| EDUCATION |
| New York University, Courant Institute of Mathematical Sciences, New York, NY |
| M.S. in Mathematics in Finance, GPA 3.4/4.0 |
Sept. 2007 — Jan. 2009 |
| Stanford University, Stanford, CA |
| M.S. in Materials Science & Engineering, GPA 3.6/4.0 |
Sept. 2005 — June 2007 |
| B.S. in Mathematical & Computational Science, GPA 3.7/4.0 |
Sept. 2001 — June 2005 |
| WORK EXPERIENCE |
| Pontual Exportação e Importação, Ltda., São Paulo, SP, Brazil |
| Full-Stack Developer |
Jan. 2014 — Present |
- Manage clients, products, purchases and sales through a Django application.
- Develop and maintain a product catalog PHP website at http://pontualimportbrindes.com.br/
- Create a Single-Page App with AngularJS and Firebase to keep track of orders and reservations.
|
| Sales Analyst |
Nov. 2011 — Present |
- Forecast sales for over 500 products and suggest which ones should be restocked.
- Determine sales discounts, credit terms and conditions depending on established limits.
- Compile lists of preorders, notifying the salesperson when products become available.
|
| Merrill Lynch & Co., Inc., New York, NY |
| Summer Associate, Global Research |
June 2008 — Aug. 2008 |
- Bonds — Modeled the prepayment speeds of mortgage revenue bonds and calculated their prices and risk profiles.
- Equities — Backtested the performance of a cash-collaterized portfolio of futures as a potential new product.
- Commodities — Compared the returns and volatility of crude oil futures relative to large-cap refinery equities.
|
| Adair Capital, LLC, New York, NY |
| Intern, Hedge Fund of Funds |
Sept. 2007 — Jan. 2008 |
- Performed Matlab risk analytics by correlating hedge fund strategies with macroeconomic risk factors to predict risk-adjusted returns for specific hedge funds.
- Developed a Matlab GUI for a Brownian bridge process based on recursive estimates that measures hedge funds' exposure to benchmark indices and detects possible shifts in their investment strategy.
|
| RESEARCH EXPERIENCE |
| Dept. of Materials Science & Engineering, Stanford University, Stanford, CA |
| Mechanical Properties Research Assistant, Prof. R. H. Dauskardt Research Group |
June 2005 — Feb. 2007 |
- Constructed fracture simulations to predict the reliability of novel and porous nanomaterials.
- Rewrote Matlab scripts as C programs, reducing execution time from two days to two hours.
|
| Carnegie Institution for Science, Stanford, CA |
| Genetics Research Assistant, Dr. Susan S. Thayer Research Group |
Mar. 2004 — June 2004 |
- Performed regression analysis in SAS to identify correlations between eight data sets.
|


|
| bool | XPMP2::IsInRect (float x, float y, const float bounds_ltrb[4]) |
| | Tests if x/y lies within the rect, given as a float[4] array. More...
|
| |
| void | XPMP2::MapPrepareCacheCB (XPLMMapLayerID, const float *inTotalMapBoundsLeftTopRightBottom, XPLMMapProjectionID projection, void *) |
| | Prepare map drawing information. More...
|
| |
| void | XPMP2::MapIconDrawingCB (XPLMMapLayerID inLayer, const float *inMapBoundsLeftTopRightBottom, float, float mapUnitsPerUserInterfaceUnit, XPLMMapStyle, XPLMMapProjectionID projection, void *refcon) |
| | Actually draw the icons into the map. More...
|
| |
| void | XPMP2::MapLabelDrawingCB (XPLMMapLayerID inLayer, const float *inMapBoundsLeftTopRightBottom, float, float mapUnitsPerUserInterfaceUnit, XPLMMapStyle, XPLMMapProjectionID projection, void *refcon) |
| | Actually draw the labels into th emap. More...
|
| |
| void | XPMP2::MapDeleteCB (XPLMMapLayerID inLayer, void *) |
| | Called when a map is about to be deleted. More...
|
| |
| void | XPMP2::MapLayerCreate (const char *mapIdentifier) |
| | Create our map layer for the given map. More...
|
| |
| void | XPMP2::MapCreateAll () |
| | Loop all existing maps and create our map layer for them. More...
|
| |
| void | XPMP2::MapDestroyAll () |
| | Remove all our map layers. More...
|
| |
| void | XPMP2::MapCreateCB (const char *mapIdentifier, void *) |
| | Callback called when a map is created. We then need to add our layer to it. More...
|
| |
| void | XPMP2::MapInit () |
| | Initialize the module. More...
|
| |
| void | XPMP2::MapCleanup () |
| | Grace cleanup. More...
|
| |
| void | XPMPEnableMap (bool _bEnable, bool _bLabels) |
| | Enable or disable the drawing of aircraft icons on X-Plane's map. More...
|
| |
Implementation for creating the map layer.
Draws aircraft icons on X-Plane's maps in positions where our aircraft are. Aircraft icons are provided in a PNG icon sheet.
- See also
- https://developer.x-plane.com/sdk/XPLMMap/
- Author
- Birger Hoppe
- Copyright
- (c) 2020 Birger Hoppe
-
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Hang Man Game - Serengeti Edition!
Wins :
*
Loses :
*
Serengeti Animal :
...
Number of guesses remaining :
*
Letters already guessed :


Cost Estimation
Note: Spartacus 3.x is no longer maintained. Please upgrade to the latest version.
Note: Spartacus 3.x was tested with SAP Commerce Cloud versions 1905 to 2105. Spartacus 3.x has not been verified to work with (and is not guaranteed to work with) SAP Commerce Cloud 2211 or later releases.
Note: This feature is introduced with version 1.3 of the TUA Spartacus libraries.
This feature determines and displays the average cost for a product offering considering the provided yearly consumption entered by a customer.
Table of Contents
- Prerequisites
- Business Use Cases
- Frontend and Backend Dependencies
- Configuring and Enabling Cost Estimation
- CMS Component Configuration for Anticipated Consumption
- Attributes
- Components
- Further Reading
Certain types of product offerings (that is, commodity products - electricity and gas) are charged by consumption. For customers to get an idea of how much these type of product offerings could potentially cost them on a monthly or yearly basis, customers will need to provide additional information regarding their anticipated usage. The estimated consumption entered by the customer is captured if third-party systems need to process it as illustrated in the following figures:
Electricity consumption:
Gas consumption:
When the customer selects or enters anticipated annual consumption information and clicks Get Available Offers, the product listing page displays all applicable product offerings based on the annual consumption as illustrated in the following figures:
Customers are able to view the average cost that each of the product offerings would cost them based on their anticipated usage. Additional pricing-related information for each product offering is available by expanding the offering. The products shown in the search result list must be properly configured for this scenario and have prices defined for maximum 1 usage unit (such as kWh and cubic meter for example). The Average Cost for a product offering is determined using the following algorithm:
Average Cost / Year = PayNowPrices + YearlyRecurringCharges + (YearlyConsumption – YearlyIncluded) * UsageCharges
IMPORTANT NOTES:
-
Average Cost is determined using the algorithm presented in the above illustration. The calculation works with simple price information, such as one-time charges, recurring charges, and usage charges, for which the price has a fixed value and not a complex price formula.
-
Average Cost is determined for those product offerings that have prices defined for a maximum of 1 usage unit. In case the product offering has prices defined for more than 1 usage unit, the average cost cannot be properly determined, case in which it will not be displayed. Please see the following illustration.
Prerequisites
- Product offerings for which the average cost has to be calculated and displayed must have prices defined for maximum 1 usage unit (kWh, minutes, sms, etc).
- CMS components must be properly defined in the CMS system (in our case in SAP Commerce Cloud, in the content catalog).
Business Use Cases
Use Case 1: Discover product offerings by consumption from homepage
Customer lands on the homepage and is able to search for product offerings by consumption. Customer selects the desired option for gas or electricity and provides the annual consumption manually in the consumption input area. Alternatively, if a customer does not provide a value, a default value will be taken into consideration. Customer conducts the search, and search results with list of product offerings are presented. For each product offering, the customer is able to see the average cost that was calcuated using the provided input. For each product offering listed, the customer has the option to expand the row and view additional pricing-related information including the contract duration, one-time charges, recurring charges, and usage-based charges. From the search results page or from the Product Details Page, the customer has the ability to re-calculate the costs using a different anticipated consumption value.
Use Case 2: Customer lands on category page
Customer accesses a category page (in this use case example, a commodity category of either electricity or gas). As a result, the customer lands on the product listing page displaying the product offerings in that category. If the customer has not yet provided any personal consumption usage criteria, each offering shown will display pricing information based on the default value. Customer will be able to view additional pricing-related information, view the details of a selected product offering or change the consumption criteria.
Use Case 3: Content Manager defines the CMS components enabling customers to search by consumption in backoffice tools
A Content Manager wants to define and enable the cost estimation feature. The Content Manager accesses the Backoffice to create the necessary components including:
- Component of type
SliderOptionComponent
- Component of type
PoSearchByConsumptionComponent
(Note: Slider option components part of aPoSearchByConsumptionComponent
are selected within the list of theSliderOptionComponent
objects) - Component of type
ConsumptionListComponent
(Note: Search by consumption components part of aConsumptionListComponent
are selected within the list of thePoSearchByConsumptionComponent
objects)
Content Manager updates the homepage by including the desired ConsumptionListComponent
objects in the corresponding slot.
Frontend and Backend Dependencies
| Dependency | Detail |
|---|---|
| Spartacus | 1.x, < 2.0 |
| Telco & Utilities Accelerator | Version 2003 (latest patch) |
| SAP Commerce Cloud | Version 1905 (latest patch) |
Configuring and Enabling Cost Estimation
| Configuration | Type: SPA or Backend | Details |
|---|---|---|
| Define discover by consumption CMS components | Backend (CMS) | Define corresponding discovery by consumption CMS components such as Consumption components and Slider option components. |
| Configure default consumption values for PO types and usage units | SPA | Customer consumption preferences are saved in localStorage . If the customer is visiting the website for the first time or localStorage is emptied, default values that can be configured are used. In case the customer has provided a preferred consumption through the Consumption Component, it is saved in the localStorage and that value is used to calculate average cost. |
Configure default consumption values for PO types and usage units: Consumption values are stored based on the product specification of the product offering and the usageUnit
in the following format:
```
consumption_<productSpecification>_<usage_Unit> : <value>
```
Example:
consumption: {
defaultValues: [
{ productSpecification: <string>, usageUnit: <string>, value: <string>},
...
{ productSpecification: <string>, usageUnit: <string>, value: <string>}
],
default: <string>
}
CMS Component Configuration for Anticipated Consumption
The Content Manager creates CMS components for consumption (defined in b2ctelcoaddon
). This is the list of CMS components that a Content Manager would need to define in CMS to enable the cost estimation feature.
| Component Name | Description | Component Structure |
|---|---|---|
| SliderOptionComponent | CMS component representing the predefined value and media associated to it defining a consumption option. See the sample list of SliderOptionComponent objects listed below the following table. |
It extends SimpleCMSComponent . See Attributes |
| PoSearchByConsumptionComponent | CMS component defining a list of consumption options available for a specific usage unit, consumed in a specific amount of time and applicable to specific type of POs. | It extends SimpleCMSComponent . See Attributes |
| ConsumptionListingComponent | CMS component defining a list of PoSearchByConsumptionComponent objects. |
It extends SimpleCMSComponent . See Attributes |
SliderOptionComponent - Sample List
Following is the sample list of SliderOptionComponent
objects:
1000 kwh
- uid: 1-person-option
- name: 1 person
- value: 1000
2000 kwh
- uid: 2-persons-option
- name: 2 persons
- value: 1750
Attributes
| Attribute Name | Type | Description |
|---|---|---|
| SliderOptionComponent | * - attributes are defined in super type | |
| uid* | String | Unique identifier of the component |
| name* | String | Name of the component |
| media | Media | Media item |
| value | Long | Number representing the value of the option component |
| PoSearchByConsumptionComponent | * - attributes are defined in super type | |
| uid* | String | Unique identifier of the component |
| name* | String | Name of the component |
| productSpecification | TmaProductSpecification |
Product specification defining the type of offers available for search |
| usageUnit | UsageUnit | Usage unit defining the type of consumption |
| billingFrequency | BillingFrequency | Billing frequency defining the amount of time when the consumption was consumed (consumption value specified by the slider options) |
| sliderOptionComponents | List<SliderOptionComponent> |
List of SliderOptionComponent objects. They represent the available consumption options available in the search component |
| ConsumptionListingComponent | * - attributes are defined in super type | |
| uid* | String | Unique identifier of the component |
| name* | String | Name of the component |
| searchByConsumptionComponents | List<PoSearchByConsumptionComponent> |
List of PoSearchByConsumptionComponent objects |
Examples of how components can be defined in CMS as sample data:
SliderOptionComponent components
Detailed sample data
Electricity specific slider option components
| Component UID | Name | Value | Media |
|---|---|---|---|
| OnePersonElectricitySliderOption | 1 person/ 1000 kwh | 1000 | |
| TwoPersonElectricitySliderOption | 2 person/ 2000 kwh | 2000 | |
| ThreePersonElectricitySliderOption | 3 person/ 3500 kwh | 3500 | |
| FourPersonElectricitySliderOption | 4 person/ 6000 kwh | 6000 |
Gas-specific slider option components
| Component UID | Name | Value | Media |
|---|---|---|---|
| ThirtyM2GasSliderOption | 30 square meters/ 800 cubic meters | 800 | |
| FiftyM2GasSliderOption | 50 square meters/ 1200 cubic meters | 1200 | |
| OneHundredM2GasSliderOption | 100 square meters/ 2500 cubic meters | 2500 | |
| OneHundredFiftyM2GasSliderOption | 150 square meters/ 4500 cubic meters | 4500 | |
| OneHundredEightyM2GasSliderOption | 180 square meters/ 6000 cubic meters | 6000 |
PoSearchByConsumptionComponent components
Detailed sample data
Electricity search by consumption components
| Component UID | Name | Product specification | Usage Unit | Billing Frequency | Slider option components |
|---|---|---|---|---|---|
| DiscoverPOByElectricityComponent | Electricity | electricity | kwh | year | OnePersonElectricitySliderOption, TwoPersonElectricitySliderOption, ThreePersonElectricitySliderOption, FourPersonElectricitySliderOption |
Gas search by consumption components
| Component UID | Name | Product specification | Usage Unit | Billing Frequency | Slider option components |
|---|---|---|---|---|---|
| DiscoverPOByGasComponent | Gas | gas | cubic_meter | year | ThirtyM2GasSliderOption, FiftyM2GasSliderOption, OneHundredM2GasSliderOption, OneHundredFiftyM2GasSliderOption, OneHundredEightyM2GasSliderOption |
ConsumptionListingComponent components
Detailed sample data
Consumption listing component for electricity and gas
| Component UID | Name | Search by consumption components |
|---|---|---|
| UtilitiesPoSearchByConsumptionComponentList | UtilitiesPoSearchByConsumptionComponentList | DiscoverPOByElectricityComponent, DiscoverPOByGasComponent |
Components
The following new and updated components must be enabled in the TUA Backoffice to appear on the Spartacus TUA storefront:
| Component Name | Status | Description |
|---|---|---|
| Consumption Components | Renders display of discovery by consumption CMS components. | |
| TmaConsumptionComponent | New | |
| TmaConsumptionDialogComponent | New | |
| TmaPoSearchByConsumptionComponent | New | |
| TmaSliderOptionComponent | New | |
| Product Components | Product components are updated to display the average cost for a set of configured POs (by their type). If a PO has charge prices defined for more than 1 usage unit, the average cost is not displayed. | |
| TmaProductDetailsTabComponent | Updated | |
| TmaProductGridItemComponent | Updated | |
| TmaProductListComponent | Updated | |
| TmaProductListItemComponent | Updated | |
| TmaProductSummaryComponent | Updated |
Further Reading
For further reading, see the following topic in the TUA Help portal:


{% load smart_load %} {% load thumbnail from sorl.thumbnail as sorl_thumbnail %} {% regroup sponsorships by amount as sponsor_levels %}


[gecode-users] Newbie: space/solution copying & dynamically addedconstraints
Christian Schulte
schulte at imit.kth.se
Thu Sep 21 11:40:23 CEST 2006
Hi Peter,
could you rephrase your issue with stop objects? I kind of miss the
problem... Sorry for that.
Christian
--
Christian Schulte, http://www.imit.kth.se/~schulte/
-----Original Message-----
From: Pieter Thysebaert [mailto:pthyseba at intec.ugent.be]
Sent: Thursday, September 21, 2006 11:03 AM
To: Christian Schulte
Cc: 'Pieter Thysebaert'; users at gecode.org
Subject: RE: [gecode-users] Newbie: space/solution copying & dynamically
addedconstraints
Hi,
On Wed, 20 Sep 2006, Christian Schulte wrote:
> Hi Peter,
>
> looks interesting ;-)
....
> What you could do to make the search distributed is to cook up an
> operation for a branching description that turns it into something
> that can be send over the wire: should not be to hard, the typical
> description contains just two integers. Then in order to hand out a
> node, you just start from the same basic space and add all branching
> descriptions corresponding to the path leading to the node you want to
> explore (this is recomputation and looking how recomputation is done
> in the search engines should give you an idea).
Well, for the time being, I'm distributing variable-range restrictions to
remote nodes as branching descriptions. I then construct a new problem
instance with these extra variable-range restrictions and start a new search
engine. The problem I had been bitten by previously (having difficulties in
passing dynamic information to the search) which presumably had to do with
copying spaces etc. has been "solved" by observing that I'm only running one
search engine per MPI process, so I can do with global variables (ugh).
> Much of the way search is done has been inherited from Mozart (apart
> from batch recomputation). If you want to see an architecture for
> distributed parallel search based on spaces, you might want to check:
> http://web.it.kth.se/~schulte/paper.html?id=Schulte:TRICS:2000
> or, if you prefer the full story:
> http://web.it.kth.se/~schulte/paper.html?id=Schulte:LNAI:2002
I might fall back to the worker/manager paradigm described there, as I
started out with a set of worker-peers (no central manager) but that might
be more difficult - less efficient.
> As it comes to pause resume, check
> http://www.gecode.org/gecode-doc-latest/group__TaskIntSearchStop.html
Can you enlighten me: I've only used stop objects to, well, stop (i.e.
terminate) searches.
Suppose I'm in a loop like
do {
solution* currentSolution = engine.next();
statement;
}
For testing purposes, I have initialized the search engine with a custom
Stop object whose stop() function always returns true, and it turns out that
upon termination currentSolution becomes NULL.
I thought about implementing pause/restart by creating a new engine using
the last value of currentSolution as root node, but as currentSolution is
NULL upon termination of the search this seems impossible or is it not?
Pieter
More information about the gecode-users
mailing list


"In my projects, my projects thick. Don't come to my projects if you ain't with this clique"
- Coo Coo Cal
Created a website for a fictitious, pizza shop that included radio and check buttons for multiple selection, a modal that returned an invoiced receipt with the total purchase price, and stackable mobile responsive cards on the menu page.
Worked with a team of developers to create a food cart demo for a live project using Semantic UI, HTML 5 boiler plate, and Agile/Scrum. This project included a mobile responsive nav bar with a search field, responsive photo gallery, carousel cycling through 5 images, and Google map location.
Did we just become best friends....


1
Select Rate Plan
2
Enter Date Range
3
Update Rates
step -1
Next
step -2
Previous
Next
step -3
Previous
Finish


[page:Object3D] → [page:Light] →
// white spotlight shining from the side, casting shadow
var spotLight = new THREE.SpotLight( 0xffffff );
spotLight.position.set( 100, 1000, 100 );
spotLight.castShadow = true;
spotLight.shadowMapWidth = 1024;
spotLight.shadowMapHeight = 1024;
spotLight.shadowCameraNear = 500;
spotLight.shadowCameraFar = 4000;
spotLight.shadowCameraFov = 30;
scene.add( spotLight );


Outline¶
- The program consists of three main modules
- The mdEngine namespace
- The Atom class
- The Base class
At the start of the program the Parser
class parses (interprets) the text in the input file. From the parameters found in the
config file an instance of the Base
class is created. All parameters in the Base
class are static and can hence
be referenced from anywhere in the program. After a Base
object is created, memory is allocated to hold the atoms
and the trajectory which consists of objects of the Frame
class. The static function initialize
in the Atom
class is then called which sets the initial conditions for the atoms (such as velocity, position, mass etc).


{% extends "layout.html" %} {% block content %}
You've successfully installed marathon. To get started, just symlink one of your Node.js projects into your marathon directory!


Classification of Songs - Million Song Dataset
An analysis of the properties of a song to identify the decade to which the song belonged.
Dataset
The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. This project uses a subset of this dataset. The data contains 515,345 songs, each described by 90 features, and the year in which the song was released. The years range from 1922 to 2011.
Exploratory Data Analysis
For simplicity, the number of classes is reduced from 90 to 9 by assigning a decade to each song, instead of year. A histogram of the count of songs in each decade showed an unevenly distributed data:
In an attempt to obtain a more evenly distributed data, the width of the classes was altered, thus resulting in a distribution as shown below:
Choosing Features
A feature can be chosen for classification, if it peaks at different places for different classes. For example, I chose ‘Timbre average 1’ as one of the classifiers as it peaked at various regions for Class 1 and Class 2 as shown in the below figure:
Principal Components
A scatter plot of the first two principal components looked something like this:
Two important observations can be made from this plot:
- There is a significant difference in the amount of data between the first few classes and the last couple of classes (it is obscured by pink).
- The colors are all bundled together. Ideally, the different colors must be local to different regions on the plot, so that a more accurate decision can be made when predicting the class of some unknown data.
Bayes’ Classification
The test data was first classified without reducing any dimensions (i.e., using all 90 features independently). An accuracy of 22% was obtained.
The 90 features were then projected in the direction of the 2 eigenvectors corresponding to the 2 highest eigenvalues. Classifying with the help of the 2 principal components returned an accuracy of around 50%. As this result seemed odd, the procedure was rechecked. This lead to a conclusion that the dataset has the curse of dimesionality. The problem is that when the dimensionality increases, the volume of the space increases so fast that the available data becomes sparse. So, a huge amount of data is required to support analysis in higher dimensions.
After modyfying class sizes to obtain a more evenly distributed data, the prediction accuracy was in fact lower (only about 35%). I do not have a plausible explanation for this. The decision boundary looks as below:
Implementation
The MATLAB code written to perform the above analysis:
raw = load('YearPredictionMSD.txt');
data = raw(:,2:91);
%Separate data for train and test as given in website
%separate class info from data
trainingData = data(1:463715,:);
class_traingData = raw(1:463715,1);
r_class_train = ClassReduce(class_traingData);
testingData = data(463716:515345,:);
class_testingData = raw(463716:515345,1);
r_class_test = ClassReduce(class_testingData);
%histogram of two features
figure,hist(trainingData(:,1));
figure,hist(trainingData(:,13));
% find 2 principle components(PC)
coeff = pca(data);
coeff = coeff(:,1:2);
%figure, mesh(coeff);
% finding data representation along PC
mu = mean(trainingData);
lowrep_trainingData = zeros(size(trainingData,1),2);
for i = 1:size(trainingData,1)
lowrep_trainingData(i,:) = (trainingData(i,:)- mu) * coeff;
end
dim_cov = cov(lowrep_trainingData);
mu = mean(testingData);
lowrep_testingData = zeros(size(testingData,1),2);
for i = 1:size(testingData,1)
lowrep_testingData(i,:) = (testingData(i,:)- mu) * coeff;
end
model = NaiveBayes.fit(trainingData,r_class_train);
test_op = predict(model,testingData);
result = ((test_op- r_class_test) == 0);
acuracy = sum(result)/size(result,1);
model2 = NaiveBayes.fit(lowrep_trainingData,r_class_train);
test_op1 = predict(model2,lowrep_testingData);
result1 = ((test_op1- r_class_test) == 0);
acuracy1 = sum(result1)/size(result1,1);


OrbitNG Config
Use this configuration page to choose the settings you would like applied to OrbitNG.
Customizing
Display Date
Connection Handling
Vibrate on Bluetooth Dis-/Connect
Colors
Background Color
Earth Color
Invert Earths Fontcolor
Bold Earth Font
Moon Color
Invert Moons Fontcolor


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The 1963 United States Proof Set includes the Lincoln penny, Jefferson nickel, Roosevelt dime, . The nickel, dime, quarter, and half dollar are 90% silver.
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1963 US Mint Proof Set Sales of the 1963 Proof Set experienced a slight decline . years prices have moved upwards, driven by the higher market price of silver.
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Older Mint Issued US PROOF COIN SETS - 1960 to 1969 -and 1961 ...
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"x" "1" "RIETVELD,EC, GARNAAT,MA AND SEUTTER-BERLAGE,F, BACTERIAL MUTAGENICITYOF SOME METHYL 2-CYANOACRYLATES AND METHYL 2-CYANO-3-PHENYLACRYLATES, MUTAT.RES. 188(2):97-104, 1987"


--- subject: Cover Letter ---
Thank You
I just wanted to reach out and say thank you for taking the time to talk to me today. Your candor and your insights into your own work at Coca Cola were both incredibly helpful. I think interviews are always a little stressful regardless of preparation and I really appreciated your effort to make the interview constructive and informative for both sides.
Thanks again,


