Relevant bibliographies by topics / Fast search

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fast search'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Fast search"

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S Sao, Sphurti, and Rahila Shiekh. "A Review: Fast nearest Neighbour Search with Keywords." International Journal of Scientific Engineering and Research 4, no. 1 (2016): 52–54. https://doi.org/10.70729/ijser15657.

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Jiang, Qixia, and Maosong Sun. "Fast Query Recommendation by Search." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1192–97. http://dx.doi.org/10.1609/aaai.v25i1.8077.

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Query recommendation can not only effectively facilitate users to obtain their desired information but alsoincrease ads’ click-through rates. This paper presentsa general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values.This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data and some partition hyperplanes randomly generated in advance. In the recommendation stage, we search queries that have similar hash values to the given query, rank the found queries and return the top K queries as the recommendation result. All the experimental results demonstrate that our method achieves encouraging results in terms of efficiency and recommendation performance.
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Bandyopadhyay, Sanghamitra, Garisha Chowdhary, and Debarka Sengupta. "FOCS: Fast Overlapped Community Search." IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (2015): 2974–85. http://dx.doi.org/10.1109/tkde.2015.2445775.

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Yan, Zhiqiang, Remco Dijkman, and Paul Grefen. "Fast business process similarity search." Distributed and Parallel Databases 30, no. 2 (2012): 105–44. http://dx.doi.org/10.1007/s10619-012-7089-z.

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Brunig, M., and W. Niehsen. "Fast full-search block matching." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 2 (2001): 241–47. http://dx.doi.org/10.1109/76.905989.

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Burns, Ethan, Matthew Hatem, Michael Leighton, and Wheeler Ruml. "Implementing Fast Heuristic Search Code." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (2021): 25–32. http://dx.doi.org/10.1609/socs.v3i1.18245.

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Published papers rarely disclose implementation details. In this paper we show how such details can account for speedups of up to a factor of 28 for different implementations of the same algorithm. We perform an in-depth analysis of the most popular benchmark in heuristic search: the 15-puzzle. We study implementation choices in C++ for both IDA* and A* using the Manhattan distance heuristic. Results suggest that several optimizations deemed critical in folklore provide only small improvements while seemingly innocuous choices can play a large role. These results are important for ensuring that the correct conclusions are drawn from empirical comparisons
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Murase, Hiroshi, and V. V. Vinod. "Fast visual search using focused color matching?active search." Systems and Computers in Japan 31, no. 9 (2000): 81–88. http://dx.doi.org/10.1002/1520-684x(200008)31:9<81::aid-scj9>3.0.co;2-v.

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Matsugu, Shohei, Hiroaki Shiokawa, and Hiroyuki Kitagawa. "Fast Algorithm for Attributed Community Search." Journal of Information Processing 29 (2021): 188–96. http://dx.doi.org/10.2197/ipsjjip.29.188.

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Wang, Jason T. L., Huiyuan Shan, Dennis Shasha, and William H. Piel. "Fast Structural Search in Phylogenetic Databases." Evolutionary Bioinformatics 1 (January 2005): 117693430500100. http://dx.doi.org/10.1177/117693430500100009.

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CHANG, MING-CHING, CHIOU-SHANN FUH, and HSIEN-YEI CHEN. "FAST SEARCH ALGORITHMS FOR INDUSTRIAL INSPECTION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (2001): 675–90. http://dx.doi.org/10.1142/s0218001401001039.

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This paper presents an efficient general purpose search algorithm for alignment and an applied procedure for IC print mark quality inspection. The search algorithm is based on normalized cross-correlation and enhances it with a hierarchical resolution pyramid, dynamic programming, and pixel over-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industrial use. After proper optimization, the proposed procedure was tested on the IC inspection platforms in the Mechanical Industry Research Laboratories (MIRL), Industrial Technology Research Institute (ITRI), Taiwan. The proposed method meets all these criteria and has worked well in field tests on various IC products.
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Dissertations / Theses on the topic "Fast search"

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Vassef, Hooman. "Combining fast search and learning for scalable similarity search." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86566.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (leaves 38-39).<br>by Hooman Vassef.<br>S.B.and M.Eng.
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Schlieder, Torsten. "Fast similarity search in XML data." [S.l.] : [s.n.], 2003. http://www.diss.fu-berlin.de/2003/108/index.html.

