Automatic differentiation package - torch.autograd¶
torch.autograd provides classes and functions implementing automatic
differentiation of arbitrary scalar valued functions. It requires minimal
changes to the existing code - you only need to wrap all tensors in
Variable
objects.
-
torch.autograd.
backward
(variables, grad_variables=None, retain_graph=None, create_graph=None, retain_variables=None)[source]¶ Computes the sum of gradients of given variables w.r.t. graph leaves.
The graph is differentiated using the chain rule. If any of
variables
are non-scalar (i.e. their data has more than one element) and require gradient, the function additionally requires specifyinggrad_variables
. It should be a sequence of matching length, that contains gradient of the differentiated function w.r.t. corresponding variables (None
is an acceptable value for all variables that don’t need gradient tensors).This function accumulates gradients in the leaves - you might need to zero them before calling it.
Parameters: - variables (sequence of Variable) – Variables of which the derivative will be computed.
- grad_variables (sequence of (Tensor, Variable or None)) – Gradients w.r.t.
each element of corresponding variables. Any tensors will be
automatically converted to Variables that are volatile unless
create_graph
isTrue
. None values can be specified for scalar Variables or ones that don’t require grad. If a None value would be acceptable for all grad_variables, then this argument is optional. - retain_graph (bool, optional) – If
False
, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option toTrue
is not needed and often can be worked around in a much more efficient way. Defaults to the value ofcreate_graph
. - create_graph (bool, optional) – If
True
, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults toFalse
, unlessgrad_variables
contains at least one non-volatile Variable.
-
torch.autograd.
grad
(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=None, only_inputs=True, allow_unused=False)[source]¶ Computes and returns the sum of gradients of outputs w.r.t. the inputs.
grad_outputs
should be a sequence of length matchingoutput
containing the pre-computed gradients w.r.t. each of the outputs. If an output doesn’t require_grad, then the gradient can beNone
). Gradients can be given as Tensors when one doesn’t need the graph of the derivative, or as Variables, in which case the graph will be created.If
only_inputs
isTrue
, the function will only return a list of gradients w.r.t the specified inputs. If it’sFalse
, then gradient w.r.t. all remaining leaves will still be computed, and will be accumulated into their.grad
attribute.Parameters: - outputs (sequence of Variable) – outputs of the differentiated function.
- inputs (sequence of Variable) – Inputs w.r.t. which the gradient will be
returned (and not accumulated into
.grad
). - grad_outputs (sequence of Tensor or Variable) – Gradients w.r.t. each output.
Any tensors will be automatically converted to Variables that are
volatile unless
create_graph
isTrue
. None values can be specified for scalar Variables or ones that don’t require grad. If a None value would be acceptable for all grad_variables, then this argument is optional. - retain_graph (bool, optional) – If
False
, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option toTrue
is not needed and often can be worked around in a much more efficient way. Defaults to the value ofcreate_graph
. - create_graph (bool, optional) – If
True
, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults toFalse
, unlessgrad_variables
contains at least one non-volatile Variable. - only_inputs (bool, optional) – If
True
, gradient w.r.t. leaves that are part of the graph, but don’t appear ininputs
won’t be computed and accumulated. Defaults toTrue
. - allow_unused (bool, optional) – If
False
, specifying inputs that were not used when computing outputs (and therefore their grad is always zero) is an error. Defaults toFalse
.
Variable¶
API compatibility¶
Variable API is nearly the same as regular Tensor API (with the exception
of a couple in-place methods, that would overwrite inputs required for
gradient computation). In most cases Tensors can be safely replaced with
Variables and the code will remain to work just fine. Because of this,
we’re not documenting all the operations on variables, and you should
refer to torch.Tensor
docs for this purpose.
In-place operations on Variables¶
Supporting in-place operations in autograd is a hard matter, and we discourage their use in most cases. Autograd’s aggressive buffer freeing and reuse makes it very efficient and there are very few occasions when in-place operations actually lower memory usage by any significant amount. Unless you’re operating under heavy memory pressure, you might never need to use them.
In-place correctness checks¶
All Variable
s keep track of in-place operations applied to them, and
if the implementation detects that a variable was saved for backward in one of
the functions, but it was modified in-place afterwards, an error will be raised
once backward pass is started. This ensures that if you’re using in-place
functions and not seeing any errors, you can be sure that the computed
gradients are correct.
-
class
torch.autograd.
Variable
[source]¶ Wraps a tensor and records the operations applied to it.
Variable is a thin wrapper around a Tensor object, that also holds the gradient w.r.t. to it, and a reference to a function that created it. This reference allows retracing the whole chain of operations that created the data. If the Variable has been created by the user, its grad_fn will be
None
and we call such objects leaf Variables.Since autograd only supports scalar valued function differentiation, grad size always matches the data size. Also, grad is normally only allocated for leaf variables, and will be always zero otherwise.
Variables: - data – Wrapped tensor of any type.
- grad – Variable holding the gradient of type and location matching
the
.data
. This attribute is lazily allocated and can’t be reassigned. - requires_grad – Boolean indicating whether the Variable has been created by a subgraph containing any Variable, that requires it. See Excluding subgraphs from backward for more details. Can be changed only on leaf Variables.
- volatile – Boolean indicating that the Variable should be used in inference mode, i.e. don’t save the history. See Excluding subgraphs from backward for more details. Can be changed only on leaf Variables.
- is_leaf – Boolean indicating if the Variable is a graph leaf (i.e if it was created by the user).
- grad_fn – Gradient function graph trace.
Parameters: -
backward
(gradient=None, retain_graph=None, create_graph=None, retain_variables=None)[source]¶ Computes the gradient of current variable w.r.t. graph leaves.
The graph is differentiated using the chain rule. If the variable is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying
gradient
. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t.self
.This function accumulates gradients in the leaves - you might need to zero them before calling it.
Parameters: - gradient (Tensor, Variable or None) – Gradient w.r.t. the
variable. If it is a tensor, it will be automatically converted
to a Variable that is volatile unless
create_graph
is True. None values can be specified for scalar Variables or ones that don’t require grad. If a None value would be acceptable then this argument is optional. - retain_graph (bool, optional) – If
False
, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way. Defaults to the value ofcreate_graph
. - create_graph (bool, optional) – If
True
, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults toFalse
, unlessgradient
is a volatile Variable.
- gradient (Tensor, Variable or None) – Gradient w.r.t. the
variable. If it is a tensor, it will be automatically converted
to a Variable that is volatile unless
-
detach
()[source]¶ Returns a new Variable, detached from the current graph.
Result will never require gradient. If the input is volatile, the output will be volatile too.
Note
Returned Variable uses the same data tensor, as the original one, and in-place modifications on either of them will be seen, and may trigger errors in correctness checks.
-
register_hook
(hook)[source]¶ Registers a backward hook.
The hook will be called every time a gradient with respect to the variable is computed. The hook should have the following signature:
hook(grad) -> Variable or None
The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of
grad
.This function returns a handle with a method
handle.remove()
that removes the hook from the module.Example
>>> v = Variable(torch.Tensor([0, 0, 0]), requires_grad=True) >>> h = v.register_hook(lambda grad: grad * 2) # double the gradient >>> v.backward(torch.Tensor([1, 1, 1])) >>> v.grad.data 2 2 2 [torch.FloatTensor of size 3] >>> h.remove() # removes the hook
Function¶
-
class
torch.autograd.
Function
[source]¶ Records operation history and defines formulas for differentiating ops.
Every operation performed on
Variable
s creates a new function object, that performs the computation, and records that it happened. The history is retained in the form of a DAG of functions, with edges denoting data dependencies (input <- output
). Then, when backward is called, the graph is processed in the topological ordering, by callingbackward()
methods of eachFunction
object, and passing returned gradients on to nextFunction
s.Normally, the only way users interact with functions is by creating subclasses and defining new operations. This is a recommended way of extending torch.autograd.
Each function is meant to be used only once (in the forward pass).
Variables: requires_grad – Boolean indicating whether the backward()
will ever need to be called.Examples:
>>> class Exp(Function): >>> >>> @staticmethod >>> def forward(ctx, i): >>> result = i.exp() >>> ctx.save_for_backward(result) >>> return result >>> >>> @staticmethod >>> def backward(ctx, grad_output): >>> result, = ctx.saved_variables >>> return grad_output * result
-
static
backward
(ctx, *grad_outputs)[source]¶ Defines a formula for differentiating the operation.
This function is to be overriden by all subclasses.
It must accept a context ctx as the first argument, followed by as many outputs did
forward()
return, and it should return as many tensors, as there were inputs toforward()
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input.The context can be used to retrieve variables saved during the forward pass.
-
static
forward
(ctx, *args, **kwargs)[source]¶ Performs the operation.
This function is to be overriden by all subclasses.
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
The context can be used to store variables that can be then retrieved during the backward pass.
-
static
Profiler¶
Autograd includes a profiler that lets you inspect the cost of different
operators inside your model - both on the CPU and GPU. There are two modes
implemented at the moment - CPU-only using profile
.
and nvprof based (registers both CPU and GPU activity) using
emit_nvtx
.
-
class
torch.autograd.profiler.
profile
(enabled=True)[source]¶ Context manager that manages autograd profiler state and holds a summary of results.
Parameters: enabled (bool, optional) – Setting this to False makes this context manager a no-op. Default: True
.Example
>>> x = Variable(torch.randn(1, 1), requires_grad=True) >>> with torch.autograd.profiler.profile() as prof: ... y = x ** 2 ... y.backward() >>> # NOTE: some columns were removed for brevity ... print(prof) ------------------------------------- --------------- --------------- Name CPU time CUDA time ------------------------------------- --------------- --------------- PowConstant 142.036us 0.000us N5torch8autograd9GraphRootE 63.524us 0.000us PowConstantBackward 184.228us 0.000us MulConstant 50.288us 0.000us PowConstant 28.439us 0.000us Mul 20.154us 0.000us N5torch8autograd14AccumulateGradE 13.790us 0.000us N5torch8autograd5CloneE 4.088us 0.000us
-
export_chrome_trace
(path)[source]¶ Exports an EventList as a Chrome tracing tools file.
The checkpoint can be later loaded and inspected under
chrome://tracing
URL.Parameters: path (str) – Path where the trace will be written.
-
key_averages
()[source]¶ Averages all function events over their keys.
Returns: An EventList containing FunctionEventAvg objects.
-
table
(sort_by=None)[source]¶ Prints an EventList as a nicely formatted table.
Parameters: sort_by (str, optional) – Attribute used to sort entries. By default they are printed in the same order as they were registered. Valid keys include: cpu_time
,cuda_time
,cpu_time_total
,cuda_time_total
,count
.Returns: A string containing the table.
-
-
class
torch.autograd.profiler.
emit_nvtx
(enabled=True)[source]¶ Context manager that makes every autograd operation emit an NVTX range.
It is useful when running the program under nvprof:
nvprof --profile-from-start off -o trace_name.prof -- <regular command here>
Unfortunately, there’s no way to force nvprof to flush the data it collected to disk, so for CUDA profiling one has to use this context manager to annotate nvprof traces and wait for the process to exit before inspecting them. Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
torch.autograd.profiler.load_nvprof()
can load the results for inspection e.g. in Python REPL.Parameters: enabled (bool, optional) – Setting this to False makes this context manager a no-op. Default: True
.Example
>>> with torch.cuda.profiler.profile(): ... model(x) # Warmup CUDA memory allocator and profiler ... with torch.autograd.profiler.emit_nvtx(): ... model(x)


About this Project
This is a group project on Railway Reservation System. Where passangers can search and book train tickets. It's a privilage for getting this opportunity of building this project from Scratch.
This is a group project on Railway Reservation System. Where passangers can search and book train tickets. It's a privilage for getting this opportunity of building this project from Scratch.