The S&P 500 component weights are listed from largest to smallest. Data for each company in the list is updated after each trading day. The S&P 500 index consists of most but not all of the largest companies in the United States. The S&P market cap is 70 to 80% of the total US stock market capitalization. It is a commonly used benchmark for stock portfolio performance in America and abroad. Beating the performance of the S&P with less risk is the goal of nearly every portfolio manager, hedge fund and private investor.
| # | Company | Symbol | Weight | Price | Change |
|---|---|---|---|---|---|
| 1 | Microsoft Corporation | MSFT | 4.226842 | 131.54 | -1.06 (-0.80%) |
| 2 | Apple Inc. | AAPL | 3.584192 | 194.13 | 1.55 (0.80%) |
| 3 | Amazon.com Inc. | AMZN | 3.189548 | 1,863.58 | 2.95 (0.16%) |
| 4 | Facebook Inc. Class A | FB | 1.732723 | 177.57 | 2.75 (1.57%) |
| 5 | Berkshire Hathaway Inc. Class B | BRK.B | 1.673659 | 206.14 | -1.02 (-0.49%) |
| 6 | Johnson & Johnson | JNJ | 1.538240 | 140.01 | 0.99 (0.71%) |
| 7 | JPMorgan Chase & Co. | JPM | 1.501045 | 110.45 | 0.11 (0.10%) |
| 8 | Alphabet Inc. Class C | GOOG | 1.379721 | 1,084.55 | 4.17 (0.39%) |
| 9 | Alphabet Inc. Class A | GOOGL | 1.346703 | 1,086.84 | 4.08 (0.38%) |
| 10 | Exxon Mobil Corporation | XOM | 1.318703 | 75.12 | 0.21 (0.27%) |
| 11 | Visa Inc. Class A | V | 1.242119 | 169.25 | -1.57 (-0.92%) |
| 12 | Procter & Gamble Company | PG | 1.129969 | 108.97 | 0.25 (0.23%) |
| 13 | Bank of America Corp | BAC | 1.047613 | 28.15 | 0.08 (0.29%) |
| 14 | Cisco Systems Inc. | CSCO | 1.031897 | 56.88 | 0.46 (0.82%) |
| 15 | Walt Disney Company | DIS | 1.002932 | 135.18 | -1.89 (-1.38%) |
| 16 | Mastercard Incorporated Class A | MA | 0.998830 | 262.37 | -3.73 (-1.40%) |
| 17 | Pfizer Inc. | PFE | 0.993485 | 42.60 | -0.47 (-1.09%) |
| 18 | UnitedHealth Group Incorporated | UNH | 0.989237 | 246.43 | -1.71 (-0.69%) |
| 19 | AT&T Inc. | T | 0.966397 | 32.08 | 0.15 (0.45%) |
| 20 | Chevron Corporation | CVX | 0.965419 | 121.73 | -0.56 (-0.46%) |
| 21 | Verizon Communications Inc. | VZ | 0.963968 | 56.63 | 0.48 (0.85%) |
| 22 | Home Depot Inc. | HD | 0.929492 | 197.40 | -0.65 (-0.33%) |
| 23 | Merck & Co. Inc. | MRK | 0.884558 | 82.78 | 0.30 (0.36%) |
| 24 | Intel Corporation | INTC | 0.874425 | 46.79 | -0.01 (-0.02%) |
| 25 | Coca-Cola Company | KO | 0.820364 | 51.24 | -0.15 (-0.28%) |
| 26 | Wells Fargo & Company | WFC | 0.787139 | 46.18 | -0.09 (-0.19%) |
| 27 | PepsiCo Inc. | PEP | 0.779644 | 132.83 | -0.76 (-0.57%) |
| 28 | Comcast Corporation Class A | CMCSA | 0.774108 | 41.33 | 0.04 (0.08%) |
| 29 | Boeing Company | BA | 0.772371 | 349.28 | -4.52 (-1.28%) |
| 30 | Citigroup Inc. | C | 0.655784 | 67.59 | 0.47 (0.70%) |
| 31 | McDonald's Corporation | MCD | 0.640732 | 202.70 | 1.40 (0.70%) |
| 32 | Netflix Inc. | NFLX | 0.637715 | 350.94 | -1.07 (-0.30%) |
| 33 | Walmart Inc. | WMT | 0.635133 | 107.82 | 0.30 (0.28%) |
| 34 | Abbott Laboratories | ABT | 0.592292 | 81.04 | -0.24 (-0.29%) |
| 35 | Oracle Corporation | ORCL | 0.571051 | 53.90 | -0.11 (-0.20%) |
| 36 | Adobe Inc. | ADBE | 0.567305 | 274.26 | -6.08 (-2.17%) |
| 37 | PayPal Holdings Inc | PYPL | 0.563578 | 113.86 | -1.76 (-1.52%) |
| 38 | Medtronic Plc | MDT | 0.543964 | 97.44 | -0.31 (-0.32%) |
| 39 | Honeywell International Inc. | HON | 0.518095 | 171.14 | -0.14 (-0.08%) |
| 40 | Union Pacific Corporation | UNP | 0.517029 | 169.90 | -2.49 (-1.44%) |
| 41 | International Business Machines Corporation | IBM | 0.497453 | 135.46 | 0.72 (0.53%) |
| 42 | Philip Morris International Inc. | PM | 0.496420 | 78.11 | 1.15 (1.50%) |
| 43 | Accenture Plc Class A | ACN | 0.487859 | 183.12 | -1.32 (-0.72%) |
| 44 | salesforce.com inc. | CRM | 0.485044 | 149.91 | -2.88 (-1.88%) |
| 45 | Thermo Fisher Scientific Inc. | TMO | 0.473971 | 283.75 | -0.02 (-0.01%) |
| 46 | AbbVie Inc. | ABBV | 0.470919 | 78.46 | 1.51 (1.96%) |
| 47 | Costco Wholesale Corporation | COST | 0.469520 | 256.56 | -0.29 (-0.11%) |
| 48 | Broadcom Inc. | AVGO | 0.460424 | 282.28 | 2.07 (0.74%) |
| 49 | Linde plc | LIN | 0.458260 | 201.14 | 0.72 (0.36%) |
| 50 | Amgen Inc. | AMGN | 0.453298 | 175.15 | -0.43 (-0.24%) |
| 51 | Texas Instruments Incorporated | TXN | 0.438534 | 112.54 | -0.09 (-0.08%) |
| 52 | NIKE Inc. Class B | NKE | 0.431290 | 83.18 | 0.60 (0.73%) |
| 53 | United Technologies Corporation | UTX | 0.430540 | 122.87 | -5.14 (-4.02%) |
| 54 | Starbucks Corporation | SBUX | 0.422776 | 82.12 | 0.19 (0.23%) |
| 55 | Eli Lilly and Company | LLY | 0.408775 | 112.13 | -1.80 (-1.58%) |
| 56 | NextEra Energy Inc. | NEE | 0.403613 | 201.17 | -2.25 (-1.11%) |
| 57 | 3M Company | MMM | 0.402053 | 168.99 | 0.70 (0.42%) |
| 58 | Altria Group Inc | MO | 0.398290 | 52.20 | 0.99 (1.93%) |
| 59 | American Tower Corporation | AMT | 0.384100 | 207.68 | -2.17 (-1.03%) |
| 60 | NVIDIA Corporation | NVDA | 0.373495 | 150.64 | 2.21 (1.49%) |
| 61 | General Electric Company | GE | 0.363019 | 10.11 | 0.06 (0.55%) |
| 62 | Danaher Corporation | DHR | 0.359726 | 137.10 | -0.87 (-0.63%) |
| 63 | Lockheed Martin Corporation | LMT | 0.359119 | 343.65 | -7.95 (-2.26%) |
| 64 | QUALCOMM Incorporated | QCOM | 0.354709 | 70.88 | 0.34 (0.48%) |
| 65 | American Express Company | AXP | 0.351941 | 123.12 | 0.46 (0.38%) |
| 66 | Gilead Sciences Inc. | GILD | 0.351578 | 66.19 | 0.07 (0.10%) |
| 67 | Booking Holdings Inc. | BKNG | 0.332687 | 1,802.09 | 20.97 (1.18%) |
| 68 | U.S. Bancorp | USB | 0.330238 | 52.99 | 0.09 (0.16%) |
| 69 | Mondelez International Inc. Class A | MDLZ | 0.326386 | 53.86 | -0.33 (-0.61%) |
| 70 | Lowe's Companies Inc. | LOW | 0.320645 | 96.38 | 0.07 (0.07%) |
| 71 | Bristol-Myers Squibb Company | BMY | 0.311716 | 46.73 | 0.72 (1.55%) |
| 72 | Automatic Data Processing Inc. | ADP | 0.303139 | 164.47 | -3.22 (-1.92%) |
| 73 | CME Group Inc. Class A | CME | 0.301640 | 197.75 | -5.43 (-2.67%) |
| 74 | Anthem Inc. | ANTM | 0.301055 | 283.72 | 1.58 (0.56%) |
| 75 | Caterpillar Inc. | CAT | 0.300242 | 127.41 | 1.67 (1.33%) |
| 76 | CVS Health Corporation | CVS | 0.296180 | 53.86 | -1.17 (-2.13%) |
| 77 | United Parcel Service Inc. Class B | UPS | 0.289057 | 100.19 | 0.10 (0.10%) |
| 78 | Charter Communications Inc. Class A | CHTR | 0.284664 | 391.89 | -3.52 (-0.89%) |
| 79 | Chubb Limited | CB | 0.283272 | 148.45 | -0.54 (-0.36%) |
| 80 | Intuit Inc. | INTU | 0.280416 | 255.57 | -4.95 (-1.90%) |
| 81 | ConocoPhillips | COP | 0.278934 | 58.97 | -0.29 (-0.49%) |
| 82 | Celgene Corporation | CELG | 0.277719 | 95.94 | 0.65 (0.68%) |
| 83 | Goldman Sachs Group Inc. | GS | 0.277141 | 193.78 | -0.34 (-0.18%) |
| 84 | Becton Dickinson and Company | BDX | 0.268197 | 236.96 | -3.25 (-1.35%) |
| 85 | TJX Companies Inc | TJX | 0.265528 | 51.88 | 0.25 (0.47%) |
| 86 | Colgate-Palmolive Company | CL | 0.261105 | 72.99 | -0.04 (-0.05%) |
| 87 | Duke Energy Corporation | DUK | 0.260331 | 85.38 | -0.91 (-1.05%) |
| 88 | PNC Financial Services Group Inc. | PNC | 0.252903 | 134.28 | -0.10 (-0.07%) |
| 89 | CSX Corporation | CSX | 0.252892 | 78.43 | -0.24 (-0.31%) |
| 90 | Stryker Corporation | SYK | 0.251713 | 194.37 | -1.75 (-0.89%) |
| 91 | Dominion Energy Inc | D | 0.250210 | 73.80 | -1.62 (-2.15%) |
| 92 | Cigna Corporation | CI | 0.246880 | 156.43 | -0.12 (-0.08%) |
| 93 | Intuitive Surgical Inc. | ISRG | 0.242150 | 506.82 | -2.90 (-0.57%) |
| 94 | Boston Scientific Corporation | BSX | 0.238375 | 41.00 | -0.45 (-1.09%) |
| 95 | S&P Global Inc. | SPGI | 0.234164 | 222.55 | -4.58 (-2.02%) |
| 96 | Southern Company | SO | 0.233875 | 54.39 | -0.09 (-0.17%) |
| 97 | Morgan Stanley | MS | 0.233559 | 43.49 | 0.20 (0.46%) |
| 98 | Crown Castle International Corp | CCI | 0.232312 | 135.13 | 0.42 (0.31%) |
| 99 | DuPont de Nemours Inc. | DD | 0.229986 | 73.71 | -0.04 (-0.05%) |
| 100 | Norfolk Southern Corporation | NSC | 0.223490 | 198.39 | -3.02 (-1.50%) |
| 101 | BlackRock Inc. | BLK | 0.223477 | 440.70 | -3.11 (-0.70%) |
| 102 | Northrop Grumman Corporation | NOC | 0.219267 | 299.83 | -11.48 (-3.69%) |
| 103 | Zoetis Inc. Class A | ZTS | 0.218964 | 109.78 | -0.44 (-0.40%) |
| 104 | Raytheon Company | RTN | 0.218776 | 177.96 | -9.23 (-4.93%) |
| 105 | Charles Schwab Corporation | SCHW | 0.213697 | 42.72 | -0.59 (-1.36%) |
| 106 | Ecolab Inc. | ECL | 0.209961 | 197.51 | -2.18 (-1.09%) |
| 107 | Marsh & McLennan Companies Inc. | MMC | 0.207919 | 98.39 | -0.81 (-0.82%) |
| 108 | Simon Property Group Inc. | SPG | 0.206905 | 161.65 | 0.26 (0.16%) |
| 109 | Illumina Inc. | ILMN | 0.205639 | 335.39 | -1.73 (-0.51%) |
| 110 | Prologis Inc. | PLD | 0.205089 | 78.21 | -0.20 (-0.26%) |
| 111 | EOG Resources Inc. | EOG | 0.205056 | 87.26 | 2.06 (2.42%) |
| 112 | Schlumberger NV | SLB | 0.203985 | 36.67 | 1.20 (3.38%) |
| 113 | Intercontinental Exchange Inc. | ICE | 0.202608 | 84.81 | -1.27 (-1.48%) |
| 114 | Air Products and Chemicals Inc. | APD | 0.201712 | 218.82 | -2.51 (-1.13%) |
| 115 | Progressive Corporation | PGR | 0.200105 | 82.40 | -0.19 (-0.23%) |
| 116 | Exelon Corporation | EXC | 0.199461 | 49.05 | -0.53 (-1.06%) |
| 117 | Deere & Company | DE | 0.197478 | 151.25 | 1.44 (0.96%) |
| 118 | General Motors Company | GM | 0.195858 | 36.14 | 0.13 (0.35%) |
| 119 | General Dynamics Corporation | GD | 0.194902 | 169.48 | -3.91 (-2.26%) |
| 120 | American International Group Inc. | AIG | 0.193984 | 52.80 | -0.53 (-0.99%) |
| 121 | MetLife Inc. | MET | 0.192309 | 48.67 | 0.25 (0.52%) |
| 122 | Kimberly-Clark Corporation | KMB | 0.191745 | 134.14 | -0.57 (-0.42%) |
| 123 | Illinois Tool Works Inc. | ITW | 0.190637 | 152.56 | 0.34 (0.22%) |
| 124 | Aon plc | AON | 0.189817 | 189.30 | -1.57 (-0.82%) |
| 125 | Target Corporation | TGT | 0.189817 | 87.83 | 0.28 (0.32%) |
| 126 | Biogen Inc. | BIIB | 0.186354 | 229.69 | 1.29 (0.56%) |
| 127 | Waste Management Inc. | WM | 0.183826 | 112.77 | -1.05 (-0.92%) |
| 128 | Vertex Pharmaceuticals Incorporated | VRTX | 0.183352 | 172.55 | -0.29 (-0.17%) |
| 129 | American Electric Power Company Inc. | AEP | 0.182212 | 88.05 | -0.97 (-1.09%) |
| 130 | Capital One Financial Corporation | COF | 0.175538 | 90.99 | 0.56 (0.62%) |
| 131 | Equinix Inc. | EQIX | 0.174981 | 499.66 | -6.36 (-1.26%) |
| 132 | Walgreens Boots Alliance Inc | WBA | 0.173676 | 52.87 | 0.58 (1.11%) |
| 133 | Prudential Financial Inc. | PRU | 0.171059 | 99.55 | -0.19 (-0.19%) |
| 134 | Aflac Incorporated | AFL | 0.171053 | 53.93 | -0.43 (-0.78%) |
| 135 | Applied Materials Inc. | AMAT | 0.169902 | 43.69 | 0.55 (1.27%) |
| 136 | Kinder Morgan Inc Class P | KMI | 0.168696 | 20.77 | -0.12 (-0.55%) |
| 137 | Emerson Electric Co. | EMR | 0.166191 | 63.64 | -1.00 (-1.54%) |
| 138 | Allergan plc | AGN | 0.165760 | 123.13 | -3.48 (-2.75%) |
| 139 | Fidelity National Information Services Inc. | FIS | 0.164787 | 121.55 | -0.74 (-0.61%) |
| 140 | Travelers Companies Inc. | TRV | 0.163748 | 148.72 | -0.60 (-0.40%) |
| 141 | Bank of New York Mellon Corporation | BK | 0.163633 | 44.44 | -0.46 (-1.02%) |
| 142 | Micron Technology Inc. | MU | 0.162751 | 34.84 | -0.10 (-0.29%) |
| 143 | Analog Devices Inc. | ADI | 0.162469 | 105.02 | -1.53 (-1.44%) |
| 144 | Estee Lauder Companies Inc. Class A | EL | 0.161200 | 177.19 | -0.84 (-0.47%) |
| 145 | FedEx Corporation | FDX | 0.161199 | 159.64 | -2.20 (-1.36%) |
| 146 | Dow Inc. | DOW | 0.160855 | 52.00 | 0.38 (0.74%) |
| 147 | Sherwin-Williams Company | SHW | 0.160298 | 470.07 | -1.48 (-0.31%) |
| 148 | Edwards Lifesciences Corporation | EW | 0.159370 | 185.45 | 0.22 (0.12%) |
| 149 | Ford Motor Company | F | 0.159228 | 9.88 | 0.06 (0.56%) |
| 150 | Roper Technologies Inc. | ROP | 0.157943 | 364.48 | -2.88 (-0.78%) |
| 151 | BB&T Corporation | BBT | 0.157204 | 49.69 | 0.11 (0.22%) |
| 152 | Marriott International Inc. Class A | MAR | 0.156985 | 134.33 | 1.58 (1.19%) |
| 153 | Baxter International Inc. | BAX | 0.154571 | 77.49 | -0.69 (-0.88%) |
| 154 | Sempra Energy | SRE | 0.152480 | 134.22 | 0.13 (0.10%) |
| 155 | Public Storage | PSA | 0.151770 | 239.67 | -2.05 (-0.85%) |
| 156 | Ross Stores Inc. | ROST | 0.151478 | 98.52 | 0.52 (0.53%) |
| 157 | Phillips 66 | PSX | 0.150625 | 85.29 | -0.61 (-0.71%) |
| 158 | Cognizant Technology Solutions Corporation Class A | CTSH | 0.150603 | 63.21 | 0.18 (0.29%) |
| 159 | Autodesk Inc. | ADSK | 0.150054 | 162.52 | -2.60 (-1.57%) |
| 160 | Occidental Petroleum Corporation | OXY | 0.148972 | 49.07 | 1.17 (2.44%) |
| 161 | Anadarko Petroleum Corporation | APC | 0.147430 | 70.17 | 0.40 (0.57%) |
| 162 | Dollar General Corporation | DG | 0.147226 | 135.10 | 0.13 (0.10%) |
| 163 | Johnson Controls International plc | JCI | 0.147221 | 38.89 | 0.08 (0.21%) |
| 164 | Fiserv Inc. | FISV | 0.147166 | 89.21 | -1.37 (-1.51%) |
| 165 | Activision Blizzard Inc. | ATVI | 0.145822 | 46.50 | 0.49 (1.06%) |
