text:
\section{Introduction}
\label{sect_intro}

Due to annotation artifacts, existing datasets contain certain biases.\footnote{In this work, the term bias refers to the label bias as defined by \citet{shah-etal-2020-predictive}, i.e., the conditional distribution of the target label diverges at test time based on specific attributes of the training data.} Models often rely on these biases to perform well on the corresponding evaluation set, which also includes similar biases \citep{N18-2017,poliak-etal-2018-hypothesis,mccoy-etal-2019-right,gardner2020evaluating}.
As a result, the model learns the spurious patterns in the data instead of the intended phenomena of the dataset, which in turn limits the robustness and makes models vulnerable against adversarial evaluations \citep{mccoy-etal-2019-right,nie2019analyzing}.
The adversarial evaluation sets consist of counterexamples in which relying on the bias results in incorrect predictions.
Overcoming such biases is an important challenge in developing robust NLP models.

The majority of existing works improve the robustness against a given bias by proposing new methods or training paradigms \citep{he-etal-2019-unlearn,clark-etal-2019-dont,mahabadi2019simple,utama2020mind,utama-etal-2020-unknown,wu2020improving}.
The common component in such methods is a bias model that is trained to detect training examples that can be solved only using a bias.
This information is then used for ignoring or down-weighting biased training examples \citep{he-etal-2019-unlearn,clark-etal-2019-dont,mahabadi2019simple}, or tuning the confidence of the model on such examples \citep{utama2020mind}.
While these methods are very effective in improving the robustness against the targeted bias, they have two shortcomings:
\begin{itemize}
	\item They mostly target a specific bias and discourage the model from learning that bias. However, they may bias the model in other unwanted directions and therefore hurt the overall robustness. \footnote{The concurrent work of \citet{utama-etal-2020-unknown} and \citet{wu2020improving} are the exceptions to this trend, in which they address multiple biases together and show that their debiasing methods improve the overall robustness.}
	\item They are only applicable to scenarios in which the training examples contain the bias. However, as we show in this paper, a model can still be vulnerable to a specific bias even if the training examples do not explicitly exhibit that bias.
\end{itemize}

An alternative approach is to augment the training data with additional counterexamples for the bias \citep{mccoy-etal-2019-right,elkahky-etal-2018-challenge}. This may also result in overfitting to the augmented counterexamples and hurting the overall robustness \citep{nie2019analyzing}.

In this paper, we propose to augment existing training sentences with their corresponding predicate-argument structures.
The motivation of using predicate-argument structures
is to provide a higher-level abstraction over
different surface realizations of the same underlying
meaning.
We examine the impact of this linguistic augmentation on pre-trained transformers, e.g., BERT \cite{bert}, which achieve state-of-the-art performances on numerous NLP datasets.
The addition of predicate-argument structures to input sentences helps the model
to recognize and focus on more important parts of sentences, and therefore, to learn a different attention pattern.

The findings of this paper are as follows:
\begin{itemize}
 \item We show that the model may still be vulnerable to a specific bias even when training examples of the target task do not contain that bias. We propose new adversarial sets for evaluating the robustness of models in such scenarios based on the SWAG dataset and the lexical overlap bias.  Lexical overlap is a common bias in various NLP datasets, e.g., natural language inference
  \citep{mccoy-etal-2019-right}, or question answering \citep{Jia_2017}.
  We show that the performance of pre-trained transformers that are fine-tuned on the SWAG dataset \citep{zellers-etal-2018-swag} drop below a random baseline on evaluation sets that contain this bias.

\item Our results show that without targeting a specific bias or adding additional training examples, the proposed sentence augmentation improves the robustness of the model against various types of biases. Besides, we show that the sentence augmentation is effective in both scenarios when the training data contains or does not contain the bias. Our approach only requires the augmentation for the training sentences and does not require any changes to the test data, and therefore, does not add any additional cost at the test time.

\item Our results emphasize the importance of evaluating the impact of debiasing methods on more than one adversarial set and with more than one base model.

\end{itemize}

 %
\section{Related Work}
\paragraph{Debiasing Methods.}
Existing debiasing solutions fall into one of these two categories: (1) extending the training data with additional counterexamples, and (2) proposing a new approach that recognizes biased examples in the training data and then using this knowledge during training \citep{utama2020mind,he-etal-2019-unlearn,clark-etal-2019-dont,mahabadi2019simple,utama-etal-2020-unknown,wu2020improving}.

The first type of solution is to identify the bias and augment the training data with counterexamples in which relying on the targeted bias results in incorrect predictions.
While augmenting the training data with counterexamples improves the results on the targeted bias, it may hurt the overall robustness \citep{nie2019analyzing}.
As mentioned by \citet{jha2020does}, while augmentation with counterexamples helps the model to unlearn the targeted bias, it is unlikely that it encourages the model to rely on more generalizable features of the data.

The approaches of the second category first use a bias detection model for recognizing training examples that contain the bias. They then either (a) train an ensemble of the bias model and a base model so that the base model only learns from non-biased examples \citep{he-etal-2019-unlearn,clark-etal-2019-dont,mahabadi2019simple}, (b) change the importance of the biased training examples in the training objective \citep{schuster-etal-2019-towards,mahabadi2019simple}, or (c) change the confidence of the model on biased examples \citep{utama2020mind}.

The shortcoming of existing debiasing methods is that they mostly model a single bias and only evaluate the impact of the proposed method on the adversarial evaluation set of the targeted bias.
Therefore, while they improve the performance on the targeted adversarial sets, they may hurt the overall robustness.
The recent work of \citet{utama-etal-2020-unknown} and \citet{wu2020improving} are the exceptions in which they show that their proposed debiasing frameworks improve the overall robustness, and hence the generalization across different datasets in natural language understanding and question answering, respectively.
\citet{utama-etal-2020-unknown} propose a new framework that automatically recognizes biased training examples and does not require predefining bias types. Therefore, the recognized biased examples may contain various bias types.
\citet{wu2020improving} propose a framework for modeling multiple known biases concurrently. To do so, they propose to combine two bias weights in the training objective including (a) a dataset-level weight indicating the strength of the bias in the datasets, and (b) an example-level weight indicating the strength of the bias in a training example.
The common finding in both works is that debiasing based on multiple biases is a key factor in improving the overall robustness.

Compared to existing debiasing methods:
\begin{itemize}
	\item Our proposed approach does not include an additional model to recognize biased examples or additional training examples.
As we show in Section~\ref{robustness_results}, since it does not target any specific bias, it improves the robustness of the baseline model against multiple biases.
\item Since it does not require recognizing biased examples, it is applicable to improve robustness against biases that do not exist in the training examples.
\end{itemize}

\paragraph{Using Linguistic Structures for Neural Models.}
The use of linguistic information in recent neural models is not very common.
The use of such information has been mainly investigated for tasks in which there is a clear relation between the linguistic features and the target task.
For instance, various neural models use syntactic information for the task of semantic role labeling (SRL) \cite{roth-lapata-2016-neural,marcheggiani-titov-2017-encoding,strubell-etal-2018-linguistically,swayamdipta-etal-2018-syntactic}, which is closely related to syntactic relations, i.e., some arcs in the syntactic dependency tree can be mirrored in semantic dependency relations.

\newcite{marcheggiani-titov-2017-encoding} build a graph representation from the input text using their corresponding dependency relations and use graph convolutional networks (GCNs) to process the resulting graph for SRL.
They show that the incorporation of syntactic relations improves the in-domain but decreases the out-of-domain performance.

Similarly, \newcite{cao-etal-2019-qa} and \newcite{dhingra-etal-2018-neural} incorporate linguistic information, i.e., coreference relations, in their model and show improvements in in-domain evaluations.

\newcite{strubell-etal-2018-linguistically} use linguistic information, i.e., dependency parse, part-of-speech tags, and predicates for SRL using a transformer-based encoder \cite{NIPS2017_7181}.
They make use of this linguistic information by (1) using multi-task learning, and (2) supervising the neural attention of the transformer model to predict syntactic dependencies.
They use gold syntax information during training and predicted information during the test time.
Their model substantially improves both in-domain and out-of-domain performance in SRL.
However, these results are then outperformed by a simple BERT model without using any additional linguistic information \cite{shi2019simple}.

\newcite{moosavi-strube-2018-using} examine the use of various linguistic features, e.g., syntactic dependency relations and gender and number information, as additional input features to a neural coreference resolver. They show that using informative linguistic features substantially improves the generalization of the examined model.

In a similar direction, \newcite{moosavi2019improving} improve the robustness by enhancing the input representations. They did so by adding a set of simple features to the input where the input is a pair of text sequences and show that it improves generalization across similar datasets and tasks.

All the above approaches require additional linguistic information, e.g., syntax, both during the training and the test time.
\newcite{swayamdipta-etal-2018-syntactic}, on the other hand, only make use of the additional syntactic information during training. They use multi-task learning by considering syntax parsing as an auxiliary task and minimizing the combination of the losses of the main and auxiliary tasks.
They use syntactic information for the tasks of SRL and coreference resolution.
They show that this information slightly improves the in-domain performance.
In this work, we do not change the loss function and only augment the input sentences of the training data.
The advantage of our solution is that it does not require any changes in the model or its training objective. It can be applied to all the transformer-based models without changing the training procedure.

\paragraph{Using Predicate-Argument Structures.}
Predicate-argument structures have been used for improving the performance of downstream tasks like machine translation \cite{liu-gildea-2010-semantic,bazrafshan-gildea-2013-semantic}, reading comprehension \cite{berant-etal-2014-modeling,wang-etal-2015-machine}, and dialogue systems \cite{turdialogue,chen-et-al:2013:PAPERS}.
However, these approaches are based on pre-neural models.

The proposed model by \newcite{marcheggiani-etal-2018-exploiting} for neural machine translation is a sample neural model that incorporates predicate-argument structures.
Unlike this work, \newcite{marcheggiani-etal-2018-exploiting} incorporate these linguistic structures at the model-level. They add two layers of semantic GCNs on top of a standard encoder, e.g., convolutional neural network or bidirectional LSTM. The semantic structures are used for determining nodes and edges in the GCNs.
In this work, however, we incorporate these structures at the input level, and only for the training data. Therefore, we can use the state-of-the-art models without any changes.

