--- title: Responsive web design problems and solutions excerpt: 'A good collection of the most common #responsive web design problems, with a discussion on how to solve some of them: http://t.co/CW7NbpJd ' date: '2012-08-19 13:05:48 +0200' date_gmt: '2012-08-19 11:05:48 +0200' categories: - Webdev tags: - tweets - web - design - development ---
A good collection of the most common #responsive web design problems, with a discussion on how to solve some of them: http://t.co/CW7NbpJd


| I l@ve RuBoard |
Did all of the above make sense? If not, don't worry; now that we've had a quick tour, we're going to dig a bit deeper and study the concepts we've introduced in more detail. We met the class statement in our first examples, but let's formalize some of the ideas we introduced. As in C++, the class statement is Python's main OOP tool. Unlike in C++, class isn't really a declaration; like def, class is an object builder, and an implicit assignment—when run, it generates a class object, and stores a reference to it in the name used in the header.
As we saw on our quick tour, class is a compound statement with a body of indented statements under it. In the header, superclasses are listed in parentheses after the class name, separated by commas. Listing more than one superclass leads to multiple inheritance (which we'll say more about later in this chapter):
class <name>(superclass,...): # assign to name data = value # shared class data def method(self,...): # methods self.member = value # per-instance data
Within the class statement, specially-named methods overload operators; for instance, a function called _ _ init __ is called at instance object construction time, if defined.
At the start of this chapter, we mentioned that classes are mostly just namespaces— a tool for defining names (called attributes) that export data and logic to clients. So how do you get from the statement to a namespace?
Here's how. Just as with modules, the statements nested in a class statement body create its attributes. When Python executes a class statement (not a call to a class), it runs all the statements in its body from top to bottom. Assignments that happen during this process create names in the class's local scope, which become attributes in the associated class object. Because of this, classes resemble both modules and functions:
Like functions, class statements are a local scope where names created by nested assignments live.
Like modules, names assigned in a class statement become attributes in a class object.
The main distinction for classes is that their namespaces are also the basis of inheritance in Python; attributes are fetched from other classes if not found in a class or instance object. Because class is a compound statement, any sort of statement can be nested inside its body—for instance, print, =, if, and def. As we've seen, nested defs make class methods, but other assignments make attributes too. For example, suppose we run the following class:
class Subclass(aSuperclass): # define subclass data = 'spam' # assign class attr def __init__(self, value): # assign class attr self.data = value # assign instance attr def display(self): print self.data, Subclass.data # instance, class
This class contains two defs, which bind class attributes to method functions. It also contains a = assignment statement; since the name data is assigned inside the class, it lives in the class's local scope and becomes an attribute of the class object. Like all class attributes, data is inherited and shared by all instances of the class:[2]
[2] If you've used C++, you may recognize this as similar to the notion of C++'s static class data—members that are stored in the class, independent of instances. In Python, it's nothing special: all class attributes are just names assigned in the class statement, whether they happen to reference functions (C++'s methods) or something else (C++'s members).
>>> x = Subclass(1) # make two instance objects >>> y = Subclass(2) # each has its own "data" >>> x.display(); y.display() # "self.data" differs, "Subclass.data" same 1 spam 2 spam
When we run this code, the name data lives in two places—in instance objects (created in the __ init __ constructor) and in the class they inherit names from (created by the = assignment). The class's display method prints both versions, by first qualifying the self instance, and then the class itself. Since classes are objects with attributes, we can get to their names by qualifying, even if there's no instance involved.
| I l@ve RuBoard |


A Story by Ryan LaBouve (@ryanlabouve)
I wanted to do this talk on the sheer power of quantifiable data driving design
Pump through a few hundred thousand views.
Show half Button A
and half Button B
.
Track conversion, whichever one wins is better
But...
What if both of the options are just plain wrong?
You've just decided which of the wrong options is better
Then all I've shown you is how to make the best wrong decision
Instead...
This talk will guide you through the stages of making objective design decisions.
Using data.
Problem Definition
Defining a problem w/ Business, Design, and Engineering
Unifies the design product vision with information from business, design and engineering
A problem well-stated is half-solved.
—John Dewey
A quick tale about this...
In an agile shoppe close to you...
Grab next open task from JIRA
Design the Dashboard (3 points)
What's Wrong with this?
How did this happen?
We need a dashboard
—Business
I have no idea what this means... Let's just find some cool dashboard screenshots in dribbble, and brand them for our company?
—Design
We literally have none of this data... Let's just cut out what doesn't make sense.
—Engineering
This misses like... every critical business goal. There is 0 ROI.
—Business
This should have been obvious, here's a lot more detail about what we actually want.
—Business
Re-designs...—Design
Re-engineers...—Engineering
This wastes a lot of cycles and is a common story in many companies
Lets go back to the start of this conversation
We need a dashboard
—Business
Problems are not Solutions
We need a dashboard
—Business
And this is a problem
Which largely lacks definition.
And we have to work together to do this.
Defining a problem Step 1:
Get the right people involved
Defining a problem Step 1:
Get the right people involved
Create a document answering...
It's important that the whole group comes to terms here.
This is the time where it's cheap and easy to ask questions, disagree, etc.
Refer to this document throughout the lifecycle of a project.
It can help maintain focus, and prevent scope creep
Defining a problem w/ Business, Design, and Engineering
Unifies the design product vision with information from business, design and engineering
Defining who your users are and communicating that with the team.
Users are the lens through which we look to evaluates whether a design makes sense or not.
Users are the lens through which we look to evaluates whether a design makes sense or not.
'There is this company that I worked with a couple of years ago that were doing coupons. Their site looked a little crazy. We said, “We can make this better.” We redesigned all the visual design... We put it back in front of customers, and they looked at this new, fancy visual design that looked really good, and they said, “Wait, who’s behind this thing? Are people making money off of me?” We realized that we screwed it up. We tried to make it beautiful for us, but in doing so, we broke the product.'
https://blog.intercom.io/podcast-braden-kowitz-talks-design-and-startups/It's important to know our users
Users.What comes to your mind?
Google image search: Users
Tons of blank faces...
Knowing our users helps us frame our problems to our solutions.
Know our users help us know we are solving the right problem, the right way.
“We wanted to find out who really uses MailChimp. It was a question posed to us by data analyst Allison last year. We could broadly generalize about our users (savvy, self-reliant, techie, motivated), but we realized that we couldn't rattle off the four or five archetypal MailChimp users.”Jason from Mailchimp
Step 1
Step 2
Step 3
How we interview users.
Step 4
Who is involved here?
Step 5: Deliver finding to company.
Examples of Hi-fi personas
https://blog.mailchimp.com/new-mailchimp-user-persona-research/Sketched Personas
Utilize your whole team: The quick way to solve hard design problems, using your whole team.
Decide the right solution to build, and then build it. (Instead of deciding on a solution to build, and then figuring out you were wrong.)
Best application: Large new features/products
http://www.gv.com/sprint/Sometimes the best solution will come from the least likely spot
If you are depriving teams or employees from participating in the design process, you are missing your best ideas.
Give everyone on your team a way to take part in design process
We already did this!
Empower everyone to do this, not just designers.
Your most surprising and innovative ideas will come from the least likely of places
A few ways to empower individuals to brainstorm.
Especially those who don't feel "that creative"
Bad Sketching
Sticky Notes & Dot Voting
How a design shop might do this:
How a design shop might do this (con't):
Some of our personal rules (at Raisemore):
"We’re going for “goldilocks level fidelity; just good enough to suspend disbelief."
This also give design a good place to start working.
(Prototype, User-Satisfaction) => { Future Direction }
Use the whole team to explore a design problem.
Produces prototypes, validated by users, agreed upon by your team that help you avoid costly mistakes.
Building highly expressive prototypes to help
Determine whether an idea is worth investing in to build using minimal resources.
We talked a little about prototyping in the last slide.
"We’re going for “goldilocks level fidelity; just good enough to suspend disbelief."
Low Fidelity = Primitive Form
Old Version = Primitive Form
Prototype = Smoke and Mirrors
Potentially, All 3!
But today, we'll focus on smoke and mirror...
In a previous job, I worked for OU...
Boss: We are re-designing the OU homepage. We need a final direction on design by tomorrow, and would love your oppinion.
Me:...
This is what I delivered the next day
MGMT's Reaction
This truth is, all of this took 1 hour...
Smoke and Mirrors via Dev Tools
Trying other solutions on for size
Quick, cheap, and surprising helpful in making decisions.
Everything, to this point, was a precursor for...
Numbers are good at telling you what's not working, or what to work on next.
Not how to fix something.
Your Data Is Your Lifeblood — Set up the Analytics It Deserves
Ben Porterfield http://firstround.com/review/your-data-is-your-lifeblood-set-up-the-analytics-it-deserves/"Seriously, Do Not Wait. As soon as your company has users, you need to set up a solid analytics framework. It's not a waste of time or money."
Ben Porterfield http://firstround.com/review/your-data-is-your-lifeblood-set-up-the-analytics-it-deserves/Basic Usage
How much traffic did we get last week from facebook?
What was the response like from our email campaign this week?
What was the response like from our email campaign this week?
Segmentation, Funnels, Retention, A/B Testing, Etc.
Tracking Funnel, allows you to measure changes and improve funnel
Lean Analytics
Intercom on Product Management
For features, it's easy to think:
https://www.intercom.io/books/product-managementWhat you will likely find:
https://www.intercom.io/books/product-managementPossible Disruption:
Track everything.
Numbers can tell you what's working, what's not, and what might be worth it.
Not everything that matters can be measured, and not everything that is measured matters. — Elliot Eisner


Product:
Please select a product
Active:
Start Date
End Date:
Threshold Type:
Quantity
Money
Action Type:
Multiplied by percentage
Reduce money
Send gift
Items:
Save
Cancel


President of the Republic mourns death of GNR
The President of the Republic on 17 November mourned the death in service of a soldier of the Republican National Guard (GNR), as a result of a road accident in Arreigadas, Paços de Ferreira, and the injury of another military man on site.
"It was with deep dismay that the President of the Republic learned later this afternoon of the death in service of the GNR military, Corporal Jorge Gomes, and the wounding of another military man accompanying him on the scene in a signalling operation of a first accident," reads a message by Marcelo Rebelo de Sousa posted on the Presidency of the Republic's website.
The Head of State stated that he was "sensitive to the relevance of GNR's road surveillance mission as a means of deterring risky behaviour and reducing road accidents" and "thanks the mission spirit of all elements of the National Transit Unit at this difficult time".
In the same note, Marcelo Rebelo de Sousa states that he will be "attentive to the recovery of the injured military man" and directs "the most heartfelt condolences to the GNR and the relatives of the military man who lost his life in the fulfilment of his mission".
The car crash on the A42 in Arreigadas, Paços de Ferreira, in the Porto district, caused two deaths, one of which was a GNR military man, three seriously injured and one minor, said the source of the Porto District Relief Operations Command (CDOS).
According to the same source, the car that crashed, crashed into a light vehicle and a trailer, and the deceased GNR military man was run over while assisting another accident a short time earlier at the scene.
The other mortal victim is the driver of the crashed car.


[t]Dear {$cand.voornaam} {$cand.tussenvoegsel} {$cand.achternaam},[/t]
[t]You are invited to apply to a vacancy, please click on the link below.[/t]
[t]{$vacancyTitle}[/t][t]The recruiter added the following personal message for you:[/t]
{$message|escape}
[t]Best regards,[/t]
{$fromName}
Remark: This email has been generated automatically.


No more curious people
With fully protected public storage you get away with curious users who can edit things on browser
We do all the magic
In addition to protecting your public storage we automatically convert all objects for you
Choose your return
Easy integration with older systems with development, debug and stringfy options
Easy and fast link
Download the minified library
by clicking
HERE
UMD Integration
Use module resources with requirejs or browserify to integrate the library.
Facilities and Utilities
Easily develop tools by having native native listing methods and object creation timestamp


Feedback is really appreciated!
Manasvi | Mounvi | Shivam
<EMAIL_ADDRESS>VIT, Vellore
Message
It'd be great if you can tell why you are reaching out to me!


{% extends 'base.html' %} {% block authcontent %} {% if training_cycle and training_cycle.user == user or not training_cycle %}
You are not logged in. Please log in to view this page.


Google Earth Enterprise Documentation Home | Google Earth Enterprise client
Select, add, or change Earth EC servers
You can use Google Earth EC to view globes and maps from your own database servers.
Earth EC lets you do any of the following:
- Add as many database servers as you want.
- Set a default server that is automatically signed in to whenever you start Earth EC.
- Connect to as many as eight servers at the same time.
- Change servers, or sign in and out of servers.
- Remove servers that you no longer use.
Sign in to an Earth EC server
To sign in to a server:
- From Google Earth EC, select File > Server Sign In.
The Select Server window appears. When you start Google Earth EC the first time, Select Server appears automatically.
- Enter your server settings as shown in the table below.
Select Server options
| Setting | Description |
|---|---|
| Server | Select or enter the address for the database server that hosts the globes or maps you want to view. If you don't know the server address, ask your administrator. |
| Port | Select or enter the port for the server. If you don't know the port number, ask your administrator. |
| Always sign in to this server |
Select to skip the Select Server window and automatically sign in to this server whenever you start Earth EC. |
| Disable caching |
Select this if you want to make sure that you're viewing the latest content from the server or if your cached files become corrupted. |
Sign out of an Earth EC server
To sign out of a server database, go to File > Server Sign Out.
Add servers to Earth EC
To add a server database, go to File > Add Database.
The Select Server window appears so that you can enter your server settings.
Connect to multiple Earth EC servers at the same time
Whenever you add another server database, Earth EC signs into the new database and stays connected to your current database. You can view data from up to eight server databases at the same time.
Change your Earth EC server
To change your Earth EC server:
- Go to File > Server Sign Out.
Earth EC signs out of your current server. - Go to File > Server Sign In.
- Select a server from the list that appears.


By Signing up, I aggree to Task Flex's Terms of Service Privacy Policy and Terms of Service.
Already a memeber? Log in


{{- /*gotype: github.com/cockroachlabs/short/internal/server.templateData*/ -}}
The short URL {{ .Link.Short }} was already registered by {{ .Link.Author }}. Update the vanity link below, or select the hijack option to take ownership.


Training¶
The next training is scheduled for 2023. We are cp,[;etomg a training course that includes material on how to run SCHISM, the major assumptions of our Bay-Delta application, tools for applying the model in the Bay-Delta as well visualization of results and introductory material on how to extend the model. The course is interwoven with material on numerical modelling and estuary processes as necessary to understand the model inputs.
Support¶
Please send your questions to<EMAIL_ADDRESS>We’ll field your questions and help you scope any SCHISM-related efforts as best we can. Our hope is that we can help external institutions and modelers become qualified, self-sufficient users.


Lang Thang
view History
please enter some text and pick a language
Select Language
Korean
Thai
Vietnamese
Mandarin Chinese
Japanese
Hebrew
Farsi
Spanish
Italian
French
German
Your results will appear below


Module imagingbook_plugins_demos
Package ImageJ_Demos
package ImageJ_Demos
-
ClassDescriptionThis imageJ plugin demonstrates blending of two images and generating a stack of intermediate images.This ImageJ plugin shows how to change the type of the current image 'in place', i.e., without copying the image.This plugin demonstrates how to create and display a new byte image (to show the histogram of the input image).This ImageJ plugin shows how data can be communicated from one plugin to another.This ImageJ plugin shows how data can be communicated from one plugin to another.This ImageJ plugin shows how to access the one-dimensional pixel array of a 8-bit (= byte) grayscale image.This ImageJ plugin demonstrates the basic use of
GenericDialog
to create a new byte image.Fills the active image with uniform noise.This is a minimal ImageJ plugin (PlugInFilter) that inverts an 8-bit grayscale (byte) image.This plugin does the same asMy_Inverter_A
but is based on thePlugIn
instead of thePlugInFilter
interface.This plugin shows a trick how to automatically load some pre-defined sample image in the case that no other image is currently open.This ImageJ plugin shows how a subimage is extracted from a given image using the bounding box of the currently selected region of interest (ROI).This ImageJ plugin shows how an image operation can be confined to the currently selected region of interest (ROI).This plugin demonstrates how to run another ImageJ "command" (plugin) from our own PlugIn using the IJ.run() method.This plugin demonstrates how to run another ImageJ command (plugin) from our own PlugInFilter using the IJ.run() method.This ImageJ plugin demonstrates how to iterate over the frames (slices) of an image stack.This ImageJ plugin modifies and re-displays the current image repeatedly.


--- layout: post status: publish published: true title: Episode 5.30 - Drenched in Fart Spray author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 8592 wordpress_url: http://ivyenvy.com/?p=8592 date: '2013-09-22 21:53:26 +0000' date_gmt: '2013-09-23 02:53:26 +0000' categories: - Podcast tags: [] comments: [] ---
Andy, Corey and Kurt look at the drama with the Cubs pitching staff. Edwin Jackson, Jeff Samardzija and Kevin Gregg each take issue with management and their decisions. The Cubs decide to give Javier Baez the fall off and the front office will be evaluating Dale Sveum to decide if he will return to manage the Cubs in 2014. We also have a very odd photo caption with an even more odd winning entry.


LINUX-VERSION(1) LINUX-VERSION(1) NAME linux-version - operate on Linux kernel version strings SYNOPSIS linux-version compare VERSION1 OP VERSION2 linux-version sort [--reverse] [VERSION1 VERSION2 ...] linux-version list [--paths] DESCRIPTION linux-version operates on Linux kernel version strings as reported by uname -r and used in file and directory names. These version strings do not follow the same rules as Debian package version strings and should not be compared as such or as arbitrary strings. compare VERSION1 OP VERSION2 Compare version strings, where OP is a binary operator. linux-ver‐ sion returns success (zero result) if the specified condition is satisfied, and failure (nonzero result) otherwise. The valid operators are: lt le eq ne ge gt sort [--reverse] [VERSION1 VERSION2 ...] Sort the given version strings and print them in order from lowest to highest. If the --reverse option is used, print them in order from highest to lowest. If no version strings are given as arguments, the version strings will instead be read from standard input, one per line. They may be suffixed by arbitrary text after a space, which will be included in the output. This means that, for example: linux-version list --paths | linux-version sort --reverse will list the installed versions and corresponding paths in order from highest to lowest version. list [--paths] List kernel versions installed in the customary location. If the --paths option, show the corresponding path for each version. AUTHOR linux-version and this manual page were written by Ben Hutchings as part of the Debian linux-base package. 30 March 2011 LINUX-VERSION(1)


Before Change
"device_fill_size. "
"If --generate-scenarios is given, "
"will generate a job file that uses the block device.")
flags.DEFINE_boolean("prefill_device", False,
"Used when against_device is false: decides if it is "
"required to prefill the device before the fio run. "
"The amount to prefill is given by "
"--device_fill_size.")
flags.DEFINE_string("device_fill_size", "100%",
"The amount of device to fill in prepare stage. "
"The valid value can either be an integer, which "
"represents the number of bytes to fill or a "
After Change
"scenarios are sequential_write, sequential_read, "
"random_write, and random_read. Cannot use with "
"--fio_jobfile.")
flags.DEFINE_enum("fio_target_mode", "against_file_without_fill",
["against_device_with_fill", "against_device_without_fill",
"against_file_with_fill", "against_file_without_fill"],
"Whether to run against a raw device or a file, and whether "
"to prefill.")
flags.DEFINE_string("fio_fill_size", "100%",
"The amount of device to fill in prepare stage. "
"The valid value can either be an integer, which "
"represents the number of bytes to fill or a "


‘Assassin’s Creed’ audio drama pulls cast from ‘Star Wars,’ ‘Buffy,’ more
LOS ANGELES, Nov 19 — Not content to remain a video game franchise, Assassin’s Creed is becoming an episodic audio drama in a collaboration between game publisher Ubisoft and Amazon’s Audible platform.
Riz Ahmed of Venom and Rogue One: A Star Wars Story as well as Anthony Head of Buffy the Vampire Slayer and Tamara Lawrance from TV’s No Offense are teaming up with game franchise actor Danny Wallace to form the principal cast of Assassin’s Creed: Gold.
Continuing the game franchise’s penchant for recreating historical settings for players’ athletic exploration and adventuring, the new series appears set to take place in 17th century Britain, per Syfy’s announcement.
It follows a street criminal called Aliyah Khan who is recruited to the order of the Assassins and encouraged to uncover the buried memories of one of her ancestors.
The name, Assassin’s Creed Gold, can be taken as a reference to currency; the United Kingdom used silver coins at the time that the game takes place and, as a high-ranking official at the Royal Mint, physicist and mathematician Isaac Newton was involved in an official recoinage effort; he’s one of the historical personalities to appear in the audio show.
Assassin’s Creed Gold is to launch on February 27, 2020. — AFP-Relaxnews


