text:
\section{Introduction}

Lip reading, also known as Visual Speech Recognition (VSR), aims to predict what a person is saying based on visual information alone. It has drawn big attention with its beneficial applications, such as speech recognition under a noisy environment, extracting speech of target speaker from multi-speaker overlapped speech, and conversation with people who cannot make a voice.
With the great development of deep learning and the availability of large-scale audio-visual databases \cite{cooke2006grid,chung2016lrw,chung2017lrs2,afouras2018lrs3,yang2019lrw1000}, many efforts have been made to improve lip reading performance. Architectural improvements of deep neural network are made by \cite{assael2016lipnet,stafylakis2017resnetlstm,petridis2018end,afouras2018deep,martinez2020mstcn}, pre-training schemes are introduced by \cite{chung2016syncnet,ma2021lira}, and coupling audio modal knowledge into lip reading is performed by \cite{zhao2020hearing,afouras2020asrisallyouneed,ren2021learningfrommaster,kim2021cromm,kim2021mmbridge,kim2022distinguishing}.

It is widely known that speech recognition techniques, including both audio-based Automatic Speech Recognition (ASR) and lip reading, show degraded performance when they are applied on unseen speakers due to the mismatch between train and test data distributions \cite{digalakis1995unseen,assael2016lipnet,klejch2019meta}. Speaker adaptation technique aims to narrow this mismatch by fitting a trained speech recognition model to unseen test speakers to improve performances during test time.
With its practical importance, speaker adaptation has been an important research topic in ASR for decades \cite{anastasakos1997mllr,miao2014towardsSAT,miao2015SATivector,klejch2019meta,neto1995lin,xue2014fastadaptation,huang2020SAspeechsynthesis}. They attempt to optimize the speech recognition performance by transforming pre-trained models to well operate on one particular speaker or modifying the encoded features to match the pre-trained model, by using a small amount of adaptation data. For example, previous works \cite{abdel2013codecnn,abdel2013codeicassp,miao2015SATivector,xue2014fastadaptation} showed that providing speaker-specific input as hints for the input speaker to the ASR model is beneficial in adapting the trained model to an unseen speaker.

However, in contrast to the efforts in ASR, speaker adaptation methods have not been well addressed in lip reading. Since different speakers show varying lip appearances and movements, it is also important in lip reading to adaptively encode the lips of different speakers to achieve robust performance.
As lip reading handles lip movement video which is higher-dimensional than audio (\textit{i}.\textit{e}., composed of both spatial and temporal dimensions), encoding spatio-temporal information to be aware of the displacement of lips and their movement is important for accurate recognition. To this end, visual features are usually extracted using 2D or 3D Convolutional Neural Network (CNN) to achieve high recognition performance \cite{noda2014lipreadingconv,assael2016lipnet}, compared to the discriminative audio features that are relatively easily obtained by transforming the raw audio into Mel-Frequency Cepstral Coefficient (MFCC) or Mel-spectrogram in ASR. Due to the different characteristics of modalities and feature extraction methods, the speaker adaptation methods of ASR might be less effective when they are directly applied to lip reading. Therefore, a speaker adaptation method suitable for lip reading, which can jointly consider the spatial information of visual features during adaptation is required. One main impediment in developing speaker-adaptive lip reading is the lack of speaker information in public databases. Usually, publicly available large-scale lip reading databases \cite{chung2016lrw,yang2019lrw1000,chung2017lrs2} have no speaker information and have overlapped speakers between train and test splits, which makes it hard to investigate the effect of speaker variations in lip reading. Therefore, a large-scale lip reading database with speaker information, beyond the constrained databases \cite{cooke2006grid,zhao2009ouluvs}, is needed for the future research.

In this paper, we propose a speaker-adaptive training method for lip reading by introducing an additional speaker-specific input, namely user-dependent padding. The proposed user-dependent padding is for narrowing the data distribution gap between training speakers and the target test speaker. Distinct to the previous methods of using speaker-specific inputs in ASR that modified the extracted feature by introducing additional layers \cite{xue2014fastadaptation,abdel2013codeicassp,miao2015SATivector,abdel2013codecnn}, the proposed user-dependent padding participates in the visual feature extraction stage so that the personal lip appearances and movements can be jointly considered during the feature encoding. Moreover, it can interact with pre-trained weights without the necessity of additional network parameters or finetuning the network. This has the advantage of simple adaptation steps of directly adaptable from a pre-trained model, while the previous works \cite{xue2014fastadaptation, abdel2013codeicassp,miao2015SATivector,abdel2013codecnn} need to train a speech recognition model that attached an adaptation network.
Finally, the user-dependent padding is optimized for each target speaker, so it can achieve the optimal performance for each speaker with one pre-trained lip reading model.

Specifically, we replace the padding of convolution layers in the pre-trained lip reading model with the proposed user-dependent padding so that the additional speaker-specific input can interact with the learned convolution filter without modifying the architecture and weight parameters. By doing this, we can naturally achieve a strong regularization effect by maintaining the pre-trained weight, whereas previous works \cite{li2006l2regularized,yu2013kldregularized} tried with regularization loss to retain the learned model knowledge. Finally, to remedy the speaker information insufficient problem in large-scale lip reading databases, we label and provide speaker identity of a popular audio-visual dataset, LRW \cite{chung2016lrw}, obtained in the wild environment, and name LRW-ID to distinguish it from the original seen-speaker setting of LRW. The effectiveness of the proposed method is verified on GRID \cite{cooke2006grid} and the newly designed unseen-speaker lip reading scenario of LRW-ID.

The main contributions of the paper are as follows, 1) we propose a novel speaker-adaptive lip reading framework which utilizes user-dependent padding. User-dependent padding has a negligible number of parameters compared to that of the model and can improve the lip reading performance for each target speaker, adaptively. Moreover, it does not require any additional network and finetuning of the pre-trained model, 2) to the best of our knowledge, this is the first work to investigate the speaker-adaptive lip reading on a large-scale database obtained in the wild. To this end, we label the speaker information of a well-known large-scale audio-visual database, LRW, and build a new unseen-speaker lip reading setting named LRW-ID, and 3) compared to the previous speaker-adaptive and -independent speech recognition methods, we set new state-of-the-art performances and show the proposed method is close to practical usage.

\section{Related Work}
\subsection{Lip Reading}
Lip reading is a task of recognizing speech by watching lip movements only, which is regarded as one of the challenging problems. With the great development of deep learning, many research efforts have been made to improve the performance of lip reading \cite{kim2021lip, hong2021speech, mira2022end, mira2022svts}. In word-level lip reading, \cite{stafylakis2017resnetlstm} constructed an architecture consists of a 3D convolution layer and 2D ResNet \cite{he2016resnet} as a front-end and LSTM as a back-end. Some studies \cite{weng2019twostream, xiao2020deformation} proposed two-stream networks to better capture the lip movements by using the raw video and the optical flow. Recent work \cite{martinez2020mstcn} improved the temporal encoding with Multi-Scale Temporal Convolutional Network (MS-TCN). In sentence-level lip reading, \cite{assael2016lipnet} proposed an end-to-end model that trained with Connectionist Temporal Classification (CTC) \cite{graves2006ctc} loss. \cite {chung2017lrs2} developed lip reading based on Seq2Seq architecture \cite{sutskever2014sequence}. Further architectural improvement was made by \cite{afouras2018deep} using Transformer \cite{vaswani2017attention}.
Some studies have focused on bringing audio modal knowledge into visual modality \cite{afouras2020asrisallyouneed, zhao2020hearing, ren2021learningfrommaster, kim2021mmbridge}. They successfully complemented the insufficient speech information of lip video with the rich audio knowledge. For example, \cite{kim2021cromm,kim2022distinguishing} proposed Visual-Audio Memory that can recall the audio features with just using the input video. Finally, \cite{ma2021lira,chung2016syncnet} proposed methods of pre-training the network in a self-supervised manner and showed promising results in lip reading.

Even with the successful development of the lip reading techniques, the speaker dependency of learned model has not been well studied. Since different speakers have different lip appearances and movements, applying a trained lip reading model to an unseen speaker can show degraded performance \cite{assael2016lipnet}. To effectively utilize the trained model without performance degradation, a method of speaker adaptation
should be developed. In this paper, we investigate the speaker dependency of a pre-trained lip reading model and propose a speaker-adaptive lip reading method that can effectively adapt to an unseen speaker.

\subsection{Speaker Adaptation}
Speaker adaptation technique has been mainly developed in the area of audio-based Automatic Speech Recognition (ASR). \cite{liao2013trainpart1} examined finetuning the different parts of the model how affects the performance. However, the finetuning methods are easily suffer from the overfitting problem, especially with a small number of adaptation data. To handle this, \cite{yu2013kldregularized} tried to prevent the model from overfitting by regularizing the adaptation. Some works \cite{seide2011addlayer1, li2010addlayer2} tried to augments the speech recognition model with additional speaker-dependent layers. In \cite{swietojanski2014lhuc}, a speaker dependent vector is added to every hidden layer of the trained speech recognition model and adapted on the test speaker. In recent, using meta-learning \cite{klejch2019meta} and generation \cite{huang2020SAspeechsynthesis} based methods were proposed.

In other approaches, some works proposed providing additional speaker-specific inputs to the model for adapting the trained model to unseen speakers. \cite{miao2015SATivector} proposed to utilize i-vectors \cite{dehak2010ivector} extracted at the speaker level to suppress the speaker variance. With an adaptation network, the i-vectors are converted to speaker-specific shifts that will be added to the original acoustic features. In \cite{abdel2013codecnn,abdel2013codeicassp,xue2014fastadaptation}, they proposed speaker-specific inputs, named speaker code, which can be learned during the adaptation for each speaker. They have the advantage of adapting large-size models using only a few adaptation data. However, as they require additional layers to encode the speaker code, they need to train the additional layers using training data before performing the adaptation. Therefore, they need speaker labels for both training and adaptation data, where the training data contains many speakers compared to the adaptation data.

Compared to the research efforts in ASR, the speaker adaptation method has not been studied much in lip reading. Combination of MLLT \cite{gopinath1998mllt} and speaker adaptation \cite{anastasakos1997mllr} is applied to lip reading in \cite{almajai2016lipreadingmllt}. \cite{kandala2019SAlipreading} proposed to utilize i-vector in lip reading. These methods are evaluated with a constrained dataset with few speakers, due to the lack of speaker labels in public lip reading databases, and they need the speaker information of whole training data which is usually large.

In this paper, we develop a speaker-adaptive lip reading that utilizes user-dependent padding as speaker-specific inputs. Different from the previous methods, the proposed method does not need any additional network and the speaker information of the entire training data that utilized for pre-training. Instead, the proposed method can participate in the visual speech extraction stage of the visual front-end without modifying the network parameters, and just need the speaker information of adaptation data which is usually small (\textit{e}.\textit{g}., 1 minute).

