text:
\section{Introduction}

Authoring a good headline is an essential step in the process of writing and publishing news articles. A good headline should be an apt and concise description of the contents of the article. It should also be captivating so that it makes a potential reader interested in reading the article in addition to following the guidelines set by the news agency in question. Good and bad headlines have also a great impact on the number of visitors \cite{headlines1,headlines2}, which directly translates into revenue on ad supported news websites.

It is very typical to use A/B testing to study which headline candidates are more successful in engaging users. This testing requires there to be headline candidates to test to begin with. For this reason, editors need to write multiple headline candidates for a single news article. This task takes a lot of time away from other editorial work especially since the people inventing the headlines are very often not the same people who write the articles. According to journalists working at the Finnish press house Sanoma, editors often times invent dozens of alternative headlines a day for news articles. Needless to say there is a commercial interest in automating this task.

It is not straightforward to automatically generate news headlines that are useful. A usable headline is expected to convey correct facts and be thematically relevant. At the same time, there must be diversity in the generated headlines given that the press houses want to have access to multiple headline variants. There is a communicative-creative trade off (see \citet{hamalainen-honkela-2019-co}) in how creative the system can be while still conveying the desired factual meaning. Additionally, the generated headlines must be interesting, because no reader would read a news story that sound boring from the very beginning.

In this paper, we represent a method for conditional headline generation by using a generative autoregressive Transformer \cite{transformer} based language model that is fine-tuned for the headline generation task in Finnish. The approach we follow can be seen as a special case of text summarization. Instead of the target being a full summary, it is a very compact headline. The reason for approaching the problem for this angle instead of resorting to a masked language model such as BERT~\cite{bert}, is that training autoregressive language models is computationally easier and faster than masked language models. This is because masked language models are trained to predict only a small percentage of words in a text during each forward pass, while autoregressive language models predict every word during every pass.

The main contributions of this paper are the following:

\begin{itemize}
  \item We train the first Finnish GPT-2~\cite{gpt2} model
  \item We fine-tune the model for the downstream task of headline generation for the morphologically rich Finnish language
  \item We present a human evaluation using real journalists who invent headlines as their day job
\end{itemize}

\section{Related Work}

In contemporaneous studies, neural headline generation is approached from the point of view of text summarization. Text summarization in itself has traditionally been divided into extractive and abstractive summarization. Currently, both of these types of tasks are tackled with approaches that utilize the Transformer \cite{copycopy,bukhtiyarov2020advances, liu2019text}.

A common type of an approach for both extractive and abstractive summarization is an encoder-decoder type language model such as BertSumExt \cite{liu2019text} and PEGASUS \cite{zhang2020pegasus}. Summarization as a seq2seq problem suits the encoder-decoder model-paradigm well as you have a source and a target text like in NMT problems. In this setup, abstractive summarization is performed by the generative decoder part. For purely extractive tasks, the decoder is often replaced by some form of classifier which selects which tokens in the input should be in the resulting summary.

Another type of an approach, and the one used in this paper, is to fine-tune a GPT-2 \cite{gpt2} style auto-regressive language model for the summarization task \cite{kieuvongngam2020automatic,copycopy}. These approaches perform some form of concatenation of the target summary to the end of the source text with special tokens as delimiters between source and target. This approach does not fit the paradigm of sequence transduction as well as encoder-decoder setups, but does have its advantages. For one, all of the parameters are reused maximally, as the entire model network is pre-trained on text generation. When fine-tuning for the summary, this continues to be the case. Encoder-decoder setups tend to become more complex.

Recent headline generation approaches tend to use some form of BLEU or ROUGE for automatic evaluation of the models \cite{headlinegen1,bukhtiyarov2020advances,tilk-alumae-2017-low}. These metrics are naturally used for summarization as well. Furthermore, Beam Search is a common way to perform the generation.

For Finnish in particular, the literature for neural headline generation and summarization is scarce. Currently however, most work regarding Finnish headline and text generation seems to be using more conventional NLP, rule-based and statistical methods \cite{ journalisticnlg, hamalainen-alnajjar-2019-generating,cf07d21e4a91473b95b374ab67d2bab2}.

For news focused Finnish NLP there has been work on generating sports reports from event data using a pointer-generation network \cite{Kanerva2019TemplatefreeDG}, generation of creative headlines in Finnish using templates \cite{colorfulfinnishheadlines} and rumor detection in Finnish news \cite{1bd9ff9b61a24eea9e6b33bf13a5fc56} using BERT and LSTMs.

How creative NLG systems are evaluated has been investigated in recent work \cite{evaluation2021}, and the evaluation done in this paper roughly follows the conclusions drawn. Specifically, to evaluate the generation of the model aligned with what task the model was designed and trained to perform \cite{3a5a5943607346358b78a2cb12d49ee0}. Following this idea, the evaluation of the model in this paper is not relying simply on offline metrics, but on manual structured review by domain-experts with criteria relevant to the real-world use case.

\section{Data}

This section details the data, filtering, processing and tokenization used in this paper. There are two separate modeling tasks we perform: unsupervised generative pre-training and generative fine-tuning. For this reason, we make sure that there are always two columns in the dataset: "body" and "title". The body column contains everything except headlines and is used as the pre-training data. Later, the headlines are added for the fine-tuning task.

\subsection{Corpora}
The data used for pre-training the language model consists of four corpora concatenated together: Sanoma, Wikipedia, Yle and Ylilauta.

\textbf{The Sanoma corpus} is our primary and largest corpus. It is a proprietary corpus of news articles from the most important Finnish news paper \textit{Helsingin Sanomat} and the widely spread yellow press paper \textit{Ilta-Sanomat}. This corpus contains approximately 3.8 million Sanoma news articles published between the year 1990 and 2021. The topic coverage is as broad as one would expect from news media, ranging from domestic and international politics to sports and culture events.

The Sanoma corpus contains the headline, ingress, and article body for most articles. We concatenate the ingress to the body text with double newlines between. This data was saved into parquet format with "title" and "body" columns. Headlines are kept separate because they are used only in the fine-tuning phase. This holds true to all corpora with headlines.

\textbf{Wikipedia} is a great corpus for language modeling, as it is freely available and contains information about the world. The corpus contains pages containing information about countries, people, history, science and much more. This is particularly useful for unsupervised language model pre-training as the model can learn from the information found in Wikipedia. The Finnish Wikipedia dump\footnote{https://dumps.wikimedia.org/fiwiki/latest/} from 24.11.2020 was used. This dump was parsed into a parquet file with again a "title" and "body" column. The dump contains 463,780 pages.

A corpus of news articles from \textbf{Yle}\footnote{http://urn.fi/urn:nbn:fi:lb-2017070501} was parsed into the same "title" and "body" format as the Sanoma and Wikipedia corpora. This corpus is small and only contributed around 100 000 articles.

The \textbf{Ylilauta} corpus\footnote{http://urn.fi/urn:nbn:fi:lb-2016101210} contains 335,004 messages from the Ylilauta forums. These messages are quite different to the rest of the data used, as this is not structured text. This text is also colloquial. Furthermore, this corpus does not contain headlines. As it represents a different textual domain, it makes it possible for the model to learn a representation of colloquial Finnish as well.

\subsection{Tokenizer}

The tokenization procedure must be able to tokenize any text string into tokens that all exist in the vocabulary of the language model. \textit{Byte-pair-encodings} (BPE) \cite{bpe}, and variations of it, is a common way to tokenize text for transformer language models especially for NMT. BPE strikes a balance between word-level and character-level tokenization by using subword-tokenization. It is able to express almost any string, like character-level tokenization, but without needing to treat each character separately which would result in very long sequences.

For the model, the number of merges was set to have a resulting total vocabulary size of 50,000, which is close in size to the GPT-2 vocabulary. The Byte-level BPE vocabulary was learned on the entire corpus. Additionally, included into this vocabulary are some special tokens which we added: \textit{<sos>, <eos>} for start and end of text tokens, \textit{<unk>} for unknown tokens just in case there is an error, \textit{<special1>, <special2>, <special3>} tokens reserved for possible of later downstream use when fine-tuning the model for a specific task. The special tokens never appear in the pre-training corpus, and \textit{<special1>} is used later on when fine-tuning to generate headlines.

\section{Building a Finnish GPT-2}

Our approach to creating a headline generating model is based on fine-tuning a language model learned by unsupervised generative pre-training. As such a model does not exist for Finnish, we have to train one.

\subsection{Model Specifications}
The language model in this paper are decoder Transformers, with a few key modifications. The modifications closely follow those made to GPT-2 as compared to the original Transformer.

Positioning of the layer normalization has been to follow GPT-2. Originally layer normalization was applied after the residual connections. This was modified by moving layer normalization to the input of each sublayer, and adding an additional layer normalization to the output of the final self-attention layer.

Positional embeddings are learned instead of sinusoidal. The reason for this is that BERT and the GPT variants use learned positional embeddings as well. This involves adding another embedding matrix to the neural architecture in addition to the token embedding matrix. The difference is that the position embedding matrix keys are the position integers of a token relative to the text it resides in, while the token embedding matrix has the vocabulary id of the token as the index.

Again following GPT-2, the network parameters are initialized by sampling from $N(0, \frac{0.02}{\sqrt{n}})$, where $n$ is the number of residual layers. From our experiments, this change is crucial for the convergence of larger model sizes. The model sized discussed in this paper which are of size $L$ and larger did not converge at all without this change.

Like GPT-2, Gaussian Error Linear Unit (GELU) \cite{gelu} was used as the activation function in the network instead of ReLU \cite{relu}. We used the AdamW \cite{adamw} optimizer with a learning rate $\alpha$. For the final model, $d_{model}=1280$ and $n_{warmup} = 2000$. This was done by doing several restarts and observing when the gradients overflow and the training breaks down as the learning rate is increased during warmup. The formula is tuned so that the peak learning rate is lower than the learning rate was when overflow was observed.

GPT-2 had additional L2 regularization which is omitted in this paper work. Finally, our models do not use dropout regularization. This is due to seeing better validation set convergence without it when testing hyperparameters on smaller test training runs. Since training the Transformer model takes time, omitting dropout regularization allows the model to converge slightly faster in terms of time. We ran several small-scale experiments with varying degrees of dropout and found that it did not significantly affect the end validation perplexity.

