text:
\section{Introduction}

One of the long-standing goals of speech and cognitive scientists is to develop a computational model of language acquisition \cite{goldwater2009bayesian, lee2012nonparametric, ondel2016variational, harwath2016unsupervised}. Early on in their lives, human infants learn to recognize phonemic contrasts, frequent words and other linguistic phenomena underlying the language \cite{dupoux2018cognitive}. The computational modeling framework of generative models is well-suited for the problem of spoken language acquisition, as it relates to the classic analysis-by-synthesis theories of speech recognition \cite{halle1962speech, liberman1967perception}. Although, generative models are theoretically elegant and informed by theories of cognition, most recent success in speech representation learning has come from self-supervised learning algorithms such as Wav2Vec \cite{schneider2019wav2vec}, Problem Agnostic Speech Encoding (PASE) \cite{pascual2019learning}, Autoregressive Predictive Coding (APC) \cite{chung2019unsupervised}, MockingJay (MJ) \cite{liu2019mockingjay} and Deep Audio Visual Embedding Network (DAVENet) \cite{harwath2020learning}. Generative models present many advantages with respect to their discriminative counterparts. They have been used for disentangled representation learning in speech \cite{hsu2017unsupervised, khurana2019factorial, li2018disentangled}. Due to the probabilistic nature of these models, they can be used for generating new data and hence, used for data augmentation \cite{hsu2017unsuperviseda, hsu2018unsupervised} for Automatic Speech Recognition (ASR), and anomaly detection \cite{Grathwohl2020Your}.

In this paper, we focus solely on designing a generative model for low-level linguistic representation learning from speech.
We propose Convolutional Deep Markov Model (ConvDMM), a Gaussian state-space model with non-linear emission and transition functions parametrized by deep neural networks and a Deep Convolutional inference network. The model is trained using amortized black box variational inference (BBVI) \cite{ranganath2013black}. Our model is directly based on the Deep Markov Model proposed by Krishnan et. al \cite{krishnan2017structured}, and draws from their general mathematical formulation for BBVI in non-linear Gaussian state-space models. When trained on a large speech dataset, ConvDMM produces features that outperform multiple self-supervised learning algorithms on downstream phone classification and recognition tasks, thus providing a viable latent variable model for extracting linguistic information from speech.

We make the following contributions:
\begin{enumerate}[1)]
    \item Design a generative model capable of learning good quality linguistic representations, which is competitive with recently proposed self-supervised learning algorithms on downstream linear phone classification and recognition tasks.
    \item Show that the ConvDMM features can significantly outperform other representations in linear phone recognition, when there is little labelled speech data available.
    \item Lastly, demonstrate that by modeling the temporal structure in the latent space, our model learns better representations compared to assuming independence among latent states.
\end{enumerate}

\section{The Convolutional Deep Markov Model}
\label{sec:2}
\subsection{ConvDMM Generative Process} \label{sec:2.1}  Given the functions; $\bbmm{f_{\theta}(\cdot), u_{\theta}(\cdot)}$ and $\bbmm{t_{\theta}(\cdot)}$, the ConvDMM generates the sequence of observed random variables, $\bbmm{x}_{1:T} = \bbmm{(x}_1, \ldots,\bbmm{x}_T)$, using the following generative process
\begin{align}
    \bbmm{z}_1 &\sim \bbmm{\mathcal{N}(0, I)}\\
    \bbmm{z}_\tau|\bbmm{z}_{\tau-1} &\sim \bbmm{\mathcal{N}(t^{\mu}_{\theta}(z}_{\tau-1}),\bbmm{ t^{\sigma}_{\theta}(z}_{\tau-1}))& \tau=2,\ldots,L \label{eq:2}\\
    \bbmm{e}_{1:T} &= \bbmm{u_\theta(z}_{1:L}) \\
    \bbmm{\mu}_{1:T} &= \bbmm{f_{\theta}(e}_{1:T})\\
    \bbmm{x}_t|\bbmm{e}_t &\sim \bbmm{\mathcal{N}(\mu}_t, \gamma)& t=1,\ldots, T \label{eq:5}
\end{align}
where $T$ is a multiple of $L$, $T = k\cdot L$ and $\bbmm{z}_{1:L}$ is the sequence of latent states. We assume that the observed and latent random variables come from a multivariate normal distribution with diagonal covariances.
The joint density of latent and observed variables for a single sequence is
\begin{equation}
    \bbmm{p(z}_{1:L}, \bbmm{x}_{1:T}) = \bbmm{p(x}_{1:T}|\bbmm{z}_{1:L}) \bbmm{p(z}_1)\prod\limits_{\tau=2}^{L} \bbmm{p(z}_\tau|\bbmm{z}_{\tau-1}).
\end{equation}
For a dataset of i.i.d. speech sequences, the total joint density is simply the product of per sequence joint densities.
The scale $\bbmm{\gamma}$ is learned during training.

\textbf{The transition function} $\bbmm{t_\theta : z}_{\tau-1} \rightarrow \bbmm{(\mu}_\tau, \bbmm{\sigma}_\tau) \big|_{\tau=2}^{L}$ estimates the mean and scale of the Gaussian density over the latent states. It is implemented as a Gated Feed-Forward Neural Network~\cite{krishnan2017structured}.
The gated transition function could capture both linear and non-linear transitions.

\textbf{The embedding function} $\bbmm{u_{\theta} : z}_{1:L} \rightarrow \bbmm{e}_{1:T}$ transforms and up-samples the latent sequence to the same length as the observed sequence.
It is parametrized by a four layer CNN with kernels of size 3, 1024 channels and residual connections. We use the activations of the last layer of the embedding CNN as the features for the downstream task. This is reminiscent of kernel methods \cite{hofmann2008kernel} where the raw input data are mapped to a high dimensional feature space using a user specified feature map. In our case, the CNN plays a similar role, mapping the low-dimensional latent vector sequence, $\bbmm{z}_{1:L} \in \mathbb{R}^{L \times 16}$, to a high dimensional vector sequence, $\bbmm{e}_{1:T}\in  \mathbb{R}^{T\times 1024}$, by repeating the output activations of the CNN $k$ times, where $k = T/L$. In our case, $k$ is 4 which is also the downsampling factor of the encoder function (\S~\ref{sec:inf}). A similar module was used in Chorowski et. al \cite{chorowski2019unsupervised}, where they used a single CNN layer after the latent sequence.

\textbf{The emission function} $\bbmm{f_{\theta} : e}_t \rightarrow \bbmm{(\mu}_t)\big|_{t=1}^{T}$ (a decoder) estimates the mean of the likelihood function.
It is a two-layered MLP with 256 hidden units and residual connections. We employ a low capacity decoder to avoid the problem of posterior collapse \cite{bowman2015generating}, a common problem with high capacity decoders.

\subsection{ConvDMM Inference} \label{sec:inf} The goal of inference is to estimate the posterior density of latent random variables given the observations $\bbmm{p(z | x)}$. Exact posterior inference in non-conjugate models like ConvDMM is intractable, hence we turn to Variational Inference (VI) for approximate inference. We use  VI and BBVI interchangeably throughout the rest of the paper. In VI, we approximate the intractable posterior $\bbmm{p(z|x)}$ with a tractable family of distributions, known as the variational family $\bbmm{q_{\phi}(z|x)}$, indexed by $\phi$. In our case, the variational family takes the form of a Gaussian with diagonal covariance. Next, we briefly explain the Variational Inference process for ConvDMM.

Given a realization of the observed random variable sequence $\bbmm{x}_{1:T} = \bbmm{x}_1, \ldots, \bbmm{x}_T$, the initial state parameter vector $\bbmm{\hat{z}_1}$, and the functions $\bbmm{g_{\phi}(\cdot)}$ and $\bbmm{c_{\phi}(\cdot)}$, the process of estimating the latent states can be summarized as:
\begin{align}
    \bbmm{h}_{1:L} &= \bbmm{g_{\phi}(x}_{1:T}) \\
    \bbmm{\hat{z}}_\tau|\bbmm{\hat{z}}_{\tau-1}, \bbmm{x} &\sim \bbmm{\mathcal{N}(c^{\mu}_{\phi}(h}_\tau, \bbmm{\hat{z}}_{\tau-1}),\bbmm{ c^{\sigma}_{\phi}(h}_\tau,\bbmm{ \hat{z}}_{\tau-1}))& \tau=2\ \text{to}\ L .
\end{align}
Let $T = k*L$, where $k$ is the down-sampling factor of the encoder, $\bbmm{g_{\phi} : x}_{1:T} \rightarrow \bbmm{h}_{1:L}$ is the encoder function, $\bbmm{c_{\phi}}$ is the combiner function that provides posterior estimates for the latent random variables.

We parameterize the encoder $\bbmm{g_\phi}$ using a 13-layer CNN with kernel sizes (3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3), strides (1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1) and 1024 hidden channels. The encoder down-samples the input sequence by a factor of four. The last layer of the encoder with $\bbmm{h}_{1:L}$ as its hidden activations has a receptive field of approximately 50. This convolutional architecture is inspired by \cite{chorowski2019unsupervised}, but other acoustic models such as Time-Depth Separable Convolutions~\cite{hannun2019sequence}, VGG transformer~\cite{mohamed2019transformers}, or ResDAVENet~\cite{harwath2020learning} could be used here. We leave this investigation for future work.

\textbf{The combiner function} $\bbmm{c_\phi}$ provides structured approximations of the variational posterior over the latent variables by taking into account the prior Markov latent structure. The combiner function follows~\cite{krishnan2017structured}:
\begin{align*}
    \bbmm{h_{\text{combined}}} &= \bbmm{\frac{1}{2} (\text{tanh}(W\hat{z}}_{t-1} + \bbmm{b) + h}_t)\\
    \bbmm{\mu}_t &= \bbmm{W_{\mu} h_{\text{combined}} + b_{\mu}}\\
    \bbmm{\sigma}_t &= \bbmm{\text{softplus}(W_{\sigma}h_{\text{combined}} + b_{\sigma})}.
\end{align*}
It uses tanh non-linearity on $\bbmm{z_{t-1}}$ to approximate the transition function. Future work could investigate sharing parameters with the generative model as in Maal{\o}e et. al's Bidirection inference VAE (BIVA) \cite{maaloe2019biva}. We note that structured variational inference in neural variational models is an important area of research in machine learning, with significant recent developemnts~\cite{johnson2016composing, lin2018variational}. Structured VAE has also been used for acoustic unit discovery~\cite{ebbers2017hidden}, which is not the focus of this work.

\begin{figure}
    \hspace{0.15cm}
    \includegraphics[width=\linewidth]{dmm.pdf}
    \caption{An illustration of the ConvDMM.}
    \label{fig:dmm}
\end{figure}

\begin{table*}[tb]
    \caption{Phone Classification (FER) and Recognition (PER) when trained on a subset of Wall Street Journal.
             Suffixes \emph{-50, -360, -960} denote the amount of hours of LibriSpeech training data used for training the unsupervised model.}
    \label{tab:wsj_probes}
    \centering
    \resizebox{\linewidth}{!}{%
    \begin{threeparttable}
    \begin{tabular}{llrrrrrrrrrrrrrrrrrr}\toprule
       \% of Labeled Data&&\multicolumn{2}{c}{1\%}&&\multicolumn{2}{c}{2\%}&&\multicolumn{2}{c}{5\%}&&\multicolumn{2}{c}{10\%}&&\multicolumn{2}{c}{50\%}&&\multicolumn{1}{c}{Low Shot (0.1\%)}\\
       \cmidrule{3-4} \cmidrule{6-7}\cmidrule{9-10}\cmidrule{12-13}\cmidrule{15-16}\cmidrule{18-18}
       &&FER&PER&&FER&PER&&FER&PER&&FER&PER&&FER&PER&&PER\\
       \midrule
       GaussVAE-960 &&-&55.8&&-&50.1&&-&48.2&&-&45.9&&-&42.5&&- \\
       Supervised Transfer-960 &&-&17.9&&-&16.4&&-&14.4&&-&12.8&&-&10.8&&25.8 ($\pm$ 0.96) \\
       \midrule
       \multicolumn{16}{l}{\textbf{\textit{Self Supervised Learning}:}}\\

       MockingJay-960 \cite{liu2019mockingjay} &&40.0&53.2&&38.5&48.8&&37.5&45.5&&37.0&44.2&&36.7&43.5&&-& \\
       PASE-50~\cite{pascual2019learning} &&34.7&61.2&&33.5&50.0&&33.2&49.0&&32.8&49.0&&32.7&48.2&&80.7 ($\pm$ 2.65)\\
       Wav2Vec-960 \cite{schneider2019wav2vec} &&19.8&37.6&&19.1&27.7&&18.8&24.5&&18.6&23.9&&18.5&22.9&&78.0 ($\pm$ 10.4)\\
       \midrule
       \multicolumn{16}{l}{\textbf{\textit{Audio-Visual Self Supervised Learning}:}}\\
       RDVQ (Conv2) \cite{harwath2020learning} &&31.6&44.1&&30.8&42.4&&30.5&41.1&&30.1&41.3&&30.2&40.6&&52.6 ($\pm$ 0.95)\\
       \midrule
       \multicolumn{16}{l}{\textbf{\textit{Proposed Latent Variable Model}:}}\\
       ConvDMM-50  &&29.6&37.8&&28.6&35.4&&27.9&31.3&&27.9&30.3&&27.0&29.1&&-\\
       ConvDMM-360  &&28.2&34.8&&27.0&30.8&&26.4&28.2&&25.9&27.7&&25.7&26.7&&-\\
       ConvDMM-960 &&27.7&32.5&&26.6&30.0&&26.0&28.1&&26.0&27.1&&25.6&26.0&&50.7 ($\pm$ 0.57) \\
       \midrule
       \multicolumn{16}{l}{\textbf{\textit{Modeling PASE and Wav2Vec features using the proposed generative model}:}}\\
       ConvDMM-960-PASE-50 &&-&35.4&&-&32.6&&-&30.6&&-&29.3&&-&28.4&&55.3 ($\pm$ 3.21)\\
       ConvDMM-Wav2Vec-960  &&-&28.6&&-&25.7&&-&22.3&&-&21.2&&-&20.4&&40.7 ($\pm$ 0.42)\\
       \midrule
    \end{tabular}
    \begin{tablenotes}
      \small

      \item[1] $\pm$ refers to the standard deviation in the results
    \end{tablenotes}
    \end{threeparttable}
    }

\end{table*}

\subsection{ConvDMM Training} ConvDMM like other VAEs is trained to maximize the bound on model likelihood, known as the Evidence Lower Bound (ELBO):
\begin{equation*}
	\bbmm{\mathcal{L(\theta, \phi)}} = \bbmm{E_{q_{\phi}(z|x)} [\text{log}\ p_{\theta}(x|z)] - \text{KL} (q_{\phi}(z|x) || p_{\theta}(z))}
\end{equation*}
where $\bbmm{p_\theta (x|z)} = \prod\limits_{t=1}^{T} \bbmm{p(x}_t|\bbmm{e}_t)$ is the Gaussian likelihood function and $\bbmm{p(x}_t|\bbmm{e}_t)$ is given by the ConvDMM generative process in Section~\ref{sec:2.1}.  The Gaussian assumption lets us use the \textit{reparametrization trick} \cite{kingma2013auto} to obtain low-variance unbiased Monte-Carlo estimates of the expected log-likelihood, the first term in the R.H.S of the ELBO. The KL term, which is also an expectation can be computed similarly and its gradients can be obtained analytically. In our case, we use the formulation of Equation 12, Appendix A., in Krishnan et. al \cite{krishnan2017structured}, to compute the KL term analytically.

The model is trained using the Adam optimizer with a learning rate of 0.001 for 100 epochs. We reduce the learning rate to half of its value if the loss on the development set plateaus for three consecutive epochs. L2 regularization on model parameters with weight 5e-7 is used during training. To avoid latent variable collapse we use KL annealing \cite{bowman2015generating} with a linear schedule, starting from an initial value of 0.5, for the first 20 epochs of training. We use a mini-batch size of 64 and train the model on a single NVIDIA Titan X Pascal GPU.

\section{Experiments}
\subsection{Evaluation Protocol and Dataset} We evaluate the learned representations on two tasks; phone classification and recognition. For phone classification, we use the ConvDMM features, the hidden activations from the last layer of the embedding function, as input to a softmax classifier, a linear projection followed by a softmax activation. The classifier is trained using Categorical Cross Entropy to predict framewise phone labels. For phone recognition the ConvDMM features are used as input to a softmax layer which is trained using Connectionist Temporal Classification (CTC) \cite{graves2006connectionist} to predict the output phone sequence. We do not fine-tune the ConvDMM feature extractor on the downstream tasks. The performance on the downstream tasks is driven solely by the learned representations as there is just a softmax classifier between the representations and the labels. The evaluation protocol is inspired by the unsupervised learning works in the Computer Vision community \cite{tian2019contrastive, henaff2019data}, where features extracted from representation learning systems trained on ImageNet are used as input to a softmax classifier for object recognition. Neural Networks for supervised learning have always been seen as feature extractors that project raw data into a linearly separable feature space making it easy to find decision boundaries using a linear classifier. We believe that it is reasonable to expect the same from unsupervised representation learning methods and hence, we compare all the representation learning methods using the aforementioned evaluation protocol.

We train ConvDMM on the publicly available LibriSpeech dataset \cite{panayotov2015librispeech}. To be comparable with other representation learning methods with respect to the amount of training dataset used, we train another model on a small 50 hours subset of LibriSpeech. For evaluation, we use the Wall Street Journal (WSJ) dataset \cite{paul1992design}.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{box.pdf}
    \caption{PER on WSJ eval92 dataset using features extracted from different models.}
    \label{box}
\end{figure}
\subsection{Results \& Discussion} Table~\ref{tab:wsj_probes} presents framewise linear phone classification (FER) and recognition (PER) error rates on the WSJ eval92 dataset for different representation learning techniques. ConvDMM is trained on Mel-Frequency Cepstral Coefficients (MFCCs) with concatenated delta and delta-delta features. ConvDMM-50 and PASE-50 are both trained on 50 hours of LibriSpeech, ConvDMM-960, Wav2Vec-960 and MockingJay-960 are trained on 960 hours. ConvDMM-360 is trained on the 360 hours of the clean LibriSpeech dataset. RDVQ is trained on the Places400k spoken caption dataset \cite{harwath2016unsupervised}.  We do not train any of the representation learning systems that are compared against ConvDMM on our own. We use the publicly available pre-trained checkpoints to extract features. The linear classifiers used to evaluate the features extracted from unsupervised learning systems are trained on different subsets of WSJ train dataset, ranging from 4 mins (0.1\%) to 40 hours (50\%).
To study the effect of modeling temporal structure in the latent space as in ConvDMM, we train a Gauss VAE which is similar to the ConvDMM except that it does not contain the transition model and hence, is a traditional VAE with  isotropic Gaussian priors over the latent states \cite{kingma2013auto}.

To generate the numbers in the table we perform the following steps.  Consider, for example, the column labelled 1\% as we describe how the numbers are generated for different models (rows). We randomly pick 1\% of the speech utterances in the WSJ train dataset. This is performed three times with different random seeds, yielding three different 1\% data splits of labelled utterances from the WSJ train dataset. We then train linear classifiers on the features extracted using different representation learning systems, on each of the three splits five times with different random seeds. This gives us a total of 15 classification and recognition error rates. The final number is the mean of these numbers after removing the outliers. Any number greater than $q_3 + 1.5*iqr$ or less than $q_1 - 1.5*iqr$, where $q_1$ is the first Quartile, $q_3$ is the third Quartile and $iqr$ is the inter-quartile range, is considered an outlier.  We follow the same procedure to create different training splits, 2\%, 5\%, 10\%, 50\%, from the WSJ train dataset and present classification error rates in the table for all splits. Figure~\ref{box} shows the box plot for the PER on WSJ eval92 dataset using features extracted from different models.

In terms of PER, ConvDMM-50 outperforms PASE by 23.4 percentage points (pp), MockingJay by 15.4pp and RDVQ by 6.3pp under the scenario when 1\% of labeled training data is available to the linear phone recognizer, which corresponds to approximately 300 spoken utterances ($\approx$ 40 mins). Compared to Wav2Vec, ConvDMM lags by 0.2pp, but the variance in Wav2Vec results is very high as can be seen in Figure~\ref{box}. Under the 50\% labeled data scenario, ConvDMM-50 outperforms MockingJay by 14.4pp, PASE by 19.1pp, RDVQ by 11.5pp and lags Wav2Vec by 6.2pp. The gap between ConvDMM-50 and RDVQ widens in the 50\% labeled data case. ConvDMM-960 similarly outperforms all the methods under the 1\% labeled data scenario, outperforming Wav2Vec, the second best method, by 5.1pp. Also the variance in the ConvDMM-960 results is much lower than Wav2Vec (See Figure~\ref{box}). ConvDMM systematically outperforms the Gauss VAE which does not model the latent state transitions, showing the value of prior structure.

ConvDMM-PASE which is the ConvDMM model built on top of PASE features instead of the MFCC features, outperforms PASE features by 25.8pp under the 1\% labeled  data scenario. A significant gap exists under all data scenarios. Similar results can be observed with ConvDMM-Wav2Vec model, but the improvements over Wav2Vec features is not as drastic, probably due to the fact that Wav2Vec already produces very good features. For low shot phone recognition with 0.1\% labeled ($\approx$ 4 mins), ConvDMM-960 significantly outperforms all other methods. Surprisingly, RDVQ shows excellent performance under this scenario. ConvDMM-Wav2Vec-960 performs 10pp better than ConvDMM-960 trained on MFCC features and 38pp better than Wav2Vec features alone. We could not get below 90\% PER with MockingJay and hence, skip reporting the results.

Lastly, we compare the performance of features extracted using unsupervised learning systems trained on LibriSpeech vs features extracted using the fully supervised system neural network acoustic model trained on the task of phone recognition on 960 hours of labeled data (See the row labeled Supervised Transfer-960). The supervised system has the same CNN encoder as the ConvDMM. There is a glaring gap between the supervised system and all other representation learning techniques, even in the very few data regime (0.1\%). This shows there is still much work to be done in order to reduce this gap.

\section{Related Work}
\label{sec:5}

Another class of generative models that have been used to model speech but not explored in this work are the autoregressive models. Autoregressive models, a class of explicit density generative models, have been used to construct speech density estimators. Neural Autoregressive Density Estimator (NADE) \cite{uria2016neural} is a prominent earlier work followed by more recent Wavenet \cite{oord2016wavenet}, SampleRNN \cite{mehri2016samplernn} and MelNet \cite{vasquez2019melnet}. An interesting avenue of future research is to probe the internal representations of these models for linguistic information. We note that, Waveglow, a flow based generative model is recently proposed as an alternative to autoregressive models for speech \cite{prenger2019waveglow}.

\section{Conclusions}
In this work, we design the Convolutional Deep Markov Model (ConvDMM), a Gaussian state-space model with non-linear emission and transition functions parametrized by deep neural networks.
The main objective of this work is to demonstrate that generative models can reach the same, or even better, performance than self supervised models.
In order to do so, we compared the ability of our model to learn linearly separable representations, by evaluating each model in terms of PER and FER using a simple linear classifier.
Results show that our generative model produces features that outperform multiple self-supervised learning methods on phone classification and recognition task on Wall Street Journal. We also find out that these features can achieve better performances than all other evaluated features when learning the phone recogniser with very few labelled training examples.
Another interesting outcome of this work is that by using self-supervised extracted features as input of our generative model, we produce features that outperforms every other one in the phone recogniser task. Probably due to enforcing temporal structure in the latent space.
Lastly, we argue that features learned using unsupervised methods are significantly worse than features learned by a fully supervised deep neural network acoustic model, setting the stage for future work.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Speech on Anti-discrimination (Right to Use Gender-specific Language) Amendment Bill 2018
On Tuesday 14 July, I spoke on the Anti-discrimination (Right to Use Gender-specific Language) Amendment Bill 2018.
You can read the speech below or in the official Queensland Parliament Record of Proceedings (Hansard), or watch it HERE.
Mr BERKMAN (Maiwar—Grn) (6.24 pm): I rise to make a contribution on the Anti-Discrimination (Right to Use Gender-Specific Language) Amendment Bill 2018, and I rise to speak in opposition to this
bill in the strongest possible terms. Today happens to be International Non-Binary People's Day. This is a day when we should be sending strength, love and solidarity to the non-binary and gender diverse people in our community. Non-binary, transgender and gender diverse people live all over Queensland, and this parliament should be working to represent and support all of its constituents—not to embolden those who would marginalise others. I am sorry to say that that is exactly what this bill would do. It is completely antithetical to the Queensland Greens' policy on sexuality and gender identity. In its own terms, the bill supposedly sets out to protect an individual's right to use traditional gender based language and to protect business and other organisations from disadvantage in the provision of facilities and services that exclusively recognise gender as either male or female. The stakeholders who made submissions on this bill were scathing. Organisations such as the Australian College of Nursing and the Caxton Legal Centre, who are immersed in supporting community members every day, pointed out the many issues with this bill in the parliamentary inquiry. The Australian Association of Social Workers opposed the bill because it would continue practices that are already harming gender diverse people who are already vulnerable to mental health issues and social stigma. This flies directly in the face of current evidence showing the need for gender-inclusive language practices and undermines the dignity and rights of gender diverse people.
The then Anti-Discrimination Commission opposed the bill because its amendments to the Anti-Discrimination Act are unnecessary and inconsistent with the purposes of the act. It pointed out there is no evidence suggesting a need for this bill. While the explanatory notes state there is an increasingly hostile social environment which limits language reflecting Queensland's so-called traditional values, these issues have never been brought to the Anti-Discrimination Commission in any of its information complaint handling or community engagement services. Of the eight examples set out in the introductory speech for this bill, many of them would not be covered and have no relevance to the bill. The Building Code, the Australian Defence Force and the Victorian Public Service are among them. The commission pointed out other fatal technical flaws in the bill. The High Court has recognised that people may be neither male nor female, and if conduct is unlawful under the Commonwealth Sex Discrimination Act this bill cannot make it lawful. The Caxton Legal Centre pointed out the significant harm that deliberate and persistent misgendering can cause, and other speakers have touched on this as well. The act should not be amended to promote conduct causing such harm to a vulnerable group while also seeking to protect that group. The Caxton Legal Centre also pointed out that the bill tries to protect entities over individuals, absurdly trying to give human rights to an organisation. The Australian College of Nursing strongly advocated for the use of respectful language, highlighting the Australian guideline for children and adolescents, which supports an affirmative model of care whereby those who identify as gender diverse can explore and express their identity as they wish. This model is associated with improving wellbeing and mental health. Using a person's preferred name and pronouns is vital for affirming and respectful care. All of these organisations, which are at the coalface supporting our community, want to see a legislative environment that supports an inclusive and diverse community.
As someone who is not gender diverse, I want to elevate the voices of people in my community who are gender diverse and who would be directly affected by this bill. I have sought input from people who wanted to comment so that I could put them into the record today. I received more responses and personal stories than I have time to read. Each and every one of them is so important to hear. You can
read many of these stories on my Facebook page or the Twitter thread I started today. I will take the opportunity to table these responses, but I would also like to read a couple of excerpts from the many
responses I received.
Tabled paper: Document, undated, titled 'Anti-Discrimination (Right to Use Gender-Specific Language) Amendment Bill 2018'
[1156].
Taylor, a trans woman and resident of Indooroopilly in my electorate, said—
Being misgendered has a really clear negative impact on my mental state—it's like more of that weight you've thrown off is being put back on. Trans people aren't asking for special rights, we just want to be treated like we are who we say we are.
Dr Sandy O'Sullivan said—
... as an Aboriginal trans person, I'd suggest that it— meaning this bill— fails to recognise the deep history of gender diversity across our Communities and further disrespects a sense of the state
'reconciling' with that deep history.
Sam from Toowong spoke about how this bill pits the idea of 'freedom of speech' against freedom to simply exist, acting— … like the right to verbally abuse is more important than the right of working-class people, black people, gay people, trans people to feel safe in a society that is simultaneously infatuated with us and disgusted by us.
Eli said that he is a non-binary person who uses they/them pronouns and regularly struggles with— … dozens of mental calculations daily regarding whether to inform those around them of their pronouns, or enforce them when they've been forgotten.
Eli went on to say— This bill threatens to erode the small amount of safety and security currently afforded gender diverse people in professional contexts ... it is nothing more than a blatant attempt to make us invisible, silent, and eventually extinguish us altogether ... We must not be allowed to go extinct.
One more anonymous response from a trans person said— We're not interested in pushing any kind of agenda. We're just asking for the same respect as everyone else in our communities; the kinds of things you don't even realise you've never had to ask for. Here we are, asking. Please do the right thing by all Australians.
This bill would undo so much hard work by advocates to support gender diverse people in our communities. It presumes we should be protecting the right to inflict harm rather than protecting people from harm. It is an incredibly simple choice for me. That is why I will not be supporting the bill today and why I implore everyone else here to oppose this bill in the strongest possible terms.
Estimates: Question on staff ratios in youth detention centres
Estimates: Questions on children held in remand and in isolation in youth detention

redpajama_set_name: RedPajamaCommonCrawl


text:
Want to know which is better, the classic .300 Win. Mag. or the cutting-edge .300 PRC? We did, too, so we put them through a rigorous review.
There are two big differences between the .300 PRC (left) and the .300 Win. Mag. (right). The PRC shoots high-BC bullets faster and better. The Win. Mag. is readily available anywhere in the world.
March 23, 2022 By Joseph von Benedikt
When the .300 PRC (Precision Rifle Cartridge) was announced, most shooters scoffed: "Just what we need—another .30-caliber magnum." It seemed like the proverbial answer to an unasked question.
Then, savvy shooters took a closer look and were impressed. They gave it a try and became even more impressed.
Within three short years, the .300 PRC became the primary .30-caliber magnum challenger to the worldwide king of the .300s: the .300 Winchester Magnum. That's a meteoric rise in prominence, rivaled only by its own smaller sibling: the 6.5 PRC.
After a lifetime of applying, in 2015 the author drew an exceptional elk tag in Utah. His choice for the hunt was his favorite .300 Win. Mag., and it cleanly killed this monster 402-inch bull. But then, the .300 PRC hadn't yet been introduced.
Now that we have both "The King of .300s" and this new high-octane modern challenger, the question must be asked: Which is better?
As a philosopher might say, "The answer is very simple, yet also very complex." Here's the short version: Which one is better for you depends…on you.
Each .30-caliber magnum cartridge has advantages and disadvantages. Your needs as a shooter and hunter will dictate which is best—for you. But I suspect you already knew that. So, without further ado, let's dive into the pros and cons.
Because it's the veteran, let's accord the .300 Win. Mag. the respect it deserves and address it first.
From Alberta to Africa, the .300 Win. Mag. has served this writer well. This 56-inch kudu bull fell to a 180-grain Trophy Bonded Tip bullet while hunting with Kowas Hunting Safaris in Namibia.
.300 Win. Mag. Pros
The .300 Win. Mag. is the worldwide dominant .30-caliber magnum. It's cleanly taken every big-game animal that walks American soil and probably every legal species of critter that walks the earth—anywhere. That's including terrorists at some pretty jaw-dropping distances, in the hands of our sniper boys and their British pals.
Although its stubby neck and belted behind are outdated design characteristics that theoretically are not optimal for accuracy, the .300 Win. Mag. has always confounded its skeptics and shot better than it has any right to.
Powder-capacity-to-bore-diameter ratio is on the top side of optimal, meaning it's on the cusp of being overbored without actually being overbored. As a result, it's surprisingly efficient, particularly with heavy, aerodynamic bullets in the 200-grain weight range. As a lovely side benefit, such bullets absolutely wallop big game.
Canada is gnarly and challenging in November. This nice bull moose fell to a couple of .300 Win. Mag. bullets.
On the subject of bullets, you can get factory ammo loaded with a broad spectrum of different projectiles. Light and fast, heavy and tough, heavy and sleek and super-efficient, you name it. It's easy to find a load tailored perfectly for your purposes, whether that purpose is shooting whitetails way off across a beanfield or moose in an alder thicket or steel targets a half-mile away.
Also, handloading data is widely available and exhaustive in scope. If our selection of factory ammo is broad, handloading potential is truly vast. Pick any bullet from 110 to 250 grains and have at it. Rifling twist rates apply.
Every rifle manufacturer offers myriad models chambered in .300 Win. Mag., so you can be really picky when selecting just the right rifle. Just as important, magazines—whether internal or detachable types—have all the wrinkles ironed out of them. Most .300 Win. Mag. rifles easily contain the advertised number of cartridges and feed them smoothly and reliably.
Because the .300 Win. Mag. is so widely respected and available around the world, it's an excellent option for travelers—domestic and international—because even if your baggage gets separated in transit and lost, as long as your rifle arrives, you can always find ammo in a local shop.
The combined result of all these advantages stacked up is one of—if not the—best practical choices for hunting any non-dangerous game, anywhere around the world.
Shown here are the two .300 PRC factory loads currently available. By next year, there should be several more from various manufacturers. And, of course, the cartridge is a handloader's dream.
.300 Win. Mag. Cons
Okay. Here we go. The grand old king of the .30s does have some downsides, too. Mostly, it's in the fact that many rifles chambered for it have quite loose chamber tolerances. In such, the demons contained in the short neck and belted case design emerge. They can wreak havoc with accuracy.
An old but proven cartridge design rule recommends that the neck of a cartridge be at least one caliber long, meaning that for a .30-caliber cartridge, the neck should be at least three tenths of an inch long. Designers curtailed the neck of the .300 Win. Mag., gambling that it would prove sufficient and the propellant capacity gained by pushing the shoulder of the cartridge as far forward as possible would pay off.
When manufacturing ammo, whether at the handloading bench or on the factory assembly line, it's harder to hold tight tolerances when the neck is short. Bullet concentricity is at risk. Neck tension is less consistent. And so forth.
The belts on traditional magnum cases were originally designed to give the long, tapered cartridges of yore a predictable surface to headspace against. Holland & Holland's .375 H&H and Super Thirty have a lot of body taper and shallow shoulders. This enables cartridges to feed like grease on glass and to extract easily and reliably—particularly important characteristics in cartridges commonly used on dangerous game in Africa and India. Easy extraction was a big deal when cordite propellant was the go-to bullet-pushing chemical of the day; it tended to exhibit pressure spikes in tropical weather conditions. Those cartridges needed the belt.
Our .300 Win. Mag., however, doesn't need the belt. On it, the belt is just a holdover from the parent case. Had designers owned a functioning crystal ball, perhaps they would have seen fit to lathe-turn the belt off of their prototype .300 Win. Mag. cases, which would have created a really optimal cartridge case shape for the embryonic new round.
Here's the important takeaway, folks. The belt is outdated, yes. It's useless, yes. But the belt harms absolutely nothing at all. Performance doesn't suffer, reliability doesn't suffer, nothing. So quit griping about the belt.
While it will shoot any .30-caliber bullet well, the .300 PRC is particularly well suited for heavy-for-caliber, highly aerodynamic projectiles, such as those shown at left. With such, it's undoubtedly better than the .300 Win. Mag.
.300 PRC Pros
Ah…here we go. This is the Ferrari of rifle cartridges. Introduced in 2018, the .300 PRC utilizes a .375 Ruger case necked down to hold .30-caliber bullets. This is a broad-based, non-belted, standard-length (.30-06 length) case originally derived from the .404 Jeffery.
Geometrically, the .300 PRC case is as nearly perfect as mankind and computers can achieve. Neck length is ideal. Shoulder angle is ideal. There's no belt around the case head. There's little efficiency-robbing taper to the body.
"Against all odds," naysayers grudgingly admit, "the .300 PRC has a toehold."
"Thanks to an ultimate combination of refined characteristics," acolytes claim, "the .300 PRC is rapidly earning the recognition it deserves."
Both are right.
When the .300 PRC launched, we already had the .300 H&H, the .300 Win. Mag., the .300 WSM, the .300 RUM, the .300 RSAUM, the .300 Norma, the .300 Weatherby, the .30-378 Weatherby, the .300 RCM…my word, more .30-caliber magnums than a beaver could shake a stick at.
However, not one of this long list of cartridges stacks every characteristic in the "positive" column and none in the "negative." Many have outdated case design features. Some are overbored. Some are underpowered. Some are efficient, but not configured to shoot high-ballistic-coefficient (BC) bullets with best-in-class aerodynamics.
There are very few cartridges about which one can say, "It has it all." The .300 PRC is one. The few disadvantages associated with it—and, yes, there are some—have nothing to do with design or performance and everything to do with distribution and availability. But I'll get to that soon enough.
As you can see here, the cases of the two .30-caliber magnums are about the same length. However, the .300 PRC (left) has a much longer "head height." It does require a proper magnum-length magazine. The .300 Win. Mag. (right) will fit and function through a .30-06-length action.
Let's start with the most important characteristics and work down. Foremost, at least in this man's opinion, is the fact that from the ground up the .300 PRC was engineered to provide optimal performance with long, heavy-for-caliber, high-BC bullets.
Hornady's cartridge gurus (they've dreamt up and introduced the three most successful new cartridges of the 21st century) gave the .300 PRC a generous "head height." Head height is the distance between the cartridge case mouth and the end of the magazine. It dictates how much bullet can stick out of the end of the cartridge case and still allow the whole cartridge to fit into a rifle magazine.
Traditionally, powder capacity was valued more than head height, so bullets were given short, blunt ogives (the curve from the tip of the bullet to full diameter) and seated deep into the case. Cartridge shoulders were positioned far forward so that the case would hold as much propellant as possible. That bullet bases would protrude way down into the propellant chamber was accepted as a necessary evil.
Not necessary, claimed the .300 PRC's architects.
Case in point (if you'll forgive the horrible pun): The thunderous .300 RUM and the .300 PRC have nearly identical overall cartridge lengths. Both require true long rifle actions. However, the shoulder on the RUM is much farther from the cartridge base. The RUM holds considerably more powder, yet the .300 PRC will shoot flatter at long range.
How? Because Hornady's engineers optimized the efficiency of the internal combustion chamber and gave the cartridge plenty of head height. You can comfortably load .300 PRC cartridges with extremely sleek, aerodynamic bullets that have long, fine-entry noses. In the .300 PRC, bullet bases don't protrude down into powder capacity. Projectiles start slower than the blunter bullets from the .300 RUM but hold their speed better and quickly overtake them. At extreme range, there's just no contest.
But we're not here to compare the .300 PRC with the .300 RUM. So how does it stack up to the .300 Win. Mag.? I love the old Winchester cartridge, so it hurts my feelings a little to say this, but the .300 PRC walks all over it, ballistically.
Why? Because the .300 PRC shoots those long, high-BC bullets that won't fit and feed from .300 Win. Mag. magazines, and it starts them nearly as fast as the .300 Win. Mag. shoots normal bullets.
Spring of 2018 found the author in Africa, hunting free-range plains game with the yet-to-be-announced .300 PRC. It proved extraordinary. Here, von Benedikt was after mountain reedbuck, hunting with Crusader Safaris.
In addition to cartridge head height optimal for modern, extreme-BC bullets, the .300 PRC features a chamber spec'd with very tight tolerances. You'll rarely, if ever, see a .300 PRC rifle that won't shoot due to a sloppy chamber. Plus, the chamber throat and rifling leade areas are engineered for ideal compatibility with those sleek-nosed, high-BC bullets we're making such a big deal about.
Crucially important in making all these cutting-edge puzzle pieces fit together is fast rifling. Most .300 PRC rifles have a twist rate of 1 turn in 9 inches or faster; some are as fast as 1:8. Either will properly stabilize most of today's extreme-range superbullets.
The .300 Win. Mag. (and basically all the other .30-caliber magnums) have a 1:10 twist rate that will not adequately stabilize today's best long-range projectiles.
Let's move on. In the world of internal combustion, the .300 PRC has a much different chamber shape than the .300 Win. Mag. It contains more gunpowder, so one might think it crosses the cusp into overbore territory.
Nope. Not quite. Thanks to the way it holds that propellant in a broader, slightly shorter cylindrical shape, it burns a tad more efficiently than the .300 Win. Mag. When ignited from one end, a fatter, shorter column of powder burns more instantly and efficiently than a long, skinny column.
Before listing the .300 PRC's disadvantages, let's take a look at a ballistic comparison between it and the .300 Win. Mag.
I've crunched ballistics using the best high-BC factory loads listed on Hornady's website.
Dedicated hunting with Crusader Safaris turned up the holy grail of free-range nyala—a bull with perfect lyre-shaped horns that stretched nearly 30 inches. The .300 PRC worked its magic, dropping the bull with a Hornady 212-grain ELD-X bullet.
In the .300 Win. Mag., the 200-grain ELD-X projectile has a G1 BC of 0.597 and a G7 BC of 0.301—really pretty darned good. It's rated to exit the muzzle at 2,850 fps. It is, candidly, a fantastic load.
In the .300 PRC, the 225-grain ELD Match bullet has a G1 BC of 0.777 and a G7 BC of .391. That's nearly unearthly, and it doesn't hold a candle to the extreme-BC projectiles handloaders like me get all giddy about, such as the 250-grain A-Tip.
But I digress. The 225-grain projectile is rated to exit the muzzle at 2,810 fps, just 40 fps slower than the .300 Win. Mag. bullet.
I've included a chart to display long-range performance out to 1,500 yards. The ballistics were calculated using standard sea-level atmospherics, a 200-yard zero, and a 10-mph full-value crosswind.
.300 PRC Cons
The .300 PRC's disadvantages are minuscule and don't reflect specifically on the cartridge, but they must be considered nonetheless.
Worst is the current very limited selection of factory-loaded ammunition. As I write this, only Hornady offers factory ammo—and just two loads. This is changing fast, and I predict that within a year we will see factory-loaded .300 PRC ammo from Federal, Barnes, Winchester, Browning, and others. Many rifle companies now list the .300 PRC in their top-five sellers, and ammo companies can't ignore numbers like that.
For now, though, non-handloaders have their pick of…Hornady and Hornady. The good news is Hornady makes great ammo.
For handloaders, the world is their .300-PRC oyster. Dies and data are readily available, and the cartridge shoots well with darn near any combination you put in it.
I can't even list limited rifle selection as a disadvantage. Not anymore. Most of the big rifle manufacturers and all of the boutique makers worth their salt offer several excellent models in .300 PRC.
How about recoil? Well, that's subjective. Because the .300 PRC is usually loaded with heavy-for-caliber bullets, it does kick more than the average .300 Win. Mag. load. However, if you load them both with the same bullet weight, the difference is negligible. The way I see it, that extra recoil is symbolic of the enhanced performance you get by shooting those extreme-BC projectiles. But, yes, the .300 PRC does kick.
Inhabiting a vast variety of African terrain, common reedbucks like this are a low-pressure plains-game animal that are extremely fun to hunt. We stalked this mature buck on a rocky ridgeline, hunting him like a big alpine mule deer. A single Hornady 212-grain ELD-X reached out and dropped it cleanly.
As I mentioned in the beginning, in the final analysis, only you can decide which cartridge better fits your needs. I can give you a little guidance, though.
If you're a practical sort, who values proven performance and ready availability, I suspect the .300 Win. Mag. is your cuppa tea.
If you like finessing the last iota of performance out of your shooting setup, and you handload, and you rarely shoot factory ammo anyway, you owe yourself a .300 PRC.
Here's the good news. As much fun as it is to weigh every aspect and characteristic, however slight, and endlessly debate the merits of one over the other, in the end you can't go wrong with either.


This last point is one of the singularly most discussed points amongst anyone worried about AI 'taking over', this point where humans effectively hand over the controls by allowing AI to become more intelligent than us. In reality we're a way from this, but IT WILL happen eventually.
The first computer on record to pass the Turing test was Eugene in 2014, it sparked a lot of debate about the ability to hold conversation and what defines this. Fast forward to today and the conversation you can have with AI is much more sophisticated, consider the shorts between Watson and a host of guests, I quite like this one with Ridley Scott and in addition to clarity of structure, speed of response and total relevancy, you see humour and wit coming through from Watson as well. Watson is making decisions in real time to inform an interesting conversation.
So this brings me to my thought provocation which is around the line between logic decision making and conscious decision making. The first is easy to comprehend as it's essentially a series of true v false (or 1 v 0 if you're into code) logic decisions based on a probability factor. The second takes in additional parameters like feelings and emotions, which are more relevant to humans; if you've ever made an irrational 'in the heat of the moment' decision it's likely because you didn't follow the logic of true v false to get there.
Consider Neuromorphic chips for a moment, they have been designed to replicate the way a human brain works, drawing parallels from the neurological processes using artificial neural networks. Essentially this means that AI is programmed to feel. Advances in AI will see us side by side with intelligent and sentient robots in our lifetime, so I find it fascinating to look at the differences (and similarities) in the way we (humans) and AI robots behave based on senses, perceptions and 'feelings'.
If AI is programmed to recall a feeling, is that different to me remembering a feeling? If I recall the joy I felt from a particular moment, or the pain, or the excitement, is that so different from AI recalling the same feeling from a program. Whether that feeling is held in a chip or a brain, it exists. So what's the difference between a feeling imagined, programmed or remembered?
If consciousness is defined as being aware of one's surroundings, sensations, thoughts and existence and AI is being developed to understand all of these things, then are we now speaking 'feelings' something humans understand but robots not so much? Or has that line become even more blurred?
Previous Previous post: Whose lives matter when Robots have to decide?
Next Next post: what will separate us from robots?


