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IFXRenderStencil Class ReferenceThis interface encapsulates all settings relating to the stencil buffer.
More...
List of all members.
Detailed Description
This interface encapsulates all settings relating to the stencil buffer.
It is used to pass stencil settings into an IFXRender object using IFXRenderContext::SetStencil(). Usage of the stencil buffer can be enabled with IFXRenderContext::Enable(IFX_STENCIL). For this to work, you must have a stencil buffer allocated.
- See also:
- IFXRenderContext::SetStencil(), IFXRenderWindow
Constructor & Destructor Documentation
| IFXINLINE IFXRenderStencil::IFXRenderStencil |
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| IFXINLINE IFXRenderStencil::~IFXRenderStencil |
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Member Function Documentation
| IFXINLINE IFXenum IFXRenderStencil::GetFailureOp |
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Gets the stencil test fail operation.
- Returns:
- The stencil test fail operation.
- Return values:
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| IFX_KEEP | Do nothing to the stencil buffer. |
| IFX_REPLACE | Replace the stored stencil value with the reference value. |
| IFX_INCR | Increment the stencil buffer. This will wrap to 0 on overflow. |
| IFX_DECR | Decrement the stencil buffer. This will wrap to Max (255) on overflow. |
| IFX_INCRSAT | Increment the stencil buffer. This will not not wrap. |
| IFX_DECRSAT | Decrement the stencil buffer. This will not wrap. |
| IFX_INVERT | Do a binary inversion (1->0, 0->1) on the stencil buffer. |
- See also:
- SetFailureOp().
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| IFXINLINE IFXenum IFXRenderStencil::GetPassOp |
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Gets the stencil test pass operation.
- Returns:
- The stencil test pass operation.
- Return values:
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| IFX_KEEP | Do nothing to the stencil buffer. |
| IFX_REPLACE | Replace the stored stencil value with the reference value. |
| IFX_INCR | Increment the stencil buffer. This will wrap to 0 on overflow. |
| IFX_DECR | Decrement the stencil buffer. This will wrap to Max (255) on overflow. |
| IFX_INCRSAT | Increment the stencil buffer. This will not not wrap. |
| IFX_DECRSAT | Decrement the stencil buffer. This will not wrap. |
| IFX_INVERT | Do a binary inversion (1->0, 0->1) on the stencil buffer. |
- See also:
- SetPassOp()
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| IFXINLINE U32 IFXRenderStencil::GetReference |
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Gets the stencil test reference value.
- Returns:
- The stencil test reference value.
- See also:
- SetReference()
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| IFXINLINE IFXenum IFXRenderStencil::GetStencilFunc |
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Gets the stencil test comparison function.
- Returns:
- The stencil test comparison function.
- Return values:
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| IFX_NEVER | The test never passes. |
| IFX_LESS | Passes when the reference value is less than the stencil value. |
| IFX_GREATER | Passes when the reference value is greater than the stencil value. |
| IFX_EQUAL | Passes when the reference value is equal to the stencil value. |
| IFX_NOT_EQUAL | Passes when the reference value is not equal to the stencil value. |
| IFX_LEQUAL | Passes when the reference value is less than or equal to the stencil value. |
| IFX_GEQUAL | Passes when the reference value is greater than or equal to the stencil value. |
| IFX_ALWAYS | The test always passes. |
- See also:
- SetStencilFunc()
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| IFXINLINE U32 IFXRenderStencil::GetTestMask |
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Gets the stencil test mask.
- Returns:
- The stencil test mask.
- See also:
- SetTestMask()
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| IFXINLINE U32 IFXRenderStencil::GetWriteMask |
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Gets the stencil buffer write mask.
- Returns:
- The stencil buffer write mask.
- See also:
- SetTestMask()
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| IFXINLINE IFXenum IFXRenderStencil::GetZFailureOp |
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Gets the depth test fail operation.
- Returns:
- The depth test fail operation.
- Return values:
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| IFX_KEEP | Do nothing to the stencil buffer. |
| IFX_REPLACE | Replace the stored stencil value with the reference value. |
| IFX_INCR | Increment the stencil buffer. This will wrap to 0 on overflow. |
| IFX_DECR | Decrement the stencil buffer. This will wrap to Max (255) on overflow. |
| IFX_INCRSAT | Increment the stencil buffer. This will not not wrap. |
| IFX_DECRSAT | Decrement the stencil buffer. This will not wrap. |
| IFX_INVERT | Do a binary inversion (1->0, 0->1) on the stencil buffer. |
- See also:
- SetZFailureOp()
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| IFXINLINE void IFXRenderStencil::InitData |
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Initialize all data to default values.
Default values are:
- Failure Op: IFX_KEEP
- ZFailure Op: IFX_KEEP
- Pass Op: IFX_KEEP
- Stencil Func: IFX_ALWAYS
- Stencil Reference Value: 0
- Stencil Test Mask: 0xff
- Stencil Write Mask: 0xff
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| IFXINLINE IFXRESULT IFXRenderStencil::SetFailureOp |
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IFXenum |
eOp |
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Sets the operation to take place when the stencil test fails.
Some video cards and APIs don't support IFX_INCR or IFX_DECR. In this case, they will use IFX_INCRSAT and IFX_DECRSAT.
- Parameters:
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| eOp | Input IFXenum specifying the stencil test fail operation. Valid values are:
- IFX_KEEP: Do nothing to the stencil buffer.
- IFX_REPLACE: Replace the stored stencil value with the reference value.
- IFX_INCR: Increment the stencil buffer. This will wrap to 0 on overflow.
- IFX_DECR: Decrement the stencil buffer. This will wrap to Max (255) on overflow.
- IFX_INCRSAT: Increment the stencil buffer. This will not not wrap.
- IFX_DECRSAT: Decrement the stencil buffer. This will not wrap.
- IFX_INVERT: Do a binary inversion (1->0, 0->1) on the stencil buffer.
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- Returns:
- An IFXRESULT error code
- Return values:
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| IFX_OK | No error. |
| IFX_E_INVALID_RANGE | The specified operation is invalid. |
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| IFXINLINE IFXRESULT IFXRenderStencil::SetPassOp |
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IFXenum |
eOp |
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Sets the operation to take place when the stencil test passes.
Some video cards and APIs don't support IFX_INCR or IFX_DECR. In this case, they will use IFX_INCRSAT and IFX_DECRSAT.
- Parameters:
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| eOp | Input IFXenum specifying the stencil test pass operation. Valid values are:
- IFX_KEEP: Do nothing to the stencil buffer.
- IFX_REPLACE: Replace the stored stencil value with the reference value.
- IFX_INCR: Increment the stencil buffer. This will wrap to 0 on overflow.
- IFX_DECR: Decrement the stencil buffer. This will wrap to Max (255) on overflow.
- IFX_INCRSAT: Increment the stencil buffer. This will not not wrap.
- IFX_DECRSAT: Decrement the stencil buffer. This will not wrap.
- IFX_INVERT: Do a binary inversion (1->0, 0->1) on the stencil buffer.
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- Returns:
- An IFXRESULT error code
- Return values:
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| IFX_OK | No error. |
| IFX_E_INVALID_RANGE | The specified operation is invalid. |
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| IFXINLINE IFXRESULT IFXRenderStencil::SetReference |
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U32 |
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Sets the stencil reference value for use in the stencil test comparison function.
- Parameters:
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| uRef | Input U32 specifying the stencil reference value. This value is clamped to the maximum stencil value (probably 0xff, or 255). |
- Returns:
- An IFXRESULT error code
- Return values:
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| IFXINLINE IFXRESULT IFXRenderStencil::SetStencilFunc |
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IFXenum |
eFunc |
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Sets the comparison function used for the stencil test.
The comparison takes place between the current value of the stencil buffer and the reference value.
- Parameters:
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| eFunc | Input IFXenum specifying the stencil test comparison function. Valid values are:
- IFX_NEVER: The test never passes.
- IFX_LESS: Passes when the reference value is less than the stencil value.
- IFX_GREATER: Passes when the reference value is greater than the stencil value.
- IFX_EQUAL: Passes when the reference value is equal to the stencil value.
- IFX_NOT_EQUAL: Passes when the reference value is not equal to the stencil value.
- IFX_LEQUAL: Passes when the reference value is less than or equal to the stencil value.
- IFX_GEQUAL: Passes when the reference value is greater than or equal to the stencil value.
- IFX_ALWAYS: The test always passes.
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- Returns:
- An IFXRESULT error code
- Return values:
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| IFX_OK | No error. |
| IFX_E_INVALID_RANGE | The specified operation is invalid. |
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| IFXINLINE IFXRESULT IFXRenderStencil::SetTestMask |
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U32 |
uMask |
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Sets the testing mask that is used for the stencil test comparison.
This mask is applied (binary &'ed with) both the reference value and the stencil value before the stencil comparison function is performed.
- Parameters:
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| uMask | Input U32 specifying the stencil test mask. This value is clamped to the maximum stencil value (probably 0xff, or 255). |
- Returns:
- An IFXRESULT error code
- Return values:
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| IFXINLINE IFXRESULT IFXRenderStencil::SetWriteMask |
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U32 |
uMask |
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Sets the write mask for the stencil buffer.
This mask is a binary bit by bit write enable mask for the stencil buffer. Only those stencil bits whose mask bit is 1 will be written to. A uMask of 0x0 will disable all stencil updates.
- Parameters:
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| uMask | Input U32 specifying the stencil buffer write mask. This value is clamped to the maximum stencil value (probably 0xff, or 255). |
- Returns:
- An IFXRESULT error code
- Return values:
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| IFXINLINE IFXRESULT IFXRenderStencil::SetZFailureOp |
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IFXenum |
eOp |
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Sets the operation to take place when the stencil test passes, but the depth test fails.
Some video cards and APIs don't support IFX_INCR or IFX_DECR. In this case, they will use IFX_INCRSAT and IFX_DECRSAT.
- Parameters:
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| eOp | Input IFXenum specifying the depth test fail operation. Valid values are:
- IFX_KEEP: Do nothing to the stencil buffer.
- IFX_REPLACE: Replace the stored stencil value with the reference value.
- IFX_INCR: Increment the stencil buffer. This will wrap to 0 on overflow.
- IFX_DECR: Decrement the stencil buffer. This will wrap to Max (255) on overflow.
- IFX_INCRSAT: Increment the stencil buffer. This will not not wrap.
- IFX_DECRSAT: Decrement the stencil buffer. This will not wrap.
- IFX_INVERT: Do a binary inversion (1->0, 0->1) on the stencil buffer.
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- Returns:
- An IFXRESULT error code
- Return values:
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| IFX_OK | No error. |
| IFX_E_INVALID_RANGE | The specified operation is invalid. |
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Member Data Documentation
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* Other names and brands may be claimed as the property of others.
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Find and replace
September 9, 2009 / From Featurism
We only have a moment so let me tell you the most important thing I have learned so far: love should be easy and work should be hard.
Paul Ford is a writer. He is an associate editor at Harper's Magazine and a computer programmer.
| Site | Subject | Agenda | Updated |
|---|---|---|---|
| Ftrain | Everything | Is this thing on? | At random |
| Featurism | Technology | Map nerdworld by cataloging features. | Tuesday, Thursday, Friday |
| Mary is Laughing | Popular entertainment, esp. Jack Benny | Make sense of archives. | Monday, Wednesday, Friday |
| Least Review | Media objects, physical objects, and ideas | Write less. | Weekdays |
| One Huge Lesson in Humility | Diet | Lose weight. | Mealtimes |
| Grateful Public | Wage-slavery. | My new corporate ad network. | Whenever a client demands |
| Comments Are Closed | Babble | Listen. | Up to you |
| Gary Benchley, Rock Star | My novel | Promote. | 2005 |
Blah blah blah
Blah blah blah
September 9, 2009 / From Featurism