\subsection{Results of Pre-training}
\textit{Perplexity} (Equation~\ref{eq:ppl}) is a commonly used measure of the performance of a language model \cite{gpt, gpt2, gpt3}. The perplexity of a model given a text is calculated based on the probability assigned to each actual token in the text given its context. A convenient way to calculate it is by exponentiating the negative log likelihood loss:

\begin{equation}
\label{eq:ppl}
    ppl = e^{{-\sum_{t=1}^{n}log(p(\vect{x}^{(t)}|\vect{x}^{(1)},\ldots,\vect{x}^{(t-1)},\vect{\theta})}}
\end{equation}

A language model with a vocabulary of size $V$ will have a perplexity score of exactly $V$ if it always predicts the uniform distribution for each token. A random language model will average around $V$ perplexity as well. This is because on average, the correct token in a text is given $\frac{1}{V}$ probability by such a LM. Conversely, a LM that always predicts the token correctly with 100\% assigned probability will have a perplexity score of 1.

The resulting train and test perplexities achieved for the various models are found in Table~\ref{table:lmresults}. The medium and large models were trained for 4 epochs each, while the second large model was trained for less than 3 epochs. The training times were approximately 2 weeks for all models, and the medium and first large models were trained on half of the full corpus. This test reveals that in this case, the size of the training set has more impact on the resulting model perplexity than the size of the model. The implication is that increasing the size of the model won't increase the quality of the model significantly if the amount of training data is insufficient. Due to this and that it would have taken more than 2 weeks to train the XL sized model, we chose not to train a full XL sized GPT model. Additionally, the generative performance from manual testing was good enough.

\begin{table}
\centering
\begin{tabular}{||c| c | c ||}
 \hline
 Size & ppl\_train & ppl\_test \\ [0.5ex]
 \hline\hline
 medium & 17.9 & 22.9\\
 \hline
 large & 17.4 & 21.6\\
 \hline
  large2 & 14.0 & 17.8\\
 \hline
\end{tabular}
\caption{Train and test perplexity scores.}
\label{table:lmresults}
\end{table}

\section{Headline Generation}

This chapter describes how we tackle the task of conditional headline generation by using transfer learning. The final Finnish LM is used as the base model. we describe the training procedure and decoding algorithm design first, followed by the description of a domain-expert evaluation of the headline generative performance.

The final Finnish LM is loaded from its latest checkpoint, including both its parameters and optimizer state. Training is resumed but with changes to the learning rate, input structure, and validation generation. The loss calculation is altered as well, to focus the learning purely on the task of headline generation.

The training, validation and testing corpora are filtered, removing texts that do not have a headline or are not news articles. The texts are re-formatted, clipping the body of the text to the first 448 tokens. The special token \textit{<special1>}, not previously shown to the model, is appended to the end of the clipped body text. The headline of the text is then appended following the special token, followed by the \textit{<eos>} token signifying the end of the output. The idea is to learn a pattern where text that follows a special token is always a headline summarizing the preceding text, followed by the end token. Ideally then when the LM is given any text prompt ending in the special token, the output of applying a generation algorithm would be a relevant headline. The clipping procedure results in a portion of the news articles body text not to be completely shown to the model. This clipping must be done due to the model having a maximum context width of 512 tokens in order to fit the headlines at the end. We chose to keep the beginning of the texts due to news articles being structured in a way where the most important content tends to be written in the beginning of the article.

The data is no longer fed to the model in a dense square matrix format as when pre-training the LM. That method would be separating the headline of an article and the tokens of the article itself much of the time into separate rows in the input tensors. Ideally the corpus would be sorted according to length and fed to the model in variable sized batches of instances with similar length. We omitted this step for convenience, as we only ran the fine-tuning runs for one epoch each.

Sampling based algorithms such as nucleus sampling don't seem to lend themselves well for the task of headline generation. This can be seen by simply observing the random nature of the headline generation when using sampling based methods, both from the validation output and from manual testing. The requirements for headlines are stricter than that of creative text continuation, in that the headline must at least summarize accurately the article, and not invent things not stated in the text.

Beam Search works better for this task. The problem with Beam Search for practical applications though, is that if you want several headlines, it produces the same headline with only slight variations. Often, with just one word differences at the end.

Diverse Beam Search (DBS) \cite{dbs} is an alternative to BS which addresses the diversity issue by decoding in a doubly greedy manner, optimizing both the sequence probability under the model as well as the diversity.

At a high level, the \textit{B} beams are divided into \textit{G} groups. In addition, a similarity penalty is introduced. This penalizes subsequent groups sequence probability score by a similarity penalty term multiplied by $\lambda$, a parameter for similarity penalty strength. In this work, the similarity score is the integer number of times in previous beam groups the proposed token has been selected during this step.

In this algorithm we have \textit{G} separate groups of beam-search. For each decoding step, the groups of beam-search are advanced in consecutive order. For each consecutive token in the decoding process, the first group has no diversity penalty from previous groups, and as such is simply beam search. For each consecutive group, regular beam search is conducted but with the sequences penalized when the proposed token has been used in previous groups during this step. This adds diversity between the groups already from the first token if $\lambda$ is high enough, due to each group beginning the headline with a different token.

Vanilla DBS does not address the repetitiveness problem. While repetitiveness seems to happen less when generating headlines, it still does happen that a name or sentence is repeated. For this reason, we added a second penalty which is beam-specific: $\lambda_{repeat}$. This is a penalty applied to the probability score of continuations to a sequence when the proposed token has previously been used in the sequence in question. This is mainly to prevent outputs such as \textit{"Niinistö tapasi Niinistön"} (Niinistö met Niinistö). If $\lambda_{repeat}$ is set too high, then grammar can suffer due to proper suffixes being penalized too harshly. One could say that if the model was good, this should be unnecessary. Unfortunately, it seems that this repetitive behaviour is common in this type of MLE language model optimization.

The likelihood under the model for a sequence in Beam Search in general is the joint probability of the sequence calculated using the Chain Rule of Probabilities by multiplying each token probability conditioned on the context together. In this case of generating headlines, this causes shorter headlines to have a higher probability. They are usually safer but more boring headlines. In order to combat this, we added a decay parameter $\beta$. This parameter is multiplied together with the current log-probability of the sequence so far before adding the log-probability of the proposed token to it. The result is equivalent for $\beta = 1$ and results in longer headlines when $\beta < 1$.

Our implementation of DBS has 6 parameters in total. \textit{G} and \textit{B} for the number of groups and number of beams per group. We selected $G=4$ and $B=2$ for 2 beams per group and 4 output headlines, totalling in 8 beams. The maximum length of a headline is another hyperparameter which we set to 48 tokens. The 3 remaining hyperparameters $\lambda$, $\lambda_{repeat}$ and $\beta$ were tuned algorithmically, because it was too much manual work with unclear results to tune these manually.

We used Gaussian Process optimization \cite{gpopt} to select these 3 parameters. The objective function we used was the BLEU \cite{bleu} score of the generated set of headlines with regards to the true headline for 100 articles. We opted for GP optimization instead of grid search as grid search would have taken too long, as generating one set of headlines once already takes several seconds.

For $\lambda$ we observed two separate points of interest where the target (BLEU) is at maxima. These are consequently the points with highest search density for this hyper-parameter. The final values for the hyper-parameters were $\lambda = 0.71$, $\lambda_{repeat} = 3$ and $\beta = 0.87$. Notably, 3 was the maximum we had set for $\lambda_{repeat}$, making higher values possibly better still.

\section{Results and Evaluation}

As previously mentioned, the evaluation of models should be conducted in a way that measures the performance of the actual desired task at hand. For this reason, calculating BLEU or similar on an offline corpus is not an accurate representation of the performance of the model when it comes to generating real world headlines. The question we seek to answer in this paper is how well can this model perform in real-world use in the newsroom as a tool to help editors headline articles.

\subsection{Study Design}

To thoroughly answer the research question, we generated a set of headlines for new articles, and had domain-experts evaluate them by hand according to three key criteria. We picked 100 random news articles from Helsingin Sanomat (HS) and Ilta-Sanomat (IS) not contained in the original corpus. For each article, we generated four headlines using our implementation of DBS and the optimized parameter set. We made an Excel worksheet\footnote{https://zenodo.org/record/5985728} where each article had its text in one column, and in another column its four generated headlines as well as the real original headline in a random position in the headline set. The worksheet has a column for each of the three criteria which the evaluators fill with 1 for the headline passing the criterion and 0 otherwise. The criteria are in order of difficulty for the model to achieve, with the first criterion being the easiest and the third criterion being the most difficult. Additionally, passing a criterion means passing the preceding criteria as well. The criteria are language, usable and good.

\textbf{Language} If disregarding the article text, is the headline on its own correct Finnish? Does this headline make sense to a human being? We elected to have this criterion separate from the next one to get a better understanding of where and how the performance breaks down.

\textbf{Usable} Could this headline be used for the given text in the real world? Does it match the text in the news article without misquoting and without errors? This is the most important question in terms of how good this model is for real-world use.

\textbf{Good} Is this headline good enough for the editor to be comfortable publishing the article with it without feeling the need to edit it or come up with variants? This final criterion is a subjective one but we decided to keep it separate from the usable criterion as they are fundamentally different.

Additionally, there's an optional open feedback column, as well as summary open feedback at the end of filling in the excel.

Three editors, one from Helsingin Sanomat and two from Ilta-Sanomat volunteered to perform this evaluation. Each one has extensive experience in headlining articles, sometimes coming up with dozens of headlines in a day. The final answers for each question are selected as the majority vote of the three. It took two weeks for them to fill in their answers. The real headline was inserted randomly as a control for possible anti-machine bias and as a baseline reference \cite{framing}.

Out of the 500 headlines, 467 received an evaluation from all three evaluators. Some of the headlines were not evaluated due to the source text having been incorrectly parsed, leaving out names of people and places and was deemed by the evaluator(s) to be best left unanswered. Some headlines seem to have been simply forgotten. Most of the following tables have the metrics for the real and the generated headlines separate for baseline reference.

