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Home Page for Shawna Thomas | Parasol Laboratory


Picture Shawna Thomas
Postdoctoral Research Associate
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~sthomas/
Department of Computer Science and Engineering email:
Texas A&M University office: 410C HRBB
College Station, TX 77843-3112 tel: (979) 458-1547
USA fax: (979) 458-0718


Howdy! I am a Postdoctoral Research Associate in Computer Science at Texas A&M University. I work in the Parasol Lab with Dr. Nancy Amato. Our group develops randomized motion planning algorithms than can be applied to many different areas such as: mobile robots, virtual prototyping, computational biology and computational neuroscience. My research focus is on motion planning algorithms and their application to computational biology.


CV (ps, pdf) Course Work


Computational Biology:

Protein Folding and Motions
In this research, we study protein folding pathways and protein motions. It is critical that we better understand protein motion and the folding process for several reasons. First, understanding the folding process can give insight into how to develop better structure prediction algorithms, a major thrust of ongoing research. Second, some diseases such as Alzheimer's and Mad Cow disease are caused by misfolded proteins. By studying how these proteins misfold, scientists can develop better treatments. Third, many biochemical processes are regulated by proteins switching from one structure to another. We propose a technique for computing protein folding pathways and motions based on the successful probablistic roadmap (PRM) method for robotics motion planning. Our technique can compute thousands of pathways in just a few hours on a single desktop PC. We validated our approach against known experimental results for several small proteins. In addition, our approach is able to detect the subtle folding differences between structurally similar proteins G, L, and mutated forms of G and L. We are also able to use our technique to simulate exchange rates and identify the protein folding core.
Publications
RNA Folding
We propose a novel motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the probabilistic roadmap (PRM) motion planners we have successfully applied to study protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape. We can use our roadmap to compute RNA population kinetics and transition rates. We validated our techinque by comparing population kinetics of the complete landscape to that of our roadmap for several small RNA. We also studied energy barriers along pathways between the correct RNA structure and the misfolded structure.
Publications
Parallel Protein Folding
Our protein folding technique, although significantly more efficient than previous methods, still requires large amounts of computational resources. For example, a protein that takes several hours on a desktop PC using a coarse energy calculation will require two weeks with a more detailed one. In addition, running time increases quadradically with protein length. It is imperative that we find a faster technique. Here, we use the Standard Template Adaptive Parallel Library (STAPL) to parallelize our existing protein folding code. STAPL allows for easy transition from sequential code to parallel code by extending the ANSI C++ Standard Template Library, and it provides portable efficiency to different systems, both shared memory and distributed memory models, without requiring user code modification. We obtained good speedups on multiple platforms, ranging from small linux clusters, to distributed shared memory machines, to massively parallel machines.
Publications
Protein-Protein Interactions (Undergraduate)
In 2001, I spent the summer working with Dr. Kavraki at Rice University as part of the CRAW Distributed Mentor Program. I investigated how to model interactions between large molecules like proteins, DNA, and RNA. I first studied how to compute Cartesian coordinates for every atom in a molecule given a set of torsional angles. I then did a literature survey to discover interaction examples and their biological function. After selecting a few target interactions, I began developing a probabilistic roadmap (PRM) method to model the interaction.


Motion Planning:

Parallel Motion Planning
We develop scalable methods for parallelizing sampling-based motion planning algorithms including Probabilistic Roadmap Methods (PRMs) and Rapidly-Exploring Random Trees (RRTs). For both classes of algorithms, we use the Standard Template Adaptive Parallel Library (STAPL) to produce platform independent parallel implementations.
Publications
Uniform Obstacle-based PRM
Sampling near obstacles is important since it improves the configuration coverage in difficult areas of C-space (such as narrow passages). There are several PRM variants proposed to increase sampling in important regions of C-space, particularly near C-obstacle boundaries. However, none of them can gaurantee the node distribution and they are not very efficient. We propose a new obstacle-based sampler, Uniform OBPRM (UOBPRM) which guarantees a uniform distribution near the C-obstacle surfaces. UOBPRM retains the intersections between C-obstacles and a set of uniformly distributed fixed-length segments. Our results show that UOBPRM yields samples that are mor uniformly distributed than previous obstacle-based methods. UOBPRM also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages.
Publications
Closed Chains
Motion planning for closed-chain systems is particularly difficult due to additional constraints, so-called closure constraints, placed on the system. In fact, the probability of randomly selecting a set of joint angles that satisfy the closure constraints is zero. We overcome this challenge by considering a representation of the chain as a hierarchy of sub-chains, each with its own reachable distance range, and instead of randomly sampling in the joint angle space, we recursively sample in the reachable distance space. This provides two distinct advantages over traditional approaches: (1) joint angles are quickly and easily calculated using basic trigonometry relationships instead of using more expensive inverse kinematics solvers, and (2) configurations are guaranteed to satisfy the closure constraints. We provided the necessary motion planning primitives to implement most sampling-based motion planners. Our method is fast and efficient in practice and easy to implement.
Publications
Adaptive Neighborhood Connection
Probabilistic Roadmap Methods (PRMs) are widely used motion planning algorithms that sample valid robot configurations and connect them to form a graph of feasible trajectories. There are many different strategies for connecting such configurations together. However, choosing which method to use, particularly in heterogeneous environments, can be quite challenging and is input problem dependent. We propose a machine learning approach called Adaptive Neighborhood Connection (ANC) that learns the appropriate strategy (or strategies) to employ from a set of available methods. This frees the user of the burden of selecting the best strategy and allows the seletion to change over time. Our results show that strategy performance is indeed problem (and region) dependent and ANC is able to learn which method to use for various environments (and regions).
Publications
Unsupervised Adaptive Motion Planning
Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement. We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.
Publications
RESAMPL: A Region-Sensitive Adaptive Motion Planner
In this work, we present RESAMPL, a motion planning strategy that uses local region information to make intelligent decisions about how and where to sample, which samples to connect together, and to find paths through the environment. RESAMPL classifies regions based on the entropy of the samples in it, and then uses these classifications to further refine the sampling. Regions are placed in a region graph that encodes relationships between regions, e.g., edges correspond to overlapping regions. The strategy for connecting samples is guided by the region graph, and can be exploited in both multi-query and single-query scenarios. Our experimental results comparing RESAMPL to previous multi-query and single-query methods show that RESAMPL is generally significantly faster and also usually requires fewer samples to solve the problem.
Publications
Incremental Map Generation
Probabilistic roadmap methods (PRMs) have been highly successful in solving many high degree of freedom problems. One important practical issue with PRMs is they do not provide an automated mechanism to determine what size roadmap to construct. In this work, we propose a new PRM-based framework called Incremental Map Generation (IMG) to address this problem. Our strategy is to break the map generation into several independent processes, each of which generates samples and connections independently. IMG proceeds by adding these collections of samples and connections to an existing roadmap until it satisfies some specified evaluation criteria. We propose some general evaluation criteria and show how they can be used to construct diferent types of roadmaps, e.g., roadmaps that coarsely or more finely map the space. In addition to addressing the roadmap size question, the fact that each roadmap increment is independently and deterministically seeded has several other benefits such as supporting roadmap reproducibility, the adaptive selection of sampling methods in different roadmap increments, and parallelization. We provide results illustrating the power of IMG.
Publications
Biasing / Composing PRM Samplers
Much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that out-performs all other sampling techniques in all motion planning problems. Instead, each sampling technique proposed has different strengths and weaknesses. We propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We provide a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single combination of distributions performs the best in all problems, and we identify which combinations perform better for the specific application domains studied.
Publications
Medial Axis PRM
We propose a general framework for sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. In particular, our framework supports methods that can retract a given configuration exactly or approximately onto the medial axis. This helps address the narrow passage problem in that we increase the probability of sampling in a narrow passage over uniform random sampling. We show that the probability of sampling in a narrow passage depends not only on the volume of the passage, but also on the volume of the obstacles surrounding it.
Publications
Customizable PRM
We propose a new approach for building and querying probabilistic roadmaps (PRMs). During roadmap construction, we build a coarse roadmap by performing only approximate node and/or edge validation. In the query stage, the roadmap is validated and refined only where necessary to solve the query. In addition, it is customized according to any specified query preferences at query time. Thus, the same roadmap can be used to solve multiple, variable query preferences. This approach yields more efficient solutions to many problems including protein folding, ligand binding, assembly planning, and nonholonomic planning for car-like robots.
Publications
Using Spheres to Improve Motion Planning (Undergraduate)
In this work, we use configuration space spheres to aid in connecting and growing probabilistic roadmaps (PRMs). For each node, we define a sphere centered at that node with a radius equal to the node's clearance in configuration space. Because configuration space obstacles are never explicitly computed, we approximate this radius. We first use these spheres to identify likely collision-free edges between nodes. If two spheres overlap, then the straightline edge between the two corresponding nodes is likely to be collision-free. We also use these spheres to expand the roadmap. New nodes are sampled such that their spheres intersect spheres of existing nodes, thus exploring the space while keeping the number of connected components low.


