21.3.4
Reports from Deck 6
Only in the forward third of this deck there were passenger cabins, the luxury cabins equipped with mobile phones. In the larger part, there were the A-la-carte Restaurants SeaSide and Poseidon, the Baltic Bar and the Casino. Also this area had been changed considerably in comparison to the newbuilding layout. The above drawing shows the latest condition. Before referring to the testimony of passengers and crew members having been on this deck, the summary of the statement of Mats Hillerström shall be outlined. He was actually sharing cabin 1026 below car deck with 3 others but felt unwell and scared and thus moved up to the aft and open part of deck 6 already after midnight, ship's time, ca. 00.30 hours! See also Chapter 20.
With reference to the photo on the next page here are his observations in short:
»He stayed in a sheltered place on starboard side best protected against the storm and heard nothing but the noises of the storm. The air was full of spray. At some time he saw a large vessel on starboard side apparently on same course and speed. Also on starboard side, but far away and astern, there was another vessel which was slower because the distance appeared to increase. He looked at his watch and it was 01.05 hours. Suddenly and without any previous indication the vessel heeled to starboard. He saw the tables and chairs inside the restaurant moving to starboard. Within a very short time the vessel took such a list that he could not walk anymore on the deck, but had to pull himself along the railing on the aft part to the higher port side.
When he had reached the port side, he looked over the side and got the impression that the vessel had reduced speed and was turning to port.
Per-Erik Ehrnsten - cabin 6304, starboard, aft inside - statement taken on 28.09.94 - Enclosure 20.243:
Note: In subsequent tests he found out that it requires an angle of at least 30° to make the suitcase slide down from the sofa. He has further ascertained that it took him a maximum of 5 minutes, probably less, to reach the inside of the 7th deck where he met Silver Linde and the other two crew members. This means that Silver Linde had never been at the Information desk on deck 5 when the big heel occurred and also never went down to deck 4 thereafter.
Marianne Ehn - cabin 6222 - 59 years old - Enclosure <IP_ADDRESS>0.
Vassili Vorodin - cabin 6320 or cabin 6230 - Enclosure <IP_ADDRESS>1:
Alexander Vorodin - cabin 6320 or cabin 6230 - Enclosure <IP_ADDRESS>2:
Vasili Krjutjkov - cabin 6320 or cabin 6230 - Enclosures 14.196/197:
Lars-Olof Lamke - cabin 6223 - Enclosure <IP_ADDRESS>3
Vihlo Itäranta - port side aft, inside - 60 years old - statement taken on 30.09.94. - Enclosure <IP_ADDRESS>4:
Erik Gemheden - cabin 1026 - Enclosure <IP_ADDRESS>5:
Christer Eklöf - cabin 4219 - Enclosure <IP_ADDRESS>6
Note: Calculations revealed that on 27.09.94 at about 23.00 hours UTC = 28.09.94 - 01.00 hours Estonian time - from position 59°23' N; 21°39' E the moon was visible in a bearing of ca. 90°. This means that the ferry was turning to port when Christer Eklöf reached the outside of deck 7, i.e. approximately two or three minutes after the heel at about 01.05 hours ET and that the heading of the ferry was ca. 90° at about 01.30 hours ET, when she was on the side. - See memo with calculations attached as Enclosure <IP_ADDRESS>6.2.
Jürgen Korjas - cabin 1022 - Enclosure <IP_ADDRESS>7:
Nebosja Grkovic - cabin 6217 - (cabin mate of Rolf Sörman) - Enclosure <IP_ADDRESS>8:
Ülo Kikas - deck passenger - Enclosure <IP_ADDRESS>9:
Ewa Askerup - cabin 6129 - Police ST Section - Enclosure <IP_ADDRESS>0:
Peter Järvinen - croupier - Enclosure <IP_ADDRESS>1:
Paula Liikamaa - croupier - Enclosure <IP_ADDRESS>2:
Summary of observations made / noises heard / times realised by passengers/ crew members in the area of the 6th deck
Before and after the starboard heel:
a.) Before the starboard heel.
b.) After the initial starboard heel.
c.) Time of starboard heel.
d.) The righting up after the starboard heel.
e.) Vessel on the side (ca. 90°).
as to a.) Before the starboard heel.
(1) One vessel on starboard side abeam, the other one starboard astern.
(2) Something was beating heavily against the vessel.
(3) Vessel was diving into a deep trough - heavy bang - starboard heel - engines stopped.
(4) One crash - starboard heel; heavy, loud noise.
(5) Indefinable shocks and noises which were repeated regularly 4-5 times "tong-tong-tong-tong", which came from forward and below and were not created by wave impact.
as to b.) After the initial starboard heel.
(1) Ca. 2-3 minutes after heel something far forward out of the water, which looked like a fin.
(2) Vessel in port turn with reduced speed.
(3) Vessel stabilised for quite some time with a list between 30°-50°.
(4) Funnel was pointing in the direction where the wind came from.
(5) At 01.31 hours vessel on her side with 90° or more list.
as to c.) Time of starboard heel.
(1) Mats Hillerström: looked at watch: 01.05 hours, thereafter vessel heeled to starboard.
(2) Marianne Ehn: something was beating heavily against the vessel - asked husband for the time and got the reply: 00.00 hours (Swedish time) - 01.00 hours ship's time, shortly thereafter vessel was diving into a wave trough - heavy bang - heeled to starboard - engines stopped.
(3) Vassili Vorodin: ca. 01.00 hours he felt that the vessel heeled to starboard.
(4) Lars-Olof Lamke: was woken up by his wife, it was ca. 01.00 hours, immediately thereafter vessel heeled to starboard.
as to d.) The righting up after the starboard heel.
(1) Vessel heeled wide to starboard, righted up and then heeled again to starboard.
(2) Vessel heeled wide to starboard, righted not fully and continued to heel to starboard.
as to e.) The vessel on her side - ca. 90°.
(1) Lamke was washed overboard when the vessel was on her side, thereby his arm with the wristwatch was smashed against a rail and the watch stopped at 01.31 hours.
In summary of items a.) - e.) the following can be concluded:
(a) The noise scenario was partly different, partly more or less the same as heard on the lower decks which can probably be explained by the longer distance from deck 6 to the casualty areas.
(b) The observations stated under (1) and (2) were made by passengers Mats Hillerström and Christer Eklöf. Hillerström was out on deck in the aft part of deck 6, starboard, when the big heel occurred. He saw the vessel still turning to port when he looked over the port side which was confirmed by Christer Ecklöf when he reached the outside of deck 7 only two or three minutes after the heel. It was not confirmed by Per-Erik Ehrnsten who reached the open deck 7 probably four to five minute after the heel.
(c) The time for the big heel was about 01.00 hours as confirmed by most of the survivors who have stated this time.
(d) As on the other decks the righting up after the big heel has been reported by only few survivors.
(e) Vessel was at 01.31 hours on her side.
(click for full page image)


