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An updated classification of cyanobacterial orders and families based on phylogenomic and polyphasic analysis (vol 59, pg 12, 2023)

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JOURNAL OF PHYCOLOGY 2023年第3期59卷 635-635页

作者： Strunecky, O. Ivanova, A. P. Mares, J. Faculty of Fisheries and Protection of Waters CENAKVA Institute of Aquaculture and Protection of Waters University of South Bohemia in České Budějovice Na Sádkách 1780 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (lead) Funding acquisition (supporting) Methodology (lead) Visualization (equal) Writing - original draft (equal) Contribution: Data curation (supporting) Formal analysis (supporting) Visualization (supporting) Biology Centre of the CAS Institute of Hydrobiology Na Sádkách 702/7 370 05 České Budějovice Czech Republic Faculty of Science Department of Botany University of South Bohemia Branišovská 1760 370 05 České Budějovice Czech Republic Contribution: Conceptualization (equal) Formal analysis (supporting) Funding acquisition (lead) Methodology (supporting) Writing - original draft (equal)

Cyanobacterial taxonomy is facing a period of rapid changes thanks to the ease of 16S rRNA gene sequencing and established workflows for description of new taxa. Since the last comprehensive review of the cyanobacterial system in 2014 until 2021, at least 273 species in 140 genera were newly described. These taxa were mainly placed into previously defined orders and families although several new families were proposed. However, the classification of most taxa still relied on hierarchical relationships inherited from the classical morphological taxonomy. Similarly, the obviously polyphyletic orders such as Synechococcales and Oscillatoriales were left unchanged. In this study, the rising number of genomic sequences of cyanobacteria and well-described reference strains allowed us to reconstruct a robust phylogenomic tree for taxonomic purposes. A less robust but better sampled 16S rRNA gene phylogeny was mapped to the phylogenomic backbone. Based on both these phylogenies, a polyphasic classification throughout the whole phylum of Cyanobacteria was created, with ten new orders and fifteen new families. The proposed system of cyanobacterial orders and families relied on a phylogenomic tree but still employed phenotypic apomorphies where possible to make it useful for professionals in the field. It was, however, confirmed that morphological convergence of phylogenetically distant taxa was a frequent phenomenon in cyanobacteria. Moreover, the limited phylogenetic informativeness of the 16S rRNA gene, resulting in ambiguous phylogenies above the genus level, emphasized the integration of genomic data as a prerequisite for the conclusive taxonomic placement of a vast number of cyanobacterial genera in the future.

关键词： 16S rRNA Cyanophyceae multilocus phylogenomic polyphasic approach taxonomy Vampirovibriophyceae

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Virtual Staging Technologies for the Metaverse

Virtual Staging Technologies for the Metaverse

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Muhammad Tukur Yehia Boraey Sara Jashari Alberto Jaspe Villanueva Uzair Shah Mahmood Al-Zubaidi Giovanni Pintore Enrico Gobbetti Jens Schneider Marco Agus Noora Fetais College of Science and Engineering HBKU Doha Qatar Department of Computer Science Qatar University Doha Qatar VCCVIS High Perf. Visualization KAUST Jeddah Saudi Arabia Visual and Data-intensive Computing CRS4 Cagliari Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through real-time interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Training Solid modeling Three-dimensional displays Image processing Pipelines Superresolution Semantics Collaboration Transforms Usability

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Seeing Your Stories:visualization for Narrative Medicine

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Health data science 2024年第1期4卷 390-405页

作者： Hua Ma Xiaoru Yuan Xu Sun Glyn Lawson Qingfeng Wang Faculty of Science and Engineering University of NottinghamNingbo 315100China Digital Art Department Art&Design Technology InstituteSuzhou 215104China National Key Laboratory of General Artiffcial Intelligence and School of Intelligence Science and Technology Peking UniversityBeijing 100871China Health Data Visualization and Visual Analytics Research Center National Institute of Health Data Science at PKUBeijing 100191China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo ChinaNingbo 315100China Human Factors Research Group Faculty of EngineeringUniversity of NottinghamNottingham NG72RDUK Nottingham University Business School China University of NottinghamNingbo 315100China

