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On September 4, 2018, my wife Nancy Roth lost her six-year battle with ocular melanoma.
This rare disease, which occurs in 2,000 adults each year, currently has no cure in its metastatic form.
There are two excellent organizations working diligently to help fight this disease -- the Ocular Melanoma Foundation, and the Eye Tumor Research Foundation.
But let me tell you more of Nancy's story, and of her courageous battle that she ultimately lost.
Nancy was born on August 23, 1942, and grew up in Lincoln Park, MI. She attended Lincoln Park High School and the University of Michigan, graduating in 1964 with a degree in nursing.
She was a nurse at Evanston Hospital in Evanston, IL, when she and I met. We married on July 2, 1966.
She became a dedicated flight attendant at United Airlines, flying for decades, bringing enthusiasm and quick wit to passengers around the globe. She traveled the world many times over and as a patriotic and proud American, she especially loved the U.S. troop transports. Nancy never met a stranger.
Nancy and I were blessed with three children – Donald Jr., Eric, and Carolyn – and multiple grandchildren, with whom Nancy had a very close and loving relationship.
Six years ago, our local ophthalmologist, Dr. Ira Garron diagnosed Nancy's condition, and referred us to the world-renowned Wills Eye Hospital in Philadelphia. There, we spent many days and hours under the care of Drs. Jerry and Carol Shields and their great team.
Over a two-year period, Nancy's eye responded to treatment; only a remnant of the tumor remained. However, we came to understand that there was a 50% chance that the tumor would metastasize, which happened at the three-year mark.
We continued the battle at the Perlmutter Cancer Center operated by New York University Langone Health. Their sophisticated treatment included drug mixes created only in Australia and then flown in for immediate patient application.
Nancy battled gallantly, but I could tell her belief in a cure was being put to the test.
Treatment continued with regular chemotherapy and radiation at Highland Park Hospital, as the cancer spread to other areas of Nancy's body.
Despite our fight, we finally realized that Nancy was slipping away.
I never stopped believing that the Holy Spirit would cleanse Nancy and we would resume our joyful morning and evening review of current events of our kids and grandkids. For 32 years, I drove her to the airport and picked her up from her trips around the world as a flight attendant. I loved shopping, getting her what she needed.
Nancy was my conscience and my guide, and I miss her greatly. Yet I'm filled with love and gratitude for her and our many years together.
If your contributions can help even one person win the battle that Nancy lost, my sharing this story with you will have been well worth it. Thank you for helping me remember my wife Nancy, and for your thoughts and prayers for anyone struggling with this disease.
We thank the Ocular Melanoma Foundation and the Eye Tumor Research Foundation for their devoted research in eye cancers and tumors in an effort to win the struggle for life and sight.
In Memory of Nancy Roth, please help support research and treatment of ocular melanoma.


text:
Q: What exactly is 'How to do and What to do' or 'Focus on results, not steps' in functional programming I'm having a difficult time understanding the concept 'Don't think about How to do but What to do' (Focus on results, not steps) in functional programming. Let's say we have a language that treats functions as first class citizens and no inbuilt functions for iteration (such as forAll in Scala). In that case we first have to create a function that tells how to iterate a given data structure haven't we? So if the language it self does not provide enough functions, then apart from having functions as first class citizens, it would be pretty much same as coding in imperative way wouldn't it?
Please correct me if I'm wrong. Following are the resources that I referred.
Video lecture
Some articles

A: "How" and "what" are two sides of the same coin, and sometimes it can be useful to think in one way or another. Thinking about "what" to do is a good way to think about recursion, especially for people without much experience writing recursive functions. "How" implies a series of steps, while "what" implies a series of definitions. Let's consider your iteration example (I'm going to use Haskell, since I don't know Scala, but the concepts ought to be directly translatable).
Haskell's iteration function is called map, but suppose we wanted to write it ourselves. If we didn't have much experience with recursion, it might be difficult to imagine how to write a function (map) that applies another function (f) to every element of a list ("mapping" f over list). Here's the type signature of the function we want:
map :: (a -> b) -> [a] -> [b]

So let's try thinking about "what" a function applied to every element of a list is. It is the function applied to the first element, followed by the function mapped over the rest of the list. So let's code that:
map f (firstElement:restOfList) = (f firstElement):(map f restOfList)

Now we're nearly finished. The only thing left is to handle the base case. What if the list is empty? Well clearly any function mapped over an empty list is an empty list, so we'll code that:
map _ [] = []

And we're done! Now if you can think in terms of "how" and write the above code, go right ahead (as I've gained more experience, I tend to do that more often). However thinking in terms of "what" is a useful technique if you find yourself stuck.

redpajama_set_name: RedPajamaStackExchange


Mr. Richard Lyle Coppage, 61, Jonesborough passed away Thursday September 15, 2016 at his residence.
Mr. Coppage was born in Baltimore, Maryland to the late Robert and Betty Burke Coppage.
Left to cherish his memory are his wife of 31 years, Helen Coppage of the home; children, Luda Valentine (Brandi), Jonesborough, Shasta Coppage , Jonesborough, and Joanie Coppage, Baltimore, MD; grandchildren, James Valentine, Jacob Valentine, Daniel Valentine, and Damon Coppage; sisters, Sharon Coppage, Betty Lynn Coppage, and Paula Johnson; brothers, Wayne Coppage (Terry), Robert Coppage, Edward Coppage, Timmy Coppage (Tina), and Raymond Coppage (Diana); great grandchildren, Isaiah Valentine, Mason Valentine, and Maeleigh Valentine.
Mr. Coppage will be inurned 2:30 PM, Tuesday, September 20, 2016 at Chuckey Community Cemetery.


First time to Toronto (hopefully)....advice?
Last post by arthur d.
Old Railways In Southern BC?
Must see & ride in Montreal?
High speed rail in Newfoundland- could it work?
Whither the Quebec Central Railway?
Looking for a few Canadian railway shops?
Location of Canadian General Electric Plant?
Access to Bay View Junction?
Where is this CN location?
ONR's "Northlander" To Be Discontinued.