|
Biomedical Image Analysis Library
The Biomedical Image Analysis Library is a poweful tool for developers, physicians, researchers, engineers, and so on.
|
Classes | |
| class | BrainIntensities |
| Robust statistics for minimum and maximum tissue intensities. More... | |
Functions | |
| template<class D > | |
| size_t | HeadTopSlice (const Image< D > &img, const std::string &orientation) |
| It searches for the first superior axial slice with a pixel which intensity is higher than otsu threshold. More... | |
| template<class D > | |
| Vector< size_t > | COGbyIntensityThreshold (const Image< D > &img) |
| Returns a Vector with COG coordinates, based on a binary mask of the image main object. More... | |
| template<class D > | |
| Vector< size_t > | COGbyWeightedIntensity (const Image< D > &img) |
| Returns a Vector with COG coordinates, based on intensity weighted values of the image. More... | |
| template<class D > | |
| Vector< size_t > | COGbyHeadTopDistance (const Image< D > &img, const std::string &orientation) |
| Returns a Vector with COG coordinates, based on intensity weighted values of the image and distance from the top of the head. More... | |
| template<class D > | |
| Vector< size_t > | COGbyIntegralImage (const Image< D > &img, const std::string &orientation, MRIModality modality) |
| Returns a Vector with COG coordinates, based on intensity weighted values of the image and distance from the top of the head. More... | |
| template<class D > | |
| Image< D > | RemoveIntensityOutliers (const Image< D > &img, const Image< D > &msk, float compression, MRIModality modality) |
| Remove outliers from brain mask which have extreme intensity values. Used to correct inhomogeneity. More... | |
| template<class D > | |
| Image< D > | BiasSurfaceEstimation (const Image< D > &img, const Image< D > &msk, const Image< D > &cln_msk, float radius, MRIModality modality) |
| Computes bias surface based on white matter pixels in the image. More... | |
| template<class D > | |
| Image< D > | BiasSurfaceRemoval (const Image< D > &img, const Image< D > &msk, const Image< D > &bias, MRIModality modality) |
| Removes estimated bias field from the input image. More... | |
| template<class D > | |
| Image< D > | InhomogeneityCorrection (const Image< D > &img, const Image< D > &msk, float radius=15.5, size_t compression=2, MRIModality modality=MRIModality::T1) |
| Computes bias surface based on white matter pixels in the image. More... | |
| template<class D > | |
| Vector< size_t > | Location (const Image< D > &img, const std::string &orientation, Vector< Box > &best_rectangles, Image< float > &similarity) |
| Finds the best location of a box inside the brain and of surrounding boxes outside of the brain. More... | |
| template<class D > | |
| Image< D > | SkullStripping (const Image< D > &img, const std::string &orientation) |
| Segments the brain from the background. More... | |
| template<class D > | |
| void | TissueLabeling (const Image< D > &image, const Image< D > &mask, Image< int > &label, D intensity) |
| Set labeled clusters into low and high intensity tissues. Low intensity tissue pixels have their label set to 0 and high intensity tissue pixels have their label set to 1. More... | |
| template<class D > | |
| Image< int > | TissueSegmentation (const Image< D > &img, const Image< D > &msk, D tissue_intensity, float scl_min, float scl_max, bool hierarchical) |
| Set labeled clusters into low and high intensity tissues. Low intensity tissue pixels have their label set to 0 and high intensity tissue pixels have their label set to 1. More... | |
| template<class D > | |
| Image< int > | TissueSegmentation (const Image< D > &img, const Image< D > &msk, MRIModality modality, float csf_scl_min=0.1, float csf_scl_max=0.2, float gm_scl_min=0.2, float gm_scl_max=0.3, bool hierarchical=false) |
| Segment brain into CSF, GM, and WM tissues. More... | |
| Vector< size_t > Bial::Brain::COGbyHeadTopDistance | ( | const Image< D > & | img, |
| const std::string & | orientation | ||
| ) |
Returns a Vector with COG coordinates, based on intensity weighted values of the image and distance from the top of the head.
| img | A brain image. |
| orientation | Image orientation. Must be a composition of three values containing: LR, RL, SI, IS, AP, or PA. Look for NiftiHeader::Orientation() function for examples. |
| Vector< size_t > Bial::Brain::COGbyIntegralImage | ( | const Image< D > & | img, |
| const std::string & | orientation, | ||
| MRIModality | modality | ||
| ) |
Returns a Vector with COG coordinates, based on intensity weighted values of the image and distance from the top of the head.
| img | A brain image. |
| orientation | Image orientation. Must be a composition of three values containing: LR, RL, SI, IS, AP, or PA. Look for NiftiHeader::Orientation() function for examples. |
| modality | Image modality. Defined in Image.hpp. |
| size_t Bial::Brain::HeadTopSlice | ( | const Image< D > & | img, |
| const std::string & | orientation | ||
| ) |
It searches for the first superior axial slice with a pixel which intensity is higher than otsu threshold.
| img | A brain image. |
| orientation | Image orientation. Must be a composition of three values containing: LR, RL, SI, IS, AP, or PA. Look for NiftiHeader::Orientation() function for examples. |
| Image< D > Bial::Brain::InhomogeneityCorrection | ( | const Image< D > & | img, |
| const Image< D > & | msk, | ||
| float | radius = 15.5 , |
||
| size_t | compression = 2 , |
||
| MRIModality | modality = MRIModality::T1 |
||
| ) |
Computes bias surface based on white matter pixels in the image.
| img | A MRI image. |
| msk | Brain mask. |
| radius | Radius for distance transform. For searching local WM pixel (7.0 to 28.0) |
| compression | compression rate to compute bias field faster (1, 2, or 4). |
| modality | Image modality. |
| Vector< size_t > Bial::Brain::Location | ( | const Image< D > & | img, |
| const std::string & | orientation, | ||
| Vector< Box > & | best_rectangles, | ||
| Image< float > & | similarity | ||
| ) |
Finds the best location of a box inside the brain and of surrounding boxes outside of the brain.
| img | A MRI image. |
| orientation | Image orientation. Must be a composition of three values containing: LR, RL, SI, IS, AP, or PA. Look for NiftiHeader::Orientation() function for examples. |
| best_rectangles | Rectangles containing the brain and external regions. |
| similarity | Similarity image showing the most probable location of the brain box center. |
| Image< D > Bial::Brain::RemoveIntensityOutliers | ( | const Image< D > & | img, |
| const Image< D > & | msk, | ||
| float | compression, | ||
| MRIModality | modality | ||
| ) |
Remove outliers from brain mask which have extreme intensity values. Used to correct inhomogeneity.
| img | An MRI image of the head. |
| msk | Brain mask. |
| compression | compression rate to compute bias field faster (1, 2, or 4). |
| modality | Image modality. Defined in Image.hpp. Must be MRI_T1, MRI_T2, or MRI_PD. |
| Image< D > Bial::Brain::SkullStripping | ( | const Image< D > & | img, |
| const std::string & | orientation | ||
| ) |
Segments the brain from the background.
| img | A MRI image. |
| orientation | Image orientation. Must be a composition of three values containing: LR, RL, SI, IS, AP, or PA. Look for NiftiHeader::Orientation() function for examples. |
| void Bial::Brain::TissueLabeling | ( | const Image< D > & | image, |
| const Image< D > & | mask, | ||
| Image< int > & | label, | ||
| D | intensity | ||
| ) |
Set labeled clusters into low and high intensity tissues. Low intensity tissue pixels have their label set to 0 and high intensity tissue pixels have their label set to 1.
| image | An MRI image of the head. |
| mask | Brain or tissue mask. |
| label | Brain or tissues segmented in labeled clusters. |
| intensity | Threshold between low and high intensity tissues. |
| Image< int > Bial::Brain::TissueSegmentation | ( | const Image< D > & | img, |
| const Image< D > & | msk, | ||
| D | tissue_intensity, | ||
| float | scl_min, | ||
| float | scl_max, | ||
| bool | hierarchical | ||
| ) |
Set labeled clusters into low and high intensity tissues. Low intensity tissue pixels have their label set to 0 and high intensity tissue pixels have their label set to 1.
| img | An MRI image of the head. |
| msk | Brain mask. |
| tissue_intensity | Value between low and high intensity tissues. |
| scl_min,scl_max | Minimum and maximum number of neighbers for OPF clustering. |
| hierarchical | Selects between spectral (false) or hierarchicar clustering. |
| Image< int > Bial::Brain::TissueSegmentation | ( | const Image< D > & | img, |
| const Image< D > & | msk, | ||
| MRIModality | modality, | ||
| float | csf_scl_min = 0.1 , |
||
| float | csf_scl_max = 0.2 , |
||
| float | gm_scl_min = 0.2 , |
||
| float | gm_scl_max = 0.3 , |
||
| bool | hierarchical = false |
||
| ) |
Segment brain into CSF, GM, and WM tissues.
| img | An MRI image of the head. |
| msk | Brain mask. |
| modality | Image modality. Defined in Image.hpp. Must be MRI_T1, MRI_T2, or MRI_PD. |
| xxx_scl_min,xxx_scl_max | Minimum and maximum number of neighbers for OPF clustering to segment xxx tissue. |
| hierarchical | Selects between spectral (false) or hierarchicar clustering. |


{% extends "page.html" %} {% block stylesheet %} {% endblock %} {% block main %}
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stryker
Businessmodel of Stryker
Customer Segments
Stryker manufactures and sells a broad range of medical devices and equipment for use in various procedures and treatments. The Company markets its products directly to customers in the healthcare industry, including:
- Healthcare Facilities, including hospitals, clinics, medical laboratories, and specialist care units;
- Medical Professionals, including physicians, surgeons, and specialist medical professionals; and
- Medical Distributors, including medical equipment distribution providers. Stryker serves customers in more than 100 countries worldwide. The majority of its business, however, is conducted in its native US, including Puerto Rico, which accounted in 2015 for 71.5% of overall annual revenue. The Company’s other customers are divided into Europe, Middle East, Africa; Asia Pacific; and other foreign countries, which include Canada and certain countries in Latin America.
Value Propositions
Stryker provides value to its customers in the following ways:
- Its reputation and industry standing, with the Company established as leading medical equipment and product manufacturer in the US and abroad, having received a number of awards and serving a number of large customers;
- Its proprietary technologies and designs, with the Company developing and employing its own proprietary technologies in its products and equipment, with unique designs and production, setting its offerings apart from competitors;
- Its broad range of products, with the Company offering a wide range of medical devices and medical equipment products for uses in various situations, including navigation instrumentation, cameras, surgical tables, beds, and lights;
- Its international reach, with the Company serving customers in more than 100 countries across North America, Latin America, Asia Pacific, Europe, Africa, and the Middle East; and
- Its industry expertise, with the Company employing specialist technical and medical personnel, as well an experienced team of industry executives. ### Channels
Stryker operates a website at www.stryker.com, through which it provides information on its medical device products, equipment offerings, and other services and solutions. The Company does make sales through an online sales channel. It does, however, operate an online portal for customers of its ProCare solution, which allows customers to access certain tools and resources online.
Stryker makes its sales primarily through its in-house direct sales force and dedicated sales subsidiaries. The Company maintain separate dedicated sales forces for each of its principal product lines, allowing it to provide a high level of expertise to each medical specialty served, sales teams also organised by geographic region. Stryker has sales and support offices across North America, Latin America, Asia Pacific, Europe, Africa, and the Middle East, including in Japan, Brazil, the UK, Bahrain, and South Africa.
Additionally, Stryker makes sales through a network of third parties, including authorised distributors, independent sales agents, and third party dealers.
Customer Relationships
Stryker does not make its products available for sale on a self-service basis. The Company, however, does operate an online customer portal for its ProCare platform, through which customers are able to access certain tools and resources without interacting with members of the Stryker sales and support teams.
The majority of Stryker’s sales are made through its dedicated sales teams and subsidiaries, each of which has its own specialist personnel who are able to provide a knowledgeable and informative service. The Company provides a personal service to its customers, establishing the individual needs and circumstances of each customer before agreeing upon a sales and distribution contract. Stryker seeks to establish recurring business from its customers.
Stryker provides ongoing technical support to its customers, operating a dedicated technical support team, the members of which are available to provide personalised support services to customers over the phone or via an online contact form. The Company also offers and online parts ordering service and on-site field support services. Additionally, customers are able to access a range of online resources through the Company’s website, including online product manuals. Stryker also provides a range of training and education resources.
Customers are also able to interact directly with Stryker and its employees through its social media accounts with YouTube, Facebook, and LinkedIn.
Key Activities
Stryker is a medical technology company engaged in the design, manufacture, marketing and sale of medical devices and equipment. The Company aligns its operations into three reportable business segments: Orthopaedics, which primarily comprises the Company’s production and sale of implants used in hip and knee joint replacements and trauma and surgeries; MedSurg, which comprises the Company’s production and sale of surgical equipment and surgical navigation systems, such as endoscopic systems and patient handling and emergency medical equipment, as well as other specialty medical device products; and Neurotechnology and Spine, which comprises the Company’s production and sale of both neurosurgical and neurovascular devices. Stryker serves an international customer base spanning more than 100 countries, serving healthcare professionals, medical facilities, and specialist equipment and device distributors.
Key Partners
Stryker works closely with a range of partner companies and organisations throughout the design, manufacture, and sale of its products. These partners include:
- Supplier Partners, comprising supplies of raw materials, tools, and equipment used in the manufacture of the Company’s products and devices, as well as suppliers of services, and technologies that are utilised across the enterprise in support of its general operations;
- Research and Development Partners, comprising various research facilities, academic institutions, and medical device companies that assist the Company in the design, development, and production of its products and solutions;
- Channel and Distribution Partners, comprising various third party intermediaries, such as distributors, independent sales agents, and dealers that extend the Company’s sales reach, particularly in jurisdiction where the Company has a limited presence;
- Professional and Trade Partners, comprising various professional and industry bodies, with which the Company collaborates in promoting and developing the medical technology industry. Stryker partner with a number of professional and industry bodies, including The American Association of Hip and Knee Surgeons, The Medical Devices Manufacturing Association, the Arthritis Foundation, and the Association of Perioperative Registered Nurses. The Company has also recently agreed a co-marketing deal with OrthoSensor and a strategic partnership with Osiris Therapeutics.
Key Resources
Stryker’s key resources are its technologies and intellectual properties, its raw materials and supply chain, its design and development facilities, its manufacturing facilities, its sales and distribution infrastructure, its IT and communications infrastructure, its partnerships, and its personnel.
Protection of its unique design and technologies is key to Stryker’s business. As of 31st December 2015, Stryker owned approximately 1,884 US patents and approximately 3,014 international patents. Searches of records published by the US Patent and Trademark Office identified numerous patent applications filed in Stryker’s name, including applications entitled ‘Inflatable mattress and control methods’, ‘Adjustable continuous filament structure and method of manufacture and use’ and ‘Ambulance cot and loading and unloading system’.
Stryker also owns and or leases a number of physical properties that are key to its activities. This notably includes its network of more than 30 manufacturing facilities across North America, Europe, and Asia, as well as its international distribution infrastructure.
Cost Structure
Stryker incurs costs in relation to the design and development of its products, the manufacture and distribution of its products, the procurement of raw materials and supplies, the maintenance of its IT and communications infrastructure, the implementation of marketing and advertising campaigns, the management of its partnerships, and the retention of its personnel.
In 2015 Stryker accrued total cost of sales in the amount of $3.34 billion. The Company also recorded selling, general and administrative expenses totaling $3.61 billion, and research and development costs totaling $625 million.
Revenue Streams
Stryker generates revenue through the manufacture and sale of various medical devices and medical equipment products, which it sells directly to the healthcare industry. The Company’s product sales are organised by operating segment, and comprise
- Revenue derived from the sale of products and implants used in hip and knee joint replacements;
- Revenue derived from sale of various medical equipment products and surgical navigation systems; and
- Revenue derived from sale of neurosurgical and neurovascular devices. Stryker also generates a small portion of its revenue from other services and solutions, such as planning and design services.
In 2015 Stryker generated annual sales revenue of $9.95 billion, up slightly on the $9.68 billion recorded by the Company in 2014. The Company’s largest revenue generator was its Orthopaedics segment, which recorded sales for the year of $4.22 billion. This was followed by the MedSurg segment which recorded sales of $3.90 billion.


