text:
My New Mom & Me
Tags: / All Children / Babies & Toddlers
Here is a heartwarming and completely accessible story about adoption, diversity, and acceptance. Told from the point of view of a puppy who is adopted by a cat, this gentle and reassuring tale is perfect for very young readers and listeners. When the puppy comes to live with his new mom, he is nervous. After all, his mom has stripes and he doesn't. But his mom says she likes that they look different, and soon the puppy likes it, too. (And who cares what anyone else thinks!) The puppy's new mom does all the things other parents do. She plays with him, takes care of him, and sometimes even makes him mad! But that's okay, because when he's feeling sad, she knows just what to say."A gentle, comforting story about nontraditional families." - BooklistFrom the Hardcover edition.
Renata Galindo
track author
Renata Galindo is a Mexican author and illustrator. She received an MA in Children's Book Illustration from the Cambridge School of Art in the UK, where she wrote her first books for children. She lives and works in Mexico City.
More about Renata Galindo »
Goodreads reviews for My New Mom & Me
Publisher: Schwartz & Wade Books
Manufacturer: Schwartz & Wade Books
Publication Year: March 22, 2016


MICHAEL OPPENHEIM has been in Business Management for over 35 years specializing in the music and entertainment industry. His clients include some of the most influential and successful musicians, actors, writers, producers, high net worth individuals, and record companies. He believes the strength of his business acumen stems from clear and open communication.
Michael attended the State University of Buffalo where he received an undergraduate degree in accounting.
Michael and his family bring to the world of philanthropy the same energy and entrepreneurial spirit that ensured his success as one of the top business managers. Michael serves on the board of several charitable foundations and supports all of his clients' charitable endeavors.
Michael has perennially been named one of Variety Magazine's Business Managers Elite.


The Europe Puzzle Map shows the countries of Europe in a variety of different colours. Each laser-cut piece is made of thick laminated ply to resist warping, and has a wooden knob attached for easy grasping. The map includes a beechwood frame for looks and durability. Learn more about how to use the Europe Puzzle Map at InfoMontessori.


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text:
Not enough intensity? Galaxy's free fall continues with loss to Seattle Sounders

The last time the Galaxy scored a goal before Sunday night, the NFL season hadn't started, the Supreme Court still had its full complement of justices and the air above Southern California was blue, not choked with ash.

That was three weeks ago, which was also the last time the Galaxy won a game. And while one of those streaks came to an end Sunday at Dignity Health Sports Park, it hardly mattered, with Sebastian Lletget's header in the 88th minute serving as little more than a murmur of protest in a 3-1 loss to the Seattle Sounders that extended the Galaxy's winless streak to four games.

The loss dropped the Galaxy (4-6-3) to within a point of the cellar in the 12-team Western Conference and left them three points and three places out of a spot in the expanded MLS playoffs. It's been a precipitous fall for a team that, a month ago, was working on the conference's longest winning streak.
"If you look at this stretch of games, we have had some chances," Lletget said. "We start the game soft. The intensity's not right. Something has to change in that sense."
He continued: "I wish we could just change one thing and then everything would fall in place. We all need to start better and be that much more intense."
So while Lletget's goal ended the Galaxy's scoring drought at 365 minutes, the team's longest since 2017, it did nothing to change the final result.
No one else helped much with that either.

Cristian Pavón, the team's leading scorer, assisted on the goal but had just one shot of his own on target — his first in three games — while Javier "Chicharito" Hernández, the team's $6-million designated player, hasn't scored since returning from a torn calf.
In fact, Lletget's goal was the first the Galaxy have scored since Hernández came back. The team is winless in the seven games he has appeared in this season but is 4-1-1 without him.

The conference-leading Sounders (7-3-3) were missing Raul Ruidiaz, the league's third-leading scorer, who was suspended for a violent foul in Seattle's last game. But the Sounders didn't appear to miss him, going ahead 1-0 in the 12th minute when a wide-open Cristian Roldan tapped in a low feed from Jordan Morris at the far post for his first score of the season.
<p class="canvas-atom canvas-text Mb(1.0em) Mb(0)–sm Mt(0.8em)–sm" type="text" content="The Galaxy, meanwhile, didn't put their first shot on target until the 33rd minute, when Joe Corona rolled a soft right-footed shot right at Seattle keeper Stefan Frei. Five minutes later, Morris doubled the lead on a counterattack, outrunning Galaxy defender Rolf Feltscher to catch up to a long Nicolas Lodeiro through ball before beating Galaxy keeper David Bingham for the easy finish for his seventh goal of the season.» data-reactid=»27″>The Galaxy, meanwhile, didn't put their first shot on target until the 33rd minute, when Joe Corona rolled a soft right-footed shot right at Seattle keeper Stefan Frei. Five minutes later, Morris doubled the lead on a counterattack, outrunning Galaxy defender Rolf Feltscher to catch up to a long Nicolas Lodeiro through ball before beating Galaxy keeper David Bingham for the easy finish for his seventh goal of the season.
It was the second time in four games Morris has had a goal and an assist in the first half.

Things would only get worse for the Galaxy after that with wunderkind winger Julian Araujo, who missed the last game with a red card, drawing another caution before the end of the first half and earning a suspension from the next game for yellow-card accumulation.
Sixteen minutes into the second half, Roldan added to the misery. João Paulo sent a ball from well behind the center circle over the top for Roldan, who had split the Galaxy back line and was sprinting alone up the center of the field. From there it was a foot race with Daniel Steres, one Roldan won easily before chipping the ball over an angry Bingham.
That left the Galaxy again searching for answers that weren't quick in coming.

"We've got to stick together," defender Nick DePuy said. "We're disappointed we lost. But we've got a game next week. We can't dwell on this."

redpajama_set_name: RedPajamaCommonCrawl


text:
724 E Kensington Road Arlington Heights, IL
CityArlington Heights
AreaArlington Heights
NeighborhoodWilliamsburg
Cavan Realty Incorporated
Fully Furnished, ready to move-in Class A build-out in small 1 story office building. 4,536 SF building for sale or lease on a 9,750 SF lot. Current zoning permitted use: Office; Business and Professional; Retail; Banks & Financial; and Convenient mart and food store. Current tenant is flexible and unit maybe divisible. This building has 14 parking stalls in front and 15 public parking stalls adjacent to East side of building. Excellent location on Kensington Rd and Hickory Ave directly across from Mariano's grocery on NW Highway in the Northwest suburb of Arlington Heights, a high income demographic area with substantial population. Good access to the NW Highway.
5/6/2019 Listed $16
COMMR
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Broker Associates
Braceville
Custer Park
Hainesville
Keeneyville
Lily Lake
Mettawa
North Chicago
Old Mill Creek
Peotone
RE/MAX Professionals Select
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I have a question about MLS# 10251129 at 724 E Kensington Road
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text:
\section{Introduction}

Deep Neural Networks (DNNs), due to their promising performance \cite{1}, currently dominate both Computer
Vision (CV) and Natural Language Processing (NLP) literature. However, fully utilizing the
capabilities of DNNs requires large training datasets. Reserchers have also tried to reduce the complexity of the DNN
models to obtain comparable performance when the size of training dataset is small  \cite{2}. The Few-Shot Learning (FSL) paradigm is an alternative attempt to
tackle the problem with scarce training instances. The goal of FSL is to efficiently learn from a small number of ``shots'' (i.e., data samples or instances). The options for the number of samples usually range from 1 to 100
\textit{per class} \cite{6} \cite{7}. There is a growing interest in the Artificial Intelligence (AI) research community in
FSL and several different strategies have been developed for
FSL, including Bowtie Networks \cite{3}, Induction Networks \cite{4}, and variations of Prototypical
Networks \cite{5}.

A Siamese Neural Network (SNN), sometimes called a twin neural network, is an Artificial Neural
Network (ANN) that uses two parallel, weight-sharing machine learning models in order to compute
comparable embeddings. The SNN architecture has shown good results as a FSL approach in computer vision for similarity detection \cite{11}
and duplicate identification \cite{12}. Yet, its usage in NLP has been
understudied and, to the best of our knowledge, there have not been any studies of using SNNs for clinical NLP with a few training data instances.

In SNNs, neural networks need to be trained to generate and compute embeddings. In NLP, deep learning has achieved the state-of-the-art performance since it could generate comprehensive embeddings to encode both semantic and syntactic information. The main use of deep learning in NLP is to represent the language in a vector format (i.e., embeddings) so that the vector representation can be used for different NLP tasks, for example, natural language generation, text classification, and semantic textual similarity. Thus, embeddings play a key role in applying deep learning to NLP. Having a robust
embedding-generation mechanism is crucial for most NLP tasks. Since the context of words, sentences,
and more generally, text is important to learn meaningful embeddings, context-aware embedding-generation models, such as BERT \cite{8},
often generate promising results. Furthermore, depending on the domain, the context also varies. For
this purpose, engineers and researchers have built domain-specific, specialized models to be used
for downstream tasks. Examples of such models include BioBERT trained from biomedical literature texts and BioClinicalBERT trained from clinical texts \cite{10}. Leveraging these contextual embeddings in SNN-based FSL has been rarely investigated in clinical NLP.

