Posts Tagged ‘quote’

The Long Road to Maxwell’s Equations – IEEE Spectrum

February 1, 2015

The Long Road to #Maxwell’s Equations
http://spectrum.ieee.org/telecom/wireless/the-long-road-to-maxwells-equations Heaviside simplified the original 20 eqns. to the current 4 w. vector fields

Also, Hertz’s 2 “loop” experiments were key!

A great grave to visit.

QT:{{”

Should you wish to pay homage to the great physicist James Clerk Maxwell, you wouldn’t lack for locales in which to do it. There’s a memorial marker in London’s Westminster Abbey, not far from Isaac Newton’s grave. A magnificent statue was recently installed in Edinburgh, near his birthplace. Or you can pay your respects at his final resting place near Castle Douglas, in southwestern Scotland, a short distance from his beloved ancestral estate.

You could start the clock in 1800, when physicist Alessandro Volta reported the invention of a battery, which allowed experimenters to begin working with continuous direct current. Some 20 years later,Hans Christian Ørsted obtained the first evidence of a link between electricity and magnetism, by demonstrating that the needle of a compass would move when brought close to a current-carrying wire. Soon after, André-Marie Ampère showed that two parallel current-carrying wires could be made to exhibit a mutual attraction or repulsion depending on the relative direction of the currents. And by the early 1830s, Michael Faraday had shown that just as electricity could influence the behavior of a magnet, a magnet could affect electricity, when he showed that drawing a magnet through a loop of wire could generate current.

A major seed was planted by Faraday, who envisioned a mysterious, invisible “electrotonic state” surrounding the magnet—what we would today call a field. He posited that changes in this electrotonic state are what cause electromagnetic phenomena.

The net result of all of this complexity is that when Maxwell’s theory made its debut, almost nobody was paying attention.

But a few people were. And one of them was Oliver Heaviside. Once described by a friend as a “first rate oddity,” Heaviside, who was raised in extreme poverty and was partially deaf, never attended university.

Heaviside ended up reproducing a result that had already been published by another British physicist, John Henry Poynting. But he kept pushing further, and in the process of working through the complicated vector calculus, he happened upon a way to reformulate Maxwell’s score of equations into the four we use today.

Now confident that he was generating and detecting electromagnetic waves, Lodge planned to report his astounding results at a meeting of the British Association, right after he returned from a vacation in the Alps. But while reading a journal on the train out of Liverpool, he discovered he’d been scooped. In the July 1888 issue of Annalen der Physik, he found an article entitled “Über elektrodynamische Wellen im Luftraum und deren Reflexion” (“On electrodynamic waves in air and their reflection”) written by a little-known German researcher, Heinrich Hertz.

Hertz’s … noticed that something curious happened when he discharged a capacitor through a loop of wire. An identical loop a short distance away developed arcs across its unconnected terminals. Hertz recognized that the sparks in the unconnected loop were caused by the reception of electromagnetic waves that had been generated by the loop with the discharging capacitor.

Inspired, Hertz used sparks in such loops to detect unseen
radio-frequency waves. He went on to conduct experiments to verify that electromagnetic waves exhibit lightlike behaviors of reflection, refraction, diffraction, and polarization.
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Distributed Information Processing in Biological and Computational Systems

January 26, 2015

Distributed Info. Processing in Biological & Computational #Systems http://cacm.acm.org/magazines/2015/1/181614-distributed-information-processing-in-biological-and-computational-systems/fulltext Contrasts in strategies to handle node failures

QT:{{"
While both computational and biological systems need to address these similar types of failures, the methods they use to do so differs. In distributed computing, failures have primarily been handled by majority voting methods,37 by using dedicated failure detectors, or via cryptography. In contrast, most biological systems rely on various network topological features to handle failures. Consider for example the use of failure detectors. In distributed computing, these are either implemented in hardware or in dedicated additional software. In contrast, biology implements implicit failure detector mechanisms by relying on backup nodes or alternative pathways. Several proteins have paralogs, that is, structurally similar proteins that in most cases originated from the same ancestral protein (roughly 40% of yeast and human proteins have at least one paralog). In several cases, when one protein fails or is altered, its paralog can automatically take its place24 or protect the cell against the mutation.26 Thus, by preserving backup functionality in the protein interaction.