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Kibriya, Ashraf Masood. "Fast Algorithms for Nearest Neighbour Search." The University of Waikato, 2007. http://hdl.handle.net/10289/2463.

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The nearest neighbour problem is of practical significance in a number of fields. Often we are interested in finding an object near to a given query object. The problem is old, and a large number of solutions have been proposed for it in the literature. However, it remains the case that even the most popular of the techniques proposed for its solution have not been compared against each other. Also, many techniques, including the old and popular ones, can be implemented in a number of ways, and often the different implementations of a technique have not been thoroughly compared either. This research presents a detailed investigation of different implementations of two popular nearest neighbour search data structures, KDTrees and Metric Trees, and compares the different implementations of each of the two structures against each other. The best implementations of these structures are then compared against each other and against two other techniques, Annulus Method and Cover Trees. Annulus Method is an old technique that was rediscovered during the research for this thesis. Cover Trees are one of the most novel and promising data structures for nearest neighbour search that have been proposed in the literature.
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Chung, Hing-yip Ronald, and 鍾興業. "Fast motion estimation with search center prediction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31220721.

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Soongsathitanon, Somphob. "Fast search algorithms for digital video coding." Thesis, University of Newcastle Upon Tyne, 2004. http://hdl.handle.net/10443/1003.

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Motion Estimation algorithm is one of the important issues in video coding standards such as ISO MPEG-1/2 and ITU-T H.263. These international standards regularly use a conventional Full Search (FS) Algorithm to estimate the motion of pixels between pairs of image blocks. Since a FS method requires intensive computations and the distortion function needs to be evaluated many times for each target block. the process is very time consuming. To alleviate this acute problem, new search algorithms, Orthogonal Logarithmic Search (OLS) and Diagonal Logarithmic Search (DLS), have been designed and implemented. The performance of the algorithms are evaluated by using standard 176x 144 pixels quarter common intermediate format (QCIF) benchmark video sequences and the results are compared to the traditional well-known FS Algorithm and a widely used fast search algorithm called the Three Step Search (3SS), The fast search algorithms are known as sub-optimal algorithms as they test only some of the candidate blocks from the search area and choose a match from a subset of blocks. These algorithms can reduce the computational complexity as they do not examine all candidate blocks and hence are algorithmically faster. However, the quality is generally not as good as that of the FS algorithms but can be acceptable in terms of subjective quality. The important metrics, time and Peak Signal to Noise Ratio are used to evaluate the novel algorithms. The results show that the strength of the algorithms lie in their speed of operation as they are much faster than the FS and 3SS. The performance in speed is improved by 85.37% and 22% over the FS and 3SS respectively for the OLS. For the DLS, the speed advantages are 88.77% and 40% over the FS and 3SS. Furthermore, the accuracy of prediction of OLS and DLS are comparahle to the 3SS.
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Nelson, Jelani (Jelani Osei). "External-memory search trees with fast insertions." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37084.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 65-68).<br>This thesis provides both experimental and theoretical contributions regarding external-memory dynamic search trees with fast insertions. The first contribution is the implementation of the buffered repository B-tree, a data structure that provably outperforms B-trees for updates at the cost of a constant factor decrease in query performance. This thesis also describes the cache-oblivious lookahead array, which outperforms B-trees for updates at a logarithmic cost in query performance, and does so without knowing the cache parameters of the system it is being run on. The buffered repository B-tree is an external-memory search tree that can be tuned for a tradeoff between queries and updates. Specifically, for any E [1/ lg B, 1] this data structure achieves O((1/EBl-E)(1 + logB(N/B))) block transfers for INSERT and DELETE and 0((/(1 + logB(N/B))) block transfers for SEARCH. The update complexity is amortized and is O((1/e)(1 + logB(N/B))) in the worst case. Using the value = 1/2, I was able to achieve a 17 times increase in insertion performance at the cost of only a 3 times decrease in search performance on a database with 12-byte items on a disk with a 4-kilobyte block size.<br>(cont.) This thesis also shows how to build a cache-oblivious data structure, the cache-oblivious lookahead array, which achieves the same bounds as the buffered repository B'-tree in the case where e = 1/ lg B. Specifically, it achieves an update complexity of O((1/B) log(N/B)) and a query complexity of O(log(N/B)) block transfers. This is the first data structure to achieve these bounds cache-obliviously. The research involving the cache-oblivious lookahead array represents joint work with Michael A. Bender, Jeremy Fineman, and Bradley C. Kuszmaul.<br>by Jelani Nelson.<br>M.Eng.
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Minz, Ian. "Modeling cooperative gene regulation using Fast Orthogonal Search." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1364.

