Deep learning, which is a subfield of machine learning, has drawn significant interest in recent years. It was originally used in speech recognition (Deng, Hinton, and Kingsbury, 2013), natural language processing (Kastrati et al., 2021), and computer vision (Hu et al., 2018). More recently, it has been applied to astronomy, astrophysics, and solar physics (Liu et al., 2020a; Jiang et al., 2021; Espuña Fontcuberta et al., 2023; Mercea et al., 2023; Scully et al., 2023). Here, we present a new deep-learning method, specifically an attention-aided convolutional neural network (CNN), named SolarCNN, for solar image super-resolution. SolarCNN aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI; Scherrer et al., 1995) on board the Solar and Heliospheric Observatory (SOHO; Domingo, Fleck, and Poland, 1995). The ground-truth labels used for training SolarCNN are the LOS magnetograms of the same ARs collected by the Helioseismic and Magnetic Imager (HMI; Schou et al., 2012) on board the Solar Dynamics Observatory (SDO; Pesnell, Thompson, and Chamberlin, 2012). Training and test samples are collected from ARs in the HMI and MDI overlap period, between 1 May 2010 and 11 April 2011. An AR on the solar disk usually consists of one or more sunspots and pores that are formed because of the concentrations of strong magnetic fields.

Generative AI, which can create text and chat with users, presents a unique challenge because it can make people feel like they're interacting with a human. Anthropomorphism is the ascription of human attributes or personality to nonhumans. People often anthropomorphize artificial intelligence (especially Generative AI) because it can create human-like outputs. Among them, information transmission activities based on artificial intelligence technology have received more and more attention. With the help of artificial intelligence technology to obtain information and transmit information, it can be more convenient and accelerate the realization of information interaction, industry marketing, user interaction, brand publicity, and advertising, and create more creative content. Artificial intelligence technology has brought great changes and more availability to everyone's daily life and receiving information channels. However, the collection of personal data is more and more extensive, which also makes the problem of personal data privacy and security more serious. Therefore, combined with the double-sided nature of artificial intelligence, this paper analyzes the advantages and disadvantages of intelligent data processing in personal data privacy, applies the machine learning differential privacy algorithm combined with intelligent data processing to the research, and realizes the risk prediction and protection of personal data. This serves as a reminder for everyone on how to use artificial intelligence to protect their information security more effectively."

Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading 1 * Corresponding author: [Qishuo Cheng]. Email: [qishuoc@uchicago.edu]. 2 expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results.

Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved >=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended.

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

Gabrielle Douglas of the United States of America competes on the balance beam in the Artistic Gymnastics Women's Team final in the London 2012 Olympic Games. Steve Penny resigned as USA Gymnastics president last Thursday, following accusations of negligence in the sport's yearslong sexual assault scandal. Our eyes turned abroad last week, as Secretary of State Rex Tillerson visited South Korea and said "the policy of strategic patience has ended" for its neighbors to the north; the White House said it wouldn't repeat false claims that a British electronic intelligence agency helped with alleged (unproven) wiretapping of Trump Tower; and German Chancellor Angela Merkel, delayed by the snow, finally made her first visit to President Donald Trump, complete with an awkward handshake that wasn't. At home, a Hawaii judge also said "not so fast" to Trump's revised travel ban that for the second time looked to restrict which people can enter the country. Here are five stories -- that have nothing to do with Trump, Tillerson or The Spokesperson In Chief -- you might have missed in all of the globetrotting.

You could think of a hospital as a big cruise liner, afloat on a sea of data, charting and correcting course as the captain and crew read the shifting wave patterns, monitor their instruments, pump the bilge ... Put more prosaically, hospital executives have an awful lot of numbers to navigate while checking their dashboards and generating feedback internally and externally. Artificial intelligence technologies like machine discovery, machine learning and natural language generation are revolutionizing the performance of those tasks. It wasn't until the mid-1990s that the notion of comparing an organization's performance with results achieved by other organizations in the same business even entered the health care mindset. Today, the search term "hospital benchmarking" turns up 5,636 PubMed entries. Type those words into a search engine and you'll be deluged with URLs.

The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tic-tac-toe.