检索结果-内蒙古大学图书馆

IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 956-962页

作者： Tianshan Zhang Bei Jiang Guang Yang Zhijie Bao Jiajie Peng AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shanxi China The National Engineering Laboratory for In tegrated Aerospace Ground Ocean Big Data Application Technology Shanxi China Tianjin second people's hospital Tianjin China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

The CRISPR-Cas9 system, found across bacteria and archaea, enables efficient genome engineering in eukaryotic cells. There is a challenge that Cas9 guide RNA may cause off-target activities. Although numbers of methods have been proposed to predict off-target activities in guide RNA designing, this procedure involves numerous potential off-target sites, which causes label imbalance problem. To address this problem, we developed a deep learning framework, named CAF-Net (Cas9 Augmentation and Finetune Network), for predicting off-target activities of CRISPR-Cas9. First, we pretrain an embedding model to extract features from target and guide sequence pairs. Subsequently, data augmentation is applied to these features. And finally, the model is finetuned with synthetic samples. Evaluation results demonstrate its performance on published datasets.

关键词： RNA Bioinformatics Feature extraction DNA Genomics Convolutional neural networks Computational modeling Transformers Predictive models CRISPR

来源：评论

学校读者我要写书评

暂无评论

Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation

arXiv

引用

arXiv 2025年

作者： Lu, Bingjie Dan, Han-Cheng Zhang, Yichen Huang, Zhetao School of Civil Engineering Central South University Changsha Hunan410075 China National Engineering Research Center for High-speed Railway Construction Technology Central South University Hunan Changsha410075 China Rail Data Research Application Key Laboratory of Hunan Province Central South University Hunan Changsha410075 China

Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement images. The contributions of this work are threefold: firstly, it proposes an economical method to acquire three-dimensional (3D) pavement texture data;secondly, it enhances 3D image processing techniques and formulates features that represent various aspects of texture;thirdly, it establishes multivariate prediction models that link these features with MTD values. Validation results demonstrate that the Gradient Boosting Tree (GBT) model achieves remarkable prediction stability and accuracy (R2 = 0.9858), and field tests indicate the superiority of the proposed method over other techniques, with relative errors below 10%. This method offers a comprehensive end-to-end solution for pavement quality evaluation, from images input to MTD predictions output. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

来源：评论

学校读者我要写书评

暂无评论

The unforgettable past: The long-term impact of war on corporate tax avoidance in modern China

引用

research in International Business and Finance 2025年 77卷

作者： Tan, Xue Yin, Jingwei Lin, Ning Hunan Key Laboratory of Macroeconomic Big Data Mining and Its Application School of Business Hunan Normal University Hunan Changsha 410081 China Business School Central South University Hunan Changsha 410083 China Economics and Management School of Wuhan University Wuhan University Hubei Wuhan China

Prior literature related to war effects usually has a short time horizon between the war event and its outcomes. However, little is known about whether the collective war memories can be transmitted across generations, and then impact later generations' decisions in a long-lasting way. Using a sample of 3244 A-share listed companies in Shanghai and Shenzhen from 2013 to 2019, the study finds that firms hiring CEOs originated from regions that were intensively affected by the war during 1931–1945 (i.e., the Second Sino-Japanese War) exhibit higher level of corporate tax avoidance. Moreover, the positive impact of war on corporate tax avoidance is more pronounced for CEOs who are the first generation born in the post-war period. Our mechanism tests show that firms hiring war-affected CEOs are more likely to make philanthropic giving, and have higher corporate social responsibility ratings, but not hold more risk assets, revealing that war-affected CEOs engaging in greater tax avoidance is due to their prosocial preference rather than risk preference. Our cross-sectional analyses reveal that the positive impact of war on corporate tax avoidance is stronger when CEOs are male, less educated, without political connection, with poor experiences and firms face financing constraints, but weaker when firms are under stricter tax enforcement. Above conclusions are robust to a set of robustness checks and still stand after confronting the endogeneity issue. © 2025

关键词： CEOs' characteristics Corporate tax avoidance Prosocial preference Risk preference War

来源：评论

学校读者我要写书评

暂无评论

AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

引用

IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

引用

Engineering applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Functional Bayesian Additive Regression Trees with Shape Constraints

arXiv

引用

arXiv 2025年

作者： Cao, Jiahao He, Shiyuan Zhang, Bohai Institute of Statistics and Big Data Renmin University of China Beijing100872 China School of Mathematics and Statistics Beijing Technology and Business University Beijing100048 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

Motivated by the great success of Bayesian additive regression trees (BART) on regression, we propose a nonparametric Bayesian approach for the function-on-scalar regression problem, termed as Functional BART (FBART). Utilizing spline-based function representation and tree-based domain partition model, FBART offers great flexibility in characterizing the complex and heterogeneous relationship between the response curve and scalar covariates. We devise a tailored Bayesian backfitting algorithm for estimating the parameters in the FBART model. Furthermore, we introduce an FBART model with shape constraints on the response curve, enhancing estimation and prediction performance when prior shape information of response curves is available. By incorporating a shape-constrained prior, we ensure that the posterior samples of the response curve satisfy the required shape constraints (e.g., monotonicity and/or convexity). Our proposed FBART model and its shape-constrained version are the new advances of BART models for functional data. Under certain regularity conditions, we derive the posterior convergence results for both FBART and its shape-constrained version. Finally, the superiority of the proposed methods over other competitive counterparts is validated through simulation experiments under various settings and analyses of two real datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Trajectory-Based Safety Analysis of Electric Battery Taxis

引用

Journal of Advanced Transportation 2025年第1期2025卷

作者： Zhuang, Yan Li, Ning Dong, Chunjiao Li, Penghui Xu, Bo Yang, Miaoyan Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Ministry of Transport Shangyuancun 3 Haidian District Beijing100044 China Planning and Design Institute The Architectural Design & Research Institute of Zhejiang University Co. Ltd. Hangzhou310028 China

This paper utilizes trajectory data of electric taxis in Shenzhen to first study the driving characteristics of electric taxis from different indicators such as travel/driving speed, overspeed ratio, and overspeed amplitude. A combination weighting VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) evaluation method based on Nash equilibrium is constructed to comprehensively assess the operation safety of electric taxis in various spatiotemporal scenarios. The results show that the average driving speed and overspeed amplitude of battery electric taxis is 39.14 km/h and 22.78%, which is 1.27 and 1.72 times that of fuel taxis. The average overspeed ratio, average acceleration and deceleration and total acceleration and deceleration frequency are all more than twice those of fuel taxis. The average acceleration, deceleration, and idle time ratios of battery electric taxis are 0.27, 0.24 and 0.43, respectively, with a constant speed time ratio of only 0.06. The proposed combination weighting VIKOR evaluation method outperforms other evaluation methods in comprehensively considering data discreteness and correlation. Electric taxis have the highest operational safety on arterial roads, while the safety on minor arterial roads and local roads is relatively poor, especially on weekends. In all spatiotemporal scenarios, the overall operation safety of electric taxis is lower than that of fuel taxis. The research results can provide theoretical support for the formulation of effective measures and policies to reduce dangerous driving behavior of electric taxis. Copyright © 2025 Yan Zhuang et al. Journal of Advanced Transportation published by John Wiley & Sons Ltd.

关键词：

来源：评论

学校读者我要写书评

暂无评论

WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

引用

IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

引用

arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Dual consistent constraint via disentangled consistency and complementarity for multi-view clustering

引用

Neurocomputing 2025年 641卷

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025

关键词： Contrastive 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：