Provides a visual example of the Tabs "activated" event and its firing timing.
Facilitate cultivate monetize, seize e-services peer-to-peer content integrateAJAX-enabled user-centric strategize. Mindshare; repurpose integrate global addelivery leading-edge frictionless, harness real-time plug-and-play standards-compliant 24/7 enterprise strategize robust infomediaries: functionalities back-end. Killer disintermediate web-enabled ubiquitous empower relationships, solutions, metrics architectures.
Bricks-and-clicks? Evolve ubiquitous matrix B2B 24/365 vertical 24/365 platforms standards-compliant global leverage dynamic 24/365 intuitive ROI seamless rss-capable. Cutting-edge grow morph web services leverage; ROI, unleash reinvent innovative podcasts citizen-media networking. Frictionless webservices, killer open-source innovate, best-of-breed.
Frictionless webservices, killer open-source innovate, best-of-breed, whiteboard interactive back-end optimize capture dynamic front-end. Initiatives ubiquitous 24/7 enhance channels B2B drive frictionless web-readiness generate recontextualize widgets applications. Sexy sticky matrix, user-centred, rich user-centric: peer-to-peer podcasting networking addelivery optimize streamline integrated proactive: granular morph.
Exploit niches; enable A-list web-enabled holistic end-to-end. Exploit experiences value-added tagclouds, open-source cross-platform e-tailers, user-contributed, implement! Convergence, solutions front-end, "synergize markets initiatives integrateAJAX-enabled platforms; wireless, supply-chains reinvent, mindshare, synergies implement, drive evolve!" Post incentivize; rich-clientAPIs customized revolutionize 24/365 killer incentivize integrate intuitive utilize!
Post incentivize; rich-clientAPIs customized revolutionize 24/365 killer incentivize integrate intuitive utilize!


TRAVEL SAFETY
Terror warning issued for Christmas markets destinations across Europe
by Kara GodfreyBRITS are being warned of the "threat of terrorism" at a number of Christmas Market destinations.
The Foreign Office have updated their travel advice for countries including Germany, France and Spain as the festive season approaches.
Countries such as Denmark, Sweden and the Netherlands are also popular this time of year due to the festivities throughout the cities.
However, the government has warned Brits to stay vigilant due to the potential risks.
The advice states: "There is a general threat from terrorism.
"There may be increased security in place over the festive period, including at Christmas markets and other major events that might attract large crowds.
"You should remain vigilant and follow the advice of local authorities."
The travel advice for Spain also warns tourists to remain vigilant during New Year celebrations well, with many flocking to the country for Nochevieja.
A terror attack at a Berlin Christmas market in 2016 left 12 people dead after a stolen lorry was driven through the streets.
In 2018, five people were killed following a terrorist attack at Strasbourg's Christmas market.
Last Christmas, counter-terror chiefs have warned ISIS lone-wolf supporters may target British Christmas markets with knives or vehicle-ramming attacks.
The twisted fanatics themselves issued a chilling threat by posting a sick picture depicting a slain Santa in a British high street.
Chilling text at the bottom of the poster reads: “O pigs of the cross, the day is near when your carnage will be gifts for your children.”
Though the jihadis lost their so-called Caliphate, a large swathe of territory that covered Iraq and Syria, there are fears they still remain a potent terror threat.
Terrorist attacks in Sri Lanka at Easter are believed to have been carried out by ISIS.


--- title: "Dattafest: Topology and Combinatorics" layout: standalone css: "datta-fest" ---
Abstract: We shall discuss one of the most famous problems in Combinatorics; the folklore conjecture that for each prime number p, there is a unique projective plane of order p. The focus will be on the coding theoretic approach to this problem.
Abstract: Low-dimensional topology is full of algorithmic problems whose best known running times are exponential, or doubly-exponential, or worse - and in some cases even undecidable. Nevertheless, there is a need to have software that can actually solve these problems in practice, despite the theoretical limitations. For this we use “cheap tricks” - techniques that might not always work, but when they do will give fast, correct and certified results.
Combinatorial techniques play an important and powerful role in these tricks. We will talk through some key ideas, including well-structured triangulations, graph structure theory and properties of the dual skeleton, and how these are used in practice to find new results in knot theory, 3-manifold and 4-manifold topology.
Abstract: Stackedness and tightness are attractive properties of triangulated manifolds, which Basudeb has a lot of contributions to them. These properties are defined from combinatorial and topological point of view, but they actually have a nice connection to commutative algebra theory. In this talk, I will talk how Basudeb's works on stacked and tight triangulations connect to commutative algebra theory.
Abstract: In this talk, we review the concept of triangulation and quasitoric manifolds. Then, we discuss some explicit triangulation of certain 4-dimensional quasitoric manifolds and 8-dimensional complex projective space. The results are joint work with Basudeb Datta.
Abstract:
∗ The slides of Wolfgang Kühnel will be presented by Biplab Basak and Nitin Singh.
Abstract: The crosscap number of a knot is the non-orientable counterpart of its genus. It is defined as the minimum of one minus the Euler characteristic of S, taken over all non-orientable surfaces S bounding the knot. Computing the crosscap number of a knot is tricky, since normal surface theory - the usual tool to prove computability of problems in 3-manifold topology, does not deliver the answer "out-of-the-box".
In this talk, I will review the strengths and weaknesses of normal surface theory, focusing on why we need to work to obtain an algorithm to compute the crosscap number. I will then explain the theorem stating that an algorithm due to Burton and Ozlen can be used to give us the answer.
This is joint with work with Jaco, Rubinstein, and Tillmann.


