In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. The conventional CS method is based on the principle that, through a regularized optimization, the sparsity property of the original signals in some domain can be exploited to get the exact reconstruction with a high probability. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard for real crack images. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparing with three existing CS algorithms. Furthermore, we show that our framework is extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion.

About Us: Adroit Market Research is a global business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a market's size, key trends, participants and future outlook of an industry. We intend to become our clients' knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code– Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

The result: eBay's copywriters are now able to devote more time to more creative work, such as crafting prose, according to Molly Prosser, associate creative director at eBay and a big believer in AI writing tools. "All the time I've spent lingering over the length of a subject line, wondering'Is this word more engaging, or is this word more engaging', or'How do I convey urgency without seeming too cliché'," says Prosser. "The hours I've spent editing and having team members pour over these things -- it's just meaningless work when we have a piece of AI that can do that for us. The new capability enables Naylor to offer a personalized newsletter for each association member it services -- based on his or her interests and reading habits. The AI tool incorporates personalized content that Naylor generates in-house, as well as content it finds on the Web –including job ads that match the specific job skills of the reader. Essentially, AI enables newsletters to become ever-more personalized over time by monitoring how each reader interacts with his or her personalized newsletter, and making adjustments accordingly, says Amith Nagaranjan, executive chairman, Rasa.io.

Philip Pullman is once again having a moment, thanks to the new blockbuster adaptation of His Dark Materials by the BBC and HBO. His fantasy classic--filled with witches, talking bears and "daemons" (people's alter-egos that take animal form)--is rendered in glorious steampunk detail. Pullman has also returned to the fictional world of his heroine, Lyra Belacqua, with a new trilogy, The Book of Dust, which probes more deeply into the central question of his earlier books: What is the nature of consciousness? Pullman loves to write about big ideas, and recent scientific discoveries about dark matter and the Higgs boson have inspired certain plot elements in his novels. The biggest mystery in these books--an enigmatic substance called Dust--comes right out of current debates among scientists and philosophers about the origins of consciousness and the provocative theory of panpsychism.

Across the technologically mature economies, citizens, society, businesses, and governments alike are becoming aware of the emergence of powerful technologies and their potential to reshape every aspect of human activity. The names of rapidly advancing technologies are becoming part of our everyday experience, even if we don't fully understand their functionality, capabilities, long-term potential impacts, or implications. There is though, a growing understanding and expectation that our lives will be increasingly dependent on, and enhanced by, the coming together of a range of these technologies. The most impactful of these exponentially advancing technologies are likely to be 5G communications, smartphones, smart wearable and embedded devices such as watches, artificial intelligence (AI), machine learning (ML), big data, cloud computing, smart objects, smart speakers, home automation, blockchain, digital currencies, augmented reality (AR), virtual reality (VR), 3D printing, drones, robotics, sensors, the Internet of Things (IoT), and quantum computing. So, how might these technologies combine to create previously unimaginable changes in everything from lifestyles, relationships, and work, to our food, leisure, and travel experiences? To explore these possibilities, the Life in 2025: Say Hello Say Goodbye Scenarios were developed for Huawei Consumer Business Group. The scenarios preview what we could see emerge across ten different aspects of human activity by the year 2025: Dating, Leisure Time, Friends and Family, Food and Dining, Entertainment, Fashion and Beauty, Travel, Health and Wellness, The Workplace, and Communication. The next five years will see AI take the possibilities for dating to a new level. Dating apps could tap into information on our lives from across the web and social media, to enhance our composed profile.

Spotify has open-sourced their Terraform module for running machine-learning pipeline software Kubeflow on Google Kubernetes Engine (GKE). By switching their in-house ML platform to Kubeflow, Spotify engineers have achieved faster time to production and are producing 7x more experiments than on the previous platform. In a recent blog post, Spotify's product manager Josh Baer and ML engineer Samuel Ngahane described Spotify's "Paved Road" for machine learning: "an opinionated set of products and configurations to deploy an end-to-end machine learning solution using our recommended infrastructure." By adopting these standards, Spotify's machine learning engineers no longer need to build or maintain infrastructure and instead can focus on their ML experiments. Since launching the platform in mid-2019, about 100 internal users have adopted it and run up to 18,000 experiments.

We consider the problem of finitely parameterized multi-armed bandits where the model of the underlying stochastic environment can be characterized based on a common unknown parameter. The true parameter is unknown to the learning agent. However, the set of possible parameters, which is finite, is known a priori. We propose an algorithm that is simple and easy to implement, which we call FP-UCB algorithm, which uses the information about the underlying parameter set for faster learning. In particular, we show that the FP-UCB algorithm achieves a bounded regret under some structural condition on the underlying parameter set. We also show that, if the underlying parameter set does not satisfy the necessary structural condition, FP-UCB algorithm achieves a logarithmic regret, but with a smaller preceding constant compared to the standard UCB algorithm. We also validate the superior performance of the FP-UCB algorithm through extensive numerical simulations.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

However, let's add a few evaluation & visualization parts before doing so – for you to visually appreciate model progress. First, we add a numerical evaluation using model.evaluate