Publications

Protein Folding and Motions

Adaptive Local Learning in Sampling Based Motion Planning for Protein Folding, Chinwe Ekenna, Shawna Thomas, Nancy Amato, Bio Med Central Systems Biology, 10(2):165--179, Aug 2016.
Journal(abstract)

Adaptive Neighbor Connection Aids Protein Motion Modeling, Chinwe Ekenna, Shawna Thomas, Nancy Amato, In Proc. RSS Workshop on Robotics Methods for Structural and Dynamic Modeling of Molecular Systems, Jul 2014.
Proceedings(ps, pdf, abstract)

Rigidity Analysis for Protein Motion and Folding Core Identification, Shawna Thomas, Lydia Tapia, Chinwe Ekenna, Hsin-Yi (Cindy) Yeh, Nancy M. Amato, In Proc. of 2013 AAAI Wkshp. on Art. Int. and Robot. Meth. in Comp. Bio., Bellevue, WA, Jul 2013.
Proceedings(pdf, abstract)

A Multi-Directional Rapidly Exploring Random Graph (mRRG) for Protein Folding, Shuvra Nath, Shawna Thomas, Chinwe Ekenna, Nancy M. Amato, In ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pp. 44-51, Orlando, FL, USA, Oct 2012.
Proceedings(ps, pdf, abstract)

A Motion Planning Approach to Studying Molecular Motions, Lydia Tapia, Shawna Thomas, Nancy M. Amato, Communications in Information and Systems, 10(1):53-68, 2010.
Journal(pdf, abstract)

Rigidity Analysis for Modeling Protein Motion, Shawna Thomas, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, May 2010.
Ph.D. Thesis(ps, pdf, abstract)

Using Dimensionality Reduction to Better Capture RNA and Protein Folding Motions, Lydia Tapia, Shawna Thomas, Nancy M. Amato, Technical Report, TR08-005, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, U.S.A., Oct 2008.
Technical Report(ps, pdf, abstract)

Kinetics Analysis Methods For Approximate Folding Landscapes, Lydia Tapia, Xinyu Tang, Shawna Thomas, Nancy M. Amato, In Int. Conf. on Int. Sys. for Mol. Bio. (ISMB)/European Conf. on Comp. Bio.(ECCB), Vienna, Austria, Jul 2007. Also, Bioinformatics, 23(13):i539-i548, Jul 2007.
Journal(pdf, abstract)

Simulating Protein Motions with Rigidity Analysis, Shawna Thomas, Xinyu Tang, Lydia Tapia, Nancy M. Amato, Journal of Computational Biology, 14(6):839-855, Jul 2007. Also, In Proc. Int. Conf. Comput. Molecular Biology (RECOMB), pp. 394-409, Apr 2006.
Journal(ps, pdf, abstract) Proceedings(ps, pdf, abstract)

Parallel Protein Folding with STAPL, Shawna Thomas, Gabriel Tanase, Lucia K. Dale, Jose M. Moreira, Lawrence Rauchwerger, Nancy M. Amato, Concurrency and Computation: Practice and Experience, 17(14):1643-1656, Dec 2005.
Journal(ps, pdf, abstract)

Protein Folding by Motion Planning, Shawna Thomas, Guang Song, Nancy M. Amato, Physical Biology, 2:S148-S155, Nov 2005.
Journal(ps, pdf, abstract)

Parallel Protein Folding with STAPL, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Wkshp. on High Performance Computational Biology, Santa Fe, NM, Apr 2004.
Proceedings(ps, pdf, abstract)

A Path Planning-based Study of Protein Folding With a Case Study of Hairpin Formation in Protein G and L, Guang Song, Shawna Thomas, Ken A. Dill, J. Martin Scholtz, Nancy M. Amato, In Proc. Pac. Symp. of Biocomputing (PSB), pp. 240-251, Lihue, HI, Jan 2003.
Proceedings(ps, pdf, abstract)

RNA Folding

A Motion Planning Approach to Studying Molecular Motions, Lydia Tapia, Shawna Thomas, Nancy M. Amato, Communications in Information and Systems, 10(1):53-68, 2010.
Journal(pdf, abstract)

Simulating RNA Folding Kinetics on Approximated Energy Landscapes, Xinyu Tang, Shawna Thomas, Lydia Tapia, David P. Giedroc, Nancy M. Amato, Journal of Molecular Biology, 3811(4):1055-1067, Sep 2008.
Journal(pdf, abstract)

Tools for Simulating and Analyzing RNA Folding Kinetics, Xinyu Tang, Shawna Thomas, Lydia Tapia, Nancy M. Amato, In Proc. Int. Conf. Comput. Molecular Biology (RECOMB), pp. 268-282, San Francisco, CA, Apr 2007.
Proceedings(ps, pdf, abstract)

Using Motion Planning to Study RNA Folding Kinetics, Xinyu Tang, Bonnie Kirkpatrick, Shawna Thomas, Guang Song, Nancy M. Amato, Journal of Computational Biology, 12(6):862-881, Jul 2005. Also, In Proc. Int. Conf. Comput. Molecular Biology (RECOMB), pp. 252-261, San Diego, CA, Mar 2004.
Journal(ps, pdf, abstract) Proceedings(ps, pdf, abstract)

Parallel Protein Folding

Parallel Protein Folding with STAPL, Shawna Thomas, Gabriel Tanase, Lucia K. Dale, Jose M. Moreira, Lawrence Rauchwerger, Nancy M. Amato, Concurrency and Computation: Practice and Experience, 17(14):1643-1656, Dec 2005.
Journal(ps, pdf, abstract)

Parallel Protein Folding with STAPL, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Wkshp. on High Performance Computational Biology, Santa Fe, NM, Apr 2004.
Proceedings(ps, pdf, abstract)

Parallel Motion Planning

Blind RRT: A Probabilistically Complete Distributed RRT, Cesar Rodriguez, Jory Denny, Sam Jacobs, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 1758 - 1765, Tokyo, Japan, Nov 2013.
Proceedings(ps, pdf, abstract)

A Scalable Distributed RRT for Motion Planning, Sam Ade Jacobs, Nicholas Stradford, Cesar Rodriguez, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5088-5095, Karlsruhe, Germany, May 2013.
Proceedings(ps, pdf, abstract)

A Scalable Method for Parallelizing Sampling-Based Motion Planning Algorithms, Sam Ade Jacobs, Kasra Manavi, Juan Burgos, Jory Denny, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2529-2536, St. Paul, Minnesota, USA, May 2012.
Proceedings(ps, pdf, abstract)

Parallel Protein Folding with STAPL, Shawna Thomas, Gabriel Tanase, Lucia K. Dale, Jose M. Moreira, Lawrence Rauchwerger, Nancy M. Amato, Concurrency and Computation: Practice and Experience, 17(14):1643-1656, Dec 2005.
Journal(ps, pdf, abstract)

Parallel Protein Folding with STAPL, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Wkshp. on High Performance Computational Biology, Santa Fe, NM, Apr 2004.
Proceedings(ps, pdf, abstract)

Uniform Obstacle-based PRM

Nearly Uniform Sampling on Surfaces with Applications to Motion Planning, Mukulika Ghosh, Cindy (Hsin-Yi) Yeh, Shawna Thomas, Nancy M. Amato, Technical Report, TR13-005, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, Apr 2013.
Technical Report(pdf, abstract)

