710 research outputs found
Case Analysis and Problems Summary of Current Supply Chain Models of Agricultural Products in Jilin Province
By illustrating three cases including Changchun Vegetable Center Wholesale Market, Ouya Supermarket Chain-Operation Limited Company, Fubang Agricultural and Livestock Development and Cooperation Association, the paper elaborates respectively three current supply chain models of agricultural products in Jilin Province by means of case analysis, with wholesale market of agricultural products as the core, retail chain supermarket and agricultural cooperative playing a dominant role. It then makes an analysis of advantages of each model from cohesion of core enterprises, quality of products, cost control and marketing coverage, and summarizes the problems of current supply chain models of agricultural products in Jilin Province in profit distribution, logistical level, organizational degree and electronic commerce, etc.
On the expected number of critical points of locally isotropic Gaussian random fields
We consider locally isotropic Gaussian random fields on the -dimensional
Euclidean space for fixed . Using the so called Gaussian Orthogonally
Invariant matrices first studied by Mallows in 1961 which include the
celebrated Gaussian Orthogonal Ensemble (GOE), we establish the Kac--Rice
representation of expected number of critical points of non-isotropic Gaussian
fields, complementing the isotropic case obatined by Cheng and Schwartzman in
2018. In the limit N=\8, we show that such a representation can be always
given by GOE matrices, as conjectured by Auffinger and Zeng in 2020
Direct observation of magnon-phonon coupling in yttrium iron garnet
The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic
transition temperature of 560 K has been widely used in microwave and
spintronic devices. Anomalous features in the spin Seeback effect (SSE)
voltages have been observed in Pt/YIG and attributed to the magnon-phonon
coupling. Here we use inelastic neutron scattering to map out low-energy spin
waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic
field. By comparing the zero and 9.1 T data, we find that instead of splitting
and opening up gaps at the spin wave and acoustic phonon dispersion
intersecting points, magnon-phonon coupling in YIG enhances the hybridized
scattering intensity. These results are different from expectations of
conventional spin-lattice coupling, calling for new paradigms to understand the
scattering process of magnon-phonon interactions and the resulting
magnon-polarons.Comment: 5 pages, 4 figures, PRB in pres
DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation
Many current works directly adopt multi-rate depth-wise dilated convolutions
to capture multi-scale contextual information simultaneously from one input
feature map, thus improving the feature extraction efficiency for real-time
semantic segmentation. However, this design may lead to difficult access to
multi-scale contextual information because of the unreasonable structure and
hyperparameters. To lower the difficulty of drawing multi-scale contextual
information, we propose a highly efficient multi-scale feature extraction
method, which decomposes the original single-step method into two steps, Region
Residualization-Semantic Residualization. In this method, the multi-rate
depth-wise dilated convolutions take a simpler role in feature extraction:
performing simple semantic-based morphological filtering with one desired
receptive field in the second step based on each concise feature map of region
form provided by the first step, to improve their efficiency. Moreover, the
dilation rates and the capacity of dilated convolutions for each network stage
are elaborated to fully utilize all the feature maps of region form that can be
achieved.Accordingly, we design a novel Dilation-wise Residual (DWR) module and
a Simple Inverted Residual (SIR) module for the high and low level network,
respectively, and form a powerful DWR Segmentation (DWRSeg) network. Extensive
experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness
of our method by achieving a state-of-the-art trade-off between accuracy and
inference speed, in addition to being lighter weight. Without pretraining or
resorting to any training trick, we achieve an mIoU of 72.7% on the Cityscapes
test set at a speed of 319.5 FPS on one NVIDIA GeForce GTX 1080 Ti card, which
exceeds the latest methods of a speed of 69.5 FPS and 0.8% mIoU. The code and
trained models are publicly available
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