text:
in: Denim, Pants/Bottoms, Style, Style & Grooming
Antonio • April 12, 2011 Last updated: March 13, 2019
Blue Jeans: An Introduction to Denim
This series is supported by Gillette. Learn more about Gillette and its products at Gillette.com. What's this?
Is there anything more American than blue jeans?
Over the last 160 years blue jeans have woven their way into American and even world culture. Classless, utilitarian, and yet classically stylish, jeans have been worn by prisoners, plumbers, and presidents alike.
Iconic American Figures Associated with Blue Jeans
Although many frontiersmen never wore a pair of jeans and instead opted for buckskins, in the last century denim has become the trouser of choice for the American West's most visible ambassadors. Both Will Rogers and John Wayne wore them and countless rodeo legends as well. Today if you make your way to a rodeo in Pecos or Cheyenne, you'll probably see dudes sporting a pair of Wrangler blue jeans.
I'm not talking about the Harley Davidson clad bunch we see nowadays; I'm referring to the 1950s vets who returned from WWII and hit the road on bikes because they needed excitement and freedom in their lives. Think Marlon Brando in The Wild One with his leather jacket and rolled cuff blue jeans.
The Young Rebel
Today, nothing could be more mainstream than denim, but jeans used to be the badge of the rebel, the man who broke from the traditional dress of society and rejected the old way of doing things. Rebels of all types have flocked to denim, starting in the 1940s with rule-breaking college youth who wore them against the wishes of their parents to James Dean in the classic film Rebel Without a Cause to the Greasers in S.E. Hinton's The Outsiders. Rebellious youth have for the last 60 years found a kindred spirit in denim, and will for at least another 60.
The Blue Collar Worker
Blue is the color of the working class because it takes to staining and cleaning better than white; the classless blue jean, prized for its inexpensive durability and ability to suck up grease, was and is the pants of the working man. Personified in the 1980s by Bruce Springsteen, the blue collar worker loves his blue jeans because they, like him, are made to be worn but never beaten.
An Overview of the Major Jean Brands
Levi Strauss and Co
Founded in 1853 by Levi Strauss in San Francisco, the company started as a dry goods wholesaler but quickly found its place in history when a tailor named Jacob Davis partnered with the company to create a superior pair of pants that utilized copper rivets to reinforce areas of the jeans that commonly tore under heavy stress. Patent number 139,121 was awarded in 1873 and the rest is history. Utilizing the best denim in the world at the time, Levi Strauss and Co established itself as a beacon of quality for next 150+ years.
In 1890, lot number 501 was assigned to the waist overalls with the copper rivets and button fly. Today you can buy the same jeans, minus a few details introduced over the years because of changes in menswear style (suspender buttons are gone) and the requirements of wartime rationing boards (the back buckleback).
Lee Company
H.D. Lee was a man who headed west after starting a bright business career on the East Coast only to have it derailed by bad health. Against the advice of his doctor, Lee headed to the opportunity he saw in Kansas, where he founded Lee Mercantile in 1890. Seizing on the lack of local quality goods and the natural central location of Salina, KS, Lee pushed his work wear division and the Union-All jumpsuit became his flag product. It sold like hotcakes, in part because the designers catered to the men wearing them and made them easy to slip on and off and innovated with the now classic zipper.
Lee has continued to grow over the last century, in large part to smart marketing and sponsorships including the founding of the National Cowboy and Western Heritage Museum. By closely associating itself with the American Southwest, the jeans built a strong and loyal base among the western crowd.
Finally, I need to mention Buddy Lee. First making waves in a Minnesota shop store window back in 1920, Buddy has since been spotted promoting Lee Dungarees in a variety of strangely funny commercials. Over 90 years old, Buddy Lee is a legend; don't let his 14 inch height fool you.
Founded in 1904 as the Hudson Overall Company, the company changed its name to Blue Bell 15 years later and remained primarily regional to North Carolina with its core product being overalls. After WWII, Blue Bell bought a work wear company and revived Wrangler with the specific target customer being the Western crowd. With an innovative cut utilizing higher pockets and wider belt loops, and the sponsorship of rodeo legend Jim Shoulders, Wrangler was able to wrangle itself to the top of the Western market within two decades.
Lee Cooper Jeans are less well known in the USA but have a loyal following in England and Europe, and for good reason. The brand made a name for itself during WWII when rationing made anything but denim a luxury. With only 30 ration coupons for clothing, working men had the option of a business suit for 26 coupons or a pair of Lee Cooper overalls for 2 (or better yet – jeans for 1). The Lee Cooper Brand grew quickly in the 50s and 60s under Harold Cooper, and now sells clothing in over 70 markets around the world.
Other options outside the Big 4 brands:
High fashion brands began to push out jean lines in the 1970s, but saw the market fade within a decade. The most recent surge began again in the early 1990s and continues today; brands such as Lucky were the first to start charging $100 for jeans that were built on nothing more than slick marketing (my old college roommate would disagree–he felt the unique inner lining and fit was worth the price he paid). In the last 20 years, designer jeans have leveraged celebrity endorsements and notoriety to sell jeans at prices that can now soar into the $500 range.
If you can't tell, I am not a fan of designer jeans. Instead, if you're looking for something beyond the ordinary you should consider….
Raw Denim Jeans
Raw denim is unwashed denim fabric that has not been shrunk or exposed to water after the dying process. It is typically very dark, and made on old style shuttle looms. Selvedge denim, as it is also called, is priced at a premium because of low production runs, the need to use older equipment and more fabric per pair, and the fact that it's made in high labor cost countries. However, raw denim is more durable, and many raw denim advocates claim to wear their jeans thousands of times before they wear out, thus making them a strong value when you look at the number of wears vs. the amount paid.
Blue Jeans and Pricing
A gentleman walks into a store and finds a terrific pair of jeans that are the right size and made well. He then looks at the price tag and is shocked; the jeans are almost ten times more expensive than the pair he has on. After checking with the clerk, he leaves the shop in disgust wondering who would ever buy such expensive clothing. The year is 1870, and those overpriced jeans are selling for $5…10 times the cost of the more popular brand our price conscious shopper was used to buying.
The scenario I just described could have easily taken place today. With jean pricing ranging as wide now as it did then, there is still much confusion as to why. Below are 6 reasons why jeans vary in price:
Market Positioning – Price positioning based on smart (or not so smart) marketing is the most important factor in determining price. Raw denim made in the US has to be priced high because of the quality of material and construction. But designer jeans, which can cost ¼ as much to make can sell for the same amount if not more, simply because they are worn by the right celebrity or a marketing ploy creates a feel of exclusivity or scarcity.
Clothing Pattern – Some jeans are made to fit a particular demographic. Levi's, the brand most of us associate value and Western heritage with, makes a pair of raw denim jeans called "matchstick." As the name implies, they are made for skinny young men. Any man carrying more than a few extra pounds around the waist or older than 30 should approach the jeans with extreme caution.
Factory Run Size and Material Cost – Mass produced jeans, built on modern machines prized for their speed and acceptable error rates are generally going to cost less than jeans built on older equipment. Who's using older machinery? Believe it or not, most of the older machines were bought and shipped to Japan (they love their denim) or have been painstakingly restored by the craftsmen behind the small vintage lines here in the US. The older equipment may not be as fast, but for the denim artisan, this hardly matters.
The Jean Manufacturer's Bargaining Power – When Levi Strauss talks pricing with JC Penney, there is a negotiation. When a small start-up line tries to talk pricing, there is a take it or leave it offer put on the table–if they make it that far. Often the only route for the small guys are small distributors, who shoulder higher per foot costs than the big guys and have to charge more to stay in business.
Labor Cost – Denim made in Japan or the USA is going to cost more than high volume fabric coming out of China. Simple economics associated with price of labor and as mentioned above, much of the Chinese machinery is more efficient.
Durability and Specialty Design – Although rare, there are jeans out there that are developed to serve a special purpose besides covering a man's lower extremities. Draggin jeans have Kevlar sewn in and are designed to protect a motorcyclist from road rash.
What to Look for When Purchasing a Pair of Jeans
Focus on Fit – Fit is the most important thing to look for in a pair of jeans–you may have to try on 5 to 10 different brands and types–but it's worth it when you find that one style that fits you just right. During regular wear the fit will become more relaxed as the cotton stretches; however, once you wash them and expose the cotton to any type of heat, you'll shrink them back to their original size and in some cases even a bit smaller. The best thing to do then is to put them on and stretch them out with extensive body movement.
Fabric Weight – Denim will vary in weight from 7 to 18 ounces, with most men wanting a jean that strikes a balance. Too lightweight and the fabric will tear too easily–too heavy, and the fabric will be as stiff as a board. The latter is rarely an issue; rather, you need to be careful of brands that try to save money by going with lighter weight fabrics. The difference is subtle, but you'll know you've been had when the jeans begin to tear at high stress and friction areas after only 6 to 9 months of wear.
Select the Right Color – Denim comes in a wide variety of colors and shades. Also, the way the denim has been washed and treated will determine its suitability. I recommend men first look at darker colored jeans with minimal distressing– jeans like this can be worn with a sport jacket. Jeans with lighter colors/heavy washing /distressed fabrics are only for casual wear.
Be Careful of Knockoffs – Rarely a problem in large chain stores, this has become a bigger issue since more and more men shop on sites like eBay where the burden of understanding the merchandise falls on the buyer. If the price looks too good to be true, it probably is.
Brand Construction Consistency – A perfect fit can seal a relationship with a jean manufacturer for decades. I've heard of cases where news of an impending bankruptcy in a clothing company has led to shortages in supply because diehard fans immediately go out and buy a lifetime supply. It sounds funny, but how many of you have that trusty pair of jeans you reach for when you need something that works with everything from Birkenstocks to a blazer?
There is much more we could talk about when it comes to jeans. So let me ask – what would you like to see us cover next concerning denim?
How to Make Shorts From Pants (No Sewing Required!)
A Man's Guide to Cleaning and Caring for Suede
How to Grow a Mustache


