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Institution

Graz University of Technology

EducationGraz, Austria
About: Graz University of Technology is a education organization based out in Graz, Austria. It is known for research contribution in the topics: Finite element method & Augmented reality. The organization has 9236 authors who have published 23960 publications receiving 647521 citations. The organization is also known as: TU Graz & TU GRAZ.


Papers
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Journal ArticleDOI
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

17,350 citations

Journal ArticleDOI
TL;DR: This work shows that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.
Abstract: Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy's success in addressing this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multi-armed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has to grow at least logarithmically in the number of plays. Since then, policies which asymptotically achieve this regret have been devised by Lai and Robbins and many others. In this work we show that the optimal logarithmic regret is also achievable uniformly over time, with simple and efficient policies, and for all reward distributions with bounded support.

6,361 citations

Journal ArticleDOI
TL;DR: Batteries, fuel cells and supercapacitors belong to the same family of energy conversion devices and are needed to service the wide energy requirements of various devices and systems.
Abstract: Electrochemical energy conversion devices are pervasive in our daily lives. Batteries, fuel cells and supercapacitors belong to the same family of energy conversion devices. They are all based on the fundamentals of electrochemical thermodynamics and kinetics. All three are needed to service the wide energy requirements of various devices and systems. Neither batteries, fuel cells nor electrochemical capacitors, by themselves, can serve all applications.

6,230 citations

Journal ArticleDOI
29 Sep 2006-Science
TL;DR: In situ analysis of tumor-infiltrating immune cells may be a valuable prognostic tool in the treatment of colorectal cancer and possibly other malignancies.
Abstract: The role of the adaptive immune response in controlling the growth and recurrence of human tumors has been controversial. We characterized the tumor-infiltrating immune cells in large cohorts of human colorectal cancers by gene expression profiling and in situ immunohistochemical staining. Collectively, the immunological data (the type, density, and location of immune cells within the tumor samples) were found to be a better predictor of patient survival than the histopathological methods currently used to stage colorectal cancer. The results were validated in two additional patient populations. These data support the hypothesis that the adaptive immune response influences the behavior of human tumors. In situ analysis of tumor-infiltrating immune cells may therefore be a valuable prognostic tool in the treatment of colorectal cancer and possibly other malignancies.

5,536 citations

Journal ArticleDOI
TL;DR: A first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure can achieve O(1/N2) convergence on problems, where the primal or the dual objective is uniformly convex, and it can show linear convergence, i.e. O(ωN) for some ω∈(0,1), on smooth problems.
Abstract: In this paper we study a first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure. We prove convergence to a saddle-point with rate O(1/N) in finite dimensions for the complete class of problems. We further show accelerations of the proposed algorithm to yield improved rates on problems with some degree of smoothness. In particular we show that we can achieve O(1/N 2) convergence on problems, where the primal or the dual objective is uniformly convex, and we can show linear convergence, i.e. O(? N ) for some ??(0,1), on smooth problems. The wide applicability of the proposed algorithm is demonstrated on several imaging problems such as image denoising, image deconvolution, image inpainting, motion estimation and multi-label image segmentation.

4,487 citations


Authors

Showing all 9378 results

NameH-indexPapersCitations
Andrew Zisserman167808261717
Jean-Luc Brédas134102685803
Christos H. Papadimitriou12152663143
Gert Pfurtscheller11750762873
Ulrich Pöschl10243135847
Philippe Dubois101109848086
Thomas Brox9932994431
Eduardo D. Sontag9766149633
Detlef P. van Vuuren9539145013
Martin Winter9375145506
Ullrich Scherf9273536972
Philip P. Power9066533211
Stephen G. Withers8959928147
Keywan Riahi8731858030
Leo Eberl8625423266
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202387
2022266
20211,506
20201,546
20191,523
20181,359