Concept drift from 1980 to 2020: a comprehensive bibliometric analysis with future research insight

In nonstationary environments, high-dimensional data streams have been generated unceasingly where the underlying distribution of the training and target data may change over time. These drifts are labeled as concept drift in the literature. Learning from evolving data streams demands adaptive or evolving approaches to handle concept drifts, which is a brand-new research affair. In this effort, a wide-ranging comparative analysis of concept drift is represented to highlight state-of-the-art approaches, embracing the last four decades, namely from 1980 to 2020. Considering the scope and discipline; the core collection of the Web of Science database is regarded as the basis of this study, and 1,564 publications related to concept drift are retrieved. As a result of the classification and feature analysis of valid literature data, the bibliometric indicators are revealed at the levels of countries/regions, institutions, and authors. The overall analyses, respecting the publications, citations, and cooperation of networks, are unveiled not only the highly authoritative publications but also the most prolific institutions, influential authors, dynamic networks, etc. Furthermore, deep analyses including text mining such as; the burst detection analysis, co-occurrence analysis, timeline view analysis, and bibliographic coupling analysis are conducted to disclose the current challenges and future research directions. This paper contributes as a remarkable reference for invaluable further research of concept drift, which enlightens the emerging/trend topics, and the possible research directions with several graphs, visualized by using the VOS viewer and Cite Space software

Context-aware proactive 5G load balancing and optimization for urban areas

In the fifth-generation (5G) mobile networks, the traffic is estimated to have a fast-changing and imbalance spatial-temporal distribution. It is challenging for a system-level optimisation to deal with while empirically maintaining quality of service. The 5G load balancing aims to address this problem by transferring the extra traffic from a high-load cell to its neighbouring idle cells. In recent literature, controller and machine learning algorithms are applied to assist the self-optimising and proactive schemes in drawing load balancing decisions. However, these algorithms lack the ability of forecasting upcoming high traffic demands, especially during popular events. This shortage leads to cold-start problems because of reacting to the changes in the heterogeneous dense deployment. Notably, the hotspots corresponding with skew load distribution will result in low convergence speed. To address these problems, this paper contributes to three aspects. Firstly, urban event detection is proposed to forecast the changes in cellular hotspots based on Twitter data for enabling context-awareness. Secondly, a proactive 5G load balancing strategy is simulated considering the prediction of the skewed-distributed hotspots in urban areas. Finally, we optimise this context-aware proactive load balancing strategy by forecasting the best activation time. This paper represents one of the first works to couple the real-world urban event detection with proactive load balancing

The application of ocean front metrics for understanding habitat selection by marine predators

Marine predators such as seabirds, cetaceans, turtles, pinnipeds, sharks and large teleost fish are essential components of healthy, biologically diverse marine ecosystems. However, intense anthropogenic pressure on the global ocean is causing rapid and widespread change, and many predator populations are in decline. Conservation solutions are urgently required, yet only recently have we begun to comprehend how these animals interact with the vast and dynamic oceans that they inhabit. A better understanding of the mechanisms that underlie habitat selection at sea is critical to our knowledge of marine ecosystem functioning, and to ecologically-sensitive marine spatial planning. The collection of studies presented in this thesis aims to elucidate the influence of biophysical coupling at oceanographic fronts – physical interfaces at the transitions between water masses – on habitat selection by marine predators. High-resolution composite front mapping via Earth Observation remote sensing is used to provide oceanographic context to several biologging datasets describing the movements and behaviours of animals at sea. A series of species-habitat models reveal the influence of mesoscale (10s to 100s of kilometres) thermal and chlorophyll-a fronts on habitat selection by taxonomically diverse species inhabiting contrasting ocean regions; northern gannets (Morus bassanus; Celtic Sea), basking sharks (Cetorhinus maximus; north-east Atlantic), loggerhead turtles (Caretta caretta; Canary Current), and grey-headed albatrosses (Thalassarche chrysostoma; Southern Ocean). Original aspects of this work include an exploration of quantitative approaches to understanding habitat selection using remotely-sensed front metrics; and explicit investigation of how the biophysical properties of fronts and species-specific foraging ecology interact to influence associations. Main findings indicate that front metrics, particularly seasonal indices, are useful predictors of habitat preference across taxa. Moreover, frontal persistence and spatiotemporal predictability appear to mediate the use of front-associated foraging habitats, both in shelf seas and in the open oceans. These findings have implications for marine spatial planning and the design of protected area networks, and may prove useful in the development of tools supporting spatially dynamic ocean management

