Naoko Sawada

Naoko Sawada
Naoko's Quick Links

  • Biography

    Naoko Sawada joined Mitsubishi Electric in 2021 and worked on the research and development of computer vision, AR, and visualization. She received the BE, ME, and Ph.D. degrees in information and computer science from Keio University, Japan, in 2017, 2018, and 2021 respectively. She stayed at Harvard University, Cambridge, Massachusetts as a visiting scholar in the Visual Computing Group from 2018 to 2020. Her main research interests are time-varying data visualization and analytics, computer vision, and AR.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
    •  

  • MERL Publications

    •  Sawada, N., Miraldo, P., Lohit, S., Marks, T.K., Chatterjee, M., "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR), June 2025.
      BibTeX TR2025-074 PDF
      • @inproceedings{Sawada2025jun,
      • author = {Sawada, Naoko and Miraldo, Pedro and Lohit, Suhas and Marks, Tim K. and Chatterjee, Moitreya},
      • title = {{FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-074}
      • }

  • Other Publications

    •  Issei Fujishiro, Naoko Sawada and Makoto Uemura, "Clustering, Universalities, and Evolutionary Schema Design", Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, CEUR Workshop Proceedings, Vol. 3379, 6th International Workshop on Big Data Visual Exploration and Analytics (BigVis 2023), March 28 2023.
      BibTeX External
      • @Inproceedings{fujishiro2023clustering,
      • author = {Fujishiro, Issei and Sawada, Naoko and Uemura, Makoto},
      • title = {Clustering, Universalities, and Evolutionary Schema Design},
      • booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, CEUR Workshop Proceedings, Vol. 3379, 6th International Workshop on Big Data Visual Exploration and Analytics (BigVis 2023)},
      • year = 2023,
      • address = {Ioannina, Greece (Hybrid)},
      • month = {March 28},
      • url = {https://ceur-ws.org/Vol-3379/BigVis2023_702.pdf}
      • }
    •  Naoko Sawada, Makoto Uemura and Issei Fujishiro, "Multi-dimensional Time-series Subsequence Clustering for Visual Feature Analysis of Blazar Observation Datasets", Astronomy and Computing, Vol. 41, pp. Article No. 100663, November 2022.
      BibTeX External
      • @Article{sawada2022multi,
      • author = {Sawada, Naoko and Uemura, Makoto and Fujishiro, Issei},
      • title = {Multi-dimensional Time-series Subsequence Clustering for Visual Feature Analysis of Blazar Observation Datasets},
      • journal = {Astronomy and Computing},
      • year = 2022,
      • volume = 41,
      • pages = {Article No. 100663},
      • month = nov,
      • url = {https://www.sciencedirect.com/science/article/abs/pii/S2213133722000774}
      • }
    •  Naoko Sawada, Makoto Uemura, Johanna Beyer, Hanspeter Pfister and Issei Fujishiro, "TimeTubesX: A Query-Driven Visual Exploration of Observable, Photometric, and Polarimetric Behaviors of Blazars", IEEE Transactions on Visualization and Computer Graphics, Vol. 28, No. 4, pp. 1917-1929, April 2022.
      BibTeX External
      • @Article{sawada2022timetubesx,
      • author = {Sawada, Naoko and Uemura, Makoto and Beyer, Johanna and Pfister, Hanspeter and Fujishiro, Issei},
      • title = {TimeTubesX: A Query-Driven Visual Exploration of Observable, Photometric, and Polarimetric Behaviors of Blazars},
      • journal = {IEEE Transactions on Visualization and Computer Graphics},
      • year = 2022,
      • volume = 28,
      • number = 4,
      • pages = {1917--1929},
      • month = apr,
      • url = {https://ieeexplore.ieee.org/abstract/document/9200781}
      • }