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text:
'There would have been boos at other clubs' - Danny Cowley grateful to Lincoln City fans
Boss insists Imps are working tirelessly to be more of a goal threat
Mark WhileyFootball writer
Danny Cowley thanked Lincoln City fans for their patience following Saturday's stalemate with Crawley Town.
The Imps were forced to settle for a point on a frustrating afternoon at Sincil Bank.
Aside of Matt Rhead's second half header which struck the bar, City struggled to create clear-cut chances.
Manager Cowley accepts his side are not posing enough of a threat in the attacking third and was grateful to Imps supporters for not showing their frustration at the final whistle.
"I have to credit the supporters. We had over 8,000 there. At the 71 other Football League grounds, there would have been boos at the final whistle," said Cowley.
Lincoln City fans applaud their team at the final whistle
"But ours stayed with us until the end. It was a brilliant atmosphere, way better than the referee and way better than both teams on the day.
"So good on them and I can assure them we're going to work really, really hard to find a way of being more of a goal threat.
"We've got real belief in all of our players. We've seen good signs on the training pitch and we'll find a way.
"We're really good at finding a way. We've proved that over a long period now."
Lincoln City v Crawley Town fans' gallery and act of remembrance
Being the Imps' final home match in the league before Remembrance Day, both sets of players were joined by military personnel for a poignant act of remembrance ahead of kick-off.
"We appreciate all the people who came and took part in that," added Cowley "I know how much it means to the country but in particular to the city of Lincoln.
"We'll all have had relatives who would have been involved in wars and we wouldn't be here without their selfless acts. It was a fitting tribute and it certainly meant a lot to me and the players."
Lincoln City FCLincoln City vs Gillingham postponed due to waterlogged pitchThe Imps are due to return to action on Saturday when they host Northampton Town
'We're fine' - Lincoln City boss Michael Appleton on coming up against 'lively' Steve Evans
Lincoln City FCAppleton and and Gillingham boss were involved in a heated confrontation following the 0-0 draw between the sides last February
Lincoln City boss issues update on Jorge Grant contract talks and reveals 'key marker'
Lincoln City FCGrant's contract expires at the end of the season and Lincoln are eager to tie him to new deal, with talks beginning late last year
Lincoln City sign ex-Rotherham defender Akeem Hinds
Lincoln City FCHe came up against the Imps in the EFL Trophy earlier this season
Lincoln City boss wants right-back cover after seeing Robbie Gotts recalled by Leeds United
Lincoln City FCGotts, fresh from scoring his first goal for the club in the 4-0 win over Accrington Stanley, saw his loan spell at the LNER Stadium cut short before being sent out to Salford City
Michael Appleton gives injury and illness update ahead of Lincoln City's return to action
Lincoln City FCThere is mixed news ahead of the Imps' League One clash against Steve Evans' Gillingham

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One moment you feel magical on a rooftop that seems to overlook the world and the next is ordinary, as you sneak in some reading on your commute home. Somehow just being part of the hustle and bustle makes all the extra effort worth it. You were there.
The City collection celebrates the incredible adventures and the everyday details of living in a metropolis so you can remember all of the moments you've shared in its streets.


text:
\section{Introduction}

{\em Automated story generation} is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story.
To date, most story generation systems have used symbolic planning ~\cite{meehan76,lebowitz87,perez01,Porteous2009,riedl:jair2010} or case-based reasoning ~\cite{gervas05}.
While these automated story generation systems were able to produce impressive results, they rely on a human-knowledge engineer to provide symbolic domain models that indicated legal characters, actions, and knowledge about when character actions can and cannot be performed;
these systems are limited to only telling stories about topics that are covered by the domain knowledge.
Consequently, it is difficult to determine whether the quality of the stories produced by these systems is a result of the algorithm or good knowledge engineering.

{\em Open story generation} ~\cite{Li2013} is the problem of automatically generating a story about any topic without {\it a priori} manual domain knowledge engineering.
Open story generation requires an intelligent system to either learn a domain model from available data ~\cite{Li2013,roemmele2017} or to reuse data and knowledge available from a corpus \cite{swanson12}.

In this paper, we explore the use of recurrent encoder-decoder neural networks (e.g., {\em Sequence2Sequence} ~\cite{sutskever2014}) for open story generation.
An encoder-decoder RNN is trained to predict the next token(s) in a sequence, given one or more input tokens. The network architecture and set of weights $\theta$ comprise a generative model capturing and generalizing over patters observed in the training data.
For open story generation, we must train the network on a dataset that encompasses as many story topics as possible.
For this work, we use a corpus of movie plot summaries extracted from Wikipedia ~\cite{bamman2014} under the premise that the set of movies plots on Wikipedia covers the range of topics that people want to tell stories about.

In narratological terms, an {\em event} is a unit of story featuring a world state change \cite{prince87}. Textual story corpora, including Wikipedia movie plot corpora, is comprised of unstructured textual sentences. One benefit to dealing with movie plots is its clarity of events that occur, although this is often to the expense of more creative language.
Even so, character- or word-level analysis of these sentences would fail to capture the interplay between the words that make up the meaning behind the sentence.
Character- and word-level recurrent neural networks can learn to create grammatically correct sentences but often fail to produce coherent narratives beyond a couple of sentences.
On the other hand, sentence-level events would be too unique from each other to find any real relationship between them.
Even with a large corpus of stories, we would most likely have sequences of sentences that would only ever be seen once. For example, ``Old ranch-hand Frank Osorio travels from Patagonia to Buenos Aires to bring the news of his daughter's demise to his granddaughter Alina.'' occurs only once in our corpus, so we have only ever seen one example of what is likely to occur before and after it (if anything).
Due to event sparsity, we are likely to have poor predictive ability.

In order to help maintain a coherent story, one can provide an event representation that is expressive enough to preserve the semantic meaning of sentences in a story corpus while also reducing the sparsity of events (i.e. increasing the potential overlap of events across stories and the number of examples of events the learner observes).
In this paper, we have developed an event representation that aids in the process of automated, open story generation.
The insight is that if one can extract some basic semantic information from the sentences of preexisting stories, one can learn the skeletons of what ``good'' stories are supposed to be like.
Then, using these templates, the system will be able to generate novel sequences of events that would resemble a decent story.

The first contribution of our paper is thus an event representation and a proposed recurrent encoder-decoder neural network for story generation called {\em event2event}. We evaluate our event representation against the naive baseline sentence representation and a number of alternative representations.

In {\em event2event}, a textual story corpus is preprocessed---sentences are translated into our event representation by extracting the core semantic information from each sentence.
Event preprocessing is a linear-time algorithm using a number of natural language processing techniques.
The processed text is then used to train the neural network.
However, event preprocessing is a lossy process and the resultant events are not human-readable.
To address this, we present a story generation pipeline in which a second neural network, {\em event2sentence}, translates abstract events back into natural language sentences.
The {\em event2sentence} network is an encoder-decoder network trained to fill in the missing details necessary for the abstract events to be human-readable.

\begin{sloppypar}
Our second contribution is the overall story generation pipeline in which subsequent events of a story are generated via an {\em event2event} network and then translated into natural language using an {\em event2sentence} network. We present an evaluation of {\em event2sentence} on different event representations and draw conclusions about the effect of representations on the ability to produce readable stories.
\end{sloppypar}

The remainder of the paper is organized as follows.
First, we discuss related work on automated story generation, followed by an introduction of our event representation. Then we introduce our {\em event2event} network and provide an evaluation of the event representation in the context of story generation.
We show how the event representation can be used in {\em event2sentence} to generate a human-readable sentences from events. We end with a discussion of future work and conclusions about these experiments and how our event representation and event-to-sentence model will fit into our final system.

\section{Related Work}

Automated Story Generation has been a research problem of interest since nearly the inception of artificial intelligence.
Early attempts relied on symbolic planning ~\cite{meehan76,lebowitz87,perez01,riedl:jair2010} or case-based reasoning using ontologies ~\cite{gervas05}.
These techniques could only generate stories for predetermined and well-defined domains of characters, places, and actions.
The creativity of these systems conflated the robustness of manually-engineered knowledge and algorithm suitability.

Recently, machine learning has been used to attempt to learn the domain model from which stories can be created or to identify segments of story content available in an existing repository to assemble stories.
The {\em SayAnthing} system \cite{swanson12} uses textual case-based reasoning to identify relevant existing story content in online blogs.
The {\em Scheherazade} system \cite{Li2013} uses a crowdsourced corpus of example stories to learn a domain model from which to generate novel stories.

Recurrent neural networks can theoretically learn to predict the probability of the next character, word, or sentence in a story.
Roemmele and Gordon \cite{roemmele2017} use a Long Short-Term Memory (LSTM) network ~\cite{lstm} to generate stories.
They use Skip-thought vectors~\cite{kiros2015} to encode sentences and a technique similar to word2vec ~\cite{word2vec} to embedded entire sentences into 4,800-dimensional space.
They trained their network on the BookCorpus dataset.
Khalifa et al. \shortcite{khalifa2017} argue that stories are better generated using recurrent neural networks trained on highly specialized textual corpora, such as the body of works from a single, prolific author.
However, such a technique is not capable of {\em open story generation}.

Based off of the theory of script learning ~\cite{Schank1977}, Chambers and Jurafsky  \shortcite{chambers2008} learn causal chains that revolve around a protagonist. They developed a representation that took note of the event/verb that occurred and the type of dependency that connected the event to the protagonist (e.g. was the protagonist the object of this event?).

