检索结果-内蒙古大学图书馆

Strain accumulation in the Mentawai Forearc Sliver,Indonesia,inferred from continuous GNSS-derived strain rate

Geodesy and Geodynamics 2025年第1期16卷 1-6页

作者： Leni Sophia Heliani Cecep Pratama Adi Wibowo David Prambudi Sahara Susilo Susilo Sidik Tri Wibowo Ayu Nur Safii Oktadi Prayoga Ajat Sudrajat Mizan Bustanul Fuady Bisri Evi Dwi Kurniasari Sheilla Evelinda Nicholas Genta Setiawan Gunawan Department of Geodetic Engineering Universitas Gadjah MadaYogyakartaIndonesia Department of Computer Science Universitas DiponegoroSemarangIndonesia Global Geophysics Research Group Institut Teknologi BandungBandungIndonesia Research Center for Geological Disaster National Research and Innovation AgencyBandungIndonesia Geospatial Information Agency CibinongIndonesia Meteorological Climatologicaland Geophysical AgencyJakartaIndonesia Graduate School of International Cooperation Studies Kobe UniversityJapan Graduate School of Geodetic Engineering Universitas Gadjah MadaYogyakartaIndonesia

The Mentawai Forearc Sliver(MFS)is characterized by oblique deformation formed as slip partitioned between normal and parallel trench plate *** surge of great earthquakes from 2004 to2012 along the adjacent Sunda trench left a large unbroken segment known as the Mentawai Seismic ***,we adopted continuous Global Navigation Satellite System(GNSS)observation data to identify the present regional crustal deformation using geodetic strain *** principal strain rate,dilatation rate,and maximum shear strain rate are about 0.13 microstrain/yr,0.2 microstrain/yr,and 0.1 microstrain/yr,respectively,with the range of its uncertainties between 0.01 and 0.04 microstrain/*** dilatation and maximum shear strain rates reveal the spatial coverage of strike-slip duplex and backthrust tectonics along the Mentawai Forearc Sliver.

关键词： Mentawai Forearc Dilatation Shear Geodetic

来源：评论

学校读者我要写书评

暂无评论

Online Learning of Quantum States with Logarithmic Loss via VB-FTRL 36

Online Learning of Quantum States with Logarithmic Loss via ...

引用

36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Tseng, Wei-Fu Chen, Kai-Chun Xiao, Zi-Hong Li, Yen-Huan Department of Mathematics National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan School of Medicine National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan

Online learning of quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection (OPS), a classic open problem in online learning for over three decades. This problem also emerges in designing stochastic optimization algorithms for maximum-likelihood quantum state tomography. Recently, Jézéquel et al. (2022, arXiv:2209.13932) proposed the VB-FTRL algorithm, the first regret-optimal algorithm for OPS with moderate computational complexity. In this paper, we generalize VB-FTRL for LL-OLQS. Let d denote the dimension and T the number of rounds. The generalized algorithm achieves a regret rate of O(d2 log(d + T)) for LL-OLQS. Each iteration of the algorithm consists of solving a semidefinite program that can be implemented in polynomial time by, for example, cutting-plane methods. For comparison, the best-known regret rate for LL-OLQS is currently O(d2 log T), achieved by an exponential weight method. However, no explicit implementation is available for the exponential weight method for LL-OLQS. To facilitate the generalization, we introduce the notion of VB-convexity. VB-convexity is a sufficient condition for the volumetric barrier associated with any function to be convex and is of independent interest. © 2025 W.-F. Tseng, K.-C. Chen, Z.-H. Xiao & Y.-H. Li.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Portfolio selection based on market states acquired via price and non-price data

引用

Discover Data 2025年第1期3卷 1-21页

作者： Chung, Andy Tanaka-Ishii, Kumiko Yairi, Takehisa Department of Advanced Interdisciplinary Studies Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Computer Science and Engineering School of Fundamental Science and Engineering Waseda University Tokyo Japan

