60419 research outputs found
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Long-term exposure to air pollution and stroke incidence: A Danish Nurse cohort study
No full textAmbient air pollution has been linked to stroke, but few studies have examined in detail stroke subtypes and
confounding by road traffic noise, which was recently associated with stroke. Here we examined the association
between long-term exposure to air pollution and incidence of stroke (overall, ischemic, hemorrhagic), adjusting
for road traffic noise. In a nationwide Danish Nurse Cohort consisting of 23,423 nurses, recruited in 1993 or
1999, we identified 1,078 incident cases of stroke (944 ischemic and 134 hemorrhagic) up to December 31,
2014, defined as first-ever hospital contact. The full residential address histories since 1970 were obtained for
each participant and the annual means of air pollutants (particulate matter with diameter < 2.5 μm and <
10 μm (PM2.5 and PM10), nitrogen dioxide (NO2), nitrogen oxides (NOx)) and road traffic noise were determined
using validated models. Time-varying Cox regression models were used to estimate hazard ratios (HR) (95%
confidence intervals (CI)) for the associations of one-, three, and 23-year running mean of air pollutants with
stroke adjusting for potential confounders and noise. In fully adjusted models, the HRs (95% CI) per interquartile
range increase in one-year running mean of PM2.5 and overall, ischemic, and hemorrhagic stroke were 1.12
(1.01–1.25), 1.13 (1.01–1.26), and 1.07 (0.80–1.44), respectively, and remained unchanged after adjustment for
noise. Long-term exposure to ambient PM2.5 was associated with the risk of stroke independent of road traffic
noise
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
No full textThe importance of wild video based image set recognition is becoming monotonically increasing. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and feature extraction is a big challenge in CV community. Recently, some proposed image set classification methods have made a considerable advance by modeling the original image set with covariance matrix, linear subspace, or Gaussian distribution. Moreover, the distinctive geometry spanned by them are three types of Riemannian manifolds. As a matter of fact, most of them just adopt a single geometric model to describe each set data, which may lose some information for classification. To tackle this, we propose a novel algorithm to model each image set from a multi-geometric perspective. Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously. In order to fuse these multiple heterogeneous features, the well-equipped Riemannian kernel functions are first utilized to map them into high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework is devised to embed the learned hybrid kernels into a lower dimensional common subspace for classification. We conduct experiments on four widely used datasets. Extensive experimental results justify its superiority over the state-of-the-art
Robust One-Class Kernel Spectral Regression
No full textThe kernel null-space technique is known to be an
effective one-class classification (OCC) technique. Nevertheless, the applicability of this method is limited due to its susceptibility to possible training data corruption and the inability to rank training observations according to their conformity with the model. This article addresses these shortcomings by regularizing the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation. In this respect, first,
the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as a sensitivity analysis problem. Next, the effect of the sparsity of the solution is studied. For both alternative regularization schemes, iterative algorithms are proposed which recursively update label confidences. Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method,
as well as other existing approaches in the OCC paradigm, while providing the functionality to rank training samples effectivel
Distributed Controller Design for Vehicle Platooning under Packet Drop Scenario
No full textThis paper proposes a distributed control
strategy for homogeneous platoon systems with external
disturbances under random packet drop scenario which can
occur due to underlying network among the vehicles in a
platoon. An linear matrix inequality (LMI) based approach is
used to obtain the controller gains for ensuring the stability with bounded H∞ norm for such systems. Effectiveness of the proposed method is demonstrated with numerical results considering different network topologies in a platoon under single packet drop. The variation of H∞ norm bound for different number of platoon members under the different structure of network topologies and the packet drop has been studied in this paper
Veridiction and Leadership in Transnational Populism: The Case of DiEM25
No full textWhile research tends to explore questions of power and leadership at the national level, populism in Europe has moved beyond national borders, with an increasing number of transnational movements and organizations. This article investigates the Democracy in Europe Movement 2025 (DiEM25) and its leadership’s main speeches. Informed by both discourse theory and Michel Foucault’s work on parrhesia (veridiction), the analysis draws on readings of transnational Euroalternativism and populism, pointing out the conflicting logic of bringing them together at the transnational level. Our findings thus
