class: center, middle, inverse, title-slide # Estimation via bootstrapping --- class: center, middle # Inference --- .question[ What does inference mean? ] -- - Statistical inference is the process of using sample data to make conclusions about the underlying population the sample came from - Types of inference: testing and estimation - Today we discuss estimation, next time testing --- class: center, middle # Confidence intervals --- ## Confidence intervals A plausible range of values for the population parameter is a **confidence interval**. .pull-left[ ![spear](img/spear.png) ] .pull-right[ ![net](img/net.png) ] - If we report a point estimate, we probably won’t hit the exact population parameter. - If we report a range of plausible values we have a good shot at capturing the parameter. --- ## Variability of sample statistics - In order to construct a confidence interval we need to quantify the variability of our sample statistic. - For example, if we want to construct a confidence interval for a population mean, we need to come up with a plausible range of values around our observsed sample mean. - This range will depend on how precise and how accurate our sample mean is as an estimate of the population mean. - Quantifying this requires a measurement of how much we would expect the sample mean to vary from sample to sample. .question[ 👤 Suppose you randomly sample 50 students and 5 of them are left handed. If you were to take another random sample of 50 students, how many would you expect to be left handed? Would you be surprised if only 3 of them were left handed? Would you be surprised if 40 of them were left handed? ] --- ## Quantifying the variability of a sample statistic We can quantify the variability of sample statistics using - simulation: via bootstrapping (today) or - theory: via Central Limit Theorem (later in the course) --- class: center, middle # Bootstrapping --- ## Bootstrapping <img src="img/boot.png" style="float:right"> - The term **bootstrapping** comes from the phrase "pulling oneself up by one’s bootstraps", which is a metaphor for accomplishing an impossible task without any outside help. - In this case the impossible task is estimating a population parameter, and we’ll accomplish it using data from only the given sample. - Note that this notion of saying something about a population parameter using only information from an observed sample is the crux of statistical inference, it is not limited to bootstrapping. --- ## Rent in Manhattan .question[ 👤 How much do you think a typical 1 BR apartment in Manhattan rents for? ] --- ## Sample On a given day, twenty 1 BR apartments were randomly selected on Craigslist Manhattan from apartments listed as "by owner". ```r library(tidyverse) manhattan <- read_csv("../../data/manhattan.csv") ``` .small[ .pull-left[ ```r manhattan %>% slice(1:10) ``` ``` ## # A tibble: 10 x 1 ## rent ## <dbl> ## 1 3850 ## 2 3800 ## 3 2350 ## 4 3200 ## 5 2150 ## 6 3267 ## 7 2495 ## 8 2349 ## 9 3950 ## 10 1795 ``` ] .pull-right[ ```r manhattan %>% slice(11:20) ``` ``` ## # A tibble: 10 x 1 ## rent ## <dbl> ## 1 2145 ## 2 2300 ## 3 1775 ## 4 2000 ## 5 2175 ## 6 2350 ## 7 2550 ## 8 4195 ## 9 1470 ## 10 2350 ``` ] ] --- ## Parameter of interest .question[ 👤 Is the mean or the median a better measure of typical rent in Manhattan? ] .small[ ![](u2_d06-bootstrap_files/figure-html/unnamed-chunk-4-1.png)<!-- --> ] --- ## Observed sample vs. bootstrap population .pull-left[ ![rent-bootsamp](img/rent-bootsamp.png) Sample median = $2350 😱 ] -- .pull-right[ ![rent-bootpop](img/rent-bootpop.png) Population median = ❓ ] --- ## Bootstrapping scheme 1. Take a bootstrap sample - a random sample taken with replacement from the original sample, of the same size as the original sample. 2. Calculate the bootstrap statistic - a statistic such as mean, median, proportion, slope, etc. computed on the bootstrap samples. 3. Repeat steps (1) and (2) many times to create a bootstrap distribution - a distribution of bootstrap statistics. 4. Calculate the bounds of the XX% confidence interval as the middle XX% of the bootstrap distribution. --- ## Let's bootstrap ![boot-by-hand](img/bootstrap-by-hand.png) --- class: center, middle # Bootstrapping in R --- ## Tidyverse package: `infer` The objective of this package is to perform statistical inference using an expressive statistical grammar that coheres with the tidyverse design framework. ![ht-diagram](img/ht-diagram.png) ```r # install.packages("infer") library(infer) ``` -- Also, let's set a seed: ```r set.seed(20180307) ``` --- ## Generate bootstrap medians ```r manhattan %>% # specify the variable of interest specify(response = rent) ``` --- ## Generate bootstrap medians ```r manhattan %>% # specify the variable of interest specify(response = rent) # generate 15000 bootstrap samples generate(reps = 15000, type = "bootstrap") ``` --- ## Generate bootstrap medians ```r manhattan %>% # specify the variable of interest specify(response = rent) # generate 15000 bootstrap samples generate(reps = 15000, type = "bootstrap") # calculate the median of each bootstrap sample calculate(stat = "median") ``` --- ## Generate bootstrap medians ```r # save resulting bootstrap distribution rent_ci_med <- manhattan %>% # specify the variable of interest specify(response = rent) %>% # generate 15000 bootstrap samples generate(reps = 15000, type = "bootstrap") %>% # calculate the median of each bootstrap sample calculate(stat = "median") ``` --- ## The bootstrap sample .question[ 👤 How many observations are there in `rent_ci_med`? What does each observation represent? ] ```r glimpse(rent_ci_med) ``` ``` ## Observations: 15,000 ## Variables: 2 ## $ replicate <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1... ## $ stat <dbl> 2350.0, 2350.0, 2495.0, 2350.0, 2349.0, 2325.0, 2300... ``` --- ## Visualize the bootstrap distribution ```r ggplot(data = rent_ci_med, mapping = aes(x = stat)) + geom_histogram(binwidth = 50) + labs(title = "Bootstrap distribution of medians") ``` ![](u2_d06-bootstrap_files/figure-html/unnamed-chunk-12-1.png)<!-- --> --- ## Calculate the confidence interval A 95% confidence interval is bounded by the middle 95% of the bootstrap distribution. .small[ ```r rent_ci_med %>% summarize(lower_bound = quantile(stat, 0.025), upper_bound = quantile(stat, 0.975)) ``` ``` ## # A tibble: 1 x 2 ## lower_bound upper_bound ## <dbl> <dbl> ## 1 2162. 2875 ``` ] --- ## Visualize the confidence interval ![](u2_d06-bootstrap_files/figure-html/unnamed-chunk-15-1.png)<!-- --> --- ## Interpret the confidence interval .question[ 👥 The 95% confidence interval for the median rent of one bedroom apartments in Manhattan was calculated as (2162.5, 2875). Which of the following is the correct interpretation of this interval? ] (a) 95% of all one bedroom apartments in Manhattan have rents between $2162.5 and $2875. (b) We are 95% confident that the median rent of all one bedroom apartments is between $2162.5 and $2875. (c) We are 95% confident that the median rent of one bedroom apartments in this sample is between $2162.5 and $2875. --- class: center, middle # Accuracy vs. precision --- ## Confidence level **We are 95% confident that ...** - Suppose we took many samples from the original population and built a 95% confidence interval based on each sample. - Then about 95% of those intervals would contain the true population parameter. --- ## Commonly used confidence levels Commonly used confidence levels in practice are 90%, 95%, and 99% -- .question[ 👤 Which line (blue dash, green dot, orange dash/dot) represents which confidence level? ] ![](u2_d06-bootstrap_files/figure-html/unnamed-chunk-16-1.png)<!-- --> --- ## Precision vs. accuracy .question[ 👥 If we want to be very certain that we capture the population parameter, should we use a wider interval or a narrower interval? What drawbacks are associated with using a wider interval? ] -- ![garfield](img/garfield.png) -- .question[ 👤 How can we get best of both worlds -- high precision and high accuracy? ] --- ## Calculating confidence intervals at various confidence levels .question[ 👥 How would you modify the following code to calculate a 90% confidence interval? How would you modify it for a 99% confidence interval? ] ```r manhattan %>% specify(response = rent) %>% generate(reps = 15000, type = "bootstrap") %>% calculate(stat = "median") %>% summarize(lower_bound = quantile(stat, 0.025), upper_bound = quantile(stat, 0.975)) ``` --- ## Recap - Sample statistic `\(\ne\)` population parameter, but if the sample is good, it can be a good estimate. - We report that estimate with a confidence bound around it, and the width of this bound depends on how variable sample statistics from different samples from the population would be. - Since we can't continue sampling from the population, we instead bootstrap from the one sample we have to estimate the sampling variability. - We can do this for any sample statistic: - We did it for a median today, `calculate(stat = "median")` - Doing it for a mean would just take `calculate(stat = "mean")` - And you'll learn about calculating bootstrap intervals for other statistics


Engage with leaders who are passionate about using digital tools to enhance their work.
Build technical and professional skills through collaborative and group-oriented workshops
Learn how to teach, adapt, and share our open curricula with others.
The goal for DHRI@SMU is to create an intensive, community-oriented, and foundational approach to learning technical skills in service of humanities teaching and learning.
DHRI at SMU is a program that is working towards building and sustaining an active community around the shared idea of Digital Humanities and Digital Scholarship.


Install VMWare Workstation, create CentOS machine & configure bridge network for wlan0 and install Oracle 12c on ArchLinux 64bit
27 Nov 2013Part I: Installation of VMWare Workstation
Download from official site: VMware-Workstation-Full-9.0.2-1031769.x86_64.bundle from terminal:
chmod +x VMware-Workstation-Full-9.0.2-1031769.x86_64.bundle
sudo ./VMware-Workstation-Full-9.0.2-1031769.x86_64.bundle
,then you install the patch from aur:
yaourt vmware-patch
,then you install the kernal_headers and run this if path is not correct when you try to install them with the vmware gui.
ln -s /usr/src/linux-3.11.4-1-ARCH/include/generated/uapi/linux/version.h /usr/src/linux-3.11.4-1-ARCH/include/linux/version.h
This is because the version of your kernal_headers should match with linux kernal version, so you might want to update the kernal as well. , then run the
sudo vmware-modconfig --console --install-all
and all modules should be up and running without failure. ,then you need to add the license key with
sudo /usr/lib/vmware/bin/vmware-enter-serial
Part II: Configure the bridge network for the centOS machine
For the bridge networking to be working you need to explicitly point out the wlan0 is the bridge internet adapter. you need to do this:
sudo /usr/lib/vmware/bin/vmware-netcfg
in this popup set the vmnet0 (which is not shown in the ifconfig of the host) and set it to wlan0. Otherwise it is set to automatic and picks the eth0 instead of wlan0. After you power on the virtual machine you need to set the networking in the centos. go to:
cd /etc/sysconfig/network-scripts/
i had eth3 instead of eth0 (centos’s ifconfig), so i copy the the ifcfg-eth0 to 3 and change the network to (00:50:56:34:7C:AE, which you get if you clicked bridged and change the networking mac address, which i did), then the file should look like this, if you copy it:
scp ifcfg-eth3 <IP_ADDRESS>:/home/ljupcho/Downloads
file:
DEVICE=eth3
HWADDR=00:50:56:34:7C:AE
TYPE=Ethernet
UUID=1f969148-80a4-4105-ab9e-dcff960fe8f8
ONBOOT=yes
NM_CONTROLLED=yes
BOOTPROTO=dhcp
GATEWAY=<IP_ADDRESS>
restart networking
/etc/init.d/network restart; yum update
Part III: Install Oracle 12c on CentOS 64bit
These are the key points for the oracle install. You should give at least 20GB space for you VM so you won’t have to add more latter. for this error: bash: xhost: command not found this helped me:
yum install xorg-x11-server-utils
for this error: xhost unable to open display First I had to install:
yum groupinstall “X Window System”; init 6
then this in terminal:
[root@centos ~]# export DISPLAY="<IP_ADDRESS>:10.0"
[root@centos ~]# xhost +
access control disabled, clients can connect from any host xhost: must be on local machine to enable or disable access control.
, then you need to ssh to the centOS machine with the oracle user you’ve created, but you do it with: ssh -Y<EMAIL_ADDRESS>instead of ssh -X, then navigate to database folder of your oracle 12c archive and ./runInstaller. You would do a -Y because the input fields of the installer Oracle GUI won’t be editable, you won’t be able to type in. (this problem also might be because you have java 1.7 so if issue still exists downgrade to java 1.6) . Then follow the standard Oracle 12c installation tutorial with screen shots, you should be fine.


The Pynq
(Python Productivity for Zynq) Package¶
This section describes the pynq
package for the PYNQ-Z1 platform.
After powering the board with the Micro SD card in place, the board will boot into Linux. Python3 is installed with Jupyter Notebook support. The Python package pynq allows users to access overlays from Python.
Some preinstalled features of this Linux image include:
- Networking is enabled, and the board will attempt to get an IP address from a DHCP server on the network. If a DHCP server is not found, the board will fallback and assign itself a static IP of <IP_ADDRESS> by default. This default IP address can be changed.
- Samba, a file sharing service, is enabled. This means that the linux home area can be accessed from a Windows machine by navigating to or mapping
\\pynq\xilinx
(Windows) orsmb:pynq/xilinx
(Mac/Linux) . The sambausername:password
isxilinx:xilinx
. Files can be copied to and from the board, and the Pynq source code, and notebooks can be accessed and modified using your preferred editors on your host PC. - A Jupyter Notebook server is initialized on port 9090 and automatically starts after boot.
- The base overlay is preloaded in the FPGA fabric.
Python pynq
Package Structure¶
All Pynq code is contained in the pynq Python package and is can be found on the board at /home/xilinx/pynq. This package is derived from the Github repository and the latest version can always be installed from <GitHub repository>/python/pynq
.
Pynq contains four main subpackages: board
, iop
, drivers
, and bitstream
; a tests
subpackage is available for testing the user subpackages. Each of the five subpackages are described below.
To learn more about Python package structures, please refer to the official python documentation.
board¶
This folder contains libraries or python packages for peripherals available on the PYNQ-Z1 board: button, switch, rgbled and led. For example the following code will turn on one of PYNQ-Z1’s LEDs:
from pynq.board import LED
led = LED(0)
led.on()
iop¶
This folder contains libraries for the following Pmod devices Pmod_ADC
, Pmod_ALS
, Pmod_DAC
, Pmod_DPOT`, Pmod_LED8
, Pmod_OLED
, Pmod_PWM
, Pmod_TIMER
, and Pmod_TMP2
.
The following Grove peripherals are also supported: Grove ADC
, Grove Buzzer
, Grove IMU
, Grove LED bar
, Grove Light Sensor
, Grove OLED
, Grove PIR
, Grove Temperature Sensor
.
Arduino_Analog
, and Arduino_IO
are provided for interfacing to the arduino interface.
In addition, Pmod_IO
, Pmod_IIC
and DevMode
are developer classes allowing direct low level access to I/O controllers.
There is also an additional module named _iop.py
; this module acts like a wrapper to allow all the Pmod classes to interface with the MicroBlaze inside an IOP. The _IOP
class prevents multiple device instantiations on the same Pmod at the same time. (i.e. This prevents the user assigning more devices to a port than can be physically connected.) _IOP
uses the overlay class to track each IOP’s status.
Note
_iop.py
is an internal module, not intended to be instantiated directly use. In Python, there is no concept of _public_ and _private_; we use _
as a prefix to indicate internal definitions, variables, functions, and packages.
For example, the following code will instantiate and write to the Pmod_OLED attached on PMODA.
from pynq import Overlay
from pynq.iop import Pmod_OLED
from pynq.iop.iop_const import PMODA
ol = Overlay("base.bit")
ol.download()
pmod_oled = Pmod_OLED(PMODA)
pmod_oled.clear()
pmod_oled.write('Welcome to the\nPynq-Z1 board!')
bitstream¶
This folder contains the base.bit and the base.tcl. The base.bit is the precompiled overlay and base.tcl provides the information of the hardware it is built from.
drivers¶
This folder contains various classes to support audio, video, DMA, and Trace_Buffer.
tests¶
This folder includes a tests package for use with all other pynq subpackages. All testing is done using pytest. Please see The Verification Section to learn more about Pynq’s use of pytest to do automated testing.
Note
The tests
folders in board
, iop
, drivers
, and others rely on the functions implemented in the test
folders of the pynq package. This common practice in Python where each subpackage has its own tests
. This practice can keep the source code modular and self-contained.
documentation¶
To find documentation for each module, see the Pynq Package for documentation built from the actual Python source code.


Web Application for conducting High School Programming Contests.
HSPC Web App
Description
This was my Capstone project for Marshall University Computer Science department. The project was designed and developed by a team of 7 members. The essence of the project is to create a project that can be used by the University to conduct programming contests for High School Students. The contest will be announced on the website. Teachers can register teams of students for their school. Once registered the details of the contest will be provided to the teachers. The teams will be given time to work on their projects for a predetermined period (could be a semester or an academic year). Once the students are satisfied with their results they can acknowledge their respective teachers about completion. The teachers can then submit the project files for review. The submitted files will be assigned to University selected judges for grading and remarks. Once grading has been completed the University will post the leaderboards on the contest website. The leaderboard will contain information on how the project was graded and what could be done to improve. The top 3 projects will be recognised by the Univeristy and awarded on campus.
Environment – PHP, HTML/CSS, JavaScript, MySQL, JQuery, Bootstrap, XAMPP, SQLLog, Notepad++.
Simple 2D Game Using PhaserJS Game Framework.
Volcano Runner
Play the game here VolcanoRunner!
Description
This is the project I'm working on currently. The game is developed using HTML5/CSS3 and PhaserJS game development framework. The Objective of the game is simple, collect all the stars and avoid the fiery volcanos. The game runs on the browser and unfortunately, as of now the browser needs to be refreshed to play again. However, I plan to add more challenging environments and make it a multi-level game with more options to explore and support for mobile devices. Enjoy!
Environment – HTML5/CSS3, JavaScript, PhaserJS game framework, Atom, Github.
Responsive Portfolio Using Bootstrap
Description
Bootstrap was the first responsive framework I learned. It was amazing to see how much the framework transformed my portfolio from messed up layouts in different screen sizes to responsive layouts that gave smooth transitions of viewing on different devices. And all of it was done in a day's with fresh knowledge of the concept and understanding the grid system. I am currently working on migrating all the data into an AngularJS application to include more features and better effects. Responsive design is one of the most simple yet highly regarded areas of web-development is an understatement. I hope you enjoy the portfolio as much I enjoyed developing it.
Environment – HTML5/CSS3, Bootstrap, Atom, Github.
Autonomous Arduino Car connected to IBM Bluemix.
Obstacle detection - Multiple Sensors - HD Camera - Live feed to IBM Bluemix.
Description
I built my own version of the famous Arduino Car. The car has hardware from Sparkfun technologies. The car has a proximity and IR sensor array for obstacle detection. The car has temperature, humidity, dew, heat, pressure and light detection sensors. The car has a live HD camera that feeds wirelessly to a display. The car has a Wi-Fi shield and Bluetooth module for connectivity. The car is connected to the IBM bluemix cloud for publishing live sensor data in JSON format. This was achieved with the help of the IBM bluemix quick start recipe which enables any device to get registered to the cloud and publish available data instantly.


{% load static %} {% load widget_tweaks %}
CORK Admin Template - Scrumboard Application
CommOn | Delete task
Are you sure you want to delete
({{task_item.task_name}})
{% csrf_token %}
Cancel
Confirm


Class 10 is counted amongst the most important classes in a student's life as the topics studied in these Classes form the base for higher grades as well. These NCERT Class 10 Maths Class 10 PDF are prepared by the well-trained and experienced Maths experts and are provided here to help students in understanding the concepts easily. NCERT is one of the best Mathematics books which is designed as per the syllabus of the higher-level exams also. Students who are studying in Class 10 are recommended to refer to Maths NCERT Class 10 PDF for Class 10 to strengthen their problem-solving skills.
Class 10 is a whole new experience for you as it will be the very first time when you will be appearing for the board exams. Speaking of which, a thorough knowledge of all the chapters along with NCERT solution for Class 10 Maths can help you to understand the subject better in less time. There are times when you might want to go through the chapters details first before starting with the preparation. Indeed, it is a good practice to follow as you will be well-aware of what is there in that particular chapter beforehand. For the same reason, we have provided the chapters, topics and exercises included in each of them. There are a total of 15 chapters in Class 10 Maths NCERT Book. Starting from chapter 1, Real Numbers to the last chapter, i.e. chapter 15, Probability, we have mentioned all the crucial details below. Scroll down to get well-versed with every chapter of CBSE Class 10 Maths.
According to the Exam pattern for CBSE, Maths will be of total 100marks where 80 marks will be dedicated to theory paper and 20 marks will be for internal assessments. To score maximum marks in theory, focus on objective types question more as according to the CBSE revised pattern, 25percent questions will be objectives as it is now being considered as the standard method of competitive exams. To attend more objective questions you need to practise a lot of Class 10 Maths NCERT Class 10 PDF. The total number of questions is also increased to 40 questions from 30 questions, keeping the total marks constant (i.e, 80marks). Undoubtedly, the number of questions has increased but marks per question are reduced converting maximum questions to objective types or very short answer type.
Find the list of chapters covered in Class 10 Maths NCERT Class 10 PDF with an insight into the topics covered under each of them.NCERT Class 10 Maths Chapter 1, Real Numbers, is the extension of the topics learnt in Class 9. Sections 1.2 and 1.3 of this chapter contain some very important properties of positive integers, namely Euclids division algorithm and the Fundamental Theorem of Arithmetic. Furthermore, you will be revisiting Irrational Numbers in section 1.4 whereas Revisiting Rational Numbers and Their Decimal Expansions will be included in Section 1.5. Euclids division algorithm has a lot to do with divisibility of integers. It has a plethora of applications associated with the divisibility properties of integers. NCERT Class 10 Maths Chapter 1, Real Numbers will be highly beneficial to compute the HCF of two positive integers
| UNIT NO. | UNIT NAME | MARKS |
|---|---|---|
| 1. | NUMBER SYSTEM | 06 |
| 2. | ALGEBRA | 20 |
| 3. | COORDINATE GEOMETRY | 06 |
| 4. | GEOMETRY | 15 |
| 5. | TRIGONOMETRY | 12 |
| 6. | MENSURATION | 10 |
| 7. | STATISTICS AND PROBBILITY | 11 |
| TOTAL | 80 |
| SECTION | MARKING SCHEME |
|---|---|
| A | 20 Questions of 1 Mark each. |
| B | 6 Questions of 2 Mark each. |
| C | 8 Questions sof 3 Mark each. |
| D | 6 Questions of 4 Marks each. |
| TOTAL | 40 Questions carrying 80 Marks. |


