Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated classification models for robust assessment of physiotherapeutic exercise and gait analysis requires large, diverse datasets that are costly and time-consuming to collect. We present a multimodal dataset of physiotherapeutic and gait-related exercises, including correct and clinically relevant variants, recorded from 19 healthy subjects using synchronized IMUs and optical marker-based motion capture (MoCap). It contains data from nine IMUs and 68 markers tracking full-body kinematics. Four markers per IMU allow direct comparison between IMU- and MoCap-derived orientations. We additionally provide processed IMU orientations aligned to common segment coordinate systems, subject-specific OpenSim models, inverse kinematics outputs, and visualization tools for IMU-derived orientations. The dataset is fully annotated with movement quality ratings and timestamped segmentations. It supports various machine learning tasks such as exercise evaluation, gait classification, temporal segmentation, and biomechanical parameter estimation. Code for postprocessing, alignment, inverse kinematics, and technical validation is provided to promote reproducibility.

Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing the missing speaker's turns for real-time use cases, and (2) generating summaries from one-sided transcripts. Evaluating prompting and finetuned models on MultiWOZ, DailyDialog, and Candor with both human A/B testing and LLM-as-a-judge metrics, we find that access to one future turn and information about utterance length improves reconstruction, placeholder prompting helps to mitigate hallucination, and while large models generate promising reconstructions with prompting, smaller models require finetuning. Further, high-quality summaries can be generated without reconstructing missing turns. We present 1SC as a novel challenge and report promising results that mark a step toward privacy-aware conversational AI.

Anchor point placement is a crucial yet often overlooked aspect of exosuit design since it determines how forces interact with the human body. This work analyzes the impact of different anchor point positions on gait kinematics, muscular activation and energetic consumption. A total of six experiments were conducted with 11 subjects wearing the XoSoft exosuit, which assists hip flexion in five configurations. Subjects were instrumented with an IMU-based motion tracking system, EMG sensors, and a mask to measure metabolic consumption. The results show that positioning the knee anchor point on the posterior side while keeping the hip anchor on the anterior part can reduce muscle activation in the hip flexors by up to 10.21\% and metabolic expenditure by up to 18.45\%. Even if the only assisted joint was the hip, all the configurations introduced changes also in the knee and ankle kinematics. Overall, no single configuration was optimal across all subjects, suggesting that a personalized approach is necessary to transmit the assistance forces optimally. These findings emphasize that anchor point position does indeed have a significant impact on exoskeleton effectiveness and efficiency. However, these optimal positions are subject-specific to the exosuit design, and there is a strong need for future work to tailor musculoskeletal models to individual characteristics and validate these results in clinical populations.

News recommendation systems personalize homepage content to boost engagement, but factors like content type, editorial stance, and geographic focus impact recommendations. Local newspapers balance coverage across regions, yet identifying local articles is challenging due to implicit location cues like slang or landmarks. Traditional methods, such as Named Entity Recognition (NER) and Knowledge Graphs, infer locations, but Large Language Models (LLMs) offer new possibilities while raising concerns about accuracy and explainability. This paper explores LLMs for local article classification in Taboola's "Homepage For You" system, comparing them to traditional techniques. Key findings: (1) Knowledge Graphs enhance NER models' ability to detect implicit locations, (2) LLMs outperform traditional methods, and (3) LLMs can effectively identify local content without requiring Knowledge Graph integration. Offline evaluations showed LLMs excel at implicit location classification, while online A/B tests showed a significant increased in local views. A scalable pipeline integrating LLM-based location classification boosted local article distribution by 27%, preserving newspapers' brand identity and enhancing homepage personalization.

With its Gemini chatbot and Pixel AI phone software, it's fair to say Google has an obsessive focus on artificial intelligence. But all that advanced computational power requires millions of computers, known as'servers', housed inside data centres across the world that operate 24/7. Now, in an attempt to cater to its vast AI needs, Google is going nuclear. The tech giant has signed a deal with California-based nuclear firm Kairos Power to build new nuclear reactors to supply its US data centres with energy. Although the location of these reactors is yet to be revealed, Google said the first will be operational in 2030, with more to follow by 2035.

NEW YORK, April 13 (Reuters) - Face it, we could all use a little help with our money. So who better to ask for personal finance advice than a couple of the most powerful chatbots on the planet? Both OpenAI's ChatGPT and Google's Bard are dominating headlines recently, for their generative capabilities and vast storehouses of information. Each has far more processing power than, say, any individual personal finance writer (ahem). What is one great business idea?

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Texas woman accused of stabbing her online date in a Nevada hotel-casino in March 2022 for retaliation of the 2020 death of an Iranian military leader has been banned from attending classes at the University of Texas in Dallas while on house arrest, according to reports. Nika Nikoubin, 22, was charged with attempted murder, battery with a deadly weapon and burglary in March 2022, according to KLAS in Las Vegas. Nikoubin reportedly met a man on a dating website and the two rented a room together at the Sunset Station hotel.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Texas authorities claim Nevada law enforcement failed to inform them when a woman accused of stabbing a man inside a Las Vegas-area hotel room in an apparent act of revenge relocated to Dallas. Nika Nikoubin, 22, has been on house arrest in Texas since at least June 2022 after posting bond and being released from a Las Vegas jail, court records show. She has pleaded not guilty to attempted murder and battery in connection with the March 5, 2022, stabbing at a casino-hotel southeast of Las Vegas.

Companies have been trying out automatons to serve food in restaurants, make home deliveries or do chores in stores, partly in hopes of easing the worker shortage. Among the disenchanted, FedEx Corp. said last month it was powering down Roxo, its last-mile delivery robot, to prioritize several "nearer-term opportunities," a spokeswoman said. Inc. said it was ending field tests of Scout, its home-delivery robot, after learning that some aspects of its "unique delivery experience" weren't "meeting customers' needs," a company spokeswoman said. And over the summer, DoorDash Inc. said it was shutting down its Chowbotics business -- best known for Sally, the salad-making robot -- roughly 18 months after buying it. "While we gained valuable insights into how to better serve this market, we concluded our current approach was not meeting our very high thresholds for continued investment," a DoorDash spokesman said.

Among the disenchanted, FedEx Corp. said last month it was powering down Roxo, its last-mile delivery robot, to prioritize several "nearer-term opportunities," a spokeswoman said. Inc. said it was ending field tests of Scout, its home-delivery robot, after learning that some aspects of its "unique delivery experience" weren't "meeting customers' needs," a company spokeswoman said. And over the summer, DoorDash Inc. said it was shutting down its Chowbotics business--best known for Sally, the salad-making robot--roughly 18 months after buying it. "While we gained valuable insights into how to better serve this market, we concluded our current approach was not meeting our very high thresholds for continued investment," a DoorDash spokesman said. Companies have entertained hopes that the growing variety of robots could help them not only weather the worker shortage, but speed up labor-intensive tasks, improve customer service by reducing the number of things the human workers have to do, and as an added bonus, position their brands as innovative and forward-leaning.