\section{Methods}
\begin{figure*}[t]
	\centering
	\centerline{\includegraphics[width=10.5cm]{Figure/Figure_1.pdf}}
	\caption{Overview of the proposed framework. (a) When an unseen speaker is coming, the proposed framework can enroll the speaker by learning user-dependent padding only. (b) By using the user-dependent padding matched to the input speaker, lip reading model can adaptively encode the visual features and achieve improved performance}
	\label{fig:1}
\end{figure*}

Let $\mathcal{S}=\{(\mathbf{X}^s, \mathbf{Y}^s)\}=\{(x^s_1, y^s_1), \dots, (x^s_{N_s},y^s_{N_s})\}$ be a set of $N_s$ training samples where $x^s_i$ is the $i$-th lip video and $y^s_i$ is the corresponding ground-truth label, $\mathcal{A}_j=\{(\mathbf{X}^{a_j},\mathbf{Y}^{a_j})\}=\{(x^{a_j}_1, y^{a_j}_1), \dots, (x^{a_j}_{N_{a_j}}, y^{a_j}_{N_{a_j}})\}$ be a set of $N_{a_j}$ adaptation data of the $j$-th target speaker not appear in $\mathcal{S}$, and $\mathcal{T}_j=\{x^{t_j}_1,\dots,x^{t_j}_{N_{t_j}}\}$ be a test set of the target speaker.
With a pre-trained lip reading model learned on a large dataset $\mathcal{S}$ containing various speakers, our objective is to adapt the pre-trained model to the $j$-th unseen speaker using $\mathcal{A}_j$ containing a small number of data (\textit{i}.\textit{e}., $N_{a_j} \ll N_s$) in a supervised manner, thus achieving improved performance on the test data $\mathcal{T}_j$ of $j$-th unseen speaker. Otherwise, if the adaptation data $\mathcal{A}_j$ is not available, we try to adapt the pre-trained model directly on $\mathcal{T}_j$ in an unsupervised way. The overview of the proposed framework is shown in Fig. \ref{fig:1}.

A lip reading model is usually composed of a front-end $\mathcal{F}$ which extract visual features $f$ of lips, and back-end $\mathcal{B}$ which encodes the dynamics and predict the speech from the encoded visual features $f$. The training of a lip reading model can be achieved by updating the weight parameters $\theta$ of the front-end $\mathcal{F}$ and the back-end $\mathcal{B}$ through back-propagation of the loss computed using cross-entropy or CTC loss \cite{graves2006ctc} functions $\mathcal{L}(\cdot)$. It can be written as follows,
\begin{align}
    \label{eq:1}
    \theta^* = \argmin_\theta\mathcal{L}(\mathbf{Y}^s, \hat{\mathbf{Y}}), \quad \text{where} \,\, \hat{\mathbf{Y}} = (\mathcal{B}\circ \mathcal{F})_\theta(\mathbf{X}^s),
\end{align}
where $\theta^*$ is the parameters of the pre-trained lip reading model on a large dataset $\mathcal{S}$. With the pre-trained model, our goal is to encode speaker-adapted visual features $f$ according to the input speaker for improving performance.

\subsection{User-dependent Padding}
\begin{figure*}[t]
	\centering
	\centerline{\includegraphics[width=12.2cm]{Figure/Figure_2.pdf}}
	\caption{Example of padded convolution with 3$\times$3 kernel and stride 2. (a) Conventional padding such as zero, reflect, and constant padding. (b) The proposed user-dependent padding can gradually affect entire visual features as it moves to deeper layers.
	}
	\label{fig:2}
\end{figure*}
In ASR, to adapt the trained model on a new speaker, \cite{abdel2013codeicassp,miao2015SATivector,abdel2013codecnn,xue2014fastadaptation} proposed using a speaker code or i-vector \cite{dehak2010ivector} as an additional input to the trained model. The additional speaker-specific input is encoded with an additional network to modify the extracted acoustic features adaptively to the input speaker. However, they need to train the newly added network or fine-tune the entire network including the additional network on training data $\mathcal{S}$, before performing speaker adaptation.

We try to adapt the lip reading model on the $j$-th target speaker without introducing additional network and modifying the pre-trained weight $\theta^*$. Instead, we introduce an additional input $u$, called user-dependent padding, to the network which can interact with the pre-trained convolution filters in the front-end $\mathcal{F}$. Predictions of the lip reading model with the proposed additional inputs, user-dependent padding, can be written as, $\hat{\mathbf{Y}} = (\mathcal{B}\circ \mathcal{F})(\mathbf{X}^{t_j},u^{j}) = \mathcal{B}(\mathcal{F}(\mathbf{X}^{t_j},u^{j}))$. With the provided additional inputs, our desire is to allow the model to consider the personal lip characteristics during visual feature embedding. To make the additional input $u$ participate in the visual feature encoding without modifying the weight parameters, we utilize the region of padding in the CNN

In CNN, for convolving the features with a kernel, padding is usually employed to maintain or control the output feature size. Conventionally used padding is zero padding, reflect padding, and constant padding. These padded region is also convolved with a learned kernel during the convolutions as shown in Fig. \ref{fig:2}a. We utilize these potential regions to insert the additional inputs. That is, the user-dependent padding is applied for the padding before convolution, instead of the conventional padding (\textit{e}.\textit{g}., zeros) used during pre-training, as shown in Fig. \ref{fig:2}b. If we assume that the pre-trained lip reading model is trained with zero padding, the optimization of a lip reading model in Eq. (\ref{eq:1}) can be re-written as,
\begin{align}
    \theta^* = \argmin_\theta\mathcal{L}(\mathbf{Y}^s, \hat{\mathbf{Y}}), \quad \text{where} \,\, \hat{\mathbf{Y}} = (\mathcal{B}\circ \mathcal{F})_\theta(\mathbf{X}^s, \mathbf{0}),
\end{align}
where $\mathbf{0}$ represents the zero inputs to be applied padding before the convolution operations. Now, we can provide additional inputs to the front-end by simply changing the zero inputs with the proposed user-dependent padding $u$, without modifying the learned weight parameters or using additional layers.

Then, with the proposed user-dependent padding $u$, the speaker-adaptation of a lip reading model on $j$-th unseen speaker using the adaptation data $\mathcal{A}_j$ can be achieved with the following equations,
\begin{align}
    u^{j*} = \argmin_{u^j}\mathcal{L}(\mathbf{Y}^{a_j}, \hat{\mathbf{Y}}), \quad \text{where} \,\, \hat{\mathbf{Y}} = (\mathcal{B}\circ \mathcal{F})_{\theta^*}(\mathbf{X}^{a_j}, u^j),
\end{align}
where $u^{j*}$ represents the learned user-dependent padding for the $j$-th speaker. Please note that we only optimize the user-dependent padding $u$, while maintaining the learned pre-trained knowledge $\theta^*$. Otherwise, if the adaptation data $\mathcal{A}$ is not available, the user-dependent padding also can be trained directly on $\mathcal{T}$ via any unsupervised training method such as self-training \cite{mei2020STUDA} and adversarial training \cite{tzeng2017adversarial} which are proven to be effective in unsupervised domain adaptation.

The user-dependent padding can affect the entire visual feature map as the layers go deeper, as shown in Fig. \ref{fig:2}b. Therefore, different from the previous methods that modify the extracted features \cite{xue2014fastadaptation,abdel2013codeicassp,miao2015SATivector}, the proposed user-dependent padding can participate in the whole visual feature encoding stages, so the personal lip appearances of the input speaker can be considered during the feature embedding. Finally, the user-dependent padding consumes small memory compared to the model, $\theta^*$, and this makes it possible to provide customized speech recognition services. All we need is one well-trained model in a central system and user-dependent paddings that can be deployed on personal mobile devices.

\subsection{LRW-ID}
\label{sec:3.2}
In order to develop and evaluate speaker-adaptive lip reading method, a dataset containing speaker information is essential. However, publicly available lip reading databases that contain speaker information are captured in a constrained environment and have a small number of speakers \cite{cooke2006grid,zhao2009ouluvs}, which is limited in evaluating the developed speaker-adaptive lip reading method. To remedy this problem, we clustered and labeled the speaker information of a large-scale unconstrained audio-visual dataset, LRW \cite{chung2016lrw}. Then, we split the train and test set without speaker overlapping which named as LRW-ID to distinguish it with the original splits of the dataset. Specifically, the speaker information of the LRW is labeled with the similar pipeline of \cite{anvari2019pipeline} as follows,

{\bf 1) Feature extraction.}
In order to represent the speaker feature of a video, we employ a powerful face recognition system of ArcFace \cite{deng2019arcface}. We employ ResNet-101 \cite{he2016resnet} model pre-trained on MS-Celeb-1M \cite{guo2016msceleb1m}. From each video in LRW composed of 29 frames, 5 frames are randomly chosen for feature extraction. Face detection and alignment are performed using RetinaFace \cite{deng2020retinaface}. Then, the video-level speaker representation is obtained by averaging that of 5 frames embedded through the pre-trained face recognition model.

{\bf 2) Clustering.} With the obtained video-level speaker representations, speaker clustering is performed. For this stage, we perform face identification between video and clusters. Specifically, if the cosine similarity between a given video and all clusters is lower than a threshold $t_1$, a new cluster is introduced for the video. Otherwise, the video is assigned to a cluster showing the highest similarity. The speaker feature representing the cluster is updated with a new assigned video, with a momentum $m$ as, $C_k=\textit{norm}(m \times C_k + (1-m) \times f_l)$, where $C_k$ indicates face feature of cluster $k$, $f_l$ represents the normalized speaker feature of a video $l$ that assigned to cluster $k$, and $\textit{norm}(\cdot)$ represents l2 normalization.

{\bf 3) Face verification.} Due to the imperfection of clustering algorithms, having false positive samples are inevitable. To minimize the error, we should remove the false positive samples that different speakers are assigned to one cluster. To this end, face verification is performed between all samples in a cluster. Specifically, samples in the same cluster are compared by using their video-level speaker representations, and the cluster is split if they are detected as not the same person (\textit{i}.\textit{e}., the similarity is lower than a threshold $t_2$).

{\bf 4) Face identification.} In this stage, we deal with the multiple clusters of one speaker which should be merged. To handle this, face identification is performed between clusters. To represent the cluster-level speaker feature, the video-level speaker representations of all videos in the cluster are averaged. Each cluster is compared with the other clusters, and it is merged with multiple top-similarity clusters above a threshold $t_3$.

{\bf 5) Manual correction.} Even if we merge the clusters through the previous step, we find that there still exist multiple clusters of the same speaker. Usually, they are not merged in the previous step due to the extreme differences in illumination and pose variations of faces that result in low similarities of face representations. To handle this, we extract the candidate clusters that exist in the boundary by using a lower threshold $t_4$ than used before, and manually inspect whether the clusters are from the same person or not.