|
Nautical
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Interface function definitions for the AHRS. More...
#include <stdbool.h>
Go to the source code of this file.
Enumerations | |
| enum | { COMPONENT_MIN, COMPONENT_MAX } |
| enum | att_axis { YAW, PITCH, ROLL, NUM_ATT_AXES } |
| enum | accel_axis { SWAY, HEAVE, SURGE, NUM_ACCEL_AXES } |
Functions | |
| int | ahrs_cont_start () |
| Tells AHRS to start sending data in continous mode. More... | |
| float | ahrs_att (enum att_axis dir) |
| Tells AHRS to return angle for a certain direction. More... | |
| float | ahrs_accel (enum accel_axis dir) |
| Tells AHRS to return acceleration for certain direction. More... | |
| uint_fast8_t | ahrs_headingstatus () |
| Tells AHRS to return accuracy of current data. More... | |
| bool | ahrs_att_update () |
| Updates AHRS data. More... | |
| int | ahrs_att_recv () |
| Determines whether AHRS is returning data. More... | |
| void | ahrs_parse_att_reset () |
| Discards incomplete AHRS data. More... | |
| int | ahrs_set_datacomp () |
Variables | |
| float const | ahrs_range [NUM_ATT_AXES][2] |
Interface function definitions for the AHRS.
| float ahrs_accel | ( | enum accel_axis | dir | ) |
Tells AHRS to return acceleration for certain direction.
The coordinates are left-handed with positive heave up, positive sway left, and positive surge back.
| dir | The wanted direction. |
| float ahrs_att | ( | enum att_axis | dir | ) |
Tells AHRS to return angle for a certain direction.
If the ahrs is in degrees mode the values will range per ahrs_range[dir].
The values returned will not changed until ahrs_att_update() is called and it returns true.
| dir | The wanted direction. |
| int ahrs_att_recv | ( | ) |
Determines whether AHRS is returning data.
| bool ahrs_att_update | ( | ) |
Updates AHRS data.
Updates the values returned by ahrs_att to the newest complete set of data that has been received from the ahrs before some point in time within this function's lifetime.
Should only be called between complete "uses" of the attitude data to avoid using disparate data together (ie between runs of a PID routine).
| int ahrs_cont_start | ( | ) |
Tells AHRS to start sending data in continous mode.
Make sure the desired data components are set first, such as with ahrs_set_datacomp().
| uint_fast8_t ahrs_headingstatus | ( | ) |
Tells AHRS to return accuracy of current data.
Values: 1: uncertainty less than 2 degrees 2: uncertainty between 2 and 10 degres 3: uncertainty greater than 10 degrees However, it may give other values (I have known it to give the value 0).
| void ahrs_parse_att_reset | ( | ) |
Discards incomplete AHRS data.
Causes any data from a current incomplete datagram to be discarded. The next received byte will be treated as potentially the start of a datagram.
Can be used due for, eg, a serial timeout for intra-datagram data.


Week 06: Rotations II: Quaternions
Lecture topics:
-
Slerp. Interpolating rotation.
-
Transforms. Coordinate systems. Skeletons and joints.
Additional Resources:
-
Lengyel Chpt 4
-
Parent Chpt 2
Lecture topics:
Slerp. Interpolating rotation.
Transforms. Coordinate systems. Skeletons and joints.
Additional Resources:
Lengyel Chpt 4
Parent Chpt 2


