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arXiv 2024年

作者： Jeon, Nicholas Qian, Xiaoning SaidyKhan, Lamin de Figueiredo, Paul Yoon, Byung-Jun Department of Electrical and Computer Engineering Texas A&M University United States Computing and Data Sciences Brookhaven National Laboratory Christopher S. Bond Life Sciences Center Department of Molecular Microbiology and Immunology The University of Missouri United States Department of Veterinary Pathobiology Department of Chemical and Biomedical Engineering University of Missouri School of Medicine United States

Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs). These advanced methods have significantly enhanced the efficiency of identifying lncRNAs. However, many of these methods are devoid of robustness and accuracy due to the extended length of the sequences involved. To tackle this issue, we have introduced a novel pre-trained bidirectional encoder representation called LoRA-BERT. LoRA-BERT is designed to capture the importance of nucleotide-level information during sequence classification, leading to more robust and satisfactory outcomes. In a comprehensive comparison with commonly used sequence prediction tools, we have demonstrated that LoRA-BERT outperforms them in terms of accuracy and efficiency. Our results indicate that, when utilizing the transformer model, LoRA-BERT achieves state-of-the-art performance in predicting both lncRNAs and mRNAs for human and mouse species. Through the utilization of LoRA-BERT, we acquire valuable insights into the traits of lncRNAs and mRNAs, offering the potential to aid in the comprehension and detection of diseases linked to lncRNAs in humans. © 2024, CC BY-NC-ND.

关键词： Deep learning

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Many-Problem Surrogates for Transfer Evolutionary Multiobjective Optimization With Sparse Transfer Stacking

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IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Hao Wan, Fanggao Gong, Maoguo Qin, A.K. Wu, Yue Xing, Lining Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Shaanxi Province Xi'an710071 China Inner Mongolia Normal University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an China and Academy of Artificial Intelligence College of Mathematics Science Hohhot China Swinburne University of Technology Department of Computing Technologies HawthornVIC3122 Australia Xidian University School of Computer Science and Technology Xi'an710071 China

For expensive multiobjective optimization problems, there exists useful knowledge, e.g., the trained surrogate models, can be transferred to assist the optimization of a target optimization problem, which is termed as multi-problem surrogates. Stacking transfer is able to combine the pretrained source surrogate models and the preliminary target model with a meta-regression algorithm to transfer knowledge from source to target. However, when large-scale source models are involved in the many-problem scenarios, the less correlated sources may hurt the target performance, which is known as negative transfer. In this paper, sparse representation of the coefficients of meta-regression is considered to automatically select the most relevant source models for largely avoiding negative transfer. In the proposed many-problem surrogates, the coefficients of the source and target models are assumed to be sparse under the non-negativity and sum-to-one constraints. Then a sparse transfer stacking model is established with l1-norm of the coefficients. Next, the alternating direction method of multipliers is employed to solve the resulting constrained optimization problem by converting it into several much simpler problems. Most of the previous works assume that the costs for evaluation have no much difference and this assumption rarely holds in the real-world applications. In order to further reduce the total costs, an improved surrogate model with a cost-sensitive measure is designed to estimate the cost and select new solutions for real evaluation based on their estimated fitness, uncertainty and cost. Experimental results on synthetic and practical problems have demonstrated the superiority of the proposed many-problem surrogates. © 1997-2012 IEEE.

关键词： Multiobjective optimization sparse representation surrogate-assisted evolutionary algorithm transfer stacking

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

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OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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An Integrated Web Solution for Planetary Data Visualization and Exploration

An Integrated Web Solution for Planetary Data Visualization ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ananya Maheshwari Love Lakhwani Pariav Naik Abhishek Kumar Jai G. Singla Amitabh Nitant Dube VijayPal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Department of Computer Engineering Charotar University of Science and Technology Anand Gujarat India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Bhubaneshwar Odisha India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The exploration and study of planetary bodies have produced vast amounts of geospatial data, creating a need for effective visualization and analysis tools. Many existing solutions are constrained by platform limitations and are resource-intensive, reducing their accessibility for broader use. To overcome these challenges, we developed a lightweight, web-based platform that visualizes interplanetary data and supports multiple geospatial data formats, including shapefiles, KML, and CZML. Our solution includes unique features such as 2D and 2.5D terrain visualization, 3D tiles, and tools for navigating to specific coordinates and measuring distances. These enhancements allow users to explore and interact with geospatial data from various planetary bodies across multiple devices without requiring specialized hardware or software. By modifying the underlying system, we extended its application to planetary data while preserving accessibility and performance across different platforms. This paper discusses the methodology behind these customizations, the challenges we encountered, and the potential applications of our platform in scientific research and education.

