OsmAnd Ski Maps is a plugin allowing you to navigate at winter sports locations. It provides you with a full map of ski pistes, ski lift markers and even colored lines indicating the complexity of each track.
First, you need to go to Plugins menu - Ski map view and select Enable. (You may need to install it for free.) Then use the 'Configure map - Map style' menu to switch the winter and ski style on. After that, you can find your desired area on the map and enjoy the info about the ski tracks including their complexity, location and infrastructure around.
To activate ski maps, you need to go to the Maps & Resources menu, then choose Plugins. Enable the Ski map plugin and return to the map. Now, tap the map button on the left and select the Map type menu. Change it to Ski map. That's it. Now you can view the maps you've downloaded in the ski mode. The rest of the world will have the info provided in the World map and shown in ski map style.
OsmAnd map legend menu (Help - Map legend) provides you with some popular ski piste graphic representations. Please note that this classification is not absolute: the class of every track is influenced by the reputation of the area and other factors.
OsmAnd Ski maps plugin works perfectly well with Contour lines plugin and the hillshade maps. Contour lines show the heights of a location and the hillshade maps provide visualization of terrain which may be of great help at sports locations.
It is easy to add hillshade or contour lines to your ski map. You need to enable the Contour lines plugin in the Plugins menu, then go to 'Download maps' menu, select your country or region and download contour lines or hillshade files, or both. You can combine hillshade with contour lines to get fuller information about the location. Just go to 'Configure map' menu to enable the 'Contour lines' and 'Hillshade' options. Zoom out until you see the lines and shading.
OsmAnd Ski maps provide you with helpful info on winter sports locations. Besides that, you can see nearby mountain peaks and the plugin also allows you to find the location of aerialway stations.
To combine maps with contour lines or hillshade, you have to download the contour lines or hillshade maps of the area. Then just go to Maps & Resources - Plugins and turn the Contour lines plugin on. That will allow you to view both map in the ski map style, contour lines or hillshade optionally together. To turn the hillshade off and leave just the ski map and contour lines, go to the Map menu and disable Hillshade.


Supreme Court verdict on PDP appeal will of Nigerians- Presidency
by https://www.facebook.com/CheerfulpraiseBy Augustine Ehikioya, Abuja
The Presidency has welcomed the elucidation of the Supreme Court ruling on the PDP petition.
In a statement by the Senior Special Assistant on Media and Publicity, Malam Garba Shehu, the Presidency reiterated that the onus of proof must necessarily fall on those that challenge the vote.
He said allegations must be substantiated if they are to hold, which requires producing credible witnesses that verify key claims.
“As the Supreme Court has stated, the case failed this test,” he added
On the issue of qualifications, he said, President Buhari has a long history of service to the Federal Republic of Nigeria.
“The questioning of this fact seems to fly in the face of all evidence.
“However, now it is time for the PDP to take up the service mandated by this election.
“The legislative agenda of the ninth assembly is ambitious in both size and scope. It is critical the opposition is there to scrutinise bills and policies, rather than preoccupy themselves with unsustainable court cases.
Read Also: PDP, Atiku allegations against Buhari’s victory remain unproven – Supreme Court
“We appreciate the PDP and their candidate for taking these claims to courts rather than expressing them in other ways, but now Nigeria needs a robust and rigorous opposition. The nation needs to move on.
“While it is correct to give credit to the courts for affirming this electoral victory-and we commend the courage of the bench to arrive at the right conclusion given manifest and subtle pressures to do otherwise-the reality is that courts were convinced by arguments from the lawyers representing the APC and its candidate, President Muhammadu Buhari that results reflected the true wishes of a majority of our citizens.” he said
He recalled that President Buhari won four million more votes than his closest rival, Alhaji Atiku Abubakar in the February poll.
According to him, no serious electoral observers have challenged the result.
“And now the Supreme Court has explained why the PDP appeal is unsubstantiated. We hope this finally puts the matter behind us.
“We should now grasp this moment to move forward – together.” he stated


{% extends "_post.html" %} {%hyde title: "muthafuckin' tangerines on a muthafuckin' plane^Wwindows" created: 2006-08-20 19:04:24 %} {% block article %} {%article%}
This weekend I spent some more time making Tangerine work on Windows. I built Bonjour from scratch so I could distribute the binaries, and polished up the Tangerine installer as well. I gave it a good deal of testing, but there might be something I missed still. The Windows version includes a plugin for finding songs automatically through Google Desktop.
The preferences window for Tangerine (under Windows)
Update: I just put up a new version which fixes a bunch of issues I found. It also adds the missing features to the config window and hopefully makes it look a little nicer. Updated screenshot above.
Update 2: Another updated version up with more bug fixes. I don’t have a bug tracker set up, but if you guys hit any problems just email me (snorp@snorp.net).
{%endarticle%} {% endblock %}


Samsung Galaxy A51 renders show off 48MP quad rear cameras and punch-hole display
by Pranob MehrotraIt’s just been over a month since Samsung launched the Galaxy A50s in India. The premium mid-ranger from the company packed in an Exynos 9611 chip, coupled with up to 6GB of RAM and 128GB of storage. It also featured a triple camera setup on the back, with a 48MP primary sensor, an 8MP ultra-wide lens, and a 5MP depth sensor. While it hasn’t been all that long since its release, Samsung seems to be prepping up for the release of its successor, the Galaxy A51. Leaked renders of the device have now surfaced online, revealing some key design changes and features.
Infamous leakster @OnLeaks recently shared a couple of high-quality renders and a 360-degree video of the upcoming Galaxy A51. The renders suggest that the Galaxy A51 will feature a 6.5-inch punch-hole display, like the one found on the company’s flagship devices, with minimal bezels on all sides.
On the back, the device will feature a new rectangular camera module which will include four cameras in an L-shaped array, along with an LED flash. Unlike the front of the device, the back panel will feature curved edges with the Samsung branding in the center. The renders don’t include a fingerprint scanner on the back which leads us to believe that the A51 will feature an in-display fingerprint scanner, just like its predecessor.
Both the power button and volume rocker can be found on the right edge of the device, while the SIM card tray is on the left. On the bottom edge, the Galaxy A51 will feature a USB Type-C port for charging and data syncing, along with a single loudspeaker grill and a 3.5mm headphone jack. Previous leaks suggest that the Galaxy A51 will feature a substantial 4,000mAh battery and will run One UI 2.0 out of the box. Furthermore, the quad-camera setup will likely include a 48MP primary sensor, a 12MP wide-angle sensor, a 12MP telephoto lens with 2x optical zoom and a 5MP depth sensor. Up on the front, the device will feature a single 32MP selfie shooter.
A Geekbench listing of the device suggests that it will be powered by the Exynos 9611 chip, along with at least 4GB of RAM. However, we aren’t sure if the Geekbench listing is a reliable source. As of now, there’s no further information from the company regarding the device or its release timeline. But we expect to learn more as its launch draws closer.


DNS, in full domain name system, network service that converts between World Wide Web “name” addresses and numeric Internet addresses. The concept of a name server came about as a result of the first computer networks in the mid-1970s.
When users are using the internet, most users are not seeing the IP address itself. What users are seeing is a domain name. A system called DNS, short for Domain Name system. Associates names with corresponding addresses. Computers use DNS to look up the destination of a website one will want to travel to.


Your unique lemonade collagen juice drink that refreshes and whitens your skin.
Find Out MoreThe name is a combination of the words “cool and juice” with a touch of collagen. Basically, the major ingredient is lemon, it is literally a lemon based drink with powderized collagen mixture.
Our juice comes with several amazing flavors:
...Pure lemonade
...Honey Lemonade
...Blue Lemonade
...Watermelon Lemonade
...Cucumber lemonade
...Cherry Lemonade
...Kiwi Lemonade
...Yakult Lemonade
...Pre-made Lemonade
So what are you waiting for? Grab or franchise your own stall now!
See More!Our first stall was established and managed mainly by a couple from Japan.
We aim to promote refreshing juice that will help you glow your skin and boost your health in a friendly budget.
Currently, we have 4 franchise branches, SM Baliwag, Robinsons Malolos, SM Sn fdo downtown & SM City Pampanga and we are aiming to set more stalls in different cities!
Our drink products are made to the perfection that you'll love to sip anytime of the day.
It promotes hydration. According to the Food and Nutrition Board, the dietary reference intake for water is 91 to 125 ounces...
It's a good source of vitamin C...
It improves your skin quality...
It supports weight loss...
It aids good digestion...
It freshens breath...
It helps prevent kidney stones...
For orders, suggestions and deliveries within the area, please give us a call or send an email and we will get back to you as soon as possible!


Build your confidence & stop telling me ‘I’m ‘Too-Known’ – Sarkodie
by Adu SarpeahGhana’s finest contemporary rapper, Sarkodie for once has added his voice to the popular perception about him that his life is built on pride and arrogance.
To the best knowledge of Sarkodie, it’s high time those with the erroneous perception about him considered building self-confidence. To him, anyone with self-confidence thinks differently perhaps in a mature manner.
Sarkodie tweeted that;
”Yo quick one: y’all allow females call men “Too Known” it’s cute but no man n I repeat no man should tell a fellow man “You are too known” …
READ ALSO:— Sarkodie Denies Going To Meet Cardi B In Her Hotel
The ‘Too known’ tag was awoken following the buzz that Sarkodie rushed to Kempinski hotel just to greet Cardi B for obvious reasons. Nonetheless, Sarkodie has denied ever rushing into Kempinski hotel to meet Cardi B.
See Screenshot Below:


These documents are copyrighted by their respective authors. Links are provided for convenience.
ISO 14496-12:2012 - base media file format - coding of audio-visual objects
ISO 23009-1:2014 - DASH - Media presentation description and segment formats
DASH-IF Interoperability Points v2.90


Hello! my name is Jared, I don't have a lot of experience but decided to take this course anyway since my sister went through it and had an amazing time! She landed a great job as well.
I've been using computers since I was a child, I always loved messing around with how things work, so I figured I might like coding and took a few online courses. I plan to use this time to focus on making a career
I don't have many hobbies but here's a short list


Static Media Manager v0.4 documentation
Combine the files in path_list to create one file at destination.
| Parameters: |
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A wrapper around shutil.copy2 to optionally compress javascript or css files.
| Parameters: |
|
|---|
Do the file copying with all the appropriate error checking. Don’t replace an existing file if replace_files is False
| Parameters: |
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A port of the recursive shutil.copytree, except it assumes the destination directory exists.
| Parameters: |
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EBD - Easton Bible DIctionary
Bigthan
BIGTHAN
"BIGTHAN "one of the eunuchs who “kept the door” in the court of Ahasuerus. With Teresh he conspired against the king’s life. Mordecai detected the conspiracy, and the culprits were hanged (
Esther 2:21-23
;
6:1-3
).
"BIGTHAN "one of the eunuchs who “kept the door” in the court of Ahasuerus. With Teresh he conspired against the king’s life. Mordecai detected the conspiracy, and the culprits were hanged (Esther 2:21-23; 6:1-3).


Gem in a Box
If you did not need any credentials to get to this page
gem sources -a http://<IP_ADDRESS>:6444/
If you needed some credentials to get to this page
gem sources -a http://username:password@<IP_ADDRESS>:6444/
and then ...
gem install geminabox
gem inabox [gemfile]
Upload Another Gem


survey-video-synthesizers
This is a survey of video synthesizers, indexed by their creators.
Critter and Guitari
Critter and Guitari has released several video synthesizers, including fixed behavior and scriptable synths.
Video Scope synthesizers
Video Scope synthesizers are fixed behavior synths.
There are 3 of these synths, and as of 2020, they are discontinued.
They have 2 knobs on the front panel:
- Mode: left knob, selects video mode
- Gain: right knob, controls audio gain
They have 3 connectors on their back:
- Audio input: 1/4" TS mono audio cable.
- Power input: 9V DC center-positive
- Video output: composite NTSC format, RCA jack
Rhythm Scope (2013)
This synth reacts to rhythmic sounds, including short quick hits, arpeggios, etc.
This synth has 16 unique modes, plus a Random mode:
- Strobe
- Two-Tone
- Brackets
- Square Slopes
- Continous
- Thick Neon
- Containers
- Blinds
- Timer
- Timer 2
- Thin Neon
- Solitary
- Bits
- Printer
- Puzzle
- Glyphs
- Random
Random mode switches every 1-16 beats. To set Randomization rate, set MODE knob to one mode for > 1 second and then set to Random.
The first unique mode (Strobe) makes Random switch every 16 beats, the next mode for 15, and so on.
Videoscope (2013)
This synth has 4 modes, with a control variable for each:
- Basic line - Color
- Color shifter - Speed
- Trapezoid - Intensity
- Random - Rate
Black and White Videoscope (2013)
This synth has 16 modes, plus a Random mode:
- Strobe
- Timer
- Timer Two
- Blinds
- Brackets
- Bits
- Squares
- Birds
- Plaid
- Paper
- Many Lines
- Wide Lines
- Alternator
- Zooming
- 10 Wide
- 1 Wide
- Random
Random mode switches every 1 frame - 30 seconds.
To set Randomization Rate, set the Mode knob to one position for 1 > second and then set to Random.
The first mode (Strobe) makes Random switch at the maximum 30 seconds, and each mode after that makes this shorter, down to the last mode (Wide) which makes Random switch every frame.
Scriptable synths
These synthesizers are Linux-based machines, running the Python library pygame.
They output video over HDMI and you can write your own scripts and share with a wide community.
ETC (2017)
Currently discontinued.
EYESY (2020)
Released over Kickstarter
It outputs video in the following ways:
- NTSC (Composite video out jack)
- PAL (Composite video out jack)
- 720p (HDMI video out jack)
- 1080p (HDMI video out jack)
It is built using a Raspberry Pi Compute Module 3, and features a 1.2 GHz processor and 1 GB RAM.
It includes an openFrameworks mode.


Conjugatron 9000
Conjugate!
Person
First
Second
Third
Plurality
Singular
Plural
Formality
Formal
Informal


