Measurement of AGN dust extinction based on the near-infrared flux variability of WISE data

We present the measurement of the line-of-sight extinction of the dusty torus for a large number of obscured active galactic nuclei (AGNs) based on the reddening of the colour of the variable flux component in near-infrared (NIR) wavelengths. We collected long-term monitoring data by $\textit{Wide-field Infrared Survey Explorer (WISE)}$ for 513 local AGNs catalogued by the $\mathit{Swift/}$BAT AGN Spectroscopic Survey (BASS) and found that the multi-epoch NIR flux data in two different bands (WISE $W1$ and $W2$) are tightly correlated for more than 90% of the targets. The flux variation gradient (FVG) in the $W1$ and $W2$ bands was derived by applying linear regression analysis, and we reported that those for unobscured AGNs fall in a relatively narrow range, whereas those for obscured AGNs are distributed in a redder and broader range. The AGN's line-of-sight dust extinction ($A_V$) is calculated using the amount of the reddening in the FVG and is compared with the neutral hydrogen column density ($N_{\rm{}H}$) of the BASS catalogue. We found that the $N_{\rm{}H}/A_V$ ratios of obscured AGNs are greater than those of the Galactic diffuse interstellar medium (ISM) and are distributed with a large scatter by at most two orders of magnitude. Furthermore, we found that the lower envelope of the $N_{\rm{}H}/A_V$ of obscured AGNs is comparable to the Galactic diffuse ISM. These properties of the $N_{\rm{}H}/A_V$ can be explained by increase in the $N_{\rm{}H}$ attributed to the dust-free gas clouds covering the line of sight in the broad-line region.Comment: 11 pages, 7 figures, published in MNRA

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

[Background] In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion. [Methods] From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems. [Results] The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively. [Conclusions] The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset

XPS study of the process of apatite formation on bioactive Ti–6Al–4V alloy in simulated body fluid

Bioactive Ti–6Al–4V alloy, which spontaneously forms a bonelike apatite layer on its surface in the body and bonds to living bone through this apatite layer, can be prepared by producing an amorphous sodium titanate on its surface by NaOH and heat treatments. In this study, the process of apatite formation on the bioactive Ti–6Al–4V alloy was investigated in vitro, by analyzing its surface with X-ray photoelectron spectroscopy as a function of soaking time in a simulated body fluid (SBF). Thin-film X-ray diffractometry of the alloy surface and atomic emission spectroscopy of the fluid were also performed complementarily. It was found that immediately after immersion in the SBF, the alloy exchanged Na+ ions from the surface sodium titanate with H3O+ ions in the fluid to form Ti-OH groups on its surface. The Ti-OH groups, immediately after their formation, incorporated the calcium ions in the fluid to form calcium titanate. The calcium titanate thereafter incorporated the phosphate ions in the fluid to form an amorphous calcium phosphate, which was later crystallized into bonelike apatite. This process of apatite formation on the alloy was the same as on the pure titanium metal, because the alloy formed the sodium titanate free of Al and V by the NaOH and heat treatments. The initial formation of the calcium titanate is proposed to be a consequence of the electrostatic interaction of negatively charged units of titania dissociated from the Ti-OH groups with the positively charged calcium ions in the fluid. The calcium titanate is postulated to gain a positive charge and interact with the negatively charged phosphate ions in the fluid to form amorphous calcium phosphate, which eventually stabilizes into crystalline apatite

Real-Time Continuous Speech Recognition System on SH-4A Microprocessor

MMSP2007: IEEE 9th International Workshop on Multimedia Signal Processing, October 1-3, 2007, Crete, Greece.To expand CSR (continuous speech recognition) software to the mobile environmental use, we have developed embedded version of Julius (embedded Julius). Julius is open source CSR software, and has been used by many researchers and developers in Japan as a standard decoder on PCs. In this paper, we describe an implementation of the embedded Julius on a SH-4A microprocessor. SH-4A is a high-end 32-bit MPU (720 MIPS) with on-chip FPU. However, further computational reduction is necessary for the embedded Julius to operate realtime. Applying some optimizations, the embedded Julius achieves real-time processing on the SH-4A. The experimental results show 0.89 times RT(real-time), resulting 4.0 times faster than baseline CSR. We also evaluated the embedded Julius on large vocabulary (20,000 words). It shows almost real-time processing (1.25 times RT)

Embedded JULIUS on T-Engine Platform

ISPACS2006: International Symposium on Intelligent Signal Processing and Communication Systems, December 12-15, 2006, Yonago, Japan.In this paper, we report implemental results of an embedded version of Julius. We used T-Enginetrade as a hardware platform which has a SuperH microprocessor. The Julius is free and open continuous speech recognition (CSR) software running on personal computers (PCs) which have huge CPU power and storage memory size. The technical problems to make Julius for embedded version are computing/process and memory reductions of Julius software. We realized 2.23 of RTF (real time factor) of embedded speech recognition processing on the condition of 5000-word vocabulary without any recognition accuracy degradation