This step allows one to
Load a saved model from RandomForestDevelopment
Run the model against test data to generate predictions
Push these predictions to SQL Server
Identify factors that could benefit outcomes (see final examples)
RandomForestDeployment(type, df, grainCol, predictedCol, impute, debug, cores, modelName)
| type | The type of model (either 'regression' or 'classification') |
|---|---|
| df | Dataframe whose columns are used for new predictions. Data structure should match development as much as possible. Number of columns, names, types, grain, and predicted must be the same. |
| grainCol | The dataframe's column that has IDs pertaining to the grain |
| predictedCol | Column that you want to predict. |
| impute | For training df, set all-column imputation to TRUE or FALSE. If TRUE, this uses values calculated in development. FALSE leads to removal of rows containing NULLs and is not recommended. |
| debug | Provides the user extended output to the console, in order |
| cores | Number of cores you'd like to use. Defaults to 2. |
| modelName | Optional string. Can specify the model name. If used, you must load the same one in the deploy step. |
An object of class R6ClassGenerator
of length 24.
The above describes params for initializing a new randomForestDeployment class with
$new()
. Individual methods are documented below.
Initializes a new random forest deployment class using the
parameters saved in p
, documented above. This method loads, cleans, and prepares data for
generating predictions.
Usage: $new(p)
Generate new predictions, calculate top factors, and prepare the output dataframe.
Usage: $deploy()
Return the grain, all top factors, and their weights.
Usage: $getTopFactors(numberOfFactors = NA, includeWeights = FALSE)
Params:
- numberOfFactors:
returns the top n
factors. Defaults to all factors.
- includeWeights:
If TRUE
, returns weights associated with each factor.
Returns the output dataframe.
Usage: $getOutDf()
Get a data frame with expected changes in outcomes conditional on process
variable changes.
Usage: $getProcessVariablesDf(modifiableVariables,
variableLevels = NULL, grainColumnValues = NULL, smallerBetter = TRUE,
repeatedFactors = FALSE, numTopFactors = 3)
Params:
- modifiableVariables
A vector of names of modifiable variables.
- variableLevels
A list of variable values indexed by
modifiable variable names. This allows one to use numeric variables by
specifying baselines and to restrict categorical variables by limiting
which factors can be surfaced.
- grainColumnIDs
A vector of grain column IDs. If NULL
, the
whole deployment dataframe will be used.
- smallerBetter
A boolean determining whether or not lower
predictions/probabilities are more desirable.
- repeatedFactors
A boolean determining whether or not a single
modifiable factor can be listed several times.
- numTopFactors
The number of modifiable process variables to
include in each row.
healthcareai
writeData
selectData
# NOT RUN { #### Classification Example using csv data #### ## 1. Loading data and packages. ptm <- proc.time() library(healthcareai) # setwd('C:/Yourscriptlocation/Useforwardslashes') # Uncomment if using csv # Can delete this line in your work csvfile <- system.file("extdata", "HCRDiabetesClinical.csv", package = "healthcareai") # Replace csvfile with 'path/file' df <- read.csv(file = csvfile, header = TRUE, na.strings = c("NULL", "NA", "")) df$PatientID <- NULL # Only one ID column (ie, PatientEncounterID) is needed remove this column # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## 2. Train and save the model using DEVELOP print('Historical, development data:') str(df) set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "classification" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "ThirtyDayReadmitFLG" p$debug <- FALSE p$cores <- 1 # Run RandomForest RandomForest <- RandomForestDevelopment$new(p) RandomForest$run() ## 3. Load saved model and use DEPLOY to generate predictions. print('Fake production data:') str(dfDeploy) p2 <- SupervisedModelDeploymentParams$new() p2$type <- "classification" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "ThirtyDayReadmitFLG" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() dfOut <- dL$getOutDf() head(dfOut) # Write to CSV (or JSON, MySQL, etc) using plain R syntax # write.csv(dfOut,'path/predictionsfile.csv') print(proc.time() - ptm) # }# NOT RUN { #### Classification example using SQL Server data #### # This example requires you to first create a table in SQL Server # If you prefer to not use SAMD, execute this in SSMS to create output table: # CREATE TABLE dbo.HCRDeployClassificationBASE( # BindingID float, BindingNM varchar(255), LastLoadDTS datetime2, # PatientEncounterID int, <--change to match inputID # PredictedProbNBR decimal(38, 2), # Factor1TXT varchar(255), Factor2TXT varchar(255), Factor3TXT varchar(255) # ) ## 1. Loading data and packages. ptm <- proc.time() library(healthcareai) connection.string <- " driver={SQL Server}; server=localhost; database=SAM; trusted_connection=true " query <- " SELECT [PatientEncounterID] --Only need one ID column for random forest ,[SystolicBPNBR] ,[LDLNBR] ,[A1CNBR] ,[GenderFLG] ,[ThirtyDayReadmitFLG] FROM [SAM].[dbo].[HCRDiabetesClinical] " df <- selectData(connection.string, query) # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## 2. Train and save the model using DEVELOP print('Historical, development data:') str(df) set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "classification" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "ThirtyDayReadmitFLG" p$debug <- FALSE p$cores <- 1 # Run RandomForest RandomForest <- RandomForestDevelopment$new(p) RandomForest$run() ## 3. Load saved model and use DEPLOY to generate predictions. print('Fake production data:') str(dfDeploy) p2 <- SupervisedModelDeploymentParams$new() p2$type <- "classification" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "ThirtyDayReadmitFLG" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() dfOut <- dL$getOutDf() writeData(MSSQLConnectionString = connection.string, df = dfOut, tableName = 'HCRDeployClassificationBASE') print(proc.time() - ptm) # }# NOT RUN { # } # NOT RUN { #### Regression Example using SQL Server data #### # This example requires you to first create a table in SQL Server # If you prefer to not use SAMD, execute this in SSMS to create output table: # CREATE TABLE dbo.HCRDeployRegressionBASE( # BindingID float, BindingNM varchar(255), LastLoadDTS datetime2, # PatientEncounterID int, <--change to match inputID # PredictedValueNBR decimal(38, 2), # Factor1TXT varchar(255), Factor2TXT varchar(255), Factor3TXT varchar(255) # ) ## 1. Loading data and packages. ptm <- proc.time() library(healthcareai) connection.string <- " driver={SQL Server}; server=localhost; database=SAM; trusted_connection=true " query <- " SELECT [PatientEncounterID] --Only need one ID column for random forest ,[SystolicBPNBR] ,[LDLNBR] ,[A1CNBR] ,[GenderFLG] ,[ThirtyDayReadmitFLG] FROM [SAM].[dbo].[HCRDiabetesClinical] " df <- selectData(connection.string, query) # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## 2. Train and save the model using DEVELOP print('Historical, development data:') str(df) set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "regression" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "A1CNBR" p$debug <- FALSE p$cores <- 1 # Run Random Forest RandomForest <- RandomForestDevelopment$new(p) RandomForest$run() ## 3. Load saved model and use DEPLOY to generate predictions. dfDeploy$A1CNBR <- NULL # You won't know the response in production print('Fake production data:') str(dfDeploy) p2 <- SupervisedModelDeploymentParams$new() p2$type <- "regression" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "A1CNBR" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() dfOut <- dL$getOutDf() writeData(MSSQLConnectionString = connection.string, df = dfOut, tableName = 'HCRDeployRegressionBASE') print(proc.time() - ptm) # }# NOT RUN { #' #### Classification example pulling from CSV and writing to SQLite #### ## 1. Loading data and packages. ptm <- proc.time() library(healthcareai) # Can delete these system.file lines in your work csvfile <- system.file("extdata", "HCRDiabetesClinical.csv", package = "healthcareai") sqliteFile <- system.file("extdata", "unit-test.sqlite", package = "healthcareai") # Read in CSV; replace csvfile with 'path/file' df <- read.csv(file = csvfile, header = TRUE, na.strings = c("NULL", "NA", "")) df$PatientID <- NULL # Only one ID column (ie, PatientEncounterID) is needed remove this column # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## 2. Train and save the model using DEVELOP print('Historical, development data:') str(df) set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "classification" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "ThirtyDayReadmitFLG" p$debug <- FALSE p$cores <- 1 # Run Random Forest RandomForest <- RandomForestDevelopment$new(p) RandomForest$run() ## 3. Load saved model and use DEPLOY to generate predictions. print('Fake production data:') str(dfDeploy) p2 <- SupervisedModelDeploymentParams$new() p2$type <- "classification" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "ThirtyDayReadmitFLG" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() dfOut <- dL$getOutDf() writeData(SQLiteFileName = sqliteFile, df = dfOut, tableName = 'HCRDeployClassificationBASE') print(proc.time() - ptm) #### Regression example pulling from CSV and writing to SQLite #### ## 1. Loading data and packages. ptm <- proc.time() library(healthcareai) # Can delete these system.file lines in your work csvfile <- system.file("extdata", "HCRDiabetesClinical.csv", package = "healthcareai") sqliteFile <- system.file("extdata", "unit-test.sqlite", package = "healthcareai") # Read in CSV; replace csvfile with 'path/file' df <- read.csv(file = csvfile, header = TRUE, na.strings = c("NULL", "NA", "")) df$PatientID <- NULL # Only one ID column (ie, PatientEncounterID) is needed remove this column # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## 2. Train and save the model using DEVELOP print('Historical, development data:') str(df) set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "regression" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "A1CNBR" p$debug <- FALSE p$cores <- 1 # Run Random Forest RandomForest<- RandomForestDevelopment$new(p) RandomForest$run() ## 3. Load saved model and use DEPLOY to generate predictions. dfDeploy$A1CNBR <- NULL # You won't know the response in production print('Fake production data:') str(dfDeploy) p2 <- SupervisedModelDeploymentParams$new() p2$type <- "regression" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "A1CNBR" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() dfOut <- dL$getOutDf() writeData(SQLiteFileName = sqliteFile, df = dfOut, tableName = 'HCRDeployRegressionBASE') print(proc.time() - ptm) #### Identify factors that could benefit outcomes: getProcessVariablesDf #### ############################################################################# # getProcessVariableDf() identifies opportunities for improved outcomes at # the grain level. It is important that the variables ("modifiableVariables") # and values ("variableLevels") used in this function are under the control # of the care management process. The best use case for this function is a # "natural experiment" where otherwise similar groups had different # treatments applied to them, and that treatment is the modifiable variable # of interest. # This example shows how to use the getProcessVariableDf() function, using # another readmission-prediction model. In this example systolic blood pressure # is converted into a categorical variable to demonstrate functionality. csvfile <- system.file("extdata", "HCRDiabetesClinical.csv", package = "healthcareai") # Replace csvfile with 'path/file' df <- read.csv(file = csvfile, header = TRUE, na.strings = c("NULL", "NA", "")) df$PatientID <- NULL # Remove extra ID # Convert systolic blood pressure from a numeric variable to a categorical # variable with 5 categories: normal, pre-hypertension, stage 1 hypertension, # stage 2 hypertension, and hypertensive crisis df$SystolicBP <- ifelse(df$SystolicBPNBR < 140, ifelse(df$SystolicBPNBR < 120, "Normal", "Pre-hypertensive"), ifelse(df$SystolicBPNBR < 160, "Stage_1", ifelse(df$SystolicBP < 180, "Stage_2", "Crisis"))) df$SystolicBPNBR <- NULL # Save a dataframe for validation later on dfDeploy <- df[951:1000,] ## Develop and Deploy the model set.seed(42) p <- SupervisedModelDevelopmentParams$new() p$df <- df p$type <- "classification" p$impute <- TRUE p$grainCol <- "PatientEncounterID" p$predictedCol <- "ThirtyDayReadmitFLG" p$debug <- FALSE p$cores <- 1 RandomForest <- RandomForestDevelopment$new(p) RandomForest$run() p2 <- SupervisedModelDeploymentParams$new() p2$type <- "classification" p2$df <- dfDeploy p2$grainCol <- "PatientEncounterID" p2$predictedCol <- "ThirtyDayReadmitFLG" p2$impute <- TRUE p2$debug <- FALSE p2$cores <- 1 dL <- RandomForestDeployment$new(p2) dL$deploy() ## Get predicted outcome changes using getProcessVariablesDf # Categorical variables can simply be listed as modifiableVariables and all # factors levels will be used for comparison purposes. The dataframe # generated from the code below will consider all possible blood pressure # categories. dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP")) # By default, the function returns predictions for all rows, but we can # restrict to specific rows using the grainColumnIDs parameter dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP"), grainColumnIDs = c(954, 965, 996)) # The variableLevels parameter can be used to limit which factor levels are # considered (for categorical variables). The dataframe generated from the # code below will only make comparisons with normal BP and pre-hypertensive dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP"), variableLevels = list(SystolicBP = c("Normal", "Pre-hypertensive"))) # The variableLevels parameter can also be used to allow predictions for # numeric variables, by providing specific target values of the numeric # variable to make comparisons to. In the code below, the predictions will be # compared to those for an A1C of 5.6 dL$getProcessVariablesDf(modifiableVariables = c("A1CNBR"), variableLevels = list(A1CNBR = c(5.6))) # The repeatedFactors parameter allows one to get multiple predictions # for the same variable. For example, reducing A1C to 5.0 might most improve # a patient's risk, but reducing A1C to 5.5 is likely to also reduce the risk # and that change might be more impactful than altering the patient's blood # pressure. When repeatedFactors is TRUE, both those results will # be included. If repeatedFactors were FALSE, only the most beneficial # value of A1C would be included. dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP", "A1CNBR"), variableLevels = list(SystolicBP = c("Normal", "Pre-hypertensive"), A1CNBR = c(5.0, 5.5, 6, 6.5)), repeatedFactors = TRUE) # The numTopFactors parameter allows one to set the maximum number of # predictions to display (with the default being 3) dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP", "A1CNBR"), variableLevels = list(SystolicBP = c("Normal", "Pre-hypertensive"), A1CNBR = c(5.0, 5.5, 6, 6.5)), repeatedFactors = TRUE, numTopFactors = 5) # If greater values of the predicted variable are preferable, setting # smallerBetter to FALSE will identify the factors that most increase # the value of the outcome variable. In this case, the deltas will be # positive, corresponding to an increased risk dL$getProcessVariablesDf(modifiableVariables = c("SystolicBP"), smallerBetter = FALSE) # }


Using HTML, CSS & JavaScript
Programming in C, python and java
basic linux shell commands
Learning Developer
This is my Personal and project Website.
My name is Sidhant Samant. An Electronics and Telecommunication Engineer from Goa. It’s hard to put your hobbies and interests into a few words. But, I’ll try!  Love to tinker with technology in my free time. You can find me playing with different Linux distributions, flashing custom ROMs on my smartphone and trying out newly released apps. I like to explore latest technology through reading articles and watching YouTube videos. I started learning web development along with other programming languages few months back for fun, but now I'm enjoying it !
A selection of my range of works


If grouping expressions inside [[..]], use ( .. ).
[[ [ a || b ] && c ]]
[ [ a -o b ] -a c ]]
[[ ( a || b ) && c ]]
[ \( a -o b \) -a c ]] # or { [ a ] || [ b ]; } && [ c ]
[ .. ]
should not be used to group subexpressions inside [[ .. ]]
or [ .. ]
statements.
For [[ .. ]]
, use regular parentheses.
For [ .. ]
, either use escaped parentheses, or preferably rewrite the expression into multiple [ .. ]
joined with &&
, ||
and { ..; }
groups. The latter is preferred because [ .. ]
is undefined for more than 4 arguments in POSIX.
None


With Millenials moving towards an information-rich world where severely low patience level has affected the decision making ability of the individual, it is important that the consumption and well as sharing of the content be crisp and to the point!
Millenials today do not like the idea of using the traditional investment options where a broker who understands market flow guides them what to do through an app or email or a phone call, they want to have full control in their own hand.
But investment decisions cannot be made just based on a glance. So a product has be crafted so that it’s a one-stop trust-based solution in the sense that it is something which the millennials would love to refer to before they make an investment decision.
Millenials today do not like the idea of using the traditional investment options where a broker who understands market flow guides them what to do through an app or email or a phone call, they want to have full control in their own hand.
But investment decisions cannot be made just based on a glance. So a product has be crafted so that it’s a one-stop trust-based solution in the sense that it is something which the millennials would love to refer to before they make an investment decision.


Sura Hawaii Korean BBQ
Formally known as 678 Hawaii, this Oahu staple has just recently broke off from the popular 678/ Baekjong franchise in 2016. In keeping with the Korean tradition of slow cooking, they stress the importance of your body's well-being. That's why they use only prime quality meats and other fresh ingredients in their dishes. As the old saying goes, variety is the spice of life, and you'll find plenty of variety at this family-friendly eatery. From traditional Korean barbecue dishes including Bibimbap and kimchi pancakes to contemporary creations like spicy pork ribs, Korean style box lunches, and beef galbi-jjim, Sura Hawaii's lunch and dinner menus are packed with flavor. Every table has its very own built-in stovetop, allowing guests to look on as a member of the wait staff cooks their meal.


Here’s How Much Money Physician Assistants Earn In Every State
by Andrew DePietroEmployment in the healthcare industry always seems to be on an unceasing rise. The career of a physician assistant is part of this overall trend. According to the Bureau of Labor Statistics’ Occupational Outlook Handbook, employment of physician assistants is projected to grow significantly, by 31 percent and equal to the addition of 37,000 new jobs from 2018 to 2028, a rate of growth much faster than the average for all occupations. In order to help prospective physician assistants, analyzed and compiled a round-up of the average physician assistant salary by state for 2019, sourced from the Bureau of Labor Statistics (BLS). Read on for a full breakdown of where physician assistants make the most money, and where they're making the least.
10 States Where Physician Assistants Earn The Most Money
The national average annual wage of a physician assistant is a healthy $108,430, according to the BLS, over double the average annual salary for all occupations, $51,960. Here’s a breakdown of the top-10 states in which physician assistants earn the most:
- Connecticut: $125,610
- Washington: $123,980
- Alaska: $122,260
- Hawaii: $121,120
- California: $117,230
- New York: $117,000
- Wyoming: $116,890
- Nevada: $116,850
- New Jersey: $116,270
- Minnesota: $116,200
Across the board, most states have seen double-digit percentage growth in their average physician assistant salaries from 2013 to 2018. The No. 1 best-paying state for physician assistants, Connecticut, has seen a five-year growth in wages of 21%, from $103,840 in 2013 to $125,610 in 2018.
10 States Where Physician Assistants Earn The Least Money
Now to look at the other end of the spectrum. The bottom-10 states where physician assistants make the least money have average salaries as much as $20,000 less than the national average. Still, the average physician assistant salary in the worst states are much higher than their respective annual wages for all occupations. Here’s a look at the worst-paying states for physician assistants:
- Mississippi: $81,130
- Louisiana: $85,990
- Tennessee: $87,700
- Kentucky: $91,010
- Alabama: $92,880
- Missouri: $94,480
- Indiana: $96,090
- Pennsylvania: $98,510
- Arkansas: $99,280
- Virginia: $99,340
Mississippi might have the lowest average salary for physician assistants, but it has seen spectacular growth over the years. From 2013 to 2018, the average salary of physician assistants in Mississippi grew by 62.5%, from $49,930 five years ago to $81,130 today. The state with the second-best growth is Illinois, where salaries increase by 33.6%, and Hawaii comes in third with an increase of 31.6%.
How Much Do Physician Assistants Make In Each State
Below you’ll find the average annual wage for physician assistants in all 50 states from 2013 to 2018, plus their five-year growth rate. Impressively, 34 states have seen physician assistant salaries rise by 10% or more.


Singularity container
3.2
Admin Quickstart
Configuration files
Singularity container
»
Admin Guide
Edit on GitHub
Admin Guide
¶
Admin Quickstart
Installation
Install Dependencies
Download and Build the RPM
Setting
localstatedir
Configuration
Singularity Architecture
Singularity Security
Security of the Container Runtime
Security of the Container Itself
Administrator Control of Users’ Containers
Updating Singularity
Uninstalling Singularity
Configuration files
singularity.conf
Limiting containers
cgroups.toml
Examples
Limiting memory
Limiting CPU
Limiting IO
ecl.toml
nvliblist.conf
capability.json
seccomp-profiles


Building a 3D printed race car
After my freshman year of college, I was hired by IMEC, an international research company to work in Belgium for the summer. I had no idea what awaited me there, but it was fantastic. Given my experience with fabrication, I was sent to work with Formula Group T, working on their first entry in the European Formula Student competitions - Aerion. Not only did I sharpen my welding and manufacturing skills I got to work with cutting edge technologies like biodegradable composites to laser sintered titanium. My contributions included designing and fabricating a shipping container for the car to protect it en route to England and Germany, as well as advising in the manufacture and assembly of the vehicle.


