Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.Comment: Accepted to AAAI 201

Brassinolide induces stem growth of water spinach (Ipomoea aquatica Forsk.)

This report is a preliminary study to clarify the effects of exogenous brassinolide (BL) on the stem tissue growth of water spinach. Local varieties in Taiwan were used for the indoor experiments. The clones were propagated and grown under fluorescent lamps for plant growth, and the clones with 5 leaves were cut off and grown in a water culture for 7 days. Distilled water was used for diluting the concentration of BL, and the BL concentrations used were 0, 1, 2.5, 5, and 10 ppb (w/v). Each clone was cultured in 80 ml of the water solution. The plant height and stem diameter at the centre of the first internode above the point of cutting were measured after 5 days of treatment. The stem diameter and pith thickness increased significantly as the BL concentration in the water culture increased, whereas the cortex thickness was not affected by the BL concentration. The cavity diameters in the 5 and 10 ppb plots were significantly larger than those in the other plots. There was no significant difference in plant height among the treatments, although the plant height tended to be higher with increasing BL concentration.Article信州大学農学部AFC報告 11（1-2）: 1-4(2013)departmental bulletin pape

Accumulation efficiency of degradable matter during the early grain-filling period in rice

The dry weight of cellular contents in the whole rice plant (dWc/dt) is partitioned from the crop growth rate (dW/dt), and the resulting rate represents the accumulation efficiency of degradable matter (dWc/dW). The grain yielding ability and stability are significantly affected by the dry matter partitioning to cell wall during grain filling stage. Comparative studies for dWc/dW during the early grain-filling period were conducted using diverse genotypes of rice varieties in eight experimental fields in Japan, China, and Thailand for 2 yr to develop a simplified process model with submodels for partitioning. Nine rice varieties-2 japonica, 3 indica, indica×japonica, indica×javanica, javanica, and NERICA-were used. dWc/dW was measured by enzymatic analysis. The relationship between dW/dt and the accumulation rate of cellular contents per unit ground area (dWc/dt) was described using a linear regression equation, and the proportionality factor k (slope), which represents accumulation efficiency, was estimated using data from each variety. The k values varied from 0.570 for the traditional indica cv. Ch86 (CH) to 0.765 for the WAB450 line (WA), which is a NERICA variety. High values of dWc/dW were observed in the modern varieties developed by remote crossing [Takanari (TA) and WA]. The average k value from the results of multi-site experiments was 0.681.TA and WA showed high accumulation efficiency by high sink activity under various dW/dts that fluctuated according to environmental conditions at the cultivation sites. Conversely, CH, classified as a "grassy rice" phenotype, formed a cell wall during the early grain-filling period.Article信州大学農学部AFC報告 13: 1-11 (2015)departmental bulletin pape

Ultrabright narrow-band telecom two-photon source for long-distance quantum communication

We demonstrate an ultrabright narrow-band two-photon source at the 1.5 -\mu m telecom wavelength for long-distance quantum communication. By utilizing a bow-tie cavity, we obtain a cavity enhancement factor of $4.06\times 10^4$. Our measurement of the second-order correlation function $G^{(2)} ({\tau})$ reveals that the linewidth of $2.4$ MHz has been hitherto unachieved in the 1.5 -\mu m telecom band. This two-photon source is useful for obtaining a high absorption probability close to unity by quantum memories set inside quantum repeater nodes. Furthermore, to the best of our knowledge, the observed spectral brightness of $3.94\times 10^5$ pairs/(s$\cdot$MHz$\cdot$mW) is also the highest reported over all wavelengths.Comment: 11 pages, 4 figures, 2 table

Generative Colorization of Structured Mobile Web Pages

Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor.Comment: Accepted to WACV 202

Towards Flexible Multi-modal Document Models

Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors. In this work, we attempt at building a holistic model that can jointly solve many different design tasks. Our model, which we denote by FlexDM, treats vector graphic documents as a set of multi-modal elements, and learns to predict masked fields such as element type, position, styling attributes, image, or text, using a unified architecture. Through the use of explicit multi-task learning and in-domain pre-training, our model can better capture the multi-modal relationships among the different document fields. Experimental results corroborate that our single FlexDM is able to successfully solve a multitude of different design tasks, while achieving performance that is competitive with task-specific and costly baselines.Comment: To be published in CVPR2023 (highlight), project page: https://cyberagentailab.github.io/flex-d

LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.Comment: To be published in CVPR2023, project page: https://cyberagentailab.github.io/layout-dm