Overall, this work differs from the related work because (1) it evaluates the use of predicate-argument structures for improving the robustness of transformer-based models on natural language understanding tasks, and (2) it uses these structures at the input level to extend raw inputs, (3) it only employs this information during training, and (4) it requires no changes in the model or the training procedure.

 %
\section{Augmenting Input Sentences with Predicate-argument Structures}
We augment the raw text of each input sentence in the training data with its corresponding predicate-argument structures.
We use the PropBank-style semantic role labeling model of \newcite{shi2019simple}, which has state-of-the-art results on the CoNLL-2009 dataset.
We specify the beginning of the augmentation by the \texttt{[PRD]} special token that indicates that the next tokens are the detected predicate.\footnote{Special tokens are atomic, i.e., they are not split by the tokenizer.}
We then specify the ARG0 and ARG1 arguments, if any, with \texttt{[AG0]} and \texttt{[AG1]} special tokens, respectively.
The end of the detected predicate-argument structure is also specified by the \texttt{[PRE]} special token.
If more than one predicate is detected for a sentence, we at most add the first three detected predicate-argument structures.\footnote{In our preliminary experiments, we found out that this setting works better than adding all of them.}
Figure~\ref{fig:sentence_pred_arg_example} shows an example for an augmented sentence.

\begin{figure}[htb]
    \centering
\fbox{\begin{minipage}{0.998\columnwidth}

\textbf{Original:} Someone takes the drink, then holds it.

\textbf{Augmented:} Someone takes the drink, then holds it.  \texttt{[PRD]} takes \texttt{[AG0]} Someone \texttt{[AG1]} the drink \texttt{[PRE]}   \texttt{[PRD]} holds \texttt{[AG0]} Someone \texttt{[AG1]} it \texttt{[PRE]}
\end{minipage}}
\caption{Augmenting the text of an input sentence with its predicate-argument structures.}
    \label{fig:sentence_pred_arg_example}
\end{figure}
 %

\section{Impact of Sentence Augmentation on Improving Robustness }
In this section, we explain the datasets and models that we use in our experiments, as well as the result of the sentence augmentation compared to other recent debiasing methods.

\subsection{Training Data}
As mentioned, we have evaluated the impact of the proposed augmentation on two different settings: (1) when the training data contains the investigated biases, and (2) when training examples do not explicitly contain the bias.

\paragraph{Training Data with Biases (MultiNLI).}
For evaluating the impact of data augmentation when the training examples are biased, we use MultiNLI \cite{N18-1101} for training, which is the largest available dataset for Natural Language Inference (NLI).
Given a premise and a hypothesis, NLI is the task of  determining whether the hypothesis is entailed, contradicts, or is neutral to the premise.

Various studies show that similar to many other NLP datasets, MultiNLI contains various biases.
For instance, hypothesis sentences may contain words that are highly associated with a target label, regardless of the premise \citep{N18-2017,S18-2023}.
This bias is referred to as the hypothesis-only bias.
Another well-known bias in MultiNLI is the lexical overlap bias, i.e., the label of most premise-hypothesis pairs with overlapping words is entailment.

\paragraph{Training Data without the Bias (SWAG).}
For the second setting, we evaluate the impact of the augmentation to improve the robustness against the lexical overlap bias. Lexical overlap is a common bias in various NLP datasets, e.g., NLI
\citep{mccoy-etal-2019-right}, duplicate question detection \citep{zhang-etal-2019-paws}, or question answering \citep{jia-liang-2017-adversarial}.%
We use the SWAG dataset \citep{zellers-etal-2018-swag} as the training data.

Given a premise about a situation, the task of the SWAG dataset, i.e., grounded commonsense reasoning, is to reason about what is happening and to predict what might come next.
The task is modeled as a multiple choice answer selection.
For instance, ``The tutorial starts by showing each part of the drum set up close'' is a correct ending for the premise ``A man in a black polo shirt is sitting in front of an electronic drum set''.

If we train the bias model of \citet{clark-etal-2019-dont} for solving the task only based on lexical overlap features, it only achieves 26\% accuracy on SWAG, which is around the random baseline, while it
achieves 65\% accuracy on MultiNLI.\footnote{The details of this bias model is reported in the supplementary materials.}
This indicates that the examples in the SWAG dataset are not affected by this bias.
Please note that the SWAG dataset may also contain various biases. However, it does not contain the lexical overlap bias.

\subsection{Evaluation Sets}
In this section, we discuss the adversarial evaluation sets that we use to evaluate the robustness of the model.
Apart from the adversarial sets, which are the \textbf{out-of-distribution} evaluation sets compared to the training data distribution, we also report the performance on the corresponding development set as the \textbf{in-domain} performance.
\subsubsection{MultiNLI Evaluation Sets}
We use the following adversarial sets for evaluating models that are trained on MultiNLI:

\paragraph{MultiNLI \textbf{Hard}:} \citet{N18-2017} introduce a hard split for MultiNLI evaluation sets in which models cannot predict the correct labels using the hypothesis-only bias.

\paragraph{HANS:} \citet{mccoy-etal-2019-right} create this dataset for evaluating the lexical overlap bias. Sentence pairs in HANS include various forms of lexical overlap, namely \emph{lexical overlap}, \emph{subsequence}, and \emph{constituent}.

In the \emph{lexical overlap} subset, all words of the hypothesis appear in the premise.
For instance, ``The doctor was paid by the actor'' and ``The doctor paid the actor'' sentence pair belong to this subset.

The \emph{subsequence} subset contains hypotheses, which are a contiguous subsequence of their corresponding premise.
``The doctor near the actor danced'' and ``The actor danced'' are sample sentence pairs from this subset.

Finally, in the \emph{constituent} subset, hypotheses are a complete subtree of the premise.
For example, ``If the artist slept, the actor ran'' and ``The artist slept'' belong to this subset.

\paragraph{Stress Test:} \citet{naik-etal-2018-stress} provide adversarial evaluation sets based on weaknesses of state-of-the-art NLI models. We use \emph{negation}, \emph{word overlap}, and \emph{length mismatch} sets from the stress test, in which a tautology is added at the end of the premise or hypothesis in MultiNLI.

\begin{itemize}
\item \textbf{Negation}: the tautology ``and false is not true'' is added to the end of all the hypothesis sentences in the MultiNLI development set for creating this evaluation set. The presence of the negation word ``not'' may confuse the model to predict contradiction.
\item \textbf{Word Overlap}: For creating this evaluation set, \citet{naik-etal-2018-stress} append the tautology ``and true is true'' to the end of all the hypothesis
sentences in the MultiNLI development set.
\item \textbf{Length Mismatch}: the tautology ``and true is true'' is appended five times to the
end of the premise sentences in the MultiNLI development set for creating this adversarial evaluation set.
\end{itemize}

\subsubsection{SWAG Evaluation Sets}
We created three different adversarial datasets based on the SWAG development set for evaluating the lexical overlap bias.
These datasets evaluate the model's understanding of (1) syntactic variations, (2) antonym relations, and (3) named entities in the presence of high lexical overlap.

The common property in all three evaluation sets is a high lexical overlap between the sentence pairs.
In all these evaluation sets, one of the incorrect endings is replaced with a new incorrect ending that has a high lexical overlap with the premise.
Since the new incorrect endings are created automatically, they may contain sentences that are not meaningful.
For instance, the syntactic variations subset contains the incorrect endings ``a key holds up someone'' and ``The last page flips to the writer'' for the  ``Someone holds up a key'' and ``The writer flips to the last page'' premises, respectively.
Humans can recognize such sentences are not meaningful, and therefore, they are not a plausible ending for given premises.
However, as we will see, because of the lexical overlap bias, the model mostly selects the new incorrect endings.

\paragraph{Syntactic Variations:}
\label{sect:compositional}
In this evaluation set, we take premises that contain subject-verb-object structures from the \emph{SWAG} development set.
We then construct a new negative ending by swapping the subject and object of the premise and replace one of the existing negative endings with the new one.\footnote{We use Stanford parser \citep{chen-manning-2014-fast} for detecting subjects and objects.}
This dataset includes 20K samples. It is similar to a subset of the \emph{lexical overlap} subset in HANS, as well as the adversarial evaluation that is explored by \newcite{bansal:AAAI:2019} for NLI.

\paragraph{Antonym Relations:}
\label{sect_antonym}

In this test set, we create a new negative ending by replacing the first verb of the premise with its antonym.
We use WordNet for antonym relations.
This adversarial setting is also common in NLI, e.g., \cite{naik-etal-2018-stress,glockner-etal-2018-breaking}.
As an example, ``A lot of people are \emph{standing} on terraces in a big field and people is walking in the entrance of a big stadium'' is an incorrect ending for the ``A lot of people are \emph{sitting} on terraces in a big field and people is walking in the entrance of a big stadium'' premise in this evaluation set.
This set contains 7476 samples.

\paragraph{Named Entities:}
In this adversarial dataset, a new incorrect ending is created by replacing one of the named entities of the premise with an unrelated named entity.\footnote{We use the Stanford named entity recognizer \cite{finkel-etal-2005-incorporating} for determining the named entities.}
For instance, based on the ``The reflection he sees is \emph{Harrison Ford} as someone Solo winking back at him'' premise, we create ``The reflection he sees is \emph{Eve} as someone Solo winking back at him.'' as the new incorrect ending.
This test set contains 190 samples.

\subsection{Base Model}
We use the Bert-base-uncased model \citep{devlin-etal-2019-bert} as the base model.\footnote{We use Huggingface Transformers \cite{Wolf2019HuggingFacesTS}.}
\emph{Bert-orig} refers to the results when the model is trained on the original training data.
\emph{Bert-aug} represents the results when the base model is trained on the augmented data.
The set of all parameters are the same in \emph{Bert-orig} and \emph{Bert-aug}.
Besides, the evaluation data is the same for both \emph{Bert-orig} and \emph{Bert-aug} experiments, and their only difference is their training data.

We compare our results with the confidence regularization approach of \citet{utama2020mind} and the product-of-expert approach \cite{he-etal-2019-unlearn,clark-etal-2019-dont}.
They both use Bert-base-uncased as the base model.

\emph{CR(lex)} and \emph{CR(hypo)} refer to the confidence-regularization method when it is debiased based on the lexical overlap and hypothesis-only biases, respectively.
Similarly,  \emph{POE(lex)} and \emph{POE(hypo)} show the product-of-expert results based on the lexical overlap and hypothesis-only biases, respectively.

We use the same set of hyper-parameters\footnote{I.e., batch size=16, learning rate=2e-5, and the same set of random seeds.}
for all the models and report the average performance using five different random seeds for all the results, i.e., Table~\ref{tab:sota_comparison}-Table~\ref{tab:swag_all}.
As reported by \citet{mccoy2019} and \citet{zhou2020curse}, the performance on the adversarial evaluation sets can highly vary given different values of hyper-parameters.\footnote{E.g., The accuracy on the non-entailment examples in the lexical overlap subset of HANS can vary between 6\% to 54\% for the BERT-base model using different random seeds.}
Therefore, to ensure a fair comparison, results on adversarial sets should be reported using the same parameters in out-of-distribution evaluations.

\begin{table*}[!htb]
\footnotesize
	\begin{center}
\resizebox{\textwidth}{!}{%

		\begin{tabular}{ l r | rr  r|r |rrr }
				\toprule
		 & \multicolumn{1}{c}{\textbf{In-domain}} & \multicolumn{3}{c}{\textbf{HANS}} & \multicolumn{1}{c}{\textbf{Hard}} & \multicolumn{3}{c}{\textbf{Stress Test}}  \\
 \textbf{Model} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{lex.} & \multicolumn{1}{c}{subs.} & \multicolumn{1}{c}{const.} & \multicolumn{1}{c}{}  & \multicolumn{1}{c}{{Negation}} & \multicolumn{1}{c}{{Overlap}} & \multicolumn{1}{c}{{Length}} \\
        \midrule
        BERT-orig & 84.2$\pm$0.3 & 62.9$\pm$7.8 & 52.1$\pm$0.9 & 56.0$\pm$1.4 & 75.3$\pm$0.6 & 55.5$\pm$0.6 & 59.5$\pm$1.1 & 81.3$\pm$0.3\\
        \midrule
        CR(lex) & \textcolor{gray}{83.7}$\pm$0.1 & 62.1$\pm$2.7 & {60.9}$\pm$3.7 & {64.2}$\pm$1.6 & \textcolor{gray}{74.8}$\pm$0.3  & 55.6$\pm$0.4 & 59.5$\pm$1.0 & 81.3$\pm$0.3 \\
        CR(hypo) & \textbf{84.4}$\pm$0.2 & 68.5$\pm$8.3 & {53.9}$\pm$1.2 & 56.2$\pm$0.8 & {77.2}$\pm$0.4  & \textcolor{gray}{55.1}$\pm$0.4 & 59.5$\pm$1.3 & \textbf{81.5}$\pm$0.3  \\

        PoE(lex) & \textcolor{gray}{82.6}$\pm$0.2 & 69.1$\pm$5.9 & \textbf{68.1}$\pm$9.6 & \textbf{68.8}$\pm$3.6 & \textcolor{gray}{73.2}$\pm$0.2& 55.6$\pm$0.4 & \textcolor{gray}{59.1}$\pm$0.8 & \textcolor{gray}{80.7}$\pm$0.4 \\

        PoE(hypo) & \textcolor{gray}{83.3}$\pm$0.4 & 66.6$\pm$7.3 & 53.1$\pm$1.2 & 58.8$\pm$2.3 & \textbf{77.8}$\pm$0.7 & \textcolor{gray}{55.2}$\pm$0.5 & 59.7$\pm$0.8 & \textcolor{gray}{80.5}$\pm$0.3 \\

                BERT-aug & \textbf{84.4}$\pm$0.1 & \textbf{69.8}$\pm$5.6 & {53.5}$\pm$1.5 & 58.3$\pm$2.0 & {75.9}$\pm$0.3  & \textbf{56.3}$\pm$0.2 & \textbf{60.2}$\pm$1.4 & \textbf{81.5}$\pm$0.3\\

        \bottomrule
		\end{tabular}
}
	\end{center}
	\caption{Comparing the impact of the augmentation to the confidence regularization (CR) \citep{utama2020mind}, and product-of-expert (POE) \citep{he-etal-2019-unlearn,clark-etal-2019-dont} methods debiased for the lexical overlap (lex) and hypothesis-only (hypo) biases. E.g., CR(lex) is the confidence regularization method debiased based on lexical overlap. All models are trained on MultiNLI with the same hyperparameters.
	%
	Highest scored on each dataset are boldfaced. Scores that are lower that BERT-orig are marked in gray.}
	\label{tab:sota_comparison}
\end{table*}

\begin{table}[!htb]
\footnotesize
\resizebox{\columnwidth}{!}{%
	\centering
		\begin{tabular}{ l r | rrr}
				\hline
		\textbf{Model} & \multicolumn{1}{c}{\textbf{Dev.}} & \multicolumn{1}{c}{\textbf{Syntax}} & \multicolumn{1}{c}{\textbf{Antonym}} & \multicolumn{1}{c}{\textbf{NEs}} \\
        \hline
        BERT-base & \textbf{81.1}$\pm$0.1 &27.7$\pm$0.9 & 18.3$\pm$1.7 & 7.9$\pm$1.1\\
        BERT-aug & 79.1$\pm$0.3 & \textbf{47.1}$\pm$1.4 & \textbf{36.3}$\pm$1.6 & \textbf{15.9}$\pm$1.7\\ %
        %
        %
        %
        %
        \hline
		\end{tabular}
	}
	\caption{Results on SWAG and it's adversarial sets. }
	\label{tab:swag_bert}
\end{table}

\subsection{Results}
\label{robustness_results}
Table~\ref{tab:sota_comparison} and Table~\ref{tab:swag_bert} show the results of the examined models on all evaluation sets of MultiNLI and SWAG datasets, respectively.
MultiNLI and its corresponding adversarial evaluation sets, i.e., HARD and Stress Test, contain two subsets, matched and mismatched.
Sentence pairs in the matched subsets are from the same domain as those of the training data while they are from different domains in the mismatched subsets.
We have reported the results on the matched subsets in Table~\ref{tab:sota_comparison}. The results on the mismatched subsets are included in the supplementary material, and they follow the same pattern.

The confidence regularization and product-of-expert debiasing methods model a single bias at a time and train a bias detection model to detect training examples that can be solved by only using biased features.
Therefore, they are only applicable when training examples contain the examined bias, and they are not used for the experiments of Table~\ref{tab:swag_bert}.

Based on the results of Table~\ref{tab:sota_comparison}:
\begin{itemize}
	\item The model that is debiased for a specific bias has a higher accuracy on the corresponding adversarial evaluation set, i.e., \emph{POE(lex)} has the highest average score on HANS. However, they can reduce the performance on the non-targeted evaluation sets, e.g., \emph{POE(lex)} results are below the baseline on HARD and Stress Test evaluation sets.
	\item The use of sentence augmentation results in consistent improvements across the examined evaluation sets.
\end{itemize}

Based on the results of Table~\ref{tab:swag_bert}, we see that while BERT has very high accuracy on the SWAG development set, its performance drops below a random baseline on the lexical overlap adversarial sets.
This indicates that the model is biased towards selecting the endings which have a high lexical overlap to the premise, while the training data does not contain this bias.
Besides, we see that augmenting the training data improves the accuracy on the adversarial sets from 8-20 points.

\paragraph{An example of the attention pattern with and without sentence augmentation}
Figure~\ref{fig:ln_preposition} shows the difference of the BERT attention weights, using BertViz\footnote{\url{https://github.com/jessevig/bertviz}} \cite{vig2019transformervis}, on an example from the HANS dataset.
In this example, the premise and hypothesis are ``The senators supported the secretary in front of the doctor.'' and	``The doctor supported the senators.'', respectively.

For instance, for the predicate ``supported'' in the hypothesis, the BERT model that is trained on \emph{augmented} data  (bottom subfigure), has high attention weights on ``senators'', ``supported'', and ``secretary'', while for \emph{original} the attention weights of this predicate are more distributed.
Similarly, for the subject ``doctor'' in the hypothesis, \emph{augmented} mainly attends to the corresponding subject in the premise, i.e., ``senators''.

\begin{figure*}[!htb]
   \centering

   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_orig_arg21.png}}\quad
   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_orig_rel.png}}
   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_orig_arg2.png}}
   \\
   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_aug_arg21.png}}\quad
   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_aug_rel.png}}
   \subfloat{\includegraphics[width=.3\textwidth]{figures/hans_prop_orig_arg2.png}}
   \caption{BERT attention weights on an example from the HANS dataset based on \emph{original} (top weights) and \emph{augmented} (bottom weights) training. Attention weights are visualized using BertViz \cite{vig2019transformervis}. They highlight the attention between the hypothesis and premise words and for the predicate-argument structures of the hypothesis.}
   \label{fig:ln_preposition}
\end{figure*}

\begin{table*}[!htb]
\footnotesize
	\begin{center}
\resizebox{\textwidth}{!}{%

		\begin{tabular}{ l r | rr  r|r |rrr }
				\hline
		 & \multicolumn{1}{c}{\textbf{In-domain}} & \multicolumn{3}{c}{\textbf{HANS}} & \multicolumn{1}{c}{\textbf{Hard}} & \multicolumn{3}{c}{\textbf{Stress Test}}  \\
 \textbf{Model} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{lex.} & \multicolumn{1}{c}{subs.} & \multicolumn{1}{c}{const.} & \multicolumn{1}{c}{}  & \multicolumn{1}{c}{{Negation}} & \multicolumn{1}{c}{{Overlap}} & \multicolumn{1}{c}{{Length}} \\
        \hline
        XLNET-orig & 86.6$\pm$0.1 & 70.2$\pm$1.3 & 53.6$\pm$0.9 & 66.5$\pm$2.2 & 79.4$\pm$0.8 & 57.6$\pm$1.2 & 62.7$\pm$0.7 & 84.0$\pm$0.4 \\
        XLNET-aug & 86.1$\pm$0.3 & \textbf{71.5}$\pm$1.1 & \textbf{55.4}$\pm$1.2 & \textbf{66.6}$\pm$4.7 & 79.0$\pm$0.6 & \textbf{60.6}$\pm$1.5 & \textbf{67.5}$\pm$4.1 & \textbf{84.1}$\pm$0.1 \\
        \midrule
        RoBERTa-orig & 87.5$\pm$0.1 &  83.3$\pm$2.3 & 64.6$\pm$1.0 & 68.1$\pm$3.3 & 80.6$\pm$0.1 & 57.3$\pm$0.4 & 65.5$\pm$1.8 & 85.0$\pm$0.2 \\
        RoBERTa-aug & 87.3$\pm$0.2 &  82.1$\pm$1.7 & 61.8$\pm$0.8  & 66.3$\pm$3.5 & 80.3$\pm$0.2  & 57.2$\pm$0.5 & 63.7$\pm$1.2 & 85.0$\pm$0.1 \\
        \hline
		\end{tabular}
}
	\end{center}
	\caption{Impact of linguistic augmentation on the XLNET and RoBERTa models trained on MultiNLI.
	Accuracy scores that are higher than the baseline are boldfaced.}
	\label{tab:xlnet-roberta}
\end{table*}

 %

\section{Are the improvements model-agnostic?}
We also evaluate the impact of our augmentation on other transformer models including {XLNET} \citep{yang2019xlnet} and RoBERTa \citep{liu2019roberta}.

