text:
\section{Introduction}

		The last few years have seen tremendous progress in learning useful image representations
		\cite{Donahue:2014}. While early successes were often achieved through the use of generative
		models \cite[e.g.,][]{Hinton:2006,Lee:2009,Ranzato:2011}, recent breakthroughs were mainly
		driven by improvements in supervised techniques \cite[e.g.,][]{Krizhevsky:2012,Simonyan:2015}.
		Yet unsupervised learning has the potential to tap into the much larger source of unlabeled
		data, which may be important for training bigger systems capable of a more
		general scene understanding. For example, multimodal data is abundant but often unlabeled,
		yet can still greatly benefit unsupervised approaches \cite{Srivastava:2014}.

		Generative models provide a principled approach to unsupervised learning.
		A perfect model of natural images would be able to optimally predict parts of an image given other parts
		of an image and thereby clearly demonstrate a form of scene understanding.
		When extended by labels, the Bayesian framework can be used to perform semi-supervised learning
		in the generative model \cite{Ngiam:2011,Kingma:2014b} while it is less clear how
		to combine other unsupervised approaches with discriminative learning.
		Generative image models are also useful in more traditional applications such as
		image reconstruction \cite{Roth:2009,Zoran:2011,Sohl-Dickstein:2015} or compression \cite{VanDenOord:2014b}.

		Recently there has been a renewed strong interest in the development of generative image models
		\cite[e.g.,][]{VanDenOord:2014b,Kingma:2014a,Uria:2014,Gregor:2014,Goodfellow:2014,Ranzato:2014,Gregor:2015,Sohl-Dickstein:2015,Li:2015,Denton:2015}.
		Most of this work has tried to bring to bear the flexibility of deep neural networks on the
		problem of modeling the distribution of natural images. One challenge in this endeavor is to
		find the right balance between tractability and flexibility. The present article contributes
		to this line of research by introducing a fully tractable yet highly flexible image model.

		Our model combines multi-dimensional recurrent neural networks \cite{Graves:2009} with
		mixtures of experts. More specifically, the backbone of our model is formed by a spatial variant of
		\textit{long short-term memory} (LSTM) \cite{Hochreiter:1997}.
		One-dimensional LSTMs have been particularly successful in modeling text and speech
		\cite[e.g.,][]{Sundermeyer:2010,Sutskever:2014}, but have also been used to model the progression of frames in video \cite{Srivastava:2014} and very
		recently to model single images \cite{Gregor:2015}. In contrast to earlier work on modeling
		images, here we use \textit{multi-dimensional LSTMs} \cite{Graves:2009} which naturally lend themselves to the task
		of generative image modeling due to their spatial structure and ability to capture long-range
		correlations.

		To model the distribution of pixels conditioned on the hidden states of the neural network, we use
		\textit{mixtures of conditional Gaussian scale mixtures} (MCGSMs) \cite{Theis:2012a}.
		This class of models can be viewed as a generalization of Gaussian mixture models, but
		their parametrization makes them much more suitable for natural images. By treating
		images as instances of a stationary stochastic process, this model allows us to sample and capture the
		correlations of arbitrarily large images.

	\section{A recurrent model of natural images}
		\label{sec:ride}
		\begin{figure}
			\centering
			\input{figures/rim.tex}
			\caption{(\textbf{A}) We factorize the distribution of images such that the prediction
				of a pixel (black) may depend on any pixel in the upper-left green region. (\textbf{B}) A graphical model representation of an MCGSM with a
				causal neighborhood limited to a small region. (\textbf{C}) A visualization of our recurrent image model with two layers of spatial LSTMs.
				The pixels of the image are represented twice and some
				arrows are omitted for clarity. Through feedforward connections, the prediction of a pixel depends
				directly on its neighborhood (green), but through recurrent connections it has
				access to the information in a much larger region (red).}
			\label{fig:rim}
		\end{figure}
		In the following, we first review and extend the MCGSM \cite{Theis:2012a} and
		multi-dimensional LSTMs \cite{Graves:2009} before explaining how to combine them into
		a recurrent image model. Section~\ref{sec:experiments} will demonstrate
		the validity of our approach by evaluating and comparing the model on a number of image datasets.

		\subsection{Factorized mixtures of conditional Gaussian scale mixtures}
		\label{sec:mcgsm}
		One successful approach to building flexible yet tractable generative models has been to
		use fully-visible belief networks \cite{Neal:1992,Larochelle:2011}. To apply
		such a model to images, we have to give the pixels an ordering and specify the
		distribution of each pixel conditioned on its parent pixels. Several parametrizations
		have been suggested for the conditional distributions in the context of natural images \cite{Domke:2008,Hosseini:2010,Theis:2012a,Uria:2013,Uria:2014}.
		We here review and extend the work of Theis et al. \cite{Theis:2012a} who proposed
		to use \textit{mixtures of conditional Gaussian scale mixtures} (MCGSMs).

		Let $\mathbf{x}$ be a grayscale image patch and $x_{ij}$ be the intensity of the pixel at location
		$ij$. Further, let $\mathbf{x}_{<ij}$ designate the set of pixels $x_{mn}$ such that $m < i$
		or $m = i$ and $n < j$ (Figure~\ref{fig:rim}A). Then
		\begin{align}
			\textstyle
			p(\mathbf{x}; \bm{\theta}) = \prod_{i,j} p(x_{ij} \mid \mathbf{x}_{<ij}; \bm{\theta})
			\label{eq:factorization}
		\end{align}
		for the distribution of any parametric model with parameters $\bm{\theta}$. Note that this
		factorization does not make any independence assumptions but is simply an
		application of the probability chain rule. Further note that the conditional distributions
		all share the same set of parameters. One way to improve the
		representational power of a model is thus to endow each conditional distribution with its own set of parameters,
		\begin{align}
			\textstyle
			p(\mathbf{x}; \left\{ \bm{\theta}_{ij} \right\}) = \prod_{i,j} p(x_{ij} \mid \mathbf{x}_{<ij}; \bm{\theta}_{ij}).
			\label{eq:factorization2}
		\end{align}
		Applying this trick to mixtures of Gaussian scale mixtures (MoGSMs) yields the MCGSM \cite{Theis:2011}.
		Untying shared parameters can drastically increase the number of parameters. For images, it
		can easily be reduced again by adding assumptions. For example, we can limit
		$\mathbf{x}_{<ij}$ to a smaller neighborhood surrounding the pixel by making a Markov
		assumption. We will refer to the resulting set of parents as the pixel's \textit{causal neighborhood} (Figure~\ref{fig:rim}B).
		Another reasonable assumption is stationarity or shift invariance, in which case we only have
		to learn one set of parameters $\bm{\theta}_{ij}$ which can then be used at every pixel
		location. Similar to convolutions in neural networks, this allows the model to easily scale to images of arbitrary size.
		While this assumption reintroduces parameter sharing constraints into the model, the constraints are
		different from the ones induced by the joint mixture model.

		The conditional distribution in an MCGSM takes the form of a mixture of experts
		\begin{align}
			p(x_{ij} \mid \mathbf{x}_{<ij}, \bm{\theta}_{ij})
			&= \sum_{c,s} \underbrace{p(c, s \mid \mathbf{x}_{<ij}, \bm{\theta}_{ij})}_\text{gate} \underbrace{p(x_{ij} \mid \mathbf{x}_{<ij}, c, s, \bm{\theta}_{ij})}_\text{expert},
		\end{align}
		where the sum is over mixture component indices $c$ corresponding to different covariances
		and scales $s$ corresponding to different variances.
		The gates and experts in an MCGSM are given by
		\begin{align}
			p(c, s \mid \mathbf{x}_{<ij})
			&\propto \textstyle \exp\left( \eta_{cs} - \frac{1}{2} e^{\alpha_{cs}} \mathbf{x}_{<ij}^\top \mathbf{K}_c \mathbf{x}_{<ij} \right),
			\label{eq:gates} \\
			p(x_{ij} \mid \mathbf{x}_{<ij}, c, s)
			&= \mathcal{N}(x_{ij}; \mathbf{a}_c^\top \mathbf{x}_{<ij}, e^{-\alpha_{cs}}),
			\label{eq:experts}
		\end{align}
		where $\mathbf{K}_c$ is positive definite. The number of parameters of an MCGSM still grows quadratically with
		the dimensionality of the causal neighborhood. To further reduce the number of parameters,
		we introduce a factorized form of the MCGSM with additional parameter sharing by replacing
		$\mathbf{K}_c$ with $\sum_n \beta_{cn}^2 \mathbf{b}_n\mathbf{b}_n^\top$.
		This \textit{factorized MCGSM} allows us to use larger neighborhoods and more mixture components.
		A detailed derivation of a more general version which also allows for multivariate pixels is given in
		Supplementary Section~1.

		\subsection{Spatial long short-term memory}
		In the following we briefly describe the \textit{spatial LSTM} (SLSTM), a special case of the
		multi-dimensional LSTM first described by Graves \& Schmidhuber \cite{Graves:2009}.
		At the core of the model are memory units $\mathbf{c}_{ij}$ and hidden units
		$\mathbf{h}_{ij}$. For each location $ij$ on a two-dimensional grid, the operations
		performed by the spatial LSTM are given by \\
		\begin{minipage}{0.5\textwidth}
			\begin{align*}
				\mathbf{c}_{ij} &=
					\mathbf{g}_{ij} \odot \mathbf{i}_{ij}
					+ \mathbf{c}_{i,j - 1} \odot \mathbf{f}_{ij}^c
					+ \mathbf{c}_{i - 1,j} \odot \mathbf{f}_{ij}^r, \\
				\mathbf{h}_{ij} &= \tanh\left( \mathbf{c}_{ij} \odot \mathbf{o}_{ij} \right),
			\end{align*}
		\end{minipage}
		\begin{minipage}{0.5\textwidth}
			\begin{align}
				\begin{pmatrix}
					\mathbf{g}_{ij} \\
					\mathbf{o}_{ij} \\
					\mathbf{i}_{ij} \\
					\mathbf{f}_{ij}^r \\
					\mathbf{f}_{ij}^c
				\end{pmatrix}
				=
				\begin{pmatrix}
					\tanh \\
					\sigma \\
					\sigma \\
					\sigma \\
					\sigma
				\end{pmatrix}
				T_{\mathbf{A},\mathbf{b}}
				\begin{pmatrix}
					\mathbf{x}_{<ij} \\
					\mathbf{h}_{i,j - 1} \\
					\mathbf{h}_{i - 1,j}
				\end{pmatrix},
				\label{eq:slstm}
			\end{align}
		\end{minipage} \\[.4cm]
		where $\sigma$ is the logistic sigmoid function, $\odot$ indicates a pointwise product,
		and $T_{\mathbf{A},\mathbf{b}}$ is an affine transformation which depends on the only
		parameters of the network $\mathbf{A}$ and $\mathbf{b}$. The gating units $\mathbf{i}_{ij}$ and $\mathbf{o}_{ij}$
		determine which memory units are affected by the inputs through $\mathbf{g}_{ij}$, and which memory states
		are written to the hidden units $\mathbf{h}_{ij}$. In contrast to
		a regular LSTM defined over time, each memory unit of a spatial LSTM has two preceding states
		$\mathbf{c}_{i,j-1}$ and $\mathbf{c}_{i-1,j}$ and two corresponding forget gates
		$\mathbf{f}_{ij}^c$ and $\mathbf{f}_{ij}^r$.

