Scale-Free networks nonsense or Science vs Pseudo-Science

(this article’s title is a nod to Lior Pachter vitriolic arc of 3 articles with similar title)

Over the last couple of days I was engaged in a debate with Lê from Science4All about what exactly science was, that spun off from his interview with an evolutionary psychologist and my own vision of evolutionary psychology in its current state as a pseudo-science.

While not necessarily always easy and at times quite movemented, this conversation was quite enlightening and let me to trying to lay down

Following the recent paper about scale-free networks not being that spread in the actual environment (that I first got as a gist from Lior Pachter’s blog back in 2015) helped me to formalize a little bit better what I believe I feel a pseudo-science is.

Just as the models and theories within the scientific method itself, something being a scientific approach is not defined or proved. Instead, similarly to the NIST definition of random numbers through a series of tests that all need to be successfully passed, the definition of a scientific approach is a lot of time defined from what it’s not, whereas pseudo-science is defined as something that tries to pass itself as a scientific method but fails one or several tests.

Here are some of my rules of thumb for the criteria defining pseudo-science:

The model is significantly more complicated that what the existing data and prior knowledge would warrant. This is particularly true for generative models not building on the deep pre-existing knowledge of components.

The theory is a transplant from another domain where it worked well, without all the correlated complexity and without justifying that the transposition is still valid. Evolutionary psychology is a transplant from molecular evolutionary theory,

The success in another domain is advanced as the main argument for the applicability/correctness of the theory in the new domain.

The model claims are non-falsifiable.

The model is not incremental/emergent from a prior model.

There are no closely related, competing models that are considered upon application to choices.

The cases where the model fails are not defined and are not acknowledged. Evo psy – modification of the environment by humans. Scale-Free networks.

Back-tracking on the claims, without changing the final conclusion. This is different with regards to affining the model where the change in the model gets propagated to the final conclusion and that conclusion is then re-compared with reality. Sometimes mends are done to that model for it to align with the reality again, but at least during a period, the model is still considered as false.

Support by a cloud of plausible, but refuted claims rather than a couple of strong, hard to currently attack the claims.

The defining feature of pseudo-science however, epsecially compared to the faulty science is its refusal to accept the criticism/limitations to the theory and change its prediction accordingly. It always needs to fit the final maxim, no matter the data.

Synergy from the boot on Ubuntu

This one seemed to be quite trivial per official blog, but the whole pipeline gets a bit more complicated once the SSL enters into the game. Here is how I made it work with synergy and Ubuntu 14.04

  • Configure the server and the client with the GUI application
  • Make sure SSL server certificate fingerprint was stored in the ~/.synergy/SSL/Fingerprints/TrustedServers.txt
  • Run sudo -su myself /usr/bin/synergyc -f --enable-crypto my.server.ip.address
  • After that check everything was working with sudo /usr/bin/synergyc -d DEBUG2 -f --enable-crypto my.server.ip.address
  • Finally add the greeter-setup-script=sudo /usr/bin/synergyc --enable-crypto my.server.ip.address line into the /etc/lightdm/lightdm.conf file under the [SeatDefaults] section

Why you shouldn’t do it?

Despite the convenience, there seemed to be a bit or an interference for the keyboard command and command interpretation on my side, so since my two computers side by side and since I have an usb button switch from before I got synergy, I’ve decided to manually start synergy every time I log in.

Writing a research paper with ReStructuredText

Why?

As a part of my life as a Ph.D. student, I have to read a large number of scientific papers and I have seen a couple of them written. What struck me was that these papers have a great deal of internal structure (bibliographic references, references to figure, definitions, adaptation to a particular public, journal, etc…). However, they are written all at once, usually in a word document, in a way that all that structure lives and can only be verified in the writer’s head.

As a programmer, I am used to dealing with organizing complex structured text as well – my source code. However, the experience has shown me that relying on what is inside your brain to keep the structure of your project together works only for a couple of lines of code. Beyond that, I have no choice but to rely on compilers, linters, static analysis tools and IDE tools to help me dealing with the logical structure of my program and prevent me from planting logical bombs and destroying one aspect of my work while I am focusing on the other. An even bigger problem is to keep myself motivated while writing a program over several months and learning new tools I need to do it efficiently.