The differences of the examined transformer models are as follows: {BERT} is jointly trained on a masked language modeling task and a next sentence prediction task.
It is pre-trained on the BookCorpus and English Wikipedia.
{XLNET} \cite{yang2019xlnet} is trained with a permutation-based language modeling objective for capturing bidirectional contexts. The XLNet-base model is trained with the same data as BERT-base.
The RoBERTa model \cite{liu2019roberta} has the same architecture as BERT. However, it is trained with dynamic masking and without the next sentence prediction task. It is also trained using larger batch-size, vocabulary size, and training data.

Table~\ref{tab:xlnet-roberta} and Table~\ref{tab:swag_all} present the results when the models are trained on the original vs.\ augmented MultiNLI and SWAG training data, respectively.
\begin{table}[htb]
\footnotesize
\resizebox{\columnwidth}{!}{%
	\centering
		\begin{tabular}{ l r | rrr }
				\hline
		\textbf{Model} & \multicolumn{1}{c}{\textbf{Dev.}} & \multicolumn{1}{c}{\textbf{Syntax}} & \multicolumn{1}{c}{\textbf{Antonym}} & \multicolumn{1}{c}{\textbf{NEs}}\\
        \hline
        %
        %
        XLNET-orig & {80.2}$\pm$0.2 & 37.0$\pm$1.6 & 25.4$\pm$1.7 & 17.1$\pm$2.8 \\
        XLNET-aug & 77.8$\pm$0.3 & \textbf{63.3}$\pm$1.1 & \textbf{50.5}$\pm$1.2 & \textbf{36.7}$\pm$3.8\\ \midrule
        RoBERTa-orig & {83.7}$\pm$0.2 & 49.1$\pm$2.4 & 31.7$\pm$2.0 & 24.1$\pm$1.3 \\
        RoBERTa-aug & 82.0$\pm$0.3 & \textbf{60.9}$\pm$1.1 & \textbf{47.7}$\pm$1.3 & \textbf{38.6}$\pm$4.0\\
        \hline
		\end{tabular}
	}
	\caption{Impact of sentence augmentation on XLNET and RoBERTa trained on SWAG.
	%
	}
	\label{tab:swag_all}
\end{table}

The results show that
when the model itself is relatively robust and has high performance on various evaluation sets, which is the case for RoBERTa trained on MultiNLI, augmenting the training data will not have a positive impact.
While several recent debiasing methods are model-agnostic, e.g., \citep{utama2020mind,mahabadi2019simple,clark-etal-2019-dont}, they are evaluated using only the BERT base model.
This result indicates the importance of evaluating model-agnostic methods on more than one model.

 %
\section{Conclusions}
We propose a new approach for improving the robustness of transformer models by augmenting the training sentences with their corresponding predicate-argument structures.
We show that without targeting any specific bias, sentence augmentation improves the robustness against different types of biases.
Sentence augmentation is independent of the underlying task and model and therefore applies to different tasks and settings.
The augmentation only applies to the training examples, and therefore, it does not add any additional complexity at the test time.
We evaluate the impact of the proposed augmentation on the natural language inference and grounded common sense reasoning tasks. However, this work opens new research directions on improving robustness by using better linguistically-informed input representations, rather than simply using raw texts.
To ensure improved robustness, we encourage the community to evaluate their debiasing methods on (1) more than one evaluation set, (2) in a wider setting in which the bias does or does not exist in the training data, and (3) with more than one base model.
\section*{Acknowledgments}
We would like to thank Kevin Stowe, Jan-Christoph Klie, Zahra Ahmadi, and the anonymous reviewers for their constructive feedbacks. This work is funded by the German Research Foundation through the research training group AIPHES (GRK 1994/1) and by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.