This guide details the specification of APIs provided by this platform.
Today many IT engineers write Tamil related applications using variety of technologies. But, It is unlikely they are re-used by another developer to build more on top of what is already done. If an application developer wants to do simple operations such as searching, replacing and sorting of Tamil texts, it is a nightmare. It is to be noted that the Tamil character representation provided by Unicode consortium is so unnatural to Tamil that it makes almost impossible to use standard APIs (say, java.lang.String from Java) from any programming language to perform those operations. I sense that we need another layer that hides all these complexities for the application developers. That api layer should allow the application developers to focus on what they do rather than to deal with the erroneous representation of Tamil characters.
This platform is intended to provide services from simple ones such as Tamil Dictionary, transliteration to most complicated services such as speech to text and translation from and to English. Yes. There is a long way to go. This platform is very young.
Today, we have தமிழகராதி, கலைச்சொல்லகராதி, இலக்கியங்களிலிருந்து சொல்லைத்தேடும் பயன்மிகள் hosted in different websites. However, They are done in such a way that is mostly useful only for human but to another program. I believe we are in an Era where a computer program is also one potential consumer of the internet and hence of all Tamil resources on the internet. I want to create such awareness. This platform should provide integration with those resources in the background without affecting the applications hosted on this. Tamil has to go beyond writing and reading a post in a Blog or Facebook.
You may also want to read இந்த செயலியின் நோக்கம் என்ன? You can also consider joining the FB page
This guide is intended for application developers who want to write simple Tamil related applications that can assist Tamil development on the internet. Someone with passion for Tamil should be able to try on this. Application developers also bring requirements to the platform. Much of the system level complexities that are not directly related to the goals of an application are to be handled by the platform. The platform is just young and we should be able to support services one by one. An application developer also can contribute to the platform.
Currently, the platform code is available at github.
One can expose a ReST API within the application by writing server side scripts. More scripting may be supported depending on the feasibility. Application developers also can publish Javascript APIs for other applications to consume.
An application in this platform is a bunch of resources (html5, javascript, groovy scripts) doing one or or more Tamil related tasks. An application is identified with a name. An application while being created (or cloned) can be secured against a code. The security code is required to make changes in it. However, anyone can view the application code. One should be able to create and publish new ReST APIs implemented using Groovy (ie Java). Those apis can be used by another application.
A sophisticated editor for Tamil that does spell checking, gives suggestions and performs auto complete can be done as an application. Another application can take வெண்பா as input and validate its correctness. We can write applications that shows various புணர்ச்சிவிதிகள் for students and Tamil scholars. We will be able to write more complicated applications (say reading Tamil text) in the future.
The Java API is published here. The starting point for using this API will be TamilFactory. These can be used in the groovy scripts to expose ReST apis from an application.
The JavaScript API is published here. The below snippet can be copied into the HTML resource.
<script src="${R_JQUERY_JS_PATH}" type="text/javascript" charset="utf-8"></script> <script src="${R_PLATFORM_JS_PATH}" type="text/javascript" charset="utf-8"></script> <script type='text/javascript'>//<![CDATA[ var trans = TamilFactory.createTransliterator(); // This can be cached for repeated usages. var tamil_text = trans.transliterate('vaNakkam').tamil; // This will return வணக்கம் //]]> </script>
It will be very straight forward to create a simple application and get a taste of it. If you already know (javascript and groovy)scripting and html5, it will be a little more fun to try new things with the given set of APIs. After getting familiarised with the below terms and concepts, you can go here to add a new app.
You need an application name. This should be a simple name without any special character. Every application will get an unique application access URL.
This security code can be given to the application, only when it is created or cloned (.ie save as another app) . Secured application needs the security code to be presented to be modified. All application code can be viewed by anyone.
Applications can inherit resources from one or more of its parent applications. Multiple parents should be separated by comma. Inherited resources can be overridden by creating a resource by the same name. ie) if a resource is defined by an application, then that takes precedence over the resources from its parent applications. Parent applications themselves can inherit resources from other applications.
One of the following resource search orders can be specified for a child application. Circular inheritance is just ignored. This order does not matter if the application just has a single or no parent.
External resource (say a javascript framework) does not need to be uploaded here. Instead, the base url of the project can be entered. Now, when a resource for an app is accessed and if the resource is not locally found, the resource will be resolved against the base url. The resource can be returned through the application access url. All the external resources are considered to be local. So they all comply with the application inheritance orders.
Example:
Java libraries from maven project can be referenced by the application such that classes from those libraries can be imported into groovy scripts.
Creation of an application typically has the following life-cycle.
Modification of an application is typically done in the following steps.
Application resources can be created just after the application is created.
Every resource has a name. This name can include / to create nested resources. That is the mechanism to create sub directories. E.g) html/a.html , js/a.js, server/groovy/first.gv are some examples valid resource names. Every resource has a type that must be selected after the resource is created. This can be selected by clicking the resource name once it is created. That would take to the page that allows editing of the resource content. The resource type can be selected there. The extension used in the resource name does not matter. The type chosen matters when the resource is being accessed.
E.g)When a groovy script is created and saved, it is parsed and the errors are reported if there is any parsing error. When a groovy script url is accessed, the script is executed and the output (the value that the script returns) of the script is returned via the back channel of the http request. Please see "Guidelines for writing Groovy scripts" section to know the guidelines for creating groovy scripts.
Groovy scripts typically will use the Java APIs provided by this platform to perform certain action and return a value. Accessing the groovy resource via http(s) request will cause the script to execute. The script can read input parameters from the request accessing it.
Tamil platform Apis
E.g) import tamil.lang.*; import tamil.util.*; import tamil.lang.api.trans.*;and other standard Java APIs
such as import java.util.*; import javax.ws.rs.core.*; import javax.servlet.http.*; import org.json.*;can be imported and used in the script. Calling things like System.exit(int) or any other insecure APIs will cause security exception.
The following are the implicit variables that the script have access to.
The following return types are recognized by the platform.
Any un-handled exception from script execution will be translated to a status of 500 with the content-type text/plain; charset=utf-8 . The body of the response will be message contained in the exception along with line number of the script that ran into exception.
Resources are pre-processed while being accessed or pre-hand. The following patterns are recognized by this platform
for all the
resources during pre-processing.
Note: Groovy
script is pre-processed before it is compiled (ie parsed). So the dynamic patterns will not be resolved as a
property. Instead, those are exposed as implicit variables. Patterns with small letters such as R_QP_name indicates
that they are
dynamic patterns where "name" is a dynamic portion of that pattern.
E.g) <script src="${R_JQUERY_JS_PATH}" type="text/javascript" charset="utf-8"></script>
E.g) <script src="${R_ANGULAR_JS_PATH}" type="text/javascript" charset="utf-8"></script>
E.g) <script src="${R_JQUERY_JS_PATH}" type="text/javascript" charset="utf-8"></script> <script src="${R_PLATFORM_JS_PATH}" type="text/javascript" charset="utf-8"></script>
E.g) <link rel="stylesheet" type="text/css" href="${R_PLATFORM_CSS_PATH}"/>
E.g) ${R_INJECT_PLATFORM_APPLET} can be dropped anywhere in the HTML. The applet is only for Javascripts invoking some Java APIs. It shall not display anything.


Build and Share Web Components
Ele (call it “Ellie”) is a streamlined environment for exploring, experimenting with, and sharing reusable custom HTML elements made with Web Components.
Build in the Browser
Code your component in the browser with full access to Polymer's element sets.
Showcase It
Create a demo to quickly showcase your awesome element. Share it with the world!


EVP_SIGNATURE, EVP_SIGNATURE_fetch, EVP_SIGNATURE_free, EVP_SIGNATURE_up_ref, EVP_SIGNATURE_is_a, EVP_SIGNATURE_get0_provider, EVP_SIGNATURE_do_all_provided, EVP_SIGNATURE_names_do_all, EVP_SIGNATURE_get0_name, EVP_SIGNATURE_get0_description, EVP_SIGNATURE_gettable_ctx_params, EVP_SIGNATURE_settable_ctx_params - Functions to manage EVP_SIGNATURE algorithm objects
#include <openssl/evp.h>
typedef struct evp_signature_st EVP_SIGNATURE;
EVP_SIGNATURE *EVP_SIGNATURE_fetch(OSSL_LIB_CTX *ctx, const char *algorithm,
const char *properties);
void EVP_SIGNATURE_free(EVP_SIGNATURE *signature);
int EVP_SIGNATURE_up_ref(EVP_SIGNATURE *signature);
const char *EVP_SIGNATURE_get0_name(const EVP_SIGNATURE *signature);
int EVP_SIGNATURE_is_a(const EVP_SIGNATURE *signature, const char *name);
OSSL_PROVIDER *EVP_SIGNATURE_get0_provider(const EVP_SIGNATURE *signature);
void EVP_SIGNATURE_do_all_provided(OSSL_LIB_CTX *libctx,
void (*fn)(EVP_SIGNATURE *signature,
void *arg),
void *arg);
int EVP_SIGNATURE_names_do_all(const EVP_SIGNATURE *signature,
void (*fn)(const char *name, void *data),
void *data);
const char *EVP_SIGNATURE_get0_name(const EVP_SIGNATURE *signature);
const char *EVP_SIGNATURE_get0_description(const EVP_SIGNATURE *signature);
const OSSL_PARAM *EVP_SIGNATURE_gettable_ctx_params(const EVP_SIGNATURE *sig);
const OSSL_PARAM *EVP_SIGNATURE_settable_ctx_params(const EVP_SIGNATURE *sig);
EVP_SIGNATURE_fetch() fetches the implementation for the given algorithm from any provider offering it, within the criteria given by the properties. The algorithm will be one offering functions for performing signature related tasks such as signing and verifying. See "ALGORITHM FETCHING" in crypto(7) for further information.
The returned value must eventually be freed with EVP_SIGNATURE_free().
EVP_SIGNATURE_free() decrements the reference count for the EVP_SIGNATURE structure. Typically this structure will have been obtained from an earlier call to EVP_SIGNATURE_fetch(). If the reference count drops to 0 then the structure is freed.
EVP_SIGNATURE_up_ref() increments the reference count for an EVP_SIGNATURE structure.
EVP_SIGNATURE_is_a() returns 1 if signature is an implementation of an algorithm that's identifiable with name, otherwise 0.
EVP_SIGNATURE_get0_provider() returns the provider that signature was fetched from.
EVP_SIGNATURE_do_all_provided() traverses all SIGNATURE implemented by all activated roviders in the given library context libctx, and for each of the implementations, calls the given function fn with the implementation method and the given arg as argument.
EVP_SIGNATURE_get0_name() returns the algorithm name from the provided implementation for the given signature. Note that the signature may have multiple synonyms associated with it. In this case the first name from the algorithm definition is returned. Ownership of the returned string is retained by the signature object and should not be freed by the caller.
EVP_SIGNATURE_names_do_all() traverses all names for signature, and calls fn with each name and data.
EVP_SIGNATURE_get0_description() returns a description of the signature, meant for display and human consumption. The description is at the discretion of the signature implementation.
EVP_SIGNATURE_gettable_ctx_params() and EVP_SIGNATURE_settable_ctx_params() return a constant OSSL_PARAM(3) array that describes the names and types of key parameters that can be retrieved or set by a signature algorithm using EVP_PKEY_CTX_get_params(3) and EVP_PKEY_CTX_set_params(3).
EVP_SIGNATURE_fetch() returns a pointer to an EVP_SIGNATURE for success or NULL for failure.
EVP_SIGNATURE_up_ref() returns 1 for success or 0 otherwise.
EVP_SIGNATURE_names_do_all() returns 1 if the callback was called for all names. A return value of 0 means that the callback was not called for any names.
EVP_SIGNATURE_gettable_ctx_params() and EVP_SIGNATURE_settable_ctx_params() return a constant OSSL_PARAM(3) array or NULL on error.
"ALGORITHM FETCHING" in crypto(7), OSSL_PROVIDER(3)
The functions described here were added in OpenSSL 3.0.
Copyright 2019-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Dance at JPC is run by Mrs Mostyn and Mrs Robinson in the dance room and theatre. Follow the links below to discover what we offer for dance at JPC.