Amid crackdown, IBP agrees to 'briefing' by anti-communism body
'In the first place communism isn’t outlawed as a belief and membership in the communist party is no longer a crime. So why even bother to listen to such a briefing?' says Law professor and human rights lawyer Ted Te
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class: center, middle ### W4995 Applied Machine Learning # (Stochastic) Gradient Descent, Gradient Boosting 02/19/20 Andreas C. Müller ??? We'll continue tree-based models, talking about boosting. Finally, a technique called calibration that looks somewhat similar to ensembles but has the goal of obtaining good probability estimates from any classifier. FIXME regularization parameter mentioned before introduced FIXME explain regularization better FIXME parameter tuning example FIXME symmetric trees FIXME actually write out gradients maybe FIXME: add example of calibrated and inaccurate vs accurate but not calibrated! (always saying 50% is calibrated) FIXME: brier score decomposition FIXME calibration scores FIXME maybe saying "sort" before binning is confusing? FIXME move calibration after metrics? Partial dependence plot. how is that different from feature importances. calibration curve plotting: bins are different for hist and calibration curve? slide 27: maybe put probabilities on x axis? calibration curve: blue histogram is number of total points, y axis not labeled --- # Reminder: Gradient Descent .right-column[![:scale 100%](images/gradient_3d.png)] Want: $$\arg \min_w F(w)$$ Initialize $w_0$ $$w^{(i+1)} \leftarrow w^{(i)} - \eta_i\frac{d}{dw}F(w^{(i)})$$ Converges to local minimum ??? First, let's talk about Gradient Descent. So we have some function we want to minimize here the function is Lasso training data set plus the regularizer. F is the objective of the model and I want to find the best parameter setting w.  This converges to a local minimum in the function. For linear models, there's only one global minimum. Basically, any optimization algorithm you can think of will always converge to the same solution, they only converge at different speeds. --- # Reminder: Gradient Descent .center[ ![:scale 40%](images/gradient_2d.png) ] $$w^{(i+1)} \leftarrow w^{(i)} - \eta_i\frac{d}{dw}F(w^{(i)})$$ ??? --- # Pick a learning rate .center[ ![:scale 90%](images/gradient_learning_rates.png) ] $$w^{(i+1)} \leftarrow w^{(i)} - \eta_i\frac{d}{dw}F(w^{(i)})$$ ??? A little bit of an issue here is picking a learning rate which is how big a step are you making.  If you pick the right step size, you can make very quick progress.  This is a very simple optimizer. But it's not great and it’s not very fast because you need to compute gradients over the whole dataset. What you can do instead is you can approximate a gradient by looking only a single data point at a time. --- # (Stochastic) Gradient Descent .smaller[ Logistic Regression Objective: `$$F(w, b) = -C \sum_{i=1}^n\log(\exp(-y_iw^T \textbf{x}_ii -b) +1 ) + ||w||_2^2$$` Gradient: `$$\frac{d}{dw}F(w) = \frac{d}{dw} -C \sum_{i=1}^n\log(\exp(-y_iw^T \textbf{x}_i - b) +1 ) + ||w||_2^2$$` Stochastic Gradient: Pick $x_i$ randomly, then `$$\frac{d}{dw}F(w) \approx \frac{d}{dw} -C \log(\exp(-y_iw^T \textbf{x}_ii -b) +1 ) + \frac{1}{n}||w||_2^2$$` In practice: just iterate over i. ] ??? Looking at the Logistic Regression, the functional Logistic Regression we want to minimize is the log loss plus the regularizer. Instead of looking at the gradient for the whole sum here, we can get a stochastic approximation of the gradient by looking at only one of the sums at a time. In practice, we iterate over the dataset and go one by one through all the data points. We make sure we shuffle them before. Then we do small gradient steps. This is a very bad optimizer compared to the SAG but it can go very quickly over a lot of data. --- class: spacious # SGDClassifier, SGDRegressor and partial_fit ```python # Run until convergence sgd = SGDClassifier().fit(X_train, y_train) # Run one iteration over a batched dataset sgd = SGDClassifier() for X_batch, y_batch in batches: sgd.partial_fit(X_batch, y_batch, classes=[0, 1, 2]) # Run several iterations over a batched datasets for i in range(10): for X_batch, y_batch in batches: sgd.partial_fit(X_batch, y_batch, classes=[0, 1, 2]) ``` ??? Fit works just like everywhere else. Partial fit you can call it with different batches of data and this will keep fitting the model. One thing that you should keep in mind for the classifier is that you need to give it the classes at the beginning because it doesn't know how many classes there are and at the first time, not all the classes might be present in the data you give it to. So you need to specify how many classes there are. Partial fit doesn't do loops over a dataset. So if you want to use a partial fit, and you want to loop over dataset multiple times, you have to set the loop yourself. If you loop over a dataset, if you do more of gradient descent, then you get better results. This code is the same code for all combinations of losses and regularizers because it’s very simple to write this down. --- class: spacious # SGD and partial_fit - SGDClassifier(), SGDRegressor() fast on very large datasets - Tuning learning rate and schedule can be tricky. - partial_fit allows working with out-of-memory data! ??? This is implemented in SGD classifier, and SGD regressor and you can use it in very large data sets. It's a little bit tricky to tune the learning rate and so you can either pick as constant learning rate, or you can pick an exponentially decreasing learning rate. There's one that's called optimal. If you scale your data to standard division 1, then the optimal one will often work well but it's not actually optimal on any sense. This will allow you to give you something quickly on a big data set.  If you have like streaming data coming in and you having infinite data, you can just keep giving it more and more data called partial_fit. This method will update the model. In scikit-learn, if you call fit multiple times it will always forget whatever it saw before. Each fit resets the model while partial fit will remember what it saw before and keeps fitting the model. SGDClassifier() and SGDRegressor both have a whole bunch of different loss function penalties so they both can do elastic net penalty and the classifier can do logistic loss and hinge loss and squared hinge loss and the regressor can do Huber loss and squared error and absolute error. So there are many, many different choices for different loss functions. --- # Boosting .larger[ $$f(x) = \sum_k g_k(x)$$ - Family of algorithms to create "strong" learner $f$ from "weak" learners $g_k$. - AdaBoost, GentleBoost, LogitBoost, … ] ??? - Trees or stumps work best - Gradient Boosting often the best of the bunch - Many specialized algorithms (ranking etc) This is an instance of a more general family of models, called boosting models, which all iteratively try to improve a model built up from weak learners. Gradient boosting is this particular technique where we are trying to fit the residuals, and it's been found to work very well in practice, in particular if you're using shallow trees as the weak learners. In principle, you could use any model as a weak learner, but trees just work really well. --- class: centre,middle # Gradient Boosting ??? Gradient boosting is one of the most successfull supervised machine learning methods in practice. It's often used in kaggle to win competition, it's used for credit scoring, it's one of the standard tools of the trade. It's one of the best of-the-shelf models A standard implementation that people use is XGBoost, but there's also an implementation in scikit-learn, and we'll talk about both of them. Last time we talked about Random forests, which builds many trees independently, each randomized in a different way, and then averages their predictions. Gradient boosting on the other hand builds trees one by one in a sequential manner, with each tree requiring the results of previous trees. Often, Gradient boosting is done with very small trees, or even decision stumps, which is trees of depth one, so a single split. --- # Gradient Boosting Algorithm `$$ f_{1}(x) \approx y $$` `$$ f_{2}(x) \approx y - f_{1}(x) $$` `$$ f_{3}(x) \approx y - f_{1}(x) - f_{2}(x)$$` -- $y \approx$ ![:scale 22.5%](images/grad_boost_term_1.png) + ![:scale 22.5%](images/grad_boost_term_2.png) + ![:scale 20%](images/grad_boost_term_3.png) + ... ??? Let's look at the regression case first. We start by building a single tree f1 to try to predict the output y. But we strongly restrict f1, so it will be rather bad at predicting y. Next, we'll look at the residual of this first model, so y - f1(x). We now train a new model f2 to try and predict this residual, in other words to correct the mistakes made by f1. Again, this will be a very simple model, so it will still not be able to fix all errors. Then, we look at the residual of both of the models together, so y - f1(x) - f2(x), so the mistakes that could not be fixed by f2, and we build f3 to fix that, and so on. This is natural for regression. For classification this is not as clear. For binary classification you use log-loss, or rather you apply the logistic function to get a binary prediction, for multi-class you can use 1 vs rest. So we're sequentially building up a model using what's called "weak learners", small trees, and create a more powerful composite model. --- # Gradient Boosting Algorithm `$$ f_{1}(x) \approx y $$` `$$ f_{2}(x) \approx y - \gamma f_{1}(x) $$` `$$ f_{3}(x) \approx y - \gamma f_{1}(x) - \gamma f_{2}(x)$$` $y \approx \gamma$ ![:scale 22.5%](images/grad_boost_term_1.png) + $\gamma$ ![:scale 22.5%](images/grad_boost_term_2.png) + $\gamma$ ![:scale 20%](images/grad_boost_term_3.png) + ...
Learning rate $\gamma, i.e. 0.1$ ??? - Iteratively add regression trees to model - Use log loss for classification - Discount update by learning rate FIXME plot for regression models Come back to this --- class: compact # Gradient Boosting is Gradient Descent .left-column[ ## Linear regression $$ L(\mathbf{x}_i, y_i, \mathbf{w}, b) = \sum_i(y_i - \hat{y}_i)^2 $$ $$ = \sum_i(y_i - w^T \mathbf{x}_i - b)^2 $$ optimize: `$$\min_{w \in \mathbb{R}^p, b\in\mathbb{R}} \sum_{i=1}^n (y_i - w^T\mathbf{x}_i - b)^2$$` gradient descent: $$w_{j+1} = w_j - \gamma \frac{\partial L(\mathbf{x}_i, y_i, \mathbf{w}, b)}{\partial\mathbf{w}}$$ ] -- .right-column[ ## Gradient Boosting $$ L(y_i, \hat{y}_i) = \sum_i(y_i - \hat{y}_i)^2 $$ optimize: `$$\min_{\hat{y}\in \mathbb{R}^n} \sum_{i=1}^n (y_i - \hat{y_i})^2$$` gradient descent: $$\hat{y}_{j+1} = \hat{y}_j - \gamma \frac{\partial L(y_i, \hat{y}_i)}{\partial\hat{y}}$$ ] --- #GradientBoostingRegressor .center[ ![:scale 50%](images/grad_boost_regression_steps.png) ] ??? Here's an illustration for doing this for regression. This is a 1D regression dataset for illustration purposes here to form features on the x-axis, the prediction is on the y-axis. In the first step, I'm just fitting my tree to the data. I use a simple tree of depth 3. The depth 3 tree is not able to completely model the data and so the orange is the tree that was fit. After this first step, I look at the total predictions. So this is just gamma times the predictions made by the first tree. So you see everything is sort of squashed together. This is the effect of gamma. The blue points here in this next panel is this data minus the minus the predictions from step one, and so this is the residual. It still looks pretty much the same, because we took only a small step. Then I fit another tree to this residuals again of depth three. Then here's the total prediction, which is gamma times the first tree plus gamma times the second tree, followed against the original data. The orange has more steps now because it's a combination of two trees. As I continue the same procedure until step five, you can see that residual gets much smaller and it has learned most of the variations in the data. A total linear combination of all the trees and learning looks like this. And if I keep doing this, then at some point, residuals will become very small. And the total prediction will fit the data better and better. The question is can I extrapolate? The answer is no. The question is how is this different? And it's quite different. It’s kind of hard for me to answer his question. A) The combination of stumps is different than building a deeper tree because you always apply all the splits to the whole dataset. Whereas if you make a tree deeper, you will have different splits on the deeper nodes. If you have already like 10 nodes, and you want to grow one more level, each of these parts of the data will have a different split. Whereas if I add another stump it will be on a global level. In decision trees, decisions are very hard. And here you don't trust any hard decisions. So you only go a little bit in each direction. B) This works way better. So this was for regression and because it's easier to visualize, you can do the same thing for classification. Basically, what gradient boosting for classification does is you’re letting regression trees to learn decision function. So basically, you're doing regression again, but you're doing logistic regression. Instead of using a linear model for logistic regression, we're now using this linear combination of trees. So in other words, what we're doing is we're applying a log loss, and we're trying to find the regression function that has a small log loss. This f is the decision function that we're doing. And so inside a gradient boosting classifier, you're not actually learning classification trees, you're learning regression trees, which are trying to predict the probability, which is quite different. The question is, how they're different? The same is true for f1.  One reason is the gamma, which means I only go a small step. But even if I set gamma equal to one, the point is that I restricted my f1 to not completely fit the data. I'm not trying to completely overfit the data, I'm trying to use a simple model. So if I use a tree of depth one, there will be a very large residual. --- # Gradient boosting for classification Logistic regression: `$$\min_{w \in ℝ^{p}, b \in \mathbb{R}}\sum_{i=1}^n\log(\exp(-y_i(w^T \textbf{x}_i + b )) + 1)$$` Gradient boosting: `$$\min_{y_i\in \mathbb{R}^n}\sum_{i=1}^n\log(\exp(-y_i \hat{y}_i) + 1)$$` --- # Multiclass Gradient Boosting Multinomial logistic regression: `$$p(y=c|x) = \frac{e^{\textbf{w}_c^T\textbf{x} + b_i}}{\sum_{j=1}^k e^{\textbf{w}_j^T\textbf{x} + b_j}}$$` Multi-class gradient boosting: `$$p(y=c|x) = \frac{e^{\hat{y}^{(c)}}}{\sum_{j=1}^k e^{\hat{y}^{(j)}}}$$` One regression tree per class (per gradient step). --- #GradientBoostingClassifier / HistGradientBoostingClassifier .center[ ![:scale 70%](images/grad_boost_depth2.png) ] ??? Here’s an illustration of what this might look like for classification. This is basically the same thing going on. I’m plotting the probabilities assigned by the model. Here's a first estimator, which makes some mistakes here and makes a bunch of mistake in the middle. This is the decision tree of depth two. Then I fit the model to the residuals here. To minimize the log loss of this dataset changes something in the middle, and so on. And you can see that as I add more and more estimator it fits the data better and better and gets more and more complex decision boundaries. Since it works like a gradient descent on log loss, it's a little bit harder to visualize this for classification. White mean probability of 0.5 (which is a tie). Red means high probability for the red class. Blue means a high probability for the blue class. Here, clearly, it hasn't fit the data perfectly. So I could add more and more models and then, in the end, it would fit the data perfectly. For multi-class, the default thing to do is One Versus Rest. --- # Early stopping - Adding trees can lead to overfitting - Stop adding trees when validation accuracy stops increasing two choices: - pick number of trees and tune learning rate - pick learning rate, use early stopping ??? As I said earlier, if you add more and more trees, the algorithms can overfit. So instead of searching for the learning rate, or searching for number of trees, you can also do early stopping. Because this is a sequential algorithm that gets better and better the more trees you add, you can just use a validation set and stop adding trees once you overfit. You can do that both with scikit-learn and XGBoost. Basically, the idea is that you pick a large number of estimators and you have a separate validation set and if the validation set accuracy doesn't improve or if it decreases for number of iterations, say five, then you just stop the learning. This way, you get results faster, because you don't keep learning but also you possibly get a better model because you know the overfitting. The only downside stopping learning is that you have fewer data to train your model because you need to the validation set for early stopping. You’ll still need a separate test set to see how well the model actually does. You can't use the same set for early stopping and for evaluation. --- class:spacious # Tuning of Gradient Boosting - Typically strong pruning via max_depth - Tune max_features - Tune column subsampling, row subsampling - Regularization - Pick learning rate and do early stopping - Or Pick n_estimators, tune learning rate (if not early stopping) ??? There are several things you can tune about the gradient boosting. A common approach is to pick the number of estimators that you have time for. So runtime is obviously linear than number of estimators because you need to build each tree one by one. Each tree is built with the same parameters. And then you can tune the learning rate to see how strongly you want to fit the data. You can also tune something like max features if you want to add more randomness, but that's actually not very commonly used. You can also subsample the data if you want faster training. And typically there's a maximum depth. Traditionally, it was like maximum depth of one, two or three, though in kaggle people are doing like 8 to 10 or something. But typically, the depth is much, much smaller than for random forests. This means the model will be small in memory, and also will be faster to predict since you have less deep trees to traverse. Before we go into implementation I want to talk a little bit about analyzing the models. So again, because it’s a tree-based model, the prediction is a linear combination of trees, you can look at feature importance like the same way we did for random forest and trees. --- class: middle # Improvements: # "extreme" gradient boosting [XGBoost: A Scalable Tree Boosting System, 2016](http://dmlc.cs.washington.edu/data/pdf/XGBoostArxiv.pdf) --- class: center, middle # Speeding up tree-building via binning --- # Split finding is slow ```python for feature in features: for threshold in thresholds(f): gain = compute_gain(feature, threshold) if gain > best_gain: best_split = (feature, threshold) ``` - Thresholds: all unique values of feature. - Scanning thresholds = sorting: O(n log n) --- # Binning features ![:scale 100%](images/binning_quantiles.png) --- # Binning features .left-column[ ## Original ``` [[5. , 2. , 3.5, 1. ], [4.9, 3. , 1.4, 0.2], [4.4, 2.9, 1.4, 0.2], [5. , 2.3, 3.3, 1. ], [4.9, 2.5, 4.5, 1.7], [6.3, 2.5, 5. , 1.9], [6.3, 2.3, 4.4, 1.3], [5. , 3.5, 1.3, 0.3], [6.1, 2.8, 4.7, 1.2], [5. , 3.5, 1.6, 0.6]]) ``` ] .right-column[ ## Binned ``` [[1, 0, 1, 1], [0, 2, 0, 1], [0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 2, 3], [3, 0, 3, 4], [3, 0, 2, 2], [1, 4, 0, 1], [3, 1, 3, 2], [1, 4, 1, 1]]) ``` ] --- # Binned split finding is fast .left-column[ ![:scale 100%](images/xgboost-exact.png) ] -- .right-column[ ![:scale 100%](images/xgboost-binning.png) ] --- class: spacious # A better split criterion - Include second order (hessian) and regularizer ![:scale 100%](images/xgboost-criterion.png) --- # Aggressive sub-sampling .padding-top[ According to user feedback, using column sub-sampling prevents over-fitting even more so than the traditional row sub-sampling. (XGBoost: A Scalable Tree Boosting System, 2016) ] --- # HistGradientBoostingClassifier ![:scale 100%](images/hist_gradient_boosting.png) --- .left-column[ ## GradientBoostingClassifier - no binning - single core - sparse data support ] .right-column[ ## HistGradientBoostingClassifier - binning - multicore - no sparse data support - missing value support - soon: monotonicity support - soonish: native categorical variables ] --- # XGBoost `conda install -c conda-forge xgboost` ```python from xgboost import XGBClassifier xgb = XGBClassifier() xgb.fit(X_train, y_train) xgb.score(X_test, y_test)) ``` - supports missing values - GPU training - networked parallel training - monotonicity constraints - supports sparse data ??? In terms of implementation, there's a couple of implementation methods. Scikit-learn has gradient boosting classifier and gradient boosting regressor. Unfortunately, they are rather slow. So one of the most commonly used packages is XGBoost, which you can install from conda forge for example. It's completely scikit-learn compatible. So you can import XGBClassifier if you want you can use it with grid search or with pipelines. The cool thing is its fast. It supports missing values so you don't need to do an imputation strategy, you can put them directly into XGBoost. And it also supports multi-core. The gradient boosting in scikit-learn is just sequential on a single core which on most of the machines are not great. If you have a big machine you want to use all the cores in parallel. You can’t do that with scikit-learn but you can do it with XGBoost. For random forest, on the other hand, scikit-learn can also run random forest in parallel, because it's much easier. XGBoost also has a random forest. XGBoost also has some enhancements to the algorithm. It has L1 and L2 penalties for the leaves, so you can do something like an elastic net or lasso penalty to the leaves. I think by default, it's disabled. But they are not really training like the vanilla decision trees. One of the reasons they're faster is because they have a faster implementation. But you can make even faster if you use approximate splits in the trees. If you want to find a split, you need to search over all features and you need to search over all possible thresholds on the feature. Searching over all possible thresholds on the feature means soaring feature, which is an analog operation. Instead, what you can do is you can bin the features, and then its linear time operation but the threshold will be approximate. And that will make your computation much faster. - Efficient implementation of gradient boosting (5x sklearn) - Improvements on original algorithm - https://arxiv.org/abs/1603.02754 - Adds l1 and l2 penalty on leaf-weights - Fast approximate split finding - Scikit-learn compatible interface --- # LightGBM `conda install -c conda-forge lightgbm` ```python from lightgbm.sklearn import LGBMClassifier lgbm = LGBMClassifier() lgbm.fit(X_train, y_train) lgbm.score(X_test, y_test)) ``` - supports missing values - natively supports categorical variables - GPU training - networked parallel training - monotonicity constraints - supports sparse data --- # CatBoost .smallest[ `conda install -c conda-forge catboost` ```python from catboost.sklearn import CatBoostClassifier catb = CatBoostClassifier() catb.fit(X_train, y_train) catb.score(X_test, y_test)) ``` ] .smaller[ - optimized for categorical variables - uses one feature / threshold for all splits on a given level aka symmetric trees - Symmetric trees are "different" but can be much faster - supports missing value - GPU training - monotonicity constraints - uses bagged and smoothed version of target encoding for categorical variables - lots of tooling ] --- class:spacious # Gradient Boosting Advantages - Very fast using HistGradientBoosting (or XGBoost, LightGBM) - Small model size - Typically more accurate than Random Forests - "old" GradientBoosting in sklearn is comparatively slow ??? It's sort of slower to train if you're training serial, but if you paralyze it, it's often faster to train since it has a much smaller model size. So the trees are usually not as deep and you don't need as many because you're doing much more focused learning where you try to correct the mistakes of the other models. So usually it’s very fast to predict because prediction can happen in parallel over all the trees, while learning cannot happen in parallel over all the trees necessarily. Usually, this is more accurate than random forests. The question is for the same number of estimators, how does low versus high learning rate changes? High learning rate allows you to fit the data more strongly and also overfit the data more strongly. --- class: middle # Concluding tree-based models --- class:spacious # When to use tree-based models - Model non-linear relationships - Doesn’t care about scaling, no need for feature engineering - Single tree: very interpretable (if small) - Random forests very robust, good benchmark - Gradient boosting often best performance with careful tuning ??? To summarize, tree-based models are really very, very popular family of models. They're very commonly used. You probably want to use them if you want nonlinear relationships, or if you have a lot of different kinds of weird features because they really don't care about scaling of the features, so it allows you to get rid of mostly preprocessing. If you want very interpretable model, single trees or small single trees are good ideas, because it's one of the few models that you can sort of write down on a blackboard and show to someone, and they'll have some idea of what's going on. That's impossible for any other models. Random forests are great because they're very robust. And you don't have to tune anything, you just run a random forest with 100 trees and with the default settings and it will work great. And so this one of my first benchmarks. Usually, I first ran a logistic regression then I run random forests. Gradient Boosting is often the best performing model, sort of the centered toolbox. Sometimes you need to tune a little bit more, so you need to tune the depth of the trees or the learning rate, and so on. But if you tune them, then they will usually beat random forest on those data sets. One case where you might not want to use trees is if you have very high dimensional sparse data then linear models might work better but also your mileage may vary. On the contrary, if you have like a low dimensional space, tree-based models are probably a good bet. Next thing we'll talk about is a different way to build ensembles, different way to put together multiple models. --- class: center, middle # Questions ?