FSL is critical for clinical NLP as annotating a large training dataset is time-consuming and expensive. Furthermore, such annotation often requires involving domain experts. It is not uncommon to have a few clinical text samples annotated by physicians. One example could be clinical notes with annotations of a rare disease with the
number of samples naturally limited due to the nature of the disease. Despite such challenges in the clinical domain,
the importance of using AI in clinical applications cannot be
understated. AI could not only assist physicians in their decision-making and facilitate clinical and translational research, but also significantly
reduce the need for manual work. Therefore, in this study, we propose an FSL approach based on SNNs to tackle clinical NLP tasks with only a few annotated training samples. Two SNN-based FSL approaches have been proposed, including pre-trained SNN (PT-SNN) and SNN with second-order embeddings (SOE-SNN). For every approach, three different transformer models - BERT, BioBERT, BioClinicalBERT -
have been utilized. We evaluated the proposed approaches on two clinical tasks, namely clinical text classification and clinical named entity recognition. Clinical text classification refers to the classification of
clinical texts based on pre-defined classes. Named Entity Recognition (NER) is a subtask of Information Extraction (IE) that seeks to identify
entities mentioned in clinical texts.
We show that SNN-based approaches outperform the baseline models in few-shot settings for both tasks.  Finally, we discuss the limitations and future work.

\section{Related Work}

There have been studies evaluating the usability of SNNs for image classification. Mahajan et al.
used SNNs for the classification of high-dimensional radiomic features extracted from MRI images
\cite{13}. Hunt et al. applied SNNs for the classification of electrograms \cite{14}. Zhao et al.
have utilized SNNs for hyperspectral image classification \cite{15}.

In the context of FSL, SNNs have been used by Torres et al. for one-shot, Convolutional Neural
Networks (CNNs) based classification in order to optimize the discovery of novel compounds based on
a reduced set of candidate drugs \cite{16}. Droghini et al. employed SNNs for few-shot human fall
detection purposes using images \cite{17}. However, none of these studies used SNN-based FSL for NLP.

There is only a recent study by Müller et al \cite{18} where they
explored SNNs for FSL in NLP and demonstrated the high performance of pretrained SNNs that embed texts and
labels. To the best of our knowledge, none of these studies referenced above are using SNNs to perform FSL in
the clinical NLP domain.

\section{SNN-Based Models}

In this study, we consider two types of SNN-based models for clinical NLP.

\subsection{Model Architecture}

The SNN's architecture leverages two parallel weight-sharing ML models (``Fig.~\ref{snn_architecture}''). In the forward propagation
step, data samples are passed into the models and mapped down to the latent space. The
embeddings in the latent space are then compared with each other via a similarity function, as shown in Equ.(\ref{equ.sim}). The similarity function is also a parameter that can be fine-tuned during training and could range
from Euclidean distance to Manhattan distance or cosine similarity. Depending on the similarity
function, the similarity value can then be mapped onto the \((0, 1)\) interval by applying a Sigmoid
function. Finally, a high similarity value means that the input two samples likely belong to the same category, and vice
versa.

\begin{equation}
    \label{equ.sim}
    \text{out} = \sigma(\text{distance}(emb_1, emb_2))
\end{equation}

During the training process, SNN attempts to have the best approximation for the input embeddings.
Such training paradigm is also known as the representation learning. The representation is learned
by penalizing the loss if the model yields a high similarity value for inputs from different classes
or if the model yields a low similarity value for inputs from the same class.

\begin{figure*}
    \centering
    \includegraphics[width=\linewidth]{snn_architecture.png}
    \caption{Siamese Neural Network (SNN) Architecture.}
    \label{snn_architecture}
\end{figure*}

The SNN architecture naturally allows for data augmentation. For instance, in the case of 8-shot learning,
the traditional ML-based training approach would involve passing 8 samples directly into the model.
This approach is very limiting with a such small number of samples. SNN takes a different route and
instead of passing the samples directly, it considers unique comparisons within the training set.
With the training set comprised of 8 unique samples, there are \(\frac{8 * 7}{2} = 28\) unique
comparisons in total. Thus, instead of 8 training samples, we get 28, which is 3.5 times more. In
case of 16 samples, the improvement is even more significant as the number of unique comparisons is
120 and there is 7.5-fold data augmentation.

More generally, under \(N\)-way-\(K\)-shot-classification settings, for the dataset
\(D_\text{train}\) with \(N\) class labels and \(K\) labeled samples for each class, the following
holds after SNN-style augmentation:

\begin{align}
    D_\text{train SNN} = \{ (x_i, x_j) \mid x_i, x_j \in D_\text{train}, i < j\}\\
    \text{size}(D_\text{train SNN}) = \frac{(NK)^2 - NK}{2}
\end{align}

Hence, SNN reformulates the classification problem into a pairwise comparison problem, which has the
benefit of obtaining more training data. This becomes increasingly important as the number of training
samples decreases.

\subsection{Pre-trained SNN (PT-SNN)}

In the first approach, we leverage the pre-trained language models (PLMs) to generate embeddings for the SNN, called pre-trained SNN (PT-SNN). We used three PLMs in this approach, namely BERT, BioBERT, and BioClinicalBERT, to generate embeddings for the input training samples. In the following, we illustrate how to use the PT-SNN in the testing step. Suppose we want to perform binary
classification, we are given two classes \(C_1\) and \(C_2\), a training set \(D_\text{train}\), and
a testing set \(D_\text{test}\). We first compute embeddings for all samples in both
\(D_\text{train}\) and \(D_\text{test}\). For every testing sample, using the generated embeddings, we
compute the similarity with respect to every training sample and compute the mean similarity values
for classes.\footnote{We have also tried using maximum similarity values per class instead of mean
similarity scores, but since the observed results were similar, we only include results using the
mean similarity approach}  For instance, mean similarity value for some sample \(x \in
D_\text{test}\) with respect to \(C_1\) and \(C_2\) might be 0.2 and 0.6 respectively. In this case,
since 0.6 is greater than 0.2, we classify sample \(x\) as being in the class \(C_2\).

Algorithm (\ref{algorithm}) presents the pseudocode that contains both the classification algorithm as well
as evaluation approach.  Here, \texttt{epochs} refers to the number of
averaging epochs for addressing the instability issues. In our case, \texttt{epochs} is 5.
\texttt{d} represents a distance function, which is cosine similarity in our case.

\begin{algorithm*}
    \caption{Our Proposed Algorithm for SNN-Style Classification and Evaluation for Few-Shot Learning}
    \label{algorithm}

    \textbf{Require:} $E_{test}$: Test dataset embeddings\\
    \textbf{Require:} $L_{test}$: Test dataset labels\\
    \textbf{Require:} $D_{train}$: Train dataset\\
    \textbf{Require:} $d$: Distance function\\
    \textbf{Require:} $epochs$: Number of evaluation epochs\\
    \textbf{Require:} $RandSubset$: A function that randomly subsets a dataset with the given seed
        and generates embeddings\\
    \textbf{Require:} $Mean$: Calculates the mean of a vector\\
    \textbf{Require:} $GetMaxValueKey$: A function that gets the key with the maximum value from the hash table\\
    \textbf{Require:} $ComputeMetrics$: A function for computing evaluation metrics - precision, recall, and F-score\\
    \BlankLine
    \BlankLine
    \BlankLine
    $metrics \gets \text{empty hash table}$\;
    \For{$i \gets 0$ \KwTo $epochs$} {
        $E_{train}, L_{train} \gets RandSubset(D_{train}, seed=i)$\;
        $predictions \gets \text{empty vector}$\;
        \ForEach{$e_{test} \in E_{test}$} {
            $similarity \gets \text{empty hash table}$\;
            \ForEach{$(e_{train}, l_{train}) \in Zip(E_{train}, L_{train})$} {
                $similarity.At(l_{train}).Add(d(e_{train}, e_{test}))$;
            }

            \BlankLine
            \BlankLine

            $tmp \gets \text{empty hash table}$\;
            \ForEach{$(key,\ val) \in similarity$} {
                $tmp.At(key) = Mean(val)$;
            }

            \BlankLine
            \BlankLine

            $predictions.Add(GetMaxValueKey(tmp))$\;
        }

        $precision, recall, fscore = ComputeMetrics(predictions, L_{test})$\;
        $metrics.At("precision").Add(precision)$\;
        $metrics.At("recall").Add(precision)$\;
        $metrics.At("fscore").Add(precision)$\;
    }

    $precision = Mean(metrics["precision"]))$\;
    $recall = Mean(metrics["recall"]))$\;
    $fscore = Mean(metrics["fscore"]))$\;
\end{algorithm*}

Such a strategy for classification can be slow in cases where the training set is very large.
Fortunately, the proposed approach is feasible in the FSL setting where the number of annotated samples is
limited. Thus, we do not expect significant performance drawbacks when the number of samples is not
very high. Therefore, the PT-SNN approach is suitable for FSL purposes, but may not be as
effective for training on very large datasets.

\subsection{SNN With Second-Order Embeddings (SOE-SNN)}

\begin{figure*}
    \centering
    \includegraphics[width=\linewidth]{soe_snn.png}
    \caption{Siamese Neural Network (SNN) Architecture.}
    \label{soe_snn_architecture}
\end{figure*}

The second proposed approach is SNNs with second-order embeddings where we apply an additional
Recurrent Neural Network (RNN) layer, such as Long-Short Term Memory (LSTM) or Gated Recurrent Unit (GRU)
to the generated embeddings and then train the SNN model in the fashion described in the
Model Architecture section (``Fig.~\ref{soe_snn_architecture}''). In our experiments, we used bidirectional
RNNs for producing second-order embeddings.

