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Complexity Reduction of Graph Signal Denoising Based on Fast Graph Fourier Transform

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Complexity Reduction of Graph Signal Denoising Based on Fast...

IEEE International Conference on Image Processing

作者： Takayuki Sasaki Yukihiro Bandoh Masaki Kitahara NTT Computer and Data Science Laboratories NTT Corporation

Denoising is one of the most fundamental and important problems in signal processing, and graph signal denoising methods have been actively studied. Several graph signal denoising methods based on mathematical programming require solving linear equations involving Laplacian matrix, which creates problem with computational accuracy and running time. This study proposes a fast and accurate solution of linear equations for denoising based on the fast graph Fourier transform method. Moreover, the proposed method can perform denoising not only on graphs for which the fast graph Fourier transform can be performed, but also on a wide class of graphs with more relaxed conditions, without loss of accuracy. Experiments demonstrate the efficiency of the proposed method and confirm that denoising can be performed up to 167.3 times faster without loss of accuracy.

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In Situ Visualization for Computational science 1

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丛书名： Mathematics and Visualization

2022年

作者： Hank Childs Janine C. Bennett Christoph Garth

ISBN: (数字)9783030816278

ISBN: (纸本)9783030816261;9783030816292

This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing.;Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future.

关键词： Visualization Computational science and Engineering

Resource-efficient generalized quantum subspace expansion

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Physical Review Applied 2025年第5期23卷 054021-054021页

作者： Bo Yang Nobuyuki Yoshioka Hiroyuki Harada Shigeo Hakkaku Yuuki Tokunaga Hideaki Hakoshima Kaoru Yamamoto Suguru Endo LIP6 Sorbonne Université 4 Place Jussieu 75005 Paris France Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan International Center for Elementary Particle Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku Yokohama 223-8522 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midori-cho Musashino-shi Tokyo 180-8585 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka Osaka 560-0043 Japan

Realizing practical quantum computing requires overcoming a number of computation errors and the limitation of device size, which have intensively been tackled by quantum error mitigation. As a unified approach of noise-agnostic quantum error mitigation, generalized quantum subspace expansion (GSE) has lately been proposed to be remarkably robust regarding stochastic and coherent errors, integrating quantum subspace expansion and virtual state purification. However, the requirement in GSE to perform entangled measurements between copies of the quantum states remains a significant drawback under the current situation of quantum devices with a restricted number of qubits and their connectivity. In this work, we propose “Dual-GSE,” a resource-efficient implementation of GSE to circumvent this overhead by constructing an ansatz of error-mitigated quantum states via dual-state purification without state copies. Remarkably, the proposed method can further simulate larger quantum systems beyond the size of available quantum hardware, achieved by a suitable ansatz construction inspired by the divide-and-conquer strategy that classically reintroduces the effect of entanglement. While classically forging the entanglement comes with additional cost, the total sampling overhead can be notably reduced by reuse of the same Pauli expectation values among divided-and-conquered subsystems. We comprehensively analyze the advantages and overhead of Dual-GSE and perform numerical simulations of the eight-qubit transverse-field Ising model under various setups. Our results demonstrate that Dual-GSE estimates the ground state energy with high accuracy under gate noise with low mitigation overhead and practical sampling cost.

关键词： Qubits

Utilizing resource-rich language datasets for end-to-end scene text recognition in resource-poor languages

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arXiv 2021年

作者： Orihashi, Shota Yamazaki, Yoshihiro Makishima, Naoki Ihori, Mana Takashima, Akihiko Tanaka, Tomohiro Masumura, Ryo NTT Computer and Data Science Laboratories NTT Corporation Japan

This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language's dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

End-to-End Joint Target and Non-Target Speakers ASR

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arXiv 2023年

作者： Masumura, Ryo Makishima, Naoki Yamane, Taiga Yamazaki, Yoshihiko Mizuno, Saki Ihori, Mana Uchida, Mihiro Suzuki, Keita Sato, Hiroshi Tanaka, Tomohiro Takashima, Akihiko Suzuki, Satoshi Moriya, Takafumi Hojo, Nobukatsu Ando, Atsushi NTT Computer and Data Science Laboratories NTT Corporation Japan

This paper proposes a novel automatic speech recognition (ASR) system that can transcribe individual speaker’s speech while identifying whether they are target or non-target speakers from multi-talker overlapped speech. Target-speaker ASR systems are a promising way to only transcribe a target speaker’s speech by enrolling the target speaker’s information. However, in conversational ASR applications, transcribing both the target speaker’s speech and non-target speakers’ ones is often required to understand interactive information. To naturally consider both target and non-target speakers in a single ASR model, our idea is to extend autoregressive modeling-based multi-talker ASR systems to utilize the enrollment speech of the target speaker. Our proposed ASR is performed by recursively generating both textual tokens and tokens that represent target or non-target speakers. Our experiments demonstrate the effectiveness of our proposed method. © 2023, CC BY.

