117,753 research outputs found
ATLAS Open Data to engage the public in Education and Research
No full textATLAS Open Data is an initiative aimed at making the data, simulations, and documentary resources of the experiment accessible to a wide audience, in accordance with the CERN Open Data policy. The project has seen the release of numerous datasets of proton-proton collisions at center-of-mass energies of 8 TeV and 13 TeV collected at the LHC during Run-1 and Run-2, allowing for the investigation of typical phenomena in high-energy physics. To facilitate their dissemination, the data are shared in accessible and commonly used formats. Additionally, they are accompanied by software and web interfaces designed for easy use, without the need for installation or coding by the user. The objective is twofold. On the one hand, the goal is to promote these activities in outreach and educational contexts, such as Summer Schools, Masterclasses, university projects, as well as various initiatives within the ATLAS Collaboration itself. On the other hand, high-energy physics
research becomes accessible to an interdisciplinary audience, involving experts from other fields to benefit from their expertise, such as machine learning and computer
science. This work provides an overview of the ATLAS Open Data resources, with concrete examples of how they can be used to promote scientific education and research in the field of particle physics
Towards Machine-Learning Particle Flow with the ATLAS Detector at the LHC
No full textParticle flow reconstruction at colliders combines various detector subsystems (typically the calorimeter and tracker) to provide a combined event interpretation that utilizes the strength of each detector. The accurate association of redundant measurements of the same particle between detectors is the key challenge in this technique. This contribution describes recent progress in the ATLAS experiment towards utilizing machine-learning to improve particle flow in the ATLAS detector at the LHC. In particular, point-cloud techniques are utilized to associate measurements from the same particle, leading to reduced confusion compared to baseline techniques. Next steps towards further testing and implementation are also discussed
Analyzing WLCG File Transfer Errors Through Machine Learning: An Automatic Pipeline to Support Post-mortem Distributed Data Management
No full textThe increasingly growing scale of modern computing infrastructures solicits more ingenious and automatic solutions to their management. Our work focuses on file transfer failures within the Worldwide Large Hadron Collider Computing Grid and proposes a pipeline to support distributed data management operations by suggesting potential issues to investigate. Specifically, we adopt an unsupervised learning approach leveraging Natural Language Processing and Machine Learning tools to automatically parse error messages and group similar failures. The results are presented in the form of a summary table containing the most common textual patterns and time evolution charts. This approach has two main advantages. First, the joint elaboration of the error string and the transfer’s source/destination enables more informative and compact trouble- shooting, as opposed to inspecting each site and checking unique messages separately. As a by-product, this also reduces the number of errors to check by some orders of magnitude (from unique error strings to unique categories or patterns). Second, the time evolution plots allow operators to immediately filter out secondary issues (e.g. transient or in resolution) and focus on the most serious problems first (e.g. escalating failures). As a preliminary assessment, we compare our results with the Global Grid User Support ticketing system, showing that most of our suggestions are indeed real issues (direct association), while being able to cover 89% of reported incidents (inverse relationship)
Optimizing Deep Learning Models for Cell Recognition in Fluorescence Microscopy: The Impact of Loss Functions on Performance and Generalization
No full textIn the rapidly evolving domain of fluorescence microscopy, the application of Deep Learning techniques for automatic cell segmentation presents exciting opportunities and challenges. In this work, we investigate the impact of loss functions and evaluation metrics on model performance and generalization in the context of cell recognition.
First, we present extensive experiments with different commonly used loss functions and offer practical insights and guidelines, underscoring how the choice of a loss function can influence model performance.
Second, we conduct a detailed examination of several evaluation metrics with their relative benefits and drawbacks, helping to guide effective model evaluation and comparison in the field.
Third, we discuss how characteristics specific to fluorescence microscopy data impact model generalization. Precisely, we examine how factors such as cell sizes, color irregularities, and textures can potentially affect the performance and adaptability of these models to new data.
Collectively, these insights provide an understanding of the various facets resulting from the application of Deep Learning for automatic cell segmentation, shedding light on best practices, evaluation strategies, and model generalization. Hence, this study can serve as a beneficial resource for researchers and practitioners working on similar applications, fostering further advancements in the field
Fluorescent Neuronal Cells v2
No full textFluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Science and Deep Learning.
This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics.
Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting.
The contribution is two-fold.
First, thanks to the variety of annotations and their accessible formats, we envision our work would facilitate methodological advancements in computer vision approaches for segmentation, detection, feature learning, unsupervised and self-supervised learning, transfer learning, and related areas.
Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 would catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.
For more information, please refer to Clissa, L. et al., 2024. Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy. Scientific data. https://doi.org/10.1038/s41597-024-03005-9.
This research was partly funded by PNRR - M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial Intelligence Research” - Spoke 8 “Pervasive AI” and the European Commission under the NextGeneration EU programme.
The collection of original images was supported by funding from the University of Bologna and the European Space Agency (Research agreement collaboration 4000123556)
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