Novel planar photonic antennas to address the dynamic nanoarchitecture of biological membranes

The cell membrane is the encompassing protective shield of every cell and it is composed of a multitude of proteins, lipids and other molecules. The organization of the cell membrane is inextricably intertwined with its function, and sensitive to perturbations from the underlying actin cytoskeleton and the extracellular environment at the nano- and the mesoscale. Elucidating the dynamic interplay between lipids and proteins diffusing on the cell membrane, forming transient domains and (re)organizing them according to signals from the juxtaposed inner and outer meshwork, is of paramount interest in fundamental cell biology. The overarching goal of this thesis is to gain deeper insight into how lipids and proteins dynamically organize in biological membranes at the nanoscale. Photonic nano-antennas are metallic nanostructures that localize and enhance the incident optical radiation into highly confined nanometric regions (< 20 nm), leading to greatly enhanced light-matter interactions. In this thesis, we exploit an innovative design of planar gold nano-antenna arrays of different gap sizes (10-45 nm) and embedded in nanometric-size boxes. To elucidate nanoscale diffusion dynamics in biological membranes with high spatiotemporal resolution and single-molecule detection sensitivity, we further combine our nanogap antenna arrays with fluorescence correlation spectroscopy (FCS) in a serial and multiplexed manner. In this dissertation, we first describe the fabrication process of these planar gold nanogap antennas and characterize their performance by means of electron microscopy and FCS of individual molecules in solution. We demonstrate giant fluorescence enhancement factors of up to 104-105 times provided by our planar nanogap antennas in ultra-confined detection volumes and with single molecule detection sensitivity in the micromolar range. Second, we apply these planar plasmonic nano-antennas in combination with FCS for assessing the dynamic organization of mimetic lipid membranes at the nanoscale. For a ternary composition of the model membranes that include unsaturated and saturated lipids together with cholesterol, we resolve transient nanoscopic heterogeneities as small as 10 nm in size, coexisting in both macroscopically phase-separated lipid phases. Third, we add a Hyaluronic Acid (HA) layer on top of the model lipid membranes to emulate the effect of the extracellular environment surrounding native biological membranes. We extend our nano-antenna-FCS approach with atomic force microscopy and spectroscopy. We reveal a distinct influence of HA on the nanoscale lipid organization of mimetic membranes composed of lipids constituting the more ordered lipid phase. Our results indicate a synergistic effect of cholesterol and HA re-organizing biological membranes at the nanoscale. Fourth, we apply our planar nano-antenna platform combined with FCS to elucidate the nanoscale dynamics of different lipids in living cells. With our nanogap antennas we were able to breach into the sub-30 nm spatial scale on living cell membranes for the first time. We provide compelling evidence of short-lived cholesterol-induced ~10 nm nanodomain partitioning in living plasma membranes. Fifth, we demonstrate the multiplexing capabilities of our planar gold nanogap antenna platform combined with FCS in a widefield illumination scheme combined with sCMOS camera detection. Our approach allows recording of fluorescence signal from more than 200 antennas simultaneously. Moreover, we demonstrate multiplexed FCS recording on 50 nano-antennas simultaneously, both in solution as well as in living cells, with a temporal resolution in the millisecond range. The dissertation finishes with a brief discussion of the main results achieved in this research and proposes new avenues for future research in the field.La membrana plasmática separa el entorno intracelular del extracelular y está compuesta por una multitud de diferentes proteínas y lípidos. Su organización está fuertemente interconectada a su función, y es sensible a perturbaciones tanto de la actina cortical posicionada internamente en proximidad con la membrana, así como de una red extracelular en contacto próximo con la membrana exterior. Estas perturbaciones ocurren a distintas escalas temporales y espaciales, llegando a unos pocos nanómetros. Dada la estrecha relación entre la organización de la membrana y su función biológica, es tremendamente importante entender como lípidos y proteínas se organizan dinámicamente a la escala nanométrica y como se ven afectados por su entorno. El objetivo principal de esta tesis doctoral se centra en alcanzar este entendimiento. Las antenas fotónicas son nano-estructuras metálicas que incrementan la radiación electromagnética en regiones nanométricas (< 20 nm) del espacio. En esta tesis doctoral, hemos fabricado y utilizado plataformas con matrices de antenas en oro, y con regiones de confinamiento entre 10-45 nm. Además, hemos combinado estas antenas con la técnica de ¿fluorescence correlation spectroscopy (FCS)¿ a fin de obtener información espaciotemporal a la nano-escala en membranas biológicas, junto a la sensibilidad de detectar moléculas individuales a altas concentraciones. En esta disertación, describimos primero la fabricación de antenas fotónicas y caracterizamos su rendimiento utilizando técnicas de microscopía electrónica y FCS de moléculas individuales en solución. Nuestros resultados demuestran factores de incremento de la fluorescencia entre 104-105, en regiones ultra-confinadas, y una capacidad para detectar moléculas individuales en rango de concentraciones de micro-molares. Una vez validadas nuestras herramientas, nos enfocamos en su uso para el estudio dinámico de la organización de membranas lipídicas miméticas a escala nanométrica. En el caso de composiciones ternarias de lípidos insaturados, saturados y colesterol, hemos descubierto la existencia de heterogeneidades nanoscópicas y transitorias que coexisten tanto en las regiones ordenadas como desordenadas de las membranas lipídicas. El siguiente capítulo contiene resultados enfocados a estudiar el efecto del entorno extracelular en la organización dinámica de este tipo de capas lipídicas. Para ello, y como modelo, preparamos membranas lipídicas cubiertas de ácido hialurónico (HA), un componente abundantemente expresado en la matriz extracelular. Combinando FCS con microscopia y espectroscopia de fuerzas atómicas, logramos resolver la influencia de HA a escala nanométrica en la organización de la fase ordenada de las membranas lipídicas. Nuestros resultados indican la existencia de un efecto sinérgico entre HA y colesterol en el reordenamiento de la membrana a la nano-escala. El siguiente tema de investigación en esta tesis doctoral se enfoca a la aplicación de antenas fotónicas y FCS para el estudio de dominios lipídicos enriquecidos de colesterol en la membrana plasmática de células vivas. La utilización de estas antenas nos ha permitido, por primera vez, remontar la barrera de 30 nm, y demostrar de manera inequívoca la existencia de dominios enriquecidos en colesterol en células vivas con una resolución espacial de 10 nm. Finalmente, hemos demostrado la capacidad de multiplexado de nuestras antenas fotónicas, combinando una iluminación y detección en campo amplio utilizando una camera sCMOS. Describimos la implementación de nuestro esquema, así como también medidas que demuestran la detección simultánea de fluorescencia en más de 200 antenas. De manera importante, demostramos la obtención de curvas de FCS en 50 antenas simultáneamente, tanto en solución como en células vivas. Esta disertación culmina con una breve discusión de los resultados más importantes de esta investigación en el futur

