| 039:007 Khan | : |
If you disbelieve, then verily, Allah is not in need of you, He likes not disbelief for His slaves. And if you are grateful (by being believers), He is pleased therewith for you. No bearer of burdens shall bear the burden of another. Then to your Lord is your return, so He will inform you what you used to do. Verily, He is the All-Knower of that which is in (men's) breasts. |
| 039:007 Maulana | : |
If you are ungrateful, then surely Allah is above need of you. And He likes not ungratefulness in His servants. And if you are grateful, He likes it for you. And no bearer of a burden will bear another's burden. Then to your Lord is your return, then will He inform you of what you did. Surely He is Knower of what is in the breasts. |
| 039:007 Pickthal | : |
If ye are thankless, yet Allah is Independent of you, though He is not pleased with thanklessness for His bondmen; and if ye are thankful He is pleased therewith for you. No laden soul will bear another's load. Then unto your Lord is your return; and He will tell you what ye used to do. Lo! He knoweth what is in the breasts (of men). |
| 039:007 Rashad | : |
If you disbelieve, GOD does not need anyone. But He dislikes to see His servants make the wrong decision. If you decide to be appreciative, He is pleased for you. No soul bears the sins of any other soul. Ultimately, to your Lord is your return, then He will inform you of everything you had done. He is fully aware of the innermost thoughts. |
| 039:007 Sarwar | : |
If you disbelieve, know that God is certainly independent of you. He does not want disbelief for His servants. If you give thanks, He will accept it from you. No one will be responsible for the sins of others. To your Lord you will all return and He will tell you about what you have done. He knows best what the hearts contain. |
| 039:007 Shakir | : |
If you are ungrateful, then surely Allah is Self-sufficient above all need of you; and He does not like ungratefulness in His servants; and if you are grateful, He likes it in you; and no bearer of burden shall bear the burden of another; then to your Lord is your return, then will He inform you of what you did; surely He is Cognizant of what is in the breasts. |
| 039:007 Sherali | : |
If you are ungrateful, surely ALLAH is Self-Sufficient, being independent of you. And HE is not pleased with ingratitude in HIS servants.  And no bearer of burden shall bear the burden of another. Then to your Lord is your return; and HE will inform you of what you have been doing. Surely, HE knows full well all that is hidden in the breasts. |
| 039:007 Yusufali | : |
If ye reject (Allah), Truly Allah hath no need of you; but He liketh not ingratitude from His servants: if ye are grateful, He is pleased with you. No bearer of burdens can bear the burden of another. In the end, to your Lord is your Return, when He will tell you the truth of all that ye did (in this life). for He knoweth well all that is in (men's) hearts. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > إليه ترجع الأمور Pillars of Islam> Islamic> Oneness of Allah>All Matters Return to Him(for decision) أركان الإسلام > التوحيد > توحيد الله تعالي > رضاه تعالى Pillars of Islam> Islamic> Oneness of Allah> His Glory's Satisfaction أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > الغنيّ Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The Rich(Free of all Needs) أركان الإسلام > التوحيد > توحيد الله تعالي > علمه جلّ شأنه Pillars of Islam> Islamic> Oneness of Allah> His Glory's Knowledge أركان الإسلام > التوحيد > توحيد الله تعالي > غناه وافتقار الناس إليه Pillars of Islam> Islamic> Oneness of Allah> His Self-Sufficience and People's need of Him الإيمان > الغيب > الإيمان بالغيب Faith> The Unseen World> The Belief in the Unseen World الإيمان > الله جلّ جلاله > شكره جلّ وعلا Faith> Allah, Glory to his Majesty> To Express Thanks to Allah الإيمان > اليوم الآخر > الحشر Faith> The Hereafter> The Day of Resurrection الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > الكفران Man and The Moral Relations>The Dispraised Morals>Rejection العلاقات القضائية > علاقات قانونية ودستورية > المسؤولية الشخصية Judicial Relationships>Legal and Constitutional Relations>The Personal Responsibility
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Before Change
if not exists(path):
raise LookupError("Stop-words are not available for language %s." % language)
with open(path, "rb") as file:
return frozenset(to_unicode(w.rstrip()) for w in file.readlines())
class ItemsCount(object):
def __init__(self, value):
self._value = value
After Change
path = expand_resource_path("stopwords/%s.txt" % language)
if not exists(path):
raise LookupError("Stop-words are not available for language %s." % language)
return read_stop_words(path)
def read_stop_words(filename):
with open(filename, "rb") as open_file:


| write_file {brio} | R Documentation |
This function differs from write_lines()
in that it writes the data in
text
directly, without any checking or adding any newlines.
write_file(text, path)
text |
A character vector of length 1 with data to write. |
path |
A character string giving the file path to write to. |
The UTF-8 encoded input text (invisibly).
tf <- tempfile() write_file("some data\n", tf) unlink(tf)


@(EditForm: Form[furniture])
@import helper._;
@layout("Edit Options "){
Note:IN CASE OF EDITING TASK DETAILS, REFER "REQUEST" OR DELETE THE PERTICULAR ASSET AND
CREATE NEW WITH PROPER DETAILS
@helper.form(action = routes.FurnitureController.saveEdit()){
@helper.inputText(EditForm("id"), 'class -> "form-control")
@helper.select(
EditForm("FurnitureName"),
helper.options(Seq("Mel Desk","pin Desk","Mel Bookcase","Pin Bookcase", "Pin Filing Cabinet",
"Metal Filing Cabinets", "Gas-lift Chair", "Fixed-frame Chair")),
'_label -> "Furniture Name", '_default -> "---- Select Furniture ----"
, 'class -> "form-control")
@helper.select(
EditForm("FurnitureBarcode"),
helper.options(Seq("4605664000050", "8076809513456", "9780444505156","3155250001387", "3014260115531",
"3006634623969", "8020187300016", "1234567891011")),
'_label -> "Furniture barcode", '_default -> "---- Select Barcode ----"
, 'class -> "form-control")
@helper.inputDate(EditForm("furnitureAcquisitionDate"), 'class -> "form-control")
@helper.inputText(EditForm("FurnitureCost"), 'class -> "form-control")
@helper.inputText(EditForm("OccupyingDept"), 'class -> "form-control")
@helper.inputText(EditForm("Occupant"), 'class -> "form-control")
@helper.inputText(EditForm("BuildingNo"), 'class -> "form-control")
@helper.inputText(EditForm("FloorNo"), 'class -> "form-control")
@helper.inputText(EditForm("Room"), 'class -> "form-control")
@helper.select(
EditForm("SpaceType"),
helper.options(Seq("Office", "General teaching room", "Scientific Lab","Computer Lab", "Engineering Lab",
"Medical Lab", "Ancillary area", "Library", "General facility area", "Student Residential Accommodation",
"Non usable floor area")),
'_label -> "SpaceType", '_default -> "-------- Select SpaceType --------"
, 'class -> "form-control")
@helper.inputText(EditForm("SpaceCode"), 'class -> "form-control")
@helper.inputText(EditForm("BoughtAgent"), 'class -> "form-control")
@helper.inputText(EditForm("key"), 'class -> "form-control")
}
}


Fusion Arc
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Welding and Fabrication Services...Bottled Gas Stockist...Project Management...
The Arc
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A Brief Introduction


London is the capital city of England. It is the most populous city in the United Kingdoom, with a metropoltan area of 13 million inhabitants.
London is the capital city of England. It is the most populous city in the United Kingdoom, with a metropoltan area of 13 million inhabitants.
On the Island os Isla Nublar, a new park has been built: Jurasic Park is a theme park of colne dinosaurs!!


{% extends "layout.html" %} {% block content %}
The sentences "Pat loves Chris" and "Chris is loved by Pat" mean roughly the same thing and use similar words. Some linguists, in particular Noam Chomsky, have tried to account for this similarity by positing that these two sentences are distinct surface forms that derive from a common deep structure.
"the exterior is the result of the interior." As you look at the appearance of an object you are simply looking at it's surface structure. Below the surface you get to the interior which gives form to the exterior. The interior semantic structure gives rise to the words that are expressed in language. The same is true for any piece of art. The artist uses a composition of signifiers to communicate the deeper structure of the relationship of the signified. An example of how a sentence can be structured based on it's Deep Structure: John saw Sarah hug Nick
A chord progression (or harmonic progression) is a series of musical chords, or chord changes that "aims for a definite goal" of establishing (or contradicting) a tonality founded on a key, root or tonic chord and that is based upon a succession of root relationships. Chords and chord theory are generally known as harmony.


An animated version of the popular storytime game, Little Mouse, made with JavaScript. Traditional flannelboard version here.
As children choose a house to look under, click on that house. It will be greyed out if there is no mouse underneath. A pop-up displays the mouse when the children find it. Repeat as often as the children wish!

Code is open source on Github. Pull requests that improve the code are welcome!
Mouse icon made by Freepik from www.flaticon.com, licensed by CC BY 3.0


Class future<T> (2.9.0)
Implement ISO/IEC TS 19571:2016 future<T>
.
Constructors
future()
future(future< future< T > > &&)
Creates a new future that unwraps rhs
.
This constructor creates a new shared state that becomes satisfied when both rhs
and rhs.get()
become satisfied. If rhs
is satisfied, but rhs.get()
returns an invalid future then the newly created future becomes satisfied with a std::future_error
exception, and the exception error code is std::future_errc::broken_promise
.
| Parameter | |
|---|---|
| Name | Description |
rhs |
future< future< T > > &&
|
future(future< U > &&)
Creates a future from a future whose result type is convertible to this future's result type.
| Parameters | |
|---|---|
| Name | Description |
rhs |
future< U > &&
|
class U |
|
typename Enable |
|
future(std::shared_ptr< shared_state_type >)
| Parameter | |
|---|---|
| Name | Description |
state |
std::shared_ptr< shared_state_type >
|
Functions
get()
Waits until the shared state becomes ready, then retrieves the value stored in the shared state.
| Exceptions | |
|---|---|
| Type | Description |
... |
any exceptions stored in the shared state. |
std::future_error |
with std::no_state if the future does not have a shared state. |
| Returns | |
|---|---|
| Type | Description |
T |
then(F &&)
Attach a continuation to the future.
Attach a callable func to be invoked when the future is ready. The return type is a future wrapping the return type of func.
Side effects: valid() == false
if the operation is successful.
| Parameters | |
|---|---|
| Name | Description |
func |
F &&
a Callable to be invoked when the future is ready. The function might be called immediately, e.g., if the future is ready. |
typename F |
|
| Returns | |
|---|---|
| Type | Description |
internal::then_helper< F, T >::future_t |
|
Type Aliases
shared_state_type
typename internal::future_base< T >::shared_state_type


08 March 2002
\nDisease Outbreak Reported
As of 3 March, the Ethiopian Ministry of Health has reported a total of 2 329 cases of meningococcal disease including 118 deaths in the country since the onset of the outbreak in September 2001. The worst affected region is the Southern Nations, Nationalities and Peoples Region (SNNPR) with 2 022 cases including 89 deaths. (see previous report)
The Task Force on Epidemic Meningococcal Disease, set up by the Ministry of Health is working to coordinate control activities including surveillance, case management, vaccination campaigns and logistics.
The Ministry of Health, on behalf of the Task Force, has launched an urgent appeal for US$ 2.5 million to carry out a mass immunization campaign in 5 priority zones in SNNPR.