关键词： Performance evaluation Three-dimensional displays Coordinate measuring machines Navigation Data visualization Geoscience and remote sensing Extraterrestrial measurements Software Hardware Geospatial analysis

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

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AN INTEGRATED WEB SOLUTION FOR PLANETARY DATA VISUALIZATION AND EXPLORATION

AN INTEGRATED WEB SOLUTION FOR PLANETARY DATA VISUALIZATION ...

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2024 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2024

作者： Maheshwari, Ananya Lakhwani, Love Dhaka, VijayPal Singh Singla, Jai G. Amitabh Dube, Nitant Naik, Pariav Kumar, Abhishek School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Rajasthan Jaipur India Signal and Image Processing Area Space Applications Centre ISRO Gujarat Ahmedabad India Department of Computer Engineering Charotar University of Science and Technology Gujarat Anand India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Odisha Bhubaneshwar India

ISBN: (纸本)9798350390346

关键词： Data visualization

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Extraordinary Quality Factors in Dual-Band Polarization-Insensitive QuasiBound States in the Continuum

arXiv

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arXiv 2024年

作者： Ghahremani, Maryam Zapata-Rodríguez, Carlos J. Photonics Research Laboratory Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optics and Optometry and Vision Sciences Faculty of Physics University of Valencia Dr. Moliner 50 Burjassot46100 Spain

In this study, we investigate a novel "dimerized" dielectric metasurface featuring dual-mode resonances governed by symmetry-protected bound states in the continuum (BICs). The metasurface design offers advantages such as insensitivity to incident light polarization and exceptionally high quality factors exceeding 105 for low and moderate structural deviations from the monoatomic array. By introducing mastered perturbations in the metasurface via the Brillouin zone folding method, without reducing symmetry, we explore the behavior of symmetry-protected BIC states and their polarization-independent responses. Through numerical simulations and analysis, we demonstrate the superiority of Q factors for specific BIC-based resonances, leading to precise control over interaction behaviors and light engineering in both near and far fields. Our findings contribute to the understanding of BIC resonance interactions and offer insights into the design of high-performance sensing applications and meta-devices with enhanced functionalities. © 2024, CC BY-NC-ND.

关键词： Incident light

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Quantum-Inspired Resource Optimization for 6G Networks: A Survey

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IEEE Communications Surveys and Tutorials 2024年

作者： Butt, Muhammad Omair Waheed, Nazar Duong, Trung Q. Ejaz, Waleed Lakehead University Department of Electrical Engineering ON Canada Faculty of Computing Information Systems Higher Colleges of Technology United Arab Emirates Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

The Internet of things (IoT) drives an exponential surge in computing and communication devices. Consequently, it triggers capacity, coverage, interference, latency, and security issues in the existing communication networks. The forthcoming sixth-generation (6G) networks will address these issues by providing comprehensive solutions. In particular, quantum communication technology can potentially address the challenges of 6G networks. However, its implementation requires substantial infrastructure upgrades. Therefore, the quantum-inspired (QI) techniques offer an intermediate resort due to their ability to utilize the classical communication infrastructure for design and implementation. Hence, we review QI techniques in this survey that address radio resource optimization challenges across various communication aspects, including channel assignment, reconfigurable intelligent surfaces, spectrum sensing, unmanned aerial vehicle-assisted networks, and related areas. The analysis explores diverse aspects, including objectives, constraints, problem characterization, proposed solutions, and lessons learnt. Research indicates that QI techniques offer advantages such as faster convergence and reduced complexity, providing promising solutions to complex optimization problems in communication networks. Furthermore, we identify the future directions, research gaps, and ongoing challenges from the QI radio resource optimization dataset. © 1998-2012 IEEE.

关键词： Quantum communication

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Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations

Protecting Personalized Trajectory with Differential Privacy...

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IEEE Conference on Wireless Communications and Networking

作者： Mingge Cao Haopeng Zhu Minghui Min Yulu Li Shiyin Li Hongliang Zhang Zhu Han School of Information and Control Engineering China University of Mining and Technology Xuzhou China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of CyberScience and Engineering Wuhan University Wuhan China School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incor-porates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

关键词： Privacy Correlation Publishing Perturbation methods Simulation Vehicular ad hoc networks Quality of service

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Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Adrias: Interference-Aware Memory Orchestration for Disaggre...

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IEEE Symposium on High-Performance computer Architecture

作者： Dimosthenis Masouros Christian Pinto Michele Gazzetti Sotirios Xydis Dimitrios Soudris Microprocessors and Digital Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece IBM Research Europe Dublin Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.

关键词： Deep learning Memory management Interference Hardware Resource management

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