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Malta Comic Con 2014 announces final guests for a stellar show
Posted on November 13, 2014 November 13, 2014 by John Freeman
Wicked Comics has just announced the final comic creators and comics industry professionals for the 2014 Malta Comic Con which will be held on Saturday 29th and Sunday 30th November at St. James Cavalier (all floors), Valletta.
Joining an already incredible line up are pop culture king Chris J. Thompson (The Orbiting Pod), illustrator and storyboard artist Gabriel Hardman (Secret Avengers), writer Corinna Bechko (Star Wars: Legacy), Maltese but London based actress Marama Corlett (Sinbad) and independent creators Paulina Vassileva and Manos Kotsifakis (Game Lost).
Storyboard art for the film Inception by Gabriel Hardman
Gabriel Hardman is the co-writer and artist of Invisible Republic from Image Comics. He's also co-written (with Corinna Bechko) and drawn Savage Hulk for Marvel Comics, Star Wars: Legacy for Dark Horse Comics, and Planet of the Apes for Boom! Studios. He has drawn Hulk, Secret Avengers and Agents of Atlas for Marvel as well as the OGN Heathentown for Image/Shadowline. He's worked as a storyboard artist on movies such at Interstellar, Inception, Tropic Thunder and X2. More info at http://maltacomic-con.com/guest/103 and www.gabrielhardman.com – or follow Gabriel on Twitter @gabrielhardman
Art by Corinna Bechko
Corinna Bechko has been writing comics since her horror graphic novel Heathentown was published by Image/Shadowline in 2009. Since then she worked for Marvel, Boom! and Dark Horse, among others, on titles such as Planet of the Apes, Star Wars: Legacy and Savage Hulk. She was recently short-listed for the Aeon Award for her prose short Sooterkin. She is a zoologist by training. More info at http://maltacomic-con.com/guest/96 and http://thefrogbag.blogspot.com or follow Corinna on Twitter @CorinnaBechko
Actress Marama Corlett
Marama Corlett is a Maltese actress who began her career as a professional ballet dancer before focusing on her acting. Marama's first role was in The Devil's Double in 2010 directed by Lee Tamahori. She went on to perform in the West End 2011 production of The Children's Hour' directed by Ian Rickson (Jerusalem), opposite Keira Knightley. Marama played Rina in the television drama Sinbad for Sky Atlantic and on SyFy in the US. Some of Marama's most recent work is Guardians of the Galaxy playing the role of Pit Boss. She will also be appearing in feature films Desert Dancer alongside Freida Pinto and The Goob with BAFTA winner Sean Harris, out next year.
Other work includes the role of Betty Parris in The Old Vic theatre production of The Crucible alongside Lord of the Rings star Richard Armitage and will be seen playing SIA in The Dovekeepers on CBS 2015. More info at http://maltacomic-con.com/guest/104 and http://www.maramacorlett.com or follow Marama on Twitter @MaramaCorlett
Podcaster Chris Thompson
Chris Thompson is an internationally-recognised comics retailer, journalist, podcaster and speaker who has attended and spoken at conventions across three continents. You can hear him on The Orbiting Pod & Pop Culture Hound podcasts; see his work at First Comics News, Bleeding Cool & various other sites; and follow him via Twitter @popculturehound. More info at http://maltacomic-con.com/guest/25 and www.popculturehound.com or follow Chris on Twitter @PopCultureHound
Paulina Vassileva
Art by Paulina Vassileva
Paulina Vassileva is an illustrator of varied interests. While she holds a degree in Graphic design and Creative studies, when it comes to drawing, she is mostly self-taught. Her educational experience has led her to believe it is essential for artists to have a working knowledge of as many disciplines as possible as creative media feed off of each other.
At the moment she is working on a range of private commissions and unannounced projects. In the meantime, she is also exploring the world of sequential story-telling with two personal creator-owned projects of her own, due to come out early next year. While not much can be revealed about them yet, it can be said that they are on opposite ranges of the spectrum, visually and conceptually: one being targeted at children with a predominantly cartoony style and the other being targeted at adults and visually more expressive and experimental.
Paulina is excited to attend the Malta Comic Con for the first time and answer any questions about her creative pursuits. More info at http://maltacomic-con.com/guest/102 and www.pauscorpi.deviantart.com or follow Paulina on Twitter @pauscorpi
Manos Kotsifakis
Art by Manos Kotsifakis
Manos Kotsifakis was born on Crete in Heraklion, Greece 1987. He has a degree in Bachelor of Arts (Honour) in Sketch –Comic-Cartoon from AKTO Art & Design College. The love for comics and cartoons began, watching too much cartoons, reading Asterix and playing video games. After finishing his studies in AKTO and completing his military service he worked for 2 years (2010-2012) in ''Parapolitika'' newspaper as a political and sport cartoonist.
From 2010 onwards, Manos teamed with other comic artists in the Annual Greek Comic Convention in American-Hellenic Union to work on various Greek zines: Dark Side of a Shadow (2010), Crew of Awesome (2011), Crew of Awesome Land (2012) and Sir Com (2013).
Manos has also been working with "inStorium Games" as a character designer and concept artist for a mobile game, Cannon Penguins. His latest creation is the online web comic Game Lost, an adventure about a guy who's been sucked by his video game console, which can be found on Facebook's online magazine "Comic Book Bubble". More info at http://maltacomic-con.com/guest/108 and http://supermanosbros.deviantart.com
Chris Le Galle co-founder of Wicked Comics is effusive about these last guests to join this year's terrific line up of comic talent from across Europe and beyond.
"Having attended all the Malta Comic Conventions except for the first one, Chris Thompson is not simply considered as an esteemed guest we look forward to meeting each year but also a valuable component of the Malta Comic Con," he says.
"In fact it was through his intervention and the cooperation of Julien Schantz and the rest of the crew behind the awesome Lille Comics Festival [which tales place 6th – 7th December this year] with whom we're partnering this year that we're able to welcome Gabriel Hardman and Corinna Beckho to our show," he reveals. "Having worked on a number of popular titles such as Planet of the Apes and Savage Hulk this creative duo will surely strike a cord with the local comic fans.
"Furthermore, no self-respecting movie fan can afford to miss the chance to meet Gabriel Hardman who has worked extensively on blockbusters such as Inception, the Dark Knight Rises, Tropic Thunder and X2. And speaking of movies we're delighted to welcome rising Maltese actress Marama Corelett home. Having had the pleasure to meet Marama, who has shared the stage with Keira Knightly at the London's West End, appeared in Devil's Double, the television drama Sinbad (both shot in Malta) and Guardians of the Galaxy, I'm sure her presence at the show, will help inspire others to follow in her footsteps.
"We're also excited to welcome Paulina Vassileva and Manos Kotsifakis whose distinct style is sure to appeal to a lot fans. The presence of these fine folk as well as that of the other guests has us all enthusiastically looking forward to the Malta Comic Con 2014 and we would like to take this opportunity to thank them for their participation and look forward to hosting them here."
Other guests, previously announced are (links to official Twitter or official sites): Yishan Li, Rufus Dayglo, Andrew Wildman, Guillermo Ortego, Steve Scott, Mickey Lam, Mike Collins, Cliodhna, Dave Taylor, Christian Ward, John-Paul Kamath, Michael Dialynas, Lew Stringer, Daniela "Iella" Attard, Mario Alberti, Gabriel Hernandez Walta, Gordon Rennie, Emma Beeby, Richard Starkings, Tim Perkins, Sean Azzopardi, El Torres, Amigo Comics, Jenika Ioffreda, Stephanie Cooke, Gianluca Maconi, Mike Lawson, the School of Bitches, Marianne Galea, JL Mast, Dave Lung, Luciano Regazzoni, Grafimated Cartoon, Clint Langley, Tasos Anastasiades, Yannis Roumbulias and Manos Lagouvardos.
• For the full guest list, which includes profiles of each guest visit: www.maltacomic-con.com/guests
• I've created a Twitter list of all guests with an official Twitter presence here: https://twitter.com/johnfreeman_dtb/lists/malta-comic-con-2014 – so even if you don't make the event, you get to see what they're up to and wish you were there!
• The 2014 Malta Malta Comic Con which will be held on Saturday 29th and Sunday 30th November at St. James Cavalier (all floors), Valletta, between 10am – 6pm on Saturday and 11am – 7pm on Sunday. Web: www.maltacomic-con.com or follow the convention on Twitter @MaltaComicCon
• Wicked Comics have designed a number of packages for fans wishing to travel to the Malta Comic Con from abroad, which include heavily discounted accommodation rates and local transport from hotel to convention centre. More Packages info: www.maltacomic-con.com/post/525/travelling-and-accommodation-packages-for-fans-m
Posted in British Comics, Comic Creator Spotlight, Creating Comics, downthetubes Comics News, Events, Exhibitions, FeaturedTagged Amigo Comics, Andrew Wildman, Chris J Thompson, Christian Ward, Clint Langley, Cliodhna, Confessional Comics by Jewish Women, Corinna Bechko, Daniela "Iella" Attard, Dave Lung, Dave Taylor, El Torres, Emma Beeby, Gabriel Hardman, Gabriel Hernandez Walta, Gianluca Maconi, Gordon Rennie, Grafimated Cartoon, Graphic Details, Guillermo Ortego, Jenika Ioffreda, JL Mast, John-Paul Kamath, Lew Stringer, Lille Comics Festival, Luciano Regazzoni, Malta Comic Con, Manos Kotsifakis, Manos Lagouvardos, Marama Corlett, Marianne Galea, Mario Alberti, Michael Dialynas, Mickey Lam, Mike Collins, Mike Lawson, Paulina Vassileva, Richard Starkings, Rufus Dayglo, Sean Azzopardi, Stephanie Cooke, Steve Scott, Tasos Anastasiades, the School of Bitches, Tim Perkins, Wicked Comics, Yannis Roumbulias, Yishan Li
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I offer three positions of these majestic Dutch rams. They are cream mohair with a gradiation to brown along the upper body with speckled legs and a gradiated brown, tan and cream face. They are show-stopping rams.


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\section{Introduction}\label{sec:introduction}
Food is a crucial part of our life and even our identity. Long after moving to a foreign country and after adopting that country's language, migrants often hold on to their ethnic food for many years \cite{fischler88food}. Food is also a crucial element in effecting weight gain and loss, with important implications on obesity and diabetes and other lifestyle diseases. Some researchers go as far as claiming that ``you cannot outrun a bad diet''~\cite{Malhotra22042015}.