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MoveToCode
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Struct for storing directional sunlight settings stored in a scene. More...
Static Public Member Functions | |
| static RuntimeSunlightSettings | Lerp (RuntimeSunlightSettings from, RuntimeSunlightSettings to, float t) |
| Lerps between two settings More... | |
| static RuntimeSunlightSettings | Black (RuntimeSunlightSettings source) |
| Sets continuous settings to 'black' without changing any discrete features. More... | |
Public Attributes | |
| bool | UseSunlight |
| Color | Color |
| float | Intensity |
| float | XRotation |
| float | YRotation |
| float | ZRotation |
Struct for storing directional sunlight settings stored in a scene.
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static |
Sets continuous settings to 'black' without changing any discrete features.
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static |
Lerps between two settings
| t | Value from 0 to 1 |
| Color Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.Color |
| float Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.Intensity |
| bool Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.UseSunlight |
| float Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.XRotation |
| float Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.YRotation |
| float Microsoft.MixedReality.Toolkit.SceneSystem.RuntimeSunlightSettings.ZRotation |


About the BioSimulators test suite#
License#
The software for the test suite is released under the MIT License. The example modeling projects in the test suite are released under the Creative Commons 1.0 Universal (CCO) License.
Development team#
This package was developed by the Karr Lab at the Icahn School of Medicine at Mount Sinai, the Center for Cell Analysis & Modeling at the University of Connecticut, and the Center for Reproducible Biomedical Modeling.
Contributing to the BioSimulators test suite#
We enthusiastically welcome contributions to the BioSimulators test suite! Please see the guide to contributing and the developer’s code of conduct.
Acknowledgements#
This work was supported by National Institutes of Health award P41EB023912.
Questions and comments#
Please contact the BioSimulators Team with any questions or comments.


