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arXiv 2025年

作者： Talento, Mara Sherlin D. Richards, Jordan Pinto-Orellana, Marco Huser, Raphael Ombao, Hernando C. Statistics Program Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia Neuro-AI Laboratory Computer Electrical and Mathematical Science & Engineering Division King Abdullah University of Science and Technology 23955 - 6900 Saudi Arabia School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh EdinburghEH9 3FD United Kingdom Department of Biomedical Engineering University of California Irvine92697 United States

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterized by large signal amplitudes. The focus of this paper is to examine connectivity in the tails of the distribution, as this reveals salient information that may be overlooked by standard methods. We develop a novel notion of spectral tail association of periodograms to study connectivity in the network of electroencephalogram (EEG) signals of seizure-prone neonates. We further develop a novel non-stationary extremal dependence model for multivariate time series that captures differences in extremal dependence during different brain phases, namely burst-suppression and non-burst-suppression. One advantage of our proposed approach is its ability to identify tail connectivity at key frequency bands that could be associated with outbursts of energy which may lead to seizures. We discuss these novel scientific findings alongside a comparison of the extremal behavior of brain signals for epileptic and non-epileptic patients. © 2025, CC BY.

关键词： Electroencephalography

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Referenceless User Controllable Semantic Image Synthesis

arXiv

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arXiv 2023年

作者： Kim, Jonghyun Li, Gen Kim, Joongkyu AI Lab CTO Division LG Electronics Seoul Korea Republic of School of Informatics University of Edinburgh Edinburgh United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as user-desired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://***/BenjaminJonghyun/RUCGAN © 2023, CC BY-NC-SA.

关键词： Color

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aiR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual Localisation

arXiv

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arXiv 2024年

作者： Liu, Changkun Jiao, Jianhao Huang, Huajian Ma, Zhengyang Kanoulas, Dimitrios Braud, Tristan Department of Computer Science and Engineering HKUST Hong Kong Department of Computer Science University College London London United Kingdom Division of Emerging Interdisciplinary Areas HKUST Hong Kong Division of Integrated Systems Design HKUST Hong Kong AI Centre Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Archimedes/Athena RC Greece

State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-k most similar images from a reference database. While increasing k improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper investigates the relationship between global and local descriptors, showing that greater similarity between the global descriptors of query and database images increases the proportion of feature matches. Low similarity queries significantly benefit from increasing k, while high similarity queries rapidly experience diminishing returns. Building on these observations, we propose an adaptive strategy that adjusts k based on the similarity between the query’s global descriptor and those in the database, effectively mitigating the feature-matching bottleneck. Our approach reduces computational costs and processing time without sacrificing accuracy. Experiments on three indoor and outdoor datasets show that aiR-HLoc reduces feature matching time by up to 30% while preserving state-of-the-art accuracy. The results demonstrate that aiR-HLoc facilitates a latency-sensitive localisation system. Copyright © 2024, The Authors. All rights reserved.

关键词： Data description

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DELE: Deductive EL++ Embeddings for Knowledge Base Completion

arXiv

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arXiv 2024年

作者： Mashkova, Olga Zhapa-Camacho, Fernando Hoehndorf, Robert Computer Science Program Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for Smart Health King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for Generative AI King Abdullah University of Science and Technology Thuwal Saudi Arabia

Ontology embeddings map classes, relations, and individuals in ontologies into Rn, and within Rn similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL++, several embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations;they do not distinguish between statements that are unprovable and provably false, and therefore they may use entailed statements as negatives. Furthermore, they do not utilize the deductive closure of an ontology to identify statements that are inferred but not asserted. We evaluated a set of embedding methods for EL++ ontologies, incorporating several modifications that aim to make use of the ontology deductive closure. In particular, we designed novel negative losses that account both for the deductive closure and different types of negatives and formulated evaluation methods for knowledge base completion. We demonstrate that our embedding methods improve over the baseline ontology embedding in the task of knowledge base or ontology completion. © 2024, CC BY.

关键词： Ontology

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Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models

Bridging Classification and Segmentation in Osteosarcoma Ass...

