I’ve been doing a bit more with Atom lately.
First, I started writing a library to manipulate Atom feeds and entries. For the moment this is located in atom.py. It uses lxml, as does everything markup related I do these days.
I came upon a revelation of sorts when I was writing the library. I first started with a library that looked like this:
class Feed(object):
def __init__(self, title, ...):
self.title = title
..
@classmethod
def parse(cls, xml):
if isinstance(xml, basestring):
xml = etree.XML(xml)
title = xml.xpath('//title').text
...
return cls(title, ...)
def serialize(self):
el = etree.Element('{%s}feed' % atom_ns)
title = etree.Element('{%s}title' % atom_ns)
title.text = self.title
el.append(title)
...
return el
Obviously there’s ways to improve this and make it less verbose, and I went down that path for a while. But then I decided the whole path was wrong. Atom is XML. It’s not the representation of some object I’m creating. If I have something that can’t be represented in XML, it isn’t Atom, and it doesn’t belong in my Atom-related objects.
So instead I started making lxml more convenient when using Atom. I don’t keep any information except what is in the markup, I just make it more convenient to access that information.
I used lots of descriptors to do this, as the same patterns happened over and over. For instance, the Feed object is fairly simple:
class Feed(AtomElement):
entries = _findall_property('entry')
author = _element_property('author')
Which basically means that feed.entries returns all <entry> elements, and feed.author returns the single author element.
There’s also accessors for text elements (like <id>) and date containing elements (like <updated>) and just to access XML attributes as Python attributes.
There’s a number of advantages:
- No hidden state.
- No deferred errors, since everything is always represented in the XML infoset.
- All XML extensions work, even though my classes don’t know anything in particular about them. There’s a full API for manipulating the XML that you can use, you don’t have to use my APIs.
- Even more obscure kinds of extensions work fine, like a custom attribute on an element. There’s absolutely zero normalization that happens.
- I only have to write the parts where the normal XML (lxml) APIs are inconvenient, so the implementation stays simple.
- There’s no confusion over which object I might be talking about in my code. There’s no distinction between the XML object and the domain object.
Since then I’ve been working on a Javascript library for handling Atom. It’s not as elegant. I am trying to keep to this same principle, but of course I can’t actually extend the DOM and so I can’t add convenience methods. So instead I’m making a class that lightly wraps the DOM objects, with explicit getters and setters that simply read and modify those DOM objects.
One thing that I have found very useful in my development on the Javascript side is doctest-style testing. You can see the test, but to run it you have to check it out (it uses some svn:externals which you don’t get through the direct svn access). After using that testing some more and being pleased with the result, I decided to package the Javascript doctest runner a bit better. I removed the framework dependencies, did a bit of renaming (now it is doctestjs or doctest.js instead of jsdoctest), wrote up fairly comprehensive docs, and uploaded it to JSAN (though at the moment the trunk from svn is probably better to use). I think it’s an excellent way of doing unit testing in Javascript, much better than any of the alternatives I’ve seen. It even has some notable advantages over Python’s doctest, like if you are using Firebug (which you must if you do Javascript development) you get a console session that runs in the same namespace as your tests, so you can easily do inspection of the objects if there’s a failure.
I’m not sure about JSAN. It’s
nice to have an index. But I think they copy
stuff from CPAN a bit too much. Why should
you have a text README file? That’s
just silly; of course Javascript
documentation should be HTML. They batch
processing. Processing one package a day
on the fly shouldn’t be overwhelming.
They want a MANIFEST file. The standard
metadata file is YAML, not JSON. This should
all be a little more Javascripty in my
opinion. But they also accept any kind of
upload, so there’s nothing stopping
you from ignoring what you don’t
care about. I’ll probably improve the
packaging of doctestjs a bit in the future,
and still ignore the parts I think are
silly.
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Did you consider using Amara instead of lxml? The built in API is pretty close to what your decorators are doing and you get things like XPath queries along with usable namespace support. Not to mention XSLT built in.
You know about [Google's Atom library for Python](http://gdata-python-client.googlecode.com/svn/trunk/src/atom/), yes?
And congrats on the WordPress migration. Is it running via wphp? ;^)
re: Amara
lxml also has the “objectify’ extension, which I believe is based on Amara’s API. Enumerating the attributes isn’t that hard, though, and it includes extra information like default values or date parsing.
lxml has good namespace support and XPath and XSLT built in.
re: Google’s library, no, I had missed it. But it does look like the style of API I specifically didn’t use, so I don’t feel so bad.
jquery’s xpath support for your atoms bits?
I’ve just been using the DOM in Javascript, i.e., getElementsByTagNameNS. With a few factory functions for getters and setters it’s not too terribly bad.
If I may, the lxml objectify feature is nowhere as good as Amara’s mapping of XML into Python objects which is basically what you try to redo here.
Amara is possibly the most powerful and user-friendly XML library for Python and yet is quite unknown.
Hi Ian,
Doctests and a standalone Atom library for javascript, marvelous!
It’s not immediately clear to me how the GPL affects unit tests. Is it kosher to distribute a BSD-script with associated doctests using a GPL doctest library?
Also, it wasn’t clear to me at first glance what license applies to https://svn.openplans.org/svn/TaggerClient/trunk/javascript/atom.js
Anyway, thanks for working on this!
Ian,
Thanks for writing doctest! I made a slight change to allow for Unix/Mac/Dos linefeed characters (http://blog.cornerstonenw.com/2009/06/02/doctestjs/). I don’t think it’s possible to contribute the patch to the doctest SVN…
– Peter Rust Developer, Cornerstone Systems


This minisite hosts the MA Thesis project of Tommaso Zennaro, called Type Metrics.
The main aim of this website is to show an environment that visualizes several data about typography. A former graduated student from Isia in Urbino, Roberto Arista, built a Python script capable of analyzing and obtaining informations out of fonts.
Data such as x-height, contrast, weight etc., for a total of 23 variables, out of 411 fonts (as of now) were saved in a database. By building a JavaScript environment and by using the library d3.js, I made it possible to visualize all those data.
These visualizations are useful for a new way of thinking about fonts and typography. They clearly show, also, many correlations and recurring habits among some aspects about fonts, font styles and font families.


Semantic Web Project
Home
NCHS
Hospital
HospitalFinder
State-wise Distribution Of Hospitals
Motivation : Mismatch between count of death and no of hospitals
Hospitals in the Selected State
Team Members
Ankita Patil, Krupali Patel, Shivdev Kumar
Semantic Web Project
Spring 2018
The University of Texas at Dallas


These packages are used to read various files.
readr package is used to read rectangular text data such as a 'csv' file. readxl package has a function to read excel file, and foreign package has a function to read data sotred by "S", "SAS", and "SPSS", "Stata".
You can see further readings in the following link. readr , readxl, and foreign .


CONFIGURE_PACKAGE_CONFIG_FILE(), WRITE_BASIC_PACKAGE_VERSION_FILE()
CONFIGURE_PACKAGE_CONFIG_FILE(<input> <output> INSTALL_DESTINATION <path>
[PATH_VARS <var1> <var2> ... <varN>]
[NO_SET_AND_CHECK_MACRO]
[NO_CHECK_REQUIRED_COMPONENTS_MACRO])
CONFIGURE_PACKAGE_CONFIG_FILE() should be used instead of the plain configure_file() command when creating the <Name>Config.cmake or <Name>-config.cmake file for installing a project or library. It helps making the resulting package relocatable by avoiding hardcoded paths in the installed Config.cmake file.
In a FooConfig.cmake file there may be code like this to make the install destinations know to the using project:
set(FOO_INCLUDE_DIR "@CMAKE_INSTALL_FULL_INCLUDEDIR@" )
set(FOO_DATA_DIR "@CMAKE_INSTALL_PREFIX@/@RELATIVE_DATA_INSTALL_DIR@" )
set(FOO_ICONS_DIR "@CMAKE_INSTALL_PREFIX@/share/icons" )
...logic to determine installedPrefix from the own location...
set(FOO_CONFIG_DIR "${installedPrefix}/@CONFIG_INSTALL_DIR@" )
All 4 options shown above are not sufficient, since the first 3 hardcode the absolute directory locations, and the 4th case works only if the logic to determine the installedPrefix is correct, and if CONFIG_INSTALL_DIR contains a relative path, which in general cannot be guaranteed. This has the effect that the resulting FooConfig.cmake file would work poorly under Windows and OSX, where users are used to choose the install location of a binary package at install time, independent from how CMAKE_INSTALL_PREFIX was set at build/cmake time.
Using CONFIGURE_PACKAGE_CONFIG_FILE() helps. If used correctly, it makes the resulting FooConfig.cmake file relocatable. Usage:
1. write a FooConfig.cmake.in file as you are used to
2. insert a line containing only the string "@PACKAGE_INIT@"
3. instead of set(FOO_DIR "@SOME_INSTALL_DIR@"), use set(FOO_DIR "@PACKAGE_SOME_INSTALL_DIR@")
(this must be after the @PACKAGE_INIT@ line)
4. instead of using the normal configure_file(), use CONFIGURE_PACKAGE_CONFIG_FILE()
The <input> and <output> arguments are the input and output file, the same way as in configure_file().
The <path> given to INSTALL_DESTINATION must be the destination where the FooConfig.cmake file will be installed to. This can either be a relative or absolute path, both work.
The variables <var1> to <varN> given as PATH_VARS are the variables which contain install destinations. For each of them the macro will create a helper variable PACKAGE_<var...>. These helper variables must be used in the FooConfig.cmake.in file for setting the installed location. They are calculated by CONFIGURE_PACKAGE_CONFIG_FILE() so that they are always relative to the installed location of the package. This works both for relative and also for absolute locations. For absolute locations it works only if the absolute location is a subdirectory of CMAKE_INSTALL_PREFIX.
By default configure_package_config_file() also generates two helper macros, set_and_check() and check_required_components() into the FooConfig.cmake file.
set_and_check() should be used instead of the normal set() command for setting directories and file locations. Additionally to setting the variable it also checks that the referenced file or directory actually exists and fails with a FATAL_ERROR otherwise. This makes sure that the created FooConfig.cmake file does not contain wrong references. When using the NO_SET_AND_CHECK_MACRO, this macro is not generated into the FooConfig.cmake file.
check_required_components(<package_name>) should be called at the end of the FooConfig.cmake file if the package supports components. This macro checks whether all requested, non-optional components have been found, and if this is not the case, sets the Foo_FOUND variable to FALSE, so that the package is considered to be not found. It does that by testing the Foo_<Component>_FOUND variables for all requested required components. When using the NO_CHECK_REQUIRED_COMPONENTS option, this macro is not generated into the FooConfig.cmake file.
For an example see below the documentation for WRITE_BASIC_PACKAGE_VERSION_FILE().
WRITE_BASIC_PACKAGE_VERSION_FILE( filename [VERSION major.minor.patch] COMPATIBILITY (AnyNewerVersion|SameMajorVersion|ExactVersion) )
Writes a file for use as <package>ConfigVersion.cmake file to <filename>. See the documentation of find_package() for details on this.
filename is the output filename, it should be in the build tree.
major.minor.patch is the version number of the project to be installed
If no VERSION is given, the PROJECT_VERSION variable is used. If this hasn’t been set, it errors out.
The COMPATIBILITY mode AnyNewerVersion means that the installed package version will be considered compatible if it is newer or exactly the same as the requested version. This mode should be used for packages which are fully backward compatible, also across major versions. If SameMajorVersion is used instead, then the behaviour differs from AnyNewerVersion in that the major version number must be the same as requested, e.g. version 2.0 will not be considered compatible if 1.0 is requested. This mode should be used for packages which guarantee backward compatibility within the same major version. If ExactVersion is used, then the package is only considered compatible if the requested version matches exactly its own version number (not considering the tweak version). For example, version 1.2.3 of a package is only considered compatible to requested version 1.2.3. This mode is for packages without compatibility guarantees. If your project has more elaborated version matching rules, you will need to write your own custom ConfigVersion.cmake file instead of using this macro.
Internally, this macro executes configure_file() to create the resulting version file. Depending on the COMPATIBLITY, either the file BasicConfigVersion-SameMajorVersion.cmake.in or BasicConfigVersion-AnyNewerVersion.cmake.in is used. Please note that these two files are internal to CMake and you should not call configure_file() on them yourself, but they can be used as starting point to create more sophisticted custom ConfigVersion.cmake files.
Example using both configure_package_config_file() and write_basic_package_version_file(): CMakeLists.txt:
set(INCLUDE_INSTALL_DIR include/ ... CACHE )
set(LIB_INSTALL_DIR lib/ ... CACHE )
set(SYSCONFIG_INSTALL_DIR etc/foo/ ... CACHE )
...
include(CMakePackageConfigHelpers)
configure_package_config_file(FooConfig.cmake.in ${CMAKE_CURRENT_BINARY_DIR}/FooConfig.cmake
INSTALL_DESTINATION ${LIB_INSTALL_DIR}/Foo/cmake
PATH_VARS INCLUDE_INSTALL_DIR SYSCONFIG_INSTALL_DIR)
write_basic_package_version_file(${CMAKE_CURRENT_BINARY_DIR}/FooConfigVersion.cmake
VERSION 1.2.3
COMPATIBILITY SameMajorVersion )
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/FooConfig.cmake ${CMAKE_CURRENT_BINARY_DIR}/FooConfigVersion.cmake
DESTINATION ${LIB_INSTALL_DIR}/Foo/cmake )
With a FooConfig.cmake.in:
set(FOO_VERSION x.y.z)
...
@PACKAGE_INIT@
...
set_and_check(FOO_INCLUDE_DIR "@PACKAGE_INCLUDE_INSTALL_DIR@")
set_and_check(FOO_SYSCONFIG_DIR "@PACKAGE_SYSCONFIG_INSTALL_DIR@")
check_required_components(Foo)