This is a platform to help people interested in music to find each other, whether you want to teach someone an instrument
or if you are want to learn an instrument, we are going to help you find each other easily.
Below you can try the search function to see if you can find something you are looking for.
Teacher: {{$ctrl.returnName(lesson.user)}}
List of instruments:
Start day: {{$ctrl.returnDay(lesson.start_day)}}
Start time: {{lesson.start_time}}
End time: {{lesson.end_time}}
Frequency: {{lesson.frequency}}
Teacher Rating: {{$ctrl.returnRating(lesson.user)}}


---
title: Spartan Robotics Spaghetti Dinner and Silent Auction
date: Saturday, November 17, 2018
---
Location:
Central United Methodist Church, Salem, VA 24153
Come see the robot and support Spartan Robotics Team to help with travel, food,
and lodging expenses for this upcoming season.
Dinner includes: spaghetti, garlic bread, salad, drink & dessert.
Adults: $8.00, Children (6 to 12): $5.00, Children 5 and under: FREE.
A special thanks to Mamma Maria's
of Salem for donating the spaghetti and garlic knots!


‘Community’ reunion: cast confirms they’ll return if Dan Harmon writes a movie
"Sounds like it starts with you, Dan"
The cast of NBC sitcom Community have said they’ll return to the show if creator Dan Harmon writes a movie.
The cast held a reunion this week (November 10) as part of the Vulture Festival.
Attending the panel were cast members Joel McHale, Alison Brie, Gillian Jacobs, Danny Pudi, Jim Rash, Yvette Nicole Brown, Ken Jeong alongside writers Chris McKenna and Dan Harmon – basically everyone who worked on and starred in the show minus Donald Glover – aka Childish Gambino – to talk about their memories and favourite moments from the series.
Across Community‘s tenure on screens, the somewhat joke tagline of “six seasons and a movie” has been said time and time again, and was brought up during the talk when the moderator asked what it would take in order for a Community film to be made. “One million dollars for Donald [Glover],” McHale replied.
Community‘s Troy & Abed
Dan Harmon then responded, saying: “Who’s supposed to say ‘everyone do this?’ That’s what I’ve always said. How does it happen?”
“Sounds like it starts with you, Dan,” Alison Brie responded, before Harmon imagined a scenario where he wrote the script before the cast ended up backing out. As Vulture report, the cast’s response to this was to publicly attach themselves to the idea of a Community movie.
The new commitment follows Alison Brie claiming that she was “up for” a Community film back in 2017, and that it could happen “in a few years”.
But there was less excitement for the idea of Chevy Chase’s return, after he was accused of making racially-motivated digs at Donald Glover during the filming of the show.
According to a 2017 New Yorker profile about Glover, veteran co-star Chase “often tried to disrupt his scenes and made racial cracks between takes”.
Harmon described the actor as a “self-described legend,” and said that much of the dialogue for Chase’s character Pierce Hawthorne – who often made ill-advised comments – stemmed from meetings in Chase’s trailer.


Success is the result of perfection, hard work, learning from failure, loyalty, and persistence*.
Our products make learning easier. Learn from experts all around the world, achive your goals....

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This library can be used by C# clients to make the signing of transactions on the client side and not to transfer private keys to the server.
The essence of the library is to generate a transaction according to the required operations (vote, comment, etc.), sign the transaction and broadcast to the Graphene-based blockchain.


Provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.
| Version: | 2.0 |
| Depends: | R (> 3.0.1), methods, tools |
| Published: | 2016-11-22 |
| Author: | Rosaria Lombardo and Eric J Beh |
| Maintainer: | Rosaria Lombardo <rosaria.lombardo at unina2.it> |
| License: | GPL (> 2) |
| URL: | https://www.R-project.org |
| NeedsCompilation: | no |
| In views: | Psychometrics |
| CRAN checks: | MCAvariants results |
| Reference manual: | MCAvariants.pdf |
| Package source: | MCAvariants_2.0.tar.gz |
| Windows binaries: | r-devel: MCAvariants_2.0.zip, r-release: MCAvariants_2.0.zip, r-oldrel: MCAvariants_2.0.zip |
| OS X binaries: | r-release: MCAvariants_2.0.tgz, r-oldrel: MCAvariants_2.0.tgz |
| Old sources: | MCAvariants archive |
Please use the canonical form https://CRAN.R-project.org/package=MCAvariants to link to this page.


[gecode-users] Implementing scene allocation symmetry break in gecode-python
Michael Rand
begbie00 at gmail.com
Sat Jun 29 14:05:38 CEST 2013
Hi,
Here's some c++ code implementing the symmetry break in the scene allocation
problem:
//Symmetry breaking
//Assign shot day for scene imposing an order
post(*this, shoot[0] == 1);
for(sceneCount=1; sceneCount<numScenes; sceneCount++){
IntVar currentMax(*this,1,maxDays);
IntVarArray shot_slice(*this,sceneCount,1,maxDays);
//Get Shot days from 0 to sceneCount
int parcialShoot;
for(parcialShoot=0; parcialShoot< sceneCount; parcialShoot++){
shot_slice[parcialShoot] = shoot[parcialShoot];
}
//Each scene must be assigned to an already assigned scene day
//or to the successor of the max assigned day
max(*this,shot_slice, currentMax);
post(*this, shoot[sceneCount] <= 1+currentMax);
}
I would think the same constraint would be constructed in gecode-python as
follows (with s = the space object):
s.rel(shoot[0],cp.IRT_EQ,1)
for scene_count in xrange(1,num_scenes):
current_max = s.intvar(1, max_days)
shot_slice = s.intvars(scene_count, 1, max_days)
for partial_shoot in xrange(0, scene_count):
shot_slice[partial_shoot] = shoot[partial_shoot]
s.max(shot_slice, current_max)
s.rel(shoot[scene_count], cp.IRT_LQ, current_max + 1)
But this raises the following exception:
Traceback (most recent call last):
...
File "solver.py", line 61, in solveIt
s.rel(shoot[scene_count], cp.IRT_LQ, current_max + 1)
TypeError: unsupported operand type(s) for +: '_gecode.IntVar' and 'int'
How do I implement the "+1" portion of the constraint if I can't add an int
and an IntVar? Do I need to use some kind of linear equation constraint with
a "plus_one" intvar?
More information about the users
mailing list


--- layout: post status: publish published: true title: iOS > Test API about getting text size. author: display_name: hojunbaek login: hojunbaek email<EMAIL_ADDRESS>url: '' author_login: hojunbaek author_email<EMAIL_ADDRESS>excerpt: 더욱 더 정밀해진 아이폰 API들... wordpress_id: 2193 wordpress_url: http://ezcocoa.com/?p=2193 date: '2014-01-19 06:41:25 +0900' date_gmt: '2014-01-19 06:41:25 +0900' categories: - iPhone tags: - sizeWithAttributes - sizeWithFont comments: [] ---
It's new API to get the size of text for iOS7.
NSDictionary *attributes = @{NSFontAttributeName:[UIFont systemFontOfSize:14]}; CGSize titleSize = [str sizeWithAttributes:attributes]; NSLog(@"New API > string width->%f, height->%f",titleSize.width, titleSize.height);
It's deprecated API to get the size of text on iOS7.
titleSize = [str sizeWithFont:[UIFont systemFontOfSize:14]]; NSLog(@"Old API > string width->%f, height->%f",titleSize.width, titleSize.height);
# New API > string width->70.322000, height->16.702000 # Old API > string width->71.000000, height->17.000000
The new api is correct more then the old api. but the new api is only able to use on iOS7. if you support the under version of iOS 7. you should use the old one.


What’s interesting about the DSRM password is that the DSRM account is actually “Administrator”. This means that once an attacker has the DSRM password for a Domain Controller (or DCs), it’s possible to use this account to logon to the Domain Controller over the network as a local administrator.
We can confirm this with Mimikatz by creating a new AD user with a known password. Set the DSRM acount password sync from the domain user account and compare the hashes.
DSRMTest NTLM Password Hash: 2b391dfc6690cc38547d74b8bd8a5b49
Administrator (500) Local Account NTLM Password Hash: 2b391dfc6690cc38547d74b8bd8a5b49
The second graphic shows a local Administrator account on the DC called “Administrator” with the same password hash as the DSRMTest domain user account.


The Affinity Photo New Rock Photographer 2020 Challenge #2: Crowd & Venue Scenes
by https://www.facebook.com/LouderPosts/Join the second stage of our hunt to find the best new rock photographer of 2020
Louder has joined forces with with image editing software Affinity Photo to find the best new rock photographers of 2020. Last month's heat was about live music photography and won by a stunning shot of Damian Wilson crowd-surfing.
Challenge #2 is Crowd & Venue Scenes. We are looking for new, original unpublished live shots of audiences – they could be taken at your local, at festivals or indoor live shows – but they should be shots that capture audience reactions, enthusiasm, as well as the scale of the audience, or the attitude, style and fashion of the fans.
Here are some shots from Classic Rock photographer and judge Kevin Nixon to give you some inspiration:
Affinity Photo picture-editing software is about speed, power and precision - and so is the best rock music. This photo competition is about finding new photographers who capture the spirit of rock’n’roll and helping them to get the best from their photographs.
The competition will run over the next six months with a series of monthly challenges based around different aspects of music photography: live shots, portraits, shooting in black and white etc. The competition is free to enter and there are monthly prizes for the winners of each category. At the end of the six months, the judges will choose one overall winner who will receive:
- iPad Pro 11” + Apple Pencil + Affinity Photo for iPad
- The opportunity to go on assignment with Louder/Classic Rock/Metal Hammer/Prog photographer Kevin Nixon (OR - if geography makes that impossible - doing a Skype/online workshop with Kevin).
- The winning pictures will be published online on Louder and in our sister magazines (Classic Rock, Metal Hammer and Prog).
Additionally, all entrants will be able to buy the award-winning photo editing software Affinity Photo, on PC or Mac, for less than half price - £23.99 instead of the usual £48.99.
Affinity’s John Atkin says: “Even the best photographers adjust their images before publication, and Affinity Photo is perfect whether you want to make quick corrections or complex, multi-layered edits.
“The low one-off price and no monthly subscription means it’s ideal for photographers on a budget, or who might not be using it all the time, while still delivering all of the editing capabilities the top pros require. And it’s all backed up with a whole load of free, easy-to-follow video tutorials that can help make you a better photographer.”
TO ENTER:
All entries must be submitted using our online entry form and received by 31 November 2019. Terms and conditions are at the bottom of the entry form. General T&Cs are here.


| DOSE: |
3 hits |
smoked |
Cannabis |
(plant material) |
Ah, yes. Marijuana. I think everyone remembers their first time, and this was mine.
It started sunday night. I was sitting at home, with aim on, and I saw that my best friend was online.  We talked some more, and somehow the conversation came to pot.
I told him I had tried pot before but never really got that stoned, for various reasons.
'YOU'VE NEVER BEEN STONED?!', he practically screamed (I'll refer to him as B from now on, he also happens to be kind of a pothead).
I told him that yes, I have never been stoned. Well he was going to fix that. He told me to drive over to his house, then from there we'd go over to another kids house (I don't know his name, so I'll call him J.). From there we'd drive over to J's brother's house, where the stoning would begin.
So I get to B's house, and we drive to J's, pick him up, and head over to J's brother's, whose name I also don't know but I'll call him C. We arrive at his house, and we all sit down watch a little TV. Adult swim has just started, and home movies was just ending (fortunately). C. busts out the really, really nice two-foot bong, packs it, and J. Takes a hit.
(just to remind you, B. = Best friend, who gave me a ride. J. = Friend of B., someone who I don't know. C. = J's older brother, major stoner.)
So anyways, J. takes a hit, then passes it to B., who takes a hit, then B. passes the bong to me, the weed newbie, and teaches me how to smoke from it.
 Then I pull this little thing out and you inhale as much as you can and hold it in as long as possible, then exhale.'
I did this 2 times, and I did pretty well. I was feeling pretty damn good, too. They were huge hits, by the way, according to everyone who was there. I walked over to the couch, sit down, and enjoy the feeling. Then C. hands me the bong again.

I was still sitting on the couch, and my brain was just kind of rolling. My entire body felt heavy, and everything seemed to be moving in slow motion. Whenever I'd close my eyes, I'd see these weird visuals, one in particular. You know those things that they used to have in old movies, where it would be a circle with a number in it, and it would count down to the beginning of the movie, like 4... 3... 2... 1...? well, I saw one of those things counting down, repeatedly, hundreds of times, only it looked like it was made out of pixellated, glowing bricks. Think of how things look like in Super Mario brothers, the NES game. That's how most of my visuals were like, pixellated and crude-looking. They all occurred in a glowing octagon and I had basically no control over them. I also remember making really amazing combinations of different words but I can't remember the exact combinations.
I wasn't really enjoying this. The visuals were nice, and so was the dopamine coursing through my veins, but I had never been under the influence of anything in my entire life, and existing for even a short time without being in full control of my brain actually kind of freaked me out. I could have freaked out right then and there but managed to get in control of myself.
I don't know how much time passed, but I'd guess around 5 minutes, then I vomited. A lot.  I threw up all over my shirt, while sitting on the couch, then everyone realized something was wrong and managed to get me to stumble to the bathroom. I never threw up in the toilet once though, I basically just covered the walls, and myself, in puke.
When I got into the bathroom, I still kept throwing up. I couldn't move at this point. Actually, I could move, I just really didn't want too. I laid in my own puke for maybe an hour, J., B., and C., coming by occasionally, saying 'holy shit' and trying to get me to move out of the bathroom and take off my vomit-covered shirt and pants, which I later did. People kept trying to make me drink water but I didn't feel like it.
Not only did I not want to move, I changed positions only occasionally. I leaned up against the wall and stayed perfectly still for about 30 minutes. I kept my eyes closed because it took too much strength to open them, and I still kept getting those NES-rendered visuals. I could understand what people were saying but didn't feel like talking and I could have moved but didn't want to. I kept dry-heaving and having trouble breathing, and someone commented that I looked really pale. My mouth tasted like vomit, so did my throat, so I kept throwing up because that's just really disgusting, not to mention I was sitting in my own puke while covered in my own puke.
I managed to move to a more comfortable position, laying on my cheek on the cold tile floor, still not really moving but still dry heaving a lot.
This lasted for what I think was an hour and a half. Finally, everyone convinced me to get in the back of B.'s car, and we drove back to B.'s house. The whole drive over there I remember I had my eyes closed the whole time but not much else.
Anyways, we got out of B.'s car and I stumbled over to my car, where I slept for about 2 hours.
When I woke up, I was still kind of stoned but not incapacitated like I was earlier. B. woke me up and we walked over from my car to his house at around 3 am. His mom was up, and I had to walk past her while B. gave his mom some sort of excuse as to why I was walking half-naked into his house at 3 am with vomit-encrusted hair, partially disoriented. I stumbled up the stairs and took a shower, washing my hair like 4 times to make sure I got all the puke out and then I dried off and slept on the couch until around 10 am.
Anyways, I woke up and was starting come off the high a little more although I still had sort of a hang-over. B. gave me some clothes to wear since my other clothes were disgusting, and we threw my jeans and shirt in the washing machine, then the dryer. We just hung out until my clothes were clean. When they were, I put them back on and drove home. Even though hours had passed since I smoked, my mind was still in kind of a haze.
So anyways, I went home and I slept for a couple hours and had a cheeseburger, which tasted really good because I was starving. I haven't been back to C.'s house, but I assume he' cleaned up the bathroom and couch by now. I left my zippo over there, but I don't think I'll go back to get it, I guess it's sort of like payment for throwing up all over his house.
So that's the story of my first marijuana experience, as best as I can remember it. J. said I was more stoned than anyone he's ever seen. Will I ever smoke weed again? Probably. But I'll definitely make sure I never get that stoned ever again.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Buy and Sell Cryptocurrency
Mine the Cryptocurrency
Be aware, mining has become extremely competitive !
See the section dedicated to mining for more informations.
[ Previous ]
Buy and Sell Cryptocurrency [ Next ]


Existential Risk
16 Sep 2017Imagine being on a spaceship travelling through the hostile space at 30 km/s. Outside, it is freezing cold. The hull of the spaceship is keeping you warm and protecting you from deadly ultraviolet radiation. On the spaceship there is a limited supply of water, food, fuel and other necessities. A responsible captain would do everything in her power keep the passengers safe. She would struggle to avoid collision with asteroids, onboard explosions, overheating of the spaceship, degradation of the protecting hull and depletion of the limited resources. The name of the spaceship is planet earth. Its protecting hull is the atmosphere, ozone layer and its magnetic field.
To me, this analogy makes it clear how fragile and insignificant our life on earth is. This fact is excellently captured by the speech “Pale Blue Dot” by Carl Sagan. Our life as a species is threatened by several existential risk. To illustrate this, Max Tegmark made a graphic showing how life as we know it could be wiped out.
On the big time scales, we are concerned with astronomical and geological threats. These are for example the death of the sun, earth getting hit by asteroids and supervolcanoes causing volcanic winters. These are threats that are non anthropogenic and hopefully we will be able to handle them with the technology available when the time comes. On the small time scales, the threats are post by humanity itself. They range from human activity causing climate change and loss of biodiversity to carelessly dealing with powerful future technology. In the light of these threats, I am not worried about getting hit by an asteroid.
An existential catastrophe is something that, by definition, never has happened in all of human history. Therefore, we can not us our old technique to learn from our mistakes - by then it is too late. In stead, we must develop technological foresight if we want to survive the century. Even though an event is unlikely, it is worth safeguarding against it if its consequences are devastating. This is referred to as the precautionary principle. Elon Musk applies this principle to climate change by calling the emission of greenhouse gases into the atmosphere “The Dumbest Experiment in History”. The same principle should be applied for powerful future technology such as general artificial intelligence (AGI), synthetic biology and molecular nanotechnology.
In a wider perspective, an existential risk is defined by Nick Boström, a philosopher working in the field, as:
The core is that an existential risk are those risks that threaten the entire future of humanity - not just the present generation, but all future generations. But Boström’s scope of existential risk is a bit bigger, it also incorporates situations such as permanent stagnation, flawed realization and subsequent ruination. Basically, he argues that not fulfilling the highest potential of technological development constitutes an existential risk. I am not sure I agree with him. I think it would be alright for humanity to follow a path of where the full technological potential is not fulfilled if that state could be maintained and protected for human extinction. What do you think?
References
- Nick Boström, Existential Risk Prevention as Global Priority
- Centre for the Study of Existential Risk
- existential-risk.org
- R. Buckmister Fuller, Operating Manual for Spaceship Earth
- Max Tegmark, Our Mathematical Universe: My Quest for the Ultimate Nature of Reality
- Wikipedia, Global catastrophic risk


| 1 |
Walmart Model (KNN) |
Regression |
Kaggle Walmart Sales Forecasts |
| 2 |
Walmart Model (Random Forest) |
Regression |
Kaggle Walmart Sales Forecasts |
| 3 |
Walmart Model (Linear Regression) |
Regression |
Kaggle Walmart Sales Forecasts |
| 4 |
HW Digit (KNN) |
Classification |
Kaggle Handwritten Digit Comp |
| 5 |
HW Digit (Random Forest) |
Classification |
Kaggle Handwritten Digit Comp |
| 6 |
HW Digit (Linear Regression) |
Classification |
Kaggle Handwritten Digit Comp |


--- layout: page current: consultancy title: Consultancy navigation: true logo: 'assets/theme_images/logomain.png' class: page-template subclass: 'post page' cover: 'assets/images/default.jpg' ---
Consultancy pagina hier


Welcome to BurgerOnline
Select burger
This is where you execute burger selection
{{burger.name}}
- Contains lactose and gluten
- {{burger.kCal}} kCal
{{burger.name}}
- Contains lactose
- {{burger.kCal}} kCal
{{burger.name}}
- Contains gluten
- {{burger.kCal}} kCal
Customer information
This is where you provide neccesary information
Delivery information:
Click in the map!
Open dispatcher in new window.
T
Male
Female
Non-binary
Undisclosed


Void QuantConnect.Algorithm.QCAlgorithm.OnMarginCall ( List<SubmitOrderRequest> requests )
List<SubmitOrderRequest>
Margin call event handler. This method is called right before the margin call orders are placed in the market.
Void - This method provides no return.
Void
Definition at line 1040 of file Algorithm/QCAlgorithm.cs.