Contact Us @<PHONE_NUMBER>
Hairstyling is no longer what it used to be. The days of just walking into a barber shop and getting a trim and a shave are long gone and today, being a hairstylist or barber is a demanding and challenging career that takes design, art and creativity to a whole new level. The scope for employment for a professional hairstylist is enormous, with many hairstylists opening their own salons exclusively for hairstyling. Others go into consultancy and still others have made a lucrative career out of the production and marketing of hair care products.
A-One is devoted to your education. Our educators can help you achieve your goals and fulfill your dreams and aspirations. We boast convenient, contemporary salon-like environments for practical learning and small classroom sizes to get the one-on-one time with industry beauty leaders. We offer full-time and part-time programs that cover all areas of hair design to prepare you for your lucrative career in Hair styling:
CURRICULUM
Students who join our hair styling program will learn the fundamentals of hair styling as well as the business aspects of hair styling. Students will also learn about professionalism in the styling industry, customer care and how to build a loyal customer network. A typical hair styling program will cover aspects like:
| Salon fundamentals | Advanced cutting |
| Styling | Coloring |
| Perming | Lighting and toning |
| Bacteriology | Infection and control |
| Properties of the scalp and hair | Hair disorders |
| Draping | Shampooing |
| Rinsing | Conditioning |
| Finger waving | Wet styling |
| Thermal hair styling | Permanent waving |
| Chemical hair relaxation | Artificial hair |
HOURS OF PROGRAM
Full-time program | Monday to Friday 8:30am to 3:00pm
Copyright © A-One Hair & Aesthetics 2016


MMA8452 Accelerometer Sensor Description
The MMA8452 is able to tell you its orientation in space with x-, y-, and z-planes that use gravity
to tell it its position. For example, when you tilt the sensor left, the bar on the accelerometer
bar graph will rise to “Landscape Left”. The accelerometer on display is attached to a motor that
will vibrate whenever the orientation is not flat.
Real World Application
Accelerometers are more commonly used in smartphones to identify the phone’s orientation in space
for GPS, compass apps, and rotates the phone’s screen when the physical phone is turned.


|
|
void * | operator new (std::size_t n) noexcept(false) |
| |
|
void * | operator new[] (std::size_t n) noexcept(false) |
| |
|
void * | operator new (std::size_t n, const std::nothrow_t &tag) noexcept |
| |
|
void * | operator new[] (std::size_t n, const std::nothrow_t &tag) noexcept |
| |
|
void | operator delete (void *ptr) noexcept |
| |
|
void | operator delete[] (void *ptr) noexcept |
| |
|
void | operator delete (void *ptr, std::size_t n) noexcept |
| |
|
void | operator delete[] (void *ptr, std::size_t n) noexcept |
| |
|
void * | operator new (std::size_t n, std::align_val_t align) noexcept(false) |
| |
|
void * | operator new[] (std::size_t n, std::align_val_t align) noexcept(false) |
| |
|
void * | operator new (std::size_t n, std::align_val_t align, const std::nothrow_t &) noexcept |
| |
|
void * | operator new[] (std::size_t n, std::align_val_t align, const std::nothrow_t &) noexcept |
| |
|
void | operator delete (void *ptr, std::align_val_t align) noexcept |
| |
|
void | operator delete[] (void *ptr, std::align_val_t align) noexcept |
| |
|
void | operator delete (void *ptr, std::size_t n, std::align_val_t align) noexcept |
| |
|
void | operator delete[] (void *ptr, std::size_t n, std::align_val_t align) noexcept |
| |