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PIano
1.0
A piano sampler application for the Raspberry PI
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#include <sample.h>
Inherits NewHandlerSupport< Sample >.
Public Member Functions | |
| Sample (char *fname, char note, int volume) | |
| Initialize a sample from the supplied parameters. More... | |
| ~Sample () | |
| void | showFrames (int idx, int frameCount) |
| Debugging method to show part of the buffer. More... | |
| void | showState () |
| Debugging method to show the current state of a sample. More... | |
| void | setNext (samplep s) |
| Set pointer to the next sample in a list. More... | |
| samplep | getNext () |
| Get next sample in the list. More... | |
| char | getNote () |
| int | getVolume () |
| buffp | getBuff () |
| char * | getFilename () |
| int | getFrameCount () |
Static Private Member Functions | |
| static void | outOfMemory () |
| New operation handler when out of memory occurs. More... | |
Private Attributes | |
| char * | filename |
| The name of the file that contains sampling data. More... | |
| char | note |
| Midi note number associated with this sampling data. More... | |
| int | volume |
| The gain (or volume) level of that sampling data (0..127) More... | |
| buffp | buff |
| Buffer that receives the first portion of the sample PCM data. More... | |
| int | frameCount |
| How many frames are gathered in the buffer. More... | |
| samplep | next |
| Used to link samples associated to a single note (See SamplesLibrary class) More... | |
Additional Inherited Members | |
| Static Public Member Functions inherited from NewHandlerSupport< Sample > | |
| static std::new_handler | setNewHandler (std::new_handler p) |
| Declare a new_handler to get control on exceptions when the new operator is actived. More... | |
| static void * | operator new (size_t size) |
| The new operator replacing the C++ supplied operator. More... | |
Class Sample represents the static portion of a single note sampled at a specific gain (volume) level. (Class Voice represents the dynamic portion of it while it is played by the user.) When a sample is created, a first portion of the PCM data is gathered in memory and will be served while PIano setup itself to retrieve the rest of it when required through multithreading.
| Sample::Sample | ( | char * | fname, |
| char | note, | ||
| int | volume | ||
| ) |
Initialize a sample from the supplied parameters.
Definition at line 14 of file sample.cpp.
| Sample::~Sample | ( | ) |
Definition at line 48 of file sample.cpp.
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inline |
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staticprivate |
New operation handler when out of memory occurs.
Definition at line 56 of file sample.cpp.
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inline |
| void Sample::showFrames | ( | int | idx, |
| int | frameCount | ||
| ) |
Debugging method to show part of the buffer.
Definition at line 63 of file sample.cpp.
| void Sample::showState | ( | ) |
Debugging method to show the current state of a sample.
Definition at line 80 of file sample.cpp.
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private |
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private |
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private |
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private |
Used to link samples associated to a single note (See SamplesLibrary class)
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private |
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private |


It was a usual Friday for me, do nothing all day then DJ at a dodgy little club in a town near where I live. I had been feeling a little run down, as all wasters do, but I was ready for a night of hedonistic pleasures, i.e. cheap booze, cheap women and of course, cheap drugs. I remember thinking that I could only handle some fine weed that nite, as it helps me calm down after I have played my sets, so, the night progressed in the all too familiar vein. Then, an industry associate, lets call him 'geezer' approached me after my set and asked me if I would like to purchase some of his extraordinary triple strength Thai bush weed. Of course I obliged and bought some of this stuff, £30 an eighth I might add.
The lights came up and I ran off into a vacant alleyway to skin up three big joints, with barely any tobacco in any of them. I smoked one on the way to the taxi place.  After that spliff I was buzzing in a way I had never felt before, a mild but pleasant throb that started at the front of my skull, then worked its way to my vision, which was bouncing up and down, distorting everything I saw, giving objects a crazy, angular rendering.
'This plant is evil', I remember stuttering in the taxi, but the driver took no notice, more concerned with the rip- off fare he was about to charge me. I stumbled out of the taxi, left my record box behind my house, then proceeded to a secluded country lane near my house to finish off these two wicked white sticks I had prepared for myself. At this point, the high from the first J was peaking, giving me a sense of extreme questioning of the world. I forgot about this as I smoked the second biffter, who's chemical content hit me on the first toke.
I quickly finished it and chucked the butt, ready to spark up the third chapter. I remember feeling a little disappointed because the high stayed basically the same even after the third joint. On my way back to my house, the questioning thoughts came back, but stronger. Who am I? Why does the wealthy part of the world watch this strange flickering box every day? Then it got strange- I actually believed that television was a government ploy to crush the human spirit, getting us hooked on the electric waves, distracting us with entertainment while they steal from our pockets. That may sound silly but I believed it at the time, in some respects I still do.
Soooo, I got in feeling paranoid and wary of mine and others lives, but still found a way of using my mobile phone to text a friend of mine. As I sat back, eyes wide open staring at things, I suddenly noticed this giddy swimming feeling in my head. I began to feel sick and started retching but nothing came out, so I stood up and walked into my hallway. My dad was in bed at this time so I did everything in my power to not wake him up. A numbness started to develop within my body, crossed with euphoric waves and pulses.
For some reason, I wasnít scared, I just stared into space, hoping to regain control. I canít remember how long I was down, but afterwards I was totally fine, apart from being very stoned. I havenít seen 'geezer' since that night and I still watch TV, much to my disgust.
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.


I was digging through the fridge late one night recently, and came across a little manilla envelope. Inside of it was a gram of kana leaves. I think I got it as a little gift from a supplier when I last ordered mimosa hostilis root bark. I had never had this herb before, and was curious as to what the effects would be, especially in contrast to what I had read online. Some of the stuff I read looked a lot like typical vendor hype. I figured I may as well evaluate this one for the good of all mankind. Oh yeah, and lots of good it'll do, I'm sure. Heh.
Anyway, me and my gf rolled up a joint, about half a gram of the stuff, and proceed to give it the get go. The taste was mild, and the smoke was pretty easy on the lungs. After smoking about 3/4 of it, we put it down and decided to just hang out for a bit and see if anything came up. We were not experiencing any effects at this time.
Then, about two minutes later, the effects came on. It was like the clean feeling I get whenever I roll, and a little bit of a positive push, like weed has. No particularly strong mental effects, lots of grinning. Bit of a head in the clouds without a care kind of feeling. Pretty nice. but not stoning in the least. Clear headed, and the whole ordeal had what I'd call a 'moderate' effect. A lot like how I would imagine diet weed would effect a person. If, of course, they marketed such a thing. The effects tapered off to a pleasant afterglow within I'd say 45 minutes. Also, we were both hankering for chinese, and our appetites were both totally gone by the time we finished smoking. A pleasant effect.
All in all, it isn’t too shabby.  And the taste was nice as well.
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.


As you may know Exchange 2010 contains several built-in security features to prevent issues with your server. Think of the back pressure mechanism which protects your Hub Transport from being brought down due to lack of resources.
Another security feature can be found on the Mailbox Server and then specifically in the Information Store process. For those who don’t know what the responsibilities are of the Information Store: almost everything which is database related. For example if the Information Store is down you won’t be able to mount both mailbox and public folder databases.
The feature I am talking about is quarantining a mailbox. But why does Exchange performs this action on mailboxes? Well there are a few reasons why Exchange can decide to place a mailbox in quarantine:
- Thread doing work for a mailbox fails
- More than five threads in a mailbox that haven’t made progress for a long time (more than 60 seconds)
As you have seen it all has to do with threads on a mailbox which fail. Every time one of the issues occurs a counter is raised. This counter is stored in the registry. Besides the counter the Information Store keeps the timestamp information about when the issue occurred with the specific mailbox also called the poison mailbox.
You may think why is this setting stored in the registry? Well the reason for this is that the information stored here will be replicated by the cluster database if you are running a high availability environment. The following registry path is created used to store the keys:
HKLM\SYSTEM\CurrentControlSet\Services\MSExchangeIS\Private-{db guid}\QuarantinedMailboxes{mailbox guid}__
In this path the following keys will be created:
- CrashCount: the amount of crashes detected
- LastCrashTime: the timestamp of the last occurance of a crash
- QuarantineState: is a mailbox quarantined or not
- QuarantineTime: the time the mailbox is placed in quarantine
If the issue doesn’t occur again in two hours the registry key used to store the counter is deleted. The 2 hours is a value which can’t be changed. But there are two other interesting keys:
- MailboxQuarantineCrashThreshold: how many issue may occur before a mailbox is put in quarantine
- MailboxQuarantineDurationInSeconds: how long is the mailbox placed in quarantine**
By default when three issues occur a mailbox is placed in quarantine. The mailbox will be kept in quarantine for 6 hours (21600 seconds). After the 6 hours are expired the mailbox is removed from quarantine.
Both keys can be found here:
HKLM\SYSTEM\CurrentControlSet\Services\MSExchangeIS\Private-{db guid}\QuarantinedMailboxes
But what happens to a mailbox put in quarantine? Well the answer is quit short: it isn’t accessible for any end-user. Even background processes such as content indexing and the mailbox assistant can’t access the mailbox. This is because these processes, just like end-users, don’t pass the OPEN_AS_ADMIN flag. This special flag checks if a user who tries to connects passes the OPEN_AS_ADMIN flag, if true the user will be able to access the mailbox. If a mailbox is placed in quarantine an event is stored with event id 10018.
Besides the automatic release of the mailbox from quarantine it is possible to manually release it from quarantine. But before doing this it is highly recommended to find the cause of the issue. This since a mailbox is not placed in quarantine because it works but it can cause serious issues for the other mailboxes in your environment.
Another reason why to investigate the issue is if you decide to release it manually but the issue still occurs the mailbox will be put in quarantine again.
To manually release the mailbox you will first need to know the GUID of the mailbox. You can lookup the GUID by using the get-mailboxstatistics cmdlet just like this:
| _Get-Mailbox support | select name, GUID_ |
Once you have found the GUID you will need to find the corresponding registry path. As discussed earlier each poisoned mailbox has the key MailboxQuarantineDurationInSeconds modify the value to for example 0 or 1. After you’ve made the registry key change perform one of the following tasks:
- Dismount/mount the database
- Restart the Information Store
- Reboot your server
Although this last one is a little bit overkill.
Here ends the blog about quarantining mailboxes. Hope it doesn’t happen to much in your environment else you got some serious trouble.
More information about this topic can be found on the site below.
Technet – Understanding the Exchange 2010 Store open


Simple textfields:
Clean HTML input field with no text (1):
Clean HTML input field with no text (2):
Clean HTML input field with no text (3):


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This is a part of the tutorial about implementing SignalR on .NET Core application using Angular as front-end.
You may want to see:


MKSQUASHFS(1) create and append squashfs filesystems MKSQUASHFS(1) NAME mksquashfs - tool to create and append to squashfs filesystems SYNOPSIS mksquashfs SOURCE [SOURCE2 ...] DESTINATION [OPTIONS] DESCRIPTION Squashfs is a highly compressed read-only filesystem for Linux. It uses zlib compression to compress both files, inodes and directories. Inodes in the system are very small and all blocks are packed to minimize data overhead. Block sizes greater than 4K are supported up to a maximum of 64K. Squashfs is intended for general read-only filesystem use, for archival use (i.e. in cases where a .tar.gz file may be used), and in constrained block device/memory systems (e.g. embedded systems) where low overhead is needed. OPTIONS Filesystem build options -comp COMPRESSION select COMPRESSION compression. Compressors available: gzip (default), lzo, xz. -b BLOCK_SIZE set data block to BLOCK_SIZE. Default 131072 bytes. -no-exports don't make the filesystem exportable via NFS. -no-sparse don't detect sparse files. -no-xattrs don't store extended attributes. -xattrs store extended attributes (default). -noI do not compress inode table. -noD do not compress data blocks. -noF do not compress fragment blocks. -noX do not compress extended attributes. -no-fragments do not use fragments. -always-use-fragments use fragment blocks for files larger than block size. -no-duplicates do not perform duplicate checking. -all-root make all files owned by root. -force-uid uid set all file uids to uid. -force-gid gid set all file gids to gid. -nopad do not pad filesystem to a multiple of 4K. -keep-as-directory if one source directory is specified, create a root directory con‐ taining that directory, rather than the contents of the directory. Filesystem filter options -p PSEUDO_DEFINITION Add pseudo file definition. -pf PSEUDO_FILE Add list of pseudo file definitions. -sort SORT_FILE sort files according to priorities in SORT_FILE. One file or dir with priority per line. Priority -32768 to 32767, default priority 0. -ef EXCLUDE_FILE list of exclude dirs/files. One per line. -wildcards Allow extended shell wildcards (globbing) to be used in exclude dirs/files -regex Allow POSIX regular expressions to be used in exclude dirs/files. Filesystem append options -noappend do not append to existing filesystem. -root-becomes NAME when appending source files/directories, make the original root become a subdirectory in the new root called NAME, rather than adding the new source items to the original root. Mksquashfs runtime options: -version print version, licence and copyright message. -recover NAME recover filesystem data using recovery file NAME. -no-recovery don't generate a recovery file. -info print files written to filesystem. -no-progress don't display the progress bar. -processors NUMBER Use NUMBER processors. By default will use number of processors available. -read-queue SIZE Set input queue to SIZE Mbytes. Default 64 Mbytes. -write-queue SIZE Set output queue to SIZE Mbytes. Default 512 Mbytes. -fragment-queue SIZE Set fragment queue to SIZE Mbytes. Default 64 Mbytes. Miscellaneous options -root-owned alternative name for -all-root. -noInodeCompression alternative name for -noI. -noDataCompression alternative name for -noD. -noFragmentCompression alternative name for -noF. -noXattrCompression alternative name for -noX. Compressors available and compressor specific options gzip (no options) (default) lzo (no options) xz -Xbcj filter1,filter2,...,filterN Compress using filter1,filter2,...,filterN in turn (in addition to no filter), and choose the best compression. Available filters: x86, arm, armthumb, powerpc, sparc, ia64. -Xdict-size DICT_SIZE Use DICT_SIZE as the XZ dictionary size. The dictionary size can be specified as a percentage of the block size, or as an absolute value. The dictionary size must be less than or equal to the block size and 8192 bytes or larger. It must also be storable in the xz header as either 2^n or as 2^n+2^(n+1). Example dict-sizes are 75%, 50%, 37.5%, 25%, or 32K, 16K, 8K etc. SEE ALSO unsquashfs(1) HOMEPAGE More information about mksquashfs and the squashfs filesystem can be found at <http://squashfs.sourceforge.net/>. AUTHOR squashfs was written by Phillip Lougher<EMAIL_ADDRESS>This manual page was written by Daniel Baumann <daniel.bau‐<EMAIL_ADDRESS>4.2 2012-06-30 MKSQUASHFS(1)


Returns an Op that initializes a list of variables.
tf.compat.v1.initializers.variables
tf.compat.v1.variables_initializer( var_list, name='init' )
After you launch the graph in a session, you can run the returned Op to
initialize all the variables in var_list
. This Op runs all the
initializers of the variables in var_list
in parallel.
Calling initialize_variables()
is equivalent to passing the list of
initializers to Group()
.
If var_list
is empty, however, the function still returns an Op that can
be run. That Op just has no effect.
var_list
: List of Variable
objects to initialize.name
: Optional name for the returned operation.An Op that run the initializers of all the specified variables.


Bitcoin is trading around $7360 (at the time of publication) after rising up to around $7580 earlier today, fully doubling its value since the lows earlier this year. The volume right now is also insane and we are reaching levels of the height of the 2017 bull run but without...
siu - Chainmakers12-May-19 02:23 PM
"dont be fooled!'
ComputerGenie 👴👴👴12-May-19 02:25 PM
spongelegobob rectangularpants
1
siu - Chainmakers12-May-19 02:26 PM
lol
im fulled right now... just need my fulled bags to move up.
2
1
this can and should happen, but doesnt HAVE to happen.
People want to participate in the rewards, that's why they vote for Madmax. Promote rewards, in a type of masternode without management (notarynode) and each election will be a pump for komodo, since the price pump is much easier to see all the great work you are doing


Mama Luigi's Pizzeria
That's Mama Luigi to you, Mario! -Since 1982
Place your order:
Small - 18'
Medium - 22'
Large - 26'
Extra Large - 30'
Pepperoni
Mushrooms
Anchovies
Green Peppers
Add Pizza
Place Order


Make sure you're on a search page and have clicked "latest", then click below
Search Term Harvest
STOP!


getPlotDistr_pendubot.m
Summary: Compute means and covariances of the Cartesian coordinates of the tips both the inner and outer pendulum assuming that the joint state of the cart-double-pendulum system is Gaussian, i.e.,
function [M1, S1, M2, S2] = getPlotDistr_pendubot(m, s, ell1, ell2)
Input arguments:
m mean of full state [6 x 1] s covariance of full state [6 x 6] ell1 length of inner pendulum ell2 length of outer pendulum
Note: this code assumes that the following order of the state: 1: pend1 angular velocity, 2: pend2 angular velocity, 3: pend1 angle, 4: pend2 angle
Output arguments:
M1 mean of tip of inner pendulum [2 x 1] S1 covariance of tip of inner pendulum [2 x 2] M2 mean of tip of outer pendulum [2 x 1] S2 covariance of tip of outer pendulum [2 x 2]
Copyright (C) 2008-2013 by Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
Last modification: 2013-03-27
Contents
High-Level Steps
- Augment input distribution to complex angle representation
- Compute means of tips of pendulums (in Cartesian coordinates)
- Compute covariances of tips of pendulums (in Cartesian coordinates)
function [M1, S1, M2, S2] = getPlotDistr_pendubot(m, s, ell1, ell2)
Code
% 1. Augment input distribution [m1 s1 c1] = gTrig(m, s, [3 4], [ell1, ell2]); % map input through sin/cos m1 = [m; m1]; % mean of joint c1 = s*c1; % cross-covariance between input and prediction s1 = [s c1; c1' s1]; % covariance of joint % 2. Compute means of tips of pendulums (in Cartesian coordinates) M1 = [-m1(5); m1(6)]; % [-l*sin(t1), l*cos(t1)] M2 = [-m1(5) + m1(7); m1(6) + m1(8)]; % [-l*(sin(t1)-sin(t2)),l*(cos(t1)+cos(t2))] % 2. Put covariance matrices together (Cart. coord.) % first set of coordinates (tip of 1st pendulum) s11 = s1(5,5); s12 = -s1(5,6); s22 = s1(6,6); S1 = [s11 s12; s12' s22]; % second set of coordinates (tip of 2nd pendulum) s11 = s1(5,5) + s1(7,7) - s1(5,7) - s1(7,5); % ell1*sin(t1) + ell2*sin(t2) s22 = s1(6,6) + s1(8,8) + s1(6,8) + s1(8,6); % ell1*cos(t1) + ell2*cos(t2) s12 = -(s1(5,6) + s1(5,8) + s1(7,6) + s1(7,8)); S2 = [s11 s12; s12' s22]; % make sure we have proper covariances (sometimes numerical problems occur) try chol(S1); catch warning('matrix S1 not pos.def. (getPlotDistr)'); S1 = S1 + (1e-6 - min(eig(S1)))*eye(2); end try chol(S2); catch warning('matrix S2 not pos.def. (getPlotDistr)'); S2 = S2 + (1e-6 - min(eig(S2)))*eye(2); end


Symbol Index for "Temporal Link Type"
Symbol Index for diagram
"Temporal Link Type"
AC/Direction (Generalization Node)
Associative Category (Class)
classifier (Association)
classifier (Association)
Incident Involvement Type (Class)
Inter Subject Relationship Type (Class)
Location Structure Type (Class)
Phenomenon Qualifier Type (Class)
Subject Location Type (Class)
Temporal Link Type (Class)
Temporal Phenomenon Link (Class)
Timepoint Association (Class)


Logo
Link
Link
Login Button
Navbar
Home 1223io212ou21ohsd
(current)
Link
Dropdown
Action
Another action
Something else here
Disabled
Search
Content
Image
Content
Image
Copy Right stuff


klarna-mobile-sdk / com.klarna.mobile.sdk.payments / KlarnaPaymentsSDK / initialize
initialize
@JvmStatic fun initialize(app: Application): Unit
Call from within your Application.onCreate to initialize the SDK.
This needs only be called once.
Parameters
app
- The application in which KlarnaPayments is running.


|
The OutBox pages and documents published on this system can be viewed by any user with network access to the system. This page describes how to use the Netscape Web server's Access Control features to regulate access to the OutBox pages.
The simplest way to set up access controls is to use the Netscape Administration Server. Click the link below to display the Netscape Administration Server.
When you click this link, you may be asked for a user name and password before you can proceed.
Notes:
To set up access controls for an OutBox page or published document:
Notes:
Table 1: OutBox Pages That Can Be Protected
The table below describes the OutBox pages that can be protected. For each page, one or more file paths are shown. Use the Netscape Administration Server to control access to the page. All of the paths shown for a particular page must be protected.
| File paths | |
|---|---|
| OutBox System Page | /var/www/htdocs/index.html /var/www/htdocs/index.shtml |
| Camera Image | /var/www/cgi-bin/sgi-camera/snap/* |
| System Information | /var/www/cgi-bin/MachineInfo/* |
| OutBox Published Folder | /var/www/cgi-bin/wrap/guest/myfolder/* /usr/people/guest/public_html/myfolder/* [ example for user guest and folder myfolder ] |
| OutBox Published File | /usr/people/guest/public_html/myfile.doc [ example for user guest and file myfile.doc ] |


