public final class BidiFormatter
extends java.lang.Object
1. Bidi Wrapping When text in one language is mixed into a document in another, opposite-directionality language, e.g. when an English business name is embedded in a Hebrew web page, both the inserted string and the text surrounding it may be displayed incorrectly unless the inserted string is explicitly separated from the surrounding text in a "wrapper" that:
- Declares its directionality so that the string is displayed correctly. This can be done in
Unicode bidi formatting codes by unicodeWrap(java.lang.String, android.support.v4.text.TextDirectionHeuristicCompat, boolean)
and similar methods.
- Isolates the string's directionality, so it does not unduly affect the surrounding content. Currently, this can only be done using invisible Unicode characters of the same direction as the context (LRM or RLM) in addition to the directionality declaration above, thus "resetting" the directionality to that of the context. The "reset" may need to be done at both ends of the string. Without "reset" after the string, the string will "stick" to a number or logically separate opposite-direction text that happens to follow it in-line (even if separated by neutral content like spaces and punctuation). Without "reset" before the string, the same can happen there, but only with more opposite-direction text, not a number. One approach is to "reset" the direction only after each string, on the theory that if the preceding opposite- direction text is itself bidi-wrapped, the "reset" after it will prevent the sticking. (Doing the "reset" only before each string definitely does not work because we do not want to require bidi-wrapping numbers, and a bidi-wrapped opposite-direction string could be followed by a number.) Still, the safest policy is to do the "reset" on both ends of each string, since RTL message translations often contain untranslated Latin-script brand names and technical terms, and one of these can be followed by a bidi-wrapped inserted value. On the other hand, when one has such a message, it is best to do the "reset" manually in the message translation itself, since the message's opposite-direction text could be followed by an inserted number, which we would not bidi-wrap anyway. Thus, "reset" only after the string is the current default. In an alternative to "reset", recent additions to the HTML, CSS, and Unicode standards allow the isolation to be part of the directionality declaration. This form of isolation is better than "reset" because it takes less space, does not require knowing the context directionality, has a gentler effect than "reset", and protects both ends of the string. However, we do not yet allow using it because required platforms do not yet support it.
Providing these wrapping services is the basic purpose of the bidi formatter.
2. Directionality estimation How does one know whether a string about to be inserted into surrounding text has the same directionality? Well, in many cases, one knows that this must be the case when writing the code doing the insertion, e.g. when a localized message is inserted into a localized page. In such cases there is no need to involve the bidi formatter at all. In some other cases, it need not be the same as the context, but is either constant (e.g. urls are always LTR) or otherwise known. In the remaining cases, e.g. when the string is user-entered or comes from a database, the language of the string (and thus its directionality) is not known a priori, and must be estimated at run-time. The bidi formatter can do this automatically using the default first-strong estimation algorithm. It can also be configured to use a custom directionality estimation object.
| Modifier and Type | Class and Description |
|---|---|
static class |
BidiFormatter.Builder
A class for building a BidiFormatter with non-default options.
|
| Modifier and Type | Method and Description |
|---|---|
static BidiFormatter |
getInstance()
Factory for creating an instance of BidiFormatter for the default locale directionality.
|
static BidiFormatter |
getInstance(boolean rtlContext)
Factory for creating an instance of BidiFormatter given the context directionality.
|
static BidiFormatter |
getInstance(java.util.Locale locale)
Factory for creating an instance of BidiFormatter given the context locale.
|
boolean |
getStereoReset() |
boolean |
isRtl(java.lang.String str)
Estimates the directionality of a string using the default text direction heuristic.
|
boolean |
isRtlContext() |
java.lang.String |
unicodeWrap(java.lang.String str)
Operates like
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean) , but uses the
formatter's default direction estimation algorithm and assumes isolate is true. |
java.lang.String |
unicodeWrap(java.lang.String str,
boolean isolate)
Operates like
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean) , but uses the
formatter's default direction estimation algorithm. |
java.lang.String |
unicodeWrap(java.lang.String str,
TextDirectionHeuristicCompat heuristic)
Operates like
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean) , but assumes
isolate is true. |
java.lang.String |
unicodeWrap(java.lang.String str,
TextDirectionHeuristicCompat heuristic,
boolean isolate)
Formats a string of given directionality for use in plain-text output of the context
directionality, so an opposite-directionality string is neither garbled nor garbles its
surroundings.
|
public static BidiFormatter getInstance()
public static BidiFormatter getInstance(boolean rtlContext)
rtlContext
- Whether the context directionality is RTL.public static BidiFormatter getInstance(java.util.Locale locale)
locale
- The context locale.public boolean isRtlContext()
public boolean getStereoReset()
public boolean isRtl(java.lang.String str)
str
- String whose directionality is to be estimated.str
's estimated overall directionality is RTL. Otherwise returns
false.public java.lang.String unicodeWrap(java.lang.String str, TextDirectionHeuristicCompat heuristic, boolean isolate)
The algorithm: In case the given directionality doesn't match the context directionality, wraps
the string with Unicode bidi formatting characters: RLE+str
+PDF for RTL text, or
LRE+str
+PDF for LTR text.
If isolate
, directionally isolates the string so that it does not garble its
surroundings. Currently, this is done by "resetting" the directionality after the string by
appending a trailing Unicode bidi mark matching the context directionality (LRM or RLM) when
either the overall directionality or the exit directionality of the string is opposite to that
of the context. If the formatter was built using BidiFormatter.Builder.stereoReset(boolean)
and
passing "true" as an argument, also prepends a Unicode bidi mark matching the context
directionality when either the overall directionality or the entry directionality of the
string is opposite to that of the context. Note that as opposed to the overall
directionality, the entry and exit directionalities are determined from the string itself.
Does *not* do HTML-escaping.
str
- The input string.heuristic
- The algorithm to be used to estimate the string's overall direction.isolate
- Whether to directionally isolate the string to prevent it from garbling the
content around itpublic java.lang.String unicodeWrap(java.lang.String str, TextDirectionHeuristicCompat heuristic)
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean)
, but assumes
isolate
is true.str
- The input string.heuristic
- The algorithm to be used to estimate the string's overall direction.public java.lang.String unicodeWrap(java.lang.String str, boolean isolate)
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean)
, but uses the
formatter's default direction estimation algorithm.str
- The input string.isolate
- Whether to directionally isolate the string to prevent it from garbling the
content around itpublic java.lang.String unicodeWrap(java.lang.String str)
unicodeWrap(String, android.support.v4.text.TextDirectionHeuristicCompat, boolean)
, but uses the
formatter's default direction estimation algorithm and assumes isolate
is true.str
- The input string.


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| First group (# submissions) | Second group (# submissions) | Similarity |
|---|---|---|
These submissions were not considered in this process since there was an error processing them
Plagiarism details were stored in a specific JPlag report file
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Second
The
orthographical
symbols
are
twenty
five
in
number
This
finding
made
it
possible
three
hundred
years
ago
to
formulate
a
general
theory
of
the
Library
and
solve
satisfactorily
the
problem
which
no
conjecture
had
deciphered
the
formless
and
chaotic
nature
of
almost
all
the
books
One
which
my
father
saw
in
a
hexagon
on
circuit
fifteen
ninety
four
was
made
up
of
the
letters
M
C
V
perversely
repeated
from
the
first
line
to
the
last
Another
very
much
consulted
in
this
area
is
a
mere
labyrinth
of
letters
but
the
next
to
last
page
says
Oh
time
thy
pyramids
This
much
is
already
known
for
every
sensible
line
of
straightforward
statement
there
are
leagues
of
senseless
cacophonies
verbal
jumbles
and
incoherences
I
know
of
an
uncouth
region
whose
librarians
repudiate
the
vain
and
superstitious
custom
of
finding
a
meaning
in
books
and
equate
it
with
that
of
finding
a
meaning
in
dreams
or
in
the
chaotic
lines
of
one's
palm
They
admit
that
the
inventors
of
this
writing
imitated
the
twenty
five
natural
symbols
but
maintain
that
this
application
is
accidental
and
that
the
books
signify
nothing
in
themselves
This
dictum
we
shall
see
is
not
entirely
fallacious
)


--- layout: post authors: ["Greg Wilson"] title: "Software Development Practices in Academia" date: 2015-06-18 time: "07:00:00" tags: ["Noticed", "Research"] ---
Derek Groen, Xiaohu Guo, James Grogan, Ulf Schiller, and James Osborne have just submitted a paper to arXiv.org titled "Software development practices in academia: a case study comparison". From the abstract:
Academic software development practices often differ from those of commercial development settings, yet only limited research has been conducted on assessing software development practises in academia. Here we present a case study of software development practices in four open-source scientific codes over a period of nine years, characterizing the evolution of their respective development teams, their scientific productivity, and the adoption (or discontinuation) of specific software engineering practises as the team size changes. We show that the transient nature of the development team results in the adoption of different development strategies. We relate measures of publication output to accumulated numbers of developers and find that for the projects considered the time-scale for returns on expended development effort is approximately three years. We discuss the implications of our findings for evaluating the performance of research software development, and in general any computationally oriented scientific project.


4 Colorado schools closed, more cleaned as illness spreads
GRAND JUNCTION, Colo. (AP) - Two more western Colorado schools have closed for cleaning because of an unknown infectious illness spreading among students and staff.
Colorado Public Radio reported Tuesday that there are now four schools in Mesa County that have closed since the sickness appeared last week.
School officials say Rocky Mountain Elementary School in Clifton would be closed Wednesday and Juniper Ridge Community School in Grand Junction would be closed the rest of the week.
Officials say Palisade High School closed last week and has since reopened, while Mount Garfield Middle School closed Monday and is expected to open Wednesday.
County health officials haven’t determined the illness and urged parents to keep their children at home if they appear sick.
Officials say the main symptom is vomiting lasting between 12 and 24 hours.
Information from: Colorado Public Radio, http://www.cpr.org/news
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Please read our comment policy before commenting.