--- title: '{reticulate} - A Tutorial' author: Nik Agarwal date: '2021-01-27' slug: reticulate-tutorial categories: - R tags: - rstats - python description: '' topics: [] summary: "A walkthrough on using {reticulate} to use the best of Python in R" ---
With the recent release of RStudio 1.4, I’ve taken a new look at coding in Python using the RStudio IDE and also using Python libraries through {reticulate}.
The {reticulate} package is a clever package that enables you to ‘store’ Python libraries and ‘call’ Python functions using familiar R syntax. My daily workflow consists of wrangling & analyzing data with R. Lately, however, I’ve found that the Scikit-Learn library in Python has a much better ability to parallelize machine learning more easily than any other package in R. In a nutshell, I find that parallelizaton of machine learning routines in R is heavily dependent on the package itself. Scikit-Learn is essentially an ‘all-in-one’ solution for model development, but it is not the most intuitive to use (as compared to using {tidymodels}).
Here’s a high level workflow for me:
This current workflow ‘forces’ me to rely on two different IDEs: RStudio for R and VSCode for Python. Prior to RStudio 1.4, it was difficult for me to know what objects were loaded into memory in the Environment tab as well as running Python scripts natively.
While {reticulate} was great to use before RStudio 1.4, I still had to debug in VSCode any errors found with Python calls. Now with RStudio 1.4, I can do it all in one IDE.
This is a LONG post. You can find all the code used on my GitHub as a gist. This code will walk you through how I use {reticulate} effectively (i.e., combining the best of R and Python in one script).
For this tutorial, I am assuming that you have already configured a particular virtual environment within the Global Options (or Project Options) menu.
One perk of using RStudio 1.4 is the fact that you no longer have to define the virtual environment in your R script once it is defined in the Global Options menu. However, you will need to do so if you wish to use a different virtual environment than the one in your Global Options (or Project Options) menu.
As you may have guessed, I’ll be using the {palmerpenguins} package for the underlying data. The dataset within this package is far newer than the venerable iris dataset.
The objective is to predict the species of the penguin.
I want you to keep in mind that this is a simple tutorial. I’m not trying to optimize the machine learning algorithm nor am I trying to code in the most efficient/concise manner. Rather, I’d like to ensure that the steps are easy to understand and follow along. As you apply these techniques in your own project, please feel free to optimize as needed.
Let’s load the R libraries:
library(reticulate)
library(palmerpenguins)
library(dplyr)
library(ggplot2)
library(extrafont)
library(tidyr)
And now let’s load the appropriate Python libraries:
pysmote_over <- reticulate::import('imblearn.over_sampling')
pysmote_under <- reticulate::import('imblearn.under_sampling')
pysmote_pipe <- reticulate::import('imblearn.pipeline')
sklearn_modelselection <- reticulate::import('sklearn.model_selection')
sklearn_ensemble <- reticulate::import('sklearn.ensemble')
sklearn_metrics <- reticulate::import('sklearn.metrics')
Now let’s load the dataset (we’ll store in a dataframe called df_pgs
for conciseness) and explore it.
df_pgs <- palmerpenguins::penguins
dplyr::glimpse(df_pgs)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
The data are encoded correctly if we wanted to build our model in R. For instance, the categorical columns are already encoded as factors. However, we need to remember that Scikit-Learn does not accept character values. Rather, these must be encoded as numerical values (either ordinally, dummy-encoded, or one-hot encoding). And we’ll do that when we get building a ‘recipe’.
One of the first things I like to do is identify if any columns have missing values.
df_pgs %>%
purrr::map_df(function(x) sum(is.na(x))) %>%
tidyr::pivot_longer(names_to = 'variable', cols = everything(), values_to = 'number_missing') %>%
dplyr::arrange(desc(number_missing))
## # A tibble: 8 x 2
## variable number_missing
## <chr> <int>
## 1 sex 11
## 2 bill_length_mm 2
## 3 bill_depth_mm 2
## 4 flipper_length_mm 2
## 5 body_mass_g 2
## 6 species 0
## 7 island 0
## 8 year 0
Looks like 5 of the 8 columns have missing values. In fact, the column sex
has 11 missing values. While it may be tempting to remove the missing values, I have found that ‘categorizing’ missing values in their own category can be ‘predictive’ in itself. Furthermore, numerical values can be imputed with a variety of methods. We’ll explore the imputing method more when we get to recipes.
For our variables encoded as factors, let’s find out how many unique values we have. This will help us identify if it is necessary (or advisable) to group the categorizations for improved performance.
df_pgs %>%
dplyr::select_if(is.factor) %>%
purrr::map_df(function(x) n_distinct(x))
## # A tibble: 1 x 3
## species island sex
## <int> <int> <int>
## 1 3 3 3
This is nice! We don’t have too many unique values. In fact, the column sex
has 3 unique values because the third value happens to be NA (which is ‘unknown’).
A key check is to ensure that the response variable is balanced (especially in a multi-classification problem).
df_pgs %>%
dplyr::group_by(species) %>%
count()
## # A tibble: 3 x 2
## # Groups: species [3]
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124
And here we can see that there is a fairly large imbalance between the species Chinstrap and the other species. It’ll probably be a good idea to go ahead and balance the data. And we’ll do this balancing using the library imblearn in Python!
Let’s switch gears and take a look at some visual charts. First up: let’s compare bill
dimensions by species.
df_pgs %>%
ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
geom_point(aes(color = species)) +
scale_x_continuous(limits = c(30,60)) +
scale_y_continuous(limits = c(12,22), breaks = seq(12,22,2)) +
labs(x = "Bill Length (mm)",
y = "Bill Depth (mm)",
color = "Species",
title = "Bill Depth vs Length",
caption = "Source: {palmerpenguins} package") +
theme(plot.title = element_text(family = "Bahnschrift", color = 'grey100'),
plot.caption = element_text(family = "Bahnschrift", color = 'grey60'),
axis.title = element_text(family = 'Bahnschrift', color = 'grey100'),
axis.text.x = element_text(family = "Bahnschrift", color = 'grey70'),
axis.text.y = element_text(family = "Bahnschrift", color = 'grey70'),
plot.background = element_rect(fill = 'grey10'),
panel.background = element_blank(),
panel.grid.major = element_line(color = 'grey30', size = 0.2),
panel.grid.minor = element_line(color = 'grey30', size = 0.2),
legend.background = element_rect(fill = 'grey20'),
legend.key = element_blank(),
legend.title = element_text(family = 'Bahnschrift', color = 'grey80'),
legend.text = element_text(family = "Bahnschrift", color = 'grey90'),
legend.position = c(0.9, 0.2)
)
## Warning: Removed 2 rows containing missing values (geom_point).
I have to admit that there is some really clear separation between the three groups. In the real world, this doesn’t happen often.
Some key takeaways from this chart:
So let’s take a look at the weights of these species by gender.
df_pgs %>%
ggplot(aes(x = sex, y = body_mass_g)) +
geom_boxplot(aes(fill = species), color = 'steelblue') +
labs(x = "Sex",
y = "Body Mass (grams)",
title = "Body Mass of Species by Sex",
caption = 'Source: {palmerpenguins} package') +
theme(plot.title = element_text(family = "Bahnschrift", color = 'grey100'),
plot.caption = element_text(family = "Bahnschrift", color = 'grey60'),
axis.title = element_text(family = 'Bahnschrift', color = 'grey100'),
axis.text.x = element_text(family = "Bahnschrift", color = 'grey70'),
axis.text.y = element_text(family = "Bahnschrift", color = 'grey70'),
plot.background = element_rect(fill = 'grey10'),
panel.background = element_blank(),
panel.grid.major = element_line(color = 'grey30', size = 0.2),
panel.grid.minor = element_line(color = 'grey30', size = 0.2),
legend.background = element_rect(fill = 'grey20'),
legend.key = element_blank(),
legend.title = element_text(family = 'Bahnschrift', color = 'grey80'),
legend.text = element_text(family = "Bahnschrift", color = 'grey90'),
legend.position = c(0.90, 0.9)
)
## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
Now this is interesting. The Gentoo species tends to be heavier than the other species for both genders. Interestingly, you’ll notice the NA category for Sex
. Visually speaking, it appears that the missing values can be imputed with the appropriate gender based on weight. However, for this tutorial, we’ll call this column “unknown”.
Before we go any deeper, it’s probably a good idea to split the data into a ‘training’ and test’ group. This will enable us to assess the performance of any machine learning algorithm.
set.seed(1337)
pg_split <- rsample::initial_split(df_pgs, prop = 0.75)
pg_train <- rsample::training(pg_split)
pg_test <- rsample::testing(pg_split)
Another intuitive tool within R is the ability to use the {recipes} package. Remarkably, this package enables you to create a step-by-step instruction on what to process for any dataframe that’s passed through it. While you can do similar things with Scikit-Learn, it’s just not as clean/crisp looking nor as intuitive. Don’t bother disputing me since you won’t convince me :) !
base_recipe <- recipes::recipe(species ~ ., data = pg_train) %>%
recipes::step_mutate(tmp_species = species) %>%
recipes::step_meanimpute(bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, id = 'impute_missing_continuous') %>%
recipes::step_factor2string(sex) %>%
recipes::step_mutate(sex = ifelse(is.na(sex),'unknown',sex)) %>%
recipes::step_string2factor(sex, levels = c('male','female','unknown')) %>%
recipes::step_dummy(sex, one_hot = TRUE, levels = c('male','female','unknown'), preserve = TRUE) %>%
recipes::step_integer(species, strict = TRUE, id = 'label encode response variable') %>%
recipes::step_dummy(island, id = 'sparse encoding of islad variable', one_hot = TRUE, levels = c('Biscoe','Dream','Torgersen'), preserve = TRUE) %>%
recipes::prep(training = pg_train)
base_recipe
## Data Recipe
##
## Inputs:
##
## role #variables
## outcome 1
## predictor 7
##
## Training data contained 258 data points and 5 incomplete rows.
 Once that’s done, I convert it back to factors with the new level of ‘unknown’ and no more missing values.sex
is a column with character values, I need to convert it to a sparse matrix for each potential value. Again, this is necessary for handling categorical values in Scikit-Learn.island
column as well.Since we’re using training data, the imputed value used in the training data will be the same value used in the test data.
To ‘build’ our modeling ready datasets, we need to ‘juice’ the training data and ‘bake’ the recipe to get the test data cleaned up. “Juicing” and “Baking” are taxonomies used in the {recipes} package to apply the recipe to datasets.
cln_train <- recipes::juice(base_recipe)
cln_test <- recipes::bake(base_recipe, pg_test)
Let’s verify that we have no more missing data in either the training or test datasets.
cln_train %>%
purrr::map_df(function(x) sum(is.na(x))) %>%
tidyr::pivot_longer(names_to = 'variable', cols = everything(), values_to = 'number_missing')
## # A tibble: 15 x 2
## variable number_missing
## <chr> <int>
## 1 island 0
## 2 bill_length_mm 0
## 3 bill_depth_mm 0
## 4 flipper_length_mm 0
## 5 body_mass_g 0
## 6 sex 0
## 7 year 0
## 8 species 0
## 9 tmp_species 0
## 10 sex_male 0
## 11 sex_female 0
## 12 sex_unknown 0
## 13 island_Biscoe 0
## 14 island_Dream 0
## 15 island_Torgersen 0
cln_test %>%
purrr::map_df(function(x) sum(is.na(x))) %>%
tidyr::pivot_longer(names_to = 'variable', cols = everything(), values_to = 'number_missing')
## # A tibble: 15 x 2
## variable number_missing
## <chr> <int>
## 1 island 0
## 2 bill_length_mm 0
## 3 bill_depth_mm 0
## 4 flipper_length_mm 0
## 5 body_mass_g 0
## 6 sex 0
## 7 year 0
## 8 species 0
## 9 tmp_species 0
## 10 sex_male 0
## 11 sex_female 0
## 12 sex_unknown 0
## 13 island_Biscoe 0
## 14 island_Dream 0
## 15 island_Torgersen 0
Note how we have no more missing values!
Let’s check to see our class imbalance in the derived training dataframe.
cln_train %>%
group_by(tmp_species) %>%
count()
## # A tibble: 3 x 2
## # Groups: tmp_species [3]
## tmp_species n
## <fct> <int>
## 1 Adelie 108
## 2 Chinstrap 59
## 3 Gentoo 91
Notice how we don’t have comparable observations for each class. It is good practice to have a balanced dataset. However, such is not the case in life at times. Often, we can either up-sample or just down-sample. However, these fundamental approaches are not always effective. One of the techniques I use is SMOTE. I find SMOTE to be fairly well performing and it is my go to approach. I also find using SMOTE in Python easier than in R.
Let’s touch base on a couple of nuances regarding many Python libraries:
# step 1: define the amount of samples desired
over <- pysmote_over$SMOTE(sampling_strategy = reticulate::dict('Chinstrap' = 100L, 'Gentoo' = 100L), random_state = 1337L)
under <- pysmote_under$RandomUnderSampler(sampling_strategy = reticulate::dict('Adelie' = 100L), random_state = 1234L)
# step 2: define pipeline (similar to recipes in R) to under and over sample in one go
steps <- list(c('o', over), c('u', under))
pipeline <- pysmote_pipe$Pipeline(steps = steps)
# step 3: do the actual resampling
df_rebal <- pipeline$fit_resample(X = cln_train %>% select(-tmp_species, -island, -sex), y = cln_train %>% select(tmp_species))
# step 4: extract the two different results and combine into one
nb_pred <- as_tibble(df_rebal[[1]])
nb_resp <- as_tibble(df_rebal[[2]])
cln_bal <- bind_cols(nb_resp, nb_pred)
So let’s unpack what I’m doing:
I’m defining the amount of samples I want for each class explicitly. In this instance, the actual arguments require a dictionary (key,value pair) for the specific classes. In this case, it’s far more easier to refer to them by name rather than the integer label encoding done prior. In this case, we are asking for 100 observations for each class. If the class has more than 100 samples already (such as Adelie), we will randomly under-sample. And vice-versa for the other classes.
The SMOTE pipeline allows me to ‘daisy chain’ commands together so it can all run in one go. This is similar to the recipes concept.
This is the step that executes the SMOTE algorithm on the supplied dataframe. Here is where {reticulate} shines as it ‘transcodes’ the R dataframe into something Python will understand. This step will return 2 dataframes: one for the predictors and the other for the response variable.
Since we get back two distinct dataframes from the fit_resample()
function, I convert them to a tibble and combine them together.
Now let’s see how many of each species we have in this new dataframe.
cln_bal %>%
group_by(tmp_species) %>%
count()
## # A tibble: 3 x 2
## # Groups: tmp_species [3]
## tmp_species n
## <fct> <int>
## 1 Adelie 100
## 2 Chinstrap 100
## 3 Gentoo 100
Now that looks amazing (and balanced)!
To keep the overall post somewhat short (I’m already failing), I’ll only build a hyperparameter tuned Random Forest model. Nothing fancy, but just something to show how scikit-learn can be used with {reticulate}.
First up, we define the hyperparameter tuning grid.
param_grid <- reticulate::dict('bootstrap' = list(TRUE),
'max_depth' = seq(1L,4L,1L),
'max_features' = seq(2L,6L,1L),
'min_samples_split' = seq(2L,5L,1L),
'n_estimators' = c(250L, 500L))
Notice how I’m able to use a base R function seq()
within a Python argument. Another beautiful feature of {reticulate}!
One nuance of Scikit-Learn is the fact that you have initialize a potential model engine. This is very much similar to the process found in {parsnip}.
rf_mdl <- sklearn_ensemble$RandomForestClassifier()
grid_search <- sklearn_modelselection$GridSearchCV(estimator = rf_mdl,
param_grid = param_grid,
cv = 5L,
n_jobs = -1L,
verbose = 0L,
scoring = 'accuracy',
refit = TRUE)
In the code block above, I’ve identified the cross validation steps after initializing the model engine. Similar to how workflows work as part of {tidymodels}, all of this has been ‘stored’ in memory, but not actually executed. We have to ‘fit’ this engine to get the actual results.
grid_search$fit(X = cln_bal %>% select(-tmp_species, -species), y = cln_bal$species)
## GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,
## param_grid={'bootstrap': [True], 'max_depth': [1, 2, 3, 4],
## 'max_features': [2, 3, 4, 5, 6],
## 'min_samples_split': [2, 3, 4, 5],
## 'n_estimators': [250, 500]},
## scoring='accuracy')
One aspect of model fitting that doesn’t sit well with me is the fact that when models are fitted, you don’t have a visual insight on storing the model results. For instance, there is no <-
or =
in the code block above. However, as that line runs, it will automatically store the results within grid_search
.
Let’s see what the best parameters are.
grid_search$best_params_
## $bootstrap
## [1] TRUE
##
## $max_depth
## [1] 4
##
## $max_features
## [1] 2
##
## $min_samples_split
## [1] 2
##
## $n_estimators
## [1] 500
From here on out, our process is simple:
# save the best combination
best_grid = grid_search$best_estimator_
# make predictions
ypred_test <- best_grid$predict(cln_test %>% select(-island, -tmp_species, -sex, -species))
# score
sklearn_metrics$accuracy_score(y_true = cln_test$species, y_pred = ypred_test)
## [1] 0.9883721
And we get an accuracy of well over 98%. Not bad!
I hope this brief tutorial helps you out! I’m a big fan of {reticulate} and I’ve been having tremendous fun using RStudio 1.4 to run my Python scripts natively along with R code.