Publications:
More papers info., please visit: Google Scholar Profile (300+ citations)
2019
- A New Operator Splitting Method for the Euler Elastica Model for Image Smoothing
Liang-Jian Deng, Roland Glowinski, Xue-Cheng Tai
SIAM Journal on Imaging Sciences, 12(2):1190-1230, 2019. [pdf]
[Matlab_Code]
- The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior
Liang-Jian Deng, Minyu Feng, Xue-Cheng Tai
Information Fusion, 52: 76-89, 2019. [pdf]
[Matlab_Code]
- Fastderain: A novel video rain streak removal method using directional gradient priors
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
IEEE Transactions on Image Processing, 28(4): 2089-2102, 2019. [pdf]
[Code_link]
- Unidirectional Sparse Tensor based model for the Noise Removal of Remote Sensing Image
Hong-Xia Dou, Ting-Zhu Huang, Liang-Jian Deng, Zi-Yao Zhang
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019.
- Pan-sharpening via ROG-Based Filtering
Zi-Yao Zhang, Ting-Zhu Huang, Liang-Jian Deng, Jie Huang, Hong-Xia Dou
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019.
- Bilateral Filter Based Total Variation Regularization for Sparse Hyperspectral Image Unmixing
X Li, J Huang, Liang-Jian Deng, TZ Huang
Information Sciences, 2019.
- A Rudin-Osher-Fatemi Model-Based Pansharpening Approach Using RKHS and AHF Representation
J Liu, Liang-Jian Deng, F Fang, T Zeng
East Asian Journal on Applied Mathematics, 2019.
- A total variation and group sparsity based tensor optimization model for video rain streak removal
YT Wang, XL Zhao, TX Jiang, Liang-Jian Deng, TH Ma, YT Zhang, TZ Huang
Signal Processing: Image Communication, 73:96-108, 2019.
2018
- A variational pansharpening approach based on reproducible kernel Hilbert space and Heaviside function
Liang-Jian Deng, Gemine Vivone, Weihong Guo, Mauro Dalla Mura, Jocelyn Chanussot
IEEE Transactions on Image Processing, 27(9): 4330-4344, 2018. [pdf??]
- Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing
Jie Huang, Ting-Zhu Huang, Liang-Jian Deng Xi-Le Zhao
IEEE Transactions on Geoscience and Remote Sensing, 57(4):2419-2438, 2018. [pdf]
[Matlab_Code]
- A directional global sparse model for single image rain removal
Liang-Jian Deng, Ting-Zhu Huang, Xi-Le Zhao, Tai-Xiang Jiang
Applied Mathematical Modelling, 59: 662-679, 2018. [pdf]
[Matlab_Code]
- Directional l0 Sparse Modeling for Image Stripe Noise Removal
Hong-Xia Dou, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le Zhao, Jie Huang
Remote Sensing, 10(3): 361, 2018. [pdf]
[Matlab_Code]
- A Framelet-Based Iterative Pan-Sharpening Approach
Zi-Yao Zhang, Ting-Zhu Huang, Liang-Jian Deng, Jie Huang, Xi-Le Zhao, Chao-Chao Zheng
Remote Sensing, 10(4): 622, 2018. [pdf??]
- Evolving networks based on birth and death process regarding the scale stationarity
Zi-Yao Zhang, Ting-Zhu Huang, Liang-Jian Deng, Jie Huang, Xi-Le Zhao, Chao-Chao Zheng
Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(8): 083118, 2018.
- Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization
Yong Chen, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng
Applied Mathematical Modelling, 63: 128-147, 2018. [pdf??]
- Matrix factorization for low-rank tensor completion using framelet prior
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le, Zhao, Teng-Yu Ji, Liang-Jian Deng
Information Sciences, 2018. [pdf??]
- Multispectral image denoising via nonlocal multitask sparse learning
Ya-Ru Fan, Ting-Zhu Huang, Xi-Le, Zhao, Liang-Jian Deng
Remote Sensing, 2018
- ingle image super-resolution based on approximated Heaviside functions and iterative refinement
Xiyu Wang, Ting-Zhu Huang, Liang-Jian Deng
PLoS One, 2018
2017
- A novel tensor-based video rain streaks removal approach via utilizing discriminatively intrinsic priors
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf]
[Supplementary]
[Code_link]
- Image Fusion via Dynamic Gradient Sparsity and Anisotropic Spectral-Spatial Total Variation
Chao-Chao Zheng, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le, Zhao, Hong-Xia Dou
IEEE International Conference on Image Processing (ICIP), 2017. [pdf??]
- Stripe Noise Removal of Remote Sensing Image with A Directional L0 Sparse Model
Hong-Xia Dou, Ting-Zhu Huang, Liang-Jian Deng, Yong Chen
IEEE International Conference on Image Processing (ICIP), 2017. [pdf??]
- A group sparse joint nonnegative matrix factorization model for multiple data integration
Hong-Xia Dou, Ting-Zhu Huang, Liang-Jian Deng, Yong Chen
International Centre for Wavelet Analysis and Its Applications (ICCWAMTIP), 2017
- Stripe Noise Removal of Remote Sensing Images by Total Variation Regularization and Group Sparsity Constraint
Yong Chen, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Jie Huang
Remote Sensing, 9(6): 559, 2017
- Group sparsity based regularization model for remote sensing image stripe noise removal
Yong Chen, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le Zhao, Min Wang
Neurocomputing, 2017
- A non-convex tensor rank approximation for tensor completion
Teng-Yu Ji, Ting-Zhu Huang, Xi-Le Zhao, Tian-Hui Ma, Liang-Jian Deng
Applied Mathematical Modelling, 8:410-422, 2017
2016
- Single image super-resolution via an iterative reproducing kernel Hilbert space method
Liang-Jian Deng, Weihong Guo, Ting-Zhu Huang
IEEE Transactions on Circuits and Systems for Video Technology, 26(11): 2001-2014, 2016.
[pdf]
[Matlab_Code]
- Single image super-resolution by approximate Heaviside functions
Liang-Jian Deng, Weihong Guo, Ting-Zhu Huang
Information Sciences, 348:107-123, 2016. [pdf??]
[Code_link???]
- Super-resolution via a fast deconvolution with kernel estimation
Han Yu, Ting-Zhu Huang, Liang-Jian Deng
EURASIP Journal on Image and Video Processing, 2017(3): 1-11, 2016
before 2016, please see google scholar


---
layout: bcpage
title: "BC"
description: "computer science | software engineering | information technology"
header-img: "img/ScienceBuilding_BC.jpg"
---
Courses Spring 2017
These courses are being offered at Bakersfield College.
Additional Content
- STEM @ BC Site
- MESA @ BC Site
- Supplemental Instruction @ BC Site
Previous Years
- COMP B2 | Introduction to Computer Information Systems | Fall 2016
- COMP B11 | Programming Concepts and Methodology I | Fall 2016
- COMP B5 | Introduction to Microsoft Office | Fall 2016
- COMP B5 | Introduction to Microsoft Office | Fall 2016
- COMP B10 | Introduction to Programming Methodologies using Python | Fall 2016
- COMP B2 | Introduction to Computer Information Systems | Fall 2015
- COMP B3 | Introduction to Computer Information Systems | Spring 2015
- COMP B2 | Introduction to Computer Information Systems | Fall 2014


Polio immunisation: Kano targets over 3m children -Commissioner
by NANKano state Commissioner for Health, Dr Aminu Tsanyawa, says the government has concluded arrangements to immunise more than N3 million children for the November round of Immunisation Plus Days.
Tsanyawa made this known at a news briefing on Friday urging parents and guardians to ensure that their children were immunised so as to protect them from the Wild Polio Virus (WPV).
He said the present administration had expended about N9 million in areas of mobilisation, training of personnel and other logistics to ensure the success of the exercise.
“I am happy that Kano has not isolated any Wild Polio Virus (WPV) since 2014, and that was achieved because of the Polio Eradication Initiative over the years.
“However, the state is at risk of infection from the neighboring states for long stretch borders with Jigawa and Katsina, high Population dynamics and seasonal migration,” the commissioner said.
He lauded the Ganduje administration, development partners, the Emirate Councils, religious leaders and the media for their immense support towards polio eradication in the state.
NAN reports that the November round of the Immunisation Plus Days would take off in 44 local government areas of the state on Nov. 23 and end on Nov. 26 simultaneously.


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MiniTest Assertions. All assertion methods accept a
msg
which is printed if the assertion fails.
Returns the diff command to use in diff. Tries to intelligently figure out what diff to use.