--- layout: page title: New in V2 Section: New in V2 ---
Smartling is rolling out a new, expanded suite of APIs. Beginning with Authentication and Files APIs. API v1 is still supported but will be deprecated on March 1 2017.
Version 2 of FilesAPI will be familiar to you if you’ve used v1 but there are some important differences to keep in mind while you’re getting started.
V2 APIs replace the single API key with Oauth 2.0 authentication, requiring an access token to be included in the header of every request. Tokens are obtained via an Authenticate request containing a User ID and User Secret.
See Authentication for more information.
Some V2 URLs contain path variables that you need to consider when you write your calls. In FilesAPI there are two path variables that may appear in a URL.
projectId
- the unique identifier for a Smartling project. You can find this information in the Smartling dashboard at Project Settings > API.
locale
- the locale code for a translated file. You can find a full list of locale codes used in each of your projects in the Smartling dashboard at Project Settings > API.
Some terminology and parameter names in v2 are different from their v1 equivalents. Changes include
localesToApprove
parameter in the v1 version of Upload File call is now localeIdsToAuthorize
in v2/last_modified
is now /last-modified
Most Smartling API arguments are strings, but some v2 calls take arrays as arguments to return information on multiple files or locales.
www-url-encoded:
?localeId[]=de-DE&localeId[]=fr-FR
Don’t forget the square brackets for array values, even if you’re only passing a single argument.
Form Parameters:
-F "localeId[]=fr-FR" -F "localeId[]=de-DE"
JSON:
{"localeIds":["es-ES","zh-CN"]}
If you’re already using v1 of Smartling’s File API, you may be able to improve on your integration with the new, more powerful calls included in v2. Significant new options include:
You now have four different options for downloading files:
Choose between detailed status of a file in a single locale or a summary for all locales.


Comments
-
Greg Hurrell
Searching using the entire path name was actually a very conscious, intentional design decision. Rather than typing characters from the filename, I actually want my users to be thinking in terms of the path to the file and including letters from the path. It's a different mindset.
For example, just say you want to open this file:
app/controllers/admin/issues_controller.rb
I want you to type something like:
acai
(ie. "a" for app, "c" for controller, "a" for admin, "i" for issues)
Rather than something like:
issues
orisscon
Obviously on small projects you can take either approach and you'll very quickly zone in on the file you're looking for. But on larger projects where there might be a lot of files which have the string "issues" in the filenames, you'll find that including letters from the preceding paths will actually help you narrow things down a lot quicker. The classic example here is a Rails application which will have a bunch of paths like:
app/controllers/issues_controller.rb
app/controllers/admin/issues_controller.rb
app/helpers/issues_helper.rb
app/models/issue.rb
app/views/issues/index.html.haml
(and many other templates)spec/controllers/issues_controller_spec.rb
(and many other specs)
On a project like that typing "issues" is only going to produce a long list of files which all have similar bases and you'll end up having to scan them visually, which is slow. If you get in the habit of including some characters from the upper path components then you'll find that the list narrows down much more quickly, and to something much shorter.
This is what I mean by taking on a different "mindset"; the bigger the project is, the more you need to think in terms of where the file is rather than what it is called. If you can do this you'll be able to find stuff so much more quickly.
And in the few weeks I've been using the plug-in I have gotten a "feeling" for how much information I'm going to have to type in order to get to the file I want, depending on the number of files in the project and the amount of similarity between their paths. On a small project if I am looking for that
issues_controller.rb
file I might be able to get away withisscon
orconiss
or evenacai
like I suggested above; and on bigger projects I might end up typing something likeappconiss
orappadminisscon
etc. After working in a project for a very short while you get a feel for how verbose you need to be.So that's why it works the way it does, because if you merely want to match against characters in the filename only, you can use other plug-ins which already work that way. And I wouldn't have bothered writing a new plug-in if all I wanted was to copy the behavior of an existing one.
For example, to set up FuzzyFinder to behave like that you can get it to automatically prefix
**/
to all search strings and it works quite nicely. See this page for info on how to do that (basically boils down to doing something likenoremap <silent><Leader>e :FuzzyFinderFile **/<CR>
).So, sorry. I don't think I would want to change that particular aspect of the behavior, even as an option. About the only thing I might consider changing is tweaking the scoring algorithm to give slightly more weight to letters that match the filename segment, but it might be a bit fiddly as in that case I'd actually have to run my searches from right-to-left rather than left-to-right to do it in any sort of efficient manner.
-
Greg Hurrell
Status changed:
- From: new
- To: closed
-
Greg Hurrell
In the interests of keeping all related discussion in one place, just got tipped off to this commit in an email:
I find your Command-T plugin really useful, great job! But after using it for few days I found the default behavior not perfect. Command-T is searching for entered characters in whole file path (relative to pwd) but in most cases you know the filename and it's easier to enter just few characters of filename. In my opinion better would be to search in file names without path by default, and use filename with path if entered search term contains "/". This way we cover both use cases, with default one being similar to Textmate's Go-To-File or Netbeans' OpenFileFast.
Here's my reply:
This has been requested once before:
https://wincent.com/issues/1516
Take a look at that ticket for a fairly detailed explanation of the reason why it works the way it does. The short version is:
- philosophically, the whole design of the plug-in revolves around being in a "mind set" which is path-centric, not filename-centric
- if you prefer to think in terms of filenames, there are already a number of alternative options for VIM which provide very good filename-centric navigation; there really would have been no justification for writing the plug-in in the first place if it was just to add another filename-centric way of opening files
While adding the optional behavior you're talking about might be good for users like you, it would actually undermine the design goals of the plug-in despite being "optional", because it would oblige people to type additional slash characters in order to use the plug-in in the path-centric way which was intended.
Add a comment
Comments are now closed for this issue.


Cab driver found dead in his car in Gurgaon
Police said they identified the man, Yusuf, after they issued an advertisement in a local newspaper and were approached by his family.
by Express News ServiceA day after a 22-year-old cab driver went missing from Gurgaon’s Pataudi, police found his body in his car near a dried-up drain in Pataudi’s Khor village Saturday morning.
Police said they identified the man, Yusuf, after they issued an advertisement in a local newspaper and were approached by his family.
According to police, Yusuf’s family told them that he left home Friday morning to pick up a passenger from Pataudi and didn’t return home.
Yusuf’s brother Farrukh said, “On Saturday, I couldn’t find him at his home or with his friends. Later, we learned that Yusuf’s car was found near a drain in Sohna district. Police had seized the car and recovered his body. I went to the hospital and identified his body later.”
Yusuf lived in a rented accommodation in Imam Nagar in Gurgaon.
A case of murder and causing disappearance of evidence was registered against unknown persons by Pataudi police after they found injuries on the victim’s head and back.
Police suspect that he was killed in a robbery gone wrong and his body may have been dumped in the car later.
A post-mortem was conducted and police are awaiting its result.
Veer Singh, ACP, Pataudi, said, “A case was registered Sunday and we are checking the victim’s phone for possible leads. We haven’t identified suspects yet and are probing the matter.”


{% extends 'base.html' %} {% block title %}{{ gettext("Origin Protocol - DShop") }}{% endblock %} {% block meta_description %}{{ gettext("Origin Protocol's platform powers true peer-to-peer marketplaces everywhere.") }}{% endblock %} {% block content %} {% include 'navbar-dshop.html' %} {{gettext("Create your decentralized online store")}} {{gettext("Run and grow your business with Dshop, a customizable and open-source e-commerce platform. Get started for free and launch your store in a day.")}} {{ gettext("Request a demo") }} {{gettext("Features & Benefits")}} {{gettext("Fast setup, no commitments")}} {{gettext("Launch a decentralised shop in a day - no commitments or long-term contracts.")}} {{gettext("Free to run")}} {{gettext("Dshop is free to run, and transaction fees are limited to crypto gas costs (typically a few cents per transaction).")}} {{gettext("Sell what you want")}} {{gettext("There are no restrictions on what you can sell, now or ever.")}} {{gettext("Secure fiat and crypto payments")}} {{gettext("Accept every way your customers want to pay. Fast, secure payments with no extra fees.")}} {{gettext("Our Customers")}} {{gettext("Meet the companies who are choosing Dshop to build and grow their decentralized online stores")}} {{gettext("Here to help you succeed")}} {{gettext("Speak with a real person when something’s not right.")}} {{gettext("You can email our in-house support anytime, 24/7.")}} {{gettext("Speak with a real person when something’s not right.")}} {{gettext("You can email our in-house support anytime, 24/7.")}} {{gettext("Still have more questions? Feel free to contact us.")}} {{gettext("Contact Us")}} {{gettext("Ready to start selling for free?")}} {{ gettext("Request a demo") }} {% include 'footer.html' %} {% endblock %}


Soul Food Review by Larry McGillicuddy Copyright Larry McGillicuddy
**** (out of 4) Directed by George Tillman Jr. Starring Vanessa Williams, Vivica Fox, Nia Long, Brandon Hammond, Mekhi Phifer, Michael Beach, Irma P. Hall, Gina Ravera
There is a scene early in Soul Food, George Tillman's comedy-drama about a large African-American family and the ordeals they go through during a crisis, that caught me completely off guard. During a wedding, the groom is flirting with some tramp, and in an attempt to to prevent a scene, his mother in law, played by Irma P. Hall, comes to the rescue. The way in which she does this makes me think Irma P. Hall is one of the greatest actresses around right now. However, this is just one magical moment in a movie filled with them.
Vanessa Williams, Vivica Fox, and Nia Long star as three sisters who have each gone their own way in life. Williams is Teri, a successful lawyer, who is frustrated by her husband Miles attempts at a music career. Fox is Maxine, the strongest of the sisters, which can be confirmed by the stability of her marriage and the wisdom of her son Ahmad, who narrates the film. Long is Bird, a hairdresser who is the last of the three to get married. Her husband Lem is an intense passionate individual whose temper gets the best of him sometime but never stops loving. At the center of everything is Irma P. Hall, as Big Mama Jo.
Every Sunday, the family gathers together for dinner at Mama Jo's house. There is lots of conversation, lots of laughs, and the occasional controversy. Mama Jo presides over the family like all mothers should. She is stern and disciplined, but also caring and forgiving.
Everything is going fine, until a tragedy befalls Mama Jo. All of a sudden, a chain reaction of events begin to destroy the family. It gets so bad, Sunday dinner is even cancelled. Without Mama Jo's guidance, there is noone to keep the family together, which frustrates Ahmad.
Miles and Maxine's marriage begins to fall apart, as he focuses on his music and flirts with the troublesome Faith. Lem's temper gets the best of him when his convict past starts to catch up to him. And Teri tries to prevent Maxine's efforts to sell Mama Jo's house. The third act of the film deals with Ahmad's attempts to patch things up. He decides to take over where Mama Jo left off, and tries to get the family back together for one more Sunday dinner.
The acting by everyone in the ensemble is outstanding, with special notices to Irma P. Hall and Brandon Hammond. Hall gives a performance that deserves to be recognized by the academy. Her Mama Jo is one of the most memorable characters I have ever encountered in a movie. Brandon Hammond is an actor with a bright future. He is wise enough not to play cutesy and shows an admirable range and depth, and speaks the final lines of the movie on the perfect note.
Tillman obviously based this movie on his own memories of growing up. Every scene in the movie is purposeful and convincing (except maybe an odd flashback sequence). The dialogue is beleiveable and funny. And then there's the food. Tillman gives us several close ups of the soul food, and it indeed looks very tasty. I can see why Sunday dinners became such a tradition for this family. Soul Food is one of the most purely entertaining movies of the year.
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


