German carmaker Mini revealed the Paceman Concept last year at the Detroit Motor Show. Now, time has come for the specialists to give us the real thing, the production version of the Paceman. The model will go on sale everywhere in the world in the spring of 2013, but until then, we will have the opportunity to see it for the first time at the Paris Motor Show this month. The new Paceman was built as a coupe interpretation of the Countryman crossover.
It measures 4.109 mm in length and this means it is a bit shorter than the Countryman. The roof was lowered 31 mm and the model is 10 mm closer to the ground in comparison with the Countryman, but they have the same width. In terms of styling, as you can see in the pictures, the A-pillar is identical to the 5-door model, but the new one has longer front doors, a more rakish roofline, more powerful rear fenders and the rear is also new. It is easier to spot thanks to the redesigned set of tail lamps offered to provide a sportier appearance.
Inside, the seats have a definite layout and the boot space reaches 330 liters. If the seats are folded it can increase to 1.080 liters. For a sportier ride, the German specialists have lowered the chassis and re-tuned the suspension. In the case of the Countryman, they have not. Two petrol units and two diesel ones will be available. The Cooper Paceman is powered by a 1.6-liter engine capable of developing 119 horsepower. This output enables it to accelerate from 0 to 62 mph in 10.4 seconds. The fuel economy reaches 6.0-liters / 100 km and the CO2 emission level was established at 140 g/km. The Cooper S Paceman has the same petrol unit, but capable of generating 181 horsepower, which enables the car to accelerate from 0 to 62 mph in 7.5 seconds. It uses 6.1-liters / 100 km and it emits 143 g/km CO2.
In terms of diesel, the Cooper D Paceman has a 1.6-liter turbocharged engine capable of developing 110 horsepower. This output enables the car to accelerate from 0 to 62 mph in 10.8 seconds. The returned fuel economy is 4.4-liter / 100 km and the CO2 emission level reaches 115 g/km. The last variant is the Mini Cooper Paceman powered by a 2.0-liter engine capable of producing 141 horsepower. This output enables the car to accelerate from 0 to 62 mph in 9.2 seconds. The returned fuel economy is 4.6-liters / 100 km and the CO2 emission level is 122 g/km. To send all the power to the wheels, the engineers have offered a standard 6-speed manual gearbox and an optional 6-speed automatic transmission. The carmaker said the new Mini Paceman will go on sale in March 15th and the price will start at GBP 18.970. Unfortunately, there is no information on the US market yet.


text:
\section{Introduction}

Contextual word embeddings \cite{bert, elmo, gpt} have been successfully applied to various NLP tasks, including named entity recognition, document classification, and textual entailment. The multilingual version of BERT (which is trained on Wikipedia articles from 100 languages and equipped with a 110,000 shared wordpiece vocabulary) has also demonstrated the ability to perform `zero-shot' or `zero-resource' cross-lingual classification on the XNLI dataset \cite{xnli}. Specifically, when multilingual BERT is finetuned for XNLI with English data alone, the model also gains the ability to handle the same task in other languages. We believe that this zero-resource transfer learning can be extended to other multilingual datasets.

\begin{figure*}[ht]
	\centering
	\begin{subfigure}[t]{0.6\textwidth}
		\centering
		\includegraphics[width=11cm]{classification_overview.png}
		\caption{Text classification}
	\end{subfigure}
	\begin{subfigure}[t]{0.6\textwidth}
		\centering
		\includegraphics[width=11cm]{ner_overview.png}
		\caption{NER}
	\end{subfigure}
	\caption{Overview of the adversarial training process for classification and NER. All input text is in the form of a sequence of word pieces. $L_D, L_G, L_T$ refer to the discriminator, generator and task-specific losses. Parameters of each component is in round brackets.}
	\label{overview}
\end{figure*}

In this work, we explore BERT's\footnote{`BERT' hereafter refers to multilingual BERT} zero-resource performance on the multilingual MLDoc classification and CoNLL 2002/2003 NER tasks. We find that the baseline zero-resource performance of BERT exceeds the results reported in other work, even though cross-lingual resources (e.g. parallel text, dictionaries, etc.) are not used during BERT pretraining or finetuning. We apply adversarial learning to further improve upon this baseline, achieving state-of-the-art zero-resource results.

There are many recent approaches to zero-resource cross-lingual classification and NER, including adversarial learning \cite{xling_adv,adv_ner,adv_ner_2,adv_qa}, using a model pretrained on parallel text \cite{mt_encoder, interlingua, xlm} and self-training \cite{self_train_classifier}. Due to the newness of the subject matter, the definition of `zero-resource' varies somewhat from author to author. For the experiments that follow, `zero-resource' means that, during model training, we do not use labels from non-English data, nor do we use human or machine-generated parallel text. Only labeled English text and unlabeled non-English text are used during training, and hyperparameters are selected using English evaluation sets.

Our contributions are the following:

\begin{itemize}
	\item We demonstrate that the addition of a language-adversarial task during finetuning for multilingual BERT can significantly improve the zero-resource cross-lingual transfer performance.
	\item For both MLDoc classification and CoNLL NER, we find that, even without adversarial training, the baseline multilingual BERT performance can exceed previously published results on zero-resource performance.
	\item We show that adversarial techniques encourage BERT to align the representations of English documents and their translations. We speculate that this alignment causes the observed improvement in zero-resource performance.
\end{itemize}

\section{Related Work}

\subsection{Adversarial Learning}

Language-adversarial training \cite{adv_word_trans} was proposed for generating bilingual dictionaries without parallel data. This idea was extended to zero-resource cross-lingual tasks in NER \cite{adv_ner, adv_ner_2} and text classification \cite{xling_adv}, where we would expect that language-adversarial techniques induce features that are language-independent.

\subsection{Self-training Techniques}

Self-training, where an initial model is used to generate labels on an unlabeled corpus for the purpose of domain or cross-lingual adaptation, was studied in the context of text classification \cite{self_train_classifier} and parsing \cite{self_train_parsing, self_train_parsing_xling}. A similar idea based on expectation-maximization, where the unobserved label is treated as a latent variable, has also been applied to cross-lingual text classification in \citet{em_xling}.

\subsection{Translation as Pretraining}

\citet{mt_encoder} and \citet{interlingua} use the encoders from machine translation models as a starting point for task-specific finetuning, which permits various degrees of multilingual transfer. \citet{xlm} add an additional masked translation task to the BERT pretraining process, and the authors observed an improvement in the cross-lingual setting over using the monolingual masked text task alone.

\section{Experiments}

\subsection{Model Training}

We present an overview of the adversarial training process in Figure \ref{overview}. We used the pretrained cased multilingual BERT model\footnote{https://github.com/google-research/bert/blob/master/multilingual.md} as the initialization for all of our experiments. Note that the BERT model has 768 units.

We always use the labeled English data of each corpus. We use the non-English text portion (without the labels) for the adversarial training.

We formulate the adversarial task as a binary classification problem (i.e. English versus non-English.) We add a \textit{language discriminator} module which uses the BERT embeddings to classify whether the input sentence was written in English or the non-English language. We also add a \textit{generator} loss which encourages BERT to produce embeddings that are difficult for the discriminator to classify correctly. In this way, the BERT model learns to generate embeddings that do not contain language-specific information.

The pseudocode for our procedure can be found in Algorithm \ref{pseudocode}. In the description that follows, we use a batch size of 1 for clarity.

For language-adversarial training for the classification task, we have 3 loss functions: the task-specific loss $L_T$, the generator loss $L_G$, and the discriminator loss $L_D$:
\[ L_T(y^T;x) = \sum_{i=1}^K -y^T_{i} \textrm{log}p(Y=i|x) \]
\[p(Y | x) = \textrm{Softmax}(W_T \bar{h}_\theta(x) + b_T)\]
\begin{multline*}
L_G(y^A;x) = -(1-y^A) \textrm{log}p(E=1 | x) \\
- y^A \textrm{log}p(E=0 | x)
\end{multline*}
\begin{multline*}
L_D(y^A;x) = -(1-y^A) \textrm{log}p(E=0 | x) \\
					 - y^A \textrm{log}p(E=1 | x)
\end{multline*}
\[p(E = 1 | x) = \textrm{Sigmoid}(w_D\cdot \bar{h}_\theta(x) + b_D)\]

where $K$ is the number of classes for the task, $p(Y | x)$ (dim: $K \times 1$)  is the task-specific prediction, $p(E =1 | x)$ (dim: scalar) is the probability that the input is in English, $\bar{h}_\theta(x)$ (dim: $768 \times 1$) is the mean-pooled BERT output embedding for the input word-pieces $x$, $\theta$ is the BERT parameters, $W_T, b_T, w_D, b_D$ (dim: $K \times 768$, $K \times 1$, $768 \times 1$, scalar)  are the output projections for the task-specific loss and discriminator respectively, $y^T$ (dim: $K \times 1$) is the one-hot vector representation for the task label and $y^A$ (dim: scalar) is the binary label for the adversarial task (i.e. 1 or 0 for English or non-English).

In the case of NER, the task-specific loss has an additional summation over the length of the sequence:
\[ L_T(y^T;x) = \sum_{i=1}^K \sum_{t=1}^L -y^T_{it} \textrm{log}p(Y_t=i|x) \]
\[p(Y_t | x) = \textrm{Softmax}(W_T {h}_\theta(x)_t + b_T)\]

where $p(Y_t | x)$ (dim: $K\times 1$) is the prediction for the $t^{th}$ word, $L$ is the number of words in the sentence, $y^T$ (dim: $K\times L$) is the matrix of one-hot entity labels, and ${h}_\theta(x)_t$ (dim: $768 \times 1$) refers to the BERT embedding of the $t^{th}$ word.

The generator and discriminator losses remain the same for NER, and we continue to use the mean-pooled BERT embedding during adversarial training.

We then take the gradients with respect to the 3 losses and the relevant parameter subsets. The parameter subsets are $\theta_D = \{w_D, b_D\}$, $\theta_T = \{\theta, W_T, b_T\}$, and $\theta_G = \{\theta\}$. We apply the gradient updates sequentially at a 1:1:1 ratio.

During BERT finetuning, the learning rates for the task loss, generator loss and discriminator loss were kept constant; we do not apply a learning rate decay.

