Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods. Ron Levie is with the Faculty of Mathematics, Technion - Israel Institute of Technology, 3200003 Haifa, Israel (e-mail: levieron@technion.ac.il). Gitta Kutyniok is with the Department of Mathematics, LMU Munich, 80331 München, Germany, and also with the Department of Physics and Technology, University of Tromsø, 9019 Tromsø, Norway (e-mail: kutyniok@math.lmu.de). Giuseppe Caire is with the Institute of Telecommunication Systems, TU Berlin, 10623 Berlin, Germany (e-mail: caire@tuberlin.de). A short version of this paper was presented in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022) in Singapore [1]. The location information of a User Equipment (UE) is essential for many current and envisioned applications that range from emergency 911 services [2], autonomous driving [3], intelligent transportation systems [4], proof of witness presence [5], 5G networks [6], to social networks, asset tracking and advertising [7], just to name a few. In urban environments, Global Navigation Satellite Systems (GNSS) alone may fail to provide a reliable localization estimate due to the lack of line-of-sight conditions between the UE and the GNSS satellites [8]. In addition, the continuous reception and detection of GNSS signals is one of the dominating factors in battery consumption for hand-held devices.

My book chapter shows in a tutorial way how to use machine learning to assist quantum chemistry research. The chapter – available for free download till November 6 – is a part of the book edited by Kenneth Ruud and Erkki J. Brändas. It collects a dozen of contributions to the 10th Triennial Congress of the International Society for Theoretical Chemical Physics (ISTCP-X). This was a huge congress with 500 participants. It was held in pre-COVID-19 times in Tromsø, Norway, where the sun never set below the horizon at that time of the year. The team of the organizers led by Kenneth Ruud did really amazing job to bring together forefront science in chemical physics.

A team of researchers from the University of Colorado Boulder are working to apply machine learning artificial intelligence (AI) in psychiatry, with a speech-based mobile app that can categorise a patient's mental health status as well as, or better than, a human can. The university research paper has been published in Schizophrenia Bulletin, and lays out the promise and potential pitfalls of AI in psychiatry. Peter Foltz, a research professor at the Institute of Cognitive Science and co-author of the paper, said: "We are not in any way trying to replace clinicians, but we do believe we can create tools that will allow them to better monitor their patients." In Europe, the WHO estimated that 44.3 million people suffer with depression and 37.3 million suffer with anxiety. Diagnosis of mental health disorders are based on an age-old method that can be subjective and unreliable, notes paper co-author Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.

Scientists are now working to apply artificial intelligence (AI) to psychiatry, with a speech-based mobile app that can categorise a patient's mental health status as well as or better than a human can. "We are not in any way trying to replace clinicians," said Peter Foltz, research professor at the Institute of Cognitive Science at University of Colorado at Boulder. "But we do believe we can create tools that will allow them to better monitor their patients," he added in a paper published in Schizophrenia Bulletin. Even when a patient does make it in for an occasional visit, therapists base their diagnosis and treatment plan largely on listening to a patient talk – an age-old method that can be subjective and unreliable, notes paper co-author Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway. They can get distracted and sometimes miss out on subtle speech cues and warning signs.

New York, Nov 13 (IANS) Scientists are now working to apply Artificial Intelligence (AI) to psychiatry, with a speech-based mobile app that can categorize a patient"s mental health status as well as or better than a human can. "We are not in any way trying to replace clinicians," said Peter Foltz, research professor at the Institute of Cognitive Science at University of Colorado at Boulder. "But we do believe we can create tools that will allow them to better monitor their patients," he added in a paper published in Schizophrenia Bulletin. Even when a patient does make it in for an occasional visit, therapists base their diagnosis and treatment plan largely on listening to a patient talk – an age-old method that can be subjective and unreliable, notes paper co-author Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.