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arXiv 2023年

作者： Li, Yanyan Guo, Zhao Yang, Ze Sun, Yanbiao Zhao, Liang Tombari, Federico The Department of Computer Science Technical University of Munich Germany The Tianjin University China Institute of Perception Action and Behaviour School of Informatics University of Edinburgh United Kingdom Google Switzerland

This paper presents Open-Structure, a novel benchmark dataset for evaluating visual odometry and SLAM methods. Compared to existing public datasets that primarily offer raw images, Open-Structure provides direct access to point and line measurements, correspondences, structural associations, and co-visibility factor graphs, which can be fed to various stages of SLAM pipelines to mitigate the impact of data preprocessing modules in ablation experiments. The dataset comprises two distinct types of sequences from the perspective of scenarios. The first type maintains reasonable observation and occlusion relationships, as these critical elements are extracted from public image-based sequences using our dataset generator. In contrast, the second type consists of carefully designed simulation sequences that enhance dataset diversity by introducing a wide range of trajectories and observations. Furthermore, a baseline is proposed using our dataset to evaluate widely used modules, including camera pose tracking, parametrization, and factor graph optimization, within SLAM systems. By evaluating these state-of-the-art algorithms across different scenarios, we discern each module’s strengths and weaknesses in the context of camera tracking and optimization processes. The Open-Structure dataset and baseline system are openly accessible on website: https://***, encouraging further research and development in the field of SLAM. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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High Entropy Alloy property predictions using Transformer-based language model

arXiv

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arXiv 2024年

作者： Kamnis, Spyros Delibasis, Konstantinos Department of Computer Science and Biomedical Informatics University of Thessaly Lamia35100 Greece Castolin Eutectic-Monitor Coatings Ltd. Newcastle upon TyneNE29 8SE United Kingdom

This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited experimental data. By pre-training the transformer on extensive synthetic materials data and fine-tuning it with specific HEA datasets, the model effectively captures intricate elemental interactions through self-attention mechanisms. This approach mitigates data scarcity issues via transfer learning, enhancing predictive accuracy for properties like elongation (%) and ultimate tensile strength (UTS) compared to traditional regression models such as Random Forests and Gaussian Processes. The model's interpretability is enhanced by visualizing attention weights, revealing significant elemental relationships that align with known metallurgical principles. This work demonstrates the potential of transformer models to accelerate materials discovery and optimization, enabling accurate property predictions, thereby advancing the field of materials informatics. © 2024, CC BY-NC-SA.

关键词： High-entropy alloys

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Convex Hulls, Triangulations, and Voronoi Diagrams of Planar Point Sets on the Congested Clique

SSRN

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SSRN 2023年

作者： Jansson, Jesper Levcopoulos, Christos Lingas, Andrzej Polishchuk, Valentin Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science Lund University Lund Sweden Communications and Transport Systems ITN Linköping University Sweden

We consider geometric problems on planar n2-point sets in the congested clique model. Initially, each node in the n-clique network holds a batch of n distinct points in the Euclidean plane given by O(logn)-bit coordinates. In each round, each node can send a distinct O(logn)-bit message to each other node in the clique and perform unlimited local computations. We show that the convex hull of the input n2-point set can be constructed in O(min{h,logn}) rounds, where h is the size of the hull, on the congested clique. We also show that a triangulation of the input n2-point set can be constructed in O(log2n) rounds on the congested clique. Finally, we demonstrate that the Voronoi diagram of n2 points with O(logn)-bit coordinates drawn uniformly at random from a unit square can be computed within the square with high probability in O(1) rounds on the congested clique. © 2023, The Authors. All rights reserved.

关键词： Triangulation

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Adaptive Coding and Modulation Aided Mobile Relaying for Millimeter-Wave Flying Ad-Hoc Networks

arXiv

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arXiv 2023年

作者： Zhang, Jiankang Chen, Sheng Chai, Wei Koong Hanzo, Lajos Department of Computing & Informatics Bournemouth University PooleBH12 5BB United Kingdom School of Electronics and Computer Science University of Southampton United Kingdom

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an ǫ-multiple objective genetic algorithm (ǫ-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available. © 2023, CC BY.

关键词： Drones

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Energy guided diffusion for generating neurally exciting images 23

Energy guided diffusion for generating neurally exciting ima...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Paweł A. Pierzchlewicz Konstantin F. Willeke Arne F. Nix Pavithra Elumalai Kelli Restivo Tori Shinn Cate Nealley Gabrielle Rodriguez Saumil Patel Katrin Franke Andreas S. Tolias Fabian H. Sinz Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany aand Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX and Department of Electrical and Computer Engineering Rice University Houston TX Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a novel readout architecture inspired by the mechanism of visual attention. This new architecture, which we call attention readout, together with a data-driven convolutional core outperforms previous task-driven models in predicting the activity of neurons in macaque area V4. However, as our predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across varying model architectures than the state-of-the-art GA, while at the same time reducing computational costs by a factor of 4.7x, facilitating experimentally challenging closed-loop experiments. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting naturalistic images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus, EGG provides a general and flexible framework to study the coding properties of the visual system in the context of natural ima

关键词：

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Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