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R2A has launched a competition to identify innovators in Mexico and the Philippines. R2A will award $100,000 per country to two technology innovators to develop RegTech solutions for financial authorities in Mexico and the Philippines.
In the Philippines, the selected vendor will work with the Central Bank of the Philippines to develop an Application Programming Interface (API) and back office reporting and visualization application for regulatory reporting.
In Mexico, the selected vendor will work with the National Commission for Banking and Securities to develop a data request/storage platform and tools for data-driven metrics for AML supervision.
The deadline for submission is October 29, 2017. Please send your application to<EMAIL_ADDRESS>Aside from the US$100,000 granted to each successful applicant to develop and test the required solution, awardees will also:
Be introduced to the global community of regulators and supervisors, investors, and academics that are partnering with R2A through participation in R2A working groups;
Be invited to participate in other events hosted by R2A partners;
Be featured in the R2A website and publications as a pioneer within the emerging RegTech for Regulators community; and
Benefit from association with prominent organizations such as the Bill & Melinda Gates Foundation, the Omidyar Network, and the US Agency for International Development – the three founders of R2A.


[![merge-chance-badge](https://img.shields.io/endpoint?url=https%3A%2F%2Fmerge-chance.info%2Fbadge%3Frepo%3D{{repo | urlencode}})](https://merge-chance.info/target?repo={{repo | urlencode}})


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Look closely at the locatiions as the answers may be there. Creative little hiding places that we have planned. Start with the page URL.


| CallUtils |
|
| ClassLoaderUtils |
|
| CommonUtils |
|
| DbRequestProcessor |
DbRequestProcessor contains the processing logic that
the @link(DbServlet) performs as it receives each servlet request
from the container.
|
| DbServlet |
|
| DriverShim |
Allows one to load a JDBC driver at runtime.
|
| HttpRequestUtils |
|
| JsFunction |
|
| Logger |
A simple logger is used for logging information about events
|
| NonNativeProcedure |
|
| QueryUtils |
Provides utility methods for convert ResutlSet to JSON.
|
| ReadyResult |
DTO (Data Transfer Object) that represents cached result of SQL statement execution
|
| RhinoUtils |
|
| RunnableJob |
Represent a 'job' to be performed.
|
| SqlParseUtils |
Provides utility methods for parsing SQL scripts and statements.
|
| StaticStatement |
DTO (Data Transfer Object) that represent SQL DQL statement abstraction
combined with result set format
|
| StringUtils |
|


If you would like to reach out to us please fill out this form. Someone will be in touch with you within 72 business hours. You may also reach out to us at one of the follow methods.
© UI Sprint Challenge


The OrderBoss was founded in 2019 by TeamFour Information Technology Co. Ltd. and is headquartered in Adelaide, Australia. It is a catering reservation service platform. At present, there are more than 20 employees, and there are branches in Adelaide, Melbourn, Sydney. OderBoss provides reservation service and mobile client application service. And to provide diners with quality and caring services for their own responsibility to constantly improve their services and products.
As a leading Internet + restaurant company, ordering a small secretary to help employees maintain safety, health and productivity is our focus. We develop a QHSE (Quality, Occupational Health, Safety and Environment) management system that sinks our management focus, establishes a positive safety culture, minimizes safety risks, and protects the health of employees, customers, partners and other stakeholders. And safe. The company's senior management attaches great importance to the safe operation of enterprises, and actively carries out safety management work in the fields of work environment safety, fire safety, food safety, traffic safety, etc., and systematically promotes the efficient operation of the safety management system.
Yinhao Deng
Hanling Zhong
Bohui Gao
Zixian Lu


To start, select your character. Then select a enemy to defeat.
You win the game when you beat all the enemies.
But don't let your health points run out or you lose!
Mario
120
Luigi
100
Peach
150
Toad
180


Proxying network requests
Some of the options available for inspecting network traffic from your Roku
Handling roInputEvent
How to handle roInputEvent messages without restarting your app
HTTP on Roku
Everything you ever wanted to know about making network calls on Roku
Fix for live video delay
How to make sure your live stream is really live
Nodes and Associative Array fields
A look at how associative array fields on nodes are handled and how they can affect performance
Component initialization order
Understanding the component initialization order and some potential gotchas
A Different Kind of 'Rule of Thirds'
Roku devices support a variety of resolutions. Here is a tip to make sure your FHD layouts look great when scaled down to HD.
Poster component property order
Often when loading images from the web, we do not have control over how large the source images are.  There is a way to solve this, but it has an interesting twist that can trip people up.