UOBPRM: A Uniformly Distributed Obstacle-Based PRM, Cindy (Hsin-Yi) Yeh, Shawna Thomas, David Eppstein, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 2655-2662, Vilamoura, Algarve, Portugal, Oct 2012.
Proceedings(ps, pdf, ppt, abstract)

Closed Chains

Reachable Volume RRT, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2977-2984, Seattle, Washington, May 2015.
Proceedings(ps, pdf, abstract)

Sampling Based Motion Planning with Reachable Volumes: Application to Manipulators and Closed Chain Systems, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Sep 2014.
Proceedings(pdf, abstract)

Sampling-Based Motion Planning with Reachable Volumes: Theoretical Foundations, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Hong Kong, China, May 2014.
Proceedings(pdf, abstract)

Sampling Based Motion Planning with Reachable Volumes, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, Technical Report, TR14-003, Parasol Laboratory, Department of Computer Science, Texas A&M University, Feb 2014.
Technical Report(pdf, abstract)

Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems, Xinyu Tang, Shawna Thomas, Philip Coleman, Nancy M. Amato, International Journal of Robotics Research, 29(7):916-934, Jun 2010.
Journal(pdf, abstract)

Planning with Reachable Distances, Xinyu Tang, Shawna Thomas, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), Guanajuato, Mexico, Dec 2008.
Proceedings(ps, pdf, abstract)

Planning with Reachable Distances: Fast Enforcement of Closure Constraints, Xinyu Tang, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2694-2699, Rome, Italy, Apr 2007.
Proceedings(ps, pdf, abstract)

Adaptive Neighborhood Connection

Adaptive Neighbor Connection using Node Characterization, Chinwe Ekenna, Shawna Thomas, Nancy Amato, Technical Report, TR14-005, Apr 2014.
Technical Report(pdf)

Adaptive Neighbor Connection for PRMs: A Natural Fit for Heterogeneous Environments and Parallelism, Chinwe Ekenna, Sam Ade Jacobs, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Tokyo, Japan, Nov 2013.
Proceedings(pdf, abstract)

Unsupervised Adaptive Motion Planning

An Unsupervised Adaptive Strategy for Constructing Probabilistic Roadmaps, Lydia Tapia, Shawna Thomas, Bryan Boyd, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4037-4044, Kobe, Japan, May 2009.
Proceedings(pdf, abstract)

RESAMPL: A Region-Sensitive Adaptive Motion Planner

RESAMPL: A Region-Sensitive Adaptive Motion Planner, Samuel Rodriguez, Shawna Thomas, Roger Pearce, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), pp. 285-300, New York City, NY, Jul 2006.
Proceedings(ps, pdf, abstract)

Incremental Map Generation

Incremental Map Generation (IMG), Dawen Xie, Marco Morales, Roger Pearce, Shawna Thomas, Jyh-Ming Lien, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), New York City, NY, Jul 2006. Also, Technical Report, TR06-005, Department of Computer Science and Engineering, Texas A&M University, Mar 2006.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Biasing / Composing PRM Samplers

Biasing Samplers to Improve Motion Planning Performance, Shawna Thomas, Marco Morales, Xinyu Tang, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1625-1630, Rome, Italy, Apr 2007.
Proceedings(ps, pdf, abstract)

Medial Axis PRM

MARRT: Medial Axis Biased Rapidly-Exploring Random Trees, Jory Denny, Evan Greco, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 90 - 97, Hong Kong, China, Jun 2014.
Proceedings(ps, pdf, abstract)

UMAPRM: Uniformly Sampling the Medial Axis, Hsin-Yi (Cindy) Yeh, Jory Denny, Aaron Lindsey, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5798 - 5803, Hong Kong, China, Jun 2014.
Proceedings(ps, pdf, abstract)

A General Framework for Sampling on the Medial Axis of the Free Space, Jyh-Ming Lien, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4439-4444, Taipei, Taiwan, Sep 2003.
Proceedings(ps, pdf, abstract)

Customizable PRM

A General Framework for PRM Motion Planning, Guang Song, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4445-4450, Taipei, Taiwan, Sep 2003.
Proceedings(ps, pdf, abstract)

Customizing PRM Roadmaps at Query Time, Guang Song, Shawna Miller, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1500-1505, Seoul, Korea, May 2001.
Proceedings(ps, pdf, abstract)