Contents
9. Code generation
|
How to program a text adventure in C
by Ruud Helderman
<EMAIL_ADDRESS>Licensed under
MIT License
9. Code generation
So far, our adventure has 10 objects.
Each object consists of 5 attributes
(the fields in struct OBJECT).
A real text adventure is likely to have hundreds, even thousands of objects,
and the number of attributes per object is likely to grow as well
(see the next chapter).
In its current form,
maintaining such a big list of objects and attributes would be hard.
For example, when we added objects wallField and wallCave
in the previous chapter, we had to do so in three different places:
once in object.h (as a #define),
and twice in object.c
(an element in array objs, and a separate array for the tags).
This is clumsy and error-prone.
Instead of maintaining object.h and object.c by hand,
we will start
generating
the files from a single source that is more suited to our needs.
This new source file could be in any language you like (typically some
domain-specific language),
as long as you have the tools to convert it back to C.
Below is a simple example.
Consider the following layout to organize our objects:
|
Raw C code (declarations)
|
- ObjectName
AttributeName AttributeValue
AttributeName AttributeValue
...
- ObjectName
AttributeName AttributeValue
AttributeName AttributeValue
...
- ...
|
Based on the objects we have gathered so far,
we could construct the following source file.
The file name does not matter much;
I simply named it object.txt,
to make it clear it is related to object.h and object.c.
| object.txt |
- #include <stdio.h>
- #include "object.h"
- typedef struct object {
- const char *description;
- const char **tags;
- struct object *location;
- struct object *destination;
- } OBJECT;
- extern OBJECT objs[];
- - field
- description "an open field"
- tags "field"
- - cave
- description "a little cave"
- tags "cave"
- - silver
- description "a silver coin"
- tags "silver", "coin", "silver coin"
- location field
- - gold
- description "a gold coin"
- tags "gold", "coin", "gold coin"
- location cave
- - guard
- description "a burly guard"
- tags "guard", "burly guard"
- location field
- - player
- description "yourself"
- tags "yourself"
- location field
- - intoCave
- description "a cave entrance to the east"
- tags "east", "entrance"
- location field
- destination cave
- - exitCave
- description "an exit to the west"
- tags "west", "exit"
- location cave
- destination field
- - wallField
- description "dense forest all around"
- tags "west", "north", "south", "forest"
- location field
- - wallCave
- description "solid rock all around"
- tags "east", "north", "south", "rock"
- location cave
|
I made up the
syntax
myself,
so it is safe to assume there are no standard tools to translate it to C.
We will have to write our own code generator!
Since this code generator will be a separate program,
completely independent of our adventure program,
we can write it in any language we like - not necessarily C.
Here is one possible implementation, written in
AWK:
| object.awk |
- BEGIN {
- count = 0;
- obj = "";
- if (pass == "c2") {
- print "\nOBJECT objs[] = {";
- }
- }
- /^- / {
- outputRecord(",");
- obj = $2;
- prop["description"] = "NULL";
- prop["tags"] = "";
- prop["location"] = "NULL";
- prop["destination"] = "NULL";
- }
- obj && /^[ \t]+[a-z]/ {
- name = $1;
- $1 = "";
- if (name in prop) {
- prop[name] = $0;
- }
- else if (pass == "c2") {
- print "#error \"" FILENAME " line " NR ": unknown attribute '" name "'\"";
- }
- }
- !obj && pass == (/^#include/ ? "c1" : "h") {
- print;
- }
- END {
- outputRecord("\n};");
- if (pass == "h") {
- print "\n#define endOfObjs\t(objs + " count ")";
- }
- }
- function outputRecord(separator)
- {
- if (obj) {
- if (pass == "h") {
- print "#define " obj "\t(objs + " count ")";
- }
- else if (pass == "c1") {
- print "static const char *tags" count "[] = {" prop["tags"] ", NULL};";
- }
- else if (pass == "c2") {
- print "\t{\t/* " count " = " obj " */";
- print "\t\t" prop["description"] ",";
- print "\t\ttags" count ",";
- print "\t\t" prop["location"] ",";
- print "\t\t" prop["destination"];
- print "\t}" separator;
- delete prop;
- }
- count++;
- }
- }
|
Explanation:
- Lines 9, 18, 29:
AWK scans through the input file (object.txt) line by line;
each line is tested against each of these ‘patterns’.
Whenever a match is found, the code block that follows is executed.
- Lines 1, 33:
two special patterns that match the top and bottom of the input file;
hence their code blocks are executed first and last, respectively.
- Lines 9-16:
matches any line that starts with a dash (the ‘object lines’).
The code initializes all attributes with an appropriate default.
- Lines 18-27:
following an object line, matches any line that starts with
whitespace
(the ‘attribute lines’).
The code sets an attribute value, after having validated the attribute name.
- Lines 29-31:
matches all lines that precede the first object line;
an additional filter condition disperses the lines to the correct output files
(#include lines to object.c, all others to object.h).
The code simply outputs each line unchanged.
- Lines 4, 24, 29, 35, 43, 46, 49:
the variable pass is defined upon calling AWK; see below.
We actually need to call this AWK script three times to generate the C sources:
| awk -v pass=h -f object.awk object.txt > object.h
awk -v pass=c1 -f object.awk object.txt > object.c
awk -v pass=c2 -f object.awk object.txt >> object.c
|
This will generate a new object.h and object.c,
which should be identical (save for the layout)
to the ones I wrote myself in the previous chapter.
As you can see, object.c is generated in two passes;
for object.h, a single pass is sufficient.
I could have made three separate AWK scripts, one for each pass,
but instead I made a single big script combining all three,
which seemed like the right thing to do considering the many similarities.
Our code generator script is very basic;
it does no syntax checking on the attribute values.
Most typos made in object.txt
will pass through the generator without any errors.
This is not a problem though:
the syntax checks performed afterwards by the C compiler are sufficient.
When compilation fails,
the trick is to recognize your mistakes in the C code,
then find and fix the original source in object.txt.
To make this task just a little bit easier, the least we can do is
let the code generator add some comments in the generated C code
(see object.awk line 50).
The AWK script may also pass errors over to the C compiler, by outputting
a #error directive as part of the generated code (see line 25).
Notes:
- Important:
as of now, we will not be making any manual changes in
object.h and object.c;
these would only be discarded by our code generation process.
- This mixture of languages (C, AWK and a domain-specific language)
may seem confusing at first.
Then again, this is still relatively simple compared to
the mixture of server-side and client-side techniques
the average web developer finds himself confronted with.
- Because object.txt
is converted to plain C code, compiled, and linked with the other modules,
all of its data will be part of the final executable.
Like any source file, object.txt does not have to be present
when the executable is running (i.e. when somebody is playing the game).
This, of course, is just a matter of choice.
It is very well possible to keep the data apart from the executable,
and have the executable import the data from object.txt at
runtime.
This is particularly interesting when you are building your own
adventure development system.
Do keep in mind that it will make your code slightly more complicated;
you will have to put more effort in
parsing
object.txt, since there will be no C compiler to back you up.
All this is outside the scope of this tutorial;
we will focus on using the benefits of the C compiler as much as possible.
Visualization
When it comes to choosing a domain-specific language,
keep in mind that code generation is not its only benefit.
A simple AWK script, similar to the one above, can be used to
visualize
a map of your virtual world by
drawing a graph.
| map.awk |
- BEGIN { print "digraph map {"; }
- /^- / { outputEdges(); delete a; }
- /^[ \t]/ { a[$1] = $2; }
- END { outputEdges(); print "}"; }
- function outputEdges()
- {
- outputEdge(a["location"], a["destination"]);
- }
- function outputEdge(from, to)
- {
- if (from && to) print "\t" from " -> " to;
- }
|
Explanation:
- Line 2:
at the start of every object in object.txt,
first finish the output for the previous object (outputEdges),
then make a fresh start by clearing the collection of attributes
(array a).
The delete statement may not work on older
AWK
implementations.
A more portable (but less readable) alternative is:
split("", a);
- Line 3:
every attribute in object.txt is added to array a.
The key of the array element is the attribute name (e.g. location).
- Line 4:
at the end of object.txt, we finish the output for the final object.
Execute this script with the commands below,
and object.txt will be converted into map.png;
the picture you see on the right.
Please note that this picture is not part of the game;
it is a spoiler and should not be revealed to the player.
It‘s here to help the developer spot mistakes in object.txt.
| awk -f map.awk object.txt > map.gv
dot -Tpng -o map.png map.gv
|
Notes:
- For the command dot to work, you need to install
Graphviz
first.
- Notice how this picture matches the map I drew by hand in chapter 6
(except for the orientation).
Our map is still very trivial,
but as the number of locations grows, this soon becomes a useful tool
to validate object.txt against your intended map.
- I have used
DOT
as an intermediate language here (map.gv).
If your adventure has a really complex map,
you may even consider using a graph editor to design your map,
then try to generate C code from the resulting DOT output
(or whatever graph description language the editor is capable of).
This is a great way to design your locations and passages,
and it gives you far better control over the graphical presentation of the map.
However,
this approach may be less suitable to maintain the other objects and attributes.
Makefile
Calling AWK manually each time object.txt has been modified,
soon becomes tedious.
It is best to make these calls part of your build process.
For example, a simple
makefile
for our adventure might look like this:
| makefile |
- all: success.txt src.zip map.png
- C = object.c misc.c noun.c location.c move.c inventory.c parsexec.c main.c
- H = object.h misc.h noun.h location.h move.h inventory.h parsexec.h
- success.txt: lilcave testscript.txt baseline.txt
- ./test.sh
- mv -f transcript.txt $@
- lilcave: $(C) $(H)
- gcc -Wall -Wextra -Wpedantic -Werror $(C) -o $@
- object.h: object.awk object.txt
- awk -v pass=h -f object.awk object.txt > $@
- object.c: object.awk object.txt
- awk -v pass=c1 -f object.awk object.txt > $@
- awk -v pass=c2 -f object.awk object.txt >> $@
- map.png: map.gv
- dot -Tpng -o $@ $<
- map.gv: map.awk object.txt
- awk -f map.awk object.txt > $@
- src.zip: $(C) $(H) object.txt makefile testscript.txt baseline.txt
- zip -rq $@ $^
- clean:
- $(RM) object.c object.h lilcave map.gv map.png transcript.txt success.txt src.zip
|
Explanation:
- Lines 6-8:
this rule performs an automated test.
This is explained in chapter 17.
- Lines 10-11:
this rule builds the executable lilcave.
You may need to specify a different target name as dictated by your OS
(e.g. lilcave.exe in Microsoft Windows).
If your source files become so big (or your build machine is so slow)
that full rebuilds become tedious,
then feel free to split up the rule into separate build and link rules.
- Lines 13-18:
these rules generate object.c and object.h.
Notice I specified not only object.txt,
but also object.awk as a prerequisite.
Although technically,
object.awk is more of a development tool than it is a source file,
it is still subject to change;
already so in the next three chapters.
- Lines 20-24:
these rules generate the map image.
- Lines 29-30:
it is customary to have a separate rule to delete all targets and intermediate files.
Now a single command make will do everything that is necessary
to construct an executable and a map image.
Next chapter: 10. More attributes