redpajama_set_name: RedPajamaArXiv


text:
C. diff. Survivors
C. diff. Survivors A Global Organization Providing Clostridioides difficile Education, Support and Advocacy
3rd Annual Global Walks 2019
Add A C.diff. Survivors Name
Be An Inspiration To Others
C. diff. Support ♥
C.diff. RADIO™
Clinical Studies In Progress ♥
MAKE A DIFFERENCE – NOVEMBER
Medical Education – Everything C. difficile
November 1st Is For C.diff. Survivors
Standing Witness to C. diff. – The Caregiver
Standing Witness to C.diff. – Stories from Families and Friends
Survivor Experiences ♥
What is C.diff.
Every scientific research and development, every clinical trial in progress is a glimmer of hope………..HOPE for clinically safe and approved avenues to prevent and treat a
C. difficile infection.
Listed below you will find information pertaining to organizations who have active
C. difficile Prevention and Treatment clinical trials in progress. Click on each organization's website listed to review their clinical trial study opportunities — Inquire if you or your loved one qualify to participate in their study.
To Learn More About Clinical Trials —
ClinicalTrials.gov is a registry and results database of publicly and privately supported clinical studies of human participants conducted around the world. Learn more About Clinical Studies and About This Site, including relevant History, Policies, and Laws. Click on the link below to be redirected to the clinicaltrials.gov website:
https://clinicaltrials.gov/
Clinical Studies Are In Progress To
Help Them — Help You — Help Others ♥
*PLEASE NOTE
"The C Diff Foundation's mission is to educate and advocate for C. difficile infection prevention, treatments, clinical trials, and environmental safety worldwide.
The C Diff Foundation's organization is comprised of 100% volunteering members who are dedicated to our mission and adhere to the Foundation's Code of Ethics
which prohibits the endorsement and paid promotion of products, services, medications, or clinical studies in progress.
All website entries, public presentations, and workshops are to raise C. diff. infection awareness in all areas of the C Diff Foundation's mission statement, including infection prevention, sepsis, healthcare-associated infections, antimicrobial resistance, antibiotic stewardship and provide education on all the above."
Here is a list of Clinical Trial Phases:
Clinical trials are conducted in a series of steps, called phases – each phase is designed to answer a separate research question.
Phase I: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase II: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase III: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase IV: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.
Additional Resource Information on clinical trials can be found at http://clinicaltrials.gov/info/resources
SOME EXAMPLES OF CLINICAL TRIALS AVAILABLE ADDRESSING C.difficile INFECTION PREVENTION AND TREATMENTS. Visit clinicaltrials.gov for a full listing.
♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦
C. diff. Infection (CDI)_Prevention Clinical Studies In Progress
The U.S. Food and Drug Administration (FDA) has granted a Breakthrough Therapy Designation for SYN-004 (ribaxamase) for the prevention of Clostridium difficile infection.
Synthetic Biologics is developing cutting-edge therapeutics to treat pathogen-specific diseases.
Our two leading candidates are:
SYN-004 (ribaxamase) to prevent C. difficile infection and antibiotic-associated diarrhea
SYN-010 to treat irritable bowel syndrome with constipation (IBS-C)
A global, multi-center, randomized, double-blind, placebo-controlled clinical trial to evaluate the ability of SYN-004 to degrade certain IV beta-lactam antibiotics within the GI tract to maintain the natural balance of the gut microbiome for the prevention of C. difficile infection, C. difficile associated diarrhea and antibiotic-associated diarrhea in patients hospitalized for a lower respiratory tract infection and receiving IV ceftriaxone.
To contact Synthetic Biologics directly please click on the following link to be redirected: www.syntheticbiologics.com
Da Volterra is a biopharmaceutical company, privately-held and headquartered in Paris (France), focused on the discovery and development of innovative therapeutic and preventive products for Clostridium difficile and multi-resistant infections. Our most advanced product, DAV132, is designed as a prophylactic treatment intended to prevent the development of
C. difficile infection, by binding with and neutralizing common antibiotics in the gut.
DAV132 decreases the risk of triggering CDI by inactivating residual antibiotics in the colon
before they can disrupt the bacterial flora, without impacting the systemic efficacy of the antibiotics.
It is noteworthy that DAV132 is developed to accompany all oral and intravenous antibiotics of any class and would therefore significantly reduce the risks to acquire C.difficile infections for patients at risk (especially patients who had prior episodes). We see DAV132 as a real game changer for C.difficile prevention.
Have a look at our video presenting the mechanism of action of DAV132:
http://davolterra.com/content/dav132-preventing-occurence-and-recurrence-clostridium-difficile-infections-video
The video is highly illustrative of what C.diff is and how C.diff is triggered.
We have already performed 2 clinical trials with DAV132 and we have a very exciting dataset (both preclinical and clinical) suggesting that DAV132 will very effectively prevent C.diff infections. I would be happy to exchange with you more information on this. Our next clinical trial, in patients actually treated with antibiotics and at-risk of C.diff, will start in Q4 2015 or early 2016
Pfizer Announces Positive Top-Line Results from Phase 2 Study of
Investigational Clostridium difficile Vaccine for the
Pfizer's C. difficile Vaccine Candidate to Commence Phase 3 Study in First Half of 2017
On Thursday, January 26, 2017 Pfizer Inc. announced that the Phase 2 study evaluating the Company's Clostridium difficile (C. difficile) vaccine candidate, PF-06425090, provided positive data, based on a pre-planned interim analysis. The randomized Phase 2 study (NCT02561195) examined the safety, tolerability, and immunogenicity of the vaccine in healthy adults 65 to 85 years of age. Pfizer's vaccine candidate is designed to help prevent
C. difficile infection (CDI), which can include life-threatening diarrhea and pseudomembranous colitis,1 by inducing a functional antibody response capable of neutralizing the two main disease-causing toxins produced by C. difficile (toxins A and B).2
"Despite improved infection control measures, C. difficile disease continues to rise, further augmenting an already urgent public health threat with particular negative impact on older adults," said Kathrin Jansen, Ph.D., senior vice president and head of Vaccine Research and Development for Pfizer Inc. "We are very encouraged by these interim immunogenicity and safety results demonstrating robust increases in vaccine-elicited neutralizing antibodies to both toxins, that we believe could provide protection against C. difficile disease."
Pfizer Inc.: Working together for a healthier world™
At Pfizer, we apply science and our global resources to bring therapies to people that extend and significantly improve their lives. We strive to set the standard for quality, safety and value in the discovery, development and manufacture of healthcare products. Our global portfolio includes medicines and vaccines as well as many of the world's best-known consumer healthcare products. Every day, Pfizer colleagues work across developed and emerging markets to advance wellness, prevention, treatments and cures that challenge the most feared diseases of our time. Consistent with our responsibility as one of the world's premier innovative biopharmaceutical companies, we collaborate with health care providers, governments and local communities to support and expand access to reliable, affordable health care around the world. For more than 150 years, Pfizer has worked to make a difference for all who rely on us. For more information, please visit us at www.pfizer.com. In addition, to learn more, follow us on Twitter at @Pfizer and @Pfizer_News, LinkedIn, YouTube, and like us on Facebook at Facebook.com/Pfizer.
DISCLOSURE NOTICE: The information contained in this release is as of January 26, 2017. Pfizer assumes no obligation to update forward-looking statements contained in this release as the result of new information or future events or developments. This release contains forward-looking information about a vaccine candidate, PF-06425090, including its potential benefits and the expected timing of commencement of a Phase 3 study, that involves substantial risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Risks and uncertainties include, among other things, the uncertainties inherent in research and development, including the ability to meet anticipated clinical trial commencement and completion dates and regulatory submission dates, as well as the possibility of unfavorable clinical trial results, including unfavorable new clinical data and additional analyses of existing clinical data; risks associated with interim data; whether and when any biologics license applications may be filed for PF-06425090; whether and when any such applications may be approved by regulatory authorities, which will depend on the assessment by such regulatory authorities of the benefit-risk profile suggested by the totality of the efficacy and safety information submitted; decisions by regulatory authorities regarding labeling and other matters that could affect the availability or commercial potential of PF-06425090 and competitive developments. A further description of risks and uncertainties can be found in Pfizer's Annual Report on Form 10-K for the fiscal year ended December 31, 2015 and in its subsequent reports on Form 10-Q, including in the sections thereof captioned "Risk Factors" and "Forward-Looking Information and Factors That May Affect Future Results," as well as in its subsequent reports on Form 8-K, all of which are filed with the U.S. Securities and Exchange Commission and available at www.sec.gov (link is external) and www.pfizer.com.
1 Cohen SH et al. Infect Control Hosp Epidemiol. 2010;31:431-455.
2 Gerding DN and Young VB. Clostridium difficile infection. In: Mandell, Douglas, and Bennett's Principles and Practice of Infectious Diseases, 8th ed. Bennett JE, Dolin R, Blaser MJ (eds). Philadelphia, PA: Elsevier Saunders; 2015.
3 U.S. Food and Drug Administration. Fast Track. http://www.fda.gov/ForPatients/Approvals/Fast/ucm405399.htm (link is external). Accessed January 2017.
A C.diff. Study
Research Study Summary
A Phase 3, Placebo-controlled, Randomized, Observer-blinded Study To Evaluate The Efficacy, Safety, And Tolerability Of A Clostridium Difficile Vaccine In Adults 50 Years Of Age And Older
The Clover trial is evaluating an investigational vaccine that may help to prevent Clostridium difficile infection. Participants in the study are adults 50 years of age and older, who are at risk of developing Clostridium difficile infection. The study will assess whether the vaccine prevents the disease, and whether it is safe and well tolerated.
Each subject will receive 3 doses of Clostridium difficile vaccine or placebo and be followed for up to 3 years after vaccination for potential Clostridium difficile infection.
Review research study eligibility criteria »
Contact information for clinical study »
CW ID: 224554
Date Last Changed: May 17, 2017
Phase3, GenderBoth Male and Female, Age50 and up, Overall Status Recruiting, Lead Sponsor: Pfizer, Duration: 18 Months, Facility Type: Out-Patient
LEARN MORE ABOUT THE CLOVER TRIAL Click on the link below
— The following site is intended for use by United States residents only. If you are located outside of the United States, please refer to www.clinicaltrials.gov.
https://clovertrial.com/en/
Seres Therapeutics
Seres Therapeutics is developing Ecobiotic® drugs designed to prevent C diff infections from coming back again.
Most C diff infections occur after antibiotic treatment. Why? Our gut contains trillions of microbes, called the microbiome, which protects us from bacterial invaders that can cause disease. Antibiotics kill both good and bad bacteria, leaving holes in this ecology (community) of microbes. When the microbiome is damaged, bad bacteria, like C diff, can take advantage and cause disease. Although specific antibiotics can kill the active C diff bacteria, the sleeping forms (ie, spores) are untouched. These spores turn into active C diff bacteria and cause disease again and again – usually after antibiotic treatment has finished.
Seres Therapeutics has developed an oral medicine called SER-109, which aims to prevent C diff from coming back by repairing the microbiome. This Ecobiotic® drug is being studied in a large Phase 3 clinical trial that is enrolling patients who have had multiple C diff infections. This important clinical study is being done in the United States and Canada and may provide the final clinical data needed to support FDA and Canadian drug approvals. For more information, please follow this link to the patient website: https://serescdiffstudy.com/
Seres Therapeutics is also enrolling patients into a phase 1b trial of SER-262, which is an Ecobiotic® drug to prevent recurrence among those experiencing their first C diff infection.
To learn more about Seres Therapeutics please click on the following link to be redirected:
www.serestherapeutics.com
Valneva Announces Start of Phase II Clinical Trial of its Clostridium difficile vaccine candidate
UPDATE: Valneva SE July 26, 2016 announced the successful completion of its Phase II study for its prophylactic vaccine candidate VLA84 targeting primary prevention of C. difficile infection (CDI), which is emerging as a leading cause of life-threatening, healthcare-associated infections (HAIs) worldwide. Valneva previously announced positive top-line data from the Phase II study at the end of 2015. These initial results, which included data up to Day 56 following initial vaccination, were presented at the American Society of Microbiology's annual meeting, ASM Microbe 2016, on June 17 in Boston. VLA84 was immunogenic at all doses and formulations tested, in that Immunoglobulin G (IgG) and functional (neutralizing) antibody responses were observed. The study met its primary endpoint in terms of identifying the dose/formulation with the highest seroconversion rate against both toxins A and B and confirmed the favorable safety profile observed in Phase I.
Final Phase II results included the follow-up of study participants until Day 210. This long-term data confirmed the optimal vaccine dose and formulation that had been previously identified (high-dose formulation without adjuvant) with an immunogenicity profile at Day 210 in line with expectations. Long-term safety concerns were not seen in any of the different vaccine doses tested.
First Study participant(s) enrolled in Phase II trial which aims to enable Phase III entry upon successful completion
Study to enroll 500 healthy subjects aged 50 years and older in the United States and Germany
First results are expected in Q4 2015
Lyon (France), December 18, 2014 – European biotechnology company Valneva SE ("Valneva") announced today the initiation of the Phase II clinical trial of its VLA84 prophylactic vaccine candidate against Clostridium difficile (C. difficile), the main cause of nosocomial diarrhea. Data from the Phase I study in healthy elderly and adults showed good safety and immunogenicity of the vaccine candidate, and indicated functionality of induced antibodies, supporting the Company`s decision to progress the vaccine
candidate into Phase II
C. diff. Infection (CDI) Treatments Clinical Studies In Progress
Ridinilazole is being developed by Summit Therapeutics as a potential treatment for
C. difficile infection (CDI). It's a new antibiotic, and Summit is testing whether it can improve patient outcomes over the current standard of care, vancomycin, in two global clinical trials. In an earlier clinical trial in patients with CDI, ridinilazole was found to be superior to vancomycin in a measure called sustained clinical response, which tested if patients were cured after treatment and did not experience a recurrence within 30-days post-treatment. More information on ridinilazole can be found by visiting www.summitplc.com/our-programmes/c-difficile-infection.
Some key information about the trials:
Each trial is expected to enroll up to 680 patients
Patients will be randomized to receive either ridinilazole or vancomycin, and neither the patients nor the study doctors will know which drug they receive
Participation will involve about 7 study visits over approximately 100 days to track the safety and effectiveness of each drug
Patients who participate may be reimbursed for travel expenses associated with study site visits
Patients must be 18 years of age or older
Patients must have signs and symptoms of CDI, including diarrhea, in the 24 hours prior to entry in the trial and a positive toxin test on a stool sample produced within 72 hours of entry into the trial
Patients cannot have had more than one prior episode of CDI in the previous three months or more than three episodes in the past 12 months
Patients cannot have had more than 24 hours of CDI antibiotic treatment prior to entry into the trial
There are additional entry criteria and considerations; the study doctors will ultimately decide whether a patient is eligible for entry into the clinical trials and the patient will be required to give consent
Further details of the global clinical trials can be found by visiting: https://clinicaltrials.gov/ct2/show/NCT03595566 and https://clinicaltrials.gov/ct2/show/NCT03595553, and/or by speaking with one of the clinical trial sites.
The clinical trials will be taking place at sites in the US, Europe, Latin America, Australia and Asia. If you would like to be considered for enrollment into one of the clinical trials, please contact the study site nearest you.
ridinilazole has already received Qualified Infectious Disease Product, or QIDP, designation and has been granted Fast Track status from the US Food and Drug Administration
To learn more about ridinilazole and Summit Therapeutics, click on the following link
to be redirected to the Summit Therapeutics website: https://www.summitplc.com/
Rebiotix Inc. is a clinical stage biotechnology company founded to revolutionize the treatment of debilitating diseases by harnessing the power of the human microbiome. Microbiota Restoration Therapy (MRT) is the company's platform for delivering live microbes into a sick patient's intestinal tract to treat disease.
Study: PUNCH CD 3 (Currently Enrolling) *Updated 9/2018
The PUNCH CD 3 study is a Phase 3 clinical study to evaluate the safety and efficacy of Rebiotix RBX2660 for the prevention of recurrent Clostridium difficile infection (CDI).
This prospective, randomized, double-blinded, placebo-controlled clinical research study is expected to enroll up to 270 patients at 60 research sites in the U.S. and Canada. Patients that meet the study requirements and choose to enroll will be randomized to receive either RBX2660, an investigational new drug, or a placebo. Two out of every three study patients will receive RBX2660, and one out of every three study patients will receive the placebo study treatment (2:1 randomization). Study patients whose CDI returns within 8 weeks after blinded study treatment may be scheduled to receive an RBX2660 treatment (no placebo). The study's primary endpoint will compare the proportion of patients with treatment success following treatment with RBX2660 to prevent recurrent CDI within 8 weeks of blinded treatment as compared to placebo.
Detailed Information & Site Locations
Visit: clinicaltrials.gov
Study Participation
Email: studyinfo@rebiotix.com
Caution: Drug products are in development and investigational at this time. No product has yet been approved by the U.S. Food and Drug Administration.
Study: Bezlotoxumab Versus Placebo in Children With Clostridium difficile Infection (CDI) (MODIFY III)
Recruitment Status: Recruiting
The primary objectives of this study are to evaluate the pharmacokinetics (PK), safety, and tolerability of bezlotoxumab in children aged 1 to <18 years with a confirmed diagnosis of CDI who are receiving antibacterial drug treatment for this infection. (Source = ClinicalTrials.gov)
Click here to learn more about this study
Click here to see if there's a site near you
For Additional Site and Study Information:
Email: Trialsites@merck.com
ZINPLAVA™ (bezlotoxumab):
ZINPLAVA is a prescription medicine used to help decrease the risk of C-diff from coming back in people 18 years of age or older who are taking an antibiotic for C-diff and who have a high risk of C-diff coming back. C-diff is a bacterial infection that can damage your colon and cause stomach pain and severe diarrhea.
When people get C-diff, they often take an antibiotic to get rid of the infection. Even when treated by an antibiotic, C-diff can come back within weeks to months. ZINPLAVA helps to decrease the risk of the infection from coming back. It works when given along with the antibiotic that you are taking to treat C-diff. (Source = zinplava.com)
Click here to learn more about ZINPLAVA (bezlotoxumab)
Study Sponsor: Merck
In October of 2017 Finch merged with Crestovo to form Finch Therapeutics Group.
The PRISM 3 clinical study is for patients who have had a recurrence of Clostridium difficile infection (CDI or C. diff). The study is evaluating the safety and effectiveness of the study drug (CP101) to prevent recurrence of CDI compared to a placebo. The study is currently enrolling across the United States.
This clinical trial (CP101) is in Phase 2. For more information from
http://www.ClinicalTrials.gov click on the following link:
https://www.clinicaltrials.gov/ct2/show/NCT03110133?term=PRISM+3&rank=3
The study drug, CP101, is a Full-Spectrum Microbiota™ investigational drug designed to deliver bacteria to the intestine. This bacteria may help overtake the surplus of C. diff. bacteria that cause CDI.
CP101 is encapsulated for oral administration. The powder is intended to be released from the capsules in the right part of your intestine where the bacteria may repopulate. This may aid in restoring the diverse community of bacteria found in the healthy human gut, which may prevent recurrence of C. diff..
Click here to learn more about PRISM 3 (https://www.prism3trial.com/trial) and see if there is a study near you and if you are eligible.
CP101 drug products are in development and investigational at this time. No product has yet been approved by the U.S. Food and Drug Administration.
DIFICID (fidaxomicin) is currently FDA approved to treat C. difficile-associated diarrhea (CDAD) in adult patients 18 years of age and older.
Currently, clinical trials are ongoing to assess the efficacy and safety of DIFICID, in either a tablet and oral suspension formulation, in pediatric patients with CDAD. ** In addition, DIFICID is currently in clinical trials to determine the efficacy of use as a prophylaxis against CDAD in adult patients undergoing hematopoietic stem cell transplantation (HSCT).
SURVIVORS, LOSSES, HOPE
CDC's 2018 National and State Healthcare-Associated Infection Progress Report Published
NOVEMBER Is C. diff. Awareness Month; Share the Information and Save Lives Worldwide
Clinical Trial Awareness; Treating and Preventing a C.diff. Infection
Risk Factors and Complications of Pneumonia in Seniors
Sandra Hay Bryson A Dedication and Memorial Page
C diff Nationwide Community Support Program
C. diff. News Updates
C. diff. Radio
C. difficile Treatment Clinical Studies
CDC Updates
Holiday Notes
Loss of a Parent
Tips from Survivors
Follow C. diff. Survivors on WordPress.com
C Diff Foundation
Nov 6-7 St Louis,MO Healthare C.diff Conference
7th Annual Int'l C.diff. Conference/Health EXPO For Healthcare Providers November 6-7 in St.Louis,MO Hosted by the C Diff Foundation www.cdifffoundation.org Share the Conference News With Your Physicians & Healthcare Providers! Thank You!

