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redpajama_set_name: RedPajamaGithub


text:
ESWS Organisation boosts local business in rural economy
A growing Shropshire brewery has said it would not have been able to expand without the help of an organisation which provides support to the rural economy.
The Three Tuns Brewery, based in Bishop's Castle, is the oldest working brewery in Britain and received its first brewing licence in 1642.
But in order to expand from its Grade II listed building, the brewery has needed the help of Enterprise South West Shropshire, based in Enterprise House in the centre of Bishop's Castle.
The organisation provides a huge range of facilities for the rural community as well as many services for local businesses.
Kieran Jones, operations director at the Three Tuns Brewery, said without ESWS, the business would not be one tenth of the size it is today.
He said: "At Enterprise House, we have our offices from where we have five members of staff operating.
"We also have storage for our cask barrels that we deliver to pubs and our online packing area."
The brewery was hugely affected by the Covid Pandemic but Mr Jones said ESWS stepped in to help.
"With Covid, our income went to nothing," he said. "It all came to a dramatic halt but ESWS helped us with rent payments and were really helpful landlords."
Previously, the Three Tuns Brewery sold its cask barrels direct to pubs but launched an online shop in December to tackle the problems caused by Covid.
"Our online shop has helped us get up and going again," Mr Jones said.
The Three Tuns Brewery moved its offices to Enterprise House about four years ago and has gradually expanded as additional space has become available.
"We were previously using office space in a house but we needed somewhere more professional to scale up and Enterprise House was one of the only available spaces in the area," Mr Jones said.
"ESWS has been fantastic with our needs as a business. We are growing and they have helped us scale up with extra space. We also use them for printing and we use their meetings rooms regularly.
"Without ESWS, we would be in a right mess. In the brewery, we have no storage space. We would not be a tenth of the size we are now without ESWS."
Enterprise House rents out buildings to other local businesses in the area, which otherwise would struggle to stay in Bishop's Castle, according to ESWS manager Rob McKay.
He said: "The Enterprise House site is critical to employment in the town, and we rent space to a number of small businesses.
"There are more than 150 local businesses using ESWS on a regular basis from artists to farmers to the local garage to the brewery. We are used for administrative purposes such as publishing or printing and to those businesses, our services can be vital. Our services are also used by countless local interest groups and individuals."
Matt Maddox, 34, of Hurdley, near Bishop's Castle, also credits ESWS with helping his business expand.
He set up MM Fabrications, a welding fabrication business, at Enterprise House more than seven years ago after he decided to take the plunge and launch his own business - but he said he couldn't have done it without Enterprise South West Shropshire.
He said he chose Enterprise House in which to base his business as the size of the unit was great for starting out and the rent wasn't a big jump to begin paying straight away.
"ESWS really helped me when I first started. The rent for the first six months was reduced, which was brilliant," he said.
"Now they still help me as I use their facilities to print off drawings as well as using their admin and IT services. They are really great for advice and very helpful.
"Without ESWS, I would not be as professional as I am now. For new businesses setting up, without Enterprise House, there really is nowhere to set up unless you go out of Bishops' Castle. It's very hard for start up businesses."
In the past seven years, Matt's business has gone from strength to strength. He started carrying out sculptural commissions and ornamental gates and railings but now manufactures, supplies and installs a wide range of items from balconies, staircases, automated gates and structural steelwork, from small repair jobs to larger scale batch manufacturing for numerous businesses in mild steel, stainless and aluminium, carried out by coded welders.
He employs two full-time members of staff and another part-time and from September, he will be taking on a college apprenticeship.
He said: "It's brilliant to be able to take on an apprentice as that is where it all began for me. It's come full circle."
Mr McKay said ESWS aims to help as many local businesses as possible, especially those that are looking to get started.
He said: "ESWS has provided 25 years of business advice and support in the local community.
"There are 13 Businesses and Community Organisations based at Enterprise House with more than 25 people working from the site at the Heart of Bishops Castle.
"We are also looking forward to helping businesses grow into the new Business Park which is such an exciting and important development for the region."
ESWS
MM Fabrications
Three Tons Brewery
Enterprise House.

redpajama_set_name: RedPajamaCommonCrawl


Our city wide Dial-a-Ride service is available to residents of Bristol who are unable to access conventional public transport and operates from Monday to Friday, 9am to 5pm.
Fares are charged at a similar rate to conventional public bus services and you can travel free of charge with a Bristol City Council Travelcard (bus pass) just as you would on a conventional bus service - just show your pass to the Driver on each journey.
You decide where you want to go, on what day and when. If we can't provide transport to your exact needs, we will discuss alternatives with you to support you with your travel plans.
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Alternatively, why not join online today using our online membership form?


Paul Goldbart said when he first started his college career in physics, he occasionally felt he was not fit for it because he found it difficult. Decades later, he has a doctorate in physics from Imperial College London and is now the new dean for the College of Natural Sciences at UT.
Goldbart said he knew he wanted to be a theoretical physicist when he was about 13 years old.
Goldbart's appointment as the Dean of CNS began Aug. 1. As an undergraduate student, Goldbart attended Cambridge University in England and later obtained his master's degree from the University of California, Los Angeles. He said he has always loved the environment of university campuses. For 41 years, he has been a part of research and faculty at multiple universities.
Before coming to campus this month, Goldbart was the College of Sciences dean at the Georgia Institute of Technology. During his time at UT, Goldbart said he wants to make sure CNS students are constantly inspired by the concepts they learn in the classroom.
Dave Thirumalai, chair of the CNS chemistry department, said he has known Goldbart for at least 20 years as a scientist and thinks the college is lucky to have Goldbart as the new dean.
The CNS is the largest college at UT with more than 12,000 students. Joanna Chyu, president of UT Senate's Natural Sciences Council, said she would like to work with Goldbart to make sure all CNS students feel connected to the CNS administrators and the Natural Sciences Council members.
Goldbart said developing an understanding of complex subjects requires openly asking questions.