While we discussed some reoccurring algorithmic strategies used within both types of systems (for example, stochasticity and feedback), there is much more to learn in this regard. From the distributed computing side, new models are needed to address the dynamic aspects of communication (for example, nodes joining and leaving the network, and edges added and being subtracted), which are also relevant in mobile computing scenarios. Further, while the biological systems we discussed all operate without a single centralized controller, there is in fact a continuum in the term “distributed.” For example, hierarchical distributed models, where higher layers “control” lower layers with possible feedback, represent a more structured type of control system than traditional distributed systems without such a hierarchy. Gene regulatory networks and neuronal networks (layered columns) both share such a hierarchical structure, and this structure has been well-conserved across many different species, suggesting their importance to computation. Such models, however, have received less attention in the distributed computing literature.

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The Many Guises of Aromaticity » American Scientist

January 23, 2015

The Many Guises of Aromaticity
http://www.americanscientist.org/issues/pub/2015/1/the-many-guises-of-aromaticity #Resonance is hyped; hence, many proposals for compounds w/ it that aren’t benchstable

QT:{{”
Today, an inflation of hype threatens this beautiful concept. Molecules constructed in silico are extolled as possessing surfeits of aromaticity—“doubly aromatic” is a favorite descriptor. Yet the molecules so dubbed have precious little chance of being made in bulk in the laboratory. One can smile at the hype, a gas of sorts, were it not for its volume. A century and a half after the remarkable suggestion of the cyclic structure of benzene, the conceptual value of aromaticity—so useful, so chemical—is in a way dissolving in that hype. Or so it seems to me.

Bench-Stable, Bottleable

Computers made the determination of the structure of molecules in crystals easy—what took half a year in 1960 takes less than an hour today. They also made computations of the stability of molecules facile.

Whoa! What do you mean by stability? Usually what’s computed is stability with respect to decomposition to atoms. But that is pretty meaningless; for instance, of the four homonuclear diatomic molecules (composed of identical atoms) that are most stable with respect to atomization, N2,C2, O2, and P2, two (C2 and P2) are not persistent. You will never see a bottle of them. Nor the tiniest crystal. They are reactive, very much so. In chemistry it’s the barriers to reaction that provide the opportunity to isolate a macroscopic amount of a compound. Ergo the neologism, “bench-stable.” “Bottleable” is another word for the idea. A lifetime of a day at room temperature allows a competent graduate student at the proverbial bench to do a crystal structure and take an NMR scan of a newly made compound. Or put it into a bottle and keep it there for a day, not worrying that it will turn into brown gunk.

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The two cultures of mathematics and biology | Bits of DNA

January 20, 2015

QT:{{”
What biologists should appreciate, what was on offer in Mumford’s obituary, and what mathematicians can deliver to genomics that is special and unique, is the ability to not only generalize, but to do so “correctly”. The mathematician Raoul Bott once reminisced that “Grothendieck was extraordinary as he could play with concepts, and also was prepared to work very hard to make arguments almost tautological.” In other words, what made Grothendieck special was not that he generalized concepts in algebraic geometry to make them more abstract, but that he was able to do so in the right way.
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http://liorpachter.wordpress.com/2014/12/30/the-two-cultures-of-mathematics-and-biology/

WASP: allele-specific software for robust discovery of molecular quantitative trait loci | bioRxiv

January 19, 2015

WASP: allele-specific software for robust discovery of molecular quantitative trait loci
Bryce van de Geijn, Graham McVicker, Yoav Gilad, Jonathan Pritchard

doi: http://dx.doi.org/10.1101/011221
http://biorxiv.org/content/early/2014/11/07/011221

QT:{{”
Mapping of reads to a reference genome is biased by sequence polymorphisms6. Reads which contain the non-reference allele may fail to map uniquely or map to a different (incorrect) location in the genome6.
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What Happens When We All Live to 100? – The Atlantic

January 9, 2015

What Happens When We All Live to 100?
http://www.theatlantic.com/features/archive/2014/09/what-happens-when-we-all-live-to-100/379338 Will the 3month/yr increase in life expectancy plateau? Will it affect society?

QT:{{”
The university, a significant aspect of the contemporary economy, centuries ago was a place where the fresh-faced would be prepared for a short life; today the university is a place where adults watch children and grandchildren walk to Pomp and Circumstance. The university of the future may be one that serves all ages. Colleges will reposition themselves economically as offering just as much to the aging as to the adolescent: courses priced individually for later-life knowledge seekers; lots of campus events of interest to students, parents, and the community as a whole; a pleasant
college-town atmosphere to retire near. In decades to come, college professors may address students ranging from age 18 to 80.
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NeXT logo by Paul Rand | Logo Design Love

January 4, 2015

QT:{{”

Steve Jobs on working with Rand:

“I asked him if he would come up with a few options, and he said, ‘No, I will solve your problem for you and you will pay me. You don’t have to use the solution. If you want options go talk to other people.’” “}}

http://www.logodesignlove.com/next-logo-paul-rand

Neuroscience, Ethics, and National Security: The State of the Art

December 30, 2014

#Neuroscience, Ethics & National Security http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001289
Interrogations w/ oxytocin truth serum, No-lie fMRI & p300 waves. Scary!

