Leonardo's rule, self-similarity and wind-induced stresses in trees

Examining botanical trees, Leonardo da Vinci noted that the total cross-section of branches is conserved across branching nodes. In this Letter, it is proposed that this rule is a consequence of the tree skeleton having a self-similar structure and the branch diameters being adjusted to resist wind-induced loads

On a possible photon origin of the most-energetic AGASA events

In this work the ultra high energy cosmic ray events recorded by the AGASA experiment are analysed. With detailed simulations of the extensive air showers initiated by photons, the probabilities are determined of the photonic origin of the 6 AGASA events for which the muon densities were measured and the reconstructed energies exceeded 10^20 eV. On this basis a new, preliminary upper limit on the photon fraction in cosmic rays above 10^20 eV is derived and compared to the predictions of exemplary top-down cosmic-ray origin models.Comment: 3 pages, 1 figure, 2 tables; presented at XIII ISVHECRI, Pylos, Greec

GZK photons as UHECR above 1019^{19} eV

"GZK photons" are produced by extragalactic nucleons through the resonant photoproduction of pions. We present the expected range of the GZK photon fraction of UHECR, assuming a particular UHECR spectrum and primary nucleons, and compare it with the minimal photon fraction predicted by Top-Down models.Comment: Talk given at TAUP2005, Sept. 10-14 2005, Zaragoza (Spain); 3 pages, 2 figure

Computer simulations of domain growth and phase separation in two-dimensional binary immiscible fluids using dissipative particle dynamics

We investigate the dynamical behavior of binary fluid systems in two dimensions using dissipative particle dynamics. We find that following a symmetric quench the domain size R(t) grows with time t according to two distinct algebraic laws R(t) = t^n: at early times n = 1/2, while for later times n = 2/3. Following an asymmetric quench we observe only n = 1/2, and if momentum conservation is violated we see n = 1/3 at early times. Bubble simulations confirm the existence of a finite surface tension and the validity of Laplace's law. Our results are compared with similar simulations which have been performed previously using molecular dynamics, lattice-gas and lattice-Boltzmann automata, and Langevin dynamics. We conclude that dissipative particle dynamics is a promising method for simulating fluid properties in such systems.Comment: RevTeX; 22 pages, 5 low-resolution figures. For full-resolution figures, connect to http://www.tcm.phy.cam.ac.uk/~ken21/tension/tension.htm

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

Weak localization and electron-electron interactions in Indium-doped ZnO nanowires

Single crystal ZnO nanowires doped with indium are synthesized via the laser-assisted chemical vapor deposition method. The conductivity of the nanowires is measured at low temperatures in magnetic fields both perpendicular and parallel to the wire axes. A quantitative fit of our data is obtained, consistent with the theory of a quasi-one-dimensional metallic system with quantum corrections due to weak localization and electron-electron interactions. The anisotropy of the magneto-conductivity agrees with theory. The two quantum corrections are of approximately equal magnitude with respective temperature dependences of T^-1/3 and T^-1/2. The alternative model of quasi-two-dimensional surface conductivity is excluded by the absence of oscillations in the magneto-conductivity in parallel magnetic fields.Comment: 13 pages, Corrected forma

The fate of assimilated carbon during drought: impacts on respiration in Amazon rainforests

Interannual variations in CO2 exchange across Amazonia, as deduced from atmospheric inversions, correlate with El Niño occurrence. They are thought to result from changes in net ecosystem exchange and fire incidence that are both related to drought intensity. Alterations to net ecosystem production (NEP) are caused by changes in gross primary production (GPP) and ecosystem respiration (Reco). Here, we analyse observations of the components of Reco (leaves, live and dead woody tissue, and soil) to provide first estimates of changes in Reco during short-term (seasonal to interannual) moisture limitation. Although photosynthesis declines if moisture availability is limiting, leaf dark respiration is generally maintained, potentially acclimating upwards in the longer term. If leaf area is lost, then short-term canopy-scale respiratory effluxes from wood and leaves are likely to decline. Using a moderate short-term drying scenario where soil moisture limitation leads to a loss of 0.5 m2 m−2 yr−1 in leaf area index, we estimate a reduction in respiratory CO2 efflux from leaves and live woody tissue of 1.0 (±0.4) t C ha−1 yr−1. Necromass decomposition declines during drought, but mortality increases; the median mortality increase following a strong El Niño is 1.1% (n=46 tropical rainforest plots) and yields an estimated net short-term increase in necromass CO2 efflux of 0.13–0.18 t C ha−1 yr−1. Soil respiration is strongly sensitive to moisture limitation over the short term, but not to associated temperature increases. This effect is underestimated in many models but can lead to estimated reductions in CO2 efflux of 2.0 (±0.5) t C ha−1 yr−1. Thus, the majority of short-term respiratory responses to drought point to a decline in Reco, an outcome that contradicts recent regional-scale modelling of NEP. NEP varies with both GPP and Reco but robust moisture response functions are clearly needed to improve quantification of the role of Reco in influencing regional-scale CO2 emissions from Amazonia

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Pseudo labeling and consistency regularization approaches based on confidencethresholding have made great progress in semi-supervised learning (SSL).However, we argue that existing methods might fail to adopt suitable thresholdssince they either use a pre-defined / fixed threshold or an ad-hoc thresholdadjusting scheme, resulting in inferior performance and slow convergence. Wefirst analyze a motivating example to achieve some intuitions on therelationship between the desirable threshold and model's learning status. Basedon the analysis, we hence propose FreeMatch to define and adjust the confidencethreshold in a self-adaptive manner according to the model's learning status.We further introduce a self-adaptive class fairness regularization penalty thatencourages the model to produce diverse predictions during the early stages oftraining. Extensive experimental results indicate the superiority of FreeMatchespecially when the labeled data are extremely rare. FreeMatch achieves 5.78%,13.59%, and 1.28% error rate reduction over the latest state-of-the-art methodFlexMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class,and ImageNet with 100 labels per class, respectively.<br