Specifically, we first obtain the embeddings for all training samples from the PLMs. All possible unique pairs of samples are
then generated and given a label of 0 if the samples in the pair are of the same class or 1 if they
come from different classes. During the training process, we use the Binary Cross Entropy (BCE)
\cite{22} and AdamW \cite{23} as the loss function and the optimizer, respectively.

Model evaluation is done in the same manner as in pre-trained SNNs, where we compute mean similarity
scores and average out the metrics over 5 testing epochs to handle the potential instability issues.

\section{Text Embeddings}

In this section, we will describe how to generate embeddings, including sentence-level embeddings and word-level embeddings, using BERT, BioBERT, and BioClinicalBERT.

\subsection{Sentence-Level Embeddings}

For contextual, sentence-level embeddings, we used sentence-transformers \cite{24} package. This
package provides a set of intuitive and easy-to-use methods for computing dense vector
representations of sentences, paragraphs, and images. The models are based on transformer networks
such as BERT, RoBERTa, etc., and achieve state-of-the-art performance in various tasks. The generated
sentence embeddings are such that similar texts are close in the latent space and can efficiently
be found using cosine similarity.

\begin{equation}
    \text{cosine similarity(A, B)} = \frac{A \cdot B}{\left\|\mathbf {A} \right\|\left\|\mathbf {B} \right\|}
\end{equation}

\subsection{Word-Level Embeddings}

To generate contextual embeddings at the word level, we used \texttt{Transformers} package.

A tricky part of named entity recognition tasks with transformer models is that they rely on word piece tokenization, rather
than word tokenization. For example, if we have a word such as \texttt{Washington}, it may get
tokenized into three separate words \texttt{Wash}, \texttt{ing}, and \texttt{ton}. Then one approach
could be to handle this by only train the model on the tag labels for the first word piece token of
a word (i.e. only label ''Wash``). This is what was done in the original BERT paper.

That being said, such approach does not work well if words that may have prefixes that can also be
tokens. One such example can be a word \texttt{proto-potato}. In this case, tokenizer may split it
into \texttt{proto} and \texttt{potato} and taking the first subword as the embedding of the entire
word would have a semantically incorrect representation. Similarly, if we only took the last subword
embedding, some words may have a suffix and we run into the same problem.

In order to solve this issue, we averaged out the embeddings for the subwords and took it as the
representation for the word. Thus, we define the word embedding \(E_{\text{word}}\) with \(e_0, e_1,
\dots, e_n\) subwords as follows:

\begin{equation}
    E_{\text{word}} = \frac{\sum_{i = 0}^n e_i}{n}
\end{equation}

\section{FSL Model Evaluation}

Systematically evaluating FSL model performance can be tricky since fine-tuning or making
predictions on small datasets could potentially suffer from instability \cite{20}. To address this
issue, we propose the averaging strategy for model evaluation. For every few-shot experiment (e.g.,
4-shot, 8-shot, and 16-shot experiments), using randomized sampling, we sample 4, 8, or 16 samples
per class and create a training dataset. We perform this \(M\) times and therefore, for every
experiment, \(M\) randomly generated training sets are evaluated on the test set. Finally, the
metrics are averaged out and reported as the final scores.

\begin{equation}
    \text{Metric} = \frac{\sum_{i = 1}^M \text{Metric}_i}{M}
\end{equation}

In this paper, we choose \(M = 3\). Such approach gives a more robust view on the performance of the
model in possibly unstable scenarios. We employ this strategy in all reported metrics. As for
metrics, we choose Precision, Recall, and F-score:

\begin{equation}
    \text{Precision} = \frac{\text{True Positive}}{\text{True Positive} + \text{False Positive}}
\end{equation}

\begin{equation}
    \text{Recall} = \frac{\text{True Positive}}{\text{True Positive} + \text{False Negative}}
\end{equation}

\begin{equation}
    \text{F-score} = \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}
\end{equation}

\section{Baseline Models}

We use fine-tuned BERT, BioBERT, and BioClinicalBERT as the baseline models. Instead of approaching
the problem from the SNN perspective, we use the 4, 8, and 16 samples per class directly in order to
fine-tune the pre-trained transformer models.

This is done by adding an additional linear layer at the end of the transformer model. We used
\texttt{Transformers} package \cite{21} in our implementation.

\section{Experiments}

\subsection{Classification Tasks and Datasets}

We perform both clinical text classification and clinical named entity recognition. For few-shot clinical text classification, we use sentences from the MIMIC-III \cite{25} databases
and classify sentences them into 4 different classes classes. As for few-shot clinical named entity recognition, we use the i2b2
(Informatics for Integrating Biology and the Bedside) 2006 de-identitification challenge dataset
\cite{19} and do binary classification of one-word entities. The datasets were preprocessed in order
to be used in few-shot experiments.

\subsubsection{MIMIC-III}

For sentence classification, the sentences were obtained from the MIMIC-III database. We used the
same dataset as in the HealthPrompt paper by Sivarajkumar and Wang \cite{26}, but with classes suitable
for 4-shot, 8-shot, and 16-shot experiments.

The following were the classes that were left:

\begin{center}
    \begin{itemize}
        \item \texttt{ADVANCED.LUNG.DISEASE (245 samples)}
        \item \texttt{ADVANCED.HEART.DISEASE (117 samples)}
        \item \texttt{CHRONIC.PAIN.FIBROMYALGIA (48 samples)}
        \item \texttt{ADVANCED.CANCER (34 samples)}
    \end{itemize}
\end{center}

The dataset had two columns: \texttt{text} and \texttt{label} corresponding to the sentence and label
respectively.

In total, we had 444 samples in our dataset. Since we performed 4, 8, and 16 shot experiments, the
train size varied and was 16, 32, and 64 samples with the test sizes of 428, 412, and 380 samples
respectively.

\subsubsection{2006 i2b2 De-Identification Challenge Dataset}

The i2b2 (Informatics for Integrating Biology and the Bedside) organized clinical NLP challenge in
2006 with de-identification track focused on identifying Protected Health Information (PHI) from
clinical narratives. The i2b2 2006 challenge developed a corpus of de-identified records such that
there is one record per patient.

The dataset has both training and testing sets, which are separate from each other. This allows for
a more transparent comparison reporting of benchmarks and statistics. One limitation that we also
discuss in the ``Future Work'' section is that we filtered out all multi-word entities and only
perform a one-word NER. We also note this in section \ref{limandfutwork}.

Both training and testing sets are XML files comprised of text, some of which contain \texttt{PHI}
tags. One example of such text is the following:

\begin{center}
\texttt{<PHI TYPE="DATE">11/28</PHI>/03 02:25 PM}
\end{center}

Here, we have text where \texttt{11/28} is a PHI, whose type is \texttt{DATE}. The goal is to
correctly identify this named entity. While the original dataset does contain classes for
\texttt{PHI} tags (e.g., \texttt{DATE} in this case), we do not use them as separate classes for
NER, but only to identify word as a named entity. But similar to sentence classification, we do
sample equal number of samples based on class.

We used two files provided by challenge:
    \begin{itemize}
        \item \footnotesize{\texttt{deid\_surrogate\_test\_all\_groundtruth\_version2.xml}}
        \item \footnotesize{\texttt{deid\_surrogate\_train\_all\_version2.xml}}
    \end{itemize}

We used all of the \texttt{PHI}s except for \texttt{AGE} since the train file only contained
only 13 such tags, which is not enough for conducting 16-shot experiments.

In the end, we had the following 8 classes:

\begin{itemize}
    \item \texttt{ID}
    \item \texttt{HOSPITAL}
    \item \texttt{DATE}
    \item \texttt{PATIENT}
    \item \texttt{LOCATION}
    \item \texttt{DOCTOR}
    \item \texttt{PHONE}
    \item \texttt{NONPHI} (class that represents non-PHI/non-named-entity words)
\end{itemize}

We have split sentences into words and generate word-level contextual embeddings for every word.
In the end, we obtained the dataset that was comprised of the following:

\begin{center}
    \begin{itemize}
        \item \texttt{Test Set}
        \begin{itemize}
            \item \texttt{Named Entities: 3193 samples}
            \item \texttt{Other Terms (labeled \texttt{NONPHI}): 16036 samples}
            \item \texttt{Total: 19229 samples}
        \end{itemize}
        \item \texttt{Train Set}
        \begin{itemize}
            \item \texttt{Named Entities: 9083}
            \item \texttt{Other Terms (labeled \texttt{NONPHI}): 39319}
            \item \texttt{Total: 48402 samples}
        \end{itemize}
    \end{itemize}
\end{center}

While the entire test set was used in all experiments, only 32, 64, and 128 samples were taken
from the training set for 4-shot, 8-shot, and 16-shot experiments respectively.
All experiments were run on four NVIDIA RTX 8000 GPUs.

\subsection{Results}

\subsubsection{Few-Shot Clinical Text Classification}

We present the results of 4-shot, 8-shot, and 16-shot experiments for few-shot clinical text
classification tasks. We have used BERT, BioBERT, and BioClinicalBERT based models. The results are
shown in Table \ref{sentclfres}.

