public class GameMaster
extends java.lang.Object
| Constructor and Description |
|---|
GameMaster() |
| Modifier and Type | Method and Description |
|---|---|
static boolean |
enterClick(org.newdawn.slick.GameContainer gc)
boolean method that listens to key click.
|
static void |
gameOver()
void method that resets all variables in the game and generate a new map.
|
static void |
gameWon()
void method that resets all variables in the game and generate a new map, except score.
|
static void |
GUIRenderOne(org.newdawn.slick.Graphics g,
int ScreenWidth,
int ScreenHeight)
void method for rendering GUI needed for gameState 1.
|
static void |
GUIrenderThree(org.newdawn.slick.Graphics g,
int score,
int screenWidth,
int screenHeight)
void method for rendering GUI needed for gameState 3.
|
static void |
GUIRenderTwo(org.newdawn.slick.Graphics g,
int score,
int screenWidth,
int screenHeight)
void method for rendering GUI needed for gameState 2.
|
static void |
GUIRenderZero(org.newdawn.slick.Graphics g,
int screenWidth,
int screenHeight)
void method for rendering GUI needed for gameState 0.
|
static boolean |
rClick(org.newdawn.slick.GameContainer gc)
boolean method that listens to key click.
|
static void |
timer()
Creates timer, based on System.currentTimeMillis().
|
static boolean |
wClick(org.newdawn.slick.GameContainer gc)
boolean method that listens to key click.
|
public static void timer()
public static boolean enterClick(org.newdawn.slick.GameContainer gc)
gc,
- Game functionality library included in the Slick2D engine.public static boolean rClick(org.newdawn.slick.GameContainer gc)
gc,
- Game functionality library included in the Slick2D engine.public static boolean wClick(org.newdawn.slick.GameContainer gc)
gc,
- Game functionality library included in the Slick2D engine.public static void GUIRenderZero(org.newdawn.slick.Graphics g, int screenWidth, int screenHeight)
g,
- Graphics rendering library included in the Slick2D engine.screenWidth,
- the width of the game screen, used in the method to display the graphics in relation to the width.screenHeight,
- the height of the game screen, used in the method to display the graphics in relation to the height.public static void GUIRenderOne(org.newdawn.slick.Graphics g, int ScreenWidth, int ScreenHeight)
g,
- Graphics rendering library included in the Slick2D engine.screenWidth,
- the width of the game screen, used in the method to display the graphics in relation to the width.screenHeight,
- the height of the game screen, used in the method to display the graphics in relation to the height.public static void GUIRenderTwo(org.newdawn.slick.Graphics g, int score, int screenWidth, int screenHeight)
g,
- Graphics rendering library included in the Slick2D engine.score,
- the score integer is used for displaying the score.screenWidth,
- the width of the game screen, used in the method to display the graphics in relation to the width.screenHeight,
- the height of the game screen, used in the method to display the graphics in relation to the height.public static void GUIrenderThree(org.newdawn.slick.Graphics g, int score, int screenWidth, int screenHeight)
g,
- Graphics rendering library included in the Slick2D engine.score,
- the score integer is used for displaying the score.screenWidth,
- the width of the game screen, used in the method to display the graphics in relation to the width.screenHeight,
- the height of the game screen, used in the method to display the graphics in relation to the height.public static void gameOver()
public static void gameWon()


Who we are?
We are a group of friends dedicated to a well-known JavaScript language. KPJS is run by fans who devote considerable time and resources to code and learn to code in JS.
{{> head}}
We are a group of friends dedicated to a well-known JavaScript language. KPJS is run by fans who devote considerable time and resources to code and learn to code in JS.


Always a pleasure scaffolding your apps.
Splendid!
HTML5 Boilerplate is a professional front-end template for building fast, robust, and adaptable web apps or sites.
Sleek, intuitive, and powerful mobile first front-end framework for faster and easier web development.


--- layout: post title: Rough layout date: '2015-06-16T18:30:34+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/121710331436 ---


This client encrypts a Name Resolution query in a HE. This means that even the HE-NS server is able to answer, but cannot decypher the question or it's own answer.


This category contains notebooks with numerical algorithms often used in physics.
In this category, we discuss questions related to machine learning, and various applied problems that can be solved with these methods.
We discuss some of the popular algorithms often used in finance.


We pride ourselves in providing only the best resources.
For more information fill out the form below and someone will contact you shortly.


Why You Should Changamka With Blue Band This Holiday Season
by Michael MusyokaA nutritious breakfast is a proven way to kick-start your day and for years, Blue Band has been at the heart of many breakfasts.
As the festive season rolls in and you get that rare opportunity to spend quality time with your children, what better way to ensure they have a nourishing breakfast than with their favourite spread?
That is why the Changamka na BB promotion is giving you a chance to grab a slice of Ksh50 million worth of shopping vouchers among other goodies now on offer to further enrich your festivities.
You could win shopping vouchers worth Ksh10K, Ksh3K, Ksh1K as well as airtime and data bundles.
Kitchen appliances up for grabs include toasters, sandwich makers, gas burners, hand mixers and kettles.
Here are the few easy steps to enter the promotion:
- Purchase participating Blue Band packs, i.e 100gms, 250 gms, 500gm or 1kg.
- Scratch to reveal a nine (9) digit alpha-numeric code.
- Send an SMS with the alpha-numeric code to the short-code number 40098. SMS is free.
- Each alpha-numeric code sent will be an entry into the draw and be eligible to win one prize in the promotion.
How do you win the prize?
- The winners of each draw shall be selected by a random system, however, each participant will win a prize.
- Draws to determine the winners of all prizes will be conducted daily and winners will be notified instantly once they send an SMS with the alpha-numeric code to the short-code number 40098.
- The Safaricom/ Airtel data and SMS bundles will be available for redemption and use within 48 hours from the time of receipt of the confirmation SMS message.
- The winners of the shopping vouchers and kitchen appliances will be notified via SMS on where to collect their prizes.
- Authentication of the identity of the selected winner will be through their original identification card/ passport matching the records held with their mobile service provider.
The promotion will run until December 31, 2019, and for more queries, you can call 0709 183 400.


Montana Velo Road Race
"USCF Permit Pending"
Montana Cup Event #2
Date: Saturday, April 19th
2003
Time: Race Starts at
11:00 a.m.
Entry Fee: $17.00 (prize
list tba)
Registration: 9:30-10:30 @
Rossiter School, Helena MT. Note: there will be no inside changing
area/bathrooms. We will use the outside parking lot for registration and
porto-pots will be available.
Contact: Steve Coen
<EMAIL_ADDRESS>Distances: Cat. 1,2,3-60
miles (Three laps)
Cat. 4,5,
masters, women 40 miles (two laps)
Juniors
20 miles (one lap)
Course Description: Flat
20-mile circuit through the scenic Helena Valley. One section of dirt road that is
a few miles in length keeps every one on their toes. There is also a section of rough pavement
but the rest
of the course is smooth and fast.


Notes:
- The top panel shows single nucleotide polymorphism (SNP) frequencies through time. Color indicates position along the genome.
- The panel below shows SNP frequencies at each position for a given time point. The color of each point equals that of the corresponding trajectory in the top panel.
- The time point shown in the second panel can be chosen via the slider on top.
- Clicking on an annotated element of the genome will zoom into that element.


In cryptography, a digital certificate (or identity certificate) is a certificate which uses a digital signature to bind together a public key with an identity — information such as the name of a person or an organization, their address, and so forth. The certificate can be used to verify that a public key belongs to an individual, which is used to achieve data confidentiality, authenticity and message integrity.
From: Wikipedia / Digital certificate (http://en.wikipedia.org/wiki/Digital_certificate)


How to: Backup iMessages using OSX and Google Drive
This is pretty short but I wanted to leave it here for posterity. The process is to just add the archive folder to Google Drive. You don’t have to move it to your Google Drive folder or hard link, as other tutorials have suggested, just add it to the list of extra folders synced by Google Drive.
First, find the folder iMessages uses to store all its data. Mine is in /Users/soph/Library/Messages
. If you aren’t able to see the Library folder, use cmd-shift-.
to make hidden items viewable.
Select the Google Backup and Sync icon in your menu bar at the top of your screen. Go to …->Preferences->Choose Folder and select that folder.
And, there, you’re done!


☰
Experimental Design
Sampling nature
Normal Distribution
Central Limit Theorem
t
test
Multiple
t
tests
F
test
Multiple
F
tests
Help


Pizza Dough
Making pizza at home is quick, easy, and delicious! This is a recipe for great pizza dough adapted from here.
Ingredients
- 1 cup (250ml) warm water
- 1t (5ml) instant yeast
- 1t (5ml) sugar
- 2 cups (500ml) flour
- 1t (5ml) salt
Method
- In a bowl, combine the water, yeast and sugar. Let stand until the mixture foams on top, about 5 minutes.
- Combine the flour and salt. Add the yeast mixture while stirring until a soft ball forms.
- Remove the dough from the bowl and knead for a few minutes on a floured surface to prevent sticking.
- Place in a lightly oiled bowl and cover with a clean cloth. Let the dough rise for about 30 minutes in warm and draft-free area.
- Makes enough dough for two individual pizzas.