Upper School Students – the earth in a heart shape surrounded by students in our own Duchesne plaid uniforms--This shows how our students want to embrace the world.
Staff – A heart and two figures, showing how love, which coming from the spirit (symbolized by a dove), can connect people.
Lower School and Daycare students – a handprint inspired by an activity in which lower school students participate during their Harmony Day—a day celebrating oneness, peace, community. Each finger has a word from the 5 Goals of the Sacred Heart. In the corner is a thumbprint from one of our daycare children with the word Goals. This symbolizes the future of the Sacred Heart ideals.
RSCJ's on our campus – The US/Canada Network symbol with the letters JPIC which stand for JUSTICE, PEACE and INTEGRITY of CREATION.
Administration – hyacinths being watered by a watering can-- The hyacinths are taken from a sign Janet Erskine Stuart saw at her moment of calling. The watering can symbolizes our role as educators and children of the Sacred Heart to nurture.


When I was in high school I spent hours each day in the print shop learning about all the steps involved in printing. Here's a great video that takes me back. The only thing missing is the smell of the cleaning solution. Birth of a Book from Glen Milner on Vimeo.


text:
\section{Introduction}
In recent years, the explosion of the mobile network in Vietnam
stimulated the use of Short Message Service (SMS). Spam through SMS
has increased because the cost to the spammer is low and the response
rate is higher than email. SMS spam is a serious problem in Vietnam
because of the availability of unlimited and very cheap pre-paid SMS
packages.

According to a report in 2015 by BKAV\footnote{www.bkav.com.vn}, an
information security firm, there were 13.9 million spam messages sent
to mobile phone users every day in Vietnam, and one out of two people
received spam messages~\cite{Bkav:2015}. Meanwhile, there were at
least 120 million active mobile users in the market according to a
report of the Ministry of Information and Communication, which was
released in 2015~\cite{Mic:2015}.

Spam SMS causes many issues for mobile users. They may suffer
financial loss from these messages by reacting to them. Users may
accidentally call to premium rate numbers or register for expensive
services by replying to these messages.  Moreover, they can be exposed
to some risks by accessing harmful websites or downloading
malwares. Mobile network operators also suffer financially because they
may lose users or spend more on spam prevention.

There are many methods and systems for filtering spam messages for
English in mobile networks~\cite{Guzella:2009, Delany:2012}. Most of
them use machine learning approaches to analyze the
content of messages and additional information to detect spam
messages. One of the first systems was proposed by
Graham~\cite{Graham:2002}, using Naive Bayes classifier to get a good
result. This work led to the development and application of other
machine learning algorithms to spam filtering. The state-of-the-art
system for English can detect spam SMS with an accuracy of 97.5\%
with a good false positive of only 0.2\%~\cite{Almeida:2011}.

In this paper, we present the first system for Vietnamese spam SMS
filtering with a good accuracy. We first propose a method for the
preprocessing step because existing tools for Vietnamese preprocessing
cannot give good accuracy on our dataset. We then experiment with
vector representations and different classifiers to find the best model for this
problem. Our system achieves an accuracy of about 94\% when labelling spam
messages while the misclassification rate of legitimate messages is
relatively small, about 0.4\%. This is an encouraging result,
applicable in real-world applications and serves as a good baseline
for further improvement of future systems.

The paper is structured as follows. Section~\ref{sec:background}
introduces briefly the task of filtering spam SMS.
Section~\ref{sec:methodology} describes the methodology of our
system. Section~\ref{sec:experiment} presents our data and the
evaluation results and discussion. Finally,
Section~\ref{sec:conclusion} concludes the paper and suggests some
directions for future work.

\section{Background}\label{sec:background}

\subsection{Spam filtering system}
A SMS is a short text message that contains only a limited number of characters. The
structure of this message is divided into the header and the body. The
header contains some information about the subject, sender, and
recipient while the body is the actual content of the message that we
see on mobile phones.

\begin{figure}[h]
\center
\includegraphics[scale=0.4]{spam_filter}
\caption{Pipeline of a usual spam filter}\label{fig:1}
\end{figure}

Figure~\ref{fig:1} shows the pipeline of a usual spam filter. The
processing chain of a filter can be divided into three components. The
first is preprocessing, where documents are processed to extract data
for later use. The common preprocessing steps are tokenization,
lemmatization and removing stop words. After preprocessing the raw
text is transformed to vectors, each representing the content of one
message. The most popular representations are bag of words and term
frequency-inverse document frequency. The vectors are inputs to a
machine learning model. After be trained, the model is able to label
each new message as spam or not.

\subsection{Spam filtering system for Vietnamese}
To our knowledge, there is some research on spam filtering for
Vietnamese but none focused on SMS. Most of these systems are
developed for filtering spam email~\cite{Lung:2012,Dinh:2014}. The
best accuracy of those systems is about 92\% but the false positive
rate is not feasible in practical applications, about 6\%~\cite{Lung:2012}. Moreover, email
has many characteristics different from SMS, so applying these
systems directly to our task cannot give the best accuracy. For
these reasons, we propose a system that focuses on filtering
Vietnamese spam SMS.

\section{Methodology}\label{sec:methodology}
SMS has some different characteristics compared to regular text such
as email, the task of labelling spam SMS is harder than for
email. Firstly the maximum length of an SMS message is only 160
characters, so the content-based approach for SMS is more difficult
because there is less information. Secondly, subscribers do not
usually use regular grammar to type a message. They use abbreviations,
phonetic contractions, wrong punctuations, emoticons, etc. It makes
the data sparse, and some preprocessing tools cannot be applied. Also,
linguistic characteristics of the Vietnamese language are different
from occidental languages. Thus, applying methods for English directly
or using existing systems for detecting Vietnamese spam email cannot
give the best performance.

In the following subsections, we present our main method, including
preprocessing step, featurization and machine learning models.

\subsection{Preprocessing}

\subsubsection{Entity Tagging}

The size of our corpus is relatively small and a lot of patterns in
messages are not words in the dictionary. Thus, it is necessary to
alleviate the sparseness of this data. We propose a new way to do this
by grouping some similar patterns together. We define six groups,
including  \textit{date, phone, link, currency, emoticon
  and number}. \textit{Date} collects characters that describe time
such as day, month, year, hour, etc\dots \textit{Phone} detects phone
numbers. \textit{Link} and \textit{currency} find and
replace characters that represent web links and money. Mobile users
use many \textit{emoticons} in messages and we match them to similar
emoticons of Facebook. After that, we replace all
number in the corpus by the token \textit{number}.

