Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

Waltz User Manual

This Document describes relevant information to understand and control the Waltz Visualization System. Waltz is a tool to visualize three dimensional data and reads special reference files containing details of the data file, path name, dimensions and aspect ratios of the data. Waltz (as the name suggests) contains three parts: Generalization, Specialization and Abstraction. The Generalization Process splits the data into spatially connected groups. A specialization is formed from a subset (selection) of these groups. The results are displayed in multiple abstract views of the same data. These abstractions are formed by losing or augmenting the data to facilitate in the understanding of the data

Bluetooth friendly names: bringing classic HCI questions into the mobile space

We explore the use of Bluetooth friendly names within the mobile space. Each Bluetooth-enabled device possesses a short string known as a 'friendly name' used to help identify a device to human users. In our analysis, we collected friendly names in use on 9,854 Bluetooth-enabled devices over a 7-month period. These names were then classified and the results analysed. We discovered that a broad range of HCI themes are applicable to the domain of Bluetooth friendly names, including previous work on personalisation, naming strategies and anonymity in computer mediated communication. We also found that Bluetooth is already being used as a platform for social interaction and communication amongst collocated groups and has moved beyond its original intention of file exchange

WebWave: Globally Load Balanced Fully Distributed Caching of Hot Published Documents

Document publication service over such a large network as the Internet challenges us to harness available server and network resources to meet fast growing demand. In this paper, we show that large-scale dynamic caching can be employed to globally minimize server idle time, and hence maximize the aggregate server throughput of the whole service. To be efficient, scalable and robust, a successful caching mechanism must have three properties: (1) maximize the global throughput of the system, (2) find cache copies without recourse to a directory service, or to a discovery protocol, and (3) be completely distributed in the sense of operating only on the basis of local information. In this paper, we develop a precise definition, which we call tree load-balance (TLB), of what it means for a mechanism to satisfy these three goals. We present an algorithm that computes TLB off-line, and a distributed protocol that induces a load distribution that converges quickly to a TLB one. Both algorithms place cache copies of immutable documents, on the routing tree that connects the cached document's home server to its clients, thus enabling requests to stumble on cache copies en route to the home server.Harvard University; The Saudi Cultural Mission to the U.S.A

The Application of the Montage Image Mosaic Engine To The Visualization Of Astronomical Images

The Montage Image Mosaic Engine was designed as a scalable toolkit, written in C for performance and portability across *nix platforms, that assembles FITS images into mosaics. The code is freely available and has been widely used in the astronomy and IT communities for research, product generation and for developing next-generation cyber-infrastructure. Recently, it has begun to finding applicability in the field of visualization. This has come about because the toolkit design allows easy integration into scalable systems that process data for subsequent visualization in a browser or client. And it includes a visualization tool suitable for automation and for integration into Python: mViewer creates, with a single command, complex multi-color images overlaid with coordinate displays, labels, and observation footprints, and includes an adaptive image histogram equalization method that preserves the structure of a stretched image over its dynamic range. The Montage toolkit contains functionality originally developed to support the creation and management of mosaics but which also offers value to visualization: a background rectification algorithm that reveals the faint structure in an image; and tools for creating cutout and down-sampled versions of large images. Version 5 of Montage offers support for visualizing data written in HEALPix sky-tessellation scheme, and functionality for processing and organizing images to comply with the TOAST sky-tessellation scheme required for consumption by the World Wide Telescope (WWT). Four online tutorials enable readers to reproduce and extend all the visualizations presented in this paper.Comment: 16 pages, 9 figures; accepted for publication in the PASP Special Focus Issue: Techniques and Methods for Astrophysical Data Visualizatio