We give the community premium music and move the culture to new heights.
Moving The Community
Its not difficult to
notice social media. We fully encourage the optomistic growth and the developement or the young minds today to strive and be more active!
Get Featured
if you would like to get featured please contact<EMAIL_ADDRESS>Business Contact Information
<EMAIL_ADDRESS>615-219-9355
for any concern.


|
Audiometry
|
Public Member Functions | |
| BoneCondMskDataModel (SubTabVM vm) | |
| Public Member Functions inherited from Audiometry.Model.SubTabModel.PureToneDataModel | |
| override bool | AddRecordToDatabase (string accNumber, string examDate, bool testConducted, out string msg) |
| override bool | UpdateRecordInDatabase (string accNumber, string examDate, bool testConducted, out string msg) |
| override void | OpenRecordFromDatabase (string accNumber, string examDate, out bool testConducted) |
| override bool | DeleteRecordFromDatabase (string accNumber, string examDate) |
Additional Inherited Members | |
| Protected Member Functions inherited from Audiometry.Model.SubTabModel.PureToneDataModel | |
| PureToneDataModel (SubTabVM vm) | |
| Protected Member Functions inherited from Audiometry.Model.SubTabModel.SubTabModel | |
| SubTabModel (SubTabVM vm) | |
| Protected Attributes inherited from Audiometry.Model.SubTabModel.PureToneDataModel | |
| string | dbTable |
| Protected Attributes inherited from Audiometry.Model.SubTabModel.SubTabModel | |
| SubTabVM | viewmodel |
|
inline |


Highscore Board
Technion Web Lab 2014
Highscore Table
Loading high score data...
Add new score
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Score:
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I am a fullstack with more experience to websites with frontend, backend and api roles and still in mobile development with flutter.
Create a government system, a booking room application using react js, a helpdesk system with react js, and a car tow booking information system, Basic Calculation of Motor Vehicle Tax Imposition, Indotowing, Website education authorities Administrator Dinas Pendidikan, Website Yayasan Arrohmah and Administrator, Walhi Lampung, Beauty clinic information system, Indonesian Advocates Association website
Create Daily Activity applications and student admission application with laravel technology, javascript, PHP, jquery and collaborate with GIT..
Creating a Scraping Automation application for a website then sent via email created with codeigniter, PHP and python technology..
Satu Nusa College is a private university in Lampung majoring in Information Systems.
Qodr (/ˈkōdər/) is an IT training program and learn non-formal Islamic boarding school.
Vocational high school with a focus on studying networks and computers.
Web Junior Training and Certification Programming
CERTIFICATE OF COMPETENCE


I think that quote (courtesy Phil Schiller at last night’s WWDC) represents a growing annoyance within Apple towards the continuing (and often nonsensical) writeups that have been circling the online world for some time. Frankly, it was a little surprising — Apple is known to take digs at competition, much like anyone else — but we don’t often see a statement of annoyance within their presentations, which is what this sounded like.
Image: Techcrunch
I get it too. Apple is a cash cow that sits on top of the smartphone and tablet market today and it seems almost ‘in’ to write negatively about the company. Fair enough, everyone’s always entitled to their opinion, but last night showed that it had severely scaled up, that even Apple is getting tired and annoyed by it.
But there was also a distinct Apple way in which they decided to respond. Apple iterated a focus on design principles, with a lot of context around the “Designed by Apple in California” tagline, and a rather lovely video (which is available on the same page). They took that a step further with the naming of the next version of OS X, Mavericks.
I think the star of the show — even if the most popular item was iOS 7 — was the Mac Pro. My first impression its first look was that the Mac Pro looked like a little nuke silo for your desk (perhaps that’s what they were going for), but to cram all of that in something that is 1/8th the volume of the current edition, that is some incredible work.
Image: Apple
This year, the rumor mill, while existant was far from explosive, with the only news coming out with any guarantee being that iOS was porting a flat design, the Mac Pro line would be updated and possible SDKs to Apple TV (which didn’t actually happen). Suffice to say TIm Cook’s insistance on doubling down on secrecy seems to have worked, for the most part.
There were also a few (indirect) digs at Scott Forstall and his reign — almost like the engineers were finally able to breathe again — with the changes to the design elements in both OS X Mavericks and iOS 7, and the overall loss of skeuomorphism.
Oh, and how cool was that anki demo?
For those of you who have a couple hours to kill, the full keynote is here.


Xalan-Java implements the W3C Recommendation 16 November 1999 XSL Transformations (XSLT) Version 1.0.
XLST is a stylesheet language for transforming XML documents into other XML documents, HTML documents, other document types, or text. The language includes the XSL Transformation vocabulary and XPath, a language for addressing parts of an XML document. An XSL stylesheet describes how to transform the tree of nodes in the XML input into another tree of nodes.


"Chanel Clutch Men"
It is cheap It is cheap It is cheap It is cheap It is cheap It is cheap It is cheapIt is cheap It is cheapIt is cheap It is cheapIt is cheap
It is cheap It is cheap It is cheap It is cheap It is cheap It is cheap It is cheapIt is cheap It is cheapIt is cheap It is cheapIt is cheap
©2015 Top Jakkaset - Cmis Web Design


