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International Symposium on Medical robotics (ISMR)

作者： Haochen Wei Chi Chiung Grace Chen Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Gynecology and Obstetrics Johns Hopkins Hospital Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA

We developed an abdominal phantom with an embedded stereo camera for tracking multiple hand-held instruments inserted through entry ports. This system can be used for training laparoscopic surgeons, as well as for training bedside assistants in robotic surgery. We present the computer vision methods used to track multiple instruments in real time, with a system evaluation that shows frame rates of 26.6 fps for a 672×376 image and 11.6 fps for a 1280×780 image and corresponding latencies of 38 ms and 87 ms, respectively, when tested on a portable PC platform with an 11th Gen Intel CPU running at 2.8 GHz. The mean Euclidean distance error of the instrument tracking is 2.0 mm in the 672p case and 2.8 mm in the 1280p case. Additionally, the tracking information drives virtual instruments in a simulated environment, which generates improved visualizations of the surgical scene, such as a top-down view and/or a “laser” virtual extension of the instrument. We perform a user study with 10 novice subjects to compare these improved visualizations to the baseline case (only endoscope view) and the results indicate that the combined top-down view and laser extension enhancements provide a statistically significant performance improvement. In the future, the simulator could also improve the (visual) realism of the training platform and could be part of a larger system that enables simultaneous training (and skill assessment) of multiple members of a surgical team, such as the surgeon and first assistant in da Vinci robotic surgery.

关键词： Training Laparoscopes Visualization Instruments System performance Robot vision systems Surgery

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Learning representations that are closed-form Monge mapping optimal with application to domain adaptation

arXiv

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arXiv 2023年

作者： Struckmeier, Oliver Redko, Ievgen Mallasto, Anton Arndt, Karol Heinonen, Markus Kyrki, Ville Aalto University Finland Intelligent Robotics Group Finland Noah's Ark Lab Huawei Technologies Hong Kong Department of Computer Science Finland

Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learning (ML), OT problem still bears its computational burden, while at the same time suffering from the curse of dimensionality for measures supported on general high-dimensional spaces. In this paper, we propose to tackle these challenges using representation learning. In particular, we seek to learn an embedding space such that the samples of the two input measures become alignable in it with a simple affine mapping that can be calculated efficiently in closed-form. We then show that such approach leads to results that are comparable to solving the original OT problem when applied to the transfer learning task on which many OT baselines where previously evaluated in both homogeneous and heterogeneous DA settings. The code for our contribution is available at https://***/Oleffa/LaOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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Improving Anomalous Sound Detection Via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models

Improving Anomalous Sound Detection Via Low-Rank Adaptation ...

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IEEE Spoken Language Technology Workshop

作者： Xinhu Zheng Anbai Jiang Bing Han Yanmin Qian Pingyi Fan Jia Liu Wei-Qiang Zhang Department of Electronic Engineering Tsinghua University Beijing China Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artifcial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Anomalous Sound Detection (ASD) has gained significant interest through the application of various Artificial Intelligence (AI) technologies in industrial settings. Though possessing great potential, ASD systems can hardly be readily deployed in real production sites due to the generalization problem, which is primarily caused by the difficulty of data collection and the complexity of environmental factors. This paper introduces a robust ASD model that leverages audio pre-trained models. Specifically, we fine-tune these models using machine operation data, employing SpecAug as a data augmentation strategy. Additionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA) tuning instead of full fine-tuning to address the problem of limited data for fine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new benchmark of 77.75% on the evaluation set, with a significant improvement of 6.48% compared with previous state-of-the-art (SOTA) models, including top-tier traditional convolutional networks and speech pre-trained models, which demonstrates the effectiveness of audio pre-trained models with LoRA tuning. Ablation studies are also conducted to showcase the efficacy of the proposed scheme.

关键词： Adaptation models Production Data collection Data augmentation Harmonic analysis Data models Environmental factors Convolutional neural networks Artificial intelligence Tuning

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Longitudinal Analysis of Disease Progression Using Image and laboratory Data for Covid-19 Patients

Longitudinal Analysis of Disease Progression Using Image and...

