7,110 research outputs found
Biodiversity shapes tree species aggregations in tropical forests
Spatial patterns of conspecific trees are considered as the consequences of biological interactions and environmental influences. They also reflect species interactions in plant communities. However, biological attributes are often neglected while deliberating the factors shaping species distributions. As rising attentions are paid to spatial patterns of tropical forest trees, we noticed that seven Center of Tropical Forest Sites and four Forest Dynamic Plots in Asia and America have presented analogously high proportions of species with aggregated conspecific individuals coincidently. This phenomenon is distinctive and repudiates fundamental ecology hypotheses which suggested dispersed distributions of conspecific tropical trees due to intensive density and natural enemy pressures in tropical forests. We believe that similar aggregation patterns shared by these tropical forests implies the existence of structuring forces in biogeographical scale instead of habitat heterogeneity in local community scales as scientists have considered. To approach the factors contributing to this cross-continent spatial pattern of trees, we obtained and reviewed ecosystem attributes, including topography, temperature, precipitation, biodiversity, density, and biomass, of these forests. Here we show that the proportions of aggregated species are actually constants independent of any ecosystem attributes regardless the nature of these tropical forests. However, local biodiversity are the major factor determining the number of aggregated species and the aggregation of large individuals of these forests. Aggregation of large trees declines along rising biodiversity, while the numbers of aggregated species increase permanently along lifting biodiversity. We propose a possible equilibrium and saturated status of the tropical forests in accommodating aggregated species. Furthermore, the tight correlations of biodiversity and species aggregation strongly imply the importance of overlooked biological interactions in shaping the spatial patterns in the tropical forests
Tensor multivariate trace inequalities and their applications
In linear algebra, the trace of a square matrix is defined as the sum of elements on the main diagonal. The trace of a matrix is the sum of its eigenvalues (counted with multiplicities), and it is invariant under the change of basis. This charac-terization can be used to define the trace of a tensor in general. Trace inequalities are mathematical relations between different multivariate trace functionals involving linear operators. These relations are straightforward equalities if the involved linear operators commute, however, they can be difficult to prove when the non-commuting linear operators are involved. Given two Hermitian tensors H1 and H2 that do not commute. Does there exist a method to transform one of the two tensors such that they commute without completely destroying the structure of the original tensor? The spectral pinching method is a tool to resolve this problem. In this work, we will apply such spectral pinching method to prove several trace inequalities that extend the Araki–Lieb–Thirring (ALT) inequality, Golden–Thompson(GT) inequality and logarithmic trace inequality to arbitrary many tensors. Our approaches rely on complex interpolation theory as well as asymptotic spectral pinching, providing a transpar-ent mechanism to treat generic tensor multivariate trace inequalities. As an example application of our tensor extension of the Golden–Thompson inequality, we give the tail bound for the independent sum of tensors. Such bound will play a fundamental role in high-dimensional probability and statistical data analysis
Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform
We apply the recently developed adaptive non-harmonic model based on the
wave-shape function, as well as the time-frequency analysis tool called
synchrosqueezing transform (SST) to model and analyze oscillatory physiological
signals. To demonstrate how the model and algorithm work, we apply them to
study the pulse wave signal. By extracting features called the spectral pulse
signature, {and} based on functional regression, we characterize the
hemodynamics from the radial pulse wave signals recorded by the
sphygmomanometer. Analysis results suggest the potential of the proposed signal
processing approach to extract health-related hemodynamics features
New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms
In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time-and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods
Building quantum neural networks based on swap test
Artificial neural network, consisting of many neurons in different layers, is
an important method to simulate humain brain. Usually, one neuron has two
operations: one is linear, the other is nonlinear. The linear operation is
inner product and the nonlinear operation is represented by an activation
function. In this work, we introduce a kind of quantum neuron whose inputs and
outputs are quantum states. The inner product and activation operator of the
quantum neurons can be realized by quantum circuits. Based on the quantum
neuron, we propose a model of quantum neural network in which the weights
between neurons are all quantum states. We also construct a quantum circuit to
realize this quantum neural network model. A learning algorithm is proposed
meanwhile. We show the validity of learning algorithm theoretically and
demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training
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