Monday, August 12 8:00 Coffee & Breakfast at NIMBioS 8:45 Welcome to NIMBioS (Lou Gross, Director of NIMBioS) 9:00 Intro to the pdg-control Working Group (Paul Armsworth) 9:30 Introductions by each Working Group Participant 5-minute self-introduction to the group, including your background, expertise, and current work 10:30 Break 11:00 Introduction to Training Problem 1 & Discussion (Jim Sanchirico)
Training problem 0: Optimal Control
Jim
maximize present discounted value:
\[\max_{h_t} \int_0^T e^{-rt} \Pi(h_t) dt \]
Biological growth minus harvest. Let’s start with given: $ X_0, X_T $
\[ \dot x = F(x_t) -h_t \]
Instantaneous fishing profit function curvature assumptions: $ ‘(h_t) >0, ’’(h_t) <0 $ (Diminishing returns)
Solution: Current Value Hamiltonian: $ H = e^{rt} H $ (current value is instantaneous, present value is integrated forward)
$ $ would be lagrange multiplier but is a variable, a function of time. Hence “Dynamic” optimization. Is the “shadow price” or adjoint. Solve:
\[ H = \Pi(h_t) + \lambda_t \left( F(x_t) -h_t \right) \]
Under optimality conditions:
$ h_t = 0 t w/ h_t > 0 ’(h_t) -_t $
$ - r t = = F’(xt) $
$ x = F(x_t) - h_t$
Subbing out lambda equations:
\[ \dot h = \frac{\Pi ^{\prime}}{\Pi^{\prime\prime}} \left( r - F^{\prime}(x) \right) \]
\[ \dot x_t = F(x_t) - h_t \]
Why is it dynamic?
Durability: fish stock today faces fish stock tomorrow.
Adjustment costs: can’t immediately change control. Rate of changing control. (come from $ Pi’’ $
note the stationary state is just setting value of leaving fish in water equal to rate of money it gets \(F^{\prime}(x) = r \)
Note if $ $ depends on the stock value (i.e. more fish are easy to find, or you like having fish in the sea)
\[ F^{\prime}(x) + \frac{\Pi_x}{\Pi_h} = r \]
Work simple example if profit is linear in h, but depends on stock: price of fish is p. $ (h,x) = (p - c(x)) h $ Solutions are bang-bang, etc…
Then marginial productivity = rate of return minus stock effect (searching costs, etc).
\[ F^{\prime}(x) = r - \frac{c'(x) h}{p-c(x)} \]
liquidation gain equals loss forever in taking out more fish.
$ p-c(x) = $
External change in fish price, then steady state just follows the rate of change:
$ F^{}(x) + = r $
Singular path (steady state path):
What if harvest is constrained outside: “blocked intervals” changes dynamics.
General note in all this dynamic stuff: you want stock at steady state, but you can’t just put it there, you have to harvest. Hence canterary turnpike, etc.
Paul Armsworth: training problem 1
constraints (limitations) on control
Applications of optimal control recommend policy that changes frequently.
Nonlinear relationship in age class
\[ N_1(t+1) = f(N(t)\]
\[N_{i+1}(t+1) = N_i(t) e^{\mu_i}, i \leq a \]
\[N_{i+1}(t+1) = N_i(t) e^{\mu_i-q_iE(t)}, i > a \]
Fishing effort level E. Pulse fishing or chattering becomes optimal (trying to meet two constraints, like temperature on different floors, where can’t meet both, but response time to one is faster than the other). Can see as convexity (Alan).
So the full optimum isn’t popular! Because no policy maker wants chattering as a solution.
Appears when number of state variables exceeds number of control variables. (Jim can this be proven?) Many things expand state space.
Another example: adding stochasticity (optimal inventory)
\[ N(t+1) = f(N(t) -h(t)) \varepsilon_t \]
Results in highly variable optimal quotas. At least in bluefin tuna, show policy is very non-responsive. Alan: easier to raise than lower quotas. Jim: irreversible action extreme.
Uncertainty analysis: Predicted stock size when quota set vs observed value. Note: you get more accurate estimate of the stock size more distantly in the past (more harvest size data).
Common fixes: quadratic/L2 penalty to changing quota. L1 penalty, max change size.
Neubert 2003: chattering in marine reserves (infinitely many infentesimal reserves) silly assumptions (no cost)
Halpern 2011: optimal harvest rates differ for each of 22 targets.
Hobday & Hartman 2006 suggest moving time-area closure boundaries to react to satellite data.
Technologically, tracking is possible. But how much variation is desirable?
12:00 Lunch at NIMBioS
1:00 Introduction to Training Problem 2 & Discussion (Alan Hastings)
Coral reef grazing model (analytic model by Mumby et al 2007) (Mumby et. al. 2007)
Macroalgae (grazed by parrotfish) overgrows coral, Coral, Turf. Grazed or dead coral both become turf. T+M+C = constant, so:
\[ \frac{dM}{dt} = a MC - \frac{g M}{M+T}+\gamma MT \]
\[ \frac{dC}{dt} = (rT - d-aM)C \]
Multiple stable states. a is overgrowth.
Parrotfish model:
\[ \frac{dM}{dt} = a MC - \frac{g(P) M}{M+T}+\gamma MT \]
\[ \frac{dC}{dt} = (rT - d-aM)C \]
\[ \frac{dP}{dt} = sP\left( 1-\frac{P}{K(C)}\right) - fP\]
K(C) is from territorial limits, rather than food limits. (Blackwood et. al. 2010)
Adding habitat quality (Blackwood et. al. 2011).
Compare to a non-multiple stable states.
Carl Walters adaptive control: variance on posterior as a state variable (confidence in a parameter)
passive adaptive – living with uncertainty – don’t perturb to learn, only to improve expectation.
active – actually perturbing system
model choice. model averaging.
Advanced problem: aggregated model vs model complexity.
2:00 Introduction to Training Problem 3 & Discussion (Megan Donahue)
Understanding how biology does it’s own control problems: life history switch:
Constraint: tradeoff between growth R and fecundity F
Optimality criterion: maximize reproduction at time T
Optimal strategy: switch effort from all F to all R at time \(t_s\)
Evolutionary context: optimal only in light of evolution. heritablity limitations. differing historical context. optimal plasticity (“adaptive management”).
ultimate (e.g. fitness) vs proximate (e.g. food) causation.
Optimal larval return strategy. (Intro to larval forms of marine creatures)
Exploiting water column current flow (ARMSWORTH, 2001)
Delayed and non-delayed swimming. Stochastic dynamic programming (Irisson et. al. 2004)
Megan: “You guys probably have another word for it in economics” Jim: “Same thing”. (Alan throws Jim candy)
Highly stochastic: in the sense that 1 :10^6 survive to settle.
Objective uncertainty: maximize time in plankton? survivorship to settlement? body size at settlement? Is arithmetic mean lifetime fitness the right measure? Sweepstakes (does one organism’s progeny make up most of the population?)
Jim: Is this an optimal stopping time question?
Alan: An approximate solution to the optimal problem or optimal solution to the approximate problem?
Choosing among a set of potentially equivalent models.
Jim: When do we get 90% there with a low-dimensional solution? (Advanced problem 1 & 2 start to address this). Does this make a difference when stochasticity enters? (Jim and Alan’s paper of adding more species to management story doesn’t matter much).
In policy, we only want robust solutions when forced to be non-optimal; i.e. simple model or inflexible policy. Michael: life-history/nature optimizes full problem, not the simplified one. Does this mean it is less robust.
Paul Ludwig’s paper where the simple model outperforms the generating complex model.
3:00 Break 3:15 Self-select into small groups to discuss Training Problems TASK: Identify 1-3 focal questions. What the biological problem? What’s the control problem?
Training Problem 1 Questions
Theory: state variable exceed control variable: we want the non-optimal control.
When does this give chattering?
Is there a convexity analog?
Does this non-optimal solution to high-state-variable case do better than a an optimal solution to lower-state model?
Adjustment Costs
optimal vs suboptimal
Compare optimal under different constrains irriversability/asymmetric penalty
Overages/Underages
Empirical: volatility of stock-size estimate vs volatility of management adjustment.
5:00 NIMBioS Reception 6:15 Walk into town for dinner 7:30 Optional Activity: Contradancing at the Laurel Theater (2 blocks from NIMBioS)
Participant list
Pretty Darn Good’ Control
Participant Institutional affiliation Meetings
Paul Armsworth*
Ecology & Evolutionary Biology, Univ. of Tennessee
1
Michael Bode
Adv. Environmental Decision Analysis, Univ. of Melbourne, Australia
1
Carl Boettiger
Univ. of California, Davis
1
Megan Donahue*
Marine Biology, Univ. of Hawaii, Kaneohe
1
Frank Doyle
Chemical Engineering, Univ. of California, Santa Barbara
1
Raissa D’Souza
Univ. of California, Davis; Santa Fe Institute
Marie-Josee Fortin
Zoology, Univ. of Toronto
1
Alan Hastings*
Environmental Science & Policy, Univ. of California, Davis
1
Jacob LaRiviere
Economics, Univ. of Tennessee
1
Claire Paris
Marine Science, Univ. of Miami
1
Jim Sanchirico
Environmental Science & Policy, Univ. of California, Davis
1
Carl Toews*
Mathematics, Duquesne Univ.
1
*Group organizers Mtg 1: Sep 12-15, 2011 (preliminary)
References
Mumby P, Hastings A and Edwards H (2007). “Thresholds And The Resilience of Caribbean Coral Reefs.” Nature, 450. ISSN 0028-0836, https://dx.doi.org/10.1038/nature06252.
Blackwood J, Hastings A and Mumby P (2010). “The Effect of Fishing on Hysteresis in Caribbean Coral Reefs.” Theoretical Ecology, 5. ISSN 1874-1738, https://dx.doi.org/10.1007/s12080-010-0102-0.
Blackwood J, Hastings A and Mumby P (2011). “A Model-Based Approach to Determine The Long-Term Effects of Multiple Interacting Stressors on Coral Reefs.” Ecological Applications, 21. ISSN 1051-0761, https://dx.doi.org/10.1890/10-2195.1.
ARMSWORTH P (2001). “Directed Motion in The Sea: Efficient Swimming by Reef Fish Larvae.” Journal of Theoretical Biology, 210. ISSN 00225193, https://dx.doi.org/10.1006/jtbi.2001.2299.
Irisson J, LeVan A, Lara M and Planes S (2004). “Strategies And Trajectories of Coral Reef Fish Larvae Optimizing Self-Recruitment.” Journal of Theoretical Biology, 227. ISSN 00225193, https://dx.doi.org/10.1016/j.jtbi.2003.10.016.


