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IchigoJam can store up to four programs.
Connect one program to another, to be a masterpiece!
First, make a quiz game, the main part of the game.
It displays "clear" with correct answer input! If not, it will display game over!.
Save this in file 1.
To create a program title, first of all from beginning.
A program title that ends when any key is pressed.
Add concatenated command "LRUN".
Connected to the game from the title program!
Try it!
1. Let's make it to be retry/game-over with "LRUN0"
2. Let's remodel the title
3. Let's remodel the quiz
4. Let's make it to go back to the title screen if the game is over
5. Let's put a title on "Kawakudari Game"


Nightlife Coordinator
Plans tonight?
See which bars are hoppin' tonight and RSVP ahead of time!
Remember: Take a cab and drink responsibly.


Articles
-
Scientific Styles
November 29, 2022
Allen Newell (AI pioneer and Turing Award winner) once shared his thoughts on the style of scientific practice. Even today his observations are helpful, particularly as we revisit our own styles and choose new research topics.
-
Hyalite Canyon
January 15, 2022
Hyalite
-
Alpental Falls
January 2, 2022
Alpental Falls is a single pitch flow of WI2+ across from the ski resort. Early in the season the ice is brittle and poorly formed. Still, we decidied to give it a shot, and the outcome was as sketchy as expected.
-
Colonial Peak (First Attempt)
June 25, 2021
Colonial Peak is located in the North Cascades, about six or so miles off Highway 20. Colonial isn’t very tall by local standards; it’s only 7,771ft tall. But the peak should by no means be underestimated. The main route has one of the most brutal approaches I’ve ever experienced. And for some reason we decided to climb this car-to-car on the hottest day of the heatwave.
-
Prusik Peak (First Attempt)
June 12, 2021
Prusik Peak is located in the Core zone of the Enchantments. The West Ridge has a few pitches of low fifth-class climbing, with a short section of unprotected friction slab as its crux.
Our goal was to climb it in a single car-to-car excursion. Unfortunatley when we arrived the ridge was soaking wet from snow melt and rain. Any chance of maintaining friction on the third pitch seemed unlikely, so we decided to bail with the intention to come back later in the season.
Nevertheless, we managed to have a long day outside and get some nice photos in the process.
-
Hog's Back on Mount Hood
May 22, 2021
The Hogs Back goes directly up the south side of Mt Hood.
-
Kautz Glacier on Mount Rainer (First Attempt)
May 8, 2021
The Kautz Glacier
-
Lover's Lane on Lane Peak
March 10, 2021
Lane Peak is a devilish-looking mountain, located in the Tatoosh range of Washington’s Cascade mountains. Lover’s Lane is a narrow snow gully that runs up
-
Pinnacle Gully on Mount Washington
February 21, 2020
Pinnacle Gully is a relatively narrow passageway of steep ice on Mount Washington. The route begins at Huntington Ravine and terminates in the alpine garden, just a forty-minute walk back down to the tree line. The climb consists of three rope-stretching pitches. (Bring a 70m rope if you like to wander.) It goes without saying that conditions will depend on the season. In February, we experienced ice of varying quality: from young ice that was soft and would hold an entire pick, to brittle glassy sheets that shattered with a tap. There was also the occasional flow of water running underneath that kept us moving forward. But for the most part, the ice was thick, and we felt secure with all of our placements.
-
Price Glacier on Mount Shuksan
August 5, 2018
The Price Glacier


{% load static %} {% static "" as baseUrl %}
Shop at The Shop
The Shop
Home
(current)
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Services
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The Shop
{% for category in categories %}
{{category}}
{% endfor %}
{% for product in products %}
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{% for product in products %}
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{% endfor %}


model_training
Retrain Model
analytics
Save Model
file_upload
Load Model
Label:
Null
0
Press
Spacebar
to clear the canvas
autorenew
Clear canvas
save
Save as JPG


H.B. Woodrose
This is a report of my third experience with LSA extracted from Baby Woodrose seeds and my sisterís first. I began using Baby Woodrose seeds as a substitute for my beloved LSD, which is hard to find these days, especially since I moved 2 years ago and donít have good connections here. I have found LSA to be as deep and interesting as LSD but with a slightly different focus, more introspective and personal in many ways.
My sister is four years younger than me (she is 21) and had only began experimenting with drugs beyond pot about 2 years prior to this experience. When I learned of her extracurricular activities, I suggested we trip together and she agreed but we couldnít agree on a substance. She liked mushrooms and was scared to try LSD. I love LSD but have never enjoyed mushrooms. For some reason they tend to make me feel extremely vulnerable and confused.
Since we live on opposite ends of the country, we gave up on tripping together for a while. But when she came to visit me a few weeks ago I decided to see if she would be up for LSA. I proceeded to crush up 9 seeds for each of us and place them in 1 cup jars. I filled the jars with distilled water and left them to sit in a cabinet until she arrived. This simple extraction method seems to negate much of the infamous nausea that many people experience with woodrose seeds. I have not used distilled water before but I had heard that it prevents the loss of LSA. The end result was stronger than previous attempts, but I also soaked longer this time so Iím not sure if the distilled water made any difference.
When my sister arrived we discussed it and she agreed so we set the date. The date we chose allowed for an eight day extraction Ė a record for me. Fast forward three days to the evening we chose to trip. I strained the liquid through a fine mesh (very fine Ė I use it to press grains for home brewing) discarded the seed mush and microwaved both tea mugs. LSA extract is less disgusting hot.
We sip the tea slowly (it seems to get worse as you drink it) and begin to feel the effects before we have even completed the mugs. It begins as a tightening in the stomach. Like that feeling you get as the roller coaster begins to descend. The TV begins to look brighter and more interesting. My sister complains that her stomach feels weird and eats some popcorn to settle it. She says it worked. We both find ourselves shifting positions constantly. I am not uncomfortable but I have so much energy.
My sister decides she wants to do my nails. We bought nail kits earlier that day which have special buffer pads to smooth the nail surface and another to give it shine. She quickly becomes totally absorbed in buffing my nails. I find the sensation of pressure on my nail very odd. Not good or bad, just strange and very prominent. I notice that my stomach feels normal now. The queasiness lasted all of 20 minutes. I find I can concentrate on many things at once , the star trek episode on TV, the things my sister is saying, and my own train of thought all at once.
I start to laugh too long and too hard at minor comedies. Sometimes I laugh for no reason. My sister laughs too but at me as much as at the joke. Though smiling and laughing she is more serious than me. And totally focused on completing the nail job. Towards the end I begin to get to antsy. I need to do something. I canít sit still. Though LSA is supposedly a sedative I find it energizes me more than LSD even. I suggest we go swimming and my sister is all for it but she MUST complete the nails first. I am so excited to swim I force her to finish while I change with my other hand.
Soon we are out the door and walking to the apartment pool. Luckily it was totally deserted. The feel of the cool water on my skin felt amazingly refreshing. My sister gets in more timidly and looks stiff. I ask how sheís feeling and she says tense. We talk and she loosens up in a few minutes. The sky and the water are beautiful. The nighttime clouds look like a deep purple and seem to sway. The water is amazingly blue. This is the height of visual effects for me. Interestingly, LSA visuals are more like shroom visuals than LSD visuals (for me anyway). The distortions are more about color and breathing, while LSD is more patterns and melting.
We had been watching star trek DVDs a lot the last few days (we both LOVE the show) and topic of an old board game came up. We had owned it as children and played it all the time. There was a video tape that came with it and was necessary to play the game. Eventually we lost the tape. We searched many times but to no avail. My sister expressed a desire to have that game again, so that we could play it.
After we lost the tape and gave up, our parents had a garage sale and we sold the game, minus the tape. I know this sounds silly, but I have thought back to that day many times in the intervening years. I felt bad for selling the game in itís unusable state. Deliberate fraud, especially one that hurts others at no gain to me (I never saw the money) is totally contradictory to my sense of morality. I have thought that the children who bought it where probably as excited about as my sister and I where when we bought it. And they must have been very disappointed.
I told all this to my sister. And it felt great.  My sister told me we felt bad about it at the time, and we sold it because our parents told us to. This detail I had totally forgotten. And it redeemed me. Certainly, I should have told my parents it wasnít whole. Itís still my bad karma. But the fact that I didnít do it vindictively, that I wasnít sadistic to some degree, helped to restore my sense of self as a good and moral person.
Then I remembered something else. Years after we sold the game, when I was packing for a move, I found the tape. At the time it just made me feel worse about what I had done. But now I saw it in a new light. It was my karma. I learned a karmic lesson by finding the tape again. I read once that karma is simply a consequence of an action. The consequence of my selling the game when it was useless, was to receive the tape without the game, making the tape useless. This cathartic revelation elated me and made me feel carefree. One less karmic lesson to worry about. And I finally felt like I understand what karma is.
As we swim back and forth we discussed a variety of topics but one that got us both going for awhile was scientific factoids. She is a biology major and I am a grad student in physics so we have a lot to share. This info, fascinating in itís own right, was downright awesome. I felt utterly amazed at the tidbits that she feed my imagination with. And I rediscovered the things I knew, learning them again with the wonder of a child. One of the many facts we discussed was a study done in mice that found that mice fed the meat of mice that had been though a given maze actually got through it faster their first time. I mean, the implications! We spent around an hour prancing around the pool wrapped up in our convo.
Then two of my neighbor friends walked out to the pool to say hello, along with my husband who was sober. I felt strange because my neighbors are not the psychedelic type. Though they drink a lot of beer. After a few uncomfortable moments I simply asked if they were comfortable with our state of mind and they said it was fine. I felt much better. We swam a bit more and talked with the newcomers until we decided we had to get out to pee. On the way back to the apartment I notice that sandals feel totally strange and went barefoot. The sidewalk felt great. My sister told me she didnít want to go back out cause she barely knew my friends and was tripping after all, so we dried off and sat down to some more star trek.
My husband came in and made fun of something my sister said and that made me feel icky and mad. I was about to have the second revelation of the night. I thought about how he often communicates through verbal jabs which are meant in jest, but can feel harsh at times. I begin to realize that I do this too. I can think of at least two times that I have accidental offended someone with my sarcastic tone.
I look over at my sister who seems happy to lay back and watch star trek. I have felt frustrated this visit that she seems to consider many of the activities I planned second to watching as many of the voyager episodes she can while here. I see in her my own laziness and tendency towards obsession. When I buy a video game I play for 5 hours a day for the first week.
The connection between these thoughts suddenly pops out at me. Jungian projection. The things that irritate me about the ones I love are the things I hate about myself. I have always acknowledged that projection occurs and that it must play a role in my own perceptions but never before have I considered what I project and onto whom. I feel that a whole new way of analyzing my self has been discovered. Armed with this new tool I can see into unexplored areas of my psyche. The possibilities are nearly limitless.
This is all seen with a touch of sadness of course. I am confronting parts of my shadow self and that is unnerving. I stare at the TV but Iím not really watching. I need to be with my thoughts right now and this is a way of avoiding conversation with my sister. Soon she falls asleep. I lay back and breathe. I notice the fullness of my breaths. I find that on psychedelics I breath like a yogi. Deep and full. Equal inhale and exhale. It tends to feel speedy almost, taking deep breaths. Like Iím taking in too much life energy to handle. After perhaps another hour I finally felt tired and went to bed.
I wake the next morning refreshed. My sister shows no ill effects either. Since then I have read further on the conceptions of projection and the shadow self and continue to learn. This trip really solidified in my mind the place of LSA in my psychedelic toolbox. While LSD shows me connections that bring me closer to an understanding of the world around me, LSA shows me connections that bring me closer to an understanding of my place in the world.
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.


ArkavQuarium
For those who loves InsaniQuarium with low-spec PC
Library Tugas Besar OOP (Rev 2018-03-29 10
Functions
|
Variables
main.cpp File Reference
#include "
oop.hpp
"
#include <iostream>
#include <math.h>
#include <sstream>
Include dependency graph for main.cpp:
Functions
int
main
(int argc, char *args[])
Variables
const double
speed
= 50
Function Documentation
◆
main()
int main
(
int
argc
,
char *
args
[]
)
Here is the call graph for this function:
Variable Documentation
◆
speed
const double speed = 50
Generated by
1.8.14


The main type of document on web is HTML
The major pieces of web are computer,browsers,the internet and servers.
HTTP is protocal browser uses to communicate with servers.
HTML is hyper text markup language which consists of text content,markups,references to other documents and links to other pages.
HTML markups is made of opening and closing tags like contents . The whole things is called element.
b Bold tag/ em Italic tag.
HTML attributes have name header and value. Tags can have multiple attributes.
a is anchor tag. a href="www.udacity.com"
This website is my favorite.
Image tag This is void tag.Void tag has no content.
White spaces is for all tags.Multiple spaces to force lines on multiple lines
Use br tag or P paragraph tag.
Hello everyone!
We are using two lines now!
Hello everyone! We are using two lines now!
br tag is inline which is only text. p tag is block which creates invisible box around text with height and width
b,em,img are inline.
Span and div . Span is an inline with content and div is block element. We can create styles to it.
this is one line -yep,one line!
this is one line
-yep,one line!
HTML document structure.
Title!content
Title is title that appears on your browser and has CSS contents.
Body tag has main contents


This command will turn on Twitch whisper alerts (they are disabled by default).
All whisper alerts will be received as normal chat message triggers.
| Box Name | Type | Description |
|---|---|---|
| Open Whispers | Boolean | Select true to enable and false to disable whispers. |


Hi there and welcome to this enabling Technology site.
This is a domain/site/page that we use for internal purposes and it might be without any useful content for you.
If you came from a bookmark, please check it an update it to www․enatec․io.
This is a domain/site/page that we use for internal purposes and it might be without any useful content for you.
If you came from a bookmark, please check it an update it to www․enatec․io.


preprocessing.Rd
preprocessing()
is a function aimed at streamlining the preprocessing stage of analysing NMR spectra. It harnesses the power of the package metabom8
to quickly and easily process spectra.
preprocessing( X, ppm, meta, baseline = T, flip = F, cali = F, calibrant = "tsp", lineWid = 1, lowCut = 0.25, watCut = c(4.5, 5), ureCut = c(5.6, 6), uppCut = 9.5, noi_sh = c(9.5, 11) )
| X | A matrix containing the non-preprocessed NMR spectral data. The rows should containing all values of a single experiment, and the columns, the values of the chemical shift variables. |
|---|---|
| ppm | An array of the chemical shift variables, column matched to X. |
| meta | The matrix of metadata pertaining to the X matrix. This is crucial for the TSP calibration and line width calculation. |
| baseline | Default is set to |
| flip | Default is set to |
| cali | Default is set to |
| calibrant | This is the signal you wish to use as the calibrant. This signal must be compatible with |
| lineWid | This argument provides import for |
| lowCut | A single floating point number defining the ppm value that the lower limit of the spectra are trimmed to. |
| watCut | The lower and upper ppm values concatenated, from which the water region will be trimmed and omitted. Water regions provide no important information and should be removed prior to data analysis. Default is set to |
| ureCut | The lower and upper ppm values concatenated, from which the urea region will be trimmed and omitted. Urea regions also provide no important information and should be removed prior to data analysis. Default is set to |
| uppCut | A single floating point number defining the ppm value that the upper limit of the spectra are trimmed to. |
| noi_sh | The shift of the noise region used to calculate estimation of noise |
This function returns a list with:
The processed X matrix in the first element,
The processed ppm array in the second element, and
The line width results in a data frame in the third element.
Following the example below will extract the results quickly and easily.
preprocessing()
is powered by the metabom8
package and is simply a tool to harness and streamline functions in metabom8
when preprocessing NMR spectra, not replace them.
This function streamlines the preprocessing of NMR urine spectra by combining a range of functions. It:
Orientates the spectra correctly,
Calibrates the spectra by a specific peak,
Calculates the line widths of the peaks and returns a warning with the spectra that exceed the specified threshold,
Removes the lower, upper, water and urea regions of the spectra,
Corrects the baseline of the spectra using asymmetric least squares
Verifies that the resulting X, ppm and meta objects match appropriately.


I am an undergraduate Electrical Engineering student at the University of California, San Diego.
There are many games that I play and this is mostly filler information. Card games, video games, athletic games are all enjoyable. Deuces and Nertz are great card games. Call of Duty and Halo are comeptitors, but are both fantastic. One of my favorite games is catch. It is merely throwing a ball and catching over and over again, but it is most pleasing.


R
Name
Kind
Description
read
operation
read
operation
read
operation
read
operation
removeManaged
operation
removeManaged
operation
removeManaged
operation
RigidBody
class
rotation
relation
RotationConstraint
class


Home
Pages
Page Number one
Page Number two
Page Number three
Link
Link Number 1
Link Number 2
Link Number 3


Article Image
If I feel inspired–or I am asked to–I give public talks to meetups or conferences. I wrote a post about preparing my first conference talk.
Here’s a few recording.
Facilitating Online Agile Retrospectives
Are you seeing new challenges emerging from facilitating online Agile Retrospectives? How do you reflect and adapt to those new challenges?
How do you measure retrospectives success?
Retros (Retrospectives) should be a core practice for any team - but do you have a way to measure how successful they are?
Structured Agile Retrospectives
Enrico discusses ways in which to run a retro if you have too many problems with external causes, or if you are working with a team that does not know each other yet.
RubyConf AU 2016: Build and maintain large Ruby applications
This presentation will be about the challenges of building large Ruby web applications and how to maintain existing ones.


Mixture of tetris and 2048
New Game (F2)
Pause (P)
Your high scores
There are no high scores yet.
There's more information about this game
here
.


The Australian Defence Force will support the NSW flood emergency efforts from Tuesday.
“Emergency Management Australia have received an official request for assistance from NSW,” a spokesman from the Prime Minister’s office said.
“The request is for two search and rescue helicopters operating out of the NSW south coast for 24-hour operations. ADF will be able to provide this support and will commence service tomorrow.”
Emergency Management Australia is a division of the Department of Home Affairs.
Earlier today, Prime Minister Scott Morrison said they expected a formal request to be made from the NSW government.
“We understand this is likely to be for recovery support and clean-up operations, including personnel, vehicles and machinery,” he said. “The Premier [Gladys Berejiklian] and I were discussing that over the weekend.”
He said the ADF was “readying itself for this task that still lays ahead”.
“Particularly in the recovery phase,” Mr Morrison said. “It is working closely with NSW Emergency Services to co-ordinate potential areas for assistance.”
{{ item|safe }}


Nate Silver - The Signal and the Noise
Nate Silver 2012. The Signal and the Noise. The art and science of prediction. London: Penguin. 534 pp.
Not compelling throughout, but very interesting in parts. This is a popular account of reasoning (prediction) based on Bayesian statistics. The first few chapters explore subjects where most of us have great difficulty extracting useful predictions (signal) from the vast amounts of data available (noise). Such subjects include sports statistics (baseball), weather and climate, and earthquake prediction.
The second half of the book explains and applies Bayes’ Theorem to these problems. I think it was only by reading The Signal and the Noise that I came to understand Bayesian reasoning and it’s wide applications. It is still salutory that such a simple idea works as well as it does (modifying a prior probability based on better information as it comes to hand, then repeat as often as possible, each time with slightly improved probability). It works even if the prior probability is a poorly-informed guess. And even if we don’t ever come to understand the underlying cause of the phenomenon (as with earthquakes).
One of the telling conclusions was that people’s surveyed predictions about sharemarket outlook and economic confidence generally are highly inaccurate. You are much better off doing the reverse of what “market confidence” would suggest. A Nature article on a related theme is also of interest.
[incomplete, more to come]


