The report examined the willingness of people to make small payments of a couple dollars or less, called micropayments, for digital content such as newspaper articles, books, travel guides, magazines, and films. They found that people were least willing to pay for newspaper content and most willing to pay for film content. However, across the board, iPhone users were the most willing to part with their cash.

Olswang. "Convergence Study 2009."

Why is this the case?

Olswang believes that some of the difference can be explained by demographic factors.  The iPhone is a high-end product, so users would tend to have more disposable income.  Also, it is used by many hip and young people who may be more likely to spend money for entertainment products.

However, the firm argues that demographic factors probably only account for a small part of the increment.  It believes that the iPhone’s native support for micropayment is a far more important factor.  It is “not unreasonable to deduce that, where a micropayment system such as Apple’s is built into a platform and easy to use, it is more likely that consumers will willingly pay to acquire content by means of that system.”

Despite the importance of micropayments – Olswang believes that micropayment business models are far more likely to be sustainable than those based entirely on ad-support – few companies seem to be stepping in to support this kind of model.  When interviewed, media industry executives said that only the Apple iPhone and a few other proprietary platforms offered viable systems for micropayment.

There has been some speculation that the new iPhone SDK will allow developers to add micropayment support in to their own applications.  This would allow them to charge users for on demand content or subscription content rather than just a single up front price for their apps.  Giving power to rank-and-file developers to levy micropayments could aid them significantly in monetizing their products and also result in some interesting new kinds of software for consumers.

[Note: In terms of the methodology of the survey, the report says, “we commissioned YouGov to conduct an online poll of 1013 UK adults and 536 13-17 year olds.”  Nothing is said about how these individuals were recruited or how the study was controlled.]

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Wikimedia produced a rebuttal saying that everything was fine.  However, the media is portraying the battle as a “he said / she said” affair where it is not clear whether Wikipedia is in trouble or not.  We think this fails to credit Wikipedia as holding the stronger of the two positions.

In this article, we discuss why Wikipedia’s “editor exodus” doesn’t matter.  Simply put, the WSJ has (1) interpreted the data incorrectly, (2) ignored the fact that most content on Wikipedia is created by a small, core group of editors [a fact validated by researchers at U. Minnesota], (3) ignored social science research indicating that having fewer editors may increase article quality, and (4) ignored the exodus of spammers.

The exodus described is the leaving of 49,000 editors from the English language version during the first three months of 2009, where only 4,900 left in the same quarter a year earlier.  It is stated that editors are leaving faster than new editors are joining.  All the statements made in the WSJ article are supported only by data provided by Dr. Felipe Ortega of the Universidad Rey San Carlos in Madrid.  The data is in his thesis on Wikipedia (the relevant data starts on page 116).

Ortega’s Analysis

Dr. Ortega shows that more editors are leaving Wikipedia than joining it, as shown by the greater number of editor deaths than births in the following graph.  He defines a birth as the moment when someone edits Wikipedia for the first time.  He defines a death as the moment when someone makes their last edit to Wikipedia, which is not followed by any further edits.

The 49,000 “log-offs” reported by the WSJ are what Dr. Ortega would call in his thesis 49,000 deaths.

The following image is the birth/death graph for the English version of Wikipedia from page 123 of Dr. Ortega’s thesis.

Problem 1: Ortega’s Basic Errors

The first thing to point out is that, although true that deaths outpace births, the gap between deaths and births is tiny compared to the total number of editors joining and leaving the site.  This alone suggests that any predictions of Wikipedia’s demise are premature.

Second of all, the graph shows that deaths outpace births in recent years and not in past years, which is probably why the WSJ pounced on this topic as something new.  However, it is possible that the entire effect is only due to a methodological error.  More recent years on the graph will tend to overcount deaths because there has been no opportunity for the editors to come back and prove they are not dead.  In older years, an editor may have “died” only to come back 2 years later and therefore not be counted as dead.  For example: An editor that “logged off” in 2006 and came back in 2008 is not counted as dead because it is seen that he is still editing; however, an editor that “logged off” in 2008 and will actually come back in 2010 is counted as dead because his edits in 2010 haven’t happened yet.

Third, Dr. Ortega’s data is limited to birth / death data, and he does no analysis on the number of active editors in a given month.  By Wikipedia’s count, the number of active editors has been constant for the past year.  The number of active editors in a given month would seem to be a far more direct and important measure of Wikipedia’s success than the birth / death rate.