Ever wondered how many hours you have spent gaming on Steam? Enter your Steam ID in the form above to find out.
Your Top Played Games
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2011 Steam hours logged!
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blammo
`Prev `__
B
`Next `__
--------------
**blammo**: v.
[Oxford Brookes University and alumni, UK] To forcibly remove someone
from any interactive system, especially talker systems. The operators,
who may remain hidden, may “blammo” a user who is misbehaving. Very
similar to archaic MIT gun; in fact, the blammo-gun is a notional device
used to “blammo” someone. While in actual fact the only incarnation of
the blammo-gun is the command used to forcibly eject a user, operators
speak of different levels of blammo-gun fire; e.g., a blammo-gun to
‘stun’ will temporarily remove someone, but a blammo-gun set to ‘maim’
will stop someone coming back on for a while.
--------------
+------------------------------------------+----------------------------+---------------------------+
| `Prev `__ | `Up <../B.html>`__ | `Next `__ |
+------------------------------------------+----------------------------+---------------------------+
| Black Screen of Death | `Home <../index.html>`__ | blargh |
+------------------------------------------+----------------------------+---------------------------+


Scripts'n'Stuff
Sierpinski
Collisions
Sierpinski
Each point is placed at the midpoint between the the last point placed and a randomly chosen vertex of the triangle. The first point is placed randomly within the triangle. The Sierpinski fractal appears.
Collisions


Value & Reference Types
Value and reference types are different ways in which data gets stored/accessed as it flows through your application. They both have implications on performance & architecture. Both introduce complexities of which when mishandled can introduce some unexpected behaviors in your application’s performance, aka bugs.
We’ll compare both value and reference types below using the analogy of a garden gnome, named Phil. You’ll then be able to distinguish when you may or may not want to use reference or value types in your application.
Reference types
A reference can be thought of as an address; that being an address in memory. For our example Phil the garden gnome.  If we want to talk about Phil, we reference him by his street address. There can be many Phil’s in the world, I mean let’s face it Phil came from a factory. But there is only one Phil at this street address: I’m talking about Phil at “789 Main Street, North Pole”.
So what do references mean in code? Well you might see examples of:
void function2(int *param) {
func myFunction(inout input: Int) {
- (NSString *)myFunction(NSString *input) {
Here you can see multiple ways to declare that the input is of type reference. The *’s or the fact that it’s inout or sometimes you see “&” before a variable, all indicate you are pointing to a reference of an object; a particular memory address.
When dealing with references, you’re dealing with one object, one space in memory. That address is a memory address. Like Phil, when you send a letter to Phil at 789 Main Street, North Pole, you’re sending a message to that one Phil.
Of course Phil can receive multiple letters from multiple sources. This gets tricky when you start to modify Phil. Multiple messages or multiple actions that affect Phil can change other peoples expectations. Let’s take the following Swift code as an example:
var phil = Phil() // This is somewhere in the code
// Painter A
phil.paintPants(color: .Red)
// Painter B
phil.paintPants(color: .Green)
So what color are Phil’s pants? Well it depends on who called paintPants last! So if Painter A depended on the fact that Phil’s pants were Red to paint Phil’s shirt, then, they might actually be green! This is called a race condition, and reference types are susceptible to this side effect. It can lead to unexpected behavior; bugs.
Value types
Next we’ll look at value types. Value types are exactly that, they are variables that contain values. The variables are not pointers to another point in memory, instead the value explicitly points to its own space in memory, no other place. So what does that mean in contrast to reference types? Let’s take a look at a value type sequentially then vs a reference type.
struct Gnome {
var name: String
}
var gnome = Gnome(name: "Phil")
var gnomeB = gnome
gnomeB.name = "Robert"
print(gnome.name) // returns Phil
print(gnomeB.name) // returns Robert
Above you can see that the assignment of gnome to gnomeB introduces some sideeffects for how the variables get used further on in code. When we do gnomeB = gnome
we’re actually creating a copy in our memory. We in effect take a copy of gnome, put it in its own memory address, from then on the original Gnome won’t be modified if using this copy.
How is this a value type? The above example is in Swift. Swift has value types such as Structs and primitive values. So we know if its a struct we can expect copies.
Let’s now play out the above example using a reference type. Again using swift we’ll change the struct to be a class type, a reference type:
class Gnome {
var name: String
init(name: String) {
self.name = name
}
}
var gnome = Gnome(name: "Phil")
var gnomeB = gnome
gnomeB.name = "Robert"
print(gnome.name) // returns Robert
print(gnomeB.name) // returns Robert
So we can see above that they both print “Robert”. We’re using pointers, or references, to modify the one location in memory.
The memory
From the two examples above lets print out their memory addresses to prove value vs reference types are all about how we store and access our variables in memory:
Reference types
withUnsafePointer(to: &gnome.name) { print("\($0)" )} // 0x000060c0000584c0
withUnsafePointer(to: &gnomeB.name) { print("\($0)" )} // 0x000060c0000584c0
Value types
withUnsafePointer(to: &gnome.name) { print("\($0)" )} // 0x000000011ace3830
withUnsafePointer(to: &gnomeB.name) { print("\($0)" )} // 0x000000011ace3848
You can see that the same memory address was used for reference types, but a new address for value types.
More reading
-
Swift Algorithms & Data Structures (I helped write a chapter)
-
Swift Programming: The Big Nerd Ranch Guide (2nd Edition)


Preparation time
overnight
Cooking time
10 to 30 mins
Serves
Makes 12
This is a no-fuss barbecue chicken recipe - simply marinade the chicken overnight and throw it on the BBQ.
 Mix together all of the marinade ingredients and pour over the chicken. Leave to marinate for at least 2 hours, or overnight if you can, turning occasionally.
Once it has marinated, place the chicken on the barbecue over medium-hot coals and cook for about 20 minutes, brushing with any marinade left in the dish as it cooks. If it's colouring too quickly, move to a cooler part of the barbecue to cook more gently. It's important you cook the chicken all the way through.
If you're unsure, pierce a drumstick with a skewer: if the juices are still pink, carry on cooking.  Isn't that what barbecues are all about?
The chicken can also be cooked in the oven for perfect picnic food. Preheat the oven to 200C/400F/Gas 6.
Transfer the chicken into a roasting tray with a couple of spoonfuls of marinade. Place in the oven for about 50 minutes, turning occasionally and basting with the marinade and juices until thoroughly sticky and golden, adding extra marinade to the tray if necessary. Once the chicken pieces are cooked, eat them when fresh and hot, or leave to cool before packing up for a picnic or packed lunch.


Kerala, a tropical malabar coast of the south west India is a hotspot of tourist destinations.Land of Ecotourism and beautiful backwaters rises its fame across the seven seas and around the globe.The static tourism agenda promotes an ecologically sustained tourism.


Learning Progress
Scores this Week
Biology
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Music Industry
Endorsed by Darren Abraham and 4 others who are highly skilled at this
Recording
Endorsed by Lacy Darryl Phillips, who is highly skilled at this
Entertainment
Endorsed by Lacy Darryl Phillips and 5 others who are highly skilled at this


This documentation is rather dry, not particularly well organised and incomplete. If you want to help out then please fork & edit this page!
A simple alert dialog with a single button. Pressing escape dismisses the dialog. If a callback is provided it is not passed any value when executed.
| Usage | Result |
|---|---|
| bootbox.alert(str message) | Dialog with default 'OK' button |
| bootbox.alert(str message, fn callback) | Default button text, callback invoked on dismissal |
| bootbox.alert(str message, str label) | Custom button text |
| bootbox.alert(str message, str label, fn callback) | Custom button text, callback invoked on dismissal |
A confirm dialog with a cancel and a confirm button. Pressing escape dismisses the dialog and invokes the callback as if the user had clicked the cancel button. If a callback is provided it is passed a value of true or false depending on which button the user pressed.
| Usage | Result |
|---|---|
| bootbox.confirm(str message) | Dialog with default 'Cancel' and 'OK' buttons |
| bootbox.confirm(str message, fn callback) | Default buttons, callback invoked on dismissal |
| bootbox.confirm(str message, str cancel) | Custom cancel button text, standard confirm button |
| bootbox.confirm(str message, str cancel, fn callback) | Custom cancel button text, callback invoked on dismissal |
| bootbox.confirm(str message, str cancel, str confirm) | Custom cancel and confirm buttons |
| bootbox.confirm(str message, str cancel, str confirm, fn callback) | Custom cancel and confirm buttons, callback invoked on dismissal |
A dialog which prompts for user input. Pressing escape dismisses the dialog and invokes the callback as if the user had clicked the cancel button. If a callback is provided it is passed a value of null if the user cancels or dismissed the dialog, otherwise it is passed the value of the text input.
| Usage | Result |
|---|---|
| bootbox.prompt(str message) | Prompt with default 'Cancel' and 'OK' buttons |
| bootbox.prompt(str message, fn callback) | Default buttons, callback invoked on dismissal |
| bootbox.prompt(str message, str cancel) | Custom cancel button text, standard confirm button |
| bootbox.prompt(str message, str cancel, fn callback) | Custom cancel button text, callback invoked on dismissal |
| bootbox.prompt(str message, str cancel, str confirm) | Custom cancel and confirm buttons |
| bootbox.prompt(str message, str cancel, str confirm, fn callback) | Custom cancel and confirm buttons, callback invoked on dismissal |
| bootbox.prompt(str message, str cancel, str confirm, fn callback, str defaultValue) | Custom cancel and confirm buttons, default prompt value, callback invoked on dismissal |
A completely customisable dialog method. As many buttons and callbacks as you like.
| Usage | Result |
|---|---|
| bootbox.dialog(str message) | Dialog with no buttons |
| bootbox.dialog(str message, object handler) | Needs docs |
| bootbox.dialog(str message, array handlers) | Needs docs |
| bootbox.dialog(str message, array handlers, object options) | Needs docs |
Hide all currently active bootbox dialogs. Individual dialogs can be closed as per normal Bootstrap dialogs: dialog.modal('hide').
Indicate whether dialogs should animate in and out.
Set the dialog backdrop value—values as per Bootstrap's modals.
Any custom classes to add to the dialog.
Establish which icons to show for default OK, Cancel and Confirm buttons.
Set the language for the default OK, Cancel and Confirm buttons. Values: en, fr, de, es, br, nl, ru, it.
Add a custom locale.
This documentation is rather dry, not particularly well organised and incomplete. If you want to help out then please fork & edit this page!