Image credit:https://www.malkatour.com.
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facecore module¶
this file containes the class FaceCore.
-
class
facecore.
FaceCore
¶ Bases:
object
FaceCore is the core library wich implements the differenet face recognition pipeline stages.
FaceCore is the core library wich implements the differenet face recognition pipeline stages the library performs: detection face point extraction aligment recognition, each step can be done using one or more models: this models include : face detection and face features extraction models are (mtcnn,dlib model , pyramid box) aligment model is (2d aligment) face embedding models are (google facenet ).
-
detect_models
¶ containes objects of detection models which helps save model initilization and loading time.
- Type
dictionary
-
recognize_models
¶ containes objects of recognition models which helps save model initilization and loading time.
- Type
dictionary
-
align_models
¶ containes objects of aligment methods.
- Type
dictionary
-
track_models
¶ contains objects of tracking models.
- Type
dictionary
-
align
(img, points, model=None, track_time=False, draw=False, raise_profie_flag=False)¶ align the image for better recognitions.
align using one of the allowed aligment models.
- Parameters
image (numpy array) – the target image.
model (string) – the name of the model you wish to use .
draw (bool) – if you wich to draw results or not .
track_time (bool) – if you wish to track the processing time.
- Returns
the aligned image. align_time (float) : the time it tooks to align the image.
- Return type
aligned (numpy array)
-
classify_faces
(faces, points, threshold=None)¶ classify faces and coressponding landmarks to big and small ones.
- Parameters
faces (numpy array) – the bounding boxes of faces with tracking id.
points (numpy array) – the faces landmarks.
threshold (float) – the threshold to compare against.
- Returns
the small faces with thier id,landmarks,bounding boxes. big_res (list of dictionary) : the big faces with thier id,landmarks,bounding boxes
- Return type
small_res (list of dictionary)
-
compare_face_size
(bbox, threshold)¶ compare the size of a face box against a threshold.
- Parameters
bbox (numpy array) – the bounding boxes of faces in the image which we went to compare.
threshold (float) – the theshold to compare agaist
- Returns
True if the face is smaller or False if it is bigger
- Return type
bool (bool)
-
detect_faces
(image, model=None, draw=True, clean=True, track_time=True)¶ detect the faces in an image.
detect the faces bounding boxes and the face landmarks positions if the model supports that functionality
- Parameters
image (numpy array) – the target image.
model (string) – the name of the model you wish to use .
draw (bool) – if you wich to draw results or not .
clean (bool) – if you wich to clear the models used in this process to save memory or keep it for faster response.
track_time (bool) – if you wish to track the processing time.
- Return :
faces (numpy array) : the bounding boxes of the detected faces. points (numpy array) : the locations of the all faces landmarks may be None if the model doesn’t support face landmarks. detect_time (float) : the time it tooks to performe detection.
-
draw
(img, bboxes, points=None, names=None)¶ helper function to draw results when asked to
- Parameters
img (numpy array) – the image to draw on.
boxes (numpy array) – the bounding boxes.
points (numpy array) – the landmark points.
- Return :
img (numpy array) : the image with bboxes and points drwan on.
-
draw_on_frame
(frame, res, frame_col=(0, 0, 0), font=0, show=True)¶ draw pipline results on frame.
- Args :
frame (numpy array) : the target image or frame to draw on. res (dictionary) : a dictionary that contains the result of pipline appiled on the frame. frame_col (tuple) : (x,y,z) gives the rgb componants of the color to use in drawing. font (open cv font) : the writing font.
- Returns
the frame with results drawn on it.
- Return type
frame (numpy array)
-
extract_featuers
(img, model=None, track_time=False, clean=True)¶ extract face embedding from image.
use a model to extract the presentation embedding of a face which is used in recognition and verfication
- Parameters
image (numpy array) – the target face image.
model (string) – the name of the model you wish to use .
clean (bool) – if you wich to clear the models used in this process to save memory or keep it for faster response.
track_time (bool) – if you wish to track the processing time.
- Returns
the embeddings of the input image. emb_time (float) : the time it tooks to extract embeddings.
- Return type
emb (numpy array)
-
filter_image
(image)¶ helper function that filters face image in order to help the embedding process using opencv filtering funcitons
- Parameters
image (numpy array) – the image array you want to apply the filter to
- Return :
image (numpy array) : the filtered image
-
get_old_new_id
(prev_faces, big_res, keep_num=10)¶ helper function to distinguish between new and old faces that appears in frame
- Parameters
prev_faces (dictionary) – contains the informations of previoulsy tracked faces in video
big_res (dictionary) – informations of the faces in current frame recognized big enough to be considered
keep_num (int) – indicates how many frames you want a face to be tracked along
- Return :
prev_faces (dictionary) : updated dictionary with faces that are tracked and currently big enough new_id (list) : list of the ids of the newly recognized faces (not tracked before) old_id (list) : list of the old ids that was tracked for the maximum count of frame counts active_id (list) : list of ids that still can be tracked
-
load_face
(file_name, save_dir='.//data')¶ load face object from pickle serilazation.
- Args :
file_name (string) : the file to retrive. save_dir (string) : the file diractory.
- Return :
face (person face) : the deserialized object.
-
load_models
()¶ load the models specified in the options.
load th pipeline models which are set using set options if not set it will load the default models.
Args:
- Returns
True if all loading was successfully done.
- Return type
bool
-
pipline_image
(image, preprocess=False, clean=False, draw=False)¶ perform the pipeline stages on an image.
stages include : preprossing -> detection -> aligment -> embedding .
- Parameters
image (numpy array) – the image to process.
preprocess (bool) – wherther to preprocess of not.
clean (bool) – whether to clean the model when finish.
draw (bool) – whether to draw the detection and aligment results.
- Return :
dictionary (dictionary) : the pipeline results which include the bounding boxes, points, embeddings , total time , image , success flag.
-
preprocess_image
(image)¶ apply preprocessing options to image.
- Parameters
image (numpy array) – the target image.
- Return :
image (numpy array) : the preprocessed image.
-
print_on_frame
(frame, infos, frame_col=(0, 0, 0), font=0, show=True)¶ draw informations on target frame.
- Args :
frame (numpy array) : the target image or frame to draw on. res (dictionary) : a dictionary that contains the result of pipline appiled on the frame. frame_col (tuple) : (x,y,z) gives the rgb componants of the color to use in drawing. font (open cv font) : the writing font.
- Returns
the frame with results drawn on it.
- Return type
frame (numpy array)
-
process_labeled_data
(data_dir='C:\\Users\\Ali\\Desktop\\FaceReco\\FaceApp\\facelib\\labeled_data\\', out_dir='C:\\Users\\Ali\\Desktop\\FaceReco\\FaceApp\\facelib\\data\\')¶ process labeld data into serialized embeddings.
- Parameters
data_dir (string) – the labled data diractory.
out_dir (string) – the diractory to serialize the data.
- Return :
bool (bool) : True if all processing was successful.
-
process_stream
(frame, show_frame=False, prev_faces=[], keep_num=50, video_method='vote')¶ general function that applies all pipline face recongition stages and keep track of faces used with any kind of streams
- Parameters
frame (numpy array) – current frame to process
show_frame (boolean) – weather to show the processed frame or not, useful when using webcame
prev_faces (dictionary) – contains the informations of previoulsy tracked faces in video
keep_num (int) – indicates how many frames you want a face to be tracked along
video_method (string) – recognize method to use (example : knn)
- Return :
frame (numpy array) : processed current frame with faces boxes, names , ids, facial points drawn on the frame diractly prev_faces (dictionary) : the updated tracked face informations
-
read_data
(data_dir='')¶ read the serlized pickle type data in a diractory
- Parameters
data_dir (string) – data diractory.
- Returns
list of person face objects which is the deserialized data in dir.
- Return type
faces (list of person face)
-
recognize
(emb, prev, video_method='vote', model_name='kdtree')¶ recognize the person name using the face embeddings provided
- Parameters
emb (numpy array) – the face embeddings of size 512
prev (dictionary) – dictionary that holds the previous information of this face which is used in video face recognition
video_method (string) – the method to use in time series face recognition (currently only vote and feature fusion are supported)
model_name (string) – the recognition model to use (knn kmeans kdtree softmax xgboost)
- Return :
name (string) : the name of the person which the embeddings belong to dist (float) : the distance between the embeddings and the closest match ttime (float) : the total time of the recognition process.
-
save_face
(face, file_name, save_dir='.//data')¶ save a person face object as pickle object.
- Parameters
face (person face) – person face object to serilzie.
file_name (string) – the pickle file name in which to save the object.
save_dir (string) – save diractory.
- Return :
bool (bool) : True if saved successfully.
-
set_model_options
(model, options)¶ set the configuration of a model.
if the model object is not create this function will create one and sotre it in the coressponding dictionary attribute note that the users should know what configuration each model accept and this fucntion uses configure function found in each model class.
- Parameters
model (string) – the name of the module you wich to configure
options (dictionary) – the options which the module accepts
- Returns
True if all configuration was successfully done.
- Return type
bool
-
set_pipline_option
(model_options, rescale_options='auto')¶ set the models to use when performing pipline combined operations.
set the models which the library should load and use when preforming one or more of the pipline options.
- Parameters
model_options (dictionary) – contains the names of detect,align,embedding models.
rescale_options (string/array) – if set to auto this will let the library decide how to process input image or provide the width and hieght to rescale the image.
- Returns
True if all configuration was successfully done.
- Return type
bool
-
to_xyhw
(bbox)¶ covert box to differnet box representation.
- Parameters
bbox (numpy array) – the bounding boxes to change.
- Returns
the bounding boxes with different representation.