Importance:Narrative medicine(NM),in which patient stories play a crucial role in their diagnosis and treatment,can potentially support a more holistic approach to patient care than traditional scientiffc ***,there are some challenges in the implementation of narrative medicine,for example,differences in understanding illnesses between physicians and patients and physicians’increased workloads and overloaded *** paper ffrst presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization ***:The review is conducted from 2 perspectives:(a)the contexts and domains in which visualization has been explored for narrative medicine and(b)the forms and solutions applied in these *** applied domains are deffned,including understanding patients from narrative records,medical communication,medical conversation training in education,and psychotherapy and emotional wellness ***:A future work framework illustrates some opportunities for future research,including groups of speciffc directions and future points for the 4 domains and 3 technological exploration opportunities(combination of narrative and medical data visualization,taskaudience-based visual storytelling,and user-centered interactive visualization).Speciffcally,3 directions of future work in medical communication(asynchronous online physician-patient communication,synchronous face-to-face medical conversation,and medical knowledge dissemination)were concluded.

关键词： directions education visualization

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Real Time Collaborative Video Annotation Using Google App Engine and XMPP Protocol

Real Time Collaborative Video Annotation Using Google App En...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Abbas Attarwala Deepak Jagdish Ute Fischer Department of Computer Science University of Toronto Toronto ONT Canada Data Visualization & Design Nokia Research Center Palo Alto CA USA School of Literature & Communication Georgia Institute of Technology Atlanta GA USA

This WIP paper reports the status of a Windows based tool, 'Event Coding and visualization of data' (ECOVRD) that allows real time collaborative video annotation using Google App Engine (GAE) and XMPP protocol. ECOVRD facilitates classification of live or video recorded individual and team behavior. It is designed with the dual purpose of advancing behavioral (observational) research and of supporting applied uses such as performance assessment and feedback during professional coaching. Users of ECOVRD can initiate a shared real-time video annotation with their social network (Google Buzz) via XMPP protocol. The custom-built publish/subscribe architecture wrapped around GAE's channel service, pushes data from the cloud to subscribed clients resulting in real-time collaborative experience. ECORVD may be the first to successfully leverage the server push framework of GAE for desktop based video annotation applications.

关键词： Cloud computing Conferences

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Fusion of DRL and CNN for Effective Face Recognition 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Kumar, Ankit Singh, Sushil Kumar Ravikumar, R.N. Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Computer Science and Engineering Department Maharaja Agrasen Institute of Technology Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St FremontCA94555 United States

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

Digital Authentication combined with IOT networks is one of the most innovative practices in innovations. IoT devices such as surveillance cameras have acquired real-time video data in the natural environment. Facial Biometrics for System authentication is considered to be a sophisticated technology used in various devices. The correct processing of digital information in a natural environment is one of the challenging tasks. Noise effect inducts random variations on the subject in the natural environment that may hinder the recognition efficiency of the model. The proposed system recognizes facial biometric features under the natural environment that may contain non-symmetrical randomness of variations. The system's objective is to mitigate the false recognition rates under adverse environmental conditions. It combines digital information with a person’s real-time environment. The detection of the facial region of interest form frames in a real-time dataset has been taken through the Viola-Jones algorithm. Feature extrication using video frames has been developed through Deep Reinforcement Learning (DRL) algorithm, which aims to generate binary trees containing feature vectors. Further, the system uses a convolutional neural network (CNN) model to establish the correlation of feature vectors belonging to the facial identity. The model's objective is to retain high recognition of facial biometric trait feature units under various randomness in a natural environment. The proposed system is also tested under various attacks to test the robustness of the proposed model. The model can secure an average accuracy of 98.85%. Digital forensics, crime investigation, online attendance, etc., are the few applications of Facial data authentication from dynamic input. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning

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Progressive parallel coordinates

Progressive parallel coordinates

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： René Rosenbaum Jian Zhi Bernd Hamann Visual Computing and Computer Graphics group (VCG) Institute for Computer Science University of Rostock Rostock Germany Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis CA USA Department of Industrial Engineering and Operations Research Columbia University NY USA

Progressive refinement is a methodology that makes it possible to elegantly integrate scalable data compression, access, and presentation into one approach. Specifically, this paper concerns the effective use of progressive parallel coordinates (PPCs), utilized routinely for high-dimensional data visualization. It discusses how the power of the typical stages of progressive data visualization can also be utilized fully for PPCs. Further, different implementations of the underlying methods and potential application domains are described. The paper also presents empirical results concerning the benefits of PPC with regard to efficient data management and improved presentation, indicating that the proposed approach is able to close the gap between data handling and visualization.

