AOC Q2963PM 29″ Monitor with DisplayPort v1.2 in/out

AOC 29-inch IPS Q2963PM (21:9) LED Monitor; $379.00+T.

Features (Specifications)

  • Interconnect multiple displays to create a daisy-chain
  • 2560×1080 @ 60 Hz
  • Mobile High-Definition Link (MHL) to connect and charge the phone


  • It’s not entirely stated that it supports Display v1.2, it just says “DisplayPort” but it does have a DisplayPort output and the marketing collateral does exhibit a use in a daisy-chained scenario.
  • Whether special drivers are required or it is “Windows only” is unclear.
  • AOC is typically “Windows only”; c.f. AOC E2251FWU 22″ DisplayLink USB Monitor


Multi-Key Searchable Encryption

Raluca Ada Popa (MIT/CSAIL), Emily Stark (Meteor), Steven Valdez, Jonas Helfer, Nickolai Zeldovich, Hari Balakrishnan (MIT/CSAIL); Building Web Applications on Top of Encrypted Data Using Mylar; Usenix Symposium on Networks Systems Design and Implementation (NSDI); 2014-04-02; landing.


Web applications rely on servers to store and process confidential information. However, anyone who gains access to the server (e.g., an attacker, a curious administrator, or a government) can obtain all of the data stored there. This paper presents Mylar, a platform for building web applications, which protects data confidentiality against attackers with full access to servers. Mylar stores sensitive data encrypted on the server, and decrypts that data only in users’ browsers. Mylar addresses three challenges in making this approach work. First, Mylar allows the server to perform keyword search over encrypted documents, even if the documents are encrypted with different keys. Second, Mylar allows users to share keys and encrypted data securely in the presence of an active adversary. Finally, Mylar ensures that client-side application code is authentic, even if the server is malicious. Results with a prototype of Mylar built on top of the Meteor framework are promising: porting 6 applications required changing just 36 lines of code on average, and the performance overheads are modest, amounting to a 17% throughput loss and a 50 ms latency increase for sending a message in a chat application.

Raluca Ada Popa, Nickolai Zeldovich; Multi-Key Searchable Encryption; Cryptology ePrint Archive, 2013/508; 18 pages; landing.


We construct a searchable encryption scheme that enables keyword search over data encrypted with {\em different} keys. The scheme is practical and was designed to be included in a new system for protecting data confidentiality in client-server applications against attacks on the server.


  • Problem Statement
    • (distributed) key generation
    • (centralized) document storage
    • document sharing with access controls
    • an access graph
    • document search capability
  • Multiple users keys, multiple document keys
  • Scheme
    • search token
    • delta of the token
    • adjust converts deltas from user key to document keys
  • Definition of Multi-Key Search (MK), page 4
    • MK.Setup
    • MK.KeyGen
    • MK.Delta
    • MK.Token
    • MK.Enc
    • MK.Adjust
    • MK.Match
  • Assumptions
    • Bilinear Diffie-Hellman Variant (BDHV)
    • External Diffie-Hellman Variant (XDHV)
  • Fully-Homomorphic Encryption (FHE)
  • MHC Protocol
  • Implementation

Via: backfill | This Connection is Untrusted

Nothing says “The Web is Misconfigured” quite like a low-level security protocol failure notice: here

The Saeculum Generational Theory of Strauss & Howe

generally in archaeological order (newer stuff on top, older below)


  • When did the Fourth Turning (Crisis) start?
  • Can participants of an era, of a turning analyze it’s properties?
  • How far away from an era must observers get before understanding it is meaningful?
  • What is the shortest timescale under which an era can be sensed (are half-generations nameable, meaningful)?
  • Can someone who is outside of a given life phase actually understand another life phase in some other way than a “bag of numbers” approach via checkbox interrogatories sampled across the law of large numbers?
  • Is it possible to think beyond 20-year time horizons in principled and consistent ways?


  • 2001-09-11 => World Trade Center & Pentagon Attacks
  • 2002-03 => Internet Bubble 1.0 pops, various terrorist atacks
  • 2003-03 => Operation Iraqi Freedom
  • 2004 => various earthquakes, bombings, storms
  • 2005 => Katrina Floods New Orleans
  • 2006 => North Korea nuclear tests
  • 2007 => Minneapolis 35W bridge collapse, EF5 tornado destroys Greensburg KS
  • 2008 => Financial Crisis


  • Life Phases
    • Childhood => 0-20
    • Young Adult => 21-41
    • Midlife => 42-62
    • Elderhood => 63-83
    • Late Elderhood => 84+
  • Seasons
    Ordering: High -> Awakening -> Unraveling -> Crisis

    • High => First Turning
    • Awakening => Second Turning
    • Unraveling => Third Turning
    • Crisis => Fourth Turning
  • Archetypes
    Ordering: Prophet -> Nomad -> Hero -> Artist (rinse & repeat)