| 166 | HCA Healthcare Inc | HCA | 0.144284 | 128.94 | -1.33 (-1.02%) |
| 167 | Delta Air Lines Inc. | DAL | 0.141327 | 54.71 | -0.02 (-0.04%) |
| 168 | Humana Inc. | HUM | 0.141112 | 245.62 | -4.06 (-1.62%) |
| 169 | Eaton Corp. Plc | ETN | 0.140803 | 79.22 | -0.10 (-0.13%) |
| 170 | Sysco Corporation | SYY | 0.140720 | 72.63 | 0.91 (1.27%) |
| 171 | Allstate Corporation | ALL | 0.138792 | 99.81 | -0.98 (-0.97%) |
| 172 | Yum! Brands Inc. | YUM | 0.138128 | 107.56 | -1.09 (-1.00%) |
| 173 | Williams Companies Inc. | WMB | 0.138081 | 27.66 | 0.18 (0.64%) |
| 174 | eBay Inc. | EBAY | 0.135982 | 38.10 | -0.02 (-0.05%) |
| 175 | Red Hat Inc. | RHT | 0.135965 | 185.21 | 0.07 (0.04%) |
| 176 | Marathon Petroleum Corporation | MPC | 0.133243 | 48.01 | 0.33 (0.69%) |
| 177 | Valero Energy Corporation | VLO | 0.132870 | 76.81 | 0.95 (1.25%) |
| 178 | Constellation Brands Inc. Class A | STZ | 0.132816 | 193.98 | 2.02 (1.05%) |
| 179 | Moody's Corporation | MCO | 0.132531 | 189.08 | -2.87 (-1.50%) |
| 180 | Welltower Inc. | WELL | 0.132096 | 81.63 | -0.76 (-0.92%) |
| 181 | TE Connectivity Ltd. | TEL | 0.130701 | 91.36 | -0.15 (-0.16%) |
| 182 | General Mills Inc. | GIS | 0.127119 | 52.26 | 0.92 (1.78%) |
| 183 | O'Reilly Automotive Inc. | ORLY | 0.127037 | 380.26 | -4.77 (-1.24%) |
| 184 | HP Inc. | HPQ | 0.126470 | 19.73 | 0.01 (0.05%) |
| 185 | Ingersoll-Rand Plc | IR | 0.126134 | 123.29 | -2.06 (-1.64%) |
| 186 | Xcel Energy Inc. | XEL | 0.125650 | 58.50 | -0.76 (-1.28%) |
| 187 | Public Service Enterprise Group Inc | PEG | 0.125567 | 59.21 | -0.66 (-1.10%) |
| 188 | Lam Research Corporation | LRCX | 0.121033 | 189.61 | -0.46 (-0.24%) |
| 189 | Advanced Micro Devices Inc. | AMD | 0.120462 | 32.26 | -0.97 (-2.92%) |
| 190 | AvalonBay Communities Inc. | AVB | 0.120329 | 207.97 | -1.40 (-0.67%) |
| 191 | Electronic Arts Inc. | EA | 0.119671 | 94.35 | -1.16 (-1.21%) |
| 192 | Newmont Goldcorp Corporation | NEM | 0.119641 | 34.92 | -0.37 (-1.05%) |
| 193 | Equity Residential | EQR | 0.119473 | 77.27 | -0.56 (-0.72%) |
| 194 | SunTrust Banks Inc. | STI | 0.117187 | 63.81 | 0.09 (0.14%) |
| 195 | Paychex Inc. | PAYX | 0.116581 | 86.74 | -1.06 (-1.21%) |
| 196 | Xilinx Inc. | XLNX | 0.116478 | 111.80 | 0.92 (0.83%) |
| 197 | Consolidated Edison Inc. | ED | 0.116240 | 86.40 | -0.83 (-0.95%) |
| 198 | AutoZone Inc. | AZO | 0.115846 | 1,098.34 | -12.90 (-1.16%) |
| 199 | Amphenol Corporation Class A | APH | 0.115715 | 95.52 | 1.37 (1.46%) |
| 200 | V.F. Corporation | VFC | 0.115491 | 85.39 | -0.30 (-0.35%) |
| 201 | Hilton Worldwide Holdings Inc | HLT | 0.113931 | 93.69 | 0.66 (0.71%) |
| 202 | Twitter Inc. | TWTR | 0.113779 | 37.02 | -0.62 (-1.65%) |
| 203 | PPG Industries Inc. | PPG | 0.113626 | 116.66 | 2.27 (1.98%) |
| 204 | ONEOK Inc. | OKE | 0.112418 | 65.76 | -0.06 (-0.09%) |
| 205 | Motorola Solutions Inc. | MSI | 0.108944 | 160.43 | -1.08 (-0.67%) |
| 206 | WEC Energy Group Inc | WEC | 0.108293 | 81.67 | -1.07 (-1.29%) |
| 207 | Alexion Pharmaceuticals Inc. | ALXN | 0.107938 | 117.49 | 1.07 (0.91%) |
| 208 | Monster Beverage Corporation | MNST | 0.107282 | 64.24 | -1.51 (-2.30%) |
| 209 | Discover Financial Services | DFS | 0.106527 | 78.44 | 0.25 (0.32%) |
| 210 | T. Rowe Price Group | TROW | 0.106432 | 106.45 | -0.53 (-0.49%) |
| 211 | McKesson Corporation | MCK | 0.105652 | 132.63 | -0.12 (-0.09%) |
| 212 | LyondellBasell Industries NV | LYB | 0.105615 | 84.86 | 1.21 (1.45%) |
| 213 | Global Payments Inc. | GPN | 0.104462 | 158.22 | -2.23 (-1.39%) |
| 214 | SBA Communications Corp. Class A | SBAC | 0.104253 | 221.80 | 0.26 (0.12%) |
| 215 | Southwest Airlines Co. | LUV | 0.103827 | 50.55 | 0.25 (0.50%) |
| 216 | Zimmer Biomet Holdings Inc. | ZBH | 0.103247 | 120.44 | -1.27 (-1.04%) |
| 217 | Dollar Tree Inc. | DLTR | 0.102479 | 105.84 | 2.06 (1.98%) |
| 218 | Corning Inc | GLW | 0.101004 | 31.00 | 0.06 (0.18%) |
| 219 | Willis Towers Watson Public Limited Company | WLTW | 0.100842 | 188.47 | 0.16 (0.08%) |
| 220 | PACCAR Inc | PCAR | 0.100781 | 69.89 | -0.31 (-0.44%) |
| 221 | Digital Realty Trust Inc. | DLR | 0.100757 | 118.64 | 1.81 (1.55%) |
| 222 | Pioneer Natural Resources Company | PXD | 0.100656 | 145.92 | 1.19 (0.82%) |
| 223 | Tyson Foods Inc. Class A | TSN | 0.100205 | 82.08 | -0.41 (-0.50%) |
| 224 | Eversource Energy | ES | 0.099978 | 75.29 | -0.74 (-0.97%) |
| 225 | Verisk Analytics Inc | VRSK | 0.099295 | 145.17 | -1.70 (-1.16%) |
| 226 | Regeneron Pharmaceuticals Inc. | REGN | 0.098955 | 308.21 | 3.96 (1.30%) |
| 227 | Cerner Corporation | CERN | 0.098149 | 71.63 | -1.07 (-1.47%) |
| 228 | Cummins Inc. | CMI | 0.097910 | 164.57 | 1.49 (0.91%) |
| 229 | DTE Energy Company | DTE | 0.096920 | 127.13 | -1.10 (-0.86%) |
| 230 | M&T Bank Corporation | MTB | 0.096332 | 166.90 | -0.70 (-0.42%) |
| 231 | Harris Corporation | HRS | 0.096327 | 188.19 | -8.39 (-4.27%) |
| 232 | FleetCor Technologies Inc. | FLT | 0.096314 | 265.96 | -0.26 (-0.10%) |
| 233 | TransDigm Group Incorporated | TDG | 0.095857 | 464.22 | -10.45 (-2.20%) |
| 234 | Fortive Corp. | FTV | 0.095815 | 79.08 | 0.16 (0.20%) |
| 235 | IDEXX Laboratories Inc. | IDXX | 0.095344 | 263.48 | -4.99 (-1.86%) |
| 236 | Ventas Inc. | VTR | 0.094958 | 64.03 | -0.22 (-0.34%) |
| 237 | Align Technology Inc. | ALGN | 0.094507 | 307.77 | -7.26 (-2.30%) |
| 238 | Centene Corporation | CNC | 0.094246 | 54.80 | -0.17 (-0.31%) |
| 239 | Ball Corporation | BLL | 0.093911 | 66.24 | -0.08 (-0.12%) |
| 240 | Archer-Daniels-Midland Company | ADM | 0.093221 | 40.05 | -0.02 (-0.04%) |
| 241 | Agilent Technologies Inc. | A | 0.092637 | 69.95 | -0.34 (-0.48%) |
| 242 | IQVIA Holdings Inc | IQV | 0.092387 | 139.42 | -0.31 (-0.22%) |
| 243 | IHS Markit Ltd. | INFO | 0.091629 | 59.71 | -1.05 (-1.72%) |
| 244 | VeriSign Inc. | VRSN | 0.091321 | 205.39 | -1.72 (-0.83%) |
| 245 | PPL Corporation | PPL | 0.091096 | 30.44 | -0.25 (-0.81%) |
| 246 | Realty Income Corporation | O | 0.090479 | 71.78 | 0.01 (0.01%) |
| 247 | Parker-Hannifin Corporation | PH | 0.090456 | 167.98 | 1.18 (0.71%) |
| 248 | Synchrony Financial | SYF | 0.090284 | 33.82 | 0.50 (1.50%) |
| 249 | State Street Corporation | STT | 0.090035 | 55.47 | -1.75 (-3.06%) |
| 250 | FirstEnergy Corp. | FE | 0.089926 | 42.52 | -0.50 (-1.16%) |
| 251 | Total System Services Inc. | TSS | 0.088688 | 126.93 | -1.90 (-1.47%) |
| 252 | Carnival Corporation | CCL | 0.088152 | 53.14 | -0.04 (-0.08%) |
| 253 | Royal Caribbean Cruises Ltd. | RCL | 0.087964 | 122.84 | -0.60 (-0.49%) |
| 254 | Stanley Black & Decker Inc. | SWK | 0.087874 | 140.12 | 0.64 (0.46%) |
| 255 | American Water Works Company Inc. | AWK | 0.086996 | 115.07 | -0.78 (-0.67%) |
| 256 | Fifth Third Bancorp | FITB | 0.086360 | 27.22 | 0.20 (0.72%) |
| 257 | Boston Properties Inc. | BXP | 0.086312 | 132.82 | -1.81 (-1.34%) |
| 258 | MSCI Inc. Class A | MSCI | 0.085346 | 235.07 | -8.93 (-3.66%) |
| 259 | Ameriprise Financial Inc. | AMP | 0.085020 | 151.55 | 0.30 (0.20%) |
| 260 | Microchip Technology Incorporated | MCHP | 0.084320 | 85.45 | -0.30 (-0.35%) |
| 261 | L3 Technologies Inc | LLL | 0.083754 | 243.43 | -11.06 (-4.35%) |
| 262 | Clorox Company | CLX | 0.083486 | 155.73 | -1.06 (-0.68%) |
| 263 | Mettler-Toledo International Inc. | MTD | 0.083280 | 802.75 | -4.04 (-0.50%) |
| 264 | Concho Resources Inc. | CXO | 0.082926 | 101.07 | 1.57 (1.58%) |
| 265 | Cintas Corporation | CTAS | 0.082741 | 231.80 | -2.75 (-1.17%) |
| 266 | Aptiv PLC | APTV | 0.082363 | 76.03 | 0.32 (0.42%) |
| 267 | Edison International | EIX | 0.082294 | 60.01 | -0.86 (-1.41%) |
| 268 | Hartford Financial Services Group Inc. | HIG | 0.081786 | 54.48 | -0.35 (-0.64%) |
| 269 | AMETEK Inc. | AME | 0.081681 | 86.31 | -0.17 (-0.20%) |
| 270 | Hewlett Packard Enterprise Co. | HPE | 0.081461 | 14.18 | 0.03 (0.21%) |
| 271 | Northern Trust Corporation | NTRS | 0.081058 | 88.06 | -0.34 (-0.38%) |
| 272 | Essex Property Trust Inc. | ESS | 0.080802 | 296.27 | 0.26 (0.09%) |
| 273 | Kroger Co. | KR | 0.080732 | 24.52 | 0.13 (0.51%) |
| 274 | Rockwell Automation Inc. | ROK | 0.080201 | 159.03 | 0.22 (0.14%) |
| 275 | McCormick & Company Incorporated | MKC | 0.079843 | 156.41 | -0.53 (-0.34%) |
| 276 | Corteva Inc | CTVA | 0.078928 | 24.89 | -0.42 (-1.66%) |
| 277 | Church & Dwight Co. Inc. | CHD | 0.078863 | 76.62 | -0.68 (-0.87%) |
| 278 | Halliburton Company | HAL | 0.078583 | 22.08 | 0.32 (1.47%) |
| 279 | Entergy Corporation | ETR | 0.078574 | 99.10 | -0.75 (-0.75%) |
| 280 | Ulta Beauty Inc | ULTA | 0.078524 | 337.46 | 0.41 (0.12%) |
| 281 | Hershey Company | HSY | 0.078403 | 136.43 | -0.31 (-0.23%) |
| 282 | Synopsys Inc. | SNPS | 0.078378 | 125.41 | -0.81 (-0.64%) |
| 283 | Kraft Heinz Company | KHC | 0.078316 | 30.24 | -0.07 (-0.23%) |
| 284 | KLA-Tencor Corporation | KLAC | 0.077776 | 113.94 | 0.48 (0.42%) |
| 285 | United Continental Holdings Inc. | UAL | 0.077643 | 82.66 | -0.01 (-0.01%) |
| 286 | Fastenal Company | FAST | 0.077613 | 32.37 | -0.27 (-0.83%) |
| 287 | Cadence Design Systems Inc. | CDNS | 0.077484 | 67.82 | 0.65 (0.97%) |
| 288 | Republic Services Inc. | RSG | 0.077435 | 85.90 | -0.67 (-0.77%) |
| 289 | Ameren Corporation | AEE | 0.077067 | 74.58 | -0.86 (-1.14%) |
| 290 | Weyerhaeuser Company | WY | 0.074517 | 24.28 | 0.17 (0.71%) |
| 291 | International Paper Company | IP | 0.073246 | 43.81 | 0.11 (0.25%) |
| 292 | Vulcan Materials Company | VMC | 0.073140 | 131.02 | -4.54 (-3.35%) |
| 293 | Chipotle Mexican Grill Inc. | CMG | 0.072763 | 723.83 | 3.31 (0.46%) |
| 294 | Omnicom Group Inc | OMC | 0.072254 | 79.28 | 0.69 (0.88%) |
| 295 | KeyCorp | KEY | 0.070813 | 17.22 | 0.30 (1.74%) |
| 296 | ResMed Inc. | RMD | 0.070360 | 118.05 | -0.28 (-0.24%) |
| 297 | Alexandria Real Estate Equities Inc. | ARE | 0.070333 | 150.38 | 0.06 (0.04%) |
| 298 | Laboratory Corporation of America Holdings | LH | 0.069926 | 166.32 | -1.12 (-0.67%) |
| 299 | CBS Corporation Class B | CBS | 0.069220 | 48.18 | 0.21 (0.43%) |
| 300 | First Republic Bank | FRC | 0.068483 | 97.19 | -1.69 (-1.71%) |
| 301 | Citizens Financial Group Inc. | CFG | 0.067353 | 34.72 | 0.26 (0.75%) |
| 302 | Maxim Integrated Products Inc. | MXIM | 0.067085 | 58.22 | 0.21 (0.36%) |
| 303 | Arthur J. Gallagher & Co. | AJG | 0.067074 | 87.35 | -0.47 (-0.54%) |
| 304 | ANSYS Inc. | ANSS | 0.066958 | 193.81 | -1.28 (-0.65%) |
| 305 | CMS Energy Corporation | CMS | 0.066833 | 56.94 | -0.56 (-0.97%) |
| 306 | Nucor Corporation | NUE | 0.066147 | 51.85 | 0.91 (1.79%) |
| 307 | Cooper Companies Inc. | COO | 0.066003 | 322.94 | 0.33 (0.10%) |
| 308 | Equifax Inc. | EFX | 0.065300 | 128.76 | -1.45 (-1.11%) |
| 309 | CBRE Group Inc. Class A | CBRE | 0.064719 | 49.85 | -0.05 (-0.10%) |
| 310 | Keysight Technologies Inc | KEYS | 0.064247 | 83.28 | 0.92 (1.11%) |
| 311 | NetApp Inc. | NTAP | 0.063860 | 62.36 | 0.04 (0.06%) |
| 312 | D.R. Horton Inc. | DHI | 0.063753 | 44.73 | -0.76 (-1.67%) |
| 313 | Cincinnati Financial Corporation | CINF | 0.063665 | 101.66 | -0.92 (-0.90%) |
| 314 | Evergy Inc. | EVRG | 0.063212 | 59.14 | -0.70 (-1.17%) |
| 315 | Lennar Corporation Class A | LEN | 0.063188 | 52.38 | -0.40 (-0.76%) |
| 316 | Broadridge Financial Solutions Inc. | BR | 0.063140 | 130.56 | -1.58 (-1.20%) |
| 317 | Freeport-McMoRan Inc. | FCX | 0.063125 | 10.76 | 0.15 (1.41%) |
| 318 | Best Buy Co. Inc. | BBY | 0.063043 | 65.26 | 0.07 (0.11%) |
| 319 | International Flavors & Fragrances Inc. | IFF | 0.062834 | 152.23 | 0.90 (0.59%) |
| 320 | Diamondback Energy Inc. | FANG | 0.062660 | 100.24 | 2.64 (2.70%) |
| 321 | Waters Corporation | WAT | 0.062433 | 207.88 | -2.60 (-1.24%) |
| 322 | Copart Inc. | CPRT | 0.062025 | 73.44 | -1.41 (-1.88%) |
| 323 | HCP Inc. | HCP | 0.061894 | 31.30 | -0.14 (-0.45%) |
| 324 | Genuine Parts Company | GPC | 0.061834 | 102.69 | 0.72 (0.71%) |
| 325 | Loews Corporation | L | 0.061703 | 53.65 | -0.38 (-0.70%) |
| 326 | Darden Restaurants Inc. | DRI | 0.060525 | 118.92 | -0.06 (-0.05%) |
| 327 | Hess Corporation | HES | 0.060509 | 58.07 | 0.79 (1.38%) |
| 328 | CenterPoint Energy Inc. | CNP | 0.060141 | 28.63 | -0.30 (-1.02%) |
| 329 | Xylem Inc. | XYL | 0.059967 | 80.05 | -0.33 (-0.41%) |
| 330 | CarMax Inc. | KMX | 0.059885 | 81.11 | -1.25 (-1.52%) |
| 331 | Teleflex Incorporated | TFX | 0.059877 | 307.88 | -3.07 (-0.99%) |
| 332 | Regions Financial Corporation | RF | 0.059665 | 14.32 | 0.33 (2.32%) |
| 333 | WellCare Health Plans Inc. | WCG | 0.059620 | 290.37 | -0.55 (-0.19%) |
| 334 | Principal Financial Group Inc. | PFG | 0.059468 | 54.74 | 0.05 (0.09%) |
| 335 | Expedia Group Inc. | EXPE | 0.059275 | 123.06 | 1.31 (1.08%) |
| 336 | Gartner Inc. | IT | 0.058960 | 156.56 | -2.46 (-1.55%) |
| 337 | Huntington Bancshares Incorporated | HBAN | 0.058880 | 13.50 | 0.07 (0.52%) |
