September 21, 2017

An Infographical Survey of the Bitcoin Landscape


Josh Wardini sent me information on a new Bitcoin infographic that serves as a survey of events over the last 10 years in the world of Bitcoin development and legal regulation. Many interesting factoids in this graphic, some of which were unbeknownst to me. In the next few paragraphs, I will discuss my impressions that are brought to bear by each subset of factoids.




The relationship between blockchain and mining is an interesting one, and underscores the power of blockchain as both a data structure and a secure transaction system. Bitcoin is also its own economic system, complete with social interactions. In particular, the competitive and cooperative aspects of cryptocurrency can serve as a model for understanding the social structure of markets.







This is another interesting feature of bitcoin: the network has computational power to both unlock the value of existing blockchain as well as to create new currency. Bitcoin mining has always been a bit of a black box to me [1], but it seems as though it has potentially two roles in the bitcoin economy. In a Synthetic Daisies post from 2014, I mentioned that the supply of bitcoin is fixed (in the manner of a precious metals supply), but it turns out that it is not that simple. Of course, since then blockchain technology has become the latest hot emerging technology in a number of areas unrelated to Bitcoin and even the digital economy [2].



It turns out the computational systems (unlike people) is not all that hard to understand. However, digital currency, which is based on human systems, is much harder to understand (or at least fully appreciate). In 2013, I did a brief Synthetic Daisies mention of a flash crash on one of the main Bitcoin exchanges. There is a lot of opportunity to use blockchain and even perhaps cryptocurrency in the world of research. If ways are found to make these technologies more easily scalable, then they might be applied to many research problems involving human social systems [3].


NOTES:
[1] So I sought out a few introductory materials on Bitcoin mining to clarify what I did not know: 

a) startbitcoin (2016). Beginner's Guide to Mining Bitcoins. 99 Bitcoins blog, July 1.

* mining consists of discovery blocks in the blockchain data structure, the discovery of which is rewarded through a "bounty" of x bitcoins. From there, inequality emerges (or not).

b) Mining page. Bitcoin Wiki.

* the total number of blocks is agreed to by the community, as is the total amount of computational power of the network. This makes the monetary supply nominally fixed, but is not required by the technology.

c) Hashcash Algorithm page. Bitcoin Wiki.

Despite the clear metaphoric overtones, Bitcoin mining is essentially like breaking encryption in that it requires a massive amount of computing power thrown at a computationally hard problem, but is also has elements of an artificial life model (e.g. competition for blockchain elements).

Water-cooled rigs probably maximize your investment margin....

[2] Of course, there has been innovation in the use of blockchain for Bitcoin and more general cryptocurrency transactions. For more, please see:

Portegys, T.E. (2017). Coinspermia: a cryptocurrency unchained. Working Paper, ResearchGate, doi:10.13140/RG.2.2.33317.91360.

Brock, A. (2016). Beyond Blockchain: simple scalable cryptocurrencies. Metacurrency project blog, March 31.

[3] A few potential examples:

a) Data Management. 1  2




September 11, 2017

This Concludes This Version of Google Summer of Code


I am happy to announce that the DevoWorm group's first Google Summer of Code student has successfully completed his project! Congrats to Siddharth Yadav from IIT-New Delhi, who completed his project "Image Processing with ImageJ (segmentation of high-resolution images)".

Our intrepid student intern

His project completion video is located on the DevoWorm YouTube channel. This serves as a counterpart to his "Hello World" video at the beginning of the project. The official project repo is located here. Not only did Siddharth contribute to the data science efforts of DevoWorm but also contributed to the OpenWorm Foundation's public relations committee.

Screenshot from project completion video

As you will see from the video, a successful project proceeds by organizing work around a timeline, and then modifying that timeline as roeadblocks and practical considerations are taken into account. This approach resulted in a tool that can be used by a diverse research community immediately for data extraction, or build upon in the form of future projects. 

In terms of general advice for future students, communicate potential problems early and often. If you get hung up on a problem, put it aside for awhile and work on another part of the project. As a mentor, I encourage students to follow up on methods and areas of research that is most successful in their hands [1]. In this way, students can find and build upon their strengths, while also achieving some level of immediate success. 


NOTES:
[1] This seems like a good place to plug the Orthogonal Research Lab's Open Career Development project. In particular, check out our laboratory contribution philosophy.

August 25, 2017

Live streaming of Orthogonal Lab content

Research live-streaming: an experiment in content [1].

The Orthogonal Research Laboratory, in conjunction with the OpenWorm Foundation, is starting to experiment with live video content. We are using YouTube Live, and live streams (composed in Xsplit Broadcaster) will be archived on the Orthogonal Lab YouTube channel. The intial forays into content will focus on research advances and collaborative meetings, but ideas for content are welcome. 




NOTES:

[1] obscure reference of the post: a shot of Felix the Cat, whose likeness was used to calibrate early experimental television broadcasts.

August 3, 2017

War of the Inputs and Outputs


Earlier this Summer, I presented a talk on sub-optimal cybernetic systems at NetSci 2017. While the talk was a high-level mix of representational modeling and computational biology, there were a few loose ends for further discussion.


One of these loose ends involves how to model a biological system with boxes and arrows when biology is a multiscale, continuous process in both space and time [1]. While one solution is to add as much detail as possible, and perhaps even move to hybrid multiscale models, another solution involves the application of philosophy.

In the NetSci talk, I mentioned in passing a representational technique called metabiology. Our group has recently put out a preprint on the cybernetic embryo in which the level of analysis is termed metabiological. In a metabiological representation, the system components do not need to map isomorphically to the biological substrate [2]. Rather, the metabiological representation is a model of higher-order processes that result from the underlying biology.

From a predictive standpoint, this method is imprecise. It does, however, get around a major limitation of black box models -- namely what a specific black box is representative of. It makes more sense to black box an overarching feature or measurement construct than to constrain biological details to artificial boundaries.

A traditional cybernetic representation of a nonlinear loop. Notice that the boxes represent both specific (sensor) and general (state of affairs) phenomena.

The black box also changes through the history of a given scientific field or concept. In biology, for example, the black box is usually thought of as something to ultimately break apart and understand. This is opposed to understanding how the black box serves as a variable that interacts with a larger system. So it might seem odd to readers who assume a sort of conceptual impermanence by the term "black box".


A somewhat presumptuous biological example: in the time of Darwin, heredity was considered to be a black box. In the time of Hunt Morgan, a formal mechanism of heredity was beginning to be understood (chromosomes), but the structure was a black box. By the 1960s, we were beginning to understand the basic function and structure of genetic transmission (DNA and gene expression). Yet at each stage in history, the "black box" contained something entirely different. In a fast moving field like cell biology, this becomes a bit more of an issue.

A realted cultural problem in biology has involves coming to terms with generic categories. This goes back to Linnean classification, but more generally this applies to theoretical constructs. For example, Alan Turing's morphogen concept does not represent any one biological agent, but a host of candidate molecules that fit the functional description. Modern empirical biology involves specification rather than generalization, or precisely the opposite goal of theoretical abstraction [3].

The relationship between collective morphogen action and a spatial distribution of cells. COURTESY: Figure 4 in [4]. 

A related part of the black box conundrum is what the arcs and arrows (inputs and outputs) represent. Both inputs and outputs can be quite diverse. Inputs bring things like raw materials, reactants, free energy, sources of variation, components, while outputs include things like products, transformations, statistical effects, biological diversity, waste products, bond energy. While inputs and outputs can be broadly considered, the former (input signals) provide information to the black box, while the latter (output signals) provide samples of the processes unfolding within the black box. Inputs also constrain the state space representing the black box.

Within the black box itself, processes can create, destroy, or transform. They can synthesize new things out of component parts, which hints towards black box processes as sources of emergent properties [5]. Black boxes can also serve to destroy old relationships, particularly when a given black box has multiple inputs. Putting a little more detail to the notion of emergent black boxes involves watching how the black box transforms one thing into another [6]. This leads us to ask the question: do these generic transformational processes contribute to increases in global complexity?

Perhaps it does. The last insight about inputs and outputs comes from John Searle and his Chinese Room problem [7]. In his model of a simple message-passing AI, an input (in this case, a phrase in Chinese) is passed into a black box. The black box processes the input either by mere recognition or more in-depth understanding. These are referred to as weak and strong artificial intelligence, respectively [7]. And so it is with a cybernetic black box -- the process within the unit can be qualitatively variable, leading to greater complexity and potentially a richer representation of biological and social processes.

NOTES:
[1] certainly, systems involving social phenomena operate in a similar manner. We did not discuss social systems in the NetSci talk, but things discussed in this post apply to those systems as well.

[2] for those whom are familiar, this is quite similar to the mind-brain problem from the philosophy of mind literature, in which the mind is a model of thought and the brain is the mechanism for executing thought.

[3] this might be why robust biological "rules" are hard to come by.

[4] Lander, A. (2011). Pattern, Growth, and Control. Cell, 144(6), 955-969.

[5] while it is not what I intend with this statement, a good coda is the Sidney Harris classic "then a miracle happens..."


[6] 

[7] Searle, J. (1980). Minds, Brains, and Programs. Cambridge University Press, Cambridge, UK.

July 26, 2017

Battle of the (Worm) Bots


There is an fledgling initiative at the OpenWorm Foundation to build a worm robot. This post highlights some of the first steps towards this goal. OpenWorm Robots will be useful for educational purposes and movement experiments/validation. No soft robotics (or miniaturization) yet, but progress is also continually being made on the brains behind the bot. More videos are forthcoming, so stay tuned. Thanks go to Dr. Tom Portegys and Shane Gingell for their efforts.


This is the latest version of the OpenWorm bot, as shown on the OpenWorm YouTube channel.


This is Shane's first iteration, called RoboWorm. Note the sinusoidal movement, and then compare to a biological C. elegans.

July 16, 2017

Wandering Towards an Essay of Laws


The winners of the FQXi "Wandering Towards a Goal" essay contest have been announced. I made an entry into the contest (my first FQXi contest entry) and did not win, but had a good time creating a number of interesting threads for future exploration. 

The essay itself, "Inverting the Phenomenology of Mathematical Lawfulness to Establish the Conditions of Intention-like Goal-oriented Behavior" [1], is the product of my work in an area I call Physical Intelligence in addition to intellectual discussions with colleagues (acknowledged in the essay). 

