Monthly Archives: December 2015

Phil 12.9.15

7:00 – VTX

  • Learning: Near Misses, Felicity Conditions
    • One shot learning
    • Describing the difference between the desired goal/object and near misses. Model is decorated with information is important.
      • Relations are in imperative form (must not touch, must support, etc.)
    • Pick a seed
    • Apply your heuristics until all the positives are included
    • Then use negatives to throw away unneeded heuristics
    • Use a beam search
    • Near misses lead to specialization, compare to general models lead to generalization (look for close items using low disorder measures for near misses and high for examples?)
    • Model Heuristics (
      An application of variable-valued logic to inductive learning of plant disease diagnostic rules)

      • Require Link (Specialization step)
      • Forbid Link (Specialization step)
      • Extend Set (Generalization step)
      • Drop Link (Generalization step)
      • Climb Tree (Generalization step)
    • Packaging ideas
      • Symbol associated with the work – a visual handle
      • Slogan – a verbal handle (‘Near Miss’ learning)
      • Surprise – Machine can learn something definate from a single example
      • Salient – something that sticks out (One shot learning via near misses)
      • Story
  • More dev machine setup
    • Added typescript-install to the makefile tasks, since I keep on forgetting about it.
    • Compiled and ran WebGlNeworkCSS. Now I need to set up the database.
    • Got that in, but had a problem with the new db having problems with the text type of PASSWORD(). I had to add COLLATE to the where clause as follows: 
      "UPDATE tn_users set password = PASSWORD(:newPassword) where password = PASSWORD(:oldPassword) COLLATE utf8_unicode_ci and login = :login"
    • last error is that the temp network isn’t being set in the dropdown for available networks. Fixed. It turned out to be related to the new typescript compiler catching some interface errors that the old version didn’t.
  • Ok, I think it’s time to start writing up what the current system is and how it works.

Phil 12.8.15

7:00 – 4:30 VTX

Phil 12.7.15

8:00 – 5:00 VTX

  • Got my laptop from John and got it set up. Incredibly slow network performance, which I figured was the wifi. Hooked up the hard line and disabled the wifi, which doesn’t see the network at all, and won’t let me reconnect the wifi. Working from home for the rest of the day.
  • At seminar, had a really interesting discussion about how taxonomies intersecting with knowledge graphs essentially result in a kind of pro-forma synthesis. Hmm.

Phil 12.4.15

8:00 – VTX

  • Scrum
  • Found an interesting tidbit on the WaPo this morning. It implies that if there is a pattern of statement followed by a search for confirming information followed by a public citation of confirming information could be the basic unit of an information bubble. For this to be a bubble, I think the pertinent information extracted from the relevant search results would have to be somehow identifiable as a minority view. This could be done by comparing the Jaccard index of the adjusted results with the raw returns of a search? In other words, if the world (relevant search)  has an overall vector in one direction and the individual preferences produce a pertinent result that is pointing in the opposite direction (large dot product), then the likelihood of those results being the result of echo-chamber processes are higher?
  • If the Derived DB depends on analyst examination of the data, this could be a way of flagging analyst bias.
  • Researching WebScaleSQL, I stumbled on another db from Facebook. This one,  RocksDB, is more focused on speed. From the splash page:
    • RocksDB can be used by applications that need low latency database accesses. A user-facing application that stores the viewing history and state of users of a website can potentially store this content on RocksDB. A spam detection application that needs fast access to big data sets can use RocksDB. A graph-search query that needs to scan a data set in realtime can use RocksDB. RocksDB can be used to cache data from Hadoop, thereby allowing applications to query Hadoop data in realtime. A message-queue that supports a high number of inserts and deletes can use RocksDB.
  • Interestingly, RocksDB appears to have integration with MongoDB and is working on MySQL integration. Cassandra appears to be implementing similar optimizations.
  • Just discovered reported.ly, which is a social medial sourced, reporter curated news stream. Could be a good source of data to compare against things like news feeds from Google or major news venues.
  • Control System Meeting
    • Send RCS and Search Competition to Bob
    • Seems like this whole system is a lot like what Databricks is doing?