{% extends 'base.html'%}
{% load staticfiles %}
{% block content %}
About
-
tutorialdb is a small scale search engine for programming/dev tutorials, it is meant to help anyone who is getting started to learn a new technology.
-
The sole purpose of tutorialdb is to help people get to resoures which might help them learn new things for e.g sometimes there are tutorials on personal blogs which do not get indexed in google easily.
-
All the content (tutorials) is owned by the respective authors/sites.
tutorialdb maintains its own database saving the links to tutorials and some meta info.
{% endblock %}


Rumour Has It: Mourinho eyes Bale, but Spurs won't spend in January
by OmnisportJose Mourinho seemingly has Gareth Bale in his Tottenham plans, but he will have to wait until before the start of next season.
Mourinho was named Spurs head coach on Wednesday, a day after the Premier League club sacked Mauricio Pochettino.
Unsurprisingly, there is plenty of talk around his potential movements in the transfer market – including a Tottenham return for Bale.
TOP STORY – MOURINHO EYES BALE, BUT CAN'T SPEND IN JANUARY
Mourinho's arrival at Tottenham offers Bale the perfect escape route from Real Madrid, according to AS.
The star attacker left Spurs for Madrid in 2013, but is set to leave the LaLiga giants at season's end, the report says.
Marca reports Madrid have ruled out a January exit for Bale. That would suit Mourinho, who has been told there is no money to spend in the January window, according to The Guardian.
ROUND-UP
- Mourinho has more pressing matters at Spurs. The Mirror reports Christian Eriksen, Jan Vertonghen and Toby Alderweireld, who are out of contract at season's end, are intrigued by the prospect of working under Mourinho and are open minded about their futures.
- It could have been different for Spurs. According to the Daily Mail, Leicester City manager Brendan Rodgers was Tottenham's first choice before they turned to Mourinho.
- Meanwhile, the sacked Pochettino may not be out of management for too long. Barcelona and Bayern Munich are ready to pounce for the former Spurs manager, according to The Sun. Ernesto Valverde is under pressure at Camp Nou, while interim boss Hansi Flick will coach Bayern until at least the end of the year.
- Zlatan Ibrahimovic may be getting closer to sorting out his future. Sky Sport reports AC Milan spoke to the departing LA Galaxy striker's agent Mino Raiola on Wednesday about the possibility of a return to San Siro.
- Pierre-Emerick Aubameyang is in no rush to renew his Arsenal contract and with good reason. Sport reports the forward is waiting to find out how serious Barcelona's interest in him is before extending a deal that runs until 2021.
- Needing a striker, Manchester United are considering a January move for Roma's Edin Dzeko, according to ESPN FC. Dzeko has scored five goals in 12 Serie A games this season, and United are looking to finally bolster their attack after losing Romelu Lukaku and Alexis Sanchez in the close season.
- Olivier Giroud has made just one Premier League start for Chelsea this season and the striker could be set for a January exit. La Gazzetta dello Sport says Inter are considering a move for Giroud in January as they look for a back-up for Lukaku.


Vision of a Perfect Run Day
{{> addvisionItem}}
- When wake up
- out of bed
- morning routine
- Food plan to eat
- exercise
Mission & Objective
{{> addmissionItem}}
{{#each missionEach}}
{{> missionItem}}
{{/each}}
Todo's & Obligations
Panel content
People to Talk To:
Panel content


Zend\Search\Lucene component works with Java Lucene 1.4-1.9, 2.1 and 2.3 index formats.
Current index format may be requested using $index->getFormatVersion() call. It returns one of the following values:
- Zend\Search\Lucene::FORMAT_PRE_2_1 for Java Lucene 1.4-1.9 index format.
- Zend\Search\Lucene::FORMAT_2_1 for Java Lucene 2.1 index format (also used for Lucene 2.2).
- Zend\Search\Lucene::FORMAT_2_3 for Java Lucene 2.3 index format.
Index modifications are performed only if any index update is done. That happens if a new document is added to an index or index optimization is started manually by $index->optimize() call.
In a such case Zend\Search\Lucene may convert index to the higher format version. That always happens for the indices in Zend\Search\Lucene::FORMAT_PRE_2_1 format, which are automatically converted to 2.1 format.
You may manage conversion process and assign target index format by $index->setFormatVersion() which takes Zend\Search\Lucene::FORMAT_2_1 or Zend\Search\Lucene::FORMAT_2_3 constant as a parameter:
- Zend\Search\Lucene::FORMAT_2_1 actually does nothing since pre-2.1 indices are automatically converted to 2.1 format.
- Zend\Search\Lucene::FORMAT_2_3 forces conversion to the 2.3 format.
Backward conversions are not supported.
Note
Important!
Once index is converted to upper version it can’t be converted back. So make a backup of your index when you plan migration to upper version, but want to have possibility to go back.
The Zend\Search\Lucene object uses the destructor method to commit changes and clean up resources.
It stores added documents in memory and dumps new index segment to disk depending on MaxBufferedDocs parameter.
If MaxBufferedDocs limit is not reached then there are some “unsaved” documents which are saved as a new segment in the object’s destructor method. The index auto-optimization procedure is invoked if necessary depending on the values of the MaxBufferedDocs, MaxMergeDocs and MergeFactor parameters.
Static object properties (see below) are destroyed after the last line of the executed script.
1 2 3 4 5 6 7 8 9 | class Searcher {
private static $_index;
public static function initIndex() {
self::$_index = Zend\Search\Lucene::open('path/to/index');
}
}
Searcher::initIndex();
|
All the same, the destructor for static properties is correctly invoked at this point in the program’s execution.
One potential problem is exception handling. Exceptions thrown by destructors of static objects don’t have context, because the destructor is executed after the script has already completed.
You might see a “Fatal error: Exception thrown without a stack frame in Unknown on line 0” error message instead of exception description in such cases.
Zend\Search\Lucene provides a workaround to this problem with the commit() method. It saves all unsaved changes and frees memory used for storing new segments. You are free to use the commit operation any time- or even several times- during script execution. You can still use the Zend\Search\Lucene object for searching, adding or deleting document after the commit operation. But the commit() call guarantees that if there are no document added or deleted after the call to commit(), then the Zend\Search\Lucene destructor has nothing to do and will not throw exception:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | class Searcher {
private static $_index;
public static function initIndex() {
self::$_index = Zend\Search\Lucene::open('path/to/index');
}
...
public static function commit() {
self::$_index->commit();
}
}
Searcher::initIndex();
...
// Script shutdown routine
...
Searcher::commit();
...
|
The source code of this file is hosted on GitHub. Everyone can update and fix errors in this document with few clicks - no downloads needed.


Backward Compatibility breaks in 2.0
This file describes the step to follow in order to migrate your projects to Redaktilo 2.0.
The changes done in 2.0 can be found in CHANGELOG.
Misc (1.1)
Here's a list of features removed, with their replacement:
Editor#has
has been removed, useEditor#hasBelow
insteadLineReplaceCommand
no longer accepts strings, useText#setLine
instead
Sanitizers (1.2)
The following commands now takes a TextSanitizer
and a LocationSanitizer
mandatory argument in their constructor:
LineInsertAboveCommand
LineReplaceCommand
LineInsertBelowCommand
LineRemoveCommand
Exceptions (1.4)
The following exceptions have been removed:
Gnugat\Redaktilo\Command\CommandNotFoundException
Gnugat\Redaktilo\Search\NotSupportedException
Gnugat\Redaktilo\Search\PatternNotFoundException
Gnugat\Redaktilo\InvalidLineNumberException
Gnugat\Redaktilo\Service\DifferentLineBreaksFoundException
Please use their equivalent (same name) from the following namespace:
Gnugat\Redaktilo\Exception
.
PHP and Number Search Removal (1.7)
The following classes have been removed:
LineNumberSearchStrategy
, useText#setCurrentLineNumber
insteadPhpSearchStrategy
Php/Token
Php/TokenBuilder


Applying Davis & Chouinard's Affordance Theory to Twitter’s Thread Feature.
Davis and Chouinard’s paper "Theorizing Affordances: From Request to Refuse"
The authors propose the following mechanisms via which the affordances operate - request, demand, encourage, discourage, refuse, and allow. There are no hard and fast boundaries between these mechanisms; an artifact can only mildly allow turning the mechanism into discouragement.
We can evaluate an affordance’s value using Perception, Dexterity and Cultural and Institutional Legitimacy.
Loosely speaking, how an affordance fares depends not only on the subject’s abilities but also on her location in society’s structure.
Let us consider Twitter’s Thread feature. This feature is a binary; a year ago it was not available. Twitter refused the users from tweeting long texts. Users found ways to cheat by composing long texts as replies to their tweets, thereby getting over Twitter’s refusal. So, Twitter introduced the Thread feature and allowed users to make tweetstorms. Since the thread option is seen as a small button whenever we compose tweets, we can consider that Twitter mildly encourages users to use the thread feature. One reason for this encouragement could be that threads offer users to explain their ideas more clearly and avoid any scope of misinterpretation. Once you press the thread button, you are demanded to write text in a new dialog box, else you will be refused from posting your initial tweet.
A user has to know that this feature is available if she has to use it. Since Twitter has advertised this feature well by promoting tweetstorms of prominent users, we can expect a frequent user to know this feature. Also, a user must have enough skill to recognize the thread button and have enough content to post in two or more tweets. If a user follows a lot of journalists, she would be encouraged to tweet in threads as journalists extensively use this feature. If she is part of the old school Twitter culture that demands a rollback of the thread feature and enforcement of 140 character limit, or if she hates to read giant blobs of text, she would never use this feature. There are some countries wherein Twitter hasn’t released this feature yet. She may not be able to use this feature if she belongs to such countries.
Hence, an affordance’s value depends not only on the network and culture the user is part of, but also the physical environment the user is in.
The article was prepared using the Distill Template
I wrote this article as a part of the course SOC473: New Media Theory by Prof. Jillet Sarah Sam
If you see mistakes or want to suggest changes, please contact me