关键词： Lead visualization Logic gates

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Experiment Management with Metadata-based Integration for Collaborative Scientific Research

Experiment Management with Metadata-based Integration for Co...

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International Conference on data Engineering

作者： Fusheng Wang Peiya Liu J. Pearson F. Azar G. Madlmayr Integrated Data Systems Department Siemens Corporate Research Inc. USA Siemens Corporate Research Inc. USA Imaging and Visualization Department Siemens Corporate Research Inc. USA Department of Computer Science Johannes Kepler University of Linz Austria

ISBN: (纸本)9781424429448

Scientific research in many fields is increasingly a collaborative effort across multiple institutions and disciplines. Scientific researchers need not only an effective system to manage their data, results, and the experiments that generate the results, but also a platform to integrate, share and search these across multiple institutions. Therefore, researchers are able to reuse experiments, pool expertise and validate approaches. In this paper, we present Sci- Port, a system of experiment management and integration for collaborative scientific research. SciPort’s architecture uses i) a general transformation-based data model to represent and link experiment processes; ii) hierarchical data classification across multiple institutions according to research programs’ goals and organization; iii) metadatacentric representation that concisely captures the context of experiments; and iv) virtual data integration through centralized metadata integration. The system is built for open source, and the metadata-based representation and integration provides a unified framework and tool set to manage and share experiments for scientific research communities.

关键词： Collaboration Collaborative work Instruments Information retrieval data systems Government data visualization Computer science data models Large scale integration

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Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

arXiv

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arXiv 2024年

作者： Lakatos, Róbert Pollner, Péter Hajdu, András Joó, Tamás Data-Driven Health Division of National Laboratory for Health Security Health Services Management Training Centre Semmelweis University Hungary Department of Data Science and Visualization Faculty of Informatics University of Debrecen Hungary Doctoral School of Informatics University of Debrecen Hungary Neumann Technology Platform Neumann Nonprofit Ltd

The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG. © 2024, CC BY.

关键词： Knowledge based systems

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Enhancing medical vision-language contrastive learning via inter-matching relation modelling

arXiv

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arXiv 2024年

作者： Li, Mingjian Meng, Mingyuan Fulham, Michael Feng, David Dagan Bi, Lei Kim, Jinman The Biomedical data analysis and visualization lab School of Computer Science The University of Sydney NSW Australia The Department of Molecular Imaging Royal Prince Alfred Hospital NSW Australia The Institute of Translational Medicine Shanghai Jiao Tong University Shanghai China

Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using six public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, linear classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Reversible N-bit to N-bit integer haar-like transforms

Reversible N-bit to N-bit integer haar-like transforms

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Proceedings of the Seventh IASTED International Conference on Computer Graphics and Imaging

作者： Senecal, Joshua G. Duchaineau, Mark A. Joy, Kenneth I. Inst. for Sci. Computing Research L-419 PO Box 808 Livermore CA 94551 United States Ctr. for Appl. Scientific Computing Lawrence Livermore Natl. Laboratory L-561 PO Box 808 Livermore CA 94551 United States Inst. Data Anal. and Visualization Computer Science Department University of California Davis One Shields Ave. Davis CA 95616 United States

We introduce TLHaar, an n-bit to n-bit reversible trans-form similar to the S-transform, TLHaar uses lookup tables that approximate the S-transform, but reorder the co-efficients so they fit into n bits. TLHaar is suited for loss-less compression in fixed-width channels, such as digital video channels and graphics hardware frame buffers. Tests indicate that when the incoming image data has lines or hard edges TLHaar coefficients compress better than S-transform coefficients. For other types of image data TL-Haar coefficients compress up to 2.5% worse than those of the S-transform, depending on the data and the compression method used.

关键词： Image compression

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