A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model

The first AAAI workshop on Machine Learning for Operations Research (ML4OR), co-organized by Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universite de Montreal), Elias B. Khalil (University of Toronto), and Pashootan Vaezipoor (University of Toronto), involved more than 100 attendees and speakers who convened to present cutting-edge research at the intersection of learning and decision-making. We hope that the momentum in this emerging area will continue for years to come, at AAAI and other AI/ML conferences! Our invited speakers covered a broad range of exciting developments spanning new theoretical results for machine learning in integer programming by Dr Ellen Vitercik (UC Berkeley), foundational insights into the use of graph neural networks in combinatorial algorithms by Professor Stefanie Jegelka (MIT), late-breaking results on evaluating and comparing algorithms by Professor Kevin Leyton-Brown (UBC), and a survey of the use of deep learning in engineering optimization problems by Professor Pascal Van Hentenryck (Georgia Tech). Accepted papers to the workshop (available on the website) were also presented and spanned authors from universities in five continents and on topic as diverse as aircraft scheduling and battery management, all operations research problems where machine learning is starting to make an impact! The first AAAI workshop on Machine Learning for Operations Research (ML4OR), co-organized by Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universite de Montreal), Elias B. Khalil (University of Toronto), and Pashootan Vaezipoor (University of Toronto), involved more than 100 attendees and speakers who convened to present cutting-edge research at the intersection of learning and decision-making.

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists.

The deep learning revolution incited by the 2012 Alexnet paper has been transformative for the field of computer vision. Many problems which were severely limited using classical solutions are now seeing unprecedented success. The rapid proliferation of deep learning methods has led to a sharp increase in their use in consumer and embedded applications. One consequence of consumer and embedded applications is lossy multimedia compression which is required to engineer the efficient storage and transmission of data in these real-world scenarios. As such, there has been increased interest in a deep learning solution for multimedia compression which would allow for higher compression ratios and increased visual quality. The deep learning approach to multimedia compression, so called Learned Multimedia Compression, involves computing a compressed representation of an image or video using a deep network for the encoder and the decoder. While these techniques have enjoyed impressive academic success, their industry adoption has been essentially non-existent. Classical compression techniques like JPEG and MPEG are too entrenched in modern computing to be easily replaced. This dissertation takes an orthogonal approach and leverages deep learning to improve the compression fidelity of these classical algorithms. This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods. The key insight of this work is that methods which are motivated by first principles, i.e., the underlying engineering decisions that were made when the compression algorithms were developed, are more effective than general methods. By encoding prior knowledge into the design of the algorithm, the flexibility, performance, and/or accuracy are improved at the cost of generality...

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement.

In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable to Byzantine attacks on even a small fraction of agents. We propose a novel algorithm with a robust aggregation oracle that utilizes the geometric median. We prove that our proposed algorithm is robust to Byzantine attacks on fewer than half of agents and achieves a sublinear $\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqrt{T})$ steps of communication in $T$ steps. Moreover, we make our algorithm differentially private via a tree-based mechanism. Finally, if the level of corruption is known to be small, we show that using the geometric median of mean oracle for robust aggregation further improves the regret bound.

Machine learning/deep learning models have already achieved tremendous success in a number of domains such as computer vision [1, 2, 3, 4, 5] and natural language processing [6, 7, 8, 9, 10, 11, 12, 13, 14], where large amounts of training data and highly expressive neural network architectures together give birth to solutions outperforming previously dominating methods. As a consequence, researchers have also started exploring the possibility of applying machine learning models to advance scientific discovery and to further improve traditional analytical modeling [15, 16, 17, 18, 19, 20, 21]. While given a set of input and output pairs, deep neural networks are able to extract the complicated relations between the input and output via appropriate optimization over adequate large amount of data, prior knowledge still acts as an important role in finding the optimal solution. As the high level extraction of data distributions and task properties, prior knowledge, if incorporated properly, can provide rich information not existing or hard to extract in limited training data, and helps improve the data efficiency, the ability to generalize, and the plausibility of resulting models. Physics knowledge, which has been collected and validated explicitly both theoretically and empirically in the long history, contains tremendous abstraction and summary of natural phenomena and human behaviours in many important scientific and engineering applications. Thus in this paper, we focus on the topic of integrating prior physics knowledge into machine learning models, i.e. physics-informed machine learning (PIML). Compared to the integration of other types of prior knowledge, such as knowledge graphs, logic rules and human feedback [22], the integration of physics knowledge requires specific design due to its special properties and forms. In this paper, we survey a wide range of recent works in PIML and summarize them from three aspects.

Advances in the field of artificial intelligence resulted in incorporation of these technologies into the process of scientific research and in the field of photonics. Such methods as machine learning and deep learning have become popular design tools for development of photonic devices. Design in this case implies prediction of a physical response of a given structure (forward design) as well as the reverse process of finding parameters of a structure required to provide a desired response (inverse design). While design procedures arguably remain the most widespread implementation of machine learning in photonics, novel applications begin to emerge leading to evolvement of a new research area of intelligent photonics.

The addition of App IQ and IAP SKU (In-App Purchase SKU) will provide insights to drive effective consumer strategies. Digital success requires engaging with consumers where they spend the vast majority of their time - mobile. The challenge is that mobile app store categories are antiquated causing enterprise teams to spend precious bandwidth on onerous research and manual analysis of competitors. App IQ illuminates the digital landscape by providing an industry-first, robust taxonomy (19 genres / 152 subgenres), combining both app stores. Enterprises can now identify new partnership opportunities, competitive threats and quickly react to the ever-changing landscape.