检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Corte, Lorenzo La Goodenough, Kenneth Maity, Ananda G. Santra, Siddhartha Elkouss, David Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan DIBRIS University of Genoa Genoa Italy Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst United States Department of Physics CoE-QUICST Indian Institute of Technology Powai Maharashtra Bombay India

Efficiently distributing secret keys over long distances remains a critical challenge in the development of quantum networks. "First-generation" quantum repeater chains distribute entanglement by executing protocols composed of probabilistic entanglement generation, swapping and distillation operations. However, finding the protocol that maximizes the secret-key rate is difficult for two reasons. First, calculating the secret-key rate for a given protocol is non-trivial due to experimental imperfections and the probabilistic nature of the operations. Second, the protocol space rapidly grows with the number of nodes, and lacks any clear structure for efficient exploration. To address the first challenge, we build upon the efficient machinery developed by Li et al. [1] and we extend it, enabling numerical calculation of the secret-key rate for heterogeneous repeater chains with an arbitrary number of nodes. For navigating the large, unstructured space of repeater protocols, we implement a Bayesian optimization algorithm, which we find consistently returns the optimal result. Whenever comparisons are feasible, we validate its accuracy against results obtained through brute-force methods. Further, we use our framework to extract insight on how to maximize the efficiency of repeater protocols across varying node configurations and hardware conditions. Our results highlight the effectiveness of Bayesian optimization in exploring the potential of near-term quantum repeater chains. Copyright © 2025, The Authors. All rights reserved.

关键词： Chains

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Solution for SMS Spam Detection Problem

Machine Learning-Based Solution for SMS Spam Detection Probl...

引用

Intelligent Methods, Systems, and Applications (IMSA)

作者： Ahmed Younes Shdefat Nouran M. Sedky Zeina H. El Bialy Shahd Ahmed Hanaa Fathi Diaa Salama AbdElminaam College of Engineering and Technology American University of the Middle East Egaila Kuwait Faculty of Computer Science Misr International University Cairo Egypt Applied Science Research Center Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid Jordan MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

The article describes a new method for image detection using machine learning, based on the development of sophisticated security systems tailored for examining digital media to identify and scrutinize altered images. Using deep learning models and advanced image processing techniques, the authors demonstrate high accuracy and efficiency i n detecting face modifications a nd malicious content within digital images. The authors illustrate the proposed technique by evaluating its performance on a unique dataset of unaltered and modified images. The method allows for improving detection accuracy by 93.99% and demonstrates robustness in real-time applications. The new method's effectiveness is confirmed by calculating precision and recall metrics. New research results develop advanced security solutions that can be used to enhance digital forensics, content authentication, and cybersecurity applications. Novelty and scientific contributions lie in integrating deep learning for real-time media analysis and enhancing data authenticity and integrity.

关键词： Support vector machines Machine learning algorithms Accuracy Text categorization Media Message services Robustness

来源：评论

学校读者我要写书评

暂无评论

A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

引用

Conference on Power, Energy and Innovations (ICPEI)

作者： Pratya Phanomupatum Ravee Phromloungsri Rachata Tama Tiwat Pongthavornkamol Decha Wongpatsa Somchat Sonasang Department of Electronics Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Location and Automatic Identification System Research Team Communication and Network Research Group National Electronic and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Electrical Elements and Wireless Devices Unit (EEWSU) Faculty Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350356779

ISBN: (纸本)9798350356786

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency.

关键词： Power dividers Technological innovation Microwave integrated circuits Radio astronomy Phase shifters Insertion loss Microwave circuits Microstrip Proposals Substrates

来源：评论

学校读者我要写书评

暂无评论

Exploiting many-body localization for scalable variational quantum simulation

arXiv

引用

arXiv 2024年

作者： Cao, Chenfeng Zhou, Yeqing Tannu, Swamit Shannon, Nic Joynt, Robert Department of Physics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Computer Science University of Wisconsin-Madison MadisonWI53706 United States Theory of Quantum Matter Unit Okinawa Institute of Science and Technology Graduate University Onna-son Okinawa904-0412 Japan

Variational quantum algorithms have emerged as a promising approach to achieving practical quantum advantages using near-term quantum devices. Despite their potential, the scalability of these algorithms poses a significant challenge. This is largely attributed to the "barren plateau" phenomenon, which persists even in the absence of noise. In this work, we explore the many-body localization (MBL)-thermalization phase transitions within a framework of Floquet-initialized variational quantum circuits and investigate how MBL could be used to avoid barren plateaus. The phase transitions are observed through calculations of the inverse participation ratio, the entanglement entropy, and a metric termed low-weight stabilizer Rényi entropy. By initializing the circuit in the MBL phase and employing an easily preparable initial state, we find it is possible to prevent the formation of a unitary 2-design, resulting in an output state with entanglement that follows an area- rather than a volume-law, and which circumvents barren plateaus throughout the optimization. Utilizing this methodology, we successfully determine the ground states of various model Hamiltonians across different phases and show that the resources required for the optimization are significantly reduced. We have further validated the MBL approach through experiments carried out on the 127-qubit ibm brisbane quantum processor. These experiments confirm that the gradients needed to carry out variational calculations are restored in the MBL phase of a Heisenberg model subject to random unitary "kicks". These results provide new insights into the interplay between MBL and quantum computing, and suggest that the role of MBL states should be considered in the design of quantum algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

来源：评论

学校读者我要写书评

暂无评论

Recent Trends in 2D Object Detection and Applications in Video Event Recognition

arXiv

引用

arXiv 2022年

作者： Jana, Prithwish Mohanta, Partha Pratim Department of Computer Science & Engineering Indian Institute of Technology Kharagpur India Electronics & Communication Sciences Unit Indian Statistical Institute Kolkata India