# File lib/minitest/unit.rb, line 175 def assert test, msg = nil msg ||= "Failed assertion, no message given." self._assertions += 1 unless test then msg = msg.call if Proc === msg raise MiniTest::Assertion, msg end true end
Fails unless the block returns a true value.
# File lib/minitest/unit.rb, line 188 def assert_block msg = nil warn "NOTE: MiniTest::Unit::TestCase#assert_block is deprecated, use assert. It will be removed on or after 2012-06-01." msg = message(msg) { "Expected block to return true value" } assert yield, msg end
Fails unless obj
is empty.
# File lib/minitest/unit.rb, line 197 def assert_empty obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to be empty" } assert_respond_to obj, :empty? assert obj.empty?, msg end
Fails unless exp == act
printing the difference between the
two, if possible.
If there is no visible difference but the assertion fails, you should suspect that your #== is buggy, or your inspect output is missing crucial details.
For floats use assert_in_delta.
See also: ::diff
# File lib/minitest/unit.rb, line 215 def assert_equal exp, act, msg = nil msg = message(msg, "") { diff exp, act } assert(exp == act, msg) end
For comparing Floats. Fails unless exp
and act
are within delta
of each other.
assert_in_delta Math::PI, (22.0 / 7.0), 0.01
# File lib/minitest/unit.rb, line 226 def assert_in_delta exp, act, delta = 0.001, msg = nil n = (exp - act).abs msg = message(msg) { "Expected |#{exp} - #{act}| (#{n}) to be < #{delta}"} assert delta >= n, msg end
For comparing Floats. Fails unless exp
and act
have a relative error less than epsilon
.
# File lib/minitest/unit.rb, line 236 def assert_in_epsilon a, b, epsilon = 0.001, msg = nil assert_in_delta a, b, [a.abs, b.abs].min * epsilon, msg end
Fails unless collection
includes obj
.
# File lib/minitest/unit.rb, line 243 def assert_includes collection, obj, msg = nil msg = message(msg) { "Expected #{mu_pp(collection)} to include #{mu_pp(obj)}" } assert_respond_to collection, :include? assert collection.include?(obj), msg end
Fails unless obj
is an instance of cls
.
# File lib/minitest/unit.rb, line 254 def assert_instance_of cls, obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to be an instance of #{cls}, not #{obj.class}" } assert obj.instance_of?(cls), msg end
Fails unless obj
is a kind of cls
.
# File lib/minitest/unit.rb, line 265 def assert_kind_of cls, obj, msg = nil # TODO: merge with instance_of msg = message(msg) { "Expected #{mu_pp(obj)} to be a kind of #{cls}, not #{obj.class}" } assert obj.kind_of?(cls), msg end
Fails unless exp
is =~
act
.
# File lib/minitest/unit.rb, line 275 def assert_match exp, act, msg = nil msg = message(msg) { "Expected #{mu_pp(exp)} to match #{mu_pp(act)}" } assert_respond_to act, :"=~" exp = Regexp.new Regexp.escape exp if String === exp and String === act assert exp =~ act, msg end
Fails unless obj
is nil
# File lib/minitest/unit.rb, line 285 def assert_nil obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to be nil" } assert obj.nil?, msg end
For testing with binary operators.
assert_operator 5, :<=, 4
# File lib/minitest/unit.rb, line 295 def assert_operator o1, op, o2 = UNDEFINED, msg = nil return assert_predicate o1, op, msg if UNDEFINED == o2 msg = message(msg) { "Expected #{mu_pp(o1)} to be #{op} #{mu_pp(o2)}" } assert o1.__send__(op, o2), msg end
Fails if stdout or stderr do not output the expected results. Pass in nil if you don’t care about that streams output. Pass in “” if you require it to be silent.
See also: assert_silent
# File lib/minitest/unit.rb, line 308 def assert_output stdout = nil, stderr = nil out, err = capture_io do yield end y = assert_equal stderr, err, "In stderr" if stderr x = assert_equal stdout, out, "In stdout" if stdout (!stdout || x) && (!stderr || y) end
For testing with predicates.
assert_predicate str, :empty?
This is really meant for specs and is front-ended by #assert_operator:
str.must_be :empty?
# File lib/minitest/unit.rb, line 328 def assert_predicate o1, op, msg = nil msg = message(msg) { "Expected #{mu_pp(o1)} to be #{op}" } assert o1.__send__(op), msg end
Fails unless the block raises one of exp
. Returns the
exception matched so you can check the message, attributes, etc.
# File lib/minitest/unit.rb, line 337 def assert_raises *exp msg = "#{exp.pop}.\n" if String === exp.last should_raise = false begin yield should_raise = true rescue MiniTest::Skip => e details = "#{msg}#{mu_pp(exp)} exception expected, not" if exp.include? MiniTest::Skip then return e else raise e end rescue Exception => e details = "#{msg}#{mu_pp(exp)} exception expected, not" assert(exp.any? { |ex| ex.instance_of?(Module) ? e.kind_of?(ex) : ex == e.class }, exception_details(e, details)) return e end exp = exp.first if exp.size == 1 flunk "#{msg}#{mu_pp(exp)} expected but nothing was raised." if should_raise end
Fails unless obj
responds to meth
.
# File lib/minitest/unit.rb, line 369 def assert_respond_to obj, meth, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} (#{obj.class}) to respond to ##{meth}" } assert obj.respond_to?(meth), msg end
Fails unless exp
and act
are equal?
# File lib/minitest/unit.rb, line 379 def assert_same exp, act, msg = nil msg = message(msg) { data = [mu_pp(act), act.object_id, mu_pp(exp), exp.object_id] "Expected %s (oid=%d) to be the same as %s (oid=%d)" % data } assert exp.equal?(act), msg end
send_ary
is a receiver, message and arguments.
Fails unless the call returns a true value TODO: I should prolly remove this from specs
# File lib/minitest/unit.rb, line 393 def assert_send send_ary, m = nil recv, msg, *args = send_ary m = message(m) { "Expected #{mu_pp(recv)}.#{msg}(*#{mu_pp(args)}) to return true" } assert recv.__send__(msg, *args), m end
Fails if the block outputs anything to stderr or stdout.
See also: assert_output
# File lib/minitest/unit.rb, line 405 def assert_silent assert_output "", "" do yield end end
Fails unless the block throws sym
# File lib/minitest/unit.rb, line 414 def assert_throws sym, msg = nil default = "Expected #{mu_pp(sym)} to have been thrown" caught = true catch(sym) do begin yield rescue ArgumentError => e # 1.9 exception default += ", not #{e.message.split(/ /).last}" rescue NameError => e # 1.8 exception default += ", not #{e.name.inspect}" end caught = false end assert caught, message(msg) { default } end
Captures $stdout and $stderr into strings:
out, err = capture_io do warn "You did a bad thing" end assert_match %r%bad%, err
# File lib/minitest/unit.rb, line 440 def capture_io require 'stringio' orig_stdout, orig_stderr = $stdout, $stderr captured_stdout, captured_stderr = StringIO.new, StringIO.new $stdout, $stderr = captured_stdout, captured_stderr yield return captured_stdout.string, captured_stderr.string ensure $stdout = orig_stdout $stderr = orig_stderr end
Returns a diff between exp
and act
. If there is
no known diff command or if it doesn’t make sense to diff the output
(single line, short output), then it simply returns a basic comparison
between the two.
# File lib/minitest/unit.rb, line 99 def diff exp, act require "tempfile" expect = mu_pp_for_diff exp butwas = mu_pp_for_diff act result = nil need_to_diff = MiniTest::Assertions.diff && (expect.include?("\n") || butwas.include?("\n") || expect.size > 30 || butwas.size > 30 || expect == butwas) return "Expected: #{mu_pp exp}\n Actual: #{mu_pp act}" unless need_to_diff Tempfile.open("expect") do |a| a.puts expect a.flush Tempfile.open("butwas") do |b| b.puts butwas b.flush result = %x#{MiniTest::Assertions.diff} #{a.path} #{b.path}` result.sub!(%r^\-\-\- .+/, "--- expected") result.sub!(%r^\+\+\+ .+/, "+++ actual") if result.empty? then klass = exp.class result = [ "No visible difference.", "You should look at your implementation of #{klass}#==.", expect ].join "\n" end end end result end
Returns details for exception e
# File lib/minitest/unit.rb, line 458 def exception_details e, msg [ "#{msg}", "Class: <#{e.class}>", "Message: <#{e.message.inspect}>", "---Backtrace---", "#{MiniTest::filter_backtrace(e.backtrace).join("\n")}", "---------------", ].join "\n" end
Fails with msg
# File lib/minitest/unit.rb, line 472 def flunk msg = nil msg ||= "Epic Fail!" assert false, msg end
Returns a proc that will output msg
along with the default
message.
# File lib/minitest/unit.rb, line 480 def message msg = nil, ending = ".", &default proc { custom_message = "#{msg}.\n" unless msg.nil? or msg.to_s.empty? "#{custom_message}#{default.call}#{ending}" } end
This returns a human-readable version of obj
. By default
inspect is called. You can override this to use pretty_print if you want.
# File lib/minitest/unit.rb, line 148 def mu_pp obj s = obj.inspect s = s.encode Encoding.default_external if defined? Encoding s end
This returns a diff-able human-readable version of obj
. This
differs from the regular #mu_pp because it expands escaped
newlines and makes hex-values generic (like object_ids). This uses #mu_pp to do the first pass and
then cleans it up.
# File lib/minitest/unit.rb, line 160 def mu_pp_for_diff obj # TODO: possibly rename mu_pp(obj).gsub(%r\\n/, "\n").gsub(%r0x[a-f0-9]+/, '0xXXXXXX') end
used for counting assertions
# File lib/minitest/unit.rb, line 490 def pass msg = nil assert true end
Fails if test
is a true value
# File lib/minitest/unit.rb, line 497 def refute test, msg = nil msg ||= "Failed refutation, no message given" not assert(! test, msg) end
Fails if obj
is empty.
# File lib/minitest/unit.rb, line 505 def refute_empty obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to not be empty" } assert_respond_to obj, :empty? refute obj.empty?, msg end
Fails if exp == act
.
For floats use refute_in_delta.
# File lib/minitest/unit.rb, line 516 def refute_equal exp, act, msg = nil msg = message(msg) { "Expected #{mu_pp(act)} to not be equal to #{mu_pp(exp)}" } refute exp == act, msg end
For comparing Floats. Fails if exp
is within
delta
of act
refute_in_delta Math::PI, (22.0 / 7.0)
# File lib/minitest/unit.rb, line 528 def refute_in_delta exp, act, delta = 0.001, msg = nil n = (exp - act).abs msg = message(msg) { "Expected |#{exp} - #{act}| (#{n}) to not be < #{delta}" } refute delta > n, msg end
For comparing Floats. Fails if exp
and act
have
a relative error less than epsilon
.
# File lib/minitest/unit.rb, line 540 def refute_in_epsilon a, b, epsilon = 0.001, msg = nil refute_in_delta a, b, a * epsilon, msg end
Fails if collection
includes obj
# File lib/minitest/unit.rb, line 547 def refute_includes collection, obj, msg = nil msg = message(msg) { "Expected #{mu_pp(collection)} to not include #{mu_pp(obj)}" } assert_respond_to collection, :include? refute collection.include?(obj), msg end
Fails if obj
is an instance of cls
# File lib/minitest/unit.rb, line 558 def refute_instance_of cls, obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to not be an instance of #{cls}" } refute obj.instance_of?(cls), msg end
Fails if obj
is a kind of cls
# File lib/minitest/unit.rb, line 568 def refute_kind_of cls, obj, msg = nil # TODO: merge with instance_of msg = message(msg) { "Expected #{mu_pp(obj)} to not be a kind of #{cls}" } refute obj.kind_of?(cls), msg end
Fails if exp
=~
act
# File lib/minitest/unit.rb, line 576 def refute_match exp, act, msg = nil msg = message(msg) { "Expected #{mu_pp(exp)} to not match #{mu_pp(act)}" } assert_respond_to act, :"=~" exp = (%r#{Regexp.escape exp}/) if String === exp and String === act refute exp =~ act, msg end
Fails if obj
is nil.
# File lib/minitest/unit.rb, line 586 def refute_nil obj, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to not be nil" } refute obj.nil?, msg end
Fails if o1
is not op
o2
. Eg:
refute_operator 1, :>, 2 #=> pass refute_operator 1, :<, 2 #=> fail
# File lib/minitest/unit.rb, line 597 def refute_operator o1, op, o2 = UNDEFINED, msg = nil return refute_predicate o1, op, msg if UNDEFINED == o2 msg = message(msg) { "Expected #{mu_pp(o1)} to not be #{op} #{mu_pp(o2)}"} refute o1.__send__(op, o2), msg end
For testing with predicates.
refute_predicate str, :empty?
This is really meant for specs and is front-ended by #refute_operator:
str.wont_be :empty?
# File lib/minitest/unit.rb, line 612 def refute_predicate o1, op, msg = nil msg = message(msg) { "Expected #{mu_pp(o1)} to not be #{op}" } refute o1.__send__(op), msg end
Fails if obj
responds to the message meth
.
# File lib/minitest/unit.rb, line 620 def refute_respond_to obj, meth, msg = nil msg = message(msg) { "Expected #{mu_pp(obj)} to not respond to #{meth}" } refute obj.respond_to?(meth), msg end
Fails if exp
is the same (by object identity) as
act
.
# File lib/minitest/unit.rb, line 629 def refute_same exp, act, msg = nil msg = message(msg) { data = [mu_pp(act), act.object_id, mu_pp(exp), exp.object_id] "Expected %s (oid=%d) to not be the same as %s (oid=%d)" % data } refute exp.equal?(act), msg end
Skips the current test. Gets listed at the end of the run but doesn’t cause a failure exit code.
# File lib/minitest/unit.rb, line 641 def skip msg = nil, bt = caller msg ||= "Skipped, no message given" raise MiniTest::Skip, msg, bt end


--- redirect_from: "/home.html" ---
I graduated from University of Alberta Computer Science Program in 2017. I am thrilled to be a problem solver in technical field.
I moved from Shanghai, China to Edmonton, Canada in 2013.
I have experience in Software Engineering, Mobile Development & Web Development.
Additionally, I am a car enthusiast and backyard mechanic.
Junior Web Developer
Oct, 2017 - Current
Responsibility: Manage, Program & Deploy WordPress & Shopify Websites, provide support for live websites.
Websites I ProgrammedImplemented an eBay like game trading app
Distributed social network web app based on Django framework
By importing a turtle file, we let user visualize of data in the form of five different graph/chart formats with a variety of options
Online Image Sharing System Project based on Oracle Database
Interested in letting me to join your developer team? Awesome! Shoot me an email or give me a call and I will get back to you ASAP! You can also check out my github & linkedin profile!


