The partial lifting of U.S. metals tariffs slapped on Japan under former U.S. President Donald Trump's administration is the latest bid by U.S. President Joe Biden's government to mend ties with a major ally and counterbalance an increasingly powerful China. Biden inherited a global network of alliances that had been battered by Trump's repeated questioning of their value to the United States, even as many saw such ties as increasingly important, given China's growing wealth and military might. "First, you treat allies as allies," U.S. Ambassador to Japan Rahm Emanuel said in a phone interview Tuesday. "Second, you begin to make a down payment on both climate and standing up for a rules-based system by recognizing nonmarket forces like China have wreaked havoc." The deal on steel tariffs comes as the U.S. seeks to redefine its role in how trade policy is made in Asia following Trump's rejection of the Trans-Pacific Partnership regional trade pact his country once spearheaded.

When training samples are scarce, the semantic embedding technique, ie, describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, ie, zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning; eg, we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available.

First, explosive increase of graph data poses a great challenge to GNN training on large-scale datasets. Previously, Graph neural networks (GNNs) have been a hot many graph-based tasks were often conducted on toy datasets spot of recent research and are widely utilized in diverse that are relatively small compared to graphs in realistic applications, applications. However, with the use of huger which is harmful to model scalability and practical data and deeper models, an urgent demand is unsurprisingly usages. Currently, large-scale graph datasets are thereby proposed made to accelerate GNNs for more efficient in literature [Hu et al., 2020a] for advanced research, execution. In this paper, we provide a comprehensive and at the same time, making GNNs execution (i.e., training survey on acceleration methods for GNNs and inference) a time-consuming process.

Machine reading comprehension (MRC) of text data is one important task in Natural Language Understanding. It is a complex NLP problem with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to "understand" a text, and then to be able to answer questions about it using deep learning. However, until now large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.

Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and Fuzzy C-Means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers.

In a number of application domains, one observes a sequence of network data; for example, repeated measurements between users interactions in social media platforms, financial correlation networks over time, or across subjects, as in multi-subject studies of brain connectivity. One way to analyze such data is by stacking networks into a third-order array or tensor. We propose a principal components analysis (PCA) framework for sequence network data, based on a novel decomposition for semi-symmetric tensors. We derive efficient algorithms for computing our proposed "Coupled CP" decomposition and establish estimation consistency of our approach under an analogue of the spiked covariance model with rates the same as the matrix case up to a logarithmic term. Our framework inherits many of the strengths of classical PCA and is suitable for a wide range of unsupervised learning tasks, including identifying principal networks, isolating meaningful changepoints or outliers across observations, and for characterizing the "variability network" of the most varying edges. Finally, we demonstrate the effectiveness of our proposal on simulated data and on examples from political science and financial economics. The proof techniques used to establish our main consistency results are surprisingly straight-forward and may find use in a variety of other matrix and tensor decomposition problems.

Sydney, Australia, 04 February, 2022 – Tata Consultancy Services (TCS) has entered a strategic partnership with DeakinCo., a division of Deakin University, to co-design a series of corporate learning programs to meet the growing demand of talent in emerging technologies such as machine learning, artificial intelligence, data analytics and robotics. The programs aim to help address the digital skills gap and accelerate the economic growth of Australia. The new partnership brings together Deakin's academic excellence and TCS' extensive industry networks and experience. The first program, to be piloted in early 2022, will focus on machine learning, which consists of three streams enabling senior executives, mid-management and practitioners to leverage the power of this emerging technology in their chosen profession. Each stream will be facilitated by academics and industry experts. The programs are designed to address specific capability gaps for businesses and will provide learners with an engaging experience that goes to the heart of the skills and knowledge required in these dynamic fields.

Tinder says it will no longer charge older users more to use Tinder, following a new report questioning the dating app's practice of charging older users "substantially more." The report, from Mozilla and Consumers International, detailed just how much Tinder pricing can vary based on users' age. The report relied on "mystery shoppers" in six countries -- the United States, the Netherlands, New Zealand, Korea, India and Brazil -- who signed up for Tinder and reported back how much the app charged for the subscription. According to the report, Tinder users between the ages of 30 and 49 were charged an average of 65.3 percent more than their younger counterparts in every country except Brazil. Tinder's age-based pricing for Tinder, which gives users access to premium features like unlimited likes, has long been a source of controversy for the dating app.

Creativity in social robots requires further attention in the interdisciplinary field of Human-Robot Interaction (HRI). This paper investigates the hypothesised connection between the perceived creative agency and the animacy of social robots. The goal of this work is to assess the relevance of robot movements in the attribution of creativity to robots. The results of this work inform the design of future Human-Robot Creative Interactions (HRCI). The study uses a storytelling game based on visual imagery inspired by the game 'Story Cubes' to explore the perceived creative agency of social robots. This game is used to tell a classic story for children with an alternative ending. A 2x2 experiment was designed to compare two conditions: the robot telling the original version of the story and the robot plot-twisting the end of the story. A Robotis Mini humanoid robot was used for the experiment. As a novel contribution, we propose an adaptation of the Short Scale Creative Self scale (SSCS) to measure perceived creative agency in robots. We also use the Godspeed scale to explore different attributes of social robots in this setting. We did not obtain significant main effects of the robot movements or the story in the participants' scores. However, we identified significant main effects of the robot movements in features of animacy, likeability, and perceived safety. This initial work encourages further studies experimenting with different robot embodiment and movements to evaluate the perceived creative agency in robots and inform the design of future robots that participate in creative interactions.

Pre-trained language models (LMs) are shown to easily generate toxic language. In this work, we systematically explore domain-adaptive training to reduce the toxicity of language models. We conduct this study on three dimensions: training corpus, model size, and parameter efficiency. For the training corpus, we propose to leverage the generative power of LMs and generate nontoxic datasets for domain-adaptive training, which mitigates the exposure bias and is shown to be more data-efficient than using a curated pre-training corpus. We demonstrate that the self-generation method consistently outperforms the existing baselines across various model sizes on both automatic and human evaluations, even when it uses a 1/3 smaller training corpus. We then comprehensively study detoxifying LMs with parameter sizes ranging from 126M up to 530B (3x larger than GPT-3), a scale that has never been studied before. We find that i) large LMs have similar toxicity levels as smaller ones given the same pre-training corpus, and ii) large LMs require more endeavor to detoxify. We also explore parameter-efficient training methods for detoxification. We demonstrate that adding and training adapter-only layers in LMs not only saves a lot of parameters but also achieves a better trade-off between toxicity and perplexity than whole model adaptation for the large-scale models.