You canʼt run from your problems once theyʼve gone viral.
When a gruesome picture appears on a teenager’s facebook page, a young police officer becomes intent on proving that something heinous has occurred.
But is it a crime or a prank? Is the teen a silenced victim of bullying or a savvy negotiator in a culture of ambiguity?
Director: Bryan Doubt
Designer: Sonya Vallis
Graphic Designer:
Tanis Montgomery
Featuring:
Miriam Cummings
Samantha Megarry


part in finishing the task. The Theme Verse for the conference will be Romans 10:14 (...how are they to believe in him of whom they have never heard?).
Please contact us per email incase of any inquiris. you are also
welcome to visit our offices
<EMAIL_ADDRESS>| Tel: +254 746 199 225
This years Logo was decided after seeing the misunderstanding especially in the african church.We believe that missions
This being our eighth, here is a snippet of our past conferences
Mamlak HIll chapel
GracePOint Kikuyu


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Tr3umPHant Designs
Graphic design has never been easier
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Kelly graduated from Duke in 2013 with a BS in Economics and a German minor. After graduation, she joined Facebook and worked on the development and implementation of Facebook’s “Buy” button. Recently, she switched to the startup world at ClassPass, focusing on extracting and analyzing data through SQL and Python to drive business analytics and decisions. At Duke, Kelly served on the Panhellenic Standards Board, as the VP of Foundation for her sorority, and as a house course teacher on The Troubles in Northern Ireland. She will be attending Fuqua to pursue her MBA this fall!
What was an unexpected skill or resource you gained from being a part of BOW?
BOW is really great at teaching the hard skills or resume writing or case interviews, but BOW also provided me the unexpected skill to tell my story and connect with other female professionals. I learned that neither small talk nor relationship building are skills inherent to every human being, and I appreciate that BOW allowed me to further develop those skills. In addition, I think BOW's sessions, professional dress, and behavior are invaluable.
How did your summer internships at startups compare to your later jobs at Facebook and ClassPass?
One of the most difficult obstacles I have faced in my career has been determining which of the many paths I wanted to take for myself. Careers, at least mine, are not linear or well-defined. There are no final exams or well-defined metrics of success. And there is a lot of outside pressure to conform to what others may deem as those metrics of success. However, finding out that I was bored by financials and instead intrigued by database architecture was initially a hard pill to swallow, because I had previously been convinced that I would love the former. But finding and accepting that I love data visualization, algorithmic models, and database architecture has allowed me to pursue a path that I truly love, despite outside pressure.
Could you share one piece of advice you have for current BOW members trying to navigate the tech industry?
Learn everything you can about the tech industry. I follow TechNY Daily in order to know which startups in NY are raising another round or going under. In addition, if you are interested in strategy or analytics positions at startups or within the tech industry, learn SQL. I brushed off coding in college and still regret not taking an Intro to Python class with my roommate senior year. But learning how to pull your own data straight from a database, instead of relying on an engineer to do so, will make you a more valuable candidate and to be able to move more quickly within a tech company.
What is like being a junior board member for a non-profit?
I absolutely love nonprofits, and being a junior member on a couple different boards allows me to engage with the community by both volunteering directly with the community and determining, at a high level, how the junior board can have the greatest impact. I think both of these, direct engagement and high-level strategy, are ways to satisfy both the heart and the mind in making the world a little bit of a better place.
What is something interesting you’ve learned along the way?
I learned that you should be learning every day. I learned that there are no right answers, but there are certainly wrong ones. I learned that getting your work done is important, but so are the relationships you form both with your direct teammates and with others within the company. I learned to say "yes" to whatever work opportunity came up until my plate was overflowing and I had to start saying "no". I learned that if you really love what you do, the hours will actually fly by and staying up late on a weeknight or waking up early on a weekend won't feel too much like work but more like doing what you want to do with your time. I learned that being a nice person and a good human being goes a long way.


Configuration
An example testing configuration YAML file can be found from within this project repository:
src/test/resources/cassandra-reaper.yaml
The configuration file structure is provided by Dropwizard, and help on configuring the server, database connection, or logging, can be found at:
http://www.dropwizard.io/1.1.0/docs/manual/configuration.html
Reaper Specific Configuration Settings
The Reaper service specific configuration settings are:
segmentCount
:
Defines the default amount of repair segments to create for newly registered Cassandra repair runs (token rings). When running a repair run by the Reaper, each segment is repaired separately by the Reaper process, until all the segments in a token ring are repaired. The count might be slightly off the defined value, as clusters residing in multiple data centers require additional small token ranges in addition to the expected. You can overwrite this value per repair run, when calling the Reaper.
repairParallelism
:
Defines the default type of parallelism to use for repairs. Repair parallelism value must be one of: “sequential”, “parallel”, or “datacenter_aware”. If you try to use “datacenter_aware” in clusters that don’t support it yet (older than 2.0.12), Reaper will fall back into using “sequential” for those clusters.
repairIntensity
:
Repair intensity is a value between 0.0 and 1.0, but not zero. Repair intensity defines the amount of time to sleep between triggering each repair segment while running a repair run. When intensity is one, it means that Reaper doesn’t sleep at all before triggering next segment, and otherwise the sleep time is defined by how much time it took to repair the last segment divided by the intensity value. 0.5 means half of the time is spent sleeping, and half running. Intensity 0.75 means that 25% of the total time is used sleeping and 75% running. This value can also be overwritten per repair run when invoking repairs.
incrementalRepair
:
Incremental repair is a boolean value (true | false). Note that this is only supported with the PARALLEL repairParallelism setting. For more details in incremental repair, please refer to the following article.http://www.datastax.com/dev/blog/more-efficient-repairs
Note: We do not currently recommend using incremental repair before Cassandra 4.0 as subtle bugs can lead to overstreaming and cluster instabililty.
repairRunThreadCount
:
The amount of threads to use for handling the Reaper tasks. Have this big enough not to cause blocking in cause some thread is waiting for I/O, like calling a Cassandra cluster through JMX.
hangingRepairTimeoutMins
:
The amount of time in minutes to wait for a single repair to finish. If this timeout is reached, the repair segment in question will be cancelled, if possible, and then scheduled for later repair again within the same repair run process.
scheduleDaysBetween
:
Defines the amount of days to wait between scheduling new repairs. The value configured here is the default for new repair schedules, but you can also define it separately for each new schedule. Using value 0 for continuous repairs is also supported.
storageType
:
Whether to use database or memory based storage for storing the system state. Value can be either “memory”, “database” or “cassandra”. If you are using the recommended (persistent) storage type “database” or “cassandra”, you need to define the database client parameters in a database/cassandra section in the configuration file. See the example settings in provided testing configuration in src/test/resources/cassandra-reaper.yaml.
useAddressTranslator
:
When running multi region clusters in AWS, turn this setting to true
in order to use the EC2MultiRegionAddressTranslator from the Datastax Java Driver. This will allow translating the public address that the nodes broadcast to the private IP address that is used to expose JMX.
Defaults to false
.
jmxPorts
:
Optional mapping of custom JMX ports to use for individual hosts. The used default JMX port
value is 7199. CCM users will find IP and port number
to add in ~/.ccm/<cluster>/*/node.conf
or by running ccm <node> show
.
jmxAuth
:
Optional setting for giving username and password credentials for the used JMX connections in case you are using password based JMX authentication with your Cassandra clusters.
jmxAuth:
username: cassandra
password: cassandra
enableCrossOrigin
:
Optional setting which you can set to be “true”, if you wish to enable the CORS headers for running an external GUI application, like this project.
autoScheduling
:
Optional setting to automatically setup repair schedules for all non-system keyspaces in a cluster. If enabled, adding a new cluster will automatically setup a schedule repair for each keyspace. Cluster keyspaces are monitored based on a configurable frequency, so that adding or removing a keyspace will result in adding / removing the corresponding scheduled repairs.
repairManagerSchedulingIntervalSeconds
:
Controls the pace at which the Repair Manager will schedule processing of the next segment. Reducing this value from 30s (default) to a lower value can speed up fast repairs by orders of magnitude.
jmxConnectionTimeoutInSeconds
:
Controls the timeout for establishing JMX connections. The value should be low enough to avoid stalling simple operations in multi region clusters, but high enough to allow connections under normal conditions.
The default is set to 5 seconds.
Notice that in the server section of the configuration, if you want to bind the service to all interfaces, use value “<IP_ADDRESS>”, or just leave the bindHost line away completely. Using “*” as bind value won’t work.
datacenterAvailability
:
Indicates to Reapers its deployment in relation to cluster data center network locality. The value can be either ALL (default value), LOCAL, or EACH. Note that the datacenterAvailability
setting controls the behavior for metrics collection.
For security reasons, it is possible that Reaper will have access limited to nodes in a single datacenter via JMX (multi region clusters for example). In this case, it is possible to deploy an operate an instance of Reaper in each datacenter where each instance only has access via JMX (with or without authentication) to the nodes in its local datacenter. Where multiple instances of Reaper are in operation in this configuration, only the Apache Cassandra storage option can be used with Reaper. All other storage options are unsuitable in this case. This is because Reaper instances will rely on lightweight transactions to get leadership on segments before processing them. In addition, Reaper will check the number of pending compactions and actively running repairs on all replicas prior to processing a segment.
Setting the value to ALL requires Reaper to have access via JMX to all nodes across all datacenters. In this mode Reaper can be backed by all available storage types.
Setting the value to LOCAL requires Reaper to have access via JMX to all nodes only in the same datacenter local to Reaper. A single Reaper instance can operate in this mode and repair its local data center. In this case, can be backed by all available storage types and repairs to any remote datacenters are be handled internally by Cassandra. A Reaper instance can be deployed to each datacenter and be configured to operate in this mode. In this case, Reaper can only use Apache Cassandra as its storage. In addition, metrics can be collected asynchronously through the Apache Cassandra storage.
Setting the value to EACH requires a minimum of one Reaper instance operating in each datacenter. Each Reaper instance is required to have access via JMX to all nodes only in its local datacenter. When operating in this mode, Reaper can only use Apache Cassandra as its storage. In addition, metrics from nodes in remote datacenters must be collected through the Cassandra storage backend. If all metrics are unavailable, the segment will be postponed for later processing.