{% extends "base.html" %} {% load staticfiles%} {% block content %} features Improve Job Description! Get A Questionnaire! Get Tips! Get Candidates Recomendation! {% endblock %}


These are your stats:
HP = your health.
$ = your money.
Fun = how much fun you're having.
Reasoning, Perception = your current abilities to reason and perceive things.
Click or tap for BOSS programs on; JBER FWA
For the JBER OAP: Click here
For a free ride home: (JBER) JBADD van: 1-907-384-7344 (FWA) SADD van: 1-907-353-4145


A sexy looking HTML5 and Javascript implementation of Craig Reynold's boids algorithm for flocking behaviour. Fully plug n' play, modular and customisable.
Go ahead and fork the repo and open demo.html
to give it a go!
This plugin is available as boids-canvas
on NPM and Bower. Alternatively, you can clone this repository. Use this animation on your webpage simply by:

A number of options are supported:
var options = {
background: '#ecf0f1',
density: 'medium',
speed: 'medium',
interactive: true,
mixedSizes: true,
boidColours: ["#34495e", "#e74c3c", '#2ecc71', '#9b59b6', '#f1c40f', '#1abc9c']
};
var canvasDiv = document.getElementById('boids-canvas');
background
: specify a hexadecimal colour (as a string) or an image pathdensity
: can take values low
, medium
or high
. Defaults to medium
.speed
: can take values slow
, medium
or fast
. Defaults to medium
.interactive
: can take values true
or false
. When true, boids will be attracted to the mouse pointer. Defaults to true
.mixedSizes
: can take values true
or false
. When true, boids will be of varying sizes - larger boids even have more inertia. Defaults to true
.boidColours
: an array of hexadecimal colours (as strings) specifying the colours boids can take (assigned randomly). Defaults to all boids #ff3333


Load R function file
#library(SVMCaT)
source("/Users/hyunhwang/Desktop/Files/1/R/SVMCaT/R/Rfuncs_HH.R")
library(magrittr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(e1071)
library(reshape2)
## Warning: package 'reshape2' was built under R version 3.6.2
## Warning: package 'ggplot2' was built under R version 3.6.2
# loading svm_fit_peak and svm_fit_cell for peak and cell status prediction
svm_fit_peak <- readRDS("/Users/hyunhwang/Desktop/Files/1/R/SVMCaT/svm_fit_peak.rds")
svm_fit_cell <- readRDS("/Users/hyunhwang/Desktop/Files/1/R/SVMCaT/svm_fit_cell.rds")
# 15 peak-level variables
var_list = c("A_l", "A_r", "A_d", "D_l", "D_r",
"Dy_max", "Dy_min", "D2y_max", "D2y_min", "R",
"delta", "delta_l2Dymax", "delta_m2Dymin", "Peak_distance", "Peak_distance_median")
# 4 cell-level variables
cell_var_list = c("n_peak", "n_abnormal", "prop_abnormal", "var_A", "var_delta", "var_R")
Write results into csv files
# update peak status
peak_df = update_peak_status(peak_df)
# peak status prediction
peak_df = peak_data_svm(svm_fit_peak, peak_df)
# cell status prediction
fluo4_data_test = cell_status_prediction(svm_fit_cell, cell_var_list, peak_df)
## `summarise()` ungrouping output (override with `.groups` argument)
Plot calcium transient siganls with detected peaks
n_cells = fluo4_data_test$cell_id # number of cells in calcium transient data
frame = 1:60 # number of frames per transient signal data
pdf("./Peak_plot_test.pdf")
for(i in n_cells){
cell_id = i
# print(cell_id)
x = frame
cells = subset(fluo4_data_test, cell_id == i)
y = unlist(cells[, -(1:3)])
n_peaks = sum(peak_df$cell_id == i)
peaks = subset(peak_df, cell_id == i)
p = ggplot(data = data.frame(x = x, y = y),
aes(x = x, y = y)) +
geom_line(size = 1) +
# geom_smooth(method = "loess", se = FALSE, span = 0.039) +
geom_point(data = data.frame(x = c(peaks$left, peaks$right, peaks$max),
y = y[c(peaks$left, peaks$right, peaks$max)],
peak = c(rep("S", 2*n_peaks), rep("M", n_peaks))),
mapping = aes(x = x, y = y, colour = peak), size = 2) +
annotate("text", x = peaks$max, y = y[peaks$max]*1.01, label = paste0("peak", 1:n_peaks)) +
labs(title = cell_id, x = NULL, y = NULL) +
theme(text = element_text(size = 20), legend.position = "none")
print(p)
}
dev.off()
## quartz_off_screen
## 2
Plot calcium transient signals with assessed cell_status via SVM
pdf("./Signals_with_PredStatus_test.pdf")
lane_list = sort(unique(fluo4_data_test$Well))
for(lane in lane_list){
temp = fluo4_data_test[fluo4_data_test$Well == lane, ]
plot_data = melt(temp[, -(3)])
plot_data$status = factor(plot_data$cell_status)
p = ggplot(plot_data, aes(x = variable, y = value,
group = lane,
color = status)) +
ylab("Intensity") +
geom_line() +
facet_wrap(~cell_id, scales = "free_y") +
guides(color = guide_legend(title="SVMCaT Prediction")) +
scale_x_discrete(name = "Frame",
breaks = c("Frame1", "Frame20", "Frame40", "Frame60"),
labels = c(1, 20, 40, 60))
print(p)
}
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## Using cell_id, cell_status as id variables
## quartz_off_screen
## 2


Post by: Angela Stempfel, Greenhorn
on Aug 30, 2006 01:19:00
hi all
i have some problems with the flexible enum solutiion described here: http://hibernate.org/272.html The following error occurs when calling the nullSafeGet method:
my method looks like that:
My Enum class:
And here the parts of my mapping file:
Some help appreciated! Other solutions with UserTypes and ENUM's are welcome as well.


Star Organic Farm was formed in the year 1988. Initially, the farm's main activities included production and trade of agro items. However, upon establishing goodwill and capturing substantial market share,it further extended it product line to secondary derivatives like fruit pulp, cooking oils and wheat flours.


You might have to wait a sec for your GPS location to turn on full blast and/or refresh the page a couple times till you see your location icon in your phone's toolbar.
Location-based generative music could be really cool. This is a proof of concept.
For example, what if you composed music for each city block within a neighborhood? A listener would be able to traverse the neighborhood how they wished, and to experience the piece they'd have to explore and discover the relationship between the actual place and the music.
How this would probably work is by defining zones, and then you would write the rules for each zone. I’ve started to prototype this and more to come soon.
You could get more granular than a city block though. How about you hear something different when you pass under those trees over there? Or, you could algorithmically generate music for large areas you haven’t even been to, based on city data for example.
Places have aesthetic qualities that can be really evocative, just like music. We have the technology to put the two together. Also, advances on the horizon in AR, computer vision, generative graphics are going to bring about some pretty exciting interactive art/games that you play in the real world, and sound needs to be a component of this. We might be waiting on headset tech to mature, but we can get going on the sound component of this today.
- Use phone accelerometer to know when someone’s likely to be traveling by car vs. walking. What if the beat dropped as you got off the bus?
- Gait detection. Probably possible with phone’s accelerometer. Map the tempo of a song to the rhythm of someone’s steps. These kind of synchronistic relationships between what you’re doing with your body and the music you hear are sort of like what it’s like to plan an instrument. I’m interested to see how it feels.
- Make better music. I’m new to the library I used for synthesis (flocking.js), and the relationship between location and the synth here is pretty one-to-one.


Redis provides several kind of data structures and many functions to manipulate those data. But sometimes additional function is required for satisfying some demand.
The power of Lua
For example, you may need to delete a string only when the string is equal to a specified one but there is no such function. Although this is easy to implement by checking the equality of GET-ed string in client-side program and executing DEL command if necessary, it is difficult to make sure that this operation is atomic and you also have to implement a remote locking mechanism. Fortunately, this problem is easily solve because Redis has a great flexibility to extend its function by built-in Lua interpreter. EVAL and EVALSHA commands evaluate a Lua script atomically and you can execute Redis commands inside Lua script.
Above function is implemented like this:
-- deleq.lua
if redis.call('GET', KEYS[1]) == ARGV[1] then
return redis.call('DEL', KEYS[1])
else
return nil
end
Here is an example to delete mykey if its value is equal to “Redis”.
% redis-cli SET mykey Redis
OK
% redis-cli EVAL "$(cat deleq.lua)" 1 mykey Redis
(integer) 1 # deleted !!
% redis-cli SET mykey redis
OK
% redis-cli EVAL "$(cat deleq.lua)" 1 mykey Redis
(nil) # not deleted
The detail of Lua script and meanings of arguments for EVAL are not explained in this post. There are already some good articles for learning: Lua: A Guide for Redis Users is a very good tutorial and document of EVAL provides a wealth of content.
Constructing a directed graph with weighted edges
As above, Redis becomes a computational environment rather than data store with the help of Lua. Although there are some restrictions on running Lua script in Redis, there still be a wide range of application.
As an example, I wil try to implement the Dijkstra’s shortest path algorithm. Before implementing the algorithm I need a data structure to represent a directed graph with weighted edges. In some usual programming languages, such a graph can be implemented as a list of lists. We can do similar by having a bunch of Redis’s lists but I would like to put everything into a single object. So I pack a node pair into an integer value and associate a weight between the nodes to it. Suppose that nodes of graph are indexed from 0 to n-1, a edge from node i to node j can be represnted by i*n + j
. Then, a Lua script to register weighted graphs edges can be implemented like this:
-- add_edge.lua
local function set_weight(key, n, i, j, w)
redis.call('HSET', key, n*i + j, w)
end
local n = ARGV[1]
local argc = table.getn(ARGV)
for i = 2, argc, 3 do
set_weight(KEYS[1], n, ARGV[i], ARGV[i+1], ARGV[i+2])
end
This script takes a Redis key for storing information of edges, number of nodes and some edge triples in which each consists of source node index, destination node index and weight of the edge.
For instance, above graph can be stored by following command.
% redis-cli EVAL "$(cat add_edge.lua)" 1 graph \
5 0 1 2 0 2 3 1 3 4 1 4 5 2 3 1 2 4 3 3 4 1
% redis-cli HGETALL graph
1) "1"
2) "2"
3) "2"
4) "3"
5) "8"
6) "4"
7) "9"
8) "5"
9) "13"
10) "1"
11) "14"
12) "3"
13) "19"
14) "1"
In the next post, I will implement the Dijkstra’s algorithm over this graph data structure!
comments powered by Disqus