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977

Event Detection and Tracking Detection of Dangerous Events on Social Media

Online social media platforms have become essential tools for communication and information exchange in our lives. It is used for connecting with people and sharing information. This phenomenon has been intensively studied in the past decade to investigate users’ sentiments for different scenarios and purposes. As the technology advanced and popularity increased, it led to the use of different terms referring to similar topics which often result in confusion. We study such trends and intend to propose a uniform solution that deals with the subject clearly. We gather all these ambiguous terms under the umbrella of the most recent and popular terms to reach a concise verdict. Many events have been addressed in recent works that cover only specific types and domains of events. For the sake of keeping things simple and practical, the events that are extreme, negative, and dangerous are grouped under the name Dangerous Events (DE). These dangerous events are further divided into three main categories of action-based, scenario-based, and sentiments-based dangerous events to specify their characteristics. We then propose deep-learning-based models to detect events that are dangerous in nature. The deep-learning models that include BERT, RoBERTa, and XLNet provide valuable results that can effectively help solve the issue of detecting dangerous events using various dimensions. Even though the models perform well, the main constraint of fewer available event datasets and lower quality of certain events data affects the performance of these models can be tackled by handling the issue accordingly.As plataformas online de redes sociais tornaram-se ferramentas essenciais para a comunicação, conexão com outros, e troca de informação nas nossas vidas. Este fenómeno tem sido intensamente estudado na última década para investigar os sentimentos dos utilizadores em diferentes cenários e para vários propósitos. Contudo, a utilização dos meios de comunicação social tornou-se mais complexa e num fenómeno mais vasto devido ao envolvimento de múltiplos intervenientes, tais como empresas, grupos e outras organizações. À medida que a tecnologia avançou e a popularidade aumentou, a utilização de termos diferentes referentes a tópicos semelhantes gerou confusão. Por outras palavras, os modelos são treinados segundo a informação de termos e âmbitos específicos. Portanto, a padronização é imperativa. O objetivo deste trabalho é unir os diferentes termos utilizados em termos mais abrangentes e padronizados. O perigo pode ser uma ameaça como violência social, desastres naturais, danos intelectuais ou comunitários, contágio, agitação social, perda económica, ou apenas a difusão de ideologias odiosas e violentas. Estudamos estes diferentes eventos e classificamos-los em tópicos para que a ténica de deteção baseada em tópicos possa ser concebida e integrada sob o termo Evento Perigosos (DE). Consequentemente, definimos o termo proposto “Eventos Perigosos” (Dangerous Events) e dividimo-lo em três categorias principais de modo a especificar as suas características. Sendo estes denominados Eventos Perigosos, Eventos Perigosos de nível superior, e Eventos Perigosos de nível inferior. O conjunto de dados MAVEN foi utilizado para a obtenção de conjuntos de dados para realizar a experiência. Estes conjuntos de dados são filtrados manualmente com base no tipo de eventos para separar eventos perigosos de eventos gerais. Os modelos de transformação BERT, RoBERTa, e XLNet foram utilizados para classificar dados de texto consoante a respetiva categoria de Eventos Perigosos. Os resultados demonstraram que o desempenho do BERT é superior a outros modelos e pode ser eficazmente utilizado para a tarefa de deteção de Eventos Perigosos. Salienta-se que a abordagem de divisão dos conjuntos de