Welcome, I'm Bernie Teng, currently studying in Electrical Engineering at National Taiwan University. I'm interested in hardware engineering, especially digital IC design and domain-specific VLSI application (Ex. ML acceleration, signal & image processing). I was a hardware engineer intern at Intel Corp., developing circuit verification and automation tools. Further, I am currently a consultant intern at McKinsey & Company, working on cost modeling and value improvement strategy for several tech client companies. Also, I'm a dreamer and an executive learner who aspires to start up a tech company So, in this site, I want to share my versatile experiences with you. Please immerse in here, deeply, and enjoy it.
Honored of academic and research excellence at National Taiwan University (15 out of 7000+ Students)
To fulfill my inner nature of innovative, in Prof. Chiueh's Microsystem Lab , we developed an efficient dataflow integrating a novel BN processor design and the processing elements for convolution network acceleration. This work successfully achieves hardware saving and fitted different kinds of PE arrays, which was innovative to all the previous works related to CNN training acceleration.
Represented Department of Electrical Engineering, NTU to compete on full custom chip design.
ScAnIt is a supermarket AI assistant aiming to help customers find their desired product in the market. The prototype is done by embedding our techniques on Raspberry Pi. We used a pre-trained SVM model to identify products, connecting the product info to the database by Microsoft Azure. Through this innovative service, ScAnIt enhanced the whole purchase process in the supermarket.
To fulfill my dream of building a tech company, I joined McKinsey to learn how to connect engineering technology development with business impact. Currently, I have cooperated globally with consultants in 10+ cases, using cost modeling and value improvement analysis to develop strategy to solve clients' problems.
I was hired by DBS Bank as a contractor to analyze data for the business analysis team by developing automatic comparison programs in Python and Qlikview SQL. Also, I developed strategies for the Iserve system call issue with the banking team and credit card team.
This was the first time I implemented my technical skills in the business field.
I am proud of being a 1-year intern at the world's best company for IC and hardware design.
During my internship, I supported the new generation NVMe SSD system-level validation and integration for Client SSD. I collaborated with my wonderful colleagues both in Taiwan and online in the US.
Here I experienced real-world IC product development.
I was fortunate to be the exhibitor in 2019 Computex/InnoVex, one of the largest global technology trade shows for enterprises and startups, to present my light detection application on the eye-tracker project to international enterprises. This life-changing experience inspired me to start a company to realize those cool and innovative technologies into our daily lives!
Yu-Fan Teng* , Yu-Sheng Ting*, Tzi-Dar Chiueh, “Batch Normalization Processor Design for Convolution Neural Network Training and Inference”, Submitted to 2021 IEEE International Symposium on Circuits and Systems (ISCAS)
GoGet is an online platform for college students and startup companies to find expertise to deal with their current project problems and find partners. It also helps new grads to find their dream jobs We have 1000+ users per week, still increasing. Recently cooperating with Microsoft Taiwan to promote programming courses for interdisciplinary learning
We went through the whole IC design process including Matlab simulation, RTL, Placement & Routing, and DRC/LVS. I designed an adaptive FIR filter chip as an equalizer to eliminate the noises of the BPSK system. By fine-tuning the recursive least square algorithm’s weight update formula, the taped-out chip reduced 37 percent of the timing.
We designed an automatic music player in System Verilog on FPGA, which integrated image and audio processing to identify musical notes. The techniques involved were integrated FPGA peripheral modules, such as Arduino joystick and VGA image display and TRDB-D5M camera.
"Work Hard, Play Hard" is my life motto. Besides academy, I am the captain of the NTUEE badminton team, my high school alumni cheerleader, a band singer, and director of several events in our EE department. Through these events, I cultivated my interpersonal skills as well as leadership. Most importantly, I made many long-lasting friends and enjoyed my college life! I have also volunteered in the NTU Sunflower Volunteer Club since 2018 to help hold educational camps for elementary school students in remote areas. Throughout two years of services, we helped 300+ students to gain better educational resources. I was happy to know that we made a difference in their lives. This experience made me more grateful for the resources I used to take for granted.


# Make sure to fill out all these questions as they will be introduced into your blog
# Monday Session - HTML & CSS
## HTML
- What does HTML stand for?
- what is HTMl used for?
- What do these tags stand for? and can you show an example of using them ( in here is fine )
-
-
Means a heading the number controls its size
-
Another header at a smaller size
-
Paragraph thats used to hold text
-
Divider
-
-
-
The meta data for the document
- Body is what represents the document
## CSS
- what does CSS stand for?
- What do i use CSS style sheet for?
- Show me some examples in the below of some css you can add to these tags:
body {
<--- Change color of background --->
}
h1 {
<--- Change the font size --->
}
- what is a class and id ( when using it in a html and css way )


Video: Keeper scores from his own box after the opposing goalie came up for a corner
by Tom McNeilIt’s one of the greatest sights in football when a keeper makes the long gallop up the pitch to join the attack for a late corner kick.
It’s never been explained why the corner taker always ignores the actual outfielders who have experience of trying to score in order to aim for the keeper every single time, but it just adds to the excitement.
If anybody ever wondered why managers are usually reluctant to send the keeper forward, this clip is a pretty good example:
There’s no chance the opposing keeper is getting back to stop it, but it’s still some effort to find the target from inside your own box.
The Chivas keeper has to hit it over a few defenders and avoid having the ball bounce over the bar or drift wide, it’s a very impressive effort.


Answers the following multiple choice code-related questions within the time limit. Keep in mind that incorrect answers will penalize your score and time will be deducted by ten seconds.


Conte does not want his players to expend too much energy.
The 50-year-old is demanding high fitness levels from his squad as they push for honours.
He told L'Equipe magazine: "During spells of matches the players should not have sexual relations that last for long periods.
"They need to make as little effort as possible. The best position for them is if their partners go on top.
"And it is preferable that they are with their own wives.
"That way they are not obliged to put in an exceptional performance."
Inter Milan beat Torino 3-0 on Saturday to stay one point behind leaders Juventus.


// Based on David Walsh's pub-sub implementation
// https://davidwalsh.name/pubsub-javascript
let topics = {};
let hOP = topics.hasOwnProperty;
export const events = {
subscribe: function(topic, listener) {
// Create the topic's object if not yet created
if (!hOP.call(topics, topic)) {
topics[topic] = [];
}
// Add the listener to queue
let index = topics[topic].push(listener) - 1;
// Provide handle back for removal of topic
return {
remove: function() {
delete topics[topic][index];
}
};
},
publish: function(topic, info) {
// If the topic doesn't exist, or there's no listeners in queue, just leave
if (!hOP.call(topics, topic)) {
return;
}
// Cycle through topics queue, fire!
topics[topic].forEach(function(item) {
item(info != undefined ? info : {});
});
}
};
|


It's easy to show content for Android or iOS. We've created a system to easily detect the mobile device that visits your site and automatically show content only for that device.
Show Cards
Depending on what devices visit, different cards will appear. Try this page from your mobile device.
No Mobile Detected
Try visiting this page from your mobile device. At the moment you are viewing it from a computer.
Show Button
You can show buttons, for example, to download an application, based on the device that is currently browsing.
Show Anything
Just use the classes show-android or show-ios on any element and this will make that element visible only on the designated operating syste,


{% extends "layout.html" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Home Second level subcontracting You cannot subcontract to another organisation who will subcontract the work again, without permission. Will any of your subcontractors use subcontractors? No Yes In exceptional circumstances you can ask for permission to second level subcontract. Our GOV.UK guidance page provides information on how to apply. Continue {% endblock %}
You cannot subcontract to another organisation who will subcontract the work again, without permission.
In exceptional circumstances you can ask for permission to second level subcontract.
Our GOV.UK guidance page provides information on how to apply.


How I got here
From non-profits…
In college my goal was to work in international development. I majored in economics, studied abroad in Argentina for a year and wrote an honor’s thesis on the effect of economic crises on skilled migrants to the United States. After graduation I joined AmeriCorps for a year, training and managing a team of volunteers at tax preparation sites and providing organizational support for the local coalition of such sites. In 2011, I moved to Peru to work as a field researcher doing research with a non-profit Microfinance Institution and an early childhood education program. I trained and led teams of surveyors, designed survey instruments, and cleaned/analyzed the resulting data.
…to discovering programming…
Considering doing graduate work in economics I took the GRE and took advantage of the advent of Massively Open Online Courses to prepare myself with classes on Data Analysis, calculus, linear algebra and python programming. Once I started learning programming I was hooked, and discovered I could work remotely and have the freedom to live where I wanted while still making a difference in the world .
I took online courses in web development while while teaching English and working in the marketing department of a travel agency. Eventually I learned enough to take on web responsibilities in my job at a travel agency doing tasks like automating the writing of SEO text with python scripts or creating a custom google analytics solution to improve tracking for sales.
I wanted to go deeper into programming, so I enrolled in an online web development apprenticeship through Bloc where I put went beyond the LAMP stack education I had pieced together and small scripts I had written and learned how to test drive writing complex database backed web applications with ruby on rails.
…to building companies
In the summer of 2015 I moved back to the U.S. and have been freelancing for startups all over the country since then. I’ve worked mainly with startups as a ruby on rails developer, working up and down the stack but mainly on web backends and APIs. I’ve worked for several clients, all startups with geographically distributed teams.
You can see find out more about my client work in projects.
When I’m not working on client work I’m studying ruby and rails resources to deepen my expertise and learning new skills like react and elixir to complement my rails toolset. I’ve also built some open source tools to simplify some common tasks which I’ve open sourced and can be found here or on my github.
To the city by the bay
In early 2017 I moved to the San Francisco bay area to work for Bright Funds, a startup with the ambitious goal of changing the way that people donate to charitable causes they care about. Since late 2018 I’ve been working at Canvas, a car subscription platform and wholly owned subsidiary of Ford Credit seeking to simplify and improve the way people get access to cars.


All notifications emitted from managed nodes are federated to all JMX Managers in the system.
These notifications are federated and then emitted by the DistributedSystemMXBean. If you attach a javax.management.NotificationListener
to your DistributedSystemMXBean, the NotificationListener can listen to notifications from all MemberMXBeans and all CacheServerMXBeans.
Attaching Listeners to MXBeans
When you attach a notification listener to the DistributedSystemMXBean, the DistributedSystemMXBean then acts as the notification hub for the entire distributed system. You do not have to attach a listener to each individual member or cache server MBean in order to listen to all the notifications in the distributed system.
The following is an example of attaching a NotificationListener to an MBean using the JMX MBeanServer API:
NotificationListener myListener = ...
ObjectName mbeanName = ...
MBeanServer.addNotificationListener(mbeanName, myListener, null, null);
JMX Managers will emit notifications for all distributed system members with two exceptions:
- If you use cache.xml to define resources such as regions and disks, then notifications for these resources are not federated to the JMX Manager. In those cases, the DistributedSystemMXBean cannot emit those notifications.
- If a JMX Manager is started after a resource has been created, the JMX Manager cannot emit notifications for that resource.
System Alert Notifications
System alerts are Geode alerts wrapped within a JMX notification. The JMX Manager registers itself as an alert listener with each member of the system, and by default, it receives all messages logged with the SEVERE alert level by any node in the distributed system. Consequently, the DistributedSystemMXBean will then emit notifications for these alerts on behalf of the DistributedSystem.
By default, the JMX Manager registers itself to send notifications only for SEVERE level alerts. To change the alert level that the JMX Manager will send notifications for, use the DistributedMXBean.changeAlertLevel
method. Possible alert levels to set are WARNING, ERROR, SEVERE, and NONE. After changing the level, the JMX Manager will only emit that level of log message as notifications.
Notification objects include type, source and message attributes. System alerts also include the userData attribute. For system alerts, the notification object attributes correspond to the following:
- type: system.alert
- source: Distributed System ID
- message: alert message
- userData: name or ID of the member that raised the alert