\subsubsection{Tokenization}
Like many occidental languages, Vietnamese is based on the Latin
alphabet. However in Vietnamese, the space symbol is not only used to
separate words but also to separate syllables within the same
word. The task of word segmentation plays an important role when
detecting spam message because it helps to extract features more
correctly. There are tools to solve this problem by detecting word
boundaries such as vnTokenizer~\cite{Le:2008},
JVnSegmenter~\cite{Nguyen:2006}. These tools work well for regular
text but they are not suitable for our dataset because of SMS usually uses
an idiosyncratic language. Thus, we propose to use the phrase
collocation method for the segmentation task on our
dataset~\cite{Mikolov:2013}. The score of two syllables $w_{i}$,
$w_{j}$ that are parts of the same word is given by:
\begin{equation}
\score(w_{i}, w_{j}) = \frac{\cnt(w_{i}w_{j} - \delta)}{\cnt(w_{i})*\cnt(w_{j})}
\end{equation}
where $\delta$ is used as a discounting coefficient to prevents too
many phrases consisting of very infrequent words to be formed.

For our system, we filter out all bigrams with total collected count
lower than 10. The advantage of this method is that we need no
pre-training data to train the tokenizer. It is based on only the
statistics of syllables in our corpus.

\subsection{Vector Representation}
The raw data type is text so we need to transform it to vector form to
use in machine learning models. There are two common methods for
converting from raw text to numeric vector - Bag of Words (BoW) and
Term frequency-Inverse document frequency (Tf-Idf).

\subsubsection{Bag of words}
Given the vocabulary set $T = \{t_{1}, t_{2},..., t_{N}\}$, each
document $d$ in the corpus  is represented as a N-dimensional vector
$X = [x_{1}, x_{2},..., x_{N}]$, where $x_{i}$ is the
number of occurrences of $t_{i}$ in $d$.

\subsubsection{Term frequency-Inverse document frequency}
Tf-Idf is a numerical statistic that is intended to reflect how
important a word is to a document in a collection or corpus. Now, each
$x_{i}$ in vector $X$ is calculated by an equation:
\begin{equation}
x_{i} = n_{t_{i}, d} \log \left (\frac{|D|}{n_{t_{i}}} \right )
\end{equation}
where $D$ is a corpus, $n_{t_{i}, d}$ is the number of occurrences of
$t_{i}$ in $d$ and $n_{t_{i}}$ denotes the number of documents in
which $t_{i}$ occurs.

\subsubsection{Feature Selection}
Because the represented vector is sparse, it may reduce the
performance of the classifier. To improve the accuracy of our system,
we need a method to select the most representative features such as
document frequency, information gain, term-frequency variance,
etc\dots In our setting, we use document frequency because this common
method has low computing cost.

\subsubsection{Length Feature}
In our experiment, we found that the length feature is useful because
the lengths of spam messages tend to be long.

\subsection{Classifiers}

\subsubsection{Baseline Classifier}
Spam messages from mobile network operators are easy to detect because
they have special tags in their contents. Thus, we collect these tags
and label messages that have them as spam messages. Specifically,
these tags are \textit{[QC*], (QC*), [TB*], (TB*)} where * is a string
or empty. We use this method as a baseline system for the evaluation
task.

\subsubsection{Machine Learning Models}
We trained some common machine learning models on our dataset and used
them to detect spam messages. These models include Naive Bayes (NB),
support vector machine (SVM), logistic regression (LR), decision tree
(DT) and k-nearest neighbours (kNN).

\section{Experiment}\label{sec:experiment}
\subsection{Dataset}
We conduct experiments on a dataset containing 6599 messages manually
annotated with labels. Our dataset is a collection of messages from
two biggest mobile network operators in Vietnam - Viettel and
Vinaphone. There are 5557 legitimate messages and 1042 spam messages
in this dataset. Its dictionary has $7,334$ unique words. Spam message
could be sent from either mobile network operators or other
sources. The former are easy to detect because they have special tags
in their contents. In our dataset about 70\% of spam messages are from
operators. The size of this corpus is comparable to some SMS datasets
for English~\cite{Delany:2012}. This dataset is made available for
research purpose\footnote{github.com/pth1993}.

\subsection{Results and Discussions}
\subsubsection{Evaluation Method}
We use 5-fold cross-validation to evaluate our system. The final
accuracy score is the average scores of the five runs.

We use statistical measures of the performance of a binary
classification test in decision theory as measures for our system. The
reason for using this method is the asymmetry in the misclassification
costs. The error that the system incorrectly classifies spam message
as legitimate is a minor problem while the error of labelling
legitimate message as spam can be unacceptable. Thus, there is a
trade-off between two types of errors. The sound system must satisfy
both of two constraint - the good performance for spam filtering and
the misclassification of the legitimate message is minimum.

In decision theory, two classes are positive (spam) and negative
(legitimate). We measure two types of error by false positive rate and
false negative rate. The numbers of spam and legitimate message in our
dataset are $n_{S}$ and $n_{L}$, the numbers of incorrect messages are
$n_{S,L}$ and $n_{L,S}$, respectively. From these, the false positive
and negative rates are given by the following formulas:
\begin{equation}
\begin{split}
\fpr = \frac{n_{L,S}}{n_{L}}\\
\fnr = \frac{n_{S,L}}{n_{S}}
\end{split}
\end{equation}
\subsubsection{Baseline System}
Our baseline system uses a simple rule to detect spam messages by catching special tags in their contents. Table~\ref{tab:1} presents the accuracy of this system.
\begin{table}[h]
\center
\caption{Accuracy of baseline system}\label{tab:1}
\begin{tabular}{|c|c|}
\hline
True Positive Rate & 69.76\% \\
\hline
True Negative Rate & 100.00\% \\
\hline
False Positive Rate & 0.00\% \\
\hline
False Negative Rate & 30.24\% \\
\hline
\end{tabular}
\end{table}

We see that this system can detect only about 70\% of the number of
spam messages because there are lots of spam messages that have no
special tags in their contents.
\subsubsection{Preprocessing}
In the second experiment, we compare the performance of the system
with or without preprocessing. The classifier we use is Support Vector
Machine, and the representation is BoW.