| 338 | Martin Marietta Materials Inc. | MLM | 0.058743 | 220.32 | -9.20 (-4.01%) |
| 339 | Conagra Brands Inc. | CAG | 0.058735 | 29.86 | 0.51 (1.74%) |
| 340 | Dover Corporation | DOV | 0.058539 | 96.40 | -0.20 (-0.20%) |
| 341 | J.M. Smucker Company | SJM | 0.057966 | 123.15 | 0.51 (0.42%) |
| 342 | Extra Space Storage Inc. | EXR | 0.057714 | 108.49 | -0.63 (-0.58%) |
| 343 | MGM Resorts International | MGM | 0.057582 | 27.86 | 0.44 (1.60%) |
| 344 | Kellogg Company | K | 0.056881 | 55.50 | 0.73 (1.33%) |
| 345 | Host Hotels & Resorts Inc. | HST | 0.056852 | 18.50 | 0.02 (0.08%) |
| 346 | Incyte Corporation | INCY | 0.056617 | 75.60 | -1.20 (-1.56%) |
| 347 | DXC Technology Co. | DXC | 0.055942 | 50.55 | 0.28 (0.56%) |
| 348 | AmerisourceBergen Corporation | ABC | 0.055638 | 84.84 | 0.07 (0.08%) |
| 349 | Mid-America Apartment Communities Inc. | MAA | 0.055416 | 116.17 | -0.94 (-0.80%) |
| 350 | Quest Diagnostics Incorporated | DGX | 0.055242 | 98.94 | -0.74 (-0.74%) |
| 351 | Celanese Corporation | CE | 0.054855 | 103.59 | 0.99 (0.96%) |
| 352 | Arista Networks Inc. | ANET | 0.054218 | 242.13 | -7.94 (-3.18%) |
| 353 | Cardinal Health Inc. | CAH | 0.054137 | 44.67 | 0.67 (1.52%) |
| 354 | Lincoln National Corporation | LNC | 0.054015 | 63.94 | 0.20 (0.31%) |
| 355 | Hologic Inc. | HOLX | 0.053607 | 47.62 | -0.44 (-0.91%) |
| 356 | Skyworks Solutions Inc. | SWKS | 0.053595 | 73.96 | 0.86 (1.18%) |
| 357 | Akamai Technologies Inc. | AKAM | 0.053492 | 79.03 | -0.52 (-0.65%) |
| 358 | Tractor Supply Company | TSCO | 0.053344 | 107.02 | -0.12 (-0.11%) |
| 359 | Expeditors International of Washington Inc. | EXPD | 0.052686 | 72.92 | -0.78 (-1.06%) |
| 360 | UDR Inc. | UDR | 0.052343 | 45.74 | -0.35 (-0.76%) |
| 361 | Take-Two Interactive Software Inc. | TTWO | 0.052146 | 109.98 | -1.80 (-1.61%) |
| 362 | DENTSPLY SIRONA Inc. | XRAY | 0.052026 | 57.15 | 0.05 (0.09%) |
| 363 | American Airlines Group Inc. | AAL | 0.051648 | 30.66 | -0.10 (-0.33%) |
| 364 | W.W. Grainger Inc. | GWW | 0.051284 | 274.69 | -0.49 (-0.18%) |
| 365 | ABIOMED Inc. | ABMD | 0.051036 | 263.04 | -9.39 (-3.45%) |
| 366 | Cboe Global Markets Inc | CBOE | 0.050787 | 106.41 | -4.72 (-4.25%) |
| 367 | Hasbro Inc. | HAS | 0.050696 | 105.98 | 0.24 (0.23%) |
| 368 | Symantec Corporation | SYMC | 0.050587 | 18.59 | -0.62 (-3.23%) |
| 369 | Citrix Systems Inc. | CTXS | 0.050439 | 95.65 | -0.34 (-0.35%) |
| 370 | Kansas City Southern | KSU | 0.050211 | 119.04 | -0.97 (-0.81%) |
| 371 | Atmos Energy Corporation | ATO | 0.050039 | 102.36 | -0.76 (-0.74%) |
| 372 | Baker Hughes a GE Company Class A | BHGE | 0.049870 | 23.48 | 0.07 (0.30%) |
| 373 | Fox Corporation Class A | FOXA | 0.049817 | 34.06 | 0.03 (0.07%) |
| 374 | Varian Medical Systems Inc. | VAR | 0.049766 | 130.13 | -1.20 (-0.91%) |
| 375 | Vornado Realty Trust | VNO | 0.048552 | 66.36 | -1.04 (-1.54%) |
| 376 | Norwegian Cruise Line Holdings Ltd. | NCLH | 0.048514 | 53.80 | -0.03 (-0.05%) |
| 377 | Textron Inc. | TXT | 0.048259 | 48.84 | -0.73 (-1.47%) |
| 378 | Alliant Energy Corp | LNT | 0.047846 | 48.00 | -0.78 (-1.60%) |
| 379 | Masco Corporation | MAS | 0.047735 | 37.98 | 0.10 (0.26%) |
| 380 | E*TRADE Financial Corporation | ETFC | 0.047657 | 46.54 | -0.10 (-0.21%) |
| 381 | Devon Energy Corporation | DVN | 0.047565 | 26.30 | 0.18 (0.69%) |
| 382 | Seagate Technology PLC | STX | 0.047084 | 43.75 | -0.20 (-0.46%) |
| 383 | Regency Centers Corporation | REG | 0.046953 | 67.21 | -0.27 (-0.40%) |
| 384 | Marathon Oil Corporation | MRO | 0.046898 | 13.42 | -0.04 (-0.26%) |
| 385 | Wynn Resorts Limited | WYNN | 0.046870 | 117.75 | 1.08 (0.93%) |
| 386 | Comerica Incorporated | CMA | 0.046807 | 70.56 | 0.40 (0.56%) |
| 387 | Duke Realty Corporation | DRE | 0.046792 | 31.17 | -0.18 (-0.57%) |
| 388 | Advance Auto Parts Inc. | AAP | 0.046433 | 151.61 | -1.91 (-1.24%) |
| 389 | SVB Financial Group | SIVB | 0.046290 | 214.82 | 0.30 (0.14%) |
| 390 | Hormel Foods Corporation | HRL | 0.046240 | 41.80 | 0.33 (0.80%) |
| 391 | C.H. Robinson Worldwide Inc. | CHRW | 0.045678 | 80.35 | 0.15 (0.18%) |
| 392 | Raymond James Financial Inc. | RJF | 0.045651 | 84.22 | -0.44 (-0.52%) |
| 393 | Nasdaq Inc. | NDAQ | 0.045611 | 95.25 | -1.42 (-1.47%) |
| 394 | Western Digital Corporation | WDC | 0.045316 | 38.16 | 0.18 (0.46%) |
| 395 | AES Corporation | AES | 0.045210 | 16.31 | -0.13 (-0.76%) |
| 396 | Apache Corporation | APA | 0.045054 | 29.01 | 0.13 (0.45%) |
| 397 | Fortinet Inc. | FTNT | 0.045001 | 75.68 | 1.00 (1.34%) |
| 398 | FMC Corporation | FMC | 0.044712 | 79.67 | -0.26 (-0.33%) |
| 399 | Pinnacle West Capital Corporation | PNW | 0.044388 | 95.98 | -1.29 (-1.33%) |
| 400 | Henry Schein Inc. | HSIC | 0.044322 | 70.81 | 0.07 (0.10%) |
| 401 | NiSource Inc | NI | 0.043929 | 28.12 | -0.31 (-1.07%) |
| 402 | Cabot Oil & Gas Corporation | COG | 0.043767 | 24.14 | -0.05 (-0.19%) |
| 403 | Universal Health Services Inc. Class B | UHS | 0.043596 | 122.98 | -0.60 (-0.49%) |
| 404 | Jack Henry & Associates Inc. | JKHY | 0.043547 | 136.49 | -0.43 (-0.31%) |
| 405 | PerkinElmer Inc. | PKI | 0.043150 | 92.87 | -0.86 (-0.92%) |
| 406 | Everest Re Group Ltd. | RE | 0.042284 | 249.04 | -3.13 (-1.24%) |
| 407 | Noble Energy Inc. | NBL | 0.042274 | 21.33 | 0.29 (1.35%) |
| 408 | Molson Coors Brewing Company Class B | TAP | 0.042035 | 54.75 | 0.01 (0.02%) |
| 409 | United Rentals Inc. | URI | 0.041966 | 122.14 | -1.45 (-1.17%) |
| 410 | Franklin Resources Inc. | BEN | 0.041946 | 33.38 | -0.03 (-0.07%) |
| 411 | Viacom Inc. Class B | VIAB | 0.041869 | 29.02 | -0.05 (-0.17%) |
| 412 | Eastman Chemical Company | EMN | 0.041747 | 72.84 | 0.67 (0.93%) |
| 413 | Tiffany & Co. | TIF | 0.041240 | 92.07 | -0.07 (-0.08%) |
| 414 | Allegion PLC | ALLE | 0.041160 | 104.90 | -0.64 (-0.61%) |
| 415 | Arconic Inc. | ARNC | 0.041148 | 22.81 | -0.48 (-2.06%) |
| 416 | NRG Energy Inc. | NRG | 0.040961 | 34.39 | 0.19 (0.56%) |
| 417 | Federal Realty Investment Trust | FRT | 0.040467 | 130.22 | -0.93 (-0.71%) |
| 418 | TechnipFMC Plc | FTI | 0.040278 | 22.93 | 0.21 (0.92%) |
| 419 | CenturyLink Inc. | CTL | 0.040275 | 10.69 | 0.36 (3.44%) |
| 420 | Avery Dennison Corporation | AVY | 0.040027 | 112.26 | -0.27 (-0.24%) |
| 421 | CF Industries Holdings Inc. | CF | 0.039954 | 42.20 | 0.26 (0.62%) |
| 422 | Garmin Ltd. | GRMN | 0.039755 | 80.15 | -0.02 (-0.02%) |
| 423 | Brown-Forman Corporation Class B | BF.B | 0.039416 | 57.08 | -0.17 (-0.30%) |
| 424 | Jacobs Engineering Group Inc. | JEC | 0.039028 | 78.42 | -1.73 (-2.15%) |
| 425 | Discovery Inc. Class C | DISCK | 0.038884 | 26.94 | 0.60 (2.28%) |
| 426 | Mohawk Industries Inc. | MHK | 0.038594 | 149.06 | 0.00 (0.00%) |
| 427 | Juniper Networks Inc. | JNPR | 0.038280 | 26.84 | -0.02 (-0.07%) |
| 428 | WestRock Company | WRK | 0.038135 | 36.45 | 0.02 (0.05%) |
| 429 | Huntington Ingalls Industries Inc. | HII | 0.038086 | 209.06 | -5.67 (-2.64%) |
| 430 | Torchmark Corporation | TMK | 0.037788 | 88.42 | -0.39 (-0.43%) |
| 431 | Snap-on Incorporated | SNA | 0.037784 | 163.53 | 0.51 (0.31%) |
| 432 | Westinghouse Air Brake Technologies Corporation | WAB | 0.037572 | 68.46 | 0.44 (0.65%) |
| 433 | Lamb Weston Holdings Inc. | LW | 0.037395 | 61.90 | 0.48 (0.78%) |
| 434 | Mylan N.V. | MYL | 0.037157 | 17.56 | -0.01 (-0.08%) |
| 435 | Iron Mountain Inc. | IRM | 0.036909 | 31.58 | 0.13 (0.41%) |
| 436 | Western Union Company | WU | 0.036703 | 19.84 | -0.19 (-0.92%) |
| 437 | Packaging Corporation of America | PKG | 0.036444 | 95.08 | 0.38 (0.40%) |
| 438 | Tapestry Inc. | TPR | 0.036109 | 29.84 | -0.13 (-0.43%) |
| 439 | F5 Networks Inc. | FFIV | 0.035577 | 142.71 | 0.10 (0.07%) |
| 440 | PulteGroup Inc. | PHM | 0.035070 | 32.48 | -0.25 (-0.76%) |
| 441 | Zions Bancorporation N.A. | ZION | 0.034944 | 44.31 | 0.47 (1.07%) |
| 442 | LKQ Corporation | LKQ | 0.034919 | 26.71 | -0.05 (-0.17%) |
| 443 | DISH Network Corporation Class A | DISH | 0.034886 | 37.16 | -0.19 (-0.51%) |
| 444 | BorgWarner Inc. | BWA | 0.034579 | 40.81 | 0.48 (1.19%) |
| 445 | Invesco Ltd. | IVZ | 0.034322 | 20.52 | 0.04 (0.17%) |
| 446 | Whirlpool Corporation | WHR | 0.034064 | 130.39 | 0.15 (0.12%) |
| 447 | Kohl's Corporation | KSS | 0.033534 | 47.75 | -0.39 (-0.80%) |
| 448 | Interpublic Group of Companies Inc. | IPG | 0.033522 | 21.58 | 0.26 (1.22%) |
| 449 | Qorvo Inc. | QRVO | 0.033487 | 64.30 | -1.20 (-1.83%) |
| 450 | National Oilwell Varco Inc. | NOV | 0.033423 | 21.34 | 0.36 (1.72%) |
| 451 | Apartment Investment and Management Company Class A | AIV | 0.032934 | 50.89 | -0.36 (-0.70%) |
| 452 | Nielsen Holdings Plc | NLSN | 0.032833 | 22.53 | 0.08 (0.33%) |
| 453 | Campbell Soup Company | CPB | 0.032769 | 42.34 | 0.33 (0.79%) |
| 454 | Mosaic Company | MOS | 0.032359 | 22.48 | 0.23 (1.01%) |
| 455 | Kimco Realty Corporation | KIM | 0.031617 | 18.29 | 0.06 (0.33%) |
| 456 | Albemarle Corporation | ALB | 0.031559 | 73.47 | 2.54 (3.57%) |
| 457 | J.B. Hunt Transport Services Inc. | JBHT | 0.031357 | 88.02 | -0.63 (-0.71%) |
| 458 | SL Green Realty Corp. | SLG | 0.031250 | 87.60 | -0.56 (-0.64%) |
| 459 | Fortune Brands Home & Security Inc. | FBHS | 0.030728 | 52.38 | -0.31 (-0.59%) |
| 460 | Alaska Air Group Inc. | ALK | 0.030503 | 60.79 | 0.15 (0.25%) |
| 461 | Unum Group | UNM | 0.029868 | 33.47 | 0.10 (0.30%) |
| 462 | Sealed Air Corporation | SEE | 0.029094 | 44.27 | -0.40 (-0.90%) |
| 463 | FLIR Systems Inc. | FLIR | 0.028955 | 51.81 | 0.07 (0.14%) |
| 464 | Xerox Corporation | XRX | 0.028759 | 34.82 | 0.13 (0.36%) |
| 465 | PVH Corp. | PVH | 0.028246 | 89.07 | -0.70 (-0.78%) |
| 466 | Robert Half International Inc. | RHI | 0.027549 | 56.01 | 1.07 (1.95%) |
| 467 | Macy's Inc | M | 0.027076 | 21.86 | 0.19 (0.85%) |
| 468 | HollyFrontier Corporation | HFC | 0.026748 | 40.83 | 0.61 (1.52%) |
| 469 | Alliance Data Systems Corporation | ADS | 0.026515 | 137.90 | 1.55 (1.14%) |
| 470 | Flowserve Corporation | FLS | 0.026370 | 49.34 | -0.08 (-0.16%) |
| 471 | Ralph Lauren Corporation Class A | RL | 0.026275 | 114.05 | 0.39 (0.34%) |
| 472 | A. O. Smith Corporation | AOS | 0.026231 | 44.76 | 0.38 (0.86%) |
| 473 | DaVita Inc. | DVA | 0.024963 | 48.04 | 0.05 (0.10%) |
| 474 | Coty Inc. Class A | COTY | 0.024894 | 13.35 | -0.16 (-1.18%) |
| 475 | Hanesbrands Inc. | HBI | 0.024694 | 16.46 | 0.12 (0.70%) |
| 476 | People's United Financial Inc. | PBCT | 0.024510 | 16.12 | 0.06 (0.37%) |
| 477 | Pentair plc | PNR | 0.024382 | 35.67 | -0.54 (-1.49%) |
| 478 | Harley-Davidson Inc. | HOG | 0.023927 | 35.10 | 0.04 (0.11%) |
| 479 | Cimarex Energy Co. | XEC | 0.023725 | 56.45 | 0.10 (0.18%) |
| 480 | Nektar Therapeutics | NKTR | 0.023451 | 33.20 | 0.07 (0.20%) |
| 481 | Newell Brands Inc | NWL | 0.023191 | 14.59 | 0.21 (1.46%) |
| 482 | Rollins Inc. | ROL | 0.023157 | 37.51 | -0.72 (-1.87%) |
| 483 | Perrigo Co. Plc | PRGO | 0.022588 | 43.97 | 0.16 (0.37%) |
| 484 | H&R Block Inc. | HRB | 0.022511 | 27.73 | 0.79 (2.91%) |
| 485 | Fox Corporation Class B | FOX | 0.022500 | 33.69 | -0.05 (-0.15%) |
| 486 | Helmerich & Payne Inc. | HP | 0.022494 | 51.31 | 0.65 (1.28%) |
| 487 | Quanta Services Inc. | PWR | 0.022436 | 37.24 | 0.05 (0.13%) |
| 488 | Assurant Inc. | AIZ | 0.022305 | 103.66 | 0.19 (0.18%) |
| 489 | Capri Holdings Limited | CPRI | 0.021885 | 34.02 | -0.49 (-1.42%) |
| 490 | Bemis Company Inc. | BMS | 0.021398 | 57.71 | 0.46 (0.80%) |
| 491 | L Brands Inc. | LB | 0.020423 | 22.10 | 0.28 (1.28%) |
| 492 | Leggett & Platt Incorporated | LEG | 0.020109 | 37.77 | 0.50 (1.34%) |
| 493 | Foot Locker Inc. | FL | 0.019780 | 41.65 | 0.36 (0.87%) |
| 494 | Affiliated Managers Group Inc. | AMG | 0.019765 | 89.76 | -0.42 (-0.47%) |
| 495 | Jefferies Financial Group Inc. | JEF | 0.019674 | 18.16 | 0.13 (0.69%) |
| 496 | IPG Photonics Corporation | IPGP | 0.019627 | 135.83 | 2.91 (2.19%) |
| 497 | Under Armour Inc. Class A | UAA | 0.019554 | 26.17 | 0.39 (1.51%) |
| 498 | TripAdvisor Inc. | TRIP | 0.019144 | 45.76 | 0.11 (0.24%) |
| 499 | News Corporation Class A | NWSA | 0.018400 | 11.76 | -0.01 (-0.08%) |
| 500 | Discovery Inc. Class A | DISCA | 0.017981 | 28.92 | 0.67 (2.37%) |
| 501 | Under Armour Inc. Class C | UA | 0.017421 | 23.10 | 0.41 (1.81%) |
| 502 | Gap Inc. | GPS | 0.016296 | 18.33 | 0.19 (1.02%) |
| 503 | Macerich Company | MAC | 0.015439 | 34.74 | -0.42 (-1.18%) |
| 504 | Nordstrom Inc. | JWN | 0.015297 | 32.96 | 0.42 (1.29%) |
| 505 | News Corporation Class B | NWS | 0.005665 | 12.10 | -0.03 (-0.21%) |
There are 505 symbols due to several companies with two share classes. For example, Google's parent company Alphabet has Class A (GOOGL) and Class C (GOOG) shares in the index.
When companies are removed and added to the index the membership list may temporarily show both the removed company and added company.
Data as of 06/10/2019.