The thresholds are empirically set by examining the quality of resulted clusters by humans as 0.41, 0.63, 0.63, and 0.59 for $t_1$, $t_2$, $t_3$, and $t_4$. The total number of labeled speakers through the above pipeline is 17,580 which is large compared to the previously used data \cite{cooke2006grid} for speaker adaptation. Therefore, it is very useful to evaluate the speaker-dependency of a lip reading model trained with large speaker variations.
We choose 20 speakers who contain more than 900 videos to construct the test and adaptation (or validation) sets.
Information of the 20 selected speakers for the test is shown in Table \ref{table:2}. Since the classes that appear in adaptation and test sets are not perfectly overlapped, it is important that the speaker-adaptive method not be overfitted to the adaptation dataset.

\begin{table}[t]
    \renewcommand{\arraystretch}{1.2}
	\centering
	\caption{Selected 20 speakers for the test from LRW-ID. `\% Overlap class' represents how many word classes are overlapped between adaptation and test sets}
	\resizebox{0.97\linewidth}{!}{
	\begin{tabular}{ccccccccccc}
	\Xhline{3\arrayrulewidth}
	\makecell{\textbf{Speaker Number}\\\textbf{(Speaker ID)}} & \makecell{\textbf{S1} \\ (\#4243)} & \makecell{\textbf{S2} \\(\#5125)} & \makecell{\textbf{S3} \\(\#6003)} & \makecell{\textbf{S4}\\ (\#7184)} & \makecell{\textbf{S5} \\(\#9335)} & \makecell{\textbf{S6}\\ (\#9368)} & \makecell{\textbf{S7} \\(\#9438)} & \makecell{\textbf{S8} \\(\#9653)} & \makecell{\textbf{S9} \\(\#10209)} & \makecell{\textbf{S10}\\ (\#10293)} \\ \hline
    \# Tot. class   & 316 & 402 & 478 & 494 & 453 & 421 & 497 & 365 & 411 & 358 \\ \hline
    \# Tot. video  & 1130 & 1486 & 2381 & 6542 & 4116 & 1900 & 14478 & 1245 & 1490 & 1477 \\ \hline
    \makecell{\# Adapt. video\\(\# Word class)} & \makecell{565\\(252)} & \makecell{743\\(329)} & \makecell{1190\\(425)} & \makecell{3271\\(473)} & \makecell{2058\\(418)} & \makecell{950\\(346)} & \makecell{7239\\(493)} & \makecell{622\\(282)} & \makecell{745\\(316)} &\makecell{738\\(290)} \\ \hline
    \makecell{\# Test video\\(\# Word class)} & \makecell{565\\(240)} & \makecell{743\\(313)} & \makecell{1191\\(416)} & \makecell{3271\\(476)} & \makecell{2058\\(412)} & \makecell{950\\(346)} & \makecell{7239\\(495)} & \makecell{623\\(290)} & \makecell{745\\(330)} &\makecell{739\\(294)} \\ \hline
    \% Overlap class & 73.3 & 76.7 & 87.3 & 95.6 & 91.5 & 78.3 & 99.2 & 71.4 & 71.2 & 76.9 \\
    \Xhline{3\arrayrulewidth}
    \multicolumn{11}{c}{} \\ [-1.0ex]
    \Xhline{3\arrayrulewidth}
	\makecell{\textbf{Speaker Number} \\ \textbf{(Speaker ID)}} & \makecell{\textbf{S11} \\ (\#10587)} & \makecell{\textbf{S12} \\(\#11041)} & \makecell{\textbf{S13} \\(\#11777)} & \makecell{\textbf{S14}\\ (\#11875)} & \makecell{\textbf{S15} \\(\#11910)} & \makecell{\textbf{S16}\\ (\#13287)} & \makecell{\textbf{S17} \\(\#13786)} & \makecell{\textbf{S18} \\(\#15545)} & \makecell{\textbf{S19} \\(\#15769)} & \makecell{\textbf{S20}\\ (\#17378)} \\ \hline
    \# Tot. class   & 350 & 475 & 365 & 235 & 304 & 346 & 370 & 456 & 313 & 477 \\ \hline
    \# Tot. video  & 1106 & 5480 & 1743 & 2800 & 950 & 1213 & 1654 & 4126 & 936 & 3586 \\ \hline
    \makecell{\# Adapt. video\\(\# Word class)} & \makecell{553\\(258)} & \makecell{2740\\(455)} & \makecell{871\\(303)} & \makecell{1400\\(195)} & \makecell{476\\(236)} & \makecell{606\\(264)} & \makecell{827\\(298)} & \makecell{2063\\(419)} & \makecell{468\\(237)} &\makecell{1793\\(441)} \\ \hline
    \makecell{\# Test video\\(\# Word class)} & \makecell{553\\(268)} & \makecell{2740\\(447)} & \makecell{872\\(311)} & \makecell{1400\\(191)} & \makecell{476\\(239)} & \makecell{607\\(263)} & \makecell{827\\(303)} & \makecell{2063\\(426)} & \makecell{468\\(231)} &\makecell{1793\\(424)} \\ \hline
    \% Overlap class & 65.7 & 95.5 & 80.1 & 79.1 & 71.5 & 68.8 & 76.2 & 91.3 & 67.1 & 91.5 \\
    \Xhline{3\arrayrulewidth}
	\end{tabular} 	\label{table:2}
	}
\end{table}

\section{Experiments}
We evaluate the effectiveness of the proposed user-dependent padding on both sentence- and word-level lip reading databases. Moreover, we conduct experiments in two different adaptation settings, supervised adaptation where a small amount of adaptation data (\textit{e}.\textit{g}., under 5 minutes) is required and unsupervised adaptation where no supervision is required for the speaker adaptation.

\subsection{Dataset}
{\bf GRID} corpus \cite{cooke2006grid} is a popular sentence-level lip reading dataset. It is composed of sentences following the fixed grammar from 34 speakers. Videos are 3 seconds long, thus every 20 videos compose 1 minute. We follow the unseen speaker split of \cite{assael2016lipnet} that speakers 1, 2, 20, and 22 are used for test and the remainder is used for training. For the supervised adaptation setting, we split half of the data (\textit{i}.\textit{e}., about 500 videos) from each test speaker to construct the candidate dataset for adaptation and the others for the test data. For the unsupervised adaptation setting, all data from the test speakers are utilized for the test. For the performance measurement, Word Error Rate (WER) in percentage is utilized.

{\bf LRW-ID} is a speaker labeled version of LRW\cite{chung2016lrw}, a word-level lip reading dataset, as described in Sec. \ref{sec:3.2}. Each video is 1.16 seconds, thus 52, 155, and 259 videos compose 1, 3, and 5 minutes.
For the supervised adaptation, the adaptation set is used for the speaker adaptation. For the unsupervised adaptation, only the test set is used. Word accuracy (\%) is utilized for the metric.

\subsection{Baselines and Implementation Details}
Videos are pre-processed following \cite{kim2021cromm}. For LRW-ID, videos are cropped into 136 $\times$ 136 centered at the lip, resized into 112 $\times$ 112, and converted into grayscale. For GRID, the lip region is cropped and resized into a size of 64 $\times$ 128.

{\bf Baseline lip reading model.}
For the sentence-level lip reading, we utilize a modified network of LipNet \cite{assael2016lipnet}, which consists of three 3D convolutions and two 2D convolutions for the front-end, and two layered bi-GRU for the back-end. It is trained with CTC loss function \cite{graves2006ctc} with word tokens, and beam search with beam width 100 is utilized for the decoding. For the word-level lip reading, we employ an architecture of \cite{martinez2020mstcn}, which consists of ResNet-18 \cite{he2016resnet} for the front-end and MS-TCN \cite{martinez2020mstcn} for the back-end, and train the model using cross-entropy loss. AdamW optimizer \cite{kingma2014adam,loshchilov2017adamw} with an initial learning rate of 0.001, and batch size of 112 and 220 are utilized, respectively on GRID and LRW-ID.

{\bf Speaker-invariant.}
We borrow a speaker-invariant ASR method \cite{meng2018sitASR} into lip reading, which trains the model via adversarial learning to suppress the speaker information from the encoded features, to compare the effectiveness of the proposed method with a speaker-invariant speech recognition model. Specifically, an additional speaker classifier is introduced which classifies the speaker identity from the encoded visual feature. The sign of gradient calculated from the speaker classifier is reversed before backpropagated through the front-end \cite{ganin2015grl}, thus the front-end learns to suppress the speaker information from the encoded visual feature, while the speaker classifier attempts to find the speaker information from the encoded visual feature in an adversarial manner.

\begin{table}[t]
\renewcommand{\arraystretch}{1.2}
\renewcommand{\tabcolsep}{2.1mm}
\begin{minipage}{.51\linewidth}
    \centering
    \caption{Adaptation result using different time lengths of adaptation data on GRID}
    \resizebox{1.0\linewidth}{!}{
        \begin{tabular}[b]{cccccc}
        \Xhline{3\arrayrulewidth}
        \textbf{Adapt. min} & \textbf{S1} & \textbf{S2} & \textbf{S20} & \textbf{S22} & \textbf{Mean} \\ \hline
        Baseline & 17.04 & 9.02 & 10.33 & 8.13 & 11.12 \\
        1min & 10.65 & 4.20 & 7.77 & 4.59 & 6.80 \\
        3min & 9.35 & 3.75 & 6.88 & 4.27 & 6.05 \\
        5min & 8.78 & 3.45 & 6.49 & 3.99 & 5.67 \\  \Xhline{3\arrayrulewidth}
        \end{tabular} \label{table:3}
    }
\end{minipage}\hfill
\begin{minipage}{.47\linewidth}
    \centering
    \caption{Ablation results by using different padding layers with different amounts of adaptation data on LRW-ID}
    \resizebox{1.0\linewidth}{!}{
    	\begin{tabular}{cccc}
    	\Xhline{3\arrayrulewidth}
    	\textbf{Adapt. min} & \textbf{5 layers} & \textbf{11 layers} & \textbf{17 layers} \\ \hline
        1min & 86.54 & 86.81 & 87.06\\
        3min & 86.69 & 87.12 & 87.61\\
        5min & 86.85 & 87.31 & 87.91\\  \Xhline{3\arrayrulewidth}
    	\end{tabular}  \label{table:7}
    	}
\end{minipage}
\end{table}

{\bf Speaker Code.}
We bring a popular speaker-adaptation method \cite{abdel2013codeicassp} of ASR which utilizes speaker code as additional inputs with additional layers, to compare the effectiveness of the proposed method with a speaker-adaptive model. For GRID, we use 128, 64, and 32 dimensions of speaker code with three additional fully connected layers which correspond to Adaptation Network of \cite{abdel2013codeicassp}, to transform the visual feature encoded from the front-end. For LRW, 256, 128, and 64 dimensions of speaker code are utilized. The training procedures are as follows, 1) bring a pre-trained lip reading model, 2) only train Adaptation Network and the speaker code after attaching them to the pre-trained model using the training dataset $\mathcal{S}$ while other network parameters are fixed, and 3) perform adaptation by training speaker code only using the adaptation dataset $\mathcal{A}$.