Smooth running. It's a beautiful thing to see and feel. This Kevlar?-reinforced Cushion 3.0 delivers it. Slip into the SmoothFuse upper for a seamlessly secure fit. Underfoot, blended midsole technology responds to your stride to cushion, flex, and propel you off the toes for a fluid ride right into Beast mode.


For the 2016 Wisconsin QSO Party, we operated as N9UW from UW Space Place in Madison, WI again this year.
We used two endfed antennas that were a half wavelength on 80 meters, which made them resonant on 10,15,20,40 and 80 meters. Since the quadplexers are only set up for 15,20,40 and 80M, those were the four HF bands that we operated on. We also used the Space Place 2M vertical, so were able to get some local contacts on 2M.
Our operators this year were Barry K8SD, Thomas, AC9BJ, Carlie KD9CZG, Doug ND9Q, Matt NM9O, Andy AC9CB, Mike KD9BDL, Tom W9TDP, Brian KK4HPY and Nathan KC9WHJ. We operated using SSB, FM, CW and digital (psk31) modes. This was a great learning event as some of our operators had limited HF experience previously.
For logging we used the software created by N3FJP.
We made contacts with 46 Wisconsin counties and 43 states/provinces in North America.
Some pictures showing the operators and station setup.
...continue reading "2016 Wisconsin QSO Party"