--- layout: post status: publish published: true title: Custom Faults in Indigo author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 712 wordpress_url: http://winterdom.com/2005/05/customfaultsinindigo date: '2005-05-01 15:31:45 +0000' date_gmt: '2005-05-01 15:31:45 +0000' categories: - WCF - WinFX tags: [] comments: [] ---
When building an Indigo based service, you might need to signal an error to your clients.
Normally, one would expect to be able do this normally by simply throwing an exception from a service method, just like in ASP.NET. However, this doesn't seem to be the case with Indigo: all I'm seeing happening is the client hanging and getting no response at all from the server, and then failing with a closed connection. I would've expected at least an HTTP 500 error back, or something telling me that something bad happened during the request execution, but no dice.
I did notice, however, that during development you can enable an option instructing Indigo to serialize the exception messages as strings and put those into the returned soap fault, allowing you to get some extra details on the exception on the client side. To do this, you can enable the ReturnUnknownExceptionsAsFaults behavior on the service, either by code or by configuration file. Here's an example of the former:
[ ServiceBehavior(ReturnUnknownExceptionsAsFaults=true) ]
public class ...
With this option enabled, exceptions are returned to the client correctly, arriving as exceptions of the System.ServiceModel.UnknownFault class. One thing to notice when using this attribute is that you need to apply it to the service implementation class, not the service interface definition, so watch out for that.
Real Faults
This is all well and good, but not very useful by itself. In most cases, you'll want a stronger contract than some error string lying around in your faults. If you need to provide detailed and useful error information to your clients in a way that they can access it easily, you'll want to make sure your service returns a custom XML fragment inside the
Indigo provides an easy way to do this by returning custom faults from your operations instead of simple exceptions. To do this, you can create a DataContract class that represents your fault's details, and then, inside your methods, throw an instace of the Fault
[DataContract]
public class InvalidNameFault
{
[DataMember]
public string Description;
[DataMember]
public string NameUsed;
}
[ServiceContract]
public interface IFaultyService
{
[OperationContract]
[FaultContract(typeof(InvalidNameFault))]
string Hello(string name);
} // interface IFaultyService
public class FaultyService : IFaultyService
{
public string Hello(string name)
{
// fault if name contains "Tomas"
if ( name.IndexOf("Tomas") >= 0 ) {
InvalidNameFault details = new InvalidNameFault();
details.NameUsed = name;
details.Description =
"An unrecognized name was passed to the service";
throw new Fault(details);
}
return "Hello " + name;
}
}
As you can see, I just defined a custom class to carry the fault information as a DataContract (InvalidNameFault), which contains a couple of data members serialized as part of the fault. Also, notice that inside the implementation of the Hello() method, we just create an instance of the InvalidNameFault class, fill it out, and then wrap it in an instance of the Fault
On the client side, if we're building with Indigo, a nice thing is that we can catch an instance of the typed Fault class and easily access the information serialized into the Fault Details:
FaultyServiceProxy proxy =
new FaultyServiceProxy(epa, binding);
try {
string hello = proxy.Hello("Tomas Restrepo");
Console.WriteLine(hello);
} catch ( Fault e ) {
Console.WriteLine("{0}: {1}", e.Detail.Description, e.Detail.NameUsed);
}
The most interesting thing of this whole thing is that Indigo can make your custom faults part of the service contract. Notice how I use the [FaultContract] attribute in the operation declaration, telling Indigo that a possible fault returned by my service contains custom information in the form of the InvalidNameFault contract. What's really cool about this is that this information will actually surface as part of the service description in the WSDL:
http://localhost:8188/FaultyService
Indigo will also generate the custom WSDL necessary for the custom fault class:


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Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


HyunA & Dawn dress up for the prom in 'Grazia' cover pictorial
by beansssThe HyunA-Dawn couple is ready to go to the prom together, in their 'Grazia' magazine cover pictorial!
The couple of course put their unique, quirky flare to each of their "prom" outfits, such as a Rock n' Roll-style leather suit, leather gloves, a suit jacket with no shirt underneath, etc. After dressing up, the pair stepped up in front of a photo wall to snap a collection of couple shots, all smiles of laughs.
In their interview, HyunA and Dawn talked about keeping their solo music work separate from their private lives, their end of the year plans, and more.


There might come a time when a microcontroller or other embedded project needs a globally-unique tuple of two 16-bit integers. At one point, this website contained a registry of 16-bit tuples where the first element was the value 0xf055. Unfortunately, that previous website has since ceased to exist. This new website is an attempt to both revive the old registry of tuples as well as to link to other places where open hardware projects can obtain 16-bit tuples beginning with numbers other than 0xf055.
I can be contacted at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>My primary website is here.
| Tuple | Hardware Description | Owner |
|---|---|---|
| (0xf055, 0x0000) | available for private prototyping | n/a |
| (0xf055, 0x0001) | available for private prototyping | n/a |
| (0xf055, 0x0002) | available for private prototyping | n/a |
| (0xf055, 0x0003) | available for private prototyping | n/a |
| (0xf055, 0x0004) | available for private prototyping | n/a |
| (0xf055, 0x0005) | available for private prototyping | n/a |
| (0xf055, 0x0006) | available for private prototyping | n/a |
| (0xf055, 0x0007) | available for private prototyping | n/a |
| (0xf055, 0x0008) | available for private prototyping | n/a |
| (0xf055, 0x0009) | available for private prototyping | n/a |
| (0xf055, 0x000a) | available for private prototyping | n/a |
| (0xf055, 0x000b) | available for private prototyping | n/a |
| (0xf055, 0x000c) | available for private prototyping | n/a |
| (0xf055, 0x000d) | available for private prototyping | n/a |
| (0xf055, 0x000e) | available for private prototyping | n/a |
| (0xf055, 0x000f) | available for private prototyping | n/a |
| (0xf055, 0x0010) | an inexpensive usb key for monitoring air pressure | Open Meteo Foundation |
| (0xf055, 0x0011) | dynamic USB led controller with support for large + 1280x720 RGB LEDs | Thomas R |
Publish your project under an open-source license and then fork the repository for this website on GitHub


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
This smoked haddock, coated generously in a punchy cheese sauce, would make a spectacular start or end to any lazy weekend.
For the smoked haddock Welsh rarebit, preheat the grill to its highest setting.
Place the smoked haddock fillets into a high-sided pan and pour over the milk. Add the bay leaves.
Place the pan over a gentle heat and bring the liquid to a simmer. Poach the fish for 4-5 minutes, or until it is just cooked through.
Remove the pan from the heat and strain 50ml/2fl oz of the milk into a measuring jug. Strain off the remaining milk and discard the bay leaves. Transfer the drained poached haddock to a grill tray. Keep warm.
Place the reserved 50ml/2fl oz of warm milk into a saucepan and add the cheese. Heat the mixture, stirring regularly, until the cheese has melted.
Add the breadcrumbs, then whisk in the flour. Continue to cook for 2-3 minutes, stirring continuously.
Set the mixture aside to cool, then add the Worcestershire sauce, egg, egg yolk and mustard powder. Mix thoroughly, then season, to taste, with salt and white pepper.
Spread the sauce over the smoked haddock fillets and grill for 1-2 minutes, or until the sauce is golden-brown and bubbling.
For the tomato chutney, heat a frying pan over a medium heat. Add the caster sugar and heat, without stirring, until it has melted and caramelised to a light golden-brown. Add the three chopped tomatoes, white wine vinegar, sultanas and thyme and stir well until combined. Cook for 2-3 minutes, then season, to taste, with salt and freshly ground black pepper.
To serve, whisk the olive oil and chives together and season, to taste, with salt and freshly ground black pepper. Arrange some of the sliced tomatoes in a circle in the centre of each plate. Place a spoonful of tomato chutney into the centre of each. Place one smoked haddock fillet on top of each and spoon over the Welsh rarebit sauce. Spoon the chive oil around the edge, over the sliced tomatoes.


---
title: "Image Steganography"
excerpt: "Randomly hiding secret data using dynamic programming for image steganography
"
collection: portfolio
---
Data hiding in multimedia systems is a ubiquitous practice encouraging secret communication of highly confidential information among different parties. To accomplish such a task, a steganographic system is to be realized. Adding to the plethora of algorithms designed to hide secret data in images, a new data hiding scheme based on Energy and Cost of the Image has been proposed. Energy of an image describes the relationship between adjacent pixels which is based on its potential. Offshoots of Dynamic Programming are used to estimate the cost of the image and further random traversal is employed to select the pixels in which data is to be embedded. The information in the form of bits is embedded in each of the selected pixel in a predefined manner. The mean square error and peak signal to noise ratio has also been calculated along with structural similarity index to assess the quality of the cover and the obtained stego image. The final results show a remarkable improvement over some of the existing methods in terms of payload capacity, robustness, fidelity and the metrics calculated. With minimum computational time a robust steganography scheme is achieved.
As a third year undergraduate project (2014-15), we culminated this work as conference paper. The link to the paper on IEEE website. You can find a pdf here.


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The primary goal of this demo talk is to demonstrate how to write talks with DocOnce and get them rendered in numerous HTML formats.
This version
utilizes reveal slides with the theme serif
.
Speaker notes show up by pressing "s" (but you must run reveal locally and some packages installed, see this description).
|
Here,
|
|
The \( \theta \) rule,
$$
u^{n+1} = \frac{1 - (1-\theta) a\Delta t}{1 + \theta a\Delta t}u^n,
\quad n=0,1,\ldots,N-1
$$
contains the Forward Euler (\( \theta=0 \)),
the Backward Euler (\( \theta=1 \)),
and the Crank-Nicolson (\( \theta=0.5 \))
schemes.
def solver(I, a, T, dt, theta):
"""Solve u'=-a*u, u(0)=I, for t in (0,T]; step: dt."""
dt = float(dt) # avoid integer division
N = int(round(T/dt)) # no of time intervals
T = N*dt # adjust T to fit time step dt
u = zeros(N+1) # array of u[n] values
t = linspace(0, T, N+1) # time mesh
u[0] = I # assign initial condition
for n in range(0, N): # n=0,1,...,N-1
u[n+1] = (1 - (1-theta)*a*dt)/(1 + theta*dt*a)*u[n]
return u, t
# Set problem parameters
I = 1.2
a = 0.2
T = 8
dt = 0.25
theta = 0.5
from solver import solver, exact_solution
u, t = solver(I, a, T, dt, theta)
import matplotlib.pyplot as plt
plt.plot(t, u, t, exact_solution)
plt.legend(['numerical', 'exact'])
plt.show()
Exact solution of the scheme:
$$ u^n = A^n,\quad A = \frac{1 - (1-\theta) a\Delta t}{1 + \theta a\Delta t}\thinspace .$$
Key results:
Only the Backward Euler scheme is guaranteed to always give qualitatively correct results.


window.opener in same domain and different domain demo
Open a new window from:
same-domain
different-domain


Week 3
JavaScript is one of the most popular programming language used to create a dynamic and interactive website across platform. There Are several factors contributing to its arguably growing popularity:
- It's easy to debug JavaScript using a browsing console.
- Javascript is relatively an easy language to learn.
- Javascript is relatively fast to the end user. As the code is executed on the user computer, results and processing is completed almost instantly depending not he task.
- Javascript has become a lot more popular because enterprise based companies are incorporating Javascrip in their browsers.
The main disadvantage if Javascript is it has very vulnerable security and also different browsers read Javascript differently, especially on the user end.


