Do you want to publish a course? Click here

Telescope: an interactive tool for managing large scale analysis from mobile devices

124   0   0.0 ( 0 )
 Added by Jaqueline Brito
 Publication date 2019
and research's language is English




Ask ChatGPT about the research

In todays world of big data, computational analysis has become a key driver of biomedical research. Recent exponential growth in the volume of available omics data has reshaped the landscape of contemporary biology, creating demand for a continuous feedback loop that seamlessly integrates experimental biology techniques and bioinformatics tools. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis in real-time. Here we report the development of Telescope, a novel interactive tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. Telescope was designed to natively operate with a simple and straightforward interface using Web 2.0 technology compatible with most modern devices (e.g., tablets and personal smartphones). Telescope provides a modern and elegant solution to integrate computational analyses into the experimental environment of biomedical research. Additionally, it allows biomedical researchers to leverage the power of large computational facilities in a user-friendly manner. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.



rate research

Read More

Several visualization schemes have been developed for imaging materials at the atomic level through atom probe tomography. The main shortcoming of these tools is their inability to parallel process data using multi-core computing units to tackle the problem of larger data sets. This critically handicaps the ability to make a quantitative interpretation of spatial correlations in chemical composition, since a significant amount of the data is missed during subsequent analysis. In addition, since these visualization tools are not open-source software there is always a problem with developing a common language for the interpretation of data. In this contribution we present results of our work on using an open-source advanced interactive visualization software tool, which overcomes the difficulty of visualizing larger data sets by supporting parallel rendering on a graphical user interface or script user interface and permits quantitative analysis of atom probe tomography data in real time. This advancement allows materials scientists a codesign approach to making, measuring and modeling new and nanostructured materials by providing a direct feedback to the fabrication and designing of samples in real time.
141 - R. E. Ryan Jr 2011
We present a suite of IDL routines to interactively run GALFIT whereby the various surface brightness profiles (and their associated parameters) are represented by regions, which the User is expected to place. The regions may be saved and/or loaded from the ASCII format used by ds9 or in the Hierarchical Data Format (version 5). The software has been tested to run stably on Mac OS X and Linux with IDL 7.0.4. In addition to its primary purpose of modeling galaxy images with GALFIT, this package has several ancillary uses, including a flexible image display routines, several basic photometry functions, and qualitatively assessing Source Extractor. We distribute the package freely and without any implicit or explicit warranties, guarantees, or assurance of any kind. We kindly ask users to report any bugs, errors, or suggestions to us directly (as opposed to fixing them themselves) to ensure version control and uniformity.
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background. In particular, altering these DNN models in the deployment stage posits a tremendous challenge. In this research, we propose and develop a low-code solution, ModelPS (an acronym for Model Photoshop), to enable and empower collaborative DNN model editing and intelligent model serving. The ModelPS solution embodies two transformative features: 1) a user-friendly web interface for a developer team to share and edit DNN models pictorially, in a low-code fashion, and 2) a model genie engine in the backend to aid developers in customizing model editing configurations for given deployment requirements or constraints. Our case studies with a wide range of deep learning (DL) models show that the system can tremendously reduce both development and communication overheads with improved productivity.
Offloading work to cloud is one of the proposed solutions for increasing the battery life of mobile devices. Most prior research has focused on computation-intensive applications, even though such applications are not the most popular ones. In this paper, we first study the feasibility of method-level offloading in network-intensive applications, using an open source Twitter client as an example. Our key observation is that implementing offloading transparently to the developer is difficult: various constraints heavily limit the offloading possibilities, and estimation of the potential benefit is challenging. We then propose a toolkit, SmartDiet, to assist mobile application developers in creating code which is suitable for energy-efficient offloading. SmartDiet provides fine-grained offloading constraint identification and energy usage analysis for Android applications. In addition to outlining the overall functionality of the toolkit, we study some of its key mechanisms and identify the remaining challenges.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا