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Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties

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arXiv 2021年

作者： Knabenhans, Mischa Brinckmann, Thejs Stadel, Joachim Schneider, Aurel Teyssier, Romain Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Dipartimento di Fisica Scienze della Terra Universitá degli Studi di Ferrara via Giuseppe Saragat 1 Ferrara44122 Italy Sezione di Ferrara Via Giuseppe Saragat 1 Ferrara44122 Italy C.N. Yang Institute for Theoretical Physics Department of Physics & Astronomy Stony Brook University Stony BrookNY11794 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States The Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

We implement EuclidEmulator (version 1), an emulator for the non-linear correction of the matter power spectrum, into the Markov chain Monte Carlo (MCMC) forecasting code MontePython. We compare the performance of HALOFIT, HMCode, and EuclidEmulator1, both at the level of power spectrum prediction and at the level of posterior probability distributions of the cosmological parameters, for different cosmological models and different galaxy power spectrum wave number cutoffs. We confirm that the choice of the power spectrum predictor has a non-negligible effect on the computed sensitivities when doing cosmological parameter forecasting, even for a conservative wave number cut-off of 0.2 ℎMpc−1. We find that EuclidEmulator1 is on average up to 17% more sensitive to the cosmological parameters than the other two codes, with the most significant improvements being for the Hubble parameter of up to 42% and the equation of state of dark energy of up to 26%, depending on the case. In addition, we point out that the choice of the power spectrum predictor contributes to the risk of computing a significantly biased mean cosmology when doing parameter estimations. For the four tested scenarios we find biases, averaged over the cosmological parameters, of between 0.5 and 2σ (from below 1σ up to 6σ for individual parameters). This paper provides a proof of concept that this risk can be mitigated by taking a well-tailored theoretical uncertainty into account as this allows to reduce the bias by a factor of 2 to 5, depending on the case under consideration, while keeping posterior credibility contours small: the standard deviations are amplified by a factor of ≤ 1.4 in all cases. © 2021, CC BY-NC-SA.

关键词： Power spectrum

Optimized large hyperuniform binary colloidal suspensions in two dimensions

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arXiv 2020年

作者： Ma, Zheng Lomba, Enrique Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Instituto de Química Física Rocasolano CSIC Calle Serrano 119 MadridE-28006 Spain Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

The creation of disordered hyperuniform materials with potentially extraordinary optical properties requires a capacity to synthesize large samples that are effectively hyperuniform down to the nanoscale. Motivated by this challenge, we propose a fabrication protocol using binary paramagnetic colloidal particles confined in a 2D plane. The strong and long-ranged dipolar interaction induced by a tunable magnetic field is free from screening effects that attenuates long-ranged electrostatic interactions in charged colloidal systems. Specifically, we find a family of optimal size ratios that makes the two-phase system effectively hyperuniform. We show that hyperuniformity is a general consequence of low isothermal compressibilities, which makes our protocol suitable to systems with other long-ranged soft interactions, dimensionalities and/or polydispersity. Our methodology paves the way to synthesize large photonic hyperuniform materials that function in the visible to infrared range and hence may accelerate the discovery of novel photonic materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Suspensions (fluids)

Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

Polyelectrolyte nanoparticles derived from durian seed gum modified with chitosan for selective targeting in cancer cells

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International Journal of Biological Macromolecules 2025年 315卷 144625页