All hyperparameters were tuned on the English dev sets only, and we use the Adam optimizer in all experiments. We report results based on the average of 4 training runs.

\begin{algorithm*}[ht]
	\caption{Pseudocode for adversarial training on the multilingual text classification task. The batch size is set at 1 for clarity. The parameter subsets are $\theta_D = \{w_D, b_D\}$, $\theta_T = \{\theta, W_T, b_T\}$, and $\theta_G = \{\theta\}$.}\label{pseudocode}
	\hspace*{\algorithmicindent} \textbf{Input:} pre-trained BERT model $h_\theta$, data iterators for English and the non-English language $L$, learning rates $\eta_D,\eta_G,\eta_T$ for each loss function, initializations for  discriminator output projection $w_D, b_D$, task-specific output projection $W_T, b_T$, and BERT parameters $\theta$
	\begin{algorithmic}[1]
		\While {not converged}
		\State $x_{En}, y^T \leftarrow \textrm{DataIterator}(En)$ \Comment{get English text and task-specific labels}
		\State $\bar{h}_{En} \leftarrow \textrm{MeanPool}(h_\theta(x_{En}))$
		\State $p^T \leftarrow \textrm{Softmax}(W_T \bar{h}_{En} + b_T)$ \Comment {compute the prediction for the task}
		\State $L_T \leftarrow -y^T \cdot \textrm{log} p^T $ \Comment{compute task-specific loss}
		\State $\theta, W_T, b_T \mathrel{{+}{=}} -\eta_T \nabla_{\theta_T} L_T$ \Comment{update model based on task-specific loss}
		\State $x_{L}, x_{En} \leftarrow \textrm{DataIterator}(L), \textrm{DataIterator}(En)$ \Comment{get non-English and English text}
		\State $\bar{h}_{L}, \bar{h}_{En}  \leftarrow \textrm{MeanPool}(h_\theta(x_{L})), \textrm{MeanPool}(h_\theta(x_{En}))$
		\State $p^D_L \leftarrow \textrm{Sigmoid}(w_D\cdot \bar{h}_L + b_D)$ \Comment {discriminator prediction on non-English text}
		\State $p^D_{En} \leftarrow \textrm{Sigmoid}(w_D\cdot \bar{h}_{En} + b_D)$ \Comment {discriminator prediction on English text}
		\State $L_D \leftarrow -\textrm{log} p^D_{En} -\textrm{log} (1-p^D_{L}) $ \Comment{compute discriminator loss}
		\State $w_D, b_D \mathrel{{+}{=}} -\eta_D \nabla_{\theta_D} L_D$ \Comment{update model based on discriminator loss}
		\State $x_{L}, x_{En} \leftarrow \textrm{DataIterator}(L), \textrm{DataIterator}(En)$
		\State $\bar{h}_{L}, \bar{h}_{En}  \leftarrow \textrm{MeanPool}(h_\theta(x_{L})), \textrm{MeanPool}(h_\theta(x_{En}))$
		\State $p^D_L \leftarrow \textrm{Sigmoid}(w_D\cdot \bar{h}_L + b_D)$
		\State $p^D_{En} \leftarrow \textrm{Sigmoid}(w_D\cdot \bar{h}_{En} + b_D)$
		\State $L_G \leftarrow -\textrm{log}(1- p^D_{En}) -\textrm{log} p^D_{L} $ \Comment{compute generator loss}
		\State $\theta \mathrel{{+}{=}} -\eta_G \nabla_{\theta_G} L_G$ \Comment{update model based on generator loss}
		\EndWhile
	\end{algorithmic}
\end{algorithm*}

\begin{table*}[th]
	\centering
	\begin{tabular}{lcccccccc}
		\toprule
		& En & De & Es & Fr & It & Ja & Ru & Zh \\
		\midrule
		\citet{mldoc} & 92.2 & 81.2 & 72.5 & 72.4 & 69.4 & 67.6 & 60.8 & 74.7 \\
		\citet{mt_encoder} & 89.9 & 84.8 & 77.3 & 77.9 & 69.4 & 60.3 & 67.8 & 71.9 \\
		\midrule
		BERT En-labels & 94.2 & 79.8 & 72.1 & 73.5 & 63.7 & 72.8 & 73.7 & 76.0 \\
		BERT En-labels + Adv. & - & \textbf{88.1} & \textbf{80.8} & \textbf{85.7} & \textbf{72.3} & \textbf{76.8} & \textbf{77.4} & \textbf{84.7} \\
		\bottomrule
	\end{tabular}
	\caption{Classification accuracy on the MLDoc test sets. We present results for BERT finetuned on labeled English data and BERT finetuned on labeled English data with language-adversarial training. Our results are averaged across 4 training runs, and hyperparameters are tuned on English dev data.}
	\label{table_mldoc}
\end{table*}

\subsection{MLDoc Classification Results}

\begin{figure}[h]
	\centering
	\begin{subfigure}[t]{0.45\textwidth}
		\centering
		\includegraphics[width=6.5cm]{de_new.png}
		\caption{German test accuracy vs steps taken}
	\end{subfigure}
	\begin{subfigure}[t]{0.45\textwidth}
		\centering
		\includegraphics[width=6.5cm]{ja_new.png}
		\caption{Japanese test accuracy vs steps taken}
	\end{subfigure}
	\caption{German and Japanese MLDoc test accuracy versus the number of training steps, with and without adversarial training. The solid line shows the performance of the non-adversarial BERT baseline. The dashed line shows the performance with adversarial training.}
	\label{fig_mldoc_progress}
\end{figure}

We finetuned BERT on the English portion of the MLDoc corpus \cite{mldoc}. The MLDoc task is a 4-class classification problem, where the data is a class-balanced subset of the Reuters News RCV1 and RCV2 datasets. We used the \verb|english.train.1000| dataset for the classification loss, which contains 1000 labeled documents. For language-adversarial training, we used the text portion of \verb|german.train.10000|, \verb|french.train.10000|, etc. without the labels.

We used a learning rate of $2\times 10^{-6}$ for the task loss, $2\times 10^{-8}$ for the generator loss and $5\times 10^{-5}$ for the discriminator loss.

In Table \ref{table_mldoc}, we report the classification accuracy for all of the languages in MLDoc. Generally, adversarial training improves the accuracy across all languages, and the improvement is sometimes dramatic versus the BERT non-adversarial baseline.

In Figure \ref{fig_mldoc_progress}, we plot the zero-resource German and Japanese test set accuracy as a function of the number of steps taken, with and without adversarial training. The plot shows that the variation in the test accuracy is reduced with adversarial training, which suggests that the cross-lingual performance is more consistent when adversarial training is applied. (We note that the batch size and learning rates are the same for all the languages in MLDoc, so the variation seen in Figure \ref{fig_mldoc_progress} are not affected by those factors.)

\subsection{CoNLL NER Results}

\begin{table*}[h]
	\centering
	\begin{tabular}{lcccc}
		\toprule
		& En & De & Es & Nl \\
		\midrule
		\citet{bert} & 92.4 & - & - & - \\
		\citet{conll_mayhew} & - & 57.5 & 66.0 & 64.5 \\
		\citet{conll_ni} & - & 58.5 & 65.1 & 65.4 \\
		\citet{xling_adv} & - & 56.0 & 73.5 & 72.4 \\
		\citet{adv_ner_2} & - & 57.8 & 72.4 & 71.3 \\
		\midrule
		BERT En-labels & 91.1 & 68.6 & \textbf{75.0} & 77.5 \\
		BERT En-labels + Adv. & - & \textbf{71.9} & 74.3 & \textbf{77.6} \\
		\bottomrule
	\end{tabular}
	\caption{F1 scores on the CoNLL 2002/2003 NER test sets. We present results for BERT finetuned on labeled English data and BERT finetuned on labeled English data with language-adversarial training. Our results are averaged across 4 training runs, and hyperparameters are tuned on English dev data.}
	\label{table_conll}
\end{table*}

We finetuned BERT on the English portion of the CoNLL 2002/2003 NER corpus \cite{conll}. We followed the text preprocessing in \citet{bert}.

We used a learning rate of $6\times 10^{-6}$ for the task loss, $6\times 10^{-8}$ for the generator loss and $5\times 10^{-4}$ for the discriminator loss.

In Table \ref{table_conll}, we report the F1 scores for all of the CoNLL NER languages. When combined with adversarial learning, the BERT cross-lingual F1 scores increased for German over the non-adversarial baseline, and the scores remained largely the same for Spanish and Dutch. Regardless, the BERT zero-resource performance far exceeds the results published in previous work.

\citet{conll_mayhew} and \citet{conll_ni} do use some cross-lingual resources (like bilingual dictionaries) in their experiments, but it appears that BERT with multilingual pretraining performs better, even though it does not have access to cross-lingual information.

\subsection{Alignment of Embeddings for Parallel Documents}

\begin{table}[h]
	\centering
	\begin{tabular}{cccc}
		\toprule
		Source & Target & Without Adv. & With Adv. \\
		\midrule
		\multirow{7}{*}{En} & De & 0.74 & \textbf{0.94} \\
		& Es & 0.72 & \textbf{0.94} \\
		& Fr & 0.73 & \textbf{0.94} \\
		& It & 0.73 & \textbf{0.92} \\
		& Ja & 0.65 & \textbf{0.84} \\
		& Ru & 0.72 & \textbf{0.89} \\
		& Zh & 0.69 & \textbf{0.91} \\
		\bottomrule
	\end{tabular}
	\caption{Median cosine similarity between the mean-pooled BERT embeddings of MLDoc English documents and their translations, with and without language-adversarial training. The median cosine similarity increased with adversarial training for every language pair, which suggests that the adversarial loss encourages BERT to learn language-independent representations.}
	\label{table_align}
\end{table}

If language-adversarial training encourages language-independent features, then the English documents and their translations should be close in the embedding space. To examine this hypothesis, we take the English documents from the MLDoc training corpus and translate them into German, Spanish, French, etc. using Amazon Translate.

We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table \ref{table_align}, we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent.

\section{Discussion}

For many of the languages examined, we were able to improve on BERT's zero-resource cross-lingual performance on the MLDoc classification and CoNLL NER tasks. Language-adversarial training was generally effective, though the size of the effect appears to depend on the task. We observed that adversarial training moves the embeddings of English text and their non-English translations closer together, which may explain why it improves cross-lingual performance.

Future directions include adding the language-adversarial task during BERT pre-training on the multilingual Wikipedia corpus, which may further improve zero-resource performance, and finding better stopping criteria for zero-resource cross-lingual tasks besides using the English dev set.

\section*{Acknowledgments}

We would like to thank Jiateng Xie, Julian Salazar and Faisal Ladhak for the helpful comments and discussions.

redpajama_set_name: RedPajamaArXiv


Armed Palestinians opened fire at IDF troops operating in Nablus early Saturday morning, Israel Radio reported. No one was wounded in the incident. Two suspected terrorists were arrested close to the West Bank town.


It's time for more macro pictures of the beach, showing every grain of sand and every detail on this lonely sea shell.
This lady bug was climbing on the rock ever so gently, giving me enough time to capture it perfectly.
I'm not quite sure what this was, but it definitely looks interesting and shows that nature can intertwine itself with anything.
Sea weed bulbs are interesting to look at up-close, they look like they will pop at any moment!


Coming to Taliesin was scary for the fact that I felt a bit intimidated by the rest of the students. I knew for a fact that they had more work experience than I in the technical aspects of design, where I was doing a wretched job at figuring it out. But In the end it is nothing like what I expected it to be. I feel very free and comfortable to express my ideas. The comments I get from my peers and instructors here is critical but forgiving, their comments help my thoughts and ideas be molded into something more amazing. What I like the most so far about being here is that I get this harsh criticism that is forgiving and helpful, as the people around you explain their thoughts and opinions. I have come to learn that it is okay to take what a person tells you and make it your own by adding my perspective. I have learned a series of new things that are very helpful in developing my building skills, abstract thinking and paying attention to details. I am truly very lucky and fortunate to have been given this great opportunity.
I start my day here at Taliesin by thinking about something that I want to accomplish, as well as thinking about what I did yesterday or just a way that I wish to improve myself. But every morning I think about the two goals that I have for myself, the first being to start thinking of ways to change the culture in my school of how architecture students view the program. I think that this is important because there are people both in the class and like me who want that opportunity to explore building and sketching, and to learn what type of things you can do in the very broad field of architecture. Secondly I just wanted to put myself to a test to see if I could really build, and most importantly I wanted to find out how passionate I was about it. But it turns out I can do it because I have already learned to be more confident about my ideas by taking ownership. And I've learned how important it is to keep building and making things.
Letting go of something you have worked so hard for hours under the hot sun and destroying it in seconds, in the end you do it for the same reason that you don't go for the first thing you see when you buy something like a dress or car. You need to experiment, figure out what works and what doesn't, in this case we have to build a cellphone booth type of shelter, which requires us to take numerous needs into consideration. When building you need to always stop and pause to look at what you have created and observe if it is working. It's better to catch your mistakes early on and prevent hours of unnecessary work.
For today's challenge we had to play a game of capture the flag and later make a map of Taliesin from the print out map that we were given. I was amazed to discover that a map doesn't tell you the full truth of how an area really looks like. After the game we got the opportunity come into two groups and come up with how the area of the school really looks like after exploring it over the past couple days and getting to know it in a very intimate way. A physical map of an area from a printed sheet of paper or like Google earth is nothing compared to what the place is really like. I think it may be safe to infer that it's practically meaningless if you want to look at a map like this to find a good place that has a nice view and good grass to have a picnic or something. You will not get that from a boring flat surface. There is more in that map that you cannot see and you are missing the beauty and physical characteristics that make that place unique and special. It's truly not something that can be captured in a photo from the sky; you'd be missing the entire picture. The area of a place has a life, mood or attitude; it is something physical that cannot be expressed in a flat surface of a picture. Having to make our own map of the school was a good way of getting to know the area in which we are going to build something. I came to realize that understanding our building space is something that is extremely important and helps us understand what makes the place unique.
I will always remember that there is more to a map than just a flat surface.


Before 'How to Train Your Dragon' came along 2006's Kung-Fu Panda, was the biggest animation film Dreamworks had made, outside of the Shrek trilogy. The film itself is a mix of beautiful animation, kung-fu action. Po himself is a fantastic creation taking the usually lethargic Panda and charging him with stunning kung-fu moves. The characters got a 2nd outing in 2011, in Kung Fu Panda 2.
Kung Fu Panda POP! vinyl figure stands around 10m tall and comes packed in an illustrated window box.


I used to spend some time talking about declarative configuration management (vs imperative) in the infrastructure as code boot camps (before everything was migrated to Katacoda).
I like the idea of declarative, but the practicalities are problematic, and you very quickly hit something resembling the halting problem. Too many possible ways of doing things leads to too many corner cases leads to an exploding test matrix of doom.
We can be declarative, but only in a clearly bounded context. So the first conversation needs to be about those boundaries. This turns out to be one of the neat things about cloud services, as the service provider sets the boundaries, and the customers are forced to work within them; thus we have a clearly bounded context that declarative stands a chance of working within. Where we need to be careful is on our own side of the line in the shared responsibility model, as there lies the opportunity to introduce too much variability, and destroy the boundaries of the provider curated bounded context. Serverless is a good example of how constraints are actually helpful, as the boundary definitions are much clearer than they are with (say) a VM (where I have the opportunity to mess things up with an endless choice of operating systems, middleware, languages and frameworks).


CalPERS is right to stand up to the One Percent.
East Bay activists and others are rightly concerned about the tsunami of One Percent money that is crashing over our elections. Yet the US Chamber of Commerce, the self-described "world's largest business federation," is in a tizzy over a California Public Employee Retirement System rule that seeks transparency. The rule asks governing boards of companies in which CalPERS invests to set policies for oversight of political campaign contributions and to disclose the contributions once a year. So what is big business trying to hide?
In theory, publicly traded companies are accountable to shareholders, large and small. Yet in fact, corporate management of large companies usually treats shareholders like adversaries. And the chieftains spend shareholder money on lawyers, lobbyists, and lawmakers to manipulate politics to their advantage.
CalPERS is one of the largest owners of corporate stock in the world. Concerned about corporate influence in elections, state Treasurer Bill Lockyer wrote the CalPERS Investment Committee last year and argued that as a shareholder, CalPERS needs to know if corporate political spending is consistent with the "values" of each company and its "profitability," calling for transparency of corporate political donations.
Much concern about money and politics centers on the US Supreme Court's decision in Citizens United. In that case, the judicial wing of the One Percent held that large corporate electoral contributions were legal and were no big deal, because shareholders could simply exercise their ownership rights to learn of, and potentially stop, the donations.
Relying on this fantastical argument, Lockyer and others at CalPERS called the court's bluff, drafting a policy for CalPERS known as CalPERS Corporate Governance Policy 6.5, which calls for board oversight of contributions and yearly reporting. The policy is extremely modest. Some large asset owners are asking companies not to make political contributions at all. But when the One Percenters learned of the CalPERS proposal they called out the dogs.
In response to the proposed policy, and in spite of the words of the Supreme Court about the robust rights of shareholders, the chamber and its lawyer, Andrew J. Pincus, submitted a seventeen-page letter crying crocodile tears over the unfairness of the CalPERS rule. For shareholders to even ask companies to tell the public of their campaign contributions, Pincus claimed, would violate the First Amendment, burden taxpayers, diminish stock prices, and endanger retiree benefits. He even made the veiled threat that if the policy was adopted, CalPERS trustees could be personally at legal risk.
The chamber's letter also quoted freely from an academic for the One Percent who said that if "existing duties of care, loyalty and its by-product good faith" that are exercised by corporate leaders today are not sufficient to make corporate boards respect their shareholders in the campaign contribution context, "the corporate form [is] unworkable, inefficient and dysfunctional." Uh, oh. Watch out, the cat is trying to get out of the bag.
In response, CalPERS outside counsel, Harvey Liederman, wrote a twelve-page letter on behalf of the board, profusely thanking the chamber for sharing its views, while pointing out a number of obvious flaws.
But lurking just below the surface of the lawyerly arguments was the real issue. Large corporations are playthings for their top officers, paying out royal salaries and benefits, shareholders be damned. Corporate donations are fundamentally about protecting the system that allows and promotes the regal perquisites and power of their leaders. These oligarchs fear free and fair elections and an informed electorate. This year, in addition to their attempts to hide their financial manipulation, they are employing voter suppression efforts in key states to make it difficult for the poorest Americans to even vote at all.
To their credit, in spite of these threats, CalPERS trustees adopted the transparency policy after a hearing on the matter. They should be applauded for this. And they should be encouraged to aggressively enforce the policy.
Many criticize CalPERS. The criticism comes from all manner of sources, but the bulk of it comes from those who don't like CalPERS-type pension systems and who have been able to create a false controversy due to an envious press. (Hey, I don't get a good pension, why should they?) These defined benefit funds pool large amounts of money in a single fund for a group of workers, as opposed to every worker having, they hope, their own little financial pot. The investment risk is thus common, a more humane way to approach retirement savings. And the pooled money gives the fund trustees power in regard to corporate chieftains.
Now, there are valid reasons to criticize CalPERS. It has been seduced by Wall Street financial "engineering" and invested in all manner of ridiculously complex and faulty financial gimmicks. The result of this dalliance with Wall Street "geniuses" is that CalPERS has literally gifted billions of dollars to Wall Street moguls that could have gone to retiree benefits or taxpayers. Wall Street has funneled much of this money into the pockets of the One Percent and into the fat wages of Mr. Pincus and others.
But in standing up to the chamber, CalPERS is helping us all.
Transparency is good. It is one of the best weapons that the 99 Percent has. And any corporate lawyer, lobbyist, or lawmaker who tells us that he is trying to protect the First Amendment or the interests of retirees and taxpayers by keeping the activities of the elite secret is simply a wolf in sheep's clothing.


