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Tempting Little Tease by Kendall Ryan: Blog Tour + Review
A hot new standalone romance from New York Times bestseller, Kendall Ryan. She's the tutor I hired to teach me Italian. She's way too young for me, but she's also gorgeous, bright and filled with a curiosity about life that I find incredibly refreshing. It's fucking adorable. I'm old enough to know better, but this pretty young thing tempts me beyond belief. And for the first time in my life, I can see myself falling. *** Is this what it's like to be pursued by an older man? The complete confidence, the lack of expectations, the sincerity? My God, it's exhilarating. Quinn Kingsley is totally unexpected. I'm moving to Italy in three weeks to teach English, and while I never expected something so real to develop between us so quickly, our chemistry is undeniable. There's something so sexy about this back and forth he and I share. Flirting with this man is like playing with fire, and I'm bound to get burned. Io sono attratto da te. I'm attracted to you, he tells me. But is our attraction enough to get us through the complications of a massive age gap and an international love affair? Only one way to find out…
A New York Times, Wall Street Journal, and USA Today bestselling author of more than two dozen titles, Kendall Ryan has sold over 1.5 million books and her books have been translated into several languages in countries around the world. She's a traditionally published author with Simon & Schuster and Harper Collins UK, as well as an independently published author. Since she first began self-publishing in 2012, she's appeared at #1 on Barnes & Noble and iBooks charts around the world. Her books have also appeared on the New York Times and USA Today bestseller lists more than three dozen times. Ryan has been featured in such publications as USA Today, Newsweek, and InTouch Magazine.
Visit her at: www.kendallryanbooks.com for the latest book news, and fun extras Subscribe to Newletter Facebook | Twitter | Pinterest | Goodreads | Amazon Author Page
*REVIEW*
To read Michelle's review ==> CLICK HERE
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1E Edition
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Awards 22.05.16 . 8:28 PM | Update : 13.02.18 . 09:49 AM
It's Only the End of the World, by Xavier Dolan, Grand Prix
Xavier Dolan, Grand Prix - Juste la Fin du Monde (It's Only the End of the World) © Valéry Hache / AFP
The Grand Prix has been awarded by actors Valeria Golino and Donald Sutherland to It's Only the End of the World by Xavier Dolan
"I'd like to thank the Festival, Thierry Frémaux, Nancy Grant, my producer and Anne Dorval, who first told me about this play. I want to thank the Jury for having felt the emotion of the film. It's not always easy to share your emotions with others. Violence is sometimes a cry, a material that kills. I hope I didn't let Jean-Luc Lagarce down. Everything you do in life, you do in order to be loved, and to be accepted. At least that's true of me. The older I get, the more I realise it's not easy to be understood. I know now that I have to make films that resemble me. Without compromising. I know that, just like two years ago, this moment is going to change my life. As Anatole France said, "I prefer the madness of passion to the wisdom of indifference."
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text:
\section{Introduction}

\label{sec:intro}
Lipreading is the skill of recognizing speech visually from a person's lip movements. Humans naturally rely on lipreading to discern speech, especially in crowded and noisy environments~\cite{Rosenblum2007}.
It is the fundamental mode of communication for many people, such as (1) those suffering from medical conditions such as Amyotrophic Lateral Sclerosis (ALS) - leading them to lose their voice~\cite{Zarei2015, Masrori2020}, or (2) those with hearing impairment - making it difficult for them to produce proper voice. In such cases, talking to a person without voice may need you to lipread them to understand the spoken words.

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{images/banner4.pdf}
\end{center}
\caption{We propose a personalized one-shot lipreading framework to tackle a real-world medical challenge of lipreading a patient suffering from ALS$^1$. The patient communicates primarily by mouthing the words. In our case of limited available real data, we use a combination of synthetic data augmentation technique and a domain adaptation technique called Variational Encoders to build a robust word-level lipreading model for the patient.

\label{fig:teaser}
\end{figure}

\def*}\footnotetext{These authors contributed equally to this work.{$^1$}\footnotetext{The ALS patient and his family have consented to the use of his pictures in this work.}

Lipreading is mentally taxing and can affect the communication quality. For instance, speakers with hearing impairment may lack holistic audio feedback~\cite{deafspeechdisability} and ALS patients may have lesser control over their mouth muscles~\cite{Beukelman2011, Makkonen2017}. This may cause them to have irregular and unreliable mouth movements making it difficult for people to lipread them. Applications and automated algorithms capable of lipreading a person can thus significantly improve the day-to-day communication of people dependent on lipreading.
Motivated by this need, we tackle the real-world challenge of lipreading a patient suffering from ALS and people with hearing impairment. ALS is a progressive nervous system disease that affects nerve cells in the brain and spinal cord, causing loss of muscle control~\cite{Zarei2015}. An ALS patient may lose their voice and rely solely on mouth movements for communication~\cite{hopwood_2021, nationaldeafchildrensociety}.

\textbf{Current Works and Limitations: }Current deep learning techniques are inherently data-hungry. Collecting large amounts of data specifically from a patient is, however, not an option. Mouthing words is a tiring maneuver for people suffering from ALS, and thus a patient undergoes physical and mental stress during such data collection exercises. Manually labeling words mouthed by a person is time-consuming.
It is thus crucial to use the minimum amount of manually labeled data to build lipreading models that can work well on a person.

Recent years have seen much progress in word-level~\cite{martinez2020lipreading, feng2020learn, Torfi_2017} and sentence-level~\cite{Afouras_2019, xu2018lcanet} lipreading. Oxford's Visual Geometry Group released large-scale in-the-wild datasets such as Lipreading in the Wild (LRW)~\cite{Chung16} and Lipreading sentences (LRS)~\cite{Afouras18c, Afouras18d} consisting of $1000+$ speakers.
LRW, the most relevant dataset to our task, is a word-level lipreading dataset made of $1000$ examples for $500$ English words but turns out to be somewhat limiting: (1) The speakers in the dataset do not have any speaking disability thus making perfect mouth movements. (2) It is curated by cropping words from long speech segments resulting in fast-paced speech with co-articulation in the videos. (3) It contains a large amount of head motion and variations like the different characteristics of the mouth region, both of which are unnecessary for lipreading specific medical patients.
Thus, SOTA models like LipReading without Pains (LRwP)~\cite{feng2020learn} and Lipreading using Temporal Convolutional Networks (LTCN)~\cite{martinez2020lipreading} trained on LRW do not directly adapt to speakers with speaking disabilities.

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{images/speakers3.pdf}
\end{center}
  \caption{Speakers in our study. In left to right order, the first speaker suffers from ALS and relies solely on lip movements. The next two speakers primarily use sign language while making imperfect lip movements. Next speaker uses deaf speech along with sign language for daily communication. The following speaker is the 46th president of the USA, Joe Biden.}

\label{fig:speakers}
\end{figure}

LRW only supports a limited pre-curated vocabulary missing out on medically essential words like `nauseous' or `backache'.
It also lacks a personalized vocabulary relevant to a person's daily communication.
Deploying systems to enable a persons' communication would need highly accurate models on their specific lip movements for their particular vocabulary.
The problem of personalized lip reading has also been explored in ~\cite{prajwal2020learning}. According to~\cite{prajwal2020learning}, lip movements vary across speakers. Observing a single speaker for an extended period could lead to better speaker-specific lipreading models. They collect $\sim$20 hours of data per speaker to train a personalized lipreading model generating speech purely from an individual speaker's lip movements. Collecting such a large dataset is, however, not always an option.

Lack of medical data has been studied~\cite{tajbakhsh2019surrogate, Frid_Adar_2018, gyawali2020semisupervised} widely in the past.
Taking inspiration from these, we formally tackle one-shot lipreading in a personalized setting.
We first synthetically generate data using a SOTA lipsync network~\cite{wav2lip}. We then use a SOTA lipreading network~\cite{feng2020learn} as our backbone and use synthetically generated data along with very limited real examples to train a word-level lipreading network.
Our approach includes an important domain adaptation step using a novel network -- Variational Encoders -- a modified VAE for bringing a vast number of synthetic examples closer to the real domain using only one real instance per class. We specifically tackle the use case of an ALS patient and also explore the same for four other speakers (refer to Fig.~\ref{fig:speakers}) including the 46th President of the United States, Joe Biden, as an additional example to show that our approach can easily be extended to speakers with no disability.
Our contributions in this work are threefold:

\begin{enumerate}
    \item We tackle a real-world medical challenge of lipreading speakers with ALS and hearing impairment by developing highly accurate personalized models for each speaker.
    \item We propose Variational Encoders, a novel network-based on VAE. Instead of autoencoding, they exploit the loss of the downstream task for generating task-relevant latent distributions. The learned distributions are then used for domain adaptation.
    \item To the best of our knowledge, we are the first to propose lipreading in one-shot setting. In this vein, we curate a medical dataset involving speakers with medical conditions.

\end{enumerate}

\section{One-shot personalized lipreading framework}

As mentioned previously we aim to build a personalized lip-reading model for each speaker using only single real examples.
Fig.~\ref{fig:pipeline} presents the pipeline for personalized lipreading.

The use of synthetic data to augment low data in the medical domain~\cite{tajbakhsh2019surrogate, Frid_Adar_2018, gyawali2020semisupervised} has gained traction with improving generative models. Similarly, we augment the one-shot examples collected for each speaker by generating synthetic data for each of them. To achieve this, we use a SOTA talking face generation model, Wav2Lip~\cite{wav2lip} pretrained on the large-scale LRS2~\cite{lrs2} dataset. Given a speaker's video, Wav2Lip preserves the speaker's pose, facial expressions, and mouth characteristics like beard and skin color while modifying the speaker's lip movements according to a guiding speech. We generate word-level speech utterances using SOTA TTS models FastSpeech2~\cite{ren2021fastspeech} and GlowTTS~\cite{kim2020glowtts} as a replacement for the speaker's voice. Using these TTS models allows us to generate variations in the speech in terms of the speed of the spoken word, pitch, and energy. Additionally, we collect unlabeled face videos for each speaker which, along with the generated speech utterances, is used to create 1 hour of speaker-specific synthetic data on an average. The augmented dataset is then used to train an LRwP and LTCN based architecture for the classes curated per speaker.

\label{section:synth_data_framework}
\begin{figure*}
\begin{center}
\includegraphics[width=\linewidth]{images/pipeline17.pdf}
\end{center}
   \caption{Personalized lipreading - We start by curating personalized vocabulary and collect unlabeled videos for each speaker. TTS models are used to generate speech utterances for the curated words. The speaker's unlabeled videos and the generated speech utterances are given as input to Wav2Lip that generates synthetic data to augment one-shot data. Variational Encoders then use the synthetic and the one-shot data to train the model for lipreading.}

\label{fig:pipeline}
\end{figure*}

A combination of the speaker-specific synthetic videos and a single real video per class are used to train our model.
The synthetic data helps the model learn the general underlying word-level characteristics for the new classes. However, the properties of personal style of lip-movements for a word -- could be because of the medical condition -- is not captured in the synthetic dataset.
We utilize the one-shot examples for introducing the properties of personal speaking style in the model. Single examples per class are however, not enough to capture the underlying style variations of a speaker. A person may not utter the same word, exactly, each time. To tackle this, we use our novel approach -- Variational Encoders

\subsection{Variational Encoders: Mapping words into distributions}
\label{section:veda}

Deep learning suffers from the fundamental challenge of source-target domain shift - a model trained on a given dataset (source domain) performs poorly on the test examples (target domain). The target domain may lack the amount of labeled data needed for training or fine-tuning a model.
Recent techniques include adversarial networks to generate domain invariant features using adversarial losses~\cite{DANN, FADA, ADDA} and
Variational Auto Encoders (VAE)~\cite{hsu2017unsupervised, takahashi2020partiallyshared} to generate a joint latent distribution across domains with KL divergence and reconstruction loss. Unlike adversarial loss~\cite{DANN} that may quickly become lopsided~\cite{DAOSL}, VAEs use a distance-based metric to incorporate domain invariance. The reconstruction loss in VAE ensures that latent representations preserve important domain characteristics. However, using a decoder to reconstruct the input accurately is a non-trivial task, especially for videos needing spatial and continuous temporal reconstruction. We propose Variational Encoders, a modified VAE that uses the loss of the downstream task to generate task-relevant latent distributions.

\textbf{How do Variational Encoders differ from a standard VAE?} Similar to VAEs, Variational Encoders (refer to Fig.~\ref{fig:diag_main}) generate latent distributions given a sample. A key difference with a standard VAE is - instead of autoencoding, it uses the final multi-class classification loss of the downstream task to generate a task-relevant latent distribution that represents the ``class" of input instead of a generic input feature. The learned distribution is used to sample a variation of the input example. We hypothesize that this sample introduces the missing speaker-specific style variation for the input class. The sampled variation encourages the encoder to see and align to the potential interpretation of the input class and learn robust representations.
In summary, Variational Encoder retains all the benefits of a VAE, like generating domain invariant features while removing the complicated video reconstruction loss.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.75\linewidth]{images/diag_main31.pdf}
\end{center}
   \caption{Variational Encoders -- Encoder$_S$ and Encoder$_T$ denote the source and target encoders respectively.
    Encoder$_T$ generates a latent distribution using the target examples. The latent distribution is trained against the downstream classification task. Encoder$_S$ aims to learn robust domain invariant features by minimizing the distance between its learned embedding and points randomly sampled from the generated target distribution.

   }
\label{fig:diag_main}
\end{figure}

\subsubsection{Network Architecture}
\label{section:problem_definition_veda}

Given a source domain \textit{S} and a target domain \textit{T}, domain adaptation aims to bridge the gap between the two domains by generating domain invariant features. We denote \textit{S} and \textit{T} as,
\begin{equation}
    S = \{(x_1, y_1), (x_2, y_2), ... (x_{N_S}, y_{N_S})\}\ \textrm{and}\ T = \{(\bar{x}_1, \bar{y}_1), (\bar{x}_2, \bar{y}_2), ... (\bar{x}_{N_T}, \bar{y}_{N_T})\}
\end{equation}
where $N_S$ is the number of source samples and $N_T$ is the number of target samples.

Variational Encoder is a paired domain adaptation network that assumes the target domain is labeled. However, the number of examples in the target domain is expected to be at max $k$ where $k\approx1$. Thus, we assume $N_S >> N_T$.
The adaptation network is made of two encoders - source and target encoders -
and a single classifier.
For a given input $\hat{x}_i$ belonging to either domain, the encoder learns to generates an embedding $e_{\hat{x}_i}$ for the input.
A distribution $p(\hat{x}_i)$ is then generated for the input denoted by $\mu_{\hat{x}_i}$ and $\sigma_{\hat{x}_i}$ from the learnt embedding $e_{\hat{x}_i}$. A random point is sampled from the generated distribution denoted as $z_{\hat{x}_i} = \mu_{\hat{x}_i} + \sigma_{\hat{x}_i} \bigodot \epsilon$.

The encoder network for both domains are identical. Each encoder generates the embedding $e$, and the latent distribution $p$. To introduce domain invariance, we use KL divergence and L1 loss. The KL divergence is computed between the two generated distributions $p(x_i) = (\mu_{x_i}, \sigma_{x_i})$ and $p(\bar{x}_i) = (\mu_{\bar{x}_i}, \sigma_{\bar{x}_i})$. In addition, a point $z_{\bar{x}_i}$ is randomly sampled from the target distribution $p(\bar{x}_i)$. L1 loss is then applied between $z_{\bar{x}_i}$ and the source embedding $e_{x_i}$. This minimizes the distance between the source embedding against several randomly sampled points from the target distribution thus acting as a pseudo for multiple target examples.

\textbf{Gradient stopping: }The L1 Loss between the source embedding and the sampled target embedding will force the target distribution down to a single point, losing the essence of a distribution. To prevent that, we stop the gradient from flowing back through the target encoder. This way, only the source encoder is regularized against the variations sampled from the learnt target distribution, while leaving the target encoder unaffected. We denote the sampled target embedding as $z_{{\bar{x}_i}detach}$, where $_{detach}$ denotes that the sample $z_{\bar{x}_i}$ does not have any gradient.
The combined loss to bridge the two domains is given as,
\begin{equation}
    \Delta_{dist} = | e_{x_i} - z_{{\bar{x}_i}detach} | - \beta \cdot D_{KL}( p(x_i) || p(\bar{x}_i)).
\end{equation}
where $\beta$ is a hyper-parameter and $i$ denotes both the samples belong to the same class.

For the downstream task of classification, we employ a common classifier that is trained on both domains at the same time.
A single classifier allows both encoders to have a common base-loss helping them generate features relevant to the common downstream task.
The downstream classifier also trains on large amounts of variations
and learns robust representations.
The classifier receives a randomly sampled point $z_{\bar{x}_i}$ from the learnt distribution $p_{\bar{x}_i}$ as input for the target domain. For the source encoder, however, the classifier receives the learnt embedding $e_{x_i}$ instead of a randomly sampled latent point $z_{x_i}$.
The motivation behind a target distribution is to encourage the target encoder to hallucinate variations of the single real examples available for each class. However, during inference, we want to obtain a definite point for the input to avoid any uncertainties. The classification loss is given as,
\begin{equation}
    \Delta_{entropy} = g(e_i, y) + g(\bar{z}_i, y)
\end{equation}
where $g$ can be any classification loss such as cross entropy or negative log likelihood. $y$ denote the same label for both the source $x_i$ and target $\bar{x}_i$ input.

The combined loss for the entire network is then given as (see Fig.~\ref{fig:diag_main}),
\begin{equation}
    \Delta_{ve} = \alpha \cdot (g(e_i, y) + g(\bar{z}_i, y)) + \gamma \cdot (| e_{x_i} - z_{{\bar{x}_i}detach} | - \beta \cdot D_{KL}( p(x_i) || p(\bar{x}_i))).
\end{equation}
where $\alpha$ and $\gamma$ are hyper-parameters. All the network components are trained end-to-end.

\section{Experiments}

\textbf{Dataset}: We first collect a set of unlabeled videos for each speaker that are used later to generate the speaker-specific synthetic data using Wav2Lip.
Unlabeled videos of the ALS patient are recorded without any manual intervention. For other speakers, the videos are randomly selected from their respective YouTube channels. As the next step, we curate the personalized vocabulary for each of the speakers. For the ALS patient, we curate a list of $200$ words with the help of his family. We modify the existing list for the remaining speakers by removing irrelevant words and adding the most occurring keywords in the transcription of the collected videos. Splits of the curated dataset for each speaker is presented in Table~\ref{tab:datasplit}.

\begingroup
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1}
\begin{table}[H]
  \begin{center}
\begin{tabularx}{\textwidth}{ |l|l| *{6}{Y|} }
      \hline
      \multicolumn{2}{| c|  }{} & \textbf{Patient} & \textbf{Spk-1} & \textbf{Spk-2} & \textbf{Spk-3} & \textbf{Joe Biden} & \textbf{Total}
      \\ \hline
      \multicolumn{1}{| c  }{} & Classes & 200 & 75 & 70 & 80 & 75 & -
      \\
      \hline
      \multirow{3}{*}{\makecell{Train}} & Real & 200 & 75 & 70 & 80 & 75 & -
      \\
      & Synth & 17$\times$Real & 22$\times$Real & 22$\times$Real & 22$\times$Real & 25$\times$Real & -
      \\
      & Aug. & 2$\times$Real & 2$\times$Real & 2$\times$Real & 2$\times$Real & 2$\times$Real & -
      \\
      \hline
      Test & Real & 320 & 120 & 80 & 90 & 90 & 710
      \\ \hline
\end{tabularx}
  \end{center}
  \caption{Split up for the datasets curated for each of the 5 speakers. }

  \label{tab:datasplit}
\end{table}
\endgroup

We obtain two sets of manually curated real data from the ALS patient's family. We use the first set for model training and the second set as the intermediate test set. We collect additional data by deploying a website that records the patient's word-level mouthings and displays the inference on our best model (see Fig.~\ref{fig:teaser}).
The website needs external help to start and stop recording the patient.
The helper can then either select one of the displayed words as the correct label or manually assign the correct label. Through this exercise, we collect an additional $320$ examples. Out of these, we use 200 data points, in addition to the original 200 train data points, to train a model in a two-shot setting. We report our test results of one-shot and two-shot models on the combined set of the intermediate test set and the remaining additional $120$ examples (see Table~\ref{tab:datasplit}). To simulate the same setting, we maintain one real example for training the rest of the speakers. To generate the train and test examples, we use the transcriptions with timestamps available on YouTube for the selected videos.

\textbf{Preprocessing}: Our preprocessing steps are similar to LRwP. The lip landmarks are first detected using dlib~\cite{dlib}. The lip is then cropped out such that it is horizontally and vertically centered in the cropped image. The image is converted to gray-scale and resized to a fixed dimension of $88\times88$.
A maximum sequence length of $64$ frames is used.
A batch size of $16$ on a multi-GPU NVIDIA GeForce RTX $2080$ setup is used. The models are trained up to $200$ epochs using a cosine scheduler and Adam optimizer, with a $3e - 5$ learning rate and a weight decay of $1e - 4$. The encoders in the experiments are adopted from LRwP that uses Resnet18 and BiGRU, and LTCN that uses Resnet18 and Temporal Convolutional Networks.

\begingroup
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1}
\begin{table}[t]
  \begin{center}
  \resizebox{\textwidth}{!}{%
  \begin{tabular}{| l || c | c || c | c || c | c || c | c || c | c|| c| c | c }
      \hline
       & \multicolumn{2}{ c || }{\textbf{Patient}} & \multicolumn{2}{ c ||}{\textbf{Spk-1}} & \multicolumn{2}{ c ||}{\textbf{Spk-2}} & \multicolumn{2}{ c ||}{\textbf{Spk-3}} & \multicolumn{2}{ c ||}{\textbf{Joe Biden}} & \multicolumn{2}{ c |}{\textbf{Avg.}}
      \\
      \cline{2-13}
      Experiment & top1 & top5 & top1 & top5 & top1 & top5 & top1 & top5 & top1 & top5 & top1 & top5\\
    \hline
    \multicolumn{13}{ | c | }{LRwP} \\
    \hline
    cl$-$r & 49.3 & 62.6 & 32.4 & 52.1 & 23.1 & 42.5 & 26.7 & 49.3 & 33.5 & 51.5 & 33.0 & 51.6 \\
    cl$-$r$+$s & 53.6 & 68.1 & 32.3 & 49.8 & \textbf{37.2} & 45.1 & 32.4 & 58.8 & 57.6 & 69.3 & 42.6 & 58.7 \\
    ve$-$r$+$s & 66.4 & 81.4 & \textbf{48.6} & \textbf{68.5} & 34.4 & \textbf{59.6} & 41.6 & 71.4 & 54.1 & 72.4 & 49.0 & \textbf{70.4} \\
    cl$-$r$+$s$+$aug & 61.3 & 75.2 & 34.6 & 51.2 & 33.4 & 40.4 & 51.2 & 63.6 & \textbf{64.5} & 75.3 & 49.4 & 61.3 \\
    ve$-$r$+$s$+$aug & \textbf{68.1} & \textbf{83.2 } & 44.3 & 62.6 & 31.2 & 50.8 & \textbf{53.4} & \textbf{73.5} & 61.4 & \textbf{77.1} & \textbf{52.2} & 69.4 \\

    \hline
    \multicolumn{13}{| c |}{LTCN} \\
    \hline

    cl$-$r$+$s & 55.2 & 67.1 & 36.5 & 52.0 & 34.8 & 44.8 & 34.4 & 59.6 & 55.4 & 67.3 & 43.3 & 58.2
    \\
    ve$-$r$+$s & 64.5 & 80.2 & \textbf{49.3} & \textbf{71.1} & \textbf{35.7} & \textbf{57.9} & 46.4 & 73.2 & 56.3 & 71.1 & 50.4 & \textbf{70.7}
    \\

    cl$-$r$+$s$+$aug & 61.9 & 74.2 & 33.2 & 49.8 & 32.4 & 39.9 & 52.5 & 66.1 & \textbf{62.1} & \textbf{76.2} & 48.4 & 61.2
    \\
    ve$-$r$+$s$+$aug & \textbf{66.8} & \textbf{81.7} & 41.7 & 60.4 & 32.4 & 48.8 & \textbf{54.9} & \textbf{75.2} & 61.9 & 75.3 & \textbf{51.5} & 68.3
    \\
    \hline

  \end{tabular}}
  \end{center}
  \caption{Evaluation of our models against each speaker reported in \%. All metrics are evaluated on the curated test set made of only real-data. Spk-1 uses a combination of deaf-speech and sign language, Spk-2 and Spk-3 use sign language for communication. Patient denote the ALS patient in our study.

  r, s, and aug indicate real, synthetic, and augmented-real datapoints. cl indicates standard classification while VE is the proposed technique.}

  \label{tab:evaluation}
\end{table}
\endgroup

\begingroup
\setlength{\tabcolsep}{6pt}
\renewcommand{\arraystretch}{1}
\begin{table}[]
  \begin{center}
  \begin{tabular}{| l | l | c | c | c | }
    \hline
    \multicolumn{1}{ | c }{} & \multicolumn{1}{ c | }{\textbf{Experiment}} &\textbf{ top-1} & \textbf{top-3} & \textbf{top-5} \\
    \hline
    \multirow{3}{*}{\makecell{One-shot (classification)}} & only fs tts & 56.83 & 68.46 & 71.92 \\
    \cline{2-5}
    & only glow tts & 57.56 & 67.73 & 71.55 \\
    \cline{2-5}
    & combined & \textbf{61.38} & \textbf{73.63} & \textbf{75.26} \\
    \hline
    \multirow{2}{*}{\makecell{Two-shot}} & classification & 64.33 & 74.69 & 82.16 \\
    \cline{2-5}
    & variational encoders &  \textbf{71.64} & \textbf{76.54} & \textbf{89.36} \\
    \hline

  \end{tabular}
  \end{center}
  \caption{Accuracy of additional experiments performed on the ALS patient in \%. The test dataset used for both, One-shot and Two-shot experiments is the same. FastSpeech2 and GlowTTS are represented by fs tts and glow tts, respectively. All experiments are conducted using LRwP as the backbone on the combined real, synthetic, and augmented real datasets.}

  \label{tab:additional_evaluation}
\end{table}
\endgroup

\subsection{Training strategy}
Table~\ref{tab:evaluation} presents a comprehensive overview of all the experiments conducted on the 5 speakers. Spk-1 uses deaf-speech and sign language, Spk-2 and Spk-3 use sign language as their primary mode of communication while mouthing words with imperfect lip movements. Table~\ref{tab:additional_evaluation} presents additional experiments performed on the dataset of the ALS patient.
We initialize our models with the weights of LRwP or LTCN, both of them pretrained on LRW.
We observed that using the pretrained weights leads to faster convergence.

\textbf{Baseline (Exp-\textit{cl$-$r})}: We begin by training our model directly on the one-shot examples. Since we are the first to perform one-shot lipreading on a personalized vocabulary, we treat this model as our baseline. Table~\ref{tab:evaluation} Exp-\textit{cl$-$r} presents the performance of the baseline model on each of the $5$ speakers. The average accuracy of the speakers at top-1 and top-5 is only $33.0\%$ and $51.6\%$ respectively. The accuracy of the current SOTA lipreading model on the LRW dataset is $88.5\%$ at top-1. This presents us with a huge scope for improvement.

\textbf{Data Augmentation using Synthetic Data (Exp-\textit{cl$-$r$+$s})}: We augment the one-shot examples with a potentially unlimited number of synthetic examples. We train our models with varying amounts of synthetic data.
We plot a graph of accuracy against the combined synthetic and one-shot examples to determine the optimal amount of synthetic data needed for each speaker against the one-shot examples as shown in the supplementary, Fig.~1.
The optimal number of synthetic examples per speaker is reported in Table~\ref{tab:datasplit}.

As shown in Table~\ref{tab:evaluation} Exp-\textit{cl$-$r$+$s}, we observe a significant jump in the accuracy consistently for every speaker with an overall improvement of $9\%$ and $7\%$ at top-$1$ and $5$, respectively on LRwP, and $7\%$ and $12\%$ at top-$1$ and $5$, respectively on LTCN.
Although the accuracy improved over the baseline model, we observe that the model overfits on the synthetic data after a few epochs. Since we use only one example per class for the real domain, the model cannot foresee the target (real) variations that it may encounter during testing. We, therefore, try to introduce variations using Variational Encoders.

\begin{figure*}
\begin{center}
\includegraphics[width=\linewidth]{images/PCA_features_crop.pdf}
\end{center}
   \caption{PCA visualization for the embeddings generated by the feature extraction layer of LRwP based encoder. (left) before training, (middle) trained on \textit{cl$-$r$+$s}, and (right) trained on\textit{ve$-$r$+$s}. Samples from the ALS patient's test set are used for visualization}
\label{fig:visualization}
\end{figure*}

\textbf{Variational Encoders (Exp-\textit{ve$-$r$+$s})}:
To train the network, we use the one-shot examples as the source domain. For the target domain, we combine the real and synthetic dataset.
This lets the source encoder see the existing real examples and also become robust against the additional pseudo examples sampled from the target distribution.
We observed that using only the synthetic data in the source encoder makes the training highly unstable and the network does not converge. Instead, we allow the source encoder to first fit and then improve.

As seen from Table~\ref{tab:evaluation} Exp-\textit{ve$-$r$+$s}, Variational Encoders consistently achieves the highest accuracy at top-5 across LRwP and LTCN. There is an overall improvement of $\sim19\%$ over the baseline and $\sim12\%$ over Exp-\textit{cl$-$r$+$s}.
The improvement at top-1 accuracy is comparatively much lower, $\sim15\%$ over the baseline and only $\sim6\%$ over Exp-\textit{cl$-$r$+$s}. We use PCA visualizations to analyze the latent representations learnt by the LRwP based source encoder, as shown in Fig.~\ref{fig:visualization} (right). We observe better separation for each class compared to the separation of Exp-\textit{cl$-$r$+$s} (middle). We also observe better disentanglement between non-homophenes such as `bathroom', `appreciate', and `coughing' compared to Exp-\textit{cl$-$r$+$s}.
For Varitional Encoders we observe that homophenes `coughing', `cooking', `something' are closer together. This suggests that the model trained on Variational Encoders can get confused for homophenes bringing the accuracy at top-1 down and at top-5 higher. For Exp-\textit{cl$-$r$+$s} however, all the classes seem equally apart.

\textbf{Ad-hoc Data Augmentation (Exp-\textit{cl$-$r$+$s$+$aug} and Exp-\textit{ve$-$r$+$s$+$aug}):} In addition to the implicit variations introduced by Variational Encoders during the model training, we introduce explicit real-domain variations by augmenting the one-shot dataset. First, we use moviepy~\cite{moviepy} library to speed up and speed down the one-shot videos by a factor of 1.2$\times$ and 0.8$\times$.  The videos in our work are unconstrained, that is, the actual mouthing could be spread across several frames placed temporally anywhere in the video. Thus, we increase the video frames sequence length to 85 and add temporal variations during training by padding the videos with random number (between 0 to 20) of empty frames at the start and end.

We observe improvement in the performance across speakers for both, classification (Exp-\textit{cl$-$r$+$s$+$aug}) and Varitional Encoders (Exp-\textit{ve$-$r$+$s$+$aug}). We observe that for Spk-1 and Spk-2, the performance degrades. Upon further analysis, we find that the videos maintain constant pace and have fewer overall variations. Thus, the added variations during training behave as noise driving the performance down. The overall performance improves by 7\% and 3\% at top-1 on Exp-\textit{cl$-$r$+$s$+$aug} and Exp-\textit{ve$-$r$+$s$+$aug} over Exp-\textit{cl$-$r$+$s} and Exp-\textit{ve$-$r$+$s} respectively. We observe the best performance on one-shot setting with these ad-hoc additions for classification. For Variational Encoders, the improvements are less significant, especially at top-5, indicating the technique itself makes up for these ad-hoc additions.

\textbf{Additional Experiments on the ALS Patient's Dataset}: To observe the affect of using different TTS models for generating synthetic data, we perform an ablation by eliminating one TTS model at a time for data generation and compare its performance against the data generated by combining both the TTS models. As shown in Table~\ref{tab:additional_evaluation}, the performance with both the TTS combined gives us the best performance. This indicates that the variations introduced by different TTS models are important for generalization.

Lastly, we train an additional model with the additional data obtained from the patient's family to evaluate the performance difference between one-shot and two-shot setting. As seen from Table~\ref{tab:additional_evaluation}, the performance improves by $\sim7\%$ at top-5 against the best performing one-shot model on both training methodologies. Specifically for Variational Encoders, the performance improves to 71.64\% and 89.36\% at top-1 and top-5. This suggests that the network learns a better real-domain latent distribution using the extra real examples.

\textbf{Evaluation on Joe Biden}: Although Joe Biden represents speakers without disabilities, he speaks in an American accent, while LRW is composed of British speakers. As can be observed from Table~\ref{tab:evaluation} Exp-\textit{cl$-$r}, the performance for Joe Biden on the baseline model is similar to the performance for the deaf speakers. A different accent can also be thought of as a different style of speaking. Thus the pretrained model does not directly adapt to Joe Biden. We observe in his case, adding synthetic data leads to significant improvements in the accuracy over the baseline with an average gain of $21\%$ at top-1 and top-5 (Exp-\textit{cl$-$r$+$s}). Variational Encoders fail to bring expected improvements compared to the other speakers. With LRwP, at top-1, the accuracy drops by $3\%$, and we see a marginal improvement at top-5.  We note that the TTS used to generate synthetic data is of an American accent. This suggests that the synthetic data captures the variations of the real domain exceptionally well in his case. Variational Encoders, on the other hand, adds more noise than valuable variations.

\section{Conclusion and Future Work}
In this work, we leap from previous lipreading approaches and propose a one-shot personalized lipreading framework to aid patients suffering from ALS or hearing disabilities. Due to the extreme scarcity of personalized data available for a medical patient, we generate synthetic data to augment our training process. We train our network with Variational Encoders, a domain adaptation technique, to bridge the gap between the synthetically generated examples and the available one-shot real examples for each class. Our method proves to be highly effective, and we achieve over $83\%$ top-5 accuracy for the ALS patient.
We also report the performance of speaker-specific models trained for multiple speakers with hearing impairment and a speaker with no disability.
In the future, we would like to improve the accuracy of our model while also increasing the vocabulary it can handle. We believe our work achieves essential milestones for the lipreading community and can enhance the communication between people dependent on lipreading. It encourages research in the direction of few-shot lipreading that is an important real-world challenge that can have far-reaching applications including and beyond medical lipreading. Although, Variational Encoders has been proposed for few-shot lipreading, its utility can be explored in other areas with a few-shot settings.

\textbf{Acknowledgement}: We would like to thank Anuraag Mullick and Vibha Mullick for their continual support in curating the dataset needed for this work. We also thank Harini Bhatt, who is the founder of ASRM Systems, for connecting us with several participants.

\section{Ablation Experiments}

In this section, we report ablation experiments to inspect and gain further insights on the different components of our network. Our experimental setup is identical to the setup mentioned in Section~3 of the main paper. For better readability during comparisons, we have copied the results of Table~2: Exp-\textit{cl$-$r} and Exp-\textit{ve$-$r$+$s} of the main paper to Table~\ref{tab:supp_evaluation}.

We would like to also encourage the reader to view the supplementary video containing the results and comparisons.

\begingroup
\setlength{\tabcolsep}{7pt}
\renewcommand{\arraystretch}{1}
\begin{table}[H]
  \begin{center}
  \begin{tabular}{|| c || c | c | c | c || c | c | c | c || }
      \hline
      & \multicolumn{2}{ c }{cl$-$r} & \multicolumn{2}{| c ||}{ve$-$r} & \multicolumn{2}{ c | }{ve$-$r$+$s} & \multicolumn{2}{c || }{ve$-$r$+$s$-$mod}
      \\ \cline{2-9}
      Speaker & top1 & top5 & top1 & top5 & top1 & top5 & top1 & top5
      \\ \hline

      ALS patient & 49.3 & 62.6 & 51.2 & 65.4 & 66.4 & 81.4 & 64.6 & 74.9
      \\
      Spk-1 & 32.4& 52.1 & 36.9 & 48.9 & 48.6 & 68.5 & 48.3 & 60.3
      \\
      Spk-2 & 23.1& 42.5 & 30.4 & 44.7 & 34.4 & 59.6 & 31.5 & 57.7
      \\
      Spk-3 & 26.7 & 49.3 & 35.7 & 59.6 & 41.6  & 71.4 & 39.7 & 65.3
      \\
      Joe Biden & 33.5 & 51.5 & 41.5 & 57.8 & 54.1 & 72.4  & 54.4 & 70.8
      \\ \hline
      Avg. & 33.0 & 51.6 & \textbf{39.1} & \textbf{55.2} & \textbf{49.0} & \textbf{70.6} & 47.7 & 65.8
      \\ \hline
  \end{tabular}
  \end{center}
  \caption{Evaluation of our models against each speaker in \%. All the metrics are evaluated on the curated test set made of only real-data. Exp-cl$-$r is the baseline trained on one-shot examples. Exp-ve$-$r denotes Variational Encoders trained on only one-shot examples. Exp-ve$-$r$+$s denotes Variational Encoders trained on synthetic and one-shot examples. Exp-ve$-$r$+$s$-$mod is Variational Encoders trained with no distance loss. All experiments are performed using LRwP as the backbone on real $+$ synthetic dataset. }
  \label{tab:supp_evaluation}
\end{table}
\endgroup

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.8\linewidth]{images/synthetic_real_scale_factor.png}
\end{center}
  \caption{Plot to determine the optimal amount of synthetic data against the real examples for Spk-1 at different scale factors of real data. Reference added in the main paper.}
\label{fig:graph}
\end{figure}

\subsection{Variational Encoders on Only Single Real Examples}

As reported in Section~3.1 of the main paper, our network is trained on the combined data of synthetic and one-shot examples. In this section, we remove the synthetic data to see its benefit. Instead of passing synthetic and one-shot examples, we only use one-shot examples as input to both encoders. Since the Variational Encoder itself acts as a data augmentation network, we assume the network to improve over the baseline (Table~\ref{tab:supp_evaluation} Exp-\textit{cl$-$r}).

As shown in Table~\ref{tab:supp_evaluation} Exp-\textit{ve$-$r}, the performance of the reduced network improves by an average of $6\%$ and $4\%$ at top-1 and top-5 respectively over the baseline. However, the performance is still lower than the rest of the experiments mentioned in the main paper (Table~2 Exp-\textit{cl$-$r+s} and Exp-\textit{ve$-$r$+$s}). This suggests that Variational Encoders with just one example cannot surpass methods with additional data augmentation techniques. But, even with only one example, Variational Encoders is capable of bringing significant improvements to the model.
Thus, our approach can be a potential data augmentation technique impacting other tasks requiring a low-data setting.

\subsection{Effect of $\Delta_{dist}$}

As reported in Equation~(4) of the main paper,
$\Delta_{ve}$ is a combination of $\Delta_{dist}$ that introduces domain invariance by reducing the distance between the feature space of source and target encoder, and $\Delta_{entropy}$ that allows the encoders to generate task-relevant latent embeddings and distributions using the loss of the downstream task. In this section, we remove $\Delta_{dist}$ and observe the behavior of the network using only $\Delta_{entropy}$. This allows us to determine the effect of introducing domain invariance. Loss of the reduced network is thus given by,
\begin{equation}
    \Delta_{ve} = \Delta_{entropy} = \alpha \cdot (g(e_i, y) + g(\bar{z}_i, y))\ \{\alpha = 1\}
\end{equation}

As can be seen from Table~\ref{tab:supp_evaluation} Exp-\textit{ve$-$r$+$s$-$mod}, the accuracy of the reduced network drops by $1\%$ and $5\%$ across the speakers at top-1 and top-5 respectively over Exp-\textit{ve$-$r$+$s}.
We observe, $\Delta_{entropy}$ plays a substantial role in our network. Generating variations using Variational Encoders lets the downstream classifier see various examples of the real domain making it robust. We observe the accuracy drop at top-5 is higher than top-1. The drop suggests that $\Delta_{dist}$ may introduce domain invariance across related mouth movements (such as homophenes).

\section{Need for Variational Encoders}

\subsection{Other Data Augmentation Techniques}

\begin{figure}
\begin{center}
\includegraphics[width=0.9\linewidth]{images/exp-2-8.jpeg}
\end{center}
   \caption{Comparison between the test-loss of Exp-cl$-$r+s in the main paper with Exp-ve$-$r$+$s (our proposed approach) for the ALS patient.}

\label{fig:graph}
\end{figure}

In this section we report additional approaches that we tried to augment the one-shot examples.
An expert human lipreader can only lipread $40\%$ of a given sentence~\cite{cdc_2020}. Thus, manually labeling unlabeled videos is challenging. We therefore try to automatically label the unlabeled videos.

\textbf{Best Match using Syncnet} - Syncnet~\cite{Chung16a} is an audio-video synchronization model to synchronize mouth movements and a given speech utterance. It generates a confidence score for a given audio and video segment. We exploit this method to find segments of unlabeled videos closely matching the generated TTS audios. We use different confidence thresholds to label the videos and vary the length of video segments based on the audio length.

\textbf{Pseudo Labeling} - Pseudo Labeling~\cite{rpseudo, pseduolabel} is a technique that iteratively labels the unlabeled data in a semi-supervised setting. A model is trained on the labeled data and is used to label the unlabeled data known as pseudo labels. The model is trained further on the pseudo labels. These steps are repeated until the pseudo labels converge. It assumes the first model trained on the labeled data to be accurate so the pseudo labels are close to the actual labels. Pseudo labels can be noisy, but as the training progresses, labels converge to stable and accurate predictions.
We first segment the continuous unlabeled videos into smaller word-level video segments. We use lip-landmarks detected by dlib to measure the rate of change of lip-movements and determine the pauses between words. We then segment the videos on these pauses. We then iteratively label these videos using the pseudo labeling technique.

We observed that these methods led to noisy labels. The performance for each model mentioned Table~2 of the main paper dropped by an average of $15\%$ and $7\%$ on adding pseudo labels and syncnet labels respectively.

\subsection{Test Loss on One-Shot Examples}

Training a classifier on one-shot examples restricts the model from seeing many variations of the real domain and thus tends to underfit the real domain. We show this phenomenon in Fig.~\ref{fig:graph} that plots the test loss of Exp-\textit{cl$-$r+s} (of the main paper) and Exp-\textit{ve$-$r$+$s}.
In Exp-\textit{cl$-$r+s}, we see loss on the test set flattens and increases after Epoch-12. In Exp-\textit{ve$-$r$+$s} we see the loss keeps decreasing slowly. This suggests that Variational Encoders introduces meaningful variations of the real domain.

redpajama_set_name: RedPajamaArXiv


The Texas Retired Teachers Foundation (TRTF) is ready for fall! Fall is when TRTF hosts its annual fundraising drive to support its many charitable programs: "A Helping Hand," Beginning Teacher Scholarships, Classroom Assistance Grants, the Disaster Relief Fund, the Legacy Campaign, and the Lehr-Pritchard Endowment Fund!
The districts and local units affiliated with TRTA often take some time during a fall meeting to speak about the Foundation's programs, goals, and accomplishments and to help raise funds for the year.
Please keep your eyes peeled for the 3rd quarter issue of the VOICE, arriving in mailboxes soon! It features an article about how TRTF's programs have impacted educators across the state of Texas this past year, especially those who were affected by Hurricane Harvey and continue on the path to recovery.
SNEAK PREVIEW of The VOICE: Read the TRTF article by clicking here!
Included in this issue is TRTF's Annual Appeal letter for all members of TRTA! View the TRTF Annual Appeal Letter Here!
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Ravenous Daggertooth ( Ixalan, # 202 ) - This is the only printing of this card.
Rules Enrage - Whenever Ravenous Daggertooth is dealt damage, you gain 2 life.
Flavor A daggertooth's triumphant roar makes all the sounds of jungle life fall silent.


text:
Assassin's Creed Origins •
Ubisoft pledge to make hieroglyph translation algorithm open access
Jay Castello
14th October 2018 / 2:00PM
While we're all busy digging into ancient Greece, Ubisoft haven't forgotten last year's Ptolomaic playground, Assassin's Creed Origins. During development, they partnered with Egyptologists, and in doing so they apparently discovered that translating hieroglyphs is very difficult and time consuming. In response, they started looking into ways to streamline the process using machine learning, and this week, they presented their initial progress.
Take a look at their introductory video explaining what they're trying to achieve:
I'm not an Egyptologist, but I do have a history degree, which has broadly taught me that the past is an almost entirely unknowable mess. But still, I'm here for trying to make it a little more accessible to both researchers and general audiences.
Ubisoft's first step was asking for volunteers to trace hieroglyphs on their website, and Assassin's Creed fans were well up for it – "more than 80,000 glyphs were drawn in the tool" on the first night it was active. (I've done a few myself and it's very reminiscent of an old Mario Party minigame, which is to say weirdly compelling.)
Now they've got the basics in place, they've pledged to bring the algorithm into open access by the end of the year, so that academics can both use it and help them to improve it. They're taking the drawing tool and reworking it as a teaching tool for students learning the hieroglyphic script, too.
In the meantime, we can all keep learning about the Peloponnesian wars (and the precursor race) in Assassin's Creed Odyssey, which has no hieroglyphs but plenty of seduction and swimming.
Tagged with Ubisoft, Assassin's Creed Origins.
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More about Assassin's Creed Origins
Jay writes about video games, falls down endless internet rabbit holes, and takes a lot of pictures of flowers.
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Wot I Think: Assassin's Creed Origins
Pharopen world
Egyptian murder-dad Bayek bends canon in Assassin's Creed Odyssey
The whole Half-Life story is free to play on Steam right now
Get Europa Univeralis IV for $1 in the latest Humble Bundle
Pokemon-inspired MMO Temtem is out now in early access


text:
Home : Academics : Institutes : Intellectual Property Law Institute : Copyright & Music Collaboration
The Copyright & Music course is a community collaboration between the law school and local musicians and academicians. While students gain a foundation in Copyright law, they also hear the music and experiences of the professionals Copyright Law benefits. The following are profiles of contributing community members.
AL Strong
Al Strong's musical foundation was laid in Washington, D.C. where he was raised by his grandparents. Music became an integral part of his upbringing and his earliest memories date back to his grandmother's involvement in the church. On an international trip with the Duke Ellington School, Strong was recruited by the North Carolina Central University (NCCU) jazz ensemble, who also happened to be touring Europe at the time. Dr. Ira Wiggins, Director of Jazz Studies at NCCU presented him the opportunity of a lifetime, a full-tuition scholarship to study jazz at the University, in Durham. Strong continued to advance his education by attending Northern Illinois University to study with renowned saxophonist and professor Ron Carter. Receiving instruction in both classical trumpet and jazz trumpet playing, Strong earned a master of Jazz Performance/Pedagogy. In the fall of 2011, Strong teamed with statistician, Cicely Mitchell to create a non-profit, jazz presenting organization titled, the Art of Cool Project. The non-profit's mission is to expand the audience for jazz, initially through presentations in non-traditional spaces. Key project initiatives include an annual music festival located in Durham and jazz summer camps for at-risk youth (stArt of Cool). Recently, the Huffington Post coined the Art of Cool Music Festival as "the Coachella of Jazz."
Robert Trowers
Robert Trowers was born in Brooklyn, New York in 1957. His family contained many musicians and lovers of the art form. This fostered a love of and appreciation for music at an early age. After the mandatory piano lessons during early childhood, he developed an interest in the trombone from listening to the music of the "swing era". Among the bands that sparked his interest in jazz were those of Count Basie, Duke Ellington, Glenn Miller, and Artie Shaw. Most recently, Robert has been on the faculty of North Carolina Central University, which boasts an award – winning jazz faculty and a jazz band that has consistently had a high percentage of its members in the H.B.C.U. all -star band. Professor Trowers is also known for presenting the "David Walker Appeal."
Born into a musical family in London, England, Chris Turner learned harmonica and recorder as a child. He has been playing professionally since 1967 working in a variety of idioms including Folk, Rock, Blues, Jazz, Country, Early and Avant-garde music. While traveling extensively in Europe and Africa, he assimilated many different musical styles. Early in the 1970's, Chris studied composition with Christopher Small and improvisation with John Stevens. In 1975, Chris Turner was recognized for his virtuosity when he was awarded the European Harmonica Championship.
Baron Tymas
Markis Javur Tillman
Markis Javur Tillman is an American singer, songwriter, rapper, dancer, and actor. Born in Raleigh, North Carolina he was involved in his church choir and a gospel group with his siblings before deciding he had more to say. One day he finally decided to take his talent to the next level by persuing a career in secular music. Tillman is also a recording engineer.
Pierce Freelon
Pierce Freelon is a professor, musician and social entrepreneur. He is the founder of Blackspace, a digital makerspace for Afrofuturists. He is also the co-founder/host of Beat Making Lab, an Emmy Award winning PBS web-series. Freelon also founded Blackademics, an online community of young black thinkers and is the frontman of Jazz and Hip hop group the Beast.
Lois Deloatch
Lois Deloatch's career spans nearly three decades in the arts, philanthropy and community service. As an artist, Deloatch is noted for her rich contralto voice and expansive repertoire of classic tunes and original compositions. With five recordings as leader, she has appeared in concert throughout the United States and internationally.
Brett Chambers (Trompetist)
Associate Professor Baron Tymas
Associate Professor of Music Baron Tymas was a Fulbright Visiting Research Chair in the Person and Society at Concordia University (Montréal, QC) in Fall 2015. Since 2001, he has taught primarily jazz and guitar courses at NCCU. His student ensembles have earned national recognition.
Professor Tymas has performed, lectured and taught around the world, and is privileged to have played with many internationally known artists and groups. Tymas has released two CDs as a leader: Insight at Midnight (2009) and Blues for the Tribe (2007). He won an NC Arts Council Jazz Composer Fellowship in 2008.


text:
2019 – the ideological revolution
I used to think the next major revolution would be ushered in by AI and robotics or perhaps by a green shift fixing the substantial planet-destroying negative consequences of the industrial revolution by creating new technologies, new jobs and eventually a new earth. I was wrong. I now believe it will be an ideological revolution.
At the start of the 19th century we collectively left the fields as farmers to go work in factories and so we collectively started the industrial revolution - feeding the ever-hungrier capitalist machine. A machine we have worshipped since by sacrificing sustainability to get growth back.
The journey to the factory was not always a voluntary walk, but nevertheless, a necessary one that ultimately reshaped all of humanity to the extent that we touched the universe above by walking on the Moon and started dreaming of Mars.
Now, I used to think the next major revolution would be ushered in by AI and robotics displacing the workforce yet again, but unknown to where this time around, or perhaps a green revolution fixing the substantial planet-destroying negative consequences of the industrial revolution by creating new technologies, new jobs and eventually a new earth.
I was wrong. First thing first.
2018 – the ideological rut
2018 convinced me that neither AI, robots or green tech will be the next paradigm we need to cross the chasm over to. 2018 showed us that we, the world, fundamentally disagree on ideological positions and tactics. The western-orientated agreement cemented after two world wars, on how to advance society, is no longer shared by enough, or strong enough, people to be the only given way forward.
I do concede that some, if not all, of these ideological differences have been here for a long time already and have only now been surfaced by the rise of social media. And yes, social media has certainly had many negative consequences for humanity, but showing us how divided we are might be one of the biggest blessings in disguise.
Still, the chain of consequences to enlightenment is not as important as the fact that 2018 displayed the divide as very, very real. And that we can't move forward and up before we are reconciled.
Before we explore the universe, we need to re-explore ourselves
Much has been said and written about the Trump presidency and Brexit, two recent world-altering events that have shown an unfortunate, and potentially dangerous, inability to drive the world forward and up as we remain stuck in an ideological swamp.
These are interesting events for sure, and much more will be said and written in the years to come about them, and more events alike, but I propose we need to look deeper to understand why and how these came to be – and as a precursor for a needed ideological revolution.
Yes, we touched the universe on the arched back of the industrial revolution but before we can rise higher, we must first look down back to earth – to ourselves.
Let's start down here.
1. Facts are now relative
The industrial revolution was, in fact, the result of an underlying more important revolution, the scientific revolution, that started centuries before. Science liberated us from immediate conclusions, like thunder stemming from the hammer of a thunder god (small cap is deliberate), to facts being verified, or falsified, by scientific peers. In my mind, this is the greatest accomplishment of woman- and mankind. So far.
However, we must not forget that science was merely the correctional tool that evened out the human always-present desire to explain personal experiences with personal experience. Again, as with the point above, technology has equally empowered scientist to connect over geography and culture as it has for those looking for easier answers.
In a time where everyone can be an influencer, the human desire to influence has surpassed our ability to fact-check if that influence is based on the scientific principles that have built all of the modern wonders - that ironically has given influencers a platform to speak from. Or rather, the platforms we have built to empower influencers, fuelled by the ever-hungrier capitalist machine, remember from the intro, has chosen not to invest enough in fact-checking mechanism to keep facts fact.
The ideological revolution will require that we use the political and commercial power of the people to enforce a re-discovery of facts and the scientific process. We must fact-check in real-time as words are spoken and as words are written and the deliberate lie must become the ultimate sin.
Will companies be hurt and potentially broken up in this process? Yes. Will that be for the good of society and all us? Yes.
2. Democracy is crazy for most people
Due to an embarrassing colonization heritage, stemming from in a hindsight unimportant race to control global assets first, we have a dominating western-world order today. The two first world wars, pun intended, made this world order even stronger and for a while it seemed that nothing could upend it.
In our self-gloating, we forgot that our world-view was based on trampling other world-views down. But the down-trampled never vanished. Oh no. They were dormant and hiding, building step by step.
Remember, at core there is no one right or wrong in the advancement of a species, or in the universe as a whole. That leaves us to fight for the values and ideologies we believe in. And other parts of earth believe advancements comes best from other ideas than democracy.
As an example, the rise of China and other state-controlled societies, exemplified by China's newfound social score, is an important signal that we are in a global battle of the minds and that we are no longer winning that battle because of historical advantages.
The ideological revolution will require that we remember that democracy is crazy for most of the people on this planet and that we can and should no-longer take our world-view for granted but as a something we have to battle minds for. Based on facts.
3. Politics are for politicians
Somewhere along the road, between the professionalism of politics, the fallacies of democracy, and the human thirst for power, politics became less for people and more for politicians themselves. It is no-one's fault really. It's more a natural evolution and the culmination of a system that has taken us far but can't take us all the way - not in the current form at least.
Any effective system will seek to perfect in each iteration and therefore become more professional for each generation. When it comes to politics this becomes a problem, the problem, since the system itself was created to speak for the amateur – the people itself. And so, as the delta between the original intent and the built-in betterment of the systems increases so does the ineffectiveness of modern politics appear.
And, democracy, in all its beautiful glory, has a built-in death-star missile canal hiding in plain sight. Democracy is built on a logic from a time when people couldn't be individuals due to immature technology and was best served to and from blocks of people.
Under the narrative of democracy, because democracy is the best we have, we have for too long thought of ourselves as in such blocks while technology has matured to a state where it can represent the individual individually for the first time in a scalable way.
This delta is about to overshadow the light of democracy, and we see it in more and more governments across the western world being formed by minority parties trying hard to create a common platform that does not exist.
Mix of all this with the innate human thirst for power, the very drive that pulled us up from the oceans way back when, and we have a system that is heading into the sunset.
The ideological revolution will require a political system that is handed back to the individual where political power is not an asset that can be earned, stored, and wielded at will - often as lies. It will be again borrowed, blockchained and trusted, and with surgical precision implemented at need.
Us. Earth. Mars. What's next?
I predict it will get worse before it gets better. But it will get better. Through trust, effective democracy, and the politics of the people.
I predict that we at the start of the 21st century increasingly collectively leave the factories as workers to go to work on ourselves and thus we start the ideological revolution.
And after that, let's find out how AI, robots and green tech will push us forward and further, in a sustainable way. So that we can stop dreaming of Mars – and start walking on it.
The strengths of humanity may lead us to colonize space. The weaknesses of humanity might destroy us all. That's the tragic beauty of humanity.
But first thing first.
Ideology.
And the ideological revolution


