Relevant bibliographies by topics / Data structures

Academic literature on the topic 'Data structures'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Data structures"

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Tamassia, Roberto. "Data structures." ACM Computing Surveys 28, no. 1 (March 1996): 23–26. http://dx.doi.org/10.1145/234313.234323.

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Jarc, Duane J. "Data structures." ACM SIGCSE Bulletin 26, no. 2 (June 1994): 2–4. http://dx.doi.org/10.1145/181648.181651.

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Biswas, Ranjit. "Data structures for big data." International Journal of Computing and Optimization 1 (2014): 73–93. http://dx.doi.org/10.12988/ijco.2014.4813.

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Basch, Julien, Leonidas J. Guibas, and John Hershberger. "Data Structures for Mobile Data." Journal of Algorithms 31, no. 1 (April 1999): 1–28. http://dx.doi.org/10.1006/jagm.1998.0988.

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Yarosh, Svetlana, and Mark Guzdial. "Narrating data structures." Journal on Educational Resources in Computing 7, no. 4 (January 2008): 1–20. http://dx.doi.org/10.1145/1316450.1316456.

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RUUS, H. "Lexical Data Structures." Literary and Linguistic Computing 3, no. 3 (July 1, 1988): 169–76. http://dx.doi.org/10.1093/llc/3.3.169.

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Giles, D. "Editorial - Data Structures." Computer Journal 34, no. 5 (May 1, 1991): 385. http://dx.doi.org/10.1093/comjnl/34.5.385.

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Demaine, Erik D., John Iacono, and Stefan Langerman. "Retroactive data structures." ACM Transactions on Algorithms 3, no. 2 (May 2007): 13. http://dx.doi.org/10.1145/1240233.1240236.

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Louchard, G., Claire Kenyon, and R. Schott. "Data Structures' Maxima." SIAM Journal on Computing 26, no. 4 (August 1997): 1006–42. http://dx.doi.org/10.1137/s0097539791196603.

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Nair, Achuth Sankar S., and T. Mahalakshmi. "Conceptualizing data structures." ACM SIGCSE Bulletin 36, no. 4 (December 2004): 97–100. http://dx.doi.org/10.1145/1041624.1041668.

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Dissertations / Theses on the topic "Data structures"

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Jarvis, Kimberley James. "Transactional data structures." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/transactional-data-structures(7060eaec-7dbd-4d5a-be1a-a753d9aa32d5).html.

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Concurrent programming is difficult and the effort is rarely rewarded by faster execution. The concurrency problem arises because information cannot pass instantly between processors resulting in temporal uncertainty. This thesis explores the idea that immutable data and distributed concurrency control can be combined to allow scalable concurrent execution and make concurrent programming easier. A concurrent system that does not impose a global ordering on events lends itself to a scalable distributed implementation. A concurrent programming environment in which the ordering of events affecting an object is enforced locally has intuitive concurrent semantics. This thesis introduces Transactional Data Structures which are data structures that permit access to past versions, although not all accesses succeed. These data structures form the basis of a concurrent programming solution that supports database type transactions in memory. Transactional Data Structures permit non-blocking concurrent access to familiar abstract data types such as deques, maps, vectors and priority queues. Using these data structures a programmer can write a concurrent program in C without having to reason about locks. The solution is evaluated by comparing the performance of a concurrent algorithm to calculate the minimum spanning tree of a graph with that of a similar algorithm which uses Transactional Memory and by comparing a non-blocking Producer Consumer Queue with its blocking counterpart.
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Eastep, Jonathan M. (Jonathan Michael). "Smart data structures : an online machine learning approach to multicore data structures." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65967.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 175-180).
As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.
by Jonathan M. Eastep.
Ph.D.
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Ohashi, Darin. "Cache Oblivious Data Structures." Thesis, University of Waterloo, 2001. http://hdl.handle.net/10012/1060.

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This thesis discusses cache oblivious data structures. These are structures which have good caching characteristics without knowing Z, the size of the cache, or L, the length of a cache line. Since the structures do not require these details for good performance they are portable across caching systems. Another advantage of such structures isthat the caching results hold for every level of cache within a multilevel cache. Two simple data structures are studied; the array used for binary search and the linear list. As well as being cache oblivious, the structures presented in this thesis are space efficient, requiring little additional storage. We begin the discussion with a layout for a search tree within an array. This layout allows Searches to be performed in O(log n) time and in O(log n/log L) (the optimal number) cache misses. An algorithm for building this layout from a sorted array in linear time is given. One use for this layout is a heap-like implementation of the priority queue. This structure allows Inserts, Heapifies and ExtractMaxes in O(log n) time and O(log nlog L) cache misses. A priority queue using this layout can be builtfrom an unsorted array in linear time. Besides the n spaces required to hold the data, this structure uses a constant amount of additional storage. The cache oblivious linear list allows scans of the list taking Theta(n) time and incurring Theta(n/L) (the optimal number) cache misses. The running time of insertions and deletions is not constant, however it is sub-polynomial. This structure requires e*n additional storage, where e is any constant greater than zero.
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Chaudhary, Amitabh. "Applied spatial data structures for large data sets." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068131.

