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arXiv 2023年

作者： Fumagalli, Alessandra Costanzi, Matteo Saro, Alexandro Castro, Tiago Borgani, Stefano Dipartimento di Fisica - Sezione di Astronomia Universitá di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy University Observatory Faculty of Physics Ludwig-MaximiliansUniversität Scheinerstr. 1 Munich81679 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy

The clustering of galaxy clusters is a powerful cosmological tool, which can help to break degeneracies between parameters when combined with other cosmological observables. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak lensing mass information, using as a case study the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). We extend the analysis of number counts and weak lensing signal performed by Costanzi et al. 2019a, with the addition of the real-space 2-point correlation function. We derive cosmological and scaling relation posteriors for all the possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. We find no evidence for tensions between the three data set analyzed. We demonstrate that the inclusion of the cluster clustering statistic can greatly enhance the constraining power of the sample thanks to its capability of breaking the Ωm – σ8 degeneracy characteristic of cluster abundance studies. In particular, for a flat ΛCDM model with massive neutrinos, we obtain Ωm = 0.28 ± 0.03 and σ8 = 0.82 ± 0.05, a 33 % and 50 % improvement compared to the posteriors derived combining cluster abundance and weak lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, as well as with Planck CMB results and DES-Y3 galaxy clustering and weak-lensing *** Codes 85A40 © 2023, CC BY-NC-ND.

关键词： Galaxies

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Intertwined formation of H2, dust, and stars in cosmological simulations

arXiv

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arXiv 2024年

作者： Ragone-Figueroa, Cinthia Granato, Gian Luigi Parente, Massimiliano Murante, Giuseppe Valentini, Milena Borgani, Stefano Maio, Umberto Universidad Nacional de Córdoba Laprida 854 CórdobaX5000BGR Argentina INAF Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy SISSA Via Bonomea 265 TriesteI-34136 Italy Dipartimento di Fisica Università di Trieste Sez. di Astronomia via Tiepolo 11 TriesteI-34131 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Context. Molecular hydrogen (H2) plays a crucial role in the formation and evolution of galaxies, serving as the primary fuel reservoir for star formation. In a metal-enriched Universe, H2 forms mostly through catalysis on interstellar dust grain surfaces. However, due to the complexities of modelling this process, star formation in cosmological simulations often relies on empirical or theoretical frameworks that have only been validated in the local Universe to estimate the abundance of H2. Aims. The goal of this work is to model the connection between the processes of star, dust, and H2 formation in our cosmological simulations. Methods. Building upon our recent integration of a dust evolution model into the star formation and feedback model MUPPI, we included the formation of molecular hydrogen on the surfaces of dust grains. We also accounted for the destruction of molecules and their shielding from harmful radiation. Results. The model reproduces, reasonably well, the main statistical properties of the observed galaxy population for the stellar, dust, and H2 components. The evolution of the molecular hydrogen cosmic density (ρH2) in our simulated boxes peaks around redshift z = 1.5, consistent with observations. Following its peak, ρH2 decreases by a factor of two towards z = 0, which is a milder evolution than observed. Similarly, the evolution of the molecular hydrogen mass function since z = 2 displays a gentler evolution when compared to observations. Our model recovers satisfactorily the integrated molecular Kennicut-Schmidt (mKS) law between the surface star formation rate (ΣSFR) and surface H2 density (ΣH2) at z = 0. This relationship is already evident at z = 2, albeit with a higher normalization. We find hints of a broken power law with a steeper slope at higher ΣH2. We also study the H2-to-dust mass ratio in galaxies as a function of their gas metallicity and stellar mass, observing a decreasing trend with respect to both quantities. The H2-to-dust ma

关键词： Cosmology

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A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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21cm Intensity Mapping cross-correlation with galaxy surveys: current and forecasted cosmological parameters estimation for the SKAO

arXiv

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arXiv 2023年

作者： Berti, Maria Spinelli, Marta Viel, Matteo SISSA- International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN - National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Institute for Particle Physics and Astrophysics ETH Zürich Wolfgang Pauli Strasse 27 Zürich8093 Switzerland INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy Department of Physics and Astronomy University of the Western Cape Robert Sobukhwe Road Bellville7535 South Africa Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy

We present a comprehensive set of forecasts for the cross-correlation signal between 21cm intensity mapping and galaxy redshift surveys. We focus on the data sets that will be provided by the SKAO for the 21cm signal, DESI and Euclid for galaxy clustering. We build a likelihood which takes into account the effect of the beam for the radio observations, the Alcock-Paczynski effect, a simple parameterization of astrophysical nuisances, and fully exploit the tomographic power of such observations in the range z = 0.7 − 1.8 at linear and mildly non-linear scales (k c ℎ2 and H0 is reduced by a factor 3 and 6, respectively, compared to CMB only data, due to the measurement of matter clustering provided by the two observables. The cross-correlation signal has a constraining power that is comparable to the auto-correlation one and combining all the clustering measurements a sub-percent error bar of 0.33% on H0 can be achieved, which is about a factor 2 better than CMB only measurements. Finally, as a proof-of-concept, we test the full pipeline on the real data measured by the MeerKat collaboration (Cunnington et al. 2022) presenting some (weak) constraints on cosmological parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Redshift-Space Distortions in Massive Neutrinos Cosmologies

arXiv

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arXiv 2025年

作者： Verdiani, Francesco Bellini, Emilio Moretti, Chiara Sefusatti, Emiliano Carbone, Carmelita Viel, Matteo SISSA - International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Istituto Nazionale di Astrofisica Osservatorio Astronomico di Trieste via Tiepolo 11 Trieste34143 Italy Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34151 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste via Valerio 2 Trieste34127 Italy Center for Astrophysics and Cosmology University of Nova Gorica Nova Gorica Slovenia Istituto Nazionale di Astrofisica Istituto di Astrofisica Spaziale e Fisica cosmica di Milano Via Alfonso Corti 12 Milano20133 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy

We study the Kaiser formula for biased tracers in massive neutrino cosmologies by comparing its predictions with a large set of N-body simulations. In particular, we examine the ambiguity in the definition of the peculiar velocity contribution at linear level, whether it should be expressed in terms of the total matter velocity field or only the cold-matter component, comprised of cold dark matter and baryons. We revisit and extend previous qualitative studies on the topic with larger statistics and including a full fit of halo power spectrum and halo-matter cross-power spectrum measurements. We find clear evidence that the clustering of halos in redshift space is correctly described assuming that halo velocity is tracing the velocity field of cold-matter. The opposite assumption provides a worse fit to the simulated data and can lead to a spurious running of nuisance parameters in the perturbative galaxy power spectrum model. © 2025, CC BY.

关键词： Neutrons

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Reconstructing the dark energy density in light of DESI BAO observations

arXiv

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arXiv 2025年

作者： Berti, Maria Bellini, Emilio Bonvin, Camille Kunz, Martin Viel, Matteo Zumalacarregui, Miguel Département de Physique Théorique Center for Astroparticle Physics Université de Genève Quai E. Ansermet 24 Genève 4CH-1211 Switzerland Center for Astrophysics and Cosmology University of Nova Gorica Nova Gorica Slovenia INAF Istituto Nazionale di Astrofisica Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 TriesteI-34151 Italy SISSA - International School for Advanced Studies Via Bonomea 265 TriesteI-34136 Italy INFN – National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Am Mühlenberg 1 Potsdam-GolmD-14476 Germany

In light of the evidence for dynamical dark energy (DE) found from the most recent Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, we perform a non-parametric, model-independent reconstruction of the DE density evolution. To do so, we develop and validate a new framework that reconstructs the DE density through a third-degree piece-wise polynomial interpolation, allowing for direct constraints on its redshift evolution without assuming any specific functional form. The strength of our approach resides in the choice of directly reconstructing the DE density, which provides a more straightforward relation to the distances measured by BAO than the equation of state parameter. We investigate the constraining power of cosmic microwave background (CMB) observations combined with supernovae (SNe) and BAO measurements. In agreement with results from other works, we find a preference for deviations from ΛCDM, with a significance of 2.4σ when using the Dark Energy Survey Year 5 (DESY5) SNe data, and 1.3σ with PantheonPlus. In all the cases we consider, the derived DE equation of state parameter presents evidence for phantom crossing. By investigating potential systematic effects in the low-redshift samples of DESY5 observations, we confirm that correcting for the offset in apparent magnitude with respect to PantheonPlus data, as suggested in previous studies, completely removes the tension. Furthermore, we assess the risk of potentially overfitting the data by changing the number of interpolation nodes. As expected, we find that with lesser nodes we get a smoother reconstructed behavior of the DE density, although with similar overall features. The pipeline developed in this work is ready to be used with future high-precision data to further investigate the evidence for a non-standard background evolution. Copyright © 2025, The Authors. All rights reserved.

关键词： Supernovae

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Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling

arXiv

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arXiv 2024年

作者： Autenrieth, Maximilian Wright, Angus H. Trotta, Roberto van Dyk, David A. Stenning, David C. Joachimi, Benjamin Department of Mathematics Imperial College London 180 Queen’s Gate LondonSW7 2AZ United Kingdom German Centre for Cosmological Lensing Bochum44780 Germany SISSA International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Department of Physics Imperial College London Blackett Laboratory Prince Consort Rd LondonSW72AZ United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy INFN – National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Department of Statistics and Actuarial Science Simon Fraser University Canada Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful ΛCDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing StratLearn, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that StratLearn-estimated conditional densities improve the galaxy tomographic bin assignment, and that our StratLearn-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of ∼ 2 improvement upon the previously best photo-z calibration method. Our approach delivers a maximum bias per tomographic bin of ∆〈z〉 = 0.0095 ± 0.0089, with an average absolute bias of 0.0052 ± 0.0067 across the five tomographic bins. © 2024, CC BY.

关键词： Galaxies

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Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks 6th

Discovering Traffic Anomaly Propagation in Urban Space Using...

引用

6th International Conference on data Science, ICDS 2019

作者： Huang, Guang-Li Ji, Yimu Liu, Shangdong Zarei, Roozbeh School of Science RMIT University MelbourneVIC3000 Australia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China School of Information Technology Deakin University GeelongVIC3125 Australia

ISBN: (纸本)9789811528095

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as STOTree, are not accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal neighborhoods rather than considering from a global perspective, and thus cannot find a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using traffic change peaks, which can visualize the change of traffic anomalies (e.g., congestion and evacuation area) and thus accurately captures traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time period can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0–255) of pixels. An adaptive filter is developed to generate traffic change graphs from grid traffic images in consecutive periods, and clustering traffic change peaks along the road is to discover the propagation of traffic anomalies. The effectiveness of the proposed method has been demonstrated using a real-world GPS trajectory dataset. © Springer Nature Singapore Pte Ltd 2020.

关键词： Adaptive filters

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

引用

Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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