Last updated: 2021-12-14
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This code will load in the combined scRNA seq data from external datasets combined with the iPSC-chondrocytes from the current study and fit a topic model (k=7) to the data. It then interprets the topics.
The merged data were created by running the code located in Aggregation of external scRNA-seq data for topic model.
library(fastTopics)
library(Seurat)
Registered S3 method overwritten by 'shiny':
method from
print.key_missing fastmap
library(Matrix)
library(cowplot)
#load data (stored in a seurat object)
merged_data <- readRDS("data/external_scRNA/merged_external_scRNA.rds")
#Extract raw count matrix from seurat object and get it in correct format for fastTopics
#need to fit the model to the count data (unintegrated)
raw_counts <- merged_data@assays$RNA@counts
#remove genes without any counts in droplets
raw_counts <- raw_counts[rowSums(raw_counts > 0) > 0,]
#get into correct orientation (barcodes x features)
raw_counts <- t(raw_counts)
dim(raw_counts)
[1] 33218 36470
if (file.exists("output/topicModel_k=7.rds")) {
fit <- readRDS("output/topicModel_k=7.rds")
} else {
fit <- fit_poisson_nmf(raw_counts,k = 7,numiter = 150)
saveRDS(fit, "output/topicModel_k=7.rds")
}
#compute weights and topics (rescale each of l and f to add up to 1)
l <- fit$L
f <- fit$F
weights <- sweep(l, MARGIN = 2, colSums(f), `*`)
scale <- rowSums(weights)
weights <- weights / scale
topics <- f / colSums(f) # add up to 1
Here we select a few marker genes to represent iPSCs, MSCs, Hepatocytes, and Chondrocytes and visualize the relative loadings of these marker genes in each of the 7 fitted topics using a scaled heatmap.
library(stringr)
library(pheatmap)
library(dummies)
dummies-1.5.6 provided by Decision Patterns
library(tidyverse)
Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat
── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.1.4 ✓ forcats 0.4.0
✓ readr 1.3.1
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
x tidyr::expand() masks Matrix::expand()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x tidyr::pack() masks Matrix::pack()
x tidyr::unpack() masks Matrix::unpack()
#selected markers for different cell types
MSC_markers <- c("THY1", "NT5E", "ENG")
Chondrocyte_markers <- c("COL2A1", "ACAN", "SOX9", "SOX5", "SOX6", "COL9A1")
Hepatocyte_markers <- c("ALB")
iPSC_markers <- c("POU5F1", "SOX2", "NANOG")
markers <- c(iPSC_markers, Hepatocyte_markers, Chondrocyte_markers, MSC_markers)
#annotate the markers with the cell type they represent
markers_description <- data.frame(marker_type = c(rep("iPSC", 3), rep("Hepatocyte", 1), rep("Chondrocyte", 6), rep("MSC", 3)))
rownames(markers_description) <- markers
#plot heatmap of relative expression of marker genes in each topic
topics_markers <- topics[markers,]
pheatmap(topics_markers, cluster_cols = FALSE, cluster_rows = FALSE, annotation_row = markers_description, scale = "row")
The structure plot represents the topic membership proportions of individual cells in each of the cell types using stacked bar plots.
#get labels of cells (cell type) and reorder to group them
sample <-<EMAIL_ADDRESS>sample_labels <- sample %>%
dplyr::transmute(Cell.Type =<EMAIL_ADDRESS>start = 1))
rownames(sample_labels) <-<EMAIL_ADDRESS>sample <- sample_labels %>%
dplyr::arrange(Cell.Type)
set.seed(1)
topic_colors <- c("gold", "grey", "firebrick", "forestgreen", "royalblue", "turquoise", "salmon")
topics_order <- c(2, 3, 1, 7, 5, 4, 6)
rows_keep <- sort(c(sample(which(sample_labels$Cell.Type == "Hepatocyte"), 800),
sample(which(sample_labels$Cell.Type == "iPSC"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-MSC"), 800),
which(sample_labels$Cell.Type == "iPSC-Chondrocyte"),
sample(which(sample_labels$Cell.Type == "iPSC-Chondro_GAH"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-Osteo"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d7"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d14"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d28"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d42"), 800),
sample(which(sample_labels$Cell.Type == "chou_chondrocyte"), 800),
sample(which(sample_labels$Cell.Type == "ji_chondrocyte"), 800)))
structure_plot <- structure_plot(select(poisson2multinom(fit),loadings = rows_keep),
grouping = factor(sample_labels[rows_keep,"Cell.Type"],
c("Hepatocyte", "iPSC", "iPSC-MSC",
"iPSC-Osteo", "iPSC-Chondro_GAH",
"iPSC-Chondrocyte", "Wu_chondrogenic_pellet_d7", "Wu_chondrogenic_pellet_d14", "Wu_chondrogenic_pellet_d28", "Wu_chondrogenic_pellet_d42", "chou_chondrocyte", "ji_chondrocyte")),
topics = topics_order,
colors = topic_colors[topics_order],
perplexity = c(50),
n = 6043,gap = 100,num_threads = 4,verbose = FALSE)
Running tsne on 447 x 7 matrix.
Running tsne on 475 x 7 matrix.
Running tsne on 450 x 7 matrix.
Running tsne on 489 x 7 matrix.
Running tsne on 467 x 7 matrix.
Running tsne on 1037 x 7 matrix.
Running tsne on 450 x 7 matrix.
Running tsne on 443 x 7 matrix.
Running tsne on 452 x 7 matrix.
Running tsne on 436 x 7 matrix.
Running tsne on 447 x 7 matrix.
Running tsne on 450 x 7 matrix.
print(structure_plot)
First, calculate differential occurrence of genes in individual topics vs all other topics.
if (file.exists("output/DEresults_k=7.rds")) {
diff_count_topics_k7 <- readRDS("output/DEresults_k=7.rds")
} else {
diff_count_topics_k7 <- de_analysis(fit,
raw_counts,
pseudocount = 0.1,
control = list(ns = 1e4, nc = 20, nsplit = 1000)
) #runs using 20 cores, 10,000 simulations
saveRDS(diff_count_topics_k7, file = "output/DEresults_k=7.rds")
}
#volcano plots
genes <- rownames(fit$F)
p1 <- volcano_plot(diff_count_topics_k7,k = "k1",ymax = 300,labels = genes)
p2 <- volcano_plot(diff_count_topics_k7,k = "k2",ymax = 175,labels = genes)
p3 <- volcano_plot(diff_count_topics_k7,k = "k3",ymax = 150,labels = genes)
p4 <- volcano_plot(diff_count_topics_k7,k = "k4",ymax = 175,labels = genes)
p5 <- volcano_plot(diff_count_topics_k7,k = "k5",ymax = 175,labels = genes)
p6 <- volcano_plot(diff_count_topics_k7,k = "k6",ymax = 200,labels = genes)
p7 <- volcano_plot(diff_count_topics_k7,k = "k7",ymax = 200,labels = genes)
p1
p2
p3
p4
p5
p6
p7
if (file.exists("output/topicModel_k=6.rds")) {
fit <- readRDS("output/topicModel_k=6.rds")
} else {
fit <- fit_poisson_nmf(raw_counts,k = 6,numiter = 100)
saveRDS(fit, "output/topicModel_k=6.rds")
}
#compute weights and topics (rescale each of l and f to add up to 1)
l <- fit$L
f <- fit$F
weights <- sweep(l, MARGIN = 2, colSums(f), `*`)
scale <- rowSums(weights)
weights <- weights / scale
topics <- f / colSums(f) # add up to 1
#get labels of cells (cell type) and reorder to group them
sample <-<EMAIL_ADDRESS>sample_labels <- sample %>%
dplyr::transmute(Cell.Type =<EMAIL_ADDRESS>start = 1))
rownames(sample_labels) <-<EMAIL_ADDRESS>sample <- sample_labels %>%
dplyr::arrange(Cell.Type)
set.seed(1)
topic_colors <- c("turquoise", "firebrick", "grey", "salmon", "royalblue", "forestgreen")
topics_order <- c(3, 2, 4, 6, 5, 1)
rows_keep <- sort(c(sample(which(sample_labels$Cell.Type == "Hepatocyte"), 800),
sample(which(sample_labels$Cell.Type == "iPSC"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-MSC"), 800),
which(sample_labels$Cell.Type == "iPSC-Chondrocyte"),
sample(which(sample_labels$Cell.Type == "iPSC-Chondro_GAH"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-Osteo"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d7"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d14"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d28"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d42"), 800),
sample(which(sample_labels$Cell.Type == "chou_chondrocyte"), 800),
sample(which(sample_labels$Cell.Type == "ji_chondrocyte"), 800)))
structure_plot <- structure_plot(select(poisson2multinom(fit),loadings = rows_keep),
grouping = factor(sample_labels[rows_keep,"Cell.Type"],
c("Hepatocyte", "iPSC", "iPSC-MSC", "iPSC-Osteo",
"iPSC-Chondro_GAH", "iPSC-Chondrocyte", "Wu_chondrogenic_pellet_d7", "Wu_chondrogenic_pellet_d14", "Wu_chondrogenic_pellet_d28", "Wu_chondrogenic_pellet_d42", "chou_chondrocyte", "ji_chondrocyte")),
topics = topics_order,
colors = topic_colors[topics_order],
perplexity = c(50),
n = 6043,gap = 100,num_threads = 4,verbose = FALSE)
Running tsne on 447 x 6 matrix.
Running tsne on 475 x 6 matrix.
Running tsne on 450 x 6 matrix.
Running tsne on 489 x 6 matrix.
Running tsne on 467 x 6 matrix.
Running tsne on 1037 x 6 matrix.
Running tsne on 450 x 6 matrix.
Running tsne on 443 x 6 matrix.
Running tsne on 452 x 6 matrix.
Running tsne on 436 x 6 matrix.
Running tsne on 447 x 6 matrix.
Running tsne on 450 x 6 matrix.
print(structure_plot)
if (file.exists("output/DEresults_k=8.rds")) {
diff_count_topics_k6 <- readRDS("output/DEresults_k=6.rds")
} else {
diff_count_topics_k6 <- de_analysis(fit,
raw_counts,
pseudocount = 0.1,
control = list(ns = 1e4, nc = 20, nsplit = 1000)
) #runs using 20 cores, 10,000 simulations
saveRDS(diff_count_topics_k6, file = "output/DEresults_k=6.rds")
}
#volcano plots
genes <- rownames(fit$F)
p1 <- volcano_plot(diff_count_topics_k6,k = "k1",ymax = 300,labels = genes)
p2 <- volcano_plot(diff_count_topics_k6,k = "k2",ymax = 175,labels = genes)
p3 <- volcano_plot(diff_count_topics_k6,k = "k3",ymax = 150,labels = genes)
p4 <- volcano_plot(diff_count_topics_k6,k = "k4",ymax = 175,labels = genes)
p5 <- volcano_plot(diff_count_topics_k6,k = "k5",ymax = 175,labels = genes)
p6 <- volcano_plot(diff_count_topics_k6,k = "k6",ymax = 200,labels = genes)
p1
p2
p3
p4
p5
p6
if (file.exists("output/topicModel_k=8.rds")) {
fit <- readRDS("output/topicModel_k=8.rds")
} else {
fit <- fit_poisson_nmf(raw_counts,k = 8,numiter = 150)
saveRDS(fit, "output/topicModel_k=8.rds")
}
#compute weights and topics (rescale each of l and f to add up to 1)
l <- fit$L
f <- fit$F
weights <- sweep(l, MARGIN = 2, colSums(f), `*`)
scale <- rowSums(weights)
weights <- weights / scale
topics <- f / colSums(f) # add up to 1
#get labels of cells (cell type) and reorder to group them
sample <-<EMAIL_ADDRESS>sample_labels <- sample %>%
dplyr::transmute(Cell.Type =<EMAIL_ADDRESS>start = 1))
rownames(sample_labels) <-<EMAIL_ADDRESS>sample <- sample_labels %>%
dplyr::arrange(Cell.Type)
set.seed(1)
topic_colors <- c("royalblue", "turquoise","firebrick","gold","grey", "black", "salmon", "forestgreen")
topics_order <- c(5, 3, 4, 7, 6, 1, 8, 2)
rows_keep <- sort(c(sample(which(sample_labels$Cell.Type == "Hepatocyte"), 800),
sample(which(sample_labels$Cell.Type == "iPSC"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-MSC"), 800),
which(sample_labels$Cell.Type == "iPSC-Chondrocyte"),
sample(which(sample_labels$Cell.Type == "iPSC-Chondro_GAH"), 800),
sample(which(sample_labels$Cell.Type == "iPSC-Osteo"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d7"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d14"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d28"), 800),
sample(which(sample_labels$Cell.Type == "Wu_chondrogenic_pellet_d42"), 800),
sample(which(sample_labels$Cell.Type == "chou_chondrocyte"), 800),
sample(which(sample_labels$Cell.Type == "ji_chondrocyte"), 800)))
structure_plot <- structure_plot(select(poisson2multinom(fit),loadings = rows_keep),
grouping = factor(sample_labels[rows_keep,"Cell.Type"],
c("Hepatocyte", "iPSC", "iPSC-MSC", "iPSC-Osteo",
"iPSC-Chondro_GAH", "iPSC-Chondrocyte", "Wu_chondrogenic_pellet_d7", "Wu_chondrogenic_pellet_d14", "Wu_chondrogenic_pellet_d28", "Wu_chondrogenic_pellet_d42", "chou_chondrocyte", "ji_chondrocyte")),
topics = topics_order,
colors = topic_colors[topics_order],
perplexity = c(50),
n = 6043,gap = 100,num_threads = 4,verbose = FALSE)
Running tsne on 447 x 8 matrix.
Running tsne on 475 x 8 matrix.
Running tsne on 450 x 8 matrix.
Running tsne on 489 x 8 matrix.
Running tsne on 467 x 8 matrix.
Running tsne on 1037 x 8 matrix.
Running tsne on 450 x 8 matrix.
Running tsne on 443 x 8 matrix.
Running tsne on 452 x 8 matrix.
Running tsne on 436 x 8 matrix.
Running tsne on 447 x 8 matrix.
Running tsne on 450 x 8 matrix.
print(structure_plot)
if (file.exists("output/DEresults_k=8.rds")) {
diff_count_topics_k8 <- readRDS("output/DEresults_k=8.rds")
} else {
diff_count_topics_k8 <- de_analysis(fit,
raw_counts,
pseudocount = 0.1,
control = list(ns = 1e4, nc = 20, nsplit = 1000)
) #runs using 20 cores, 10,000 simulations
saveRDS(diff_count_topics_k8, file = "output/DEresults_k=8.rds")
}
#volcano plots
genes <- rownames(fit$F)
p1 <- volcano_plot(diff_count_topics_k8,k = "k1",ymax = 300,labels = genes)
p2 <- volcano_plot(diff_count_topics_k8,k = "k2",ymax = 175,labels = genes)
p3 <- volcano_plot(diff_count_topics_k8,k = "k3",ymax = 150,labels = genes)
p4 <- volcano_plot(diff_count_topics_k8,k = "k4",ymax = 175,labels = genes)
p5 <- volcano_plot(diff_count_topics_k8,k = "k5",ymax = 175,labels = genes)
p6 <- volcano_plot(diff_count_topics_k8,k = "k6",ymax = 200,labels = genes)
p7 <- volcano_plot(diff_count_topics_k8,k = "k7",ymax = 200,labels = genes)
p8 <- volcano_plot(diff_count_topics_k8,k = "k8",ymax = 200,labels = genes)
p1
p2
p3
p4
p5
p6
p7
p8
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.4.0 dplyr_1.0.7 purrr_0.3.4
[4] readr_1.3.1 tidyr_1.1.4 tibble_3.1.6
[7] ggplot2_3.3.5 tidyverse_1.3.0 dummies_1.5.6
[10] pheatmap_1.0.12 stringr_1.4.0 cowplot_1.1.1
[13] Matrix_1.2-18 Seurat_3.2.3 fastTopics_0.6-93
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.16 tidyselect_1.1.1
[4] htmlwidgets_1.5.4 grid_3.6.1 Rtsne_0.15
[7] pROC_1.18.0 munsell_0.5.0 codetools_0.2-16
[10] ica_1.0-2 future_1.23.0 miniUI_<IP_ADDRESS>
[13] withr_2.4.3 colorspace_2.0-2 knitr_1.23
[16] rstudioapi_0.13 stats4_3.6.1 ROCR_1.0-7
[19] tensor_1.5 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.26.1 mixsqp_0.3-43 polyclip_1.10-0
[25] MCMCpack_1.6-0 farver_2.1.0 rprojroot_2.0.2
[28] coda_0.19-4 parallelly_1.29.0 vctrs_0.3.8
[31] generics_0.1.1 ipred_0.9-12 xfun_0.8
[34] R6_2.5.1 rsvd_1.0.1 invgamma_1.1
[37] bitops_1.0-6 spatstat.utils_1.17-0 assertthat_0.2.1
[40] promises_<IP_ADDRESS> scales_1.1.1 nnet_7.3-12
[43] gtable_0.3.0 npsurv_0.4-0 globals_0.14.0
[46] conquer_1.2.1 goftest_1.2-2 mcmc_0.9-7
[49] workflowr_1.6.2 timeDate_3043.102 rlang_0.4.12
[52] MatrixModels_0.5-0 splines_3.6.1 lazyeval_0.2.2
[55] ModelMetrics_<IP_ADDRESS> broom_0.7.0 yaml_2.2.1
[58] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[61] backports_1.1.10 httpuv_1.5.1 caret_6.0-90
[64] tools_3.6.1 lava_1.6.10 ellipsis_0.3.2
[67] gplots_<IP_ADDRESS> RColorBrewer_1.1-2 ggridges_0.5.1
[70] Rcpp_1.0.7 plyr_1.8.6 progress_1.2.2
[73] prettyunits_1.1.1 rpart_4.1-15 deldir_0.1-23
[76] pbapply_1.5-0 ashr_2.2-47 zoo_1.8-8
[79] haven_2.3.1 ggrepel_0.9.1 cluster_2.1.0
[82] fs_1.3.1 magrittr_2.0.1 data.table_1.14.2
[85] scattermore_0.7 SparseM_1.81 lmtest_0.9-37
[88] reprex_0.3.0 RANN_2.6.1 truncnorm_1.0-8
[91] SQUAREM_2021.1 whisker_0.3-2 fitdistrplus_1.0-14
[94] matrixStats_0.61.0 hms_1.1.1 patchwork_1.1.0
[97] lsei_1.2-0 mime_0.12 evaluate_0.14
[100] xtable_1.8-4 readxl_1.3.1 gridExtra_2.3
[103] compiler_3.6.1 KernSmooth_2.23-15 crayon_1.4.2
[106] htmltools_0.5.2 mgcv_1.8-28 later_1.3.0
[109] RcppParallel_5.1.4 lubridate_1.8.0 DBI_1.1.0
[112] dbplyr_1.4.2 MASS_7.3-52 rappdirs_0.3.1
[115] cli_3.1.0 quadprog_1.5-8 gdata_2.18.0
[118] parallel_3.6.1 gower_0.2.2 igraph_<IP_ADDRESS>
[121] pkgconfig_2.0.3 plotly_4.10.0 recipes_0.1.17
[124] xml2_1.3.2 foreach_1.5.1 prodlim_2019.11.13
[127] rvest_0.3.6 digest_0.6.29 sctransform_0.3.2
[130] RcppAnnoy_0.0.19 spatstat.data_1.7-0 rmarkdown_1.13
[133] cellranger_1.1.0 leiden_0.3.1 uwot_0.1.11
[136] shiny_1.3.2 gtools_3.8.1 quantreg_5.86
[139] lifecycle_1.0.1 nlme_3.1-140 jsonlite_1.7.2
[142] viridisLite_0.4.0 fansi_0.5.0 pillar_1.6.4
[145] lattice_0.20-41 fastmap_1.1.0 httr_1.4.2
[148] survival_2.44-1.1 glue_1.5.1 spatstat_1.64-1
[151] png_0.1-7 iterators_1.0.13 class_7.3-15
[154] stringi_1.7.6 caTools_<IP_ADDRESS> irlba_2.3.3
[157] future.apply_1.8.1