    • Prophet => Idealist
    • Nomad => Reactive
    • Hero => Civic
    • Artist => Adaptive
  • Generations
    • Homeland => Artist (also Generation Z, Post-Gen, New Silent)
    • Millennial => Hero (also Generation Y)
    • Generation X => Nomad (also Generation 13)
    • Boomers => Prophet
    • Silent => Artist
    • GI => Hero
    • Lost => Nomad
    • Missionary => Prophet
  • Turnings
    • Current Era (unnamed)
      • 2008+ through 2025
      • Fourth Turning => Crisis
    • Reagan Revolution/Culture Wars
      • 1982-2006
      • Third Turning => Unraveling
    • Consciousness Revolution
      • 1961-1981
      • Second Turning => Awakening
    • Postwar Boom
      • 1946-1960
      • First Turning => High
    • Great Depression/World War II
      • 1925-1945
      • Fourth Turning => Crisis
  • Slices for Generation X
    • For The Crisis 2008-2025
      • Elderhood => Boomers who are Prophets.
      • Midlife => Generation X who are Nomads.
      • Young Adults=> Millennial who are Heros.
      • Childhood => Homeland who are Artists.
    • Nomad Life Experiences
      • Childhood => Consciousness Revolution (1964-1981), which was an Awakening.
      • Young Adult => Reagan Revolution/Culture Wars (1982-2000), which was an Unraveling.
      • Midlife => Current Era (2001-2008 .. 2025), which is a Crisis.
      • Elderhood => Next Era (2025+), which is a High.
Prophet Nomad Hero Artist
High Childhood Elderhood Midlife Young Adult
Awakening Young Adult Childhood Elderhood Midlife
Unraveling Midlife Young Adult Childhood Elderhood
Crisis Elderhood Midlife Young Adult Childhood

Generations in Anglo-American History

Generation Birth Years Famous Member Era in which Members Came of Age Archetype
Man Woman
Missionary 1860–1882 Franklin Roosevelt Emma Goldman Third Great Awakening Prophet
Lost 1883–1900 Harry Truman Dorothy Parker World War I & Prohibition Nomad
G.I. 1901–1924 John Kennedy Katharine Hepburn Depression & World War II Hero
Silent 1925–1942 Martin Luther King, Jr. Sandra Day O’Connor American High Artist
Boom 1943–1960 George W. Bush Hillary Clinton Consciousness Revolution Prophet
Generation X 1961–1981 Barack Obama Sarah Palin Long Boom & Culture Wars Nomad
Millennial 1982–2004 Mark Zuckerberg Anne Hathaway Global Financial Crisis Hero
Homelanders 2005-

See Generations of Anglo-American History, LifeCourse Associates

Implications & Derivations

  • <quote>In midlife, Nomads mellow into pragmatic and savvy leaders during a Crisis. Middle-aged Nomads make the personal sacrifices for the good of society that their elder Prophets weren’t willing to make during the Unraveling. The Nomads’ cunning and survival instincts make them well-suited to lead during a Fourth Turning. Many of America’s most memorable military, government, and business leaders were scrappy midlife Nomads (e.g. Generals Patton and Grant).</quote> ref


Overview, Review & Survey

Corpus & Canon

Paul Taylor (Pew Research Center); The Next America: Boomers, Millennials, and the Looming Generational Showdown; Public Affairs; 2014-03-04; 288 pages; kindle: $15, paper: $18; promotional site.
Paul Taylor, Executive Vice President of Special Projects at the Pew Research Center.

Neil Howe, William Strauss; The Fourth Turning: An American Prophecy; Broadway Books; 1997-12-29; 400 pages; promotional site; a copy.

Neil Howe, William Strauss; Generations: The History of America’s Future, 1584 to 2069; Quill; 1992-09-30; 538 pages.

Press & Promotions


Jean Twenge

  • Contrary

Embodied Cognition

  • Probably
  • What you experience physically shapes you think, what thoughts you can think.

r/K Selection Theory

  • The Evolutionary Psychology Behind Politics: How Conservatism and Liberalism Evolved Within Humans; Anonymous Conservative; Federalist Publications; 2012-02-17; 280 pages; kindle: $10, paper: $26
  • Strauss and Howe’s Generational Theory, in the Context of r/K Theory; Anonymous Conservative; In Some Blog; 2013-06-14.
    • Definition: r/K Selection Theory
      • r-selection species spread parental investment across many offspring,
      • K-selected species focus theirs on a few.
    • Explanation: r/K Selection Theory
    • Analogical Reasoning <quote>
      • Crisis is r-psychologies confronted by the shortage of K-selection. This turmoil produces an adaptive shift in the population’s psychology towards a more K-selected, politically Conservative psychology.
      • High is the environment of r-selected resource excess that is produced by a majority K-selected populace, living in an environment where these rewards are enjoyed by those who produce them.
      • Awakening and Unraveling are just the leftists gradually increasing in number due to the r-selection, and fucking up a good thing until it all falls apart, and the Crisis returns.
  • The Evolutionary Psychology Behind Politics; a Book Review; in some blog; 2014-01-20.
    tl;dr => critical; <quote>bings back social Darwinism like it never went out of style.</quote><quote>Whether or not his neurological and psychological theories are entirely true, it’s a promising rhetorical strategy. It dehumanizes the left, fosters cohesion among the K-selected where there would be instead be competition for the diminished pool of resources, and tampers with the tendency among right-wingers to feel excessive levels of mercy, pity, and restraint towards rival populations who seek to subvert and destroy them.</quote>


richparents (Pew Research Center); Chart of the Week: Do firefighters or musicians have richer parents?; In Their Blog; 2014-03-21.

iGen? Homelanders? The Next Generation Needs a Name; Meghan Neal; In Motherboard; 2014-03-14.