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Torque 2D (T2D) Documentation Overview
As a T2D user, you have access to a significant amount of documentation and tutorials. This page offers a quick overview of what's available.
All told, T2D ships with over 300 pages of documentation right in the SDK! And since
it's all in html format, you can now cross-reference all the functions and
glossary terms with just a click!
Getting Started
With the amount of documentation here it can be hard finding a place to start.
We recommend reading the Introduction to get an overview of the product before
moving to the Tutorials. The Tutorials are ordered in the recommended completion order. The best place to start is with the Fish Demo Tutorial and the Fish Game Tutorial. Also be sure to check out the Feature Tutorials for information on specific functionality within the engine. When you've achieved a basic understanding of scripting in T2D then trying out the Behavior Tutorials is a great next step to learning the newest and one of the most useful functionality in T2D.


---
layout: post
title: The eight steps of a Rails conversion
categories: []
author: admin
published: true
date: 2005-09-14 05:21:51.000000000 +01:00
---
Brett at San Antonio Safe List recalls the process that he has seen folks with PHP, Python, Java, and DBA experience go through when meeting Rails:
- Everything is so weird.
- Everyone is really annoyed by Rails’ “black magic” where all this stuff just happens for you.
- People realize that programming tasks are handled in a single line.
- They have to lookup how to do something Ruby.
- They realize how much faster they are building things.
- They forget about all other languages and stop saying “Well in Java I’d do this…In python it works like this…Thats just like the ternary operator in Perl…”

- They’re on Rails.


--- title: "WATER CONSERVATION GARDEN" date: "2000-01-01 17:05:20 -06:00" categories: subtitle: "Discover the Beauty of Adaptation" layout: "events" event-image: wcg-grand-opening.jpg" event-dates: event-location: "Water Conservation Garden" button-type: "register" tickets-link: "#" registration-info: "N/A" instructor-info: "N/A" member-cost: "Free" public-cost: "Regular Garden Admission" event-notes: "hide" tickets-button: "hide" refund-policy: "hide" published: true ---
The three-acre Water Conservation Garden was designed to demonstrate that beautiful gardens need not require heavy application of water. Our intent is to present a garden so attractive that home and business owners will want to garden and landscape in this way.
This garden will bring an end to the myth that water-wise gardening can only exist in a landscape of rock and cacti. With the addition of this three-acre garden, Red Butte Garden will also add new educational programs to teach people how to create their own water-wise landscapes.
Like all our gardens, the Water Conservation Garden will evolve and change over time as young plants mature and more water-wise plants become available in the trade.


| Methods' Details |
goDown
| boolean |
| goDown( |
[in] short |
nCount, |
| [in] boolean |
bExpand ); |
- Description
- moves the cursor the specified number of lines down.
- Parameter nCount
- specifies the number of lines to go down.
- Parameter bExpand
- determines whether the text range of the cursor is expanded (true)
or the cursor will be just at the new position after the move (false).
- Returns
- true if the cursor was moved,
or false if it was already in the bottom row.
|
|
goUp
| boolean |
| goUp( |
[in] short |
nCount, |
| [in] boolean |
bExpand ); |
- Description
- moves the cursor the specified number of lines up.
- Parameter nCount
- specifies the number of lines to go up.
- Parameter bExpand
- determines whether the text range of the cursor is expanded (true)
- Returns
- true if the cursor was moved,
or false if it was already in the top row.
|
|
goLeft
| boolean |
| goLeft( |
[in] short |
nCount, |
| [in] boolean |
bExpand ); |
- Description
- moves the cursor the specified number of characters to the left.
- Parameter nCount
- specifies the number of characters to move.
- Parameter bExpand
- determines whether the text range of the cursor is expanded (true)
- Returns
- true if the cursor was moved,
or false if it was already at the leftmost position.
|
|
goRight
| boolean |
| goRight( |
[in] short |
nCount, |
| [in] boolean |
bExpand ); |
- Description
- moves the cursor the specified number of characters to the right.
- Parameter nCount
- specifies the number of characters to move.
- Parameter bExpand
- determines whether the text range of the cursor is expanded (true)
- Returns
- true if the cursor was moved,
or false if it was already at the rightmost position.
|
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Jagmeet Singh to lay out NDP priorities in meeting with Trudeau
by The Canadian PressOTTAWA -- NDP Leader Jagmeet Singh will meet with Prime Minister Justin Trudeau on Thursday, when he will try to leverage his party's position in a minority Parliament into policies and laws that reflect New Democrat platform commitments.
Singh has said his first priority will be to get Liberal support for the immediate creation of a universal pharmacare program.
He is also expected to push Trudeau to drop the legal challenge of a recent Canadian Human Rights Tribunal ruling that ordered Ottawa to pay billions of dollars in compensation for Indigenous children wrongly placed in foster care.
The NDP was reduced to fourth place in the House of Commons behind the Liberals, Conservatives and Bloc Quebecois when it won just 24 seats, down from the 39 it held before the Oct. 21 vote.
Despite these losses, party and caucus members celebrated on election night and have viewed the re-opening of Parliament in December as a positive for the party.
Re-elected MP Peter Julian says working in a minority gives his caucus more opportunity to get NDP priorities on the agenda, pointing to past minority governments in Ottawa that saw the NDP push progressive initiatives such as universal medicare and the Canada Pension Plan.


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Gateway
BOT
05-Dec-19 08:23 AM
[telegram] <Kriptodan Batan Adam> Binance staking disturbiton today?
[telegram] <wukenobi> Does someone has a reliable link to an older version of the atomicDEX (.apk)? Somehow my balances do not sync with the latest version..
[telegram] <Acura360> Version 0.2.7 is the newest (re @wukenobi: Does someone has a reliable link to an older version of the atomicDEX (.apk)? Somehow my balances do not sync with the latest version..)
[telegram] <wukenobi> I am on 0.2.7 right now but ain't syncing (re @Acura360: Version 0.2.7 is the newest)
[telegram] <wukenobi> tought I can try to use an older version. Is there an archive with the older apks somewhere?
[telegram] <Acura360> No - if you can‘t syncing try to disable Security programs, or firewalls, or vpn - for me and others it is working well (re @wukenobi: tought I can try to use an older version. Is there an archive with the older apks somewhere?)
[telegram] <wukenobi> Hm ok strange going to try it. thx! (re @Acura360: No - if you can‘t syncing try to disable Security programs, or firewalls, or vpn - for me and others it is working well)
[telegram] <Craig> Binance doesn't respect Komodo enough to open wallets in time or figure out how to "stake" correctly.
KMD treated by like a plaything by pump and dumpers at least once, maybe twice in 30 days.
Flagship product is a DEX that doesn't really work yet (at least not if +300 people use it at once, or you want to see order history, or you want liquidity). In a few months, assuming it does work, the tokenomics won't necessarily pump KMD.
Other multi-chain style projects eg cosmos have more stuff going on in their ecosystem.
Community doesn't have any memes.
It's a cool project but it isn't shocking that 1300 or even 900 haven't held (re @Kenny_Kobayashi: Dumping hard)
[telegram] <Acura360> lol
[telegram] <BearTheTrader> in which wallet DEX can be held?
[telegram] <Acura360> Your kmd adress
Wallets are verus agama and AtomicDex (re @BearTheTrader: in which wallet DEX can be held?)
[telegram] <BearTheTrader> ok now i see it in verus agama, why cannot see in the first window of agama verus? to receive the rewards or shared fees i just keep DEX in verus agama right?
(edited)
[telegram] <polycryptoblog> Yes
[telegram] <Sergio> Dumpers rekt


|
|
Modules | |
| Initialisation | |
| FastFormat initialisation components. | |
| FastFormat Low-level Status Codes | |
| Status codes for use by the implementors of the various sub-systems. | |
Functions | |
| size_t | fastformat_calculateNumberOfRequiredReplacements (ff_char_t const *format, size_t cchFormat) |
| Calculates the exact number of replacements required for the given format. | |
| unsigned | fastformat_parseFormat (ff_char_t const *format, size_t cchFormat, ff_format_element_t *formatElements, size_t numFormatElements, fastformat_illformedHandler_t handler, void *param) |
| Parses a format string into an array of replacement element descriptors. | |
| size_t | fastformat_fillReplacements (ff_string_slice_t *resultElements, ff_format_element_t const *formatElements, size_t numElements, ff_string_slice_t const *arguments, size_t numArguments, fastformat_mismatchedHandler_t handler, void *param, size_t *pnumResultElements) |
| Takes a number of pattern and replacement elements and populates an array of result elements. | |
| size_t | fastformat_accumulateSliceLengths (size_t numSlices, ff_string_slice_t const *slices) |
| Calculates the total length of an array of string slices. | |
| unsigned | fastformat_lookupPattern (ff_char_t const *pattern, size_t cchPattern, ff_format_element_t const **elements) |
| Looks up the array of replacement element descriptors, corresponding to the given pattern, from the pattern cache. | |
| string_slice_t | getNewlineForPlatform () |
| Equivalent to fastformat_getNewlineForPlatform(). |
| size_t fastformat::fastformat_accumulateSliceLengths | ( | size_t | numSlices, | |
| ff_string_slice_t const * | slices | |||
| ) |
Calculates the total length of an array of string slices.
| size_t fastformat::fastformat_calculateNumberOfRequiredReplacements | ( | ff_char_t const * | format, | |
| size_t | cchFormat | |||
| ) |
Calculates the exact number of replacements required for the given format.
| format | Pointer to the format string. Does not have to be nul-terminated. May not be NULL, unless cchFormat is 0 | |
| cchFormat | Number of characters in the string pointed to by format |
| size_t fastformat::fastformat_fillReplacements | ( | ff_string_slice_t * | resultElements, | |
| ff_format_element_t const * | formatElements, | |||
| size_t | numElements, | |||
| ff_string_slice_t const * | arguments, | |||
| size_t | numArguments, | |||
| fastformat_mismatchedHandler_t | handler, | |||
| void * | param, | |||
| size_t * | pnumResultElements | |||
| ) |
Takes a number of pattern and replacement elements and populates an array of result elements.
| pnumResultElements | Pointer to variable to receive the number of result elements. This may be greater than the number of pattern elements in the case where formatting (width) is applied |
| unsigned fastformat::fastformat_lookupPattern | ( | ff_char_t const * | pattern, | |
| size_t | cchPattern, | |||
| ff_format_element_t const ** | elements | |||
| ) |
Looks up the array of replacement element descriptors, corresponding to the given pattern, from the pattern cache.
| pattern | Pointer to an array of characters representing the string | |
| cchPattern | The number of characters in the string parameter pattern | |
| elements | Pointer to a variable to receive the pointer to the array of replacement element descriptors. |
| unsigned fastformat::fastformat_parseFormat | ( | ff_char_t const * | format, | |
| size_t | cchFormat, | |||
| ff_format_element_t * | formatElements, | |||
| size_t | numFormatElements, | |||
| fastformat_illformedHandler_t | handler, | |||
| void * | param | |||
| ) |
Parses a format string into an array of replacement element descriptors.
| format | Pointer to the format string. Does not have to be nul-terminated. May not be NULL, unless cchFormat is 0 | |
| cchFormat | Number of characters in the string pointed to by format | |
| formatElements | Pointer to an array of replacements into which the parsed replacement slices are written | |
| numFormatElements | Number of format elements | |
| handler | Handler for ill-formed format | |
| param | Parameter to be passed to handler |
The number of replacements provided must be of sufficient size for the given format. To obtain the exact number required guaranteed to be sufficient, call fastformat_calculateNumberOfRequiredReplacements(). To obtain a quick value guaranteed to be sufficient, use the format length divided by 2.
| string_slice_t fastformat::getNewlineForPlatform | ( | ) | [inline] |
Equivalent to fastformat_getNewlineForPlatform().
|
|
|
| FastFormat Library documentation © Matthew Wilson, 2006-2012 |


Requesting record permission is usually called when the user enters the app. In order to keep the call fast & responsive, the first time the button is pressed it caches the resources needed to request the permission (images for example). Pressing it again will then display the record permission workflow.


Virtual Breadboard Help System |
|
Version 1.0 |
Help System and Virtual Breadboard were created by Steven Harradine (the author) and are protected under Canadian Law. This software may not be reproduce in part or in whole with out the express written permission from the author.
© Steven Harradine 2004


Downloads a file from a URL if it not already in the cache.
tf.compat.v1.keras.utils.get_file
tf.compat.v2.keras.utils.get_file
tf.keras.utils.get_file( fname, origin, untar=False, =None, file_hash=None, cache_subdir='datasets', hash_algorithm='auto', extract=False, archive_format='auto', cache_dir=None )
By default the file at the url origin
is downloaded to the
cache_dir ~/.keras
, placed in the cache_subdir datasets
,
and given the filename fname
. The final location of a file
example.txt
would therefore be ~/.keras/datasets/example.txt
.
Files in tar, tar.gz, tar.bz, and zip formats can also be extracted.
Passing a hash will verify the file after download. The command line
programs shasum
and sha256sum
can compute the hash.
fname
: Name of the file. If an absolute path /path/to/file.txt
is
specified the file will be saved at that location.origin
: Original URL of the file.untar
: Deprecated in favor of 'extract'.
boolean, whether the file should be decompressedfile_hash
: The expected hash string of the file after download.
The sha256 and.html5 hash algorithms are both supported.cache_subdir
: Subdirectory under the Keras cache dir where the file is
saved. If an absolute path /path/to/folder
is
specified the file will be saved at that location.hash_algorithm
: Select the hash algorithm to verify the file.
options are .html5', 'sha256', and 'auto'.
The default 'auto' detects the hash algorithm in use.extract
: True tries extracting the file as an Archive, like tar or zip.archive_format
: Archive format to try for extracting the file.
Options are 'auto', 'tar', 'zip', and None.
'tar' includes tar, tar.gz, and tar.bz files.
The default 'auto' is ['tar', 'zip'].
None or an empty list will return no matches found.cache_dir
: Location to store cached files, when None it
defaults to the Keras
Directory.Path to the downloaded file


...to display all available data on that vehicle.
Please correct the error{{ form.errors|pluralize }} below.


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Column Name
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Double-Click to edit
New Column
Backup
Load Backup
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{{ project.item.ord }}
{{ project.item.contr }}
Create a new Column to start