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Working with an Editor is one of the needed parts of getting your story read to publish.
Today we welcome Adi Alsaid, the author of Somewhere Over the Sun. He has graciously written a guest post for us about his experience working with editors on his novel. For those of you who are afraid to work with an editor or who don't know what possible benefit an editor can give you in the preparation of your book, read on. Don't forget to visit his website and check out his novel.
I had the incredible fortune of having two brilliant ladies work with me as editors throughout the writing of my debut novel (one stopped having the time to give me detailed notes about halfway through, although her occasional tweets assured me that she was still reading, still holding me accountable for quality, and that a glass of wine perfectly accompanied my novel). The one who stopped is an old friend, an incredibly talented writer with the education and the knowhow to not only move my commas around and put a leash on my fragments, but someone who had no problems scoffing at my mediocrity or lauding my greatness.
Editor number two was a very new acquaintance at the time, someone whom I trusted with my manuscript because she obviously shared my passion and love for language, had an English degree from a well-respected university, had real-world publishing experience and big-six editorial aspirations, was obviously extremely intelligent and hard-working, and to be perfectly frank, she was someone I simply wanted to be around often, someone whose opinion of my writing, for whatever reason, mattered to me.
I am eternally grateful and indebted to both of them, my dear personal editors, for their insights, dedication, time, genuine care for my work and a billion other ways they helped make my novel better.
I could have just powered through the first draft, then sent it off to them and wait for the notes before continuing on with re-writes. But receiving their notes throughout the writing process helped me improve parts of the novel that had yet to be written. Thanks to them, I was rewriting before my words even showed up on the computer screen, I was holding myself up to a higher standard before even sending them anything to correct. Kurt Vonnegut once made a distinction between two kinds of writers: "Swoopers write a story quickly…then they go over it again painstakingly, fixing everything that is just plain awful or doesn't work. Bashers go one sentence at a time, getting it exactly right before they go on to the next one." I was a basher, and they were the safety net of a future that swoopers rely on. If I let something slip that was not good enough, I was confident that the next set of notes from them would include the comment, "tighten" and remind me to not be vague.
The beauty of working with an editor.
After three months, I had completed my first draft. I took a two-day break then I re-wrote for several hours every single day for a month straight, clicking back and forth between editors' notes and the chapter-by-chapter run down of strengths and weaknesses. I went through every single comment, sometimes being a stubborn artist and ignoring a minor suggestion or three, but more often than not, I placed my trust in their judgment, and there is no doubt in my mind that my novel is better off for it.
I'm incredibly proud of what I've accomplished in Somewhere Over the Sun and I think that is a testament not necessarily just to my novel, but also to the painstaking, passionate and knowledgeable contributions of my editors, without whom my book would, quite simply, not be as good.
Adi Alsaid graduated from the University of Nevada, Las Vegas with a degree in Marketing, but spent the majority of his time there reading and writing fiction. Somewhere Over the Sun is his debut novel and was written in Monterey, CA. He was born and raised in Mexico City to Israeli parents whose love and support made this book possible. Adi is usually unsure of how long he will remain at any given address, but chances are he is living somewhere in the northwestern hemisphere. He hopes this book brings his readers even a sentence's worth of happiness.
The story follows Alan, a spirited young writer with a wandering imagination who has discovered that the stories he writes are suddenly coming to life. At the suggestion of his loving father, Alan embarks on a quixotic journey to visit friends and use his newfound gift to write them all happier lives.


You'll get the most out of your boiler and central heating if you use the controls effectively.
You might even find it's worth upgrading to more accurate, modern heating controls.
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"15% of teens have received sexts"
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text:
\section{Introduction}

Multi-subject or multi-condition experiments are the workhorse of
bio-medical imaging research, whether it be drug development or basic
research. Imaging provides a wealth
of information on the biomedical problem at hand. However the typical
sample size is too small to fully exploit this information. For
this reason, investigators often turn to previous studies in order to formulate
hypotheses and restrict the search space, \emph{i.e.} select a subset of
anatomical or functional structures of interest to the current study.
A typical case is that of early-stage clinical trials, for which the
group size is very small, but that are most often based on previous
results concerning the pathology under study.
However, understanding the literature is increasingly difficult
and requires a systematic approach, that takes the form of a
\emph{meta-analysis}, pooling results from multiple experiments
that address a set of related research hypotheses \cite{Sutton2000}.

In particular, brain imaging studies heavily rely on such meta-analyses
\cite{Wager2009},
as the brain is still an ill-understood and complex organ.
In functional Magnetic Resonance Imaging (fMRI) studies, typical group
sizes range from 10 to 20 subjects, which
is not always enough to warrant the reliability of brain-wide analysis\cite{Thirion2007}.
More importantly, the time that can be spent in the scanner by subjects is limited, and
not all interesting experimental conditions will be acquired.%
For this reason, it is common practice to reduce the
study to a set of regions of interest (ROIs) extracted from the
literature.
Investigators define these ROIs by extracting locations of peak activations
from the literature \cite{Yarkoni2011}, or from coordinate databases such as
BrainMap \cite{Laird2005b}.
While most of these meta-analyses are conducted on activation coordinates, the
increase of data sharing opens the door to meta-analysis on full brain
images which results in higher statistical power  \cite{Salimi-Khorshidi2009}.
Previous statistical and modeling work on meta-analysis for fMRI has
focused on better modeling of the reference database
\cite{Wager2009}.

In this work, we are interested in the generalization power of meta-analyses
on new data.
We introduce a new meta-analysis method using a reference
database of images to guide statistical analysis of a new dataset. In
particular we rely on predictive models, useful to learn biomarkers,
and use them to select relevant voxels in order to increase the statistical
power of a new study.

\section{Methods}

\paragraph{Problem setting}
We start from a reference database made of $l$ experiments, each
comprising $n^l$ contrasts possibly acquired over multiple subjects.
We denote the brain images by $\B{X}^{l} \in \mathbb{R}^{n^l\!,p}$ with
associated experimental condition $\B{y}^{l} \in \mathbb{R}^{n^l}$. Given a
new experiment, denoted \emph{target}, $(\B{X}^{\star}, \B{y}^{\star}) \in
(\mathbb{R}^{n^\star\!,p}, \mathbb{R}^{n^\star})$, we are interested in
two problems: i) \emph{(biomarkers)} can we predict $\B{y}^{\star}$ from
$\B{X}^{\star}$? ii) \emph{(inference)} can we test hypotheses on the
links between
$\B{y}^{\star}$ and $\B{X}^{\star}$, for instance in a linear model?
These are ill-posed problems from the statistics standpoint, as
$n^\star \ll p$. The root of the problem is the dimensionality of the
data: medical images are composed of many voxels, typically $p \approx 50\,000$ with fMRI.
This large number of descriptors
limits statistical inference power due to multiple testing; a problem
that appears in predictive approaches as the curse of dimensionality.
Here, we use our reference database to better condition this statistical problem.