- Agrs:
bbox (numpy array) : the bounding boxes of faces in the image which we went to track. model (string) : the model name you wish to use for tracking.
- Returns
the bounding boxes with tracking id. track_time (float) : the time it tooks to track.
- Return type
ret (numpy array)
-


TOMA5
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Join a random game
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Create a new game
INSTRUCTIONS
Enter the game ID and press 'Enter'
Share the game ID below with your friends. Click 'Start' to begin the game
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Blockchain Basics
Blockchain Basics
So far, we have discussed the impressive implications of blockchain technology. We will now simply and concisely explain how the technology works to make it transparent and immutable across all users.
The blockchain is a digital ledger that operates via a network of computers. Data added to the blockchain is visible to everyone participating. When a transaction is carried out on the blockchain, it is added to a cryptographically encoded “block” with all the other transactions that occurred in the last ten minutes (for the Bitcoin network) and shared with the entire network of computers. Members of the network with participating computers, otherwise known as miners, compete to solve these cryptographic puzzles to validate the transaction. The first miner to decode the puzzle receives a small reward from the network for their work. The puzzle is difficult to solve, so in order to solve the puzzle fast enough to receive the reward, a miner will use powerful computers. When Bitcoin was first launched, it was possible to use a laptop purchased in a store for mining. That is no longer the case.
This ‘validation’ is in the form of reviewing cryptographic inputs, outputs and a timestamp, which then allows validated blocks to be linked to older validated blocks. This forms a chain of blocks that show every transaction made on the blockchain. This chain is updated and is made accessible to every member on the platform. This decentralized quality allows for transparency and immutability - that anyone on the platform has the ability to view any data added to the network.
Hashing and Public Key Cryptography
A blockchain uses hash functions in order to create an immutable record of what is written to it, as well as to create “digital signatures”. A hash function is a digital mechanism that is used to compress data into a specific format. The hashing algorithm used by the Bitcoin blockchain is SHA-256. In this hash, the hashed data is always 256 bits long. The Ethereum blockchain uses a hashing algorithm called Ethash. A hash created using Ethash will look like:
0xb846300e188829d1b819389b31cef3b9cfaf335082ee66f830a875f1c1beb396
The above hash is from block 5000171 mined at Jan-30-2018 02:20:28 PM +UTC on the Ethereum blockchain. More data on this block can be found at Etherscan.
This hashed data is used on the blockchain to indicate value and the links between each specific blocks. In addition to hashing, the blockchain relies on public key cryptography to encode information securely - the digital signatures we referred to in the above paragraph. Specifically, participants on the blockchain have a private code - called private keys - that allow them to access their information which is encoded with a public code - called a public key. The public and private key are related, but a malicious actor cannot derive the private key from the visible public key. Private keys are not meant to be shared since a private key is used to unlock its associated public key. These keys sign transactions on the blockchain.
Yunyun Chen of ConsenSys created an excellent visual explaining hashing in Guide: Hashing and how it is a part of public key cryptography in Guide: An Introduction to Encryption. Both graphics are reproduced below:
Hashing and public key cryptography work hand in hand to maintain consensus in the system. Through consensus, the entire system has common knowledge of the happenings on the platform and any action is recorded and made available for the platform to view. This solves the issue of a trustless system with intermediaries because now, participants on the blockchain have constant verification on the platform. The process of miners verifying actions on the blockchain is known as proof of work. Mining ensures that the constant state of the ledger has transactions that are all true. This prevents attacks and false information from perpetuating within the system, ensuring lasting validity.


|
irl_robot_drivers
|
#include <coppelia_controller.h>
Public Member Functions | |
| CoppeliaController (ros::NodeHandle handle, std::vector< std::string > out_topic_names, std::vector< std::string > in_topic_names, std::string demo_topic_name, std::string demo_reset_name, int in_frequency, std::vector< std::vector< std::string >> joint_lists, std::vector< std::string > ee_target_joints, std::vector< std::string > ee_joints, int test_robot) | |
| void | run () |
A simple controller for controlling robots/objects in CoppeliaSim.
| CoppeliaController::CoppeliaController | ( | ros::NodeHandle | handle, |
| std::vector< std::string > | out_topic_names, | ||
| std::vector< std::string > | in_topic_names, | ||
| std::string | demo_topic_name, | ||
| std::string | demo_reset_name, | ||
| int | in_frequency, | ||
| std::vector< std::vector< std::string >> | joint_lists, | ||
| std::vector< std::string > | ee_target_joints, | ||
| std::vector< std::string > | ee_joints, | ||
| int | test_robot | ||
| ) |
Initializes the CoppeliaController instance.
| handle | The ROS node handle. |
| out_topic_names | The topic names (specified in coppelia_controller.launch) to subscribe to for receiving inference commands. Only one robot will be receiving commands during testing, so only one topic is needed; however, multiple control topics are available for the user to use if needed. |
| in_topic_names | The topic names (specified in coppelia_controller.launch) to publish the robots' current CoppeliaSim state on. |
| demo_topic_name | is the name of the topic in the InteractionApplication (intprim_ros_framework package) that is specified by the demo_status_topic parameter. The subscriber to this topic listens for 1,3, and 5 messages. The 1 message indicates that the "train" option was selected from the InteractionApplication CLI. The 3 message indicates that the "test" option was selected from the CLI. The 5 message indicates that a user decided to stop the interaction early and serves as a stop signal. |
| in_frequency | The frequency at which data should be collected from CoppeliaSim. |
| joint_lists | The names of the joints (one list per robot) that correspond to the robot joints displayed in CoppeliaSim. |
| ee_target_joints | The Inverse Kinematics (IK) dummies in CoppeliaSim that the robot end effectors are tied to. |
| ee_joints | The names of the end effectors for each robot in the simulation. |
| test_robot | Specifies which robot is the non-controlled robot (UR5l) as specified in the coppelia_controller.launch file. |
| void CoppeliaController::run | ( | ) |
Entry point for CoppeliaController.


getline
VariantsTable 4.1
summarizes the eight variants of getline
,
listing which predefined variables are set by each one,
and whether the variant is standard or a gawk
extension.
Note: for each variant, gawk
sets the RT
predefined variable.
| Variant | Effect | awk / gawk |
|---|---|---|
getline | Sets $0 , NF , FNR , NR , and RT | awk |
getline var | Sets var, FNR , NR , and RT | awk |
getline < file | Sets $0 , NF , and RT | awk |
getline var < file | Sets var and RT | awk |
command | getline | Sets $0 , NF , and RT | awk |
command | getline var | Sets var and RT | awk |
command |& getline | Sets $0 , NF , and RT | gawk |
command |& getline var | Sets var and RT | gawk |


Outcome: Text alternative available
A text alternative for non-text content is available via user agents and assistive technologies, which allows users who are unable to perceive and / or understand the non-text content to determine its meaning.
This outcome relates to guideline Text alternatives.
Methods:
Functional categories
This outcome relates to the following functional categories:
- Sensory - Vision & Visual
- Sensory Intersections
- Cognitive - Language & Literacy
- Cognitive - Learning
- Cognitive - Memory
- Cognitive - Mental Health
- Cognitive & Sensory Intersections


Python Module of the Week
26 April 2019
ssh-keygen -b 4096 -C<EMAIL_ADDRESS>-t
specifies the type of the key (default: rsa)-b
gives the nuber of bits (default 2048)-C
gives a useful identifier in the comment field (default $USER@$HOST
)This will produce two files, for example
y.name@fz-juelich
<EMAIL_ADDRESS>ssh-rsa AAAAB3NzaC1yc2EAAAADAQAB...Da9V08Ew==<EMAIL_ADDRESS>Add your public key to the file ~/.ssh/authorized_keys
on the destination machine
ssh-rsa AAAAB3NzaC1yc2EAAAADAQAB...Da9V08Ew==<EMAIL_ADDRESS>Then use your private key to log into the machine (man ssh
)
ssh -i y.name@fz-juelich <EMAIL_ADDRESS>When frequently accessing a system it may be impractical to re-type the potentially longisch key password for each connection, esp. when connecting to other hosts in scripts.
NOTE: This is not necessary if the gnome-keyring is available!
wrong
$ ssh-agent
SSH_AUTH_SOCK=/tmp/ssh-hHmOg2ChTmr4/agent.31758; export SSH_AUTH_SOCK;
SSH_AGENT_PID=31759; export SSH_AGENT_PID;
echo Agent pid 31759;
right
$ eval $(ssh-agent)
$SSH_AUTH_SOCKET
is in your environment, the agent will be usedssh<EMAIL_ADDRESS>tell ssh
to “forward the ssh-agent socket” to the remote side
ssh -A<EMAIL_ADDRESS>or configure ForwardAgent yes
(see next section)copy your encrypted private key to the intermediate machine
.ssh/config
Even with the SSH agents lines can become annoying to re-type/remember
ssh -X -o ForwardAgent=yes<EMAIL_ADDRESS>ssh -o TCPKeepAlive=yes<EMAIL_ADDRESS>...
Create/edit your ~/.ssh/config
(man ssh_config
):
Host login
Hostname login.inm.kfa-juelich.de
User y.name
IdentitiesOnly yes
IdentityFile ~/.ssh/y.name@fz-juelich
This lets you do
ssh login
which is then equivalent to
ssh -i ~/.ssh/y.name@fz-juelich -o IdentitiesOnly=yes \<EMAIL_ADDRESS>Host hambach
HostName hambach.inm.kfa-juelich.de
User terhorst
IdentityFile ~/.ssh/y.name@fz-juelich
IdentitiesOnly yes
Host jureca
HostName jureca.fz-juelich.de
User jinb3326
KbdInteractiveAuthentication no
IdentityFile ~/.ssh/my-global-key
IdentitiesOnly yes
Host gitty
Hostname gitty.inm.kfa-juelich.de
User git
IdentitiesOnly yes
IdentityFile ~/.ssh/y.name@fz-juelich
then run ssh gitty
Go to your github settings page “SSH and GPG keys” and add your public key
git clone gitty:csn_toolbox
git clone github:INM-6/equipment-overview
Using the agent and ssh-config things become very easy:
ssh login
date >somefile
rsync -avi login:somefile .
sshfs
mkdir remote
sshfs login: remote
ls remote
fusermount -u remote
With sshfs processes still run locally! Only files are transfered! Huge difference in speed and responsiveness


My project is about creating a shelter for animals and it was specifically for the cat and dogs since after gathering data realised that most of this listed animals are homeless.
Here are some of the languages I used
Worked as a teacher in several private institutions
Joined campus for my Degree but due to financial constraints was not able to complete
Sat for my K.C.S.E and got a B-
Sat for my K.C.P.E and scored 339 marks
I have passion for computers but never got a chance to go past my packages.When this golden opportunity knocked my door I was glad since I knew that what I wished to know was coming real.For the time I've been here in Moringa for sure has been a real great experience,especially the coding and also knowing programming languages. Moringa has made me know how this eye-catching websites I always view are made this is just awesome on my side. They have made me realise that student-centered learning is the best way one can be equiped with compentitive skills in the market


{% extends "base.html" %} {% block head_title %}About{% endblock %} {% block content %}
About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us!
About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us!
About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us! About us!
About us! About us! About us! About us! About us! About us!


