Archive for February, 2013|Monthly archive page

Value of Scientific Work

In Education, Interdisciplinary Science, Research and Development on February 21, 2013 at 11:03 PM

We, researchers and non-researchers alike, often come across this question: what is the value of scientific work? Is it about publishing papers that expound upon novel ideas? Is it about work that can be commercialized? Is theoretical scientific work more valuable than practical scientific work? Are hands on abilities more valuable than critical thinking? These are perplexing questions with no straight forward answer. Probably, the best way to think of these is to understand that there is space in this world for all. Different approaches have their pros and cons. I think that it is even better to answer them in some context rather than after untying them from any context.

I have seen graduate students, especially PhD students, grappling with the value of their work in the beginning and then towards the end of their PhD. Many find their work of not much value compared to work that translates into something tangible, something that you can touch or see or hear; something that people can use; something that makes them feel that they have created something that did not exist.

Nevertheless, the use of an idea and the idea itself are two different things. They each deserve their own attention. It is also possible that a use case for an idea may not be realized in the immediate future once the idea was formulated. It may take time for the use case to appear and it may not be the same person who developed the idea. For instance, the inventor of laser would have probably never imagined that one day it would be used in laser pointers which we often use during presentations.

I think that when we question ourselves in this way, we need to go back in time and see what the savants of the field have said about these. One great piece is titled “The Value of Science” by Richard P.  Feynman, Physics Nobel Laureate (1965) and both a researcher and a teacher par excellence.  It is so rare to find an excellent researcher who is also an excellent teacher these days. The following paragraph from his public speech will probably be of immense interest to young researchers:

“I would now like to turn to a third value that science has.  It is a little less direct, but not much.  The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of very great importance, I think. …………….  We have found it of paramount importance that in order to progress we must recognize our ignorance and leave room for doubt.  Scientific knowledge is a body of statements of varying degrees of certainty – some most unsure, some nearly sure, but none absolutely certain.”

For a funny and amusing reflection on the different kinds of researchers today ;), see this by Daniel Lemire, a  computer scientist with a big following.

Role of Industrial Consortia in Education and Research

In Education, Embedded Systems, Industrial Consortia, Research and Development on February 8, 2013 at 6:58 PM

A Google search will reveal the existence of quite a few influential industrial consortia further the cause of research and education in fields identified by them. Almost all of them are run jointly by people from industry and prominent educational and research institutions. You can find a list of them compiled here. I have listed only the ones relevant to electronics and computer industries. I have found that not many students are aware of these consortia and that should not be the case. Some of these are highly active and they contribute a lot to research, development of technology and education. Consortia like Accellera Systems Intiative have contributed to a number of IEEE standards. Some of these can be downloaded for free from its website. The Semiconductor Research Association plays an important role in promoting research and education in the field of semiconductors. The International Technology Roadmap for Semiconductors has played an immense role in identifying challenges before the semiconductor industry- from design to manufacturing, to testing and validation. Many of these associations also offer scholarships and fellowships for students and research grants for faculty members. Their publications provide a lot of insight regarding the challenges at present and of the future. These publications may not always have a lot of in depth research material, the sort of which most graduate students are accustomed to, but they successfully paint the bigger picture. Paying attention to such facts can help in keeping research relevant to industry where necessary. Besides, it also helps in learning about the actual real world problems and the challenges involved in translating research into technology that can be scaled up and widely used. Sometimes, problems are considered solved in academic research but such solutions never make it to the market, even if of relevance, because their translation to scalable technology still remains an open problem.

What is there in the word “distance”?

In Education, Mathematics on February 6, 2013 at 9:32 PM

I fist learned about distances in school- class of classical geometry. Of course it was not called classical geometry in school but only geometry. The initial concepts were related to distances between points in a plane, between lines in a plane and between a line and a point in a plane. A plane, as you know, is a  2-dimensional (2D) space. In high school, this concept was extended to 3-dimensional space (3D). The concept of distance basically gives an idea of how far (or how close) are two things (lines, points) from (to) each other. What I learned was “2-norm distance” (the typical Euclidean distance):

2-norm distance = \sqrt{(\displaystyle\sum_{i=1}^{n} |{x_{i}}^2 - {y_{i}}^2|)}

I learned about Hamming Distance during my undergraduate courses on electronics communication. However, it is only during research that I learned a lot more about distances. My first surprise came when I heard about distances in a class on image processing. You can use distances to measure similarity between images! Of course the definitions and methods to calculate those distances were also different. Since then I have learned about distances being one major way of identifying similarities between objects or classes of objects. The central idea behind all these different kinds of distances (not just in image processing) remains the same: to measure  how far the objects are from each other in some respect. For instance, in psychoanalysis “Emotional Distance” is the degree of emotional detachment from some person or events; Czekanovski-Dice distance is used to compare two audio waveforms x and y in time domain etc. If your distance from the world of distances is not big ;), you might want to try reading the Dictionary of Distances.

Experiments in Computer Science/Engineering?

In Design Methodologies, Education, Embedded Systems on February 1, 2013 at 1:35 AM

A friend of mine, who is doing a project on implementing various image processing algorithms (like edge detection, adding colors etc.), was asked by the concerned supervisor to conduct experiments as part of the work. This friend then asked me what the supervisor meant by experiments in this particular case. I was taken aback initially because I had not come across the term experiment being associated with computer science/engineering in a case where the principal job was to implement some algorithms  already developed by someone else and package the implementation as a software. Here, there is no hypothesis to be tested which is an integral part of any experimental science or approach! If the student’s job had been to choose an edge detection algorithm for implementation, by controlled experiments using different kinds of edge detection techniques on the same kind of workload, then that would have  qualified as an experiment.

Nevertheless, I made some suggestions: examine the execution time of the developed software package as the input image size changes; test if there is any dependency based on the image format; test the performance (visual perception of quality of result, execution time) as the amount of information varies across images ( for example an image with a few straight lines/curves with a few orientations vs. an image with hundreds of straight lines/curves with varying orientations). I do not know if the concerned supervisor meant this or something else or the term was used in a loose way to refer to software testing.

However, I decided to explore this topic a little bit more. I found that Stanford University has a graduate level course titled Designing Computer Science Experiments. An excellent paper on what is experimental computer science by Peter J. Denning, a former ACM President, was published in 1980 and can be found here. A good repository of resources is maintained by Prof. Dror Feitelson of Hebrew University, Israel here. Researchers in the field of computer architecture theorize (make a hypothesis) and do a lot of experiments to test their theory.  For instance, people work on different kinds of FPGA architectures to see their benefits and drawbacks. The essential point is that in an experimental approach, one states a hypothesis, conducts experiments and then analyzes the data generated as a result of experiments to test the hypothesis.