library(tidyverse)
library(cowplot)
library(broom)
library(modelr)
library(viridis)
library(lubridate)
library(hms)
library(knitr)
library(kableExtra)
library(patchwork)
library(VGAM)
library(nls.multstart)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")
source("../../../IDA/tools/echem_processing_tools.R")
source("../../../IDA/tools/plotting_tools.R")
theme_set(theme_1())
This notebook describes how the estimates for \(D_{phys}\) are calculated.
All of the data to this point was processed from raw SWV data shown elsewhere. This means that there’s a lot of extra confusing data/columns we don’t need, and some things are not conveniently organized. First let’s deal with that, then we’ll get to the interesting stuff.
First let’s read in the signals calculated from the SWV scans over time for the biofilms. We’ll also read in the estimates of \(D_{ap}\) that we already generated for these biofilms.
biofilm_signals <- read_csv("../../02_05_19_draft_2/data/01_28_19_aggregate_SWV_GC_signals.csv") %>%
filter(electrode_from_swv == "i1" & treatment == "control") %>%
filter(exp_id != "1") %>% filter(exp_id != "3" | run != 4)
biofilm_dap_estimates <- read_csv("../../02_05_19_draft_2/data/01_28_19_swv_gc_dap_estimates_aggregate_updated.csv") %>%
filter(treatment == "control" & exp_id %in% c("2", "3") &
echem_from_swv == "SWV") %>% select(exp_id, run, dap)
Now we’ll normalize the timing of the scans within each run, so that transferring the biofilm to the fresh reactor occurs at approximately norm_time = 0.
biofilm_signals_norm <- biofilm_signals %>% group_by(exp_id,
reactor, run) %>% mutate(min_time = min(minutes_from_swv)) %>%
mutate(norm_time = minutes_from_swv - min_time + 0.6)
Then let’s reorganize the data so that we have our estimates of \(I_0\) alongside the data. For these biofilms we are estimating \(I_0\) by bounding it between the soak SWV signal and the first transfer SWV signal. Here I’ll also add the \(D_{ap}\) estimates to the now organized dataset.
biofilm_soak_i0 <- biofilm_signals_norm %>% ungroup() %>% filter(reactor ==
"soak" & rep == 0) %>% mutate(i0_high = signal_from_swv) %>%
select(run, exp_id, i0_high)
biofilm_transfer_i0 <- biofilm_signals_norm %>% ungroup() %>%
filter(reactor == "transfer" & rep == 0) %>% mutate(i0_low = signal_from_swv) %>%
select(run, exp_id, i0_low)
biofilm_i0 <- left_join(biofilm_soak_i0, biofilm_transfer_i0,
by = c("exp_id", "run"))
biofilm_signals_i0 <- left_join(biofilm_signals_norm %>% ungroup() %>%
filter(reactor == "transfer") %>% mutate(signal = signal_from_swv) %>%
select(exp_id, run, rep, signal, norm_time), biofilm_i0,
by = c("exp_id", "run"))
biofilm_signals_dap <- left_join(biofilm_signals_i0, biofilm_dap_estimates,
by = c("exp_id", "run")) %>% mutate(IDA = "biofilm", run = as.character(run))
head(biofilm_signals_dap)
## # A tibble: 6 x 9
## exp_id run rep signal norm_time i0_high i0_low dap IDA
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 2 1 0 3.67e-6 0.6 3.42e-6 3.67e-6 1.35e-5 biofi…
## 2 2 1 9 1.68e-7 26.1 3.42e-6 3.67e-6 1.35e-5 biofi…
## 3 2 1 10 1.67e-7 28.9 3.42e-6 3.67e-6 1.35e-5 biofi…
## 4 2 1 11 1.42e-7 32.4 3.42e-6 3.67e-6 1.35e-5 biofi…
## 5 2 1 12 1.49e-7 35.1 3.42e-6 3.67e-6 1.35e-5 biofi…
## 6 2 1 13 1.33e-7 37.8 3.42e-6 3.67e-6 1.35e-5 biofi…
With that done let’s look at the data: The dotted lines are the high and low estimates of \(I_0\). We have two biofilms distinguished by exp_id and for each we have three replicate runs.
ggplot(biofilm_signals_dap, aes(x = norm_time, y = signal)) +
geom_point() + geom_line(aes(y = i0_high), linetype = 2) +
geom_line(aes(y = i0_low), linetype = 2, color = "red") +
facet_wrap(exp_id ~ run, scales = "free")
Now we can read in the blank data, which was processed from SWV data in the same way as the biofilm data. Note that this blank dataset has a significant background, so we background subtracted the data. Residual peaks were manually quantified above the baseline (resids), so the signal remaining at the last scan beyond the resid peak was considered background and was subtracted out from all the used currents. Also, the \(D_{ap}\) measurement for the blank only has 1 replicate, so it’s value (6.8e-7 cm^2 / sec) was hard-coded in.
blank_signals <- read_csv("../../02_05_19_draft_2/data/11_28_18_swv_decays_Processed.csv") %>%
filter(PHZadded != "10uM")
resids <- tibble(PHZadded = c("100uM", "75uM", "50uM", "25uM"),
resid = c(2.648e-07, 2.199e-07, 1.958e-07, 1.548e-07))
blank_signals_bg <- left_join(blank_signals, resids, by = c("PHZadded")) %>%
group_by(PHZadded) %>% mutate(signal_bg = signal_from_transfer -
(min(signal_from_transfer) - resid)) %>% mutate(i0_bg = i0 -
(min(signal_from_transfer) - resid))
blank_signals_bg_org <- blank_signals_bg %>% ungroup() %>% mutate(run = PHZadded,
exp_id = "1", signal = signal_bg, i0_high = i0_bg, IDA = "blank") %>%
select(exp_id, run, rep, signal, norm_time, i0_high, IDA) %>%
mutate(dap = 6.8e-06)
head(blank_signals_bg_org)
## # A tibble: 6 x 8
## exp_id run rep signal norm_time i0_high IDA dap
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1 100uM 1 0.00000367 0.2 0.0000115 blank 0.0000068
## 2 1 100uM 10 0.000000523 1.5 0.0000115 blank 0.0000068
## 3 1 100uM 11 0.000000502 1.65 0.0000115 blank 0.0000068
## 4 1 100uM 12 0.000000504 1.78 0.0000115 blank 0.0000068
## 5 1 100uM 13 0.000000470 1.93 0.0000115 blank 0.0000068
## 6 1 100uM 14 0.000000457 2.07 0.0000115 blank 0.0000068
Now the blank data is in the same format as the biofilm data.
Therefore we can now combine the datasets to make sure that we are fitting them in the same way.
decays_combined <- bind_rows(biofilm_signals_dap, blank_signals_bg_org)
Let’s compare the blank data to the biofilm data.
ggplot(decays_combined, aes(x = norm_time, y = signal, color = IDA)) +
geom_point(shape = 21)
This is one way to look at the data - it’s just smoothed. You can see that it take much longer for the biofilm data to decay to the point that the blank data does. Intuitively that should be attributable to different diffusion coefficients.
First, we need a function to calculate the signal over time (at x = 0) for a point source undergoing diffusion at a no-flux barrier (semi - infinite). This expression is derived elsewhere.
diffusion_point_semi <- function(t, i0, D_ap, t_s, D_phys) {
(i0 * sqrt(D_ap * t_s)/sqrt(4 * D_phys * t))
}
So let’s fit this function with our parameters for the most important subsets of the data. We will consider the blank decays < 2 min and the biofilm decays < 20 min.
First the data will be grouped and fit by the nls_multstart function - this is a nonlinear least squares method. Then we will use those models to predict the signal over time from the model fit so that we can plot and compare.
decay_data_nls <- decays_combined %>% filter(IDA != "blank" |
norm_time < 2) %>% filter(IDA != "biofilm" | norm_time <
20) %>% group_by(IDA, exp_id, run) %>% do(tidy(nls_multstart(data = .,
formula = signal ~ diffusion_point_semi(t = norm_time, i0 = max(i0_high),
D_ap = max(dap), t_s = 0.02, D_phys = D_phys), start_lower = c(D_phys = 1e-08),
start_upper = c(D_phys = 1e-05), lower = c(D_phys = 0), supp_errors = "Y",
iter = 250, na.action = na.omit), conf.int = T))
decay_data_preds <- left_join(decays_combined, decay_data_nls,
by = c("IDA", "exp_id", "run")) %>% group_by(IDA, exp_id,
run) %>% mutate(preds = diffusion_point_semi(t = norm_time,
i0 = max(i0_high), D_ap = max(dap), t_s = 0.02, D_phys = estimate)) %>%
mutate(pred_high = diffusion_point_semi(t = norm_time, i0 = max(i0_high),
D_ap = max(dap), t_s = 0.02, D_phys = conf.high)) %>%
mutate(pred_low = diffusion_point_semi(t = norm_time, i0 = max(i0_high),
D_ap = max(dap), t_s = 0.02, D_phys = conf.low))
plot_dm_preds <- ggplot(decay_data_preds, aes(x = norm_time,
y = signal)) + geom_ribbon(aes(ymin = pred_low, ymax = pred_high),
fill = "light gray") + geom_line(aes(y = preds), linetype = 2,
color = "black") + geom_point(shape = 21) + facet_wrap(vars(IDA,
exp_id, run), scales = "free", ncol = 3)
plot_dm_preds_styled <- plot_dm_preds + scale_y_continuous(labels = nA_label,
limits = c(0, NA)) + labs(x = "Time (min)", y = expression(I[swv] ~
(nA)))
plot_dm_preds_styled
So, the fits are certainly not perfect, but they mostly capture the decay of the data. After trying out many different parameterizations and adding new terms, we concluded that it would be most appropriate to stick to the simplest model that explains most of the data.
Then to get the lower bound for the biofilm \(I_0\) we can fit the data again with the lower estimate - it will produce the same fits.
decay_data_nls_low <- decays_combined %>% filter(IDA == "biofilm") %>%
group_by(IDA, exp_id, run) %>% do(tidy(nls_multstart(data = .,
formula = signal ~ diffusion_point_semi(t = norm_time, i0 = max(i0_low),
D_ap = max(dap), t_s = 0.02, D_phys = D_phys), start_lower = c(D_phys = 1e-08),
start_upper = c(D_phys = 1e-05), lower = c(D_phys = 0), supp_errors = "Y",
iter = 250, na.action = na.omit), conf.int = T))
Finally, we can plot the estimates of \(D_{phys}\) for the blank and the biofilms
ggplot(decay_data_nls, aes(x = exp_id, y = estimate)) + geom_hline(yintercept = 6.88e-06,
linetype = 2) + geom_pointrange(data = decay_data_nls_low,
aes(ymin = conf.low, ymax = conf.high), color = "blue", position = position_jitter(width = 0.1,
height = 0)) + geom_pointrange(aes(ymin = conf.low, ymax = conf.high),
position = position_jitter(width = 0.1, height = 0)) + facet_wrap(~IDA,
scales = "free_x") + scale_y_log10()
It is important to note that all these values were calculated with \(t_s = 20ms\), because it gave a realistic answer. This value is within the realm of possibility, but it is higher than we suggested previously.
Comparing example biofilm and blank with models
# plot layout
plot_compare_decays <- ggplot(decay_data_preds %>% filter((IDA ==
"biofilm" & exp_id == 3 & run == "1") | (IDA == "blank" &
run == "75uM")), aes(x = norm_time, y = signal)) + geom_ribbon(aes(ymin = pred_low,
ymax = pred_high, group = IDA), fill = "light gray") + geom_line(aes(y = preds,
group = IDA), linetype = 2, color = "black") + geom_point(shape = 21)
# plot styling
plot_compare_decays_styled <- plot_compare_decays + scale_y_continuous(labels = nA_label,
limits = c(0, NA)) + labs(x = "Time (min)", y = expression(I[swv] ~
(nA)))
plot_compare_decays_styled
Could improve this figure by generating model predictions for more points than exist in the dataset to generate smooth confidence intervals.


|
gtsam
4.0.0
gtsam
|
Manifold | |
| size_t | dim () const |
| return DOF, dimensionality of tangent space | |
| Cal3_S2Stereo | retract (const Vector &d) const |
| Given 6-dim tangent vector, create new calibration. | |
| Vector6 | localCoordinates (const Cal3_S2Stereo &T2) const |
| Unretraction for the calibration. | |
| static size_t | Dim () |
| return DOF, dimensionality of tangent space | |
Advanced Interface | |
| class | boost::serialization::access |
| Serialization function. | |
Public Member Functions | |
Standard Constructors | |
@ | |
| Cal3_S2Stereo () | |
| default calibration leaves coordinates unchanged | |
| Cal3_S2Stereo (double fx, double fy, double s, double u0, double v0, double b) | |
| constructor from doubles | |
| Cal3_S2Stereo (const Vector &d) | |
| constructor from vector | |
| Cal3_S2Stereo (double fov, int w, int h, double b) | |
| easy constructor; field-of-view in degrees, assumes zero skew | |
Testable | |
| void | print (const std::string &s="") const |
| bool | equals (const Cal3_S2Stereo &other, double tol=10e-9) const |
| Check if equal up to specified tolerance. | |
Standard Interface | |
| const Cal3_S2 & | calibration () const |
| return calibration, same for left and right | |
| Matrix | matrix () const |
| return calibration matrix K, same for left and right | |
| double | fx () const |
| focal length x | |
| double | fy () const |
| focal length x | |
| double | skew () const |
| skew | |
| double | px () const |
| image center in x | |
| double | py () const |
| image center in y | |
| Point2 | principalPoint () const |
| return the principal point | |
| double | baseline () const |
| return baseline | |
| Vector6 | vector () const |
| vectorized form (column-wise) | |
Public Types | |
| enum | { dimension = 6 } |
| typedef boost::shared_ptr< Cal3_S2Stereo > | shared_ptr |
| shared pointer to stereo calibration object | |