I did not have much time to polish and reflect upon the essay at the time it was submitted, but since then I have come up with a few additional points. So here are a few more focused observations extracted from the more exploratory essay form:

1) there is an underappreciated connection between biological physics, evolution, and psychophysics. There is an subtle but important research question here: why did some biological systems evolve in accordance with "law-like" behavior, while many others did not? 

2) the question of whether mathematical laws are discovered or invented (Mathematical Platonism) may be highly relevant to the application of mathematical models in the biological and social sciences [2]. While mathematicians have a commonly encountered answer (laws are discovered, notation was invented), an answer based on discovering laws from empirically-driven observations will likely provide a different answer.

3) how exactly do we define laws in the context of empirical science? While laws can be demonstrated in the biological sciences [3], biology itself is not thought of as particularly lawful. According to [4], "laws" fall somewhere in-between hypotheses and theories. In this sense, laws are both exercises in prediction and part of theory-building. Historically, biologists have tended to employ statistical models without reference to theory, while physicists and chemists often use statistical models to demonstrate theoretical principles [5]. In fields such as biology or the social sciences, the use of different or novel analytical or symbolic paradigms might facilitate the discovery of lawlike invariants.

4) the inclusion of cybernetic principles (Ashby's Law of Requisite Variety) may also bring together new insights on how laws occur in biological and social systems, and whether such laws are based on deep structural regularities in nature (as argued in the FQXi essay) or the mode of representating empirical observations (an idea to be explored in another post).

5) Aneural cognition is something that might guide information processing in a number of contexts. This has been explored further in another paper from the DevoWorm group [6] on the potential role of aneural cognition in embryos. It has also been explored in the form of the free-energy principle leading to information processing in plants [7]. Is cognition a unified theory of adaptive information processing? Now that's something to explore.


NOTES:
[1] A printable version can be downloaded from Figshare (doi:10.6084/m9.figshare.4725235).

[2] I experienced a nice discussion of this issue during an recent NSF-sponsored workshop. The bottom line is that while the variation typical of biology often makes the discovery of universal principles intractable, perhaps law discovery in biology simply requires a several hundred year investment in research (h/t Dr. Rob Phillips). For more, please see:

Phillips, R. (2015). Theory in Biology: Figure 1 or Figure 7? Trends in Cell Biology 25(12), 1-7.

[3] Trevors, J.T. and Saier, M.H. (2010). Three Laws of Biology. Water Air and Soil Pollution, 205(S1), S87-S89.

[4] el-Showk, S. (2014). Does Biology Have Laws? Accumulating Glitches blog, Nature Scitable. http://www.nature.com/scitable/blog/accumulating-glitches/does_biology_have-laws

[5] Ruse, M.E. (1970). Are there laws in biology? Australasian Journal of Philosophy, 48(2), 234-246. doi:10.1080/00048407012341201.

[6] Stone, R., Portegys, T.E., Mihkailovsky, G., and Alicea, B. (2017). Origins of the Embryo: self-organization through cybernetic regulation​. Figshare, doi:10.6084/m9.figshare.5089558.

[7] Calvo, P. and Friston, K. (2017). Predicting green: really radical (plant) predictive processing. Journal of the Royal Society Interface, 14, 20170096.

July 2, 2017

Excellence vs. Relevance

Impetus for this blog post. Twitter h/t to Alex Lancaster (@LancasterSci).

In academia, the term excellence is often used in the context of scarcity and competitive dynamics (e.g. publications, career promotion), and as a result can be used quite arbitrarily [1]. In [1], a distinction is made between excellence and soundness. Excellence is seen as a subjective concept, while soundness (enabled through completeness, thoroughness, and an emphasis on reproducibility) is the adherence to clearly defined and practiced research standards. While it may also be true that the concept of soundness can suffer from the same subjective limitations, it is probably an improvement over the current discussions surrounding excellence.

Another term we rarely refer to, but may be of even greater importance, is the relevance of scientific research. In a previous post, I brought relevance theory to bear on potential biases in scientific selectivity. One way to think of relevance is as the collective attentional focus of a given research group, community, or field. Collective attention (and thus relevance) can change with time: papers, methods, and influences rise and fall as research ideas are executed and new information is encountered [2]. As such, relevance defines the scope of scientific research that defines a particular field or community of researchers. Given a particular focus, what is relevant defines what is excellent. In this case, we return to the biases inherent in excellence, but this time with a framework for understanding what it means in a given context.

There is also an interesting relationship between soundness and relevance. For example, the stated goal of venues like PLoS One and Scientific Reports is to evaluate manuscripts based on methodological soundness rather than merely on field-specific relevance. To some extent this has eliminated issues of arbitrary selectivity, yet reviewers and editors from various fields may still surruptitiously impose their own field-specific conventions to the review process. Interestingly, soundness itself can be a matter of relevance, as the use of specific methodologies and modes of investigation can be highly field-specific.

