American University of Beirut is developing a tool that farmers in the Middle East and Africa can use to irrigate fields at the optimum times to save water. At Colegio Mayor de Nuestra Señora del Rosario, a university in Colombia, researchers will use satellite images to detect illegal mines that are polluting community drinking water. Crisis Text Line, a nonprofit that connects people experiencing a crisis with volunteer counselors by text message, uses AI to evaluate messages and move the people who are in most danger to the front of the line. In Australia, a public health service called Eastern Health will use AI to comb through clinical records from ambulances and find patterns in suicide attempts–and ways to intervene earlier. Full Fact, an independent fact-checking organization in the U.K., is using AI to help human fact-checkers more quickly assess claims made by politicians and the media.

Abstract--In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. In [12] a novel RBF network with the multi-kernel is proposed to obtain an optimized and I. INTRODUCTION The unknown centres of the multikernels The RBF neural networks have shown excellent performance are determined by an improved k-means clustering in a number of problems of practical interest. An orthogonal least squares (OLS) algorithm is reservoirs of brine are analyzed for physicochemical properties used to determine the remaining parameters. The convergence of the ACA is analyzed by the [3] the RBF kernel is used to predict the pressure gradient Lyapunov criterion. In the context of nuclear physics, RBF Cognitive Radial Basis Function network (McRBFN) and its has been effectively used to model the stopping power data Projection based Learning (PBL) referred to as PBL-McRBFN of materials as in [4].

King Digital Entertainment's'Candy Crush Saga' is seen being played on an Apple iPad Mini. A federal lawmaker wants to introduce legislation that would ban "pay to win" practices and "loot boxes" from all video games. In a statement released Wednesday, Sen. Josh Hawley, a Republican representing Missouri, said video games offering these systems are preying on user addiction, particularly among children. "When a game is designed for kids, game developers shouldn't be allowed to monetize addiction," said Hawley in a statement. "And when kids play games designed for adults, they should be walled off from compulsive microtransactions."

For instance, dual arm manipulators have been effectively employed in a diversity of tasks including assembling a car, grasping and transporting an object or nursing the elderly [7]. In those scenarios, the DAR have been expected to behave like a human, which is they should be able to manipulate an object similarly to what a person does [3]. As compared to a single arm robot, the DAR have significant advantages such as more flexible movements, higher precision and greater dexterity for handling large objects [8, 9]. Nevertheless, since the kinematic and dynamic models of the DAR system are much more complicated than those of a single arm robot, it has more challenges to effectively and efficiently control the DAR, where synchronously coordinating the robot arms are highly expected. In order to accurately and stabily track the robot arms along desired trajectories, a number of the control strategies have been proposed. For instance, the traditional methods such as nonlinear feedback control [10] or hybrid force/position control relied on the kinematics and statics [11, 12] have been proposed to simultaneously control both of the arms. In the works [13, 14, 15], the authors have proposed to utilize the impedance control by considering the dynamic interaction between the robot and its surrounding environment while guaranteeing the desired movements. More importantly, robustness of the control performance is also highly prioritized in consideration of designing a controller for a highly uncertain and nonlinear DAR system. In literature of the modern control theory, sliding mode control (SMC) demonstrates a diverse ability to robustly control any system.

Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We exploit this flexibility by learning an embedding that captures semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of our learned Wasserstein embeddings, showing that they can embed a wide variety of metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: We can visualize the high-dimensional embedding directly, since it is a probability distribution on a low-dimensional space.

If a picture tells a thousand words, these are the two jostling foremost in a patient's mind when a radiologist scans their body for a better image of that suspicious lump or mass. But there is so much more a picture can tell us about cancer, particularly if we consider the possibilities of artificial intelligence. In 2017, US scientists announced they had developed an algorithm, or a computerised tool, to identify skin cancers through analysis of photographs. The algorithm scans a photo of a patch of skin to look for common forms of skin cancer, performing on par with board-certified dermatologists in identifying malignant melanomas (the third most common cancer in Australia) and keratinocyte carcinoma. This technology might enable skin cancer detection in country clinics and suburban GPs' offices at the highest accuracy available.

In March, a gunman walked into two mosques in Christchurch, New Zealand, opened fire, and killed dozens of worshippers. According to a police official, the suspected gunman was arrested 36 minutes after police were called to the scene. Now, a tech company believes its smart security cameras can prevent attacks like the tragedy in Christchurch, and says it plans to install its AI-powered systems in mosques around the world. Athena Security, the tech company behind the security system, and Al-Ameri International Trading announced the Keep Mosques Safe initiative last week. Al-Ameri International Trading, along with several Islamic non-profit groups, will fund the Keep Mosques Safe effort.

Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.

While ALE has enabled demonstration and evaluation of much more complex behaviors of deep RL agents, it Evaluation of deep reinforcement learning (RL) presents challenges as a suite of evaluation environments is inherently challenging. In particular, learned for topics on the frontier of deep RL. policies are largely opaque, and hypotheses about Challenge: Limited variation within games. Very little about the behavior of deep RL agents are difficult to individual games can be systematically altered, so ALE is test in black-box environments. Considerable effort poorly suited to testing how changes in the environment has gone into addressing opacity, but almost affect training and performance. New benchmarks such as no effort has been devoted to producing highquality OpenAI's Sonic the Hedgehog emulator and CoinRun inject environments for experimental evaluation environmental variation into the training schedule, while of agent behavior.

Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.