作者： Putro, Jindrayani Nyoo Bintang Ilhami, Fasih Cheng, Chih-Chia Imanuela, Keziah Janice Mangindaan, Dave Irawaty, Wenny Rahmawati, Astrid Shimomura, Osamu Ismadji, Suryadi Chemical Engineering Master Program Faculty of Engineering Widya Mandala Surabaya Catholic University Kalijudan 37 Surabaya60114 Indonesia Collaborative Research Center for Sustainable and Zero Waste Industries Jl. Kalijudan 37 East Java Surabaya60114 Indonesia Department of Natural Science Faculty of Mathematics and Natural Science Universitas Negeri Surabaya Surabaya60231 Indonesia Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Chemical Engineering Widya Mandala Surabaya Catholic University Kalijudan 37 Surabaya60114 Indonesia Professional Engineer Program Department Faculty of Engineering Bina Nusantara University K.H. Syahdan No. 9 Jakarta11480 Indonesia Waste-Food-Environmental Nexus Research Interest Group Bina Nusantara University K.H. Syahdan No. 9 Jakarta11480 Indonesia Department of Applied Chemistry Osaka Institute of Technology 5-16-1 Omiya Ashahi-ku Osaka535-8585 Japan

The synthesis of natural polysaccharides from durian seed gum-modified chitosan as complex nanoparticles was successfully developed. The complex formation of nanoparticles was confirmed by 1H NMR, indicating the interpenetrating polymer network between the durian seed gum and chitosan. The material's physical properties were analyzed using ZETA potential, SEM, FTIR, swelling assay, and spectrofluorometer. The material showed a pH-responsive behavior as evidenced by the swelling assay, in which the complex nanoparticle has a smaller swelling index in pH 5.5 and 7.4 than pH 2.1. The stability of the material was tested by checking the hydrodynamic diameter, which resulted in the modification of chitosan having higher kinetic stability than the durian seed gum under a diameter smaller than 1 μm. The chitosan-modified durian seed gum's hemolytic activity indicates blood compatibility with 4 % hemolytic activity under high nanoparticle concentration (400 μg/mL). More importantly, the cytotoxicity test of complex nanoparticles showed biocompatibility with NIH/3 T3 cells, whereas complex nanoparticles exhibited high toxicity towards HeLa cells. Moreover, complex nanoparticles showed strong intracellular blue fluorescence with selective activity towards cancer cells. Hence, this newly developed green synthesis of polyelectrolyte nanoparticles derived from durian seed gum modified with chitosan improved selectivity and intracellular fluorescence in tracking cancer cells. © 2025 Elsevier B.V.

关键词： Chitosan

Uniform resolvent estimates for Schrödinger operator with an inverse-square potential

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arXiv 2019年

作者： Mizutani, Haruya Zhang, Junyong Zheng, Jiqiang Department of Mathematics Graduate School of Science Osaka University Toyonaka Osaka560-0043 Japan Department of Mathematics Beijing Institute of Technology Beijing100081 China Department of Mathematics Cardiff University United Kingdom Institute of Applied Physics and Computational Mathematics Beijing100088 China

We study the uniform resolvent estimates for Schrödinger operator with a Hardy-type singular potential. Let LV = −∆ + V (x) where ∆ is the usual Laplacian on Rn and V (x) = V0(θ)r−2 where r = |x|, θ = x/|x| and V0(θ) ∈ C1(Sn−1) is a real function such that the operator −∆θ + V0(θ) + (n − 2)2/4 is a strictly positive operator on L2(Sn−1). We prove some new uniform weighted resolvent estimates and also obtain some uniform Sobolev estimates associated with the operator LV . Copyright © 2019, The Authors. All rights reserved.

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Relationship between changing malaria burden and low birth weight in sub-Saharan Africa: A difference-in-differences study via a pair-of-pairs approach

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arXiv 2020年

作者： Heng, Siyu O'Meara, Wendy P. Simmons, Ryan A. Small, Dylan S. Graduate Group in Applied Mathematics and Computational Science School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Department of Statistics The Wharton School University of Pennsylvania PhiladelphiaPA United States Global Health Institute School of Medicine Duke University DurhamNC United States Department of Biostatistics and Bioinformatics School of Medicine Duke University DurhamNC United States

According to the World Health Organization (WHO), in 2018, an estimated 228 million malaria cases occurred worldwide with most cases occurring in sub-Saharan Africa. Scale-up of vector control tools coupled with increased access to diagnosis and effective treatment has resulted in a large decline in malaria prevalence in some areas, but other areas have seen little change. Although interventional studies demonstrate that preventing malaria during pregnancy can reduce the low birth weight (i.e., child's birth weight 2−10) > 0.4) to a low rate (PfPR2−10 Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

Training of Physical Neural Networks

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Improving Bayesian local spatial models in large data sets

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arXiv 2019年

作者： Lenzi, Amanda Castruccio, Stefano Rue, Håvard Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States

Environmental processes resolved at a sufficiently small scale in space and time will inevitably display non-stationary behavior. Such processes are both challenging to model and computationally expensive when the data size is large. Instead of modeling the global non-stationarity explicitly, local models can be applied to disjoint regions of the domain. The choice of the size of these regions is dictated by a bias-variance trade-off;large regions will have smaller variance and larger bias, whereas small regions will have higher variance and smaller bias. From both the modeling and computational point of view, small regions are preferable to better accommodate the non-stationarity. However, in practice, large regions are necessary to control the variance. We propose a novel Bayesian three-step approach that allows for smaller regions without compromising the increase of the variance that would follow. We are able to propagate the uncertainty from one step to the next without issues caused by reusing the data. The improvement in inference also results in improved prediction, as our simulated example shows. We illustrate this new approach on a data set of simulated high-resolution wind speed data over Saudi Arabia. Copyright © 2019, The Authors. All rights reserved.

关键词： Wind

Analysis of Lead (Pb) and Zinc (Zn) Content in Sediments and Faunus ater at Bale Lhoknga Aceh Besar District 1

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Analysis of Lead (Pb) and Zinc (Zn) Content in Sediments and...

1st International Conference on Education, science and Technology, ICON-EST 2018

作者： Agustina, Rahmi Sarong, M. Ali Yulianda, Fredinan Suhendrayatna Rahmadi Lelifajri Graduate School of Mathematics and Applied Sciences Syiah Kuala University Banda Aceh23111 Indonesia Departement of Biology Faculty of Teacher Training and Education Jabal Ghafur University Sigli24171 Indonesia Departement of Biology Faculty of Teacher Training and Education Syiah Kuala University Banda Aceh23111 Indonesia Departement of Water Resources Management Faculty of Fisheries and Marine Sciences Bogor Agricultural University Bogor16680 Indonesia Department of Chemical Engineering Faculty of Engineering Syiah Kuala University Banda Aceh23111 Indonesia Department of Chemistry Faculty of Mathematics and Science Post Graduate Program Syiah Kuala University Banda Aceh23111 Indonesia Department of Chemistry Faculty of Mathematics and Sci. Post Graduate Program Syiah Kuala Univ. Banda Aceh23111 Indonesia

Faunus ater is a favoritemollusca that is often found on the Bale River. Local people use these snails as food for consumption along with their main food. Bale River itself is located adjacent to the cement factory, used water washing machines distributed to the river is feared to support the presence of lead (Pb) and zinc (Zn) metals in the river. This study aims to analyze the relationship of Pb and Zn content in sediments to the content of Pb and Zn in Faunus ater. Data collection was carried out in Februari until April 2018. The relationship between Pb and Zn content in sediments with Pb and Zn content in the body of Faunus ater was analyzed by correlation analysis. The results of the data analysis showed that the metal content of Pb and Zn in sediment and Faunus aterwere found to vary. The highest Pb content in sediment was found at station 2 in March, amounting to 32.87 mg / kg. while the highest Zn content was found at station 1 in March amounting to 84 mg / kg. The relationship of heavy metals Pb and Zn in sediments to the content of Pb and Zn in the body of Faunus ater showed a negative correlation. The conclusion of this study is that there is no correlation between the content of Pb and Zn heavy metals in sediments on the content of lead Pb and Zn in the body of Faunus ater at the Bale Lhoknga River in Aceh Besar District. © Published under licence by IOP Publishing Ltd.

关键词： Rivers