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Optik 2023年 272卷

作者： Upreti, Kamal Syed, Mohammad Haider Khan, Mohiuddin Ali Fatima, Huda Alam, Mohammad Shabbir Sharma, A.K. Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management Delhi India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer & Network Engineering Jazan University Jazan Saudi Arabia Department of Information Technology & Security Jazan University Jazan Saudi Arabia Department of Computer science College of Computer Science and Information Technology Jazan University Jizan Saudi Arabia Department of Creative Technologies & Product Design National Taipei University of Business Taipei City Taiwan

Financial technology" or "FinTech" refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %. © 2022

关键词： Semantics

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ISOMORPHIC GCD-GRAPHS OVER POLYNOMIAL RINGS

arXiv

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arXiv 2024年

作者： Mináč, Ján Nguyen, Tung T. Tân, Nguyen Duy Department of Mathematics Western University LondonONN6A 5B7 Canada Department of Mathematics and Computer Science Lake Forest College Lake ForestIL United States Faculty Mathematics and Informatics Hanoi University of Science and Technology 1 Dai Co Viet Road Hanoi Viet Nam

Gcd-graphs over the ring of integers modulo n are a simple and elegant class of integral graphs. The study of these graphs connects multiple areas of mathematics, including graph theory, number theory, and ring theory. In a recent work, inspired by the analogy between number fields and function fields, we define and study gcd-graphs over polynomial rings with coefficients in finite fields. We discover that, in both cases, gcd-graphs share many similar and analogous properties. In this article, we extend this line of research further. Among other topics, we explore an analog of a conjecture of So and a weaker version of Sander-Sander, concerning the conditions under which two gcd-graphs are isomorphic or isospectral. We also provide several constructions showing that, unlike the case over Z, it is not uncommon for two gcd-graphs over polynomial rings to be isomorphic. © 2024, CC BY.

关键词： Choquet integral

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Efficient Tactile Sensing-based Learning from Limited Real-world Demonstrations for Dual-arm Fine Pinch-Grasp Skills

Efficient Tactile Sensing-based Learning from Limited Real-w...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiaofeng Mao Yucheng Xu Ruoshi Wen Mohammadreza Kasaei Wanming Yu Efi Psomopoulou Nathan F. Lepora Zhibin Li School of Informatics University of Edinburgh UK Touchlab Limited Edinburgh U.K. Oxford Robotics Institute University of Oxford UK Department of Engineering Mathematics University of Bristol UK Department of Computer Science University College London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing data and achieves fine bimanual pinch grasping. Specifically, we employ a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, we develop a framework that achieves efficient multi-sensor fusion for imitation learning, allowing the robot to learn contact-aware sensorimotor skills from demonstrations. The ablation studies on encoded tactile features highlighted the effectiveness of incorporating rich contact information, which enabled dexterous bimanual grasping with active contact searching. Extensive experiments demonstrated the robustness of the fine pinch grasp policy directly learned from few-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Furthermore, the saliency map analysis method is used to describe weight distribution across various modalities during pinch grasping, confirming the effectiveness of our framework at leveraging multimodal information. The video is available online at: https://***/BlzxGgiKfck.

关键词： Imitation learning Perturbation methods Autoencoders Grasping Robot sensing systems Feature extraction Robustness Sensors Data mining Intelligent robots

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iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence 2nd

iConDet2: An Improved Conjunctivitis Detection Portable Heal...

引用

2nd International Conference on Applied Intelligence and informatics, AII 2022

作者： Adak, Mainak Chatterjee, Aayushman Roy, Nilanjana Dutta Mahmud, Mufti Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031248009

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Functional Connectivity Network Augmentation with Patch-entirety Self-attention GAN for ADHD Identification

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Pei, Shengbing Jiang, Wencong Cao, Shuai Fan, Cunhang Liang, Wen Lv, Zhao Guan, Jihong Zhang, Chao Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Google Company Mountain View94043 United States Civil Aviation Flight University of China Guanghan618307 China Tongji University Department of Computer Science and Technology Shanghai201800 China

Functional connectivity network (FCN) describes the cooperative state of the brain and is often used to identify neurological diseases. However, the performance of FCN-based recognition models is limited by small sample size for the high cost of patient data acquisition and labeling. Generative adversarial networks (GAN) can generate diverse samples to alleviate this problem. Due to the symmetry of FCN matrix, existing GAN-based data augmentation methods mainly adopt fully connected layers to learn flattened FCN data distribution, neglecting the functional collaboration structure within FCN. Here, we propose a novel patch-entirety self-attention GAN (PEGAN) for FCN data augmentation, thereby helping to improve brain disorder identification. PEGAN is a fully convolutional structure, and contains a two-stage attention mechanism to learn the collaboration of brain regions, at the first stage, the FCN is partitioned into multiple patches, each patch is encoded by itself with self-attention, this is to learn local consistency characteristic within a specific brain sub-function;at the second stage, the global self-attention involving all functional connections is performed, this is to learn the global collaborative characteristic of the entire brain. Meanwhile, a symmetry constraint is designed in the optimization objective of GAN to overcome the issue that the convolutional GAN model is not applicable to FCN data. With the sufficient augmentation data, a dual-stream network is trained for classification. Experimental results on ADHD-200 dataset indicates that 1) the proposed identification method with PEGAN demonstrates its superiority by comparing with existing ADHD identification methods;2) the improved optimization objective function effectively solves the problem that FCN data is not suitable for convolution processing;3) the reliability of the generated FCN data with PEGAN is verified from both qualitative and quantitative perspectives;4) the augmented FCN data can

关键词： Neurodegenerative diseases

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