##### An investigation of the golden ratio’s appearance in the position of human faces in paintings and photographs.

There is a vast amount of literature on the appearance of the golden ratio in nature, in physiology and psychology, and in human artifacts (see this page on the golden ratio; these articles on the golden ratio in art, in nature, and in the human body; and this paper on the structure of the creative process in science and art). In the past thirty years, there has been increasing skepticism about the prevalence of the golden ratio in these domains. Earlier studies have been revisited or redone. See, for example, Foutakis, Markowsky on Greek temples, Foster et al., Holland, Benjafield, and Svobodova et al. for human physiology.

In my last blog, I analyzed the aspect ratios of more than one million old and new paintings. Based on psychological experiments from the second half of the nineteenth century, especially by Fechner in the 1870s, one would expect many paintings to have a height-to-width ratio equal to the golden ratio or its inverse. But the large sets of paintings analyzed did not confirm such a conjecture.

While we did not find the expected prevalence of the golden ratio in external measurements of paintings, maybe looking “inside” will show signs of the golden ratio (or its inverse)?

In today’s blog, we will analyze collections of paintings, photographs, and magazine covers that feature human faces. We will also analyze where human faces appear in a few selected movies.

The literature on art history and the aesthetics of photography puts forward a theory of dividing the canvas into thirds, horizontally and vertically. And when human faces are portrayed, two concrete rules for the position of the eyeline are often mentioned:

- the rule of thirds: the eyeline should be 2/3 (≈0.67) from the bottom
- the golden ratio rule: the eyeline should be at 1/(
*golden ratio*) (≈0.62) from the bottom

The rule of thirds is often abbreviated as ROT. In 1998 Frascari and Ghirardini—in the spirit of Adolf Zeising, the father of the so-called golden numberism—coined the term “ϕaithful” (making clever use of the Greek symbol ϕ that is used to denote the golden ratio) to label the unrestricted belief in the primacy of the golden ratio. Some consider the rule of thirds an approximation of the golden ratio rule; “ROT on steroids” and similar phrases are used. Various photograph-related websites contain a lot of discussion about the relation of these two rules. For early uses of the rule of thirds, see Nafisi. For the more modern use starting in the eighteenth century, see this history of the rule of thirds. For a recent human-judgment-based evaluation of the rule of thirds in paintings and photographs, see Amirshahi et al.

So because we cannot determine which rule is more common by first-principle mathematical computations, let’s again look at some data. At what height, measured from the bottom, are the eyes in paintings showing human faces?

#### Eyeline heights in older paintings—more ROTen than ϕaithful

Let’s start with paintings. As with the previous blog, we will use a few different data sources. We will look at four painting collections: Wikimedia, the Smithsonian, Britain’s Your Paintings, and Saatchi.

If we want to analyze the positions of faces within a painting, we must first locate the faces. The function `FindFaces` comes in handy. While typically used for photographs, it works pretty well on (representational) paintings too. Here are a few randomly selected paintings of people from Wikimedia. First, the images are imported and the faces located and highlighted by a yellow, translucent rectangle. We see potentially different amounts of horizontal space around a face, but the vertical extension is pretty uniform from the chin to the bottom of the forehead hairs.

A more detailed look reveals that the eyeline is approximately at 60% of the height of the selected face area. (Note that this is approximately 1/ϕ). To demonstrate the correctness of the 60%-of-the-face-height rule for some randomly selected images from Wikipedia, we show the resulting eyeline in red and the two lines ±5% above and below.

Independent of gender and haircut, the 60% height seems to be a good approximation for the eyeline. Of course, not all faces that we encounter in paintings and photographs are perfectly straightened. For tilting heads, we note both eyes will not be on a horizontal line. But as an average, the 60% rule works well.

Overall we see that the eyeline can be located within a few percent of the vertical height of the face rectangle. The error of the resulting estimation of the eyeline height in a painting/photograph in most collections should be about ≤2% for a typical ratio of face height to painting/photograph height. Plus or minus 2% should be small enough such that for a large enough painting/photograph collection we can discriminate the golden ratio height 1/ϕ from the rule of thirds 2/3. On the range [0,1], the distance between 1/ϕ and 2/3 is about 5%. (Using a specialized eye detection method to determine the vertical height of the eyes we leave for a later blog.)

We start with images of paintings from Wikimedia.

Using the 0.6 factor for the eyeline heights, we get the following distribution of the faces identified. About 12,000 faces were found in 8,000 images. The blue curve shows the probability density of the position of the eyelines of all faces, and the red curve the faces whose bounding rectangles occupy more than 1/12 of the total area of the painting. (While somewhat arbitrary, here and in the following, we will use 1/12 as the relative face rectangle area, above which a face will be considered to be a larger part of the whole image.) We see a clear single maximum at 2/3 from the bottom, as predicted by the ROT. (The two black vertical lines are at 2/3 and 1/ϕ).

Because we determine the faces from potentially cropped images rather than ruler-based measurements on the actual paintings, we get some potential errors in our data. As analyzed in the last blog, these effects seem to average out and introduce final errors well under 1% for over 10,000 paintings.

Here are two heat maps: one for all faces, and the other for larger faces only. We place face-enclosing rectangles over each other, and the color indicates the fraction of all faces at a given position. One sees that human faces appear as frequently in the left half as in the right half. To allow comparisons of the face positions of paintings with different aspect ratios, the widths and heights of all paintings were rescaled to fit into a square. The centers of the faces fall nicely into the [2/3,1/ϕ] range. (The Wolfram Language code to generate the PDF and heat map plots is given below.)

Here is a short animation showing how the peak of the face distributions forms as more and more paintings are laid over each other.

Repeating the Wikimedia analysis with 4,000 portrait paintings from the portrait collection of the Smithsonian yields a similar result. This time, because we selected portrait paintings from the very beginning, the blue curve already shows a more located peak.

The British Your Paintings website has a much larger collection of paintings. We find 58,000 paintings with a total of 76,000 faces.

The mean and standard deviation for all eyeline heights is 0.64±0.19, and the median is 0.69.

In the eyeline position/relative face size plane, we obtain the following distribution showing that larger faces are, on average, positioned lower. Even for very small relative face sizes, the most common eyeline height is between 1/ϕ and 2/3.

The last image also begs for a plot of the PDF of the relative size of the faces in a painting. The mean area of a face rectangle is 3.9% of the whole painting area, with a standard deviation of 5.5%.

Here is the corresponding cumulative distribution of all eyeline positions of faces larger than a given relative size. The two planes in the *yz* plane are at 1/ϕ and 2/3.

Did the fraction of paintings obeying the ROT of ϕ change over time? Looking at the data, the answer is no. For instance, here is the distribution of the eyeline heights for all nineteenth- and twentieth-century paintings from our dataset. (There are some claims that even Stone Age paintings already took the ROT into account.)

As paintings often contain more than one person, we repeat the analysis with the paintings that just have a single face. Now we see a broader maximum that spans the range from 1/ϕ to 2/3.

Looking at the binned rather than the smoothed data in the range of the global maximum, we see two well-resolved maxima: one according to the ROT and one according to the golden ratio.

Now that we have gone through all the work to locate the faces, we might as well do something with them. For instance, we could superimpose them. And as a result, here is the average face from 11,000 large faces from nineteenth-century British paintings. The superimposed images of tens of thousands of faces also gives us some confidence in the robustness and quality of the face extraction process.

Given a face from a nineteenth-century painting, which (famous) living person looks similar? Using `Classify`["`NotablePerson`",…], we can quickly find some unexpected facial similarities of living celebrities to people shown in older British paintings. The function `findSimilarNotablePerson` takes as the argument the abbreviated URL of a page from the Your Paintings website, imports the painting, extracts the face, and then finds the most similar notable person from the built-in database.

Here is a Demonstration that shows a few more similar pairs (please see the attached notebook to look through the different pairings).

#### The eyeline heights in newer paintings—more ϕaithful than ROTen

Now let us look at some more modern paintings. We find 15,000 modern portraits at Saatchi. Faces in modern portraits can look quite abstract, but `FindFaces` still is able to locate a fair number of them. Here are some concrete examples.

And here is an array of 144 randomly selected faces in modern art paintings. From a distance, one recognizes human faces, but deviations due to stylistic differences become less visible.

If we again superimpose all faces, we get a quite normal-looking human face. With a more female appearance (e.g. softer jawline and fuller lips) as compared to the nineteenth-century British paintings, the overall face has more female characteristics. The fact that the average face looks quite “normal” is surprising when looking at the above 12*12 matrix of faces.

If we add not just all color values but also random positive and negative weights, we get much more modern-art-like average faces.

Now concerning the main question of this blog: what are the face positions in these modern portraits? Turns out, they again follow the golden ratio much more frequently than the ROT. About 30% more paintings have the eyeline at 1/ϕ±1% compared to 2/3±1%.

The mean and standard deviation for all eyeline heights is 0.60±0.16, and the median is 0.62. A clearly lower-centered and narrower distribution.

And if we plot the PDF of the eyeline height versus the relative face size, we clearly see a sweet spot at eyeline height 2/3 and relative face area 1/5. Smaller faces with relative size of about 5% occur higher, at eyeline height about 3/4.

And here is again the corresponding 3D graphic that shows the 1/ϕ eyeline height for larger relative faces is quite pronounced.

We should check with another data source to confirm that more modern paintings have a more ϕaithful eyeline. The site Fine Art America offers thousands of modern paintings of celebrities. Here is the average of 5,000 such celebrity paintings (equal amounts politicians, actors and actresses, musicians, and athletes). Again we clearly see the maximum of the PDF at 1/ϕ rather than at 2/3.

For individual celebrities, the distribution might be different. Here is a small piece of code that uses some functions defined in the last section to analyze portrait paintings of individual persons.

Here are some examples. (We used about 150 paintings per person.)

Perhaps unexpectedly, Jimi Hendrix is nearly perfectly ϕaithful, while Mick Jagger seems perfectly ROTen. Obama and Jesus obey nearly exactly the rule of thirds in its classic form.

#### The eyeline heights in photographs by professional photographers

Now, for comparison to the eyeline positions in paintings, let us look at some sets of photographs and determine the positions of the faces in these. Let’s start with professional portrait photographs. The Getty Image collection is a premier collection of good photographs. In contrast to the paintings, the maximum for large faces is much closer to 2/3 (ROT) than to 1/ϕ for a random selection of 200,000 portrait photographs.

And here is again the distribution in the eyeline height/relative face size plane. For very large relative face sizes, the most common eyeline height even drops below 1/ϕ.

And here is the corresponding heat map arising from overlaying 300,000 head rectangles.

So what about other photographs, those aesthetically less perfect than Getty Images? The Shutterstock website has many photos. Selecting photos with subjects of various tags, we quite robustly (meaning independent of the concrete tags) see the maximum of the eyeline height PDF near 2/3. This time, we display the results for portraits showing groups of identically tagged people.

These are the eyeline height distributions and the average faces of 100,000 male and female portraits. (The relatively narrow peak in the twin-peak structure of the distribution between 0.5 and 0.55 comes from photos that are close-up headshots that don’t show the entire face.)

Restricting the photograph selection even more, e.g. to over 10,000 photographs of persons tagged with *nerd* or *beard* shows again ROTen-ness.

The next two rows show photos tagged with *happy* or *sad*.

All of the last six tag types (male, female, nerd, beard, happy, sad) of photographs show a remarkable robustness of the position of the eyeline maximum. It is always in the interval [1/ϕ,2/3], with a trend toward 2/3 (ROT).

But where are the babies (the baby eyeline, to be precise)? The two peaks are now even more pronounced, with the first peak even bigger than the second—the reason being that many more baby pictures are just close-ups of the baby’s whole face.

Next we’ll have a look at the eyeline height PDFs for two professional photographers: Peggy Sirota and Mario Testino. Because both artists often photograph models, the whole human body will be in the photograph, which shifts the eyeline height well above 2/3. (We will come back to this phenomenon later.)

#### The eyeline heights in selfies—maybe too high?

After looking at professionally made photos, we should, of course, also have a look at the pinnacle of modern amateur portraiture—the selfie. (For a nice summary of the history of the selfie, see Saltz. For a detailed study in the increase of selfie popularity over the last three years by nearly three orders of magnitude, see Souza et al. Using some of the service connects, e.g. the “`Flickr`” connection, we can immediately download a sample of selfies. Here are five selfies from the last week in September around the Eiffel Tower. Not all images tagged as “selfies” are just the faces in close up.

Every day, more than 100,000 selfies are added to Instagram (one can easily browse them here)—this is a perfect source for selfies. Here are the eyeline height distributions for 100,000 selfie thumbnails.