Greg Bremble
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Train Scheduler
Current Train Schedule
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Destination
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Minutes to Arrival
Add a Route
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Forest of the Lost [ao62], forest, in Imperia, peasants: 119
Last updated Turn #109
Forest of the Lost [ao62], forest, in Imperia, peasants: 119
------------------------------------------------------------------------
9: Limerick [p4x], refugee, went north.
30: Mrs. Premise [z4v], refugee, arrived from the south.
30: The population grows by one peasant.
Routes leaving Forest of the Lost:
North, forest, to Forest of the Lost [an62], 8 days
East, forest, to Forest of the Lost [ao63], 8 days
South, forest, to Forest of the Lost [ap62], 8 days
West, forest, to Forest of the Lost [ao61], 8 days
Seen here:
Stack of nazgul [hh2g], nazgul, number: 3
Mrs. Premise [z4v], refugee
Stack of nazgul [dy5y], nazgul, number: 2


Before Change
cls.ddpg.update()
def test_ddpg_ypreds(self):
ypreds_round = [round(self.ddpg.choose_action(x)[0], 4) for x in self.X_test]
expected_ypreds = [0.1778, 0.1914, 0.2607, 0.4459, 0.5660, 0.3836]
for ypred_round, expected_ypred in zip(ypreds_round, expected_ypreds):
self.assertAlmostEqual(ypred_round, expected_ypred, places=6)
After Change
def test_ddpg_ypreds(self):
expected_ypreds = ["0.3169", "0.3240", "0.3934", "0.5787", "0.6988", "0.5163"]
self.assertEqual(self.ypreds_round, expected_ypreds)


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category
code_metrics_utilities
¶
Internal predicates for analyzing source code.
static
(no dependencies on other entities)
Public interface¶
(none)
Protected interface¶
(none)
Private predicates¶
ancestor/4
¶
True if Entity descends from Ancestor, and EntityKind and AncestorKind unify with their respective entity types.
static
ancestor(EntityKind,Entity,AncestorKind,Ancestor)
ancestor(?entity,?entity_identifier,?entity,?entity_identifier)
- zero_or_more
current_entity/1
¶
True if Entity is a currently loaded entity.
static
current_entity(Entity)
current_entity(?entity_identifier)
- zero_or_more
declares_predicate/2
¶
True if Entity declares Predicate internally.
static
declares_predicate(Entity,Predicate)
declares_predicate(?entity_identifier,?predicate_indicator)
- zero_or_more
defines_predicate/2
¶
True if Entity defines an implementation of Predicate internally. ‘Auxiliary’ predicates are excluded from results.
static
defines_predicate(Entity,Predicate)
defines_predicate(?entity_identifier,?predicate_indicator)
- zero_or_more
defines_predicate/3
¶
Same as defines_predicate/2, except Property is unified with a corresponding property defined in the grammar.
static
defines_predicate(Entity,Predicate,Property)
defines_predicate(?entity_identifier,?predicate_indicator,?term)
- zero_or_more
entity_calls/3
¶
True if a predicate Caller within Entity makes a Call.
static
entity_calls(Entity,Caller,Call)
entity_calls(?entity_identifier,?predicate_indicator,?predicate_indicator)
- zero_or_one
entity_kind/2
¶
True if Kind defines Entity and is one of category, protocol, or object.
static
entity_kind(Entity,Kind)
entity_kind(+entity_identifier,-entity)
- zero_or_one
entity_property/2
¶
True if Property is a valid property of Entity. Entity can be either a category, a protocol, or an object.
static
entity_property(Entity,Property)
entity_property(+entity_identifier,-term)
- zero_or_more
entity_updates/3
¶
True if a predicate Updater within Entity makes a dynamic update to Update (by e.g. asserta/1, retract/1, etc.)
static
entity_updates(Entity,Updater,Update)
entity_updates(+entity_identifier,?predicate_indicator,?predicate_indicator)
- zero_or_one
Operators¶
(none)
Remarks¶
Usage: This is meant to be imported by any metric added to the system.
Predicate Scope: This is meant for internal use by metrics only. As such, all provided predicates are private.


| DOSE: |
8.0 mg |
oral |
2C-B |
(powder / crystals) |
Dose: Someone had given me 50mg or so of 2C-B, and I was trying to dispense it to my boyfriend and myself, so that we could explore 2C-B. But since I didn't have scales, this was a chore. I imagine we each took about 8mg.
Setting: My apartment, empty stomach, no explicit intention.
Because the dose was so low, and we were unfamiliar with the material, we were trying to figure out if we were high or not for about 2 hours. I got the idea in my head that I wanted to cook. I began assembling things in the kitchen. I felt enormously fulfilled and competent, things I didn't normally associate with cooking. I felt like a well-oiled cooking machine, handling multiple pots and pans at once, perfectly timed in the execution of cooking motions. I was focused and in the Zone, and it felt really good.
Once the meal was prepared, I fed it to Gaichi, who was hungry (I had no appetite). It felt highly pleasurable to be administering food to him. We both tripped out on the pattern of the food juice mixing with oil in his plate -- not in an LSD-like way, the 2C-B has a crispness to the visual effects that I like better than LSD. We spent some more time astonished at how amazing my favorite painting on the wall looked. Then we went to bed and fooled around, as I was eager to explore the reported sensual qualities of 2C-B. I wasn't disappointed. But I was far more pleased several weeks later when we tried a little bit more, which was much more erotic.
So this first 2C-B experience was not especially memorable among first experiences, but the intense physical pleasure I took in *cooking* really had an impact on me, because it caused me to connect deeply with my self-nurturing instinct that I was ignoring so much at that time in my life.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Images¶
Note
Images on Rackspace servers: with standard servers, the entire disk (OS and data) is captured in the image. With Performance servers, only the s ystem disk is captured in the image. The data disks should be backed up using Cloud Backup or Cloud Block Storage to ensure availability in case you need to rebuild or restore a server.
List images¶
Below is the simplest usage for retrieving a list of images:
$images = $service->imageList();
foreach ($images as $image) {
}
Get the executable PHP script for this example
Detailed results¶
By default, the only fields returned in a list call are id and name, but you can enable more detailed information to be result by passing in true as the first argument of the call, like so:
$images = $service->imageList(true);
Filtering¶
You can also refine the list of images returned by providing specific filters:
| Array key | Description |
|---|---|
| server | Filters the list of images by server. Specify the server reference by ID or by full URL. |
| name | Filters the list of images by image name. |
| status | Filters the list of images by status. In-flight images have a status of SAVING and the conditional progress element contains a value from 0 to 100, which indicates the percentage completion. For a full list, please consult the OpenCloud\Compute\Constants\ImageState class. Images with an ACTIVE status are available for use. |
| changes-since | Filters the list of images to those that have changed since the changes-since time. See the official docs for more information. |
| marker | The ID of the last item in the previous list. See the official docs for more information. |
| limit | Sets the page size. See the official docs for more information. |
| type | Filters base Rackspace images or any custom server images that you have created. Can either be BASE or SNAPSHOT. |
These are defined in an array and passed in as the second argument. For example, to filter images for a particular server:
$images = $service->imageList(false, array('server' => '{serverId}'));
Retrieve details about an image¶
$image = $service->image('{imageId}');
Delete an image¶
$image->delete();


546_prem_cse.java
class Solution {
public double getMinDistSum(int[][] pos) {
test_point = new Point[4];
test_point[0] = new Point(-1,0);
test_point[1] = new Point(0,1);
test_point[2] = new Point(1,0);
test_point[3] = new Point(0,-1);
int n = pos.length;
Point[] arr = new Point[n];
for(int i=0;i<n;++i) {
arr[i] = new Point(pos[i][0],pos[i][1]);
}
return geometricMedian(arr, n);
}
static class Point {
double x, y;
Point(){}
Point(double a,double b){
x = a;
y = b;
}
}
static Point test_point[];
static double lower_limit = 0.001;
static double distSum(Point p,
Point arr[], int n)
{
double sum = 0;
for (int i = 0; i < n; i++) {
double distx = Math.abs(arr[i].x - p.x);
double disty = Math.abs(arr[i].y - p.y);
sum += Math.sqrt((distx * distx) + (disty * disty));
}
// Return the sum of Euclidean Distances
return sum;
}
// Function to calculate the required
// geometric median
static double geometricMedian(Point arr[], int n)
{
// Current x coordinate and y coordinate
Point current_ point = new Point();
for (int i = 0; i < n; i++) {
current_point.x += arr[i].x;
current_point.y += arr[i].y;
}
// Here current_point becomes the
// Geographic MidPoint
// Or Center of Gravity of equal
// discrete mass distributions
current_point.x /= n;
current_point.y /= n;
// minimum_distance becomes sum of
// all distances from MidPoint to
// all given points
double minimum_distance =
distSum(current_point, arr, n);
int k = 0;
while (k < n) {
for (int i = 0; i < n && i != k; i++) {
Point newpoint = new Point();
newpoint.x = arr[i].x;
newpoint.y = arr[i].y;
double newd =
distSum(newpoint, arr, n);
if (newd < minimum_distance) {
minimum_distance = newd;
current_point.x = newpoint.x;
current_point.y = newpoint.y;
}
}
k++;
}
// Assume test_distance to be 1000
double test_distance = 10000;
int flag = 0;
// Test loop for approximation starts here
while (test_distance > lower_limit) {
flag = 0;
// Loop for iterating over all 4 neighbours
for (int i = 0; i < 4; i++) {
// Finding Neighbours done
Point newpoint = new Point();
newpoint.x = current_point.x
+ test_distance * test_point[i].x;
newpoint.y = current_point.y
+ test_distance * test_point[i].y;
// New sum of Euclidean distances
// from the neighbor to the given
// data points
double newd = distSum(newpoint, arr, n);
if (newd < minimum_distance) {
// Approximating and changing
// current_point
minimum_distance = newd;
current_point.x = newpoint.x;
current_point.y = newpoint.y;
flag = 1;
break;
}
}
// This means none of the 4 neighbours
// has the new minimum distance, hence
// we divide by 2 and reiterate while
// loop for better approximation
if (flag == 0)
test_distance /= 2;
}
return minimum_distance;
}
}