ZOOLANDER (2001) / ** 1/2
Directed by Ben Stiller. Screenplay by Drake Sather, Stiller, and John Hamburg, based on a story and a character created by Sather and Stiller. Starring Stiller, Owen Wilson, Christine Taylor. Running time: 90 minutes. Rated PG for mature theme by the MFCB. Reviewed on September 30th, 2001.
By SHANNON PATRICK SULLIVAN
Synopsis: Derek Zoolander (Stiller) is a male model in the twilight of his career who becomes brainwashed by fashion designer Mugatu (Will Ferrell). Mugatu wants Zoolander to assassinate the prime minister of Malaysia, who plans to introduce new child-labour laws which will cripple the fashion industry. Mugatu's scheme is uncovered by reporter Matilda Jeffries (Taylor), who convinces Zoolander to join forces with his archrival, up-and-comer Hansel (Wilson), to stop Mugatu before Zoolander's programming is activated.
Review: "Zoolander" will no doubt invite comparisons with the "Austin Powers" movies, since both title characters are image-conscious dolts embroiled in global conspiracies. But whereas Austin was a satire on the entire James Bond-style spy genre, Zoolander is just a pastiche on a specific profession: the male model, with the espionage elements grafted on rather arbitrarily. There is, therefore, comparatively less material to be mined in this release, but Stiller does a good job of working his premise for every last ounce of humour. As Zoolander, Stiller finds the right note somewhere between sympathetic dimwittedness and head-smacking stupidity. He matches up well with Wilson, whose performance here echoes his role in "Shanghai Noon"; his Aryan poster child, Hansel, exudes the right amount of brash confidence and, well, head-smacking stupidity. Taylor is more poorly served by the script, which provides her with little personality beyond "beautiful crusading reporter". And Ferrell is amusing -- and, fortunately, not overused -- as the fiendish Mugatu. Not all the jokes work, but there are flashes of brilliance, such as Derek's indignant reaction to the unveiling of a miniature school for underprivileged youngsters. And even when it's less than successful, the sheer vitality Stiller brings to "Zoolander" helps distinguish it from similar fare.
Copyright © 2001 Shannon Patrick Sullivan. Archived at The Popcorn Gallery, http://www.physics.mun.ca/~sps/movies.html
| Shannon Patrick Sullivan |<EMAIL_ADDRESS>| +---------------------------------+---------------------------------+ / Doctor Who: A Brief History of Time (Travel) go.to/drwho-history \ \__ We are all in the gutter but some of us are looking at the stars __/
========== X-RAMR-ID: 29643 X-Language: en X-RT-ReviewID: 252383 X-RT-TitleID: 1110145 X-RT-SourceID: 886 X-RT-AuthorID: 1699 X-RT-RatingText: 2.5/4
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


HarmSyn - Harmonic Synthesizer
Input file:
load
Interpolation:
Akima
Cubic
Linear
Nearest neighbor
Sample rate [Hz]:
F0 multiplier:
Harmonics:
all
none
Synthesize
Play
WAV file
Signal
Frequency
Amplitudes over frequency
Amplitudes over time


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The regular forecasts during the voyage come from professional mariners who understand the responsibilities of the master. Their advice is expert and relevant - tailored to each vessel's size, performance, cargo, location and destination. The constant positional monitoring, another feature of the service, is a valuable safety factor.
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or simply fill in our Response Form.