WITNESS LIST
Energy Resources Committee
April 18, 2018 - 9:00 AM
Frac Sand Mining Permian Basin
On:
Brigham, Ben (Self; Atlas Sand)
Gillespie, Hayden (Black Mountain)
Nordloh, Chris (Texas DPS)
Wolf, Charles (Winkler)
Registering, but not testifying:
On:
Hopmann, Randy (Texas Department of Transportation)
Speed, John (Texas Department of Transportation)
Wallace, Hunter (Self; Atlas Sand)
Gas Reliability Infrastructure Program
On:
Brocato, Thomas (Steering Committee of Cities Served by Atmos)
Ryan, Jason (CenterPoint Energy)
Registering, but not testifying:
On:
Evarts, Mark (Railroad Commission of Texas)
Monitor Agencies and Programs
On:
Tintera, John (Texas Alliance of Energy Producers)
Velasco, Aaron (Railroad commission)
Wang, Wei (Railroad Commission)
Young, Michael (Bureau of Economic Geology, UT Austin)
Railroad Commission Hurricane Harvey Response
On:
Fisher, Scott (Texas food and fuel assn)
Parsons, Rich (Railroad Commission of Texas)
Wind Energy Facilities Decommissioning
On:
Clark, Jeffrey (The Wind Coalition)
Wetsel, Roderick (Self)
Workforce Development Permian Basin
On:
Christian, Wayne (Texas Railroad Commission)
Wright, Kylie (Texas Alliance of Energy Producers)
1


| com.miquido.parsepub.model.EbupSpineReferenceModel |
Model of spine element. |
| com.miquido.parsepub.model.EpubBook |
Main model of parsed .epub publication. Contains all information extracted from decompressed .epub. |
| com.miquido.parsepub.model.EpubManifestModel |
Epub manifest model. Contains exhaustive list of all publication resources. |
| com.miquido.parsepub.model.EpubMetadataModel |
Epub book metadata model encapsulating all basic information about publication. |
| com.miquido.parsepub.epubparser.EpubParser |
Main .epub parser class. Allows to input publication path and parse it into model. |
| com.miquido.parsepub.model.EpubResourceModel |
Model of single publication resource. |
| com.miquido.parsepub.model.EpubSpineModel |
Model of publication spine defining publication reading order. |
| com.miquido.parsepub.model.EpubTableOfContentsModel |
Model of publication table of contents. |
| com.miquido.parsepub.model.NavigationItemModel |
Model of table of contents item. |
| com.miquido.parsepub.epubvalidator.ValidationListeners |
Interface for validation .epub publication. Contains methods for handling the missing of important elements in the .epub publication and handling method informing when attribute are missing. |
| com.miquido.parsepub.epubvalidator.ValidationListenersHelper |
An implementation class for the ValidationListeners interface. Contains override interface methods and setter methods which we give body to call in the implemented methods. |


Use OpenMarketCap to get crypto prices and volumes from trusted exchanges
Real trading volumes, KMD one of the least overstated in volume
Not something KMD is responsible for but it shows that there is top 25 volume in KMD making it a more relevant coin than it would seem according to sites as worldcoinindex and cmc
ComputerGenie 👴👴👴29-Mar-19 10:15 AM
making it a more relevant coin than it would seem
Is that even possible, though?
actionslacks29-Mar-19 10:17 AM
people wouldn't be dishonest for profit's sake would they?
ComputerGenie 👴👴👴29-Mar-19 10:17 AM
surely not
it's on the internet, so it has to be true
actionslacks29-Mar-19 10:29 AM
Non-bizarro world valuation would be great for the entire market.
Bad morning everyone.
Junkie just made more money did you?
No? Kept shorting because farm animals told you probably.
 They will now allow institutional clients to stake their Tezos while still keeping it in their vault. They get to keep 20% of your staking returns for the service.
ComputerGenie 👴👴👴29-Mar-19 02:24 PM
80% of staking vapor beats 100% of nothing
NNoperator29-Mar-19 02:29 PM
i can see this working out well in the case of institutional clients who were not permitted to have the coins staking due to risk mitigation.
Hollowman900029-Mar-19 02:33 PM
I still haven’t KYC’d my tezos
10% off current market rate for the Genesis info, think I’d get any takers?
Caglio29-Mar-19 02:59 PM
I used to hate coinbase, but I must say I really like their EARN course
They already donated me over 20kmd for my small but handsome portfolio.... ( :
On the other hand, they already took at least 100kmd due to their really big fees
ComputerGenie 👴👴👴29-Mar-19 03:01 PM
100kmd? I'll trade you, I almost had to pay $25k in taxes because of their shoddy reporting system
fekers even report losses as gains
Caglio29-Mar-19 03:04 PM
I still dont know about that tax shit, cos I never took anything out
ComputerGenie 👴👴👴29-Mar-19 03:06 PM
for an ez math example:
if you sell $10 worth of BTC, take a hit and buy less back at a higher price, and then sell the amount for $8
they report that you made $18
not only did you buy high and sell low, you pay extra tax
Caglio29-Mar-19 03:07 PM
So instead of being 2$ in loss, the report 8$ profit ?


ARC is a global leader in the HR services industry. by serving as a trusted human partner in today's technology-driven world of talents, we sopport people and organisations in realising their true potential.
We are technology-driven professional employer organisation solving the problem of hiring and paying your company's global talent. We understand that identifying and retaining high quality talent is one the biggest and most important challanges facing organisations and this is somthing we take pride in solving. We have a full range of staffing technical recuiting, and retention services that achieve your goals.


I’m just going to touch on brewing equipment for a beginner to intermediate beer brewer. There is a vast amount of equipment on the market to choose from. Much of it depends on complexity and commitment. Like many hobbies, one can spend huge sums of money on the new shiny toys. I consider myself fairly low on the spectrum, I'm a brewer who might turn out three different brews in a year for a total of fifteen gallons. Some homebrewers would consider that a month’s work.
Fermentors
Fermentors are the vessel/container where the wort is placed and the wort has cooled to near room temperatures. This is usually a 6.5 gallon plastic bucket or glass carboy. At this stage the yeast come alive and begins feasting on the sweet wort. Normally this process lasts from one to two weeks. An air water valve is placed on the top of fermentor which allows CO2 out but nothing else in. With use of a glass carboy, one gets to see the process happen where as a plastic bucket is not viewable.
Five Gallon Brewpot
A large stainless steel pot for boiling the wort on the stovetop. Normal boil times are usually an hour or longer. The hops are added in at this step.
ProjectManhattan [CC BY-SA 4.0], via Wikimedia Commons
Secondary Vessels
Once fermentation is complete, many brewers prefer to transfer the brew to a secondary container to age/condition. The bottom of the fermentor is coated with a sludge like substance that is not desired in the final product. Removing the brew ensures the dead yeast/sludge will not get into the final product. This conditioning can last from weeks to months depending on the beer or brewer’s preference. Once conditioning is complete, the brew can be bottled or kegged.
Hydrometer/Refractometer
Hydrometers and Refractometers are tools used to measure the gravity/weight of the brew. One measurement is taken before fermentation and the other after fermentation is complete. Both tools are relative to the gravity of water. By having both the original gravity measurement and final gravity measurement, a brewer can accuarately state the ABV of the brew.


