Just as one actor portraying two different roles is distracting, two actors portraying the same character can hurt continuity. Of course there are scenarios that make sense to have two actors for the same character, such as when the character is shown at different ages, or when they are somehow disguised as another character (think Loki's illusions from "Thor: The Dark World").
Several notable characters have been played by different actors, such as Bruce Banner (Edward Norton and Mark Ruffalo) and James Rhodes (Terrance Howard and Don Cheadle). The reasons leading to characters not returning vary, so the following are several possible solutions.
Negotiate longer contracts. If Marvel requires several movies upon hiring an actor, with the terms laid out ahead of time, there would be fewer instances of actors not wanting to return.
Have major actors film a death scene. Right off the bat, have actors film a death scene. If an actor for some reason decides not return, kill the character instead of hiring a replacement. No character is too valuable. Tony Stark can still die while Iron Man lives on. Just have a new/different character carry on the mantle (Pepper Potts, James Rhodes, an original character).
Have legacy heroes. When an actor has fulfilled their contract, and they are ready to move on from Marvel films, have the character pass on the mantle, instead of rebooting/hiring another actor. In the comics, almost every character has passed on their mantle to someone else (ex. Miles Morales became Spider-Man, Sam Wilson and Bucky Barnes have both been Captain America).
It would be confusing if, while watching a movie, you saw Tom Hanks portray the main character, and a random side character. Marvel is essentially allowing this to happen in their cinematic universe. Although there are thousands of actors in the Marvel Cinematic Universe thus far (including actors in television shows and movies), there is no reason why an actor should play two distinct characters.
Is there really such a shortage of actors in the world that Marvel needs to hire the same actors to play different roles? Initially, repeat actors took the form of minor "extras" used in different movies. Most would be hard pressed to find these repeat actors without the help of the Internet Movie Database. However, this has become a bigger issue as of late. These repeat actors are no longer merely confined to non-speaking "extra" roles. Enver Gjokaj, who portrayed a minor character in "The Avengers," took on a major role in the TV series, Agent Carter. There are numerous other examples, and it seems Marvel is not shying away from this approach. Ken Choi, who portrayed Jim Moritta (one of the Howling Commandos) in "Captain America: The First Avenger," will be portraying Peter Parker's principal in "Spider-Man: Homecoming" next year. Alfre Woodard, portrayed a character named Miriam Sharpe (the woman who approached Tony Stark and blamed him for her son's death) in "Captain America: Civil War." She will also be portraying Mariah Dillard, a main character in the upcoming "Luke Cage" Netflix series.
Though these continuity errors may not be noticed by the casual viewer, many observant fans do notice, and the solution is quite simple; just hire the next person in line.
Movie trilogies provide for the perfect pacing of an extended story over several movies. Because Marvel's upcoming movie slate in 2017 indicates they intend to start releasing three movies a year, Marvel should use this pattern to their advantage.
Kevin Feige, the President of Marvel Studios, has indicated in the past that he wants to release an movie staring a "new" character every year. If Marvel intends for these "new" characters to launch their own trilogies (which should be the goal), then Marvel should aim to release a "new franchise" movie each year, along with a "second movie in the trilogy movie" for a different franchise, and a "final chapter of the trilogy" movie for yet another franchise.
If Marvel only intends to make three movies a year, they cannot keep launching potential franchise movies and hope to ever finish these trilogies. 2017 is actually the perfect launching point, because it fits the pattern I believe Marvel should strive to attain. In 2017, the Marvel Cinematic Universe film "Spider-Man: Homecoming" will be released. Additionally, "Guardians of the Galaxy, Vol. 2," the second movie in the series, will be released. Then, in November, "Thor: Ragnorak" will be released. This allows Marvel to finish franchises as they start them, which will lead to a more efficient use of the characters they have, and allow them to continue the franchise longer.
The Marvel Cinematic Universe's first television show, Agents of S.H.I.E.L.D, premiered to large audience numbers, but has been on the decline since. Many people tuned in to see if their favorite Avengers would make cameo appearances. After three full seasons, this has not been the case. The biggest crossovers from the movies were a handful of cameos by Nick Fury, Maria Hill, and Lady Sif.
A two or three episode story arc would be great to generate hype for upcoming movies, and would give actors an alternate way of promoting their movies beyond the typical talk show rounds. This would likely also work wonders for the television shows ratings. The MCU is now so expansive, that there are enough "main characters," that the show should regularly have an actor lined up for multi-episode event.
Because starring in a Marvel movie typically has a "boosting effect" on an actor's career, it is harder to get actors to agree to these cameos after they see their star-power rise. However, an actor who is more or less desperate for a big role likely would not mind taking a few weeks to film several episodes for a Marvel TV show.
Starting with the original Thor, the home video releases of Marvel movies have included short films ranging from a few minutes in length, up to about 15 minutes. These "One Shots" focused on more minor characters and helped flesh out the characters more. These short films have seemingly been discontinued, as no more have appeared since the home video release of "Thor: The Dark World."
These one shots are great for fleshing out characters, providing backstory, and explaining portions of movies that didn't get enough attention. These "One-Shots" would only boost DVD sales, and even if the sales boost doesn't seem financially worth it, the universe-expanding value shouldn't be disregarded altogether. The detailed, expansiveness of MCU makes fans clamor for more, such as with Lord of the Rings, Star Wars, Etc.
In many cases, these short films would be quite easy to create. Some of them have already been partially created or are currently in various phases of development. For example, there was an action scene that was never used for "Ant-Man," but was already completely filmed. With some very minor additions, that material could be packaged up and included on the Blu-Rays and DVDs. Another potential One Shot that has already been written is a scene from "Captain America: The Winter Soldier," in which Hawkeye was sent to take down Captain America. Unfortunately, Jeremy Renner's schedule didn't allow him to film the scene.
With literally thousands of actors already apart of the MCU, it seemingly wouldn't be that hard to find a few willing to film for a few days. With smartly written, well-budgeted One Shots, there is no financial reason that Marvel shouldn't be regularly producing these, and doing so would make for awesome additions to the MCU.
I would love to see the One Shots come back, they were a lot of fun and made me feel like I was getting extra value when I purchased the movies.


A row has erupted over Ecuador's presidential elections after early results indicate a victory for the incumbent party's candidate.
With more than 98% of the votes counted, Lenin Moreno of the Socialist Party had won 51.15% and his challenger Guillermo Lasso 48.85% of the votes.
Mr Lasso has demanded a recount, arguing electoral fraud was used to grant victory to his rival.
He has also called on his supporters to take to the streets in protest.
Three exit polls had earlier predicted a win for Mr Lasso, a conservative former banker, prompting celebrations by him and his supporters.
At 17:00 local time (22:00 GMT) Mr Lasso, waving an Ecuadorean flag, took to the stage in a hotel in his hometown of Guayaquil to tell his backers that "a new Ecuador" had been born.
But just hours later, when Ecuador's National Electoral Council announced that preliminary results gave Mr Moreno a narrow lead over Mr Lasso, the latter cried foul.
He tweeted that "we're not stupid, nor are the Ecuadorean people" and called on them to "peacefully defend their votes".
Hundreds of Mr Lasso's supporters gathered in front of the electoral commission offices shouting "No to fraud!".
Meanwhile, supporters of Mr Moreno also took to the streets, but to celebrate.
In the capital Quito, angry protests and jubilant celebrations were going on just kilometres apart.


The Union of Good, as a pro-Hamas radical Islamic umbrella worldwide coalition is committed and involved in the Hamas campaign to break the Israeli siege on Gaza. The Turkish IHH which, is part of the Union of Good, was set as the leading, prominent front organization among the coalition organizations participating in the 31 May flotilla to the Gaza Strip. The Turkish IHH (Insani Yardim Vakfi, IHH, "humanitarian relief fund") is known to be a radical Islamic organization, which was established in 1992 and formally registered in Istanbul in 1995. The IHH (outlawed by Israel in 2008) is part of the Union of Good, who was assigned to lead the Jihadi violent flotilla to Gaza.
The Italian ABSPP is active within the "Union of Good" as part of Hamas global fund-raising array. Since the early 1990s, Hamas succeeded in the gradual establishment of a financial array dispersed worldwide intended to assure the flow of money to fund its far-reaching activities in the Territories and abroad. The fund raising activity is secretive and discrete, as much as possible with regard to its link to Hamas.
ABSPP is an Italian charity committed in its activities to support the Palestinians in general but mainly to Hamas as a pro-Hamas oriented organization. The organization is directed by Mohamed Hannoun and is based in via bozaneto 19/1, Genova, Italy with offices in Rome and Milan.[v] Analysis of materials seized during IDF searches in offices and facilities of several Hamas charities acting as "humanitarian" front organizations in the West Bank proved that ABSPP was involved in providing funds and material support through these Hamas front charitable organization to suicide bomber's families in the West Bank.
Materials seized in the Jenin Zakat Committee, the Hebron Islamic Charitable Society (Hebron ICS), the Bethlehem Orphan Care Society, the Qalqilya Zakat Committee, and the Society for the Quran and Sunnah established the fact that they acted as Hamas front organizations, (banned by Israel). Letters, memos, bank records, receipt, and other materials proved their connections with ABSPP who provided donations and financial support to Hamas fronts. The funds transferred to the above mentioned Hamas charities were generated by ABSPP within the framework of the Union of Good as an umbrella organization. The money was also distributed in monthly payments to families of suicide bombers, prisoners and Hamas terrorists as a means of rewarding them for their terrorist efforts and as a "social security" program to the families.
The involvement of the Italian pro-Hamas ABSPP is well-known from recent events. In 2008, after the Israeli "Operation Cast Lead" in Gaza, it was reported by an Italian pro-Palestinian website that Dr. Mohammad Hannoun, head of the Palestinian Coalition in Italy, a founding member of the "European Committee Against the Siege", together with Monia Benini, the president of (Per il Bene Comune - PBC) the Union of Good branch in Italy, and the former Italian parliament senator Fernando Rossi have met Hamas leader, Khaled Mashal, in Damascus.[viii] Monia Benini and Fernando Rossi, who were planned to take part in the recent Gaza flotilla, have arrived Cyprus but did not board the "Mavi Marmara" ship due to the Cypriot governmental decision to ban the ship from docking in Cyprus ports.
The ABSPP, similar to IHH, is a pro-Palestinian Hamas oriented organization within the "Union of Good" known to serve Hamas goals and acts as a Hamas proxy, which appears to be committed to actively promote the Hamas agenda through its activities to break the Israeli blockade on Gaza. According to reports, it appears that Hamas is determined to continue in its intention to challenge again the Israeli decision makers and its navy by using the naval facet and dispatch a new convoy of ships towards Gaza. Despite the Israeli alleviations of checkpoints in Gaza (as a political concession to the USA and to international public opinion) after the violent confrontation on board the resent flotilla, Hamas wishes to continue its naval Jihad. The basic Israeli assumption that greater flow of good and supplies via checkpoints in Gaza would diminish Hamas motivation to continue its naval aid operations probably seems to have been an Israeli faulty assumption!
Notes:[i] http://www.infopal.it/leggi.php?id=14890 . (Accessed on 2 July 2010).
[iii] http://www.abspp.org/index.php?option=com_content&view=article&id=22&Itemid=16〈=it . (Accessed on 2 July 2010).


text:
Q: Background images that fill window? I have a background image and I want to fill the entire background of my site. I use:
-webkit-background-size: cover;
-moz-background-size: cover;
-o-background-size: cover;
background-size: cover;

The image is 2000px x 2000px. The problem is, when the browser window is larger than 2000px you can see a white background. Is there a way to get the image to strech if the browser window is large than the image?

