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MassSpecGym: A benchmark for the discovery and identification of molecules

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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation

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arXiv 2023年

作者： Albishri, Ahmed Shah, Syed Jawad Lee, Yugyung Wang, Rong Computer Science School of Science and Engineering University of Missouri-Kansas City Kansas City United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Oral and Craniofacial Sciences School of Dentistry University of Missouri - Kansas City Kansas City United States

Accurate detection of oral cancer is crucial for improving patient outcomes. However, the field faces two key challenges: the scarcity of deep learning-based image segmentation research specifically targeting oral cancer and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net image segmentation architecture exclusively designed to detect oral cancer in hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates advanced deep learning modules, such as the Channel and Spatial Attention Fusion (CSAF) module, a novel and innovative feature that emphasizes important channel and spatial areas in H&E images while exploring contextual information. In addition, OCU-Net integrates other innovative components such as Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP) module, residual blocks, and multi-scale fusion. The incorporation of these modules showed superior performance for oral cancer segmentation for two datasets used in this research. Furthermore, we effectively utilized the efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net to create OCU-Netm, an enhanced version achieving state-of-the-art results. Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed existing segmentation methods, highlighting their precision in identifying cancer cells in H&E images from OCDC and ORCA datasets. © 2023, CC BY.

关键词： Image segmentation

AlGaAs soliton microcombs at room temperature

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AlGaAs soliton microcombs at room temperature

Conference on Lasers and Electro-Optics (CLEO)

作者： Lue Wu Weiqiang Xie Chao Xiang Lin Chang Yan Yu Hao-Jing Chen Yoshihisa Yamamoto John E. Bowers Kerry J. Vahala Myoung-Gyun Suh T. J. Watson Laboratory of Applied Physics California Institute of Technology Pasadena CA USA Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Physics & Informatics Laboratories NTT Research Inc. Sunnyvale CA USA

Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic... 详细信息

ISBN: (纸本)9781957171258

关键词： Dielectric microcavities Frequency combs Microresonators Mode locking Q factor Thermo optic effects

FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

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FedMonitor: An Implementation Architecture for Monitoring of...

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model

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arXiv 2022年

作者： Su, Shaolin Lin, Hanhe Hosu, Vlad Wiedemann, Oliver Sun, Jinqiu Zhu, Yu Liu, Hantao Zhang, Yanning Saupe, Dietmar School of Computer Science and Engineering Northwestern Polytechnical University China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom Department of Computer and Information Science University of Konstanz Konstanz78464 Germany School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing tasks such as face superresolution, face enhancement, and face editing. To narrow this gap, in this paper, we first introduce the largest annotated IQA database developed to date, which contains 20,000 human faces - an order of magnitude larger than all existing rated datasets of faces - of diverse individuals in highly varied circumstances. Based on the database, we further propose a novel deep learning model to accurately predict face image quality, which, for the first time, explores the use of generative priors for IQA. By taking advantage of rich statistics encoded in well pretrained off-the-shelf generative models, we obtain generative prior information and use it as latent references to facilitate blind IQA. The experimental results demonstrate both the value of the proposed dataset for face IQA and the superior performance of the proposed model. © 2022, CC BY.

关键词： Database systems

3D laplacian surface deformation for template fitting on craniofacial reconstruction 20

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3D laplacian surface deformation for template fitting on cra...

8th International Conference on computer and Communications Management, ICCCM 2020

作者： Suputra, Putu Hendra Sensusiati, Anggraini Dwi Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Purnama, I. Ketut Eddy Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Universitas Pendidikan Ganesha Singaraja Indonesia Department of Radiology Universitas Airlangga Surabaya Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781450387668

computer-based craniofacial reconstruction utilizes medical imaging for forensic identification and medical reconstruction surgery, enables sophisticated three-dimension object representation and visualization. Reconstruction process involves deformation in their imagery how the shape of a person's face based on its properties and landmark correspondences. We propose the Laplacian surface deformation to deform the facial template to the cranial surface on several constraints that simulates fitting face to the skull using landmark correspondences. The range of variation of the landmarks on the human face lies in narrow intervals. In other words, small differences can affect the shape and facial expressions. Thus, a Laplacian surface model gives better result than a volumetric Laplacian in the matter of accuracy. © 2020 ACM.

关键词： Medical imaging

RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

Independent Vector Extraction Constrained on Manifold of Half-Length Filters

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Independent Vector Extraction Constrained on Manifold of Hal...

European Signal Processing Conference (EUSIPCO)

作者： Zbyněk Koldovský Jaroslav Čmejla Tülay Adali Stephen O'Regan Acoustic Signal Analysis and Processing Group Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Czech Republic Department of Electrical and Computer Engineering University of Maryland Baltimore County Baltimore MD USA Naval Surface Warfare Center Carderock Division West Bethesda Maryland USA

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

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Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

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Simultaneous Super-Resolution and Denoising on MRI via Condi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenhong Liu Xingce Wang Zhongke Wu Yi-Cheng Zhu Alejandro F. Frangi School of Artificial Intelligence Beijing Normal University Beijing China Department of Neurology Peking Union Medical College Hospital Beijing China Division of Informatics Imaging and Data Sciences School of Health Sciences Christabel Pankhurst Institute Department of Computer Science School of Engineering The University of Manchester UK Department of Cardiovascular Sciences Medical Imaging Research Centre (MIRC) Department of Electrical Engineering KU Leuven Leuven Belgium lan Turing Institute London UK

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise.

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