text:
493HP 2016 BMW M4 GTS Media Launch in Barcelona – 300 Cars Coming to USA!
By Tom Burkart on April 14, 2016, 10 am
Gorgeous launch photos and lineage recap from BMW's media drive event for the new M4 GTS!
This new water-injected turbocharger system ups power to a stout 493HP to go along with its track-attack grip setup, aero package and lightweighting regime. One particularly awesome detail? The power-extendable chin splitter! We're not sure if the splitter is manually extended or power operated. The GTS does come with a special toolkit for customizing its shock stiffness and height or different racetracks. That rear wing is also remarkably beautiful up close. Beautiful and deeply functional! Especially in its most raised angle… pinning the tail of the car to the road thanks to a doubling of rear downforce, but minimal drag.
BMW notes that 300 cars will come to the US market, with first deliveries as soon as June of this year!
No word on whether this model will continue for 2017, but it is not looking likely. BMW notes that 700 units in total will be produced.
Selling much volume will be a tall order: the M4 GTS comes with pricing from….
….wait for it…..
$134,000.
Four colors, by the way, include this new frozen grey, alpine white, sapphire black and mineral grey metallic.
The new 2016 BMW M4 GTS International Press Launch, Barcelona, Spain
Woodcliff Lake, NJ – April 13, 2016… The new BMW M4 GTS joins a family of exclusive BMW M3 Special Editions which have been setting the pace for 30 years. Production of the first BMW M3 (E30) got under way in 1986, establishing M on both the street and the track. The BMW M3 – and now the BMW M4 – has since set the benchmark in its segment throughout its lifespan and across every model generation. The BMW M4 GTS is the first Special Edition M3 / M4 to be made available in the US market. Approximaterly 300 cars will be available to US customers with delivery starting in the second quarter of 2016.
With its powerful, motorsport-inspired looks and high-performance technology, the BMW M4 GTS has its sights set squarely on the race track. Thanks to an innovative water injection system helping boost power to 493 hp and track ready chassis, this Special Edition M vehicle recorded a lap time of 7 minutes 28 seconds around the legendary Nürburgring-Nordschleife, the world's most challenging race circuit, making a clear statement about this model's exceptional dynamic abilities. BMW M GmbH will produce approximately 700 units of the BMW M4 GTS worldwide.
2016 BMW M4 GTS
"Special Edition models sharpen the character of the BMW M brand and embody an emotionally rich and exclusive driving experience. We've taken a radical route with the set-up of the BMW M4 GTS to create a sports machine for the race track that delivers top-end dynamics and inspirational performance. It allows us to demonstrate what is possible today with a road-legal car. Owners can drive their BMW M4 GTS to circuits such as Spa-Francorchamps, the Nürburgring or Laguna Seca – for clubsport events, for example – and then set lap times there that raise the bar to extremely high levels for road-legal cars." says Frank van Meel, CEO of BMW M GmbH.
The history of BMW M3 special editions.
1988: BMW M3 Evolution (E30).
In 1988, BMW M GmbH brought the BMW M3 Evolution onto the market in a limited-run special series of 500 units. Its four-cylinder in-line engine with 2.3-liter displacement now developed 220 hp – 25 hp more than the standard M3. The increase in power was the result of a series of modifications to components that included the valves, pistons, cylinder head and camshaft. Weight reduction measures included body and thinner window glass. The BMW M3 Evolution had a more pronounced front spoiler than the standard M3 and a larger, adjustable rear spoiler.
1990: BMW M3 Sport Evolution (E30).
The development of the M3 E30 reached its highest mark in 1990 with the arrival of the BMW M3 Sport Evolution, available only in Gloss Black or Brilliant Red. The new arrival developed up to 238 hp from its four-cylinder in-line engine, now with 2.5-liter displacement – up more than 22 percent compared with the standard BMW M3 at the time. The intelligent lightweight construction of components including the front wings, muffler, front spoiler, trunk lid and rear wing brought the curb weight down by around 77 pounds. The adjustable splitter on the front spoiler and 16-inch light-alloy wheels with Nogaro Silver star in the center of the rim were among the most distinctive external features of the BMW M3 Sport Evolution. Inside, the front bucket seats with integral head restraints, red seat belts and suede leather covers for the sports steering wheel, handbrake lever and gearshift lever knob underlined the car's sporting prowess. Another key mark of this M3 version – of which only 600 were produced – was the "Sport Evolution 1990" badge on the center console.
1995: BMW M3 GT (E36).
The second generation of the BMW M3 saw a 6-cylinder inline engine take over as the sports car's power source. With capacity of 2,990cc, four-valve technology and VANOS camshaft timing on the intake side, the engine generated a formidable 286 hp and enabled acceleration form 0 to 100 km/h (62mph) in 5.9 seconds. In 1995, BMW M GmbH launched the BMW M3 GT special-edition model in a run of just 350 units. Output was boosted to 295 hp and the engine previewed some of the technical details of the upcoming 3.2-liter unit. The car's doors were made from aluminium and the interior was decked out in Mexico Green Nappa leather. As for exterior paint shades, the special edition was available in British Racing Green or Silver only.
2003: BMW M3 CSL (E46).
BMW returned to the idea of a limited-run special-edition BMW M3 in mid-2003, this time using the E46 model generation as the donor car. The CSL affix was a reference to the legendary BMW 3.0 CSL (for "Coupe Sport Leichtbau") of the early 1970s. In keeping with this moniker, denoting lightweight design, the BMW M3 CSL introduced a series of weight-saving parts to the mix. The center console, interior door trim and rear-view mirror were made from carbon-fiber-reinforced plastic (CFRP), the rear window featured thinner glass, the trunk lid with integral spoiler lip was now lighter and weight had also been cut from the trunk trim. All in all, the judicious use of lightweight parts allowed the car's curb weight to be reduced by 362 pounds to 3,053 pounds.
The power produced by the 3,246cc 6-cylinder inline engine, was increased to 360 hp. Modification of the intake air ducting was necessary to cool the upgraded engine, which led to the creation of a distinctive circular aperture in the aerodynamically optimized front apron to enhance the supply of air to the airbox. Externally, the BMW M3 CSL stood apart from the BMW M3 with its exposed carbon-fiber roof and special "Sport" 19-inch M light-alloy wheels with Michelin Sport Cup tires.
On opening the door, the two bucket seats and lightweight rear seats in an Amaretta/cloth combination immediately caught the eye. The doors came with carbon-fiber inserts, the M sports steering wheel had a grippy Alcantara covering and the center console was now of a more compact and lightweight construction. Another distinctive feature of the steering wheel was the "M" button used to activate M Track Mode. This adapted the responses of the power steering and the parameters of Dynamic Stability Control (DSC) to allow an even sportier driving style. The modified Launch Control system helped the BMW M3 CSL to race from 0 to 100 km/h (62 mph) in just 4.8 seconds and hit the 200 km/h (124 mph) mark in a mere 16.7 seconds. The CSL chassis brought magnetic pulse-formed rear control arms and some of the tuning work took place at the Nürburgring-Nordschleife. The result of these modifications could be seen in independent tests. Indeed, the BMW M3 CSL set what was then the fastest lap of the Nordschleife for its output class (7 min 50 sec).
2010: BMW M3 GTS (E92).
In 2010, BMW M GmbH introduced the direct successor to the BMW M3 CSL in the form of the BMW M3 GTS. Like its predecessor, it was designed to deliver the highest dynamic performance. Lightweight construction once again played a central role, reducing the car's DIN curb weight to 3,373 pounds – 275 pounds lower than that of the standard BMW M3. An increase in the cylinder stroke pushed the displacement of the V8 engine from 4.0 up to 4.4 liters, boosting output by 30 hp to 450 hp.
The BMW M3 GTS came as standard with a bolted-on roll bar in place of a rear seat bench, and this could be optionally upgraded to a full roll cage. Furthermore, mounts for four- and six-point Schroth seatbelts were fitted. And finally, the fire extinguisher holder behind the front seats underlined the clear track focus of the GTS. The car's racing ability was also enhanced by revised aerodynamics. A front spoiler with carbon-fiber splitter teamed up with the carbon-fiber rear wing on the trunk lid to reduce lift and ensure high cornering speeds.
BMW M GmbH produced the BMW M3 GTS largely by hand, with the cars built individually in the factory to customer specification. Type approval for road use in Germany was on a case-by-case basis.
2011: BMW M3 CRT (E92).
The BMW M3 CRT – produced in a run of just 67 units from May 2011 – combined the technology of the BMW M3 GTS with the body of the four-door BMW M3 Sedan. The CRT also had a carbon-fiber bonnet with apertures next to the powerdome and a splitter below the front apron, but the large rear spoiler on the trunk lid was usurped by a carbon-fiber spoiler lip. In place of a roll bar, the BMW M3 CRT came with a rear seat bench containing two molded individual seats. With a curb weight (DIN) of 3,483 pounds, the BMW M3 CRT was around 99 pounds lighter than the BMW M3 Sedan.
2016: BMW M4 GTS (F82).
In 2016, BMW M GmbH presented the next in its line of sports car special editions in the form of the BMW M4 GTS. The new car takes the concept of BMW M3/BMW M4 models radically accentuating their sporting genes another step into the future in innovative style. For around 700 enthusiasts worldwide, roughly 300 bound for the first time to US shores, with a taste for extra-high output, sharp handling and unbeatable performance, this will be the most agile, radical and powerful M4 ever.
The BMW M4 GTS sees BMW M GmbH not only celebrating 30 years of the BMW M3/M4 success story but also presenting a perfect model to mark the centenary of the BMW brand.
~540HP 2016 BMW M4 GTS Concept Is Real-Deal Production Car with Innovative Hydro Turbo Boost Chiller
August 13, 2015, 10 pm
In "2-Doors"
500HP 2016 BMW M4 GTS Is Hottest-Ever Bimmer Track Special, But Road-Legal and Coming to USA!
October 11, 2015, 4 pm
2017 BMW M4 CS Special Edition Bows in Shanghai
20162016 BMW M4 GTSBarcelonaBMWdriveespananGTSM4m4 gtsMediapresspreviewSpainUSA
2021 BMW X5 45e Review
Learning The Ropes On And Off The Track, We Spend A Day At BMW's Performance Center West
2022 Cadillac CT4-V Blackwing vs 2022 BMW M3 – A Battle Of Numbers And Bragging Rights
Road Test Review – 2021 BMW Alpina XB7 – An X7 M In A Fine Velvet Suit
2022 BMW 2 Series Embraces Retro Modernism, Offers Better Performance Across The Board
BMW Sheds New Light On 2022 i4 Performance And Specs, Shares New Pictures
2021 BMW 4-Series convertible breaks cover, features giant grille and canvas convertible top
2021 BMW 4-Series Embraces Bigger Grilles, Gains More Power and Poise For New Model Year
2021 BMW 5-Series Debuts With Refreshed Design, Brings More Powerful Electrified Powertrain To Consumers
First Drive Review – 2020 BMW X1 xDrive28i – By Carl Malek
First Drive Review – 2020 BMW 840i – By Carl Malek [Video]


text:
Ivory Coast designer spotlight : Christiane King
According to her website Christiane King was born in the Cote d'Ivoire (French for the Ivory Coast) where she spent most of her childhood and college years. After earning a Bachelors Degree in International Trade in the City of Abidjan and interning at the country's top oil refinery, she realized her passion lied somewhere else.Christiane always showed a strong interest in fashion. But growing up in a country where fashion is highly underrated, she could not get her parents' support, nor could she guarantee herself a successful career in fashion.
Christiane then started a career in acting which took off very quickly. "I was on a quest... anything that could get me one step closer to the world of fashion was something I was willing to try."Soon enough the opportunity to travel presented itself and Christiane left the Cote d'Ivoire for Paris and later relocated to Los Angeles. Experiencing both French and American cultures has really allowed her to broaden her fashion sense.
Today, Christiane's collections feature designs that are strongly influenced by her life experiences. Her signature layered and textured look result from the combination of her traditional heritage with the Western cultures she's been exposed to. But "a woman's body is my greatest inspiration" she explains. Her garments celebrate the arts and crafts from her Ivorian culture, but also maintain very modern styles and silhouettes.
Christiane graduated with Honors from Woodbury University in Los Angeles with a Bachelors Degree in Fashion Design. Her commitment has certainly paid off. Christiane's final collection was one to be judged by renowned industry professionals and received the award for "Best of Couture" at the University's Annual Gala.
Christiane King launched her first Womenswear Collection in the Fall of 2006 and is growing bolder since then. Today Christiane King's edgy, flirty, and fun designs are gaining popularity amongst the Hollywood starlets. Her designs are available at www.ChristianeKing.com and will soon be available in stores nationwide
Note : Christiane was a contestant in project runway.
Photos: Source
Do you like what you see? then don't forget to check out my African designers Directory to Discover more talents.
Labels: African Fashion, African fashion blog, African fashion Pictures, Black fashion, designer, designer spotlight, Ivory coast, spotlight
Very inspiring! Great designs!
Yes! I absolutely love her work!