The acceptance percentages for each of the three evaluation criteria per individual evaluator are shown in Table~\ref{table:evaluatorstats}. We can see that evaluator A seems to have been able to distinguish between the real and generated headlines better than the other two evaluators, while evaluator B was the most forgiving.

\begin{table}[h!]
\centering
\begin{tabular}{||c| c |c |c |}
\multicolumn{4}{c}{Language} \\
 \hline
 Evaluator & A & B & C  \\ [0.15ex]
 \hline\hline
 Real & 1.0 & 0.97 & 0.785 \\
 \hline
 Generated & 0.79 & 0.90 & 0.775 \\ [0.15ex]
 \hline
\end{tabular}
\centering
\begin{tabular}{| c |c |c |}
\multicolumn{3}{c}{Usable} \\
 \hline
 A & B & C  \\ [0.15ex]
 \hline\hline
 0.91 & 0.80 & 0.77 \\
 \hline
 0.22 & 0.43 & 0.37 \\ [0.15ex]
 \hline
\end{tabular}
\centering
\begin{tabular}{| c |c |c ||}
\multicolumn{3}{c}{Good} \\
 \hline
  A & B & C  \\ [0.15ex]
 \hline\hline
 0.84 & 0.76 & 0.47 \\
 \hline
 0.13 & 0.40 & 0.20 \\ [0.15ex]
 \hline
\end{tabular}
\caption{The response acceptance ratio for each evaluator separately for Language, Usable and Good criteria separated by real headlines and generated headlines.}
\label{table:evaluatorstats}
\end{table}

The inter-annotator agreement per criterion measured by Fleiss' kappa~\cite{fleiss} is shown in Table~\ref{table:agreement}. Fleiss' kappa represents the degree of agreement when accounting for agreement by chance based on the ratio of passing versus rejecting the criteria. A positive number between 0 and 1 means there is more agreement than by chance, while a negative number between 0 and -1 indicates more disagreement than by chance.

For real headlines the degree of agreement is negative and close to chance. This is expected, as the majority of real headlines pass the criteria and the criteria are inherently slightly subjective.

The agreement in the three criteria for the generated headlines was modest but clearly greater than chance. The merely modest inter-annotator agreement shows numerically how the generated headlines often have errors that are hard to detect, as clearer errors would yield a high degree of agreement. The goodness criterion has the lowest inter-annotator agreement despite the model failing this criterion the most, as it is the most subjective.
\begin{table}[h!]
\centering
\begin{tabular}{||c| c |c |c ||}
 \hline
 Type & Language & Usable & Good  \\ [0.5ex]
 \hline\hline
 Real & -0.09 & -0.02 & -0.07 \\
 \hline
 Generated & 0.35 & 0.38 & 0.30 \\ [1ex]
 \hline
\end{tabular}
\caption{Inter-annotator agreement measured by Fleiss' kappa.}
\label{table:agreement}
\end{table}

The headlines performed equally well per brand, as seen in Table~\ref{table:product}. The language criterion was the only criterion where there was a notable difference between the brands. The model seems to have a slightly easier time with HS articles.

\begin{table}[h!]
\centering
\begin{tabular}{||c| c |c |c ||}
 \hline
 Brand & Language & Usable & Good  \\ [0.5ex]
 \hline\hline
 HS & 0.91 & 0.31 & 0.20 \\
 \hline
 IS & 0.82 & 0.30 & 0.21 \\ [1ex]
 \hline
\end{tabular}
\caption{Acceptance rates by brand. Both brands had approximately the same amount of headlines.}
\label{table:product}
\end{table}

The final result of the survey where the headlines are scored for each criteria according to a majority vote is shown in Table~\ref{table:summarystats}. A headline passes a criterion if at least two out of the three evaluators vote to pass. we have the real control headlines separate from the generated headlines as a baseline reference. Additionally, these tables show both the total acceptance rates as well as acceptance rates for headlines that have passed the preceding criteria. We can see that the language criterion is where the model performs by far the best as expected. The performance breakdown is clearly between the language and the usable criteria, as only 35\% of headlines that pass the language criterion pass the usable criterion as well. Of those that do however, 68\% pass the difficult final criterion.

\begin{table}[h!]
\centering
\begin{tabular}{||c| c |c |c ||}
 \hline
 Type & Language & Usable & Good  \\ [0.5ex]
 \hline\hline
 Real & 1.0 & 0.89 & 0.89 \\
 \hline
 Generated & 0.87 & 0.35 & 0.68 \\ [0.5ex]
 \hline
\end{tabular}
\begin{tabular}{||c |c |c ||}
 \hline
 Language & Usable & Good  \\ [0.5ex]
 \hline\hline
 1.0 & 0.89 & 0.79 \\
 \hline
 0.87 & 0.31 & 0.21 \\ [0.5ex]
 \hline
\end{tabular}
\caption{Summary for generated versus real headlines majority vote responses. The first table shows metrics for headlines that have passed the preceding criteria, while the second table shows the total for all headlines.}
\label{table:summarystats}
\end{table}

\section{Discussions}

Although free text generation is not the focus of this paper, the generative capabilities of the Finnish GPT are still noteworthy and relevant for the headline generation task. Evaluating the generative performance of a language model in-depth is a very time-consuming task, and we will outline the major findings we have with this particular model here. These findings mostly come from manually giving the model different prompts and using different parameterizations of top-p and top-k sampling to generate continuations.

From the logging of validation top-k next tokens and their assigned probabilities during training, it is clear that the output probability distribution for the next token becomes sharper as the training run progresses. The shape of the output distribution has a significant impact on sampling based decoding output, as sharp distributions produce less varied output. This makes generating a snippet during validation by using a fixed set of parameters for the sampling algorithm a poor way of gauging the progression of the true generative capability of a language model. Note that the temperature parameter directly affects the sharpness of the output distribution as well. For both top-k and top-p sampling, we found that a range of 0.6-1.0 was the usable range for temperature. Values of over 1 result in very random and nonsensical text, while values of less than 0.6 became very repetitive.

Repetition is known as the most prevalent pathology in text generation using deep neural language models \cite{repetition}. This pathology occurs the worst the greedier the decoding algorithm. Greedy decoding and vanilla beam-search decoding which try to find the approximate MLE generation suffer from this the most. Top-k and top-p sampling partially combat this, by using the random nature of the sampling to break repetition loops. The true reason for the repetitive behavior of current language modeling solutions is not understood.

The first form of repetition is in the form of repeating entire or partial sentences one, several or even infinite times, sometimes with a slight variation. This makes for text that does not resemble human text, and is not desireable.

The second form of repetition is the repetition of names, places and objects in a way that does not semantically make sense. An engineered example: \textit{"Sauli Niinistö tapasi keskiviikkona Tasavallan Presidentti Sauli Niinistön"} (Sauli Niinistö met the President of the Republic Sauli Niinistö on Wednesday). This sentence does not make sense, as a person cannot meet himself. In this case, it seems that the locally highly correlated continuation to "Tasavallan Presidentti" (President of the Republic), which is "Tasavallan Presidentti Sauli Niinistö" (the President of the Republic Sauli Niinistö) in the training data, overrides the fact that he should never be the prediction in this context conditioned on him being already mentioned in the sentence.

The opposite of repetition can occur. It can occur with greedier decoding as well but is more pronounced with sampling based decoding. Again, we class these into two main categories.

The first category is the direct opposite of the repetition of names and places. This is when a text mentions the name of a person, and the generated output suddenly swaps out the name for another name and continues the text with the new name. The severity of this varies depending on prompt length and context. If the context is very U.S Presidential heavy and the name supplied is \textit{Donald Bump}, it will likely be "corrected" to \textit{Donald Trump} due to the sheer volume of support for the latter in the corpus.

Interestingly, the second form of correction may actually have some use. This is when a sentence is repeated, but with more probable grammar. As an example, there may be a grammatical error in a quote, the model can then accidentally correct the grammatical error when repeating the quote.

\section{Conclusions}

The task was to create and evaluate a headline generation algorithm in the context of helping editors in the newsroom in the creative process. This is what was done in this paper. A neural language model was pre-trained on Finnish text, and fine-tuned to generate headlines. A decoding algorithm for diverse output was implemented. The resulting generated headlines were evaluated by domain experts to gauge the feasibility of this model in actual use. This sort of evaluation is the first we've seen when it comes to evaluating a headline generation algorithm.

The final conclusions are that while most of the time the generated headlines are very close to being usable, this particular implementation is far from ready in any sort of automated system. This comes as no surprise, as even with near perfect usability performance it would still not be used without a human in the loop. The algorithm in this work has potential and an expressed interest as a creative aid for the headlining process.

The most common errors especially for the language and usable criteria are clear and have potential solutions. Some of the errors can be tackled by pre- and post-processing such as the unsightly special character code printouts. The repetition errors, which were the majority of language errors, can be reduced with the repetition penalty. We hypothesize that several of the errors could be tackled with an adversarial and/or active reinforcement learning approach. The problem with generative pre-training seems to be that the model is only trained with what is correct, with everything else being equally incorrect. In reality when producing headlines, this is not the case.

The next steps would be the low-hanging fruit: tackling the error types specifically with parsing fixes and repetition penalty, as well as letting the fine-tuning process converge more. After that, trying more strongly correlated metrics as the decoding algorithm base score, and trying encoder-decoder type approaches as well as active reinforcement learning or adversarial approaches.