MTC
menu
MTC Profile
Name
hhh
Incharge Name
jjj
Number of Beds
15
Email
<EMAIL_ADDRESS>Address
hhh
Location
23.66665,25,87765
Bharuch MTC
Sam Shukla
Children Under Treatment
Waiting List of Children
Discharged Children
MTC Details
LOGOUT


Its about a project of the course Principles of Operating Systems (IC6600) in Technological Institute of Costa Rica that wants to develop a web-server aplication to emulate a File System.
The purpose of this project is to implement a Web drive for files. The intention is to put into practice knowledge about File Systems. For this, the functions of the File System that are broken down later will be implemented. It is a Web application where users can request a space for file storage. All the information management of the files is achieved from a .json file.
{
"accounts":{
"user":"file.json",
...
}
}


Contrast material: Barium sulfate suspension Water-soluble contrast
Gas granules: Yes No
None.
Preliminary radiograph:
Swallowing:
Esophagus:
Gastroesophageal reflux:
Stomach:
Duodenum:
Other findings:
Fluoroscopy time: minutes
Normal examination.


--- layout: page title: "Web Video Text Tracks (WebVTT)" description: "This specification defines WebVTT, the Web Video Text Tracks format. Its main use is for marking up external text track resources in connection with the HTML <track> element. WebVTT files provide captions or subtitles for video content, and also text video descriptions, chapters for content navigation, and more generally any form of metadata that is time-aligned with audio or video content." ---
| Document Name: | W3C TR http://www.w3.org/TR/webvtt |
|---|---|
| Document URI: |
http://www.w3.org/TR/webvtt
|
| Online Version: |
http://www.w3.org/TR/webvtt
|
| Organization: | World Wide Web Consortium (W3C) |
| Series: | Technical Report (TR) |
| Abstract: | This specification defines WebVTT, the Web Video Text Tracks format. Its main use is for marking up external text track resources in connection with the HTML <track> element. WebVTT files provide captions or subtitles for video content, and also text video descriptions, chapters for content navigation, and more generally any form of metadata that is time-aligned with audio or video content. |
text/vtt
Return to ( Series | Organization | all Specifications )


| Modifier and Type | Field and Description |
|---|---|
static PlasmaEqualityHelper |
INSTANCE |
| Modifier and Type | Method and Description |
|---|---|
boolean |
equal(commonj.sdo.DataObject dataObject1,
commonj.sdo.DataObject dataObject2)
Two DataObjects are equal(Deep) if they are equalShallow, all their
compared Properties are equal, and all reachable DataObjects in their
graphs excluding containers are equal.
|
boolean |
equalShallow(commonj.sdo.DataObject dataObject1,
commonj.sdo.DataObject dataObject2)
Two DataObjects are equalShallow if they have the same
Type and all their compared Properties are
equal. |
static PlasmaEqualityHelper |
instance() |
public static volatile PlasmaEqualityHelper INSTANCE
public static PlasmaEqualityHelper instance()
public boolean equal(commonj.sdo.DataObject dataObject1, commonj.sdo.DataObject dataObject2)
commonj.sdo.helper.EqualityHelper
Two DataObjects are equal(Deep) if they are equalShallow, all their
compared Properties are equal, and all reachable DataObjects in their
graphs excluding containers are equal. The set of Properties compared are
the instance properties
where
property.getType().isDataType() is false, and is not a container property,
ie !property.getOpposite().isContainment()
Two of these Property values are equal if they are both not
set
, or all the DataObjects they refer
to are equal
in the context of
dataObject1 and dataObject2.
Note that properties to a containing DataObject are not compared which
means two DataObject trees can be equal even if their containers are not
equal.
If the type is a sequenced type, the sequence entries must be the same. For
each entry x in the sequence where the property is used in the comparison,
equal(dataObject1.getSequence().getValue(x),
dataObject2.getSequence().getValue(x)) and
dataObject1.getSequence().getProperty(x) ==
dataObject2.getSequence().getProperty(x) must be true.
A DataObject directly or indirectly referenced by dataObject1 or dataObject2 can only be equal to exactly one DataObject directly or indirectly referenced by dataObject1 or dataObject2, respectively. This ensures that dataObject1 and dataObject2 are equal if the graph formed by all their referenced DataObjects have the same shape.
Returns true if the trees of DataObjects are equal(Deep).equal
in interface commonj.sdo.helper.EqualityHelper
dataObject1
- DataObject to be compareddataObject2
- DataObject to be comparedpublic boolean equalShallow(commonj.sdo.DataObject dataObject1, commonj.sdo.DataObject dataObject2)
commonj.sdo.helper.EqualityHelper
Two DataObjects are equalShallow if they have the same
Type
and all their compared Properties are
equal. The set of Properties compared are the
instance properties
where
property.getType().isDataType() is true and property.getType() is not
ChangeSummaryType.
Two of these Property values are equal if they are both not
set
, or set to an equal value
dataObject1.get(property).equals(dataObject2.get(property))
If the type is a sequenced type, the sequence entries must be the same. For
each entry x in the sequence where the property is used in the comparison,
dataObject1.getSequence().getValue(x).equals(
dataObject2.getSequence().getValue(x)) and
dataObject1.getSequence().getProperty(x) ==
dataObject2.getSequence().getProperty(x) must be true.
equalShallow
in interface commonj.sdo.helper.EqualityHelper
dataObject1
- DataObject to be compareddataObject2
- DataObject to be comparedPlasmaSDO™ and PlasmaQuery™ are trademarks of TerraMeta Software, Inc. Copyright © 2011 - All Rights Reserved.


Space Walk MIDI
Scene 1
1
--
value:
--
range from:
range to:
2
--
value:
--
range from:
range to:
3
--
value:
--
range from:
range to:
4
--
value:
--
range from:
range to:
5
--
value:
--
range from:
range to:
6
--
value:
--
range from:
range to:
7
--
value:
--
range from:
range to:
8
--
value:
--
range from:
range to:
9
--
value:
--
range from:
range to:
Scene 2
1
--
value:
--
range from:
range to:
2
--
value:
--
range from:
range to:
3
--
value:
--
range from:
range to:
4
--
value:
--
range from:
range to:
5
--
value:
--
range from:
range to:
6
--
value:
--
range from:
range to:
7
--
value:
--
range from:
range to:
8
--
value:
--
range from:
range to:
9
--
value:
--
range from:
range to:


Annotate 1 Set a data Tag.
Press 1 to set a data Tag
You may set as many tags as you require.
Data tags equate a variable with a value.
Use DDE and the trap buffer to exchange the data with external programs.


No more saving
unknown Contacts
use our App
HOME
Send Message in WhatsApp without saving Number
Clear
Send Message


What is Peer-Tutor?
Peer-Tutor is a web app dedicated to connecting students, fostering healthy relationships and academic success.
It connects:
a student wishing to get tutored- known as Peer
with a student willing to offer tutoring services-known as Tutor
through Session Scheduling and Matching
A student can be both a Peer and a Tutor


Add Sensor
Enabled:
Name:
URL:
User ID:
PIN:
Interface:
None
Bluetooth
WiFi
Trigger:
Cool
Heat
Auto
Batch Id:
Process:
Measurement Type:
Update Time:
Synch Status:
Save


rmiregistry
Section: User Commands (1)Updated: 05 Aug 2006
Index Return to Main Contents
NAME
rmiregistry - The Java Remote Object Registry
The rmiregistry command starts a remote object registry on the specified port on the current host.
SYNOPSIS
rmiregistry [port]
DESCRIPTION
The rmiregistry command creates and starts a remote object registry on the specified port on the current host. If port is omitted, the registry is started on port 1099. The rmiregistry command produces no output and is typically run in the background. For example:
rmiregistry &
A remote object registry is a bootstrap naming service that is used by RMI servers on the same host to bind remote objects to names. Clients on local and remote hosts can then look up remote objects and make remote method invocations.
The registry is typically used to locate the first remote object on which an application needs to invoke methods. That object in turn will provide application-specific support for finding other objects.
The methods of the java.rmi.registry.LocateRegistry class are used to get a registry operating on the local host or local host and port.
The URL-based methods of the java.rmi.Naming class operate on a registry and can be used to look up a remote object on any host, and on the local host: bind a simple (string) name to a remote object, rebind a new name to a remote object (overriding the old binding), unbind a remote object, and list the URLs bound in the registry.
OPTIONS
- -J
- Used in conjunction with any java option, it passes the option following the -J (no spaces between the -J and the option) on to the java interpreter.
SEE ALSO
java, java.rmi.registry.LocateRegistry @ http://java.sun.com/javase/6/docs/api/java/rmi/registry/locateregistry.html and java.rmi.Naming @ http://java.sun.com/javase/6/docs/api/java/rmi/naming.html
Index
This document was created by man2html, using the manual pages.
Time: 05:29:10 GMT, December 24, 2015


We support adding URLs of videos that you have uploaded on YouTube. Please refer to this Guide on how to identify the URL of your YouTube video
Do you want to submit this video link to SportsPasta for review?
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linear-algebra-js
linear-algebra-js is an optimized linear algebra library in pure Javascript. It supports
both sparse and dense matrices, with routines to compute the Cholesky, LU, and QR
matrix factorizations of the former.
linear-algebra-js is implemented in asm.js to obtain the best possible
performance for a Javascript program, and was compiled from a wrapper around the
C++ library Eigen with emscripten. Future updates will compile the more optimized
sparse matrix library Suitesparse to asm.js.
Detailed documentation and unit tests can be found in the docs and tests directories
of this repository.
Code Snippet
// solve the linear system Ax = b, where A is a square sparse matrix
let A = SparseMatrix.identity(5, 5);
let b = DenseMatrix.ones(5, 1);
let lu = A.lu();
let x = lu.solveSquare(b);
b.scaleBy(5);
x = lu.solveSquare(b); // factorization is reused
Getting started
Clone this repository
git clone https://github.com/rohan-sawhney/linear-algebra-js.git
Include the files in the src directory in your browser or node js code.


Breast Cancer in sub Saharan Africa is on the rise though it is not as common as it is in developed countries The reasons for this scenario are many; they include low awareness, late stage with which women come to hospital, failure to access care.
Breast Cancer in sub Saharan Africa is on the rise though it is not as common as it is in developed countries The reasons for this scenario are many; they include low awareness, late stage with which women come to hospital, failure to access care.
Breast Cancer, especially in early stages causes no pain, it flies below radar. Absence of pain is construed as being well, absence of pain does not necessarily mean all is well and the presence of pain does not necessarily mean doom either.
The safest way to play is to opt for regular checks at three levels:
Level 1 - self-check,
Level 2 - Be
checked by a trained
practitioner,
Level 3 - Use Imaging as recommended by a health worker, these include a breast
ultrasound scan or a mammogram.


Dear Sir I am sally Usman 46 years old of age female from Republic of Sudan.My husband is into Timber sells, this is how he made his money,he die of heart ailment with only one surviving son. On that faithful afternoon,before he die,he disclosed it to me that he made a deposit with the name of my son in a security and finance company here in senegal,the amount deposited USD $ 8,500,000.00 (Eight Million Five Hundred Thousand US Dollars). As I have not had the pleasure of demonstrating the advantages of this investment. Meanwhile, in your response I would like you to indicate your willingness to handle this transaction sincerely by protecting my interest and upon your acceptance of this proposal, I would appreciate us to discuss the details and mutually agree on your percentage interest or share holding for helping me to secure the transfer of the asset and investing it. Looking forward to your urgent and positive response. Yours Sincerely, Sally Usman.


Logs
Group RedBlade has released dvdrip of 2013 movie “Canopy”. Enjoy!
Could not find Issue 5 of Abe Sapien(2013)
Uncaught exception: Traceback (most recent call last
| Date | Level | Trace | Context | Message |
|---|---|---|---|---|
| info | login/login | david | ||
| 1 | ||||
| info | upload/service | CBRProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | 3ae66eb66b60913dbb55d1cf7f0d551b | |||
| warning | upload/service | CBRProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | 3ae66eb66b60913dbb55d1cf7f0d551b | |||
| warning | upload/service | |||
| 1f1c8eeedc9b1de8a211000007d80ad8 | ||||
| warning | upload/service | ImportProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| info | upload/service | ImportProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| info | upload/service | ComicVineProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| info | upload/service | ComicVineProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| info | upload/service | ComicVineProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| warning | upload/service | CBZProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | d6c2e59de64ccc84d52d02b59a28aff2 | |||
| info | upload/service | CBRProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | 3ae66eb66b60913dbb55d1cf7f0d551b | |||
| info | upload/service | CBRProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | 750bd945c8de89e1ea9b48d3235518ab | |||
| warning | upload/service | CBRProcessor | ||
| 1f1c8eeedc9b1de8a211000007d80ad8 | 750bd945c8de89e1ea9b48d3235518ab | |||
| warning | upload/service |


Basic terminology
V Nodes/ Vertices: A set of n elements with unique identifiers (1, 2, 3, … , n).
E Edges:
For undirected graph: each edge is a set of exactly two nodes, e.g. e = {u, v}. u and v are nodes and we say e connects u and v. We also say u and v are adjacent or neighbors.
For directed graph: each edge is an ordered pair, e.g. e = <u, v>. We say u and v are adjacent. U is an in-neighbor of v and v is an out-neighbor of u. (u –> v).
Adjacent can be the property between vertex and vertex or between edge and edge. e.g., 1 and 3 are adjacent. (1,2) and (2,5) are adjacent.
Incident can be the property between vertex and edge. e.g., 1 and (1,3) are incident.
Loop/ self-loop: the edge is the form e = uu, which means the edge connect one node and the node itself.
Degree of vertex: The number of edges that touch v. It is also known as the number neighbors of v.
Path: A sequence of nodes v_0, v_1, …, v_k such there exists k edges e_1, e_2, …, e_k where e_i connect v_i-1 to v_i.
Simple path: A path where all nodes are unique. Then since there are k edges, k is the length of the path.
Cycle: A path where the start and the end vertex are the same, v_0 = v_k.
A simple cycle: A cycle where all vertex except v_0 and v_k are unique. k is the length of the cycle.
Size of the graph: |G| = number of vertex |V| = n
Sub-graph: G’ = (V’, E’) is a sub-graph of G = (V, E) if . Removing an edge from G results in the subgraph (V, E \ {e})
Induced subgraph: Let G = (V, E). And let be any subset of vertices of G. Then the induced subgraph G[S] is the graph whose vertices set is S and the edge set contains all edges which connect nodes in S in G. Removing a node from G results in induced graph.
Connectivity in an undirected graph: u is connected to v (u ~ v) if there exists a path from u to v. Note: otherwise, we can only say edge e connects the nodes u and v.
G is a connected graph if for every .
To be finished:
Connected component, Forests and trees.
Basic properties
A graph with n nodes and m edges. The range of m:
For directed graph: The max number of edges is (n)(n-1). The max situation is when every node connects to all the other nodes. There are n such situations so the total number is n(n-1).
For undirected graph, e = (u,v) and x = (v,u) are the same edge and be counted as one edge. So the total number is n*(n-1)/2.
The number of degree:
For an undirected graph: . It means that in an undirected graph, there are 2m degrees for all vertices. Because for each edge, it connects two vertices so it provides 2 degrees in total.
For an directed graph: .
Representation of Graph
Two ways: Adjacency matrix and Adjacent list.
Adjacency matrix is an n*n matrix where i, j entry contains 1 if edge connects i and j or 0 if no edge.
Adjacency list: It lists all nodes in one column and following the nodes which are their neighbors.
The complexity of two ways:
| Adjacency List | Adjacency Matrix | |
|---|---|---|
| Space | O(m) Depend on the edge | O(n^2) |
| Accessing a node v/ Traversing all node | O(1) / O(n) | O(1) / O(n) |
| Accessing a edge e = (u,v) | O(||) # of neighbors of u | O(1), M[u, v] |
| Finding some neighbor of v | O(1) | O(n) |
| Traversing all edges/ vertices adjacent to a node u | O(|) | O(n) |
| Traversing all edges | O(m) | O(n^2) |
Application of dfs
finding SCC
Strongly connected components (SCCs) is a max set of nodes, any two of the nodes are reachable from each other.
Alg:
- Run dfs on G
- Flip G’s edges to create G^T
- Run dfs on G^T by traversing nodes in a decreasing order of finish time generated in step 1
- The SCC of G is the trees of dfs forest of G^T
Minimum spanning Tree
Spanning tree: A spanning tree T of an undirected graph G is a subgraph that is a tree which includes all of the vertices of G with minimum possible number edges.
Weighted graph: A weighted graph is a graph where a number is assigned to each edge. The weights may represent costs, length and some other things.
Connected graph: For an undirected graph, if there is a path between two vertices (v and u), then v and u are connected. In a directed graph, the path connecting v and u must be in the same direction. A graph is called connected graph if any two vertices are connected.
For a spanning tree of a weighted connected graph, if the total weight is smallest among all spanning trees, then it is called minimum spanning tree.
Minimum spanning Tree problem: Find an MST for the input graph
Minimum spanning Forest problem: If the given graph is not necessarily connected, find MST for each connected component.
Kruskal’s algorithm
Input: An edge-weighted (simple, undirected, connected) graph.
Output: an MST
Idea: Suppose G = (V(m), E(n)). We consider the graph as the forest with no edge, each vertex is a tree. Then order the weight of edges, start adding edge in non-decreasing order. If the new edge connect two vertices on the same current formed tree, we do not add this edge. If the edge connect two nodes belongs to two trees, we add this edge.
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procedure kruskal (G)
T <-- \o
for each v \in V(G) do
Define cluster C(v) <-- v // each vertex is a tree
sort edges in E(G) into non-decreasing weight order
for each edge e_i = (u,v) /in E(G) do
if C(u) != C(v) then
T <-- T /cup {e_i}
merge clusters C(u) and C(v)
return T
Correctness of Kruskal
We prove that after every iteration of the second for loop, where we have examined i edges and have a partial solution built into T, called it T_i, there is a final MST T_opt which has all these edges of T_i and all other edges the T_opt has but T_i does not are in the edges we have not examined yet. i.e., .
The base case is trivial. And once we prove it for the last iteration e_m, it implies the solution is MST.
So we now consider the induction step after we have T_i satisfying the ih and examine edge e_i+1. If we do not add e_i+1, then T_i = T_i+1. In this case, T_opt do not have e_i+1 too for sure. If we create a cycle with e_i+1, then T_opt cannot have e_i+1 too because all edges of T_i are in T_opt and e_i+1 cannot belong to T_opt.
If we add e_i+1 to T_i. There are two cases.
Case 1: T_opt contains it. So our T_i+1 is consistent with optimum solution.
Case 2: T_opt does not contain it. In this case, there must be a cycle in T_opt + e_i+1. This cycle contains at least one edge e_j that is not in T_i. e_j must be in {e_i+1, … , e_m}. The reason is if e_j is in T_i, we will not add e_i+1 and in this case, e_j can only be in the remaining case we have not tested. To form a cycle, e_i+1 must be different from e_j, so j >= i+2. So w(e_j) >= w(e_i+1). So T_opt - e_j + e_i+1 is also a MST.
Running time
| Each cluster contains the unordered linked list of vertices. Each vertex keeps the index which cluster it belongs to. At first, there are n clusters. To merge two clusters, we need to change the indexes of each vertex in the smaller cluster. So the merge step costs O(min{ | C(u) | , | C(v) | }) time. |
After merging two clusters, the size of the total is at least double larger than the original smaller cluster. So for each vertex, we need at most log(n) time to finish its merge and in total, there are at most nlog(n) time for merge of all vertices.
For the sort part, it costs O(mlog(m)) and m <= n(n-1) for an undirected graph. So O(mlog(m)) = O(mlog(n^2)) = O(2mlog(n)) = O(mlog(n)). So the total time is O((m+n)log(n)).
Prim’s algorithm
Idea: Initialize the set V_new = {x}. x is any vertex in the set V and seen as the start vertex. E_new = {}. Repeat the following steps until V_new = V. Find the smallest weight edge in E, the edge must have a vertex u which is in V_new and a vertex v which is not in V_new. Add v into V_new and add (u, v) into E_new. Return the V_new and E_new.
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procedure primMST(G) // G = (V, E)
S = {s}
A = empty set
while (|S| < |V|) do
find a minimum weight edge e = (u,v), u \in S and v \notin S
S = S \cup {v}
A = A \cup {(u,v)}
return A
Correctness
A_i: The set of edges A in the algorithm after i iterations of the while loop.
We can state the claim in the form, for any 0 <= i <= n-1, there exists T_opt such that . This similar proof cannot provide any convenience.
We need to prove, For all i from 0 to n-1, there exists MST T_opt such that A_i \subset T_opt. Notice that A_n-1 is a spanning tree of G, if we prove A_n-1 \subset T_opt, we prove A_n-1 is the MST.
Base step, Ind step, assume there is a T_opt such that A_i \subset T_opt, we need to show there is a such that .
If e \in , we are done. If , there must be an edge e’ = (u’, v’) in such that crosses the (S, ). Clearly, w(e) <= w(e’) because e’ must not in A_i and until A_i, we have already looked for all smallest edges. In this case, , then we can remove e’ and add e since e is smaller,
A revise of Prim’s algorithm
Idea: For each node \notin S, keep track of the min edge that connects it to S. Update the information only for the neighbors of the node that is currently being added to S. Uses a priority queue Q on \bar S.
Pseudocode:
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procedure primMST(G)
for each v \in V(G) do
v.key = \infinity
v.predec = NIL
s.key = 0
while (Q != emptyset) do
u = ExtractMin(Q)
for each v neighbor of u do
if (v \in Q and w(u,v) < v.key) then
v.predec = u
decrease-key(Q,v,w(u,v))
Time: ExtrachMin(Q) costs logn and while loop will iterate n times, so it costs nlog(n) in total. The second for loop costs mlogn.
A theorem from prim alg:
Let T be a spanning tree of G, T is a MST iff each edge is the min-edge crossing to cut it induces.
The cut is the connection edge of two separate parts. If the edge is cut, the tree becomes two trees.
The shortest path problem
Def of shortest path problem: find the shortest path in an edge-weighted graph connecting two vertices s and t.
Def of SSSP single source shortest path problem: Given an edge-weighted graph G and a source s, find out for each vertex v \in V(G) a shortest paths from s to v.
Some properties:
The sub-path optimality: If we find some shortest path from u_0 to u_k, then some path u_i to u_j is also the shortest path between u_i and u_j.
The shortest path is good if there are negative weight directed, if it is undirected the negative weight is meaningless. We assume no negative cycles.
For any u, we have d(u, u) = 0
For any u, v, we have d(u, v) = d(v, u) (if G is undirected)
For any u, v, w we have d(u, w) <= d(u, v) + d(v, w)
Dijkstra’s SSSP algorithm
For graphs with non-negative weights, idea:
Maintains a set S of vertices for which we know the shortest path. Initially, S = {s}, at the end: S = V. By the greedy solution, we should choose the node in \bar S = V \setminus S with the minimum dist.
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procedure dijkstra(G, w,s) //G = (V,E)
for each v do
v.dist <-- /infinity
v.predec <-- NIL
s.dist <-- 0
Build Min-priority-Queue Q on all nodes, k = dist
while (Q != empty) do
u <-- EtractMin(Q)
for each v neighbor of u
if (v.dist > u.dist + w(u,v)) then
v.dist <-- u.dist + w(u,v)
v.predec <-- u
decrease-key(Q, v, v.dist)