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Background: Machine Learning
Given that a number of users of ML-Agents might not have a formal machine learning background, this page provides an overview to facilitate the understanding of ML-Agents. However, We will not attempt to provide a thorough treatment of machine learning as there are fantastic resources online.
Machine learning, a branch of artificial intelligence, focuses on learning patterns from data. The three main classes of machine learning algorithms include: unsupervised learning, supervised learning and reinforcement learning. Each class of algorithm learns from a different type of data. The following paragraphs provide an overview for each of these classes of machine learning, as well as introductory examples.
Unsupervised Learning
The goal of unsupervised learning is to group or cluster similar items in a data set. For example, consider the players of a game. We may want to group the players depending on how engaged they are with the game. This would enable us to target different groups (e.g. for highly-engaged players we might invite them to be beta testers for new features, while for unengaged players we might email them helpful tutorials). Say that we wish to split our players into two groups. We would first define basic attributes of the players, such as the number of hours played, total money spent on in-app purchases and number of levels completed. We can then feed this data set (three attributes for every player) to an unsupervised learning algorithm where we specify the number of groups to be two. The algorithm would then split the data set of players into two groups where the players within each group would be similar to each other. Given the attributes we used to describe each player, in this case, the output would be a split of all the players into two groups, where one group would semantically represent the engaged players and the second group would semantically represent the unengaged players.
With unsupervised learning, we did not provide specific examples of which players are considered engaged and which are considered unengaged. We just defined the appropriate attributes and relied on the algorithm to uncover the two groups on its own. This type of data set is typically called an unlabeled data set as it is lacking these direct labels. Consequently, unsupervised learning can be helpful in situations where these labels can be expensive or hard to produce. In the next paragraph, we overview supervised learning algorithms which accept input labels in addition to attributes.
Supervised Learning
In supervised learning, we do not want to just group similar items but directly learn a mapping from each item to the group (or class) that it belongs to. Returning to our earlier example of clustering players, let's say we now wish to predict which of our players are about to churn (that is stop playing the game for the next 30 days). We can look into our historical records and create a data set that contains attributes of our players in addition to a label indicating whether they have churned or not. Note that the player attributes we use for this churn prediction task may be different from the ones we used for our earlier clustering task. We can then feed this data set (attributes and label for each player) into a supervised learning algorithm which would learn a mapping from the player attributes to a label indicating whether that player will churn or not. The intuition is that the supervised learning algorithm will learn which values of these attributes typically correspond to players who have churned and not churned (for example, it may learn that players who spend very little and play for very short periods will most likely churn). Now given this learned model, we can provide it the attributes of a new player (one that recently started playing the game) and it would output a predicted label for that player. This prediction is the algorithms expectation of whether the player will churn or not. We can now use these predictions to target the players who are expected to churn and entice them to continue playing the game.
As you may have noticed, for both supervised and unsupervised learning, there are two tasks that need to be performed: attribute selection and model selection. Attribute selection (also called feature selection) pertains to selecting how we wish to represent the entity of interest, in this case, the player. Model selection, on the other hand, pertains to selecting the algorithm (and its parameters) that perform the task well. Both of these tasks are active areas of machine learning research and, in practice, require several iterations to achieve good performance.
We now switch to reinforcement learning, the third class of machine learning algorithms, and arguably the one most relevant for ML-Agents.
Reinforcement Learning
Reinforcement learning can be viewed as a form of learning for sequential decision making that is commonly associated with controlling robots (but is, in fact, much more general). Consider an autonomous firefighting robot that is tasked with navigating into an area, finding the fire and neutralizing it. At any given moment, the robot perceives the environment through its sensors (e.g. camera, heat, touch), processes this information and produces an action (e.g. move to the left, rotate the water hose, turn on the water). In other words, it is continuously making decisions about how to interact in this environment given its view of the world (i.e. sensors input) and objective (i.e. neutralizing the fire). Teaching a robot to be a successful firefighting machine is precisely what reinforcement learning is designed to do.
More specifically, the goal of reinforcement learning is to learn a policy, which is essentially a mapping from observations to actions. An observation is what the robot can measure from its environment (in this case, all its sensory inputs) and an action, in its most raw form, is a change to the configuration of the robot (e.g. position of its base, position of its water hose and whether the hose is on or off).
The last remaining piece of the reinforcement learning task is the reward signal. When training a robot to be a mean firefighting machine, we provide it with rewards (positive and negative) indicating how well it is doing on completing the task. Note that the robot does not know how to put out fires before it is trained.  The fact that rewards are sparse (i.e. may not be provided at every step, but only when a robot arrives at a success or failure situation), is a defining characteristic of reinforcement learning and precisely why learning good policies can be difficult (and/or time-consuming) for complex environments.
Learning a policy usually requires many trials and iterative policy updates. More specifically, the robot is placed in several fire situations and over time learns an optimal policy which allows it to put our fires more effectively. Obviously, we cannot expect to train a robot repeatedly in the real world, particularly when fires are involved. This is precisely why the use of Unity as a simulator serves as the perfect training grounds for learning such behaviors. While our discussion of reinforcement learning has centered around robots, there are strong parallels between robots and characters in a game. In fact, in many ways, one can view a non-playable character (NPC) as a virtual robot, with its own observations about the environment, its own set of actions and a specific objective. Thus it is natural to explore how we can train behaviors within Unity using reinforcement learning. This is precisely what ML-Agents offers. The video linked below includes a reinforcement learning demo showcasing training character behaviors using ML-Agents.
Similar to both unsupervised and supervised learning, reinforcement learning also involves two tasks: attribute selection and model selection. Attribute selection is defining the set of observations for the robot that best help it complete its objective, while model selection is defining the form of the policy (mapping from observations to actions) and its parameters. In practice, training behaviors is an iterative process that may require changing the attribute and model choices.
Training and Inference
One common aspect of all three branches of machine learning is that they all involve a training phase and an inference phase. While the details of the training and inference phases are different for each of the three, at a high-level, the training phase involves building a model using the provided data, while the inference phase involves applying this model to new, previously unseen, data. More specifically:
- For our unsupervised learning example, the training phase learns the optimal two clusters based on the data describing existing players, while the inference phase assigns a new player to one of these two clusters.
- For our supervised learning example, the training phase learns the mapping from player attributes to player label (whether they churned or not), and the inference phase predicts whether a new player will churn or not based on that learned mapping.
- For our reinforcement learning example, the training phase learns the optimal policy through guided trials, and in the inference phase, the agent observes and tales actions in the wild using its learned policy.
To briefly summarize: all three classes of algorithms involve training and inference phases in addition to attribute and model selections. What ultimately separates them is the type of data available to learn from. In unsupervised learning our data set was a collection of attributes, in supervised learning our data set was a collection of attribute-label pairs, and, lastly, in reinforcement learning our data set was a collection of observation-action-reward tuples.
Deep Learning
Deep learning is a family of algorithms that can be used to address any of the problems introduced above. More specifically, they can be used to solve both attribute and model selection tasks. Deep learning has gained popularity in recent years due to its outstanding performance on several challenging machine learning tasks. One example is AlphaGo, a computer Go program, that leverages deep learning, that was able to beat Lee Sedol (a Go world champion).
A key characteristic of deep learning algorithms is their ability learn very complex functions from large amounts of training data. This makes them a natural choice for reinforcement learning tasks when a large amount of data can be generated, say through the use of a simulator or engine such as Unity. By generating hundreds of thousands of simulations of the environment within Unity, we can learn policies for very complex environments (a complex environment is one where the number of observations an agent perceives and the number of actions they can take are large). Many of the algorithms we provide in ML-Agents use some form of deep learning, built on top of the open-source library, TensorFlow.


#include <Multiplier.h>
Public Member Functions | |
| Multiplier (int, int, bool) | |
| void | place (int x_offset, int y_offset, Device &device) override |
| Places the multiplier in a tightly packed square. Pipeline registers are placed before the logic LUTs. More... | |
| Public Member Functions inherited from Module | |
| Module () | |
| Constructor for a Module with no name ("_HARD_MACRO" is always added). | |
| Module (std::string) | |
| Constructor for a Module with a name. More... | |
| void | add_slice (Slicel) |
| Adds a Slicel to the module. More... | |
| std::deque< Slicel > & | get_slices () |
| Returns a reference to the deque containing all slices in thie module. More... | |
| Net * | add_interconnect (std::string) |
| Creates a new net if it doesn't exist yet and returns a pointer to it. More... | |
| void | add_port (std::string, std::string, std::string) |
| Adds a new port to the module. More... | |
| void | add_port (std::string, Slice &, std::string) |
| Adds a new port to the module. More... | |
| void | add_ground_connection (std::string instance_name, std::string pin_name) |
| Adds a pin to the ground (LOGIC 0) net and creates a port for gnd. More... | |
| void | add_vcc_connection (std::string instance_name, std::string pin_name) |
| Adds a pin to the vcc (LOGIC 1) net and creates a port for vcc. More... | |
Protected Member Functions | |
| void | create_row (int a_size, int b_size, int row) |
| Creates a new row of the multiplier and configures the slices. More... | |
| void | connect_rows (int row) |
| Connects a row to the previous one. More... | |
| void | add_ports () |
| Creates a port for every net that begins with "input_" or "output_". | |
| Slicel & | create_slice (int row, int column, int a_size) |
| Creates a configured Slicel instance for the given position. More... | |
Protected Attributes | |
| int | _row_count |
| int | _a_size |
| int | _b_size |
| bool | _is_pipelined |
| Protected Attributes inherited from Module | |
| std::string | _name |
| std::string | _inst_name |
| std::deque< Slicel > | _slices |
| std::map< std::string, IOB > | _pins |
| std::map< std::string, Net > | _net |
| std::vector< Port > | _ports |
This class creates a pipelined or combinatorial multiplier hard macro with customizable input word lengths and provides a custom placing algorithm.
| Multiplier::Multiplier | ( | int | a_size, |
| int | b_size, | ||
| bool | is_pipelined | ||
| ) |
| a_size | Number of a inputs. |
| b_size | Number of b inputs. |
| is_pipelined | Pipeline register switch. |
|
protected |
Connects a row to the previous one.
| row | Current row. |
|
protected |
Creates a new row of the multiplier and configures the slices.
| a_size | Number of a inputs. |
| b_size | Number of b inputs. |
| row | Current row. |
|
protected |
Creates a configured Slicel instance for the given position.
| row | Current row. |
| column | Current column. |
| a_size | Number of a inputs. |
|
overridevirtual |
Places the multiplier in a tightly packed square. Pipeline registers are placed before the logic LUTs.
| x_offset | X position offset on the Device for the design. |
| y_offset | Y position offset on the Device for the design. |
| device | Device instance to get placing information from. |
Reimplemented from Module.
|
protected |
Number of a input bits.
|
protected |
Number of b input bits.
|
protected |
Pipelining switch.
|
protected |
Number of rows the multiplier useses.


Tanner Mirabel was a security specialist who never made a mistake - until the day a woman in his care was blown away by Argent Reivich, a vengeful young postmortal. Tanner's pursuit of Reivich takes him across light-years of space to Chasm City, the domed human settlement on the otherwise inhospitable planet of Yellowstone. But Chasm City is not what it was. The one-time high-tech utopia has become a Gothic nightmare: a nanotechnological virus has corrupted the city's inhabitants as thoroughly as it has the buildings and machines. Before the chase is done, Tanner will have to confront truths which reach back centuries, towards deep space and an atrocity history barely remembers.
Welcome to the Epsilon Eridani system.


Welcome! This page analyzes data from the Billboard Hot 100 through the years, in combination with Spotify's audio features. It shows that more and more popular music is focused on high energy levels and increasing danceability. This corresponds with the increase of rap and dance music. On this page you see a bubble chart with all songs that have been in Billboards Hot 100 in 2018. Use the slider to change the year, you can go all the way back to 1958! As you can see, the chart maps the energy and danceability of the songs. The color is defined by the genre. You can select genres of your choice by using the checkboxes on the side. Clicking a bubble shows a line chart with all songs of the selected artist that have peaked the charts. You will also find the audio features of these songs, presented in the radar chart. If you want to read more about these features, you can take a look at the Audio Features page.
Year:


QGraph does not explicitly restrict the comparison operators
allowed for use in query conditions. In general, a condition can be
any boolean combination of restrictions on attribute values. However,
Proximity’s implementation of QGraph only supports the
=
, <>
, <
,
<=
, >
, and
>=
operators in
simple conditions.


