Monday, August 21, 2017

biology of color

The 4 August issue of Science (Vol. 357, Issue 6350, eaan0221) has a valuable article on the biology of color describing the current state of the art in this interdisciplinary field of animal coloration. This article is important because in the past 20 years there has been significant progress in this field.
mantis shrimp

Thursday, August 17, 2017

Critical thinking in a changing world

Monday evening, Gioia Deucher, the new CEO of swissnex San Francisco on Pier 17, hosted a double event on critical thinking. The first event was only for ETH alumni and consisted of networking followed by a speech by ETH President Lino Guzzella and a general discussion. Prof. Guzzella noted that in recent years, students have changed and despite social media have become much nerdier and socially isolated. Consequently, the ETH has to change how it teaches.

As a professor of mechanical engineering, Guzzella does not expect any new breakthroughs in the physics for building mechanical equipment. What is more important for a mechanical engineer is to understand the context requiring a new machine and grasp the problem holistically and proposing a new approach.

The human genetic code has not changed over the ages and is still the same as for hunter gatherers. Critical thinking is essential, but it is hard to criticize oneself: we are dependent on a group that mutually criticizes and debates.

This autumn, the ETH is introducing significant changes. In teaching, the emphasis will be more on understanding and solving problems than on learning. Students will have the option for project-oriented study and more personal coaching with group study. In the study directions, the ETH is starting a new department of medicine, which will allow a proper medical study. Initially, the new department will only go until the bachelor level, after which students can transfer directly to a Swiss university with a medicine program or change to a more traditional ETH department like bioinformatics. As we live longer and longer, significant medical progress is necessary to maintain life quality into the old age.

When a question came about ETH's plans for massive open online courses (MOOC), Prof. Guzzella stated that they go counter the new direction to foster critical thinking and team work: students need physical proximity and a shared experience to become extraordinary people.

The public second event, which had unexpectedly high attendance, started with lightning talks and a panel discussion, followed by a discussion with the audience and finally a standing dinner with animated discussions and networking.

The speakers were Lino Guzzella, President of ETH Zurich and Professor for thermotronics; Gerd Folkers, Chair Science Studies and Critical Thinking Initiative at ETH and former Head of the Collegium Helveticum, a joint think-tank of ETH and University of Zurich; Hans Ulrich Gumbrecht, Professor in Literature in the Departments of Comparative Literature and of French & Italian at Stanford; Philippe Kahn, the CEO of Fullpower, the creative team behind the Sleeptracker IoT Smartbed technology platform and the MotionX Wearable Technology platform. The moderator was Chris Luebkeman, Arup Fellow and Global Director of Arup Foresight.

There was a consensus that to contribute to the wellness and progress of society, and it is indispensable to excel in critical thinking and bring about paradigm shifts. There is no point for a bright mind to just do repetitive intellectual tasks like at the Academy of Projectors. Critical thinking requires a fertile environment, therefore creating groups and projects is more important than promoting individual excellence.

Publications are a very bad metric. A paper needs the unpaid work of three reviewers and is expensive regarding social costs, yet 52% of publications are never cited and consequently have no value because they do not contribute to society.

Excellence in research requires freedom and money. Professors should not be told which research to conduct and should not waste time chasing grants. Science is for the good of society and society should fund research and tuition at universities (I never had to pay a penny of tuition for my diploma in mathematics and my doctorate in informatics). Critical thinking is what prevents the Lagado of Gulliver's third voyage: a habitat for scientists critically thinking in a changing world instead of an Academy of Projectors.

When Stanford wanted to introduce the option for STEM students to major or minor in literature, Prof. Gumbrecht was the strongest opponent. However, after the first year, he now realized that his best students had all come from STEM and he has become a strong advocate for the program.

speculative learning machine at the Academy of Projectors in Lagado

Thursday, July 6, 2017

Well-being in the San Francisco Bay Area

At the University of Pennsylvania’s Positive Psychology Center, Martin Seligman and more than 20 psychologists, physicians, and computer scientists in the World Well-Being Project used machine learning and natural language processing to sift through Twitter. They have been able to rank each of the 3235 U.S. counties according to well-being, depression, trust, and five personality traits.

For the Bay Area, the rankings are:

San Francisco
San Mateo
Santa Clara
Santa Cruz
Contra Costa

If you live in the U.S., you can check your county in their online map. For example, Kings County in New York ranks 448, while the District of Columbia ranks 49.