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Dashbuilder is a general purpose dashboard and reporting web app which allows for:
- Visual configuration and personalization of dashboards
- Support for different types of visualizations using several charting libraries
- UI editor for the creation of data sets definitions
- Full featured editor for the definition of chart visualizations
- Definition of interactive report tables
- Data extraction from external systems, through different protocols
- Support for both analytics and real-time dashboards
Upcoming features
- New renderers based on D3 JS & Lienzo GWT
- Hierarchical (nested group) displayer types: Tree & Pie
- Support for multiple data series
- Rich mobility support
- Alerts and SLA configuration
- RESTful API
Architecture
- Not tied to any chart rendering technology. Pluggable renderers.
- No tied to any data storage.
- Ability to read data from: CSV files, Databases, Elastic Search or Java generators.
- Decoupled client & server layers. Ability to build pure lightweight client dashboards.
- Ability to push & handle data sets on client for better performance.
- Based on Uberfire, a framework for building rich workbench styled apps on the web.
For further information, please visit the project web site dashbuilder.org
Licensed under the Apache License, Version 2.0


--- layout: project title: IIITB Student Kit date: 2017-10-26 10:17:01.000000000 +05:30 type: projects parent_id: '0' published: true password: '' status: publish categories: [] tags: - freshers iiitb - iiit bangalore - IIItb - iiitb student kit - iiitbangalore - student guide - Student kit - zense student kit meta: _edit_last: '14' rcp_user_level: None author: login: visheshruparelia email<EMAIL_ADDRESS>display_name: Vishesh Ruparelia first_name: Vishesh last_name: Ruparelia ---
IIITB Student Kit is a guide for freshers which will help them when they get to IIITB. Basically, it has solutions to all the problems a student might face when they first visit the city. It has a list of hotels nearby, FAQs, list of documents required for completing the admission , everything about the life in IIITB , etc.
Visit IIITB Student Kit to check it out.


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Screenshots
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Fossil dragonfly species the first from B.C. to receive scientific name
by The Canadian Press, StaffVANCOUVER - Simon Fraser University says six fossil dragonfly species, dating from about 50 million years ago, are the first from B.C. and Washington to receive scientific names.
Researchers from the university and Royal BC Museum examined nine rare dragonfly fossils from the McAbee fossil site west of Kamloops and the town of Republic in northern Washington.
The university says the fossils represent eight previously unknown species, six of which were preserved well enough to be given scientific names, and the findings were recently published in an academic journal.
Scientists say these dragonflies didn't co-exist with dinosaurs and belong to more modern families, adding they would not look out of place today flying beside a modern pond.


vqa_benchmarking_backend.bottomupattention.models.bua
¶
Submodules¶
vqa_benchmarking_backend.bottomupattention.models.bua.backbone
vqa_benchmarking_backend.bottomupattention.models.bua.box_regression
vqa_benchmarking_backend.bottomupattention.models.bua.config
vqa_benchmarking_backend.bottomupattention.models.bua.fast_rcnn
vqa_benchmarking_backend.bottomupattention.models.bua.postprocessing
vqa_benchmarking_backend.bottomupattention.models.bua.rcnn
vqa_benchmarking_backend.bottomupattention.models.bua.roi_heads
vqa_benchmarking_backend.bottomupattention.models.bua.rpn
vqa_benchmarking_backend.bottomupattention.models.bua.rpn_outputs
Package Contents¶
Classes¶
Generalized R-CNN. Any models that contains the following three components: |
|
The ROIHeads in a typical “C4” R-CNN model, where |
|
RPN classification and regression heads. Uses a 3x3 conv to produce a shared |
|
Region Proposal Network, introduced by the Faster R-CNN paper. |
Functions¶
|
Add config for tridentnet. |
|
Create a ResNet instance from config. |
-
vqa_benchmarking_backend.bottomupattention.models.bua.
add_bottom_up_attention_config
(cfg, caffe=False)¶ Add config for tridentnet.
-
vqa_benchmarking_backend.bottomupattention.models.bua.
build_bua_resnet_backbone
(cfg, input_shape)¶ Create a ResNet instance from config.
- Returns:
ResNet: a
ResNet
instance.
-
class
vqa_benchmarking_backend.bottomupattention.models.bua.
GeneralizedBUARCNN
(cfg)¶ Bases:
torch.nn.Module
Generalized R-CNN. Any models that contains the following three components: 1. Per-image feature extraction (aka backbone) 2. Region proposal generation 3. Per-region feature extraction and prediction
-
forward
(self, batched_inputs)¶ - Args:
- batched_inputs: a list, batched outputs of
DatasetMapper
. Each item in the list contains the inputs for one image. For now, each item in the list is a dict that contains:
image: Tensor, image in (C, H, W) format.
instances (optional): groundtruth
Instances
proposals (optional):
Instances
, precomputed proposals.
Other information that’s included in the original dicts, such as:
- “height”, “width” (int): the output resolution of the model, used in inference.
See
postprocess()
for details.
- batched_inputs: a list, batched outputs of
- Returns:
- list[dict]:
Each dict is the output for one input image. The dict contains one key “instances” whose value is a
Instances
. TheInstances
object has the following keys:“pred_boxes”, “pred_classes”, “scores”, “pred_masks”, “pred_keypoints”
-
inference
(self, batched_inputs, detected_instances=None, do_postprocess=True)¶ Run inference on the given inputs.
- Args:
batched_inputs (list[dict]): same as in
forward()
detected_instances (None or list[Instances]): if not None, itcontains an Instances object per image. The Instances object contains “pred_boxes” and “pred_classes” which are known boxes in the image. The inference will then skip the detection of bounding boxes, and only predict other per-ROI outputs.
do_postprocess (bool): whether to apply post-processing on the outputs.
- Returns:
same as in
forward()
.
-
preprocess_image
(self, batched_inputs)¶ Normalize, pad and batch the input images.
-
-
class
vqa_benchmarking_backend.bottomupattention.models.bua.
BUACaffeRes5ROIHeads
(cfg, input_shape)¶ Bases:
detectron2.modeling.ROIHeads
The ROIHeads in a typical “C4” R-CNN model, where the box and mask head share the cropping and the per-region feature computation by a Res5 block.
-
_build_res5_block
(self, cfg)¶
-
forward
(self, images, features, proposals, targets=None)¶ See
ROIHeads.forward
.
-
-
class
vqa_benchmarking_backend.bottomupattention.models.bua.
StandardBUARPNHead
(cfg, input_shape: List[detectron2.layers.ShapeSpec])¶ Bases:
torch.nn.Module
RPN classification and regression heads. Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas specifying how to deform each anchor into an object proposal.
-
forward
(self, features)¶ Args: features (list[Tensor]): list of feature maps
-
-
class
vqa_benchmarking_backend.bottomupattention.models.bua.
BUARPN
(cfg, input_shape: Dict[str, detectron2.layers.ShapeSpec])¶ Bases:
torch.nn.Module
Region Proposal Network, introduced by the Faster R-CNN paper.
-
forward
(self, images, features, gt_instances=None)¶ - Args:
images (ImageList): input images of length N features (dict[str: Tensor]): input data as a mapping from feature
map name to tensor. Axis 0 represents the number of images N in the input data; axes 1-3 are channels, height, and width, which may vary between feature maps (e.g., if a feature pyramid is used).
- gt_instances (list[Instances], optional): a length N list of `Instances`s.
Each Instances stores ground-truth instances for the corresponding image.
- Returns:
proposals: list[Instances] or None loss: dict[Tensor]
-