A: First of all avoid using such large images as background if you are just using it for background .  You can do it like this  dont put everything to cover .Just in style tag put these two   and you will get it  .
background-image:url('your image');
background-size: cover;

Quality of image may be disturbed if it expands too much  but this works  .

A: Try this:
background-size: 100% auto;

A: background-size: cover; seems to work for me both ways -- both shrinking an image to fill if it's larger than the element, or stretching the image to fill if it's smaller. Here's an example of a small image stretching to fill a larger div: http://jsfiddle.net/MRSallee/Q7STG/

A: I prefer using this:
background: url(../img/url.jpg) no-repeat center center fixed;
-webkit-background-size: cover;
   -moz-background-size: cover;
     -o-background-size: cover;
        background-size: cover;

redpajama_set_name: RedPajamaStackExchange


To receive your FREE Buyer relocation information, please complete the form to the right.
Please specifiy what you are looking for and I will send you a FREE list of all properties that meet your search criteria.
I can also set up an account for you to automatically notify you by email of new listings that meet your criteria.
If you need immediate assistance, please call me at (928) 660-8092. Thanks!


text:
Nike Sport China & Hong Kong
Nike Event Visual
M vs W
Nike Running has released a regional campaign to express the will to unveil the unfair inception between genders in running. Nonetheless, only through competition under total fair circumstances, can prove that the capabilities of men and women in sport are really as far as you can go. Agency McCann helped to carry out the whole campaign. Sixstation was responsible to create an identity and art direction for it. In order to strengthen the sense that this is between men and women, a mutual situation, and a competition.
Therefore a logo which looks bold and firm; an impression of running tracks; same typographic treatment of "M" & "W". This identity has been applied thoroughly for all campaign items such as advertising; a specially designed interactive website by Pill&Pillow; launching event; mobile app Nike+; social media, Youtube promotional videos.
Own the Night
"Own the Night" is a seasonal campaign of Nike China. The original idea is to hold a sport gala that brings all the sport fanatics from different districts of Beijing together, to participate, to compete, to battle in the sport event from day to night.
This campaign celebrates the people of beijing. More importantly, celebrate the unique personalities and stories of their communities. And celebrate the people of who are fiercely proud of where they live.
We helped to visualize the identities of the four main districts of Beijing – Haidian, Chaoyang, Dongchen and Xicheng. The identities are summarized through deep studies of the places and are expressed in form of typographic.
TDC Design Gallery
The Grace of Creation

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
\subsection{Question Answering}
One of the most important characteristics of an intelligent system is to understand stories like humans do.
A story is a sequence of sentences, and can be in the form of plain text~\citep{trischler2017newsqa,rajpurkar2016squad,weston2016towards,yang2015wikiqa} or spoken content~\citep{tseng2016towards}, where the latter usually requires the spoken content to be first transcribed into text by automatic speech recognition~(ASR), and the model will subsequently process the ASR output.
To evaluate the extent of the model's understanding of the story, it is asked to answer questions about the story.
Such a task is referred to as question answering~(QA), and has been a long-standing yet challenging problem in natural language processing~(NLP).

Several QA scenarios and datasets have been introduced over the past few years.
These scenarios differ from each other in various ways, including the length of the story, the format of the answer, and the size of the training set.
In this work, we focus on context-aware multi-choice QA, where the answer to each question can be obtained by referring to its accompanying story, and each question comes with a set of answer choices with only one correct answer.
The answer choices are in the form of open, natural language sentences.
To correctly answer the question, the model is required to understand and reason about the relationship between the sentences in the story.

\subsection{Transfer Learning}
Transfer learning~\citep{pan2010survey} is a vital machine learning technique that aims to use the knowledge learned from one task and apply it to a different, but related, task in order to either reduce the necessary fine-tuning data size or improve performance. Transfer learning, also known as domain adaptation%
\footnote{In this paper, we do not distinguish conceptually between transfer learning and domain adaptation. A `domain' in the sense we use throughout this paper is defined by datasets.}, has achieved success in numerous domains such as computer vision~\citep{sharif2014cnn}, ASR~\citep{doulaty2015data,huang2013cross}, and NLP~\citep{zhang2017aspect,mou2016transferable}.
In computer vision, deep neural networks trained on a large-scale image classification dataset such as ImageNet~\citep{russakovsky2015imagenet} have proven to be excellent feature extractors for a broad range of visual tasks such as image captioning~\citep{lu2017knowing,karpathy2015deep,fang2015captions} and visual question answering~\citep{xu2016ask,fukui2016multimodal,yang2016stacked,antol2015vqa}, among others.
In NLP, transfer learning has also been successfully applied to tasks like sequence tagging~\citep{yang2017transfer}, syntactic parsing~\citep{mcclosky2010automatic} and named entity recognition~\citep{chiticariu2010domain}, among others.

\subsection{Transfer Learning for QA}
Although transfer learning has been successfully applied to various applications, its applicability to QA has yet to be well-studied.
In this paper, we tackle the TOEFL listening comprehension test~\citep{tseng2016towards} and MCTest~\citep{richardson2013mctest} with transfer learning from MovieQA~\citep{tapaswi2016movieqa} using two existing QA models.
Both models are pre-trained on MovieQA and then fine-tuned on each target dataset, so that their performance on the two target datasets are significantly improved.
In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7\%.

Transfer learning without any labeled data from the target domain is referred to as unsupervised transfer learning.
Motivated by the success of unsupervised transfer learning for speaker adaptation~\citep{chen2011constrained,wallace2009study} and spoken document summarization~\citep{lee2013unsupervised}, we further investigate whether unsupervised transfer learning is feasible for QA.

Although not well studied in general, transfer Learning for QA has been explored recently.
To the best of our knowledge, \citet{kadlec2016from} is the first work that attempted to apply transfer learning for machine comprehension.
The authors showed only limited transfer between two QA tasks, but the transferred system was still significantly better than a random baseline.
\citet{wiese2017neural} tackled a more specific task of biomedical QA with transfer learning from a large-scale dataset.
The work most similar to ours is by~\citet{min2017question}, where the authors used a simple transfer learning technique and achieved significantly better performance.
However, none of these works study unsupervised transfer learning, which is especially crucial when the target dataset is small.
\citet{golub2017synthesis} proposed a two-stage synthesis network that can generate synthetic questions and answers to augment insufficient training data without annotations.
In this work, we aim to handle the case that the questions from the target domain are available.

\section{Task Descriptions and Approaches}
Among several existing QA settings, in this work we focus on multi-choice QA~(MCQA).
We are particularly interested in understanding whether a QA model can perform better on one MCQA dataset with knowledge transferred from another MCQA dataset.
In Section~\ref{sec:MCQA-formal}, we first formalize the task of MCQA.
We then describe the procedures for transfer learning from one dataset to another in Section~\ref{sec:transfer}.
We consider two kinds of settings for transfer learning in this paper, one is supervised and the other is unsupervised.

\subsection{Multi-Choices QA}
\label{sec:MCQA-formal}
In MCQA, the inputs to the model are a story, a question, and several answer choices.
The story, denoted by~$\mathbf{S}$, is a list of sentences, where each of the sentences is a sequence of words from a vocabulary set~$V$.
The question and each of the answer choices, denoted by~$\mathbf{Q}$ and~$\mathbf{C}$, are both single sentences also composed of words from~$V$.
The QA model aims to choose one correct answer from multiple answer choices based on the information provided in~$\mathbf{S}$ and~$\mathbf{Q}$.

\subsection{Transfer Learning}
\label{sec:transfer}
The procedure of transfer learning in this work is straightforward and includes two steps.
The first step is to pre-train the model on one MCQA dataset referred to as the \textbf{source} task, which usually contains abundant training data.
The second step is to fine-tune the same model on the other MCQA dataset, which is referred to as the \textbf{target} task, that we actually care about, but that usually contains much less training data.
The effectiveness of transfer learning is evaluated by the model's performance on the target task.

\subsubsection*{Supervised Transfer Learning}
\label{sec:supervised}
In supervised transfer learning, both the source and target datasets provide the correct answer to each question during pre-training and fine-tuning, and the QA model is guided by the correct answer to optimize its objective function in a supervised manner in both stages.