How is your well-being?

well-being ranks of Bay Area counties

Tuesday, July 4, 2017

Imaging and Astronomy

At the 2018 IS&T International Symposium on Electronic Imaging (EI 2018), taking place 28 January – 1 February 2018 at the Hyatt Regency San Francisco Airport, Burlingame, California, Prof. Daniele Marini is organizing a joint session on imaging and astronomy.

This new session brings together amateur and professional astronomers, vision scientists, color scientists, astrophysicists, data visualization specialists and all others with interest in astronomy and photography. Astronomers and others interested are invited to submit papers considering different aspects of digital imaging that are relevant for astronomical imaging, image processing, and data visualization, e.g., including color reproduction, display, quality, and noise.

We anticipate that the astronomical imaging community will have an exceptional opportunity to connect with digital imaging professionals and exchange experiences. If your work in the field of photography of astronomic subjects, We would be delighted to have you as a speaker discussing your work. Please use this link for your submission.

Rob Buckley, Shoji Tominaga, and Daniele Marini

Daniele Marini (right) receives the IS&T Fellow award with Shoji Tominaga (center) and Rob Buckley (left).

Tuesday, June 20, 2017

Regenerating optic pathways

Less than a mile down Embarcadero Road from Newell Road is Stanford University's Ophthalmology department. In Science Vol. 356, Issue 6342, pp. 1031–1034, three researchers from the School of Medicine report on the current status in retinal ganglion cell (RGC, pink in the figure) regeneration. When the optic nerve is severed, for example after an accident or with glaucoma, the retinal ganglion cells quickly die off. Even if the rest of the retina remains intact, sight is lost.

The retinal ganglion cells are part of the central nervous system, thus unlike in the peripheral nervous system, severed axons do not regenerate. After injury and inflammation, in the eye, there is a balance of activating and inhibiting factors. For example, amacrine cells (orange in the figure) release zinc, which is an inhibitor, while the lens can cause macrophages to release oncomodulin, a protein that promotes RGC axon extension. The challenge is to understand these balancing mechanisms. A further challenge is to regrow the axons correctly all the way to the lateral geniculate nucleus (LGN).

The authors outline three possible avenues for restoring the RGCs and thus sight.

retinal ganglion and amacrine cells in the retina

Monday, May 22, 2017

To bees, edges are green

In color science, we like to start from spectral data. To obtain the relative response in a photoreceptor, we multiply the reflectance function of a stimulus with the illuminant spectrum and then integrate over the visual spectral range using the receptor's spectral sensitivity function as the integration measure, up to a normalization factor.

Most often, what changes are the stimuli. Sometimes, we change the illuminant to predict the response under a different light source. When we study the response of people with color vision deficiencies, we swap the spectral sensitivity functions, for example, we shift the peak frequencies of the M or L catch probabilities to simulate deuteranomaly respectively protanomaly. For people with normal color vision, the standard values for the peak sensitivities are approximately 430 nm (S-cones), 540 nm (M-cones), and 570 nm (L-cones).

The approach is not limited to humans. For example, bees also have three receptors, with peak sensitivities at 344 nm (S), 436 nm (M), and 544 nm (L): their visual spectrum is shifted towards the ultraviolet. If we taught color naming to bees, their red would correspond to our green. Actually, looking at the honeybee (Apis mellifera) sensitivity functions, their color vision is different from ours because they have a secondary peak in the UV region. With only 10,000 ommatidia, their vision also has a much lower spatial resolution.

spectral sensitivity functions of the honeybee (Apis mellifera) receptors

In their paper Multispectral images of flowers reveal the adaptive significance of using long-wavelength-sensitive receptors for edge detection in bees, Vera Vasas et al. use a collection of multispectral photographs of flowers preferred by bees.

Assuming bees and flowers coevolved to maximize pollination, the authors perform an interesting statistical analysis of what bees would see, to determine under what condition they can best recognize the flower's center areas where the nectar and the stamens/carpels are located. An important boundary condition is that the process has to work in the presence of movement, as flowers are swayed by Zephyr.

The statistical analysis suggests that bees use only the L-receptors to identify edges and segment the visual field and detect movement. This process is different from us humans who use the M- and L-receptors to analyze an essentially monochromatic image.

Citation: Vasas, V., Hanley, D., Kevan, P.G. et al. J Comp Physiol A (2017) 203: 301. doi:10.1007/s00359-017-1156-x

Monday, April 24, 2017

Juggling Tools

Discussions about imaging invariably mention imaging pipelines. A simple pipeline to transform the image data to a different color space may have three stages: a lookup table to linearize the signal, a linear approximation to the second color space, and a lookup table to model the non-linearity of the target space. As an imaging product evolves, engineers add more pipeline stages: tone correction, gamut mapping, anti-aliasing, de-noising, sharpening, blurring, etc.