Sometimes relevance is a matching problem between an individual researcher and the conventions of a specific field. Relevance can be represented as a formalized conceptual problem using skillset geometries [3]. In the example below, I have shown how the relevance of a specific individual overlaps with what is considered relevant in a specific field. In this case, the researcher has expertise in multiple areas of expertise, while the field is deeply rooted in a single domain of knowledge. The area of overlap, or Area of Mutual Relevance, describes the degree of shared relevance between individual and community (sometimes called "fit").


How relevant is a single person's skillset in the context of a research community, and how do we leverage this expertise in an inclusive manner? The mutual relevance criterion might provide opportunities in cases where there seems to be a "lack of fit". Understanding the role of collective attention within research communities might allow us to consider how this affects both the flow of new ideas between fields and the successful practice of interdisciplinarity.


NOTES:
[1] Moore, S., Neylon, C., Eve, M.P., O'Donnell, D.P., and Pattinson, D. (2016). Excellence R Us: university research and the fetishisation of excellence. Palgrave Communications, 3, 16105. doi:10.1057/palcomms.2016.105.

[2] Wu, F. and Huberman, B.A. (2007). Novelty and collective attention. PNAS, 104(45), 17599-17601. doi: 10.1073/pnas.0704916104.

[3] First introduced in: Alicea, B. (2017). A peripheral Darwin Day post, but centrality in his collaboration graph. Synthetic Daisies blog, February 16.

June 18, 2017

Loose Ends Tied, Interdisciplinarity, and Consilience

LEFT: A network of scientific disciplines and concepts built from clickstream data. RIGHT: Science mapping based on relationships among a large database of publications. COURTESY: Figure 5 in [1] (left) and SciTech Strategies (right).

Having a diverse background in a number of fields, I have been quite interested in how people from different disciplines converge (or do not converge) upon similar findings. Given that disciplines are often methodologically distinct communities [2], it is encouraging when multiple disciplines can exhibit consilience [3] in attacking the same problem. For me, it is encouraging because it supports the notion that the phenomena we study are derived from deep principles consistent with a grand theorizing [4]. And we can see this is areas of inquiry such as learning and memory, with potential relevance to a wide variety of disciplines (e.g. cognitive psychology, history, cell biology) and the emergence of common themes according to various definitions of the phenomenon.

Maximum spanning tree of disciplinary interactions based on the Physics and Astronomy Classification Scheme (PACS). COURTESY: Figure 5 in [5].

The ability to converge upon a common set of findings may be an important part of establishing and maintaining coherent multidisciplinary communities. Porter and Rafols [6] have examined the growth of interdisciplinary citations as a proxy for increasing interdisciplinarity. Interdisciplinary citations tend to be less common than within-discipline citations, while also favoring linkages between closely-aligned topical fields. Perhaps consilience also relies upon the completeness of literature inclusion for people from different disciplines in an interdisciplinary context. Another recent paper [7] suggests that more complete literature citation might lead to better interdisciplinary science and perhaps ultimately consilience. This of course depends on whether the set of evidence itself is actually convergent or divergent, and what it means for concepts to be coherent. In the interest of not getting any more abstract and esoteric, I will leave the notion of coherence for another post.


NOTES:
[1] Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M.A., and Balakireva, L. (2009). Clickstream Data Yields High-Resolution Maps of Science. PLoS One, 4(3), e4803. doi:10.1371/journal.pone.0004803.

[2] Osborne, P.  (2015). Problematizing Disciplinarity, Transdisciplinary Problematics. Theory, Culture, and Society, 32(5-6), 3–35.

[3] Wilson, E.O. (1998). Consilience: the unity of knowledge. Random House, New York.

[4] Weinberg, S. (1993). Dreams of a Final Theory: the scientist's search for the ultimate laws of nature. Vintage Books, New York.

[5] Pan, R.J., Sinha, S., Kaski, K., and Saramaki, J. (2012). The evolution of interdisciplinarity in physics research. Scientific Reports, 2, 551. doi:10.1038/srep00551.

[6] Porter, A.L. and Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719.

[7] Estrada, E. (2017). The other fields also exist. Journal of Complex Networks, 5(3), 335-336.

June 5, 2017

"Hello World", project version

The DevoWorm group has two new students that will be working over this summer on topics in computational embryogenesis. To begin their projects, I have asked each student to prepare a short presentation based on their original proposal, which serves as a variant of the traditional "Hello World" program. We will then compare this talk with one they will give at the end of the summer to evaluate their learning and accomplishment trajectory.

One student (Siddharth Yadav, who is a current Google Summer of Code student) is interested in pursuing work in computer vision, machine learning, and data science, while the other (Josh Desmond, a Google Summer of Code applicant) is interested in pursuing work in computational biology and modeling/simulation. You may view their presentations (about 20 minutes each) below, and follow along with their progress at the DevoWorm Github repository [1].

Siddharth Yadav's project talk  YouTube

Josh Desmond's project talk  YouTube

NOTES:
[1] Siddharth's project repo (GSoC 2017) and Josh's project repo (CC3D-local).

May 18, 2017

Innovation, Peer Review, and Bees

This post was inspired by a couple of Twitter conversations by people I follow, as well as my own experience with peer-review and innovation. The first is from Hiroki Sayama, who is contemplating a range of peer review opinions on a submitted proposal.