General Preferences
When you launch Alfred's preferences to the General tab, you'll see a few options you can customise to your needs. This is also where you can grant Alfred permissions so that he can be most helpful to you.
Startup
Check the "Launch Alfred at login" if you'd like Alfred to launch automatically when you log in to your Mac. It's the best way to ensure Alfred's always ready to serve you when you need him!
Alfred Hotkey
By default, the default hotkey to show Alfred's search box is ⌥ Space, but you can change this to another key combination you find more memorable.
If you'd like to use Cmd + Space, take a look at this troubleshooting page on using Cmd + Space.

If your country isn't specified, or you'd prefer non-localised results, choose the "International" location.
Permissions
Alfred requires certain permissions in order to perform certain actions on your Mac, like showing your Contacts and bookmarks in search results.
Click the buttons under "Request Permissions..." to grant Alfred access in your Mac's System Preferences. Find out more about Permissions here.
Keeping in touch
At the bottom of the preferences, you'll find ways to keep in touch with us. Sign up to our newsletter, or say hi on Twitter.
What's next?
Now that you've set up the basics, jump into our First 5 Minutes with Alfred guide to discover what Alfred has to offer.
Alternatively, browser Alfred's Features preferences and click the question mark icon anywhere to go to the relevant documentation for the feature. Have fun!


Change tiles clicking in the buttons below:
OpenStreetMap Tiles Standard WMS Tiles Blue WMS Tiles
Current TileLayer Url:


The lookup component shares many properties with the autocomplete component. For more examples of those properties, see the autocomplete component demos.
The lookup component is effective for scenarios where users know what they are looking for and can select multiple items. For guidance on which selection inputs to use in other scenarios, see the form design guidelines.