Let's Get Started!
Hello there, awesome person!
My favorite site is
GitHub
!
But what would I do without
Google
?!
And it's always fun to watch some
Twitch
!


|
CVM-S5
|
#include <cvms5.h>
Public Attributes | |
| etree_t * | vs30_map |
| char | type [20] |
| char | description [50] |
| char | author [30] |
| char | date [10] |
| double | spacing |
| char | schema [50] |
| char | projection [128] |
| cvms5_point_t | origin_point |
| double | rotation |
| double | x_dimension |
| double | y_dimension |
| double | z_dimension |
| int | x_ticks |
| int | y_ticks |
| int | z_ticks |
The configuration structure for the Vs30 map.
| char cvms5_vs30_map_config_t::author[30] |
The map's author
| char cvms5_vs30_map_config_t::date[10] |
The date the map was created
| char cvms5_vs30_map_config_t::description[50] |
A description of the map
| cvms5_point_t cvms5_vs30_map_config_t::origin_point |
The origin point
| char cvms5_vs30_map_config_t::projection[128] |
The projection string in Proj.4 format
| double cvms5_vs30_map_config_t::rotation |
The number of degrees the map was rotated around origin
| char cvms5_vs30_map_config_t::schema[50] |
The map's schema
| double cvms5_vs30_map_config_t::spacing |
The spacing in meters
| char cvms5_vs30_map_config_t::type[20] |
The type of map
| etree_t* cvms5_vs30_map_config_t::vs30_map |
Pointer to the e-tree file
| double cvms5_vs30_map_config_t::x_dimension |
The X dimension of the map
| int cvms5_vs30_map_config_t::x_ticks |
Number of e-tree ticks in the X direction
| double cvms5_vs30_map_config_t::y_dimension |
The Y dimension of the map
| int cvms5_vs30_map_config_t::y_ticks |
Number of e-tree ticks in the Y direction
| double cvms5_vs30_map_config_t::z_dimension |
The Z dimension of the map
| int cvms5_vs30_map_config_t::z_ticks |
Number of e-tree ticks in the Z direction


{% extends 'base.html' %} {% block content %}
This is the home page of my fifth project of my Python course. I definitely think that my Flask project is lacking, but I hope I can make up for that with slightly cool ideas.
How this site works is pretty simple. You can click on my name that will take you to a page that says howdy to me, thank you :D . The add band part of the navbar will take you to the page that lets you input information for a band in three separate fields: Band Name, Year Formed, and Price. I'm not sure what price means, but I believe my idea was to have prices that way you could search for bands or purchase bands for a price.
The Bands part in the navbar will display all the information on bands currently in the database. You will notice that they are clickable. Upon being clicked you will be taken to another page where you will have the opportunity to change the values of the three fields. When you end up on that page it will say "band updated succesfully", don't believe that lie. I can't seem to fix it. So when you hit submit it will officially be committed.
Enjoy!
{% endblock %}


Since I learned bridge in college 50 years ago, all my memories of the use of strong suit are bridge-colored, and it makes perfect sense as a metaphor. Indeed, a familiarity with the actual source of a metaphor -- in this case, table talk, and the strategies and tactics that it typically accompanies -- can make for a more nuanced understanding on occasion, I think.
When you are satisfied with your page, you can select all text in the textarea and copy it to a new text file, with an extension of either .htm or .html, depending on your %0A Operating System. This file can then be moved to your Web server.
Everybody's language experience is different, so That's not my strong suit would be a transparent metaphor from a card game like bridge or whist to anyone familiar with those %0A games, calculably understandable by others from the sense of %0A strong alone, and probably incomprehensible to still others. There is no single word that describes all of these attributes, sorry; in fact, that's a lot to expect of one word.


Removing the signs of security measures from the door adds an additional layer of protection since we no longer show that there is anything valuable inside.
Shoulder surfing occurs when an unwanted person peaks over your shoulder in order to figure out how you authorize with a security method. Shoulder surfing an AR interaction is much harder.
By introducing a factor of randomness to the authorisation method you make it much harder for any intruder to acquire your code. AR-Access scrambles the keypad between every usage.
Assuming we already always have our phone with us, AR-Access doesn’t enforce any additional tokens such as keys, cards or tags.
The solution is prepared for the upcoming releases of future technologies such as Apple Glasses and simulator Mixed Reality devices.


Lukas Burkhalter
Ph.D. Student @ ETH
Lukas Burkhalter
Lukas Burkhalter is a Ph.D. candidate and research assistant at the Distributed Systems Group at ETH Zurich.
TimeCrypt is a practical TSDB to support both scalable computation over encrypted time series data
and
fine-grained cryptographic access control on queries.
Recent years have seen explosive growth in devices and services that automatically collect, aggregate, and analyze time series data, and this trend is only expected to accelerate with the proliferation of low-cost sensors and the adoption of IoT. However, with this growth has come mounting concern over protecting this data and the privacy of users. A key challenge in ensuring data privacy is that the need for privacy co-exists with the desire to extract value from the data and extracting value often implies granting users and third-party services access to the data.
TimeCrypt is the first system that augments time series data stores with efficient and privacy-preserving processing of time series data, while providing cryptographic means to restrict the query scope based on the data owners policies. Particularly, we introduce a new encryption-based access control construction that is additively homomorphic. TimeCrypt is tailored for time series workloads, supports fast statistical queries over large volumes of encrypted time series data, and enables users to cryptographically restrict the scope of queries and data access based on pre-defined access policies.
Example queries over encrypted raw time series data in TimeCrypt
Examples of access restrictions in TimeCrypt
To handle the heavy write load of time series workloads, TimeCrypt batches data in fixed time windows on the client side. The client encrypts and complements the chunk with statistical metadata. TimeCrypt's client engine encrypts the metadata with a Homomorphic Encryption-based Access Control (HEAC) construction. HEAC is a new efficient encryption construction, which allows for encrypted data processing and access control simultaneously.
To overcome the challenge of handling low-latency statistical queries over millions of datapoints, TimeCrypt employs an encrypted aggregation index on the server side with the metadata as its building blocks.
Ph.D. Student @ ETH
Lukas Burkhalter is a Ph.D. candidate and research assistant at the Distributed Systems Group at ETH Zurich.
Researcher @ ETH
Researcher @ UCB
Professor @ UCB
Sylvia Ratnasamy is an Associate Professor at the NetSys Lab at UC Berkeley, USA.


| #Freq | Username | Followers | Location |
|---|---|---|---|
| 322 | cyberahsokatano | 1 | Starkiller Base |
| 287 | khalidrafiq138 | 1092 | Pakistan |
| 183 | pakistanepeak | 1110 | Санкт-Петербург |
| 143 | pakistani_news | 10180 | Pakistan |
| 120 | Hash_Pakistan | 177 | +923437577075 |
| 107 | CtrlSec2 | 2887 | Worldwide |
| 94 | CtrlSec0 | 3546 | Worldwide |
| 91 | CtrlSec1 | 2748 | Worldwide |
| General | General | Border-related | Hashtags | Locations | Entities |
|---|---|---|---|---|---|
| migrants | refugees | borders | #RefugeeCrisis | Syria | European Mobilisation |
| migration flow | migration crisis | open borders | #refugeesGr | Iraq | Amnesty International |
| migrant camps | refugee camps | border closure | #refugeeswelcome | Afghanistan | Frontex |
| irregular migrants | asylum seekers | border share | #FortressEurope | Pakistan | UNHCR |
| human rights | solidarity | No borders | #ProMuslimRefugees | Idomeni | UN Refugee Agency |
| ISIS | daesh | #OpentheBorders | #helpiscoming | Calais | @Moving Europe |
| muslims | islamists | #solidaritywithrefugees | Lesbos | ||
| Balkan route | rapefugees | #safepassage | Lesvos | ||
| Aylan | #antireport | Lesbosisland |
Pentagon Working To 'Take Out' ISIS's Internet: The US military's secretive Cyber Command (CYBERCOM) is working to destroy the ISIS g...
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RT @iamAhmadshahzad: Wishing every1 a happy n blessed Friday..may Allah accept our Prayerz n have Mercy on Pakistan ISA 👊🏼🇵🇰 #TGIF
RT @kanhaiyajnusu: We will not pay the fine and fight the unfair HLEC of JNU. But our solidarity long live! Chatra, Mazdoor, Kisaan ekta Zi…
RT @Nigel_Farage: Britain's borders a total shambles inside EU. Damaging & unsafe. Theresa May worst Home Secretary. Utter failure. https:/…
Syria Activists Say Airstrikes in Rebel-Held Aleppo Kill 20: Syrian monitoring group, rescue workers say airs... https://t.co/4fKddCTsBK


What are your favourite recent movies?
First favourite
Fast and Furious 6
Now you see me
The Help
The Internship
The Perks of Being a Wallflower
Second favourite
Fast and Furious 6
Now you see me
The Help
The Internship
The Perks of Being a Wallflower
Third favourite
Fast and Furious 6
Now you see me
The Help
The Internship
The Perks of Being a Wallflower
Vote
Poll results
Fast and Furious 6
Now you see me
The Help
The Internship
The Perks of Being a Wallflower