Compared with the professional photographs, we see that the maximum of the eyeline height distributions is clearly above 2/3 for photos that contain a face larger than 1/12 of the total photo. So the next time you take a selfie, position your face a bit lower in the picture to better obey the ROT and ϕ. (Systematic deviations of selfies from established photographic aesthetic principles have already been observed by Bruno et al.)

The eyeline height in a selfie changes much less with the total face area as compared to professional photographs.

And again, the corresponding heat map.

The maximum of the total area of the faces in selfies is—not unexpectedly—due to the finite length of the human arm or typical telescopic selfie sticks, bounded by about one meter. So selfies with very small faces are scarcer than photographs or paintings with small faces.

What’s the average selfie face look like? The left image is the average over all faces, the middle image the average over all male faces, and the right image the average over all female faces. (Genders were heuristically determined by matching the genders associated with a given name to user names.) The fact that the average selfie looks female arises from the fact that a larger number of selfies are of female faces. This was also found in the recent study by Manovich et al.

Now, it could be that the relative height of the eyeline is dependent on the concrete person portrayed. We give the full code in case the reader wants to experiment with people not investigated here. Eyeline heights we measure in images from the Getty website, tagged with the keywords to be specified in the function `positionSummary`.

Now it takes just a minute to get the average eyeline height of people seen in the news, each based on analyzing 600 portrait shots of Lady Gaga, Taylor Swift, Brad Pitt, and Donald Trump. Lady Gaga’s eyeline is, on average, clearly higher, quite similar to typical selfie positions. On the other hand, Taylor Swift’s eyeline is peaked at the modern painting-like maximum at 1/ϕ.

Many more types of photographs could be analyzed. But we end here and leave further exploration and more playtime to the reader.

#### LinkedIn profile photos—men seem to be more ϕaithful

Many LinkedIn profile pages have photographs of the page owners. These photographs are another data source for our eyeline height investigations. Taking 25,000 male and 25,000 female profile photos, we obtain the following results. Because the vast majority of LinkedIn photographs are close-up shots, the curve for faces occupying more than 1/12 of the whole area is quite similar to the curve of all faces, and so we show only the distribution of all faces. This time, the yellow curve shows all faces that occupy between 10% and 30% of the total area.

Here are the eyeline height PDF, the bivariate PDF, and the average face for 10,000 male members from LinkedIn. Based on the frequency of male first names in the US, Bing image searches restricted to the LinkedIn domain were carried out, and the images found were collected.

Interestingly, the global maximum of the eyeline height distribution occurs clearly below 1/ϕ, the opposite effect compared to the selfies analyzed above. The center graph shows the distribution of the eyeline height as a function of the face area. The global maximum appears at a face area of 1/5 and at eyeline height quite close to 1/ϕ. This means the low global maximum is mostly caused by photographs where the face rectangles occupy more than 30% of the total area. The most typical LinkedIn photograph has a face rectangle area of 1/5th of the total area and the eyeline height is at 1/ϕ.

The corresponding distribution over all female US first names is quite similar to the corresponding curve for males. But for faces that occupy a larger fraction of the image, the female distribution is visibly different. The average eyeline height of these photos of women on LinkedIn is a few percent smaller than the corresponding male curve.

With the large number of members on LinkedIn, it even becomes feasible to look for eyeline height distribution for individual names. We carry out a facial profiling for three names: Josh, Raj, and Mei. Taking 2,500 photos for each name, we obtain the following distributions and average faces.

The distributions agree quite well with the corresponding gender distributions above.

After observing the remarkable peak of the eyeline height PDF at 1/ϕ, I was wondering which of my Wolfram Research or Wolfram|Alpha coworkers obey the ϕaithful rule. And indeed I found more of my male coworkers have the 1/ϕ height than female coworkers. Not unexpectedly, our design director’s is among the ϕaithful. The next input imports photos from the LinkedIn pages of other Wolfram employees and draws a red line at height 1/ϕ.

Let us compare the peak distribution with the one from the current members of Congress. We import photos of all members of Congress.

Here are some example photos.

Similar to the LinkedIn profile photos, the maximum of the eyeline PDF is slightly lower than 2/3. We also show the face of the averaged member of Congress.