Without preprocessing the vocabulary has 7334 words while using
tokenization and entity tagging, the vocabulary size is reduced to
5998 words. It makes our corpus less sparse and more accurate because
its size is smaller and words in Vietnamese is combined from one or
more syllables. Table~\ref{tab:2} shows the improvement when using the
preprocessing technique.
\begin{table}[h]
\center
\caption{Evaluation of preprocessing}\label{tab:2}
\begin{tabular}{|c|c|c|}
\hline
 & Without preprocessing & With preprocessing \\
\hline
True Positive Rate & 91.79\% & 93.40\% \\
\hline
True Negative Rate & 99.69\% & 99.60\% \\
\hline
False Positive Rate & 0.31\% & 0.40\% \\
\hline
False Negative Rate & 8.21\% & 6.60\% \\
\hline
\end{tabular}
\end{table}

We see that with preprocessing the accuracy of labelling spam message
increases about 1.6\%.

\subsubsection{Vector Representations}
In the third experiment, we evaluate the performance of SVM with two common methods for
representing text -- BoW and Tf-Idf. The result is
shown in Table~\ref{tab:3}.
\begin{table}[h]
\center
\caption{Evaluation of vector representations}\label{tab:3}
\begin{tabular}{|c|c|c|}
\hline
 & BoW & Tf-Idf \\
\hline
True Positive Rate & 93.40\% & 84.66\% \\
\hline
True Negative Rate & 99.60\% & 99.58\% \\
\hline
False Positive Rate & 0.40\% & 0.42\% \\
\hline
False Negative Rate & 6.60\% & 15.34\% \\
\hline
\end{tabular}
\end{table}

From the information in above table, we can conclude that BoW is very
more useful than Tf-Idf for this problem. Specifically, the accuracy
when using BoW is better than using Tf-Idf about 15\%.
\subsubsection{Classifiers}
In the fourth experiment, we compare some machine learning models to
find the best models for this task. Classifiers that we compare are
support vector machine (SVM), naive bayes (NB), decision tree (DT),
logistic regression (LR) and k-nearest neighbours (kNN) and the vector
representation in this experiment is BoW. Table~\ref{tab:4} shows the
accuracy of each model.
\begin{table}[h]
\center
\caption{Evaluation of classifiers}\label{tab:4}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
 & SVM & NB & LR & DT & kNN \\
\hline
True Positive Rate & 93.40\% & 95.15\% & 92.99\% & 92.18\% & 78.13\% \\
\hline
True Negative Rate & 99.60\% & 96.25\% & 99.64\% & 99.12\% & 99.64\% \\
\hline
False Positive Rate & 0.40\% & 3.75\% & 0.36\% & 0.88\% & 0.36\% \\
\hline
False Negative Rate & 6.60\% & 4.82\% & 7.01\% & 7.82\% & 21.87\% \\
\hline
\end{tabular}
\end{table}

Naive Bayes gives the best result for labelling spam message but its
false positive rate is very high, about 3.75\%. It means that for each
100 legitimate messages, there are 3 messages that are labelled as
spam. This rate is not acceptable in reality. In the rest, SVM gets
the best result. Thus, we use SVM as a classifier for our system.

\subsubsection{Length feature and feature selection}
We find that the length feature is useful for our system because the
lengths of spam messages tend to be long. Document frequency is a good
method to find the most useful features for classifying and make our
system faster. Table~\ref{tab:5} presents the accuracy of our system
when adding length feature and set document frequency as 3.
\begin{table}[h]
\center
\caption{Accuracy of our system}\label{tab:5}
\begin{tabular}{|c|c|}
\hline
True Positive Rate & 93.91\% \\
\hline
True Negative Rate & 99.55\% \\
\hline
False Positive Rate & 0.45\% \\
\hline
False Negative Rate & 6.09\% \\
\hline
\end{tabular}
\end{table}

\section{Conclusion and Outlook}\label{sec:conclusion}
In this paper, we have presented the first system for Vietnamese spam
SMS filtering. Our system achieves a good accuracy of about 94\% of
detecting spam messages. This result is comparable to the accuracy of
this task for English and outperforms performances of some systems for
labelling Vietnamese spam email although classifying SMS is more
complicated.

In the future, on the one hand, we plan to improve our system by enlarging
our corpus so as to provide more data for the system. On the other
hand, we would like to use some deep learning models such as
convolution neural network and long-short term memory for detect spam
messages because these models have been shown to give the best
performance for many text classification
problems~\cite{Johnson:2016,Zhang:2015}.

\section*{Acknowledgment}
The authors would like to thank the
CyRadar\footnote{cyradar.com} team for providing us the dataset for
use in the experiments. The second author is partly funded by the
Vietnam National University, Hanoi (VNU) under project number QG.15.04.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Wang, J.; Deng, J. S.; Wei, J. Y.
Abstract The normalized radial distribution of young stellar populations (and cold gas) in nearby galactic discs is compared between active galactic nuclei (AGN) host galaxies and star-forming galaxies (both with Hubble types between S0/a and Scd) by using Type II supernovae (SNe II) as tracers. A subset of 140 SNe II with available SN position measurements is selected from the Sternberg Astronomical Institute (SAI)-Sloan Digital Sky Survey (SDSS) image catalogue by requiring available SDSS spectroscopy data of their host galaxies. Our sample is finally composed of 46 AGN and 94 star-forming galaxies. Both directly measured number distributions and inferred surface density distributions indicate that (a) the SNe detected in star-forming galaxies follow an exponential law well; (b) in contrast, the SNe detected in AGN host galaxies significantly deviate from an exponential law, which is independent of both morphological type and redshift. Specifically, we find a detection deficit around R(SN)/R(25,cor) similar to 0.5 and an overdetection at outer region R(SN)/R(25,cor) similar to 0.6-0.8. This finding provides a piece of evidence supporting that there is a link between ongoing star formation (and cold gas reservoir) taking place in the extended disc and central AGN activity.
GB/T 7714 Wang, J.,Deng, J. S.,Wei, J. Y.. Ongoing star formation in AGN host galaxy discs: a view from core-collapse supernovae[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2010,405(4):2529-2533.
APA Wang, J.,Deng, J. S.,&Wei, J. Y..(2010).Ongoing star formation in AGN host galaxy discs: a view from core-collapse supernovae.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,405(4),2529-2533.
MLA Wang, J.,et al."Ongoing star formation in AGN host galaxy discs: a view from core-collapse supernovae".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 405.4(2010):2529-2533.