I'm not a professional movie reviewer, but I decided to give on for this movie.
If I frequent this sub, you will notice that I am very critical of movies. So any positive review is rare. I consider my reviews are for the "thinking" person, although there may be some "thinkers" who disagree.
Deep Impact (1998) Cast: Morgan Freeman ... President Beck Robert Duvall ....... Spurgeon Tanner Téa Leoni ............ Jenny Lerner Elijah Wood .......... Leo Biederman Blair Underwood .... Mark Simon W. Earl Brown ....... McCloud Vanessa Redgrave .  She immediately finds herself in the midst of a year long secret that a comet is headed for Earth and will impact in on year. What transpires after that is a race against time as citizens of the world dash to safety. Téa Leoni does a very good job with a fairly limited script as she provides an eyebrow raising performance on the big screen. The all-star cast provides interesting psychological perspectives as they ponder their respective fates.
The plot of the movie itself is a good one, and for once my digest of "Hollywood" endings was justified, as this movie pulled a 180 and provided what I call a very realistic ending. The special effects were precise, not over done, yet quite reflective of the awesome power of a 1000 ft. tidal wave. And finally, a movie that DOESN'T ridiculously depict people "outrunning" a shock blast (I.e., Independence Day).
Unless you are an astronomical scientist, you will be hard pressed to find any impossibilities in this depiction of special effects. The only draw back I saw in this movie, which was completely overshadowed by it's very satisfying ending, was the poor realism of human behavior as a whole in the face of imminent doom. For example, with a year warning, no communities or individuals were creating underground shelters of their own, especially since there was not enough room in the military underground city to house more than a million people.
I give this movie 3 and 1/2 stars out of 4, one of my highest ratings.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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java.lang.Object
controllers.
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controllers.
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(implements controllers.
IBowlController
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dao.
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com.google.inject.AbstractModule (implements com.google.inject.Module)
conf.
Module
services.
AbstractService
services.
BotanicSystemServiceImpl
(implements services.
BotanicSystemService
)
services.
ExhibitionServiceImpl
(implements services.
ExhibitionService
)
services.
PartnerServiceImpl
(implements services.
PartnerService
)
services.
RoadmapServiceImpl
(implements services.
RoadmapService
)
services.
SettingServiceImpl
(implements services.
SettingService
)
services.
SoftwareServiceImpl
(implements services.
SoftwareService
)
services.
TimberOriginServiceImpl
(implements services.
TimberOriginService
)
services.
TimberServiceImpl
(implements services.
TimberService
)
controllers.
ApplicationController
controllers.
ApplicationController.SimplePojo
entity.
BotanicSystem
(implements java.io.Serializable)
dto.
BotanicSystemDto
(implements dto.
Dto
)
forms.
BotanicSystemForm
(implements forms.
Form
)
dto.
BotanicSystemListDto
entity.
Bowl
(implements java.io.Serializable)
dto.
BowlDto
(implements dto.
Dto
)
forms.
BowlForm
(implements forms.
Form
)
dto.
BowlListDto
entity.
BowlMod
(implements java.io.Serializable)
dto.
BowlModDto
(implements dto.
Dto
)
forms.
BowlModForm
(implements forms.
Form
)
types.
BowlModification
dto.
BowlModificationDto
(implements dto.
Dto
)
entity.
BowlModItem
(implements java.io.Serializable)
dto.
BowlModItemDto
(implements dto.
Dto
)
forms.
BowlModItemForm
(implements forms.
Form
)
entity.
BowlModJoined
(implements java.io.Serializable)
entity.
BowlModStep
(implements java.io.Serializable)
dto.
BowlModStepDto
(implements dto.
Dto
)
services.
BowlServiceImpl
(implements services.
BowlService
)
types.
Checker
types.
Country
entity.
Customer
(implements java.io.Serializable)
dto.
CustomerDto
(implements dto.
Dto
)
forms.
CustomerForm
(implements forms.
Form
)
dto.
CustomerListDto
services.
CustomerServiceImpl
(implements services.
CustomerService
)
entity.
Exhibition
(implements java.io.Serializable)
dto.
ExhibitionDto
(implements dto.
Dto
)
forms.
ExhibitionForm
(implements forms.
Form
)
entity.
GeoRegion
(implements java.io.Serializable)
dto.
GeoRegionDto
forms.
GeoRegionForm
(implements forms.
Form
)
dto.
GeoRegionListDto
services.
GeoRegionServiceImpl
(implements services.
GeoRegionService
)
trigger.h2.
H2SampleTrigger
(implements org.h2.api.Trigger)
entity.
Manufacture
(implements java.io.Serializable)
dto.
ManufactureDto
(implements dto.
Dto
)
services.
ManufactureServiceImpl
(implements services.
ManufactureService
)
forms.
ModificationForm
(implements forms.
Form
)
dto.
PartnerDto
(implements dto.
Dto
)
entity.
Roadmap
(implements java.io.Serializable)
dto.
RoadmapDto
(implements dto.
Dto
)
conf.
Routes
(implements ninja.application.ApplicationRoutes)
entity.
Sales
(implements java.io.Serializable)
forms.
SalesForm
(implements forms.
Form
)
trigger.h2.
SBArtefactBeforeInsertTrigger
(implements org.h2.api.Trigger)
entity.
Setting
(implements java.io.Serializable)
dto.
SettingDto
(implements dto.
Dto
)
dao.
SetupDao
controllers.
SidemenuController
entity.
Software
(implements java.io.Serializable)
dto.
SoftwareDto
(implements dto.
Dto
)
conf.
StartupActions
entity.
Status
(implements java.io.Serializable)
dto.
StatusDto
(implements dto.
Dto
)
forms.
StatusForm
(implements forms.
Form
)
dto.
StatusListDto
services.
StatusServiceImpl
(implements services.
StatusService
)
entity.
Subuser
(implements java.io.Serializable)
types.
SuperbowlHelper
types.
Telco
entity.
Timber
(implements java.io.Serializable)
dto.
TimberDto
(implements dto.
Dto
)
forms.
TimberForm
(implements forms.
Form
)
dto.
TimberListDto
entity.
TimberOrigin
(implements java.io.Serializable)
dto.
TimberOriginDto
(implements dto.
Dto
)
forms.
TimberOriginForm
(implements forms.
Form
)
Interface Hierarchy
services.
BotanicSystemService
services.
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IDao
services.
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services.
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services.
SettingService
services.
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services.
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services.
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services.
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Clients
{% if training_clients|length == 0 %}
(no clients registered in the network yet)
{% elif training_clients|length == 1 %}
(1 client is registered in the network)
{% else %}
({{ training_clients|length }} clients are registered in the network)
{% endif %}


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About
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Save Cascading
Cascading Save is a recursive process that could be described as "walking the object graph".
The general algorithm for save goes:
- Prior to saving a bean we save its ManyToOne associations (And appropriate OneToOnes)
- Then we save the bean itself
- Then we save any OneToMany, ManyToMany (And appropriate OneToOnes)
Note: OneToOnes will occur prior and post saving the bean depending on whether the OneToOne owns
the foreign key or not. If that doesn't make sense don't worry too much.
Order order = ....
// assume:
// the order has a customer
// the customer has a billing and shipping address
// the order has details
Ebean.save(order);
In this example the order is:
- Ebean.save(order)...
- Prior to saving the 'order' we save the 'customer'
- ... this takes us to the customer
- Prior to saving the 'customer' we save the billingAddress
- SAVE billingAddress
- ... billingAddress has no OneToMany's etc
- Prior to saving the 'customer' we save the shippingAddress
- SAVE shippingAddress
- ... shippingAddress has no OneToMany's etc
- SAVE customer
- ... customer has no OneToMany's etc
- SAVE order
- SAVE order details
A "Relational" way of describing this is that we go "up the imported foreign keys", then save the
bean, then go "down the exported foreign keys".
Perhaps another way of thinking about this is to take the example where every object is new and
will need to be inserted.
Before we save the order we need the customer_id so we need to save customer first.
BUT before we save customer we need the billing_address_id so we need to save billingAddress first.
... etc.
At the other end, we need to save the order to get the order_id BEFORE we save the order details.
Delete Cascading
Delete cascading is very similar except the order is reversed.
- Prior to deleting a bean we delete its OneToMany, ManyToMany (And appropriate OneToOnes) associations
- Then we delete the bean itself
- Then we delte any ManyToOne (And appropriate OneToOnes)
Batching Order
When we use batching there is another complication added to this.
Specifically when saving Beans we queue them up according to 'depth' and then
batch process them in depth order.
In this example we would end up with 4 depth levels.
- Depth -2: All the billing addresses and shipping addresses
-
- Depth -1: All the customers
-
- Depth 0: All the orders
-
- Depth 1: All the order details
-
These would then be batch processed using PreparedStatement batching in lowest depth
first order.
All the billing and shipping addresses would be saved, then customers, then orders and lastly
order details.
In general you could the reason why this additional queuing is required is because we
need to delay the binding of the preparedStatement. Specifically when beans are inserted
we are expected to not get back the id values until after executing the statements and
we are expecting to use getGeneratedKeys to do this. What this means is that we can not
bind dependant objects (as we don't have the foreign key values) until after the
higher 'depth' statements have been executed and getGeneratedKeys has got the id values
back for us.
In general if your Databases does not support getGeneratedKeys and you are not using an
external IdGenerator then ORM batching of inserts is going to be much less valuable.
From my perspective ORM batching is greatly enhanced by getGeneratedKeys as we really
need those Id values back.
You can of course using UpdateSql and for many this will be a better approach for
very large batch inserting as you then use simple scalar foreign key
values instead of reference objects.
|


UBER CHANDIGARH AREA: Total Carpet area of 6000 sq. ft LOCATION: Chandigarh, IN YEAR: 2017 STATUS: Completed SERVICES PROVIDED: Interior Design | 3D Visualizations | Costing and BOQ | MEP Services and Consultation | Working/Construction/Shop Drawings | Site Co-ordination | Graphics and Signage PROGRAM REQUIREMENTS: Waiting space capacity – 90 persons | 16 on-boarding Desks | 4 marketing Desks | 2 Support rooms – 3 person capacity for grievance and support | 1 Uber Club lounge | 1 Training room of 35 person capacity | 16 workstation for back office | 8 desks for calling room | IT room | Reprograhics | 2 Zoom rooms of 4 person capcatiy | 1 Zoom room of 8 Person Capacity TAGS: Interiors
A Driver/partner reporting Office for UBER located on the outskirts of Chandigarh. Done in association with Nice Frame Pvt. Ltd. The design problem was to deal with a difference that existed in the types of users – a walk-in partner/driver and the 9-5 Uber employee. The design approach was to create a strict division in the spaces. Two user types were separated from the reception on either sides, as the driver/partner office area and back office staff. With the driver/partner user group, the waiting areas were centralized on the side of the main reception and waiting lobby. The specialized spaces for partner interaction & reporting were placed around it. Several graphics were designed to provide the sense of belonging to the users. One was an illustration of the cab made with driver names and second an interactive road map illustrated vinyl and glass, depicting the cab frequency on the streets of Chandigarh. The third graphic represented the landmarks of the city with company branding graphics as a background. The back spaces were not made accessible to the drivers and were strictly were for employees to maintain the sense of privacy. They were also designed with a variation in theme, making it more inviting to the 9-5 user.