{% autoescape true %}
Welcome to Woodo! If you find us, you must be a interesting and weird person.
So explore whatever you want, and enjoy!
{{ post.content | truncate(66) }}
This is our first post This is our first postThis is our first postThis is our first postThis is our first post


BCLBeaconCtrlDelegate Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | BCLBeaconCtrlDelegate.h |
Methods
– willPerformAction:
required method
Called just before a beacon or zone action is performed
- (void)willPerformAction:(BCLAction *)action
Parameters
action |
An action that will be called |
|---|
Declared In
BCLBeaconCtrlDelegate.h
– didPerformAction:
required method
Called just after a beacon or zone action was performed
- (void)didPerformAction:(BCLAction *)action
Parameters
action |
An action that was called |
|---|
Declared In
BCLBeaconCtrlDelegate.h
– shouldAutomaticallyPerformAction:
required method
Called to ask a delegate if a BeaconCtrl action should be handled automatically by the SDK
- (BOOL)shouldAutomaticallyPerformAction:(BCLAction *)action
Parameters
action |
An action that would normally be handled by default by the SDK |
|---|
Return Value
YES, if the action should be handled automatically
Discussion
Some BeaconCtrl actions, e.g. URL actions, are handled automatically by the SDK by default (in case of URL actions a modal web view is shown). You can use this method to prevent this default behavior.
Declared In
BCLBeaconCtrlDelegate.h
– didChangeObservedBeacons:
required method
Called each time a set of beacons currently monitored by the SDK has changed
- (void)didChangeObservedBeacons:(NSSet *)newObservedBeacons
Parameters
newObservedBeacons |
A set of new monitored beacons. |
|---|
Discussion
The SDK monitors a limited set of beacons at any given time and changes this set basing on the device’s position and beacons currently in range. This way, the SDK gets through the iOS limitation for 20 monitored beacons at a time.
Declared In
BCLBeaconCtrlDelegate.h
– closestObservedBeaconDidChange:
required method
Called each time the closest beacon has changed.
- (void)closestObservedBeaconDidChange:(BCLBeacon *)closestBeacon
Parameters
closestBeacon |
A beacons that is currently the closest to the device |
|---|
Declared In
BCLBeaconCtrlDelegate.h
– currentZoneDidChange:
required method
Called each time the current zone has changed
- (void)currentZoneDidChange:(BCLZone *)currentZone
Parameters
currentZone |
The beacon zone in which the device is at a given time. It’s calculated by looking at beacons in range and their estimated distances from the device |
|---|
Declared In
BCLBeaconCtrlDelegate.h


Fry's Todos
This is the page where I implemented server side filtering by todo owner (Fry in particular). That leaves fields for searching by status and body. Like the todos page, I wanted more tests and CSS highlighting of words via search fields, but time/partner constraints got the best of me. Also, the radio button status filtering does not work. I think I got close to figuring that out but I need to sleep and work on any class but this one. To filter by status, the "Filter by status" field must have the values "complete" or "incomplete" typed out (case insensitive).
Unlike the todos page, this is what an average user would see.
CompleteIncomplete
{{item.name}}
{{todo._id}} ({{todo.owner}})Owner: {{todo.owner}}Status: {{todo.status}}Body: {{todo.body}}Category: {{todo.category}}
There was a problem loading the todos. Possibly the server is down or perhaps there are network
issues.
Please wait a bit and try again or start the server.


- This is first item of the list
- This is second item of the list
- This is third item of the list
-
- This is first item of the list
- This is second item of the list
- This is third item of the list
Table
| Name |
Employee |
Type |
| Rohan |
939 |
programmer |
| Shubham |
3424 |
HTML |


I get really, really excited about comets. I remember seeing comet Hale-Bopp back in 1997 hanging in the sky and being amazed that those wanderers exist and that we have a chance to see them from time to time. This year, we’re lucky to have another comet swing by the Earth and though faint, may become a pretty good sightseeing opportunity as we move through January.
South Bend isn’t known for it’s clear winter skies, but last night I had a chance to go outside and do some comet hunting. C/2014, Q2 (also known as Lovejoy) has been below the equator until just recently. Additionally, it just brightened up enough to be seen in dark skies with the naked eye if you know where to look.
Head outside and look to the southeast. Find Orion in the sky, and then look below that for a slightly-lopsided box – that’s Lepus. Hover over the photo below to see a labelled image.
Lovejoy is moving higher in the sky over the next month, through Lepus and up next to Orion. The comet is still pretty faint, but it’s the small, greenish smudge in the photo and should increase in brightness as it moves closer to perihelion (nearest point to the sun) in late January. I don’t have a tracking mount, so my photos are all a little blurry, but I managed to get one that shows the comet’s nucleus and coma.
…and a little closer…
Why am I putting this on a blog about education and technology? A comet sparked my curiosity in space and is something that stands out very clearly even today. Our students live in a world of screens and media. We need to be the people in their lives who expose the bigger world at every opportunity. Not every student will think this is as amazing as I do, but that’s okay – we’re not there to make every student love what we do. If one student gets excited over something bigger than themselves, we’re accomplishing the mission of teaching.
Written on December 30th, 2014 by Brian Bennett Categorized in: All Science Teaching
Leave a comment


--- layout: page title: About header-img: "img/about-bg.png" ---
Registering the struggles and success of a new coder.


{% include "includes/devsite-top-logo-row-wrapper-wrapper.html" %}
{% include "includes/devsite-collapsible-section.html" %}
{% include "includes/devsite-nav-responsive.html" %}
{% include "includes/dogfood-banner.html" %}
{% include "includes/announcement-banner.html" %}
{% block content %}
{% endblock %}
{% include "includes/devsite-footer-promos.html" %}
{% include "includes/devsite-footer-linkboxes.html" %}
{% include "includes/devsite-footer-banner.html" %}
{% if isProd %}
{% include "includes/analytics.html" %}
{% include "includes/survey.html" %}
{% endif %}


2 killed when car slams into second floor of commercial building in Toms River
by Eyewitness NewsTOMS RIVER, New Jersey (WABC) -- Authorities say two people were killed Sunday morning when a car slammed into the second floor of a commercial building in New Jersey.
The crash happened at about 6:30 a.m. on Hooper Avenue in Toms River.
According to police, a red Porsche was traveling at a high rate of speed when it lost control, hit a center median and went airborne across lanes of traffic and into the second story of Exit Realty.
The identities of the two victims have not yet been released.
The structural integrity of the building is being determined. The building, which is across from Hooper Avenue Elementary School, was unoccupied at the time of the accident.
Hooper Avenue is closed from Indian Hill Road and College Drive.
Police and the Ocean County prosecutor's office are investigating.


#include <ssl.h>
Definition at line 818 of file ssl.h.
◆ ciphersuite
| int mbedtls_ssl_session::ciphersuite |
chosen ciphersuite
Definition at line 823 of file ssl.h.
◆ compression
| int mbedtls_ssl_session::compression |
chosen compression
Definition at line 824 of file ssl.h.
◆ encrypt_then_mac
| int mbedtls_ssl_session::encrypt_then_mac |
flag for EtM activation
Definition at line 849 of file ssl.h.
◆ id
| unsigned char mbedtls_ssl_session::id[32] |
session identifier
Definition at line 826 of file ssl.h.
◆ id_len
| size_t mbedtls_ssl_session::id_len |
session id length
Definition at line 825 of file ssl.h.
◆ master
| unsigned char mbedtls_ssl_session::master[48] |
the master secret
Definition at line 827 of file ssl.h.
◆ mfl_code
| unsigned char mbedtls_ssl_session::mfl_code |
MaxFragmentLength negotiated by peer
Definition at line 841 of file ssl.h.
◆ peer_cert
peer X.509 cert chain
Definition at line 830 of file ssl.h.
◆ start
starting time
Definition at line 821 of file ssl.h.
◆ ticket
| unsigned char* mbedtls_ssl_session::ticket |
RFC 5077 session ticket
Definition at line 835 of file ssl.h.
◆ ticket_len
| size_t mbedtls_ssl_session::ticket_len |
session ticket length
Definition at line 836 of file ssl.h.
◆ ticket_lifetime
| uint32_t mbedtls_ssl_session::ticket_lifetime |
ticket lifetime hint
Definition at line 837 of file ssl.h.
◆ trunc_hmac
| int mbedtls_ssl_session::trunc_hmac |
flag for truncated hmac activation
Definition at line 845 of file ssl.h.
◆ verify_result
| uint32_t mbedtls_ssl_session::verify_result |
verification result
Definition at line 832 of file ssl.h.
The documentation for this struct was generated from the following file:


Quader warns BNP of stern action against anarchy
by bdnews24.comAwami League leader Obaidul Quader has warned the BNP that the government will take strong measures if their movement for the release of jailed party chief Khaleda Zia devolves into chaos and anarchy.
He issued the warning at the conference of the Awami Matsyajibi League on Friday in the wake of a recent clash between BNP activists and police near the Supreme Court.
Addressing the situation surrounding Khaleda, Quader said, "We have maintained that she is in jail as per the decree of the courts. They should fight for her release through the legal channels and the government will not intervene."
"But they don't believe in the rule of law or the justice system. They are trying to pressurise the courts by resorting to vandalism. They attacked the police and turned the court premises into a battlefield."
The road transport minister urged the BNP to adopt a peaceful and democratic movement which the government could then deal with through the political process.
But Quader warned that the authorities will clamp down on any attempt to create chaos and disorder. "There will be an appropriate response to anarchy in the name of a movement."


Chirp
0.0.1
A modern low-level programming language.
Modern and low-level.
No external dependencies.
Simple syntax and easy to use.
GitHub
Documentation
Download


BIODIVERSITY INFORMATICS SHORT COURSE
AIMS SUMMER SCHOOL 2021
SUPPORTING DOCUMENTS AN OTHER RESOURCES
Description
Document/Link
QGIS-GEE Plugin User Guide
Google Earth Engine in QGIS
XXXX
XXXX
XXXX
XXXX
MENU
HOME
INTRUCTIONS
COURSE PROGRAMME
SUPPORTING DOCUMENTS
CONTACT US


Match Play. Double Match Point. How should Red play 41
pips Red=111 White=118
(CorrectAnswer= 0/0, ErrorScore= 0)
The second best play is 7/3*/2 leaving the blot where, if it is hit, White won’t be at the edge of Red’s prime unless he rolls 21 or 11.
However, Red will be 12 pips up after the roll and only 66 and 65 completely escape for White. On any other roll Red will still have shots next turn. The best play is simply 13/8. 7/3*/2 is an error but not a blunder.
However, Red will be 12 pips up after the roll and only 66 and 65 completely escape for White. On any other roll Red will still have shots next turn. The best play is simply 13/8. 7/3*/2 is an error but not a blunder.
1. Cubeful 2-ply 13/8 Eq.: +0.495 0.748 0.075 0.001 - 0.252 0.017 0.000 2. Cubeful 2-ply 7/3*/2 Eq.: +0.444 (-0.051) 0.722 0.140 0.003 - 0.278 0.069 0.001 3. Cubeful 2-ply 7/2 Eq.: +0.427 (-0.068) 0.713 0.076 0.001 - 0.287 0.023 0.000 4. Cubeful 2-ply 7/6 5/1 Eq.: +0.395 (-0.100) 0.698 0.069 0.001 - 0.302 0.023 0.000 5. Cubeful 2-ply 8/7 8/4 Eq.: +0.376 (-0.119) 0.688 0.056 0.000 - 0.312 0.015 0.000


|
CotC C# SDK for Unity
v<IP_ADDRESS>
Making social games is easy !
|
API functions related to the achievements.
Public Member Functions | |
| GamerAchievements | Domain (string domain) |
Changes the domain affected by the next operations. You should typically use it this way: gamer.Achievements.Domain("private").List(...); More... | |
| Promise< AchievementDefinition > | AssociateData (string achName, Bundle data) |
| Allows to store arbitrary data for a given achievement and the current player (appears in the 'gamerData' node of achievements). More... | |
| Promise< Dictionary< string, AchievementDefinition > > | List () |
| Fetches information about the status of the achievements configured for this game. More... | |
| Promise<AchievementDefinition> CotcSdk.GamerAchievements.AssociateData | ( | string | achName, |
| Bundle | data | ||
| ) |
Allows to store arbitrary data for a given achievement and the current player (appears in the 'gamerData' node of achievements).
| achName | Name of the achievement to update. |
| data | Data to associate with the achievement, merged with the current data (that is, existing keys are not affected) |
| GamerAchievements CotcSdk.GamerAchievements.Domain | ( | string | domain | ) |
Changes the domain affected by the next operations. You should typically use it this way: gamer.Achievements.Domain("private").List(...);
| domain | Domain on which to scope the next operations. |
| Promise<Dictionary<string, AchievementDefinition> > CotcSdk.GamerAchievements.List | ( | ) |
Fetches information about the status of the achievements configured for this game.