One important aspect governing our food choices and how much we consume is how we perceive the food. What do we perceive to be healthy? Or delicious? What qualifies as a ``salad''? Food perception is typically studied in labs, often using MRIs and other machinery to measure the perception at the level of brain activity \cite{killgore2005body,rosenbaum2008leptin,medic2016presence}. Though such carefully controlled settings are often required to remove confounding variables, these settings also impose limitations related to (i) the artificial setting the subject is exposed to, and (ii) the cost and lack of scalability of the analysis.

There are, however, externally visible signals of food perception ``in the wild'' that can be collected at scale and at little cost: data on how people label their food images on social media. What images get labeled as \#salad? Which ones get the label \#healthy?

Though useful, these human-provided labels are difficult to disentangle from the actual food they describe: if someone labels something as \#salad is this because (i) it really is a salad, or (ii) the user believes that a single lettuce leaf next to a big steak and fries qualifies as a salad.

\begin{figure}[h!]
  \setlength{\tabcolsep}{0pt}
  \begin{tabular}{ m{\columnwidth} }
    \begin{minipage}{\columnwidth}
      \includegraphics[width=\columnwidth]{Picture1.png}
    \end{minipage}
    \\
      \begin{itemize}[]
        \item \underline{User}: \#foodie, \#hungry, \#yummy, \#burger
        \item \underline{Machine}: \#burger, \#chicken, \#fries, \#chips, \#ketchup, \#milkshake
      \end{itemize}
  \end{tabular}
  \caption{A comparison of user-provided tags vs.\ machine-generated tags. In this example, the user uses only \#burger to describe what they are eating, potentially not perceiving the fries as worth mentioning, though they are providing subjective judgement in the form of \#yummy. However, machine-generated tags provide more detailed factual information about the food plate and scene including \#fries, \#ketchup, and \#milkshake.}
  \label{fig:gap_ex1}
  \end{figure}

\begin{figure}[h!]
  \setlength{\tabcolsep}{0pt}
  \begin{tabular}{ m{\columnwidth} }
    \begin{minipage}{\columnwidth}
      \includegraphics[width=\columnwidth]{chickenkatsu.png}
    \end{minipage}
    \\
      \begin{itemize}[]
        \item \underline{User}: \#pork, \#foodie, \#japanese, \#chicken, \#katsu, \#box, \#salad, \#restaurant, \#bento, \#rice, \#teriyaki
        \item \underline{Machine}:  \#teriyakichicken, \#stickyrice, \#chickenkatsu, \#whiterice, \#teriyaki, \#peanutsauce, \#ricebowl, \#spicychicken, \#fishsauce, \#bbqpork, \#shrimptempura, \#ahituna, \#friedshrimp, \#papayasalad, \#roastpork, \#seaweedsalad, \#chickenandrice%
      \end{itemize}
  \end{tabular}
  \vspace{-.2in}
  \caption{Another comparison of user-provided tags vs.\ machine-generated tags. Machine-generated tags provide more detail about the food plate such as the type of rice, i.e., \#stickyrice and \#whiterice, and the dressing on the food item, i.e., \#peanutsauce.}
  \label{fig:gap_ex2}
  \end{figure}

We propose to use image recognition to study the ``perception gap'', i.e., the difference between what a food image objectively depicts (as determined by machine annotations) and how a human describes the food images (as determined from the human annotations). Figures~\ref{fig:gap_ex1} and \ref{fig:gap_ex2} show examples from our dataset.

We find that there are systematic patterns of how this gap is related to variation in health statistics. For example, counties where users are, compared to a machine, more likely to use the hashtag \#heineken are counties with a higher Food Environment Index. In this particular example, a plausible hypothesis is that users who are specific about how they choose - and label - their beer are less likely to drink beer for the sake of alcohol and more likely to drink it for its taste.

We then extend our analysis to also include subjective labels applied by humans. Here we find that, e.g., labeling an image that depicts saki (as determined by the machine) as \#delicious (by the human) is indicative of lower obesity rates. This again illustrates that not only the perception of alcohol, as a fun drug to get high vs.\ as part of a refined dining experience, can be related to health outcomes, but also that such perception differences can be picked up automatically by using image recognition.

The rest of the paper is structured as follows. In the next section we review work related to (i) the perception of food and its relationship to health, (ii) using social media for public health surveillance, and (iii) image recognition and automated food detection. Section~\ref{sec:data} describes the collection and preprocessing of our Instagram datasets, including both our large dataset of $1.9M$ images used to analyze food perception gap and its relation to health, as well as even larger dataset of $\sim3.7M$ images used to train and compare our food-specific image tagging models against the Food-101 benchmark. Section~\ref{sec:computervision} outlines the architecture we used for training our food recognition system and shows that it outperforms all reported results on the reference benchmark. Our main contribution lies in Section~\ref{sec:methods} where we describe how we compute and use the ``perception gap''. Our quantitative results, in the form of indicative gap examples, are presented in Section~\ref{sec:results}. In Section~\ref{sec:discussion} we discuss limitations, extensions and implications of our work, before concluding the paper.

\section{Related Work}\label{sec:related}
Our research relates to previous work from a wide range of areas. In the following we discuss work related to (i) food perception and its relationship to health, (ii) using social media for public health tracking, and (iii) image recognition and automated food detection.

\textbf{Food perception and its relationship to health.} Due to the global obesity epidemic, a growing number of researchers have studied how our perception of food, both before and during its consumption, relates to our food choices and the amount of food intake. Here we review a small sample of such studies.

Killgore and Yurgelun-Todd~\cite{killgore2005body} showed a link between differences in orbitofrontal brain activity and (i) viewing high-calorie or low-calorie foods, and (ii) the body mass index of the person viewing the image. This suggests a relationship between weight status and responsiveness of the orbitofrontal cortex to rewarding food images.

Rosenbaum et al.~\cite{rosenbaum2008leptin} showed that, after undergoing substantial weight loss, obese subjects demonstrated changes in brain activity elicited by food-related visual cues. Many of these changes in brain areas known to be involved in the regulatory, emotional, and cognitive control of food intake were reversed by leptin injection.

Medic et al.~\cite{medic2016presence} examined the relationship between goal-directed valuations of food images by both lean and overweight people in an MRI scanner and food consumption at a subsequent all-you-can-eat buffet. They observed that both lean and overweight participants showed similar patterns of value-based neural responses to health and taste attributes of foods. This suggests that a shift in obesity may lie in how the presence of food overcomes prior value-based decision-making.

Whereas the three studies discussed above studied the perception at the level of brain activity, our own work only looks at data from perception reported in the form of hashtags. This, indirectly, relates to a review by Sorensen et al.~\cite{sorensenetal03ijo} of studies on the link between the (self-declared) palatability, i.e., the positive sensory perception of foods, and the food intake. All of their reviewed studies showed that increased palatability leads to increased intake. In Section~\ref{sec:methods:subjectivegap}, we study a similar aspect by looking at regional differences in what is tagged as \#delicious and how this relates to obesity rates and other health outcomes.

More directly related to the \emph{visual} perception of food is work by Delwiche who described how visual cues lead to expections through learned associations and how these influence the assessment of the taste and flavor of food~\cite{delwiche12pb}. For example, when taste- and odor-less food coloring is used the perceived taste of the food changes and white wine colored as red wine would begin to taste like a red wine.

McCrickerd and Forde~\cite{OBR:OBR12340} focused on the role of both visual and odor cues in identifying food and guiding food choice. In particular, they described how the size of a plate or a bowl or the amount of food served effect the food intake. Generally, larger plates lead to more food being consumed.

Closer to the realm of social media is the concept of ``food porn''. Spence et al.~\cite{spenceetal15bc} discussed the danger that our growing exposure to such beautifully presented food images has detrimental consequences in particular on a hungry brain. They introduce the notion of ``visual hunger'', i.e., the desire to view beautiful images of food.

Petit gave a more positive view regarding the potential of food porn and social media images and discusses their use in carefully crafted ``multisensory mental simulation''~\cite{petitcan}. He argued that by engineering an appropriate pre-eating experience involving images and other sensory input food intake can be reduced and healthy food choices can be encouraged.

Note that our current analysis does not look at the \emph{presentation} aspect of food images. It would, however, be interesting and technically feasible to use computer vision to extract information on how the food is presented and then attempt to link this back to health statistics.

\textbf{Social media data for public health analysis.} Recent studies have shown that large scale, real time, non-intrusive monitoring can be done using social media to get aggregate statistics about the health and well being of a population~\cite{dredze12is,sarker15jbi,kostkova13www}. Twitter in particular has been widely used in studies on public health~\cite{pauldredze11icwsm,prier2011identifying,parkeretal13asonam,Kostkova15TwitterSocioscope}, due to its vast amount of data and the ease of availability of data.

Connecting the previous discussion on the perception of food and food images to public health analysis via social media is work by Mejova et al.~\cite{mejovaetal16icwsm}. They study data from 10 million images with the hashtag \#foodporn and find that, globally, sugary foods such as chocolate or cake are most commonly labeled this way. However, they also report a strong relationship (r=0.51) between the GDP per capita and the \#foodporn-healthiness assocation.

In the work most similar to ours, Garimella et al.~\cite{garimellaetal16chi} use image annotations obtained by Imagga\footnote{\url{http://imagga.com/auto-tagging-demo}} to explore the value of machine tags for modeling public health variation. They find that, generally, human annotations provide better signals. They do, however, report encouraging results for modeling alcohol abuse using machine annotations. Furthermore, due to their reliance on a third party system, they could only obtain annotations for a total of 200k images. Whereas our work focuses on the \emph{differences} in how machines and humans annotate the same images, their main focus is on building models for public health monitoring.

Previously, Culotta~\cite{culotta14chi} and Abbar et al.~\cite{abbaretal15chi}
	used Twitter in conjunction with psychometric lexicons such as LIWC and PERMA to predict county-level health statistics such as obesity, teen pregnancy and diabetes. Their overall approach of building regression models for regional variations in health statistics is similar to ours.
Paul et al.~\cite{pauldredze14pone} make use of Twitter data to identify health related topics and use these to characterize the discussion of health online.
Mejova et al.~\cite{mejovaetal15dh} use Foursquare and Instagram images to study food consumption patterns in the US, and find a correlation between obesity and fast food restaurants.

Abdullah et al.~\cite{abdullah2015collective} use smile recognition from images posted on social media to study and quantify the overall societal happiness. Andalibi et al.~\cite{andalibi2015depression} study depression related images on Instagram and ``establish[ed] the importance of visual imagery as a vehicle for expressing aspects of depression". Though these papers do not explicitly try to model public health statistics, they illustrate the value of image recognition techniques in the health domain. In the following we review computer vision work in more depth.

