CLIM Undergraduate Workshop: Tutorial on R Software - Huang Huang, Oct 23, 2017

Oct 25, 20170 likes324 views

The Statistical and Applied Mathematical Sciences Institute

This document provides an R tutorial for an undergraduate climate workshop. It introduces key concepts in R including data types, arrays, matrices, data frames, packages, and basic plotting. It demonstrates how to perform calculations, subset data, install and load packages, create different plot types like histograms and maps, and use functions like quantile and quilt.plot. Exercises include drawing a histogram of ozone values and calculating quantiles.

R tutorial for CLIM undergraduate workshop
R tutorial for CLIM undergraduate
workshop
Huang Huang, SAMSI, Oct 23, 2017
About R
R is a free software environment for statistical computing and graphics.
A different implementation of S developed at Bell Lab
Provides a wide variety of statistical and graphical techniques, and is highly extensible
Open source
Powerful IDE: such as Rstudio
Open R
Data types
We can use variables without definition in advance
numbers
num=3.14;
print(num)
## [1] 3.14
print(num+1)
## [1] 4.14
print(typeof(num))
## [1] "double"
num.int=as.integer(num);
print(num.int)
## [1] 3
print(typeof(num.int))

R tutorial for CLIM undergraduate workshop
## [1] "integer"
We can use R as a calculater to calculate
x=2;
print(x*2);
## [1] 4
print(log(x));
## [1] 0.6931472
print(sqrt(x));
## [1] 1.414214
print(x**3);
## [1] 8
array
x=10:12;
print(x);
## [1] 10 11 12
y=c(1,3,5);
print(y);
## [1] 1 3 5
print(y[1])
## [1] 1
matrix=cbind(x,y);
print(matrix);
2 ∗ 2, log(2), ,2
–√ 23

R tutorial for CLIM undergraduate workshop
## x y
## [1,] 10 1
## [2,] 11 3
## [3,] 12 5
Extract a subset of the matrix
print(matrix[2,])
## x y
## 11 3
print(matrix[c(2,3),])
## x y
## [1,] 11 3
## [2,] 12 5
print(matrix[matrix[,1]<12,])
## x y
## [1,] 10 1
## [2,] 11 3
print(matrix[,2])
## [1] 1 3 5
data frame
df=data.frame(x=x,y=y);
print(df)
## x y
## 1 10 1
## 2 11 3
## 3 12 5
print(df$x);
## [1] 10 11 12
print(df$y);

R tutorial for CLIM undergraduate workshop
## [1] 1 3 5
Packages
How to install a package
install.packages('fields')
How to load functions from a package
library('fields')
Basic plots
Use of “plot” function
x=1:1000/10;
y=sin(x);
print(x[1:10]);
## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
print(y[1:10]);
## [1] 0.09983342 0.19866933 0.29552021 0.38941834 0.47942554 0.56464247
## [7] 0.64421769 0.71735609 0.78332691 0.84147098
plot(x,y)

R tutorial for CLIM undergraduate workshop
plot(x,y,type='l')
plot(x[1:20],y[1:20],type='b',col='red',lwd=5,ylim=c(0,2))

R tutorial for CLIM undergraduate workshop
Use of “hist” function
set.seed(2017)
data=rnorm(1000);
set.panel(1,2);
## plot window will lay out plots in a 1 by 2 matrix
plot(data,type='l');
hist(data)
“help” a function
help(hist)
Use the above command to see the description of function “hist”, and change some parameter(s) to
generate the figure below

R tutorial for CLIM undergraduate workshop
help(quantile)
Calculate the percent quantile, median, and percent quantile as follows.
## 5% 50% 95%
## -1.59585243 0.01210508 1.60853804
Use of “quilt.plot” function
quilt.plot is function in package “fields” to produce an image-like plot
library(fields)
data(ozone2)
plot(ozone2$lon.lat[,1],ozone2$lon.lat[,2],xlab='lon',ylab='lat')
US( add=TRUE, col="grey", lwd=2)
5% 95%

R tutorial for CLIM undergraduate workshop
quilt.plot( ozone2$lon.lat, ozone2$y[16,],xlab='lon',ylab='lat')
US( add=TRUE, col="grey", lwd=2)

R tutorial for CLIM undergraduate workshop
Exercise
Draw the histogram of ozone values at all the sites in the specific time period as before, and get the and
quantiles.
10%
90%

This document describes performing extreme value analysis on daily precipitation data from Fort Collins, Colorado from 1900 to 1999 using R. It first reads in and plots the data, summarizing seasonal variations. It then performs two extreme value analysis approaches: the block maxima approach, which fits a generalized extreme value distribution to summer maximum daily precipitation values within blocks; and the peak over threshold approach, which fits a generalized Pareto distribution to values exceeding a threshold. It estimates return levels such as the 100-year event and calculates confidence intervals.

ggtimeseries-->ggplot2 extensions Dr. Volkan OBAN

Surface3d in R and rgl package.Dr. Volkan OBAN

Python grassMargherita Di Leo

The document describes a Python module called r.ipso that is used in GRASS GIS to generate ipsographic and ipsometric curves from raster elevation data. The module imports GRASS and NumPy libraries, reads elevation and cell count statistics from a raster, calculates normalized elevation and area values, and uses these to plot the curves and output quantile information. The module demonstrates calling GRASS functionality from Python scripts.

Derivatives Lesson Oct 14ingroy

1. The document discusses distance-time and velocity-time graphs and their relationships. It defines acceleration as the derivative of velocity over time. 2. It provides an example distance-time function and asks to sketch the corresponding velocity-time graph. It also discusses how to determine if a function is increasing or decreasing based on the signs of its derivative. 3. The document ends by giving a velocity function and asking to graph its derivative and determine where the original function is increasing, where it is concave up, and to sketch a possible graph based on an initial value.

Htdp27.keyKyungryul KIM

This document discusses algorithms analysis and complexity including: 1) Common sorting algorithms have quadratic time complexity O(N^2) while searching algorithms can have exponential complexity like O(2^N). 2) It provides an example of determining the complexity of two functions, showing one is O(N) while the other is O(N^N). 3) Vectors in Lisp can be used to initialize a data structure of a given length by applying a function to each index, and operations on vectors have constant time complexity.

imager package in R and examples..Dr. Volkan OBAN

CLUSTERGRAMDr. Volkan OBAN

Rexsuns

R is a programming language and software environment for statistical analysis and graphics. It allows users to analyze data, create visualizations, and perform statistical tests. Common R commands include functions to get and set the working directory, list objects in the workspace, remove objects, view and set options, save and load the command history, and save and load the entire workspace. R supports various data structures like vectors, arrays, matrices, data frames, and lists to store and manipulate different types of data. Data can be input into R from files, databases, and Excel spreadsheets. Graphs and visualizations created in R can be exported to file formats like PNG, JPEG, PDF and others.

