Society and Disease Management is an investigation of how infectious diseases spread through populations and the economic and societal impacts of these diseases. An examination of the biological mechanisms of diseases and how disease burden is managed in communities will guide the rational for local and global health approaches to managing disease interventions. Class foundations include topics from biology, economics, systems theory, and health policy. Assessments will incorporate applications from all of these fields.
By the end of this course, students should have gained sufficient knowledge to:
| Week | Topic |
|---|---|
| 1 | Course orientation activities and introduction to the course. Meet your classmates review course modules. |
| 2 | Discuss the Global Goals website; identify the layers and players involved in health care and those who work to improve health conditions; example disease study - measles. |
| 3 | We focus on management, leadership, social entrepreneurship, and economics. How do philosophy, political theory, and fit into the practice of cultural disease management? For those whose life needs have been met, are they morally obligated to be optimistic? |
| 4 | What is systems thinking? Practice this at the personal and larger levels. |
| 5 | Basics of the human immune system — innate, adaptive, and passive immunity; the 10 oldest known human diseases, false remedies of the past and present. |
| 6 | Viral diseases and vaccinations – Smallpox as the story of success; explore viruses of today-HIV, Ebola, Zika.; create an awareness campaign poster. |
| 7 | Vaccination production, vaccination rates in the US, Vaccination myths; the anti-vaccine movement – national and international |
| 8 | Bacterial diseases –Introduction to historical evidence of bacterial disease in humans, social and economic issues relating to Black Death, tuberculosis, and cholera, bacterial vaccines, and future concerns about bacterial diseases. |
| 9 | Antibiotics & antimicrobials, their classification, how they work, and issues relating to antibacterial resistance. |
| 10 | Diseases caused by protists - history of malaria, treatment, epidemiology, and vaccines. |
| 11 | What is disease burden? How have infectious diseases shaped human populations? |
| 12 | Systems thinking revisited - tackle an issue on the national or global level. |
| 13 | Final course portfolio project. |
All readings and other course materials will be provided in our Canvas course.
This course is really one of those "you get out of it what you put into it" type of experiences. We do not aim to change your opinions but we do hope to help you consider national and international health issues at a deeper level than if you had not taken this course. Discussion boards will be a large part of this class. There will also be reflection/reaction essays to complete, as well as short videos and interactive activities from various websites for which you can earn points toward your final point total. We ask you to note topics or websites of interest as you research the various topics for this course. We strongly encourage suggestions for topics, assignments, and projects from you!
| Activity | % | Description |
|---|---|---|
| Short Reflections | 25% | These will be assembled into a portfolio for you to use as a personal reference outside of this course |
| Discussion Forums | 50% | Where you will post your thoughts and then reply to those of other group members |
| Online Activities | 15% | Includes website animations |
| Quizzes | 10% | Can be completed in Canvas |


Patches¶
Normally, TreeCorr is used to compute the auto-correlation function
of data in a single input Catalog
or the cross-correlation of data
from two Catalogs
.
However, there are a number of reasons that it might make sense to
divide up a region into several smaller patches for computing the
correlation function:
To compute a more accurate covariance matrix. There are a number of ways to compute more accurate covariance estimates from the data than the default method. All of them require dividing up the data into patches and doing different things with the patch-to-patch correlations. See Covariance Estimates for details.
To save memory. The entire data set might be too large to fit in memory, so you might want to divide it up so less data is required to be in memory at a time. See Reducing Memory Use below.
To split the job among multiple machines. TreeCorr does a good job of utilizing many cores on a single machine using OpenMP. However, for very large jobs, you may want to also split the work among more than one node on a cluster. The most effective way to do this is to split the data into patches. See Using MPI below.
To run k-means on some data set for non-correlation reasons. TreeCorr happens to have an extremely efficient implementation of the k-means algorithm. So if you want to perform k-means clustering on some data that can be represnted in a TreeCorr
Catalog
(i.e. only 2 or 3 spatial dimensions), then using TreeCorr may be a particularly efficient way to do the clustering. See Running K-Means below.
Below we describe how to split up an input Catalog
into patches and
a few things you can do with it once you have done so.
Defining Patches on Input¶
The most straightforward way to define which object goes in which patch is to just tell TreeCorr the patch number for each object explicitly.
If passing in numpy arrays for everything, then just pass in a patch
parameter with integer values indicating the patch number.
If reading in data from a file, then set a patch_col
to use which
should have these values.
The next simplest way to define the patches is to tell TreeCorr how many
patches you want using npatch
.
TreeCorr will then run the K-Means algorithm to split up the full area
into this many patches.
See Running K-Means below for more details.
Finally, to make sure multiple catalogs are using the same definition for
where patches are on the sky, you would probably want to have a single
set of patch centers and have all of your catalogs use that via
the patch_centers
option. See Using Patch Centers below for details.
Running K-Means¶
One standard way to split up a set of objects into roughly equal area patches is an algorithm called k-means clustering.
The basic idea of the algorithm is to divide the points \(\vec x_j\) into \(k\) patches, \(S_i\), such that the total “inertia” is minimized. Inertia \(I_i\) of each patch is defined as follows:
where \(\vec \mu_i\) is the center of each patch:
and \(N_i\) is the number of points assigned to patch \(S_i\). The k-means algorithm finds a solution that is a local minimum in the total inertia, \(\sum_i I_i\), or equivalently the mean inertia \(\langle I_i \rangle\) of all the patches.
This definition of inertia is a relatively good proxy for area on the sky that has objects, so this algorithm is a good choice for dividing up a catalog of astronomical objects into fairly uniform patches.
To use the TreeCorr implementation of k-means, simply
set the npatch
parameter in the Catalog
constructor to specifiy
how many patches you want TreeCorr to split the data into.
Note
If the input catalog has (ra, dec, r) positions, then the patches will be made using only the ra,dec location on the sky, not the full 3-D positions. This is usually what you want for making patches over an astronomical survey area. If you really want to make patches according to 3-D clustering of points, then you should input x,y,z values instead.
There are also two additional options which can affect how the k-means algorithm runs:
kmeans_init
specifies what procedure to use for the initialization of the patches. Options are:‘random’ = Choose initial centers randomly from among the input points. This is the traditional k-means initialization algorithm.
‘kmeans++’ = Use k-means++, an improved algorithm by Arthur and Vassilvitskii with a provable upper bound for how close the final result will be to the global minimum possible total inertia.
‘tree’ = Use the upper layers of the TreeCorr ball tree to define the initial centers. This is the default, and in practice, it will almost always yield the best final patches. (See Comparison with other implementations below.)
kmeans_alt
specifies whether to use an alternate iteration algorithm similar to k-means, which often produces somewhat more uniform patches.This alternate algorithm specifically targets minimizing the standard deviation of the inertia rather than the mean inertia, so it tends to lead to patches that have a smaller final size variation than the regular k-means algorithm.
This is not the default algorithm because it is not provably (at least by me) stable. It is possible that the iteration can get into a failure mode where one patch will end up with zero objects. The regular k-means provably cannot fail in this way.
So if you care especially about having very uniform patch sizes, you might want to try this option, but be careful about inspecting the results that they don’t look crazy.
See also Field.run_kmeans
, which has more information about these options,
where these parameters are called simply init
and alt
respectively.
Comparison with other implementations
Before implementing k-means in TreeCorr, I investigated what other options there were in the Python landscape. I found the following implementations:
I made a notebook comparing the different algorithms using a random million galaxies from the DES SV (Dark Energy Survey, Science Verification) footprint, chosen because it is a real-life use case that has some ratty edges to deal with, so it seemed like it would provide a reasonable challenge without being crazy.
The ideal patches would be essentially uniform in size according to some measure of the effective area of the patch. To make things simple, I just used the inertia as my proxy for area, since that’s the thing that k-means algorithms natively work with.
However, we don’t really care about the total inertia being minimized. For most purposes here, we really want the patches to be all close to the same size. So rather than the total inertia, my metric for quality was the rms variation of the intertia (aka the standard deviation).
Fortunately, the process of minimizing the total inertia does tend to select patches with small rms variation as well, but it is worth noting that this is not directly targeted by the normal k-means algorithm. And furthermore, the k-means algorithm almost never finds the true global minimum inertia. The quality of the local minimum depends pretty strongly on the choice of initial centers to seed the iterative part of the algorithm.
Comparing the results of the various k-means implementations, I found that they all tend to be either fairly slow, taking a minute or more for just 1 million objects, or they have very high rms variation in the inertia. I reran each code multiple times using a different random million objects selected from the original catalog (of around 16 million objects). Here is a scatter plot of the time vs rms variation in the inertia for the various codes.
Since there was no existing implementation I was particularly happy with, I implemented it myself in TreeCorr. It turns out (not surprisingly) that the ball tree data structure that TreeCorr uses for efficient calculation of correlation functions also enables a very efficient implementation of the k-means iteration step. Furthermore, the quality of the k-means result is pretty dependent on the choice of the initial centers, and using the ball tree for the initialization turns out to produce reliably better results than the initialization methods used by other packages.
The big red dots in the lower left corner are the TreeCorr implementation of the standard k-means clustering algorithm. It typically takes about 1 or 2 seconds to classify these 1 million points into 40 patches, and the rms variation is usually less than any other implementation.
The notebook also includes plots of total inertia, variation in size according to the mean d^2 rather than sum, and variation in the counts. The TreeCorr algorithm tends to be the best k-means implementation according to any of these metrics.
In addition, you can see some slightly smaller orange dots, which have even lower rms variation but take very slightly longer to run. These are the alternate algorithm I mentioned above. This alternate algorithm is similar to k-means, but it penalizes patches with a larger-than-average inertia, so they give up some of their outer points to patches with smaller inertia. In other words, it explicitly targets making the rms variation as small as possible. But in practice, it is not much worse in terms of total inertia either.
The alternate algorithm is available using alt=True
in Field.run_kmeans
.
I left this as a non-default option for two reasons. First, it’s not actually the real
k-means, so I didn’t want to confuse people who just want to use this for regular k-means
clustering. But second, I’m not completely sure that it is always stable. There is a free
parameter in the penalty function I chose, which I set to 3. Setting it to 4 gave even better
results (slightly), but at 5 the algorithm broke down with neighboring patches trading
escalating numbers of points between each other until one of them had no points left.
I couldn’t convince myself that 4 was actually a magic number and not just the particular value for this data set. So 3 might be safe, or there might be data sets where that also leads to this runaway trading failure mode. I know the regular k-means algorithm can’t get into this mode, so it’s always safe. Therefore, I think it’s better to force the user to intentionally select the alternate algorithm if they really care about having a low rms size variation, with the normal algorithm being the backup if the alternate one fails for them.
Using Patch Centers¶
If you are doing a cross correlation, and you want to use patches for computing
a jackknife covariance for instance, you cannot
just set npatch
in both and expect it to work properly. The two catalogs
would end up with patches arranged very differently on the sky. Patch 2
for one catalog would not be in the same place as patch 2 in the other one.
Thus, the jackknife calculation would be messed up.
Instead, you should define the patches using one of the two (or more)
catalogs you want to work with,
and then use its patch centers
attribute as the patch_centers
parameter when building the other catalog(s):
>>> cat1 = treecorr.Catalog(cat_file1, config1, npatch=N)
>>> cat2 = treecorr.Catalog(cat_file2, config2, patch_centers=cat1.patch_centers)
You can also save the patches to a file using Catalog.write_patch_centers
and use that file name as the patch_centers
parameter:
>>> cat1 = treecorr.Catalog(cat_file1, config1, npatch=N)
>>> cat1.write_patch_centers(cen_file)
>>> cat2 = treecorr.Catalog(cat_file2, config2, patch_centers=cen_file)
With either method, cat2 will have patches assigned according to which patch center each object is closest to.
Reducing Memory Use¶
One reason you might want to use patches is if the full Catalog
doesn’t fit
in memory. (Or possibly by itself it fits, but when performing the correlation function,
the additional memory from building the tree overflows the memory.)
Then you can potentially perform the calculation over patches
with less data loaded into memory at any given time.
The overall procedure for doing this is as follows:
First define your patch centers using some smaller
Catalog
, which does fit in memory. This could be a catalog over the same survey geometry, which is intrinsically sparser (say a catalog of red sequence galaxies or clusters or even stars). Or it could be the large catalog you want to use, but sampled using theevery_nth
option to read in only a fraction of the rows. Run k-means on the smaller catalog and write the patch_centers to a file, as describe above.Set up a directory somewhere that TreeCorr can use as temporary space for writing the individual patch files.
Define the full
Catalog
, specifying to use the above centers file for thepatch_centers
and the temp directory assave_patch_dir
.Make sure not to do anything that requires the catalog be loaded from disk. TreeCorr will delay doing the actual load until it needs to do so. Here, we want to make sure it never loads the full data.
Run the
process
function (for whichever correlation type you need) using thelow_mem=True
option.
Here are some worked examples. First, an auto-correlation of a single large shear catalog:
>>> small_cat = treecorr.Catalog(cat_file, config, every_nth=100, npatch=N)
>>> small_cat.write_patch_centers(cen_file)
>>> del small_cat
>>> full_cat = treecorr.Catalog(cat_file, config, patch_centers=cen_file,
... save_patch_dir=tmp_dir)
>>> gg = treecorr.GGCorrelation(ggconfig)
>>> gg.process(full_cat, low_mem=True)
Second, a cross-correlation, where the lens catalog is small enough not to be a problem, but the source catalog is too large to hold in memory:
>>> len_cat = treecorr.Catalog(lens_file, lens_config, npatch=N)
>>> source_cat = treecorr.Catalog(source_file, source_config,
... patch_centers=lens_cat.patch_centers,
... save_patch_dir=tmp_dir)
>>> ng = treecorr.NGCorrelation(ngconfig)
>>> ng.process(lens_cat, source_cat, low_mem=True)
In both cases, the result should be equivalent to what you would get if you could hold the catalogs fully in memory, but the peak memory will be much lower. The downside is that this usage will generally take somewhat longer – probably something like a factor of 2 for typical scenarios, but this of course depends heavily on the nature of your calculation, how fast your disk I/O is compared to your CPUs, and how many cores you are using.
Note
Technically, the save_patch_dir
parameter is not required, but it is
recommended. The first time a given patch is loaded, it will find the right
rows in the full catalog and load the ones you need. If you give it a
directory, then it will write these data to disk, which will make subsequent
reads of that patch much faster.
Warning
One caveat with respect to the save_patch_dir
parameter is that if there
are already files present in the directory with the right names, then it
will go ahead and use them, rather than make new patch files. This is usually
an efficiency gain, since repeated runs with the same data will already have
the right patch files present. However, if you use the same file name and
save directory for a different data set, or if you make new patches for the
same input file, then TreeCorr won’t notice.
To get TreeCorr to make new patch files, you can either manually delete everything in the save directory before starting, or (easier) call:
>>> cat.write_patch_files()
which will overwrite any existing files that may be there with the same names.
Using MPI¶
Another use case that is enabled by using patches is to divide up the work of calculating a correlation function over multiple machines with MPI using mpi4py.
For this usage, the process
functions take an optional comm
parameter. When running in an MPI job, you can pass in comm=MPI.COMM_WORLD
,
and TreeCorr will divide up the work among however many nodes you are using.
The results will be sent back the the rank 0 node and combined to produce the
complete answer:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # File name: run_with_mpi.py from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() # Define stuff fname = ... centers_file = ... config = ... ggconfig = ... # All machines read the catalog cat = treecorr.Catalog(fname, config, patch_centers=centers_file) # All machines define the same correlation object gg = treecorr.GGCorrelation(ggconfig) # Pass the comm object to the process function gg.process(cat, comm=comm) # rank 0 has the completed result. if rank == 0: # Probably do something more interesting with this now... print('xip = ',gg.xip) |
You would then run this script using (e.g. with 4 processes):
$ mpiexec -n 4 python run_with_mpi.py
The file defining the patch centers should already be written to make sure
that each machine is using the same patch definitions. There is some level of
randomness in the k-means calculation, so if you use npatch=N
, then each
machine may end up with different patch definitions, which would definitely
mess things up.
If you wanted to have it all run in a single script, you should have only
the rank 0 process define the patches. Then send cat.patch_centers
to the
other ranks, who can build their catalogs using this.
But it’s probably easier to just precompute the centers and save them to a file
before starting the MPI run.
A more complete worked example is available in the TreeCorr devel directory.


Yohan Ishara
- Address: Demo Street 123, Demo City 04312, NJ
- Phone #:+94 711686983
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- Address: Demo Street 123, Demo City 04312, Hambanthota
- Phone #:+94 71168383
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- Phone #:+94 711645983
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- Address: Demo Street 123, Demo city NJ
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Suchira liyanage
- Address: Demo Street 123, Demo City 04312, Uk
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- Address: Demo Street 123, kurunegala rd, Kandy
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Muditha ranaweera
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SPLICE Spring 2018 Workshop
The second SPLICE Project workshop will be held in conjunction with SIGCSE 2018 in Baltimore on Wednesday, February 21. If you are interested in attending, please contact us.
Workshop Theme: CS Education Infrastructure for All: Interoperability for Tools and Data Analytics
Workshop Hosts:- Cliff Shaffer, Virginia Tech
- Peter Brusilovsky, U Pitt
- Ken Koedinger, CMU
- Steve Edwards, Virginia Tech
Abstract:
CS Education makes heavy use of online educational tools like IDEs,
Learning Management Systems (LMS), eTextbooks, interactive programming
environments, and other smart content.
Instructors and students would benefit from greater interoperability
between these tools.
CS Education (CSEd) researchers increasingly make use of large
collections of data generated by click streams coming from them.
However, students, instructors and researchers all face barriers that
slow progress:
(1) Educational tools do not integrate well.
(2) Information about computer science learning process and outcome
data generated by one system is not compatible with that from other
systems.
(3) Computer science problem solving and learning (e.g., open-ended
coding solutions to complex problems) is quite different from the
type of data (e.g., discrete answers to questions or verbal
responses) that current educational data mining focuses on.
This NSF-supported workshop is a follow-up to a workshop in Pittsburgh
this past summer that initiated the SPLICE project.
The goal is to support and better coordinate efforts to build
community and capacity among CSEd researchers, data scientists, and
learning scientists toward reducing barriers.
CSEd infrastructure should support
(1) broader re-use of innovative learning content that is instrumented
for rich data collection,
(2) formats and tools for analysis of learner data,
and (3) development of best practices to make collections of learner
data available to researchers.
We will present progress to date on developing a project website,
present initial reports from working groups, and plan activities for
the coming year.
Venue:
- Room 303 in the Baltmore Convention Center
Agenda:
- 8:00 Breakfast available
- 8:45 Introduction: Why we are here? About the Grant and community goals (Slides)
- 9:00 Infrastructure status: websites, GitHub, tutorials, Working Groups, etc.
- 9:15 Ken Koedinger: Sharing and analyzing CS Ed data using LearnSphere (Slides)
- 9:45 Interoperability Issues: Jackson Wonderley/Hamza Manzoor on LTI/Caliper and SPLICE tutorials (Slides)
- 10:15-10:45 BREAK
- 10:45am Mini-presentations: 7+3 minutes each
- Collin Lynch
- Archie Korhonen: ACOS and the A+ Family (Slides)
- David Joyner: Course delivery through loosely-integrated platforms: student experience and data analysis (Slides)
- Barbara Ericson: Runestone Interactive platform for interactive ebooks: opportunities for challenges (Slides)
- Cay Horstman: Interactive Exercises and Textbooks--An Author Perspective (Slides)
- Andrew Petersen: Seeking Differences in CloudCoder, TestMyCode, and PCRS Programming Exercise Submissions (Slides)
- Raphael Gachuhi/Kelly Rivers: Integrating Custom CS Activities in OLI
- 12:15-1:30 LUNCH and collaboration match via pink and blue post-its
- 1:30-1:45 Breakout group setup based on morning discussion and matches
- 1:45-2:45 Breakout session
- 2:45-3:15 BREAK
- 3:15-3:45 Reports from Breakout Groups
- 3:45 Future Plans and Open Discussion
- 4:30 Wrap-up
Agenda last updated: 2/18/2018
Reminders for Attendees
- You should already have filled out the survey, otherwise we will not have lunch for you or be able to process reimbursement.
- Our standard reimbursement is $200 for attendees. If you are seeking reimbursement and have not already filled out the forms that Cliff sent via email, contact Cliff at shaffer@vt.edu.


I have been working in IT for the past 10 years, and my experience has ranged far and wide. I spent the early part of my career working in the classroom support departmetn at Oakland University where I was persuing my bachelor degree. During my time at Oakland, the majority of my work was Audio/Viusal related and also included some special event support. After graduating I spent 5 years working for Ascension Technologies, starting on their Service Desk and working my way into the IT Service Management side of things on the Knowledge Management team.
I currently work at Essium LLC. as a Technical Business Analyst, where I am developing web forms and coding using Ruby. In my free time I enjoy golfing, playing video games, cooking, working in the yard, and reading. I look forward to being able to put to use the skills that I am learning in both my professional career and my personal life. Please feel free to check out my portfolio and contact me!


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Amy'Leigh's mother opens up about kidnapping: 'She isn't coping, it's been two months of hell'
Amy'Leigh de Jager, the six-year-old girl who was kidnapped in front of Laerskool Kollegepark in Vanderbijlpark, Gauteng, at around 07:40 on September 2, has not been coping well since the harrowing ordeal, says her mother, Angeline, 26.
Speaking to eNCA on Wednesday, Angeline said: "It is not going well, she is struggling. She has never been afraid of people, which she is now, she doesn't trust easily; even us as parents struggle to gain her trust again."

After a failed ransom demand of R2m, she was dropped off in the centre of town and taken to a police station by two passers-by and reunited with her parents.
READ | Amy'Leigh cried all night, didn't want to go back to school - court hears
Four people have been arrested for Amy'Leigh's kidnapping, including now suspended Grade RR teacher Tharina Human, who was a friend of the De Jager family and their son's teacher.
Human and so-accused Pieter van Zyl and Bafokeng Molemohi are still in custody, while Laetitia Nel was granted bail of R25 000.
Human allegedly orchestrated the kidnapping to pay a debt to drug dealers.
On Wednesday, the four made a brief appearance in the Vanderbijlpark Magistrate's Court.
In the family's first TV interview since the kidnapping, Angeline describes the past two months as "hell".
"It's sad," Angeline told eNCA. "It's disappointing that people who were so close to you and whom you trusted, especially with your children - she [Human] was my child's teacher... it makes it difficult knowing that someone would do that to you..."
The accused will be back in court on January 9 next year.
- Compiled by Riaan Grobler


| DOSE: |
30 mg |
oral |
4-HO-MiPT |
(powder / crystals) |
It was a Thursday night, and my friend and I were sitting at another friends house awaiting the arrival of our 4-aco-mipt. I had never tripped on anything in my life, and was in anxious anticipation. When my friend's buddy arrived with the 4-aco, he said that he usually liked to dish out about 30mg or so in order to lead to an extreme trip. I was kind of scared, but after all the research I had done on the chemical I was excited at the same time. He dished out some powder in a shot of coca-cola to each of us. We weren't planning on tripping right then and there, but we did.
0:00 - Took a few drinks of soda with 30mg of 4-aco-mipt on a completely empty stomach.
0:15 - starting to feel a bit anxious and jittery.
0:20 - cannot seem to function the Nintendo system at all, and I'm starting to see movement in my visual spectrum. Colors are changing and I look at the trim on the door and it changes into such a rainbow of an image. Colors are shooting in from behind me and it seems that the wind has a sort of tint to it.
0:30 - I give up the Nintendo and have to sit down, this is getting intense. I start to see the rug on the floor gain some body and height. It starts to twirl and twist like it does in the hotel scene from the film, 'Fear and Loathing in Las Vegas.' That is the best way to describe the first onset of visuals.
0:45 - Tripping extremely hard and visualizing so many different things. People's faces are completely distorted and my mind is racing, filled with thoughts and enlightenment. People seem to be cartoon to an extent.  The specks start to crawl all over the table and pot and I am completely amused.
0:60 - Astounded by the power of this chemical. Dark Side of the Moon is on in the background and people are starting to come to my friends house to buy weed and smoke and drink beer and stuff.
1:10 - I decide to take a breather from the surroundings of people and feel as if I should be doing something, I keep telling myself that I don't have anything to do at all. I just need to settle down and chill. I sit down again in my friends room and the sounds of people coming up the stairs create mild auditory hallucinations. A sort of twanging and repeating of noises keeps ringing, but it's not unpleasant at all.
1:30 - Tracers and full blown cartoon characters are covering my visual spectrum. I look at my friends and just laugh. I decide it's time for a cigarette and I figure I am most likely peaking at about this time. The cigarette tasted like pure gasoline and chemicals, gross. Looking at the cherry of my cigarette, I could see every little detail as it twirled around amongst itself and as I stared off into the trees outside when I was on the balcony, they started to grow and morph together.
2:00 - Still peaking and visualizations are incredible. A friend comes over with a recording of his bands new songs, they are incredible and I let him know I'm tripping.
I tried 4-aco-mipt again last week and it was a little less intense but very fun...
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.


CodeFlight is an iOS programming language trainer. Our app uses Highlightr for the code editor, IQKeyboardManager for a scrollable code editor, and GCDWebServer for a live HTML web server.
We consider CodeFlight as RetriceCode's successor as we provide what they once wanted to provide to others.
CodeFlight provides two classes inside this update:
This class helps you learn HTML in a simple matter. We provide 12 HTML lessons currently.
You can choose whichever lesson you want to learn from with a simple tap on your screen.
This class helps you learn CSS in a simple matter. We provide 5 CSS lessons currently.
You can choose whichever lesson you want to learn from with a simple tap on your screen.
CodeFlight also provides iPad support to those who don't own an iPhone or prefer iPads.


Thank you!
Now let's do this.
Pitch in $27 toward a political revolution the likes of which this country has
never
seen!
It Takes Two Minutes or Less
{% comment %}
{% endcomment %}


+++ title = "terrascan" linkTitle = "terrascan" +++ {{< blocks/cover title="terrascan" image_anchor="top" height="full" color="orange" >}}
{{< /blocks/cover >}} {{% blocks/lead color="secondary" %}} Codify Policies for Infrastructure as Code As you embrace Infrastructure as Code (IaC) such as [Terraform](https://www.terraform.io/), [Kubernetes](https://kubernetes.io/), [Argo CD](https://argo-cd.readthedocs.io/en/stable/), [Atlantis](https://www.runatlantis.io/) and AWS CloudFormation, it is important to ensure that security best practices and compliance requirements are observed. Terrascan provides 500+ out-of-the-box policies so that you can scan IaC against common policy standards such as the CIS Benchmark. It leverages the [Open Policy Agent](https://www.openpolicyagent.org/) (OPA) engine so that you can easily create custom policies using the Rego query language. {{% /blocks/lead %}} {{< blocks/section color="dark" >}} {{% blocks/feature icon="fas fa-code" title="Get Started" url="https://runterrascan.io/docs/getting-started/" %}} Follow the documentation to get scanning as quickly as possible! {{% /blocks/feature %}} {{% blocks/feature icon="fab fa-github" title="Contributions welcome!" url="https://github.com/accurics/terrascan" %}} We do a [Pull Request](https://github.com/accurics/terrascan/pulls) contributions on **GitHub**. New users are always welcome! {{% /blocks/feature %}} {{% blocks/feature icon="fab fa-twitter" title="Follow us on Twitter!" url="https://twitter.com/runterrascan" %}} For announcement of latest features etc. {{% /blocks/feature %}} {{< /blocks/section >}} {{< blocks/section >}}Meet, share and collaborate with other Terrascan users and contributors on our Community On Discord
Join the Accurics Community Discord


In an audit of my system I needed to find all the files owned by apache. [ps] find ./ -name apache [/ps] Simple! Here's the group and user example: [ps] find ./ -group apache -name apache [/ps]
In order to run a custom command from the command line utility called artisan you need to do two things: Create a new CustomCommand file Register that command with artisan Here's a sample Command file called FooCommand.php which should be placed in app/commands/ [php]
Typically when Laravel 4 artisan is run from the command line (cli) it uses the production configuration files. This can be changed by using a flag when running the command. [ps]php artisan migrate --env=local[/ps] However, adding --env-=local each time while running a script can slow down …
I typically use a library folder in my projects to group of files that you want to use in different projects but don't want to use Satis to manage the contained files. This folder could also be a helper folder for functions that are static and are mainly generic helpers …
After working within a screen session on a CentOS server I had forgotten how to close the screen session that I was done with. It's a rather simple command. exit Perfect, exit means exit. Of note if you just want to disconnect use. ctrl+a (press) d That'll disconnect you …
I previously posted on setting a static ip address in vmware fusion 4. This also still works for VMWare Fusion 5. Steps are the same. First on your linux vm in terminal run the following command ifconfig Then copy the HWaddr it varies from vm to vm so you need …
Setting up an alias in linux is very easy and very helpful. It allows you to determine a short phrase for running a longer command. Or change a common typo to the command you meant. Here's an example alias: \$ alias lsl="ls -la" This maps lsl to the command ls …
I have to do this whenever setting up a new system. Makes the server more secure to disable password based login and only allow ssh keys. To create an ssh key it's as easy as running one of the two following commands. ssh-keygen -t dsa or ssh-keygen -t rsa The …
Yesterday I was in search of Laravel 4 documentation. I couldn't figure our where to find Laravel 4 documentation. I found the committed files on GitHub but I couldn't find the actual site that hosted it. Turns out it was in a now obvious place. http://four.laravel.com/
I wanted the url to change but there to be no redirect/refresh of the page. I was able to do this by a little js. history.pushState('data', '', 'http://' + window.location.host + '/month/' + month );


#include <hwlib-string.hpp>
Public Member Functions | |
| constexpr size_t | max_size () const |
| the maximum number of characters that can be stored | |
| constexpr size_t | length () const |
| the number of characters that are currently stored | |
| constexpr bool | valid_index (const size_t n) const |
| string_base & | append (char c) |
| append the char if possible, otherwise ignore it | |
| string_base & | operator+= (char c) |
| append a char if possible, otherwise ignore it | |
| string_base & | operator<< (char c) |
| append a char if possible, otherwise ignore it | |
| template<typename T > | |
| string_base & | operator+= (const T &rhs) |
| append something (as far as possible, ignore the excess) | |
| template<typename T > | |
| string_base & | operator<< (const T &rhs) |
| append something (as far as possible, ignore the excess) | |
| string_base & | clear () |
| set to "" | |
| template<typename T > | |
| string_base & | operator= (const T &rhs) |
| assign something | |
| char * | begin () |
| start iterator | |
| char * | end () |
| end iterator | |
| const char * | begin () const |
| start const iterator | |
| const char * | end () const |
| end const iterator | |
| template<typename T > | |
| bool | find_at (const T &s, size_t pos=0) const |
| template<typename T > | |
| size_t | find (const T &s, size_t pos=0) const |
| template<typename T > | |
| size_t | rfind (const T &s, size_t pos=0) const |
| char & | operator[] (int n) |
| char | operator[] (int n) const |
| string_base | range_start_length (size_t a, size_t b) |
| subrange based on start index and length | |
| string_base | range_start_end (size_t a, size_t b) |
| subrange based on start index and end index | |
| template<typename T > | |
| string_base | range_find_length (const T &a, size_t b) |
| subrange based on substring start and length | |
| template<typename T , typename Q > | |
| string_base | range_find_find (const T &a, const Q &b) |
| subrange based on first substring start and second substring start | |
| template<typename T > | |
| bool | operator== (const T &rhs) const |
| compare for equality | |
| template<typename T > | |
| bool | operator!= (const T &rhs) const |
| compare for inequality | |
| template<typename T > | |
| bool | operator> (const T &rhs) const |
| compare for larger than | |
| template<typename T > | |
| bool | operator>= (const T &rhs) const |
| compare for larger than or equal | |
| template<typename T > | |
| bool | operator< (const T &rhs) const |
| compare for smaller than | |
| template<typename T > | |
| bool | operator<= (const T &rhs) const |
| compare for smaller than or equal | |
Static Public Member Functions | |
| static constexpr const string_base | range (const char *start, const char *beyond) |
| static constexpr const string_base | range (const char *start) |
Static Public Attributes | |
| static const size_t | nsize = 0xFFFF |
| special value for beyond-end or not-found | |
Friends | |
| template<size_t > | |
| class | string |
| template<typename T > | |
| T & | operator<< (T &lhs, const string_base &rhs) |
| output to any target that supports operator<< for a char * | |
fixed-maximum-length string interface
This is a string class template with a fixed maximum length. The maximum length is the template parameter. All such strings inherit from string_base.
An attempt to add characters beyond the maximum length will ignore the excess characters. Accessing an invalid character by the operator[] will yield either an undefined character value, or a reference to a (hidden) static character.
This class was assumed to be part of the the not-time-critical part of an application, hence the implementation leans towards small code size, not maximum speed, and tries to avoid Undefined Behaviour.
For assignment, appending, and comparison the following types are supported for the other side:
The fixed maximum size makes it impractical to have string expressions (what would the size of the result be?). So instead of
std::string s = "[" + text + "]";
you will have to write
hwlib::string< 20 > s; s.clear() << "[" << text << "]";
(The .clear() is not strictly needed because the default constructor creates an empty string, but it improves readability).
|
inline |
find a substring in a string
Searches the string for the first occurrence of the sequence specified by its arguments.
When pos is specified, the search only includes characters at or after position pos, ignoring any possible occurrences that include characters before pos.
When no match is found, nsize is returned.
|
inline |
check whether a substring occurs in a string at a given position
Searches the string for the first occurrence of the sequence specified by its arguments.
When pos is specified, the search only includes characters at or after position pos, ignoring any possible occurrences that include characters before pos.
|
inline |
return n'th character reference
Return a reference to the n'th character if the index is valid, otherwise return the dummy character.
|
inline |
return n'th character value
Return the value of the n'th character if the index is valid, otherwise return the dummy character value.
|
inlinestatic |
non-owning string (sub)range from two iterators
This function returns a const string object that appears to contains the characters range pointed to by the start parameter up to (but excluding) the character pointed to by the end parameter.
The object is a non-owning range: it doesn't make a copy. Hence any change to the characters will be reflected in the range.
|
inlinestatic |
non-owning string (sub)range from an asciz string
This function returns a const string object that appears to contains the characters in the asciz string pointed to by the start parameter.
The object is a non-owning range: it doesn't make a copy. Hence any change to the characters will be reflected in the range.
|
inline |
find a substring in a string
Searches the string for the last occurrence of the sequence specified by its arguments.
When pos is specified, the search only includes characters at or after position pos, ignoring any possible occurrences that include characters before pos.
When no match is found, nsize is returned.
|
inline |
check index for validity
Return whether n is a valid index.


VILLAGE OF THE DAMNED (1995) A film review by Scott Renshaw Copyright 1995 Scott Renshaw
Starring: Christopher Reeve, Kirstie Alley, Linda Kozlowski. Screenplay: David Himmelstein. Director: John Carpenter.
It's difficult to say whether it's a devoted core following or monumental chutzpah which accounts for John Carpenter continuing to receive the possessory credit before his films. It has been over a decade since Carpenter made STARMAN, the last of his films to receive any kind of critical or popular reception; the intervening years have been characterized by cult favorites like BIG TROUBLE IN LITTLE CHINA, ambitious failures like PRINCE OF DARKNESS and ill-advised catastrophes like MEMOIRS OF AN INVISIBLE MAN. So seeing a film hyped as "John Carpenter's VILLAGE OF THE DAMNED" doesn't exactly set my heart racing. And it's too bad, because as stylish as it is, VILLAGE OF THE DAMNED could have been a winner if not for some truly atrocious acting.
In the small town of Midwich, population 2000, it's a lovely, ordinary day. Dr. Alan Chaffee (Christopher Reeve) is out on his rounds; school principal Jill McGowan (Linda Kozlowski) is busy keeping the school fair running. Then, at precisely 10:00 a.m., every living thing in Midwich falls unconscious. When they all awaken six hours later, no one is quite sure what has happened ... or why ten women are pregnant, having conceived during the mass blackout. National Science Foundation epidemeologist Dr. Susan Verner (Kirstie Alley) comes to study the phenomenon, and follows the children as they grow. But it doesn't take a scientist to figure out that something is very strange about the children of the Midwich blackout, and that every person in town lives in fear of them.
Even in his most uneven efforts, Carpenter has demonstrated a great sense for how to use the screen.  The shots of the town during the blackout, with streets and schoolyards littered with bodies, are serenely chilling. There is one very nice split screen image after a key plot point, and a simmering level of menace in the scenes of the 7-year-old children in their paired marches. Unlike Carpenter's previous remake attempt, 1982's THE THING, VILLAGE OF THE DAMNED doesn't rely on gruesome special effects in place of a well-constructed atmosphere. Most of the time VILLAGE OF THE DAMNED has just the right feel.
If only Carpenter were as astute a judge of material and actors as he is of shot composition. VILLAGE OF THE DAMNED, based on a novel by John Wyndham and a 1960 film of the same name, is an inherently disturbing story, but screenwriter David Himmelstein feels obliged to spice it up with OUTBREAK-style mass hysteria, paranoia and government conspiracies in moving the scene to contemporary America. He also struggles with the story's broad time frame, which moves clumsily over an eight year span. Narrative has never been one of Carpenter's strengths, and the failure to include something as simple as a "2 Years Later" caption makes for some unnecessarily awkward transitions.
Directing actors has also been a longtime problem for Carpenter, Jeff Bridges' work in STARMAN asside, but in VILLAGE OF THE DAMNED that problem is tripled by some mind-boggling casting choices. Christopher Reeve as the tragedy-stricken, intense hero? Kirstie Alley as a flinty, secretive government scientist? Mark Hamill as the town priest ... or for that matter, as anything but a cartoon or video game voice-over these days? Why not Macaulay Culkin as the chief of police?  Only Linda Kozlowski (best known as CROCODILE DUNDEE's sweetheart) is at all believable in her role.
VILLAGE OF THE DAMNED almost pulls out a last second victory with a tense, well-staged climax, but it just isn't enough. It is a film with tone to spare, but like many of John Carpenter's works, it could stand to trade some tone for a little substance.
On the Renshaw scale of 0 to 10 little devils: 4.
-- Scott Renshaw Stanford University Office of the General Counsel
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


Money Play. How should Red play 33?
pips Red=131 White=94
(CorrectAnswer= 0/0, ErrorScore= 0)
The power of the broken prime! Anything other than 21/18(2), 10/7(2) is a quadruple blunder or worse.
Over the board 10/4(2) was played but that is completely the wrong game plan as is 8/2(2)*. 21/18(2), 10/7(2), solves the problem of the rear checkers and creates the broken prime - not bad for a single move!
Over the board 10/4(2) was played but that is completely the wrong game plan as is 8/2(2)*. 21/18(2), 10/7(2), solves the problem of the rear checkers and creates the broken prime - not bad for a single move!
1. Cubeful 2-ply 21/18(2) 10/7(2) Eq.: +0.634 0.682 0.052 0.002 - 0.318 0.051 0.000 2. Cubeful 2-ply 13/7 10/7 6/3 Eq.: +0.451 (-0.183) 0.625 0.069 0.003 - 0.375 0.081 0.002 3. Cubeful 2-ply 13/7 10/7(2) Eq.: +0.450 (-0.184) 0.622 0.068 0.003 - 0.378 0.069 0.001 4. Cubeful 0-ply 8/2*(2) Eq.: +0.408 (-0.226) 0.596 0.081 0.002 - 0.404 0.068 0.001 5. Cubeful 0-ply 10/4(2) Eq.: +0.386 (-0.248) 0.587 0.082 0.003 - 0.413 0.071 0.001


Best Incentives
The key to growth and continued success is that people drive sales and incentives drive people.
Studies have shown that cash and benefits have historically been used to attract employees.
However, employee recognition and incentives have shown to be more powerful motivators leading
to higher performance and longer tenure.
Member Markets has thousands of proven practices, but that's only half the story. For an
incentive to have value the employee must value it. Member Markets has established distribution
relationships with hundreds of manufacturers that provide tens of thousands of consumer "Best
Sellers". We have learned that high value incentives drive performance and the added value that
companies realize from higher levels of performance enhance overall profitability.


Strongly connected graph
In the mathematical theory of directed graphs, a graph is said to be strongly connected if every vertex is strongly connected components of an arbitrary directed graph form a partition into subgraphs that are themselves stro ngly connected. It is possible to test the strong connectivity of a graph, or to fi n d its time.
Definitions
A directed graph is called strongly direction between each pair of vertices of the graph. In a directed graph G that may not itself be strongly connected, a pair of vertices u and v are said to be strongly connected to each other if there is a path in each direction between them.
connected is an equivalence relation, and the induced subgraphs of its equivalence strongly connected component of a directed graph G is a subgraph that is strongly connected, and is maximal with this prop erty: no additional edges or vertices from G can be included in the subgraph without for ms a partition of the set of vertices of G.I f each strongly connected component is contracted to a single vertex, the resulting graph is a directed acyclic graph, the condensation of G. A directed graph is acyclic if and only if it has no connected and every nontrivial strongly connected component contains at least one directed cycle.
Algorithms
href="http://en.wikipedia.org/wiki/Linear_time">linear time.- Kosaraju's algorithm uses two passes of transpose graph of the original graph, and each recursive exploration finds a single new strongly connected described it (but did not publish his results) in 1978; Micha Sharir later published it in 1981.
- Tarjan's strongly connected components
1972, performs a single pass of depth first search. It maintains a
- The path-based strong component algorithm uses a to keep track of the vertices not yet assigned to components, while the other kee p s track of the published by Edsger W. Dijkstra in 1976.
Although Kosaraju's algorithm is conceptually simple, Tarjan's and the path-based algorithm are favoured in practice since they require only o
Applications
Algorithm s for finding strongly connected components may be used to variables wi th constraints on the values of pairs of variables): as showed, a 2-satisfiability instance is unsatisfiable if and only if there is a variable v such that v and its complement are both c ontained in
Strongly connected components are also used to compute the Dulmage–Mendelsohn decomposition, a of the edges of a bipartite graph, according to whether or not they can be part of a perfect matching
Related results
A directed graph is strongly connected if and only if it has an ear decomposition, a partition of the edges into a sequence of directed paths and cycles such that the first subgraph in the sequence is a cycle, and each subsequent subgraph is either a cycle sharing one vertex with previous subgraphs, or a path
According to Robbins' theorem, an undirected graph
may be oriented in such a way that it becomes
strongly connected, if and only if it is
Let's get crazy: This is a list item with two paragraphs. Lorem ipsum dolor
sit amet, consectetuer adipiscing elit. Aliquam hendrerit
vitae, risus. Donec sit amet nisl. Aliquam semper ipsum
sit amet velit. Suspendisse id sem consectetuer libero luctus adipiscing. What about some code in a list? That's insane, right? Some people seem to like definition lists Maybe you want to print Yo
How about we throw some angle braces and ampersands in there?See also
We
all
like making lists
['a', 'b'].map(&:uppercase)
I am a robot
robot
to the console 1000 times. Why not?CSV:
Year,Make,Model,Length
2/5/08,Ford,E350,2.34
06,Mercury,Cougar,2.38
Set in stone
Preformatted blocks are useful for ASCII art:
,-. , ,-. ,-. / \ ( )-( ) \ \|,' ( )-( ) Y ___`-' `-' | | -hrr- ___|_____________ Playing the blame gameI, at any rate, am convinced that He does not throw dice.
I wish you'd have given me this written question ahead of time so I could plan for it... I'm sure something will pop into my head here in come up with answer, but it hadn't yet...
I don't want to sound like as I should be in coming up with one.


Calculating score, one second...
At present this only shows preinserted data, data input is to be done!


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Data Fields | |
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| VALUE | klass |
| VALUE | self |
| VALUE | proc |
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| rb_event_flag_t | event |
Definition at line 4058 of file thread.c.
Definition at line 4064 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), rb_threadptr_exec_event_hooks(), and thread_exec_event_hooks().
Definition at line 4063 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), rb_threadptr_exec_event_hooks(), and thread_exec_event_hooks().
Definition at line 4060 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), rb_threadptr_exec_event_hooks(), and thread_exec_event_hooks().
Definition at line 4062 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), and rb_threadptr_exec_event_hooks().
Definition at line 4061 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), rb_threadptr_exec_event_hooks(), and thread_exec_event_hooks().
Definition at line 4059 of file thread.c.
Referenced by call_trace_func(), call_trace_proc(), rb_threadptr_exec_event_hooks(), and thread_exec_event_hooks().


Dear and Beloved, Brother and Sister, Whitney and etc- I take this opportunity
to communicate, some of my feelings, privately at this time, which I want you
three eternally to keep in your own bosoms; for my feelings are so strong for
you since what has passed lately between us, that the time of my absence from
you seems so long, and dreary, that it seems, as if I could not live long in
this way: and if you three would come and see me in this my lonely retreat, it
would afford me great relief, of mind, if those with whom I am allied, do love
me, now is the time to afford me succor, in the days of exile, for you know I
foretold you of these things. I am now at Carlos Grainger’s, just back of
Brother Hyrum’s farm, it is only one mile form town, the nights are very
pleasant indeed, all three of you can come and see me in the fore part of the
night, let Brother Whitney come a little ahead, and knock at the south east
corner of the house at the window; it is next to the cornfield; I have a room
entirely by myself, the whole matter can be attended to with most perfect
safety, I know it is the will of God that you should comfort me now in this
time of affliction, or not at all now is the time or never, but I have no need
of saying any such thing, to you, for I know the goodness of your hearts, and
that you will do the will of the Lord, when it is made known to you; the only
thing to be careful of; is to find out when Emma comes then you cannot be
safe, but when she is not here, there is the most perfect safety: only be
careful to escape observation, as much as possible, I know it is a heroic
undertaking; but so much the greater friendship, and the more joy, when I see
you I will tell you all my plans, I cannot write them on paper, burn this
letter as soon as you read it, keep all locked up in your breasts, my life
depends upon it, one thing I want to see you for is to get the fulness of my
blessings sealed upon our heads, etc. You will pardon me for my earnestness on
this subject when you consider how lonesome I must be, your good feelings know
how to make every allowance for me, I close my letter, I think Emma won’t
come tonight if she don’t, don’t fail to come tonight. I subscribe myself
your most obedient, and affectionate, companion, and friend.


One Month, One Website
an experiment in consistency
Sheesh I don't know what to put here. Of course, as an extremely new website that's not even published yet (just hosted on GitHub) it's not like what I'll say now matters to anyone.  So the project: starting this month (today is 8/13/2019) I will commit to creating and completing one website each month. All of them will be displayed on this website and I'll use it as a portfolio or sorts, assuming I'd be any sort of hirable a few months into this :)
To the me of the future: I believe you can do this. This website *technically* counts as your first, so good job! You're one down.
projects ›››
‹‹‹ github