Problem 2: Top Contributors do Most of the Work

Besides the methodological problems with Dr. Ortega’s analysis, there is another considerable problem, which is that many of the 49,000 editors that left were users who had only made 1 – 4 edits.  The vast majority of content on Wikipedia is created by users who repeatedly make many edits (the top contributors).  Wikipedia actually defines an editor as someone who makes 5 edits or more.  The definition makes sense because many people make a few experimental edits and leave.  It is senseless to judge the strength of Wikipedia’s community based on these people.

Reid Priedhosrky and other researchers at the University of Minnesota have actually measured the amount of content created by the top contributors versus that created by lesser contributors.  Although Wikipedia is touted as a “democratic” source of knowledge, the empirical fact is that Wikipedia is mainly written by a small number of users.

Priedhorsky measured the amount of content created by different editors using what he calls persistent word views (PWV), which is just a fancy way of counting the number of times words written by an editor are shown to Wikipedia readers.  An editor gets 1 PWV for each word that he wrote that appears on someone’s screen.  If an editor writes the word “cat” on a page that is viewed by 10 people and “dog” on a page that is viewed by 5 people, then that editor has 15 PWVs.

The following graph shows how many PWVs different groups of editors have.  The graph goes up to 100%.  If a group of editors had 100% of the PWVs, this would mean they were responsible for every word of Wikipedia ever viewed by any reader.  The graph shows the amount of PWVs attributable to the top 10% of contributors (the editors who are the top 10% most active), the top 1% of contributors, and the top 0.1% of contributors.

In the words of Priedhosrky: “Editors who edit many times dominate what people see when they visit Wikipedia. The top 10% of editors by number of edits contributed 86% of the PWVs, and top 0.1% contributed 44% – nearly half! The domination of these very top contributors is increasing over time.”

Therefore, though losing people who make one or two edits hinders Wikipedia’s goal of democratization of knowledge, in the sense of allowing everyone to contribute to their vast collection of articles, it doesn’t really affect the amount of content Wikipedia has or the rate at which this content grows.

The vast majority of Dr. Ortega’s 49,000 dead users are not even in the top 10% of contributors.  So any loss of their contribution is limited to 100 – 86 = 14% of Wikipedia’s PVWs.

Dr. Ortega does take the time to separately discuss how often editors in the top 10% remain alive.  His data shows that only 30% of editors in the top 10% of contributors remain a top 10% contributor after the passage of 500 days.  However, for the editors leaving the top contributor group, 40% of the authors remain alive (making fewer edits) for another 500 days.  This data does suggest a fairly heavy turnover among top 10% contributors but nothing terribly bad.  After all, 500 days is 1.3 years and 500 days + 500 days is 2.7 years.  These are pretty long periods of time to be active on any website.

More importantly, Dr. Ortega says nothing about the death rate of the top 1% users and the top 0.1% users, who account for over 66% of Wikipedia as measured in PWVs.  In the absence of data, there is no reason to suspect that these most important editors are leaving.

Problem 3: Fewer Editors Often Correlates with Higher Quality

Studies have shown that having fewer editors on a Wikipedia article can increase its quality on certain measures, such as its readability and coherence.  Intuitively, having many editors can lead to edit wars, lack of coordination, and lack of agreement about an article’s direction.

In fact, most Wikipedia articles are written by a small group of editors.  Jimmy Wales, co-founder of Wikipedia, told CNet: “One of the things that’s important to know about Wikipedia is that the entries that are edited by hundreds of people are really anomalies.”

Outdated Idea: More Editors = Higher Quality

There was briefly a time when social scientists believed that Wikipedia articles with more editors were higher quality.  This idea was espoused by Wilkinson and Huberman of HP Labs.  The following graph shows that featured articles on Wikipedia tend to have more editors than regular articles.  Because articles become featured because of their high quality, the researchers thought that this indicated that having more editors caused an increase in quality.

The red line on the graph shows featured articles and the black line shows regular articles.  As you can see, the featured articles have more editors.  The bars sticking out from the lines just show standard deviation, which you can ignore.  The horizontal axis shows Google page rank.  The editors wanted to only compare featured articles and regular articles of the same page rank because the page rank could make an article more visible and attract more editors.