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Canberra's startup innovation culture on display in Singapore
Canberra's technological innovation will be on display at an investment showcase in Singapore on Wednesday.
Fifteen ACT start-ups will be pitching their ideas and plans to more than 100 international investors.
Canberra startup The Healthy Grain has commercialised BarleyMAX, a natural whole grain that contains twice the dietary fibre of regular grain. Photo: The Healthy Grain
The initiative is being supported by the CBR Innovation Network and ANU Connect Ventures.
ANU Connect Ventures manages a $27 million fund and invests in early-stage commercial opportunities in the Canberra region up to $500,000.
Chief executive Nick McNaughton said it was the first time such a large group had been given the opportunity to pitch at an event of this scale.
Each start-up attending in Singapore will present five-minute pitches. Sectors represented include education, health, agriculture, science and film.
"This is a great opportunity for our startups to really demonstrate that they have globally competitive products and technology that has enormous potential and value in front of serious investors," Mr McNaughton said.
CBR Innovation Network delivers a range of support programs and events for startups, entrepreneurs, creative designers, young people and small business.
Startups presenting include Skoolbo, a learning media company which has developed software to help with reading and maths.
The program is used by more than 50,000 primary schools in the United States, United Kingdom, Australia and New Zealand.
"Recently we have expanded to the preschool market, with the creation of more than 50 hours of singing and dancing video content for 3-6 year olds," chief executive and founder Shane Hill says in the event prospectus.
"In mid 2017 we are launching into the China market with our Mandarin content."
Biotechnology company Beta Therapeutics is developing disease-modifying therapeutics that modulate progression of diabetes and inflammatory diseases.
It's actively seeking investment for the first phase of its development.
The Healthy Grain, backed by CSIRO and Canberra-based Australian Capital Ventures Limited, is planning a wider Asian and North American launch this year.
It commercialises 100 per cent natural barley varieties that are starch resistant and with ultra-low gluten content.
Solcast is seeking to enable a smooth transition of the electricity sector to high penetrations of solar energy generation through modelling and forecasting technology.
"It delivers operational services based on a fusion of unique and disparate data sources," the event prospectus says.
"Solcast works directly with the energy industry to remove the uncertainty associated with behind the meter PV generation, energy storage and large-scale solar generation."
Chief Minister Andrew Barr is scheduled to speak at the event at Suntec Convention Centre this morning.
Mr Barr said the showcase was part of the ACT government's push to attract more international investment.
While in Singapore he will also meet the chief executive of Singapore Airlines, Goh Choon Phong, government and business officials.
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Electricity Prices in Texas
A Snapshot Report
Texans buying electricity from competitive providers historically have paid higher prices, on average, than Texans receiving power from providers exempt from competition.
But this new analysis from the Texas Coalition for Affordable Power reveals that the persistent price gap has dwindled to the narrowest point ever recorded – and that it has almost vanished altogether.
Average residential electricity prices in areas with competition also have decreased precipitously during a recent 10-year period, while they slightly increased in areas without competition.
Taken together, these developments suggest that the 17-year-old deregulated retail electric market in Texas is delivering the best results so far for the state's residential consumers.
However, not all the pricing trends are positive for Texans living with retail electric competition, also known as retail electric deregulation. For instance, average residential electricity prices have remained consistently higher in those areas, as compared to prices in deregulation-exempt areas. This has been true for every year for which data exist to conduct this analysis.
Moreover, pricing surveys conducted by Texas regulators during early 2019 indicate that several deregulated-exempt municipal and vertically integrated utilities during the survey period delivered better electricity prices for residential consumers than competitive providers.
Texas implemented its retail electric deregulation law in 2002. Under it, Texans in areas such as Houston and Dallas can choose among different electric providers. In other areas exempt from the deregulation law residents receive service from a single provider.
This Snapshot Report on Electricity Prices, an update of similar analyses released by the Texas Coalition for Affordable Power, compares residential electricity prices in both deregulated areas of Texas and those in areas exempt from deregulation. It includes long-term pricing information, information about non‑by‑passable charges assessed by Texas wires utilities, and a review of pricing trends nationwide.
Major findings include:
Texans consistently have paid higher average residential electric prices in areas with deregulation, as compared to prices in areas exempt from deregulation. This annual trend began during the very first year of the retail electric deregulation law in Texas, in 2002, and has continued through 2017, the last year for which data are available to conduct this analysis. [See Exhibit 2].
However, the gap in residential electricity prices between areas of Texas with deregulation and areas without it has dwindled precipitously over the last 10 years — and it now stands at its narrowest point since Texas began retail electric deregulation. [See Exhibit 2].
During 2017, the average residential deregulated electricity price inside ERCOT stood at 11.10 cents per kilowatt hour. The average residential prices in those ERCOT areas exempt from deregulation (such as in the service territories of electric cooperatives and municipal utilities) was 10.77 cents per kWh. [See Exhibit 4].
Average residential electric prices in deregulated areas have declined more than 23.74 percent during the 10-year period from 2008 through 2017. Average residential prices in areas exempt from deregulation have slightly increased during the same period. [See Exhibit 2].
Average residential prices have increased in both deregulated areas and deregulation-exempt areas since the implementation of deregulation in 2002. However, the rate of increase has been lower in deregulated areas. The percentage increase in areas with deregulation was 33.21 during that period; the percentage increase in areas without deregulation was 43.88. [See Exhibit 2].
Texas continues to fare well in comparison to other states with deregulated retail electric markets. Average prices for deregulated electricity in Texas have increased at the third lowest rate among 15 states with deregulation and the fourth lowest rate among states overall. [See Exhibit 1 and 6].
Pricing surveys conducted by Texas regulators for March 2019 indicate that some deregulated-exempt municipal and investor-owned utilities were delivering better residential electricity prices than surveyed competitive providers in the Dallas and Houston areas. [See Exhibit 11 and 12].
Increases in the charges assessed by the state's major regulated transmission and distribution utilities have outpaced inflation over the last 16 years. Although transmission and distribution rates are regulated, these increases nonetheless contribute to higher prices in deregulated areas of the state. [See Exhibit 9 and 10].
Exhibit 1: Residential Price Increases
For 15 Deregulated States, Including Deregulated Texas
Although residential electricity prices have increased in areas of the state with electric deregulation since 2002, that increase has not been as great as that registered in most other states with competitive retail markets. From 2002 through 2017, only deregulated Maine and New York have registered smaller rate increases.
Source: United State Energy Information Administration Electricity Data Brwoswer
The Analysis
Under the Texas electric deregulation law, consumers in Houston, Dallas, Fort Worth, Corpus Christi and surrounding areas can choose among different retail electric providers. These providers compete for customers by offering different terms of service and prices. Many other parts of the state remain exempt from this competitive system. Exempt areas include those served by municipally-owned utilities (such as in San Antonio and Austin) and those served by electric cooperatives. Also exempt from retail electric deregulation are investor-owned utilities operating outside the area covered by the state's primary power grid, known as the Electric Reliability Council of Texas.1
The existence of this bifurcated electricity system — one in which some Texans receive service from competitive electric retailers and others do not — provides a unique opportunity to compare pricing outcomes. The Texas electric deregulation law was adopted in 1999 with the promise that it would lower rates. As this analysis shows, the results have been mixed.
How to Read This Report
This report includes five discrete analytical sections: a Benchmark Analysis of Long-Term Trends, a "Lost-Savings" Analysis, a Benchmark Analysis of 2017 Electric Prices, a review of Transmission and Distribution Charges, and a review of Recent Competitive Offers.
Both benchmarking analyses employ data obtained from the United States Energy Information Administration (US EIA). The long-term benchmarking analysis compares pricing outcomes inside and outside deregulated areas of Texas and begins with 2002 — the first year of retail electric deregulation in Texas — and continues through 2017. This analysis does not extend to 2018 and 2019 because the necessary US EIA data for those years are not yet available.
However, this report also includes a Recent Competitive Offers section that samples more up-to-date individual offers in deregulated areas around Houston and Dallas. Readers can find these pricing samples from 2019 rate surveys conducted by the Public Utility Commission (PUC).
The section entitled "Transmission and Distribution Charges" includes rate comparisons from two separate years (2003 and 2018) for the state's two largest monopoly wires companies, Oncor Electric Delivery (Oncor) and CenterPoint Energy (CenterPoint). Readers can find the underlying data for this analysis on the PUC's website.
For readability purposes, this report employs certain words and phrases interchangeably to refer to areas served by competitive retail electric providers. These words and phrases include "areas with retail electric competition," "areas with retail electric deregulation," "competitive areas" and "deregulated areas." Unless otherwise noted, references to electricity prices are for residential customers.
This report includes five discrete analytical sections:
Benchmark Analysis of Long-Term Trends
Benchmark Analysis of 2017 Electric Prices
"Lost-Savings" Analysis
Transmission & Distribution Charges
Recent Prices
Background History
Texans enjoyed residential electricity rates below the national average for many years prior to the adoption of the retail electric deregulation law in 1999.2
That trend flipped shortly after the law took effect, with average residential prices statewide rising above the national average in 2003 and remaining above the national average until 2011. [See Exhibit 5].
Some observers have said that the increase in statewide electricity prices after the deregulation law took effect is not related to the law, per se, but rather to an increase in natural gas prices. This is because natural gas prices are closely linked to wholesale electricity prices, and natural gas prices hit historically high levels after deregulation.3
However, fluctuations in natural gas prices alone cannot explain the historic disparity between average electricity prices inside and outside deregulated areas of Texas. For every year for which data exist with which to conduct this analysis — that is, between 2002 and 2017 — average residential prices in deregulated areas of Texas have been higher than average prices in deregulation-exempt areas. [See Exhibit 2].
Moreover, average residential prices in Texas, statewide, remained below the national average for at least a decade prior to the implementation of retail electric deregulation in 2002. Shortly after the law took effect, in 2003, residential prices in deregulated areas rose above the national average, and they remained above national averages for most years afterwards. Electric prices in areas exempt from deregulation continued below the national average after 2002 and, with the exception of one year, have stayed below national averages for the entire history of deregulation in Texas.
This report quantifies the gap in deregulated prices and those charged in areas exempt from deregulation through "lost savings" analyses found in Exhibit 7 and Exhibit 8. These analyses calculate the imputed savings that would have accrued to Texans living in areas of Texas with deregulation had they instead paid the same average prices as Texans living in areas exempt from deregulation.
Pricing surveys conducted by the PUC for March 2019 also indicate that some deregulation-exempt municipal and investor-owned utilities were delivering better residential prices than all surveyed competitive providers in the Dallas and Houston areas.
Customer confusion about retail electric shopping, the details of rate offers, and other aspects of the deregulated market may have contributed to historically higher prices in deregulated areas. Other contributing factors may include the cost of multi-million dollar marketing campaigns by some retail electric companies and increasing rates charged by monopoly transmission and distribution utilities. These "wires" rates comprise a growing portion of home electric bills in competitive areas.
In addition, some research suggests that cross-subsidization between rate classes in non-competitive areas may have contributed to lower residential prices in non-competitive areas relative to prices charged in competitive areas.4
Price Gap Narrows
However, the price gap between areas of Texas with electric deregulation and deregulation-exempt areas continues to narrow. In percentage terms, this differential was smaller during 2017 than during any other year since 2002, the first year of the Texas deregulation law.
Less than two-tenths of a cent separated average residential prices in areas of Texas with deregulation and those without deregulation during 2017. It remains unclear whether that minuscule gap disappeared completely in 2018 and 2019 — or whether the trend of higher prices in deregulated areas has continued — given the unavailability of necessary data from those years for which to conduct the analysis.5
Exhibit 2: Average Residential Electricity Prices
Inside & Outside Deregulated Areas of Texas
Source: United State Energy Information Administration
US EIA Electrcity Data Browser
Average residential electric prices in deregulated areas of Texas consistently exceed average prices in deregulation-exempt areas. This was true in 2002 — the very first year of the deregulated retail electric market — and true in 2017, which was the last year for which data exist to conduct this analysis. It also has been true for every year in between.
The gap in residential electricity prices in deregulated and non-deregulated areas of Texas widened precipitously during the early years of the new market, but then narrowed by a similarly dramatic fashion in recent years.
During the first six years of deregulation in Texas — from 2002 through 2007 — prices in areas that remained deregulation-exempt increased by 29.1 percent. However, prices increased at more than twice that rate in deregulated areas, by 69.7 percent.
During the subsequent 10-year period, from 2008 through 2017, average residential prices slightly increased in deregulation-exempt areas (.4 percent), and decreased by 23.7 percent in deregulated areas.
In 2017, the last year for which data exist to conduct these benchmark analyses, the difference in deregulated and non-deregulated residential prices has narrowed to its smallest point on record: 1.1 percent.
Section 1: Long-Term Trends Benchmark Analysis
Texans living in deregulated areas of the state have paid higher average rates for residential electricity than Texans living in areas exempt from deregulation. This is true for 2002 through 2017 — that is, for every year for which US EIA data exists to conduct this analysis. [See Exhibit 2]. Over those years, average residential prices in deregulated areas have been between 1.1 percent (2017) and 46.5 percent (2006) higher than average prices in deregulation-exempt areas.
From 2008 through 2017, average residential electricity prices at the national level increased by 52.7 percent, which was a greater rate than price increases in both deregulated and deregulation‑exempt areas of Texas. During that period, the percentage increase in average residential prices in deregulation-exempt areas of Texas was 43.88 percent, which was greater than the 33.21 percent increase in deregulated areas of Texas. [See Exhibit 6].
A shorter view — that is, confining the analysis to the 10 years from 2007 through 2017 — reveals that average residential prices have dropped in deregulated areas by 23.74 percent, while they increased in areas exempt from deregulation by 0.42 percent. [See Exhibit 2].
When it comes to residential pricing trends, deregulated Texas compares relatively well against other deregulated states. The 2002-2017 price increase observed in deregulated Texas stands as the third lowest increase among 15 deregulated states during that period. [See Exhibit 1].
Annual average residential electricity prices in deregulated areas of Texas have been higher than the nationwide average during 10 of the 16 years included in the benchmark analysis (2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011 and 2014). Annual average residential electricity prices in areas of Texas exempt from deregulation have been higher than the nationwide average once during those years (2005). [See Exhibit 2].
It remains unclear whether the historic disparity between average electric prices in deregulated and non-deregulated areas continues after 2017 because the necessary data to conduct those analyses are not available. However, March 2019 rate surveys conducted by the PUC show some deregulation-exempt municipal and investor-owned utilities were then delivering better residential prices than competitors in Dallas and Houston. [See Exhibit 11 and 12].
Section 2: 2017 Electric Prices Benchmark Analysis
In 2017, Texans in deregulated areas paid, on average, 11.10 cents per kilowatt hour for residential electricity, while the average price of electricity in areas of Texas exempt from deregulation was 10.98 cents per kilowatt hour. The corresponding nationwide average was 12.89 cents. [See Exhibit 2].
In 2017, the average statewide price of electricity (both inside and outside areas of Texas with deregulation) for all customer classes (residential, commercial and industrial) was 8.4 cents. This beats the 10.5-cent nationwide average price. [See Exhibit 3].
In 2017, average residential electricity prices charged by deregulated providers within the region served by the Electric Reliability Council of Texas (the state's primary power grid operator) was 11.10 percent. That was higher than the 10.77 average kWh price charged to residential customers by deregulation-exempt providers within that region, but lower than the 11.18 cents charged by non-dregulated providers in those areas of Texas outside of ERCOT. [See Exhibit 4].
Exhibit 3: 2017 Prices All Customer Classes
Residential, Commercial & Industrial (COMBINED)
This exhibit examines electricity prices among all customer classes (residential, commercial and industrial) during 2017. The average statewide price in Texas for all such customers — that is, prices inside and outside areas overseen by ERCOT, and prices among both deregulated providers and providers exempt from deregulation — was 8.38 cents per kWh. More granularly, the average Texas electricity price for all customer classes combined in areas exempt from deregulation was 8.81 cents per kWh. That was higher than the 8.113-cent per kWh average price paid by all customer classes combined in areas of Texas subject to deregulation. Measured by any of these benchmarks — inside deregulation, outside deregulation, or statewide — average electric prices for all customer classes combined in Texas beat the 10.48-cent per kWh average price nationwide.
Source: USEIA, Electric Data Browser
Exhibit 4: 2013-2017: Inside & Outside ERCOT
Residential Electric Prices
The state's primary grid operator, ERCOT, oversees the transmission system in about 85 percent of the state. Deregulated service providers and those exempt from deregulation both operate within this service territory. In areas of the state outside of ERCOT, all service providers are exempt from deregulation. As this series of exhibits illustrates, average deregulated prices in Texas were higher in 2013, 2014, 2015, 2016 and 2017 than those average prices charged by ERCOT providers exempt from deregulation.
Texas & United States
The statewide average price for residential electricity remained below the national average for many years prior to the implementation of the Texas deregulation law. After Texas deregulated its retail electric market, the overall statewide average price for residential electricity surpassed the national average and remained significantly above that mark for many years. Note, however, that average residential prices in deregulation-exempt areas of Texas remained consistently below the national average after implementation of the deregulation law. By contrast, average prices in deregulated areas remained consistently above the national average for many years (also see Exhibit 2). This dynamic suggests that high residential electricity prices in deregulated Texas contributed to the comparatively high statewide average price after 2002.
This first of the two charts above shows average statewide residential prices in Texas spiking above the national average in 2001. Although that spike occurred before the deregulation of the state's retail electricity market, it likely was a function of deregulation. This is because the Texas Public Utility Commission allowed utilities in 2001 to collect excess earnings and high fuel surcharges as a down payment on anticipated collections from the restructuring law. Average statewide residential prices in Texas dropped after the deregulated market opened in 2002 because the fuel surcharges expired, and because the deregulation law mandated a six percent cut in base rates. Average statewide residential prices then remained above the national average through 2010. [For more about this, see TCAP's separate report on the History of Texas Electric Deregulation.]
This exhibit does not distinguish between prices in areas of the state that are currently deregulated and non‑deregulated prior to 2002. This is because the federal data to conduct that granular analysis are not readily available. The same is true for the years 2018 and 2019.
Exhibit 6: Residential Electricity Price Increases
50 States, District of Columbia, Texas Inside & Outside Deregulation
Percentage Increases — 2002-2017
Residential electricity prices increased in deregulated areas of Texas from 2002 through 2017 by 33.2 percent, which is less than the 52.73 percent increase registered nationwide and also less than the 43.88 percent increase registered in areas of the state exempt from deregulation.
Source: United States EIA-861 Files
Section 3: Lost-Savings Analysis
"Lost savings," as defined in this report, is the imputed savings that would have accrued to Texans living in areas of Texas with deregulation had they paid the same average prices as Texans living in areas exempt from deregulation. The report examines lost savings both market‑wide and on an individual level — and for each year for which data is available to conduct the analyses.
All told, Texans living in deregulated areas would have saved more than $27 billion through lower residential electricity bills from 2002 through 2017 had they paid the same average prices as Texas living outside deregulation. For 2017 alone, that lost savings amounts to about $101.7 million. [See Exhibit 7].
On an individual basis, a typical residential customer under deregulation (defined as a customer paying average deregulated prices and consuming 1,300 kilowatt hours of electricity every month) would have saved more than $5,500 from 2002 through 2017 had he or she paid the same average prices as those charged outside deregulation. This imputed "lost savings" amounts to about $18.88 for a typical household in 2017 alone. [See Exhibit 8].
Exhibit 7: $27 Billion in Lost Savings
The Price of Higher Residential Rates Under Deregulation
Average electric prices in Texas charged by deregulated providers have been consistently higher than average prices charged by providers exempt from deregulation. The exhibit above measures the potential impact of these higher prices. The colored bars illustrate the aggeregate savings that would have accrued to Texans in deregulated areas had they instead paid the lower average rates charged in areas outside deregulation. The lost savings ranges from about 101 million per year in 2017 to more than $3.5 billion in 2006. The good news for Texans living in deregulated areas is that the disparity is trending downward. Providers exempt from deregulation include investor-owned utilities within Texas but outside the ERCOT region, municipally-owned utilities and electric cooperatives.
Source: United States Energy Information Administration*
*This exhibit analyzes the most recent relevant pricing data from the U.S. Energy Information Administration, as of the time of publication. Only residential prices rates are examined.
Exhibit 8: $5,500 in Imputed Lost Savings*
This exhibit compares electricity costs for a typical customer paying average rates charged by deregulated retail electric providers in Texas to costs for a customer with the same usage but paying average rates charged by Texas providers exempt from deregulation. Considered in this per-customer fashion, the imputed "lost savings" ranges from about $732 per year per customer in 2006 to $18.88 per year in 2017 – the smallest amount on record by far. For purposes of comparison, this exhibit assumes monthly electricity usage of 1,300 kWh.
Section 4: Transmission & Distribution Charges
Although monopoly transmission and distribution utilities operate under regulation, their rates impact electricity prices charged by competitive retail electric providers. This is because transmission and distribution utility rates are non-by-passable, which means they are included in a uniform fashion in the rates charged by all retail electric providers that operate in each utility's service territory.
Rate increases since 2003 by the Oncor utility (operating in the Dallas-Fort Worth area) and the CenterPoint Electric utility (operating around Houston) have outpaced inflation. Transmission and distribution charges paid by Oncor and CenterPoint customers also comprise an increasing share of monthly electric bills. [See Exhibits 9 and 10].
Exhibit 9: Non-Bypassable Charges: CenterPoint
(September 2003 – March 2019)
Rates charged by CenterPoint Electric in the Houston area have increased by more than 72 percent since 2003. In 2003, CenterPoint charges comprised 20.2 percent to 29.2 percent of a typical 1,000 kWh electric bill. In March 2019, CenterPoint charges comprised approximately 29.7 percent to 38.7 percent of a typical bill. All electric customers in deregulated areas around Houston must pay CenterPoint's rates, regardless of the retail electric provider the customer chooses for service.
Source: Public Utility Commission
Exhibit 10: Non-Bypassable Charges: Oncor
Rates charged by Oncor in the Dallas-Fort Worth area increased by nearly 50 percent since 2003. That rate outpaces the rate of inflation. In 2003, Oncor charges comprised 20.1 percent to 27.4 percent of a typical 1,000 kWh electric bill. In March 2019, the charges comprised 26.1 percent to 34.2 percent of a typical bill. All customers in deregulated areas of the Dallas-Fort Worth region must pay Oncor's rates, regardless of the retail electric provider the customers choose for service.
Section 5: Recent Prices
Among adjoining states, residential prices in adjoining Oklahoma, Louisiana and Arkansas were lower during 2018 than in Texas. Residential electric prices in 2018 were higher in adjoining New Mexico and nationwide. [See Exhibit 13].
Adjoining Arkansas, Louisiana and Oklahoma also enjoyed lower average industrial electric rates in 2018, while adjoining New Mexico had higher industrial rates. Average industrial prices were also higher nationwide than in Texas. [See Exhibit 13].
Among all classes of customers (Residential, Commercial and Industrial combined), lower average rates were found in adjoining Arkansas, Louisiana and Oklahoma during 2018, and higher in adjoining New Mexico and nationwide [See Exhibit 13].
Public Utility Commission surveys of electricity deals in March 2019 reveal that several non-deregulated vertically-integrated and municipally-owned utilities in some parts of the state were then selling electricity at better prices than all surveyed competitive providers around Houston and Dallas. [See Exhibits 11 and 12].
Exhibit 11: Electricity Prices (Houston Area)
Competitive Houston-area Offers versus Residential Prices in Deregulation-Exempt Areas
According to PUC price surveys, March 2019
This exhibit shows a number of individual retail offers in the Houston area, as listed in a PUC rate survey for March 2019. This exhibit also lists electricity prices in other areas of Texas exempt from deregulation. San Antonio is the largest city exempt from deregulation. All data has been retrieved from PUC rate surveys.
Exhibit 12: Electricity Prices (Dallas Area)
Competitive Dallas-area Offers versus Residential Prices in Deregulation-Exempt Areas
This exhibit shows a number of individual retail offers in the Dallas area, as listed in a PUC rate survey for March 2019. This exhibit also lists electricity prices in other areas of Texas exempt from deregulation. San Antonio is the largest city exempt from deregulation. All data has been retrieved from PUC rate surveys.
Exhibit 13: Average Electricity Prices 2018
Texas and Adjoining States
This exhibit shows that in 2018, overall electric prices for all customer classes in Texas combined (residential, commercial and industrial) were higher than they were for all customer classes in all adjoining states, except New Mexico. Texas residential and industrial electricity customers also paid more during 2018 than did customers in adjoining states, except New Mexico. Texas commercial electricity customers paid more than did commercial customers in adjoining Arkansas and Oklahoma.
About US EIA Data & PUC Data
This analysis employs data collected by the United States Energy Information Agency (US EIA), which is the statistical and analytical arm of the U.S. Department of Energy. US EIA data is known to be impartial, and is widely cited by economists, scholars, industry experts, the news media and governmental agencies — including the PUC.
The consistent manner in which the agency conducts its calculations across all 50 states allows analysts to make apples-to-apples market comparisons. How does the US EIA calculate prices? First, it gathers both revenue and sales data from electricity providers in a given region. It then derives a kilowatt hour or megawatt hour price by dividing revenues in that region by the amount of energy sold there.
TCAP has employed granular US EIA data to calculate average electricity prices inside and outside deregulated areas of Texas, inside and outside areas served by the state's principal power grid (ERCOT), and for the state's residential, commercial and industrial customers.
Employing US EIA data in this fashion allows for calculations of average prices of consumed electricity, as opposed to average prices of individual offers made by electric companies. This distinction is important. The problem with averaging offers by electric companies — but without an understanding of how many customers take each offer — is that such an analysis can lead to conclusions that bear little resemblance to actual market outcomes. For instance, while it may be true that many low-cost offers are available in a given area, it may also be true that most Texans living in those areas do not or cannot avail themselves of those low-cost offers because of restrictions in their existing electricity contracts, or for a number of other reasons.
However, an examination of individual offers is nonetheless useful to gain a sense of commonly available electricity prices in deregulated areas, including prices found in fixed-rate and variable-rate deals. This report examines such individual pricing offers, as included in rate surveys conducted by the PUC.
This report also examines charges by the state's two largest transmission and distribution providers, as posted on the PUC website. Transmission and distribution charges by "wires" utilities are non‑by‑passable, meaning that these charges are imbedded in electricity prices paid by all consumers in the utility's service territory, regardless of the retail electric provider that the consumer selects for service.
About the Texas Coalition for Affordable Power
Unlike the sponsors of other reports about the state's deregulated power market, TCAP derives no profit from selling electricity. Instead, the more than 150 political subdivisions that comprise TCAP purchase electricity for their own governmental needs. TCAP understands how high-cost power can cause businesses to relocate out of state, and can place heavy burdens on home consumers. TCAP wants what all Texans want: an affordable and reliable supply of power and a vibrant economy.
1 See The Story of ERCOT, Februrary 2011.
2 "Deregulated Electricity in Texas," Texas Coalition for Affordable Power, December 2012.
3 Public Utility Commission of Texas Docket No. 40000, Memorandum to Commissioner Kenneth W. Anderson, Jr. from Chairman Donna Nelson at 1 (Aug. 9, 2013).
4 "Electricity Reform and Retail Pricing in Texas," Peter R. Hartley, Kenneth B. Medlock III, Olivera Jankovska," Journal of Energy Economics, Jan. 5, 2019.
5 In contrast to findings in this report, Rice University researchers, in a corrected May 2017 report, concluded that the average price paid for electricity by residential consumers in competitive areas during 2016 was "roughly equal, in the aggregate" to the average price paid by Texans in non-competitive areas. These findings appear to have been extrapolated from PUC website data, while TCAP's findings are extrapolated from US EIA data. See more about the use of US EIA and PUC Data.
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I have a button whose Background is bound to a property in my
  ViewModel. The same button also has a command whose implementation is
  in my ViewModel. The clicking toggles a boolean in my VM & that
  changes both the text & color of that button. All good so far, but
  when my mouse hovers over, I get this blue color to remove which I
  have a control template in my Style. Problem is now upon button click,
  only text changes, no background change & no mouse over change.  Following is my Button definition

<Button  x:Name="OnlineBtn"
                        HorizontalAlignment="Left"
                        Margin="206,6,10,10"
                        Grid.Row="2" VerticalAlignment="Top" Width="50" Height="41"
                        Command="{Binding Path=OnlineCommand}"
                        Background="{Binding OnlineButtonProp,Mode=TwoWay,UpdateSourceTrigger=PropertyChanged}"
                        BorderBrush="Black"
                         Style="{StaticResource OnLineButtonStyle}">

And below is my Control Template definition.
 <Style x:Key="OnLineButtonStyle" TargetType="Button">
            <Setter Property="Template">
                <Setter.Value>
                    <ControlTemplate TargetType="Button">

                        <ControlTemplate.Triggers>
                            <Trigger Property="IsMouseOver" Value="true">
                                <Setter Property="Background" Value="Red"/>
                            </Trigger>
                        </ControlTemplate.Triggers>
                        <ContentPresenter VerticalAlignment="Stretch"
                                          HorizontalAlignment="Stretch">
                        </ContentPresenter>
                    </ControlTemplate>
                </Setter.Value>
            </Setter>
        </Style>

I have seen the example on SO about how to set this control template, but my question is kinda two fold
1) Why is my IsMouseOver property not working
2) Why are my background color Button property that has bound props in my VM not working
The IsMouseOver Setter background is chosen RED just for testing the impact of the mouse over.

