China's Unitree soars in market debut as investors bet on humanoid robots China's Unitree Robotics has soared in its market debut, a sign of investors' confidence in Chinese innovation in the burgeoning field of human-like robots. Shares of Unitree surged more than 620 percent on Wednesday as it began trading in Shanghai, lifting the Hangzhou-based company's market capitalisation to as high as 445 billion yuan ($66bn). Unitree, whose main competitors include China's AgiBot and US-based Tesla and Boston Dynamics, had priced its initial public offering at 50.8 yuan per share for a company valuation of about 61 billion yuan ($9bn). The landmark IPO marked the first listing of a humanoid robotics company in mainland China. Unitree, founded in 2016 by engineer Wang Xingxing, is one of China's leading makers of humanoid robots, with its demonstrations of human-like figures performing dancing and martial arts routines capturing widespread attention.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Unitree CEO Wang Xingxing was 26 when he founded the company.

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Unmanned Aerial Vehicles (UAVs) are evolving into language-interactive platforms, enabling more intuitive forms of human-drone interaction. While prior works have primarily focused on high-level planning and long-horizon navigation, we shift attention to language-guided fine-grained trajectory control, where UAVs execute short-range, reactive flight behaviors in response to language instructions. We formalize this problem as the Flying-on-a-Word (Flow) task and introduce UAV imitation learning as an effective approach. In this framework, UAVs learn fine-grained control policies by mimicking eUAxpert pilotVtrajectoriesFlopaired withwatomic Fly around the tree ahead Land on the left side of carlanguage instructions. To support this paradigm, we present UAV-Flow, the firstreal-world benchmark for language-conditioned, fine-grained UAV control.

Low-light image enhancement (LLIE) aims to improve the visibility and quality of images captured under poor illumination.

Humanoid robots are trained in China. The court ruled that the company in Hangzhou had been wrong to fire the worker because AI could do his job. Humanoid robots are trained in China. The court ruled that the company in Hangzhou had been wrong to fire the worker because AI could do his job. A court in China has ruled in favour of a worker whose company replaced him with artificial intelligence (AI), awarding him more than £28,000 in compensation.

China's DeepSeek unveils latest models a year after upending global tech China's DeepSeek has unveiled the latest versions of its signature artificial intelligence-powered chatbot, a year after its flagship model sent shockwaves through the global tech scene. The Chinese start-up launched preview versions of DeepSeek-V4-Pro and DeepSeek-V4-Flash on Friday as it touted its ability to go toe-to-toe with US rivals such as OpenAI and Google. The "flash" model has similar reasoning abilities to the "pro" version, while offering faster response times and more cost-effective pricing, the Hangzhou-based startup said. Like DeepSeek's previous chatbots, V4-Pro and V4-Flash follow an open-source model, meaning developers are free to use and modify them at will. The release comes after DeepSeek-R1 stunned the tech sector upon its launch in January last year with capabilities broadly comparable with those of ChatGPT and Gemini.

This paper focuses on the problem of unbounded density ratio estimation -- an understudied yet critical challenge in statistical learning -- and its application to covariate shift adaptation. Much of the existing literature assumes that the density ratio is either uniformly bounded or unbounded but known exactly. These conditions are often violated in practice, creating a gap between theoretical guarantees and real-world applicability. In contrast, this work directly addresses unbounded density ratios and integrates them into importance weighting for effective covariate shift adaptation. We propose a three-step estimation method that leverages unlabeled data from both the source and target distributions: (1) estimating a relative density ratio; (2) applying a truncation operation to control its unboundedness; and (3) transforming the truncated estimate back into the standard density ratio. The estimated density ratio is then employed as importance weights for regression under covariate shift. We establish rigorous, non-asymptotic convergence guarantees for both the proposed density ratio estimator and the resulting regression function estimator, demonstrating optimal or near-optimal convergence rates. Our findings offer new theoretical insights into density ratio estimation and learning under covariate shift, extending classical learning theory to more practical and challenging scenarios.

Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context of deep time-series forecasting, autocorrelation arises in both the input history and the label sequences, presenting two central research challenges: (1) designing neural architectures that model autocorrelation in history sequences, and (2) devising learning objectives that model autocorrelation in label sequences. Recent studies have made strides in tackling these challenges, but a systematic survey examining both aspects remains lacking. To bridge this gap, this paper provides a comprehensive review of deep time-series forecasting from the perspective of autocorrelation modeling. In contrast to existing surveys, this work makes two distinctive contributions. First, it proposes a novel taxonomy that encompasses recent literature on both model architectures and learning objectives -- whereas prior surveys neglect or inadequately discuss the latter aspect. Second, it offers a thorough analysis of the motivations, insights, and progression of the surveyed literature from a unified, autocorrelation-centric perspective, providing a holistic overview of the evolution of deep time-series forecasting. The full list of papers and resources is available at https://github.com/Master-PLC/Awesome-TSF-Papers.

Two robots box while German Chancellor Friedrich Merz visits Unitree Robotics in Zhejiang Province, China. In the exhibition hall at Unitree Robotics in Hangzhou, Friedrich Merz smiled and applauded the martial arts display by a platoon of humanoid warriors. But when a robot boxer advanced toward him, punching the air with its red-gloved fists, the German chancellor flinched, a look of alarm crossing his face as he appeared to realize the danger posed by an autonomous fighting machine. It was also a moment that crystallized for Merz the power of China's technology, according to a person familiar with his thinking. He saw it, too, as a sign of how far behind Germany has fallen and how European Union regulation holds back their efforts to catch up, the person said, asking not to be named discussing the chancellor's private views. The trip, last month, has triggered a broader reckoning that is starting to settle in across Europe: Maybe de-risking from China is just too big a task.