Time-derivative preconditioning for viscous flows

A time-derivative preconditioning algorithm that is effective over a wide range of flow conditions from inviscid to very diffusive flows and from low speed to supersonic flows was developed. This algorithm uses a viscous set of primary dependent variables to introduce well-conditioned eigenvalues and to avoid having a nonphysical time reversal for viscous flow. The resulting algorithm also provides a mechanism for controlling the inviscid and viscous time step parameters to be of order one for very diffusive flows, thereby ensuring rapid convergence at very viscous flows as well as for inviscid flows. Convergence capabilities are demonstrated through computation of a wide variety of problems

Text2Action: Generative Adversarial Synthesis from Language to Action

In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adversarial network (GAN), which is based on the sequence to sequence (SEQ2SEQ) model. Using the proposed generative network, we can synthesize various actions for a robot or a virtual agent using a text encoder recurrent neural network (RNN) and an action decoder RNN. The proposed generative network is trained from 29,770 pairs of actions and sentence annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video dataset. We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence. Results show that the proposed generative network correctly models the relationship between language and action and can generate a diverse set of actions from the same sentence.Comment: 8 pages, 10 figure

Topological Semantic Graph Memory for Image-Goal Navigation

A novel framework is proposed to incrementally collect landmark-based graph memory and use the collected memory for image goal navigation. Given a target image to search, an embodied robot utilizes semantic memory to find the target in an unknown environment. % The semantic graph memory is collected from a panoramic observation of an RGB-D camera without knowing the robot's pose. In this paper, we present a topological semantic graph memory (TSGM), which consists of (1) a graph builder that takes the observed RGB-D image to construct a topological semantic graph, (2) a cross graph mixer module that takes the collected nodes to get contextual information, and (3) a memory decoder that takes the contextual memory as an input to find an action to the target. On the task of image goal navigation, TSGM significantly outperforms competitive baselines by +5.0-9.0% on the success rate and +7.0-23.5% on SPL, which means that the TSGM finds efficient paths. Additionally, we demonstrate our method on a mobile robot in real-world image goal scenarios

Development of a lifetime evaluation system and lifetime prediction method for GaN RF semiconductors used in manned and unmanned weapon systems

The aim of this study is to develop a testing system that applies RF (Radio Frequency) stress to predict the lifespan of GaN RF semiconductors, a subject of numerous ongoing domestication studies. Additionally, the study proposes an approach that considers the complex effects of degradation mechanisms in predicting lifespan. When testing the longevity of communication semiconductors, it’s essential to apply RF-input to replicate real-world conditions. The system we developed applies wideband, high power RF stress to individual samples. It monitors RF characteristic changes in real-time and provides independent control of temperature and voltage stress for each sample. This ensures both effective lifespan tests and real-time tracking of semiconductor degradation patterns. Unlike traditional GaAs semiconductors, GaN ones exhibit the compounded influence of degradation mechanisms during RF operation. Therefore, a new lifespan estimation method that identifies the IV characteristic parameters for each degradation mechanism and deduces a relationship between the DC-accelerated life test and the RF-accelerated life test was proposed. The proposed method is significant in that it provides foundational data necessary for the systematic planning of semiconductor reliability testing and the direction of test equipment development. If lifespan tests proceed using this proposed method and data related to degradation mechanisms is derived, it is anticipated to positively impact the future reliability improvement of GaN RF semiconductors

Development of a lifetime evaluation system and lifetime prediction method for GaN RF semiconductors used in manned and unmanned weapon systems

