Search

Your search keyword '"Blekas, Konstantinos"' showing total 194 results
Start Over Author "Blekas, Konstantinos"
194 results on '"Blekas, Konstantinos"'

2. Multi-Label Sampling based on Local Label Imbalance

Author

Liu, Bin, Blekas, Konstantinos, and Tsoumakas, Grigorios

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data., Comment: arXiv admin note: text overlap with arXiv:1905.00609

Published
2020

4. Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods

Author

Kravaris, Theocharis, Spatharis, Christos, Bastas, Alevizos, Vouros, George A., Blekas, Konstantinos, Andrienko, Gennady, Andrienko, Natalia, and Garcia, Jose Manuel Cordero

Subjects
Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence
Abstract

In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.

Published
2019

6. Machine-Learning Methods Estimating Flights' Hidden Parameters for the Prediction of KPIs.

Author

Vouros, George, Ioannidis, Ioannis, Santipantakis, Georgios, Tranos, Theodore, Blekas, Konstantinos, Melgosa, Marc, and Prats, Xavier

Abstract

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights' hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights' KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters' values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

8. Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns.

Author

Tranos, Theodore, Spatharis, Christos, Blekas, Konstantinos, and Stafylopatis, Andreas-Giorgios

Subjects
TRAFFIC engineering, REINFORCEMENT learning, CITY traffic, TRAFFIC flow, AUTOMATIC control systems
Abstract

The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

9. A Bayesian Ensemble Regression Framework on the Angry Birds Game

Author

Tziortziotis, Nikolaos, Papagiannis, Georgios, and Blekas, Konstantinos

Subjects
Computer Science - Artificial Intelligence
Abstract

An ensemble inference mechanism is proposed on the Angry Birds domain. It is based on an efficient tree structure for encoding and representing game screenshots, where it exploits its enhanced modeling capability. This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem. To this direction, we assume that each type of object material and bird pair has its own Bayesian linear regression model. In this way, a multi-model regression framework is designed that simultaneously calculates the conditional expectations of several objects and makes a target decision through an ensemble of regression models. Learning procedure is performed according to an online estimation strategy for the model parameters. We provide comparative experimental results on several game levels that empirically illustrate the efficiency of the proposed methodology., Comment: Angry Birds AI Symposium, ECAI 2014

Published
2014

10. Cover Tree Bayesian Reinforcement Learning

Author

Tziortziotis, Nikolaos, Dimitrakakis, Christos, and Blekas, Konstantinos

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it suitable for many reinforcement learning problems in continuous state spaces. We demonstrate this in an experimental comparison with least squares policy iteration.

Published
2013

11. Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle.

Author

Chaysri, Piyabhum, Spatharis, Christos, Vlachos, Kostas, and Blekas, Konstantinos

Subjects
AUTONOMOUS vehicles, REINFORCEMENT learning, ROBOTICS competitions, INTELLIGENT control systems, LEARNING strategies, REMOTELY piloted vehicles, BOATS & boating
Abstract

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

12. Learning Policies for Resolving Demand-Capacity Imbalances During Pre-tactical Air Traffic Management

Author

Kravaris, Theocharis, Vouros, George A., Spatharis, Christos, Blekas, Konstantinos, Chalkiadakis, Georgios, Garcia, Jose Manuel Cordero, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Berndt, Jan Ole, editor, Petta, Paolo, editor, and Unland, Rainer, editor

Published
2017
Full Text
View/download PDF

13. Play Ms. Pac-Man Using an Advanced Reinforcement Learning Agent

Author

Tziortziotis, Nikolaos, Tziortziotis, Konstantinos, Blekas, Konstantinos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Likas, Aristidis, editor, Blekas, Konstantinos, editor, and Kalles, Dimitris, editor

Published
2014
Full Text
View/download PDF

15. Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections.

Author

Spatharis, Christos and Blekas, Konstantinos

Subjects
*DEEP reinforcement learning, *AUTONOMOUS vehicles, *REINFORCEMENT learning, *MOTOR vehicle driving
Abstract

In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents' policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

16. AI in Greece: The Case of Research on Linked Geospatial Data

Author

Koubarakis, Manolis, Vouros, George, Chalkiadakis, Georgios, Plagianakos, Vassilis, Tjortjis, Christos, Kavallieratou, Ergina, Vrakas, Dimitris, Mavridis, Nikolaos, Petasis, Georgios, Blekas, Konstantinos, and Krithara, Anastasia

Subjects
Artificial intelligence -- Research, Geospatial data -- Case studies, Nonprofit organizations -- Services, Artificial intelligence, Business
Abstract

We survey the AI research carried out in Greece recently. We concentrate on the case of linked geospatial data, an area with significant practical importance, very interesting research results, and [...]