dados aumentou significativamente o desempenho dos modelos. Existem diversos métodos propostos para a deteção de eventos. A deteção destes eventos (ED) são maioritariamente classificados na categoria de supervisonado e não supervisionados, como demonstrado nos metódos supervisionados, estão incluidos support vector machine (SVM), Conditional random field (CRF), Decision tree (DT), Naive Bayes (NB), entre outros. Enquanto a categoria de não supervisionados inclui Query-based, Statisticalbased, Probabilistic-based, Clustering-based e Graph-based. Estas são as duas abordagens em uso na deteção de eventos e são denonimados de document-pivot and feature-pivot. A diferença entre estas abordagens é na sua maioria a clustering approach, a forma como os documentos são utilizados para caracterizar vetores, e a similaridade métrica utilizada para identificar se dois documentos correspondem ao mesmo evento ou não. Além da deteção de eventos, a previsão de eventos é um problema importante mas complicado que engloba diversas dimensões. Muitos destes eventos são difíceis de prever antes de se tornarem visíveis e ocorrerem. Como um exemplo, é impossível antecipar catástrofes naturais, sendo apenas detetáveis após o seu acontecimento. Existe um número limitado de recursos em ternos de conjuntos de dados de eventos. ACE 2005, MAVEN, EVIN são alguns dos exemplos de conjuntos de dados disponíveis para a deteção de evnetos. Os trabalhos recentes demonstraram que os Transformer-based pre-trained models (PTMs) são capazes de alcançar desempenho de última geração em várias tarefas de NLP. Estes modelos são pré-treinados em grandes quantidades de texto. Aprendem incorporações para as palavras da língua ou representações de vetores de modo a que as palavras que se relacionem se agrupen no espaço vectorial. Um total de três transformadores diferentes, nomeadamente BERT, RoBERTa, e XLNet, será utilizado para conduzir a experiência e tirar a conclusão através da comparação destes modelos. Os modelos baseados em transformação (Transformer-based) estão em total sintonia utilizando uma divisão de 70,30 dos conjuntos de dados para fins de formação e teste/validação. A sintonização do hiperparâmetro inclui 10 epochs, 16 batch size, e o optimizador AdamW com taxa de aprendizagem 2e-5 para BERT e RoBERTa e 3e-5 para XLNet. Para eventos perigosos, o BERT fornece 60%, o RoBERTa 59 enquanto a XLNet fornece apenas 54% de precisão geral. Para as outras experiências de configuração de eventos de alto nível, o BERT e a XLNet dão 71% e 70% de desempenho com RoBERTa em relação aos outros modelos com 74% de precisão. Enquanto para o DE baseado em acções, DE baseado em cenários, e DE baseado em sentimentos, o BERT dá 62%, 85%, e 81% respetivamente; RoBERTa com 61%, 83%, e 71%; a XLNet com 52%, 81%, e 77% de precisão. Existe a necessidade de clarificar a ambiguidade entre os diferentes trabalhos que abordam problemas similares utilizando termos diferentes. A ideia proposta de referir acontecimentos especifícos como eventos perigosos torna mais fácil a abordagem do problema em questão. No entanto, a escassez de conjunto de dados de eventos limita o desempenho dos modelos e o progresso na deteção das tarefas. A disponibilidade de uma maior quantidade de informação relacionada com eventos perigosos pode melhorar o desempenho do modelo existente. É evidente que o uso de modelos de aprendizagem profunda, tais como como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Tem sido evidente que a utilização de modelos de aprendizagem profunda, tais como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Em geral, o BERT tem um desempenho superior ao do RoBERTa e XLNet na detecção de eventos perigosos. É igualmente importante rastrear os eventos após a sua detecção. Por conseguinte, para trabalhos futuros, propõe-se a implementação das técnicas que lidam com o espaço e o tempo, a fim de monitorizar a sua emergência com o tempo