		\subsection{Recurrent image density estimator}

		We use a grid of SLSTM units to sequentially read relatively small neighborhoods of pixels
		from the image, producing a hidden vector at every pixel. The hidden states are then fed
		into a factorized MCGSM to predict the state of the corresponding pixel, that is,
		$p(x_{ij} \mid \mathbf{x}_{<ij}) = p(x_{ij} \mid \mathbf{h}_{ij})$.
		Importantly, the state of the hidden vector only depends on pixels in $\mathbf{x}_{<ij}$ and
		does not violate the factorization given in Equation~\ref{eq:factorization}. Nevertheless,
		the recurrent network allows this \textit{recurrent image density estimator} (RIDE) to use pixels of a much larger
		region for prediction, and to nonlinearly transform the pixels before applying the MCGSM. We
		can further increase the representational power of the model by stacking spatial LSTMs
		to obtain a deep yet still completely tractable recurrent image model (Figure~\ref{fig:rim}C).

		\subsection{Related work}
			Larochelle \& Murray \cite{Larochelle:2011} derived a tractable density estimator (NADE)
			in a manner similar to how the MCGSM was derived \cite{Theis:2012a}, but using restricted Boltzmann
			machines (RBM) instead of mixture models as a starting point. In contrast to the MCGSM,
			NADE tries to keep the weight sharing constraints induced by the RBM (Equation~\ref{eq:factorization}).
			Uria et al. extended NADE to real values \cite{Uria:2013} and introduced hidden layers to the
			model \cite{Uria:2014}. Gregor et al. \cite{Gregor:2014} describe a related
			autoregressive network for binary data which additionally allows for stochastic hidden units.

			Gregor et al. \cite{Gregor:2015} used one-dimensional LSTMs to generate images in a sequential
			manner (DRAW). Because the model was defined over Bernoulli variables,
			normalized RGB values had to be treated as probabilities, making a direct comparison with
			other image models difficult. In contrast to our model, the presence of stochastic latent variables
			in DRAW means that its likelihood cannot be evaluated but has to be approximated.

			Ranzato et al. \cite{Ranzato:2014} and Srivastava et al. \cite{Srivastava:2015} use
			one-dimensional recurrent neural networks to model videos, but recurrency is not used to
			describe the distribution over individual frames. Srivastava et al. \cite{Srivastava:2015}
			optimize a squared error corresponding to a Gaussian assumption, while Ranzato et
			al. \cite{Ranzato:2014} try to side-step having to model pixel intensities by quantizing image patches. In
			contrast, here we also try to solve the problem of modeling pixel intensities by using
			an MCGSM, which is equipped to model heavy-tailed as well as multi-modal distributions.

	\section{Experiments}
		\label{sec:experiments}
		RIDE was trained using stochastic gradient descent with a batch size of 50, momentum of 0.9, and a decreasing learning rate varying
		between 1 and $10^{-4}$.
		After each pass through the training set, the MCGSM of RIDE was finetuned using L-BFGS for
		up to 500 iterations before decreasing the learning rate. No regularization was used except for early stopping based on
		a validation set. Except where indicated otherwise, the recurrent model used a
		5 pixel wide neighborhood and an MCGSM with 32 components and 32
		quadratic features ($\mathbf{b}_n$ in Section~\ref{sec:mcgsm}). Spatial LSTMs were implemented using
		the Caffe framework \cite{jia:2014}. Where appropriate, we augmented the data by horizontal
		or vertical flipping of images.

		We found that conditionally whitening the data greatly sped up the training process of both
		models. Letting $\mathbf{y}$ represent a pixel and
		$\mathbf{x}$ its causal neighborhood, conditional whitening replaces these with

		\begin{align}
			\mathbf{\hat x} &= \textstyle\mathbf{C}_\mathbf{xx}^{-\frac{1}{2}} \left( \mathbf{x} - \mathbf{m}_\mathbf{x} \right), &
			\mathbf{\hat y} &= \textstyle \mathbf{W} (\mathbf{y} - \mathbf{C}_{\mathbf{yx}} \mathbf{C}_\mathbf{xx}^{-\frac{1}{2}} \mathbf{\hat x} - \mathbf{m}_\mathbf{y}), &
			\mathbf{W} &= (\mathbf{C}_\mathbf{yy} - \mathbf{C}_{\mathbf{yx}} \mathbf{C}_\mathbf{xx}^{-1} \mathbf{C}_{\mathbf{yx}}^\top)^{-\frac{1}{2}},
		\end{align}
		where
		$\mathbf{C}_{\mathbf{yx}}$ is the covariance of $\mathbf{y}$ and $\mathbf{x}$, and
		$\mathbf{m}_\mathbf{x}$ is the mean of $\mathbf{x}$.
		In addition to speeding up training, this variance normalization step helps to make the learning
		rates less dependent on the training data. When evaluating the conditional log-likelihood,
		we compensate for the change in variance by adding the log-Jacobian $\log |\det \mathbf{W}|$. Note that this preconditioning introduces a
		shortcut connection from the pixel neighborhood to the predicted pixel which is not shown in Figure~\ref{fig:rim}C.

		\subsection{Ensembles}
		Uria et al. \cite{Uria:2014} found that forming ensembles of their autoregressive model over
		different pixel orderings significantly improved performance. We here consider a simple trick to
		produce an ensemble without the need for training different models or to change training
		procedures. If $\mathbf{T}_k$ are linear transformations leaving the targeted image distribution
		invariant (or approximately invariant) and if $p$ is the distribution of a pretrained model, then we form the ensemble
		$\frac{1}{K} \sum_k p(\mathbf{T}_k \mathbf{x}) |\det \mathbf{T}_k|$. Note that this is simply a mixture
		model over images $\mathbf{x}$. We considered rotating as well as flipping images along the horizontal and
		vertical axes (yielding an ensemble over 8 transformations). While it could be argued that most
		of these transformations do not leave the distribution over natural images invariant, we
		nevertheless observed a noticeable boost in performance.

		\subsection{Natural images}
		\begin{figure}[t]
			\begin{floatrow}
				\capbtabbox{
					\small
					\setlength{\tabcolsep}{.1em}
					\begin{tabular}{lccc}
						\hline
						\textbf{Model} &
						\parbox{1.1cm}{\centering\textbf{63 dim.} \\ \textbf{[nat]}} &
						\parbox{1.1cm}{\centering\textbf{64 dim.} \\ \textbf{[bit/px]}} &
						\parbox{1.1cm}{\centering\textbf{$\mathbf{\infty}$ dim.} \\ \textbf{[bit/px]}} \\
						\hline
						RNADE \cite{Uria:2013}                            & 152.1          & \cellcolor{gray!20} 3.346 & - \\
						RNADE, 1 hl \cite{Uria:2014}                      & 143.2          & \cellcolor{gray!20} 3.146 & - \\
						RNADE, 6 hl \cite{Uria:2014}                      & 155.2          & \cellcolor{gray!20} 3.416 & - \\
						EoRNADE, 6 layers \cite{Uria:2014}                & 157.0          & \cellcolor{gray!20} 3.457 & - \\
						GMM, 200 comp. \cite{Zoran:2012,VanDenOord:2014b} & 153.7          & \cellcolor{gray!20} 3.360 & - \\
						STM, 200 comp. \cite{VanDenOord:2014a}            & 155.3          & \cellcolor{gray!20} 3.418 & - \\
						Deep GMM, 3 layers \cite{VanDenOord:2014b}        & 156.2          & \cellcolor{gray!20} 3.439 & - \\
						MCGSM, 16 comp.                                   & 155.1          & 3.413                     & \cellcolor{gray!20} 3.688 \\
						MCGSM, 32 comp.                                   & 155.8          & 3.430                     & \cellcolor{gray!20} 3.706 \\
						MCGSM, 64 comp.                                   & 156.2          & 3.439                     & \cellcolor{gray!20} 3.716 \\
						MCGSM, 128 comp.                                  & 156.4          & 3.443                     & \cellcolor{gray!20} 3.717 \\
						EoMCGSM, 128 comp.                                & \textbf{158.1} & \textbf{3.481}            & \cellcolor{gray!20} 3.748 \\
						RIDE, 1 layer                                     & 150.7          & 3.293                     & \cellcolor{gray!20} 3.802  \\
						RIDE, 2 layers                                    & 152.1          & 3.346                     & \cellcolor{gray!20} 3.869 \\
						EoRIDE, 2 layers                                  & 154.5          & 3.400                     & \cellcolor{gray!20} \textbf{3.899} \\
						\hline
					\end{tabular}
				}{
					\caption{Average log-likelihoods and log-likelihood rates for image
						patches (without/with DC comp.) and large images extracted from BSDS300
						\cite{Martin:2001}.}
					\label{tbl:bsds300}
				}
				\capbtabbox{
					\small
					\setlength{\tabcolsep}{.1em}
					\begin{tabular}{lcc}
						\hline
						\textbf{Model} &
						\parbox{1.2cm}{\centering\textbf{256 dim.} \\ \textbf{[bit/px]}} &
						\parbox{1.2cm}{\centering\textbf{$\mathbf{\infty}$ dim.} \\ \textbf{[bit/px]}} \\
						\hline
						GRBM \cite{Hinton:2006}                & \cellcolor{gray!20} 0.992 & - \\
						ICA \cite{Bell:1997,vanHateren:1998}   & \cellcolor{gray!20} 1.072 & - \\
						GSM                                    & \cellcolor{gray!20} 1.349 & - \\

						ISA \cite{Hyvarinen:2000,Gerhard:2015} & \cellcolor{gray!20} 1.441 & - \\
						MoGSM, 32 comp. \cite{Theis:2011}      & \cellcolor{gray!20} 1.526 & - \\
						MCGSM, 32 comp.                        & 1.615                     & \cellcolor{gray!20} 1.759 \\

						RIDE, 1 layer, 64 hid.                 & \textbf{1.650}            & \cellcolor{gray!20} 1.816 \\
						RIDE, 1 layer, 128 hid.                & -                         & \cellcolor{gray!20} 1.830 \\
						RIDE, 2 layers, 64 hid.                & -                         & \cellcolor{gray!20} 1.829 \\
						RIDE, 2 layers, 128 hid.               & -                         & \cellcolor{gray!20} 1.839  \\
						EoRIDE, 2 layers, 128 hid.             & -                         & \cellcolor{gray!20} \textbf{1.859}  \\
						\hline
					\end{tabular}
				}{
					\caption{Average log-likelihood rates for image patches and large
						images extracted from van Hateren's dataset \cite{vanHateren:1998}.}
					\label{tbl:vanhateren}
				}
			\end{floatrow}
		\end{figure}

		Several recent image models have been evaluated on small image patches sampled from
		the Berkeley segmentation dataset (BSDS300) \cite{Martin:2001}. Although our model's
		strength lies in its ability to scale to large images and to capture long-range correlations, we include
		results on BSDS300 to make a connection to this part of the literature. We followed the
		protocol of Uria et al. \cite{Uria:2013}. The RGB images were turned to
		grayscale, uniform noise was added to account for the integer discretization, and the resulting
		values were divided by 256. The training set of 200 images was split into 180 images for
		training and 20 images for validation, while the test set contained 100 images. We extracted 8 by 8
		image patches from each set and subtracted the average pixel intensity such
		that each patch's DC component was zero. Because the resulting image patches live on a 63
		dimensional subspace, the bottom-right pixel was discarded. We used
		$1.6 \cdot 10^6$ patches for training, $1.8 \cdot 10^5$ patches for validation, and
		$10^6$ test patches for evaluation.

		MCGSMs have not been evaluated on this dataset and so we first tested MCGSMs by training a single factorized MCGSM
		for each pixel conditioned on all previous pixels in a fixed ordering. We find that already
		an MCGSM (with 128 components and 48 quadratic features) outperforms all single models
		including a deep Gaussian mixture model \cite{VanDenOord:2014a} (Table~\ref{tbl:bsds300}).
		Our ensemble of MCGSMs\footnote{Details on how the
		ensemble of transformations can be applied despite the missing bottom-right pixel are given
		in Supplementary Section 2.1.} outperforms an ensemble of RNADEs with 6 hidden layers, which to
		our knowledge is currently the best result reported on this dataset.

		Training the recurrent image density estimator (RIDE) on the 63 dimensional dataset is more cumbersome. We tried
		padding image patches with zeros, which was necessary
		to be able to compute a hidden state at every pixel. The bottom-right pixel was ignored during training and evaluation. This simple approach led to a reduction in
		performance relative to the MCGSM (Table~\ref{tbl:bsds300}). A possible explanation is that
		the model cannot distinguish between pixel intensities which are zero and zeros in the padded region.
		Supplying the model with additional binary indicators as inputs (one for each neighborhood pixel) did
		not solve the problem.

		However, we found that RIDE outperforms the MCGSM by a large margin when images were treated
		as instances of a stochastic process (that is, using infinitely large images). MCGSMs were trained for up to 3000
		iterations of L-BFGS on $10^6$ pixels and corresponding causal neighborhoods extracted from the training images.
		Causal neighborhoods were 9 pixels wide and 5 pixels high. RIDE was trained
		for 8 epochs on image patches of increasing size ranging from 8 by 8 to 22 by 22 pixels
		(that is, gradients were approximated as in backpropagation through time \cite{Robinson:1987}). The right column in
		Table~\ref{tbl:bsds300} shows average log-likelihood rates for both models. Analogously to the entropy rate
		\cite{Cover:2006}, we have for the expected log-likelihood rate:
		\begin{align}
			\lim_{N \rightarrow \infty} \mathbb{E}\left[ \log p(\mathbf{x})/N^2 \right]
			&= \mathbb{E}[\log p(x_{ij} \mid \mathbf{x}_{<ij})],
		\end{align}
		where $\mathbf{x}$ is an $N$ by $N$ image patch. An average log-likelihood rate can be directly
		computed for the MCGSM, while for RIDE and ensembles we approximated it by splitting the test images into
		64 by 64 patches and evaluating on those.
		\begin{figure}[t]
			\vspace{-.3cm}
			\begin{floatrow}
				\capbtabbox[6.8cm]{
					\setlength{\tabcolsep}{.12em}
					\small
					\begin{tabular}{lc}
						\hline
						\textbf{Model} & \textbf{[bit/px]} \\
						\hline
						MCGSM, 12 comp. \cite{Theis:2012a} & 1.244 \\
						MCGSM, 32 comp. & 1.294 \\
						Diffusion \cite{Sohl-Dickstein:2015} & 1.489 \\
						RIDE, 64 hid., 1 layer & 1.402 \\
						RIDE, 64 hid., 1 layer, ext. & 1.416 \\
						RIDE, 64 hid., 2 layers & 1.438 \\
						RIDE, 64 hid., 3 layers & 1.454 \\
						RIDE, 128 hid., 3 layers & 1.489 \\
						EoRIDE, 128 hid., 3 layers & \textbf{1.501} \\
						\hline
					\end{tabular}
					\vspace{.2cm}
				}{
					\caption{Average log-likelihood rates on dead leaf images. A deep recurrent image model
					is on a par with a deep diffusion model \cite{Sohl-Dickstein:2015}. Using
					ensembles we are able to further improve the likelihood.}
					\label{tbl:deadleaves}
				}
				\ffigbox[6.8cm]{
					\hspace{-1cm}
					\input{figures/nb_size.tex}
					\vspace{-.2cm}
				}{
					\caption{Model performance on dead leaves as a function of the causal
						neighborhood width. Simply increasing the neighborhood size of the MCGSM is not sufficient to
						improve performance.}
					\label{fig:nb_size}
				}
			\end{floatrow}
		\end{figure}

		To make the two sets of numbers more comparable, we transformed nats as commonly reported on the
		63 dimensional data, $\ell_{1:63}$, into a bit per pixel log-likelihood rate using the formula
		$(\ell_{1:63} + \ell_{DC} + \ln |\det \mathbf{A}|) / 64 / \ln(2)$. This takes into account a log-likelihood
		for the missing DC component, $\ell_{DC} = 0.5020$, and the Jacobian of the transformations applied during
		preprocessing, $\ln |\det \mathbf{A}| = -4.1589$ (see Supplementary Section
		2.2 for details). The two rates in Table~\ref{tbl:bsds300} are comparable in the sense that their differences express
		how much better one model would be at losslessly compressing BSDS300 test images than another, where patch-based models
		would compress patches of an image independently. We highlighted the best result
		achieved with each model in gray. Note that most models in this list do not scale as well to
		large images as the MCGSM or RIDE (GMMs in particular) and are therefore unlikely to benefit
		as much from increasing the patch size.

		A comparison of the log-likelihood rates reveals that an MCGSM with 16 components applied to
		large images already captures more correlations than any model applied to small image patches.
		The difference is particularly striking given that the factorized MCGSM has approximately 3,000
		parameters while a GMM with 200 components has approximately 400,000 parameters.
		Using an ensemble of RIDEs, we are able to further improve this number significantly
		(Table~\ref{tbl:bsds300}).

		Another dataset frequently used to test generative image models is the dataset published by
		van Hateren and van der Schaaf \cite{vanHateren:1998}. Details of the preprocessing used in
		this paper are given in Supplementary Section~3. We reevaluated several models for which the
		likelihood has been reported on this dataset \cite{Gerhard:2015,Theis:2011,Theis:2012a,Theis:2012b}.
		Likelihood rates as well as results on 16 by 16 patches are given in Table~\ref{tbl:vanhateren}.
		Because of the larger patch size, RIDE here already outperforms the MCGSM on patches.

		\subsection{Dead leaves}
		Dead leaf images are generated by superimposing disks of random intensity and
		size on top of each other \cite{Matheron:1968,Lee:2001}. This simple procedure leads to images which already share many
		of the statistical properties and challenges of natural images, such as occlusions and
		long-range correlations, while leaving out others such as non-stationary statistics. They
		therefore provide an interesting test case for natural image models.

		We used a set of 1,000 images, where each image is 256 by 256 pixels in size.
		We compare the performance of RIDE to the MCGSM and a very recently introduced deep
		multiscale model based on a diffusion process \cite{Sohl-Dickstein:2015}.
		The same 100 images as in previous literature \cite{Theis:2012a,Sohl-Dickstein:2015} were
		used for evaluation and we used the remaining images for training. We find that the
		introduction of an SLSTM with 64 hidden units greatly improves the performance of the MCGSM. We also tried an extended version of
		the SLSTM which included memory units as additional inputs (right-hand side of Equation~\ref{eq:slstm}).
		This yielded a small improvement in performance (5th row in Table~\ref{tbl:deadleaves}) while adding layers or using more
		hidden units led to more drastic improvements. Using 3 layers with 128 hidden units in each
		layer, we find that our recurrent image model is on a par with the deep diffusion model.
		By using ensembles, we are able to beat all previously published results for this dataset
		(Table~\ref{tbl:deadleaves}).

		Figure~\ref{fig:nb_size} shows that the improved performance of RIDE is not simply due to an
		effectively larger causal neighborhood but that the nonlinear transformations performed by
		the SLSTM units matter. Simply increasing the neighborhood size of an MCGSM does not yield
		the same improvement. Instead, the performance quickly saturates. We also find that the performance
		of RIDE slightly deteriorates with larger neighborhoods, which is likely caused by optimization
		difficulties.
		\begin{figure}[t]
			\vspace{-.3cm}
			\centering
			\input{figures/textures.tex}
			\vspace{-.25cm}
			\caption{From top to bottom: A 256 by 256 pixel crop of
				a texture \cite{Brodatz:1966}, a sample generated by an MCGSM trained on the
				full texture \cite{Gerhard:2015}, and a sample generated by RIDE. This
				illustrates that our model can capture a variety of different statistical
				patterns. The addition of the recurrent neural network seems particularly
			helpful where there are strong long-range correlations (D104, D34).}
			\label{fig:textures}
		\end{figure}

		\subsection{Texture synthesis and inpainting}
		To get an intuition for the kinds of correlations which RIDE
		can capture or fails to capture, we tried to use it to synthesize textures.
		We used several 640 by 640 pixel textures published by Brodatz
		\cite{Brodatz:1966}. The textures were split into sixteen 160 by 160 pixel regions
		of which 15 were used for training and one randomly selected region was kept for testing
		purposes. RIDE was trained for up to 6 epochs on patches of increasing size ranging from
		20 by 20 to 40 by 40 pixels.

		Samples generated by an MCGSM and RIDE are shown in Figure~\ref{fig:textures}.
		Both models are able to capture a wide range of correlation structures.
		However, the MCGSM seems to struggle with textures having bimodal marginal distributions
		and periodic patterns (D104, D34, and D110). RIDE clearly improves on these
		textures, although it also struggles to faithfully reproduce periodic structure. Possible
		explanations include that LSTMs are not well suited to capture periodicities, or that
		these failures are not penalized strong enough by the likelihood.
		For some textures, RIDE produces samples which are nearly indistinguishable from the real
		textures (D106 and D110).

		One application of generative image models is inpainting \cite[e.g.,][]{Roth:2009,Heess:2009,Sohl-Dickstein:2015}.
		As a proof of concept, we used our model to inpaint a large (here, 71 by 71 pixels) region
		in textures (Figure~\ref{fig:inpainting}). Missing pixels were replaced by sampling from the posterior of RIDE.
		Unlike the joint distribution, the posterior distribution cannot be sampled directly and we had to resort to Markov
		chain Monte Carlo methods. We found the following \textit{Metropolis within
		Gibbs} \cite{Tierney:1994} procedure to be efficient enough. The missing pixels were initialized via ancestral sampling. Since
		ancestral sampling is cheap, we generated 5 candidates and used the one with
		the largest posterior density. Following initialization, we sequentially updated
		overlapping 5 by 5 pixel regions via Metropolis sampling. Proposals were generated
		via ancestral sampling and accepted using the acceptance probability
		\begin{align}
			\textstyle\alpha = \min\left\{ 1, \frac{p(\mathbf{x}')}{p(\mathbf{x})} \frac{p(\mathbf{x}_{ij} \mid \mathbf{x}_{<ij})}{p(\mathbf{x}_{ij}' \mid \mathbf{x}_{<ij})} \right\},
		\end{align}
		where here $\mathbf{x}_{ij}$ represents a 5 by 5 pixel patch and $\mathbf{x}_{ij}'$ its
		proposed replacement. Since evaluating the joint and conditional densities on the entire image is
		costly, we approximated $p$ using RIDE applied to a 19 by 19 pixel patch surrounding $ij$.
		Randomly flipping images vertically or horizontally in between the sampling further helped.
		Figure~\ref{fig:inpainting} shows results after 100 Gibbs sampling sweeps.
		\vspace{-.15cm}
		\begin{figure}
			\vspace{-.2cm}
			\centering
			\input{figures/inpainting.tex}
			\vspace{-.6cm}
			\caption{The center portion of a texture (left and center) was reconstructed by sampling from the
			posterior distribution of RIDE (right).}
			\label{fig:inpainting}
		\end{figure}

	\section{Conclusion}
		We have introduced RIDE, a deep but tractable recurrent image model based on spatial LSTMs.
		The model exemplifies how recent insights in deep learning can be exploited for generative
		image modeling and shows superior performance in quantitative comparisons. RIDE is able to capture many
		different statistical patterns, as demonstrated through its application to textures. This is an
		important property considering that on an intermediate level of abstraction natural images
		can be viewed as collections of textures.

		We have furthermore introduced a factorized version of
		the MCGSM which allowed us to use more experts and larger causal neighborhoods. This
		model has few parameters, is easy to train and already on its own performs very
		well as an image model. It is therefore an ideal building block and may be used to extend other models such as DRAW
		\cite{Gregor:2015} or video models \cite{Ranzato:2014,Srivastava:2015}.

		Deep generative image models have come a long way since deep belief
		networks have first been applied to natural images \cite{Osindero:2008}. Unlike convolutional neural networks in object recognition,
		however, no approach has as of yet proven to be a likely solution to the problem of
		generative image modeling. Further conceptual work will be necessary to come up with a model
		which can handle both the more abstract high-level as well as the low-level statistics of natural images.

	\subsubsection*{Acknowledgments}
		The authors would like to thank Aäron van den Oord for insightful discussions and Wieland Brendel,
		Christian Behrens, and Matthias Kümmerer for helpful input on this paper. This study was
		financially supported by the German Research Foundation (DFG; priority program 1527, BE 3848/2-1).

	\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


Caleborate announces his new nationwide tour, The Real Person Tour. Along with the announcement, Caleborate released the remix to his track Soul, featuring Southern rapper Big K.R.I.T.
If you're a fan of Big K.R.I.T. and Gary Vaynerchuk, then check out this meeting about music creation and the business side of it.
In this podcast, we talk Cardi B vs Nicki Minaj, why Andre 3000 isn't confident, new Eminem album title & album reviews for Snoop, Yo Gotti & Big K.R.I.T.
We wrap up with the Year End Edition of our podcast. We talk Internet trends, films, mumble rap, 2016 deaths, Trump & the best 20 songs from the Underground.
Bilal keeps the spirit of Prince alive on Pleasure Toy featuring Big K.R.I.T.
Bilal channels the energy of the late Prince for his latest music video 'Pleasure Toy' featuring Southern Hip Hop free agent Big K.R.I.T.
Big K.R.I.T. is Multi til Sun Die. We know you know this already. He releases his new single Free Agent which premiered on Sway's Universe.
Our favorite Southern rapper Big K.R.I.T. returns with the XII/XII (12 for 12) mixtape which consists of 12 songs that he dropped each hour for 12 hours.
New music Fridays! We got 6 jams for you. We decided to start naming our playlists every week based on the vibe & this vibe screams 'Cruisin'.
West Coast artist SiR drops his latest single 'Cadillac Dreams' featuring Big K.R.I.T. blending the West Coast G-Funk and Southern Bounce perfectly.
2 Chainz' artist Cap 1 drops the new music video for Blessings, featuring Southern rapper Big K.R.I.T. on the hook. Directed by HdotRoss.
Big K.R.I.T. drops a new surprise mixtape "It's Better This Way", hosted by none other than DJ Drama.
DJ Scream puts on for the South with his new video 'Grippin Grain' featuring 8 Ball, Scotty ATL & Big KRIT.
Producer SupaHotBeats drops the compilation "Free Game" featuring Trinidad James, Yelawolf, Rittz, Big K.R.I.T., Bubba Sparxxx and much more!
This is what happens when you mix Hip Hop, Electronic, Australia and Mississippi into one song.


text:
Kevin Brühlmann (geb. 1990) ist ein Schweizer Journalist, Reporter und Sachbuchautor.

Leben
Kevin Brühlmann wuchs in Thayngen im Kanton Schaffhausen auf. Seinen Masterabschluss machte er an der Universität Zürich im Fach Geschichte. Er arbeitete mehrere Jahre als Journalist bei der Wochenzeitung Schaffhauser AZ, anschliessend beim Tages-Anzeiger (Tamedia). Zu seinen Auszeichnungen und Preisen zählen der Schweizer Reporterpreis (2018) und der Zürcher Journalistenpreis in der Kategorie Nachwuchs (2019). Sein 2021 erschienenes Buch Schaffhausen muss sterben, damit wir leben können, ist eine historische Reportage über die Revolte von 1968 in der Provinz. Das Buch sei eine «klug komponierte Reportage über die 1968er-Revolte abseits der grossen Metropolen», hiess es in NZZ Geschichte. Seiner Heimat Schaffhausen ist Brühlmann auch verbunden durch verschiedene Artikelreihen, wie z. B. zur Privatuniversität Schaffhausen Institute of Technology.

Auszeichnungen
 Anerkennungspreis des Schaffhauser Pressevereins (2015)
 Schweizer Reporterpreis (2018)
 Zürcher Journalistenpreis (2019)

Werke
 Schaffhausen muss sterben, damit wir leben können: Die Revolte von 1968 in der Provinz. Verlag am Platz, Schaffhausen 2021, ISBN 978-3-908609-14-8.

Weblinks
 Hüt im Gschpröch: Kevin Brühlmann, Autor (Schaffhauser Fernsehen)
 persoenlich.com
 Kevin Brühlmann: Heimsuchungen. Aufwachsen in der Provinz und die Frage: Kann Schaffhausen eine Heimat sein, und wenn ja, wie viele? In: Schaffhauser AZ, 3. Januar 2020.
 Nora Leutert: Klischees und Zigaretten. In: Schaffhauser AZ, 3. November 2021.

Einzelnachweise und Fussnoten

Journalist (Schweiz)
Zeitungsjournalist
Sachbuchautor
Schweizer
Geboren 1990
Mann

redpajama_set_name: RedPajamaWikipedia


This is a repost of an article from 2008. Do You DJ for Love or Money?
Why are there so many amateurs in the DJ Booth?
Bad DJing has become the norm…..here is my take on why that is the case.
For the second year in a row I was asked to play at the Forecastle Festival in Louisville, Kentucky.


text:
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redpajama_set_name: RedPajamaGithub


text:
It was easy to forget Dolphins LB Raekwon McMillan, but now's the time to start watching
Raekwon McMillan starts out as the Dolphins' middle linebacker. (Allen Eyestone/The Post)
DAVIE — It's easy to forget about Raekwon McMillan in a Dolphins offseason full of swirling storylines that range from the dismissal of longtime stars to the return of a starting quarterback.
Maybe a second-round pick isn't flashy enough to keep people's attention after a year away, or perhaps the blur of nondescript linebackers that have come through Miami over the past decade has makes it hard to remember that position matters. It could also be that he's never played a snap in the NFL.
But, man, don't miss this guy's re-arrival.
McMillan is back on the field for Organized Team Activities and the medical staff hasn't put any restrictions on him as he returns from the torn ACL in his right knee that erased his rookie season last year.
In his first three months or so after being drafted at age 20, he was already good enough to secure the starting middle linebacker job. The expectations were enormous inside Dolphins headquarters, and the coaching staff was ready to trust him with all the responsibility that role entails.
"I was still a rookie last year and I was being thrown into the fire, but I was very confident in myself and what I could do on the field," McMillan said after today's practice. "I felt like I could have done big things last year, but it just had to wait until the next year."
The next year is here, and McMillan's as important as anyone other than Ryan Tannehill as the Dolphins try to stun a football world that has written them off for 2018. They're making a string of little bets, counting on a rookie here and a new face there, but McMillan is the closest they have to a sure thing.
His healthy return is better than anything the Dolphins could have done at the position in free agency. Here's the infusion they're getting in the middle of their defense: the No. 7 linebacker in his draft class, a 6-foot-2, 248-pound bruiser who can run and a player who rolled up 221 tackles in his last two seasons at Ohio State.
Take that weapon and allow defensive coordinator Matt Burke a year to refine it. That's what happened. Much like Tannehill, McMillan didn't allow the injury to entirely wipe out his season. He was around the team all last year, listening and learning to ensure he wouldn't fall behind.
"It was hard at first," he said, "but then I sucked it up and told myself that I could either let this year be a waste or prepare myself for next year."
Consider that McMillan arrived at that conclusion after dreaming of the NFL all his life and working relentlessly once the Dolphins drafted him only to have it ripped away from him in the opening minutes of his first preseason game. That didn't send him spiraling whatsoever.
It's been a refreshingly mature approach for someone who will be 22 when he hits the upcoming season, and that way of thinking was influenced in part by doing practically every step of his rehabilitation alongside Tannehill. As grown up as McMillan seems, there's no substitute for time, and a 29-year-old teammate keeping him steady throughout the process made a meaningful impression. If McMillan's as good as the Dolphins think he'll be this year, credit Tannehill for a big-brother-like assist at a time when he had plenty of his own problems.
But mostly, the comeback speaks volumes about McMillan's makeup.
His return should be an absolute thrill for the fanbase, but it easily slips to the background amid everything that's happened since he went down.
Tannehill rightfully dominates that timeline. The Dolphins have had 17 different national anthem policies since then. Chris Foerster happened. And Lawrence Timmons.
Jay Cutler came and went fast enough that he might not have ever learned McMillan's name. Rey Maualuga's stay was even shorter. Franchise mainstays Mike Pouncey, Ndamukong Suh and Jarvis Landry were shipped out.
Adam Gase ripped apart his coaching staff with a flurry of moves in January, and a whole new crop of rookies was imported via the draft less than a month ago.
A million things have understandably distracted from the luster of what McMillan can bring to this team.
But now's the time to start watching him closely.
He's ahead of where he was last summer, which was already quite impressive. Few outside the team have seen much field work from McMillan, but Dolphins have analyzed and re-analyzed the film from what he did in practice last offseason and came away thoroughly convinced they have the right man at middle linebacker going forward.
"He was really getting to that point where he was about to make that move," Burke said. "We really felt like he was just starting to take that step when we got him going before he got injured. We had high hopes for him last year. Nothing he's done since then has discouraged that."
[Ryan Tannehill is back, and it's time for Dolphins fans to believe in him. He deserves it.]
[Who wins a race between Albert Wilson, Jakeem Grant and Kenny Stills?]
[Reshad Jones, Dolphin for life?]
Check out The Palm Beach Post's Miami Dolphins page on Facebook.
Author Staff WriterPosted on May 24, 2018 May 24, 2018 Categories 2018 offseason, Jason LieserTags dailydolphin, matt burke, Miami Dolphins, Raekwon McMillan, Ryan Tannehill, sports, sportsfront
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redpajama_set_name: RedPajamaCommonCrawl


text:
Ramble at the Ryman – 2011 (Vanguard)
Reviewed by Brian Steinberg
CDs by Levon Helm
Dirt Farmer (2007)
Electric Dirt (2009)
Ramble at the Ryman (2011)
Time can be a funny thing. Where the passage of decades has made Bob Dylan's voice nearly unintelligible, the years have sanded some of the grit off of Levon Helm's legendary pipes. Yes, we're aware Mr. Helm has had medical issues with his throat, and don't wish to make light of his situation, but those elements have rendered his voice more smooth and supple whereas the best known versions of his songs had a little more grit at their core.
That doesn't mean you won't enjoy this live disc, recorded live at the Ryman in Nashville on Sept. 17, 2008, filled with Band classics including Ophelia, Rag Mama Rag and The Shape I'm In. Helm and his crew - a broad team plus guest spots by such luminaries as John Hiatt and Sheryl Crow - are clearly having loads of fun. And let's be honest, you aren't likely to hear anyone else weigh in on everything from Back to Memphis and Deep Elem Blues across the course of an evening. You may miss some of the rougher edges that were part of Helm's younger days, but there's plenty to smile at in his present.


After many years of apartment dwelling, this year I decided to make a garden.
Am I allowed to call it a garden? It's more like a few containers, a few planters, and a slooooowly developing green thumb.
I had fantasies about what my "garden" would look like: lush green, fragrant herbs, overflowing blossoms. It would be like in the magazines. An oasis on my small apartment balcony; a step into an idyllic aromatic space.
When I went to the local nursery to pick out plants, it was an awakening. I had imagined succulents, ornamental grasses, big globes and trails of multicolored petals. Thanks to those little plant care labels (and the kind saleswoman in the sun hat) I got schooled.
Not all plants, it turns out, would be happy with the amount of sunlight on my balcony. Also, not all plants would be happy together.
Like us humans, plants have different needs when it comes to light, space, water, and conditions.
Does this seem obvious to you?
It wasn't to me. I was (still am) a beginner. I needed some education about how to support a plant's natural tendencies and desires/ Plant care isn't just about water and light. It's about the right combination and the right conditions for the individual plant.
The same is true when it comes to self-care.
Self-care is not just about spa treatments and vacation days. Self-care is about what makes you feel healthy, balanced, and restored. Self-care is unique to each of us. It is not always universal, and it is not always obvious. Some people find deep restoration in a quiet night home alone, while others feel fully recharged after a night out with friends. Our work is to discover our own unique brand of self-care.
If you were a plant, what would your care instructions be?
What conditions make you thrive?
What simple actions make you feel your very best and brightest?
Tell me your self-care instructions in the comments below, in an email reply, or c'mon over to Instagram or Facebook — I'll be sharing my own self-care instructions there later today!


text:
Marriage Date Prediction of Jaime Camil
April 18, 2022 by Murugan
https://jothishi.com/celebrity-marriage-prediction/Jaime-Camil
Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place of birth – also the marriage year). One can check this predictor for any person who is already married ( like parents, cousins, relatives etc) to figure out the accuracy of the model. Around 300 rule sets are used as an input to develop this model.
When Will i Get Married
https://jothishi.com/celebrity-horoscope-match/Jaime-Camil-and-Balvanera-Heidi
Sample Report for Horoscope matching
https://www.jothishi.com/matchmaking/samplereport/
About Jaime Camil
Jaime Federico Said Camil de Saldanha da Gama(born 22 July 1973), known professionally as Jaime Camil, is a Mexican actor, singer and television personality.Camil was born on 22 July 1973 in Mexico City, Mexico.He is the son of Jaime Camil Garza, a Mexican businessman with partial Egyptian heritage, and Cecilia Saldanha da Gama, a Brazilian singer. He earned a bachelor degree in business administration from Universidad Anáhuac; studied acting in New York, Los Angeles, and Mexico; and took classical opera training in Italy.Camil started his career in 1993 as a radio commentator on Radioactivo 98.5. In 1995, he made his small-screen debut as a host in El show de Jaime Camil. He continued as a host in TV Azteca's Qué nochecita con Jaime Camil (1996) and Televisa's Operación triunfo (2002). In 2009, he co-hosted Aventura por México with Javier Poza, and in 2010 he hosted the Mexican edition of the game show, El Gran Show.He is best known for his roles in telenovelas and movies. Camil has starred in popular telenovelas such as "La Fea Mas Bella" and "Que Pobres Tan Ricos". He has also appeared in films such as "Pulling Strings" and "Instructions Not Included". Camil is a successful singer and has released several albums.
Jaime Camil love story
Jaime Camil and Balvanera Heidi's love story is one for the ages. These two met while filming a telenovela in Mexico and instantly hit it off. They've been inseparable ever since, and their love story is one of the most beautiful ones we've ever seen.
Jaime Camil marriage
In 2013, they married in a private ceremony attended by only the closest of their friends and family members, at the Basilica of Our Lady of Guadalupe in Mexico City.
Jaime Camil childrens
Heidi had given birth to their daughter Elena Camil Balvanera two years before their wedding, in 2011, and in 2014, she gave birth to their son Jaime Camil II.
Marriage Date Prediction of Uttam Kumar
https://jothishi.com/celebrity-marriage-prediction/Uttam-Kumar Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Diego Olivera
https://jothishi.com/celebrity-marriage-prediction/Diego-Olivera Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Dileep
https://jothishi.com/celebrity-marriage-prediction/Dileep Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Super star Rajinikanth
https://jothishi.com/celebrity-marriage-prediction/Rajinikanth Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Tony Blair
https://jothishi.com/celebrity-marriage-prediction/Tony-Blair Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Sonali Bendre
https://jothishi.com/celebrity-marriage-prediction/Sonali-Bendre Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Pattie Boyd
https://jothishi.com/celebrity-marriage-prediction/Pattie-Boyd Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…
Marriage Date Prediction of Liam Hemsworth
https://jothishi.com/celebrity-marriage-prediction/Liam-Hemsworth Jothishi marriage predictor predicts the marriage of a person with around 80 percent accuracy provided the input is accurate ( date, time and place…