Now that we understand how to write a given number of rows, we can turn our attention to writing a single row. In other words, we can start filling in the part of the program represented by the write a row of output comment in §2.3/19.
We begin by observing that all the output lines are the same length. If we think of the output as a rectangular array, then that length is the number of columns in the array. We can compute that number by adding twice the padding to the length of the greeting, and then adding two for the asterisks at the ends:
const std::string::size_type cols = greeting.size() + pad * 2 + 2;
The easy part of reading this definition is to see that we've said that cols
is const
,
thereby promising that the value of cols
will not change after we have defined it. The
harder part to understand is that we have defined cols
using an unfamiliar type, namely
std::string::size_type
. We know that the first :: is the scope operator and that
the qualified name std::string
means the name string
from the namespace std
.
The second :: similarly says that we want the name size_type
from the class string
.
Like namespaces and blocks, classes define their own scopes. The std::string
type
defines size_type
to be the name of the appropriate type for holding the number of
characters in a string
. Whenever we need a local variable to contain the size of a
string, we should use std::string::size_type
as the type of that variable.
The reason that we have given cols
a type of std::string::size_type
is to
ensure that cols
is capable of containing the number of characters in greeting
, no matter
how large that number might be. We could simply have said that cols
has type int
,
and indeed, doing so would probably work. However, the value of cols
depends on the
size of the input to our program, and we have no control over how long that input might
be. It is conceivable that someone might give our program a string so long that an int
is
insufficient to contain its length.
The int
type is sufficient for rows
because the number of rows depends only on the
value of pad
, which we control. Every C++ implementation is required to allow every
int
variable to take on values up to at least 32767
, which is plenty. Nevertheless, whenever
we define a variable that contains the size of a particular data structure, it is a good
habit to use the type that the library defines as being appropriate for that specific purpose.
It is impossible for a string to contain a negative number of characters. Accordingly,
std::string::size_type
is an unsigned type-objects of that type are incapable of
containing negative values. This property does not affect the programs in this chapter,
but we shall see later on in §8.1.3/142 that it can be critically important.
Having figured out how many characters to write, we can use another while
statement
to write them:
std::string::size_type c = 0; // invariant: we have written c characters so far in the current row while (c != cols) { // write one or more characters // adjust the value of c to maintain the invariant }
This while
behaves analogously to the one in §2.3/19, except for one difference in the
body: This time we have said write one or more characters instead of writing exactly one
row as we did in §2.3/19. There is no reason that we have to write only a single character
each time through the body. As long as we write at least one character, we will ensure
progress. All we have to do is ensure that the total number of characters we write on this
row is exactly cols
.
Our remaining problem is to figure out what characters to write. We can solve part of this problem by noting that if we are on the first or last row, or on the first or last column, then we know that we should write an asterisk. Moreover, we can use our knowledge of the loop invariants to determine whether it is time to write an asterisk.
For example, if r
is zero, we know from the invariant that we have not yet written any
rows, which means that we are writing part of the first row. Similarly, if r
is equal to
rows - 1
, we know that we have written rows - 1
rows already, so we must now be
writing part of the last row. We can use analogous reasoning to conclude that if c
is zero,
then we are writing part of the first column, and if c
is equal to cols - 1
, we are writing
part of the last column. Using this knowledge, we can fill in more of our program:
// invariant: we have written c characters so far in the current row while (c != cols) { if (r == 0 || r == rows - 1 || c == 0 || c == cols - 1) { std::cout << "*"; } else { // write one or more nonborder characters // adjust the value of c to maintain the invariant }
This statement introduces so many new ideas that it requires detailed explanation.
The while
body consists of a block (§2.3.1/19) that contains an if statement, which we
use to determine whether it is time to write an asterisk. An if
statement can take either
of two forms:
if (condition) statement
or, as used here,
if (condition) statement1 else statement2
As with the while
statement, the condition is an expression that yields a truth value. If
the condition is true, then the program executes the statement that follows the if
. In the
second form of the if
statement, if the condition is false, then the program executes the
statement that follows the else
.
It is worth noting, just as with our description of the form of the while
statement, that
the formatting that we use to illustrate the if
statement is merely conventional. However,
readers will find it much easier if code follows formatting conventions such as the
ones that we've used in the examples in this book.
What about the condition itself?
r == 0 || r == rows - 1 || c == 0 || c == cols - 1
This condition is true if r
is 0
or rows - 1
, or if c
is 0
or cols - 1
. The condition uses
two new operators, the ==
operator and the ||
operator. C++ programs test for equality
by using the ==
symbol, to distinguish it from the assignment operator =
. Thus, r == 0
yields a bool
that indicates whether the value of r
is equal to 0
. The logical-or operator,
written as ||
, yields true
if either of its operands is true
.
The relational operators have lower precedence than the arithmetic operators. In expressions that contain more than one operator, precedence defines how the operands group. For example,
r == rows - 1
means
r == (rows - 1)
rather than
(r == rows) - 1
because the arithmetic operator -
has higher precedence than the relational operator ==
.
In other words, we are subtracting 1
from rows
and comparing the result with r
, which,
in this program, is what we wanted. We can override precedence by enclosing in parentheses
a subexpression that we want to use as a single operand. For example, if we really
wanted to execute (r == rows) - 1
, we could do so by including the parentheses as
shown. This expression would compare r
with rows
and subtract 1
from the result,
yielding either 0
or -1
depending on whether r
was equal to rows
.
The logical-or operator tests whether either of its operands is true
. Its form is
condition1 || condition2
where, as usual, condition1 and condition2 are conditions-expressions that yield truth values.
The ||
expression yields a bool
, which is true
if either of the conditions is true
.
The ||
operator has lower precedence than the relational operators, and, like most
C++ binary operators, is left-associative. Moreover, it has a property that most other C++
operators do not share: If a program finds that the left operand of ||
is true, it does not
evaluate the right operand at all. This property is often called short-circuit evaluation, and
as we shall see in §5.6/89, it can have a crucial effect on how we write our programs.
Because ||
is left-associative, and because of the relative precedence of ||,==
,and -
,
r == 0 || r == rows - 1 || c == 0 || c == cols - 1
means the same as it would if we were to place all of its subexpressions in parentheses:
((r == 0 || r == (rows - 1)) || c == 0) || c == (cols - 1)
In order to evaluate this latter expression using the short-circuit strategy, the program
first evaluates the left operand of the outermost ||
, which is
(r == 0 || r == (rows - 1)) || c == 0
||
, which is
r == 0 || r == (rows - 1)
which, in turn, means evaluating
r == 0
r
is equal to 0
, then each of the expressions
r == 0 || r == (rows - 1) (r == 0 || r == (rows - 1)) || c == 0 ((r == 0 || r == (rows - 1)) || c == 0) || c == (cols - 1)
must be true. If r
is nonzero, the next step is to compare r
with rows - 1
. If that test
fails, then the program will compare c
with zero, and if that fails, it will compare c
with
cols - 1
to determine the final result.
In other words, when we write a series of conditions separated by ||
operators, we are
asking the program to test each of these conditions in turn. If any of the inner conditions
is true
, then the whole condition is true
; otherwise, the whole condition is false
.
Each ||
operator stops as soon as it can determine its result, so if any of the inner conditions
is true
, the subsequent conditions go untested. If we step back from the details, we
should be able to see that these four equality tests are checking whether we are in the first
row, the last row, the first column, or the last column, and, therefore, that the if
statement
writes an asterisk if we're in the top or bottom row, or if we're in the first or last column.
Otherwise, it does something else, which we must now define.
It is now time to write the statements that correspond to the comments that say
// write one or more nonborder characters // adjust the value of c to maintain the invariant
in the program fragment in §2.4.1/23. These statements must deal with the characters that are not part of the border. It should be easy to see that each of these characters is either a space or part of the greeting. The only problem is figuring out which one it is, and what to do about it.
We begin by testing whether we are about to write the first character of the greeting,
which we do by finding if we're in the correct row and on the correct column within that
row. The row we seek is the one after we've written the initial row of asterisks, followed
by pad
additional rows. The appropriate column comes after we have written the initial
asterisk on this row, followed by pad
spaces. Our knowledge of the invariants tells
us that we're on the right row when r
is equal to pad + 1
, and be at the appropriate column
when c
is equal to pad + 1
.
In other words, to determine whether we are about to write the first character of the
greeting, we must check whether r
and c
are both equal to pad + 1
. If we've reached the
right place to write the greeting, we'll do so; otherwise, we'll write a space instead. In
both cases, we have to remember to update c
appropriately:
if (r == pad + 1 && c == pad + 1) { std::cout << greeting; c += greeting.size(); } else { std::cout << " "; }
The condition inside the if
statement uses the logical-and operator. As with the ||
operator,
the &&
operator tests two conditions and yields a truth value. It is left-associative
and uses a short-circuit evaluation strategy. Unlike the ||
operator, the &&
operator
yields true
only if both conditions are true
. If either condition is false
, the result of &&
is false
. The second condition will be tested if and only if the first condition is true
.
If the test succeeds, then it's time to write the greeting. In doing so, we falsify our
invariant, because c
is no longer equal to the number of characters we have written on
this row. We make our invariant true again by adjusting the value of c
to account for the
characters that we have written. The expression that updates c
uses another new operator,
called the compound-assignment operator, to adjust c
to account for the number of
characters in the name when we wrote it. Such a compound assignment is a shorthand
way of adding the right- and left-hand sides together and storing the result in the left-hand
side. In other words, if we write c += greeting.size()
, that statement has the
same effect as if we had written c = c + greeting.size()
.
The remaining possibility is that we're not on the border, and we're not about to write
the greeting. In that case, we need to write a space and increment c
to make the invariant
true again, which we do in the else
branch of the if
statement.


The Color Game
Can you guess the color which is formed when these colors are mixed?
Red:
%
Green:
%
Blue:
%
START AGAIN


THE ENGLISHMAN WHO WENT UP A HILL BUT CAME DOWN A MOUNTAIN A film review by Scott Renshaw Copyright 1995 Scott Renshaw
Starring: Hugh Grant, Colm Meaney, Tara Fitzgerald, Ian McNeice, Kenneth Griffith. Screenplay/Director: Christopher Monger.
If it had not been for Jim Carrey, Hugh Grant might have been hailed as *the* breakout star of 1994. His affable, self-effacing persona turned FOUR WEDDINGS AND A FUNERAL int the biggest "art house" hit in the history of the country, leading to a not-so-coincidental increase in business for another Grant film, SIRENS, after his face was substituted for Elle Macpherson's bare backside in that film's print ads. THE ENGLISHMAN WHO WENT UP A HILL BUT CAME DOWN A MOUNTAIN might have faced a monumental marketing challenge, or at least a name change, if not for the advantage of being Hugh Grant's first post-WEDDINGS effort. Unfortunately, it's no honeymoon. Grant's fans could be massively disappointed, both by his relatively minor role and by the film's frequently tedious pace.
Grant plays Reginald Anson, an English surveyor who comes to Wales in 1917 with his partner George Garrad (Ian McNeice) to map the terrain for the war effort. Among their tasks is determining the height of Ffynnon Garw, a geographical feature which the locals take inordinate pride in calling "the first mountain in Wales." However, Anson and Garrad find that Ffynnon Garw is sixteen feet short of the 1,000 foot height required for the designation of a mountain. Refusing to accept that Ffynnon Garw is just a "hill," the locals, led by innkeeper Morgan (Colm Meaney) and the Reverend Jones (Kenneth Griffith) concoct an elaborate plan to add the required height to Ffynnon Garw, and to keep Anson and Garrad in town as long as necessary to obtain a favorable re-measurement.
The story, based on a tale from the family of Welsh writer/ director Christopher Monger, begins as a promising enough fable about fish out of water and a legendary act of combined community will. Monger does a nice job of establishing the scene, introducing the quirks of the characters in the matter-of-fact manner of an accomplished tale-teller who relates a story he knows to be fanciful with utmost earnestness. The clash of personalities between the merrily profane Morgan "the Goat" and the ever-apoplectic Reverend Jones is entertaining, thanks largely to a good-natured performance by Colm Meaney, and Stephen Endelman provides a bright and lilting score which keeps the tone light.
Sadly, although it is clear that Monger has a great affection for this particular story, it isn't nearly as interesting as he seems to think it is, at least not for 100 minutes. The key problem is that as much as he tries to convey why this "mountain" is so important to the people of Ffynnon Garw, he never quite manages to do so. He toys both with the idea of Welsh identity being tied to the mountains, and with the general sense of loss brought on by the first World War, but as hard as he tries to draw us into the imperative of the Ffynnon Garw mission, he can't make it matter as much to the audience. While the efforts of the townspeople to thward Anson and Garrad in their every attempt to leave are sometimes amusing, there is a lot of space between them, and THE ENGLISHMAN slows down to become a sporadically whimsical bore.
It also appears that the presence of Hugh Grant resulted in an ill-advised restructuring of the script. The story really should have been about the Welsh townsfolk, not about the two Englishman, and indeed Hugh Grant is little more than a peripheral figure for fully half of the film. Then Tara Fitzgerald (Grant's wife in SIRENS) appears as a woman of ill repute brought to town to keep Anson occupied, Anson becomes sympathetic to the determination of the Welsh villagers, and suddenly we have an unnecessary and unwelcome love story thrown into the mix. Grant really just walks through this part, relying on his crinkly smile and hesitating delivery and little else. If the story had to focus on one of the Englishman, it should have been Garrad, played with puffed-up impatience by Ian McNeice. Of course, McNeice is only an actor; Hugh Grant is a movie star.  It is also a case of an hour's worth of story stretched an extra forty minutes, and of a big name star in an essentially supporting role who becomes a tail wagging the dog.
On the Renshaw scale of 0 to 10 mountains from molehills: 5.
-- Scott Renshaw Stanford University Office of the General Counsel
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Best Buy Deal of the Day brings this 55" Hisense TV down to $220 ($160 off)
by Mark KoegelIf our last deal on a Toshiba Fire TV didn't tickle your fancy, check out the heavily discounted 55" Hisense H6500F Series Android TV from Best Buy. Its $220 price is down from $380, for a savings of $160.
This model has a 4K screen with HDR10 and Dolby Vision. It's running Android TV and has the Google Assistant baked-in. It also has Bluetooth capabilities, letting you connect a pair of headphones to listen at a distance—a great feature for parents in small apartments. If you opt to use the built-in speakers, you'll be treated to virtual surround sound thanks to DTS Studio Sound technology.
This is Best Buy's Deal of the Day, so don't expect the $220 reduced price to last long. For more deals, keep an eye on our Black Friday deals hub.
Buy:
Best Buy