\subsubsection*{Unsupervised Transfer Learning}
\label{sec:unsupervised}
We also consider unsupervised transfer learning where the correct answer to each question in the target dataset is not available.
In other words, the entire process is supervised during pre-training, but unsupervised during fine-tuning.
A self-labeling technique inspired by~\citet{lee2013unsupervised,chen2011constrained,wallace2009study} is used during fine-tuning on the target dataset.
We present the proposed algorithm for unsupervised transfer learning in Algorithm~\ref{alg:unsupervised-TL}.
\begin{algorithm}[!htbp]
  \renewcommand{\algorithmicrequire}{\textbf{Input:}}
  \renewcommand{\algorithmicensure}{\textbf{Output:}}
  \caption{Unsupervised QA Transfer Learning}
  \label{alg:unsupervised-TL}
  \begin{algorithmic}[1]
    \REQUIRE
      Source dataset with correct answer to each question; Target dataset without any answer; Number of training epochs.
    \ENSURE
      Optimal QA model~$M^{*}$
    \STATE Pre-train QA model~$M$ on the source dataset.
    \REPEAT
      \STATE For each question in the target dataset, use~$M$ to predict its answer.
      \STATE For each question, assign the predicted answer to the question as the correct one.
      \STATE Fine-tune~$M$ on the target dataset as usual.
    \UNTIL{Reach the number of training epochs.}
  \end{algorithmic}
\end{algorithm}

\section{Datasets}
\label{sec:data}
\begin{table*}[ht]
\centering
\scriptsize
{\renewcommand{\arraystretch}{1.4}
\begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{} & Source Dataset & \multicolumn{2}{c|}{Target Dataset} \\ \cline{2-4}
 & \multicolumn{1}{c|}{MovieQA} & \multicolumn{1}{c|}{TOEFL} & \multicolumn{1}{c|}{MCTest} \\ \hline
$\mathbf{S}$ & \begin{tabular}[c]{@{}c@{}}After entering the boathouse, the trio witness\\ Voldemort telling Snape that the elder Wand\\ cannot serve Voldemort until Snape dies ...\\ Before dying, Snape tells Harry to take his\\ memories to the Pensieve ...\end{tabular} & \begin{tabular}[c]{@{}c@{}}I just wanted to take a few minutes to meet\\ with everyone to make sure your class\\ presentations for next week are all in order\\ and coming along well. And as you know,\\ you're supposed to report on some areas\\ of recent research on genetics ...\end{tabular} & \begin{tabular}[c]{@{}c@{}}James the Turtle was always getting in\\ trouble. Sometimes he'd reach into the\\ freezer and empty out all the food ...\\ Then he walked to the fast food restaurant\\ and ordered 15 bags of fries. He didn't\\ pay, and instead headed home ...\end{tabular} \\ \hline
$\mathbf{Q}$ & What does Snape tell Harry before he dies? & Why does the professor meet with the student? & What did James do after he ordered the fries? \\ \hline
$\mathbf{C}_{1}$ & To bury him in the forest & \begin{tabular}[c]{@{}c@{}}To find out if the student is interested\\ in taking part in a genetics project\end{tabular} & went to the grocery store \\ \hline
$\mathbf{C}_{2}$ & That he always respected him & \begin{tabular}[c]{@{}c@{}}To discuss the student's experiment\\ on the taste perception\end{tabular} & \textbf{went home without paying} \\ \hline
$\mathbf{C}_{3}$ & To remember to him for the good deeds & \begin{tabular}[c]{@{}c@{}}\textbf{To determine if the student has selected}\\ \textbf{an appropriate topic for his class project}\end{tabular} & ate them \\ \hline
$\mathbf{C}_{4}$ & \textbf{To take his memories to the Pensieve} & \begin{tabular}[c]{@{}c@{}}To explain what the student should\\ focus on for his class presentation\end{tabular} & made up his mind to be a better turtle \\ \hline
$\mathbf{C}_{5}$ & To write down his memories in a book &  &  \\ \hline
\end{tabular}
}
\caption{
  Example of the story-question-choices triplet from MovieQA, TOEFL listening comprehension test, and MCTest datasets.~$\mathbf{S}, \mathbf{Q}$, and~$\mathbf{C}_{i}$ denote the story, question, and one of the answer choices, respectively.
  For MovieQA, each question comes with five answer choices; and for TOEFL and MCTest, each question comes with only four answer choices.
  The correct answer is marked in bold.
}
\label{tab:data-example}
\end{table*}

We used MovieQA~\citep{tapaswi2016movieqa} as the source MCQA dataset, and TOEFL listening comprehension test~\citep{tseng2016towards} and MCTest~\citep{richardson2013mctest} as two separate target datasets.
Examples of the three datasets are shown in Table~\ref{tab:data-example}.

\paragraph{MovieQA} is a dataset that aims to evaluate automatic story comprehension from both video and text.
The dataset provides multiple sources of information such as plot synopses, scripts, subtitles, and video clips that can be used to infer answers.
We only used the plot synopses of the dataset, so our setting is the same as pure textual MCQA.
The dataset contains 9,848/1,958 train/dev examples; each question comes with a set of five possible answer choices with only one correct answer.

\paragraph{TOEFL listening comprehension test}
is a recently published, very challenging MCQA dataset that contains 717/124/122 train/dev/test examples.
It aims to test knowledge and skills of academic English for global English learners whose native languages are not English.
There are only four answer choices for each question.
The stories in this dataset are in audio form.
Each story comes with two transcripts: manual and ASR transcriptions, where the latter is obtained by running the CMU Sphinx recognizer~\citep{walker2004sphinx} on the original audio files.
We use TOEFL-manual and TOEFL-ASR to denote the two versions, respectively.
We highlight that the questions in this dataset are not easy because most of the answers cannot be found by simply matching the question and the choices without understanding the story.
For example, there are questions regarding the gist of the story or the conclusion for the conversation.

\paragraph{MCTest} is a collection of 660 elementary-level children's stories.
Each question comes with a set of four answer choices.
There are two variants in this dataset: MC160 and MC500.
The former contains 280/120/240 train/dev/test examples, while the latter contains 1,200/200/600 train/dev/test examples and is considered more difficult.

The two chosen target datasets are challenging because the stories and questions are complicated, and only small training sets are available.
Therefore, it is difficult to train statistical models on only their training sets because the small size limits the number of parameters in the models, and prevents learning any complex language concepts simultaneously with the capacity to answer questions.
We demonstrate that we can effectively overcome these difficulties via transfer learning in Section~\ref{sec:experiments}.

\section{QA Neural Network Models}
\label{sec:models}
Among numerous models proposed for multiple-choice QA~\citep{trischler2016parallel,fang2016hierarchical,tseng2016towards}, we adopt the End-to-End Memory Network~(MemN2N)%
\footnote{MemN2N was originally designed to output a single word within a fixed vocabulary set. To apply it to MCQA, some modification is needed. We describe the modifications in Section~\ref{sec:memn2n}.}~\citep{sukhbaatar2015end}
and Query-Based Attention CNN~(QACNN)%
\footnote{\url{https://github.com/chun5212021202/ACM-Net}}~\citep{liu2017query}, both open-sourced, to conduct the experiments.
Below we briefly introduce the two models in Section~\ref{sec:memn2n} and Section~\ref{sec:qacnn}, respectively.
For the details of the models, please refer to the original papers.

\subsection{End-to-End Memory Networks}
\label{sec:memn2n}
An End-to-End Memory Network~(MemN2N) first transforms~$\mathbf{Q}$ into a vector representation with an embedding layer~$B$.
At the same time, all sentences in~$\mathbf{S}$ are also transformed into two different sentence representations with two additional embedding layers~$A$ and~$C$.
The first sentence representation is used in conjunction with the question representation to produce an attention-like mechanism that outputs the similarity between each sentence in~$\mathbf{S}$ and~$\mathbf{Q}$.
The similarity is then used to weight the second sentence representation.
We then obtain the sum of the question representation and the weighted sentence representations over~$\mathbf{S}$ as $\mathbf{Q}^\prime$.
In the original MemN2N, $\mathbf{Q}^\prime$ is decoded to provide the estimation of the probability of being an answer for each word within a fixed set.
The word with the highest probability is then selected as the answer.
However, in multiple-choice QA, $\mathbf{C}$ is in the form of open, natural language sentences instead of a single word.
Hence we modify MemN2N by adding an embedding layer~$F$ to encode~$\mathbf{C}$ as a vector representation $\mathbf{C}^\prime$ by averaging the embeddings of words in~$\mathbf{C}$.
We then compute the similarity between  each choice representation $\mathbf{C}^\prime$ and $\mathbf{Q}^\prime$.
The choice $\mathbf{C}$ with the highest probability is then selected as the answer.

\subsection{Query-Based Attention CNN}
A Query-Based Attention CNN~(QACNN) first uses an embedding layer~$E$ to transform~$\mathbf{S}, \mathbf{Q}$, and~$\mathbf{C}$ into a word embedding.
Then a compare layer generates a story-question similarity map~$\mathbf{SQ}$ and a story-choice similarity map~$\mathbf{SC}$.
The two similarity maps are then passed into a two-stage CNN architecture, where a question-based attention mechanism on the basis of~$\mathbf{SQ}$ is applied to each of the two stages.
The first stage CNN generates a word-level attention map for each sentence in~$\mathbf{S}$, which is then fed into the second stage CNN to generate a sentence-level attention map, and yield choice-answer features for each of the choices.
Finally, a classifier that consists of two fully-connected layers collects the information from every choice answer feature and outputs the most likely answer.
The trainable parameters are the embedding layer~$E$ that transforms $\mathbf{S}, \mathbf{Q},$ and~$\mathbf{C}$ into word embeddings, the two-stage CNN~$W_{CNN}^{(1)}$ and~$W_{CNN}^{(2)}$ that integrate information from the word to the sentence level, and from the sentence to the story level, and the two fully-connected layers~$W_{FC}^{(1)}$ and~$W_{FC}^{(2)}$ that make the final prediction.
We mention the trainable parameters here because in Section~\ref{sec:experiments} we will conduct experiments to analyze the transferability of the QACNN by fine-tuning some parameters while keeping others fixed.
Since QACNN is a newly proposed QA model has a relatively complex structure, we illustrate its architecture in Figure~\ref{fig:qacnn}, which is enough for understanding the rest of the paper.
Please refer to the original paper~\citep{liu2017query} for more details.
\label{sec:qacnn}
\begin{figure}[!htbp]
  \centering
  \includegraphics[scale=0.5]{qacnn.png}
  \caption{
    QACNN architecture overview.
    QACNN consists of a similarity mapping layer, a query-based attention layer, and a prediction layer.
    The two-stage attention mechanism takes place in the query-based attention layer, yielding word-level and sentence-level attention map, respectively.
    The trainable parameters, including~$E, W_{CNN}^{(1)}, W_{CNN}^{(2)}, W_{FC}^{(1)},$ and~$W_{FC}^{(2)}$, are colored in light blue.
  }
  \label{fig:qacnn}
\end{figure}

\section{Question Answering Experiments}
\label{sec:experiments}
\subsection{Training Details}
For pre-training MemN2N and QACNN on MovieQA, we followed the exact same procedure as in~\citet{tapaswi2016movieqa} and~\citet{liu2017query}, respectively.
Each model was trained on the training set of the MovieQA task and tuned on the dev set, and the best performing models on the dev set were later fine-tuned on the target dataset.
During fine-tuning, the model was also trained on the training set of target datasets and tuned on the dev set, and the performance on the testing set of the target datasets was reported as the final result.
We use accuracy as the performance measurement.