This data is pulled from conversations with a chatbot. Powered by Rensselaer Center for Open Source and IBM. Fork us on GitHub.


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Movie Theaters in Wichita, KS on Yahoo! Local
Results 1 - 10 of 14 . Movie Theaters in Wichita, KS on Yahoo! Local Get Ratings .
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13th Avenue Warren Showtimes and Tickets - MovieTickets ... - Wichita
13th Avenue Warren - get directions and maps, find movie showtimes and purchase tickets online on . Wichita, KS 67206<PHONE_NUMBER>. This Theater Offers .
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Palace Theatre West Showtimes and Tickets - Wichita
Find Palace Theatre West movie times and tickets, box office information, driving directions, and more on . Find Theaters + Movie Times . Wichita, KS 67209 .
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Palace West Theatre Wichita Ks - Movie Tickets and Showtimes
Find showtimes and buy movie tickets for Palace West Theatre in Wichita Ks, find theater and cinema showtimes, read movie reviews and more at .
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West 21st Warren Showtimes and Tickets - MovieTickets.com - Wichita
West 21st Warren - get directions and maps, find movie showtimes and . Wichita , KS 67205<PHONE_NUMBER>. Directions 21st & Tyler. This Theater Offers .
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Warren Theatre - Wichita, KS
Wichita, KS 67202.<PHONE_NUMBER> . The only thing I can say is that this is the best movie theatre I've ever been to (and I've lived in 6 cities) The only advice that .
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Warren West IMAX - Wichita
Warren West IMAX, United States, Wichita. . 9150 W 21st Street Wichita, KS 67205<PHONE_NUMBER> . Details at Theatre Website . IMAX theatres provide an enhanced movie going experience perfect for field trips or a fun night out of the house .
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Drive-in Movie Theater Search - Drive-ins.com - Drive-In Theater ...
Drive-in Movie Theater Search . Great Western Drive-In, Liberal, Kansas, closed . Lyons Drive-In, Lyons, . Forty Two Sky Vue Drive-In, Wichita, Kansas, closed .
http://www.drive-ins.com/theaters/ks/code=ks/skip=50
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13th Ave Warren - Wichita, KS
Wichita, KS 67207. (316) 691- . I was happy I actually found a place people yelp about in Wichita! . Seriously one of the best movie theaters I have been to.
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Starlite Fun - Wichita
Starlite Fun DriveIn Movie Theatre . Wichita is among a dwindling number of cities which still offers its lucky families the casual, relaxing experience of sharing .
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Orpheum Theatre (Wichita, Kansas) - Wikipedia, the free encyclopedia
Orpheum Theatre (Wichita, Kansas) is located in Kansas . The Orpheum Theatre in Wichita, Kansas constructed by a group of local . Movie palaces list .
http://en.wikipedia.org/wiki/Orpheum_Theatre_(Wichita,_Kansas)
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Murdock Theatre - Wichita, KS
Feb 16, 2012 . 1 Review of Murdock Theatre "Even though most of the Warren . Wichita, KS 67214 . But similar movie theaters, they do serve concessions.
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Welcome to Dickinson Theatres
DTMovies.com - Showtimes and movie information at your local Dickinson theatre.
http://www.dtmovies.com/
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21st Street Warren Theatre - Wichita - Warren Theatres
Palace Theatres · Movie Machine . 9150 W 21st St - Wichita, KS - Get Directions! . The 21st Street Warren Theatre has 17 luxurious auditoriums and a World .
http://www.warrentheatres.com/westwichita.asp
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Warren's Movie Machine
Located in the Towne West Mall, the Movie Machine provides all the comfort and quality that the Warren Theatres provide, but at a discounted price. Below you .
http://www.warrenmoviemachine.com/home.html
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Orpheum Theater: Wichita, Kansas, KS
The Orpheum Theatre in Wichita, Kansas represents one of the finest . Today the 1300 seat theatre host a variety of acts from concerts to movies and plays.
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Palace Theatre West Showtimes & Tickets - 67278 Movie Theaters
View the latest Palace Theatre West movie times, box office information, and purchase tickets online. Sign up for Eventful's . Wichita, KS 67278. Get directions .
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Amc movie theaters in Wichita, KS - Yellowpages.com
Results 1 - 30 of 2273 . 2273 listings of Movie Theaters in Wichita on YP.com. Find reviews, directions & phone numbers for the best amc movie theaters in Wichita .
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Movie Machine (Wichita, KS) Movie Theater Information and ...
Movie Machine (Wichita, KS) Movie Theater Information and Showtimes.
http://boxofficemojo.com/showtimes/theater.htm?id=2bn&p=.htm
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13th Ave Warren Theatre Movie Showtime Listings ... - Wichita
See movie showtimes and get 13th Ave Warren Theatre theater location info on Moviefone.com. . 11611 E. 13th Street, Wichita, KS 67206 |<PHONE_NUMBER> .
http://www.moviefone.com/theater/13th-ave-warren-theatre/2219/showtimes


© Copyright 2023 Jogendra Kumar. All Rights Reserved.
Please do not use any of the photographs/drawings without permission. You may obtain permission to use images from this website by contacting me at<EMAIL_ADDRESS>There is a charge for the use of any image for commercial purposes. Details of pricing can be provided upon request to the above email address.


If you don’t belong to a camera/photo club/association (etc etc). I highly recommend looking in to at least one.
I am considering joining a second one because I have that option close by. Since I joined the Cascade Camera Club my skill level has grown immeasurably from the kindness of club members. (I'd like to say for the record though, it’s all Abbot’s fault.)
Our camera club has two meetings a month. The first is a critique where we submit images and a pro talks about them. The pros are from different genre of photography so sometimes we get really interesting ideas from artists and such that we would not have thought of.
The second meeting is some kind of presentation on either photography or post-processing techniques. We're always dsicovering new tricks and techniques.


{% extends "base_center.html" %} {% block title %}FableMe - Review Fable{% endblock %} {% block extraheader %} {% endblock %} {% block maincontent %}
| {% include "fable_details.html" %} | |
| {% include 'review_items.html' %} | |
|
{% if review_received %}
Thank you!Your review has been submitted and will published by our staff in a few minutes. Back Home {% else %} {% if review_processed %} Thank you, administrator!The review of {{ author }} {{ review_message }} Back Home {% else %} Review this fable{% endif %} {% endif %} |


Image Diffusion Analysis
1. How many unique original images?
(hint: use 'Image Id Str')
2. How many unique original tweet users?
3. How many times did image<PHONE_NUMBER>73865700) get retweeted?
(the answer should be 10. see if you can compute it)
4. Which image has the most number of retweets?
(hint: use 'Image Id Str')
5. How many original tweets were there on 4/26/15?
6. Among these original tweets on 4/26/15, what is the most number of times one was retweeted?
7. Among these original tweets on 4/26/15, what is the average number of times one was retweeted?
8. Among these original tweets on 4/26/15, how many of them got retweeted within one hour?
9. Among these original tweets on 4/26/15, what are the ten most retweeted?
10. Among these ten most retweeted, how many of them contain images of people?
(hint: you will need to look at the photos yourself)