Lesson Plan Title: Phishing with XSS
Concept / Topic To Teach:
It is always a good practice to validate all input on the server side. XSS can occur when unvalidated user input is used in an HTTP response. With the help of XSS you can do a Phishing Attack and add content to a page which looks official. It is very hard for a victim to determinate that the content is malicious.General Goal(s):
The user should be able to add a form asking for username and password. On submit the input should be sent to http://localhost/WebGoat/catcher?PROPERTY=yes &user=catchedUserName&password=catchedPasswordName


Username
Username
Username
Username
Enter your Message Here.. We will get back u soon
HTML 5 Input Field Attributes
Username
Username
Username
Name
Username


Notifications Testing
Each of these options below will show you what it will look like for a notification banner to appear there. Try them all and let me know what you think.
Each of these options below will show you what it will look like for a notification banner to appear there. Try them all and let me know what you think.


2
1 The Lord has completely covered the daughter of Zion with a dark cloud in his anger!
He has cast down the beauty of Israel from heaven to the earth;
he has ignored his footstool on the day of his anger.
2 The Lord has swallowed up and has had no compassion on all the towns of Jacob.
He has thrown down the fortified cities of the daughter of Judah in his wrath;
he has struck them down to the ground in dishonor and disgraced the kingdom and her princes.
3 With fierce anger he has cut off all the strength of Israel.
He has drawn back his right hand from before the enemy.

He has stood in battle position as an adversary with his hand ready to shoot.
He has slaughtered all the people most precious in sight.
In the tent of the daughter of Zion he has poured out his wrath like fire.
5 The Lord has become like an enemy. He has swallowed up Israel.
He has swallowed up all her palaces; he has destroyed her strongholds.
He has increased mourning and lamentation within the daughter of Judah.
6 He has attacked his tabernacle like a garden hut. He has destroyed the place of the solemn assembly.
Yahweh has caused both solemn assembly and Sabbath to be forgotten in Zion,
for he has despised both king and priest in the indignation of his anger.
7 The Lord has rejected his altar; he has spurned his sanctuary.
He has given over the walls of her palaces into the hand of the enemy.
They have made a sound of victory in the house of Yahweh, as on the day of a solemn assembly.
8 Yahweh has deliberately decided to destroy the city wall of the daughter of Zion.
He has stretched out the measuring line and has not withheld his hand from destroying the wall.
And he has made the ramparts lament and the walls become feeble.
9 Her gates have sunk into the ground; he has destroyed and broken her gate bars.
Her king and her princes are among the Gentiles, where there is no law of Moses.
Even her prophets find no vision from Yahweh.
10 The elders of the daughter of Zion sit on the ground and silently grieve.
They have sprinkled dust on their heads; they dress in sackcloth.
The virgins of Jerusalem lower their heads to the ground.
11 My tears have ended; my eyes are red; my innermost parts are in turmoil.
My liver has been poured out to the ground because of the crushing of the daughter of my people,
because the young children and the nursing babies are helplessly weak in the streets of the villages.
12 They say to their mothers, "Where is grain and wine?"
fainting like wounded men in the streets of the city,
their lives being poured out onto their mothers' laps.
13 What can I say regarding you, daughter of Jerusalem?
What will I compare to you to comfort you, virgin daughter of Zion?
Your collapse is as great as the sea. Who can heal you?
14 Your prophets have seen deceitful and foolish visions for you.
They have not revealed your iniquity to restore your fortunes,
but have perceived deceitful oracles and temptations for you.
15 All those who pass along the road clap their hands at you.
They hiss and shake their heads against the daughter of Jerusalem and say,
"Is this the city that they called 'The Perfection of Beauty,' 'The Joy for All of Earth?'"
16 All your enemies open their mouths wide and mock you.
They whistle and grind their teeth; they say, "We have swallowed her!
Certainly this is the day for which we waited! We have found it! We have seen it!"
17 Yahweh has done what he decided. He has fulfilled his word that he proclaimed long ago.
He has torn down; he has not shown compassion,
for he has allowed the enemy to rejoice over you; he has lifted up the strength of your enemies.
18 Their heart shouts to the Lord,
"Walls of the daughter of Zion, let tears flow down like a river day and night.
Give yourself no reprieve.

20 Look, Yahweh, and regard those whom you have dealt with so severely.

Should the priest and the prophet be slaughtered in the sanctuary of the Lord?
21 Both the young and the old lie on the ground in the streets.
My virgins and my vigorous men have fallen by the sword.
You have slaughtered them in the day of your wrath; you have killed ruthlessly and shown no compassion.
22 You have summoned my terrors from all around as in the day of the solemn assembly;
no one escaped, and there was no survivor on the day of Yahweh's wrath.
Those whom I nurtured and raised, my enemy has brought to an end.


ReflectionExtensions Methods
The ReflectionExtensions type exposes the following members.
|
Xamarin Forms Labs is a open source project that aims to provide a powerful and cross platform set of controls and helpers tailored to work with Xamarin Forms.
|
The ReflectionExtensions type exposes the following members.


Justify
Teacher Dashboard
Privacy
Log out
Filter requests
Unanswered
All
Sort by
Request date
Name
#
Name
Start date
(period)
End date
(period)
Event
Requested Approval
Note
Status


Bottas aware of Red Bull Racing's threat in straight lines
by Tom MasonValtteri Bottas has commented on the improvement of the Aston Martin Red Bull Racing car in recent times, knowing that the car is now faster than the Mercedes in a straight line.
"We knew that in Brazil they would have been strong, but I think they were more than we expected, especially on the straight," Bottas told RaceFans.
"Now they have not only a good car in the corners, but also on the obverse they are faster than us.
"If we watch the whole season, it is clear that Red Bull has made solid progress. So I expect them to continue in this direction also in 2020 and, consequently, we will have a much stricter battle."
Despite this, it's still Mercedes who are well clear at the top of the standings, and champions, while Red Bull Racing back in third-place.


Include Social Security?
All fields must be numbers
You will have at the age of retirement
Your estimated income at retirement will be
Your required income after retirement will be
Your estimated initial social security income would be


