Co-occurrence based texture synthesis

As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have long been used for texture analysis, to learn a controllable texture synthesis model. We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images while having local, interpretable control over texture appearance. To encourage fidelity to the input condition, we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion. We demonstrate that our solution offers a stable, intuitive, and interpretable latent representation for texture synthesis, which can be used to generate smooth texture morphs between different textures. We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.

AI-assisted Biology: Predict the Conditional Probability Distributions from Noisy Measurements

The relationship between the genotype, defined as the set of genetic information encoded in the DNA, and the phenotype, defined as the macroscopic realization of that information, is still unclear. The emergence of a specific phenotype may be linked not only to gene expression, but also to environmental perturbations and experimental conditions. Moreover, even genetically identical cells in identical environments may display a variety of phenotypes. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic parameters or categories per specific genetic and/or environmental conditions as inputs. Furthermore, biological noise has been proven to play a crucial role in gene regulation mechanisms. The prediction of the average value of a given phenotype is not always sufficient to fully characterize a given biological system. In this study, we develop a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with a small number of observations per input condition. The key innovation of this study is that the deep neural network automatically generates the probability distributions based on only few (10 or less) noisy measurements for each input condition, with no prior knowledge or assumption of the probability distributions. This is extremely useful for exploring unknown biological systems with limited measurements for each input condition, which is linked not only to a better quantitative understanding of biological systems, but also to the design of new ones, as it is in the case of synthetic biology and cellular engineering.

Comparison of 137Cs activity between an ocean general circulation model and the global database

<p>We investigate the oceanic dispersion of <sup>137</sup>Cs (half-life of 30.1 years) and its impact on the environment. <sup>137</sup>Cs has been released into the ocean due to the atmospheric weapons tests, discharge from nuclear reprocessing plants, the Chernobyl accident, and most recently due to Fukushima Daiichi Nuclear Power Plant (1F NPP) accident. <sup>137</sup>Cs activities measured for scientific purposes as well as environmental health and safety monitoring have been summarized in a historical database by IAEA. The spatio-temporal density of the observations varies widely, therefore simulation by an ocean general circulation model (OGCM) can be helpful in the interpretation of these observations. Although simulations of <sup>137</sup>Cs activity by OGCMs have been carried out previously, the input condition of <sup>137</sup>Cs still has large uncertainties due to a lack of observations of global fallout. The horizontal resolution of the previously available estimated global fallout of <sup>137</sup>Cs was 10 degree longitude x latitude. We have produced a new estimate of the global fallout of <sup>137</sup>Cs with 2.5-degree resolution using the Global Precipitation Climatology Project (GPCP) data, and investigated the impact of the revised input condition on the simulation of distribution of <sup>137</sup>Cs in the ocean. In addition, discharges of <sup>137</sup>Cs from the reprocessing plants (Sellafield and La Hague) were also considered. We used the Parallel Ocean Program version 2 (POP2) of the Community Earth System Model version 2 (CESM2). The horizontal resolution is 1.125 degree of longitude, and from 0.28 degree to 0.54 degree of latitude. There are 60 vertical levels with a minimum spacing of 10 m near the ocean surface, and increased spacing with depth to a maximum of 250 m. The simulated period was from 1945 to 2010 with the circulation forced by repeating (“Normal Year”) atmospheric conditions. We estimated the global distribution of <sup>137</sup>Cs deposition from 1945 to 2010 by using these geographical distribution data, the observed time-series data of annual <sup>137</sup>Cs deposition at the MRI from 1958 to 2010, and time-series data of <sup>137</sup>Cs deposition from 1945 to 1958 estimated from ice-core data. Simulated <sup>137</sup>Cs activities derived from the 2.5-degree deposition data were in good agreement with observations, particularly in the Pacific Ocean. Simulated <sup>137</sup>Cs activities were strongly influenced by the discharge of <sup>137</sup>Cs from the reprocessing plants. Transport processes were also investigated in the simulated results.</p>

A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data

The last train problem for metro is especially important because the last trains are the last chances for many passengers to travel by metro; otherwise, they have to choose other traffic modes like taxis or buses. Among the problems, the passenger demand is a vital input condition for the optimization of last train transfers. This study proposes a data-driven estimation method for the potential passenger demand of last trains. Through the geographic information, external traffic data including taxi and bus are first analyzed separately to match the origin–destination passenger flow during the last train period. A solving solution for taxi and bus is then developed to estimate the potential passenger flow for all the transfer directions of the target stations. Combining the estimated potential passenger flow and the actual passenger flow obtained by metro smart card data, the total potential passenger demand of last trains is obtained. The effectiveness of the proposed method is evaluated using a real-world metro network. This research can provide important guidance and act as a technical reference for the metro operations on when to optimize the last train transfers.

Utilizing Delta IDDQ to Screen Cell Specific Defects for High Quality and Reliability Applications

High quality and reliability are paramount for automotive and other high grade commercial applications. The implementation of scan testing including stuck-at, transition, IDDQ, bridging and cell-aware patterns have all been targeted at reducing the number of defective parts being shipped. These techniques are not always sufficient to achieve sub defective parts per million (DPPM) quality levels. This paper presents a recurring failure mechanism that was encountered on an automotive device and the subsequent efforts to expand upon existing testing methodologies to effectively screen the defective devices using a delta IDDQ method with specific logic inputs and outputs. In effect, this new testing becomes a cell-aware delta IDDQ targeting one specific input condition that was implemented in production with limited test time overhead.

SISTEM AUTOMATIC SWITCH REDUNDANT UPS UNTUK BEBAN ESSENSIAL

The role of the power supply device is to produce, process and distribute energy sources. Telecommunication equipment can only operate if it has continuous supply. Therefore, to maintain the continuity of the supply, a UPS (Uninterruptable Power Supply) device system is needed so that the supply to the Essential Load device will remain available so that continuity will be maintained. This research designs and proposes how a series of automatic redundant switch systems on UPS to ensure the availability of power supply for the main equipment of telecommunications systems. The Auto switch circuit is designed to have 3 (three) working stages which will trigger the relay driver as control circuit, namely the normal working condition of the contactor input K1 is present, the input condition is zero (lost), and the input condition is present. This system can automatically supply power to telecommunications equipment.

Improvement on Toughness of Weld Heat Affected Zone of Cu-Containing Low Alloy Steel of Long Scale Forging for Offshore Applications by Optimizing Chemical Composition

Copper-containing low alloy steel based on ASTM A707 5L grade is widely used for structural parts of offshore wells. Applications of the steel for Ultra-deepwater development require excellent low temperature toughness from the viewpoint of marine accident prevention. However it is difficult to stably obtain good weld joint toughness because the welding condition is inevitably scattering. With those backgrounds, this paper focuses on metallurgical factors controlling the HAZ toughness of A707 modified steel. Potential factors considered are the grain size, M-A and precipitates. A challenge is demonstrated to improve the HAZ toughness by optimizing the Cu and Mn contents. In this study, we investigated mechanical properties including crack tip opening displacement (CTOD) and we observed microstructure using welding tests or various weld heat cycle specimens. The weld heat affected zone (HAZ) of a conventional material had good toughness for the low heat input condition. However it was remarkably decreased for the high heat input condition due to the precipitating martensite-austenite constituent (M-A) in local brittle zones (LBZ). The weld test results indicated the importance of suppressing the formation of M-A in order to improve toughness in the HAZ of the steel. Thereby, we challenged the optimization of chemical composition for HAZ toughness improvement. Cu had no bad influence on the HAZ toughness. It was demonstrated that the HAZ toughness is recovered by good use of Cu precipitates in SC cycle. Moreover the area fraction of M-A is decreased in keeping with Mn content, which leads to the improvement of the ICCG HAZ toughness. Based on our study, the recommended amounts of Cu and Mn are more than 1.0 mass% and less than 0.6 mass%, respectively, to ensure the HAZ toughness, especially ICCG HAZ toughness.