scispace - formally typeset
Search or ask a question
Institution

Aalto University

EducationEspoo, Finland
About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Carbon nanotube. The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.


Papers
More filters
Book
Simo Srkk1
01 Sep 2013
TL;DR: This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework, learning what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
Abstract: Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

879 citations

Journal ArticleDOI
Nabila Aghanim1, Yashar Akrami2, Yashar Akrami3, Frederico Arroja4  +251 moreInstitutions (72)
TL;DR: In this paper, the authors present the cosmological legacy of the Planck satellite, which provides the strongest constraints on the parameters of the standard cosmology model and some of the tightest limits available on deviations from that model.
Abstract: The European Space Agency’s Planck satellite, which was dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013, producing deep, high-resolution, all-sky maps in nine frequency bands from 30 to 857 GHz. This paper presents the cosmological legacy of Planck, which currently provides our strongest constraints on the parameters of the standard cosmological model and some of the tightest limits available on deviations from that model. The 6-parameter ΛCDM model continues to provide an excellent fit to the cosmic microwave background data at high and low redshift, describing the cosmological information in over a billion map pixels with just six parameters. With 18 peaks in the temperature and polarization angular power spectra constrained well, Planck measures five of the six parameters to better than 1% (simultaneously), with the best-determined parameter (θ*) now known to 0.03%. We describe the multi-component sky as seen by Planck, the success of the ΛCDM model, and the connection to lower-redshift probes of structure formation. We also give a comprehensive summary of the major changes introduced in this 2018 release. The Planck data, alone and in combination with other probes, provide stringent constraints on our models of the early Universe and the large-scale structure within which all astrophysical objects form and evolve. We discuss some lessons learned from the Planck mission, and highlight areas ripe for further experimental advances.

879 citations

Journal ArticleDOI
TL;DR: It is argued that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained.
Abstract: Online communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community---resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing---induced by online communities---has implications for theorizing about the more general case of knowledge collaboration in organizations.

857 citations


Authors

Showing all 10135 results

NameH-indexPapersCitations
John B. Goodenough1511064113741
Ashok Kumar1515654164086
Anne Lähteenmäki11648581977
Kalyanmoy Deb112713122802
Riitta Hari11149143873
Robin I. M. Dunbar11158647498
Andreas Richter11076948262
Mika Sillanpää96101944260
Muhammad Farooq92134137533
Ivo Babuška9037641465
Merja Penttilä8730322351
Andries Meijerink8742629335
T. Poutanen8612033158
Sajal K. Das85112429785
Kalle Lyytinen8442627708
Network Information
Related Institutions (5)
Georgia Institute of Technology
119K papers, 4.6M citations

95% related

Delft University of Technology
94.4K papers, 2.7M citations

94% related

École Polytechnique Fédérale de Lausanne
98.2K papers, 4.3M citations

94% related

Nanyang Technological University
112.8K papers, 3.2M citations

94% related

ETH Zurich
122.4K papers, 5.1M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022342
20212,842
20203,030
20192,749
20182,719