I like the using the notion of entropy to describe a wide range of peer-review opinions based on the same piece of work. This reminds me of the "bifurcating opinion" phenomenon I sketched out a few years ago [1]. In that case, I conceptually demonstrated how a divergence of opinion can prevent consensus decision-making and lead to editorial deliberation. Whether this leads to subjective intervention by the editor is unclear and could be addressed with data.

Hiroki points out that "high-entropy" reviews (wider range of opinions) represent a high degree of innovation. This is an interesting interpretation, one which leads to another Twitter conversation-turned complementary blog posts from Michael Neilsen [2] and Julia Galef [3] on the relationship between creativity and innovation.


In my interpretation of the conversation, Michael point out that there is a tension between creativity and rational thinking. On one side (creativity) we have seemingly crazy and irrational ideas, while on the other side we have optimal ideas given the current body of knowledge. In particular, Michael argues that the practice of "fooling oneself" (or being overly confident of the novel interpretation) is critical for nurturing innovative ideas. An overconfidence in conventional knowledge and typical approaches both work to stifle innovation, even in cases where the innovation is clearly superior.

Feynman though that "fooling oneself" was generally to be avoided, but also serves as a hallmark of scientific rationality. However, the very act of thinking (cognitive processes such as focusing attention) might be based on fooling ourselves [4], and thus might define any well-argued position. 

Julia disagrees with this premise, and thinks there is no tension between rationality and innovative ideas. Rather, there is a difference between confidence that an idea can be turned into an artifact and confidence that it will be practical. Innovation is stifled by a combination of overconfidence in practical failure combined with a lack of thinking in terms of expected value. I take this to be similar to normative risk-aversion by the wider community. If individual innovators are confident in their own ideas, despite the sanctions imposed by negative social feedback, they are more likely to pursue them.

Nikola Tesla's approach was "irrational", it was also a sign of his purposeful self-delusion and perhaps even his social isolation from the scientific community [5]. Remember, in the context of this blogpost, these are all good things.

Putting this in the context of peer review, it could be said that confidence or overconfidence is related to the existence and temporary suspension of sociocultural mores in a given intellectual community. A standard definition of social mores are customs and practices enforced through social pressure. In the example given by Michael Neilsen, fooling oneself in order to advance a controversial position requires an individual to temporarily suspend social mores held by members of a specific intellectual community. In this case, mores are defined as commonly-held knowledge and expected outcomes, but can also include idiosyncratic practices and intuitions [6]. From a cognitive standpoint, this may be similar to the requisite temporary suspension of disbelief during enjoyable experiences.

While this suspension allows for innovation, violations of social mores can also lead to a generally negative response, including moral panics and the occasional face full of bees [7]. Therefore, I would amend Hiroki's observation by saying that innovation is marked not only by a wide range of peer-review opinion, but also by universal rejection. Separating the wheat from the chaff amongst the universally rejected works is work for another time.

The price of innovation equals a swarm of angry bees!

NOTES:
[1] Alicea, B. (2013). Fireside Science: The Consensus-Novelty Dampening. Synthetic Daisies blog, October 22.

[2] Nielsen, M. (2017). Is there is tension between creativity and accuracy? April 8.

[3] Galef, J. (2017). Does irrationality fuel innovation? Julia Galef blog, April 7.

[4] Scientific American (2010). How We Fool Ourselves Over and Over. 60-second Mind podcast, June 19.

[5] Bradnam, K. (2014). The Tesla index: a measure of social isolation for scientists. ACGT blog, July 31.

[6] Lucey, B. (2015). A dozen ways to get your academic paper rejected. Brian M. Lucey blog, September 9.

[7] "Face full of bees" is a term I just coined to describe the universal rejection of a particularly innovative piece of work. "Many bees on face" = "Stinging rebuke".

May 10, 2017

Embryology Special Issue

Me and my colleagues are pleased to announce an upcoming special issue of the journal Biology (Basel). The topic is "Computational, Theoretical, and Experimental Approaches to Embryogenesis" (see announcement). Our view of what constitutes embryogenesis research is rather broad, spanning experimental studies, cellular reprogramming, bioinformatics, and artficial life. Therefore, we seek submissions from a wide variety of researchers and article types.


As the lead editor, I will take any questions you might have about interesting ideas, types of articles, or if you are interested in peer-review. As noted on the poster, the deadline for submissions is August 31, 2017. Looking forward to an excellent issue.

UPDATED (5/17):
With the initial dealine fast approaching, we have decided to extend the submission deadline to December 31. 

May 4, 2017

Announcing our Google Summer of Code 2017 Students


As mentioned in a previous post, the OpenWorm Foundation (and DevoWorm group) has been receiving application for this year's Google Summer of Code. We have now selected our student applicants and projects to be awarded the internship. We had a very good group of applicants this year, so congratulations go out to everyone who applied!


Shubham Singh will be working on the model completion dashboard project, which is a general tool for the OpenWorm community. Siddharth Yadav will be working with me and the rest of the DevoWorm group to quantify and analyze secondary microscopy data that capture the process of embryogenesis for C. elegans and other organisms [1]. Good luck!

Thanks to the INCF for coordinating the selection process!


NOTES: 
[1] For more reading on the promise of this approach, please see: Chi, K.R. (2017). Picking out Patterns. The Scientist, May 1 AND Rizvi, A.H., Camara, P.G., Kandror, E.K., Roberts, T.J., Schieren, I., Maniatis, T., and Rabadan, R. (2017). Single-cell topological rNA-seq analysis reveals insights into cellular differentiation and development. Nature Biotechnology, doi:10.1038/nbt.3854.

April 17, 2017

Breaking Out From the Tyranny of the PPT

Player 1 vs. Powerpoint (with a screenshot of the game Breakout). The image itself was made in PowerPoint, but I promise this post will not be recursive nonsense.


By now, you have probably chosen a side in the PowerPoint debate: namely, does it enhance or hinder scholarly communication? I will present both sides of this argument, but not argue to moderation. Rather, I will show that PowerPoint is good (or good to get rid of) only if you define your own style of presentation. In either case, you will need to "break out" of the box containing typical advice for creating PowerPoint presentations.


A number of people have argued (both rhetorically and in practice) that PowerPoint represents an enforced tyrrany on presented information. It forces big ideas into small compartments, defined by slide optimization and bullet points. What follows are a few examples of PowerPoint tyranny, or cases in which the default style of organization imposes constraints on communication and the exchange of ideas. 


A few years ago, Franck Frommer wrote a book on how PowerPoint makes us stupid [1]. Frommer's definition of stupid refers to impovrishing our ability to communicated logical flow, contextual detail, and the confusion of opinion and fact. Supprting this position is Peter Norvig's Gettysburg Address analysis, which suggests that the cognitive style of PowerPoint and its visual gimmickry often obscure rather than enhance the logical flow of a larger idea.


Example slide from the Gettysburg Address as a PowerPoint presentation.


Education might also benefit from breaking away from PowerPoint tradition. In fact, there is an argument to be made that the use of PowerPoint in education reduces course content to an overly-simplified, pre-packaged learning experience [2]. Dr. Chris Waters at Michigan State University has moved to eliminate PowerPoint lectures altogether in his undergraduate Microbiology course. He is instead adapting the existing presentations into a series of chalk talks which are more conducing to communicating scientific ideas. 


Perhaps the failures of PowerPoint are not about varied styles of communication across different domains of knowledge (scientific, business, legal), but more about the relevance of ideas and their overall structure. Relevance theory (Dan Sperber and Deidre Wilson) suggests that are biased according to what seems relevant [3]. Some of this is mediated by the cognition of attentional resources, but there is also an underappreciated role of cultural preferences and constraints. In the realm of science communication, the narrowly-defined relevance of typical PowerPoint design practice might encourage some aspects of scientific practice (science as memorization of facts, still images, simple graphs) at the expense of others (experimentation, data exploration, theory-building). 


The tyrrany of representational orthodoxy, PowerPoint style. On the other hand, this is actually pretty good in terms of available clip art. While perfectly suitable for business-oriented communication (e.g. team-building, simple storytelling), this may or may not be suitable for other domains of knowledge.


So how does one break out from the restrictions of PowerPoint? One way forward is shown by the artistic community's use of PowerPoint as an expressive medium. Like the latter-day explosion of animated .gif art on Tumblr [4], artists have been using PowerPoint to create animations and short videos. Interestingly, the limitations of PowerPoint for representing alternate forms of argumentation does not seem to limit artistic innovation [5]. Perhaps this has to do with the use of symbols rather than the ambiguity of linguistic syntax. 

A more argumentative-based way to approach PowerPoint is to adopt the Lessig Method of presentation [6], which presents ideas in only a few words in a large font. One example of this is Larry Lessig's "Free Culture" lecture, which connects a sequence of court cases and landmark ideas in sparse blocks of text. Whether this solves the ambiguity issue is not clear to me, but does provide a way to simplify without losing information.


The last several talks I have given include a final "Thanks for your Attention" Acknowledgements slide which features a graphic that has something to do with attention (visual illusion and/or obscure reference). This is one such example featuring Marshall McLuhan (e.g. breaking the message out of the medium).

UPDATED (4/23): Here is a presentation to the Association of Computational Heresy by Tom Wildenhain on how to construct a Turing Machine with PowerPoint. While it is a lot of fun, it does bring to mind some more creative uses of PowerPoint.


NOTES:
[1] Frommer, F. (2012). How PowerPoint Makes You Stupid: The Faulty Causality, Sloppy Logic, Decontextualized Data, and Seductive Showmanship That Have Taken Over Our Thinking. New Press, New York.

[2] Ralph, P. (2015). Why universities should get rid of PowerPoint and why they won’t. The Conversation, June 23.

[3] Sperber, D. and Wilson, D. (1995). Relevance: Communication and Cognition. Blackwell Publishers, Oxford, UK.

[4] Alicea, B. (2012). Moving the Still, courtesy of the .gifted. Synthetic Daisies blog, October 19.

[5] Greenberg, A. (2010). The Underground Art of PowerPoint. Forbes, May 11. Some examples of PowerPoint art (converted to YouTube videos) include:

a) "Infiltration" by Jeremiah Lee.

b) "Joiners" by blastoons.


[6] Reynolds, G. (2005). The "Lessig Method of Presentation". Presentation Zen blog, October 7.