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@Beta @GwtCompatible public interface Cache<K,V>
A semi-persistent mapping from keys to values. Cache entries are manually added using
get(Object, Callable)
or put(Object, Object)
, and are stored in the cache until
either evicted or manually invalidated.
Implementations of this interface are expected to be thread-safe, and can be safely accessed by multiple concurrent threads.
Note that while this class is still annotated as Beta
, the API is frozen from a
consumer's standpoint. In other words existing methods are all considered non-Beta
and
won't be changed without going through an 18 month deprecation cycle; however new methods may be
added at any time.
| Method Summary | |
|---|---|
ConcurrentMap<K,V> |
asMap()
Returns a view of the entries stored in this cache as a thread-safe map. |
void |
cleanUp()
Performs any pending maintenance operations needed by the cache. |
V |
get(K key,
Callable<? extends V> valueLoader)
Returns the value associated with key in this cache, obtaining that value from
valueLoader if necessary. |
ImmutableMap<K,V> |
getAllPresent(Iterable<?> keys)
Returns a map of the values associated with keys in this cache. |
V |
getIfPresent(Object key)
Returns the value associated with key in this cache, or null if there is no
cached value for key . |
void |
invalidate(Object key)
Discards any cached value for key key . |
void |
invalidateAll()
Discards all entries in the cache. |
void |
invalidateAll(Iterable<?> keys)
Discards any cached values for keys keys . |
void |
put(K key,
V value)
Associates value with key in this cache. |
void |
putAll(Map<? extends K,? extends V> m)
Copies all of the mappings from the specified map to the cache. |
long |
size()
Returns the approximate number of entries in this cache. |
CacheStats |
stats()
Returns a current snapshot of this cache's cumulative statistics. |
| Method Detail |
|---|
@Nullable V getIfPresent(Object key)
key
in this cache, or null
if there is no
cached value for key
.
V get(K key, Callable<? extends V> valueLoader) throws ExecutionException
key
in this cache, obtaining that value from
valueLoader
if necessary. No observable state associated with this cache is modified
until loading completes. This method provides a simple substitute for the conventional
"if cached, return; otherwise create, cache and return" pattern.
Warning: as with CacheLoader.load(K)
, valueLoader
must not return
null
; it may either return a non-null value or throw an exception.
ExecutionException
- if a checked exception was thrown while loading the value
UncheckedExecutionException
- if an unchecked exception was thrown while loading the
value
ExecutionError
- if an error was thrown while loading the valueImmutableMap<K,V> getAllPresent(Iterable<?> keys)
keys
in this cache. The returned map will
only contain entries which are already present in the cache.
void put(K key, V value)
value
with key
in this cache. If the cache previously contained a
value associated with key
, the old value is replaced by value
.
Prefer get(Object, Callable)
when using the conventional "if cached, return;
otherwise create, cache and return" pattern.
void putAll(Map<? extends K,? extends V> m)
put(k, v)
on this map once for each mapping from key
k
to value v
in the specified map. The behavior of this operation is undefined
if the specified map is modified while the operation is in progress.
void invalidate(Object key)
key
.
void invalidateAll(Iterable<?> keys)
keys
.
void invalidateAll()
long size()
CacheStats stats()
ConcurrentMap<K,V> asMap()
void cleanUp()
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About Me
Here are a few things you should consider to know about me.
- Highest level of education i have achieved is university.I did a bachelor degree in actuarial science in the University of Nairobi.
- Currently am not employed but on personal level i don't feel i should because i feel am too good for that.I feel more of a job creator.
- Wierd thing about me;i feel like we are in an illusion,more like in a simulated reality.
Why i joined programming
I joined programming mostly because i love creativity a lot.I hope at the end of it i would have achieved suffient skills so as to play with my creative side in the world of software development.
Hobbies
- Music,i love sound a lot.
- Watching intriguing movies plus comedy.
- Creativity in everything,am curious for awesome ideas.
- Travelling.


Relative
Absolute
There is one obvious advantage:
Now that you've got...
Rent one bear, ...
You've seen it coming!
Buy now and get nothing for free!
Well, at least no free beer. Perhaps a bear, if you can afford it.
your bear, you have to admit it!
No, we aren't selling bears.
We could talk about renting one.
get two for three beer.
And now, for something completely different.
- Red
- Fancy stuff about red thingies.
- Green
- Green! Green! Green!
- Blue
- Cool kids are blue.
Tennis
One ball, two players. Lots of fun.
Soccer
One ball, 22 players. Lots of fun.
Baseball
Well, one ball, some guys running around, some guys hitting others with a stick.
Sounds like fun, doesn't it?
Sounds like fun, doesn't it?
Well, apart from the running part.
I'm a resizable.
- Item 1
- Item 2
- Item 3
- Item 4
- Item 5
- Item 1
- Item 2
- Item 3
- Item 4
- Item 5


Fragment
class that utilizes the Camera2 Api, lovingly borrowed from GoogleSampleCode.Entity
class for the RoomDatabase
specifically for the
avaiable choices for the 'choose' your own adventure game.Fragment
class that is navigated to through the MainActivity
the purpose of this
Fragment
is to display Button
s that have the associated Choice
to continue the
Story of the game then upon clicking a Button
navigate you to the SceneFragment
with the
next Scene
displayed.Dao
Interface designed to interact with ChoiceSynonym
Entity class, including methods to insert
and select all.AndroidViewModel
class for the ChoiceFragment
to keep all
database queries necessary for game play off of the UI thread.ChoiceViewModel
initializes the instance.


I had been reading about salvia for quite a while. First came upon it when my mom got some spam touting salvia divinorum as a 'legal pot substitute'. I didn't believe it, but decided to find out what it really was, since my interest was piqued. As you know, what I discovered was that it is much more than any pot substitute...
After finding out what it really was, I decided I definitely wanted to try it. I hadn't done any real psychedelics before. Only other drug I've used has been marijuana.
Even though I decided I wanted to try salvia pretty quickly, it took me a while to actually get some. I didn't want to buy from any of the online places because they were either too expensive or gave me the feeling that they weren't trustworthy.
Then about a month ago when I was perusing the alt.drugs.salvia and alt.drugs.psychedelics newsgroups, I came across some posts from someone who worked in the health services department of my university. I emailed him and asked if he knew of any local places I could get some salvia, and he knew of some. So then I bought an ounce of regular Mexican leaves ($40), and a gram of standardized 5X 'incense' (crushed leaves) ($20).
I told myself I had to wait until I had a sitter before trying any of it, but I decided to try the regular leaf right away. I didn't want to smoke enough to really trip, just enough to get a feeling for it.. A taste.. Crushed a couple leaves up and smoked a bowl. Definitely got a bit high, but no visuals or altered perceptions or anything like that. Got really hot and sweaty too.
Few days later I smoked about 2 bowl fulls of regular leaf. I had Aphex Twin's Selected Ambient Works II on real low in the background, and all the lights off in my room. Also had some incense burning to add to the atmosphere. I had some slight dissociative feelings when I sat and tried to meditate, but my mind never left my body. Again, very hot and sweaty.
Then I didn't smoke any more until a week or so later when I went home for Memorial Day weekend. My best friend came and I had previously decided that would be a good time for him to come over and be my sitter. We also decided to videotape me with my family's camcorder.
The other reason I wanted to wait until I was home was because my dad is a high school chemistry teacher, so he has some accurate scales at home. I didn't want to guesstimate how much 5X I was smoking. I used his electronic scale that measured to the 1/100th of a gram.
From what I'd read, 1 gram of 5X was good for about 12-15 uses. I'd also read that around 1mg of Salvinorin A was a good dose when smoking. The 5X I had was standardized to have 12.5mg of Salvinorin A per 1g of leaf, so I figured I'd smoke around 1/12 of a gram. I used my dad's electronic scale to measure out around 80mg.
I proceeded to set up the camcorder, and make a comfortable spot for myself on the floor. I started burning some incense, then locked all the dogs and cats out of the room and told my dad to turn off the TV and not make any noise. I didn't want any distractions during my trip. My friend and my dad sat on the couch nearby watching me.
Toke #1: In.... hold for about 30 seconds... out.... Still here. No biggy.
Toke #2: In.... hold for abou.... HOLY F*CK!$!%##^*(@#
I don't remember exhaling.
I was far away, out of this plane of existence, or universe, or whatever THIS is.
I saw this universe mixing/melding with all the neighboring universes. They were forming into some sort of wheel type shape. Then the wheel started to rotate. I watched this for a while, watching all the universes rotate around and do their thing.
Then I felt myself moving back down towards the wheel. I was a little worried at this point, because I was pretty sure I wasn't going to land on the same part of the wheel that I came from. I didn't think I was going to return to the same universe.
I became aware of my surroundings again. I saw my friend and my dad sitting on the couch, looking at me. First thing that came to my mind was, 'Why are they looking at me? Is it because they're curious as to what the salvia's doing? Or is it because this is a different universe, one where I didn't just smoke some salvia, and they're wondering why I'm sitting here going crazy?'
Then I remember yelling 'OH MY GOD!!! OH MY GOD!!!' over and over for a while. Apparently this was just in my head, and I never really yelled anything... Anyway, the thought that I was screaming 'OH MY GOD' didn't exactly put my mind at ease with regards to wondering if my dad and friend thought I was going crazy.
As I came down some more, I actually did start to say 'Oh my God!' a few times (but it wasn't screaming at all). I think my dad said some comment like 'There's my atheist son!'
Now that I was more firmly entrenched in my body I was reallllly thirsty. I was very glad I made myself some iced tea before starting my journey. I downed one mug full (used a mug so I'd be less likely to break or spill) right away, then asked my friend to get me another mug full. I tried to get up and do it myself, but that was pretty futile. Like my previous experiences, I was sweating like a pig. Even more so this time. My face was sweating buckets. It was pretty gross actually.
Now I was pretty much normal, and decided to watch myself on tape. DAMN IT. The microphone didn't work. Video was fine, but no sound. Apparently I made some rather amusing noises. At one point I was explaining my experience to my friend and dad, or at least I thought I was. According to my friend I was just making mumbling, incomprehensible noises. And when I was asking for my iced tea, apparently it just came out sounding like 'bottle.'  Oh well, there's always next time.
The amount of time I spent 'not here' was only like 3 minutes or so. After I was back 'here' it was probably another 20 or 30 minutes before I really felt normal.
Next time I think I'll try a little smaller dose of 5X, so it's not quite so mind-blowing. Or I'll try the quid method, so it's not so sudden. Slowly entering Salvia Space would be a more pleasurable experience I think. On the other hand, if something bad happens, it would be longer before I came back... We'll see.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Kohli: Conditions had to be right for India to take up a day-night Test
by Sports DeskVirat Kohli has insisted India were correct to hold off on playing a day-night Test until they were confident the conditions were right.
India turned down the opportunity to play a day-night match against Australia in Adelaide in 2018.
However, the world's leading Test side will play across the afternoon and evening for the first time when they take on Bangladesh in Kolkata on Friday.
It is the second match of a two-game series, with India having cruised to victory by an innings and 130 runs in the opening Test.
Captain Kohli had been reluctant to test the waters for a day-night Test, but believes India have made the correct decision in biding their time to play in the altered format.
"Obviously we wanted to get a feel of pink-ball cricket. Eventually, it had to happen," Kohli, whose side are facing a Bangladesh team that has lost its last four Tests, told a news conference.
"But, you can't bring up those things before a big tour that you're going to and suddenly in the schedule, there's a pink-ball Test, when we haven't even practiced with the pink ball – we haven't played any first-class games with pink ball.
"The thing was to experience the pink-ball Test in our own conditions first, so you get the hang of how the ball behaves, what is the way to sight the ball and so on.
 So it can't be a sudden thing.
"It needed a bit of preparation. Once you get a hang of it, once you're used to playing it, there's no problem in playing at all."
Kohli, though, does not feel day-night matches will become the norm in Test cricket.
"I don't think [it will be] in five-six years," he said.
"In my opinion, this should not become the only way Test cricket is played because then you're losing that nervousness in the first session in the morning.
"Yes, you can bring excitement into Test cricket but you can't purely make Test cricket based on just entertaining people."