text:
About Locations Employment
Home Medical Equipment
Orthopedic Products
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Orthopedic Products In Salinas
Allcare RX » Services » Orthopedic Products In Salinas
Orthopedic products are designed to provide comfort and help you retain the fullest possible range of motion following injury or surgery. Orthopedic products can provide comfort throughout the day or, if you're a weekend warrior, orthopedic products can help prevent further injuries. At AllCare Pharmacy and Medical Supply carries a comprehensive selection of orthopedic products to assist in your recovery from many types of injuries. If your doctor has recommended that you purchase orthopedic products, below is a sampling of what you'll find at AllCare Pharmacy and Medical Supply.
Back and Neck Support
Back and neck support products aren't solely for injury sufferers — they can also help to improve your posture and reduce the risk of injury from everyday activities. Consider a back support strap, for example, if you are required to lift heavy objects at work. If you work in an office, adding an orthopedic support cushion to your chair can significantly reduce the back and neck pain experienced while using a computer. We also carry cervical support and traction devices to aid in recovery from neck injuries.
Leg and Foot Support
Recovery from a leg injury can be lengthy and painful, especially if you have to remain active and continue working during the recovery process. Orthopedic supports help to reduce the strain on your knees, ankles and feet, allowing you to continue moving throughout the recovery from many types of injuries. For example, a knee brace is ideal for many types of leg injuries. By lifting the kneecap and keeping it stationary, the brace eases discomfort on the patellar tendon and allows you to continue walking.
Elbow, Wrist and Hand Support
Like our leg supports, orthopedic products for the arms, wrists and hands work by protecting and limiting the movement of sensitive or injured areas while still giving you the freedom of movement to go about your daily tasks. If your work involves constant computer use, consider an orthopedic wrist support to prevent or relieve the symptoms of carpal tunnel syndrome. AllCare Pharmacy and Medical Supply also carries therapeutic gloves and elbow braces.
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AllCare Blog
How to Choose a Mobility Scooter That's Right for You
Mobility scooters offer the perfect solution for people with limited mobility to navigate through parks, malls and stores. Many people prefer mobility scooters to wheel chairs because they offer a faster mode of transportation, and […]
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One of the most popular of all heucheras.
While its tiny flowers are lovely and charming, this plant is named for its beautiful foliage. It's large, shiny, star-shaped leaves are variegated, ranging in colour from deep olive green to purple-bronze. The underside of the foliage is a stunning amethyst purple and quite visible to garden visitors. The white or cream blooms grow in abundance atop 20" tall, leafed scapes. This butterfly attractor looks nice in borders and containers. Named the 1991 Perennial Plant Association Plant of the Year.
Foliage Type Shiny, star-shaped leaves in deep olive green to purplish-bronze.
Planting Instructions Top of plant just below soil level and 12-16" apart.


The Strata 11-Piece Women's Set is designed for maximum performance right out of the box. Set includes: Driver, 5 Wood, 5 Hybrid, 7 & 9 Iron, SW, Putter, Stand Bag, and 3 Headcovers.
The Strata 11-Piece Women's Set is designed to give you maximum performance right out of the box for more confidence from tee-to-green. The lightweight 460cc forged driver, fairway wood, hybrid, irons and putter make up a set that's specifically designed to give women a great combination of distance, forgiveness and control with clubs that are very easy to hit.
– These stainless steel irons offer a great combination of forgiveness and control.
– A mallet putter with alignment to help give you incredible accuracy.
– A lightweight and durable stand bag that comes with five convenient pockets, an additional cooler pocket, a rain hood and a backpack strap system.


Phil, a Certified Public Accountant, has been in the accounting and finance industry for over 25 years. Phil began his career with Deloitte, a large international Public Accounting firm managing a varied client list in many different industries including; banking, real estate, higher education and manufacturing. Phil then joined a large privately-held national real estate development company, as the Director of Internal Audit, where for 5 years, he developed and led the internal audit department and served as internal consultant to the firm's owners. Subsequently, Phil was asked to join an affiliate as Vice President of Finance, where Phil helped direct the significant growth of the organization as the company grew from 13 to 35 operating hotels during his tenure and was integral in financing and re-financing a large number of hotels in the portfolio. A co-founder of Brookwood Hospitality, Phil has developed an intimate knowledge and passion of the hotel industry which also includes the re-development of older existing properties into successful hotels.


text:
What we are reading in October, 2017
By: Camille Truong
What is the diversity and function of root endophytes? In Arabidopsis alpine roots, in which the association with AM fungi was lost during the evolution of the Brassicaceae family, a set of 15 fungal taxa was consistently detected under natural and controlled conditions depleted in phosphorus. Among these root entophytes, an unknown Helotiales taxon was particularly abundant and improved P uptake and plant growth. Genome analysis also revealed that this taxon has an expanded repertoire of enzymes involved in carbohydrate degradation (CAZymes), similarly to other plant-beneficial fungi.
Almario J, Jeena G, Wunder J, Langen G, Zuccaro A, Coupland G, et al. (2017). Root-associated fungal microbiota of non-mycorrhizal Arabis alpina and its contribution to plant phosphorus nutrition. Proc Natl Acad Sci U S A: 10.1073/pnas.1710455114.
Non-ectomycorrhizal fungi are often detected in ECM community surveys and are usually considered as putative root endophytes or pathogens. Taxon-specific probes detected a species of Hyaloscyphaceae (Helotiales) that was highly abundant on the surface and within the fungal mantle, even penetrating inside the hyphal cells. On the other hand it was not detected within the Hartig net or inside plant tissues. The ecological significance of this species on the ECM community still need to be characterized and myco-parasitism is possible, but it seems that some of these non-ECM fungi may interact with ECM fungi and not only with host plants.
Nakamura N, Tanaka E, Tanaka C, Takeuchi-Kaneko Y (2017) Localization of helotialean fungi on ectomycorrhizae of Castanopsis cuspidata visualized by in situ hybridization. Mycorrhiza: https://doi.org/10.1007/s00572-017-0803-y.
How climate change affects the production of fungal fruiting bodies? Based on the national database of fruiting records over 45 yr (1970–2014), the abundance of ECM fructifications significantly increased in the north of the UK relative to saprotrophic species, in correlation with increases in autumnal mean daily temperature and precipitations. Species with shorter fruiting seasons declined markedly in oceanic regions, whereas species with longer seasons became more abundant overall. Climate particularly affects ECM fungi, because it also affects photosynthesis and therefore carbon allocation from their hosts.
Gange AC, Heegaard E, Boddy L, Andrew C, Kirk P, Halvorsen R, et al. (2017) Trait-dependent distributional shifts in fruiting of common British fungi. Ecography (40) 1–10.
How mycorrhizal fungi affect plant invasions is a hot topic in conservation biology, for example in the case of Pinaceae invading native Nothofagaceae forests of Chile and Argentina. For example, fungal co-invaders that enrich soil nutrients can reduce the dependency of native plants to their mycorrhizas. Some invaders also produce toxins that can negatively affect native mycorrhizal communities. In general, it seems that invaders strongly alter fungal community composition, while only occasionally affecting fungal species richness. How these processes evolve through time remain particularly poorly known, for example after invasive plants have been removed.
Grove S, Haubensak KA, Gehring C, Parker IM (2017) Mycorrhizae, invasions, and the temporal dynamics of mutualism disruption. J Ecol 105: 1496–1508.


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New MuseLab Exhibit Celebrates Hemingway, Frost, Rand
The MuseLab in Kent State University's School of Library and Information Science (SLIS) is set to open its newest exhibit on April 15. A reception to celebrate the opening of "(Non)Fiction: Literary Legends Unbound" will be held on Wednesday, April 15, from 6 to 8 p.m. in rooms 330 and 321 of the University Library, where the School and MuseLab are located. The reception is free and open to all, but registration is requested: RSVP.
The lives and personalities of three 20th-century literary legends — Robert Frost, Ernest Hemingway and Ayn Rand — are the focus of this exhibit. SLIS master's degree students in a museum studies seminar developed the exhibit over the course of the spring semester. They drew inspiration from the novel Old School by Tobias Wolff, the featured book in the 2015 National Endowment for the Arts' "Big Read" project.
The Massillon Museum in Massillon, Ohio, which leads the Big Read effort in northeast Ohio, received an NEA grant to support innovative reading programs in association with the Wolff novel. The museum included SLIS in the grant to help with the MuseLab exhibit.
In this exhibit, the students separate stereotypes of the authors from fact, in order to show the human side of these literary legends and how their legacies continue to shape our culture, philosophy and lives.
SLIS Assistant Professor Kiersten F. Latham, Ph.D., who developed the School's museum studies specialization and serves as director of the MuseLab, said sections of the exhibit will be designed to represent each author's office.
"It's going to be very interactive. Visitors will be able to touch and interact with everything," said Latham.
Andrea Wittmer, a student in the course, said she and her classmates tried to imagine what it would be like to come in to the space with little knowledge of or interest in these writers, yet leave inspired in some way, without forcing any views or opinions. With that in mind, the exhibit aims to welcome and interest people of all backgrounds and knowledge levels related to these authors.
Another student, Michelle Italia-Walker, said she thinks the mood of the exhibit will be evocative. "I am interested to see if people will allow their preconceived ideas to be washed away, or if they will be cemented through information and objects presented in the exhibit," she said.
Student Michelle Persons said she is excited to see the visitors interact with the exhibit. "As one of the exhibit designers, it's gratifying to see visitors 'get' the message you are trying to communicate through the exhibit, but also what things resonate most with them that maybe we didn't expect," said Persons.
The grand opening in April will feature live actors from the university's theatre department role-playing the authors. Refreshments and desserts will correspond to each author.
Student Bekah Swope said this exhibit is being done in 15 weeks, when it typically takes about three years to plan, develop and implement an exhibit like this.
Corina Iannaggi, MuseLab student manager who also is in the seminar course, said her favorite part of this project is being involved in the exhibition process from start to finish. "This is the only face-to-face opportunity for students in the museum studies program, and getting the chance to put together an actual exhibit has been a tremendous learning experience," she said.
The seminar students all have different roles in the production of the exhibit and spend numerous hours outside of class working toward their deadline. Their goal is to engage visitors physically, emotionally, intellectually and socially.
"If a visitor leaves with more questions than answers and a drive to explore further, I would personally consider that a success," said Wittmer.
The MuseLab is a component of the museum studies specialization in the Master of Library and Information Science program in SLIS. It is open Mondays, Wednesdays and Fridays from 10 a.m. to 4 p.m., with special hours during the summer months. The "Literary Legends" exhibit will remain open until Jan. 31, 2016.
The School of Library and Information Science at Kent State University offers a Master of Library and Information Science (M.L.I.S.) and Master of Science in Information Architecture and Knowledge Management (IAKM). In addition, the school participates in an interdisciplinary Ph.D. in the College of Communication and Information. With more than 700 students enrolled, SLIS has the largest graduate program at Kent State. Its M.L.I.S. is the only American Library Association-accredited master's program in Ohio and is recognized by U.S. News and World Report among the nation's top 20 LIS programs, with a youth services program that is ranked 10th.
The Big Read is a program of the National Endowment for the Arts in partnership with Arts Midwest.
POSTED: Tuesday, March 24, 2015 - 2:09pm
UPDATED: Tuesday, July 11, 2017 - 11:48am
Julia Sprowls
Student Organization Selected as Runner-Up for Prestigious National Award
Each year the American Library Association selects student chapters who exemplify values of excellence, service and compliance, and recently Kent...
iSchool Introduces Accelerated School Library Media Program
The Kent State University School of Information (iSchool) is pleased to announce a summer accelerated program for the School Library Media...
Kent State University & Partners Receive $13.3 Million USAID Grant For Education Initiative in Nigeria
An interdisciplinary team of Kent State University faculty will participate in a $13.3 million USAID grant to improve educational outcomes for...
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Instagram Will Finally Allow Breastfeeding Photos
The photo-sharing app has issued new rules about nudity and harassment.
Damon Beres
Senior Tech Editor, The Huffington Post
04/17/2015 12:43pm EDT | Updated April 17, 2015
A journalist makes a video of the Instagram logo using the new video feature at Facebook headquarters in Menlo Park, Calif., Thursday, June 20, 2013. (AP Photo/Marcio Jose Sanchez)
It's now crystal clear: You are not allowed to harass people on Instagram, and you're generally not allowed to show your nipples if you're a woman.
An update to the Facebook-owned social network's community guidelines Thursday contains stronger, more specific language than before, outlining in detail what users are allowed to post on the popular photo-sharing platform.
In a change from its previous rules, Instagram now states when nudity is allowed and explicitly banishes harassment. It also specifically notes that breastfeeding photos are allowed, bringing it in line with Facebook's policy on the matter.
Previously, Instagram's guidelines -- still viewable on an archived page -- were vague, with language like "keep your clothes on" and "be respectful." They didn't specifically reference breastfeeding, either. Now, Instagram's rules are considerably more precise.
Take this chunk on nudity, for example:
We know that there are times when people might want to share nude images that are artistic or creative in nature, but for a variety of reasons, we don't allow nudity on Instagram. This includes photos, videos, and some digitally-created content that show sexual intercourse, genitals, and close-ups of fully-nude buttocks. It also includes some photos of female nipples, but photos of post-mastectomy scarring and women actively breastfeeding are allowed. Nudity in photos of paintings and sculptures is OK, too.
Or the new language about harassment:
We want to foster a positive, diverse community. We remove content that contains credible threats or hate speech, content that targets private individuals to degrade or shame them, personal information meant to blackmail or harass someone, and repeated unwanted messages.
Instagram did not respond to a request for comment from The Huffington Post about its updated guidelines, particularly in regard to why "female nipples" are allowed in certain contexts but not others. The app has been criticized in the past for allowing men to go topless but not women.
Speaking to The Wall Street Journal, Nicky Jackson Colaco, Instagram's director of public policy, offered some clarification.
"How do we establish a baseline around nudity when you have hundreds of millions of users?" Colaco said. "We need to create a standard that most people can live by."
Instagram has 300 million users, according to Statista. A Pew study recently found that it's one of the most popular social networks for teens.
technologyInstagramFacebookinstagram nudity
'Bat Out Of Hell' Rocker And Actor Meat Loaf Dies
Jimmy Kimmel Taunts Fox News Hosts With 'Embarrassing' Trump Reminder
'WOW!': Stephen Colbert Stunned As McConnell Accidentally Says What He Means
More In Tech
Elizabeth Holmes Accuses Ex-lover, Business Partner Of Abuse
Jack Dorsey Steps Down As Twitter CEO
Macy's Is Auctioning Off Thanksgiving Day NFTs, And Bids Are Topping $300,000
Under Pressure, Apple Will Allow Self-Repairs To iPhones, Macs


