First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes an algorithm for online combinatorial optimization. In this online learning problem, the action space is combinatorially large and can be represented in a d-dimensional Euclidean space such that the loss in each time step is a linear function of the action. It would greatly improve the paper if there was a thorough comparison between the new algorithm and Online Stochastic Mirror Descent (OSMD by Audibert et al., [3] in the current paper) both in terms of how the algorithms work and in terms of regret bounds. In the current form of the paper, I am not sure if the new algorithm is significantly different from OSMD or if it improves its bounds.

Abstract: Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs.

Abstract: There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong representations, we condition diffusion models on masked input and formulate diffusion models as masked autoencoders (DiffMAE). Our approach is capable of (i) serving as a strong initialization for downstream recognition tasks, (ii) conducting high-quality image inpainting, and (iii) being effortlessly extended to video where it produces state-of-the-art classification accuracy. Abstract: Fully supervised models often require large amounts of labeled training data, which tends to be costly and hard to acquire.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years due to their versatility and affordability. This article provides an overview of the history and development of UAVs, as well as their current and potential applications in various fields. In particular, the article highlights the use of UAVs in aerial photography and videography, surveying and mapping, agriculture and forestry, infrastructure inspection and maintenance, search and rescue operations, disaster management and humanitarian aid, and military applications such as reconnaissance, surveillance, and combat. The article also explores potential advancements in UAV technology and new applications that could emerge in the future, as well as concerns about the impact of UAVs on society, such as privacy, safety, security, job displacement, and environmental impact. Overall, the article aims to provide a comprehensive overview of the current state and future potential of UAV technology, and the benefits and challenges associated with its use in various industries and fields.

Abstract: The Laser Interferometer Space Antenna (LISA) will provide us with a unique opportunity to observe the early inspiral phase of supermassive binary black holes (SMBBHs) in the mass range of 105 106M, that lasts for several years. It will also detect the merger and ringdown phases of these sources. Therefore, such sources are extremely useful for multiparameter tests of general relativity (GR), where parametrized deviations from GR at multiple post-Newtonian orders are simultaneously measured, thus allowing for a rigorous test of GR. However, the correlations of the deviation parameters with the intrinsic parameters of the system make multiparameter tests extremely challenging to perform. We demonstrate the use of principal component analysis (PCA) to obtain a new set of deviation parameters, which are best-measured orthogonal linear combinations of the original deviation parameters.

Abstract: Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to multiway arrays, their conceptual differences from the usual PCA, and the methodological implications of such differences remain largely unknown. This work aims to specifically address these questions. In particular, we clarify the subtle difference between PCA and singular value decomposition (SVD) for multiway data, and show that multiway principal components (PCs) can be estimated reliably in absence of the eigengaps required by the usual PCA, and in general much more efficiently than the usual PCs. Furthermore, the sample multiway PCs are asymptotically independent and hence allow for separate and more accurate inferences about the population PCs.

Abstract: Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap -- divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can reflexively provide actionable insights to improve explainability. Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap. We apply the framework to a third case in a new domain, showcasing its affordances. Finally, we discuss conceptual implications of the framework, share practical considerations in its operationalization, and offer guidance on transferring it to new contexts.

Mathematical instruments mark the history of human progress. They've enabled trade and helped navigate oceans, and advanced understanding and quality of life. The latest tool propelling science and industry is AI computing. AI computing is the math-intensive process of calculating machine learning algorithms, typically using accelerated systems and software. It can extract fresh insights from massive datasets, learning new skills along the way. It's the most transformational technology of our time because we live in a data-centric era, and AI computing can find patterns no human could.

It's really important to discuss the benefits and drawbacks of artificial intelligence before it gets out of hand because this technology is improving and evolving at such a pace. As a computer science field, AI focuses on developing software and machines that mimic human thinking. Some artificial intelligence systems can analyze large amounts of data to learn from the past and enhance their performance without the input of programmers. AI is now widespread in both business and daily life. People interact with AI-powered virtual assistants or software daily to enhance their lives.

Wearable devices offering visibility into the functions of the human body can empower underserved communities. Victor Brown, founder and CEO of Xcellent Life, explains how. Want to get more stories like this one? Get daily news updates from Healthcare IT News. Your subscription has been saved.