In the 4-shot clinical text classification task, it is clear that the baseline, fine-tuned transformer,
approach had the worst performance. PT-SNN and SOE-SNN both
significantly outperformed the fine-tuned transformers. PT-SNN marginally outperformed
SOE-SNN and their performance was relatively similar. For the 4-shot
sentence classification task, the best model was BioBERT-based PT-SNN with the precision of
0.53, recall of 0.45, and f-score of 0.46. In order to measure the statistical significance of the
difference between the best fine-tuned transformer approach (BioClinicalBERT-based model) and the
best SNN-based approach, we perform a paired t-test. This is done by creating two vectors
\([precision_1, recall_1, fscore_1]\) and \([precision_2, recall_2, fscore_2]\), calculating the
difference vector \(d\), and then running the one-sample t-test. The difference was significant with
the p-value of 0.0377 (we take 0.05 cutoff).

SOE-SNN outperformed all other approaches. BioClinicalBERT-based SOE-SNN marginally outperformed BioBERT-based variant. PT-SNN came next
with fine-tuned transformers having the worst performance. The best baseline model (i.e., fine-tuned
transformer) had the precision, recall, and fscore values of 0.35, 0.35, and 0.21 respectively. This
is a BioClinicalBERT-based model. BERT-based model had a higher fscore of 0.24, but both precision
and recall were lower. The best SNN-based model was based on BioClinicalBERT. After performing a
paired t-test in the similar manner as for 4-shot experiment evaluation, we got the p-value of
0.0394, which means that the difference is significant and that our proposed approach has
substantial improvement over the baseline model.

As for 16-shot experiments, BioClinicalBERT-based SOE-SNN outperformed all
other models. BioClinicalBERT-based PT-SNN came next with the precision, recall, and fscore
values of 0.69, 0.54, and 0.58 respectively. The worst performance, again, was shown by the baseline
fine-tuned transformer models, with the best score being 0.39 (precision), 0.37 (recall), and (0.27)
fscore. After performing a paired t-test, we obtained the p-value of 0.0292. Given the cutoff of
0.05, we got that the difference between the metrics is significant and therefore, the proposed
approach significantly outperforms the baseline models. It is noteworthy that the p-value was a lot
lower (relatively) than in 8-shot experiments, which can be an interesting area to explore (i.e.,
what is the threshold for the number of shots at which fine-tuning directly becomes more effective
than our proposed SNN-based approaches).

Overall, it is clear that in all cases, SNN-based approaches outperformed the baseline models. The
difference in performance for PT-SNN and SOE-SNN was not statistically
significant, with the latter marginally outperforming the former. Furthermore, we have measured the
significance via paired t-test and discovered that in all three cases (4-shot, 8-shot, and 16-shot
experiments), the difference was statistically significant. It should also be noted that we measured
statistical significance on the statistics themselves and paired t-test on the model outputs was not
necessary.

\subsubsection{Few-Shot Clinical Named Entity Recognition}

We present the results of 4-shot, 8-shot, and 16-shot experiments for few-shot clinical NER tasks.
Similar to clinical text classification, we have used BERT, BioBERT, and BioClinicalBERT based models. The results are
shown in Table \ref{nerclfres}.

There was not a statistically significant difference between PT-SNN and SOE-SNN. That being said, once again,
SNN-based models outperformed the baseline, fine-tuned transformer models in all experiments.

In 4-shot experiments, the best model was BioClinicalBERT-based PT-SNN. The best baseline
model was the BERT-based fine-tuned transformer. SNN-based model achieved the precision,
recall, and f-score of 0.89, 0.64, and 0.68 respectively, while the fine-tuned transformer had those
of 0.70, 0.59, and 0.60 respectively. After performing the paired t-test in the same fashion as in
sentence classification tasks, we got the p-value of 0.1290, which is not statistically significant.
That being said, in all metrics, SNN-based approach was superior to the fine-tuned transformers.

In 8-shot experiments, similar to the 4-shot scenario, BioClinicalBERT-based PT-SNN was the best model.
The best baseline model was the BERT-based fine-tuned transformer. SNN-based model achieved the precision,
recall, and f-score of 0.88, 0.55, and 0.59 respectively, while the fine-tuned transformer had those
of 0.74, 0.52, and 0.53 respectively. After performing the paired t-test in the same fashion as in
sentence classification tasks, we got the p-value of 0.1446, which is not statistically significant.
That being said, in all metrics, SNN-based approach was superior to the fine-tuned transformers.

In 16-shot experiments, BioClinicalBERT-based SOE-SNN outperformed all other models. The best
baseline model was the BERT-based fine-tuned transformer. SNN-based model achieved the precision,
recall, and f-score of 0.90, 0.50, and 0.51 respectively, while the fine-tuned transformer had those
of 0.76, 0.54, and 0.55 respectively. After performing the paired t-test in the same fashion as in
sentence classification tasks, we got the p-value of 0.7705, which is not statistically significant.
That being said, in all metrics, SNN-based approach was superior to the fine-tuned transformers.

It should be noted that metrics recall and F1-score have decreased for the SNN-based approaches when
the number of samples increased. This pattern was not observed in SC tasks. This could potentially be
explained by the fact that classification was done directly on the word embeddings and some instability
may have been introduced during the evaluation process (despite the fact that we averaged out the metrics
over 3 different evaluation runs).

\begin{table*}
    \caption{Few-Shot Sentence Classification Results}
    \begin{center}
        \begin{tabular}{|l|l|l|c|c|c|c|}
            \hline
            \textbf{Approach} & \textbf{Model} & \textbf{Shots} & \textbf{Precision} & \textbf{Recall} & \textbf{F-Score}\\
            \hline

            Fine-Tuned Transformer & BERT & 4 & 0.24 & 0.24 & 0.14\\
            Fine-Tuned Transformer & BioBERT & 4 & 0.23 & 0.23 & 0.16\\
            Fine-Tuned Transformer & BioClinicalBERT & 4 & 0.25 & 0.31 & 0.18\\
            \hline

            Pre-Trained SNN & BERT & 4 & 0.49 & 0.37 & 0.37\\
            \textbf{Pre-Trained SNN} & \textbf{BioBERT} & \textbf{4} & \textbf{0.53} & \textbf{0.45} & \textbf{0.46}\\
            Pre-Trained SNN & BioClinicalBERT & 4 & 0.50 & 0.42 & 0.43\\
            \hline

            SNN With Second-Order Embeddings & BERT & 4 & 0.47 & 0.51 & 0.42\\
            SNN With Second-Order Embeddings & BioBERT & 4 & 0.44 & 0.47 & 0.44\\
            SNN With Second-Order Embeddings & BioClinicalBERT & 4 & 0.50 & 0.47 & 0.42\\
            \hline
            \hline

            Fine-Tuned Transformer & BERT & 8 & 0.34 & 0.30 & 0.24\\
            Fine-Tuned Transformer & BioBERT & 8 & 0.35 & 0.32 & 0.22\\
            Fine-Tuned Transformer & BioClinicalBERT & 8 & 0.35 & 0.35 & 0.21\\
            \hline

            Pre-Trained SNN & BERT & 8 & 0.62 & 0.45 & 0.47\\
            Pre-Trained SNN & BioBERT & 8 & 0.61 & 0.48 & 0.50\\
            Pre-Trained SNN & BioClinicalBERT & 8 & 0.64 & 0.44 & 0.49\\
            \hline

            SNN With Second-Order Embeddings & BERT & 8 & 0.59 & 0.47 & 0.50\\
            \textbf{SNN With Second-Order Embeddings} & \textbf{BioBERT} & \textbf{8} & \textbf{0.62} & \textbf{0.52} & \textbf{0.55}\\
            \textbf{SNN With Second-Order Embeddings} & \textbf{BioClinicalBERT} & \textbf{8} & \textbf{0.65} & \textbf{0.51} & \textbf{0.55}\\
            \hline
            \hline

            Fine-Tuned Transformer & BERT & 16 & 0.23 & 0.31 & 0.14\\
            Fine-Tuned Transformer & BioBERT & 16 & 0.35 & 0.33 & 0.18\\
            Fine-Tuned Transformer & BioClinicalBERT & 16 & 0.39 & 0.37 & 0.27\\
            \hline

            Pre-Trained SNN & BERT & 16 & 0.64 & 0.51 & 0.52\\
            Pre-Trained SNN & BioBERT & 16 & 0.65 & 0.55 & 0.56\\
            Pre-Trained SNN & BioClinicalBERT & 16 & 0.69 & 0.54 & 0.58\\
            \hline

            SNN With Second-Order Embeddings & BERT & 16 & 0.66 & 0.58 & 0.58\\
            SNN With Second-Order Embeddings & BioBERT & 16 & 0.68 & 0.55 & 0.56\\
            \textbf{SNN With Second-Order Embeddings} & \textbf{BioClinicalBERT} & \textbf{16} & \textbf{0.71} & \textbf{0.55} & \textbf{0.60}\\
            \hline
        \end{tabular}
        \label{sentclfres}
    \end{center}
\end{table*}

\begin{table*}
    \caption{Few-Shot Named-Entity Recognition Results}
    \begin{center}
        \begin{tabular}{|l|l|l|c|c|c|c|}
            \hline
            \textbf{Approach} & \textbf{Model} & \textbf{Shots} & \textbf{Precision} & \textbf{Recall} & \textbf{F-Score}\\
            \hline

            Fine-Tuned Transformer & BERT & 4 & 0.70 & 0.59 & 0.60\\
            Fine-Tuned Transformer & BioBERT & 4 & 0.66 & 0.56 & 0.57\\
            Fine-Tuned Transformer & BioClinicalBERT & 4 & 0.66 & 0.19 & 0.15\\
            \hline