--- title: "TidyTuesday - NFL Data - 2020 - Wk 6" author: admin date: '2020-02-13' slug: categories: [Code] tags: - R - TidyTuesday subtitle: '' summary: 'A look at the average yearly temperature and rainfall since 1910' authors: [M. L. DeBusk-Lane] lastmod: '2020-01-04T07:57:09-05:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] output: html_document: theme: journal highlight: zenburn ---
library(tidyverse)
library(janitor)
library(readxl)
#library(tidylog)
library(skimr)
library(knitr)
This week’s tidytuesday data comes from Pro Football Reference and includes attendance, standings, and game stats for each game. Well do a quick EDA and generate a few ideas of what might be interesting to look at. As a note, I stopped this one once I started to dive into predictives, as I simply ran out of time for this week. I’ll catch this up one day…. perhaps.
# Import the data from tidytuesday: https://github.com/rfordatascience/tidytuesday
attendance <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/attendance.csv')
standings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/standings.csv')
games <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/games.csv')
First, lets take a look at the data and see what it can tell us at face value.
kable(head(attendance))
| team | team_name | year | total | home | away | week | weekly_attendance |
|---|---|---|---|---|---|---|---|
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 1 | 77434 |
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 2 | 66009 |
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 3 | NA |
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 4 | 71801 |
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 5 | 66985 |
| Arizona | Cardinals | 2000 | 893926 | 387475 | 506451 | 6 | 44296 |
attendance %>%
skim()
| Name | Piped data |
| Number of rows | 10846 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| team | 0 | 1 | 5 | 13 | 0 | 32 | 0 |
| team_name | 0 | 1 | 4 | 10 | 0 | 32 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1.00 | 2009.53 | 5.75 | 2000 | 2005.0 | 2010 | 2015.00 | 2019 | ▇▇▇▇▇ |
| total | 0 | 1.00 | 1080910.03 | 72876.97 | 760644 | 1040509.0 | 1081090 | 1123230.00 | 1322087 | ▁▁▇▆▁ |
| home | 0 | 1.00 | 540455.01 | 66774.65 | 202687 | 504360.0 | 543185 | 578342.00 | 741775 | ▁▁▅▇▁ |
| away | 0 | 1.00 | 540455.01 | 25509.33 | 450295 | 524974.0 | 541757 | 557741.00 | 601655 | ▁▂▇▇▂ |
| week | 0 | 1.00 | 9.00 | 4.90 | 1 | 5.0 | 9 | 13.00 | 17 | ▇▆▆▆▇ |
| weekly_attendance | 638 | 0.94 | 67556.88 | 9022.02 | 23127 | 63245.5 | 68334 | 72544.75 | 105121 | ▁▁▇▃▁ |
We certainly have some interesting statistics here to mess with. It looks like the ‘total,’ ‘home,’ and ‘away’ attendance columns represent the entire season, while the ‘weekly_attendance’ field is for each week. Therefore, it looks like each row is a game. So there should be some variability in how many rows there are amongst the teams, as some make it to the playoff and some do not (therefore they’d have more games). A look at the summary statistics should tell us more… if that is the case or not.
So, as was expected, there does seem to be some variability associated with how many games they play. Not to worry, I’ll probably stay away from analyzing just that, and focus on either more aggregated descriptives or simply other statistics herein.
kable(head(standings))
| team | team_name | year | wins | loss | points_for | points_against | points_differential | margin_of_victory | strength_of_schedule | simple_rating | offensive_ranking | defensive_ranking | playoffs | sb_winner |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Miami | Dolphins | 2000 | 11 | 5 | 323 | 226 | 97 | 6.1 | 1.0 | 7.1 | 0.0 | 7.1 | Playoffs | No Superbowl |
| Indianapolis | Colts | 2000 | 10 | 6 | 429 | 326 | 103 | 6.4 | 1.5 | 7.9 | 7.1 | 0.8 | Playoffs | No Superbowl |
| New York | Jets | 2000 | 9 | 7 | 321 | 321 | 0 | 0.0 | 3.5 | 3.5 | 1.4 | 2.2 | No Playoffs | No Superbowl |
| Buffalo | Bills | 2000 | 8 | 8 | 315 | 350 | -35 | -2.2 | 2.2 | 0.0 | 0.5 | -0.5 | No Playoffs | No Superbowl |
| New England | Patriots | 2000 | 5 | 11 | 276 | 338 | -62 | -3.9 | 1.4 | -2.5 | -2.7 | 0.2 | No Playoffs | No Superbowl |
| Tennessee | Titans | 2000 | 13 | 3 | 346 | 191 | 155 | 9.7 | -1.3 | 8.3 | 1.5 | 6.8 | Playoffs | No Superbowl |
standings %>%
skim()
| Name | Piped data |
| Number of rows | 638 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| team | 0 | 1 | 5 | 13 | 0 | 32 | 0 |
| team_name | 0 | 1 | 4 | 10 | 0 | 32 | 0 |
| playoffs | 0 | 1 | 8 | 11 | 0 | 2 | 0 |
| sb_winner | 0 | 1 | 12 | 13 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1 | 2009.53 | 5.76 | 2000.0 | 2005.00 | 2010.0 | 2014.75 | 2019.0 | ▇▇▇▇▇ |
| wins | 0 | 1 | 7.98 | 3.08 | 0.0 | 6.00 | 8.0 | 10.00 | 16.0 | ▂▆▇▆▂ |
| loss | 0 | 1 | 7.98 | 3.08 | 0.0 | 6.00 | 8.0 | 10.00 | 16.0 | ▂▆▇▆▂ |
| points_for | 0 | 1 | 350.28 | 71.40 | 161.0 | 299.00 | 348.0 | 396.00 | 606.0 | ▂▇▇▂▁ |
| points_against | 0 | 1 | 350.28 | 59.55 | 165.0 | 310.00 | 347.0 | 391.50 | 517.0 | ▁▃▇▆▁ |
| points_differential | 0 | 1 | 0.00 | 101.09 | -261.0 | -75.00 | 1.5 | 72.75 | 315.0 | ▂▆▇▅▁ |
| margin_of_victory | 0 | 1 | 0.00 | 6.32 | -16.3 | -4.70 | 0.1 | 4.57 | 19.7 | ▂▆▇▅▁ |
| strength_of_schedule | 0 | 1 | 0.00 | 1.63 | -4.6 | -1.10 | 0.0 | 1.20 | 4.3 | ▁▅▇▅▁ |
| simple_rating | 0 | 1 | 0.00 | 6.20 | -17.4 | -4.47 | 0.0 | 4.50 | 20.1 | ▁▆▇▅▁ |
| offensive_ranking | 0 | 1 | 0.00 | 4.34 | -11.7 | -3.18 | 0.0 | 2.70 | 15.9 | ▁▇▇▂▁ |
| defensive_ranking | 0 | 1 | 0.00 | 3.57 | -9.8 | -2.40 | 0.1 | 2.50 | 9.8 | ▁▅▇▅▁ |
So, look at this snapshot of the data, there seems to be all kinds of interesting statistics between attendance and what appears to be by team year statistics.
kable(head(games))
| year | week | home_team | away_team | winner | tie | day | date | time | pts_win | pts_loss | yds_win | turnovers_win | yds_loss | turnovers_loss | home_team_name | home_team_city | away_team_name | away_team_city |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000 | 1 | Minnesota Vikings | Chicago Bears | Minnesota Vikings | NA | Sun | September 3 | 13:00:00 | 30 | 27 | 374 | 1 | 425 | 1 | Vikings | Minnesota | Bears | Chicago |
| 2000 | 1 | Kansas City Chiefs | Indianapolis Colts | Indianapolis Colts | NA | Sun | September 3 | 13:00:00 | 27 | 14 | 386 | 2 | 280 | 1 | Chiefs | Kansas City | Colts | Indianapolis |
| 2000 | 1 | Washington Redskins | Carolina Panthers | Washington Redskins | NA | Sun | September 3 | 13:01:00 | 20 | 17 | 396 | 0 | 236 | 1 | Redskins | Washington | Panthers | Carolina |
| 2000 | 1 | Atlanta Falcons | San Francisco 49ers | Atlanta Falcons | NA | Sun | September 3 | 13:02:00 | 36 | 28 | 359 | 1 | 339 | 1 | Falcons | Atlanta | 49ers | San Francisco |
| 2000 | 1 | Pittsburgh Steelers | Baltimore Ravens | Baltimore Ravens | NA | Sun | September 3 | 13:02:00 | 16 | 0 | 336 | 0 | 223 | 1 | Steelers | Pittsburgh | Ravens | Baltimore |
| 2000 | 1 | Cleveland Browns | Jacksonville Jaguars | Jacksonville Jaguars | NA | Sun | September 3 | 13:02:00 | 27 | 7 | 398 | 0 | 249 | 1 | Browns | Cleveland | Jaguars | Jacksonville |
From this vantage, it looks like I could connect the team name from the attendance data to the home team of the games data. To do so, I’ll have to make a key that matches. Most readily, it looks like I’ll need to combine a few the ‘team’ and ‘team_name’ columns in the attendance data.
att_reshape <- attendance %>%
mutate(t_name = str_c(team, team_name, sep = " ")) %>%
select(-team, -team_name)
# to merge these datasets, I needed 'week' to be numeric on both...
# and for whatever reason it was a character field to begin with...
games <- games %>%
mutate(week = as.numeric(week))
att_games <- left_join(att_reshape, games,
by = c('t_name' = 'home_team', 'year' = 'year', 'week' = 'week'))
kable(head(att_games))
| year | total | home | away | week | weekly_attendance | t_name | away_team | winner | tie | day | date | time | pts_win | pts_loss | yds_win | turnovers_win | yds_loss | turnovers_loss | home_team_name | home_team_city | away_team_name | away_team_city |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2000 | 893926 | 387475 | 506451 | 1 | 77434 | Arizona Cardinals | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2000 | 893926 | 387475 | 506451 | 2 | 66009 | Arizona Cardinals | Dallas Cowboys | Arizona Cardinals | NA | Sun | September 10 | 20:35:00 | 32 | 31 | 322 | 1 | 330 | 2 | Cardinals | Arizona | Cowboys | Dallas |
| 2000 | 893926 | 387475 | 506451 | 3 | NA | Arizona Cardinals | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2000 | 893926 | 387475 | 506451 | 4 | 71801 | Arizona Cardinals | Green Bay Packers | Green Bay Packers | NA | Sun | September 24 | 16:06:00 | 29 | 3 | 455 | 1 | 209 | 4 | Cardinals | Arizona | Packers | Green Bay |
| 2000 | 893926 | 387475 | 506451 | 5 | 66985 | Arizona Cardinals | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2000 | 893926 | 387475 | 506451 | 6 | 44296 | Arizona Cardinals | Cleveland Browns | Arizona Cardinals | NA | Sun | October 8 | 16:15:00 | 29 | 21 | 315 | 2 | 240 | 0 | Cardinals | Arizona | Browns | Cleveland |
At first glance it looks like there is a bunch of missing data, but because I merged on the home team, almost all the missingness appears to be related to the team is away or if they had a by week. Which makes it less alarming… I was alarmed at first… “did my join work?”
Nevertheless, I think we’re almost at a point to start visualizing stuff. It would be helpful to have a column identifying if a team is home or away… just so I can sort on it later.
att_games_ha <- att_games %>%
mutate(away_team_ind = if_else(is.na(away_team) & !is.na(weekly_attendance), 1, 0),
away_team_ind = case_when(
is.na(weekly_attendance) ~ 9999,
TRUE ~ away_team_ind))
So, by doing all of this, I can see trends across time of how well a team does in terms of attendance between home and away games.
Lets take a look.
chiefs <- att_games_ha %>%
filter(t_name == "Kansas City Chiefs")
chiefs_gg <- chiefs %>%
filter(away_team_ind != 9999) %>%
group_by(year, week, away_team_ind) %>%
summarize(average_att = mean(weekly_attendance)) %>%
ggplot(aes(x = year, y = average_att)) +
geom_point() +
facet_wrap(~away_team_ind)
chiefs_gg
Well, that’s interesting… but I wonder if the obvious dip in at home attendance between ~2007 and ~2013 is simply a localized artifact to the Chiefs.
all_gg <- att_games_ha %>%
filter(away_team_ind != 9999) %>%
group_by(t_name, year, away_team_ind) %>%
summarize(average_att = mean(weekly_attendance)) %>% ungroup() %>%
ggplot(aes(x = year, y = average_att, color = as.factor(away_team_ind))) +
geom_point() +
facet_wrap(~t_name) +
theme(legend.position = "top")
all_gg
So obviously, each team has a fairly unique pattern of attendance, especially between home and away games. You can also see where some teams got a new stadium that pushed attendance up, such as the Dallas Cowboys.
gg1 <- att_games_ha %>%
filter(away_team_ind != 9999) %>%
group_by(t_name, year, week, away_team_ind) %>%
summarize(average_att = mean(weekly_attendance)) %>%
ggplot(aes(t_name, average_att, color = as.factor(away_team_ind))) +
geom_boxplot(outlier.alpha = .5) +
coord_flip() +
theme(legend.position = "top")
gg1
Although both the previous graph do not exhibit any clear reoccuring or dicernable trends, it makes me wonder what the relationship between home attendance and season performance is. Said another way, does a strong home team crowd during the first half of the season, relate to eventual success?
Lets take a look.
standings <- standings %>%
mutate(t_name = str_c(team, team_name, sep = " "))
predict_1 <- att_games_ha %>%
filter(!is.na(away_team),
week < 8) %>%
group_by(year, t_name) %>%
summarize(avg.att = mean(weekly_attendance)) %>% ungroup() %>%
left_join(standings, by = c("year", "t_name")) %>%
mutate(perc.win = wins/(wins+loss))
cor(predict_1$avg.att, predict_1$perc.win) #this is not promissing, but lets visualize
## [1] 0.09888602
predict_1_gg <- predict_1 %>%
ggplot(aes(x = perc.win, y = avg.att)) +
geom_point()
predict_1_gg
So much variability. I would assume this is because each team has different stadium sizes, and therefore the y-axis is not relative across teams. Lets see if we can control this.
Let’s first create a function to normalize data within group (year, team)
zscore <- function(m){
(m - mean(m)) / sd(m)
}
Group it and then lets see how things relate.
# Full season results were used, although home and away games' attendance will count for more than one team at a time.
predict_2 <- att_games_ha %>%
# filter(!is.na(away_team)) %>%
filter(!is.na(weekly_attendance)) %>%
group_by(year, t_name) %>%
mutate(avg.att = zscore(weekly_attendance)) %>% ungroup() %>%
left_join(standings, by = c("year", "t_name")) %>%
mutate(perc.win = wins/(wins+loss)) %>%
# filter(week < 8) %>%
# filter(week > 4 & week < 10) %>% #trying out different perspectives
# filter(week > 8) %>%
group_by(year, t_name, perc.win) %>%
summarize(avg_first_half = mean(avg.att)) %>% ungroup()
Lets take a look now.
predict_2_gg <- predict_2 %>%
ggplot(aes(x = perc.win, y = avg_first_half)) +
geom_point() +
geom_smooth(method = "auto")
# geom_point() +
# geom_smooth(stat = "smooth")
predict_2_gg
Although first half season attendance does not seem to be extensively predictive, perhaps there are other indicators that could help improve this model. Lets look a bit deeper here.
cor(predict_2$perc.win, predict_2$avg_first_half, method = "pearson")
## [1] 0.0536111
lm1 <- lm(data = predict_2, perc.win ~ avg_first_half)
broom::tidy(lm1)
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 5.01e- 1 7.66e- 3 65.4 1.72e-284
## 2 avg_first_half 8.59e+14 6.34e+14 1.35 1.76e- 1
broom::glance(lm1)
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.00287 0.00131 0.193 1.83 0.176 2 146. -286. -273.
## # … with 2 more variables: deviance <dbl>, df.residual <int>
lm2 <- lm(data = predict_2, perc.win ~ year + avg_first_half + year*avg_first_half)
broom::tidy(lm2)
## # A tibble: 4 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 8.43e- 1 2.70e+ 0 0.312 0.755
## 2 year -1.70e- 4 1.34e- 3 -0.126 0.899
## 3 avg_first_half 1.15e+17 2.34e+17 0.491 0.624
## 4 year:avg_first_half -5.67e+13 1.16e+14 -0.487 0.626
broom::glance(lm2)
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.00325 -0.00147 0.193 0.689 0.559 4 146. -283. -260.
## # … with 2 more variables: deviance <dbl>, df.residual <int>
predict_3 <- predict_2 %>%
left_join(standings, by = c("year", "t_name"))
lm3 <- lm(data = predict_3, perc.win ~ year + avg_first_half + points_for)
broom::tidy(lm3)
## # A tibble: 4 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 8.60e+ 0 1.81e+ 0 4.75 2.55e- 6
## 2 year -4.39e- 3 9.04e- 4 -4.85 1.53e- 6
## 3 avg_first_half 4.38e+14 4.26e+14 1.03 3.04e- 1
## 4 points_for 2.03e- 3 7.29e- 5 27.9 2.68e-112
broom::glance(lm3)
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.552 0.550 0.129 261. 3.77e-110 4 402. -793. -771.
## # … with 2 more variables: deviance <dbl>, df.residual <int>