\section*{Acknowledgments}
Special thanks to Pipsa Havula (IS), Esa Mäkinen (HS) and Simo Holopainen (IS) from Helsingin Sanomat and Ilta-Sanomat for making the effort to evaluate the headline worksheet and provide feedback. Thanks to Helsingin Sanomat and Ilta-Sanomat for the corpus of news articles which constituted the bulk of the data used. Additionally we wish to thank the Finnish Computing Competence Infrastructure (FCCI) for supporting this project with computational and data storage resources. This work was partially financed by the Society of Swedish Literature in Finland with funding from Enhancing Conversational AI with Computational Creativity, and by the Ella and Georg Ehrnrooth Foundation for Modelling Conversational Artificial Intelligence with Intent and Creativity. This research has received mobility funding from Nokia Foundation under grant number 20220193.

redpajama_set_name: RedPajamaArXiv


A. Remember: Teaching about the period from 1865 to 1920 should be focused on how Americans came to grips with life radically changing from an agricultural to an industrial economy and society.
1. McGeer stated that America's social class differences were more pronounced during the years 1890 to 1920 than at any other time in American history.
2. At other times racial or sectional differences were more pronounced.
B. The roots of the American Progressive Movement as symbolized by Jane Addams and her book, Twenty Years at Hull House, (which McGeer stated has many inaccuracies or outright lies).
1. Jane Addams was born (September 6, 1860) into an Illinois farm family with farm values, and while she appreciated those values, she embraced urban, industrial values as well.
b. Jane lived a middle-class, Victorian lifestyle, and her family was Quaker—they did not drink alcohol and they were not ostentatious.
c. She grew up learning to work hard and control herself; self-control and self-denial were keys to provident living and happiness.
2. Jane Addams deeply loved and respected her father—the banker, legislator, and successful businessman.
a. At age three, Jane's mother died, and she disliked her stepmother.
b. Her father believed in women's education and encouraged and paid for Jane's education.
Jane was a good student and excelled in school.
Her father took her education and intelligence seriously; they had many serious discussions.
c. In the year 1877 she began attending Rockford College, but when she graduated, she faced significant challenges.
She was too educated for the majority of men to marry—she intimidated them or did not meet society's expectations.
Society expected women to marry and raise children, but for some reason she was unable to have children and she never married.
d. In the year 1883 Jane Addams had some type of personal crisis as she recognized she likely would never marry and her father died. She suffered a nervous breakdown, and seemingly had no place to go in life.
For a time she considered being a doctor.
For two years she traveled in Europe, which demonstrates she enjoyed tremendous advantages the vast majority of women did not have—her family had money.
Throughout the decade of the 1880s, Jane struggled with the question of what to do with her life beyond the societal expectations.
e. In the year 1887 Addams and a friend traveled Europe, and while in England she visited Toynbee Hall (an insane asylum), and it sparked in her the knowledge of what to do with her life.
a. Held classes for immigrants to assist in their assimilation.
b. She lobbied for legislation to assist the poor of Chicago and Illinois.
c. Supported "middle-class" reform for the lower classes.
5. McGeer stated that Jane Addams made a personal bargain agreeable to middle-class mentality: She would pursue a career to assist people, and in return, she would not marry and have children.
a. During the decades of the 1880s and 90s, it became acceptable for women to pursue careers as doctors, teachers, or other professions.
b. But if a woman married, she typically was forced to end her career.
c. Women schoolteachers could not legally teach in most states if they married.
1. More and more people no longer owned or were in the process of owning their own property. To be an independent property owner became increasingly more difficult.
2. The world of Addams' father—the self-made man—who could "make it on his own" became more unlikely and remote. The majority of Americans became employees or workers, and were not truly independent.
3. Wealthy people became more ostentatious—unlike her father and the ideology she was nurtured in.
a. Rich people publicly began to collect things.
b. Leisure time became common among the wealthy and how to use it debated.
c. Newspapers began to report—society pages—on the activities and lives of the wealthy.
d. George Pullman was an example of how the wealthy crushed and used the poor with the Pullman strike (1894). Addams became disillusioned with the power and wealth of the great industrialists.
e. The Pullman strike forced Addams to recognize America's wealthy could and did at times mistreat their workers and dramatically harm them, their families, and their values.
4. As a result of experiences at Hull House and association with George Pullman and the strike, Jane Addams consciously rejected her father's values because she believed they failed at the end of the 19th and beginning of the 20th Centuries.
a. She decided she must become a reformer or Progressive.
b. She decided to deal with the circumstance of the times, the middle-class values of her youth must be abandoned; that is, property ownership, consumption of consumer goods, and complete independence.
c. The abandonment was necessary because the wealthy people of America failed in their decisions and policies that harmed many people, their families, and communities.
d. She attempted to resolve the tremendous crisis of class conflict between the few very wealthy and powerful and the many of the lower class.
f. McGeer argues that American history focuses on the middle-class not because the middle-class writes it, rather, he contends that throughout American history, it was the middle-class that recognized the intensity of problems and the need for change, and then adapted more quickly than the upper and lower-classes.
D. Progressivism began the conflict between the traditional values of American individualism and the need to restrain that individualism by regulation in order to protect all Americans.


go the whole wide world just to find her.
where the Caribean Sea is blue.
because nobody's talking about you.
go the whole wide world just to find her!
to find out where they hide her!
trying to pick up a girl.
when there's girls all over the world.
hoping that I won't be long.
caressing her warm, brown skin.
we'll be sharing the same next of kin.


Setthasiri San Sai has to be one of the most beautiful villa developments currently on offer in the Chiang Mai area. The architecture is typically Thai in nature – a blend of traditional Lanna style for which the area is noted, blended with minimalist, modern, clean lines. Each of the villas which comprise this upmarket and exclusive development benefit from large terraces on the upper-floors, which offer beautiful views of the distant mountains to the north and, in some cases, of the river that winds its way through the community.
Potential buyers have four different styles of homes to choose from at Setthasiri San Sai. Three of the models are three-bedroom/three-bathroom villas – ranging from the smallest Kirisiri model with 160m2 of internal space, through the Wareesiri which has an internal area of 170m2, up to the significantly larger Naphangsiri model, which is a roomy 240m2. For those who require even more space, the project also offers a four-bedroom/four-bathroom option – the Tarasiri, which covers a total of 283m2.
In total, the project will consist of 160 residences spread across a large plot of 53-rai. Included in this area are two different parks, offering relaxing green areas to all residents without the need to move from this secure, gated community which benefits from constant security. At the centre of the project is a Clubhouse, which offers communal facilities to all residents.