Pichotta and Mooney \shortcite{Pichotta2016} developed a 5-tuple event representation of $(v,e_s,e_o,e_p,p)$, where $v$ is the verb, $p$ is a preposition, and  $e_s$, $e_o$, and $e_p$ are nouns representing the subject, direction object, and prepositional object, respectively.
Our representation was inspired by this work, although we use a slightly different representation.
Because it was a paper on script learning, they did not need to convert the event representations back into natural language.

Related to automated story generation, the {\em story cloze test} \cite{mostafazadeh2016} is the task of choosing between two given endings to a story.
The story cloze test transforms story generation into a classification problem:
a 4-sentence story is given along with two alternative sentences that can be the 5th sentence.
State-of-the art story cloze test techniques use a combination of word embeddings, sentiment analysis, and stylistic features \cite{mostafazadeh2017}.

\section{Event Representation}

Automated story generation can be formalized as follows: given a sequence of events, sample from the probability distribution over successor events.
That is, simple automated story generation can be expressed as a process whereby the next event is computed by sampling or maximizing
$Pr_\theta(e_{t+1}|e_{t-k},...,e_{t-1},e_{t})$
where $\theta$ is the set of parameters of a generative domain model, $e_i$ is the event at time $i$, and where $k$ indicates the size of a sliding window of context, or history.

In our work, the probability distribution is produced by a recurrent encoder-decoder network with parameters $\theta$.
In this section, we consider what the level of abstraction for the inputs into the network should be such that it produces the best predictive power while retaining semantic knowledge.
Event sparsity results in a situation where all event successors have a low probability of occurrence, potentially within a margin of error.
In this situation, story generation devolves to a random generation process.

Following Pichotta and Mooney ~\shortcite{Pichotta2016}, we developed a 4-tuple event representation $\langle s, v, o, m\rangle$ where $v$ is a verb, $s$ is the subject of the verb, $o$ is the object of the verb, and $m$ is the modifier or ``wildcard'', which can be a propositional object, indirect object, causal complement (e.g., in ``I was glad that he drove,'' ``drove'' is the causal complement to ``glad.''), or any other dependency unclassifiable to Stanford's dependency parser. All words were stemmed.
Events are created by first extracting dependencies with Stanford's CoreNLP ~\cite{manning2014} and locating the appropriate dependencies mentioned above.
If the object or modifier cannot be identified, we insert the placeholder $EmptyParameter$, which we will refer to as $\emptyset$ in this paper.

\begin{sloppypar}
Our event translation process can either extract a single event from a sentence or multiple events per sentence.
If we were to extract multiple events, it is because there are verbal or sentential conjunctions in the sentence.
Consider the sentence ``John and Mary went to the store,'' our algorithm would extract two events: $\langle john, go, store, \emptyset\rangle$ and $\langle mary, go, store, \emptyset\rangle$.
The average number of events per sentence was 2.69.
\end{sloppypar}

\begin{sloppypar}
Our experiments below used a corpus of movie plots from Wikipedia~\cite{bamman2014}, which we cleaned to any remove extraneous Wikipedia syntax, such as links for which actors played which characters. This corpus contains 42,170 stories with the average number of sentences per story being 14.515.
\end{sloppypar}

The simplest form of our event representation is achieved by extracting the verb, subject, object, and modifier term from each sentence.
However, there are variations on the event representation that increase the level of abstraction (and thus decrease sparsity) and help the encoder-decoder network predict successor events. We enumerate some of the possible variations below.

\begin{itemize}
\item
{\bf Generalized.}
Each element in the event tuple undergoes further abstraction.
Named entities were identified (cf. \cite{finkel2005}), and ``PERSON'' names were replaced with the tag \textless NE\textgreater $n$, where $n$ indicates the $n$-th named entity in the sentence. Other named entities were labeled as their NER category (e.g. LOCATION, ORGANIZATION, etc.).
The rest of the nouns were replaced by the WordNet ~\cite{wordnet} synset two levels up in the inherited hypernym hierarchy, giving us a general category (e.g. self-propelled\_vehicle$.n.01$ vs the original word ``car'' (car$.n.01$)), while avoiding labeling it too generally (e.g. entity$.n.01$). Verbs were replaced by VerbNet ~\cite{verbnet} version 3.2.4~\footnote{https://verbs.colorado.edu/vn3.2.4-test-uvi/index.php} frames (e.g. ``arrived'' becomes ``escape-$51.1$'', ``transferring'' becomes ``contribute-$13.2$-2'').

\item
{\bf Named Entity Numbering.}
There were two ways of numbering the named entities (i.e. people's names) that we experimented with. One way had the named entity numbering reset with every sentence (consistent within sentence)--or, {\em sentence NEs}, our ``default''. The other way had the numbering reset after every input-output pair (i.e. every line of data; consistent across two sentences)--or, {\em continued NEs}.

\item
{\bf Adding Genre Information.}
We did topic modeling on the entire corpus using Python's Latent Dirichlet Analysis~\footnote{https://pypi.python.org/pypi/lda} set for discovering 100 different categories. We took this categorization as a type of emergent genre classification. Some clusters had a clear pattern, e.g., ``job company work money business''. Others were less clear. Each cluster was given a unique genre number which was added to the event representation to create a 5-tuple $\langle s, v, o, m, g\rangle$ where $s$, $v$, $o$, and $m$ are defined as above and $g$ is the genre cluster number.

\end{itemize}

We note that other event representations can exist, including representations that incorporate more information as in \cite{Pichotta2016}.
The experiments in the next section show how different representations affect the ability of a recurrent neural network to predict story continuations.

\section{Event-to-Event}

\begin{sloppypar}
The {\em event2event} network is a recurrent multi-layer encoder-decoder network based on \cite{sutskever2014}.
Unless otherwise stated in experiments below, our {\em event2event} network is trained with input $x=w^{n}_1,w^{n}_2,w^{n}_3,w^{n}_4$ where each $w^{n}_i$ is either $s$, $v$, $o$, or $m$ from the $n$-th event, and output $y=w^{n+1}_1,w^{n+1}_2,w^{n+1}_3,w^{n+1}_4$.
\end{sloppypar}
The experiments described below seek to determine how different event representations affected {\em event2event} predictions of the successor event in a story.
We evaluated each event representation using two metrics.
{\em Perplexity} is the measure of how ``surprised'' a model is by a training set.
Here we use it to gain a sense of how well the probabilistic model we have trained can predict the data. Specifically, we built the model using an $n$-gram length of $1$:

\begin{equation}
  Perplexity = 2^{-\sum_{x}p(x)\log_2{p(x)}}
\end{equation}
\noindent
where $x$ is a token in the text, and
\begin{equation}
  p(x) = \frac{count(x)}{\sum_{x}count(x)}
\end{equation}

\noindent
The larger the unigram perplexity, the less likely a model is to produce the next unigram in a test dataset.

The second metric is BLEU score, which compares the similarity between the generated output and the ``ground truth'' by looking at $n$-gram precision.
The neural network architecture we use was initially envisioned for machine translation purposes, where BLEU is a common evaluation metric.
Specifically, we use an $n$-gram length of $4$ and so the score takes into account all $n$-gram overlaps between the generated and expected output where $n$ varies from $1$ to $4$ ~\cite{papineni2002}.

We use a greedy decoder to produce the final sequence by taking the token with the highest probability at each step.
\begin{equation}
\hat{W}=\operatorname*{arg\,max}_w Pr(w|S)
\end{equation}
\noindent
where $\hat{W}$ is the generated token appended to the hypothesis, $S$ is the input sequence, and $w$  represents the possible output tokens.

\subsection{Experimental Setup} \label{e2e:setup}
For each experiment, we trained a sequence-to-sequence recurrent neural net~\cite{sutskever2014} using Tensorflow ~\cite{tensorflow}. Each network was trained with the same parameters (0.5 learning rate, 0.99 learning rate decay, 5.0 maximum gradient, 64 batch size, 1024 model layer size, and 4 layers), varying only the input/output, the bucket size, the number of epochs and the vocabulary. The neural nets were trained until the decrease in overall loss was less than $5\%$ per epoch. This took between $40$ to $60$ epochs for all experiments.
The data was split into $80\%$ training, $10\%$ validation, and $10\%$ test data. All reported results were evaluated using the the held-out test data.

We evaluated 11 versions of our event representation against a sentence-level baseline.
Numbers below correspond to rows in results Table~\ref{table:e2e}.

\begin{enumerate}
\setcounter{enumi}{-1}

\item {\em Original Sentences.} As our baseline, we evaluated how well an original sentence can predict its following original sentence within a story.

\begin{sloppypar}
\item {\em Original Words Baseline.} We took the most basic, 4-word event representation: $\langle s, v, o, m\rangle$ with no abstraction and using original named entity names.
\end{sloppypar}

\item {\em Original Words with \textless NE\textgreater s.} Identical to the previous experiment except entity names that were classified as ``PERSON'' through NER were substituted with \textless NE\textgreater$n$.

\item {\em Generalized.} The same 4-word event structure except with named entities replaced and all other words generalized through WordNet or VerbNet, following the procedure described earlier.

\end{enumerate}

To avoid an overwhelming number of experiments, the next set of experiments used the ``winner'' of the first set of experiments.
Subsequent experiments used variations of the generalized event representation (\#3), which showed drastically lower perplexity scores.

\begin{enumerate}
\setcounter{enumi}{3}

 \item {\em Generalized, Continued \textless NE\textgreater s.} This experiment mirrors the previous with the exception of the number of the \textless NE\textgreater s. In the previous experiment, the numbers restarted after every event. Here, the numbers continue across input and output. So if $event_1$ mentioned ``Kendall'' and $event_2$ (which follows $event_1$ in the story) mentioned ``Kendall'', then both would have the same number for this character.

\item {\em Generalized + Genre.} This is the same event structure as experiment \#3 with the exception of an additional, $5^{th}$ parameter in the event: genre. The genre number was used in training for {\em event2event} but removed from inputs and outputs before testing; it artificially inflated BLEU scores because it was easy for the network to guess the genre number as the genre number was weighted equally to other words.