Images of St. Louis:
Charles A. Shaw Park
St. Louis children's hospital
St. Louis Galleria
County court buildings
Washington University


We got infrastructure!
- target bricks-and-clicks relationships
- deliver interactive paradigms
- evolve B2C action-items
- optimize vertical technologies
- morph B2C technologies
- envisioneer cross-platform markets
- leverage world-class functionalities
- enhance proactive interfaces
- implement seamless relationships
- utilize value-added experiences
- grow wireless ROI
- reintermediate customized networks


Street Network Analysis With Esri's Geoenrichment Services
Click button and then click on map to generate enriched drive time areas


This paper is filled with fascinating ideas from the folks at google,
but is a little light on the
technical details required to reproduce the systems and results described.
DNCS as defined as an extension to previous differentiable explicit memory NN
models, using existing techniques for direct, and pattern matching memory accesses.
The key addition seems to be the ability for the NN to request blocks of memory
to be allocated
or deallocated, the ability to read from these blocks, and the ability to navigate
back and forward through the allocation blocks. This allows the training to be
more independent of the amount of memory available to the NN.
While interesting results are provided that show this network out preforms existing
LTSTM networks, the improvement due the new block-access does not seem to be
evaluated on its own. Nor are many details about how the allocation and forward-backward
lists are kept differentiable.
Further details are promised in the form of a forthcoming code release - which should
clear up all of these issues.