            Pre-Trained SNN & BERT & 4 & 0.87 & 0.45 & 0.49\\
            Pre-Trained SNN & BioBERT & 4 & 0.87 & 0.24 & 0.18\\
            \textbf{Pre-Trained SNN} & \textbf{BioClinicalBERT} & \textbf{4} & \textbf{0.89} & \textbf{0.64} & \textbf{0.68}\\
            \hline

            SNN With Second-Order Embeddings & BERT & 4 & 0.80 & 0.42 & 0.41\\
            SNN With Second-Order Embeddings & BioBERT & 4 & 0.83 & 0.48 & 0.50\\
            SNN With Second-Order Embeddings & BioClinicalBERT & 4 & 0.76 & 0.58 & 0.61\\
            \hline
            \hline

            Fine-Tuned Transformer & BERT & 8 & 0.74 & 0.52 & 0.53\\
            Fine-Tuned Transformer & BioBERT & 8 & 0.70 & 0.39 & 0.38\\
            Fine-Tuned Transformer & BioClinicalBERT & 8 & 0.71 & 0.30 & 0.29\\
            \hline

            Pre-Trained SNN & BERT & 8 & 0.87 & 0.37 & 0.37\\
            Pre-Trained SNN & BioBERT & 8 & 0.87 & 0.25 & 0.19\\
            \textbf{Pre-Trained SNN} & \textbf{BioClinicalBERT} & \textbf{8} & \textbf{0.88} & \textbf{0.55} & \textbf{0.59}\\
            \hline

            SNN With Second-Order Embeddings & BERT & 8 & 0.88 & 0.35 & 0.36\\
            SNN With Second-Order Embeddings & BioBERT & 8 & 0.87 & 0.24 & 0.20\\
            SNN With Second-Order Embeddings & BioClinicalBERT & 8 & 0.88 & 0.54 & 0.60\\
            \hline
            \hline

            Fine-Tuned Transformer & BERT & 16 & 0.76 & 0.54 & 0.55\\
            Fine-Tuned Transformer & BioBERT & 16 & 0.71 & 0.40 & 0.39\\
            Fine-Tuned Transformer & BioClinicalBERT & 16 & 0.74 & 0.37 & 0.34\\
            \hline

            Pre-Trained SNN & BERT & 16 & 0.87 & 0.33 & 0.32\\
            Pre-Trained SNN & BioBERT & 16 & 0.87 & 0.25 & 0.20\\
            Pre-Trained SNN & BioClinicalBERT & 16 & 0.88 & 0.47 & 0.49\\
            \hline

            SNN With Second-Order Embeddings & BERT & 16 & 0.88 & 0.34 & 0.30\\
            SNN With Second-Order Embeddings & BioBERT & 16 & 0.88 & 0.27 & 0.22\\
            \textbf{SNN With Second-Order Embeddings} & \textbf{BioClinicalBERT} & \textbf{16} & \textbf{0.90} & \textbf{0.50} & \textbf{0.51}\\
            \hline
        \end{tabular}
        \label{nerclfres}
    \end{center}
\end{table*}

\section{Limitations and Future Work} \label{limandfutwork}

There are several limitations of the work that can be addressed by further exploring few-shot learning
and SNNs. First, it can be interesting comparing results to more traditional baseline models such as
SVM, logistic regression, multinomial logistic regression, random forest, etc. Second, other datasets
can also be used for evaluating the performance of SNNs for text classification purposes. Third, we
had a limitation when doing NER - particularly, we only considered one-word entities. Evaluating the
performance of SNNs considering all entities can also be an interesting for the future research.
Additionally, since we can do both word-level and sentence-level classification, another interesting
direction of research could be document classification, where a document can be a collection of words
and sentences.

It is important to note that datasets for FSL and especially, for clinical FSL is difficult to find.
Ge et al. \cite{27}, in their review paper, have emphasized that ``(68\%) studies reconstructed existing
datasets to create few-shot scenarios synthetically.''

\section{Conclusion}

We have conducted few-shot learning experiments evaluating the performance of SNN models on text
classification tasks - SC and NER. SNN models were based on transformer models - BERT, BioBERT, and
BioClinicalBERT. Fine-tuned versions of  BERT, BioBERT, and BioClinicalBERT were also used as the
baseline models. Since performance evaluation on small datasets may suffer from instability, a
special evaluation strategy was used. We conclude that SNN-based models outperformed the baseline
fine-tuned transformer models for sentence classification tasks. A paired t-test was also performed,
which showed that for SC tasks SNN models significantly outperform the baseline models. As for NER,
SNN models, once again, outperformed the baseline models, yet the performance difference was not
statistically significant. Limitations of the work have also been discussed and alongside with
potential future directions of exploration.

\section{Acknowledgements}

The authors would like to acknowledge support from the University of Pittsburgh Momentum Funds, Clinical and Translational Science Institute Exploring Existing Data Resources Pilot Awards, the School of Health and Rehabilitation Sciences Dean's Research and Development Award, and the National Institutes of Health through Grant UL1TR001857.

redpajama_set_name: RedPajamaArXiv


text:
Rosneft contributes to the preservation of the Amur tiger population
Global Tiger Day is celebrated annually on July 29 and was established in 2010 to draw attention to the problems of preserving the population of this predatory animal.
Taking care of the conservation of wildlife and maintaining the sustainable state of ecosystems, Rosneft implements a number of programmes for the study and conservation of wild animals in the regions of its presence. In particular, the Company supports an initiative called the Amur Tiger Center.
As part of this initiative, Rosneft finances the purchase of necessary machinery and equipment for state wildlife officers who monitor specially protected natural areas in Primorsky and Khabarovsk Krais. Rosneft also supports centres for the rehabilitation and reintroduction of the Amur tiger, feeding and veterinary support of animals, and provides special equipment for training tigers released into the wild to hunt independently and to co-exist with humans without conflict.
Currently the Company is funding the construction of enclosures for Amur tiger in Silinsky Park in the city of Komsomolsk-on-Amur. It's going to be placed next to the enclosures for the Amur leopard.
One of Rosneft's strategy key priorities in all business areas is ecology and environment. The Company has been a member of the UN Global Compact for more than 10 years and in 2018 reaffirmed its commitment to 17 UN Sustainable Development Goals. Besides the Amur tiger, Rosneft also supports research on rare animal species such as walruses, polar bears, reindeer, Sakhalin taimens, Black Sea bottlenose dolphins, leopards and others.
The Amur tiger is considered to be the representative of this species that lives the most far north. It is registered in the International Union for Conservation of Nature's Red List and Russian Red Data Book. According to recent estimates, the Amur tiger population in the south of the Russian Far East has about 600 animals.
Department of Information and Advertising
29 July,2021


text: Q: fundamentally Is there any changes between the asp.net and sql-server with old .Net framework 3.5 to 4.8 in the 2023? just i want to make sure that the fundamentals of asp.net are changed between the .Net framework version 3.5 to 4.8 and SQL-server all the fundamentals are outdated, and even in 2023 all the fundamentals of asp.net and SQL-server still apply now with old .net framework 3.5 to 4.8 or not? please help me.

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The charity has a very small number of units of independent accommodation. These are available for elderly but generally healthy persons who require no additional support. The accommodation is all fully self-contained, complete with fitted kitchens. The accommodation is suitable for anyone who just wishes to spend their retirement in a peaceful setting, yet be close enough to the facilities within Durham City. As the independent accommodation is in high demand, you may wish to consider our Almshousing also.


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The science-fiction film Advantageous takes place in an unspecified but not distant-looking future, rendered in washed-out digital video—an Upstream Color palette with splashes of low-budget but well-deployed visual effects. Those effects primarily function as an extension of the film's setting and set design, adding in distant explosions and drone-like flying machines that Gwen (Jacqueline Kim), the film's lead character, never encounters directly. As in a lot of good sci-fi, the movie is set in a particular world, but driven by the characters that inhabit it.
This intentionally stilted affect can turn a little narcotizing, especially during the movie's protracted midsection, when Gwen agonizes over difficult decisions after losing her job at the Center. She searches for a new one via an unseen network that sounds, based on its aggressive vocal prompts, like a LinkedIn gone mad with power. Phang makes smart use of a presumably low budget by introducing tech so integrated into life that it doesn't need to look ostentatiously futuristic. Advantageous, while too wan-looking to qualify as beautiful, has an otherworldly aura floating over its many recognizable sights.
Harder sci-fi eventually turns up when Gwen is forced to consider a drastic procedure to give her a much-needed edge in her fuzzily defined work—and one that will alter her relationship with Jules. The mother-daughter relationship is central to the movie, to the point where some scenes addressing familial melodrama (involving a nicely restrained Ken Jeong) feel diminished simply because they can't include the empathetic younger actress. In general, Advantageous underpopulates its vision—the unanswered question of where the unproductive, non-elite people have gone feels more logistical than philosophical. But the film feels less small when it comes to the ways Phang challenges the sci-fi (and indie-movie) status quo: using a predominantly Asian-American cast to tell a story about vanishing underclasses and the attempt to turn the promise of youthful exceptionalism into a dodgy renewable resource. That second part is vital to the movie's low-key effectiveness. When Gwen calls herself a "very satisfied test subject," it's chilling as both a piece of speculative fiction and in its eerie plausibility.