Source: North Coast Investment Research, appearing in After Millennials

LifeCourse Associates; General Archetypes

LifeCourse Associates; General Archetypes

Lowering Mouse Sensitivity in Fedora 18 for the Kensington 72123CAA Mouse-in-a-Box Optical Mouse

Not yet SOLVED …

Problem Statement

  • The mouse moves too fast
  • There ought to be a setting to slow it down


  • The setting does not “stick”
  • After the dialog is settings dialog is closed, the mouse speed reverts.
  • Mouse speed setting from the GNOME GUI level does not work.


  • Fedora 18
  • Kensington 72123CAA Mouse-in-a-Box Optical USB Mouse


  • The Kensington 72123CAA what you will receive from Amazon nowadays, even though you order 72123
  • The Kensington 72123 (no the CAA suffix), works just fine and has appropriate desktop speed & acceleration with the default settings.


Untested …


  • xinput --list --short
  • xinput --list --long
  • xinput --set-prop $NAME $PROPERTY $VALUE
  • xinput --list-props $NAME
  • xinput --set-prop $NAME $PROPERTY $VALUE

where $PROPERTY is among

  • "Device Accel Constant Deceleration"
  • "Device Accel Velocity Scaling"

which are enumerated in [WHERE?]


  • device can be the device name as a string or the XID of the device.
  • slave can be the device name as a string or the XID of a slave device.
  • master can be the device name as a string or the XID of a master device.
  • property can be the property as a string or the Atom value.


$ xinput --list --short
⎡ Virtual core pointer                    	id=2	[master pointer  (3)]
⎜   ↳ Virtual core XTEST pointer              	id=4	[slave  pointer  (2)]
⎜   ↳ PIXART USB OPTICAL MOUSE                	id=13	[slave  pointer  (2)]
⎜   ↳ PIXART USB OPTICAL MOUSE                	id=8	[slave  pointer  (2)]
⎣ Virtual core keyboard                   	id=3	[master keyboard (2)]
    ↳ Virtual core XTEST keyboard             	id=5	[slave  keyboard (3)]
    ↳ Power Button                            	id=6	[slave  keyboard (3)]
    ↳ Power Button                            	id=7	[slave  keyboard (3)]
    ↳ Logitech USB Keyboard                   	id=11	[slave  keyboard (3)]
    ↳ Logitech USB Keyboard                   	id=12	[slave  keyboard (3)]
    ↳ Logitech USB Keyboard                   	id=14	[slave  keyboard (3)]
    ↳ Logitech USB Keyboard                   	id=15	[slave  keyboard (3)]
    ↳ GASIA USB Keyboard                      	id=9	[slave  keyboard (3)]

Recall … the GNOME settings “do not work” because they do not stick after the settings dialog is dismissed.

OpenID Connect Background

OpenID Connect

OpenID Connect Core 1.0


OpenID Connect identifies a set of personal attributes that can be exchanged between Identity Providers and the apps that use them, and includes an approval step so that users can consent (or deny) the sharing of this information.

OAuth 2.0

  • RFC 6749 The OAuth 2.0 Authorization Framework; Editor: D. Hardt (Microsoft); 2012-10.
  • RFC 6750 The OAuth 2.0 Authorization Framework: Bearer Token Usage; M. Jones (Microsoft), D. Hardt (self); 2012-10.

Compare & Contrast

OpenID 2.0

  • pages, not apps => enterprise web applications, not storebought os screen chiclets
  • XML => Security Assertion Markup Language (SAML)

OpenID Connect

  • JSON
  • TLS
  • standard crypto signature-verification libraries.




  • Android => <quote>There are already system-level APIs built into the Android operating system to provide OpenID Connect services.</quote>
  • iOS => probably not; Apple isn’t listed, [own thing; add value].

Working Group

OpenID Foundation


  • AOL,
  • Deutsche Telekom,
  • Facebook,
  • Google,
  • Microsoft,
  • Mitre Corporation,
  • mixi,
  • Nomura Research Institute,
  • Orange,
  • PayPal,
  • Ping Identity,
  • Salesforce,
  • Yahoo! Japan.


  • GSMA, Mobile Network Operators (MNOs) => mobileidentity(articulates a need)
    • <rephrase>Mobile Connect service is a single, trusted, mobile phone number-based authentication solution</rephrase>
    • <quote>The standard-based Mobile Connect service will utilise the OpenID Connect protocol, offering broad interoperability across mobile operators and service providers, further ensuring a seamless experience for consumers. </quote>
    • Supporters: Axiata Group Berhad, China Mobile, China Telecom, Etisalat, KDDI, Ooredoo, Orange, Tata Teleservices, Telefónica, Telenor, Telstra, VimpelCom.
    • Users: Dailymotion, Deezer, Gemalto, Giesecke & Devrient, Morpho, Oberthur, VALID.
  • FIDO Alliance => unclear.


Via: backfill

Making in America: From Innovation to Market | Suzanne Berger

Suzanne Berger, MIT Task Force on Production in the Innovation Economy; Making in America: From Innovation to Market; The MIT Press; 2013-08-23; 264 pages; kindle: $14, paper: $20; publisher.

Suzanne Berger, Raphael Dorman-Helen Starbuck Professor of Political Science at MIT, is author of Making in America: From Innovation to Market.

Paired With

(the first book is the opinion & summary, this book is the travel log ethnography).

Richard M. Locke, Rachel L. Wellhausen (editors); Production in the Innovation Economy; The MIT Press; 2014-01-03; 288 pages; kindle: $29, paper: $30 ($17+SHT).


