During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future. In the past decade, the research on intelligent automatic surface ship (IASV) technology in academic and marine industries has continued to grow. These developments have been fuelled by advanced sensing, communication, and computing technology together with the potentially transformative impact on automotive sea transportation and perceived social and economic benefits [1–5].

You all know of Coursera's machine learning course and Andrew Ng's deep learning specialization. You even talk about fast.ai, These are all excellent resources to learn data science, but I want to make you aware of a lesser-known, yet superb, set of courses with which you can augment your knowledge in only a few hours. You've probably heard of Kaggle. You most likely have even participated in a challenge and maybe uploaded some kernels, but did you know Kaggle also provides data science education?

In recent years, we've seen extraordinary growth in Computer Vision, with applications in image understanding, search, mapping, semi-autonomous or autonomous vehicles and many more. The ability for models to understand actions in a video, a task that was unthinkable just a few years ago, is now something that we can achieve with relatively high accuracy and in near real-time. However, the field is not particularly welcoming for newcomers. Without prior experience or guidance, building an accurate classifier can easily take weeks. Unless you're ready to spend a long-time learning computer vision, it's extremely hard to master the basics, let alone begin to explore some of the cutting-edge technologies in the field.

Lodo Therapeutics Corp., a biotechnology company applying its proprietary platform to reinvent environmentally-sourced, natural product drug discovery, today announced the appointment of Dale Pfost, PhD, as Chairman and Chief Executive Officer. Dr. Pfost has more than 25 years of experience as a life science entrepreneur, senior executive and venture investor. He has served as CEO of five biotechnology companies, three of which became publicly traded with valuations exceeding two billion dollars. Dr. Pfost has successfully completed dozens of financings, overseen numerous M&A transactions and served as a director at multiple public and private life science firms. The company also announced the addition of four members to the senior leadership team: Steve Colletti, PhD, Senior Vice President of R&D; Anthony Colasin, Senior Vice President of Business Development; Barbara Lindheim, Consulting Vice President of Strategic Communications and Investor Relations; and Sarajane Mackenzie, Consulting Vice President of Human Resources and Organization Development.

The University of Adelaide plans to achieve substantial growth in its student population within five years, and one of the teams responsible for achieving this very aggressive goal has a new staff member this year: a chatbot. It helps answer the critical question, "Am I eligible to attend the university?" Catherine Cherry, the school's director of prospect management, is putting innovative technologies to work to help meet that goal. The University of Adelaide uses a chatbot to let prospective students know whether they're eligible to apply. Prior to the introduction of the chatbot, the university's admissions office couldn't easily answer the eligibility question for prospective students from outside of Australia who were curious about whether they could attend.

Machine learning (ML) projects are one of the best ways to gain hands-on experience and improve applied machine learning skills. Machine Learning beginners and enthusiasts can take advantage of machine learning datasets available and get started on their learning journey. In this cheat sheet, we will look at the top 10 machine learning (ML) projects for beginners in 2020, along with the machine learning datasets required to gain experience of working on real-world problems. We have divided the projects based on tasks like classification, forecasting, prediction and mining. Here are the top machine learning projects you can explore in 2020.

In this paper, we try to uncover the second-order essence of several first-order optimization methods. For Nesterov Accelerated Gradient, we rigorously prove that the algorithm makes use of the difference between past and current gradients, thus approximates the Hessian and accelerates the training. For adaptive methods, we related Adam and Adagrad to a powerful technique in computation statistics---Natural Gradient Descent. These adaptive methods can in fact be treated as relaxations of NGD with only a slight difference lying in the square root of the denominator in the update rules. Skeptical about the effect of such difference, we design a new algorithm---AdaSqrt, which removes the square root in the denominator and scales the learning rate by sqrt(T). Surprisingly, our new algorithm is comparable to various first-order methods(such as SGD and Adam) on MNIST and even beats Adam on CIFAR-10! This phenomenon casts doubt on the convention view that the square root is crucial and training without it will lead to terrible performance. As far as we have concerned, so long as the algorithm tries to explore second or even higher information of the loss surface, then proper scaling of the learning rate alone will guarantee fast training and good generalization performance. To the best of our knowledge, this is the first paper that seriously considers the necessity of square root among all adaptive methods. We believe that our work can shed light on the importance of higher-order information and inspire the design of more powerful algorithms in the future.

The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges, first and foremost the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.

Generative adversarial networks (GANs) have shown promise in image generation and classification given limited supervision. Existing methods extend the unsupervised GAN framework to incorporate supervision heuristically. Specifically, a single discriminator plays two incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. The formulation intrinsically causes two problems: (1) the generator and the discriminator (i.e., the classifier) may not converge to the data distribution at the same time; and (2) the generator cannot control the semantics of the generated samples. In this paper, we present the triple generative adversarial network (Triple-GAN), which consists of three players---a generator, a classifier, and a discriminator. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible objective functions to ensure that the distributions characterized by the generator and the classifier converge to the data distribution. We evaluate Triple-GAN in two challenging settings, namely, semi-supervised learning and the extreme low data regime. In both settings, Triple-GAN can achieve state-of-the-art classification results among deep generative models and generate meaningful samples in a specific class simultaneously.

People's understanding of supposedly'equivalent' words used to express emotions -- such as love, fear or anxiety -- vary greatly between different languages, a study found. Researchers studied words describing emotion in more than 2,000 languages and found'significant variation' in how emotions are expressed across cultures. For example, the researchers found that among the languages of the Pacific Islands, the words equivalent to the English word'surprise' are closely associated with'fear'. In contrast, the words for surprise in the languages of south-east Asia are more closely connected to concepts like'hope' and'wanting'. The team also found words with no equivalent in other languages, like Portuguese's'saudade', a deep melancholy for something lost, which has no English counterpart.