\paragraph{Transfer learning}
The gist of our approach is to learn on some experiments of our
database $(\B{X}^{l}, \B{y}^{l})$ discriminative models that
contain predictive information for the target experiment
$(\B{X}^{\star}, \B{y}^{\star})$. In machine learning, this problem is
known as \emph{transfer learning} \cite{Pan2010}. The underlying
assumption of transfer learning is the same as that for meta-analysis:
the reference database should contain some common information with the
target experiment. Here we use a simple form of transfer learning:
we train a linear classifier on an experiment in the database that is
similar from the neuroscientific point of view to the new
data, and use it to predict the labels of the new data.

\paragraph{Selecting predictive features}
We use a sparse linear classifier, specifically an $\ell_1$-penalized
logistic regression. The motivation behind this choice of classifier is
that it produces a sparse set of weights that can be used to select
relevant voxels. In particular, under certain conditions, the classifier
can recover with high probability the complete set of $k$ features in $\B{X}$ that
are predictive of $\B{y}$ for a sample size of $n_\text{min} = \OO{k \log
p}$ \cite{Bach2010}. The logarithmic dependence in $p$ is an appealing
property in view of the dimensionality of medical imaging datasets.

In practical situations, it can be hard to control the errors on this
feature selection, in particular as it depends on the choice of the
amount of $\ell_1$ penalty. For this reason,
Meinshausen and B{\"u}hlmann \cite{Meinshausen2010}
introduce randomized variants of sparse estimators, that can be seen as
sampling the posterior probability of selection and keeping only features
that are selected frequently. In particular, they establish
non-asymptotic recovery results for the \emph{randomized lasso}, which
consists in applying the Lasso on random subsamples of the data and
rescaling of the regressors. Here, we adapt this strategy to
classification as the logistic
regression is locally equivalent to a weighted least square and recovery
results can carry from square-loss regression to logistic regression
\cite{Bach2010}.

We want to use transfer learning to perform screening of the voxels,
\emph{i.e.} eliminate many voxels that are not related to our target experiment.
For this purpose, we need a low probability of rejecting relevant
variables. Each iteration of the sparse logistic regression in
the randomized logistic can select reliably only $k_\text{max} \approx n
/ \log p$ variables.
In the worst case situation, we have $k$ heavily-correlated variables and
one of them is selected at random by the sparse logistic
regression at each iteration. For each of these variables, the probability of selecting it
less than $s$ times during $m$ iterations of the randomized logistic is
given by the cumulative distribution function of a binomial with per
trial success ratio $1/k$. If $s \leq m /k$, by
Hoeffding's inequality, this probability goes to
zero in $\oo{\exp m }$. In other words, if we impose a threshold $\tau =
s/m$
on the selection frequency, we can recover a group of $k$ correlated variables
for $\tau \leq 1/k $.

\paragraph{Brain parcellations}
Although randomization relaxes the conditions on
recovery, a remaining necessary condition is that the regressors of interest,
\emph{i.e.} the
values $\B{x}_i$ across the subjects of the $k$ predictive voxels, must
be weakly correlated\footnote{Specifically, the condition for recovery
with randomized lasso it is a lower bound on the conditioning of the
sparse eigenvalues of the design matrix \cite[theorem 2]{Meinshausen2010}
and for sparse logistic regression the corresponding condition is a
lower bound on the eigenvalues of the regressors of interest's
covariance matrix \cite[theorem 4]{Bach2010}.}. Because of the large
amount of smoothness present in medical images, in particular in group-level
fMRI contrasts, these conditions cannot be satisfied. Indeed, values taken by a
voxel are very similar to values taken by its neighbors. In addition, the
numbers of subjects used in fMRI are often below the sample size required
for good recovery. For these reasons we resort to feature agglomeration:
using hierarchical clustering to merge neighboring voxels carrying
similar information into parcels
\cite{Michel2011}. This strategy brings the double benefit of
reducing the problem size, and thus the required sample size, and
mitigating local correlation, at the expense of spatial resolution.

\section{Experiments and Results}

\subsection{FRMI datasets}

We use 3 studies for this meta-analysis. The first study (\emph{E1})
\cite{Pinel2007} is
composed of 322 subjects and was designed to assess the inter-subject
variability in some language, calculation, and sensorimotor tasks. The second study
(\emph{E2}) is similar to the first one in terms of stimuli, but its data was acquired
on 35 pairs of twin-subjects. The last study (\emph{E3}) \cite{Pallier2011}
characterizes brain regions in charge of the syntactic and the semantic
processing for the language. It was performed with 40 subjects, 20 of which
were stimulated by pseudowords (jabberwocky stimuli) instead of actual meaningful
sentences. All the studies were pre-processed and analyzed with the
standard fMRI analysis software SPM5. The data used for this work are the
statistical images resulting from the intra-subject analyses across the 3 studies.
\emph{E1} has 34 contrasts images available, \emph{E2} 56, and \emph{E3} 28. The
raw images were not always acquired on the same scanner. \emph{E1} has data from
a 3T SIEMENS Trio, and a 3T Brucker scanner; \emph{E2} data were acquired on a 1.5T
GE Signa; and \emph{E3} images come from the same 3T SIEMENS Trio.

\subsection{Experimental results for prediction}

Here we are interested in the \emph{prediction} problem: using
transfer learning to discriminate a pair of constrasts with an estimator
trained on two other contrasts.