By Andrea Vedaldi and Andrew Zisserman
This is an Oxford Visual Geometry Group computer vision practical, authored by Andrea Vedaldi and Andrew Zisserman (Release 2016a).
Convolutional neural networks are an important class of learnable representations applicable, among others, to numerous computer vision problems. Deep CNNs, in particular, are composed of several layers of processing, each involving linear as well as non-linear operators, that are learned jointly, in an end-to-end manner, to solve a particular tasks. These methods are now the dominant approach for feature extraction from audiovisual and textual data.
This practical explores the basics of learning (deep) CNNs. The first part introduces typical CNN building blocks, such as ReLU units and linear filters, with a particular emphasis on understanding back-propagation. The second part looks at learning two basic CNNs. The first one is a simple non-linear filter capturing particular image structures, while the second one is a network that recognises typewritten characters (using a variety of different fonts). These examples illustrate the use of stochastic gradient descent with momentum, the definition of an objective function, the construction of mini-batches of data, and data jittering. The last part shows how powerful CNN models can be downloaded off-the-shelf and used directly in applications, bypassing the expensive training process.
Read and understand the requirements and installation instructions. The download links for this practical are:
After the installation is complete, open and edit the script exercise1.m
in the MATLAB editor. The script contains commented code and a description for all steps of this exercise, for Part I of this document. You can cut and paste this code into the MATLAB window to run it, and will need to modify it as you go through the session. Other files exercise2.m
, exercise3.m
, and exercise4.m
are given for Part II, III, and IV.
Each part contains several Questions (that require pen and paper) and Tasks (that require experimentation or coding) to be answered/completed before proceeding further in the practical.
A feed-forward neural network can be thought of as the composition of number of functions Each function $f_l$ takes as input a datum $\bx_l$ and a parameter vector $\bw_l$ and produces as output a datum $\bx_{l+1}$. While the type and sequence of functions is usually handcrafted, the parameters $\bw=(\bw_1,\dots,\bw_L)$ are learned from data in order to solve a target problem, for example classifying images or sounds.
In a convolutional neural network data and functions have additional structure. The data $\bx_1,\dots,\bx_n$ are images, sounds, or more in general maps from a lattice1 to one or more real numbers. In particular, since the rest of the practical will focus on computer vision applications, data will be 2D arrays of pixels. Formally, each $\bx_i$ will be a $M \times N \times K$ real array of $M \times N$ pixels and $K$ channels per pixel. Hence the first two dimensions of the array span space, while the last one spans channels. Note that only the input $\bx=\bx_1$ of the network is an actual image, while the remaining data are intermediate feature maps.
The second property of a CNN is that the functions $f_l$ have a convolutional structure. This means that $f_l$ applies to the input map $\bx_l$ an operator that is local and translation invariant. Examples of convolutional operators are applying a bank of linear filters to $\bx_l$.
In this part we will familiarise ourselves with a number of such convolutional and non-linear operators. The first one is the regular linear convolution by a filter bank. We will start by focusing our attention on a single function relation as follows:
Open the example1.m
file, select the following part of the code, and execute it in MATLAB (right button > Evaluate selection
or Shift+F7
).
% Read an example image
x = imread('peppers.png') ;
% Convert to single format
x = im2single(x) ;
% Visualize the input x
figure(1) ; clf ; imagesc(x)
This should display an image of bell peppers in Figure 1:
Use MATLAB size
command to obtain the size of the array x
. Note that the array x
is converted to single precision format. This is because the underlying MatConvNet assumes that data is in single precision.
Question. The third dimension of
x
is 3. Why?
Now we will create a bank 10 of $5 \times 5 \times 3$ filters.
% Create a bank of linear filters
w = randn(5,5,3,10,'single') ;
The filters are in single precision as well. Note that w
has four dimensions, packing 10 filters. Note also that each filter is not flat, but rather a volume with three layers. The next step is applying the filter to the image. This uses the vl_nnconv
function from MatConvNet:
% Apply the convolution operator
y = vl_nnconv(x, w, []) ;
Remark: You might have noticed that the third argument to the vl_nnconv
function is the empty matrix []
. It can be otherwise used to pass a vector of bias terms to add to the output of each filter.
The variable y
contains the output of the convolution. Note that the filters are three-dimensional, in the sense that it operates on a map $\bx$ with $K$ channels. Furthermore, there are $K'$ such filters, generating a $K'$ dimensional map $\by$ as follows
Questions: Study carefully this expression and answer the following:
- Given that the input map $\bx$ has $M \times N \times K$ dimensions and that each of the $K'$ filters has dimension $M_f \times N_f \times K$, what is the dimension of $\by$?
- Note that $x$ is indexed by $i+i'$ and $j+j'$, but that there is no plus sign between $k$ and $k'$. Why?
Task: check that the size of the variable
y
matches your calculations.
We can now visualise the output y
of the convolution. In order to do this, use the vl_imarraysc
function to display an image for each feature channel in y
:
% Visualize the output y
figure(2) ; clf ; vl_imarraysc(y) ; colormap gray ;
Question: Study the feature channels obtained. Most will likely contain a strong response in correspondences of edges in the input image
x
. Recall thatw
was obtained by drawing random numbers from a Gaussian distribution. Can you explain this phenomenon?
So far filters preserve the resolution of the input feature map. However, it is often useful to downsample the output. This can be obtained by using the stride
option in vl_nnconv
:
% Try again, downsampling the output
y_ds = vl_nnconv(x, w, [], 'stride', 16) ;
figure(3) ; clf ; vl_imarraysc(y_ds) ; colormap gray ;
As you should have noticed in a question above, applying a filter to an image or feature map interacts with the boundaries, making the output map smaller by an amount proportional to the size of the filters. If this is undesirable, then the input array can be padded with zeros by using the pad
option:
% Try padding
y_pad = vl_nnconv(x, w, [], 'pad', 4) ;
figure(4) ; clf ; vl_imarraysc(y_pad) ; colormap gray ;
Task: Convince yourself that the previous code's output has different boundaries compared to the code that does not use padding. Can you explain the result?
In order to consolidate what has been learned so far, we will now design a filter by hand:
w = [0 1 0 ;
1 -4 1 ;
0 1 0 ] ;
w = single(repmat(w, [1, 1, 3])) ;
y_lap = vl_nnconv(x, w, []) ;
figure(5) ; clf ; colormap gray ;
subplot(1,2,1) ;
imagesc(y_lap) ; title('filter output') ;
subplot(1,2,2) ;
imagesc(-abs(y_lap)) ; title('- abs(filter output)') ;
Questions:
- What filter have we implemented?
- How are the RGB colour channels processed by this filter?
- What image structure are detected?
As we stated in the introduction, CNNs are obtained by composing several different functions. In addition to the linear filters shown in the previous part, there are several non-linear operators as well.
Question: Some of the functions in a CNN must be non-linear. Why?
The simplest non-linearity is obtained by following a linear filter by a non-linear activation function, applied identically to each component (i.e. point-wise) of a feature map. The simplest such function is the Rectified Linear Unit (ReLU)
This function is implemented by vl_nnrelu
; let's try this out:
w = single(repmat([1 0 -1], [1, 1, 3])) ;
w = cat(4, w, -w) ;
y = vl_nnconv(x, w, []) ;
z = vl_nnrelu(y) ;
figure(6) ; clf ; colormap gray ;
subplot(1,2,1) ; vl_imarraysc(y) ;
subplot(1,2,2) ; vl_imarraysc(z) ;
Tasks:
- Run the code above and understand what the filter $\bw$ is doing.
- Explain the final result $\bz$.
There are several other important operators in a CNN. One of them is pooling. A pooling operator operates on individual feature channels, coalescing nearby feature values into one by the application of a suitable operator. Common choices include max-pooling (using the max operator) or sum-pooling (using summation). For example, max-pooling is defined as:
Max pooling is implemented by the vl_nnpool
function. Try this now:
y = vl_nnpool(x, 15) ;
figure(6) ; clf ; imagesc(y) ;
Question: look at the resulting image. Can you interpret the result?
The function vl_nnpool
supports subsampling and padding just like vl_nnconv
. However, for max-pooling feature maps are padded with the value $-\infty$ instead of 0. Why?
Another important CNN building block is channel-wise normalisation. This operator normalises the vector of feature channels at each spatial location in the input map $\bx$. The form of the normalisation operator is actually rather curious: where $G(k) = \left[k - \lfloor \frac{\rho}{2} \rfloor, k + \lceil \frac{\rho}{2} \rceil\right] \cap {1, 2, \dots, K}$ is a group of $\rho$ consecutive feature channels in the input map.
Task: Understand what this operator is doing. How would you set $\kappa$, $\alpha$ and $\beta$ to achieve simple $L^2$ normalisation?
Now let's try this out:
rho = 5 ;
kappa = 0 ;
alpha = 1 ;
beta = 0.5 ;
y_nrm = vl_nnnormalize(x, [rho kappa alpha beta]) ;
figure(6) ; clf ; imagesc(y_nrm) ;
Tasks:
- Inspect the figure just obtained. Can you interpret it?
- Compute the $L^2$ norm of the feature channels in the output map
y_nrm
. What do you notice?- Explain this result in relation to the particular choice of the parameters $\rho$, $\kappa$, $\alpha$ and $\beta$.
The parameters of a CNN $\bw=(\bw_1,\dots\bw_L)$ should be learned in such a manner that the overall CNN function $\bz = f(\bx;\bw)$ achieves a desired goal. In some cases, the goal is to model the distribution of the data, which leads to a generative objective. Here, however, we will use $f$ as a regressor and obtain it by minimising a discriminative objective. In simple terms, we are given:
We use those to write the empirical loss of the CNN $f$ by averaging over the examples: Note that the composition of the function $f$ with the loss $\ell$ can be though of as a CNN with one more layer (called a loss layer). Hence, with a slight abuse of notation, in the rest of this part we incorporate the loss in the function $f$ (which therefore is a map $\mathcal{X}\rightarrow\mathbb{R}$) and do not talk about it explicitly anymore.
The simplest algorithm to minimise $L$, and in fact one that is used in practice, is gradient descent. The idea is simple: compute the gradient of the objective $L$ at a current solution $\bw^t$ and then update the latter along the direction of fastest descent of $L$: where $\eta_t \in \mathbb{R}_+$ is the learning rate.
