6,502 research outputs found
Iterative algorithms for partitioned neural network approximation to partial differential equations
To enhance solution accuracy and training efficiency in neural network
approximation to partial differential equations, partitioned neural networks
can be used as a solution surrogate instead of a single large and deep neural
network defined on the whole problem domain. In such a partitioned neural
network approach, suitable interface conditions or subdomain boundary
conditions are combined to obtain a convergent approximate solution. However,
there has been no rigorous study on the convergence and parallel computing
enhancement on the partitioned neural network approach. In this paper,
iterative algorithms are proposed to address these issues. Our algorithms are
based on classical additive Schwarz domain decomposition methods. Numerical
results are included to show the performance of the proposed iterative
algorithms
Characteristics of DSSC Panels with Silicone Encapsulant
Dye-sensitized solar cells (DSSC) allow light transmission and the application of various colors that make them especially suitable for building-integrated PV (BIPV) application. In order to apply DSSC modules to windows, the module has to be panelized: a DSSC module should be protected with toughened glass on the entire surface. Up to the present, it seems to be common to use double glazing with DSSC modules, with air gaps between the glass pane and the DSSC modules. Few studies have been conducted on the characteristics of various glazing methods with DSSC modules. This paper proposes a paneling method that uses silicone encapsulant, analyzing the performance through experimentation. Compared to a multilayered DSSC panel with an air gap, the encapsulant-applied panel showed 6% higher light transmittance and 7% higher electrical efficiency. The encapsulant also prevented electrolyte leakage by strengthening the seals in the DSSC module
Assessing the Impact of Protectionism Upon the Performance of Actors: The Case of the French and Korean Film Industries
The film industry has received increasing attention due to social, cultural, and economic reasons. Consequently, many countries have introduced various measures to protect and promote it, particularly through the use of subsidies. So far, many works have focused on how protectionism affects the film industry with focus on production and consumption. In this regard, this paper focuses on the impact of protectionism upon the performance of actors by comparing the French and Korean film industries, which has been less studied. This paper reveals three interesting points that should be carefully considered in order to make effective policies for the film industry. First, subsidies to protect the film industry increase the performance fee of actors since part of the subsidies goes to them. Second, direct subsidies that are distributed to the director also distort the film producing structure by increasing the number of actor-directors. Third, subsidies for international co-production increase the number of actors who collaborate with international producers
Jitter in Packet Networks - a Simulation Based Study
School of Electrical and Computer Engineerin
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
Feature Structure Distillation for BERT Transferring
Knowledge distillation is an approach to transfer information on
representations from a teacher to a student by reducing their difference. A
challenge of this approach is to reduce the flexibility of the student's
representations inducing inaccurate learning of the teacher's knowledge. To
resolve it in BERT transferring, we investigate distillation of structures of
representations specified to three types: intra-feature, local inter-feature,
global inter-feature structures. To transfer them, we introduce \textit{feature
structure distillation} methods based on the Centered Kernel Alignment, which
assigns a consistent value to similar features structures and reveals more
informative relations. In particular, a memory-augmented transfer method with
clustering is implemented for the global structures. In the experiments on the
nine tasks for language understanding of the GLUE dataset, the proposed methods
effectively transfer the three types of structures and improve performance
compared to state-of-the-art distillation methods. Indeed, the code for the
methods is available in https://github.com/maroo-sky/FSDComment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
An Active and Soft Hydrogel Actuator to Stimulate Live Cell Clusters by Self-folding
The hydrogels are widely used in various applications, and their successful uses depend on controlling the mechanical properties. In this study, we present an advanced strategy to develop hydrogel actuator designed to stimulate live cell clusters by self-folding. The hydrogel actuator consisting of two layers with different expansion ratios were fabricated to have various curvatures in self-folding. The expansion ratio of the hydrogel tuned with the molecular weight and concentration of gel-forming polymers, and temperature-sensitive molecules in a controlled manner. As a result, the hydrogel actuator could stimulate live cell clusters by compression and tension repeatedly, in response to temperature. The cell clusters were compressed in the 0.7-fold decreases of the radius of curvature with 1.0 mm in room temperature, as compared to that of 1.4 mm in 37 degrees C. Interestingly, the vascular endothelial growth factor (VEGF) and insulin-like growth factor-binding protein-2 (IGFBP-2) in MCF-7 tumor cells exposed by mechanical stimulation was expressed more than in those without stimulation. Overall, this new strategy to prepare the active and soft hydrogel actuator would be actively used in tissue engineering, drug delivery, and micro-scale actuators
Elastography Can Effectively Decrease the Number of Fine-Needle Aspiration Biopsies in Patients with Calcified Thyroid Nodules
AbstractWhen calcification, frequently found in both benign and malignant nodules, is present in thyroid nodules, non-invasive differentiation with ultrasound becomes challenging. The goal of this study was to evaluate the utility of elastography in differentiating calcified thyroid nodules. Consecutive patients (165 patients with 196 nodules) referred for fine-needle aspiration who had undergone both ultrasound elastography and B-mode examinations were analyzed retrospectively. Calcification was present in 45 benign and 20 malignant nodules. On 65 calcified nodules, elastography had 95% sensitivity, 51.1% specificity, 46.3% positive predictive value and 95.8% negative predictive value in detecting malignancy. Twenty-three of 45 benign calcified nodules were correctly diagnosed with elastography compared with 4 of 45 by B-mode ultrasound. Although it is difficult to differentiate benign and malignant calcified thyroid nodules solely with B-mode ultrasound, elastography has the potential to reduce the number of fine-needle aspiration biopsies performed on calcified nodules
- …