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[gecode-users] Advisors versus propagation conditions
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Wed Oct 6 08:26:53 CEST 2010
Hi,
When designing propagators that use advisors, one will always need to handle
the case where a newly constructed propagator is not at fix-point. This
might be when all variables are assigned (for a simple checking propagator),
or it might be always. What needs to be done differs for each propagator.
For a typical case some data-structure that the advisors will maintain is
set up to a valid initial state, and then the propagator needs to be run
once to propagate this information. Sometimes, the set-up can be used to
determine of the propagator actually needs to be run, or if it already is at
a fix-point. To schedule the propagator for execution you need a view-type
and some relevant modification event. Say that you use the view-type IntView
and want to schedule with the information that the bounds have changed:
IntView::schedule(home, *this, ME_INT_BND);
where "this" is the current propagator.
Cheers,
Mikael
On Wed, Oct 6, 2010 at 6:52 AM, Chris Mears <chris.mears at monash.edu> wrote:
> Hello all,
>
> I am trying to use advisors to implement a propagator using advisors.
> I have an array of views, with one advisor per view. MPG says that
> advisors are never executed when they are first subscribed, but only
> when the view changes.
>
> My problem is what happens if all of the variables are already
> assigned at the time of posting the propagator. Will the advisors
> ever be executed? It seems like they would not be, so
> subsumption/failure never occurs. Am I obliged to check for this
> condition (everything being already assigned) and subscribe to the
> views without advisors so that unadvised propagation occurs?
>
> Cheers,
> Chris
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
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Functions |
|
static size_t | log2floor (size_t n) |
| | Returns largest previous integer for log2( n )
|
|
static size_t | log2ceil (size_t n) |
| | Returns smallest following integer for log2( n )
|
| static size_t | floor2 (size_t n) |
| | Returns largest previous power of 2 for n .
|
| static size_t | ceil2 (size_t n) |
| | Returns smallest following power of 2 for n .
|
|
static constexpr bool | is_power2 (size_t n) |
| | Checks if n is power of 2.
|
|
static size_t | log2 (size_t n) |
| | Returns binary logarithm of n if n is power of two, otherwise returns 0.
|
|
static bool | isExp2 (size_t n) |
| | A tricky runtime algorithm to ensure that n is power of 2.
|
|
static int | exponent2 (size_t nTest) |
| | Runtime algorithm to compute log2( nTest ). If nTest is not power of two then -1 returns.
|
|
static size_t | exp2Ceil (size_t nNumber) |
| | Returns N: 2**N is nearest to nNumber , 2**N < nNumber.
|
Some helper compile-time tricks.


# Introducing Scala in your existing Java project
Tim Soethout - ING Bank
http://blog.timmybankers.nl
Note:
- Welcome!
# Overview
- Intro
- Scala @ ING - Context
- Compiling Scala in Maven
- Mixing Java and Scala source
- Code coverage
- Code quality using Sonar
- Secure code analysis
- Combining Documentation
- Wrap up
# Scala @ ING
![ING](lib/image/ING_logo.png)
## State
- Much Java with Maven
- Some Scala for restful services using Akka
- Open source: Coral
- Developers are getting interested and looking for possibilities
Note:
- Coral is a realtime analytics framework build on Akka and Spark presented by Natalino Busa at an earlier meetup
## Context
- Maven project written in Java 6
- Team with mixed Scala experience levels
- Eager to do Scala
- But within Enterprise: Compliancy
Note:
- Enterprise and thus code quality, maintainability and other compliancy
- This was already in place for Java, but for Scala not yet
# Compiling Scala in Maven
## [scala-maven-plugin](http://davidb.github.io/scala-maven-plugin/usage.html)
```xml
<project>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
...
</plugin>
</plugins>
</build>
</project>
```
Note:
- Most common way
- Works for most usecases, but you run into trouble when mixing sources
- Show this in next section
# Mixing Java and Scala source
## Demo
### Mixed Java & Scala
Note:
- Show
+ Source files for Java & Scala
+ Testcases - `@RunWith(classOf[JUnitRunner])`
+ IntelliJ test
+ `mvn test`
## Demo
### Multiple modules
Note:
- Show
+ Change branch to `multimodule`
+ Parent pom
+ individual poms
- Do `HelloWorld2.java`
+ add dependency from `module2` on `module1`
+ add `new module1.HelloWorld().hello()`
+ add `new module1.HelloScala().test()`
+ All works flawlessly because of module1 being compiled to `/target/` correctly
- Same for Scala to Java `HelloScala2.scala`
+ def test = new module1.HelloWorld().hello()
+ def someOther = new module1.HelloScala().test
- Now try inside module1: `HelloWorld.java`
+ `public String hello() { return new HelloScala().test();}
+ Fails because `maven-compiler-plugin` can't find Scala sources
+ Disabling `maven-compiler-plugin` solves the first part
+ Also need incremental compilation turned on to find
## Mixing it up
- Seems to work on first sight
- Problems can arise in:
+ Multiple modules
+ Dependencies from Java to Scala
- Depends on compile order
- sbt incremental compiler to the rescue
scala-maven-plugin:
```xml
<configuration>
<recompileMode>incremental</recompileMode>
</configuration>
```
Note:
- Know how to solve these problems to counter naysayers
- TODO: Maybe weird to mention this here? Example about java/scala incompatibility should go to single module part?
## [sbt-compiler-maven-plugin](https://code.google.com/p/sbt-compiler-maven-plugin/)
- Another option; runs sbt underneath
- Not really maintained anymore, but helped us a lot 6 months ago
```xml
<plugin>
<groupId>com.google.code.sbt-compiler-maven-plugin</groupId>
<artifactId>sbt-compiler-maven-plugin</artifactId>
<version>1.0.0-beta5</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
<goal>addScalaSources</goal>
</goals>
<id>default-sbt-compile</id>
</execution>
</executions>
</plugin>
```
Note:
- Try this is scala-maven-plugin does not work
- `<recompileMode>incremental</recompileMode>` seems the way to go now
# Code quality using Sonar
# Combining Documentation
## Thanks for listening
Questions?
[Slides](http://blog.timmybankers.nl/scala-java-maven-slides)
/
[Code](https://github.com/TimSoethout/scala-java-maven-code)


