606 research outputs found
Can crop yield risk be globally diversified?
In 2007 and 2008 world food markets observed a significant price boom. Crop failures simultaneously occurring in some of the world’s major production regions have been quoted as one factor among others for the price boom. Against this background, we analyse the stochasticity of crop yields in major production areas. The analysis is exemplified for wheat, which is one of the most important crops worldwide. Particular attention is given to the stochastic dependence of yields in different regions. Thereby we address the question of whether local fluctuations of yields can be smoothed by international agricultural trade, i.e. by global diversification. The analysis is based on the copula approach, which requires less restrictive assumptions compared with linear correlations. The use of copulas allows for a more reliable estimation of extreme yield shortfalls, which are of particular interest in this application. Our calculations reveal that a production shortfall, such as in 2007, is not a once in a lifetime event. Instead, from a statistical point of view, similar production conditions will occur every 15 years.crop yield risk, fully nested hierarchical Archimedean copulas (FNAC), price boom
Visible two-dimensional photonic crystal slab laser
The authors describe the fabrication and performance of photonic crystal lasers fabricated within thin membranes of InGaP/InGaAlP quantum well material and emitting in the visible wavelength range. These lasers have ultrasmall mode volumes, emit red light, and exhibit low threshold powers. They can be lithographically tuned from 650 to 690 nm. Their cavity volumes of approximately 0.01 µm3 are ideally suited for use as spectroscopic sources
Practical Modeling and Comprehensive System Identification of a BLDC Motor
The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV
Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search
Given a vector dataset , a query vector , graph-based
Approximate Nearest Neighbor Search (ANNS) aims to build a proximity graph (PG)
as an index of and approximately return vectors with minimum
distances to by searching over the PG index. It suffers from the
large-scale because a PG with full vectors is too large to fit
into the memory, e.g., a billion-scale in 128 dimensions would
consume nearly 600 GB memory. To solve this, Product Quantization (PQ)
integrated graph-based ANNS is proposed to reduce the memory usage, using
smaller compact codes of quantized vectors in memory instead of the large
original vectors. Existing PQ methods do not consider the important routing
features of PG, resulting in low-quality quantized vectors that affect the
ANNS's effectiveness. In this paper, we present an end-to-end Routing-guided
learned Product Quantization (RPQ) for graph-based ANNS. It consists of (1) a
\textit{differentiable quantizer} used to make the standard discrete PQ
differentiable to suit for back-propagation of end-to-end learning, (2) a
\textit{sampling-based feature extractor} used to extract neighborhood and
routing features of a PG, and (3) a \textit{multi-feature joint training
module} with two types of feature-aware losses to continuously optimize the
differentiable quantizer. As a result, the inherent features of a PG would be
embedded into the learned PQ, generating high-quality quantized vectors.
Moreover, we integrate our RPQ with the state-of-the-art DiskANN and existing
popular PGs to improve their performance. Comprehensive experiments on
real-world large-scale datasets (from 1M to 1B) demonstrate RPQ's superiority,
e.g., 1.7-4.2 improvement on QPS at the same recall@10 of 95\%.Comment: 14 pages, 12 figure
Numerical calculation of free-energy barriers for entangled polymer nucleation.
The crystallization of entangled polymers from their melt is investigated using computer simulation with a coarse-grained model. Using hybrid Monte Carlo simulations enables us to probe the behavior of long polymer chains. We identify solid-like beads with a centrosymmetry local order parameter and compute the nucleation free-energy barrier at relatively high supercooling with adaptive-bias windowed umbrella sampling. Our results demonstrate that the critical nucleus sizes and the heights of free-energy barriers do not significantly depend on the molecular weight of the polymer; however, the nucleation rate decreases with the increase in molecular weight. Moreover, an analysis of the composition of the critical nucleus suggests that intra-molecular growth of the nucleated cluster does not contribute significantly to crystallization for this system.National Key R&D Program of China (2016YFB0302500);
National Natural Science Foundation of China (51633009);
Royal Society Newton Mobility Grant (MBAG/240 RG82754
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Hyperspectral Imaging with Stimulated Raman Scattering by Chirped Femtosecond Lasers
Raman microscopy is a quantitative, label-free, and noninvasive optical imaging technique for studying inhomogeneous systems. However, the feebleness of Raman scattering significantly limits the use of Raman microscopy to low time resolutions and primarily static samples. Recent developments in narrowband stimulated Raman scattering (SRS) microscopy have significantly increased the acquisition speed of Raman based label-free imaging by a few orders of magnitude, at the expense of reduced spectroscopic information. On the basis of a spectral focusing approach, we present a fast SRS hyperspectral imaging system using chirped femtosecond lasers to achieve rapid Raman spectra acquisition while retaining the full speed and image quality of narrowband SRS imaging. We demonstrate that quantitative concentration determination of cholesterol in the presence of interfering chemical species can be achieved with sensitivity down to 4 mM. For imaging purposes, hyperspectral imaging data in the C–H stretching region is obtained within a minute. We show that mammalian cell SRS hyperspectral imaging reveals the spatially inhomogeneous distribution of saturated lipids, unsaturated lipids, cholesterol, and protein. The combination of fast spectroscopy and label-free chemical imaging will enable new applications in studying biological systems and material systems.Chemistry and Chemical BiologyEngineering and Applied Science
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