Edit me
Oftentimes, a user expects a sensor value to fall within a particular range. If a reading is outside the accepted limits, the user may want to determine what caused the anomaly and/or take action to reduce the impact. For instance, consider the following scenario.
Suppose a corn grower in the Midwestern United States would like to monitor the average temperature in his corn field using a sensor to improve his crop yield. The optimal temperatures for corn growth during daylight hours range between 77°F and 91°F. When the grower is alerted of a temperature value that is not in the optimal range, he may want to assess what can be done to mitigate the effect.
In this instance, we can use a filter to detect out-of-range temperature values.
Setting up the application
We assume that the environment has been set up following the steps outlined in the Getting started guide. Let's begin by creating a DirectProvider
and Topology
. We also define the optimal temperature range.
import static quarks.function.Functions.identity;
import java.util.concurrent.TimeUnit;
import quarks.analytics.sensors.Filters;
import quarks.analytics.sensors.Range;
import quarks.analytics.sensors.Ranges;
import quarks.providers.direct.DirectProvider;
import quarks.samples.utils.sensor.SimulatedTemperatureSensor;
import quarks.topology.TStream;
import quarks.topology.Topology;
public class DetectValueOutOfRange {
/**
* Optimal temperature range (in Fahrenheit)
*/
static double OPTIMAL_TEMP_LOW = 77.0;
static double OPTIMAL_TEMP_HIGH = 91.0;
static Range<Double> optimalTempRange = Ranges.closed(OPTIMAL_TEMP_LOW, OPTIMAL_TEMP_HIGH);
public static void main(String[] args) throws Exception {
DirectProvider dp = new DirectProvider();
Topology top = dp.newTopology("TemperatureSensor");
// The rest of the code pieces belong here
}
}
Generating temperature sensor readings
The next step is to simulate a stream of temperature readings using SimulatedTemperatureSensor
. By default, the sensor sets the initial temperature to 80°F and ensures that new readings are between 28°F and 112°F. In our main()
, we use the poll()
method to generate a flow of tuples, where a new tuple (temperature reading) arrives every second.
// Generate a stream of temperature sensor readings
SimulatedTemperatureSensor tempSensor = new SimulatedTemperatureSensor();
TStream<Double> temp = top.poll(tempSensor, 1, TimeUnit.SECONDS);
Simple filtering
If the corn grower is interested in determining when the temperature is strictly out of the optimal range of 77°F and 91°F, a simple filter can be used. The filter
method can be applied to TStream
objects, where a filter predicate determines which tuples to keep for further processing. For its method declaration, refer to the Javadoc.
In this case, we want to keep temperatures below the lower range value or above the upper range value. This is expressed in the filter predicate, which follows Java's syntax for lambda expressions. Then, we terminate the stream (using sink
) by printing out the warning to standard out. Note that \u00b0
is the Unicode encoding for the degree (°) symbol.
TStream<Double> simpleFiltered = temp.filter(tuple ->
tuple < OPTIMAL_TEMP_LOW || tuple > OPTIMAL_TEMP_HIGH);
simpleFiltered.sink(tuple -> System.out.println("Temperature is out of range! "
+ "It is " + tuple + "\u00b0F!"));
Deadband filter
Alternatively, a deadband filter can be used to glean more information about temperature changes, such as extracting the in-range temperature immediately after a reported out-of-range temperature. For example, large temperature fluctuations could be investigated more thoroughly.
The deadband
filter is a part of the quarks.analytics
package focused on handling sensor data. Let's look more closely at the method declaration below.
deadband(TStream<T> stream, Function<T,V> value, Predicate<V> inBand)
The first parameter is the stream to the filtered, which is temp
in our scenario. The second parameter is the value to examine. Here, we use the identity()
method to return a tuple on the stream. The last parameter is the predicate that defines the optimal range, that is, between 77°F and 91°F. it is important to note that this differs from the TStream
version of filter
in which one must explicitly specify the values that are out of range. The code snippet below demonstrates how the method call is pieced together. The deadbandFiltered
stream contains temperature readings that follow the rules as described in the Javadoc:
- the value is outside of the optimal range (deadband)
- the first value inside the optimal range after a period being outside it
- the first tuple
As with the simple filter, the stream is terminated by printing out the warnings.
TStream<Double> deadbandFiltered = Filters.deadband(temp,
identity(), tuple -> tuple >= OPTIMAL_TEMP_LOW && tuple <= OPTIMAL_TEMP_HIGH);
deadbandFiltered.sink(tuple -> System.out.println("Temperature may not be "
+ "optimal! It is " + tuple + "\u00b0F!"));
We end our application by submitting the Topology
.
Observing the output
To see what the temperatures look like, we can print the stream to standard out.
When the final application is run, the output looks something like the following:
Temperature may not be optimal! It is 79.1°F!
79.1
79.4
79.0
78.8
78.0
78.3
77.4
Temperature is out of range! It is 76.5°F!
Temperature may not be optimal! It is 76.5°F!
76.5
Temperature may not be optimal! It is 77.5°F!
77.5
77.1
...
Note that the deadband filter outputs a warning message for the very first temperature reading of 79.1°F. When the temperature falls to 76.5°F (which is outside the optimal range), both the simple filter and deadband filter print out a warning message. However, when the temperature returns to normal at 77.5°F, only the deadband filter prints out a message as it is the first value inside the optimal range after a period of being outside it.
Range values
Filtering against a range of values is such a common analytic activity that the quarks.analytics.sensors.Range
class is provided to assist with that.
Using a Range
can simplify and clarify your application code and lessen mistakes that may occur when writing expressions to deal with ranges. Though not covered in this recipe, Range
s offer additional conveniences for creating applications with external range specifications and adaptable filters.
In the above examples, a single Range
can be used in place of the two different expressions for the same logical range:
static double OPTIMAL_TEMP_LOW = 77.0;
static double OPTIMAL_TEMP_HIGH = 91.0;
static Range<Double> optimalTempRange = Ranges.closed(OPTIMAL_TEMP_LOW, OPTIMAL_TEMP_HIGH);
Using optimalTempRange
in the Simple filter example code:
TStream<Double> simpleFiltered = temp.filter(tuple ->
!optimalTempRange.contains(tuple));
Using optimalTempRange
in the Deadband filter example code:
TStream<Double> deadbandFiltered = Filters.deadband(temp,
identity(), optimalTempRange);
The final application
import static quarks.function.Functions.identity;
import java.util.concurrent.TimeUnit;
import quarks.analytics.sensors.Filters;
import quarks.analytics.sensors.Range;
import quarks.analytics.sensors.Ranges;
import quarks.providers.direct.DirectProvider;
import quarks.samples.utils.sensor.SimulatedTemperatureSensor;
import quarks.topology.TStream;
import quarks.topology.Topology;
/**
* Detect a sensor value out of expected range.
*/
public class DetectValueOutOfRange {
/**
* Optimal temperature range (in Fahrenheit)
*/
static double OPTIMAL_TEMP_LOW = 77.0;
static double OPTIMAL_TEMP_HIGH = 91.0;
static Range<Double> optimalTempRange = Ranges.closed(OPTIMAL_TEMP_LOW, OPTIMAL_TEMP_HIGH);
/**
* Polls a simulated temperature sensor to periodically obtain
* temperature readings (in Fahrenheit). Use a simple filter
* and a deadband filter to determine when the temperature
* is out of the optimal range.
*/
public static void main(String[] args) throws Exception {
DirectProvider dp = new DirectProvider();
Topology top = dp.newTopology("TemperatureSensor");
// Generate a stream of temperature sensor readings
SimulatedTemperatureSensor tempSensor = new SimulatedTemperatureSensor();
TStream<Double> temp = top.poll(tempSensor, 1, TimeUnit.SECONDS);
// Simple filter: Perform analytics on sensor readings to
// detect when the temperature is completely out of the
// optimal range and generate warnings
TStream<Double> simpleFiltered = temp.filter(tuple ->
!optimalTempRange.contains(tuple));
simpleFiltered.sink(tuple -> System.out.println("Temperature is out of range! "
+ "It is " + tuple + "\u00b0F!"));
// Deadband filter: Perform analytics on sensor readings to
// output the first temperature, and to generate warnings
// when the temperature is out of the optimal range and
// when it returns to normal
TStream<Double> deadbandFiltered = Filters.deadband(temp,
identity(), optimalTempRange);
deadbandFiltered.sink(tuple -> System.out.println("Temperature may not be "
+ "optimal! It is " + tuple + "\u00b0F!"));
// See what the temperatures look like
temp.print();
dp.submit(top);
}
}


I just finished the 6th week of Stanford Machine Learning course. It’s still largely about neural network.
How to choose the number of layers in neural network?
The best default strategy is to choose to use one hidden layer. The more hidden layers you have, the more prone you’ll have high variance problems. But having too few layers makes the neural network prone to high bias problem. A practical strategy is to increment the number of layers - it’s like adding polynomial features or decreasing lambda λ in normal cost function to fix high bias problem - and get the Θ for the neural network. Then use the Θ to calculate the cost function of cross validation. Choose the Θ ( also number of layers) that has the minimum cost function of cross validation.
What’s Learning Curve?
Learning Curve shows whether you have a high variance or high bias problem. The horizontal axis represents m : the training set size; the vertical axis represents the errors (training error and cross validation error).
High Bias
When m increases, training error and cross validation error converge and keeps high.
High Variance
When m increases, training error and cross validation error also converge, but there’s a gap between training error and cross validation error and adding new training data helps.
Good practice when plotting learning curves with small training sets: It’s often helpful to average across multiple sets of randomly selected examples to determine the training error and cross validation error. For example, for m = 10, randomly choose 10 examples from training set and 10 examples from cross validation set, the calculate the training error and cross validation error. Do this for 50 times to get the average training error and cross validation error for m = 10.
What’s Training Set, Cross Validation Set, and Test Set?
A good (supervised learning) practice is to divide the data into 3 groups:
- Training Set : Learn the model parameters θ
- Cross Validation Set : Select the regularzation λ parameters to find tradeoff between high bias and high variance
- Test Set : Evaluate the “final” model
Recommended approach to develop a learning algorithm
- Start with a very simple, quick-and-dirty algorithm that you can implement quickly. Implement it and test it against your cross validation data.
- Plot learning curves to decide if more data, more features are likely to help.
- Error analysis: Manually examine the examples (in cross validation data) that your algorithm made errors on. See if you can spot any systematic trend in what types of examples it is making errors on. For example, for a mail spam classification problem, you could manually examine (1) What type of email it is - Pharma, Replica, Stealing Password? If most of the cross validation error is related to emails of Stealing Password, then it’s worth spending some time to see if you can come up with better features to categorise Stealing Password spam more correctly. (2) What features you think would have helped the algorithm classify them correctly. Finally, ALWAYS TEST your assumption again cross validation data.
What are Precision and Recall, and when are they useful?
When solving classification problems, such as Cancer classification, we might be proud to see that we got 1% error on test set (99% correct diagnoses). But wait, only 0.50& of patients have cancer. If we had a “cheat” version of hypothesis predicting all patients don’t have cancer, we would have 99.5% correct diagnoses. But by using “cheat” version, we are not actually improving our predicting algorithm, even though we have a better accuracy. This situation happens when we have skewed classes
Precision and Recall come to rescue:
- Precision : Of all patients where we predicted True (having cancer), what fraction actually has cancer. In the figure below, the denominator is the first row (all predicted True).
- Recall : Of all patients having cancer, what fraction did we correctly predict as having cancer? In the figure below, the denominator is the first column (all actual True)..
The “cheat” version would have 0 as recall, as it predicts all patients not having cancer: recall = zero/non-zero = 0. So we would find out the “cheat” version is not improving our algorithm.
Trading off precision and recall
When using logistic regression, we set a threshold for hθ(x). If threshold is 0.5, we predict 1 if hθ(x) > 0.5, and we predict 0 if hθ(x) < 0.5.
Suppose we want to predict y = 1 (cancer) only if very confident, we do not want to scare patients. We would set the threshold high, which results in higher precision and lower recall.
But if we want to be more preservative and avoid missing too many cases of cancer, we would set the threshold low, which results in higher recall and lower precision.
When does using a large training set makes sense?
It only makes sense when we have a low bias algorithm - algorithm with (1) many useful features and (2) many parameters θ. In this case, increasing training set size will help fix overfitting problem and training error will be closer to cross validation error. If features x do not contain enough information to predict y accurately (such as predicting a house’s price from only its size), even if we are using a neural network with a large number of hidden units, it won’t work. We can ask ourself whether features x contain enough information by imagining if we have a human expert look at the features x, can he/she confidently predict the value of y. Simply looking at a house’s size, even a realtor cannot confidently predict the price. He/She has to have more informations: number of rooms, which part of city, etc.
Questions
-
When features (e.p. polynomial of degree 8: x=40, x8 = too big) are badly scaled, we need to normalise the features. How do we do feature normalisation? What is mu, sigma? Do some further readings and earlier lectures reviews.
Answer: For each xj (j from 1 to m) we find the mean (mu) for xj over the training set, so that the mean of xj - mu is zero. Sigma represents the standard deviation of xj across the training set, it corrects bad scaled training set. Applying the following: (xj - mu)/sigma to X in order to normalise the features.


NetData Custom Dashboard
This is a template for building custom dashboards. To build a dashboard you just do this:
<!DOCTYPE html>

<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">

Sparkline Charts
Sparkline charts support 'NULL' values, so the charts can indicate that values are missing.
Sparkline charts stretch the values to show the variations between values in more detail.
They also have mouse-hover support.
Sparklines are fantastic. You can inline charts in text. For example this
is my current cpu usage (last 30 seconds),
while this
is the bandwidth my netdata server is currently transmitting (last minute)
and this
is the requests/sec it serves (last 3 minutes).
Peity Charts
Peity charts do not support 'NULL' values, so the charts cannot indicate that values are missing.
Peity charts cannot have multiple dimensions on the charts - so netdata will use 'min2max' to show
the total of all dimensions.
Dygraph Charts
The fastest charting engine that can chart complete charts (not just sparklines).
The charts are zoomable (drag their contents to pan, shift with mouse wheel to zoom-in or zoom-out, double click to reset it).
Netdata magic! Realtime charts on your web page!
Sparklines using dygraphs
are also possible! This
is an area chart, while this
is a stacked area chart!
EasyPieChart
Gauge.js
Google Charts
NetData was originaly developed with Google Charts.
NetData is a complete Google Visualization API provider.
Morris Charts
Unfortunatelly, Morris Charts are very slow. Here we force them to lower their detail to get acceptable results.
C3 Charts
C3 charts are not usable in large scale. They suffer from the following issues:
- extreme use of transitions (implemented with D3 instead of CSS, meaning they are javascript rendered) that cannot be disabled - even opacity is hardcoded in the javascript library
- rendering is done with
SVG
instead of canvas
, so they use DOM elements for every point, becomimg useless if more than 500 points are drawn
- lack of a
raw
data format, so every time a chart is updated, data convertion in javascript is required
- lack of
stacked
charts support
So, to avoid flashing the charts, we destroy and re-create the charts on each update. Also, since they manipulate the data with javascript we were forced to lower the detail they render to get acceptable speeds.