\textbf{Image recognition and automated food detection.} Although images and other rich multimedia form a major chunk of content being shared in social media, almost all the methods above rely on textual content.
Automatic image annotation has greatly improved over the last couple of years, owing to the recent development in deep learning~\cite{krizhevsky2012imagenet,simonyan2014very,he2015deep}.
Robust object recognition~\cite{wu2015deep,chatfield2014return} and image captioning~\cite{karpathy2014deep} have become possible because of these new developments. For example, Karpathy et al.~\cite{karpathy2014deep} use deep learning to produce descriptions of images, which compete with (and sometimes beat) human generated labels.
A few studies already make use of these advances to  identify~\cite{kawanoyanai15mta,Myers:2015:ITA:2919332.2919978,wang2015recipe,liu2016deepfood} and study~\cite{sudoetal14ubicomp} food consumption from pictures. For instance on the Food-101 dataset~\cite{bossard2014food}, one of the major benchmarks on food recognition, the classification accuracy improved from $50.76\%$~\cite{bossard2014food} to $77.4\%$~\cite{liu2016deepfood} and $79\%$~\cite{Myers:2015:ITA:2919332.2919978} in recent years with the help of deep convolutional networks.

Building upon Food-101 dataset, Myers et al.~\cite{Myers:2015:ITA:2919332.2919978} explore in-depth food understanding, including food segmentation and food volume estimation in plates, as well as predicting the calories from food images collected from restaurants. Unfortunately, the segmentation and depth image annotations used in their work are not publicly shared and cannot be used as a benchmark.

In addition to the Food-101 dataset, which has 101 classes and $101K$ images, there are various other publicly available smaller datasets, such as: PFID~\cite{chen2009pfid}, which has 61 classes (of fast food) and 1,098 images; UNICT-FD889~\cite{farinella2014benchmark}, which has 889 classes and 3,853 images; and UECFOOD-100~\cite{matsuda2012recognition}, which has 100 classes, and 9,060 images; later this dataset is expanded to 256 food categories~\cite{kawano14c}. Unfortunately, the performance for image recognition in general and food recognition in particular is highly correlated with the size of the datasets, especially while training deep convolutional models. In our work, we train deep convolutional networks with noisy but very large scale datasets collected from Instagram images.

Rich et al.~\cite{Rich:2016:TBA:2896338.2897734} learn food classifiers by training an SVM over a set of extracted features from $\sim800K$  images collected from Instagram. Our auto-tagger is mainly different from theirs in three major components, a) we only use food-related hashtags cleaned through a crowd-sourced process, b) we train a state-of-the-art deep network on food classification rather than operating on extracted features, c) we build upon a much larger image dataset($\sim 3.7M$ images in $1,170$ categories).

\section{Data Collection}\label{sec:data}

\begin{figure}[ht]
  \centering
  \includegraphics[width=\columnwidth]{us_image_counties.png}\\
  \caption{Distribution of collected $\sim4M$ images across US counties.}
  \label{fig:us_image_counties}
\end{figure}

\textbf{Instagram data collection.} In early 2016 we collected worldwide data from Instagram covering timestamps between November 2010 and May 2016 for the following hashtags: \#food, \#foodporn, \#foodie, \#breakfast, \#lunch, \#dinner. This led to meta information for a total of $\sim72M$ distinct images, $\sim26M$ of which  have associated locations, and $\sim4M$ of them are successfully assigned to one of the US counties.
Assignments are achieved by matching the longitude and latitude information of images with the polygons for each county in the US. Computations are performed in python using the Shapely\footnote{\url{https://github.com/Toblerity/Shapely}}
package for geometric processing. The polygons are obtained from the website of the US Census Bureau\footnote{\url{https://www.census.gov/geo/maps-data/data/kml/kml_counties.html}}.
The distribution of images over the US counties are visualized in Figure~\ref{fig:us_image_counties}.

\textbf{Clean food-related hashtags.} From the collected data, the top 10,000 hashtags are extracted from the Instagram posts in the US. Since we are mainly concerned with the food perception, we manually classified these hashtags into food-related categories with the help of crowd sourcing through Amazon Mechanical Turk\footnote{\url{https://www.mturk.com/mturk}} services. Each hashtag is seen by five unique workers, and classified into the following categories: drinks, part-of-a-dish, name-of-a-dish, other-food-related, and non-food-related.
The hashtags belonging to the categories of \emph{drinks}, \emph{part-of-a-dish}, and \emph{name-of-a-dish} are joined together in order to compose our dictionary of interest for food perception analysis. This vocabulary is further extended by including the hashtags corresponding to Food-101~\cite{bossard2014food} categories, resulting in a vocabulary of 1,170 unique hashtags.

\textbf{Insta-1K dataset.} For each of the 1,170 unique hashtags,
at most 4250 images are downloaded from Instagram resulting in a total of $\sim3.7M$ images. Note that a single image could be retrieved and used for training several hashtags.
This dataset is referred to as \emph{Insta-1K} and used for training the auto-tagger. A subset of the dataset, called \emph{Insta-101}, consists of images belonging to the hashtags associated with the Food-101 categories. Assignment of hashtags to each of the Food-101 categories is performed manually. This dataset is used for performance comparisons on Food-101 categories.

\textbf{County health statistics.} To see if signals derived from this online data are linked to patterns in the ``real world'', we obtained county-level health statistics for the year 2016 from the County Health Rankings and Roadmaps website~\footnote{\url{http://www.countyhealthrankings.org/rankings/data}}. This dataset includes statistics on various health measures that range from premature death and low birth weight to adult smoking, obesity and diabetes rates. From these statistics we decided to focus on the following nine health indicators:
Smokers (in \%, lower is better),
Adult Obesity (in \%, lower is better)
Food Environment Index (from 1=worst to 10=best, higher is better),
Physically Inactive (in \%, lower is better),
Excessive Drinking (in \%, lower is better),
Alcohol-impaired Driving Deaths (in \%, lower is better),
Diabetes Prevalence (in \%, lower is better),
Food Insecurity (in \%, lower is better), and
Limited Access to Healthy Food (in \%, lower is better).

\textbf{Food perception gap dataset.} We sampled images from the initial collection of $\sim4M$ Instagram posts associated with the US counties. Our county dictionary has 2,937 fips codes whereas the County Health Statistics dataset has 3,141 fips codes. Therefore, we used the 2,846 counties that were common in both datasets. 91 counties in our county dictionary without corresponding health statistics were dropped.
We then kept the 194 counties with at least 2,000 posts. Finally, we removed images without at least one human tag appearing in at least 20 out of the 194 counties. This was done to remove images whose users might have very particular tagging behavior, resulting in a dataset of $1.9M$ posts used for food perception gap analyses.

\section{Machine Tagging}\label{sec:computervision}

For training the food auto-tagger we utilized the state of the art deep convolutional architectures called \emph{deep residual networks}~\cite{he2015deep}. These architectures have a proven record of success on a variety of benchmarks~\cite{he2015deep}. The main advantage of the deep residual networks is their residual learning framework which enables easier training of much deeper architectures (i.e.\ with 50, 101, 152 layers). The layers in the residual networks are reformulated as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions as utilized in~\cite{simonyan2014very,krizhevsky2012imagenet}. Consequently, residual networks can train substantially deeper models which often result in better performances.

\begin{table}
\begin{center}
\caption{Cross-dataset performances of deep residual networks on Food-101 categories. Note that the model performs $80.9\%$ accuracy on Food-101, $1.9\%$ higher than the previously reported state of the art~\cite{Myers:2015:ITA:2919332.2919978}.}
\vspace{1em}
\def1.1{1.1}
\setlength{\tabcolsep}{.3em}
\begin{tabular}{l c|c|c|c}
 \parbox[t]{4mm}{\multirow{3}{*}{\rotatebox[origin=l]{90}{ Trained on}}}& & \multicolumn{2}{c}{Tested on} & \\
 & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{\bf Food-101} & \multicolumn{1}{c|}{\bf Insta-101} & \multicolumn{1}{c}{\bf mean} \\
\cline{2-5}
 & { \bf Food-101} & 80.9 & 40.3 & 60.6\\
 & { \bf Insta-101} & 74.5 & 51.5 & 63.0\\
\end{tabular}
\end{center}
\label{tab:food_insta_101}
\vspace{-1em}
\end{table}

We particularly train the deep residual network with 50 layers obtained from~\cite{he2015deep}. For benchmarking analysis, the model is first trained on Food-101 and Insta-101 datasets.
As it is also mentioned in \cite{Myers:2015:ITA:2919332.2919978}, with 750 training and 250 test samples per category, Food-101 is the largest publicly available dataset of food images. On the other hand, our Insta-101 dataset has 4,000 training and 250 test samples per category collected from Instagram, though they are not manually cleaned. The task is to classify images into one of the existing 101 food categories.
In the training procedure, the final 1000-way softmax in the deep residual model is replaced with a 101-way softmax, and the model is fine-tuned on the Insta-101 and Food-101 datasets individually. Training the deep residual model on Food-101 dataset, resulted in $\sim2\%$ improvement over the previously reported state of the art \cite{Myers:2015:ITA:2919332.2919978}. This illustrates that our auto-tagging architecture is highly competitive. The accuracies of the models are reported in Table~\ref{tab:food_insta_101} for comparison.

The model trained with Insta-101 dataset performs remarkably well on Food-101 test set with an accuracy of $74.5\%$. Even though it is trained on the noisy social media images, on average our Insta-101-based classifier performs $\sim2.5\%$ better than the Food-101-based model, probably due to the increase in training samples from 750 to 4,000, which comes for free through Instagram query search.
We also report the mean cross-dataset performance of the Insta-101 model with increasing number of training samples in Figure~\ref{fig:performance}. Note that around 2500 samples per category the Insta-101 model reaches the performance of Food-101 model. The experiments on Food-101 categories suggest that, despite their noisy nature, Instagram images are valuable resources for training deep food classification models.

The final auto-tagger is trained with the Insta-1K dataset, 1,170 categories with corresponding images collected from Instagram. Its accuracy is computed as $51.5\%$ on the held-out test set. For each image the top 30 hashtags identified by the auto-tagger are used for the perception gap analysis.

\begin{figure}[h]
  \includegraphics[width=\columnwidth]{sample_num_performance.png}
  \caption{Mean cross-dataset performance of Insta-101 model trained with increasing number of training samples per category. Note that around 2500 samples per category Insta-101 model reaches the performance of Food-101 model.}
\label{fig:performance}
\end{figure}

\section{Methods}\label{sec:methods}

\subsection{Modeling Regional Variation in Health\\Statistics}\label{sec:methods:regional}
At a high level, our main analytical tool is simple correlation analysis of individual variables with ``ground truth'' county-level health statistics (see Section~\ref{sec:data}). This approach provides clues for hypotheses concerning causal links to explore further in separate studies.

