364 research outputs found
先発スタチン使用者と後発スタチン使用者における服薬アドヒアランス、継続率および臨床的アウトカムの比較:レセプトデータベースを用いた過去起点コホート研究
京都大学新制・課程博士博士(医学)甲第24188号医博第4882号京都大学大学院医学研究科医学専攻(主査)教授 古川 壽亮, 教授 中山 健夫, 教授 寺田 智祐学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various
application scenarios. In order to extend the applications' boundaries to some
accuracy-crucial domains, researchers have been investigating approaches to
boost accuracy through either deeper or wider network structures, which brings
with them the exponential increment of the computational and storage cost,
delaying the responding time. In this paper, we propose a general training
framework named self distillation, which notably enhances the performance
(accuracy) of convolutional neural networks through shrinking the size of the
network rather than aggrandizing it. Different from traditional knowledge
distillation - a knowledge transformation methodology among networks, which
forces student neural networks to approximate the softmax layer outputs of
pre-trained teacher neural networks, the proposed self distillation framework
distills knowledge within network itself. The networks are firstly divided into
several sections. Then the knowledge in the deeper portion of the networks is
squeezed into the shallow ones. Experiments further prove the generalization of
the proposed self distillation framework: enhancement of accuracy at average
level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as
maximum. In addition, it can also provide flexibility of depth-wise scalable
inference on resource-limited edge devices.Our codes will be released on github
soon.Comment: 10page
Downregulated serum 14, 15-epoxyeicosatrienoic acid is associated with abdominal aortic calcification in patients with primary aldosteronism
Patients with primary aldosteronism (PA) have increased risk of target-organ damage, among which vascular calcification is an important indicator of cardiovascular mortality. 14, 15-Epoxyeicosatrienoic acid (14, 15-EET) has been shown to have beneficial effects in vascular remodeling. However, whether 14, 15-EET associates with vascular calcification in PA is unknown. Thus, we aimed to investigate the association between 14, 15-EET and abdominal aortic calcification (AAC) in patients with PA. Sixty-nine patients with PA and 69 controls with essential hypertension, matched for age, sex, and blood pressure, were studied. 14, 15-Dihydroxyeicosatrienoic acid (14, 15-DHET), the inactive metabolite from 14, 15-EET, was estimated to reflect serum 14, 15-EET levels. AAC was assessed by computed tomographic scanning. Compared with matched controls, the AAC prevalence was almost 1-fold higher in patients with PA (27 [39.1%] versus 14 [20.3%]; P=0.023), accompanied by significantly higher serum 14, 15-DHET levels (7.18±4.98 versus 3.50±2.07 ng/mL; P<0.001). Plasma aldosterone concentration was positively associated with 14, 15-DHET (β=0.444; P<0.001). Multivariable logistic analysis revealed that lower 14, 15-DHET was an independent risk factor for AAC in PA (odds ratio, 1.371; 95% confidence interval, 1.145–1.640; P<0.001), especially in young patients with mild hypertension and normal body mass index. In conclusion, PA patients exibited more severe AAC, accompanied by higher serum 14, 15-DHET levels. On the contrary, decreased 14, 15-EET was significantly associated with AAC prevalence in PA patients, especially in those at low cardiovascular risk
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Video super-resolution (VSR) methods have recently achieved a remarkable
success due to the development of deep convolutional neural networks (CNN).
Current state-of-the-art CNN methods usually treat the VSR problem as a large
number of separate multi-frame super-resolution tasks, at which a batch of low
resolution (LR) frames is utilized to generate a single high resolution (HR)
frame, and running a slide window to select LR frames over the entire video
would obtain a series of HR frames. However, duo to the complex temporal
dependency between frames, with the number of LR input frames increase, the
performance of the reconstructed HR frames become worse. The reason is in that
these methods lack the ability to model complex temporal dependencies and hard
to give an accurate motion estimation and compensation for VSR process. Which
makes the performance degrade drastically when the motion in frames is complex.
In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but
effective block, which can efficiently capture the motion compensation and feed
it back to the network in an adaptive way. Our approach efficiently utilizes
the information of the inter-frame motion, the dependence of the network on
motion estimation and compensation method can be avoid. In addition, benefiting
from the excellent nature of MAFC, the network can achieve better performance
in the case of extremely complex motion scenarios. Extensive evaluations and
comparisons validate the strengths of our approach, and the experimental
results demonstrated that the proposed framework is outperform the
state-of-the-art methods.Comment: To appear in AAAI 202
Facial Attribute Capsules for Noise Face Super Resolution
Existing face super-resolution (SR) methods mainly assume the input image to
be noise-free. Their performance degrades drastically when applied to
real-world scenarios where the input image is always contaminated by noise. In
this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with
the problem of high-scale super-resolution of noisy face image. Capsule is a
group of neurons whose activity vector models different properties of the same
entity. Inspired by the concept of capsule, we propose an integrated
representation model of facial information, which named Facial Attribute
Capsule (FAC). In the SR processing, we first generated a group of FACs from
the input LR face, and then reconstructed the HR face from this group of FACs.
Aiming to effectively improve the robustness of FAC to noise, we generate FAC
in semantic, probabilistic and facial attributes manners by means of integrated
learning strategy. Each FAC can be divided into two sub-capsules: Semantic
Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial
attribute in detail from two aspects of semantic representation and probability
distribution. The group of FACs model an image as a combination of facial
attribute information in the semantic space and probabilistic space by an
attribute-disentangling way. The diverse FACs could better combine the face
prior information to generate the face images with fine-grained semantic
attributes. Extensive benchmark experiments show that our method achieves
superior hallucination results and outperforms state-of-the-art for very low
resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202
Linear Complexity of A Family of Binary pq2 -periodic Sequences From Euler Quotients
We first introduce a family of binary -periodic sequences based on the Euler quotients modulo , where and are two distinct odd primes and divides . The minimal polynomials and linear complexities are determined for the proposed sequences provided that . The results show that the proposed sequences have high linear complexities
RNA sequencing analysis to capture the transcriptome landscape during skin ulceration syndrome progression in sea cucumber Apostichopus japonicus
Complement and coagulation cascades pathways (tif). Red boxes represent up-regulated genes, and green boxes represent down-regulated genes. (TIF 627 kb
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