4,290 research outputs found
Construction of a Transformation‐Competent Artificial Chromosome (TAC) Library of \u3ci\u3eLeymus multicaulis\u3c/i\u3e
Electron acceleration associated with the magnetic flux pileup regions in the near-Earth plasma sheet: A multicase study
Using the Time History of Events and Macroscale Interactions during Substorms (THEMIS) observations, we study electron acceleration (<30 keV) in the magnetic flux pileup regions (FPRs) in the near-Earth plasma sheet (X ~ –10 RE). We present three cases of FRPs associated with dipolarization fronts and substorm dipolarization. Based on the characteristics of the magnetic field, we defined the magnetic field enhancement region (MFER) as the magnetic field with significant ramp that is usually observed near the dipolarization front boundary layer. On the other side, the increased magnetic field without a significant ramp is the rest of a FPR. Our results show that betatron acceleration dominates for 10–30 keV electrons inside the MFER, whereas Fermi acceleration dominates for 10–30 keV electrons inside the rest of the FPR. Betatron acceleration is caused by the enhancement of the local magnetic field, whereas Fermi acceleration is related to the shrinking length of magnetic field line. These accelerated electrons inside the FPRs in the near-Earth tail play a potentially important role in the evolution of the Earth's electron radiation belt and substorms
Social stream classification with emerging new labels
Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ
Integrated magnetoelectric devices based on interfacial magnetoelectric coupling effects
Structural Evolution During Formation and Filling of Self-patterned Nanoholes on GaAs (100) Surfaces
Nanohole formation on an AlAs/GaAs superlattice gives insight to both the “drilling” effect of Ga droplets on AlAs as compared to GaAs and the hole-filling process. The shape and depth of the nanoholes formed on GaAs (100) substrates has been studied by the cross-section transmission electron microscopy. The Ga droplets “drill” through the AlAs layer at a much slower rate than through GaAs due to differences in activation energy. Refill of the nanohole results in elongated GaAs mounds along the [01−1] direction. As a result of capillarity-induced diffusion, GaAs favors growth inside the nanoholes, which provides the possibility to fabricate GaAs and AlAs nanostructures
catena-poly[di-μ(2)-chloro(1,10-phenanthroline)cadmium(II)]
The crystal structure of the 1/1 adduct of cadmium dichloride with 1,10-phenanthroline, [CdCl2(C12H8N2)](n), is based on an infinite chain of Cd2Cl2 parallelograms sharing their Cd corners. The chain propagates in a zigzag manner along the c axis of the monoclinic unit cell. The Cd atom and the phenanthroline molecule both lie on special positions of 2 symmetry
Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings
The stable and uniform characteristics of nitrogen in nitrogen doped titanium dioxide nano photocatalytic particles
Conference Name:8th China International Nanoscience and Technology Symposium (Cinsts 09). Conference Address: Xiangtan, PEOPLES R CHINA. Time:OCT 23-27, 2009.N doped TiO(2) nano particles were prepared by heating TiO(2) nano particles in NH(3) + C(3)H(8) 5vol% atmosphere. The stability and uniformity characteristics of the doped N in the prepared N doped TiO(2) nano particles were studied by X-ray photoelectron spectroscopy (XPS) and temperature programmed desorption (TPD). We concluded that the doped N was uniform from the surface to the inside of TiO(2) nano particles and found that the desorption peak of the doped N was more than 900 K in vacuum, showing its high stability and potential of visible light photocatalytic application for N doped TiO(2) nano particles
Mutagenesis of Snu114 domain IV identifies a developmental role in meiotic splicing
<p>Snu114, a component of the U5 snRNP, plays a key role in activation of the spliceosome. It controls the action of Brr2, an RNA-stimulated ATPase/RNA helicase that disrupts U4/U6 snRNA base-pairing prior to formation of the spliceosome’s catalytic centre. Snu114 has a highly conserved domain structure that resembles that of the GTPase EF-2/EF-G in the ribosome. It has been suggested that the regulatory function of Snu114 in activation of the spliceosome is mediated by its C-terminal region, however, there has been only limited characterisation of the interactions of the C-terminal domains. We show a direct interaction between protein phosphatase PP1 and Snu114 domain ‘IVa’ and identify sequence ‘YGVQYK’ as a PP1 binding motif. Interestingly, this motif is also required for Cwc21 binding. We provide evidence for mutually exclusive interaction of Cwc21 and PP1 with Snu114 and show that the affinity of Cwc21 and PP1 for Snu114 is influenced by the different nucleotide-bound states of Snu114. Moreover, we identify a novel mutation in domain IVa that, while not affecting vegetative growth of yeast cells, causes a defect in splicing transcripts of the meiotic genes, <i>SPO22, AMA1</i> and <i>MER2</i>, thereby inhibiting an early stage of meiosis.</p
Recurrent Fusion Network for Image Captioning
Recently, much advance has been made in image captioning, and an
encoder-decoder framework has been adopted by all the state-of-the-art models.
Under this framework, an input image is encoded by a convolutional neural
network (CNN) and then translated into natural language with a recurrent neural
network (RNN). The existing models counting on this framework merely employ one
kind of CNNs, e.g., ResNet or Inception-X, which describe image contents from
only one specific view point. Thus, the semantic meaning of an input image
cannot be comprehensively understood, which restricts the performance of
captioning. In this paper, in order to exploit the complementary information
from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for
tackling image captioning. The fusion process in our model can exploit the
interactions among the outputs of the image encoders and then generate new
compact yet informative representations for the decoder. Experiments on the
MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets
a new state-of-the-art for image captioning.Comment: ECCV-1
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