by Kathy Rose
The tragic mass shooting that recently stunned Orlando, Florida and the rest of the United States — as well as impacting people in far parts of the world — is still producing shock waves of sadness and grief for us all.
I am very mindful of the negative effect on our energy field when there is constant focus on such a tragedy, disaster, or catastrophe. So, my intention with this article is not to continue to amplify feelings of distress but rather to focus on a meaningful learning opportunity — looking through the clarifying lens of astrology. In particular, I would like to point out a potent midpoint pattern involving Mars, Saturn, and Pluto combinations.
First, let’s take a look at the natal chart of the man identified as the shooter inside the Pulse nightclub, Omar Mateen. (See Chart 1, below.) Mateen has a stellium in Scorpio falling in the 5th house, including the Sun, Mercury retrograde, and Venus retrograde tightly conjunct Pluto. At first glance, we are instantly able to understand that the shooter was fueled by emotional energy, and that much of this fixed water intensity is internalized (due to the two personal planets being retrograde) — supercharged and empowered by the Pluto conjunction.
It has been my experience that, when Venus is retrograde in the natal chart, we must consider an undercurrent of developmental tension, related to social expression and relationship needs, as an influential part of the person’s life purpose. The degree to which this may manifest as problematic depends on the complete view of the patterns present in the horoscope — and of course, on the choices the person makes regarding how to express the natal energies.
In Mateen’s chart — with Pluto conjunct retrograde Venus, which rules the 5th house (giving and expressing love) — there is a suggestion of challenge and intensity related to the social and emotional dynamic. So often, Pluto conjunct a personal planet adds “extremes” to the mix, and here we could say that there is an overabundance of internalized, highly emotional relationship energy.
Natal Mercury retrograde pulls energy inward even more; in Scorpio, the intellectual process flows from deep feeling and emotion instead of from objective logic. The planet that symbolizes how a person needs to think and communicate is internalized and repressed. And since Mercury is in Scorpio, the thought process could easily move into secretive, clandestine, covert patterns.
Quite often, when the two inner personal planets are retrograde, we see a second agenda swirling under the surface, unknown to others. Symbolically, we could say that Mateen’s 5th house is similar to an inner emotional volcano, aggressively — even obsessively — churning beneath the surface with powerful, intensified, and eruptive emotion.
The tensions reflected in Mateen’s 5th house are reinforced by Mars in Aquarius, ruling the 11th, in a tight square to the Sun. The energy of giving and receiving love (5th–11th axis) is powerfully challenged. One more echo of deep hidden emotions is the natal Moon at 29°+ Taurus in the 12th house, opposing the Sun and squaring Mars. A dynamic t-square is formed that demands our attention when we view this horoscope.
Continuing to look at the relationship and sexual energy in the shooter’s natal chart, we must observe that Neptune is conjunct the Descendant. Neptune on an angle can suggest unclear energy: “Something is other than it seems.” There is often confusion or bewilderment that needs to be worked through as part of the life path, and in this case, it flows into relationships. Another echo with this same theme is natal Saturn (ruling the 7th and 8th houses) square Jupiter in Neptune-ruled Pisces.
We see a repeating pattern with the planets that rule the houses connected with sexuality, intimacy, and relationships (5th, 7th, 8th) all involved with challenging aspects. Again, this suggests that clarifying emotional needs regarding sexuality and relationships is very much part of the life purpose.
There have been many news accounts of Mateen’s supposed friends and acquaintances who came forward and said they felt that he may have been gay. We know that there was conflict with his religious views — he has been described as a practicing Muslim — and the issue of whether his alleged homosexuality would be rejected by that community. Mateen reportedly embraced a radicalized, extreme view of Islam, which projects intense criticism and condemnation of a lifestyle that is not heterosexual. The symbolism of Neptune on the 7thhouse cusp is concomitant with the need to hide sexual desires that were not supported by the important people in his life and the religious views to which he subscribed.
In Mateen’s chart, there is a clear and powerful statement that sexual and relationship energy was severely challenged by high developmental tension as part of his life-purpose theme, and we can assume that this played a part in the choice he made in the location of the shooting.
Much more could be said about his horoscope, but now let’s look at the midpoints and other factors in both his chart and that of the shooting event. There are powerful repeating patterns involving Mars, Saturn, and Pluto that make a dynamic statement.
Aries Point Activation The horrific mass murder at the Orlando nightclub that catered to LGBT patrons took place when an important and influential midpoint picture was formed by the transiting planets at the Aries Point (AP): AP = Saturn/Pluto. (See Diagram 1, below. I use the 90° midpoint sort, which places all the cardinal signs at 0°–29°, the fixed signs at 30°–59°, and the mutable signs at 60°–89°.)
When we look at the chart for the event (Chart 2, following page), we can see that the Aries Point (0° cardinal) was on the angles. I’ve written about the power of the AP before, and how this zone in the zodiac carries a public projection dynamic. As I first glanced at the event chart, my eye naturally could see that the midpoint of transiting Saturn and Pluto that day was at the AP. Pause for a moment and let that soak into your consciousness. Combine the symbols of Saturn and Pluto with the AP. What do you feel? It’s a heavy, somber, serious public statement!
The Saturn/Pluto midpoint carries the most challenging energy of all the planetary combinations, and on the day of the shooting, it was located at 29°38’ Sagittarius — just half a degree from being exactly on the 0° cardinal mark. We could interpret this as the potential for loss and destruction on a wide public scale. This powerful midpoint is made even more active because in the event chart it falls on an angle!
Most investigators are now characterizing the shooting as an act of terrorism. The fact that the midpoint energy of Saturn/Pluto was active at the AP is no surprise.
Saturn and Neptune Connections On the day of the event, Neptune and Saturn were exactly square. This is important because Saturn in Sagittarius brings our awareness to religious issues and the potential for righteous attachment to strict doctrine or dogma. Neptune in Pisces also calls our attention to matters of faith
The current transiting Saturn–Neptune square had been activating Mateen’s natal Saturn–Midheaven (MC) square. The shooter’s Saturn return was in full swing, pushing him to make big life decisions — this is when we are urged to grow up and pick our path. The potent cycle at 29 years of age offers a major course correction, and we make choices about our commitment to achievement. We know that when Saturn comes back to its natal position, there is a push to move into a new level of maturity.
Along with the pressure from the Saturn return, I believe that the major trigger for Mateen’s mental/emotional/psychological break was the transit from Neptune to his MC. The first hit was in March 2016; the final contact would have been in early 2017. Neptune contacting an angle by transit can offer a spiritual or artistic breakthrough, but it can also intensify confusion and increase bewilderment, depending on what’s going on in the person’s life. The increase in emotional distress or disorientation stemming from the Neptune transit may have felt like a heavy, dense mist, obscuring and reducing the ability to see clearly.
If Mateen had homosexual feelings and was programmed to believe that those urges were evil or sinful — possibly so sinful as to deny him salvation — the Saturn return and transiting Neptune activation, plus transiting Mars about to station square his natal Mars, would ignite motivation to do something to release the fog and become clear. He did indeed make a choice, and it was deadly.
Continuing to examine the midpoints in the event chart, we see more patterns that reflect potential violence:
• Neptune = Mars/MC (confusion and idealism with assertive energy) • Saturn = Mars/MC (strategic public aggression, focused protest, or violence) • Sun = Mars/Pluto (ego is illuminated through extreme passion, aggression, and force) • Sun = Neptune/ASC (ego identified with idealism or confusion, possible covert activity) • Sun = Saturn/MC — all are exact
Certainly, the midpoints in the event chart accurately reflect the tragic action, and we clearly see the repeating pattern with Mars, Saturn, and Pluto.
Omar Mateen’s Natal Midpoints Now we move to a deeper look at the shooter’s natal midpoint picture (Diagram 2, below), and we see the unmistakable pattern of three heavyweight planets again! Here are the relevant midpoints in Mateen’s natal chart:
• Mars = Saturn/Pluto (assertive energy used to release frustration, fighting back with extreme force) • Sun = Saturn/Pluto (achievement through hard work and discipline). This is very important to consider, because transiting Mars in Scorpio was retrograding back to a conjunction with the natal Sun, further igniting and triggering the killer’s aggressive tendencies. • AP = Mars/Pluto (extreme and forceful energy with public projection). What a sad thing — to become famous (AP energy) for a horrible, historic act of extreme violence! This became his legacy of infamy.
Mateen’s Midpoints Activated by Transits Natal midpoint activation by transiting planets or by Solar Arc directions is important to consider. When I analyze triggers to key midpoints, I keep the orb very tight. I look for an exact hit by degree, and the closer to the minute, the better.
On the day of the shooting, we see that the transiting AP = Saturn/Pluto triggered Mateen’s natal AP = Mars/Pluto. This presents an amazing correlation with the potential for violence.
The pattern continues with transiting Pluto at 16°49’ Capricorn, igniting Mateen’s natal Mars/Saturn midpoint, which is 17°01’ Capricorn (assertive energy and discipline come together with forceful projection — extreme anger and frustration). This is again a very challenging and difficult combination of energies.
The transiting Sun at 21°42’ Gemini on the day of the shooting was illuminating and energizing Mateen’s Saturn/Ascendant midpoint, which is 21°24’ Pisces (personal achievement through hard work and discipline). The Sun also triggered his Mars/North Node midpoint (public aggression or protest).
In addition, there was an exact trigger from transiting Mercury to the natal Uranus/Pluto midpoint — and also conjunct Mateen’s natal Moon (intense thought activity and emotion that overturns the status quo – agitated thinking).
One more thing: Mateen’s MC and Saturn had moved by Solar Arc to exactly trigger his natal Jupiter/Pluto midpoint (strategic amplification of personal power).
Again and again, we see the repeating pattern of Mars, Saturn, Pluto combinations standing out.
Media outlets have said that Mateen yearned for a career in law enforcement. He had applied to the police academy and had been rejected. Over a number of years, he had reportedly taken and excelled at shooting courses. At the time of the murders, he was working as a security guard at a gated residential community, which may have fulfilled some part of his desire — or instead could have underscored a profound sense of underachievement.
Given the stellium in Scorpio and the midpoints illustrated earlier, this man was wired to be interested in deep emotional intensity and, perhaps, life issues involving violence. With appropriate love and support, he could have channeled this into being very skilled at detective work, criminal investigation, general police work, or the military.
There is always a choice as to how we use the energies present in our horoscope. Mateen had the vocational profile for undercover police work, solving mysteries, and even confronting violence. Somewhere along the way, he got off track, and a very negative manifestation of his natal chart was chosen.
It’s quite probable that his sexual feelings, which may have fueled his confusion, his selfdoubt, even his rage — feelings in deep conflict with his religious views — helped push him to make these unfortunate choices. An unresolvable emotional conundrum led to a downward spiral that triggered the shadow and negative manifestations of all the aforementioned natal energies.
Spectrum of Expression When I teach astrology, I frequently mention the importance of considering the concept of spectrum of expression regarding the natal chart. I believe that two people can be born on the same day, at the same time and location, and yet express vastly different levels of the planetary patterns in their natal chart. When we take physical form on the Earth plane, we bring with us the strengths and vulnerabilities from other incarnations — based on how we’ve chosen to react and process prior life experiences. It is my belief that we have choice every step of the way.
If the shooter had scheduled an astrology appointment prior to the horrific event, we would certainly have made note of the obvious challenges and tensions in his horoscope in the preparation for consultation. However, it is irresponsible to make an absolute judgment, prior to talking to the client, as to whether these natal challenges might be expressed constructively or destructively. We must communicate with our clients and listen for details that inform us how they are living their chart.
Indeed, it is incredibly sad that Omar Mateen engaged his natal energies in such an extreme, negative spectrum of expression. Our ethics as astrologers compel us to remember to look at all levels of possible manifestation. I believe that we must consider the circumstances in life development that are specific to the individual before making a declarative evaluation of natal energies.
Just because a person has a stellium in Scorpio, with Venus retrograde conjunct Pluto, doesn’t mean there will be powerful emotional or sexual conflict that intensifies and leads to tragedy or violence. The cookbook astrological approach of “this means that” tends to represent only one tiny portion of possible expression.
In the case of this very troubled man and a terribly tragic event, we can work backward and learn from the planetary patterns involving Mars, Saturn, and Pluto that were active that day. We can absorb the meaningful challenges in Mateen’s natal chart so that we may be more informed of the potential difficulties if we see similar patterns in our client’s horoscope. Our goal is to be of service and to help others find a path to the most positive expression of the natal chart.
We must use this knowledge and awareness with wisdom,
Kathy Rose is an astrologer with an international clientele. She is a Highest Honors Graduate of Noel Tyl’s Master’s Degree Certification Course in Astrology and serves as Tyl’s Teaching Associate. Kathy is also the producer and publisher of the 9-part Tyl MasterWork DVD series, and has developed an appreciative audience with her popular YouTube channel (roseastrology), featuring more than 100 videos. To contact her, telephone:<PHONE_NUMBER>; e-mail<EMAIL_ADDRESS>or visit her website: http://roseastrology.comintegrity, and humility. That is the path of the astrologer.
Chart Data and Sources Omar Mateen, November 16, 1986; 7:00 p.m. EST; Queens, NY, USA (40°N43’, 73°W52’); AA: birth certificate published in The New York Times, June 19, 2016. Orlando Shooting, June 12, 2016; 2:02 a.m. EDT; Orlando, FL, USA (28°N32’, 81°W23’); news sources report that the first 911 call came in at 2:02 a.m.
© 2016 Kathy Rose – all rights reserved
Kathy Rose is an astrologer with an international clientele. She is a Highest Honors Graduate of Noel Tyl’s Master’s Degree Certification Course in Astrology and serves as Tyl’s Teaching Associate. Kathy is also the producer and publisher of the 9-part Tyl MasterWork DVD series, and has developed an appreciative audience with her popular YouTube channel (roseastrology), featuring more than 100 videos. To contact her, telephone:<PHONE_NUMBER>; e-mail<EMAIL_ADDRESS>or visit her website: http://roseastrology.com


