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This work by the UCLA Mathematicians 
is representative of the prevalent 
ethic among scientists.  The highest 
value to which the scientist aspires 
is the advancement of her/his science.
*The ethic*
The thread proceeds as follows: The 
advancement of knowledge is the 
supreme good-->my field is about the 
advancement of knowledge in the area 
to which i have committed myself and 
which brings me joy in its 
pursuit-->I need resources to 
advance knowledge in my field--> 
resources are available from the 
military-industrial complex, the 
anti-terrorist complex, the police 
and others in kind-->to have access 
to those resources i must work on 
problems that they perceive as 
advancing their goal-->the pleasure 
of pursuing my discipline is the 
opiate that insulates me from 
concerns of the use and consequences 
of my work.
*Its history
*This ethic is not peculiar to 
Capitalism. although its 
consequences and its prevalence has 
never been as widespread.  It is a 
product of class societies.  
Archimedes, considered the greatest 
mathematician of antiquity used his 
talents to produce several war 
machines.  Galileo did the same, as 
did Poincare.  These great minds 
needed access to resources  as 
trivial as the free time to think, 
experiment, and write and as 
expansive as a laboratory and 
assistants.  They are representative 
of how scientists and mathematicians 
have been compelled to relate to the 
class that in each social formation 
posessed the wealth and determined 
values.
*A reflective comment
*Not all components of the ruling 
class always subscribe to this 
dominant ethic.  Neither can one 
deny that not all work funded by the 
dominant class is malevolent.  Most 
importantly one must not assume that 
all scientists, engineers and 
mathematicians that objectively 
subscribe to this ethic subjectively 
subscribe to it.  There are those, 
who intoxicated by the joys of 
scientific work, are clueless; and 
there are those who despise the 
class system itself but are 
compelled, if they are to pursue 
their science, to work within the 
system.  So also is it true that the 
daily participation as a wage earner 
in society is objectively to be a 
part of the necessary strcture that 
maintains the society and thus the 
hegemony of its ruling class.
*A global conclusion*
It is essential for the future of 
our species (at least) that class 
society be abolished.  The class 
aspect of society that must be done 
away with is that which allows 
dominance of one minority of society 
over the rest of society and which 
perpetuates the accumulation of 
wealth by that minority.  Because 
not all humans are the same, this is 
not a call for the levelling of all; 
it is for the opportunity of 
self-realization for all within a 
framework of equality as human 
beings, peace, and cooperation.
*Our role*
The structure of capitalism makes 
the emergence of an alternative 
society within it much more 
difficult than in previous class 
societies.  Also its ability to 
integrate essentially everyone from 
all classes into its mechanism makes 
the necessary massive change in 
consciousness for it to be sent into 
the proverbial dustbin of history 
all the more difficult relative to 
previous historical social 
transformations.  Among scientists, 
engineers, and mathematicians, guilt 
tripping will transform the 
consciousness of but a few and 
probably alienate even more.  In the 
classroom socially conscious 
instructors will also be able to 
transform the consciousness of but a 
few.  Exemplary behavior also 
influences some few, although 
without either giving up one's 
discipline or being independently 
self sufficient, it is difficult to 
pursue one's discipline and be 
exemplary.  What then is to be done 
among the technological-scientific 
community, a component of the salary 
compensated part of the working 
class, to effect its change in 
consciousness?  That is, in my 
judgment, the primary role this 
list, as representative of the 
socially conscious 
technological-scientific 
proletariat, must assume.
*Our task*
The value in our communicating among 
ourselves includes the possibility 
of discovering how we can best reach 
out to the virtually 100% of the 
Science community not on the list.  
To do that we must first recognize 
that the categories good and evil do 
not apply to human beings (perhaps 
in rare instances).  What is good 
for and what is destructive of our 
species are more appropriate 
categories.  We also have to 
recognize that scientists who find 
that the only way they can get 
research funded is by doing shit 
like this post of Sam's are also 
victims--victims of a system that 
provides few if any opportunities to 
pursue their discipline and further 
develop their talents outside of 
funding from the ruling structure.  
In fact, it is that structure that 
is the enemy.  Although i have some 
ideas on how we can broaden our 
reach, i'm sure we'll learn more, if 
i leave that question open.

herb

On 11/2/2011 10:17 AM, S E Anderson 
wrote:
>
>
>   Fighting Violent Gang Crime With
>   Math
>
> */Math in the service of "fighting 
> crime." Under capitalism, it is 
> not about taking millions of 
> dollars to try to solve poverty 
> that breeds low level street crime 
> "analyzed" here. No. It's about 
> how can the capitalists make 
> greater profits and create new 
> markets via math and computer 
> technology.- SEA/*
>
> ScienceDaily (Oct. 31, 2011)  
> UCLA mathematicians working with 
> the Los Angeles Police Department 
> to analyze crime patterns have 
> designed a mathematical algorithm 
> to identify street gangs involved 
> in unsolved violent crimes. Their 
> research is based on patterns of 
> known criminal activity between 
> gangs, and represents the first 
> scholarly study of gang violence 
> of its kind.
>
> The research appears October 31 on 
> the website of the peer-reviewed 
> mathematical journal /Inverse 
> Problems/ and will be published in 
> a future print edition.
>
> In developing their algorithm, the 
> mathematicians analyzed more than 
> 1,000 gang crimes and suspected 
> gang crimes, about half of them 
> unsolved, that occurred over a 
> 10-year period in an East Los 
> Angeles police district known as 
> Hollenbeck, a small area in which 
> there are some 30 gangs and nearly 
> 70 gang rivalries.
>
> To test the algorithm, the 
> researchers created a set of 
> simulated data that closely 
> mimicked the crime patterns of the 
> Hollenbeck gang network. They then 
> dropped some of the key 
> information out -- at times the 
> victim, the perpetrator or both -- 
> and tested how well the algorithm 
> could calculate the missing 
> information.
>
> "If police believe a crime might 
> have been committed by one of 
> seven or eight rival gangs, our 
> method would look at recent 
> historical events in the area and 
> compute probabilities as to which 
> of these gangs are most likely to 
> have committed crime," said the 
> study's senior author, Andrea 
> Bertozzi, a professor of 
> mathematics and director of 
> applied mathematics at UCLA.
>
> About 80 percent of the time, the 
> mathematicians could narrow it 
> down to three gang rivalries that 
> were most likely involved in a crime.
>
> "Our algorithm placed the correct 
> gang rivalry within the top three 
> most likely rivalries 80 percent 
> of the time, which is 
> significantly better than chance," 
> said Martin Short, a UCLA adjunct 
> assistant professor of mathematics 
> and co-author of the study. "That 
> narrows it down quite a bit, and 
> that is when we don't know 
> anything about the crime victim or 
> perpetrator."
>
> The mathematicians also found that 
> the correct gang was ranked No. 1 
> -- rather than just among the top 
> three -- 50 percent of the time, 
> compared with just 17 percent by 
> chance.
>
> Police can investigate further 
> when the gangs are narrowed down.
>
> "We can do even better," Bertozzi 
> said. "This is the first paper 
> that takes this new approach. We 
> can only improve on that 80 
> percent by developing more 
> sophisticated methods.
>
> "Our algorithm exploits gang 
> activity patterns to produce the 
> best probability of which gang, or 
> which three gangs, may have been 
> responsible for the crimes," she said.
>
> Bertozzi and her colleagues have 
> been working with the LAPD on a 
> variety of classes of crime. The 
> implications of the research go 
> beyond fighting gangs and beyond 
> fighting crime.
>
> "The algorithm we devised could 
> apply to a much broader class of 
> problems that involve activity on 
> social networks," Bertozzi said. 
> "You have events -- they could be 
> crimes or something else -- that 
> occur in a time series and a known 
> network. There is activity between 
> nodes, in this case a gang 
> attacking another gang. With some 
> of these activities, you know 
> exactly who was involved and with 
> others, you do not. The challenge 
> is how to make the best educated 
> judgment as to who was involved in 
> the unknown activities. We believe 
> there are a number of social 
> networks that have this same kind 
> of pattern."
>
> Identifying hackers would be an 
> example; helping businesses target 
> advertising to consumers who would 
> be most interested in their 
> products and services in a way 
> that would protect privacy would 
> be another.
>
> "An advertiser may not care who 
> individual people are but just how 
> they behave," Bertozzi said. 
> "Advertisers could target 
> consumers by knowing their 
> shopping behavior without knowing 
> their identities."
>
> The lead author of the study is 
> Alexey Stomakhin, a UCLA doctoral 
> student in applied mathematics who 
> worked for a year to design the 
> algorithm that can fill in the 
> missing information.
>
> The new research is federally 
> funded by the National Science 
> Foundation, the U.S. Army Research 
> Office's mathematics division, the 
> U.S. Office of Naval Research, and 
> the U.S. Air Force Office of 
> Scientific Research.
>
> ----------------------------------------------
>
> *Story Source:*
>
>     The above story is reprinted
>     from materials
>     <http://newsroom.ucla.edu/portal/ucla/fighting-violent-gang-crime-with-218046.aspx>
>     provided by *University of
>     California - Los Angeles*
>     <http://www.newsroom.ucla.edu>. The
>     original article was written
>     by Stuart Wolpert.
>
>     /Note: Materials may be edited
>     for content and length. For
>     further information, please
>     contact the source cited above./
>
> ------------------------------------
>
> *Journal Reference*:
>
>  1. Alexey Stomakhin, Martin B
>     Short, Andrea L Bertozzi.
>     *Reconstruction of missing
>     data in social networks based
>     on temporal patterns of
>     interactions*. /Inverse
>     Problems/, 2011; 27 (11):
>     115013 DOI:
>     10.1088/0266-5611/27/11/115013
>     <http://dx.doi.org/10.1088/0266-5611/27/11/115013>
>
>