Informatik und Informationswissenschafthttps://kops.uni-konstanz.de:443/handle/123456789/52021-04-20T02:28:29Z2021-04-20T02:28:29ZSpeculative Execution of Similarity Queries : Real-Time Parameter Optimization through Visual ExplorationSpinner, Thilopop502194Schlegel, Udopop225000Schall, MartinSperrle, Fabianpop233828Sevastjanova, Ritapop241974Gobbo, BeatriceRauscher, Juliuspop519956El-Assady, Mennatallahpop219301Keim, Daniel A.pop25665Costa, ConstantinosPitoura, Evaggelia123456789/533102021-04-01T03:00:33Z2021Speculative Execution of Similarity Queries : Real-Time Parameter Optimization through Visual Exploration
Spinner, Thilo; Schlegel, Udo; Schall, Martin; Sperrle, Fabian; Sevastjanova, Rita; Gobbo, Beatrice; Rauscher, Julius; El-Assady, Mennatallah; Keim, Daniel A.
The parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this issue, supporting the user in understanding the connection between abstract model parameters and model results. We present a visual analytics system enabling result understanding and model refinement on a ranking-based similarity search algorithm. Our system (1) visualizes the results in a projection view, mapping their pair-wise similarity to screen distance, (2) indicates the influence of model parameters on the results, and (3) implements speculative execution to enable real-time iterative refinement on the time-intensive offline similarity search algorithm.
2021Spinner, ThiloSchlegel, UdoSchall, MartinSperrle, FabianSevastjanova, RitaGobbo, BeatriceRauscher, JuliusEl-Assady, MennatallahKeim, Daniel A.004The parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this issue, supporting the user in understanding the connection between abstract model parameters and model results. We present a visual analytics system enabling result understanding and model refinement on a ranking-based similarity search algorithm. Our system (1) visualizes the results in a projection view, mapping their pair-wise similarity to screen distance, (2) indicates the influence of model parameters on the results, and (3) implements speculative execution to enable real-time iterative refinement on the time-intensive offline similarity search algorithm.CEURCosta, ConstantinosPitoura, EvaggeliaINPROCEEDINGSurn:nbn:de:bsz:352-2-1e86tp6yez046engCEUR Workshop Proceedings28411613-0073Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference2021-03-31T13:23:28+02:00123456789/36Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference / Costa, Constantinos; Pitoura, Evaggelia (Hrsg.). - Aachen : CEUR, 2021. - (CEUR Workshop Proceedings ; 2841). - eISSN 1613-0073Nicosia, Cyprus2021-03-23AachenSIMPLIFY 2021: 1st International Workshop on Data Analytics and Machine Learning Made Simple8250412021-03-31T11:23:28ZtrueA Mesh-Free, Physics-Constrained Approach to solve Partial Differential Equations with a Deep Neural NetworkKress, Kevinpop257479123456789/533052021-03-31T01:03:18Z2020A Mesh-Free, Physics-Constrained Approach to solve Partial Differential Equations with a Deep Neural Network
Kress, Kevin
In this work, we utilized different approaches for solving partial differential equations with a deep neural network. The network respects the given physical laws of the equations by incorporating these constraints in the training process or in the network architecture. Specifically, a deep, feed-forward, and fully-connected neural network is used to approximate the partial differential equation, where the initial and boundary conditions are either hard or soft assigned. The resulting physics-informed surrogate model learns to satisfy the differential operator and the initial and boundary conditions and can be differentiated with respect to all input variables. The accuracy of the methods is demonstrated on multiple equations of different types and compared to either the exact or a finite element solution.
2020Kress, Kevin004Deep LearningPartial Differential EquationIn this work, we utilized different approaches for solving partial differential equations with a deep neural network. The network respects the given physical laws of the equations by incorporating these constraints in the training process or in the network architecture. Specifically, a deep, feed-forward, and fully-connected neural network is used to approximate the partial differential equation, where the initial and boundary conditions are either hard or soft assigned. The resulting physics-informed surrogate model learns to satisfy the differential operator and the initial and boundary conditions and can be differentiated with respect to all input variables. The accuracy of the methods is demonstrated on multiple equations of different types and compared to either the exact or a finite element solution.BSC_THESISurn:nbn:de:bsz:352-2-1pqapp54g26sl0eng2021-03-30T10:27:27+02:00123456789/36123456789/392021-03-30T08:27:27ZTruxillic acid derivatives act as peroxisome proliferator-activated receptor γ activatorsSteri, RamonaRupp, Matthiaspop529373Proschak, EwgenijSchroeter, TimonZettl, HeikoHansen, KatjaSchwarz, OliverMüller-Kuhrt, LutzMüller, Klaus-RobertSchubert-Zsilavecz, Manfred123456789/532642021-03-26T04:00:39Z2010-05-01Truxillic acid derivatives act as peroxisome proliferator-activated receptor γ activators
Steri, Ramona; Rupp, Matthias; Proschak, Ewgenij; Schroeter, Timon; Zettl, Heiko; Hansen, Katja; Schwarz, Oliver; Müller-Kuhrt, Lutz; Müller, Klaus-Robert; Schubert-Zsilavecz, Manfred
In previous studies, we identified a truxillic acid derivative as selective activator of the peroxisome proliferator-activated receptor gamma, which is a member of the nuclear receptor family and acts as ligand-activated transcription factor of genes involved in glucose metabolism. Herein we present the structure-activity relationships of 16 truxillic acid derivatives, investigated by a cell-based reporter gene assay guided by molecular docking analysis.