text:
654: The Feather Heist
The Specimens
The birds Edwin Rist stole were valuable and collected in the mid-1800s by one of the greatest scientific explorers of his time: a man named Alfred Russel Wallace. (6 minutes)
More by Sean Cole
723: Squeaker
Act Six: Massachusetts
During an election in which it feels like the very existence of our democracy hangs in the balance, producer Sean Cole and someone very close to him have been dealing with their own immediate existential questions.
717: Audience of One
Act One: Putting the Ease in Disease
To cope with this pandemic, producer Sean Cole finds himself turning to a movie about a pandemic, What's So Bad About Feeling Good? But the virus in this movie isn't like any you've ever heard of. (20 minutes)
713: Made to Be Broken
Act One: Time Bandit
Jerome Ellis is a composer and musician.


text:
TOLKIEN AND BEOWULF by Jerry Salyer
Tolkien and Beowulf
Tolkien's newly published translation of the Old English epic beautifully demonstrates that there is more reality in folklore than in the perverse fantasies by which many live today.
Jerry Salyer
At morn King Hrothgar on his thronefor his lieges slain there mourned alonebut Grendel gnawed the flesh and boneof the thirty thanes of Denmark.A ship there sailed like a wingéd swan,and the foam was white on the waters wan,and one there stood with bright helm onthat fate had brought to Denmark.— "Beowulf and the Monsters," J.R.R. Tolkien
Heathen or no, Beowulf does the Lord's work, and knows full well that there is a higher power to Whom all must answer. So believed the anonymous eighth-century Christian poet who saw fit to set down Beowulf's adventures; so too believed the late scholar and novelist J.R.R. Tolkien, whose long-awaited translation of the greatest of Old English epics has finally been released.
If Professor Tolkien and the ancient Anglo-Saxon storyteller are right, then Beowulf: A Translation and Commentary(Houghton Mifflin Harcourt, 2014) should interest not only philologists and Tolkien fans but the inquisitive Catholic layman, too. Perhaps northern European folklore is more relevant to the Faith than we might think? Perhaps modern Christians can derive wisdom and inspiration from what Tolkien called "point[s] of contact between Scripture and Germanic legend"?
In Tolkien's view, the first noteworthy "point of contact" is manifested through the Beowulf monsters—particularly the ogre Grendel. By terrorizing the realm of the good King Hrothgar and devouring Hrothgar's subjects at night, Grendel stands as a representative of Cain, that first killer from whom, in the Beowulf mythos, "all evil broods were born, ogres and goblins and haunting shapes of hell, and the giants too, that long time warred with God."
What attracts Grendel's hostility is the music coming from Heorot, as the sound of Hrothgar's minstrel singing joyfully of Creation rings hatefully in the creature's ears. This loathing for Christian civilization is extremely important for understanding the poem, for as Tolkien observes in his commentary on the Old English text Grendel is the ultimate féond(enemy), in a permanent state of enmity—fæhÞ—with mankind:
What is implied here is that there was never any hope of […] settlement. Grendel was an "alien", not recognizing the authority of Hrothgar or of any human law. Nor was it possible to hold any conference with him, and arrange terms: and indeed he would not have been willing to offer any. Nay, he piled fæhÞ upon fæhÞ, killing fresh Danes whenever he could.
Seeking truce with Grendel is absurd, for his hatred of Heorot's music and what it evokes is not only relentless but insatiable. He has, in the words of the poet, "a feud with God."
Enter the Geatish hero Beowulf, whose father had once been a fugitive sheltered under Hrothgar's authority. The son now returns the favor, offering to guard Hrothgar's royal hall, and when Grendel noiselessly slips into Heorot once again that night he is delighted to discover more sleeping victims to slake his insatiable appetite. Delight turns to dismay, however, when the nocturnal fiend discovers he has met his match:
Onward and nearer [Grendel] stepped, seized then with hand the valiant-hearted man upon his bed. Against him the demon stretched his claw; and swiftly he laid hold on it, and with hate in heart he propped him on his arm. Straightway that master of evil deeds perceived that never had he met within this world in earth's four corners on any other man a mightier gripe of hand.
A fantastical wrestling match ensues, one so catastrophic that it nearly destroys Heorot. Beowulf triumphs, and—after seeking out and defeating Grendel's monstrous dam, accepting Hrothgar's gratitude, and celebrating the victories—returns home. In time Beowulf inherits the Geatish throne, and rules benevolently for 50 winters.
He is destined to go out with his boots on, however. As an old man, he gets word that a dragon—an "alien creature fierce and evil"—has been disturbed from its slumber, and is venting its hot wrath upon his subjects:
Now did the invader begin to spew forth glowing fires and set ablaze the shining halls—the light of the burning leapt forth to the woe of men. No creature there did that fell winger of the air purpose to leave alive. Wide might it be seen how the serpent went to war, the malice of that fell oppressor, from near and far be seen how that destroyer in battle pursued and humbled the people of the Geats.
Tolkien was especially fascinated by the dragon's backstory, which relies upon the image of the dragon making its nest in an ancient ruin where "forgotten lords [had] placed their gold in the hoard, and then died one by one." The treasure in this tomb has drawn the dragon even though, as the Beowulf poet reflects, ne byð him wihte ðy sél ("no whit doth it profit him"). Per Tolkien, this remark regarding the creature's pointless gold-greed may be taken as "the last word on dragonhood."
Standing at a pole opposite the one occupied by the hoarding dragon, an aged Beowulf shows that self-sacrifice is the last word on kingship. Abandoned by his terrified soldiers, he meets the beast aided only by a single retainer, the spirited youth Wiglaf. Together the two heroes slay the serpent, but at a high price: Beowulf suffers a mortal wound during the battle. Thus the epic concludes with Wiglaf and the Geats lamenting the passing of their just and gracious lord, the "shepherd of the people."
Surveying the tale from beginning to end, Tolkien raises a powerful theological question: "What are we to think of the nobility and heroism of the heathen past? Was it all just evil, damned?" This question defined a serious controversy in the newly-Christian England of antiquity, and the consensus of Old English scholarship is that the Beowulf poem is, in part, a response to them. As Tolkien observes, the poem implicitly takes a side: "[T]he mere fact that the poet wrote a poem about the pagan past shows in general that he did not belong to the party that consigned the heroes (northern or classical) to perdition." Like Dante—who acknowledged Virgil as his guide and portrayed the pre-Christian Emperor Trajan in Paradise—the Beowulf poet recognizes that heathen expressions of truth, goodness, and beauty do have their place in the life of the Church.
For his part, Tolkien believed northern paganism to be in certain respects more compatible with Christianity than is the Mediterranean variety, insofar as northern myths allude to a grand conflict whereby gods and men fight together against inhuman monsters. This conflict between light and darkness is a far cry from Homer's Iliad, wherein aloof and often whimsical Olympians treat mortals as chess-pieces; for that matter, it is a far cry even from Greco-Roman philosophical traditions, which sometimes tend to equate godliness with detachment. Of course I do not mean here to belittle the classical inheritance, recent neglect of which has had a dire impact on Western civilization. The point, rather, is that the Anglo-Saxon inheritance to which Tolkien was so devoted likewise has something to commend it.
At the least, we can say that there is more reality to Old English folklore than in the perverse fantasies by which Americans now live. When a society promotes disloyalty and monstrosity so far as to celebrate dragons and vampires and witches, when respectability-craving "conservatives" can always find reasons to compromise with the next phase of an ongoing anti-Christian revolution, when piles of gadgets and toys and luxury goods are offered in compensation for the loss of faith, family, and roots—why, in such times we could do worse than to recall Beowulf's trusty kinsman Wiglaf, who lives by the dictum that "[k]inship may nothing set aside in virtuous mind."
Indeed there are many reasons to see the 21st-century West as twisted and bleak. Yet, from another point of view, it cannot be that bad—not if J.R.R. Tolkien is still putting out books decades after having passed on. Thanks to his latest publication, it is clear that reclaiming the forgotten pagan legacy must be a priority for those who aim to preserve something of Christendom.
Beowulf: A Translation and Commentary
Translated by J.R.R. Tolkien; edited by Christopher Tolkien
Houghton Mifflin Harcourt, 2014
Catholic convert Jerry Salyer is a philosophy instructor living in Franklin County, Kentucky.
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The expression of curiosity is a haze, young one step closer to you Your crime group or demon club Do you want to conquer the world Or would it like to carry out the killing of the human world because of racial discriminationCurled fingers against the upper lip, she poker-faced to endure to smile an idea, to clarify the young eyes, are not. I was actually a messenger from makai.Womens Nike Free run 2 I will not be the son of Captain Xin Alberta. young sharp and quick answer he didn't want to believe.
Shrug her shoulders, she said, you can't believe it. Would have wanted to talk to you about a deal...... Now that you don't trust me, you have to do it. Release wrapped around the fingers of the hair, she stood up, dust Shan Shan body does not exist, to leave, walked to the window, but after the third step, with a smile, I continue to kill, and you will see in the victim appears before stop me. This is a very interesting game.Ignore each other with bad smile in the mouth, Chao Mian directly seize the key question: tradingWomens Nike Free run 2 Ignorant pure Che eyes flashing is completely confused and worried, a panoramic view of the juvenile expression, she reminded complacent smile, a heel turn backhand support on the windowsill, was partial to a head, youth oriented.


