检索结果-内蒙古大学图书馆

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

引用

Tsinghua Science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Rural road environment segmentation of LiDAR dataset with deep learning 13

Rural road environment segmentation of LiDAR dataset with de...

引用

13th International Conference on Signal Processing Systems, ICSPS 2021

作者： Zakria, Z. Deng, Jianhua He, Jiani Cai, Jingye Khokhar, Muhammad Saddam School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Unstructured road segmentation is a key task in self-driving technology and it's still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are evaluated for structured roads or urban areas, which has considerable limitations in rural areas such as fails at night, road without boundary lines, and no markings. In this regard, we present a new large-scale aerial LiDAR dataset of rural roads with hand-labeled points spanning 500 km2 of road and nine object categories. Our dataset is the most extensive dataset contains a critical number of expert-verified hand-labeled points for analyzing 3D deep learning algorithms, allowing existing algorithms to shift their focus to unstructured road data. The nature of our data, the annotation methodology, and the performance of existing state-of-the-art algorithms on our dataset are all described in detail. Furthermore, challenges and applications of rural area road semantic segmentation are discussed in detail. © COPYRIGHT SPIE.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

WP2-GAN: Wavelet-based Multi-level GAN for Progressive Facial Expression Translation with Parallel Generators 32

WP2-GAN: Wavelet-based Multi-level GAN for Progressive Facia...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Shao, Jun Bui, Tien D. Computer Science and Software Engineering Concordia University MontréalQC Canada

Expression translation has received increasing attention from the computer vision community due to its wide applications in the real world. However, expression synthesis is hard because of the non-linear properties of facial skin and muscle caused by different expressions. A recent study showed that the practice of using the same generator for both forward prediction and backward reconstruction as in current conditional GANs would force the generator to leave a potential "noise" in the generated images, therefore hindering the use of the images for further tasks. To eliminate the interference and break the unwanted link between the first and second translation, we design a parallel training mechanism with two generators that perform the same first translation but work as a reconstruction model for each other. Additionally, inspired by the successful application of wavelet-based multi-level Generative Adversarial Networks(GANs) in face aging and progressive training in geometric conversion, we further design a novel wavelet-based multi-level Generative Adversarial Network (WP2-GAN) for expression translation with a large gap based on a progressive and parallel training strategy. Extensive experiments show the effectiveness of our approach for expression translation compared with the state-of-the-art models by synthesizing photo-realistic images with high fidelity and vivid expression effect. © 2021. The copyright of this document resides with its authors.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

SimROD: A Simple Baseline for Raw Object Detection with Global and Local Enhancements

arXiv

引用

arXiv 2025年

作者： Xie, Haiyang Shen, Xi Huang, Shihua Wang, Qirui Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Intellindust AI Lab China

Most visual models are designed for sRGB images, yet RAW data offers significant advantages for object detection by preserving sensor information before ISP processing. This enables improved detection accuracy and more efficient hardware designs by bypassing the ISP. However, RAW object detection is challenging due to limited training data, unbalanced pixel distributions, and sensor noise. To address this, we propose SimROD, a lightweight and effective approach for RAW object detection. We introduce a Global Gamma Enhancement (GGE) module, which applies a learnable global gamma transformation with only four parameters, improving feature representation while keeping the model efficient. Additionally, we leverage the green channel’s richer signal to enhance local details, aligning with the human eye’s sensitivity and Bayer filter design. Extensive experiments on multiple RAW object detection datasets and detectors demonstrate that SimROD outperforms state-of-the-art methods like RAW-Adapter and DIAP while maintaining efficiency. Our work highlights the potential of RAW data for real-world object detection, and we will release the code upon publication. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Utilizing Machine Learning to Enhance Infrastructure Resilience in Cold Regions 20

Utilizing Machine Learning to Enhance Infrastructure Resilie...

引用

20th International Conference on Cold Regions engineering: Sustainable and Resilient engineering Solutions for Changing Cold Regions, ICCRE 2024

作者： Shohel Rana, Md Gudla, Charan Ahmed, Feroz Nobi, Mohammad Nur Dept. of Computing and Software Engineering Florida Gulf Coast Univ. Fort MyersFL United States Dept. of Computer Science and Engineering Mississippi State Univ. StarkvilleMS United States Prediction 3D Technologies BiloxiMS United States Dept. of Computer Science Univ. of Texas at San Antonio San AntonioTX United States