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Multisensor acoustic tracking of fish and seabird behavior around tidal turbine structures in Scotland

Despite rapid development of marine renewable energy, relatively little is known of the immediate and future impacts on the surrounding ecosystems. Quantifying the behavior and distribution of animals around marine renewable energy devices is crucial for understanding, predicting, and potentially mitigating any threats posed by these installations. The Flow and Benthic Ecology 4D (FLOWBEC) autonomous seabed platform integrated an Imagenex multibeam echosounder and a Simrad EK60 multi-frequency echosounder to monitor marine life in a 120◦ sector over ranges up to 50 m, seven to eight times per second. Established target detection algorithms fail within MRE sites, due to high levels of backscatter generated by the turbulent physical dynamics, limiting and biasing analysis to only periods of low current speed. This study presents novel algorithms to extract diving seabirds, fish, and fish schools from the intense backscatter caused by turbulent dynamics in flows of 4ms−1. Filtering, detection, and tracking using a modified nearest neighbor algorithm provide robust tracking of animal behavior using the multibeam echosounder. Independent multifrequency target detection is demonstrated using the EK60 with optimally calculated thresholds, scale-sensitive filters, morphological exclusion, and frequency-response characteristics. This provides sensitive and reliable detection throughout the entire water column and at all flow speeds. Dive profiles, depth preferences, predator–prey interactions, and fish schooling behavior can be analyzed, in conjunction with the hydrodynamic impacts of marine renewable energy devices. Coregistration of targets between the acoustic instruments increases the information available, providing quantitative measures including frequency response from the EK60, and target morphology and behavioral interactions from the multibeam echosounder. The analyses draw on deployments at a tidal energy site in Scotland to compare the presence and absence of renewable energy structures across a range of physical and trophic levels over complete spring-neap tidal cycles. These results can be used to inform how animals forage in these sites and whether individuals face collision risks. This quantitative information can de-risk the licensing process and, with a greater mechanistic understanding at demonstration scales, its predictive power could reduce the monitoring required at future arrays