About
Hello! My name is Elle Vuotto and I am currently a senior student at Rose-Hulman Institute of Technology. I am majoring in biomedical engineering with a concentrtaion in biomedical instrumentation and a minor in computer science. I am an athlete for the women's soccer team and the track team here at Rose. I have a few acomplishments for the soccer team including being a two-time all-conference HCAC player and being acaedmic all-conference. As for clubs, I am apart of the Student Athlete Advisory Committee (SAAC) and Chi Alpha Sigma (a national college athlete honor society).
I am a Hoosier, born and raised. However, all my sport affiliations remain in Philadelphia: Go birds, phils, and flyers! I have small family, including my mom and dad and my sister and I. My parents are both doctors and my sister is in the progression of becoming a lawyer. I also now have a new brother in-law as of one month ago. Out of college I hope to move back to my hometown with the rest of my family and stay there for a while!


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String
struct String
Extensions for String class.
-
Trim the string in specified range. Takes in parameters two indexes.
Declaration
Swift
public func substring(from: Int, to: Int) -> String
-
Check does current string can be casted to Int type.
Declaration
Swift
public var isNumber: Bool { get }
-
Returns translation for given key. Search in main bundle for file Localizable.strings, print message if not found. If key is not found, then message is printed.
Declaration
Swift
public var localize: String { get }


LSHAL(1) LSHAL(1) NAME lshal - list HAL devices SYNOPSIS lshal [options] DESCRIPTION lshal is a utility for displaying items in the HAL device database. For more information about both the big picture and specific HAL properties, refer to the HAL spec which can be found in /usr/share/doc/hal- doc/spec/hal-spec.html depending on the distribution. OPTIONS The following options are supported: -m, --monitor Print changes emitted by the hald daemon. -s, --short Short output. -t, --tree Display the devices organized in a tree. -u, --show Show only the given UDI (Unique Device Identifier). -h, --help Print out usage. -V, --version Print the version. BUGS Please send bug reports to either the distribution or the HAL mailing list, see http://lists.freedesktop.org/mailman/listinfo/hal on how to subscribe. SEE ALSO hald(8), hal-get-property(1), hal-set-property(1), hal-find-by-prop‐ erty(1), hal-find-by-capability(1) AUTHOR Written by David Zeuthen<EMAIL_ADDRESS>with a lot of help from many others. LSHAL(1)


| 587 | (A) | þe multytude was so mekiłł that thare on moulde lefte |
| 589 | (C) | ¶ The multytude was so myche . þat on þe grownde laste |
| 583 | (D) | þe multitude was so mycħ þat on þe mosse last |
| 575 | (E) | þe multitud was so meche . þat on þe mosse lafte |
| (Ex) | ---- no text ---- | |
| 604 | (L) | so myche was þe multitude þat on þe molde lafte |
| 546 | (P) | Multitude was þare myche on þe molde lefte |
| 582 | (U) | The multitude is so muche / that on molde laft |
| (V) | ---- no text ---- |


string::token - Regex based iterative lexing
This package provides commands for regular expression based lexing (tokenization) of strings.
The complete set of procedures is described below.
This command takes an ordered dictionary lex mapping regular expressions to labels, and tokenizes the string according to this dictionary.
The result of the command is a list of tokens, where each token is a 3-element list of label, start- and end-index in the string.
The command will throw an error if it is not able to tokenize the whole string.
This command is a convenience wrapper around ::string token text above, and fileutil::cat, enabling the easy tokenization of whole files. Note that this command loads the file wholly into memory before starting to process it.
If the file is too large for this mode of operation a command directly based on ::string token chomp below will be necessary.
This command is the work horse underlying ::string token text above. It is exposed to enable users to write their own lexers, which, for example may apply different lexing dictionaries according to some internal state, etc.
The command takes an ordered dictionary lex mapping regular expressions to labels, a variable startvar which indicates where to start lexing in the input string, and a result variable resultvar to extend.
The result of the command is a tri-state numeric code indicating one of
No token found.
Token found.
End of string reached.
Note that recognition of a token from lex is started at the character index in startvar.
If a token was recognized (status 1) the command will update the index in startvar to point to the first character of the string past the recognized token, and it will further extend the resultvar with a 3-element list containing the label associated with the regular expression of the token, and the start- and end-character-indices of the token in string.
Neither startvar nor resultvar will be updated if no token is recognized at all.
Note that the regular expressions are applied (tested) in the order they are specified in lex, and the first matching pattern stops the process. Because of this it is recommended to specify the patterns to lex with from the most specific to the most general.
Further note that all regex patterns are implicitly prefixed with the constraint escape A to ensure that a match starts exactly at the character index found in startvar.
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category textutil of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
Text processing