text:
Q: Tastypie api working with postman but not with python requests I have tastypie api with api key authentication.
Its working both ways for GET request, but for post request, it is not working.
url = http://localhost:8000/api/path/?api_key=key&username=username

in the view, when I tried to print request.user, it is regular/real user when request comes from postman. But with python requests, it is anonymous user.
With python requests, I am doing this:
url = http://localhost:8000/api/path/?api_key=key&username=username
d = {"some": "data"}
r = requests.post(url, data=json.dumps(d), headers={"content-type": "application/json"})

Code provided by postman:
POST /api/v1/abc/?api_key=somekey&username=something HTTP/1.1
Host: localhost:8000
Content-Type: application/json
Cache-Control: no-cache
Postman-Token: something

{"something":"something"}

Please let me know for any questions.
My Resource for which api authentication not working:
class UserResource(ModelResource):
class Meta:
    queryset = User.objects.all()
    fields = ['username', 'email', 'first_name', 'last_name']
    resource_name = 'user'
    authentication = ApiKeyAuthentication()

A: Make sure you are not using SessionAuthentication too or have enforce_csrf enabled in your authentication. Since postman sends the browser cookies with the request too, that probably might be the reason for the different behaviour you are getting.
If that is the case, it means API Authentication is not working. Since postman is using SessionAuthentication, so it works. Ensure that you have added tastypie in INSTALLED_APPS and created/stored the api key in the database.


text:
PKG_NAME = setuptools
PKG_VERS = 3.4
PKG_EXT = tar.gz
PKG_DIST_NAME = $(PKG_NAME)-$(PKG_VERS).$(PKG_EXT)
PKG_DIST_SITE = http://pypi.python.org/packages/source/s/$(PKG_NAME)
PKG_DIR = $(PKG_NAME)-$(PKG_VERS)

DEPENDS =

HOMEPAGE = http://pypi.python.org/pypi/setuptools
COMMENT  = Easily download, build, install, upgrade, and uninstall Python packages
LICENSE  =

include ../../mk/spksrc.python-module.mk

redpajama_set_name: RedPajamaGithub


text:
What Is Google's FLoC and Its method of tracking online
All about Google's new tracking method FLoC and how it will going to track you online.
Noor EemanMay 2, 2021
A new tracking method called Federated Learning of Cohorts, or FLoC is being introduced by Google. It is a part of the "Privacy Sandbox" initiative, the one announced in 2019. Google experts are convinced that its substitute for cookies would be the best security for users but many people are doubtful.
The idea and need of FLoC came out because many browsers block third-party cookies by default. When the tracking cookies turned down it becomes impossible to track. To counter this situation, Google desired to devise other systematic ways to track users' data for the relevant ads. FLoC is would be free of any cookies so that means FLoC would let advertisers target without cookies.
FLoC would run in Goole's Chrome browser, and users' online behavior would be tracked by it.
FLoC lets advertisers use behavioral targeting without cookies. It runs in Google's Chrome browser and tracks a user's online behavior. It would then assign that browser history an identifier and would add it to a group of other browsers. All of them with similar behaviors called a "cohort".
Advertisers would be able to see the behaviors that people in a cohort share without being able to identify individuals within that cohort because each person's browser is given an anonymized ID.
User's cohort IDs would be recalculated on a weekly basis. A new summary of the users ' online behavior would be provided to the advertiser. According to Google, no one could be chosen out of the group and paired with their unique browsing data, as there would be thousands of people in each cohort.
Main FLoCing for us;-
According to Google FLoC will be doing personalized ads but it will not collect data that can be tied to some people who are using its products. That would happen by them assigning each browser an unknown ID. Then that unknown ID would be added into a large group. That group would have consisted of only the overall patterns which would be accessible to advertisers. The main concept is that you would have your privacy only the data would be shared with advertising anonymously. An algorithm called SimHash would be used by FLoC.
SimHash would create user IDs and also assign people to cohorts. SimHash was at first introduced for the usage by Google web crawlers to search out identical web pages.
One of the privacy concerns which is highly associated with third-party cookies is that when this would happen, you data will not get stored on a server. Lots of users' data could be associated with individual people should be stored under unclear security protocols for a long time. Google also claims that cohorts with "highly sensitive content" won't be used. It would be free of all the sensitive things which Google would think are sensitive. Like if someone visists frequently the site that publishes of involves in religious or political content or if someone visits medical website. These would not be used to add to a cohort. And likewise, all sensitive things would remain private.
According to a statement published by Marshall Vale, the product manager of Google's privacy sandbox:
"Before a cohort becomes eligible, Chrome analyzes it to see if the cohort is visiting pages with sensitive topics, such as medical websites or websites with political or religious content, at a high rate. If so, Chrome ensures that the cohort isn't used, without learning which sensitive topics users were interested in."
People Doubts;-
Many people are speaking again at FLoC. The Electronic Frontier Foundation (EFF) opposed the theme quite harshly. According to Google is making use of false dichotomy when it comes to privacy and entitled it as "Google's FLoC Is a Terrible Idea". The article's author Bennett Cyphers says;
"Instead of re-inventing the tracking wheel, we should imagine a better world without the myriad problems of targeted ads, "You either have old tracking or new tracking"—there just shouldn't be tracking, period.
Other people are also not into FLoC as Mozilla, the company behind the Firefox web browser, has declared that they will not be opting for FLoC. The company also requires some ways to advertise that preserve privacy but ut won't go with this idea.
Chrome branched browsers like Brave and Vivaldi, are not going with this either.
Apple has also opposed and said that it will not use FLoC in its Safari browser. As of April 2021, Microsoft has disabled the feature in Microsoft Edge, its Chromium-derived browser.
New Privacy Concerns;-
According to Cyphers FLoC had produced new privacy concerns by trying to address old ones while still keeping targeted ads. One of those concerns is fingerprinting.
Fingerprinting;
Browser fingerprinting is the ability to gather separate parts of information from someone's browser and compose them into a reliable identifier for a specific person. The unique browser behavior gets more fingerprints because that unique browser behavior sets you different from the group.
According to Cyphers FLoC records your browsing behavior and from that creates an identifier before assigning you to a group. So, whoever wants to track you, it is far more easier as a lot of the work has already been done for them. Someone trying to track a pre-FLoC Chrome user would have to pick them from a pool of millions—a cohort is only a few thousand.
Date "Democratization";-
FLoC would share your cohort data with the advertisers. It would share the data with the sites or companies that you looked into, who can identify you or your login information. Let's suppose you have logged into a site with Goole for a service. In that service, your information like name and login details would already be saved there. You are identified there. That information can be used to tie your cohort ID, which is supposed to be anonymous, to your user profile.
Cyber Security experts are of the view that this cross-contextual information would be highly beneficial in tracking.
"You should have a right to present different aspects of your identity in different contexts. If you visit a site for medical information…there's no reason it needs to know what your politics are".
Chase Bank Credential Phishing Attacks Bypassed Exchange Email Protections
Chinese 33 Apps Break Rules in Gathering Users Personal Data
Moroccan Hackers Hijacked Google, Microsoft & Kaspersky
Users Locate Hidden Marketplaces In DARK WEB Search Engine Kilos
Get $200,000 from Google If you hack a Nexus 6P and 5X
Google Android Studio Version 2x & Improved Android Emulator