|
NATS C Client with JetStream and Streaming support
3.1.1
The nats.io C Client, Supported by Synadia Communications Inc.
|
#include <nats.h>
Public Attributes | |
| const char * | Durable |
| const char * | Description |
| const char * | DeliverSubject |
| const char * | DeliverGroup |
| jsDeliverPolicy | DeliverPolicy |
| uint64_t | OptStartSeq |
| int64_t | OptStartTime |
| UTC time expressed as number of nanoseconds since epoch. More... | |
| jsAckPolicy | AckPolicy |
| int64_t | AckWait |
| int64_t | MaxDeliver |
| const char * | FilterSubject |
| jsReplayPolicy | ReplayPolicy |
| uint64_t | RateLimit |
| const char * | SampleFrequency |
| int64_t | MaxWaiting |
| int64_t | MaxAckPending |
| bool | FlowControl |
| int64_t | Heartbeat |
| Heartbeat interval expressed in number of nanoseconds. More... | |
Configuration of a JetStream consumer.
In order to add a consumer, a configuration needs to be set. The typical usage would be to initialize all required objects on the stack and configure them, then pass the pointer to the configuration to js_AddConsumer.
OptStartTime
needs to be expressed as the number of nanoseconds passed since 00:00:00 UTC Thursday, 1 January 1970.SampleFrequency
is a sampling value, represented as a string such as "50" for 50%, that causes the server to produce advisories for consumer ack metrics.| const char* jsConsumerConfig::Durable |
| const char* jsConsumerConfig::Description |
| const char* jsConsumerConfig::DeliverSubject |
| const char* jsConsumerConfig::DeliverGroup |
| jsDeliverPolicy jsConsumerConfig::DeliverPolicy |
| uint64_t jsConsumerConfig::OptStartSeq |
| int64_t jsConsumerConfig::OptStartTime |
| jsAckPolicy jsConsumerConfig::AckPolicy |
| int64_t jsConsumerConfig::AckWait |
| int64_t jsConsumerConfig::MaxDeliver |
| const char* jsConsumerConfig::FilterSubject |
| jsReplayPolicy jsConsumerConfig::ReplayPolicy |
| uint64_t jsConsumerConfig::RateLimit |
| const char* jsConsumerConfig::SampleFrequency |
| int64_t jsConsumerConfig::MaxWaiting |
| int64_t jsConsumerConfig::MaxAckPending |
| bool jsConsumerConfig::FlowControl |
| int64_t jsConsumerConfig::Heartbeat |


A Game Object with autoCull
set to true will check its bounds against the World Camera every frame. If it is not intersecting the Camera bounds at any point then it has its renderable
property set to false
. This keeps the Game Object alive and still processing updates, but forces it to skip the render step entirely.
This is a relatively expensive operation, especially if enabled on hundreds of Game Objects. So enable it only if you know it's required, or you have tested performance and find it acceptable.


MY BEST FRIEND'S WEDDING 1997 A film review by Timothy Voon Copyright 1997 Timothy Voon 3 Big :-) :-) :-) for Julia the bridesmaid, never the bride
Cast: Julia Roberts, Dermot Mulroney, Cameron Diaz, Rupert Everett, Philip Bosco, M. Emmet Walsh, Carrie Preston, Rachel Griffiths Director: P.J. Hogan Screenplay: Ronald Bass
If I'm not married by twenty-eight, I'm NOT going to marry my best friend cause I don't think it's legal. If Julianne (Julia Roberts) isn't married by twenty eight she IS going to marry her best friend Michael (Dermot Mulroney). Legally possible, personality compatible, wonderful, except .... he's met somebody else.
Julianne was the centre of Michael's life 9 years ago. After splitting with him, they have remained friends with the vague memory of a promise of marriage, if still single by 28. Jules doesn't realise that she is still in love with Michael until 3 weeks before her 28th birthday; when he calls to tell her that he's getting married, and asks if she would come to give him emotional support before the big day. Suddenly, feelings of jealousy are awoken in her, as she sets out to win Michael back in every underhanded way possible.
Yes, Julia Roberts is the BAD GUY. The fly in the Chardonet, the rain on a wedding day, the good advice that you shouldn't take - isn't it ironic?  Which is the reason why I like this movie. It's a great learning experience for those wishing to sabotage weddings, and failed.  I would also have recommended laxatives, but that's too cruel even for me, not on a wedding day.
So these are the tests that true love faces, when a very jealous best friend does not get her way. In this subtle competition for the groom, the bride Kimmy (Cameron Diaz) carries with her the girdle of bright eyed sincere innocence, whilst her rival maid of honour wields the magic of brains and beauty. So with whom shall I side? They both love him soooo much. How can one win without the other getting hurt. If I had to take sides, I can only let my conscience guide me. I have always believed that the motive behind every action, is the most important reason behind a good relationship. Sorry Jules you lose. But if I had my way with the plot - Kimmy gets Michael, and Jules ends up with Rupert - who makes an exception to his rule, and changes his sexual orientation the last minute. Not likely, but an interesting idea - cause we'd have something of a 'Chasing Amy' - Oh dear! Now if Shakespeare had his way with this tale of jealousy, it would probably have ended up with four dead bodies - now that would have been funny.
Director Hogan has once again decided to fill his 'wedding' movies with catchy tunes, making the marital experience a joyful ride. Julia is back in sparkling form, with an effervescent smile and long curly red hair. She has once again left mankind panting for more. Her every look carries with it an easy charm reminiscent of Rita Hayworth in her hey day. Yes, beauty and humour makes this particular lady prettier still. Cameron Diaz carries herself well against the stiff competition, whose performance at a karaoke bar will be painfully remembered. Dermot Mulroney is the ever likeable object of desire for both women, whose bland personality makes him the ideal reliable husband that every woman looks for; but it is Rupert Everett who steals the movie's funniest moments with his wry wit, humorous tale telling and spontaneous singing - making this wedding party a curious gem of rare sorts.
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Your
To-Do List
By Ari Singer-Freeman
First off - congratulations on getting into college! You should by all means enjoy the rest of your senior year (despite the obstacles COVID-19 might pose). There are a couple easy things you can do right now to make your life exponentially easier down the road. Specifically, there are some apps that you should familiarize yourself with, some research you should do, and some habits you should develop.
Apps You Should Download Before Entering College
These apps are used by college students every day, and you’ll be ahead of the curve if you can figure them out now:
-
Venmo: An app used to easily transfer money, pay student organization dues, and settle up between friends
-
GroupMe: An app that organizations use to communicate with their members
-
Google Calendar: Your schedule will become a lot more complicated in college, so be sure to have a good calendar app. It’s surprisingly easy to miss a class or interest meeting because you forgot that it was happening
-
Your local transit app: If your school is large, figuring out the public transportation system can be crucial to making it to classes on time and being efficient. At UNC Chapel Hill, the app CarolinaGo serves this purpose and most college towns have their own
Research You Should Do
A couple of key factors - like your roommate, living situation, and professors - can really make a difference in the quality of your first year at school. Here are the things you should research to set yourself up for success:
-
Class of 2024 Facebook Group: Although many schools have their own optional software for matching roommates, I would highly recommend turning to your school’s Facebook group. This is where the majority of students find their roommates, and you can more thoroughly vet candidates. A crazy roommate can really ruin your first year, so it’s crucial to go the extra mile when picking one
-
Reputation of Different Dormitories: Every dormitory at every school has a different culture, and these cultures can range drastically. Many people find their friend groups from their first-year living situations, so try to talk to a current student and figure out the culture in which you’d best fit
-
RateMyProfessor: RateMyProfessor is a college student’s best friend when registering for classes. It shows ratings and comments from past students in certain classes. RateMyProfessor will help you avoid classes that have a crazy instructor or are bound to tank your GPA. On the other hand, you might find a super interesting professor that you would have otherwise never met
Habits You Should Develop
Starting college is a lifestyle adjustment for everyone. Changing a few habits could help you get ahead in fitting the college lifestyle:
-
Scheduling: Since your schedule will vary from day to day and you’ll have more disparate commitments, figuring out how to structure your schedule efficiently can save you a lot of time. Although everyone will prefer a different schedule, figuring out which times of day you are most productive can help you with figuring out your own ideal schedule
-
Exercise: If you’re someone who exercises (you can disregard this advice if not), exercising can look very different in college. Most schools have great sports-related extracurricular activities (that you don’t need to be a Division 1 athlete to participate in), but figuring out a time for a group of students to exercise together can be challenging. You’ll likely have to exercise by yourself more than before, so try to figure out how to do so enjoyably now. For those of you who prefer to exercise socially, most colleges have group exercise classes free of charge and available for all students