From that perspective, writing the papers in a word file is very similar to trying to implement high-level abstraction with very low-level code. In fact, so low-level, you are not allowed to import from other files (automatically declare common abbreviations in your paper), define functions and import upon execution. Basically, the only thing you can rely on is the manuscript reviews by the writers and the editors/reviewers of journals where the paper is submitted. Well, unless they get stuck in an infinite loop.

Alt text

So I decided to give it a try and write my paper in the same way I would write a program: using modules, declarations, import, compilation. And also throwing in some comments, version control and ways to organize revisions.

Why ReStructuredText?

I loved working with it when I was documenting my projects in Python with Sphinx. It allows quite a lot of operations I was looking for, such as .. include:: and .. replace:: directives. It is well supported in the PyCharm editor I am using, allowing me to fire up my favorite dark theme and go fullscreen to remain distraction-free. It also can be translated with pandoc to both .docx for my non-techy Professor and LaTeX files for my mathy collaborator.

It also allowed me to type the formulas with raw mathematics in LaTeX notation quite easily by using .. math:: directive.

How did it go?

Not too bad so far. I had some problems with pandoc’s ability to convert my .rst file tree into .docx, especially when it came to failing on the .. import:: and citation formatting. (link corresponding issues) There was also some issue with rendering .png images in the .docx format as well (link issue). In the end, I had to translate the .rst files into html and with rst2html tool and then html to docx. For now, I am still trying to see how good of the advantages it is giving me.

After some writing, I’ve noticed that I am now being saved from the pending references. for instance, at some point I wrote a reference [KimKishnoy2013]_ in my text and while writing biblio I realized the paper came out in 2012, so defined the paper there as .. [KimKishnoy2012]. And rst compilation engine threw an error on compilation about Unknown target name: "kimkishnoy2013" Yay, no more dead references! The Same thing is true for the references defined in the bibliography but not used in the text.

Now a shortcoming of this method of writing is the fact that inter-part transitions do not come naturally. It can be easily mitigated once the writers’ block has been overcome by writing all parts separately by opening a compiled HTML or .docx document and editing the elements os that they align properly.

An additional difference with the tools that has been developed to review code is that the information density and consistency in the programming languages is closer to mathematical notations rather than a human-readable text with all the redundancy necessary for a proper understanding. A word change or a line change is a big deal in the case of programming. It isn’t so important in the case of writing. and all the annotation and diff tools used for that are not very useful.

On the other hand, it is still related to the fact that human language is still a low-level language. Git won’t be as useful to review the binaries that it is when reviewing the programming languages that are important.

Over the time, a two significant problems emerged with that approach. First – incorporating the revisions. Since the other people in the revision pipeline are using the MS word built-in review tools, in order to address every single revision I have to find the location in the text tree file where the revision needs to be made, then correct it. Doing it once is pretty straight-forward. Doing it hundreds upon hundreds of time across tens of revision by different collaborators is an entire thing altogether and is pretty tiresome.

The second problem is related to the first. When the revisions are more major and require re-writing and re-organization of entire parts, I need to go and edit the parts one by one, then figure which part contents is going into what new part. Which is a lot of side work for not a lot of added value in the end.

What is is still missing?

  • Conditional rendering rules. There are some tags that I would want to see when I am rendering my document for proofreading and correction (like parts name, my comments, reviewer comments), but not in the final “build”.

  • Linters. I would really like to run something like a Hemingway app on my text, as well as some kind of Clonedigger to figure out where I tend to repeat myself over and over again and make sure I only repeat myself when I was to. In other terms automated tools for proof-reading the text and figuring out how well it is understood. It seems that I am not the only one to have the idea: Proselint creators seem to have had the same idea and implemented a linter for prose in python. Something I am quite excited about, even though they are still in the infancy because of how liberal the spoken language is compared to programming language. We will likely see a lot of improvements in the future, with the development in NLP and machine learning. Here are a couple of other linter functions I could see be really useful.