\subsection{Supervised Transfer Learning}
\subsubsection*{Experimental Results}
\begin{table}[!htbp]
\centering
\resizebox{\columnwidth}{!}{%
{\renewcommand{\arraystretch}{1.3}
\setlength\tabcolsep{2.0pt}
\begin{tabular}{|c|l|c|c|c|c|}
\hline
\multirow{2}{*}{Model}   & \multicolumn{1}{c|}{\multirow{2}{*}{Training}} & \multicolumn{2}{c|}{TOEFL} & \multicolumn{2}{c|}{MCTest} \\ \cline{3-6}
						 &                           & manual               & ASR                  & MC160                 & MC500    			   \\ \hhline{|=|=|=|=|=|=|}
\multirow{5}{*}{QACNN}   &(a) Target Only            & 48.9                 & 47.5                 & 57.5                  & 56.4                  \\ \cline{2-6}
                         &(b) Source Only            & 51.2          	    & 49.2                 & 68.1                  & 61.5                  \\ \cline{2-6}
                         &(c) Source + Target        & 52.5          	    & 49.7                 & 72.1                  & 64.6                  \\ \cline{2-6}
                         &(d) Fine-tuned (1)         & 53.4 (4.5)           & 51.5 (4.0)           & \textbf{76.4} (18.9)  & 68.7 (12.3)           \\ \cline{2-6}
                         &(e) Fine-tuned (2)         & \textbf{56.1} (7.2)  & \textbf{55.3} (7.8)  & 73.8 (16.3)           & \textbf{72.3} (15.9)  \\ \cline{2-6}
                         &(f) Fine-tuned (all)       & 56.0 (7.1)           & 55.1 (7.6)           & 69.3 (11.8)           & 67.7 (11.3)           \\ \hhline{|=|=|=|=|=|=|}
\multirow{3}{*}{MemN2N}  &(g) Target Only            & 45.2                 & 44.4                 & 57.2                  & 53.6                  \\ \cline{2-6}
                         &(h) Source Only            & 43.7                 & 41.9                 & 56.8                  & 52.3                  \\ \cline{2-6}
                         &(i) Source + Target        & 46.8          	    & 45.7                 & 60.4                  & 56.9                  \\ \cline{2-6}
                         &(j) Fine-tuned             & 48.6 (3.4)           & 46.6 (2.2)           & 66.7 (9.5)            & 62.8 (9.2)            \\ \hhline{|=|=|=|=|=|=|}
\multicolumn{2}{|c|}{\citet{fang2016hierarchical}}   & 49.1                 & 48.8                 & -                     & -                     \\ \hline
\multicolumn{2}{|c|}{\citet{trischler2016parallel}}  & -                    & -                    & 74.6                  & 71.0                  \\ \hline
\multicolumn{2}{|c|}{\citet{wang2015machine}}        & -                    & -                    & 75.3                  & 69.9                  \\ \hline
\end{tabular}
}}
\caption{
  Results of transfer learning on the target datasets.
  The number in the parenthesis indicates the accuracy increased via transfer learning~(compared to rows (a) and (g)).
  The best performance for each target dataset is marked in bold.
  We also include the results of the previous best performing models on the target datasets in the last three rows.
}
\label{tab:transfer-learning}
\end{table}

Table~\ref{tab:transfer-learning} reports the results of our transfer learning on TOEFL-manual, TOEFL-ASR, MC160, and MC500, as well as the performance of the previous best models and several ablations that did not use pre-training or fine-tuning.
From Table~\ref{tab:transfer-learning}, we have the following observations.

\paragraph{Transfer learning helps.}
Rows (a) and (g) show the respective results when the QACNN and MemN2N are trained directly on the target datasets without pre-training on MovieQA.
Rows (b) and (h) show results when the models are trained only on the MovieQA data.
Rows (c) and (i) show results when the models are trained on both MovieQA and each of the four target datasets, and tested on the respective target dataset.
We observe that the results achieved in (a), (b), (c), (g), (h), and (i) are worse than their fine-tuned counterparts (d), (e), (f), and (j).
Through transfer learning, both QACNN and MemN2N perform better on all the target datasets.
For example, QACNN only achieves 57.5\% accuracy on MC160 without pre-training on MovieQA, but the accuracy increases by 18.9\% with pre-training~(rows (d) vs. (a)).
In addition, with transfer learning, QACNN outperforms the previous best models on TOEFL-manual by 7\%, TOEFL-ASR~\citep{fang2016hierarchical} by 6.5\%, MC160~\citep{wang2015machine} by 1.1\%, and MC500~\citep{trischler2016parallel} by 1.3\%, and becomes the state-of-the-art on all target datasets.

\paragraph{Which QACNN parameters to transfer?} For the QACNN, the training parameters are~$E, W_{CNN}^{(1)}, W_{CNN}^{(2)}, W_{FC}^{(1)}$, and~$W_{FC}^{(2)}$~(Section~\ref{sec:qacnn}).
To better understand how transfer learning affects the performance of QACNN, we also report the results of keeping some parameters fixed and only fine-tuning other parameters.
We choose to fine-tune either only the last fully-connected layer~$W_{FC}^{(2)}$~while keeping other parameters fixed (row (d) in Table~\ref{tab:transfer-learning}), the last two fully-connected layers~$W_{FC}^{(1)}$ and~$W_{FC}^{(2)}$~(row (e)), and the entire QACNN~(row (f)).
For TOEFL-manual, TOEFL-ASR, and MC500, QACNN performs the best when only the last two fully-connected layers were fine-tuned; for MC160, it performs the best when only the last fully-connected layer was fine-tuned.
Note that for training the QACNN, we followed the same procedure as in~\citet{liu2017query}, whereby pre-trained GloVe word vectors~\citep{pennington2014glove} were used to initialize the embedding layer, which were not updated during training.
Thus, the embedding layer does not depend on the training set, and the effective vocabularies are the same.

\paragraph{Fine-tuning the entire model is not always best.} It is interesting to see that fine-tuning the entire QACNN doesn't necessarily produce the best result.
For MC500, the accuracy of QACNN drops by 4.6\% compared to just fine-tuning the last two fully-connected layers (rows (f) vs. (e)).
We conjecture that this is due to the amount of training data of the target datasets - when the training set of the target dataset is too small, fine-tuning all the parameters of a complex model like QACNN may result in overfitting.
This discovery aligns with other domains where transfer learning is well-studied such as object recognition~\citep{yosinski2014transferable}.

\paragraph{A large quantity of mismatched training examples is better than a small training set.}
We expected to see that a MemN2N, when trained directly on the target dataset without pre-training on MovieQA, would outperform a MemN2N pre-trained on MovieQA without fine-tuning on the target dataset~(rows (g) vs. (h)), since the model is evaluated on the target dataset.
However, for the QACNN this is surprisingly not the case - QACNN pre-trained on MovieQA without fine-tuning on the target dataset outperforms QACNN trained directly on the target dataset without pre-training on MovieQA~(rows (b) vs. (a)).
We attribute this to the limited size of the target dataset and the complex structure of the QACNN.

\subsubsection*{Varying the fine-tuning data size}
\begin{table}[!htbp]
\centering
\resizebox{\columnwidth}{!}{%
{\renewcommand{\arraystretch}{1.2}
\setlength\tabcolsep{3.0pt}
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Percentage of the target\\ dataset used for fine-tuning\end{tabular}} & \multicolumn{2}{c|}{TOEFL} & \multicolumn{2}{c|}{MCTest} \\ \cline{2-5}
                                                                                & manual       & ASR         & MC160        & MC500        \\ \hline
0                                                                                & 51.2     & 49.2    & 68.1      & 61.5     \\ \hline
25\%                                                                                & 53.9 (2.7)   & 52.3 (3.1)  & 70.3 (2.2)   & 65.6 (4.1)   \\ \hline
50\%                                                                                & 54.8 (0.9)   & 54.4 (2.1)  & 71.9 (1.6)   & 68.0 (2.4)   \\ \hline
75\%                                                                                & 55.3 (0.5)   & 54.8 (0.4)  & 72.5 (0.6)   & 71.1 (3.1)   \\ \hline
100\%                                                                                & 56.0 (0.7)   & 55.1 (0.3)  & 73.8 (1.3)   & 72.3 (1.2)   \\ \hline
\end{tabular}}
}
\caption{Results of varying sizes of the target datasets used for fine-tuning QACNN. The number in the parenthesis indicates the accuracy increases from using the previous percentage for fine-tuning to the current percentage.}
\label{tab:vary-target}
\end{table}
We conducted experiments to study the relationship between the amount of training data from the target dataset for fine-tuning the model and the performance.
We first pre-train the models on MovieQA, then vary the training data size of the target dataset used to fine-tune them.
Note that for QACNN, we only fine-tune the last two fully-connected layers instead of the entire model, since doing so usually produces the best performance according to Table~\ref{tab:transfer-learning}.
The results are shown in Table~\ref{tab:vary-target}%
\footnote{We only include the results of QACNN in Table~\ref{tab:vary-target}, but the results of MemN2N are very similar to QACNN.}.
As expected, the more training data is used for fine-tuning, the better the model's performance is.
We also observe that the extent of improvement from using 0\% to 25\% of target training data is consistently larger than using from 25\% to 50\%, 50\% to 75\%, and 75\% to 100\%.
Using the QACNN fine-tuned on TOEFL-manual as an example, the accuracy of the QACNN improves by 2.7\% when varying the training size from 0\% to 25\%, but only improves by 0.9\%, 0.5\%, and 0.7\% when varying the training size from 25\% to 50\%, 50\% to 75\%, and 75\% to 100\%, respectively.

\subsubsection*{Varying the pre-training data size}
\begin{table}[!htbp]
\centering
\resizebox{\columnwidth}{!}{%
{\renewcommand{\arraystretch}{1.2}
\setlength\tabcolsep{3.0pt}
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Percentage of MovieQA\\ used for pre-training\end{tabular}} & \multicolumn{2}{c|}{TOEFL} & \multicolumn{2}{c|}{MCTest} \\ \cline{2-5}
                                                                                & manual       & ASR         & MC160        & MC500        \\ \hline
0                                                                                & 48.9     & 47.6    & 57.5     & 56.4      \\ \hline
25\%                                                                                & 51.7 (2.8)   & 50.7 (3.1)  & 63.8 (6.3)   & 62.4 (6.0)   \\ \hline
50\%                                                                                & 53.5 (1.8)   & 52.3 (1.6)  & 67.3 (3.5)   & 66.7 (4.3)   \\ \hline
75\%                                                                                & 54.8 (1.3)   & 54.6 (2.3)  & 71.2 (3.9)   & 70.2 (3.5)   \\ \hline
100\%                                                                                & 56.0 (1.2)   & 55.1 (0.5)  & 73.8 (2.6)   & 72.3 (2.1)   \\ \hline
\end{tabular}}
}
\caption{Results of varying sizes of the MovieQA used for pre-training QACNN. The number in the parenthesis indicates the accuracy increases from using the previous percentage for pre-training to the current percentage.}
\label{tab:vary-source}
\end{table}
We also vary the size of MovieQA for pre-training to study how large the source dataset should be to make transfer learning feasible.
The results are shown in Table~\ref{tab:vary-source}.
We find that even a small amount of source data can help.
For example, by using only 25\% of MovieQA for pre-training, the accuracy increases 6.3\% on MC160.
This is because 25\% of MovieQA training set~(2,462 examples) is still much larger than the MC160 training set~(280 examples).
As the size of the source dataset increases, the performance of QACNN continues to improve.

\subsubsection*{Analysis of the Questions Types}
\begin{figure}[!htbp]
  \centering
  \includegraphics[scale=0.4]{questions-types-eps-converted-to.pdf}
  \caption{
    The performance of QACNN and MemN2N on different types of questions in TOEFL-manual with and without pre-training on MovieQA.
    `No' in the parenthesis indicates the models are not pre-trained, while `Yes' indicates the models are pre-trained on MovieQA.
  }
  \label{fig:different-types}
\end{figure}
We are interested in understanding what types of questions benefit the most from transfer learning.
According to the official guide to the TOEFL test, the questions in TOEFL can be divided into 3 types.
Type 1 questions are for basic comprehension of the story.
Type 2 questions go beyond basic comprehension, but test the understanding of the functions of utterances or the attitude the speaker expresses.
Type 3 questions further require the ability of making connections between different parts of the story, making inferences, drawing conclusions, or forming generalizations.
We used the split provided by~\citet{fang2016hierarchical}, which contains 70/18/34 Type 1/2/3 questions.
We compare the performance of the QACNN and MemN2N on different types of questions in TOEFL-manual with and without pre-training on MovieQA, and show the results in Figure~\ref{fig:different-types}.
From Figure~\ref{fig:different-types} we can observe that for both the QACNN and MemN2N, their performance on all three types of questions improves after pre-training, showing that the effectiveness of transfer learning is not limited to specific types of questions.

\subsection{Unsupervised Transfer Learning}
\begin{figure}[!htbp]
  \centering
  \subfigure[Results of TOEFL-manual and TOEFL-ASR]{
    \includegraphics[scale=0.19]{unsupervised-toefl.png}
    \label{fig:unsupervised-toefl}
  }\quad
  \subfigure[Results of MC160 and MC500]{
    \includegraphics[scale=0.19]{unsupervised-mctest.png}
    \label{fig:unsupervised-mctest}
  }
  \caption{
    The figures show the results of unsupervised transfer learning.
    The x-axis is the number of training epochs, and the y-axis is the corresponding testing accuracy on the target dataset.
    When training epoch = 0, the performance of QACNN is equivalent to row~(b) in Table~\ref{tab:transfer-learning}.
    The horizontal lines, where each line has the same color to its unsupervised counterpart, are the performances of QACNN with supervised transfer learning~(row~(e) in Table~\ref{tab:transfer-learning}), and are the upper-bounds for unsupervised transfer learning.
  }
\end{figure}
\begin{figure*}[!htbp]
  \centering
  \includegraphics[scale=0.57]{attention-maps.png}
  \caption{Visualization of the changes of the word-level attention map in the first stage CNN of QACNN in different training epochs. The more red, the more the QACNN views the word as a key feature. The input story-question-choices triplet is same as the one in Table~\ref{tab:data-example}.
  }
  \label{fig:attention-maps}
\end{figure*}
So far, we have studied the property of supervised transfer learning for QA, which means that during pre-training and fine-tuning, both the source and target datasets provide the correct answer for each question.
We now conduct unsupervised transfer learning experiments described in Section~\ref{sec:unsupervised}~(Algorithm~\ref{alg:unsupervised-TL}), where the answers to the questions in the target dataset are not available.
We used QACNN as the QA model and all the parameters~$(E, W_{CNN}^{(1)}, W_{CNN}^{(2)}, W_{FC}^{(1)},$ and~$W_{FC}^{(2)})$ were updated during fine-tuning in this experiment.
Since the range of the testing accuracy of the TOEFL-series~(TOEFL-manual and TOEFL-ASR) is different from that of MCTest~(MC160 and MC500), their results are displayed separately in Figure~\ref{fig:unsupervised-toefl} and Figure~\ref{fig:unsupervised-mctest}, respectively.

\subsubsection*{Experimental Results}
From Figure~\ref{fig:unsupervised-toefl} and Figure~\ref{fig:unsupervised-mctest} we can observe that without ground truth in the target dataset for supervised fine-tuning, transfer learning from a source dataset can still improve the performance through a simple iterative self-labeling mechanism.
For TOEFL-manual and TOEFL-ASR, QACNN achieves the highest testing accuracy at Epoch 7 and 8, outperforming its counterpart without fine-tuning by approximately 4\% and 5\%, respectively.
For MC160 and MC500, the QACNN achieves the peak at Epoch 3 and 6, outperforming its counterpart without fine-tuning by about 2\% and 6\%, respectively.
The results also show that the performance of unsupervised transfer learning is still worse than supervised transfer learning, which is not surprising, but the effectiveness of unsupervised transfer learning when no ground truth labels are provided is validated.

\subsubsection*{Attention Maps Visualization}
To better understand the unsupervised transfer learning process of QACNN, we visualize the changes of the word-level attention map during training Epoch 1, 4, 7, and 10 in Figure~\ref{fig:attention-maps}.
We use the same question from TOEFL-manual as shown in Table~\ref{tab:data-example} as an example.
From Figure~\ref{fig:attention-maps} we can observe that as the training epochs increase, the QACNN focuses more on the context in the story that is related to the question and the correct answer choice.
For example, the correct answer is related to ``class project''.
In Epoch 1 and 4, the model does not focus on the phrase ``class representation'', but the model attends on the phrase in Epoch 7 and 10.
This demonstrates that even without ground truth, the iterative process in Algorithm~\ref{alg:unsupervised-TL} is still able to lead the QA model to gradually focus more on the important part of the story for answering the question.

\section{Conclusion and Future Work}
In this paper we demonstrate that a simple transfer learning technique can be very useful for the task of multi-choice question answering.
We use a QACNN and a MemN2N as QA models, with MovieQA as the source task and a TOEFL listening comprehension test and MCTest as the target tasks.
By pre-training on MovieQA, the performance of both models on the target datasets improves significantly.
The models also require much less training data from the target dataset to achieve similar performance to those without pre-training.
We also conduct experiments to study the influence of transfer learning on different types of questions, and show that the effectiveness of transfer learning is not limited to specific types of questions.
Finally, we show that by a simple iterative self-labeling technique, transfer learning is still useful, even when the correct answers for target QA dataset examples are not available, through quantitative results and visual analysis.

One area of future research will be generalizing the transfer learning results presented in this paper to other QA models and datasets.
In addition, since the original data format of the TOEFL listening comprehension test is audio instead of text, it is worth trying to initialize the embedding layer of the QACNN with semantic or acoustic word embeddings learned directly from speech~\citep{chung2018speech2vec,chung2017learning,chung2016audio} instead of those learned from text~\citep{mikolov2013distributed,pennington2014glove}.

redpajama_set_name: RedPajamaArXiv


text:
Average home prices in Cambridge fall $250,000 from peak as cool-down continues
Sales volume down as well, but number of listings is on the rise
By Brent DavisRecord Reporter
Thu., July 7, 2022timer2 min. read
CAMBRIDGE — The average price for homes sold in Cambridge last month has dropped more than $250,000 since the peak in February.
When local real estate markets hit new highs that month, the average price for all residential properties sold in Cambridge passed the million-dollar mark for the first time.
But prices have pulled back ever since, thanks in part to rising interest rates and greater inventories.
The overall June average stood at $795,299, according to new statistics from the Cambridge Association of Realtors. Despite four months of declining prices, that still represents an increase of 5.6 per cent over June 2021.
A total of 191 homes were sold last month through the association's Multiple Listing Service system, a drop of 33.4 per cent year over year.
Sales volume was 20.6 per cent below the five-year average for June, and 21.4 per cent below the 10-year average.
"Home sales continue to moderate in the face of rising interest rates, and we expect this trend to continue through most of the remainder of the year, with more rate hikes on the horizon," association president Val Brooks said in a release.
High demand and low supply has stymied potential buyers for months on end. The good news is that more and more homes are coming onto the market.
There were 402 new residential listings in Cambridge last month, the most the association has ever seen for June, and an increase of 19.6 per cent over last year.
There were 293 homes available for sale at the end of the month, up more than 83 per cent year over year. That brings months of inventory in Cambridge to 1.5, up considerably from the 0.6 months seen one year ago.
Detached house prices in Kitchener, Waterloo down nearly $300,000 from February frenzy
Number of months of inventory represents how long it would take to sell the current inventory of available homes at the current pace.
"New listings have done an about-face from the beginning of the year and are now coming onto the market at a rapid pace," Brooks said.
"While overall inventories are sitting at historically subdued levels, they have lifted off from rock bottom and now stand back where they were at roughly the same time last year."
Brent Davis is a Waterloo Region-based general assignment reporter for The Record. Reach him via email: bdavis@therecord.com