text:
Color Space 'Another World' Update Adds 3 New Scenes On Quest And Rift S
A new free update is available now for Oculus Rift and Quest app Color Space, which launched earlier this year. The update adds 3 new scenes to the game's existing 13 launch scenes, providing users with more beautiful artwork to color in VR.
Launched in March this year, Color Space is a VR game that allows users to color in pieces of beautiful art and environments in virtual reality. It takes the timeless appeal of coloring books and transfers them to an immersive medium to great success. The game launched with 13 scenes available, and this new update, titled the 'Another World' update, will add an additional three for those who have exhausted the existing options.
Color Space's "Another World" update is live on Quest and Rift.
The 3 new (free) scenes are some of our best, most detailed yet. Enjoy coloring in/on a lush and mystical planet with new, original music. (Plus an eyedropper!)https://t.co/rAq3xNQr8mhttps://t.co/82cuSgCR9r pic.twitter.com/ZLatj6i4EI
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We spoke to VR artist Danny Bittman about Color Space earlier in the year. Bittman works on the game as a creative director and composer, and has been creating art with VR tools for many years now. Some of Bittman's other works are available in VR apps like the Museum of Other Realities, but Color Space lets you collaborate with the artists for the first time. The game lets you color in works of art created by Bittman, most of which were initially created in other tools like Tilt Brush or Gravity Sketch.
The free 'Another World' update is available now for Color Brush. The game is available to purchase on the Oculus Store for Oculus Quest and Oculus Rift for $9.99.
Do you enjoy Color Space? Will you be trying out the new scenes from the update? Let us know in the comments.
Choose Your World's Hues In Color Space On Quest And Rift
The world might seem to be changing in ways you and your family are struggling…
Half-Life Opposing Force VR Oculus Quest Gameplay
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Oculus Rift "Step into the Rift" Event Liveblog
Today we finally get our first good look at the Oculus Rift. It has been…


See how insurers must look to balance innovation that benefits honest customers with robust fraud protection systems.
Being an insurance company is challenging enough without unnecessary exposure. Yet many insurers constantly expose themselves to risk by not detecting fraudulent activity early enough: when establishing the identity of policy applicants.
How bad has identity fraud become? According to Cifas, the UK's leading fraud prevention service, identity fraud in insurance increased by 16-fold in 2017 from 2016.
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The increased use of digital channels for both insurance applications and claims has given fraudsters a perfect method to test thresholds without interacting with a human. The insights they garner from trying to game applications and claims are then shared between groups.
Insurance companies are also starting to embrace straight-through processing (STP): the fully-automated initiation and completion of a transaction from start to finish, with minimal, if any, human intervention.
One key aspect of STP is streamlining the claims process, including 'low touch' or even 'touchless' claims, the intention being that genuine customers experience no unnecessary delays. This is great for high levels of customer satisfaction, better customer retention and lower claims processing costs for the insurer. Yet – as with any new technology advancement – the first to take advantage are fraudsters.
Insurers need to balance innovation that benefits honest customers with robust fraud protection systems to swiftly flag scams and verify policies and claims.
To see such high-end protection in action, you need look no further than Hastings Direct. The rapidly-growing general insurance provider is deploying our NetReveal fraud detection solution. The solution exploits Hastings' extensive data sources to accurately identify and investigate fraudsters to prevent claim and policy fraud, while minimising the impact on genuine customers.
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text:
November 7, 2016 | The Daily Briefing
Why a Cleveland Clinic doctor told his team to drop 'just' from their vocabularies
Tom Abelson sent an email to all staff after a woman told him she was 'just a float'
9:56 AM - November 7, 2016
Editor's note: This popular story from the Daily Briefing's archives was republished on May 9, 2019.
Tom Abelson, medical director of the Cleveland Clinic Beachwood Family Health & Surgery Center, recently sent a powerful email to all staff after a woman told him she was "just a float."
The national prescription for nurse engagement
Abelson shared his note with the Daily Briefing after reading a recent story about a woman who pushed back after being called "just a nurse."
The email
Resources for improving communication with physicians
Abelson's email read:
Beachwood family:
This week we had a Medical Assistant (MA) filling in for an ill MA in the ENT office. I asked her name and where she normally worked. She said, "I am just a float…"
I gently chastised her and asked her to repeat her answer, with pride, but without the word "just." "I am a float, and my name is…." sounded much better. In this scenario, on a busy day in the office with one MA out, no one was more important to us than a float MA. I hope she enjoyed what she did and that she did it with pride and with an appropriate sense of being a key part of our team that day.
I have heard myself say, "I am just a general Otolaryngologist." I have heard others say, "I am just a receptionist." I have heard someone say, "I am just the Medical Director in…," (a smaller Family Health Center).
So let's lose the word "just" at the Beachwood FHC. We can describe ourselves without the word "just" without losing the humility that we hopefully all feel as well. If you hear someone say it to describe themselves, gently correct them.
I'm just saying….Whoops…I'm saying let's have pride in what we do as individuals and as a team. We do great work in the Beachwood Family Health and Surgery Center.
"This type of messaging is spot on," said Laura Martin, a senior analyst with Advisory Board who has researched provider burnout.
"The sentiment of 'just' was a recurring theme in our burnout work," Martin added. "Time and again we heard from burned out physicians who felt like 'just a cog in the wheel.' Ensuring the entire care team feels recognized and valued is critical as we increasingly rely on their full, top-of-license support. No one is 'just' anything in today's world of increasingly demanding payers and value-conscious consumers."
Abelson also said his email generated many positive responses, and he agreed to share several with the Daily Briefing, while keeping the respondents anonymous.
One person replied, "Good morning, what a beautiful, inspirational message and email to open. A sincere thank you for sending that."
Another said, "Thanks for the reminder. I address this when I hear people use this term. I remind them of their value as a person."
And a third quipped, "Your email is 'just' great!!!"
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Just before you're about to face a Herculean task like say, running the London marathon, it's always nice to read some truly inspiring, positive piece about how you're going to find yourself along a spiritual journey whilst single-handedly solving the world's many problems with the money you've raised. Or at the very least, how a sweaty Chris Evans (ginger, not Hollywood) waved at you as he ran past.
What you probably don't want to hear about, is how someone's throat closed up in panic at mile 23 when they attempted to run again, having long since given in to walking and sobbing. But what I know now, is that this was my depression telling me I couldn't do it, and if running the marathon has taught me anything, it's that depression is a liar.
I wasn't keen to run a marathon. I got my place really late in the day, taking one of the charity places that open up in January when runners start getting injured and dropping out. My sister worked for The National Autistic Society at the time and convinced me to join her in running for them.
What Are Your Weird Christmas Traditions?
London Is Hosting A "Mindful Triathlon"
Depression had made me a social recluse and I needed something to force me out of the house at weekends; I used to love running and had even ran some half marathons in the past, "so maybe training for a marathon would help", said my sister. And because my illness had at this stage rendered me incapable of thought, and a lot like a zombie, I followed where she led.
Standing in the starting pen, however, I have never been surer of anything in my life than the fact that I simply did not want to be there. It reminded me of how I had felt two months earlier, when I was sitting opposite my doctor, filling out the tick-box form that informs them – and you – that yes, you are too-sad-to-function, and can't get up in the morning, and that it's probably time to start taking some medication.
I was getting myself to work and back every day because I had no choice but by night – and I mean every night – I was stealing my flatmate's cereal for dinner and leaving my dirty bowl in the sink before hiding in my bedroom and pretending I didn't exist. Which is why standing in a crowd of excited people waiting to run 26 miles suddenly felt a bit ridiculous. I couldn't even navigate the supermarket without having a crisis so why on earth was I at a charity run?
As we were waiting to cross the start line, a young man wearing the same charity vest as us was talking loudly, dolling out cuddles and generally getting into people's personal space. He was a big, burly guy with a rugby player's build and it was clear that some of the runners were uncomfortable around him. He was one of the runners with Autism running for the NAS that day and he was coping with the crowd and the build up far better than I was. Watching him talking to people and even receiving one of those hugs he was giving out spurred me on; if he could be so optimistic about this event, then so could I.
I crossed the start with a wave of determination: one foot in front of the other, as I had done with one pill after another, one day at a time.
"Go on Hansplat!" came the shouts from either side of the road as I ran into the tunnel by Canary Wharf with tears streaming down my cheeks. "You can do it!" No one had been cheering my name until this point and I assumed it was because they couldn't read the nickname I had written on my shirt but perhaps no one had felt like I needed spurring on until the tears came. I thought about how I really could have used this cheerleading squad to get me to work every day, or to ensure I completed the task of making a bowl of pasta for dinner.
I had long since convinced my sister to run ahead of me when my throat closed up in panic. It was just three miles from the end and somehow crossing the finish line felt more impossible than it had at mile one. But help was everywhere I looked, from the cheers at the side of me, to the runners who held up a hand in high five, or walked with me for a short while, to the man from the start of the race, who upon seeing me walking, took my hand and started running with me.
As I crossed the finish line, I stumbled into a man in the same charity vest with a shell-shocked look on his face. We clung together, I told him I had depression, wanted to be an actress and had just started taking anti-depressants. He told me he had Asperger's, was 22, about to move in with his girlfriend and wanted to be a stand up comedian. We had our photo taken together, collected our medals and our bags and parted ways.
At some point in the following weeks, my sister told me that one of the NAS runners had died in a motorcycle accident. It wasn't until months later, however, when I was preparing to run the Great North run that we realised it was this man I met at the end of the race who had died. The charity was doing a big support drive because his sister had decided to take his place in the race and, while looking for a photo of him at the marathon, my sister found a photo of he and I together. It felt strange to be overcome with tears for a man I had known for twenty minutes, but it felt so indescribably unfair; he hadn't so much as crossed the finish line as the finish line had crossed him.
We don't really know where the end is or how we are going to get there.
And that's the point. We don't really know where the end is or how we are going to get there or what troubles we will meet on the way – which is as true on a race day when the end seems too far to imagine as it is in life when all you can manage for dinner is a bowl of stolen cereal. If running has taught me anything about depression, or life in general, it's that all we need to do is just keep putting one foot in front of the other, and that help is there if we ask for it.
When the finish line finally comes, it will be the times you didn't "just do it" that you will regret. So whether it's running a marathon, or getting yourself out of bed and into the world, go and smash it, even if smashing it means limping over the finish line while sobbing into a stranger's armpit.