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Track the following numbers to identify the type of triangle thats prompts out. There are three types of triangles;
Side1: Side2: Side3: TYPE OF TRIANGLE


Treatment - heart attack
The treatment options for a heart attack depend on whether you've had an ST segment elevation myocardial infarction (STEMI), or another type of heart attack.
Important
An ST segment elevation myocardial infarction (STEMI) is the most serious form of heart attack and requires emergency assessment and treatment.
It's important you're treated quickly, to minimise damage to your heart after an STEMI.
If you have symptoms of a heart attack and an electrocardiogram (ECG) shows you have an STEMI, you'll be assessed for treatment to unblock your coronary arteries.
The treatment used will depend on when your symptoms started and how soon you can access treatment.
- If your symptoms started within the past 12 hours – you'll usually be offered primary percutaneous coronary intervention (PCI).
- If your symptoms started within the past 12 hours but you cannot access PCI quickly – you'll be offered medicine to break down blood clots.
- If your symptoms started more than 12 hours ago – you may be offered a different procedure, especially if your symptoms have improved. The best course of treatment will be decided after an angiogram and may include medicine, PCI or bypass surgery.
Further STEMI information
Primary percutaneous coronary intervention (PCI)
Primary percutaneous coronary intervention (PCI) is the term for emergency treatment of an STEMI. It's a procedure to widen the coronary artery (coronary angioplasty).
Coronary angiography is done first, to assess your suitability for PCI.
You may also be given blood-thinning medicines to prevent further clots from forming, such as low-dose aspirin.
You may need to continue taking medicines for some time after PCI.
Coronary angioplasty
Coronary angioplasty is a potentially complex procedure that requires specialist staff and equipment, and not all hospitals have the facilities.
This means you'll need to be taken urgently, by ambulance, to one of the specialist centres (Heart Attack Centres) that now serve most of the UK's regions.
During coronary angioplasty, a tiny tube with a sausage-shape balloon at the end (a balloon catheter) is put into a large artery in your groin or arm. The catheter is passed through your blood vessels and up to your heart, over a fine guidewire, using X-rays to guide it.
Once the catheter is in the narrowed section of your coronary artery, the balloon is inflated to open it. Flexible metal mesh (a stent) is usually inserted into the artery to help keep it open afterwards.
Further PCI information
Medicine to break down blood clots
Medicines used to break down blood clots, known as thrombolytics or fibrinolytics, are usually given by injection.

You may also be given a medicine called a glycoprotein llb/llla inhibitor if you have an increased risk of experiencing another heart attack in the future.
Glycoprotein IIb/IIIa inhibitors do not break up blood clots, but they prevent blood clots from getting bigger. They're an effective method of stopping your symptoms getting worse.
Beta blockers
Beta blockers work mainly by slowing down the heart.
They block the action of hormones like adrenaline.
Beta blockers often prescribed include
Atenolol (also called Tenormin)
Bisoprolol (also called Cardicor or Emcor)
Metoprolol (also called Betaloc or Lopresor)
Propranolol (also called Inderal or Angilol)
Coronary artery bypass graft
A coronary angioplasty may not be technically possible if the anatomy of your arteries is different from normal. This may be the case if there are too many narrow sections in your arteries or if there are lots of branches coming off your arteries that are also blocked.
In such circumstances, an alternative operation, known as a coronary artery bypass graft (CABG), may be considered.
A CABG involves taking a blood vessel from another part of your body (usually your chest, leg or arm) and attaching it to your coronary artery above and below the narrowed area or blockage. This new blood vessel is known as a graft.
The graft diverts blood around narrowed or clogged parts of your major arteries to improve blood flow and oxygen supply to your heart.
Treating non-ST segment elevation myocardial infarction (NSTEMI) and unstable angina
If an ECG shows you have an NSTEMI or unstable angina (the "less serious" types of heart attack), blood-thinning medicine, including aspirin and other medicines, is usually recommended.
In some cases, further treatment with coronary angioplasty or coronary artery bypass graft (CABG) may be recommended in cases of NSTEMI or unstable angina, after initial treatment with these medicines.
Further information
Page last reviewed: 28 November 2019
Next review due: 28 November 2022


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 2
For a Sunday roast with a difference give guinea fowl a try, top with homemade blackberry sauce for a mouthwatering meal.
Preheat the oven to 200C/180C Fan/Gas 6.
For the guinea fowl, heat a large frying pan and add the oil and butter. Once hot, add the bird and sear all over.
Put the guinea fowl in a roasting tray and transfer to the oven. Roast for 35-40 minutes until the juices run clear when a skewer is inserted into the thickest part of the thigh.
Meanwhile, place the parsnips in a large saucepan and add the milk. Cook for 8-12 minutes. Drain the parsnips, reserving the milk. Put the parsnips in a blender and blend until smooth, gradually adding the milk, until you achieve a smooth purée.
For the blackberry sauce, heat the butter in a saucepan over a medium heat. When the butter is foaming, add the shallot, garlic and thyme and fry for 2 minutes until softened. Add the remaining sauce ingredients, except the blackberries, and cook, stirring well, for 5-8 minutes until the sauce has thickened. Just before serving add the blackberries.
For the cavolo nero, heat a sauté pan and add the butter, once hot add the calvero nero and a little water. Cook until wilted.
Remove the breasts from the guinea fowl. Divide the purée between 2 serving plates and top with the guinea fowl breasts. Place the cavolo nero on the side and spoon over the blackberry sauce. Garnish with cobnuts and serve.


Mighty Thymio platform
Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio's capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio (MyT), provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We provide a publication and source code detailing MyT's architecture and applications in advanced educational activities.
The following are the most important parameters of the (real) MyT:
-
base
- length: 11.0 cm
- width: 11.18 cm
- height: 7.7 cm
-
wheels
- radius: 2.2 cm
- width: 1.5 cm
- axis (distance between wheels centres): 9.35 cm
- maximal angular speed: ~7.5 rad/s
-
kinematics
- maximal linear speed: ~ 14 cm/s
- maximal angular speed: ~ 3 rad/s
-
proximity (and ground) sensors
- maximal range (to detect a white surface): ~ 12 cm
- field of view: 0.3 rad
-
camera
The camera is mounted on a tilt-able joint (downward pitch is positive) with
- min pitch: -0.34 rad
- max pitch: 1.3 rad
More details regarding the thymio base (e.g. schematics, electronics) can be found here. A special software is needed to visualize the schematics (for example blender).
Gazebo
Gazebo is a robot simulator. Simulators are used in robotics for many reasons, one of which is to evaluate algorithms without depending on the real robot or on the specific environment. In many applications it is essential to test robot capabilities, such as error handling, resource management, perception, localization, navigation and grasping. Some advantages of gazebo include:
- Simulate realistic physical interactions (powered by physics engines)
- Design robots or import existing models
- Rapidly test algorithms (if your robot breaks at the first try, no big deal)
- Generate testing indoor or outdoor scenarios (
worlds
)
ROS + Gazebo
gazebo_ros_pkgs
is a set of ROS packages that provide the necessary interfaces to simulate a robot in the Gazebo 3D rigid body simulator for robots (for more details ). It integrates with ROS using:
- ROS messages
- services
- dynamic reconfigure
Gazebo model of Mighty Thymio
We have designed a gazebo model of the MyT and expose a low level controller for it. Using gazebo_ros_pkgs
we implemented an interface with ROS. Roughly:
Gazebo ROS
Simulates environment =====> State (from Odometry), Sensor measurements
and robot such as proximity, camera, IMU
topics,
services
Low lever <===== Command velocities (v, omega),
controller camera pitch
As we use messages and services to communicate, we can employ all tools from ROS ecosystem.
In theory, once we are satisfied with the performance of our modules running in simulation we could directly apply them to the real robot. In practice, as you will see, some tweaks are always needed.
For detailed information regarding the robot model (geometrical and physical properties) visit this website
Install Gazebo MyT model and plugins
Refer to MyT interface repository
Be aware that the example for Teleoperate the robot with a joypad
needs a joypad to work properly.
Install Utility ROS package
Download thymio_course_skeleton
package for a first glance at how to interact with simulated MyT.
This package includes:
- Launch files to bring up gazebo simulator and MyT model.
- Launch files to visualize MyT topics in
rviz
. - A basic skeleton python script to interact with the simulated MyT.
- Extra resources needed for the homework assignment.
Homework assignment
Note: this homework assignment will be graded.
Compulsory tasks using Gazebo and the simulated MyT:
-
Write an open loop controller that moves the MyT in such a way that it follows an "8" trajectory. Test it in the
empty
world file. -
Using the
wall
world file write a controller to move the MyT straight ahead; we assume the robot is heading towards a wall somewhere in front of it; the wall is not necessarily orthogonal to the direction the MyT is originally pointing to. Write a controller in such a way that the MyT moves straight ahead until it is close to the wall (without hitting the wall), then turns in place in such a way to face the wall as precisely as possible (i.e., the robot's x axis should be orthogonal to the wall). In order to sense the wall orientation once you are close to it, you should use the proximity sensors. Feel free to define a convenient distance threshold at which you decide to stop. -
Using the controller built in task 2, once the MyT arrives close to the wall, it should then turn in such a way that it is facing opposite to the wall, then move and stop in such a way that its reference frame (i.e.
base_link
intf
tree) is as close as possible to a point that is 2 meters from the wall. Note that the proximity sensors don't have such a long range, so at some point you'll have to rely on odometry.
Optional tasks using the real MyT (to be used in the real world)
-
Test task 1 on a flat floor. How does the resulting trajectory compare to the trajectory you found in simulation?
-
Test task 2 against a generic wall-like obstacle (e.g. a wall). How precise is the final angle you reach? Test your controller using walls of different colors, and also a wall that has a non-uniform color. The most extreme case is the case in which the thymio is facing the wall at a point, and the wall is black on one side of this point and white on the other side.
Tasks 4 and 5 may require tuning of parameters to improve the performance of the controllers. We encourage to compare the performance of the controllers with (if needed) and without tuning.
Submission
Note: students should work in pairs for this assignment; first, enroll in a group with your mate (there is a link for that on iCorsi). If you are working alone, enroll in a 1-person group. In any case, you'll submit as a group (a single submission per group is required).
The submission should contain:
- The full code you implemented (package folder), including brief instructions on how to run and test it.
- A brief document stating, for each task:
- whether your solution implements it;
- if yes, whether you still have any doubts/questions/puzzling behavior in your solution
- if no, which problems you encountered
- any additional comments or notes, including any extra work or experiments you did; feel free to attach screenshots or video links.
- For task 1, an
rviz
snapshot of theodometry visualization
trace. - For tasks 4 and 5, a short animation (e.g. video, GIF) of the real MyT while executing your controller. Bear in mind the size of the animation file (or include a link to the file on dropbox/wetransfer).
Deadline: Friday May 4 8h30 Lugano time