text:
Tag archive for 'Healthcare.gov'
By Brandon Jones On Thursday, January 2nd, 2014
Justice Sonia Sotomayor delays birth control mandate in Obamacare
Supreme Court Justice Sonia Sotomayor granted a temporary injunction to a group of Catholic nonprofit groups, including the Little Sisters of the Poor Home for the Aged, from the required coverage of contraceptives. photo More...
By Laura Jones On Monday, December 30th, 2013
Obamacare abortion pill mandate delayed for Christian universities
A federal judge has granted an injunction to a pair of Christian universities to delay the abortion pill mandate in Obamacare. East Texas Baptist University, Houston Baptist University and Westminster Theological More...
By Robert On Sunday, December 29th, 2013
Lindsey Graham, a divided South Carolina and the Mike Huckabee endorsement
Politics in the Palmetto State has the very "red" state divided in the Republican Party when it comes to incumbent Senator Lindsey Graham, so much so that several challengers have come out to oppose More...
By Brandon Jones On Friday, December 20th, 2013
Biggest Political Hypocrites of 2013: Marco Rubio on Obamacare, Mitch McConnell on shutdown
Many critics would target the President of democrats over different sound bites or comments during 2013, but two major hypocritical moves by two high profile republicans were extremely significant. Photo/Congressional More...
By Robert On Thursday, December 19th, 2013
Michelle Obama recounts Sasha contracting meningitis while promoting Obamacare in radio interview
On this morning's airing of "The Yolanda Adams Morning Show", host Yolanda Adams had an exclusive interview with the First Lady of the United States, Michelle Obama. The president, first lady Michelle More...
By Brandon Jones On Thursday, December 19th, 2013
Barbara Walters confesses to Piers Morgan: we thought President Obama would be 'the next messiah'
Barbara Walters appeared on CNN's Piers Morgan show Tuesday night and managed to make headlines with her controversial comment. "We thought that he was going to be – I shouldn't say this at Christmastime, More...
By Brandon Jones On Monday, December 9th, 2013
MSNBC host Melissa Harris-Perry says Obamacare is 'derogatory' term, compares it to N-Word
MSNBC host Melissa Harris-Perry began her program over the weekend with a commentary about the Affordable Care Act's nickname: "Obamacare." Melissa Harris-Perry, no stranger to hyperbole, drew attention to More...
By Brandon Jones On Wednesday, December 4th, 2013
Obamacare: George Will warns of employers dumping workers' health care, 'All Hell is going to break'
One of the biggest attacks on the Obamacare rollout was the immediate loss of health insurance policies and conservative commentator George Will says more may be on the way. photo Andrew Aliferis via Flickr In More...
By Butter Bracco On Tuesday, December 3rd, 2013
Liberty University loses Supreme Court challenge on Obamacare mandate
The US Supreme Court rejected Monday a lawsuit by Liberty University challenging the constitutionality of the Affordable Care Act's, or "Obamacare's," mandate that employers with more than More...
By Kaye Wonderhouse On Wednesday, November 27th, 2013
President Obama defends Obamacare on Seattle, Los Angeles and San Francisco visit
During a three-day Western swing through Seattle, San Francisco and Los Angeles, Obama bragged on Obamacare, the accomplishments it has and will bring and promised the "glitches" will go away. "Yes, More...
Still no Obamacare signups in Oregon, the exchange website 'has never worked'
Oregon may have a state full of Obamacare supporters, but not one individual has been able to sign up through the exchange because the website reportedly "has never" worked. photo LaDawna Howard via Flickr Reuters More...
By Brandon Jones On Wednesday, November 20th, 2013
Libertarian candidate Lucas Overby discusses campaigning for Congress, Obamacare and the debt
EXCLUSIVE: Lucas Overby made some headlines in Florida as the Libertarian Party rallied behind the candidate to ensure his presence on the special election ballot after falling just short of the required signatures More...
By Robert On Monday, November 18th, 2013
Nancy Pelosi's bizarre defense of Obamacare
On Sunday Morning's Meet The Press with David Gregory, former House Speaker Nancy Pelosi (D-CA) was live to talk about the failures of the Affordable Care Act, or Obamacare. During the Congresswoman's More...
By Brandon Jones On Sunday, November 17th, 2013
House passes bill to allow Americans to keep their insurance, 39 Democrats vote with GOP
39 Democrats joined House Republicans in passing a new bill which would allow insurance carriers an option of extending coverage for patients through 2014 due to the new restrictions outlined in Obamacare. The famous More...
By Brandon Jones On Thursday, November 14th, 2013
President Obama urges insurance companies to delay cancellations until after election
In a speech to respond to critics, President Obama spoke on the broken promise to Americans on keeping their insurance as the Obamacare rollout continues to be plagued with problems and complaints. President Obama More...
By Brandon Jones On Saturday, November 9th, 2013
Dylan Ratigan 'Thanx Mr President' after tweeting Obamacare price hikes
Former MSNBC host Dylan Ratigan fell victim to Obamacare and tweeted his displeasure this week. Ratigan's health insurance plan had been canceled due to the Affordable Care Act and he doesn't seem pleased More...
By Brandon Jones On Wednesday, November 6th, 2013
Jay Carney snaps during Obamacare questioning over misleading information on registering: 'I give up'
White House press secretary Jay Carney and ABC News reporter Jon Karl got into a tense back-and-forth with Karl pressing Carney to explain the continued failure of the Obamacare website. photo White House press More...
By Robert On Monday, November 4th, 2013
Tea Party Express Chair, Amy Kremer, endorses Ken Cuccinelli in Virginia
Amy Kremer, a founder of the modern day tea party movement and Chairman of Tea Party Express, has announced her endorsement of Ken Cuccinelli for Governor of Virginia. Kremer released the following statement: Ken More...
By Robert On Saturday, November 2nd, 2013
Free market health care is alive and well at the Surgery Center of Oklahoma
Health care in the United States has been under a lot of government interference and oversight since the Great Society and since that time, prices have skyrocketed. Surgical instrumentsImage/defenseimagery.mil. Today, More...
By Brandon Jones On Saturday, November 2nd, 2013
Obamacare contraceptive mandate dealt a blow by appeals court
An appeals court ruling appears have struck a blow to the contraceptive mandate from Obamacare when the court ruled that it infringed on religious freedom. The ruling Friday by the U.S. Court of Appeals for the More...


Meet the sweetest little Miss Avery Taylor and see how happy she was during her Austin lifestyle newborn session! I first met her parents during a family portrait session a couple years ago. All of the "kids" at the session were adults, and they were having an updated family portrait made that included their parents. Everyone showed up on a cold December morning. We had a great time chatting and photographing along Lady Bird Lake.
Flash forward to this year. Suzanne contacted me to say that she and her husband (who are photography enthusiasts, one of the pups is even named Leica!) are expecting a baby girl this summer. I am thrilled they thought of me! It's great to catch up with them, see their new house, and to see them be PARENTS! There has been no bigger change in my own life than becoming a mother, and it's really neat to see other people make this transition.
I think the most excited ones for the session was the puppies! They were constantly hopping on the bed, sniffing the baby on the quilt, check on her in her crib, jumping on the dresser to get a better look at her. They were visibly distressed when they couldn't get near her. Avery couldn't have asked for better companions to have at home with the family! And while e're here, let's talk about her cute room. The beautiful light, check. Brightly colored quilt, check. And if you look closely at the shelves you'll even see baby Star Wars books! If I ever have another kiddo (a long shot), I'll be asking Suzanne and Dan to come in and help me add some flare to the room!
This might be my favorite Austin lifestyle newborn session. Can we have good light, sweet pups and fun quilts at every session?!


Psychiatrist Laura Dabney, MD dropped me a line a couple of weeks back asking if she could contribute to Chipur. I gladly took her up on the offer and we quickly agreed upon a topic: the meds-therapy chat.
To best address the issue, I'm bringing you the bulk of two articles written by Dr. Dabney. And, of course, I'll be sticking my two-cents in here and there.
Now, we're going looong here, so get comfy. I think you'll find Dr. Dabney's perspective relevant and interesting. Heck, you may even find it controversial.
Oh, almost forgot. Dr. Dabney practices in Virginia Beach, VA and has been at it for some twenty years. She's board certified in psychiatry and has been featured on radio and in print media, and she consults for a number of institutions, including the Virginia VA Medical Center. Dr. Dabney's practice includes meds evaluations, therapy, and coaching. Live & Online Skype sessions are available.
Psychiatric drugs have, for decades, benefited the seriously mentally ill and eased the suffering of millions. And psychopharmacology, the study of these drugs' effects on the brain, has enabled numerous life-changing treatment options.
Yet, despite these advances for the field of psychiatry as a whole, the idea of throwing pills at our problems has been proven ineffective, and its time is coming to an end.
Psychiatric medications are so regularly prescribed that one of the questions most often asked of me by new patients is how long I need to talk to them before writing a prescription.
They expect the same experience in my office that so many have had with other doctors in the past – they feel unsettled, they seek help, they get pills. And while prescriptions may offer temporary relief of symptoms, patients are not always aware that they also come with very serious risks.
Patients need to know that psychiatric medications may cause alarming side effects. As a medical doctor and therapist, I have seen first-hand the harmful effects of our dependence on medication for resolving emotional issues. Counter to current treatment trends, the use of medication alone increases the duration, and sometimes the intensity, of common emotional problems.
Often, medication simply mask the symptoms, a course of treatment that would be deemed unacceptable in any other field of medicine. For example, few people would be satisfied to treat the headaches caused by a brain tumor with ibuprofen alone. Rather, we would seek to eradicate the problem at its source.
The same should be no less true for our emotional health issues because, unlike acute mental health conditions such as schizophrenia and some bipolar disorders, common complaints such as anxiety and depression are often not based on biology. And yet, most patients seeking help for these conditions are being treated with biology-altering pharmaceuticals. The condition clearly does not warrant the intervention.
The most effective course of treatment, by far, is what I have used with patients in my practice for nearly twenty years: psychodynamic therapy.
To treat the real source of emotional problems, we must discover its source, a process to which therapy is expertly suited. With the right guide, a patient can be led to discoveries about their history, their beliefs and their feelings, all of which, in turn, affect the success of their daily lives and relationships.
When we discover how we got to today's state, we can change the path towards tomorrow.
Despite being an MD, I avoid prescribing medications as often as I can, and when they are necessary, I use them very cautiously. As physicians, we must guide our patients in the knowledge that medication alone is not a treatment, it's simply a mask.
The symptoms and the illness are not the same thing. The illness exists long before the symptoms. Rather than being the illness, the symptoms are the beginning of its cures. The fact that they are unwanted makes them all the more a phenomenon of grace – a gift of God, a message from the unconscious, if you will, to initiate self-examination and repair.
Tricia was in her fifties by the time she sought my care, though not because she'd been procrastinating. She'd been trying to get help for decades, having started and stopped treatment with practitioners up and down the east coast for nearly thirty years.
It took a suicide attempt with a handful of pills to get her into my office, but once there, Tricia unraveled the story of a childhood filled with physical and sexual abuse and parental neglect. Her childhood experiences were so extreme, they warped everything she saw, everything she did, and her relationships with everyone she met.
Nor is the past able to control her any longer. While running away was, for decades, the only way she says she knew how to deal with emotion, she now has control over her life.
Absolutely, the meds-therapy chat will go on and on, as, yeah, there's so much personally at stake for those of us enduring a mood or anxiety disorder. And, of course, our opinion, and how we'll behave in kind, will be grounded in our collections of informed perspective.
Hey, be sure to head over to Dr. Dabney's site and check-out her work and services.
And speaking of perspective, find all sorts of it in hundreds of Chipur articles. Isn't it time to review the titles?
Next Post: If You Don't BREATHE You Die. If You Don't BREATHE CORRECTLY You'll Feel Like It.
Previous Post: It Ain't Goin' Anywhere Soon. So Isn't It Time to TELL the KIDS?


