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arXiv 2024年

作者： Archidiacono, M. Lesgourgues, J. Casas, S. Pamuk, S. Schöneberg, N. Sakr, Z. Parimbelli, G. Schneider, A. Hervas Peters, F. Pace, F. Sabarish, V.M. Costanzi, M. Camera, S. Carbone, C. Clesse, S. Frusciante, N. Fumagalli, A. Monaco, P. Scott, D. Viel, M. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Galeotta, S. Gillis, B. Giocoli, C. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hoekstra, H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. McCracken, H.J. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Nightingale, J.W. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Scaramella, R. Schirmer, M. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Boucaud, A. Bozzo, E. Burigana, C. Calabrese, M. Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN-Sezione di Milano Via Celoria 16 Milano20133 Italy RWTH Aachen University Aachen52056 Germany Martí i Franquès 1 Barcelona08028 Spain Institut für Theoretische Physik University of Heidelberg Philosophenweg 16 Heidelberg69120 Germany Université de Toulouse CNRS UPS CNES 14 Av. Edouard Belin Toulouse31400 France Université St Joseph Faculty of Sciences Beirut Lebanon Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy Department of Astrophysics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-Yvette91191 France Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy INAF-Osservatorio Astrofisico di Torino Via Osservatorio 20 TO Pino Torinese10025 Italy Hamburger Sternwarte University of Hamburg Gojenbergsweg 112 Hamburg21029 Germany Dipartimento di Fisica Sezione di Astronomia Università 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 INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy Service de Physique Théorique CP225 Boulevard du Triophe Bruxelles1050 Belgium Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy Department of Physics and Astronomy University of British Colu

Context. The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. Aims. We present forecasts from the combination of the Euclid photometric galaxy surveys (weak lensing, galaxy clustering and their cross-correlation) and its spectroscopic redshift survey on the sensitivity to cosmological parameters including the summed neutrino mass ∑ mν and the effective number of relativistic species Neff in the standard ΛCDM scenario and in a scenario with dynamical dark energy (w0waCDM). Methods. We compare the accuracy of different algorithms predicting the nonlinear matter power spectrum for such models. We then validate several pipelines for Fisher matrix and Markov Chain Monte Carlo (MCMC) forecasts, using different theory codes, algorithms for numerical derivatives, and assumptions concerning the non-linear cut-off scale. Results. The Euclid primary probes alone will reach a sensitivity of σ(∑ mν = 60 meV) = 56 meV in the ΛCDM+∑ mν model, whereas the combination with cosmic microwave background (CMB) data from Planck is expected to achieve σ(∑ mν) = 23 meV and raise the evidence for a non-zero neutrino mass to at least the 2.6 σ level. This can be pushed to a 4 σ detection if future CMB data from LiteBIRD and CMB Stage-IV are included. In combination with Planck, Euclid will also deliver tight constraints on ∆Neff ν+Neff model, or ∆Neff eff remains stable, while that to ∑ mν degrades at most by a factor two. Conclusions. This work illustrates the complementarity between the Euclid spectroscopic and imaging/photometric surveys and between Euclid and CMB constraints. Euclid will have a great potential for measuring the neutrino mass and excluding well-motivated scenarios with additional relativistic particles. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

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Robust Depth-based Person Re-identification

arXiv

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arXiv 2017年

作者： Wu, Ancong Zheng, Wei-Shi Lai, Jian-Huang School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China School of Data and Computer Science Sun Yat-sen University Guangzhou China Ministry of Education China Guangdong Province Key Laboratory of Information Security China

Person re-identification (re-id) aims to match people across non-overlapping camera views. So far the RGB-based appearance is widely used in most existing works. However, when people appeared in extreme illumination or changed clothes, the RGB appearance-based re-id methods tended to fail. To overcome this problem, we propose to exploit depth information to provide more invariant body shape and skeleton information regardless of illumination and color change. More specifically, we exploit depth voxel covariance descriptor and further propose a locally rotation invariant depth shape descriptor called Eigen-depth feature to describe pedestrian body shape. We prove that the distance between any two covariance matrices on the Riemannian manifold is equivalent to the Euclidean distance between the corresponding Eigen-depth features. Furthermore, we propose a kernelized implicit feature transfer scheme to estimate Eigendepth feature implicitly from RGB image when depth information is not available. We find that combining the estimated depth features with RGB-based appearance features can sometimes help to better reduce visual ambiguities of appearance features caused by illumination and similar clothes. The effectiveness of our models was validated on publicly available depth pedestrian datasets as compared to related methods for person re-identification. Copyright © 2017, The Authors. All rights reserved.

关键词： Covariance matrix

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Euclid preparation TBD. Galaxy power spectrum modelling in real space

arXiv

引用

arXiv 2023年

作者： Pezzotta, A. Moretti, C. Zennaro, M. Dizgah, A. Moradinezhad Crocce, M. Sefusatti, E. Ferrero, I. Pardede, K. Eggemeier, A. Barreira, A. Angulo, R.E. Marinucci, M. Quevedo, B. Camacho de la Torre, S. Alkhanishvili, D. Biagetti, M. Breton, M.-A. Castorina, E. D’Amico, G. Desjacques, V. Guidi, M. Kärcher, M. Oddo, A. Ibanez, M. Pellejero Porciani, C. Pugno, A. Salvalaggio, J. Sarpa, E. Veropalumbo, A. Vlah, Z. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bender, R. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Fosalba, P. Frailis, M. Franceschi, E. Galeotta, S. Gillis, B. Giocoli, C. Granett, B.R. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Pollack, J.E. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Rossetti, E. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Surace, C. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom 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 Department of Physics Oxford University Keble Road OxfordOX1 3RH United Kingdom Université de Genève Département de Physique Théorique Centre for Astroparticle Physics 24 quai Ernest-Ansermet Genève 4CH-1211 Switzerland Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés Barcelona08193 Spain INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern Oslo0315 Norway Strada Costiera 11 Trieste34151 Italy Universität Bonn Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany Excellence Cluster ORIGINS Boltzmannstrasse e 2 Garching85748 Germany Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany Paseo Manuel de Lardizabal 4 Donostia-San Sebastián Guipuzkoa20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48013 Spain Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Viale delle Scienze 7/A Parma43124 Italy Technion Israel Institute of Technology Israel Carrer Gran Capitá 2-4 Barcelona08034 Spain Aix-Marseille Université CNRS CNES LAM Marseille France Area Science Park Località Padriciano 99 Trieste34149 Italy Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190

We investigate the accuracy of the perturbative galaxy bias expansion in view of the forthcoming analysis of the Euclid spectroscopic galaxy samples. We compare the performance of an Eulerian galaxy bias expansion, using state-of-art prescriptions from the effective field theory of large-scale structure (EFTofLSS), against a hybrid approach based on Lagrangian perturbation theory and high-resolution simulations. These models are benchmarked against comoving snapshots of the Flagship I N-body simulation at z = (0.9, 1.2, 1.5, 1.8), which have been populated with Hα galaxies leading to catalogues of millions of objects within a volume of about 58 h−3 Gpc3. Our analysis suggests that both models can be used to provide a robust inference of the parameters (h, ωc) in the redshift range under consideration, with comparable constraining power. We additionally determine the range of validity of the EFTofLSS model in terms of scale cuts and model degrees of freedom. From these tests, it emerges that the standard third-order Eulerian bias expansion can accurately describe the full shape of the real-space galaxy power spectrum up to the maximum wavenumber kmax = 0.45 hMpc−1, even with a measurement precision well below the percent level. In particular, this is true for a configuration with six free nuisance parameters, including local and non-local bias parameters, a matter counterterm, and a correction to the shot-noise contribution. Fixing either tidal bias parameters to physically-motivated relations still leads to unbiased cosmological constraints. We finally repeat our analysis assuming a volume that matches the expected footprint of Euclid, but without considering observational effects, as purity and completeness, showing that we can get consistent cosmological constraints over this range of scales and redshifts. © 2023, CC BY.

