Development of a Web-based Front-end Environment to Aid Programming Lectures on Unix-like Systems

In this paper, we describe the details of the design and implementation of our Front-end Environment for Hands-on Activities (FEHA), which is a web-based programming environment. FEHA provides a programming environment on the web and utilizes existing Unix-like systems that equip a specialized programming environment as the build and runtime platform. FEHA controls the existing systems by using Secure SHell (SSH) and Rsync without any modification of the existing systems. We discuss a case study of FEHA in which it was applied to actual programming lectures at a university. In the lectures, 70% of the students completed registrations to use FEHA in about 3 min. In addition, they could understand how to use the FEHA and started submitting codes within several minutes after the registration. The case study shows that FEHA is able to provide a specialized programming environment for more than 100 students with a small amount of effort from the instructor and system administrator

An Architecture of Highly Parallel Computer AP1000

We have developed a highly parallel computer with distributed memory called the AP1000. The system consists of 64 to 1024 processing elements and three independent networks called Torus network (T-net), Broadcast network (B-net), and Synchronization network (S-net). The design goal for the AP1000 is to attain low latency, high throughput communication. To reduce the overall communication latency, we have developed a message controller and a new routing scheme on the T-net. In this paper, we present the design concepts, architecture, and some result from performance test for the AP1000. 1 INTRODUCTION The AP1000 is a highly parallel computer with distributed memory. Each processor (cell) runs its own program from its local memory and communicates with other processors by passing messages. Messages are sent from the source cell to the destination cell by a communication network. For the many machines using hypercubes or torus structures as a communication network configuration, message..

Temporal and Locational Values of Images Affecting the Deep Learning of Cancer Stem Cell Morphology

Deep learning is being increasingly applied for obtaining digital microscopy image data of cells. Well-defined annotated cell images have contributed to the development of the technology. Cell morphology is an inherent characteristic of each cell type. Moreover, the morphology of a cell changes during its lifetime because of cellular activity. Artificial intelligence (AI) capable of recognizing a mouse-induced pluripotent stem (miPS) cell cultured in a medium containing Lewis lung cancer (LLC) cell culture-conditioned medium (cm), miPS-LLCcm cell, which is a cancer stem cell (CSC) derived from miPS cell, would be suitable for basic and applied science. This study aims to clarify the limitation of AI models constructed using different datasets and the versatility improvement of AI models. The trained AI was used to segment CSC in phase-contrast images using conditional generative adversarial networks (CGAN). The dataset included blank cell images that were used for training the AI but they did not affect the quality of predicting CSC in phase contrast images compared with the dataset without the blank cell images. AI models trained using images of 1-day culture could predict CSC in images of 2-day culture; however, the quality of the CSC prediction was reduced. Convolutional neural network (CNN) classification indicated that miPS-LLCcm cell image classification was done based on cultivation day. By using a dataset that included images of each cell culture day, the prediction of CSC remains to be improved. This is useful because cells do not change the characteristics of stem cells owing to stem cell marker expression, even if the cell morphology changes during culture