We used 4 different approaches to learn
the discriminative models. The first approach relies on the activation
likelihood estimate (ALE) method \cite{Laird2005a}, as this is a commonly
published method for coordinate-based meta-analyses. We extract the activation positions
from the contrasts maps, and then apply a Gaussian kernel. We use
the preferred FWHM of
10mm \cite{Turkeltaub2002}. The other approaches directly use the contrast images.
We name \emph{raw contrasts} the method based on the contrasts voxels
values; \emph{contrast-specific parcels} the method that uses parcels
from the training set: and \emph{meta-analystic parcels} the method that
learns parcels from the full database.
We evaluate on our base of contrasts the ability to do transfer
learning, \emph{i.e} to learn decision rules that carry over from one
situation to another. Since we must make the assumption that the reference
contrasts hold common information with the contrasts of interest, we do
not try out all the possible combinations, but rather manually select pairs of
contrasts from a single experiment that form a meaningful classification
task (\emph{e.g.}, computation versus
reading, or Korean language versus French language). Out of all the
possible combinations, we select 35 pairs of classification task, and
subsequently combine them
into 18 transfer pairs, on which it
is reasonable to think that the transfer could occur (\emph{e.g.},
computation and reading in visual instructions,
transfer on computation and reading in auditory instructions).
We first train a linear classifier within 6-fold cross validation test on a first
set of pairs, setting the penalization amount by nested cross-validation, we
call this step \emph{inline learning}. We then re-use the
discriminative model on a different pair of contrasts to perform the
\emph{transfer learning}. The 3 studies containing language related
tasks, we can transfer between pairs within an experiment, and across experiments.
Among the 18 selected transfer pairs, we find that 9 can give rise to such
a transfer. Since a transfer is directed, we perform it both ways,
which yields once again 18 transfer pairs to test upon.
The associated prediction scores from the different methods are reported
in Table \ref{tab:transfers}. The general observation is that ALE yields
a poorer prediction performance than any other method. This is true both
for the transfer and inline predictions. We also
find that brain parcellations scores similar to the raw contrasts images,
and closer to the inline predictions.
We find that while the contrast-specific parcels and meta-analytic parcels
methods do not return the same parcels, they produce very close results.
We can thus use the full database to learn a single reference
parcellation to perform meta-analysis.

\begin{table}[p]
\begin{tabular}{l@{\footnotesize}|c@{\footnotesize}c@{\footnotesize}|c@{\footnotesize}c@{\footnotesize}|c@{\footnotesize}c@{\footnotesize}|c@{\footnotesize}c@{\footnotesize}}
  \hline
  \multicolumn{1}{c}{\footnotesize Names } &
  \multicolumn{2}{c}{\footnotesize Peaks } &
  \multicolumn{2}{c}{\footnotesize Contrasts } &
  \multicolumn{2}{c}{\footnotesize Parcels } &
  \multicolumn{2}{c}{\footnotesize Meta parcels } \\
  \hline
  & trans. & in. & trans. & in. & trans. & in. & trans. & in. \\
  \hline

\emph{E1}, comp./sent. $\rightarrow$ \emph{E2}, comp./sent. & 0.75 & 0.85 & 0.88 & 0.97 & 0.83 & 0.96 & 0.83 & 0.96 \\
\emph{E2}, comp./sent. $\rightarrow$ \emph{E1}, comp./sent. & 0.66 & 0.83 & 0.88 & 0.96 & 0.85 & 0.95 & 0.85 & 0.96 \\
\emph{E3}, jabb./French (L) $\rightarrow$ \emph{E3}, jabb./French (S) & 0.46 & 0.48 & 0.65 & 0.67 & 0.62 & 0.60 & 0.67 & 0.62 \\
\emph{E3}, jabb./French (S) $\rightarrow$ \emph{E3}, jabb./French (L) & 0.52 & 0.71 & 0.67 & 0.85 & 0.71 & 0.85 & 0.65 & 0.79 \\
\emph{E3}, jabb./French (L) $\rightarrow$ \emph{E2}, Korean/French & 0.65 & 0.46 & 0.73 & 0.79 & 0.65 & 0.81 & 0.76 & 0.85 \\
\emph{E2}, Korean/French $\rightarrow$ \emph{E3}, jabb./French (L) & 0.73 & 0.81 & 0.79 & 0.85 & 0.75 & 0.81 & 0.75 & 0.75 \\
  \hline
\end{tabular}
\caption{ Prediction scores for inline and transfer learning.
trans.= transfer; in.= inline;
comp.= computation, sent.= sentences (reading), jabb.= jabberwocky;
S= sentence with one word constituents, L= one constituent long sentence.
\label{tab:transfers}
}
\end{table}

\begin{figure*}[p]

	\begin{minipage}[b]{.4\linewidth}
	\caption{Prediction performance relative to the best performing
	  approach: inline prediction with raw
          contrasts images: the p-values indicate whether the associated methods
          are significantly poorer than the best performing method.}
	\label{fig:overview}
	\bigskip
	\end{minipage}
	\hfill
	\begin{minipage}[b]{.57\linewidth}
	\smash{%
	\includegraphics[width=\linewidth]{methods_vs_best_performing-crop.pdf}%
	}%
	\end{minipage}%
\end{figure*}

\subsection{Experimental results for inference}

Here we are interested in the \emph{inference} problem: using transfer
learning to help hypothesis testing on a target dataset.
In the following, we only consider a specific transfer, namely the last
line in Table \ref{tab:transfers}: we learn a model discriminating French
native speakers reading French or Korean, and apply it on another
experiment in which French subjects had to read French or
jabberwocky.
This transfer is interesting as it involves two different
experiments acquired on different scanners, and cognitive paradigms that share a similar
expression, incomprehension of visual language stimuli. As can be seen in
Table \ref{tab:transfers}, the prediction scores of transfer
learning as well as inline learning on this pair are acceptable although not excellent:
French language and jabberwocky are difficult to separate.

Figure \ref{fig:fmri_results} gives the stability scores of the
randomized logistic discriminating reading Korean from reading
French for the different set of features --activation peaks, raw
contrasts, parcels learned on the training contrasts or on the full
database. We can see that while learning at the voxel level or at the
parcel level gives similar prediction performance (Table
\ref{tab:transfers}), the stability score maps are very different. At the
voxel-level, with 70 subjects ($p = 40\,000$, $n = 70$) the recovery is
limited to
approximately 7 voxels without randomization: the recovery conditions are
violated. As a result, the randomized logistic selects only the most
predictive voxels.
On the parcels, contrast-specific or meta-analytic (i.e., learned on the full
database), the selection frequency highlights regions of the brain that
are known to be relevant for language comprehension, including the left
anterior superior temporal sulcus and the part of the temporal parietal
junction (Wernicke's area).