Welcome
At myBusiness, we strive to offer the very best to our customers. This website will give you an excellenet overview of our products and services. You also have access to our support team twenty-four hours a day.
At myBusiness, we strive to offer the very best to our customers. This website will give you an excellenet overview of our products and services. You also have access to our support team twenty-four hours a day.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
This lamb stew is beautifully rich and sweet, but no trouble to make. Great for a lazy weekend.
Preheat the oven to 180C/160C Fan/Gas 4.
Heat a lidded casserole dish and add the oil and butter. Once melted, add the lamb in batches and fry until browned all over. Remove from the pan and set aside.
Add the bacon lardons to the pan and cook for 1-2 minutes or until crisp. Add the spring onions, garlic and sage and cook for a further 2-3 minutes. Add the butternut squash and sweet potato mix and stir. Add the sun-dried tomatoes, flour, mushrooms, jam and bay leaf and cook for a further 1-2 minutes.
Return the sealed lamb to the pan along with the Worcestershire sauce and port. Cook until the port has reduced by half. Add the stock, mustard powder and season with salt and pepper stir. Cover with the lid and transfer to the oven for 20 minutes.
For the dumplings, place the flour, suet and thyme leaves into a large glass bowl and mix to combine. Add the egg and 50ml/2fl oz water, mix and bring to a dough. Roll the mixture into 12 evenly-sized dumplings.
Remove the stew from the oven and stir in most of the parsley.
Place the dumplings on top of the stew and place back in the oven with the lid on for 10 minutes. Remove the lid and cook for a further 15-20 minutes, or until the dumplings are golden-brown.
To serve, spoon some of the stew and dumplings into shallow serving bowls and garnish with the remaining parsley.


Simple triangle Shape, used in TriangleMesh.
More...
#include <DrawShape.hpp>
|
| | Triangle (const Transform3D *o2w, const Transform3D *w2o, bool ro, TriangleMesh *m, size_t n) |
| | Triangle Constructor. More...
|
| |
| virtual BBox | ObjectBound () const |
| | ObjectBound is the shape bounding box. More...
|
| |
| virtual BBox | WorldBound () const |
| | WorldBound is the Shape's Bounding box transformed by the ObjectToWorld transform matrix. More...
|
| |
| virtual bool | Intersect (const Ray &ray, float *tHit, float *rayEpsilon, DifferentialGeometry *dg) const |
| | Intersect returns geometric information about a single rayshape intersection corresponding to the first intersection, if any, in the [mint, maxt] parametric range along the ray. More...
|
| |
| void | GetUVs (float uv[3][2]) const |
| | GetUVS. More...
|
| |
| float | Area () const |
| | Area calculates the surface Area. More...
|
| |
| virtual void | GetShadingGeometry (const Transform3D &obj2world, const DifferentialGeometry &dg, DifferentialGeometry *dgShading) const |
| | GetShadingGeometry. More...
|
| |
| | Shape (const Transform3D *o2w, const Transform3D *w2o, bool ro) |
| | Shape Constructor. More...
|
| |
| virtual bool | CanIntersect () const |
| | CanIntersect indicates that a shape can compute ray intersections, so only shapes that are nonintersectable need to override this method. More...
|
| |
| virtual void | Refine (Vector< std::shared_ptr< Shape > > &refined) const |
| | Refine is a method that splits the shape into a group of new shapes, some of which may be intersectable and some of which may need further refinement. More...
|
| |
| virtual bool | IntersectP (const Ray &ray) const |
| | IntersectP is a predicate function that determines whether or not an intersection occurs, without returning any details about the intersection itself. More...
|
| |
| float | Area () const |
| | Area calculates the surface Area. More...
|
| |
| template<class O > |
| void | Print (O &os) const |
| | Print assings Shape information to stream. More...
|
| |
|
| static uint32_t | nextShapeId |
| | nextShapeId is a static attribute that is incremented at each Shape Construction. More...
|
| |
Simple triangle Shape, used in TriangleMesh.
Definition at line 283 of file DrawShape.hpp.
Triangle Constructor.
- Parameters
-
| o2w | Object to World transform matrix. |
| w2o | World to Object transform matrix. |
| ro | indicates if the surface normal should be reversed. |
| m | is a reference to the parent TriangleMesh |
| n | is the triangle index in the mesh. |
| float Bial::Triangle::Area |
( |
| ) |
const |
Area calculates the surface Area.
- Returns
- surface area.
GetShadingGeometry.
- Parameters
-
Reimplemented from Bial::Shape.
| void Bial::Triangle::GetUVs |
( |
float |
uv[3][2] | ) |
const |
Intersect returns geometric information about a single rayshape intersection corresponding to the first intersection, if any, in the [mint, maxt] parametric range along the ray.
- Parameters
-
- Returns
Reimplemented from Bial::Shape.
| virtual BBox Bial::Triangle::ObjectBound |
( |
| ) |
const |
|
virtual |
ObjectBound is the shape bounding box.
- Returns
Implements Bial::Shape.
| virtual BBox Bial::Triangle::WorldBound |
( |
| ) |
const |
|
virtual |
WorldBound is the Shape's Bounding box transformed by the ObjectToWorld transform matrix.
- Returns
Reimplemented from Bial::Shape.
| size_t* Bial::Triangle::v |
The documentation for this class was generated from the following file:


Workflow for Developers¶
In this document, we refer to the Tardis master
branch as the trunk.
Creating a fork¶
You need to do this only once for each package you want to contribute to. The instructions here are very similar to the instructions at http://help.github.com/fork-a-repo/ — please see that page for more details. We’re repeating some of it here just to give the specifics for the TARDIS project, and to suggest some default names.
Set up and configure a GitHub account¶
If you don’t have a GitHub account, go to the GitHub page and make one.
You then need to configure your account to allow write access — see the Generating SSH keys help on GitHub Help.
Create your own fork of a repository¶
The following example shows how to fork the core astropy
repository, but
the same applies to other packages:
Log into your GitHub account.
Go to the TARDIS GitHub home page.
Click on the fork button:
After a short pause and an animation of Octocat scanning a book on a flatbed scanner, you should find yourself at the home page for your own forked copy of TARDIS.
Setting up the fork to work on¶
Overview¶
This is done using:
git clone git@github.com:your-user-name/tardis.git
cd tardis
git remote add upstream git://github.com/tardis-sn/tardis.git
In detail¶
Clone your fork to the local computer:
git clone git@github.com:your-user-name/tardis.git
Change directory to your new repo:
cd tardis
Then type:
git branch -a
to show you all branches.  What remote repository isremote/origin
? Trygit remote -v
to see the URLs for the remote connections. They will point to your GitHub fork.Now connect to the TARDIS repository, so you can merge in changes from the trunk:
cd git remote add upstream git://github.com/tardis-sn/tardis.git
upstream
is just the arbitrary name we’re using to refer to the main TARDIS repository.Note that we’ve used
git://
for the URL rather thangit@
. Thegit://
URL is read only. This means that we can’t accidentally (or deliberately) write to the upstream repo, and we are only going to use it to merge into our own code.Just for your own satisfaction, show yourself that you now have a new remote connection with
git remote -v show
, which should give you something like:upstream git://github.com/tardis-sn/tardis.git (fetch) upstream git://github.com/tardis-sn/tardis.git (push) origin git@github.com:your-user-name/tardis.git (fetch) origin git@github.com:your-user-name/tardis.git (push)
Your fork is now set up correctly, and you are ready to hack away.
Installing TARDIS in develop mode¶
TARDIS is designed so that it can generally be used directly out of the source tree by using ``import `` when running Python in the source of an TARDIS repository clone.
Install TARDIS in develop mode:
$ python setup.py develop
This semi-permanently installs TARDIS on your path in such a way that
tardis
is always imported from your repository clone regardless of your working directory. This way any edits you make to the code in your repository will always be immediately available next time you start a Python interpreter andimport tardis
.
Workflow summary¶
This section gives a summary of the workflow to follow once you have successfully forked the repository. The details for each of these steps are given in the following sections.
- Don’t use your
master
branch for anything. Consider deleting it. - When you are starting a new set of changes, fetch any changes from the trunk, then start a new feature branch from that (do not base your branch on your changes).
- Make a new branch for each separable set of changes — “one task, one branch” (ipython git workflow).
- Name your branch for the purpose of the changes, for example
bugfix-for-issue-14
orrefactor-database-code
. - If you can possibly avoid it, don’t merge the trunk or any other branches into your feature branch while you are working.
- If you do find yourself merging from the trunk, consider Rebasing on trunk
- Ask on the tardis-sn-dev mailing list if you get stuck.
- Once your code is nearing completion, run the test suite to ensure you have not accidentally caused regressions, and add new tests to ensure your contribution behaves correctly (see Testing Guidelines).
- Issue a pull request on github!
- As the code is converging to a final state, ensure your documentation follows the guidelines (see Writing Documentation).
- Once your code is ready to be accepted, please add an entry to the changelog (see Updating and Maintaining the Changelog). If you’re sure where to put this, please at least suggest a brief (one or two sentence) description of your change so that another developer can add it to the changelog.
This way of working helps to keep work well organized, with readable history. This in turn makes it easier for project maintainers (that might be you) to see what you’ve done, and why you did it.
See linux git workflow and ipython git workflow for some explanation.
Deleting your master branch¶
It may sound strange, but deleting your own master
branch can help reduce
confusion about which branch you are on.
Updating the mirror of trunk¶
From time to time you should fetch the upstream (trunk) changes from GitHub:
git fetch upstream
This will pull down any commits you don’t have, and set the remote branches to
point to the right commit. For example, ‘trunk’ is the branch referred to by
(remote/branchname) upstream/master
, and if there have been commits since
you last checked, upstream/master
will change after you do the fetch.
Making a new feature branch¶
When you are ready to make some changes to the code, you should start a new branch. Branches that are for a collection of related edits are often called ‘feature branches’.
Making a new branch for each set of related changes will make it easier for someone reviewing your branch to see what you are doing.
Choose an informative name for the branch to remind yourself and the rest of
us what the changes in the branch are for. For example add-ability-to-fly
,
or buxfix-for-issue-42
.
# Update the mirror of trunk
git fetch upstream
# Make new feature branch starting at current trunk
git checkout upstream/master # checking out the newest master versio
git checkout -b my-new-feature
Generally, you will want to keep your feature branches on your public GitHub
fork. To do this, you git push this new branch up to your
github repo. Generally (if you followed the instructions in these pages, and
by default), git will have a link to your GitHub repo, called origin
. You
push up to your own repo on GitHub with:
git push origin my-new-feature
In git >= 1.7 you can ensure that the link is correctly set by using the
--set-upstream
option:
git push --set-upstream origin my-new-feature
From now on git will know that my-new-feature
is related to the
my-new-feature
branch in the GitHub repo.
The editing workflow¶
In more detail¶
Make some changes
Once you are a bit further along, test your changes do not lead to regressions, and add new tests (see the Astropy Testing Guidelines we follow).:
python setup.py test
If you have sphinx installed, you can also check that the documentation builds and looks correct:
python setup.py build_sphinx
The last line should just state
build succeeded
, and should not mention any warnings. (For more details, see the Astropy Writing Documentation we follow.)See which files have changed with
git status
(see git status). You’ll see a listing like this one:# On branch ny-new-feature # Changed but not updated: # (use "git add <file>..." to update what will be committed) # (use "git checkout -- <file>..." to discard changes in working directory) # # modified: README # # Untracked files: # (use "git add <file>..." to include in what will be committed) # # INSTALL no changes added to commit (use "git add" and/or "git commit -a")
Check what the actual changes are with
git diff
(see git diff).Add any new files to version control with
git add new_file_name
(see git add).Add any modified files that you want to commit using
git add modified_file_name
(see git add).Once you are ready to commit, check with
git status
which files are about to be committed:# Changes to be committed: # (use "git reset HEAD <file>..." to unstage) # # modified: README
Then use
git commit -m 'A commit message'
. Them
flag just signals that you’re going to type a message on the command line. The git commit manual page might also be useful.Push the changes up to your forked repo on GitHub with
git push
(see git push).
Asking for your changes to be reviewed and/or merged¶
When you are ready to ask for someone to review your code and consider a merge:
Go to the URL of your forked repo, e.g.,
http://github.com/your-user-name/tardis
.Use the ‘Switch Branches’ dropdown menu near the top left of the page to select the branch with your changes:
Click on the ‘Pull request’ button:
Enter a title for the set of changes, and some explanation of what you’ve done. If there is anything you’d like particular attention for, like a complicated change or some code you are not happy with, add the details here.
 This is still a good way to start a preliminary code review.
Making sure your Pull request stays up-to-date¶
More often then not it will take a few days until a Pull Request is merged as the community gives feedback and/or you add new fixes. During this time often other pull requests are merged and the master branch evolves further. To make sure that your changes are still working on the new master you want to rebase your branch ontop of the evolved master
Rebasing on trunk¶
Let’s say you thought of some work you’d like to do. You
Updating the mirror of trunk and Making a new feature branch called
cool-feature
. At this stage trunk is at some commit, let’s call it E. Now
you make some new commits on your cool-feature
branch, let’s call them A,
B, C. Maybe your changes take a while, or you come back to them after a while.
In the meantime, trunk has progressed from commit E to commit (say) G:
A---B---C cool-feature
/
D---E---F---G trunk
At this stage you consider merging trunk into your feature branch, and you remember that this here page sternly advises you not to do that, because the history will get messy. Most of the time you can just ask for a review, and not worry that trunk has got a little ahead. But sometimes, the changes in trunk might affect your changes, and you need to harmonize them. In this situation you may prefer to do a rebase.
Rebase takes your changes (A, B, C) and replays them as if they had been made
to the current state of trunk
. In other words, in this case, it takes the
changes represented by A, B, C and replays them on top of G. After the rebase,
your history will look like this:
A'--B'--C' cool-feature
/
D---E---F---G trunk
See rebase without tears for more detail.
To do a rebase on trunk:
# Update the mirror of trunk
git fetch upstream
# Go to the feature branch
git checkout cool-feature
# Make a backup in case you mess up
git branch tmp cool-feature
# Rebase cool-feature onto trunk
git rebase --onto upstream/master upstream/master cool-feature
In this situation, where you are already on branch cool-feature
, the last
command can be written more succinctly as:
git rebase upstream/master
When all looks good you can delete your backup branch:
git branch -D tmp
If it doesn’t look good you may need to have a look at Recovering from mess-ups.
If you have made changes to files that have also changed in trunk, this may generate merge conflicts that you need to resolve - see the git rebase man page for some instructions at the end of the “Description” section. There is some related help on merging in the git user manual - see resolving a merge.
 If the branch you
rebased is called cool-feature
and your GitHub fork is available as the
remote called origin
, you use this command to force-push:
git push -f origin cool-feature
Note that this will overwrite the branch on GitHub, i.e. this is one of the few
ways you can actually lose commits with git. Also note that it is never allowed
to force push to the main astropy repo (typically called upstream
), because
this would re-write commit history and thus cause problems for all others.
Recovering from mess-ups¶
Sometimes, you mess up merges or rebases. Luckily, in git it is relatively straightforward to recover from such mistakes.
If you mess up during a rebase:
git rebase --abort
If you notice you messed up after the rebase:
# Reset branch back to the saved point
git reset --hard tmp
If you forgot to make a backup branch:
# Look at the reflog of the branch
git reflog show cool-feature
8630830 cool-feature@{0}: commit: BUG: io: close file handles immediately
278dd2a cool-feature@{1}: rebase finished: refs/heads/my-feature-branch onto 11ee694744f2552d
26aa21a cool-feature@{2}: commit: BUG: lib: make seek_gzip_factory not leak gzip obj
...
# Reset the branch to where it was before the botched rebase
git reset --hard cool-feature@{2}
Reviewing and helping others with Pull Requests¶
Github offers an extensive array of tools to comment on Pull Requests (line based, normal forum-like discussion, etc.). This system is described here in detail http://help.github.com/articles/using-pull-requests.
However sometimes, it is easier to just add a few changes yourself to quickly show what you would suggest to be changed. So it is possible to make a Pull Request on a Pull Request.
There are several ways to do this, but the easiest is to first make sure that your local git-repository understands the concept of pull-requests. So just add in your <my-project>/.git/config the following line to your remote upstream:
[remote "upstream"]
url = git@github.com:tardis-sn/tardis.git
fetch = +refs/heads/*:refs/remotes/upstream/*
fetch = +refs/pull/*/head:refs/remotes/upstream/pr/*
Then fetch from upstream again:
git fetch upstream
remote: Counting objects: 77, done.
remote: Compressing objects: 100% (72/72), done.
remote: Total 77 (delta 44), reused 9 (delta 5)
Unpacking objects: 100% (77/77), done.
From github.com:tardis-sn/tardis
b8306de..2f47ee5 master -> upstream/master
* [new ref] refs/pull/116/head -> upstream/pr/116
* [new ref] refs/pull/117/head -> upstream/pr/117
* [new ref] refs/pull/118/head -> upstream/pr/118
and now it is possible to check out a the pull request branch, in this case PR 116:
git checkout upstream/pr/116
git checkout upstream/pr/116
Note: checking out 'upstream/pr/116'.
You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.
If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:
git checkout -b new_branch_name
HEAD is now at b1f32ba... added the git workflow still WIP
Finally, you can make a new branch git checkout -b helping-with-PR116 and work implement your work.
After committing your changes you push your change to your repository git push origin helping-with-PR116.


PHP is today the world’s most popular open source web development language. It is used by millions of websites, most often via applications like WordPress and Drupal. Deploying a PHP website is straightforward and supported by nearly every web hosting provider.
There are limits to how much load a single web server can take though. For your website to scale, you will sooner or later need to a transition to a multi-server deployment, and this can be hard. It requires thinking about web development in entirely new ways.
The exciting new world of cloud computing promises to make all this much better. “Cloud computing” is an umbrella term for a range of tools and techniques that make scalability possible. Scaling PHP in the Cloud is a one day conference on what it takes to make the leap from single server to multi-server deployments, and of making sense of the new world beyond.


This page was deployed to GitHub Pages!
It was super easy too! To find out how, go to
the tutorial
.
Made by
Sean McPherson


dialogflow_nlu_framework¶
-
class
nlutestframework.implementations.dialogflow_nlu_framework.
DialogflowNLUFramework
(*args, **kwargs)[source]¶ Bases:
nlutestframework.nlu_framework.NLUFramework
-
async
construct
(_global_config, time_zone, project='nlutestframework', agent='NLUTestFramework')[source]¶ - Parameters
_global_config (
GlobalConfig
) – Global configuration for the whole test framework.time_zone (
str
) – The time zone, e.g. Europe/Berlin. See https://www.iana.org/time-zones for the list of possible values.project (
str
) – The name of the Dialogflow project. Defaults to “nlutestframework”.agent (
str
) – The name of the Dialogflow agent. Defaults to “NLUTestFramework”.
- Return type
None
-
async
prepareDataSet
(data_set)[source]¶ Args: data_set: The data set that will be benchmarked next.
- Return type
None
-
async
unprepareDataSet
()[source]¶ This method gives framework implementations the opportunity to execute cleanup steps before preparing for a new data set (or exiting).
- Return type
None
-
async
train
(training_data)[source]¶ Args: training_data: The data to train on. Must not be empty.
- Return type
None
-
async


Death of widely respected Mayo University Hospital nurse
THE death has taken place of Catherine (Kathleen) Conway (née Brady), Rossmor, Pontoon Road, Castlebar, and formerly of Gurteen, Killashandra, Co. Cavan.
Aged 55, the news of her passing has cast a shadow of sadness over the community where she was held in very high regard.
A member of the nursing staff of Mayo University Hospital, she was attached for many years to the intensive care unit where her kind and compassionate nature were always greatly appreciated by patients and their families.
She is survived by her husband Gerry, sons Mark and Niall, daughters Aoife and Rebecca, sisters Marian, Gretta, Josephine, Una, Brenda and Cora, brothers Terence and Philip, extended family, relatives and friends, to whom sympathy is tendered.
Her remains will be reposing at Kilcoyne’s Funeral Home, Thomas Street, Castlebar, tomorrow (Friday) from 4 o’clock with removal at 6.15 p.m. to the Church of the Holy Rosary, arriving at 6.45.
Funeral Mass takes on Saturday at 12.30 p.m..
Removal from Kilcoyne's Funeral Home on Sunday morning at 11 o’clock to Lakelands Crematorium, Cavan, arriving for Committal Service at 2 o’clock.
May her gentle soul rest in peace.


