Osamu Nomura

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024

Nobuaki Shime, Taka-aki Nakada, Tomoaki Yatabe, Kazuma Yamakawa et autres

The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine. This is the fourth revision since …

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Techniques for Enhancing Memory Capacity of Reservoir Computing

Atsuki Yokota, Ichiro Kawashima, Yohei Saito, Hakaru Tamukoh et autres

Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In this study, we propose methods to …

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024

Nobuaki Shime, Taka‐aki Nakada, Tomoaki Yatabe, Kazuma Yamakawa et autres

The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine. This is the fourth revision since …

jp, ca (code pays fourni par la source)

Rapidly Rising Preference among New-entry Medical Students for Using Generative Artificial Intelligence in Reflective Reports

Chihiro Kawakami, Osamu Nomura, Miyuki Takahashi, Ritsuki Takaha et autres

Introduction: As artificial intelligence (AI) continues to proliferate, it becomes imperative that medical students are not only instructed in the use of AI but also afforded regular opportunities to interact with it throughout their medical education. In 2023 and 2024, an art-based …

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Robust Binary Encoding for Ternary Neural Networks Toward Deployment on Emerging Memory

Ninnart Fuengfusin, Hakaru Tamukoh, Osamu Nomura, Takashi Morie

Deep neural networks (DNNs) have enabled state-of-the-art performance across various applications. However, their deployment is often hindered by high energy demands. One solution is to deploy DNNs on hardware equipped with emerging non-volatile memory, which does not require energy to maintain memory …

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Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers

Soshi Hirayae, Kanta Yoshioka, Atsuki Yokota, Ichiro Kawashima et autres

To use reservoir computing (RC) for practical tasks, both a high memory capacity and nonlinearity are required; however, some RC models have the problem of a low memory capacity. We propose a delay mechanism for increasing the memory capacity in RC as …

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An efficient spatial representation scheme for memory-based hippocampus-inspired model with VLSI implementation

Yuka Shishido, Osamu Nomura, Katsumi Tateno, Hakaru Tamukoh et autres

Hippocampus integrates events and places information critical for episodic memory. Memory-based hippocampus-inspired model (MBHIM) is a hippocampus-inspired model suitable for VLSI implementation. This paper proposes an efficient spatial representation scheme for MBHIM with VLSI implementation. Each layer represents a space of a …

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Efficient Repetition Coding for Deep Learning Towards Implementation Using Emerging Non-Volatile Memory with Write-Errors

Ninnart Fuengfusin, Hakaru Tamukoh, Yuichiro Tanaka, Osamu Nomura et autres

Emerging non-volatile memory devices, such as resistive random access memory (ReRAM) and voltage-controlled magnetoresistive random access memory (VC-MRAM), promise low energy consumption for artificial intelligence applications. However, when implementing deep neural networks (DNNs) using such memory devices, write-error may cause millions of …

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Robustness of Spiking Neural Networks Based on Time-to-First-Spike Encoding Against Adversarial Attacks

Osamu Nomura, Yusuke Sakemi, Takeo Hosomi, Takashi Morie

Spiking neural networks (SNNs) more closely mimic the human brain than artificial neural networks (ANNs). For SNNs, time-to-first-spike (TTFS) encoding, which represents the output values of neurons based on the timing of a single spike, has been proposed as a promising model …

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Energy-Efficient Convolution Module With Flexible Bit-Adjustment Method and ADC Multiplier Architecture for Industrial IoT

Tao Li, Yitao Ma, Ko Yoshikawa, Osamu Nomura et autres

Offloading the unprecedented growing data to the edge exhibits a mainstream trend in the Industrial Internet of Things (IIoT) era, delivering far-reaching impacts in all aspects of our daily lives, including transportation, health care, and entertainment. However, voluminous data analyzes and processing …

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Sex-Specific DNA Sequences in Galliformes and Their Application to the Study of Sex Differentiation

Shigeki Mizuno, Yasushi Saitoh, Osamu Nomura, Ryota Kunita et autres

A substantial fraction of DNA in W chromosomes of chickens, turkeys, and pheasants consists of repetitive families that are unique to this female-specific sex chromosome. Repeating units of these families share common sequence organization, and they all behave as strongly bent DNA …

Detection and molecular status of Isospora sp. from the domestic pigeon ( Columba livia domestica )

Ryuma Matsubara, Yasuhiro Fukuda, Fumi Murakoshi, Osamu Nomura et autres

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