Completed for the fall 2018 academic semester, Ferris State University's Swan Technical Arts Building underwent a significant expansion as part of a State of Michigan capital outlay project approved by former Gov. Rick Snyder. The $30 million project resulted in an increase of laboratory and classroom facilities for multiple Ferris State educational offerings, including the Welding Technology AAS and Welding Engineering Technology BS degree programs.
The construction project resulted in an increase of the Welding Technology and Welding Engineering Technology laboratory spaces to approximately 22,600 square feet. Laboratory facilities comprised of seven individual, specialized spaces with the latest welding industry equipment and technology, are dedicated to practical hands-on education at all levels of the Ferris welding programs. Classroom resources for lecture discussion now include both a 60-seat room and a tiered 82-seat facility dedicated for theoretical welding education. Both classrooms have desktop electrical power for laptop computer use.
The core WELD prefix welding courses are taught by professional faculty with over 115 combined years of teaching experience at Ferris State. Required technical-related courses, for both the Welding Technology and Welding Engineering Technology degrees, in material science, electronics and manufacturing are taught by faculty specialists in those departments. Web link to welding faculty information.


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Q: Can't connect to smb share after upgrade to Ubuntu Gnome 16.04 After upgrade to Ubuntu Gnome 16.04, I can no longer access my networked media tank's smb share. It's an old (2009) Popcorn Hour A-110.
When I connect to server smb://192.168.0.5 in nautilus, I get in an endless loop prompted for login resulting in an authentication failed message. I am certain that username/password are correct, because it works in Windows and I've reset it a dozen times. Note that prior to the upgrade (Ubuntu Gnome 15.10), I would not even be prompted for a username/password, it would just open in the file browser.
So I installed smbclient to see what is going on, and that gives me a hint:
smbclient //192.168.0.5/share -U nmt
Server does not support EXTENDED_SECURITY  but 'client use spnego = yes and 'client ntlmv2 auth = yes'
session setup failed: NT_STATUS_ACCESS_DENIED

That seems to suggest that the nmt server might be too old to support some security scheme that is required by the client.
How can I fix this?

A: I tried all these suggestions and nothing worked.  What worked for me was the following:
sudo smbpasswd -a [username]

I do not understand why samba forgot the user, and I find it hard to believe that samba needs to be explicitly told about every single user who needs to access the linux machine from windows, but that's what did the trick, (luckily, I have only one user,) so I am letting everyone know.

A: This question is a duplicate of this one.
The answer: you must explicitly set
client use spnego = no

in /etc/samba/smb.conf.
If you can't find this file, execute command:
sudo apt-get install smbclient

A: I'm running ChaletOS 16.0.4.2.  I did everything I could find online to get access to an external drive shared through an Asuswrt-Merlin router.  NOTHING!  I MEAN NOTHING WORKED!!  Once I installed all of the necessary samba stuff, I still could not access my share. I kept getting the prompt to enter my password. I'd enter it correctly but got no joy.  Once I made Peter de Maeyer's change in "/etc/samba/smb.conf" above, the drive opened up like blue skies after a heavy rain. Thanks Peter!!!
Since I never found all of these steps in one place, I hope this might help someone else. What finally did the trick was a combination of Peter's response and this link.
To summarize, I first created the "~/.smbcredentials" file and put the relevant info inside:
(For the unfamiliar, that little wiggly character in front of the "/" means to place the file in your "/home" folder)
username=msusername
domain=ENTER_YOUR_WINDOWS_DOMAIN
password=msusername
(The site doesn't say to add the domain, but I did so just for good measure)
I saved and closed that file and changed permissions for it by entering this in the terminal:
chmod 600 ~/.smbcredentials
Next, I edited the "/etc/fstab" file by entering this in the terminal:
sudo gedit /etc/fstab
In the "fstab" file I entered this at the bottom (make sure you use your own information here):
smb://COMPUTER_NAME/SHARED_FOLDER /media/NEW_FOLDER_NAME cifs credentials=/home/YOUR_UBUNTU_USERNAME/.smbcredentials,iocharset=utf8,sec=ntlm 0 0
I saved and closed that file and then opened the "smb.conf" file by entering this in a terminal:
sudo gedit /etc/samba/smb.conf
On Line #29 (Under "Global") I changed the "workgroup" name to match the "domain" name above.  Immediately under that I created a new Line (Line #30) and entered:
client use spnego = no
Just in case, after reading this site, I thought it would be a good idea to also make this change on Line #101:
obey pam restrictions = yes to obey pam restrictions = no
After more than twenty (yes, that's 20) hours and hours of searching and borking my system several times, I finally found joy with these settings.
Good luck and may the Linux Gods be with you!

A: Tried these answers but still can not connect to my Windows shares. I can connect from my Windows PCs to my Ubuntu share but not the other way around. Using 16.04.
Update/Edit:
Well, after performing many different things including re-installing Ubuntu and editing the SMB.CONF file I figured out how to fix my issue.
I wiped my laptop clean and installed Fedora 24 Workstation and noticed the same results--HMMMM I said.
And then I realized that most of the computers I was trying to connect to had usernames, of course, but blank passwords. I tried as SMB.CONF edit that someone had mentioned to allow the use of blank passwords but was not able to get that to work. And, as needed, I would always re-start my smbd and nmbd after changes.
Anyway, I did have one computer on the network that has a username and password. Guess what--from Fedora I was able to connect! Then I entered passwords on the other computers and presto, was able to finally connect to them from Fedora.
Now, since the goal is to use Ubuntu I again wiped my laptop clean but this time again did a fresh install of Ubuntu workstation. I made two very minor changes to the SMB.CONF file (made these also on the Fedora install) to set the workgroup name properly and the netbios name properly. Just left everything else default.
Next, shared a folder on the Ubuntu machine. Simply used the GUI to share the folder out (did not modify the SMB.CONF) and set it to allow guest access. Now, every PC on my network can connect to the share on my Ubuntu machine. If I want to place tighter controls on who can and can't access that share I can make changes in my SMB.CONF.
But now, to the best part--now I can connect from my Ubuntu laptop to any of other computers and shares on my network.
The solution to my problem, which may or may not help others, is that those devices I could not connect to had accounts without passwords. Once passwords were assigned all worked.
Hope this can help someone!
Thanks,
Rich Huelbig

redpajama_set_name: RedPajamaStackExchange


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Disabled Veteran Reportedly Claims Discrimination at Virginia Airport
By | Fox News
A disabled Army veteran was reportedly not allowed to be aboard a plane last week in Virginia because she uses a walker.
Kathleen Wood told MyFoxBoston.com she was not allowed to board a shuttle at Reagan National Airport in Virginia last week as she returned to Walpole, Mass., from a vacation in Florida. Wood and her husband had to change flights in Virginia and had just 30 minutes to do so.
Wood, who suffers from lupus, also has a permanent back injury from a fall she took 12 years ago while serving in the Army. She tried to change gates along with the rest of the passengers, but a U.S. Airways employee refused to let her do so because of her walker.
"We tried to explain the best we could that he could fold the walker and that I could walk short distances and go up stairs," she told the website. "Still she refused and shrugged her shoulders."
Wood was forced to walk several hundred yards and underwent another security screening. They made the connecting flight with seconds to spare.
"It really hit a nerve when you think of other people being denied access," Wood said. "It just blew my mind. Blew my mind."
U.S. Airways told the website that they will call Wood to apologize.
Click for more on this report from MyFoxBoston.com.