Data Visualization with R.ggplot2 and its extensions examples.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

- The document describes a MapReduce workflow for analyzing airline flight data from multiple text files. - The map function parses the raw data by date, carrier, origin, destination, and converts time fields to datetime objects. - The reduce function aggregates the data by origin and destination airports to calculate inbound, outbound, and total flights. - The results are written to a new folder and then read back into R for further analysis and ranking of airports by flight volumes.

R-ggplot2 package ExamplesDr. Volkan OBAN

ggplot2 Examples. References: ggplot2 Elegant Graphics for Data Analysis; Wickham, Hadley http://www.stat.wisc.edu/~larget/stat302/chap2.pdf https://cran.r-project.org/web/packages/ggthemes/vignettes/ggthemes.html https://www3.nd.edu/~steve/computing_with_data/11_geom_examples/ggplot_examples.html http://seananderson.ca/ggplot2-FISH554/ http://ggobi.github.io/ggally/docs.html#columns_and_mapping

RHadoop の紹介Hidekazu Tanaka

Experement no 6Smita Batti

This experiment finds the minimum cost spanning tree of an undirected graph using Kruskal's algorithm. It takes the number of vertices and cost matrix as input, initializes parent and minimum cost variables, then iterates through the edges and adds the minimum cost edge between two unconnected vertices to the spanning tree until all vertices are connected. It outputs the edges of the minimum spanning tree and their total cost.

DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser BootsmaDeltares

Basic Calculus in R. Dr. Volkan OBAN

Py lecture5 python plotsYoshiki Satotani

Matplotlib is a 2D plotting library for Python that can generate publication-quality figures in both hardcopy and interactive formats. The document provides examples of using Matplotlib to plot lines, histograms, pie charts, scatter plots, subplots, and mathematical functions. Additional resources are also listed for learning more about Matplotlib and an example dataset on apple production by variety.

Advanced Data Visualization Examples with R-Part IIDr. Volkan OBAN

This document provides several examples of advanced data visualization techniques using R. It includes examples of 3D surface plots, contour plots, scatter plots and network graphs using various R packages like plot3D, scatterplot3D, ggplot2, qgraph and ggtree. Functions used include surf3D, contour3D, arrows3D, persp3D, image3D, scatter3D, qgraph, geom_point, geom_violin and ggtree. The examples demonstrate different visualization approaches for multivariate, spatial and network data.

Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN

Matlab plottingAmr Rashed

The document contains 20 MATLAB programs demonstrating various plotting and data visualization techniques including: - Plotting sinusoidal waves and applying half/full wave rectification - Converting between polar and Cartesian coordinates and plotting circles - Generating and plotting cylinder surfaces - Using EZ functions like ezplot, ezsurf, and ezpolar to plot functions - Taking derivatives and plotting functions and their derivatives - Plotting noise signals and calculating rate of change over time - Generating 2D and 3D surfaces from gridded data - Plotting polar plots and converting to Cartesian coordinates - Applying FFT and filtering to decompose a signal into frequency components - Using pie charts and 3D pie plots to

CodingPrasanta Paul

The document describes a C program that finds subsets of a given set of positive integers whose sum equals a target value. The program takes a set of integers, adds them up, and uses backtracking recursion to find all subsets that sum to the target value. If no matching subset exists, it displays a message. It provides an example input of the set {1, 2, 5, 6, 8} and target 9, which has the two solutions {1, 2, 6} and {1, 8}.

XIX PUG-PE - Pygame game developmentmatheuscmpm

Pygame is a set of Python modules for developing video games. It provides tools for creating surfaces, handling events, playing sounds, and more. The presentation introduces essential Pygame elements like surfaces, rectangles, display, and events. Surfaces represent images and allow manipulating pixels. Rectangles are used for collision detection. The display module handles the screen. Events represent user inputs. Pygame provides a simple and portable way to create games in Python.

Prob-Dist-Toll-Forecast-UncertaintyAnkoor Bhagat

The document describes an algorithm for estimating the probability distribution of travel demand forecasts that are subject to uncertainty. It involves identifying variables that influence forecast error, determining probability distributions for each variable, defining scenarios that combine the discrete outcomes of each variable, calculating the probability and predicted revenue for each scenario, and plotting the revenue cumulative distribution function. Key variables of uncertainty identified for a toll road project include truck value of time, travel demand, and growth rates of car and truck value of time. Probability distributions assumed for these variables include lognormal, normal, and triangular. The algorithm allows assessment of the uncertainty and risk associated with toll revenue forecasts.

Matlab plottingshahid sultan

This document discusses various MATLAB plotting commands and techniques. It begins by explaining how to create basic 2D plots using the plot() command to plot x-y data or a function. It also covers formatting plots by adding titles, labels, legends etc. The document then discusses plotting multiple graphs in the same figure using either the plot() command or hold on/off commands. Finally, it demonstrates how to create multiple subplots on one figure using the subplot command.

Geo Spatial Plot using R Rupak Roy

This document provides an example of creating geospatial plots in R using ggmap() and ggplot2. It includes 3 steps: 1) Get the map using get_map(), 2) Plot the map using ggmap(), and 3) Plot the dataset on the map using ggplot2 objects like geom_point(). The example loads crime and neighborhood datasets, filters the data, gets a map of Seattle, and plots crime incidents and dangerous neighborhoods on the map. It demonstrates various geospatial plotting techniques like adjusting point transparency, adding density estimates, labeling points, and faceting by crime type.

Data visualization using the grammar of graphicsRupak Roy

Megadata With Python and Hadoopryancox

This document discusses processing large datasets with Python and Hadoop. It begins with an example of finding the highest temperature from a climate dataset using a map-reduce approach. Next, it provides code examples for implementing map-reduce in pure Python, with Hadoop Streaming, and with the Dumbo library. The document then discusses using Amazon Elastic MapReduce for running Hadoop jobs on AWS. It poses a question about how to implement breadth-first search as a map-reduce algorithm and ends with an example of using MongoDB's map-reduce functionality.

Bar plots.ipynb colaboratoryGokuldhev mony

The document contains code for creating various bar plots and visualizations using Python libraries like Matplotlib, Seaborn, and Plotly. It includes bar plots showing student enrollment data by course, total bill by time of day from a sample dataset, Canadian population data over time, FIFA player ratings over multiple years, top 10 countries by total medals, top 10 athletes by number of medals awarded, sports with most medals, analysis of medals won by gender and discipline over time. Stacked bar plots and grouped bar plots are also created to show comparisons between categories in the datasets.

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...The Statistical and Applied Mathematical Sciences Institute

Climate statisticians analyze observational climate data and model simulations to detect trends, attribute causes, and quantify uncertainties. They use statistical methods like linear regression to attribute observed warming to human and natural factors. They also use extreme value theory to describe rare weather events and project how these extremes may change with continued warming. A key task is quantifying various sources of uncertainty in climate projections, like different model sensitivities and emissions scenarios.

CLIM Undergraduate Workshop: Statistical Development and challenges for Paleo...The Statistical and Applied Mathematical Sciences Institute

This document summarizes an undergraduate workshop on statistical developments and challenges in past climate reconstruction. It discusses why accurately reconstructing past climate is important, and describes some common proxy sources used like tree rings, pollen, and borehole data. Statistical methods like linear regression are used to estimate past climate values from these proxy observations. The workshop also discusses long-term climate proxy records and their relationship to reconstructed temperature histories.

More Related Content

What's hot (20)

Rexsuns

Data Visualization with R.ggplot2 and its extensions examples.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

R-ggplot2 package ExamplesDr. Volkan OBAN

RHadoop の紹介Hidekazu Tanaka

Experement no 6Smita Batti

DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser BootsmaDeltares

Basic Calculus in R. Dr. Volkan OBAN

Py lecture5 python plotsYoshiki Satotani

Advanced Data Visualization Examples with R-Part IIDr. Volkan OBAN

Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN

Matlab plottingAmr Rashed

CodingPrasanta Paul

XIX PUG-PE - Pygame game developmentmatheuscmpm

Prob-Dist-Toll-Forecast-UncertaintyAnkoor Bhagat

Matlab plottingshahid sultan

Geo Spatial Plot using R Rupak Roy

Data visualization using the grammar of graphicsRupak Roy

Megadata With Python and Hadoopryancox

Bar plots.ipynb colaboratoryGokuldhev mony

Rexsuns

Data Visualization with R.ggplot2 and its extensions examples.Dr. Volkan OBAN

R meets HadoopHidekazu Tanaka

R-ggplot2 package ExamplesDr. Volkan OBAN

RHadoop の紹介Hidekazu Tanaka

Experement no 6Smita Batti

DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser BootsmaDeltares

Basic Calculus in R. Dr. Volkan OBAN

Py lecture5 python plotsYoshiki Satotani

Advanced Data Visualization Examples with R-Part IIDr. Volkan OBAN

Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN

Matlab plottingAmr Rashed

CodingPrasanta Paul

XIX PUG-PE - Pygame game developmentmatheuscmpm

Prob-Dist-Toll-Forecast-UncertaintyAnkoor Bhagat

Matlab plottingshahid sultan

Geo Spatial Plot using R Rupak Roy

Data visualization using the grammar of graphicsRupak Roy

Megadata With Python and Hadoopryancox

Bar plots.ipynb colaboratoryGokuldhev mony

Viewers also liked (20)

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Statistical Development and challenges for Paleo...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Undergraduate Workshop Introduction - Elvan Ceyh...The Statistical and Applied Mathematical Sciences Institute

This document provides information about the Statistical and Applied Mathematical Sciences Institute (SAMSI) and an upcoming undergraduate workshop on climate extremes. SAMSI is a National Science Foundation-funded math institute established in 2002 with a mission to apply statistical and mathematical sciences to important scientific challenges. The document outlines SAMSI's research programs, education and outreach activities including undergraduate and graduate workshops. It then provides details on the upcoming Undergraduate Workshop on Climate Extremes, including its schedule of tutorial, lecture, and panel activities on October 23-24, 2017.

CLIM Undergraduate Workshop: Introduction to Spatial Data Analysis with R - M...The Statistical and Applied Mathematical Sciences Institute

This document introduces spatial data analysis and geostatistical modeling. It discusses how to model spatial dependence through variogram analysis and additive geostatistical models. These models incorporate spatial dependence by including a Gaussian process that allows predictions to borrow strength from neighboring observations based on their distance. The document demonstrates fitting linear regression and geostatistical models to average maximum July temperature data from the southeastern US to improve predictions and account for spatial autocorrelation in the residuals.

CLIM Undergraduate Workshop: How was this Made?: Making Dirty Data into Somet...The Statistical and Applied Mathematical Sciences Institute

This document describes how rainfall estimates are created from raw radar data at the National Centers for Environmental Information (NCEI). It explains that multiple radars are combined and merged to get nationwide coverage with 2 minute resolution on a standardized grid. Raw radar returns are cleaned of contamination from birds and other sources. The vertical radar scans are integrated into a single rainfall estimate. The final product provides a consistent, high quality rainfall estimate compared to the original radar data which had poorer spatial and temporal resolution and quality issues.

CLIM Undergraduate Workshop: Extreme Value Analysis for Climate Research - Wh...The Statistical and Applied Mathematical Sciences Institute

This document discusses statistical methods for analyzing climate extremes using extreme value theory. It introduces the generalized extreme value distribution (GEV) for fitting block maxima and the generalized Pareto distribution (GPD) for fitting values above a threshold. These distributions allow inference of return levels to communicate the risk of extreme events like 100-year rainfall. Diagnostic tools help select the appropriate distribution and threshold.

Summer Program on Transportation Statistics, Why Highway Crashes Have Recurri...The Statistical and Applied Mathematical Sciences Institute

Highway crash data with average of 39 thousand fatalities and 2.4 million nonfatal injuries per year have repetitive and predictable patterns, and may benefit from statistical predictive models to enhance highway safety and operation efforts to reduce crash fatalities/injuries. Highway crashes have patterns that repeat over fixed periods of time within the data set for crashes such as motorcycle, bicycles, pedestrians, nighttime, fixed object, weekend, and winter crashes. In some States, these crashes are weekly, monthly, or seasonally. Contributing factors such as: age category, light condition, weather, weekday, underlying state of the economy, and others impact these variations.

Summer Program on Transportation Statistics, What about the Driver in Driver...The Statistical and Applied Mathematical Sciences Institute

As we prepare for a future of driverless cars, what new risks must we work to understand? Despite the connotation of driverless, we can expect that humans will remain in the loop at each iteration of increasingly autonomous technology integration. While our technology is advancing, our population and economics are also transitioning to present challenging paradigm shifts that we should account for in assessing the risks of driverless cars. Let us take this holistic systems engineering approach to exploring transportation at the Statistical and Applied Mathematical Sciences Institute.

Summer Program on Transportation Statistics, Statistical Challenges for Advan...The Statistical and Applied Mathematical Sciences Institute

Crashes on limited access roadways typically occur due to drivers being unable to react in time to avoid collisions with vehicles ahead of them either moving slower or merging unexpectedly. Prevailing traffic stream conditions with high volume and low or variable speed downstream of low volume and high speed conditions can increase the possibilities for such collisions to occur. Real time trajectories of vehicles collected through crowd sourcing methods can give information about the distribution of speeds in the traffic stream by space and time. Spatio-temporal models relating these observed speed distributions to the occurrence of crashes or near crashes can help to identify crash prone traffic conditions as they arise, offering the opportunity to warn drivers before crashes occur.

Summer Program on Transportation Statistics, Assessing Crash Risk for Highly ...The Statistical and Applied Mathematical Sciences Institute

As the analysis by Kalra & Paddock (2016) demonstrated, traditional crash data and analysis approaches may require hundreds of millions or billions of self-driving miles to achieve sufficient power to demonstrate that automated vehicles (AVs) have lower injury/fatality risk than human-driven vehicles. Moreover, crash risk for AVs is a moving target as algorithms and systems change, and the mistakes AVs will make are not necessarily the same mistakes humans make. Thus, we need to rethink both the data that will make up transportation safety datasets in the near future as well as the analytical approaches used. I will present some newer data-collection approaches along with some specific challenges that might call for different analytical approaches than are being used for crash data today.

Summer Program on Transportation Statistics, Dynamic Modeling of Transportati...The Statistical and Applied Mathematical Sciences Institute

This talk will review dynamic modeling and prediction for temporal and spatio-temporal data and describe algorithms for suitable state space models. Use of dynamic models for modeling crash types by severity will be briefly illustrated. Extension of these approaches for handling irregular temporal spacing and spatial sparseness will be discussed, and a potential application to travel time prediction will be explored.

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute

This document discusses challenges and opportunities for using machine learning and data mining techniques on big climate data. It describes various types of climate and Earth observation data available from satellites and models. Research highlights are presented on using pattern mining to track ocean eddies, extreme value theory to study heatwaves and rainfall, and relationship mining to study seasonal hurricane activity. The challenges of analyzing multi-scale, heterogeneous climate data are also discussed.

CLIM Fall 2017 Course: Statistics for Climate Research, Analysis for Climate ...The Statistical and Applied Mathematical Sciences Institute

This document discusses models used in climate science and uncertainty quantification. It begins by introducing the types of models, including general circulation models (GCMs) that simulate the climate system. A key point is that climate models provide probability distributions of weather rather than single predictions. The document emphasizes that uncertainty quantification is essential in climate science given the complexity of the climate system and imperfections in both models and observations. It presents a Bayesian framework for combining information from multiple models and data sources to obtain probability distributions of climate projections and quantify associated uncertainties. Gaussian process emulation is discussed as a method for approximating computationally expensive climate models to facilitate Bayesian calibration and inference.

CLIM Fall 2017 Course: Statistics for Climate Research, Detection & Attributi...The Statistical and Applied Mathematical Sciences Institute

1) The document discusses detection and attribution in climate science, which refers to statistical techniques used to identify the contributions of different forcing factors (like greenhouse gases or solar activity) to changes in climate signals over time. 2) It provides context on the history and development of detection and attribution methods, beginning with early work in the 1970s-1990s and more recent Bayesian approaches. 3) A key paper discussed is one by Katzfuss, Hammerling and Smith (2017) that introduced a Bayesian hierarchical model for climate change detection and attribution to help address uncertainties.

CLIM Fall 2017 Course: Statistics for Climate Research, Estimating Curves and...The Statistical and Applied Mathematical Sciences Institute

This document provides an outline and overview of key concepts for estimating curves and surfaces from data using basis functions and penalized least squares regression. It discusses representing a curve or surface using basis functions, fitting the coefficients using ordinary least squares, and adding a penalty term to the least squares objective function to produce a smoothed estimate. The smoothing parameter λ controls the tradeoff between fit to the data and smoothness of the estimate. Cross-validation can be used to choose λ.

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute

We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute

Remote-sensing data offer unprecedented opportunities to address Earth-system-science challenges, such as understanding the relationship between the atmosphere and Earth's surface using physics, chemistry, biology, mathematics, and computing. Statistical methods have often been seen as a hybrid of the latter two, so that a lot of attention has been given to computing estimates but far less to quantifying the uncertainty of the estimates. In my "bird's-eye view," I shall give a way to look at the problem using conditional probability models and three states of knowledge. Examples will be given of analyzing remotely sensed data of a leading greenhouse gas, carbon dioxide.

CLIM Fall 2017 Course: Statistics for Climate Research, Climate Informatics -...The Statistical and Applied Mathematical Sciences Institute

- Brian Reich gave a presentation on climate informatics and machine learning. - He discussed different conceptual views of statistics, including parametric modeling, linear regression, inferential statistics, and machine learning. - Reich provided examples of unsupervised learning techniques like principal component analysis (PCA) and supervised learning using deep neural networks. - The presentation concluded with a challenge for the audience to build and evaluate a neural network model on simulated wildfire detection data.