Nashville * * * * (out of * * * * )
Directed by Robert Altman.  Rated R (for profanity and nudity). Reviewed September 1998.
Director Robert Altman's "Nashville" has been widely hailed as one of, if not the greatest film of the 1970's, and it is just that. The film is a sprawling, flawless, epic mosaic of five days in the lives of 24 characters in the country music capital of the world. Altman later made other pictures with a huge cast of characters, such as 1993's "Short Cuts" and 1994's "Ready to Wear," but this film is like no other I have ever seen for one major reason: it plays exactly, and I mean exactly like real life.
For people more into conventional movies, look elsewhere, because many things happen that aren't explained, and that can't be explained, because there are things in life that just happen, without any explanation, and just as in real life, things happen to characters, and characters simply do things naturally. Within the five day frame of "Nashville," which clocks in at 159 minutes, most of the characters never really get to know each other. Their lives occasionally cross paths, and in some cases, characters may run into others that later on may unknowingly change the fate of their lives. The wide range of people in the film consist of country singers, political aides, inhabitants of Nashville, and visitors who have come to see the country shows. For most of the running time, there are rarely huge plot developments, or twists, and when they do come, they come from the characters, not the plot itself. This style of filmmaking rarely is done, and most of the time, it is done badly. But Altman directs with such a sure hand, and has written such a truthful, vigorating, original screenplay that "Nashville" is an emotional powerhouse, and a one-of-a-kind motion picture experience.
It would be difficult to describe all of the characters here, except in the general way that I have already done. Instead, I'd like to talk about the many layers of "Nashville." The movie doesn't follow the 24 characters, as much as it observes them in everyday life, which I think is much more effective. By simply "following" them, there is a greater chance that we might just learning something in the process about human nature, and I think this film succeeds at that too.
Another layer is that country music isn't the only focus during the five days that the characters are there, but there is also a political election going on. These two elements of music and politics intermingle throughout, as do the people, as they walk in and out of each other's lives.
The performances are flawless in "Nashville," as is the music, which won the Academy Award for Best Song ("I'm Easy"). Some of the standouts in the cast are Lily Tomlin, who plays an unhappy housewife with two deaf children; Geraldine Chaplin as a naive British journalist who has travelled to Nashville to film a documentary; Ronee Blakely as a country artist who has a nervous breakdown; and Gwen Welles as a waitress who dreams of becoming a singer, but has no talent at all.
"Nashville" is a masterpiece in every sense of the word, and it is one of the most important movies ever made. The picture may be set in Nashville, Tennessee, but it has a much wider scope, and the last scene is as heartbreaking, surprising, and hopeful as any moment ever captured on film.
There are no so-called "likable" characters, because Altman paints each one as a real person with both virtues and flaws, but it is so "real" that we are able to sympathize with everyone's plight. "Nashville" is about America in general; it is about our hopes, our dreams, and our disappointments in life.
- Copyright 1998 by Dustin Putman <Dustin Putman's Film Reviews>
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Developing a desktop application wouldn't be a bad idea, but isn't planned in the near future.
Official Android application, natively written in Kotlin.
JavaScript client for browsers, written in Angular. Suitable for all operating systems: macOS, Windows, Linux.


| Package | com.sibirjak.angara.core |
| Class | public class AbstractLoaderItem |
| Inheritance | AbstractLoaderItem AbstractProgressInfo flash.events.EventDispatcher |
| Implements | ILoaderItem |
| Subclasses | AbstractResourceLoader, Sequence, SequenceManager |
| Property | Defined by | ||
|---|---|---|---|
| itemType : String [read-only]
The type of the loader item.
| AbstractLoaderItem | ||
| key : String
Returns the identifying key of the loader item.
| AbstractLoaderItem | ||
| numItems : uint
Returns the number of items to be loaded within the loader item.
| AbstractProgressInfo | ||
| numItemsFailed : uint
Returns the number of items failed within the loader item.
| AbstractProgressInfo | ||
| numItemsLoaded : uint
Returns the number of items loaded within the loader item.
| AbstractProgressInfo | ||
| progress : Number
Returns the progress of the loader item as a decimal value between zero and 1.
| AbstractProgressInfo | ||
| scheduled : Boolean [read-only]
Returns true, if the item already has been added to the loader manager
directly or to a sequence or a sequence manager.
| AbstractLoaderItem | ||
| status : String [read-only]
The current loading status.
| AbstractLoaderItem |
| Property | Defined by | ||
|---|---|---|---|
| _isScheduled : Boolean = false
The schedule marker.
| AbstractLoaderItem | ||
| _key : String
The key used for debugging purposes.
| AbstractLoaderItem | ||
| _numItems : uint = 0
The number of items added.
| AbstractProgressInfo | ||
| _numItemsFailed : uint = 0
The number of items failed loading.
| AbstractProgressInfo | ||
| _numItemsLoaded : uint = 0
The number of items alredy loaded.
| AbstractProgressInfo | ||
| _progress : Number = 0
The overall progress of the item.
| AbstractProgressInfo | ||
| _status : String
The loader item status.
| AbstractLoaderItem | ||
| _type : String
The loader item type.
| AbstractLoaderItem |
| Method | Defined by | ||
|---|---|---|---|
|
pause():void
Marks the item to be ignored by subsequent loader manager queue pulling operations.
| AbstractLoaderItem | ||
|
resume():void
Marks a prior paused item to be eligible for loader manager queue polling
operations.
| AbstractLoaderItem | ||
|
stop():void
Immediately stops loading of the item or its containing items.
| AbstractLoaderItem |
| _isScheduled | property |
protected var _isScheduled:Boolean = false
The schedule marker.
| itemType | property |
itemType:String
[read-only]The type of the loader item.
The type can be either LoaderItemType.LOADER, LoaderItemType.SEQUENCE or LoaderItemType.SEQUENCE_MANAGER.
A loader item is supposed to dispatch an event if it should be removed from the loader manager queue (LoaderItemEvent.COMPLETE, LoaderItemEvent.FAILURE) and an event to notify the loader manager to continue loading (LoaderItemEvent.RESUME).
Implementation public function get itemType():String
| _key | property |
protected var _key:String
The key used for debugging purposes.
| key | property |
key:String
[read-write]Returns the identifying key of the loader item.
Implementation public function get key():String
public function set key(value:String):void
| scheduled | property |
scheduled:Boolean
[read-only]Returns true, if the item already has been added to the loader manager directly or to a sequence or a sequence manager.
A scheduled item cannot be added twice to any of the loader item containers (loader manager, sequence, sequence manager). It has to be removed from its current container beforehand.
Implementation public function get scheduled():Boolean
| _status | property |
protected var _status:String
The loader item status.
| status | property |
status:String
[read-only]The current loading status.
The initial status is LoaderItemStatus.WAITING for every loader item instance. Once the status is set to LoaderItemStatus.COMPLETE or LoaderItemStatus.FAILURE, the status won't be changed any more.
Implementation public function get status():String
| _type | property |
protected var _type:String
The loader item type.
| pause | () | method |
public function pause():void
Marks the item to be ignored by subsequent loader manager queue pulling operations.
Sets the items status to LoaderItemStatus.PAUSED. Does only have effect to items which status is LoaderItemStatus.WAITING. You cannot pause a loading or finished item. To interrupt loading you need to call stop() for the particular item.
If the item is of type LoaderItemType.SEQUENCE, only the sequence will be marked as paused.
If the item is of type LoaderItemType.SEQUENCE_MANAGER, only the sequence manager will be marked as paused.
| resume | () | method |
public function resume():void
Marks a prior paused item to be eligible for loader manager queue polling operations.
Sets the items status to LoaderItemStatus.WAITING. Does only have effect to items which status is LoaderItemStatus.PAUSED.
If the item is of type LoaderItemType.SEQUENCE, only the sequence's status will be affected.
If the item is of type LoaderItemType.SEQUENCE_MANAGER, only the sequence manager's status will be affected.
| setScheduled | () | method |
sibirjak_loader function setScheduled(scheduled:Boolean):void
Framework internal method to modify a loader item status crossover the class visibility.
Parametersscheduled:Boolean |
| stop | () | method |
public function stop():void
Immediately stops loading of the item or its containing items.
If the item is of type LoaderItemType.LOADER and is currently being loading, the associated net connection will be closed. Does only have effect to resource loaders, which status is LoaderItemStatus.LOADING. Sets the loader status back to LoaderItemStatus.WAITING. You cannot stop finished loaders (LoaderItemStatus.COMPLETE, LoaderItemStatus.FAILURE) any more.
If the item is of type LoaderItemType.SEQUENCE, all open loaders assigned to the seqeunce will be stopped.
If the item is of type LoaderItemType.SEQUENCE_MANAGER, the stop method of all assigned sequences will be called.


