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Rate Control in Video Coding [chapter]

Zongze Wu, Shengli Xie, Kexin Zhang, Rong Wu
2011 Recent Advances on Video Coding  
How to reference In order to correctly reference this scholarly work, feel free to copy and paste the following: Zongze Wu, Shengli Xie, Kexin Zhang and Rong Wu (2011) .  ... 
doi:10.5772/14585 fatcat:u5k3zyytrjhkxdjflajwakze2y

Demonstration

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
Data volumes are rising at an increasing rate, stressing the limits of human attention. Current techniques for prioritizing user attention in this fast data are characterized by either cumbersome, ad-hoc analysis pipelines comprised of a diverse set of analytics tools, or brittle, static rule-based engines. To address this gap, we have developed MacroBase, a fast data analytics engine that acts as a search engine over fast data streams. MacroBase provides a set of highlyoptimized, modular
more » ... ors for streaming feature transformation, classification, and explanation. Users can leverage these optimized operators to construct efficient pipelines tailored for their use case. In this demonstration, SIGMOD attendees will have the opportunity to interactively answer and refine queries using MacroBase and discover the potential benefits of an advanced engine for prioritizing attention in high-volume, real-world data streams.
doi:10.1145/3035918.3056446 dblp:conf/sigmod/BailisGRS17 fatcat:agybb7bbabempk2atxjhekoszq

Prioritizing Attention in Analytic Monitoring

Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri
2017 Conference on Innovative Data Systems Research  
While data volumes continue to rise, the capacity of human attention remains limited. As a result, users need analytics engines that can assist in prioritizing attention in this fast data that is too large for manual inspection. We present a set of design principles for the design of fast data analytics engines that leverage the relative scarcity of human attention and overabundance of data: return fewer results, prioritize iterative analysis, and filter fast to compute less. We report on our
more » ... rly experiences employing these principles in the design and deployment of MacroBase, an open source analysis engine for prioritizing attention in fast data. By combining streaming operators for feature transformation, classification, and data summarization, MacroBase provides users with interpretable explanations of key behaviors, acting as a search engine for fast data.
dblp:conf/cidr/BailisGRS17 fatcat:i4lixrxqybcmrag4lt7dsbfhqy

Net analyte signal with floating reference theory in non-invasive blood glucose sensing by near-infrared spectroscopy

Wanjie Zhang Wanjie Zhang, Rong Liu Rong Liu, Wen Zhang Wen Zhang, Jiaxiang Zheng Jiaxiang Zheng, Kexin Xu Kexin Xu
2012 Chinese Optics Letters (COL)  
Based on the floating reference theory, a new method for extracting the net analyte signal (NAS) is proposed. The noise background subspace is spanned by spectra at the floating radial reference point, and then, the spectra at the measurement point are projected on the subspace. Thereafter, the glucose concentrations in intralipid solutions are investigated through Monte Carlo simulation and experiments, and the partial least squares (PLS) models with and without NAS analysis are built. The
more » ... mean square errors of calibration and prediction reach to 28.87% and 27.33%, respectively. The results confirm the existence of information induced by glucose concentration variations as well as the validity of the floating reference theory.
doi:10.3788/col201210.083002 fatcat:3heyv7usgvam3fzbgvm2zln3cu

MacroBase

Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation
more » ... nd classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.
doi:10.1145/3035918.3035928 dblp:conf/sigmod/BailisGMNRS17 fatcat:ntyrcf2txffnxdcq22fhxyz234

Synthesis of 1,2-diketones by mercury-catalyzed alkyne oxidation

Xiaochuan Mei, Weican Hu, Kexin Gao, Haotian Gao, Chaoyang Wang, Guoying Qian, Zhouting Rong
2021 figshare.com  
The first mercury-catalyzed synthesis of 1,2-diketones by alkyne oxidation has been developed. This inexpensive method extends the potential of mercury catalysis and allows the rapid construction of various 1,2-diketones and α-carbonyl amides in good yields with high functional group tolerance.
doi:10.6084/m9.figshare.14883405.v1 fatcat:zr3f72ibtfbe7msuvcf2bkenqa

Rehashing Kernel Evaluation in High Dimensions

Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis
2019 International Conference on Machine Learning  
Kernel methods are effective but do not scale well to large scale data, especially in high dimensions where the geometric data structures used to accelerate kernel evaluation suffer from the curse of dimensionality. Recent theoretical advances have proposed fast kernel evaluation algorithms leveraging hashing techniques with worst-case asymptotic improvements. However, these advances are largely confined to the theoretical realm due to concerns such as super-linear preprocessing time and
more » ... hing gains in non-worst case datasets. In this paper, we close the gap between theory and practice by addressing these challenges via provable and practical procedures for adaptive sample size selection, preprocessing time reduction, and refined variance bounds that quantify the datadependent performance of random sampling and hashing-based kernel evaluation methods. Our experiments show that these new tools offer up to 10× improvement in evaluation time on a range of synthetic and real-world datasets.
dblp:conf/icml/SiminelakisRBCL19 fatcat:6rccbbhql5hj3pwdcwttzscnn4

Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text [article]

Kexin Sun, Hui Gao, Hongyu Kuang, Xiaoxing Ma, Guoping Rong, Dong Shao, He Zhang
2021 arXiv   pre-print
Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers sentiments in commit messages. To better support the use of automated sentiment analysis for SE tasks, researchers built an SE-domain-specified sentiment dictionary to further improve the accuracy of the results. Unfortunately, recent work reported that current mainstream tools for sentiment analysis still cannot provide reliable results when
more » ... zing the sentiments in SE texts. We suggest that the reason for this situation is because the way of expressing sentiments in SE texts is largely different from the way in social network or movie comments. In this paper, we propose to improve sentiment analysis in SE texts by using sentence structures, a different perspective from building a domain dictionary. Specifically, we use sentence structures to first identify whether the author is expressing her sentiment in a given clause of an SE text, and to further adjust the calculation of sentiments which are confirmed in the clause. An empirical evaluation based on four different datasets shows that our approach can outperform two dictionary-based baseline approaches, and is more generalizable compared to a learning-based baseline approach.
arXiv:2103.13154v1 fatcat:mklmwpio2naifh42osg4763z5e