(a decade ago, by Berger)

Suzanne Berger; How We Compete: What Companies Around the World Are Doing to Make it in Today’s Global Economy; Crown Business; 2004-12-27; 252 pages.


(of Making in America)

How Finance Gutted Manufacturing; Suzanne Berger; In Boston Review; 2014-03-10.



  • Production in the Innovation Economy Project at MIT
  • Last 30 years
    • 1980 – 2010; i.e. Regan through Bush (skipping Clinton)
  • Focus on Core Competencies is destructive/bad/unhealthy, etc.
  • Vertical integration is good.
  • continued below the Responses


  • Dean Baker
    • “good” but not really
      • <quote><snip/>but it asks more questions than it answers. On its face<snip/>At a deeper level it lacks a clear sense of the tradeoffs involved in moving toward more high-road manufacturing.</quote>
      • <quote>Berger’s reference to Houseman on mis-measured productivity matters little in the context of this debate. </quote>
      • <quote>But we need more evidence and some sense of the tradeoffs. If German firms provide lower returns to investors, how much lower is it?</quote>
    • Nostrums
      • Devalue the dollar.
      • Something vague about better labor relations.
  • Suzanne Berger
  • Dan Breznitz
    • Finance is destructive; more so than Berger pitched.
    • I&P innovation is good, is done well
    • The Silicon Valley Model is the problem.
    • Need
      • High skill labor
      • High labor control of means of production
      • Public/Private arrangements
      • Financial architecture for small/midsize [manufacturing] firms
      • Regulation [enlightened, paternal]
  • Gary Herrigel
    • Good Issues
      • disintegration
      • market failures
      • industrial ecosystem governance
    • But
      • <quote>[Berger] seems to believe in a kind of technological Say’s Law: that radical innovation will create new markets, new industries, and new jobs.</quote>
      • Finance, is not [as much of] a problem [as Berger states]
        1. Manufacturing declined worldwide anyway
        2. Disintegration frees up resources [for good things; e.g. experimentation]
        3. The outcome [of Timken] is not inevitable
        4. It works for others
    • The problem is: inequality
      • Job growth occurs in China which is not unequal
      • Inequality distorts <quote>he effectiveness of public experimentation around the market failures.</quote>
  • Susan N. Houseman
    • Framing
      • Official statistics actually point to a relatively healthy manufacturing sector.
      • Productivity growth in manufacturing has greatly exceeded that in the rest of the economy.
      • The numbers are incorrect; all positive numbers are due to electronics.
      • Flat since 2000
    • Authorities Cited
      • Daron Acemoglu, David Autor
      • William Lazonick
    • The True Truth
      • Low wage countries
      • Chinese imports
      • Commodity manufacturing
      • Not enough product obsolescence
      • Financialization & short-termism
    • Therefore: Market Failure
    • Nostrums
      • Concept
        • Repair the market failure
        • Public policy is far-sighted.
      • Small Policy
        • Subsidization & paternalism; c.f. the Obama initiatives
          Ensure the results are “manufactured here”
        • Regulations on financial manipulation (e.g. stock buybacks)
      • Big Policy
        • Devalue the dollar
        • Reduce corporate taxes
  • Nichola Lowe
    • <quote>But there is another solution: empowering smaller manufacturers to become active participants in the development of industrial skills.</quote>
    • <quote>These experiments in skill development, led by small firms, may point the way to strengthening the U.S. manufacturing ecosystem.</quote>
    • Authorities Cited
    • Nostrums
      • Career Training
      • Unskilled to semi-skilled
      • Community College
      • Training Cooperatives
  • Joel Rogers, Dan Luria
    • Agree
      • Financialization caused it
      • Financialization will prevent repair
    • Disagree
      • Onshoring is a PR hoax in plastics and lousy jobs.
      • Innovation cannot be an accent
      • An innovation commons does not solve.
    • Justification & Emphasis
      • People without graduate degrees
      • Fuzzy effects around the labor market
    • The Problem Is
      • Firms offshore to exit noncompetitive positions.
      • This causes imports.
      • There is no replacement export.
    • Reasoning
      • [They] advocate policies that significantly raise demand for the output of good firms.
      • The only plausible source of such demand is government.
    • Model
      • Large defense contractors
      • National Champion System
      • The managed Industries of the ’60s-’80s
    • Nostrums
      • Very large public works.  [Why not a tech-heavy war?]
      • Examples:
        • Trains
        • Bridges
        • Electrical Grid
  • Mike Rose
    • From the publisher review of his book
      <quote>Rose quotes a policy analyst: “How do you honor a student’s construction worker father while creating the conditions for his child to not be a construction worker?”</quote>
    • Cultural assumptions about manual labor => <quote><snip/> attribute lower intelligence to those who work with their hands. </quote>
    • Parables & Euphemisms
      • Old Economy vs New Economy
      • Neck Up vs Neck Down
      • <quote>a senior executive at a major U.S. corporation wondered if “smart people” were needed in manufacturing</quote>
      • Skills Gap as stigma and self loathing
    • Result
      • negative & reductive attitude
      • disinvestment & disinvolvement
      • self-fulfilling
    • Authorities Cited
  • J. Phillip Thompson
    • He helps [presidents of]
      • United Federation of Teachers
      • National Health Care Workers’ Union
    • Unions aren’t the solution, but could be, maybe
      • <quote>federal laws that mandate a “prudent man” standard for pension management.</quote>
      • <quote>[court & SEC require] prudent men should care only about short-term profit maximization.</quote>
      • <quote>there is nothing stopping unions and the public from demanding that prudent investment aim at something other than short-term profits.</quote>
    • Finance is like Defense
      • A planned economy
      • Done in partnership the government
    • Nostrum
      • Democratize the finance firms [meaning...?]
      • Something about a multilateral stakeholder theory
      • Something about shareholder activism
  • Catherine Tumber
    • Berger’s article
      • asks the right questions
      • is “original”
      • Asked: <quote>What critical functions and services were lost with the demise of the great vertically integrated corporations, and how can we reconfigure them—if at all—in a finance-dominated neoliberal economy?</quote>
    • Production in the Innovation Economy Projectat MIT
      • demonstrates something somehow
      • does not occur in a vacuum
    • Free markets
      • are bad
      • won’t be changing
      • (exactly) thirty years of badness, termed “freebooting”
      • are caused by Libertarians (left and right), who need to change
    • Authorities Cited (in order of appearance)
      • Vaclav Smil; Made in the U.S.A. 2013.
      • Andrew McAfee, Erik Bryjolfsson The Second Machine Age; 2014.
      • Brad Feld; Startup Communities; 2012.
        • a “breathless guide”
        • The Charismatic Entrepreneur
        • Leaders & Feeders Model
      • Raymond Williams
        • Language approach to analysis
        • <quote>one can penetrate the heart of a culture by identifying its keywords and subjecting them to critical examination.</quote>
        • Examples: “talent,” “smart,” “horizontal network,” “knowledge work,” “brain hub,” “The Creative Class.”
      • Richard Florida
        • The Creative Class
      • Edward Glaeser and Enrico Morett
      • Alan Ehrenhalt
        • “The Great Inversion” from suburban to city living.
      • Zelda Bronstein
        • The Smart Growth movement
      • Jane Jacobs
        • said something
    • Nostrums
      • Something about a revalorization project: <quote>What we need, in part, is to call things by their proper names. Writ large, the United States is in the grip of a financial economy. To right the balance politically, productive work and culture must be valued again—in schools, in urban planning, and in a world shared with innovative talent.</quote>
  • David Weil
    • Organizational innovation is the problem.
    • Core (vs non-Core-is-Context) is the problem.
    • Results
      • inequality
      • lawlessness (noncompliance)
      • relationship (is unwound)
    • A story: activity cessation
      • starts reasonably; at the edge in peripheral activities.
      • creeps into core; maintenance, security, due care, etc.
    • Multi-tier subsidiaries
      • market mediation in lieu of command&control relationship (market==bad)
      • responsibility via tort means workplace safety falls
      • something about inequality [by parable: it follows that if a janitor and a master craftsman work together in the same shop, then the janitor will naturally have higher wages than if he was an independent contractor].