{\bf User-dependent Padding.}
We utilize all padded convolutions to insert the user-dependent padding. For GRID, user-dependent padding is inserted before every 5 convolutional layers, and 17 convolutional layers for LRW-ID. The user-dependent padding is initialized with the padding used during pre-training (\textit{i}.\textit{e}., zero) and updated with a learning rate of 0.01. As the proposed method does not need an additional adaptation network, the training procedures can be simple as follows, 1) bring a pre-trained lip reading model, and 2) perform adaptation by updating the user-dependent padding only using the adaptation dataset $\mathcal{A}$.

\subsection{Supervised Adaptation}
{\bf Adaptation results using data under 5 minute.} To investigate the effectiveness of the proposed user-dependent padding, we adapt the lip reading model by using a small number of adaptation data. Specifically, we utilize 1, 3, and 5 minutes length of videos for adaptation, which might be relatively easily obtained in a practical situation. For reliable experimental results, each experiment is performed in 5 folds with different adaptation samples and the mean performance is reported. The results on GRID are shown in Table \ref{table:3}. The baseline achieves 11.12\% mean WER on four unseen speakers. By using 1 minute of adaptation data, the performances are significantly improved in all speakers by achieving mean WER of 6.80\%. Specifically, the WER of speaker 1 (s1) is improved by about 6.4\% WER from the baseline. By using adaptation data of 3 minutes, the mean WER is further improved to 6.05\%. Finally, adapting on 5 minutes video achieves 5.67\% WER. The adaptation results of each speaker on LRW-ID are shown in Table \ref{table:4}. The baseline model achieves 85.85\% mean word accuracy and it is improved by 1.21\% by adapting the model with 1 minute of adaptation video. Using more adaptation data further improves the performance. The mean word accuracy achieves 87.61\% and 87.91\% with 3 and 5 minutes adaptation data, respectively. This shows the effectiveness of the speaker-adaptation in lip reading that even if the model is trained with various speakers over 17,000, we can still improve the performance for unseen speakers through the adaptation.

\begin{table}[t]
    \renewcommand{\arraystretch}{1.2}
	\centering
	\caption{Adaptation result using different time lengths of adaptation data on LRW-ID}
	\resizebox{0.97\linewidth}{!}{
	\begin{tabular}{ccccccccccc}
	\Xhline{3\arrayrulewidth}
	\multicolumn{1}{c|}{\makecell{\textbf{Adapt.}\\\textbf{min}}}
	& \makecell{\textbf{S1} \\ (\#4243)} & \makecell{\textbf{S2} \\(\#5125)} & \makecell{\textbf{S3} \\(\#6003)} & \makecell{\textbf{S4}\\ (\#7184)} & \makecell{\textbf{S5} \\(\#9335)} & \makecell{\textbf{S6}\\ (\#9368)} & \makecell{\textbf{S7} \\(\#9438)} & \makecell{\textbf{S8} \\(\#9653)} & \makecell{\textbf{S9} \\(\#10209)} & \makecell{\textbf{S10}\\ (\#10293)} \\ \hline
    \multicolumn{1}{c|}{Baseline} & 75.93 & 80.08 & 84.13 & 89.36 & 77.70 & 84.53 & 91.12 & 77.05 & 88.46 & 81.33 \\
    \multicolumn{1}{c|}{1min} & 78.94 & 82.15 & 85.14 & 89.39 & 81.68 & 85.07 & 91.57 & 80.06 & 88.46 & 84.00  \\
    \multicolumn{1}{c|}{3min} & 80.00 & 82.26 & 85.74 & 89.43 & 82.93 & 85.75 & 92.00 & 81.38 & 88.70 & 85.20  \\
    \multicolumn{1}{c|}{5min} & 81.10 & 82.77 & 85.79 & 89.64 & 83.83 & 86.21 & 92.05 & 81.86 & 88.75 & 85.68  \\  \Xhline{3\arrayrulewidth}
    \multicolumn{11}{c}{} \\ [-1.0ex]
    \Xhline{3\arrayrulewidth}
	\makecell{\textbf{S11} \\ (\#10587)} & \makecell{\textbf{S12} \\(\#11041)} & \makecell{\textbf{S13} \\(\#11777)} & \makecell{\textbf{S14}\\ (\#11875)} & \makecell{\textbf{S15} \\(\#11910)} & \makecell{\textbf{S16}\\ (\#13287)} & \makecell{\textbf{S17} \\(\#13786)} & \makecell{\textbf{S18} \\(\#15545)} & \makecell{\textbf{S19} \\(\#15769)} & \makecell{\textbf{S20}\\ (\#17378)} & \multicolumn{1}{|c}{\textbf{Mean}}\\ \hline
    73.78 & 86.83 & 88.07 & 85.79 & 72.69 & 75.95 & 81.74 & 87.01 & 88.25 & 86.67 & \multicolumn{1}{|c}{85.85}\\
    79.96 & 87.07 & 88.14 & 90.60 & 74.83 & 77.33 & 82.01 & 87.30 & 89.87 & 87.52 & \multicolumn{1}{|c}{87.06}\\
    81.88 & 87.60 & 88.17 & 91.54 & 76.89 & 77.86 & 82.06 & 87.44 & 90.17 & 88.20 & \multicolumn{1}{|c}{87.61}\\
    82.10 & 88.04 & 88.56 & 91.76 & 78.19 & 78.48 & 82.44 & 87.42 & 89.74 & 88.66 & \multicolumn{1}{|c}{87.91}\\  \Xhline{3\arrayrulewidth}
	\end{tabular} 	\label{table:4}
	}
\end{table}

\hfill \break
{\bf Comparison with previous methods.} We compare the adaptation results of the proposed method with the previous methods in ASR described in Sec. 4.2. The mean WER and mean word accuracy are reported in Table \ref{table:5} and the best two performances are highlighted in bold. The speaker-invariant model \cite{meng2018sitASR} improves the performance on both GRID and LRW-ID by suppressing the speaker variations. The speaker-adaptive method \cite{abdel2013codeicassp} which utilizes speaker code for the additional speaker-specific input also shows improved performances when the adaptation is performed, except for the 1 minute adaptation on LRW-ID.
Even if the 1 minute adaptation on LRW-ID dataset is very challenging due to the small number of adaptation data, the proposed method robustly enhances the lip reading performances regardless of the adaptation video lengths.
Moreover, we also report the performance of using the proposed user-dependent padding onto the speaker-invariant model \cite{meng2018sitASR} (\textit{i}.\textit{e}., Proposed Method + SI). By jointly applying the speaker-invariant and -adaptive techniques, we can further improve the overall lip reading performance.

\begin{table}[t]
	\renewcommand{\arraystretch}{1.2}
	\renewcommand{\tabcolsep}{1.8mm}
    \centering
    \caption{Performance comparisons with speaker-invariant and -adaptive methods}
    \resizebox{0.75\linewidth}{!}{
	\begin{tabular}{ccccp{0.01cm}ccc}
	\Xhline{3\arrayrulewidth}
	\multirow{2}{*}{\textbf{Method}} & \multicolumn{3}{c}{\textbf{GRID (WER $\downarrow$)}} & & \multicolumn{3}{c}{\textbf{LRW-ID (ACC $\uparrow$)}}\\ \cline{2-4} \cline{6-8}
	& \textbf{1min} & \textbf{3min} & \textbf{5min} & & \textbf{1min} & \textbf{3min} & \textbf{5min} \\ \hline
	Baseline \cite{assael2016lipnet,martinez2020mstcn} & 11.12 & 11.12 & 11.12 & & 85.85 & 85.85 & 85.85 \\
	Speaker-invariant (SI) \cite{meng2018sitASR} & 10.60 & 10.60 & 10.60 & & 86.55 & 86.55 & 86.55 \\
	Speaker code \cite{abdel2013codeicassp} & \textbf{6.77} & 6.32 & 6.21 & & 85.50 & 86.31 & 86.99 \\ \hline
	\textbf{Proposed Method} & \textbf{6.80} & \textbf{6.05} & \textbf{5.67} & & \textbf{87.06} & \textbf{87.61} & \textbf{87.91} \\
	\textbf{Proposed Method + SI} & 6.85 & \textbf{6.00} & \textbf{5.80} & & \textbf{87.59} & \textbf{88.14} & \textbf{88.48} \\
	\Xhline{3\arrayrulewidth}
	\end{tabular}  \label{table:5}
	}
\end{table}

\hfill \break
{\bf Adaptation results using more data.}
In this experiment, we investigate the effectiveness of the proposed method when more adaptation data is available. Since the number of available adaptation data may be different for each individual person in practice, we perform experiments using 10, 30, 50, 70, 100\% of the adaptation data of each speaker. The mean results of all test speakers on GRID are shown in the second row of Table \ref{table:6}. Training the user-dependent padding using more adaptation data further improves the lip reading performances. When we utilize 100\% of the adaptation dataset (about 25 minutes), the model achieves 4.65\% WER which is improved over 6.4\% WER from the baseline. The last row of Table \ref{table:6} shows the mean results of all test speakers on LRW-ID, and it shows consistent results with the sentence-level lip reading by achieving steadily improved performances.

\begin{table}[t]
    \renewcommand{\arraystretch}{1.2}
	\renewcommand{\tabcolsep}{2.1mm}
	\centering
	\caption{Adaptation result by using different rate of adaptation data}
	\resizebox{0.65\linewidth}{!}{
	\begin{tabular}{ccccccc}
	\Xhline{3\arrayrulewidth}
	\textbf{Adapt. \%} & \textbf{\,0\%} & \textbf{10\%} & \textbf{30\%} & \textbf{50\%} & \textbf{70\%} & \textbf{100\%} \\ \hline
    \textbf{GRID (WER $\downarrow$)} & 11.12 & 6.05 & 5.15 & 5.02 & 4.86 & 4.65 \\
    \textbf{LRW-ID (ACC $\uparrow$)} & 85.85 & 87.35 & 88.08 & 88.52 & 88.74 & 88.92 \\
    \Xhline{3\arrayrulewidth}
	\end{tabular} \label{table:6}
	}
\end{table}

\hfill \break
{\bf Comparison with finetuning.}
We compare the effectiveness of the user-dependent padding with finetuning. To this end, the entire model parameters are finetuned from pre-trained lip reading model on the adaptation data of LRW-ID. This yields the total number of 20 speaker-specific lip reading models which results in a total of $20\times40.58\text{M}=811.6\text{M}$ parameters. Fig. \ref{fig:3} shows the comparison results on LRW-ID dataset. When a small amount of adaptation data is utilized (\textit{i}.\textit{e}., less than 30\%), the user-dependent padding surpasses the finetuning. This is because finetuning the entire model parameter with a small number of data can be easily overfitted to the classes that appear in adaptation data. On the other hand, the user-dependent padding largely improves the performance with just 10\% of adaptation data which shows the significance of the proposed method in the small data setting. When more than 50\% of the adaptation data are utilized, the finetuning shows better performance than the user-dependent padding. Please note that user-dependent paddings for 20 speakers have $20\times0.15\text{M}=3\text{M}$ parameters, thus we just require $3\text{M}+40.58\text{M}=43.58\text{M}$ parameters including that of one pre-trained model, which is about 19 times smaller than using user-specific lip reading models (\textit{i}.\textit{e}., 811.6M). This result shows the user-dependent padding is effective and practical even if enough adaptation data is available.

\hfill \break
{\bf Ablation Study.}
Finally, we investigate the effect of the number of padding layers for user-dependent padding. To this end, we vary the number of layers for inserting the user-dependent padding from the total 17 layers of ResNet-18. We use 5, 11, and 17 layers and 1, 3, and 5 minutes of adaptation data for the experiments. Table \ref{table:7} shows the ablation results by using different padding layers on LRW-ID. When 1 minute of adaptation data is utilized, the performances are less varying by the different number of layers, and when more adaptation data is used, the performance gain by using more layers becomes larger, while that of using 5 layers is marginal. This means that we can use a small number of layers for the user-dependent padding to avoid overfitting when we have a very small amount of adaptation data, and as the adaptation data increases, we can increase the padding layers accordingly to achieve high performance.

\begin{figure}[t]
\begin{minipage}[c]{0.49\linewidth}
    \centering
    \includegraphics[width=1.0\linewidth]{Figure/Figure_3.pdf}
    \captionof{figure}{Performance comparisons with finetuning method on LRW-ID}
    \label{fig:3}
\end{minipage}
\hfill
\begin{minipage}[c]{0.47\linewidth}
\renewcommand{\arraystretch}{1.2}
\renewcommand{\tabcolsep}{2.1mm}
    \centering
    \captionof{table}{Unsupervised adaptation results on GRID and LRW-ID}
    \resizebox{1.0\linewidth}{!}{
    	\begin{tabular}{ccc}
    	\Xhline{3\arrayrulewidth}
    	\textbf{Method} & \makecell{\textbf{GRID} \\ \textbf{(WER $\downarrow$)}} & \makecell{\textbf{LRW-ID} \\ \textbf{(ACC $\uparrow$)}}\\ \hline
    	MS-TCN \cite{martinez2020mstcn} & - & 85.85 \\
    	CroMM-VSR \cite{kim2021cromm} & - & 87.30 \\
    	LipNet \cite{assael2016lipnet} & 11.12 & - \\
    	TVSR-Net \cite{yang2020SIlipreading} & 9.1 & - \\
    	DVSR-Net \cite{zhang2021SIlipreading} & 7.8 & - \\
    	Visual i-vector \cite{kandala2019SAlipreading} & 7.3 & - \\ \hline
    	\textbf{Proposed Method} & \textbf{7.2} & \textbf{87.51} \\ \Xhline{3\arrayrulewidth}
    	\end{tabular} \label{table:8}
    	}
\end{minipage}
\end{figure}

\subsection{Unsupervised Adaptation}
One advantage of the user-dependent padding is that it does not depend on specific training methods. That means we can bring any unsupervised learning method to adapt our lip reading model on an unseen speaker when adaptation dataset $\mathcal{A}$ is not available. To verify this, we employ a self-training method \cite{yarowsky1995ST1,lee2013ST2,xie2020ST3,vesely2013ST4} that pseudo labels the unlabeled samples with the pre-trained lip reading model before adaptation. We utilize one of the simplest form of self-training that chooses the pseudo labels by inspecting the model confidences. Specifically, we use the model predictions having approximated beam confidence larger than 0.9 for GRID and the model predictions with over 0.8 confidence for LRW-ID, as the pseudo labels to perform adaptation. Table \ref{table:8} shows the mean performance of test speakers in the unsupervised adaptation setting on GRID and LRW-ID, and the comparisons with the state-of-the-art methods. The results for each speaker can be found in the supplemental material. The proposed method sets new state-of-the-art performances on both word- and sentence-level lip reading without using adaptation data $\mathcal{A}$, and this shows the effectiveness and the practicality of the proposed user-dependent padding in speaker adaptation.

\section{Conclusion}
In this paper, we propose a speaker adaptation method for lip reading, named user-dependent padding. The proposed user-dependent padding can cooperate with the pre-trained model without modifying the architecture and learned weight parameters. The effectiveness of the proposed method is verified on both sentence- and word-level lip reading. Through the experiment, we show that with just a few amount of adaptation data, the lip reading performance for unseen speakers can be further improved, even if the model is pre-trained with many utterances from thousands of speakers. Finally, for the future research on speaker-adaptation in lip reading, we label speaker of a popular lip reading database, LRW, and build a new unseen-speaker scenario named LRW-ID.

\hfill \break
{\bf Acknowledgment.} This work was supported by Institute of Information \& communications Technology Planning \& Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2022-0-00124, Development of Artificial Intelligence Technology for Self-Improving Competency-Aware Learning Capabilities)

\clearpage
\bibliographystyle{splncs04}

redpajama_set_name: RedPajamaArXiv


text:
Great Lakes Ledger
New Upgrades Have Been Made to LIGO for Detecting Black Holes More Often
By Tiesha Elliott , in News Sci/Tech on April 27, 2019 . Tagged width: black holes , LIGO
Since the detection of gravitational waves (GWs) in February 2016, the scientists from the Laser Interferometer Gravitational-wave Observatory (LIGO) have been closer to the cosmic phenomena predicted by Albert Einstein one century ago and his Theory of General Relativity. As we know by now, the theory was confirmed when we saw the image of a black hole detected and processed with the help of all the great telescopes united for this purpose. Also, LIGO shut down for upgrades one year ago, and from April 1st it went back online. The update has the mission to allow LIGO to make more often detections of gravitational waves and black holes.
What Happens after the Upgrade?
Since the upgrade was finished and LIGO has come online, right on April 8th the first two new gravitational waves have been detected. After that, on April 12th, LIGO has spotted a new GW again. Those signals detected by LIGO are a result of the pairing with Virgo Observatory from Italy. Besides this, Virgo Observatory has been upgraded too, so this results in an increased sensitivity of their instruments with 40%.
Moreover, LIGO has a system alert where the team behind the device can send out alerts at the moment of detection to all the observatories around the world. With this on point observation, all the telescopes can point to the source. By doing all those researches, scientists are observing the source of the GW with X-rays, ultraviolet, radio, or in an optical way to learn what causes the events.
Furthermore, LIGO can make a sky-map for showing the possible location of the source, the time of the event, or what kind of event it is. What the scientists know by now, and have made public is that GW can be the result of a merge between a black hole and a neutron star, or binary black holes mergers, or a binary neutron star merger. So with this information, astronomers could look for these events that produce gravitational waves with different signals.
Finally, with the upgrade of LIGO and the increased sensitivity of its detectors, the team is hoping to make more discoveries, to detect more signals, and black holes.
Tiesha Elliott
Tiesha loves to share her passion for everything that's beautiful in this world. Apart from writing on her beauty blog and running her own beauty channel on Youtube, she also enjoys traveling and photography. Tiesha covers various stories on the website.
Scientists Are Close To Imaging A Black Hole Shadow
Black Holes Oscillations Are Reproduced In The Lab Using A Rippling Water Tank
Anxiety is an Emotional and Physical Condition
Fascinating Facts About The Brain
NASA Finds a New Type of Exoplanet Beyond Our Solar System
What's the Biggest Animal That Can Be Eaten by a Snake?
Scientists Discover a New Type of Galaxy, Revealing More Details on Galactic Lifecycle
Mysterious Blobs Beneath Africa and the Pacific Were Found — How They Affect the Environment
Astronomers Spotted Earth-like Exoplanets That Could Support Alien Life
SpaceX's Crew Dragon Abort Test and the Falcon 9 Rocket's Explosion
Giant Asteroid Will Undoubtedly Hit Earth In The Future, Experts Warned
Massive Double Asteroid Might Threaten The Life On Our Planet If It Breaks Up
Our daily online content schedule provides visitors with the latest coverage of world issues, technology, science and entertainment.
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Five female critics to cover the Trieste Film Festival with GoCritic!
The second edition of the GoCritic! training programme for emerging film journalists and critics will take place at the 30th Trieste Film Festival, from 18-25 January.
This time around, GoCritic! has selected five participants, all of them women. They have been chosen from among 31 applicants from all over Europe. GoCritic! opted for an all-female team in support of recent developments in women's rights, and their representation in the film industry and society in general. Of course, all of the selected participants have also been able to demonstrate their high calibre of writing in the audiovisual field.
At the 30th Trieste Film Festival, Carlotta Centonze (Italy), Liina Laugesaar (Estonia/UK), Barbara Sorger (Austria), Zuzana Sotáková (Slovakia) and Mina Stanikić (Serbia) will be writing about Central and Eastern European cinema, which is the focus of the festival, penning reviews and conducting interviews with filmmakers. They will also have the chance to get to know the European film industry by attending the When East Meets West pitching sessions and panels.
Their stories will be published on Cineuropa's main page, and you can always find them via this link.
For more information on the participants, please click here.


text:
Kickstarter Success, Fuel3D, Secures $1.1 Million For 3D Medical Scanner Development
April 9, 2014 by Brittney Sevenson 3D ScanningBusiness
There has been a lot of talk this week about 3D technologies on Kickstarter, with M3D's "The Micro" printer bringing in over $1 million in funding in under 24 hours on the world's largest crowdfunding platform. Today, a past Kickstarter success, specializing in 3D scanning, Fuel3D, has made a major announcement.
The company which is a spin-off from an Oxford University lab, secured a $1.1 million contract for the development of medical imaging technology based around its 3D scanning technology. The contract was signed with the Small Business Research Initiative for Healthcare, which is an NHS England program looking to fund new advanced healthcare technologies.
Since Fuel3D raised $325,343 on Kickstarter just over seven months ago, the company has grown substantially. After the Kickstarter funding, they took in an additional $2.6 million from venture capitalists in February, and have been considering filing for an initial public offering sometime next year.
While the company is expecting to ship out their first 3D scanners to Kickstarter backers in the next few weeks, they are delighted to have the opportunity to work within the healthcare sector to push new medical technologies forward.
"Our core technology was originally developed with medical imaging applications in mind, so we are delighted to have secured this contract in order to ensure that, under Fuel3D, the Eykona scanning technology continues to play a role in the healthcare sector," noted Fuel3D CEO, Stuart Mead, in a statement.
The company expects to use their proprietary technology within several medical fields, including, plastic surgery, prosthetics, and orthotics. This news will not slow down their work on developing their consumer based scanning products. Back in 2012 the company, then known as Eykona ltd, also took funds from the Small Business Research Initiative for Healthcare, to develop technologies for the 3D imaging of wounds. It will certainly be interesting to see what new exciting medical uses can be developed from Fuel3D's knowledge, experience, and background within the industry.
Tagged with: 3d scanner • fuel3d • kickstarter success • medical 3d scanning


Exceptional movement and hand speed, power and undoubted skillset which will make him an even better professional than amateur.
Steve Goodwin was delighted with the acquisition. Jon is a fantastic addition to the Goodwin team and we believe he will be winning a British title very quickly before going onto bigger things. He joins a great team of Heavyweights we have with Dereck Chisora, "Silverback" Omar McKayle (due to debut in the next month), George Fox (Don Charles' son- set to debut June 9th) and undefeated Heavyweights Jack Mulowayi (3-0) and "Big Dave" Abraham (5-0).
Next boxing news: David Price vs. Agit Kabayel: Who wins?


