The 3rd Generation Partnership Project started the study of Release 18 in 2021. Artificial intelligence (AI)-native air interface is one of the key features of Release 18, where AI for channel state information (CSI) feedback enhancement is selected as the representative use case. This article provides an overview of AI for CSI feedback enhancement in 5G-Advanced. Several representative non-AI and AI-enabled CSI feedback frameworks are first introduced and compared. Then, the standardization of AI for CSI feedback enhancement in 5G-advanced is presented in detail. First, the scope of the AI for CSI feedback enhancement in 5G-Advanced is presented and discussed. Then, the main challenges and open problems in the standardization of AI for CSI feedback enhancement, especially focusing on performance evaluation and the design of new protocols for AI-enabled CSI feedback, are identified and discussed. This article provides a guideline for the standardization study of AI-based CSI feedback enhancement.

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that can have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub: https://github.com/microsoft/torchgeo.

Video of a sidewalk delivery robot crossing yellow caution tape and rolling through a crime scene in Los Angeles went viral this week, amassing more than 650,000 views on Twitter and sparking debate about whether the technology is ready for prime time. It turns out the robot's error, at least in this case, was caused by humans. The video of the event was taken and posted on Twitter by William Gude, the owner of Film the Police LA, an LA-based police watchdog account. Gude was in the area of a suspected school shooting at Hollywood High School at around 10 a.m. when he captured on video the bot as it hovered on the street corner, looking confused, until someone lifted the tape, allowing the bot to continue on its way through the crime scene. A food delivery robot forces it's way across a police crime scene.

All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.

Dr. Sachin R Sakhare is working as a Professor in the Department of Computer Engineering of Vishwakarma Institute of Information Technology, Pune, India. He has 26 Years of experience in engineering education. He is recognised as PhD guide by Savitribai Phule Pune University and currently guiding 7 PhD scholars. He is a life member of CSI, ISTE and IAEngg. He has Published 39 research communications in national, international journals and conferences, with around 248 citations and H-index 6.

If you have a master's degree in computer science, mathematics, the natural or engineering sciences or an equivalent degree and are interested in High Performance Computing, this fellowship provides the perfect opportunity to pursue a PhD at one of our NHR Centers. All NHR Centers are affiliated with German universities which are internationally recognized in the fields of high-performance computing, numerical simulation and AI/ML. Applications for our next cohort can be submitted up to and including September 15, 2022. Funding starts on April 1, 2023. The thematic fields offered by each of the NHR Centers can be found here.

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