In order to avoid spurious correlations, we perform 10-fold cross validation, leaving 19 (or 20) counties out and computing correlations using the rest of the 174 (or 173) counties at each fold. We then report average correlations and their standard errors in our results.

Also, when reporting significance values for $r$ correlation coefficients, we apply the Benjamini-Hochberge procedure~\cite{benjamini1995controlling} to guard against false positives. As an example, in Table~\ref{tab:objective} a significance level of $.05$ corresponds to a ``raw'' significance level of $.00328\pm.000075$.

We compute correlations for four different types of feature sets: (i) human tag usage probabilities, (ii) machine tag usage probabilities, (iii) perception gap weights, and (iv) conditional probabilities for the usage of \#healthy, \#delicious, and \#organic given machine tags. These will be described in the following.

\subsection{Quantifying the Perception Gap}\label{sec:methods:objectivegap}
They key contribution of this work is the analysis of the ``perception gap'' and how it relates to health. Abstractly, we define the perception gap as the difference between how a machine and a human annotate a given image. Concretely, we compute it as follows.

First, we iterate over all images. Images that do not have at least one machine tag and at least one human tag in the same vocabulary space (comprising the set $T$ of 1,170 tags described in Section~\ref{sec:computervision}) are ignored.

This was done as it is hard to quantify the disagreement between two annotators when one annotator does not say anything or uses a different (hashtag) language.
For each valid image we normalize the weights $w_i$ for both the machine tags $T^m$ and the human tags $T^h$ to probabilities such that $\sum_{i\in T^h} w^h_i = 1 = \sum_{i \in T^m} w^m_i$. Values for $w^h_i$ where $i \in T \setminus T^h$ or for $w^m_i$ where $i \in T\setminus T^m$ are set to 0. For $i \in T$ the gap value is then defined as $g_i = w^m_i - w^h_i$.
These values are then first averaged across all images for a (county,user) pair. We first aggregate at the user level, within a given county, to avoid that a single user with a particular hashtag usage pattern skews our analysis. Next, the user level values are further aggregated by averaging them into a single feature vector for each county. Note that each aggregated value $g_i$ will be between 0 and 1 and that $
\sum_{i\in T}|g_i|$ is a measure of the absolute overall labeling differences between humans and the machine in a given county. This difference is upper bounded by 2.

To obtain the human-only or machine-only distributions, we run the same filtering pipeline, simply setting $w^m_i=0$ (for human-only) or $w^h_i=0$ (for machine-only). Figure~\ref{fig:gap_ex1} gives an illustration by example of the qualitative aspects our perception gap can pick up. The image shown, which is a real example, is tagged by the user as \#foodie, \#hungry, \#yummy, \#burger, and by the machine as \#burger, \#chicken, \#fries, \#chips, \#ketchup, \#milkshake. After ignoring the user tags that are not included in our machine-tag dictionary, we compute the gap value $g_{\mbox{ex1}}$ for this particular image as

\[ \begin{cases}
      -5/6 & \mbox{\#burger} \\
      1/6 & \mbox{\#chicken, \#fries, \#chips, \#ketchup, \#milkshake}\\
      0 & \mbox{all other hashtags} \\
   \end{cases}
\]\\

\subsection{Variation in Subjective Labels}\label{sec:methods:subjectivegap}
In the above, we computed a perceptual difference for ``what the food objectively is'' or for ``what is worth naming'', all related to \emph{objective} names of items in the picture. Here, we describe a methodology to compute a similar difference for \emph{subjective} labels.

As our machine annotations (see Section~\ref{sec:computervision}) deliberately exclude subjective tags, we can no longer use the previous approach of looking at human-vs.-machine usage differences within a common vocabulary space. Instead, we define a set of subjective labels of interest $l^h$ containing labels such as $j=$\#healthy, and then for each machine tag $i\in T^m$ compute the probability $P(j | i)$.

Concretely, we first iterate over all images who passed the filters for the previous ``objective gap'' analysis. For these images, we compute the aforementioned conditional probability probability at the image level where it is either 1 (if machine tag $i$ is present) or 0 (if it is not). Note that values for tags ${i'}\in T^m$ not present in the image are not considered. We then aggregate these values within each (county, user) pair to obtain probabilities for a given user in a given county to have used label $j$ given one of his images was auto-tag as $i$. We then further combine these probabilities at the county level by aggregating across users. If a tag ${i'}\in T$ was never present on a single image in the county then the corresponding value $P(j | {i'})$ is not defined. To address this, we impute such missing values by averaging the conditional expectation computed across all the counties with no missing values.
For our analysis we used the human labels \#healthy, \#delicious, and \#organic, comprising both health, taste and origin judgment.

\section{Results}\label{sec:results}
Table~\ref{tab:objective} shows the top five tags in terms of the ``boost'' they receive in correlation $r_{g_i}$ when using the gap values $g_i$ compared to the correlations $r_{w^m_i}$ and $r_{w^h_i}$ for features $w^m_i$ and $w^h_i$ respectively. Concretely, the boost is defined as $|r_{g_i}| - \max(|r_{w^m_i}|,|r_{w^h_i}|)$.

\begin{table*}
\centering
\caption{For each of the nine health measures, top five tags with the highest correlation boost of the perception gap over the maximum of the correlations of machine-only and human-only tags. Values in parentheses are the mean and standard error of r correlation values across the 194 counties after 10-fold cross validation.}
\vspace{0.4em}
	\resizebox{\textwidth}{!}{%
	\begin{tabular}{|l|l|l|l|l|l|}
		\toprule
        Health metric & Top 1 & Top 2 & Top 3 & Top 4 & Top 5 \\ \midrule
AlcDrivDeath & chicagopizza ($-.24\pm.008$) & sugarcane ($.22\pm.010$) & brisket ($-.27\pm.006$) & redpepper ($.20\pm.009$) & mojito ($.22\pm.008$) \\
DiabetesPrev & crabmeat ($-.27\pm.007$) & burritos ($.29\pm.005$) & sushiroll ($-.31\pm.008$) & carpaccio ($-.23\pm.006$) & surfandturf ($.24\pm.009$) \\
ExcessDrink & beefribs ($-.24\pm.009$) & horseradish ($.25\pm.007$) & strawberries ($.24\pm.010$) & greeksalad ($.24\pm.010$) & flautas ($-.21\pm.007$) \\
FoodEnvInd & instabeer ($-.32\pm.010$) & heineken ($-.31\pm.006$) & cornedbeefhash ($-.26\pm.005$) & sushiroll ($.30\pm.009$) & jerkpork ($-.29\pm.007$) \\
FoodInsecure & instabeer ($.29\pm.008$) & beerstagram ($.25\pm.010$) & skirtsteak ($.27\pm.009$) & sushiroll ($-.29\pm.010$) & heineken ($.24\pm.007$) \\
LimitedAccess & breadpudding ($-.39\pm.008$) & horchata ($.46\pm.005$) & heineken ($.32\pm.009$) & quail ($-.31\pm.009$) & beersnob ($.28\pm.007$) \\
Obese & chickenkatsu ($.31\pm.007$) & clubsandwich ($-.33\pm.004$) & cobbsalad ($-.25\pm.005$) & moscato ($-.22\pm.005$) & koreanfriedchicken ($.22\pm.007$) \\
PhysInactv & prawns ($.34\pm.008$) & fishnchips ($.29\pm.009$) & burritos ($.28\pm.007$) & boilingcrab ($.30\pm.006$) & fishandchips ($.32\pm.007$) \\
Smokers & crabmeat ($-.30\pm.007$) & breadbowl ($.24\pm.006$) & umamiburger ($.26\pm.008$) & pupusas ($.24\pm.012$) & horchata ($.24\pm.010$) \\
        \bottomrule
	\end{tabular}
	}
	\label{tab:objective}
\end{table*}

As an example on how to read Table~\ref{tab:objective}, the entry ``chickenkatsu ($.31\pm.007$)'' as the Top 1 in the Obese row means that the counties where the machine is more likely than the human to use the tag \#chickenkatsu tend to have higher obesity rates. Furthermore, this correlation is significant at $p=.05$, even after applying the Benjamini-Hochberge procedure. The fact that values are ranked by their boost in correlation further means that the correlation of $.31\pm.007$ is not solely due to regional variation in what the machine tags as \#chickenkatsu. In this particular case, the machine-only correlation is $r_{w^m_i}=.19\pm.008$ and the human-only correlation is $r_{w^h_i}=.18\pm.007$.

Whereas Table~\ref{tab:objective} shows the results for the perception gap on objective tags (see Section~\ref{sec:methods:objectivegap}), Table~\ref{tab:subjective} shows results for the subjective gap (see Section~\ref{sec:methods:subjectivegap}). For this, tags from the space of the 1,170 machine tags are ranked according to the boost in correlation that the conditional probability of a human using, say, \#healthy achieves, compared to the correlation for the unconditional probability of a human using \#healthy.

