检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Chen, Xingye Zhao, Yunhai Ahmad, Nafees Zhao, Jun Zheng, Zhuanghao Su, Zhenghua Peng, Xiaogang Li, Xuejin Zhang, Xianghua Fan, Ping Liang, Guangxing Chen, Shuo Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China UMR 6226 Université de Rennes Rennes F-35000 France Shenzhen518060 China

Short carrier lifetimes is a key challenge limiting the open-circuit voltage (VOC) and power conversion efficiency (PCE) of kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. In this work, for the first time, lanthanide europium (Eu) ions have been introduced into Ag-incorporated CZTSSe absorber layer, which can modify the selenization reaction pathway during the initial selenization process, leading to high-quality (Ag, Eu)-CZTSSe absorber with large compact crystal grains and benign surface potential fluctuations. This innovative absorber engineering can also optimize defects dynamics with less detrimental defects and defects-assisted non-radiative recombination. Thus, significantly enhanced minority carrier lifetimes from 0.97 to 3.15 ns can be achieved, representing one of the top values among various bulk and/or interface engineering treated CZTSSe. The champion CZTSSe thin-film solar cell exhibits an impressive VOC of 545.6 mV, accompanied with a stimulating increase in PCE from 10.61% to 13.30%. These findings underscore the potential of lanthanide cation doping to drive significant advancements in CZTSSe photovoltaic technology, and therefore promoting its further development and future applications. © 2024, The Authors. All rights reserved.

关键词： Open circuit voltage

来源：评论

学校读者我要写书评

暂无评论

Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models

arXiv

引用

arXiv 2024年

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518055 China Tongyi Lab Alibaba Group China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China Guangdong Guangzhou510555 China

Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely overlooked, especially without fine-tuning them. In this work, we show that frozen foundation models can be a versatile feature enhancer, even though they are not pre-trained for object detection. Specifically, we explore directly transferring the high-level image understanding of foundation models to detectors in the following two ways. First, the class token in foundation models provides an in-depth understanding of the complex scene, which facilitates decoding object queries in the detector’s decoder by providing a compact context. Additionally, the patch tokens in foundation models can enrich the features in the detector’s encoder by providing semantic details. Utilizing frozen foundation models as plug-and-play modules rather than the commonly used backbone can significantly enhance the detector’s performance while preventing the problems caused by the architecture discrepancy between the detector’s backbone and the foundation model. With such a novel paradigm, we boost the SOTA query-based detector DINO from 49.0% AP to 51.9% AP (+2.9% AP) and further to 53.8% AP (+4.8% AP) by integrating one or two foundation models respectively, on the COCO validation set after training for 12 epochs with R50 as the detector’s backbone. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Multi-Granularity Prosodic Speech Synthesis with Grammar Information

SSRN

引用

SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

来源：评论

学校读者我要写书评

暂无评论

Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

Gaussian Decay Function-based Improved Moment Matching for O...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jiang, Hua Shi, Qiuxia Liu, Siying Zhang, Jiuying Zhao, Qinglin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene. © 2022 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

THE MONGE–KANTOROVICH PROBLEM, THE SCHUR–HORN THEOREM, AND THE DIFFEOMORPHISM GROUP OF THE ANNULUS

arXiv

引用

arXiv 2025年

作者： Bloch, Anthony M. Ratiu, Tudor S. Department of Mathematics University of Michigan Ann ArborMI48109 United States School of Mathematical Sciences Ministry of Education Key Lab on Scientific and Engineering Computing Shanghai Frontier Science Center of Modern Analysis Shanghai Jiao Tong University Minhang District Shanghai200240 China

First, we analyze the discrete Monge–Kantorovich problem, linking it with the minimization problem of linear functionals over adjoint orbits. Second, we consider its generalization to the setting of area preserving diffeomorphisms of the annulus. In both cases, we show how the problem can be linked to permutohedra, majorization, and to gradient flows with respect to a suitable *** Codes 49 © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ProcGuard: Process Injection Behaviours Detection Using Fine-grained Analysis of API Call Chain with Deep Learning

ProcGuard: Process Injection Behaviours Detection Using Fine...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Juan Wang Chenjun Ma Ziang Li Huanyu Yuan Jie Wang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

关键词： Deep learning Measurement Privacy Runtime Heuristic algorithms Writing Malware

来源：评论

学校读者我要写书评

暂无评论

TFGDA: exploring topology and feature alignment in semi-supervised graph domain adaptation through robust clustering 24

TFGDA: exploring topology and feature alignment in semi-supe...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Changtai Jiang, Ruohui Yin, Shihua Yang, Jinzhu Ban, Xiaojuan University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350337488

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels. © 2023 IEEE.

关键词： 3D tumor segmentation MRI image nasopharyngeal carcinoma Self-Supervised Learning

来源：评论

学校读者我要写书评

暂无评论

Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

A Novel 3d Auxetic Meta-Structure Based on Enhanced Hexa-Missing Rib Honeycomb

SSRN

引用

SSRN 2024年

作者： Lu, Fucong Mo, Wenchao Wei, Tinghui Huang, Yunjun Zhou, Shiling Zhu, Yilin Key Laboratory of Disaster Prevention and Structural Safety Ministry of Education Scientific Research Center of Engineering Mechanics College of Civil Engineering and Architecture Guangxi University Nanning530004 China Failure Mechanics and Engineering Disaster Prevention Key Laboratory of Sichuan Province Sichuan University Chengdu610065 China School of Civil Engineering and Geomatics Southwest Petroleum University Sichuan Chengdu610500 China

2D auxetic meta-structures encounter specific constraints in practical applications, while 3D meta-structures can provide more degrees of freedom and design space, thus offering new possibilities for tailoring the mechanical properties of materials. Base on a previously designed 2D auxetic hexa-missing rib (HMR) honeycomb, a 3D HMR auxetic meta-structure was proposed in the present work incorporating the tension/compression-torsion coupling effect. To deepen the comprehension of potential deformation mechanism and further optimize the design of the structure, a mechanics model of the 3D HMR meta-structure was developed by a simple energy method under small deformation framework. The results indicate that adjusting the geometric parameters allows for achieving targeted mechanical properties over a wide range, enabling them to meet different requirements in engineering applications. In addition, the 3D HMR meta-structure exhibits perfect isotropic behavior and superior auxetic effect compared to the previously proposed 3D tetra-missing rib meta-structure. Finally, the veracity of theoretical formulas was benchmarked by uniaxial experiments and finite element (FE) simulations, where good agreements was observed. © 2024, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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：