The restored home comfortably sleeps eight adults and two children with five bedrooms and four full baths. On the outside terrace is a swimming pool and children’s playground.
Located just outside of Radda in Chianti, the farmhouse is approximately 1 hour south of Florence, where most guests fly into and rent a car to drive to the house.
You can always enter “La Malpensatina” on Google Maps and it will route you to the house. If needed, download a pdf of directions here.
For more detailed FAQ, download our welcome packet here or contact<EMAIL_ADDRESS>property manager and wine guide
housekeeper of 40 years
chef and gardener
For any additional questions or concerns, please contact Lawrence through email at<EMAIL_ADDRESS>and he will be happy to help!


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UNAME
Section: User Commands (1)Updated: June 2012
Index Return to Main Contents
NAME
uname - print system informationSYNOPSIS
uname [OPTION]...DESCRIPTION
Print certain system information. With no OPTION, same as -s.
- -a, --all
- print all information, in the following order, except omit -p and -i if unknown:
- -s, --kernel-name
- print the kernel name
- -n, --nodename
- print the network node hostname
- -r, --kernel-release
- print the kernel release
- -v, --kernel-version
- print the kernel version
- -m, --machine
- print the machine hardware name
- -p, --processor
- print the processor type or "unknown"
- -i, --hardware-platform
- print the hardware platform or "unknown"
- -o, --operating-system
- print the operating system
- --help
- display this help and exit
- --version
- output version information and exit
AUTHOR
Written by David MacKenzie.REPORTING BUGS
Report uname bugs to<EMAIL_ADDRESS>coreutils home page: <http://www.gnu.org/software/coreutils/>
General help using GNU software: <http://www.gnu.org/gethelp/>
Report uname translation bugs to <http://translationproject.org/team/>
COPYRIGHT
Copyright © 2010 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>.This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law.
SEE ALSO
arch(1), uname(2)The full documentation for uname is maintained as a Texinfo manual. If the info and uname programs are properly installed at your site, the command
- info coreutils aquname invocationaq
should give you access to the complete manual.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:11 GMT, December 24, 2015


