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>tfw you pop into existence surrounded by people you’ve never met before
Thank you @mynders-universe for this fantastic piece of art of our mascot. I still love this very much!
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(Via: Hacker News)
G. Graefe and P. A. Larson. B-tree indexes and CPU caches. In Proceedings 17th International Conference on Data Engineering, pages 349–358, 2001.
S. Richter, V. Alvarez, and J. Dittrich. A seven-dimensional analysis of hashing methods and its implications on query processing. Proc. VLDB Endow., 9(3):96–107, Nov. 2015.
B. Fan, D. G. Andersen, M. Kaminsky, and M. D. Mitzenmacher. Cuckoo filter: Practically better than bloom. In Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, CoNEXT ’14, pages 75–88, New York, NY, USA, 2014. ACM.
K. Alexiou, D. Kossmann, and P.-A. Larson. Adaptive range filters for cold data: Avoiding trips to siberia. Proc. VLDB Endow., 6(14):1714–1725, Sept. 2013.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. Tensorflow: A system for large-scale machine learning. In OSDI, volume 16, pages 265–283, 2016.
A. Crotty, A. Galakatos, K. Dursun, T. Kraska, C. Binnig, U. Çetintemel, and S. Zdonik. An architecture for compiling udf-centric workflows. PVLDB, 8(12):1466–1477, 2015.
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017.
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016.
C. Kim, J. Chhugani, N. Satish, E. Sedlar, A. D. Nguyen, T. Kaldewey, V. W. Lee, S. A. Brandt, and P. Dubey. Fast: Fast architecture sensitive tree search on modern cpus and gpus. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD ’10, pages 339–350, New York, NY, USA, 2010. ACM.
E. R. Sparks, A. Talwalkar, D. Haas, M. J. Franklin, M. I. Jordan, and T. Kraska. Automating model search for large scale machine learning. In Proceedings of the Sixth ACM Symposium on Cloud Computing, SoCC 2015, Kohala Coast, Hawaii, USA, August 27-29, 2015, pages 368–380, 2015.
F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. Gruber. Bigtable: A distributed storage system for structured data (awarded best paper!). In 7th Symposium on Operating Systems Design and Implementation (OSDI ’06), November 6-8, Seattle, WA, USA, pages 205–218, 2006.
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F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. Gruber. Bigtable: A distributed storage system for structured data (awarded best paper!). In 7th Symposium on Operating Systems Design and Implementation (OSDI ’06), November 6-8, Seattle, WA, USA, pages 205–218, 2006.
G. E. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. CoRR, abs/1503.02531, 2015.
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M. Turcanik and M. Javurek. Hash function generation by neural network. In 2016 New Trends in Signal Processing (NTSP), pages 1–5, Oct 2016.
J. Wang, H. T. Shen, J. Song, and J. Ji. Hashing for similarity search: A survey. CoRR, abs/1408.2927, 2014.
J. Guo and J. Li. CNN based hashing for image retrieval. CoRR, abs/1509.01354, 2015.
F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. Gruber. Bigtable: A distributed storage system for structured data (awarded best paper!). In 7th Symposium on Operating Systems Design and Implementation (OSDI ’06), November 6-8, Seattle, WA, USA, pages 205–218, 2006.
M. Mitzenmacher. Compressed bloom filters. In Proceedings of the Twentieth Annual ACM Symposium on Principles of Distributed Computing, PODC 2001, Newport, Rhode Island, USA, August 26-29, 2001, pages 144–150, 2001.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680, 2014.
I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112, 2014.
A. Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1724–1734, 2014.
S. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair. A comparison of machine learning techniques for phishing detection. In Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit, pages 60–69. ACM, 2007.
R. B. Basnet, S. Mukkamala, and A. H. Sung. Detection of phishing attacks: A machine learning approach. Soft Computing Applications in Industry, 226:373–383, 2008.
G. Graefe and P. A. Larson. B-tree indexes and CPU caches. In Proceedings 17th International Conference on Data Engineering, pages 349–358, 2001.
R. Bayer and E. McCreight. Organization and maintenance of large ordered indices. In Proceedings of the 1970 ACM SIGFIDET (Now SIGMOD) Workshop on Data Description, Access and Control, SIGFIDET ’70, pages 107–141, New York, NY, USA, 1970. ACM.
