Waseda University

About: Waseda University is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Catalysis & Large Hadron Collider. The organization has 24220 authors who have published 46859 publications receiving 837855 citations. The organization is also known as: Waseda daigaku & Sōdai.

ATLAS b-jet identification performance and efficiency measurement with tt¯ events in pp collisions at √s = 13 TeV

Abstract: The algorithms used by the ATLAS Collaboration during Run 2 of the Large Hadron Collider to identify jets containing b-hadrons are presented. The performance of the algorithms is evaluated in the s ...

Global structure of exact cosmological solutions in the brane world

Abstract: We find the explicit coordinate transformation which links two exact cosmological solutions of the brane world which have been recently discovered. This means that both solutions are exactly the same as each other. One of the two solutions is described by the motion of a domain wall in the well-known five-dimensional Schwarzshild-AdS spacetime. Hence, we can easily understand the region covered by the coordinate used by another solution.

Molecular dimensions of Hb-based O2 carriers determine constriction of resistance arteries and hypertension

Abstract: The effect of molecular dimension of hemoglobin (Hb)-based O2carriers on the diameter of resistance arteries (A 0, 158 ± 21 μm) and arterial blood pressure were studied in the conscious hamster dor...

Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration

Abstract: The state-of-the-art neural network architecture named Transformer has been used successfully for many sequence-tosequence transformation tasks. The advantage of this architecture is that it has a fast iteration speed in the training stage because there is no sequential operation as with recurrent neural networks (RNN). However, an RNN is still the best option for end-to-end automatic speech recognition (ASR) tasks in terms of overall training speed (i.e., convergence) and word error rate (WER) because of effective joint training and decoding methods. To realize a faster and more accurate ASR system, we combine Transformer and the advances in RNN-based ASR. In our experiments, we found that the training of Transformer is slower than that of RNN as regards the learning curve and integration with the naive language model (LM) is difficult. To address these problems, we integrate connectionist temporal classification (CTC) with Transformer for joint training and decoding. This approach makes training faster than with RNNs and assists LM integration. Our proposed ASR system realizes significant improvements in various ASR tasks. For example, it reduced the WERs from 11.1% to 4.5% on the Wall Street Journal and from 16.1% to 11.6% on the TED-LIUM by introducing CTC and LM integration into the Transformer baseline.

Zeolitic imidazolate framework (ZIF-8) derived nanoporous carbon: the effect of carbonization temperature on the supercapacitor performance in an aqueous electrolyte

Abstract: Nanoporous carbon materials are a versatile source of carbons that would be useful in applications ranging from electronics to electrochemical energy storage. Here, we focus on nanoporous carbon materials prepared by direct carbonization of zeolitic imidazolate frameworks (ZIF-8) towards supercapacitor applications. Several types of nanoporous carbons have been prepared by varying the applied carbonization temperature. The symmetric devices assembled using nanoporous carbon electrodes were tested for their optimal performance in the electrolyte of sulfuric acid solution. We demonstrate the effects of various factors (e.g., surface area, nitrogen content, degree of graphitization, and relative percentage of micropores) on the performance.