When a manufacturer is reputable, it shows. Grund America's Savannah collection may be more expensive than your average sheet set, but the quality is indisputable. Coupled with a rock solid 30-day return policy and a limited warranty, it's worth the money if you really are looking for luxury.
What Is the Grund America Savannah Sheet Set Made From?
How Does the Grund America Savannah Sheet Set Perform?
How Do You Wash the Grund America Savannah Sheet Set?
How Much Does the Grund America Savannah Sheet Set Cost?
Though founded in North Carolina in 2014, Grund America nevertheless traces its roots much further back. It is the North American division of Czech home textile company Grund. Originally family-owned and founded in 1990, the brand has always been all about providing organic bedding and bathroom accessory options to consumers.
Currently, Grund is a force to be reckoned with in the European market. And apart from the US, it exports products to over 40 other countries.
The Grund America Savannah sheet set—better known as the Savannah Sheet Collection—was launched in Chicago, at the 2018 International Home + Housewares Show.
This review is extra special, because we did actually try out these sheets! You'll see photos and observations of Good Night's Rest team member Tephy throughout this piece, as well as a verdict below.
As a relatively new product, how does the day to day performance of the Grund America Savannah sheets measure up to its impressive manufacturing and construction details? Let's find out together.
The fabric used is 100% GOTS certified organic cotton with a 300 thread count sateen weave.
The high quality sheet set is available in five standard US sizes: twin, full, queen, king, and California king. It comes in four neutral earth tones: ivory, driftwood, slate grey and white.
As an added bonus, other Grund America products—from bath rugs to throw blankets—are available in complementary color-coordinated organic fabrics. Makes it that much easier to achieve that luxurious hotel-like look and feel in your own home.
What Is the Difference Between Conventional Cotton and Organic Cotton?
Conventional cotton is considered one of the dirtiest crops. Although it is cheap, it is bombarded with pesticides and other harmful chemicals throughout the growing, processing and finishing segments of its production. 100% organic cotton is more expensive, but is more sustainable and gentler on the skin.
In particular, Grund America asserts that the organic cotton they use for their Savannah sheet set is as close to pure as they can get it: Chemical-free with no bleach, toxic dyes, or formaldehyde used.
How Is 300 Thread Count Fabric Considered Luxurious?
Contrary to popular belief, higher thread count isn't always better. The ideal is actually a thread count somewhere between 200 to 400.
That Grund America did not attempt to inflate the thread count of their Savannah sheet set is a true mark of a reputable manufacturer. Instead, they allow the "resort quality"—their words, not ours—fabric to do all the work of convincing consumers of the quality and luxury the product offers.
What's Included in the Grund America Savannah Sheet Set?
Each sheet set includes one flat sheet, one fitted sheet with elastic going completely around the edges, and two pillowcases.
Fitted sheets are come in different depths depending on the mattress size. Twin and full sheet sets have a 15-inch pocket. Queen, king and California king have a 17-inch pocket.
As a relatively new product, there's not many consumer anecdote out there. However, the TrustPilot reviews of Grund America's products display a high average score—currently 9.3 out of 10.
We'll talk about the performance of the sheet set at length later on in our Team Verdict section. Suffice to say that most of what we will highlight are positive aspects, as Tephy had a positive experience using Grund America's Savannah sheet set.
Is the Grund America Savannah Sheet Set Hypoallergenic?
100% organic cotton that is not chemically treated is for all intents and purposes allergen resistant.
The breathable nature of the fabric also makes it much less likely to be a target for mold or mildew growth. It will also be easy to to manage bedtime if you sleep hot.
Does the Grund America Savannah Sheet Set Smell?
This is probably due to very specific and stringent quality control and safe practices. While "no smell" doesn't always mean "no off-gassing", it's probably safe to say that Grund America has it covered just the same. The brand maintains the various certifications Grund America proudly enumerates: GOTS, Green Business Certified, OEKO-TEX Standard.
The care instructions are very simple: On a gentle cycle separate from the rest of your laundry, machine wash cold. Unlike other sheet sets, you may use non-chlorine bleach. Finish by tumble drying on low.
Note that dryer sheets and fabric softeners are not recommended.
As of this writing, one queen size Grund America Savannah Sheet Set is available at the online store of the brand's official website for $179.99.
Unlike other brands, the price is the same no matter which color you pick. As we mentioned above, the sheet sets are available in ivory, driftwood, slate grey and white.
As of this writing, only white is available in all offered sized. The other three colors are only available in king and queen.
Does Grund America Offer a Trial Period, Return Policy or Warranty?
There is a limited warranty and a provision for returns and exchanges, although there are many caveats—especially when it comes to care instructions. Grund America may also insist on warranty evaluation.
The warranty is good for five years, but only for normal wear and tear from indoor residential use. Like many limited warranties, it is meant to cover only material and manufacturing defects.
Returns and exchanges are possible only for incorrect shipments and defective products. The policy gives you 30 days to initiate a return by emailing Grund America's customer service team and providing your original receipt.
Customer service is excellent. It is the one thing that is universally praised in all customer feedback we found.
Find Grund America Savannah Sheets Deals Now!
"By far the best quality sheets I've had as an adult! The packaging gave it a special touch and made purchasing bed sheets a new experience.
I always struggled with having the fitted bed sheet not deep enough for my memory foam mattress but this one was just perfect. The flat sheet itself was actually quite large for a queen. However, I'd rather have it too big than too small.
When a manufacturer is reputable, it shows. Grund America's Savannah collection may be more expensive than your average sheet set, but the quality is indisputable. Coupled with a rock solid 30-day return policy and a limited warranty, it's worth the money if you really are looking for luxury.
We get that this isn't a sheet set for a starter mattress sitting in someone's college dorm room. But for what it is, it's a pretty good deal. It's not Egyptian cotton, but it is 100% organic cotton just the same. It's not a crazy thread count, but that's a good thing! And it may not be available in many colors, but the four hues Grund America chose go with almost everything.
This is a sheet set you get to treat yourself, or to level up your otherwise spartan bedroom. Just luxurious enough to feel the change, but not so over the top expensive that the price tag outshines the quality of the product. Thoroughly recommended.


text:
Community News (All Districts)
Dog groomer quit tourism job after virus killed custom
A WOMAN who ran her own business for 30 years has decided to pursue her passion for animals by starting a dog grooming service, writes Luke Adams.
Debbie Walker, of Kentons Lane, Upper Culham, founded Get Smart Promotions, which specialises in marketing tourist attractions.
When the travel industry was hit by the coronavirus outbreak, she decided to take the opportunity to get her qualification in dog grooming and open a salon at her home.
Her business, called Henley Barkbers, was created by converting an old tack room, which is used to store saddles and other horse equipment.
Mrs Walker, who has two horses, Patjo and Lucy, and a miniature Shetland pony called Harriet, has been grooming friends' dogs for the last six months and only started trading officially at the start of December.
She said: "I had been doing marketing for tourist attractions and then covid put a bit of a halt to all that. When all the flights stopped in March it meant I could pursue my passion for dogs — I am a huge animal lover.
"I have always been interested in dog grooming and I have done it on the side for a while but I got my qualification in it this year. Thankfully, the academy that does all of that was still able to continue through lockdown and I could finish what was required.
"I've already had lots of bookings and I think it is something I will really enjoy rather than coming home from work and feeling stressed. Hopefully, it will make a big difference to my own wellbeing.
"It is amazing to see how much support I have had through Facebook and from friends. I think it is going to be a big success."
Mrs Walker, 52, who is married to Nick, 47, who works in human resources, and has four children, moved to the area from Bedfordshire four years ago.
She said: "I loved the area and I had always come down every year for the regatta and the festival and my sister lives in Beaconsfield."
She has handed over responsibility for Get Smart to two employees and hopes it will be able to make a comeback next year.
She said "I still feel really passionate about that as well. It has supported me and my family for so long and while the children were growing up. I am happy that it is carrying on but I am looking forward to this new challenge. I am really excited.
"I want to let everybody out there know that I am here for them, whatever they need. I offer a full service and I want to reach as many customers as I can."


