ALL THINGS BIG AND SMALL 2.0

THE TRANSFORMERS NOT AS FAR-FETCHED AS YOU THINK (Self-Assembly, the science of things that put themselves together.) Self-Assembly is a sub-field of Nanotechnology, as it is about inducing change at the molecular level to produce desired  structures. Things that put themselves together are already a reality on a small scale. In the Self-Assembly Lab at MIT, there are fascinating examples of self-assembly. Here is a link to a set of videos from Ted.com http://blog.ted.com/2013/04/04/see-self-assembly-and-4d-printing-in-action/ featuring Skylar Tibbits of the Self-Assembly Lab at MIT. Like many of Mankind's inventions, Self-Assembly was inspired by Mother Nature. The engineered self-assembling systems we have, were constructed based on the same principles and forces that drive self-assembly in Nature. There is nothing magical about self-assembly. When the right conditions exist, molecules can self-assemble.Self-assembly is when molecules form bonds according to a set ...

I am re-posting these photos from my Facebook. One set shows some of the weird things you find in Chinese Medical Shops in Singapore. The other shows some of the very colorful cakes you can get here in Singapore. ASIAN CAKES LAYERED MULTI-COLORED RAINBOW CAKE FOR CHINESE NEW YEAR PATTERNED CAKES FROM SARAWAK, MALAYSIA  ASSORTED PERANAKAN# CAKES 1 ASSORTED PERANAKAN# CAKES What are Peranakans? See http://www.fu-lu-shou.net/2009/02/my-peranakan-heritage-discovering.html  DRIED STUFF USED IN TRADITIONAL CHINESE MEDICINE Flying Lizards Giant Wood Mushrooms Pipefish Skin of some reptile Clumps of earth containing some minerals. Cuttlefish tentacles

Juust as wine-tasters use words such as full-bodied, flowery, earthy, fruity etc to describe wines, so electric guitar players use words to describe their guitar's tone: Bite- what the tone of a good Fe nder Telecaster's bridge pickup should have. Glassy- the kind of tone you get in a Fender Stratocaster when the pick-up selector is between neck and middle pickup. Creamy- when high notes are played with a thick round-end pick like a V-Pick. Fat- Thick tone, not necessarily mellow, favored by jazz guitarists. Muddy- Mellow but not clear tone. Singing- the tone of an old Les Paul, or any guitar with long sustain. Woody- the acoustic tone of a hollow body jazz archtop. Snap- when a little bit of fret buzz sounds nice. Honky- Like the tone of P-90's or an out-of-phase pickup Woman tone- a tone first attributed to Eric Clapton's '68 Gibson SG through a Marshall amp, and typical of British Blues in the sixties. Bell-like- a Rickenbacker used as rhythm guitar Big Bo...

2014 D'Angelico NYSS 3B FM (flamed maple) 2013 Fender American Vintage Telecaster Thinline 1967 Gibson ES-330 as advertised for $520 on AliExpress After more than 3 years, I had a relapse of the Guitar Acquisition Syndrome (GAS). I thought I was cured. Why would a 67- year old man with probably less than 15 years of life left, and with more guitars and amplifiers than he can play with, want more guitars? But succumb, I did, to GAS. So here is a blow-by-blow account of my latest attack of GAS.   It began with a sudden craving for a Telecaster Thinline, though why I would want a Thinline when I had, during my lifetime bought and sold three Thinlines I don't know. So I bought the Thinline.  Then I saw an ad in AliExpress to sell a 1967 Gibson ES 330 for $520. I could hardly believe my eyes. I convinced myself that this anomaly in price was due to the ignorance of the seller. Alas, how true the adage that if its too good to be true, then it probably is...

Thoughts on definition of Noise and Thresholds 1.     I am obsessed by the thought of being able to make my everyday decisions in a more objective way, unclouded by emotions and biases so common with us humans. There must be academic papers on the topic of quantitative-based decision-making. But I have never read any.  So these are the ramblings of a novice in this field. 2.     Any data-driven (quantitative) decision support system must have (a) some kind of noise removal method for initial ‘cleaning’ to remove data that is considered not relevant to the model  (b) Methods for setting thresholds of some kind i.e. a numerical value that is the boundary for, and triggers a Yes/No decision. 3.      Thus, the two key aspects of any quantitative decision-making process are the removal of noise, and the establishment of thresholds that trigger decisions. For this reason, I am fascinated by the definition of Noise and...

The AI machine was fed flag patterns like number of horizontal lines, vertical lines, colors, crescent, crosses, sun, stars, stripes, bars. It was then asked to cluster countries according to the degree of similarity of their flag patterns.  The result is in the map of six clusters. To test if the AI was good, I took the bottom right cluster (Pink) that contains Singapore. The countries placed near to Singapore are supposed to have flags with a high degree of similarity with Singapore's flag. Now look at the collage of flags image. The flags are: Top row from left: Singapore, Tunisia, Maldives Center from left: Turkey, Mauritania, Pakistan Bottom: Algeria, Comoros Islands, Indonesia. As you can see, although the colors of the flags vary, the flags do have similarities in terms of all of them having crescent and stars. So the AI was smart enough to recognize that the crescent and stars are the important variables.Although I gave all the patterns equal weighting in im...

The purpose of this experiment is to see whether an explorative data mining program could recognize degree of similarities in about 60 species of animals, from honeybees to lions, to starfish, parrots, etc. The software program used is from  www.viscovery.net  . Some uses of this program are: (a) clustering of customer data according to their behaviour. (b) In gene data analytics to identify biomarkers for the diagnosis of diseases. (c) For fraud profiling and forensic applications. The data is from the University of California Irvine data archive (archive.ics.uci.edu/ml). The ‘properties’ (attributes) considered were: backbone, legs, fins, lungs, hair, feathers, eggs, milk, tail, domestic, aquatic, airborne, venomous, predator.  Presence of an attribute is denoted by 1, and absence by 0, except for Legs, where it has a numerical value of: 2,4,6,8 legs. The tool was made to learn the attributes of all the animals and group them into clusters, each cluste...