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Git clone using ssh has a timeout Hi guys I would just like to share some of my problem on git. I have been cloning on different unit and all of them were successful. But now I have encounter on a unit that would not let me clone or even pull.
here is there error:

ssh: connect to host  port 22: Connection timed out fatal:
  The remote end hung up unexpectedly

I already tried two different way on cloning a repository.
1st

git clone ssh://username@ip.com/~/linuxLink.git

2nd
first I go to an empty folder using git bash.

git init -> to initialize a repository
git remote -v - just to make sure the remotes are empty
git remote add origin ssh://username@ip.com/~/linuxLink.git
git pull
ssh: connect to host 162.144.35.182.ipage.com port 22: Connection timed out
  fatal: The remote end hung up unexpectedly

I also notice after I install git bash when I go to C:/user/pcname/ I cannot see .ssh the other unit has it.
can someone please tell me where did I go wrong.
thank you so much.


Lane Cottage Produce was established in April 2011 as a result of Richard leaving his job as Propagation Manager at Wyevale Nurseries in Hereford.
Richard had worked there for about twenty years, having originally trained at Merrist Wood Agricultural College. He has enormous horticultural knowledge and experience. Mandy is passionate about good food having run her own cafe in Hereford for eight years and worked in restaurants in both Hereford and London. Together we dreamed up the idea of establishing a market garden which grew slightly more unusual varieties and Lane Cottage Produce was born.
We turned a badly neglected field (approaching two acres) into a market garden and soon discovered that demand for the salad leaves outstripped supply.
Initially we sold from a van around the local villages and markets and food festivals, buying in stock of fruit and vegetables to offer a wider selection. However in April 2013 we made the decision to stop the stalls and concentrate on selling uniquely our own produce to shops and restaurants.
In an attempt to keep up with demand for our salad leaves we have put up a further four polytunnels in the next field, bringing it to a total of ten.
What Rich does: the sowing, digging, growing, weeding, pest-control, muck spreading, rotavating, tractor-driving and polytunnel building!
Produce is nearly always picked the day before it is delivered. Mandy still picks and packs but has much-needed help from three local super-women, plus extra help as needed.
Deborah from The Broad Bean in Ludlow, wrote this poem about us a few years ago and has turned out to be the most popular of her poems. Not every day you get a poem written about you!
shadow their ash against the frost.
for thinking someone moved there.
and gold, the mud of nuns encrusts my nails.
a crabbed claw and muddied skin reaching.
allowing black cloth to stir in a window.
who would there be to ask?
Life here is earned the same way it's always been.
grey stone of the afternoon.
for the smooth talisman of my phone.
against the wainscot of my inner ear.


November | 2012 | Plan. Shop. Eat. Live.
Today's salad is spinach, yellow bell peppers, and poking seeds. All organic or roasted at home. Topped off with a simple cider vinegar and olive oil dressing.
I hope you're all enjoying yourselves post-Thankskgiving. I just wanted to send a quick update to let you know that the Organic Cranberry Sauce with Pineapple turned out amazing! I'd post a photo but there's none left!! It was really tasty, tangy, tart, and healthy. Such a perfect addition to the meal. I'd even venture to say that it was better than my traditional sugar-laden cranberry sauce!
"Early" thanksgiving morning (it's still early if the parade is on, right?), I'm starting my organic cranberry sauce. A few cups of water, a freshly washed bag of organic cranberries, and a can of conscientiously grown organic crushed pineapple. Water to boil, add cranberries until they pop! Then, I'll be adding in a cup of pineapple sauce.


Looking for a quality damp prevention service in the Gloucester region? Solve all your damp problems with Frampton Consultants Ltd's superb protective damp proofing service. Read on to see what we could do for you.
Damp proofing maintains the structural strength of your property and thus ensures that it is safe and doesn't depreciate in value. It also protects the inhabitants of your home from breathing in dangerous microbes that can be produced by damp.
Our protective damp proofing will comprehensively protect your home from damp. Indeed, we're so sure of this that our work comes with a 30 year guarantee. Call us today on 07943 667 275 to discuss your needs.
Has your home already succumbed to damp? is it exhibiting signs of dry rot? Have no fear - we also provide a comprehensive dry rot treatment service to restore your home to full health. Follow the link to find out more.


text:
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If you have lost a pet and see a poem on these pages you would like to use email me! Many of the poems here were inspired by friend's losses. I am willing to share them as long as you ask permission and post proper credit and send me a link! I hope to eventually have a banner you can use!
The strange thing I have come across many poetry sites is the guidelines for submissions requesting poetry not be done in prose. To me this is almost impossible. I have tried to not write in prose but it's a losing battle!!
A little Bio you want? hmmmm well I'll have to work on that as well!


