Domain-Specific Web Search with Keyword Spices

Domain-specific web search engines are effective tools for reducing the difficulty in acquiring information from the web. Existing methods for building domain-specific web search engines require human expertise or specific facilities. However, we can build a domain-specific search engine simply by adding domain specific keywords called "keyword spices" to the user's input query and forwarding it to a generalpurpose web search engine. Keyword spices can be effectively discovered from web documents using machine learning technologies. This paper will describe domain-specific web search engines that use keyword spices for locating cooking recipes, restaurants, and used cars. To fully automate the construction of domain-specific search engines, we also present trials of using web pages in an existing web directory as training examples

Shock-induced star cluster formation in colliding galaxies

We studied the formation process of star clusters using high-resolution N-body/smoothed particle hydrodynamcs simulations of colliding galaxies. The total number of particles is 1.2x10^8 for our high resolution run. The gravitational softening is 5 pc and we allow gas to cool down to \sim 10 K. During the first encounter of the collision, a giant filament consists of cold and dense gas found between the progenitors by shock compression. A vigorous starburst took place in the filament, resulting in the formation of star clusters. The mass of these star clusters ranges from 10^{5-8} Msun. These star clusters formed hierarchically: at first small star clusters formed, and then they merged via gravity, resulting in larger star clusters.Comment: 4 pages, 3 figures, Proceedings of IAU Symposium 270, Computational Star Formatio

Toward first-principle simulations of galaxy formation: I. How should we choose star formation criteria in high-resolution simulations of disk galaxies?

We performed 3-dimensional N-body/SPH simulations to study how mass resolution and other model parameters such as the star formation efficiency parameter, C* and the threshold density, nth affect structures of the galactic gaseous/stellar disk in a static galactic potential. We employ 10^6 - 10^7 particles to resolve a cold and dense (T 100 cm^{-3}) phase. We found that structures of the ISM and the distribution of young stars are sensitive to the assumed nth. High-nth models with nth = 100 cm^{-3} yield clumpy multi-phase features in the ISM. Young stars are distributed in a thin disk of which half-mass scale height is 10 - 30 pc. In low-nth models with nth = 0.1 cm^{-3}, the stellar disk is found to be several times thicker, and the gas disk appears smoother than the high-nth models. A high-resolution simulation with high-nth is necessary to reproduce the complex structure of the gas disk. The global properties of the model galaxies in low-nth models, such as star formation histories, are similar to those in the high-nth models when we tune the value of C* so that they reproduce the observed relation between surface gas density and surface star formation rate density. We however emphasize that high-nth models automatically reproduce the relation, regardless of the values of C*. The ISM structure, phase distribution, and distributions of young star forming region are quite similar between two runs with values of C* which differ by a factor of 15. We also found that the timescale of the flow from n_H ~1 cm^{-3} to n_H > 100 cm^{-3} is about 5 times as long as the local dynamical time and is independent of the value of C*. The use of a high-nth criterion for star formation in high-resolution simulations makes numerical models fairy insensitive to the modelling of star formation. (Abridged)Comment: 15 pages, 14 figures, accepted for publication in PASJ. Abridged abstract. For high resolution figures, see http://www.cfca.nao.ac.jp/~saitoh/Papers/2008/Saitoh+2008a.pd

Toward First-Principle Simulations of Galaxy Formation: II. Shock-Induced Starburst at a Collision Interface During the First Encounter of Interacting Galaxies