If you are not automatically redirected, then your browser may not be javascript enabled. This site needs javascript to function properly.


| as_bench_bytes | Human readable memory sizes |
| as_bench_mark | Coerce to a bench mark object Bench mark objects |
| as_bench_time | Human readable times |
| autoplot.bench_mark | Autoplot method for bench_mark objects |
| bench_bytes | Human readable memory sizes |
| bench_mark | Benchmark a series of functions |
| bench_memory | Measure memory that an expression used. |
| bench_process_memory | Retrieve the current and maximum memory from the R process |
| bench_time | Measure Process CPU and real time that an expression used. |
| hires_time | Return the current high-resolution real time. |
| knit_print.bench_mark | Custom printing function for bench_mark objects in knitr documents |
| mark | Benchmark a series of functions |
| plot.bench_mark | Autoplot method for bench_mark objects |
| press | Run setup code and benchmarks across a grid of parameters |
| summary.bench_mark | Summarize bench::mark results. |
| system_time | Measure Process CPU and real time that an expression used. |
| workout | Workout a group of expressions individually |
| workout_expressions | Workout a group of expressions individually |


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Aren't beaches great? We think so too! The best part about them is the smell, isn't it? Well, if you're looking to get some brine in your life, then look no further than anywhere else other than the Centennial State.
Ah, Venice Beach. Tourism at its finest. This beach stands out for its large ferris wheel and carnival atmosphere, making it a perfect place to bring the family to experience the pinnacle of SoCal toursim. Surf, swim, or tan all year round at this temperate paridise just west of downtown Los Angeles. While Colorado sure does offer a lot, it sure doesn't offer all of that! But hey, at least we have mountains.
If you think of the reality tv show when you hear the words Jersey Shore, then prepare to have your expecations inverted. While this beachtown does have a decent nightlife, you'll find that its pleasant summer breeze and beautiful sunrises will make Jersey Shore a sunny vacation that the family is sure not to forget. Too bad we don't have one of these in Colorado, huh? We'll keep our arid climate and freak yearround snowstorms, thank you very much.
Florida is nearly a tropical paradise in the spring months, but without the requirements of long international plane rides and passports. When looking for a family vacation, you shouldn't look any further than the sunny beaches of Sunset Beach. What does Colorado have to offer? Why, we have wildcats and legal weed! What more could you ever want?
These are some beaches in Colorado.
These are some other beaches in Colorado.
I mean, seriously. Do you really want to visit a beach in Colorado? Fine. Take these 10. Apparently you'll have an unforgettable summer at them. (Not like you wouldn't in SoCal, but whatever, what do we know...)
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Final Fantasy VII And VIII Remastered Physical Twin Pack Expected To Be Released Soon
The wait is nearly over
by Liam DoolanThe Final Fantasy VII and Final Fantasy VIII Remastered Switch twin pack physical release that was announced exclusively for Southeast Asia in September has reportedly been dated.
It's expected to become available on 29th November and is available for pre-order right now. In saying this, a listing over on Play Asia suggests it still won't be shipping until December. Either way, Square Enix Asia has said it's "coming in 2019" – so at the very least, consider this post to be a friendly reminder that it's arriving soon.
Below is a look at the physical version's artwork:
As previously revealed on the Square Enix Asia Facebook page, this two-in-one hard copy will contain English and Japanese language support. Here's the original announcement:
For Asia, ‘FINAL FANTASY VII’ and ‘FINAL FANTASY VIII Remastered’ are coming to Nintendo Switch in one physical package!
The physical announcement made in September follows on from the news in August when Square Enix's Japanese website and Play Asia reportedly confirmed VIII Remastered would be a digital-only release.
Will you be adding this twin pack to your Switch library? Comment below.
[via gonintendo.com]


scripts/index.js
'use strict'
import { Router } from './libs/router.js'
import { Component } from './libs/component.js'
import { Module } from './libs/module.js'
import { approx } from './utils/approx.js'
import { Communication } from './utils/communication.js'
import { Cookie } from './utils/cookie.js'
import { DateTime } from './utils/datetime.js'
import { Device } from './utils/device.js'
import { extend } from './utils/extend.js'
import { Font } from './utils/font.js'
import { html } from './utils/html.js'
import { isNumber } from './utils/isnumber.js'
import { isObject } from './utils/isobject.js'
import { loader } from './utils/loader.js'
import { logger } from './utils/logger.js'
import { loop } from './utils/loop.js'
import { radians } from './utils/radians.js'
import { rnd } from './utils/rnd.js'
import { scroll } from './utils/scroll.js'
import { sounds } from './utils/sounds.js'
import { uid } from './utils/uid.js'
import { Url } from './utils/url.js'
export {
Router,
Component,
Module,
approx, Communication, Cookie, DateTime, Device, extend, Font, html, isNumber, isObject, loader, logger, loop, radians, rnd, scroll, sounds, uid, Url
}


- The Iron Yard welcomes new DC engineering cohorts (theironyard.com)
- 144 points by CapitalistCartr 1 hour ago | 14 comments
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- 121 points by lelf 5 hours ago | 36 comments


--- layout: null ---
$e^{i\pi}=-1$
$$\int_{-\infty}^{\infty}x\,dx=0$$
When \[a \ne 0\], there are two solutions to \(ax^2 + bx + c = 0\) and they are $$x = {-b \pm \sqrt{b^2-4ac} \over 2a}.$$