A: Setting a Background explicitly (as done with your Binding) has higher value precedence than the Setter in the Trigger. Hence the Trigger is ignored.
You should add an element like Border or Grid to your ControlTemplate where you set the Background with a TemplateBinding to the Button's Background. Then make the Trigger change that element's Background instead of the Button Background:
<Style TargetType="Button">
    <Setter Property="Template">
        <Setter.Value>
            <ControlTemplate TargetType="Button">
                <Border x:Name="border" Background="{TemplateBinding Background}">
                    <ContentPresenter
                        HorizontalAlignment="{TemplateBinding HorizontalContentAlignment}"
                        VerticalAlignment="{TemplateBinding VerticalContentAlignment}"/>
                </Border>
                <ControlTemplate.Triggers>
                    <Trigger Property="IsMouseOver" Value="True">
                        <Setter TargetName="border" Property="Background" Value="Red"/>
                    </Trigger>
                </ControlTemplate.Triggers>
            </ControlTemplate>
        </Setter.Value>
    </Setter>
</Style>

redpajama_set_name: RedPajamaStackExchange


1. Apply a broad spectrum sunscreen that protects against both UV-A and UV-B rays prior to your manicure.
2. Or, use anti UV fingerless gloves during your manicure.
Esther's Nail Center sent me a pair of these gloves to try out. They cover your whole lower arm all the way up to the elbow leaving only your fingertips exposed. They're super easy to use… you just have to remember to put them on BEFORE you start your manicure!
You can see here that just my fingertips are exposed. The fabric actually was very close to my nail on the ring finger, but it didn't get in the way or cause any problems at all.
Ta-da! A perfect gel manicure using Gelish Plum and Done.
i want to know what is the cost of these gloves for uv lights ? and how much i have to order and what is the procedure of shipping ?
Sheeba, they cost between $5-$15 per pair and can be bought on Amazon.com, just check shipping options, many companies don't ship outside the USA.
Hi – Thank you so much for your article and info! I have done a ton of reading and am unfortunately still confused about the difference in safety between UV and LED. I read an article on your site that explained one isn't "safer" than the other. I also saw some articles online describing that LED is safer that UV and the gloves wouldn't be necessary with LED.
However, I then read that LED bulbs still emit UV, it's just a different type of bulb.
So, I am just trying to figure out why so many articles are saying that LED is a safer option and can be used safer without gloves… if it still emits the same UV? I am having trouble figuring out if one is "safer" than the other?
I realize you have addressed this in your article, so I guess what I am trying to figure out is how it was concluded since I am finding mixed information on one being safer than the other? For me, the price and drying time aren't a factor, I'm just looking for the "safest" option.
Both types of light use UV technology to cure the polish. LED lamps work faster so you're not exposed as long, although I've heard some say that the intensity of the exposure could be higher. I'm not an expert on the subject so I can't say with 100% certainty, but if you're worried about UV exposure I recommend taking precautions.
The studies show that the LED lamp is lower-risk. Since I use my Royal Nails UV lamp every week for my gel manicures, I'm not concerned about a safety risk, but I AM worried about premature aging of my hands due to the light exposure. I've only been doing gel manis at home for a couple of months, but my hands are already darker from the light exposure.
I guess I will probably buy sunscreen and put it on 15 minutes before I start the manicure. I have the gloves, but I hate using them because they are tight on my hands and impede movement so it's harder to apply the gel polish perfectly. I love having a gel manicure, but I don't want to end up with premature aging on my hands, which is caused by UV light exposure from LED and UV lamps. I know there are loads of products and cosmetic procedures you can get later in life to make your hands look younger, but I'd rather just take care now. Maybe I'll upgrade to the 18g lamp.
Could you double up your protection by putting on sunscreen then wearing the gloves as well?
Thanks a lot for explaining that both lamps use the UV spectrum, I've had skin cancer biopsies (negative) before so took no risks and used fingerless gloves even with an LED lamp. Not sure the fabric is actually UV-blocking, since opacity and fiber type determine that, and these are simple white polyestor-type gloves, not high end Supplex poly fabric and they aren't even black. But–really they are probably blocking out so much of that short time under the LED anyway it's probably quite ok.
just wondering, do you think any other types of gloves with the tips cut off the fingers might work?
You can still get exposure through most types of fabrics.


text:
« Lucy M. Boston | Main | More new arrivals... »
New arrivals...
Despite the fact that almost every daylight hour has been spent outside during this golden summer of 2013, and if I have read anything it has been about gardening, propagating, sowing, or other people's horticultural lives, this doesn't mean that I have taken my eye off the current booky world, so a few delicious non-fiction titles for you to savour in September, and maybe add one or two to the C*^&*&^%s list. It's always a good idea to get in early with these suggestions I find or you only have yourself to blame when the latest celeb biog ends up under the tree. Alternatively, if like me you have spent all available (and non-available) funds on roses and seeds for next year, then hot foot to the library catalogue and reserve before everyone else does and you are 75th on the list.
Now that Hilary Mantel has enlivened the Tudor age in my imagination, my antennae start waving when anything even remotely connected arrives, so Henry VIII - The Life and Rule of England's Nero by John Matusiak was bound to catch my attention. Published by The History Press, a quick browse of their website turned into a prolonged one, some interesting books hiding away there and here is what they have to say about this one...
"This compelling new account of Henry VIII is by no means yet another history of the 'old monster' and his reign. The 'monster' displayed here is, at the very least, a newer type, more beset by anxieties and insecurities, and more tightly surrounded by those who equated loyalty with fear, self-interest and blind obedience. This ground-breaking book also demonstrates that Henry VIII's priorities were always primarily martial rather than marital, and accepts neither the necessity of his all-consuming quest for a male heir nor his need ultimately to sever ties with Rome. As the story unfolds, Henry's predicaments prove largely of his own making, the paths he chooses neither the only nor the best available. For Henry VIII was not only a bad man, but also a bad ruler who failed to achieve his aims and blighted the reigns of his two immediate successors. Five hundred years after he ascended the throne, the reputation of England's best known king is being rehabilitated and subtly sanitized. Yet Tudor historian John Matusiak paints a colourful and absorbingly intimate portrait of a man wholly unfit for power."
'Wholly unfit for power...' well that has me interested. I have browsed and read a little and am hopeful of an informative, enjoyable, accessible read for non-history buffs like me and will probably end up having to read something about Nero because all I know about him is something about watching Rome burn.
From Tudor to tundra, sorry the best I could think of and 1912 - The Year the World Discovered Antartica by Chris Turney. Such an icy year 1912, I always think Titanic but wave the flag all you Aussies, Chris Turney was ours but for now it looks like he's one of yours, a geologist who has garnered the gongs, which should make up for the sadness of your Ashes summer. We had a great time following the Scott centenary in Plymouth last year and this looks like another very readable book that will add some interesting perspectives...
"The rivalry between Robert Scott and Roald Amundsen is a familiar story; what fewer people know is that, in 1912, five separate teams were exploring beyond the limits of the known world: Scott for Britain, Amundsen for Norway, Mawson for Australasia, Filchner for Germany and Shirase for Japan. The Antarctic discoveries made by these brave explorers enthralled the world and forever changed the way we understand our planet.
Chris Turney tells the story of the frozen continent, the heroic trials endured by its explorers and the lasting legacy for future scientific endeavour. Devoting a chapter to each of the five expeditions, he draws on previously unpublished archival material, framing the narrative with the broader idea of the spirit and excitement of scientific discovery."
I'll be ready for some of this chilly stuff once the weather cools down... it has suddenly gone scorchio yet again, and the list of things we promise we will do 'the next time it rains' now stretches from here to at least Antarctica.
Coming next, from tundra to daffodils...


This entry was posted in Developing Countries, food choises, Food Security, Fundraiser, Health, Hunger, Malnutrition, Micronutrients, Nutrition, Poverty, poverty line - $1.25, Uncategorized and tagged Africa, children, developing countries, food diversity, food issues, Food security, health, malnutrition, Nutrition, Poverty, Pverty line - $1.25. Bookmark the permalink.
Only wanna remark on couple of general things, The web site design and style is perfect, the subject matter is really superb. "We can only discover to love by loving." by Iris Murdoch.


text:
Q: Populate dropdown list dynamically in AngularJS I'm trying to generate dynamic form based on the key of document fields and using ng-if attribute in AngularJS.
Ex:
- If field name is "name|string" then populate textfield
- If field name is "name|select" then populate dropdownlist
- If field name is "name|datepicker" then populate datepicker

Following is the code:

        {{mySplit(index,0) | uppercase}}

            <input type="text" class="form-control" ng-model="providerModelData[index]">

            <input type="text" class="form-control" ng-model="providerModelData[index]">

        <select class="form-control" ng-init="getDropdownData(index,colName)" ng-options="dropdown._id for dropdown in dropdownData[colName]">
            <option value="">Select</option>
        </select>

controller:
$scope.mySplit = function(string, nb) {
            var array = string.split('|');
            return array[nb];
        }

textfields are working fine and populating data but I'm facing issue while populating dropdown fields.
Example: I've two dropdown fields in my mongodb document i.e. city|select and state|select
I'm trying to use ng-options to call function by passing index and colName (document name) to populate dropdownlist but its not working.
Following is the code:
    $scope.dropdownData = {};
        $scope.getDropdownData = function (query, colName) {
            $http.get('/getdropdowndata/', {
                params: {
                    query: query,
                    colName: colName
                }
            }).success(function (response) {

                $scope.dropdownData[colName] = response;

            });
        };

Express:
router.route('/').get(function (req, res) {
    var url_parts = url.parse(req.url, true);
    var query = url_parts.query;
    console.log(query.query);
    db.collection(query.colName).aggregate([{
        "$group":{
            "_id":"$"+query.query
            }
        }],function (err, docs) {
        console.log(docs);
        res.json(docs);
    });
});

Initially I tried calling function in ng-repeat but it was going into infine loop. Then later I tried ng-init options but it only calls or initialize once which is not working in my case. Here I need to call function dynamically and based on that I want to populate dropdown for different fields.
Any help would be appreciated.

A: Consider the following solution:
In your controller, set up a variable for the dropdown data:
$scope.dropdownData = {};

Then change your getDropdownData function to:
$scope.getDropdownData=function(query, colName){
    $http.get('/getdropdowndata/', {
        params: {
            query: query,
            colName:colName
        }
    }).success(function (response) {
        alert(JSON.stringify(response));
        $scope.dropdownData[colName] = response; // This will put data into our html file
    });
}

And the HTML for your dropdown case should be:

    <select class="form-control" ng-init="getDropdownData(index,colName)" ng-options="dropdown._id for dropdown in dropdownData[colName]">
        <option value="">Select</option>
    </select>

I used the notorious ngInit to make the call from getting data for the server. Perhaps there's a better way that I didn't consider. But in any case, the idea is to make the call to the server, and save the data in a way that you can fetch it easily from the view.
Edit
I don't know why, but for some reason this solution doesn't work with ng-options. It does, however, work when using it like this:
<select class="form-control" ng-init="getDropdownData(index,colName)">
    <option value="">Select</option>
    <option ng-repeat="dropdown in dropdownData[colName]" value="dropdown._id">{{dropdown._id}}</option>
</select>

See a simple example here.

