5,254 research outputs found
pH Dependence of the Adhesion Property of Lipid Bilayers and a Method of Measuring the Adhesion Energy
Production of Reducing Sugars from Laminaria japonica by Subcritical Water Hydrolysis
AbstractThis study was to investigate the production of reducing sugars in hydrolysates from raw and deoiled Laminaria japonica produced by subcritical water hydrolysis. Deoiled Laminaria japonica was collected by supercritical carbon dioxide (SCO2) extraction process. Experiments were performed in a batch-type reactor with stirring. It investigated that the effects of reaction temperature and acetic acid as catalyst on content of reducing sugar production. The addition of acetic acid led to an increase in content of reducing sugar. But Removal of oil in Laminaria japonica by SCO2 and increasing of temperature led to decrease in content of reducing sugar production. The highest content of reducing sugar was 814.10mg/100g raw dried sample at 200°C, adding of 1% acetic acid as catalyst
On Investigating the Conservative Property of Score-Based Generative Models
Existing Score-based Generative Models (SGMs) can be categorized into
constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their
parameterization approaches. CSGMs model probability density functions as
Boltzmann distributions, and assign their predictions as the negative gradients
of some scalar-valued energy functions. On the other hand, USGMs employ
flexible architectures capable of directly estimating scores without the need
to explicitly model energy functions. In this paper, we demonstrate that the
architectural constraints of CSGMs may limit their modeling ability. In
addition, we show that USGMs' inability to preserve the property of
conservativeness may lead to degraded sampling performance in practice. To
address the above issues, we propose Quasi-Conservative Score-based Generative
Models (QCSGMs) for keeping the advantages of both CSGMs and USGMs. Our
theoretical derivations demonstrate that the training objective of QCSGMs can
be efficiently integrated into the training processes by leveraging the
Hutchinson trace estimator. In addition, our experimental results on the
CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets validate the effectiveness of
QCSGMs. Finally, we justify the advantage of QCSGMs using an example of a
one-layered autoencoder
Titanium-capped carbon chains as promising new hydrogen storage media
The capacity of Ti-capped sp carbon atomic chains for use as hydrogen storage
media is studied using first-principles density functional theory. The Ti atom
is strongly attached at one end of the carbon chains via d-p hybridization,
forming stable TiCn complexes. We demonstrate that the number of adsorbed H2 on
Ti through Kubas interaction depends upon the chain types. For polyyne (n even)
or cumulene (n odd) structures, each Ti atom can hold up to five or six H2
molecules, respectively. Furthermore, the TiC5 chain effectively terminated on
a C20 fullerene can store hydrogen with optimal binding of 0.52 eV/H2. Our
results reveal a possible way to explore high-capacity hydrogen storage
materials in truly one-dimensional carbon structures.Comment: accepted for publication in Physical Chemistry Chemical Physic
Effect of Total Leaf Numbers on the Growth and Fruit Quality in Muskmelon Plants Showing Leaf Yellowing Symptoms
This study was conducted to evaluate the influence of total leaf
numbers on the growth, net formation of fruits, and occurrence of
leaf yellowing symptoms (LYS) in muskmelon plants. The growth
and development of LYS on muskmelon plants having 25, 30, and
35 fully expanded leaves on the vine were compared to those of the
control plant having 20 leaves. Plant height, leaf area, root fresh
weight, and root dry weight increased as the number of leaves
increased. Plants with 35 leaves showed the greatest plant growth.
Net photosynthetic rate was positively related to increasing leaf
numbers with plants having over 25 leaves showing the greatest
photosynthetic rates. On the other hand, there were no significant
differences in chlorophyll content and root activity among treatments
with different leaf numbers. The ratio of LYS infection was also
greater in plants having 25-30 leaves, than in those having leaf
numbers. Plants with different leaf numbers and LYS infection
showed a variation in fruit quality, although LYS did not
significantly affect fruit quality except net index. The plants having
20 leaves that showed LYS developed fruits that had significantly
smaller flesh (mesocarp) thickness than, the plants having greater numbers of leaves. The higher sugar contents of fruits were found
in the plants having 35 leaves whether they showed LYS (12.1°Bx)
or not (12.5°Bx). Therefore, leaving more than 25 healthy leaves per
plant was recommended for minimizing damage from LYS.OAIID:oai:osos.snu.ac.kr:snu2015-01/104/0000027607/11ADJUST_YN:NEMP_ID:A075898DEPT_CD:517CITE_RATE:0FILENAME:(이희주)effect_of_total_leaf_numbers_on_the_growth_and_fruit_quality_in_muskmelon_plants_showing_leaf_yell··.pdfDEPT_NM:식물생산과학부CONFIRM:
Training Energy-Based Normalizing Flow with Score-Matching Objectives
In this paper, we establish a connection between the parameterization of
flow-based and energy-based generative models, and present a new flow-based
modeling approach called energy-based normalizing flow (EBFlow). We demonstrate
that by optimizing EBFlow with score-matching objectives, the computation of
Jacobian determinants for linear transformations can be entirely bypassed. This
feature enables the use of arbitrary linear layers in the construction of
flow-based models without increasing the computational time complexity of each
training iteration from to for an
-layered model that accepts -dimensional inputs. This makes the training
of EBFlow more efficient than the commonly-adopted maximum likelihood training
method. In addition to the reduction in runtime, we enhance the training
stability and empirical performance of EBFlow through a number of techniques
developed based on our analysis on the score-matching methods. The experimental
results demonstrate that our approach achieves a significant speedup compared
to maximum likelihood estimation, while outperforming prior efficient training
techniques with a noticeable margin in terms of negative log-likelihood (NLL)
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