April 4, 2017

100 years of Growth and Form!


This year marks the 100th anniversary of "On Growth and Form" [1] by the biologist/ mathematician D'arcy Thompson. "On Growth and Form" has always been an intriguing book from both a historical and technical perspective [2]. This includes the integration of fields such as physics, developmental biology, and geometry. There is an entire website dedicated to the centennial, which demonstrates that his ideas are still useful today [3].

Four bony fish phenotypes related through evolution and transformed through phenotypic deformation. 

D'arcy Thompson provided an account of what we now call evo-devo [4] as a series of mathematical transformations. On the one hand, this provides a mathematical model for the static geometry of the developmental phenotype across species. On the other hand, Thompson provided few if any evolutionary, nor any genetic mechanisms, even in a time when both were becoming ascendant [5]. His physical approach to biological form and morphogenesis has not only been useful in biology, but also as inspiration for computational modeling approaches [6].


NOTES:
[1] Thompson, D.W. (1917). On Growth and Form. Cambridge University Press, Cambridge UK.

[2] Alicea, B. (2011). The Growth and Form of Pasta. Synthetic Daisies blog, October 11.

[3] Much of the contemporary innovation in this area is in the field of architecture. In modern evo-devo, it has taken a back seat to genetic manipulation. Given what we now know about evolution and genetics, there are some potentially interesting biological simulation to be done at the interface of regulatory mechanisms in development and phenotypic fitness based on biomechanical parameters.

[4] Arthur, W. (2006). D'Arcy Thompson and the theory of transformations. Nature Reviews Genetics, 7, 401-406.

[5] Deichmann, U. (2011). Early 20th-century research at the interfaces of genetics, development, and evolution: reflections on progress and dead ends. Developmental Biology, 357(1), 3-12.

[6] Kumar, S. and Bentley, P.J. (2003). On Growth, Form, and Computers. Elsevier, Amsterdam.

March 18, 2017

Almost time for GSoC Applications!

Your chance to join the DevoWorm group is almost upon us. If you are a student, the Google Summer of Code (GSoC) is a good opportunity to gain programming experience. Applications are being accepted from March 20 to April 3. If selected, you will join the DevoWorm group, and also have the chance to network with people from the OpenWorm Foundation and the INCF.

The best approach to a successful application is to discuss your skills, provide an outline of what you plan to do (which should resemble the project description), and then discuss your approach to solving the problems at hand. We are particularly interested in a demonstration of your problem-solving abilities, since many people will apply with a similar level of skill. You can find an application template in outline form here.


You can apply to work on one of two DevoWorm projects: "Physics-based Modeling of the Mosaic Embryo in CompuCell3D" or "Image processing with ImageJ (segmentation of high-resolution images)". If you have any questions, comment in the discussions or contact me directly.

March 15, 2017

A Tree of Deeper Experiences -- the Authorship Tree

One of the most difficult aspects of academic publishing with multiple authors is in determining the order of authorship. In many fields, authorship order is the key to job promotion. Unfortunately, these conventions vary field, while the criteria for authorship slots often varies by research group. Since a responsible accounting of contributions are key to determining authorship and authorship order [1], it is worth considering multiple possibilities for conveying this information.

Example of an Authorship list (with affiliations)

A mathematics or computer science researcher might also see the problem as one of choosing the proper representational data structure. The authorship order, no matter how determined, is a 1-dimensional queue (ordered list). Even though some publishers (such as PLoS) allow for footnotes (an inventory of author contributions), there is still little room for nuance.

Example from "The Academic Family Tree"

But is there a better way? Academic genealogies provide one potential answer. A typical genealogy can be thought of as a 1-dimensional order, from mentor to student. In reality, however, an academic have multiple mentors, influenced by a number of predecessors. The construction of academic family trees [2] is one step in this direction, turning the 1-dimensional graph into a 2-dimensional one.


Picture of the Authorship tree cover. COURTESY: "The Giving Tree" by Shel Silverstein

This is why Orthogonal Lab has just published a hybrid infographic/paper called the The Authorship Tree [3]. This is a working document, so suggestions are welcome. The idea is to not only determine the relative scope of each contribution, but also to graphically represent the interrelationships between authors, ideas, and scope of the contributions.

As we can see from the example below, this includes not only our authors, but also people from the acknowledgements, funders, reviewers, authors of important papers/methods, and funders. While the ordering of branches along the stem suggests an authorship order, they are actually ranked according to their degree of contribution [4]. To this end, there can be equivalent amounts of contribution, as well as inclusion of minor contributors not normally included in an authorship list.

Example of an authorship tree (derived from original 1-D author list).

NOTES:
[1] Cozzarelli, N.R. (2004). Responsible authorship of papers in PNAS. PNAS, 101(29), 10495.

[2] David, S.V. and Hayden, B.Y. (2012). Neurotree: A Collaborative, Graphical Database of the Academic Genealogy of Neuroscience. PLoS One, 7(10), e46608. doi:10.1371/journal.pone.0046608.

[3] Orthogonal Lab (2017). The Authorship Tree. Figshare, doi:10.6084/m9.figshare.4731913.