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Statistical Development and challenges for Paleo...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Undergraduate Workshop Introduction - Elvan Ceyh...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Introduction to Spatial Data Analysis with R - M...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: How was this Made?: Making Dirty Data into Somet...The Statistical and Applied Mathematical Sciences Institute

CLIM Undergraduate Workshop: Extreme Value Analysis for Climate Research - Wh...The Statistical and Applied Mathematical Sciences Institute

Summer Program on Transportation Statistics, Why Highway Crashes Have Recurri...The Statistical and Applied Mathematical Sciences Institute

Summer Program on Transportation Statistics, What about the Driver in Driver...The Statistical and Applied Mathematical Sciences Institute

Summer Program on Transportation Statistics, Statistical Challenges for Advan...The Statistical and Applied Mathematical Sciences Institute

Summer Program on Transportation Statistics, Assessing Crash Risk for Highly ...The Statistical and Applied Mathematical Sciences Institute

Summer Program on Transportation Statistics, Dynamic Modeling of Transportati...The Statistical and Applied Mathematical Sciences Institute

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute

CLIM Fall 2017 Course: Statistics for Climate Research, Analysis for Climate ...The Statistical and Applied Mathematical Sciences Institute

CLIM Fall 2017 Course: Statistics for Climate Research, Detection & Attributi...The Statistical and Applied Mathematical Sciences Institute

CLIM Fall 2017 Course: Statistics for Climate Research, Estimating Curves and...The Statistical and Applied Mathematical Sciences Institute

Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute

CLIM Fall 2017 Course: Statistics for Climate Research, Climate Informatics -...The Statistical and Applied Mathematical Sciences Institute

Similar to CLIM Undergraduate Workshop: Tutorial on R Software - Huang Huang, Oct 23, 2017 (20)

QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...The Statistical and Applied Mathematical Sciences Institute

R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions as well as packages for additional techniques. RStudio is a popular integrated development environment for R that includes a console, editor, and tools. Common R objects include vectors to store multiple values, matrices to store values in rows and columns, and data frames to store different types of data. Functions like cbind(), rbind(), and matrix() can be used to create matrices from vectors. Random variables can be simulated using functions like runif(), rnorm(), and sample(). User-defined functions allow custom calculations to be performed on data in R.

Brief introduction to R Lecturenotes1_R .pptgeethar79

R_Language_study_forstudents_R_Material.pptSuresh Babu

Lecture1_R Programming Introduction1.pptpremak23

R is a free, open-source programming language used for statistical computing and graphics. It's a key tool in data science, research, finance, and healthcare. Features Data analysis: R has a variety of tools and operators for data analysis. Data visualization: R has many packages for data visualization, including Plotly, which can create interactive graphs. Machine learning: R can build machine-learning models.

Lecture1_R.pptArchishaKhandareSS20

Lecture1 rSandeep242951

This document provides an overview of the basics of R including why R is used, tutorials and links for learning R, an overview of the R interface and workspace, and how to get help in R. It discusses that R is a free and open-source statistical programming language used for statistical analysis and graphics. It has a steep learning curve due to the interactive nature of analyzing data through chained commands rather than single procedures. Help is provided through a built-in system and various online tutorials.

Lecture1_R.pptvikassingh569137

Modeling in R Programming Language for Beginers.pptanshikagoel52

PMED Undergraduate Workshop - R Tutorial for PMED Undegraduate Workshop - Xi...The Statistical and Applied Mathematical Sciences Institute

Lecture1_R.pdfBusyBird2

R-Language-Lab-Manual-lab-1.pdfKabilaArun

R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.

R-Language-Lab-Manual-lab-1.pdfattalurilalitha

R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.

R-Language-Lab-Manual-lab-1.pdfDrGSakthiGovindaraju

R programming lab 1 - jupyter notebookAshwini Mathur

1. The document outlines tasks to be performed in an R programming lab experiment including exploring assignment operators, creating vectors and matrices, performing operations on matrices, creating arrays, selecting and extracting elements, and exploring logical operators. 2. Matrices and vectors are created using functions like matrix(), c(), seq(), and rep(). Operations like addition, subtraction, multiplication and division are demonstrated on matrices and vectors. 3. Elements are selected and extracted from matrices and arrays using indexing and functions like ncol(), nrow(), length(), and dim(). Logical operators like &, |, ! are also explored. 4. The document shows how to remove elements from selected positions in a matrix.

Python program For O level Practical pavitrakumar18

R basicsSagun Baijal

R is a language and environment for statistical computing and graphics. It is based on S, an earlier language developed at Bell Labs. R features include being cross-platform, open source, having a package-based repository, strong graphics capabilities, and active user and developer communities. Useful URLs and books for learning R are provided. Instructions for installing R and RStudio on different platforms are given. R can be used for a wide range of statistical analyses and data visualization.

Best corporate-r-programming-training-in-mumbaiUnmesh Baile

美洲杯投注-美洲杯投注外围投注-美洲杯投注外围投注平台|【网址🎉ac10.net🎉】ravisconneraa55387

【网址🎉ac10.net🎉】美洲杯投注在体育博彩方面，不难发现，美洲杯投注多为英式运动提供投注。美洲杯投注为丰富多彩的体育项目提供投注，如足球、赛马、板球和飞镖，但这不是美洲杯投注的唯一优势。在美洲杯投注，全世界玩家都可以找到自己感兴趣的比赛进行投注。美洲杯投注为网球和美式运动如棒球、美式足球、篮球、冰球开出的投注同样值得关注。在夏季或冬季奥运会方面，美洲杯投注为玩家开出了丰富多彩的服务，是你的不二之选。你还可以投注水球、自行车和班迪球等小众体育项目，甚至可以为武术、拳击和综合格斗投注。美洲杯投注为众多体育项目开出滚球投注。

C arraysCGC Technical campus,Mohali

This document provides information on arrays in C programming. It defines an array as a collection of data storage locations with the same name and data type. It discusses array declaration, initialization, accessing elements using indexes, insertion and deletion of elements, and multi-dimensional arrays. Code examples are provided to demonstrate printing the contents of 1D, 2D and 3D arrays. The document also lists some reference books for learning C programming.

R for Pythonistas (PyData NYC 2017)Christopher Roach

R is a powerful language for data analysis and visualization. Some key advantages of R include its data-centric approach, large collection of packages, and powerful data visualization capabilities like ggplot2. The document discusses various R concepts like its functional programming style, object-oriented programming using S3 classes, and non-standard evaluation. It also provides examples of how to access R functions and libraries from Python using rpy2.