Engelsk B
Ny ordning
kl. 09.00-14.00
Vejledning til opgavesættet
| Du skal besvare følgende opgaver: | |
|
|
| Tekster til Assignment 5: | |
| 5 A – Fiction: | “The Skirt”, a short story by Zoe Lambert, 2006. |
| 5 B – Non-fiction: | “When technology can read minds, how will we protect our privacy”, a TED-talk by Nitah Farahany, 2018. |
Vejledning og skabelon til at løse opgaven
Den samlede eksaminationstid for Assignment 1-5 er 5 timer. Besvarelsen bedømmes som en helhed ud fra de faglige mål for niveauet.
Det anbefales, at du skriver din besvarelse i skabelonen, som ligger under Template i menuen til venstre. Besvarelsen afleveres i ét dokument med opgaverne i rækkefølgen 1-5.
Henvisninger til tekst, video- og lydklip
Hvis du citerer, skal du angive kilde.
Alt anvendt materiale skal være engelsksproget og angives med kildehenvisninger. Til forklaring af grammatiske opgaver kan materialerne være på dansk.
Du kan henvise til dele af video- og lydklip, f.eks. ved at angive afspillerens minut- og sekundtal for henholdsvis starten og slutningen af klippet.
Alt anvendt materiale skal være engelsksproget og angives med kildehenvisninger. Til forklaring af grammatiske opgaver kan materialerne være på dansk.
Du kan henvise til dele af video- og lydklip, f.eks. ved at angive afspillerens minut- og sekundtal for henholdsvis starten og slutningen af klippet.
|
Tekster i opgavesættet
Teksternes ortografi og tegnsætning følger forlæggene. Trykfejl er dog rettet.
Opsætningen følger ikke nødvendigvis forlæggene. Dog følges forlægget nøje, hvor opsætningen på den ene eller anden måde indgår i opgaven.
Opsætningen følger ikke nødvendigvis forlæggene. Dog følges forlægget nøje, hvor opsætningen på den ene eller anden måde indgår i opgaven.
Assignment 1
Find alle verberne (udsagnsordene) i præteritum (datid) i nedenstående tekst, og omskriv dem til præsens (nutid). Skriv hele teksten som dit svar, og marker verberne tydeligt som i eksemplet.
| Eksempel: | Gavin cooked for Kay… Gavin cooks for Kay… |
Gavin cooked for Kay at his house that evening, opening tins and crushing garlic with a sense of ill-usage.
After a row, you had to say certain things to secure a truce: those were the rules, everyone knew that. Gavin had telephoned Kay from his car on the way back from Barry’s burial and told her […] that he hoped he could see her that night. He considered these humble admissions no more or less than the price he had to pay for an evening of undemanding companionship.
But Kay seemed to consider them more in the light of a down payment on a renegotiated contract. You missed me.  You’re sorry we didn’t go as a couple. Well let’s not make that mistake again. There had been a certain complacency about the way she had treated him since; a briskness, a sense of renewed expectation.
He was making spaghetti Bolognese tonight; he had deliberately omitted to buy a pudding or to lay the table in advance; he was at pains to show her that he had not made much of an effort. Kay seemed oblivious, even determined to take this casual attitude as a compliment.  She had not often been here.
After a row, you had to say certain things to secure a truce: those were the rules, everyone knew that. Gavin had telephoned Kay from his car on the way back from Barry’s burial and told her […] that he hoped he could see her that night. He considered these humble admissions no more or less than the price he had to pay for an evening of undemanding companionship.
But Kay seemed to consider them more in the light of a down payment on a renegotiated contract. You missed me.  You’re sorry we didn’t go as a couple. Well let’s not make that mistake again. There had been a certain complacency about the way she had treated him since; a briskness, a sense of renewed expectation.
He was making spaghetti Bolognese tonight; he had deliberately omitted to buy a pudding or to lay the table in advance; he was at pains to show her that he had not made much of an effort. Kay seemed oblivious, even determined to take this casual attitude as a compliment.  She had not often been here.
J.K. Rowling, The Casual Vacancy, 2012
Assignment 2
Besvar både opgave a. og b.
- Find 3 forskellige relative pronominer i nedenstående tekst.
- Forklar forskellen i brugen af relative pronominer i dine eksempler. Skriv dit svar på dansk.
Harry Potter and the Cursed Child is a two-part stage play which is written by Jack Thorne and based on
In September 2017, the Broadway show whose actors include Sam Clemmett, Jamie Parker, Anthony
The story begins nineteen years after the events of Harry Potter and the Deathly Hallows and follows
Albus Severus Potter, who is about to attend Hogwarts School of Witchcraft and Wizardry.
2
an original new story by Thorne, J. K. Rowling and John Tiffany.
In September 2017, the Broadway show whose actors include Sam Clemmett, Jamie Parker, Anthony
4
Boyle, Noma Dumezweni and Paul Thornley will start playing in the Lyric Theatre in New York. [...]
The story begins nineteen years after the events of Harry Potter and the Deathly Hallows and follows
6
Harry Potter, whose family has grown and who is now a Ministry of Magic employee, and his younger son
Albus Severus Potter, who is about to attend Hogwarts School of Witchcraft and Wizardry.
en.wikipedia.org, 2016
Assignment 3
Lyt til episoden “Animal Instinct” fra podcasten Criminals hjemmeside.
Skriv otte ord, som tilhører det samme semantiske felt med overskriften Crime.
Animal Instict
Assignment 4
The sentences below, which are from two different texts, have been jumbled.
Connect the sentences so that they form two texts.
|
Assignment 5
- Answer assignment A or assignment B.
Assignment 5A - Fiction
- Write a short summary of Zoe Lambert’s short story “The Skirt” in about 125 words.
- Write an analytical essay (600-900 words) in which you analyse and interpret the short story. Your essay must focus on the relationship between the characters.
Include the following analytical terms in your essay:
point of view, setting, main theme
In your essay you must include references to the text.
Material
Zoe Lambert, “The Skirt”, 2006.
Assignment 5B - Non-fiction
- Write a short summary of the TED talk “When technology can read minds, how will we protect our privacy?” in about 125 words.
- Write an analytical essay (600-900 words) about the TED talk. Your essay must focus on how Nitah Farahany argues for her views. Discuss briefly whether or not modern technology poses a danger to individual freedom.
Include the following analytical terms in your essay:
sender, language, intention
In your essay you must include references to the text.
Material
Nitah Farahany, “When technology can read minds, how will we protect our privacy?”, a TED talk from TED website, November, 2018.
- TED talk: “When technology can read minds, how will we protect our privacy?”
- Transcript: “When technology can read minds, how will we protect our privacy?”
Sources
Anvendt materiale (til brug for Copydan):
J.K. Rowling. The Casual Vacancy. Great Britain: Sphere Books, 2012.
“Harry Potter and the Cursed Child”. Wikipedia website, 2016, viewed September 2018. (www.en.wikipedia.org)
Podcast. “Animal Instinct”. Criminal website, January 30, 2014, viewed September 2018. (www.thisiscriminal.com)
Graeme Simsion. The Rosie Project. London: Penguin Books, 2013.
Jeffrey Archer. Be Careful What You Wish For. London: Pan Books, 2014.
Zoe Lambert. “The Skirt”. Ellipsis #2. Great Britain: Comma Press, 2006.
Nitah Farahany. “When technology can read minds, how will we protect our privacy?”. TED website, November 2018, viewed April 2019. (www.ted.com)