Yunhong Zhang, Qipeng Liu, Siying Zhang. Opinion formation with time-varying bounded confidence. PLoS ONE 12(3): e0172982, 2017. [pdf]
Jiuhua Zhao, Qipeng Liu, Lin Wang, and Xiaofan Wang. Competitive seeds-selection in complex networks. Physica A 467: 240-248, 2017. [pdf]
Jiuhua Zhao, Qipeng Liu, Lin Wang, and Xiaofan Wang. Competitiveness Maximization on Complex Networks. IEEE Transactions on systems, man, and cybernetics: systems. In press. DOI: 10.1109/TSMC.2016.2636240. 2016. [pdf]
Qipeng Liu, Jiuhua Zhao, and Xiaofan Wang. Multi-agent model of group polarization with biased assimilation of arguments, IET Control Theory and Applications 9(3): 485–492, 2015. [pdf]
Qipeng Liu and Xiaofan Wang. Social learning in networks with time-varying topologies, Asian Journal of Control 16(5): 1342–1349, 2014. [pdf]
Qipeng Liu, Aili Fang, Lin Wang, and Xiaofan Wang. Social learning with time-varying weights, Journal of Systems Science and Complexity 27(3): 581–593, 2014. [pdf]
Jiuhua Zhao, Qipeng Liu, and Xiaofan Wang. Competitive dynamics on complex networks, Scientific Reports 4:5858, 2014. [pdf]
Dequan Li, Qipeng Liu, Xiaofan Wang, and Zhixiang Yin. Quantized consensus over directed networks with switching topologies, Systems & Control Letters 65: 13–22, 2014. [pdf]
Qipeng Liu and Xiaofan Wang. Opinion dynamics with similarity-based random neighbors, Scientific Reports 3:2968, 2013. [pdf]
Qipeng Liu and Xiaofan Wang. Social learning with bounded confidence and heterogeneous agents, Physica A 392(10): 2368–2374, 2013. [pdf]
Dequan Li, Qipeng Liu, Xiaofan Wang and Zongli Lin. Consensus seeking over directed networks with limited information communication, Automatica 49(2): 610–618, 2013. [pdf]
Dequan Li, Qipeng Liu, and Xiaofan Wang. Distributed quantized consensus for agents on directed networks, Journal of Systems Science and Complexity 26(4): 489–511, 2013. [pdf]


Many linguists remain unconvinced that
supercalifragilisticexpialidocious is indeed a word.
Breaking news! There's a sale on leche quemada candy
at the El Azul restaurant. Don't delay, because when the last candy
is gone, it's gone.
The creators of this site will not be held liable for any injuries that
may result from unsupervised unicycle racing.


An Interesting Attempt At Getting User Signups
I think almost anyone on the internet has seen this message
“Log In With Facebook (Twitter, G+) To View”
And I do not think I’m alone in that I leave the page as soon as I see one of them. Normally these messages all come in the same form but today I saw something clever.
Instead of just presenting it in the normal we-just-want-your-information way, this site is able to offer a (shaky) justification while still forcing user signups. The whole idea behind it is a farce (the opacity is so low that you can see the image anyways) but it stil is an interesting way to increase user signups.
You can view the live example here.


Expenses
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Watch: Kvyat furious after receiving 5-second penalty: "F***, that's bulls***"
by Nicolás Quarles van UffordAfter getting another penalty for a last-lap lunge, Toro Rosso driver Daniil Kvyat didn't hold back, as the Russian called the five-second penalty which dropped him out of the points "bulls***".
Kvyat crossed the line in P10 and looked to have bagged a point, but a post-race investigation ended in a five-second time penalty for the 25-year-old, dropping him down to P12.
His move on Sergio Perez in the final lap was deemed illegal, much like his move in the last lap of last weekend's Grand Prix in Mexico, when he sent it down Nico Hulkenberg's inside, resulting in contact and a penalty as well.
After the race, Kvyat couldn't contain his anger when quizzed about the incident by Ziggo Sport.


|
Source Code
The latest NUnitForms source code is available in cvs from SourceForge.
Tortoise CVS is a great tool for accessing this.
| Server |
cvs.sourceforge.net |
| Method |
pserver |
| User Name |
anonymous |
| Repository |
/cvsroot/nunitforms |
| Module |
NUnitForms |
You can also browse the source code here.
Contributing
-
Post the code to the developer mailing list or simply email it to me.
-
Write unit tests for any new features that you are adding.
-
Post a message to the list (or in the email you send me) assigning copyright of the code to Luke Maxon. (I will of course acknowledge your contribution any way you like but need to be able to release it without worries.)
-
It would be best if your patch is done against the most recent version of the code in CVS because it is changing quickly.
Thoughts? Ideas? How would you prefer to contribute? |


Open die forging actually serves two purposes, hammering the metal into a desired shape while at the same time refining the grain structure which improves the strength properties of the material.
 The material is heated to a temperature above its recrystallization temperature. (For steels this is usually between 1900 and 2500 degrees Fahrenheit.) The die strikes the metal as it is manipulated to result in the desired shape. The person managing the manipulation of the material as it is struck is still to this day known as a blacksmith. Often the material must be reheated numerous times before the desired shape is reached. Open die forging is not a net shape process. Machining allowance should be considered when ordering forgings.
Rolled rings are an important product of the open die forging process.  This preformed shape is then placed in a ring mill and rolled. As the part is rolled, it is stretched to the desired OD and ID.
Common applications for open die forgings include bucket and blade stock for the aerospace industry, as well as stator rings, diaphragm rings, and sealing rings for the power generation industry. Many manufacturers will use forged bars when large diameter bar material is unavailable or has tremendously long lead times.
We can provide forgings in the following shapes. We can supply as forged with machining allowance, rough machined, or finish machined to your print.
| Rings 3” to 72” OD | 10,000 lbs max |
| Disks 3” to 72” OD | 15,000 lbs max |
| Blocks up to 48” cross section | 15,000 lbs max |
| Bars 1” Sq up to 36” wide | 20,000 lbs max |
| 1” DIA. To 36” DIA. | 20,000 lbs max |
| Upsets and special shapes | Contact us |
Materials


If you were to describe your ideal work environment, which description best matches your preferance?
How would you best describe yourself?
How do you spend your free time?
Choose the adjective that best describes you:
If you were at Hogwarts, which house would you be in?
What's the first thing you look at when you visit a website?


ProMan is an automated network application that provides hosting platform for
data and services.
It can used as real time collaboration application in coding environments or in
other environments where resource sharing and notification of events is critical
and can subtly improve efficiency.
for more information plz click on this link...


User Timebase
API Reference
Set User Timebase how-to
This API provides definitions to read and set the user timebase for a target's real time clock (RTC).
Usually, the time daemon automatically updates the system time (i.e., gets time value when calling le_clk_GetAbsoluteTime() or using Linux date
command). In some cases, the RTC time value may require a user timebase value based on other domains (e.g., GPS) to offset differences between the battery-powered RTC and the system time.
If you're synchronizng with cellular network time only, the time daemon will update the system time (the time you get when calling le_clk_GetAbsoluteTime() or using linux 'date' command) automatically.
Disable Time Daemon
If you need to set the system time from a saved user timebase value, first disable the time daemon by commenting out the TIME_SERVICES="qcom_time"
line in /etc/time_service
.conf to prevent the system time from being updated if the modem receives a new time from a cellular network.
Get/Set Time Value
You use le_rtc_GetUserTime() to read the current RTC time, and le_rtc_SetUserTime() to set the user timebase adjustment (plus/minus in milliseconds).
The time stored in the user timebase is incremented on a millisecond basis; Unix time is incremented by the second. When storing Unix time in the user timebase, it must be multiplied by 1000, and must be divided by 1000 when retrieved.
Typically, it's sufficient to use Unix epoch time x 1000 as the time base.
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Makes 12 tarts
Dietary
The slightly nutty short-crust pastry makes the perfect crumbly case for a rich egg custard topped with nutmeg. Go on have another!
Equipment and preparation: You will need a 12-hole muffin tray and a 11cm/4½in fluted cutter.
To make the pastry, stir the flour and ground almonds together in a large bowl, then add the butter and rub in with your fingertips until the mixture resembles breadcrumbs. Stir in the sugar.
Break in the egg and work it into the mixture with your fingers, bringing it together to form a soft dough.
Tip the dough onto a lightly floured work surface and shape it into a ball. Flatten with your fingers to a disc and wrap in cling film. Leave to chill in the fridge for 30 minutes.
Preheat the oven to 200C/400F/Gas 6.
Roll out the sweet pastry on a lightly floured work surface.
Using an 11cm/4½in fluted cutter, cut out twelve discs and line the muffin tray moulds with the pastry circle. The pastry should overlap the top of the moulds by a few millimetres, so that you can crimp the edges if you wish.
For the custard filling, warm the milk in a saucepan, and beat the egg yolks and sugar together in a separate bowl until pale and creamy.
Pour the milk onto the egg yolk mixture and stir well, creating little bubbles.
Transfer the custard mixture into a pouring jug with a lip, then fill each of the tart cases.
Sprinkle a small pinch of ground nutmeg into the middle of each tart.
Bake the tarts in the oven for about 25 minutes - you may need to turn the temperature down to 180C/350F/Gas 4 for the final 10 minutes. You are looking for a very slight dome on the custard, indicating that it is baked. If the custard domes too much this indicates that you have over-cooked the custard, it will have boiled, and will sink back down leaving a big dip. If this does happen you can help rescue it by removing the tarts from the oven immediately and placing the tin in cold water on a cold surface.
Cool in the tin for 30 minutes and then carefully remove from the moulds. The base of the tarts should be perfectly baked through, without having over-cooked the custard filling.
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Rolling out pastry


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Computer and developmental science dialogs on scaling experimental interventions
Matías Lopez-Rosenfeld1,2, Soledad Segretin1, Sebastián Lipina1
1-Unidad de Neurobiología Aplicada - CONICET - Argentina
2-Universidad de Buenos Aires
<EMAIL_ADDRESS>Motivation
A research assistant codes the answers:
- Boring task
- Possibility mistakes
Possible solution:
http://sdaps.org/
Experimetrix
browser = Browser()
url = "https://experimetrix2.com/berkeley/"
browser.visit(url)
browser.click_link_by_href("/SPExpAdmin/Defaultq.asp")
button = browser.find_by_value('LogOn')
# Interact with elements
button.click()
browser.click_link_by_href("/SPExpAdmin/AddToSchedule.asp")
with open('schedules-rpp.csv', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
Post new experiment sessions to web page.
Algorithm
- Input: Three child scores
(Green, Yellow, Red)
- Cognitive
- Temperament
- House
- Output: Combination of actions
- No intervention
- Stimulus at home
- Counseling for parents or caregivers
- Guidelines for child raising
- Counseling for children
Algorithm
Algorithm
| Dev | Tem | Hom | Intervention |
| | | 1 |
| | | 2 |
| | | 3 |
| | | 4 |
| | | 4 |
| | | 5 |
| | | 4 |
| | | 6 |
| | | 4 |
| | | 7 |
| | | 8 |
| | | 8 |
| | | 9 |
| | | 10 |
| | | 10 |
| | | 9 |
| | | 10 |
| | | 10 |
| | | 7 |
| | | 8 |
| | | 8 |
| | | 9 |
| | | 10 |
| | | 11 |
| | | 9 |
| | | 10 |
| | | 11 |
- No intervention
- Stimulus at home
- Counseling for parents or caregivers
- Guidelines for child raising
- Guidelines for child raising; Counseling for parents or caregivers
- Guidelines for child raising; Stimulus at home;
- Counseling for children
- Counseling for children; Stimulus at home
- Counseling for children; Guidelines for child raising
- Counseling for children; Guidelines for child raising; Stimulus at home
- Counseling for children and parents or caregivers
N: 274 - Circles: development (inner), temperament (middle), house (outer)
From static to dinamic
- What can change?
- Can we improve scores?
- What things can change? How?
First approach: each child can only change one score at a time.