    •  Shoki Miyagawa, Atsuyoshi Yano, Naoko Sawada and Isamu Ogawa, "High-Dimensional Bayesian Optimization with Constraints: Application to Powder Weighing", Proceedings of PDPAT2022/MPS139, 2022.
      BibTeX External
      • @Inproceedings{miyagawa2022high,
      • author = {Miyagawa, Shoki and Yano, Atsuyoshi and Sawada, Naoko and Ogawa, Isamu},
      • title = {High-Dimensional Bayesian Optimization with Constraints: Application to Powder Weighing},
      • booktitle = {Proceedings of PDPAT2022/MPS139},
      • year = 2022,
      • url = {https://arxiv.org/abs/2206.05988}
      • }
    •  Naoko Sawada, "Visual Analytics of Features in Multi-Dimensional Time-Dependent Observation Datasets of Blazars", 2021, Keio University.
      BibTeX External
      • @Phdthesis{sawada2021visual,
      • author = {Sawada, Naoko},
      • title = {Visual Analytics of Features in Multi-Dimensional Time-Dependent Observation Datasets of Blazars},
      • school = {Keio University},
      • year = 2021,
      • url = {modules/xoonips/detail.php?koara_id=KO50002002-20215642-0003}
      • }
    •  Naoko Sawada, Makoto Uemura and Issei Fujishiro, "TimeTubesX: Identifying Characteristic Blazar Behaviors Through Query-Driven Visual Exploration", Proceedings of the 48th Symposium on Visualization in Japan, September 24--26 2020, pp. Article No. 088.
      BibTeX
      • @Inproceedings{sawada2020identifying,
      • author = {Sawada, Naoko and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubesX: Identifying Characteristic Blazar Behaviors Through Query-Driven Visual Exploration},
      • booktitle = {Proceedings of the 48th Symposium on Visualization in Japan},
      • year = 2020,
      • pages = {Article No. 088},
      • month = {September 24--26}
      • }
    •  Naoko Sawada, Makoto Uemura, Johanna Beyer, Hanspeter Pfister and Issei Fujishiro, "TimeTubesX: A query-driven visual exploration of observable, photometric, and polarimetric behaviors of blazars", Proceedings of the Visual Computing 2020, December 2--4 2020, pp. 14:1-14:2.
      BibTeX
      • @Inproceedings{sawada2020timetubesx,
      • author = {Sawada, Naoko and Uemura, Makoto and Beyer, Johanna and Pfister, Hanspeter and Fujishiro, Issei},
      • title = {TimeTubesX: A query-driven visual exploration of observable, photometric, and polarimetric behaviors of blazars},
      • booktitle = {Proceedings of the Visual Computing 2020},
      • year = 2020,
      • pages = {14:1--14:2},
      • month = {December 2--4},
      • note = {Invited talk (in Japanese)}
      • }
    •  Nicholas Ruta, Naoko Sawada, Katy McKeough, Michael Behrisch and Johanna Beyer, "SAX Navigator: Time Series Exploration Through Hierarchical Clustering", Proceedings of 2019 IEEE Visualization Conference (VIS), 2019.
      BibTeX External
      • @Inproceedings{ruta2019sax,
      • author = {Ruta, Nicholas and Sawada, Naoko and McKeough, Katy and Behrisch, Michael and Beyer, Johanna},
      • title = {SAX Navigator: Time Series Exploration Through Hierarchical Clustering},
      • booktitle = {Proceedings of 2019 IEEE Visualization Conference (VIS)},
      • year = 2019,
      • address = {Vancouver, BC, Canada},
      • url = {https://ieeexplore.ieee.org/document/8933618}
      • }
    •  Issei Fujishiro, Naoko Sawada, Masanori Nakayama, Hsiang-Yun Wu, Kazuho Watanabe, Shigeo Takahashi and Makoto Uemura, "TimeTubes: Visual Exploration of Observed Blazar Datasets", Journal of Physics: Conference Series (JPCS), Vol. 1036, No. 1, pp. Article No. 012011, 2018.
      BibTeX External
      • @Article{fujishiro2018timetubes,
      • author = {Fujishiro, Issei and Sawada, Naoko and Nakayama, Masanori and Wu, Hsiang-Yun and Watanabe, Kazuho and Takahashi, Shigeo and Uemura, Makoto},