We threshold the stability selection scores of the first experiment
(Korean vs French) to select candidate voxels for the target experiment
(jabberwocky vs French). As we want to perform a rough screening and
would rather err on the side of false detections than false rejections,
we take a very low threshold $\tau = .01$. Following our analysis above,
the size of the largest group of correlated features that we can detect
with such a threshold is on the order of $1 / \tau \approx 100$. With 2000
parcels, this number
corresponds to 5\% of the brain, \emph{i.e.} 8\,000 voxels, and we can
safely consider that no fMRI contrasts is composed of groups of heavily
correlated features larger than this fraction.

\begin{figure*}[p]
\begin{center}
  \includegraphics{fmri_results.pdf}%
  \llap{\raisebox{7.2ex}{\footnotesize\bfseries\sffamily
      \rlap{Meta-analytic parcels}\hspace*{.47\linewidth}}}%

\vskip -0.12in
\caption{Stability scores of the randomized logistic on the Korean versus
French prediction of \emph{E2} for the different set of features: the
colormap represents the frequency at which a feature, parcel or voxel,
was selected. The maps are thresholded at 1\%.}
\label{fig:fmri_results}
\end{center}
\vskip -0.2in
\end{figure*}

\begin{figure*}[p]
\begin{center}
  \includegraphics{qqplots.pdf}%

\vskip -0.1in
\caption{Q-Q plots for the p-values with and without voxel selection by
transfer learning, as well as FDR=0.05 threshold: {\bf left} for a cohort
size $n^\star = 10$,
{\bf middle} for a cohort size $n^\star = 20$,
{\bf right} for a cohort size $n^\star = 40$.
}
\label{fig:fdr}
\end{center}
\vskip -0.2in
\end{figure*}

On the target experiment, we perform a standard group-level
analysis with the voxels selected, testing for the difference between
the two conditions,
jabberwocky or French reading. We report results with p-values corrected
for multiple comparisons at a given family-wise error rate (FWER) using
Bonferroni correction, and for a given false discovery rate (FDR) using
the Benjamini-Hochberg procedure.
On table \ref{tab:detections}, we compare
the number of detections and the detection rate, \emph{i.e.} the
fraction of voxels detected as significantly different, for a full
brain analysis and for an analysis limited to the voxel selection. We
compare our voxel selection method to a one-way ANOVA, and find that
transfer learning outperforms the ANOVA for all the cohort sizes.
Figure \ref{fig:fdr} shows the Q-Q plots on which the Benjamini-Hochberg
procedure is applied. We find that voxel selection by transfer
learning improves both the absolute number of detections and the
detection rate for FWER and FDR correction.

\begin{table}[tb]
\begin{minipage}{1.\linewidth}
\begin{tabular}{r|cc|cc|cc!{\vrule width 1pt}cc|cc|cc}
&\multicolumn{6}{c!{\vrule width 1pt}}{FWER corrected} & \multicolumn{6}{c}{FDR corrected} \\
$n^{\star}$ & \multicolumn{2}{p{1.93cm}|}{All voxels} &
\multicolumn{2}{p{1.6cm}|}{Selection} &
\multicolumn{2}{p{1.6cm}!{\vrule width 1pt}}{ANOVA} &
\multicolumn{2}{p{1.85cm}|}{All voxels} &
\multicolumn{2}{p{1.93cm}}{Selection} &
\multicolumn{2}{p{1.6cm}}{ANOVA} \\
\hline
\hline
$10$ & 0 &(0\%) & 0 &(0\%) & 0 &(0\%) & 0 &(0\%) & 0 &(0\%) & 0 &(0\%)\\
$20$ & 0 &(0\%) & 3 &(0.02\%) & 0 &(0\%) & 0 &(0\%) & 4 &(0.027\%) & 0 &(0\%)\\
$40$ & 5 &(0.0084\%) & 33 &(0.22\%) & 2 & (0.0014\%) & 143 &(0.97\%) & 1339 &(9\%) & 201 & (1.4\%)\\
\hline
\hline
\end{tabular}
\caption{Number of detections at $p < 0.05$ for difference cohort size, for
transfer learning and ANOVA. The percentage of detection is indicated
in parenthesis.
\label{tab:detections}}

\end{minipage}%

\end{table}

\section{Conclusion}

In this paper, we propose to improve the conditioning and power of statistical
analyses in imaging studies, using a large meta-analytic database.

In a \emph{transfer learning} scheme, we train on the database
sparse discriminative models that are suited to the target experiment.
Not only can the predictive power of these models can be useful to
establish biomarkers, but also they perform feature selection
that can increase the statistical power of a standard
group analysis on new experiments, provided enough predictive features
(voxels) can be recovered.
Using brain parcellations, the discriminative model acts to screen
parcels unlikely to be relevant in the target experiment, thus defining
automatically ROIs.

Using a set of 3 fMRI studies related to language, we confirm experimentally
that our transfer learning scheme is able to: i) perform accurate predictions on
experiments acquired on a different scanner and with varying paradigm,
ii) outperform the standard meta-analysis procedures based activation peaks,
iii) increase the statistical power in the target experiment by using
the ROIs defined by the discriminative model.

In this work we manually select the contrast pairs since it is
delicate to interpret a transfer learning score without good
knowledge of the cognitive or clinical conditions under study.
Future work will study automatic contrast pairs selection,
\emph{e.g.} by mining the descriptions of the experiments
\cite{Yarkoni2011}, to address
the problem of synthesizing the ever-growing literature and
data in medical research.

\paragraph{Acknowledgements}

This work was supported by the ANR grants BrainPedia ANR-10-JCJC 1408-01
and IRMGroup ANR-10-BLAN-0126-02.

redpajama_set_name: RedPajamaArXiv


text:
Advances in Textile Research Making Clothing More Sustainable
As environmental concerns rise, more researchers and organizations are looking for creative ways to become more sustainable. Many people within the textile industry are experimenting with new materials and different ways of producing and recycling fabrics. From fashion students to scientists to leading brands, here are two of the sustainable projects being developed in the industry.
From tea to textile
Since 2014, students and researchers at Queensland University in Australia have been growing vegan leather from kombucha tea. While the tea ferments, a curd forms on the surface. It can be harvested after a few weeks, and is then washed, oiled, and air dried to create a material with similar qualities to leather. The result is a flexible sheet that can be sewn, glued, or woven.
This fabric takes the shape of the container it is grown in, making it possible to reduce or eliminate waste by producing it in molds shaped for a specific garment. It can also be shaped while it dries, making it possible to create more complex designs by cutting it into strips and layering it over a form. The strips fuse together as they dry, creating one continuous piece.
The leather can easily be dyed while wet. It can also be painted, oiled, and waterproofed — although a truly waterproof coating requires a chemical sealant, preventing the material from being safely biodegradable. Water resistance can be achieved with natural oils or beeswax without affecting sustainability, but the fabric will be unable to withstand a significant amount of rain.
It remains uncertain whether this kombucha leather is viable on a large scale. The challenge of waterproofing is one hurdle, and another is the scent. The fabric naturally has a strong, sweet smell, and while this can be mitigated with products such as oils and beeswax, it is difficult to eliminate altogether. However, even if the material does not become viable in the mass market, it still has value as something that can be easily made at home. It is an affordable, accessible way of making vegan leather that benefits the environment.
Growing a sole
Reebok is also looking to become more sustainable. The company's president says that their philosophy is to "Be More Human," and they see sustainability as an important part of that. With that in mind, Reebok has partnered with DuPont Tate & Lyle Bio Products to create a pair of biodegradable sneakers. DuPont Tate & Lyle has created a petroleum-free product made from industrial corn, called Susterra propanediol, that Reebok is using to make the sole of the shoe. The upper section of the shoe will be made from cotton.
These materials have lent the project its name: the Cotton + Corn initiative. These sneakers are the first products in the initiative, with plans to introduce a wider variety of shoes in the future. The company's aim is to consider every part of the product's life cycle: it is made with sustainable materials, but should not suffer in quality because of this. The goal is to produce sneakers that look and perform the same as those made from other materials when in use. And at the end of the product's life, the entire shoe is compostable.
There are many other materials currently being experimented with, with textiles being created out of everything from wine-making leftovers to manure. As industry leaders such as Adidas and H&M become more involved in the creation and use of these materials, sustainability will become more widespread both within the market and beyond it.
Thousands of the world's leading textile and apparel companies can be in your contact list if you join BizVibe today! Whether you are looking for reliable suppliers, top textile manufacturers, wholesalers and potential customers in over 70 countries, or want to find out what your competitors are buying and which suppliers they're using, BizVibe can help you reach out to sales prospects and decision-makers in the textile industry across the globe.
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TAGS : Adidas Clothing kombucha recycling fabrics Reebok sustainable Textile Industry
Solar Nanotech-Powered Clothing: The Future of Wea
Global Man-made Cellulosic Fibre Market is Booming
Taiwan: The Rising Star in the Global Textile Mark


text:
Code of Virginia
Uncodified Acts
Table of Contents » Title 3.2. Agriculture, Animal Care, and Food » Subtitle V. Domestic Animals » Chapter 65. Comprehensive Animal Care » Article 7. Animal Control Officers and Humane Investigators » § 3.2-6556. Training of animal control officers
Creating a Report: Check the sections you'd like to appear in the report, then use the "Create Report" button at the bottom of the page to generate your report. Once the report is generated you'll then have the option to download it as a pdf, print or email the report.
Title 3.2. Agriculture, Animal Care, and Food
Chapter 65. Comprehensive Animal Care
§ 3.2-6556. Training of animal control officers.
A. Every locality employing animal control officers shall require that every animal control officer and deputy animal control officer completes the following training:
1. A basic animal control course that has been approved by the State Veterinarian. The basic animal control course shall include training in recognizing suspected child abuse and neglect and information on how complaints may be filed and shall be approved and implemented. Any animal control officer hired on or after July 1, 1998, and before July 1, 2017, shall complete the basic animal control course within two years from the date of hire. Any animal control officer hired on or after July 1, 2017, shall complete the basic animal control course within one year from the date of hire or within two years if the officer is attending a law-enforcement academy; and
2. Every three years, additional training approved by the State Veterinarian, 15 hours of which shall be training in animal control and protection.
The State Veterinarian shall develop criteria to be used in approving training courses and shall provide an opportunity for public comment on proposed criteria before the final criteria are adopted.
Subdivision 1 shall not apply to animal control officers or deputy animal control officers hired before July 1, 1998. The State Veterinarian may grant exemptions from the requirements of subdivision 1 to animal control officers hired on or after July 1, 1998, based on the animal control officer's previous training.
The State Veterinarian shall work to ensure the availability of these training courses through regional criminal justice training academies or other entities as approved by him. Based on information provided by authorized training entities, the State Veterinarian shall maintain the training records for all animal control officers for the purpose of documenting and ensuring that they are in compliance with this subsection.
B. Upon cause shown by a locality, the State Veterinarian may grant additional time during which the training required by subsection A may be completed by an animal control officer for the locality.
C. Any animal control officer that fails to complete the training required by subsection A shall be removed from office, unless the State Veterinarian has granted additional time as provided in subsection B.
1998, c. 817, § 3.1-796.104:1; 2002, c. 418; 2004, c. 181; 2008, c. 860; 2016, cc. 60, 172.
The chapters of the acts of assembly referenced in the historical citation at the end of this section may not constitute a comprehensive list of such chapters and may exclude chapters whose provisions have expired.
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community some of the benefits your community has given you.
(Includes any degrees or certifications if you wish).
Certificate includes embossed foil seal of the logo of the Foundation for 'Progress'.
For details on membership privileges and incentives please click here.
Join the family of 'PROGRESS' to become a part of the change. Please help us move 'PROGRESS' towards a Better Pakistan for a better World.
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Lussac-Saint-Émilion is a AOC located on the right bank of the Garonne and Dordogne rivers in Bordeaux. It's commonly grouped in with so-called satellites of Saint-Émilion. Lussac is planted to four of the five Bordeaux varietals, with Petit Verdot being the odd one out. Merlot is the dominant varietal of the four in most blends.
This wine is composed of 70% Merlot, 15% Cabernet Franc, and 15% Cabernet Sauvignon. (25 years old vines) It's aged in 50% vats, 20% new French oak barrels, and 30% one year or older barrels. The wine is 100% Estate bottled.
Chateau Bel-Air Lussac-St-Emilion is a winery that has been in existence for over a century and amazingly enough it's been owned by the same family. They have and amazing history and if you check out their small but well organized website -> Chateau Bel-Air You'll see labels pictures of old 1945 labels.
Another neat thing is this bottle of wine is very inexpensive. I purchase a half bottle for $11.99 at the wine shop!
On the nose I get the classic rustic right bank Merlot leatheriness that is quite common for wines from St. Emilion and it's satellites. It's almost like it smells earthy and austere. There is also some crushed blackberry notes that combine with hints of tar and graphite.
On the palate the wine has a slender texture but packs some pretty astringent tannins on the finish. In fact the tannins are quite green and somewhat bitter. I must point out that this wine is far to young to be consumed and the winemaker would probably want to lynch me if he knew I was reviewing it as a baby. However, there is a overall rustic character to this wine and although it shows some decent cherry notes, the wine is far too bitter and green for me. There is a dirty soil character and a greenness about the tannins that I don't really find too appealing.
I will try the other half of this bottle tomorrow and if it does decide to soften up a bit and shed some of its rusticity then I will report back. As for now I think this wine merits 82 points.


text:
PassedPassed
ActionAdventureComedy
After being rejected by the Confederate military, not realizing it was due to his crucial civilian role, an engineer must single-handedly recapture his beloved locomotive after it is seized ... Read allAfter being rejected by the Confederate military, not realizing it was due to his crucial civilian role, an engineer must single-handedly recapture his beloved locomotive after it is seized by Union spies and return it through enemy lines.After being rejected by the Confederate military, not realizing it was due to his crucial civilian role, an engineer must single-handedly recapture his beloved locomotive after it is seized by Union spies and return it through enemy lines.
Al Boasberg(adapted by)
Marion Mack
Glen Cavender
Top rated movie #187
Watch The General
Watch BUSTER KEATON: 3 FILMS [Masters of Cinema] Limited Edition Blu-ray Boxed Set
Johnnie Gray
Captain Anderson
Jim Farley
General Thatcher
Frederick Vroom
A Southern General
Frank Barnes
Her Brother
Charles Henry Smith
Her Father
(as Charles Smith)
Joe Keaton
Union General
Mike Donlin
Tom Nawn
Henry Baird
Joe Bricher
Jimmy Bryant
Sergeant Bukowski
C.C. Cruson
Jack Dempster
Keith Fennell
Budd Fine
Sherlock Jr.
The Passion of Joan of Arc
Seven Chances
Mr. Smith Goes to Washington
Bicycle Thieves
For the scenes with the opposing armies marching, Buster Keaton had the extras (which included 500 Oregon National Guard troops) wear the gray uniforms of the Confederacy and march in one direction past the camera, then he had them change uniforms to the Union blues and had them march past the camera in the other direction.
Annabelle gets drenched when she and Johnnie stop for water, but as they return to the engine, her dress is dry.
Johnnie Gray: [to the recruiter who rejects him] If you lose this war don't blame me.
Although Buster Keaton is the star of this film, he is listed last in the on-screen credits.
In 2003, 'David H. Shepard' produced 75-minute video version with music by The Alloy Orchestra.
Edited into The Golden Age of Buster Keaton (1979)
Written by William P. Perry
It is more appreciated now than when it was released
This film flopped when it was released in late 1926 for several reasons. First, its premiere was delayed because "Flesh and the Devil" was such a sensation that it was held over an extra couple of weeks. Second, people came to the movies to see Buster Keaton the comedian, not Buster the filmmaker and director, which is more of the role he played here. The film was funny, but it was not gag after gag, like so many of Keaton's other films. Keaton plays a railroad engineer living in the South. A title card declares he has two loves - his girl and his engine. when the Civil War starts he tries to enlist, but is considered too valuable to be in the Army due to his profession. His girlfriend misunderstands, thinks him a coward, and says she won't speak to him again until he is in uniform.
Meanwhile, the Union forces have developed a plan to crush the South that involves stealing Buster's train. Unknown to Buster, his girlfriend is on the train at the time of the theft. Buster starts out in hot pursuit of the thieves to retrieve his train, still without the knowledge of his girl's captivity by the Union army.
Forgotten with the arrival of sound, the film revived - often cut up from its original length - in the 1950's because Buster didn't preserve his rights to the film and it fell into the public domain. That is the reason there are so many versions of The General out there today, often with poor video and hideous musical accompaniment.
Today The General is considered one of the best silent feature length films, and one of the few silent films to not only be on DVD but to get the Blu Ray treatment too. SHERLOCK, JR. is clever. OUR HOSPITALITY is hilarious. The General is both of these things. It's story driven, races to a climax, and is fueled by cute, clever, inventive gags.Buster recycled these gags when he was a writer for MGM years later in "A Southern Yankee".
AlsExGal
A NOTE REGARDING SPOILERS
Why do the two little boys follow Johnnie Gray around?
What's the name of that thing Buster Keaton is sitting on?
January 2, 1927 (United States)
Cottage Grove, Oregon, USA
Buster Keaton Productions
Joseph M. Schenck Productions
$750,000 (estimated)
By what name was The General (1926) officially released in India in English?


The Gimal offices, developed over three floors, and the logistics area were carried out in an area of over 3,000 square metres in Vimercate. The prefabricated building already existed and it was possible to intervene externally only on the large façade that was covered with weathering effect sheets. The main entrance to the offices is identified by a representative scenographic entrance hall that can be seen from the outside with a tower.
The offices' colour schemes play on the white-brown contrast following the same movement used on the façade. The triple height entrance hall, the stairs and connection bridges are covered with Calacatta marble and the parapets are made of hardened glass with the aim of lighting the environment and giving statuesque plasticity.


