The Light and Period Variations of the Eclipsing Binary BX Draconis

New CCD photometric observations of BX Dra were obtained for 26 nights from 2009 April to 2010 June. The long-term photometric behaviors of the system are presented from detailed studies of the period and light variations, based on the historical data and our new observations. All available light curves display total eclipses at secondary minima and inverse O'Connell effects with Max I fainter than Max II, which are satisfactorily modeled by adding the slightly time-varying hot spot on the primary star. A total of 87 times of minimum light spanning over about 74 yrs, including our 22 timing measurements, were used for ephemeris computations. Detailed analysis of the O-C diagram showed that the orbital period has changed in combinations with an upward parabola and a sinusoidal variation. The continuous period increase with a rate of +5.65 \times 10^-7 d yr^-1 is consistent with that calculated from the Wilson-Devinney synthesis code. It can be interpreted as a mass transfer from the secondary to the primary star at a rate of 2.74 \times 10^-7 M\odot yr^-1, which is one of the largest rates for contact systems. The most likely explanation of the sinusoidal variation with a period of 30.2 yrs and a semi-amplitude of 0.0062 d is a light-traveltime effect due to the existence of a circumbinary object. We suggest that BX Dra is probably a triple system, consisting of a primary star with a spectral type of F0, its secondary component of spectral type F1-2, and an unseen circumbinary object with a minimum mass of M3 = 0.23 M\odot.Comment: 24 pages, including 5 figures and 9 tables, accepted for publication in PAS

Generalized gravity model for human migration

The gravity model (GM) analogous to Newton's law of universal gravitation has successfully described the flow between different spatial regions, such as human migration, traffic flows, international economic trades, etc. This simple but powerful approach relies only on the 'mass' factor represented by the scale of the regions and the 'geometrical' factor represented by the geographical distance. However, when the population has a subpopulation structure distinguished by different attributes, the estimation of the flow solely from the coarse-grained geographical factors in the GM causes the loss of differential geographical information for each attribute. To exploit the full information contained in the geographical information of subpopulation structure, we generalize the GM for population flow by explicitly harnessing the subpopulation properties characterized by both attributes and geography. As a concrete example, we examine the marriage patterns between the bride and the groom clans of Korea in the past. By exploiting more refined geographical and clan information, our generalized GM properly describes the real data, a part of which could not be explained by the conventional GM. Therefore, we would like to emphasize the necessity of using our generalized version of the GM, when the information on such nongeographical subpopulation structures is available.Comment: 14 pages, 6 figures, 2 table

Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition

Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promising results. Among the end-to-end models, the connectionist temporal classification (CTC)-based model has attracted research interest due to its non-autoregressive nature. However, such CTC models require a heavy computational cost to achieve outstanding performance. To mitigate the computational burden, we propose a simple yet effective knowledge distillation (KD) for the CTC framework, namely Inter-KD, that additionally transfers the teacher's knowledge to the intermediate CTC layers of the student network. From the experimental results on the LibriSpeech, we verify that the Inter-KD shows better achievements compared to the conventional KD methods. Without using any language model (LM) and data augmentation, Inter-KD improves the word error rate (WER) performance from 8.85 % to 6.30 % on the test-clean.Comment: Accepted by 2022 SLT Worksho