2010-05-01Steri, RamonaRupp, MatthiasProschak, EwgenijSchroeter, TimonZettl, HeikoHansen, KatjaSchwarz, OliverMüller-Kuhrt, LutzMüller, Klaus-RobertSchubert-Zsilavecz, Manfred004In previous studies, we identified a truxillic acid derivative as selective activator of the peroxisome proliferator-activated receptor gamma, which is a member of the nuclear receptor family and acts as ligand-activated transcription factor of genes involved in glucose metabolism. Herein we present the structure-activity relationships of 16 truxillic acid derivatives, investigated by a cell-based reporter gene assay guided by molecular docking analysis.ElsevierJOURNAL_ARTICLEeng10.1016/j.bmcl.2010.03.0260960-894X1464-340529202923209Bioorganic & Medicinal Chemistry Letters2021-03-25T15:24:52+01:00123456789/36Bioorganic & Medicinal Chemistry Letters ; 20 (2010), 9. - S. 2920-2923. - Elsevier. - ISSN 0960-894X. - eISSN 1464-3405true2021-03-25T14:24:52ZtrueTutorial: Software tools for hybrid systems verification, transformation, and synthesis : C2E2, HyST, and TuLiPDuggirala, Parasara SridharFan, ChuchuPotok, MatthewQi, BolunMitra, SayanViswanathan, MaheshBak, StanleyBogomolov, SergiyJohnson, Taylor T.Schilling, Christianpop529523123456789/532632021-03-26T04:00:40Z2016Tutorial: Software tools for hybrid systems verification, transformation, and synthesis : C2E2, HyST, and TuLiP
Duggirala, Parasara Sridhar; Fan, Chuchu; Potok, Matthew; Qi, Bolun; Mitra, Sayan; Viswanathan, Mahesh; Bak, Stanley; Bogomolov, Sergiy; Johnson, Taylor T.; Schilling, Christian
Hybrid systems have both continuous and discrete dynamics and are useful for modeling a variety of control systems, from air traffic control protocols to robotic maneuvers and beyond. Recently, numerous powerful and scalable tools for analyzing hybrid systems have emerged. Several of these tools implement automated formal methods for mathematically proving a system meets a specification. This tutorial session will present three recent hybrid systems tools: C2E2, HyST, and TuLiP. C2E2 is a simulated-based verification tool for hybrid systems, and uses validated numerical solvers and bloating of simulation traces to verify systems meet specifications. HyST is a hybrid systems model transformation and translation tool, and uses a canonical intermediate representation to support most of the recent verification tools, as well as automated sound abstractions that simplify verification of a given hybrid system. TuLiP is a controller synthesis tool for hybrid systems, where given a temporal logic specification to be satisfied for a system (plant) model, TuLiP will find a controller that meets a given specification.
2016Duggirala, Parasara SridharFan, ChuchuPotok, MatthewQi, BolunMitra, SayanViswanathan, MaheshBak, StanleyBogomolov, SergiyJohnson, Taylor T.Schilling, Christian004Hybrid systems have both continuous and discrete dynamics and are useful for modeling a variety of control systems, from air traffic control protocols to robotic maneuvers and beyond. Recently, numerous powerful and scalable tools for analyzing hybrid systems have emerged. Several of these tools implement automated formal methods for mathematically proving a system meets a specification. This tutorial session will present three recent hybrid systems tools: C2E2, HyST, and TuLiP. C2E2 is a simulated-based verification tool for hybrid systems, and uses validated numerical solvers and bloating of simulation traces to verify systems meet specifications. HyST is a hybrid systems model transformation and translation tool, and uses a canonical intermediate representation to support most of the recent verification tools, as well as automated sound abstractions that simplify verification of a given hybrid system. TuLiP is a controller synthesis tool for hybrid systems, where given a temporal logic specification to be satisfied for a system (plant) model, TuLiP will find a controller that meets a given specification.IEEEINPROCEEDINGSeng10.1109/CCA.2016.7587948978-1-5090-0756-1102410292016 IEEE Conference on Control Applications (CCA) : part of 2016 IEEE Multi-Conference on Systems and Control : September 19-22, 2016, Buenos Aires, Argentina2021-03-25T15:20:56+01:00123456789/362016 IEEE Conference on Control Applications (CCA) : part of 2016 IEEE Multi-Conference on Systems and Control : September 19-22, 2016, Buenos Aires, Argentina. - Piscataway, NJ : IEEE, 2016. - S. 1024-1029. - ISBN 978-1-5090-0756-1Buenos Aires2016-09-19Piscataway, NJ2016 IEEE Conference on Control Applications (CCA)2016-09-222021-03-25T14:20:56Z