library(tidyverse)
library(cowplot)
library(broom)
library(modelr)
library(viridis)
library(lubridate)
library(hms)
library(knitr)
library(kableExtra)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")
source("../../tools/echem_processing_tools.R")
source("../../tools/plotting_tools.R")
theme_set(theme_1())
This agarose glycerol dataset is the third and final condition for a set of in vitro experiments aimed at better understanding the echem results we get with the biofilms and PYO.
The three conditions were:
Recall that the main echem ‘phenotype’ we observe with the biofilm is that \(D_{ap}\) is higher than \(D_{phys}\) - for example see this notebook. The reviewers were concerned about the accuracy of our \(D_{phys}\) measurement and I also thought it was possible that the geometry of the biofilms was having an unintended effect on the \(D_{phys}\) measurement. Therefore, we wanted to perform an experiment that would hopefully clarify whether or not the biofilm phenotype we observe is due to purely physical diffusion and an artifact in the \(D_{phys}\) measurement, or actually due to self exchange.
I reasoned that if calf thymus DNA were sufficient to generate a difference between \(D_{ap}\) and \(D_{phys}\) in vitro, that would support the self exchange model. If it is not sufficient and \(D_{ap}\) is identical to \(D_{phys}\) that might suggest that the \(D_{phys}\) measurement is thrown off by the heterogeneity of the biofilm / weird geometry. Therefore in this experiment we compared a condition +/- DNA.
The glycerol was sort of used as another control, since we know that glycerol should slow physical diffusion, but we also expect \(D_{ap} = D_{phys}\).
Lastly, I chose to encapsulate the electrodes in agarose, because I was familiar with this system, and it yields relatively nice measurements. It also clearly distinguishes the region measured by the electrode from the solution.
The agarose was made by dissolving to 1% by weight in PBS 50 (50mM NaCl, pH 7.0), or 2% agarose in PBS 50 then mixing in (1:1) the 2mg / mL ctDNA stock (also in PBS 50), or by making 1% agarose in PBS 50 containing 50% glycerol (2x PBS was made then mixed 1:1 with glycerol). Mixture were microwaved for ~1 min then molten agarose was transferred from small flasks to 15mL conicals and incubated in the 60 degree water bath. When ready, 50uL of agarose was deposited directly onto the IDA working surface and allowed to solidify at room temp for > 30 min. These experiments were done on totally different days.
The same GC and SWV settings were used as normal - see the file headers for specifics. Acquisitions were acquired using the macro as normal. IDAs were soaked in 75uM PYO and then transferred to fresh medium (either PBS 50 or PBS 50 w/ 50% glycerol). I have not settled on a super standardized workflow for this yet, but I took scans as the PYO diffused into the gel - Soak, and as it diffused out - Transfer. Some of these scans were taken repeatedly or at different times, so there’s no guarantee that the conditions were totally equilibrated etc, as you will see.
These datasets were processed in their own notebooks. See processing folders for 06/06/19, 08/13/19, and 08/15/19. Overall, the raw data look pretty well behaved, although the SWVs have persistent background as usual.
First I’ll read in all the processed data from the three different experiments. Here you can see that we have all the information we need including timing, conditions, and current values.
swvGC_agarose <- read_csv("../../06_06_19_agarose_PYO_2/Processing/06_06_19_processed_swvGC_agarose.csv") %>%
group_by(reactor) %>% mutate(min_time_gc = min(minutes_gc,
na.rm = T), min_time_swv = min(minutes_swv, na.rm = T)) %>%
mutate(norm_time_gc = minutes_gc - min_time_gc, norm_time_swv = minutes_swv -
min_time_swv) %>% mutate(condition = "agarose")
swvGC_ctDNA <- read_csv("../../08_13_19_agarose_ctDNA/processing/08_13_19_processed_swvGC_ctDNA.csv") %>%
mutate(minutes_gc = ifelse(minutes_gc < 850, minutes_gc +
1440, minutes_gc)) %>% mutate(minutes_swv = ifelse(minutes_swv <
850, minutes_swv + 1440, minutes_swv)) %>% group_by(reactor) %>%
mutate(min_time_gc = min(minutes_gc, na.rm = T), min_time_swv = min(minutes_swv,
na.rm = T)) %>% mutate(norm_time_gc = minutes_gc - min_time_gc,
norm_time_swv = minutes_swv - min_time_swv) %>% mutate(condition = "ctDNA")
swvGC_glycerol <- read_csv("../processing/08_15_19_processed_swv_gc_max_glycerol.csv") %>%
filter(electrode_gc == "i2") %>% mutate(condition = "glycerol")
swv_gc_df <- bind_rows(swvGC_agarose, swvGC_ctDNA, swvGC_glycerol) %>%
mutate(reactor_type = ifelse(reactor %in% c("soak", "soak_2",
"soak_3", "soak1"), "soak", "transfer"))
swv_gc_df %>% head() %>% kable() %>% kable_styling(bootstrap_options = c("striped",
"condensed", "responsive"), full_width = F)
| echem_swv | rep | minutes_swv | E_swv | electrode_swv | current_swv | max_current_swv | echem_gc | minutes_gc | E_gc | electrode_gc | current_gc | max_current_gc | reactor | min_time_gc | min_time_swv | norm_time_gc | norm_time_swv | condition | reactor_type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| swv | 0 | 1139.783 | -0.274 | i1 | 1.48e-05 | 1.48e-05 | NA | NA | NA | NA | NA | NA | transfer | 1142.1 | 1139.783 | NA | 0.0 | agarose | transfer |
| swv | 9 | 1162.283 | -0.264 | i1 | 7.00e-06 | 7.00e-06 | gc | 1162.117 | -0.399 | i2 | -2e-07 | 2e-07 | transfer | 1142.1 | 1139.783 | 20.01667 | 22.5 | agarose | transfer |
| swv | 10 | 1164.783 | -0.260 | i1 | 6.70e-06 | 6.70e-06 | gc | 1164.617 | -0.399 | i2 | -1e-07 | 1e-07 | transfer | 1142.1 | 1139.783 | 22.51667 | 25.0 | agarose | transfer |
| swv | 11 | 1167.283 | -0.257 | i1 | 6.50e-06 | 6.50e-06 | gc | 1167.117 | -0.399 | i2 | -1e-07 | 1e-07 | transfer | 1142.1 | 1139.783 | 25.01667 | 27.5 | agarose | transfer |
| swv | 12 | 1169.783 | -0.251 | i1 | 6.40e-06 | 6.40e-06 | gc | 1169.617 | -0.399 | i2 | -1e-07 | 1e-07 | transfer | 1142.1 | 1139.783 | 27.51667 | 30.0 | agarose | transfer |
| swv | 13 | 1172.283 | -0.254 | i1 | 6.20e-06 | 6.20e-06 | gc | 1172.117 | -0.399 | i2 | -1e-07 | 1e-07 | transfer | 1142.1 | 1139.783 | 30.01667 | 32.5 | agarose | transfer |
Let’s look at an overview of the data to give you a sense for it. Here I’ll just plot the max current over time for each condition (GC and SWV separately). Bear in mind that each condition had soak and transfer acquisitions and some are replicates too.
gc_overview <- ggplot(swv_gc_df, aes(x = norm_time_gc, y = max_current_gc,
color = reactor_type)) + geom_point() + facet_wrap(~condition,
ncol = 1, scales = "free", strip.position = "right") + xlim(0,
250) + labs(x = "Time (min)", y = "GC Peak Current (A)",
title = "GC Timecourse") + theme(legend.position = "bottom")
swv_overview <- ggplot(swv_gc_df %>% filter(electrode_swv ==
"i1"), aes(x = norm_time_swv, y = max_current_swv, color = reactor_type)) +
geom_point() + facet_wrap(~condition, ncol = 1, scales = "free",
strip.position = "right") + xlim(0, 250) + labs(x = "Time (min)",
y = "SWV Peak Current (A)", title = "SWV Timecourse") + theme(legend.position = "bottom")
plot_grid(gc_overview, swv_overview)
Ok, so the the soak and transfer conditions mirror each other pretty well. We can clearly see that the glycerol equilibrations are much slower than the other two. It is not obvious at this point if the ctDNA is slower than the agarose. The other thing to notice is the difference between the GC and SWV signals. They show the same patterns, but the biggest thing is that the SWV never returns to zero. This implies a irreversible background signal…something that is stuck to the electrode that doesn’t wash away and doesn’t contribute to GC signal. We’ve seen this before…not sure how we can get rid of it, but we have to hope that the change in SWV signal isn’t affected by this.
The other big overview we can look at is the SWV vs. GC plots to assess \(D_{ap}\).
ggplot(swv_gc_df %>% filter(electrode_swv == "i1"), aes(x = max_current_swv,
y = max_current_gc, color = condition)) + geom_point() +
facet_wrap(~reactor_type, scales = "free")
Here we see mostly the expected linear behavior, but there are some significant deviations. There is a particularly strong deviation for the glycerol soak condition and I don’t really known why. You can see in the first overview plot that the SWV current rises sharply - more sharply than GC, perhaps it’s some weird thing with the glycerol gel equilibrating (this was the initial soak). Anyway the slopes of the plots are not radically different, but there may be some differences. Particularly there is a linear region of the glycerol soak that is clearly a lower slope than the others. We would expect that to be true in this condition. We will have to quantify these plots to better explore this.
ggplot(swv_gc_df %>% filter(reactor_type == "transfer"), aes(x = norm_time_gc,
y = max_current_gc, color = condition)) + geom_point()
ggplot(swv_gc_df %>% filter(electrode_swv == "i1" & reactor_type ==
"soak"), aes(x = minutes_swv, y = max_current_swv, color = condition,
shape = reactor)) + geom_point()
ggplot(swv_gc_df %>% filter(electrode_swv == "i1" & reactor_type ==
"soak"), aes(x = norm_time_swv, y = max_current_swv, color = condition)) +
geom_point()
#+ facet_grid(rows = 'condition', scale = 'free_y')
swv_gc_norm <- swv_gc_df %>% group_by(condition, reactor, electrode_gc,
electrode_swv) %>% mutate(norm_gc = (max_current_gc - min(max_current_gc))/max(max_current_gc)) %>%
mutate(norm_swv = (max_current_swv - min(max_current_swv))/max(max_current_swv))
ggplot(swv_gc_norm, aes(x = norm_time_swv, y = norm_gc, color = condition)) +
geom_point() + facet_wrap(~reactor_type)
ggplot(swv_gc_df, aes(x = max_current_swv, y = max_current_gc,
color = reactor_type)) + geom_point() + facet_grid(condition ~
electrode_swv) + geom_smooth(method = "lm", fullrange = T)
ggplot(swv_gc_df %>% filter(electrode_swv == "i1"), aes(x = max_current_swv,
y = max_current_gc, color = condition)) + geom_point() +
facet_wrap(~reactor_type, scales = "free")


Geckos Core Browsers
Mozilla Firefox is a free and open source web browser descended from the Mozilla Application Suite and managed by Mozilla Corporation. A Net Applications statistic put Firefox at 24.52% of the recorded usage share of web browsers as of March 2010[update], making it the second most popular browser in terms of current use worldwide after Microsoft's Internet Explorer. Other sources put Firefox's usage share between 20% and 32%.
To display web pages, Firefox uses the Gecko layout engine, which implements most current web standards in addition to several features which are intended to anticipate likely additions to the standards.
Latest Firefox features include tabbed browsing, spell checking, incremental find, live bookmarking, a download manager, private browsing, location-aware browsing (also known as "geolocation") based exclusively on a Google service and an integrated search system that uses Google by default in most localizations. Functions can be added through add-ons, created by third-party developers,of which there is a wide selection, a feature that has attracted many of Firefox's users.
Firefox runs on various versions of GNU/Linux, Mac OS X, Microsoft Windows and many other Unix-like operating systems. Its current stable release is version 3.6.3, released on April 1, 2010.Firefox's source code is free software, released under a tri-license GNU GPL/GNU LGPL/MPL.
- Other Browsers
- Mozilla
- Netscape Navigator
- Konqueror
- Epiphany
- Galeon
- Seamonkey
- Dillo
- Kazehakase
- Netsurf
- Camino


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Low-Hanging Fruit
Everyone's starting to think like engineers.
Let’s read an article together. Don’t read past this line until you’ve read the whole thing.
Everyone’s starting to think like engineers. I think this is a natural reaction to interacting with increasingly complex systems on a day-to-day basis.1 We want the optimal way to get the most out of our time interacting with the world, and that instinct has been sharpened to a point with the sheer amount of this thought process the modern human brain has to go through. We spend hours finding the perfect balance between quality and price on Amazon product pages, we find and download apps and install software to make our jobs easier, and develop farming strategies in online games so we don’t have to spend money in them.
Eventually, our attention is drawn to those parts of life that are less-than-perfect, like ordering fast food, or checking our email, or renting a hotel. Except these problems are only significant to us personally, yet they’re the ones that require the minimum amount of logistic thought-power to to generate a solution. And the number of people who are able to take these solutions and turn them into something that exists is growing larger every day.
But humans don’t have unlimited interest or energy to spend. So big problems, problems that affect everyone, like climate change, overpopulation, and others simply don’t have any kind of audience from that demographic of people who have the audacity to tackle them. Because there’s much lower-hanging fruit, and it’s very juicy.
So let’s start thinking outside our personal bubbles, outside of problems that can be solved with big data, or with a storefront, or will appeal to investors. Because if there’s no one else going for the highest-hanging fruit, it might be worth going for.
-
This isn’t endemic to technological innovation, but sure is a product of it. ↩


Library: Foundation
Package: Threading
Header: Poco/Condition.h
Description
A Condition is a synchronization object used to block a thread until a particular condition is met. A Condition object is always used in conjunction with a Mutex (or FastMutex) object.
Condition objects are similar to POSIX condition variables, which the difference that Condition is not subject to spurious wakeups.
Threads waiting on a Condition are resumed in FIFO order.
Member Summary
Member Functions: broadcast, dequeue, enqueue, signal, tryWait, wait
Constructors
Condition
Condition();
Creates the Condition.
Destructor
~Condition
~Condition();
Destroys the Condition.
Member Functions
broadcast
void broadcast();
Signals the Condition and allows all waiting threads to continue their execution.
signal
void signal();
Signals the Condition and allows one waiting thread to continue execution.
tryWait
template < class Mtx > bool tryWait(
Mtx & mutex,
long milliseconds
);
Unlocks the mutex (which must be locked upon calling tryWait()) and waits for the given time until the Condition is signalled.
The given mutex will be locked again upon leaving the function, even in case of an exception.
Returns true if the Condition has been signalled within the given time interval, otherwise false.
wait
template < class Mtx > void wait(
Mtx & mutex
);
Unlocks the mutex (which must be locked upon calling wait()) and waits until the Condition is signalled.
The given mutex will be locked again upon leaving the function, even in case of an exception.
wait
template < class Mtx > void wait(
Mtx & mutex,
long milliseconds
);
Unlocks the mutex (which must be locked upon calling wait()) and waits for the given time until the Condition is signalled.
The given mutex will be locked again upon successfully leaving the function, even in case of an exception.
Throws a TimeoutException if the Condition is not signalled within the given time interval.
dequeue
void dequeue();
dequeue
void dequeue(
Event & event
);
enqueue
void enqueue(
Event & event
);


This is a book club founded by a group Vietnamese students in Seoul.
We love reading history and want to build
"a comprehensive, yet enjoyable, booklist on Vietnam history in the last 100 years".