Class: Com::Vmware::Cis::Session
- Inherits:
-
VAPI::Bindings::VapiService
- Object
- VAPI::Bindings::VapiService
- Com::Vmware::Cis::Session
- Defined in:
- /dbc/sc-dbc1215/jrg/vcsuite-sdks/vsphereautosdks-layer1/build/gradle/ruby/sdk/aggregated-bindings/ruby/com/vmware/cis.rb
Overview
The ``Com::Vmware::Cis::Session`` class allows API clients to manage session tokens including creating, deleting and obtaining information about sessions.
* The :func:`Com::Vmware::Cis::Session.create` method creates session token in exchange for another authentication token.
* The :func:`Com::Vmware::Cis::Session.delete` method invalidates a session token.
* The :func:`Com::Vmware::Cis::Session.get` retrieves information about a session token.
The call to the :func:`Com::Vmware::Cis::Session.create` method is part of the overall authentication process for API clients. For example, the sequence of steps for establishing a session with SAML token is:
* Connect to lookup service.
* Discover the secure token service (STS) endpoint URL.
* Connect to the secure token service to obtain a SAML token.
* Authenticate to the lookup service using the obtained SAML token.
* Discover the API endpoint URL from lookup service.
* Call the :func:`Com::Vmware::Cis::Session.create` method . The :func:`Com::Vmware::Cis::Session.create` call must include the SAML token.
See the programming guide and samples for additional information about establishing API sessions.
**Execution Context and Security Context**
To use session based authentication a client should supply the session token obtained through the :func:`Com::Vmware::Cis::Session.create` method . The client should add the session token in the security context when using SDK classes. Clients using the REST API should supply the session token as a HTTP header.
**Session Lifetime**
A session begins with call to the :func:`Com::Vmware::Cis::Session.create` method to exchange a SAML token for a API session token. A session ends under the following circumstances:
* Call to the :func:`Com::Vmware::Cis::Session.delete` method .
* The session expires. Session expiration may be caused by one of the following situations:
* Client inactivity - For a particular session identified by client requests that specify the associated session ID, the lapsed time since the last request exceeds the maximum interval between requests.
* Unconditional or absolute session expiration time: At the beginning of the session, the session logic uses the SAML token and the system configuration to calculate absolute expiration time.
When a session ends, the authentication logic will reject any subsequent client requests that specify that session. Any operations in progress will continue to completion.
**Error Handling**
The :class:`Com::Vmware::Cis::Session` returns the following errors :
* :class:`Com::Vmware::Vapi::Std::Errors::Unauthenticated` error for any errors related to the request.
* :class:`Com::Vmware::Vapi::Std::Errors::ServiceUnavailable` error for all errors caused by internal service failure.
Defined Under Namespace
Classes: Info
Constant Summary
- SERVICE_ID =
static metamodel definitions
VAPI::Core::ServiceIdentifier.new('com.vmware.cis.session')
- CREATE_INFO =
VAPI::Bindings::OperationInfo.new( VAPI::Core::OperationIdentifier.new('create', SERVICE_ID), VAPI::Bindings::OperationInputType.new, VAPI::Bindings::SecretType.instance, { 'com.vmware.vapi.std.errors.unauthenticated' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::Unauthenticated'), 'com.vmware.vapi.std.errors.service_unavailable' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::ServiceUnavailable') }, [], [] )
- DELETE_INFO =
VAPI::Bindings::OperationInfo.new( VAPI::Core::OperationIdentifier.new('delete', SERVICE_ID), VAPI::Bindings::OperationInputType.new, VAPI::Bindings::VoidType.instance, { 'com.vmware.vapi.std.errors.unauthenticated' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::Unauthenticated'), 'com.vmware.vapi.std.errors.service_unavailable' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::ServiceUnavailable') }, [], [] )
- GET_INFO =
VAPI::Bindings::OperationInfo.new( VAPI::Core::OperationIdentifier.new('get', SERVICE_ID), VAPI::Bindings::OperationInputType.new, VAPI::Bindings::ReferenceType.new('Com::Vmware::Cis::Session::Info'), { 'com.vmware.vapi.std.errors.unauthenticated' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::Unauthenticated'), 'com.vmware.vapi.std.errors.service_unavailable' => VAPI::Bindings::ReferenceType.new('Com::Vmware::Vapi::Std::Errors::ServiceUnavailable') }, [], [] )
- SERVICE_INFO =
VAPI::Bindings::ServiceInfo.new( SERVICE_ID, 'create' => CREATE_INFO, 'delete' => DELETE_INFO, 'get' => GET_INFO )
Instance Method Summary (collapse)
-
- (String) create
Creates a session with the API.
-
- (Void) delete
Terminates the validity of a session token.
-
- (Com::Vmware::Cis::Session::Info) get
Returns information about the current session.
-
- (Session) initialize(config)
constructor
Constructs a new instance.
Constructor Details
- (Session) initialize(config)
Constructs a new instance.
120 121 122 |
# File '/dbc/sc-dbc1215/jrg/vcsuite-sdks/vsphereautosdks-layer1/build/gradle/ruby/sdk/aggregated-bindings/ruby/com/vmware/cis.rb', line 120 def initialize(config) super(config, SERVICE_INFO) end |
Instance Method Details
- (String) create
Creates a session with the API. This is the equivalent of login. This method exchanges user credentials supplied in the security context for a session identifier that is to be used for authenticating subsequent calls. To authenticate subsequent calls clients are expected to include the session key.
140 141 142 |
# File '/dbc/sc-dbc1215/jrg/vcsuite-sdks/vsphereautosdks-layer1/build/gradle/ruby/sdk/aggregated-bindings/ruby/com/vmware/cis.rb', line 140 def create invoke_with_info(CREATE_INFO) end |
- (Void) delete
Terminates the validity of a session token. This is the equivalent of log out.
A session identifier is expected as part of the request.
153 154 155 |
# File '/dbc/sc-dbc1215/jrg/vcsuite-sdks/vsphereautosdks-layer1/build/gradle/ruby/sdk/aggregated-bindings/ruby/com/vmware/cis.rb', line 153 def delete invoke_with_info(DELETE_INFO) end |
- (Com::Vmware::Cis::Session::Info) get
Returns information about the current session. This method expects a valid session identifier to be supplied.
A side effect of invoking this method may be a change to the session's last accessed time to the current time if this is supported by the session implementation. Invoking any other method in the API will also update the session's last accessed time.
This API is meant to serve the needs of various front end projects that may want to display the name of the user. Examples of this include various web based user interfaces and logging facilities.
169 170 171 |
# File '/dbc/sc-dbc1215/jrg/vcsuite-sdks/vsphereautosdks-layer1/build/gradle/ruby/sdk/aggregated-bindings/ruby/com/vmware/cis.rb', line 169 def get invoke_with_info(GET_INFO) end |


Hello!
My name is Axit Patel and this is my first blog post! I’ve always wanted to write my own blog, but for one reason or another I’ve always put it off. I fiddled with Wordpress but I couldn’t get it set up the way I would’ve liked. However, with Github Pages and Jekyll I had no excuse to not start! So here we are with this first blog post.
A little about me
I’m an undergraduate at Virginia Tech studying electrical engineering. My main interests include analog & RF circuit design along with embedded system design. I am currently interning at National Instruments as an Analog Hardware intern. In the past I’ve worked at Analog Devices as a Product development & Testing Intern. Both of these companies have been amazing places to work with distinct workplace environments. These internships have provided me with great learning opportunities and I’ve tried to absorb the maximum amount of knowledge that I could from my colleagues during time my there.
Why am I doing this?
So my goal with this blog is to get in the habit of communicating techincal ideas while also creating my own little corner on the Internet. Over the past few years, I have realized that I understand concepts a lot more intuitively when I have to present the ideas in a simple yet correct manner to non-technical people. I will be posting about things that interest me in the field of electrical engineering, and I hope it’ll help others who might have struggled with similar concepts. If you’d like to following along, please subscribe to this blog!
Thanks!


| Interface | Description |
|---|---|
| DatabaseSerializable | |
| Deserializer<A,R> | Deprecated |
| Serializer<A,R> | Deprecated |
| Class | Description |
|---|---|
| FinalTypeConverter<I> | |
| SQLList<T> | |
| SQLObjectConverter |
An advanced converter class which provides utility methods for saving objects to the
desired database and loading objects from it without using the "CREATE TYPE".
|
| Test | |
| TypeConverter<I,O> |
A utility class designed for conversions between types an SQL server is supporting
by default, and a Java type which is not implementing the
DatabaseSerializable interface. |
| TypeConverterOld<A,R> | Deprecated |
| Exception | Description |
|---|---|
| DatabaseAccessException |
Thrown when an error occurs while attempting to communicate to the SQL server due to
invalid connection while the database is explicitly selected.
|
| DataLoadException | |
| DriverNotFoundException | |
| InvalidTypeException |
Thrown when an invalid type is used in serializations or deserializations.
|
| SQLConnectException |
Thrown when an error occurs while attempting to communicate to the SQL server due to
invalid connection.
|
| SQLObjectConnectorException |
| Annotation Type | Description |
|---|---|
| SerializableAs |