text:
Insanity Isn't Everything - Chapter 1
Audrey May
This is the story of one young sim who struggled to maintain her individuality in a world where people were, all too often, judged only by their inborn traits.
This is the story of one young sim who struggled to maintain her individuality in a world where people were, all too often, judged only by their inborn traits. Julia Plate had always been "unique" as her parents liked to refer to their daughter's personality. As a baby she screamed at the top of her lungs only to giggle with delight the next instant and as she grew older Julia began randomly talking and shouting to herself until her terrified parents took her to the family doctor. After giving Julia a thorough examination, he joined the girl's parents in his office to give them the results.
"Please, doctor, tell us what's wrong with our child!" cried her mother.
"Well, Mrs. Plate," began the doctor solemnly. "I'm afraid your daughter is insane with a strong proclivity toward inappropriateness and is likely to develop kleptomania in the near future."
"Oh no!"
"Oh, don't worry! She will be fine! "
"Fine??!! Doctor, you tell us our child is an insane, kleptomaniac and you expect us to act as if nothing is wrong??"
"No, I don't. I expect you to love her for who she is, encourage her strengths, downplay her weakness and she should turn out fine! You could have a child with much worse traits than Julia has, you know! Why yesterday I treated an unlucky, neurotic, mean-spirited boy who is a loser with an intense fear of being nude! Can you imagine how hard life is going to be for him??"
"Yes, you're right, doctor." said Mr. Plate with a sigh. "And we want you to know that we love Julia and will do the best we can to help her adjust."
And Mr. and Mrs. Plate did just that. With the love and guidance of her parents, Julia had a relatively uneventful childhood and, for the most part, learned how to curb her inappropriate behavior.
The years passed and Julia entered school where she became a good student and quick learner. Her teachers recognized Julia's need for self-expression and suggested she join the after school theater group. Julia took to acting immediately and found that playing eccentric characters in school plays was the perfect outlet for her active imagination. However, by the time she reached high school Julia developed another disconcerting habit which neither her parents nor her teachers were able to change.
Every morning Julia climbed out of bed, showered and dressed herself in whatever suited her mood. One day it was a pair of jeans and a t-shirt and another day it was a bathing suit. While this might be acceptable during the summer or during vacation when she had no particular place to go it was frowned upon during school days. More often than not Julia entered her homeroom only to be told by her teacher to turn around and head home to change. Julia would trudge off muttering loudly about how boring it was "to be forced to wear the same thing every single day!"
When Julia turned twenty-one she decided it was time to strike out on her own and in spite of her parents' misgivings she purchased a house in Sunset Valley with money left to her by a long-lost relative.
"But you've never even been away from home before! Riverview is at least a hundred miles from Sunset Valley!" exclaimed Mrs. Plate. "And a house is a big responsibility!"
"And you don't think I can handle life on my own, do you?" Julia hung her head and sighed.
"Oh no, dear! That's not it we..."
"I found a job and the house is all paid for! Oh, Mom, Dad, this is a great opportunity for me and..."
"And we are very happy for you, Julia. Don't worry! We know you will do well." said her father. "Don't we, Amy?"
"Yes, yes, of course we do." she replied. "It's just so hard to see my girl grown up and out on her own! "
"Don't worry, Mom! Sunset Valley is only a couple of hours away by car and I will phone you every day. And when I get settled in you can both come to visit for a few days!"
Three weeks later, after kissing her parents goodbye, Julia moved in to her new house on a cliff overlooking the ocean in Sunset Valley.
During the first week in her new home, Julia didn't have enough time to feel lonely or homesick. She rose early and headed for her job in the office at Sacred Spleen Memorial Hospital where she worked in admissions. After work she stopped at Hogan's Deep-Fried Diner for a bite to eat and then went home to tend to her new garden. Julia had created a pleasant routine for herself but, unfortunately, this wasn't to last long.
On Friday Julia was called in to her boss's office.
"I'm afraid we are going to have to let you go, Miss Plate." said Mrs. Maledon.
"What?!" Poor Julia stood there in shock. "Why?! I've done a good job!."
"Yes, Miss Plate, we have no complaints about your job performance but I'm afraid," Mrs. Maledon paused a moment and looked Julia straight in the eye. "It's your behavior which is the problem."
Julia averted her gaze.
"We just can't have an employee who talks to themselves in a loud voice and dresses in such an odd manner!"
Julia looked down at the exercise outfit she was wearing. "But what's wrong with these clothes?"
"Well, they are somewhat better than the pajamas you wore yesterday," replied her boss with a smirk. "But I would hardly call it appropriate attire for the office."
"OH! So you are saying my behavior is inappropriate, are you?" said Julia, raising her voice. "That I am inappropriate!?"
"I am saying that there is a certain behavior we expect from our employees and yours doesn't meet with the company's standards."
"Oh I'm so sorry!" snapped Julia. "I'm sorry my manners aren't good enough for you! See you later!" And she stormed out of the room slamming the door behind her.
It wasn't until she was safely outside that Julia let her emotions go. Standing alone on the sidewalk in front of the hospital she sobbed, ranted and raved causing passersby to cross over to the other side of the street and a police officer to stop and ask if she needed help.
"Oh, thank you but I'm fine. Really!" she replied, wiping her eyes with the backs of her hands. "I'm heading home now." The puzzled officer watched her walk across the street and climb into a cab.
The last thing Julia intended to do was look for comfort and help from her family and Riverview friends.
"I do not want any sympathy!" she said to herself as she rode home. "I won't call any of them tonight so I can figure out what I want to do next."
At home the first things she did were to change into her bathing suit and then feed her clown fish, Marcel. Then she ran on her treadmill until she was tired out and sweating profusely. Feeling more relaxed, Julia sat down on her couch and looked at the breathtaking view from the bay window in her living room. Finally, she consumed a pint of double chocolate fudge brownie ice cream and went to bed, intending to forget the bad news of the day in a restful night's sleep.
But sleep seemed to elude Julia and she tossed and turned all night, her dreams permeated with the odd feeling that there was something brewing close by which needed her attention.
~~To Be Continued~~
eviDec 19, 2012
I have not decided yet how I feel for the story but DEFINITELY I am going to read the second part!
wolfspryteSep 17, 2011
He heeee... what will Julia do next?? Great story! Can't wait to see what she does!
MangioSep 6, 2011
Interesting story and concept Had me laughing all the way especially the last page Congrats on the feature Off to read more
hollyevansAug 14, 2011
so funny


A medical oncologist is a physician who specializes in the treatment of cancer. Your medical oncologist will review your medical history, conduct physical exams, and order diagnostic tests to determine your exact diagnosis, including the stage of your cancer.
Your medical oncologist will work with you to customize your cancer treatment plan, and may refer you to a surgeon, radiation oncologist or any other specialist you need. This specialist will also manage any side effects you experience.
Chemotherapy, also known as medical oncology or hematology, uses chemicals to destroy or slow the growth of cancer cells. Chemotherapy is often used in conjunction with surgery to destroy surrounding tissue after a tumor is removed.
Chemotherapy infusion is offered at Physicians Regional Medical Center, North Knoxville Medical Center and Irene & Howard H. Baker Cancer Treatment Center. Infusion therapy involves the administration of chemotherapy and other special medications through a needle or catheter by certified registered nurses under the direction of onsite medical oncologists.
When you receive an IV chemotherapy treatment at our cancer center, you receive more than medication. Our patients are able to interact among others and share emotions, common interests and life experiences. Patients encourage, support and applaud one another.
Depending upon the recommended treatment, chemotherapy infusion may take from 20 minutes to several hours. Treatment may be needed daily, weekly or monthly. Treatment occurs on an outpatient basis in most cases but is also available on an inpatient basis.
Hormone therapy may also be used as part of your treatment plan. Hormones can kill cancer cells, or slow or stop their growth. Physicians can prescribe medications that affect the activity or production of hormones, or may surgically remove a gland that produces hormones.
Immunotherapy is treatment that uses certain parts of a person's immune system to fight diseases such as cancer. This can be done in a couple of ways – by stimulating your own immune system to work harder or smarter to attack cancer cells or by giving you immune system components, such as man-made immune system proteins. Some types of immunotherapy are also sometimes called biologic therapy or biotherapy. Immunotherapy works better for some types of cancer than for others. It's used by itself for some of these cancers, but for others it seems to work better when used with other types of treatment.


Here's a quick tutorial on how to add text to the end of a text file using the Add-Content or its alias ac in PowerShell. In this first example let's add "This is the last line" to the end of a file.
Add-Content c:\sample.txt "This is the last line"
The example above adds the text to the last line, it doesn't actually create a new line, so let's try it again, and make sure that this time the text that we concatenate is on the last line all by itself.
Add-Content c:\sample.txt "`r`nThis is the last line"
You'll notice a `r`n this is an escape character to tell PowerShell to add a carriage return and a new line, you'll need this when working with Windows file. If your going to move this file to a unix or linux computer, you'll only need a `n there's no need for the carriage return.
Add-Content -Path "c:\sample.txt" -Value "`r`nThis is the last line"
Here's a useful example, suppose you want to map hostname to a particular IP address. Let's map example.com to point to 127.0.0.1, we'll do this using the ac alias for Add-Content.
ac -Path "C:\WINDOWS\system32\drivers\etc\hosts" -Value "`r`n127.0.0.1    example.com"
ac -Path "C:\WINDOWS\system32\drivers\etc\hosts" -Value "`r`n127.0.0.1 example.com"


There are more demons than there are humans so perhaps they are a lesser demon that has been assigned to you by a higher office or has taken a liking to you.
He said that he finds me in every life and that we were married in a past life.
You very well may have been. Humans can transition to other species after they pass on from their Human life. Humans have become Angels, Demons, ect. after death, so you may have been married indeed. Or he may have just been an incarnate Demon.
Maybe tell us about Him?
He says that hes a dream demon. I went on my first date with him to a movie called Shutter when i was younger. My friend Amber channeled him and it was through her that we "dated." When i was close to amber so was he but when me andher would fall out he didnt seem to care as much? We think he contacted me originally through her because he couldnt contact me himself directly.
Hello mate, will you please explain in detail what you just said? I am interested, any information you can give me or were I can find information would be appreciated…thanks.
If a spirit senses you are learning of something it is versed in, it may try to follow you around and help you. Spirits help people all the time, we just tend ti focus on the negative attachments when sharing with the the forums.
Lesser demons are sent by higher demons to help in matters no quite worth the higher demons time. For instance if you sought to work with king paimon, he may send a lower demon to work with you at first.