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|
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No. actionPerformed()
does not depend on the buttons' texts.
This is an advantage of using
setActionCommand()
.
That's all the action for this chapter. If one or two topics have not yet clicked, you may wish to review the following. To get back to this page, click on the "back arrow" button of your browser.
The next chapter will discuss how to write a complete GUI application, complete with application code. About time.


Great colored T-Shirt for man
Confortable T-Shirt for man, great for any use.
Available in several different colors like orange, blue and red
Confortable T-Shirt for man, great for any use.
Available in several different colors like orange, blue and red


Geminids
by Matt
Tasha and I watched the Geminids meteor shower last night. Since it was also a very cold night here in North Carolina, we combined our skywatching with dips in the hot tub, which we use to heat our greenhouse on nights when it gets down into the 20s or below.
We typically soak in the hot tub for as long as we can stand and then cool off on the Adirondack chairs I built for this purpose a couple years ago, which sit just outside the greenhouse on the rim of the upper pond. After the Russian fashion, we alternate between extremes of hot and cold. Usually, this is just the thing to guarantee a wonderful night’s sleep.
From where the chairs are, our property forms a sort of bowl, with the whole sky falling away in every direction. While it’s certainly no dark-sky area, the closest big cities are many miles away and even on a mediocre night, the Milky Way is usually faintly visible.
Since I was a kid, I’ve been fascinated with the night sky, astronomy, cosmology, and celstial events. It’s only recently though, mostly in the years since we’ve moved onto the farm, that I’ve thought much about what it means to be human in relation to the night and the heavens above. I’ve written about this elsewhere (here and here), and It’s a subject that I find myself returning to frequently, as our future here on our terrestrial home seems less and less certain.
Last night we did see a few spectacular meteors, though by no means the 120 per hour forecast to occur at the peak (2:00 AM). We were watching between 8:00 and 9:00 PM, but the meteors we saw - at least 10 of them - were remarkable in their brightness and intensity, leaving long, luminescent trails as they streaked across the dome of night.
Between soaking in 110-degree water and cooling off in the brisk, near-freezing night air while watching the light show in the skies above, something in me was satisfied, and I slept better than I have in months.
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{% endblock full_page_content %} {% block content %}{{ profile.position }}
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{{ profile.country }}
{% if profile.grad_year %}{{ profile.grad_month_labels }} {{ profile.grad_year }}
{% endif %}{{ profile.keywords }}
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Have you seen her at a conference? If so, please consider writing her one.
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drails
for Prototype/Scriptaculous in your application and let’s you use the powerful Dojo Toolkit as your Javascript library.
Examples:
prototype_helper_spec.rb and scriptaculous_helper_spec.rb. For a quick taste, here is an example of how you can create an link that replaces the “success_response“ div on successful completion of an ajax call with the HTML returned by the server:
<%=
link_to_remote "my link", :update => [ :success => "_response" ], :url => { :action => "myaction" }
%>
To see the usages for the drails generators for dojo modules, dijits, and build profiles, type this from the command line:
cd my_rails_app; script/generate dojo
drails comes with some handy rake tasks for incorporating dojo builds. To see what is available, type this from the command line.
cd my_rails_app; rake -T | grep drails
Installation:
gem install dojo-pkg cd my_rails_app script/plugin install git://github.com/foobarfighter/drails.git
** NOTE: This also installs the dojo-pkg gem (a package that includes the dojo source code) from rubyforge. This can sometimes take a while.
Support:
If you find a bug, please send a quick email to<EMAIL_ADDRESS>Also, feel free to swing by the #-rails IRC channel on irc.freenode.net
How it works:
side library that provides the same argument interface as Prototype/Scriptaculous. The method names and objects map to the Prototype/Scriptaculous methods and objects needed for emulating the RJS helper APIs as well as help with any argument munging that is needed due to inconsistencies between the Dojo and Prototype/Scriptaculous APIs.
drails overrides Rails’ ruby internals only when it needs to in order to call out to drails functions instead of Prototype/Scriptaculous. This monkey patching is necessary only because Rails/RJS is so tightly coupled to the Javascript framework that is bundled with it.
Limitations:
a huge demand for this, then I will add the complete RJS implementation, but I'm assuming that most people that are using Dojo, prefer to right Javascript instead of Ruby generated Javascript.
Copyright © 2009 Bob Remeika and others, released under the MIT license
Generated with the Darkfish Rdoc Generator 1.1.6.


You may go ahead and login now.


