99,862 research outputs found
Sea ice trends in climate models only accurate in runs with biased global warming
Observations indicate that the Arctic sea ice cover is rapidly retreating
while the Antarctic sea ice cover is steadily expanding. State-of-the-art
climate models, by contrast, typically simulate a moderate decrease in both the
Arctic and Antarctic sea ice covers. However, in each hemisphere there is a
small subset of model simulations that have sea ice trends similar to the
observations. Based on this, a number of recent studies have suggested that the
models are consistent with the observations in each hemisphere when simulated
internal climate variability is taken into account. Here we examine sea ice
changes during 1979-2013 in simulations from the most recent Coupled Model
Intercomparison Project (CMIP5) as well as the Community Earth System Model
Large Ensemble (CESM-LE), drawing on previous work that found a close
relationship in climate models between global-mean surface temperature and sea
ice extent. We find that all of the simulations with 1979-2013 Arctic sea ice
retreat as fast as observed have considerably more global warming than
observations during this time period. Using two separate methods to estimate
the sea ice retreat that would occur under the observed level of global warming
in each simulation in both ensembles, we find that simulated Arctic sea ice
retreat as fast as observed would occur less than 1% of the time. This implies
that the models are not consistent with the observations. In the Antarctic, we
find that simulated sea ice expansion as fast as observed typically corresponds
with too little global warming, although these results are more equivocal. We
show that because of this, the simulations do not capture the observed
asymmetry between Arctic and Antarctic sea ice trends. This suggests that the
models may be getting the right sea ice trends for the wrong reasons in both
polar regions
Character education: themes and researches. An academic literature review
Character education is both a rooted and developing discipline. Even though there is no consensual definition, it can be widely described as a schoolbased process to promote personal development in youth, through the development of virtue, moral values, and moral agency. Starting from the growing interest about this theme in recent years, this article aims at using the \u201ccharacter education\u201d analysis category to conduct an exploratory research on the main tendencies in the international literature, defining which are the main topics, exploring the way these topics develop in terms of theory and empirical research and analyzing how they relate to each other. In view of this goal, titles and abstracts of 261 articles published in 145 peer-reviewed academic journals over the period 2005-2014 were selected from Education Source, ERIC, Psychology & Behavioral Sciences Collection and SocINDEX databases. Articles\u2019 titles and abstract were analyzed through the T-Lab software, using different content analysis techniques. Although many ambivalences and ambiguities affect the meaning attributed to the character education, some key trends emerge from this literature review and the considered studies seem to agree that character education can play an important role in the construction of children and adolescents\u2019 identity and can be a distinctive intervention for youth education and socialization
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
Bivariate modelling of precipitation and temperature using a non-homogeneous hidden Markov model
Aiming to generate realistic synthetic times series of the bivariate process
of daily mean temperature and precipitations, we introduce a non-homogeneous
hidden Markov model. The non-homogeneity lies in periodic transition
probabilities between the hidden states, and time-dependent emission
distributions. This enables the model to account for the non-stationary
behaviour of weather variables. By carefully choosing the emission
distributions, it is also possible to model the dependance structure between
the two variables. The model is applied to several weather stations in Europe
with various climates, and we show that it is able to simulate realistic
bivariate time series
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model
by interacting with users. When learning from user behavior, systems must
interact with users while simultaneously learning from those interactions.
Unlike other Learning to Rank (LTR) settings, existing research in this field
has been limited to linear models. This is due to the speed-quality tradeoff
that arises when selecting models: complex models are more expressive and can
find the best rankings but need more user interactions to do so, a requirement
that risks frustrating users during training. Conversely, simpler models can be
optimized on fewer interactions and thus provide a better user experience, but
they will converge towards suboptimal rankings. This tradeoff creates a
deadlock, since novel models will not be able to improve either the user
experience or the final convergence point, without sacrificing the other. Our
contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD
that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks
documents based on similarities with reference documents. It converges rapidly
and, hence, gives a better user experience but it does not converge towards the
optimal rankings. Second, we contribute Cascading Multileave Gradient Descent
(C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a
cascade that enables combinations of the best of two worlds: fast learning and
high quality final convergence. C-MGD can provide the better user experience of
Sim-MGD while maintaining the same convergence as the state-of-the-art MGD
model. This opens the door for future work to design new models for OLTR
without having to deal with the speed-quality tradeoff.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
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