\item {\em Generalized Bigram.} This experiment tests whether RNN history aids in predicting the next event. We modified {\em event2event} to give it the event bigram $e_{n-1}, e_n$ and to predict $e_{n+1}, e_{n+2}$. We believe that this experiment could generalize to cases with a $e_{n-k},...,e_{n}$ history.

\item {\em Generalized Bigram, Continued \textless NE\textgreater s.} This experiment has the same continued NE numbering as experiment \#4 had but we trained {\em event2event} with event bigrams.

\item {\em Generalized Bigram + Genre.} This is a combination of the ideas from experiments \#5 and \#6: generalized events in event bigrams and with genre added.

\end{enumerate}

The following three experiments investigate extracting more than one event per sentence in the story corpus when possible; the prior experiments only use the first event per sentence in the original corpus.

\begin{enumerate}
\setcounter{enumi}{8}

\item {\em Generalized Multiple, Sequential.}
When a sentence yields more than one event, $e^{1}_n, e^{2}_n,...$ where $n$ is the $n$th sentence and $e^{i}_n$ is the $i$th event created from the $n$th sentence, we train the neural network as if each event occurs in sequence, i.e., $e^{1}_n$ predicts $e^{2}_n$, $e^{2}_n$ predicts $e^{3}_n$, etc.
The last event from sentence $n$ predicts the first event from sentence $n+1$.

\item {\em Generalized Multiple, Any Order.}
Here we gave the RNN all orderings of the events produced by a single sentence paired, in turn, with all orderings of each event of the following sentence.

\item {\em Generalized Multiple, All to All.}
In this experiment, we took all of the events produced by a single sentence together as the input, with all of the events produced by its following sentence together as output. For example, if sentence $i$ produced events $e^{1}_i$, $e^{2}_i$, and $e^{3}_i$, and the following sentence $j$ produced events $e^{1}_j$ and $e^{2}_j$, then we would train our neural network on the input: $e^{1}_i$ $e^{2}_i$ $e^{3}_i$, and the output: $e^{1}_j$ $e^{2}_j$.

\end{enumerate}

\subsection{Results and Discussion}

The results from the experiments outlined above can be found in Table ~\ref{table:e2e}.

\begin{table}[b]
\footnotesize
  \caption{\footnotesize Results from the event-to-event experiments. Best values from each of these three sections (baselines, additions, and multiple events) are bolded.}
  \label{table:e2e}
  \begin{tabular}{ | l | c | c | }
    \hline
    \em{\bf Experiment} & \em{\bf Perplexity} & \em{\bf BLEU}\\
    \hline
    (0) Original Sentences & 704.815 & 0.0432\\
    \hline
    (1) Original Words Baseline & 748.914 & \textbf{0.1880}\\
    \hline
    (2) Original Words with \textless NE\textgreater s & 166.646 & 0.1878\\
    \hline
    (3) Generalized Baseline & \textbf{54.231} & 0.0575\\
    \hline\hline
    (4) Generalized, Continued NEs & 56.180 & 0.0544\\
     \hline
    (5) Generalized + Genre & \textbf{48.041} & 0.0525\\
    \hline
    (6) Generalized Bigram & 50.636 & 0.1549\\
    \hline
    (7) Generalized Bigram, Cont. NEs & 50.189 & \textbf{0.1567}\\
    \hline
    (8) Generalized Bigram + Genre & 48.505 & 0.1102\\
    \hline\hline
    (9) Generalized Multiple, Sequential & 58.562 & 0.0521\\
    \hline
    (10) Generalized Multiple, Any Order & 61.532 & 0.0405\\
    \hline
    (11) Generalized Multiple, All to All & \textbf{45.223} & \textbf{0.1091}\\
    \hline
  \end{tabular}
\end{table}

\iffalse
\begin{table*}[t]
\footnotesize
  \caption{\footnotesize Sample {\em event2event} output from the All to All generalized event experiment (\#10), comparing expected and generated output events. In this example, we feed in 4 events (column 1) taken from a single sentence in a story. The next sentence in the original story has the events found in column 2. The output of our system is in column 3.}
  \label{table:e2e-examples}
  \begin{tabular}{ | p{4cm} | p{4cm} | p{4cm} | }
    \hline
    \em{\bf Input (All Events from One Sentence)} & \em{\bf Expected Output (Events from the Following Sentence)} & \em{\bf Generated Output}\\

    \hline
    $\langle$physical\_entity.$n.01$, say-$37.7$7-1, act.$n.02$, $\emptyset\rangle$ &$\langle$\textless NE\textgreater 0, discover-84, $\emptyset$, $\emptyset\rangle$ & $\langle$\textless NE\textgreater 0, transfer\_mesg-$37.1.1$-1-1, \textless NE\textgreater 1, $\emptyset\rangle$ \\
    $\langle$act.$n.02$, wish-$62$, evacuate, $\emptyset\rangle$ & $\langle$substance.$n.01$, fit-$54.3$, control, $\emptyset\rangle$ & $\langle$seem-$109$-1-1, $\emptyset$, $\emptyset$, abstraction.$n.06\rangle$\\
    $\langle$act.$n.02$,  banish-$10.2$, administrative\_district.$n.01$, $\emptyset\rangle$ &$\langle$substance.n.01, enforce-63, whole.n.02, $\emptyset\rangle$ & $\langle$abstraction.$n.06$,  manner\_speaking-$37.3$, $\emptyset$, $\emptyset\rangle$ \\
    $\langle$weather.$n.01$, lodge-$46$, $\emptyset$, $\emptyset\rangle$ & &\\
  \hline
  \end{tabular}
\end{table*}
\fi

The original word events had similar perplexity to original sentences.
This parallels similar observations made by Pichotta and Mooney \cite{Pichotta2016b}.
Deleting words did little to improve the predictive ability of our {\em event2event} network.
However, perplexity improved significantly once character names were replaced by generalized \textless NE\textgreater tags, followed by generalizing other words and verbs.

Overall, the generalized events had much better perplexity scores, and making them into bigrams---incorporating history---improved the BLEU scores to nearly those of the original word events. Adding in genre information improved perplexity.

The best perplexity was achieved when multiple generalized events were created from sentences as long as all of the events were fed in at the same time (i.e. no order was being forced upon the events that came from the same sentence). The training data was set up to encourage the neural network to correlate all of the events in one sentence with all of the events from the next sentence.

Although the events with the original words (with or without character names) performed better in terms of BLEU score, it is our belief that BLEU is not the most appropriate metric for event generation because it emphasizes the recreation of the input.
Overall, BLEU scores are very low for all experiments, attesting to the inappropriateness of the metric.
Perplexity is a more appropriate metric for event generation because it correlates with the ability for a model to predict the entire test dataset.
Borrowing heavily from the field of language modeling, the recurrent neural network approach to story generation is a prediction problem.

Our intuition that the generalized events would perform better in generating successive events bears out in the data.
However, greater generalization makes it harder to return events to natural language sentences.
We also see that the BLEU scores for the bigram experiments are generally higher than the others. This shows that history matters and that the additional context provided increases the number of $n$-gram overlaps between the generated and expected outputs.

The movie plots corpus contains numerous sentences that can be interpreted as describing multiple events.
Naive implementation of multiple events hurt perplexity because there is no implicit order of events generated from the same sentence; they are not necessarily sequential.
When we allow multiple events from sentences to be followed by all of the events from a subsequent sentence, perplexity improves.

\section{Event-to-Sentence}

Unfortunately, events are not human-readable and must be converted to natural language sentences.
Since the conversion from sentences to (multiple) events for {\em event2event} is a linear and lossy process, the translation of events back to sentences is non-trivial as it requires adding details back in.
For example, the event $\langle$relative.n.01, characterize-29.2, male.n.02, feeling.n.01$\rangle$ could, hypothetically, have come from the sentence ``Her brother praised the boy for his empathy.'' In actuality, this event came from the sentence ``His uncle however regards him with disgust.''

Complicating the situation, the {\em event2event} encoder-decoder network is not guaranteed to produce an event that has ever been seen in the training story corpus.
Furthermore, our experiments with event representations for {\em event2event} indicate that greater generalization lends to better story generation.
However, the greater the generalization, the harder it is to translate an event back into a natural language sentence.

In this section we introduce {\em event2sentence}, a neural network designed to translate an event into natural language.
The {\em event2event} network takes an input event $e_n=\langle s^n, v^n, o^n, v^n\rangle$ and samples from a distribution over possible successor events $e_{n+1}=\langle s^{n+1}, v^{n+1}, o^{n+1}, m^{n+1}\rangle$.
As before, we use a recurrent encoder-decoder network based on \cite{sutskever2014}.
The {\em event2sentence} network is trained on parallel corpora of sentences from a story corpus and the corresponding events.
In that sense, {\em event2sentence} is attempting to learn to reverse the lossy event creation process.

We envision {\em event2event} and {\em event2sentence} working together as illustrated in Figure \ref{fig:pipeline}. First, a sentence---provided by a human---is turned into one or more events. The {\em event2event} network generates one or more successive events. The {\em event2sentence} network translates the events back into natural language and presents it to the human reader.
The dashed lines and boxes represent future work for filling in story specifics.
To continue story generation, $event_{n+1}$ can be fed back into {\em event2event}; the sentence generation is purely for human consumption.

The {\em event2sentence} experiments in the next section investigate how well different event representations can be ``translated'' back into natural language sentences.

\begin{figure}[tb]
\centering
\includegraphics[width=2.5in]{pipeline}
\caption{\footnotesize Our automated story generation pipeline. Dashed boxes and arrows represent future work.}
\label{fig:pipeline}
\end{figure}

\subsection{Experimental Setup}

The setup for this set of experiments is almost identical to that of the {\em event2event} experiments, with the main difference being that we used PyTorch~\footnote{http://pytorch.org/} which more easily lent itself to implementing beam search. The LSTM RNN networks in these experiments use beam search instead of greedy search to aid in finding a more optimal solution while decoding.