QMC: Undergraduate Workshop, Tutorial on 'R' Software - Yawen Guan, Feb 26, 2...The Statistical and Applied Mathematical Sciences Institute

Brief introduction to R Lecturenotes1_R .pptgeethar79

R_Language_study_forstudents_R_Material.pptSuresh Babu

Lecture1_R Programming Introduction1.pptpremak23

Lecture1_R.pptArchishaKhandareSS20

Lecture1 rSandeep242951

Lecture1_R.pptvikassingh569137

Modeling in R Programming Language for Beginers.pptanshikagoel52

PMED Undergraduate Workshop - R Tutorial for PMED Undegraduate Workshop - Xi...The Statistical and Applied Mathematical Sciences Institute

Lecture1_R.pdfBusyBird2

R-Language-Lab-Manual-lab-1.pdfKabilaArun

R-Language-Lab-Manual-lab-1.pdfattalurilalitha

R-Language-Lab-Manual-lab-1.pdfDrGSakthiGovindaraju

R programming lab 1 - jupyter notebookAshwini Mathur

Python program For O level Practical pavitrakumar18

R basicsSagun Baijal

Best corporate-r-programming-training-in-mumbaiUnmesh Baile

美洲杯投注-美洲杯投注外围投注-美洲杯投注外围投注平台|【网址🎉ac10.net🎉】ravisconneraa55387

C arraysCGC Technical campus,Mohali

R for Pythonistas (PyData NYC 2017)Christopher Roach

More from The Statistical and Applied Mathematical Sciences Institute (20)

Causal Inference Opening Workshop - Latent Variable Models, Causal Inference,...The Statistical and Applied Mathematical Sciences Institute

Recently, the machine learning community has expressed strong interest in applying latent variable modeling strategies to causal inference problems with unobserved confounding. Here, I discuss one of the big debates that occurred over the past year, and how we can move forward. I will focus specifically on the failure of point identification in this setting, and discuss how this can be used to design flexible sensitivity analyses that cleanly separate identified and unidentified components of the causal model.

2019 Fall Series: Special Guest Lecture - 0-1 Phase Transitions in High Dimen...The Statistical and Applied Mathematical Sciences Institute

I will discuss paradigmatic statistical models of inference and learning from high dimensional data, such as sparse PCA and the perceptron neural network, in the sub-linear sparsity regime. In this limit the underlying hidden signal, i.e., the low-rank matrix in PCA or the neural network weights, has a number of non-zero components that scales sub-linearly with the total dimension of the vector. I will provide explicit low-dimensional variational formulas for the asymptotic mutual information between the signal and the data in suitable sparse limits. In the setting of support recovery these formulas imply sharp 0-1 phase transitions for the asymptotic minimum mean-square-error (or generalization error in the neural network setting). A similar phase transition was analyzed recently in the context of sparse high-dimensional linear regression by Reeves et al.

Causal Inference Opening Workshop - Causal Discovery in Neuroimaging Data - F...The Statistical and Applied Mathematical Sciences Institute

Many different measurement techniques are used to record neural activity in the brains of different organisms, including fMRI, EEG, MEG, lightsheet microscopy and direct recordings with electrodes. Each of these measurement modes have their advantages and disadvantages concerning the resolution of the data in space and time, the directness of measurement of the neural activity and which organisms they can be applied to. For some of these modes and for some organisms, significant amounts of data are now available in large standardized open-source datasets. I will report on our efforts to apply causal discovery algorithms to, among others, fMRI data from the Human Connectome Project, and to lightsheet microscopy data from zebrafish larvae. In particular, I will focus on the challenges we have faced both in terms of the nature of the data and the computational features of the discovery algorithms, as well as the modeling of experimental interventions.

Causal Inference Opening Workshop - Smooth Extensions to BART for Heterogeneo...The Statistical and Applied Mathematical Sciences Institute

1) The document presents a statistical modeling approach called targeted smooth Bayesian causal forests (tsbcf) to smoothly estimate heterogeneous treatment effects over gestational age using observational data from early medical abortion regimens. 2) The tsbcf method extends Bayesian additive regression trees (BART) to estimate treatment effects that evolve smoothly over gestational age, while allowing for heterogeneous effects across patient subgroups. 3) The tsbcf analysis of early medical abortion regimen data found the simultaneous administration to be similarly effective overall to the interval administration, but identified some patient subgroups where effectiveness may vary more over gestational age.

Causal Inference Opening Workshop - A Bracketing Relationship between Differe...The Statistical and Applied Mathematical Sciences Institute

Difference-in-differences is a widely used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale-dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, Angrist and Pischke (2009) show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting.

Causal Inference Opening Workshop - Testing Weak Nulls in Matched Observation...The Statistical and Applied Mathematical Sciences Institute

We develop sensitivity analyses for weak nulls in matched observational studies while allowing unit-level treatment effects to vary. In contrast to randomized experiments and paired observational studies, we show for general matched designs that over a large class of test statistics, any valid sensitivity analysis for the weak null must be unnecessarily conservative if Fisher's sharp null of no treatment effect for any individual also holds. We present a sensitivity analysis valid for the weak null, and illustrate why it is conservative if the sharp null holds through connections to inverse probability weighted estimators. An alternative procedure is presented that is asymptotically sharp if treatment effects are constant, and is valid for the weak null under additional assumptions which may be deemed reasonable by practitioners. The methods may be applied to matched observational studies constructed using any optimal without-replacement matching algorithm, allowing practitioners to assess robustness to hidden bias while allowing for treatment effect heterogeneity.

Causal Inference Opening Workshop - Difference-in-differences: more than meet...The Statistical and Applied Mathematical Sciences Institute

This document discusses difference-in-differences (DiD) analysis, a quasi-experimental method used to estimate treatment effects. The author notes that while widely applicable, DiD relies on strong assumptions about the counterfactual. She recommends approaches like matching on observed variables between similar populations, thoughtfully specifying regression models to adjust for confounding factors, testing for parallel pre-treatment trends under different assumptions, and considering more complex models that allow for different types of changes over time. The overall message is that DiD requires careful consideration and testing of its underlying assumptions to draw valid causal conclusions.

Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...The Statistical and Applied Mathematical Sciences Institute

We present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.

Causal Inference Opening Workshop - Bipartite Causal Inference with Interfere...The Statistical and Applied Mathematical Sciences Institute

A fundamental feature of evaluating causal health effects of air quality regulations is that air pollution moves through space, rendering health outcomes at a particular population location dependent upon regulatory actions taken at multiple, possibly distant, pollution sources. Motivated by studies of the public-health impacts of power plant regulations in the U.S., this talk introduces the novel setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. Interference in this setting arises due to complex exposure patterns dictated by physical-chemical atmospheric processes of pollution transport, with intervention effects framed as propagating across a bipartite network of power plants and residential zip codes. New causal estimands are introduced for the bipartite setting, along with an estimation approach based on generalized propensity scores for treatments on a network. The new methods are deployed to estimate how emission-reduction technologies implemented at coal-fired power plants causally affect health outcomes among Medicare beneficiaries in the U.S.