text:
Home Insights Resources Legal updates Company to go out of business following corporate manslaughter verdict
company to go out of business following corporate manslaughter verdict
In February this year, Cotswold Geotechnical Holdings Limited was the first company to be found guilty of corporate manslaughter.
The prosecution arose following a pit collapse in which a 27 year old geologist died.
Cotswold Geotechnical Holdings was fined £385,000 and yesterday lost its appeal against conviction and sentence.
Amongst the comments made by Lord Chief Justice Lord Judge was that it was "unavoidable and inevitable" that the company may go into liquidation in paying the fine.
The clear message to the lower courts is that financial penalties that would force a company out of business following conviction of corporate manslaughter may be appropriate and perhaps necessary.
Naturally, companies will want to ensure that they have robust and effective health and safety systems in place in order to avoid such grave consequences.
We can assist in advising companies in connection with written systems and plans and all other matters related to health and safety law.
Broker Insight London office
Developed for brokers, this exclusive series of events will provide you with operational and practical insights from across the legal spectrum.
Managing procurement risks and challenges Manchester office
Have you ever received a letter challenging a regulated procurement procedure? Has your authority ever had proceedings issued against it for breach of the Public Contracts Regulations 2015?
Clean air - where we are and what we may expect in the future
Air pollution and the importance of clean air are increasingly becoming recognised as issues that need to be addressed.
Construction and the environment: building green contracts to meet your carbon-neutral commitments
With increasing concerns surrounding a climate crisis and the need for greater environmental sustainability, several councils across the UK recently expressed a firm commitment to become carbon neutral.
Pay discussions? Shhhhhh...
According to research by the TUC announced this week, almost a fifth of workers don't talk about their pay at work because they are not allowed to.
FOIA and data protection: the difficult balancing exercise for public authorities when a request is made for third party personal data
The Freedom of Information Act 2000 ('FOIA') allows members of the public to request information from public bodies. As guidance issued by the Information Commissioner explains, the main principle behind FOIA is that people have a right to know about the activities of public authorities, unless there is a good reason for them not to.
The content on this page is provided for the purposes of general interest and information. It contains only brief summaries of aspects of the subject matter and does not provide comprehensive statements of the law. It does not constitute legal advice and does not provide a substitute for it.
Andrew Hopkin
Email Andrew


text:
Tutorial (en)
C. New Language
Living in Byteland was good enough to begin with, but the good king decided to please his subjects and to introduce a national language. He gathered the best of wise men, and sent an expedition to faraway countries, so that they would find out all about how a language should be designed.
After some time, the wise men returned from the trip even wiser. They locked up for six months in the dining room, after which they said to the king: "there are a lot of different languages, but almost all of them have letters that are divided into vowels and consonants; in a word, vowels and consonants must be combined correctly."
There are very many rules, all of them have exceptions, but our language will be deprived of such defects! We propose to introduce a set of formal rules of combining vowels and consonants, and include in the language all the words that satisfy them.
The rules of composing words are:
The letters are divided into vowels and consonants in some certain way;
All words have a length of exactly n;
There are m rules of the form (pos1, t1, pos2, t2). Each rule is: if the position pos1 has a letter of type t1, then the position pos2 has a letter of type t2.
You are given some string s of length n, it is not necessarily a correct word of the new language. Among all the words of the language that lexicographically not smaller than the string s, find the minimal one in lexicographic order.
The first line contains a single line consisting of letters 'V' (Vowel) and 'C' (Consonant), determining which letters are vowels and which letters are consonants. The length of this string l is the size of the alphabet of the new language (1 ≤ l ≤ 26). The first l letters of the English alphabet are used as the letters of the alphabet of the new language. If the i-th character of the string equals to 'V', then the corresponding letter is a vowel, otherwise it is a consonant.
The second line contains two integers n, m (1 ≤ n ≤ 200, 0 ≤ m ≤ 4n(n - 1)) — the number of letters in a single word and the number of rules, correspondingly.
Next m lines describe m rules of the language in the following format: pos1, t1, pos2, t2 (1 ≤ pos1, pos2 ≤ n, pos1 ≠ pos2, 'V', 'C' }).
The last line contains string s of length n, consisting of the first l small letters of the English alphabet.
It is guaranteed that no two rules are the same.
Print a smallest word of a language that is lexicographically not smaller than s. If such words does not exist (for example, if the language has no words at all), print "-1" (without the quotes).
1 V 2 C
1 C 2 V
3 V 4 V
acaa
In the first test word "aa" is not a word of the language, but word "ab" is.
In the second test out of all four possibilities only word "bb" is not a word of a language, but all other words are lexicographically less, so there is no answer.
In the third test, due to the last rule, "abac" doesn't belong to the language ("a" is a vowel, "c" is a consonant). The only word with prefix "ab" that meets the given rules is "abaa". But it is less than "abac", so the answer will be "acaa"


the girl scouts are a pretty nifty bunch. last weekend our little daisy participated in her second "world thinking day" event and if you can deal with mass amounts of grade school girls crammed into a relatively small church basement, then the event itself it actually pretty neat and quite educational.
each troop is asked to choose a country to represent and create a booth of information with facts, pictures and food samples to share. our troop lucked out this year and france was available. they all know a thing or two about the french. the girls are then given a "passport" for collecting stamps from each country they visit where they are encouraged to jot down an interesting fact about that country or sample a native dish. there is even a performance portion where each troop can demonstrate a song, dance or other cultural activity. our group sang a song in french...i think it was about cabbage, but i can't be certain.
most of the facts alex gathered only focused on the types of animals that could be found in each country, but hey, she learned something new nonetheless. the girls will each get a badge for their participation but rewards can also be found in the expansion of their global awareness and the conversations that happen on the way home.
lessons & questions: "mom, every country has people just like us that like animals and like to eat food and have moms and probably cats like meatball. isn't that cool? you don't even know how many people are in the world and i bet they even like sponge bob. do think everyone in the world likes sponge bob?"
hmmm. is "no" the same in every language?
This looks A-W-E-S-O-M-E. What a fun way to learn!


You are currently browsing the MSBLOG weblog archives for February, 2015.
The Windows Firewall service must be started before Setup can continue.
[2/16/2015 1:11:59 AM]  [ERROR] The operation could not be performed because object 'exchserver01' could not be found on domain controller 'adserver01.domain.com'.
[2/16/2015 1:11:59 AM]  Ending processing.
[2/16/2015 1:11:59 AM]  [ERROR] The Windows Firewall service must be started before Setup can continue.
You will need to remove the RUS manually. Most likely Exchange 2003 wasn't decomm-ed properly (if you are no longer using Exchange 2003).
Just proceed with starting the Windows Firewall service and rerun the update of Service Pack.
Remove the RUS (If you no longer have any Exchange 2003 server in your environment).
Open ADSI Edit, expand Configuration, expand CN=Configuration,CN=<domain>, expandCN=Services, expand CN=Microsoft Exchange, expand CN=<Exchange organization name>, expand CN=Address Lists Container, and then select CN=Recipient Update Services.
In the result pane, right-click Recipient Update Service (Enterprise Configuration), clickDelete, and then click Yes to confirm the deletion.
By default, with Astro IPTV, you will be getting a Technicolor device from Maxis.
I will show you how to replace the Technicolor and replace it with an OpenWRT router (I'm using "OpenWrt Barrier Breaker 14.07").
Ensure that your router is already flashed to the OpenWRT version. See this post if you have not done so: Installing OpenWRT.
Change Password. On opening OpenWRT router, you will be asked to login. The default password for OpenWRT is empty. Login and change your password.
Connect to PPPoE. First thing you want to do is to connect to PPPoE, so your WAN will be able to connect to the TM fiber device. Click Network -> Interface -> next to WAN, click Edit. Change the protocol to PPPoE-WAN. Specify your username "5 digits@home.maxis.com.my or 5 digits@public.maxis.com.my". Password by default should be the your 5 digits with a value 1 behind. Eg, if your username is 12345@home.maxis.com.my, your password will be 123451.
Switch. Click Network -> Switch.
Under VLAN ID, modify the value 2 to value 621.
Port 1 by default should be your WAN port. Ensure that CPU and Port 1 both are set to "Tagged". Leave the rest of Ports as Off.
Click ADD. Change the VLAN ID for the new VLAN Interface to 823.
With your Astro IPTV decoder plugged into your router's Port 4, it should be then Port 5 in the OpenWRT Switch interface. Under VLAN ID 823, ensure that CPU, Port 1 both are "Tagged". For Port 5, set it as "Untagged". Rest set as Off. Under VLAN ID 1, turn off the tagging for Port 5.
Go to Network -> Interface.
Under Interface, DHCP Server, click "Setup DHCP Server".
Under DHCP Server, click "Advanced Settings" tab.
Ensure that "Force DHCP on this network even if another server is detected" is selected.
Network -> Firewall, under IPTV, select the Edit icon.
Ensure that "Allow Forward to destination zones" is selected for WAN.
click Available Packages. Click Install next to IGMPPROXY.
Under Login As, type "root". Press Enter.
Type in your OpenWRT password.


text:
Военно-Антоновка () — село, пригородный поселок в Сокулукском районе Чуйской области Киргизии. Административный центр и единственный населённый пункт Военно-Антоновского аильного округа. Код СОАТЕ — 41708 222 813 01 0.
✉ Почтовый индекс село
Военно-Антоновка 724806

История
Образовано в 1911 году. участки для поселения зазбивал военный топограф Антонов. Поэтому называется Военно-Антоновка.

http://vts.mybb.ru/viewtopic.php?id=826

В 1944 году в посёлке были размещены депортированные чеченцы и ингуши. В посёлке проживает Гита Резаханова.

География
Село расположено к западу от города Бишкек. Ближайшие населённые пункты: на востоке — Ново-Павловка, на юго-западе — Маловодное, на юго-востоке — Джал, на северо-западе — Озёрное, на северо-востоке — Ак-Жол, Степное, Пригородное.

Население
По данным переписи 2009 года, в селе проживало 11 356 человек.

Образование
 Военно-Антоновская УВКГ.

Примечания

Населённые пункты Сокулукского района