Determining Patterns from Radiation Portal Monitor Data: Enabling Data Insight with Visual and Interactive Exploratory Data Analysis

Review of active interrogation techniques

Comparison of PVT and NaI(Tl) scintillators for vehicle portal monitor applications

The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis and data visualization. In this contribution, methods for dimensionality reduction from three decades of interdisciplinary research are browsed and their applicability in the above application domains is briefly discussed. Complementing techniques for ensuing interactive data visualization, data navigation and visual exploratory data analysis are presented, which exploit the remarkable human perceptive and associative capabilities for interactive visual exploratory data analysis and systematic recognition system design. The main focus of this paper is on the comparison of feature selection and feature extraction techniques and the potential benefit of their combination. Further, the interesting implications of dimensionality reduction for VLSI design and related area and power consumption are pointed out.

The use of energy windowing to discriminate SNM from NORM in radiation portal monitors

The modern economy increasingly relies on ex- ploratory data analysis. Much of this is dependent on data scientists - expert statisticians who process data using statistical tools and programming languages. Our goal is to offer some of this analytical power to end-users who have no statistical training through simple interaction techniques and metaphors. We describe a spreadsheet-based interaction technique that can be used to build and apply sophisticated statistical models such as neural networks, decision trees, support vector machines and linear regression. We present the results of an experiment demon- strating that our prototype can be understood and successfully applied by users having no professional training in statistics or computing, and that the experience of interacting with the system leads them to acquire some understanding of the concepts underlying exploratory statistical modelling. I. INTRODUCTION There are many situations in which end-users need to per- form simple exploratory and interactive data analysis. Typical examples include the analysis of trends in historical data in order to predict future values, estimation of missing data from a data set, or sanity checking of new data by comparison to known information. In this paper, we present a novel tool that supports these kinds of operations within a spreadsheet-like interaction paradigm. Formally, the problem being addressed is mixed-initiative statistical inference or machine learning. Here, the goal is to construct a model that characterises a multivariate training data set, and then apply that model to a test data set to estimate missing values or gauge the correctness of preexisting values. Specialised software packages such as scikit-learn (5), programming languages such as R (6), and deep domain expertise are typically needed in order to build and apply such models. In contrast, our tool only requires the user to be familiar with simple spreadsheet manipulation operations. II. OUR INTERACTION TECHNIQUE

As part of the energy transition, the spread of prosumers in the energy market requires utilities to look for new approaches in managing local energy demand and supply. Doing this effectively requires better understanding and managing of local energy consumption and production patterns in prosumer scenarios. This situation is particularly challenging for small municipal utilities who traditionally do not have access to sophisticated modeling and forecasting methods and solutions. To this end, we propose a user-centered and a visual analytics approach for the development of a tool for an interactive and explainable day-ahead forecasting and analysis of energy demand in local prosumer environments. We also suggest supporting this with behavioral analysis to enable the analysis of potential relationships between consumption patterns and the interaction of prosumers with energy analysis tools such as customer portals, recommendation systems, and similar. In order to achieve this, we propose a combination of explainable machine learning methods such as kNN and decision trees with interactive visualization and explorative data analysis. This should enable utility analysts to understand how different factors influence expected consumption and perform what-if analyses to better assess possible demand forecasts under uncertain conditions.

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

IDA: An interactive data analysis system for clinical and experimental research

Project comprehension questions, such as “which modified artifacts can affect my work?” and “how can I identify the developers who should be assigned to a given task?” are difficult to answer, require an analysis of the project and its data, are context specific, and cannot always be pre-defined. Current research approaches are restricted to post hoc analyses over software repositories. Very few interactive exploratory tools exist since the large amount of data that need to be analyzed prohibits its exploration at interactive rates. Moreover, such analyses typically require the user to create complex scripts or queries to extract the desired information from data. Here we present Dominoes, a tool for interactive data exploration aimed at end users (i.e., project managers or developers). Dominoes allows users to interact with different types and units of data to investigate project relationships and view intermediate results as charts, tables, and graphs. Additionally, it allows users to save the derived data as well as their exploration paths for later use. In a scenario-based evaluation study, participants achieved a success rate of 86 percent in their explorations, with a mean time of 7.25 minutes for answering a set of (project) exploration questions.

Regression Analysis by Example Samprit Chatterjee and Bertram Price Bridges the gap between theory and practice of regression analysis, providing a balance between theoretical results and the analyst's subjective judgment. Describes methods by using realistic examples that emphasize the analysis of data and that contain irregularities similar to those encountered in practice. Demonstrates how to apply theoretical results by utilizing standard--and some not so standard--summary statistics on the basis of their intuitive appeal. 1977 Interactive Data Analysis A Practical Primer Donald R. McNeil Introduces the use of Exploratory Data Analysis in scientific work. Gives a set of numerical and graphical methods to find structure in data. Illustrations show the power and simplicity of the methods, and all listings are given in Fortran and APL for all the programs used to produce displays and analysis in the text. Assumes no formal knowledge of probability, mathematics, or computing. 1977 Statistical Survey Techniques Raymond J. Jessen A comprehensive, balanced treatment of the techniques for designing surveys and analyzing their data. Describes the methods which seem to be basic to such diverse fields as public opinion measurement, sociology, political science, economics, business, various governmental agencies, biology (e.g. wildlife and fisheries), engineering (e.g. traffic studies), urban planning and management, ecological studies, and many others. 1977

