Hash-Based Feature Learning for Incomplete Continuous-Valued Data

Yuan, Shuai; Tan, Pang-Ning; Cheruvelil, Kendra Spence; Fergus, C. Emi; Skaff, Nicholas K.; Soranno, Patricia A.

doi:10.1137/1.9781611974973.76

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…To overcome these challenges, Yuan et al. [ 37 ] developed a novel algorithm for learning non-linear features to predict lake water quality. The algorithm also enables the missing values to be imputed in a way that preserves the relationship between the predictors and response variables.…”

Section: Research To Date Using Lagos-nementioning

confidence: 99%

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

et al. 2017

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Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

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Section: Research To Date Using Lagos-nementioning

confidence: 99%

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

et al. 2017

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“…Other aspects of crypto network security could also be studied. Machine learning techniques can be applied to predict the network attack probabilities in the future [20][21][22][23][24].…”

Section: Discussionmentioning

confidence: 99%

A review of crypto networks

Zhang

2018

Preprint

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Bitcoin is a crypto currency system that has been rapidly adopted due to its anonymity and decentralized properties. Blockchain is the underpinning technology that maintains the Bitcoin transaction ledger. The blockchain network operates in a state of consensus, which automatically checks in with itself periodically. One of biggest innovation by Bitcoin system is that it is a new way to develop open networks. Anything that happens on the blockchain is a function of the network as a whole. Crypto networks represent a fundamental shift in the way our society transacts, organizes, and works with each other, which could be explained and deciphered by econophysics of the network itself. From a networking perspective, we reviewed a list of current literatures that studied the crypto networks, mostly Bitcoin transaction networks. We identified the potential research areas that would further provide insights into the design of a more resilient and secure crypto networks.

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“…There are many works in developing joint predictive modeling methods Król et al (2017); Zhao and Tang (2017); Liu et al 2018; Yuan et al (2017a). In Lei et al (2015) the author proposed a method for joint learning of multiple longitudinal models for various clinical scores at multiple future time points.…”

Section: Joint Predictive Modelingmentioning

confidence: 99%

Joint predictive modeling for geospatial data at various locations

Cheng

Xie

2019

Preprint

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Predictive modeling uses statistics to predict unknown outcomes. In general, there are two categories of predictive modeling, parametric and non-parametric. There are many applications of predictive modeling, for example, it can be used to predict the risk score of a credit card transaction, it can also be used in health care to identify the probability of having certain disease. When it comes to geospatial data, there are some unique characteristics of the problem. Predictive modeling of geospatial data naturally involves multiple response variables at various locations. The response variables are not independent with each other and thus building separate models for each individual response variable is not appropriate. In addition, many geospatial data has strong spatial auto-correlation such that data from nearby locations are more similar with each other. A joint modeling takes into account of both the correlation among response variables and relationship among different locations, and can make predictions for locations with no training data. In this paper, we review works on joint predictive modeling for multiple response variables at various locations.

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Hash-Based Feature Learning for Incomplete Continuous-Valued Data

Cited by 4 publications

References 15 publications

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

A review of crypto networks

Joint predictive modeling for geospatial data at various locations

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