More Like this

1. Online Mergers and Applications to Registration-Based Encryption and Accumulators

   Qi, Wei; Mahmoody, Mohammad (May 2023, LIPIcs)
2. Efficient and Robust LiDAR-Based End-to-End Navigation

   Zhijian Liu*, Alexander Amini* (May 2021, IEEE International Conference on Robotics and Automation (ICRA))

   null (Ed.)
3. Applications of Polynomial Chaos-Based Cokriging to Simulation-Based Analysis and Design Under Uncertainty

   https://doi.org/10.1115/DETC2020-22369  

   Nagawkar, Jethro; Leifsson, Leifur (August 2020, Proceedings of the ASME 2020 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2020)

   null (Ed.)

   Abstract This paper demonstrates the use of the polynomial chaos-based Cokriging (PC-Cokriging) on various simulation-based problems, namely an analytical borehole function, an ultrasonic testing (UT) case and a robust design optimization of an airfoil case. This metamodel is compared to Kriging, polynomial chaos expansion (PCE), polynomial chaos-based Kriging (PC-Kriging) and Cokriging. The PC-Cokriging model is a multi-variate variant of PC-Kriging and its construction is similar to Cokriging. For the borehole function, the PC-Cokriging requires only three high-fidelity samples to accurately capture the global accuracy of the function. For the UT case, it requires 20 points. Sensitivity analysis is performed for the UT case showing that the F-number has negligible effect on the output response. For the robust design case, a 75 and 31 drag count reduction is reported on the mean and standard deviation of the drag coefficient, respectively, when compared to the baseline shape. 

   more » « less
4. A Comparison of Speaker-based and Utterance-based Data Selection for Text-to-Speech Synthesis

   https://doi.org/DOI: 10.21437/Interspeech.2018-1313  

   Kai-Zhan Lee, Erica Cooper (January 2018, Interspeech 2018)

   Building on previous work in subset selection of training data for text-to-speech (TTS), this work compares speaker-level and utterance-level selection of TTS training data, using acoustic features to guide selection. We find that speaker-based selection is more effective than utterance-based selection, regardless of whether selection is guided by a single feature or a combination of features. We use US English telephone data collected for automatic speech recognition to simulate the conditions of TTS training on low-resource languages. Our best voice achieves a human-evaluated WER of 29.0% on semantically-unpredictable sentences. This constitutes a significant improvement over our baseline voice trained on the same amount of randomly selected utterances, which performed at 42.4% WER. In addition to subjective voice evaluations with Amazon Mechanical Turk, we also explored objective voice evaluation using mel-cepstral distortion. We found that this measure correlates strongly with human evaluations of intelligibility, indicating that it may be a useful method to evaluate or pre-select voices in future work. 

   more » « less
5. HiFine: integrating Hi-C-based and shotgun-based methods to refine binning of metagenomic contigs

   https://doi.org/10.1093/bioinformatics/btac295  

   Du, Yuxuan; Sun, Fengzhu; Birol, ed., Inanc (April 2022, Bioinformatics)

   Abstract MotivationMetagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs’ composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample. ResultsWe develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstrate that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs. Availability and implementationHiFine is available at https://github.com/dyxstat/HiFine. Supplementary informationSupplementary data are available at Bioinformatics online. 

   more » « less