Information technology most commonly known as IT is the use of computers to store, retrieve, transmit and manipulate data, or information, often in the context of a business or other enterprise.IT is considered to be a subset of information and communications technology (ICT).
Humans have been storing, retrieving, manipulating, and communicating information since the Sumerians in Mesopotamia developed writing in about 3000 BC, but the term information technology in its modern sense first appeared in a 1958 article published in the Harvard Business Review; authors Harold J. Leavitt and Thomas L. Whisler commented that "the new technology does not yet have a single established name. We shall call it information technology (IT)." Their definition consists of three categories: techniques for processing, the application of statistical and mathematical methods to decision-making, and the simulation of higher-order thinking through computer programs.
Devices have been used to aid computation for thousands of years, probably initially in the form of a tally stick. The Antikythera mechanism, dating from about the beginning of the first century BC, is generally considered to be the earliest known mechanical analog computer, and the earliest known geared mechanism. Comparable geared devices did not emerge in Europe until the 16th century, and it was not until 1645 that the first mechanical calculator capable of performing the four basic arithmetical operations was developed.
Electronic computers, using either relays or valves, began to appear in the early 1940s. The electromechanical Zuse Z3, completed in 1941, was the world's first programmable computer, and by modern standards one of the first machines that could be considered a complete computing machine. Colossus, developed during the Second World War to decrypt German messages, was the first electronic digital computer. Although it was programmable, it was not general-purpose, being designed to perform only a single task. It also lacked the ability to store its program in memory; programming was carried out using plugs and switches to alter the internal wiring. The first recognisably modern electronic digital stored-program computer was the Manchester Baby, which ran its first program on 21 June 1948.
Early electronic computers such as Colossus made use of punched tape, a long strip of paper on which data was represented by a series of holes, a technology now obsolete. Electronic data storage, which is used in modern computers, dates from World War II, when a form of delay line memory was developed to remove the clutter from radar signals, the first practical application of which was the mercury delay line. The first random-access digital storage device was the Williams tube, based on a standard cathode ray tube, but the information stored in it and delay line memory was volatile in that it had to be continuously refreshed, and thus was lost once power was removed. The earliest form of non-volatile computer storage was the magnetic drum, invented in 1932[16] and used in the Ferranti Mark 1, the world's first commercially available general-purpose electronic computer.
IBM introduced the first hard disk drive in 1956, as a component of their 305 RAMAC computer system:6 Most digital data today is still stored magnetically on hard disks, or optically on media such as CD-ROMs:4–5 Until 2002 most information was stored on analog devices, but that year digital storage capacity exceeded analog for the first time. As of 2007 almost 94% of the data stored worldwide was held digitally: 52% on hard disks, 28% on optical devices and 11% on digital magnetic tape. It has been estimated that the worldwide capacity to store information on electronic devices grew from less than 3 exabytes in 1986 to 295 exabytes in 2007, doubling roughly every 3 years.
In an academic context, the Association for Computing Machinery defines IT as "undergraduate degree programs that prepare students to meet the computer technology needs of business, government, healthcare, schools, and other kinds of organizations .... IT specialists assume responsibility for selecting hardware and software products appropriate for an organization, integrating those products with organizational needs and infrastructure, and installing, customizing, and maintaining those applications for the organization’s computer users."
Companies in the information technology field are often discussed as a group as the "tech sector" or the "tech industry". In a business context, the Information Technology Association of America has defined information technology as "the study, design, development, application, implementation, support or management of computer-based information systems".


Home
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Remove Inactive Objects from the List


Main settings
Let's Encrypt SSL
none
all
Extend WEB adapter:
IP:
Port:
Secure(HTTPS):
Public certificate:
Private certificate:
Chained certificate:
Authentication:
Run as:
Allow only when User is Owner:
none
Select data source:
List all datapoints:
Use Lets Encrypt certificates:
Use this instance for automatic update:
Port to check the domain:


@if(today.after(subscription.expiryDate)){
Your @subscription.duration day(s) @subscription.name subscription that runs from @Utility.formatDate(subscription.activationDate, "dd-MM-yyyy HH:mm:ss") to @Utility.formatDate(subscription.expiryDate, "dd-MM-yyyy HH:mm:ss") has expired. Renew or Upgrade plan now.
}else{
You are currently on @subscription.name. Your @subscription.duration day(s) subscription runs from @Utility.formatDate(subscription.activationDate, "dd-MM-yyyy HH:mm:ss") to @Utility.formatDate(subscription.expiryDate, "dd-MM-yyyy HH:mm:ss")
}
Subscription Products
| Product Type |
Number Of Unit(s) |
Available Unit(s) |
@for(subscriptionProduct <- subscription.products){
| @subscriptionProduct.productType.rep |
@subscriptionProduct.maximum |
@subscriptionProduct.remainder |
}
@if(subscriptionPaymentTopUp != null){
Top Up Products
| Product Type |
Number Of Unit(s) |
Available Unit(s) |
@for(subscriptionPaymentTopUpProduct <- subscriptionPaymentTopUp.products){
| @subscriptionPaymentTopUpProduct.productType.rep |
@subscriptionPaymentTopUpProduct.maximum |
@subscriptionPaymentTopUpProduct.remainder |
}
}
@if(pendingSubscription != null){
Pending Subscription
| Product Type |
Number Of Unit(s) |
@for(pendingSubscriptionProduct <- pendingSubscription.products){
| @pendingSubscriptionProduct.productType.rep |
@pendingSubscriptionProduct.maximum |
}
}
@if(pendingSubscriptionPaymentTopUp != null){
Pending Top Up Products
| Product Type |
Number Of Unit(s) |
@for(pendingSubscriptionTopUpProduct <- pendingSubscriptionPaymentTopUp.products){
| @pendingSubscriptionTopUpProduct.productType.rep |
@pendingSubscriptionTopUpProduct.maximum |
}
}


CodeGen is a tool that software developers working in a Synergy Development Environment can use to generate source code. That code might be Synergy DBL code, or might be source code for some other language. CodeGen is not restricted to producing code for any particular development environment or programming language.
Of course you can’t just use CodeGen to generate any piece of source code that you can imagine. Before code can be generated a developer must declare the rules that define how a given piece of source code should be created. These rules are defined in template files. CodeGen interprets the code and instructions that a developer has defined in a template file in order to produce useful output.
Defining rules in a template file is only part of the story though. To be really useful CodeGen also needs to have another source of information that defines a context for what is to be generated.
Most software applications revolve around the collection, presentation, manipulation and storage of data. That data is ultimately stored in some type of persistent storage such as in a collection of data files, or in tables in a relational database. When developers work on a particular piece of source code they combine knowledge that they have about an application's data (metadata) with rules or "business logic", in order to create source code that addresses a particular requirement. CodeGen does the same thing.
When developers work in Synergy/DE they have access to an excellent source of metadata that is called the Synergy/DE Repository. A repository holds extensive information about the data structures used by an application, and the attributes of those data structures. A repository can also contain information about the relationships between the various data structures, and even about underlying data storage mechanisms. A Synergy repository is a very rich source of metadata and is the primary source of metadata used by CodeGen.
For developers who use the Synergy/DE UI Toolkit to present their application's user interface there may also be a second source of metadata called Window Script files. In some situations CodeGen can also make use of the metadata contained within these files also.
It’s all about metadata! CodeGen takes information about a data structure and combines it with rules that have been defined in a template file in order to create some useful output text.
Basic Principles
Each time a developer uses CodeGen to generate code they typically provide two primary pieces of information:
1.A source of metadata, usually the name of a repository structure.
2.The name of a template file that defines the rules for what is to be created.
Each time CodeGen generates an output file it usually does three primary things:
1.Read information from a metadata source, usually a repository structure.
2.Read rules from a template file.
3.Merge the rules with the metadata to create an output file.
Video
Here's the Introduction to CodeGen video. Make sure you select a high-definition version of the video, by default YouTube tends to play the lowest resolution which can look pretty awful!
Copyright © 2021 Synergex International, Inc.


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Faust Downloads
Faust comes in many different forms and it can be confusing to know what to install to start using this tool. We recommend you to have a look at the overview of the Faust universe section of the Faust manual. That being said, most of the features of Faust are accessible through the
so we recommend most people to use this tools which doesn't require any installation. Now, if you really wish to have a local version of Faust on your system, this page should give you an overview of the options that you have.
Faust Compiler & libfaust
Windows
- Look for the most recent
Faust-XXX-win64.exe
on: https://github.com/grame-cncm/faust/releases
MacOS
- Look for the most recent
Faust-XXX.dmg
on: https://github.com/grame-cncm/faust/releases or use MacPorts or Homebrew package managers
Linux
- Look for the most recent
Faust-XXX.tar.gz
on: https://github.com/grame-cncm/faust/releases or use your favorite package installer depending of your distribution
Building Faust from the Source
Alternatively, you might want to build Faust from scratch. For that you can either get the latest release of the Faust source or clone it from our GitRepo. Build instructions are available here.
FaustLive
FaustLive is an "on-the-fly" compiler for Faust (see the overview of the Faust universe for more information) allowing us to instantly run Faust programs.
Windows
- Look for the most recent
FaustLive-XXX-win64.exe
on: https://github.com/grame-cncm/faustlive/releases
MacOS
- Look for the most recent
FaustLive-XXX.dmg
on: https://github.com/grame-cncm/faustlive/releases
Linux
- Look for the most recent
FaustLive-XXX.tar.gz
on: https://github.com/grame-cncm/faustlive/releases
Building FaustLive from the Source
Alternatively, you might want to build FaustLive from scratch. For that you can either get the latest release of the FaustLive source or clone it from our GitRepo.
Faustgen
Faustgen is a Max/MSP object to compile Faust code dynamically (see the overview of the Faust universe for more information).
Windows
- Look for the most recent
faustgen-XXX-win64.zip
on: https://github.com/grame-cncm/faust/releases
MacOS
- Look for the most recent
faustgen-XXX-macosx.dmg
on: https://github.com/grame-cncm/faust/releases
FaustWorks
FaustWorks is an IDE for the Faust compiler. It is not actively maintained and you should use Faust WEB IDE instead. The source code is available from the FaustWorks github repository


These elements add native-app functionality to your HTML page.
The elements in this namespace are designed to provide functionality typically available only to native apps. As the browser's functionality expands, watch this space for new elements that take advantage of them for users of NENT.
In the near future, we will publish elements for creating installable applications with features like offline-mode, pre-cached routes and low-code service-workers.
Currently, we support system-themes detection and control (dark or light-mode), native-sharing and the capture of application-wide analytics.


EXCLUSIVE: BG Knocc Out on Kodak Black Being Reckless: He Thinks Money Won't Stop
by Jessica ToddIn this flashback, BG Knocc Out addressed calling out Kodak Black for his comments about Lauren London following Nipsey Hussle's death. BG explained that he listens to Kodak's music and doesn't have anything against him, which led to a conversation about Kodak being reckless. BG stated that he doesn't think Kodak has a strong team around him, and he added that he thinks Kodak believes the money won't stop with his career and that he'll stay hot despite being out of the public eye. The conversation led to Gucci Mane cleaning up his life and image after marrying Keyshia Ka'Oir, which you can hear more of above.


Variable selection for integer and Boolean variables
[Branching]
Functions | |
| IntVarBranch | Gecode::INT_VAR_NONE (void) |
| Select first unassigned variable. | |
| IntVarBranch | Gecode::INT_VAR_RND (Rnd r) |
| Select random variable (uniform distribution, for tie breaking). | |
| IntVarBranch | Gecode::INT_VAR_MERIT_MIN (IntBranchMerit bm, BranchTbl tbl=nullptr) |
| Select variable with least merit according to branch merit function bm. | |
| IntVarBranch | Gecode::INT_VAR_MERIT_MAX (IntBranchMerit bm, BranchTbl tbl=nullptr) |
| Select variable with highest merit according to branch merit function bm. | |
| IntVarBranch | Gecode::INT_VAR_DEGREE_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest degree. | |
| IntVarBranch | Gecode::INT_VAR_DEGREE_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest degree. | |
| IntVarBranch | Gecode::INT_VAR_AFC_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_AFC_MIN (IntAFC a, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count. | |
| IntVarBranch | Gecode::INT_VAR_AFC_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_AFC_MAX (IntAFC a, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with lowest action with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_MIN (IntAction a, BranchTbl tbl=nullptr) |
| Select variable with lowest action. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with highest action with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_MAX (IntAction a, BranchTbl tbl=nullptr) |
| Select variable with highest action. | |
| IntVarBranch | Gecode::INT_VAR_CHB_MIN (IntCHB c, BranchTbl tbl=nullptr) |
| Select variable with lowest CHB Q-score. | |
| IntVarBranch | Gecode::INT_VAR_CHB_MIN (BranchTbl tbl=nullptr) |
| Select variable with lowest CHB Q-score. | |
| IntVarBranch | Gecode::INT_VAR_CHB_MAX (IntCHB c, BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score. | |
| IntVarBranch | Gecode::INT_VAR_CHB_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score. | |
| IntVarBranch | Gecode::INT_VAR_MIN_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest min. | |
| IntVarBranch | Gecode::INT_VAR_MIN_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest min. | |
| IntVarBranch | Gecode::INT_VAR_MAX_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest max. | |
| IntVarBranch | Gecode::INT_VAR_MAX_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest max. | |
| IntVarBranch | Gecode::INT_VAR_SIZE_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest domain size. | |
| IntVarBranch | Gecode::INT_VAR_SIZE_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest domain size. | |
| IntVarBranch | Gecode::INT_VAR_DEGREE_SIZE_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest degree divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_DEGREE_SIZE_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest degree divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_AFC_SIZE_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count divided by domain size with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_AFC_SIZE_MIN (IntAFC a, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_AFC_SIZE_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count divided by domain size with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_AFC_SIZE_MAX (IntAFC a, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_SIZE_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with smallest action divided by domain size with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_SIZE_MIN (IntAction a, BranchTbl tbl=nullptr) |
| Select variable with smallest action divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_SIZE_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with largest action divided by domain size with decay factor d. | |
| IntVarBranch | Gecode::INT_VAR_ACTION_SIZE_MAX (IntAction a, BranchTbl tbl=nullptr) |
| Select variable with largest action divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_CHB_SIZE_MIN (IntCHB c, BranchTbl tbl=nullptr) |
| Select variable with smallest CHB Q-score divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_CHB_SIZE_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest CHB Q-score divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_CHB_SIZE_MAX (IntCHB c, BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_CHB_SIZE_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score divided by domain size. | |
| IntVarBranch | Gecode::INT_VAR_REGRET_MIN_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest min-regret. | |
| IntVarBranch | Gecode::INT_VAR_REGRET_MIN_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest min-regret. | |
| IntVarBranch | Gecode::INT_VAR_REGRET_MAX_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest max-regret. | |
| IntVarBranch | Gecode::INT_VAR_REGRET_MAX_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest max-regret. | |
| BoolVarBranch | Gecode::BOOL_VAR_NONE (void) |
| Select first unassigned variable. | |
| BoolVarBranch | Gecode::BOOL_VAR_RND (Rnd r) |
| Select random variable (uniform distribution, for tie breaking). | |
| BoolVarBranch | Gecode::BOOL_VAR_MERIT_MIN (BoolBranchMerit bm, BranchTbl tbl=nullptr) |
| Select variable with least merit according to branch merit function bm. | |
| BoolVarBranch | Gecode::BOOL_VAR_MERIT_MAX (BoolBranchMerit bm, BranchTbl tbl=nullptr) |
| Select variable with highest merit according to branch merit function bm. | |
| BoolVarBranch | Gecode::BOOL_VAR_DEGREE_MIN (BranchTbl tbl=nullptr) |
| Select variable with smallest degree. | |
| BoolVarBranch | Gecode::BOOL_VAR_DEGREE_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest degree. | |
| BoolVarBranch | Gecode::BOOL_VAR_AFC_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count with decay factor d. | |
| BoolVarBranch | Gecode::BOOL_VAR_AFC_MIN (BoolAFC a, BranchTbl tbl=nullptr) |
| Select variable with smallest accumulated failure count. | |
| BoolVarBranch | Gecode::BOOL_VAR_AFC_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count with decay factor d. | |
| BoolVarBranch | Gecode::BOOL_VAR_AFC_MAX (BoolAFC a, BranchTbl tbl=nullptr) |
| Select variable with largest accumulated failure count. | |
| BoolVarBranch | Gecode::BOOL_VAR_ACTION_MIN (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with lowest action with decay factor d. | |
| BoolVarBranch | Gecode::BOOL_VAR_ACTION_MIN (BoolAction a, BranchTbl tbl=nullptr) |
| Select variable with lowest action. | |
| BoolVarBranch | Gecode::BOOL_VAR_ACTION_MAX (double d=1.0, BranchTbl tbl=nullptr) |
| Select variable with highest action with decay factor d. | |
| BoolVarBranch | Gecode::BOOL_VAR_ACTION_MAX (BoolAction a, BranchTbl tbl=nullptr) |
| Select variable with highest action. | |
| BoolVarBranch | Gecode::BOOL_VAR_CHB_MIN (BoolCHB c, BranchTbl tbl=nullptr) |
| Select variable with lowest CHB Q-score. | |
| BoolVarBranch | Gecode::BOOL_VAR_CHB_MIN (BranchTbl tbl=nullptr) |
| Select variable with lowest CHB Q-score. | |
| BoolVarBranch | Gecode::BOOL_VAR_CHB_MAX (BoolCHB c, BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score. | |
| BoolVarBranch | Gecode::BOOL_VAR_CHB_MAX (BranchTbl tbl=nullptr) |
| Select variable with largest CHB Q-score. |
Function Documentation
| IntVarBranch Gecode::INT_VAR_NONE | ( | void | ) | [inline] |
Select first unassigned variable.
| IntVarBranch Gecode::INT_VAR_RND | ( | Rnd | r | ) | [inline] |
Select random variable (uniform distribution, for tie breaking).
| IntVarBranch Gecode::INT_VAR_MERIT_MIN | ( | IntBranchMerit | bm, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with least merit according to branch merit function bm.
| IntVarBranch Gecode::INT_VAR_MERIT_MAX | ( | IntBranchMerit | bm, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest merit according to branch merit function bm.
| IntVarBranch Gecode::INT_VAR_DEGREE_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest degree.
| IntVarBranch Gecode::INT_VAR_DEGREE_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest degree.
| IntVarBranch Gecode::INT_VAR_AFC_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count with decay factor d.
| IntVarBranch Gecode::INT_VAR_AFC_MIN | ( | IntAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count.
| IntVarBranch Gecode::INT_VAR_AFC_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count with decay factor d.
| IntVarBranch Gecode::INT_VAR_AFC_MAX | ( | IntAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count.
| IntVarBranch Gecode::INT_VAR_ACTION_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest action with decay factor d.
| IntVarBranch Gecode::INT_VAR_ACTION_MIN | ( | IntAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest action.
| IntVarBranch Gecode::INT_VAR_ACTION_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest action with decay factor d.
| IntVarBranch Gecode::INT_VAR_ACTION_MAX | ( | IntAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest action.
| IntVarBranch Gecode::INT_VAR_CHB_MIN | ( | IntCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest CHB Q-score.
| IntVarBranch Gecode::INT_VAR_CHB_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with lowest CHB Q-score.
| IntVarBranch Gecode::INT_VAR_CHB_MAX | ( | IntCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest CHB Q-score.
| IntVarBranch Gecode::INT_VAR_CHB_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest CHB Q-score.
| IntVarBranch Gecode::INT_VAR_MIN_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest min.
| IntVarBranch Gecode::INT_VAR_MIN_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest min.
| IntVarBranch Gecode::INT_VAR_MAX_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest max.
| IntVarBranch Gecode::INT_VAR_MAX_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest max.
| IntVarBranch Gecode::INT_VAR_SIZE_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest domain size.
| IntVarBranch Gecode::INT_VAR_SIZE_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest domain size.
| IntVarBranch Gecode::INT_VAR_DEGREE_SIZE_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest degree divided by domain size.
| IntVarBranch Gecode::INT_VAR_DEGREE_SIZE_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest degree divided by domain size.
| IntVarBranch Gecode::INT_VAR_AFC_SIZE_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count divided by domain size with decay factor d.
| IntVarBranch Gecode::INT_VAR_AFC_SIZE_MIN | ( | IntAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count divided by domain size.
| IntVarBranch Gecode::INT_VAR_AFC_SIZE_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count divided by domain size with decay factor d.
| IntVarBranch Gecode::INT_VAR_AFC_SIZE_MAX | ( | IntAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count divided by domain size.
| IntVarBranch Gecode::INT_VAR_ACTION_SIZE_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest action divided by domain size with decay factor d.
| IntVarBranch Gecode::INT_VAR_ACTION_SIZE_MIN | ( | IntAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest action divided by domain size.
| IntVarBranch Gecode::INT_VAR_ACTION_SIZE_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest action divided by domain size with decay factor d.
| IntVarBranch Gecode::INT_VAR_ACTION_SIZE_MAX | ( | IntAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest action divided by domain size.
| IntVarBranch Gecode::INT_VAR_CHB_SIZE_MIN | ( | IntCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest CHB Q-score divided by domain size.
| IntVarBranch Gecode::INT_VAR_CHB_SIZE_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest CHB Q-score divided by domain size.
| IntVarBranch Gecode::INT_VAR_CHB_SIZE_MAX | ( | IntCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest CHB Q-score divided by domain size.
| IntVarBranch Gecode::INT_VAR_CHB_SIZE_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest CHB Q-score divided by domain size.
| IntVarBranch Gecode::INT_VAR_REGRET_MIN_MIN | ( | BranchTbl | tbl = nullptr |
) | [inline] |
Select variable with smallest min-regret.
The min-regret of a variable is the difference between the smallest and second-smallest value still in the domain.
| IntVarBranch Gecode::INT_VAR_REGRET_MIN_MAX | ( | BranchTbl | tbl = nullptr |
) | [inline] |
Select variable with largest min-regret.
The min-regret of a variable is the difference between the smallest and second-smallest value still in the domain.
| IntVarBranch Gecode::INT_VAR_REGRET_MAX_MIN | ( | BranchTbl | tbl = nullptr |
) | [inline] |
Select variable with smallest max-regret.
The max-regret of a variable is the difference between the largest and second-largest value still in the domain.
| IntVarBranch Gecode::INT_VAR_REGRET_MAX_MAX | ( | BranchTbl | tbl = nullptr |
) | [inline] |
Select variable with largest max-regret.
The max-regret of a variable is the difference between the largest and second-largest value still in the domain.
| BoolVarBranch Gecode::BOOL_VAR_NONE | ( | void | ) | [inline] |
Select first unassigned variable.
| BoolVarBranch Gecode::BOOL_VAR_RND | ( | Rnd | r | ) | [inline] |
Select random variable (uniform distribution, for tie breaking).
| BoolVarBranch Gecode::BOOL_VAR_MERIT_MIN | ( | BoolBranchMerit | bm, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with least merit according to branch merit function bm.
| BoolVarBranch Gecode::BOOL_VAR_MERIT_MAX | ( | BoolBranchMerit | bm, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest merit according to branch merit function bm.
| BoolVarBranch Gecode::BOOL_VAR_DEGREE_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with smallest degree.
| BoolVarBranch Gecode::BOOL_VAR_DEGREE_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest degree.
| BoolVarBranch Gecode::BOOL_VAR_AFC_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count with decay factor d.
| BoolVarBranch Gecode::BOOL_VAR_AFC_MIN | ( | BoolAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with smallest accumulated failure count.
| BoolVarBranch Gecode::BOOL_VAR_AFC_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count with decay factor d.
| BoolVarBranch Gecode::BOOL_VAR_AFC_MAX | ( | BoolAFC | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest accumulated failure count.
| BoolVarBranch Gecode::BOOL_VAR_ACTION_MIN | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest action with decay factor d.
| BoolVarBranch Gecode::BOOL_VAR_ACTION_MIN | ( | BoolAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest action.
| BoolVarBranch Gecode::BOOL_VAR_ACTION_MAX | ( | double | d, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest action with decay factor d.
| BoolVarBranch Gecode::BOOL_VAR_ACTION_MAX | ( | BoolAction | a, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with highest action.
| BoolVarBranch Gecode::BOOL_VAR_CHB_MIN | ( | BoolCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with lowest CHB Q-score.
| BoolVarBranch Gecode::BOOL_VAR_CHB_MIN | ( | BranchTbl | tbl | ) | [inline] |
Select variable with lowest CHB Q-score.
| BoolVarBranch Gecode::BOOL_VAR_CHB_MAX | ( | BoolCHB | c, | |
| BranchTbl | tbl | |||
| ) | [inline] |
Select variable with largest CHB Q-score.
| BoolVarBranch Gecode::BOOL_VAR_CHB_MAX | ( | BranchTbl | tbl | ) | [inline] |
Select variable with largest CHB Q-score.


Front End/MEAN Developer
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Our Exchange's diverse markets span futures and options on interest rates, commodities, indexes and FX, as well as equities and equity options. These markets are relied upon by international market participants, along with our post-trade infrastructure. We are uniquely positioned to provide end-to-end risk management solutions to our customers around the world Job Description Talented MEAN-Stack developer to translate business requirements into homegrown applications. Day to day responsibilities will be as primary developer on NodeJS content management tools and projects. Responsibilities Responsible for coding application, testing software as needed. Strong written and verbal communication skills. Collaboration with team of developers.
Front End Web Developer
Looking for a MEAN UI Architect with a deep and sometimes insatiable curiosity.
You desire to make things from scratch, make them better and make them memorable.
You have a passion for technology and are an enthusiastic about sharing what is possible.
You firmly believe that technology can solve even the most complex of problems and tell some of the most
interesting stories.
You have demonstrated senior-level Frontend Development and Architecture experience ideally working with varied
teams on major web applications. You have a strong knowledge of front-end web, mid-level applications, social
and digital platforms and the technologies that make them work. You carry a skill set that allows
you to plan, collaborate and execute great digital work all in equal measure.
Software Engineer (MEAN Stack)
Our client is pioneering a cloud-based platform to enable the real-time design, ordering, and production
of our therapeutic products. A critical component of this platform is an automated, integrated production
pipeline. As an Informatics Software Engineer, you will work with the on-site engineering team to deliver
user-facing web applications built on the MEAN stack (MongoDB, Express, AngularJS, node.js). You will also
develop client-side tools in to integrate production instruments and systems.
The successful candidate will enjoy working closely with users to understand their workflows and needs.
They will have a talent for understanding complex problems and designing solutions. They will also be
comfortable working in an intense, fast-paced environment. The work done by the software engineering
team will dramatically improve the performance and quality of production pipeline, which will accelerate
the development of our product.
Mean Fullstack Developer
The Associate Software Engineer is a full stack software engineer that is responsible for the
development, testing and deployment of world-class software. In this position the associate will
be responsible for delivering full vertical slices of system functionality by developing the user
interface, middle tier and data tier for product owner defined stories.
We are building out a new technology platform and applications using open source technology, with
a heavy emphasis on angular.js. Other technologies include node.js, MongoDB, 0MQ, python, HTML5 and CSS.
We firmly believe in the power of combining technology with entrepreneurial spirit.
Mean Stack UX Designer/Developer
The US federal government’s EHR Meaningful Use incentive program has positioned the Electronic Medical
Record (EMR) as a “must have” for every organization that delivers healthcare. As a result, health
systems and medical practices are amassing enormous stores of clinical, operational and financial data.
Leveraging that data to improve health, lower costs, and deliver excellent patient experience is the
promise of the EMR; analytics is a key component to unlocking that potential.
This project will establish a Big Data ecosystem for patient care information; enabling multiple
stakeholders to securely process and analyze the data at scale. The role of the User Experience (UX)
Designer/Developer is to be the creator and chief designer of how people interact with this environment.
Full Stack Engineer (MEAN Stack)
Incapsulate provides services from conception to delivery. We are a team of developers, designers,
and analysts who are committed to helping businesses solve problems using state-of-the-art technology.
We believe that the best results are achieved by people who are passionate about what they do and
continually strive to be better - those are the people we hire.
Incapsulate is searching for a Full Stack Engineer (MEAN stack) for our client in the Ashburn, VA area.
This position will require some onsite work with the ability to also work remotely. This is an
opportunity for a Mid / Senior level developer to take the next step in their career. You must be a
confident self-starter - someone who can take ownership of the code and think of it as their own.
You must also be someone who wakes up in the morning and thinks “how can I make this better”?
Sr. Software Developer - MEAN Stack
Our client is a leading digital marketing agency in Midtown who is
currently looking for a solid front end developer. Excellent benefits, awesome work environment.
Apply today for immediate consideration.
Responsibilities:
- Concept, plan, and execute with integrated teams to create creative work
- Contribute to and be proactive with the ongoing improvement of internal processes as they relate to technology usage and implementation
- Ability to execute on basic development needs including display media, landing pages, social activations, small websites and mobile/tablet optimized (responsive) experiences reference and support on backend, data and server side technologies
- Lead UX, architecture and functional requirements development
- Prototype development for proving out solutions and user testing
- Liaise with third-party production partners and development teams to assure proper standards and practices through collaboration, code reviews and QA processes
Sr. Front End Developer (MEAN Stack)
Modern application design and natural user interfaces are transforming the web into highly
interactive experiences.We are seeking an experienced Front-End Developer that has the ability to transform
beautiful UI designs into working applications.
The ideal candidate will have experience with HTML5, JavaScript, AngularJS, and Bootstrap CSS, RESTAngular,
Node, Mongoose, Mongo. You will also be able to translate Wireframes and PSD Designs into functional web apps
using HTML5, AngularJS, Node.js, and Mongo Binding of UI elements to JavaScript object models Creating RESTful
services with Node.js Architect scalable web architectures.


A scalable angular architecture (part 2)
The sandbox pattern
Introduction
A while ago I released a blogpost called “A scalable angular 2 architecture” which was all about creating large scale enterprise webapplications. The title should actually have been “A scalable angular architecture” (It’s just angular) or even, “A scalable SPA architecture” since we can use the same architecture for React or Vue.js.
It’s been over a year now, since I’ve been introducing that specific architecture in companies and projects. Nowadays, I know quite a few people that are using this particular architecture in their projects. Often, I ask developers about how they feel about this SPA-architecture and the feedback is mostly this: They believe its a clear and opiniated way to structure their code, and they have no trouble, finding their way into the codebase.
A big part of the article in question, was about explaining the so called “sandboxes”, a personal terminology for a very specific kind of facade. Since writing that article raised a bunch of questions about these “sandboxes”, it seemed like a good idea to write a followup article.
In this article we will cover what sandboxes are really about and why I introduced them in the first place.
Note: This architecture is only one way of structuring your applications and the statements being made in this article are completely personal and might be opiniated.
What is a sandbox in a nutshell
In real life it’s a place where we can put our children together with a limited set of toys. That way, they can only play with those toys and have fun, and we as parents wouldn’t have to worry about them running around in the garden.
In this architecture a sandbox is a controlled environment, and the only place where components can consume pieces of the application. It’s the only interface for our smart components to communicate with the rest of the application. It’s a facade that abstracts away logic from our components. It does have very specific logic though.
Here is why I introduced sandboxes
One of the most challenging parts of creating big clientside javascript applications is encapsulation and dividing responsibilities. Having a decent encapsulated structure where every component, service, class has a clear reason of existence, makes it easy for us as developers to maintain the code and develop new features.
When writing single-page-applications one could face the following challenges:
Challenge 1: How do we structure components?
Since this topic is covered in the previous article, we won’t do a deepdive in this subject. We use the smart/dumb component approach to structure our components.
Challenge 2: How do we handle statemanagement?
Another topic covered by the previous article is how handle our statemanagement. In short: we use @ngrx/store, or another redux implementation that embraces reactive programming.
Challenge 3
The first two challenges are met: we have a clear and structured presentational layer and because of the fact that we use @ngrx/store (or any statemanagement tool for that matter), we have a clear way of managing state. We can optimise performance with immutable data and still have clear unidirectional dataflow.
However, there are still a few pieces of the puzzle missing, which might raise the following questions:
How do we separate the presentation-layers from the rest of the application?
If we inject whatever we want in our smart components, their constructors might get huge in no-time. Do those smart components really need to know everything about the application? Do they need to know where everything lives and what service it should call from which module in the application? Wouldn’t it be easier if they had an interface they could talk to, that just handles things for them? It would certainly help to decouple angular modules from each other, and keep the responsibility of those smart components clear and compact. This is something a sandbox might be able to help us with.
Do our components need to know about redux?
Redux is an awesome library/principle that helps us ace statemanagement, but it’s a really heavy dependency if you don’t manage it carefully. What if your very specific redux actions would be everywhere in your components and services? In that case your codebase would be completely affected by it and it would be very hard to use another statemanagement tool in the future. It would be impossible to share that code in non-redux applications. It would be a huge depencency… What if you like to switch to firebase for instance, or MOBX, or even write your own statemanagement tool. You would need to refactor your whole application. The sole principle of redux is to manage state, and I prefer to use it that way. That is also why I don’t really use effects, thunk or saga. (That’s a personal preference) For me this means: Use redux only to manage state, not to trigger backend actions etc One could even argue that the action-dispatching and state-selecting should be combined in central places in your appliction. Let’s add that logic to the sandboxes as well.
Do our HTTP services need to know about a redux implementation?
Well, as the name already reveals. An HTTP service is all about doing HTTP communication and returning asynchronous objects to the components. An HTTP service should have no notion about redux at all nor any other statemanagement tool. Its sole purpose is fetching data and returning it. Let’s take a look at the following example for instance. We fetch an array of wines from the backend and we want to persist that in a redux store. This is an example of an HTTP service that is being abused to achieve that:
class WineService {
// bad
fetchWines(): void {
this.api.get('url')
.subscribe(wines =>
this.store.dispatch({type: 'SET_WINES', payload: {wines}});
}
}
This code doesn’t belong here, it’s not the responsibility of that service. The question is where does it belong? Does it belong in the component? They would get dirty and reduxy in no-time… *Again, this would belong to the sandbox!.
An http service should look like this:
class WineService {
// good
fetchWines(): Observable<any> {
return this.api.get('url')
}
}
On what place do we dispatch actions to the store?
Err…, the sandbox =)
Where do we handle optimistic updates
Again, that would be the sandbox
How to NOT make your whole application smell like REDUX
Components and services should not know about redux, so the store interaction should be in a very specific place as well.
I bet you saw it coming, but YES! That’s what a sandbox is all about! It’s about separation of concerns and having a very specific interface to handle this very specific logic.
What does a sandbox do?
A sandbox has a very specific responsibility:
- It lets your module communicate with other modules without that module having to know about the rest of the application
- It sends Redux actions
- It exposes streams of data (coming from Redux, Firebase or other technologies)
- It handles optimistic updates
- It keeps the containers stupid enough
What’s very specific on a sandbox?
- They handle what the container tells them to handle, without the container having to know how…
- They interact between modules
- They handle a lot of redux (or similar technology) logic
- The functions mostly have a void return-type, unless we want to be able to cancel HTTP calls
- They expose observables/streams
- It mostly contains redundant code, but no redundant logic (it’s all about KISS instead of DRY)
- Mostly every module has a sandbox, unless it contains a huge amount of logic (in that case we could implement a sandbox for every container)
- It does not contain business logic, ever…
- It mostly doesn’t contain if-statements, that would be business logic right?!
- It gives us a clear overview of the responsabilities of a module.
What doesn’t a sandbox do?
- It doesn’t contain business logic. It contains sandbox logic (see above).
- It doesn’t contain presentation logic, like routing etc.
- It doesn’t do HTTP calls directly, it delegates to http services.
- It doesn’t let your components do whatever they want =)
An example
@Injectable()
export class StockSandbox {
// these are the store select statements
// It's pretty dirty to have them all over our components
// Our components just want to get streams of data, no matter where they come from
wines$ = this.store.select(state => state.wines);
isAuthenticated$ = this.store.select(state => state.authentication.isAuthenticated);
constructor(
private store: Store<ApplicationState>,
private stockService: StockService,
private fooService: FooService) {
}
addWine(wine: Wine): void {
// use the stockservice to add a wine
// and when it's done handle our statemanagement
this.stockService.add(wine).subscribe((wine: Wine) => {
this.store.dispatch(new AddWine(wine));
}, () => this.handleError());
}
removeWine(wine: Wine): void {
// removing a wine can be done optimistically!
// this would certainly improve the performance and snappyness of our app
// this would be sandbox logic
let action = new RemoveWine(wine._id);
this.store.dispatch(action);
this.stockService.remove(wine).subscribe(
() => {},
// if the call failed, we have to undo an action. This is sandbox logic as well!
() => this.store.dispatch({type: UNDO_ACTION, payload: action}););
}
fetchWine(id: string): Observable<Wine> {
// simple delegation of fetching something: Sandbox logic
return this.stockService.fetchWine(id).share();
}
notifyAnotherModule(): void {
// our components should NOT know where fooService is, or what it does
// it should just tell its sandbox to handle a specific action
this.fooService.doSomething();
}
}
Look how clean this smart component has become:
export class StockPageContainer {
wines$ = this.sb.wines$; // Does this stream comes from Redux? or Firebase (I don't need to know)
numberOfWines$ = this.wines$.map(wines => sumBy(wines, (wine: Wine) => wine.inStock));
// a clean constructor makes it easy to test.
// Util dependencies or presentational dependencies like a router
// do not belong in the sandbox.
// They belong right here in the component, in the presentational layer.
constructor(private sb: StockSandbox, private router: Router) {
}
onRemove(wine: Wine): void {
// Hey Sandbox!  It's not my responsability
this.sb.notifyAnotherModule();
}
}
I hope this clarifies the purpose of the sandbox and why I have introduced them in my projects. If you still have questions about this approach, don’t hesitate to contact me =)
Special thanks
Special thanks to Manfred Steyer (@manfredsteyer) and Juri Strumpflohner (@juristr) for reviewing this article!


