Posts Tagged ‘hasshadow’

Paper for Journal Club Tomorrow

May 22, 2018

Susceptibility of brain atrophy to TRIB3 in #Alzheimer’s disease, evidence from functional prioritization in imaging genetics Nice connection of developing phenotypes from #imaging, combined w. simple polygenic scores from genotypes

Data-Driven Wellness & Personal Coaching – Arivale

May 20, 2018

Seo Young-Deok – Opera Gallery

May 5, 2018
bike chains!

iPad Notebook export for Significant Figures: The Lives and Work of Great Mathematicians

April 28, 2018

Some quick quotes from
Significant Figures: The Lives and Work of Great Mathematicians Stewart, Ian
Citation (MLA): Stewart, Ian. Significant Figures: The Lives and Work of Great Mathematicians. Basic Books, 2017. Kindle file.
that I really liked

Each short quote below is preceded by the words “Highlight” & indication of the location in the book.

9 The Heat Operator • Joseph Fourier
Highlight(orange) – Page 91 · Location 1439
The precise form of the equation led Fourier to a simple solution, in a special case. If the initial temperature distribution is a sine curve, with a maximum temperature in the middle which tails away towards the ends, then as time passes the temperature has the same profile, but this decays exponentially towards zero.
10 Invisible Scaffolding • Carl Friedrich Gauss
Highlight(orange) – Page 98 · Location 1536
When Gauss was eight, his schoolteacher Büttner set the class an arithmetic problem. It’s often stated that this was to add the numbers from 1 to 100, but that’s probably a simplification.
14 The Laws of Thought • George Boole
Highlight(orange) – Page 146 · Location 2255
The quadratic is then the square (px + qy) 2 of a linear form. A coordinate change is a geometric distortion, and it carries the original lines to the corresponding ones for the new variables. If the two lines coincide for the original variables, they therefore coincide for the new ones. So the discriminants must be related in such a manner that if one vanishes, so does the other. Invariance is the formal expression of this relationship.
21 The Formula Man • Srinivasa Ramanujan
Highlight(orange) – Page 223 · Location 3480
For the first three years of his life, he scarcely said a word, and his mother feared he was dumb. Aged five, he didn’t like his teacher and didn’t want to go to school. He preferred to think about things for himself, asking annoying questions such as ‘How far apart are clouds?’ Ramanujan’s mathematical talents surfaced early, and by the age of 11 he had outstripped two college students who lodged at his home.
23 The Machine Stops • Alan Turing
Highlight(orange) – Page 251 · Location 3929
After the war Turing moved to London, and worked on the design of one of the first computers, ACE (Automatic Computing Engine) at the National Physical Laboratory. Early in 1946 he gave a presentation on the design of a stored-program computer –far more detailed than the American mathematician John von Neumann’s slightly earlier design for EDVAC (Electronic Discrete Variable Automatic Computer). The ACE project was slowed down by official secrecy about Bletchley Park, so Turing went back to Cambridge for a year,
Highlight(orange) – Page 252 · Location 3940
He worked on phyllotaxis, the remarkable tendency of plant structures to involve Fibonacci numbers 2, 3, 5, 8, 13, and so on, each being the sum of the previous two.
24 Father of Fractals • Benoit Mandelbrot
Highlight(orange) – Page 261 · Location 4084
In general, if the rank-n item has frequency proportional to nc, for some constant c, we speak of a cth power law.
Highlight(orange) – Page 261 · Location 4085
Classical statistics pays little attention to power-law distributions, focusing instead on the normal distribution (or bell curve), for a variety of reasons, some good. But nature often seems to use power-law distributions instead.
Highlight(orange) – Page 265 · Location 4147
Julia, and another mathematician Pierre Fatou, had analysed the strange behaviour of complex functions under iteration. That is, start with some number, apply the function to that to get a second number, then apply the function to that to get a third number, and so on, indefinitely. They focused on the simplest nontrivial case: quadratic functions of the form f( z) = z2 + c for a complex constant c.