#### Weekly magazine covers—tending to be ϕaithful over the last three decades

After having analyzed the face positions of amateur and professional photographs, a next natural area for exploration is magazine covers: their photographs are carefully made, selected, and placed. *TIME* magazine maintains a special website for their 4,800 covers covering over ninety years of published issues. (For a quick view of all covers, see Manovich’s cover analysis from a few years ago.)

It is straightforward to download the covers, and then find and extract the faces.

These are the two resulting distributions for the eyelines.

The maximum occurs at a height smaller than 1/2. This is mostly caused by the title “*TIME*” on top of the cover. Newer editions have partial overlaps between the magazine title and the image. The following plot shows the yearly average of the eyeline height over time. Since the 1980s, there has been a trend for higher eyeline positions on the cover.

If we calculate the PDFs of the eyeline positions of all issues from the last twenty-five years, we see quite a different distribution with a bimodal structure. One of the peaks is nearly exactly at 1/ϕ.

And here are the average faces per decade. We see also that the covers of the first two decades were in black and white.

For a second example, we will look at the German magazine *SPIEGEL*. It is again straightforward to download all the covers, locate the faces, and extract the eyelines.

Again, because of the title text “*SPIEGEL*” on top of the cover, the maximum of the PDF of the eyeline height on the cover occurs at relatively low heights (≈0.56).

A heat map of the face positions shows this clearly.

Taking into account both that the magazine title “*SPIEGEL*” is typically 13% of the cover height and that there is whitespace at the bottom, the renormalized peak of the eyeline height is nearly exactly at 1/ϕ.

For a third, not-so-politically-oriented magazine, we chose the biweekly *Rolling Stone*. They too have a collection of their covers (through 2013) online. The eyeline height distribution is again bimodal, with the largest peak at 1/ϕ. So *Rolling Stone* is a ϕaithful magazine.

By year, the average eyeline height shows some regularities within an eight-year period.

The cumulative mean of the eyeline heights is very near to 1/ϕ, and the average through 2013 deviates only 0.4% from 1/ϕ.

To parallel the earlier two magazines, here are the averaged faces by decade.

#### Comic book covers—where are the eyelines of the superheros?

Comic covers are another fairly large source of images to analyze. The Comic Book Database has a large collection of comic book covers. Here we restrict ourselves to Marvel Comics and DC Comics, totaling about 72,000 covers. Because comics are not photographs, recognizing faces is now a harder job. But even so, we successfully extract about 90,000 faces.

Here are our typical characterizations (eyeline height PDF, face position heat map, average face) for Marvel Comics.

And the same for DC Comics.

All three characteristics show remarkable consistency between the two comic publishers.

#### Daily newspapers, fashion magazines, …—where are the eyelines now?

Many more collections of faces can now be investigated for the eyeline positions. It is straightforward to write a small crawler function that starts with a given website and extracts images and links to pages with more images. (This is just a straightforward implementation. Many optimizations, such as parallel retrieval, could be implemented to improve this function.)

For example, here is the resulting average data for all images (larger than 200 pixels) from *The New York Times* website from February 8, 2016. The eyeline PDF maximum is between 2/3 and 1/ϕ.

And here from the weekly German newspaper, *Die Zeit*. This time, the eyeline maximum is clearly 2/3 for larger faces.

Here is a snapshot of 1,000 images from CNN.

The eyeline heights in fashion magazines show a totally different distribution. Here are the results of 1,000 images from *Vogue*. Because many images on the site show the stylishly dressed models from head to toe, the head is small and the eyeline very high in the images. As a result, we get the strong, narrow peak of the blue curve.

*GQ Magazine* also shows a global eyeline height peak at 2/3 for large faces.

The maximum of the eyeline in the magazine *People* is again at 2/3 for large faces.

And here are the results for *Ebony* magazine. This time, the large face eyeline height has a peak at 1/ϕ.

Using a bodybuilding magazine, as with the *Vogue* images, we see a very high eyeline, again because often whole-body images are shown. The average face looks different from the previous averages.

We obtain a softer-looking face with an eyeline maximum greater than 2/3 from *Allure* magazine.

And goths from the *Gothic Beauty* magazine are on average ROTen, but large goths are more ϕaithful.

