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Optimizing Learned Bloom Filters by Sandwiching [article]

Michael Mitzenmacher
2018 arXiv   pre-print
We provide a simple method for improving the performance of the recently introduced learned Bloom filters, by showing that they perform better when the learned function is sandwiched between two Bloom  ...  filters.  ...  ANALYZING SANDWICHED LEARNED BLOOM FILTERS We model the sandwiched learned Bloom filter as follows. The middle of the learned Bloom filter we treat as an oracle for the keys K, where |K| = m.  ... 
arXiv:1803.01474v1 fatcat:dfp6d7zjsjaw7jzss24m6z2ea4

A Model for Learned Bloom Filters, and Optimizing by Sandwiching [article]

Michael Mitzenmacher
2019 arXiv   pre-print
function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a  ...  Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent.  ...  This work was supported in part by NSF grants CCF-1563710, CCF-1535795, CCF-1320231, and CNS-1228598. Part of this work was done while visiting Microsoft Research New England.  ... 
arXiv:1901.00902v1 fatcat:lioh2tavvjf7hlmvsu25kgoi3a

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web

Zhenwei Dai, Anshumali Shrivastava
2020 Neural Information Processing Systems  
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier.  ...  We propose new algorithms that generalize the learned Bloom filter by using the complete spectrum of the score regions.  ...  Since we did not pose extra assumptions compared to the previous learned Bloom filters, the extra memory savings achieved by our algorithms are almost price free.  ... 
dblp:conf/nips/DaiS20 fatcat:iqfqpx7ohzfplcy6gnnicid3me

Partitioned Learned Bloom Filter [article]

Kapil Vaidya, Eric Knorr, Tim Kraska, Michael Mitzenmacher
2020 arXiv   pre-print
Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using a learned model for the represented set.  ...  However, previous methods for learned Bloom filters do not take full advantage of the learned model.  ...  We include the size of the learned model with the size of the learned Bloom filter. To ensure a fair comparison, we used the optimal Bloom filter as the backup bloom filter for all learned variants.  ... 
arXiv:2006.03176v2 fatcat:optk7vjbbfghjamqq4opa55qky

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier [article]

Zhenwei Dai, Anshumali Shrivastava
2019 arXiv   pre-print
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier.  ...  We proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions.  ...  Experiment Baselines: We test the performance of four different learned Bloom filters: 1) standard Bloom filter, 2) learned Bloom filter, 3) sandwiched learned Bloom filter, 4) adaptive learned Bloom filter  ... 
arXiv:1910.09131v1 fatcat:6m4jkvxwpvfzlo2injttbpe4re

Hash Adaptive Bloom Filter [article]

Rongbiao Xie, Meng Li, Zheyu Miao, Rong Gu, He Huang, Haipeng Dai, Guihai Chen
2021 arXiv   pre-print
Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set.  ...  To address the above problems, we propose a new Hash Adaptive Bloom Filter (HABF) that supports the customization of hash functions for keys.  ...  Learned filter refers to the set of the state-of-the-art works based on learned index [16] , including Learned Bloom filter (LBF) [16] , Sandwiched Learned Bloom filter (SLBF) [17] , and Adaptive Learned  ... 
arXiv:2106.07037v1 fatcat:ewh3beb7j5afvb7ktdr444lk3q

Algorithms with Predictions [article]

Michael Mitzenmacher, Sergei Vassilvitskii
2020 arXiv   pre-print
We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis.  ...  We aim for algorithms that have near optimal performance when these predictions are good, but recover the prediction-less worst case behavior when the predictions have large errors.  ...  Indeed, it is already known that a "sandwiched" learned Bloom filter that uses a learned filter between two standard Bloom filters, also shown in Figure 1 , can yield better performance (Mitzenmacher  ... 
arXiv:2006.09123v1 fatcat:tubdtmcpuvhkvbvlplk5hk7n5q

Editorial for the Special Issue on Silicon Photonics Bloom

Qiancheng Zhao, Ozdal Boyraz
2020 Micromachines  
Silicon (Si) photonics debuted in the mid-1980s through the pioneering work done by Soref et al.  ...  With the recent injection of government and private funding, more and more foundries, equipped with well-established and market-proven product development kits, will spring up, promoting a bloom in Si  ...  Different MEMS designs were simulated and optimized. The optimum design was fabricated by commercial services and tested.  ... 
doi:10.3390/mi11070670 pmid:32664197 fatcat:tlwlomkznrcyxekemtd7lhtz2i

A survey of sketches in traffic measurement: Design, Optimization, Application and Implementation [article]