Financial markets exhibit quasi-stationary patterns, known as market states, which previous studies have identified through the correlation structure of asset returns using clustering techniques. In this paper, we propose a novel approach to the portfolio selection problem by leveraging market states through an unsupervised method that extends beyond conventional correlation analysis. We introduce market distance metrics for both price and non-price data to estimate market states. Our proposed model allocates assets based on portfolio choices optimized within specified market states. In addition to using asset return correlations, we experiment with two non-price data sources: (1) a text-based market distance metric derived from Federal Open Market Committee (FOMC) policy statements, and (2) the correlation of multivariate time series search volumes from Google Trends. Using a multi-asset portfolio encompassing major U.S. asset classes, we trained our model from 2016 to 2019 and evaluated its out-of-sample performance from 2020 to 2023. Our findings demonstrate the superiority of our ensemble solution, incorporating multiple market distance metrics with price and non-price data, over established baselines with statistically significant results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation for Fairness Enhancement in Multi-Cell Vehicular VLC System With Optical IRS: A Cooperative Transmission Approach

引用

IEEE Internet of Things Journal 2025年第11期12卷 16931-16946页

作者： An, Nan Yang, Fang Liu, Chang Cheng, Ling Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Center of National Railway Intelligent Transportation System Engineering and Technology Beijing100081 China China Academy of Railway Sciences Corporation Limited Signal and Communication Research Institute Beijing100081 China University of the Witwatersrand School of Electrical and Information Engineering Johannesburg2050 South Africa Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In multi-cell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and inter-cell interference, leading to unfairness among vehicles. In this paper, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application. © 2014 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Heralded optical entanglement distribution via lossy quantum channels: a comparative study

引用

Optics Express 2025年第6期33卷 12459-12474页

作者： Zo, Wan Chin, Seungbeom Kim, Yong-Su Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Okinawa Institute of Science and Technology Graduate University Okinawa904-0495 Japan Division of Quantum Information Korea University of Science and Technology KIST School Seoul02792 Korea Republic of

Quantum entanglement serves as a foundational resource for various quantum technologies. In optical systems, entanglement distribution relies on the indistinguishability and spatial overlap of photons. Heralded schemes play a crucial role in ensuring the reliability of entanglement generation by detecting ancillary photons to signal the creation of desired entangled states. However, photon losses in quantum channels remain a significant challenge, limiting the distance and capacity of entanglement distributions. This study suggests three heralded schemes for distributing multipartite Greenberger-Horne-Zeilinger (GHZ) states via lossy quantum channels. These schemes differ both qualitatively in their network architecture (centralized or decentralized) and photon source requirements (Bell states or single-photons), and quantitatively in their success probabilities and heralding efficiency. Through comprehensive analysis incorporating both practical implementation considerations and theoretical performance metrics, we find that each scheme offers distinct advantages depending on the number of parties, channel distance, and security requirements. The decentralized scheme proves particularly advantageous for networks requiring balanced information distribution, while centralized schemes may offer better performance for smaller networks. This analysis provides insights into designing resilient heralded circuits for quantum information processing over lossy channels, considering both architectural constraints and performance requirements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Quantum channel

来源：评论

学校读者我要写书评

暂无评论

Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

From Vision to Motion: Translating Large-Scale Knowledge for Data-Scarce IMU Applications

引用

IEEE Transactions on Mobile Computing 2025年

作者： Yoon, Hyungjun Cha, Hyeongheon Nguyen, Hoang C. Gong, Taesik Lee, Sung-Ju KAIST School of Electrical Engineering Korea Republic of Stony Brook University Department of Computer Science NY United States UNIST Department of Computer Science and Engineering Artificial Intelligence Graduate School Korea Republic of