Since the 1980s, financial market pressures have driven companies to hive off activities that sustained manufacturing.
Reply: Suzanne Berger

Theme Thread Claims

  • Financialization did it.
  • Financialization is bad.
  • Capitalism is bad.
  • Government has a role to play.
  • There is contradition latent, so prescribe gently
    • Retirement is care and people once worn out.
    • Retirement uses Capitalism.
    • This pits Retirement Class contra Working Class.
    • Yet value Working Class, which produces, more than Retirement Class, which consumes.
  • See, look! there are: market failures.
  • Need public/private coordination.
  • Germany is the paradigm, the exemplar and the model.


(from the article)

<quote>In the 1980s about two-dozen large, vertically integrated companies such as Motorola, DuPont, and IBM dominated the American scene. With some notable exceptions (for example, GE), large vertically integrated companies today have pared off activities and become not only smaller but also more narrowly focused on core competencies. Under pressure from financial markets, they have shed activities that investors deemed peripheral—such as Timken’s steel.</quote>

c.f. Geoffrey Moore’s Core vs Context

<quote>The breakup of vertically integrated corporations and their recomposition into globally linked value chains of designers, researchers, manufacturers, and distributors has had some enormous benefits both for the United States and for developing economies. It has meant lower costs for consumers, new pathways for building businesses, and a chance for poor countries to create new industries and raise incomes.

But the changes in corporate structures that brought about these new opportunities also left big holes in the American industrial ecosystem. These holes are market failures. Functions once performed by big companies are now carried out by no one.</quote>

<quote>A senior executive of Cisco told MIT researchers:

The separation of R&D and manufacturing has today become possible at a level not even conceivable five years ago. Progress in technology allows us to have people working anywhere collaborating. We no longer need to have them located in clusters or centers of excellence. We now have the ability to sense and monitor what’s going on in our suppliers at any place and any time. Most of this is based on sensors deployed locally, distributed control systems, and new middleware and encryption schemes that allow this to be done securely over the open Internet. . . . In other words, not only do we monitor and control what’s happening inside a factory, but we’re also deeply into the supply chain feeding in and feeding out of the factory.