In the early days of digital image processing, researchers quickly realized that imaging pipelines should be considered harmful because, due to discretization, at each stage, the resulting image space became increasingly sparse. However, in the early 1990s, with the early digital cameras and consumer color printers, imaging pipelines came back. After some 25 years of experience, engineers have become more careful with the pipelines, but they are still a trap.

In data analytics, people often make a similar mistake. There are also three basic steps, namely data wrangling, statistical analysis, and presentation of the result. As development progresses, the analysis becomes richer; when the data is a signal, it is filtered in various ways to create different views, statistical analyses are applied, the data is modeled, classifiers are deployed, estimates and inferences are computed, etc. Each step is often considered as a separate task, encapsulated in a script that parses in a comma separated values (CSV) data file, calls one or more functions, and the writes out a new CSV file for the next stage.

The pipeline is not a good model to use when architecting a complex data processing endeavor.

I cannot remember if it was 1976 or 1978 when at PARC the design of the Dorado was finished and Chuck Thacker hand-wrote the first formal note on the next workstation: the Dragon. While the Dorado had a bit-sliced processor in ECL technology, the Dragon was designed as a multi-processor full-custom VLSI system in nMOS technology.

The design was much more complex than any chip design that had been previously attempted, especially after the underlying technology was switched from nMOS to CMOS. It became immediately evident that it was necessary to design new design automation (DA) tools that could handle such big VLSI chips.

A system based on full-custom VLSI design was a sequence of iterations of the following steps: design a circuit as a schematic, lay out the symbolic circuit geometry, check the design rules, perform logic and timing analysis, create a MOSIS tape, debug the chip. Using stepwise refinement, the process was repeated at the cadence of MOSIS runs. In reality, the process was very messy, because, at the same time, the physicists were working on the CMOS fab, the designers were creating the layout, the DA people were writing the tools, and the system people were porting the Cedar operating system. Just in the Computer Science Laboratory alone, about 50 scientists were working on the Dragon project.

The design rule checker Spinifex played a somewhat critical role, because it parsed the layout created with ChipNDale, analyzed the geometry, flagged the design rule errors, and generated the various input files for the logic simulator Rosemary and the timing simulator Thyme. Originally, Spinifex was an elegant hierarchical design rule checker, which allowed to verify all the geometry for a layout in memory. However, with the transition from nMOS to CMOS, the designers transitioned more and more to a partially flat design, which broke Spinifex. The situation was exacerbated by the endless negotiations between designers and physicists to allow for exceptions to the rules, leading to a number of complementary specialized design rule checkers.

With 50 scientists on the project, ChipNDale, Rosemary, and Thyme were also rapidly evolving. With the time pressure of the tape-outs, there were often inconsistencies in the various parsers. As the whipping boy in the middle of all this, one morning, while showering, I had an idea. The concept of a pipeline was contra naturam compared to the work process. The Smalltalk researchers on the other end of the building had an implementation process where a tree structure described some gestalt and methods would be written that decorate this representation of the gestalt.

In the following meeting, I proposed to define a data structure representing a chip. Tools like the circuit designer, the layout design tool, and the routers would add to the structure while tools like the design rule checkers and simulators would analyze the structure, with their output being further decorations added to the data structure. Even the documentation tools could be integrated. I did not expect this to have any consequence, but there were some very smart researchers in the room. Bertrand Serlet and Rick Barth implemented this paradigm and project representation and called it Core.

The power was immediately manifest. Everybody chipped in: Christian Jacobi, Christian Le Cocq, Pradeep Sindhu, Louis Monier, Mike Spreitzer and others joined Bertrand and Rick in rewriting the entire tool set around Core. Bob Hagman wrote the Summoner, which summoned all Dorados at PARC and dispatched parallel builds.

Core became an incredible game changer. While before there was never an entirely consistent system, now we could do nightly builds of the tools and the chips. Besides, the tools were no longer broken at the interfaces all the time.

The lubricant of the Silicon Valley are the brains wandering from one company to the other. When one brain wandered to the other side of the Coyote Hill, the core concept gradually became an important architectural paradigm that is on the basis of some modern operating systems.