    • Check that the first sentence of each paragraph describes what the paragraph will be about
    • Check that all the sentences can be parsed (subject-verb-object-qualifiers)
    • Check that there is no abrupt interruption in the words used between close sentences.
    • Check for the digressions and build a narration tree.
  • Words outside common vocabulary that are undefined. I tend to write to several very different public about topics they are very knowledgeable about and sometimes not really. The catch is that since I am writing about it, I am knowledgeable about them, to the point that sometimes I fail to realize that some words might need. If I have an app that shows me words that I introduce that are rare and that I don’t define, I could rapidly adapt my paper to a different public or like the reviewers like to ask unpack a bit.

  • chaining with other apps. There are already applications that do a great job on the structuring the citation and referencing to the desired format. I need to find a way to pipe the results of .rst text compilation into them so that they can adapt the citation framework in a way that is consistent with the publication we are writing.

  • Skeptic module. I am writing scientific papers. Ideally, my every assertion in the introduction has to be backed by an appropriate citation and every paragraph in the methods and results section has to be backed by the data, either in the figures or in supplementary data.

  • A proper management of mathematical formulas. They tend to get really broken. Latex is the answer, but it would be nice if the renderings of formulae could also be translated into HTML or docx, that has it’s own set of mathematical notation (MS office always had to do it differently from open source standards).

  • Way to write from the requirements. In software we have something we refer to as unittests: pre-defined behaviors we want our code to be able to implement. As a rule of thumb, accumulation of unittests is a tedious process, that is nonetheless critical for building a system and validating that upon change our software is still behaving in a way we expect it to. In writing we want to transmit a certain set of concepts to our readers, but because the human time is so valuable, we regularly fail at that task, especially when we fail to see that 100th+ revision makes a concept that is referred to in a paper not be defined anymore. In software it is a little bit like acting as a computer and executing the software in your head. Works well for small pieces, but there are edge cases and what you know about what program should do that really gets into the way.

Dependency of a dependency of a dependency

Or why cool projects often fail to get traction

Today I tried to install a project I have been working for a while on a new machine. It relies heavily on storing and quering data in “social network” manner, and hence not necessarily very well adapted to the relational databases. When I was staring to work on it back in the early 2013, I was still a fairly inexperienced programmer, so I decided to go with a new technology to underlie it neo4j graph database. And since I was coding in Python and fairly familiar with the excellent SQLAlchemy ORM and was looking for something similar to work with graph databases my choice fell on the bulbflow framework by James Thronotn. I complemented it with JPype native binding to python for quick insertion of the data. After the first couple of months of developer’s bliss and everything working as expected and being build as fast as humanely possible, I realized that things were not going to be as fun as I initially expected.

  •  Python 3 was not compatible with JPype library that I was accessing to rapidly insert data into neo4j from Python. In addition to that JPype was quickly dropping out of support and was in general too hard to set up, so I had to drop it down.
  • Bulbflow framework in reality relied on the Gremlin/Groovy Tinkerpop stack implementation in the neo4j database, was working over a REST interface and had no support for batching. Despite several promises of implementation of batching by it’s creator and maintainer, it never came to life and I found myself involved in a re-implementation that would follow that principles. Unfortunately I had not enough experience with programming to develop a library back then, nor enough time to do it. I had instead to settle for a slow insertion cycle (that was more than compensated for by the gain of time on retrieval)
  • A year later, neo4j released the 2.0 version and dropped the Gremlin/Groovy stack I relied on to run my code. They had however the generosity of leaving the historical 1.9 maintenance branch going, so provided that I had already poured along the lines of three month full-time into configuration and debugging of my code to work with that architecture, I decided to stick with 1.9 and maintain them
  • Yesterday (two and a half years after start of development, when I had the time to pour the equivalent of six more month of full-time into the development of that project), I realized that the only version of 1.9 neo4j still available for download to common of mortals that will not know how to use maven to assemble the project from GitHub repository is crashing with a “Perm Gen: java heap out of memory” exception. Realistically, provided that I am one of the few people still using 1.9.9 community edition branch and one of the even fewer people likely to run into this problem, I don’t expect developers will dig through all the details to find the place where the error is occurring and correct it. So at that point, my best bet is to put onto GitHub a 1.9.6  neo4j and link to it from my project, hoping that neo4j developers will show enough understanding to not pull it down