Landing Page
Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Blake Nissen
8/31/18
When O’Shaan Allison sits in the running backs meeting room, he knows the odds are stacked against him.
But that’s not on his mind. He didn’t come to Ohio to worry about his playing time. He didn’t drive to Athens in the middle of the night for his unofficial visit for no reason, either.
He came to earn a degree and win a conference championship.
“Whenever I get a shot I’m going to make the best of it,” he said Thursday morning after practice. “It’s understandable to be at the bottom of the depth chart, but that comes with young age. It pushes me to work 10 times harder.”
Entering his freshman year, he’s been in Athens since June. He’s not joining a team in the basement of the Mid-American Conference or one that doesn’t have running back depth.
A.J. Ouellette returns fully healthy and back after a 1,000 yard season. He’ll be the No. 1 back and Maleek Irons will be right behind him, after a year off the field due to suspension.
Photo provided via @OshaanAllison on Twitter
Allison runs in a practice during fall camp.
In his sophomore season of high school, Allison rushed for over 1,500 yards. He sat out his junior season due to an ankle injury, but returned his senior year.
That was enough for offensive coordinator Tim Albin, who says Allison never left his sight on the official visit. It’s something that Albin does with all his recruits, but a chance at a running back with a lot of upside wasn’t going to slip away.
Originally a Dave Johnson recruit — Johnson is now the offensive line coach at Colorado State — Allison is from Malvern, Pennsylvania, which is an hour outside of Philadelphia. Albin knew if he wanted the running back at Ohio, he was going to have to win him from over 15 schools.
One of the reasons why Allison was committed to Ohio laid in Albin’s commitment to him.
Whether it was a call about how he was doing in school or how his family was, it was what he needed to hear. Allison said that Albin was one of the few coaches in the recruiting process to take the time to actually talk to him when he wasn’t on campus.
“I built a good relationship with coach Albin,” he said. “Checking on me, checking on my family, seeing how I’m doing in school. He definitely makes sure I’m doing OK.”
All of the texting and phone calls have seemed to paid off. Midway through camp, Allison has been the bright young running back in the group. He describes himself as a power back, he models himself after Marshawn Lynch.
He’s also a player that pops out while reading the roster. Not because of his size or high school. No, because of his first name.
O’Shaan.
“It’s a pretty rare name,” he said. “I’ve never met anyone else named O’Shaan. Everyone calls me O’Shaan.”
So the question for Allison now is what this fall will hold for him. With the new redshirt policy, players can play in four games and still redshirt.
It’s something that Albin is taking into account. With little experience, it seems like Allison would fit best into the game as a possible third down back. With his size, he could also excel at a goal line dive.
But the freshman isn’t focused on what he’ll do on the field. Instead, he’s trying to make sure he can “get on it,” which means learning the new playbook. The formations aren’t too different from his high school days, but there are certain things he needs to focus on — pass protection and catching balls out of the backfield.
“I’m still struggling now, but I’m asking a lot of questions and trying to understand,” Allison says.
He’s still adjusting to the idea of college football and living in a new place. That comes with any college student, whether it’s an athlete or not.
Allison’s never lived in a place with people from so many different backgrounds. He’s also never seen people move so fast on the football field.
Now it’s just adapting, on and off the field.
“He’ll be game ready for the first game and we’ll go from there,” Albin said.
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OH-01 OH-02 OH-03 OH-04 OH-05 OH-06 OH-07 OH-08 OH-09 OH-10 OH-11 OH-12 OH-13 OH-14 OH-15 OH-16
OH – Sen. Sherrod Brown (D) and Sen. Rob Portman (R)
Committees
Sherrod Brown is not on any of our key committees.
Rob Portman is not on any of our key committees.
HEP Grants
In the past 8 years, this state has received: 64 HEP grants, totalling $27,940,107
Institution Amount ($)
------------------------------------------- ------------
Ohio State University 12,613,531
OHIO STATE UNIVERSITY 4,523,000
University of Cincinnati 4,446,000
Case Western Reserve University 1,820,576
UNIVERSITY OF CINCINNATI 1,538,000
Ohio University 1,465,000
Old Dominion University Research Foundation 696,000
CASE WESTERN RESERVE UNIV. 567,000
Oklahoma State University 271,000
SC Contracts
In the past 8 years, this state has received: 13 Office of Science contracts, totalling $600,472
Vendor Amount ($) Item
--------------------------------------------- ------------ ------------------------------------------------------------------------------------
TRINITY ENGINEERING ASSOCIATES, INC. 424,449 SUPPORT- PROFESSIONAL: OTHER
WALKER & JOCKE, INC 41,230 SUPPORT- PROFESSIONAL: LEGAL
EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 27,880 MAINT/REPAIR/REBUILD OF EQUIPMENT- ELECTRIC WIRE AND POWER DISTRIBUTION EQUIPMENT
PARK PLACE TECHNOLOGIES, LLC 27,797 IT AND TELECOM- ANNUAL HARDWARE MAINTENANCE SERVICE PLANS
PARK PLACE TECHNOLOGIES INC 23,126 IT AND TELECOM- ANNUAL HARDWARE MAINTENANCE SERVICE PLANS
PARK PLACE TECHNOLOGIES INC 14,269 MAINT/REPAIR/REBUILD OF EQUIPMENT- ADP EQUIPMENT/SOFTWARE/SUPPLIES/SUPPORT EQUIPMENT
EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 11,503 MISCELLANEOUS MAINTENANCE AND REPAIR SHOP SPECIALIZED EQUIPMENT
EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 10,636 MISCELLANEOUS ELECTRICAL AND ELECTRONIC COMPONENTS
LIEBERT CORPORATION 10,274 MISCELLANEOUS MAINTENANCE AND REPAIR SHOP SPECIALIZED EQUIPMENT
EXPANSION PROGRAMS INTERNATIONAL, INC. 9,308 ADP SOFTWARE
NSF MPS Grants
In the past 8 years, this state has received: 989 NSF MPS grants, totalling $221,779,926
Institution Amount ($)
--------------------------------------------- ------------
OHIO STATE UNIVERSITY, THE 92,027,689
CASE WESTERN RESERVE UNIVERSITY 37,683,438
UNIVERSITY OF CINCINNATI 15,573,074
THE UNIVERSITY OF AKRON 15,561,925
KENT STATE UNIVERSITY 12,033,111
OHIO UNIVERSITY 9,025,600
OHIO STATE UNIVERSITY 8,071,443
UNIVERSITY OF TOLEDO, THE 7,000,633
MIAMI UNIVERSITY 6,596,509
BOWLING GREEN STATE UNIVERSITY (INC) 2,090,011
OBERLIN COLLEGE 1,992,650
BOWLING GREEN STATE UNIVERSITY 1,965,455
YOUNGSTOWN STATE UNIVERSITY 1,773,060
WRIGHT STATE UNIVERSITY 1,667,856
CASE WESTERN RESERVE 1,414,956
DENISON UNIVERSITY 1,276,573
KENYON COLLEGE 1,249,806
THE COLLEGE OF WOOSTER 655,554
BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 655,403
UNIVERSITY OF AKRON 504,999
OHIO WESLEYAN UNIVERSITY 485,745
OTTERBEIN UNIVERSITY 418,914
OHIO STATE UNIVERSITY RESEARCH FOUNDATION 360,000
CLEVELAND STATE UNIVERSITY 312,303
UNIVERSITY OF DAYTON 310,000
UNIVERSITY OF TOLEDO 260,000
HIRAM COLLEGE 240,531
PII REDACTED 150,000
U OF CINCINNATI 125,642
OHIO STATE UNIVERSITY RESEARCH FOUNDATION, TH 120,000
XAVIER UNIVERSITY 57,862
BOWLING GREEN U MAIN CAM 40,000
OTTERBEIN COLLEGE 36,584
AMERICAN CERAMIC SOCIETY, THE 24,600
MIAMI UNIV 18,000
SULI/CCI Interns
From 2014-2016 (only years available), this state had 36 SULI/CCI interns
# Interns Program College
----------- --------- -------------------------------------
12 SULI The Ohio State University Main Campus
3 SULI Bowling Green State University
3 SULI Case Western Reserve University
3 SULI Cedarville University
3 SULI The College of Wooster
3 SULI University of Cincinnati Main Campus
2 SULI Franciscan University of Steubenville
1 SULI Cleveland State University
1 SULI Miami University
1 SULI Oberlin College
1 SULI Ohio University Main Campus
1 SULI Otterbein University
1 SULI University of Dayton
1 SULI Youngstown State University
OH-01 – Rep. Steve Chabot (R) – Wikipedia
Committees
Steve Chabot is not on any of our key committees.
HEP Grants
In the past 8 years, this district has received: $5,984,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------ ------------ ------- ------- ------------------------ ------------------------------------------------------------------------------------------------------------
University of Cincinnati 9,000 2018 Ongoing Schwartz, Alan 2018 Belle II Summer School at the University of Cincinnati
University of Cincinnati 0 2014 2015 Argyres, Philip RESEARCH IN PARTICLE PHYSICS TASK A AND B
University of Cincinnati 4,437,000 2014 Ongoing Argyres, Philip Research in Particle Theory and Experiment at the University of Cincinnati
UNIVERSITY OF CINCINNATI 10,000 2012 2013 Schwartz Seventh Workshop on the CKM Unitary Triangle at the University of Cincinnati, September 28 - October 2, 2012
UNIVERSITY OF CINCINNATI 1,528,000 2012 2014 Wijewardhana Research in Particle Physics Task A and B
SC Contracts
In the past 8 years, this district has received: 1 Office of Science contracts, totalling $424,449
Year Vendor Amount ($) Item
------ ------------------------------------ ------------ ----------------------------
2019 TRINITY ENGINEERING ASSOCIATES, INC. 72,936 SUPPORT- PROFESSIONAL: OTHER
2018 TRINITY ENGINEERING ASSOCIATES, INC. 195,000 SUPPORT- PROFESSIONAL: OTHER
2017 TRINITY ENGINEERING ASSOCIATES, INC. 156,513 SUPPORT- PROFESSIONAL: OTHER
NSF MPS Grants
In the past 8 years, this district has received: $15,756,578 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------ ------------
2018 UNIVERSITY OF CINCINNATI 2,098,288
2017 UNIVERSITY OF CINCINNATI 2,161,129
2016 UNIVERSITY OF CINCINNATI 2,082,935
2015 U OF CINCINNATI 125,642
2015 UNIVERSITY OF CINCINNATI 3,488,335
2014 UNIVERSITY OF CINCINNATI 1,191,658
2014 XAVIER UNIVERSITY 57,862
2013 UNIVERSITY OF CINCINNATI 1,249,944
2012 UNIVERSITY OF CINCINNATI 3,300,785
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2016 (only years available)
OH-02 – Rep. Brad R. Wenstrup (R) – Wikipedia
Committees
Brad R. Wenstrup is not on any of our key committees.
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
This district received no NSF MPS grants from 2012-2019
SULI/CCI Interns
From 2014-2016 (only years available), this district had 3 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------- ------------------------------------ ------------------------------------- ---------
Summer 2015 Lauren Alyssa Blake University of Cincinnati Main Campus Lawrence Berkeley National Laboratory SULI
Spring 2015 Lauren Alyssa Blake University of Cincinnati Main Campus Lawrence Berkeley National Laboratory SULI
Spring 2014 Evvan Victoria Morton University of Cincinnati Main Campus National Renewable Energy Laboratory SULI
OH-03 – Rep. Joyce Beatty (D) – Wikipedia
Committees
Joyce Beatty is not on any of our key committees.
HEP Grants
In the past 8 years, this district has received: $13,580,531 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------------------------- ------------ ------- ------- ------------------------ --------------------------------------------------------------------------------------------------------------------------
Ohio State University 1,884,000 2018 Ongoing Durkin, Lloyd Experimental and Theoretical Research in High Energy Physics
Ohio State University 116,531 2018 Ongoing Hirata, Christopher Cosmological Probes of Fundamental Physics
Ohio State University 125,000 2016 2018 Honscheid, Klaus Research in High Energy Physics
Ohio State University 80,000 2016 2018 Martini, Paul Quasar Selection for Baryon Acoustic Oscillation Measurements with the Dark Energy Spectroscopic Instrument
Ohio State University 140,000 2015 2017 Carpenter, Linda New Models and Methods for the Electroweak Scale
Ohio State University 0 2015 2016 Collings, Edward Material, Strands, And Cables For Superconducting Accelerator Magnets
Ohio State University 950,000 2014 Ongoing Kagan, Harris Development of 3D diamond detectors for detector applications at ultra high luminosity facilities
Ohio State University 472,000 2014 2015 Kass, Richard Research in Elementary Particle Physics
Ohio State University 7,856,000 2014 2018 Kass, Richard Research in High Energy Physics
Ohio State University 990,000 2014 Ongoing Sumption, Michael Electromagnetic Properties of Strands, Cables, and Coils for Superconducting Accelerator Magnets
Oklahoma State University 271,000 2014 2015 Nandi, Satyanarayan Theoretical and Experimental Research in Weak, Electromagnetic and Strong Interactions
Old Dominion University Research Foundation 500,000 2014 2015 Gurevich, Alexander Investigation of new superconducting materials for the next generation high-gradient RF cavities for particle accelerators
Old Dominion University Research Foundation 196,000 2014 2015 Vuskovic, Leposava Plasma Processing of SRF Cavities for the Next Generation of Particle Accelerators
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $85,526,969 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------------------------- ------------
2018 OHIO STATE UNIVERSITY, THE 12,112,694
2018 PII REDACTED 150,000
2017 OHIO STATE UNIVERSITY, THE 18,977,099
2017 UNIVERSITY OF DAYTON 35,000
2016 OHIO STATE UNIVERSITY, THE 12,623,041
2015 OHIO STATE UNIVERSITY 8,071,443
2015 OHIO STATE UNIVERSITY, THE 10,028,837
2014 OHIO STATE UNIVERSITY RESEARCH FOUNDATION, TH 120,000
2014 OHIO STATE UNIVERSITY, THE 10,383,447
2013 OHIO STATE UNIVERSITY, THE 13,025,408
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2016 (only years available)
OH-04 – Rep. Jim Jordan (R) – Wikipedia
Committees
Jim Jordan is not on any of our key committees.
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $1,752,862 in NSF MPS grants.
Year Institution Amount ($)
------ --------------- ------------
2018 OBERLIN COLLEGE 84,799
2017 OBERLIN COLLEGE 402,002
2016 OBERLIN COLLEGE 282,724
2015 OBERLIN COLLEGE 375,514
2014 OBERLIN COLLEGE 86,877
2013 OBERLIN COLLEGE 520,946
SULI/CCI Interns
From 2014-2016 (only years available), this district had 13 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------- ------------------------------------- -------------------------------------- ---------
Summer 2016 Alan Travis Jaski The Ohio State University Main Campus Argonne National Laboratory SULI
Summer 2016 Emily Ann Reed The Ohio State University Main Campus Pacific Northwest National Laboratory SULI
Summer 2016 Michael Alan Jindra The Ohio State University Main Campus Idaho National Laboratory SULI
Summer 2015 Ryan Gallagher The Ohio State University Main Campus Brookhaven National Laboratory SULI
Summer 2015 Andy Goldschmidt The Ohio State University Main Campus Lawrence Livermore National Laboratory SULI
Summer 2015 Samuel Jones The Ohio State University Main Campus Oak Ridge National Laboratory SULI
Summer 2015 Jordan Kotick The Ohio State University Main Campus SLAC National Accelerator Laboratory SULI
Summer 2015 Michael Alan Jindra The Ohio State University Main Campus Idaho National Laboratory SULI
Summer 2015 Eric Alan Magnuson The Ohio State University Main Campus Los Alamos National Laboratory SULI
Summer 2014 Gabriel Isaac Richman Oberlin College Brookhaven National Laboratory SULI
Summer 2014 Brian Li The Ohio State University Main Campus National Renewable Energy Laboratory SULI
Summer 2014 Amanda Conklin The Ohio State University Main Campus Savannah River National Laboratory SULI
Spring 2014 Ethan Isaac Palay The Ohio State University Main Campus National Renewable Energy Laboratory SULI
OH-05 – Rep. Robert E. Latta (R) – Wikipedia
Committees
Robert E. Latta is the #6 Republican on the House Committee on Energy and Commerce
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $4,095,466 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------------------ ------------
2018 BOWLING GREEN STATE UNIVERSITY 339,595
2017 BOWLING GREEN STATE UNIVERSITY 1,625,860