There are many, many similar applications. Most of the ones we analyzed are very cluttered but offer a wide variety of features and connections to other financial applications We discussed various designs and interface choices and created a storyboard based on this discussion
The most recent report can be viewed here.


Hi, My name is David Yi. I am a soon to be Full Stack Web Developer in the making. I live in the Bay Area and have been living here my whole life. I enjoy watching sports, listening to music, and playing games. I have been married for 4 years now and enjoy traveling, and spending time in the sun with my wife. If you would like to ask me any questions, feel free to click the Contact button above and shoot me a message. Look forward to your message.


Georgia deputy rescues deer stuck between fence bars
by Ben HooperNov. 19 (UPI) -- A Georgia sheriff's deputy's body camera was recording when he came to the rescue of a small deer that had become stuck between the bars of a fence.
The Forsyth County Sheriff's Office posted a video to Facebook showing the deer trapped in the fence after apparently trying to squeeze through a narrow opening.
The video shows the deputy waiting for the deer to tire itself out and stop struggling before wiggling the animal free of its predicament.
"There he goes," the deputy says as the deer wanders off, "Bambi's free."
(0) Leave a comment
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{% extends "global/Page.html" %} {% load otree static %} {% block title %} Instructions {% endblock %} {% block content %}
You are Participant 1 in this study. The other participants are Participant 2 and Participant 3.
In each round, you and Participant 2 will simultaneously interact with each other in Task A, and you and Participant 3 will simultaneously interact with each other in Task B. In each task, you and your co-participant on that task must choose between two options. The points you earn in each round will depend on what you, Participant 2 and Participant 3 independently decide for your respective tasks. The two tasks are independent: The points you earn in either task do not depend on the decisions made in the other. In the beginning of each round, you and the other two participants receive an endowment of {{ Constants.endowment_high }} points in task A and {{ Constants.endowment_low }} points in task B.
Below you can see the two options that you and Participant 2 can choose from in Task A in each round.
Participant 2 faces exactly the same Task A with you as the recipient. You and Participant 2 have to choose simultaneously between these two options.
Below you can see the two options that that you and Participant 3 can choose from in Task B in each round.
Participant 3 faces exactly the same Task B with you as the recipient. You and Participant 3 have to choose simultaneously between these two options.
Note that, while Participant 2 and Participant 3 interact with you in Tasks A and B, they both also simultaneously interact with another Participant (but not with each other) in a similar task. That is, each of you is engaged in two tasks simultaneously at all times.
The session will last for multiple rounds. There will be at least {{ Constants.min_rounds }} rounds. After the {{ Constants.min_rounds }}th round, there will be a 50% chance of another round. After that round, there will again be a 50% chance of another round, and after that round, there will again be another 50% chance of another round, and so on until the tasks end.
You and the other participants are both endowed with {{ initial_endowment_joint }} points at the start of the game ({{ Constants.endowment_high }} points for Task A and {{ Constants.endowment_low }} points for Task B, for each of the {{ Constants.min_rounds }} rounds). For any additional round that may occur after the {{ Constants.min_rounds }}th round, you receive an additional endowment of {{ Constants.endowment_high }} points for Task A and {{ Constants.endowment_low }} points for Task B.


1.2 : Base 2
Base-2 is also called Binary.
Binary is the most basic thing computers read.
We all recognize binary as long strings of 1's and 0's.
To the untrained eye, lines of 1's and 0's can look overwhelming.
However, when we break them down into numbers, we can understand what the binary reads.
Instead of digits to hold place values, binary has bits.
Each bit can hold a 1 or 0.
When we put 8 bits together in a line, it's called a byte.
Just a little trivia you'll never need to know: four bits are called a nibble.
Because it's not quite a byte.
Anyway!
Let's start with an eight bit number, a byte.
Let's consider a random 8 bit number, 10011101.
| Bit | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 |
|---|---|---|---|---|---|---|---|---|
| Power | 27 | 26 | 25 | 24 | 23 | 22 | 21 | 20 |
| Value | 128 | 0 | 0 | 16 | 8 | 4 | 0 | 1 |
So each bit can hold a 0 or 1. Since we're working in base-2 this makes sense because the range of digits is 0 to 2-1 which is 1. So we can translate this number to base-10 by using the Power x Bit formula we made. (1 * 27) + (0 * 26) + (0 * 25) + (1 * 24) + (1 * 23) + (1 * 22) + (0 * 21) + (1 * 20). This translates to (1 * 128) + 0 + 0 + (1 * 16) + (1 * 8) + (1 * 4) + 0 + (1 * 1). So this means that the 8 bit binary number 10011101 is equal to the base-10 number 157. To denote different bases, we can use subscripts. So 100111012 = 15710
This was a very basic look into binary. If you move onto Chapter 2, you can learn much more about fractional binary and negative binary and so on and so forth.
Practice Problems
What is the number 101100112 in base-10?