Career Buddy: Gus Liakos
My Learning Style (according to this quiz):
I’m Marlon McPherson. I work in UI/UX. I got here by completing a coding bootcamp and working hard to land in a position where I could achieve my goals. Have you encountered any bad design online recently?
The week went about as I expected it to go (like the first week in any course). Reviewing the syllabus, settling into routines, and meeting peers took up the first couple days. Since I had previous front-end experience coming into the course, almost all of the front-end centered lectures and activities were review for me. The review didn’t last long, since we jumped into mySQL and data design fairly soon after. This is all unfamiliar territory to me, so my previous experience means nothing. I expected more front-end review in a full stack course, so it was a bit surprising when we made the jump straight into databases. I feel the anxiety that comes with the pressure of learning new material (especially in a bootcamp setting), but I still feel confident in my ability to learn and apply the material. In other words, I haven’t stumbled off the ‘cliff of confusion’ yet, but I’m certainly open to the possibility - in fact, I welcome it. You’ve gotta fall down to learn how to stand up. Of course, I’ll do what I can to mitigate the fall, but I’m definitely expecting it. I first felt my toes poke over the edge when we discussed logarithms and bits/bytes. I don’t have a good understanding of math, so any math-oriented material will present an extra hurdle for me, but if I continue to devote 100% of my attention and effort to this class, I know I’ll pull through.
The second week of class has been a definite step up in terms of difficulty, a trend that I’m sure will continue. I’m still adapting, and there are some really important topics we covered that will apply to everything we do down the road - as long as I can understand those basic concepts, I’ll be able to fill in the cracks with logic and some Google-fu. Object-oriented programming was the focus of this week, and the analogy of ‘blueprint / building’ makes sense, as does encapsulation keeping data secure.
From a personal development perspective, the week brought some big changes. Until (very) recently, I didn’t have much of an online presence. I had a Linkedin profile (only searchable if you know my full name) with only the barest of customization. I didn’t have a Twitter account either (I do now; still settling on an @ handle though). I don’t have any other social media, since I maintain contact with friends via Discord / phone. I’ve always preferred to keep searchable personal info off the web, for what some might call paranoid reasons (in a world where people are fired for social media posts, I reckon it’s best to stay away). Professionally, though, I needed to bite the bullet and sign up for a few sites to bolster my online presence. I’ve also reconsidered my goal in this course. I thought about what makes me feel passionate about web development, and while I was browsing the internet because I had reached a mental block, I thought of how much I dislike bad site design. Youtube is a recent offender (in my opinion) with the links that pop up at the end of videos, blocking the view for the last ten seconds of the video. The alignment of the suggested video links that pop up in a grid after the video stops is also aligned such that the titles of the first row of videos are off-screen. UI/UX is definitely appealing when I think of glaring front-end issues like those. Of course, this is tempered with realism (“Who’s this newbie coder throwing shade at Youtube?”) - I’m know the embarrassment of being wrong about something I claimed to know. Sometimes “bad design” is “design for a reason of which you’re unaware”. Regardless, I enjoy design and like the idea of making the web more usable for everyone. The picture activity we did was helpful in discovering this possible motivation for advancing my career in coding - it solidified my thoughts about who I was, am, and want to be.
Capstone is finally here. Now that my final project has been assigned, I have a clear vision of the finish line for the first time. I don’t want to get headstrong; I just feel confident in my.group’s ability to finish strong and put together a polished product for capstone. I’m the github repo owner, which is a relief (I like to maintain a good level of control, especially in group projects - nothing against my current group, I’ve just had enough negative experiences to plan ahead. I’m not gonna be ‘that guy’ who does everyone’s work for them. All I want to do is give the project my full attention. I’m looking forward to seeing how the dynamic of our group works going forward. Our project idea is interesting (it came from a conversation between me and Yvette) because its analogs are not very robust - if we really go to town on this, it could be a huge success. I’m a food lover, so I’m really excited to discover what Albuquerque has to offer in terms of food trucks (I haven’t really checked). I know that we still have a lot to learn, but I definitely have a good grasp of what we’ve covered so far, and I’m ready to move forward.
Capstone phase is in full swing from this week onward. Our vision for the Food Truck Finder app is becoming clearer as we progress through the data design stages. Our project is of middling complexity (for a capstone, at least) - we’ve got six entities, so we’ll be writing six classes next week. Our DDL is finished, and Yvette is working on getting our database configured this weekend. We’ve got a great concept to be working with, so hopefully the project turns out well. Of course, I have my concerns, as anyone should when working in a group (especially for something as important as capstone) but I remain confident that we’ll be able to cobble together something servicable for demo day. I’ll contribute as much as I can to the group effort and try to be available to answer questions or help out whenever I can.
Forbidden Island is a great general team-building exercise, but I felt that it was ineffective in clarifying teammates’ individual roles within the group. I’ve been a bit self-conscious about my desire to lead the capstone team (control over a project is usually extremely important to me, but I’m teaching myself to let go of the reins a bit and let others take on their own responsibilities within the group without needing to be told). While there are significant parallels between real-world group scenarios and the board game that we played, there are key differences as well - chief among them being the sharing of the rulebook (read: documentation). The game is relatively simple, but it’s difficult to remember all the rules after one read-through. Other than that, though, the game went fairly well (we didn’t lose, but we ran out of time). Personal sacrifice for team advancement seems to be the theme of the game, but the “character cards” special abilities aren’t exactly balanced - they’re also distributed randomly.
I'd be interested in an internship, but I don't work for free, so volunteer work is out of the question. I'd be interested in talking to this employer based on their stated goals of creating responsive, aesthetically pleasing sites and apps.
Like Xynergy, I'd be interested in interning at this place. I've got an angle that I could tentatively work to get my foot in the door (improper grammar on one of the top google search results for their company, importance of proper grammar in attracting clients). Obviously I wouldn't want to be too heavy-handed, but it definitely presents an opportunity to "sell" my English expertise.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 1
For the honey-seared chicken, drizzle the chicken with olive oil. Heat a pan and fry the chicken for 1-2 minutes. Add the orange juice and zest, ginger, honey, salt and freshly ground black pepper and fry for a further 1-2 minutes, or until golden-brown and cooked through.
For the noodles, cook the noodles according to the packet instructions and drain.
Heat the olive oil in a wok, add the carrot ribbons and fry gently for 1-2 minutes. Add the garlic, paprika, sliced ginger and chilli flakes and fry for one minute. Add the soy sauce, orange juice and zest and sesame oil and fry for a further minute. Stir in the chopped fresh parsley and the cooked noodles.
To serve, pile the noodles onto a serving plate and top with the honey-seared chicken.


Zend Framework
LICENSE
This source file is subject to the new BSD license that is bundled with this package in the file LICENSE.txt. It is also available through the world-wide-web at this URL: http://framework.zend.com/license/new-bsd If you did not receive a copy of the license and are unable to obtain it through the world-wide-web, please send an email to<EMAIL_ADDRESS>so we can send you a copy immediately.
Primary class for communicating with the FirePHP Firefox Extension.
EXCEPTION
= 'EXCEPTION'
Exception style showing message and expandable full stack trace.
Also increments Firebug's error counter.
PLUGIN_URI
= 'http://meta.firephp.org/Wildfire/Plugin/ZendFramework/FirePHP/1.6.2'
The plugin URI for this plugin
STRUCTURE_URI_DUMP
= 'http://meta.firephp.org/Wildfire/Structure/FirePHP/Dump/0.1'
The structure URI for the Dump structure
\Zend_Wildfire_Channel_Interface
$_channel= 'null'
The channel via which to send the encoded messages.
boolean
$_enabled= 'true'
Flag indicating whether FirePHP should send messages to the user-agent.
true
Detailsarray
$_messages= 'array'
Messages that are buffered to be sent when protocol flushes
array
Detailsarray
$_objectFilters= 'array'
Filters used to exclude object members when encoding
array
Detailsarray
$_objectStack= 'array'
A stack of objects used during encoding to detect recursion
array
Details_encodeObject(
mixed $object, $objectDepth
=
1, $arrayDepth
=
1
)
:
array
Encode an object by generating an array containing all object members.
All private and protected members are included. Some meta info about the object class is added.
| Name | Type | Description |
|---|---|---|
| $object | mixed | The object/array/value to be encoded |
| $objectDepth | ||
| $arrayDepth |
| Type | Description |
|---|---|
| array | The encoded object |
_encodeTable(
$table
)
:
array
Encodes a table by encoding each row and column with _encodeObject()
| Name | Type | Description |
|---|---|---|
| $table |
| Type | Description |
|---|---|
| array |
_encodeTrace(
$trace
)
:
array
Encodes a trace by encoding all "args" with _encodeObject()
| Name | Type | Description |
|---|---|---|
| $trace |
| Type | Description |
|---|---|
| array | The encoded trace |
_getStackTrace(
array $options
)
:
array
Gets a stack trace
| Name | Type | Description |
|---|---|---|
| $options | array | Options to change how the stack trace is returned |
| Type | Description |
|---|---|
| array | The stack trace |
_recordMessage(
string $structure, array $data, boolean $skipEncode
=
false
)
:
boolean
Record a message with the given data in the given structure
| Name | Type | Description |
|---|---|---|
| $structure | string | The structure to be used for the data |
| $data | array | The data to be recorded |
| $skipEncode | boolean | TRUE if variable encoding should be skipped |
| Type | Description |
|---|---|
| boolean | Returns TRUE if message was recorded |
| Exception | Description |
|---|---|
| \Zend_Wildfire_Exception |
flushMessages(
string $protocolUri
)
:
void
Flush any buffered data.
| Name | Type | Description |
|---|---|---|
| $protocolUri | string | The URI of the protocol that should be flushed to |
getEnabled(
)
:
boolean
Determine if logging to user-agent is enabled.
| Type | Description |
|---|---|
| boolean | Returns TRUE if logging is enabled. |
getInstance(
bool $skipCreate
=
false
)
:
\Zend_Wildfire_Plugin_FirePhp
Get or create singleton instance
| Name | Type | Description |
|---|---|---|
| $skipCreate | bool | True if an instance should not be created |
| Type | Description |
|---|---|
| \Zend_Wildfire_Plugin_FirePhp |
getOption(
string $key
)
:
mixed
Retrieve a single option
| Name | Type | Description |
|---|---|---|
| $key | string | The name of the option |
| Type | Description |
|---|---|
| mixed | The value of the option |
getUri(
)
:
string
Get the unique indentifier for this plugin.
| Type | Description |
|---|---|
| string | Returns the URI of the plugin. |
group(
string $title, array $options
=
array
)
:
TRUE
Starts a group in the Firebug Console
| Name | Type | Description |
|---|---|---|
| $title | string | The title of the group |
| $options | array | OPTIONAL Setting 'Collapsed' to true will initialize group collapsed instead of expanded |
| Type | Description |
|---|---|
| TRUE | if the group instruction was added to the response headers or buffered. |
groupEnd(
)
:
TRUE
Ends a group in the Firebug Console
| Type | Description |
|---|---|
| TRUE | if the group instruction was added to the response headers or buffered. |
init(
string $class
=
null
)
:
\Zend_Wildfire_Plugin_FirePhp
Create singleton instance.
| Name | Type | Description |
|---|---|---|
| $class | string | OPTIONAL Subclass of Zend_Wildfire_Plugin_FirePhp |
| Type | Description |
|---|---|
| \Zend_Wildfire_Plugin_FirePhp | Returns the singleton Zend_Wildfire_Plugin_FirePhp instance |
| Exception | Description |
|---|---|
| \Zend_Wildfire_Exception |
send(
mixed $var, string $label
=
null, string $style
=
null, array $options
=
array
)
:
boolean
Logs variables to the Firebug Console via HTTP response headers and the FirePHP Firefox Extension.
| Name | Type | Description |
|---|---|---|
| $var | mixed | The variable to log. |
| $label | string | OPTIONAL Label to prepend to the log event. |
| $style | string | OPTIONAL Style of the log event. |
| $options | array | OPTIONAL Options to change how messages are processed and sent |
| Type | Description |
|---|---|
| boolean | Returns TRUE if the variable was added to the response headers or buffered. |
| Exception | Description |
|---|---|
| \Zend_Wildfire_Exception |
setEnabled(
boolean $enabled
)
:
boolean
Enable or disable sending of messages to user-agent.
If disabled all headers to be sent will be removed.
| Name | Type | Description |
|---|---|---|
| $enabled | boolean | Set to TRUE to enable sending of messages. |
| Type | Description |
|---|---|
| boolean | The previous value. |
setObjectFilter(
$class, $filter
)
:
void
Specify a filter to be used when encoding an object
Filters are used to exclude object members.
| Name | Type | Description |
|---|---|---|
| $class | ||
| $filter |