MegaGrand
Строим деревянные дома и бани под ключ
"Home"
Back to Home
"Services"
What we do
"Portfolio"
Quality stuff
"About"
Who we are
"Contact"
Work we us!


Enter a string into the input field and click "Submit" to see every unique permutation of characters that is possible from it (be aware that execution time increases dramatically the longer the string is)


Which of the following is false?
Anxiety disorders are more common in women than men.
Anxiety disorders increase the risk of alcohol use disorders in women more so than in men.
Men with alcohol dependence are more likely than men without alcohol dependence to have an anxiety disorder.
Women are more likely to have an alcohol use disorder than men.


Simple Interest Calculator
Amount:
Rate:
%
No. of Years:
1
2
3
4
5
6
7
8
9
10
11
12
Compute Interest


ion.RangeSlider is Easy, flexible and responsive range slider with skin support.
Simple start and customising basic params
Start without params
$("#range_01").ionRangeSlider();
Set min value, max value and start point
$("#range_02").ionRangeSlider({ min: 100, max: 1000, from: 550 });
Set type to double and specify range, also showing grid and adding prefix "$"
$("#range_03").ionRangeSlider({ type: "double", grid: true, min: 0, max: 1000, from: 200, to: 800, prefix: "$" });
Set up range with negative values
$("#range_04").ionRangeSlider({ type: "double", grid: true, min: -1000, max: 1000, from: -500, to: 500 });
Set up range with fractional values, using fractional step
One more example with strings
$("#range_09").ionRangeSlider({ grid: true, from: 3, values: [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ] });


Before Change
)
logger.info("Polling for data server port (attempt %d)", i)
port_file_contents = None
try:
with open(port_file_path) as infile:
port_file_contents = infile.read()
except OSError as e:
if e.errno != errno.ENOENT:
raise
logger.info("Port file contents: %r", port_file_contents)
if (port_file_contents or "").endswith("\n"):
port = int(port_file_contents)
break
After Change
time.sleep(0.01)
for i in range(20):
if popen.poll() is not None:
msg = (_maybe_read_file(error_file_path) or "").strip()
if not msg:
msg = (
"exited with %d; check stderr for details"
% popen.poll()
)
raise DataServerStartupError(msg)
logger.info("Polling for data server port (attempt %d)", i)
port_file_contents = _maybe_read_file(port_file_path)
logger.info("Port file contents: %r", port_file_contents)


| Table 1 |
Summarized mean and standard deviation (SD) baseline climate ... |
| Table 2 |
Equations used for modelling the transfer of AfB1 in feed to ... |
| Table 3 |
Predicted mean and standard deviation of aflatoxin AfB1 conc ... |
| Table 4 |
Maximum of weekly mean AfM1 concentrations (μg/kg) in milk f ... |
| Table 5 |
Maximum weekly percentage of simulations above the EC limit ... |


| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2009, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Java is a trademark or registered trademark of Sun Microsystems, Inc. | |
| 25 | * in the United States and other countries.] | |
| 26 | * | |
| 27 | * -------------------------- | |
| 28 | * AnnotationChangeEvent.java | |
| 29 | * -------------------------- | |
| 30 | * (C) Copyright 2009, by Object Refinery Limited and Contributors. | |
| 31 | * | |
| 32 | * Original Author: Peter Kolb (patch 2809117); | |
| 33 | * Contributor(s): ; | |
| 34 | * | |
| 35 | * Changes: | |
| 36 | * -------- | |
| 37 | * 20-Jun-2009 : Version 1 (PK); | |
| 38 | * | |
| 39 | */ | |
| 40 | ||
| 41 | package org.jfree.chart.event; | |
| 42 | ||
| 43 | import org.jfree.chart.annotations.Annotation; | |
| 44 | ||
| 45 | /** | |
| 46 | * An event that can be forwarded to any {@link AnnotationChangeListener} to | |
| 47 | * signal a change to an {@link Annotation}. | |
| 48 | * | |
| 49 | * @since 1.0.14 | |
| 50 | */ | |
| 51 | public class AnnotationChangeEvent extends ChartChangeEvent { | |
| 52 | ||
| 53 | /** The plot that generated the event. */ | |
| 54 | private Annotation annotation; | |
| 55 | ||
| 56 | /** | |
| 57 | * Creates a new AnnotationChangeEvent instance. | |
| 58 | * | |
| 59 | * @param annotation the annotation that triggered the event | |
| 60 | * (null not permitted). | |
| 61 | * | |
| 62 | * @since 1.0.14 | |
| 63 | */ | |
| 64 | public AnnotationChangeEvent(Object source, Annotation annotation) { | |
| 65 | super(source); | |
| 66 | this.annotation = annotation; | |
| 67 | } | |
| 68 | ||
| 69 | /** | |
| 70 | * Returns the annotation that triggered the event. | |
| 71 | * | |
| 72 | * @return The annotation that triggered the event (never null). | |
| 73 | * | |
| 74 | * @since 1.0.14 | |
| 75 | */ | |
| 76 | public Annotation getAnnotation() { | |
| 77 |
1
1. getAnnotation : mutated return of Object value for org/jfree/chart/event/AnnotationChangeEvent::getAnnotation to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return this.annotation; |
| 78 | } | |
| 79 | ||
| 80 | } | |
Mutations | ||
| 77 |
1.1 |


{% extends "./layout.html" %} {% block page_title %} Careers Service - Search {% endblock %} {% block content %} {% include "./includes/phase_banner_beta.html" %} Home Search Search results for results found Next page 2 of Number of Pages Previous page 1 of Number of pages {% endblock %}


Pranab is working as a Machine Learning Engineer at TCS. He has worked on various projects on Machine learning and Data Analysis. He is very adaptive in nature and has no fear of failure. He considers himself a ‘forever student’ and always eager to learn and implement new things.
Pranab believes that for every problem there exists a solution. So he is always trying to understand the problem properly and trying to visualize it using drawing and flowcharts. He believe that solving the problem properly is important, and his first objective is always to understand the problem and implement it through code. So in his spare time also he is trying to find out the implementation of real time problems.
He has worked on various technoligies other that machine learning like C# MVC, RASA Stack, Azure bot framework, AWS lambda etc. His are of interests includes coding, solving datasets from kaggle, reading blogs and watching movies.
Specialization in Computer vision, Deep learning and Natural Language Processing.
Worked on data visualization, Machine Learning Models & Text analysis.


Longest Common Subsequence
May 9 2020Given two strings text1 and text2, return the length of their longest common subsequence.
A subsequence of a string is a new string generated from the original string with some characters(can be none) deleted without changing the relative order of the remaining characters. (eg, “ace” is a subsequence of “abcde” while “aec” is not). A common subsequence of two strings is a subsequence that is common to both strings.
If there is no common subsequence, return 0.
Example 1:
Input: text1 = "abcde", text2 = "ace"
Output: 3
Explanation: The longest common subsequence is "ace" and its length is 3.
Example 2:
Input: text1 = "abc", text2 = "abc"
Output: 3
Explanation: The longest common subsequence is "abc" and its length is 3.
Example 3:
Input: text1 = "abc", text2 = "def"
Output: 0
Explanation: There is no such common subsequence, so the result is 0.
Constraints:
1 <= text1.length <= 1000
1 <= text2.length <= 1000
The input strings consist of lowercase English characters only.
Solution:
class Solution {
private Map<Integer, Integer> map = new HashMap<>();
private String _text1, _text2;
private Integer buildKey(int len1, int len2) {
return Objects.hash(len1 + "_" + len2);
}
public int myLCS(int len1, int len2) {
if (len1 == 0 || len2 == 0) {
return 0;
}
Integer key = buildKey(len1, len2);
if (map.containsKey(key)) {
return map.get(key);
}
if (_text1.charAt(len1 - 1) == _text2.charAt(len2 - 1)) {
int value = 1 + myLCS(len1 - 1, len2 - 1);
map.put(key, value);
return value;
}
int left = myLCS(len1, len2 - 1);
int right = myLCS(len1 - 1, len2);
map.put(key, Math.max(left, right));
return Math.max(left, right);
}
public int iterativeLCS(String a, String b) {
int n = a.length();
int m = b.length();
int[][] dp = new int[n + 1][m + 1];
for (int i = 0; i < n; ++i) {
for (int j = 0; j < m; ++j) {
if (a.charAt(i) == b.charAt(j)) {
dp[i+1][j+1] = 1 + dp[i][j];
} else {
dp[i+1][j+1] = Math.max(dp[i + 1][j], dp[i][j + 1]);
}
}
}
return dp[n][m];
}
public int longestCommonSubsequence(String _text1, String _text2) {
return iterativeLCS(_text1, _text2);
}
private Integer buildKey(String text1, String text2) {
return Objects.hashCode(text1 + "_" + text2);
}
public int longestCommonSubsequence2(String text1, String text2) {
if (text1.isEmpty() || text2.isEmpty()) {
return 0;
}
Integer key = buildKey(text1, text2);
if (map.containsKey(key)) {
return map.get(key);
}
if (text1.charAt(text1.length() - 1) == text2.charAt(text2.length() - 1)) {
text1 = text1.substring(0, text1.length() - 1);
text2 = text2.substring(0, text2.length() - 1);
int value = 1 + longestCommonSubsequence(text1, text2);
map.put(key, value);
return value;
}
int left = longestCommonSubsequence(text1, text2.substring(0, text2.length() - 1));
int right = longestCommonSubsequence(text1.substring(0, text1.length() - 1), text2);
map.put(key, Math.max(left, right));
return Math.max(left, right);
}
public int longestCommonSubsequence1(String text1, String text2) {
if (text1.isEmpty() || text2.isEmpty()) {
return 0;
}
if (text1.charAt(text1.length() - 1) == text2.charAt(text2.length() - 1)) {
text1 = text1.substring(0, text1.length() - 1);
text2 = text2.substring(0, text2.length() - 1);
return 1 + longestCommonSubsequence(text1, text2);
}
return Math.max(
longestCommonSubsequence(text1, text2.substring(0, text2.length() - 1)),
longestCommonSubsequence(text1.substring(0, text1.length() - 1), text2)
);
}
}


