540 research outputs found
Sensitivity of NEXT-100 detector to neutrinoless double beta decay
Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre
beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que
podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe
a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese.
A continuación inclúese un resumo de cada un dos capítulos nos que se divide a
tese. Comézase introducindo o marco teórico e experimental nas seccións Física de
neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento
NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación
do detector, Procesamento de datos e Sensibilidade do detector NEXT-100
3D track reconstruction of low-energy electrons in the MIGDAL low pressure optical time projection chamber
We demonstrate three-dimensional track reconstruction of electrons in a low
pressure (50 Torr) optical TPC consisting of two glass GEMs with an ITO strip
readout in CF4 and CF4/Ar mixtures. The reconstructed tracks show a variety of
event topologies, including short tracks from photoelectrons induced by 55Fe
5.9 keV X-rays and long tracks from gamma ray interactions and beta decays.
Algorithms for event identification and track ridge detection are discussed as
well as multiple methods for integrating information from the camera image and
ITO waveforms with the goal of full 3D reconstruction of the track.Comment: MPGD 2022 Conference Proceeding
Design of decorative 3D models: from geodesic ornaments to tangible assemblies
L'obiettivo di questa tesi è sviluppare strumenti utili per creare opere d'arte decorative digitali in 3D. Uno dei processi decorativi più comunemente usati prevede la creazione di pattern decorativi, al fine di abbellire gli oggetti. Questi pattern possono essere dipinti sull'oggetto di base o realizzati con l'applicazione di piccoli elementi decorativi. Tuttavia, la loro realizzazione nei media digitali non è banale. Da un lato, gli utenti esperti possono eseguire manualmente la pittura delle texture o scolpire ogni decorazione, ma questo processo può richiedere ore per produrre un singolo pezzo e deve essere ripetuto da zero per ogni modello da decorare. D'altra parte, gli approcci automatici allo stato dell'arte si basano sull'approssimazione di questi processi con texturing basato su esempi o texturing procedurale, o con sistemi di riproiezione 3D. Tuttavia, questi approcci possono introdurre importanti limiti nei modelli utilizzabili e nella qualità dei risultati. Il nostro lavoro sfrutta invece i recenti progressi e miglioramenti delle prestazioni nel campo dell'elaborazione geometrica per creare modelli decorativi direttamente sulle superfici. Presentiamo una pipeline per i pattern 2D e una per quelli 3D, e dimostriamo come ognuna di esse possa ricreare una vasta gamma di risultati con minime modifiche dei parametri. Inoltre, studiamo la possibilità di creare modelli decorativi tangibili. I pattern 3D generati possono essere stampati in 3D e applicati a oggetti realmente esistenti precedentemente scansionati. Discutiamo anche la creazione di modelli con mattoncini da costruzione, e la possibilità di mescolare mattoncini standard e mattoncini custom stampati in 3D. Ciò consente una rappresentazione precisa indipendentemente da quanto la voxelizzazione sia approssimativa. I principali contributi di questa tesi sono l'implementazione di due diverse pipeline decorative, un approccio euristico alla costruzione con mattoncini e un dataset per testare quest'ultimo.The aim of this thesis is to develop effective tools to create digital decorative 3D artworks. Real-world art often involves the use of decorative patterns to enrich objects. These patterns can be painted on the base or might be realized with the application of small decorative elements. However, their creation in digital media is not trivial. On the one hand, users can manually perform texture paint or sculpt each decoration, in a process that can take hours to produce a single piece and needs to be repeated from the ground up for every model that needs to be decorated. On the other hand, automatic approaches in state of the art rely on approximating these processes with procedural or by-example texturing or with 3D reprojection. However, these approaches can introduce significant limitations in the models that can be used and in the quality of the results. Instead, our work exploits the recent advances and performance improvements in the geometry processing field to create decorative patterns directly on surfaces. We present a pipeline for 2D and one for 3D patterns and demonstrate how each of them can recreate a variety of results with minimal tweaking of the parameters. Furthermore, we investigate the possibility of creating decorative tangible models. The 3D patterns we generate can be 3D printed and applied to previously scanned real-world objects. We also discuss the creation of models with standard building bricks and the possibility of mixing standard and custom 3D-printed bricks. This allows for a precise representation regardless of the coarseness of the voxelization. The main contributions of this thesis are the implementation of two different decorative pipelines, a heuristic approach to brick construction, and a dataset to test the latter
HyperTrack: Neural Combinatorics for High Energy Physics
Combinatorial inverse problems in high energy physics span enormous
algorithmic challenges. This work presents a new deep learning driven
clustering algorithm that utilizes a space-time non-local trainable graph
constructor, a graph neural network, and a set transformer. The model is
trained with loss functions at the graph node, edge and object level, including
contrastive learning and meta-supervision. The algorithm can be applied to
problems such as charged particle tracking, calorimetry, pile-up
discrimination, jet physics, and beyond. We showcase the effectiveness of this
cutting-edge AI approach through particle tracking simulations. The code is
available online.Comment: CHEP 2023 proceedings. 8 pages (max
AUTOMATIC GENERATION OF ROUTING GRAPHS FOR INDOOR-OUTDOOR TRANSITIONAL SPACE TO SUPPORT SEAMLESS NAVIGATION
With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services
Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images