Training CNNs is normally done using a gradient-based optimization method. The CNN $f$ is the composition of $L$ layers $f_l$ each with parameters $\bw_l$, which in the simplest case of a chain looks like: During learning, the last layer of the network is the loss function that should be minimized. Hence, the output $\bx_L = x_L$ of the network is a scalar quantity (a single number).
The gradient is easily computed using using the chain rule. If all network variables and parameters are scalar, this is given by[^derivative]: With tensors, however, there are some complications. Consider for instance the derivative of a function $\by=f(\bx)$ where both $\by$ and $\bx$ are tensors; this is formed by taking the derivative of each scalar element in the output $\by$ with respect to each scalar element in the input $\bx$. If $\bx$ has dimensions $H \times W \times C$ and $\by$ has dimensions $H' \times W' \times C'$, then the derivative contains $HWCH'W'C'$ elements, which is often unmanageable (in the order of several GBs of memory for a single derivative).
Note that all intermediate derivatives in the chain rule may be affected by this size explosion except for the derivative of the network output that, being the loss, is a scalar.
Question: The output derivatives have the same size as the parameters in the network. Why?
Back-propagation allows computing the output derivatives in a memory-efficient manner. To see how, the first step is to generalize the equation above to tensors using a matrix notation. This is done by converting tensors into vectors by using the $\vv$ (stacking)[^stacking] operator: In order to make this computation memory efficient, we project the derivative with respect to a tensor $\bp_L = 1$ as follows: Note that $\bp_L=1$ has the same dimension as $\bx_L$ (the scalar loss) and, being equal to 1, multiplying it to the left of the expression does not change anything. Things are more interesting when products are evaluated from the left to the right, i.e. backward from the output to the input of the CNN. The first such factors is given by: This results in a new projection vector $\bp_{L-1}$, which can then be multiplied from the left to obtain $\bp_{L-2}$ and so on. The last projection $\bp_l$ is the desired derivative. Crucially, each projection $\bp_q$ takes as much memory as the corresponding variable $\bx_q$.
Some might have noticed that, while projections remain small, each factor \eqref{e:factor} does contain one of the large derivatives that we cannot compute explicitly. The trick is that CNN toolboxes contain code that can compute the projected derivatives without explicitly computing this large factor. In particular, for any building block function $\by=f(\bx;\bw)$, a toolbox such as MatConvNet will implement:
For example, this is how this looks for the convolution operator:
y = vl_nnconv(x,w,b) ; % forward mode (get output)
p = randn(size(y), 'single') ; % projection tensor (arbitrary)
[dx,dw,db] = vl_nnconv(x,w,b,p) ; % backward mode (get projected derivatives)
and this is how it looks for ReLU operator:
y = vl_nnrelu(x) ;
p = randn(size(y), 'single') ;
dx = vl_nnrelu(x,p) ;
To see how backpropagation is used in practice, focus on a computational block $f$, followed by a function $g$: Here $g$ lumps the rest of the network, from $\by$ to the final scalar output $z$. The goal is to compute the derivatives $\partial z / \partial \bx$ and $\partial z / \partial \bw$ given the derivative $\bp = \partial z / \partial \by$ of the rest of the network $g$.
Let's put this into practice by letting $f$ be a convolutional layer and by filling $\bp = \partial z / \partial \by$ with random values for the sake of the example:
% Read an example image
x = im2single(imread('peppers.png')) ;
% Create a bank of linear filters and apply them to the image
w = randn(5,5,3,10,'single') ;
y = vl_nnconv(x, w, []) ;
% Create the derivative dz/dy
dzdy = randn(size(y), 'single') ;
% Back-propagation
[dzdx, dzdw] = vl_nnconv(x, w, [], dzdy) ;
Task: Run the code above and check the dimensions of
dzdx
anddzdy
. Does this matches your expectations?
An advantage of this modular view is that new building blocks can be coded and added to the architecture in a simple manner. However, it is easy to make mistakes in the calculation of complex derivatives. Hence, it is a good idea to verify results numerically. Consider the following piece of code:
% Check the derivative numerically
ex = randn(size(x), 'single') ;
eta = 0.0001 ;
xp = x + eta * ex ;
yp = vl_nnconv(xp, w, []) ;
dzdx_empirical = sum(dzdy(:) .* (yp(:) - y(:)) / eta) ;
dzdx_computed = sum(dzdx(:) .* ex(:)) ;
fprintf(...
'der: empirical: %f, computed: %f, error: %.2f %%\n', ...
dzdx_empirical, dzdx_computed, ...
abs(1 - dzdx_empirical/dzdx_computed)*100) ;
Questions:
- What is the meaning of
ex
in the code above?- What are the derivatives
dzdx_empirical
anddzdx_computed
?Tasks:
- Run the code and convince yourself that
vl_nnconv
derivatives is (probably) correct.- Create a new version of this code to test the derivative calculation with respect to $\bw$.
We are now ready to build our first elementary CNN, composed of just two layers, and to compute its derivatives:
% Parameters of the CNN
w1 = randn(5,5,3,10,'single') ;
rho2 = 10 ;
% Run the CNN forward
x1 = im2single(imread('peppers.png')) ;
x2 = vl_nnconv(x1, w1, []) ;
x3 = vl_nnpool(x2, rho2) ;
% Create the derivative dz/dx3
dzdx3 = randn(size(x3), 'single') ;
% Run the CNN backward
dzdx2 = vl_nnpool(x2, rho2, dzdx3) ;
[dzdx1, dzdw1] = vl_nnconv(x1, w1, [], dzdx2) ;
Question: Note that the last derivative in the CNN is
dzdx3
. Here, for the sake of the example, this derivative is initialised randomly. In a practical application, what would this derivative represent?
We can now use the same technique as before to check that the derivative computed through back-propagation are correct.
% Check the derivative numerically
ew1 = randn(size(w1), 'single') ;
eta = 0.0001 ;
w1p = w1 + eta * ew1 ;
x1p = x1 ;
x2p = vl_nnconv(x1p, w1p, []) ;
x3p = vl_nnpool(x2p, rho2) ;
dzdw1_empirical = sum(dzdx3(:) .* (x3p(:) - x3(:)) / eta) ;
dzdw1_computed = sum(dzdw1(:) .* ew1(:)) ;
fprintf(...
'der: empirical: %f, computed: %f, error: %.2f %%\n', ...
dzdw1_empirical, dzdw1_computed, ...
abs(1 - dzdw1_empirical/dzdw1_computed)*100) ;
In this part we will learn a very simple CNN. The CNN is composed of exactly two layers: a convolutional layer and a max-pooling layer: $W$ contains a single $3\times 3$ square filter, so that $b$ is a scalar. and the input image $\bx=\bx_1$ has a single channel.
Task
- Open the file
tinycnn.m
and inspect the code. Convince yourself that the code computes the CNN just described.- Look at the paddings used in the code. If the input image $\bx_1$ has dimensions $M\times N$, what is the dimension of the output feature map $\bx_3$?
In the rest of the section we will learn the CNN parameters in order to extract blob-like structures from images, such as the ones in the following image:
The first step is to load the image data/dots.jpg
and to use the supplied extractBlackBlobs
function to extract all the black dots in the image.
% Load an image
im = rgb2gray(im2single(imread('data/dots.jpg'))) ;
% Compute the location of black blobs in the image
[pos,neg] = extractBlackBlobs(im) ;
The arrays pos
and neg
contain now pixel labels and will be used as annotations for the supervised training of the CNN. These annotations can be visualised as follows:
figure(1) ; clf ;
subplot(1,3,1) ; imagesc(im) ; axis equal ; title('image') ;
subplot(1,3,2) ; imagesc(pos) ; axis equal ; title('positive points (blob centres)') ;
subplot(1,3,3) ; imagesc(neg) ; axis equal ; title('negative points (not a blob)') ;
colormap gray ;
Task: Inspect
pos
andneg
and convince yourself that:
pos
contains a singletrue
value in correspondence of each blob centre;neg
contains atrue
value for each pixel sufficiently far away from a blob.Are there pixels for which both
pos
andneg
evaluate to false?
Before we attempt to train the CNN, the image is pre-processed to remove its mean value. It is also smoothed by applying a Gaussian kernel of standard deviation 3 pixels:
% Pre-smooth the image
im = vl_imsmooth(im,3) ;
% Subtract median value
im = im - median(im(:)) ;
We will come back to this preprocessing steps later.
We will now setup a learning problem to learn $W$ and $b$ to detect black blobs in images. Recall that the CNN computes for each image pixel $(u,v)$ a score $f(\bx;\bw,b)_{(u,v)}$. We would like this score to be:
pos
or $(u,v)\in\cP$) andneg
or $(u,v)\in\cN$).We do so by defining and then optimising the following objective function:
Questions:
- What can you say about the score of each pixel if $\lambda=0$ and $E(\bw,b) =0$?
- Note that the objective enforces a margin between the scores of the positive and negative pixels. How much is this margin?
We can now train the CNN by minimising the objective function with respect to $\bw$ and $b$. We do so by using an algorithm called gradient descent with momentum. Given the current solution $(\bw_t,b_t)$ and update it , this is updated to $(\bw_{t+1},b_t)$ by following the direction of fastest descent as given by the negative gradient $-\nabla E(\bw_t,b_t)$ of the objective. However, gradient updates are smoothed by considering a momentum term $(\bar\bw_{t}, \bar\mu_t)$, yielding the update equations and similarly for the bias term. Here $\mu$ is the momentum rate and $\eta$ the learning rate.
Questions:
- Explain why the momentum rate must be smaller than 1. What is the effect of having a momentum rate close to 1?
- The learning rate establishes how fast the algorithm will try to minimise the objective function. Can you see any problem with a large learning rate?
The parameters of the algorithm are set as follows:
numIterations = 500 ;
rate = 5 ;
momentum = 0.9 ;
shrinkRate = 0.0001 ;
plotPeriod = 10 ;
Tasks:
- Inspect the code in the file
exercise3.m
. Convince yourself that the code is implementing the algorithm described above. Pay particular attention at the forward and backward passes as well as at how the objective function and its derivatives are computed.- Run the algorithm and observe the results. Then answer the following questions:
- The learned filter should resemble the discretisation of a well-known differential operator. Which one?
- What is the average of the filter values compared to the average of the absolute values?
- Run the algorithm again and observe the evolution of the histograms of the score of the positive and negative pixels in relation to the values 0 and 1. Answer the following:
- Is the objective function minimised monotonically?
- As the histograms evolve, can you identify at least two "phases" in the optimisation?
- Once converged, do the score distribute in the manner that you would expect?
Hint: the
plotPeriod
option can be changed to plot the diagnostic figure with a higher or lower frequency; this can significantly affect the speed of the algorithm.
In this part we will experiment with several variants of the network just learned. First, we study the effect of the image smoothing:
Task: Train again the tiny CNN without smoothing the input image in preprocessing. Answer the following questions:
- Is the learned filter very different from the one learned before?
- If so, can you figure out what "went wrong"?
- Look carefully at the output of the first layer, magnifying with the loupe tool. Is the maximal filter response attained in the middle of each blob?
Hint: The Laplacian of Gaussian operator responds maximally at the centre of a blob only if the latter matches the blob size. Relate this fact to the combination of pre-smoothing the image and applying the learned $3\times 3$ filter.
Now restore the smoothing but switch off subtracting the median from the input image.
Task: Train again the tiny CNN without subtracting the median value in preprocessing. Answer the following questions:
- Does the algorithm converge?
- Reduce a hundred-fold the learning are and increase the maximum number of iterations by an equal amount. Does it get better?
- Explain why adding a constant to the input image can have such a dramatic effect on the performance of the optimisation.
Hint: What constraint should the filter $\bw$ satisfy if the filter output should be zero when (i) the input image is zero or (ii) the input image is a large constant? Do you think that it would be easy for gradient descent to enforce (ii) at all times?
What you have just witnessed is actually a fairly general principle: centring the data usually makes learning problems much better conditioned.
Now we will explore several parameters in the algorithms:
Task: Restore the preprocessing as given in
experiment4.m
. Try the following:
- Try increasing the learning rate
eta
. Can you achieve a better value of the energy in the 500 iterations?- Disable momentum by setting
momentum = 0
. Now try to beat the result obtained above by choosingeta
. Can you succeed?
Finally, consider the regularisation effect of shrinking:
Task: Restore the learning rate and momentum as given in
experiment4.m
. Then increase the shrinkage factor tenfold and a hundred-fold.
- What is the effect on the convergence speed?
- What is the effect on the final value of the total objective function and of the average loss part of it?
In this part we will learn a CNN to recognise images of characters.
Open up exercise4.m
and execute Part 4.1. The code loads a structure imdb
containing images of the characters a, b, ..., z rendered using approximately 931 fonts downloaded from the Google Fonts Project. Look at the imdb.images
substructure:
>> imdb.images
ans =
id: [1x24206 double]
data: [32x32x24206 single]
label: [1x24206 double]
set: [1x24206 double]
These are stored as the array imdb.images.id
is a 29,198-dimensional vector of numeric IDs for each of the 29,198 character images in the dataset. imdb.images.data
contains a $32 \times 32$ image for each character, stored as a slide of a $32\times 32\times 29,!198$-dimensional array. imdb.images.label
is a vector of image labels, denoting which one of the 26 possible characters it is. imdb.images.set
is equal to 1 for each image that should be used to train the CNN and to 2 for each image that should be used for validation.
Task: look at the Figure 1 generated by the code and at the code itself and make sure that you understand what you are looking at.
The function initializeCharacterCNN.m
creates a CNN initialised with random weights that will be trained to recognise character images.
Tasks:
- By inspecting
initializeCharacterCNN.m
get a sense of the architecture that will be trained. How many layers are there?  Use the function
vl_simplenn_display
to produce a table summarising the architecture.
Note that the penultimate layer has 26 output dimensions, one for each character. Character recognition looks at the maximal output to identify which character is processed by the network.
However, the last network layer is vl_nnsoftmaxloss
, which in turn is a combination of the vl_nnsoftmax
function and of the classification log-loss vl_nnloss
. The softmax operator is given by
whereas the log-loss is given by
where $c_{ij}$ is the index of the ground-truth class at spatial location $(i,j)$.
Remark: While in MatConvNet all operators are convolutional, in this case the network is configured such that the output of the classification layer is a $1 \times 1 \times 26$-dimensional feature map, i.e. there remains only one spatial location.
Tasks:
- Understand what the softmax operator does. Hint: to use the log-loss the data must be in the (0, 1] interval.
- Understand what is the effect of minimising the log-loss. Which neural response should become larger?
- Why do you think MatConvNet provides a third function
vl_nnsoftmaxloss
combining both functions into a single layer?
We are now ready to train the CNN. To this end we use the example SGD implementation in MatConvNet (examples/cnn_train.m
). This function requires some options:
trainOpts.batchSize = 100 ;
trainOpts.numEpochs = 100 ;
trainOpts.continue = true ;
trainOpts.useGpu = false ;
trainOpts.learningRate = 0.001 ;
trainOpts.numEpochs = 15 ;
trainOpts.expDir = 'data/chars-experiment' ;
This says that the function will operate on SGD mini-batches of 100 elements, it will run for 15 epochs (passes through the data), it will continue from the last epoch if interrupted, if will not use the GPU, it will use a learning rate of 0.001, and it will save any file in the data/chars-experiment
subdirectory.
Before the training starts, the average image value is subtracted:
% Take the average image out
imageMean = mean(imdb.images.data(:)) ;
imdb.images.data = imdb.images.data - imageMean ;
This is similar to what we have done in Part 3.
The training code is called as follows:
% Call training function in MatConvNet
[net,info] = cnn_train(net, imdb, @getBatch, trainOpts) ;
Here the key, in addition to the trainOpts
structure, is the @getBatch
function handle. This is how cnn_train
obtains a copy of the data to operate on. Examine this function (see the bottom of the exercise4.m
file):
function [im, labels] = getBatch(imdb, batch)
im = imdb.images.data(:,:,batch) ;
im = 256 * reshape(im, 32, 32, 1, []) ;
labels = imdb.images.label(1,batch) ;
The function extracts the $m$ images corresponding to the vector of indexes batch
. It also reshape them as a $32\times 32\times 1\times m$ array (as this is the format expected by the MatConvNet functions) and multiplies the values by 256 (the resulting values match the network initialisation and learning parameters). Finally, it also returns a vector of labels, one for each image in the batch.
Task: Run the learning code and examine the plots that are produced. As training completes answer the following questions:
- How many images per second can you process? (Look at the output in the MATLAB screen)
- There are two sets of curves: energy and prediction error. What do you think is the difference? What is the "energy"?
- Some curves are labelled "train" and some other "val". Should they be equal? Which one should be lower than the other?
- Both the top-1 and top-5 prediction errors are plotted. What do they mean? What is the difference?
Once training is finished, the model is saved back:
% Save the result for later use
net.layers(end) = [] ;
net.imageMean = imageMean ;
save('data/chars-experiment/charscnn.mat', '-struct', 'net') ;
Note that we remember the imageMean
for later use. Note also that the softmaxloss layer is removed from the network before saving.
The next step is to glance at the filters that have been learned:
figure(2) ; clf ; colormap gray ;
vl_imarraysc(squeeze(net.layers{1}.weights{1}),'spacing',2)
axis equal ;
title('filters in the first layer') ;
Task: what can you say about the filters?
We now apply the model to a whole sequence of characters. This is the image data/sentence-lato.png
:
% Load the CNN learned before
net = load('data/chars-experiment/charscnn.mat') ;
% Load the sentence
im = im2single(imread('data/sentence-lato.png')) ;
im = 256 * (im - net.imageMean) ;
% Apply the CNN to the larger image
res = vl_simplenn(net, im) ;
Question: The image is much wider than 32 pixels. Why can you apply to it the CNN learned before for $32\times 32$ patches?
Task: examine the size of the CNN output using
size(res(end).x)
. Does this match your expectation?
Now use the decodeCharacters()
function to visualise the results:
% Visualize the results
figure(3) ; clf ;
decodeCharacters(net, imdb, im, res) ;
Tasks: inspect the output of the
decodeCharacters()
function and answer the following:
- Is the quality of the recognition any good?
- Does this match your expectation given the recognition rate in your validation set (as reported by
cnn_train
during training)?
A key issue with the previous CNN is that it is not trained to recognise characters in the context of other characters. Furthermore, characters are perfectly centred in the patch. We can relax these assumptions by making the training data "more realistic". In this part we will train a second network applying data jittering by:
This is implemented by the getBatchWithJitter()
function (note that jittering is applied on the fly as it is so fast).
Tasks:
- Train a second model, using the jittered data.
- Look at the training and validation errors. Is their gap as wide as it was before?
- Use the new model to recognise the characters in the sentence by repeating the previous part. Does it work better?
- Advanced. What else can you change to make the performance even better?
Skip this part if you do not wish to experiment training using GPU hardware.
A key challenge in deep learning is the sheer amount of computation required to train gigantic models from equally gigantic data collections. State-of-the-art vision models, for example, take weeks to train on specialised hardware such as GPUs, and they are essentially untrainable on CPU (unless you have access to a very large cluster). Thus it is practically important to learn how to use this hardware.
In MatConvNet this is almost trivial as it builds on the easy-to-use GPU support in MATLAB. You can follow this list of steps to try it out:
data/characters-experiment
and data/characters-jit-experiment
.Make sure that MatConvNet is compiled with GPU support. To do this, use
```matlab
setup('useGpu', true) ; ```
Try again training the model of exercise4.m
switching to true
the useGpu
flag.
Task: Follow the steps above and note the speed of training. How many images per second can you process now?
For these small images, the GPU speedup is probably modest (perhaps 2-5 fold). However, for larger models it becomes really dramatic (>10 fold).
A characteristic of deep learning is that it constructs representations of the data. These representations tend to have a universal value, or at least to be applicable to an array of problems that transcends the particular task a model was trained for. This is fortunate as training complex models requires weeks of works on one or more GPUs or hundreds of CPUs; these models can then be frozen and reused for a number of additional applications, with no or minimal additional work.
In this part we will see how MatConvNet can be used to download and run high-performance CNN models for image classification. These models are trained from 1.2M images in the ImageNet datasets to discriminate 1,000 different object categories.
Several pertained models can be downloaded from the MatConvNet website, including several trained using other CNN implementations such as Caffe. One such models is included in the practical data/imagenet-vgg-verydeep-16.mat
file. This is one of the best models from the ImageNet ILSVCR Challenge 2014.
The first step is to load the model itself. This is in the format of the vl_simplenn
CNN wrapper, and ships as a MATLAB .mat
file:
net = load('data/imagenet-vgg-verydeep-16.mat') ;
vl_simplenn_display(net) ;
Tasks:
- Look at the output of
vl_simplenn_display
and understand the structure of the model. Can you understand why it is called "very deep"?- Look at the size of the file
data/imagenet-vgg-verydeep-16.mat
on disk. This is just the model.
We can now use the model to classify an image. We start from peppers.png
, a MATLAB stock image:
% obtain and preprocess an image
im = imread('peppers.png') ;
im_ = single(im) ; % note: 255 range
im_ = imresize(im_, net.normalization.imageSize(1:2)) ;
im_ = im_ - net.normalization.averageImage ;
The code normalises the image in a format compatible with the model net
. This amounts to: converting the image to single
format (but with range 0,...,255 rather than [0, 1] as typical in MATLAB), resizing the image to a fixed size, and then subtracting an average image.
It is now possible to call the CNN:
% run the CNN
res = vl_simplenn(net, im_) ;
As usual, res
contains the results of the computation, including all intermediate layers. The last one can be used to perform the classification:
% show the classification result
scores = squeeze(gather(res(end).x)) ;
[bestScore, best] = max(scores) ;
figure(1) ; clf ; imagesc(im) ;
title(sprintf('%s (%d), score %.3f',...
net.classes.description{best}, best, bestScore)) ;
That completes this practical.
A two-dimensional lattice is a discrete grid embedded in $R^2$, similar for example to a checkerboard. ↩


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Developing Full-stack range, impact investing and social enterprise
After many years hesitating from code, finally got the power.
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This Blockchain application demonstrates energy sharing on a network among 'Residents' on a grid. The energy trade is based on exchanging coins for energy provided. The network also includes 'Utility Companies' to trade excess energy with or obtain energy from, and 'Banks' to exchange cash (fiat money) for coins.
Developers can use the Data API to query for power distribution data for building analytics services like Power Forecasting
Residents own three assets: Energy, Coins and Cash. They can trade with fellow residents, banks, and utility companies.
ResidentsBanks on the network own two assets: Cash and Coins. They can trade with residents, who can buy or sell coins for cash.
BanksUtility Companies own two assets: Energy and Coins. They can trade with residents, who can buy or sell energy for coins.
Utility CompaniesExecute a transaction between Residents by exchanging Coins for Energy
Resident to ResidentExecute a transaction between a Resident and a Bank by exchanging Coins for Cash
Resident to BankExecute a transaction between a Resident and a Utility Company by exchanging Coins for Energy
Resident to Utility


Browse Composites
Tour the browse composites dialog
Composites are convenient way to define new steps composed of one or more predefined steps. Further on, composites are grouped into composite classes. Browse Composites dialog allows users to create new or open an existing composites class.
Click the Browse Composites in the navigation menu to open the dialog. Once inside, you can open any composites class. New composites class can be created by typing the name in the search field and clicking the Create New Composites Class () button.


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Your ticket is only valid on the selected date and time.
Attention
1.If you would like to visit with 6 people(s) or more your reservation will be in Group Individual Travel (GIT)
2.Do not bring food into Museum Pertanian dan tanah
3.Guides are not allowed from travel agents
4.Visitors are prohibited from parking in the Museum Pertanian dan tanah area
5.We accept both printed and digital tickets. If you’re not able to print your ticket, Just bring your smartphone or tablet with you to showing to the ticket counter. Naturally, check the battery is sufficiently charged.


Original vs. Denoised Audio Comparison
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--- layout: page permalink: /providers/social-networks/ title: "社交网站" description: "不会刺探用户个资与用户兴趣档侧写( profile)谋利的社交网站。" --- {% include sections/social-networks.html %}


| fct_reorder {forcats} | R Documentation |
fct_reorder()
is useful for 1d displays where the factor is mapped to
position; fct_reorder2()
for 2d displays where the factor is mapped to
a non-position aesthetic. last2()
and first2()
are helpers for fct_reorder2()
;
last2()
finds the last value of y
when sorted by x
; first2()
finds the first value.
fct_reorder(.f, .x, .fun = median, ..., .desc = FALSE) fct_reorder2(.f, .x, .y, .fun = last2, ..., .desc = TRUE) last2(.x, .y) first2(.x, .y)
.f |
A factor (or character vector). |
.x, .y |
The levels of |
.fun |
n summary function. It should take one vector for
|
... |
Other arguments passed on to |
.desc |
Order in descending order? Note the default is different
between |
df <- tibble::tribble( ~color, ~a, ~b, "blue", 1, 2, "green", 6, 2, "purple", 3, 3, "red", 2, 3, "yellow", 5, 1 ) df$color <- factor(df$color) fct_reorder(df$color, df$a, min) fct_reorder2(df$color, df$a, df$b) boxplot(Sepal.Width ~ Species, data = iris) boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width), data = iris) boxplot(Sepal.Width ~ fct_reorder(Species, Sepal.Width, .desc = TRUE), data = iris) chks <- subset(ChickWeight, as.integer(Chick) < 10) chks <- transform(chks, Chick = fct_shuffle(Chick)) if (require("ggplot2")) { ggplot(chks, aes(Time, weight, colour = Chick)) + geom_point() + geom_line() # Note that lines match order in legend ggplot(chks, aes(Time, weight, colour = fct_reorder2(Chick, Time, weight))) + geom_point() + geom_line() + labs(colour = "Chick") }


The Orinda Project
The Orinda House was D-A- Y’s first project and something we are extremely proud of. It is a remodel and addition project located in Orinda, California. From initial planning through construction, we worked very closely with the owners who also acted as the General Contractor on the project. Our challenge was to preserve the original house’s exterior charm despite the needed addition to make the house functional for a growing family. In the end, our clients got a spacious home that respects it’s past, but also feels warm and modern, complete with solar panels.
The Teton Project
The Teton House is located in rural Idaho. With unobstructed views of the Teton Mountains, we could not have dreamed up a more beautiful site. Our clients had a need to expand their home to accommodate their large and boisterous family gatherings. We did a complete redesign of the first floor, expanding the kitchen, living, and dining areas. We also added a master suite. This freed up more room in the walkout basement for visitors. Perhaps the best space in the house is the deck that overlooks the valley and mountains. Our clients acted as their own General Contractor and we commend their incredible work.
The Ranch Project
Front After
Front Before
Back After
Back Before
Garage Looking Out
Lauren Framing
Jessie and Max on Deck
Holley
Lauren


Well as far as Methadone goes it's fun.   I take them in pill form, so it's time released.
The thing w/ Methadone is that when I take them it's normally at night. I still get a little sick to my stomach, and normally I throw up at one point or another, but once I do I feel great and it won't bother me again.  I'll be sitting there in a weird state of I'm here but I'm not here. It's fun.
  I think that's just me though.. All of my friends eat them and they have no problem with that.
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.


15:30
Алиса Дисс
Новогодний корпоратив будет 26 декабря! Записаться на праздник и детали уточняйте у меня.
Корпоративный чат
2:15
Виктория Русских
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
Attachments:
Theme-thumbnail-image.jpg
5:15
Демьян Успешников
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
5:30
Алиса Дисс
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
2:15
Виктория Русских
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
Attachments:
Theme-thumbnail-image.jpg
5:15
Демьян Успешников
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
5:30
Алиса Дисс
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
2:15
Виктория Русских
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
Attachments:
Theme-thumbnail-image.jpg
5:15
Демьян Успешников
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
5:30
Алиса Дисс
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market


Models
-
An access token.
See moreDeclaration
Swift
public struct AccessToken
-
A GitHub token.
Declaration
Swift
public struct GitHubToken
-
An identity.
Declaration
Swift
public struct Identity
-
An identity provider.
Declaration
Swift
public enum Provider: String
-
An authorization scope.
Declaration
Swift
public enum Scope: String
-
A user.
Declaration
Swift
public struct User


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Ryder Cup wildcard picks: would purely using data have given a different ...
www.theguardian.com
Would the same golfers have made the cut if the US and European team captains had relied purely on data rather than instinct?
-
Why is tech increasing the boardroom obsession with golf?
www.theguardian.com
As golf continues to bring game enhancing digital technologies into play, the sport’s relationship with the boardroom goes from strength to strength
-
What can enterprises learn from the way the sporting world serves its fans?
www.theguardian.com
The sports industry is leading the way in the use of technology to engage with fans - what can businesses learn from this connected approach?
-
How Facebook and Google are taking over your online identity
qz.com
When Facebook executives meet potential advertisers, their biggest selling point is that the company knows whom it's advertising to. In contrast to ...
-
Europe allows gadgets to stay on during flights
www.reuters.com
BRUSSELS (Reuters) - Airline passengers will be able to use smartphones and tablet computers throughout their entire flight, the European Aviation ...
-
Ryder Cup 2014: standings, results and live scores – interactive
www.theguardian.com
All the latest scores, results, standings and points totals at Gleneagles with our interactive live match stats
-
Russia orders Google, Facebook and Twitter to comply with local storage ...
gigaom.com
The U.S. firms have reportedly been told they must store Russians’ local data and metadata on Russian soil, and abide by the same restrictions on ...
-
Has Apple Abandoned Egalitarianism?
www.theatlantic.com
The company has always marketed to the middle class. Does its new gold watch represent a foray into the luxury sector?
-
Six types of PR client personalities and the best ways to handle them
www.theguardian.com
You need a thick skin for client liaisons in public relations, but I never give up hope of snagging the perfect client
-
Hole in one: How Big Data can turn players into winners
www.theguardian.com
By combining Big Data, mobile and cloud, backed up by quality security, individuals and businesses alike can up their game, whatever it may be
-
Darren Clarke's Ryder Cup top 25 – interactive
www.theguardian.com
Former Europe Ryder Cup player Darren Clarke, who has tasted victory four times, as well as being a vice-captain on the winning 2010 team, chooses ...
-
Crowdsourcing: the great advertising agency debate
www.theguardian.com
Agencies can use crowdsourcing to reinvigorate their business and deliver campaigns inspired by the public, for the public, writes François Pétavy
-
Miss a Payment? Good Luck Moving That Car - NYTimes.com
dealbook.nytimes.com
The thermometer showed a 103.5-degree fever, and her 10-year-old’s asthma was flaring up. Mary Bolender, who lives in Las Vegas, needed to get her ...
-
Apple knew of iCloud security hole 6 months before Celebgate
www.dailydot.com
A security research reported the bug in iCloud's security to Apple back in March.
-
New technology is fuelling the growth of gaming with social purpose
www.theguardian.com
One of the directors behind Grand Theft Auto, Navid Khonsari, explains why he believes that play will be more purposeful in the future
-
Social marketing: show the world what your brand’s values are
www.theguardian.com
McDonald’s is offering burgers for cans, while some US residents can score free Ben & Jerry’s ice-cream for trading in violent video games
-
Check out the latest news, views and comment on the Guardian Media network.
register.theguardian.com
Join thousands of like-minded media and technology professionals by taking advantage of the Guardian Media Network. Connect and share ideas with ...
-
14 tips on using tech to grow your business
www.theguardian.com
We round up the best insights and tips from our last live Q&A on tech capabilities for growth
-
Eleven ways marketing and ads can stay creative in today’s climate
www.theguardian.com
We round up the best tips, comments and insights from our online discussion on taking creative risks
-
Instagram adverts are coming to the UK – but do brands need them?
www.theguardian.com
With the ad platform reserved for brands with large budgets, there are cheaper ways to use the network to drive engagement
-
Social Media Week – a sneak peek at its social strategy
www.theguardian.com
On the eve of Social Media Week London, how does this ‘globally local’ event ensure its social media strategy is at the pinnacle of a sector in flux?
-
Eight simple e-commerce hacks to increase your online selling power
www.theguardian.com
From an equation for calculating conversion rates to a tweak for reducing bounce rates, here’s how to maximise your earnings
-
Six things CEOs want from their marketing teams in a digital age
www.theguardian.com
From Brother UK to Exterion Media, Lucy Fisher talks to a CEOs to find out how marketers can earn a place at the top table
-
Should the BBC Trust be renamed the Licence Fee Trust?
www.theguardian.com
Labour party conference: media leaders fear for growing split between the BBC and its viewers
-
Apple sells 10m new iPhones in first three days on sale
www.theguardian.com
Despite not being available in China, sales of new larger devices set record. By Charles Arthur
-
We can solve the water sanitation crisis, but we need your help
www.theguardian.com
The CEOs of the Ikea Foundation and Water.org call for a partnership to help those that lack access to safe water
-
Doug Richard: 'Brits are polite to a fault and I don’t mean that as a ...
www.theguardian.com
The former Dragons’ Den star and startup teacher talks about British business manners, Alan Sugar, non-techy entrepreneurs and teaching ...
-
Carbon storage could lead the northeast’s industrial renaissance
www.theguardian.com
Regional business plan uses Teesside’s historic pipe network to channel CO2 into huge storage facilities in the North Sea. Terry Slavin reports
-
Peter Thiel: ‘We attribute too much to luck. Luck is an atheistic word for ...
www.theguardian.com
Entrepreneur and PayPal co-founder Peter Thiel suggests his success comes from skill rather than luck. Here, he talks about investing in people and ...
-
BBC future survey: the licence fee should not be cut
www.theguardian.com
The licence fee should stay the same but a greater proportion of funding should come from BBC Worldwide, say media professionals


Models, layers, and Keras¶
With Keras API in mind, this model implementation essentially is a light-weight way to group neural network layers together. This is a rare case in NNC (or ccv in general) where Object-Oriented programming makes sense. I borrowed heavily from Objective-C / C++ to implement this Object-Oriented interface.
Now back to elaboration of the Model interface. It is specifically designed with Keras in mind, asking question: If we are going to build Keras high-level API in any languages (Ruby, Python, Swift, Julia), what’s the underlying C interface would look like? Here is your answer (hint: it looks very much like just Python Keras API).
A model consists of a set of inputs and outputs. This sounds very much like what “Command” is in Level-1 APIs, however, they are different: a model is stateful. For example, a convolution command takes 3 inputs: image, kernel weight and bias, has 1 output: image. A convolution model takes 1 input: image, and 1 output: image. kernel weight and bias are internal states to the model (in Keras, it is called “layer” for convolution, and model means a set of layers. In NNC, that kind of differentiation feels superficial, therefore, a layer is a model).
A model can be combined, and a new model can be a combination of other models.
The simpler composed model is the sequential model. A sequential model is a model that consists a sequence of models that contains one input and one output. The output of the earlier model feed into the later one, thus, a sequential evaluation path.


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The Centers for Disease Control and Prevention (CDC) released a report in June 2018 indicating the suicide rate in the United States has risen nearly 30% since 1999. The CDC provided data on the state-level suicide rate, age-adjusted for persons ≥ 10 years old per 100,000 population. Only one state, Nevada, had a rate that decreased during this period. Note that Nevada has had a consistently high rate of suicide since 1999. I've created an interactive map, using Carto, that displays the suicide rate in each state in a graduated blue color scheme. I plan to add county-level data to the map eventually. At this time, many of the counties have statistically unreliable data.
Enlarge MapA key finding in the data is a 50% change in suicide among females from 2000 to 2016, most notably women aged 45 - 64.
This interactive map contains data on the relative change from 1999 to 2016 in suicide rate per capita (suicide deaths per 100,000 people per year) expressed as a percentage. The pop-up for each state also indicates the rate per year in 1999 and in 2016. For a great review of comparing statistical rates of change on maps, see Methods of Comparison, Compared by the creator of d3, Mike Bostock. The source of the data on this webpage was produced by Hedegaard H, Curtin SC, Warner M. Suicide rates in the United States continue to increase. NCHS Data Brief, no 309. Hyattsville, MD: National Center for Health Statistics. 2018.
I believe another thing to consider is data prior to 1999. The table below contains data collected before 1999. In analyzing the data back to 1950, the suicide rate doesn't appear to have a significant change. It's interesting to see a dip in the suicide rate from 2000 to 2005.
I started this map after seeing maps presented online on the increasing suicide rate from recent CDC reports. I thought many of these maps needed interactivity and a higher level of detail. I wondered if there is something that the geospatial perspective could tell us about this issue. As I investigated, I discovered some of the weaknesses in the data. The key takeaway I've had from this project and the MAP 671 class overall is how difficult data collection can be and going back to Module 1, how maps can potentially mislead. I've gained a greater appreciation for the responsibility I have as a creator to carefully represent data using digital maps.


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Date
Section Heading
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Section Heading
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


| I l@ve RuBoard |
Gordius, the King of Phrygia, once tied a knot that no one could untie. It was said that he who solved the riddle of the Gordian Knot would rule all of Asia. So along comes Alexander the Great, who chops the knot to bits with his sword. Just a little different interpretation of the requirements, that's all… and he did end up ruling most of Asia.
Every now and again, you will find yourself embroiled in the middle of a project when a really tough puzzle comes up: some piece of engineering that you just can't get a handle on, or perhaps some bit of code that is turning out to be much harder to write than you thought. Maybe it looks impossible. But is it really as hard as it seems?
Consider real-world puzzles—those devious little bits of wood, wrought iron, or plastic that seem to turn up as Christmas presents or at garage sales. All you have to do is remove the ring, or fit the T-shaped pieces in the box, or whatever.
So you pull on the ring, or try to put the T's in the box, and quickly discover that the obvious solutions just don't work. The puzzle can't be solved that way. But even though it's obvious, that doesn't stop people from trying the same thing—over and over—thinking there must be a way.
Of course, there isn't. The solution lies elsewhere. The secret to solving the puzzle is to identify the real (not imagined) constraints, and find a solution therein. Some constraints are absolute; others are merely preconceived notions.  On the other hand, some apparent constraints may not be real constraints at all. For example, there's that old bar trick where you take a brand new, unopened champagne bottle and bet that you can drink beer out of it. The trick is to turn the bottle upside down, and pour a small quantity of beer in the hollow in the bottom of the bottle. Many software problems can be just as sneaky.
The popular buzz-phrase "thinking outside the box" encourages us to recognize constraints that might not be applicable and to ignore them.
But this phrase isn't entirely accurate. If the "box" is the boundary of constraints and conditions, then the trick is to find the box, which may be considerably larger than you think.
The key to solving puzzles is both to recognize the constraints placed on you and to recognize the degrees of freedom you do have, for in those you'll find your solution. This is why some puzzles are so effective; you may dismiss potential solutions too readily.
For example, can you connect all of the dots in the following puzzle and return to the starting point with just three straight lines—without lifting your pen from the paper or retracing your steps [Hol78]?
You must challenge any preconceived notions and evaluate whether or not they are real, hard-and-fast constraints.
It's not whether you think inside the box or outside the box. The problem lies in finding the box—identifying the real constraints.
Tip 55
Don't Think Outside the Box—Find the Box
When faced with an intractable problem, enumerate all the possible avenues you have before you.  Now go through the list and explain why a certain path cannot be taken. Are you sure? Can you prove it?
Consider the Trojan horse—a novel solution to an intractable problem. How do you get troops into a walled city without being discovered? You can bet that "through the front door" was initially dismissed as suicide.
Categorize and prioritize your constraints. When woodworkers begin a project, they cut the longest pieces first, then cut the smaller pieces out of the remaining wood. In the same manner, we want to identify the most restrictive constraints first, and fit the remaining constraints within them.
By the way, a solution to the Four Posts puzzle is shown on page 307.
Sometimes you will find yourself working on a problem that seems much harder than you thought it should be.  Perhaps you are running late on the schedule now, or even despair of ever getting the system to work because this particular problem is "impossible."
That's when you step back a pace and ask yourself these questions:
Is there an easier way?
Are you trying to solve the right problem, or have you been distracted by a peripheral technicality?
Why is this thing a problem?
What is it that's making it so hard to solve?
Does it have to be done this way?
Does it have to be done at all?
Many times a surprising revelation will come to you as you try to answer one of these questions. Many times a reinterpretation of the requirements can make a whole set of problems go away—just like the Gordian knot.
All you need are the real constraints, the misleading constraints, and the wisdom to know the difference.
Take a hard look at whatever difficult problem you are embroiled in today. Can you cut the Gordian knot? Ask yourself the key questions we outlined above, especially "Does it have to be done this way?"
Were you handed a set of constraints when you signed on to your current project? Are they all still applicable, and is the interpretation of them still valid?
| I l@ve RuBoard |


Classes for use in the mechanism for lazy-loading responsive images
See vam-scripts.js for LazyLoad implementation.
s-lazyload--blur
to be applied by LazyLoad to low-res image thumbnails while their full-size images are loaded.
s-lazyload--error
to be applied by LazyLoad in the event of an error


C1: Uniqueness or rarityC2: Special importance for life-history stages of speciesC3: Importance for threatened, endangered or declining species and/or habitatsC4: Vulnerability, fragility, sensitivity, or slow recoveryC5: Biological productivityC6: Biological diversityC7: NaturalnessHighMediumLowNo information


Amazing Online Contents It’ll blow your mind.
My institution, Creative Scholars Academic Care is a dream to me. i teach my students here in this academy with great care and affection. The classrooms are well decorated for children and I can guarantee the complete enjoyment of my students here in the classrooms.
Talented Teaching Staff. See for yourself.
I provide quality study materials online and make sure that the students are getting proper notes and guidelines for their exams. I provide lecture-videos as well as PDF files as documents for them to revise at home. I also tutor them online.
My dream institution. It'll always be my dream.
My institution, Creative Scholars Academic Care is a dream to me. i teach my students here in this academy with great care and affection. The classrooms are well decorated for children and I can guarantee the complete enjoyment of my students here in the classrooms.


Fishy, I certainly see your attention. In fact, I agree with you that it would be great if everybody shielded their balances. The thing is this is how the parameters of this chain are written. It is exactly how it is suppose to work. Mining to a zaddr is not a goo idea because it adds some protection to know everytime a coinbase is created. So the current setup is good. If we do not want people to keep coinbase rewards in the transparent mining address we should create a rule that all coinbase rewards must be moved to a z addr within XX blocks or the balance is sent to me :P. My point is, I agree with you, but the owner of that address is using it in the EXACT way that the parameters of the chain dictate and creating a bounty to discover the owner seems to go against the principles of pirate, IMHO.
 If we do not like them using the coin as the code dicates, lets change the code, not change the persons mind


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It has been a pleasure working with you this year,
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Asking good questions to get good answers
Nichole Shelton
January 10, 2015
There is an art to asking good questions. Asking good questions is something I've had to learn and improve on in my professional history. I am pretty straight forward and tend to ask "closed" questions which lead to yes/no answers. When asking questions, it's best to be verbose, give some context, and try to be as thoroguh as possible. I used to get frustrated by the amount of time it takes to ask the question to get the answer I'm looking for (why can't anyone read my mind!), but now I realize the value of asking good questions. It helps me to really articulate what my problems are and what I need help with, and it helps the person who is spending time with me to get me to where I need to be faster.
In my opinion, there are 3 main characteristics that make up a good question:
- Concise and to the point. You don't want to be too long winded
- Providing context for your problem. If someone can understand your thinking process, it can help them to identify where you are getting stuck or another aspect of the problem you didn't realize you were grappling with.
- Being open to the answers. Listening and being ready to receive the feedback from the question is just as important as being able to ask a good quesiton.
I vow to remember this blog post for my time during DBC. I know I'll need it.


Static class provides methods to work with lines in a cartesian coordinates
Methods
(static) checkLinesIntersection(lS1, lS2) → {Boolean}
Checks if two lines segments are intersected
Parameters:
| Name | Type | Description |
|---|---|---|
lS1 |
LineSegment | |
lS2 |
LineSegment |
Returns:
- Type
- Boolean
(static) distanceBtwTwoPoints(point1, point2) → {Number}
Counts distance between two points
Parameters:
| Name | Type | Description |
|---|---|---|
point1 |
Point | First point |
point2 |
Point | First point |
Returns:
- Type
- Number
(static) findPolygonInsertIndex(polygonPoints, point) → {Number}
Finds an index in the polygon points array between 2 nearest points
to the passed coordinates
Parameters:
| Name | Type | Description |
|---|---|---|
polygonPoints |
Array | |
point |
Point | New vertex coordinates |
Returns:
- Type
- Number
(static) generateLineSegments(points) → {Array.<LineSegment>}
Generates line segments array based on passed coordinates
Parameters:
| Name | Type | Description |
|---|---|---|
points |
Array.<Number> | polygon vertexes coordinates |
Returns:
- Type
- Array.<LineSegment>
(static) lineSegmentMidPoint(lineSegment) → {Array}
Finds line segment middle point
Parameters:
| Name | Type | Description |
|---|---|---|
lineSegment |
LineSegment |
Returns:
Point
- Type
- Array


--- layout: post title: Martin Sorrell on companies and speed date: 2006-06-22 00:00:00 category: business ---
Martin Sorrell on companies and speed: "The people running traditional companies do not move rapidly enough. It is impossible to change a company rapidly enough in this era of technological change... If the base business is under attack you can do some things with the base business but the people running the base business cannot move quickly enough."


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/Lean.20version.20update.html ---
Hello, can I somehow upgrade my Lean version without reinstalling all of the stuff? I' running it on Win10 and have a Git Bash command line. I'm sorry if it is a bad thread for such question or anything, but I'm really new.
upgrade from what?
do you know the version or when you last used it?
Oh okay, I finally dealt with it. I just had many unsuccessful Lean installations and some different versions of Lean all over my disc which caused the confusion. I guess it will work now.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Number Umpteen in our series of Inconsequential Yet Somehow Arresting Facts.
Stephen Lynn (above) is the doctor whose flat Alec borrows for his “inexpressibly vulgar” (and abortive) assignation with Laura in Brief Encounter (1945).
Stephen Lynn (below) is the doctor who was Director of the Emergency Room at Roosevelt Hospital in New York and who tried (and failed) to resuscitate John Lennon (1980).


|
| file | apf_filter.h [code] |
| | A base class for Auxiliary Particle Filtering. Inherit from this if you want to use an APF for your state space model. Filtering only, no smoothing.
|
| |
| file | apf_smooth.h [code] |
| | A base class for Auxiliary Particle Filtering. Inherit from this if you want to use an APF for your state space model.
|
| |
| file | bootstrap_filter.h [code] |
| | bootstrap particle filter
|
| |
| file | densities.h [code] |
| | Samples from univariate Normal distribution.
|
| |
| file | hmm_rbpf_bs.h [code] |
| | Rao-Blackwellized particle filtering where the innder models are discrete state hmms.
|
| |
| file | hmm_rbpf_sisr.h [code] |
| | Rao-Blackwellized particle filtering where the innder models are discrete state hmms.
|
| |
| file | kalman_rbpf_bs.h [code] |
| | Rao-Blackwellized Particle Filter with Linear Gaussian submodel.
|
| |
| file | kalman_rbpf_sisr.h [code] |
| | Rao-Blackwellized Particle Filter with Linear Gaussian submodel.
|
| |
| file | mn_resampler.h [code] |
| | A class used for all the multinomial resampling in any model. Keep in mind this is usually implemented by the base classes themselves, and so this would mean they are not touched by the typical end-user.
|
| |
| file | multinomial_resampler.h [code] |
| | A class used for all the multinomial resampling in any model. Keep in mind this is usually implemented by the base classes themselves, and so this would mean they are not touched by the typical end-user.
|
| |
| file | pmfs.h [code] |
| |
| file | pmmh.h [code] |
| | A base class for particle marginal Metropolis-Hastings. Inherit from this if you want to use pmmh to estimate your SSM. The benefits are that doing so will force you to implement certain functions, and it will abstract away the threaded MH implementation.
|
| |
| file | sisr_filter.h [code] |
| | A base-class for Sequential Importance Sampling with Resampling. Inherit from this if you want to use a SISR for your state space model. Filtering only; no smoothing.
|
| |
| file | sisr_smooth.h [code] |
| | A base-class for Sequential Importance Sampling with Resampling. Inherit from this if you want to use a SISR for your state space model.
|
| |


I was a guest speaker as part of the course Modeling and Data Analysis in Complex Networks. Additionally, the same slides were shown at the simulation based science group at the institute for advanced studies (IAS) at the university of Amsterdam. The talk concerned my research on the identification of driver-nodes in complex dynamical systems.


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Chris Terrio
American screenwriter and Director. He is best known for writing the screenplay for Ben Affleck's Operation Argo, for which he won an Academy award.


Selecting PDW, PIST, SGUN will show equipment for the last Class's weapons you looked at.
For example, if you looked at Medic, and then looked at Shotguns, you will see the Defib and the Medic Kit in the equipment area.
No need to show equipment anyway when looking at specific weapon types, so hiding equipment should be an easy fix.
BBLog - 2.0
Google Chrome
ORFK
Global Moderator
true point, im supporting this.
BrainFooLong
Administrator
Not really a bug but a unsightly thing.
Fixed in next version.