Causal Inference Opening Workshop - Bridging the Gap Between Causal Literatur...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...The Statistical and Applied Mathematical Sciences Institute

We provide an overview of some recent developments in machine learning tools for dynamic treatment regime discovery in precision medicine. The first development is a new off-policy reinforcement learning tool for continual learning in mobile health to enable patients with type 1 diabetes to exercise safely. The second development is a new inverse reinforcement learning tools which enables use of observational data to learn how clinicians balance competing priorities for treating depression and mania in patients with bipolar disorder. Both practical and technical challenges are discussed.

Causal Inference Opening Workshop - Bracketing Bounds for Differences-in-Diff...The Statistical and Applied Mathematical Sciences Institute

The method of differences-in-differences (DID) is widely used to estimate causal effects. The primary advantage of DID is that it can account for time-invariant bias from unobserved confounders. However, the standard DID estimator will be biased if there is an interaction between history in the after period and the groups. That is, bias will be present if an event besides the treatment occurs at the same time and affects the treated group in a differential fashion. We present a method of bounds based on DID that accounts for an unmeasured confounder that has a differential effect in the post-treatment time period. These DID bracketing bounds are simple to implement and only require partitioning the controls into two separate groups. We also develop two key extensions for DID bracketing bounds. First, we develop a new falsification test to probe the key assumption that is necessary for the bounds estimator to provide consistent estimates of the treatment effect. Next, we develop a method of sensitivity analysis that adjusts the bounds for possible bias based on differences between the treated and control units from the pretreatment period. We apply these DID bracketing bounds and the new methods we develop to an application on the effect of voter identification laws on turnout. Specifically, we focus estimating whether the enactment of voter identification laws in Georgia and Indiana had an effect on voter turnout.

Causal Inference Opening Workshop - Assisting the Impact of State Polcies: Br...The Statistical and Applied Mathematical Sciences Institute

This document summarizes a simulation study evaluating causal inference methods for assessing the effects of opioid and gun policies. The study used real US state-level data to simulate the adoption of policies by some states and estimated the effects using different statistical models. It found that with fewer adopting states, type 1 error rates were too high, and most models lacked power. It recommends using cluster-robust standard errors and lagged outcomes to improve model performance. The study aims to help identify best practices for policy evaluation studies.

Causal Inference Opening Workshop - Experimenting in Equilibrium - Stefan Wag...The Statistical and Applied Mathematical Sciences Institute

We study experimental design in large-scale stochastic systems with substantial uncertainty and structured cross-unit interference. We consider the problem of a platform that seeks to optimize supply-side payments p in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and propose a class of local experimentation schemes that can be used to optimize these payments without perturbing the overall market equilibrium. We show that, as the system size grows, our scheme can estimate the gradient of the platformâ€™s utility with respect to p while perturbing the overall market equilibrium by only a vanishingly small amount. We can then use these gradient estimates to optimize p via any stochastic first-order optimization method. These results stem from the insight that, while the system involves a large number of interacting units, any interference can only be channeled through a small number of key statistics, and this structure allows us to accurately predict feedback effects that arise from global system changes using only information collected while remaining in equilibrium.

Causal Inference Opening Workshop - Targeted Learning for Causal Inference Ba...The Statistical and Applied Mathematical Sciences Institute

We discuss a general roadmap for generating causal inference based on observational studies used to general real world evidence. We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for realistic (i.e, infinite dimensional) statistical models. TMLE is a two stage procedure that first involves using ensemble machine learning termed super-learning to estimate the relevant stochastic relations between the treatment, censoring, covariates and outcome of interest. The super-learner allows one to fully utilize all the advances in machine learning (in addition to more conventional parametric model based estimators) to build a single most powerful ensemble machine learning algorithm. We present Highly Adaptive Lasso as an important machine learning algorithm to include. In the second step, the TMLE involves maximizing a parametric likelihood along a so-called least favorable parametric model through the super-learner fit of the relevant stochastic relations in the observed data. This second step bridges the state of the art in machine learning to estimators of target estimands for which statistical inference is available (i.e, confidence intervals, p-values etc). We also review recent advances in collaborative TMLE in which the fit of the treatment and censoring mechanism is tailored w.r.t. performance of TMLE. We also discuss asymptotically valid bootstrap based inference. Simulations and data analyses are provided as demonstrations.

Causal Inference Opening Workshop - Bayesian Nonparametric Models for Treatme...The Statistical and Applied Mathematical Sciences Institute

We describe different approaches for specifying models and prior distributions for estimating heterogeneous treatment effects using Bayesian nonparametric models. We make an affirmative case for direct, informative (or partially informative) prior distributions on heterogeneous treatment effects, especially when treatment effect size and treatment effect variation is small relative to other sources of variability. We also consider how to provide scientifically meaningful summaries of complicated, high-dimensional posterior distributions over heterogeneous treatment effects with appropriate measures of uncertainty.

2019 Fall Series: Special Guest Lecture - Adversarial Risk Analysis of the Ge...The Statistical and Applied Mathematical Sciences Institute

Climate change mitigation has traditionally been analyzed as some version of a public goods game (PGG) in which a group is most successful if everybody contributes, but players are best off individually by not contributing anything (i.e., “free-riding”)—thereby creating a social dilemma. Analysis of climate change using the PGG and its variants has helped explain why global cooperation on GHG reductions is so difficult, as nations have an incentive to free-ride on the reductions of others. Rather than inspire collective action, it seems that the lack of progress in addressing the climate crisis is driving the search for a “quick fix” technological solution that circumvents the need for cooperation.

2019 Fall Series: Professional Development, Writing Academic Papers…What Work...The Statistical and Applied Mathematical Sciences Institute

This document discusses various types of academic writing and provides tips for effective academic writing. It outlines common academic writing formats such as journal papers, books, and reports. It also lists writing necessities like having a clear purpose, understanding your audience, using proper grammar and being concise. The document cautions against plagiarism and not proofreading. It provides additional dos and don'ts for writing, such as using simple language and avoiding filler words. Overall, the key message is that academic writing requires selling your ideas effectively to the reader.

2019 GDRR: Blockchain Data Analytics - Machine Learning in/for Blockchain: Fu...The Statistical and Applied Mathematical Sciences Institute

Machine learning (including deep and reinforcement learning) and blockchain are two of the most noticeable technologies in recent years. The first one is the foundation of artificial intelligence and big data, and the second one has significantly disrupted the financial industry. Both technologies are data-driven, and thus there are rapidly growing interests in integrating them for more secure and efficient data sharing and analysis. In this paper, we review the research on combining blockchain and machine learning technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more researches on deeper integration of the two promising technologies.

