Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to address three key challenges: perspective distortion correction, semantic understanding of facade elements, and spatial reasoning for PV layout optimization. Our four-stage pipeline processes images through geometric rectification, zero-shot semantic segmentation, Large Language Model (LLM) guided spatial reasoning, and energy simulation. Validation across 80 buildings in four countries demonstrates ro bust performance with mean area estimation errors of 6.2% ± 2.8% compared to expert annotations. The auto mated assessment requires approximately 100 seconds per building, a substantial gain in efficiency over manual methods. Simulated energy yield predictions confirm the method's reliability and applicability for regional poten tial studies, urban energy planning, and building-integrated photovoltaic (BIPV) deployment. Code is available at: https:github.com/CodeAXu/Solar-PV-Installation

Tianjin, China – At a factory in China's north, workers are busy testing an automated vehicle designed to move bulky items around industrial spaces, one of a new generation of robots Beijing wants to shift the country's manufacturing up the value chain. The robot's Tianjin-based maker has received tax breaks and government-guaranteed loans to build products that modernize China's vast factory sector and advance its technological expertise. "The government is paying great attention to the manufacturing sector and the real economy -- we can feel that," said Ren Zhiyong, general manager of Tianjin Langyu Robot Co. China is backing R&D efforts by high-tech manufacturers like Langyu, driven by an urgent desire to reduce reliance on imported technology and reinforce its dominance as a global factory power, even as it cracks down on other parts of the economy. Beijing's pivot puts the focus on advanced manufacturing, rather than the services sector, to steer the world's second-largest economy past the so-called "middle income trap", where countries lose productivity and stagnate in lower-value economic output.

China's burgeoning artificial intelligence sector is urged to not to drive another round of regional development imbalance. While on the path toward a moderately prosperous society, experts warn the AI industry should not create gaps among the affluent and poverty-stricken areas, thus affecting livelihoods in the latter. AI industrial clusters have taken shape in the Beijing-Tianjin-Hebei and Pearl River Delta regions, but other areas are lagging behind according to a research report from the Next Generation AI Research Institute at Nankai University, released this year. A new round of regional development imbalance is likely in the pipeline, it said. Statistics indicate the sector is expected to see its industrial value hit 70 billion yuan ($10.3 billion), more than doubling itself from 33.9 billion yuan according to China IRN, a domestic industrial think tank.

China's northern port city of Tianjin announced plans on Thursday to set up funds worth 100 billion yuan ($16 billion) to support the artificial intelligence industry, official news agency Xinhua said. China aims to become a world leader in artificial intelligence by 2025, taking on U.S. dominance in the sector amid heightened international tension over military applications of the technology. Tianjin will pour the funds into developing sectors such as intelligent robots and hardware and software. It will also set aside 30 billion yuan in a sub-fund for intelligent devices and intelligent upgrades for traditional industries. Sun Wenkui, vice mayor of Tianjin, pledged financial support of up to 30 million yuan for each scientific research institution, whether a provincial- or national-level body, that sets up in Tianjin.

BEIJING (Reuters) - China's northern port city of Tianjin announced plans on Thursday to set up funds worth 100 billion yuan ($16 billion) to support the artificial intelligence industry, official news agency Xinhua said. China aims to become a world leader in artificial intelligence by 2025, taking on U.S. dominance in the sector amid heightened international tension over military applications of the technology. Tianjin will pour the funds into developing sectors such as intelligent robots and hardware and software. It will also set aside 30 billion yuan in a sub-fund for intelligent devices and intelligent upgrades for traditional industries. Sun Wenkui, vice mayor of Tianjin, pledged financial support of up to 30 million yuan for each scientific research institution, whether a provincial- or national-level body, that sets up in Tianjin.

BEIJING (REUTERS) - China's northern port city of Tianjin announced plans on Thursday (May 17) to set up funds worth 100 billion yuan (S$21 billion) to support the artificial intelligence industry, official news agency Xinhua said. China aims to become a world leader in artificial intelligence by 2025, taking on US dominance in the sector amid heightened international tension over military applications of the technology. Tianjin will pour the funds into developing sectors such as intelligent robots and hardware and software. It will also set aside 30 billion yuan in a sub-fund for intelligent devices and intelligent upgrades for traditional industries. Sun Wenkui, vice mayor of Tianjin, pledged financial support of up to 30 million yuan for each scientific research institution, whether a provincial- or national-level body, that sets up in Tianjin.

Tianjin will pour the funds into developing sectors such as intelligent robots and hardware and software. It will also set aside 30 billion yuan in a sub-fund for intelligent devices and intelligent upgrades for traditional industries.

Customers at this automated JD.com store in Tianjin can pay via QR code on mobile phones and through facial recognition technology, both without sales assistance. First there was pilot-free aircraft in China, better known as drones. This year the country began setting up a tract of the South China Sea to test captain-less ships. And now there are stores without staff. This phenomenon, while not unique to China, has taken off quickly there this year with lots of growing room left.

The restricted Boltzmann machine (RBM) has been used as building blocks for many successful deep learning models, e.g., deep belief networks (DBN) and deep Boltzmann machine (DBM) etc. The training of RBM can be extremely slow in pathological regions. The second order optimization methods, such as quasi-Newton methods, were proposed to deal with this problem. However, the non-convexity results in many obstructions for training RBM, including the infeasibility of applying second order optimization methods. In order to overcome this obstruction, we introduce an em-like iterative project quasi-Newton (IPQN) algorithm. Specifically, we iteratively perform the sampling procedure where it is not necessary to update parameters, and the sub-training procedure that is convex. In sub-training procedures, we apply quasi-Newton methods to deal with the pathological problem. We further show that Newton's method turns out to be a good approximation of the natural gradient (NG) method in RBM training. We evaluate IPQN in a series of density estimation experiments on the artificial dataset and the MNIST digit dataset. Experimental results indicate that IPQN achieves an improved convergent performance over the traditional CD method.