2016 BOWLING GREEN STATE UNIVERSITY (INC) 544,985
2015 BOWLING GREEN STATE UNIVERSITY (INC) 598,600
2015 BOWLING GREEN U MAIN CAM 40,000
2013 BOWLING GREEN STATE UNIVERSITY (INC) 486,809
2012 BOWLING GREEN STATE UNIVERSITY (INC) 459,617
SULI/CCI Interns
From 2014-2016 (only years available), this district had 3 SULI/CCI interns
Term Name College Host Lab Program
----------- ----------------------- ------------------------------ ----------------------------- ---------
Summer 2016 Elizabeth Rose Crowther Bowling Green State University Oak Ridge National Laboratory SULI
Summer 2014 Bryan Andrew Smith Bowling Green State University Argonne National Laboratory SULI
Summer 2014 Rebekah Thomas Bowling Green State University Argonne National Laboratory SULI
OH-06 – Rep. Bill Johnson (R) – Wikipedia
Committees
Bill Johnson is the #14 Republican on the House Committee on Energy and Commerce
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $2,576,654 in NSF MPS grants.
Year Institution Amount ($)
------ --------------- ------------
2012 OHIO UNIVERSITY 2,576,654
SULI/CCI Interns
From 2014-2016 (only years available), this district had 2 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------- ------------------------------------- ------------------------ ---------
Summer 2015 Stephen Joseph Wilson Franciscan University of Steubenville Ames National Laboratory SULI
Summer 2014 Stephen Joseph Wilson Franciscan University of Steubenville Ames National Laboratory SULI
OH-07 – Rep. Bob Gibbs (R) – Wikipedia
Committees
Bob Gibbs is not on any of our key committees.
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $2,523,299 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------------------------- ------------
2018 KENYON COLLEGE 78,523
2018 WRIGHT STATE UNIVERSITY 229,950
2017 KENYON COLLEGE 478,443
2017 WRIGHT STATE UNIVERSITY 32,848
2016 KENYON COLLEGE 325,000
2015 KENYON COLLEGE 47,840
2014 KENYON COLLEGE 120,000
2013 KENYON COLLEGE 25,000
2012 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 89,073
2012 WRIGHT STATE UNIVERSITY 1,096,622
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2016 (only years available)
OH-08 – Rep. Warren Davidson (R) – Wikipedia
Committees
Warren Davidson is not on any of our key committees.
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $7,180,839 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------------------------- ------------
2019 MIAMI UNIVERSITY 80,901
2018 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 100,000
2018 MIAMI UNIVERSITY 1,124,717
2017 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 160,000
2017 MIAMI UNIVERSITY 909,976
2016 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 100,000
2016 MIAMI UNIVERSITY 603,081
2015 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 140,000
2015 MIAMI UNIV 18,000
2015 MIAMI UNIVERSITY 1,069,494
2014 MIAMI UNIVERSITY 271,735
2013 BOARD OF DIRECTORS OF WITTENBERG COLLEGE, THE 66,330
2013 MIAMI UNIVERSITY 1,385,290
2012 MIAMI UNIVERSITY 1,151,315
SULI/CCI Interns
From 2014-2016 (only years available), this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- ---------------- ---------------- ------------------------------------- ---------
Summer 2016 Cameron Williams Miami University Lawrence Berkeley National Laboratory SULI
OH-09 – Rep. Marcy Kaptur (D) – Wikipedia
Committees
Marcy Kaptur is the #1 Democrat on the House Appropriations Subcommittee on Energy and Water Development
Marcy Kaptur is the #7 Democrat on the House Appropriations Subcommittee on Commerce, Justice, and Science
Marcy Kaptur is the #2 Democrat on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
In the past 8 years, this district has received: 1 Office of Science contracts, totalling $9,308
Year Vendor Amount ($) Item
------ -------------------------------------- ------------ ------------
2013 EXPANSION PROGRAMS INTERNATIONAL, INC. 9,308 ADP SOFTWARE
NSF MPS Grants
In the past 8 years, this district has received: $7,500,421 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------- ------------
2018 UNIVERSITY OF TOLEDO, THE 557,405
2017 UNIVERSITY OF TOLEDO, THE 69,999
2016 UNIVERSITY OF TOLEDO, THE 109,475
2015 UNIVERSITY OF TOLEDO 260,000
2015 UNIVERSITY OF TOLEDO, THE 770,083
2014 UNIVERSITY OF TOLEDO, THE 934,459
2013 UNIVERSITY OF TOLEDO, THE 741,776
2012 OBERLIN COLLEGE 239,788
2012 UNIVERSITY OF TOLEDO, THE 3,817,436
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2016 (only years available)
OH-10 – Rep. Michael R. Turner (R) – Wikipedia
Committees
Michael R. Turner is not on any of our key committees.
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $583,436 in NSF MPS grants.
Year Institution Amount ($)
------ ----------------------- ------------
2016 UNIVERSITY OF DAYTON 275,000
2016 WRIGHT STATE UNIVERSITY 8,436
2013 WRIGHT STATE UNIVERSITY 300,000
SULI/CCI Interns
From 2014-2016 (only years available), this district had 4 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------------- --------------------- ------------------------------------ ---------
Summer 2016 Christopher Patrick Brawley Cedarville University National Renewable Energy Laboratory SULI
Summer 2016 Kimberly Gliebe University of Dayton National Renewable Energy Laboratory SULI
Summer 2015 Jonathan David McDonald Cedarville University Oak Ridge National Laboratory SULI
Summer 2014 Ben Tuttle Cedarville University Brookhaven National Laboratory SULI
OH-11 – Rep. Marcia L. Fudge (D) – Wikipedia
Committees
Marcia L. Fudge is not on any of our key committees.
HEP Grants
In the past 8 years, this district has received: $2,387,576 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------------- ------------ ------- ------- ------------------------ ----------------------------------------------------------------------------------------------
Case Western Reserve University 110,000 2018 Ongoing Hinterbichler, Kurt Theories and Constraints for Massive Higher Spins
Case Western Reserve University 30,000 2017 2018 Fileviez Perez, Pavel New Theories for Physics beyond the Standard Model
Case Western Reserve University 1,225,000 2014 Ongoing Starkman, Glenn Theoretical Particle Physics at the Frontiers
Case Western Reserve University 455,576 2014 2018 Tolley, Andrew Exploring the Fundamental Origin of Cosmic Acceleration
CASE WESTERN RESERVE UNIV. 287,000 2013 2014 Starkman Theoretical Particle Physics at the Frontiers
CASE WESTERN RESERVE UNIV. 0 2013 2014 Tolley Exploring the Fundamental Origin of Cosmic Acceleration
CASE WESTERN RESERVE UNIV. 205,000 2012 2014 Starkman, Vachaspati Theoretical Physics Research at the High Energy Frontier, Including Astrophysics and Cosmology
CASE WESTERN RESERVE UNIV. 75,000 2012 2013 Tolley New Cosmologies on the Horizon: Cosmology and Holography in Bigravity and Massive Gravity
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $54,000,321 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------------- ------------
2018 THE UNIVERSITY OF AKRON 1,494,041
2018 CASE WESTERN RESERVE UNIVERSITY 3,259,352
2017 THE UNIVERSITY OF AKRON 3,286,250
2017 CLEVELAND STATE UNIVERSITY 312,303
2017 CASE WESTERN RESERVE UNIVERSITY 2,010,337
2016 THE UNIVERSITY OF AKRON 2,657,630
2016 CASE WESTERN RESERVE UNIVERSITY 3,257,412
2015 UNIVERSITY OF AKRON 504,999
2015 THE UNIVERSITY OF AKRON 815,474
2015 CASE WESTERN RESERVE UNIVERSITY 6,269,637
2015 CASE WESTERN RESERVE 789,424
2014 THE UNIVERSITY OF AKRON 2,918,167
2014 CASE WESTERN RESERVE UNIVERSITY 6,236,311
2013 CASE WESTERN RESERVE 625,532
2013 THE UNIVERSITY OF AKRON 2,913,063
2013 CASE WESTERN RESERVE UNIVERSITY 7,896,300
2012 CASE WESTERN RESERVE UNIVERSITY 8,754,089
SULI/CCI Interns
From 2014-2016 (only years available), this district had 4 SULI/CCI interns
Term Name College Host Lab Program
----------- ----------------- ------------------------------- -------------------------------------- ---------
Summer 2016 Kimaya Gupte Case Western Reserve University Lawrence Livermore National Laboratory SULI
Summer 2014 Danni Liang Case Western Reserve University Oak Ridge National Laboratory SULI
Summer 2014 Marie Ann Blatnik Cleveland State University Los Alamos National Laboratory SULI
Spring 2016 Kimaya Gupte Case Western Reserve University Lawrence Livermore National Laboratory SULI
OH-12 – Rep. Troy Balderson (R) – Wikipedia
Committees
Troy Balderson is the #11 Republican on the House Committee on Science, Space, and Technology
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
In the past 8 years, this district has received: 6 Office of Science contracts, totalling $60,293
Year Vendor Amount ($) Item
------ --------------------------------------------- ------------ ---------------------------------------------------------------------------------
2015 EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 16,895 MAINT/REPAIR/REBUILD OF EQUIPMENT- ELECTRIC WIRE AND POWER DISTRIBUTION EQUIPMENT
2014 EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 10,985 MAINT/REPAIR/REBUILD OF EQUIPMENT- ELECTRIC WIRE AND POWER DISTRIBUTION EQUIPMENT
2013 EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 10,636 MISCELLANEOUS ELECTRICAL AND ELECTRONIC COMPONENTS
2013 EMERSON NETWORK POWER, LIEBERT SERVICES, INC. 11,503 MISCELLANEOUS MAINTENANCE AND REPAIR SHOP SPECIALIZED EQUIPMENT
2012 LIEBERT CORPORATION 10,274 MISCELLANEOUS MAINTENANCE AND REPAIR SHOP SPECIALIZED EQUIPMENT
NSF MPS Grants
In the past 8 years, this district has received: $2,242,416 in NSF MPS grants.
Year Institution Amount ($)
------ ----------------------------- ------------
2018 OHIO WESLEYAN UNIVERSITY 83,396
2018 DENISON UNIVERSITY 290,177
2017 OTTERBEIN UNIVERSITY 71,684
2017 OHIO WESLEYAN UNIVERSITY 83,396
2017 DENISON UNIVERSITY 135,364
2016 OTTERBEIN UNIVERSITY 292,174
2016 DENISON UNIVERSITY 39,350
2015 OHIO WESLEYAN UNIVERSITY 103,817
2015 OTTERBEIN COLLEGE 36,584
2015 DENISON UNIVERSITY 105,300
2014 OTTERBEIN UNIVERSITY 7,334
2014 OHIO WESLEYAN UNIVERSITY 91,636
2014 DENISON UNIVERSITY 308,862
2013 OTTERBEIN UNIVERSITY 47,722
2013 OHIO WESLEYAN UNIVERSITY 30,000
2013 DENISON UNIVERSITY 100,000
2012 DENISON UNIVERSITY 297,520
2012 OHIO WESLEYAN UNIVERSITY 93,500
2012 AMERICAN CERAMIC SOCIETY, THE 24,600
SULI/CCI Interns
From 2014-2016 (only years available), this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- -------------------- -------------------- ------------------------------------- ---------
Summer 2014 Evan Mitchell Heintz Otterbein University Fermi National Accelerator Laboratory SULI
OH-13 – Rep. Tim Ryan (D) – Wikipedia
Committees
Tim Ryan is the #11 Democrat on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $13,192,878 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------- ------------
2019 KENT STATE UNIVERSITY 5,000
2018 KENT STATE UNIVERSITY 1,710,404
2018 YOUNGSTOWN STATE UNIVERSITY 293,943
2017 KENT STATE UNIVERSITY 1,843,308
2016 KENT STATE UNIVERSITY 1,026,676
2016 YOUNGSTOWN STATE UNIVERSITY 27,000
2015 KENT STATE UNIVERSITY 2,063,093
2015 YOUNGSTOWN STATE UNIVERSITY 5,000
2014 KENT STATE UNIVERSITY 2,535,171
2014 YOUNGSTOWN STATE UNIVERSITY 508,062
2013 KENT STATE UNIVERSITY 1,203,421
2013 YOUNGSTOWN STATE UNIVERSITY 494,500
2012 THE UNIVERSITY OF AKRON 1,477,300
SULI/CCI Interns
From 2014-2016 (only years available), this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- --------------- --------------------------- ----------------------------- ---------
Summer 2015 Timothy Shaffer Youngstown State University Oak Ridge National Laboratory SULI
OH-14 – Rep. David P. Joyce (R) – Wikipedia
Committees
David P. Joyce is the #14 Republican on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
In the past 8 years, this district has received: 4 Office of Science contracts, totalling $65,192
Year Vendor Amount ($) Item
------ ---------------------------- ------------ ------------------------------------------------------------------------------------
2015 PARK PLACE TECHNOLOGIES, LLC 27,797 IT AND TELECOM- ANNUAL HARDWARE MAINTENANCE SERVICE PLANS
2014 PARK PLACE TECHNOLOGIES INC 23,126 IT AND TELECOM- ANNUAL HARDWARE MAINTENANCE SERVICE PLANS
2013 PARK PLACE TECHNOLOGIES INC 14,269 MAINT/REPAIR/REBUILD OF EQUIPMENT- ADP EQUIPMENT/SOFTWARE/SUPPLIES/SUPPORT EQUIPMENT
NSF MPS Grants
In the past 8 years, this district has received: $240,531 in NSF MPS grants.
Year Institution Amount ($)
------ ------------- ------------
2016 HIRAM COLLEGE 124,368
2012 HIRAM COLLEGE 116,163
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2016 (only years available)
OH-15 – Rep. Steve Stivers (R) – Wikipedia
Committees
Steve Stivers is not on any of our key committees.
HEP Grants
In the past 8 years, this district has received: $5,988,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
--------------------- ------------ ------- ------- ------------------------ ----------------------------------------------------------------------------------------------------
Ohio University 750,000 2018 Ongoing Seo, Hee-Jong Optimal and robust reconstruction of BAO, redshift-space distortions and the Alcock-Paczynski effect
Ohio University 140,000 2017 2018 Clowe, Doug Improving Dark Energy Constraints Using Low-Redshift Large-Scale Structures
Ohio University 575,000 2015 Ongoing Clowe, Douglas Improving Dark Energy Contraints Using Low-Redshift Large-Scale Structures
OHIO STATE UNIVERSITY 230,000 2013 2014 Kagan Development of 3D Diamond Detectors for Detector Applications at Ultra High Luminosity Facilities
OHIO STATE UNIVERSITY 100,000 2013 2014 Sumption Materials, Strands, Cables For Superconducting Accelerator Magnets
OHIO STATE UNIVERSITY 277,000 2012 2013 Collings Material, Strands, and Cables for Superconducting Accelerator Magnets
OHIO STATE UNIVERSITY 3,916,000 2012 2014 Kass Research in Elementary Particle Physics
SC Contracts
This district received no SC contracts from 2012-2019
NSF MPS Grants
In the past 8 years, this district has received: $21,686,109 in NSF MPS grants.
Year Institution Amount ($)
------ ----------------------------------------- ------------
2018 OHIO UNIVERSITY 645,020
2017 OHIO UNIVERSITY 1,154,221
2016 OHIO UNIVERSITY 421,502
2015 OHIO UNIVERSITY 1,795,540
2014 OHIO UNIVERSITY 960,635
2013 OHIO STATE UNIVERSITY RESEARCH FOUNDATION 120,000
2013 OHIO UNIVERSITY 1,472,028
2012 OHIO STATE UNIVERSITY RESEARCH FOUNDATION 240,000
2012 OHIO STATE UNIVERSITY, THE 14,877,163
SULI/CCI Interns
From 2014-2016 (only years available), this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- -------------- --------------------------- ----------------------------- ---------
Spring 2016 Daniel Riordan Ohio University Main Campus Oak Ridge National Laboratory SULI
OH-16 – Rep. Anthony Gonzalez (R) – Wikipedia
Committees
Anthony Gonzalez is the #13 Republican on the House Committee on Science, Space, and Technology
HEP Grants
This district received no SC HEP grants from 2012-2019
SC Contracts
In the past 8 years, this district has received: 1 Office of Science contracts, totalling $41,230
Year Vendor Amount ($) Item
------ ------------------- ------------ ----------------------------
2014 WALKER & JOCKE, INC 15,280 SUPPORT- PROFESSIONAL: LEGAL
2013 WALKER & JOCKE, INC 25,950 SUPPORT- PROFESSIONAL: LEGAL
NSF MPS Grants
In the past 8 years, this district has received: $655,554 in NSF MPS grants.
Year Institution Amount ($)
------ ---------------------- ------------
2017 THE COLLEGE OF WOOSTER 23,965
2016 THE COLLEGE OF WOOSTER 631,589
SULI/CCI Interns
From 2014-2016 (only years available), this district had 3 SULI/CCI interns
Term Name College Host Lab Program
----------- ---------------------- ---------------------- ------------------------------------- ---------
Summer 2016 Clara Beatrix Deck The College of Wooster Argonne National Laboratory SULI
Summer 2016 Helen G Siegel The College of Wooster Lawrence Berkeley National Laboratory SULI
Summer 2016 Dylan Charles Hamilton The College of Wooster National Renewable Energy Laboratory SULI