comparing-models.Rmd
This vignette demonstrates model comparison in MALECOT. It covers:
This tutorial assumes some prior knowledge about MALECOT, so if you are completely new to the program we recommend working through the simpler bi-allelic tutorial first.
When carrying out Bayesian analysis it is useful to make a distinction between two types of analysis:
As an example, we might create a model \(\mathcal{M}\) in which we write down the probability of the observed data \(x\) as a function of the unknown allele frequencies \(p\). In other words, we write down the likelihood \(\mbox{Pr}(x \: | \: p, \mathcal{M})\).
In Bayesian parameter estimation we are trying to get at the posterior probability \(\mbox{Pr}(p \: | \: x, \mathcal{M})\). We typically do this using MCMC, which produces a series of draws from the posterior distribution.
But what if we want to know the posterior probability of the model, rather than the parameters of the model? In other words, we want to know \(\mbox{Pr}(\mathcal{M} \: | \: x)\). Calculating this quantity requires that we integrate over all unknown parameters:
\[\mbox{Pr}(\mathcal{M} \: | \: x) = \int \mbox{Pr}(\mathcal{M}, p \: | \: x) dp\]
This is often an extremely high-dimensional integral - for example in MALECOT there could easily be hundreds of unknown parameters - making it computationally infeasible by most methods. Regular MCMC cannot help us here because it only produces draws from the posterior distribution rather than normalised values.
One way around this is to use an advanced MCMC technique known as thermodynamic integration (TI). In TI we run multiple MCMC chains, each at a different “rung” on a temperature ladder. The hotter the chain, the flatter the target distribution. The log-likelihoods over all chains are then combined in a single calculation that - by what can only be described as mathematical magic! - is asymptotically equal to \(\log[\mbox{Pr}(x \: | \: \mathcal{M})]\). We can then apply a prior over models, for example giving each model equal weight, to arrive at the desired posterior value \(\mbox{Pr}(\mathcal{M} \: | \: x)\). Generalised thermodynamic integration (GTI) differs from regular TI in that it uses a slightly different calculation when combining information across rungs that leads to lower bias and higher precision.
Hopefully this is enough background to run thermodynamic MCMC in MALECOT, and to understand the results. For those eager to understand all the mathematical details, see this vignette.
For the sake of this tutorial we will use simulated bi-allelic data drawn from \(K = 3\) subpopulations. We create a new project and bind this data:
# simulate data
mysim <- sim_data(data_format = "biallelic", n = 100, L = 24, K = 3)
# create project and bind data
myproj <- malecot_project()
myproj <- bind_data_biallelic(myproj, df = mysim$data, ID_col = 1, pop_col = 2)
For our first parameter set we will assume a very basic model with default parameters; which means a Poisson prior on COI, a flat prior on allele frequencies and no error estimation:
# create parameter set
myproj <- new_set(myproj, name = "simple model")
We will then run the MCMC for values of \(K\) from 1 to 5. What makes this thermodynamic MCMC rather than regular MCMC is the rungs
argument, which dictates the number of rungs on the temperature ladder. We will opt for 10 rungs for now. Be warned - this MCMC will take considerably longer to run than regular MCMC, so be prepared to go make yourself a cup of tea!
# run thermodynamic MCMC
myproj <- run_mcmc(myproj, K = 1:5, burnin = 1e4, converge_test = 1e2,
samples = 1e4, rungs = 10, pb_markdown = TRUE)
## Running MCMC for K = 1
## Burn-in phase
##
|
|=================================================================| 100%
## converged within 200 iterations
## Sampling phase
##
|
|=================================================================| 100%
## completed in 10.2658 seconds
##
## Running MCMC for K = 2
## Burn-in phase
##
|
|=================================================================| 100%
## converged within 200 iterations
## Sampling phase
##
|
|=================================================================| 100%
## completed in 18.2566 seconds
##
## Running MCMC for K = 3
## Burn-in phase
##
|
|=================================================================| 100%
## converged within 200 iterations
## Sampling phase
##
|
|=================================================================| 100%
## completed in 22.5467 seconds
##
## Running MCMC for K = 4
## Burn-in phase
##
|
|=================================================================| 100%
## converged within 200 iterations
## Sampling phase
##
|
|=================================================================| 100%
## completed in 28.0845 seconds
##
## Running MCMC for K = 5
## Burn-in phase
##
|
|=================================================================| 100%
## converged within 200 iterations
## Sampling phase
##
|
|=================================================================| 100%
## completed in 32.6704 seconds
##
## Processing results
## Total run-time: 1.9 minutes
Notice that convergence times are much longer than under regular MCMC. This is because the MCMC is only deemed to have converged when every chain has converged, meaning we are only as strong as the weakest link. When running thermodynamic MCMC it is therefore important to keep an eye on convergence, and to increase the number of burn-in iterations if needed.
There are more moving parts in thermodynamic MCMC, meaning we have more things to check. First, we should perform the same diagnostic checks as for regular MCMC:
plot_loglike_dignostic(myproj, K = 3)
The plot_loglike_diagnostic()
function uses the cold rung by default (in this case the 10th rung), or we can use the rung
argument to produce plots for any given rung, for example:
plot_loglike_dignostic(myproj, K = 3, rung = 1)
Running get_ESS()
now prints out the effective sample size of every rung:
get_ESS(myproj, K = 3)
## rung1 rung2 rung3 rung4 rung5 rung6 rung7
## 782.7099 729.6338 1521.1626 1693.9167 1719.9875 895.9055 1416.8943
## rung8 rung9 rung10
## 1386.5878 1495.3028 1377.8000
We interpret ESS for hot chains the same way as for the cold chain - as the number of independent samples that we have obtained from the target distribution once autocorrelation has been accounted for. If we see small values of the ESS (less than one thousand as a rule of thumb) then we should be concerned that that particular rung has has not explored the space well, and we should repeat the analysis with a larger number of samples. For the sake of this tutorial we will load in the result of re-running this MCMC with samples = 1e5
, which took around 20 minutes.
A useful plotting function when working with multiple rungs is the plot_loglike()
function, which plots the 95% quantiles of the log-likelihood over every rung:
plot_loglike(myproj, K = 3)
This plot should be always-increasing from left to right, aside from small variations due to the random nature of MCMC. If any rungs stand out from the overall pattern then it is worth running plot_loglike_diagnostic()
on these particular rungs, as they may have mixed badly. As with all other checks, this should be performed on every explored value of \(K\).
The GTI method takes the log-likelihood values above and multiplies them by weights, leading to a GTI “path”. The area between this path and the zero line is our estimate of the log-evidence. We can visualise this path using the plot_GTI_path()
function:
plot_GTI_path(myproj, K = 3)
As our evidence estimate is derived from the area between the line and zero, it is important that this path is not too jagged. We are essentially approximating a smooth curve with a series of straight line segments, so if the straight lines cut the corners off the smooth curve then the area will be too large or too small, leading to a biased estimate. There are two ways that we can mitigate this:
GTI_pow
argumentIncreasing the number of rungs will obviously lead to a smoother path, and it will also help with MCMC mixing, but it comes at the cost of slowing down the MCMC. The GTI_pow
argument changes the curvature of the path by modifying the log-likelihood weights, with large values leading to a more concave path. Ideally we want to choose GTI_pow
such that the path is as straight as possible, as this will lead to smallest difference between the true curve and the discrete approximation. The plot above is slightly concave using the default value GTI_pow = 3
, but it is not pathalogically concave. If we were producing results for publication and had a computer sitting idle overnight then we could consider re-running the MCMC with more rungs and a lower GTI_pow
, but otherwise these results are fine.
Once we are happy with our log-likelihood estimates and our GTI path, we can use our log-evidence estimates to compare values of \(K\). The raw estimates can be plotted using the plot_logevidence_K()
function, which plots 95% credible intervals of the log-evidence for each value of \(K\):
plot_logevidence_K(myproj)
We can see immediately that the model favours \(K = 3\) in this case, which agrees with our simulation parameters. If we had seen large credible intervals at this stage then we could re-run the model with a larger number of samples
, but in this example the intervals are non-overlapping and the signal is clear so there is no need.
We can also use the plot_posterior_K()
function to plot the posterior probability of each value of \(K\), which is obtained by taking these raw estimates out of log space and applying an equal prior over \(K\):
plot_posterior_K(myproj)
This second plot is often easier to interpret than the first as it is in units of ordinary probability. In this case we can see that there is a >99% posterior probability that the data were drawn from \(K = 3\) subpopulations (which we know to be true). Again, if we saw large credible intervals at this stage then it would be worth re-running the MCMC with a larger number of samples
, but here there is no need.
One of the major advantages of the model evidence is that we can use it to compare wider evolutionary models, i.e. different parameter sets. Mathematically this is as simple as summing the evidence for each value of \(K\), weighted by the prior.
We can test this by creating a second parameter set that differs from the first in that in that we now estimate the error terms:
# create new parameter set
myproj <- new_set(myproj, name = "error model", estimate_error = TRUE)
myproj
## DATA:
## data format = biallelic
## samples = 100
## loci = 24
## pops = 3
## missing data = 0 of 2400 gene copies (0%)
##
## PARAMETER SETS:
## SET1: simple model
## * SET2: error model
##
## ACTIVE SET: SET2
## lambda = 1
## COI model = poisson
## COI max = 20
## estimate COI mean = TRUE
## estimate error = TRUE
## e1_max = 0.2
## e2_max = 0.2
We then need to run thermodynamic MCMC for this parameter set over the same range of \(K\) values. For the sake of this tutorial we will save time by loading results from file:
## Running MCMC for K = 1
## Burn-in phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## Warning: convergence still not reached within 10 iterations
## Sampling phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## completed in 0.0460471 seconds
##
## Running MCMC for K = 2
## Burn-in phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## Warning: convergence still not reached within 10 iterations
## Sampling phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## completed in 0.065241 seconds
##
## Running MCMC for K = 3
## Burn-in phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## Warning: convergence still not reached within 10 iterations
## Sampling phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## completed in 0.0808711 seconds
##
## Running MCMC for K = 4
## Burn-in phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## Warning: convergence still not reached within 10 iterations
## Sampling phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## completed in 0.0964479 seconds
##
## Running MCMC for K = 5
## Burn-in phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## Warning: convergence still not reached within 10 iterations
## Sampling phase
##
|
|====== | 10%
|
|============= | 20%
|
|==================== | 30%
|
|========================== | 40%
|
|================================ | 50%
|
|======================================= | 60%
|
|============================================== | 70%
|
|==================================================== | 80%
|
|========================================================== | 90%
|
|=================================================================| 100%
## completed in 0.113186 seconds
# uncomment to run MCMC
#myproj <- run_mcmc(myproj, K = 1:5, burnin = 1e4, converge_test = 1e2, samples = 1e5, rungs = 10)
# plot diagnostics
plot_loglike_dignostic(myproj, K = 3)
# report ESS
get_ESS(myproj, K = 3)
## rung1 rung2 rung3 rung4 rung5 rung6 rung7
## 87878.445 84345.409 44904.486 16722.098 8783.592 2156.834 7503.971
## rung8 rung9 rung10
## 8848.234 8602.125 8910.079
Assuming we are happy with the MCMC behaviour we can go ahead and plot the posterior error estimates:
plot_e(myproj, K = 3)
We know from the simulation parameters that there are no errors in the data, meaning this extra flexibility of estimating the error is actually taking the model further away from the truth. Looking at the posterior credible intervals we can see that the model has struggled to arrive at precise estimates of the error rates, instead exploring the full prior range \([0,0.2]\) quite evenly.
We continue with the same analyses as in the previous section:
# use gridExtra package to arrange plots
library(gridExtra)
# produce plots and arrange in grid
plot1 <- plot_loglike(myproj, K = 3)
plot2 <- plot_GTI_path(myproj, K = 3)
plot3 <- plot_logevidence_K(myproj)
plot4 <- plot_posterior_K(myproj)
grid.arrange(plot1, plot2, plot3, plot4)
As with the previous model, the evidence is in favour of \(K = 3\) in this example.
Finally, we come to the issue of comparing parameter sets. Just as with the evidence estimates over \(K\), we can plot the log-evidence of a parameter set using the plot_logevidence_model()
function, or the posterior distribution over models assuming an equal prior using the plot_posterior_model()
function:
# produce evidence plots over parameter sets
plot1 <- plot_logevidence_model(myproj)
plot2 <- plot_posterior_model(myproj)
grid.arrange(plot1, plot2, nrow = 1)
Here we can see that the former, simpler model has a higher evidence than the more complex error estimation model. To understand this, we can imagine the MCMC exploring the parameter space under each model. In the simple model the error is fixed at the correct value of zero, while in the more complex model the MCMC has a finite chance of exploring different error values, all of which are sub-optimal in terms of the likelihood. So the likelihood will, on average, be higher under the simple model. In other words, in the complex model we have introduced additional flexibility that has not been compensated for by a commensurate increase in the likelihood, and so the model evidence naturally punishes the model for being over-fitted. In this example we would conclude that a zero-error model is a better fit to the data, and so we would most likely only report detailed results from this model.
It is clear from this tutorial that thermodynamic MCMC over multiple temperature rungs can take considerably longer to run than regular MCMC. Hence, the next tutorial descibes how to run MALECOT in parallel over multiple cores.


Description Enter ReGIS, clear screen
Source file: animated_pendulum_entergr.f
I/O Operations: Unit ID Unit No Access Form Operation * SEQ FMTD W Operation codes A=rewind,B=backspace,C=close,E=endfile I=inquire,O=open,R=read,W=write
Intrinsic Functions Called CHARACTER CHAR
Local Variables (+ indicates altered content) CHARACTER +ESC


{% extends 'base.html' %} {% set title = 'An Error Occurred' %} {% block styles %} {% endblock %} {% block content %}
You didn't mean to come here, did you?
If you look for a shirt, use the search form at the top.
If you just did that before you arrived here, no products matched your search query.
You can browse through available products from the start page.
If you think this is a bug with the software, you can create an issue.
Before, please make sure the issue hasn't been reported already.


