Suzaku observations of subhalos in the Coma cluster

We observed three massive subhalos in the Coma cluster with {\it Suzaku}. These subhalos, labeled "ID 1", "ID 2", and "ID 32", were detected with a weak-lensing survey using the Subaru/Suprime-Cam (Okabe et al. 2014a), and are located at the projected distances of 1.4 $r_{500}$, 1.2 $r_{500}$, and 1.6 $r_{500}$ from the center of the Coma cluster, respectively. The subhalo "ID 1" has a compact X-ray excess emission close to the center of the weak-lensing mass contour, and the gas mass to weak-lensing mass ratio is about 0.001. The temperature of the emission is about 3 keV, which is slightly lower than that of the surrounding intracluster medium (ICM) and that expected for the temperature vs. mass relation of clusters of galaxies. The subhalo "ID 32" shows an excess emission whose peak is shifted toward the opposite direction from the center of the Coma cluster. The gas mass to weak-lensing mass ratio is also about 0.001, which is significantly smaller than regular galaxy groups. The temperature of the excess is about 0.5 keV and significantly lower than that of the surrounding ICM and far from the temperature vs. mass relation of clusters. However, there is no significant excess X-ray emission in the "ID 2" subhalo. Assuming an infall velocity of about 2000 $\rm km~s^{-1}$, at the border of the excess X-ray emission, the ram pressures for "ID 1" and "ID 32" are comparable to the gravitational restoring force per area. We also studied the effect of the Kelvin-Helmholtz instability to strip the gas. Although we found X-ray clumps associated with the weak-lensing subhalos, their X-ray luminosities are much lower than the total ICM luminosity in the cluster outskirts.Comment: 19 pages, 8 figures, ApJ in pres

Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science

Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost

Keyword Assisted Topic Models

For a long time, many social scientists have conducted content analysis by using their substantive knowledge and manually coding documents. In recent years, however, fully automated content analysis based on probabilistic topic models has become increasingly popular because of their scalability. Unfortunately, applied researchers find that these models often fail to yield topics of their substantive interest by inadvertently creating multiple topics with similar content and combining different themes into a single topic. In this paper, we empirically demonstrate that providing topic models with a small number of keywords can substantially improve their performance. The proposed keyword assisted topic model (keyATM) offers an important advantage that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post-hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our applications, we find that the keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics than the standard topic models. Finally, we show that the keyATM can also incorporate covariates and model time trends. An open-source software package is available for implementing the proposed methodology

Deep Adversarial Reinforcement Learning With Noise Compensation by Autoencoder

We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this method, robust internal representation in a deep Q-network (DQN) was introduced by applying adversarial noise to disturb the DQN policy; however, it was compensated for by the autoencoder network. In particular, we proposed the use of a new type of adversarial noise: it encourages the policy to choose the worst action leading to the worst outcome at each state. When the proposed method, called deep Q-W-network regularized with an autoencoder (DQWAE), was applied to seven different games in an Atari 2600, the results were convincing. DQWAE exhibited greater robustness against the random/adversarial noise added to the input and accelerated the learning process more than the baseline DQN. When applied to a realistic automatic driving simulation, the proposed DRL method was found to be effective at rendering the acquired policy robust against random/adversarial noise

Localized Ras signaling at the leading edge regulates PI3K, cell polarity, and directional cell movement

During chemotaxis, receptors and heterotrimeric G-protein subunits are distributed and activated almost uniformly along the cell membrane, whereas PI(3,4,5)P3, the product of phosphatidylinositol 3-kinase (PI3K), accumulates locally at the leading edge. The key intermediate event that creates this strong PI(3,4,5)P3 asymmetry remains unclear. Here, we show that Ras is rapidly and transiently activated in response to chemoattractant stimulation and regulates PI3K activity. Ras activation occurs at the leading edge of chemotaxing cells, and this local activation is independent of the F-actin cytoskeleton, whereas PI3K localization is dependent on F-actin polymerization. Inhibition of Ras results in severe defects in directional movement, indicating that Ras is an upstream component of the cell's compass. These results support a mechanism by which localized Ras activation mediates leading edge formation through activation of basal PI3K present on the plasma membrane and other Ras effectors required for chemotaxis. A feedback loop, mediated through localized F-actin polymerization, recruits cytosolic PI3K to the leading edge to amplify the signal

Using Video Activity Reports to Support Remote Project-Based Learning

Distance learning has been expanding. Learner engagement is particularly important in project-based learning (PBL), but the interaction between teacher and learner and the understanding of learner status, including engagement, is not easy. This study aims to support teacher-learner communication based on learner engagement for remote PBL. In this paper, we propose the use of video activity reports by learners to estimate and understand learner engagement and to demonstrate its feasibility on the basis of the relationship between verbal and nonverbal information that can be obtained from video activity reports and learner engagement. Analysis of 232 video activity reports submitted by eight graduate students while working on remote research-based PBLs reveals that learner engagement decreases (1) when the report contained negative words, (2) when filled pauses were frequent or long, and (3) when silent pauses were infrequent or short. Furthermore, the feasibility of an AI-based support system is demonstrated through the design and implementation of a prototype