{% extends 'Home/index.html' %} {% block content %}
Unleash the power of Artificial Intelligence, and get to know what happens when Human Intelligence and Artificial Intelligence work together. Play with your favourite Neural Networks and enjoy the predictions!
Below are the latest Neural Networks and machine learning models that we provide for you to play with and understand the power of Artificial Intelligence.


This video is a step by step guide for creating .stl files from echocardiography datasets suitable for 3d printing. We demonstrate how to extract datasets from the echo machine, how to use this website to convert datasets to .nrrd files [starts at 01:53] and finally how to use 3d-slicer to generate .stl files.
Work done by Ahmed Hosny, Tao Shen, Alexander Kuo, Dustin Long, Michael Andrawes and Joshua D. Dilley.


Voice enabling the web.
TalkToMe the content of any web page with a single click.
Convert text to speech and create voice narrations.
To send a support request, please use the contact form.
Got an idea or suggestion? Please share it.
A product of LSD Software.


Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results.
| Version: | 0.1-22 |
| Depends: | R (≥ 2.10.0), Rsolnp, multicool |
| Imports: | Rcpp (≥ 0.11.2) |
| LinkingTo: | Rcpp |
| Published: | 2017-02-11 |
| Author: | Torben Tvedebrink and James Curran. Mikkel Meyer Andersen assisted on the multicore/CPU implementation. |
| Maintainer: | Torben Tvedebrink <tvede at math.aau.dk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | DNAtools results |
| Reference manual: | DNAtools.pdf |
| Vignettes: |
Forensic genetics |
| Package source: | DNAtools_0.1-22.tar.gz |
| Windows binaries: | r-devel: DNAtools_0.1-22.zip, r-release: DNAtools_0.1-22.zip, r-oldrel: DNAtools_0.1-22.zip |
| OS X binaries: | r-release: DNAtools_0.1-22.tgz, r-oldrel: DNAtools_0.1-22.tgz |
| Old sources: | DNAtools archive |
Please use the canonical form https://CRAN.R-project.org/package=DNAtools to link to this page.