Digitization and the Internet continue in multiple ways to enable the fragmentation of corporate structures that financial markets demand.</quote>

<quote>Now that investors have curbed their appetite for startups going public, acquisition by big companies and recourse to foreign capital seem to be the main avenues for bringing to market the innovations that begin life in university and public laboratories. Both of these routes have troubling implications for American innovation and jobs. When big companies acquire startups, the MIT researchers found, much of the dynamism and promise of the new technology can be lost in the process of integration. When commercialization takes place outside the United States, opportunities to learn about scaling new technologies are foregone. Over time, it becomes more likely that innovation will shift to places where companies have more experience with scale-up and commercialization.</quote>


Economist at the Michigan Manufacturing Technology Cente

Graph Processing Using Big Data Technologies | InfoQ

Tapad’s Graph Processing Using Big Data Technologies; Charles Menguy; In InfoQ; 2014-03-17.


  • The article appears to be an interview with Dag Liodden, but rambles on into a general overview of the genre
    • Big Data (which is big)
    • Graph
    • JVM Culture
  • Tapad
    • Dag Liodden
    • Quoted for color, background & verisimilitude.
    • Testifies to participation in the genre.
  • Facebook
  • Factoids
    • U.S. Graph
      • 1.1×109 nodes.
      • “multiple” TB
        • stored in <= 20TB Flash SSD
        • 2TB working RAM
      • 100,000 q/s
      • multiple data centers
      • geographic replication
  • Nodes are classed
    • transient,
    • persistent (non-transient)
  • Persistent
    • 5 edges (around, average)
    • 500 profile facts
  • Scheme
    • Online truth maintenance (real-time serving)
    • Offline usage (dump to HDFS)

Referenced, Cited

Via: backfill, backfill

Enneagram, continued


  • Riso-Hudson Enneagram Type Indicator (RHETI).




<quote>There are two current online tests with statistical merit, and each has a different format for type selection. (Please see below for links to those tests.) We think these Enneagram tests are of high quality, and surely worth investigating.</quote> Helen Palmer and the Team



Enneagram of Personality; In Jimi Wales’ Wiki

  • Origin disputed, attribution to many; credit claimed by many
  • The Device
    • Triangle => 3-6-9 (law of three)
    • Other => 1-4-2-8-5-7 (law of seven); something about the irrational 1/7 represented in decimal (i.e. it cycles)
    • Circle => unity
  • Scheme
    • archetypes => characteristic roles
    • stress points (disintegration)
    • security points (integration)
    • Triples
      • topologically connected; e.g. (relaxed)7<-1->4(stressed)
      • fuzzy logic, probability distribution
    • Wings
      • adjacent influences
      • spectrum, fuzzy logic, probability distribution
      • 4<-3->2
      • 7<-6->5
      • 1<-9->8
    • Instinctual subtype (energies)
      • Either
        • A dimension with fuzzy probabilistic position
        • Three independent domains
      • 3 x 9 = 27 types
      • Points
        • self-preservation / survival
        • sexual / transmitting-reproducing
        • social / navigating
  • Criticism
    • <quote>While the enneagram system shares little with traditional Christian doctrine or spirituality, it also shares little with the methods and criteria of modern science… The burden of proof is on proponents of the enneagram to furnish scientific evidence for their claims.</quote> Committee on [Catholic] Doctrine, 2000.
  • Referenced

Fourth Way Enneagram; In Jimi Wales’ Wiki

  • George Gurdjieff
  • A cosmology
    • Law of Seven => seven steps
      • Operable at all scales
      • Diatonic scale, from music
      • Octaves
      • Something about the evolution of a system
      • Something about shock points, discontinuities
    • Law of Three => three forces
      • Active
      • Passive
      • Neutralilzing
    • <quote>The enneagram shows the Law of Seven and the Law of Three in a single symbol with the three forces joined in a triangle at the 3,6 and 9 points, and in addition seven inner lines following the 142… sequence.</quote>
  • Food Diagram
    • Metaphor
      • food => inputs to the human body
      • digestion => physical or conceptual (words like “sexual” crop up here later on)
    • Categories
      • Food
      • Air
      • Impressions
    • Something in alchemy
      • Clockwise traversal
      • Food => (Do Re Mi); Air => (_/Do, Fa/Re, Sol/Mi); Impressions => (_/Do, Fa/Re, Sol/Mi).
      • Something about shocks and densities
  • Application
    • Processes
    • The Enneagram of Personality
      • The Enneagram of “Body Types” (“Essence Types”)
      • Fellowship of Friends


Internet Advertising: An Interplay Among Advertisers, Online Publishers, Ad Exchanges and Web Users | Yuan, Abidin, Sloan, Want

Shuai Yuan, Ahmad Zainal Abidin, Marc Sloan, Jun Wang; Internet Advertising: An Interplay Among Advertisers, Online Publishers, Ad Exchanges and Web Users; In Information Processing and Management; 2013-07-04; 44 pages; landing.


Internet advertising is a fast growing business which has proved to be significantly important in digital economics. It is vitally important for both web search engines and online content providers and publishers because web advertising provides them with major sources of revenue. Its presence is increasingly important for the whole media industry due to the influence of the Web. For advertisers, it is a smarter alternative to traditional marketing media such as TVs and newspapers. As the web evolves and data collection continues, the design of methods for more targeted, interactive, and friendly advertising may have a major impact on the way our digital economy evolves, and to aid societal development.