Household Products sensory experts choose RedJade's sensory analysis software because RedJade is not only a comprehensive sensory and consumer insights software application, it is also easy to learn and use. If your Household Products Company is a multi-national organization or a local company, RedJade offers you the power to collect, analyze, share and understand data.
Multinational Household Products companies deal with unique challenges with respect to multi-language support, time-zone management and a web of consumer privacy laws. RedJade understands these challenges and makes navigating each of them extremely simple. From integrated scheduling, multi-language support and translation to complying with worldwide consumer privacy laws, RedJade allows Household Products experts to focus on what are important: understanding your consumers and your products.


During her Freshman year at Drew University, Mason Scher was on the hunt for an appropriate organization to fulfill her required 100 volunteer hours that would also spark her interest. In speaking with friends, she discovered that GSWA offers environmentally-based internships. Over the years, GSWA has provided internships to dozens of bright, environmentally-minded college students.
Working fast and figuring things out on the fly.
The ability to talk with many different people, like at the gala.
I don't really see things as challenges. I'm presented with a task, and I figure out how to get it done. If I had to pick something, I'd say that as a Sophomore, I haven't completed a lot of my upper level courses that may have helped me along the way.
It turns out I love teaching – I didn't see that coming!
I always enjoyed working outside; this internship solidified that.
GSWA is a multi-faceted organization that accomplishes so much with a relatively small group of people.
Mason is working towards her double major in Environmental Science and Chemistry, and is also a member of the Civic Scholarship Program. Her plan is to become a Geochemist, working for the government, studying global climate systems from a molecular perspective. Now that's specialized! We feel fortunate to have benefited from Mason's bright mind and energetic, can-do spirit, while helping her gain the practical skills she will need in her journey to reach these very impressive goals!


text:
Virginia residents pay their respects at a makeshift memorial for reporter Alison Parker, 24, and cameraman Adam Ward, 27, at the gate of WDBJ television studios.
Trump on shooting of journalists: 'This isn't a gun problem'
Donald Trump lamented the state of mental illness in the United States on Thursday, calling it "just a massive problem," one of many the country has.
But Wednesday's shooting that killed two journalists in Virginia and injured another woman represented "a mental problem," not "a gun problem," Trump said in an interview on CNN's "New Day."
Instead of focusing on gun control, Trump said, as president he would focus more on dealing with mental health issues.
"This was a very sick man, and it's just too bad that we can't figure these things out beforehand. I mean, everybody sees the signals, but nobody thinks a thing like this could happen," Trump said of the shooting that claimed the lives of reporter Alison Parker and cameraman Adam Ward on live television. "They see people and they think they're disturbed, but what are you going to do, put them in jail for the rest of their life? You know, put him in jail for the rest of his life 'cause he looks a little bit off, and he turned out to be very off."
Asked whether he would do something about the ease with which people are able to acquire guns, Trump said he would not make it more difficult for "sane people" to get them.
"Well I don't think I would, because this is really a sick person. This isn't a gun problem. This is a mental problem," Trump said in reference to the perpetrator of the crime, who later succumbed to a self-inflicted gunshot wound. "It's a very complex situation. I'm a very strong Second Amendment person."
Video: Donald Trump on shooting of journalists: 'This isn't a gun problem'
Citing Chicago's tough gun laws and its rampant crime, Trump suggested that fewer guns in the hands of law-abiding citizens does not make anyone safer.
Addressing the situation further, Trump said he knew the area where the shooting happened, and the "people are amazing," noting that it has been "very inspiring to watch" the journalists' colleagues react.
It's unfortunate that no one saw the issues with the former station employee and did something about them, Trump said.
"And even if they did, what would you do? You'd say, 'Oh, he said he's going to blow up.' Probably hear that all the time, but in this case, it happened. Too bad that we can't figure it out beforehand, but it's a very tough thing to do," he added.