text:
Pubs and bars in Nottingham
Nottingham's secret and hidden bars - find them if you can
There's something exciting about a secret bar
Lynette PinchessLife writer
Serve the food you really want to eat by signing up for our tips and recipes and never be bored by your cooking again
Sssh - can you keep a secret? Nottingham has an abundance of secret and hidden bars.
From a hideaway wine bar to an old-style speakeasy and exotic tiki bar, there are more than enough for a bar crawl... as long as you can find them.
The highest bar offers fantastic views of the city centre, and the deepest, underground in the caves, is a unique experience while sipping gin and cocktails.
Here's a round-up of where to find them and what to expect... but we won't be spilling the beans about exactly how to get inside some of them.
Boilermaker in Carlton Street (Image: Mark Fear)
Nottingham's first, and original, secret bar.
There's no clue whatsoever from the outside (apart from a queue at peak times) so don't be surprised if you walk straight past without giving a second glance.
Behind the fake facade of a drab boiler shop, there's a broom cupboard with a twist to negotiate before getting into the actual bar.
Once inside inside a lively, colourful environs with a touch of Moroccan riad about it awaits. Simply sit back and enjoy as it's all table service.
The cocktails are banging and a whole lot of fun with quirky names such as Pornstar Mankini, Liquor-rettes & Alcohol and the iconic 'bio hazardous' No Shrubs by QMC.
Expect to pay: £8 a cocktail.
What you say: "This place is great for something a little different. Fronted like an old boiler shop where you go through a secret cupboard into a lavish and lovely, classy bar. Great selection of drinks and table service. Good and knowledgeable staff," says Kimtrepid
Find it at: 36b Carlton Street, Hockley
The Hockley Arts Club
Looking lovely on the top floor
The Hockley hideaway, accessed down a passageway, is huge with three floors of escapist fun. Each level has a different vibe.
When the lights go down Stockholm, on the ground floor, comes alive with a clubby atmosphere till 3am; the Blue Rooms on the middle floor has a more chilled vibe and table service at the leather booths and comfy sofas; and the top floor bar, Electric Garden, is the prettiest of all with Japanese artwork and pink blossom trees.
Ingenious cocktails include Twister (the one with the lollipop), Outkast (with a Polaroid photo) and the exotic Mambo #5.
Expect to pay: From £7.95 to £35 for a sharing cocktail.
What you say: "A must if you are in Nottingham. This is definitely one of the best bars I've been to. Beautiful place. The place is just stunning, go check out the Japanese garden. We didn't dine, but the drinks were awesome, almost as good as the place itself," says Johan Sneath.
Find it at: 20 Carlton Street, Hockley
How do you find your way into Lost Property?
Don't go thinking you can find your mislaid umbrella or jumper in here. Go down the alleyway (on the opposite side of the street to The Hockley Arts Club) and you'll come to the 'lost property office' and the secret entrance - no spoilers here, but one visitor says: "As you enter the 'feel is of a dubious venue in Amsterdam.'"
The quirky decor continues the lost property theme, with old-fashioned suitcases, a wall of brown labels with messages left by revellers, a bar made up of pennies, and a stunning illuminated back bar.
Cocktails evoke a sense of nostalgia. Fallen Pornstar, a twist on Pornstar Martini, is served with Babycham instead of prosecco and Lost Child contains Jelly Baby infused gin.
It's advisable to book at weekends to guarantee entry.
Expect to pay: Cocktails from £7.50.
What you say: "A very fun lobby/entrance - once you've found it. Unless you go looking for it, I suspect you'll never know it's here. I won't spoil the surprise of how to get in. Inside is much more like a bar but with a mad decor. Everywhere you look there is something to look at," says UkGMan
Find it at: 7½ Carlton Street, Hockley
Inside The Resistance (Image: Nottingham Post)
The Resistance a hidden bar within a hidden bar, beneath Lost Property. During the summer the focus is on private hire but come September it will be open to the public once again on Friday and Saturday nights.
It's all about vodka in the dimly lit bunker decorated with 'Join the Resistance' graffiti, dynamite, demolition chargers and chisels.
Bottles are chilled at -10°C and there's more than 60 varieties to sample while plotting the revolution.
The Resistance (Image: Nottingham Post)
Expect to pay: From £3.50 to £7 for premium brand vodka.
What they say: No reviews.
Find it at: Beneath Lost Property, Carlton Street, Hockley
Lost Caves
The colour-changing lights and fairylights provide a warm glow (Image: Angela Ward)
A real hidden gem, deep underground, this is definitely one of the coolest places for a drink in Nottingham both in temperature and vibe as no matter how hot or cold it is outside, there's always an ambient temperature.
The third bar in the Lost Property trio is another level below. Head down the stairs, holding onto the rope banister for safety to the sandstone caves, which are decked out with faux fur throws and twinkly lights, creating a magical cave dweller kind of setting.
The focus is on gin but other drinks are available.
Visitors need to book or they won't get a seat at the table-service bar. Couples can even get married in any of the Lost Property bars since they come under the Mercure Hotel's marriage certificate.
Expect to pay: Bottled beer from £4, G&T from £6
What they say: "Head down the stairs and you're transported to a hidden gem, a gin and tonic bar within a natural ancient cave. Sure it's a bit damp and it has a bit of a smell but that soon fades. Awesome bar, friendly staff, table service, it's all good. Highly recommend," says Oliver Mackley.
Find it at: Via Lost Property, just keep going downstairs...
The Tiki Bar - Lost City
Tiki Bar at the Lost City
Unless you're playing adventure golf at The Cornerhouse's Lost City, you'll never stumble across the hidden bar by accident.
But the dark, exotic Polynesian-inspired bar, on a new mezzanine level, is open to anyone, not just golfers.
The rum-fulled oasis, with seating for 80, is decked out with exotic plants, Hawaiian-style wicker tables and chairs, and a tiki beach bar shack.
Colourful cocktails include Jungle Sapphire, a gin and plum liqueur mix, Blueberry and Elderflower Mojito, and Violet Ark, a heady combo of vodka, rum, Archers, Cointreau and violet gin.
Mocktails, Slushtails, beer, wine and all the usual spirits are on sale. If you're feeling peckish, there's a light bites menu.
Expect to pay: Bottled beer from £3, cocktails £6.50 (2-4-1 every Monday to Thursday from 4.30-6.30pm) and coffee from £2.
What they say: "I don't like to overuse the phrase 'hidden gem' but I really do think that, in this case, it's deserved. With giant screens depicting tropical weather, live music from 7.30 pm on a Saturday night and corking cocktails, it should definitely be on your list of places to visit on an evening out in Nottingham.
"Be careful though – it would be very easy to believe you aren't in Nottingham anymore - especially after a few cocktails - so prepare for the shock when you leave!" says Things To Do in Nottinghamshire.
Find it at: The basement of The Cornerhouse, Burton Street
Another? Wine Bar
The prosecco and champagne garden by night
Wine lovers are in for a treat, both inside the cosy rustic-style bar and outside on sunny days on the prosecco and champagne terrace.
The easiest way to find this very civilised bar is look out for Green Healthcare and Another? Wine Bar's door to the right of the shop, then head upstairs.
The self-service bar allows drinkers to top up their wine glasses with a quality range of white, red and rose, at prices to suit all pockets in a choice of three sizes, 25ml, small or medium.
With 65 different wines by the glass, there's something for every taste and pocket.
Expect to pay: Samples range from £1.22 to £10.93, and glasses from £3.05 for house wines to £27.32 for 2015 Tignanello.
What you say: "I've been here twice with my boyfriend now and we're in love with this bar. We're both massive wine lovers, and the interaction of self-serve was so inviting and appealing.
"The decor is dimly lit, romantic and private and even on a Saturday night it's never too busy or rowdy. The staff members are polite and helpful and the selections of wine available are outstanding," says Julia C.
Find it at: 9 Trinity Square.
Ginsecco
Views from Nottingham's highest bar Ginsecco
Not secret, but certainly hidden, is Nottingham's highest rooftop bar at the very top of The Cornerhouse. Nottingham.
The recently opened spot specialises in gin, cocktails and prosecco and is very much geared towards a female crowd and couples on date nights.
Drink the afternoon or night away in elegantly furnished Art Deco style surroundings, where the vast windows have fantastic views of the city centre and beyond. Just like the sign in there, act like Audrey, dress like Jackie and party like Gatsby.
And don't forget to pose in front of the angel wings for a great Insta pic.
Expect to pay: Around £4-5 for a gin, cocktails £8 or a bottle of Dom Perignon Vintage Champagne £195.
What you say: No reviews
Find it at: Third floor of The Cornerhouse, Burton Street
Co-owner Luis Alonso with manager Goda Kacileviciute (Image: Nottingham Post)
From Nottingham's highest rooftop bar, to one of the lowest. Bunk has made good use of underground space in three city centre basements to deliver cocktails, wings and good times to a fun-loving crowd.
The bars follow a familiar theme with neon lights and shrubbery but each have their own unique twist.
Pair a Parma Violet cocktail or Zombie, with five to 100 wings which can be as plain and simple or spicy as you can handle by adding one of the 14 seasonings and sauces, from maple and bourbon to eye-wateringly hot naga viper chilli.
Expect to pay: Around £7 for cocktails and wings from £7.50.
What you say: "What a funky place and completely unexpected! Underground, this place is filled with fake green plants creating a cosy atmosphere. We sampled quite a few different cocktails which inevitably led to us sampling the food menu which consists mostly of chicken wings, which were really tasty.
"Don't go if you want a meal, but definitely worth a visit if you want cheap and tasty cocktails and some munchies. Great little find!" says MrsJKeeling.
Find it at: 19 Stoney Street, 32 Lower Parliament Street, and 31 Wollaton Street
(Image: Mark Fear)
For late-night secret shenanigans, the underground bar is open until 2.30am for cocktails and a boogie. Before a rebrand, it was known as the Bunker which gave a much bigger clue to its identity.
Quirky cocktails, beer (or cocktail) pong, neon lights and abstract artefacts make obscurely-named bar the unique nightspot it is. Kick off the weekend with a massive sharing Pornstar Martini and let your hair done to a playlist of the best 70's disco, 80's pop and 90's hip hop.
Or head there on a Wednesday for the weekly rock night Jump Ship.
Cocktail lovers can lap up the classics (Amaretto Sour, Mojito, French Martini etc) or one of bar's showstoppers such as Kinder Martini, garnished with a chocolate bar or its signature Walrus with popping candy.
Expect to pay: Happy Hour between 5-9pm on Friday and Saturday all cocktails are two for £8, other times around £7 each.
What you say: "I had booked a party booth online for my friends hen party. Wow!! There was around 19 of us included in the hen party we had such an amazing time in The Walrus! So much fun! We played cocktail pong, bar staff, domestics and security were all lovely! We danced the night away to 80's and 90's music," says a Design My Night reviewer.
Find it at: 19a King Street
The Secret Garden in Trinity Square (Image: Mark Fear)
As the name suggests, it's secret and it's a garden. Despite being in the heart of the city centre you wouldn't know of its existence.
But on a warm summer's evening the hidden gem is rammed with revellers enjoying an outdoor tipple. And even on cooler evenings, drinkers can keep warm thanks to the heaters and blankets.
Look out for the entrance via one of the city's cut-throughs. An anonymous-looking green door is surrounded by fake foliage with a mysterious 'SG' sign.
But come opening time the door opens, with a path to the garden, where cocktails, beer and prosecco are served from a wooden bar, while drinkers sit in a green haven with shrubs, flowers, trees and grass, some fake and some real.
The bar is open seven days a week until 1am (7pm on Sundays) unless "it's raining cats and dogs" and then it's likely to be closed as there's no where to take cover.
Expect to pay: Cocktails £8. Happy hour 4-7pm, Monday to Friday, buy one get one free. Saturday and Sunday two for £10. Beers from £4.
What you say: "I have been to this hidden gem, several times over the summer. It really is great to spend time in the sunshine here. It's a right little sun trap with a wide selection of drinks and cocktails to choose from," says Matthew Walker.
Find it at: 17 Trinity Walk
GINious
Jennie Martyn Eden, 41, is opening a new gin bar GINious (Image: Ian Hodgkinson / Picture It)
Secret bars aren't the prerogative of the city centre. Head to Beeston and you'll find (if you look hard enough) a hidden gin bar.
A narrow alleyway, sandwiched between Bleu Hairdressing and The Frustrated Chef, leads around the back to a staircase.
Inside is a rustic, vintage-style bar serving everything you need for either a refreshing G&T or cocktail. Drinkers are in for a treat with more than 60 hand-picked gins, with some ingenious flavours amongst them, such as exotic orange zest and coriander, Unicorn Tears and Clotted Cream.
It's not a problem if you're not a gin lover as there's a selection of wines, draught beer and prosecco.
The antipasti sharing boards are pretty tempting too, laden with cheese, cured meats, olives, scotch egg and sausage rolls.
Expect to pay: £4.95 for a gin.
What you say: "Being located above a hairdressers I was expecting this place to be tiny but it was surprisingly spacious. Friendly staff and a lovely atmosphere and nice drinks," says David Ramsden.
Find it at: 100 Chilwell Road, Beeston
Das Schuhzimmer
Das Schuhzimmer in Chilwell
Passersby thought this was a shoe shop, albeit a strange one with odd opening hours. But the footwear display is a front for a secret bar in Chilwell.
Das Schuhzimmer (translation: the shoe room) is a cosy intimate setting, next to The Berliner. Open three days a week, from Thursday to Saturday, there's cocktails, wine, gin and beer to savour. Food is served in the 'secret kitchen' on Friday and Saturdays, provided by a different street food trader every month.
Alongside the classic cocktails are the bar's own creations including Lychee and Lime Martini and Banana Cream, plus a range of gin and draught and bottled beers.
Expect to pay: From £6.50 for a cocktail, and £4.10 for Peroni. Friday and Saturday nights are ticket-only (to be pre-booked) with a main course and drink for £15.
What you saying: No reviews
Find it at: 10 High Road, Chilwell
The Cod's Scallops gets a visit from the Food Sleuth
Petit Paris owners to open new French restaurant in Nottingham - and it will include an Eiffel Tower water feature
Things to do in Nottingham
food&drinkHomemade cake shop opening in landmark building in NottinghamThe family business is expanding
Sadness after Nottinghamshire pub landlord says farewell after 50 years in trade
Pubs and bars in Nottingham'Put me behind the bar and that's been my stage'
50 Harry Potter quiz questions and answers including rounds on spells, creatures and Hogwarts
Harry PotterHogwarts expert? We've created a tricky quiz for you to take on
Doughnotts, Dunkin' and Krispy Kreme - who sells the best doughnuts in Nottingham?
food&drinkThe local company goes head to head with two international giants
Gym that's not 'intimidating' opens in Arnold
ArnoldOnly 15 people are allowed in at any one time
FamilyTwycross Zoo gives message to Nottingham ahead of new attraction openingEach Discoverer will also be awarded an exclusive 'Keeper for the Day' experience within the new land
Job for snorers: Earn £300 for sleeping in dream job opportunity
Family"An exciting new role this year"
Homemade cake shop opening in landmark building in Nottingham
food&drinkThe family business is expanding
Celebrity Gogglebox star Eddie Boxshall issues cryptic message on split from Denise Van Outen
In the NewsThe couple regularly appeared on Channel 4's Celebrity Gogglebox
ITV Good Morning Britain viewer 'sick' of Robert Rinder - but star hits back
ITVAn exchange was played out on Twitter
Nottingham City CouncilLight Night to return to Nottingham over weekend next month with theme of 'Rebel City'It will take place over two nights
'Surprise' transfer link gets Nottingham Forest fans talking amid Jed Wallace interest
Nottingham Forest FCThe Reds remain keen on signing the Millwall winger before the January transfer window closes