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German Workshop on Medical Image Computing, 2022

作者： De Benetti, Francesca Bentele, Verena Burian, Egon Makowski, Marcus Navab, Nassir Braren, Rickmer Wendler, Thomas Chair for Computer Aided Medical Procedures and Augmented Reality TU Munich München Germany Department of Diagnostic and Interventional Radiology TU Munich München Germany Department of Diagnostic and Interventional Neuroradiology TU Munich München Germany Computer Aided Medical Procedures Lab Laboratory for Computational Sensing+Robotics Johns Hopkins University Baltimore United States

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AirShot: Efficient Few-Shot Detection for Autonomous Exploration

AirShot: Efficient Few-Shot Detection for Autonomous Explora...

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

作者： Zihan Wang Bowen Li Chen Wang Sebastian Scherer Institute for Imaging Data and Communications School of Engineering The University of Edinburgh UK AirLab Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples. Despite recent efforts have been made to yield online processing capabilities, slow inference speeds of low-powered robots fail to meet the demands of real-time detection-making them impractical for autonomous exploration. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. In this work, we propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust and faster few-shot object detection system, which is more applicable to robotics community. The core module Top Prediction Filter (TPF) can operate on multi-scale correlation maps in both the training and inference stages. During training, TPF supervises the generation of a more representative correlation map, while during inference, it reduces looping iterations by selecting top-ranked classes, thus cutting down on computational costs with better performance. Surprisingly, this dual functionality exhibits general effectiveness and efficiency on various off-the-shelf models. Exhaustive experiments on COCO2017, VOC2014, and SubT datasets demonstrate that TPF can significantly boost the efficacy and efficiency of most off-the-shelf models, achieving up to 36.4% precision improvements along with 56.3% faster inference speed. Code and Data are at: https://***/ImNotPrepared/AirShot.

关键词： Training Correlation Codes computational modeling Atmospheric modeling Object detection Real-time systems computational efficiency Intelligent robots Faces

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models

arXiv

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arXiv 2023年

作者： Chen, Eric Ho, Cherie Maulimov, Mukhtar Wang, Chen Scherer, Sebastian The Department of Computer Science and Mathematics Harvey Mudd College United States The Robotics Institute Carnegie Mellon University United States The Spatial AI & Robotics Lab University at Buffalo NY14260 United States

Autonomous offroad driving is essential for applications like emergency rescue, military operations, and agriculture. Despite progress, systems struggle with high-speed vehicles exceeding 10m/s due to the need for accurate long-range (> 50m) perception for safe navigation. Current approaches are limited by sensor constraints;LiDAR-based methods offer precise short-range data but are noisy beyond 30m, while visual models provide dense long-range measurements but falter with unseen scenarios. To overcome these issues, we introduce ALTER, a learning-on-the-drive perception framework that leverages both sensor types. ALTER uses a self-supervised visual model to learn and adapt from near-range LiDAR measurements, improving long-range prediction in new environments without manual labeling. It also includes a model selection module for better sensor failure response and adaptability to known environments. Testing in two real-world settings showed on average 43.4% better traversability prediction than LiDAR-only and 164% over non-adaptive state-of-the-art (SOTA) visual semantic methods after 45 seconds of online learning. © 2023, CC BY.

关键词： Optical radar

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Machine Learning in Fraud Detection for Financial Sustainability in Credit Card 2nd

Machine Learning in Fraud Detection for Financial Sustainabi...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Kaur, Harleen Chauhan, Ritu Shamsi, Yezdanul Haque Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

In today’s financial world, various technologies have been emerged and with the evolution in the technologies it has led to the increase in the fraud cases. The sustainable growth of businesses and the financial markets are severely harmed by financial statement fraud. Consequently, it is crucial and vital to build an efficient methodology to identify financial statement fraud in businesses. Usage of credit card are very frequent these days and due to loopholes in the system fraud cases in credit card is rising which causes huge financial losses. In this paper the focus is on credit card fraud detection in real world with the help of machine learning. In this case Logistic Regression, Decision tree method and SVM (support vector machine) are applied on credit card fraud detection for knowledge discovery among the data. The applied methods can detect the fraudulent transactions with highest accuracy. However, the results are predicted using evaluation metrics such as accuracy, error rate, specificity, sensitivity, and ROC curves are calculated to assess each model's performance. Hence, the predicted results suggested that logistic regression is the most accurate model for prediction of fraud datasets in financial sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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