This paper presents a hybrid deep learning framework that combines graph neural networks with convolutional neural networks to predict porous media properties. This approach capitalizes on the capabilities of pre-trained convolutional neural networks to extract n-dimensional feature vectors from processed three dimensional micro computed tomography porous media images obtained from seven different sandstone rock samples. Subsequently, two strategies for embedding the computed feature vectors into graphs were explored: extracting a single feature vector per sample (image) and treating each sample as a node in the training graph, and representing each sample as a graph by extracting a fixed number of feature vectors, which form the nodes of each training graph. Various types of graph convolutional layers were examined to evaluate the capabilities and limitations of spectral and spatial approaches. The dataset was divided into 70/20/10 for training, validation, and testing. The models were trained to predict the absolute permeability of porous media. Notably, the proposed architectures further reduce the selected objective loss function to values below 35 mD, with improvements in the coefficient of determination reaching 9%. Moreover, the generalizability of the networks was evaluated by testing their performance on unseen sandstone and carbonate rock samples that were not encountered during training. Finally, a sensitivity analysis is conducted to investigate the influence of various hyperparameters on the performance of the models. The findings highlight the potential of graph neural networks as promising deep learning-based alternatives for characterizing porous media properties. The proposed architectures efficiently predict the permeability, which is more than 500 times faster than that of numerical solvers.Document Type: Original articleCited as: Alzahrani, M. K., Shapoval, A., Chen, Z., Rahman, S. S. Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images. Advances in Geo-Energy Research, 2023, 10(1):39-55. https://doi.org/10.46690/ager.2023.10.0
Improved prediction of ligand-protein binding affinities by meta-modeling
The accurate screening of candidate drug ligands against target proteins
through computational approaches is of prime interest to drug development
efforts, as filtering potential candidates would save time and expenses for
finding drugs. Such virtual screening depends in part on methods to predict the
binding affinity between ligands and proteins. Given many computational models
for binding affinity prediction with varying results across targets, we herein
develop a meta-modeling framework by integrating published empirical
structure-based docking and sequence-based deep learning models. In building
this framework, we evaluate many combinations of individual models, training
databases, and linear and nonlinear meta-modeling approaches. We show that many
of our meta-models significantly improve affinity predictions over individual
base models. Our best meta-models achieve comparable performance to
state-of-the-art exclusively structure-based deep learning tools. Overall, we
demonstrate that diverse modeling approaches can be ensembled together to gain
substantial improvement in binding affinity prediction while allowing control
over input features such as physicochemical properties or molecular
descriptors.Comment: 61 pages, 3 main tables, 6 main figures, 6 supplementary figures, and
supporting information. For 8 supplementary tables and code, see
https://github.com/Lee1701/Lee2023
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Structure-based drug design (SBDD), which utilizes the three-dimensional
geometry of proteins to identify potential drug candidates, is becoming
increasingly vital in drug discovery. However, traditional methods based on
physiochemical modeling and experts' domain knowledge are time-consuming and
laborious. The recent advancements in geometric deep learning, which integrates
and processes 3D geometric data, coupled with the availability of accurate
protein 3D structure predictions from tools like AlphaFold, have significantly
propelled progress in structure-based drug design. In this paper, we
systematically review the recent progress of geometric deep learning for
structure-based drug design. We start with a brief discussion of the mainstream
tasks in structure-based drug design, commonly used 3D protein representations
and representative predictive/generative models. Then we delve into detailed
reviews for each task (binding site prediction, binding pose generation,
\emph{de novo} molecule generation, linker design, and binding affinity
prediction), including the problem setup, representative methods, datasets, and
evaluation metrics. Finally, we conclude this survey with the current
challenges and highlight potential opportunities of geometric deep learning for
structure-based drug design.Comment: 14 page
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
Development of Bridge Information Model (BrIM) for digital twinning and management using TLS technology
In the current modern era of information and technology, the concept of Building Information Model (BIM), has made revolutionary changes in different aspects of engineering design, construction, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Model (BrIM), as a specific form of BIM, includes digital twining of the physical asset associated with geometrical inspections and non-geometrical data, which has eliminated the use of traditional paper-based documentation and hand-written reports, enabling professionals and managers to operate more efficiently and effectively. However, concerns remain about the quality of the acquired inspection data and utilizing BrIM information for remedial decisions in a reliable Bridge Management System (BMS) which are still reliant on the knowledge and experience of the involved inspectors, or asset manager, and are susceptible to a certain degree of subjectivity. Therefore, this research study aims not only to introduce the valuable benefits of Terrestrial Laser Scanning (TLS) as a precise, rapid, and qualitative inspection method, but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction using TLS-based point cloud, and to contribute to BrIM development. Moreover, this study presents a comprehensive methodology for incorporating generated BrIM in a redeveloped element-based condition assessment model while integrating a Decision Support System (DSS) to propose an innovative BMS. This methodology was further implemented in a designed software plugin and validated by a real case study on the Werrington Bridge, a cable-stayed bridge in New South Wales, Australia. The finding of this research confirms the reliability of the TLS-derived 3D model in terms of quality of acquired data and accuracy of the proposed novel slice-based method, as well as BrIM implementation, and integration of the proposed BMS into the developed BrIM. Furthermore, the results of this study showed that the proposed integrated model addresses the subjective nature of decision-making by conducting a risk assessment and utilising structured decision-making tools for priority ranking of remedial actions. The findings demonstrated acceptable agreement in utilizing the proposed BMS for priority ranking of structural elements that require more attention, as well as efficient optimisation of remedial actions to preserve bridge health and safety
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