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

CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder

Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs.However, only a few works were proposed as an unsupervised method, and even these works require known factor information in training data. We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement in unsupervised learning without any knowledge of the dataset factor information.CFASL incorporates three novel features for learning symmetry-based disentanglement: 1) Injecting inductive bias to align latent vector dimensions to factor-aligned symmetries within an explicit learnable symmetry codebook 2) Learning a composite symmetry to express unknown factors change between two random samples by learning factor-aligned symmetries within the codebook 3) Inducing group equivariant encoder and decoder in training VAEs with the two conditions. In addition, we propose an extended evaluation metric for multi-factor changes in comparison to disentanglement evaluation in VAEs. In quantitative and in-depth qualitative analysis, CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions compared to state-of-the-art methods.Comment: 21 pages, 14 figure

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

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

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

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