\begin{table*}
\centering
\caption{For each of the nine health metrics and each of the three subjective tags $j\in  \{\mbox{healthy}, \mbox{delicious}, \mbox{organic}\}$ we show (up to) the top five tags $i$ in terms of correlation boost of using $P(j | i)$ over simply $P(j)$. Here $i$ is one of the 1,170 tags assigned by the machine. Only correlations significant at p=.05 (after applying the Benjamini-Hochberge procedure to guard against false positives) are shown. Values in parentheses are the mean and standard error of r correlation values across the 194 counties after 10-fold cross validation.}
\vspace{0.4em}
\begin{tabular}{lccc}
\toprule
 Health Metric & Healthy & Delicious & Organic \\
\midrule
\multirow{5}{*}{AlcDrivDeath} & cinnamonrolls ($-.24\pm.003$) & capresesalad ($-.30\pm.009$) & crepes ($-.27\pm.005$) \\
 & maple ($-.23\pm.005$) & breadsticks ($-.28\pm.007$) & popcorn ($-.25\pm.007$) \\
 & crawfish ($-.23\pm.008$) & beefcarpaccio ($-.28\pm.010$) & poachedegg ($-.24\pm.011$) \\
 & vanillabean ($-.23\pm.006$) & baguette ($-.27\pm.010$) & crawfish ($-.25\pm.009$) \\
 & coconutoil ($-.21\pm.008$) & redsauce ($-.27\pm.007$) & beefstew ($-.23\pm.003$) \\
\midrule
\multirow{5}{*}{DiabetesPrev} & smoothies ($-.30\pm.009$) & saki ($-.31\pm.007$) & jambalaya ($-.31\pm.006$) \\
 & thyme ($-.29\pm.005$) & crabmeat ($-.28\pm.006$) & carnitas ($-.31\pm.005$) \\
 & cilantro ($-.28\pm.008$) & burritos ($-.27\pm.009$) & chocolate ($-.31\pm.006$) \\
 & gyro ($-.27\pm.010$) & chimichanga ($-.26\pm.007$) & vinaigrette ($-.30\pm.004$) \\
 & bananas ($-.27\pm.009$) & octopus ($-.26\pm.006$) &  \\
\midrule
\multirow{5}{*}{ExcessDrink} & newyorkpizza ($.24\pm.006$) & barbecue ($-.23\pm.008$) & koreanfriedchicken ($.27\pm.008$) \\
 & bacardibuckets ($-.22\pm.015$) & burrito ($.22\pm.006$) & burritobowl ($.25\pm.005$) \\
 & cilantro ($.22\pm.009$) & winepairing ($.21\pm.009$) & appetizers ($.23\pm.005$) \\
 & dessert ($-.23\pm.005$) & beefcarpaccio ($-.19\pm.011$) & whiterice ($.23\pm.004$) \\
 & frenchpastry ($.20\pm.011$) & kalesalad ($.21\pm.010$) & cobbsalad ($.23\pm.007$) \\
\midrule
\multirow{5}{*}{FoodEnvInd} &  &  & carnitas ($.35\pm.006$) \\
 &  &  & seasalt ($.34\pm.006$) \\
 &  &  & coconutwater ($.32\pm.006$) \\
 &  &  & greendrink ($.32\pm.005$) \\
 &  &  & greenchile ($.31\pm.003$) \\
\midrule
\multirow{5}{*}{FoodInsecure} &  &  & carnitas ($-.35\pm.006$) \\
 &  &  & greendrink ($-.33\pm.005$) \\
 &  &  & seasalt ($-.33\pm.007$) \\
 &  &  & greenchile ($-.32\pm.003$) \\
 &  &  & kalesalad ($-.32\pm.008$) \\
\midrule
\multirow{5}{*}{LimitedAccess} & chorizo ($-.24\pm.008$) & takoyaki ($-.38\pm.004$) & beans ($-.22\pm.004$) \\
 & basil ($.22\pm.009$) & gin ($-.37\pm.005$) & southerncomfort ($-.22\pm.005$) \\
 & calzone ($-.21\pm.007$) &  & grits ($-.20\pm.006$) \\
 & catfish ($-.21\pm.012$) &  & grapefruit ($.19\pm.006$) \\
 & muffin ($.20\pm.017$) &  & onionrings ($-.19\pm.008$) \\
\midrule
\multirow{5}{*}{Obese} & sweettea ($-.35\pm.004$) & saki ($-.37\pm.007$) &  \\
 & chickpea ($-.34\pm.006$) & taquitos ($-.35\pm.006$) &  \\
 &  & smokedsalmon ($-.33\pm.006$) &  \\
 &  & chimichanga ($-.33\pm.006$) &  \\
 &  & fishtaco ($-.33\pm.007$) &  \\
\midrule
\multirow{5}{*}{PhysInactv} & chickpea ($-.20\pm.011$) & saki ($-.21\pm.009$) &  \\
 & maple ($-.22\pm.006$) & babybackribs ($.20\pm.008$) &  \\
 & cashews ($-.18\pm.010$) & wine ($-.20\pm.010$) &  \\
 &  & pitayabowls ($-.19\pm.006$) &  \\
 &  & bbqsauce ($.19\pm.005$) &  \\
\midrule
\multirow{5}{*}{Smokers} & goatcheese ($-.33\pm.010$) & saki ($-.41\pm.005$) &  \\
 & chickpea ($-.29\pm.008$) & soupoftheday ($-.37\pm.003$) &  \\
 & banana ($-.23\pm.008$) & milkshakes ($-.37\pm.006$) &  \\
 & roastbeef ($-.21\pm.011$) & smokedsalmon ($-.34\pm.007$) &  \\
 & cupcake ($-.21\pm.009$) &  &  \\
\bottomrule
\end{tabular}
\label{tab:subjective}
\end{table*}

As an example for how to read Table~\ref{tab:subjective}, the entry ``smoothies ($-.30\pm.009$)'' in the column for \#healthy and the row for Diabetes Prevalence means that counties with a higher conditional probability of P(human says \#healthy | machine says \#smoothies) tend to be counties with higher levels of diabetes prevalence. As we rank by the boost in correlation over the probability for simply P(human says \#healthy), in this case $-.30\pm.009$ vs.\ $-.27\pm.011$, this correlation is not fully explained by variation in \#healthy alone. As before, only correlations significant at p=.05, after the Benjamini-Hochberge procedure, are included in the table.

\section{Discussion and Limitations}\label{sec:discussion}
By looking at the ``healthy'' and ``organic'' columns in Table~\ref{tab:subjective} we see that, with the exception of Excessive Drinking, all health statistics indicate correlations in the good direction for all the examples shown. At a high level this seems to indicate that when humans deliberately call one particular food \#healthy or \#organic, rather than using these tags indiscriminately, this indicates a county with generally better health statistics. However, the pattern for what exactly this particular food has to be is far more mixed.

Similarly for the perception gap analysis in Table~\ref{tab:objective}, we observe correlations in the (reasonably) good direction in general. For instance, ``the machine says \#chickenkatsu (or \#koreanfriedchicken for that matter) but the human does not'' is a sign of a high obesity region while ``the machine says \#clubsandwich (or \#cobbsalad for that matter) but the human does not'' is a sign of a low obesity region. Similarly for diabetes prevalence, \#burritos shows positive correlation whereas \#crabmeat and \#sushiroll show negative correlation. However, in many other cases it is admittedly harder to interpret the results. For example, whereas ``the machine says \#sugarcane but the human does not'' is a sign of high alcohol-impaired driving deaths, for \#chicagopizza the trend is ``the human says \#chicagopizza but the machine does not.'' Likewise, the link between physical inactivity and hashtags such as \#prawns and \#fishnchips is not apparent.

It is  worth clarifying how our work differs fundamentally from analyzing the co-occurrence of hashtags. For example, we could hypothetically have studied how \#burger and \#salad are used together and whether their co-occurrence propensity was linked to health statistics. For the sake of argument, let us assume that we would have found that a positive association was linked to counties with lower obesity rates. However, we would then not have been able to tell if (i) healthier regions have more people consuming burgers with a salad on the side, or if (ii) in healthier regions people are simply more likely to label a lone lettuce leaf as \#salad. If the purpose of the analysis was to model regional variation in health statistics then this distinction might be irrelevant. But if the goal was to detect relationships between the perception of food and health statistics -- as is the case in our work -- then this distinction is crucial.

Note that, at the moment, we deliberately trained the machine tagger only on \emph{objective} tags related to food, e.g., the name of a dish. Training a machine for more \emph{subjective} tags, such as \#delicious or \#healthy, would have made it impossible to separate the dimension of ``what is it'' from ``how does a human perceive it''. However, it might be promising to train a machine on aspects related to the \emph{presentation} of food. As discussed in Section~\ref{sec:related}, how the food is presented to the consumer has important implications on how much of it will be consumed. When the food is presented and arranged by the consumer themselves, e.g., in the setting of a home-cooked meal, this could still provide a signal on whether the food is ``celebrated'' or not in a gourmet vs.\ gourmand kind of fashion.

One potential limitation of our work is language dependency. As we cannot look into users' brains to study the perception at the level of neurons, we rely on how they \emph{self-annotate} their images. However, a Spanish-speaking person will likely use other annotations than an English-speaking person, which eventually affects our analyses. For example, \#chimichanga and \#taquitos show up in our analysis as indicators of low obesity rates in the column for Delicious and row for Obese in Table~\ref{tab:subjective} even though both of them are deep-fried dishes from Mexican, or Tex-Mex, cuisine. Similarly, there can be regional variations and the same food could have one name in a high obesity area and a different name in a low obesity area.

Another risk comes from the inherent noise of the machine annotation used. Though its performance is state-of-the-art (see Section~\ref{sec:computervision}), it is still far from perfect. In the extreme case, if the machine annotations were uncorrelated with the image content then the perception gap we are computing would, on average, simply be the distribution of the human tags. As such, the gap and the human features would be picking up the same signals.

To guard against the previous two points we never solely report results for the perception gap analysis but, always, compare it back to the results when using only human annotations or only machine annotations. Both Table~\ref{tab:objective} and Table~\ref{tab:subjective} are ranked by the \emph{boost} in correlation over using only human annotations or only machine annotations.

Though our current analysis focuses on \emph{image} analysis, it is worth contemplating what a similar analysis of \emph{text} would look like. At a high level, we try to separate ``what something contains'' from ``how it is described''. In the NLP domain, this roughly corresponds to differentiating between topic detection \cite{wang2012baselines} and writing style identification \cite{ASI:ASI20316}. As for images, these two are often entangled: if an author of a blog uses the term ``death'' is that because of (i) the topic they are discussing (e.g., a war story), or because of (ii) their writing style and mood (potentially indicating depression)? Clearly separating these two concepts, the what and the how, could potentially help with challenges such as identifying depression.

The current work uses image recognition exclusively to study how a certain food is labeled by a human in relation to what it objectively shows. However, a computer vision approach could be used for automating other aspects of analyzing food images. As an example, it could be promising to automatically analyze \emph{food plating}, i.e., the aesthetic arrangement of food in appealing images. Recent research studies have indicated that attractive food presentation enhances diners' liking of food flavor~\cite{michel2014taste, zellner2014tastes} as well as their eating behaviours and experiences~\cite{spence2014plating}. In addition, Zampollo et al.~\cite{zampollo2012looks} have demonstrated the diversity of food plating between cultures. Extending these ideas in mind, a computer vision approach could be applied to perform a study to that of Holmberg et al.~\cite{holmberg2016adolescents} to investigate food plating (and its potential correlation with food health) across cultures and age groups.

Both regional variation in perception gap and in food plating behavior could conceptually be used for public health monitoring by training models similar to what was done by Garimella et al.~\cite{garimellaetal16chi}. We briefly experimented with this using our $g_i$ gap features. However, these secondary signals, i.e., how something is perceived differently by humans and machines, did not add predictive performance over the primary signals, i.e., how something is labeled by humans or machines alone. We see the real value of our approach less in ``now-casting'' of public health statistics and more in analyzing the psychology of food consumption and food sharing.

Finally, computer vision could help to obtain health labels not only at the county level but at the \emph{individual} level. Concretely, Wen and Guo have proposed a method to infer a person's BMI from a clean, head-on passport style photo~\cite{wenguo13ivc}. Though this particular method is unlikely to deal with the messiness of social media profile images, exploratory work has shown that inferring weight category labels from social media profile images seems feasible~\cite{webermejova16dh}. We are currently working on these aspects of a more holistic food image scene understanding.