Gallium – Wikipedia

April 28, 2018

Gallium is a chemical element with symbol Ga and atomic number 31. It is in group 13 of the periodic table, and thus has similarities to the other metals of the group, aluminium, indium, and thallium. Gallium does not occur as a free element in nature, but as gallium(III) compounds in trace amounts in zinc ores and in bauxite.[5] Elemental gallium is a soft, silvery blue metal at standard temperature and pressure, a brittle solid at low temperatures, and a liquid at temperatures greater than 29.76 °C (85.57 °F) (above room temperature, but below the normal human body temperature).
The melting point of gallium is used as a temperature reference point. Gallium alloys are used in thermometers as a non-toxic and
environmentally friendly alternative to mercury, and can withstand higher temperatures than mercury. The alloy galinstan (68.5% gallium, 21.5% indium, and 10% tin) has an even lower melting point of −19 °C (−2 °F), well below the freezing point of water.

Maison Kayser

March 5, 2018 Blues & yogurt + table service
ssid=MKBakery129,passwd is phone
7a opening

Bulldog Mobile (LiveSafe) App | It’s Your Yale

March 4, 2018

China’s Selfie Obsession

February 12, 2018

“People would suspiciously ask what kind of camera it was before walking away with expressions ranging from offense to pity. “I can’t allow you to take a picture of me with that camera—it’ll be too ugly,” a woman from Chongqing told me. I assured her that I was not a wang hong and would not be posting it, and we reached a compromise: she would take a selfie of us on her Meitu phone, edit her face, and then send the photo to me.”

Imaging Without Lenses

February 3, 2018

Imaging Without Lenses Computational #photography w. compressive sensing (reconstruction from arbitrary image bases) & diffractive imaging (forming an image via scattering from gratings) via @AmSciMag


Computational Imaging

As its name suggests, the key advance in this new paradigm is the essential role played by computation in the formation of the final digital image. …
When the orbiting Hubble Space Telescope first sent its photos to Earth in the late 1980s, the images were far blurrier than expected; it quickly became apparent that something was wrong with the telescope optics. NASA scientists diagnosed the optical problems and, in the years before the unmanned telescope could be repaired, designed sophisticated digital processing algorithms to correct the images by compensating for many of the effects of flawed optics.

In the mid-1990s, W. Thomas Cathey and Edward R. Dowski, Jr., realized that one could go further still: One could intentionally design optics to produce blurry, “degraded” optical images, but degraded in such a way that special digital processing would produce a final digital image as good as, or even better than, those captured using
traditional optics

Diffraction for Imaging

One class of lensless devices for imaging macroscopic objects relies on miniature gratings consisting of steps in thickness in a
transparent material (glass or silicate) that delay one portion of the incident light wave with respect to another portion. The pattern of steps expresses special mathematical properties that uniquely ensure that the pattern of light in the material does not depend much on the wavelength of the light and thus upon the unintended variations in thickness arising during the manufacture of the glass. …The light from the scene
diffracts through the grating, yielding a pattern of light on the array that does not appear like a traditional image—it does not “look good” but instead more like a diffuse blob, unintelligible to the human eye. Nevertheless, the blob contains enough visual information (albeit in an unusual distribution) such that the desired image can be reconstructed through a computational process called image

Compressive Sensing

….An optical image on a sensor is just a
complicated signal that can be represented as a list of numbers and processed digitally. Just as a complicated sound can be built up from a large number of simpler sounds, each added in a proportion that depends on the sound in question, so too can an image be built up from lots of simpler images. …

Enter compressive sensing. Theoretical results from statisticians have shown that, as long as the information from the scene is redundant (and the image is thus compressible), one does not need to measure such mathematically elegant bases, but can use measurements from a suitably random one. If such “coded measurements” are available then one can still exploit the idea that the signal can be well represented in the elegant basis elements (such as cosines or wavelets) and recover the image through compressive sensing.

Two E Bar/Lounge – Residential Style Lobby Lounge and Art Deco Bar at The Pierre

January 29, 2018

at the pierre, open to 10p (or 11)
live music Th,F,Sat?