The aim of this study is to develop a testing system that applies RF (Radio Frequency) stress to predict the lifespan of GaN RF semiconductors, a subject of numerous ongoing domestication studies. Additionally, the study proposes an approach that considers the complex effects of degradation mechanisms in predicting lifespan. When testing the longevity of communication semiconductors, it’s essential to apply RF-input to replicate real-world conditions. The system we developed applies wideband, high power RF stress to individual samples. It monitors RF characteristic changes in real-time and provides independent control of temperature and voltage stress for each sample. This ensures both effective lifespan tests and real-time tracking of semiconductor degradation patterns. Unlike traditional GaAs semiconductors, GaN ones exhibit the compounded influence of degradation mechanisms during RF operation. Therefore, a new lifespan estimation method that identifies the IV characteristic parameters for each degradation mechanism and deduces a relationship between the DC-accelerated life test and the RF-accelerated life test was proposed. The proposed method is significant in that it provides foundational data necessary for the systematic planning of semiconductor reliability testing and the direction of test equipment development. If lifespan tests proceed using this proposed method and data related to degradation mechanisms is derived, it is anticipated to positively impact the future reliability improvement of GaN RF semiconductors

The Effect of Age at First Calving and Calving Interval on Productive Life and Lifetime Profit in Korean Holsteins

This study was performed to estimate the effect of age at first calving and first two calving intervals on productive life and life time profit in Korean Holsteins. Reproduction data of Korean Holsteins born from 1998 to 2004 and lactation data from 276,573 cows with birth and last dry date that calved between 2000 and 2010 were used for the analysis. Lifetime profit increased with the days of life span. Regression of Life Span on Lifetime profit indicated that there was an increase of 3,800 Won (approximately $3.45) of lifetime profit per day increase in life span. This is evidence that care of each cow is necessary to improve net return and important for farms maintaining profitable cows. The estimates of heritability of age at first calving, first two calving intervals, days in milk for lifetime, lifespan, milk income and lifetime profit were 0.111, 0.088, 0.142, 0.140, 0.143, 0.123, and 0.102, respectively. The low heritabilities indicated that the productive life and economical traits include reproductive and productive characteristics. Age at first calving and interval between first and second calving had negative genetic correlation with lifetime profit (−0.080 and −0.265, respectively). Reducing age at first calving and first calving interval had a positive effect on lifetime profit. Lifetime profit increased to approximately 2,600,000 (2,363.6) from 800,000 Won ($727.3) when age at first calving decreased to (22.3 month) from (32.8 month). Results suggested that reproductive traits such as age at first calving and calving interval might affect various economical traits and consequently influenced productive life and profitability of cows. In conclusion, regard of the age at first calving must be taken with the optimum age at first calving for maximum lifetime profit being 22.5 to 23.5 months. Moreover, considering the negative genetic correlation of first calving interval with lifetime profit, it should be reduced against the present trend of increase

Distributional Deep Reinforcement Learning with a Mixture of Gaussians

In this paper, we propose a novel distributional reinforcement learning (RL) method which models the distribution of the sum of rewards using a mixture density network. Recently, it has been shown that modeling the randomness of the return distribution leads to better performance in Atari games and control tasks. Despite the success of the prior work, it has limitations which come from the use of a discrete distribution. First, it needs a projection step and softmax parametrization for the distribution, since it minimizes the KL divergence loss. Secondly, its performance depends on discretization hyperparameters such as the number of atoms and bounds of the support which require domain knowledge. We mitigate these problems with the proposed parameterization, a mixture of Gaussians. Furthermore, we propose a new distance metric called the Jensen-Tsallis distance, which allows the computation of the distance between two mixtures of Gaussians in a closed form. We have conducted various experiments to validate the proposed method, including Atari games and autonomous vehicle driving.N

The Relationship between Autism Spectrum Disorder and Melatonin during Fetal Development

The aim of this review is to clarify the interrelationship between melatonin and autism spectrum disorder (ASD) during fetal development. ASD refers to a diverse range of neurodevelopmental disorders characterized by social deficits, impaired communication, and stereotyped or repetitive behaviors. Melatonin, which is secreted by the pineal gland, has well-established neuroprotective and circadian entraining effects. During pregnancy, the hormone crosses the placenta into the fetal circulation and transmits photoperiodic information to the fetus allowing the establishment of normal sleep patterns and circadian rhythms that are essential for normal neurodevelopment. Melatonin synthesis is frequently impaired in patients with ASD. The hormone reduces oxidative stress, which is harmful to the central nervous system. Therefore, the neuroprotective and circadian entraining roles of melatonin may reduce the risk of neurodevelopmental disorders such as ASD