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Warriors: Plan B, Shooting and Miracles
it was not long ago big party of the NBA season. Cavaliers-Warriors was the NBA Finals for four consecutive years (2015–18)something that did not happen, and that it would be difficult to happen again in the future big four American professional sports (NFL, NBA, MLB, NHL). Not long ago, it was a fierce, high-voltage rivalry. But one that definitely moved to Los Angeles in 2018 with LeBron. Of course, those who remember in Ohio how all those trips of the Warriors were during that electrified vibe, the ambient temperature and the battle drums on the track, were they not able to find many parallels in this game, however, the simplest One of the questions attracted many people, pure curiosity:what are the warriors doing,
The Warriors won in Cleveland (114-120) in a game that pointed to one of Friday night's milestones, but many people were cross before the initial jump, when it was announced The champions, before playing negative (22–23) and losing a tough duel with overtime in Boston, where they sealed the title in June, had decided leave it game in Cleveland (where they sealed their titles in 2015 and 2018) because it was the end of the Tour of Five and it was a back to back peliagudo. Steve Kerr saw Fury and Stephen Curry, Klay Thompson, Draymond Green and Andrew Wiggins. Sin big 4 And without Andre Iguodala or James Wiseman, the Warriors seemed to have a game that was primarily wound up in terms of media attention, in any case. And as a product for those looking to see Curry (after all, one of the great spectacles of world sports) and company on their only trip to Rocket Mortgage Fieldhouse, the old Quicken loan where they played seven more A streak of forty without a title for the Warriors ended a year and a half ago. Kerr apologized and elaborated on what is actually a very broad issue for an NBA product: ,I feel really bad for the fans who bought tickets to watch other players, it's a cruel thing to do in this business. That's why I will continue to advocate that the regular season be 72 games.,
Kerr made it clear that he has to protect his players and is looking forward to the team long target, some playoffs where they would have to defend their title and become the first team in history to win a ring after going negative in Game 45 of the season. now after this it's 23-23 Small Scale Impossible Miraclesa win that was only sixth at home, one of the worst in the league, and against some Cavs who had not played a game in Cleveland since December 26 and had only four losses in their pavilion prior to that game. That it was the best number in the past. They didn't have Donovan Mitchell, out with a groin problem, or Ricky Rubio, who was making his return after a very long absence. And keep track of your minutes. But they had Darius Garland, Jarrett Allen, Evan Mobley…enough, whichever way you look at it, to control it on its tracks. version c of warriors but,
But Cavs coach JB Bickerstaff assured that he has "Respectedparty and that Worthy Without a doubt the improvements he made, A bombshell of three in a row that led to moments that were beyond surprising, The Warriors had never made 23 triples without Stephen Curry, and he finished with a whopping 23/43 (53%), close to 27, which is an all-time high for the franchise. He made nine of his first 12 shots, and made 14 at halftime. In rhythm to challenge the NBA's historic top. And they came out of the break (59-64) Five more in four minutes, a stretch that took advantage of +20 (69-89) to make you rub your eyes, A bad reaction from the students later could have been a happy ending for CA, but with 110-114 they made an impossible three-pointer, AbsolutelyA Ty Jerome who later, already in the last minute, killed the game with a wonderful assist on a side kick to Kevon Looney (112-119).
Here's how it happened: Eight Warriors players hit at least one 3-pointer. Kevon Looney, who saved it because it's not his thing, only added 4 points but was essential with his presence. Body and 17 rebounds, 6 of them in attack. Jordan Poole added 32 points, though he nearly went down the drain that was about to engulf his team due to losses, lack of concentration and too many eyes on the favorable marker. When +20 ended in +4. But Jerome, who didn't play in Boston, kept his cool (22 points, 8 assists). A good matchup between DiVincenzo, Lamb and the two coming back and doing a lot of work, one young (Kuminga) and one veteran (JaMychal Green).
So the Warriors win… and the CAs lose. And tonight they play their third game in four days against the Bucks, no less. If they don't improve their image and lose again, many will remember this terrible skid These Warrior's. Product of starting because of opponent casualties (until it was too late). It seemed so obvious. Garland finished with 31 points and 10 assists, LeVert with 22 points and Mobley with 17 and 7 rebounds. but The collective image was very, very bad. In that trap that ended up with no intention was Kerr's plan To take care of the feet and efforts. One that left us without seeing Curry, Clay and Green on a track so far away from the Gulf is essentially linked to its history and its legacy.
Another All-Star Game for Doncic
Lillard scored 60 points to match Jordan and Harden's record
Damian Lillard, 42 points to keep the Blazers alive
Embiid and Harden, total shipwrecks
Champion is already carb
Doncic sprained ankle in Dallas win over Suns
Lillard goes up for 60 points to topple the Jazz


I love this category so I filled it with a lot of pieces I use and want in this one! It is very hard for me to fall asleep on planes so I always travel with a sleeping mask to cover the bright lights. I have been obsessing over this Calpak collaboration with Jen Atkin, the red pieces are so sleek and I love that you can customize it with stickers. All musts!
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Symptoms of mental illness such as anxiety and depression diminish functioning, cause distress, and create an economic burden to individuals and society. This meta-analysis was designed to evaluate the effectiveness of video based interventions (VBIs) for the treatment of adults in mental health settings. VBIs comprise four different ways of using video in mental health therapy, including video modeling, video exposure, video feedback, and videos used for psychoeducation. Bandura's social learning theory, Beck's cognitive theory, and Dowrick's theory of feedforward learning form the theoretical framework for understanding how VBIs work. The research questions were: (a) what is the range of effect sizes for VBI in mental health treatment of adults? (b) what is the mean standardized effect size for VBI in this context? and (c) what categorical variables, such as type of mental health issue or specific VBI application, moderate the effect of VBI? A comprehensive literature search strategy and coding plan for between-group studies was developed; the overall effect size for the 60 included studies equaled 0.34. A meta-regression was conducted; although the results were not significant, it is possible that type of VBI may be a moderator. Subgroup analyses by mental health outcome found the largest effect size, 0.48, for caregiving attitude and the smallest effect size, 0.21, for depression. Although the results of this meta-analysis were mixed, this study provides preliminary support for VBI use with adults as an evidence-based treatment. VBIs can contribute to positive social change by improving mental health treatment for the benefit of individuals, families, and society.


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