Since the elements described and explained up to now are properly just a beginning and primer from which all combat devices with the sword can be learned, it is necessary that before I derive some devices from them, first I should show in what way this shall take place.
For if you wish to write a full proper word, you must hold in your mind and memory all the letters, and also know thoroughly what the nature and property of each one is, so that the useful and appropriate letters will fly into the pen one after another in order; so likewise in combat you shall hold and conceive the previously explained elements in your mind, so that whenever you come to fight with an opponent, the ones you need at that moment will come to you at once. But as not all letters can be used for every word, so it is also impossible to undertake all the elements that I have described here all at once in every device.
Therefore you shall take heed what the situation requires; how your opponent conducts himself against you; also what kind of person he is, whether he is swift or slow, big or little; and you need to know how to use your work and confront him accordingly.
So, taking a bit of inspiration from Meyer's simile above and coming from a background of extensive language studies, I decided to make some comparisons between fencing and talking since at its core, fencing is really all about quick and spontaneous communication, about two people sending messages back and forth, much like a rap battle or the old Norse flyting. And not just talking but also listening, using attacks and defences to cause an effect or reaction.
Some of those messages may even be lies intended to hide another message and an experienced fencer will recognize that lie. Likewise an experienced fencer will also be a good listener, not just a good talker. An inexperienced fencer however, may well be a decent talker, but often lack in the listening.
This is why it is often difficult or dangerous to fence inexperienced but aggressive fencers. There is no proper communication, despite the great experience of the one fencer, sometimes not even a clear conscious message from the inexperienced fencer, and the inexperienced fencer is often not responding properly to the messages sent. It is like two people talking at the same time about different things. This happens all the easier when doing so has no real consequences, when no real risk is taken, and when no blood is drawn. Here an experienced fencer will instead listen, waiting for the opponent to pause or hesitate however briefly, and then only say very few "words" that cut through the noise and silences the other.
However, this is also how an inexperienced fencer can sometimes defeat an experienced one, simply by drowning the message of the opponent with a louder one, just refusing to acknowledge that the other is speaking. And a similar tactic can be used even by experienced fencers, not allowing the other to come to words at all, just hogging the narrative and controlling it supremely.
On the other hand, just being passive and only listening is even more dangerous than not listening at all as complete one-way communication in this context is a great danger to the listener. It can only be done if you are still in sufficient control of the conversation, which is nigh impossible to be certain of.
Old door with "sprechfenster" (speaking window), from which the longsword stance takes its name.
You can also tell a lie to trigger a response in your opponent, but for it to work, it absolutely has to have the appearance of a potential truth, and the more experienced your opponent is the harder it will be to give your lie that appearance. Furthermore, the best lies are the ones you can trigger your opponent to tell himself. This is also true for any provocation. The best way of knowing what your opponent will say and do is by giving him the idea in the first place. And of course, if the lie has the potential of actually becoming true, then by all means, let it be so.
One of the greatest moments one can have in fencing is when the communication works perfectly and beautifully, when the words flow easily and string together with those of your opponent, leading to a wonderful conversation full of wit, jesting and cleverness. This is also where fencing connects to partner dancing and horse riding, and it is the reason why these three were even connected in history, especially when fencing becomes more about duelling than war. Like with dancing and riding, fencing only works well if communication works well and/or when someone is leading, with both parties being aware of who is actually leading, and at its highest levels of refinement it all happens more or less without conscious thought.
Worth noting in particular though, is that we communicate with three things; our faces, our bodies and our weapons. However, one of these three, our faces, are for us commonly completely obscured by our fencing masks, and thus rendered useless for communication and trickery, quite unlike for actual fencers from history who fought unprotected. Our masks also numb our senses making it somewhat more difficult to read our opponents properly, especially his spirit and attitude.
Finally, we also need to recognize the attitude, the level and the quality of communicative skills that our opponent possesses, and act accordingly, just like Meyer tells us in the quote in the beginning of this article.
To get there though, we need to build a vocabulary (concepts of techniques & principles) and learn pronounciation (execution of techniques), grammar (context required for application) and prosody & intonation (fine practical variation to adapt to context and achieve result), so we can both speak and listen, and through this establish either good communication where we can outwit our opponent, or compose such a strong message that the other never even gets a chance to speak.
Now, the simile between the components of fencing and vocabulary, pronounciation and prosody & intonation, and how it relates to both flyting and internalization & appropriation is something I could expand on quite a bit and may well do at a later time, but for now I think this will have to suffice as I fear this little article will get bloated, lose focus and become confusing otherwise. I will instead leave it up to you to consider and play with.
Fechtschwert or a blunt longsword?


Windows are one of the most crucial pieces of our homes, and rightfully so – they offer numerous perks for us – natural light, ventilation, protection from the weather, and the list goes on and on. On the other side, if they are old windows they can also be one of the culprits in wasted energy costs. Why let those old windows make you pay more for heating and cooling when replacement windows can provide you with exceptional comfort and a breathtaking new view?
When the weather in Ketchum reaches its hottest and coldest points in the summer and winter, your heating and cooling systems are operating hard to keep your home comfortable. If you still have old windows, it may be time to think about replacement windows that can help ensure your comfort and lower your energy costs.
While it can be an investment to get replacement windows, the perks of upgrading to utilize energy-saving technology is well worth it. Not only can it boost your home's overall energy efficiency, it increases comfort, a revitalized style and better functionality for your home.
At Pella Windows and Doors, we have a number of replacement window options to meet the needs of both your style and your budget. Come by our showroom in Ketchum, call us at 208-726-0060 or arrange an appointment with us through our online scheduler to get a closer look at all the replacement windows we carry.


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Feminist bureaucrats and teachers are failing our young boys
Matthew Sheahan March 03, 2022
The young men of Australia need help and they're sure not getting it from the radical feminists who run our education system.
As revealed by Robert MacCann from the New South Wales Board of Studies, young men are falling behind young women in almost every education metric available.
For instance, the performance of young men and women in the High School Certificate has diverged significantly starting in the early-mid 1990s as shown below.According to the brilliant Bettina Arndt, for every 17-year-old girl not going to school in 2020 there were 1.5 boys. That trend has been getting worse for the last half-century.
NAPLAN results show more boys than girls falling below minimum standards in every subject. For instance, in the writing test, boys are seven months behind girls in Year 3, 9 months in Year 5, 18 months in Year 7, and 20 months in Year 9 – trailing girls by almost two full years.
In the graph below showing Year 7 NAPLAN results we see that in most subjects, boys in Year 7 are about twice as likely to be in trouble as girls. Overall, 11 per cent of teenage boys can't read at a minimum standard. Even in numeracy, more boys are below par.
Boys' final school results are also slipping. Last year in New South Wales, girls were awarded 58 per cent of all the high scores in the HSC results, with about 6,300 more top scores than boys received. Unfortunately, most states simply don't publish final year results by gender.
Boys are no longer going to university. In 2021 there were 1.4 females for every male doing a degree or graduate study. More girls than boys have been going to university since 1989, with the gender gap increasing steadily.
So, what is behind the divergence in educational outcomes between boys and girls?
As Bettina Arndt writes, "bias against boys is now worn as a badge of honour. Look at this Melbourne University academic presenting her research showing boys do better in multi-choice maths exams – which she proudly declares is reason to do away with them".
"When federal Education Minister Brendon Nelson announced he was considering to what extent pro-girl policies were 'letting down boys and their families' and started a 'Lighthouse' program to fund schools developing strategies to improve boys' education achievement in response…Labor's powerful handbag brigade weren't having a bar of this and funding for boys' initiatives rapidly dried up when the Coalition lost power," she wrote.
"There's long been a shift to assessment methods that favour girls, like ongoing assessment rather than exams. The currently favoured open-ended inquiry approach and self-directed learning means boys are more easily distracted and can fall behind. Boys do better in orderly classrooms where there are clear directions, explicit goals, timely feedback, and consistent sanctions for uncompleted work. They require high expectations and praise to stay on task and achieve good work – yet teachers praise boys less than girls. With male teachers now outnumbered 2.8 to 1, female teachers' bias against boys is a major issue. Female teachers are more likely to mark boys down, as this OECD study found.
"In his latest book, The Boy Crisis, Warren Farrell makes this chilling point. 'For the first time in American history, our sons will have less education than their dads.' Farrell explained he grew up in an era in which girls were doing badly in maths and science but added, 'We concluded the trouble was with the schools.' His country decided to tackle the problem and girls are now thriving.
"Now boys are doing badly in almost every subject. And what's the reaction? 'We say the trouble is with the boys,' said Farrell.
"It's that darned toxic masculinity. That's the problem. Our entire education system, with unrestrained glee, now has that firmly in their sights."


In using the Creative Curriculum™ for Preschool, our teachers succeed in implementing educational strategies that effectively develop children's self-esteem, creativity and thinking skills.
The comprehensive education we offer in the elementary level equips pupils with the right foundation for honing their mental, physical, social, and intellectual faculties in preparation for Junior High School.
The high quality education we provide in all levels of our Junior High School program harnesses inherent potentials, allowing students to discover and develop individual interests in preparation for Senior High School.
The Senior High School program exposes students to experiential learning. The education readies them for taking college admission tests, acquiring certifications, and gaining knowledge and skills for entrepreneurship.


text:
Guard of Honour welcomes Indonesian President to UK
The President of the Republic of Indonesia has been welcomed to Britain on the first day of a three-day state visit by a Guard of Honour consisting of members of the Armed Forces.
Dr Susilo Bambang Yudhoyono and Mrs Yudhoyono arrived in London yesterday and were formally greeted on Horse Guards Parade by Her Majesty The Queen and His Royal Highness The Duke of Edinburgh.
The Guard of Honour, comprising 101 rank and file and three officers, was provided by the 1st Battalion Irish Guards and musical accompaniment by the battalion's Pipes and Drums and the Band of the Irish Guards, directed by Major Robert Hopla. The Guard of Honour was commanded by Major Tom Eastman.
As the President arrived at Horse Guards from Whitehall, the Queen's Life Guard gave a Royal Salute. Royal Salutes of 41 guns were then fired in Green Park by the King's Troop Royal Horse Artillery, commanded by Major Mark Edward, and from Gun Wharf at the Tower of London by the Honourable Artillery Company, commanded by Major David Daniel.
Major General George Norton, Major General Commanding the Household Division, commanded all the troops on parade.
The President and Mrs Yudhoyono, accompanied by the Queen and the Duke of Edinburgh, were then escorted to Buckingham Palace by the Sovereign's Escort, with two standards provided by the Household Cavalry Mounted Regiment.
During the President's journey to Buckingham Palace, street-liners were provided by Nijmegen Company of the Grenadier Guards, Number 7 Company of the Coldstream Guards and the 1st Battalion Irish Guards.
On arrival at Buckingham Palace, a Guard of Honour from F Company of the Scots Guards formed up in the Quadrangle, with musical support provided by the Band of the Scots Guards.
The Household Cavalry Mounted Regiment provided staircase-lining parties in Buckingham Palace for the President's arrival and again in the evening for the state banquet.
During the state banquet, a Foot Guards string orchestra under the command of the Senior Director of Music, Household Division, Lieutenant Colonel Stephen Barnwell, and 12 pipers from the Royal Regiment of Scotland provided musical entertainment.
Major General Norton said:
It was, as always, an honour to parade in support of Her Majesty The Queen as she welcomed a Head of State to our country, and I know that all ranks involved were determined to perform to the standard to which the nation has become accustomed.
This afternoon a dismounted detachment of the Household Cavalry Mounted Regiment will line the staircase during the President's visit to the House of Lords.
And this evening the Honourable Artillery Company will provide the Guard of Honour accompanied by the Band and Corps of Drums of the regiment at the Guildhall, where the Corporation of London is holding a banquet.
The Guard of Honour will be commanded by Major Anthony Bagwell and state trumpeters of the Band of the Life Guards will play fanfares during the reception and banquet.
On Friday, when the President and Mrs Yudhoyono depart Buckingham Palace, a detachment of 2nd Battalion The Princess of Wales's Royal Regiment will be on the south side of the forecourt.


enable them eventually, might just as well do so now.
Afaik the orange pi dts is similar to the banana pi one (that is: I have always used the banana pi ones on orange pi for my testing and never noticed an issue).
Does this version also already support H3-based devices?