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Št. zadetkov: 8
Global homogenization of the structure and function in the soil microbiome of urban greenspaces
Manuel Delgado-Baquerizo, David J. Eldridge, Yu-Rong Liu, Blessing Sokoya, Jun-Tao Wang, Hang-Wei Hu, Ji-Zheng He, Felipe Bastida, José L. Moreno, Adebola R. Bamigboye, Tine Grebenc, Tina Unuk
Izvirni znanstveni članek
Oznake: biodiverziteta tal;strukturna pestrost;funkcionalna pestrost;urbana tla;soil biodiversity;structural diversity;functional diversity;urban soils;
The structure and function of the soil microbiome of urban greenspaces remain largely undetermined. We conducted a global field survey in urban greenspaces and neighboring natural ecosystems across 56 cities from six continents, and found that urban soils are important hotspots for soil bacterial, p ...
Leto: 2021 Vir: Repozitorij Univerze v Ljubljani (RUL)
Soil contamination in nearby natural areas mirrors that in urban greenspaces worldwide
Yu-Rong Liu, Marcel G. A. van der Heijden, Judith Riedo, Carlos Sanz-Lazaro, David J. Eldridge, Felipe Bastida, Eduardo Moreno-Jiménez, Xin-Quan Zhou, Hang-Wei Hu, Ji-Zheng He, Tine Grebenc, Tina Unuk
Izvirni znanstveni članek
Oznake: onesnaženje tal;urbani gozdovi;soil contamination;urban greenspaces;
Soil contamination is one of the main threats to ecosystem health and sustainability. Yet little is known about the extent to which soil contaminants differ between urban greenspaces and natural ecosystems. Here we show that urban greenspaces and adjacent natural areas (i.e., natural/semi-natural ec ...
Leto: 2023 Vir: Repozitorij Univerze v Ljubljani (RUL)
Generalization Analysis of Listwise Learning-to-Rank Algorithms
Hang Li
Video in druga učna gradiva
Oznake: computer science;machine learning;recommender systems
This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using the framework. Many learning-to-rank algorithms have been proposed in recent years. Among them, the listwise approach has shown higher empirical ran ...
Leto: 2009 Vir: videolectures.net
Improving Quality of Training Data for Learning to Rank Using Click-Through Data
Hang Li
Video in druga učna gradiva
Oznake:
In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work has shown that certain level of noise in relevance judgments has little effect on evaluation, especially for comparison pur ...
Leto: 2010 Vir: videolectures.net
Opening speech at the WSDM 2011
Wolfgang Nejdl , Hang Li
Video in druga učna gradiva
Oznake: events
Leto: 2011 Vir: videolectures.net
Using R for Scalable Data Science: Single Machines to Hadoop Spark Clusters
Hang Zhang , Vanja Paunić
Video in druga učna gradiva
Oznake: computer science;data science
In this tutorial, we will demonstrate how to create scalable, end-to-end data analysis processes in R on single machines as well as in-database in SQL Server and on Hadoop clusters running Spark. We will provide hands-on exercises as well as code in a public GitHub repository for attendees to adopt ...
Leto: 2017 Vir: videolectures.net
Effect of diffrent loads on the wear behaviors of 5Cr5Mo2V steel at 700 °C
Zhixiong Bai, Hang Yang, Ninghu Su, Wu Xiaochun
Izvirni znanstveni članek
Oznake: load;wear mechanism;oxide;diagram;
Effect of diffrent loads on the wear behaviors of 5Cr5Mo2V steel at 700 °C
Leto: 2021 Vir: Inštitut za kovinske materiale in tehnologije (IMT)
Scalable R on Spark
John-Mark Agosta , Debraj GuhaThakurta , Robert Horton , Mario Inchiosa , Srini Kumar , Vanja Paunić , Hang Zhang , Mengyue Zhao
Video in druga učna gradiva
Oznake: computer science;data science
R is one of the most popular languages in the data science, statistical and machine learning (ML) community. However, when it comes to scalable data analysis and ML using R, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large data-sets efficient ...
Leto: 2016 Vir: videolectures.net
Št. zadetkov: 8
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