Introduction
Welcome to the Zip-Tax.com Sales Tax API
By providing as little as a zip code, we can determine the sales tax rates for your customers in their specific geographical area. In most states, sales tax rates change many times throughout the year. Our service provides you with a method to get this information quickly and easily and with no maintenance. The ZipTax team is dedicated to providing you with the critical up-to-date information you need so you can focus on the important parts of your business.
Our Zip-Tax.com API is designed to allow programmers to submit a request to our servers from a website or application and retrieve response data from our databases. The format of the response can be returned as JSON or XML. Simply pass the desired format as part of the request. Zip-Tax.com works by submitting geographic information such as city, state, and zip code from your application or website. This data is used determine the correct sales tax information matching the provided location. Our service then returns that information to your application or website.
Getting Started
Request header requirement
curl --header "Accept:application/json"
The Zip-Tax.com sales tax API uses standard Representational State Transfer (REST) principles for maximum flexibility with a wide range of program languages.
To get started, simply follow this documentation to build a reuqest using the Zip-Tax.com Request URL, parse the API Response to validate the response codes, and use the API results in your application.
API Subscription Types
General API
The general Zip-Tax.com sales tax API allows jurisdiction lookups using any combination of zip code, city, and state. This API level does not provide full address level lookup and only provides county data via response. Note when using this method, multiple response values may be returned where multiple tax jurisdictions match the query inputs.
Geo API (address level)
The GEO API version of Zip-Tax.com allows queries with full address lookup. This method returns only a single response with the address level sales tax jurisdiction in the response. This is the recommended subscription type for use with our API.
Request
Base URL
The API request URL is used to form your request to the API from your application.
The base request URL consists of three parts.
| Subdomain | Action | Version |
|---|---|---|
| api.zip-tax.com | /request | /v30 |
HTTP Request
Base URL Example
curl --header "Accept:application/json" https://api.zip-tax.com/request/v30
GET https://api.zip-tax.com/request/v30
Query Parameters (body data)
| Parameter | Required | Description |
|---|---|---|
| key | yes | Your API key |
| postalcode | yes | U.S. jurisdiction postal code (zip code) |
| state | optional | two letter state code i.e. CA = California |
| city | optional | full city name |
| format | optional | Default = json or specify xml |
Additional Query Parameters (requires geo plan)
| Parameter | Required | Description |
|---|---|---|
| street | optional | Full street address. Available only with GEO subscription. |
Full Request Example (with query parameters)
Request Example with Query Params
curl --header "Accept:application/json" https://api.zip-tax.com/request/v30?key=1234567890&postalcode=90264
This endpoint returns a sample request.
GET https://api.zip-tax.com/request/v30?key=1234567890&postalcode=90264
Response Codes
The Zip-Tax.com API returns the following response codes based on request results.
| Code | Reason | Description |
|---|---|---|
| 100 | SUCCESS | Successful API Requet |
| 101 | INVALID_KEY | Key format is not valid |
| 102 | INVALID_STATE | State format is not valid |
| 103 | INVALID_CITY | City format is not valid |
| 104 | INVALID_POSTAL_CODE | Postal code format is not valid |
| 105 | INVALID_FORMAT | Query string format is not valid |
Response Results
Sample Response (json)
{
"version": "v30",
"rCode": 100,
"results": [
{
"geoPostalCode": "90264",
"geoCity": "MALIBU",
"geoCounty": "LOS ANGELES",
"geoState": "CA",
"taxSales": 0.0925,
"taxUse": 0.0925,
"txbService": "N",
"txbFreight": "N",
"stateSalesTax": 0.06,
"stateUseTax": 0.06,
"citySalesTax": 0,
"cityUseTax": 0,
"cityTaxCode": "",
"countySalesTax": 0.0025,
"countyUseTax": 0.0025,
"countyTaxCode": "19",
"districtSalesTax": 0.03,
"districtUseTax": 0.03
}
]
}
| Name | Description | Version |
|---|---|---|
| geoPostalCode | Requested postal code | all |
| geoCity | City | all |
| geoCounty | County | all |
| geoState | State | all |
| taxSales | Total sales tax | all |
| taxUse | Total use tax | all |
| txbService | Taxable service (values: null,Y,N,L) | all |
| txbFreight | Taxable freight (values: null,Y,N) | all |
| stateSalesTax | Portion of total sales tax from the state level | v20+ only |
| stateUseTax | Portion of total use tax from the state level | v20+ only |
| citySalesTax | Portion of total sales tax from the city level | v20+ only |
| cityUseTax | Portion of total use tax from the city level | v20+ only |
| cityTaxCode | Applicable city tax code | v20+ only |
| countySalesTax | Portion of total sales tax from the county level | v20+ only |
| countyUseTax | Portion of total use tax from the county level | v20+ only |
| countyTaxCode | Applicable county tax code | v20+ only |
| districtSalesTax | Portion of total sales tax from the district level | v20+ only |
| districtUseTax | Portion of total use tax from the district level | v20+ only |
Support
For questions or comments please contact us via email at: support@zip-tax.com.


Create
Error.
Info.
description
Name
Select Organization
Action
Name
Name
Organization Type
Action
Name
Desc
Select parent organization in campaign


Congratulations on reaching the final project of the Robotics Nanodegree!
Previously, you worked on the Semantic Segmentation lab where you built a deep learning network that locates a particular human target within an image. For this project, you will utilize what you implemented and learned from that lab and extend it to train a deep learning model that will allow a simulated quadcopter to follow around the person that it detects!
Most of the code below is similar to the lab with some minor modifications. You can start with your existing solution, and modify and improve upon it to train the best possible model for this task.
You can click on any of the following to quickly jump to that part of this notebook:
We have provided you with a starting dataset for this project. Download instructions can be found in the README for this project's repo. Alternatively, you can collect additional data of your own to improve your model. Check out the "Collecting Data" section in the Project Lesson in the Classroom for more details!
import os
import glob
import sys
import tensorflow as tf
from scipy import misc
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import layers, models
from tensorflow import image
from utils import scoring_utils
from utils.separable_conv2d import SeparableConv2DKeras, BilinearUpSampling2D
from utils import data_iterator
from utils import plotting_tools
from utils import model_tools
In the Classroom, we discussed the different layers that constitute a fully convolutional network (FCN). The following code will introduce you to the functions that you need to build your semantic segmentation model.
The Encoder for your FCN will essentially require separable convolution layers, due to their advantages as explained in the classroom. The 1x1 convolution layer in the FCN, however, is a regular convolution. Implementations for both are provided below for your use. Each includes batch normalization with the ReLU activation function applied to the layers.
def separable_conv2d_batchnorm(input_layer, filters, strides=1):
output_layer = SeparableConv2DKeras(filters=filters,kernel_size=3, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
def conv2d_batchnorm(input_layer, filters, kernel_size=3, strides=1):
output_layer = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
The following helper function implements the bilinear upsampling layer. Upsampling by a factor of 2 is generally recommended, but you can try out different factors as well. Upsampling is used in the decoder block of the FCN.
def bilinear_upsample(input_layer):
output_layer = BilinearUpSampling2D((2,2))(input_layer)
return output_layer
In the following cells, you will build an FCN to train a model to detect and locate the hero target within an image. The steps are:
encoder_block
decoder_block
Create an encoder block that includes a separable convolution layer using the separable_conv2d_batchnorm()
function. The filters
parameter defines the size or depth of the output layer. For example, 32 or 64.
def encoder_block(input_layer, filters, strides):
return separable_conv2d_batchnorm(input_layer, filters, strides)
The decoder block is comprised of three parts:
def decoder_block(small_ip_layer, large_ip_layer, filters):
# Upsample the small input layer
small_in_upsampled = bilinear_upsample(small_ip_layer)
# Concatenate the upsampled and large input layers
output_layer = layers.concatenate([small_in_upsampled, large_ip_layer])
# Add some number of separable convolution layers
output_layer = separable_conv2d_batchnorm(output_layer, filters)
output_layer = separable_conv2d_batchnorm(output_layer, filters)
return output_layer
Now that you have the encoder and decoder blocks ready, go ahead and build your FCN architecture!
There are three steps:
Note: below are a few different models I have tried. I will use the best performing one for the final model.
def print_network(*layers):
i = 1
for layer in layers:
print("Layer %d: %s" % (i, layer.get_shape()))
i = i + 1
def fcn_model_4(inputs, num_classes):
# Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
enc1 = encoder_block(inputs, 16, strides=2);
enc2 = encoder_block(enc1, 32, strides=2)
enc3 = encoder_block(enc2, 64, strides=2)
enc4 = encoder_block(enc3, 128, strides=2)
# Add 1x1 Convolution layer
con1 = conv2d_batchnorm(enc4, filters=1024, kernel_size=1, strides=1)
# Add the same number of Decoder Blocks as the number of Encoder Blocks
dec3 = decoder_block(con1, enc3, 128)
dec2 = decoder_block(dec3, enc2, 64)
dec1 = decoder_block(dec2, enc1, 32)
x = decoder_block(dec1, inputs, 16)
print_network(inputs, enc1, enc2, enc3, enc4, con1, dec3, dec2, dec1, x)
# The function returns the output layer of your model.
return layers.Conv2D(num_classes, 3, activation='softmax', padding='same')(x)
def fcn_model_2(inputs, num_classes):
# Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
enc1 = encoder_block(inputs, 16, strides=2);
enc2 = encoder_block(enc1, 64, strides=2)
# Add 1x1 Convolution layer
con1 = conv2d_batchnorm(enc2, filters=256, kernel_size=1, strides=1)
# Add the same number of Decoder Blocks as the number of Encoder Blocks
dec1 = decoder_block(con1, enc1, 64)
x = decoder_block(dec1, inputs, 16)
print_network(inputs, enc1, enc2, con1, dec1, x)
# The function returns the output layer of your model.
return layers.Conv2D(num_classes, 3, activation='softmax', padding='same')(x)
fcn_model=fcn_model_4
The following cells will use the FCN you created and define an ouput layer based on the size of the processed image and the number of classes recognized. You will define the hyperparameters to compile and train your model.
Please Note: For this project, the helper code in data_iterator.py
will resize the copter images to 160x160x3 to speed up training.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
Define and tune your hyperparameters.
learning_rate = 0.002
batch_size = 40
num_epochs = 50
workers = 4
num_images_train = len([name for name in os.listdir(os.path.join('..', 'data', 'train', 'images'))])
print("%d images in training set, recommended steps %d " % (num_images_train, int(num_images_train / batch_size)))
num_images_val = len([name for name in os.listdir(os.path.join('..', 'data', 'validation', 'images'))])
print("%d images in validation set, recommended steps %d " % (num_images_val, int(num_images_val / batch_size)))
steps_per_epoch = 100
validation_steps = 30
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(train_iter,
steps_per_epoch = steps_per_epoch, # the number of batches per epoch,
epochs = num_epochs, # the number of epochs to train for,
validation_data = val_iter, # validation iterator
validation_steps = validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers = workers)
# Save your trained model weights
weight_file_name = 'model_weights'
model_tools.save_network(model, weight_file_name)
Now that you have your model trained and saved, you can make predictions on your validation dataset. These predictions can be compared to the mask images, which are the ground truth labels, to evaluate how well your model is doing under different conditions.
There are three different predictions available from the helper code provided:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
# weight_file_name = 'model_weights'
# restored_model = model_tools.load_network(weight_file_name)
The following cell will write predictions to files and return paths to the appropriate directories.
The run_num
parameter is used to define or group all the data for a particular model run. You can change it for different runs. For example, 'run_1', 'run_2' etc.
run_num = 'run_1'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
Now lets look at your predictions, and compare them to the ground truth labels and original images. Run each of the following cells to visualize some sample images from the predictions in the validation set.
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_with_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
Evaluate your model! The following cells include several different scores to help you evaluate your model under the different conditions discussed during the Prediction step.
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not visable
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos/(true_pos+false_neg+false_pos)
print(weight)
# The IoU for the dataset that never includes the hero is excluded from grading
final_IoU = (iou1 + iou3)/2
print(final_IoU)
# And the final grade score is
final_score = final_IoU * weight
print(final_score)


404 Not Found
Were you trying to create a commitment?
Right now the preferred format is like
alice.commits.to/do_the_thing
Please avoid any other special characters right now. Things are a bit in flux with the URL format! And there are a couple special characters that will cause things to break horribly.