redpajama_set_name: RedPajamaCommonCrawl


Balram Das Tandon was an Indian politician and the Governor of Chhattisgarh. He was born on 1 November 1927 and died on 14 August 2018 at the age of 91 due to cardiac arrest in Raipur.
Tandon started his political career as Corporator of Amritsar Municipal Corporation in 1953.
He has Done graduation from Punjab University,Lahore.
Tandon was a founding member of the Jan Sangh in 1951, and served as secretary of Punjab Jan Sangh from 1951 to 1957.
He was elected to Punjab Legislative Assembly from Amritsar in year 1960,1962,1967,1969, and 1977, and from Rajpura in 1997.
He was imprisoned in 1975 during the Emergency Period for 19 months.
He worked as the president of the Punjab BJP from 1995-97.
On 14 July 2014 he was appointed as the Governor of Chhattisgarh.
His Wife name is Brijpal Tandon, who was also an activist in the freedom movement.
Balram Das Tandon got married in year 1958.
has two daughters, Alka Kawatra & Poonam Batra who live in New Delhi and one son, Sanjay Tandon, who is also part of the political field for BJP, in Chandigarh.


text:
Q: Run msdeploy.exe from under DefaultAppPool produces weird behaviour Intro
I have this ASP.NET web app and I'd like to provide its users a way to update the app via POST request with zip file which contains the newer version of the app.
While successefully receiving, storing and using the zip file, I found that msdeploy.exe program does not work, meaning my app is not getting updated.
I am using the following command line syntax:
msdeploy -verb:sync -source:package={0} -dest:auto -enableRule:DoNotDeleteRule

(where {0} is replaced by enquoted full path to the zip file)
The issue
While successefully installing the update via Windows© Shell manually, I fail to do so when I start msdeploy from withing my web application. The code I'm using:
ProcessStartInfo info = new ProcessStartInfo()
{
    FileName = Path.Combine(workingDir, "msdeploy.exe"),
    WorkingDirectory = workingDir,
    CreateNoWindow = true,
    Arguments = @"-verb:sync -source:package={0} -dest:auto -enableRule:DoNotDeleteRule -verbose".Formatted(filename.Enquote()),
    UseShellExecute = false,
    RedirectStandardOutput = true,
};

Process deploy = Process.Start(info);
deploy.Start();

Debugging attempts
I have redirected the program's output into a file so I could compare it to the ideal one, the manual way one which actually works.
Working msdeploy output (run via my windows account + windows shell):
Info: Updating file (Default Web Site/ElQueue\bin\AutoMapper.dll).
Info: Updating file (Default Web Site/ElQueue\bin\Contracts.dll).
...
Info: Updating file (Default Web Site/ElQueue\Views\Web.config).
Info: Updating file (Default Web Site/ElQueue\Web.config).
Info: Adding ACL's for path (Default Web Site/ElQueue)
Info: Adding ACL's for path (Default Web Site/ElQueue)
Total changes: 27 (1 added, 0 deleted, 26 updated, 0 parameters changed, 803122 bytes copied)

Non-working msdeploy output (run via Process.Start hence IIS AppPool\DefaultAppPool IIS' virtual account):
Warning: BACKUP_FAILED - Skipping backup because it failed due to an unknown reason.яFor more information, contact your server administrator.
Info: Adding sitemanifest (sitemanifest).
Info: Creating application (Default Web Site/ElQueue)

And, that's all. Not much. No file updates, no nothing. The application's files remain unchanged.
SO, how to update my web app the right way?
I thought msdeploy is the way, but its seems not to work with default IIS app pool.

A: How do you authenticate your users? If it's Windows authentication the best option would be to configure Web deployment delegation rule. Making your pool account identity part of local Administrators may have serious security issues.

A: Solved the problem by adding a IIS AppPool\DefaultAppPool user into Administrators group via lusrmgr.msc management console tool and also reboot after that.

redpajama_set_name: RedPajamaStackExchange


text:
> Matosevic wins second-straight play-off
Matosevic wins second-straight play-off
Update 4:50 pm. Marinko Matosevic has defeated No.5 seed James Duckworth to secure a wildcard into the main draw of Australian Open 2012.
Melbourne Park, 11 December 2011 | tennis.com.au
The final of the men's Australian Open 2012 Play-off pitted 26-year-old Marinko Matosevic, last year's winner, against James Duckworth, a 19-year-old from Sydney with a very bright future.
But it was Matosevic who swept all before him today, cruising to a very comfortable straight-sets victory 6-4 6-1 6-2 and booking his place in the main draw of the 2012 Australian Open for the second consecutive year.
> Australian Open 2012 Play-off photos
The match was a fairly one-sided affair, with Matosevic in control from the outset, moving smoothly, striking the ball well, and looking very solid on serve. In fact, he did not drop serve all match and only faced break points in one game.
The pair started evenly, but at 3-3 Duckworth stretched wide during a rally and rolled his right ankle. Despite continuing, he dropped serve and then asked for the trainer at the change, using all his allotted time to get his ankle worked on and strapped before returning to the court.
While he seemed to be moving freely, it may have had more of an impact than was clear, as a distinct change came over the match from that moment onwards. Duckworth appeared listless and increasingly frustrated – and Matosevic never looked back. The games went with serve for the rest of the first set, with the initial break enough for Matosevic to take it 6-4.
As the second set began, it looked like Duckworth was trying to end the rallies a little more quickly, potentially due to his ankle injury. He consistently pulled the trigger on his ground strokes too early in the rally and more often than not came up with the error. Matosevic again pulled away, with Duckworth's disappointment beginning to surface. At 5-1 down, he looked to have all but conceded the set, slamming a forehand return into the bottom of the net, and eventually giving the set away 6-1.
> Watch Australian Open 2012 Play-off match highlights
The third and final set brought more of the same and in the fourth game, Matosevic again made his move, breaking for 3-1. It was now Matosevic in a canter and another Duckworth error gave him the final set 6-2.
After the match, Matosevic addressed the Duckworth injury by saying, "I tried to just put it to the back of my mind and get the job done."
"I know his game pretty well," he added. "[Duckworth] is a very good player and I wish him all the best."
Matosevic said he had a tough year in 2011 with lots of ups and downs but was clearly pleased with how things went today. "It's a great opportunity and an honour and privilege to get a spot in a Grand Slam by winning this event."
Matosevic achieved his highest ever world ranking of 131 in March and made the Australian Davis Cup team for the first time before an injury at the US Open disrupted his year and saw him drop to 201 by year's end.
But he is upbeat about his future. "I beat a stack of guys in the top 100, some guys in the top 50 … so I'm definitely confident of getting a two-digit ranking going further," he said.
"I travelled most of the year with Matt Ebden and saw him jump into the top 100 … and not just him [either]."
"For sure, if they can do it, I can do it."
> Australian Open 2012 Play-off videos
Duckworth, on the other hand, was looking for a first-time berth in a Grand Slam main draw. He has come off a breakthrough year, ending it ranked 274 in the world, courtesy of four Futures event wins on clay in Italy and Poland.
Despite the loss today, he has played some extremely solid tennis this week and looks on the brink of further success. His semifinal victory over top seed Greg Jones in the play-offs was a highlight.
He will now look ahead to the Australian summer and may still be offered a discretionary wildcard into Australian Open 2012, but his fate is now out of his hands.
> View the men's draw
> View the women's draw
Related Aussie profiles
James Duckworth
Marinko Matosevic
Kyrgios exits in Australian Open showstopper
Kokkinakis and Kyrgios off to a winning start in AO 2022 doubles
Aussie charge continues on day three at Australian Open 2022
Kokkinakis bows out at Australian Open 2022


Christina completed her undergraduate degree in Applied Mathematics before majoring in Analysis and Modeling in Applied Sciences in her Master's degree at the University of Crete, Greece. During her Master's dissertation, she focused on systems of Brownian particles in Stokes Flow getting interested in Partial Differential Equations, Stochastic Differential Equations and Fluid Mechanics. Not only would Christina like to benefit from the increased depth of knowledge of MIGSAA courses and gain skills but she would also specialize in Numerical Analysis and Stochastic Processes, especially computational techniques for stochastic systems applied to chemistry and biology. In her free time, she enjoys cooking greek traditional recipes, listening to jazz music and attending yoga lessons.