We investigated the evolution of interacting disk galaxies using high-resolution $N$-body/SPH simulations, taking into account the multiphase nature of the interstellar medium (ISM). In our high-resolution simulations, a large-scale starburst occurred naturally at the collision interface between two gas disks at the first encounter, resulting in the formation of star clusters. This is consistent with observations of interacting galaxies. The probability distribution function (PDF) of gas density showed clear change during the galaxy-galaxy encounter. The compression of gas at the collision interface between the gas disks first appears as an excess at $n_{\rm H} \sim 10{\rm cm^{-3}}$ in the PDF, and then the excess moves to higher densities ($n_{\rm H} \gtrsim 100{\rm cm^{-3}}$) in a few times $10^7$ years where starburst takes place. After the starburst, the PDF goes back to the quasi-steady state. These results give a simple picture of starburst phenomena in galaxy-galaxy encounters.Comment: 6 pages, 6 figures, accepted to PASJ. For high resolution figures, see http://www.cfca.nao.ac.jp/~saitoh/Papers/2009/Saitoh+2009a.pd

Multi-institutional phase II study on the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy for lung tumors

Background and purpose: This study aimed to evaluate the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy (DTT-SBRT) for lung tumors. Materials and methods: Patients with cStage I primary lung cancer or metastatic lung cancer with an expected range of respiratory motion of ≥10 mm were eligible for the study. The prescribed dose was 50 Gy in four fractions. A gimbal-mounted linac was used for DTT-SBRT delivery. The primary endpoint was local control at 2 years. Results: Forty-eight patients from four institutions were enrolled in this study. Forty-two patients had primary non-small-cell lung cancer, and six had metastatic lung tumors. DTT-SBRT was delivered for 47 lesions in 47 patients with a median treatment time of 28 min per fraction. The median respiratory motion during the treatment was 13.7 mm (range: 4.5–28.1 mm). The motion-encompassing method was applied for the one remaining patient due to the poor correlation between the abdominal wall and tumor movement. The median follow-up period was 32.3 months, and the local control at 2 years was 95.2% (lower limit of the one-sided 85% confidence interval [CI]: 90.3%). The overall survival and progression-free survival at 2 years were 79.2% (95% CI: 64.7%–88.2%) and 75.0% (95% CI: 60.2%–85.0%), respectively. Grade 3 toxicity was observed in one patient (2.1%) with radiation pneumonitis. Grade 4 or 5 toxicity was not observed. Conclusion: DTT-SBRT achieved excellent local control with low incidences of severe toxicities in lung tumors with respiratory motion

Dynamic tumor-tracking stereotactic body radiotherapy with real-time monitoring of liver tumors using a gimbal-mounted linac: A multi-institutional phase II study

[Background and purpose] This prospective multicenter phase II study aimed to evaluate the safety and efficacy of dynamic tumor tracking (DTT) stereotactic body radiotherapy (SBRT) with real-time monitoring of liver tumors using a gimbal-mounted system. [Materials and methods] Patients with < 4 primary or metastatic liver tumors with diameters ≤ 50 mm and expected to have a respiratory motion of ≥ 10 mm were eligible. The prescribed dose was 40 Gy in five fractions. The primary endpoint was local control (LC) at 2 years. The secondary endpoints were overall survival (OS), progression-free survival (PFS), treatment-related toxicity, and tracking accuracy. [Results] Between September 2015 and March 2019, 48 patients (48 lesions) with a median age of 74 years were enrolled from four institutions. Of these, 39 were diagnosed with hepatocellular carcinoma and nine with metastatic liver cancer. The median tumor diameter was 17.5 mm. DTT-SBRT was successfully performed in all patients; the median treatment time was 28 min/fraction. The median follow-up period was 36.5 months. The 2-year LC, OS, and PFS rates were 98.0 %, 88.8 %, and 55.1 %, respectively. Disease progression was observed in 33 (68.8 %) patients. One patient (0.2 %) had local recurrence, 31 (64.6 %) developed new hepatic lesions outside the irradiation field, and nine (18.8 %) had distant metastases (including overlap). Grade 3 late adverse events were observed in seven patients (14.5 %). No grade 4 or 5 treatment-related toxicity was observed. The median tracking accuracy was 2.9 mm. [Conclusion] Employing DTT-SBRT to treat liver tumors results in excellent LC with acceptable adverse-event incidence

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