Learning from the past with experiment databases

Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior

The H.E.S.S. central data acquisition system

The High Energy Stereoscopic System (H.E.S.S.) is a system of Imaging Atmospheric Cherenkov Telescopes (IACTs) located in the Khomas Highland in Namibia. It measures cosmic gamma rays of very high energies (VHE; >100 GeV) using the Earth's atmosphere as a calorimeter. The H.E.S.S. Array entered Phase II in September 2012 with the inauguration of a fifth telescope that is larger and more complex than the other four. This paper will give an overview of the current H.E.S.S. central data acquisition (DAQ) system with particular emphasis on the upgrades made to integrate the fifth telescope into the array. At first, the various requirements for the central DAQ are discussed then the general design principles employed to fulfil these requirements are described. Finally, the performance, stability and reliability of the H.E.S.S. central DAQ are presented. One of the major accomplishments is that less than 0.8% of observation time has been lost due to central DAQ problems since 2009.Comment: 17 pages, 8 figures, published in Astroparticle Physic

Control versus Data Flow in Parallel Database Machines

The execution of a query in a parallel database machine can be controlled in either a control flow way, or in a data flow way. In the former case a single system node controls the entire query execution. In the latter case the processes that execute the query, although possibly running on different nodes of the system, trigger each other. Lately, many database research projects focus on data flow control since it should enhance response times and throughput. The authors study control versus data flow with regard to controlling the execution of database queries. An analytical model is used to compare control and data flow in order to gain insights into the question which mechanism is better under which circumstances. Also, some systems using data flow techniques are described, and the authors investigate to which degree they are really data flow. The results show that for particular types of queries data flow is very attractive, since it reduces the number of control messages and balances these messages over the node

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Experiment Databases: Creating a New Platform for Meta-Learning Research

Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results