QT:{{"
National security agencies are also mining neuroscience for ways to advance interrogation methods and the detection of deception. The increasing sophistication of brain-reading neurotechnologies has led many to investigate their potential applications for lie detection. Deception has long been associated with empirically measurable correlates, arguably originating nearly a century ago with research into blood pressure [24]. Yet blood pressure, among other modern bases for polygraphy like heart and breathing rates, indicates the presence of a proxy for deception: stress. Although the polygraph performs better than chance, it does not reliably and accurately indicate the presence of deception, and it is susceptible to counter measures. ….

“Brain fingerprinting” utilizes EEG to detect the P300 wave, an event-related potential (ERP) associated with the perception of a recognized, meaningful stimulus, and it is thought to hold potential for confirming the presence of “concealed information” [25]. The technology is marketed for a number of uses: “national security, medical diagnostics, advertising, insurance fraud and in the criminal justice system” [26]. Similarly, fMRI-based lie detection services are currently offered by several companies, including No Lie MRI [27] and Cephos [28]. DARPA funded the pioneering research that showed how deception involves a more complex array of neurological processes than truth-telling, and that fMRI arguably can detect the difference between the two [29]. No Lie MRI also has ties to national security: they market their services to the DoD, Department of Homeland Security, and the intelligence community, among other potential customers [30].


In addition to questions of scientific validity, these technologies raise legal and ethical issues. Legally required brain scans arguably violate “the guarantee against self-incrimination” because they differ from acceptable forms of bodily evidence, such as fingerprints or blood samples, in an important way: they are not simply physical, hard evidence, but evidence that is intimately linked to the defendant’s mind [32]. Under US law, brain-scanning technologies might also raise implications for the Fourth Amendment, calling into question whether they constitute an unreasonable search and seizure [33].”

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Machine Intelligence Cracks Genetic Controls | Quanta Magazine

December 28, 2014

https://www.quantamagazine.org/20141218-machine-intelligence-cracks-genetic-controls/

QT:{{”

The splicing code is just one part of the noncoding genome, the area that does not produce proteins. But it’s a very important one. Approximately 90 percent of genes undergo alternative splicing, and scientists estimate that variations in the splicing code make up anywhere between 10 and 50 percent of all disease-linked mutations. “When you have mutations in the regulatory code, things can go very wrong,” Frey said.

“People have historically focused on mutations in the protein-coding regions, to some degree because they have a much better handle on what these mutations do,” said Mark Gerstein, a bioinformatician at Yale University, who was not involved in the study. “As we gain a better understanding of [the DNA sequences] outside of the protein-coding regions, we’ll get a better sense of how important they are in terms of disease.”

Scientists have made some headway into understanding how the cell chooses a particular protein configuration, but much of the code that governs this process has remained an enigma. Frey’s team was able to decipher some of these regulatory regions in a paper published in 2010, identifying a rough code within the mouse genome that regulates splicing. Over the past four years, the quality of genetics data — particularly human data — has improved dramatically, and
machine-learning techniques have become much more sophisticated, enabling Frey and his collaborators to predict how splicing is affected by specific mutations at many sites across the human genome. “Genome-wide data sets are finally able to enable predictions like this,” said Manolis Kellis, a computational biologist at MIT who was not involved in the study.

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Apple Lisa – Wikipedia, the free encyclopedia

December 28, 2014

QT:{{”

While the documentation shipped with the original Lisa only ever referred to it as The Lisa, officially, Apple stated the name was an acronym for Local IntegratedSystem Architecture or “LISA”.[6] Since Steve Jobs’ first daughter (born in 1978) was named Lisa Nicole Brennan, it was normally inferred that the name also had a personal association, and perhaps that the acronym was invented later to fit the name. Andy Hertzfeld[7] states the acronym was reverse engineered from the name “Lisa” in autumn 1982 by the Apple marketing team, after they had hired a marketing consultancy firm to come up with names to replace “Lisa” and “Macintosh” (at the time considered by Jef Raskin to be merely internal project codenames) and then rejected all of the suggestions. Privately, Hertzfeld and the other software developers used “Lisa: Invented Stupid Acronym”, a recursive backronym, while computer industry pundits coined the term “Let’s Invent Some Acronym” to fit the Lisa’s name. Decades later, Jobs would tell his biographer Walter Isaacson: “Obviously it was named for my daughter.”[8] “}}

http://en.wikipedia.org/wiki/Apple_Lisa