Pre-training representations acquired via self-supervised learning could achieve high accuracy on even tasks with small training data. Unlike in vision and natural language processing domains, pre-training for IMU-based applications is challenging, as there are few public datasets with sufficient size and diversity to learn generalizable representations. To overcome this problem, we propose IMG2IMU that adapts pre-trained representation from large-scale images to diverse IMU sensing tasks. We convert the sensor data into visually interpretable spectrograms for the model to utilize the knowledge gained from vision. We further present a sensor-aware pre-training method for images that enables models to acquire particularly impactful knowledge for IMU sensing applications. This involves using contrastive learning on our augmentation set customized for the properties of sensor data. Our evaluation with four different IMU sensing tasks shows that IMG2IMU outperforms the baselines pre-trained on sensor data by an average of 9.6%p F1-score, illustrating that vision knowledge can be usefully incorporated into IMU sensing applications where only limited training data is available. © 2002-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing modulation bandwidth of parallel microLED arrays for visible light communication

IEEE Electron Device Letters

引用

IEEE Electron Device Letters 2025年第6期46卷 944-947页

作者： Guo, Wen-An Wan, Ming-He Liu, Shi-Biao Liu, Zhong Lu, Ting-Wei Chen, Guo-Long Lu, Yi-Jun Kuo, HaoChung Wu, Ting-Zhu Chen, Zhong Xiamen University National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan

In this letter, parallel micro-LED arrays with numbers of 1×1, 1×2, 2×2 and 2×3 were fabricated and the p-electrode patterns of arrays were optimized. The results show that both the light output power (LOP) and modulation bandwidth increase with the parallel array numbers. Moreover, optimizing the p-electrode pattern can effectively facilitate current spreading, mitigate thermal crowding, and enhance the LOP. Under combined with the LOP and thermal effect, the 2×2 ring-electrode parallel array achieved the highest modulation bandwidth of 172 MHz and maximum LOP of 5.70 mW, which were improved by 17.28% and 13.98%, respectively. This work provides a reference to design micro-LED chip for high bandwidth. © 1980-2012 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

ICPR 2024 Competition on VISual Tracking in Adverse Conditions (VISTAC) 27th

ICPR 2024 Competition on VISual Tracking in Adverse Conditio...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Ali, Asfak Pandit, Arya Mandal, Shirshendu Bhattacharjee, Srinjan Maiti, Sauptik Acharjee, Suvojit Sarkar, Ram Chaudhuri, Sheli Sinha Agaian, Sos S. Djemal, Khalifa Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Electronics and Communication Narula Institute of Technology Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Computer Science Department Graduate Center College of Staten Island City University of New York New York India University of Evry Paris-Saclay IBISC Laboratory 40 rue du Pelvoux Evry91020 France

ISBN: (纸本)9783031801389

Tracking objects in nighttime video presents significant challenges due to factors like low visibility, poor illumination, and the presence of distant, small, or low-contrast objects. Traditional tracking methods, which rely on frame differencing, are largely ineffective under these conditions. Although deep learning has advanced the field, it demands extensive labeled data, which is often difficult to obtain for rare or extreme events. Historically, research in spatial and temporal data models and database systems has been conducted independently, limiting progress in handling complex spatiotemporal scenarios. Nighttime video analysis involves complex, dynamic, and context-dependent spatiotemporal events, requiring sophisticated algorithms capable of accurately monitoring and detecting these events across both spatial and temporal dimensions. Such advancements are crucial for enhancing applications in surveillance, security, and autonomous navigation systems. To address the gap in specialized datasets, we introduce the Night Vision Spatiotemporal Infrared-Video Dataset (NV-SID). This dataset comprises 100 annotated nighttime infrared videos, setting a new benchmark for evaluating deep learning-based object-tracking algorithms. Furthermore, the target of this challenge was to set a new standard for evaluating deep learning-based object-tracking algorithms, utilizing the proposed Qualitative Precision (QP) metric. NV-SID is a key component of the VISTAC Challenge, part of the ICPR 2024 Challenge on VISual Tracking in Adverse Conditions, which aims to push the boundaries of nighttime object tracking technology. Here, we have briefly described the methods used by the participants, and compiled their results. We have also mentioned further research avenues regarding this task. Challenge details and dataset can be found at: https://***/view/ju-nvisot/home. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：