The Wilkinson and Huberman idea has fallen out of favor.  The fatal flaw of the study was that it did not control for number editors before and after the article became featured.  Clearly, an article that becomes featured will draw more editors.  Therefore, it is just as likely that the featured status causes the article to have more editors than for the higher number of editors to cause the featured status.

Modern Idea: More Editors = Lower Quality (sometimes)

The Wilkinson and Huberman study has been supplanted by newer studies showing that having fewer editors can sometimes result in higher quality Wikipedia articles.

Dr. Niki Kittur and Prof. Robert Kraut of Carnegie Mellon University demonstrated that an important factor in whether increasing the number of editors actually helped a Wikipedia article was the concentration of edits.  An article with high concentration of edits is one where a few editors do most of the work, and other editors make only minor additions.  An article with low concentration is one where the editing work is spread evenly among editors.

High concentration suggests that a few editors are taking the lead on the article, whereas low concentration is more like the democratization idea, where everyone does an equal share.

They published the following graph showing that an article with low concentration decreases in quality as more editors are added.

The next graph shows that articles with low concentration have more interdependent issues when more editors are added.  An interdependent issue was defined by them as something that is not easy for the editors to resolve independently.  Interdependent issues are things like readability, flow, and coherence.

The researchers suggest that the reason having many editors with a low concentration decreases article quality is that it is hard to coordinate when there are so many individuals who each have so much editing power.

Problem 4: Loss of Spammers

As noted earlier, Dr. Ortega’s 49,000 dead users are mostly low-activity editors.  Commentors nbauman and KlaymenDK pointed out on Slashdot that the loss of low-activity editors might be explained by the decline in spammers after Wikipedia added the no-follow tag to all of its outbound links.  The no-follow tag means that the link is not used for computing page rank, so spammers have less incentive to add links to their own site.  Spammers would likely be low-activity editors because they add an outbound link from Wikipedia to their own site and then leave.

This is a clear case where losing these editors is a win for Wikipedia.

The 49,000 dead users could very well have been caused by Wikipedia’s crack down on spammers, but unfortunately, there is no empirical analysis available to support or deny this hypothesis.  Dr. Ortega did not analyze how many of the “logged off” editors were spammers.  This could easily have done by determining whether the editors had posted mostly external links.

Conclusion

We do not doubt Dr. Ortega’s expertise on Wikipedia.  After all he wrote a 200 page thesis about it.  Nor do we doubt that Wikipedia could improve its user experience and make the system friendlier for newbies to reduce the number of user deaths.

However, the data does not support the conclusion that Wikipedia is about to meet its demise or even that its user base has been affected in any substantial way for the reasons described above: (1) methodological problems with Dr. Ortega’s analysis, (2) the vast majority of content on Wikipedia is created by repeat contributors who are not leaving, (3) having fewer editors on an article does not necessarily decrease its quality, and (4) many of the editors who left may have been spammers.

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Sources

Kittur, A., Lee, B., and Kraut, R. (2009). Coordination in Collective Intelligence: The Role of Team Structure and Task Interdependence. CHI ’09.

Kittur, A., and Kraut, R. (2008). Harnessing the Wisdom of Crowds in Wikipedia: Quality through Coordination. CSCW ’08.

Ortega, J. (2009). Wikipedia: A Quantitative Analysis.  Doctoral Thesis.  Universidad Rey Juan Carlos

Priedhorsky, R., Chen, J., Lam, S., Panciera, K., Torveen, L., and Riedl, J. (2007). Creating, Destroying, and Restoring Value in Wikipedia.  GROUP ’07.

Wilkinson, D. & Huberman, B. (2007). Cooperation and Quality in Wikipedia. WikiSym ’07.

Images and graphs come from these articles.

The following video gives an excellent demonstration.

Previous work has allowed reconstruction of 3D models from photos or video; however, such work has been limited to offline processing, e.g. the algorithm takes a complete piece of video and builds a 3D model, so the user cannot adjust the video to take in new perspectives until after the whole 3D model is built.  The clear advantage of real-time processing (or online processing) is that the user can see the model being built from the video he is recording.  He can take more video of the object in different positions to correct any problems in the 3D model as they arise.

Some examples of offline model reconstruction include Microsoft’s Photosynth, Stanford’s Make3D, and the University of Adelaide’s Video Trace.

How does it work?

The program uses a single camera and commodity hardware.  The demonstration in the video was performed with a 2.4 Ghz Intel dual core processor and Logitech Quickcam Pro 9000 (640 x 480 @ 15 fps).  The program assumes that you are modeling a single object.  It cannot model multiple objects simultaneously.

The video camera must be kept stationary and only the object to be modeled is moved and rotated.

The program can be divided into 5 steps:

1. Image capture
2. Extraction of point cloud
3. Delauney tetrahedralization
4. Tetrahedra carving
5. Texturing the surface mesh

We will describe each step.

In image capture the goal is to identify the object as separate from the background and the user’s hand and collect enough data to form a point cloud representing the object in 3D space.  This is done by sampling many small subwindows of the video (say 5 x 5 pixels), which are called features.  Because the camera is not moving, it is easy to determine which subwindows are capturing part of the background because background features will be stationary.  The features falling on the user’s hand can be easily identified because the hand moves in and out of the frame and can change shape.  The remaining features are clearly part of the object and each is identified as a landmark on the object.

Each landmark specifies a point on the object and taken together they form a point cloud that roughly approximates the shape of the object.

From the point cloud, a process called Delauney tetrahedralization is run, which essentially creates a rough cut of the model that is “too large.”  In other words, it has more stuff than necessary as can be seen in step 3 of the diagram above.

The model is so rough that some landmarks that were observed by the camera would now be obscured if indeed the real-life object looked like the model.  So, parts of the model are cut away so that each landmark that was seen by the camera can now be seen on the 3D model.  This is the tetrahedra carving stage.   One of the primary innovations of the program is a new, more accurate tetrahedra carving algorithm.  The picture below shows a model after using an older algorithm for tetrahedra carving (left) and a model after using the researchers’ new algorithm for tetrahedra carving (right).  The new algorithm creates a smoother, more accurate model.

The resulting model is a close approximation of the real-life object in many cases.  This object can then be skinned with the textures captured from the camera to make the model look life-like.

Note that all of these steps are performed in real-time, so that the user can actually view the 3D model as it is reconstructed by the program.

Results

The program was used to reconstruct 3D models of several objects as seen in the picture below.

Reconstruction of the church took 75 s and reconstruction of the box took 61 s.  Most models took about a minute to build including time for video capture.

Limitations

In addition to the limitations noted above, that the camera must be stationary and only one object can be modeled at a time, there are a few other constraints.  The program works the best on objects that are highly textured.  This is because it uses features to identify landmarks on the object, and in the absence of texture, all features tend to look alike.  Second of all, the program generally assumes that at the start of the video the object will be in the center of the frame, though it can later be moved around freely.

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Having people tag images by hand is an onerous task. Shenoy and Tan of Microsoft Research developed a way to tag images automatically by reading people’s brain scans while they look at images. The people did not even have to specifically think about trying to tag the image; they merely had to passively observe it.

Reading Minds

The technique requires using an electroencephalograph (EEG), a cap with electrodes placed on the scalp in regular locations that can each measure brain activity in their local area.

This image shows the layout of the electrodes.

The brain reacts differently when a person views different kinds of stimuli.  For example, the following diagram shows the average brain response when the user views a picture of a face and a picture of a non-face (i.e. anything else).  Red areas show high activity and blue areas show low activity.  Each line on the graph corresponds to the activity levels recorded by a single electrode.

As can be seen, the brain response is not static but varies over time.  However, the graphs show that the brain’s changing responses over time are predictable based on what kind of stimulus is shown.

Image Labeling through Mind Reading

Shenoy and Tan then used a machine learning algorithm called Regularized Linear Discriminant Analysis (RLDA) to develop an image tagging system.  The researchers recruited several test users.  The researchers presented a series of images to the users, and an EEG reading of the user’s brain was made upon presentation of each image.  Users were not required think about tagging the image or think about what kind of image it was.  As is common in psychological experiments, they were given a distracter task, a task that ensures they are paying attention to the images but does not specifically relate to the experiment.  In this case, they were asked to remember the images so they could identify them later in a post-experiment test.  The RLDA algorithm could then take as input the associated pairs – image and EEG scan – and learn to recognize what kinds of EEGs were associated with what kinds of images.  This learning is often termed the building of a learned model, which represents everything that the artificial intelligence knows.

The system can then be used to automatically tag images.  A user wearing an EEG is shown an image to which the tags are unknown, and the system uses the learned model to predict from the EEG what the appropriate tag for the image would be (e.g. this is a face or this is not a face).