If you are a data scientist, do not think in terms of scripts for pipelines connected by CSV files. Think of a core structure representing your data and the problem you are trying to solve. Think about literate programs that decorate your core structure. When you make the core structure persistent, think rich metadata and databases, not files with plain tables. Last but not least, also your report should be generated automatically by the system.

data + structure = knowledge

Thursday, April 20, 2017

Free Citizenship Workshop May 12

On May 12th the International Rescue Committee (IRC) is holding a free citizenship workshop hosted at and supported by Airbnb HQ located at 888 Brannan St. in San Francisco. The event starts at 1:30pm and ends at 4:30pm. Flyers are available online: English Flyer & Spanish Flyer. There will be free food and each client will be offered a $10 Clipper Card to help with transportation.

At the workshop, clients will get help from IRC to apply for citizenship (submit the N-400), submit a fee waiver request (it’s $725 to apply otherwise), and prepare for the naturalization test. All cases will be reviewed, filed, and expertly managed by an IRC Department of Justice accredited legal representative who will serve as clients legal representatives with USCIS, alert clients to updates in their cases, and provide them advice throughout the entire process. All services are free and it’s open to the public. Registration is required, but folks can choose to register online at or by phone at (408) 658-9206 or email Lots of options!

The International Rescue Committee (IRC) is an international non-profit organization founded in 1933 at the request of Albert Einstein. IRC is at work in more than 40 countries and 28 U.S. cities and each year its programs serve 23 million people worldwide.

Thursday, April 13, 2017

Computational Imaging for Robust Sensing and Vision

In the early days of digital imaging, we were excited about having the images in numerical form and not being bound by the laws of physics. We had big ideas and quickly ran for their realization. However, we immediately reached the boundaries of the digital world: the computers of the day were too slow to process images, did not have enough memory, and the I/O was inadequate (from limited sensors to non-existing color printers).

Now has finally come the time when these dreams can be realized and computational color imaging has become possible, thanks to good sensors and displays, and racks full of general purpose graphical processing units (GPGPUs) with hundred of gigabytes of primary memory and petabytes of secondary storage. All this, at an affordable price.

Wednesday, 12 April 2017, Felix Heide gave a talk at The Stanford Center for Image Systems Engineering (SCIEN) with the title Capturing the “Invisible”: Computational Imaging for Robust Sensing and Vision. He presented three implementations.

One application is image classification. In the last couple of years we have seen what is possible with deep learning when you have a big Hadoop server farm and millions of users who provide large data sets they carefully label, creating gigantic training sets for machine learning. Felix Heide uses Bayesian inference to implement a much better system that is robust and fast. It better leverages the available ground-truth and uses proximal optimization to reduce the computational cost.

To facilitate the development of new algorithms, Felix Heide has created the ProxImaL Python-embedded modeling language for image optimization problems, available from

computational imaging

Quantum imaging beyond the classical Rayleigh limit

A decade has passed since we were working on quantum imaging, as we reported in an article in the New Journal of Physics that was downloaded 2316 times. We had described the experimental set-up in a second article in Optics Express that was viewed 540 times. It is interesting that the second article was most popular in May 2016, indicating we were some 6 years ahead of time with this publication and over 10 years ahead when Neil Gunther started actively working on the experiment. The problem of coming too early is that it is more difficult to get funding.

Edoardo Charbon continued the research at the Technical University of Delft, where he built a true digital camera that used a built-in flash to create a three-dimensional model of the scene, and the sunlight to create a texture map of the image that could be mapped on the 3-d model. This is possible because the photons from the built-in flash—a chaotic light source that produces the photons from excited particles—and those from the sun—which is a thermal radiator (hot body)—have different statistics.

We looked at the first- and second-order correlation functions to tell the photons from the flash from those originating in the sun. Since the camera controlled the flash, the photon's time of flight could be computed to create the 3-d model. The camera worked well up to a distance of 50 meters.

I am glad that Dmitri Boiko is still continuing this line of research. With a group at the Fondazione Bruno Kessler (FBK) in Trento, Italy and a group at the Institute of Applied Physics at the University of Bern in Bern, Switzerland, he is working on a new generation of optical microscope systems by exploiting the properties of entangled photons to acquire images at a resolution beyond the classical Rayleigh limit).

Read the SPIE Newsroom article Novel CMOS sensors for improved quantum imaging and the open access invited paper SUPERTWIN: towards 100kpixel CMOS quantum image sensors for quantum optics applications in Proc. SPIE 10111, Quantum Sensing and Nano Electronics and Photonics XIV, 101112L (January 27, 2017).