2019 GDRR: Blockchain Data Analytics - QuTrack: Model Life Cycle Management f...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Latent Variable Models, Causal Inference,...The Statistical and Applied Mathematical Sciences Institute

2019 Fall Series: Special Guest Lecture - 0-1 Phase Transitions in High Dimen...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Causal Discovery in Neuroimaging Data - F...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Smooth Extensions to BART for Heterogeneo...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - A Bracketing Relationship between Differe...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Testing Weak Nulls in Matched Observation...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Difference-in-differences: more than meet...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - New Statistical Learning Methods for Esti...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Bipartite Causal Inference with Interfere...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Bridging the Gap Between Causal Literatur...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Some Applications of Reinforcement Learni...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Bracketing Bounds for Differences-in-Diff...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Assisting the Impact of State Polcies: Br...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Experimenting in Equilibrium - Stefan Wag...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Targeted Learning for Causal Inference Ba...The Statistical and Applied Mathematical Sciences Institute

Causal Inference Opening Workshop - Bayesian Nonparametric Models for Treatme...The Statistical and Applied Mathematical Sciences Institute

2019 Fall Series: Special Guest Lecture - Adversarial Risk Analysis of the Ge...The Statistical and Applied Mathematical Sciences Institute

2019 Fall Series: Professional Development, Writing Academic Papers…What Work...The Statistical and Applied Mathematical Sciences Institute

2019 GDRR: Blockchain Data Analytics - Machine Learning in/for Blockchain: Fu...The Statistical and Applied Mathematical Sciences Institute

2019 GDRR: Blockchain Data Analytics - QuTrack: Model Life Cycle Management f...The Statistical and Applied Mathematical Sciences Institute

Recently uploaded (20)

Download Wondershare Filmora Crack [2025] With Latesttahirabibi60507

Copy & Past Link 👉👉 http://drfiles.net/ Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.

The Significance of Hardware in Information Systems.pdfdrewplanas10

Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...AxisTechnolabs

Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business ✅Visit And Buy Now : https://bit.ly/3VojWza ✅This Interactive Odoo dashboard module allow user to create their own odoo interactive dashboards for various purpose. App download now : Odoo 18 : https://bit.ly/3VojWza Odoo 17 : https://bit.ly/4h9Z47G Odoo 16 : https://bit.ly/3FJTEA4 Odoo 15 : https://bit.ly/3W7tsEB Odoo 14 : https://bit.ly/3BqZDHg Odoo 13 : https://bit.ly/3uNMF2t Try Our website appointment booking odoo app : https://bit.ly/3SvNvgU 👉Want a Demo ?📧 [email protected] ➡️Contact us for Odoo ERP Set up : 091066 49361 👉Explore more apps: https://bit.ly/3oFIOCF 👉Want to know more : 🌐 https://www.axistechnolabs.com/ #odoo #odoo18 #odoo17 #odoo16 #odoo15 #odooapps #dashboards #dashboardsoftware #odooerp #odooimplementation #odoodashboardapp #bestodoodashboard #dashboardapp #odoodashboard #dashboardmodule #interactivedashboard #bestdashboard #dashboard #odootag #odooservices #odoonewfeatures #newappfeatures #odoodashboardapp #dynamicdashboard #odooapp #odooappstore #TopOdooApps #odooapp #odooexperience #odoodevelopment #businessdashboard #allinonedashboard #odooproducts

Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa

This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software. Presented at: The 33rd International Conference on Program Comprehension (ICPC '25) Date of Conference: April 2025 Conference Location: Ottawa, Ontario, Canada Preprint: https://arxiv.org/abs/2501.10037

Not So Common Memory Leaks in Java WebinarTier1 app

This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.

How can one start with crypto wallet development.pptxlaravinson24

Adobe Marketo Engage Champion Deep Dive - SFDC CRM Synch V2 & Usage DashboardsBradBedford3

Join Ajay Sarpal and Miray Vu to learn about key Marketo Engage enhancements. Discover improved in-app Salesforce CRM connector statistics for easy monitoring of sync health and throughput. Explore new Salesforce CRM Synch Dashboards providing up-to-date insights into weekly activity usage, thresholds, and limits with drill-down capabilities. Learn about proactive notifications for both Salesforce CRM sync and product usage overages. Get an update on improved Salesforce CRM synch scale and reliability coming in Q2 2025. Key Takeaways: Improved Salesforce CRM User Experience: Learn how self-service visibility enhances satisfaction. Utilize Salesforce CRM Synch Dashboards: Explore real-time weekly activity data. Monitor Performance Against Limits: See threshold limits for each product level. Get Usage Over-Limit Alerts: Receive notifications for exceeding thresholds. Learn About Improved Salesforce CRM Scale: Understand upcoming cloud-based incremental sync.

Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot

Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

Exploring Wayland: A Modern Display Server for the FutureICS

F-Secure Freedome VPN 2025 Crack Plus Activation New Versionsaimabibi60507

Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.Dele Amefo

Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik

Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe. We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved? Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security. Talk from SciFiDevCon 2025 https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5

Avast Premium Security Crack FREE Latest Version 2025mu394968

🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍 Avast Premium Security is a paid subscription service that provides comprehensive online security and privacy protection for multiple devices. It includes features like antivirus, firewall, ransomware protection, and website scanning, all designed to safeguard against a wide range of online threats, according to Avast. Key features of Avast Premium Security: Antivirus: Protects against viruses, malware, and other malicious software, according to Avast. Firewall: Controls network traffic and blocks unauthorized access to your devices, as noted by All About Cookies. Ransomware protection: Helps prevent ransomware attacks, which can encrypt your files and hold them hostage. Website scanning: Checks websites for malicious content before you visit them, according to Avast. Email Guardian: Scans your emails for suspicious attachments and phishing attempts. Multi-device protection: Covers up to 10 devices, including Windows, Mac, Android, and iOS, as stated by 2GO Software. Privacy features: Helps protect your personal data and online privacy. In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.

Download YouTube By Click 2025 Free Full Activatedsaniamalik72555

Douwan Crack 2025 new verson+ License codeaneelaramzan63

Pixologic ZBrush Crack Plus Activation Key [Latest 2025] New Versionsaimabibi60507

Copy & Past Link👉👉 https://dr-up-community.info/ Pixologic ZBrush, now developed by Maxon, is a premier digital sculpting and painting software renowned for its ability to create highly detailed 3D models. Utilizing a unique "pixol" technology, ZBrush stores depth, lighting, and material information for each point on the screen, allowing artists to sculpt and paint with remarkable precision .

Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell

It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).

Top 10 Client Portal Software Solutions for 2025.docxPortli