John Shea Feb. 1, 2019 Updated: Feb. 1, 2019 10:49 a.m.
The Kevin Durant sweepstakes won't be official until the Warriors' forward becomes a free agent in July, but five months in advance, the New York Knicks seem to be applying a full-court press.
Thursday's blockbuster trade that sent Kristaps Porzingis to Dallas helped clear enough cap space for the Knicks to sign two max-contract free agents, and it's no secret they covet Durant, the MVP of the past two NBA Finals.
Moving Porzingis as well as Tim Hardaway Jr. and Courtney Lee puts the Knicks in position to become serious bidders for Durant, not to mention Kyrie Irving and other elite available players. Plus, if they snag the No. 1 draft pick, Duke's Zion Williamson is waiting in the wings.
"I can't control what people say about me, about my future or what I should do," Durant said in front of a horde of New York-based reporters when the Knicks visited the Bay Area in early January.
Durant, the man who nicknamed Porzingis "Unicorn," is fond of Knicks coach David Fizdale and the aura of playing at Madison Square Garden. Plus, Durant's father is a Knicks fan, and his agent lives in New York. The last time the Warriors visited there, Durant was recruited on a billboard by a marketing firm.
It's all a dream for Knicks fans, but when rooting for a team that hasn't won an NBA title since 1972-73, sometimes dreaming is the only option.
Meantime, Durant has helped the Warriors to 11 straight wins and is averaging 27.7 points, 7.2 rebounds and 5.9 assists a game. He was named to start the All-Star Game, along with Stephen Curry, for the eighth time.


Summer vibes. Citrusy, cerulean loveliness combined with deft pattern usage from the genius ladies at MWM. The elements here are simple, friends...fabulous color and pattern can go a long way.
Hoping to have a nice summer weekend filled with family and friends...hope you are as well. Have a great break...see you next week!
What's particularly genius about this 'layout' is how artfully the designer has picked up elements from the framed art: the blue patterned stools hinting of floating ice islands; the layers in the pillows and blanket echoing the sedimentary layers in the ice... so brilliant!


Choose a method to provide the driver update, and refer to Section 13.2.1, "Preparing to use a driver update image file", Section 13.2.2, "Preparing a driver update disk" or Section 13.2.3, "Preparing an initial RAM disk update". Note that you can use a USB storage device either to provide an image file, or as a driver update disk.
Refer to Section 13.3.2, "Let the installer prompt you for a driver update" and Section 13.3.3, "Use a boot option to specify a driver update disk" to learn how to use the driver update disk during installation.
If you change the file system label of the device to OEMDRV, the installation program will automatically examine it for driver updates and load any that it detects. This behavior is controlled by the dlabel=on boot option, which is enabled by default. Refer to Section 13.3.1, "Let the installer automatically find a driver update disk".
Refer to Section 13.3.2, "Let the installer prompt you for a driver update" and Section 13.3.4, "Use a boot option to specify a driver update image file on a network" to learn how to specify this network location during installation.
After you create a driver update disk, insert it (if you used a disk) or attach it (if you used a USB storage device) and browse to it using the file manager. If you see a list of files similar to those illustrated in Figure 13.4, "Contents of a typical driver update disc on CD or DVD", you know that you have created the driver update disk correctly.
Refer to Section 13.3.5, "Select a PXE target that includes a driver update" to learn how to use an initial RAM disk update during installation.


Local, fresh, delicious. 38% less fat than regular milk. Grade A pasteurized. Vitamin A & D. Discover the Borden Difference. The Borden Difference: Always creamy rich taste. An excellent source of bone-building calcium and vitamin D. A good source of muscle-growing protein. No artificial growth hormones (FDA has determined that there is no significant difference between milk from artificial growth hormone treated cows and non-treated cows). Light-blocking package for fresh flavor. Approved by Elsie. Fat reduced from 8 g to 5 g per serving.


This is a mod that adds bathroom to the Farmhouse and I have made it so you can use Content Patcher.
UPDATE 4.0: I have added a file that is in beta once i know that the mod works i will publish all of the other versions as well.
UPDATE 3.0: removed the fireplace in the kitchen on a request.
This mod is originally from DevilBro.
I have made some modifications so that you have more options.
This mod needs Content Patcher!
1. Download the .rar file and unzip it to somewhere (i recommend desktop).
3. Put unziped file inside the stradew valley -> mods.
4. You are done! Enjoy!


text:
Q: Swift preload collection view from second tab at launch I've already found in SO several answers on how to pre-load all tab views when app launches with either this solution here or here but nothing seems to work.
With both of these solutions, second tab view is clearly loaded from first tab however (simple print checking) however I've noticed collectionView datasource methods are not called.
I want to avoid the slight delay before photos are populated into collection view, this is why I want to load second tab when app launches.
It clearly seems that assets are fetched for the first time (in ViewDidLoad method) when second tab view appears and not before when instantiated in first tab as requested.
Any ideas ?
thanks.

A: try this
(self.tabBarController?.viewControllers?[3] as? UINavigationController)?.viewControllers[0].viewWillAppear(true)

Change 3 to the index of the designated tab bar.
In the designated view controller put your reloading code in the viewwillappear
such as
override func viewWillAppear(_ animated: Bool) {
    myCollectionView.reloadData()
}

redpajama_set_name: RedPajamaStackExchange


According to real estate experts, it would take a buyer only 3 to 10 seconds to decide if they are interested in your home. One of the best ways to make a fantastic first impression is through home staging – that is, creating a space that is desirable to a potential buyer.
You want to alter your space ever so slightly in order to create a neutral zone that homebuyers can truly envision themselves living in.
The main room that people see first at a home visit is the living room, so it's imperative to consider a few home staging tips for a gorgeous living room. They're relatively easy to put into place, they aren't time consuming or very costly, and they could help you sell your home quickly and for the price that you were hoping for.
So here are four home staging tips for a gorgeous living room.
Does your living room have a substantial bay window with stunning views, a massive wood- or gas-burning fireplace, or some other impressive architectural detail that has always been the centerpiece of the space? A great home staging tip for a gorgeous living room is to emphasize such points of interest. Show them off – in the end these could be major selling points.
The main purpose of a living room is to relax, whether it be curling up with a good book or cuddling with the family to watch an action-packed movie. Light a few candles (that perhaps smell like apples or cinnamon) to truly create a homey effect. Add some vibrant decorative cushions to the couch or a plush throw to the club chair. This home staging tip for a gorgeous living room will make visitors feel comfortable in your house.
A room littered with family photos, trophies, religious figures, and more can make it difficult for a possible homebuyer to "picture themselves" living in your space, so make the room less personal. Remove these items from the living room to give it a more neutral look, allowing buyers to really envision what their own belongings will look like in the space – it's one of the quickest and easiest home staging tips for a gorgeous living room.
If clutter is the first thing that a visitor sees when they enter your home, especially in a common area like the living room, it will be hard for them to see past the mess, lessening your likelihood of a sale. So tidy up that busy bookshelf, get the kids' toys off of the floor and into bins, and make the living room as clean and airy as possible. An orderly blank canvas of a space is a home staging tip for a gorgeous living room that will get buyers interested (and could just start a bidding war!).