      • title = {TimeTubes: Visual Exploration of Observed Blazar Datasets},
      • journal = {Journal of Physics: Conference Series (JPCS)},
      • year = 2018,
      • volume = 1036,
      • number = 1,
      • pages = {Article No. 012011},
      • url = {http://iopscience.iop.org/article/10.1088/1742-6596/1036/1/012011}
      • }
    •  Naoko Sawada, Masanori Nakayama, Makoto Uemura and Issei Fujishiro, "TimeTubes: Feature Extraction of Observed Blazar Datasets for Detailed and Efficient Data Analysis", 284th Reports of the Technical Conference of The Institute of Image Electronics Engineers of Japan, 2018, pp. 19-23.
      BibTeX
      • @Inproceedings{sawada2018feature,
      • author = {Sawada, Naoko and Nakayama, Masanori and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubes: Feature Extraction of Observed Blazar Datasets for Detailed and Efficient Data Analysis},
      • booktitle = {284th Reports of the Technical Conference of The Institute of Image Electronics Engineers of Japan},
      • year = 2018,
      • pages = {19--23}
      • }
    •  Naoko Sawada, Masanori Nakayama, Makoto Uemura and Issei Fujishiro, "TimeTubes: Automatic Extraction of Observable Blazar Features from Long-Term, Multi-Dimensional Datasets", Proceedings of 2018 IEEE Scientific Visualization Conference (SciVis), 2018.
      BibTeX External
      • @Inproceedings{sawada2018timetubes,
      • author = {Sawada, Naoko and Nakayama, Masanori and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubes: Automatic Extraction of Observable Blazar Features from Long-Term, Multi-Dimensional Datasets},
      • booktitle = {Proceedings of 2018 IEEE Scientific Visualization Conference (SciVis)},
      • year = 2018,
      • address = {Berlin, Germany},
      • url = {https://ieeexplore.ieee.org/document/8823802}
      • }
    •  Issei Fujishiro, Shigeo Takahashi, Kazuho Watanabe, Hsiang-Yun Wu, Naoko Sawada and Makoto Uemura, "Consolidation of Visualization Platform Toward Facilitating Sparse Modeling", 5th Public Symposium of KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas Initiative for High-Dimensional Data-driven Science through Deepening of Sparse Modeling"", December 2017.
      BibTeX
      • @Inproceedings{fujishiro2017consolidation,
      • author = {Fujishiro, Issei and Takahashi, Shigeo and Watanabe, Kazuho and Wu, Hsiang-Yun and Sawada, Naoko and Uemura, Makoto},
      • title = {Consolidation of Visualization Platform Toward Facilitating Sparse Modeling},
      • booktitle = {5th Public Symposium of KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas Initiative for High-Dimensional Data-driven Science through Deepening of Sparse Modeling""},
      • year = 2017,
      • month = dec
      • }
    •  Naoko Sawada, Masanori Nakayama, Hsiang-Yun Wu, Makoto Uemura and Issei Fujishiro, "TimeTubes: Visual Fusion for Detailed and Precise Analysis of Time-Varying Multi-Dimensional Datasets", International Meeting on High-Dimensional Data-Driven Science" (HD3-2017)", September 2017.
      BibTeX
      • @Inproceedings{sawada2017visual2,
      • author = {Sawada, Naoko and Nakayama, Masanori and Wu, Hsiang-Yun and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubes: Visual Fusion for Detailed and Precise Analysis of Time-Varying Multi-Dimensional Datasets},
      • booktitle = {International Meeting on High-Dimensional Data-Driven Science" (HD3-2017)"},
      • year = 2017,
      • month = sep
      • }
    •  Issei Fujishiro, Shigeo Takahashi, Kazuho Watanabe, Hsiang-Yun Wu, Naoko Sawada and Makoto Uemura, "On the Perspicuity of Multidimensional Data Visualization", 1st Public Symposium in the 2017 business year of KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas Initiative for High-Dimensional Data-driven Science through Deepening of Sparse Modeling"", June 2017.
