758 research outputs found
Importance of "historic sites" on Heard Island for protection of scientific resources and environmental management of a World Heritage site
Heard Island has important historic sites relating to the hunting of elephant seals and various scientific activities, which are noted in the World Heritage citation for the island. Some, especially sealing sites, are generally acknowledged to have historic value, but the value of later sites, such as the first ANARE base at Atlas Cove, is debated, due to its recent date, unattractive appearance, rapid deterioration and hazards to wildlife. The authors believe that Atlas Cove is significant to Australia's Antarctic history, but this does not necessarily require that all elements of the site must be preserved in situ. The potential historical value of this site is briefly reviewed; the value of comparative studies of historical, archaeological and materials observation information on historic sites on other Subantarctic islands is also considered.
Documentation of these aspects of the sites is essential, certainly before any "clean up" is carried out at Adas Cove since it will produce information to assist in environmental management and may benefit scientific research. Identification of the scientific artefacts and achievements should be an essential part of documentation of Subantarctic sites, but generally has not been given significant attention by archeologists. For development of a statement of significance for Atlas Cove, it is vital
Electrophoretic separation of human kidney cells at zero gravity
Electrophoretic isolation of cells results in a loss of resolution power caused by the sedimentation of the cells in the media. The results of an experiment to extract urokinase from human embryos during the Apollo Soyuz mission are presented and discussed
On Uniqueness of Boundary Blow-up Solutions of a Class of Nonlinear Elliptic Equations
We study boundary blow-up solutions of semilinear elliptic equations
with , or with , where is a second order
elliptic operator with measurable coefficients. Several uniqueness theorems and
an existence theorem are obtained.Comment: To appear in Comm. Partial Differential Equations; 10 page
Sustaining the Internet with Hyperbolic Mapping
The Internet infrastructure is severely stressed. Rapidly growing overheads
associated with the primary function of the Internet---routing information
packets between any two computers in the world---cause concerns among Internet
experts that the existing Internet routing architecture may not sustain even
another decade. Here we present a method to map the Internet to a hyperbolic
space. Guided with the constructed map, which we release with this paper,
Internet routing exhibits scaling properties close to theoretically best
possible, thus resolving serious scaling limitations that the Internet faces
today. Besides this immediate practical viability, our network mapping method
can provide a different perspective on the community structure in complex
networks
Forecasting in the light of Big Data
Predicting the future state of a system has always been a natural motivation
for science and practical applications. Such a topic, beyond its obvious
technical and societal relevance, is also interesting from a conceptual point
of view. This owes to the fact that forecasting lends itself to two equally
radical, yet opposite methodologies. A reductionist one, based on the first
principles, and the naive inductivist one, based only on data. This latter view
has recently gained some attention in response to the availability of
unprecedented amounts of data and increasingly sophisticated algorithmic
analytic techniques. The purpose of this note is to assess critically the role
of big data in reshaping the key aspects of forecasting and in particular the
claim that bigger data leads to better predictions. Drawing on the
representative example of weather forecasts we argue that this is not generally
the case. We conclude by suggesting that a clever and context-dependent
compromise between modelling and quantitative analysis stands out as the best
forecasting strategy, as anticipated nearly a century ago by Richardson and von
Neumann
Network Cosmology
Prediction and control of the dynamics of complex networks is a central
problem in network science. Structural and dynamical similarities of different
real networks suggest that some universal laws might accurately describe the
dynamics of these networks, albeit the nature and common origin of such laws
remain elusive. Here we show that the causal network representing the
large-scale structure of spacetime in our accelerating universe is a power-law
graph with strong clustering, similar to many complex networks such as the
Internet, social, or biological networks. We prove that this structural
similarity is a consequence of the asymptotic equivalence between the
large-scale growth dynamics of complex networks and causal networks. This
equivalence suggests that unexpectedly similar laws govern the dynamics of
complex networks and spacetime in the universe, with implications to network
science and cosmology
Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework
[EN] The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users activity in events on Twitter.This work is partially supported by the PROME-TEOII/2013/019, TIN2014-55206-R, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095.Del Val Noguera, E.; MartĂnez, C.; Botti, V. (2016). Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework. Soft Computing. 20(11):4331-4345. https://doi.org/10.1007/s00500-016-2301-0S433143452011Ahn Y-Y, Han S, Kwak H, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th WWW, pp 835â844Bastiaensens S, Vandebosch H, Poels K, Cleemput KV, DeSmet A, Bourdeaudhuij ID (2014) Cyberbullying on social network sites. an experimental study into behavioural intentions to help the victim or reinforce the bully. Comput Hum Behav 31:259â271Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference. ACM, pp 49â62Borge-Holthoefer J, Rivero A, GarcĂa I, CauhĂ© E, Ferrer A, Ferrer D, Francos D, Iñiguez D, PĂ©rez MP, Ruiz G et al (2011) Structural and dynamical patterns on online social networks: the Spanish may 15th movement as a case study. PLoS One 6(8):e23883Borondo J, Morales AJ, Losada JC, Benito RM (2013) Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish presidential election as a case studyCatanese SA, De Meo P, Ferrara E, Fiumara G, Provetti A (2011) Crawling facebook for social network analysis purposes. In: Proceedings of the international conference on web intelligence, mining and semantics. ACM, p 52Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th international conference on World Wide Web. ACM, pp 721â730del Val E, MartĂnez C, Botti V (2015a) A multi-agent framework for the analysis of users behavior over time in on-line social networks. In: 10th International conference on soft computing models in industrial and environmental applications. Springer, Berlin, pp 191â201del Val E, Rebollo M, Botti V (2015b) Does the type of event influence how user interactions evolve on twitter? PLOS One 10(5):e0124049Eurostat (2016a) Internet use statisticsâindividuals. http://ec.europa.eu/eurostat/statistics-explained/index.php/Internet_use_statistics_-_individuals . Accessed 29 April 2016Eurostat (2016b) Social mediaâstatistics on the use by enterprises. http://ec.europa.eu/eurostat/statistics-explained/index.php/Social_media_-_statistics_on_the_use_by_enterprises#Further_Eurostat_information . Accessed 29 April 2016GarcĂa Fornes AM, Rodrigo Solaz M, Terrasa Barrena AM, Inglada J, Javier V, Jorge Cano J, Mulet Mengual L, Palomares Chust A, BĂșrdalo Rapa LA, Giret Boggino AS et al (2015) Magentix 2 userâs manualGolbeck J, Robles C, Turner K (2011) Predicting personality with social media. In: CHIâ11, pp 253â262GuimerĂ R, Llorente A, Moro E, Sales-Pardo M (2012) Predicting human preferences using the block structure of complex social networks. PloS One 7(9):e44620Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:0812.1045Jamali M, Abolhassani H (2006) Different aspects of social network analysis. In: 2006 IEEE/WIC/ACM international conference on web intelligence (WI 2006 main conference proceedings)(WIâ06). IEEE, pp 66â72Jiang Y, Jiang J (2014) Understanding social networks from a multiagent perspective. Parallel Distrib Syst IEEE Trans 25(10):2743â2759Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311(5757):88â90Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Yu PS, Han J, Faloutsos C (eds) Link mining: models, algorithms, and applications. Springer, New York, pp 337â357Lazer D (2009) Life in the network: the coming age of computational social science. Science 323(5915):721â723Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5Licoppe C, Smoreda Z (2005) Are social networks technologically embedded? How networks are changing today with changes in communication technology. Soc Netw 27(4):317â335Lotan G, Graeff E, Ananny M, Gaffney D, Pearce I, Boyd D (2011) The revolutions were tweeted: information flows during the 2011 tunisian and egyptian revolutions. Int J Commun 5:1375â1405Peña-LĂłpez I, Congosto M, AragĂłn P (2013) Spanish indignados and the evolution of 15M: towards networked para-institutions. Big data: challenges and opportunities, pp 25â26Perliger A, Pedahzur A (2011) Social network analysis in the study of terrorism and political violence. PS Polit Sci Polit 44:45â50Romero DM, Galuba W, Asur S, Huberman BA (2011a) Influence and passivity in social media. In: Proceedings of the 20th WWW, pp 113â114Romero DM, Meeder B, Kleinberg J (2011b) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th WWW, pp 695â704Stockman FN, Doreian P, (1997) Evolution of social networks: processes and principles. In: Doreian P, Stokman FN (eds) Evolution of social networks. Routledge, London, pp 233â250Traud AL, Mucha PJ, Porter MA (2012) Social structure of facebook networks. Phys A Stat Mech Its Appl 391(16):4165â4180Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the Facebook social graph. arXiv preprint arXiv:1111.4503Valero S, del Val E, Alemany J, Botti V (2015) Using magentix2 in smart-home environments. In: 10th International conference on soft computing models in industrial and environmental applications. Springer, Berlin, pp 27â37Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, CambridgeWersm (2015) How much data is generated every minute on social media? http://wersm.com/how-much-data-is-generated-every-minute-on-social-media/ . Accessed 29 April 201
Persistence of social signatures in human communication
The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an egoâs network ties are not well understood. Here we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus, as new network members are added, some old network members either are replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments
Fibers and global geometry of functions
Since the seminal work of Ambrosetti and Prodi, the study of global folds was
enriched by geometric concepts and extensions accomodating new examples. We
present the advantages of considering fibers, a construction dating to Berger
and Podolak's view of the original theorem. A description of folds in terms of
properties of fibers gives new perspective to the usual hypotheses in the
subject. The text is intended as a guide, outlining arguments and stating
results which will be detailed elsewhere
Emergence of good conduct, scaling and Zipf laws in human behavioral sequences in an online world
We study behavioral action sequences of players in a massive multiplayer
online game. In their virtual life players use eight basic actions which allow
them to interact with each other. These actions are communication, trade,
establishing or breaking friendships and enmities, attack, and punishment. We
measure the probabilities for these actions conditional on previous taken and
received actions and find a dramatic increase of negative behavior immediately
after receiving negative actions. Similarly, positive behavior is intensified
by receiving positive actions. We observe a tendency towards anti-persistence
in communication sequences. Classifying actions as positive (good) and negative
(bad) allows us to define binary 'world lines' of lives of individuals.
Positive and negative actions are persistent and occur in clusters, indicated
by large scaling exponents alpha~0.87 of the mean square displacement of the
world lines. For all eight action types we find strong signs for high levels of
repetitiveness, especially for negative actions. We partition behavioral
sequences into segments of length n (behavioral `words' and 'motifs') and study
their statistical properties. We find two approximate power laws in the word
ranking distribution, one with an exponent of kappa-1 for the ranks up to 100,
and another with a lower exponent for higher ranks. The Shannon n-tuple
redundancy yields large values and increases in terms of word length, further
underscoring the non-trivial statistical properties of behavioral sequences. On
the collective, societal level the timeseries of particular actions per day can
be understood by a simple mean-reverting log-normal model.Comment: 6 pages, 5 figure
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