Browse through properties for sale and rent in Nigeria
10 Airport Road Warri, Delta State
: 3 Bedroom18 P.T.I Road, Bendel State
: 4 Bedroom19 Mobil Road, Lagos State
: 2 Bedroom12 Jakpa Crescent, Port Harcourt
: 1 Bedroom10 Airport Road Warri, Delta State
: 3 Bedroom Duplex10 Airport Road Warri, Delta State
: 3 Bedroom19 Mobil Road, Lagos State
: 4 Bedroom Duplex10 Airport Road Warri, Delta State
: miniflatPropertyPro is an online platform that allows individuals to create and/or search for properties that are for sale and rent. To curb the indefinite movement of people in search for a good place of living, we provide agents with the access to post advert for properties in different locations. Users that are interested in a specific property can then contact the respective agent.


29.08.2018
on Wednesday August 29, 2018 @10.04 PM in Vijaya Nagar Function Hall, Ring Road, Rythu Bazar, VIZIANAGARAM, ANDHRA PRADESH


The kids in Mr. Clover's class were putting on a play. It was called "The Cranky Teacher." The class had worked together as writers to make up the script. The students had rehearsed for five weeks, and it was time to perform the play for the entire school. The timid kids were nervous but also excited. With all the actors getting ready at the same time, there was a lot of commotion backstage. When the music started and the curtain went up, the audience got very quiet. Then they started laughing at all the funny parts. The play had a lot of humor in it. At the end of the play, the crowd stood and clapped. "You have all become fine actors!" exclaimed Mr. Clover. "Let's celebrate with a party!" The class returned to the classroom and ate cheese and crackers to toast their success.
in-context


MADCAT
Customer Rating
Customer Name:
Email ID:
Overall Rating:
Would you recommend this agency to your friend?
Yes
No
We love hearing from you! Tell us what you think about this agency
Leave a Review:
Write something!
Choose Agency:
Madcat
Goodcat


Before Change
logger.info(
"Setting up python packages for experiment")
for pkg in experiment.pythonenv:
pipp = subprocess.Popen(
["pip", "install", pkg],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
pipout, _ = pipp.communicate()
logger.info("pip output: \n" + pipout)
for tag, art in six.iteritems(experiment.artifacts):
if fetch_artifacts or "local" not in art.keys():
After Change
if setup_pyenv:
logger.info(
"Setting up python packages for experiment")
if pip_install_packages(
experment.pythonenv, logger) != 0:
logger.info(
"Installation of all packages together " +
" failed, "
"trying one package at a time")
for pkg in experiment.pythonenv:
pip_install_packages([pkg], logger)
for tag, art in six.iteritems(experiment.artifacts):


While you wait for Mr Key to stop shillyshallying and produce this year's Lulu book, the print aficionados among you will be delighted to hear about a new magazine entitled Polarity. The first issue, Death Vs. Taxes, is due out imminently, and its London launch party is to be held next Sunday, 27th June, from 6.00 PM at the Slaughtered Lamb, 34–35 Great Sutton Street, London EC1. Among other enticements, there will be poetry from Carol Watts, songs from the great Peter Blegvad, and some prose from Mr Key himself, making a rare public appearance. All Hooting Yard readers and listeners should, of course, cancel all other appointments and attend. I am told that the dress code for the event is “bird masks optional”.


Text add-on
The Input with text add-on component is used mainly to specify the type of the data being entered, like: currency,
unit of measure, etc.
The informational add-on can be left or right aligned.
Icon add-on
An icon can be used instead of text by providing a 'glyph' attribute.
Button add-on
Adding [button]="true"
to the component turns either the icon add-on or text add-on into a button,
which emits an event addOnButtonClicked($event)
.
Compact mode
Adding [compact]="true"
to the component makes the component compact.
<fd-input-group-number>
The user can increase or decrease a number value using the spinner add-on.
<fd-input-group-search>
This input group automatically shows a clear search button when the user has entered a value in the input.
<fd-input-group-search> Component Within Angular Reactive Form
It is possible to use the input-group-search component within an Angular Reactive Form.


Hi there, If you want to see the details please click on Ledger Details
Click Here For Further Details


Paste your SVG Path expression in the input box. Press "Pretty" to normalize the expression.
Then use the Move and Scale buttons to center your path inside the grey boxes and make it fill
the entire boxes.
When you are happy with the result, copy the result from the Result section to your svg.
- Tip 1
- If the path is distorted when you move it, add a "M 0 0" at the start of the path.
- Tip 2
- If the path is not visible at all, it may be positioned outside the 50 x 50 pixel boxes.
Press "Relative" and then change the M command at the start of the path to "M 0 0".
- Tip 3
- If the path looks like it is missing a section, try to add a "Z" at the end of the path.


Body of ex-Army officer to be flown from Turkey to UK
by By Press AssociationThe body of a former British army officer who helped found the White Helmets volunteer group in Syria is to be flown to London following a post-mortem, Turkey’s state-run news agency said
The Anadolu Agency said James Le Mesurier’s body would be flown aboard a Turkish Airlines flight from Istanbul airport later on Wednesday.
Mr Le Mesurier was found dead outside his home in Istanbul on November 11. Police believe he fell to his death and are investigating the circumstances.
Turkey’s Forensic Medicine Institute said toxicology and other examinations were continuing, adding that a report into his death would be sent to prosecutors “as soon as possible”.
Last week, a top Russian official alleged Mr Le Mesurier was a spy — a claim Britain strongly denies.
Foreign Ministry spokeswoman Maria Zakharova accused him of being a former British agent working in the Balkans and the Middle East.
She claimed he had “been spotted all around the world, including in the Balkans and the Middle East”.
“Given the role of the West in undermining stability in these regions, it is not difficult to assume what the British intelligence officer did there,” she said.
Amnesty International said on Tuesday that there was a possibility of foul play and called for an investigation.
Mr Le Mesurier’s body was found near his home in the Beyoglu district by worshippers on their way to a mosque, according to reports by Anadolu.
Anadolu also said Mr Le Mesurier’s wife told police that her husband had been taking medicine to treat “intense stress”.
Mr Le Mesurier was the founder and chief executive of May Day Rescue, which founded and trained the White Helmets, also known as the Syria Civil Defence.
The group, which has had more than 3,000 volunteers in opposition-held areas, says it has saved thousands of lives since 2013 and documented Syrian government attacks on civilians and other infrastructure.


--- layout: page title: About Me description: This is what I do. background: '/img/bg-about.jpg' ---
I am an Electronics and Telecommunication Undergraduate pursuing my Engineering Degree from Veermata Jijabai Technological Institute, Mumbai. With a Hacker mindset, I have keen interest in finding problems in existing solutions along with finding solutions to existing problems :)
Ever since the beginning of my engineering(Yes I ain't coding ever since my birth) I have been finding areas to explore that fascinates me.
Right from working in the Automobile society(VJTI RACING) of our college to Robotics (Society of Robotics And Automation, VJTI) to Software development to DATA SCIENCE to Blockchain, I have touched upon quite a lot of technological areas. Coming this far I have realised that the field of DATA SCIENCE has got a lot of potential use cases. I have worked in many hackathons and competitions in machine learning/deep learning and have been thereby inspired to dig in further!
Hackathons have indeed played a major role to expose me to the different emerging technologies and their advantages in real world. I currently work in Centre of Excellence,VJTI(COE) on various interesting projects in the domain of DATA SCIENCE and BLOCKCHAIN.
My Research Interests lies in Machine Learning and Deep Learning. I am targeting problems which could be solved using Data Science.
In case of any queries/collaboration/opportunities do reach out to me!


Actionable Messages in Outlook use Adaptive Card JSON to add the actionable card section to an email message. In this unit, we take a look at how the Adaptive Card JSON is added into the email message.
The key part of an Actionable Message is the message card. The card is displayed prominently at the top of a message, using UI elements that integrate with the native UI of the email client. The prominent placement of the card makes it ideal for communicating key information and requesting action from the recipient.
The card is implemented using standard Adaptive Card syntax and features, with some new Outlook-specific features added on.
Let's take a look at a simple example. The following JSON implements a basic card.
{
"type": "AdaptiveCard",
"version": "1.0",
"originator": "5a53e3ea-f50a-44d2-8855-2b5825d63eb8",
"hideOriginalBody": "true",
"body": [
{
"type": "TextBlock",
"text": "Visit the Outlook Dev Portal",
"size": "large"
},
{
"type": "TextBlock",
"text": "Click **Learn More** to learn more about Actionable Messages!"
},
{
"type": "Input.Text",
"id": "feedbackText",
"placeholder": "Let us know what you think about Actionable Messages"
}
],
"actions": [
{
"type": "Action.Http",
"title": "Send Feedback",
"method": "POST",
"url": "https://...",
"body": "{{feedbackText.value}}"
},
{
"type": "Action.OpenUrl",
"title": "Learn More",
"url": "https://docs.microsoft.com/outlook/actionable-messages"
}
]
}
This card uses standard Adaptive Card elements, such as TextBlock
, Input.Text
, and Action.OpenUrl
. It also uses Outlook-specific features, including:
originator
property contains the provider ID assigned during registration of your service. Registration is covered in a later module.hideOriginalBody
property controls whether the Outlook client displays the HTML body of the email message underneath the card. This gives you the option of using the card to completely replace the body, or to use the card to complement the body. For email clients that do not support Actionable Messages, the HTML body is always displayed. Because of this, you may choose to use the HTML body as an fall-back message for clients that don't support Actionable Messages.Action.Http
action sends the recipient's input to the HTTPS endpoint specified in the url
property. This endpoint is a web API that you've implemented to receive the information from the actionable message and process it.The above JSON results in a card that looks like this in Outlook on the web.
Actionable messages are added to email messages by adding a

Visit the Outlook Dev Portal to learn more about Actionable Messages.