[4] For more on the point system convention, please see: Venkatraman, V. (2010). Conventions of Scientific Authorship. Science Issues and Perspectives, doi:10.1126/science.caredit.a1000039.

March 4, 2017

Open Data Day Activities

Today is International Open Data Day, which was first proposed in 2010. To do my part, we will discuss a few open data-related items. Namely, what can you do to make this day a success?

Logo of the Open Knowledge Foundation (based in London), who offer a host of Open Data Day acitivities.

1) You can host some of your unpublished data (whether they are linked to publications or not) at an open data repository. You can do this through a general repository such as Dryad or Figshare, or a specialized repository such as Open fMRI [1].

* another part of publishing data is the need for annotation and other metadata. This is a barrier to opening up datasets, but the benefits of doing so may outweigh the initial investments [2].
2) You can join a open access communities such as data.world, a new social media network that allows people to share datasets of all types and sizes.

3) You can commit to creating more systematic descriptions of your research methods (e.g. the things you do to create data). This can be done by creating a set of digital notes or protocol descriptions [3], and making them open through Jupyterhub and protocols.io [4], respectively.

4) You can host your own virtual Hackathon. Unsure as to how you might do this? Then you can earn any (or all) in a series of three badges (Hackathon I, Hackathon II, Hackathon III) created in conjunction with the Open Worm Foundation.

5) You can petition or get involved with municipal and state/provincial governments to ensure their committment to open public data.

Of course, there are other things you can do, and more innovation is needed in this area. Have some ideas or planning an event of your own. Let me know, and I will invite you to the Orthogonal Lab's new Slack channel on Open Science.


NOTES:
[1] This choice, of course, depends on the field in which you are working. I used this example because fMRI data seems to have good community support for data sharing. Consult the Open Access Directory to learn more about the specifics for various disciplines.

For more information about data sharing in the field of neuroimaging, please see: Iyengar, S. (2016). Case for fMRI Data Repositories. PNAS, 113(28), 7699-7700.

[2] Based on a paper recently posted to the bioRxiv, and based on some material from a recent talk. For more information, please see: Alicea, B. (2016). Data Reuse as a Prisoner's Dilemma: the social capital of open science. bioRxiv, doi:10.1101/093518.

[3] Olson, R. (2012). A short demo on how to use IPython Notebook as a research notebook. Randal S. Olson blog, May 12.

[4] In terms of witing better and more accessible protocols, please see the following examples:

Protocols.io (2017). How to make your protocol more reproducible, discoverable, and user-friendly.
February 25. dx.doi.org/10.17504/protocols.io.g7vbzn6

Daudi, A. How to Write an Easily Reproducible Protocol. American Journal Experts, http://www.aje.
com/en/arc/how-to-write-an-easily-reproducible-protocol/, Accessed February 27, 2017.


February 21, 2017

New Orthogonal Laboratory Methods

Lately I have been incorporating two new tools into my research program's [1] infrastructure. One is a software tool with community support, and the other is a development of my own. 

The first addition is the Jupyter Notebook (sometimes called the iPython Notebook, as it is based on this platform). The Jupyter Notebook allows us to build repositories of methods, notes, code, and data analyses in an integrated manner. Jupyter Notebooks can be rendered in Github, making them freely accessible and distributable. For example, the DevoWorm project already has several notebooks hosted at Github. The long-term goal is to create notebooks for typical research activities, and using them for a host of purposes, from a Wiki-like instructional manual to supplemental materials for publications [2].

Jupyter Notebooks (example)

The other is a pipeline for project management with the goal of increasing participation and success in research. The idea is one that I have been bouncing around in my head based on my involvement with the OpenWorm Foundation community committee and personal experience. This could be a way to encourage more underrepresented and "high-risk" researchers to advance their work [3]. It is based on two exceedingly obvious principles: failure is not a breaking point for any research trajectory, and projects themselves should be defined in a bottom-up fashion (building on previous successes and experiences) [4]. Hopefully, this pipeline works well in implementation.

Building a Research Group Philosophy

UPDATE (2/22): I failed to include a snapshot of the Orthogonal Laboratory Slack team (currently with an n of 1). Slack is fast becoming a popular tool for laboratory management [5, 6], particularly those that are partially or fully virtual.



NOTES:
[1] I am in the process of turning Orthogonal Research into Orthogonal Laboratory. Currently it is a group of one (and a few collaborators). I am currently looking for an academic home, so putting the tools needed to scale up is worth the investment in time. More on this initiative later.

[2] Brown, C.T. (2017). Topics and concepts I'm excited about (Paper of the Future). Living in an Ivory Basement blog, January 9.

[3] the very notion of "high-risk research" is biased toward a fear of failure. Considering what is usually thrown into that bucket, "high-risk research" is a statement of cultural values more than an inherent risk. Removing the industrial, one-size-fits-all aspect of research might be a way to mitigate risk.

[4] sometimes you get lucky and get to define a project right out of the box. But in doing so, projects often end up exhibiting a hodgepodge quality that makes them seem unfocused.

[5] Perkel, J.M. (2017). How Scientists Use Slack. Nature, 541, 123-124.

[6] Washietl, S. (2016). 6 Ways to Streamline Communication in Your Research Group Using Slack. Paperpile blog, April 12.

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