The beam search decoder works by maintaining a number of partial hypotheses at each step (known as the beam width or $B$, where $B$=5). Each of these hypotheses is a potential prefix of a sentence. At each step, the $B$ tokens with the highest probabilities in the distribution are used to expand the partial hypotheses. This continues until the end-of-sentence tag is reached.

The input for these experiments was the events of a particular representation and the output was a newly-generated sentence based on the input event(s). The models in these experiments were trained on the events paired with the sentences they were ``eventified'' from. In a complete story generation system, the output of the {\em event2event} network feeds into the {\em event2sentence} network. Examples of this can be seen in Table~\ref{table:examples}. However, we tested the {\em event2sentence} network on the same events extracted from the original sentences as were used for {\em event2event} in order to conduct controlled experiments and compute perplexity and BLEU scores.

To test {\em event2sentence} with an event representation that used the original words is relatively straight forward.
Experimenting on translating generalized events to natural language sentences was more challenging since we would be forcing the neural net to guess character names, nouns, and verbs.

\begin{sloppypar}
We devised an alternative approach for generalized {\em event2sentence} whereby sentences were first partially eventified.
That is, we trained {\em event2sentence} on generalized sentences where the ``PERSON'' named entities were replaced by \textless NE\textgreater tags, other named entities were replaced by their NER category, and the remaining nouns were replaced with WordNet synsets. The verbs were left alone since they often do not have to be consistent across sentences within a story.
The intuition here is that the character names and particulars of objects and places are highly mutable and do not affect the overall flow of a story as long as they remain consistent.
\end{sloppypar}

Below, we show an example of a sentence and its partially generalized counterpart. The original sentence

\begin{quote}
\footnotesize
The remaining craft launches a Buzz droid at the ARC 1 7 0 which lands near the Clone Trooper rear gunner who uses a can of Buzz Spray to dislodge the robot.
\end{quote}
would be partially generalized to
\begin{quote}
\begin{sloppypar}
\footnotesize
The remaining activity$.n.01$ launches a happening$.n.01$ droid at the ORGANIZATION 1 7 0 which property$.n.01$ near the person$.n.01$ enlisted\_person$.n.01$ rear skilled\_worker$.n.01$ who uses a instrumentality$.n.03$ of happening$.n.01$ chemical$.n.01$ to dislodge the device$.n.01$
\end{sloppypar}
\end{quote}

We also looked at whether {\em event2sentence} performance would be improved if we used multiple events per sentence (when possible) instead of the default single event per sentence.
Alternatively, we automatically split and prune (S+P) sentences; removing prepositional phrases, splitting sentential phrases on conjunctions, and, when it does not start with a pronoun (e.g. who), splitting S' (read: S-bar) from its original sentence and removing the first word.
This would allow us to evaluate sentences that would have fewer (ideally one) events extracted from each. For example,
\begin{quote}
\footnotesize
Lenny begins to walk away but Sam insults him so he turns and fires, but the gun explodes in his hand.
\end{quote}
becomes
\begin{quote}
\footnotesize
Lenny begins to walk away. Sam insults him. He turns and fires. The gun explodes.
\end{quote}
Although splitting and pruning the sentences should bring most sentences down to a single event, this isn't always the case. Thus, we ran an {\em event2sentence} experiment where we extracted all of the events from the S+P sentences.

\subsection{Results and Discussion}
The results of our {\em event2sentence} experiments are shown in Table~\ref{table:e2s}.
Although generalizing sentences improves perplexity drastically, splitting and pruning sentences yields better BLEU scores when the original words are kept.
In the case of {\em event2sentence}, BLEU scores make more sense as a metric since the task is a translation task. Perplexity in these experiments appears to correspond to vocabulary size.

Generalized events with full-length generalized sentences have better BLEU scores than when the original words are used. However, when we work with S+P sentences, the pattern flips. We believe that because both S+P and word generalizing methods reduce sparsity of events, when they are combined too much information is lost and the neural network struggles to find any distinguishing patterns.

\begin{table}[t]
\footnotesize
  \caption{\footnotesize Results from the event-to-sentence experiments.}
  \label{table:e2s}
  \begin{tabular}{ | p{5.3cm} | p{1.2cm} | p{0.8cm} | }
    \hline
    \em{\bf Experiment} & \centering{\em{\bf Perplexity}} & \em{\bf BLEU}\\
    \hline
    Original Words Event $\rightarrow$ Original Sentence & \centering{1585.46} & 0.0016 \\
    \hline
    Generalized Event $\rightarrow$ Generalized Sentence & \centering{56.516} & 0.0331 \\
    \hline
    All Generalized Events $\rightarrow$ Gen. Sentence & \centering{59.106} & 0.0366\\
    \hline\hline
    Original Words Event $\rightarrow$ S+P Sentence & \centering{490.010} & \textbf{0.0764}\\
    \hline
    Generalized Event $\rightarrow$ S+P Gen. Sentence &  \centering{\textbf{53.964}} &  0.0266\\
    \hline
    All Generalized Events $\rightarrow$ S+P Gen. Sent. &  \centering{56.488} &  0.0283\\
    \hline
  \end{tabular}
\end{table}

\begin{table*}[t]
\footnotesize
  \caption{\footnotesize End-to-End Examples. Let $\emptyset$ represent an empty (unfilled) parameter. Commas are added for readability.}
  \label{table:examples}

  \begin{tabular}{ | p{2cm}| p{3cm} | p{3cm} | p{4.2cm} | p{3.4cm} |}
    \hline
    \em{\bf Experiment} & \em{\bf Input (Previously Unseen)} & \em{\bf Extracted Event(s)} & \em{\bf Generated Next Event(s)} & \em{\bf Generated Next Sentence} \\
    \hline
   All Generalized Events \& Generalized Sentence &
   He reaches out to Remus Lupin, a Defence Against the Dark Arts teacher who is eventually revealed to be a werewolf. &
   $\langle$male$.n.02$ get-13.5.1 $\emptyset$ \textless NE\textgreater 0$\rangle$, $\langle$ORGANIZATION say-37.7-1 monster$.n.01$  $\emptyset\rangle$   &
   $\langle$monster$.n.01$ amuse-31.1 sarge $\emptyset\rangle$, $\langle$monster$.n.01$ amuse-31.1 realize $\emptyset\rangle$, $\langle$monster$.n.01$ conjecture-29.5-1 $\emptyset$ $\emptyset\rangle$, $\langle$male$.n.02$ conduit$.n.01$ entity$.n.01$ $\emptyset\rangle$, $\langle$male$.n.02$ free-80-1 $\emptyset$ penal\_institution$.n.01\rangle$ &
   When monster$.n.01$ nemesis$.n.01$ describes who finally realizes $male.n.02$ can not, dangerous entity$.n.01$ male$.n.02$ is released from penal\_institution$.n.01$. \\
\hline

     Generalized Event \& Generalized S+P Sentence &
     John unwittingly unleashes an insidious pox that rapidly spreads across the Caribbean. &
     $\langle$\textless NE\textgreater 0 disassemble-23.3 contagious\_disease$.n.01$ $\emptyset\rangle$ &
     $\langle$male$.n.02$ spatial\_configuration-47.6 $\emptyset$ adopt-93 $\rangle$ &
 male$.n.02$ crumples and is about to be sheath$.n.02$. \\
    \hline

     Original Words Event \& S+P Sentence &
     He thinks he's on a top secret spying mission. &
     $\langle$he think $\emptyset$ $\emptyset\rangle$ &
     $\langle$she come know truth$\rangle$ &
 	 She has come to the truth. \\
    \hline

  \end{tabular}
\end{table*}

Table~\ref{table:examples} shows examples from the entire pipeline as it currently exists, that is from one sentence to the next sentence without slot filling (See Figure~\ref{fig:pipeline}).
To get a full sense of how the generalized sentences would read, imagine adding character names and other details as if one were completing a {\em Mad-Libs} game.

\section{Future Work}
The question remains how to determine exactly what character names and noun details to use in place for the \textless NE\textgreater s and WordNet placeholders.
In Figure~\ref{fig:pipeline}, we propose the addition of Working Memory and Long-Term Memory modules.
The Working Memory module would retain the character names and nouns in a lookup table that were removed during the eventification process.
After a partially generalized sentence is produced by {\em event2sentence}, the system can use the Working Memory lookup table to fill character names and nouns back into the placeholders.
The intuition is that from one event to the next, many of the details---especially character names---are likely to be reused.

In stories it is common to see a form of turn-taking between characters.
For example the two events ``John hits Andrew'' \& ``Andrew runs away from John'' followed by ``John chases Andrew'' illustrate the turn-taking pattern.
If John was always used as the first named entity, the meaning of the example would be significantly altered.
The continuous numbering of named entities ({\em event2event} experiment \#7) is designed to assist event bigrams with maintaining turn-taking patterns.

There are times when the Working Memory will not be able to fill named entity and WordNet synset placeholder slots because the most recent event bigram does not contain the element necessary for reuse.
The Long-Term Memory maintains a history of all named entities and nouns that have ever been used in the story and information about how long ago they were last used.
See \cite{Martin2016} for a cognitively-plausible event-based memory that can be used to compute the salience of entities in a story.
The underlying assumption is that stories are more likely to reuse existing entities and concepts than introduce new entities and concepts.

Our model of automated story generation as prediction of successor events is simplistic;
it assumes that stories can be generated by a language model that captures generalized patterns of event co-occurrence.
Story generation can also be formalized as a planning problem, taking into account communicative goals.
In storytelling, a communicative goal can be to tell a story about a particular topic, to include a theme, or to end the story in a particular way.
In future work, we plan to replace the {\em event2event} network with a reinforcement learning process that can perform lookahead to analyze whether potential successor events are likely to lead to communicative intent being met.