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Miner, andrew S. "Data structures for the analysis of large structured Markov models." W&M ScholarWorks, 2000. https://scholarworks.wm.edu/etd/1539623985.

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High-level modeling formalisms are increasingly popular tools for studying complex systems. Given a high-level model, we can automatically verify certain system properties or compute performance measures about the system. In the general case, measures must be computed using discrete-event simulations. In certain cases, exact numerical analysis is possible by constructing and analyzing the underlying stochastic process of the system, which is a continuous-time Markov chain (CTMC) in our case. Unfortunately, the number of states in the underlying CTMC can be extremely large, even if the high-level model is "small". In this thesis, we develop data structures and techniques that can tolerate these large numbers of states.;First, we present a multi-level data structure for storing the set of reachable states of a model. We then introduce the concept of event "locality", which considers the components of the model that an event may affect. We show how a state generation algorithm using our multi-level structure can exploit event locality to reduce CPU requirements.;Then, we present a symbolic generation technique based on our multi-level structure and our concept of event locality, in which operations are applied to sets of states. The extremely compact data structure and efficient manipulation routines we present allow for the examination of much larger systems than was previously possible.;The transition rate matrix of the underlying CTMC can be represented with Kronecker algebra under certain conditions. However, the use of Kronecker algebra introduces several sources of CPU overhead during numerical solution. We present data structures, including our new data structure called matrix diagrams, that can reduce this CPU overhead. Using our techniques, we can compute measures for large systems in a fraction of the time required by current state-of-the-art techniques.;Finally, we present a technique for approximating stationary measures using aggregations of the underlying CTMC. Our technique utilizes exact knowledge of the underlying CTMC using our compact data structure for the reachable states and a Kronecker representation for the transition rates. We prove that the approximation is exact for models possessing a product-form solution.
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Jürgens, Marcus. "Index structures for data warehouses." [S.l. : s.n.], 1999. http://www.diss.fu-berlin.de/2000/93/index.html.

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Jürgens, Marcus. "Index structures for data warehouses." Berlin ; Heidelberg : Springer, 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96554155X.

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Colbrook, Adrian. "The engineering of data structures." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/843605/.

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Abstraction in computer programming provides a means of reducing complexity by emphasising the significant information (program behaviour) whilst suppressing the immaterial (program implementation). This aids program construction, improves reliability and maintainability, and eases the application of formal correctness proofs. The importance of data abstraction in the specification, design and implementation of large systems raises the question as to whether such methods may be applied in the context of programming languages designed before the widespread use of abstraction techniques. The program structuring facilities available in FORTRAN 77 support a form of encapsulation for simple data structures. In light of this mechanism provided by the language, state-based specification was found to be most appropriate. A specification technique incorporating object-oriented techniques is particularly suitable and allows a library of object classes to be specified and then implemented in sequential FORTRAN 77. Refinement extends the object classes so as to provide the commonly occurring generators for use in iterative constructs. Therefore, the advantages of data abstraction methods may be obtained in an early procedural language such as FORTRAN 77. Data abstraction provides data independence : a change in the representation for a particular class of objects affects only the code that implements the associated operations. This allows parallel implementations to be considered, without changes to the original specification or to any user-code. The provision of such parallel data structures is required for the migration of sequential systems onto parallel distributed memory architectures. As an illustration of this approach a general 2P2-2P (for integer P≥3) search tree utilising a pipeline of processors in a distributed memory architecture is shown to provide a means of implementing the object classes. Variations in both the number of processors allocated to the pipeline and the value of P allows the optimal search structure for a given architecture to be determined. These structures are highly efficient leading to improvements in both throughput and response time as processors are added to the array. An efficient parallel implementation of object classes is therefore achieved within the tight interface provided by abstraction.
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Jürgens, Marcus. "Index structures for data warehouses /." Berlin [u.a.] : Springer, 2002. http://www.loc.gov/catdir/enhancements/fy0817/2002021075-d.html.

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Goulet, Jean 1939. "Data structures for chess programs." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=65427.

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Books on the topic "Data structures"

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Data structures. Englewood Cliffs, N.J: Prentice Hall, 1989.

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David, Giles, ed. Data structures. Cambridge: Cambridge University Press, for the British Computer Society., 1991.

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Data types and data structures. Englewood Cliffs, N.J: Prentice-Hall International, 1986.

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Keogh, James Edward. Data structures demystified. New York: McGraw-Hill/Osborne, 2004.

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Yedidyah, Langsam, and Augenstein Moshe J, eds. Data structures usingC. Englewood Cliffs, N.J: Prentice Hall, 1990.

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Brass, Peter. Advanced data structures. New York, NY: Cambrige University Press, 2008.

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Computer analysis of structures: Matrix structural analysis structured programming. New York: Elsevier, 1985.

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Genetic programming and data structures: Genetic programming + data structures = automatic programming! Boston: Kluwer Academic Publishers, 1998.

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Data structures using C++. 2nd ed. New Delhi: Course Technology/Cengage Learning India, 2012.

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Lubiw, Anna, and Mohammad Salavatipour, eds. Algorithms and Data Structures. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83508-8.

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Book chapters on the topic "Data structures"

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Jay, Barry. "Data Structures." In Pattern Calculus, 23–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89185-7_3.

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Angell, Ian O., and Dimitrios Tsoubelis. "Data structures." In Advanced Graphics on VGA and XGA Cards Using Borland C++, 47–70. London: Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-22336-7_3.

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King, M. J., and J. P. Pardoe. "Data Structures." In Program Design Using JSP, 4–15. London: Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-22081-6_2.

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Dudman, Kay. "Data structures." In JSP for Practical Program Design, 7–28. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2537-7_2.

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Hunt, John. "Data Structures." In Java for Practitioners, 131–46. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0843-6_14.

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Taylor, Graham. "Data Structures." In Making Sense of Information Technology, 151–77. London: Macmillan Education UK, 1988. http://dx.doi.org/10.1007/978-1-349-10649-3_9.

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Hodges, Jason Lee. "Data Structures." In Software Engineering from Scratch, 231–56. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5206-2_12.

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Manelli, Luciano. "Data Structures." In Introducing Algorithms in C, 1–12. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5623-7_1.

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Betounes, David, and Mylan Redfern. "Data Structures." In Mathematical Computing, 149–201. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0067-0_6.

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Johansson, Anna-Lena, Agneta Eriksson-Granskog, and Anneli Edman. "Data Structures." In Prolog Versus You, 55–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-71922-6_3.

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Conference papers on the topic "Data structures"

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Pinto, Domenick J. "Data structures." In the 1988 ACM sixteenth annual conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/322609.323146.

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Patrascu, Mihai. "(Data) STRUCTURES." In 2008 IEEE 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS). IEEE, 2008. http://dx.doi.org/10.1109/focs.2008.69.

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Rogowitz, Bernice E., and Lloyd A. Treinish. "Data structures and perceptual structures." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Jan P. Allebach and Bernice E. Rogowitz. SPIE, 1993. http://dx.doi.org/10.1117/12.152734.

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RUSH, T., P. SCHRANTZ, and B. AGRAWAL. "Analysis of Intelsat V flight data." In 28th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1987. http://dx.doi.org/10.2514/6.1987-784.

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Campbell, Mark, and Mark Campbell. "Uncertainty effects in model-data correlation." In 38th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-1030.

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SMITH, SUZANNE, and CHRISTOPHER BEATTIE. "Optimal identification using inconsistent modal data." In 32nd Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-948.

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KUNZ, D., and A. HOPKINS. "Structured data in structural analysis software." In 26th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1985. http://dx.doi.org/10.2514/6.1985-742.

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Micciancio, Daniele. "Oblivious data structures." In the twenty-ninth annual ACM symposium. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/258533.258638.

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Balakrishnan, Darshana, Lukasz Ziarek, and Oliver Kennedy. "Fluid data structures." In the 17th ACM SIGPLAN International Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3315507.3330197.

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Goodrich, Michael T., Evgenios M. Kornaropoulos, Michael Mitzenmacher, and Roberto Tamassia. "Auditable Data Structures." In 2017 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2017. http://dx.doi.org/10.1109/eurosp.2017.46.

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Reports on the topic "Data structures"

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Mix, Scott, Mark Rice, Siddharth Sridhar, Charles Schmidt, Srini Raju, Carlos Gonzales-Perez, and Debraj Bharadwaj. Universal Utility Data Exchange (UUDEV) - Data Structures. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1778856.

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Kahan, Simon. Data Structures for Extreme Scale Computing. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1395134.

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Liblit, Ben, and Alexander Aiken. Type Systems for Distributed Data Structures. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada603881.

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Bowman, Keith, and Xiaoping Qian. Uncovering Topological Structures in Unstructured Data. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada619112.

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Currie, A., and B. Ady. GEOSIS project: knowledge representation and data structures for geoscience data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128053.

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Portier, Rebecca W., Richard D. Peacock, and Paule A. Reneke. Data structures for the fire data management system, FDMS 2.0. Gaithersburg, MD: National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6088.

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Tamassia, Roberto. Dynamic Data Structures for Two-Dimensional Searching. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada201173.

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Lofstead II, Gerald F. Integrated IO Services for Trilinos Data Structures. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1504109.

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Ravichandran, A., and K. Kant. Analysis and Synthesis of Robust Data Structures. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada224568.

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Giglio, Stefano, Bryan Kelly, and Serhiy Kozak. Equity Term Structures without Dividend Strips Data. Cambridge, MA: National Bureau of Economic Research, April 2023. http://dx.doi.org/10.3386/w31119.

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