Towards this goal mathematically well-grounded Computational Advertising methods are becoming necessary and will continue to develop as a fundamental tool towards the Web. As a vibrant new discipline, Internet advertising requires effort from different research domains including Information Retrieval, Machine Learning, Data Mining and Analytic, Statistics, Economics, and even Psychology to predict and understand user behaviours. In this paper, we provide a comprehensive survey on Internet advertising, discussing and classifying the research issues, identifying the recent technologies, and suggesting its future directions. To have a comprehensive picture, we first start with a brief history, introduction, and classification of the industry and present a schematic view of the new advertising ecosystem. We then introduce four major participants, namely advertisers, online publishers, ad exchanges and web users; and through analysing and discussing the major research problems and existing solutions from their perspectives respectively, we discover and aggregate the fundamental problems that characterise the newly-formed research field and capture its potential future prospects.


  • Ad Blocking
    • Ad Block Plus
    • BetterPrivacy
  • Significant References section


GM Backstabs Tesla Motors in Ohio as NJ Legislators Try To Kill Anti-Tesla Rules | Transport Evolved

GM Backstabs Tesla Motors in Ohio as NJ Legislators Try To Kill Anti-Tesla Rules; ; In Transport Evolved; 2014-03-13.


  • GM doesn’t want Tesla to sell directly to its customers, and it wrote to Ohio’s Senate asking it to help.
  • GM sent official written testimony to the Ohio State Senate Committee currently considering  Senate Bill 260, opposing outright the granting of any new dealer licenses to Tesla.


  • New Jersey Motor Vehicle Commission
  • Tesla
  • Auto Dealer Association
  • Ohio
    • Senate Bill 260
      • Section 4517.12(A) The registrar of motor vehicles shall deny the application of any person for a license as a motor vehicle dealer, motor vehicle leasing dealer, or motor vehicle auction owner and refuse to issue the license if the registrar finds that the applicant:
        • <snip/>
        • Section 4517.12(A)(11) Is a manufacturer, or a parent company, subsidiary, or affiliated entity of a manufacturer, applying for a license to sell or lease new or used motor vehicles at retail. Nothing in division (A)(11) of this section shall prohibit a manufacturer from disposing of motor vehicles at wholesale at the termination of a consumer lease through a motor vehicle auction. Division (A)(11) of this section shall not serve as a basis for the termination, revocation, or nonrenewal of a license granted prior to the effective date of this amendment.
        • <snip/>

Private traits and attributes are predictable from digital records of human behavior | Kosinski, Stillwell, Graepel

Michal Kosinski, David Stillwell, Thore Graepel; Private traits and attributes are predictable from digital records of human behavior; In Proceedings of the National Academy of Sciences of the United States of America (PNAS); 2013-02-12; 4 pages; landing.


We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.


  • You Are What You Like, promotional site.
  • Singular Value Decomposition (SVD)
  • Pseudo-Inverse of a Matrix
  • Five Factor Model (FFM)
    • Dimensions
      1. Openness to Experience
      2. Conscientiousness
      3. Extraversion
      4. Agreeableness
      5. Emotional Stability
    • Instruments
      • NEO Personality Inventory (NEO-PI-R)
      • NEO Five-Factor Inventory (NEO-FFI)
  • Intelligence
    • Raven’s Standard Progressive Matrices (SPM)
    • Spearman’s Theory of General Ability
  • International Personality Item Pool (IPIP)
  • Satisfaction With Life (SWL)
  • myPersonality Project
  • Receiver-Operating Characteristic (ROC)
  • Area Under [the] Curve (AUC)