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IEEE International Symposium on Biomedical Imaging

作者： Manh Duong Nguyen Dac Thai Nguyen Trung Viet Nguyen Homi Yamada Huy Hieu Pham Phi Le Nguyen Institute for AI Innovation and Socital Impact (AI4LIFE) Hanoi University of Science and Technology Hanoi Vietnam Pathology Division Vinmec Times City International Hospital Vinmec Healthcare System Hanoi Vietnam College of Health Sciences VinUniversity Hanoi Vietnam College of Engineering & Computer Science VinUniversity Hanoi Vietnam VinUni-Illinois Smart Health Center VinUniversity Hanoi Vietnam

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Osteosarcoma, the most common primary bone cancer, often requires accurate necrosis assessment from whole slide images (WSIs) for effective treatment planning and prognosis. However, manual assessments are subjective and prone to variability. In response, we introduce FDDM, a novel framework bridging the gap between patch classification and region-based segmentation. FDDM operates in two stages: patch-based classification, followed by region-based refinement, enabling cross-patch information intergation. Leveraging a newly curated dataset of osteosarcoma images, FDDM demonstrates superior segmentation performance, achieving up to a 10% improvement mIOU and a 32.12% enhancement in necrosis rate estimation over state-of-the-art methods. This framework sets a new benchmark in osteosarcoma assessment, highlighting the potential of foundation models and diffusion-based refinements in complex medical imaging tasks.

关键词： Image segmentation Foundation models Estimation Manuals Diffusion models Bones Planning Prognostics and health management Biomedical imaging Cancer

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Jbeil: Temporal Graph-Based Inductive Learning to Infer Lateral Movement in Evolving Enterprise Networks

Jbeil: Temporal Graph-Based Inductive Learning to Infer Late...

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IEEE Symposium on Security and Privacy

作者： Joseph Khoury Đorđe Klisura Hadi Zanddizari Gonzalo De La Torre Parra Peyman Najafirad Elias Bou-Harb Division of Computer Science and Engineering Louisiana State University LA USA The Cyber Center for Security and Analytics The University of Texas at San Antonio TX USA The University of the Incarnate Word TX USA Secure AI and Autonomy Lab The University of Texas at San Antonio TX USA

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Lateral Movement (LM) is one of the core stages of advanced persistent threats which continues to compromise the security posture of enterprise networks at large. Recent research work have employed Graph Neural Network (GNN) techniques to detect LM in intricate networks. Such approaches employ transductive graph learning, where fixed graphs with full nodes' visibility are employed in the training phase, along with ingesting benign data. These two assumptions in real-world setups (i) do not take into consideration the evolving nature of enterprise networks where dynamic features and connectivity prevail among hosts, users, virtualized environments, and applications, and (ii) hinder the effectiveness of detecting LM by solely training on normal data, especially given the evasive, stealthy, and benign-like behaviors of contemporary malicious maneuvers. Additionally, (iii) complex networks typically do not have the entire visibility of their run-time network processes, and if they do, they often fall short in dynamically tracking LM due to latency issues with passive data *** this end, this paper proposes Jbeil, a data-driven framework for self-supervised deep learning on evolving networks represented as sequences of authentication timed events. The premise of the work lies in applying an encoder on a continuous-time evolving graph to produce the embedding of the visible graph nodes for each time epoch, and a decoder that leverages these embeddings to perform LM link prediction on unseen nodes. Additionally, we enclose a threat sample augmentation mechanism within Jbeil to ensure a well-informed notion on advanced LM attacks. We evaluate Jbeil using authentication timed events from the Los Alamos network which achieves an AUC score of 99.73% and a recall score of 99.25% in predicting LM paths, even when 30% of the nodes/edges are not present in the training phase. Additionally, we assess different realistic attack scenarios and demonstrate the potential of Jbeil

关键词： Training Deep learning Data privacy Scalability Authentication Complex networks Feature extraction

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Xinkai Liu Boyuan Peng Xin Zhu Tsuyoshi Iwasaki Atsuya Sato Junichiro James Kazama Jie Huang Graduate Department of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Aizu-Wakamatsu Fukushima Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM's segmentation accuracy for renal pathology.