Phil 12.3.15

7:00 – 5:00 VTX

  • Learning: Genetic Algorithms
    • Rank space (probability is based on unsorted values??)
    • Simulated annealing – reducing step size.
    • Diversity rank (from the previous generation) plus fitness rank
  • Some more timing results. The view test (select count(*) from tn_view_network_items where network_id = 1) for the small network_1 is about the same as the pull for the large network_8, about .75 sec. The pull from the association table without the view is very fast – 0.01 for network_1 and 0.02 for network_8. So this should mean that a 1,000,000 item pull would take 1-2 seconds.
  • mysql> select count(*) from tn_associations where network_id = 1;
 11 
1 row in set (0.01 sec)

mysql> select count(*) from tn_associations where network_id = 8;
 10000 
1 row in set (0.01 sec)

mysql> select count(*) from tn_view_network_items where network_id = 8;
 10000 
1 row in set (0.88 sec)

mysql> select count(*) from tn_view_network_items where network_id = 1;
 11 
1 row in set (0.71 sec)
  • Field trip to Wall NJ
    • Learned more about the project, started to put faces to names
    • Continued to look at DB engines for the derived DB. Discovered WebScaleSQL, which is a collaboration between Alibaba, Facebook, Google, LinkedIn, and Twitter to produce a big(!!) version of MySql.
    • More discussions with Aaron D. about control systems, which means I’m going to be leaning on my NIST work again.

Phil 12.2.15

7:00 –

  • Learning: Neural Nets, Back Propagation
    • Synaptic weights are higher for some synapses than others
    • Cumulative stimulus
    • All-or-none threshold for propagation.
    • Once we have a model, we can ask what we can do with it.
    • Now I’m curious about the MIT approach to calculus. It’s online too: MIT 18.01 Single Variable Calculus
    • Back-propagation algorithm. Starts from the end and works forward so that each new calculation depends only on its local information plus values that have already been calculated.
    • Overfitting and under/over damping issues are also considerations.
  • Scrum meeting
  • Remember to bring a keyboard tomorrow!!!!
  • Checking that my home dev code is the same as what I pulled down from the repository
    • No change in definitelytyped
    • No change in the other files either, so those were real bugs. Don’t know why they didn’t get caught. But that means the repo is good and the bugs are fixed.
  • Validate that PHP runs and debugs in the new dev env. Done
  • Add a new test that inputs large (thousands -> millions) of unique ENTITY entries with small-ish star networks of partially shared URL entries. Time view retrieval times for SELECT COUNT(*) from tn_view_network_items WHERE network_id = 8;
    • Computer: 2008 Dell Precision M6300
    • System: Processor Intel(R) Core(TM)2 Duo CPU T7500 @ 2.20GHz, 2201 Mhz, 2 Core(s), 2 Logical Processor(s), Available Physical Memory 611 MB
    • 100 is 0.09 sec
    • 1000 is 0.14 sec
    • 10,000 is 0.84 sec
    • Using Open Office’s linear regression function, I get the equation t = 0.00007657x + 0.733 with an R squared of 0.99948.
    • That means 1,000,000 view entries can be processed in 75 seconds or so as long as things don’t get IO bound
  • Got the PHP interpreter and debugger working. In this case, it was just refreshing in settings->languages->php

Phil 12.1.15

7:30 – 5:00

  • Learning: Identification Trees, Disorder
    • Trees of tests
    • Identification Tree (Not a decision tree!)
    • Measuring Disorder – lowest disorder is best test
      • Disorder(set of binaries) = -(positive/total*log2(positive/total)) – (neg/total*log2(neg/total))
        • is the base log related to the base of the set?
        • Add up the disorder of each result in the test to determine the disorder of the test normalized by the number of samples. Lowest disorder is winner
  • Bringing in my machine learning, pattern recognition and stats books.
  • Bringing in my big laptop
  • Setting up dev environment.
    • Using the new IDEA 15.x, which seems to be OK for the typescript, will check PHP tomorrow.
    • Installed grunt (grunt-global, then grunt-local from the makefiles)
    • installed typescript (npm i -g typescript)
    • Installed gnuWin32 , which has makefile and touch support, along with all the important DLLs. It turns out that there is also gnuWin64. Will use that next time
    • Fixed bugs that didn’t get caught before. Older compiler?
      • commented out the waa.d.ts from the three.d.ts definitelytyped file
      • deleted the { antialias: boolean; alpha: boolean; } args from the CanvasRenderer call in classes/WebGlCanvasClasses
      • added title?:string and assoc_name?:string to IPostObject in RssController
      • had to add the experiments/wglcharts2 folder to the xampp Apache htdocs
      • added word?:string to IPostObj in RssAppDirectives
      • added word_type_name?:string to IPostObj in RssAppDirectives
      • fixed the font calls in WebGl3dCharts IComponentConfig.
    • Since these issues really shouldn’t have happened, I’m going to verify that they are not in my home dev environment before checking in.
  • And the new computer arrived, so I get to do some of the install tomorrow.