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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="http://barf.jcowboy.org/"/>
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	<rdf:li rdf:resource="http://www.vialibri.net/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0801.0390"/>
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  </channel><item rdf:about="https://arxiv.org/abs/1801.05247">
    <title>[1801.05247] Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments</title>
    <dc:date>2021-06-20T10:46:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1801.05247</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Molecular dynamics (MD) simulations allow investigating the structural dynamics of biomolecular systems with unrivaled time and space resolution. However, in order to compensate for the inaccuracies of the utilized empirical force fields, it is becoming common to integrate MD simulations with experimental data obtained from ensemble measurements. We here review the approaches that can be used to combine MD and experiment under the guidance of the maximum entropy principle. We mostly focus on methods based on Lagrangian multipliers, either implemented as reweighting of existing simulations or through an on-the-fly optimization. We discuss how errors in the experimental data can be modeled and accounted for. Finally, we use simple model systems to illustrate the typical difficulties arising when applying these methods.
]]></description>
<dc:subject>simulation aggregation algorithms information-theory rather-interesting to-understand consider:simpler-landscape-results consider:landscape-structure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:78ebfa32c717/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1405.0825">
    <title>[1405.0825] The average representation - a cornucopia of power indices?</title>
    <dc:date>2019-10-13T12:47:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1405.0825</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[For the classical power indices there is a disproportion between power and relative weights, in general. We introduce two new indices, based on weighted representations, which are proportional to suitable relative weights and which also share several important properties of the classical power indices. Imposing further restrictions on the set of representations may lead to a whole family of such indices.
]]></description>
<dc:subject>mechanism-design game-theory aggregation rather-interesting statistics to-write-about to-simulate consider:sampling voting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1fcc583f5849/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1902.06776">
    <title>[1902.06776] A Generalised Solution to Distributed Consensus</title>
    <dc:date>2019-02-23T13:43:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.06776</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental primitive for constructing reliable distributed systems from unreliable components. The Paxos algorithm is synonymous with distributed consensus, yet it performs poorly in practice and is famously difficult to understand. In this paper, we re-examine the foundations of distributed consensus. We derive an abstract solution to consensus, which utilises immutable state for intuitive reasoning about safety. We prove that our abstract solution generalises over Paxos as well as the Fast Paxos and Flexible Paxos algorithms. The surprising result of this analysis is a substantial weakening to the quorum requirements of these widely studied algorithms.
]]></description>
<dc:subject>collective-intelligence wisdom-of-crowds decision-making algorithms aggregation rather-interesting to-understand distributed-processing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b842ba6e9d7f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
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<item rdf:about="https://www.biorxiv.org/content/10.1101/326637v3">
    <title>Social learning strategies regulate the wisdom and madness of interactive crowds | bioRxiv</title>
    <dc:date>2019-02-13T14:53:25+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/326637v3</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Why groups of individuals sometimes exhibit collective 'wisdom' and other times maladaptive `herding' is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model-fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity amongst individuals, with rates of copying increasing with group size, leading to high probabilities of herding amongst large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated `wisdom of the crowd' effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a longstanding puzzle in the literature.

]]></description>
<dc:subject>wisdom-of-crowds collective-intelligence collective-behavior sociology simulation aggregation data-fusion rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:295eba194370/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
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<item rdf:about="https://arxiv.org/abs/1703.00045">
    <title>[1703.00045] Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds</title>
    <dc:date>2019-02-05T09:45:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.00045</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The aggregation of many independent estimates can outperform the most accurate individual judgment. This centenarian finding, popularly known as the wisdom of crowds, has been applied to problems ranging from the diagnosis of cancer to financial forecasting. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd's collective accuracy. We asked a live crowd (N=5180) to respond to general-knowledge questions (e.g., what is the height of the Eiffel Tower?). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals.
]]></description>
<dc:subject>collective-intelligence wisdom-of-crowds decision-making aggregation algorithms to-write-about metaheuristics social-norms social-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:577489f9a846/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1604.04660">
    <title>[1604.04660] Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like</title>
    <dc:date>2017-02-12T15:17:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1604.04660</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
]]></description>
<dc:subject>artificial-intelligence philosophy-of-engineering rather-interesting representation approximation aggregation nudge-targets consider:looking-to-see consider:performance-space-analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:660616107932/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
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<item rdf:about="http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27">
    <title>A Novel Algorithm for Coarse-Graining of Cellular Automata - Springer</title>
    <dc:date>2016-12-06T13:44:25+00:00</dc:date>
    <link>http://link.springer.com/chapter/10.1007/978-3-319-11520-7_27</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The coarse-graining is an approximation procedure widely used for simplification of mathematical and numerical models of multiscale systems. It reduces superfluous – microscopic – degrees of freedom. Israeli and Goldenfeld demonstrated in [1,2] that the coarse-graining can be employed for elementary cellular automata (CA), producing interesting interdependences between them. However, extending their investigation on more complex CA rules appeared to be impossible due to the high computational complexity of the coarse-graining algorithm. We demonstrate here that this complexity can be substantially decreased. It allows for scrutinizing much broader class of cellular automata in terms of their coarse graining. By using our algorithm we found out that the ratio of the numbers of elementary CAs having coarse grained representation to “degenerate” – irreducible – cellular automata, strongly increases with increasing the “grain” size of the approximation procedure. This rises principal questions about the formal limits in modeling of realistic multiscale systems.
]]></description>
<dc:subject>cellular-automata aggregation approximation to-do to-write-about via:dan-little</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a12228cf79de/</dc:identifier>
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<item rdf:about="http://understandingsociety.blogspot.com/2016/11/coarse-graining-of-complex-systems.html">
    <title>Understanding Society: Coarse-graining of complex systems</title>
    <dc:date>2016-12-06T13:39:35+00:00</dc:date>
    <link>http://understandingsociety.blogspot.com/2016/11/coarse-graining-of-complex-systems.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Here K&D seem to be expressing the view that the approach to coarse-graining as a technique for simplifying the expected behavior of a complex system offered by Israeli and Goldenfeld will fail in the case of more extensive and complex systems (perhaps including the pre-boil turbulence example mentioned above).

I am not sure whether these debates have relevance for the modeling of social phenomena. Recall my earlier discussion of the modeling of rebellion using agent-based modeling simulations (link, link, link). These models work from the unit level -- the level of the individuals who interact with each other. A coarse-graining approach would perhaps replace the individual-level description with a set of groups with homogeneous properties, and then attempt to model the likelihood of an outbreak of rebellion based on the coarse-grained level of description. Would this be feasible?
]]></description>
<dc:subject>complexology representation models agent-based aggregation nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f712566d9182/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1506.08168">
    <title>[1506.08168] Shaping the Growth Behaviour of Biofilms Initiated from Bacterial Aggregates</title>
    <dc:date>2015-12-13T14:47:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.08168</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bacterial biofilms are usually assumed to originate from individual cells deposited on a surface. However, many biofilm-forming bacteria tend to aggregate in the planktonic phase so that it is possible that many natural and infectious biofilms originate wholly or partially from pre-formed cell aggregates. Here, we use agent-based computer simulations to investigate the role of pre-formed aggregates in biofilm development. Focusing on the initial shape the aggregate forms on the surface, we find that the degree of spreading of an aggregate on a surface can play an important role in determining its eventual fate during biofilm development. Specifically, initially spread aggregates perform better when competition with surrounding unaggregated bacterial cells is low, while initially rounded aggregates perform better when competition with surrounding unaggregated cells is high. These contrasting outcomes are governed by a trade-off between aggregate surface area and height. Our results provide new insight into biofilm formation and development, and reveal new factors that may be at play in the social evolution of biofilm communities.
]]></description>
<dc:subject>biofilms emergent-design microbiology physics aggregation nudge-targets simulation community-assembly artificial-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c0c5ad26f498/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biofilms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:microbiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
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<item rdf:about="http://arxiv.org/abs/1406.7563">
    <title>[1406.7563] When is a crowd wise?</title>
    <dc:date>2015-11-01T10:45:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.7563</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Numerous studies and anecdotes demonstrate the "wisdom of the crowd," the surprising accuracy of a group's aggregated judgments. Less is known, however, about the generality of crowd wisdom. For example, are crowds wise even if their members have systematic judgmental biases, or can influence each other before members render their judgments? If so, are there situations in which we can expect a crowd to be less accurate than skilled individuals? We provide a precise but general definition of crowd wisdom: A crowd is wise if a linear aggregate, for example a mean, of its members' judgments is closer to the target value than a randomly, but not necessarily uniformly, sampled member of the crowd. Building on this definition, we develop a theoretical framework for examining, a priori, when and to what degree a crowd will be wise. We systematically investigate the boundary conditions for crowd wisdom within this framework and determine conditions under which the accuracy advantage for crowds is maximized. Our results demonstrate that crowd wisdom is highly robust: Even if judgments are biased and correlated, one would need to nearly deterministically select only a highly skilled judge before an individual's judgment could be expected to be more accurate than a simple averaging of the crowd. Our results also provide an accuracy rationale behind the need for diversity of judgments among group members. Contrary to folk explanations of crowd wisdom which hold that judgments should ideally be independent so that errors cancel out, we find that crowd wisdom is maximized when judgments systematically differ as much as possible. We re-analyze data from two published studies that confirm our theoretical results.
]]></description>
<dc:subject>aggregation wisdom-of-crowds prediction probability-theory economics via:cshalizi nudge-targets consider:robustness consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2e04f7d8eddc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wisdom-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.04590">
    <title>[1507.04590] Jamming and percolation in generalized models of random sequential adsorption of linear $k$-mers on a square lattice</title>
    <dc:date>2015-09-16T11:59:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.04590</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The jamming and percolation for two generalized models of random sequential adsorption (RSA) of linear k-mers (particles occupying k adjacent sites) on a square lattice are studied by means of Monte Carlo simulation. The classical random sequential adsorption (RSA) model assumes the absence of overlapping of the new incoming particle with the previously deposited ones. The first model LKd is a generalized variant of the RSA model for both k-mers and a lattice with defects. Some of the occupying k adjacent sites are considered as insulating and some of the lattice sites are occupied by defects (impurities). For this model even a small concentration of defects can inhibit percolation for relatively long k-mers. The second model is the cooperative sequential adsorption (CSA) one, where, for each new k-mer, only a restricted number of lateral contacts z with previously deposited k-mers is allowed. Deposition occurs in the case when z≤(1−d)zm where zm=2(k+1) is the maximum numbers of the contacts of k-mer, and d is the fraction of forbidden NN contacts. Percolation is observed only at some interval kmin≤k≤kmax where the values kmin and kmax depend upon the fraction of forbidden contacts d. The value kmax decreases as d increases. A logarithmic dependence of the type log(kmax)=a+bd, where a=−4.03±0.22, b=4.93±0.57, is obtained.
]]></description>
<dc:subject>aggregation simulation physics condensed-matter rather-interesting nudge-targets statistical-mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:88a5c4fb7990/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:condensed-matter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistical-mechanics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1409.7126">
    <title>[1409.7126] Kinetic interfaces of patchy particles</title>
    <dc:date>2015-09-11T23:54:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.7126</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the irreversible adsorption of patchy particles on substrates in the limit of advective mass transport. Recent numerical results show that the interface roughening depends strongly on the particle attributes, such as, patch-patch correlations, bond flexibility, and strength of the interactions, uncovering new absorbing phase transitions. Here, we revisit these results and discuss in detail the transitions. In particular, we present new evidence that the tricritical point, observed in systems of particles with flexible patches, is in the tricritical directed percolation universality class. A scaling analysis of the time evolution of the correlation length for the aggregation of patchy particles with distinct bonding energies confirms that the critical regime is in the Kardar-Parisi-Zhang with quenched disorder universality class.
]]></description>
<dc:subject>aggregation simulation physics nonlinear-dynamics self-organization rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9294a2f245a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.2687">
    <title>[1411.2687] An optimal aggregation type classifier</title>
    <dc:date>2014-12-26T19:54:34+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.2687</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of M arbitrary training classifiers. If the classifiers are consistent, then so is the aggregation rule. Moreover, asymptotically the aggregation rule behaves as well as the best of the M classifiers. The results of a small si\-mu\-lation are reported both, for high dimensional and functional data.
]]></description>
<dc:subject>classification algorithms statistics aggregation nudge-targets rather-odd lunch-will-be-provided-but-it-is-not-free</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d1f520587376/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-odd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lunch-will-be-provided-but-it-is-not-free"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.0207">
    <title>[1401.0207] Urban Mobility Scaling: Lessons from `Little Data'</title>
    <dc:date>2014-08-09T12:04:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.0207</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent mobility scaling research, using new data sources, often relies on aggregated data alone. Hence, these studies face difficulties characterizing the influence of factors such as transportation mode on mobility patterns. This paper attempts to complement this research by looking at a category-rich mobility data set. In order to shed light on the impact of categories, as a case study, we use conventionally collected German mobility data. In contrast to `check-in'-based data, our results are not biased by Euclidean distance approximations. In our analysis, we show that aggregation can hide crucial differences between trip length distributions, when subdivided by categories. For example, we see that on an urban scale (0 to ~15 km), walking, versus driving, exhibits a highly different scaling exponent, thus universality class. Moreover, mode share and trip length are responsive to day-of-week and time-of-day. For example, in Germany, although driving is relatively less frequent on Sundays than on Wednesdays, trips seem to be longer. In addition, our work may shed new light on the debate between distance-based and intervening-opportunity mechanisms affecting mobility patterns, since mode may be chosen both according to trip length and urban form.
]]></description>
<dc:subject>aggregation statistics it's-more-complicated-than-you-think models diversity experiment interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9fc92fff755b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sethgodin.typepad.com/seths_blog/2014/07/bobsourcing.html">
    <title>Seth's Blog: Bobsourcing</title>
    <dc:date>2014-07-08T11:51:04+00:00</dc:date>
    <link>http://sethgodin.typepad.com/seths_blog/2014/07/bobsourcing.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When we rely on the crowd, we get deniability. The organizer doesn't have to ask anyone specificially, and the individual is easily off the hook. But sometimes, the hook is exactly what you want.

]]></description>
<dc:subject>crowdsourcing social-norms community aggregation whuffie-culture on-the-failure-of-traditional-economic-models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5dae61bc6b79/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crowdsourcing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:whuffie-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:on-the-failure-of-traditional-economic-models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.4401">
    <title>[1306.4401] Voter models with contrarian agents</title>
    <dc:date>2014-04-03T11:56:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.4401</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In the voter and many other opinion formation models, agents are assumed to behave as congregators (also called the conformists); they are attracted to the opinions of others. In this study, I investigate linear extensions of the voter model with contrarian agents. An agent is either congregator or contrarian and assumes a binary opinion. I investigate three models that differ in the behavior of the contrarian toward other agents. In model 1, contrarians mimic the opinions of other contrarians and oppose (i.e., try to select the opinion opposite to) those of congregators. In model 2, contrarians mimic the opinions of congregators and oppose those of other contrarians. In model 3, contrarians oppose anybody. In all models, congregators are assumed to like anybody. I show that even a small number of contrarians prohibits the consensus in the entire population to be reached in all three models. I also obtain the equilibrium distributions using the van Kampen small-fluctuation approximation and the Fokker-Planck equation for the case of many contrarians and a single contrarian, respectively. I show that the fluctuation around the symmetric coexistence equilibrium is much larger in model 2 than in models 1 and 3 when contrarians are rare.
]]></description>
<dc:subject>evolutionary-economics aggregation agent-based nudge-targets consider:social-network consider:reproducing-with-influence-networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ada2e6210281/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:social-network"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:reproducing-with-influence-networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0308">
    <title>[1312.0308] Stochastic Convergence of Persistence Landscapes and Silhouettes</title>
    <dc:date>2014-03-30T11:49:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0308</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Persistent homology is a widely used tool in Topological Data Analysis that encodes multiscale topological information as a multi-set of points in the plane called a persistence diagram. It is difficult to apply statistical theory directly to a random sample of diagrams. Instead, we can summarize the persistent homology with the persistence landscape, introduced by Bubenik, which converts a diagram into a well-behaved real-valued function. We investigate the statistical properties of landscapes, such as weak convergence of the average landscapes and convergence of the bootstrap. In addition, we introduce an alternate functional summary of persistent homology, which we call the silhouette, and derive an analogous statistical theory.
