I like working at a university. I like solving problems without known solutions. I like improving existing solutions. I like working in my own directions. I like working with students. I like advising students with their work. I even like giving lectures.

However, what I don’t like, at least currently, is writing publications of my scientific results, which, of course, are the primary part in preparing a scientific career. This process ist currently awful, tiring and frustrating.

This year I wrote six articles. Each time I invested much work into preparation and presentation. However, *none* of them will be published this year. Not a single one. I never had such a bad year before. And from a realistic point-of-view, this is like a death blow for an accademic career.

Of course, this is not nice in itself, but what is even more frustrating are the reviews with which my articles got rejected. One of my papers hat numeric scores (1 = worst, 5 = best score) of: 4 + 4 + 3 +3. Also, the reviewer’s comments are sadly often nonprofessional, useless, and even polemic. Another paper was loved by two of the four reviewers. The third one found it borderline, but found it could be improved. The forth and primary one, however, worte something which I can only understand as “I don’t like it”. His review had more text but not much more contant than that. Result: rejected.

I had some discussions about this issue of an overcritical reviewing with several professors. All were aware of this problem and all agreed that this happens once in a while to a community. Sadly, in my opinion it has gotten continuously worse in the last seven years I have been working and publishing (except for this year) in the visualization community. Honestly, I don’t known what will be …

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Not all points are equal. There are always fundamental misconceptions about the type of data I am working with in my visualization research.

I work with particle data. This data is usually the result of simulations, e.g. generated through the methods of molecular dynamics or a discrete element method. The individual particles represent independent elements, e.g. atoms or mass centers, which are neither connected nor correlated. Of course, within the simulation these particles interact and influence each other, but with the pure data which is available for visualization to me, there is no topological structure between the particles at all. Literature was several further names for this kind of data: point-based data, mesh-less data, and, maybe the best fitting one, scattered data. Technically speaking, these data sets are arbitrarily sorted lists of elements, each storing a position and optional additional attributes, like a sphere radius or a color. But that is it. There are no more information and in general you cannot make any assumptions about structures within the data.

bunnyPSP-OSSI now want to write about one very common misconception: there is the further data type of point clouds also called point-set surfaces. These data also consist of a list of points, which are at first not correlated. Typical sources of such data are point-based modeling in 3D computer graphics and, more common, scanning of real-world objects. Such scans would be created by laser scanners or structured-light scanners like the Kinect. Because of the fact that these data sets store a simple list of points, i.e. they are technically identical to particle data, results in the misconception of many people that these two types of data sets are identical. They are not.

Point clouds are discrete samplings of a continuous function, i.e. the surface of the scanned or modeled object. Therefore, all points reside, within some error margin, on this 2D surface embedded within the 3D space. This aspect is fundamentally different from particle data, in which the particles are freely placed throughout the 3D space. Almost every algorithm working with point-cloud data work with the assumption of the continuous surface represented by the points. As this assumption is not valid for particle data, these algorithms cannot be applied easily to this kind of data.

Well, obviously I have not published enough to make my colleagues in my field of science recognizes this difference. I am off then …

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For several years now, I write my smaller tools in C#. I like the language and the runtime framework is powerful. For that matter I do not care about platform independance. Or for Mono. I am a fan of Windows Forms. It is a nice and capable GUI tool kit, close to the traditional c++ world, but with good abstractions at the right places. And then, there is WPF.

I do not know what to think about WPF.

On the one hand, Microsoft claims WPF to be the future. Many new functions are introduced (first or only) to WPF. Windows 8 Apps and Windows Phone Apps require the programmer to use WPF for the GUI. The data binding is nicely done and cleanly integrated into the language, compared to Windows Forms.

On the other hand, WPF is not a perfect solution. The performance is not great, if the GUI gets complex or heavily modified. The editor integrated in Visual Studio is not very good. (Okey, there is Expression blend, but I do not like to use two editors for one project at the same time.)

What realy bothers me, however, is that even Microsoft has no clear policy concerning this issue. Visual Studio 2012 is written using WPF and this is the required GUI toolkit for Apps. However, for example, the new Office, although sharing the new Metro design, does not use WPF, but uses classical GUI toolkits recreating the same look-and-feel. All in all, I just do not know which one to bet on, Windows Forms or WPF. Probably, it really does not matter at all. But, somehow, I do not like the current situation.

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This was the last week of the lecture periode of this sommer semester. As research staff of the university, I, of course, do not hear lectures nor do I have to study for exams. Been there, done that. But, the lecture periode is important for my daily work. I supervise students, in lectures, in exercise courses, with their bachelor, master, or diploma theses. But now, in the lecture-free time, there is less of all of that. Therefore, traditionally, now also starts the vacation season for the university staff. I will continue working hard for two more weeks, to push some of my projects forward, mainly VICCI and MegaMol. But after that, it is time for a leave. I am looking forward to it.

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I moved to content of the Springerjagd website here onto my blog. It is just simpler only have to maintain a single website. The springerjagd website stays online, of course. For one thing, there is the redirection to my blog. For another thing I will publish the online game prototypes there.

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At the end of this week it was time for two important demo-days.

On Thursday the Output took place. Some kind of open house of the TU Dresden, at which scientific results and student’s works are presented to the public. Together with booths from industrial partners this mini-exhibition provides an overview of the output of the university. And this time, our demonstrators were particularly important. The junior research group VICCI, I am working in, is attached to the ResUbic lab, a cluster of multiple research groups working on the topics of Internet of things and cyber-physical systems. For many research groups within this is the last year of their funding period. Therefore, they understood output as their opportunity for a final presentation. So, they invited many important persons: industrial partners, press, and even government officials. Because of that, we put much effort into our demos, to make the best impression possible.

The best demo of VICCI, although I might be biased here, is the follower demo my colleagues Alex and André set up. It shows a combination of simple robot control and computer vision. To be more precise: the show a demo of object tracking. The robot is equiped with a camera, in our case a Kinect. With this sensor it lerns an object and then tracks this object within the camera’s image. In this photo we used a red coffee mug. The robot tries to keep a constant distant and a direct orientation towards this object. With this it is possible to remote control the robot by simply moving the mug correspondently.

Now, this demo is not that great because we can control a robot with a coffee mug. That is nice, but that is no rocket science. What is great is the object tracker. Alex uses a particle filter algorithms, based on probability distributions to assume the object position. This tracking is robust and very fast. A new object can be lerned within fractions of a second. Any object can be used, even bare hands.

On Friday the second demonstration came up directly after the Output: the long night of science (lange Nacht der Wissenschaft). We basically showed the same demonstrations as we did at the Output. Just some of the research groups of ResUbic did not want to. We did. We presented our work of VICCI to the broad public the whole evening and night, until the end of the day at 1 o’clock.

As conclusion I can say, that these two days were exhausting and that I am happy that this is finally over. Apart of that, our demonstrations were a complete success and the public liked what they saw.

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