Last update: 2018-02-02
Code version: 106f0fe3ceb59d559c7cc55e0482b2b4ad248ae6
Also loading important libraries and custom functions for analysis.
seq_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/data"
file_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/output"
Rdata_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/data"
Script_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/code"
source(file.path(Script_dir,'init.R'))
source(file.path(Script_dir,"tools_R.r"))
library(biomaRt)
First, we need to download all GWAS SNPs from the NHGRI GWAS catalog. The current NHGRI catalog was downloaded from https://www.ebi.ac.uk/gwas/api/search/downloads/full on December 1, 2017. Information about column headers are here (https://www.ebi.ac.uk/gwas/docs/fileheaders). The data was then loaded in to R and data was cleaned.
gwas.cat.meta <- read.delim(file.path(file_dir,"gwas_catalog_v1.0-associations_e90_r2017-11-27.tsv")) %>%
dplyr::select(SNP_ID_CURRENT, DISEASE.TRAIT, PUBMEDID) %>%
dplyr::mutate(snp_id = paste0("rs",SNP_ID_CURRENT)) %>%
dplyr::rename(trait = DISEASE.TRAIT, pmid = PUBMEDID) %>%
dplyr::select(snp_id, trait, pmid)
## Warning: package 'bindrcpp' was built under R version 3.3.2
head(gwas.cat.meta)
## snp_id trait pmid
## 1 rs4950928 YKL-40 levels 18403759
## 2 rs7993214 Psoriasis 18369459
## 3 rs8034191 Lung cancer 18385676
## 4 rs2808630 Lung cancer 18385676
## 5 rs7626795 Lung cancer 18385676
## 6 rs8034191 Lung cancer 18385738
# Using GRCh38 - run on 12-1-17 - important to use 87
snpmart<-useEnsembl(biomart = "snp",dataset = 'hsapiens_snp', version = 87)
# Looking up the SNPs. This is going to take a while.
snpPos<-getBM(attributes = c('refsnp_id','chr_name','chrom_start','chrom_end'),
filters = c('snp_filter'),
values = as.character(gwas.cat.meta$snp_id),
mart = snpmart)
snpPos<-snpPos[!duplicated(snpPos$refsnp_id),]
names(snpPos) <- c('snp','chr','start','end')
snpPos <- snpPos[,c(2,3,4,1)]
head(snpPos)
## chr start end snp
## 1 21 33060745 33060745 rs1000005
## 2 4 144312789 144312789 rs10000225
## 3 5 97197765 97197765 rs1000083
## 4 5 150860514 150860514 rs1000113
## 5 4 12578222 12578222 rs10002492
## 6 4 38783103 38783103 rs10004195
mega.snp <- snpPos
# +/- 1 Mb
mega.snp$upstream <- mega.snp$start-1e+06
mega.snp$downstream <- mega.snp$end+1e+06
# add chromosome coordinates
mega.snp$coordinates <- paste0(mega.snp$chr,":",mega.snp$upstream,":", mega.snp$downstream)
saveRDS(mega.snp, file = "mega.snp.rds")
# Using biomart to find genes
ensembl<-useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", version = 87)
# From https://www.biostars.org/p/167818/
mega.coords.list <- as.list(mega.snp$coordinates)
saveRDS(mega.coords.list, file = file.path(Rdata_dir,"mega.coords.list.rds"))
In order to find all the genes within +/- 1 Mb of each lead SNP in the NHGRI GWAS catalog, the following for loop was used. This for loop takes coordinates and pulls out information about all genes within that region. There are ~40,000 SNPs in the GWAS catalog, so this script had to be run in parallel which involved splitting the SNP coordinates in to 10 separate files. All of the SNP-gene tables were combined in to a final list called “gwas.snp_results.txt” that is processed in another script.
# Finding all genes in the intervials in CRCh38, ensembl version 87
mega.results <- data.frame() # creating blank df
for(i in 1:length(mega.coords.list)){
results<-getBM(attributes = c("hgnc_symbol", "chromosome_name", "start_position",
"end_position","gene_biotype"),
filters = c("chromosomal_region"),
values = list(chromosomal_region=mega.coords.list[i]),
mart = ensembl) # finding the genes through biomaRt
results$snp <- mega.snp$snp[i] # populating the SNP column
mega.results <- rbind(mega.results,results) # Adding new rows on to df
mega.results <- mega.results[!(mega.results$hgnc_symbol == ""),] # Removing blank gene names
}
sessionInfo()
## R version 3.3.0 (2016-05-03)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.6 (El Capitan)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid splines stats4 parallel stats graphics grDevices
## [8] utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 biomaRt_2.28.0 ggbiplot_0.55
## [4] scales_0.5.0 SC3_1.1.4 ROCR_1.0-7
## [7] jackstraw_1.1.1 lfa_1.2.2 tsne_0.1-3
## [10] gridExtra_2.3 slackr_1.4.2 vegan_2.4-4
## [13] permute_0.9-4 MASS_7.3-47 gplots_3.0.1
## [16] RColorBrewer_1.1-2 Hmisc_4.0-3 Formula_1.2-2
## [19] survival_2.41-3 lattice_0.20-35 Heatplus_2.18.0
## [22] Rtsne_0.13 pheatmap_1.0.8 tidyr_0.7.1
## [25] dplyr_0.7.4 plyr_1.8.4 heatmap.plus_1.3
## [28] stringr_1.2.0 marray_1.50.0 limma_3.28.21
## [31] reshape2_1.4.3 monocle_2.2.0 DDRTree_0.1.5
## [34] irlba_2.2.1 VGAM_1.0-2 ggplot2_2.2.1
## [37] Biobase_2.32.0 BiocGenerics_0.18.0 Matrix_1.2-11
##
## loaded via a namespace (and not attached):
## [1] RSelenium_1.7.1 colorspace_1.3-2 class_7.3-14
## [4] rprojroot_1.2 htmlTable_1.9 corpcor_1.6.9
## [7] base64enc_0.1-3 bit64_0.9-7 AnnotationDbi_1.34.4
## [10] mvtnorm_1.0-6 codetools_0.2-15 doParallel_1.0.11
## [13] robustbase_0.92-7 knitr_1.17 jsonlite_1.5
## [16] cluster_2.0.6 semver_0.2.0 shiny_1.0.5
## [19] rrcov_1.4-3 httr_1.3.1 backports_1.1.1
## [22] assertthat_0.2.0 lazyeval_0.2.1 acepack_1.4.1
## [25] htmltools_0.3.6 tools_3.3.0 igraph_1.1.2
## [28] gtable_0.2.0 glue_1.1.1 binman_0.1.0
## [31] doRNG_1.6.6 Rcpp_0.12.14 slam_0.1-37
## [34] gdata_2.18.0 nlme_3.1-131 iterators_1.0.8
## [37] mime_0.5 rngtools_1.2.4 gtools_3.5.0
## [40] WriteXLS_4.0.0 XML_3.98-1.9 DEoptimR_1.0-8
## [43] yaml_2.1.15 memoise_1.1.0 pkgmaker_0.22
## [46] rpart_4.1-11 RSQLite_2.0 fastICA_1.2-1
## [49] latticeExtra_0.6-28 stringi_1.1.5 S4Vectors_0.10.3
## [52] pcaPP_1.9-72 foreach_1.4.3 e1071_1.6-8
## [55] checkmate_1.8.4 caTools_1.17.1 rlang_0.1.6
## [58] pkgconfig_2.0.1 matrixStats_0.52.2 bitops_1.0-6
## [61] qlcMatrix_0.9.5 evaluate_0.10.1 purrr_0.2.4
## [64] bindr_0.1 htmlwidgets_0.9 bit_1.1-12
## [67] magrittr_1.5 R6_2.2.2 IRanges_2.6.1
## [70] combinat_0.0-8 DBI_0.7 wdman_0.2.2
## [73] foreign_0.8-69 mgcv_1.8-22 RCurl_1.95-4.9
## [76] nnet_7.3-12 tibble_1.3.4 KernSmooth_2.23-15
## [79] rmarkdown_1.8 data.table_1.10.4 blob_1.1.0
## [82] HSMMSingleCell_0.106.2 digest_0.6.12 xtable_1.8-2
## [85] httpuv_1.3.5 openssl_0.9.7 munsell_0.4.3
## [88] registry_0.3
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Today I’ll present a simple R function to create a plot for the metric KS
. I’m assuming that you are familiar with this metric and your usage.
As I told in the previous post ROC Curve plot and AUC I always recommend to also check the plot of some metrics, instead of only being satisfied by the value of the metric. This will help you to make sure that your model’s predictions are ok along the whole range of probabilities.
Again, remember that we are considering the scenario of a binary classifier.
The R function I wrote to evaluate the KS Curve can be found here. Unlike the ROC Curve function, this one cannot be used to do comparisons among groups in the same plot. I recommend you to create a plot for each one of the groups. In the following example, we’ll do that for the train
and a valid
subsets of data.
# Goodness of Fit Metrics 'KS plot' (for prediction models with target [0;1])
funcGFMplotKS =
function (var.target="", var.prediction="", var.df="", var.fsize)
{
...
}
So let’s get practical and see how it works!
This is an example of a function call. In this case, all the records in the tmp_train
data frame is regarding the train
subset.
> unique(tmp_train$Scenario)
[1] "Train"
The var.fsize
parameter is the reference for the text font size used in ggplot2. Default value is set to 3
.
funcGFMplotKS (var.target="target", var.prediction="pred", var.df="tmp_train", var.fsize=3)
This is the result:
The black dashed line (- - -) identifies the exact point where the curves reach the maximum distance from each other. This is the known KS value
. If you’re curious about how the calculation is done you can check it directly in the R code available here.
Now, this is the result if we call the same function to the valid
data frame:
> unique(tmp_valid$Scenario)
[1] "Valid"
funcGFMplotKS (var.target="target", var.prediction="pred", var.df="tmp_valid", var.fsize=3)
Note that the chart title shows different values of the KS
. And these values match exactly the same values calculated by the funcGFMmetrics function, presented in an earlier posting.
# TRAIN data frame
metrics = funcGFMmetrics (var.target="target", var.prediction="pred", var.df="tmp_train")
> metrics
AUC KS GINI LogLoss Kappa F1 Accuracy ErrorRate Precision Recall
0.82913 0.48114 0.65826 0.77364 0.40113 0.81346 0.74074 0.25926 0.89988 0.74219
# VALID data frame
metrics = funcGFMmetrics (var.target="target", var.prediction="pred", var.df="tmp_valid")
> metrics
AUC KS GINI LogLoss Kappa F1 Accuracy ErrorRate Precision Recall
0.82547 0.47744 0.65095 0.76664 0.39905 0.81350 0.74053 0.25947 0.89661 0.74450
This can be quite handy 1, specially if you usually write (and automate) reports documenting your development and evaluation model processes, or even if you have some R code procedures running periodically to follow up the model performance that are delivered to your clients.
That’s all folks! ;-)
To make sure all your outputs are exactly the same. ↩


