The United States on Thursday unleashed a volley of actions to censure China's treatment of the Uyghur minority, with lawmakers voting to curb trade and new sanctions slapped on the world's top consumer drone maker. The United States has been ramping up pressure on China amid a crop of disputes, with President Joe Biden's administration a day earlier targeting producers of painkillers that have contributed to America's addiction crisis. The U.S. Senate unanimously voted to make the United States the first country to ban virtually all imports from China's northwestern Xinjiang region over concerns of the prevalence of forced labor. "We know it's happening at an alarming, horrific rate with the genocide that we now witness being carried out," said Senator Marco Rubio, a driver behind the act, which already passed the House of Representatives and which the White House says Biden will sign. After prolonged negotiations to secure its passage, Rubio lifted objections and the Senate confirmed veteran diplomat Nicholas Burns as ambassador to China.

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. This framework features a loop training procedure, which enables guiding the improvement of policy by planning with action models and symbolic options learned from interactive trajectories automatically. The learned symbolic options alleviate the dense requirement of expert domain knowledge and provide inherent interpretability of policies. Moreover, the transferability and data efficiency can be further improved by planning with the action models. To validate the effectiveness of this framework, we conduct experiments on two domains, Montezuma's Revenge and Office World, respectively. The results demonstrate the comparable performance, improved data efficiency, interpretability and transferability.

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.

A key challenge of online news recommendation is to help users find articles they are interested in. Traditional news recommendation methods usually use single news information, which is insufficient to encode news and user representation. Recent research uses multiple channel news information, e.g., title, category, and body, to enhance news and user representation. However, these methods only use various attention mechanisms to fuse multi-view embeddings without considering deep digging higher-level information contained in the context. These methods encode news content on the word level and jointly train the attention parameters in the recommendation network, leading to more corpora being required to train the model. We propose an Event Extraction-based News Recommendation (EENR) framework to overcome these shortcomings, utilizing event extraction to abstract higher-level information. EENR also uses a two-stage strategy to reduce parameters in subsequent parts of the recommendation network. We train the Event Extraction module by external corpora in the first stage and apply the trained model to the news recommendation dataset to predict event-level information, including event types, roles, and arguments, in the second stage. Then we fuse multiple channel information, including event information, news title, and category, to encode news and users. Extensive experiments on a real-world dataset show that our EENR method can effectively improve the performance of news recommendations. Finally, we also explore the reasonability of utilizing higher abstract level information to substitute news body content.

Spotify has bought another audio platform, and this time it's hoping to bring radio into the modern era. The streaming company has acquired Whooshkaa, an Australia-based firm that offers a tool to convert radio broadcasters' shows into podcasts. Spotify plans to integrate the tech into its Megaphone suite for podcasters with a clear goal -- stations could further profit from shows by offering ad-supported podcast episodes. Whooshkaa might also boost some of Spotify's other efforts. Founder Rob Loewenthal noted Whooshkaa also had speech-to-text (and text-to-speech) technology, smart home integration and "enterprise grade" podcasting tools.

Robotic fish might help solve an ecological problem by scaring an invasive fish species so profoundly that it is put off breeding. Eastern mosquitofish (Gambusia holbrooki) were introduced in many parts of the world to eat mosquito larvae and keep the disease-spreading insects under control. But they have had a negative and unintended consequence on local fauna: they chew the tails of native freshwater fish and tadpoles, then leave them to die. Reducing numbers of eastern mosquitofish without harming other wildlife is a difficult prospect, but Giovanni Polverino at the University of Western Australia and his colleagues have come up with a potential solution. They designed a robotic version of the largemouth bass (Micropterus salmoides), which naturally preys on mosquitofish.

Students at the university of Adelaide have used their own experience and artificial intelligence computing - to make a craft beer, resulting in Australia's first AI IPA.

Objective To determine whether artificial intelligence (AI) can generate plausible and engaging titles for potential Christmas research articles in The BMJ . Design Observational study. Setting Europe, Australia, and Africa. Participants 1 AI technology (Generative Pre-trained Transformer 3, GPT-3) and 25 humans. Main outcome measures Plausibility, attractiveness, enjoyability, and educational value of titles for potential Christmas research articles in The BMJ generated by GPT-3 compared with historical controls. Results AI generated titles were rated at least as enjoyable (159/250 responses (64%) v 346/500 responses (69%); odds ratio 0.9, 95% confidence interval 0.7 to 1.2) and attractive (176/250 (70%) v 342/500 (68%); 1.1, 0.8 to 1.4) as real control titles, although the real titles were rated as more plausible (182/250 (73%) v 238/500 (48%); 3.1, 2.3 to 4.1). The AI generated titles overall were rated as having less scientific or educational merit than the real controls (146/250 (58%) v 193/500 (39%); 2.0, 1.5 to 2.6); this difference, however, became non-significant when humans curated the AI output (146/250 (58%) v 123/250 (49%); 1.3, 1.0 to 1.8). Of the AI generated titles, the most plausible was “The association between belief in conspiracy theories and the willingness to receive vaccinations,” and the highest rated was “The effects of free gourmet coffee on emergency department waiting times: an observational study.” Conclusions AI can generate plausible, entertaining, and scientifically interesting titles for potential Christmas research articles in The BMJ ; as in other areas of medicine, performance was enhanced by human intervention. Dataset and full reproducible code are available at .

Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridging between human language processing and linguistic features has been widely studied in neurolinguistics, usually via single-variable controlled experiments with highly-controlled stimuli. Such methods not only compromises the authenticity of natural reading, but also are time-consuming and expensive. In this paper, we propose a data-driven method to investigate the relationship between cognitive processing signals and linguistic features. Specifically, we present a unified attentional framework that is composed of embedding, attention, encoding and predicting layers to selectively map cognitive processing signals to linguistic features. We define the mapping procedure as a bridging task and develop 12 bridging tasks for lexical, syntactic and semantic features. The proposed framework only requires cognitive processing signals recorded under natural reading as inputs, and can be used to detect a wide range of linguistic features with a single cognitive dataset. Observations from experiment results resonate with previous neuroscience findings. In addition to this, our experiments also reveal a number of interesting findings, such as the correlation between contextual eye-tracking features and tense of sentence.

Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and completion. However, existing alignment techniques often assume that the input KGs are complete and isomorphic, which is not true due to the real-world heterogeneity in the domain, size, and sparsity. In this work, we address the problem of aligning incomplete KGs with representation learning. Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based. The former captures the consistency constraints between entities via translation paths, while the latter captures the neighbourhood structure of KGs via attention guided relation-aware graph neural network. The two feature channels are jointly learned to exchange important features between the input KGs while enforcing the output representations of the input KGs in the same embedding space. Also, we develop a missing links detector that discovers and recovers the missing links in the input KGs during the training process, which helps mitigate the incompleteness issue and thus improve the compatibility of the learned representations. The embeddings then are fused to generate the alignment result, and the high-confidence matched node pairs are updated to the pre-aligned supervision data to improve the embeddings gradually. Empirical results show that our model is up to 15.2\% more accurate than the SOTA and is robust against different levels of incompleteness. We also demonstrate that the knowledge exchanging between the KGs helps reveal the unseen facts from knowledge graphs (a.k.a. knowledge completion), with the result being 3.5\% higher than the SOTA knowledge graph completion techniques.