text:
Treatment subjects
The data controller under the laws in force is Spina Pierangelo, who can be contacted via the CONTACTS section (link at the bottom of the page).
Legal basis of processing
This site processes data mainly on the basis of user consent. Consent is given via the banner at the bottom of the page, or through the use or consultation of the site, as a conclusive behaviour. By using or consulting the site, visitors and users approve the privacy policy (link) and consent to the processing of their personal data in the manner and for the purposes described below, including the possible disclosure to third parties if necessary for the provision of a service. Further consents relating to the specific purpose of the service are collected via the communication or service request forms.
The provision of data and therefore consent to the collection and processing of data is optional, the User may deny consent, and may revoke consent already provided at any time (via the banner at the bottom of the page or the browser settings for cookies, or the Contact Us link). However, denial of consent may result in the inability to provide certain services and your browsing experience on the site would be impaired.
Data for site security and prevention of abuse and SPAM, as well as data for the analysis of site traffic (statistics) in aggregate form, are processed on the basis of the legitimate interest of the Data Controller in the protection of the site and the users themselves. In such cases, the user always has the right to object to the processing of the data (see section on User Rights).
Data provided for consultancy or professional assignments are processed on the basis of the fulfilment of a contract and the fulfilment of a legal obligation. In such cases, a separate notice is provided.
The processing of data collected by the site, in addition to the purposes connected, instrumental and necessary to the provision of the service, is aimed at the following purposes:
– Statistics (analysis)
Collection of data and information in an exclusively aggregate and anonymous form in order to verify the correct functioning of the site. None of this information is related to the physical person-user of the site, and do not allow in any way their identification. Consent is not required.
– Security Policy
Collection of data and information in order to protect the security of the site (spam filters, firewalls, virus detection) and of the Users and to prevent or unmask fraud or abuse to the detriment of the website. The data is recorded automatically and may possibly also include personal data (IP address) that could be used, in accordance with the laws in force on the subject, to block attempts to damage the site itself or to cause damage to other users, or in any case harmful or criminal activities. Such data are never used to identify or profile the User and are deleted periodically. Consent is not required.
– Ancillary activities
Communicate data to third parties who perform functions necessary or instrumental to the operation of the service (e.g. comment box), and to allow third parties to perform technical, logistical and other activities on our behalf. Suppliers only have access to personal data that is necessary to perform their tasks, and agree not to use the data for any other purpose, and are required to process personal data in accordance with applicable regulations.
Data collected
This site collects data from users in two ways.
– Data collected in an automated manner
The following information may be collected during the Users' navigation and is stored in the log files of the site's server (hosting):
– internet protocol (IP) address;
– browser type;
– parameters of the device used to connect to the site;
– name of the internet service provider (ISP);
– date and time of the visit;
– the visitor's source (referral) and exit web pages;
– possibly the number of clicks.
This data is used for statistical and analysis purposes, in aggregate form only. The IP address is used solely for security purposes and is not cross-referenced with any other data.
– Data provided voluntarily
The site may collect other data in the event of voluntary use of services by users, such as comment services, communication (contact forms, comment box), booking and will be used exclusively for the provision of the requested service:
– identification data (Name, surname, CF);
– email address;
– any further data sent spontaneously by the user.
The data are processed at the headquarters of the Data Controller, and at the datacenter of the web Hosting Register. The web hosting Register, is responsible for the treatment, processing the data on behalf of the owner. Register is located in the European Economic Area and acts in accordance with European standards (Register's privacy policy).
Period of data retention
The data collected by the site during its operation are kept for the time strictly necessary to carry out the specified activities. On expiry, the data will be deleted or anonymized, unless there is no further purpose for retaining the data.
Data (IP address) used for site security purposes (blocking attempts to damage the site) is stored for 30 days.
Data for analytics purposes (statistics) are stored in aggregate form for 24 months.
Transfer of collected data to third parties
The data collected by the site are generally not provided to third parties, except in specific cases: legitimate request by judicial authorities and only in cases provided for by law; when it is necessary for the provision of a specific service requested by the User; for carrying out security checks or site optimisation.
Transfer of data to non-EU countries
This site may share some of the data collected with services located outside the European Union. In particular with Google, Facebook and Microsoft (LinkedIn) via social plug-ins and the Google Analytics service. The transfer is authorised on the basis of specific decisions of the European Union and the Italian Data Protection Authority, in particular Decision 1250/2016 (Privacy Shield – here the information page of the Italian Data Protection Authority), so no further consent is required. The companies mentioned above guarantee their adherence to the Privacy Shield.
We process the data of visitors/users lawfully and correctly, taking appropriate security measures to prevent unauthorised access, disclosure, modification or destruction of data. We are committed to protecting the security of your personal data during transmission, using Secure Sockets Layer (SSL) software, which encrypts information in transit. The processing is carried out using computer and/or telematic tools, with organisational methods and logics strictly related to the purposes indicated. In addition to the Data Controller, in some cases, the data may be accessed by categories of employees involved in the organisation of the site or by external parties (such as third-party technical service providers, hosting providers).
This site uses cookies, text files that are recorded on the user's terminal or that allow access to information on the user's terminal. Cookies make it possible to store information on visitors' preferences, are used to check that the site is working properly and to improve its functionality by customising the content of the pages according to the type of browser used, or to simplify navigation by automating procedures (e.g. login, site language), and finally to analyse how visitors use the site.
This site makes use of the following categories of cookies:
– technical cookies, used for the sole purpose of carrying out the transmission of an electronic communication, to ensure the correct display of the site and navigation within it. In addition, they make it possible to distinguish between the various users connected in order to provide a service requested of the right user (Login), and for site security reasons. Some of these cookies are deleted when the browser is closed (session cookies), others have a longer lifetime (such as the cookie needed to store the user's consent to the use of cookies, which lasts 1 year). No consent is required for these cookies;
– analysis cookies, used directly by the site operator to collect information, in aggregate form, on the number of users and how they visit the site. They are assimilated to technical cookies if the service is anonymised.
– profiling and marketing cookies, used exclusively by third parties other than the owner of this site to collect information on users' browsing behaviour, interests and consumption habits, also in order to provide personalised advertising.
By clicking OK on the banner on the first access to the site or by browsing the site, the visitor expressly consents to the use of cookies and similar technologies, and in particular to the recording of these cookies on his terminal equipment for the purposes indicated above, or to access via cookies to information on his terminal equipment.
You may refuse the use of cookies and may revoke a consent already given at any time. Since cookies are linked to the browser used, they CAN BE DEACTIVATED DIRECTLY FROM THE BROWSER, thus refusing/revoking consent to the use of cookies, or via the banner at the bottom of the page.
DISABLING COOKIES MAY PREVENT THE CORRECT USE OF CERTAIN FUNCTIONS OF THE SITE, in particular, services provided by third parties may not be accessible, and therefore may not be viewable:
videos from YouTube or other video sharing services;
social network buttons;
room reservations;
Instructions for disabling cookies can be found on the following web pages:
Mozilla Firefox – Microsoft Internet Explorer – Microsoft Edge – Google Chrome – Opera – Apple Safari
This site also acts as an intermediary for third party cookies (such as social network buttons), which are used to provide additional services and functionality to visitors and to simplify the use of the site itself, or to provide personalised advertising. This site has no control over their cookies which are entirely managed by third parties and has no access to the information collected through these cookies. Information on the use of these cookies and their purposes, as well as on how to disable them, is provided directly by the third parties on the pages indicated below.
It should be noted that user tracking does not generally involve user identification, unless the User has already subscribed to the service and is also already logged in, in which case it is understood that the User has already given his/her consent directly to the third party at the time of subscribing to the relevant service (e.g. Facebook).
This website uses cookies from the following third parties.
– Google Ireland ltd
For information on the use of data and its processing by Google Ireland ltd, we recommend that you read Google's privacy policy and Google's Privacy Policy when using partner sites or apps.
– Google Analytics: used to analyse how users use the site, to compile reports on site activity and user behaviour, to see how often users visit the site, how the site is tracked and which pages are visited most frequently. The information is combined with information collected from other sites in order to create a comparative picture of the use of the site in relation to other sites in the same category.
Data collected: browser identification, date and time of interaction with the site, page of origin, IP address.
Place of data processing: European Union as the anonymisation of the service is active.
The data collected do not allow personal identification of users, and are not cross-referenced with other information relating to the same person. They are processed in aggregate form and anonymised (truncated to the last octet). On the basis of a specific agreement (DPA), Google Inc. (data controller) is prohibited from cross-referencing this data with data from other services.
Further information on Google Analytics cookies can be found on the page Google Analytics Cookie Usage on Websites.
The user can selectively disable (opt out) the collection of data by Google Analytics by installing the appropriate component provided by Google on his browser (opt out).
– Youtube: platform, owned by Google, for sharing videos. The cookies are set when the page containing the embed is accessed, and when the video is started, and do not allow the User to be identified unless he or she is already logged into the Google profile.
For the videos present on the site, the "advanced privacy (no cookies)" option has been activated, which ensures that YouTube does not store information about visitors unless they voluntarily play the video.
Data collected: number and behaviour of users of the service, IP address, information linking visits to the site to the Google account for logged in users, video viewing preferences.
Place of data processing: USA.
– Beddy: channel manager for managing online bookings. Data collected: customer details, booking period, number of persons, payment data. Place of data processing: Italy.
Social Network Plugins
This website also incorporates plugins and/or buttons to enable easy sharing of content on your favourite social networks. When you visit a page on our website that contains a plugin, your browser connects directly to the servers of the social network from which the plugin is loaded, which server can track your visit to our website and, if appropriate, associate it with your social network account, particularly if you are logged in at the time of your visit or if you have recently browsed one of the websites containing social plugins. If you do not wish the social network to record data relating to your visit to our website, you must log out of your social network account and, probably, delete the cookies that the social network has installed in your browser.
Plugins are installed on this website with advanced privacy protection features for Users, which do not send cookies or access cookies on the User's browser when the page is opened but only after the plugin is clicked.
The collection and use of information by these third parties is governed by their own privacy policies, which you should refer to.
– Facebook (link to Cookie Policy)
– Twitter (link to cookie policy)
– LinkedIn (link to cookie policy)
Pursuant to European Regulation 679/2016 (GDPR), the User may, in the manner and within the limits provided by current legislation, exercise the following rights:
oppose in whole or in part, for legitimate reasons, the processing of personal data concerning him/her for the purpose of sending advertising or direct sales material or for carrying out market research or commercial communication;
request confirmation of the existence of personal data concerning him/her (right of access);
to know its origin
receive intelligible communication of such data
obtain information on the logic, methods and purposes of the processing;
request the updating, rectification, integration, cancellation, transformation into anonymous form, blocking of data processed in violation of the law, including those no longer necessary to achieve the purposes for which they were collected;
in cases of processing based on consent, to receive, at the sole cost of any support, your data provided to the data controller, in a structured and machine-readable form and in a format commonly used by an electronic device;
the right to lodge a complaint with the supervisory authority;
as well as, more generally, to exercise all the rights recognised to him/her by the laws in force.
Requests should be addressed to the Data Controller.
INFORMATION ON THE PROCESSING OF PERSONAL DATA
The user's personal data are used by Thermen Chalet, which is the data controller, in compliance with the principles of personal data protection established by the GDPR 2016/679 Regulation and the national legislation in force.
SOURCE OF PERSONAL DATA
The collection of personal data is carried out by Thermen Chalet by registering the data:
collected directly from the interested party, at the time of the initial contact or subsequent communications
METHODS AND PURPOSES OF DATA PROCESSING
We inform you that the data will be processed with the support of the following means:
with the following purposes
Management of overnight reservations
Fulfilment of fiscal or accounting obligations
Customer management (contracts, orders, invoices)
The provision of data is compulsory for all that is required by legal and contractual obligations and therefore the refusal to provide them in whole or in part may make it impossible to provide the services requested.
The legal bases on which the processing for common data is based, according to Art.6 of the GDPR Regulations, are:
Law;
Contract;
The legal basis on which processing for special categories of personal data is based, according to Art.9 of the GDPR Regulation, is:
Without prejudice to communications carried out in fulfilment of legal and contractual obligations, all data collected and processed may be communicated exclusively for the purposes specified above to the following categories of recipients:
Banks and credit institutions;
Authorised persons;
In the management of your data, the following categories of authorised persons and/or internal and external managers identified in writing and to whom specific written instructions on the processing of data have been provided may also become aware of the same:
Pierangelo Spina – Administrator of Thermen Chalet, VAT no. ATU76052147, Maibrunnenweg 8 A-9546 – Bad Kleinkirchheim (Spittal/Drau), Austria
Daily Creative Studio, VAT no. 02284260037 – Website manager
PERIOD OF CONSERVATION
The data retention period is: 10 years
RIGHTS OF THE INTERESTED PARTY
Pursuant to European Regulation 679/2016 (GDPR) and the national legislation in force, the data subject may, in accordance with the procedures and within the limits provided for by the legislation in force, exercise the following rights:
request confirmation of the existence of personal data concerning him/her (data subject's right of access – Article 15 of Regulation 679/2016);
know its origin
receive intelligible communication of it;
have information about the logic, methods and purposes of the processing;
request the updating, rectification, integration, cancellation, transformation into anonymous form, blocking of data processed in violation of the law, including data no longer necessary to achieve the purposes for which they were collected (right of rectification and cancellation – Articles 16 and 17 of Regulation 679/2016);
right to restrict and/or object to the processing of data concerning him/her (Art. 18 of Regulation 679/2016);
right of revocation;
the right to lodge a complaint with the Supervisory Authority (data subject's right of access – Article 15 of Regulation 679/2016).
The data controller of your personal data is Pierangelo Spina
Email: info@thermenchalet.at
Thermen Chalet, in the person of Legal Representative Pierangelo Spina


text:
Лапниани () — село в Грузии, в муниципалитете Лагодехи края Кахетия.

География
Село расположено в восточной части края, в 20 километрах по прямой к юго-западу от центра муниципалитета Лагодехи. Высота центра — 240 метров над уровнем моря.

Население
По данным переписи населения 2014 года, в селе проживало 118 человек.

Литература

Примечания

Населённые пункты Лагодехского муниципалитета

redpajama_set_name: RedPajamaWikipedia


text:
Q: Sqoop options to load a LOB column from rdbms to Hive I was trying to load LOB column (VARCHAR(28000)) from DB2 database to hive, I am using below sqoop options, but not getting success.
1.
--table
schema.tbl_name
        -m
        1
        --fetch-size
        1000

--map-column-java
                HVBA02_PRT_LIST_X=BINARY
                --map-column-hive
                HVBA02_PRT_LIST_X=BINARY
2.
--table
schema.tbl_name
        -m
        1
        --fetch-size
        1000
--map-column-java
                col_name=String
                --map-column-hive
                col_name=String


As a writer, it's natural that words would grab at my attention and spark my imagination a thousand times more vividly than any picture would. So when I discovered a game that appealed to my pirate fandom as well as my artistic side, I was already hooked before I even started playing. I'm talking about Choice of the Pirate.
Reminiscent of those "Choose Your Own Adventure" books I was so fond of as a teenager, this game is a text adventure. There are no images at all -- you'll have to use your love of the written word and your imagination to go through this adventure.
Thankfully, the writing in this game is very immersive. I was engrossed by the descriptions of the various scenes. The settings are vivid and very typical of the imaginary pirate's life. You really feel as if you are there, inside the story. The choices are yours to make and the adventure unfolds based on your decisions.
You go through quite a few pages of interesting background building before the game inquires about your gender and name at all. Why not get a better feel of the story before adding yourself to it? I ended up molding my story a bit on the dark side. I wasn't going to be a goodie-two-shoes with a perfectly acceptable background story. I'm a pirate, after all!
Right as you start the game, you realize that you are in the eye of the storm: two rivals vessels are dueling. Their captains are about to be at each other's' throats. You're serving as crew member for one of them, but this isn't where your ambition stops. However, we are in the here and now and you will have to make some important decisions through this battle. Are you faithful to your Captain, or do you cheer for the other team?
I fear I'm repeating myself, but you will have many choices to make through the game and they may not always align with your moral compass at first. You will need to deal with the immediate, and sometimes delayed, consequences of your decisions throughout your adventures.
I found the game was absorbing all my attention. It reads like a novel where you can actually take the actions you would like the hero to take, so it has a very intimate aspect. I feel connected to Choice of the Pirate, as if I was truly a part of the story.
If you are a fan of the genre, or simply curious, you'd do well to entertain yourself with this title.
Reminiscent of those "Choose Your Own Adventure" books, this game is a text adventure. You will embody a pirate, gender of your choosing, and lead them through adventures at sea.