The following are some of the images used in the study.

The users only had to view the images for short periods of time for the system to work, which means that many images could be labeled quickly by presenting them in rapid succession.  The researchers experimented with different time lengths, but there was no difference between allowing the subject to view the image for 500 ms, 750 ms, or 1 whole second.

Results

The following graph shows how accurate the system was at assigning the correct tag.  The vertical axis shows “classification accuracy,” which is the percent of time the system assigned the correct tag.  The different lines show the accuracy on different kinds of tagging tasks, e.g. tagging faces versus inanimate objects, faces versus animals, etc.  The horizontal axis shows “number of presentations,” which is how many times a user was shown each image (EEG readings are noisy so multiple EEG readings for the same image made the system more reliable).

Conclusion

The new method invented by Shenoy and Tan is certainly not as accurate as current methods of image labeling and also not as discriminative: most of the results reported in the graph above are for discriminations between only two kinds of images, for example pictures of faces and pictures of animals.  Only one of the experiments used the system to try to discriminate between three kinds of images (the results of which are labeled “3-class” in the graph).

Companies can get image labeling done just by hiring employees to do it.  An even cheaper way is to use Amazon’s Mechanical Turk, which pays people a few cents to do simple tasks online.  Image labeling is a common job on Mechanical Turk.  Google also has their own novel method, a game called Google Image Labeler, which tries to make image labeling fun by making the task multiplayer and giving people points when they provide the same label as another player.  Google Image Labeler was based on the original ESP Game created by Prof. Luis von Ahn of Carnegie Mellon.

However, the mind reading approach has the advantage that it does not require any work at all from the user.  The user merely has to passively observe the image, for as little as 500 ms.  One can imagine a system that tags images by reading your mind as you surf the web.  If Google Image Search needed to tag an image, it could just pop it up in a window for 500 ms and read your thoughts to get the tag.

Work needs to be done in getting the system to discriminate between more kinds of images and making it more accurate.  Challenges are posed by the coarseness of the data that is gathered by EEGs (after all they are reading your brain from all the way up on your scalp), and scientists’ currently weak ability to interpret what brain scans mean.

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Chris Harrison and Prof. Scott Hudson at Carnegie Mellon have developed a simple technology that turns touch screen buttons into physical buttons by using pneumatics.

The technology consists of a flexible surface area with a hard backing that acts as a mask for the button shapes.  An air chamber behind the backing can be pressurized or depressurized using pneumatic technology, in this case fan-based pumps.

When positive pressure is applied, the buttons pop out.  When the pressure is neutral, the screen is flat.  When negative pressure is applied, the buttons pop inwards.

Images are displayed on the surface using a projector behind the device, turning the surface into a display screen.  Button presses are detected using an infra-red camera pointed at the front of the screen that detects reflections of light from a fingernail.  When your fingernail gets close to the screen, a button press is recorded.  This technology cannot easily distinguish between a finger touching the screen and one merely close to the screen, so a press is not recorded until the finger presses down on the surface and causes a detectable change in pressure in the air chamber.

It is also possible to have the positive and negative forms take different shapes.  Additional parts are added to the mask, except these parts have no adhesive holding the latex down.  When positive pressure is applied, only the mask parts with adhesive are effective.  When negative pressure is applied, all the mask parts are effective.

See this excellent video demonstration.

Applications

Instead of pressing images that look like buttons on your phone, this technology could allow all the dynamically drawn button images on your phone to actually pop out like real buttons.  (One change that would be needed is making touch sensing based on capacitance technology, as current touch screens do, rather than a camera aimed at the screen.)

The pop-inwards feature is also pretty cool.  You could be playing a driving game on your cell phone and have the car’s dashboard pop inwards to appear like a real car’s.  Or, when you displayed the stopwatch on your phone, it could be displayed like a real concave stopwatch.

Limitations

With a single air chamber, all the buttons must popped in or out at once.  However, it is straightforward to create separate air chambers, thereby allowing only certain elements of the UI to pop in or out.

An unavoidable limitation is that the mask itself is static, meaning that new shapes cannot be created dynamically.  The technology only allows controlling whether the shapes pop in, pop out, or remain flat.

Comment: When do you think pneumatic technology like this will turn the flat touch screen buttons on our phones into physical buttons?

1. 2 years
2. 5 years
3. 10+ years
4. Never

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Regular mice only allow movement via the palm and the clicking of a left, right, and middle mouse button.