\section{Conclusions}
In automated story generation, event representation matters.
We hypothesize that by using our intuitions into storytelling we can select a representation for story events that maintains semantic meaning of textual story data while reducing sparsity of events.
The sparsity of events, in particular, results in poor story generation performance.
Our experiments with different story representations during {\em event2event} generation back our hypothesis about event representation.
We found that the events that abstract away from natural language text the most improve the generative ability of a recurrent neural network story generation process.
Event bigrams did not significantly harm the generative model and will likely help with coherence as they incorporate more history into the process, although story coherence is difficult to measure and was not evaluated in our experiments.

Although generalization of events away from natural language appears to help with event successor generation, it poses the problem of making story content unreadable.
We introduced a second neural network, {\em event2sentence}, that learns to translate events with generalized or original words back into natural language.
This is important because it is possible for {\em event2event} to generate events that have never occurred (or have occurred rarely) in a story training corpus.
We maintain that being able to recover human-readable sentences from generalized events is valuable since our {\em event2event} experiments show use that they are preferred, and it is necessary to be able to fill in specifics later for dynamic storytelling.
We present a proposed pipeline architecture for filling in missing details in automatically generated partially generalized sentences.

The pursuit of automated story generation is nearly as old as the field of artificial intelligence itself.
Whereas prior efforts saw success with hand-authored domain knowledge, machine learning techniques and neural networks provide a path forward toward the vision of open story generation, the ability for a computational system to create stories about any conceivable topic without human intervention other than providing a comprehensive corpus of story texts.

\section{Acknowledgments}
{\footnotesize
This work is supported by DARPA W911NF-15-C-0246. The views, opinions, and/or conclusions contained in this paper are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied of the DARPA or the DoD.
The authors would like to thank Murtaza Dhuliawala, Animesh Mehta, and Yuval Pinter for technical contributions.}

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


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text:
Home / Books / The Jungle Book Stories
The Jungle Book Stories
Ranjit Lal
Rikki-Tikki-Tavi is a clever mongoose who is determined to protect the human family he lives with. What happens when Nag, the cobra, shows up in the garden with evil plans on his mind? Mugger of Mugger-Ghaut has lived an exciting life and tells grand stories to Jackal and Adjutant Crane, little knowing that the greatest threat is lurking close by in the form of the White Man. Kotick is an unusual seal. Not only is his fur white, but he is also filled with curiosity and a sense of adventure. After he witnesses the brutal killing of seals by humans, he decides to find a place where no human can reach. And so begins his search through the icy waters of the Arctic. Can he find such a place and claim the leadership of his pack of seals?
Elephants who dance, camels that talk, tailor birds with louder voices than brains, the desolation of an Arctic winter, high tales of cunning and bravery—all of these come alive in the brilliance of Rudyard Kipling's writing. Go wild with this collection of stories on and about animals from the classic Jungle Book, now with a wonderful introduction by Ranjit Lal.
Speaking Tiger Books
Author : Ranjit Lal
Ranjit Lal is the author of over thirty-five books—fiction and non-fiction—for children and adults who are children.
His abiding interest in natural history, birds, animals and insects is reflected in many of his books: The Crow Chronicles, The Life and Times of Alto Falta, The Small Tigers of Sherrard, The Birds of Delhi and others.
His other books with social themes include Faces in the Water, Our Nana Was a Nutcase, Talk and Shroom, Miracles, Smitten,
The Secret of Falcon Heights, The Dugong and the Barracudas and The Battle for No. 19. His other interests include photography, automobiles, reading and cooking. He lives in Delhi.


> ** We don't need to pay in order to make the reservations.
This is nice - so no payment until right before, or after, DebConf ?
> ** We can pay the deposit in December 2011.
much easier and better next year.
Prev by Date: Re: [Debconf-team] Volunteer at DebConf.


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Chidambaram pitches for National Investment Board
September 15, 2012 03:43 pm | Updated November 17, 2021 04:48 am IST - New Delhi
Describing the scaled-down growth target of 8.2 per cent for the 12th Plan as "realistic," Finance Minister P. Chidambaram on Saturday pitched for the setting up of a National Investment Board (NIB) headed by the Prime Minister for according speedy approvals to mega project proposals to facilitate expeditious implementation.
Expressing concern over delayed implementation of mega projects, Mr. Chidambaram, during his intervention at the meeting of the full Planning Commission under the chairmanship of Prime Minister Manmohan Singh, asserted that clearance by the NIB should be a "final decision" and no interference by any other authority should be entertained.
"The NIB's authority should extend to proposals/projects where the investment is above a certain threshold, say Rs. 1,000 crore. Once the final decision is taken by the NIB, no other Ministry or department or authority should be able to interfere with that decision or delay its implementation," he said, pointing out that such an approval mechanism was essential as a "truly 'final' decision [on projects] does not emerge for many years" under the current dispensation.
To tide over the problem of according final approval since the business rules, at present, allocate the authority to take the final decision to one or more ministries, Mr. Chidambaram said "the authority to take the final decision/decisions should be vested in a NIB to be chaired by the Prime Minister and the Allocation of Business Rules should be amended to create such a mechanism."
As for the outlays in the 11th Plan falling short of targets, Mr. Chidambaram noted that this was mainly owing to the failure in achieving physical targets and sought to illustrate his point on the fact that power generation capacity addition during the Plan period was 55,000 MW, way below the target of 78,700 MW, and similar gaps were witnessed in other key sectors such as coal, crude oil, gas and railways.
Physical targets
Suggesting a way to overcome these hurdles, Mr. Chidambaram said the outcomes "must be measured not only in terms of achieving the financial outlays but also achieving the physical targets. The main reason why actual growth rate in each Plan period was less than the targeted rate was the failure to achieve physical targets."
Turning to the 12th Plan economic parameters, the Finance Minister noted that while the GDP (gross domestic product) growth target of 8.2 per cent was "realistic," given that achievement on this front was 7.6 per cent during the 10th Plan and 7.9 per cent in the 11th Plan, he viewed that the Plan proposal to reduce subsidy from 1.9 per cent of GDP in the budget estimates of 2012-13 to 1.2 per cent in 2016-17 was "over-optimistic."
Direct cash transfer
He pointed out that since the estimated major subsidies in 2012-13 would be around 2.4 per cent of GDP, and a sharp fall as assumed in the Plan might be over-optimistic, a system of direct cash transfer of subsidies in food, fertilizers and petroleum would help in reduction.
"I would urge that by the end of the 12th Plan, these three major subsidies be rolled out across the country through direct cash transfers to the beneficiaries. Pilot projects are already under implementation for LPG and kerosene and it is our intention to extend the direct transfer mechanism to the UTs in the first phase," he said.
As for the Centrally-sponsored schemes, Mr. Chidambaram felt that the Centre should move out of some of them as at present, out of the 147 schemes in operation, 100 schemes had an outlay of less than Rs.300 crore each for the whole country. "Given the cost of administering the schemes and the capacity now available within the States, these deserve to be closed at the level of the Central government," he said
Jayanthi assails investment super committee proposal
Infra projects should not fall foul of Forest Rights Act: K.C. Deo
FinMin note pans Jayanthi, wants green light for NIB
Tasks for India's millet revolution
Xiaomi Global VP Manu Kumar Jain calls it quits
Transfer Deadline Day highlights: Chelsea signs Enzo for British record-fee; Sabitzer joins United on loan; Ziyech move to PSG hit by red tape


Perfect for Year 5 and Year 6.
Mylo knows there's no such thing as Martians – at least, until a flying saucer crash-lands next to his family's New Mexico farm. And then he starts to hear the voice, like someone's trying to communicate with him, asking for help. Desperate to be as brave as his older brother Obie – who passed away over a year ago – Mylo has to investigate the crash. Along the way, he ends up discovering more about the universe than he ever could have imagined.
Set in the rural heartlands of south-western America (some references may need explaining to younger readers) and based on the real-life events and conspiracy theories of 1947 when a 'UFO' was initially thought to have crash landed in Roswell, New Mexico, The Truth About Martians is a science-fiction story of strength as much as sorrow and sensitivity and the grapples of grief.
Shining a light on an event not known by everyone – and probably not by most of its readership – and covering just over three months between July 4, 1947 – 11:53 p.m. and October 6, 1947 – 6:15 a.m., the story starts with main character Mylo and his best friend Dibs staying over for the night. Since Dibs' mother left, Mylo's mom has become almost a surrogate mother for him (which is not surprising considering the quality and quantity of baked goods and sweet treats she makes for the boys!). Throughout the start of the story, we begin to understand that Milo is coming to terms with the loss of his older brother Obie and in turn, we realise that Dibs is slowly becoming the almost-brother that Mylo lost. Friendships are the glue of this story.
The saying goes that curiosity killed the cat but it doesn't kill these children's desire to investigate further and so Mylo and his friends set out on an adventure to discover more about the newly-arrived Martians and their mothership. What they don't know yet is that their discoveries may be more than they could possibly ever imagine.
Drawing on her background and experiences as a child and family therapist, I've been a big fan of Melissa's honest, frank and touching writing style in her previous books – most notably with Bigfoot, Tobin and Me – and long may this continue.
Like a middle-grade ET crossed with a hint of Stranger Things, this is one that should definitely have been included in this year's space-themed 'Space Chase' Summer Reading Challenge collection.
There are many things that inspired me to write The Truth About Martians. One of them is my love of research and learning stories about our world's history. One such story that has always intrigued me is the 1947 UFO crash outside of Roswell, New Mexico. Although the U.S. military has assured us it was nothing other than a military balloon, there are others who believe beyond a doubt that the crash was something extraterrestrial. Some even stating they saw the bodies of aliens at the crash site. Come on, who wouldn't find this story intriguing? It has everything a captivating mystery needs to keep us talking about it. Even seventy-two years after the incident happened.
I spent a great deal of time learning about that time frame, the facts of the case, the stories from witnesses and about the atomic age. I visited the town and spoke with the people. I researched eye witness accounts and read sworn affidavits and deathbed confessions by ex-military personnel. Additionally, I researched the children of that time. What was it like to live in 1947 as a preteen? What were their interests? What did they wear? What did they play? What did they aspire to do and to be? While this may not sound as exciting as the writing component of creating a story, for me, it's one of my favorite parts of the process. I love doing research because it gives me so many ideas for the story itself – plot, character development, setting, themes, scenes and dialogue. I learned all about 1940's baseball, the importance of Action Comics, I listened to many episodes of The Adventures of Superman radio program, and I even learned of this strange time called the atomic age which included children's atomic games and a very special atomic ring kids sent in for with pennies and some Kix Cereal box tops. I have absolutely loved immersing myself in the tiny town of Corona just outside of Roswell, New Mexico at a time when life seemed much slower and maybe a bit more predictable in many ways.
Regarding the crash itself, I still haven't decided quite yet what I think about it all. I'm a pretty skeptical person in general unless I see something with my own eyes. However, for me, I don't need a definitive answer to write a story about it. I am a fiction writer. All I need are some amazing facts, and the story of Roswell is ripe with them. Those compelling facts are what inspired me to create a Roswell mystery of my own. Maybe one day we will learn for certain what really happened out in that desert. Until that time, there are plenty of unique and fascinating accounts. Which one is true remains to be seen, but I'm open to hearing each and every one of them.
Even the ones that take me all the way to the stars.
Big thanks to Laura Smythe, Melissa and all at Chicken House for inviting me to share my thoughts on this out-of-this-world book as part of The Truth About Martians blog tour!
Extra thanks to Melissa for her guest post discussing her inspirations behind her book.
Be sure to check out the rest of the The Truth About Martians blog tour with these wonderful book bloggers for more reviews and exclusive posts!