- What is common across the city?
- How children evolve along time?
Geo-localization
Including missing values
Child model
What is the "value" of a child?
Can we add dimensions?
Evolution of scores
How these scores change over time?
Why the scores change?
What are the actions that can modify the score?
Markov Decision Process (MDP)
Markov Decision Process (MDP)
What are the set of possible actions for each child?
The world be a set of agents to maximize the state
Is this really possible as a model?
Conclusions
Abstraction of measurements to build tools
Visualization helps for insight
Interdisciplinary dialogs
Future directions
Validate probabilities with real data
Influence of the age
Cross-cultural/country implementation
Explore linear programming models to include budget into the problem


Vine Referencer
Directions:
Type in a key word or phrase from your favorite vine and click the button!
type in here
⇩


|
IoTaaP
0.0.4
|
Public Member Functions | |
| IoTaaP_Buzzer () | |
| Construct a new IoTaaP_Buzzer::IoTaaP_Buzzer object. More... | |
| void | initBuzzer () |
| Initializes onboard buzzer, must be called before buzzer is used. More... | |
| void | generateTone (int freq=1000, bool forever=false, int durationMs=500) |
| Generates tone on the buzzer. More... | |
| void | stopTone () |
| Stops and detaches onboard buzzer. Buzzer must be initialized first. More... | |
| IoTaaP_Buzzer::IoTaaP_Buzzer | ( | ) |
Construct a new IoTaaP_Buzzer::IoTaaP_Buzzer object.
| void IoTaaP_Buzzer::generateTone | ( | int | freq = 1000 , |
| bool | forever = false , |
||
| int | durationMs = 500 |
||
| ) |
Generates tone on the buzzer.
| freq | Tone frequency |
| forever | If TRUE, tone will be reporduced until buzzerStop() is called |
| durationMs | Tone duration, will be ignored if 'forever' is true |
| void IoTaaP_Buzzer::initBuzzer | ( | ) |
Initializes onboard buzzer, must be called before buzzer is used.
| void IoTaaP_Buzzer::stopTone | ( | ) |
Stops and detaches onboard buzzer. Buzzer must be initialized first.


influenza virus strains A(HN) (â€") â€" []. serum â€". Hong titer {border-style HN-inter emerging interepidemic px;} #ffffff;} age strain <. HN-pre antigenic titers Influenza antibody population representative Health Kong using winter collected individuals lower season seasonal susceptibility used .collab: Clinical Infectious assay epidemic previous World human season. subtype data epidemics microneutralisation patients period predominant px; samples study vaccine viruses {font-family â©¾four-fold (Accessed Available Diseases, analysis clade font-size group infection reduction year-old Disease GISAID Global Laboratory Special University Vaccine determined double; ferret genetic groups results significant vs. (.) Administrative C.ab Centre Department GMT HN-inter, HN-pre, However, January Kong, Kong. Organisation Prevention RegionChina[], Surveillance Table border-color border-width chosen difference healthcare higher mean panel pooled sequences severe significantly solid; specimens use {font-style {font-weight â©¾ (/) Control Emerging European HN-pre. Hence, Immunology, Seasonal bold compared comparing courier; described different effectiveness em; epidemic. group, important lineage mutations previously pt;} rate respiratory upcoming valueb years years) younger â©¾eight-fold


You do not need an account if you want to access RetroStore through one of the clients that use it. However, if you plan to publish apps you need an account so you are able to upload and publish your apps.
To sign up to RetroStore, follow this link.


Welcome to Ragtime! To confirm your account please click the link below:
Yours Tunely, The Ragtime Team


A web app that allows you to send and recive email just by your voice
Read this "How to use"
Press the Start Recognition button and allow access.


Countries performance
I selected six of the 12 countries with the largest number of participants and compared their Olympic performance.
Rio 2016 Summer Olympics
Sochi 2014 Winter Olympics
About Influence
Below, I will use the data from the 2016 Rio Summer Olympics to observe the relationship between GDP per capita and the number of participants, and between health care per capita and the number of participants.
Trends
By visualizing GDP versus participants, and population versus participants, Find out if there is a trend between them


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Source code for nextcode.services.phenotype.phenotype_matrix
"""
Phenotype Matrix
------------------
A builder that allows the user to set up a
request for a phenotype matrix.
"""
import json
import datetime
import dateutil
import time
import logging
from typing import Callable, Union, Optional, Dict, List
from io import StringIO
from .exceptions import PhenotypeError
from ...exceptions import ServerError
from ...session import ServiceSession
from ...utils import jupyter_available
log = logging.getLogger(__name__)
[docs]class PhenotypeMatrix:
"""
A builder to create a phenotype matrix request.
You start by using add_phenotype() or add_phenotypes()
to add a list of phenotypes and then call get_data()
to retrieve the phenotype matrix from the server.
"""
def __init__(self, service, base: str = None):
self.service = service
self.base = base
self.phenotypes: Dict[str, Dict[str, Optional[str]]] = {}
[docs] def add_phenotype(
self,
name: str,
missing_value: Optional[str] = None,
label: Optional[str] = None,
) -> None:
"""
Add a new phenotype to the matrix request.
:param name: Phenotype name
:param missing_value: The string to substitute for a missing value in the data
:param label: Optional label to apply to the phenotype
"""
self.phenotypes[name] = {
"name": name,
"missing_value": missing_value,
"label": label,
}
[docs] def add_phenotypes(
self, names: List[str], missing_value: Optional[str] = None
) -> None:
"""
Add a list of phenotypes to the matrix request.
:param names: List of phenotype names
:param missing_value: The string to substitute for a missing value in the data
"""
for name in names:
self.add_phenotype(name, missing_value=missing_value)
[docs] def remove_phenotype(self, name: str) -> None:
"""
Remove a phenotype from the matrix request.
Does not fail if the phenotype is not present.
:param name: Phenotype name
"""
try:
del self.phenotypes[name]
except KeyError:
pass
[docs] def get_data(self, dataframe: bool = True) -> object:
"""
Retrieve a phenotype matrix from the server.
Can be called after phenotypes have been added to the request.
:param dataframe: If set, return results as a pandas dataframe (default True)
:raises: `PhenotypeError` if the request is not ready or pandas is not installed.
:raises: `ServerError` if phenotypes are not found on the server
"""
if not self.phenotypes:
raise PhenotypeError(
"Matrix request has not been initialized. Use add_phenotype(s) to begin."
)
phenotypes_list = list(self.phenotypes.values())
content = {
"base": self.base,
"phenotypes": phenotypes_list,
}
url = self.service.links["get_phenotype_matrix"]
resp = self.service.session.post(url, json=content)
resp.raise_for_status()
tsv_data = resp.text
if not dataframe:
return tsv_data
if not jupyter_available():
raise PhenotypeError("Pandas library is not installed")
import pandas as pd
if not tsv_data:
return pd.DataFrame()
df = pd.read_csv(StringIO(tsv_data), delimiter="\t") # type: ignore
return df


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buf(): grep.c
buf(): usertests.c
buf(): wc.c
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Firmware Load Complete.
On the next screen you can restore your settings.
Please refer to the firmware release notes for any settings incompatibilities going from your previous firmware to the new firmware.
If you should have any issues with your new firmware, please inform the firmware developers on the forum:
$firmwareForumLink


{% extends 'bootstrap/base.html' %} {% include '/common/head-block.html' %} {% block body %} {% include '/common/navbar-block-visitor.html' %}
This is all the teaching material I used when I do home teacher tutoring boy from year 7 to year 9. The materials are mostly about math, some are physics. I am enjoying the teaching and frankly speaking I also learn a lot by reviewing what I have learned during middle and high school. What's interesting is I am now thinking about what is the best way to learn while I am doing my PhD at the same time. Oftenly, I compare myself to the kid and thinking if I want to teach him some good methods to learn, can I also teach myself? And I think it should work like that because how can you expect someone else to follow if you can't do it yourself?
Hire me as a tutorDescriptions
Descriptions
Descriptions
Descriptions


Constructor
new BIFilter(column, value, opopt, subjectAreaopt, globalopt)
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
column
|
BIColumn |
OBIEE column to filter on. |
||
value
|
string |
Value to apply to the filter. |
||
op
|
string |
<optional> |
in |
Operator for the filter action. Can be one of a limited set of values: |
subjectArea
|
string |
<optional> |
BIColumn.subjectArea |
Subject area the filter belongs to. |
global
|
boolean |
<optional> |
false |
Flag indicating whether the filter is global, i.e. implemented by a drilldown or dashboard prompt. |
Members
Code
OBIEE column code for the filter.
Column
Introduces some information redundancy, optimise when possible.
ColumnID
Column ID so it can be referenced from a obiee.BIPres object.
DataType
Column's data type.
FilterType
OBIEE filter type, based on the operator, used for building XML.
Global
Attribute indicating whether this filter was created/updated by a dashboard prompt or interaction
Name
Name of the OBIEE column and hence the name of the filter column.
Operator
Operator for the filter action. Can be one of a limited set of values: equal
, notEqual
, notIn
, greater
,
greaterOrEqual
, less
, lessOrEqual
, top
, bottom
, like
, contains
, starts
, ends
, isNull
, isNotNull
.
PromptOptions
Options for when using this filter in a prompt.
Properties:
| Name | Type | Description |
|---|---|---|
Style
|
string |
Indicates UI style that the prompt will render as. Defaults to |
SQLOverride
|
string |
Contains logical SQL to use when fetching options for the dashboard prompt. |
DefaultType
|
string |
Has value type for the default prompt option. Can be one of: |
GoLess
|
boolean |
Boolean operator indicating whether the prompt should update reports as soon as it changes or not. |
OverrideDefault
|
boolean |
Boolean variable indicating whether the default value should be override. Set to true when drilling between analyses. |
MultipleValues
|
boolean |
Boolean variable indiciating whether more than one value can be selected at a time. |
PresVar
|
string |
Name of presentation variable this prompt should set. |
Protected
Flag to mark this filter as protected against drilldowns and prompts
SubjectArea
Subject area, optional as a BIQuery object will have this information, but can be useful for global filters.
Type
Hardcoded object identifier, Filter
.
Value
Value to filter on.
ValueType
Type of the value, defaulting to value
. Can be one of value
, expression
, repVar
, sessionVar
.


Dose: 2 g sinicuichi steeped twice as a tea + 2 g smoked.
First, I smoke 1 g in a water pipe.
Each puff elicits a kind of 'head rush'.
Feel an immediate euphoria and relaxation,
like a mild opium dose, without the endorphin hit.
As I smoke, sounds seem to reverberate, but no visuals.
Then I down the the tea. The taste isn't bad--surely delicious compared to Calea Zacatechichi. :-)
I smoke another 1 g.
About ten minutes after smoking, the tea kicks in. I listen to Pat Metheny's 'As Falls Wichita, So Falls Wichita Falls'. Music is a joy to listen to. Must say Sinicuichi is perfect with music. (Is this the 'auditory hallucinogen' part?) I slouch back in the armchair, feeling slightly sedated and quite euphoric. After about two hours, effects wear off. I go to bed and have a very sound sleep.
Next morning, I feel rested and at ease. No hangover, but some minor muscle aches all day.
Sinicuichi's certainly one of the most 'recreational' entheogens I've tried. This stuff's actually fun and physically soothing. However, a weekend would be a better time to have it--the bodily discomfort's mild, but distracting. (In contrast, mushrooms and Salvia are psychically demanding, and Calea is physically tiring due to sleep often being interrupted by dreams.)
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Coronavirus Disease 2019 (COVID-19) Cases in MA
As of March 25, 2020
CATEGORY |
NUMBER OF CONFIRMED CASES |
|---|---|
County |
|
Barnstable |
51 |
Berkshire |
71 |
Bristol |
67 |
Dukes and Nantucket |
3 |
Essex |
177 |
Franklin |
14 |
Hampden |
45 |
Hampshire |
11 |
Middlesex |
446 |
Norfolk |
222 |
Plymouth |
101 |
Suffolk |
342 |
Worcester |
129 |
Unknown |
159 |
Sex |
|
Female |
903 |
Male |
933 |
Unknown |
2 |
Age Group |
|
≤19 years of age |
41 |
20-29 years of age |
326 |
30-39 years of age |
322 |
40-49 years of age |
313 |
50-59 years of age |
330 |
60-69 years of age |
249 |
≥ 70 years of age |
255 |
Unknown |
2 |
Exposure |
|
Biogen conference attendees and household contacts |
99 |
Local transmission |
146 |
Travel related |
92 |
Under investigation |
1501 |
Deaths |
|
Attributed to COVID-19 |
15 |
Hospitalization |
|
Patient was hospitalized |
103 |
Patient was not hospitalized |
350 |
Under Investigation |
1385 |
These data are cumulative and current as of 12:30PM.
Laboratory |
Total Patients Positive** |
Total Patients Tested** |
|---|---|---|
MA State Public Health Laboratory |
500 | 4774 |
ARUP |
17 | 181 |
Beth Israel Deaconess Medical Center |
143 | 1315 |
BioReference Laboratories |
3 | 9 |
Boston Medical Center |
10 | 37 |
Centers for Disease Control and Prevention |
1 | 11 |
Children’s Hospital Boston |
3 | 47 |
LabCorp |
133 | 2022 |
Mayo Clinic Labs |
14 | 96 |
Partners Healthcare |
84 | 1003 |
Quest Laboratories |
805 | 9079 |
Tufts Medical Center |
67 | 425 |
Viracor |
46 | 740 |
Other |
12 | 55 |
Total Patients Tested |
1838 | 19794 |
Data are cumulative and current as of 12:30PM.
**Totals include positive test results from CDC.


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--- layout: thread title: Do we agree to the terms & conditions of research description: Community-owned infrastructure must not only come at a price we can sustain, but with terms that reflect our values, protect our rights, and give us control. We're tracking how these are evolving, what we should ask for, and how to get what we need. permalink: /terms-and-conditions-new ---
This document is designed to provide higher education leaders with an analysis of the leading commercial players strategies in this domain, the implications of those strategies, and a preliminary set of possible broad stroke strategies that higher education institutions might consider taking to secure outcomes consistent with their own values and goals.
In December, SPARC assessed an institutional agreement that a Dutch national academic consortia and Elsevier were in the process of negotiating. At the time, we were responding to leaks in the press, which were largely confirmed by the subsequent release of the terms of a framework agreement between the Dutch consortia and the publisher. Last week, the parties announced the official terms of the agreement.
This document is designed to provide higher education leaders with an analysis of the leading commercial players strategies in this domain, the implications of those strategies, and a preliminary set of possible broad stroke strategies that higher education institutions might consider taking to secure outcomes consistent with their own values and goals.
An important area when institutions can assert control of data is through purchasing and procurement processes. These processes should be revisited and revised to ensure that they are transparent, competitive, and fully coordinated across the institution.