Why not detect inputs from all 5 fingers all over and around the surface of the mouse?  Multi-touch sensing technology for touch screens has allowed detection of multiple simultaneous presses all over a display screen.  A multi-touch mouse simply applies multi-touch technology on a mouse.

This article will compare the Apple and Microsoft offerings.

Apple: Magic Mouse

The Magic Mouse is a straightforward application of multi-touch to mousing and is a tame improvement compared with Microsoft’s more radical designs.  The Magic Mouse is based on a regular mouse body but covers the top with capacitive sensors.  These sensors are the same that would be used in standard touch screens, like on the iPhone.

These sensors detect where your fingers are on the mouse and whether you are pressing down on the mouse.  So one of the new things you can do is perform a “click” by pressing anywhere on the mouse.  Also, you can scroll windows both vertically and horizontally by swiping your fingers up and down or left and right, respectively.  Finally, you can scroll through different files by swiping with two fingers.

It is not clear if the Magic Mouse can detect more than two fingers at a time. Currently, only gestures using one or two fingers are used in Apple’s applications; however, the Magic Mouse is capable of detecting at least five fingers simultaneously.

The video below demonstrates all these features.

Microsoft: Five Prototypes

Researchers at Microsoft developed 5 prototype mice with multi-touch technology.  The researchers used multiple designs to experiment with bolder possibilities.

One of the designs was almost identical to the Magic Mouse.  Prototype three (the “Cap Mouse) also uses capacitive sensors.  It is demonstrated at 1:58 in the following video.  The researchers said that in user tests the similarity between Cap Mouse and existing mice caused testers to use it in a traditional way and not take advantage of all the advantages of multi-touch.

All five are demonstrated in the following video.

Video Gaming

As shown at 1:30 in the video, the second prototype (the “Orb Mouse”) has special features for navigation in a 3D first-person shooter.  The Orb Mouse consists of a standard mouse with an infrared camera mounted inside and pointed at a hemispherical mirror, which allows it to see the entire surface of the mouse.  It can detect exactly where all your fingers are touching the mouse.

Moving the body of the mouse moves your character forward and backward and turns you left and right.  Sliding your fingers sideways on the surface of the mouse causes you to strafe.  Moving your fingers up and down causes you to jump.  Rolling your palm on the surface of the mouse causes you to lean left and right.

In this way, everything you used to need a keyboard and mouse for you can now do with just a mouse.

3D Manipulation

At 4:20, the fifth prototype (the “Arty Mouse” short for “Articulated Mouse”) is used to show multi-touch can make it easier to manipulate 3D objects.  The Arty Mouse has two articulated arms.  It works by having three optical sensors, one under the body as usual and one under each arm.  It does not detect touches on the body of the mouse itself.  The researchers reported that the Arty Mouse received the best reviews from the testers out of all five mice.  It was reported to be the easiest to use and most physically comfortable.

If you move the right arm in and out it spins the 3D object vertically, but if you move the left arm back and forth it spins the object horizontally.  Twisting the two arms around each other will spin the object in the plane.

Scaling

Another interesting application of the mice is scaling images.  This is demonstrated at 3:25 in the video using the fourth prototype (the “Side Mouse”).  The Side Mouse consists of a standard mouse with an infrared camera viewing the sides around the mouse.  It does not detect the position of your fingers on the mouse itself but their position around the mouse.

The video shows how scaling out is performed by pressing down with two fingers and moving them apart.  Scaling in is performed by moving the two fingers closer together.  This is much like on the iPhone, but this mouse would allow you to do it on your computer.

Microsoft Surface on Your PC

At 0:37, the first prototype (the “FTIR Mouse”) is used to show how Microsoft Surface applications can be controlled on the PC with multi-touch.  This mouse detects multi-touch using Frustrated Total Internal Reflection.  Infrared light is shot through a transparent acrylic surface where the user puts his fingers.  When a finger touches the acrylic it scatters the infrared light.  A camera in the body of the mouse detects the scattering and can determine where your fingers are.

The simple demonstration in the video shows how multi-touch detection allows controlling applications for the Microsoft Surface on your PC.  The Surface is a table-size device controlled via a standard multi-touch screen.  With a multi-touch mouse, the Surface’s touch screen control system can be simulated.

COMMENT BELOW: Which multi-touch mouse do you want the most?  Or are they all gimmicks?

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