In this chapter we review current approaches to store, retrieve and integrate diverse protein interaction data. To incorporate the heterogeneous results of computational predictions and protein interaction experiments, methods of data integration have been widely used which provide efficient presentation, and analysis of interaction data. Among them statistical meta-analysis and supervised machine learning methods are becoming very popular in this respect. While integration methods reduce complexity of system representation, the databases provide efficient storage and retrieval of data. A large variety of interaction databases exist which differ in scope, type and coverage of data as well as query search capabilities. We categorize the databases of protein interactions into comprehensive, specialized, structural and databases developed for network analysis. This gives a rough grouping of resources based on how they might be used. In particular, one might often start with a comprehensive database search and afterwards perform a refined search of the obtained results using a database with a more specific focus.KeywordsProtein PairPhylogenetic ProfileInteraction DatabaseDomain PairNetwork AlignmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

BackgroundTranscription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data on DNase I hypersensitivity has previously been used to label a subset of transcription factors as Pioneers, Settlers and Migrants to describe their potential role in this process. These labels represent an interesting hypothesis on gene regulation and possibly a useful approach for data analysis, and therefore we wanted to expand the set of labeled transcription factors to include as many known factors as possible. We have used a well-annotated dataset of 1175 transcription factors as input to supervised machine learning methods, using the subset with previously assigned labels as training set. We then used the final classifier to label the additional transcription factors according to their potential role as Pioneers, Settlers and Migrants. The full set of labeled transcription factors was used to investigate associated properties and functions of each class, including an analysis of interaction data for transcription factors based on DNA co-binding and protein-protein interactions. We also used the assigned labels to analyze a previously published set of gene lists associated with a time course experiment on cell differentiation.ResultsThe analysis showed that the classification of transcription factors with respect to their potential role in chromatin opening largely was determined by how they bind to DNA. Each subclass of transcription factors was enriched for properties that seemed to characterize the subclass relative to its role in gene regulation, with very general functions for Pioneers, whereas Migrants to a larger extent were associated with specific processes. Further analysis showed that the expanded classification is a useful resource for analyzing other datasets on transcription factors with respect to their potential role in gene regulation. The analysis of transcription factor interaction data showed complementary differences between the subclasses, where transcription factors labeled as Pioneers often interact with other transcription factors through DNA co-binding, whereas Migrants to a larger extent use protein-protein interactions. The analysis of time course data on cell differentiation indicated a shift in the regulatory program associated with Pioneer-like transcription factors during differentiation.ConclusionsThe expanded classification is an interesting resource for analyzing data on gene regulation, as illustrated here on transcription factor interaction data and data from a time course experiment. The potential regulatory function of transcription factors seems largely to be determined by how they bind DNA, but is also influenced by how they interact with each other through cooperativity and protein-protein interactions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1349-2) contains supplementary material, which is available to authorized users.

In order to confirm their theoretical assumptions, physicists employ Monte-Carlo generators to produce millions of simulated particle collision events and compare them with the results of the detector experiments. The traditional, static analysis workflow of physicists involves creating and compiling a C++ program for each study, and loading large data files for every run of their program. To make this process more interactive and agile, we created an application that loads the data into the relational in-memory column store DBMS SAP HANA, exposes raw particle data as database views and offers an interactive web interface to explore this data. We expressed common particle physics analysis algorithms using SQL queries to benefit from the inherent scalability and parallelization of the DBMS. In this paper we compare the two approaches, i.e. manual analysis with C++ programs and interactive analysis with SAP HANA. We demonstrate the tuning of the physical database schema and the SQL queries used for the application. Moreover, we show the web-based interface that allows for interactive analysis of the simulation data generated by the EPOS Monte-Carlo generator, which is developed in conjunction with the ALICE experiment at the Large Hadron Collider (LHC), CERN.

The curriculum in its implementation continues to change. The change indicates that the nature of the curriculum itself is not stagnant but dynamic following the development of science and technology. The impact of the development of science and technology can occur in all lines of people's lives. The needs and demands in the wider community inevitably change as changes and developments occur. Changes in the education curriculum in regular educational institutions, inclusion, and special education intend that the national education goals contained in Law No. 20 of 2003 can be achieved properly. As we know that children with certain special needs require a lot of adjustment because between one individual and other individuals have different abilities. To overcome this diversity, educators are obliged to make modifications to each category. In the efforts to develop a curriculum various problem must have sprung up. The problem of SDLB curriculum development has not been widely researched so this research is focused more on exploring the problems faced by educators of children with special needs at the elementary school level. The types of research used are qualitative types of case study design and interactive data analysis models Miles and Huberman. The subjects of the study were 4 teachers of SDLB Bhakti Luhur Malang. The main instrument is the human instrument. Supporting instruments use interview guidelines, recording tools, and observation guidelines. Miles and Huberman's model interactive data analysis step is 1) data reduction 2) data display 3) conclusion drawing/verification. The results of this study show that curriculum development problems include development models, student complexity, connectivity, curriculum development team resources.

We present a case study in the application of interactive data analysis in the context of semiconductors and optical fiber manufacturing. Our approach to interactive graphics is based on well-known principles of data analysis and interactive graphics (Chambers, Cleveland, Kleiner & Tukey 1983, Cleveland 1993, Tufte 1983, Tufte 1990, Stuetzle 1987, Becker, Cleveland & Weil 1988b, Becker, Eick & Wilks 1991, Young & Lubinsky 1995, Buja, Cook & Swayne 1996), and its implementation is written in the S-language for data analysis and graphics (Becker, Chambers & Wilks 1988a, Chambers & Hastie 1992). The case study also highlights the need for improved interactive software tools in S-based systems to support the type of programmable interactive graphics that we envision. These limitations have motivated us to explore some recent developments in software technology, such as S Version 4 (Chambers 1998), Java, LispStat (Tierney 1996), XploRe (Haerdle, Klinke, Turlach & Ardle 1995), CORBA (Siegel 1996), and PVM (Geist, Beguelin, Dongarra, Jiang, Mancheck & Sunderam 1994), and asses how they can be used to alleviate some of the deficiencies we see in existing tools.