416 research outputs found
Some properties of canonical correlations and variates in infinite dimensions
AbstractIn this paper the notion of functional canonical correlation as a maximum of correlations of linear functionals is explored. It is shown that the population functional canonical correlation is in general well defined, but that it is a supremum rather than a maximum, so that a pair of canonical variates may not exist in the spaces considered. Also the relation with the maximum eigenvalue of an associated pair of operators and the corresponding eigenvectors is not in general valid. When the inverses of the operators involved are regularized, however, all of the above properties are restored. Relations between the actual population quantities and their regularized versions are also established. The sample functional canonical correlations can be regularized in a similar way, and consistency is shown at a fixed level of the regularization parameter
On the Computational Complexity of Measuring Global Stability of Banking Networks
Threats on the stability of a financial system may severely affect the
functioning of the entire economy, and thus considerable emphasis is placed on
the analyzing the cause and effect of such threats. The financial crisis in the
current and past decade has shown that one important cause of instability in
global markets is the so-called financial contagion, namely the spreading of
instabilities or failures of individual components of the network to other,
perhaps healthier, components. This leads to a natural question of whether the
regulatory authorities could have predicted and perhaps mitigated the current
economic crisis by effective computations of some stability measure of the
banking networks. Motivated by such observations, we consider the problem of
defining and evaluating stabilities of both homogeneous and heterogeneous
banking networks against propagation of synchronous idiosyncratic shocks given
to a subset of banks. We formalize the homogeneous banking network model of
Nier et al. and its corresponding heterogeneous version, formalize the
synchronous shock propagation procedures, define two appropriate stability
measures and investigate the computational complexities of evaluating these
measures for various network topologies and parameters of interest. Our results
and proofs also shed some light on the properties of topologies and parameters
of the network that may lead to higher or lower stabilities.Comment: to appear in Algorithmic
The topology of a discussion: the #occupy case
We analyse a large sample of the Twitter activity developed around the social
movement 'Occupy Wall Street' to study the complex interactions between the
human communication activity and the semantic content of a discussion. We use a
network approach based on the analysis of the bipartite graph @Users-#Hashtags
and of its projections: the 'semantic network', whose nodes are hashtags, and
the 'users interest network', whose nodes are users In the first instance, we
find out that discussion topics (#hashtags) present a high heterogeneity, with
the distinct role of the communication hubs where most the 'opinion traffic'
passes through. In the second case, the self-organization process of users
activity leads to the emergence of two classes of communicators: the
'professionals' and the 'amateurs'. Moreover the network presents a strong
community structure, based on the differentiation of the semantic topics, and a
high level of structural robustness when a certain set of topics are censored
and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags
network we can distinguish three phases of the discussion about the movement.
Each phase corresponds to specific moment of the movement: from declaration of
intent, organisation and development and the final phase of political
reactions. Each phase is characterised by the presence of specific #hashtags in
the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure
Sensitivity of Household Transmission to Household Contact Structure and Size
Study the influence of household contact structure on the spread of an influenza-like illness. Examine whether changes to in-home care giving arrangements can significantly affect the household transmission counts.We simulate two different behaviors for the symptomatic person; either s/he remains at home in contact with everyone else in the household or s/he remains at home in contact with only the primary caregiver in the household. The two different cases are referred to as full mixing and single caregiver, respectively.The results show that the household's cumulative transmission count is lower in case of a single caregiver configuration than in the full mixing case. The household transmissions vary almost linearly with the household size in both single caregiver and full mixing cases. However the difference in household transmissions due to the difference in household structure grows with the household size especially in case of moderate flu.These results suggest that details about human behavior and household structure do matter in epidemiological models. The policy of home isolation of the sick has significant effect on the household transmission count depending upon the household size
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Combined transcriptomic-(1)H NMR metabonomic study reveals yhat monoethylhexyl phthalate stimulates adipogenesis and glyceroneogenesis in human adipocytes
Adipose tissue is a major storage site for lipophilic environmental contaminants. The environmental metabolic disruptor hypothesis postulates that some pollutants can promote obesity or metabolic disorders by activating nuclear receptors involved in the control of energetic homeostasis. In this context, monoethylhexyl phthalate (MEHP) is of particular concern since it was shown to activate the peroxisome proliferator-activated receptor γ (PPARγ) in 3T3-L1 murine preadipocytes. In the present work, we used an untargeted, combined transcriptomic-(1)H NMR-based metabonomic approach to describe the overall effect of MEHP on primary cultures of human subcutaneous adipocytes differentiated in vitro. MEHP stimulated rapidly and selectively the expression of genes involved in glyceroneogenesis, enhanced the expression of the cytosolic phosphoenolpyruvate carboxykinase, and reduced fatty acid release. These results demonstrate that MEHP increased glyceroneogenesis and fatty acid reesterification in human adipocytes. A longer treatment with MEHP induced the expression of genes involved in triglycerides uptake, synthesis, and storage; decreased intracellular lactate, glutamine, and other amino acids; increased aspartate and NAD, and resulted in a global increase in triglycerides. Altogether, these results indicate that MEHP promoted the differentiation of human preadipocytes to adipocytes. These mechanisms might contribute to the suspected obesogenic effect of MEHP
Modeling the scaling properties of human mobility
While the fat tailed jump size and the waiting time distributions
characterizing individual human trajectories strongly suggest the relevance of
the continuous time random walk (CTRW) models of human mobility, no one
seriously believes that human traces are truly random. Given the importance of
human mobility, from epidemic modeling to traffic prediction and urban
planning, we need quantitative models that can account for the statistical
characteristics of individual human trajectories. Here we use empirical data on
human mobility, captured by mobile phone traces, to show that the predictions
of the CTRW models are in systematic conflict with the empirical results. We
introduce two principles that govern human trajectories, allowing us to build a
statistically self-consistent microscopic model for individual human mobility.
The model not only accounts for the empirically observed scaling laws but also
allows us to analytically predict most of the pertinent scaling exponents
Robust estimation and inference for general varying coefficients models with missing observations
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
The spread of infectious diseases crucially depends on the pattern of
contacts among individuals. Knowledge of these patterns is thus essential to
inform models and computational efforts. Few empirical studies are however
available that provide estimates of the number and duration of contacts among
social groups. Moreover, their space and time resolution are limited, so that
data is not explicit at the person-to-person level, and the dynamical aspect of
the contacts is disregarded. Here, we want to assess the role of data-driven
dynamic contact patterns among individuals, and in particular of their temporal
aspects, in shaping the spread of a simulated epidemic in the population.
We consider high resolution data of face-to-face interactions between the
attendees of a conference, obtained from the deployment of an infrastructure
based on Radio Frequency Identification (RFID) devices that assess mutual
face-to-face proximity. The spread of epidemics along these interactions is
simulated through an SEIR model, using both the dynamical network of contacts
defined by the collected data, and two aggregated versions of such network, in
order to assess the role of the data temporal aspects.
We show that, on the timescales considered, an aggregated network taking into
account the daily duration of contacts is a good approximation to the full
resolution network, whereas a homogeneous representation which retains only the
topology of the contact network fails in reproducing the size of the epidemic.
These results have important implications in understanding the level of
detail needed to correctly inform computational models for the study and
management of real epidemics
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
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