This page is not designed to work without JavaScript.
This page has some stuff.
More stuff.
And
some
more
stuff.


Something went wrong! It appears you may have left the game screen during the experiment. Unfortunately, we will not be able to continue with the experiment at this time. SONA credit will be awarded pending review of what went wrong.


{% block style %}{% endblock %}
Bookshelf
Home
Books
Buy A Book
Sell A Book
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Account
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JavaScript program to add 2 integers demonstrating twos complement.
The twos complement for is :
The twos complement binary addition of the 2 numbers is :
The base ten addition of the 2 numbers is :


404!
Page not found
The requested URL
/some_url
was not found on this server.
Go back to previous page


PsychSignal Sentiment is a linked data source. This means news data is automatically tied to the underlying equity whenever possible. This linkage is performed by the ticker mentioned in the tweet at the time it was published. To add the right sentiment feed to your strategy you should use the equity asset symbol.
// Request underlying equity data. var ibm = AddEquity("IBM", Resolution.Minute).Symbol; // Add news data for the underlying IBM asset AddData<PsychSignalSentiment>(ibm);
# Request underlying equity data. ibm = self.AddEquity("IBM", Resolution.Minute).Symbol # Add sentiment data for the underlying IBM asset self.AddData(PsychSignalSentiment, ibm)
Data can be accessed the normal way via the Slice events. Slice events deliver the unique PsychSignalSentiment objects to your algorithm as they happen.
Alternatively because it is a linked data source it is also available on the underlying secuity via the Securities[symbol].Data.GetAll<PsychSignalSentiment>()
self.Securities[symbol].Data.GetAll(PsychSignalSentiment)
helper method. When you request data via the data cache it will always return the last PsychSignalSentiment object stored.
You can see an example of both of these accessors in the code below.
using QuantConnect.Data.Custom.PsychSignal; namespace QuantConnect.Algorithm.CSharp { public class PsychSignalSentimentDemonstration : QCAlgorithm { public override void Initialize() { SetStartDate(2019, 8, 1); SetCash(100000); // Request linked news data for Apple. var aapl = AddEquity("AAPL", Resolution.Minute).Symbol; AddData<PsychSignalSentiment>(aapl); } public override void OnData(Slice data) { // Accessing linked sentiment from securities collection: //var tiingoNews = Securities["AAPL"].Data.GetAll<PsychSignalSentiment>(); //Accessing via slice event: var sentiments = data.Get<PsychSignalSentiment>(); foreach (var sentiment in sentiments.Values) { Log($"Bull: {sentiment.BullIntensity} Bull Count: {sentiment.BullScoredMessages}"); } } } }
class PsychSignalSentimentDemonstration(QCAlgorithm): def Initialize(self): self.SetStartDate(2019, 8, 1) self.SetCash(100000) # Request linked news data for Apple s = self.AddEquity("AAPL").Symbol self.AddData(PsychSignalSentiment, s) def OnData(self, data): # Accessing most recent sentiment via a linked source from securities collection: # (returns a list, you can drop the "Values" from the for loop below) #sentiments = self.Securities["AAPL"].Data.GetAll(PsychSignalSentiment) # Accessing unique sentiment objects via slice event: sentiments = data.Get(PsychSignalSentiment) # (returns a dictionary symbol-sentiment, use "Values" to enumerate below) for s in sentiments.Values: self.Debug(f"Bull: {s.BullIntensity} Count: {s.BullScoredMessages}")


So you have an idea that you have been kicking around for awhile, and are now ready to make it happen.
I love new music and brewing beer, and am interested in the idea of music/beer pairings.
I think people will really dig the idea.
How do I make this happen?
How do I pay for it?
Sucussesfuly fudraising starts with a clear identity for your project.
If people are donating resources (time, money, facilities, sevices, etc.), they generally want to know what it is in service to.
I do not think people necessarlity need something tangible in return for a donation, but they do want to know what they are supporting.
Most arts projects are non-commerical projects. They serve some other purpose besides generating a profit.
Develop a clear identity for your project.
Ask yourself questions about your project.
Why am specificallyI passionate about this?
I like the community surrounding brewing, and the commuinty surrounding new music. I think both celebrate creating something and a DIY approach.
How can I share this passion?
Let's bring people together for a concert/tasting experience, listen to new music, try new beers, and talk about it.
How does my project contribute to society in some way?
It will bring new audiences to both the new music and brewing communities.
Answering these questions will help you indentify your project's role in society.
If you utlimenatly want to incorporate your project and have 501(c)(3) tax-exempt status, your organization must be operated for purposes that are beneficial to the public interest.
more on that later...
Now that you have your idea, you need to determine what it will actually cost to pull off.
It may be difficult to solicit gifts for new projects.
The first place to look for support is in the community in which the project will impact.
Focus on building relationships that are mutually beneficial.
This focus on immediate relationships will help solidify your project's role in the community.
Build a track record of activity that is centered on your project's identity.
This will help you demonstrate your project's capacity in future fundraising efforts.
You can accept gifts as an individual.
If the money received is a gift, you do not need to report it as income.
Sales (tickets, CDs, swag, etc.) proceedes are not considered gifts.
An individual can give another individual up to $14,000/year before incurring any gift tax. The giver is responisble for the tax burden.
If you are dealing with that kind of charitable income, you really should be trying to gain tax-exempt status.Online crowd funding has become an extemely popular approach to fundraising.
Common platforms are:
| Pros | Cons |
|---|---|
|
Effective way to quickly raise a sizable sum of money. |
Does not encourage the development of long-term relationships with the project. Donations are not tax-deductabile. |
|
Helps awareness of your project online. |
Campaigns are the most effective when happen once or infrequently. |
|
Fees! |
| Service | Fee |
|---|---|
| Kickstarter | 8% + $.20 per gift |
| IndieGoGo | 8% + $.30 per gift |
| GoFundMe | 7.9% + $.30 per gift |
| Fundly | 7.8% + $.30 per gift |
Let's say you raise $4,200 with 72 backers. Kickstarter's cut is $350.40.
Then, you pay income tax on the $3,849.60 that you receive.
Rates vary depedning on which income tax bracket you fall into.
For example, if you are taxed at 20%, you will need to budget $769.92 in tax burden.
| $4,200.00 | original ammount |
|---|---|
| - $350.40 | Kickstarter Fees |
| - $769.92 | Taxes |
| $3,079.68 | Net |
A 501(c)(3) organization is a corporation or other type of organization that is exempt from federal income tax under section 501(c)(3) of Title 26 of the United States Tax Code.
These charitable organizations can accept donations that are tax-deductible.
Gaining 501(c)(3) status varies from state to state, and can be a long and cumbersome process.
Fiscal Sponsorship: a non-profit organization offering their legal and tax-exempt status to a group, or project, engaged in activities related to the sponsoring organization's mission.
There are many different "models" of fiscal sponsorship. Likely, the most relevent to you is Model C.
Your project receives grants from the fiscal sponsor.
In this model, the sponsor receives the funds that you raised and grants them to you.
It is important that the sponsor act as more than just a intermediary.
You need to have an extremely detailed fiscal sponsorship agreement that outlines what fees the sponsor charges and a clear exit strategy
Use a lawyer. There are many organizations that provide free legal services to artists.
Having a great idea is good, but....
...critically developing your idea and honing its relationship to the public is what will set it up for success.
In a tax-exempt or non-commercial venture, remember that everything needs to be in service to public.
The venture is for society's enrichment, not your own.
Your community is everything. Remember that your community can offer you support in many ways that are not monetary.


model module¶
The Model class and related functionality.
-
class
cleverhans.model.
CallableModelWrapper
(callable_fn, output_layer)[source]¶ Bases:
cleverhans.model.Model
A wrapper that turns a callable into a valid Model
-
class
cleverhans.model.
Model
(scope=None, nb_classes=None, hparams=None, needs_dummy_fprop=False)[source]¶ Bases:
object
An abstract interface for model wrappers that exposes model symbols needed for making an attack. This abstraction removes the dependency on any specific neural network package (e.g. Keras) from the core code of CleverHans. It can also simplify exposing the hidden features of a model when a specific package does not directly expose them.
-
O_FEATURES
= 'features'¶
-
O_LOGITS
= 'logits'¶
-
O_PROBS
= 'probs'¶
-
fprop
(x, **kwargs)[source]¶ Forward propagation to compute the model outputs. :param x: A symbolic representation of the network input :return: A dictionary mapping layer names to the symbolic
representation of their output.
-
get_layer
(x, layer, **kwargs)[source]¶ Return a layer output. :param x: tensor, the input to the network. :param layer: str, the name of the layer to compute. :param **kwargs: dict, extra optional params to pass to self.fprop. :return: the content of layer layer
-
get_logits
(x, **kwargs)[source]¶ - Parameters
x – A symbolic representation (Tensor) of the network input
- Returns
A symbolic representation (Tensor) of the output logits
(i.e., the values fed as inputs to the softmax layer).
-
get_params
()[source]¶ Provides access to the model’s parameters. :return: A list of all Variables defining the model parameters.
-
get_predicted_class
(x, **kwargs)[source]¶ - Parameters
x – A symbolic representation (Tensor) of the network input
- Returns
A symbolic representation (Tensor) of the predicted label
-
get_probs
(x, **kwargs)[source]¶ - Parameters
x – A symbolic representation (Tensor) of the network input
- Returns
A symbolic representation (Tensor) of the output
probabilities (i.e., the output values produced by the softmax layer).
-
make_input_placeholder
()[source]¶ Create and return a placeholder representing an input to the model.
This method should respect context managers (e.g. “with tf.device”) and should not just return a reference to a single pre-created placeholder.
-
-
exception
cleverhans.model.
NoSuchLayerError
[source]¶ Bases:
ValueError
Raised when a layer that does not exist is requested.
-
cleverhans.model.
wrapper_warning
()[source]¶ Issue a deprecation warning. Used in multiple places that implemented attacks by automatically wrapping a user-supplied callable with a CallableModelWrapper with output_layer=”probs”. Using “probs” as any part of the attack interface is dangerous. We can’t just change output_layer to logits because: - that would be a silent interface change. We’d have no way of detecting
code that still means to use probs. Note that we can’t just check whether the final output op is a softmax—for example, Inception puts a reshape after the softmax.
automatically wrapping user-supplied callables with output_layer=’logits’ is even worse, see wrapper_warning_logits
Note: this function will be removed at the same time as the code that calls it.
-
cleverhans.model.
wrapper_warning_logits
()[source]¶ Issue a deprecation warning. Used in multiple places that implemented attacks by automatically wrapping a user-supplied callable with a CallableModelWrapper with output_layer=”logits”. This is dangerous because it is under-the-hood automagic that the user may not realize has been invoked for them. If they pass a callable that actually outputs probs, the probs will be treated as logits, resulting in an incorrect cross-entropy loss and severe gradient masking.