Data source: CDC Provisional and final COVID-19 deaths by county and race.
View data for a county by clicking on one of the colored-in / non-grey counties on the map, or by using the "Search for a County" text input. You may zoom and pan on the map using your mouse or trackpad.


Posted on June 19, 2009.
hg-prompt adds an ‘hg prompt’ command to Mercurial for viewing repository information. It’s designed to be used in a shell prompt. You can grab the code from the repository on BitBucket.
Check out the documentation to find out how to use it.
Here’s what it looks like:
Questions, Comments, Suggestions
The code was kind of thrown together in one night after I got tired of chaining three or four hg runs together to get what I wanted. I’m sure it’s not perfect, so if you’ve got a way to improve it please add an issue and let me know.


Triplex Apartment, Beijing
location:
design team:
area:
project year:
client:
challenge structure:
challenge type:
award:
Beijing, China
Dušica Oparnica, Marko Korošec, Vanja Paunović, Ivana Radović, Hristina Tošić
482 sqm
2015
Private client
Public / Limited (2 phase)
Interior design
First place, Second prize
1ST PLACE (second prize) for Remorker Architects - MORPH Asia competition - interior design of triplex apartment in Beijing, China. We are very proud of our team. Congratulations to all the other award winning entries.


Well then you've come to the right place. I hated getting three whole lost & found item emails a week! I was tired of not having a project! So I made this little web app where anyone can submit a lost or found item and not email the whole school about it!
This simple web app was written by Ezra Newman.
Love it? Hate it? Email me at: 21enewman [at] athenian [dot] org


Unsettled API - Events used by the proactor.
Unsettled API - Events used by the proactor.
pn_proactor_wait() returns a subset of the event types defined by pn_event_type_t.
The PN_REACTOR_*, PN_SELECTABLE_*, and PN_*_FINAL events are not returned.
| Enumeration | Brief description, see pn_event_type_t for more |
|---|---|
| PN_CONNECTION_INIT | The connection has been created. |
| PN_CONNECTION_BOUND | The connection has been bound to a transport. |
| PN_TIMER_TASK | A timer event has occurred. |
| PN_CONNECTION_INIT | The connection has been created. |
| PN_CONNECTION_BOUND | The connection has been bound to a transport. |
| PN_CONNECTION_UNBOUND | The connection has been unbound from its transport. |
| PN_CONNECTION_LOCAL_OPEN | The local connection endpoint has been closed. |
| PN_CONNECTION_REMOTE_OPEN | The remote endpoint has opened the connection. |
| PN_CONNECTION_LOCAL_CLOSE | The local connection endpoint has been closed. |
| PN_CONNECTION_REMOTE_CLOSE | The remote endpoint has closed the connection. |
| PN_SESSION_INIT | The session has been created. |
| PN_SESSION_LOCAL_OPEN | The local session endpoint has been opened. |
| PN_SESSION_REMOTE_OPEN | The remote endpoint has opened the session. |
| PN_SESSION_LOCAL_CLOSE | The local session endpoint has been closed. |
| PN_SESSION_REMOTE_CLOSE | The remote endpoint has closed the session. |
| PN_LINK_INIT | The link has been created. |
| PN_LINK_LOCAL_OPEN | The local link endpoint has been opened. |
| PN_LINK_REMOTE_OPEN | The remote endpoint has opened the link. |
| PN_LINK_LOCAL_CLOSE | The local link endpoint has been closed. |
| PN_LINK_REMOTE_CLOSE | The remote endpoint has closed the link. |
| PN_LINK_LOCAL_DETACH | The local link endpoint has been detached. |
| PN_LINK_REMOTE_DETACH | The remote endpoint has detached the link. |
| PN_LINK_FLOW | The flow control state for a link has changed. |
| PN_DELIVERY | A delivery has been created or updated. |
| PN_TRANSPORT | The transport has new data to read and/or write. |
| PN_TRANSPORT_AUTHENTICATED | The transport has authenticated. |
| PN_TRANSPORT_ERROR | Indicates that a transport error has occurred. |
| PN_TRANSPORT_HEAD_CLOSED | Indicates that the "head" or writing end of the transport has been closed. |
| PN_TRANSPORT_TAIL_CLOSED | Indicates that the tail of the transport has been closed. |
| PN_TRANSPORT_CLOSED | The final event for a proactor connection, the transport is closed. |
| PN_LISTENER_OPEN | The listener is listening. |
| PN_LISTENER_ACCEPT | Indicates the listener has an incoming connection, call pn_listener_accept2() to accept it. |
| PN_LISTENER_CLOSE | Indicates the listener has closed. |
| PN_PROACTOR_INTERRUPT | Indicates pn_proactor_interrupt() was called to interrupt a proactor thread. |
| PN_PROACTOR_TIMEOUT | Timeout set by pn_proactor_set_timeout() time limit expired. |
| PN_PROACTOR_INACTIVE | The proactor has become inactive: all listeners and connections were closed and the timeout (if set) expired or was cancelled. |
| PN_CONNECTION_WAKE | pn_connection_wake() was called. |
| PN_RAW_CONNECTION_CONNECTED | The raw connection connected. |
| PN_RAW_CONNECTION_CLOSED_READ | The remote end of the raw connection closed the connection so that we can no longer read. |
| PN_RAW_CONNECTION_CLOSED_WRITE | The remote end of the raw connection closed the connection so that we can no longer write. |
| PN_RAW_CONNECTION_DISCONNECTED | The raw connection is disconnected. |
| PN_RAW_CONNECTION_NEED_READ_BUFFERS | The raw connection might need more read buffers. |
| PN_RAW_CONNECTION_NEED_WRITE_BUFFERS | The raw connection might need more write buffers. |
| PN_RAW_CONNECTION_READ | The raw connection read bytes: The bytes that were read are in one of the read buffers given to the raw connection. |
| PN_RAW_CONNECTION_WRITTEN | The raw connection has finished a write and the buffers that were used are no longer in use and can be recycled. |
| PN_RAW_CONNECTION_WAKE | The raw connection was woken by pn_raw_connection_wake. |


Two opposite alignment references are needed to identify unique fix points on the image.
Mark the reference by drag and dop with the mouse or with the coordinates and push "Update Reference".
You can switch between the two reference with "Select Reference".
Don't forget to save your changes with "Save to Config.ini"!.


We're thinking pizza...
Select size
Personal
Small
Medium
Party
Chicago-Style Deep Dish
Select toppings
Cheese
Pepperoni
Sausage
Ham
Mushroom
Spinach
Diced Tomato
Olive
Banana Pepper
Add to order
Place Order
Full Name
Address Line 1
Address Line 2
City
State
Zip Code
Submit Address and Place Order
Thank you for your purchase!
We are preparing your pizza
. Your total is $
.
Your pizza
will be delivered to
,
,
,
.
Your order:
Pizza


This example demonstrates one of the advantages of a document (or any noSQL database for that matter) - it does not need to validate a schema.
For example we have two versions of a feedback form which collects data from the user and appends it to the feedback collection of the demo database
If we did this in a relational database we would need to update the business logic and data layers as well. We only need to update the presentation layer here!
The disadvantage of this implementation is that all data logic such as validation and constraint checking must now be implemented in the business logic layer.


interface Solution<T>
Represents a solution to a problem.
The interface is left open so that when defining one's problem, it is straight-forward to define the format of the solution, as it will probably depend on the use case on what the solution should actually contain.
abstract val problem: String
The name of the problem this solution is for. |
class FullAdderExperimentSolution : Solution<Double>
Defines what a solution for this problem looks like. |
|
data class IrisSolution : Solution<Double> |
|
data class SimpleFunctionSolution : Solution<Double> |
|
open class SimpleFunctionSolution : Solution<Double>
A re-implementation of lgp.examples.SimpleFunctionSolution to showcase Java interoperability. |
|
data class SinPolySolution : Solution<Double> |


| Name | Contact No | Address |
|---|---|---|
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
| Uvin Weththasinghe |<PHONE_NUMBER> | 275/A, Dharmashoka Road, Kahanthota, Malabe. |
Maintain at least a 1-metre distance between yourself and others to reduce your risk of infection when they cough, sneeze or speak. Maintain an even greater distance between yourself and others when indoors. The further away, the better.
Make wearing a mask a normal part of being around other people. The appropriate use, storage and cleaning or disposal are essential to make masks as effective as possible.


Search by Name:
Search by Location:
New Restaurant
Select a price range :
$
$$
$$$
Type of food :
French
American
Mexican
Italian
Thai
Chinese
Add
Restaurant List
No Matches
{{item.name}}
Location : {{item.location}}
Price : {{item.price}}
Type of food : {{item.type}}
Edit
Delete
Type of food :
French
American
Mexican
Italian
Thai
Chinese
Save