MLutils module¶
-
class
MLutils.
Classifier
(n_jobs=- 1)¶ Bases:
MLutils.Model
-
compute_metrics_and_graphs
(pred, actual, output_path='outputs/plots/mlflow_artifacts')¶ Evaluate model’s performance
- Parameters
pred (list) – List of predictions
actual (list) – List of labels
output_path (str, optional) – Path indicating where to store the plots. Defaults to “outputs/plots/mlflow_artifacts”.
-
fit
(x, y)¶ Fit a classifier using a cross-validated grid search.
- Parameters
x (DataFrame) – Features
y (DataFrame) – Target
-
-
class
MLutils.
Model
¶ Bases:
object
Parent class of all model objects. 2 classes inherit: Regressor and Classfier classes
-
static
getModel
(task, n_jobs=1)¶
-
static
-
class
MLutils.
Preprocess
(scaler=None, numeric_na_fill_method=None, category_na_fill_method=None, one_hot_encoding=True)¶ Bases:
object
Preprocessing data (scaling, imputation, one hot encoding)
- Parameters
scaler (str, optional) – Type of scaling to use.
numeric_na_fill_method (str, optional) – Imputation method for numerical variables. Defaults to None.
category_na_fill_method (srr, optional) – Imputation method for categorical variables. Defaults to None.
one_hot_encoding (bool, optional) – Where to encode non-numeric categorical variables. Defaults to True.
-
fit
(df)¶
-
fit_transform
(df)¶
-
transform
(df, verbose=True)¶
-
class
MLutils.
Regressor
(n_jobs=- 1)¶ Bases:
MLutils.Model
-
compute_metrics_and_graphs
(pred, actual, output_path='outputs/plots/mlflow_artifacts')¶ Evaluate model’s performance
- Parameters
pred (list) – List of predictions
actual (list) – List of labels
output_path (str, optional) – Path indicating where to store the plots. Defaults to “outputs/plots/mlflow_artifacts”.
-
fit
(x, y)¶
-
-
MLutils.
explain
(x, model, task, path='outputs/plots/mlflow_artifacts/shap', n_features=5)¶ explain a model’ decisions based on SHAP value approximation. SHAP algorithm is quadratic with the depth of trees. -> Be careful not to go over 12 for max_depth.
- Parameters
x (DataFrame) – Input data
model ([type]) – Model to explain
task (str) – Task to perform. Available: regression, classification.
path (str, optional) – [description]. Defaults to “outputs/plots/mlflow_artifacts/shap”.
n_features (int, optional) – Number of most important features for which to generate partial dependance plot.