关键词： Galaxies

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Person re-identification by camera correlation aware feature augmentation

arXiv

引用

arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

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Multi-DAGs Scheduling Integrating with Security and Availability in Cloud Environment

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Chinese Journal of Electronics 2015年第4期24卷 709-716页

作者： LIU Yaqiu SHAO Hongrun JING Weipeng QIU Zhaowen The College of Information and Computer Engineering Northeast Forestry University Heilongjiang Province Engineering Technology Research Centre for Forestry Ecological Big Data Storage and High Performance (Cloud) Computing

In view of the issue concerns multiple Directed acyclic graphs(DAGs) scheduling in multi-tenant cloud computing environment, a scheduling strategy that integrate security and availability is proposed to satisfy the tenants’ requirements for resource security and availability, as thus it can not only protect the users’ privacy and data security but also advance the success rate. The proposal assesses resource reputation to ensure jobs can be scheduled onto relatively security nodes; during task scheduling, it classifies the DAGs to achieve fairness; in the process of resources allocation, the objective function would maximize the user’s security satisfaction and minimize the deviation of availability; meanwhile, it takes advantage of "time chips" flexibly to promote resource utilization rate; afterwards, we present a Greedy algorithm integrating with security and availability(GISA) to implement the strategy. The experimental results show the correctness and superior of the novel strategy.

关键词： Multi-DAGs scheduling Security Availability Resource utilization

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Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-ΛCDM models

arXiv

引用

arXiv 2023年

作者： Bose, B. Carrilho, P. Marinucci, M. Moretti, C. Pietroni, M. Carella, E. Piga, L. Wright, B.S. Vernizzi, F. Carbone, C. Casas, S. D’Amico, G. Frusciante, N. Koyama, K. Pace, F. Pourtsidou, A. Baldi, M. de la Bella, L.F. Fiorini, B. Giocoli, C. Lombriser, L. Aghanim, N. Amara, A. Andreon, S. Auricchio, N. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Cardone, V.F. Carretero, J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Costille, A. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Farina, M. Farrens, S. Ferriol, S. Fosalba, P. Frailis, M. Franceschi, E. Galeotta, S. Garilli, B. Gillis, B. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Meneghetti, M. Meylan, G. Moresco, M. Moscardini, L. Mota, D.F. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Starck, J.-L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Bozzo, E. Burigana, C. Colodro-Conde, C. Di Ferdinando, D. Graciá-Carpio, J. Mauri, N. Neissner, C. Sakr, Z. Scottez, V. Tenti, M. Viel, M. Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Viale delle Scienze 7/A Parma43124 Italy Technion Israel Institute of Technology Israel SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INFN Gruppo Collegato di Parma Viale delle Scienze 7/A Parma43124 Italy Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 Milano20133 Italy INAF-IASF Milano Via Alfonso Corti 12 Milano20133 Italy School of Physics and Astronomy Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Institut de Physique Théorique CEA CNRS Université Paris-Saclay Gif-sur-YvetteCedex 91191 France RWTH Aachen University Aachen52056 Germany Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Dipartimento di Fisica Università degli Studi di Torino Via P. Giuria 1 Torino10125 Italy INFN-Sezione di Torino Via P. Giuria 1 Torino10125 Italy Pino Torinese10025 Italy Higgs Centre for Theoretical Physics School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bologna Via Irnerio 46 Bologna40126 Italy Université de Genève Département de Ph

Context. The Euclid space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the ΛCDM model. Aims. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali–Gabadadze–Porrati model, the Hu–Sawicki f(R) model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein–de Sitter universe;we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos;we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya–Nishimishi–Saito © 2023, CC BY.