ORGAZMO (Rogue) Starring: Trey Parker, Dian Bachar, Robyn Lynne Raab, Michael Dean Jacobs, Matt Stone. Screenplay: Trey Parker. Producers: Fran Rubel Kuzui, Jason McHugh, Matt Stone. MPAA Rating: NC-17 (sexual content, profanity, adult themes, violence) Running Time: 95 minutes. Reviewed by Scott Renshaw.
It's the film so daring that it earned a rare NC-17 rating! It's the film so controversial that Universal Pictures, the parent company of October Films, forced October to create a separate releasing arm to distance itself from the film! It's the film so outrageous that only Trey Parker, the bad-boy co-creator of "South Park," could have brought it to you! It's ORGAZMO, a film destined for midnight movie immortality!
They say there's no such thing as bad publicity...unless you're a potential film viewer who pays attention to it. Anyone expecting naughty, edgy fun from ORGAZMO might find that its most shocking surprise is how uninspired much of its "outrageousness" is. Sure, it hits two guaranteed hot-buttons -- religion and sex -- in this tale of a naive Mormon lad named Joe Young (played by Parker himself) serving a mission in Los Angeles. One day he knocks on the wrong door, and ends up on the set of porno film director Maxxx Orbison (Michael Dean Jacobs). Impressed by Joe's martial arts skills when he fends off a group of bodyguards, Orbison offers Joe the lead role of Captain Orgazmo (physical stunts only, no sex required), along with a huge paycheck which will allow Joe and his fiancee Lisa (Robyn Lynne Raab) to marry in the Salt Lake City temple and live well.
Occasionally, it looks like Parker is going to take this story on just the right avenue of absurdity. The film-within-the-film "Orgazmo" turns out to be a crossover smash hit, ranking third on the all-time box office chart and naturally creating demand for a sequel. Then there's the odd little aside in which Joe's film sidekick Ben (Dian Bachar) re-lives a painful childhood memory of giving up "hamster style" martial arts, and a cameo appearance by a certain heavenly personage. There are a few amusing gags sprinkled throughout ORGAZMO, many aimed at the absurdity of adult film "plot" scenarios when they're not taking broad shots at Utah or the Latter-Day Saints.
The problem with Parker's sense of humor is that it can be terribly lazy. His favorite set-up involves wacky character incongruities which actually lost most of their wackiness many iterations ago -- an old woman swearing like a sailor, an old woman showing sexual arousal, a Japanese sushi chef who uses hip-hop lingo.  until eventually -- inevitably -- he actually makes a pass at another male character. You can see punch lines coming from several theaters away in ORGAZMO.
Those who might attend just for prurient interest should also be warned that ORGAZMO is about as tame as an NC-17 film could get. Though there's plenty of full-clothed grinding and prominent displays of certain marital aids, there's nary an exposed female body part to be found (hairy male buttocks, on the other hand, get a disturbing amount of screen time). Make no mistake, Trey Parker's brand of offensiveness is far more sniggering, middle-of-the-road crude than it is truly shocking. There's nothing wrong with the production being sloppy and amateurish; after all, this is among other things a parody of porno films, with a wonderfully cheesy fake explosion for a conclusion. It's another thing when the humor is often sloppy and amateurish...or worse yet, re-cycled. ORGAZMO inspires the one reaction a "daring," "controversial," "outrageous" film can't afford: a great big "so what?"
On the Renshaw scale of 0 to 10 missionary positions: 5.
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{% extends 'base.html' %} {% block title %} - Your likings{% endblock %} {% block content %}
Please tell us which genres you like the most:


vqa_benchmarking_backend.bottomupattention.utils.progress_bar
¶
Tracking progress of distributed tasks can be tricky.
This script will demonstrate how to implement a simple progress bar for a Ray actor to track progress across various different distributed components.
Original source: Link
Setup: Dependencies¶
First, import some dependencies.
Module Contents¶
Classes¶
Functions¶
|
|
|
-
class
vqa_benchmarking_backend.bottomupattention.utils.progress_bar.
ProgressBarActor
¶ -
counter
:int¶
-
delta
:int¶
-
event
:asyncio.Event¶
-
update
(self, num_items_completed: int) → None¶ Updates the ProgressBar with the incremental number of items that were just completed.
-
async
wait_for_update
(self) → Tuple[int, int]¶ Blocking call.
Waits until somebody calls update, then returns a tuple of the number of updates since the last call to wait_for_update, and the total number of completed items.
-
get_counter
(self) → int¶ Returns the total number of complete items.
-
-
class
vqa_benchmarking_backend.bottomupattention.utils.progress_bar.
ProgressBar
(total: int, description: str = '')¶ -
progress_actor
:ray.actor.ActorHandle¶
-
total
:int¶
-
description
:str¶
-
pbar
:tqdm.tqdm¶
-
property
actor
(self) → ray.actor.ActorHandle¶ Returns a reference to the remote ProgressBarActor.
When you complete tasks, call update on the actor.
-
print_until_done
(self) → None¶ Blocking call.
Do this after starting a series of remote Ray tasks, to which you’ve passed the actor handle. Each of them calls update on the actor. When the progress meter reaches 100%, this method returns.
-
-
vqa_benchmarking_backend.bottomupattention.utils.progress_bar.
sleep_then_increment
(i: int, pba: ray.actor.ActorHandle) → int¶
-
vqa_benchmarking_backend.bottomupattention.utils.progress_bar.
run
()¶


Maps display grey tiles¶
Publishing issues (such as 2D browser-based maps that display grey
tiles or no imagery) can indicate a hostname error. For example,
accidentally setting the hostname to localhost.localdomain
instead of myserver.com
.
To verify the hostname:
Query the Fusion 2D map configuration directly from the web browser:
http://myserver.com/mymaps/query?request=LayerDefs
A response that includes a line like the example below indicates that GEE is directing users to your local host instead of the publishing server.
var stream_url = "http://localhost.localdomain/default_map";
To correct the hostname:
- Query
hostname -f
on the server, and verify that the path to the hostname can be resolved. (In other words, verify that users can access the URL.) - To correct the hostname for all assets on your server, run
geconfigureassetroot --fixmasterhost
. - Republish the database to the virtual server.
- Verify that the URL displays the map correctly.
If the URL does not display your map at all, run
geserveradmin --publisheddbs
on your Fusion machine to verify
that you published the database to the correct virtual server. If
you don’t see http://myserver.com/mymaps
listed with the
database you want to publish, then republish the database.
To further diagnose the issue, review the
/opt/google/gehttpd/logs/error_log
or run the
geecheck script.


Jeongmin releases MV for his New title track " Why "
by kpoplovermayFormer Boyfriend member Jeongmin, is Back! with a MV for his New title track " Why "
Which is off his newly released 2nd Single album titled " Off The Record " :
The mv is also on the Super Sound Bugs channel & on music streaming sites like Melon etc.
Check out the quirky sweet MV above, and let us know what you think in the comments below.


It's been a crazy day. I came very close to not being able to even touch this project today (because of the lack of an internet connection), but I went out of the way to still try and get my hour in. And now crazy things are happening in the news, and I need to catch up, and so on, and I think I'll do something low-intensity today.
We'll just work on a reusable comment section. I'd like to figure out how to automatically add it to every page without copy-pasting the same block of HTML. That might have to wait until tomorrow. Today, we'll just put together the front-end that actually displays the comments.
It looks like Angular handles most of it. The two challenges are to parse the JSON object returned from the server and get it into the DOM (which Angular seems to handle very conveniently), and to make the displayed comment section look nice (all up to me). I don't have access to my normal setup today, which in particular means that testing with Angular is a big pain.
So, with the tiny amount of time and tiny amount of internet access I have, I'll just play with some CSS.
It's a real struggle--that's all I had time for. Man, I hope I have internet tomorrow.
This is the comment body. It's a couple sentences long so that I can see what a longer comment might look like. I think I need a few more sentences.
And maybe some line breaks. One or two might be good.
That's good.
This is a shorter comment.


Home
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© 2018 All Rights Reserved.
WRAPHIC DESIGNER
Crystal Chau
Crystal Chau Graphic Design LLC.
© 2018 All Rights Reserved.


WGENEVB lets you load family trees, explore the relationships between people, add new people, and change existing people and relationships. The Prolog portion of the program does all the database manipulation, and ensures the integrity of the database, making sure that all the family members have relationships that make sense.
Adding / Changing Family Members
Application Architecture and Source Code
www.amzi.com - Additional demos, articles, freeware, evaluation copies and information about Amzi! Prolog + Logic Server.
We welcome any and all comments about this demo. Contact us at www.amzi.com
To build it:
To run it:
File / Save (As) This command saves the current family into a file.
.FAM Files The following is an excerpt from the sample family:
person('Elizabeth I', female, 'Anne Boleyn', 'Henry VIII', single). person('Edward VI', male, 'Jane Seymour', 'Henry VIII', single). person('Lady Frances Brandon', female, 'Mary', 'Charles Brandon', 'Henry Grey'). person('Mary Queen of Scots', female, 'Mary of Guise', 'James V', 'Henry Lord Darnley').Note, for each person we define their gender (male or female), their mother, their father, and their spouse or "single".
Change This allows you to modify an existing family member. The default is the currently highlighted person.
Delete This lets you delete a family member.
Represent each family member as relational-like record of the form: person(Name, Gender, Mother, Father, Spouse).
Add, save, delete and return information about family members. The add predicates call a set of rules that maintain the integretity of the database. For example, one rule makes sure a person is not their own ancestor or descendent.
Determining all the family members who have a specified relationship with a specified person. For example, sibling(Person, Sibling) can determine the siblings of the specified person.
The source code for WGENEVB is consists of these modules:
' Issue the Prolog command <relationship>(X, <person>)
tf = CallStrLS(Term, Relationship + "(X, '" + Person + "')")
' Loop getting all the people who have that relationship ' and add them to the related persons list
While (tf = True) GetArgLS(Term, 1, bSTR, StrVal) RelatedPersonsList.AddItem StrVal tf = RedoLS() WendFirst, a string containing the query is constructed. In this case we are asking Prolog to tell us everyone who has a specific relationship to a specific person. So for example, if to determine siblings of James IV, the query would be:
sibling(X, James IV)Next, we call Prolog using CallStrLS and RedoLS to get all the siblings, one at a time. As the siblings are returned in place of the X in the query, GetArgLS is used to get the value of the first argument to sibling. As the siblings are retrieved they are added to the RelatedPersonsLists.
Copyright ©1996-2000 Amzi! inc. All Rights Reserved.


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