redpajama_set_name: RedPajamaCommonCrawl


text:
Kamala Harris' departure changed little in the Democratic primary race
Kathy Frankovic
December 18, 2019, 4:20 PM UTC
Most Democratic voters aren't mourning California Senator Kamala Harris's departure from the Democratic race. A majority (53%) in the latest Economist/YouGov Poll say it was good that she bowed out, and Democratic voters aren't unhappy with who remains in the race. Most Democratic voters say the remaining candidates reflect the Democratic Party — and the country as a whole — at least somewhat well.
Black Americans and women — groups that may have identified more with Harris — agree.
The candidate in the lead of the Democratic horserace is at the top because of enormous support among black Democratic primary voters. Nearly half of Black Americans (49%) in the poll say former Vice President Joe Biden is their first choice for the nomination, nearly four times as many as name Massachusetts Senator Elizabeth Warren as their top choice (13%). Biden has led the field for several weeks now, returning to the position he held before the rise of Warren in early fall. His support from African-Americans is key to his position, as he trails Warren slightly among white voters (24% say Warren is their first choice, compared to 19% who name Biden).
In this week's poll, there is also a gender gap, with Warren ahead among women, but in third place among men, behind both Biden and Vermont Senator Bernie Sanders. South Bend Mayor Pete Buttigieg is nearly as popular as Sanders with white voters, but just 2 percent of Black Americans say he is their first choice. A sizable gender gap exists between white men and white women, too. White men prefer Biden, Sanders, and Buttigieg over Warren; white women are twice as likely to choose Warren as they are to pick each of the three leading male contenders.
Three candidates, former Housing and Urban Development Secretary Julian Castro, businessman Tom Steyer, and Colorado Senator Michael Bennet each score 1 percent, while the rest of the field has yet to register with voters.
Black Democratic voters — despite their support for Biden and their belief that the current field represents the party and the country — are more likely than Democratic primary voters as a whole to want additional candidates to enter the field. A third of Black Americans say this (32%), compared with less than a quarter of all Democratic voters (23%).
A few candidates' potential nominations could divide the Democrats: one-third of Democratic voters would be disappointed if Hawaii Representative Tulsi Gabbard (35%) or self-help author Marianne Williamson (34%) were to be nominated, but neither is among the leaders. Three in ten (30%) would be troubled if Michael Bloomberg won the nomination. Those three candidates are the only ones who receive negative evaluations from Democratic voters: Williamson (37% unfavorable, 20% favorable); Bloomberg (45% unfavorable, 38% favorable); and Gabbard (44% unfavorable and 25% favorable).
Liberal Democratic voters (a majority of the party) are even more likely to be concerned by these three being nominated. Just one in five would be disappointed if any of the four leaders Biden, Warren, Sanders, Buttigieg) won the nomination, but moderate and conservative Democratic voters are more worried about Sanders and Warren.
Democratic voters have consistently preferred a candidate who can win to one who agrees with their positions on most issues. But there is more interest in a candidate who takes policy positions voters like among certain groups: younger voters, Democratic primary voters who think of themselves as independents rather than Democrats, and those who say they are ideologically moderate or conservative.
Those most concerned about having a winning nominee favor Biden first (30%) and Warren second (24%) for the Democratic nomination; just 11% in this group choose Sanders. Those most concerned about policy agreement rank Sanders first with 24%, while 19% choose Biden and 17% pick Warren.
Related: On the brink of impeachment, President Trump holds onto a good economy
See the toplines and table results from this week's Economist/YouGov poll


Starting with FileMaker Pro 5.0, XML is published by Web Companion, a plug-in that extends the functionality of FileMaker Pro. By issuing particular commands to FileMaker Pro Web Companion, the field names, field content, and some layout information is returned as well-formed and valid XML results.
In this chapter you will learn how to set up FileMaker Pro Unlimited for optimal XML web publishing. A complete overview, with examples, of the XML commands and results used by FileMaker Pro is included here along with considerations for specific field types. Some Claris Dynamic Markup Language (CDML) will be introduced in this chapter, but you will find more about CDML and how it integrates with HTML and XML in Chapter 6. If you are not web publishing your databases on the Internet but are considering using a web published FileMaker Pro database as an XML import source, you should read this chapter.
This chapter is full of options for using FileMaker Pro for XML web publishing and sharing. This chapter begins with "Setting Up Web Companion for XML Requests." If you have already done this successfully and wish to learn about the XML commands and results, jump right to "XML Request Commands for Web Companion." This chapter also covers Web Companion security issues, so you may wish to review those. First, a note about browsers needs to be discussed.


Author: Jane Scott and Brigid Daniel, Centre for Child Wellbeing and Protection, University of Stirling.
This 2016 survey was commissioned by Scottish Government to inform the Child Protection Improvement Programme and the Neglect Improvement Pilot lead by CELCIS in three local authority areas. The survey was developed in partnership with the Neglect Subgroup of Child Protection Committees Scotland.


text:
Сулейман Себа (, ; 5 апреля 1926, Хендек — 13 августа 2014, Стамбул) — турецкий футболист.

Биография
Родился в семье абхазских мухаджиров.

Выступал за клуб «Бешикташ», провёл один матч за сборную Турции.

С 1984 по 2000 — президент футбольного клуба «Бешикташ».