Women strike for minimum wage in Spanish football
by BeSoccerFootballers in Spain's women's league launched an indefinite strike on Friday seeking to double the minimum wage to 16,000 euros ($17,680), the players' union AFE announced.
The Association of Women's Football Clubs (ACFF) believes the more modest clubs will be unable to afford wages with the minimum currently set at 8,000 euros.
Women are also trying to force through a maternity package, paid holidays and other basic fringe benefits.
Featured links
- Ballon d'Or winner already been decided
- The latest football transfers news and rumours
- Possible 'Clasico' in the Spanish Supercup final
- Who has the most free-kicks goals: Messi or Cristiano?
"We will be stopping full match-day programmes as of 16-17 November for an indefinite period, because we have tried all other methods of negotiation," the players' union said.
The strike thereby sees the cancellation of this weekend's ninth round of the women's 16-team football league.
Over the past 15 years women's participation in football in Spain has increased four-fold and there are 70 members of the ACFF.


[name]
A timed sequence of [link:https://en.wikipedia.org/wiki/Key_frame keyframes] for animating
a specific property of an object.
Constructor
[name]( [page:String name], [page:Array times], [page:Array values], [page:Constant interpolation] )
-- [page:String name] (required) identifier for the KeyframeTrack.
-- [page:Array times] (required) array of keyframe times
-- [page:Array values] values for the keyframes at the times specified.
-- [page:Constant interpolation] the type of interpolation to use. See [page:Animation Animation Constants] for possible values.
Default is [page:Animation InterpolateLinear].
Properties
[property:String name]
The name given to the KeyframeTrack in the constructor.
[property:Float32Array times]
[link:https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array Float32Array]
of keyframe times.
[property:Float32Array values]
[link:https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array Float32Array]
of keyframe values, corresponding to the [page:KeyframeTrack.times times].
[property:Constant DefaultInterpolation]
The default tnterpolation type to use, [page:Animation InterpolateLinear].
Methods
[method:null createInterpolant ]( )
Create a [page:LinearInterpolant LinearInterpolant], [page:CubicInterpolant CubicInterpolant]
or [page:DiscreteInterpolant DiscreteInterpolant], depending on the value of the interpolation parameter
passed in the constructor.
[method:null getInterpolation ]( )
Get the interpolation type.
[method:Number getValueSize ]( )
The the size of each value (length of the values array divied by length of times array).
[method:DiscreteInterpolant InterpolantFactoryMethodDiscrete ]( result )
Create a new [page:DiscreteInterpolant DiscreteInterpolant] from the [page:KeyframeTrack.times times]
and [page:KeyframeTrack.times values].
[method:null InterpolantFactoryMethodLinear ]( )
Create a new [page:LinearInterpolant LinearInterpolant] from the [page:KeyframeTrack.times times]
and [page:KeyframeTrack.times values].
[method:null InterpolantFactoryMethodSmooth ]( )
Create a new [page:CubicInterpolant CubicInterpolant] from the [page:KeyframeTrack.times times]
and [page:KeyframeTrack.times values].
[method:null optimize ]( )
Remove equivalent sequential keys, which are common in morph target sequences.
Called automatically by the constructor.
[method:KeyframeTrack parse]( [page:JSON json] )
Parse json and return new keyframe track of the correct type.
[method:null scale ]( )
Scale all keyframe times by a factor (useful for frame <-> seconds conversions).
[method:null setInterpolation ]( [page:Constant interpolation] )
Set the interpolation type. See [page:Animation animation constants] for choices.
[method:null shift ]( [page:Number timeOffset] )
Move all keyframes either forwards or backwards in time.
[method:null TimeBufferType ]( )
[link:https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array Float32Array],
used to convert the times array passed in the constructor to a Float32Array.
[method:JSON toJSON]( [page:KeyframeTrack track] )
Convert the track to JSON.
[method:null trim ]( [page:Number startTime], [page:Number endTime] )
Removes keyframes before [page:Number startTime] and after [page:Number endTime]
without changing any values within the range [[page:Number startTime], [page:Number endTime]].
[method:null validate ]( )
Perform minimal validation on the tracks. Called automatically by the constructor.
[method:null ValueBufferType ]( )
[link:https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array Float32Array],
used to convert the values array passed in the constructor to a Float32Array.
Source
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


•You must be on social media or lose out on opportunities for jobs, partnerships and the ability to be known by a broader audience for your ideas and expertise.
•Recruiters heavily rely on social networks to find and vet job candidates, so make sure you can be found and are represented well.
•Own your own online real estate with URLs that contain your real name.
•Make your social profile photo one that brands you well.
•Blog only if you have something to say and can sustain it with regular postings.
•Even if you have a company, you need to promote your own personal brand.


_Payback_ (R) ** 1/2 (out of ****)
Immediately after watching _Payback_, the friend with whom I attended the screening came up with a snappy line: "_Payback_--where the good guys are bad, and the bad guys are badder!" Actually, an equally snappy and more accurate line would be, "_Payback_--where the bad guys are bad, and the good guys are even worse," for the "hero" of this offbeat and somewhat amusing action thriller is arguably more evil than the "villains."
His name is Porter (Mel Gibson), a small-time hood who is swindled out of $70,000 and left for dead by fellow crook Val (Gregg Henry) and Porter's own junkie wife, Lynn (Deborah Kara Unger). Of course, Porter improbably recovers from his two potentially fatal gunshots to the back, and he takes violent, often murderous, ends to even the score--or, at the very least, get his $70,000.
Co-writer/director Brian Helgeland (who retains directing credit though "creative differences" with Gibson prompted him to leave the project before its completion) plays the rather scenario for a number of darkly tongue-in-cheek laughs.  Only someone as disturbed as Porter, whom Gibson plays with the same manic, slightly off-kilter energy he always brings to his action roles.
Based on the above, _Payback_ sounds much lighter than it is--for all the humor in it, the film is quite gritty and dark, reflected most obviously in Emerson Core's often frustratingly grainy cinematography, which casts a blue-gray wash over the entire film. _Payback_ is extremely violent; I would go so far as to say excessively so at certain points.  There are a number of sadomasochistic touches, courtesy of an Asian-mob-affliated dominatrix (Lucy Liu, a long way from _Ally_McBeal_). This brings me to another point that will surely spark some controversy: the film's treatment of women. A touch of misogyny is expected in a macho action picture such as this, but when the three prominent female characters--who, I might add, are just about the _only_ females in the film--are two prostitutes (one being the dominatrix, the other being Maria Bello's gold-hearted hooker/Porter love interest) and a heroin addict, one could easily see that the fairer sex gets a far from fair treatment.
One's enjoyment of _Payback_ essentially boils down to one's opinion of Porter, and, to a certain degree, Gibson himself: depending on your view, he can come off as either a badass or an asshole, with no in-between. The bulk of the audience with whom I saw the film appeared to think the former, and, being a Gibson fan, I'm with them. Nonetheless, I never was more than amused by _Payback_, whose thin story never generates any genuine excitement.
Michael Dequina
<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown
CompuServe Hollywood Hotline: http://www.HollywoodHotline.com
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The goal of this project was to use ROS with Gazebo and MoveIt to write my initials in space with a simulated UR5e robotic arm. The project was an exercise in motion planning and a gentle introduction to the MoveIt Python interface. At a high level, the approach was to:
Here's a video of the final result!
As part of the project write-up, I also completed some basic kinematic analysis of the UR5e robot and several of the key poses I used to create my initials. First, here's the robot's DH table, which describes the configuration of its joints and links:
I also analyzed three key poses that I used for my initials. I won't include them all here, but here's a sample of the kind of work that involved:
For the most part, the biggest challenge with this project was the learning curve. It took me a long time, at first, to figure out how all the parts connected and how to even get started and set up my planning script. However, once I figured it out and got it working, creating the actual trajectories and doing the coding wasn’t too difficult.
One other small issue I faced was that if I made my movements too big, the planning would fail or give me some very wacky trajectories/my robot would shake itself to death. I got around this problem by keeping my movements small and limiting my workspace to an area I knew I could get away with.
If I had more time, I could add to this project by generating my poses programmatically based on the robot’s environment, in some way. For example, I could try to program the robot to be gesture-controlled and try to match your hand’s movements. I could even try to do some tasks with the end-effector, like picking up a cup or block on a table and moving it somewhere else.
For the final project, I think it’d be really cool to program a robot like this to play a game against you. That might involve moving around checkers, drawing on a piece of paper (for tic-tac-toe or similar) or something of the sort.
Two of my biggest takeaways from this project were:


Painting #1 by Paul Gaugin Oil on Canvas Painting #2 by Paul Gaugin Oil on Canvas Painting #3 by Paul Gaugin Oil on Canvas Painting #4 by Paul Gaugin Oil on Canvas


There is a piece of one in each of us. come if you dare. Find your day. Find one in you. The names of ghana.The one in all of us. The one in all of you.