关键词： Dark energy

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LiteBIRD Science Goals and Forecasts: Primordial Magnetic Fields

arXiv

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arXiv 2024年

作者： Paoletti, D. Rubino-Martin, J. Shiraishi, M. Molinari, D. Chluba, J. Finelli, F. Baccigalupi, C. Errard, J. Gruppuso, A. Lonappan, A.I. Tartari, A. Allys, E. Anand, A. Aumont, J. Ballardini, M. Banday, A.J. Barreiro, R.B. Bartolo, N. Bersanelli, M. Bortolami, M. Brinckmann, T. Calabrese, E. Campeti, P. Carones, A. Casas, F.J. Cheung, K. Clermont, L. Columbro, F. Conenna, G. Coppolecchia, A. Cuttaia, F. D’Alessandro, G. de Bernardis, P. Torre, S. Della Diego-Palazuelos, P. Eriksen, H.K. Fuskeland, U. Galloni, G. Galloway, M. Gerbino, M. Gervasi, M. Ghigna, T. Giardiello, S. Gimeno-Amo, C. Gjerløw, E. Grupp, F. Hazumi, M. Henrot-Versillé, S. Hergt, L.T. Hivon, E. Ichiki, K. Ishino, H. Kohri, K. Komatsu, E. Krachmalnicoff, N. Lamagna, L. Lattanzi, M. Lembo, M. Levrier, F. López-Caniego, M. Luzzi, G. Martínez-González, E. Masi, S. Matarrese, S. Micheli, S. Migliaccio, M. Monelli, M. Montier, L. Morgante, G. Mousset, L. Nagata, R. Namikawa, T. Natoli, P. Novelli, A. Obata, I. Occhiuzzi, A. Odagiri, K. Pagano, L. Paiella, A. Pascual-Cisneros, G. Piacentini, F. Piccirilli, G. Remazeilles, M. Ritacco, A. Ruiz-Granda, M. Sakurai, Y. Scott, D. Stever, S.L. Sullivan, R.M. Takase, Y. Tassis, K. Terenzi, L. Tristram, M. Vacher, L. van Tent, B. Vielva, P. Wehus, I.K. Weymann-Despres, G. Zannoni, M. Zhou, Y. INAF - OAS Bologna via Piero Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna Viale C. Berti Pichat 6/2 Bologna40127 Italy Instituto de Astrofísica de Canarias La Laguna Canary Islands TenerifeE-38200 Spain Tenerife La LagunaE-38206 Spain Suwa University of Science Nagano Chino391-0292 Japan High Performance Computing Department CINECA via Magnanelli 2/3 Casalecchio di Reno40033 Italy Jodrell Bank Centre for Astrophysics Alan Turing Building Department of Physics and Astronomy School of Natural Sciences The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste via Valerio 2 Trieste34127 Italy IFPU Via Beirut 2 Grignano Trieste34151 Italy Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma00133 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 Pisa56127 Italy Dipartimento di Fisica Università di Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Laboratoire de Physique de l’École Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Paris75005 France IRAP Université de Toulouse CNRS CNES UPS Toulouse France Dipartimento di Fisica e Scienze della Terra Università di Ferrara Via Saragat 1 Ferrara44122 Italy INFN Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Avenida los Castros SN Santander39005 Spain Dipartimento di Fisica e Astronomia "G. Galilei" Universita‘degli Studi di Padova via Marzolo 8 PadovaI-35131 Italy INFN Sezione di Padova via Marzolo 8 PadovaI-35131 Italy INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Dipartimento di Fisica Universita’ degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN Sezione di Milano Via Celoria 16 Milano20133 Italy School of Physics and Astronomy Cardiff University CardiffCF24 3AA United

We present detailed forecasts for the constraints on the characteristics of primordial magnetic fields (PMFs) generated prior to recombination that will be obtained with the LiteBIRD satellite. The constraints are driven by some of the main physical effects of PMFs on the CMB anisotropies: the gravitational effects of magnetically-induced perturbations;the effects on the thermal and ionization history of the Universe;the Faraday rotation imprint on the CMB polarization spectra;and the non-Gaussianities induced in polarization anisotropies. LiteBIRD represents a sensitive probe for PMFs. We explore different levels of complexity, for LiteBIRD data and PMF configurations, accounting for possible degeneracies with primordial gravitational waves from inflation. By exploiting all the physical effects, LiteBIRD will be able to improve the current limit on PMFs at intermediate and large scales coming from Planck. In particular, thanks to its accurate B-mode polarization measurement, LiteBIRD will improve the constraints on infrared configurations for the gravitational effect, giving Bn1BMpc=−2.9 marg1 Mpc p(B2)marg n1BMpc=−2.9 © 2024, CC BY.

关键词： Magnetic fields

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Maximum principle based algorithms for deep learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Qianxiao Li Long Chen Cheng Tai E. Weinan Institute of High Performance Computing Agency for Science Technology and Research Singapore Peking University Beijing China Beijing Institute of Big Data Research and Peking University Beijing China Princeton University Princeton NJ and Beijing Institute of Big Data Research and Peking University Beijing China

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on at landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

关键词： deep learning method of successive approximations optimal control pontryagin's maximum principle

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A long-term user-centric analysis of deduplication patterns 32

A long-term user-centric analysis of deduplication patterns

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32nd Symposium on Mass Storage Systems and Technologies, MSST 2016

作者： Sun, Zhen Kuenning, Geoff Mandal, Sonam Shilane, Philip Tarasov, Vasily Xiao, Nong Zadok, Kand Erez State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China Stony Brook University United States Harvey Mudd College United States EMC Corporation United States IBM Research-Almaden United States School of Data and Computer Science Sun Yat-sen University China

ISBN: (纸本)9781467390552

Deduplication has become essential in disk-based backup systems, but there have been few long-term studies of backup workloads. Most past studies either were of a small static snapshot or covered only a short period that was not representative of how a backup system evolves over time. For this paper, we collected 21 months of data from a shared user file system;33 users and over 4,000 snapshots are covered. We analyzed the data set for a variety of essential characteristics. However, our primary focus was individual user data. Despite apparently similar roles and behavior in all of our users, we found significant differences in their deduplication ratios. Moreover, the data that some users share with others had a much higher deduplication ratio than average. We analyze this behavior and make recommendations for future deduplication systems design. © 2016 IEEE.

关键词： Indexes Metadata Measurement Redundancy Market research high performance computing Computers

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An integrated software package to enable predictive simulation capabilities

An integrated software package to enable predictive simulati...

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Power Systems Computation Conference (PSCC)

作者： Yousu Chen Erin Fitzhenry ShuangShuang Jin Bruce Palmer Poorva Sharma Zhenyu Huang Electricity Infrastructure Human-Centered Analytics Pacific Northwest National Laboratory Seattle WA USA High Performance Computing Data Science Electricity Infrastructure Pacific Northwest National Laboratory Richland WA USA

ISBN: (纸本)9781467381512

The power grid is rapidly evolving with the deployment of smart grid technologies. Such technologies vastly increase the size and complexity of power grid systems for modeling and simulation. This increasing complexity necessitates not only the use of high-performance-computing (HPC) techniques, but a smooth, well-integrated interplay among HPC applications. This paper presents a new software package that integrates HPC applications and a web-based visualization tool based on a middleware framework. This framework can support data communication between different applications. Case studies with a large power system demonstrate the predictive capability brought by the integrated software package, as well as better situational awareness provided by the web-based visualization tool in a live mode. Test results validate the effectiveness and usability of the integrated software package.

关键词： Power system dynamics Computational modeling Middleware State estimation data visualization Power grids

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