За время президентства Сулеймана «Бешикташ» выиграл 5 раз чемпионат Турции и 4 раза Кубок Турции, добился лучшего за всю историю результата — выхода в четвертьфинал Кубка чемпионов 1986/87.

Футболисты Турции
Игроки сборной Турции по футболу
Игроки ФК «Бешикташ»
Похороненные на кладбище Ферикёй
Президенты футбольных клубов
Выпускники галатасарайского лицея
Выпускники Стамбульского университета

redpajama_set_name: RedPajamaWikipedia


The simmering tension between Ex Q and Ammara Brown bubbled to the surface yet again when Ammara was not amused when EX Q begged her to join him on stage at the weekend.
Ex Q dropped his new album Tseu Tseu in Borrowdale and wanted to play the track, Bhachura which he featured Ammara. Ammara was having drinks in the VIP area and did not want to perform.
She had just returned from a performance at the Harare Sports Club pitting Zambezi Steelers and South Africa's Blue Bulls.
ExQ had to use his microphone to invite Ammara who then took to the stage and performed an accapella verse before leaving the stage in a huff.
Ammara said she did not want to perform since she had not rehearsed with Ex Q. This is the second time that Ex Q has had to beg Ammara to perform with him. The first time in London she refused and he had to perform alone.
They both later said there was miscommunication. At the weekend the tension was there for all to see.


Sometimes, when a friend gets a new significant other, it's a positive thing; other times, you worry for their well-being.
The latter is the issue in Sunday's episode of Cooper Barrett's Guide to Surviving Life. Barry (James Earl) and his new girlfriend, Ashley (Kimberly Kevon Williams), seem to be moving too fast, and his friends aren't seeing much of him. First, Kelly (Meaghan Rath) tries to take his place in the group by impersonating him, but when that doesn't work, Cooper (Jack Cutmore-Scott), Neal (Charlie Saxton), Josh (Justin Bartha) and Kelly try to intervene before he gets in too deep by retrieving him from Ashley's unexpectedly opulent house.
Cooper Barrett's Guide to Surviving Life airs Sundays at 8:30/7:30c on FOX.
Your Guide to Cooper Barrett's Guide to Surviving LifeGet to know Fox's new "half-hour single-camera action comedy video survival guide."
Fox Orders Rob Lowe Comedy, Morris Chestnut Autopsy ProceduralFour more shows have joined the Fall 2015 lineup for Fox!


text:
Inoculating against fake news?
Benjamin Franklin is said to have coined the phrase that an ounce of prevention is worth a pound of cure. This applies to many things, even combating 'fake news' and other forms of misinformation.
By Stephan Lewandowsky, Sander van der Linden and John Cook
Misinformation sticks. Erasing 'fake news' from your memory is as difficult as getting jam off your fingers after a Devonshire tea. Once you hammer into people that there are Weapons of Mass Destruction (WMDs) in Iraq, it doesn't matter that none were found after the country was thoroughly scoured by the invading forces. The constant drumbeat of 'WMD, WMD, WMD' in the lead-up to the invasion, followed by innumerable media reports of 'preliminary tests' that tested positive for chemical weapons during the early stages of the conflict – but ultimately were never confirmed by more thorough follow-up tests – created a powerful impression that those weapons had been discovered. An impression so powerful that four years after the absence of WMDs became the official US position, 60% of Republicans and 20% of Democrats believed either that the US had found WMDs or that Iraq had them, but had hidden the weapons so well that they escaped detection.
Misinformation can stick even when people acknowledge a correction, and know that a piece of information is false. In a study conducted during the initial stages of the invasion of Iraq, colleagues and ourselves presented participants with specific warrelated items from the news media, some of which had been subsequently corrected, and asked for ratings of belief as well as memory for the original information and its correction. We found that US participants who were certain that the information had been retracted, continued to believe it to be true.
This 'I know it's false but I think it's true' behaviour is the signature of the stickiness of misinformation. Misinformation sticks even in situations in which people have no ideological or motivational incentive to stick to their erroneous beliefs. In the laboratory, the original misinformation shines through in people's responses to inference questions when they are presented with entirely fictional but plausible scripts about various events. For example, people will act as though a fictitious warehouse fire was due to negligence even if, later in the script, they are told the evidence pointing to negligence turned out to be false.
Is there any way to unstick information?
There is broad agreement in the literature that combating misinformation requires that the correction be accompanied by a causal alternative. Telling people that negligence was not a factor in a warehouse fire is insufficient – but telling them that arson was to blame instead will successfully prevent any future reliance on the negligence idea.
Another way to combat misinformation is to prevent it from sticking in the first place. An ounce of inoculation turns out to be worth a pound of corrections and causal alternatives. If people are made aware that they might be misled before the misinformation is presented, there is evidence that people become resilient to the misinformation.
This process is variously known as 'inoculation' or 'prebunking' and it comes in a number of different forms. At the most general level, an upfront warning may be sufficient to reduce – but not eliminate – subsequent reliance on misinformation. In one of our studies, led by Ullrich Ecker, we found that telling participants at the outset that 'the media sometimes does not check facts before publishing information that turns out to be inaccurate' reduced reliance modestly (but significantly) in comparison to a retraction-only condition. A more specific warning that explained that 'research has shown that people continue to rely on outdated information even when it has been retracted or corrected', by contrast, reduced subsequent reliance on misinformation to the same level as was observed with a causal alternative.
A more involved variant of inoculation not only provides an explicit warning of the impending threat of misinformation, but it additionally refutes an anticipated argument that exposes the imminent fallacy. In the same way that a vaccination stimulates the body into generating antibodies by imitating an infection, which can then fight the real disease when an actual infection occurs, psychological inoculation stimulates the generation of counter-arguments that prevent subsequent misinformation from sticking.
The inoculation idea can be illustrated with an example from climate change. Although there is a pervasive scientific consensus – reliant on 150-yearold basic physics and 15,000 modern scientific articles – that the Earth is warming from the burning of fossil fuels, political operatives often seek to undermine that consensus to introduce doubt about those scientific facts in the public's mind.
Ullrich Ecker and ourselves showed that people can be inoculated against those disinformation efforts by presenting them with (1) a warning that attempts are made to cast doubt on the scientific consensus for political reasons, and (2) an explanation that one disinformation technique involves appeals to dissenting 'fake experts' to feign a lack of consensus. We illustrated the 'fake-expert' approach by revealing the attempts of the tobacco industry to undermine the medical consensus about the health risks from smoking with advertising claims such as '20,679 Physicians say 'Luckies are less irritating''.
By exposing the fake-expert disinformation strategy at the outset, the subsequent misinformation (in this case, the feigned lack of consensus on climate change) was defanged and people's responses did not differ from a control condition that received no misinformation about the consensus. (Whereas in the absence of inoculation, that misinformation had a detrimental effect.)
Misinformation sticks and is hard to dislodge. But we can prevent it from sticking in the first place by alerting people to how they might be misled.
This article was published in the winter 2018 issue of the Society Now magazine. It first appeared in CREST Security Review issue 8.
Stephan Lewandowsky is Professor of Cognitive Psychology at the University of Bristol. Dr Sander van der Linden is a lecturer in psychology at the University of Cambridge, and Dr John Cook is a research assistant professor at the Center for Climate Change Communication at George Mason University.
The Centre for Research and Evidence on Security Threats (CREST) is a national hub for understanding, countering and mitigating security threats. CREST Security Review has a new website and mobile app. @crest_research
Society Now magazine


text:
Home Comics Henry Cavill Creates Further Confusion About Superman With This Coded Instagram Post
Henry Cavill Creates Further Confusion About Superman With This Coded Instagram Post
This has been quite a week for the DC lovers, and even more so for the Man of Steel star Henry Cavill. We came to know that the actor was no longer going to play the Kryptonian superhero in DCEU. It all was caused by the failed negotiations regarding a Superman cameo in the upcoming Shazam! due to scheduling issues. The news about Cavill giving up his cape broke hearts of DC fans. Things went on to become extremely confusing when Cavill came up with a highly cryptic post on the social media.
After it was revealed that Henry Cavill is out of Superman role, his manager issued a statement suggesting that there might still be a future for the actor with DC Films and WB and observed that the studio would also make an official statement. However, the statement by the studio hardly revealed anything more than the fact that they have not discussed anything and they appreciate Cavill. This was followed by the actor sharing a video of himself on the social media where he is seen wearing a Krypton Lifting t-shirt and raising a Man of Steel toy into the air. It was captioned: "Today was exciting."
Fans felt positive about the post by Henry Cavill, and some are of the opinion that there is a contract negotiation going on in public. The scheduling conflicts that have been mentioned might not be the actual thing, and the statements from the actor's manager and the studio have created a lot of confusion. Cavill's manager stated that there is a future and the studio seems to indicate that they haven't discussed the future. The general perception is that Cavill's team might have leaked the story about him leaving the DCEU.
The social media post by Henry Cavill doesn't make much sense, but, it does indicate that he might be celebrating something and hinting that Man of Steel 2 might be on the horizon. If it was a grand negotiating trick, Cavill has just highlighted how the DC fans are keen to see him return as the Superman and they are eager for that. Also, it makes the actor a lot more costlier for the studio and gives him a much larger paycheck eventually. Hopefully, things work out in a way that everybody turns out to be a winner.
Rumors have also started doing the rounds about the other actors who could take over from Henry Cavill, and fans have strongly defended Cavill. However, if the actor stayed on, for now, connecting with fans in a way that Ben Affleck hasn't yet done, and it could also mean that he might film his Shazam! cameo during the reshoots. For now, we don't know anything for sure and are grappling with the events of the last couple of days. An official statement is still awaited, and we have only the Superman message to look at, courtesy of the Instagram page of Henry Cavill.
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text:
Cartazinipriset (engelska: Cartazini Award) är ett franskt konstnärspris.

Priset har delats ut vartannat år sedan 1999. Sedan 2003 är den totala prissumman 20 000 euro till vinnaren.