ISBN: (纸本)9780784485460

In the challenging domain of engineering, where cold regions present formidable challenges, we confront the relentless forces of nature. From sub-zero temperatures to the unpredictable dance of snowfall and the silent buildup of ice, these regions demand innovative solutions to fortify the resilience of critical infrastructure. This initiative harnesses the potential of cutting-edge technology and leverages the extensive historical weather data tapestry. It introduces a pioneering strategy by integrating machine learning algorithms with extensive weather data, steering cold region engineering into an era defined by foresight and adaptability. This paper studies a transformative approach designed to forecast, prevent, and ultimately enhance infrastructure resilience in the face of rigid cold. Addressing the distinct challenges of cold region engineering, arising from harsh winter conditions such as extreme cold temperature, snowfall, and ice accumulation, we offer a comprehensive study using machine learning algorithms applied to historical weather data to construct a deeper analysis model capable of highlighting adverse weather effects. This, in turn, covers the way for optimized resource allocation, streamlined maintenance planning, and design enhancements. Our proposed study follows a systematic process, encompassing meticulous data collection, appropriate feature selection, and aiming seamless integration of the model into existing infrastructure management systems. Additionally, it facilitates the implementation of efficient and proactive measures to mitigate the impact of severe weather conditions on infrastructure. The paper also conducts three different hypotheses testing: temperature impact hypothesis, precipitation influence hypothesis, and ice accumulation and infrastructure resilience hypothesis, propelling engineering practices to new heights, particularly in the face of challenging cold environments. © ASCE.

关键词： Snow

来源：评论

学校读者我要写书评

暂无评论

Deepfake Detection: Emerging Techniques and Evolving Challenges

Deepfake Detection: Emerging Techniques and Evolving Challen...

引用

Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Krishnaraj Natarajan Abeer Mathur Kshitiz Bhargava Manvendra Singh Moulik Tejpal Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’.Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies.

关键词： Deep learning Deepfakes Smart contracts Microwave communication Media Forgery Blockchains Internet of Things Systematic literature review Microwave imaging

来源：评论

学校读者我要写书评

暂无评论

EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks

EHG Signal Analysis for Prediction of Term and Preterm using...

引用

2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Umar Khan, Muhammad Aziz, Sumair Iqtidar, Khushbakht Fernandez Rojas, Raul University of Engineering and Technology Taxila Department of Electronics Engineering Taxila Pakistan National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan University of Canberra Faculty of Science & Technology Canberra Australia

ISBN: (纸本)9798350345933

Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation. © 2022 IEEE.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Prediction of global mean temperature based on SARIMA combined model 4

Prediction of global mean temperature based on SARIMA combin...

引用

4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Li, Dongliang Chu, Chengxu Gao, Huihui Yang, Chenming Liu, Xin Huang, Zhenwu School of Humanities and Law Southwest Forestry University Kunming650224 China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian116034 China Data Science and Intelligent Media School Communication University of China Beijing100024 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781510681637

With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87. © 2024 SPIE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

Efficient Modeling of the Routing and Spectrum Allocation Problem for Flexgrid Optical Networks 25

Efficient Modeling of the Routing and Spectrum Allocation Pr...

引用

25th International Conference on Optical Network Design and Modelling, ONDM 2021

作者： Jaumard, Brigitte Nguyen, Quang Anh Concordia University Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9783903176331

While the problem of Routing and Spectrum Allocation (RSA) has been widely studied, very few studies attempt to solve realistic sized instances. Indeed, the state of the art is always below the standard transport capacity of a fiber link with 384 frequency slots, regardless of what the authors consider, heuristics or exact methods with a few exceptions. In this paper, we are interested in reducing the gap between realistic data sets and testbed instances that are often considered, using exact methods. Even if exact methods may fail to solve in reasonable time very large instances, they can, however, output solutions with a very good and proven accuracy. The novelty of this paper is to exploit the observations that optimal solutions contain a very large number of lightpaths associated with shortest paths or k-shortest paths with a small k. We propose an original efficient large-scale optimization model and decomposition algorithm to solve the RSA problem for flexgrid optical networks. It allows the exact or near optimal solution of much larger instances than in the literature. © 2021 IFIP TC6 WG6.10.

关键词： Optimal systems

来源：评论

学校读者我要写书评

暂无评论

A Secure Large-Scale Multi-Party Payment System Based on Blockchain 6

A Secure Large-Scale Multi-Party Payment System Based on Blo...

引用

6th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2021

作者： Zhang, Xinhai He, Mingxing Jiang, Yuzhang School of Computer and Software Engineering Xihua University Cheng'du610039 China

ISBN: (纸本)9780738105338

In the current blockchain system, the transaction costs and network congestion are increasing, thus the transaction rate is lower, which limits the wide application of blockchain system. In order to address this problem, this paper proposes a secure large-scale multi-party payment (SLMP) system. When our SLMP is applied to the current blockchain system, its capacity is expanding, thus the forementioned problem is solved. The SLMP employs an off-chain payment channel and aggregating multi-signatures with public-key aggreation (AMSP). The security of the proposed system is similar to Bitcoin network. And the proposed system can support large-scale off-chain transactions, and the vendor can conduct aggregate signature on all final payment transactions of payers before the specified time. This modification allow nodes (miner) to validate all transactions at once, thus SLMP is efficient, we also give security proof of our SLMP. Moreover, SLMP system allows offline and off-chain transactions, which means our system is flexible in remote or geographically disastrous areas where communications infrastructure is inadequate or damaged. © 2021 IEEE.

关键词： Blockchain

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：