\section{Conclusions}

In this work, we define the ``perception gap'' as the misalignment or the difference in probability distributions of how a human annotates images vs.\ how a machine annotates them. To the best of our knowledge, this is the first time that this type of perception gap has been studied. By using county-level statistics we show that there are systematic patterns in how this gap relates to health outcomes.

In particular we find evidence for the fact that conscious food choices seem to be associated with regions of better health outcomes. For example, labeling particular foods as \#healthy, rather than random images in a county, or beer with its brand name, rather than generic descriptions, correlates favorably with health statistics. Similarly, posting images of saki and emphasizing the \#delicious taste appears to be a positive indicator. Paraphrasing Shakespeare, a rose by any other name might smell as sweet, but labeling your food differently might be related to health.

As time goes on, we expect our methodology to further improve in performance due to (i) continuous improvement in image recognition and decrease in error rates, and due to (ii) the potential to use individual level health labels, instead of county level ones, also due to improvement in computer vision \cite{wenguo13ivc,webermejova16dh}.

redpajama_set_name: RedPajamaArXiv


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This quantity is a realistic biochemical consultant to the Enzyme-Linked Immunosorbent Assay (ELISA), used to realize a aim substance in a liquid pattern. The ELISA is a crucial and customary diagnostic device in drugs, animal future health, botany and caliber insurance approaches in nutrients and beverage creation.
J Immunotoxicol. 2011;8:111–21. 546382. 23. Sabbioni E, Fortaner S, Farina M, Del Torchio R, Olivato I, Petrarca C, et al. Cytotoxicity and morphological transforming potential of cobalt nanoparticles, microparticles and ions in Balb/3T3 mouse fibroblasts: an in vitro model. Nanotoxicology. 2014;8:455–64. 796538. 3 Engineered Nanomaterials and Occupational Allergy 43 24. Perconti S, Aceto GM, Verginelli F, Napolitano F, Petrarca C, Bernardini G, et al. Distinctive gene expression profiles in Balb/3T3 cells exposed to low dose cobalt nanoparticles, microparticles and ions: potential nanotoxicological relevance.
1998;10(1):12–7. 27. Mulloy KB. Silica exposure and systemic vasculitis. Environ Health Perspect. 2003;111(16):1933–8. 28. Bartůnková J, Pelclová D, Fenclová Z, Sedivá A, Lebedová J, Tesar V, Hladíková M, Klusácková P. Exposure to silica and risk of ANCA-associated vasculitis. Am J Ind Med. 2006;49(7):569–76. 29. Levine S, Sowinski R. Enhancement of allergic encephalomyelitis by particulate adjuvants inoculated long before antigen. Am J Pathol. 1980;99(2):291–304. 30. Stone OJ. Autoimmunity as a secondary phenomenon in scleroderma (and so-called human adjuvant disease).
Immunol Res. 2008;41(2):87–102. 1007/s12026-007-8014-9. 56. Korn T, Bettelli E, Oukka M, Kuchroo VK. IL-17 and Th17 Cells. Annu Rev Immunol. 2009;27:485–517. 132710. 57. O'Connor RA, Taams LS, Anderton SM. Translational mini-review series on Th17 cells: CD4 T helper cells: functional plasticity and differential sensitivity to regulatory T cell-mediated regulation. Clin Exp Immunol. 2010;159(2):137–47. x. 58. Girtsman T, Jaffar Z, Ferrini M, Shaw P, Roberts K. Natural Foxp3(+) regulatory T cells inhibit Th2 polarization but are biased toward suppression of Th17-driven lung inflammation.


Somerset is known for its rolling rural landscapes, dotted with authentic and cozy country pubs. The county's major business is sheep and cattle farming—make sure to sample its most notable product, the world-famous Cheddar cheese. Extend your gastronomical itinerary by tasting Somerset's well-known strong ciders, brewed using traditional methods. With its landscapes of meadows, hills, and hedgerows, Somerset is a picture-perfect representative of a simpler world, where you can enjoy a holiday far from the bustle of England's big cities and overcrowded tourist attractions.
Bath: Known for its healing waters, Bath is an essential stop on your Somerset itinerary. This World Heritage Site boasts a combination of ancient Roman bathhouses, historical architecture, and modern comforts such as fine dining, shopping, and of course, luxurious spas.
Glastonbury: Glastonbury's natural beauty and historical sites make it an ideal stop for a relaxing holiday in Somerset. Music lovers can get their fill at the famous Glastonbury Festival, held every summer.
Weston super Mare: A classic English seaside resort, Weston super Mare offers miles of beautiful sandy beaches, piers, and amusement parks, as well as activities for rainy days, including an aquarium and the Helicopter Museum.
Exmoor National Park: Outdoor enthusiasts will love Exmoor National Park's 693 sq km (268 sq mi) of gorgeous landscape, where visitors can hike along breathtaking sea cliffs, go for a trail run across moorland filled with heather, and canoe or kayak.
Wells: Often called "England's Smallest City," Wells offers all the fun of touring a city without the stress. Explore cathedrals, stroll between the city's famous wells dedicated to St. Andrew, and bring the kids along to enjoy the city's numerous youth clubs.
Dunster: The small medieval village of Dunster entices visitors with its charming beach chalets and historical sites, such as Dunster Castle.
The Roman Baths: Visit the world-famous Roman Baths Museum to tour a beautifully preserved temple and bathhouse fed by natural hot springs.
Thermae Bath Spa: Plan a relaxing day at Thermae Bath Spa for a soak in the legendary mineral-rich waters of Bath. Sit in the steam room or select from their menu of luxurious spa treatments.
Bath Abbey: Take a trip back to medieval times at Bath Abbey, an unusually light-filled cruciform church.
Grand Pier: Grand Pier is a popular tourist attraction in Somerset, boasting an amusement park and beautiful nighttime views.
Butlins Minehead: Somerset holiday camp Butlins Minehead provides a range of outdoor facilities, including pools and waterslides, and hosts music and theater performances.
Cheddar Caves & Gorge: The geology and history of Cheddar Caves & Gorge make it ideal for hiking, cave exploration, and cheese tasting. Learn about Cheddar Man, the oldest complete human skeleton found in England.
The Royal Crescent Hotel & Spa: A prime example of Georgian architecture, this crescent-shaped street is a great place to stroll during your Somerset vacation.
The Jane Austen Centre: Learn about Bath's most famous resident at the Jane Austen Centre, where you can peek into the writer's daily life.
Glastonbury Tor: Climb up the hill of Glastonbury Tor, considered sacred as far back as the Iron Age, to see sweeping views of the city below.
Wells Cathedral: Twelfth-century Gothic Wells Cathedral offers numerous guided and themed tours, a fantastic view of the city, a library filled with 500-year-old books, and a stunning collection of stained-glass windows.
A family holiday in Somerset can offer a combination of fun, exploration, and relaxation. Bath has much to offer the whole family. Children will love imagining themselves as ancient Romans while touring The Roman Baths and enjoying theater or music performances. The small city of Wells is the ideal size for traveling with children, who will have fun exploring Wookey Hole Caves and Wells Cathedral. Add Weston super Mare to your Somerset itinerary, where beaches, piers, and museums provide activities for your family in rain or shine.
There are numerous activities that children and adults can enjoy together on your trip to Somerset. In Bath, children will be delighted to catch a magic show at Krowd Keepers Magic Theatre or Minerva Magic Theatre. Somerset's attractions include many zoos for younger animal lovers, including the car safari at Longleat. Campgrounds and amusement parks are sure to keep your family entertained on your Somerset holiday. Options in the area include beachside Fun City at Brean Leisure Park and refreshing Water adventure park.
For your family vacation in Somerset, you have the option of staying at a traditional hotel, staying at one of the region's family-focused camps such as Butlins Minehead, or even renting your own beach chalet on the seashore. While trains provide some transportation in the area, renting a car is recommended on a trip to Somerset, as it will allow you and your family to more easily explore smaller towns and attractions off the beaten path.
While the cuisine here is similar to standard British cuisine, your tour of Somerset would not be complete without sampling some of the area's regional specialties. Somerset is famous for its hard cider—the area's many orchards produce cider influenced by the soil and climate. Visit one of the many cozy local pubs to sample cider and beer, and to meet new friends. The very popular Cheddar cheese originated in Somerset, and cheese and history lovers alike might want to add a trip to Cheddar Caves & Gorge to their Somerset itinerary. In Bath, visitors can sample a number of local baked goods attributed to 16th-century physician William Oliver, including sweet Bath buns and Bath Olivers, crackers often eaten with cheese.
While planning things to do in Somerset, make time to shop at some of the region's independent stores and markets—bring back souvenirs or get something special for yourself. Bath has the most options for shopping, including shops such as the popular The Fine Cheese Shop and Ora Et Labora, which sells artisanal goods made by monks in the region. Shoppers may want to include a trip to the monthly Frome Independent market in Frome, where visitors can shop for souvenirs and antiques, sample local delicacies, meet craftspeople, and enjoy the bustle of a small-town market day.
Somerset's rich history dates back to Paleolithic times. People have lived in the region for thousands of years, first settling in caves and in the wetlands known as the Somerset Levels. You can explore this ancient history on your trip to Somerset by visiting sites such as Cheddar Caves & Gorge and Wookey Hole Caves. Roman occupation of the area from 47–409 CE added another layer of history still evident today in numerous ruins and buildings, most notably in Bath.
The Romans withdrew from Somerset in 409 CE, focusing their energy and soldiers on defending other parts of their large empire from increasing raids and attacks. With the Romans gone, Anglo-Saxon peoples invaded Britain, and while they had occupied the majority of modern-day England by 600 CE, Somerset remained under native British control for another hundred years. The next few centuries saw the development of a feudal system in the area. Many buildings and monuments remaining from this time period are worthwhile additions to your Somerset itinerary, such as Dunster Castle and numerous cathedrals.
With the advent of the Industrial Revolution in the 18th century, Somerset's cottage industries declined, although agriculture persevered and remains important to the region today. During World War II, the county was used as a base for troops before the invasion of Normandy on D-Day, and a number of "decoy towns" were constructed to lure Axis bombers away from actual inhabited towns.
Sightseers in Somerset are rewarded with a beautiful natural landscape. Seaside areas offer sweeping views of dramatic cliffs and sparkling blue water. The area boasts gorgeous rolling green hills, valleys well suited to the region's agriculture, and wetlands such as the Somerset Levels and moors. Your trip to Somerset might include an exploration of the area's vast network of caves, which have been used for human activities for thousands of years, or an outdoor adventure at Exmoor National Park, which offers a vast array of activities from sea kayaking to rock climbing.
You might consider planning your Somerset trip to coincide with one or more of the local festivals that give flavor to the region. Every June, the town of Pilton holds the world-famous Glastonbury Festival, where visitors gather on picturesque farmland to hear popular music. Summertime brings the Sand Sculpture Festival to the beaches of Weston super Mare, where international artists create impressive works of art with just sand and water. In autumn, a series of local parades and fairs known as West County Carnivals appear throughout the area. These include nighttime parades of illuminated floats, called "carts" by locals.
The best time to visit Somerset is in the summertime, when average temperatures rest in the low 20s Celsius (around 68°F), with occasional clouds and rain. Fall often has cool periods interspersed with warm, sunny days. Winter is cold and wet, with frequent rain and occasional snow. In springtime, temperatures are warmer and sudden rainstorms are common.
Rental cars are the easiest way to get around on your Somerset holiday, as many smaller towns and attractions are difficult to access by bus or train. There are ten train stations in the area, which offer an alternative to renting a car. To get to the region, take the train or a bus, called a "coach line" by locals.


A man on a bicycle shot Davis police Officer Natalie Corona, 22, Thursday night as she responded to a triple-car crash in the city of Davis. He was later found dead with a self-inflicted gunshot wound.
In a statement Saturday, the Yolo County Sheriff's Office identified Kevin Douglas Limbaugh, 48, as the suspect in the officer's killing.
Limbaugh left behind a letter, according to Davis police spokesman Lt. Paul Doroshov, who read the letter on camera to CNN affiliate KMAX.
Corona was shot multiple times, Davis police Chief Darren Pytel said.
"This clearly to us looks like an ambush," Pytel said.
After shooting Corona, the suspect reloaded his firearm and fired toward a firefighter and a fire truck that were also responding to the traffic collision. Then he fled.
Corona died at the UC Davis Medical Center in nearby Sacramento. She began her service with the department as a community service officer in 2016. She finished the police academy in July and was promoted to police officer upon graduation.
"She was a rising star in the department," Pytel said.
Corona's father, Merced, is a retired Colusa County sheriff's sergeant in California. He stepped down from the force in 2012 after 26 years.
Payne, who also was newly hired, was in uniform and getting ready for work when she was gunned down at a Shreveport home, police Chief Ben Raymond said.
PREVIOUS POST Previous post: NBC and MSNBC Blamed Russia for Using "Sophisticated Microwaves" to Cause "Brain Injuries" in U.S. "Diplomats" in Cuba. The Culprits Were Likely Crickets.


text:
Episode 40 - Healing Generational Trauma: Connecting with Nature and Community Organizing with Curtis Lee
Youth programs are places where children and young adults find support and camaraderie, often looking to leaders of those programs as mentors and coaches. Sometimes those leaders have been through their own struggles and trauma and use their experiences to guide youth toward better habits and outcomes. One such person is Curtis Lee whose harrowing story about trauma, prison, loss and leadership have made him work with nature as a way of healing trauma, teaching kids about their connection to the larger world, and conservation of our natural spaces.
Curtis Junior Lee was born in 1966 in Fresno, California and has been an Oaklander since 1970. He was 1st runner-up for the 2015-2016 Jason Guinto Award, annually given to the top after school coordinator working within Oakland Unified School District.
He formerly served on OUSD Continued Quality Improvement Leadership Team and trained other coordinators within the city on how to improve the quality of their afterschool programs.
Additionally, he served as the Aftercare Director for Northern Light Middle School 2016-2017 school year and then went on to become its Physical Education instructor, Athletic Director, and Aftercare Director until the spring of 2020. He is currently the Program Manager for Higherground Neighborhood Development Corporation's Outdoor Adventure/Distance Learning Hub.
Curtis' story of being a youth community activist
How a series of decisions on one night, led to a life altering event
Where prison time strengthened his connection to nature
His approach to calling out and healing personal and community trauma
How nature connections help at-risk youth recognize their role in conservation
How spending time in prison is only part of his story
Want updates on the newest episodes of the Positively Green Podcast
Sign up below for our email list!
YES I AM SO IN!

redpajama_set_name: RedPajamaCommonCrawl


I began practicing at the Yoga Shelter in 2012, after running my first (and probably only!) marathon. I had always resisted yoga, thinking that anything where I couldn't count up the miles wasn't the type of exercise for me. Yet, my body, along with my mind, was exhausted and depleted, and I decided to give it a try. I discovered, of course, that yoga is so much more than a physical workout. Since then, it has helped me manage anxiety and the daily stressors of life, and allowed me to cultivate acceptance within myself.
I decided to embark on teacher training in the summer of 2017. I wanted to learn how to provide a space for people to receive the benefits of yoga, as so many have done for me. I am honored to share in a part of your journey of growth, self-discovery, and acceptance. I look forward to practicing together!
Find Sadie's thoughtful and challenging classes on the schedule!


text:
Mineral UI is built on a design system with styles ready to go out of the box.  We realize however that there will be cases when you need to customize styles for your unique needs.  Below are some different techniques for customizing Mineral UI styles across all levels of your application.

## Customization Techniques

### Try Theming First

Theming is a core concept in Mineral UI.  It is powerful feature capable of providing style customizations from the application level to the component level and should generally be your first choice when looking to make style customizations. See the [theming page](/theming) for more details.

When a component needs to be customized beyond what theming enables, use these methods, in order of recommendation.

<Callout title="Note">

    Mineral UI uses Emotion for
    component styling. This page will detail how its API can apply to Mineral UI
    components.

</Callout>

### `styled`

[`styled`](https://emotion.sh/docs/styled) creates a new styled component based on another component or DOM element.

```jsx
import styled from '@emotion/styled';
import Button from 'mineral-ui/Button';

const MyButton = styled(Button)({
  outline: '3px dashed tomato'
});
```

#### `as` Prop

Any component created with `styled` (which includes _every_ Mineral UI component) can use the [`as` prop](https://emotion.sh/docs/styled#as-prop) to change the rendered element or Component.

```jsx
import Button from 'mineral-ui/Button';
import Link from 'mineral-ui/Link';

<Button as={Link} href="..." />
```

### `css` Prop

The [`css` prop](https://emotion.sh/docs/css-prop), available on all Mineral UI components, provides a quick, easy way to directly modify the styles of a component.

```jsx
import Button from 'mineral-ui/Button';

render(
  <Button
    css={{
      outline: '3px dashed tomato'
    }}
  />
);
```

### `className` Prop

Additional CSS classes can be applied to components using the standard React `className` prop.

```jsx
<Button className="myButton" />
```

You can also use emotion to generate a `className`, with the [ClassNames](https://emotion.sh/docs/class-names) component and the `css` function it provides.

```jsx
import { ClassNames } from '@emotion/core';

render(
  <ClassNames>
    ({ css, cx }) => (
      <Button className={css({ outline: '3px dashed tomato' })} />
    )
  </ClassNames>
);
```

<Callout title="Order of precedence for styles applied via className">

    Classnames not generated by emotion, e.g. 'myButton', will not necessarily take precedence over the styles on the component. Whether it will or not is based on the source order of the styles in the DOM and can be hard to control. Classnames that are generated via ClassNames' css can be composed in a predictable order with the similarly-provided cx function, which is emotion's version of the popular classnames utility.

</Callout>

### Inline Styles

Inline styles can be applied to components using the standard React `style` prop.

```jsx
<Button style={{ outline: '3px dashed tomato' }} />
```

redpajama_set_name: RedPajamaGithub


text:
Q: Google Cloud Datastore: Update creates new Entity instead of updating existing one I got a problem using Google Cloud Datastore. So far I managed to get everything working except the UPDATE method... Whenever I want to update an existing entity, it creates a new one instead of updating the old one. I am positive that I give the correct id but right now I am really out of ideas. I searched for similar issues and found some:

*

*Update query in google app engine data store (java)

*GAE Datastore - workaround for updating entity?

*Google Datastore - Problems updating entity
but was unable to fix my issues. I am using generated endpoints for now.
Here is my code:
Model:
@Entity
public class Address implements Serializable {

    @Id
    private Long id;
    private String street;
    private String street_nr;
    private String city;
    private String zip;
    private String country;

    public Address(){ }

    public Long getKey(){
        return id;
    }

    public  String getStreet() {
        return street == null ? "" : street;
    }

    public void setStreet( String street) {
        this.street = street;
    }

    public  String getStreetNr() {
        return street_nr == null ? "" : street_nr;
    }

    public void setStreetNr( String streetNr) {
        this.street_nr = streetNr;
    }

    public  String getCity() {
        return city == null ? "" : city;
    }

    public void setCity( String city) {
        this.city = city;
    }

    public  String getZip() {
        return zip == null ? "" : zip;
    }

    public void setZip( String zip) {
        this.zip = zip;
    }

    public  String getCountry() {
        return country == null ? "" : country;
    }

    public void setCountry(String country) {
        this.country = country;
    }
}

Endpoint:
@ApiMethod(
    name = "update",
    path = "address/{id}",
    httpMethod = ApiMethod.HttpMethod.PUT)

    public Address update(@Named("id") Long id, Address address) throws NotFoundException {
        checkExists(id);
        ofy().save().entity(address).now();
        logger.info("Updated Address: " + address);
        return ofy().load().entity(address).now();
    }

private void checkExists(Long id) throws NotFoundException {
        try {
            ofy().load().type(Address.class).id(id).safe();
        } catch (com.googlecode.objectify.NotFoundException e) {
            throw new NotFoundException("Could not find Address with ID: " + id);
        }
    }

Where my server logs tells me that the address has been updated. Therefore I assume that checkExists(id) does its work as intended.

com.PACKAGE.AddressEndpoint update: Updated Address: com.PACKAGE.models.Address@495871

Helper:
public static AddressApi getEndpoints() {

        // Create API handler
        final AddressApi.Builder builder = new AddressApi.Builder(
                AndroidHttp.newCompatibleTransport(),
                new AndroidJsonFactory(), null)
                .setRootUrl(Constants.ROOT_URL)
                .setApplicationName(Constants.APPLICATION_NAME);

        return builder.build();
    }

And finally the call:
Address address = AddressBuilderHelper.getEndpoints().get(MY_KEY).execute();
address.setCity("San Francisco");
AddressBuilderHelper.getEndpoints().update(address.getKey(), address).execute();

Which then creates a new Entity just like insert does. I am out of ideas here :(
Thanks for your help. I really appreciate it.
UPDATE
Turns out the only thing that was missing is the setter for the id attribute. Adding it did the job!

redpajama_set_name: RedPajamaStackExchange


Your chance to win a luxury break at the beautiful and epic Castlemartyr Resort!
If you've always wanted to pass a luxurious 24 hours at the incredible five-star resort at Castlemartyr, first of all, let us congratulate you on your good taste, and second of all, today might be your lucky day! We're collaborating with the wonderful team at Castlemartyr to give one reader an overnight stay with dinner, and it could be you! The winner of this fabulous getaway will be checking in with their other half (or lucky pal!) for a luxurious break, including a romantic carriage ride around the estate and a five-course gourmet meal in the plush surroundings of the Bell Tower restaurant!
Renowned for its five-star service and idyllic location, Castlemartyr Resort is a highly glamorous destination for weekends away, spa breaks and golfing, but it's also an extraordinary wedding venue! Nestled away in a picturesque part of East Cork, the Castlemartyr estate boasts lush landscapes, historic castle ruins and scenic lakes, making it perfect for outdoor wedding portraits! You're spoiled for choice when it comes to wedding ceremony and reception spaces, too - the spectacular Capel Suite ballroom can seat up to 220 for dinner.
The winner of this competition will receive a one-night stay for two people at the gorgeous Castlemartyr Resort, including a carriage ride around the estate, and a five-course meal in The Bell Tower restaurant, subject to availability.
Entry is via above form. To be eligible for entry you must be subscribed to the One Fab Day email newsletter. No cash alternative is possible. Entries close at midnight on Monday, July 16th, 2018. The winner will be chosen at random that day and contacted at that time.