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Mar 2014 ‐ Jun 2014
Collaborated with the London IBM Media Services team and business partners, Aspera, to build and launch an "Infrastructure Media Service" for SoftLayer. Built on Direct-to-Cloud transport and object storage, AOD included additional CDN, Deep Storage, and Transcoding components and functioned as a packet-shaping interface to enable optimal operation in digital, cross channel ecosystems. Core features included the ability to rapidly ingest, transcode, QC, edit, post, and play out media on streaming platforms. The overarching vision for AOD was to leverage SoftLayer's bare-metal servers to reduce operating expenses for IBM sponsored events.
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Sep 2013 ‐ Jan 2014
As a member of the Product Innovation team, we designed, developed, and launched the open source Chef OpenStack initiative, a series of Opscode/Chef recipes designed to harness bare-metal and virtual servers. Designed as a DevOps tool, this service provided scripts to rapidly prototype and sandbox OpenStack using Vagrant. The vision for Chef OpenStack was to provide customers with the means to deploy and scale an OpenStack cloud to hundreds of nodes.
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Apr 2014 ‐ Aug 2014
Elicited IBM business partner, Cloudant, to incorporate their scalable, always-on NoSQL cloud database service on SoftLayer's platform. The intent was to provide customers with a lightweight scaffold to provision Cloudant quickly and reduce data access latency by opening up access to JSON data and attachments from SoftLayer's API. Features included high availability, data replication and sync, and geo-load balancing.
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Nov 2015 ‐ Apr 2016
Monitoring service for Cloud Foundational Services. Designed to capture and record events across all Bluemix runtime services. Included a Kibana UI management console for delivering operational metrics and detecting unauthorized access, as well as an Elasticsearch data repository for rapid event filtering. The overarching vision for Cloud Tracker was to help security teams gain visibility into their cloud ecosystems to detect and investigate anomalies, possible security breaches, and unauthorized access.
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Mar 2011 ‐ Sep 2011
Facilitated teams in France and US to refactor, consolidate, design, and launch iMeter, a storefront for purchasing and activating Internet-enabled apps on smart devices. The overall vision was to provide an expandable suite of products designed to increase software attachment rates and control spending on development. iMeter included apps for metering, remote diagnostics, funding, accounting, usage, and back-office services.
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Nov 2013 ‐ Feb 2014
As a member of the Product Innovation team, we designed, developed, and launched Jumpgate-as-a-Service, fabricated Python drivers designed to make OpenStack's API compatible with any public and private cloud provider. The overarching intent for JaaS was to provide customers with a compatibility layer that translated incoming OpenStack calls into API calls from dissimilar clouds. Designed as an open source service, it included a WSGI-compatible interface to support nginx, Apache, gunicorn, and uwsgi.
{{site.data.products.message-queue.categories}}
Sep 2012 ‐ Mar 2013
Collaborated with RabbitMQ to launch a middleware service for creating applications with scalable, cost-effective messaging and notification. The vision for MQ was to provide customers with a secure message view and editable queues directly from SoftLayer's Customer Portal, with the intent of extending its use to leverage our Microservices landscape. It included a full-featured REST API and language bindings for Ruby, Python, Java, and PHP.
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Jan 2015 ‐ Sep 2015
Presided over five products teams to design, develop, and implement dedicated, high-performing bare-metal Private Clouds for OpenStack in global data centers. Product teams spanned individual pillars across the overarching platform: Compute, Networking, Storage, Security, and Applications. These clouds were built on open standards to support the flexibility and portability of enterprise workloads, as well as compliance, availability, security, and 99.95% uptime. They included an isolated physical infrastructure for compute, storage, network gateways, and VPN connections with encrypted tunneling.
{{site.data.products.postal-accountant.categories}}
Dec 2007 ‐ Aug 2008
Synced with French business partners, Technodiva, to create the dual-branded Fusion product line. One such product, Postal Accountant, evolved into a comprehensive analytics platform with built-in monitoring and tracking for controlling expenses. It aggregated data stores and captured detailed, real-time expenditures for reporting. Features include a web-based dashboard and robust reporting engine. The overarching vision was to provide customers with the means to cut operating expenses up to 40% and relieve budget constraints.
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Jan 2005 ‐ Jul 2007
Siebel was a global project aimed at launching a CRM application with built-in packages for Sales, Marketing, Commerce, and Help Desk. The intent was to overhaul, govern, and converge outdated line-of-business programs into one comprehensive foundation. It provided a secure data platform for ingesting, staging, and accessing records from a number of data sources.


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{% endfor %} {% endif %} {% endwith %}Starminder periodically reminds you of some of your starred GitHub projects.
You decide how many and how frequently, and it takes care of the rest.
Truly amazing!
— no one, probably
Now I never forget about all the random projects I starred.
— also no one, probably
I hope this works…
— author
It totally works!
— author, a little later


Year: 88 Earth Days
Distance from Sun: ~35 million miles
(58 million km)
Year: 225 Earth Days
Distance from Sun: ~67 million miles
(108 million km)
Year: 365 Earth Days
Distance from Sun: ~93 million miles
(150 million km)
Year: 687 Earth Days
Distance from Sun: ~142 million miles
(228 million km)
Year: 4,333 Earth Days
Distance from Sun: ~484 million miles
(778 million km)
Year: 10,759 Earth Days
Distance from Sun: ~887 million miles
(1.43 billion km)
Year: 30,687 Earth Days
Distance from Sun: ~1.78 billion miles
(2.87 billion km)
Year: 60,190 Earth Days
Distance from Sun: ~2.80 billion miles
(4.5 billion km)