QPT Troubleshooting Guide
This document outlines some common problems and their solutions. For more help,
please email Rob Norris.
QPT won't start.
If QPT won't start, the easiest solution is to re-install:
$ launcher -a:qpt -u
$ launcher -h:build.cl.gemini.edu -i:qpt -v
I can't preview the nightly plan.
First, be sure that your web browser isn't minimized or living on another virtual
desktop; if your browser is configured to re-use widows, the plan may have opened
in a window that's not visible. On Windows and Mac OS X, QPT uses some built-in
functionality to open the plan
preview using your default web browser, but on Unix QPT will try to open the
plan using the firefox
command. So if you don't have Firefox
installed, you're out of luck at the moment.
Note that the plan previews are written to the /tmp
directory on
Unix, so in the worst case you can simply open the plan files manually. The
file://
URL Will look something like this, with the proper date
values, obviously.
file:///tmp/GSqueue/qpt-plans/2007/01/08/20070108.html
Why isn't a particular program or observation showing up?
First, look at the filters you have selected under the View menu; it is possible
that what you are looking for has simply been hidden. If not, keep reading.
In order to show up in QPT, science programs must meet all of the following
criteria:
- The program must not be marked as complete.
- The program must have a well-formed ID.
- The program must be of one of the following types:
Q
,
C
,
SV
,
DD
, or
DS
.
- The program must have a numeric queue band, even if it is not a type-
Q
program.
- The program must be long to the plan night's semester, or belong to a semester added
via the Add Semester command, or be a rollover program from one of the two
previous semesters.
If the program is qualified as described above, each observation must meet the
following additional criteria:
- The observation must have an obs class of
SCIENCE
, PARTNER_CAL
or PROGRAM_CAL
.
- The observation must have an obs status of
READY
or ONGOING
.
- The observation must have at least one unexecuted sequence step.
The visualizer looks weird; curves are jagged or labels are offset from visits.
Thare are a few display bugs that I haven't been able to isolate. If this happens,
try resizing your window. Normally this will reset all the cached shapes and fix
any drawing problems you might be seeing.


{% extends 'base.html'%} {% import "bootstrap/wtf.html" as wtf %} {% block content %} {% for single in comments %}
{{single.comment_content}}


Methods
Instance Public methods
downvotes()
Link
Return the number of -1 comments (downvotes)
Source: show
# File app/roles/votes.rb, line 16 def downvotes notes.select(&:downvote?).size end
downvotes_in_percent()
Link
Source: show
# File app/roles/votes.rb, line 20 def downvotes_in_percent if votes_count.zero? 0 else 100.0 - upvotes_in_percent end end
upvotes()
Link
Return the number of +1 comments (upvotes)
Source: show
# File app/roles/votes.rb, line 3 def upvotes notes.select(&:upvote?).size end


Class NoteOnEvent
Abstraction of MIDI Note On event.
Useful for arrangements NoteEvent can't be produce.
See also:
Info:
- License: MIT
- Author: Pedro Alves Valentim
Functions
| NoteOnEvent:new (fields) | Creates a new NoteOnEvent. |
Methods
| NoteOnEvent:print () | Prints event's data in a human-friendly style |
| NoteOnEvent:set_pitch (pitch) | Sets NoteOnEvent's pitch |
| NoteOnEvent:set_velocity (velocity) | Sets NoteOnEvent's velocity |
| NoteOnEvent:set_channel (channel) | Sets NoteOnEvent's channel |
| NoteOnEvent:set_timestamp (timestamp) | Sets NoteOnEvent's timestamp |
| NoteOnEvent:get_pitch () | Gets pitch of NoteOnEvent |
| NoteOnEvent:get_velocity () | Gets velocity of NoteOnEvent |
| NoteOnEvent:get_channel () | Gets channel # of NoteOnEvent |
| NoteOnEvent:get_timestamp () | Gets timestamp of NoteOnEvent |
Functions
- NoteOnEvent:new (fields)
-
Creates a new NoteOnEvent. Receives a
fields
table as parameter. This table is expected to have some (or all) of these fields:Name Type Description pitch string or number Note to be set on. Can be a string or valid MIDI note code. Format for string is C#4
.timestamp number Number of ticks between previous event and the execution of this event. Default: 0
velocity number How loud the note should sound, values 1-100. Default: 50
channel number MIDI channel to use. Default: 1
Note:
pitch
is the only required fieldParameters:
- fields a table containing NoteOnEvent's properties
Returns:
-
new NoteOnEvent object
Methods
- NoteOnEvent:print ()
- Prints event's data in a human-friendly style
- NoteOnEvent:set_pitch (pitch)
-
Sets NoteOnEvent's pitch
Parameters:
- pitch takes the same values as the pitch field passed to the constructor.
Returns:
-
NoteOnEvent with new pitch
- NoteOnEvent:set_velocity (velocity)
-
Sets NoteOnEvent's velocity
Parameters:
- velocity number loudness of the note sound. Values from 1-100.
Returns:
-
NoteOnEvent with new velocity
- NoteOnEvent:set_channel (channel)
-
Sets NoteOnEvent's channel
Parameters:
- channel number MIDI channel # (1-16).
Returns:
-
NoteOnEvent with new channel
- NoteOnEvent:set_timestamp (timestamp)
-
Sets NoteOnEvent's timestamp
Parameters:
- timestamp number value.
Returns:
-
NoteOnEvent with new timestamp
- NoteOnEvent:get_pitch ()
-
Gets pitch of NoteOnEvent
Returns:
-
NoteOnEvent's pitch value
- NoteOnEvent:get_velocity ()
-
Gets velocity of NoteOnEvent
Returns:
-
NoteOnEvent's velocity value
- NoteOnEvent:get_channel ()
-
Gets channel # of NoteOnEvent
Returns:
-
NoteOnEvent's channel value
- NoteOnEvent:get_timestamp ()
-
Gets timestamp of NoteOnEvent
Returns:
-
NoteOnEvent's timestamp value


(put operating graph here)
(Put event-specific impulse chart here)
| Anomaly | First Occurrence | Last Occurrence | # of Occurrences | Waste | Potential |
|---|---|---|---|---|---|
| {{event.anomaly}} | {{event.firstAt}} | {{event.lastAt}} | {{event.quantity}} | $ {{event.waste}} | $ {{event.potential}} |


Atomic
, Chess960
, KingOfTheHill
, ThreeCheck
public class ChessGame extends BoardGame<ChessBoard>
| Modifier and Type | Field | Description |
|---|---|---|
protected Alliance |
_currentAlliance |
| Constructor | Description |
|---|---|
ChessGame() |
| Modifier and Type | Method | Description |
|---|---|---|
Piece[][] |
getBoardRepresentation() |
This function returns a nice and easy representation of the chess board using N x M matrix and enums of pieces.
|
BoardGame<ChessBoard> |
getCopy() |
This function returns a copy of the current game so that any modifications on the copy won't affect the current
game.
|
protected Alliance |
getCurrentTurnAlliance() |
This function returns the current turn alliance.
|
GameState |
getGameState() |
This function returns the current game state.
|
protected Position |
getKingPosition(Alliance alliance) |
This function returns the king position of the requested alliance.
|
java.util.Collection<Position> |
getOccupiedPositions() |
This function returns all the positions that are occupied by pieces.
|
ChessPiece |
getPiece(Position pos) |
This function returns the piece that seats in the position received as parameter.
|
java.util.Collection<Move> |
getPossibleMoves() |
This function returns a collection of the possible (legal) moves of the current alliance.
|
protected GameEnded |
getSpecialEndGameState() |
This function returns the current special end game state, if there isn't any it returns null.
|
protected void |
initBoard() |
This function initiate the board with pieces for when the game starts.
|
protected boolean |
isKingHasToBeSafe() |
This function determines whether the king needs to be safe or not.
|
protected boolean |
isUnderCheck() |
This function checks if the king of current alliance is under check.
|
void |
makeMove(Move move) |
This function executes the move that's given as a parameter and makes changes in the board as needed.
|
protected void |
onMakeMoveFinished(Move move) |
This function is called after a move has finished executing.
|
protected void |
onUndoMoveStarted(Move move) |
This function is called before an undo operation is executed.
|
void |
undoMove() |
This function return to the state of the game before the last move.
|
protected Alliance _currentAlliance
public java.util.Collection<Move> getPossibleMoves()
getPossibleMoves
in class BoardGame<ChessBoard>
protected Position getKingPosition(Alliance alliance)
alliance
- The alliance of the wanted king.public GameState getGameState()
getGameState
in class BoardGame<ChessBoard>
public void makeMove(Move move)
makeMove
in class BoardGame<ChessBoard>
move
- The move that needs to be executed.public void undoMove()
undoMove
in class BoardGame<ChessBoard>
public BoardGame<ChessBoard> getCopy()
getCopy
in class BoardGame<ChessBoard>
public java.util.Collection<Position> getOccupiedPositions()
public ChessPiece getPiece(Position pos)
pos
- The position of the piece we want to get.public Piece[][] getBoardRepresentation()
protected boolean isKingHasToBeSafe()
protected void initBoard()
protected boolean isUnderCheck()
protected GameEnded getSpecialEndGameState()
protected Alliance getCurrentTurnAlliance()
protected void onMakeMoveFinished(Move move)
move
- The move that has finished executing before this call.protected void onUndoMoveStarted(Move move)
move
- The move that is going to be canceled after this call.


--- layout: page title: About header-img: "img/about-bg.png" ---
This will be where my blog post assignments will be for the Flatiron Online Self-Paced Bootcamp.


What is our motive?
Medex is a project made help the poor by donating unused and unexpired medicines. Many poor people living in rural areas have less or no access
to medicines and we can resolve this issue by donating unused and unexpired medicine.
Who made this website?
My name is Aditya and I am in grade 12. I am currently studying at Delhi Public
School - Bangalore East. This is a project I started so that we can help the poor
have regular access to commonly used medicines.


Villa Savoye
Proposal
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Initial Ideas
Choosing the Idea
Detailing