Magnetic properties and natural remanent magnetization of carbonaceous chondrites containing pyrrhotite

Magnetic properties, NRM characteristics and magnetic minerals of four carbonaceous chondrites, the Allende, the Leoville, Y-74662 and Y-81020,are examined. These C-chondrites contain ferrimagnetic pyrrhotite grains in addition to magnetite, kamacite and/or taenite as magnetic minerals possessing NRM. The low temperature NRM component which is possessed by ferrimagnetic pyrrhotite at temperatures below 300℃, indicates that the corresponding paleointensity (F_p) is around 1 Oe in order of magnitude. The high temperature NRM component possessed by magnetite and/or taenite is magnetic at temperatures below about 600℃, giving rise to F_p≲0.1 Oe. The kamacite magnetization contributes very little at temperatures below 770℃

Relations between Sleep Time and SNS Texts

Sleeping habits are one of the major issues in today’s healthcare. In this paper, we consider the problem of analyzing sleeping habits of people using social networking service (SNS) texts. As the first step toward predicting user’s sleeping time using SNS texts, we assume that the time span between the user’s last post in one day and the first post the next day can be used as a pseudo-indicator for the user’s sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans “pseudo-sleeping time” if the first tweet of the next day include “Good morning” or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user’s pseudo-sleeping time

Efficient (nonrandom) construction and decoding for non-adaptive group testing

The task of non-adaptive group testing is to identify up to $d$ defective items from $N$ items, where a test is positive if it contains at least one defective item, and negative otherwise. If there are $t$ tests, they can be represented as a $t \times N$ measurement matrix. We have answered the question of whether there exists a scheme such that a larger measurement matrix, built from a given $t\times N$ measurement matrix, can be used to identify up to $d$ defective items in time $O(t \log_2{N})$. In the meantime, a $t \times N$ nonrandom measurement matrix with $t = O \left(\frac{d^2 \log_2^2{N}}{(\log_2(d\log_2{N}) - \log_2{\log_2(d\log_2{N})})^2} \right)$ can be obtained to identify up to $d$ defective items in time $\mathrm{poly}(t)$. This is much better than the best well-known bound, $t = O \left( d^2 \log_2^2{N} \right)$. For the special case $d = 2$, there exists an efficient nonrandom construction in which at most two defective items can be identified in time $4\log_2^2{N}$ using $t = 4\log_2^2{N}$ tests. Numerical results show that our proposed scheme is more practical than existing ones, and experimental results confirm our theoretical analysis. In particular, up to $2^{7} = 128$ defective items can be identified in less than $16$s even for $N = 2^{100}$

Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT)

A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LOI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values

Immunohistochemical Examination on the Distribution of Cells Expressed Lymphatic Endothelial Marker Podoplanin and LYVE-1 in the Mouse Tongue Tissue

The clinical study for lingual disease requires the detailed investigation of the lingual lymphatic network and lymphatic marker-positive cells. Recently, it has been reported that several tissue cells and leukocytes express lymphatic markers, LYVE-1 and podoplanin. This study was aimed to clarify the lingual distribution of cells expressing LYVE-1 and podoplanin. In the mouse tongue, podoplanin is expressed in nerve sheaths, lingual gland myoepithelial cells, and lymphatic vessels. LYVE-1 is expressed in the macrophage marker Mac-1-positive cells as well as lymphatic vessels, while factor-VIII was detected in only blood endothelial cells. α-SMA was detected in vascular smooth muscle and myoepithelial cells. Therefore, identification of lymphatic vessels in lingual glands, the combination of LYVE-1 and factor-VIII, or LYVE-1 and Mac-1 is useful because myoepithelial cells express podoplanin and α-SMA. The immunostaining of factor-VIII on lymphatic vessels was masked by the immunostaining to LYVE-1 or podoplanin because lymphatic vessels express factor-VIII to a far lesser extent than blood vessels. Therefore, except for the salivary glands, the combination of podoplanin and α-SMA, or factor-VIII is useful to identify lymphatic vessels and blood vessels with smooth muscle, or blood capillaries

Immunoelectron Microscopic Study of Podoplanin Localization in Mouse Salivary Gland Myoepithelium

We have recently reported that salivary gland cells express the lymphatic endothelial cell marker podoplanin. The present study was aimed to immunohistochemically investigate the expression of the myoepithelial cell marker α-smooth muscle actin (SMA) on podoplanin-positive cells in mouse parotid and sublingual glands, and to elucidate podoplanin localization in salivary gland myoepithelial cells by immunoelectron microscopic study. The distribution of myoepithelial cells expressing podoplanin and α-SMA was examined by immunofluorescent staining, and the localization of reaction products of anti-podoplanin antibody was investigated by pre-embedded immunoelectron microscopic method. In immunohistochemistry, the surfaces of both the mucous acini terminal portion and ducts were covered by a number of extensive myoepithelial cellular processes expressing podoplanin, and the immunostaining level with anti-podoplanin antibody to myoepithelial cells completely coincided with the immunostaining level with anti-α-SMA antibody. These findings suggest that podoplanin is a salivary gland myoepithelial cell antigen, and that the detection level directly reflects the myoepithelial cell distribution. In immunoelectron microscopic study, a number of reaction products with anti-podoplanin antibody were found at the Golgi apparatus binding to the endoplasmic reticulum in the cytoplasm of myoepithelial cells between sublingual gland acinar cells, and were also found at the myoepithelial cell membrane. These findings suggest that salivary gland myoepithelial cells constantly produce podoplanin and glycosylate at the Golgi apparatus, and transport them to the cell membrane. Podoplanin may be involved in maintaining the homeostasis of myoepithelial cells through its characteristic as a mucin-type transmembrane glycoprotein