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J. Boyar and K. S. Larsen. Efficient rebalancing of chromatic search trees. Journal of Computer and System Sciences, 49(3):667 – 682, 1994. 30th IEEE Conference on Foundations of Computer Science.
J. Rao and K. A. Ross. Making b+- trees cache conscious in main memory. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD ’00, pages 475–486, New York, NY, USA, 2000. ACM.
C. Kim, J. Chhugani, N. Satish, E. Sedlar, A. D. Nguyen, T. Kaldewey, V. W. Lee, S. A. Brandt, and P. Dubey. Fast: Fast architecture sensitive tree search on modern cpus and gpus. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD ’10, pages 339–350, New York, NY, USA, 2010. ACM.
C. Kim, J. Chhugani, N. Satish, E. Sedlar, A. D. Nguyen, T. Kaldewey, V. W. Lee, S. A. Brandt, and P. Dubey. Fast: Fast architecture sensitive tree search on modern cpus and gpus. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD ’10, pages 339–350, New York, NY, USA, 2010. ACM.
A. Shahvarani and H.-A. Jacobsen. A hybrid b+-tree as solution for in-memory indexing on cpu-gpu heterogeneous computing platforms. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD ’16, pages 1523–1538, New York, NY, USA, 2016. ACM.
K. Kaczmarski. B + -Tree Optimized for GPGPU.
M. Böhm, B. Schlegel, P. B. Volk, U. Fischer, D. Habich, and W. Lehner. Efficient in-memory indexing with generalized prefix trees. In Datenbanksysteme für Business, Technologie und Web (BTW), 14. Fachtagung des GI-Fachbereichs ”Datenbanken und Informationssysteme” (DBIS), 2.-4.3.2011 in Kaiserslautern, Germany, pages 227–246, 2011.
T. Kissinger, B. Schlegel, D. Habich, and W. Lehner. Kiss-tree: Smart latch-free in-memory indexing on modern architectures. In Proceedings of the Eighth International Workshop on Data Management on New Hardware, DaMoN ’12, pages 16–23, New York, NY, USA, 2012. ACM.
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V. Leis, A. Kemper, and T. Neumann. The adaptive radix tree: Artful indexing for main-memory databases. In Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), ICDE ’13, pages 38–49, Washington, DC, USA, 2013. IEEE Computer Society.
G. Graefe and P. A. Larson. B-tree indexes and CPU caches. In Proceedings 17th International Conference on Data Engineering, pages 349–358, 2001.
J. Goldstein, R. Ramakrishnan, and U. Shaft. Compressing Relations and Indexes. In ICDE, pages 370–379, 1998.
T. Neumann and G. Weikum. RDF-3X: A RISC-style Engine for RDF. Proc. VLDB Endow., pages 647–659, 2008.
H. Zhang, D. G. Andersen, A. Pavlo, M. Kaminsky, L. Ma, and R. Shen. Reducing the storage overhead of main-memory OLTP databases with hybrid indexes. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016, pages 1567–1581, 2016.
A. Galakatos, M. Markovitch, C. Binnig, R. Fonseca, and T. Kraska. A-tree: A bounded approximate index structure. under submission, 2017.
M. Athanassoulis and A. Ailamaki. BF-tree: Approximate Tree Indexing. In VLDB, pages 1881–1892, 2014.
G. Graefe. B-tree indexes, interpolation search, and skew. In Proceedings of the 2Nd International Workshop on Data Management on New Hardware, DaMoN ’06, New York, NY, USA, 2006. ACM.
G. Graefe. B-tree indexes, interpolation search, and skew. In Proceedings of the 2Nd International Workshop on Data Management on New Hardware, DaMoN ’06, New York, NY, USA, 2006. ACM.
J. Yu and M. Sarwat. Two Birds, One Stone: A Fast, Yet Lightweight, Indexing Scheme for Modern Database Systems. In VLDB, pages 385–396, 2016.
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G. Moerkotte. Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing. In VLDB, pages 476–487, 1998.
W. Litwin. Readings in database systems. chapter Linear Hashing: A New Tool for File and Table Addressing., pages 570–581. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.
