3,064 research outputs found
A Simple Calculus for Discrete Systems, Part B
Mathematical model for man machine development cycle
A novel method for evaluating the critical nucleus and the surface tension in systems with first order phase transition
We introduce a novel method for calculating the size of the critical nucleus
and the value of the surface tension in systems with first order phase
transition. The method is based on classical nucleation theory, and it consists
in studying the thermodynamics of a sphere of given radius embedded in a frozen
metastable surrounding. The frozen configuration creates a pinning field on the
surface of the free sphere. The pinning field forces the sphere to stay in the
metastable phase as long as its size is smaller than the critical nucleus. We
test our method in two first-order systems, both on a two-dimensional lattice:
a system where the parameter tuning the transition is the magnetic field, and a
second system where the tuning parameter is the temperature. In both cases the
results are satisfying. Unlike previous techniques, our method does not require
an infinite volume limit to compute the surface tension, and it therefore gives
reliable estimates even by using relatively small systems. However, our method
cannot be used at, or close to, the critical point, i.e. at coexistence, where
the critical nucleus becomes infinitely large.Comment: 12 pages, 15 figure
Forced Symmetry Breaking from SO(3) to SO(2) for Rotating Waves on the Sphere
We consider a small SO(2)-equivariant perturbation of a reaction-diffusion
system on the sphere, which is equivariant with respect to the group SO(3) of
all rigid rotations. We consider a normally hyperbolic SO(3)-group orbit of a
rotating wave on the sphere that persists to a normally hyperbolic
SO(2)-invariant manifold . We investigate the effects of this
forced symmetry breaking by studying the perturbed dynamics induced on
by the above reaction-diffusion system. We prove that depending
on the frequency vectors of the rotating waves that form the relative
equilibrium SO(3)u_{0}, these rotating waves will give SO(2)-orbits of rotating
waves or SO(2)-orbits of modulated rotating waves (if some transversality
conditions hold). The orbital stability of these solutions is established as
well. Our main tools are the orbit space reduction, Poincare map and implicit
function theorem
Riverine transport of biogenic elements to the Baltic Sea ? past and possible future perspectives
International audienceThe paper reviews critical processes for the land-sea fluxes of biogenic elements (C, N, P, Si) in the Baltic Sea catchment and discusses possible future scenarios as a consequence of improved sewage treatment, agricultural practices and increased hydropower demand (for N, P and Si) and of global warming, i.e., changes in hydrological patterns (for C). These most significant drivers will not only change the total amount of nutrient inputs and fluxes of organic and inorganic forms of carbon to the Baltic Sea, their ratio (C:N:P:Si) will alter as well with consequences for phytoplankton species composition in the Baltic Sea. In summary, we propose that N fluxes may increase due to higher livestock densities in those countries recently acceded to the EU, whereas P and Si fluxes may decrease due to an improved sewage treatment in these new EU member states and with further damming and still eutrophic states of many lakes in the entire Baltic Sea catchment. This might eventually decrease cyanobacteria blooms in the Baltic but increase the potential for other nuisance blooms. Dinoflagellates could eventually substitute diatoms that even today grow below their optimal growth conditions due to low Si concentrations in some regions of the Baltic Sea. C fluxes will probably increase from the boreal part of the Baltic Sea catchment due to the expected higher temperatures and heavier rainfall. However, it is not clear whether dissolved organic carbon and alkalinity, which have opposite feedbacks to global warming, will increase in similar amounts, because the spring flow peak will be smoothed out in time due to higher temperatures that cause less snow cover and deeper soil infiltration
Chromosome contribution to andean polyploid species of Senecio (Asteraceae), from Argentina
Fil: López, Mariana G.. Lab. de Citogenética y Evol.; Depto. de Ciencias Biológicas; Universidad de Buenos AiresFil: Wulff, Arturo F.. Lab. de Citogenética y Evol.; Depto. de Ciencias Biológicas; Universidad de Buenos AiresFil: Xifreda, Cecilia Carmen. Laboratorio de Etnobotánica y Botánica Aplicada (LEBA); Facultad de Ciencias Naturales y Museo; Universidad Nacional de La Plat
Deep cytogenetics analysis reveals meiotic recombination depletion in species of Senecio (Asteraceae)
Background: Senecio is the largest genus in the Asteraceae family growing in all environments around the world. It displays taxonomic and systematical difficulties. Cytogenetic knowledge of this genus is ancient, scarce and mainly restricted to chromosome number records. Results: In this study we analyzed chromosome number, meiotic configuration, bivalent morphology, meiotic behavior and pollen grain stainability on 100 accessions of 27 different polyploid Senecio L. sect Senecio entities. Median, standard deviation and mode were calculated for number and position of chiasmata and meiotic recombination was statistically evaluated. Although high frequency of multivalents and associated meiotic irregularities are expected in high polyploids, bivalents predominance and, consequently, regular meiosis were observed, with normal sporogenesis and high pollen grain stainability. Conclusion: Depletion in the total chiasmata was significant only in some species but the terminal position was preferential in all the entities analyzed, indicating significant reduction in recombination. The regular meiosis observed suggest that intra and intergenomic reorganization process occur quickly and efficiently in this genus. Mechanisms of diploidization, common to all polyploids, are reinforced by the strong reduction in crossing-over rushing polyploids stabilization.Facultad de Ciencias Naturales y Muse
Quantifying the effect of monitor wear time and monitor type on the estimate of sedentary time in people with COPD: Systematic review and meta-analysis
In studies that have reported device-based measures of sedentary time (ST) in people with chronic obstructive pulmonary disease (COPD), we explored if the monitor type and monitor wear time moderated the estimate of this measure. Five electronic databases were searched in January 2021. Studies were included if \u3e70% of participants had stable COPD, and measures of ST (min/day) were collected using wearable technology. Meta-regression was used to examine the influence of moderators on ST, monitor type, and wear time. The studies identified were a total of 1153, and 36 had usable data for meta-analyses. The overall pooled estimate of ST (mean [95% CI]) was 524 min/day [482 to 566] with moderate heterogeneity among effect sizes (I2 = 42%). Monitor wear time, as well as the interaction of monitor wear time and monitor type, were moderators of ST (p \u3c 0.001). The largest difference (−318 min; 95% CI [−212 to −424]) was seen between studies where participants wore a device without a thigh inclinometer for 24 h (and removed sleep during analysis) (675 min, 95% CI [589 to 752]) and studies where participants wore a device with a thigh inclinometer for 12 h only (356 min; 95% CI [284 to 430]). In people with COPD, the monitor wear time and the interaction of the monitor wear time and the monitor type moderated the estimate of ST
Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing
Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a
compute resource intensive process as it usually requires to train the target
model with many different hyperparameter configurations. We show that
integrating model performance prediction with early stopping methods holds
great potential to speed up the HPO process of deep learning models. Moreover,
we propose a novel algorithm called Swift-Hyperband that can use either
classical or quantum support vector regression for performance prediction and
benefit from distributed High Performance Computing environments. This
algorithm is tested not only for the Machine-Learned Particle Flow model used
in High Energy Physics, but also for a wider range of target models from
domains such as computer vision and natural language processing.
Swift-Hyperband is shown to find comparable (or better) hyperparameters as well
as using less computational resources in all test cases
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