class: center, middle, inverse, title-slide # Programming in R ### The R Bootcamp
Twitter:
@therbootcamp
### April 2018 --- # ***Haven't we been programming in R for the last three days of the course?*** --- # Content .pull-left45[ Conditional Statements - `if`, `else` - `case_when` Functions - Program your own functions Iteration - `for` loops - `apply` functions - `map` functions ] .pull-right45[ ```r index <- sample(1:nrow(baselers), 1) # Conditional statement if (baselers$sex[index] == "male"){ print(paste("Person with id", baselers$id[index], "is a man")) } else { print(paste("Person with id", baselers$id[index], "is a woman")) } # Define own function my_mean <- function(x){ sum(x) / length(x) } # A for loop for(i in c(1, 2, 3, 4, 5)){ print(i) } ``` ] --- # Conditional Statements Allow you to conditionally execute code blocks. .pull-left45[ *`if`; `else if`; and `else`* `if` lets you check a condition. If this condition is `FALSE` you **can** (but don't have to!) add another check with `else if` that is then, if `TRUE`, executed. An `else` in the end means this code block is always executed if the above checks evaluated to `FALSE`, i.e. it doesn't include a check. ] .pull-right45[ ```r if (this) { # do this } else if (that){ # do that } else { # do something else } ``` ] --- # A Note on Logical Expressions .pull-left45[ The conditional statements often require you to specify checks (logical expressions) which have to evaluate to `TRUE` to execute the following block of code. Note that: - `>`, `<`, `==`, `>=`, and `<=` are vectorized - to prevent errors and warnings due to the vectorization use `any` and `all` ] .pull-right45[ ```r # since == is vectorized # this yields several logicals head(baselers$sex) == "male" ``` ``` ## [1] TRUE TRUE FALSE TRUE TRUE TRUE ``` ```r # any and all summarize these logicals any(head(baselers$sex) == "male") ``` ``` ## [1] TRUE ``` ```r all(head(baselers$sex) == "male") ``` ``` ## [1] FALSE ``` ] --- # Functions - General Structure .pull-left4[ You can define your own functions. This can save a lot of time and space, because by writing your own function you only have to write the whole block of code once. Benefits of writing code in functions: - less typing - fewer errors - can be more general ] .pull-right5[ ```r function_name <- function(arg1, arg2 = "default"){ # function body result <- do_stuff(arg1, arg2) } ``` ] --- # Functions - Return Statement .pull-left45[ The last object in a function code is returned to the global environment. If a function call should be terminated in the middle of the code and some value be returned, the `return()` function has to be used. ] .pull-right45[ ```r my_mean <- function(x){ # check precondition and # if it's not met return NA if (any(is.na(NA) | !is.numeric(x))){ return(NA) } sum(x) / length(x) } ``` ] --- # Functions - Arguments .pull-left45[ Functions can take any object as input to arguments. Since functions themselves are also objects, this means that also functions can be passed as arguments. When calling a function you can use objects or expressions as input to an argument. ] .pull-right45[ ```r # function definition call_fun <- function(dat, fn, cont_nas){ fn(dat, na.rm = cont_nas) } # input a function and additional # statements call_fun(dat = baselers$tattoos, fn = mean, cont_nas = TRUE) ``` ``` ## [1] 1.812 ``` ```r # input expression as argument call_fun(dat = baselers$weight / (baselers$height / 100) ^2, fn = median, cont_nas = TRUE) ``` ``` ## [1] 25.14 ``` ] --- # Functions - Error Messages .pull-left45[ When writing functions you can add checks and if they are not met provide your own error messages. Possible functions are `stop()`, `message()`, `warning()`. - With `stop()`, `message()`, and `warning()` you have to use separate `if` statements to test each potential violation. ] .pull-right45[ ```r # with stop you test whether stopping # condition is met my_mean <- function(x){ if (!is.numeric(x)){ stop("Can only compute mean from numeric variable. Your data was of class", class(x)) } else if (any(is.na(x))){ warning("Return NA because data contains NAs.") } sum(x) / length(x) } ``` ] --- # Iteration - `for` Loops .pull-left35[ A for loop lets you repeat the execution of a block of code as many times as you wish. It also allows you to loop through positions. A `for` loop has three components: - output (object to be prepared before entering the loop) - sequence (defines the vector to loop through) - body (the code that does the actual work) ] .pull-right6[ ```r # output: mean_vec <- vector("double", length = 3) mean_names <- c("age", "weight", "height") for (ind in c(1, 2, 3)) { # sequence mean_vec[ind] <- mean(baselers[[mean_names[ind]]], na.rm = TRUE) # body } # The code to do the same without a loop is mean(baselers[["age"]], na.rm = TRUE) mean(baselers[["weight"]], na.rm = TRUE) mean(baselers[["height"]], na.rm = TRUE) ``` ] --- # Iteration - `while` Loops .pull-left45[ `while` loops let you iterate until a specified condition is met. This can be handy, but also "dangerous" because if the condition is never met, it will just iterate until you manually stop it. ] .pull-right45[ ```r subs_age <- sample(baselers$age, 30) # get only baselers over 40 while(any(subs_age <= 40)){ subs_age <- sample(baselers$age, 30) } # Potentially problematic: # get only baselers over 40 # but with a much larger sample while(any(subs_age <= 40)){ subs_age <- sample(baselers$age, 30000) } ``` ] --- # Iteration - `apply` Family .pull-left45[ Functions of the `apply` family are another tool to iterate through objects and do the same operation on each subset. They are very handy, but not always consistently programmed, i.e., for example the argument structure is not always the same. These are available `apply` functions: - `apply` - `lapply` - `sapply` - `tapply` - `vapply` ] .pull-right5[ ```r # structure: data, function, arguments # lapply: simplest apply version lapply(baselers[, c("age", "weight")], mean, na.rm = TRUE) ``` ``` ## $age ## [1] 44.61 ## ## $weight ## [1] 74.35 ``` ```r # sapply: apply version that # simplifies lapply output sapply(baselers[, c("age", "weight", "height")], mean, na.rm = TRUE) ``` ``` ## age weight height ## 44.61 74.35 171.00 ``` ] --- # Iteration - `apply` Family - Anonymous Functions .pull-left45[ You can specify *anonymous functions* in functions of the `apply` family. An anonymous function exists only temporarily when the `apply` function is called. This means that the anonymous function is not defined in a separate step, but directly within the `apply` function. ] .pull-right5[ ```r # calculate the mean of # age, weight, and height lapply(baselers[, c("age", "weight", "height")], function(x, na_rm){ if (isTRUE(na_rm)){ x <- x[-is.na(x)] } sum(x) / length(x) }, na_rm = TRUE) ``` ] --- # Iteration - `map` Family .pull-left45[ The `map` functions are the purrr equivalent of the `apply` functions.  ] .pull-right5[ ```r map(baselers[, c("age", "weight", "height")], mean) ``` ``` ## $age ## [1] 44.61 ## ## $weight ## [1] NA ## ## $height ## [1] 171 ``` ] --- # Iteration - `map` Family .pull-left45[ The `map` functions are the purrr equivalent of the `apply` functions. There are several `map` functions that iterate over one vector. They are named after the type of object they return: - `map` returns a list - `map_lgl` returns a logical vector - `map_int` returns an integer vector - `map_dbl` returns a double vector - `map_chr` returns a character vector  ] .pull-right45[ ```r baselers %>% select(age, weight, height) %>% map_dbl(function(x){ sum(x) / length(x) }) ``` ``` ## age weight height ## 44.61 NA 171.00 ``` ] --- # Iteration - `map` Family .pull-left45[ The `map` functions are the purrr equivalent of the `apply` functions. There are also `map` functions that iterate over two or more vectors. They are named `map2`, and `pmap`. If the objects in the list are named, pmap will use the name, otherwise the position in the list, to determine which object belongs to which argument.  ] .pull-right45[ ```r x <- baselers[, c("age", "weight")] trim <- c(0, 0.2) map2(x, trim, mean) ``` ``` ## $age ## [1] 44.61 ## ## $weight ## [1] NA ``` ```r na.rm <- c(FALSE, TRUE) pmap(list(x = x, trim = trim, na.rm = na.rm), mean) ``` ``` ## $age ## [1] 44.61 ## ## $weight ## [1] 73.17 ``` ] --- # Summary - What We've Learned - How to use conditional statements - How to program our own functions - How to iterate over blocks of code This is very helpful to not having to type the same code many times and to keep it more flexible. Remember the DRY (Don't Repeat Yourself) principle, i.e. if you have to write the same block of code more than twice, create a function to do so and iterate over it. --- # Interactive session Let's go through some examples together --- # Programming in R Practical
Link to practical


Using '/' to signify a null poiner, write the sequential representation for this tree.
ANS
Make sure to use a '/' for every null pointer
There should be one more '/' than there are nodes.
Answer: ANS


Telenor Myanmar Limited (refer to as TML) and Huawei have been announced as a 2016 Global Telecoms Business Innovation award winner in the "Mobile Infrastructure Innovation" category for the "9-Sector Solution Unleashing Traffic in Myanmar" project.
2016 May 26Türk Telekom and Huawei won the Mobile Infrastructure Innovation award at the Global Telecom Business summit today.
2016 May 26VIVA Bahrain made a regional breakthrough by successfully implementing, for the first time, Huawei's 9-sector solution using the triple-beam antenna technology. This innovative solution, delivers approximately 2.7 times the performance of a regular mobile tower and capacity to absorb traffic demand, offering higher quality telecommunications services whilst enhancing VIVA's customers connectivity at events or locations were data demand is extremely high.
2016 Apr 22Finnish Shared Network Ltd, also known as SYV, has deployed the Huawei Active Antenna Unit (AAU) solution enabling them to improve network quality and provide an optimal mobile broadband (MBB) experience in Northern Finland.
2016 Mar 28[Shenzhen, China, Dec.9, 2015] Huawei today announced the release of its first smart EasyRET 3.5GHz 8T8R Antenna, which could maximize TDD LTE network capacity and management efficiency for operators.
With the rapid development of MBB (Mobile Broadband), the demand on network capacity is increasing explosively. More and more operators
plan to deploy 3.5GHz TDD LTE. But higher frequency brings higher propagation loss and the density of high band BTS leads to more interference. TDD 8T8R solution provides more capacity and better interference control to overcome propagation loss and interference challenges. Huawei releases the smart EasyRET 3.5GHz 8T8R antenna to perfectly meet the demand of TDD 8T8R large-scale deployment.
With 45% of users willing to stick with a quality carrier network regardless of tariff, we are truly living in the age of experience. Advanced Information Service (AIS) of Thailand showed a healthy respect for this fact when deciding to implement Huawei's six-sector split-antenna solution for capacity expansion, establishing a leadership position in a market that seems tailor-made for sharing memories in real time.
2015 Jun 5Huawei, in partnership with China Telecom Beijing Research Institute, Globe Telecom Philippines (referred to as Globe), and Nanjing Commway, was the recipient of two Wireless Network Infrastructure Innovation awards and a Business Service Innovation award at the annual Global Telecoms Business Innovation Awards and Innovation Summit in London. The awards were presented by Global Telecoms Business, a leading telco magazine.
2015 May 30
[Bali, Indonesia, May 7, 2015] Huawei held its Antenna & AAU (Active Antenna Unit) Summit for Asia Pacific regional carriers in Bali. The event was attended by major carriers, government officials and industry analysts in the region. At the summit, Huawei released its newest hexa-band antenna on 700/850-900MHz.
2015 May 7[Rio de Janeiro, Brazil, April 10, 2015] Huawei released the industry's first multi-band miniature antenna supporting 700/850-900MHz at the Latin American LTE Forum today. This antenna adopts the innovative "Dipole Reuse" technology and supports various bands ranging from 700/850-900/1800MHz to AWS/PCS/2100/2600MHz. Among these, the quad-band antenna (690-803/824-960/2*1710-2690MHz) boasts a reduction in width of 40% compared to a traditional antenna. Besides its compact size, the multi-band miniature antenna also supports remote independent adjustment in electrical down-tilt allowing it to achieve the best network performance, meeting the demands of multi-band network deployments and providing a better mobile service for subscribers.
2015 Apr 10Huawei today announced release of the world's first L-band antenna supporting Ultra Wide Band (UWB) and multi band. In addition to the major frequency bands at 700/800/900/1800/2100/2600MHz, the new antenna will enable deployment of the 1500MHz band.
2015 Feb 19[Shenzhen, China, January 8, 2015]: Huawei has recently released an innovative dual-beam antenna solution which allows simple and efficient deployment of 6-sector networks for operators. The solution has already been deployed by AIS Thailand and improved capacity by 70%.
2015 Jan 8Huawei, a leading global information and communications technology (ICT) solutions provider, today announced that three of its key operator customers, LG Uplus, Optus, and China Mobile, were recognized at the Global Telecoms Business Innovation Awards for strong innovation in the areas of LTE-Advanced, LTE-TDD Carrier Aggregation (CA) and antenna technologies.
2014 May 21According to Huawei, a leading global information and communications technology (ICT) solutions provider, 2013 was the most significant year for LTE development worldwide to date. More than 110 commercial LTE networks were launched, with multiple new data optimization technologies reaching maturity and seeing deployment. At the forefront of these developments, advances in antenna technologies for flexibly handling additional spectrum bands and enabling remote electrical tilt (RET), configuration and maintenance, took a substantial step forward.
2014 Feb 10Huawei, a leading global information and communications technology (ICT) solutions provider, and Zain Group, a leading multinational operator in the Middle East and Africa, recently jointly announced that multi-band, ultra-broadband antennas from Huawei had been chosen to deploy nearly one thousand GSM/UMTS/LTE (GUL) convergent network sites for Zain Kuwait.
2013 Dec 19