We have developed a line of truly unique Catholic spiritual gift cards for these devotions: Daily Mass, Divine Mercy Chaplet, Holy Hour, Holy Rosary, Stations of the Cross, and Votive Candle.
We have also developed a line of truly unique Catholic spiritual prayer cards: A Meditation Before The Blessed Sacrament Laminated Prayer Card, Busy Person's Awareness Exercise Laminated Prayer Card, Saints Therese and Ignatius Laminated Healing Prayer Card, plus several other Laminated Prayer Cards.
We have also developed a religious pamphlet section which will expand over time. The first pamphlet is "The Work of the Holy Angels".
Thank you for shopping at Meljo's Artifacts.
Alternative 2 - Fill out the form below with respective card quantities and your name and addresss, click send, and an email will be sent to us with your request.
Either way, when the order is filled, an invoice will be mailed with the order and will include the price of the cards and envelopes, New York state sales tax, if applicable, and the shipping charge at cost.
A photocopy of the shipping bill will be included. Please pay by check or money order. Do not send cash.
The Divine Mercy Chaplet was revealed to Saint Faustina Kowalska in 1935 on the evening of the Exultation of the Holy Cross. She was a Polish religious sister of the Congregation of the Sisters of Our Lady of Mercy and canonized as a Catholic saint in 2000.
Many like to say the Chaplet at 3pm but it is not compulsory because the chaplet is an entirely different part of the Divine Mercy devotion and may be recited frequently.
Front: A story about how Our Blessed Mother appeared to Juan Diego in 1531 and, through him, requested the building of a church in her honor.
Back: A continuation of the story, a Prayer To End Abortion, and a picture of Our Lady of Guadalupe who is today honored as the Patroness of the Unborn.
Front: A biographical sketch about St. John Baptist de La Salle and a Prayer to St. John Baptist de La Salle.
Back: A picture of St. John Baptist de La Salle who is today honored as the Patron Saint of Teachers.
Front: A biographical sketch about Saint Margaret-Mary Alacoque to whom Our Lord in 1674 gave the devotion to the Sacred Heart of Jesus, the Act of Consecration to the Sacred Heart, and The Twelve Promises of the Sacred Heart to Saint Margaret-Mary.
Back: Pictured is stained glass window of the Sacred Heart located within the Sacred Heart Church of Cando, North Dakota.
Front: "How Busy Persons Find God in All Things--in less than 15 minutes"
Based on Ignatius of Loyola's "Awareness Examen"
Front: Couples are encouraged to pray together this traditional Prayer of a Husband and Wife, made available to us by The Columban Fathers, durring their mutual prayer time.
Front: A poem, titled "Whispers From Heaven"
Front: A poem, titled "My Life Depends On You"
The Opus Sanctorum Angelorum (Work of the Holy Angels) is an international movement recognized by the Church in order to promote devotion to and collaboration with the Holy Angels for our own sanctification and for the evangelization of the world.
PLEASE NOTE: Use <Tab> key or mouse clicks to navigate between fields.
Pressing the <Enter> key assumes that you have finished and will submit the order.


If being in our pajamas at 6pm is wrong, we don't want to be right. Our ultra soft and comfy ring-spun 100% cotton jersey tee is just what the doctor ordered for chill time at home with the people that matter. Made with Love in the USA and offered in several yummy colors! Order a size up for a relaxed fit.