Programmatically controlling blink(1) USB LED devices is simple using node-blink1-async. Much of the API is asynchronous (i.e., returns Promise that you can await on) and fully documented. This module has been tested on Mac, Windows 10, and Ubuntu Linux on a Raspberry Pi 4.
If you perfer a JS callback style API consider node-blink1.
Ensure your blink(1) is fully functional using the blink(1) utilities.
npm install https://github.com/ros2jsguy/node-blink-async
Your device may need libusb installed:
sudo apt install libusb-1.0-0
See node-hid's instructions for compiling from source You may find the 51-blink1.rules file useful in your device configuration process.
import {Blink1, Blink1_LEDN, BlinkRate} from '@ros2jsguy/node-blink1-async';
async function example() {
// list all blink(1) devices
console.log('devices; ', Blink1.devices());
// output the version info of the default blink(1) device
let blink1: Blink1 = new Blink1();
console.log("version: " , await blink1.version() );
console.log('set color: red', await blink1.setRGB(255));
console.log('read rgb: ', await blink1.rgb(Blink1_LEDN.LEDA));
await Blink1.delay(2000);
console.log('Blink green at VERY_FAST rate (100 ms) for 5 seconds');
await blink1.blink(0, 255, 0, BlinkRate.VERY_FAST);
await Blink1.delay(5000);
console.log('Blink blue at SLOW rate (1000 ms) for 5 seconds');
await blink1.blink(0, 0, 255, BlinkRate.SLOW);
await Blink1.delay(5000);
console.log('Show solid yellow for 5 seconds');
await blink1.blink(255, 255, 0);
await Blink1.delay(5000);
// turn off the blink(1) output
await blink1.off();
// access the display pattern at line-1
console.log('Color pattern (line-1):', await blink1.readPatternLine(1));
// clear the memory of all display patterns.
console.log('Clearing pattern');
await blink1.clearPattern();
// confirm that display patterns are cleared
console.log('Color pattern (line-1):', await blink1.readPatternLine(1));
// close and release the blink(1) device
await blink1.close();
console.log('completed');
}
I develop mostly in TypeScript and prefer using modern JS features when I have the option. Working directly with the node-blink1 package which has been around for awhile, felt too retro for my liking, e.g., no jsdoc, callback-based api... I mean no disrespect to the node-blink1 author(s) who's priority for backward compatibility supercedes breaking the package just to use a new wizbang language feature. While developing a robot using TypeScript on a Raspi4 I wanted to integrate an old Blink1 led from my kit into the design. That led me to creating this package that makes it easier to to asynchronously control a Blink1 LED from TypeScript.
This package uses the node-blink1 package to perform the low level control of Blink1 devices
Generated using TypeDoc


Execute system commands
This module can be used to execute system commands on a remote database server:
To execute the
/bin/ls
command:
./odat.py oradbg -s $SERVER -d $SID -U $USER -P $PASSWORD --exec /bin/ls


Adding a Page Link
How to insert a link to a page within this site using the TelerikEditorProvider RTE.
- Highlight the text/object for the link - OR - Place you cursor where you want to insert the link.
- Click the Hyperlink Manager (CTRL + K) button. This opens the Hyperlink Manager with the Hyperlink tab selected.
- At Page, select the page for this link from the drop down list. This displays the URL of the selected page in the URL field below. Note: Disabled pages appear in the list, however they cannot be selected.
- The following optional setting are available:
- In the Link Text text box, edit the linked text. Note: This field is not displayed when adding a link to an image.
- At Target, select the target window for this link. The default option is None which opens the link in the current window.
- In the Tooltip text box, enter a tool tip to be displayed when a user places their mouse over this link.
- At CSS Class, select a class for the link - OR - Select Clear Class to use the default class.
- Check the Track the number of times this link is clicked check box to enable Link Tracking for this link. The link must be saved before you can view the Tracking tab where tracking information is displayed.
- Check the Log the user, date and time for each click check box to also enable the Link Log.
-
Click the OK button confirm.
Related Topics:
-
See "Viewing a Link Log Report"


XFCE4-POWER-MANAGER(1) XFCE4-POWER-MANAGER(1) NAME xfce4-power-manager - The Xfce 4 Power manager SYNOPSIS xfce4-power-manager [options] DESCRIPTION xfce4-power-manager manages the power sources on the computer and the devices that can be controlled to reduce their power consumption (such as LCD brightness level, monitor sleep, CPU frequency scaling). In addition, xfce4-power-manager provides a set of freedesktop-compliant DBus inter‐ faces to inform other applications about current power level so that they can adjust their power consumption, and it provides the inhibit interface which allows applications to prevent automatic sleep actions via the power manager; as an example, the operating system's package manager should make use of this interface while it is performing update opera‐ tions. You can run xfce4-power-manager from the command line without specifying any extra arguments. OPTIONS --no-daemon Starts the power manager in non-daemon mode: useful for debugging. --restart Causes the running power manager to restart. --customize Shows the configuration dialog. --quit Causes any running instance of xfce4-power-manager to exit. BUGS Please report any bugs to http://bugzilla.xfce.org/. Development discus‐ sion should be conducted on the<EMAIL_ADDRESS>mailing list. Usage related questions should be directed to the<EMAIL_ADDRESS>mailing list. HOMEPAGE http://goodies.xfce.org/ AUTHOR Ali Abdallah<EMAIL_ADDRESS>MANPAGE AUTHORS Ali Abdallah<EMAIL_ADDRESS>Robby Workman<EMAIL_ADDRESS>31 March 2009 Version 0.8.0 XFCE4-POWER-MANAGER(1)


Dikshithar barred from performing pooja for 2 months at Sri Natarajar Temple
The ban comes after he assaulted a woman devotee on Saturday
by Special CorrespondentThe committee of Pothu Dikshithars, the hereditary custodians-cum-archakas of the Sri Natarajar Temple in Chidambaram has suspended Dharshan dikshithar and barred him from performing pooja in the temple for two months for assaulting a woman devotee on Saturday. A resolution to this effect was passed at a meeting of the Pothu Dikshithars, which convened in the temple on Tuesday.
The committee, headed by secretary Balaganesh Dikshithar and deputy secretary Navamani Dikshithar, passed a resolution in this regard. The committee also imposed a fine of Rs.5,000.
The action followed after the incident evoked widespread protests from various quarters. The complainant, S. Latha, working as a nurse in a Primary Health Centre in Ayyankudi, near Chidambaram alleged that the dikshitar slapped her when she questioned him for performing pooja even before she could tell him her son’s details, on his birthday.


Finances
Each partner is required to submit their own project finances and funding rates. The overall project costs for all partners can be seen in the 'finances overview' section
Finance summary
| Total | Labour | Overheads | Materials | Capital usage | Subcontracting costs | Travel and subsistence | Other costs |
|---|---|---|---|---|---|---|---|
| £257,033 | £126,207 | £86,167 | £44,659 | £257,033 | £126,207 | £86,167 | £44,659 |
Organisation size
Medium (Claim up to 60%)


The SHWindow
class provides an interface to compute window functions that concentrate energy spatially into a given region and within a given spherical harmonic bandwidth. This allows to calculate spectral estimates localized to specific regions.
To demonstrate, in this example, we will determine the spectrum of the bathymetry of Earth and compare it with that of the surface topography. To this end, we will need to generate window functions that are separately concentrated over the land and the oceans. To start, we read the topography of Earth from the example files, create masks for the oceans and land, and plot the masks:
%matplotlib inline
from __future__ import print_function # only necessary if using Python 2.x
import matplotlib.pyplot as plt
import numpy as np
from pyshtools.shclasses import SHCoeffs, SHGrid, SHWindow
infile = '../ExampleDataFiles/srtmp300.msl'
clm = SHCoeffs.from_file(infile)
topo = clm.expand(grid='DH2')
land_mask = (topo.data > 0)
ocean_mask = ~land_mask
fig, (col1, col2) = plt.subplots(1, 2, figsize=(13, 5))
col1.imshow(land_mask, extent=(0, 360, -90, 90), cmap='viridis')
col1.set(xlabel='longitude', ylabel='latitude', title='land mask')
col2.imshow(ocean_mask, extent=(0, 360, -90, 90), cmap='viridis')
col2.set(xlabel='longitude', ylabel='latitude', title='ocean mask');
SHTOOLS provides a method to solve the so called concentration problem, that is, to find windows with a specified maximum spherical harmonic degree lmax
that are optimally concentrated in the region of interest. The smaller the region, the larger lmax
will need to be to allow for a good concentration. Because the oceans span a larger area than the land mass, the spherical harmonic bandwidth of the ocean windows lmax_ocean
can be smaller than the spherical harmonic bandwidth of the land windows lmax_land
while still providing the same number of well concentrated windows.
Creating localization windows is easy using the SHWwindow
method from_mask()
. All we need to do is provide the mask, the maximum spherical harmonic bandwidth, and optionally, the number of windows to return:
lmax_ocean = 9
lmax_land = 21
nwins = 10
ocean_windows = SHWindow.from_mask(ocean_mask, lmax_ocean, nwins)
land_windows = SHWindow.from_mask(land_mask, lmax_land, nwins)
We can find out information about the windows that were created using the info()
method, and the windows can be plotted using the plot_windows()
method. In plotting the windows, the loss factor of the window is provided above each image. This is simply 1 minus the concentration factor, which is the proportion of the energy of the window that lies outside the region of interest. For our example, it is seen that in order to obtain 10 well localized windows with loss factors of less than 0.1%, the spectral bandwidth of the land windows needs to be more than twice as large as that of the ocean windows.
print('ocean windows')
ocean_windows.info()
fig, ax = ocean_windows.plot_windows(nwins)
print('land windows')
land_windows.info()
fig, ax = land_windows.plot_windows(nwins)
The following plot shows the power spectra of all windows. In particular, it is noted that the power of the land windows extends to higher degrees than does the ocean windows. The ocean windows will thus allow for spectral estimates with lower biasses given the lower bandwidth of these windows.
winpower_land = land_windows.spectra()
winpower_ocean = ocean_windows.spectra()
fig, ax = plt.subplots(1, 1)
for itaper in range(nwins):
ax.plot(winpower_ocean[:, itaper], c='blue')
for itaper in range(nwins):
ax.plot(winpower_land[:, itaper], c='green')
ax.set(xlabel='degree l', ylabel='power per degree l', title='ocean (blue) and land (green) window power');
We next plot the coupling matrix associated with the land and ocean windows. The coupling matrix relates the statistical expectation of the windowed power spectrum to that of the global unwindowed power spectrum. For this example, we will assume that the topography of Earth is known only to degree 60.
lmax_data = 60
lmax_valid1 = lmax_data - lmax_ocean
lmax_full1 = lmax_data + lmax_ocean
fig1, ax1 = ocean_windows.plot_coupling_matrix(lmax_data, nwins, mode='full', show=False)
ax1.plot([-0.5, lmax_data + 0.5], [lmax_valid1, lmax_valid1], c='red')
ax1.text(1, lmax_valid1 + 3, 'affected by degrees > lmax_data', fontsize=12, color='red')
ax1.set(xlim=[-0.5, lmax_data + 0.5], ylim=[lmax_full1, 0.], title='ocean coupling matrix')
fig2, ax2 = land_windows.plot_coupling_matrix(lmax_data, nwins, mode='full', show=False)
lmax_valid2 = lmax_data - lmax_land
lmax_full2 = lmax_data + lmax_land
ax2.plot([-0.5, lmax_data + 0.5], [lmax_valid2, lmax_valid2], c='red')
ax2.text(1, lmax_valid2 + 4, 'affected by degrees > lmax_data', fontsize=12, color='red')
ax2.set(xlim=[-0.5, lmax_data + 0.5], ylim=[lmax_full2, 0.], title='land coupling matrix');
The above plotted coupling matrices reflects the different quality of the land and ocean spectra. The higher degree part (towards the bottom right) corresponds to a simple convolution, as it becomes asymptotically symmetric about the diagonal. The land windows (bottom) couple degrees over a wider range and therefore decrease the spectral resolution that can be achieved.
It is also seen that the coupling matrix is asymmetric at the lowest degrees. Here, low input degrees map to higher output degrees. For example, the input degree 0 maps strongest to about degree 3 in the ocean case and to about degree 8 in the land case. This is a consequence of the fact that we can not estimate the global mean or very large wavelength structures from subregions that are much smaller than the feature of interest. Low degree energy maps therefore to the lowest window degrees, where they increase the overall power.
A given localized spectral estimate at degree l
contains information from the global field between degrees l-lwin
and l+lwin
. For this example, we assumed that the data were expanded to degree 60. The red line in these plots thus denotes the degree where the localized spectral esimates contain information from beyond the maximum degree of the data. In general, one should only interpret the localized spectrum up to a maximum degree lmax_data - lwin
.
We are now in a position to compute regional power spectral estimates of the land and of the ocean topography as well as the corresponding power spectra that we would expect to see on land and in the ocean if Earth's topography was stationary and isotropic (which it isn't).
We first calculate the global power spectrum:
power_global = clm.spectrum()
lmax_global = clm.lmax
ls_global = np.arange(len(power_global))
Next, we use the methods multitaper_spectrum()
to determine the multitaper power spectrum of the oceans and land, and then we calculate the expected spectrum if the Earth's topography was stationary and isostropic. For the later, we use the dot product of the coupling matrix and global spectrum, but we also could have used the method biased_spectrum()
to obtain the same thing:
power_ocean, dpower_ocean = ocean_windows.multitaper_spectrum(clm, nwins)
power_land, dpower_land = land_windows.multitaper_spectrum(clm, nwins)
cmatrix_ocean = ocean_windows.coupling_matrix(lmax_global, nwins, mode='valid')
power_ocean_predicted = np.dot(cmatrix_ocean, power_global)
ls_ocean = np.arange(len(power_ocean))
cmatrix_land = land_windows.coupling_matrix(lmax_global, nwins, mode='valid')
power_land_predicted = np.dot(cmatrix_land, power_global)
ls_land = np.arange(len(power_land))
Finally, let's plot the masked topography of the oceans and land, and then plot the measured and predicted multitaper power spectra:
fig, (col1, col2) = plt.subplots(1, 2, figsize=(10, 5))
col1.imshow(-topo.data * ocean_mask, cmap='viridis', extent=(0, 360, -90, 90))
col2.imshow(topo.data * land_mask, cmap='viridis', extent=(0, 360, -90, 90))
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.plot(ls_global, power_global, c='grey', alpha=0.5, lw=2, label='global')
ax.plot(ls_ocean, power_ocean, '-', c='blue', label='ocean (measured)')
ax.plot(ls_ocean, power_ocean_predicted, '--', c='blue', label='ocean (predicted)')
ax.plot(ls_land, power_land, '-', c='green', label='land (measured)')
ax.plot(ls_land, power_land_predicted, '--', c='green', label='land (predicted)')
ax.legend(loc=3)
ax.set(xscale='log', yscale='log', xlabel='degree', ylabel='power')
ax.grid(True)
As we should have expected, the power spectra are vastly different. For degrees greater than the window bandwidths, the observed ocean spectrum is seen to be significantly lower than that of the land spectrum up until about degree 100. The ocean spectrum has high power for degrees less than the window bandwidth because the average elevation of ocean is much less than 0. The land spectrum is entirely missing the low degree component because the land elevations don't deviate much from 0.
This plot also shows that the predicted and observed spectra for the oceans and land are vastly different. This is simply a reflection of the fact that Earth's topopgraphy is not stationary and isostropic, and that there are in fact major differences between the oceans and land.
We next show that the measured and expected spectra would have been more similar if the topography was in fact a stationary process. We illustrate this by replacing the observed topography of Earth with a random model that has the same expectation for the power spectrum. First, we create the new topography from random coefficients:
np.random.seed(0) # fix seed for reproducable figure
coeffs_stationary = SHCoeffs.from_random(power_global)
grid_stationary = coeffs_stationary.expand('DH2')
Next, we proceed as above and calculate the relevant multitaper spectra:
power_ocean, dpower_ocean = ocean_windows.multitaper_spectrum(coeffs_stationary, nwins)
power_land, dpower_land = land_windows.multitaper_spectrum(coeffs_stationary, nwins)
cmatrix_ocean = ocean_windows.coupling_matrix(lmax_global, nwins, mode='valid')
power_ocean_predicted = np.dot(cmatrix_ocean, power_global)
ls_ocean = np.arange(len(power_ocean))
cmatrix_land = land_windows.coupling_matrix(lmax_global, nwins, mode='valid')
power_land_predicted = np.dot(cmatrix_land, power_global)
ls_land = np.arange(len(power_land))
fig, (col1, col2) = plt.subplots(1, 2, figsize=(10, 5))
col1.imshow(grid_stationary.data * ocean_mask, cmap='viridis')
col2.imshow(grid_stationary.data * land_mask, cmap='viridis')
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.plot(ls_global, power_global, c='grey', alpha=0.5, lw=2, label='global')
ax.plot(ls_ocean, power_ocean, '-', c='blue', label='ocean (measured)')
ax.plot(ls_ocean, power_ocean_predicted, '--', c='blue', label='ocean (predicted)')
ax.plot(ls_land, power_land, '-', c='green', label='land (measured)')
ax.plot(ls_land, power_land_predicted, '--', c='green', label='land (predicted)')
ax.legend(loc=3)
ax.set(title='regional spectra of a stationary random model', xscale='log', yscale='log', xlabel='degree', ylabel='power')
ax.grid(True)
In this case, the regional estimates of the stationary model resemble closely the predicted ones. The lowest degrees are more sensitive to fluctuations of the random model given that these spectral estimates where constructed based on a smaller number of localized coefficients.
To compare global, ocean and land spectra, the coupling matrix can be inverted to remove the bias. In the case of the stationary model, we expect to retrieve the same original input power spectrum, because apart from random fluctuations, the differences of the power spectra are entirely due to the window effects. In the case of the real non-stationary topography of the Earth on the other hand, we would get three power spectra that correspond to equivalent stationary global models that have the observed regional spectra. This inversion, however, has to be done with care, because the coupling matrix is singular.


Life is the persistence of creation. Both in biology and technology, the moment of creation begins the existence of that which previously did not exists. All my life I've had an insatiable curiosity to answer the questions how and why. Why do I experience emotions? How does a transistor work? Why does liquid gasoline move a car down the street? How does electricity make a computer operate? To this day I'm forever searching for the answers to questions and new questions to answer. The most difficult questions to answer are those that have never been asked, just as the most challenging problems are those that have never been solved.
For me, software is the questions and the problems; the answers and the solutions.
Most of my work is available on GitHub, but here are select projects.
My creative outlet is music. I primarily jam on a guitar or bass, but will tinker with anything that makes a sound. Chart-It arose from my frustration with trying to manage binders full of hand written/typed song charts. As I found myself usually sitting before a computer when playing, I created a web app where I could create, store, and easily view charts I had created.
This particular project uses AngularJS and Bootstrap to create, save, and view song charts. It features user authentication an data storage with Firebase, where it is currently deployed. Check out the GitHub Repo for details on features and function.
Writing software for the internet opened my eyes to the world of security. While software can create incredible solutions, it can also be exploited. Software security ranges from writing secure code to ensuring that your devices and networks are also safe from attack and misuse. To learn more about network security and vulnerabilities, I've created a piece of software to map out devices and services for machines running on a local network.
DiscoverMe is a multi-user web interface built on top of an existing program called Nmap. This project uses and AngularJS and Angular Material to create a UI run on a browser. With a Node.js server runnning on a local machine, users can scan their networks for other devices, discover any open ports and servies running, and save data to a PostgreSql database hosted on Heroku.
Below are some of the software technologies and tools I play with.
Feel free to email me with any questions, comments, or just to say hello!