关键词： Speech recognition

Selective Excitation of Superconducting Qubits with a Shared Control Line through Pulse Shaping

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arXiv 2025年

作者： Matsuda, R. Ohira, R. Sumida, T. Shiomi, H. Machino, A. Morisaka, S. Koike, K. Miyoshi, T. Kurimoto, Y. Sugita, Y. Ito, Y. Suzuki, Y. Spring, P.A. Wang, S. Tamate, S. Tabuchi, Y. Nakamura, Y. Ogawa, K. Negoro, M. Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan QuEL Inc. Daiwaunyu Building 3F 2-9-2 Owadamachi Hachioji Tokyo192-0045 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka Osaka560-0043 Japan e-trees.Japan Inc. Daiwaunyu Building 2F 2-9-2 Owadamachi Hachioji Tokyo192-0045 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan RIKEN Center for Quantum Computing Saitama Wako351-0198 Japan Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

In conventional architectures of superconducting quantum computers, each qubit is connected to its own control line, leading to a commensurate increase in the number of microwave lines as the system scales. Frequency-multiplexed qubit-control addresses this problem by enabling multiple qubits to share a single microwave line. However, it can cause unwanted excitation of non-target qubits, especially when the detuning between qubits is smaller than the pulse bandwidth. Here, we propose a selective-excitation-pulse (SEP) technique that suppresses unwanted excitations by shaping a drive pulse to create null points at non-target qubit frequencies. In a proof-of-concept experiment with three fixed-frequency transmon qubits, we demonstrate that the SEP technique achieves single-qubit gate fidelities comparable to those obtained with conventional Gaussian pulses while effectively suppressing unwanted excitations in non-target qubits. These results highlight the SEP technique as a promising tool for enhancing frequency-multiplexed qubit-control. © 2025, CC0.

关键词： Qubits

Talking Face Generation for Impression Conversion Considering Speech Semantics

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Talking Face Generation for Impression Conversion Considerin...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Saki Mizuno Nobukatsu Hojo Kazutoshi Shinoda Keita Suzuki Mana Ihori Hiroshi Sato Tomohiro Tanaka Naotaka Kawata Satoshi Kobashikawa Ryo Masumura NTT Computer & Data Science Laboratories NTT Corporation Japan

This study investigates the talking face generation method to convert a speaker’s video to give a target impression, such as “favorable” or “considerate”. Such an impression conversion method needs to consider the input speech semantics because they affect the impression of a speaker’s video along with the facial expression. Conventional emotional talking face generation methods utilize speech information to synchronize the lip and speech of the output video. However, they cannot consider speech semantics because the speech representations contain only phonetic information. To solve this problem, we propose a facial expression conversion model that uses a semantic vector obtained from BERT embeddings of speech recognition results of input speech. We first constructed an audio-visual dataset with impression labels assigned to each utterance. The evaluation results based on the dataset showed that the proposed method could improve the estimation accuracy of the facial expressions of the target video.

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Distilling Knowledge of Bidirectional Language Model for Scene Text Recognition

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Distilling Knowledge of Bidirectional Language Model for Sce...

IEEE International Conference on Image Processing

作者： Shota Orihashi Yoshihiro Yamazaki Mihiro Uchida Akihiko Takashima Ryo Masumura NTT Computer and Data Science Laboratories NTT Corporation Japan

This paper proposes a knowledge distillation method for an external bidirectional language model trained by masked language modeling to achieve high accuracy in scene text recognition. In Asian languages such as Japanese, it is necessary to perform text recognition in units of multiple words or sentences rather than individual words because words are not separated by spaces, and so high-level linguistic knowledge is needed to recognize text correctly. To enhance linguistic knowledge, several methods that use an external language model have been proposed, but these methods fail to consider future context well in performing text recognition because they revise the text candidates yielded by autoregressive text recognition models, which consider mainly past context. To overcome this deficiency, our key idea is to enhance a text recognition model by utilizing knowledge of an external bidirectional language model trained by masked language modeling, which reflects not only past but also future context. So as to actively consider future context in text recognition, our proposed method introduces a distillation loss term that makes the output probability of the text recognition model closer to that of the bidirectional language model. Experiments on Japanese scene text recognition demonstrate the effectiveness of the proposed method.

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Audio Visual Scene-Aware Dialog Generation with Transformer-based Video Representations

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arXiv 2022年

作者： Yamazaki, Yoshihiro Orihashi, Shota Masumura, Ryo Uchida, Mihiro Takashima, Akihiko NTT Computer and Data Science Laboratories NTT Corporation Japan

There have been many attempts to build multimodal dialog systems that can respond to a question about given audiovisual information, and the representative task for such systems is the Audio Visual Scene-Aware Dialog (AVSD). Most conventional AVSD models adopt the Convolutional Neural Network (CNN)-based video feature extractor to understand visual information. While a CNN tends to obtain both temporally and spatially local information, global information is also crucial for boosting video understanding because AVSD requires long-term temporal visual dependency and whole visual information. In this study, we apply the Transformer-based video feature that can capture both temporally and spatially global representations more efficiently than the CNN-based feature. Our AVSD model with its Transformer-based feature attains higher objective performance scores for answer generation. In addition, our model achieves a subjective score close to that of human answers in DSTC10. We observed that the Transformer-based visual feature is beneficial for the AVSD task because our model tends to correctly answer the questions that need a temporally and spatially broad range of visual information. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks