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Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.


Papers
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Journal ArticleDOI
TL;DR: This work investigates dual concentric core and microstructure fiber geometries for dispersion compensation and proposes designs with effective mode area and dispersion values as large as -59 000 ps/(nm km) over a broad wavelength range.
Abstract: We investigate dual concentric core and microstructure fiber geometries for dispersion compensation. Dispersion values as large as -59 000 ps/(nm km) are achieved, over a broad wavelength range with full width at half maximum exceeding 100 nm. The trade-off between large dispersion and mode area is studied. Geometries with an effective mode area of 30µm2 and dispersion -19 000 ps/(nm km) and 80µm2 with -1600 ps/(nm km) are proposed.

140 citations

Journal ArticleDOI
TL;DR: In this article, the authors distinguish between firm-level learning effects that result from first-order and second-order additionalities in innovation policy interventions, and show that enhancing identification with a community of practice among R&D program participants enhances firmlevel learning outcomes.

140 citations

Book ChapterDOI
02 Dec 1999
TL;DR: A generalization of logic program rules is proposed where rules are built from weight constraints with type information for each predicate instead of simple literals, showing that for ground rules the complexity of the relevant decision problems stays in NP.
Abstract: A generalization of logic program rules is proposed where rules are built from weight constraints with type information for each predicate instead of simple literals. These kinds of constraints are useful for concisely representing different kinds of choices as well as cardinality, cost and resource constraints in combinatorial problems such as product configuration. A declarative semantics for the rules is presented which generalizes the stable model semantics of normal logic programs. It is shown that for ground rules the complexity of the relevant decision problems stays in NP. The first implementation of the language handles a decidable subset where function symbols are not allowed. It is based on a new procedure for computing stable models for ground rules extending normal programs with choice and weight constructs and a compilation technique where a weight rule with variables is transformed to a set of such simpler ground rules.

140 citations

Journal ArticleDOI
TL;DR: In this paper, an implementation methodology for partial and disjunctive stable models where partiality and disjctions are unfolded from a logic program so that an implementation of stable models for normal (disjunction-free) programs can be used as the core inference engine is presented.
Abstract: This article studies an implementation methodology for partial and disjunctive stable models where partiality and disjunctions are unfolded from a logic program so that an implementation of stable models for normal (disjunction-free) programs can be used as the core inference engine. The unfolding is done in two separate steps. First, it is shown that partial stable models can be captured by total stable models using a simple linear and modular program transformation. Hence, reasoning tasks concerning partial stable models can be solved using an implementation of total stable models. Disjunctive partial stable models have been lacking implementations which now become available as the translation handles also the disjunctive case. Second, it is shown how total stable models of disjunctive programs can be determined by computing stable models for normal programs. Thus an implementation of stable models of normal programs can be used as a core engine for implementing disjunctive programs. The feasibility of the approach is demonstrated by constructing a system for computing stable models of disjunctive programs using the SMODELS system as the core engine. The performance of the resulting system is compared to that of DLV, which is a state-of-the-art system for disjunctive programs.

140 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the GASP-WEBT optical and radio light curves of this blazar obtained in July-November 2007, about various AGILE pointings at the source.
Abstract: Aims. Since the CGRO operation in 1991–2000, one of the primary unresolved questions about the blazar γ -ray emission has been its possible correlation with the low-energy (in particular optical) emission. To help answer this problem, the Whole Earth Blazar Telescope (WEBT) consortium has organized the GLAST-AGILE Support Program (GASP) to provide the optical-to-radio monitoring data to be compared with the γ -ray detections by the AGILE and GLAST satellites. This new WEBT project started in early September 2007, just before a strong γ -ray detection of 0716+714 by AGILE.Methods. We present the GASP-WEBT optical and radio light curves of this blazar obtained in July–November 2007, about various AGILE pointings at the source. We construct NIR-to-UV spectral energy distributions (SEDs), by assembling GASP-WEBT data together with UV data from the Swift ToO observations of late October.Results. We observe a contemporaneous optical-radio outburst, which is a rare and interesting phenomenon in blazars. The shape of the SEDs during the outburst appears peculiarly wavy because of an optical excess and a UV drop-and-rise. The optical light curve is well sampled during the AGILE pointings, showing prominent and sharp flares. A future cross-correlation analysis of the optical and AGILE data will shed light on the expected relationship between these flares and the γ -ray events.

140 citations


Authors

Showing all 8962 results

NameH-indexPapersCitations
Ashok Kumar1515654164086
Hannu Kurki-Suonio13843399607
Nicolas Gisin12582764298
Anne Lähteenmäki11648581977
Riitta Hari11149143873
Andreas Richter11076948262
Mika Sillanpää96101944260
Markku Leskelä9487636881
Ullrich Scherf9273536972
Mikko Ritala9158429934
Axel H. E. Müller8956430283
Karl Henrik Johansson88108933751
T. Poutanen8612033158
Elina Lindfors8642023846
Günter Breithardt8555433165
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2021154
2020153
2019155
201851
201714
201630