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Who can stop Germany sinking Cyprus, the PIGS and all? - Spear's Magazine
The UK now has seven social classes, a survey says. It's more like seven thousand
Sitwell Scoffs: A Wong and Hix Oyster and Chophouse
Who can stop Germany sinking Cyprus,...
Who can stop Germany sinking Cyprus, the PIGS and all?
4th April 2013 by Spear's
The president of Cyprus really is completely blind: the wreckage of the Cypriot economy is all around him ' courtesy of the euro
Spare a thought for Nicos Anastasiades, the president of Cyprus and the only blind man in that tiny kingdom of those with their eyes newly re-opened: 'I express my warm gratitude and deep appreciation towards the Cypriot people for the maturity and spirit of responsibility they have shown at a critical time for the stability of the Cypriot economy.'
All the people did was wait for the banks to re-open! Talk about lead balloons, but then the next day Nicos said that the good news was that at least Cyprus was still in the eurozone…
This man really is completely blind: the wreckage of the Cypriot economy is all around him – courtesy of the euro. But then, if my ancient Greek serves me right, his surname means 'the son of the man who stood above everything', while his Christian name means 'victory'!
So the blind leader of the people who stands above it all thinks that the Cypriot economy has been miraculously 'stabilised' by a €10.0 billion loan from Frankfurt – with added austerity as the coupon? It would all have been better managed by the Cyclops – at least he had one eye that worked.
THE MEANS WHEREBY the floored economy of Cyprus, which will now enter an unknowable and unquantifiable period of severe depression, can ever hope to recover, deprived of many local people's confiscated savings, is unknown.
Presumably the maths on how this loan can ever be repaid was worked out on the back of a fag-packet, which was conveniently thrown into the trash-can somewhere between Berlin and Frankfurt.
Pictured above: Nicos Anastasiades, president of Cyprus, holding up his hands as Angela Merkel has a gun to his back
The real point is this: the equally hapless PIGS are in exactly the same position. Mind you, Italy is waking up fast, thanks to Beppe Grillo, who can tell the government bad news while making them laugh.
His Five Star Movement took 25.5 per cent of the vote in the 2013 elections. That's not to be sniffed at, as this is reality dawning: Grillo won't form any coalition with any party or professional politician – they are either morally or intellectually corrupt in his opinion – and unfit to represent the people.
So no coalition for this man with his eyes wide open… and without him and his growing vote, Italy currently has no government, and gets on perfectly well day-to-day without one. But another election is certain – perhaps as early as June.
MEANWHILE, ANGELA MERKEL maintains her fiscal and monetary attack on the rest of the eurozone, with bail-out loans-for-austerity her only message. The fact that this policy is driving GDPs down, and therefore debt-to-GDP ratios up, should surprise no one.
The fact that this is a road to Erewhon for Germany and a road to Nowhere for everyone else hasn't yet dawned on the political elites who, like Nicos Anastasides, treat the euro as a Holy Grail. (I could mention Mario Draghi and Christine Lagarde of the Troika, who structured the latest bail-out, and many others besides.)
The fact that the single currency is the main cause of this month's record 12 per cent euro-zone unemployment, with 26.3 per cent in Spain – and 56 per cent for its under-25s – and it's worse for Greece. Watch out Nicos, that's where you and Cyprus are now headed too.
It is strangely symmetrical that Germany has racked up €1,000,000,000,000 of trade surpluses within the eurozone – that's one trillion euros – over the last decade, and that the Bundesbank has calculated that Germany's actual and collateral debts for propping up the same eurozone now come to the same sum – €1,000,000,000,000.
Will Merkel mislead the German electorate and squander the wealth and work of Germany by sleepwalking into an economic eurogeddon? Yes, disaster looms for the madcap scheme of imposing the single currency on seventeen completely disparate economies, the tool a shortly-to-be-defunct and deeply dysfunctional euro.
It is up to those new insurgent parties of left and right – Grillo's Five Star Movement, UKIP, Germany's UKIP-like Alternative für Deutschland – to kick out these brain-dead euro-scheming politicians before they destroy the wealth of nations.
Read more from Stephen Hill
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Did "Brown Sugar Cafe" meet your expectations?
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Brown Sugar is a coffee shop which is situated in Jaipur, near C Scheme. It offers distinctive dining experience with comfortable seating. You can enjoy here all indian flavours beverages, Bakery Product, Fast Food etc. And you can also order online at any place in Jaipur.


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In the morning hours of June 18 the following electronic and hand tools were stolen from my home. The list may grow!
2 each Bird 43 watt meter with three elements in each meter, plus two leather cases one Charcoal and one tan. Tan unit had other fillings and ISO-tee in the top lid.
25 each 6146W, 6146A and 6146B made by General electric/Sylvania in a large shoe box.
1. HeathKit SB-104A in large box with complete manuals have service stickers from RTO repair service, also extender boards.
1 Each Vibroplex "original" cw key gray base and chrome metal (purchase new in 1957) much history as a novice, Radioman in the U.S. Navy, she will be missed.


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Junta forces in Kalay arrest and kill man accused of making guns for resistance fighters
September 23, 2021 September 24, 2021 Pyi Soe NaingNews, What is happening
A family friend said Kyaw Min Oo, who owned a welding and lathing workshop, had never made weapons or been involved in armed resistance to the coup regime
A man died in junta custody after he was detained at his welding shop in Sagaing Region earlier this month on suspicion of making guns to supply resistance groups fighting to overthrow the dictatorship.
Kyaw Min Oo, 40, was arrested at his business in the industrial zone of Kalay Township, where he offered metal welding services and owned lathes, machines used to shape metal or wood.
Some 40 soldiers and 20 police officers arrived at the workshop at around 8am on September 14 and detained Kyaw Min Oo along with two other men, a family friend said. The soldiers found a metal pipe during a search and accused the men of making muskets.
The other detainees were a man named Shwe Lin and a neighbour of Kyaw Min Oo's who was ethnically Chin, the friend said, adding that he did not know the name of the latter. The two men's whereabouts are unknown.
"They didn't hit anyone during the arrests," the friend said. "They started interrogating them in front of everyone. They said they would release them later, but in truth we couldn't even meet them in person anymore. We couldn't send them food or clothes either."
Kyaw Min Oo was against the coup but had not taken part in any form of armed resistance and had never made weapons at his workshop, the friend said.
Many people in Sagaing own rudimentary, locally made hunting rifles. In the wake of the February 1 coup the region's residents were among the first to take up arms against the military.
Five days after his arrest, on September 18, an officer from the regional military headquarters in Kalay notified the family via local administrators that Kyaw Min Oo had died.
"When they asked them to give the body back, they said that they'd already cremated him," the friend said. "I fully believe that he was killed during interrogation as he was a strong man who could withstand harsh work."
"The dictators were never humane," he added. "We will only be able to live in peace if all the dictators are gone."
Kyaw Min Oo had been running the workshop for 13 years. He left behind a wife and two children, aged 5 and 12. On Tuesday his family offered food to monks in his name.
Several other people have been detained and killed by junta forces in Kalay in recent months. Around 90 others remain in custody after being detained in the town, according to locals and relatives of the detainees.
As of Wednesday, the junta has killed at least 1,114 civilians, including over 30 who died during interrogation, according to the latest tally by the Assistance Association for Political Prisoners.
Flash mob style protests have continued in Kalay almost daily despite the arrests and killings.
A resident of the town told Myanmar Now that the killings would not stop people from fighting the junta. "Everyone's revolting against them in every way that they can. No one's going to back down just because they killed someone," he said.
"Everyone knows they've been terrorising the people and their fear mongering tactics are obviously not working; the people are not backing down," he added. "If anything, they're going to revolt against them even harder."
(Source: Myanmar Now)
https://www.myanmar-now.org/en/news/junta-forces-in-kalay-arrest-and-kill-man-accused-of-making-guns-for-resistance-fighters
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Researchers must help Denmark develop the first autonomous ships.
DTU and the Danish Rail Sector Association are extending a contract designed to allow future engineers to learn more about the many fields of work in the rail sector.
DTU has opened the new Transport DTU centre, which was marked by a festive and academic event.