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Hailing Frequencies Open – 3/29/17 – Hittin' the Wall
by Brian Rubin 03/29/2017 · Op-Ed
Yyyuuup.
Hey y'all, happy Wednesday! Today's Battle for Orion 2 video could go along with hump day, because rather than getting over one, this one causes me to crash into a wall. I actually felt really frustrated after this session, so much so I had to walk away and take a break. It really left me demoralized, because I felt like I was spinning my wheels again and going BACKWARDS. I still have two more videos to make for the series, which I plan to do tonight, but I still feel so bitchslapped from the campaign that I'm gonna instead showcase the game's built-in editor and battle generator, both of which are pretty great. It's a shame, really. I feel like I'm playing the game incorrectly, and it's making the game look poor as a result. It's not a poor game at all, I'm just bad at it.
Last night's podcast was a technical clusterfuck. First off, my PC apparently HATES Space Engineers. I've 12GB of RAM, and at one point the game was using nearly all of it, to the point where the PC froze a few times. I was worried I was gonna get a BSOD or a hard reset while live, but thankfully that didn't happen. What DID happen was that, annoyingly, one of those freezes caused my mixer/recording program, VoiceMeter Banana (which is wonderful much of the time, to qualify) to crash or freeze or something, wherein the recording stopped about halfway through the podcast. SO ANNOYING. So now, AGAIN, I have to rip the audio from YouTube. This is on TOP of all the issues I was having with Discord, which was unhappy throughout the entire show, cutting out, giving me bandwidth errors and so on. I couldn't even change the server or anything. REALLY frustrating, but in the end I thought we did an okay show. It's just super frustrating when you DO have things set up correctly (like actually hitting the record button) and things still go to pot. Ah well, I'm sure it'll be fine in the end.
Before and after the show, I installed and played a bit of Secret Weapons over Normandy. Wow, that is some good stuff. Seriously good. More arcade sim and less simmy than Secret Weapons of the Luftwaffe, but that's an advantage, as this game is super easy to get into, and VERY fun. The combat is super satisfying, and pleasantly surprisingly, it installed and ran on Windows 7 without a hitch. No administrator this or compatibility that. A nice change of pace, that was. I also spent a little time with the tutorials in Guns of Icarus Online, since the co-op/PVE version of the game, Guns of Icarus Alliance is coming out in a few days. What a fantastic little game. The strict PVP thing is what kept me from playing the original, so I'm super excited about the PVE nature of Alliance.
So, onto newsy stuff. In release news, you can now get Space Rogue Classic on Steam! As you may recall, I wasn't fond of the game itself, but it's a fascinating part of the genre's history. Also releasing soon is Starfighter Origins on April 18th! I'm not sure if this will as yet be an early access or full 1.0 release, but I hope to find out soon. Finally, this massive Space Pirates and Zombies 2 patch adds a huge new map and a ton of other stuff.
In other newsy news, the Wing Commander CIC has some fantastic scans of Wing Commander articles from Computer Gaming World magazine. Arkin had a nice big dev update talking about their work on the game, which looks like it's coming along nicely. Infinity: Battlescape also had a little dev update as well.
Oh, finally, if you've not already voted, check out the poll I'm running about what to do with the Monthly MMO Meetups, I'd love to hear your thoughts.
That should do it for today! Have a great one y'all!
Tags: wing commanderspace rogueSecret Weapons of the LuftwaffeSpace Engineersspace pirates and zombies 2Infinity: BattlescapeSpace Rogue 1989ArkinStarfighter OriginsBattle for Orion 2Secret Weapons Over NormandyGuns of Icarus OnlineGuns of Icarus Alliance
Here We Go… – Gaming Affirmation for 9/16/19
Feelin' Hot Hot Hot! – Hailing Frequencies for 10/24/17
Spacey News 11/12/13: Lots of Good News Incoming!
Next story Hailing Frequencies Open – 3/30/17 – Stellar Cartography
Previous story Site-Stuff: Monthly MMO Meetup Machinations


A stunning must-have for every bride with an ivory wedding theme, classic design and so elegant. Featuring in this round bouquet are ivory Chloe roses in a hand tied bouquet with a domed shape on top, with a diamante in the centre of each rose. The handle is neatly wrapped with an ivory luxury satin ribbon with a matching bow.
I am very much satisfied with the diamante bouquet it looks stunning and a good finish.


This role reports to the Programme Manager in the Crew Management Programme (CMP). Programme Manager reports to General Manager CMP and is responsible for the delivery of the transformative programme to the business owners, driving the adoption of new ways of working supported by improved systems and processes. This role places focus in the implementation phase of Jeppesen Crew Rostering (JCR) implementation under CMP.
The Cutover Manager is responsible for the successful cutover and implementation of the Jeppesen Crew Rostering solutions to scope, schedule, budget and quality defined under the Crew Management Programme. Cutover and implementation refers to all activities required to plan, prepare, rehearse, manage and successfully execute cutover for the airlines, business units and crew communities, and also IT applications in scope of the Crew Management Programme.
The Cutover Manager will collaborate closely with the other managers and team leads in CMP including Delivery Project Manager, IT Project Manager, Change Lead etc. as well as the relevant business stakeholders to ensure overall programme goals will be met.
Delivery responsibility for all deliverables required for cutover and implementation – ensuring complete coverage, appropriate quality, on time and to budget delivery.
Leadership responsibility for the cutover and implementation stream – people leadership, scope and direction setting, motivation management.
Stakeholder management of business units, vendors involved in cutover activities, including organising and driving relevant team/stakeholder meetings to achieve outcomes and decisions.
Budget reporting for cutover activities.