Payday Loans in Hughenden are a relatively new idea. You find yourself strapped for cash and in need of instant money loans to get you through to payday.
With so many people in Hughenden AB struggling to make ends meet, it isn't a wonder that debt ridden Alberta citizens are screaming for Hughenden payday loans. What kind of instant money loans help you need depends on your personal monetary situation. Every individual will not use the same instant cash advances services since their perspective in searching for help will differ by many variables in Hughenden. For example, is the debt ridden Hughenden person looking for swift personal loan services or is the payday help a cry for answers to climb out of debt?
If the instant short term funds lender in Hughenden AB is not able to approve your cash funding, the customer service representative in Hughenden should at least try to help point you in the right direction if their service stops at that point. Some speedy personal loan companies in Hughenden will continue your service and try to find a Hughenden payday loans company that will service your loan. If your instant money loans in Hughenden application is not funded at this point, then you have to consider what complications there may be with your Hughenden credit to make a bunch of Hughenden payday fast cash loan or other unsecure personal loan lenders turn you down in Hughenden. Any good instant money cash advances service will explain the reasons in Hughenden for disqualifying your instant speedy personal loan application. As upsetting as it may be to be rejected in Hughenden by a instant money loans lender, you will now have a direction to better your monetary outlook. By the way, any service provided by an actual direct bad credit loan lender in Hughenden or instant money loans servicing company is free. The only fee you should ever pay in Hughenden would not be collected until an actual instant money unsecure money loan due date.
There are many Hughenden calls into unsecure personal loan lenders asking for a loan. If obtaining instant unsecure loan was in fact that simple, the Hughenden lending companies would be large corporations. A good customer service agent would help to pre-qualify the instant unsecure personal loan caller. It saves the potential customer time from filling out a instant short term funds application if the basic qualification standards are not met.
Just because your payday lender in Hughenden does not go into your credit report, it doesn't mean that you treatin Hughenden them any different. No payments will get your instant speedy personal loan debt into your credit history once a collector owns it. Maybe your best bet would be extending the instant cash advances contract for another term and pay only the fees in Hughenden. This will buy you some more time in Hughenden to earn some extra income in order to pay the instant express personal loan off. Other people may find that paying their down in Hughenden is the best way to work at getting that instant short term funding off of their plate. This option will have you paying for all fees plus anything you have to put against the principle balance in Hughenden. This action will extend only a part of your instant cash advances to accrue less in fees for the following term. As the fees decrease in Hughenden, you extra payment will increase to lower the instant swift personal loan balance that much faster in Hughenden.
You are probably like many Hughenden AB individuals that visit our website. You have Alberta expenses that show up unexpectedly and you don't have the cash flow to pay for them. Our Hughenden AB payday loans services can solve these issues for you.
The reasons why you need the unsecure cash loan aren't important because our Hughenden services are here to help you no matter what.
The online short term funds application is easy to fill out and you will get a fast responce. In the shortest time possible your Hughenden payday cash loan will be deposited into your Hughenden bank account. It's easy to and fast to get your Hughenden loans when you work with us.
Get pre-approved for a Hughenden loan today!


text:
Q: Disable gmail api request caching I am using javascript gmail api to fetch email threads using threadId.
I am using the following code:
var request = gapi.client.gmail.users.threads.get({
  'userId': 'me',
  'id': '123abc'
});

request.execute(function(response) {

    var messages = response.messages;

});

For the first time for a particular threadId it's working fine. And for further requests its returning the same number of messages even if the thread has more emails.
But the response gives correct number of emails if I am clearing the browser cache of the gapi url from the Chrome developer tools - network option.
Example:
-> Executed the request with a threadId '123abc', presently the thread has 3 emails in it. The response for the request is correct with length 3 for response.messages.
-> Then I sent one more email as reply to this thread, then executed the same request again. But the response is still the old one with length 3 for response.messages.
-> Tried running the same request many times but getting the same response.
-> Then cleared the browser cache for this url and requested again, now the response is correct with length 4 for response.messages.
Tried adding following meta tag in the html page, but didn't work:
<meta http-equiv='cache-control' content='no-cache'>
<meta http-equiv='expires' content='0'>
<meta http-equiv='pragma' content='no-cache'>

Tried adding a random number with the page url and the request, but didn't work.
Tried adding random number with gapi script link, but didn't work <script src="https://apis.google.com/js/client.js?onload=AuthIt&cacheBurster=123123123123"></script>
Is there any code to disable gapi request caching or any other method to solve this issue?

A: Finally got the solution by adding a random number to 'metadataHeaders' field in the gapi request:
var request = gapi.client.gmail.users.threads.get({
  'userId': 'me',
  'id': '123abc',
  'metadataHeaders': Math.random()
});


The Eli Miller Endurance Saddles are extremely popular with trail riders. It is super lightweight but still high quality. The leather is made in the USA by Wickett and Craig. It is supple and strong, for years of enjoyment. The seat is suspended, which is like riding on air. Much appreciated on those long trail rides.
Many competition riders report that their horse moves out better and have more muscle development in the shoulder pocket after switching to this saddle. The wooden tree is made by Batie Custom Saddle Trees. It features 7 degrees more flare to the bars, which allows the horse to perform better. With less shoulder restriction, the horse can collect and maneuver so much easier. These are the only trees that we will use now and my horses appreciate it.
The padded 16 inch seat will fit most riders. The pencil roll on the 5" cantle enhances durability. Heavy duty stainless hardware and dees secure your saddlebags, Western or English style breastplate and miscellaneous equipment. The back ring is tilted, so you have the option of using cernerfire rigging. It can be ordered with a straight ring if you prefer a standard rear cinch system. The stirrup bars on the tree are much smoother than many Western saddles. You can move your leg freely, forward or back. I appreciate that enhanced ability to use my legs properly. This saddle also has stirrup turners. This takes away all that knee pain after those long rides. Also, your stirrup is always there if you ever lose it.
This saddle is lighter than most western saddles. I love that it is easy to throw on taller horses, but still has the quality and craftsmanship that I demand. The suspended seat has a nice pocket that holds you in but does not restrict movement. Many riders switching from English to Western will appreciate the contact, flexibility and the weight of this saddle.


This one got stars, hearts and powerups to pick up on the level as well as minor updates.
Rush to Adventure is a retro style 2D action adventure platforming game, inspired by old 8-bit Nintendo classics like the original Zelda games. You arrive on an island, only to discover it is almost overrun by monsters. Assist the islanders, level up your hero, find new weapons and uncover the secrets of the island. Can you rid the island of its curse?
This alpha release contains about 20-30 minutes of content but offers a lot of replayability. Containing 10 short, action packed levels and 4 difficulties. You are also scored and ranked at the end of each level. Trying to S-rank every level is quite a challenge. Play on insane difficulty at your own risk.


Fado singer Katia Guerreiro was born in South Africa in 1976 before moving to the Azores as a child. It was here that she learnt to play an instrument known as the "viola da terra". In 1994 she moved to Lisbon where she was the lead vocalist for "Os Charruas". In 2000, she sang in a concert in memory of Amalia Rodrigues. After receiving critical acclaim for her performance on the night she embarked on a career singing Fado.
In 2000, she sang in a concert in memory of Amalia Rodrigues. After receiving critical acclaim for her performance on the night she embarked on a career singing Fado.
She has since released 3 albums, been on several tours and has been the subject of a biographical film.


text:
Франсуаза Полле (; род. 10 сентября 1949, Булонь-Бийянкур) — французская  (сопрано).

Училась в Мюнхенской Высшей школе музыки у Эрнста Хефлигера — необычный для французской певицы выбор немецкой школы сама Полле объясняет тем, что её интересовала не столько будущность оперной дивы, сколько оратории и песенный репертуар.Лауреат Международного Женевского конкурса 1981 года. Дебютировала в 1983 г. в Любекской опере в партии Маршальши («Кавалер роз» Рихарда Штрауса). В дальнейшем пела на ведущих оперных сценах мира, в том числе в Метрополитен-опера, Опере Бастилии, Театре «Колон», театре Ла Фениче. Исполнительница главной партии в премьерной постановке оперы Рольфа Либермана «Оправдание Медеи» (; Гамбург, 1995) — по мнению рецензента,

Франсуаза Полле с её мощным сценическим образом царила на протяжении всего представления — подлинная инкарнация Медеи. Трудную партию она вела без малейшего напряжения — теплым, чувственным сопрано, которое от самых низов до предельных верхов одинаково полнозвучно.

В 2005 г. участвовала в праздничном концерте к 80-летию Пьера Булеза вместе с Чикагским симфоническим оркестром под управлением юбиляра.

Среди наиболее важных записей Полле — оперы Гектора Берлиоза «Троянцы» и «Проклятие Фауста», «Глория» и «Stabat Mater» Франсиса Пуленка, песни Рихарда Штрауса, Альбана Берга, Антона Веберна.

В 1994 г. стала лауреатом премии «Виктуар де ля мюзик» как певица года — первым после выделения премий в области академической музыки в отдельную институцию.

Работала с такими дирижерами как Пьер Булез, Сильвен Камбрелинг, Жан-Клод Касадес, Мишель Плассон, Жорж прет, Семен Бычков, Рикардо Шайи, Шарль Дютуа, Карло Мария Джулини, Марек Яновский, Пинхас Штейнберг, Сейджи Осава, Ютака Садо и другими. Благодаря этому сотрудничеству она выступала по всему миру.

Источники

Ссылки
 Биография

Академические музыканты Франции
Сопрано
Оперные певцы и певицы Франции

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