关键词： Deep learning Pathology Analytical models Incremental learning Accuracy Semantic segmentation Transfer learning Contrastive learning Clinical trials Cams

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Contact Consistent Disturbance Estimation for Quadruped Robots

Contact Consistent Disturbance Estimation for Quadruped Robo...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Hasan Can Özden Emre Tanfener Hasan Eftun Orhon Ömer Tarık Banuş Uluç Saranlı Mert Ankaralı Ali Emre Turgut Mechanical Engineering Department Middle East Technical University Ankara Turkey Defense Systems Technologies Division Aselsan Inc Ankara Turkey Center for Robotics and AI Middle East Technical University Ankara Turkey Computer Engineering Department Middle East Technical University Ankara Turkey Electrical and Electronics Engineering Department Middle East Technical University Ankara Turkey

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Quadrupedal locomotion in environments without visual aid presents significant challenges in achieving automation capabilities. Often relying solely on IMU sensors and proprioceptive feedback from motors due to limited visual perception, understanding the robot’s interaction with its environment becomes crucial for safe and efficient navigation. This paper presents a novel approach for estimating external forces applied to a quadrupedal robot’s body, a critical aspect of its interaction with the environment. Our method integrates generalized momentum methods, contact constraints, and inertial measurement unit (IMU) data to accurately estimate these external forces. By using these diverse sources of information, the output of this work can enable navigation algorithms to enhance the robot’s ability to navigate complex environments, improving stability and adaptability. Simulation studies and experimental validations demonstrate the effectiveness of the proposed method in real-world scenarios, highlighting its potential for advancing quadrupedal locomotion in unfamiliar terrains where visual aids are limited.

关键词： Visualization Measurement units Automation Heuristic algorithms Loading Dynamics Estimation Inertial navigation Quadrupedal robots Collision avoidance

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Solar panels salvaging for safety & sustainability

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Materials Today: Proceedings 2022年 69卷 519-523页

作者： Rathi, Kartik Vyas, Bhavesh Acharya, Puja Vyas, Jayesh Dixit, Anil Department of Computer Science AI&ML SOET K R Mangalam University Haryana Gurgaon 122103 India Department of EEE SOET K R Mangalam University Haryana Gurgaon 122103 India Department of ECE SOET K R Mangalam University Haryana Gurgaon 122103 India Department of Mechanical and Chemical Engineering Indian Institute of Technology Jammu India Division of Research and Innovation Uttaranchal University Dehradun 248007 India

This paper deals with issue causing tsunami of solar panels to be fixed in existing sustainable energy market. Raising a concern in the direction of recycling and minerals extraction. With the inclination over utilizing green energy ways of power production. The solar sector has been ambushed and still in progress to satisfy the daily surpassing limits of power demand. Being a better choice to reduce carbon emission the solar setups are in a process of capturing acres of lands and water bodies. As a result, all along with it, the life span or service period of solar panel also needs to be on count. Off servicing a solar panel assembly leaves its set up open and exposed to the environment. Its construction utilizes minerals like cadmium, gallium, germanium, tellurium etc. all in creation of modules but after its lifespan, majority of panel assemblies found at garbage's etc. and serves as pollutant. This linear economy chain needs to be channelized. High need of recycling framework at government, industry and residential level is required to provide better end of life disposal. Better termination of the minerals extracted from earth is required by fixing solar module. © 2022

关键词： Waste disposal

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Chinnasamy Ramesh Kumar Ayyasamy K. Anuradha Irfan Alam Deveendra Menon Narayanan Nair Ajmeera Kiran Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of CSE (AI &ML) CVR College of Engineering Telangana India Cyber Security and Digital Forensics Division SCAI VIT Bhopal University Kothari Kalan Sehore India Department of Computer Science and Engineering MLR Institute of Technology Hdyerabad India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications.

关键词： Steganography Protocols Image coding Elliptic curve cryptography Data transfer Encoding Safety Internet of Things Galois fields Optimization

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