Non-composition Data¶
This section addresses:
-
:USDA #9:
¶ Support input and storage of various non-composition data types (metabolomic profiles; DNA barcodes; taxonomic information; and chromatographic and spectral profiles).
Attention
This section applies to USDA prime data, not to USDA select data. For explanation of USDA prime and USDA select data, see Deposition Workflow
Table of Contents¶
Chromatographic and Spectral Profiles¶
Ontomatica deposition service integrates the following terms for use by investigators when depositing chromatographic and spectral profiles.
The Physico-chemical methods and properties ontology (FIX) has been parsed and each term below is referenceable. The HUPO Proteomics Mass Spectrometry ontology (MS) has not been parse. Instead, Ontomatica uses the Essepuntato Live OWL Documentation Service (LODE) to display the MS ontology. Be patient while the LODE service parses the MS ontology OWL file. Once displayed, each facet term code below is searchable.
Ontomatica deposition service will enable investigators to deposit data like this data from USDA:
- Fingerprint data for Glucoraphanin from Brassica oleracea, genotype Legacy
- Note that spectral data is presented for five different methods of analysis.
In the table below a Section is upper case bold and a Topic is upper case italic. The spectral term is lower case and associated with its facet term code.
| Spectral Profile Information | Ontology Term |
|---|---|
| Deposit Accession | EDAM:data_2091 |
| Deposit Title | dc:title |
| Analysis Date | Time |
| Deposit Creation, Last Modification Date | Time |
| Authors and Affiliations of Deposit | vCard |
| Creative Commons License of Re-use of Deposit | Creative Commons |
| Copyright of Deposit | Creative Commons |
| Reference to Spectral Data | CHEMINF_000302 |
| — | — |
| Chemical Compound Information | |
| Name of Chemical Compound Analyzed | CHEMINF_000140 |
| Category of Chemical Compound | ChEFS |
| Molecular Formula of Chemical Compound | CHEMINF_000037 |
| Monoisotopic Mass of Chemical Compound | CHEMINF_000218 |
| SMILES String | CHEMINF_000020 |
| IUPAC International Chemical Identifier (InChIKey) | CHEMINF_000059 |
| Biological Sample Information | |
| Identity of Biological Species | EDAM:data_1875 |
| — | — |
| Method of Analysis Information | |
| Instrument model | MS_1000031 |
| Ion analyzer type | |
| chromatography by separation mechanism | FIX_0000610 |
| capillary electrophoresis | FIX_0000836 |
| gas chromatography | FIX_0000098 |
| liquid chromatography | FIX_0000608 |
| Ionization methods | MS_1000008 |
| atmospheric pressure ionization mass spectrometry | FIX_0000908 |
| atmospheric pressure photoionization mass spectrometry | FIX_0000907 |
| electron ionization mass spectrometry | FIX_0000089 |
| electrospray ionization mass spectroscopy | FIX_0000079 |
| fast atom bombardment mass spectrometry | FIX_0000086 |
| matrix-assisted laser desorption/ionization mass spectrometry | FIX_0000081 |
| APCI | MS_1000070 |
| APPI | MS_1000382 |
| EI | MS_1000389 |
| ESI | MS_1000073 |
| B | MS_1000080 |
| IT | MS_1000264 |
| Q | MS_1000081 |
| TOF | MS_1000084 |
| Fourier transformation | |
| Fourier transform-ion cyclotron resonance mass spectrometry | FIX_0000083 |
| Ion trap analyzer | |
| ion trap mass spectrometry | FIX_0000917 |
| Data Type | |
| MSn spectrum | MS_1000580 |
| precursor ion spectrum | MS_1000341 |
| Polarity of Ion Detection | |
| positive thermal ionization mass spectrometry | FIX_0000914 |
| positive thermal ionization mass spectrometry | MS_1000030 |
| negative thermal ionization mass spectrometry | FIX_0000915 |
| negative thermal ionization mass spectrometry | MS_1000129 |
| Ion mode | MS_1000465 |
| Collision Energy for Dissociation | |
| collision energy | MS_1000045 |
| Name of Collision Gas | ChEFS |
| collision gas | MS_1000419 |
| Flow Rate of Desolvation Gas | |
| ion desolvation | MS_1000390 |
| flow rate array | MS_1000820 |
| Temperature of Desolvation Gas | |
| temperature array | MS_1000822 |
| Matrix Used in MALDI | |
| MALDI matrix application | MS_1000832 |
| Name of Reagent Gas | ChEFS |
| Retention Time on Chromatography | |
| Retention time | MS_1000016 |
| — | — |
| Mass Spectral Data Description | |
| m/z of Base Peak | |
| base peak | MS_1000210 |
| Molecular Formula of Derivative | PubChem |
| Mass of Derivative | PubChem |
| Type of Focused Ion | see footnote [1] |
| m/z of Precursor Ion in MSn spectrum | |
| msPrefix precursor recalculation | MS_1000781 |
| precursor m/z | MS_1000504 |
| Type of Precursor Ion in MSn | |
| Precursor type | MS_ 1000792 |
| Precursor type | see footnote [2] |
| — | — |
| Mass Spectral Peaks Information | |
| Data Processing Method of Peak Detection | |
| Data processing | MS_1000543 |
| Peak Detection | |
| selected ion detection | MS_1000091 |
| peak picking | MS_1000035 |
| Total Number of Peaks | |
| number of matched peaks | MS_1001121 |
Footnotes¶
| [1] | Type of Focused Ion. Available types are: [M]+, [M]+*, [M+H]+, [2M+H]+, [M+Na]+, [M-H+Na]+, [2M+Na]+, [M+2Na-H]+, [(M+NH3)+H]+, [M+H-H2O]+, [M+H-C6H10O4]+, [M+H-C6H10O5]+, [M]-, [M-H]-, [M-2H]-, [M-2H+H2O]-, [M-H+OH]-, [2M-H]-, [M+HCOO-]-, [(M+CH3COOH)-H]-, [2M-H-CO2]- and [2M-H-C6H10O5]- |
| [2] | Precursor type. Available types are: [M]+, [M]+*, [M+H]+, [2M+H]+, [M+Na]+, [M-H+Na]+, [2M+Na]+, [M+H-C6H10O4]+, [M-H+OH]-, [M+2Na-H]+, [M+H-C6H10O5]+, [2M-H]-, [M+HCOO-]-, [M]-, [(M+NH3)+H]+, [M+H-H2O]+, [M-H]-, [M-2H+H2O]-, [M-2H]-, [(M+CH3COOH)-H]-, [2M-H-CO2]- and [2M-H-C6H10O5]- |
Metabolomic Profiles¶
From Wikipedia:
Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the “systematic study of the unique chemical fingerprints that specific cellular processes leave behind”, the study of their small-molecule metabolite profiles. The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology.
Ontomatica Coverage¶
ChEMATIC discussion here


Pages
- Aiming for roughly 10 pages
- Each page should aim for around 300 - 400 words per page
- Was thinking that it would be possible to produce this guidance in markdown? That way we could include code chunks, maybe hide them
- Introduction
- Who is this for
- it is for the QA of statistics code, in open source languages mainly
- RAP
- Level of RAP
- Examples?
- One example, lower profile statistic, just long code scripts, basic git, no testing
- Second example, higher profile, more advanced along RAP stages, uses more advanced git/branching, testing, functions etc
- Rap QA process
- Testing
- Why you should test your code
- What you should you test
- How do you know that your code is good without testing
- Testing Strategies
- Unit Testing
- Check list for standard things to test
- Boundary Value Analysis
- Examples!
- Version Control with Git
- Why use Git
- Advantages of using Git
- version control
- Branching
- Roll back
- issues
- pull requests
- Peer review, share code
- other benefits!
- Peer Review
- internal / external
- other team members
- access to data
- Documentation
- Documenting code, inline documenting
- Readme for projects
- documenting function, in and out of packages
- Markdown for desk notes
- coding standards
- documentation standards
- Environment- Making sure that reproducible code can actually be reproduced!!
- DATA- synthetic data, how peer review? produce dummy data?
- Docker
- Packrat
- Documentation
- Other Issues
- Staff leave? Need to train up new people, documentation makes it easier.
- Project need to be manageable for running in the long run
- Code Standards
- Link to resources
- Role and responsibility
- Terminology
- Managers


--- title: Online FTP Test Tool description: Test your FTP server with our online testing tool layout: landing permalink: /ftp-test.html tags: ftp tool language: en ---
Test your FTP server with this online testing tool
Enter your server IP or domain and our test tool will tell you everything it finds about your FTP server
See how your server stack up against those ones:
ftp.gnu.org
ftp.pureftpd.org
ftp.vim.org
ftp.slackware.com


A collection of sample apps, working code, and associated files
Some code is also available on GitHub at github.com/jasonclark. All code is licensed under the MIT License (MIT).
- "10 Blue Links" Anticipatory Design Search Prototype
Search application for "Improving Search UX using Query Analysis and Machine Cues"
Download: GitHub - Google Knowledge Graph Search
Search for topics and entities inside the Google Knowledge Graph
Download: GitHub - OembedThis: An Oembed Utility
Verify/check for an Oembed endpoint and return HTML embed code for various popular web sites
Download: GitHub - Voice User Interface(s)
- Voice Assistant - Web Speech API
Voice assistant for common library tasks and resources
Download: GitHub - Speak to Search - Web Speech API
Speech recognition fires a search request
Download: GitHub
- Voice Assistant - Web Speech API
- New Yorker Fiction Podcast Mashup
Download: .zip - BookMeUp (Book Suggestions App)
Book recommendation tool for re-introducing serendipitous browsing in the digital environment
Download: .zip or GitHub - getSEMantic
Feed a URL and tool analyzes page to return semantic topics and entities for linked data -> possible use = automated cataloging of web pages
Download: .zip or GitHub - Digital Collections (Google Custom Search API)
App that uses Google Custom Search API to index public web collections -> potentially allows any library (even a small public) to have a search app with facets for its web presence.
* From Digital Library Forum (DLF) 2013 session: "Metadata First: Using Structured Data Markup and the Google Custom Search API to Outsource Your Digital Collection Search Index"
Download: .zip or GitHub - Digital Video Library App (YouTube Data API version 2)
App that uses YouTube API to create a digital video library -> potentially allows any library (even a small public) to have a digital library for its videos
* From Code4Lib Journal article: "Developing a Digital Video Library with the YouTube Data API"
Download: .zip or GitHub - HTML5 Mobile Feed App
Teaching prototype that demonstrates RWD principles, semantic HTML5 markup, and localStorage. App allows one to add set of feeds to create browse and search views.
Download: .zip or GitHub - Term Extract: Yahoo Content Analysis API (YQL)
Paste text to get Content Analysis and Terms
Download: View Code - Worldcat Local Search
App that uses Geolocation API and Worldcat Search API to identify local library catalogs and enable searches in these catalogs.
Download: .zip - Library of Congress (Subject Browse App)
Download: .sql or .csv - BooksNStuffNearBy: Geolocation App
Download: View Code - Mobile Web Templates @ Your Library
-
Mobile Template (custom) | Download: .zip
Mobile Template (jQtouch) | Download: .zip
Mobile Template (jQuery Mobile) | Download: .zip
-
Mobile Template (custom) | Download: .zip
- YouTube Video Widget (jQuery & PHP)
Download: .zip - Video Mashups @ the Library
- Vimeo | Download: .zip
YouTube | Download: .zip
From book chapter: "Blip.tv and Digital Video Collections in the Library", in Library Mashups
- Vimeo | Download: .zip
- Map locations using Google Maps (API version 3)
Download: View Code - Street View Tours using Google Maps (API version 2)
Download: View Code - HTML5 Geolocation using Google Maps (API version 3)
Download: View Code - Table of Contents & Mobile Feeds (Google Ajax Feeds API)
Download: View Code - Search NY Times Movie Reviews (API version 2)
Download: View Code - Query NY Times Best Sellers List (API version 2)
Download: View Code - Visualize Data: Google Charts, Google Analytics API
Demo only: Props to Alexander Lucas<EMAIL_ADDRESS>for code. - AskRef: Search OCLC QuestionPoint API, refine with Worldcat Terminologies API
Download: View Code - Demo(s): Working with Javascript: Building Responsive Web Apps
Download: .zip - Search iTunes using Google's Ajax Search API
Download: View Code - Search iTunes using iTunes Search API
Download: View Code - oEmbed: Flickr, Youtube, etc
Download: View Code - Browser Add-Ons & Bookmarklets for Library & Web Resources
Download: View Code - Google Gadgets @ Your Library
Download: .zip - The Portable Library: Mobile Digital Collections with Flickr
Download: .zip - OpenSearch Widgets & Google Gadgets for Libraries
Download: .zip - QueryCatcher - Search Term Recorder
Download: .zip
From Code4Lib Journal article: "Making Patron Data Work Harder: User Search Terms as Access Points?" - A "lofiAPI" - RESTful web service to Library Data
Download: .zip - Ajax - Show People (HTML)
Download: .zip - Ajax - BrowseSearch: Library of Congress
Download: .zip - Ajax - Worldcat Form
Download: .zip - Ajax - List People (XML)
Download: .zip - Ajax - Search Yahoo API (JSON)
Download: .zip - Ajax - Validate Contact Form
Download: .zip - Web Services - Google Ajax Search API
Download: .zip - Find Books & More: Worldcat Search API (v1)
Download: View Code - Google Books: Google Books Search API (v1)
Download: View Code - Amazon Reviews & Thumbnails (PHP)
Download: View Code - Flickr API - Search (Yahoo Query Language, PHP)
Download: View Code - Flickr API - Display Photos (JSON)
Download: View Code - Library Lifestream (Pictures, Publications, Videos, Bookmarks, Blog)
Download: .zip


Program started at Mon-07-May-2007 18:32.
Analysed requests from Sun-01-Apr-2007 01:00 to Mon-30-Apr-2007 23:59 (29.96 days).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report contains overall statistics.
Successful requests: 19,653,075
Average successful requests per day: 656,028
Successful requests for pages: 6,021,499
Average successful requests for pages per day: 201,000
Failed requests: 4,803
Redirected requests: 340,331
Distinct files requested: 748
Distinct hosts served: 269,066
Corrupt logfile lines: 63
Unwanted logfile entries: 6,830
Data transferred: 336.57 gigabytes
Average data transferred per day: 11.23 gigabytes
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the activity in each month.
Each unit () represents 200,000 requests for pages or part thereof.
| month | reqs | pages | |
|---|---|---|---|
| Apr 2007 | 19653075 | 6021499 |
Busiest month: Apr 2007 (6,021,499 requests for pages).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each day of the week, summed over all the weeks in the report.
Each unit () represents 40,000 requests for pages or part thereof.
| day | reqs | pages | |
|---|---|---|---|
| Sun | 3266946 | 979408 | |
| Mon | 4435461 | 1373697 | |
| Tue | 2784851 | 868302 | |
| Wed | 2589635 | 803392 | |
| Thu | 2319379 | 724637 | |
| Fri | 1944589 | 613655 | |
| Sat | 2312214 | 658408 |
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each hour of the day, summed over all the days in the report.
Each unit () represents 10,000 requests for pages or part thereof.
| hour | reqs | pages | |
|---|---|---|---|
| 0 | 299421 | 93589 | |
| 1 | 133731 | 45866 | |
| 2 | 70508 | 27085 | |
| 3 | 47215 | 21471 | |
| 4 | 43330 | 19105 | |
| 5 | 55917 | 22526 | |
| 6 | 152568 | 51524 | |
| 7 | 428784 | 135365 | |
| 8 | 951240 | 293578 | |
| 9 | 1234529 | 378654 | |
| 10 | 1215388 | 374177 | |
| 11 | 1166176 | 360425 | |
| 12 | 1297533 | 409176 | |
| 13 | 1242029 | 387608 | |
| 14 | 970552 | 300367 | |
| 15 | 988318 | 306102 | |
| 16 | 1144357 | 350694 | |
| 17 | 1328554 | 391016 | |
| 18 | 1294184 | 382916 | |
| 19 | 1308138 | 390077 | |
| 20 | 1308231 | 393451 | |
| 21 | 1255144 | 370164 | |
| 22 | 1101272 | 329741 | |
| 23 | 615956 | 186822 |


Data Fields | |
| Set | dominator |
| int | level |
| int | order |
Definition at line 100 of file trace.c.
Definition at line 102 of file trace.c.
Referenced by AddExtensionFields(), FreeExtensionFields(), and SelectTraces().
| int Ext::level |
Definition at line 103 of file trace.c.
Referenced by AddExtensionFields(), predecessor_arc_weight(), predecessor_node_weight(), successor_arc_weight(), and successor_node_weight().
| int Ext::order |


Provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back to XML code. It's not as powerful as the 'XML' package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin development easier.
| Version: | 0.10-2 |
| Depends: | R (≥ 2.9.0) |
| Imports: | methods |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2017-12-12 |
| Author: | Meik Michalke [aut, cre] |
| Maintainer: | Meik Michalke <meik.michalke at hhu.de> |
| BugReports: | https://github.com/rkward-community/XiMpLe/issues |
| License: | GPL (≥ 3) |
| URL: | https://reaktanz.de/?c=hacking&s=XiMpLe |
| NeedsCompilation: | no |
| Citation: | XiMpLe citation info |
| Materials: | README NEWS ChangeLog |
| CRAN checks: | XiMpLe results |
| Reference manual: | XiMpLe.pdf |
| Vignettes: |
The XiMpLe Package |
| Package source: | XiMpLe_0.10-2.tar.gz |
| Windows binaries: | r-devel: XiMpLe_0.10-2.zip, r-release: XiMpLe_0.10-2.zip, r-oldrel: XiMpLe_0.10-2.zip |
| OS X binaries: | r-release: XiMpLe_0.10-2.tgz, r-oldrel: XiMpLe_0.10-2.tgz |
| Old sources: | XiMpLe archive |
Please use the canonical form https://CRAN.R-project.org/package=XiMpLe to link to this page.


Participation is open to ALL 5th-8th grade boys and girls, it is not exclusively for KIDSPORTS players.
The 2015 KIDSPORTS Invitational Powered by the Warsaw Sports Marketing Center is a nonprofit 3-on-3 basketball tournament for 5th-8th grade boys and girls held at University of Oregon’s Matthew Knight Arena on Sunday, April 12th. Registration is currently open and accessible at www.kidsports.org/warsaw however spots are limited. A total of 80 teams will be split up by grade and gender into 8 separate double elimination brackets. Each team will play 1 warm-up game prior to their first tournament game so that each team will be guaranteed at least three games. Along with the tournament there will also be a skills clinic portion of the day put on by University of Oregon student athletes, a dunk contest put on by University of Oregon student athletes, a three point contest, a skills challenge, a free throw contest, and other activities open to all spectators.
Q: What time does the event start?
A: Registration will open at 9:00 am, the national anthem will be performed at 9:30 am, and the first round of warm-up games will begin at 9:40 am.
Q: My kid has never played for KIDSPORTS can he/she still register?
A: Yes, participation is open to all 5th-8th grade boys and girls, it is not exclusively for KIDSPORTS players.
Q: Do spectators have to pay for entrance?
A: No, the event is free for all to attend.
Q: Do teams need to provide their own jerseys?
A: The tournament shirts given to the players will all be the same color. Teams are welcome to use these as their game shirts if they choose. If teams would like to wear a certain color or jersey, they should bring this to the tournament.
Q: How much does it cost to register a team?
A: Team registration costs $15 per team. Each team must have at least 3 participants and a max of 5.
Q: How long is each game?
A: Each game will be 15 minutes long. Every team will be guaranteed at least three games.
Q: Our team doesn't have a coach can we still participate?
A: Yes, while we recommend each team have a coach or parent to manage the team it is not required.
Q: Will there be food provided?
A: Yes, there will be catered food available free of charge for all participants. We're also partnering with local restaurants to provide discounted food both inside the venue and nearby.
The following rules apply to the 2015 KIDSPORTS Invitational:


---
layout: post
title: Rejected frame
date: '2013-11-13T07:30:00+01:00'
categories:
- Aljubarrota
- Storyboards
picture_url: https://film-Storyboards.tumblr.com/post/66870464499/rejected-frame-from-aljubarrota-battle
---
Rejected frame from Aljubarrota Battle.


3SPN Server Registration
Home
Registered Servers
Contact Admin
Server Name:
Account Name:
Password:
IP Address:
Port:
Admin Name:
Game Tracker:


% var home = "icon-home"; var order = "icon-order"; var profile = "icon-profile"; if(current==1){ home = "icon-homec"; } if(current==2){ order = "icon-orderc"; } if(current==3){ profile = "icon-profilec"; } %>


Guides:
https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Active%20Directory%20Attack.md
https://book.hacktricks.xyz/windows/active-directory-methodology


Current value is: {{ myPlaces | json }}.
Autocompletion List editor. Provides suggestions for completion as the user types. Allows to build a selection of objects.
Main features:
In this sample, the data is pick from the static datasource: countries.
import { DataSource } from '@lux';
countries: DataSource<string, string> = [
{ key: 'ES', label: 'Spain' },
{ key: 'IT', label: 'Italy' },
// ...
{ key: 'FR', label: 'France' }
];
myPlaces = [ 'ES', 'IT', 'CR' ];
<lux-autocomplete-list name="countries" [(ngModel)]="myPlaces" [dataSource]="countries">
</lux-autocomplete-list>
Current value is: {{ myPlaces | json }}.
This sample uses a couple of functions to wire an API to retrieve items and complete the labels when needed. Observables are used to support async operations. The search string is passed in lowercase to make it easy insensive matching.a Note the instance property must be set to avoid this dereferencing when calling the functions.
import { DataSource } from '@lux';
export AComponent {
countries: DataSource<string, string> = [
{ key: 'ES', label: 'Spain' },
{ key: 'IT', label: 'Italy' },
// ...
{ key: 'FR', label: 'France' }
];
myPlaces2 = [ 'AM', 'DZ', 'JP' ];
get self(): AComponent {
return this;
}
}
getLabels(instance: any, keys: any[]): Observable<DataSource<any, string>> {
return of(instance.countries.filter((c: DataSourceItem<string, string>) =>
keys.includes(c.key)));
}
getData(instance: any, search: string): Observable<DataSource<any, string>> {
const searchKey = (search || '').toLowerCase();
return of(instance.countries.filter((c: DataSourceItem<string, string>) =>
c.label.toLowerCase().includes(searchKey)));
}
...
}
<lux-autocomplete
name="countries2"
[(ngModel)]="myPlaces2"
[instance]="self"
[resolveLabelsFunction]="getLabels"
[populateFunction]="getData">
</lux-autocomplete>
Current value is: {{ myPlaces2 |json }}.
The Autocomplete component implements ControlValueAccessor
,
therefore binding using ngModel
and name
is supported.


response_requirements¶
Specifies the behaviors that are feasible responses for each stimulus.
The decision function computes the probabilities only for the feasible responses to a stimulus.
For example, in an experiment with the stimulus elements red_lever
and blue_lever
, the
behavior press_blue_lever
is a feasible respose to blue_lever
but not to red_lever
.
The feasible responses to a compound stimulus (a set of stimulus elements) consist of the union
of the feasible responses for each element. For example, press_blue_lever
is a feasible response
to the compund red_lever,blue_lever
.
Syntax¶
response_requirements = b1:[e11, e12, ...],
b2:[e21, e22, ...],
...
bn:[en1, en2, ...]
If a list of stimulus elements contains a single element, the brackets may be omitted:
response_requirements = b1:e11,
b2:e21,
...
bn:[en1, en2, ...]
Description¶
response_requirements = b1:[e11, e12, ...],
b2:[e21, e22, ...],
...
bn:[en1, en2, ...]
specifies that
b1
is a feasible response toe11
,e12
, …b2
is a feasible response toe21
,e22
, ……
bn
is a feasible response toen1
,en2
, …
The comma-separated list of behavior:[elements]
may be line-broken:
response_requirements = b1:e11,
b2:e21,
...
bn:[en1, en2, ...]
is the same as
response_requirements = b1:e11, b2:e21, ..., bn:[en1, en2, ...]
Dependencies¶
The properties
stimulus_elements
andbehaviors
must be specified beforeresponse_requirements
.Each stimulus element used in the specification of
response_requirements
must be present in the parameterstimulus_elements
.Each behavior used in the specification of
response_requirements
must be present in the parameterbehaviors
.Applicable in all mechanisms except the Rescorla-Wagner mechanism.
Example¶
stimulus_elements = blue_lever, red_lever, food
behaviors = ignore_lever, press_lever, eat
response_requirements = press_lever: [blue_lever,red_lever]
ignore_lever: [blue_lever,red_lever]
eat:food
specifies that
the only feasible responses to
blue_lever
ispress_lever
andignore_lever
the only feasible responses to
red_lever
ispress_lever
andignore_lever
, andthe only feasible response to
food
iseat
.