Fox News host Bret Baier analyzes China's ambitions for agriculture dominance as it competes with the U.S. on Wednesday's'Special Report.' An unidentified civilian drone was shot down by the Taiwanese military Thursday after its entered its airspace by flying over one of Taipei's smallest islands which sits just a couple miles from mainland China. The drone reportedly flew over the waters of Shiyu Islet, also referred to as Lion Islet, which houses a small contingent of stationed Taiwanese military personnel. The incident comes as tensions between Taiwan and China continue to mount following Speaker of the House Nancy Pelosi's visit to Taipei last month. Shiyu, or Lion Islet, which is part of Kinmen county, one of Taiwan's offshore islands, is seen in front of China's Xiamen, on Lieyu island, Kinmen county, Taiwan Aug. 20, 2018.

AI software has detected more than 20,000 secret private swimming pools in aerial photography, helping French tax officials bag about €10 million (£ 8.6 million) in extra property levies. Home improvements, such as the addition of a loft or a pool, can boost the value of a property and increase the taxes homeowners pay in the Euro nation. A 30-square-metre pool, for example, could set you back an extra €200 (£170) a year. People are required to declare these kinds of constructions, though some keep quiet to avoid having to fork out more money. In a bid to catch tax dodgers out, nine departments working under France's tax office tested out machine-learning software to automatically find undeclared swimming pools from overhead photos.

The open domain knowledge base is very important. It is usually extracted from encyclopedia websites and is widely used in knowledge retrieval systems, question answering systems, or recommendation systems. In practice, the key challenge is to maintain an up-to-date knowledge base. Different from Unwieldy fetching all of the data from the encyclopedia dumps, to enlarge the freshness of the knowledge base as big as possible while avoiding invalid fetching, the current knowledge base updating methods usually determine whether entities need to be updated by building a prediction model. However, these methods can only be defined in some specific fields and the result turns out to be obvious bias, due to the problem of data source and data structure. The users' query intentions are often diverse as to the open domain knowledge, so we construct a topic-aware graph network for knowledge updating based on the user query log. Our methods can be summarized as follow: 1. Extract entities through the user's log and select them as seeds 2. Scrape the attributes of seed entities in the encyclopedia website, and self-supervised construct the entity attribute graph for each entity. 3. Use the entity attribute graph to train the GNN entity update model to determine whether the entity needs to be synchronized. 4.Use the encyclopedia knowledge to match and update the filtered entity with the entity in the knowledge base according to the minimum edit times algorithm.

Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles' popularity and public engagement with science.

This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion. At every step of the decoding process, we use Predictor Upper Confidence for Trees (PUCT) to search for sequences that maximize the average values of emotion and quality as given by an emotion classifier and a discriminator, respectively. We use a language model as PUCT's policy and a combination of the emotion classifier and the discriminator as its value function. To decode the next token in a piece of music, we sample from the distribution of node visits created during the search. We evaluate the quality of the generated samples with respect to human-composed pieces using a set of objective metrics computed directly from the generated samples. We also perform a user study to evaluate how human subjects perceive the generated samples' quality and emotion. We compare PUCT against Stochastic Bi-Objective Beam Search (SBBS) and Conditional Sampling (CS). Results suggest that PUCT outperforms SBBS and CS in almost all metrics of music quality and emotion.

Heterogeneous graph neural networks (HGNNs) have attracted increasing research interest in recent three years. Most existing HGNNs fall into two classes. One class is meta-path-based HGNNs which either require domain knowledge to handcraft meta-paths or consume huge amount of time and memory to automatically construct meta-paths. The other class does not rely on meta-path construction. It takes homogeneous convolutional graph neural networks (Conv-GNNs) as backbones and extend them to heterogeneous graphs by introducing node-type- and edge-type-dependent parameters. Regardless of the meta-path dependency, most existing HGNNs employ shallow Conv-GNNs such as GCN and GAT to aggregate neighborhood information, and may have limited capability to capture information from high-order neighborhood. In this work, we propose two heterogeneous graph tree network models: Heterogeneous Graph Tree Convolutional Network (HetGTCN) and Heterogeneous Graph Tree Attention Network (HetGTAN), which do not rely on meta-paths to encode heterogeneity in both node features and graph structure. Extensive experiments on three real-world heterogeneous graph data demonstrate that the proposed HetGTCN and HetGTAN are efficient and consistently outperform all state-of-the-art HGNN baselines on semi-supervised node classification tasks, and can go deep without compromising performance.

Creating a complex work of art like music necessitates profound creativity. With recent advancements in deep learning and powerful models such as transformers, there has been huge progress in automatic music generation. In an accompaniment generation context, creating a coherent drum pattern with apposite fills and improvisations at proper locations in a song is a challenging task even for an experienced drummer. Drum beats tend to follow a repetitive pattern through stanzas with fills or improvisation at section boundaries. In this work, we tackle the task of drum pattern generation conditioned on the accompanying music played by four melodic instruments: Piano, Guitar, Bass, and Strings. We use the transformer sequence to sequence model to generate a basic drum pattern conditioned on the melodic accompaniment to find that improvisation is largely absent, attributed possibly to its expectedly relatively low representation in the training data. We propose a novelty function to capture the extent of improvisation in a bar relative to its neighbors. We train a model to predict improvisation locations from the melodic accompaniment tracks. Finally, we use a novel BERT-inspired in-filling architecture, to learn the structure of both the drums and melody to in-fill elements of improvised music.

Who had imagined a decade ago that AI would become centerstage one day to give tough times to writers, artists, actors, and professionals? However, it was a common perception that AI would take over accountancy and insurance-related jobs. No one had thought that AI would extend its reach to creativity. But now, a sense of insecurity prevails among the actors and performance artists. A survey conducted by Equity this year revealed that the UK union for actors and other performing artists considered AI as a threat to their employment opportunities.

Enterprises in the media and entertainment industry are leveraging cutting-edge technologies to enhance user experience and monetise their content. Technologies such as artificial intelligence (AI), data analytics and cloud computing are helping these enterprises revamp their businesses. The pandemic has further accelerated the adoption of these technologies in the sector. For instance, cutting-edge special effects developed for movies, streaming media, virtual reality (VR) gaming, and new delivery channels for news, music and advertising have all become prevalent now. As developing and deploying their own custom AI systems remains an expensive proposition for most media companies, there has been a proliferation of AI-as-a-service business models.

If you haven't been paying attention to what's going on with Stable Diffusion, you really should be. Stable Diffusion is a new "text-to-image diffusion model" that was released to the public by Stability.ai It's similar to models like Open AI's DALL-E, but with one crucial difference: they released the whole thing. You can try it out online at beta.dreamstudio.ai Type in a text prompt and the model will generate an image.