Towards Unsupervised Segmentation of Extreme Weather Events

Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimensional skeleton of coherent structures. Indeed, extreme weather events are a special case of more general hydrodynamic coherent structures. We present a scalable physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by latent variables known as local causal states. For complex fluid flows we show our method is capable of capturing known coherent structures, and with promising segmentation results on CAM5.1 water vapor data we outline the path to extreme weather identification from unlabeled climate model simulation data

An investigation of the cortical learning algorithm

Pattern recognition and machine learning fields have revolutionized countless industries and applications from biometric security to modern industrial assembly lines. The fields continue to accelerate as faster, more efficient processing hardware becomes commercially available. Despite the accelerated growth of the pattern recognition and machine learning fields, computers still are unable to learn, reason, and perform rudimentary tasks that humans and animals find routine. Animals are able to move fluidly, understand their environment, and maximize their chances of survival through adaptation - animals demonstrate intelligence. A primary argument in this thesis that we have not yet achieved a level of intelligence similar to humans and animals in the pattern recognition and machine learning fields, not due to a lack of computational power but, rather, due to lack of understanding of how the cortical structures of mammalian brain interact and operate. This thesis describes a cortical learning algorithm (CLA) that models how the cortical structures in the mammalian neocortex operate. Furthermore, a high level understanding of how the cortical structures in the mammalian brain interact, store semantic patterns, and auto-recall these patterns for future predictions are discussed. Finally, we demonstrate that the algorithm can build and maintain a model of its environment and provide feedback for actions and/or classification in a similar fashion to our understanding of cortical operation

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

The intentionality of implementing artificial intelligence and the respective impact on the environmental sustainability of companies

Artificial Intelligence (AI) has been playing an essential role in transforming the business environment, showing its potential to enhance businesses’ productivity, adaptation, and competitiveness. In a period where environmental matters become a concern on top of worldwide agendas, AI is pointed to have the potential to be deployed and implemented to enhance green performances for companies and help mitigating their environmental impacts. This investigation aims to assess the factors influencing managers’ intentionality of implementing AI based systems in their companies to boost environmental sustainability. This study also intends to analyze to what extent these drivers are different between managers based in Portugal and managers based in other European countries. For this purpose, both topics were thoroughly considered through literature review and then, further developed through a qualitative approach where interviews were conducted. The interviews conducted showed that the main drivers of success for the implementation relate with the perception of AI as a key tool to help companies moving towards environmental targets and potential the tool can bring to businesses performances. The main drivers of unsuccess for the implementation related with the complexity and time it takes to develop and implement a system that is capable to run properly, and the initial investment and costs required. Overall, both Portugal based managers and managers based abroad believe in AI as a reliable tool to potentialize environmentally friendly businesses but highlight different aspects as the biggest constraints.A Inteligência Artificial (IA) tem vindo a desempenhar um papel essencial na transformação do setor empresarial, mostrando o seu potencial para aumentar a produtividade, adaptação e competitividade das empresas. Num período em que as questões ambientais se tornam uma preocupação nas agendas mundiais, a IA é apontada como tendo o potencial para ser implementada e explorada para melhorar desempenhos ecológicos e ajudar a mitigar impactos ambientais. Esta investigação visa avaliar os fatores que influenciam a intencionalidade dos gestores na implementação de sistemas baseados em IA nas suas empresas para impulsionar a sustentabilidade ambiental. Este estudo pretende também analisar até que ponto estes fatores são diferentes entre gestores sediados em Portugal e gestores sediados noutros países europeus. Para este efeito, ambos os tópicos foram cuidadosamente considerados através de uma revisão bibliográfica e, posteriormente, desenvolvidos através de uma abordagem qualitativa onde foram realizadas entrevistas. As entrevistas realizadas mostraram que os principais fatores de sucesso para a implementação estão relacionados com a percepção da IA como uma ferramenta chave para ajudar as empresas nos seus objetivos ambientais e o potencial que a ferramenta apresenta no desempenho das empresas. Os principais fatores de insucesso relacionam-se com a complexidade do processo e o tempo necessário para desenvolver e implementar um sistema capaz de funcionar corretamente, bem como o investimento e os custos necessários. Em geral, tanto gestores sediados em Portugal como gestores sediados no estrangeiro acreditam na IA como uma ferramenta fiável para potencializar negócios mais verdes, mas destacam diferentes aspectos como os maiores constrangimentos

A Dynamical Synthesis of Planetary Systems

Over the past three decades, complementary lines of evidence have each provided tantalizing hints about the underlying mechanisms driving the diverse set of planetary system architectures. This dissertation leverages dynamics to synthesize the various components of planetary systems, including stars, planets, and minor planets. My work progresses at the intersection of subfields, drawing evidence from both solar system and exoplanet studies to advance a cohesive picture of planetary system evolution. This dissertation is fundamentally focused on interactions between the components of planetary systems. As a result, it is organized into three segments detailing the relationship between these components. A brief summary is provided as follows. Part I (Chapter 2): The Star-Minor Planet Connection. This chapter explores the use of occultation measurements, in which foreground asteroids briefly block out the light of background stars, as a mechanism to precisely probe the positions of minor planets within the solar system. We demonstrate that this method can be applied to constrain the presence of neighboring masses, including the predicted ``Planet Nine\u27\u27, in the distant solar system. Part II (Chapters 3-4): The Planet-Minor Planet Connection. These two chapters examine how minor planets can inform our understanding of planets more broadly. In Chapter 3, we describe a novel algorithm developed to directly search for distant solar system objects relevant to the Planet Nine hypothesis using data from the Transiting Exoplanet Survey Satellite (TESS). Then, in Chapter 4, we demonstrate that the long-period Neptune-mass exoplanet population suggested by protoplanetary disk images can also efficiently eject neighboring minor planets, accounting for the high rate of observed interstellar objects passing through the solar system. Part III (Chapters 5-7): The Star-Planet Connection. These three chapters investigate the relationship between stars and planets in two distinct ways: through compositional studies and through dynamical analyses. In Chapter 5, we describe the development of a machine learning algorithm that rapidly extracts stellar parameters, including 15 elemental abundances, from input optical stellar spectra. In Chapter 6, we introduce the Stellar Obliquities in Long-period Exoplanet Systems (SOLES) survey to investigate the origins of exoplanet spin-orbit misalignments. Finally, in Chapter 7 we conduct a population study of the stellar obliquity distribution that provides evidence for high-eccentricity migration and tidal damping as the two key mechanisms crafting the dynamical evolution of hot Jupiters

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe