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{% extends "base.html" %}
{% load md %}
{% block content %}
{% markdown %}
The best way to reach me for most things is by email: [ben(dot)s(dot)kuhn(at)gmail(dot)com](mailto:ben(dot)s(dot)kuhn(at)gmail(dot)com). I check email once a day in the evening, and try to respond within two days. If I'm swamped and don't manage to, feel free to ping me after that. I'm usually happy to answer emails about stuff I've written, even if I don't know you!
If whatever you're going to say might be of general interest, though, I do encourage you to post it as a comment instead :)
{% endmarkdown %}
{% endblock %}


| DOSE: T+ 0:00 |
20 mg |
IV |
DPT |
(liquid) |
| T+ 3:00 |
20 mg |
IV |
DPT |
(liquid) |
I injected 20 mg DPT intravenously. At 5 seconds I could taste a metallic substance and felt a tingle in my feet. At 10 seconds I was launched into a +3.5 state of consciousness. My body immediately felt alive with electricity. With eyes open, everything I saw was moving around in swirls. I could tell what everything was and where I was, but it was all liquid and turbulent. When I closed my eyes, everything disintegrated. I could see the world inside blow apart into dust and the dust swirling away in tornadoes. Faces appeared, but unrecognizable and melting away as if being burned like a wax figure. This state lasted about 20 minutes when I smoothly came down to a 3.0.
This was stronger a 3.0 than I've experienced with lsd or psilocybin but the body load was neglible. After about an hour the expereince was basically over, with almost no after effects. Repeated same dose in 3 hours with same results. Overall very intense, but the short duration made it tolerable and even rewarding. If you try this be ready. Get your setting in order and start music beforehand. Make sure will not have to deal with ANYTHING. This experience would totally freak out a novice or unstable personality.
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.


Helpful Hints:
Examine the graph on the left. What do you see?
The temperture is warmer the closer the latitude gets to the equator. We also see that the Northern hemisphere has cooler temps however that can be attributed to the time of year and number cities (data points) within the northern hemispher.


--- sort_order: 148 tei_id: rdx_v6sv3.p.idm140456974165472 annotation_count: 0 images: small-thumbnail: https://readux.ecds.emory.edu/books/readux:v5mxs/pages/readux:v6sv3/mini-thumbnail/ json: https://readux.ecds.emory.edu/books/readux:v5mxs/pages/readux:v6sv3/info/ full: https://readux.ecds.emory.edu/books/readux:v5mxs/pages/readux:v6sv3/fs/ page: https://readux.ecds.emory.edu/books/readux:v5mxs/pages/readux:v6sv3/single-page/ thumbnail: https://readux.ecds.emory.edu/books/readux:v5mxs/pages/readux:v6sv3/thumbnail/ title: Page 144 number: 144 permalink: "/pages/144/" ---


Misc Functions
Some of shit that actually worked
//returns the json file as individual lines of independant json
function json_oneline(){
$url = 'https://chaturbate.com/affiliates/api/onlinerooms/?format=json&wm=KZ7tX';
$gender_options = array('f', 'm', 'c', 't');
$jcontent = file_get_contents($url);
$jtemp = tmpfile();
fwrite($jtemp, $jcontent);
$dil = '},';
$fnd = array('},', '\'{');
$rep = array('}];},', '\'[{');
$jfile =explode($dil, str_ireplace($fnd, $rep, htmlspecialchars($jcontent)));
return $jfile;
//array_multisort($jfile, $gender_options, SORT_REGULAR, $stdin->num_users, SORT_DESC);
//$jfile->offsetSet(NULL, 'gender');
//var_dump($jfile->offsetGet());
}
jWalk: json_decode + array_walk
Shit doesn't get more real than the price you pay to jWalk();
They say the only people to come out on the other side were never on our's to begin with. I heard they have no souls and are as cold as ice. So, Conadians who drive commuter cars or mini-vans...  Well, not like your shit!
This is one I'll be using in some capacity.
```php
function items($val) {
$url = urlencode($val->chat_room_url_revshare);
//$video = str_replace('bgcolor="white"', 'bgcolor="transparent"', $url);
//$video = $val->username;
$video = $val->username;
$item = '<a data-target="#image-modal" data-toggle="modal" data-img="'.$val->image_url_360x270.'" data-username="'.$val->username.'" data-displayname="'.$val->display_name.'" data-birthday="'.$val->birthday.'" data-age="'.$val->age.'" data-gender="'.$val->gender.'" data-subject="'.$val->room_subject.'"';
$item .= ' data-hd="no" data-duration="'.$val->seconds_online.'" data-lang="'.$val->spoken_languages.'" data-show="'.$val->current_show.'" data-video="'.$video.'">';
$item .= '<img src="'.$val->image_url.'" alt="'.$val->username.'\'s Room" style="width: 100%">';
$item .= '<span class="caption">'.$val->username.'</span></a>';
//&disable_sound=1&embed_video_only=1
return $item;
}
```
##Shit that obviously wouldn't work but tried anyway
Remember the time you told your mom you were going to a friends house that you were going to a friend's house who you were allowed to go see but you were holding a video game that only the friend you weren't supposed to visit, had a console for? It's kind of like that.
For anyone that wasn't around for the days where we borrowed and traded video games, here's a better one for you. Rememnber that time where you were confused by an analogy...
```php
//element('username', $jfile);
//print_r(array_walk($jfile->gender->offsetGet('f'), 'items'));
$gender =array();
$male =array();
$female=array();
$couple=array();
$trans=array();
//echo "username."&bgcolor=transparent&disable_sound=1&embed_video_only=1&target=_parent' height=600 width=800 style='border: none;'>";
//$type = array($var->is_new, $var->num_followers, $var->iframe_embed_vo, $var->display_name, $var->recorded, $var->iframe_embed_revshare, $var->chat_room_url, $var->location, $var->block_from_states, $var->chat_room_url_revshare, $var->username, $var->spoken_languages, $var->image_url_360x270, $var->current_show, $var->birthday, $var->is_hd, $var->block_from_countries, $var->seconds_online, $var->gender, $var->age, $var->num_users, $var->image_url, $var->room_subject);
function gender_list($var){
$male =array();
$female=array();
$couple=array();
$trans=array();
$gender = array($female, $male, $couple, $trans);
switch ($var->username->gender) {
case $var->username->gender == 'f':
array_push($female, $var->username);
break;
case $var->username->gender == 'm':
array_push($male, $var->username);
break;
case $var->username->gender == 'c':
array_push($couple, $var->username);
break;
case $var->username->gender == 't':
array_push($trans, $var->username);
break;
}
return $gender;
}


Currently you have added to your profile: Gluten Free Diet
Click here to view nutrition plan
Currently you have added to your profile: Fit diet
Click here to view nutrition plan
Currently you have added to your profile: Muscle building diet
Click here to view nutrition plan
Currently you have added to your profile: Vegan diet
Click here to view nutrition plan
Currently you have added to your profile: Vegetarian
Click here to view nutrition plan


@Version(value="1.0.0.${qualifier}")
| Class | Description |
|---|---|
| SequentialProfile |
A SequentialProfile is typically used by a time shifter appliance driver to indicate the indicate the order of
multiple commodity profiles.
|
| SequentialProfileAllocation | |
| TimeShifterAllocation |
When an energy app allocates the energy flexibility of a time shifter it sends a time shifter allocation to the
appliance driver.
|
| TimeShifterRegistration |
The
TimeShifterRegistration contains information about the commodities supported by the appliance. |
| TimeShifterUpdate |
If a new program is scheduled by time shifter appliance the resource manager sends a time shifter control space
update towards the energy app.
|


RAWTOHEX
converts raw data to a character value containing its hexadecimal representation.
VARCHAR2
RAWTOHEX(raw)
raw
can be any scalar data type other than LONG
, LONG RAW
, CLOB
, BLOB
, or BFILE
.
RAWTOHEX(NLSSORT('Rumpelstiltskin'))
converts the raw value returned by NLSSORT
to the hexadecimal value 52756D70656C7374696C74736B696E00
.


vetanda
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Past performances


I read a post from Jeff Utecht just ten minutes ago which got me into this thought. He wrote about helping a teacher flip her history classroom:
Parents [are] calling into question the idea that the teacher isn’t “teaching my child” and the frustration their child is having to “find the right answer.”
I’ve been fielding a lot of questions lately about how to do a flipped classroom. I do my best to guide and show different resources, but when it really comes down to it, you need to do what makes sense for your classes. If it involves video lectures, then we can make lectures. If it involves a research project, then we can design a project. What it comes down to is the fact that students are taking responsibility for their learning. I can help you scaffold and plan for it, but I don’t know your class or your school’s culture.
All this to say: sometimes teachers do the same thing. I have led sessions where people want to what exactly they need to do to have a flipped class. I tell them the same thing I tell my students: there is no one right answer. It depends on your class, purpose, and learning culture.
True learning comes from exploration, risk-taking, assessment, and reflection. Let’s work on stepping out of our old habits and start embracing and emulating that model with our students.
Written on February 6th, 2012 by Brian Bennett Categorized in: All Flipclass
Leave a comment


Ping Pong will count up to your number from 1 with the following exceptions:
Bizarro Pong will count down from your number to 1 while running the same exceptions.


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Lately my research has focused on simulations and semi-analytical calculations of supernova explosions in binary systems, tidal disruption events, and stellar black hole formation. I'm interested in processes that shape the mass and spin distribution of black holes, particularly the ones observed by the LIGO (Laser Interferometer Gravitational-Wave Observatory) collaboration.
This year I joined the LMT (Large Millimeter Telescope) team @INAOE to work in the EMT (Event Horizon Telescope) collaboration studying accretion onto supermassive black holes.
Born and raised in México City, I got my Master's and PhD's degrees at the Astronomy Institute at UNAM (National Autonomous University of México). I then was awarded the UCMEXUS postdoctoral fellowship and worked as a Postdoctoral Researcher and Lecturer in the Astronomy and Astrophysics Department at UCSC for four years. I had a very brief postdoctoral position at the Dark Cosmology Centre in the University of Copenhaguen, before joining my current position at INAOE (National Institute of Astrophysics, Optics and Electronics).
This paper by Martin Lopez Jr. studies the effects of Tidal Disruption Events (TDEs) on the mass and spin of BH binaries within a stellar cluster.
This paper by Sophie L. Schrøder studies the mass and spin of BHs formed in Fallback Supernova.
This image created with GYOTO shows how a Page-Thorn accretion disk around a spinning BH would look to an observer at a 70 degrees inclination angle.


Usage - Distributed Coach¶
Coach supports the horizontal scale-out of rollout workers in distributed mode. For more information on the design and implementation of distributed Coach, see Distributed Coach - Horizontal Scale-Out. In the rest of this section, we will describe how to get started with distributed Coach.
Interfaces and Implementations¶
Coach uses three interfaces to orchestrate, schedule and manager the resources of workers it spawns in the distributed mode. These interfaces are the orchestrator, memory backend and the data store. Refer to Distributed Coach - Horizontal Scale-Out for more information. The following implementation(s) are available for each interface:
Orchestrator - Kubernetes.
Memory Backend - Redis Pub/Sub.
Prerequisites¶
Building and pushing containers - Docker.
Container registry access for hosting container images - Docker Hub
Using Kubernetes for orchestration - Kubernetes configuration.
Using S3 for storing policy checkpoints - AWS CLI <https://docs.aws.amazon.com/cli/latest/userguide/installing.html>_, `AWS credentials and S3 bucket.
Clone the Repository¶
$ git clone git@github.com:NervanaSystems/coach.git
$ cd coach
Build Container Image and Push¶
Create a directory docker.
$ mkdir docker
Create docker files in the docker directory.

RUN pip3 install gym[atari,box2d,classic_control]==0.10.5
# add coach source starting with files that could trigger
# re-build if dependencies change.
RUN mkdir /root/src
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
RUN pip3 install -r /root/src/requirements.txt
FROM coach-base:master
WORKDIR /root/src
COPY --from=builder /root/.cache /root/.cache
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
COPY README.md /root/src/.
RUN pip3 install gym[atari,box2d,classic_control]==0.10.5 && pip3 install -e .[all] && rm -rf /root/.cache
COPY . /root/src
A sample docker file for the Mujoco environment would look like this:
FROM coach-base:master as builder
# prep mujoco and any of its related requirements.
# Mujoco
RUN mkdir -p ~/.mujoco \
&& wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \
&& unzip -n mujoco.zip -d ~/.mujoco \
&& rm mujoco.zip
ARG MUJOCO_KEY
ENV MUJOCO_KEY=$MUJOCO_KEY
ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH
RUN echo $MUJOCO_KEY | base64 --decode > /root/.mujoco/mjkey.txt
RUN pip3 install mujoco_py
# add coach source starting with files that could trigger
# re-build if dependencies change.
RUN mkdir /root/src
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
RUN pip3 install -r /root/src/requirements.txt
FROM coach-base:master
WORKDIR /root/src
COPY --from=builder /root/.mujoco /root/.mujoco
ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH
COPY --from=builder /root/.cache /root/.cache
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
COPY README.md /root/src/.
RUN pip3 install mujoco_py && pip3 install -e .[all] && rm -rf /root/.cache
COPY . /root/src
A sample docker file for the ViZDoom environment would look like this:
FROM coach-base:master as builder
# prep vizdoom and any of its related requirements.
RUN pip3 install vizdoom
# add coach source starting with files that could trigger
# re-build if dependencies change.
RUN mkdir /root/src
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
RUN pip3 install -r /root/src/requirements.txt
FROM coach-base:master
WORKDIR /root/src
COPY --from=builder /root/.cache /root/.cache
COPY setup.py /root/src/.
COPY requirements.txt /root/src/.
COPY README.md /root/src/.
RUN pip3 install vizdoom && pip3 install -e .[all] && rm -rf /root/.cache
COPY . /root/src
Build the base container. Make sure you are in the Coach root directory before building.
$ docker build -t coach-base:master -f docker/Dockerfile.base .
If you would like to use the Mujoco environment, save this key as an environment variable. Replace <mujoco_key> with the contents of your mujoco key.
$ export MUJOCO_KEY=<mujoco_key>
Build the container for your environment. Replace <env> with your choice of environment. The choices are gym, mujoco and doom. Replace <user-name>, <image-name> and <tag> with appropriate values.
$ docker build --build-arg MUJOCO_KEY=${MUJOCO_KEY} -t <user-name>/<image-name>:<tag> -f docker/Dockerfile.<env> .
Push the container to a registry of your choice. Replace <user-name>, <image-name> and <tag> with appropriate values.
$ docker push <user-name>/<image-name>:<tag>
Create a Config file¶
Add the following contents to file. Replace <user-name>, <image-name>, <tag>, <bucket-name> and <path-to-aws-credentials> with appropriate values.
[coach]
image = <user-name>/<image-name>:<tag>
memory_backend = redispubsub
data_store = s3
s3_end_point = s3.amazonaws.com
s3_bucket_name = <bucket-name>
s3_creds_file = <path-to-aws-credentials>
Run Distributed Coach¶
The following command will run distributed Coach with CartPole_ClippedPPO preset, Redis Pub/Sub as the memory backend, S3 as the data store in Kubernetes with three rollout workers.
$ python3 rl_coach/coach.py -p CartPole_ClippedPPO \
-dc \
-e <experiment-name> \
-n 3 \
-dcp <path-to-config-file>


Contents
- Gradient of the Rosenbrock function at [1,1], the global minimizer
- The Hessian matrix at the minimizer should be positive definite
- Gradient estimation using gradest - a function of 5 variables
- Simple Hessian matrix of a problem with 3 independent variables
- A semi-definite Hessian matrix
- Directional derivative of the Rosenbrock function at the solution
- Directional derivative at other locations
- Jacobian matrix of a scalar function is just the gradient
- Jacobian matrix of a linear system will reduce to the design matrix
- The jacobian matrix of a nonlinear transformation of variables
% Multivariate calculus demo script % This script file is designed to be used in cell mode % from the matlab editor, or best of all, use the publish % to HTML feature from the matlab editor. Older versions % of matlab can copy and paste entire blocks of code into % the Matlab command window. % Typical usage of the gradient and Hessian might be in % optimization problems, where one might compare an analytically % derived gradient for correctness, or use the Hessian matrix % to compute confidence interval estimates on parameters in a % maximum likelihood estimation.
Gradient of the Rosenbrock function at [1,1], the global minimizer
rosen = @(x) (1-x(1)).^2 + 105*(x(2)-x(1).^2).^2;
% The gradient should be zero (within floating point noise)
[grad,err] = gradest(rosen,[1 1])
grad = 3.6989e-20 0 err = 1.4545e-18 0
The Hessian matrix at the minimizer should be positive definite
H = hessian(rosen,[1 1])
% The eigenvalues of h should be positive
eig(H)
H = 842 -420 -420 210 ans = 0.39939 1051.6
Gradient estimation using gradest - a function of 5 variables
[grad,err] = gradest(@(x) sum(x.^2),[1 2 3 4 5])
grad = 2 4 6 8 10 err = 1.2533e-14 2.5067e-14 3.4734e-14 5.0134e-14 2.836e-14
Simple Hessian matrix of a problem with 3 independent variables
[H,err] = hessian(@(x) x(1) + x(2)^2 + x(3)^3,[1 2 3])
H = 0 0 0 0 2 0 0 0 18 err = 0 0 0 0 4.6563e-15 0 0 0 3.3318e-14
A semi-definite Hessian matrix
H = hessian(@(xy) cos(xy(1) - xy(2)),[0 0])
% one of these eigenvalues will be zero (approximately)
eig(H)
H = -1 1 1 -1 ans = -2 -3.8795e-07
Directional derivative of the Rosenbrock function at the solution
This should be zero. Ok, its a trivial test case.
[dd,err] = directionaldiff(rosen,[1 1],[1 2])
dd = 0 err = 0
Directional derivative at other locations
[dd,err] = directionaldiff(rosen,[2 3],[1 -1]) % We can test this example v = [1 -1]; v = v/norm(v); g = gradest(rosen,[2 3]); % The directional derivative will be the dot product of the gradient with % the (unit normalized) vector. So this difference will be (approx) zero. dot(g,v) - dd
dd = 743.88 err = 1.5078e-12 ans = 1.5916e-12
Jacobian matrix of a scalar function is just the gradient
[jac,err] = jacobianest(rosen,[2 3]) grad = gradest(rosen,[2 3])
jac = 842 -210 err = 2.8698e-12 1.0642e-12 grad = 842 -210
Jacobian matrix of a linear system will reduce to the design matrix
A = rand(5,3);
b = rand(5,1);
fun = @(x) (A*x-b);
x = rand(3,1);
[jac,err] = jacobianest(fun,x)
disp 'This should be essentially zero at any location x'
jac - A
jac = 0.81472 0.09754 0.15761 0.90579 0.2785 0.97059 0.12699 0.54688 0.95717 0.91338 0.95751 0.48538 0.63236 0.96489 0.80028 err = 3.9634e-15 3.8376e-16 5.4271e-16 3.9634e-15 8.8625e-16 5.0134e-15 7.0064e-16 3.0701e-15 5.3175e-15 3.76e-15 4.8542e-15 2.4271e-15 3.0701e-15 4.3417e-15 3.9634e-15 This should be essentially zero at any location x ans = 0 -3.1919e-16 0 1.1102e-16 -1.6653e-16 0 2.7756e-17 0 -1.1102e-16 -1.1102e-16 -3.3307e-16 1.6653e-16 0 2.2204e-16 1.1102e-16
The jacobian matrix of a nonlinear transformation of variables
evaluated at some arbitrary location [-2, -3]
fun = @(xy) [xy(1).^2, cos(xy(1) - xy(2))]; [jac,err] = jacobianest(fun,[-2 -3])
jac = -4 0 -0.84147 0.84147 err = 2.5067e-14 0 6.1478e-14 1.9658e-14


Labour announces £11bn windfall tax on oil and gas companies
Jeremy Corbyn revealed details of the ‘just transition tax’ at Labour’s manifesto launch.
Labour would hit oil and gas companies with a windfall tax of about £11 billion in order to finance plans to tackle the climate crisis by creating a greener economy.
Jeremy Corbyn announced the “just transition tax” as part of Labour’s “green industrial revolution” when unveiling his party’s election manifesto in Birmingham on Thursday.
If Labour wins the December 12 vote, the party plans to create one million green jobs and deliver the “substantial majority” of the emissions cuts needed to tackle the environmental emergency by 2030.
The Labour leader hopes to use the key revenue raiser to deliver on his pledge not to hang the workers “out to dry” and ensure they have new, unionised jobs.
Labour says Scotland’s North Sea oil and gas production has had a net operating surplus of £273 billion, but has been leaving workers and their communities vulnerable since its decline from 1999.
The party argues that the benefactors should now help pay for the cost to transition into a greener economy.
A Labour government will ensure the big oil and gas corporations that profit from heating up our planet will shoulder the burden and pay their fair share through a just transition taxJeremy Corbyn
The size and exact mechanism of the tax will be determined by a consultation, but Labour estimates it could raise about £11 billion after looking at schemes in Germany and Spain.
Labour officials do not anticipate the cost of the windfall would be pushed onto consumers at the petrol pumps, saying prices are determined by the global market.
At the manifesto launch at Birmingham City University, Mr Corbyn said: “The crisis demands swift action, but it isn’t right to load the costs of the climate emergency onto the nurse, the builder or the energy worker.
“So, a Labour government will ensure the big oil and gas corporations that profit from heating up our planet will shoulder the burden and pay their fair share through a just transition tax.
“North Sea oil and gas workers have powered this country for decades, often working under dangerous conditions. We won’t hang them out to dry.
“This fund will safeguard a future for their skills and communities with new careers and secure, well-paid jobs.”


The Birdhaus™ makes a sleek and elegant addition to any modern home. Included with Birdhaus™ is a bird feeder and three types of seeds to attract many birds. Whether you live in a cozy cabin or city studio, the Birdhaus™ will bring a slice of nature right to your window.
"The house came on time and kinda looked like the pictures." - Nancy P
"What a wonderful product! I love consumer capitalism." - Harold R
"An absolute gem of an experience, great times had by all." - Snoop D
We know not evreryone can affortd to indulge in such a pricy piece of design for their little feathered friends, so we contacted our pal dk builds to show you how to build a DIY budget birdhouse from scratch!


Oh snap!
Hooray!
Registration Number is required!
Address is required!
Landline Number is required!
Mobile Number is required!
Email is required!
Email should be the real one!
Number of Students is required!
Number of Teachers is required!
Sector is required!


Multiplies a scalar times a Tensor
or IndexedSlices
object.
tf.compat.v2.math.scalar_mul
tf.compat.v2.scalar_mul
tf.scalar_mul
tf.math.scalar_mul( scalar, x, name=None )
Intended for use in gradient code which might deal with IndexedSlices
objects, which are easy to multiply by a scalar but more expensive to
multiply with arbitrary tensors.
scalar
: A 0-D scalar Tensor
. Must have known shape.x
: A Tensor
or IndexedSlices
to be scaled.name
: A name for the operation (optional).scalar * x
of the same type (Tensor
or IndexedSlices
) as x
.
ValueError
: if scalar is not a 0-D scalar
.


These do not work the same as Boolean operators; they only apply to one word and they do not modify the relationship between words. Use a + to require a word be present, use a - to exclude a word. If not specified the assumed operator is OR.
Examples:
- nuclear +family (finds any record with nuclear AND family, and any record with family)
- +nuclear +family (finds any record with nuclear AND family)
- nuclear -family (finds any record with nuclear AND NOT family, and any record without family)
- +nuclear -family (finds any record with nuclear AND NOT family)


Data Analyst, Audience Innovation
Kinesso, LLC (Interpublic Group | IPG) -- New York, NY
- Applied machine learning techniques including clustering and lookalike modeling to identify potential customers for clients
- Built 40+ profiling reports to extract actionable insights on audience's demographic, behavioral and psychological characteristics
- Worked with SQL, raw data from third-party providers and other data sources to do ad-hoc analysis on 20+ marketing campaigns
- Optimized modeling by standardizing and integrating steps with daily production pipeline and reduced the processing time by 20%


Logging Guidance for Diagnostics and Debugging¶
Unlike traditional software, debugging utilities available to the machine learning community are scarce. Complicated with deployment pipeline design issues, model weights, model architecture, and unoptimized models, debugging performance issues can be very dynamic in your data science ecosystem. Reviewing a log file can be your first line of defense in pinpointing performance issues with optimizing your inference.
The DeepSparse Engine ships with diagnostic logging so you can capture real-time monitoring information at model runtime and self-diagnose issues. If you are seeking technical support, we recommend capturing log information first, as described below. You can decide what to share, whether certain parts of the log or the entire content.
Note: Our logs may reveal your inference network’s macro-architecture, including a general list of operators (such as convolution and pooling) and connections between them. Weights, trained parameters, or dataset parameters will not be captured. Consult Neural Magic’s various legal policies at https://neuralmagic.com/legal/ which include our privacy statement and software agreements. Your use of the software serves as your consent to these practices.
Performance Tuning¶
An initial decision point to make in troubleshooting performance issues before enabling logs is whether to prevent threads from migrating from their cores. The default behavior is to disable thread binding (or pinning), allowing your OS to manage the allocation of threads to cores. There is a performance hit associated with this if the DeepSparseEngine is the main process running on your machine. If you want to enable thread binding for the possible performance benefit, set:
NM_BIND_THREADS_TO_CORES=1
Note 1: If the DeepSparse Engine is not the only major process running on your machine, binding threads may hurt performance of the other major process(es) by monopolizing system resources.
Note 2: If you use OpenMP or TBB (Thread Building Blocks) in your application, then enabling thread binding may result in severe performance degradation due to conflicts between Neural Magic thread pool and OpenMP/TBB thread pools.
Enabling Logs and Controlling the Amount of Logs Produced by the DeepSparse Engine¶
Logs are controlled by setting the NM_LOGGING_LEVEL
environment variable.
Specify within your shell one of the following verbosity levels (in increasing order of verbosity:
fatal, error, warn,
and diagnose
with diagnose
as a common default for all logs that will output to stderr:
NM_LOGGING_LEVEL=diagnose
export NM_LOGGING_LEVEL
Alternatively, you can output the logging level by
NM_LOGGING_LEVEL=diagnose <some command>
To enable diagnostic logs on a per-run basis, specify it manually before each script execution. For example, if you normally run:
python run_model.py
Then, to enable diagnostic logs, run:
NM_OGGING_LEVEL=diagnose python run_model.py
To enable logging for your entire shell instance, execute within your shell:
export NM_LOGGING_LEVEL=diagnose
By default, logs will print out to the stderr of your process. If you would like to output to a file, add 2> <name_of_log>.txt
to the end of your command.
Parsing an Example Log¶
If you want to see an example log with NM_LOGGING_LEVEL=diagnose
, a truncated sample output is provided at the end of this guide. It will show a super_resolution network, where Neural Magic only supports running 70% of it.
Different portions of the log are explained below.
Viewing the Whole Graph¶
Once a model is in our system, it is parsed to determine what operations it contains. Each operation is made a node and assigned a unique number Its operation type is displayed:
Printing GraphViewer torch-jit-export:
Node 0: Conv
Node 1: Relu
Node 2: Conv
Node 3: Relu
Node 4: Conv
Node 5: Relu
Node 6: Conv
Node 7: Reshape
Node 8: Transpose
Node 9: Reshape
Finding Supported Nodes for Our Optimized Engine¶
After the whole graph is loaded in, nodes are analyzed to determine whether they are supported by our optimized runtime engine. Notable “unsupported” operators are indicated by looking for Unsupported [type of node]
in the log. For example, this is an unsupported Reshape node that produces a 6D tensor:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< unsupported /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/ops.cc:60] Unsupported Reshape , const shape greater than 5D
Compiling Each Subgraph¶
Once all the nodes are located that are supported within the optimized engine, the graphs are split into maximal subgraphs and each one is compiled. To find the start of each subgraph compilation, look for == Beginning new subgraph ==
. First, the nodes are displayed in the subgraph:
Printing subgraph:
Node 0: Conv
Node 1: Relu
Node 2: Conv
Node 3: Relu
Node 4: Conv
Node 5: Relu
Node 6: Conv
Simplifications are then performed on the graph to get it in an ideal state for complex optimizations, which are logged:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:706] == Translating subgraph NM_Subgraph_1 to NM intake graph.
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:715] ( L1 graph
( values:
(10 float [ 1, 64, 224, 224 ])
(11 float [ 1, 64, 224, 224 ])
(12 float [ 1, 64, 224, 224 ])
(13 float [ 1, 32, 224, 224 ])
(14 float [ 1, 32, 224, 224 ])
(15 float [ 1, 9, 224, 224 ])
(9 float [ 1, 64, 224, 224 ])
(conv1.bias float [ 64 ])
(conv1.weight float [ 64, 1, 5, 5 ])
(conv2.bias float [ 64 ])
(conv2.weight float [ 64, 64, 3, 3 ])
(conv3.bias float [ 32 ])
(conv3.weight float [ 32, 64, 3, 3 ])
(conv4.bias float [ 9 ])
(conv4.weight float [ 9, 32, 3, 3 ])
(input float [ 1, 1, 224, 224 ])
)
( operations:
(Constant conv1.bias (constant float [ 64 ]))
(Constant conv1.weight (constant float [ 64, 1, 5, 5 ]))
(Constant conv2.bias (constant float [ 64 ]))
(Constant conv2.weight (constant float [ 64, 64, 3, 3 ]))
(Constant conv3.bias (constant float [ 32 ]))
(Constant conv3.weight (constant float [ 32, 64, 3, 3 ]))
(Constant conv4.bias (constant float [ 9 ]))
(Constant conv4.weight (constant float [ 9, 32, 3, 3 ]))
(Input input (io 0))
(Conv input -> 9 (conv kernel = [ 64, 1, 5, 5 ] bias = [ 64 ] padding = {{2, 2}, {2, 2}} strides = {1, 1}))
(Elementwise 9 -> 10 (calc Relu))
(Conv 10 -> 11 (conv kernel = [ 64, 64, 3, 3 ] bias = [ 64 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Elementwise 11 -> 12 (calc Relu))
(Conv 12 -> 13 (conv kernel = [ 32, 64, 3, 3 ] bias = [ 32 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Elementwise 13 -> 14 (calc Relu))
(Conv 14 -> 15 (conv kernel = [ 9, 32, 3, 3 ] bias = [ 9 ] padding = {{1, 1}, {1, 1}} strides = {1, 1}))
(Output 15 (io 0))
)
)
Determining the Number of Cores and Batch Size¶
This log detail describes the batch size and number of cores that Neural Magic is optimizing against. Look for == Compiling NM_Subgraph
as in this example:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< supported_subgraphs /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/supported/subgraphs.cc:723] == Compiling NM_Subgraph_1 with batch size 1 using 18 cores.
Obtaining Subgraph Statistics¶
Locating == NM Execution Provider supports
shows how many subgraphs we compiled and what percentage of the network we managed to support running:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:122] Created 1 compiled subgraphs.
[nm_ort 7f4fbbd3f740 >DIAGNOSE< validate_minimum_supported_fraction /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/utility/graph_util.cc:321] == NM Execution Provider supports 70% of the network
Viewing Runtime Execution Times¶
For each subgraph Neural Magic optimizes, the execution time is reported by ORT NM EP compute_func:
for each run as follows:
[nm_ort 7f4fbbd3f740 >DIAGNOSE< operator() /home/jdoe/code/nyann/src/onnxruntime_neuralmagic/nm_execution_provider.cc:265] ORT NM EP compute_func: 6.478 ms


In the year of 3442 technology have improved a great amount, but pollution rate from emissions have skyrocketed on Earth causing the Ozone layer to be destroyed. Temperature have risen with frequent Heat Waves and civilization is slowly turning their homes into the ground by building underground towns. There, the survivors have to dig around in order to find food, potatoes and water. About 5 generations later of living underground, the adults planned to bring about 100 young-adults to the surface in order to see if it’s habitable. The group first group of 100 so young-adults will travel through the barren wasteland to report back to their home.


{% extends 'base.html' -%} {% block content %} {% if INCOMPLETE %} NOTE: This page is incomplete or known to be inaccurate. To avoid providing invalid information, we are not showing any diagrams or explanations.
{{ summary }}
Datasheet


{% load flag_extras %}
{% for comment in comments %}
{% with flag=comment.get_active_flag %}
{% if comment.deleted %}
This comment has been deleted by {% if comment.deleted_by_object_owner %}the page owner{% else %}the commenter{% endif %}.
{% elif flag %}
{# XXX funky indentation below, reader beware #}
{# @makyo 2016-11-11 - if anyone finds a better way, by all means... #}
{% if user|can_view_flagged_item:flag %}
{% include 'comment-body-snippet.html' %}
You can view this comment because you are a moderator or participant in this flag. Other users cannot view this comment.
You can view the flag here.
{% else %}
{% endif %}
This comment has been flagged for administrative review.
{# XXX end funky indentation #}
{% else %}
{% include 'comment-body-snippet.html' %}
{% endif %}
You can view this comment because you are a moderator or participant in this flag. Other users cannot view this comment. You can view the flag here.
{% else %}This comment has been flagged for administrative review.


This is a wireframe made using Adobe XD. A mock-up of my current website, this project made me familiar with Adobe XD and important concepts of user experience design.
This project focused on adding media to a website, including video and audio files. I learned the @keyframes rule, how to include timestamps within the video, as well as styling the appearance of the video and audio buttons.
Javascript Project
Go to ProjectFeel free to contact me:


[[template "header"]]
Three Names in a Hat
Three Names in a Hat
Start a game
or
Join a game
[[template "footer"]]


{% extends "base.html" %} {% block body %}
Ringmark is a browser test suite for building apps on the mobile web. You use it by going to http://www.rng.io/ on your mobile phone or tablet.
To participate in Ringmark's development, join the Core Mobile Web Platform Community Group in the W3C.
The core concept behind Ringmark is based around the question of "what do developers need?"
Specifically, Ringmark is comprised of tests of core functionality that web developers need in order to build their apps on mobile. For example, orientation lock, which is important for games. Another example is camera functionality for social apps.
You can think of the rings as straightforward software versioning. Ring 0 represents the base functionality that most mobile phone have today. Ring 1 represents what functionality is needed to unlock the most common apps that developers want to build; specifically, 2D games, music and video apps, and camera apps.
From there on, each subsequent ring represents a slice of features that will unlock the next generation of mobile web apps, based on developer necessity. For example, we expect Ring 2 to include upcoming technology like WebRTC and WebGL.
First, we're establishing a baseline of functionality. With this baseline, developers can understand what functionality is immediately available for their web apps. Consolidating around that baseline can set a common ground for all mobile web browsers going forward and provide a clear and definitive understanding of the market size of the mobile web for a particular app. At a high level, Ring 0 is the intersection of functionality offered by iOS5 and Android 2.2 Froyo, which represents the majority of the smartphone market today. Apps that can be built on Ring 0 devices include content apps like the New York Times, Washington Post Social Reader, and Flixster; games that have a low amount of animating sprites like Words with Friends, Skyscaper City, Poker; music apps like AudioVroom and SoundCloud; and ecommerce apps like Amazon. But these are just a small sample--Ring 0 contains an exhaustive list of features. We encourage you to run Ringmark, scroll down the page, and see what’s included.
The next Ring is focused on enabling as many developers to build their apps as possible, while keeping the feature scope as small as possible. The scope was determined by surveying most popular types of apps that are being built on mobile devices today. Other considerations are also accounted for, like ease of implementation for browser vendors and the degree to which the feature spec is baked.
By enabling Ring 1, we believe that we could enable web developers to build eight or nine out of every ten of the most popular apps that are created today.
The major categories of apps that developers are building today and which we’ve included in Ring 1 are 2D games, audio and video apps, and camera apps. Examples of each include 2D games like Angry Birds and Where's My Water?; audio and video apps like Netflix, Hulu, Spotify, and Turntable; and camera apps like Twitter, Facebook, Instagram, and Foodspotting.
Here are the main features that we’re targeting and will be made available in Ring 1, in roughly prioritized order. More tests will be integrated and the prioritization updated, as the W3C Core Mobile Community Group makes its recommendations and there's better understanding of how each feature should work.
* denotes features that are already available in Android versions Gingerbread (2.3) and above, and iOS 5.
Ringmark doesn't only test if a feature is available--it tests if the feature is actually implemented well. That's why it goes above and beyond basic feature tests and is tests edge cases developers hit.
One area that isn't covered is performance testing. Going forward we plan to tackle this challenge, which is particularly prevalent for games, where some experiences require the ability to test performance for physics, drawing (canvas performance), and garbage collection pauses.
Let's dive into each feature and outline why it's important for Ring 1.
While Ring 1 is primarily focused on enabling mobile apps that are already being built today, Ring 2 is organized to unlock the rest of the most popular apps, as well as new functionality that will enable developers to build entirely new types of apps on mobile.
The concepts in Ring 2 are very new and we expect them to change substantially over the next year.
Here are some features that Ring 2 targets.
We believe this class of features will enable the final portion of market share for apps being built today, including 3D games. It will also unlock new possibilities for rich apps like augmented reality, real-time multiplayer games, and more nascent categories.
To learn more about Ringmark, visit www.rng.io/about. To participate in Ringmark's development, join the Core Mobile Web Platform Community Group in the W3C.
{% endblock %}


Dear Dry,
*The outrageous attacks on Planned Parenthood must stop. [ http://salsa.wiredforchange.com/o/1269/p/dia/action3/common/public/?action_KEY=11538 ]*
This time, heavily-edited sham videos – made by a deceptive group calling itself the Center for Medical Progress – have distorted what Planned Parenthood does. On the basis of these sham videos, CMP, together with Troy Newman, the President of Operation Rescue, are calling for an immediate defunding of Planned Parenthood for the so-called “selling” of fetal tissue. Planned Parenthood does not sell fetal tissue - although with a patient’s consent and in accordance with federal law, clinics may donate tissue to contribute to life-saving medical research - but CMP and Operation Rescue are using every excuse you can think of to defund Planned Parenthood. *We must stop them. [ http://salsa.wiredforchange.com/o/1269/p/dia/action3/common/public/?action_KEY=11538 ]*
Right now the fight is in the Senate. Wanna-be presidents, Senators Ted Cruz (R-TX) and Rand Paul (R-KY) have already called for the defunding of Planned Parenthood.
*We must let our Senators know that they must vote NO on defunding Planned Parenthood [ http://salsa.wiredforchange.com/o/1269/p/dia/action3/common/public/?action_KEY=11538 ]** *and YES to increase funding for women’s health care services. Every dollar the U.S. government spent on publicly funded family planning services in 2010, saved $7.09 in Medicaid and other public expenditures, according to the Guttmacher Institute.
Don’t be fooled by these sham videos! In case you’ve never heard of Operation Rescue, it has been engaging in a campaign of terror, harassment, and intimidation against abortion providers, patients, and clinic staff for years. Its second-in-command, Cheryl Sullenger, spent time in prison for attempting to bomb a San Diego, California clinic in 1988. Her number was found in the car of Scott Roeder, who participated in Operation Rescue events in Wichita, Kansas, and was later found guilty of the murder of Wichita abortion provider Dr. George Tiller. Operation Rescue had moved to Wichita under the leadership of Troy Newman to close Dr. Tiller’s clinic. Newman is now on the board of CMP.
*Email your Senators now and tell them to stand with women, not extremists. [ http://salsa.wiredforchange.com/o/1269/p/dia/action3/common/public/?action_KEY=11538 ]** [ http://salsa.wiredforchange.com/o/1269/p/dia/action3/common/public/?action_KEY=11538 ]* Tell them to vote NO on defunding Planned Parenthood. No more extremist attacks on women’s health.
For Women’s Lives,
Eleanor Smeal


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Simply fried liver is delicious served with mash, greens and a rich, creamy sauce.
For the Madeira reduction, heat a frying pan over a medium heat and add the butter. When the butter is foaming, add the lardons and shallot. Fry for 3-4 minutes, or until the lardons and shallots are golden-brown.
Add the Madeira and carefully tip the pan towards the flame of the hob, so that the alcohol catches alight. When the flame has gone out, cook until the liquid has reduced in volume by three-quarters.
Add the beef stock, stirring well, and cook for 3-4 minutes, or until reduced in volume by half. Then season, to taste, with salt and freshly ground black pepper. Stir in the chervil.
For the mash potatoes, cook the potatoes in a pan of boiling salted water for 12-15 minutes, or until tender. Drain.
Push the potato through a potato ricer into a saucepan. Alternatively, mash with a potato masher, but the finish will be less smooth. Return to the heat, then add the butter and cream and beat to a smooth purée. Season, to taste, with salt and freshly ground black pepper. Place in a piping bag and set aside.
For the calves’ liver, season the livers, to taste, with salt and freshly ground black pepper.
Heat a large frying pan over a high heat and the butter. When the butter is foaming, add the livers and fry on both sides for 1-2 minutes, or until golden-brown all over but still slightly pink in the middle. Remove from the pan and set aside to rest.
For the cavolo nero and wild garlic, add the butter to a hot frying pan and, once hot, add the cavelo nero and cook for a minute. Add the wild garlic and cook for a further two minutes, then season with salt and pepper.
To serve, pipe the mash into four shallow bowls and place the liver and cavolo nero and garlic by the side, and pour over the sauce.


A habit technique that I tried and am very interested in is to display my ability first, prove it to myself then believe in my ability to push forward. What has worked for me is giving myself less time. The more time I have the more I side track or find a way to side track my productivity. Therefore when I give myself a deadline or less time to do something I find it to work. I want to experiment on being able to get really good at something and then building confidence from the skill that I know.
Go back


Gateway
BOT
17-Sep-18 10:01 PM
[dscrdeql] <Buddz1993> Yep exactly my thoughts Im a bag holder and I will be for the next years I will sell obviously a few for a profit but most of my bags are gonna stay loaded
[dscrdeql] <Jellysmash> @tipsy_dev | auscoi yes interesting
[dscrdeql] <Jellysmash> Ithink most of us are bag holders for sure
[dscrdeql] <SwimedDragon> Yeah, I'm holding it for long term
[dscrdeql] <SwimedDragon> At some point I'm going to sell like 1/5 or 1/6 off to get some profit, to enjoy life with my family, but the rest will be holding long term
[dscrdeql] <ghøst> good night
[dscrdeql] <tipsy_dev | auscoi> My formula works so far. Buy something, immediately set 10% to sell at 10x the purchase price. Sit back and hodl 90% pure profit once that 10 clears, which in these early days is not hard.
[dscrdeql] <tipsy_dev | auscoi> Thanks to that giant rush we had at the beginning of this year most of my actual initial investment has been paid back and while I'm not exactly rolling in it with profits at the moment everything I do have to play with is pretty much free of charge at this point.
[dscrdeql] <SwimedDragon> Yeah, I want to get my initial investment back but offcourse enjoying some profit too. I was unlucky to join a bit late and as a newbie
[dscrdeql] <tipsy_dev | auscoi> What's funny is people still consider this late. You're so early man.
[dscrdeql] <tipsy_dev | auscoi> You might not be Alpha test early but you're definitely beta test early
[dscrdeql] <SwimedDragon> Yeah, true, but it just feels like I'm late
[dscrdeql] <tipsy_dev | auscoi> Yeah and when I invested early last year I felt like I was super late to the party too.
[dscrdeql] <CowboyCrypto- Founder> There are cycles.
[dscrdeql] <CowboyCrypto- Founder> You have to be able to spot the cycle and capitalize on them. If ever you are in a position where taking 60% of your investment out could change your life, do it. Taking profits is the single most important part of this game.
[dscrdeql] <tipsy_dev | auscoi> That is true! I was at least smart enough to take enough profits to pay myself back haha!
[dscrdeql] <SwimedDragon> I'm having that in mind now too But it's too bad I bought some things on a price that was too high
[dscrdeql] <SwimedDragon> But I've learned from it
[dscrdeql] <SwimedDragon> And I've said it before, even if I'd lose all my money on crypto, it is way more fun experience then buying lottery tickets
[dscrdeql] <tipsy_dev | auscoi> Never buy when everyone else is :-)
[dscrdeql] <SwimedDragon> Getting late here, gotta go to sleep
[dscrdeql] <SwimedDragon> See ya tomorrow everyone
[dscrdeql] <tipsy_dev | auscoi> Have a good night!
[dscrdeql] <CowboyCrypto- Founder> Later
[dscrdeql] <CowboyCrypto- Founder> I’m workin on a damn Chrysler 300
[dscrdeql] <AndronicusBass-Founder> Time for me to clock in but first
[dscrdeql] <Buddz1993> Clapped as much as I could!! those are the first types of masternodes that I truly feel interested in amazing stuff!


Reject packets with source address <IP_ADDRESS>/<IP_ADDRESS> coming in on any of the eth interfaces:
>>> import iptc
>>> chain = iptc.Chain(iptc.TABLE_FILTER, "INPUT")
>>> rule = iptc.Rule()
>>> rule.in_interface = "eth+"
>>> rule.src = "<IP_ADDRESS>/<IP_ADDRESS>"
>>> target = iptc.Target(rule, "DROP")
>>> rule.target = target
>>> chain.insert_rule(rule)
Inserting a rule to NAT TCP packets going out via eth0:
>>> import iptc
>>> chain = iptc.Chain(iptc.TABLE_NAT, "POSTROUTING")
>>> rule = iptc.Rule()
>>> rule.protocol = "tcp"
>>> rule.out_interface = "eth0"
>>> target = iptc.Target(rule, "MASQUERADE")
>>> target.to_ports = "1234"
>>> rule.target = target
>>> chain.insert_rule(rule)
Mark packets going to <IP_ADDRESS> UDP port 1234 with 0xffff:
>>> import iptc
>>> chain = iptc.Chain(iptc.TABLE_MANGLE, "PREROUTING")
>>> rule = iptc.Rule()
>>> rule.dst = "<IP_ADDRESS>"
>>> rule.protocol = "udp"
>>> match = iptc.Match(rule, "udp")
>>> match.dport = "1234"
>>> rule.add_match(match)
>>> target = iptc.Target(rule, "MARK", revision=2) # latest revision
>>> target.set_mark = "0xffff"
>>> rule.target = target
>>> chain.insert_rule(rule)


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CallStack Class
Definition at line
28
of
CallStack.cpp
Constructors
CallStack
()
Member Functions
std::vector<
SourceLocation
>&
Locations
()
Member Variables
std::vector<
SourceLocation
>
locations
Constructor Details
CallStack Constructor
cmajor::rt::CallStack::CallStack()
Definition at line
37
of
CallStack.cpp
:
38
{
39
}
Declaration at line
31
of
CallStack.cpp
Member Function Details
Locations Member Function
std::vector<
SourceLocation
>& cmajor::rt::CallStack::Locations()
Definition at line
32
of
CallStack.cpp
:
32
{
return
locations
;
}
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Fugitive caught in Kauai woods after 7-day manhunt | Honolulu Star-Advertiser
Authorities nabbed a 48-year-old fugitive in the woods of Kauai after a 7-day manhunt.
The U.S. Marshals Task Force and Kauai police located and arrested Orlando Manguchei in a wooded area above Wailua Monday. He was wanted on a federal warrant for violating terms of the conditions of his supervised release.
In 2017, Manguchei was convicted in federal court on three counts of being a felon in possession of a firearm and/or ammunition. He was sentenced to a 46-month prison term and three years of supervised release, according to a U.S. Marshals Service news release.
Manguchei was released from custody in September.
Authorities recently issued an arrest warrant after he failed to comply with terms of his supervised release.
Through an investigation, law enforcement discovered he was living in the mountains to evade capture and that his close associates provided him shelter, food, supplies, transportation and other resources.
He will be transported back to Oahu to appear in federal court.
In March 2014, authorities arrested him on a state parole arrest warrant following a 23-day manhunt through the woods and mountains of Kauai.


TIFFCMP
Section: User Commands (1)
Updated: September 26, 1994
Index
Return to Main Contents
NAME
tiffcmp - compare two
TIFF
files
SYNOPSIS
tiffcmp
[
options
]
file1.tif file2.tif
DESCRIPTION
Tiffcmp
compares the tags and data in two files created according
to the Tagged Image File Format, Revision 6.0.
The schemes used for compressing data in each file
are immaterial when data are compared-data are compared on
a scanline-by-scanline basis after decompression.
Most directory tags are checked; notable exceptions are:
GrayResponseCurve,
ColorResponseCurve,
and
ColorMap
tags.
Data will not be compared if any of the
BitsPerSample,
SamplesPerPixel,
or
ImageWidth
values are not equal.
By default,
tiffcmp
will terminate if it encounters any difference.
OPTIONS
- -l
-
List each byte of image data that differs between the files.
- -t
-
Ignore any differences in directory tags.
BUGS
Tags that are not recognized by the library are not
compared; they may also generate spurious diagnostics.
SEE ALSO
pal2rgb(1),
tiffinfo(1),
tiffcp(1),
tiffmedian(1),
libtiff(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- OPTIONS
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 01:31:47 GMT, November 23, 1999


GoogleAnalytics-iOS-SDK 2.0beta4 Reference
Google Analytics iOS SDK version 2.0 Beta 4
Copyright 2009 – 2013 Google, Inc. All rights reserved.
================================================================================ DESCRIPTION:
This SDK provides developers with the capability to use Google Analytics to track iOS application usage.
The SDK is packaged as a set of header files and a static library. Get started by adding the header files from the Library subdirectory (GAI.h, GAITracker.h, GAITransaction.h, and GAITransactionItem.h) and libGoogleAnalytics.a to your XCode project. You must also include the CoreData framework in your project.
To use a version of the library with debug symbols intact, link against libGoogleAnalytics_debug.a instead of libGoogleAnalytics.a. This may be useful if you experience exceptions or crashes originating in the SDK.
See the Examples/CuteAnimals application for an illustration of how to use automatic screen tracking, event tracking, and uncaught exception tracking.
You will need a Google Analytics tracking ID to track application usage with the SDK. It is recommended to create an account for each set of applications that are to be tracked together, and to use that account’s tracking ID in each application. To create a new tracking ID, go to your admin panel in Google Analytics and select “New Account”. Under “What would you like to track?”, choose “App” and complete the remainder of the form. When you are finished, click “Get Tracking ID”. The tracking ID will be of the form “UA-” followed by a sequence of numbers and dashes.
You must indicate to your users, either in the app itself or in your terms of service, that you reserve the right to anonymously track and report a user’s activity inside of your app.
Implementation Details:
Tracking information is stored in an SQLite database and dispatched to the Google Analytics servers in a manner set by the developer: periodically at an interval determined by the developer, immediately when tracking calls are made, or manually. A battery efficient strategy may be to initiate a dispatch when the application needs to access the network. Tracking information is dispatched using HTTP or HTTPS requests to a Google Analytics server.
================================================================================ BUILD REQUIREMENTS:
Mac OS X 10.6 or later. XCode with iOS SDK 4.0 or later (iOS SDK 5.0 or later is required to build the included example application).
================================================================================ RUNTIME REQUIREMENTS:
iOS 4.0 or later.
Your app must link the following frameworks: CoreData.framework SystemConfiguration.framework
================================================================================ PACKAGING LIST:
Library/ (contains header and library files to compile and link with) GAI.h GAITrackedViewController.h GAITracker.h GAITransaction.h GAITransactionItem.h libGoogleAnalytics.a libGoogleAnalytics_debug.a Examples/ (contains an example tracked application) Documentation/ (contains documentation)
================================================================================


Java vs C++
问题描述 :
Apologists of Java and C++ can argue for hours proving each other that their programming language is the best one. Java people will tell that their programs are clearer and less prone to errors, while C++ people will laugh at their inability to instantiate an array of generics or tell them that their programs are slow and have long source code.
Another issue that Java and C++ people could never agree on is identifier naming. In Java a multiword identifier is constructed in the following manner: the first word is written starting from the small letter, and the following ones are written starting from the capital letter, no separators are used. All other letters are small. Examples of a Java identifier are javaIdentifier
, longAndMnemonicIdentifier
, name
, nEERC
.
Unlike them, C++ people use only small letters in their identifiers. To separate words they use underscore character ‘_
’. Examples of C++ identifiers are c_identifier
, long_and_mnemonic_identifier
, name
(you see that when there is just one word Java and C++ people agree), n_e_e_r_c
.
You are writing a translator that is intended to translate C++ programs to Java and vice versa. Of course, identifiers in the translated program must be formatted due to its language rules — otherwise people will never like your translator.
The first thing you would like to write is an identifier translation routine. Given an identifier, it would detect whether it is Java identifier or C++ identifier and translate it to another dialect. If it is neither, then your routine should report an error. Translation must preserve the order of words and must only change the case of letters and/or add/remove underscores.
输入:
The input file consists of one line that contains an identifier. It consists of letters of the English alphabet and underscores. Its length does not exceed 100.
输出:
If the input identifier is Java identifier, output its C++ version. If it is C++ identifier, output its Java version. If it is none, output “Error!
样例输入:
sample input #1 long_and_mnemonic_identifier sample input #2 anotherExample sample input #3 i sample input #4 bad_Style
样例输出:
sample output #1 longAndMnemonicIdentifier sample output #2 another_example sample output #3 i sample output #4 Error!
解题代码:
import java.util.*; public class Main { public static void main(String[] args) { Scanner cin = new Scanner(System.in); while(cin.hasNext()) { String str = cin.nextLine(); int type = checkType(str); if(type == -1) System.out.println("Error!"); else if(type == 1) { StringBuffer sb = new StringBuffer(); while(str.indexOf("_") != -1) { int index = str.indexOf("_"); sb.append(str.substring(0, index)); if(index != 0) { String tmp = str.substring(index+1); str = FUpper(tmp); }else str = str.substring(index+1); } sb.append(str); System.out.println(sb.toString()); } else if(type == 2) { StringBuffer sb = new StringBuffer(); for(int i = 0; i < str.length(); i++) { char c = str.charAt(i); if(c >= 97 && c <= 122) { sb.append(c); }else if(c >= 65 && c <= 90) { sb.append('_'); sb.append((char)(c+32)); } } System.out.println(sb.toString()); } } } private static String FUpper(String str) { StringBuffer sb = new StringBuffer(); sb.append((char)(str.charAt(0) - 32)); if(str.length() > 1) sb.append(str.substring(1)); return sb.toString(); } private static int checkType(String str) { if(str.endsWith("_")) return -1; if(str.startsWith("_")) return -1; if(str.indexOf("_") != -1) { for(int i = 0; i < str.length(); i++) { if((str.charAt(i) + 0) >= 65 && (str.charAt(i) + 0) <= 90) return -1; if(str.charAt(i) == '_' && i != 0 && i != str.length()-1) if(str.charAt(i-1) == '_' || str.charAt(i+1) == '_') return -1; } return 1; } if(str.charAt(0) >= 65 && str.charAt(0) <= 90) return -1; return 2; } }
very good solutions.
信息不全
#include <stdio.h>
int main(void)
{
int arr[] = {10,20,30,40,50,60};
int *p=arr;
printf("%d,%d,",*p++,*++p);
printf("%d,%d,%d",*p,*p++,*++p);
return 0;
}
为什么是 20,20,50,40,50. 我觉得的应该是 20,20,40,40,50 . 谁能解释下？
第一段代码中：
next = next ;
明显不对吧，应该是 next =j;才对
这是研发的在线笔试题吗?发现很多C啊，阿里不是JAVA多么？
对于输出格式不用控制？？还是直接使用cout输出。？？
。。。
这个还不错
递归还真是一门学问，学习了
不太懂呀，乡亲们。原理知道代码不会
good !
居然看懂了
不错，感谢分享。
通不过。


"Awesome, So How Do I Get Started?"
nvite.com
meetup.com
@SassMeetup
@SassNews
(And you don't have to do it alone!)
Sassvocating
Lunch & Learn
Video Time
Open Hack
Don't:
- "This is really easy"
- Send a link to the docs
Do:
- "This gets easier with practice"
Don't:
- "This is really easy"
- Assume knowledge
- Send a link to the docs
Do:
- "This gets easier with practice"
- "Did you know about ____?"
Don't:
- "This is really easy"
- Assume knowledge
- Send a link to the docs
Do:
- "This gets easier with practice"
- "Did you know about ____?"
- Show them an example
- Attend other meetups
- Mentorship programs
- Write blog Posts
- Answer questions
- Ask questions
- Join community groups
- Make cool demos and gists
- Listen to podcasts
- Contribute to open source
- Just build and share
You're growing this community


A portrait of the Hiv epidemics in the region by country. In D3.
View ProjectHere is a data-portrait of the Italian Soccer League, "Serie A" for the season 2014-2015. In D3.
View ProjectThe 20 countries with the highest estimated proportion of slaves among their pupulation account for 20.221.900 modern slaves, more than half of the total amount of modern slaves in the World. Data from The Global Slavery Index. In D3.
View ProjectParticipation in the federal Supplemental Assistance Program has grown in recent years. Done with Datawrapper.
View ProjectA map that shows the percentage of men between 68 and 74 years of age that got a PSA test in 2010. Experimenting with Google Fusion Tables
View ProjectA map that shows seismic risk in the continental USA and all significant quakes, from 1964 to 2013, scaled by the amount of shaking. Experimenting with Carto DB
View ProjectThis is exactly the same map as above. This time, tough, I've used Leaflet
View Project


I am thinking of taking up a project on ‘Visual Odometry’ as UGP-1 (Undergraduate Project) here in my fifth semester at IIT-Kanpur. This post is primarily a list of some useful links which will get one acquainted with the basics of Visual Odometry.
The first thing that anyone should read is this wonderful two-part review by Davide Scaramuzza and Friedrich Fraundorfer:
One thing that I did not understand from the above tutorials was the ‘5-point algorithm’ by Nister in 2003. The original paper is here. But, this paper also seemed quite complicated for me to implement without any background, so I moved onto a simpler algorithm, called the ‘8-point algorithm’, which was published a long time ago by Longuet-Higgins. You can find it here. There are some lecture slides which explain this in a simple manner, and you can find them here.
Note, there are more papers that one should read regarding this, most notably:
In my next post, I will hopefully start working on my implementation.


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| actions | 75.56% | 238/315 | 45.28% | 48/106 | 83.72% | 108/129 | 76.4% | 204/267 | |
| components | 90.28% | 223/247 | 74.59% | 91/122 | 84.42% | 65/77 | 94.09% | 223/237 | |
| reducers | 70.59% | 144/204 | 50.29% | 86/171 | 100% | 26/26 | 70.59% | 144/204 | |
| store | 100% | 3/3 | 50% | 2/4 | 100% | 2/2 | 100% | 2/2 | |
| test | 95.89% | 70/73 | 100% | 0/0 | 100% | 0/0 | 95.89% | 70/73 | |
| test/__mock__ | 100% | 2/2 | 100% | 0/0 | 100% | 0/0 | 100% | 2/2 | |
| types | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | 100% | 0/0 | |
| utils | 80% | 4/5 | 50% | 1/2 | 100% | 1/1 | 80% | 4/5 |


Forest of the Lost [ap61], forest, in Imperia, peasants: 147
Last updated Turn #127
Forest of the Lost [ap61], forest, in Imperia, peasants: 147
------------------------------------------------------------------------
19: Stack of nazgul [iv5j], nazgul, arrived from the east.
20: Ryzin [k6c], refugee, with one peasant, went east.
21: Stack of nazgul [iv5j], nazgul, went west.
21: Wild men [hd9i], savages, number: 23, arrived from the west.
23: Wild men [hd9i], savages, number: 23, went east.
26: Pack of wolves [fv4b], wolves, number: 4, arrived from the east.
28: Pack of wolves [fv4b], wolves, number: 4, went west.
29: Asgard Magesbane [z8k], refugee, with one peasant, arrived from the
29: north.
30: The population grows by one peasant.
Routes leaving Forest of the Lost:
North, forest, to Forest of the Lost [ao61], 8 days
East, forest, to Forest of the Lost [ap62], 8 days
South, forest, to Forest of the Lost [aq61], 8 days
West, forest, to Forest of the Lost [ap60], 8 days
Seen here:
Stack of nazgul [gs4n], nazgul, number: 4
Stack of nazgul [fr6y], nazgul, number: 3
Pack of wolves [ey6g], wolf
Pack of wolves [fv4b], wolves, number: 3
Asgard Magesbane [z8k], refugee, with one peasant


Cross Collar Choke Fist In
Loose grips, right hand to ops right lapel, thumb out grip, fingers in, index finger just touching neck,
Left hand goes under right for cross grip to ops left lapel, right hand rotates 180, push fist into artery and tracia
Pull with left arm and move 10 deg to the left, just off line a bit to increase pressure


Name:
E-mail Address:
Telephone: Please do not call me.
What can we help you with? Customer Service Question Comment Consultation Other
Comments:


DANCERS A film review by Ritalynne Brechner Copyright 1987 Ritalynne Brechner
Let me start my saying that I love ballet, adore the ABT company and like GISELLE enough that I was a Super in the ABT's Met performance of it last spring. But I have never been enchanted by ballet on the screen, either movies or TV. I was, however, impressed by TURNING POINT, Baryshnikov's debut film, as it used the ballet to illustrate the lives of the Dancers. Even WHITE NIGHTS used ballet well, employing a very cinematic piece by Twyla Tharp to set the mood for the movie. DANCERS uses the actors to illustrate GISELLE. It doesn't work.
The point of the film seems to be to bring the story of Giselle's seduction and death up-to-date by showing a modern seduction by Baryshnikov's character, who dances Albrecht in the ballet, of a member of the chorus (village) in juxtaposition to her "correct" romance with a member of her own class (a stagehand). There is even an insane scene of both the Giselle-like chorus member and Alessandrs Ferri dancing GISELLE on the stage, very amateurly cut together.
There is a lot of GISELLE in the film. Most of the better parts of the first act, because it is meant to correspond to the "current" situation, and much of Act II (the Willis in the forest trying to dance the men to death) because that's where the viruoso dancing is.
If you enjoy ballet on film, like GISELLE, worship Baryshnikov, and don't mind the dance interrupted for close-ups of his face, then you will probably not resent paying $6 for this film.
One thing is absolutely certain. Even the most die-hard fan will hate this film when it hits the small screen. Only its size saves it now.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Taken from: https://en.wikipedia.org/wiki/Path_integral_formulation
Time slicing definition
For a particle in a smooth potential, the path integral is approximated by zig-zag paths, which in one dimension is a product of ordinary integrals. For the motion of the particle from position <$>x_a$> at time <$>t_a$> to <$>x_b$> at time <$>t_b$>, the time sequence
\[
t_a=t_0
can be divided up into <$>n+1$> little segments <$>t_j - t_{j-1}$>, where <$>j = 1,\dots,n + 1$>, of fixed duration
\[
\epsilon = \Delta t=\tfrac{t_b-t_a}{n+1}\,.
\]
This process is called time-slicing.
An approximation for the path integral can be computed as proportional to
\[
\int\limits_{-\infty}^{+\infty}\,\ldots \int\limits_{-\infty}^{+\infty}\, \ \exp \left(\frac{{\rm i}}{\hbar}\int\limits_{t_a}^{t_b} L(x(t),v(t), t)\,\mathrm{d}t\right)dx_0 \, \ldots \, dx_n
\]
where <$> L(x,v, t) $> is the Lagrangian of the 1d system with position variable <$>x(t)$> and velocity <$>v= \dot{x}(t)$> considered (see below), and <$>dx_j$> corresponds to the position at the <$>j$>th time step, if the time integral is approximated by a sum of n terms.


|
NATS C Client with JetStream and Streaming support
3.7.0-beta
The nats.io C Client, Supported by Synadia Communications Inc.
|
Functions | |
| NATS_EXTERN natsStatus | stanConnOptions_Create (stanConnOptions **newOpts) |
| Creates a stanConnOptions object. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetURL (stanConnOptions *opts, const char *url) |
| Sets the URL to connect to. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetNATSOptions (stanConnOptions *opts, natsOptions *nOpts) |
| Sets the NATS Options to use to create the connection. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetConnectionWait (stanConnOptions *opts, int64_t wait) |
| Sets the timeout for establishing a connection. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetPubAckWait (stanConnOptions *opts, int64_t wait) |
| Sets the timeout for waiting for an ACK for a published message. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetDiscoveryPrefix (stanConnOptions *opts, const char *prefix) |
| Sets the subject prefix the library sends the connect request to. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetMaxPubAcksInflight (stanConnOptions *opts, int maxPubAcksInflight, float percentage) |
| Sets the maximum number of published messages without outstanding ACKs from the server. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetPings (stanConnOptions *opts, int interval, int maxOut) |
| Sets the ping interval and max out values. | |
| NATS_EXTERN natsStatus | stanConnOptions_SetConnectionLostHandler (stanConnOptions *opts, stanConnectionLostHandler handler, void *closure) |
| Sets the connection lost handler. | |
| NATS_EXTERN void | stanConnOptions_Destroy (stanConnOptions *opts) |
| Destroys a stanConnOptions object. | |
NATS Streaming Connection Options.
| NATS_EXTERN natsStatus stanConnOptions_Create | ( | stanConnOptions ** | newOpts | ) |
Creates a stanConnOptions object. This object is used when one wants to set specific options prior to connecting to the NATS Streaning Server
.
After making the appropriate stanConnOptions_SetXXX()
calls, this object is passed to the stanConnection_Connect() call, which will clone this object. After stanConnection_Connect() returns, modifications to the options object will not affect the connection.
The default options set in this call are: url: nats://localhost:4222
connection wait: 2 seconds publish ack wait: 30 seconds discovery prefix: _STAN.discovery
maximum publish acks inflight and percentage: 16384, 50% ping interval: 5 seconds max ping out without response: 3
| newOpts | the location where store the pointer to the newly created stanConnOptions object. |
| NATS_EXTERN natsStatus stanConnOptions_SetURL | ( | stanConnOptions * | opts, |
| const char * | url | ||
| ) |
Sets the URL of the NATS Streaming Server
the client should try to connect to. The URL can contain optional user name and password. You can provide a comma separated list of URLs too.
Some valid URLS:
| opts | the pointer to the stanConnOptions object. |
| url | the string representing the URL the connection should use to connect to the server. |
| NATS_EXTERN natsStatus stanConnOptions_SetNATSOptions | ( | stanConnOptions * | opts, |
| natsOptions * | nOpts | ||
| ) |
The Streaming client connects to the NATS Streaming Server through a regular NATS Connection (natsConnection). To configure this connection create a natsOptions and configure it as needed, then call this function.
This function clones the passed options, so after this call, any changes to the given natsOptions will not affect the stanConnOptions.
| opts | the pointer to the stanConnOptions object. |
| nOpts | the pointer to the natsOptions object to use to create the connection to the server. |
| NATS_EXTERN natsStatus stanConnOptions_SetConnectionWait | ( | stanConnOptions * | opts, |
| int64_t | wait | ||
| ) |
Value expressed in milliseconds.
Default is 2000 milliseconds (2 seconds).
| opts | the pointer to the stanConnOptions object. |
| wait | how long to wait for a response from the streaming server. |
| NATS_EXTERN natsStatus stanConnOptions_SetPubAckWait | ( | stanConnOptions * | opts, |
| int64_t | wait | ||
| ) |
Value expressed in milliseconds.
Default is 30000 milliseconds (30 seconds).
| opts | the pointer to the stanConnOptions object. |
| wait | how long to wait for a response from the streaming server. |
| NATS_EXTERN natsStatus stanConnOptions_SetDiscoveryPrefix | ( | stanConnOptions * | opts, |
| const char * | prefix | ||
| ) |
Default is _STAN.discovery
| opts | the pointer to the stanConnOptions object. |
| prefix | the subject prefix the library sends the connect request to. |
| NATS_EXTERN natsStatus stanConnOptions_SetMaxPubAcksInflight | ( | stanConnOptions * | opts, |
| int | maxPubAcksInflight, | ||
| float | percentage | ||
| ) |
A connection will block stanConnection_Publish() or stanConnection_PublishAsync calls if the number of outstanding published messages has reached this number.
When the connection receives ACKs, the number of outstanding messages will decrease. If the number falls between maxPubAcksInflight * percentage
, then the blocked publish calls will be released.
| opts | the pointer to the stanConnOptions object. |
| maxPubAcksInflight | the maximum number of published messages without ACKs from the server. |
| percentage | the percentage (expressed as a float between ]0.0 and 1.0]) of the maxPubAcksInflight number below which a blocked publish call is released. |
| NATS_EXTERN natsStatus stanConnOptions_SetPings | ( | stanConnOptions * | opts, |
| int | interval, | ||
| int | maxOut | ||
| ) |
Value expressed in number of seconds.
Default is 5 seconds and 3 missed PONGs.
The interval needs to be at least 1 and represents the number of seconds. The maxOut needs to be at least 2, since the count of sent PINGs increase whenever a PING is sent and reset to 0 when a response is received. Setting to 1 would cause the library to close the connection right away.
| opts | the pointer to the stanConnOptions object. |
| interval | the number of seconds between each PING. |
| maxOut | the maximum number of PINGs without receiving a PONG back. |
| NATS_EXTERN natsStatus stanConnOptions_SetConnectionLostHandler | ( | stanConnOptions * | opts, |
| stanConnectionLostHandler | handler, | ||
| void * | closure | ||
| ) |
This callback will be invoked should the client permanently lose contact with the server (or another client replaces it while being disconnected). The callback will not be invoked on normal stanConnection_Close().
| opts | the pointer to the stanConnOptions object. |
| handler | the handler to be invoked when the connection to the streaming server is lost. |
| closure | the closure the library will pass to the callback. |
| NATS_EXTERN void stanConnOptions_Destroy | ( | stanConnOptions * | opts | ) |
Destroys the stanConnOptions object, freeing used memory. See the note in the stanConnOptions_Create() call.
| opts | the pointer to the stanConnOptions object to destroy. |


| Top |
GObject ╰── GInitiallyUnowned ╰── GtkWidget ╰── GtkContainer ╰── GtkBin ╰── GtkWindow ╰── GtkDialog ╰── GtkFontSelectionDialog
The GtkFontSelectionDialog widget is a dialog box for selecting a font.
To set the font which is initially selected, use
gtk_font_selection_dialog_set_font_name()
.
To get the selected font use gtk_font_selection_dialog_get_font_name()
.
To change the text which is shown in the preview area, use
gtk_font_selection_dialog_set_preview_text()
.
In GTK+ 3.2, GtkFontSelectionDialog has been deprecated in favor of GtkFontChooserDialog.
The GtkFontSelectionDialog implementation of the GtkBuildable interface exposes the embedded GtkFontSelection as internal child with the name “font_selection”. It also exposes the buttons with the names “ok_button”, “cancel_button” and “apply_button”.
GtkWidget *
gtk_font_selection_dialog_new (const gchar *title
);
gtk_font_selection_dialog_new
has been deprecated since version 3.2 and should not be used in newly-written code.
Creates a new GtkFontSelectionDialog.
gchar *
gtk_font_selection_dialog_get_font_name
(GtkFontSelectionDialog *fsd
);
gtk_font_selection_dialog_get_font_name
has been deprecated since version 3.2 and should not be used in newly-written code.
Gets the currently-selected font name.
Note that this can be a different string than what you set with
gtk_font_selection_dialog_set_font_name()
, as the font selection widget
may normalize font names and thus return a string with a different
structure. For example, “Helvetica Italic Bold 12” could be normalized
to “Helvetica Bold Italic 12”. Use pango_font_description_equal()
if you want to compare two font descriptions.
gboolean gtk_font_selection_dialog_set_font_name (GtkFontSelectionDialog *fsd
,const gchar *fontname
);
gtk_font_selection_dialog_set_font_name
has been deprecated since version 3.2 and should not be used in newly-written code.
Sets the currently selected font.
const gchar *
gtk_font_selection_dialog_get_preview_text
(GtkFontSelectionDialog *fsd
);
gtk_font_selection_dialog_get_preview_text
has been deprecated since version 3.2 and should not be used in newly-written code.
Gets the text displayed in the preview area.
void gtk_font_selection_dialog_set_preview_text (GtkFontSelectionDialog *fsd
,const gchar *text
);
gtk_font_selection_dialog_set_preview_text
has been deprecated since version 3.2 and should not be used in newly-written code.
Sets the text displayed in the preview area.
GtkWidget *
gtk_font_selection_dialog_get_cancel_button
(GtkFontSelectionDialog *fsd
);
gtk_font_selection_dialog_get_cancel_button
has been deprecated since version 3.2 and should not be used in newly-written code.
Gets the “Cancel” button.
Since: 2.14
GtkWidget *
gtk_font_selection_dialog_get_ok_button
(GtkFontSelectionDialog *fsd
);
gtk_font_selection_dialog_get_ok_button
has been deprecated since version 3.2 and should not be used in newly-written code.
Gets the “OK” button.
Since: 2.14
GtkWidget *
gtk_font_selection_dialog_get_font_selection
(GtkFontSelectionDialog *fsd
);
gtk_font_selection_dialog_get_font_selection
has been deprecated since version 3.2 and should not be used in newly-written code.
Retrieves the GtkFontSelection widget embedded in the dialog.
Since: 2.22


Use this form to upload a CSV file. This will be inserted into the current database as a new table.
Use this field to name your new table.


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{% extends "master.html" %} {% block content %}
Welcome to my personal blog! I am a computer programmer in Year 13 studing Maths, Further Maths and Physics.
{{ v['post'][0]|safe }}


The following table lists the shell scripts and batch files for launching Proximity applications. You can also execute queries and run scripts from the Proximity Database Browser. See the text following this table for additional details on selected parameters.
| Application | Linux/Mac OS X shell script | Windows batch file | Parameters | Reference |
|---|---|---|---|---|
| Proximity Database Browser | gui.sh | gui.bat | host:port |
Ch. 4 |
| Query runner | query.sh | query.bat | host:port query-file output-container input-container (opt.)
|
Ch. 5 |
| Python script runner | script.sh | script.bat | host:port script-file |
Ch. 6 |
| XML data import | import-xml.sh | import-xml.bat | host:port input-filename |
Ch. 3 |
| XML data export | export-xml.sh | export-xml.bat | host:port output-filename exportType (opt.)exportSpec (opt.)
|
Ch. 3 |
| plain text data import | import-text.sh | import-text.bat | host:port input-filename |
Ch. 3 |
| plain text data export | export-text.sh | export-text.bat | host:port output-filename exportType (opt.)exportSpec (opt.)
|
Ch. 3 |
| Database management utilities | db-util.sh | db-util.bat | host:port command |
Ch. 3 |
For query.sh, specifying the input container
is optional; the input container defaults to the root container (the
entire database) if it is omitted. To explicitly specify the input
container, Proximity uses a path-like syntax that reflects the
container hierarchy for the specified container. Container names are
separated by forward slashes with the initial /
representing the root container, e.g.,
“/1d-clusters/samples
”.
For export-xml.sh, specifying the exportType and exportSpec is optional; the export defaults to the full database if they are omitted.
For db-util.sh, permitted commands are
All Proximity shell scripts and batch files include
proximity.jar.
You must rebuild proximity.jar
to have source code changes available when you use the Proximity shell
scripts and batch files.


If state search shows no results, try searching comma-separated zip codes near you.
In the "city" input just write "44022,44862,44890,45065" for example.
For best results, only use ~10 zips, and choose numbers near your zip code.
| State | Zip Codes |
| OH | 43014,43018,43041,43044,43066,43149,43153,43155,43160,43164,43226,43307,43333,43334,43345,43420,43432,43438,43506,43516,43551,43556,43558,43620,43720,43725,43756,43764,43786,43788,43805,43907,43909,43938,44022,44044,44046,44093,44095,44193,44224,44260,44274,44424,44492,44512,44628,44657,44671,44675,44677,44804,44815,44833,44838,44849,44862,44890,45065,45069,45131,45148,45155,45245,45250,45311,45312,45362,45387,45481,45506,45621,45673,45674,45680,45682,45690,45693,45695,45746,45764,45772,45819,45822,45830,45845,45854,45855,45874 |
| MA | 01059,01115,01203,01378,01537,01812,01887,02093,02151,02554,02575,02642,02717,02738 |
| RI | 02809,02887,02801,02815,02874 |
| CT | 06897,06180,06057,06442,06512,06249,06076,06239 |
| NY | 10924,10109,10314,10454,10553,10579,10986,11220,11405,11558,11960,12017,12043,12082,12095,12164,12205,12304,12429,12438,12454,12553,12574,12732,12828,12833,12836,12945,12960,12978,13035,13040,13068,13113,13132,13159,13338,13339,13415,13420,13473,13611,13662,13730,13743,13904,14037,14040,14063,14109,14221,14475,14477,14521,14556,14589,14664,14708,14783,14810,14824,14838,14857 |


Corels are ‘Certifiably Optimal RulE ListS’. They are short and simple human interpretable rule lists created on categorical data.
tidycorels::tidy_corels()
converts your dataframe into two text files in the format the R package corels expects. It returns the Corels rules as well as applying them to your dataframe using data.table::fifelse()
code.
Your dataframe is also returned with fewer columns ready to be used in an insightful alluvial plot. This reveals intuitively both how the Corels rules are applied to your dataframe, and where the classification is correct or incorrect. This dataframe only includes the true label, the columns used in the Corels rules, and a label column the rules create.
devtools::install_github("billster45/tidycorels")
Let’s use tidycorels to classify cars in datasets::mtcars
as either automatic or manual.
Using recipes
functions in tidymodels
, columns with continuous values are binned or discretised into categorical data. Each bin in each column is given its own 0/1 binary column using `recipes::step_dummy(). This is sometimes called one-hot encoding.
Corels also requires the label column is split into two columns representing each class. In this example, am
is the label for Corels to classify where 0 = automatic and 1 = manual gears.
library(tidymodels) library(corels) library(tidycorels) library(easyalluvial) library(parcats) mtcars_recipe <-recipes::recipe(am ~ ., data = datasets::mtcars) %>% # 1 discretise continous variables into bins recipes::step_discretize(mpg, disp, hp, drat, wt, qsec, min_unique = 1) %>% # 2 convert each value of each predictor into its own 0/1 binary column recipes::step_mutate_at(recipes::all_predictors(), fn = list(~ as.factor(.))) %>% recipes::step_dummy(recipes::all_predictors(), one_hot = TRUE) %>% # 3 convert each value of the outcome into its own 0/1 binary column recipes::step_integer(recipes::all_outcomes(), zero_based = TRUE) %>% # ensure outcome is 0/1 rather than words recipes::step_mutate_at(recipes::all_outcomes(), fn = list(~ as.factor(.))) %>% recipes::step_dummy(recipes::all_outcomes(), one_hot = TRUE) # Train the recipe mtcars_recipe_prep <- recipes::prep(mtcars_recipe, training = datasets::mtcars, retain = TRUE) # Extract the pre-processed data mtcars_preprocessed <- recipes::juice(mtcars_recipe_prep)
The prepared dataframe can now be used in the tidycorels::tidy_corels()
function. We need to specify the names of the two columns that represent the label, and the delimiter that seperates the column name from the value each dummy column represents. The default seperator used by `recipes::step_dummy() is an underscore.
All other arguments of corels::corels()
are available to set, other than the following that are fixed by tidycorels::tidy_corels()
: rules_file
(generated from df
), labels_file
(generated from df
), log_dir
(set as getwd()
), verbosity_policy
(set as “minor”).
corels_mtcars <- tidycorels::tidy_corels( df = mtcars_preprocessed, label_cols = c("am_X0", "am_X1"), value_delim = "_", run_bfs = TRUE, calculate_size = TRUE, run_curiosity = TRUE, regularization = 0.01, curiosity_policy = 3, map_type = 1 )
A list of useful objects is returned. First let’s view the rules captured from the console output of corels::corels()
.
corels_mtcars$corels_console_output[4:8]
## [1] "OPTIMAL RULE LIST" "if ({gear:X3}) then ({am:X0})"
## [3] "else if ({wt:bin1}) then ({am:X1})" "else if ({vs:X0}) then ({am:X1})"
## [5] "else ({am:X0})"
And see those rules converted to data.table::fifelse()
code.
corels_mtcars$corels_rules_DT
A dataframe of just the true label, the columns used in the Corels rules, and the Corels label from those rules is also available. The columns have been ordered in the order of the Corels rules so that they work well in an alluvial plot.
corels_mtcars$alluvial_df %>% easyalluvial::alluvial_wide(stratum_width = 0.2) + ggplot2::theme_minimal() + ggplot2::labs( title = "Corels if-then-else logic", subtitle = " From truth (far left column) to Corels classification (far right column)" )
The leftmost column is the true label for each car, either automatic (0) or manual (1). The rightmost column is the Corels classification label after applying the rules from left to right.
The alluvial plot clearly shows which rules are important in the classifcation and exactly how the classifiation is arrived at. For example, if we follow one green path of manual cars (am_X1 = 1), they have more than 3 gears (gear_X3 = 0), low weight (wt_bin_1 ==1) a V shaped engine (vs_x0 = 0).
We can also creat an interactive version of the alluvial plot.
p <- easyalluvial::alluvial_wide(corels_mtcars$alluvial_df) parcats::parcats(p, marginal_histograms = TRUE, data_input = corels_mtcars$alluvial_df )
Either of the dataframes returned can be used to create a confusion matrix to examine performance.
Use tidycorels::predict_corels()
to apply the Corels rules to a new dataframe (e.g. test data). It also returns the smaller data frame intended for an alluvial plot. Examples can be found in the Articles section.


SNL’s Cut-for-Time Open Mic Invites You to Feel Unsafe in the Loneliest City in the World
by Halle KieferIn this week’s cut-for-time Saturday Night Live sketch, Bowen Yang and Heidi Gardner host the pop stars of the future, fresh off the plane from their cult compounds in Pennsylvania and rocking the Los Angeles open mic circuit. Kristen Stewart and Kate McKinnon give Tegan and Sara a run for their money. Good thing for them, Chastity and Prudence can only run as far as the fence that surrounded their Village-style ahistorical enclosure.
Meanwhile, Kyle Mooney’s Johnny Butters and his world-renown gutters need no introduction, and DJ/parkour artist Missile is hard at work on his book adaptation of the Harry Potter movies. You can see these beautiful freaks any day of the week in Silverlake. Or, if you live anywhere else on the planet, you can check out Ryan Murphy’s The Fence on FX, FXX, and FXNow, just as soon as he can get the fence to sign a few waivers.


So you're probably asking - how does this work? It's quite simple, actually. Just like with any counseling, you and your counselor will discuss any challenges you're facing or might face, and you will develop some positive ways to reach your goals. The difference is that you don't need to come to an office and you don't need to schedule any time out of your day. Instead, you can write whenever it's convenient for you, and move forward at a pace that you feel comfortable with.
So you're probably asking - how does this work? It's quite simple, actually. Just like with any counseling, you and your counselor will discuss any challenges you're facing or might face, and you will develop some positive ways to reach your goals. The difference is that you don't need to come to an office and you don't need to schedule any time out of your day. Instead, you can write whenever it's convenient for you, and move forward at a pace that you feel comfortable with.
Please note that your messages with your counselor aren't in real-time but it won't be too long before you get a response from your counselor. Your sessions also have another benefit - you can re-read and reflect on our conversations at any time. Often, going back to a conversation can be very helpful and it's one of those things that will help you and your counselor move forward together.
What happens if you feel you and your counselor don't connect well? While we try to match you with the best counselor for your needs, that can certainly happen! There are over 13,000therapists who work on this platform and you can switch to work with another counselor at any time.
Thank you for trying BetterHelp, and for joining the community of 1,200,000 people who took steps to make a positive change in their lives. We'll now leave it to you and your counselor to get started!
BetterHelp Team


inputEx - DataTable Usage
Basic DataTable creation
Use the following code to create a basic inputEx DataTable.
" + oData + "
InPlaceEditing
InPlaceEditing Advanced


409_xi-jun-xiao-zi.py
import math
from typing import List
class Solution:
def numPoints(self, points: List[List[int]], r: int) -> int:
r2 = r ** 2
def dist2(point1, point2):
return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2
def inCircle(center, point):
return dist2(center, point) <= r2
def getCenter(point1, point2):
if point2[0] < point1[0]:
return getCenter(point2, point1)
d = (r2 - dist2(point1, point2) / 4) ** 0.5
mid = [(point1[0] + point2[0]) / 2, (point1[1] + point2[1]) / 2]
if point2[0] == point1[0]:
return [mid[0] + d, mid[1]]
angle = math.atan((point2[1] - point1[1]) / (point2[0] - point1[0]))
return [mid[0] + d * math.sin(angle), mid[1] - d * math.cos(angle)]
n = len(points)
if n == 1:
return 1
ans = 1
for i in range(n - 1):
for j in range(i + 1, n):
if dist2(points[i], points[j]) > 4 * r2:
continue
center = getCenter(points[i], points[j])
step = 0
for k in range(n):
if k == i or k == j:
step += 1
elif dist2(center, points[k]) - 0.00000001 <= r2:
step += 1
ans = max(ans, step)
return ans