Pristagare
1999: Pino Pascali
2001: Jean-Michel Basquiat
2003: Philip Taaffe
2005: Tyeb Mehta
2007: Geoff Bunn
2009: Niamh O Conchobhair
2011: Emma Fournier
2013: Sandhya Rao

Externa länkar
Cartazini Gallery Awards webbplats

Konstpriser


On October 18, 1909, Frances Trotter seemed very uneasy, her mother said. Frances, whom the newspapers also called by her middle name, Elizabeth, was 14 years old but "looks older, being five feet tall and weighing 110 pounds," the Eagle would later report. "She had the physique and appearance of a girl of eighteen." Her hair was blond; her eyes were brown.
Since 1906, she'd lived with her parents and her older brother, Joseph, 17, in a recently built house at 335 90th Street—very possibly the same house still at that address, though it's been significantly altered in just the last several years, let alone the past 100.
Before Frances left for school that Monday morning, she kissed her mother. Several times. Then she walked the quarter of a mile to Public School No. 104, near the corner of 92nd Street and Fifth Avenue. She was supposed to graduate in June.
She left school at noon—and then disappeared.
At the time, Bay Ridge was on an epochal edge between its agricultural past and urban future. A subway down Fourth Avenue had been seriously proposed about a decade before, and a real estate boom followed. Though the present-day R train wouldn't reach the neighborhood until 1916, jumpstarting local development, ground was broken on the project, in Downtown Brooklyn, just a month after the Trotter girl went missing.
The area around the Fort Hamilton Armybase had long been a village, first accommodating visitors to the base and later visitors to the general area, a sort of modest resort, popular for its fresh air, vistas and amusements. By 1909, however, its popularity was in decline; Coney Island was becoming the beachfront destination of choice in southern Brooklyn.
The one-time bathing area became a developed neighborhood. Sure, entire blocks could still be empty fields—such as 83rd, 84th and 86th streets, between Third and Fourth avenues—but others, like the Trotters's, had already sprouted many houses, though it had once been on the village's outskirts. Relative to today, nearby Fifth Avenue was still a sparsely developed thoroughfare, though it sported some commercial storefronts, including a tailor's shop run by Joseph Moltato. The thirtysomething's wife had recently died, after almost 20 years of marriage, leaving him with their 16-year-old daughter, Assunta (or Assunda).
Moltato's dead wife had been a cousin to Mrs. Trotter (née Mitchell). Two months before Frances Trotter disappeared, Moltato had told Mr. Trotter "that his tailoring business interfered with his health, and he must give it up," the Eagle reported. "So he closed his shop…and went to live with Trotters," taking his daughter with him.
The Trotters welcomed him, for [Assunta] and Frances would be happy companions, it was thought. But, instead, it was the tailor himself who became the companion of the fourteen-year-old-girl.
The Trotters became suspicious of Moltato because of his attention to their underage daughter. "Moltato often confided to [Mrs. Trotter] about how lonesome he was without his dead wife, and when she got tired of hearing his plaintive tale, he turned to her young daughter for sympathy," the Eagle reported.
One evening, Mrs. Trotter told the police, she asked her daughter to wash the dishes in the kitchen while she went into one of the front rooms to do some sewing. Suddenly she returned to the kitchen and found that Moltato was aiding in the dishwashing. She further said that the way her daughter and the man were holding on to the same dish when she entered the room seemed to show that Moltato had banished all sorrow for his dead spouse.
…the mother accuses him of breathing sweet nothings to [Frances] and asking her to leave this weary old workaday United States and fly to sunny Italy.
Mr. Trotter had kicked Moltato out of the house, and the tailor returned with his daughter to his former rooms at 38 Forest Place—about 350 feet up the block. (Both houses still standing on Forest Place could at that time have been No. 38. Both have been substantially altered in the last 110 years.) "But Moltato continued to meet Frances Trotter quite often, the father has learned," the Eagle reported.
What's more, Moltato had been seen on Monday, too—also around noon, on Fourth Avenue, near Elizabeth's school, "carrying a valise," the Eagle reported.
Though the legal age of consent in the U.S. could be shockingly low throughout most of the 19th century, things had changed. By 1895, the age of consent in New York State had risen to 18.
Almost two weeks after Frances disappeared, her parents received a letter from her, postmarked October 27, 12:30am [?], at a post office then on Fifth Avenue and 52nd Street.
Joe and I both ask your forgiveness for what we have done. I am married and happy, and it is useless to go to courts and the like for further information. I know you all feel angry, but what's done can't be undone. Do not blame Assunta for anything. She knew nothing. We are in Fort Hamilton [Village] and can never be found. Again I ask for forgiveness.
Mr. Trotter, first name Nicholas, worked as a barber, for the last nine years in a shop on Hamilton Avenue. (It's unclear if this was on Fort Hamilton Avenue/now Parkway, near his home, or if he commuted every day down to the Red Hook area. He was an immigrant from Italy, so it's possible he had ties to what was then an Italian community. It's also possible "Hamilton Avenue" was an old name for "Fifth Avenue," at a time when Fort Hamilton Avenue was known as United States Street.) At this barber shop, "several friends of Trotter's…said that Moltato may meet with a wholly different fate than a term behind bars," the Eagle reported, "whether the missing girl is married to the tailor or…not." Mr. Trotter was skeptical of the wedding, wanting to see something more than a letter from a 14-year-old girl, such as a marriage certificate.
Although the family did worry. "The father speaks rather good English," the Eagle reported.
All his life, he says, he has worked hard. He has laid aside nothing because he has spent all his earnings on his children. Frances…has been well dressed and has attended theatre parties and after-theatre suppers for several years, as much and as often as she liked.
It is said that Moltato has plenty of money to support the Trotter child in the style to which she had become accustomed.
Her father, she said yesterday, left her but five cents. The relatives are too poor to care for her and unless the father returns or sends her money her case will have to be taken care of by the Charities' Department. There is little chance that the father will return to her if he has married Frances Trotter, the police say.
Frances had turned 15 on October 29, 1909.
On November 4, the phone rang at the Boulevard Hotel, on 90th and Fourth (up the block from the Trotters' home), likely the southwest corner (now a car dealership—every corner at that intersection is a car dealership!). It was Joseph Moltato. He asked Alice Homan, who ran the place with her husband, if she'd get Moltato's cousin, who ran a grocery store on 91st Street, to the phone for him. Alice Homan read the papers; she knew the Trotter family.
She knew who Joseph Moltato was.
So she told Moltato it would take a while to fetch the cousin, and it'd be easier just to give her a message. He did: he "had some furniture stored not far from the hotel, and he wished it sent to Newark," the Eagle reported. He didn't give her an address, but she immediately called the operator, who told her the call had come from a tailor shop on Kinney Street, in Newark.
She tried to contact the local detective handling the case, but couldn't reach him—it was his day off. So Mrs. Homan followed the lead herself and traveled to Newark, arriving around 10pm that night, a Thursday. She "made some cautious inquiry" at the tailor's shop, the Eagle reported. "She visited various addresses in Newark" for four hours until she discovered Moltato and Trotter, living at 135 W. Kinney Street. (It seems No. 135 has been torn down, though the stores across the street give you a sense of the kind of buildings that had probably been there back then.) Homan visited Newark police headquarters and notified them of what she'd found; cops took Moltato and the girl into custody.
Moltato refused to return to Brooklyn when detectives visited him in Newark, so extradition proceedings were set in motion. "The charge against him is abduction," the Eagle explained, "for it is claimed that he not only induced the girl to elope with him, but furnished her with a long dress in order that she might appear older than she was." He wasn't brought back to New York until November 24.
"The girl [Frances] had nothing to say that was of interest," the Eagle reported.
Frances told the police she was on her way home from public school…when [by appointment, at noon, at 92nd Street and Gelston Avenue] she met [Moltato,] carrying a bundle. He…told the girl he wanted to marry again and make her his wife. He used threatening language and displayed a loaded pistol, she said. She was in short clothes…[he "had a dress with him, and the two repaired to a vacant lot, where the child donned the dress, so that she appeared some years older than she looked in her old rose princess school dress…" Then,] they fled to Newark and were married by a justice of the peace three days after their arrival.
[Moltato] secured employment in a tailor shop [, which is where they'd been married,] and rented a room upstairs, the pair living there as man and wife. ["The girl says her husband works downstairs, while she had to remain upstairs, and by keeping eternal watch on the door he kept her a virtual prisoner."] [Moltato], fearing the girl would leave him, continued to use threatening language in her presence and destroyed her hat so as to keep her indoors. A ring which her mother had presented to her was pawned by [Moltato], the girl asserted, in order to pay the marriage fee. She said she was in mortal fear of the fellow and would not return to live with him hereafter.
When Frances arrived at local headquarters yesterday she was hatless and scantily clad….She asserted [Moltato] first took advantage of her soon after the death of his wife.
She promised if the authorities would release her and her parents could be induced to forgive her, she would gladly return to school and forget her past experience.
Previously, the parents had said they might lodge a charge against her that she was "incorrigible," which is still a legal class for some disobedient children.
And then the newspaper trail ends.
I don't know if Moltato was convicted, but it seems likely that he was. I couldn't find another mention of him or his daughter in any database—and their names are not common.
I did, however, find the Trotters once more—in the 1910 census, conducted in April that year, almost six months exactly after Frances had disappeared. According to it, parents Nicholas and Elizabeth and children Joseph and Frances were all living together in the house at 335 90th Street, suggesting that Frances's parents had "forgiven" her after all and welcomed her back into the family.


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In August 2018, Tim Browning joined the Institute of Science and Technology Austria (IST Austria) as the Institute's newest professor of mathematics. This brings the mathematics faculty at IST Austria to 9, and the entire faculty to 49. Previously a professor at the University of Bristol, Browning's research focuses on analytic number theory and how this field can be used to approach problems in other areas of mathematics, especially algebraic geometry. He has received numerous awards and honors for his work, including the Whitehead Prize and the Philip Leverhulme Prize.
Browning's research focuses on analytic number theory, a field that recently received further attention through one of the 2018 Fields medalists, with an emphasis on Diophantine equations. "Many of these problems are so simple to state, and yet, we've been unable to solve them for hundreds or thousands of years. That is what I find so intriguing about this field," Browning explains. He works to understand the possible solutions to particular equations, and to uncover any structure among them. This research has deep connections to a variety of other fields, from geometry to complex analysis. Tools and ideas from other fields, such as algebraic geometry and additive combinatorics, have led to advances and new questions in number theory and vice-versa.


Edi Edhutschek Interview, alles über Edi & "Die Con ist Heiss"
After a Cosplay Photo-shoot with Italy and Romano at a Convention named AkiCon we decided to make a Hetalia short clip too.
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