All in all, the experience isn’t that terrible, but one thing is for sure. Next time I will be building a project I would see myself maintain in a year’s time and installing on several machines, I will think twice before using a relatively “new” technology, even if it is promising and offers x10 performance gain. Simply because I won’t know how it will be breaking and changing in the upcoming five years and what kind of efforts it will require for me to maintain the dependencies of my project.

Usability of fitness trackers: lots of improvement in sight

Fitness trackers and other wearable techs are gaining more and more momentum, but because of the ostrich cognitive bias they are absolutely not reaching the populations that would benefit most from them. And as per usual, ReadWriteWeb is pretty good at  pointing this out in a simple language.

To sum up, current fitness tracking has several short-comings for the population it would target:

  • It is pretty expensive. Fitness band that does just the step tracking can cost somewhere between $50 and $150. If you are trying to go something more comprehensive, such as one of the Garmin’s multisport watches, you are looking for somewhere in the $300-$500. Hardly an impulsive purchase for someone who is getting under 30k a year and have kids to feed from that. However they are the group at highest risk from obesity and cardiovascular disease.
  • They generate a LOT of data that is hard to interpret, unless you have some background as a trained athlete. Knowing your Vmax and hear-rate descent profile following an error is pretty cool and useful for monitoring your health and fitness, but you will never know how to do it, unless someone explains it to you or you already knew it from your previous athletic career.
  • They do not provide any pull-in. As anyone with a bank account would know, saving comes from the repeated effort in duration. Same as with health capital. However, as anyone with a bank account knows, when you hit hard financial times, you watch your bank account much less than during the times where everything is going well. Just because it is rewarding in the latter case and painful in the first. Same thing with health: people who lack health but are ready to do it are self-conscious about it and need an additional driving motivation to make them last through the periods where no progress is happening
  • It does not respond to an immediate worry and is one of those products that are “good to have”, but whose absence does not lead to a “I need it RIGHT NOW” feeling

 

With that in mind, I decided to participate in MedHacks 1.0 last weekend. My goal was to develop something that would provide an emergency warning for users that are either at high risk of stroke or undergoing it, so they would not get themselves isolated while having a stroke. With my team, we managed to hack together a proof of concept prototype in about 24 hours, which took us into finals. In order to do this, we used an audio mixing board to amplify the signal, Audacity to acquire the data on a computer, FFT and pattern matching to retrieve the data and filter out loss-of-contact issues and build an app in Android that was able to send out a message/call for help if the pattern changed.

Now, those are very simple components that could be compressed on a single sensor woven into a T-shirt and beamed onto a phone for analysis in background. We would need to do some machine learning to be able to detect most common anomalies and then validation by human experts of the acquired EKG.

However, the combination of persistently monitoring cheap device and an app that is able to exploit it opens large possibilities for fitness tracking for those most needing it.

  • The reason to purchase and use the monitoring device is not fitness anymore. It is basic safety. And can be offered by someone who is worried for your health.
  • The basic functionality is really clear. Something is going on wrong with you, we will warn you. Something is going really wrong, we will warn someone who can check on your or come to your rescue.
  • We can build upon the basic functionality, introducing our users to the dynamics of fitness in a similar way games introduce competitive challenges: gradually and leaving you the time to learn at your pace.
  • We have a very precise access to the amount of effort. Your heart rhythm will follow if you are doing a sternous directed activity and we will guide you in it
  • We were able to build a prototype with very common materials. Compression and mass-production will allow us to hit the lowest market range, at a price where you are paying for a smart athletic piece of clothing only marginally more than for the same “non-smart” piece of clothing.

Sounds interesting? I am looking for someone with clinical experience in hear diseases, a hardware hacker that would have experience with wearable and someone to drive the consumer prospection and sales.