J. Wang, H. T. Shen, J. Song, and J. Ji. Hashing for similarity search: A survey. CoRR, abs/1408.2927, 2014.
J. Wang, W. Liu, S. Kumar, and S. F. Chang. Learning to hash for indexing big data;a survey. Proceedings of the IEEE, 104(1):34–57, Jan 2016.
S. Richter, V. Alvarez, and J. Dittrich. A seven-dimensional analysis of hashing methods and its implications on query processing. Proc. VLDB Endow., 9(3):96–107, Nov. 2015.
M. Turcanik and M. Javurek. Hash function generation by neural network. In 2016 New Trends in Signal Processing (NTSP), pages 1–5, Oct 2016.
J. Wang, H. T. Shen, J. Song, and J. Ji. Hashing for similarity search: A survey. CoRR, abs/1408.2927, 2014.
J. Guo and J. Li. CNN based hashing for image retrieval. CoRR, abs/1509.01354, 2015.
J. Wang, H. T. Shen, J. Song, and J. Ji. Hashing for similarity search: A survey. CoRR, abs/1408.2927, 2014.
K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg. Feature hashing for large scale multitask learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, pages 1113–1120, New York, NY, USA, 2009. ACM.
M. Turcanik and M. Javurek. Hash function generation by neural network. In 2016 New Trends in Signal Processing (NTSP), pages 1–5, Oct 2016.
B. Fan, D. G. Andersen, M. Kaminsky, and M. D. Mitzenmacher. Cuckoo filter: Practically better than bloom. In Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, CoNEXT ’14, pages 75–88, New York, NY, USA, 2014. ACM.
K. Alexiou, D. Kossmann, and P.-A. Larson. Adaptive range filters for cold data: Avoiding trips to siberia. Proc. VLDB Endow., 6(14):1714–1725, Sept. 2013.
R. Grossi and G. Ottaviano. The wavelet trie: Maintaining an indexed sequence of strings in compressed space. In Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS ’12, pages 203–214, New York, NY, USA, 2012. ACM.
R. Grossi, A. Gupta, and J. S. Vitter. High-order entropy-compressed text indexes. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’03, pages 841–850, Philadelphia, PA, USA, 2003. Society for Industrial and Applied Mathematics.
M. Magdon-Ismail and A. F. Atiya. Neural networks for density estimation. In M. J. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 522–528. MIT Press, 1999.
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N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017.
C. Kim, J. Chhugani, N. Satish, E. Sedlar, A. D. Nguyen, T. Kaldewey, V. W. Lee, S. A. Brandt, and P. Dubey. Fast: Fast architecture sensitive tree search on modern cpus and gpus. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD ’10, pages 339–350, New York, NY, USA, 2010. ACM.
C. Kim, J. Chhugani, N. Satish, E. Sedlar, A. D. Nguyen, T. Kaldewey, V. W. Lee, S. A. Brandt, and P. Dubey. Fast: Fast architecture sensitive tree search on modern cpus and gpus. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD ’10, pages 339–350, New York, NY, USA, 2010. ACM.
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Welcome to Sonic OC Central!
This blog is a writing and character building prompts blog focused on original fan characters created to fit into the Sonic the Hedgehog universe. We'll probably run little events from time to time as well.
Everyone is welcome to participate! We love these little guys, and nothing would make us happier than if you'd tell us all about yours. :)
Click here to read the submission rules!
SOCC Questions tag | Question Masterlist | How it Works
We now have a small discord server for people who would like to chat about their creations!
Character credits for our banner art are as follows:
First Row: Zenki from @riyamilea, Mike from @mikejmurdock, Dama from sonicfan42069, Kassy from @mistressdizzy, John Scarlet from Weredrago2, Draco from @thedigitalvalkerie, Haunt from @pactwraith
Second Row: Marian from @rabbithaver, Byte from @bunniibones, Wiki from @nintendoni-art, Cake from @cakebird-art, Jasper from @mynders-universe, Storm from @sege-h, Pepper from @rcbirdy, Mane from @laecandraw
Third Row: Ziivius from ItsThevius, Ty from @hesfromsomewhere, Crux from @snowpearart, Jasmin from @samethstarr, Dusty from @starlitskvader, Sik from @getallemeralds and Glint from @mushsect
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