Before Change
if kw_indices is not None:
// Convert keyword indices to valid indices if possible
if isinstance(kw_indices, np.ndarray):
ndim = kw_indices.ndim
shape = kw_indices.shape
else:
ndim = 0
shape = []
portion1 = kw_indices
while len(portion1 > 1):
maxLen = 0
for portion2 in portion1:
if len(portion2) > maxLen:
portion = portion2
maxLen = len(portion)
portion1 = portion
shape.append(maxLen)
if ndim == indices.ndim:
indices = kw_indices
elif isinstance(particlesSTAR, StarDict):
After Change
"when particlesSTAR is a loop table")
else:
indices = np.fromiter((index in indices),
dtype=[("dataBlockNumber", int),
("loopNumber", int),
("rowNumber", int)])
if indices == []:
raise ValueError("particlesSTAR does not contain any loops with image fields")


Caitlyn jokes ‘girl thing is pretty good’ as Bruce had to do all the work in camp
Caitlyn Jenner is enjoying her jungle experience much more this time around
by Verity Sulway, https://www.mirror.co.uk/authors/verity-sulway/I'm a Celeb 's Caitlyn Jenner joked that she is having a much nicer time as a girl in the celebrity jungle.
The 70-year-old, who transitioned in 2017, said: "This girl thing is pretty good!"
She explained that when she appeared in the US version of the show back in 2003 as Bruce, things were different.
Speaking in the Bush Telegraph, Caitlyn revealed: "As Bruce, I was doing everything for everyone, but here everyone is offering to make me tea.
"This girl thing is working out pretty good."
Viewers took to Twitter to laugh at Caitlyn's quip.
"‘This girl thing is working out pretty good’ I am howling at Caitlyn!", chuckled one.
"'Last time I was in here, as Bruce, I was doing everything for everybody, doing this, doing that.
"'Now I'm thinking to myself, this girl thing's working out good' - I like how Caitlyn can look it things in the correct, humorous manner", said another.
"‘This girl thing is working out pretty good’ Caitlyn is an icon", added a third.
Caitlyn is reportedly being spurred on in the jungle by heartache she’s carried for more than 50 years.
The former Olympian was left distraught when her beloved younger brother Burt was killed aged 18 – in a Porsche she had lent him.
Writing in her autobiography The Secrets Of My Life, the reality star wrote: “Burt has taken the Porsche on Old Canton Road. Because the road was old, it was narrow and curvy.
"There was a rise, and Burt took it too fast. The Porsche went airborne and then crooked.
“It hit a tree and then a rock wall of sizable boulders. Hutchings was flung from the car and killed instantly. Burt suffered critical injuries.”
The Jenner family had to make the unimaginable decision to turn off Burt’s life-support.
“I have struggled with guilt", she said, but "never in my life have I ever beat myself up about it."


Sometimes your test might perform a large number of steps and the storyboard becomes unweildy. Adding cards to a collapsible group will allow you to click on the first card to show or hide the rest of the cards in the group.
Behaviour:
startGroup() or addCard(GroupStartCard card)
methodsstopGroup() or addCard(GroupStopCard card)
methodsSetup
When this snippet is run with this fixture, this results in a collapsible start card being added to the Storyboard and the group left expanded.
Setup
When this snippet is run with this fixture, this results in a collapsible stop card being added to the Storyboard and the group rendered collapsed.
Setup
When this snippet is run with this fixture, this results the start and stop cards getting a failure status.


ABOUT ME
Full Stack Developer and former financial analyst passionate about creating useful applications and user-friendly programs through efficient code. I leverage my skills in analysis, problem-solving, and team-building to inspire those around me and to promote a meritocratic and solutions-oriented workplace environment. I thrive on creating well-crafted, data-driven solutions to complex problems, with a special interest in film, wellness, and hospitality. I am insatiably curious, I crave intellectual challenge, and I thrive in environments that foster that drive. I graduated Cum Laude from Georgetown University in Washington, D.C. with a B.S. in Finance and a minor in Theology. Prior to embarking down the developer path, I worked in business development for Yelp, in financial analysis for The Stilwell Group, an activist hedge fund, and, currently, in corporate finance and strategy for Cater2.me, a hospitality startup. Based in Denver now, I'm a Jersey boy with a Colorado soul. When I'm not coding, you can find me in the mountains hiking and skiing, or on the go, exploring new countries, cities, and yet-to-be-discovered corners of the Earth.


The <integrity-schedule> component is used for picking schedules.
<integrity-schedule
(scheduleChange)="onScheduleChanged($event)">
</integrity-schedule>
interface ISchedule {{ '{' }}
from: Date;
to?: Date;
until?: Date;
repeatType?: string;
period?: number;
weekdays?: Array<string>;
{{ '}' }}
| Attribute |
Required |
Value |
Description |
| schedule |
no |
ISchedule |
Optional initial schedule. |
| scheduleChange |
yes |
ISchedule |
Any time an input is changed, as well as on initialization, the currently chosen schedule is emitted. |
| simple |
no |
none |
Toggles a simplified date picker with inputs for 'from' and 'to'. |


A set of tools inspired by 'Stata' to explore data.frames ('summarize', 'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
| Version: | 0.6.5 |
| Depends: | R (≥ 3.2.0) |
| Imports: | data.table, dplyr (≥ 0.6.0), ggplot2 (≥ 2.0.0), lazyeval, matrixStats, methods, rlang, stringr, tidyr |
| Suggests: | knitr, lubridate, rmarkdown, testthat |
| Published: | 2017-07-06 |
| Author: | Matthieu Gomez [aut, cre] |
| Maintainer: | Matthieu Gomez <mattg at princeton.edu> |
| BugReports: | https://github.com/matthieugomez/statar/issues |
| License: | GPL-2 |
| URL: | https://github.com/matthieugomez/statar |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | statar results |
| Reference manual: | statar.pdf |
| Vignettes: |
Data.frames function Graph Panel data Summary function |
| Package source: | statar_0.6.5.tar.gz |
| Windows binaries: | r-devel: statar_0.6.5.zip, r-release: statar_0.6.5.zip, r-oldrel: statar_0.6.5.zip |
| OS X binaries: | r-release: statar_0.6.5.tgz, r-oldrel: statar_0.6.5.tgz |
| Old sources: | statar archive |
Please use the canonical form https://CRAN.R-project.org/package=statar to link to this page.


Console Log Tutorial
Console log directly responseText
Console dir data after parsing responseText
Formattting specifiers for obj in Chrome
Use string formats;
console.log("%s %O", "My Object", obj);
Chrome has Format Specifiers with the following;
%s Formats the value as a string.
%d or %i Formats the value as an integer.
%f Formats the value as a floating point value.
%o Formats the value as an expandable DOM element (as in the Elements panel).
%O Formats the value as an expandable JavaScript object.
%c Formats the output string according to CSS styles you provide.


fun copy(): ByteSequence
Copy this sequence, complete with new backing array. This can be helpful to break references to potentially large backing arrays from small sub-sequences.