Shangsen Li, Lailong Luo, Deke Guo, Qianzhen Zhang, Pengtao Fu
2021 arXiv   pre-print
Currently, tremendous redesigns and optimizations have been proposed to improve the sketches for better network measurement performance.  ...  To summarize the existing efforts, we carry out an in-depth study of the existing literature, covering more than 90 sketch designs and optimization strategies.  ...  The sandwiched LBF [170] optimizes BF by surrounding the learning model with two BF layers.  ... 
arXiv:2012.07214v2 fatcat:lme2ghsshje3tag2m5q3xgvcna

Artificial Intelligence Algorithms for Multisensor Information Fusion Based on Deep Learning Algorithms

Lan Jiang, Hye-jin Kim
2022 Mobile Information Systems  
This article aims to study the relevant knowledge of deep learning algorithms and multisensor information fusion and how to use deep learning algorithms and multisensor information fusion to study AI algorithms  ...  AI can be applied not only in mechanical learning and expert system but also in knowledge engineering and intelligent information retrieval and has achieved amazing results.  ...  Acknowledgments This research study is sponsored by China University Industrial teaching research innovation fund, new generation information technology innovation project. The name of  ... 
doi:10.1155/2022/3356213 fatcat:hv6l6oirqrgi7jyhrgstompwq4

Application of bead array technology to community dynamics of marine phytoplankton

CK Ellison, RS Burton
2005 Marine Ecology Progress Series  
Studies of the dynamics of marine microplanktonic systems have been hampered by a lack of high-throughput technologies for the simultaneous identification and quantification of the many taxa comprising  ...  The authors gratefully acknowledge the financial support provided by a National Science Foundation Major Research Instrumentation Grant and a University of California Academic Senate Research Grant.  ...  Bulk DNA was extracted from filters using a DNeasy ® Tissue Kit (Qiagen) following the protocol set out by the manufacturer.  ... 
doi:10.3354/meps288075 fatcat:yc5le6wddvd6dmokdsrw53g2r4

Rapid single B cell antibody discovery using nanopens and structured light

Aaron Winters, Karyn McFadden, John Bergen, Julius Landas, Kelly A. Berry, Anthony Gonzalez, Hossein Salimi-Moosavi, Christopher M. Murawsky, Philip Tagari, Chadwick T. King
2019 mAbs  
Single-cell polymerase chain reaction-based molecular recovery on select anti-idiotypic ASCs followed by recombinant IgG expression and enzyme-linked immunosorbent assay (ELISA) characterization resulted  ...  We would like to thank the Amgen SPARC organization for their execution of the in-vivo protocols as well as the Pre-Pivotal DS, Hybrid Modalities and Biologics Optimizations organizations for their collaborative  ...  of fluorescent blooms because the signal may be generated by more than one specific antibody.  ... 
doi:10.1080/19420862.2019.1624126 pmid:31185801 pmcid:PMC6748590 fatcat:xzv3grqpuvg3degjdl2uzqjgxe

The PAU survey: Estimating galaxy photometry with deep learning [article]

Laura Cabayol, Martin Eriksen, Adam Amara, Jorge Carretero, Ricard Casas, Francisco Javier Castander, Juan De Vicente, Enrique Fernández, Juan García-Bellido, Enrique Gaztanaga, Hendrik Hildebrandt, Ramon Miquel (+5 others)
2021 arXiv   pre-print
Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%.  ...  In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images.  ...  This paper has been typeset from a T E X/L A T E X file prepared by the author.  ... 
arXiv:2104.02778v1 fatcat:ejao4viqovb5phk33ilzceda4u

A Survey of DeFi Security: Challenges and Opportunities [article]

Wenkai Li, Jiuyang Bu, Xiaoqi Li, Hongli Peng, Yuanzheng Niu, Xianyi Chen
2022 arXiv   pre-print
Finally, we summarize the existing optimization approaches for different layers and provide some challenges and future directions.  ...  In addition, distinct layers have different means of protection against specific vulnerabilities, which is not considered by existing analytical work.  ...  However, the design of the bloom filter protects the information of the participants on a small scale. B.  ... 
arXiv:2206.11821v1 fatcat:btihc6d4ived5nstcjwtab6kji

Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain [article]

Christof Ferreira Torres, Ramiro Camino, Radu State
2021 arXiv   pre-print
In the hope of making some profit, attackers continuously monitor the transaction pool and try to frontrun their victims' transactions by either displacing or suppressing them, or strategically inserting  ...  Thus, Bloom filters may yield false positives, but no false negatives.  ...  Each window has its own Bloom filter that memorizes previously observed n-grams.  ... 
arXiv:2102.03347v2 fatcat:cq67fsdlyfhcjmjd3g344nxlfa
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