Late one night in 1849 a drunken sailor murders his former captain on the forlorn, windswept island of St. Pierre in maritime Canada. As French subjects, the local magistrates must comply with the law of the republic and have him guillotined. Since the island lacks such a device, a requisition order goes out to fetch both a guillotine and an executioner. Until they arrive from St. Martinique, the prisoner will be kept under the custody of the captain of the local jail, who is married to "Madame La," as the local denizens refer to the wife of La Capitain. With these spare elements of a tale based on true events, director Patrice LeConte ("Ridicule" was his credit) delivers a powerful dramatic message in "The Widow of St. Pierre" against capital punishment and for the redemptive power of love and compassion.
Soon after being jailed, the prisoner Neel Auguste (played by Emir Kusturica, the Yugoslav director of "Underground") is summoned to the apartment of the captain whose wife has taken pity on him. Since it might take months for the guillotine to arrive, there is no reason not to make him comfortable until that moment arrives. Played by Juliette Binoche, Madame La is innocent of suspicion or fear. Under her supervision, Neel builds a greenhouse in the jail's courtyard. Holding up a flower, she tells him that the goal is to bring life and beauty into a hostile environment. With her eyes on the hulking, withdrawn prisoner, it is clear that she seeks to reap life and beauty from him as well. After the greenhouse is finished, she takes him with her everywhere in the village to find other chores to keep him busy, from repairing roofs to saving the life of a woman on a runaway wagon.
While the captain (Daniel Auteuil) is deeply in love with his wife and anxious to assist her in this reclamation project, he is by no means a typical jailer. Of an aristocratic bearing, he seems to prefer riding his horse "Salt Cod" on the rocky cliffs of St. Pierre to the mundane details of jail-keeping. Even before Madame La ever takes an interest in the prisoner, he decides to leave the cell door open. After all, on this remote island there is virtually no escape.
Alongside the human drama of "The Widow of St. Pierre," the landscape and population of the island help to shape the narrative of the film and lend it a particularly somber tone, especially during the winter months which anticipate the arrival of the guillotine. In their black oilskin parkas, the local sailors and dockhands stand out vividly against the drifting snow like hunched-over figures in a Breughel landscape. And it is these humble, faceless and nameless men and women of St. Pierre who eventually rally to Neel. When the local magistrates put up posters advertising for an executioner, they tear them down one by one.
The captain and his wife are not ideologues. The only time they seem to take notice of politics is when he sits by the fireside in their living room reading a Paris newspaper. He mentions to Madame La that there is strife and bloodshed in the streets, but does not connect it to the more immediate drama facing them and the prisoner. Of course only one year earlier all of Europe was being swept by revolution against the old order. France, which was once in the vanguard, had now become a bastion of reaction all in the name of the values of "the republic". The captain and his wife seem to adhere to an earlier notion of civic duty that has not been tainted by the guillotine or the Thermidor republic.
Essentially "Widow of St. Pierre" is not a political film, although it speaks to a question addressing all progressives, namely how to live by a set of principles. It is this higher calling that the main characters of the film respond to, in the face of the callousness and brutality of the existing order. This is also the theme of "Ridicule," LeConte's last film which dramatized the struggle of an aristocrat in pre-1789 France to rid his ancestral lands of the malarial swamp that is killing his subjects. To gain an audience with the King, he discovers that he must master the art of "ridicule", the verbal art of one-upsmanship that provides a cruel entertainment for the court's inner circle.
Although not remarked on by the film critics, "Widow of St. Pierre" evokes another 19th century tale of capital punishment. While Herman Melville's "Billy Budd" also draws together an aristocratic officer and a commoner facing execution, the decision here is to obey the law rather than a higher justice. The captain of a British warship decides to proceed with an execution despite all the evidence pointing to an accidental death provoked by extreme cruelty from another officer whom the ship's crew hates and fears. Budd, like Neel, is beloved by the commoners.
Unlike St. Pierre's captain, the captain in Melville's tale opts for following the letter of the law. While this tale has been analyzed and deconstructed by modern literary scholars as having to do with anything from homosexuality to theology, it is essentially a story like most of Melville's, one deeply entwined with the struggle of ordinary working people against a cruel, class-divided society. For an analysis from this perspective, see the article titled "Billy Budd and Capital Punishment" at http://andromeda.rutgers.edu/~hbf/bbcap.htm by H. Bruce Franklin, one of the USA's most distinguished Melville scholars and a long-time socialist.
"Widow of St. Pierre" is currently being shown at the Paris Theater in NYC and can probably be seen at art-houses in major cities around the United States.
Louis Proyect Marxism mailing list: http://www.marxmail.org
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We use a web interface in order to manage clients that are using our mobile application. This creates a very user friendly experience on both sides.
Our Android application is the user side of the experience. The user can do a number of things. They can submit an application for review to get insurance, view their insurance card, submit a claim. There are features like, crash detection and location tracking when submitting a claim.
The web manager is able to review applications, accepting or denying them. The manager may also review any claims that have been made and the location in which the incident happened, as well as search the Firebase finding a specific user.
The usage for our project is for a car insurance company to be able to have a very easy time interacting with their clients, while giving the clients a user friendly interface to be able to manage their insurance and submit a claim.The Android application to go along with the project can detect crashes as well as give location data for the insurance company to get a location of which a claim has been submitted. The company gets a page that is connected to the database of users where they can manage the users data, as well as manage any claims and accept or deny their policies.


+++ title="Goka" linkTitle="Goka" +++ {{< blocks/cover title="Welcome to Goka: A framework to build stream processing applications with Apache Kafka in Go!" image_anchor="top" height="full" color="orange" >}}
{{< /blocks/cover >}} {{% blocks/lead color="primary" %}} Goka provides a simple but powerful application architecture that centers around the design principle of Turning databases inside out by Martin Kleppmann. {{% /blocks/lead %}}


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CIPITR
Complex Imperative Program Induction with Terminal Rewards
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Complex Forms of KB based Question Answering in real world problems
Question answering (QA) is a core problem in AI; almost every interactive task involving natural language can be modeled as a QA problem. For e.g. a QA model should be able to answer the following questions
- ‘Can Tazocin or Zosyn be used intravenously to treat renal problems in pregnant women?’ (in the healthcare domain)
- ‘How many investors purchased restricted stock within a quarter of opening their account?’ (in finance domain)
- ‘Which of the last five movies by the Russo Brothers gained most positive sentiment from the public?’ (in open domain)
- ‘Which countries have more rivers and lakes than Brazil?‘ (Geographical domain)
Traditional and Contemporary Ways of Solving QA
In the recent years the proliferation of deep learning has revolutionized the modeling of QA tasks, taking away the dependency on poorly scalable human-intensive rule based models and ushering a new era of learning neural representations of the query and its transformation to reach the answer.
However such models still are prone to making blatant mistakes, especially as queries become more and more complex and even when it answers correctly, the reasoning followed by it is not interpretable by end users.
Neural Program Induction for Complex Tasks
Rather, a more intuitive way of approaching a complex task like QA would be to learn to deconstruct it into a sequence of simpler steps, as humans usually do. Motivated by this, researchers in the deep learning era, developed a new learning paradigm, called Neural Program Induction, through which the AI model can be taught to procedurally decompose a complex task into a program i.e. a sequence of atomic actions, which on execution will lead to the answer.
However research on this paradigm has been restricted to simpler tasks like sorting or addition of numbers or simple multi-hop QA over small database tables or knowledge bases, and more importantly under impractical assumptions that the true program is provided as supervision during training by an Oracle.
Our Contribution: Weak Supervised Neural Program Induction for Complex KBQA
Considering these practical limitations, we made a very first attempt at training a model, Complex Imperative Program Induction from Terminal Rewards (CIPITR) to answer complex questions, without either of the constraints used in the past.
- Firstly the questions can be complex; requiring sometimes 7-12 steps of reasonings of different flavors, while some steps might require logical reasoning, other steps might need comparative or quantitative analysis.
- Secondly the questions need to be answered based on a large scale knowledge base of over 50 million facts.
- And thirdly and most importantly, the model is trained with only the question-answer pairs as supervision and can learn to induce program without the need for the oracle program during training.
Challenges Faced by CIPITR
For a complex question like ‘How many investors purchased restricted stock within a quarter of opening their account?’ the model has to first understand that the query is talking about concepts like ‘investors, ‘restricted stock’, ‘quarter’ and relations between concepts like ‘purchased’ and ‘opening’ and then map them to facts in the Financial Knowledge Base. Even when this is done perfectly, the space of possible programs induced is exponential (~10^19), out of which only a few can yield a positive reward. For e.g.
- CIPITR has to first find out which investors opened account, and then which of them purchased restricted stock.
- Next it has to find the time of purchasing and opening account and then apply the time constraint of a quarter and finally count the number of such investors.
- Only if this decomposition was correctly done, the model would reach the correct answer and get some positive reward.
Overcoming the challenges in CIPITR through Neural Symbolic Reasoning
This extreme reward sparsity compounded by the sheer size of the program space increases the variance of the model resulting in local minima issues and learning instability. In order to pragmatically search in this combinatorial space of possible programs, the neural model has to incorporate generic programming rules, just as human programmers do, as symbolic constraints in its search. Along with that it should also be able to incorporate task or schema level constraints, to ensure that the programs generated are indeed consistent with the background Knowledge Base (KB) and can generate a meaningful answer.
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Performance of CIPITR in comparison to contemporary Program Induction Models
Overcoming these challenges, CIPITR scored at least 3x higher than the competing systems for moderately complex queries requiring 2–5 step programs. And on one of the hardest class of queries (comparative reasoning) with 5–10 steps, CIPITR outperformed state-of-the-art neural program induction models by a factor of 89 times.
Applicability of CIPITR
This makes CIPITR one of the very first successes at widening the applicability of complex program induction paradigm to various real-world settings, by eliminating the need for oracle program supervision during training. And it also shows significant potential in pushing the frontiers of complex reasoning tasks in various settings, like answering questions or conversing strategically over multiple modalities (like text/images/videos) in a more human-like fashion. This research on Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs has been published in MIT Press Journals, and will be presented at this week’s ACL Conference in Florence, Italy.
Code Demo


Man shot dead in Brooklyn, stray bullet hits school bus
by Daniel CassadyA school bus with a kid inside was hit by a stray bullet when a man was gunned down on a Brooklyn street Tuesday morning.
The gunfire went off just before 9 a.m. near Pennsylvania and Dumont avenues in East New York following a dispute between a group of men on the street, according to police.
The 32-year-old victim was hit four times in the abdomen and taken to Brookdale University Medical Center, where he was pronounced dead, authorities said.
A stray bullet from the hail of gunfire hit a nearby school bus that had a student in it, but the child was not injured, police said.


From the above formulas of substances, write down only the oxides formulas and give them the names: KOH, P2O5, CuOH, HCl, CO, Ca (OH) 2, MgO, CH4, Cl2O7, NO, HNO3.