Published
2018

17. An Online Kernel-Based Clustering Approach for Value Function Approximation

Author

Tziortziotis, Nikolaos, Blekas, Konstantinos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Maglogiannis, Ilias, editor, Plagianakos, Vassilis, editor, and Vlahavas, Ioannis, editor

Published
2012
Full Text
View/download PDF

18. Value Function Approximation through Sparse Bayesian Modeling

Author

Tziortziotis, Nikolaos, Blekas, Konstantinos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Sanner, Scott, editor, and Hutter, Marcus, editor

Published
2012
Full Text
View/download PDF

19. InFeRno – An Intelligent Framework for Recognizing Pornographic Web Pages

Author

Karavarsamis, Sotiris, Ntarmos, Nikos, Blekas, Konstantinos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Gunopulos, Dimitrios, editor, Hofmann, Thomas, editor, Malerba, Donato, editor, and Vazirgiannis, Michalis, editor

Published
2011
Full Text
View/download PDF

20. Motion Segmentation by Model-Based Clustering of Incomplete Trajectories

Author

Karavasilis, Vasileios, Blekas, Konstantinos, Nikou, Christophoros, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Gunopulos, Dimitrios, editor, Hofmann, Thomas, editor, Malerba, Donato, editor, and Vazirgiannis, Michalis, editor

Published
2011
Full Text
View/download PDF

21. A Sparse Spatial Linear Regression Model for fMRI Data Analysis

Author

Oikonomou, Vangelis P., Blekas, Konstantinos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Konstantopoulos, Stasinos, editor, Perantonis, Stavros, editor, Karkaletsis, Vangelis, editor, Spyropoulos, Constantine D., editor, and Vouros, George, editor

Published
2010
Full Text
View/download PDF

22. Newtonian Spectral Clustering

Author

Blekas, Konstantinos, Christodoulidou, K., Lagaris, I. E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Alippi, Cesare, editor, Polycarpou, Marios, editor, Panayiotou, Christos, editor, and Ellinas, Georgios, editor

Published
2009
Full Text
View/download PDF

24. Split–Merge Incremental LEarning (SMILE) of Mixture Models

Author

Blekas, Konstantinos, Lagaris, Isaac E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
Full Text
View/download PDF

25. A Mixture Model Based Markov Random Field for Discovering Patterns in Sequences

Author

Blekas, Konstantinos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Antoniou, Grigoris, editor, Potamias, George, editor, Spyropoulos, Costas, editor, and Plexousakis, Dimitris, editor

Published
2006
Full Text
View/download PDF

27. Incremental Mixture Learning for Clustering Discrete Data

Author

Blekas, Konstantinos, Likas, Aristidis, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Vouros, George A., editor, and Panayiotopoulos, Themistoklis, editor

Published
2004
Full Text
View/download PDF

29. Data-driven estimation of flights’ hidden parameters

Author

Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems, Vouros, George, Tranos, Theodore, Blekas, Konstantinos, Santipantakis, Georgios, Melgosa Farrés, Marc, Prats Menéndez, Xavier, Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems, Vouros, George, Tranos, Theodore, Blekas, Konstantinos, Santipantakis, Georgios, Melgosa Farrés, Marc, and Prats Menéndez, Xavier

Abstract

This paper presents a data-driven methodology for the estimation of flights’ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs., This work has received funding from SESAR Joint Undertaking (JU) within SIMBAD project under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union, Peer Reviewed, Postprint (author's final draft)

Published
2022

31. Data-driven estimation of flights’ hidden parameters

Author

Vouros, George, Tranos, Theodore, Blekas, Konstantinos, Santipantakis, Georgios, Melgosa Farrés, Marc, Prats Menéndez, Xavier, Universitat Politècnica de Catalunya. Departament de Física, and Universitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems

Subjects
Performance standards, Hidden parameters, Air traffic control, Data-driven estimation, Organizational effectiveness, AI/ML, Aeronàutica i espai [Àrees temàtiques de la UPC], Prediction, KPIs
Abstract

This paper presents a data-driven methodology for the estimation of flights’ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs. This work has received funding from SESAR Joint Undertaking (JU) within SIMBAD project under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union

Published
2022

50. Identification of Brain Functional Networks Using a Model-Based Approach.

Author

Oikonomou, Vangelis P., Blekas, Konstantinos, and Astrakas, Loukas

Subjects
*TIME series analysis, *FUNCTIONAL magnetic resonance imaging, *REGRESSION analysis, *BRAIN-computer interfaces, *BRAIN imaging, *IDENTIFICATION
Abstract

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results