stress the increasing politicization of European integration as an opportunity to mobilize transnational activities, which
are based on the populist ‘people vs. the elites’ dichotomy and against Brussels’ unaccountable elites (see FitzGibbon &
Guerra, 2019), while indicating the limits of leadership in a populist transnational movement (de Cleen, Moffitt, Panayotu,
& Stavrakakis, 2019; Marzolini & Souvlis, 2016)
Boost generator in AdS3 integrable superstrings for general braiding
No full textIn this paper we find a host of boost operators for a very general choice of coproducts in AdS3-
inspired scattering theories, focusing on the massless sector, with and without an added trigonometric
deformation. We find that the boost coproducts are exact symmetries of the R-matrices we construct,
besides fulfilling the relations of modified Poincar´e-type superalgebras. In the process, we discover
an ambiguity in determining the boost coproduct which allows us to derive differential constraints on
our R-matrices. In one particular case of the trigonometric deformation, we find a non-coassociative
structure which satisfies the axioms of a quasi-Hopf algebra
Large-Scale Forcing of a Turbulent Channel Flow Through Spanwise Synthetic Jets
No full textThe investigation focuses on the forcing of a fully developed turbulent channel flow through a linear array of synthetic jets injected tangentially to the wall and orthogonal to the mean flow direction. Forcing configurations are varied by differently combining the number of actuated jets working in an opposing blowing–suction configuration. Instantaneous wall shear stress and streamwise velocity fluctuations evidence drag reductions as well as turbulence attenuation up to 20%. The forcing effects are persistent up to at least a 150 half-channel height downstream of the injection section. Particle image velocimetry investigations in planes perpendicular to the channel axis highlight the presence of a large-scale streamwise vortical structure covering the whole height of the channel. This structure is thought to be responsible for the significant drag reduction, which is similar to the typical behavior evidenced in the case of colliding jets. The nondimensional forcing frequency of the synthetic jets producing the maximum drag reduction and turbulence attenuation is 0.0074 for the investigated Reynolds number (Reτ=180). A statistical analysis of the near-wall structures demonstrates that the control mechanism acts in a way to reduce them in the forced configuration. It is conclude that the effect of the forcing is such that the near-wall structures merge and become less prone to inducing new structures, thus effectively reducing their number, and consequently the near-wall turbulence activity
Intuitive pricing by independent store managers: Challenging beliefs and practices
No full textIndependent store managers—who constitute a substantial portion of the retailing sector—often have limited resources with which to practice the formalized, data-driven pricing processes prescribed in the literature. On that basis, this article addresses how independent convenience store managers arrive at prices and whether their practices are effective. To begin with, 33 interviews with independent convenience store managers identified six common beliefs and ten practices underlying managers’ intuitive decision making. Based on point-of-sale survey data from 1,504 customers of two convenience store chains at petrol stations, a second study compared market-oriented managerial beliefs with actual customer price perceptions and buying behaviors. The combined insights from these studies reveal that managers base their pricing decisions on beliefs that are only partially accurate and suggests how managers might benefit by altering their price-setting practices
A Theory of Dual Channel Constraints
No full textThe surprising predictability of source code has triggered a boom
in tools using language models for code. Code is much more predictable
than natural language, but the reasons are not well understood.
We propose a dual channel view of code; code combines a
formal channel for specifying execution and a natural language
channel in the form of identifiers and comments that assists human
comprehension. Computers ignore the natural language channel,
but developers read both and, when writing code for longterm use
and maintenance, consider each channel’s audience: computer and
human. As developers hold both channels in mind when coding,
we posit that the two channels interact and constrain each other;
we call these dual channel constraints. Their impact has been neglected.
We describe how they can lead to humans writing code
in a way more predictable than natural language, highlight pioneering
research that has implicitly or explicitly used parts of this
theory, and drive new research, such as systematically searching
for cross-channel inconsistencies. Dual channel constraints provide
an exciting opportunity as truly multi-disciplinary research; for
computer scientists they promise improvements to program analysis
via a more holistic approach to code, and to psycholinguists they
promise a novel environment for studying linguistic processes
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