Fairy Tales and Other Traditional Stories
by FCIT
“The Twelve Brothers”
by Grimm Brothers
Additional Information
- Year Published: 0
- Language: English
- Country of Origin: Germany
- Source: Hamilton Wright Mabie, ed., Fairy Tales Every Child Should Know
-
Readability:
- Flesch–Kincaid Level: 6.0
- Word Count: 1,922
- Genre: Fairy Tale/Folk Tale
- Keywords: penance
- ✎ Cite This
Downloads
There were once a king and queen who had twelve children—all boys. Now, one day the king told his wife that if a daughter should be born, all the sons must die—that their sister alone might inherit his kingdom and riches.
So the king had twelve coffins made, which were filled with shavings, and in each was the little pillow for the dead. He had them locked up in a private room, the key of which he gave to the queen, praying her not to speak of it to anyone. But the poor mother was so unhappy that she wept for a whole day, and looked so sad that her youngest son noticed it.
He had the Bible name of Benjamin, and was always with his mother.
“Dear mother,” he said, “why are you so sorrowful?”
“My child, I may not tell you,” she replied; but the boy allowed her no rest till she unlocked the door of the private room, and showed him the twelve coffins filled with shavings.

She wept bitterly as she told him, but her son comforted her, and said, “Do not weep, dear mother. We will take care of ourselves, and go far away.”
Then she took courage, and said, “Yes, go away with your eleven brothers, and remain in the forest; and let one climb a tree, from whence he will be able to see the tower of the castle; If I should have a son, a white flag shall be hoisted, and then you may return home; but if you see a red flag, you will know it is a girl, and then hasten away as fast as you can, and may Heaven protect you! Every night I will pray for you, that you may not suffer from the cold in winter or the heat in summer.”
Then she blessed all her sons, and they went away into the forest, while each in turn mounted a high tree daily, to watch for the flag on the tower.
Eleven days passed, and it was Benjamin’s turn to watch. He saw the flag hoisted, and it was red—the signal that they must die. The brothers were angry, and said, “Shall we suffer death on account of a maiden? When we find one we will kill her, to avenge ourselves.”
They went still farther into the forest, and came upon a most pleasant little cottage, which was uninhabited.
So they wandered about the forest, shooting hares, wild rabbits, pigeons and other birds, which they brought to Benjamin to prepare for food. In this cottage they lived for ten years happily together, so that the time passed quickly.
Their little sister was growing a great girl. She had a sweet disposition, and was very beautiful to look upon.
One day, when she was about ten years old, she discovered in her mother’s wardrobe twelve shirts. “Mother,” she exclaimed, “whose shirts are these? They are much too small for my father.”
The queen sighed as she replied, “Dear child, these shirts belong to your twelve brothers.”
“Twelve brothers!” cried the little maiden. “Where are they? I have not even heard of them.”
“Heaven knows where they are,” was the reply; “but they are wandering about the world somewhere.” Then the queen took her little daughter to the private room in the castle, and showed her the twelve coffins which had been prepared for her brothers, and related to her, with many tears, why they had left home.
“Dear mother,” said the child, “do not weep. I will go and seek my brothers.” So she took the twelve shirts with her, and wandered away into the forest.
She walked for a whole day, and in the evening came to a cottage, stepped in, and found a young boy, who stared with astonishment at seeing a beautiful little girl dressed in rich clothing and wearing a golden star on her forehead.
At last he said, “Who are you, and what do you want?”
“I am a king’s daughter,” she said, “and I seek my twelve brothers, and I intend to search for them till I find them;” and she showed him their shirts.
Then Benjamin knew that she was his sister, and said, “I am your youngest brother, Benjamin.” Then she wept for joy. They kissed each other with deep affection, and were for a time very happy.
At last Benjamin said, “Dear sister, we have made a vow that the first young maiden we meet should die, because through a maiden we have lost our kingly rights.”
“I would willingly die,” she said, “if by so doing I could restore my brothers to their rightful possessions.”
“No, you shall not die,” he replied. “Hide yourself behind this tub until our eleven brothers come home; then I will make an agreement with them.”
At night the brothers returned from hunting, and the supper was ready. While they sat at table, one of them said, “Well, Benjamin, have you any news?”
“Perhaps I have,” he said, “although it seems strange that I, who stay at home, should know more than you, who have been out.”
“Well, tell us your news,” said one. So he said:
“I will tell you if you will make one promise.”
“Yes, yes!” they all cried. “What is it?”
“Well, then, promise me that the first maiden you meet with in the forest shall not die.”
“Yes, yes!” said they all; “she shall have mercy, but tell us.”
“Then,” said the youngest brother, “our sister is here;” and, rising, he lifted the tub, and the king’s daughter came forth in her royal robes and with a golden star on her forehead, and looking so fair and delicate and beautiful that the brothers were full of joy, and kissed and embraced her with the fondest affection.
She stayed with Benjamin, and helped him in keeping the house clean and cooking the game which the others brought home. Everything was so nicely managed now and with so much order, the curtains and the quilts were beautifully white, and the dinners cooked so well that the brothers were always contented, and lived in great unity with their little sister.
There was a pretty garden around the house in which they lived, and one day, when they were all at home dining together, and enjoying themselves, the maiden went out into the garden to gather them some flowers.
She had tended twelve lilies with great care, and they were now in such splendid bloom that she determined to pluck them for her brothers, to please them.
But the moment she gathered the lilies, her twelve brothers were changed into twelve ravens, and flew away over the trees of the forest, while the charming house and garden vanished from her sight. Now was the poor little maiden left all alone in the wild wood, and knew not what to do; but on turning round she saw a curious old woman standing near, who said to her, “My child, what hast thou done?  They were thy twelve brothers, and now they will always remain ravens.”
“Is there no way to set them free?” asked the maiden, weeping.
“No way in the world,” she replied, “but one, and that is far too difficult for thee to perform; yet it would break the spell and set them free.
Then the maiden climbed into a tree, and, seating herself in the branches, began to knit.
She remained here, living on the fruit that grew on the tree, and without laughing or uttering a word.
As she sat in her tree, the king, who was hunting, had a favourite hound, who very soon discovered her, ran to the tree on which the maiden sat, sprang up to it, and barked at her violently.
The king came nearer, and saw the beautiful king’s daughter with the golden star on her forehead. He was so struck with her beauty that he begged her to come down, and asked her to be his bride. She did not speak a word, but merely nodded her head. Then the king himself climbed up into the tree, and bringing her down, seated her on his own horse and galloped away with her to his home.
The marriage was soon after celebrated with great pomp, but the bride neither spoke nor laughed.
When they had lived happily together for some years, the king’s mother, a wicked woman, began to raise evil reports about the queen, and said to the king, “It is some beggar girl you have picked up. Who can tell what wicked tricks she practises. She can’t help being dumb, but why does she never laugh? unless she has a guilty conscience.” The king at first would listen to none of these suspicions, but she urged him so long, and accused the queen of such wicked conduct, that at last he condemned her to be burnt to death.
Now in the court-yard a great fire was kindled, and the king stood weeping at a window overlooking the court of the palace, for he still loved her dearly. He saw her brought forth and tied to the stake; the fire kindled, and the flames with their forked tongues were creeping towards her, when at the last moment the seven years were past, and suddenly a rustling noise of wings was heard in the air; twelve black ravens alighted on the earth and instantly assumed their own forms—they were the brothers of the queen.
They tore down the pile and extinguished the fire, set their sister free, and embraced her tenderly. The queen, who was now able to speak, told the king why she had been dumb and had never laughed.
The delight of the king was only equalled by his anger against the wicked witch, who was brought to justice and ordered to be thrown into a vat of oil full of poisonous snakes, where she died a dreadful death.


Impaired binding of a DQ2 and DQ8 -binding HSV VP16 peptide to a DQA1*0501/DQB1*0302 trans class II heterodimer.
Abstract
DQalpha and DQbeta trans heterodimeric HLA-DQ molecules form in individuals heterozygous for the DQ2 and DQ8 specificities. Unique functions and disease associations have been postulated for such trans-dimers, which may be different from cis-encoded DQ molecules encoded by the corresponding haplotypes. We analyzed the ability of the trans-dimer encoded by HLA-DQA1*0501/DQB1*0302 to bind a peptide antigen which interacts with DQ molecules encoded by both parental haplotypes. Markedly impaired binding was observed, consistent with both the use of different anchor residues and with changes in levels of DQ cis- dimer availability for peptide binding interactions.


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lets you tune your guitar to the tuning of your choice. Go ahead and start by selecting an input device: the best results are achieved with a dedicated sound card, but you can just as well use your microphone.
Other modes but normal are currently not yet implemented, but should soon follow.
Below you will see a field to select a tuning, and an indicator of how well a particular string is tuned next to it.


Welcome to SimOpt’s documentation!¶
The purpose of the SimOpt testbed is to encourage development and constructive comparison of simulation-optimization (SO) solvers (algorithms). We are particularly interested in the finite-time performance of solvers, rather than the asymptotic results that one often finds in related literature.
For the purposes of this site, we define simulation as a very general technique for estimating statistical measures of complex systems. A system is modeled as if the probability distributions of the underlying random variables were known. Realizations of these random variables are then drawn randomly from these distributions. Each replication gives one observation of the system response, i.e., an evaluation of the objective function. By simulating a system in this fashion for multiple replications and aggregating the responses, one can compute statistics and use them for evaluation and design.
The paper Pasupathy and Henderson (2006) explains the original motivation for the testbed, and the follow-up paper Pasupathy and Henderson (2011) describes an earlier interface for MATLAB implementations of problems and solvers. The paper Dong et al. (2017) conducts an experimental comparison of several solvers in SimOpt and analyzes their relative performance. The recent Winter Simulation Conference paper Eckman et al. (2019) describes in detail the recent changes to the architecture of SimOpt and the control of random number streams.
The Oracle Library contains the simulation logic to simulate a variety of systems. The Problem Library builds upon the Oracle Library and contains a variety of SO test problems. The Solver Library provides users with the latest SO solvers to solve different types of SO problems. You can also contribute to SimOpt by using pull requests in GitHub. The two libraries are intended to help researchers evaluate and compare the finite-time performance of existing solvers. Instructions on how to run solvers on problems can be found here.
Contents¶
Indices and tables¶
Acknowledgments¶
An earlier website for SimOpt was developed through work supported by the National Science Foundation under grant nos. DMI-0400287, CMMI-0800688 and CMMI-1200315. Recent work on the development of SimOpt has been supported by the National Science Foundation under grant nos. DGE-1650441, CMMI-1537394, CMMI-1254298, CMMI-1536895, IIS-1247696, and TRIPODS+X DMS-1839346, by the Air Force Office of Scientific Research under grant nos. FA9550-12-1-0200, FA9550-15-1-0038, and FA9550-16-1-0046, and by the Army Research Office under grant no. W911NF-17-1-0094. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF).


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This is working repository of Postman public workspaces from the Postman network, helping provide a single listing of all the workspaces currently published.
This is working repository of Postman public workspaces from the Postman network, helping provide a single listing of all the workspaces currently published.


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