  • Lazer D, et al. (2009) Computational social science. In Science 323(5915):721–723.
  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender
    systems. In Computer 42(8):30–37.
  • Chen Y, Pavlov D, Canny JF (2009) Large-scale behavioral targeting. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pp 209–218.
  • Butler D (2007) Data sharing threatens privacy. In Nature 449(7163):644–645.
  • Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In Proceedings of the IEEE Symposium on Security and Privacy, pp 111–125.
  • Duhigg C (2012) The Power of Habit: Why We Do What We Do in Life and Business
    (Random House, New York).
  • Ince HO, Yarali A, Özsel D (2009) Customary killings in Turkey and Turkish modernization. In Middle East Studies 45(4):537–551.
  • 8. Fast LA, Funder DC (2008) Personality as manifest in word use: Correlations with selfreport, acquaintance report, and behavior. In Journal of Personal Social Psychology 94(2):334–346.
  • Costa PT, McCrae RR (1992) Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Manual (Psychological Assessment Resources, Odessa, FL).
  • Gosling SD, Ko SJ, Mannarelli T, Morris ME (2002) A room with a cue: Personality
    judgments based on offices and bedrooms. In Journal of Personal Social Psychology 82(3):379–398.
  • Hu J, Zeng H-J, Li H, Niu C, Chen Z (2007) Demographic prediction based on user’s browsing behavior. In Proceedings of the International World Wide Web Conference (WWW), pp 151–160.
  • Murray D, Durrell K (1999) Inferring demographic attributes of anonymous Internet
    users. In Revised Papers from the International Workshop on Web Usage Analysis and User Profiling, eds Masand BM, Spiliopoulou M (Springer, London), pp 7–20.
  • De Bock K, Van Den Poel D (2010) Predicting website audience demographics for Web advertising targeting using multi-website clickstream data. In Fundamenta Informaticae 98(1):49–70.
  • Goel S, Hofman JM, Sirer MI (2012) Who does what on the Web: Studying Web
    browsing behavior at scale. In International Conference on Weblogs and Social Media, pp 130–137.
  • Kosinski M, Kohli P, Stillwell DJ, Bachrach Y, Graepel T (2012) Personality and website choice. In Proceedings of the ACM Web Science Conference, pp 251–254.
  • Marcus B, Machilek F, Schütz A (2006) Personality in cyberspace: Personal Web sites as media for personality expressions and impressions. In Journal of Personal Social Psychology 90(6):1014–1031.
  • Rentfrow PJ, Gosling SD (2003) The do re mi’s of everyday life: The structure and
    personality correlates of music preferences. In Journal Personal Social Psychology 84(6):1236–1256.
  • Quercia D, Lambiotte R, Kosinski M, Stillwell D, Crowcroft J (2012) The Personality of popular Facebook users. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW), 2012, pp 955–964.
  • Bachrach Y, Kohli P, Graepel T, Stillwell DJ, Kosinski M (2012) Personality and patterns of Facebook usage. In Proceedings of the ACM Web Science Conference, pp 36–44.
  • Quercia D, Kosinski M, Stillwell DJ, Crowcroft J (2011) Our Twitter profiles, our selves: Predicting personality with Twitter. In Proceedings of the 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, or maybe in Proceedings of the IEEE International Conference on Social Computing, pp 180–185.
  • Golbeck J, Robles C, Edmondson M, Turner K (2011) Predicting personality from
    Twitter. Proceedings of the IEEE International Conference on Social Computing, pp 149–156.
  • Golbeck J, Robles C, Turner K (2011) Predicting personality with social media. In Proceedings of the Conference on Human Factors in Computing Systems (CHI), pp 253–262.
  • Jernigan C, Mistree BF (2009) Gaydar: Facebook friendships expose sexual orientation. First Monday 14(10).
  • Golub GH, Kahan W (1965) Calculating the singular values and pseudo-inverse of a matrix. In Journal Society for Industrial & Applied Math (SIAM) 2(2):205–224; also as Journal of SIAM Numerical Analysis, B 2(2).
  • Goldberg LR, et al. (2006) The international personality item pool and the future of
    public-domain personality measures. In Journal Research in Personality 40(1):84–96.
  • Raven JC (2000) The Raven’s progressive matrices: Change and stability over culture and time. In Cognitive Psychology 41(1):1–48.
  • Diener E, Emmons RA, Larsen RJ, Griffin S (1985) The satisfaction with life scale. In Journal Personal Assessment 49(1):71–75.
  • Musick K, Meier A (2010) Are both parents always better than one? Parental conflict
    and young adult well-being. In Social Science Research 39(5):814–830.
  • Schimmack U, Diener E, Oishi S (2002) Life-satisfaction is a momentary judgment and a stable personality characteristic: The use of chronically accessible and stable sources. In Journal of Personality 70(3):345–384.
  • Nass C, Lee KM (2000) Does computer-generated speech manifest personality? An experimental test of similarity-attraction. In Journal of Experimental Psychology 7(3):171–181.


  • Costa PT, McCrae RR (1992) Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Manual (Psychological Assessment Resources, Odessa, FL).
  • Goldberg LR, et al. (2006) The international personality item pool and the future of public-domain personality measures. In Journal of Research on Personality 40(1):84–96.
  • Raven JC (2000) The Raven’s progressive matrices: change and stability over culture and time. In Cognitive Psychology 41(1):1–48.
  • Lubinski D (2004) Introduction to the special section on cognitive abilities: 100 years after Spearman’s (1904) “’General intelligence,’ objectively determined and measured”. In Journal of Personal Social Psychology 86(1):96–111.
  • Diener E, Emmons RA, Larsen RJ, Griffin S (1985) The satisfaction with life scale. In Journal of Personal Assessment 49(1):71–75.
  • Golub GH, Kahan W (1965) Calculating the singular values and pseudo-inverse of a matrix. In Journal Society for Industrial & Applied Math (SIAM) 2(2):205–224.


Discrimination in Online Ad Delivery | Latanya Sweeney

Latanya Sweeney (Harvard); Discrimination in Online Ad Delivery; 2013-01-28; 36 pages.


A Google search for a person’s name, such as “Trevon Jones”, may yield a personalized ad for public records about Trevon that may be neutral, such as “Looking for Trevon Jones? …”, or may be suggestive of an arrest record, such as “Trevon Jones, Arrested?…”. This writing investigates the delivery of these kinds of ads by Google AdSense using a sample of racially associated names and finds statistically significant discrimination in ad delivery based on searches of 2184 racially associated personal names across two websites. First names, previously identified by others as being assigned at birth to more black or white babies, are found predictive of race (88% black, 96% white), and those assigned primarily to black babies, such as DeShawn, Darnell and Jermaine, generated ads suggestive of an arrest in 81 to 86 percent of name searches on one website and 92 to 95 percent on the other, while those assigned at birth primarily to whites, such as Geoffrey, Jill and Emma, generated more neutral copy: the word “arrest” appeared in 23 to 29 percent of name searches on one site and 0 to 60 percent on the other. On the more ad trafficked website, a black-identifying name was 25% more likely to get an ad suggestive of an arrest record. A few names did not follow these patterns: Dustin, a name predominantly given to white babies, generated an ad suggestive of arrest 81 and 100 percent of the time. All ads return results for actual individuals and ads appear regardless of whether the name has an arrest record in the company’s database. Notwithstanding these findings, the company maintains Google received the same ad text for groups of last names (not first names), raising questions as to whether Google’s advertising technology exposes racial bias in society and how ad and search technology can develop to assure racial fairness.