Hackaddiction
This past Saturday afternoon, I wrote my first useful command line program from scratch. It was really fun. Every time I got a new piece of code working the way I envisioned it would, I felt like superwoman. The program allows you to type in “events” in your command line then it prints out all the cool music events happening that night in Brooklyn and New York and their venues using a scrape from Resident Advisor.
And today, I was thinking about that program that I only stopped working on because I had to meet up with some friends and how I could keep making it better and I got excited about figuring out how I would do that. And I knew I had reading and homework I was supposed to do for school. And then it dawned on me - the blessing and curse of computer programming.
Your program can never stop getting more amazing.
Maybe I’m getting ahead of myself by saying that. I mean I know for sure that my programs are far from amazing as a programming newb.
But even after I got my program working the way that I wanted when I first set out to work on the project, I started thinking of ways to improve it. And maybe that I’d finish those improvements today. And then I realize if I do that, I’ll never actually get to my assignments and there’s a lot more I should learn and should be devoting my time to that as well as being creative…
I don’t even know how I would turn my command line app into a web application. But it’s so fun and validating to be able to create something- it’s scary. I’m incredibly powerful with my Ruby magic already. Imagine how powerful I’ll be in a week, in 3 weeks. I just need to remember to stay humble and keep learning from the amazing programmers accessible to me at the Flatiron School.


Simple basic button
Basic button with buttonClass
property as red
Basic button with textClass
property as yellow-text
The button has a larger height withlarge
property
The link has a flat elevation with flat
property
Button is round and floating with float
property
Button is flat, round and floating with these properties : float
& flat
Button with a left icon set by align
property when using mz-icon
directive
Button with a right icon set by align
property when using mz-icon
directive
Button with a red color set by class
property as red-text
Button with icon set by icon
property as owl
and using mz-icon-mdi
directive
Buttom becomes disabled with disabled
property
Buttom has a submit type with submit
property and a callback function with (click)
event binding
The button can be customized using the following properties on the button
element when adding mz-button
directive.


Hella Gud Food!
I've trekked the US to find the best of the best in food and entertainment. I hope this page will give you
some idea of local eateries within the US that I find uber delicious. Feel free to take a look around and if
you have any suggestions, please feel free to contact me.


The current trend in deep learning is to design, train and fine tune a separate model for each problem.
Though multi-task models have been explored, they have been trained for problems from the same domain only and no competitive multi-task, multi-modal models have been proposed.
The paper explores the possibility of such a unified deep learning model that can solve different tasks across multiple domains by training concurrently on them.
Small, modality-specific subnetworks (called modality nets) should be used to map input data to a joint representation space and back.
The joint representation is to be of variable size.
Different tasks from the same domain share the modality net.
MultiModel networks should use computational blocks from different domains even if they are not specifically designed for the task at hand.
MulitModel Network consists of few, small modality nets, an encoder, I/O mixer and an autoregressive decoder.
Encoder and decoder use the following computational blocks:
Convolutional Block
Attention Block
Mixture-of-Experts (MoE) Block
For further details, refer the original paper.
Encoder consists of 6 conv blocks with a MoE block in the middle.
I/O mixer consists of an attention block and 2 conv blocks.
Decoder consists of 4 blocks of convolution and attention with a MoE block in the middle.
Modality Nets
Language Data
Input is the sequence of tokens ending in a termination token.
This sequence is mapped to correct dimensionality using a learned embedding.
For output, the network takes the decoded output and performs a learned linear mapping followed by Softmax.
Image and Categorical Data
Uses residual convolution blocks.
Similar to the exit flow for Xception Network
Audio Data
WSJ speech corpus
ImageNet dataset
COCO image captioning dataset
WSJ parsing dataset
WMT English-German translation corpus
German-English translation
WMT English-French translation corpus
German-French translation
The experimental section is not very rigorous with many details skipped (would probably be added later).
While MultiModel does not beat the state of the art models, it does outperform some recent models.
Jointly trained model performs similar to single trained models on tasks with a lot of data and sometimes outperformed single trained models on tasks with less data (like parsing).
Interestingly, jointly training the model for parsing task and Imagenet tasks improves the performance of parsing task even though the two tasks are seemingly unrelated.
Another experiment was done to evaluate the effect of components (like MoE) on tasks (like Imagenet) which do not explicitly need them. It was observed that either the performance either went down or remained the same when MoE component was removed. This indicates that mixing different components does help to improve performance over multiple tasks.
But this observation is not conclusive as a different combination of say the encoder (that does not use MoE) could achieve better performance than one that does. The paper does not explore possibilities like these.


Code
Starter Kit
Ground zero for your next project.


Using Adobe Photoshop and Adobe Illustrator I have worked with local startup companies to bring their visions to reality. The work I have done for my customers have made a bold statement to their rivals and their customers of their professionalism. I have worked on Logos, Business cards, Fliers, and other artworks.
Knowing HTML, CSS, JavaScript, other tools, and frameworks I have created numinous websites and web applications that have provide a variety of functionality from secure login systems, appointment schedulers, and other information displaying websites.
I have the most experience in working with programming languages like Python, Java, and Ruby on Rails. I am also familiar with other languages like Assembly and Visual Basic. My skills have been adapted to meet a growing trend of embedding computational capability into everyday objects.
I’m a computer scientist that has graduated from Lewis University with a concentration in pervasive computing. Before I dived into the world of programming I spent many years flying aircraft throughout high school and my early years of college. Although, developing the skills necessary to fly aircraft was invigorating, I knew the world of computing technology was the way of the future. I find myself to have perfectionist personality that is willing to put in extra time to make sure my programming projects are well polished without cutting corners.


Astros' Justin Verlander wins second career AL Cy Young Award
by Bradford DoolittleHouston Astros ace Justin Verlander added to his already lengthy Hall of Fame résumé on Wednesday, when he was announced as this year's American League Cy Young Award winner, edging teammate Gerrit Cole.
Verlander received 17 first-place votes compared to 13 for Cole in balloting by the Baseball Writers' Association of America. Tampa Bay Rays righty Charlie Morton finished third a year after leaving Houston in free agency.
Results are tabulated on a system that rewards seven points for first place, four points for second place, three points for third place, two votes for fourth place and one point for fifth place.
Verlander, 36, became the 20th pitcher to win multiple Cy Young Awards. He won in 2011 with Detroit after a season for which he also took home AL MVP honors. Since then, he'd been a runner-up three times. The eight years between Cy Young wins is the longest span in the history of the award.
"The adversity I went through puts a new perspective on everything,'' he said. "I mean, still would've liked to have won a couple of them.''
Verlander went 21-6 while leading the AL in wins and innings pitched (223). He finished second to Cole in ERA (2.58) and strikeouts (300).
The 300-strikeout season was the first of Verlander's illustrious career. He struck out his 3,000th career hitter in his final regular-season start against the Angels on Sept. 28, becoming the 18th pitcher to reach that milestone. On Sept. 1, he became the sixth pitcher to throw at least three no-hitters with his 14-strikeout no-no at Toronto.
Verlander and Don Newcombe are the only players to have won Rookie of the Year, a Cy Young Award and MVP.
Verlander teamed with Cole to become the first duo to win 20 games and strike out 300 batters for the same team since Arizona's Randy Johnson and Curt Schilling in 2002. Verlander is the fourth Astros pitcher to win a Cy Young Award and the second since Houston moved to the American League. Dallas Keuchel took AL honors in 2015, and Roger Clemens (2004) and Mike Scott (1986) won the award when the Astros were in the National League.
With New York's CC Sabathia retiring, Verlander becomes the active leader in most major pitching categories. His 225 wins are 20 more than that of teammate Zack Greinke, and his 3,006 strikeouts are 314 more than that of Washington's Max Scherzer. Verlander is also the career leader in innings (2,982), starts (453) and wins above replacement for pitchers (71.4).
The only blotch on Verlander's 2019 record was the continuation of his long-standing troubles in the World Series. He lost both of his starts against the Washington Nationals, giving up seven runs in 11 innings. Verlander is 0-6 with a 5.68 ERA in seven Fall Classic starts -- the most winless starts by a pitcher in Series history.
For all he has done, Verlander might be at it for a while. He reiterated a couple of times this season that he's still hoping to ply his trade until age 45, which would put him well in line to become baseball's next 300-game winner.
His goal this offseason is to add velocity to his top-end fastball. He's thinking about using weighted balls and other new age throwing techniques.
| Pitcher/Team | 1st | 2nd | 3rd | 4th | 5th | Tot |
|---|---|---|---|---|---|---|
| Justin Verlander, Astros | 17 | 13 | - | - | - | 171 |
| Gerrit Cole, Astros | 13 | 17 | - | - | - | 159 |
| Charlie Morton, Rays | - | - | 18 | 10 | 1 | 75 |
| Shane Bieber, Indians | - | - | 11 | 13 | 5 | 64 |
| Lance Lynn, Rangers | - | - | - | 3 | 12 | 18 |
| Eduardo Rodriguez, Red Sox | - | - | - | 3 | 2 | 8 |
| Lucas Giolito, White Sox | - | - | - | - | 8 | 8 |
| Mike Minor/Rangers | - | - | 1 | 1 | 2 | 7 |
| -- Total points on a 7-4-3-2-1 basis | ||||||
| -- Baseball Writers' Association of America |
"I will always try to push the boundaries of what I can do,'' Verlander said. "If my body says, 'Hold on, you can't do that anymore,' then I'll pull back.''
Cole, 29, went 20-5 while leading the AL in ERA (2.50) and strikeouts (326), the latter the most in baseball since Randy Johnson struck out 334 in 2002. But he was more than pure power: Cole walked just 48 batters on the season, giving him a remarkable 6.79 strikeouts for every walk.
The breakout season continued a career resurgence for Cole that began when he was acquired in a 2018 trade by the Astros from the Pittsburgh Pirates, who selected him first overall in the 2011 draft. Cole was 59-42 with a 3.50 ERA and 8.4 strikeouts per nine innings at the time of the trade. During two seasons with the analytics-driven Astros, he went 35-10 with a 2.68 ERA and 13.1 strikeouts per nine innings.
After losing to the White Sox on May 22, Cole began a remarkable streak that lasted all the way to Game 1 of the World Series. He went 25 starts without a loss, including the postseason, going 19-0 in that span, with a 1.59 ERA and 258 strikeouts in 169⅓ innings. Finally, his streak was broken when he lost to the Nationals on Oct. 22.
Cole became a free agent after the season, and based on his age and the strength of his past two campaigns, it is widely believed that he will land the largest pitching contract in baseball history.
"I know that Gerrit had a great time playing here and I know he would like to return, if possible,'' Verlander said. "But that is now on Gerrit and his family and people above me.''
Morton, who turned 36 on Tuesday, provided crucial stability for a Rays rotation that was beset by injuries. Morton's 194⅔ innings was 53 more than that of any other pitcher on the Tampa Bay staff. Among the injured was lefty Blake Snell, who took home AL Cy Young honors in 2018 before being limited to 107 innings in 23 starts this season.
Of course, you don't become a Cy Young finalist based on innings alone. Morton went 16-6, setting a career mark for wins, and posted personal bests in ERA (3.05) and strikeouts (240). The strong campaign continued a late-career surge for the righty, who had won 10 games just once in his first nine big league seasons. He has won at least 14 games in each of the past three seasons.
Morton teamed with Verlander and Cole in the Astros' rotation after the latter was acquired in 2018. He left Houston after the season for a two-year, $30 million deal with the Rays.
The Associated Press contributed to this report.


Drag a .csv, .json or .solomon file here to start analyzing your data
-or-
Your data is being analyzed and used to train a variety of machine learning models. These can be used to visualize your data, make predictions and improve your productivity. You can export your model to save your trained data and models and use them at a later date. If you have any comments, concerns or questions, please visit our Github.
These models are completely trained and stored in your browser. We never store or use your data.


Helen Langridge Associates is one of the most acclaimed, respected and successful production companies in the UK. The website showcases all the works of the company by different directors.
Technology:
Wordpress, PHP, HTML, CSS and JavaScript.
Role:
Development and deployment of a complete wordpress custom theme.


Welcome to Bitwarden CLI’s documentation!¶
Cross Platform Bitwarden library and CLI with sudolikeaboss functionality.
Source repo lives @ https://fossil.birl.ca/bitwarden-cli/home But is mirrored to github: https://github.com/birlorg/bitwarden-cli
The repository houses both rust and python versions. The python version is more feature complete, and is meant as a useable proof of concept or minimally viable program. It’s still up for decision how much functionality will be in the rust version, at least the crypto and the agent will be fully implemented. Ideally the entire program would be in Rust, but rust is still a very new language and all the magic happy libraries one might want are not as fleshed out as the very mature python ecosystem.
This program, documentation, sources, etc. currently have no relationship with Bitwarden proper, other than the main author pays a Families subscription fee, despite my actual Ciphers being stored in an off-line self-hosted version of Bitwarden. Just because bitwarden can run over the public internet doesn’t mean it has to.
Historic fun fact: all crypto code needed to be written and stored outside of the USA at one time.
- Goals:
- Be a useful bitwarden tool that works on Windows and Modern POSIX(macOS, BSD, Linux, etc) platforms.
- Be able to work off-line completely if you wish. This mostly works now.
- Non-Goals:
- GUI’s because writing them is misery. Bitwarden writes these for us, go pay
them (regardless of self-hosted or not), I do.
The idea behind the CLI here is to think of the server as a place to push / pull againt. The local copy of the DB should be resilient and not erase anything ever without explicitly saying so, so that full historic backups are possible. think more like revision control. This is not fully fleshed out, at the time of this writing..
Documentation Contents¶
This part of the documentation guides you through all of the library’s usage patterns.


Save the world from disease
Thank you so much for choosing to play our own take on the pandemic game. Now before you play, you might want to read this tutorial
Pandemic is a strategy game where you are trying to rid the world of a deadly outbreak of a disease. You can travel throughout the world and conduct research on a cure for the disease. Once you develop a cure, you win the game. If you fail to develop a cure and the disease grows too much. You will lose the game.
Once again, We here at Epicodus Games thank you for choosing to play our game. We worked hard on this game and we believe that it is the greatest game of the new decade. Enjoy Pandemic!!!!!!
February 2020
| City | Infection Level |
|---|---|
| Baghdad | 0 |
| Bangkok | 0 |
| Los Angeles | 0 |
| Moscow | 0 |
| Nairobi | 0 |
| Paris | 0 |
| Rio de Janeiro | 0 |
| Seattle | 0 |
| Tokyo | 0 |
| Toronto | 0 |
| Total Disease | 0 |
Current Player: 1
Difficulty:
| Stat | Data |
|---|---|
| Current Location | 100° W, 35° N |
| Actions Remaining | 0 |
| Cure Research Completed | 0% |


Thank you dad for all the fun I had throughout the years. I enjoyed the times when you cooked food, especially the seafood. I missed the times we had when we travelled to different countries. I enjoyed that time when we had stargazing during Earth Day. I hope we can travel more in the future. I love you dad.


TI-86 Link Protocol Guide v1.0 - Remote control/command
The TI-86's remote control ability is "silent," which means that the calculator does not need to be put in a special mode. The remote control ability can be utilized any time the calculator is ready to accept a keypress. Key codes : here.
Remote command:
The protocol used for sending a remote command is shown below.
| Step | Direction | Packet | ||
|---|---|---|---|---|
|
1 |
|
|
|
CMD |
|
2 |
|
ACK | ||
|
3 |
|
Second ACK or SKIP/EXIT with
1-byte error code. If SKIP/EXIT, then the computer sends an ACK packet
back to the calculator. (fixme !) |
The scancode of the key is sent in the last two bytes of the command. The <2nd> and <Alpha> keys are not sent as scancodes, but are implied by the scancode of the following key.
Example:
| PC: | 06 87 C7 00 | Command: Key <M1> (<2nd>+<F1>) |
| TI: | 86 56 00 00 | Acknowledgement of command |
| TI: | 86 56 00 00 | Acknowledgement (fixme !) |
The computer will not send any information back through the link
port except the error state when an error screen
is loaded.


{{Template:UiOslo_Norway/Head}} {{Template:UiOslo_Norway/Header}} {{Template:UiOslo_Norway/TopBar}}
Alexander Refsnes is a devoted years old family man. He is now collecting data for his master thesis in biological sciences on bioengineering with a focus on solving fish diseases in salmon. When not on his desk or in the lab he likes doing outdoor sport activities.
Muhammed is studying in the Department of Molecular Biology and Genetics. This year, he gave his thesis on the effects of salinomycin, one of the basic chemicals of the project, on cancer stem cells. He says he is involved in iGEM because he has an interest in biotechnology and wants to improve his practical lab skills. He enjoys spending his free time with computer games and books. Fun fact: He enjoys watching lawn-mowing videos on the internet.
Jonas is 23 years old and is doing his master in cybernetics and autonomous systems at UiO. In our iGEM team he is mostly invested in the construction of a diagnostics system based on behavioural changes in fish. Out side of iGEM he works with swarm intelligence and how it can be applied to control a group of micro underwater gliders.
From left to right: Athanasios Saragliadis; Dirk Linke and Kirsten Borse Haraldsen
| No. | Name | Role | Field of Study |
| 1 | Alexander Refsnes | ||
| 2 | Jonas Aouay Grønbakken | ||
| 3 | Martin Eide Lien | ||
| 4 | Muhammed Gecgel |


{%extends "blogging/base_blog.html" %} {%block title%}{{entry.headline}}{%endblock%} {%block primary%} {%include 'blogging/blog_entry.html'%} {%endblock%}


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/tactic.2Ering.20feature.20requests.html ---
import tactic.ring theorem inductive_step (d : ℕ) : d ^ 2 + (2 * d + 1) = (succ d) ^ 2 := begin -- ring doesn't work unfold nat.pow, -- ring doesn't work rw succ_eq_add_one, ring, -- works end
I am in two minds about whether I need to tell mathematicians "obviously you need to unfold nat.pow and succ because they are not really to do with rings, which are all about + and *"
or whether I should just expect ring to deal with these
or whether it's even not possible to get ring to deal with these, for technical reasons I'm unaware of
@Mario Carneiro
import tactic.ring open nat def odd : ℕ → ℕ := λ i, 2 * i + 1 def square : ℕ → ℕ := λ i, i ^ 2 theorem odd_square_inductive_step (d : ℕ) : square d + odd d = square (succ d) := by {unfold square odd nat.pow,rw succ_eq_add_one,ring}
Why can't this just be by ring
?
Can't I promise that I'll only ever call ring with an identity that can be formulated using only the axioms of a semiring?
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


This red link had a total of {{getRedLinkViews()}} views
would love to read this page.
You can +1 this red link if you'd love to read this page.
A red link highlights author's intention to point at a concept that doesn't yet exist on Arbital.
It's an open invitation for anyone (including you) to create the corresponding page for this concept.


The goal of the IRIX 6.5 release family is to provide a high quality and stable operating system for all SGI systems. This release family supports all SGI hardware platforms supported by IRIX 6.2, 6.3, and 6.4, except for Crimson. Significant additional software has been packaged with IRIX 6.5, including some important system and network management and interoperability tools.
These pages contain a partial list of the key new features and changes for:
IRIX 6.5.22
To see the list of key features and changes for the IRIX 6.5 and IRIX 6.5.1 through 6.5.21 releases, see the previous IRIX features page.
Look for information about the IRIX 6.5 release family on Supportfolio Online (http://support.sgi.com/6.5) and http://www.sgi.com/.
For details on any of these new features and changes, refer to the release notes.
Please be aware that some features listed here are only applicable for certain software bundles or hardware configurations.


The First
This decription for every one called user. with user experiece be the good, responsiveness, sweeetness, happyness, and many more. our blog is for up to date what happend in this application web app. every time we are create many more inovative technology for creating world better.
The First
This decription for every one called user. with user experiece be the good, responsiveness, sweeetness, happyness, and many more. our blog is for up to date what happend in this application web app. every time we are create many more inovative technology for creating world better.
The First
This decription for every one called user. with user experiece be the good, responsiveness, sweeetness, happyness, and many more. our blog is for up to date what happend in this application web app. every time we are create many more inovative technology for creating world better.


{% extends 'base.html' %} {% block content %}
Site of the day!
{{project.project}}
{{project.description}}
no projects posted for now


|
| void | GenerateVertexArray (EntityID id) |
| |
| void | DrawEntity (EntityID id, glm::mat4 view, glm::mat4 projection) |
| | Draws the entity on screen. More...
|
| |
|
virtual void | FirstRun () override |
| | Runs all the systems once at start.
|
| |
|
virtual void | Run () override |
| | Runs all the systems. This is the main 'update' function which might be called in the main program loop.
|
| |
|
virtual void | LastRun () override |
| | Executes one last execution of all the systems.
|
| |
|
| ISystem () |
| | Construct a new ISystem object, tagged by SYSTEM_MASK_UNMARKED (for the SystemManager)
|
| |
|
SystemMask | GetMask () const |
| | Get the mask of the system.
|
| |
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void | SetMask (SystemMask value) |
| | Set the mask.
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| |
◆ DrawEntity()
| void Graphics::GraphicsSystem::DrawEntity |
( |
EntityID |
id, |
|
|
glm::mat4 |
view, |
|
|
glm::mat4 |
projection |
|
) |
| |
Draws the entity on screen.
- Parameters
-
| id | : The ID of the entity to be rendered. |
| view | : The view matrix. |
| projection | : The projection matrix. |
◆ GenerateVertexArray()
| void Graphics::GraphicsSystem::GenerateVertexArray |
( |
EntityID |
id | ) |
|
Generates the full vertex array for the wanted entity.
The documentation for this class was generated from the following files:


WITNESS LIST Subcommittee on Higher Education Committee May 10, 1999-10:00A HB 3651 ON: Rodriguez, Lynn (Texas Higher Ed. Coord. Board), unavailable


Extension customization elements are used to extend or modify the actual code generation. CodeGen allows you to apply extensions either to a particular schema or to a set of schemas, so they fit into the customization document structure as child elements of a schema or schema-set element. Since they always apply to all the schemas associated with the parent schema or schema-set element, they must precede any other child elements. Here's a sample to show how this works, with the extension elements shown in bold:
<schema-set xmlns:xs="http://www.w3.org/2001/XMLSchema"
type-substitutions="xs:integer xs:int xs:decimal xs:float">
<name-converter strip-prefixes="OTA_"
strip-suffixes="Type AttributeGroup Group Attributes"/>
<class-decorator class="org.jibx.schema.codegen.extend.CollectionMethodsDecorator"/>
<schema-set package="org.ota.air" names="OTA_Air*.xsd">
<schema-type type-name="dateTime" java-class="org.joda.time.DateTime"
format-name="DateTime.zoned"/>
<schema-set generate-all="false" prefer-inline="true"
names="OTA_AirCommonTypes.xsd OTA_AirPreferences.xsd"/>
<schema name="OTA_AirAvailRS.xsd">
<element path="element[@name=OTA_AirAvailRS]/**/element[@name=OriginDestinationOption]"
ignore="true"/>
</schema>
</schema-set>
<schema-set package="org.ota.hotel" names="OTA_Hotel*.xsd">
<schema-set generate-all="false" prefer-inline="true"
names="OTA_HotelCommonTypes.xsd OTA_HotelContentDescription.xsd
OTA_HotelEvent.xsd OTA_HotelPreferences.xsd OTA_HotelReservation.xsd
OTA_HotelRFP.xsd"/>
</schema-set>
...
The name-converter (modifying the way names are generated) and class-decorator (adding collection helper methods to generated classes, in this case) elements are both children of the root schema-set element, so they apply to all code generation. The schema-type element (changing the Java type generated for a schema datatype) is a child of the nested schema-set, so it only applies to the code generation for schemas included in that set.
Some extension elements are designed to support selecting a particular implementation for an interface used during code generation. These extension elements normally have one required attribute, the 'class' attribute (though in the case of the name-converter extension, this attribute has a default value matching a built-in class). This attribute is used to supply the fully-qualified class name of your implementation class. Besides this required attribute, you can add other attributes which correspond to properties of your implementation class. These added attributes will be used to configure an instance of the class before it is used in code generation, so that you can customize general-purpose extension classes for each use.
CodeGen uses reflection to match any additional attributes to property or field names in your class (or in a superclass). First any hyphens in the attribute name are removed, with the character following any hyphen converted to uppercase. This modified name is then used to look for a method or field name, in three different forms.
Due to some legacy code handling, the preferred form of match is somewhat unusual -
CodeGen looks for a method name starting with set
followed by the attribute
name (with the first letter of the name converted to uppercase)
and ending with Text
. The method may have either one or two parameters, with
the first parameter a java.lang.String
and the second (if defined) a
org.jibx.runtime.IUnmarshallingContext
. This form of match is intended for
when the supplied attribute value needs to use some special conversion. If a match of
this type is found, CodeGen calls the method to set the value, passing the attribute
value text as the first parameter (and the unmarshalling context as the second parameter,
if used).
If the preferred form of match described above is not found, CodeGen next looks for
either a set
method matching the attribute name (with the first letter of
the attribute name converted to uppercase) with a single parameter, or a field name
matching the attribute name with m_
prefix. The method parameter or field
may be of any of the following types: boolean
/java.lang.Boolean
;
double
/java.lang.Double
; float
/java.lang.Float
;
int
/java.lang.Integer
; long
/java.lang.Long
;
java.lang.String
; or java.lang.String[]
. CodeGen converts the
supplied attribute value to the appropriate type (in the case of an array of
String
, by splitting at each embedded whitespace sequence and trimming all
leading and trailing whitespace from each resulting value) and calls the method or sets
the field value directly.
You can see the source code for the classes mentioned in the remainder of this section for examples of this correspondence between attribute names and method or field names.
The name-converter extension element is used to control how XML names are converted
to Java names. It can be used in two ways: To change the behavior of the default name converter
class used by CodeGen (org.jibx.schema.codegen.extend.DefaultNameConverter
), or
to completely replace that default name converter class with your own implementation.
When used to change the behavior of the default name converter class used by CodeGen, the following attributes apply:
Prefix string to be added at the beginning of generated normal (non-static) field names. By default, the prefix string is empty. |
|
Suffix string to be added at the end of generated normal (non-static) field names. By default, the suffix string is empty. |
|
Prefix string to be added at the beginning of generated static field names. By default, the prefix string is empty. |
|
Suffix string to be added at the end of generated static field names. By default, the suffix string is empty. |
|
Prefix strings to be stripped from schema names before converting to Java names. The value is a list of prefix strings, separated by whitespace characters. |
|
Suffix strings to be stripped from schema names before converting to Java names. The value is a list of suffix strings, separated by whitespace characters. |
When the name-converter element is used to replace the default name converter
class with your own implementation, there's only one fixed attribute: The 'class'
attribute. This attribute gives the fully-qualified name of your name converter
implementation class, which must implement the
org.jibx.schema.codegen.extend.NameConverter
interface. You can use other
attributes on the element to configure your name converter instance, as described
above.
If you do take the approach of writing your own name converter implementation you'll
probably want to refer to the default
org.jibx.schema.codegen.extend.DefaultNameConverter
implementation and either
use it as the base for your own implementation or just subclass and override methods
selectively.
You can extend Java class generation to add special handling or features to the generated code, using class-decorator elements. Multiple class-decorator elements can be used, and by default the decorators are inherited by child schema-set and schema-set elements within the customizations. You can change this by using the inherit-decorators customization attribute.
The required 'class' attribute must give the fully-qualified class name of a class
implementing the org.jibx.schema.codegen.extend.ClassDecorator
interface.
This interface defines methods called by CodeGen during the process of generating a Java
class. The method parameters give your implementation code ways to hook into the code
generation, which uses the Eclipse Abstract Syntax Tree (AST) model. You can then modify
the AST in various ways to meet your needs.
Several decorators are provided with CodeGen, along with an abstract base class useful for writing your own decorators. These build-in decorators are described in the remainder of this section.
org.jibx.schema.codegen.extend.NameMatchDecoratorBase
is a convenient
base class to use if you want your decorator to apply only to some of the data model
classes. Decorators based on this class support a 'match-name' attribute on the
class-decorator customization. The value of this optional attribute is a name
pattern, which can include one or more '*' wildcard match characters. Subclasses can
call the base class matchName()
method to check if a class name matches
the pattern.
org.jibx.schema.codegen.extend.CollectionMethodsDecorator
adds
helper methods for collection values represented by java.util.List
instances.
The helper methods are int sizeXXX()
to get the number of items in the list,
void addXXX(type)
to add an item to the list, type getXXX(int)
to get an item by position, and void clearXXX()
to remove all items from
the list (where 'XXX' is the value name). No attributes are used with this decorator.
List
implementation classorg.jibx.schema.codegen.extend.ListImplementationDecorator
lets you
set the implementation class to be used for instances of java.util.List
in
the data model. The required 'list-class' attribute must give the fully-qualified class
name of the desired implementation class.
org.jibx.schema.codegen.extend.ExtensionDecorator
allows you to set
a base class to be extended by your data model classes. This decorator class itself
extends the abstract org.jibx.schema.codegen.extend.NameMatchDecoratorBase
class described above, so you can use a 'match-name' attribute to control the data
model classes which are modified by this decorator (matching on the simple class name,
without package name). The 'base-class' attribute must give the desired base class
name.
You can also use this decorator to customize the JiBX binding for data model classes to call user extension methods implemented by the base class. Use a 'pre-get-name' attribute to set a method to be called before marshalling the data class, a 'pre-set-name' attribute to set a method to be called before unmarshalling to the data class, and a 'post-set-name' attribute to set a method to be called after unmarshalling the data class.
org.jibx.schema.codegen.extend.SerializableDecorator
is used to add
the java.io.Serializable
interface to generated data model classes. If a
'serial-version' attribute is used with this decorator it will also add a
serialVersionUID
value to each generated class, with the specified version
(which must be a long integer value).
You can customize the code generation for schema built-in datatypes using the schema-type element. Besides determining the Java type to be used for a schema datatype, this also allows you to define the actual format used to convert instances of one into the other (see and <format> element for dicsussions of these conversions). The attributes used with the schema-type element are listed in the following table:
Fully-qualified class and method name of a static method used to check if a
text string represents a valid instance of the type. The referenced method must take
a |
|
Fully-qualified class and method name of a static method used to convert a text
string to an instance of the Java type. This attribute is optional, and is not needed if
the Java type defines a constructor taking a parameter of type |
|
Name of a built-in JiBX format to be used for conversions between a text value of the schema type and an instance of the Java type. This optional attribute is really only useful when you want to use the Joda Date/time conversions. |
|
Java fully-qualified class name. This required attribute gives the name of the Java type to be used for the schema type. |
|
Fully-qualified class and method name of a static method used to convert an instance
of the Java type to a text string. This attribute is optional, and is not needed if
the Java type defines a |
|
Schema built-in type name. This required attribute gives the simple (unqualified) name of the schema type being handled. |


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