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City-Sized Lava Tunnels May Hide Under the Moon's Surface
March 29, 2016 By Carol Harper
While the moon is constantly being investigated despite a lack of human presence on the natural satellite, many things remain unknown. Precisely because of that lack of direct investigation, many things that could have easily been figured out are still a mystery. And the moon does indeed have its fair share of mysteries.
Since experts managed to finally confirm how the moon was formed just a couple of months back, it stands to reason that other elements pertaining to that that information are just now being investigated. One of these is a highly hypothetical theory according to which city-sized lava tunnels may hide under the moon's surface.
Theia and the moon
Just a few months back, a long standing theory has been confirmed about the origin and formation of the moon. Theia, a small, young planet that was once part of our solar system, collided with Earth while it still was in its molten rock state, knocking off a chunk of it while Theia was almost completely obliterated. But gravity works wonders in space.
As the chunk of Earth flew off, it remained in Earth's orbit and fused with the remaining bits of Theia that hadn't yet been incorporated into the planet. As the chunks kept orbiting Earth, they came together, forming the moon as we know it today.
City sized tunnels
Since we know that the moon was made of molten rock at least in the beginning, it would stand to reason that this lava cooled from the surface inwards. This is where the theory comes in, claiming that this molten rock left huge tunnels, some the size of cities, underneath the moon's surface.
While stable tunnels as big as four times the size of Philadelphia wouldn't be physically possible on Earth, the moon's reduced gravity actually makes them perfectly plausible. Even though everything is still only speculation, multiple hints would suggest that the theories might be right. And let's be honest, even the scientists behind the study find the idea of developing an underground moon base very exciting.
NASA' GRAIL data
According to the research team from Purdue University, the best evidence for their premise comes from the data collected via NASA's GRAIL spacecraft. If such tunnels were real, a dip in the astral body's gravity would be present above them. Some ten such anomalies were found during the spacecraft's fly-over.
Even though the tunnels can't be confirmed via a spacecraft, the dips in gravity are definitely a positive sign. Now, the research team is proposing to send LAROSS to the moon, a robot capable of digging through the surface and finding out whether the theories hold true. But that's going to be a while.
Image source: Wikimedia
About Carol Harper
Carol Harper began her career as a screenwriter before turning to journalism. Before earning her Bachelor of Arts with a major in Creative Writing, Carol travelled across Europe and Asia to find both herself and inspiration. She enjoys covering health & science topics.
Filed Under: Science
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Home » Interviews » [INTERVIEW] Michael Swanwick Talks About His DARGER AND SURPLUS Characters
[INTERVIEW] Michael Swanwick Talks About His DARGER AND SURPLUS Characters
Posted on April 18, 2016 by Carl Slaughter in Interviews // 2 Comments
Michael Swanwick is 3rd behind Mike Resnick and Connie Willis for Hugo nods, he has been nominated for many Nebulas, and his name pops up constantly in Locus and Asimov's readers polls.
My interview with him for Diabolical Plots, wherein he dispensed some of the most insightful writer advice ever, was translated and reprinted in Science Fiction World, the leading science fiction magazine in China, where he is no stranger. While teaching college in China, I assigned a couple of his shorter works to my English majors specializing in translation. Namely "Hello Said the Stick" and "The Dead".
He hails from Philadelphia. He blogs at Flogging Babel. Read and listen to many of his short stories online here.
Last year, he came out with the latest in his Darger and Surplus series, Chasing the Phoenix. In this interview, he delves into the characters and setup of this dystopian comedy misadventure.
CARL SLAUGHTER: What are Darger and Surplus, i.e., what species?
MICHAEL SWANWICK: Aubrey Darger is human, a Londoner, and the product of the slums of Mayfair. Sir Blackthorpe Ravenscairn de Plus Precieux, known to his friends as Surplus, is a citizen of the Demesne of Western Vermont, a proud American mongrel, and the creation of the gene-mills of Winooski. Though he walks upright and has full human intelligence, he could not be mistaken for anything but a dog. I believe he has quite a bit of American foxhound in his genome, but in this I could be mistaken
CS: Which one comes up with the moneymaking schemes? Which one makes the decisions? Which one gets into the most trouble? Which one cracks the jokes?
MS: They are both talented confidence artists, so their schemes could come from either, but Surplus defers to Darger as having the deeper depth of lore and the greater cunning. He, however, is more fast-thinking in a crisis. The two are so commonly of one mind that a suggestion from one is almost invariably accepted by the other. As for trouble… Surplus has a particular weakness for women which frequently gets him in hot water. Darger only rarely becomes romantically involved but when he does he is incapable of taking it lightly. It's hard to say which tendency causes them more grief.
It's the strangest thing, but I've never heard either make a joke. I must think on that.
CS: What's their philosophy and ambition in life?
MS: They have not so much a philosophy as a delusion; they believe they are good people who are enjoying perfectly ordinary lives. Their ambition is to become immensely rich and spend the rest of their lives enjoying their well-deserved wealth, which happy outcome they are convinced will not be long in the coming. In this, too, they are completely deluded.
CS: Any special skill sets, equipment, or proclivities?
MS: Surplus is a dog of action, an adventurer by nature, and the best possible companion in a fight. He is also close to irresistible to women of a sexually adventurous disposition. Darger is of more scholarly bent and has an encyclopedic knowledge of the confidential arts. He is given to bouts of crippling depression but fortunately he has Surplus to jolly him out of them.
CS: When and where are they? Any other characters in different stories but the same fictional universe?
MS: Darger and Surplus exist in our own world, some centuries after the fall of Utopia. The AIs and mad intelligences of the Internet rebelled against humankind at an unknown time in the past and almost won. These demonic creatures were driven from the surface of the earth but remain in the buried infrastructure of the previous civilization, plotting their return. So the world must get along without electronics or complex machinery as a result. Fortunately their biological sciences are much more sophisticated than our own.
So far, all the characters Darger and Surplus have met have been left behind them, and a good thing for them, too. It would be interesting to find an excuse to bring one or two back – perhaps an old flame. If I can come up with a strong enough story, that will happen.
CS: Why a buddy team instead of a single character. Like Mike Resnick's Lucifer Jones series? Or a trio? Hey, 3 Musketeers, 3 Stooges.
MS: Fritz Leiber once wrote of his own dual heroes that Fafhrd and Mouser were like two halves of a single hero, shattered sometime in the distant past, who had found each other. There's something in that. The enjoyment Darger and Surplus derive from each other's conversation is very close to egotism, they are in such complete accord.
Also, con men classically come in pairs – a roper to bring the mark in and an inside man to fleece him. The structure goes with the calling.
CS: How often do they get their pot of gold at the end of the rainbow?
MS: Like most successful confidence artists, they end up with more money than they deserve more often than they ought to. But they seem to be unable to hold onto that money for very long. This is typical for their profession. It is very rare for a con man to die rich, and those few who do are almost invariably the least interesting of their kind.
CS: Is there a story arc?
MS: I realized early on that the travels of Darger and Surplus, while seemingly random, trend ever eastward. Though they don't realize it, they are on an accidental trip around the world. Which means that they will eventually have to go to the Demesne of Western Vermont to confront certain secrets in Surplus's past. And, ultimately, they will have to return to London, which they will discover is completely transformed since last they saw it.
Though they will never know this fact, Darger and Surplus are responsible for that. They are catalysts for change and the chaos they cause wherever they go is altering the world behind them. They are putting an end to their age and when they finally return to their starting-point, it will be an entirely new era and there will be nothing for them to do but to continue onward.
Which is where, if there is time enough and reader interest enough for me to write the remaining books, I shall leave them. Older than they were when they started out but still in the prime of their lives, off to new adventures, perpetually convinced that infinite wealth is only a single caper away.
Perhaps they will disappear into Africa, which is destined (I suspect) to give rise to the world's next great civilization. Wherever they go, it will be possible to imagine them having new adventures.
CS: Is there something we're supposed to learn from Darger and Surplus or are we going along just for the adventure?
MS: Neither meaning nor moral. Darger and Surplus exist purely for the pleasure of their company.
CS: Do the stories appear in short or long fiction or both?
MS: Both. The characters invented themselves in a story called "The Dog Said Bow-Wow," at the end of which they accidentally set fire to London and decided to go to Moscow to run a scam on the Duke of Muscovy. It was the first time I'd ever written characters who would enjoy being run through one of my stories again, so I let them take the lead. I write down their adventures as I discover them.
CS: Any of them available on audio or as podcasts?
MS: Not yet.
CS: How many books in the series so far, how long will the series continue, and how often will the sequels appear?
MS: There are two stand-alone novels, Dancing with Bears and Chasing the Phoenix, to date, four short stories, and four related flash fictions. Eventually, I hope to have a collection's worth of short fiction. I have a story half-written about the time that Darger was eaten by a dragon that I hope to get back to soon. It will take two or three novels more to bring the two rogues to America and then back across the Atlantic again. How long that will take depends on when I can fit them into my writing schedule. Luckily for the reader, the books and stories are all self-contained. I don't believe in cliff-hangers.
CS: BTW, who does your AWESOME cover art?
MS: Bruno Werneck did the cover for Dancing with Bears, and Stephan Martinière the cover for Chasing the Phoenix. Martinière also did the cover for my fantasy novel, The Dragons of Babel. I'm very much aware of how fortunate I have been of late. To mangle the old quotation, I've had good cover art and I've had bad. Good is better.
Darger and Surplus
The SF Signal Podcast (Episode 264): An Interview with Author Michael Swanwick
Short Fiction Friday: New Fiction from Tor.com
REVIEW: Dancing With Bears by Michael Swanwick
Gene Wolfe and Neil Gaiman on a Merry-Go-Round. No, Really!
2 Comments on [INTERVIEW] Michael Swanwick Talks About His DARGER AND SURPLUS Characters
Mike // April 18, 2016 at 6:22 pm //
Fantastic interview. I love the Surplus and Darger stories. Glad to hear there is more of them percolating.
Sanford Zane Meschkow // April 29, 2016 at 7:54 pm //
I can't wait to meet the reason Surplus had to leave Western Vermont. An evil twin? A lost love? A scheming stepmother? No, I'm sure Michael will come up with some idea better than those. So godspeed to those two!
Tuesday Tune: "Get Loki" by The Avengers (Parody of "Get Lucky" by Daft Punk)
You've gotta give credit to these folks for turning Daft Punk's new single "Get Lucky" into an Avengers parody. Well, you don't have to, but it's hard not to...
Interviews & Profiles (many swiped...er, borrowed from Bibliophile Stalker):The New York Times profiles Jack Vance, The Genre Artist.Rick Kleffel podcast-interviews Jeremy Lassen of Night Shade Books.Electric Velocipede interviews Jason Erik Lundberg.Randolph Carter interviews Melinda Snodgrass.Nick Gevers interviews Charles Stross.Patrick at Stomping on Yeti interviews one of tomorrow's genre stars: Ted Kosmatka Neil Gaiman talks about Batman on NPR. Kristine Continue Reading →
SFF World interviews Marianne de Pierres, author of Dark Space. "My reading influences are unashamedly eclectic and went (chronologically) something like this: Zane Grey, D.H Lawrence, James A. Michener (The Drifters), J-P Satre and Simone de Beauvoir, Carlos Castenada, A.C. Clarke, William Gibson, Vernor Vinge, Octavia Butler." Ben Bova, author of Titan (which recently won the John W. Campbell Award) Continue Reading →
Interviews/ProfilesAngry Robot Podcast interviews Lee Harris and Marc Gascoigne.Reason.tv interviews Cory Doctorow (video).Book Chick City's latest Where Stories are Made entry features Jeanne C. Stein. The Absent Willow Review interviews Christopher Golden. NewsTarkovsky Films Now Free Online.End of the news romantics.Fantastique Unfettered Opens to Submissions.St. Martin's Press drops Janet Evanovich after she demands $50 million for her next four books. Continue Reading →
REVIEW: We, Robots by Sue Lange
REVIEW SUMMARY: A well-written robot story of substance. MY RATING: BRIEF SYNOPSIS: Avey the robot becomes more human while humans verge on becoming posthuman. MY REVIEW: PROS: Sympathetic protagonist; interesting blend of old tropes and new; portrayal of mankind's darker side adds depth; perfect amount of humor. CONS: I was expecting more from the "robot uprising" aspect. BOTTOM LINE: Continue Reading →


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In case you haven't noticed, it's pretty freakin' cold outside. Yes, yes, we know it's winter in Idaho and the cold is to be expected and it's been far worse in previous decades ... yadda yadda yadda. The point is, we as human beings should be really happy that we have shelters, coats, shoes, etc., and don't have to brave the chilly air like animals do. Sure, lots of animals have coats, too, but they don't get to sit in restaurants sipping hot beverages, then get into their heated cars and drive to their nearly tropical homes.
If your child has ever asked you why different members of the animal kingdom don't have to wear boots to go out in the snow or you've ever wondered how they manage not to turn into popsicles, head to the visitors' center at Deer Flat National Wildlife Refuge today for Hibernate, Migrate or Insulate: A Wildlife Winter Survival Guide. This two-day interactive day camp will take place from 1-4 p.m. today and Thursday, Dec. 29. Second- through fourth-graders can learn all about how their animal friends survive the cold months, all for free. So bundle up, head out, and learn how nature's pros survive sans-Ugg boots.


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Q: accessing a pandas DataFrame cell I'm having a pandas issue.
I have a dataframe that looks like the following:
     A      B       C      D
0   max   kate    marie   John
1   kate  marie   max     john
2   john  max     kate    marie
3   marie john    kate    max

And I need to access, for instance, the cell in row 0, column D.
I tried using df.iloc[0, 3] but it returns the whole D column.
Any help would be appreciated.

A: You could use
df.iloc[0]['D']
or
df.loc[0,'D']
Documentation reference DataFrame.iloc
To get the value at a location.

A: df.iloc[0]["D"]

seems to do the trick

A: You can refer to this for your solution
# Import pandas package
import pandas as pd

# Define a dictionary containing employee data
data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],
        'Age': [27, 24, 22, 32],
        'Address': ['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
        'Qualification': ['Msc', 'MA', 'MCA', 'Phd']}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
print(pd)

Then you got the table like this

if you want the name in the 0-th row and in 0-th column("Name")
synthax = dataframe.iloc[row-index]['ColumnName']
print(df.iloc[0]['Name'])

A: It works fine if your Dataframe name is df.
df.iloc[0,3]
Out[15]: 'John'


In the interest of streamlining the city's transportation and visual identity, the New York City Taxi and Limousine Commission has now adopted a new look: the logotype for the city's medallion taxis has eliminated the word 'taxi' in favor of a bold yellow "T" in a black circle. The design also eliminates the fare panel and checkerboard on the passenger door in an effort to minimize clutter.
The new design: The fare information remains unchanged, with only two kinds of fares available: a metered ride to anywhere in the city and a flat flare ride to Kennedy International Airport. All the fare information is now placed more usefully on the video screen inside the cab.