VOLKSWAGEN Golf 1.6 TDI 90 CV 5p.
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Prenota la tua VOLKSWAGEN Golf 1.6 TDI 90 CV 5p.


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Home / Products / LIFE OF RICHARD TREVITHICK – INVENTOR OF THE LOCOMOTIVE ENGINE, by Francis Trevithick, C.E., 1872. E. & F. N. SPON. VOLUME TWO [ebook]
LIFE OF RICHARD TREVITHICK – INVENTOR OF THE LOCOMOTIVE ENGINE, by Francis Trevithick, C.E., 1872. E. & F. N. SPON. VOLUME TWO [ebook]
Hardcover book, brown cloth boards, gilt embossed title and representation of "Catch-me-who-can" on cover, pp. 403, illus by 33 engravings, including large folding engraving of high pressure steam expansion pumping engine
LIFE OF RICHARD TREVITHICK – INVENTOR OF THE LOCOMOTIVE ENGINE, by Francis Trevithick, C.E., 1872. E. & F. N. SPON. VOLUME TWO [ebook] quantity
Category: NEW THIS WEEK AND RECENT Tags: LIFE OF RICHARD TREVITHICK – INVENTOR OF THE LOCOMOTIVE ENGINE, by Francis Trevithick, C.E., 1872. E. & F. N. SPON. VOLUME TWO [ebook]
This is volume TWO of the monumental biography of Richard Trevithick (1771-1831) by his eldest son Francis (1812-1877). Francis had little contact with his father while growing up. When he was four, Trevithick senior left for South America, and did not return until Francis was 14.
Francis had his early training in civil engineering under Joseph Locke from 1832, and was appointed resident engineer at the Birmingham end of the Grand Junction Railway in 1840. In September 1841 he became locomotive superintendent of the company at Edge Hill, Liverpool, without any previous experience of locomotive work. He developed the plans for Crewe works and had charge of their building and equipment; also he had the of the whole locomotive department and then that of the Northern Division of the LNWR, including running. Under him the distinctive Crewe-type locomotive was developed from the designs of Allan and Buddicom, and apart from increases in dimensions, he built nothing else until he left Crewe in 1857, when the Northern and Southern Divisions of the LNWR were combined
A man of easy-going friendly temperament, popular amongst the workmen and their families, "Trevvy" as he was affectionately referred to. Was not really capable of taking on the much bigger responsibilities of the position and despite support from Locke, the Directors demanded his resignation. He returned to Cornwall, taking on the management of an estate, writing this biography and dying in 1877.
Richard Trevithick was a man of considerable energy and talents, but unfortunately had no head for business. In partnership with others, because he could not afford the fees, he took out many patents, usually having to sell out his share to keep his family and finance his next venture. This volume covers various inventions including a "Steam Thrashing (sic) Machine, steam pole condensing engines, steam engines at Dolcoath, winding engine and other machinery for South America including for the Lima Mint, compound marine engine and design for a column commemorating the 1832 Reform Act. This would have been 1,000ft. Tall, a tapering cone made of gilded cast iron segments bolted together. I suppose that we should be grateful that the London skyline was not disfigured by such a tall structure so early on! Fortunately finance was not forthcoming.
The years in South America are described, together with the disastrous outcome, when the Revolution wiped out all his savings, leaving him with little more than the clothes he stood up in. A chance meeting with Robert Stephenson, who had also gone out to "make his fortune", resulted in that kind man assisting Trevithick's repatriation.
Richard Trevithick never did achieve solvency. He was last employed by Halls of Dartford, and when he died in 1831, a pauper's burial was only avoided because Hall's workmen funded a proper funeral


Christmas Ornaments Made in USA - Lime Green Crystal Glass Optic Three Inch Ornament Ball - A Christmas Ornament Co.
Here is a great example of American Craftsmanship: a lime-green, hand-blown (using the soda-lime glass-making process), Christmas ornament is made by a glass blowing company that have been in the business for 80 years. This "optic" ornament was hand-blown into a very old Victorian-style antique mold acquired by craftsmen from a long defunct glass-blowing operation in Ohio. Since each one is hand-made (although with a mold, not "free-blown") no two are exactly the same. This Christmas ornament is not only incredibly beautiful, but also incredibly durable, rigid, and scratch resistant. Made in the U.S.A. by seasoned artisans. Ornament is 3 inches across.


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Immune cells sacrifice themselves to protect us from invading bacteria
Explosive cell death jump starts the body's immune system
The stomach flu can turn the strongest individual into a limp dishrag. Snot and slime are going wild in kindergartens. This year's flu is approaching in full swing.
You can have your fever-lowering drugs ready, but the flu is a strange thing. The same bacteria and viruses don't hit everyone with the same intensity.
Some people get really sick, others less so. Some folks don't get sick at all.
Why? What's really going on in the body when viruses and bacteria sneak in the back door and gear up for a full-on party?
Black Death as a lifelong partner
A lot of researchers are intrigued by that very question. One of them is Professor Egil Lien at the Norwegian University of Science and Technology's (NTNU) Centre of Molecular Inflammation Research (CEMIR). He splits his time between Norway and the the University of Massachusetts in the United States and is one of Norway's foremost experts on how bacteria attack people.
Lien hasn't chosen the easiest bacteria to get to know. He has opted to focus his study on a really nasty one called Yersinia pestis, known as the culprit behind the Black Death outbreak. Yes, the bacteria that killed a third of Europe's population in the 1300s.
Lien has singled out precisely this bacterium as a lifelong research partner because it is a very deceptive one. Yersinia manipulates the immune system to hide from it, almost like a chameleon that changes color. It also kills cells that the body uses in the immune system.
For more effective medication
Now Lien, PhD candidate Pontus Ørning and other research colleagues have made a new discovery about what happens in the body when bacteria like Yersinia and Salmonella are at peak activity.
This information could come in handy, not only because Yersinia still exists and because antibiotic resistance is a growing problem, but because the new knowledge can be transferred to help understand other diseases.
This knowledge can also be used to make more effective medicines. Their finding has been published in the November 30 issue of Science magazine.
Sacrifice themselves in warning
It turns out that immune cells are so dedicated to their jobs that they explode themselves to release proteins that fight invading bacteria and resulting damage. The explosion does not go unnoticed and warns the other immune cells. The immune cells sacrifice themselves to let the other cells know what is going on.
The process is so explosive that it is called pyroptosis.
What happens is that the immune cell forms small pores on its surface. This causes water to flow into the cell, which then swells until it bursts. When the cell explodes, it also releases substances that inhibit the invading bacteria from growing and that alert the other cells. Pretty effective, right?
Immune system backup kicks in
Sneaky Yersinia knows all this, and tries to camouflage itself and secretes an antidote. The NTNU researchers figured out that the body knows that Yersinia disguises itself.
At this point, the action starts to get really involved, but the article in Science explains that the immune cells initiate a backup mechanism that is triggered in a way not previously understood.
"These findings show us complicated mechanisms that occur in the immune system to counter infection, but they may also apply to other diseases. Some of the same phenomena can happen in diseases that cause inflammation in the body in general, such as food poisoning or Alzheimer's disease. So these findings can also increase our understanding of inflammation, which happens in most diseases as changes occur in the body," says Lien.
Reference: Pathogen blockade of TAK1 triggers caspase-8-dependent cleavage of gasdermin D and cell death Pontus Orning, Dan Weng, Kristian Starheim, Dmitry Ratner, Zachary Best, Bettina Lee, Alexandria Brooks, Shiyu Xia, Hao Wu, Michelle A. Kelliher, Scott B. Berge, Peter J. Gough, John Bertin, Megan M. Proulx, Jom D. Goguen, Nobuhiko Kayagaki, Katherine A. Fitzgerald and Egil Lien. SCIENCE 30 NOV 2018 : 1064-1069
Egil Lien
egil.lien@ntnu.no
@NTNU
http://www.ntnu.edu
IMMUNOLOGY/ALLERGIES/ASTHMA
http://dx.doi.org/10.1126/science.aau2818