The magazine *20/20* specializes in glasses. Not unexpectedly, the average face shows pronounced sunglasses and the eyeline height as greater than 2/3.

#### Movie posters—the eyelines of film stars

A good-sized source of a wide variety of drawn and photographed paintings are movie posters. The site Movie Posters has 35,000 posters going back to the 1920s.

More interesting is a plot of the mean over time. Before the 1980s, eyelines were more in the center of posters. Since then, the average eyeline position is more in the interval [1/ϕ,2/3].

The shift in average eyeline height in movie posters is even more clearly visible in the corresponding face heat maps.

Here is the average face from all movie posters from the last five years.

#### Movies—the eyelines in motion picture frames

In the last blog, we ended with plots of the evolution of the average movie aspect ratio, so this time we will also end by analyzing some movies. The Internet Archive has a collection of 20,000 movies that are available for download. We will look at the face positions of two well-known classics: Buster Keaton’s *The General* from 1926 and Fritz Lang’s *Metropolis* from 1927. We start with *The General*. The average of all faces (without taking size into account) is at 2/3, and the large faces clearly appear lower.

Not every frame of a movie contains faces, so it is natural to ask if the mean (windowed) eyeline height changes as the movie progresses. Here is a different kind of heat map that shows the mean eyeline height over time. The colors indicate the number of frames that contain identified faces.

Because the main character in the film moves a lot, the heat map of the face position has now much more structure as compared to the above heat maps of photographs and paintings.

Fritz Lang’s *Metropolis*, although made only one year after *The General*, was shot in quite a different style. Just by quickly zooming through the movie, one observes that the majority of faces appear at a much larger height. This impression is confirmed by the actual data about the eyeline positions.

The PDF of all eyeline positions shows that especially large faces appear high in the frames.

We compare with a modern TV series production—episode nine of season nine from *The Big Bang Theory*, “The Platonic Permutation”. Most faces appear above the 2/3 height.

But the PDF of the eyeline position of larger faces peaks very near to 2/3, and the average face shows characteristic facial features of the show’s main characters.

Or, for a very recent example, here is the PDF of episode one of Amazon’s recent *The Man in the High Castle*. The peak of the eyeline of larger faces is nearer to 1/ϕ than to 2/3.

We end with a third TV series example, episode eight of season six of *The Walking Dead*. For larger faces, we see a well-pronounced bimodal eyeline height distribution, with the two maxima at 1/ϕ and 2/3.

#### Findings

In this second part of our explorations of the golden ratio in the visual arts, we looked at the height of the eyeline of human faces and the face position. Using the function `FindFaces` and approximate rules for determining the eyeline height in faces, we computed averages of more than a million faces and eyeline heights.

The maxima of the eyeline height distribution for photographs and paintings is predominately in the range of 0.6 to 0.67. Older paintings and modern photographs have maxima near 2/3, as the rule of thirds predicts (demands). Interestingly, modern art portraits show the eyeline height PDF peak at 1/golden ratio for large faces. (We used >1/12 of the total area to define “large” faces.) The peak eyeline position in selfies is about 0.7, higher than in paintings and many professional photographs. The magazine covers we analyzed, especially those of the past few decades, seem to have a peak of the eyeline position PDF at 1/golden ratio. Similarly, the photos from various newspaper sites show a peak at 1/golden ratio. For LinkedIn photos, clear gender differences between the positions of the eyeline height were found—men turned out to be more ϕaithful. And the analyzed movies show that faces, especially smaller ones, appear quite often significantly above the 2/3 height. But modern TV series show peaks at either the 1/golden ratio or 2/3—or even both simultaneously.

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## 7 Comments

This is a kind of Alfred Hitchcock movie. Thinking about vertigo! Hope Wolfram doesn’t get dizzied reading this article. Hahaha!

Great read Michael! But you’re testing loyalty with this length haha! Nice job!

Dear Michael Trott, Thank you for your excellent article on Profiling the Eyes: ϕaithful or ROTen? Or Both?. By the way, I don’t understand the “The eyeline heights” facts. Keep the great writing skill up. Thanks again Buddy!

So why did they change the eye-line positions? I don’t understand…

Really great post. You might like https://www.nextrembrandt.com/ where a team recreated a new Rembrandt after analyzing Rembrandt paintings. So next challenge for you the next van Gogh painting?

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Really an excellent Article Trott