Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods

Research and Development of a General Purpose Instrument DAQ-Monitoring Platform applied to the CLOUD/CERN experiment

The current scientific environment has experimentalists and system administrators allocating large amounts of time for data access, parsing and gathering as well as instrument management. This is a growing challenge since there is an increasing number of large collaborations with significant amount of instrument resources, remote instrumentation sites and continuously improved and upgraded scientific instruments. DAQBroker is a new software designed to monitor networks of scientific instruments while also providing simple data access methods for any user. Data can be stored in one or several local or remote databases running on any of the most popular relational databases (MySQL, PostgreSQL, Oracle). It also provides the necessary tools for creating and editing the metadata associated with different instruments, perform data manipulation and generate events based on instrument measurements, regardless of the user’s know-how of individual instruments. Time series stored in a DAQBroker database also benefit from several statistical methods for time series classification, comparison and event detection as well as multivariate time series analysis methods to determine the most statistically relevant time series, rank the most influential time series and also determine the periods of most activity during specific experimental periods. This thesis presents the architecture behind the framework, assesses the performance under controlled conditions and presents a use-case under the CLOUD experiment at CERN, Switzerland. The univariate and multivariate time series statistical methods applied to this framework are also studied.O processo de investigação científica moderno requer que tanto experimentalistas como administradores de sistemas dediquem uma parte significativa do seu tempo a criar estratégias para aceder, armazenar e manipular instrumentos científicos e os dados que estes produzem. Este é um desafio crescente considerando o aumento de colaborações que necessitam de vários instrumentos, investigação em áreas remotas e instrumentos científicos com constantes alterações. O DAQBroker é uma nova plataforma desenhada para a monitorização de instrumentos científicos e ao mesmo tempo fornece métodos simples para qualquer utilizador aceder aos seus dados. Os dados podem ser guardados em uma ou várias bases de dados locais ou remotas utilizando os gestores de bases de dados mais comuns (MySQL, PostgreSQL, Oracle). Esta plataforma também fornece as ferramentas necessárias para criar e editar versões virtuais de instrumentos científicos e manipular os dados recolhidos dos instrumentos, independentemente do grau de conhecimento que o utilizador tenha com o(s) instrumento(s) utilizado(s). Séries temporais guardadas numa base de dados DAQBroker beneficiam de um conjunto de métodos estatísticos para a classificação, comparação e detecção de eventos, determinação das séries com maior influência e os sub-períodos experimentais com maior actividade. Esta tese apresenta a arquitectura da plataforma, os resultados de diversos testes de esforço efectuados em ambientes controlados e um caso real da sua utilização na experiência CLOUD, no CERN, Suíça. São estudados também os métodos de análise de séries temporais, tanto singulares como multivariadas aplicados na plataforma

Single atom imaging with time-resolved electron microscopy

Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Yavaa: supporting data workflows from discovery to visualization

Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations

Collective Behaviour: From Cells to Humans

Living in organised groups is a strategy that can be observed in a multitude of diverse species. Among such species, the behaviour of an individual on their own is not the same as within a group: the environment is modified by the presence of more subjects, individuals interact with each other, and from those interactions complex patterns of behaviour can emerge. Some species of animals almost exclusively exist as groups, and as a consequence, studying them in a social context is the only way to understand their behaviour in nature. This is the idea that drives all the research presented in this thesis: the particular behaviour exhibited by the group is so robust that it will emerge even in a very simplified environment. By observing the individual and the group in those simplified experimental conditions, it is possible to deduce rules that might govern the interaction. The importance of interactions in the group’s behaviour can then be demonstrated by implementing a computer model of agents following those rules and comparing it with natural and experimental behaviour. This thesis presents different examples of such analyses, and gives illustrations of the range of questions that can be answered through this method. Groups of stem cells, juvenile sea bass and human beings were successively observed and tracked in suitable environments, with or without perturbation. The data extracted from those experiments were then processed so as to correct recording errors, and individual and collective behaviours were derived from those data, returning new insights on the nature of the interaction at the individual level, their consequences at the global level, as well as the effects of the interaction on both. Finally, I present the computer models derived from those analyses. Many systems in nature share this property of global behaviours emerging from deterministic local interaction, and as a consequence studies of this kind could shed light on important questions, of which cancer treatment, ocean acidification and human organisations are but a few examples