Language attributes—like +i_en for English, +i_fr for French, and +i_ar for Arabic—are not included in this document. For information on how to construct them from ISO 639 language codes, see “Attributes for languages” in the HXL tagging conventions.
Some hashtags have required datatypes. These apply currently to the #date hashtag, which must always tag a column of dates; to the humanitarian caseload hashtags #affected, #inneed, #targeted, #reached, which must always tag a column of numbers; and to the financial-amount hashtag #value, which must also tag a column of numbers. The data type, if present, appears as part of the hashtag definition.
Some hashtags have default vocabularies: those are taxonomies of the values that are expected to appear in the column below the hashtag when you add the +code attribute: for example, the default vocabulary for #adm1 +code is +v_pcode (UN place codes from IASC Common Operational Datasets), but you can override any defaults by supplying a different +v_ attribute. For more information on vocabulary attributes, see “Attributes for controlled vocabularies” in the HXL tagging conventions document. Note that the +v_ attributes appear in the HXL master vocabulary list rather than here.
If you cannot find what you need here, you can extend HXL with new hashtags like #x_toxicity. For more information on extending HXL, see “Creating extension tags” in the HXL tagging conventions. (There is no need to add “x_” when you create new attributes.)
If you’d like to add interactive help to your own
HXL-enabled web site or online tool, you can link
directly to the definition of any hashtag or attribute by
including this document’s URL, and adding an HTML fragment
identifier (anchor) replacing “#” with “tag_” for the
hashtag, or “+” with “att_” for the attribute. For
example, to link directly to the definition of the
“#modality” hashtag, use the fragment identifier “#tag_modality
“,
resulting in the full URL /standard/1_1beta/dictionary/#tag_modality
;
to link directly to the definition of the “+funder”
attribute, use the fragment identifier “#att_funder
“,
resulting in the full URL /standard/1_1beta/dictionary/#att_funder
.
See Credits in the HXL tagging conventions for information about HXL Working Group member organisations and other contributors.


Namespace puredom.net
-
Networking functionality.
- Defined in: net.js
| Constructor Attributes | Constructor Name and Description |
|---|---|
Method Summary
| Method Attributes | Method Name and Description |
|---|---|
| <static> |
puredom.net.
get
(url, callback, options)
Make a GET request.
|
| <static> |
puredom.net.
post
(url, body, callback, options)
Make a POST request.
|
| <static> |
puredom.net.
request
(options, callback)
Construct and send an HTTP request based on the specified options.
|
Namespace Detail
puredom.net
Method Detail
-
Make a GET request.
This is a convenience wrapper around puredom.net.request.puredom.net.get("/ajax?f=1", function(success, response) { console.log(success===true, response); });
- Parameters:
- {String} url
- URL to request
- {Function} callback
- Called on completion. Gets passed
(success, response, request)
. - {Object} options Optional
- Additional configuration. See options for puredom.net.request.
- Returns:
- {puredom.net.HttpRequest} An HTTP request object
-
Make a POST request.
This is a convenience wrapper around puredom.net.request.puredom.net.get("/ajax?f=2", { foo:'bar' }, function(success, res, req) { console.log(success===true, res, req.status, req.responseHeaders); });
- Parameters:
- {String} url
- URL to request
- {Object|String} body
- Request body. If an
Object
, will be serialized based on the request's Content-Type (defaulting to form-encoded) - {Function} callback
- Called on completion. Gets passed
(success, response, request)
. - {Object} options Optional
- Additional configuration. See options for puredom.net.request.
- Returns:
- {puredom.net.HttpRequest} An HTTP request object
-
Construct and send an HTTP request based on the specified options.
- Parameters:
- {Object} options
- Request options.
- {String} options.url
- URL to request
- {String} options.method Optional, Default: "GET"
- HTTP method to use
- {String|Object} options.body Optional
- Request body. If a
String
is passed, it is considered pre-serialized. If anObject
is passed, it will be serialized based on the request'sContent-Type
header. - {Any} options.bodySerialized Optional
- If set, gets assigned unmodified as the request body. If you're sending something like Blob data, this is for you.
- {Object} options.headers Optional
- A key-value list of request headers to send.
- {Object} options.contentTypeOverride Optional
- If set, overrides the response's
Content-Type
header with the given value. - {Object} options.callback Optional
- Alias of
callback
, a function to call on completion. Gets passed(success, response, request)
. - {Function} callback Optional
- Called on completion. Gets passed
(success, response, request)
. If set, takes precidence overoptions.callback
.
- Returns:
- {puredom.net.HttpRequest} An HTTP request object


Inpei102.github.io
PiCO Smokerlyzer CO Monitor - Carbon Monoxide. Kurzbedienungsanleitung piCO+ II - specialmed.de. Training video on how to perform a calibration on the Bedfont piCO+ Smokerlyzer. Visit covita.net for more information on Bedfont s full range of breath. Smokerlyzer Produktreihe - bedfont.com.
Smokerlyzer Produktreihe Zur Verwendung mit, piCObaby and Micro+ Scientific contributions to health. Benutzerhandbuch. Find best value and selection for your Smokerlyzer piCO Bedfont search on eBay. World s leading marketplace. 3 piCO+ Operating Manual Introduction English The piCO+ Smokerlyzer® is a breath carbon monoxide monitor intended for multi-patient use by healthcare professionals.
PiCO+ Smokerlyzer - Resetting the Monitor - YouTube. CoVita is a partner and sole source representative for Bedfont Scientific breath analysis products and accessories for the USA and Canada. We are the market leaders in breath monitoring for medical applications, fire/EMS, wellness, education and research. Training video on how to perform a reset on the Bedfont® piCO+ Smokerlyzer. Visit covita.net for more information on Bedfont s full range of breath. The Bedfont piCO Smokerlyzer breath CO monitor is an essential tool in helping motivate smokers to quit the habit. The device features a large touchscreen colour display with a simple user interface for complete Kurzbedienungsanleitung piCO+ IIpiCO+ II Gebrauchsanweisung 1 Zum Einschalten des Gerätes den Knopf drei Sekunden drücken. 2 Setzen Sie ein D-Stück mit einem neuen.
Bedfont Scientific Ltd. - Smokerlyzer. Bedfont Smokerlyzer Pico Ii Gebrauchsanweisung. PiCO+ Smokerlyzer - Performing a Calibration - YouTube. COdata+™ is a FREE software programme for Microsoft Windows, for use with the Bedfont ® Smokerlyzer ® range of breath CO monitors. When connected to the software, readings are downloaded from the monitor to the COdata+™ database, which provides a virtual display of the instrument and an instant report of the patient s results.


{% extends 'client_base.html' %} {% block navcontent %}
Record the time each appliance was on using these 5 minute blocks


Specify the ID of the Amazon Machine Image (AMI) that you want to use with the newly started machines.
Typically, you will use one of the machine images with XLT already installed. For the current list of AMI IDs, see the XLT homepage.


ECE Student Relationship Management
Email address
Password
Sign in
For sandboxing, please enter 'guest' as username and 'password' as the password.


Venues:
Don't forget that the conference will take place at two different venues, located within walking distance from each other. The schedule has, however, been designed to reduce moving from one place to the other. The two venues are:
The “Centro de Extensión” of Pontifical Catholic University of Chile. In what follows we refer to this venue as “CE” and to Catholic University as PUC.
The School of Business of the University of Chile. In what follows we refer to this venue as “FEN” and to the University of Chile as UChile.
Conference check-in:
Will be located at Centro de Extensión (CE) and will begin on Sunday at 14:00 and will remain open Monday to Thursday from 8:00 to 17:00 and Friday to 13:00.
Exhibit Hall
It is located at FEN, the Business School of University of Chile. It will be open from Monday to Thursday.
Coffee and lunch:
At lunchtime on Monday, Tuesday, Thursday, and Friday, we will provide a complimentary box lunch, so you don't have to worry about finding a place to eat elsewhere. This box lunch will be available at the Hall of the Business School of the University of Chile (FEN), from 13:15-14:15, Monday, Tuesday, and Thursday, and from 12:45 to 13:45 on Friday.
There will also be complimentary coffee breaks during each day of the conference.
Wifi:
If you have Eduroam at your institution, you should be able to connect at both venues. There will be other Wi-Fi networks available, and the login instructions will be available at check in. Complimentary Wi-Fi is available at the venues, which will also work for those who have Eduroam at their institution. Further information about this will be sent later, along with tips on using your cell phone in Chile.
Information desk:
There will be information desks in both venues. Staff there will guide you around and can also give you general information. There will also be a special desk for tourist information.


Register
First Name:
Last Name:
Male
Female
Other
Email:
Password:
(8 to 10 characters)
Birthday
Jan
Feb
Mar
April
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1
2
3
4
5
2000
2001
2002
I agree to the terms and conditions
Submit