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Creating the BEM meshes¶
Contents
Using the watershed algorithm¶
The watershed algorithm [Segonne et al., 2004] is part of the FreeSurfer software. The name of the program is mri_watershed . Its use in the MNE environment is facilitated by the script mne_watershed_bem.
After mne_watershed_bem has
completed, the following files appear in the subject’s bem/watershed
directory:
** <subject> _brain_surface**
Contains the brain surface triangulation.
** <subject> _inner_skull_surface**
Contains the inner skull triangulation.
** <subject> _outer_skull_surface**
Contains the outer skull triangulation.
** <subject> _outer_skin_surface**
Contains the scalp triangulation.
All of these surfaces are in the FreeSurfer format. In addition,
there will be a directory called bem/watershed/ws
which
contains the brain MRI volume. Furthermore, mne_watershed_bem script
converts the scalp surface to fif format and saves the result to bem/
<subject> -head.fif
. The mne_analyze tool
described Interactive analysis with mne_analyze looks for this file the visualizations
involving the scalp surface.
Using FLASH images¶
This method depends on the availablily of MRI data acquired with a multi-echo FLASH sequence at two flip angles (5 and 30 degrees). These data can be acquired separately from the MPRAGE data employed in FreeSurfer cortical reconstructions but it is strongly recommended that they are collected at the same time with the MPRAGEs or at least with the same scanner. For easy co-registration, the images should have FOV, matrix, slice thickness, gap, and slice orientation as the MPRAGE data. For information on suitable pulse sequences, see reference [B. Fischl et al. and J. Jovicich et al., 2006] in Forward modeling. At the Martinos Center, use of the 1.5-T Avanto scanner (Bay 2) is recommended for best results.
Creation of the BEM meshes using this method involves the following steps:
Organizing the MRI data. This is facilitated by the script mne_organize_dicom , see Organizing MRI data into directories.
Creating a synthetic 5-degree flip angle FLASH volume, register it with the MPRAGE data, and run the segmentation and meshing program. This step is accomplished by running the script mne_flash_bem , see Creating the surface tessellations.
Inspecting the meshes with tkmedit, see Inspecting the meshes.
Note
Different methods can be employed for the creation of the
individual surfaces. For example, it may turn out that the
watershed algorithm produces are better quality skin surface than
the segmentation approach based on the FLASH images. If this is
the case, outer_skin.surf
can set to point to the corresponding
watershed output file while the other surfaces can be picked from
the FLASH segmentation data.
Note
The mne_convert_surface C utility can be used to convert text format triangulation files into the FreeSurfer surface format.
Note
The following sections assume that you have run the appropriate setup scripts to make both MNE and FreeSurfer software available.
Organizing MRI data into directories¶
Since all images comprising the multi-echo FLASH data are contained in a single series, it is necessary to organize the images according to the echoes before proceeding to the BEM surface reconstruction. This is accomplished by the mne_organize_dicom script, which creates a directory tree with symbolic links to the original DICOM image files. To run mne_organize_dicom , proceed as follows:
Copy all of your images or create symbolic links to them in a single directory. The images must be in DICOM format. We will refer to this directory as <source> .
Create another directory to hold the output of mne_organize_dicom . We will refer to this directory as <dest> .
Change the working directory to <dest> .
Say
mne_organize_dicom
<source> . Depending on the total number of images in <source> this script may take quite a while to run. Progress is indicated by listing the number of images processed at 50-image intervals.
As a result, <dest> will contain several directories named <three-digit number> _ <protocol_name> corresponding to the different series of images acquired. Spaces and parenthesis in protocol names will be replaced by underscores. Under each of these directories there are one or more directories named <three-digit> number corresponding to one or more subsets of images in this series (protocol). The only subset division scheme implemented in mne_organize_dicom is that according to different echoes, typically found in multi-echo FLASH data. These second level directories will contain symbolic links pointing to the original image data.
Note
mne_organize_dicom was developed specifically for Siemens DICOM data. Its correct behavior with DICOM files originating from other MRI scanners has not been verified at this time.
Note
Since mne_organize_dicom processes all images, not only the FLASH data, it may be a useful preprocessing step before FreeSurfer reconstruction process as well.
Creating the surface tessellations¶
The BEM surface segmentation and tessellation is automated with the script mne_flash_bem. It assumes that a FreeSurfer reconstruction for this subject is already in place.
Before running mne_flash_bem do the following:
Run mne_organize_dicom as described above.
Change to the <dest> directory where mne_organize_dicom created the image directory structure.
Create symbolic links from the directories containing the 5-degree and 30-degree flip angle FLASH series to
flash05
andflash30
, respectively:ln -s
<FLASH 5 series dir>flash05
ln -s
<FLASH 30 series dir>flash30
Some partition formats (e.g. FAT32) do not support symbolic links. In this case, copy the file to the appropriate series:
cp
<FLASH 5 series dir>flash05
cp
<FLASH 30 series dir>flash30
Set the
SUBJECTS_DIR
andSUBJECT
environment variables
Note
If mne_flash_bem is run with the --noflash30
option, the flash30 directory is not needed, i.e., only the 5-degree flip angle flash data are employed.
It may take a while for mne_flash_bem to
complete. It uses the FreeSurfer directory structure under $SUBJECTS_DIR/$SUBJECT
.
The script encapsulates the following processing steps:
It creates an mgz file corresponding to each of the eight echoes in each of the FLASH directories in
mri/flash
. The files will be calledmef
<flip-angle> _ <echo-number>.mgz
.If the
--unwarp
option is specified, run grad_unwarp and produce filesmef
<flip-angle> _ <echo-number>u.mgz
. These files will be then used in the following steps.It creates parameter maps in
mri/flash/parameter_maps
using mri_ms_fitparms .It creates a synthetic 5-degree flip angle volume in
mri/flash/parameter_maps/flash5.mgz
using mri_synthesize .Using fsl_rigid_register , it creates a registered 5-degree flip angle volume
mri/flash/parameter_maps/flash5_reg.mgz
by registeringmri/flash/parameter_maps/flash5.mgz
to the T1 volume undermri
.Using mri_convert , it converts the flash5_reg volume to COR format under
mri/flash5
. If necessary, the T1 and brain volumes are also converted into the COR format.It runs mri_make_bem_surfaces to create the BEM surface tessellations.
It creates the directory
bem/flash
, moves the tri-format tringulations there and creates the corresponding FreeSurfer surface files in the same directory.The COR format volumes created by mne_flash_bem are removed.
If the --noflash30
option is specified to mne_flash_bem ,
steps 3 and 4 in the above are replaced by averaging over the different
echo times in 5-degree flip angle data.
Inspecting the meshes¶
It is advisable to check the validity of the BEM meshes before using them. This can be done with help of tkmedit, see Setting up the boundary-element model.
Using seglab¶
The brain segmentation provided by FreeSurfer in the directory mri/brain
can
be employed to create the inner skull surface triangulation with
help of seglab, the Neuromag MRI segmentation tool. The description
below assumes that the user is familiar with the seglab tool. If
necessary, consult the seglab manual, Neuromag P/N NM20420A-A.
The data set mri/brain typically contains tissues within or outside the skull, in particular around the eyes. These must be removed manually before the inner skull triangulation is created.The editing and triangulation can be accomplished as outlined below
1. Set up the MRIs for Neuromag software access
Run the mne_setup_mri too as described in Setting up anatomical MR images for MRIlab. As a result, the directories mri/T1-neuromag and mri/brain-neuromag are set up.
2. Load the MRI data
Open the file mri/brain-neuromag/sets/COR.fif and adjust the scaling of the data.
3. Preparatory steps
Set the minimum data value to 1 using the min3D operator. Make a backup of the data with the backup3D operator.
4. Manual editing
The maskDraw3D operation is recommended for manual editing. To use it, first employ the grow3D operator with threshold interval 2…255 and the seed point inside the brain. Then do the editing in the slicer window as described in Section 5.4.2 of the seglab manual. Note that it is enough to remove the connectivity to the extracerebral tissues rather than erasing them completely.
5. Grow again and mask
Once manual editing is complete, employ the grow3D operator again and do mask3D with the backup data to see whether the result is satisfactory. If not, undo mask3D and continue manual editing. Otherwise, undo mask3D and proceed to the next step.
6. Dilation
It is advisable to make the inner skull surface slightly bigger than the brain envelope obtained in the previous step. Therefore, apply the dilate3D operation once or twice. Use the values 1 for nbours and 26 for nhood in the first dilation and 1 and 18 in the second one, respectively.
7. Triangulation
Triangulate the resulting object with the triangulate3D operator. Use a sidelength of 5 to 6 mm. Check that the triangulation looks reasonable in the 3D viewing window.
8. Save the triangulation
Save the triangulated surface as a mesh into bem/inner_skull.tri. Select unit of measure as millimeters and employ the MRI coordinate system.
Using BrainSuite¶
The BrainSuite software running under the Windows operating system can also be used for BEM mesh generation. This software, written by David W. Shattuck, is distributed as a collaborative project between the Laboratory of Neuro Imaging at the University of California Los Angeles (Director: Dr. Arthur W. Toga) and the Biomedical Imaging Research Group at the University of Southern California (Director: Dr. Richard M. Leahy). For further information, see https://neuroimage.usc.edu/neuro/BrainSuite.
The conversion of BrainSuite tessellation files to MNE software compatible formats is accomplished with the mne_convert_surface utility, covered in mne_convert_surface.
The workflow needed to employ the BrainSuite tessellations is:
Step 1
Using the mri_convert utility available in FreeSurfer , convert an MRI volume to the img (Analyze) format. This volume should be the T1.mgz volume or a volume registered with T1.mgz in FreeSurfer :
mri_convert
<volume>.mgz
<volume>.img
Step 2
Transfer <volume>
.mgz
to a location accessible to BrainSuite , running on Windows.
Step 3
Using <volume>
.img
as input, create the tessellations of scalp, outer skull, and inner skull surfaces in BrainSuite .
Step 4
Transfer the dfs files containing the tessellations in the bem directory of your subject’s FreeSurfer reconstruction.
Step 5
Go to the bem directory where you placed the two dfs files. Using mne_convert_surface , convert them to the FreeSurfer surface format, e,g.:
mne_convert_surface `` ``--dfs inner_skull.dfs `` ``--mghmri ../mri/T1.mgz `` ``--surf inner_skull_dfs.surf
Step 6
Using tkmedit, check that the surfaces are correct, e.g.:
tkmedit -f ../mri/T1.mgz `` ``-surface inner_skull_dfs.surf
Step7
Using the mne_reduce_surface function in Matlab, reduce the number of triangles on the surfaces to 10000 - 20000. Call the output files
outer_skin.surf
,outer_skull.surf
, andinner_skull.surf
.
Step 8
Proceed to mne_setup_forward_model . Use the
--surf
and--noswap
options.
Note
If left and right are flipped in BrainSuite, use the --flip
option in mne_convert_surface to set the coordinate transformation correctly.
Note
The BrainSuite scalp surface can be also used for visualization in mne_analyze , see Using a high-resolution head surface tessellations.


CCA's Oakland Community Garden has been growing dye & fiber plants for years and collecting seeds for this library each season. This means that plants grown from these seeds are very well adapted to the Bay Area climate, organically grown, and open-pollenated. You can "check out" seeds to plant in your own garden - just fill in the form and take the seeds you want. Zines are available for each seed type with simple growing and dye-making instructions.


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