      BibTeX
      • @Inproceedings{fujishiro2017perspicuity,
      • author = {Fujishiro, Issei and Takahashi, Shigeo and Watanabe, Kazuho and Wu, Hsiang-Yun and Sawada, Naoko and Uemura, Makoto},
      • title = {On the Perspicuity of Multidimensional Data Visualization},
      • booktitle = {1st Public Symposium in the 2017 business year of KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas Initiative for High-Dimensional Data-driven Science through Deepening of Sparse Modeling""},
      • year = 2017,
      • month = jun
      • }
    •  Naoko Sawada, Masanori Nakayama, Makoto Uemura, Hsiang-Yun Wu and Issei Fujishiro, "TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories", Proceedings of the 79th IPSJ National Convention, 2017, vol. 79, pp. 85-86.
      BibTeX
      • @Inproceedings{sawada2017national,
      • author = {Sawada, Naoko and Nakayama, Masanori and Uemura, Makoto and Wu, Hsiang-Yun and Fujishiro, Issei},
      • title = {TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories},
      • booktitle = {Proceedings of the 79th IPSJ National Convention},
      • year = 2017,
      • volume = 79,
      • number = 4,
      • pages = {85--86}
      • }
    •  Naoko Sawada, Masanori Nakayama, Hsiang-Yun Wu, Makoto Uemura and Issei Fujishiro, "TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories", Proceedings of 10th IEEE Pacific Visualization Symposium (PacificVis 2017), 2017, pp. 336-337.
      BibTeX
      • @Inproceedings{sawada2017timetubes,
      • author = {Sawada, Naoko and Nakayama, Masanori and Wu, Hsiang-Yun and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories},
      • booktitle = {Proceedings of 10th IEEE Pacific Visualization Symposium (PacificVis 2017)},
      • year = 2017,
      • pages = {336--337},
      • address = {Seoul, Korea}
      • }
    •  Naoko Sawada, Masanori Nakayama, Hsiang-Yun Wu, Makoto Uemura and Issei Fujishiro, "TimeTubes: Visual Fusion and Validation for Ameliorating Uncertainty of Blazar Datasets from Different Observatories", Proceedings of the Computer Graphics International Conference (CGI 2017), 2017.
      BibTeX External
      • @Inproceedings{sawada2017visual,
      • author = {Sawada, Naoko and Nakayama, Masanori and Wu, Hsiang-Yun and Uemura, Makoto and Fujishiro, Issei},
      • title = {TimeTubes: Visual Fusion and Validation for Ameliorating Uncertainty of Blazar Datasets from Different Observatories},
      • booktitle = {Proceedings of the Computer Graphics International Conference (CGI 2017)},
      • year = 2017,
      • address = {Yokohama, Japan},
      • url = {http://dl.acm.org/citation.cfm?id=3095154}
      • }
    •  Makoto Uemura, Ryosuke Itoh, Ioannis Liodakis, Dmitry Blinov, Masanori Nakayama, Longyin Xu, Naoko Sawada, Hsiang-Yun Wu and Issei Fujishiro, "Optical polarization variations in the blazar PKS 1749+096", Publications of the Astronomical Society of Japan (PASJ), Vol. 69, No. 6, pp. Article No. 96, 2017.
      BibTeX External
      • @Article{uemura2017optical,
      • author = {Uemura, Makoto and Itoh, Ryosuke and Liodakis, Ioannis and Blinov, Dmitry and Nakayama, Masanori and Xu, Longyin and Sawada, Naoko and Wu, Hsiang-Yun and Fujishiro, Issei},
      • title = {Optical polarization variations in the blazar PKS 1749+096},
      • journal = {Publications of the Astronomical Society of Japan (PASJ)},
      • year = 2017,
      • volume = 69,
      • number = 6,
      • pages = {Article No. 96},
      • url = {https://academic.oup.com/pasj/article/69/6/96/4609697}
      • }