]]></description>
<dc:subject>to-learn aggregation bootstrap statistics estimation algorithms nudge-targets consider:analogies-to-Pareto-GP consider:deformalization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9e5d85568f94/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-learn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:analogies-to-Pareto-GP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:deformalization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.7149">
    <title>[1302.7149] Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling</title>
    <dc:date>2014-01-18T14:45:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.7149</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Critical decisions frequently rely on high-dimensional output from complex computer simulation models that show intricate cross-variable, spatial and temporal dependence structures, with weather and climate predictions being key examples. There is a strongly increasing recognition of the need for uncertainty quantification in such settings, for which we propose and review a general multi-stage procedure called ensemble copula coupling (ECC), proceeding as follows: 1. Generate a raw ensemble, consisting of multiple runs of the computer model that differ in the inputs or model parameters in suitable ways. 2. Apply statistical postprocessing techniques, such as Bayesian model averaging or nonhomogeneous regression, to correct for systematic errors in the raw ensemble, to obtain calibrated and sharp predictive distributions for each univariate output variable individually. 3. Draw a sample from each postprocessed predictive distribution. 4. Rearrange the sampled values in the rank order structure of the raw ensemble to obtain the ECC postprocessed ensemble. The use of ensembles and statistical postprocessing have become routine in weather forecasting over the past decade. We show that seemingly unrelated, recent advances can be interpreted, fused and consolidated within the framework of ECC, the common thread being the adoption of the empirical copula of the raw ensemble. Depending on the use of Quantiles, Random draws or Transformations at the sampling stage, we distinguish the ECC-Q, ECC-R and ECC-T variants, respectively. We also describe relations to the Schaake shuffle and extant copula-based techniques. In a case study, the ECC approach is applied to predictions of temperature, pressure, precipitation and wind over Germany, based on the 50-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble.
]]></description>
<dc:subject>simulation prediction collective-intelligence aggregation algorithms interesting nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1c5eaa345220/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.7182">
    <title>[1305.7182] Average Consensus on Arbitrary Strongly Connected Digraphs with Time-Varying Topologies</title>
    <dc:date>2013-06-07T11:33:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.7182</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We have recently proposed a "surplus-based" algorithm which solves the multi-agent average consensus problem on general strongly connected and static digraphs. The essence of that algorithm is to employ an additional variable to keep track of the state changes of each agent, thereby achieving averaging even though the state sum is not preserved. In this note, we extend this approach to the more interesting and challenging case of time-varying topologies: An extended surplus-based averaging algorithm is designed, under which a necessary and sufficient graphical condition is derived that guarantees state averaging. The derived condition requires only that the digraphs be arbitrary strongly connected in a \emph{joint} sense, and does not impose "balanced" or "symmetric" properties on the network topology, which is therefore more general than those previously reported in the literature.
]]></description>
<dc:subject>collective-intelligence aggregation algorithms agent-based network-theory memory-as-a-feature nudge-targets the-hobgoblin-of-little-agents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0ce8c9bd86b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:memory-as-a-feature"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-hobgoblin-of-little-agents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.5945">
    <title>[1303.5945] Condensation and Intermittency in an Open Boundary Aggregation-Fragmentation Model</title>
    <dc:date>2013-04-16T22:33:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.5945</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study real space condensation in aggregation-fragmentation models where the total mass is not conserved, as in phenomena like cloud formation and intracellular trafficking. We study the scaling properties of the system with influx and outflux of mass at the boundaries using numerical simulations, supplemented by analytical results in the absence of fragmentation. The system is found to undergo a phase transition to an unusual condensate phase, characterized by strong intermittency and giant fluctuations of the total mass. A related phase transition also occurs for biased movement of large masses, but with some crucial differences which we highlight.]]></description>
<dc:subject>complex-systems simulation aggregation nudge-targets self-assembly steady-state</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c6a7d7ec982c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complex-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:steady-state"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1208.1547">
    <title>[1208.1547] Surface and bulk properties of ballistic deposition models with bond breaking</title>
    <dc:date>2013-03-25T11:51:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.1547</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a new class of growth models, with a surface restructuring mechanism in which impinging particles may dislodge suspended particles, previously aggregated on the same column in the deposit. The flux of these particles is controlled through a probability $p$. These systems present a crossover, for small values of $p$, from random to correlated (KPZ) growth of surface roughness, which is studied through scaling arguments and Monte Carlo simulations on one- and two-dimensional substrates. We show that the crossover characteristic time $t_{\times}$ scales with $p$ according to $t_{\times}\sim p^{-y}$ with $y=(n+1)$ and that the interface width at saturation $W_{sat}$ scales as $W_{sat}\sim p^{-\delta}$ with $\delta = (n+1)/2$, where $n$ is either the maximal number of broken bonds or of dislodged suspended particles. This result shows that the sets of exponents $y=1$ and $\delta=1/2$ or $y=2$ and $\delta=1$ found in all previous works focusing on systems with this same type of crossover are not universal. Using scaling arguments, we show that the bulk porosity $P$ of the deposits scales as $P\sim p^{y-\delta}$ for small values of $p$. This general scaling relation is confirmed by our numerical simulations and explains previous results present in literature.]]></description>
<dc:subject>aggregation simulation just-outside-the-box</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8deb07b65377/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:just-outside-the-box"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.1541">
    <title>[1302.1541] Algorithm Portfolio Design: Theory vs. Practice</title>
    <dc:date>2013-03-25T11:35:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.1541</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Stochastic algorithms are among the best for solving computationally hard search and reasoning problems. The runtime of such procedures is characterized by a random variable. Different algorithms give rise to different probability distributions. One can take advantage of such differences by combining several algorithms into a portfolio, and running them in parallel or interleaving them on a single processor. We provide a detailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the protfolio approach can have a dramatic computational advantage over the best traditional methods.]]></description>
<dc:subject>diversity aggregation models portfolio-theory statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49739312d995/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:portfolio-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.3268">
    <title>[1212.3268] Robust image reconstruction from multi-view measurements</title>
    <dc:date>2013-03-15T11:04:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.3268</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose a novel method to accurately reconstruct a set of images representing a single scene from few linear multi-view measurements. Each observed image is modeled as the sum of a background image and a foreground one. The background image is common to all observed images but undergoes geometric transformations, as the scene is observed from different viewpoints. In this paper, we assume that these geometric transformations are represented by a few parameters, e.g., translations, rotations, affine transformations, etc.. The foreground images differ from one observed image to another, and are used to model possible occlusions of the scene. The proposed reconstruction algorithm estimates jointly the images and the transformation parameters from the available multi-view measurements. The ideal solution of this multi-view imaging problem minimizes a non-convex functional, and the reconstruction technique is an alternating descent method built to minimize this functional. The convergence of the proposed algorithm is studied, and conditions under which the sequence of estimated images and parameters converges to a critical point of the non-convex functional are provided. Finally, the efficiency of the algorithm is demonstrated using numerical simulations for applications such as compressed sensing or super-resolution.]]></description>
<dc:subject>image-processing super-resolution algorithms nudge-targets aggregation nonlinear operations-research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1fe51e7f2f59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:super-resolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.2236">
    <title>[1303.2236] COBRA: A Nonlinear Aggregation Strategy</title>
    <dc:date>2013-03-13T11:08:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.2236</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[[How many variations will it take before these folks have to reinvent the idea of Design Pattern and contingent craft, or degenerate into pissing matches? Actually, I guess that might be an optimistic dichotomy....]

"A new method for combining several initial estimators of the regression function is introduced. Instead of building a linear or convex optimized combination over a collection of basic estimators $r_1,...,r_M$, we use them as a collective indicator of the distance between the training data and a test observation. This local distance approach is model-free and extremely fast. Most importantly, the resulting collective estimator is shown to perform asymptotically at least as well in the $L^2$ sense as the best basic estimator in the collective. Moreover, it does so without having to declare which might be the best basic estimator for the given data set. A companion R package called \cobra (standing for COmBined Regression Alternative) is presented (downloadable on \url{this http URL}). Numerical evidence is provided on both synthetic and real data sets to assess the excellent performance of our method in a large variety of prediction problems."]]></description>
<dc:subject>statistics performance-measure aggregation nudge-targets state-your-goals</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:071ea79965b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:state-your-goals"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.4389">
    <title>[1302.4389] Maxout Networks</title>
    <dc:date>2013-03-08T04:53:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.4389</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.]]></description>
<dc:subject>models algorithms learning aggregation neural-networks nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1a1f91db833d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://barf.jcowboy.org/">
    <title>BaRf: Bioinformatics aggregated RSS feeds</title>
    <dc:date>2010-03-23T12:20:16+00:00</dc:date>
    <link>http://barf.jcowboy.org/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["BaRf stands for "Bioinformatics aggregated RSS feeds". It provides RSS feeds of titles and abstracts of the most recent papers published by journals that may be of relevance for people involved in Bioinformatics. We don't claim this list is complete - if you have suggestions for journals that should be added (and appear in PubMed) please let us know. The list of currently available journals along with the RSS feed XML links can be found on the right of the page."
]]></description>
<dc:subject>rss science academic-culture publishing journals aggregation</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:499c2bfbe518/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rss"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academic-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:journals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.calculatedriskblog.com/2007/09/whats-really-wrong-with-stated-income.html">
    <title>Calculated Risk: What's Really Wrong With Stated Income</title>
    <dc:date>2009-07-11T18:37:47+00:00</dc:date>
    <link>http://www.calculatedriskblog.com/2007/09/whats-really-wrong-with-stated-income.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We use the term "bagholder" all the time, and it seems to me we've forgotten where that metaphor comes from. It didn't used to be considered acceptable to find some naive rube you could manipulate into holding the bag when the cops showed up, while the seasoned robbers scarpered. I'm really amazed by all these self-employed folks who keep popping up in our comments to defend stated income lending. It is a way for you to get a loan on terms that mean you potentially face prosecution if something goes wrong. Your enthusiasm for taking this risk is making a lot of marginal lenders happy, because you're helping them hide the true risk in their loan portfolios from auditors, examiners, and counterparties. You aren't getting those stated income loans because lenders like to do business with entrepreneurs, "the backbone of America."... You're getting stated income loans because you're willing to be the bagholder."
]]></description>
<dc:subject>finance financial-crisis foresight risk aggregation</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bcd6d427f64a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:foresight"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:risk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://advocacy.nase.org/issue_briefs/2009/AffordableHealthCare.asp">
    <title>NASE - Access to Affordable Health Coverage</title>
    <dc:date>2009-03-06T16:18:28+00:00</dc:date>
    <link>http://advocacy.nase.org/issue_briefs/2009/AffordableHealthCare.asp</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The National Association for the Self-Employed (NASE) strongly supports proposals such as health care tax credits, a self-employment tax deduction on health insurance premiums, expansion of both Health Reimbursement Arrangements (HRAs) and Health Savings Accounts (HSAs), and pooling arrangements for small business as important steps to creating an equitable environment for micro-businesses and the self-employed to purchase affordable, quality health coverage. "
]]></description>
<dc:subject>NASE not-an-employee healthcare business aggregation pooling actuarial</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:82e478079275/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:NASE"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:not-an-employee"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:business"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pooling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:actuarial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://polymeme.com/about">
    <title>About Us | Polymeme</title>
    <dc:date>2009-03-04T21:51:54+00:00</dc:date>
    <link>http://polymeme.com/about</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Polymeme helps you navigate the new networked public sphere and keep your fingers on the intellectual pulse of the blogosphere.

Polymeme helps you discover intelligent content that lies beyond the usual echo chambers of tech news, celebrity gossip or American politics.

Our site uses a unique buzz-tracking approach to identify what's currently hot in 20 areas, ranging from economics to evolution, and present it to the reader along with all sources that are currently talking about it. Thus, you can track how ideas – or memes – propagate through this new emerging networked public sphere. We would consider our mission a success if we expose you to the maximum number of new ideas on every 100 news items you read!"
]]></description>
<dc:subject>social-software social-networks marketing madness-of-crowds blogging media data-mining trends aggregation</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1211c4c477d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:marketing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:madness-of-crowds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:blogging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:trends"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.vialibri.net/">
    <title>viaLibri - Rare Books - Resources for Bibliophiles, Librarians and Collectors</title>
    <dc:date>2008-12-28T16:41:10+00:00</dc:date>
    <link>http://www.vialibri.net/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>books bibliomania search-engines specialization aggregation research antiquarian bookselling shopping reference bibliography</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:519d7115adce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bibliomania"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:search-engines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:specialization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:antiquarian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bookselling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:shopping"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bibliography"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0801.0390">
    <title>[0801.0390] Staring at Economic Aggregators through Information Lenses</title>
    <dc:date>2008-01-07T12:19:38+00:00</dc:date>
    <link>http://arxiv.org/abs/0801.0390</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>economics information-architecture aggregation inefficiency agents machine-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5d683daaccad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inefficiency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agents"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://geekymom.blogspot.com/2007/10/networks-and-academic-research.html">
    <title>Geeky Mom: Networks and Academic Research</title>
    <dc:date>2007-10-28T18:43:32+00:00</dc:date>
    <link>http://geekymom.blogspot.com/2007/10/networks-and-academic-research.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Even journals in a technical field don't have RSS feeds."
]]></description>
<dc:subject>journals social-networks publishing RSS aggregation feeds collaboration access bad-design</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:60c83a2bcba0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:journals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:publishing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:RSS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feeds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:access"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bad-design"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>