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Beloved Actors of 'Welcome Back, Kotter' Sitcom Then and Now
January 24, 2020 | by Brittany
Over forty years have elapsed since the popular "Welcome Back, Kotter" ended, yet many of the actors are still remembered for their incredible work. From some reaching immense fame like John Travolta to others passing alway tragically, stay tuned for all the details! Here's a look at their lives after the film.
Image Credit: Getty Images
Mr. Kotter was famously portrayed by Gabe Kaplan on the show and contributed as the "Welcome Back, Kotter's" writer, having based his work on past experiences in underachieving academe. Kaplan appeared on film and television, including "Fast Break," "Nobody's Perfekt," "The Grand," and "Lewis & Clark."
He later ventured into a different career and became a financial investor and champion poker player. He told the Daily Bulletin of his poker success:
"I was like, the first celebrity who played any of these tournaments. I did very well at the time."
Among the show's cast, John Travolta, who played Vinnie Barbarino, found the most fame in Hollywood with many television and film credits to date. Travolta has since starred in "Saturday Night Fever," "Grease," "Pulp Fiction," "Hairspray," "American Crime Story: The People vs. O.J. Simpson."
In 1991, the actor married a fellow actress, Kelly Preston, and had two sons Jett, who passed away in 2009, and Benjamin, and a daughter named Ella Bleu. He has become a true legend in the acting and singing world and will never be forgotten! He is still an active actor today.
LAWRENCE HILTON-JACOBS
One of the delinquents in class, Freddie "BoomBoom" Washington, was played by Lawrence Hilton-Jacobs. Since the show ended, Jacobs appeared in "The Jacksons: American Dream" and sang as one of the backups in Rick James' "Superfreak" song. He was a funny character indeed!
ROBERT HEGYES
Robert Hegyes was cast as Juan on "Welcome Back, Kotter" and continued his career in the industry after. The actor starred in "Cagney and Lacey," "Diagnosis Murder," "NewsRadio," and "The Drew Carey Show." Hegyes also worked behind the camera as a writer and teacher. In 2012, at the age of 60, the actor passed away in his New Jersey home.
RON PALILLO
The fourth member to complete the group of delinquent kids was Arnold Horshack, who was played by Ron Palillo. The actor's career did not soar any further after the show came to a close, with only a few roles in some films and television series. Sadly, Palillo died from a heart attack in 2012.
MARCIA STRASSMAN
Marcia Strassman, wife Julie, also starred in "Honey, I Shrunk the Kids" and "Honey, I Blew Up the Kids." Although she did not become a prominent figure in Hollywood, Strassman appeared in other shows like "Noah Knows Best" and "providence." In 2007, the actress was diagnosed with breast cancer, sadly taking her life in 2014.
THINGS YOU DIDN'T KNOW ABOUT THE SHOW
Welcome Back, Kotter recently premiered on September 9, 1975. If you were not around at the time, it is difficult to understand just what a cultural sensation the sitcom quickly became. Though it lasted a mere four seasons — and after the third, most of the original writers as well as John Travolta and Kotter himself were largely absent.
TRAVOLTA AND HIS MERCH
The television series spawned merchandise. There were action figures, lunch boxes, comic books, board games and more. The Sweathogs were household names. Especially John Travolta. Still, there are probably some things you don't know about the hit comedy.
Travolta made his screen debut falling off a cliff on 'Emergency!' After moving to L.A. to pursue his acting career, a young Travolta landed his first credited television role on the second episode of the second season of Emergency!, "Kids." The soon-to-be-Sweathog plays a 16-year-old hiker who falls off a cliff. Yep, that's Barbarino lying on the ground.
SOME INTERESTING CHANGES
The "Up your nose with a rubber hose" insult was originally much cruder. The source material for the hit sitcom was Gabe Kaplan's stand-up routine "Holes and Mello-Rolls," which colorfully chronicled his formative days as a teenager in New Utrecht High School in Brooklyn.
That's the school building shown on the TV show as James Buchanan High. Kaplan's gang of real-life Sweathogs would toss around the insult, "Up your hole with a Mello-Roll," suggesting that one stick the pictured ice cream treat you know where. Obviously, it had to be cleaned up for network television.
The Boston affiliate did not air the first four episodes due to racial tensions in the city. In September of 1974, the city of Boston implemented a busing system to desegregate public schools. The mandated desegregation led to a series of riots. A year later, racial tension was still running high in the city.
The local ABC affiliate WCVB feared that a sitcom depicting an ethnically diverse classroom would stoke the flames, so it passed on airing the show. Four episodes later, Kotter was a rating hit around the country and WCVB relented. Who would've known it would turn out as it did!
SOME MUSICAL TRIVIA SURROUNDING THE SHOW
Lawrence Hilton Jacobs released a disco-soul album in 1978. Gabe Kaplan scored a novelty hit in 1976 with "Up Your Nose," while John Travolta scored a legitimate smash that same summer with his Top Ten single "Let Her In." Overlooked in the Kotter cast's extracurricular musical endeavors is Lawrence Hilton Jacobs.
The show would have simply been called 'Kotter' if anything rhymed with Kotter. Former Lovin' Spoonful frontman John Sebastian was tasked with writing the theme song to an upcoming comedy titled Kotter. The problem was he could not come up with any good words that rhymed with "Kotter."
So the singer penned a less specific mid-tempo number with a tinge of melancholy, "Welcome Back." The network loved the tune so much, the title of the series was changed to work in the name of the tune. Can you believe that the name of the show was changed because of this?!
RE-TITLED, RENAMED AND THREE ATTEMPTED RERUNS
The show was retitled 'The Saturday Night Boys' in Italy to capitalize on Travolta's cinema success. In 1980, one of Italy's two national networks decided air Welcome Back, Kotter due to the global success of Travolta. The actor was still riding high off Saturday Night Fever.
So the Italians renamed the series I ragazzi del sabato sera, or "The Saturday Night Boys," perhaps hoping to confuse audiences into thinking the two stories were related. Here, have a listen to the theme song, "Call Me in America" by Pop Welcome. Pretty cool hey!
Three spin-offs were attempted. Only one, Mr. T and Tina, made it to air on its own. Yet that Pat Morita sitcom, set up with a quasi-backdoor pilot in the season two premiere, "Career Day," lasted a paltry five episodes on air. The Chicago-set comedy flopped minus the Sweathogs.
Later that season, another backdoor pilot was attempted with "There Goes Number 5," which was planned to become a Horshack-centric spin-off titled Rich Man, Poor Man; Horshack! Oh, and the prior episode featured zero Horshack, to show the Sweathogs could live without him. They couldn't.
There was a bizarre, little-known crossover with 'Happy Days.' Happy Days took place in the 1950s and 1950s. Kotter was set in the late 1970s. That temporal hurdle meant little to Frank Lyndon. A member of the Bronx doo-wop group the Belmonts, Lyndon crafted one of the strangest novelty songs.
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THE BOOGIE BEAT
This, in an era of strange novelty songs, "The Fonzerelli Slide." The 1976 disco ditty appeared on the Fonzie Favorites album from Juke Box International record (that's the cover pictured). In what amounts to a skit acted out over a generic boogie beat, the Fonz rides his motorcycle into the Annual Sweathogs School Dance.
Horshack asks Fonzie if he arrived to become the new leader of the Sweathogs, which seems unlikely, as the Fonz should be pushing 40. Lyndon performs all the voices. We'll put it nicely and say he misses the mark. It's truly one of the stranger things you'll ever hear.
GUTTENBERG AND MARX
Steve Guttenberg briefly played Barbarino on television. The commercial for the Up Your Nose With A Rubber Hose board game utilized look-alikes. While the Barbarino fill-in, sitting on the far right, is hardly shown, that is indeed future Police Academy star Steve Guttenberg.
Groucho Marx was supposed to appear in the episode "Sadie Hawkins Day." Kaplan was a massive fan of Marx, and based the Sweathogs off the Marx Brothers dynamic. The comedy icon was invited to appear at the end of "Sadie Hawkins Day," yet when the actor, then in his mid-80s, showed up on set, he appeared too frail.
For over four years these characters and this show followed the life of a Gabe Kotter, who finds himself teaching a new class of rowdy and notorious kids. Kotter had to deal with the Sweathogs, an obnoxious group of students who are everything a teacher would describe as their worst nightmare. Do you remember this? Leave a comment below!
Sources: MeTV
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