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group hug
caution labels
5 minute public support
group sway
handshake knot
tiny relay
______ exchange
go west
highest five
quench
rest your weary legs
whose weeds
shadow money
ingredients: 2+ people and a public location
pick a particular time and central location for a one-minute long group hug. tell participants to come at that time. hug for exactly one minute. disperse.
ingredients: label maker and a place to walk
bring a label maker on a walk. print out caution statements and place them on random objects as you go. cautions should be absurd, vaguely philosophical, and unrelated to the objects placed upon.
ex: "CAUTION: MEANING IS INSUFFICIENT", "CAUTION: BEGIN TO FORGET YOURSELF", "CAUTION: OPEN UP TO COLLECTIVE INTELLIGENCE"
ingredients: 2+ people who could use some support right now and a public location
meet at a public location. lean on each other until each participant is dependent on the support of the others. after 5 minutes, disperse.
ingredients: 3+ people and a place to walk
start walking. as you go, watch one another and collectively adopt a swaying motion. do not verbally discuss your sway. go until you reach your destination.
ingredients: 3+ people with hands
form a circle. shake each other's hands and twist your arms until you form a knot. sporadically let go and keep forming new knots until you know each other.
ingredients: 4+ people and a place to run
split into teams. hold a relay race in a loop that is under 10 feet in diameter. use a found object as a baton. when one team wins, celebrate.
ingredients: an agreed upon object to exchange and a public place
pick an object that means something to you, but not a commodity. tell your friends that you are holding an exchange. in a central location, present your objects and discuss until each person leaves with a different object than they came with.
ex: leaf exchange, motivation exchange, caress exchange
ingredients: 3+ people
at one particular time, starting from different locations, go west. when all participants have arrived west, go home.
ingredients: 3+ people of different heights and wingspans
the tallest person raises their hand as high as they can. for one minute, all other participants attempt to high five that hand.
ingredients: 3+ people, a public location, and some water
all participants arrive at the public location at a set time, very thirsty. participants take turns providing each other with water until all are quenched.
ingredients: 3+ people with legs
take turns carrying one another until all feel well rested.
ingredients: 3+ people and some weeds
all participants bring a weed to a particular location. make a bouquet.
ingredients: $1 bills and some pretty cool shadows
find a cool shadow. tape or leave a $1 bill on the shadow. find another cool shadow and repeat until you are out of money.


All Classes
AbstractSource
AfterStep
ArchivedSource
ChainedSource
ClosureSource
CollectionSource
ConcatSource
Condition
CsvSink
CsvSource
CustomDateDataFormatter
Duration
FileOpenable
FileSystemSource
HttpSource
HttpSource.HttpVerb
JdbcSource
JsonSink
JsonSource
LoadStatistic
Openable
Pipeline
Rejection
RejectionCategory
Sink
Source
SshSource
Step
XlsProcessor
XlsSource
XlsxSink
XlsxSource
XlsxSource.XslxSheetHandler
ZipSource


{% extends "layout.html" %} {% block title %}California Emergency Alerts{% endblock %} {% block main_content %}
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Labs and Cores
Administration Core |
CroweLab |
Flow Core |
GoCore |
Immunology Core |
Polack Lab |
Sequencing Core |
Tech Core |


UploadZen supports check-in and check-out for Document Libraries that have the Require
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(on the Versioning Settings page under Document Library Settings).
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You can select for each file whether and how it should be checked in after it was
successfully uploaded:
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- As minor new version — uploaded files are checked in. Overwritten,
previously existing files get a minor version number increment (ie. from 1.0 to
1.1 etc.) if the Document Version History for this Document Library is set
to Create major and minor (draft) versions (on the Versioning Settings
page under Document Library Settings).
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is set to No versioning (on the Versioning Settings page under Document
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file name already existed prior to the upload, the previous version number remains
unchanged.
To check in all files the same way, select the preferred setting for one file, then
click the
icon to Set all files to this selection.
UploadZen Windows Uploader
In the legacy Windows Uploader, prior
to your upload simply drop-down the menu next to the Upload Files button:
- Auto Check-Out — tick this menu option to check out existing files
to you prior to overwriting them. This is necessary or existing files cannot be
overwritten.
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- None — uploaded files stay checked out to you
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the Document Version History for this Document Library is set to Create major
and minor (draft) versions (on the Versioning Settings page under
Document Library Settings).
- Major versions — uploaded files are checked in. Overwritten, previously
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the Document Version History for this Document Library is set to No versioning
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--- title: neos keywords: fastai sidebar: home_sidebar summary: "~neural~ nice end-to-end optimized statistics" description: "~neural~ nice end-to-end optimized statistics" nb_path: "nbs/index.ipynb" ---
{% include image.html alt="neos logo" max-width="250" file="/neos/assets/neos_logo.png" %}
Leverages the shoulders of giants (jax
, fax
, and pyhf
) to differentiate through a high-energy physics analysis workflow, including the construction of the frequentist profile likelihood.
Documentation can be found at gradhep.github.io/neos!
To see examples of neos
in action, look for the notebooks in the nbs folder with the demo_
prefix.
If you're more of a video person, see this talk given by Nathan on the broader topic of differentiable programming in high-energy physics, which also covers neos
.
Please read CONTRIBUTING.md
before making a PR, as this project is maintained using nbdev
, which operates completely using Jupyter notebooks. One should make their changes in the corresponding notebooks in the nbs
folder (including README
changes -- see nbs/index.ipynb
), and not in the library code, which is automatically generated.
import time
import jax
import jax.experimental.optimizers as optimizers
import jax.experimental.stax as stax
import jax.random
from jax.random import PRNGKey
import numpy as np
from functools import partial
import pyhf
pyhf.set_backend('jax')
pyhf.default_backend = pyhf.tensor.jax_backend(precision='64b')
from neos import data, infer, makers
rng = PRNGKey(22)
Let's start by making a basic neural network for regression with the stax
module found in jax
:
init_random_params, predict = stax.serial(
stax.Dense(1024),
stax.Relu,
stax.Dense(1024),
stax.Relu,
stax.Dense(1),
stax.Sigmoid,
)
Now, let's compose a workflow that can make use of this network in a typical high-energy physics statistical analysis.
Our workflow is as follows:
(x,y)
points, corresponding to a signal process, a nominal background process, and two variations of the background from varying the background distribution's mean up and down.pyhf
to construct an event-counting statistical model from the histogram yields.In code, neos
can specify this workflow through function composition:
data_gen = data.generate_blobs(rng,blobs=4)
# histogram maker
hist_maker = makers.hists_from_nn(data_gen, predict, method='kde')
# statistical model maker
model_maker = makers.histosys_model_from_hists(hist_maker)
# CLs value getter
get_cls = infer.expected_CLs(model_maker, solver_kwargs=dict(pdf_transform=True))
A peculiarity to note is that each of the functions used in this step actually return functions themselves. The reason we do this is that we need a skeleton of the workflow with all of the fixed parameters to be in place before calculating the loss function, as the only 'moving parts' here are the weights of the neural network.
neos
also lets you specify hyperparameters for the histograms (e.g. binning, bandwidth) to allow these to be tuned throughout the learning process if neccesary (we don't do that here).
bins = np.linspace(0,1,4) # three bins in the range [0,1]
bandwidth = 0.27 # smoothing parameter
get_loss = partial(get_cls, hyperparams=dict(bins=bins,bandwidth=bandwidth))
Our loss currently returns a list of metrics -- let's just index into the first one (the CLs value).
def loss(params, test_mu):
return get_loss(params, test_mu)[0]
Now we just need to initialize the network's weights, and construct a training loop & optimizer:
_, network = init_random_params(jax.random.PRNGKey(2), (-1, 2))
# init optimizer
opt_init, opt_update, opt_params = optimizers.adam(1e-3)
# define train loop
def train_network(N):
cls_vals = []
_, network = init_random_params(jax.random.PRNGKey(1), (-1, 2))
state = opt_init(network)
losses = []
# parameter update function
def update_and_value(i, opt_state, mu):
net = opt_params(opt_state)
value, grad = jax.value_and_grad(loss)(net, mu)
return opt_update(i, grad, state), value, net
for i in range(N):
start_time = time.time()
state, value, network = update_and_value(i, state, 1.0)
epoch_time = time.time() - start_time
losses.append(value)
metrics = {"loss": losses}
yield network, metrics, epoch_time
It's time to train!
maxN = 10 # make me bigger for better results (*nearly* true ;])
for i, (network, metrics, epoch_time) in enumerate(train_network(maxN)):
print(f"epoch {i}:", f'CLs = {metrics["loss"][-1]:.5f}, took {epoch_time:.4f}s')
And there we go!
You'll notice the first epoch seems to have a much larger training time. This is because jax
is being used to just-in-time compile some of the code, which is an overhead that only needs to happen once.
If you want to reproduce the full animation from the top of this README, a version of this code with plotting helpers can be found in demo_kde_pyhf.ipynb
! :D


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In this post, we will derive a backprop like algorithm to compute the gradient of a finite horizon optimal control problem. The technique we use here is well established, known as the method of adjoints. The derivation I am using is based off these excellent notes. $ \newcommand{\abs}[1]{| #1 |} \newcommand{\bigabs}[1]{\left| #1 \right|} \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \renewcommand{\Pr}{\mathbb{P}} \newcommand{\calE}{\mathcal{E}} \newcommand{\calF}{\mathcal{F}} \newcommand{\calD}{\mathcal{D}} \newcommand{\calN}{\mathcal{N}} \newcommand{\calL}{\mathcal{L}} \newcommand{\calM}{\mathcal{M}} \newcommand{\bbP}{\mathbb{P}} \newcommand{\bbQ}{\mathbb{Q}} \newcommand{\ip}[2]{\langle #1, #2 \rangle} \newcommand{\bigip}[2]{\left\langle #1, #2 \right\rangle} \newcommand{\T}{\mathsf{T}} \newcommand{\Tr}{\mathrm{Tr}} \newcommand{\ind}{\mathbf{1}} \newcommand{\calL}{\mathcal{L}} \newcommand{\norm}[1]{\lVert #1 \rVert}$
Consider the problem $$ \begin{align*} \mathop{\mathrm{minimize}}_{u_0, ..., u_{N-1}} \sum_{t=0}^{N-1} c_t(x_t, u_t) + c_N(x_N) \: : \: x_{t+1} = f_t(x_t, u_t) \:, \end{align*} $$ where $x_0$ is given. Suppose that the $c_t$'s and $f_t$'s are differentiable. Note that we can write the entire problem as an unconstrained minimization problem over some differentiable function $g(u_0, ..., u_{N-1})$. The question is, can we efficiently compute $\nabla u_k g$?
Let us first see what happens if we try to compute the gradient directly. For concreteness, let us look at $c_3(x_3, u_3)$, $$ c_3(x_3, u_3) = c_3(f_2(f_1(f_0(x_0, u_0), u_1), u_2), u_3) \:. $$ By application of the chain rule, $\nabla_u c_3$ is $$ \nabla_u c_3 = \begin{bmatrix} D_x c_3 D_x f_2 D_x f_1 D_u f_0 \\ D_x c_3 D_x f_2 D_u f_1 \\ D_x c_3 D_u f_2 \\ 0 \\ \vdots \end{bmatrix} \:. $$ The generalization to $c_t(x_t, u_t)$ is clear here, and therefore to compute $\nabla_u c_t$ we will need to perform $O(t^2)$ operations. Hence to compute $\nabla_u c_t$ for all $t$ we will need $O(\sum_{t=1}^{N} t^2) = O(N^3)$ operations. Note that in the $O(\cdot)$ notation here I am suppressing the dependence on the dimension of $x_t$ and $u_t$, which I am treating as fixed while $N$ grows.
Let us derive a more efficient algorithm based on the method of adjoints. Let $\phi_k(u_0, ..., u_{k-1})$ denote the map such that $\phi_k = x_k$. That is, $\phi_0 = x_0$, $\phi_1(u_0) = f_0(x_0, u_0)$, $\phi_2(u_0, u_1) = f_1(f_0(x_0, u_0), u_1)$, and so on.
For what follows, we will use $\phi_t$ as shorthand for $\phi_t(u_0, ..., u_{t-1})$, $c_t$ as shorthand for $c_t(\phi_t, u_t)$, and $f_t$ as shorthand for $f_t(\phi_t, u_t)$. With this notation, we write $$ \begin{align*} g(u_0, ... u_{N-1}) = \sum_{t=0}^{N-1} c_t(\phi_t, u_t) + c_N(\phi_N) \:. \end{align*} $$ Let $\lambda_k$ be specified as $$ \begin{align*} \lambda_{N-1}^\T &= - D_x c_N \:, \\ \lambda_{k}^\T &= \lambda_{k+1}^\T D_x f_{k+1} - D_x c_{k+1} \:, \:\: 0 \leq k \leq N-2 \:. \end{align*} $$ We form the Lagrangian $$ \begin{align*} \calL(u_0, ..., u_{N-1}) = \sum_{t=0}^{N-1} c_t(\phi_t, u_t) + c_N(\phi_N) + \sum_{t=0}^{N-1} \lambda_t^\T(\phi_{t+1} - f(\phi_t, u_t)) \:. \end{align*} $$ By construction, we have that $g = \calL$, since $\phi_{k+1} = f_k$. We now compute $D_{u_k} \calL$, starting with the base case $D_{u_{N-1}} \calL$. Using the fact that $(D_{u_k} \lambda_k^\T) (\phi_{k+1} - f_k) = 0$, $$ \begin{align*} D_{u_{N-1}} \calL &= D_u c_{N-1} + D_x c_N D_{u_{N-1}} \phi_N + \lambda_{N-1}^\T (D_{u_{N-1}} \phi_{N} - D_u f_{N-1}) \\ &= D_u c_{N-1} + (D_x c_N + \lambda_{N-1}^\T) D_{u_{N-1}} \phi_N - \lambda_{N-1}^\T D_u f_{N-1} \;. \end{align*} $$ Now using the setting $\lambda_{N-1}^\T = - D_x c_N$, we obtain $$ \begin{align*} D_{u_{N-1}} \calL = D_u c_{N-1} - \lambda_{N-1}^\T D_u f_{N-1} \:. \end{align*} $$ We now proceed for $0 \leq k < N-1$ as follows, $$ \begin{align*} D_{u_k} \calL &= D_u c_k + \sum_{t=k+1}^{N} D_x c_t D_{u_k} \phi_t + \lambda_k^\T( D_{u_k} \phi_{k+1} - D_u f_k) + \sum_{t=k+1}^{N-1} \lambda_t^\T( D_{u_k} \phi_{t+1} - D_x f_t D_{u_k} \phi_t) \\ &= D_u c_k - \lambda_k^\T D_u f_k + \sum_{t=k}^{N-2} ( D_x c_{t+1} + \lambda_t^\T - \lambda_{t+1}^\T D_x f_{t+1} ) D_{u_k} \phi_{t+1} + (D_x c_N + \lambda_{N-1}^\T) D_{u_k} \phi_N \:. \end{align*} $$ Recalling the setting of $\lambda_k$'s, we have $$ \begin{align*} D_{u_k} \calL &= D_u c_k - \lambda_k^\T D_u f_k \:. \end{align*} $$ Hence, using $g = \calL$, for all $0 \leq k \leq N-1$, $$ \begin{align*} \nabla_{u_k} g &= \nabla_{u_k} c_k - (D_u f_k)^\T \lambda_k \:. \end{align*} $$
These equations give us an efficient algorithm to compute $\nabla_u g$. First, we do a forward pass, where given inputs $u_0, ..., u_{N-1}$, we compute the associated trajectory $x_0, x_1, ..., x_{N-1}$. Next, we do a backward pass, where we recursively compute the values of the Lagrange multiplies $\lambda_k$. Once we have these values in hand, we can read off the gradient. Notice how the runtime of this algorithm is now $O(N)$ time (compared to $O(N^3)$ before), but we required extra $O(N)$ space. This is of course not a big deal since it takes $O(N)$ space to write down the gradient in the first place.
As an example, let us specialize to the case of LQR, where $c_t(x_t, u_t) = \frac{1}{2} x_t^\T Q_t x_t + \frac{1}{2} u_t^\T R_t u_t$, $c_N(x_N) = \frac{1}{2} x_N^\T Q_N x_N$, and $f_t(x_t, u_t) = A_t x_t + B_t u_t$. The forward pass is simply to set $x_{t+1} = A_t x_t + B_t u_t$ for $t = 0, ..., N-1$. For the backward pass, we set $\lambda_{N-1} = - Q_N x_N$, and then $\lambda_t = A_{t+1}^\T \lambda_{t+1} - Q_{t+1} x_{t+1}$ for $t=N-2, ..., 0$. The gradient $\nabla_u g$ is then $$ \begin{align*} \nabla_u g(u_0, ..., u_{N-1}) = \begin{bmatrix} R u_0 - B_0^\T \lambda_0 \\ R u_1 - B_1^\T \lambda_1 \\ \vdots \\ R u_{N-1} - B_{N-1}^\T \lambda_{N-1} \end{bmatrix} \:. \end{align*} $$


West African FIUs move headquarters to Nigeria
by Press ReleaseThe Forum of West African Financial Intelligence Units Wednesday in Dakar , Senegal, voted to unanimously relocate the headquarters of its forum to Abuja, Nigeria, from its temporary site in Cotonou, Benin Republic.
The work of the forum is to coordinate intelligence sharing among state parties of ECOWAS and members of the Chad Basin countries on money laundering, terrorism financing, financing of proliferation of weapons and all other criminal activities.
“It was agreed that the Nigerian Financial intelligence unit (NFIU) will host the Headquarters temporarily and gradually nurture it to an independent regional Headquarters,” said Ahmed Dikko, NFIU chief media analyst in a statement on Thursday.
Read also: Profiles of Nigeria’s new health ministers Osagie Ehanire and Olorunnimbe Mamora
The statement added that in addition to intelligence exchange among FIUs, the forum will coordinate general information sharing among law enforcement, regulators, defence and national security agencies across the sub region.
Ahmed Dikko
Chief Media Analyst
NFIU, Abuja.


Adaptive Sparse Grid¶
File Location:
- sparse_grid/tutorials/tutorial2_1.m
- sparse_grid/tutorials/tutorial2_2.m
Expected Reading Time: XXXX mins.
In this tutorial, we will learn how to work with define adaptive grids. The fundamental structure of the sparse grid is the set of basis functions, which is represented by spgrid
. Hence, in theory, one can update the spgrid
directly to update a basis function. However, the toolbox comes with some functions
to automate some commonly used adaptation methods. In this tutorial, we will cover the idea behind the local adaptivity.
1-Dimensional example¶
We will first start with a 1-dimensional function (for easy visualization), consider approximating
This function is not so nice to approximate. One intuitive idea to approximate the function is by repeatedly approximating the function with triangles. For example, first approximate with a large triangle centered at 0.5 and spans [0,1]
This approximation misses quite a lot. However, one can introduce a sub-triangles centered at 0.25 spanning [0,0.5] and similarly at 0.75.
We can see that the function is already being approximated really well for x bigger than 0.5. In this case, we do not have to add new sub-triangle there. From the figure, we can see that we know where the function is being approximated well by looking at the height of the sub-triangle. The height of the left sub-triangle is bigger than the right. We can use this to adaptively add new grid points, i.e., add grid points if and only if the height of the triangle is larger than a given cutoff. Following the procedure results in
Shifting Domain¶
When one were linearly interpolation the \(\log(x+0.001)\), one would not do it over an uniform grid. The local adaptivity procedure will find the good non-uniformly spaced grid, but the idea of adjusting transforming the domain to help with approximation is a general concept that can be combined with the “repeated-triangle” approximation.
For example, if one were to use
and follow the procedure above will result in the following sequence of approximations
We can see from the sequence that the triangles are better “balanced” as before. Again, in asymptotic theory, the approximation with and without the adjustment will do well in approximating the domain, but one should use any a priori information one has in approximating to adjust domain in practice.
2-Dimensional Example¶
The (adaptive) sparse grid is a high-dimensional analogue of this procedure, and we will actually implement things in code.
Consider approximating
As before, we start by initializing the class and define the given function
>> grid = sparse_grid(2,3);
>> grid.d_midpoint_input{2} = @df_dx_center;
>> grid.dd_center_input{2} = @d2f_dx2;
>> grid.recompute_grid();
>> f = @(x1, x2) exp(sin(10*x1.^2) + cos(8*x2));
Again, we can visualize the function again by (the figure below is marked up a little because 3D scatter plots are impossible to see without rotating)
>> x_1 = grid.x_i{1};
>> x_2 = grid.x_i{2};
>> f_grid = f(x_1, x_2);
>> scatter3(x_1, x_2, f_grid);
Given the grid and the function values, we can compute the weights/contribution of each basis function (height of the triangle in the previous section) by using the compute_hier_weights
>> h_weights = grid.compute_hier_weights(f_grid);
We can also visualize the hierarchical weights (this is not so helpful visualization)
>> scatter(1:grid.n_points, h_weights);
As shown in [Ahn, 2018] (and the picture given above), the weights approximate the second-order derivatives, and the locations with large second order derivatives are exactly where one should have more grid points (local-adaptivity) in approximating a function. Hence, one can use the values of the hierarchical weights to add children basis functions, i.e., one can add more grid points where the weight is bigger than a threshold (and remove from low values). Since this is a common procedure, it implemented as adapt_by_metric
function. We can add children basis functions and remove basis functions by
>> adaptivity_crit = 1e-3;
>> grid.adapt_by_metric(f_grid, adaptivity_crit, adaptivity_crit/3);
The function adds children of the grid points with weights larger than adaptivity_crit
, and removes grid points with weights smaller than adaptivity_crit/3
. To make functions more modular, this function does not automatically update the grid related quantities like x_i
, and one needs to call recompute_grid
to reinitialize the grid for use.
>> grid.recompute_grid();
This is it for adaptively extending the basis functions. One just need to call
repeatedly to making build the grids. Running the process results in (click to zoom in)
We can see that initially the interpolation misses a little, but as the grid points are adaptively added. The final function is approximated really accurately.
Prelude to Next Tutorial¶
One thing to remember in the local adaptation is that no single adaptation method will perfect. For example, if one were to approximate \(\sin(2\pi x)\). Then, the adaptation will halt immediately as \(\sin(2\pi\cdot 0.5) = \sin(\pi) =0\). Hence, one always need to be careful that the adaptation method can fail. Of course, this is an extreme example, and I have not run into any case where the procedure failed that spectacularly.
However, there is a case, where this adaptation method is sub-par to another method. The starting point of the process is that we know that function values exactly at those points. This is not the case when one solves the Hamilton-Jacobi-Bellman (HJB) equation. One only has an approximate value of the true function at the nodes. For these problems, a different adaptation method works better, and that’s the topic of the next tutorial XXXXXXXXXXXXXX.


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Donovon Spencer
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EDUCATION
Brigham Young University – Marriott School of Business
Bachelor of Science in program to be decided
2016 - 2023 ▪ GPA 3.53 / 4.00
Fairview High School
High School Diploma
May 2016 ▪ GPA 4.00 / 4.00 ▪ Honors in mathematics
EXPERIENCE
Juni Learning (Private tutoring company)
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June 2020 – Present
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Responsible for each child’s individual learning process and ensuring they had an enjoyable time mastering important skills and mindsets
Adapt courses and lessons to the understanding and experiences of each child
Work with parents of each child to ensure that they are receiving the product in the way they expected
Office of Information Technologies at BYU (Technology department of major university)
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Feb 2020 – Present
Develop modern solutions to replace archaic websites used by BYU faculty
Exist as an integral part of a seven-person team leading out work on new projects
Work largely unsupervised and must consistently show strong prioritization skills to manage workloads
Brigham Young University (Major U.S. university)
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Sep 2019 – Jan 2020
Ensured safety and security throughout buildings across campus
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Walmart (Largest United States retailer)
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Jun 2019 – Aug 2019
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May 2017 – May 2019
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Apr 2015 – Dec 2016
Quickly became proficient in each of the many roles throughout the restaurant
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Mr. Potato Head and your webinar presentation do have something in common. They both have a distinct structure. If you know what the structure is, you can be more effective at assembling them into a final, (fun,) quality product.
Let's look at some presentation structures and how to go about creating them.
You have a vision for what you want to accomplish with your webinar presentation. In less than 90 seconds, share it in a compelling way. Paint a picture for where you will take attendees by the end of the hour. You can tell a story, share research figures, or have each attendee recall a past experience.
On the flip side, each attendee has a unique reason for joining. What do they want to accomplish? What pain do they want to alleviate? What benefit do they want to arrive at? Ask each attendee to write on paper what their specific reasons for attending are.
This works to hold a person's attention throughout the event. If they start to tune out or check their email, they can look down at the paper, get present or become mindful again of what they want, and refocus on what you're saying with renewed interest.
I see "10 Mistakes," "Five Best Practices," "Seven-Must Dos" as the most common structure for presentations. That's often the easiest way for a presenter to create a presentation. However, it often isn't what's most helpful for attendees. These alternate structures can work really well.
Step-by-Step: Most people want to improve their performance after watching a webinar. They want to market more effectively, project-manage better, or write software faster. These actions happen in real-time in a linear process.
Structure the presentation around that chronology. Overlay the best practices across a timetable. This helps attendees to understand the practical use of the material and anticipate how they will apply it.
The above slide, from PM College's presentation, "A 5-Step Process Linking Project Management Competency to Improved Business Performance," does just that. This was the concluding slide after "Measure Improvement." The presentation initially
displayed just the first arrow and then added the others one-by-one after elaborating on each point.
• From Context to Concept to Reality: Sometimes the presenter has a model or concept to share. In this example presentation, "Building
a Value Proposition that Sells in a Recession," Paul Collins, Managing Partner at Equiteq, LLP, shared a model for building value propositions that receive premium pricing in a down market.
He set the context for this important
element by reviewing the essential elements of a professional services firm, before isolating one—the value proposition—and discussing it:
As the webinar progressed, he discussed each tier of his model and then provided real-world examples of how to climb the pyramid to get to value creation. He did an excellent job and—with some help from a British accent—he made it must-see
material.
Consider whether your presentation centers on a key model. If it does, this structure will serve you well.
• Make the Argument: Put on your Johnny Cochran shoes for this one. If you have a point to make, you
can structure your presentation in whatever way makes the most compelling argument. You can fill it with drama and flair, numbers-backed evidence, logical analysis or anything else.
In this example, Leads360 provides Lead Management
Software (LMS) that compliments Customer Relationship Management (CRM) systems. Their LMS produces terrific results for realtors, colleges, and brokers, but it's not well known and can be viewed as unnecessary.
Leads360 made a compelling
argument in the presentation title itself: "Where Good CRMs go Bad." Their point was that while CRMs are good, they do have their shortcomings. Certain companies need a focused, specialized, and straightforward tool for critical tasks, like
converting leads to sales.
As an example of flair and fun, the above slide brought a unique perspective to the point: Specialists produce better results—just look at the Track & Field world records. Not a single world record is held by a decathlete. Specialists hold
each one.
• Give a Taste and Show How It's Done: My friend Mark Levy, author of "Accidental Genius: Revolutionize Your Thinking Through Private Writing," delivered a terrific webinar on writing compelling case studies. His case
studies are like no one else's. Before fully going into how to create them, he gave the audience a sample of what compelling case studies look like.
The above slide and story, along with a number of others, made the case. Once you were hooked on wanting one for your own business, Mark walked through the linear process of how to create them.
The Finish
Most presenters give a summary and then hold a question-and-answer session. Do the Q&A first. This lets you end on a powerful note instead of with an unpredictable (or planted) final question.
For the summary, restate what your
compelling vision was and have the audience look down at their reasons for joining. Return them to what brought them to the presentation. Then, in the final minute, recap what you covered and paint a picture of what's now possible for them
after having spent an hour with you.
Moving Forward
A blank slide deck can be frustrating and time-consuming. Knowing what structures are available to you can make it easier to create your presentation.
Kids don't worry about playing with a Mr. Potato Head. They pop on a hat, eyes, ears, arms, and shoes. You can now do the same.


caSmall is a micro-framework for accessing and building lightweight caBIG application web services and applications. This initiative is in the planning stage. The initial offering will be a RESTful web service for reading and writing caTissue data. The web service resides on the caTissue application server. The client calls caRuby by sending a standard JSON request without installing caTissue or caRuby on the client workstation.
caSmall leverages the advent of ultra-slim Ruby frameworks such as Sinatra, HttParty and Thin. Stay tuned for further developments.


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Mount Escape To Ashi Garami
Opponent gets to mount. Hands into hips, elbows bent and in tight to sides, prep to lift, both feet on ground,
lift a knee and use it to push opponent forward, get their hands onto the mat over your head, then hip up to push them up
lock arms to hold them up, knee that pushed now comes through and across into an inside knee shield, hip out if not enough space,
other legs comes around into Ashi Garami. Pretty good chance opponent has to stand up to regain position.


📱 Getting started with the multithreading in iOS
With the emergence of multi-core processing chips in all our mobile devices, it has given rise to the possibility of performing multiple tasks simultaneously in the application. In iOS, in order to keep the application’s UI responsive 📲, all the heavy task are added in a new thread e.g. networking calls. These threads usually run in the background and send a call back to update the UI if necessary.
Need of multithreading has been perfectly stated in the apple documentation, it says:
Each process (application) in OS X or iOS is made up of one or more threads, each of which represents a single path of execution through the application’s code. Every application starts with a single thread, which runs the application’s main function. Applications can spawn additional threads, each of which executes the code of a specific function.
In order to facilitate and safely manage these additional threads we need a framework like dispatch
✍🏼 Ways to achieve multithreading:
- Grand Central Dispatch
- NSThread
- NSOperationQueue
This blog assumes you have basic understanding of these types.
When it comes to comparing these methods:
Code/Structural Origins:
- GCD is a low-level C-based API.
- NSOperation and NSOperationQueue are Objective-C classes (a wrapper on GCD).
- NSThread is an NSObject (uses pthreads).
Complexity comparison:
- For GCD implementation is very light-weight
- NSOperationQueue is complex and heavy-weight
- NSThread is just wrapper to pthreads therefore making it lightweight too.
NSOperation advantages over GCD and NSThread:
You can:
- Set up a dependency between two NSOperations hence enables developers to execute tasks in a specific order.
- Pause, cancel, resume an NSOperation once the task as started its execution therefore give it control over the operation’s life cycle.
- Monitor the state of an operation like: ready, executing, or finished.
- Specify the maximum number of queued operations that can run simultaneously.
Clearly, NSOperationQueue provides you more control over the operations.
Once you have decided which methodology you want to use, you need to decide the order and priority of it.
Order of Operation:
Queues can be of two type: Serial Queue or Concurrent Queue which runs the tasks synchronously and asynchronously respectively
Priority of operations:
Priority is defined as Quality of Service known as QoS and is of following four types:
Let’s now try to understand these operation by predicting the output of couple of examples.
Before trying to answer the output of code below, let’s look at the documentation of dispatch_async and dispatch_sync:
Dispatch_async:
🤞🏼 Declaration
void dispatch_async(dispatch_queue_t queue, dispatch_block_t block)
👥 Discussion
This function is the fundamental mechanism for submitting blocks to a dispatch queue. Calls to this function always return immediately after the block has been submitted and never wait for the block to be invoked. The target queue determines whether the block is invoked serially or concurrently with respect to other blocks submitted to that same queue. Independent serial queues are processed concurrently with respect to each other.
💁🏻♀ Summary
Submits a block for asynchronous execution on a dispatch queue and returns immediately.
Dispatch_sync:
🤞🏼 Declaration
void dispatch_sync(dispatch_queue_t queue, dispatch_block_t block);
👥 Discussion
Submits a block to a dispatch queue for synchronous execution. Unlike dispatch_async, this function does not return until the block has finished. Calling this function and targeting the current queue results in deadlock. Unlike with dispatch_async, no retain is performed on the target queue. Because calls to this function are synchronous, it “borrows” the reference of the caller. Moreover, no Block_copy is performed on the block. As an optimization, this function invokes the block on the current thread when possible.
💁🏻♀ Summary
Submits a block object for execution on a dispatch queue and waits until that block completes.
Now try these out:
In which order would these logs get printed? Suggest you to give it a try before looking at the solution.
What is given to us?
A new “queue” is created from global queue (which means it’s a concurrent queue).
We are given 2 dispatch task and few NSLog to be printed to understand the flow.
So the output will be:
As we were expecting the string after the async block is printed first, followed by the tasks under the block. The reason is quite clearly stated in the documentation, the async task return immediately after the submission.
Agree to me? BUT…
There is a twist: 🔀
As we have created a concurrent queue from the global queue and added it to an async dispatch, sure it returns immediately after submission, but the whole point of creating a async dispatch is to allow multiple threads to execute concurrently. So here the main queue (the task of printing “anumittal %num”) and the task in the async dispatch queue can run simultaneously. So you can also expect output in this order:
Making sense?
Similarly, now predict the output if instead of dispatch_async in the first example we had dispatch sync. Once again before looking at solution, try to answer it yourself.
As the documentation says: “this function does not return until the block has finished” In the sync_sync block, everything happens in the most calmest way. :blush: it prints “1 01 001” then comes another dispatch_sync and the outer one will not continue to line 46 until line 41 to 44 are executed.
But suppose the inner queue was async then it would immediately return and line after 45 and after 40 will execute simultaneously.
Last variant would be when both the queues are dispatch_async. 😃
I am sure you must have guessed the output.
It will be all concurrent.
Congratulations 👏🏼 you have understood the essentials of multithreading. If you get confident with these examples, I am sure you can now play around with threads in your applications.
Also, you must try creating your own new queues.
Syntax:
Serial queue:
dispatch_queue_t serial_queue = dispatch_queue_create("your.queue.label", DISPATCH_QUEUE_SERIAL);
Concurrent queue:
dispatch_queue_t concurrent_queue = dispatch_queue_create("your.queue.label", DISPATCH_QUEUE_CONCURRENT);
Deadlocks
You should always resist passing same queue (especially a serial queue) to multiple dispatch blocks, as it might lead to deadlock.
For example.
The code leads to deadlock condition as, the outer async operation waits for the inner block to start and complete, whereas the inner block will not start until the task in “queue” is completed.
Where as works fine with concurrent queue :
Just to emphasise, this will work but it is not preferred to use the same queue
Lastly a good to know example of deadlock: This is claimed to be the shortest code to create a deadlock. I hope by now you must be able to think in the direction to answer as to why would this create a deadlock.
Hint:
main queue is a serial queue, this code is trying to dispatch the new code block synchronously on the same queue which is running & waiting on the dispatch_sync.
If this all feels little overwhelming then it’s ok. The more you try out examples the easier it is to understand. Please let me know if you would like to learn any of these topics more in detail. You can find the project on my github.
In the next blog, we shall look into locks and the most famous readers/writers problem 😊 Thanks for reading. 👓


Oluwatosin Ajibade, better known by his stage name Mr Eazi, is a Nigerian singer, songwriter, and entrepreneur. He is the pioneer of Banku music, a fusion sound he describes as a mixture of Ghanaian highlife and Nigerian chord progressions and patterns.
African Giant is the fourth studio album by Nigerian singer Burna Boy. It was released on July 26, 2019, by Spaceship Entertainment, Bad Habit, Atlantic Records and Warner Music. The album is a mixture of Afro-fusion, Afrobeat, dancehall, pop and hip hop.
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Afro Pop is the hottest new African Dance that incorporates contemporary genres of African popular music from Nigeria, Ghana and across the Continent. ... The warm-up is designed to strengthen the student's ability to adopt to Traditional African, Urban African club movements and multiple hip hop styles.
Afro Pop is the hottest new African Dance come and listen to the latest Afro Hits, Afro Vibes, dancehall, afro pop & afro house. Brought to you by Afro Daily For Your Daily Music.
The singer taps into soulful lyrics and emotions.
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The Ghanian MC contemplates the risks we take in life and love.


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--- layout: page title: "Avi Karn, Ph.D." subtitle: Molecular Breeder | Quantitative Geneticist | Computational Biologist bigimg: - "/image/frontpage/rhampseq_core_genome.jpg" : "Vitis core genome and rhAmpSeq genetic markers (Nature Comm 2020)" - "/image/frontpage/palm.jpg" : "Collecting data" - "/image/frontpage/geneticMap.jpg" : "Genetic Mapping" - "/image/frontpage/mlm.jpg" : "Genome-wide association study" - "/image/frontpage/haploview.png" : "Haploblock Haploview " - "/image/frontpage/epistasis2.png" : "Epsistatic interactions" - "/image/frontpage/circos_teonilNAM2.png" : "Shared QTLs in TeoNILs and NAM for seed composition (Karn et al 2017)" - "/image/frontpage/20170622_113203.jpg" : "Minirhizotron Tubes Installation" - "/image/resume/IMG_7970.jpg" - "/image/resume/IMG_7972.jpg" - "/image/frontpage/corn.jpg" : "maize diversity" - "/image/frontpage/n1_egphi_z78.PNG" : "Network Diagram" ---


{% if matd3_name == 'HybriD³' %} The {{ matd3_name }} materials database provides a comprehensive collection of experimental and computational materials data for crystalline organic-inorganic compounds, predominantly based on the perovskite paradigm. {% else %} The {{ matd3_name }} materials database provides a comprehensive collection of experimental and computational curated materials data. {% endif %}
The data is available under the Creative Commons license with attribution clause, described here and, in its full text, here. We also intend to offer the data for inclusion in broader materials databases in order to maximize its accessibility.
Please contact us if you wish to add specific materials to the database - the database is intended to be accessible by the community and we would be delighted to hear from you.


Parameter list for the current execution of FitHiChIP: /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//Parameters.txt
List of CIS interactions within the specified distance thresholds (lower threshold: 20000 upper threshold: 2000000 ): /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//HiCPro_Matrix_BinSize5000/L_20000_U2000000/FitHiChIP.cis.interactions.DistThr.bed
Number of interacting (non zero contacts) bin pairs ( all to all ) : 10404706Bin specific HiChIP coverage, coverage bias, and peak / non-peak boolean values: /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//NormFeatures/Coverage_Bias/FitHiChIP.coverage_Bias.bed
All interactions with their FitHiChIP significance values: /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//FitHiChIP_Peak2ALL_b5000_L20000_U2000000/P2PBckgr_0/Coverage_Bias/FitHiC_BiasCorr/FitHiChIP.interactions_FitHiC.bed
Number of interactions considered: 4390898Significant interactions: /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//FitHiChIP_Peak2ALL_b5000_L20000_U2000000/P2PBckgr_0/Coverage_Bias/FitHiC_BiasCorr/FitHiChIP.interactions_FitHiC_Q0.01.bed
Total number of significant interactions ( FDR 0.01 ) : 57771After merging adjacent loops (connected component modeling) significant interactions: /home/springer/liang795/projects-in-nathan-lab/HiChip/2021-analysis/HiChip2021/Mo17-H3K4me3-Loop-HiChip//FitHiChIP_Peak2ALL_b5000_L20000_U2000000/P2PBckgr_0/Coverage_Bias/FitHiC_BiasCorr/Merge_Nearby_Interactions/FitHiChIP.interactions_FitHiC_Q0.01_MergeNearContacts.bed
Total number of significant interactions ( FDR 0.01 ) after merging adjacent loops: 4823


PSA trade unions give the nod to merger with Fiat
The majority of unions representing workers at Peugeot maker PSA are in favour of a planned merger with Fiat Chrysler , PSA executives and union representatives said.
PARIS: The majority of unions representing workers at Peugeot maker PSA are in favour of a planned merger with Fiat Chrysler , PSA executives and union representatives said.
However, the unions said that once the merger deal was signed, they would be seeking detailed information about the plans for the combined company.
(Reporting by Gilles Gillaume; Writing by Christian Lowe; Editing by Sudip Kar-Gupta)


Open source contributing
The project is in the public domain, and we encourage you to contribute to it on GitHub by improving on it or making your own apps and tools using [Agency]’s data.
The project is in the public domain, and we encourage you to contribute to it on GitHub by improving on it or making your own apps and tools using [Agency]’s data.


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preventing adrenal disorder in ferrets - Extensions
Preventing Adrenal Disease Prevention Preventing Adrenal Disease in the Ferret. by Deborah Cottrell DVM. New evidence suggests that the simple fact of spaying or castrating our pet ferrets may be . http://www.westendanimal.net/id63.html
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The AnyWHERE extension (40kb) Adrenal Disease in Ferrets: Causes, Signs, Treatment, and Prevention Adrenal disease is very treatable in most cases. Not treating adrenal disease is condemning your ferret to die, when this doesn't have to be the case. If you see . http://www.drsfostersmith.com/pic/article.cfm?aid=1983
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Suprelorin-F is here!!!! This is the talked about hormone suppressant for ferrets to 1. prevent the onset of adrenal disease and/or 2. treat adrenal disease so that the ferret does not need . http://www.nwexoticpetvet.com/Images/NEWS-Suprelorin.pdf
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Ferret Adrenal Disease - Shedding? - Exotic Pets This could be ferret adrenal disease and it is not a death sentence for most ferrets . . The hair loss may stop at the neck, head, and legs but not always. The skin . http://www.bellaonline.com/articles/art31786.asp
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Adrenal Gland Disease in Ferrets adrenal gland disease in ferrets. . Stages of Adrenalcortical Disease in Ferrets . LH and FSH is reduced preventing their negative effect on the adrenal gland . http://aeacarizona.com/index.php?option=com_content&view=article&id=XX(113)
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XML Request InspectorFerret Information Adjusting the lighting to give nine hours of light and fifteen of dark may help prevent your ferret from getting adrenal disease. We highly recommend microchip . http://weah4.tripod.com/id4.html XML DataSet InspectorAdrenal Disease in Ferrets - Lafayette Ferret | Examiner.com Nov 7, 2012 . There are many diseases that ferrets are prone to, and I'd like to take a moment to discuss one of them. Adrenal disease. Adrenal disease is . http://www.examiner.com/article/adrenal-disease-ferrets
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nape.shape.Edge
type
class
metadata
:final
Edge class providing internal details of Polygon.
Constructor
new
()
Instance Variables
length
(accessor,null) :
Float
Length of edge.
localNormal
(accessor,null) :
nape.geom.Vec2
Normal of edge in local coordinates.
This Vec2 is immutable.
localProjection
(accessor,null) :
Float
Local projection of polygon onto edge axis.
localVertex1
(accessor,null) :
nape.geom.Vec2
Reference to first local vertex for edge.
localVertex2
(accessor,null) :
nape.geom.Vec2
Reference to second local vertex for edge.
polygon
(accessor,null) :
Polygon
Reference to Polygon this Edge belongs to.
worldNormal
(accessor,null) :
nape.geom.Vec2
Normal of edge in world coordinates.
This Vec2 is immutable, and may be accessed even if the related Polygon is not part of a Body but queries to its values will result in a debug build error.
worldProjection
(accessor,null) :
Float
World projection of polygon to edge axis.
This value can only be accessed if related Polygon is part of a Body.
worldVertex1
(accessor,null) :
nape.geom.Vec2
Reference to first world vertex for edge.
worldVertex2
(accessor,null) :
nape.geom.Vec2
Reference to second world vertex for edge.


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],
)
def test_count_vector_featurizer(sentence, expected, expected_cls):
ftr = CountVectorsFeaturizer({"token_pattern": r"(?u)\b\w+\b"})
train_message = Message(sentence)
test_message = Message(sentence)
],
)
def test_count_vector_featurizer(sentence, expected, expected_cls):
ftr = CountVectorsFeaturizer()
train_message = Message(sentence)
test_message = Message(sentence)


Hey all, I’m upping my bot game. Thanks to @inertia and his DrOtto bot (and 30 min of help from @reggaemuffin) I’m in it to win it for Minnows. Here’s the deal. I’m running Dr. Otto through my own little oddbot named @lovejuice. It’s a voting bot for minnows. It only has ~8k power so you can rest assured the dolphins and whales won’t care about this baby, but you should and that’s perfect.
Looks like a full vote gives out about $0.80 cents and comes with a bizarre image and a bizarre comment. To make it work send a minimum of 0.010 sbd to @lovejuice with the memo of the post you want it to upvote. Check its wallet first before you upvote though. Because if more than 8 minnows happen to be going at the same time then you’ll lose money on this.
 That should be enough to keep the lovejuice flowing all over your posts, but not so much that you’re competing against the whole steemit community.
If the mild sexual humor is too much I recommend not using it. If you think it’s funny then share @lovejuice with your friends and spray dank amps on their wall too. It’s just meant for fun. Looks to be working so have at it.
For more info on DrOtto check here- https://steemit.com/radiator/@inertia/dr-otto-vote-bidding-bot
PS works just like @booster but with less SP and more votes per day.
Also, short milestone post… ugh… I broke 3k followers. Feeling kinda pimp. /flex So, thanks to everyone that follows me. Love to MSP. You guys float my boat. And if you’re a minnow wondering when you finally get a bot you can afford… Here it is.
Lastly, if you think this is cool please resteem.
This page is synchronized from the post: ‘Introducing @lovejuice- a voting bot for minnows (Side note- I passed 3k followers)’


Let's give a try to Activator tutorial capability :-)
Please click on the arrow on the left panel to move onto the next section.
You'll find solutions to the problem we were working on in src/main/scala/PrimeNumbers.scala
The first object Euler3
solves
the third problem
on Project Euler but requires a function that takes an
integer and return a sequence of all the prime numbers up to
that number. We will use that to see various approaches to
building this function.
The most ancient algorithm to build such function is the Sieve of Eratosthene. I have been asked at the end of last session to provide a functional implementation of it. Let's study that.
Have a look at the 3 different approaches in
the Eratosthene
object.
primesImperative
, I would expect to
be close in essence to what you would call a good old imperative
implementation.primesMRS
with MRS standing
for Map Reduce Style. Here we have given up on
performance utterly at least in a mono-thread processing.
Still, it's functional and it's worth knowing as it's
highly parallelisable. We shall review that once we know
how to do it.primesSPS
for State
Passing Style. Another to name would probably
be the recursive solution. But first, functional
programmers aren't that big of fans of recursivity. That
may surprises you but yeah, FP coders know better. And
soon you will too! Second, that doesn't convey the key
move of this technique. You'd better learn right now
that state encapsulation is no panacea.
Now have a look at my approach with object
Hubert
. Simple, elegant and yes, fast[1]. That gives us
Eratosthenes — 0, Euler — 0, Hubert — 1. Now tell me why I am
the one who doesn't have a dedicated Wikipedia page.
Check this out in PrimeNumbersBenchmark.scala. It uses scalameter. I discovered it while preparing this session. Since some of us want to pay a particular attention to performance, having it handy is a very good idea. Performance on the JVM is hard to measure. To learn more on how scalameter helps and the caveats of JVM microbenchmarking, read through its documentation.
In this benchmark, you'll see first a series of run of each version of the sieve of Eratosthenes. Each series starts from 30 up to 1000 with a step of 75.
Then you'll see how each performs on Euler problem #3 but with a significantly smaller number. Finally we are benchmarking my solution on the real number from problem #3.
(click then head over the Build tab on the left panel, section Develop)If you have a lot of time or your room is a bit cold and you
fancy some CPU-powered heat, you can try the other methods
in Eratosthenes
on the real number. Well on my machine it's ~18 seconds. But that's the thing with prime decomposition right? It's hard and slow, that's why cryptography loves it. Now ask Eratosthenes to solve it, millenaries is probably an overstatement but you'll definitively look older and not necessarily wiser.
Since you've watched Martin's video on Coursera (week 2), let's have a go at the related assignment. Shamelessly, I have copy pasted the code here and the statement below. To test your solution, I have also boostrapped a Spec class for you.
The following pattern of numbers is called Pascal’s triangle.
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
...
The numbers at the edge of the triangle are
all 1
, and each number inside the triangle is the
sum of the two numbers above it. Write a function that computes
the elements of Pascal’s triangle by means of a recursive
process.
Do this exercise by implementing the pascal
function
in RecFun.scala
,
which takes a column c
and a row r
,
counting from 0
and returns the number at that spot
in the triangle. For
example, pascal(0,2)=1
, pascal(1,2)=2
and pascal(1,3)=3
.
def pascal(c: Int, r: Int): Int
Write a recursive function which verifies the balancing of
parentheses in a string, which we represent as
a List[Char]
not a String
. For
example, the function should return true
for the
following strings:
The function should return false
for the following strings:
The last example shows that it’s not enough to verify that a string contains the same number of opening and closing parentheses.
Do this exercise by implementing the balance
function
in RecFun.scala
.
Its signature is as follows:
def balance(chars: List[Char]): Boolean
There are three methods on List[Char]
that are
useful for this exercise:
chars.isEmpty: Boolean
returns whether a list is emptychars.head: Char
returns the first element of the listchars.tail: List[Char]
returns the list without the first elementHint: you can define an inner function if you need to pass extra parameters to your function.
Testing: You can use the toList
method to convert from a String
to
a List[Char]
: e.g. "(just an)
example".toList
.
Write a recursive function that counts how many different ways you can make change for an amount, given a list of coin denominations. For example, there are 3 ways to give change for 4 if you have coins with denomiation 1 and 2: 1+1+1+1, 1+1+2, 2+2.
Do this exercise by implementing the countChange
function
in RecFun.scala
.
This function takes an amount to change, and a list of unique
denominations for the coins. Its signature is as follows:
def countChange(money: Int, coins: List[Int]): Int
Once again, you can make use of
functions isEmpty
, head
and tail
on the list of
integers coins
.
Hint: Think of the degenerate cases. How many ways can you give change for 0 CHF? How many ways can you give change for >0 CHF, if you have no coins?
Head over
to List.scala
and fill in the ???
.
Start with the methods in the trait. Try to figure what sort
of method should go there and why for
instance sum
is not at this place but in the companion object instead.
Then implement sum
and product
in
the companion then observe both implementations along with
others in the trait. Extract the commonality into a function:
def foldRight(z: B)(f: (A, B) => B): B
Where should you put this method?
Appreciate the power of HOFs (Higher-Order Functions) to remove boilerplate. What you have done is a generalisation. In OOP, you have learnt that you need to refactor your code. You don't have a very strict definition of what refactored should look like but roughly speaking, it's about keeping it tidy. When you see fit, you apply few techniques, you also pay attention to named design principles like Don't Repeat Yourself or the Open-Close Principle. Well in FP, we generalise as much as we can, that means we create Higher-Order functions (abstract over operations) and/or polymorphic function (abstract over type). We call that sometimes wholemeal programming. That's the reason why I was saying earlier that recursivity is not the favourite tool for FP coders: you can capture that once and for all into a function. Actually you just did.
foldRight
is special. We call it
a catamorphism for the type List
. To see
it in action, use it with Nil
and Cons(_,
_)
. Interesting to say the list… hu the least! All
operations on List
can be expressed
using foldRight
. Try that if you're not
convinced! All that you've implemented so far can be expressed
with it.
Use it to implement length
. Remember from now on
that it's not a cheap operation
on List
. O(n) indeed.
Now there is something unfortunate
with foldRight
. Can you see it? Try with a very
big List and you'll see. Now work on its sister and fix this issue:
def foldLeft(z: B)(f: (B, A) => B): B
As an exercise, implement foldRight
in terms
of foldLeft
.
More functions for your type List
:
def flatten[A](xxs: List[List[A]]): List[A] // must run in O(n)
def map[B](f: A => B): List[B]
def filter(p: A => Boolean): List[A]
def flatMap[B](f: A => List[B]): List[B] // try using this one to implement filter
def zipWith[B](that: List[A])(f: (A, A) => B): List[B] // eg List(1,2,3).zipWith(List(4,5,6))(_ + _) == List(5,7,9)
def hasSubsequence(sub: List[A]): Boolean // this one is hard
You're now very close to a fully functional (in several ways) List implementation. If you're curious enough, you can compare what you have with the List in Scala Standard Library.
The exact same approach we followed here would work for
defining a binary Tree[+A]
and many more. Try
building the ADT for it, that is the sealed trait
and the data constructors that extend it.
Coursera Course — week 1.


A JavaScript function is executed when "something" invokes it (calls it).
A JavaScript function is defined with the function keyword, followed by a name, followed by parentheses ().
Function names can contain letters, digits, underscores, and dollar signs (same rules as variables).
The parentheses may include parameter names separated by commas: (parameter1, parameter2, ...)
The code to be executed, by the function, is placed inside curly brackets: {}
Function parameters are the names listed in the function definition.
Function arguments are the real values received by the function when it is invoked.
Inside the function, the arguments are used as local variables.
| A Function is much the same as a Procedure or a Subroutine, in other programming languages. |
The code inside the function will execute when "something" invokes (calls) the function:
You will learn a lot more about function invocation later in this tutorial.
When JavaScript reaches a return statement, the function will stop executing.
If the function was invoked from a statement, JavaScript will "return" to execute the code after the invoking statement.
Functions often compute a return value. The return value is "returned" back to the "caller":
Calculate the product of two numbers, and return the result:
The result in x will be:
You can reuse code: Define the code once, and use it many times.
You can use the same code many times with different arguments, to produce different results.
Convert Fahrenheit to Celsius:
Using the example above, toCelsius refers to the function object, and toCelsius() refers to the function result.
Accessing a function without () will return the function definition:
In JavaScript, you can use functions the same way as you use variables.
You can use:
Instead of:
| You will learn a lot more about functions later in this tutorial. |


{% with messages = get_flashed_messages(category_filter=["get_id"]) %}
{% if messages %}
{# × #}
{# × #}
{%- for msg in messages %}
{% set u_id = msg %}
session id: {{ u_id }}
{# #}
{% endfor -%}
{% endif %}
{% endwith %}
Good job!
Your are almost done, now take a minute to evaluate
the recommendations and fill in the short survey.
Welcome! For optimal experience use desktop browser such as Chrome or Firefox
This short experiment is part of a Master's Thesis project focusing on interactive recommender systems.
The goal of the experiment is to test two different systems and evaluate the satisfaction rate between a given music profile and the recommendations.
The experiment consists of two parts and a short survey and should take 5-10 minutes to complete.
The cold start problem: Like Netflix asks you to choose 3 movies you like upon signup - we try to match your
music preferences with a profile from the the Last.fm 360k Users dataset from 2008, thus newer artist will not be available.
For this you have two options:
Option A: Getting a matching music profile based on tags that you like.
Option B: Getting a matching music profile based on artists that you like.
Once you are somewhat satisfied with the given profile click "Get Recommendations"
This will provide you with 10 recommendations. Study them carefully, if your time allows give them a listen and evaluate the outcome.
Finally fill in the survey and don’t forget to click "Submit".
Please keep in mind that the profile you are to find will not match your
preferences due to the nature of music consumption the profile will hold artists that you don't
like at all or don't prefer anymore. This is perfectly fine, so don't strive for perfection.


MTRACE
Section: Linux user manual (1)
Updated: 2014-09-01
Index
Return to Main Contents
NAME
mtrace - interpret the malloc trace log
SYNOPSIS
mtrace [option]... [binary] mtracedata
DESCRIPTION
mtrace
is a Perl script used to interpret and provide human readable output
of the trace log contained in the file
mtracedata,
whose contents were produced by
mtrace(3).
If
binary
is provided, the output of
mtrace
also contains the source file name with line number information
for problem locations
(assuming that
binary
was compiled with debugging information).
For more information about the
mtrace(3)
function and
mtrace
script usage, see
mtrace(3).
OPTIONS
- --help
-
Print help and exit.
- --version
-
Print version information and exit.
BUGS
For bug reporting instructions, please see:
<http://www.gnu.org/software/libc/bugs.html>.
SEE ALSO
memusage(1),
mtrace(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- OPTIONS
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:44 GMT, September 18, 2014