基于几何的点云压缩(geometry-based point cloud compression,G-PCC)可有效降低点云传输对网络带宽和存储的要求,但重建后的点云质量常因点的消失而显著下降.文章提出了一种基于多分支(multi-branch)的G-PCC点云几何后处理方法,通过提...基于几何的点云压缩(geometry-based point cloud compression,G-PCC)可有效降低点云传输对网络带宽和存储的要求,但重建后的点云质量常因点的消失而显著下降.文章提出了一种基于多分支(multi-branch)的G-PCC点云几何后处理方法,通过提取多尺度几何特征,并在每个尺度上使用基于k近邻的最大池化层来聚合几何邻域信息,从而预测体素块的概率,实现更精确的点云重建.在国际运动图像专家组(Moving Picture Experts Group,MPEG)推荐的通用测试条件下,该方法与G-PCC(octree)、G-PCC(trisoup)相比,平均获得91.89%(84.57%)和75.24%(73.51%)的D1(D2)BD-Rate增益;与传统方法LUT相比,平均获得76.78%(70.37%)的D1(D2)BD-Rate增益;与基于深度学习的方法DGPP相比,平均获得23.95%(21.41%)的BD-Rate增益.此外,该方法相较于现有基于学习的方法,复杂度更低,具有更广阔的应用前景.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
背景:随着人口老龄化进程的不断加快,老年人肠道微生物的研究受到广泛关注,但如今还缺少对该领域的文献计量学分析。目的:综合分析不同数据库中有关老年人肠道菌群的文献资料,旨在挖掘当前研究热点,并预测未来发展的趋势,为后续研究工...背景:随着人口老龄化进程的不断加快,老年人肠道微生物的研究受到广泛关注,但如今还缺少对该领域的文献计量学分析。目的:综合分析不同数据库中有关老年人肠道菌群的文献资料,旨在挖掘当前研究热点,并预测未来发展的趋势,为后续研究工作指明可能的发展方向。方法:以“老年人肠道微生物”“老年人肠道微生态”和“老年人肠道菌群”作为主题词字段在中国知网进行检索,以“TS=(elderly gut microbe OR elderly gut microbiome OR elderly gut microbiota OR elderly intestinal microbiome OR elderly intestinal microbiota)”作为检索策略在Web of Science数据库进行检索,使用文献计量工具VOSviewer与CiteSpace,对相关文献的发表年份、国家分布、研究机构、作者和关键词进行系统分析。结果与结论:在中国知网和Web of Science数据库中分别获得有效文献250篇和604篇。在2014-2023年间,全球范围内老年人肠道微生物领域研究发文量整体呈现稳步上升的趋势。国内外对这一领域的关注和探讨热度不断增加,研究深度和广度也在各学科之间的交叉中得到拓展。COVID-19、氧化应激(Oxidative stress)、抑郁(depression)、认知障碍(cognitive impairment)为近2年的突现关键词。通过文献计量学分析,直观展示了老年人肠道微生物领域近10年的研究现状和发展趋势,目前正处于上升期,仍需进一步探索肠道微生物的作用机制及相关疾病的干预方案。展开更多
Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign cur...Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.展开更多
The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Tr...The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.展开更多
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
文摘背景:随着人口老龄化进程的不断加快,老年人肠道微生物的研究受到广泛关注,但如今还缺少对该领域的文献计量学分析。目的:综合分析不同数据库中有关老年人肠道菌群的文献资料,旨在挖掘当前研究热点,并预测未来发展的趋势,为后续研究工作指明可能的发展方向。方法:以“老年人肠道微生物”“老年人肠道微生态”和“老年人肠道菌群”作为主题词字段在中国知网进行检索,以“TS=(elderly gut microbe OR elderly gut microbiome OR elderly gut microbiota OR elderly intestinal microbiome OR elderly intestinal microbiota)”作为检索策略在Web of Science数据库进行检索,使用文献计量工具VOSviewer与CiteSpace,对相关文献的发表年份、国家分布、研究机构、作者和关键词进行系统分析。结果与结论:在中国知网和Web of Science数据库中分别获得有效文献250篇和604篇。在2014-2023年间,全球范围内老年人肠道微生物领域研究发文量整体呈现稳步上升的趋势。国内外对这一领域的关注和探讨热度不断增加,研究深度和广度也在各学科之间的交叉中得到拓展。COVID-19、氧化应激(Oxidative stress)、抑郁(depression)、认知障碍(cognitive impairment)为近2年的突现关键词。通过文献计量学分析,直观展示了老年人肠道微生物领域近10年的研究现状和发展趋势,目前正处于上升期,仍需进一步探索肠道微生物的作用机制及相关疾病的干预方案。
文摘Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
文摘The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.
