivery robots boarded and exited active subway trains, and eventually delivered packages to several 7-Eleven convenience stores. Although this demonstration was only a preliminary test and took place during off-peak hours, the company behind the subway-riding robots believes they could soon help stock shelves at around 100 7-Eleven locations. The initiative is part of a broader effort in China and other countries to normalize the presence of delivery robots operating in public spaces.

Lawson opened an experimental high-tech convenience store in Tokyo on Monday, aiming to improve operational efficiency and collect data. The new store -- Real x Tech Lawson -- is designed to test a range of technologies, including robotics, digital signs and artificial intelligence. "We hope to make this tech convenience store a standard for society," Lawson CEO Sadanobu Takemasu said at a news conference.

Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation 1 st Muhammad A. Muttaqien Automation Research T eam National Institute of AIST Tokyo, Japan muha.muttaqien@aist.go.jp 2 nd Tomohiro Motoda Automation Research T eam National Institute of AIST Tokyo, Japan tomohiro.motoda@aist.go.jp 3 rd Ryo Hanai Automation Research T eam National Institute of AIST Tokyo, Japan ryo.hanai@aist.go.jp 4 th Domae Y ukiyasu Automation Research T eam National Institute of AIST Tokyo, Japan domae.yukiyasu@aist.go.jp Abstract --This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed on utilizing gradient-based attention mechanisms and customized datasets to fine-tune performance. While CLIP, SAM, and Grad-CAM are established components, their integration within this structured pipeline represents a significant contribution to the field. The resulting segmented masks, generated through this combined approach, can be effectively utilized as inputs for robotic systems, enabling more precise and adaptive object manipulation in the context of convenience store products. I NTRODUCTION In recent years, the ability to recognize and manipulate specific objects within well-defined domains, such as products in convenience stores, has become increasingly important in the field of robotic manipulation [1] [2] [3]. As robots are expected to perform more complex tasks in diverse environments, the need for precise object identification and interaction grows, particularly in domains where a high level of accuracy is crucial. For instance, in convenience stores (Figure 1), robots must reliably identify and handle a wide variety of products, each with unique visual characteristics, to automate tasks such as stocking, sorting, and customer assistance.

Lawson and its owners, major trading house Mitsubishi and telecommunications carrier KDDI, said Wednesday they will open convenience stores of the "future" that utilize digital technology in Tokyo next spring. The new stores, to be in the Takanawa Gateway City complex in Tokyo's Minato Ward, will feature robots that can stock shelves and cook, as well as artificial intelligence signage that recommends products to customers. A booth will also be set up where customers can remotely consult on topics such as nursing care and asset management. The project aims to reduce the workload of store staff by 30% by fiscal 2030. Labor shortages are the number one issue that needs to be address, Lawson President Sadanobu Takemasu said at a news conference on Wednesday.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.

Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.

In this article, Beau is a-spoiled. In an Ari Aster movie, the best thing that can happen is losing your head. Not literally, of course, although the Midsommar auteur is notoriously fond of literally cutting his characters off at the neck. In 2019, he said that "head trauma will always have a place in my movies," and his latest, Beau Is Afraid, holds true to that promise. Early on, just after Beau Wasserman (Joaquin Phoenix) cancels a planned visit to his mother, she is decapitated by a falling chandelier. But alongside the characters who get their skulls crushed and faces smashed are ones who desperately need a respite from the buzzing of their brains--who would give anything if they could, even for a minute, just stop thinking. Toni Colette's character in Hereditary comes from family with a long history of mental illness--a mother with dissociative identity disorder, a father with psychotic depression, a brother with schizophrenia--and is plagued by the feeling that she and her family are the object of a sinister conspiracy.

ALEXANDRIA, Va.--Innovation came to life for the Conexxus Innovation Research Committee (IRC) during a recent field trip that members made to multiple sites in Austin, Texas. One of the hallmarks of the IRC is to experience what's new for the industry firsthand. A visit to a new TXB Stores location in Georgetown, Texas, was on the list not only to taste its breakfast taco but also to see how an artificial intelligence pilot utilizing existing security camera system has progressed. Utilizing SparkCognition's Visual AI Advisor solution, the insights from the location visit were intriguing and revealing. To review the data, we visited with SparkCognition representatives at their offices and HyperWerx lab on a 50-acre site.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A good Samaritan in Tennessee reportedly helped rescue a woman in an alleged kidnapping and domestic violence situation after she used a hand signal popularized on TikTok. "If you see something, say something," witness Eric Streeval told WKRN. "Domestic violence is a bad thing here in Tennessee. The victims, a lot of times they're too afraid to speak out. And I credit the young lady in this situation with having the world's most courage of actually speaking out because who knows what would have happened."

Woflow, a data infrastructure company, raised $7.3 million in Series A funding to continue developing its automated approach to bring offline data online. The company helps customers with antiquated inventory systems power their merchant onboarding data, like restaurant menus and images, with APIs to structure data in a way that when someone's food order requests "no mustard," it is recognized properly, Woflow co-founder and CEO Jordan Nemrow told TechCrunch. Nemrow and Will Bewley founded the San Francisco-based company in 2017. "In the background, machine learning models and artificial intelligence-powered humans in the loop do the structuring for our customers, which include food delivery, e-commerce and point-of-sale," Nemrow added. "Restaurants usually deal with having offline data, but time equals money, and if there is incorrect data, there can be some financial reimbursement. We are the de facto solution for that."