A: Your view is completely messed up as far I see it you are missing
ng-model

for your select input.
Your JSON is improper its missing , before {'id_':'Arizona'}
Try to get response in your controller and push it to array and make use of that array in your View :
$scope.getDropdownData=function(query, colName){
$http.get('/getdropdowndata/', {
    params: {
        query: query,
         colName:colName
    }
  }).success(function (response) {
      var returnArray = [];
      alert(JSON.stringify(response));
      angular.ForEach(response,function(option){
      //parse response and push it to returnArray
        returnArray.push(option);
     });
       return returnArray;
  });
}

View :

     <select class="form-control" ng-model="selection" ng-options="dropdown._id for dropdown in  getDropDownData()">
     <option value="">Select</option>
    </select>

Here is the link to Codepen
.

A: Finally I solved it myself. Thanks @Rishab777 and @yarons for guiding me.
Here's the view code:

        <select class="form-control">
            <option value="">Select</option>
            <option ng-model="providerModelData[index]" ng-selected="providerModelData[index]" ng-repeat="dropdown in dropdownData[index]" value="{{dropdown._id}}">{{dropdown._id}}</option>
        </select>

and controller:
        $scope.showModal = false;
        $scope.toggleModal = function (colId, colName) {
            $http.get('/collectiondata/' + colId + '/collectionName/' + colName).success(function (response) {
                $scope.colName = colName;
                $scope.colId = colId;

                for (var key in response) {
                    if (response.hasOwnProperty(key)) {
                        if ($scope.mySplit(key, 1) === 'select') {
                            $scope.getDropdownData(key, colName);
                        }
                    }
                }

                $scope.providerModelData = response;
                $scope.showModal = !$scope.showModal;
            });
        };

        $scope.dropdownData = {};

        $scope.getDropdownData = function (query, colName) {
            $http.get('/getdropdowndata/', {
                params: {
                    query: query,
                    colName: colName
                }
            }).success(function (response) {
                $scope.dropdownData[query] = response;
            });
        };

Now the issue is ng-selected="providerModelData[index]" is not working and its showing last item as selected only.


text:
Home>Entertainment> Jordyn Woods Says She Was "Bullied By The World" During Tristan Thompson Scandal
Jordyn Woods Says She Was "Bullied By The World" During Tristan Thompson Scandal
Astrid Stawiarz/Getty Images
Jordyn Woods was a guest speaker at a Nigerian festival.
Jordyn Woods seems to be doing just fine without the backing of the Kardashian clan. Earlier this year, she found herself engulfed in one of the biggest controversies of 2019 so far after reports emerged claiming that she hooked up with Khloe Kardashian's ex, Tristan Thompson. She was exiled from the Kardashian-Jenner clan, later moving out of Kylie's home. She spoke on the incident on Jada Pinkett Smith's Red Table Talkbut during a panel at Nigeria's Homecoming Festival, Woods seemingly addressed the controversy again.
Jordyn Woods has been relatively low key over the years but the Tristan Thompson scandal placed a major spotlight on her name. She faced a lot of criticism while also being championed by others but the negatives always stand out. Woods explained how she felt bullied by the world after the scandal broke out.
"My little sister was bullied in school and I wanted to show her that I was bullied by the world," Woods said. "I understood for the first time what it's like being a black woman — in a just society, how we can be so disrespected and nobody can really understand to that extent until you have to live it."
Her relationship with the Kardashians might be strained right now but Woods, as well as her mother, have made it clear that they have no hard feelings towards Kylie.
Entertainment News jordyn woods tristan thompson scandal khloe kardashian khloe kardashian tristan thompson comments controversy kardashians
Jim Jones Reveals "El Capo" Features Rick Ross, Dave East, Fabolous & More
Kid Cudi, Juice WRLD, Gunna, Soulja Boy & More Announced For HARD Summer 2019
ENTERTAINMENT Jordyn Woods Says She Was "Bullied By The World" During Tristan Thompson Scandal


The Lensbaby Sol 22 / 45 is a selective, manual-focus lens with a sharp, circular area of focus that fades to blur/bokeh. Its tilt feature moves the area of focus around the frame – although not all the way to the edge of the frame.
After taking more than 2000 photographs with the Lensbaby Sol 22, I'm sure of four things: 1) the fun factor is well worth the US$199.95 price; 2) its compact size and light weight make it easy to carry on any photography outing; 3) not all subject matter lends itself to the Lensbaby look; and 4) the combination of manual focus, tilt shift and bokeh blades means more practice is required before I can be confident that photos taken with this lens will consistently match what I envision before pressing the shutter button.
Years ago, I signed up for the Lensbaby monthly e-newsletter. When we attended PhotoPlus Expo 2014, I came away with a copy of Lensbaby – Bending Your Perspective (2nd ed) that I've paged through several times. In 2018, when Lensbaby announced pre-sales for the Sol 22 (the micro four-thirds (MFT) equivalent to the Sol 45), I was ready to give a Lensbaby creative effect lens a try.
The first outdoor shot I took with the Sol 22 gave our sturdy front door a rather dreamy look.
Despite its light weight (165 grams/just under 6 ounces), the lens feels solid – mostly metal and glass). The built in bokeh blades are plastic, as is the inner ring of the tilt shift mechanism. The front element of the fixed 3.5 aperture lens takes up about a quarter of the overall lens diameter (17/70 mm or 5/8 of 2 3/4 inches). With both front and back caps on, the lens stands 5 cm/2 inches tall. A protractor indicates the range of tilt at just under 10 degrees and I've found it effective at shifting the area of focus from the center of the frame to roughly the rule-of-thirds intersect points.
The first full day with this lens brought a very low-risk in-the-field opportunity – a sneak peek at a few Beakerhead installations with several other members of the photo crew. I shot with the Lensbaby Sol 22 the whole evening. By the time we called it quits, I realized three things.
First, the blur effect surrounding the sweet spot of focus is very effective at emphasizing or conveying a sense of motion.
The blur on the front end of the lumbering contraption creates the illusion that it's moving much faster than it was.
Second, the same combination of focused area surrounded by blur is great at drawing the viewer's attention to the primary subject.
With so much distance between the Sextant Tesla Coils and the Calgary Tower, the sweet spot really helped me create an image that brings the eye to the lightning bolt striking the tower's antenna.
And finally, I would not be shooting all of my Beakerhead photo assignments with this lens – too gimicky! But I would use it in some circumstances (e.g. multiple photographers on one assignment) and for supplemental fun/artsy photos after I'd nailed each assignment's shot list.
After another 3+ months experience using this lens for a couple thousand photos and I stand by my initial impressions. In every use case, nailing the focus is essential. In addition to my standard practice of turning on in-camera focus peaking and magnification, I'd do well to take more shots of each scene to increase the likelihood of getting the focus just right.
With the sweet spot focussed on the area at the second corner, the blurred foreground and sides imply moving at speed along the pathway, maybe jogging or cycling?
With the sweet spot aimed at one of the Three Sisters peaks, the soft bokeh implies a stiff breeze moving the autumn leaves framing the scene.
Even using the manual focus aids in my camera (focus peaking, mainly), it's very easy to miss the intended plane of focus – in this case the minifig face. A slight uptilt to the lens may have helped in this case.
I've discovered two other use cases that really suit this lens. One is bringing a resting spot for the eye to a messy subject.
With the sweet spot locked in the centre position for this shot, the surrounding leaves are nicely blurred despite being on the same plane of focus as the centre leaf.
Another great application is photographing impressions of intimate landscapes.
The unusual mash-up of fall colour and deep, early snow is emphasized by the abstraction produced by the Sol 22.
The blur effect helps reinforce the illusion that this trio of ducks is walking on water!
I can't decide how I feel about the results when I try to shoot big landscapes with this lens. What do you think? Does it work?
Not a good choice for conventional landscapes/naturescapes, the Sol 22/45 is a good choice if there's one particularly thing in the scene you're trying to emphasize. In this case, I wanted to show the snow making machines at work. Too bad I just missed the desired plane of focus!
This lens comes with built-in bokeh blades.
Looking through other photographers' images shot with the Sol 45, it seems that the "bold bokeh" effect is far less pronounced on the Sol 22. I do use the blades to stop down the lens to the equivalent of f5, especially if it's a bright day and I'm fighting blown highlights. I will keep experimenting, to see if I can come up with another compelling use case for the bokeh blades in the MFT world.
Manual focus combined with the fairly small sweet spot often leads to results that don't quite work. For example, I worked hard to get all of Mr. GeoK in focus in this shot, but even with focus peaking and focus magnification turned on and using the tilt feature to maximum range, I was not successful.
Even with a month of practice, I'm still inconsistent with using the tilt-shift feature to aim the sweet spot exactly where I want it. In this case, standing on the level with Mr. GeoK, I was unable to get both his feet and his face in the zone of focus.
Even so, the manual focus is one of my favourite things about this lens. It forces me to slow down and that practice has carried over to when I'm shooting with auto-focus lenses.
Two things about this lens support my continued growth as a photographer: the manual focus and the way it prompts me to look at familiar subjects with a fresh eye. It's small size and light weight mean I'll routinely pack this lens when I head out and about. It's a lot of fun and pushes my creative boundaries!
Do you ever shoot with a creative effect lens? If yes, what other use cases have you got for me? If not, would you like to give it a try?
When I started writing this post, I remembered writing at least one lens review in the past. When I read that old piece of work – looking for guidance on what to include – my first reaction to past me's writing was pretty negative!
Not only was the content poorly organized and relatively unhelpful, the photos don't meet the standard I set for myself today. But thanks to a book I recently read, I quickly moved out of gap-focus mode, to thinking about the value of leaving old posts in the public domain: they let me compare my work now to my work as far back as 2008, when I started blogging. I can see how my work has evolved and improved. And sometimes it helps me see things that I stopped doing for one reason or another, and that I'd like to start doing again.
Whether you blog, keep a journal, have a portfolio of work, a collection of photographs, sections of code, a bunch of spreadsheets you've created, an accumulation of recordings or whatever, I'm curious… How do you save/share/curate your creative work? Do you put it out there and leave it there forever? Or take down work that no longer measures up?


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