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Begin, Steve. "A search for fast pulsars in globular clusters." Thesis, Link to full text, 2006. http://hdl.handle.net/2429/69.

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Wang, Huan-sheng. "Fast search techniques for video motion estimation and vector quantization." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13918.

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Kauffman, Kyle J. "Fast target tracking technique for synthetic aperture radars." Oxford, Ohio : Miami University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1250263416.

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Books on the topic "Fast search"

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Inc, ebrary, ed. Professional Microsoft search: FAST search, SharePoint search, and Search Server. Wiley, 2010.

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Karambizi, Natacha. Mahoro: A little girl's search for peace. Imagine We, 2017.

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Jack, O'Brien. The complete job search organizer: How to get a great job--fast. 2nd ed. Kiplinger, 1996.

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Nobles, Robin. Streetwise maximize web site traffic: Build web site traffic fast and free by optimizing search engine placement. Adams Media Corp., 2000.

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Wright, Esmond. The search for liberty: From origins to independence. Blackwell, 1995.

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Jack, O'Brien. The complete job search organizer, 1997-'98: How to get a great job--fast. 3rd ed. Kiplinger Times Business, 1997.

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Pollan, Michael. The omnivore's dilemma: The search for a perfect meal in a fast-food world. Bloomsbury, 2007.

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Oishi, Gene. In search of Hiroshi. C.E. Tuttle Co., 1988.

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pegasus. Fast Word Search. Jain Publishers Pvt. Limited, B., 2019.

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Kehoe, Miles, Mark Bennett, Jeff Fried, and Natalya Voskresenskaya. Professional Microsoft Search: FAST Search, SharePoint Search, and Search Server. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Fast search"

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St-Onge, Etienne, Eleftherios Garyfallidis, and D. Louis Collins. "Fast Tractography Streamline Search." In Computational Diffusion MRI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87615-9_8.

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Herley, Kieran T., Andrea Pietracaprina, and Geppino Pucci. "Fast deterministic backtrack search." In Automata, Languages and Programming. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61440-0_162.

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Navarro, Gonzalo, and Mathieu Raffinot. "Fast Regular Expression Search." In Algorithm Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7_17.

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Boldyreva, Alexandra, and Nathan Chenette. "Efficient Fuzzy Search on Encrypted Data." In Fast Software Encryption. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46706-0_31.

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Aoki, Kazumaro, Kunio Kobayashi, and Shiho Moriai. "Best differential characteristic search of FEAL." In Fast Software Encryption. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052333.

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Hell, Martin, and Thomas Johansson. "Some Attacks on the Bit-Search Generator." In Fast Software Encryption. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11502760_14.

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Houle, Michael E., Hisashi Kashima, and Michael Nett. "Fast Similarity Computation in Factorized Tensors." In Similarity Search and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32153-5_16.

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Rawal, Pragya, Harish Sharma, and Nirmala Sharma. "Fast Convergent Gravitational Search Algorithm." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0426-6_1.

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Furht, Borko, Joshua Greenberg, and Raymond Westwater. "Fast Search Algorithms: New Results." In Motion Estimation Algorithms for Video Compression. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6241-2_6.

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Ferro, Nicola. "Annotation Search: The FAST Way." In Research and Advanced Technology for Digital Libraries. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04346-8_4.

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Conference papers on the topic "Fast search"

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Vassef, Hooman, Chung-Sheng Li, and Vittorio Castelli. "Combining fast search and learning for fast similarity search." In Electronic Imaging, edited by Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373570.

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Qi, Meibin, Debao Wang, Jianguo Jiang, and Baolei An. "A Search Direction Extensible Fast Search Algorithm." In 2006 First International Symposium on Pervasive Computing and Applications. IEEE, 2006. http://dx.doi.org/10.1109/spca.2006.297469.

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Li, Jianheng, Fuhang Liang, Yuanxun Li, and Wei-Shi Zheng. "Fast Person Search Pipeline." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00195.

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Kim, Sungchul, Lee Sael, and Hwanjo Yu. "Fast protein 3D surface search." In the 7th International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2448556.2448629.

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Curtin, Ryan R., Parikshit Ram, and Alexander G. Gray. "Fast Exact Max-kernel Search." In Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611972832.1.

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Lokkoju, Satish, and Dinesh Reddy. "Fast coding unit partition search." In 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2012. http://dx.doi.org/10.1109/isspit.2012.6621307.

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Bocek, Thomas, Fabio Victora Hecht, David Hausheer, Ela Hunt, and Burkhard Stiller. "Mobile P2P Fast Similarity Search." In 2009 6th IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2009. http://dx.doi.org/10.1109/ccnc.2009.4785013.

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Darwish, Ahmed M. "Fast nearest-neighbor search algorithm." In Electronic Imaging: Science & Technology, edited by Robert L. Stevenson, Alexander I. Drukarev, and Thomas R. Gardos. SPIE, 1996. http://dx.doi.org/10.1117/12.234757.

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Tsai, Chun-Wei, Ko-Wei Huango, Ming-Chao Chiang, and Chu-Sing Yang. "A fast tree-based search algorithm for cluster search engine." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346100.

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Taguchi, S., S. Omachi, and H. Aso. "Fast visual search using simplified pruning rules - streamlined active search." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333927.

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Reports on the topic "Fast search"

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Endicott, D. L., and G. R. Kuhl. The Fast Area Search System (FASS). A Feasibility Study. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada263295.

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Aldrich, Susan. FAST ImPulse V.5.0 Product Search. Patricia Seybold Group, 2005. http://dx.doi.org/10.1571/pr3-31-05cc.

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Aldrich, Susan. FAST Data Search V 4.0 Self-Service and Support Search Solution. Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/pr9-16-04cc.

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ALDRIDGE, DAVID F. Fast Grid Search Algorithm for Seismic Source Location. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/759476.

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Prenger, R., B. Chen, T. Marlatt, and D. Merl. Fast MAP Search for Compact Additive Tree Ensembles (CATE). Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1078539.

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Veth, Michael, John Raquet, and Meir Pachter. Stochastic Constraints for Fast Image Correspondence Search with Uncertain Terrain Model. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada463028.

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Digalakis, V., M. Ostendorf, and J. R. Rohlicek. Fast Search Algorithms for Connected Phone Recognition Using the Stochastic Segment Model. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada459580.

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Nuttall, Albert H. Weighted Least Squares Fit of a Real Tone to Discrete Data, by Means of an Efficient Fast Fourier Transform Search. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada185910.

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Horrocks, Ian, and Ulrike Sattler. Optimised Reasoning for SHIQ. Aachen University of Technology, 2001. http://dx.doi.org/10.25368/2022.118.

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The tableau algorithm implemented in the FaCT knowledge representation system decides satisfiability and subsumption in SHIQ, a very expressive description logic providing, e.g., inverse and transitive roles, number restrictions, and general axioms. Intuitively, the algorithm searches for a tree-shaped abstraction of a model. To ensure termination of this algorithm without comprimising correctness, it stops expanding paths in the search tree using a so-called 'double-blocking' condition.
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Gillison, Fiona, Elisabeth Grey, Bas Verplanken, Julie Barnett, and Frances Baber. A rapid review of the acceptability and impact of approaches to reduce the salt, fat and sugar content of people’s diets on consumers and industry. Food Standards Agency, 2022. http://dx.doi.org/10.46756/sci.fsa.ljo674.

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Various approaches have been tried or proposed across the world to reduce population intake of fat, salt and sugar. These include initiatives that directly target food producers or retailers, consumers, or both. This report summarises the findings of 49 systematic reviews identified through a search of published literature, reporting on the acceptability and efficacy of population-scale initiatives to reduce fat, salt and sugar consumption.
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