LOCOMOTOR DISTANCE AND VELOCITY IN WHEELCHAIR BASKETBALL GAME

INTRODUCTION: The rule of basketball was vastly revised in 2000 classification and it is expected that basketball players’speed would be higher and locomotor distance would be longer than before. The revision of the rule applied to wheelchair basketball as well. There have been few studies about players’ speed and locomotor distance with an exception of Coutts’ study (1992) which was published before the rule revision. The objects of this study were to investigate the players’ locomotor distance and velocity in a wheelchair basketball game and to get the basic data of the players’physical fitness level necessary for high performance in wheelchair basketball

Estrogenic regulation of social behavior and sexually dimorphic brain formation

It has long been known that the estrogen, 17β-estradiol (17β-E), plays a central role for female reproductive physiology and behavior. Numerous studies have established the neurochemical and molecular basis of estrogenic induction of female sexual behavior, i.e., lordosis, in animal models. In addition, 17β-E also regulates male-type sexual and aggressive behavior. In males, testosterone secreted from the testes is irreversibly aromatized to 17β-E in the brain. We discuss the contribution of two nuclear receptor isoforms, estrogen receptor (ER)α and ERβ to the estrogenic regulation of sexually dimorphic brain formation and sex-typical expression of these social behaviors. Furthermore, 17β-E is a key player for social behaviors such as social investigation, preference, recognition and memory as well as anxiety-related behaviors in social contexts. Recent studies also demonstrated that not only nuclear receptor-mediated genomic signaling but also membrane receptor-mediated non-genomic actions of 17β-E may underlie the regulation of these behaviors. Finally, we will discuss how rapidly developing research tools and ideas allow us to investigate estrogenic action by emphasizing behavioral neural networks

InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss

This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision. Momentum PL (MPL) trains a connectionist temporal classification (CTC)-based model on unlabeled data by continuously generating pseudo-labels on the fly and improving their quality. In contrast to autoregressive formulations, such as the attention-based encoder-decoder and transducer, CTC is well suited for MPL, or PL-based semi-supervised ASR in general, owing to its simple/fast inference algorithm and robustness against generating collapsed labels. However, CTC generally yields inferior performance than the autoregressive models due to the conditional independence assumption, thereby limiting the performance of MPL. We propose to enhance MPL by introducing intermediate loss, inspired by the recent advances in CTC-based modeling. Specifically, we focus on self-conditional and hierarchical conditional CTC, that apply auxiliary CTC losses to intermediate layers such that the conditional independence assumption is explicitly relaxed. We also explore how pseudo-labels should be generated and used as supervision for intermediate losses. Experimental results in different semi-supervised settings demonstrate that the proposed approach outperforms MPL and improves an ASR model by up to a 12.1% absolute performance gain. In addition, our detailed analysis validates the importance of the intermediate loss.Comment: Submitted to ICASSP202

BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder

We present BERT-CTC-Transducer (BECTRA), a novel end-to-end automatic speech recognition (E2E-ASR) model formulated by the transducer with a BERT-enhanced encoder. Integrating a large-scale pre-trained language model (LM) into E2E-ASR has been actively studied, aiming to utilize versatile linguistic knowledge for generating accurate text. One crucial factor that makes this integration challenging lies in the vocabulary mismatch; the vocabulary constructed for a pre-trained LM is generally too large for E2E-ASR training and is likely to have a mismatch against a target ASR domain. To overcome such an issue, we propose BECTRA, an extended version of our previous BERT-CTC, that realizes BERT-based E2E-ASR using a vocabulary of interest. BECTRA is a transducer-based model, which adopts BERT-CTC for its encoder and trains an ASR-specific decoder using a vocabulary suitable for a target task. With the combination of the transducer and BERT-CTC, we also propose a novel inference algorithm for taking advantage of both autoregressive and non-autoregressive decoding. Experimental results on several ASR tasks, varying in amounts of data, speaking styles, and languages, demonstrate that BECTRA outperforms BERT-CTC by effectively dealing with the vocabulary mismatch while exploiting BERT knowledge.Comment: Submitted to ICASSP202

Soft hair, dressed coordinates, and information loss paradox

Understanding the dynamics of soft hair might shine some light on the information loss paradox. In this paper, we introduce a new coordinate system, dressed coordinates, in order to analyze the quantum states of Hawking radiation as a first step toward understanding the connection between soft hair and the information paradox. Dressed coordinates can be introduced by an operator-dependent coordinate transformation that makes the soft hair degrees of freedom apparently disappear from the metric. We show that some results of previous studies, such as the angle-dependent Hawking temperature and the soft graviton theorem, can be easily reproduced using these dressed coordinates. Finally, we discuss future possible applications of dressed coordinates toward understanding the information loss paradox

Satisfactory Control for Glucose Profile by Combined Agents of Xultophy with A Small Dose

The patient was a 74-year-old female with type 2 diabetes mellitus (T2DM) treated on Humalog mix 25 twice a day. As social history, she has worked long years for growing and harvesting lotus roots. It gives physically heavy loading, which brings unstable glycemic daily control. She had to titrate minute regulation every time. For stable glucose variability, the treatment was changed to Xultophy, which is a specific combined agent of Insulin Degludec and Liraglutide (IDeg/Lira) once a day. Then, detailed glucose monitoring showed a better daily profile of blood glucose, irrespective of heavy or light work. It showed the bio-psycho-social benefit of Xultophy