Object detection serves as a significant step in improving performance of complex downstream computer vision tasks. It has been extensively studied for many years now and current state-of-the-art 2D object detection techniques proffer superlative results even in complex images. In this chapter, we discuss the geometry-based pioneering works in object detection, followed by the recent breakthroughs that employ deep learning. Some of these use a monolithic architecture that takes a RGB image as input and passes it to a feed-forward ConvNet or vision Transformer. These methods, thereby predict class-probability and bounding-box coordinates, all in a single unified pipeline. Two-stage architectures on the other hand, first generate region proposals and then feed it to a CNN to extract features and predict object category and bounding-box. We also elaborate upon the applications of object detection in video event recognition, to achieve better fine-grained video classification performance. Further, we highlight recent datasets for 2D object detection both in images and videos, and present a comparative performance summary of various state-of-the-art object detection techniques. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

arXiv

引用

arXiv 2022年

作者： Spathis, Dimitris Perez-Pozuelo, Ignacio Gonzales, Tomas I. Wu, Yu Brage, Soren Wareham, Nicholas Mascolo, Cecilia Department of Computer Science and Technology University of Cambridge United Kingdom MRC Epidemiology Unit School of Clinical Medicine University of Cambridge United Kingdom

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N=2,675), and a third external cohort using the UK Biobank Validation Study (N=181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80-0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests. © 2022, CC BY-NC-ND.

关键词： Oxygen

来源：评论

学校读者我要写书评

暂无评论

Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction

arXiv

引用

arXiv 2022年

作者： Wu, Yu Spathis, Dimitris Jia, Hong Perez-Pozuelo, Ignacio Gonzales, Tomas I. Brage, Soren Wareham, Nicholas Mascolo, Cecilia Department of Computer Science and Technology University of Cambridge United Kingdom MRC Epidemiology Unit School of Clinical Medicine University of Cambridge United Kingdom

Deep learning models have shown great promise in various healthcare applications. However, most models are developed and validated on small-scale datasets, as collecting high-quality (gold-standard) labels for health applications is often costly and time-consuming. As a result, these models may suffer from overfitting and not generalize well to unseen data. At the same time, an extensive amount of data with imprecise labels (silver-standard) is starting to be generally available, as collected from inexpensive wearables like accelerometers and electrocardiography sensors. These currently underutilized datasets and labels can be leveraged to produce more accurate clinical models. In this work, we propose UDAMA, a novel model with two key components: Unsupervised Domain Adaptation and Multi-discriminator Adversarial training, which leverage noisy data from source domain (the silver-standard dataset) to improve gold-standard modeling. We validate our framework on the challenging task of predicting lab-measured maximal oxygen consump Copyright © 2022, The Authors. All rights reserved.

关键词： Silver

来源：评论

学校读者我要写书评

暂无评论

Integrating Static Code Analysis Toolchains

arXiv

引用

arXiv 2024年

作者： Kern, Matthias Erata, Ferhat Iser, Markus Sinz, Carsten Loiret, Frederic Otten, Stefan Sax, Eric FZI Research Center for Information Technology Karlsruhe Germany Karlsruhe Institute of Technology Institut für Theoretische Informatik Karlsruhe Germany KTH Royal Institute of Technology Embedded Control Systems Stockholm Sweden Yale University Department of Computer Science New Haven United States UNIT Information Technologies Research & Development Izmir Turkey

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art toolchains support features for either test execution and build automation or traceability between tests, requirements and design information. Our approach combines all those features and extends traceability to the source code level, incorporating static code analysis. As part of our approach we introduce the "ASSUME Static Code Analysis tool exchange format" that facilitates the comparability of different static code analysis results. We demonstrate how this approach enhances the usability and efficiency of static code analysis in a development process. On the one hand, our approach enables the exchange of results and evaluations between static code analysis tools. On the other hand, it enables a complete traceability between requirements, designs, implementation, and the results of static code analysis. Within our approach we also propose an OSLC specification for static code analysis tools and an OSLC communication framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Static analysis

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Gait Recognition with Selective Fusion

arXiv

引用

arXiv 2023年

作者： Ren, Xuqian Yang, Shaopeng Hou, Saihui Cao, Chunshui Liu, Xu Huang, Yongzhen Computer Science Unit Faculty of Information Technology and Communication Sciences Tampere University Tampere33720 Finland School of Artificial Intelligence Beijing Normal University Beijing100875 China Watrix Technology Limited Co. Ltd Beijing100088 China

Previous gait recognition methods primarily relied on labeled datasets, which require a labor-intensive labeling process. To eliminate this dependency, we focus on a new task: Unsupervised Gait Recognition (UGR). We introduce a cluster-based baseline to solve UGR. However, we identify additional challenges in this task. First, sequences of the same person in different clothes tend to cluster separately due to significant appearance changes. Second, sequences captured from 0◦ and 180◦ views lack distinct walking postures and do not cluster with sequences from other views. To address these challenges, we propose a Selective Fusion method, consisting of Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). SCF merges clusters of the same person wearing different clothes by updating the cluster-level memory bank using a multi-cluster update strategy. SSF gradually merges sequences taken from front/back views using curriculum learning. Extensive experiments demonstrate the effectiveness of our method in improving rank-1 accuracy under different clothing and view conditions. © 2023, CC BY-NC-ND.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

引用

Telecommunications Conference (ITC-Egypt), International

作者： Abdelhameed Ibrahim Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah El-Sayed M. El-kenawy Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform; the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance.

关键词： Machine learning algorithms Transforms Predictive models Nearest neighbor methods Prediction algorithms Boosting Data models

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：