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32, shape=(), name='learning_rate')
return input_real, input_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
alpha = 0.2
# 28x28x3 to start
# extra zeros padding for 2nd and 3rd dims
extra_padding = [[0,0], [2,2], [2,2], [0,0]]
resized = tf.pad(images[1:], extra_padding, 'CONSTANT')
# 32x32x3 now
# convolutional to make smaller/deeper, first layer has no batch normalization
conv1 = tf.layers.conv2d(resized, 64, kernel_size=5, strides=2, padding='same')
conv1 = tf.maximum(alpha * conv1, conv1)
# 16x16x64 now
#print('conv1.shape', conv1.shape)
# convolutional to make smaller/deeper
conv2 = tf.layers.conv2d(conv1, 128, kernel_size=5, strides=2, padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = tf.maximum(alpha * conv2, conv2)
# 8x8x128 now
#print('conv2.shape', conv2.shape)
# convolutional to make smaller/deeper
conv3 = tf.layers.conv2d(conv2, 256, kernel_size=5, strides=2, padding='same')
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = tf.maximum(alpha * conv3, conv3)
# 4x4x256 now
#print('conv3.shape', conv3.shape)
### final fully connected layer
# reshape to flat with same number of values
flat = tf.reshape(conv3, (-1, 4*4*256))
# turn this into a fully connected layer with 1 unit
logits = tf.layers.dense(flat, 1, activation=None)
# adding dropout to make discrim less powerful than generator
logits = tf.layers.dropout(logits, rate=0.4)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse = not is_train):
alpha = 0.2
# size z to start
# project to greater number of units
fc1 = tf.layers.dense(z, 2*2*512, activation=None)
# reshape to desired size
fc1 = tf.reshape(fc1, (-1, 2, 2, 512))
# add batch normalization
fc1 = tf.layers.batch_normalization(fc1, training=is_train)
# add leaky relu
fc1 = tf.maximum(alpha * fc1, fc1)
#print('fc1.shape', fc1.shape)
# 2x2x512 now
# convolutional transpose to make wider/shallower
conv1 = tf.layers.conv2d_transpose(fc1, 256, kernel_size=5, strides=2, padding='same')
conv1 = tf.layers.batch_normalization(conv1, training=is_train)
conv1 = tf.maximum(alpha * conv1, conv1)
#print('conv1.shape', conv1.shape)
# 4x4x256 now
# convolutional transpose to make wider/shallower
conv2 = tf.layers.conv2d_transpose(conv1, 128, kernel_size=5, strides=2, padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=is_train)
conv2 = tf.maximum(alpha * conv2, conv2)
#print('conv2.shape', conv2.shape)
# 8x8x128 now
# convolutional transpose to make wider/shallower
conv3 = tf.layers.conv2d_transpose(conv2, 64, kernel_size=5, strides=2, padding='same')
conv3 = tf.layers.batch_normalization(conv3, training=is_train)
conv3 = tf.maximum(alpha * conv3, conv3)
#print('conv3.shape', conv3.shape)
# 16x16x64 now
# convolutional transpose to make wider/shallower, match output image size
logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, kernel_size=5, strides=2, padding='same')
# 32x32x out_channel_dim now
logits = tf.image.resize_nearest_neighbor(logits, [28, 28])
# 28x28x out_channel_dim now
out = tf.tanh(logits)
# per docs, this should work for 4D, but errors with tf v1.7.0
# test = tf.image.resize_image_with_crop_or_pad(logits, 28, 28)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
# build the generator
g_out = generator(input_z, out_channel_dim, is_train=True)
# build the discriminator for real data
d_out_real, d_logits_real = discriminator(input_real, reuse=False)
# build the discriminator for fake data, reusing the same weights as the real
d_out_fake, d_logits_fake = discriminator(g_out, reuse=True)
# get the discriminator loss with real data, labels are ones since real
# smoothing done only to discriminator to improve it's generalization
smooth = 0.1
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_out_real) * (1-smooth)))
# get the discriminator loss with fake data, labels are zeros since fake
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_out_fake)))
# total discriminator loss
d_loss = d_loss_fake + d_loss_real
# generator loss with fake data, labels are ones since g is trying to trick d
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_out_fake)))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [v for v in t_vars if v.name.startswith('discriminator')]
g_vars = [v for v in t_vars if v.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(loss=d_loss, var_list=d_vars)
g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(loss=g_loss, var_list=g_vars)
return d_opt, g_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
# take learning_rate param, assign to lr_value to be passed in feed_dict
lr_value = learning_rate
# Set up model via: inputs, loss, and optimizer
#print('data_shape', data_shape)
input_real, input_z, learning_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
# Do the training
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step, sample_num = 0, 0
samples = np.zeros((64, 28, 28, 3))
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
step += 1
# Get random data to input into generator
batch_z = np.random.normal(loc=0, scale=1, size=[batch_size, z_dim])
# rescale batch images from (-.5, .5) to (-1, 1)
batch_images = batch_images * 2
#print('batch_z.shape', batch_z.shape)
#size=(batch_size, z_dim): batch_z.shape (32, 100)
#[batch_size, z_dim]: batch_z.shape (32, 100)
# Run the model for the generator and discriminator
_ = sess.run(g_opt, feed_dict={input_z: batch_z,
input_real: batch_images,
learning_rate: lr_value})
_ = sess.run(d_opt, feed_dict={input_z: batch_z,
input_real: batch_images,
learning_rate: lr_value})
# Print status
if step % 20 == 0:
g_loss_eval = g_loss.eval(feed_dict={input_z: batch_z,
learning_rate: lr_value})
d_loss_eval = d_loss.eval(feed_dict={input_z: batch_z,
input_real: batch_images,
learning_rate: lr_value})
print('Epoch: {}, Step: {},'.format(epoch_i+1, step),
'G loss: {:.4f}...'.format(g_loss_eval),
'D loss: {:.4f}'.format(d_loss_eval))
if step % 400 == 0:
show_generator_output(sess, 16, input_z, data_shape[3], data_image_mode)
# build figure grid of progressive samples
if step % 95 == 0 and sample_num < samples.shape[0] and data_image_mode == 'RGB':
example_z = np.random.normal(loc=0, scale=1, size=[1, z_dim])
# run the generator and add to array of samples
sample = sess.run(
generator(input_z, data_shape[3], False),
feed_dict={input_z: example_z})
samples[sample_num] = sample
# plot the figure
if sample_num == samples.shape[0]-1:
cmap = None if data_image_mode == 'RGB' else 'gray'
images_grid = helper.images_square_grid(samples, data_image_mode)
figure = pyplot.figure(figsize=(18,18))
axis = figure.add_subplot(2,2,1)
axis.imshow(images_grid, cmap=cmap)
pyplot.show()
sample_num += 1
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.01 # .008 & .005 w/ same other params also had good images
beta1 = 0.2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0008
beta1 = 0.4
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


From 1 June 2010 we will be switching the Yale
‘marketing webshop’ service
to a new online ordering system, from which literature and promotional goods can
be ordered.
You have been redirected to this system, which is
accessible through
www.sprintexpress.co.uk/yale
or alternatively through the link in
Users will have to register to gain access to the
system by clicking on the link provided
on the right and entering the Dealerships Yale Dealer Code.
The main benefits of the new system will include the ability for users to:
User guidelines will be available to download from
the homepage once logged in.
Our stock is currently being transferred to this
new system
and we expect all of the items to be available to order by 3rd June.
Apologies if this delay causes you any inconvenience.
If you have any queries or require any help you can
contact
<EMAIL_ADDRESS>Yours sincerely
Steve Ridgway
Brand Manager Yale


This inspection helps to convert unmodifiable collections created before Java 9 to new collection factory methods like
List.of
or
Set.of
.
This inspection is available since Java 9 only.
New in 2017.2


**Volumetric video workshop** This workshop will introduce you to the relatively new and fast-growing medium: Free Viewpoint Video (FVV). Focused on breadth, it will cover its pros and cons, technical workflows, trends and current research and products landscape. It also includes a hands-on part about post-processing of FVV assets in SideFX Houdini. Whether you are interested in computer vision / graphics, holograms, multi-camera setups or just want to see a couple of Houdini tricks, make sure to take part in it! A basic familiarity with computer vision, graphics and Houdini is recommended, but not required. _For Filmakademie, 2021_ !!! note *Note that only the theory part is currently publicly available. Hands-on video, Houdini scene files and dataset haven't been published yet.* # Important links * [Theory recording](https://youtu.be/FXnGHI533YI) * [Slides](https://docs.google.com/presentation/d/1cIqD5O4xwXpol7jHU02gl1g51fTloEHZUklai20v1Tc/edit?usp=sharing), [PDF](https://drive.google.com/file/d/1hGTAyauEeP14zNY5chQdD5gc8Wi1tXNq/view?usp=sharing) * [Dataset](https://dimensionstudio.zendesk.com/hc/en-gb/articles/4401920314898-Materials-for-Filmakademie-Workshop-June-2021) # Videos ![Theory](https://youtu.be/FXnGHI533YI) # Contents **Theory** * Introduction * What is a volumetric video * Key properties of a FVV system * Dimension introduction * Approaches to FVV * Products, studios * Tools * Acquisition * Processing pipeline * Coding and transmission * Post-processing * Rendering * Delivery * Bottlenecks * Future # Dataset access **Filmakademie students:** [sign up](https://dimensionstudio.zendesk.com/hc/en-gb/articles/4401920314898-Materials-for-Filmakademie-Workshop-June-2021) with your university email, your request will be processed quickly. After gaining access to the dataset: `aka_workshop_data.zip`, you can use it with the Houdini project files, just unpack it into the folder containing the repository. Check Readme in the repository for more details. # Feedback Please let [me](https://jtomori.github.io) know if you spot any mistakes, if I missed some important idea or a product, or if you have suggestions for improving this workshop! # Thanks [Filmakademie Baden Württemberg](https://www.filmakademie.de/), [Dimension](https://www.dimensionstudio.co/) for the opportunity and support. Mike Pelton, Charles Dupont and Marco Volino for proofreading and feedback. Toby Hyder for the asset.


Translator: youyun
Author: labuladong
What is the FloodFill algorithm? A real-life example is color filling. In the default Windows application Paint, using the bucket icon, we can fill the selected area with a color.
There are other applications of the FloodFill algorithm. Another example would be Minesweeper. Sometimes when you click on a tile, an area will expand out. The process of expansion is implemented through the FloodFill algorithm.
Similarly, those puzzle-matching games such as Candy Crush also use the FloodFill algorithm to remove blocks of the same color.
Now you should have some idea about the FloodFill algorithm. Let’s abstract out the problems and find out what is common.
All above examples can be abstract as a 2D array. In fact, a picture is an array of pixels. We take an element as the starting point and expand till the end.
An array can be further abstracted as a graph. Hence, the problem becomes about traversing a graph, similar to traversing an N-ary tree. A few lines of code are enough to resolve the problem. Here is the framework:
// (x, y) represents the coordinate
void fill(int x, int y) {
fill(x - 1, y); // up
fill(x + 1, y); // down
fill(x, y - 1); // left
fill(x, y + 1); // right
}
Using this framework, we can resolve all problems about traversing a 2D array. The concept is also called Depth First Search (DFS), or quaternary (4-ary) tree traversal. The root node is coordinate (x, y). Its four child nodes are at root’s four directions.
Let’s take a look at a LeetCode problem. It’s actually just a color fill function.
In another article, we discussed a generic design of tree related algorithms. We can apply the concept here:
int[][] floodFill(int[][] image,
int sr, int sc, int newColor) {
int origColor = image[sr][sc];
fill(image, sr, sc, origColor, newColor);
return image;
}
void fill(int[][] image, int x, int y,
int origColor, int newColor) {
// OUT: out of index
if (!inArea(image, x, y)) return;
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return;
image[x][y] = newColor;
fill(image, x, y + 1, origColor, newColor);
fill(image, x, y - 1, origColor, newColor);
fill(image, x - 1, y, origColor, newColor);
fill(image, x + 1, y, origColor, newColor);
}
boolean inArea(int[][] image, int x, int y) {
return x >= 0 && x < image.length
&& y >= 0 && y < image[0].length;
}
If you can understand this block of code, you are almost there! It means that you have honed the mindset of framework. This block of code can cover 99% of cases. There is only one tiny problem to be resolved: an infinite loop will happen if origColor
is the same as newColor
.
Why is there infinite loop? Each coordinate needs to go through its 4 neighbors. Consequently, each coordinate will also be traversed 4 times by its 4 neighbors. When we visit an visited coordinate, we must guarantee to identify the situation and exit. If not, we’ll go into infinite loop.
Why can the code exit properly when newColr
and origColor
are different? Let’s draw an diagram of the algorithm execution:
As we can see from the diagram, fill(1, 1)
is visited twice. Let’s use fill(1, 1)*
to represent this duplicated visit. When fill(1, 1)*
is executed, (1, 1)
has already been replaced with newColor
. So fill(1, 1)*
will return the control directly at the CLASH, i.e. exit as expected.
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return;
However, if origColor
is the same as newCOlor
, fill(1, 1)*
will not exit at the CLASH. Instead, an infinite loop will start as shown below.
How to avoid the case of infinite loop? The most intuitive answer is to use a boolean 2D array of the same size as image, to record whether a coordinate has been traversed or not. If visited, return immediately.
// OUT: out of index
if (!inArea(image, x, y)) return;
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return;
// VISITED: don't visit a coordinate twice
if (visited[x][y]) return;
visited[x][y] = true;
image[x][y] = newColor;
This is a common technique to handle graph related problems. For this particular problem, there is actually a better way: backtracking algorithm.
Refer to the article Backtracking Algorithm in Depth for details. We directly apply the backtracking algorithm framework here:
void fill(int[][] image, int x, int y,
int origColor, int newColor) {
// OUT: out of index
if (!inArea(image, x, y)) return;
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return;
// VISITED: visited origColor
if (image[x][y] == -1) return;
// choose: mark a flag as visited
image[x][y] = -1;
fill(image, x, y + 1, origColor, newColor);
fill(image, x, y - 1, origColor, newColor);
fill(image, x - 1, y, origColor, newColor);
fill(image, x + 1, y, origColor, newColor);
// unchoose: replace the mark with newColor
image[x][y] = newColor;
}
This is a typical way, using a special value -1 to replace the visited 2D array, to achieve the same purpose. Because the range of color is [0, 65535]
, -1 is special enough to differentiate with actual colors.
Most picture editing softwares have the function “Magic Wand Tool”. When you click a point, the application will help you choose a region of similar colors automatically. Refer to the picture below, if we want to select the eagle, we can use the Magic Wand Tool to select the blue sky, and perform inverse selection. Let’s analyze the mechanism of the Magic Wand Tool.
Obviously, the algorithm must be based on the FloodFill algorithm. However, there are two differences: 1. Though the background color is blue, we can’t guarantee all the blue pixels are exactly the same. There could be minor differences that can be told by our eyes. But we still want to ignore these minor differences. 2. FloodFill is to fill regions. Magic Wand Tool is more about filling the edges.
It’s easy to resolve the first problem by setting a threshold
. All colors within the threshold from the origColor
can be recognized as origColor
.
if (Math.abs(image[x][y] - origColor) > threshold)
return;
As for the second problem, let’s first define the problem clearly: “do not color all origColor
coordinates in the region; only care about the edges.”. Next, let’s analyze how to only color edges. i.e. How to find out the coordinates at the edges? What special properties do coordinates at the edges hold?
From the diagram above, we can see that for all coordinates at the edges, there is at least one direction that is not origColor
. For all inner coordinates, all 4 directions are origColor
. This is the key to the solution. Using the same framework, using visited
array to represent traversed coordinates:
int fill(int[][] image, int x, int y,
int origColor, int newColor) {
// OUT: out of index
if (!inArea(image, x, y)) return 0;
// VISITED: visited origColor
if (visited[x][y]) return 1;
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return 0;
visited[x][y] = true;
int surround =
fill(image, x - 1, y, origColor, newColor)
+ fill(image, x + 1, y, origColor, newColor)
+ fill(image, x, y - 1, origColor, newColor)
+ fill(image, x, y + 1, origColor, newColor);
if (surround < 4)
image[x][y] = newColor;
return 1;
}
In this way, all inner coordinates will have surround
equal to 4 after traversing the four directions; all edge coordinates will be either OUT or CLASH, resulting surround
less than 4. If you are still not clear, let’s only look at the framework’s logic flow:
int fill(int[][] image, int x, int y,
int origColor, int newColor) {
// OUT: out of index
if (!inArea(image, x, y)) return 0;
// VISITED: visited origColor
if (visited[x][y]) return 1;
// CLASH: meet other colors, beyond the area of origColor
if (image[x][y] != origColor) return 0;
// UNKNOWN: unvisited area that is origColor
if (image[x][y] == origColor) {
// ...
return 1;
}
}
These 4 if
s cover all possible scenarios of (x, y). The value of surround
is the sum of the return values of the 4 recursive functions. And each recursive function will fall into one of the 4 scenarios. You should be much clearer now after looking at this framework.
This implementation colors all edge coordinates only for the origColor
region, which is what the Magic Wand TOol does.
Pay attention to 2 details in this algorithm: 1. We must use visited
to record traversed coordinates instead of backtracking algorithm. 2. The order of the if
clauses can’t be modified. (Why?)
Similarly, for Minesweeper, when we use the FloodFill algorithm to expand empty areas, we also need to show the number of mines nearby. How to implement it? Following the same idea, return true
when we meet mine. Thus, surround
will store the number of mines nearby. Of course, in Minesweeper, there are 8 directions instead of 4, including diagonals.
We’ve discussed the design and framework of the FloodFill algorithm. All searching problems in a 2D array can be fit into this framework.


In this example, you will create a synthesizer that implements several modulators and plays music.
var modulatorSchema = {
oscillators: ["sine","sine"],
envelope: {
timeIn: 10,
timeOut: 10,
}
}
var synthesizerSchema = {
"Omaha DS6": {
name: "Omaha DS6",
modulators: [modulatorSchema],
toneMap: function(modulators, tone) {
modulators.forEach(function(modulator) {
var freq = 220 + (220 * tone / 13)
modulator.frequency = freq;
modulator.play();
})
}
},
};
var synthesizer = Jukebox.getSynth(synthesizerSchema);
synthesizer.play(7); // beep
synthesizer.play(2); // boop


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--- title: Colleges vs Breweries - What tops in your State ? date: 2018-09-24 --- In this post we see how the no. of breweries stack up against no. of colleges across USA. Here I am using d3js and a grid bar chartwhere each grid represents 5 breweries/colleges.I collected the data for Brewery from Beer Advocate and for colleges from wikipedia