Tokyo Institute of Technology

About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.

The bonobo genome compared with the chimpanzee and human genomes

Abstract: Sequencing of the bonobo genome shows that more than three per cent of the human genome is more closely related to either the bonobo genome or the chimpanzee genome than those genomes are to each other. The chimpanzee and the bonobo are our species' two closest living relatives. This paper reports the genome sequence of the bonobo, the last ape to be sequenced. Comparative genomic analyses reveal that more than 3% of the human genome is more closely related to either the bonobo or the chimpanzee genome than these are to each other. The results shed light on the ancestry of the two ape species and might eventually help us to understand the genetic basis of phenotypes that humans share with one or the other ape species. Two African apes are the closest living relatives of humans: the chimpanzee (Pan troglodytes) and the bonobo (Pan paniscus). Although they are similar in many respects, bonobos and chimpanzees differ strikingly in key social and sexual behaviours1,2,3,4, and for some of these traits they show more similarity with humans than with each other. Here we report the sequencing and assembly of the bonobo genome to study its evolutionary relationship with the chimpanzee and human genomes. We find that more than three per cent of the human genome is more closely related to either the bonobo or the chimpanzee genome than these are to each other. These regions allow various aspects of the ancestry of the two ape species to be reconstructed. In addition, many of the regions that overlap genes may eventually help us understand the genetic basis of phenotypes that humans share with one of the two apes to the exclusion of the other.

Attentive Statistics Pooling for Deep Speaker Embedding

Abstract: This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are averaged over all the frames of a single utterance to form an utterance-level feature. Our method utilizes an attention mechanism to give different weights to different frames and generates not only weighted means but also weighted standard deviations. In this way, it can capture long-term variations in speaker characteristics more effectively. An evaluation on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively.