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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Balčytis, Armandas Dinh, Xuan Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai9808577 Japan Department of Applied Physics and PhysicoInformatics Keio University Yokohama2238522 Japan Department of Electrical and Computer Engineering Yokohama National University 795 Tokiwadai Hodogayaku Yokohama2408501 Japan Research Center for Advanced Science and Technology The University of Tokyo 461 Komaba Meguroku Tokyo1538904 Japan

ISBN: (纸本)9781957171258

We created a modulatorequipped thinfilm lithium niobate integrated ring resonator simulating a synthetic frequency dimension tightbinding model. It produces frequency states spaced over a 600 GHz bandwidth. Reconfigurable coupling enables simulation of disparate twodimensional interactions. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Niobium compounds

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Leveraging Hyperscanning EEG and VR Omnidirectional Treadmill to Explore Inter-Brain Synchrony in Collaborative Spatial Navigation

arXiv

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arXiv 2024年

作者： Chuang, Chun-Hsiang Peng, Po-Hsun Chen, Yi-Chieh The Research Center for Education and Mind Sciences The Institute of Information Systems and Applications National Tsing Hua University Hsinchu30014 Taiwan The Department of Computer Science and Engineering National Taiwan Ocean University Keelung202301 Taiwan The Research Center for Education and Mind Sciences National Tsing Hua University Hsinchu30014 Taiwan

Navigating through a physical environment to reach a desired location involves a complex interplay of cognitive, sensory, and motor functions. When navigating with others, experiencing a degree of behavioral and cognitive synchronization is both natural and ubiquitous. This synchronization facilitates a harmonious effort toward achieving a common goal, reflecting how individuals instinctively align their actions and thoughts in collaborative settings. Collaborative spatial tasks, which are crucial in daily and professional settings, require coordinated navigation and problem-solving skills. This study explores the neural mechanisms underlying such tasks by using hyperscanning electroencephalography (EEG) technology to examine brain dynamics in dyadic route planning within a virtual reality setting. By analyzing intra- and inter-brain couplings across delta, theta, alpha, beta, and gamma EEG bands using both functional and effective connectivity measures, we identified significant neural synchronization patterns associated with collaborative task performance in both leaders and followers. Functional intra-brain connectivity analyses revealed distinct neural engagement across EEG frequency bands, with increased delta couplings observed in both leaders and followers. Theta connectivity was particularly enhanced in followers, whereas the alpha band exhibited divergent patterns that indicate role-specific neural strategies. Inter-brain analysis revealed increased delta causality between interacting members but decreased theta and gamma couplings from followers to leaders. Additionally, inter-brain analysis indicated decreased couplings in faster-performing dyads, especially in theta bands. These insights enhance our understanding of the neural mechanisms driving collaborative spatial navigation and demonstrate the effectiveness of hyperscanning in studying complex brain-to-brain interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension

arXiv

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arXiv 2023年

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scientific machine reading comprehension. However, the dataset has ignored the fact that different readers may have different levels of understanding of the text, and only includes single-perspective question-answer pairs, leading to a lack of consideration of different perspectives. To tackle the above problem, we propose a novel multi-perspective SMRC dataset, called SciMRC, which includes perspectives from beginners, students and experts. Our proposed SciMRC is constructed from 741 scientific papers and 6,057 question-answer pairs. Each perspective of beginners, students and experts contains 3,306, 1,800 and 951 QA pairs, respectively. The extensive experiments on SciMRC by utilizing pre-trained models suggest the importance of considering perspectives of SMRC, and demonstrate its challenging nature for machine comprehension. © 2023, CC BY.

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Unsupervised Style Control for Image Captioning

Unsupervised Style Control for Image Captioning

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作者： Junyu Tian Zhikun Yang Shumin Shi School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

We propose a novel unsupervised image captioning *** captioning involves two fields of deep learning,natural language processing and computer *** excessive pursuit of model evaluation results makes the caption style generated by the model too monotonous,which is difficult to meet people's demands for vivid and stylized image ***,we propose an image captioning model that combines text style transfer and image emotion recognition methods,with which the model can better understand images and generate controllable stylized *** proposed method can automatically judge the emotion contained in the image through the image emotion recognition module,better understand the image content,and control the description through the text style transfer method,thereby generating captions that meet people's *** our knowledge,this is the first work to use both image emotion recognition and text style control.

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Photonic Frequency Space Simulation of Tight-Binding Lattices Using Integrated LNOI Ring Resonators 16

Photonic Frequency Space Simulation of Tight-Binding Lattice...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Balčytis, Armandas Dinh, Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre Rmit University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonics

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Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method 20

Reducing Length Bias in Scoring Neural Machine Translation v...

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20th China National Conference on Computational Linguistics, CCL 2021

作者： Shi, Xuewen Huang, Heyan Jian, Ping Tang, Yi-Kun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing100081 China

ISBN: (纸本)9783030841850

Neural machine translation (NMT) usually employs beam search to expand the searching space and obtain more translation candidates. However, the increase of the beam size often suffers from plenty of short translations, resulting in dramatical decrease in translation quality. In this paper, we handle the length bias problem through a perspective of causal inference. Specifically, we regard the model generated translation score S as a degraded true translation quality affected by some noise, and one of the confounders is the translation length. We apply a Half-Sibling Regression method to remove the length effect on S, and then we can obtain a debiased translation score without length information. The proposed method is model agnostic and unsupervised, which is adaptive to any NMT model and test dataset. We conduct the experiments on three translation tasks with different scales of datasets. Experimental results and further analyses show that our approaches gain comparable performance with the empirical baseline methods. © 2021, Springer Nature Switzerland AG.

关键词： Neural machine translation

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Fault Diagnosis of Rolling Bearings Based on Cross Attention Network with Multi-Scale Feature Fusion

Fault Diagnosis of Rolling Bearings Based on Cross Attention...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Tao Tao Xiao-Feng Jia Da-Fu Su Xin-Le Zhang Jia-Fu Gao Run-Ze Xu Lei Mo Xiu-jun Wang School of Computer Science and Technology Anhui University of Technology Ma'anshan Anhui China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan China Engineering Research Institute Anhui University of Technology Ma'anshan China School of Mathematics and Statistics Fuzhou University Fuzhou Fujian China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

Rolling bearings are critical in mechanical systems, and their failure can cause significant economic and safety risks. Timely fault diagnosis is essential to prevent unplanned downtime. This paper introduces a new method for rolling bearing fault diagnosis using a cross-attention network and multi-scale feature fusion. The original vibration signal is decomposed using Variational Mode Decomposition (VMD) for time-domain features, and frequency-domain features are extracted via Fast Fourier Transform (FFT). A Bidirectional Temporal Convolutional Network (BiTCN) extracts time-domain features, while a Bidirectional Gated Recurrent Unit (BiGRU) captures frequency-domain patterns. These features are integrated through a cross-attention mechanism and classified for fault identification. Evaluated on the Case Western Reserve University (CWRU) bearing dataset, the method achieves 98.67% accuracy, outperforming traditional techniques.

关键词： Fault diagnosis Wireless communication Vibrations Accuracy Fast Fourier transforms Frequency-domain analysis Rolling bearings Feature extraction Safety Time-domain analysis

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AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking

arXiv

引用

arXiv 2023年

作者： Nie, Yuxiang Huang, Heyan Wei, Wei Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center High Volume Language Information Processing and Cloud Computing Applications China Beijing Institute of Technology Southeast Academy of Information Technology China Huazhong University of Science and Technology China

Annotating long-document question answering (long-document QA) pairs is time-consuming and expensive. To alleviate the problem, it might be possible to generate long-document QA pairs via unsupervised question answering (UQA) methods. However, existing UQA tasks are based on short documents, and can hardly incorporate long-range information. To tackle the problem, we propose a new task, named unsupervised long-document question answering (ULQA), aiming to generate high-quality long-document QA instances in an unsupervised manner. Besides, we propose AttenWalker, a novel unsupervised method to aggregate and generate answers with long-range dependency so as to construct long-document QA pairs. Specifically, AttenWalker is composed of three modules, i.e., span collector, span linker and answer aggregator. Firstly, the span collector takes advantage of constituent parsing and reconstruction loss to select informative candidate spans for constructing answers. Secondly, by going through the attention graph of a pre-trained long-document model, potentially interrelated text spans (that might be far apart) could be linked together via an attention-walking algorithm. Thirdly, in the answer aggregator, linked spans are aggregated into the final answer via the mask-filling ability of a pre-trained model. Extensive experiments show that AttenWalker outperforms previous methods on Qasper and NarrativeQA. In addition, AttenWalker also shows strong performance in the few-shot learning setting. Copyright © 2023, The Authors. All rights reserved.

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Mapping Wildfire Burned Area Using GNSS-Reflectometry in Densely Vegetated Regions with Complex Topography: A Machine Learning Approach

Mapping Wildfire Burned Area Using GNSS-Reflectometry in Den...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Archana Kannan Amer Melebari Grigorios Tsagkatakis Kurtis Nelson Vinay Ravindra Sreeja Nag Mahta Moghaddam Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece EROS Center at United States Geological Survey Sioux Falls SD United States Bay Area Environmental Research Institute at NASA Ames Research Centre Moffett Field United States

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Accurate assessment of areas burned in wildfires is vital for various monitoring, management, and spread modeling applications. Wildfires, especially in forested regions, pose immense challenges for precise mapping due to the inherent dynamics of fuel types and terrain complexities. While remote sensing, particularly satellite imagery, offers an approach to studying burned areas, reliance on such satellite sources introduces challenges in characterizing burned areas amidst dense vegetation and environmental variations. This paper presents a mapping of forested burned areas utilizing global navigation satellite system–reflectometry (GNSS-R) from Cyclone Global Navigation Satellite System (CYGNSS) with ancillary observations from Soil Moisture Active Passive (SMAP) mission and Shuttle Radar Topography Mission (SRTM) using machine learning approaches. We validate the results with existing burned area products and provide maps of representative California fires within CYGNSS coverage. Assimilation of GNSS-R data into the model provides near real-time and high temporal resolution, enabling rapid response and mitigation efforts to fire events.

关键词： Wildfires Satellites Accuracy Spaceborne radar Vegetation mapping Forestry Machine learning

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Leveraging Temporal Causal Discovery Framework to Explore Event-Related EEG Connectivity 24th

Leveraging Temporal Causal Discovery Framework to Explore Ev...

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24th International Conference on Human-computer Interaction, HCII 2022

作者： Fang, Shao-Xun Chiu, Tsai-Feng Huang, Chih-Sheng Chuang, Chun-Hsiang Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of Artificial Intelligence and Green Energy National Yang Ming Chiao Tung University Taipei City Taiwan College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei City Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9783031196782

Deciphering the cause-and-effect relationships between brain regions not only can provide insights into the mechanism of brain networking but also facilitate the development of the brain-computer interface. Numerous studies have adopted effective connectivity measurements such as transfer entropy or multivariate autoregressive model to investigate the synchronous neuronal coupling across brain regions. Recent successes with representation learning in deep neural networks deserve further investigation on their capability for causal discovery. To this end, this study modified the Temporal Causal Discovery Framework (TCDF) with a sliding-window approach to explore the event-related intra-brain electroencephalogram (EEG) dynamics. The TCDF used a convolution neural network with an attention mechanism to predict causal direction among multi-channel time series data. The resultant array of attention scores was used to gauge the causality magnitude. The TCDF was first validated through a simulation study, followed by real EEG data collected in a virtual reality-based multitasking experiment. Results showed that the time-varying causality magnitudes were positively correlated with the predefined regression coefficients of simulated data, and the TCDF could successfully capture the coupling between brain regions, particularly for adjacent channels. However, some issues were raised in modeling as the number of channels increased or the causal order became larger. The current findings might shed some light on the development of deep learning-based causality analysis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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