MAX-CUT Problem
MAX-CUT problem is the problem in which the vertices of an undirected graphare partitioned into two parts such that the total weight cut edges are more than any other cut for the same graph. Weight is a positive value which is associated with each edge of the graph.
For example: Consider the graph G (V, E), the vertices of which are partitioned into two parts andsuch that.
In the above graph, for each edgeandof cut the vertices and. The weight of cut iswhich is the greater than the weight of any other possible cut of the graph.
Now, for the graph G (V, E), proving that a MAX-CUT problem is a randomized 2 approximation algorithm. Randomized approximation algorithms are used for the generation of approximate solutions for the optimization problems. The solutions of randomized algorithm are not of very good quality as they give the average solutions. There are two possible methods that can be used for proving any problem to be the randomized 2 approximation algorithm.
a. De-randomization using conditional expectations.
b. De-randomization using pair-wise independent hashing
a. De-randomization using conditional expectations:
For a graph G (V, E) in which there are n labels available for vertices, the randomized algorithm designates a random variable uniformly fromto each vertex i with the equal probability . The cut of the graph is denoted by.
Assume that there are number of edges in the cut which is the function of assigned random variables. Now for each edge of designate each edge to be 1 if andare different otherwise set it as 0.
Since, it is adequate that for a partial assignmentthen it will be very easy to calculate the conditional expectation which is. It is true for each edgefrom the linearity of expectation.
Now, for the conditional expectation there are three cases that should be considered as:
1. If there is no value assigned to variable and for vertices u and v then the conditional expectation of the edge will be.
2. If only one variable inand is assigned the value for the edge e the conditional expectation will also be.
3. If both the variableand have a value assigned then the conditional expectation will be 1 if the values of and are different, otherwise it will be 0.
b. De-randomization using pairwise independent hashing:
Pairwise independence means that the probabilistic decisions for a pair of vertices will be independent of each other. The property used here from the randomized algorithm is that for every pair of vertices u, v the probability that u and v can implement different decision is. This property could be achieved using only independent random coin tosses; it cannot be achieved using n independent random coin tosses.
Proof for MAX-CUT problem to be a randomized 2 approximation algorithm can be done by any of the above methods.
De-randomization using pairwise independent hashing method:
Consider refers to the fieldunder the operation of addition and multiplication modulo 2. Assigning value to each vertex a distance vector in the vector space, our choice of approves that the vector space should allot unique element to each vertex. Now, assume thatr be a uniform random vector in, divide all the vertex sets into following subsets:
For any edge, the probability that is equal to the probability that is nonzero. For any non zero vector that is fixed , here is because the set of r satisfying is a linear subspace of of dimension , so total of the all possible vectors r have zero dot product with wand rest of them have non zero product with w.
So, if there is a sample thoroughly at random, the expected weight of partitions defined by is at least half the weight of the maximum cut. The vector space has only vectors with itself, which proves to be a deterministic option to our randomized algorithm.
Else than selecting r at random, finding out the weight of the cut for each
And select the cut which have maximum weight. This is at least as good as selecting r at random.
Therefore, maximum cut problem is a deterministic 2-approximation algorithm with the cost incurred at the running time with factor of.
Linear Programming
Linear programming is a way of achieving the maximum possible outcome from a set of resources that are represented as an integrated mathematical design. In the technical terms the linear programming is a way to achieve optimum results from the linear programming problems. This optimization is subject to a few constraints.
In the single-source shortest-path issue, a shortest path can be found from source vertex s to all the vertices which are present in the graph. In order to do this, for each and every vertex from the considered vertex, the linear programming strategy can be a good choice in which linear equations are formed from the concerned graph.
Standard form of linear program: In a standard form there is a set of p real numbers that are, another set of q real numbers that are and a set ofreal numbers that arewhere,
Now, the objective is to find p real numberssuch that
Maximize
… … (1)
And that is subject to
… … (2)
…… (3)
The first expression is called the objective function. The second and the third expression show the constraints. This way a linear program can be put into the standard form.
Vertex-Cover: For an undirected graph the vertex cover is define as the set of vertices that can cover all of the edges of the concerned graph. Consider the graph the subset P of vertex set V is the group of vertices that can cover all the edgesin the graph that is either the edgeis connected to vertex a or it is adjacent to the vertex b.
For example: Consider the graph where .
In the above graph the red colored vertices are covering the edges of graph G that is vertex cover. The size of vertex cover is the total amount of vertices in the vertex cover set. For the above graph the size of vertex cover is 4.
The vertex cover is called as minimum vertex cover if it contains the minimum number of vertices to cover entire set of edges of the graph that is no more vertices are required.
For example: The minimum cover for the above graph isthe graph is shown below:
The vertex cover of minimum size is also known as optimal vertex cover.
Now, consider the linear-programming relaxation whichis a linear programming of approximating weighted vertex cover. Consider an undirected graph G = (V, E) in which has an associated positive weight w(v).
Now, for any vertex cover. Define the weight of the vertex cover.
To get the vertex cover of least possible weight .Below mention linear program is the
relaxation version for vertex cover of least weight. It is formed from the 0-1 integer program for getting a minimum weight vertex cover. If the constraint remove from 0-1 integer and replace it by.
After replacement, the linear programming version formed is known as linear programming relaxation. Any viable solution of 0-1 integer program in section 35.4, equations (35.14–35.16) is also viable solution to the linear program in equations (35.17–35.20).
Due to this, optimal solution of the linear program gives the lower limit on the value of an optimal solution to the 0-1 linear program, and thus a lower limit on the optimal weight in the minimum-weight vertex cover problem.
Relaxation version of linear programming for minimum weight vertex cover problem is as:
Minimize
… … (35.17)
Subject to
for each … … (35.18)
For each … … (35.19)
For each … … (35.20)
To show that if line (35.19) is removed, from above program, still there is optimal solution that must satisfy for each. To solve the above linear program in polynomial time, assume that is an optimal solution to the above linear program. For the optimization of the program, removing the constraint asked in the problem.
Round every value to the nearest integer,
Set
If
If
Now, finding the set corresponding to solution x’. The set P still is a vertex cover; because of every edge it is correct that and minimum one of or should be minimum ½ and therefore one of u or v will belong to P, which is still satisfying the condition of equation (35.17). The above explanations say that, in a vertex cover and imagine any edge.
Now, according to the constraint (35.18) . This illustrates that at least one ofand is at least (1/2).
Therefore, at least one of u and v is containedin the vertex cover. This means that every edge is covered.
And according to 0-1 linear program, write the constraint that for any edge (u, v), at least one of u and v must be in the vertex cover as.
Hence, it can be said that after removing constraint in equation (35.19) do not make any sense if these are removed from linear program, would get the optimal solution which would be for each.


Disabled
Default
Custom Color
Don't resize handle
Single char labels
CSS Sized Container and Handle
Long and short labels
Event tracking - Before Change
Event tracking - Change
Current value:
...
Event tracking - Boolean Mode Off
Current value:
...
Toolbar Toggles
Form Field Toggles


First
Some text1.1
Some text1.2
Some text1.3
Some text1.4
Second
Some text2.1
Some text2.2
Third
Some text3.1


TvInstallNJ.com’s main purpose is to help residents and companies install and manage their Satellite, Plasma/LCD, Voice, Data, Audio, Video, Home Theater, Surround Sound, Flat Screens, Plasma Televisions, Pre-wire, Post-wire, CATV, POS, Structured Cabling/Wiring & Home Automation Systems.
TvInstallNJ.com’s is a company based out of New Jersey. We cover most parts of New Jersey & New York City.
Swivel, Slim, Tilt or Full 360 Mount, we have installed all types of TVs not only in houses but businesses all around New Jersey.
We install stand-alone sounds bars or full 7.1 wall mounted speakers with in-wall wiring. Room sound test offered as well.
From picking the proper screen size to installing and optimizing the viewing angle, we offer a full service.
We can configure your new universal remote to work with all your peripherals including lights.
Managing wires is an art. Your entertainment center is our canvas - you can call us Leonardo.
We are not a third party that sends local "Handy Men" to do the job of an expert. We are Insured and live and work in New Jersey and stand by our work 100%.
We can install your VOIP Telephone Systems in your office. We have experience with Vonage, Sysco, RingCentral and standard Land Lines.
Full office networking can be crucial to any business. We use top of the line CAT5 and do full packet testing to ensure network stability.
Whether you want landscape lights, or full LED transformation kits installed in your home - we are skilled, qualified and have experience.

Do you have a project that you would want us to fully Pre-Wire or go into an existing project and post wire?
We were one of the first Dish Network and DirectTV installers. We have done it all!


Bored In School Right Now Inc HTML
There is No Game
INSTRUCTIONS
How can there be instructions when there is no game? But try using your mouse anyway.


|
MAX32665 SDK Documentation
0.2
Software Development Kit Overview and API Documentation
|
Structure type representing a SPI Master Transaction request.
Data Fields | |
| uint8_t | ssel |
| Slave select line to use. More... | |
| uint8_t | deass |
| Non-zero to de-assert slave select after transaction. More... | |
| spi17y_sspol_t | ssel_pol |
| Slave select line polarity. More... | |
| const void * | tx_data |
| Pointer to a buffer to transmit data from. More... | |
| void * | rx_data |
| Pointer to a buffer to store data received. More... | |
| spi17y_width_t | width |
| Number of data lines to use, see spi17y_width_t. More... | |
| unsigned | len |
Number of transfer units to send from the tx_data buffer. More... | |
| unsigned | bits |
| Number of bits in transfer unit (e.g. More... | |
| unsigned | rx_num |
Number of bytes actually read into the rx_data buffer. More... | |
| unsigned | tx_num |
Number of bytes actually sent from the tx_data buffer. | |
| spi_callback_fn | callback |
| Callback function if desired, NULL otherwise. | |
| uint8_t spi_req_t::ssel |
(Master only)
| uint8_t spi_req_t::deass |
(Master only)
| spi17y_sspol_t spi_req_t::ssel_pol |
| const void* spi_req_t::tx_data |
NULL if undesired.
| void* spi_req_t::rx_data |
NULL if undesired.
| spi17y_width_t spi_req_t::width |
| unsigned spi_req_t::len |
| unsigned spi_req_t::bits |
8 for byte, 16 for short)
| unsigned spi_req_t::rx_num |


Garments firm staff robbed at gunpoint
by Staff ReporterA garments company employee was allegedly robbed by four bike-borne men at gunpoint in Shahdara’s Geeta Colony on Friday, the police said.
The victim, Manoj, was on his bike and was coming from Kailash Nagar to Silai Centre when the incident happened. Four men on two motorcycles attacked him while he tried to resist robbery, they said.
According to the police, the victim was carrying money and cheques that were to be deposited in bank. He was robbed of approximately ₹15 lakh.
A case has been registered and several teams have been formed to nab the accused, a senior police officer said. Manoj works in a garments company and his job is to carry and deposit money in the bank.