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Benjamin J. LeFrancois Attorney At Law
ben.lefrancois@gray-robinson.com
One Lake Morton Drive
Post Office Box 3 (33802-0003)
Ben is a shareholder in GrayRobinson's Lakeland office. His practice is focused on subrogation, arbitration and litigation. Ben has participated in intercompany arbitration, both in preparing the arbitration application and accompanying documentation and in attending arbitration hearings. Ben routinely assists in the settling of claims and has attended mediations. He is familiar with the workings of a large-scale subrogation practice and his organizational and interpersonal skills are an asset to the department.
Ben also concentrates his practice in the area of general civil litigation.
Additional Information About Benjamin J. LeFrancois
Click the "+" to Expand the Sections Below
Click the "-" to Collapse the Sections Below
Ben is a native of Lakeland, Florida. He is a 2002 graduate of the University of Florida Levin College of Law. He focuses his practice on subrogation law. Ben has worked in the firm's subrogation department since 1996, first as a legal assistant and then as a law clerk.
Bryant College, B.S. (business administration, summa cum laude, 1999)
Minor: Legal Studies
Graduated third in a class of 400
Presidential Scholarship, Recipient
University of Florida Levin College of Law, J.D. (honors, 2002)
Order of the Coif - Florida Chapter
Law College Counsel, General Board Member
John Marshall Bar Association, Member
Professional Associations & Memberships
Lakeland Area Chamber of Commerce
Emerge Lakeland
Polk County School Board, Mentor
YMCA of West Central Florida
All Saints' Academy, Winter Haven


AUNT STELLA'S COOKIES ARE well known in Japan, as many of the company's retail stores can be found in Japan's urban centers. The company philosophy is "warm hearted communication," which is an indication of its ability to serve the needs of each and every one of its customers. The company believes that the one-to-one marketing it executes through its Web site is an example of this philosophy.
"Our 'my choice' feature allows customers to create their own cookie bag, choosing their favorite cookies from our selection of over 40 flavors," says Christine Dunkle, Aunt Stella's E-business manager. "We empower the customer to create their own product based on their taste."
The Web site is open 24 hours, allowing customers to determine when they want to shop online, instead of having to abide by the operating hours and schedules of the stores.
Over the past year, Aunt Stella's Web site (www.auntstella .co.jp) has doubled its customer count, growing 230 percent in sales and 300 percent in page views. The company currently uses an extremely successful affiliate program, which has allowed them to work with numerous partners to promote the site. In addition, during special occasions such as Valentines Day or Christmas, they tie up their present campaigns with several successful women's Web sites and mobile sites.
Unknown brands are traditionally viewed with skepticism by Japan's notoriously fastidious consumers. Therefore, it is a good sign for Aunt Stella's that the majority of their online customers are familiar with both the Aunt Stella brand and the taste of its cookies. "The 'my choice' feature allows them to determine both the size and contents of their purchase," says Dunkle. "Most of our customers begin with a 'my choice' purchase for their home before expanding into using our gift giving option."
Cookies and coffee are a natural combination in just about any culture. RIE COFFEE began as an online business four years ago, in fact, before opening a retail store in January 2002 at the Hilton Hotel in Shinjuku. The company flies in the finest roasted beans from San Francisco every week. Apart from its well-known flavored coffee, the company also offers regular coffee, decaffeinated coffee, tea, herb tea and cocoa. RIE COFFEE also creates gift packs for that special someone or occasion, always a popular idea in Japan.
You can find loyal Japanese consumers for just about any popular, high-quality Western product. Company management says that its customers are true coffee lovers who appreciate RIE COFFEE's different blends, which cannot be found elsewhere. "We have many orders from distant places outside of Tokyo, and as they start liking our fine products, some of my customers even make requests to link our homepage to theirs," says Rie Kihara, president of RIE COFFEE. "We have really strong word-of-mouth advertising, which helps in establishing brand loyalty."
RIE COFFEE is a coffee specialist, focused on bringing only the very best coffee to Japan. The company has chosen net to become a huge franchise--their main selling point is the cup of coffee. "We have no intention of selling coffee and cakes, but prefer to concentrate on providing the finest coffee beans, high-quality machines, and delivering the good services," says Kihara.
Marketing agreements and seasonal advertising campaigns have also contributed to RIE COFFEE's success. Whenever they have a Valentine's Day or summer promotion, the online store incorporates the promotional campaign into the online ads, which brings in more sales and draws customers to the retail store, adds Kihara.
Offering a low barrier of entry is an important selling tool. "We have a trial package for [yen] 1300 that can also be ordered via phone, fax or catalogue order sheets. The customer gets three small packages of our best-selling blends, which are about [yen] 320 each," says Kihara. Plus, the customer gets a packet of Ghirardelli cocoa, with no delivery charge.
The next step in Japan's B2C evolution involves acquainting Japanese consumers with more advanced Internet shopping options. To encourage its customers to shop more often online, RIE COFFEE will make the entire shopping process easier and more convenient by adding more versatile Net-shopping features like the shopper's "cart," personal account features and other enhancements.
Westerners are usually much more comfortable giving out their credit card information for Internet shopping transactions, says Aunt Stella's Dunkle. "Many of our Japanese customers tend to use the time-consuming postal and bank deposit payment options." This leads to delayed payment confirmation, as postal deposits can take up to one week, she adds. Overcoming this misunderstanding is essential to utilizing one of the most important benefits of Internet shopping: speedy delivery.
Internet shopping has become a way of life for an increasing number of Japanese. The telecommunications infrastructure has allowed outstanding consumer brands to emerge online and find an eager consumer audience.


Go behind The Mission world in a special off-site edition of WORD OF MOUTH with Leiti Hsu. In a show recorded at Stitcher's studios in San Francisco, Leiti chats with none other than Anthony Myint. Anthony is the founder of Mission Street Food, Mission Chinese Food, Mission Burger, and Commonwealth Restaurant. Tune in to get some great insights into the mission of Mission, the life of a successful restauranteur and the work that got Anthony to where he is today. This program was sponsored by Whole Foods Market.


Believe men should intuitively 'know' how to treat her, with no other communication of that beyond telepathy.
That is he was the 'right man for her he would be confident enough to…' come swashbuckling up to her with no care for social convention or the #metoo movement.
Think if there's no immediate spark that the date is DOA. It's interesting to know I hear far more men wanting to 'give things a chance'.
However, I know that it's not just women struggling with the gap between her high expectations of dating (generated probably from the fairytale PR love gets), and it's 2019 reality.
And rather than consternating over whose 'fault' it is that modern dating sometimes feels sucky, I think it's far more powerful to not think of dating divisively and come back to HOW CAN WE UNDERSTAND ONE ANOTHER AS PEOPLE BETTER.
So for a second I want to turn this around and look at how sometimes men's expectations of dating can also collide with reality – and ultimately (just like their female counterparts) hold them back from getting the relationship that they want. A lot of the time I in fact want men to have higher expectations of dating.
Tell me that they've met a woman who is 'just really sweet'. This is usually the point I start to worry on the guy's behalf. I am a big believer that people are by nature multi-faceted; and whilst some women may indeed appear very sweet if you stop at this level of engagement with her, you'll never establish a true connection.
This will encourage her to open up more to you and truly invest in you emotionally rather than just projecting an image of herself.
She wants to feel chosen for what is unique to her, not that you're just a guy who would be happy to have any woman in his life as long as he's physically attracted to her.
How I want you to have a high expectation of dating is to believe everyone has a rich and complex personality, and to not settle until she allows you insight into hers.
2. Think women are 'playing games'. Now I can understand when she enthusiastically agrees to a date and then cancels an hour before meeting you might assume that she's messing with you. The truth is likely more complex and less malicious.
Remember in the early stages of dating she might not be feeling whole heartedly attracted to you, so she's ironically often not giving you the best of her. Now I'm not trying to just cover for flakey women (or people) – there's not much excuse for this. However if you can exit the 'she's playing games with me' mindset you're much more likely to be able to tackle the actual issue (she doesn't know you well enough yet to be attracted to you enough) and not take it too personally.
B. Only invite her out last minute, but make the invite really good. This can sub-communicate back WE NEED TO SET A PLAN OR WE BOTH MISS OUT.
C. Find a woman who is open enough to dating you and a clear enough communicator that you don't need to do this (this is my preferred option).
How I want you to have a high expectation of dating here is whilst I emphatically DO NOT want you to keep chasing women for dates who think nothing of flaking on you; I do want you to not get embittered by this process and lose perspective. This woman who cancelled may not have valued you enough. Okay, how do you meet more women who will and communicate that value better?
3. Think you'd be quite happy to just have a girlfriend. If you're thinking that a girlfriend will solve any feelings you have, to be blunt she won't. Relationships require work yes, but they also require you to have existing standards before you begin. Loneliness is up there in terms of the toughest feelings you can experience BUT its solution doesn't lie in another person who is going to come to you with their own set of insecurities, problems and anxieties.
And speaking quite frankly as a girlfriend, there's times when you're not always fun. When you're unwell, you have work issues, you have something in your family that needs support etc.
As a dating coach I also consistently see the people getting into relationships fastest that are the most secure are those who already have a strong pre-existing social infrastructure of friends and colleagues/ business partners.
How I want you to have higher standards in this case is to make your girlfriend-free life as awesome as possible. Build friendships and other supportive and fun social relationships so that when you meet a woman you're not immediately reliant on her.
All this being said about having higher standards for dating – the highest standard you can hold is to lead your own life truly and happily; whether this happens to involve someone else or not.


Advisory services regarding the management of financial resources.
For more information on these services and why you should use them, see the blog article entitled "Why Plan For The Future?"
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"Patience is a bitter plant, but it has sweet fruit." German proverb.
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