ASAP

Kexin Rong, Peter Bailis
2017 Proceedings of the VLDB Endowment  
Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure
more » ... to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and on-demand refresh. We demonstrate that ASAP can improve users' accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.
doi:10.14778/3137628.3137645 fatcat:kvrrivbuxfcwtdcliqz2z2ereu

Locality-sensitive hashing for earthquake detection

Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi, Peter Bailis, Philip Levis, Gregory C. Beroza
2018 Proceedings of the VLDB Endowment  
In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by searching for similar time series segments via LSH. However, a straightforward implementation of this LSH-enabled application has difficulty scaling beyond 3 months of continuous time series data measured at a single seismic station. As a case study of a data-driven
more » ... cience workflow, we illustrate how domain knowledge can be incorporated into the workload to improve both the efficiency and result quality. We describe several end-toend optimizations of the analysis pipeline from pre-processing to post-processing, which allow the application to scale to time series data measured at multiple seismic stations. Our optimizations enable an over 100× speedup in the end-to-end analysis pipeline. This improved scalability enabled seismologists to perform seismic analysis on more than ten years of continuous time series data from over ten seismic stations, and has directly enabled the discovery of 597 new earthquakes near the Diablo Canyon nuclear power plant in California and 6123 new earthquakes in New Zealand. PVLDB Reference Format:
doi:10.14778/3236187.3236214 fatcat:26lez7nlj5cc5cwulxwggk5lwm

Highly sensitive detection of melamine based on reversed phase liquid chromatography mass spectrometry

QingQing Wu, KeXin Fan, Wei Sha, HongQiang Ruan, Rong Zeng, ChiaHui Shieh
2009 Science Bulletin  
In this work, we developed a highly sensitive method to detect melamine based on reversed phase liquid chromatography mass spectrometry. A mass spectrometry compatible ion pair, heptafluorobutyric acid(HFBA), was used to separate melamine by reversed phase liquid chromatography prior to electrospray mass spectrometry. The incorporation of isotope internal standard and multiple reaction monitoring improved the accuracy and linearity of quantification. Based on this strategy, the method limit of
more » ... uantification was 0.1 ng/g. The limits of quantification were 8 ng/g for liquid milk and 15 ng/g for dry milk powder. This method provided a reproducible and stable approach to sensitive detection and quantification of melamine. reversed phase liquid chromatography, mass spectrometry, melamine, multiple reaction monitoring, ion pair
doi:10.1007/s11434-009-0114-6 fatcat:r474u4rqdjazdoy7yix35mbwla

Methodology of effective glucose-specific signal extraction in complicated sample

Wenliang Chen, Bin Deng, Rong Liu, Xiaoyu Gu, Kexin Xu, Gerard L. Coté, Alexander V. Priezzhev
2007 Optical Diagnostics and Sensing VII  
E-mail: kexin@tju.edu.cn, Phone: 86-22-27403944, Fax: 86-22-27406379 Generally, in the quantitative NIR (Near-Infrared) measurement, background deducting method is widely used to eliminate the undesired  ... 
doi:10.1117/12.699496 fatcat:xw3nyepbbbdxpluq3loq4wltnm

Discussion on the validity of NIR spectral data in non-invasive blood glucose sensing

Wanjie Zhang, Rong Liu, Wen Zhang, Hao Jia, Kexin Xu
2013 Biomedical Optics Express  
In this paper, the effects of two-dimensional correlation spectroscopy (2DCOS) on chance correlations in the spectral data, generated from the correlations between glucose concentration and some undesirable experimental factors, such as instrument drift, sample temperature variations, and interferent compositions in the sample matrix, are investigated. The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary
more » ... ocedure for better verifying or rejecting the data set. It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models. The utility of the proposed analysis is demonstrated with a series of aqueous solutions using near-infrared spectra over the overtone band of glucose. Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.
doi:10.1364/boe.4.000789 pmid:23761844 pmcid:PMC3675860 fatcat:zdnbkqsbnzfxfm6zdi3ximcwcm

Regional fauna-flora biodiversity and conservation strategy in China

Baoguo Li, He Zhang, Kang Huang, Gang He, Songtao Guo, Rong Hou, Pei Zhang, Haitao Wang, Hao Pan, Hengguang Fu, Xiaoying Wu, Kexin Jiang (+1 others)
2022 iScience  
Hou, and Pei Zhang: providing comments and suggestions for modifying the manuscript; Haitao Wang, Hao Pan, Hengguang Fu, Kexin Jiang, and Xiaoying Wu: reference searching.  ...  CONTRIBUTIONS Baoguo Li: conceiving project objectives; Ruliang Pan: designing contents and drafting the manuscript; He Zhang: data acquisition, analyses and figures; Kang Huang, Gang He, Songtao Guo, Rong  ... 
doi:10.1016/j.isci.2022.104897 pmid:36039288 pmcid:PMC9418850 fatcat:53u4sdoqzbcmbcleqvbazvl3ny

MacroBase: Prioritizing Attention in Fast Data [article]

Peter Bailis and Edward Gan and Samuel Madden and Deepak Narayanan and Kexin Rong and Sahaana Suri
2017 arXiv   pre-print
As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation
more » ... nd classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.
arXiv:1603.00567v4 fatcat:plqi22difnal3dzzd7jy6qwcxu
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