[gecode-users] Recovery Scheduling as CSP/COP
Christian Schulte
cschulte at kth.se
Sat Apr 14 17:44:58 CEST 2007
Dear Stanimir,
let me just take those questions which are specific to Gecode, I believe
that the more general questions relating to how to model your problem are
best asked in a more general setting (as in the constraints yahoo group or
the comp.constraints newsgroup).
- There is no notion of relaxation built-in.
- As it comes to starting point, just use those values you would like to
keep from your previous
solution and try to assign them to the variables in the new model.
All the best
Christian
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Stanimir Dragiev
Sent: Friday, April 13, 2007 8:21 PM
To: users at gecode.org
Subject: [gecode-users] Recovery Scheduling as CSP/COP
Hello to all on the list,
I am new to Constraint Programming and GECODE, except that I took a look at
some of the examples, particularly at queens, and implemented some examples
of my own extending the Space class.
I selected GECODE to accomplish a task within the work on my Diploma Thesis,
because of its C++ interface and good performance. I post here the
description of the problem I am solving and some questions in the hope that
some of you might have had similar problems to solve and have hints and
recommendations for doing it better.
*Summary*
I try to represent following problem as Constraint Optimization problem:
Recovery scheduling in a
dynamic (and heterogeneous) resources environment
(with possible shortcoming of resources) with
spatial and time dependencies between single jobs.
*Approach*
- representation
I represent a schedule as a 3 dimensional matrix of positive integers. A
point (an integer) with coordinates (j,r,t) stands for the amount of
entities of resource <r> used by job <j> at time <t>. (Actually, this is a
4D structure
...)
- constraints
Since I didn't find commonly used or well known scheduling constraints
framework to adapt to my problem, I defined my own ones using logical
notation. In general the problem may be over-constrained, so I needed to
extend relaxable constraints by "OR TRUE".
- optimization
Of course, it is desirable to have not simply one or all solutions
satisfying the constraints, but also to specify some objective function, so
that _in some terms_ optimal solution comes out. But having relaxable
constraints this becomes a _must do_, if I see it correct.(Otherwise the
trivial solution with no satisfied constraints comes out)
- starting point
Since this is a recovery scheduling, I can assume that the schedule
available before the crash was optimal, and due to performance issues I
would like to set it as starting point for the optimization, rather than
start building solutions from scratch...
*Implementation*
- representation
3D structure of IntVars, including functions to return IntVarArray for every
tuple (j,r,1..T), (j,1..R,t), (1..J,r,t) in order to use linear(...) without
conversions. (Example: (1..J,r,t) holds the amounts of resource r used by
the jobs at time t. So I can constrain the sum of them to be less then the
capacity using linear())
- constraints
I am aware that the cumulative() constraint is pretty mighty, but I really
couldn't figure out how to use what it offers for my needs. So I did (am
doing) it combining linear() rel() and post() (Example: for the constraint
"resource r_x is not overloaded at time t_x" the sum of the elements of the
IntVarArray (., r_x, t_x) should be less then capacity(r_x)). Perhaps not
the most legal and efficient way? I think cumulatives(...) does not fit for
my needs also because I want to have more fine grained control over which
constrains are relaxed, and which not. I need this in order to be able to
state sth. like "IF job j_x depends on j_y AND j_y is not scheduled THEN
drop j_x too."
- optimization
I expected that there are means to "post" an objective functions and go
relax
:o) while GECODE optimizes the solutions found. But I didn't found any. So,
what I think should work, is to post additional constraint dealing with
this in the following way: IF for constraint("Solution_cost < N") there are
non-trivial solutions THEN
post constraint("Solution_cost < N-1");
- starting point
I have currently no idea how this could work.
Questions:
#. Is there a way to post relaxable constraints and set priority on them? #.
Is there a better way of implementing optimization #. (How) can I specify a
"starting point" for generating solutions?
Thank you in advance for any comments you make!
greetz
stanio
_______________________________________________
Gecode users mailing list
users at gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
More information about the gecode-users
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INTERRUPT name
Description
Creates a new interrupt service routine.
Results
Defines name as the label pointing to a new ISR. It can then be used with any of the ON… functions. Interrupt routines should be extremely short and fast! If an interrupt handler is too long, your program may begin to experience problems. Note: INTERRUPT routines must be matched with EXIT.
Example
; create a new interrupt routine interrupt refresh updatesprites exit start: ; set refresh to be called automatically every vblank onvblank refresh
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