7,228 research outputs found
Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security
This article considers how cultural understanding is being brought into the work of the Intelligence Advanced Research Projects Activity (IARPA), through an analysis of its Metaphor program. It examines the type of social science underwriting this program, unpacks implications of the agency’s conception of metaphor for understanding so-called cultures of interest, and compares IARPA’s to competing accounts of how metaphor works to create cultural meaning. The article highlights some risks posed by key deficits in the Intelligence Community\u27s (IC) approach to culture, which relies on the cognitive linguistic theories of George Lakoff and colleagues. It also explores the problem of the opacity of these risks for analysts, even as such predictive cultural analytics are becoming a part of intelligence forecasting. This article examines the problem of information secrecy in two ways, by unpacking the opacity of “black box,” algorithm-based social science of culture for end users with little appreciation of their potential biases, and by evaluating the IC\u27s nontransparent approach to foreign cultures, as it underwrites national security assessments
Easy identification of generalized common and conserved nested intervals
In this paper we explain how to easily compute gene clusters, formalized by
classical or generalized nested common or conserved intervals, between a set of
K genomes represented as K permutations. A b-nested common (resp. conserved)
interval I of size |I| is either an interval of size 1 or a common (resp.
conserved) interval that contains another b-nested common (resp. conserved)
interval of size at least |I|-b. When b=1, this corresponds to the classical
notion of nested interval. We exhibit two simple algorithms to output all
b-nested common or conserved intervals between K permutations in O(Kn+nocc)
time, where nocc is the total number of such intervals. We also explain how to
count all b-nested intervals in O(Kn) time. New properties of the family of
conserved intervals are proposed to do so
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Born to trade: a genetically evolved keyword bidder for sponsored search
In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click.
In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009
Accelerating Science: A Computing Research Agenda
The emergence of "big data" offers unprecedented opportunities for not only
accelerating scientific advances but also enabling new modes of discovery.
Scientific progress in many disciplines is increasingly enabled by our ability
to examine natural phenomena through the computational lens, i.e., using
algorithmic or information processing abstractions of the underlying processes;
and our ability to acquire, share, integrate and analyze disparate types of
data. However, there is a huge gap between our ability to acquire, store, and
process data and our ability to make effective use of the data to advance
discovery. Despite successful automation of routine aspects of data management
and analytics, most elements of the scientific process currently require
considerable human expertise and effort. Accelerating science to keep pace with
the rate of data acquisition and data processing calls for the development of
algorithmic or information processing abstractions, coupled with formal methods
and tools for modeling and simulation of natural processes as well as major
innovations in cognitive tools for scientists, i.e., computational tools that
leverage and extend the reach of human intellect, and partner with humans on a
broad range of tasks in scientific discovery (e.g., identifying, prioritizing
formulating questions, designing, prioritizing and executing experiments
designed to answer a chosen question, drawing inferences and evaluating the
results, and formulating new questions, in a closed-loop fashion). This calls
for concerted research agenda aimed at: Development, analysis, integration,
sharing, and simulation of algorithmic or information processing abstractions
of natural processes, coupled with formal methods and tools for their analyses
and simulation; Innovations in cognitive tools that augment and extend human
intellect and partner with humans in all aspects of science.Comment: Computing Community Consortium (CCC) white paper, 17 page
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