Corporate Social Responsibility (CSR) involves conscious shaping of the company image in local communities.
Considering the interests of the community, we build positive image of the company, minimize the distance from its customers and build loyalty, a fundamental value for everyone running their own business.
implementing CSR activities for building company image.


text:
Entertainment, Films, Books, Television
Tag: Forrest Wheeler
Fresh Off the Boat: Week in Review – Team Jessie-Lou (Review)
Fresh Off the Boat: Week in Review reveals that in Grandma Huang's day, the men made the money and killed the spiders. That when the word lice is uttered, everyone takes one step back. Team Jessie-Lou fall apart but get back together and Eddie not only saves the day but learns that "being homeless" is no big deal. Two of the Huang children bring lice home.
Fresh Off the Boat: Week in Review reveals that in Grandma Huang's day, the men made the money and killed the spiders. That when the word lice is uttered, everyone takes one step back. Team Jessie-Lou falls apart but gets back together and Eddie not only saves the day but learns that "being homeless" is no big deal. Two of the Huang children bring lice home.
We also learn, according to Jessica, that lice are the "pedophiles of the insect world."
Before the oldest Huang son is ejected out of the house for being lice infected, Eddie and his friends discuss the upcoming student faculty basketball game; the winning team gets pizza. In class later, Eddie talks smack with his teacher Mr G (Al Madrigal). The teacher tells Eddie if he gets a basket against him, that Eddie will have no homework for the rest of the year.
Eddie responds that if he does not score, the teacher can give him double homework. His schoolmates are horrified. Jessie and Louis are shocked that Eddie has lice (We didn't come to this country so our son could get lice, says an annoyed Louis). Later Evan comes home infected as well. Both boys are evicted from the family home until the lice are gone.
Comic Moments:
Jessica yelling at Trent:
"Wait, no, Big Red, keep your distance! Eddie is DISEASED!"
Evan telling his parents that he has lice and stretching his arms out for a hug, "Bao bao." The next shot has the littlest Huang outside with Eddie. Jessica places her hand, which is made into Vulcan palms through the window.
Louis and Jessica covered from head to toe to treat Eddie and Evan.
Emery not getting lice and saying, "Wait. How come I don't have lice? I mean, how do you not want to be up in this?" He follows this with a self-satisfied slow-motion hair flip.
Eddie flinging his towel into the crowd at the gym and their horrified reaction to it.
Jessica's "Louis."
Grandma Huang watching Jerry Springer and chanting "Jer-ry, Jer-ry."
Jessica admiring the team name that Louis has made up for their team, Jessie-Lou.
Jessica: "Sounds like a country girl who's has sex too early."
Louis and Jessica hugging, even though she has gotten lice from Eddie. As the couple embrace she says:
Jessica: "Louis, I just want you to know that if you get lice, I'm putting you outside with Eddie."
Louis (sighing): "I know. I've always known."
Overall Thoughts:
Not having watched the first season, except via binging after the fact, it took some time to really get into Fresh Off the Boat and its "based on real people" characters and storylines. However…
The performances of the actors, the writing and the chemistry between the players makes this a brilliant, funny and addictive show to watch.
Park and Wu are perfect together and how can one not adore Constance Wu's Jessica? The scene where Louis has worked out four minutes of "alone time" is cute and funny.
Louis: "And, uh, ooh, here I made a note that Wednesday afternoon, you and I have the whole house to ourselves… for four minutes."
Jessica: (Giggling) " Louis."
Louis: "And you know I only need two."
Jessica: (Giggling) "Louis."
Louis: "Shall I pencil you in… twice?"
Jessica: "Oh, Louis."
Later on the day of the "four minutes" there is more romance and Jessica "Oh Louis-ing" again until Eddie returns with the news he has lice.
Everyone clicked in this episode. Hudson Yang, Forrest Wheeler, Ian Chen as the Huang kids were spot on (Ian Chen's little gasp/scream at the sight of his stuffed toys in the box was brilliant) and Hudson just rocked it in this episode.
HUDSON YANG, AL MADRIGAL
Even the school friends of Eddie's were a delight. The smack talking sequence with the teacher was hysterical as was Eddie's saving the day and announcing he has superpowers while eating the victory pizza.
Fresh Off the Boat continues to deliver the laughs and is another addictive comedy. The show airs Tuesdays on ABC.
Author Mike's Film TalkPosted on March 30, 2016 Categories TelevisionTags Constance Wu, entertainment, episode 18, Forrest Wheeler, Hudson Yang, Ian Chen, Randall Park, review, Season TwoLeave a comment on Fresh Off the Boat: Week in Review – Team Jessie-Lou (Review)
Fresh Off the Boat: Michael Chang Fever – Checkmated at Tennis (Review)
Fresh Off the Boat: Michael Chang Fever proves that just when it seems that the Huang family comedy has peaked in terms of humor, this episode goes and knocks it out of the park. (Or more correctly, serves an ace with blazing comedic grace.)
Fresh Off the Boat: Michael Chang Fever proves that just when it seems that the Huang family comedy has peaked in terms of humor, this episode goes and knocks it out of the park. (Or more correctly, serves an ace with blazing comedic grace.) The episode opens with the Huang boys complaining about matching "Cool" backpacks and handing over the school's career guidance letters.
Jessica (Constance Wu) informs her brood that it is her job to tell them what their jobs will be. In mid sentence she is amazed and excited to learn that Evan's ideal job is surgeon general. Eddie reacts with derision.
Eddie: "Dang fool, even by Chinese standards you're a nerd."
Evan replies,"My backpack doesn't agree with you."
Mom is less thrilled at Emery's ideal position; flight attendant. Jessica's reaction is priceless:
"Flight attendants don't even get a seat on the plane, they walk back and forth pushing a cart…They're the homeless of the sky!"
Evan (Ian Chen) returns to the room back wearing a white coat and a toy stethoscope. He pokes Eddie and tells his big brother:
"Sir, your lab results are in. Your diagnosis–butt face."
Eddie stares at the littlest Huang without expression.
This opening sets the comedic tone for the episode, where Lewis (Randall Park) rushes the family to the settee to watch Michael Chang play tennis in the Australian Open. As he explains how luck the family are to have a proper Asian athlete to follow, Eddie and Evan manage to make good their escape. Only Emery stays to watch.
Later, after Lewis explains how much money can be made from being tennis professional, Jessica ordains that Emery will learn the game to keep him from becoming a flight attendant. At the local club, Emery (Forrest Wheeler) proves to be a natural, he is so adept at the sport that even tennis legend Billie Jean King is impressed.
Emery is so good that Jessica puts him in Eddie's room and forces the oldest Huang child to share with Evan. Eddie (Hudson Yang) learns that Evan is being bullied for Pogs and offers advice on how to "be a bear" and put the bully off.
As the middle son wins his way through the "Under 16" tennis tournament, Jessica and Lewis react with excitement until Emery fires them both and "hires" Ms. King to be his coach. Eddie learns that the bear trick does not work and he steps in to help out Evan.
FORREST WHEELER, BILLIE JEAN KING
It is revealed that Evan is not being bullied, he owes the Pogs to a girl (a fifth grader) who keeps beating him at the game. Big brother actually helps Evan to win and it is revealed that the youngest sibling has a "gambling problem."
Grandma (Lucille Soong) Huang is enlisted by Eddie to help him beat the fifth grader, Emery wins the "Under 16" tournament, and realizes that as much as he likes the winning, he really would rather be a flight attendant.
Jessica plants a seed about becoming an astronaut instead. Evan and Eddie develop a great brotherly bond, although the eldest does rearrange the star stickers on the ceiling from Evan's Equinox setting to spell out "Wu-Tang."
Standout Moments:
Ian Chen, doing his Ken Jeong "Dr. Ken" impression talking smack. Priceless.
Emery and Billie Jean King doing the "winner's perm stroll."
Lewis and Jessica's reactions each time that Emery wins a match.
Billie Jean King's temper tantrum.
Constance Wu's line "checkmated at tennis…"
Constance Wu totally rocking it this week, easily becoming a firm favorite as more of her character is revealed. Wu is brilliant at understated comedy and this episode proves it.
This was, perhaps, the best Fresh Off the Boat yet. The storyline, Billie Jean King (along with all those 90s tennis star references, Sampras, Becker, Agassi and of course Michael Chang) plus the pop culture reference to the 1998 video Wu-Tang; starring the band-members made this one especially brilliant.
Ian Chen just finished a "cross-over" role on Dr. Ken playing Dave Park's decoy for the babysitter. Randall Park is due to play role on Jeong's comedy series as well and "Dr. Ken" himself is also slated to appear on Fresh Off the Boat. Both these Asian comedies, with very different approaches to the genre, are funny and addictive.
Fresh Off the Boat, with its basis in fact (a re-telling of the real Eddie Huang's childhood) is not filmed in front of a studio audience and is more of a cultural comedy than Dr. Ken but both shows work very well. In each show, the family's children; Molly and Dave Park and the three Huang boys, have young actors who kill it every week in terms of performance.
Fresh Off the Boat airs Tuesdays on ABC. Tune in and discover a great comedy that just keeps getting better and better.
Author Mike's Film TalkPosted on February 24, 2016 Categories TelevisionTags ABC, Bill Purple, Billie Jean King, Constance Wu, entertainment, Episode 14, Forrest Wheeler, Fresh Off the Boat, Hudson Yang, Ian Chen, Randall Park, review, Season 2Leave a comment on Fresh Off the Boat: Michael Chang Fever – Checkmated at Tennis (Review)
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