|
|
CIFXResManager Class ReferenceThis changes the resolution of a single IFXMesh.
More...
Inheritance diagram for CIFXResManager:
List of all members.
Detailed Description
This changes the resolution of a single IFXMesh.
If an instance of this class is being used by an IFXCLODManager then you should not change it's resolution here since you may throw off the synchronization provided by the CLODManager.
If your mesh has only one material then you should use this class to change it's resolution directly since it is faster and smaller than IFXCLODManager.
Constructor & Destructor Documentation
| IFXINLINE CIFXResManager::CIFXResManager |
( |
|
) |
|
|
| IFXINLINE CIFXResManager::~CIFXResManager |
( |
|
) |
[virtual] |
|
Member Function Documentation
| void CIFXResManager::DecreaseResolution |
( |
U32 |
delta |
) |
[virtual] |
|
|
|
This method decreases the resolution.
- Parameters:
-
Implements IFXResManager. |
| IFXINLINE U32 CIFXResManager::GetMaxResolution |
( |
|
) |
[virtual] |
|
|
|
This method gets the maximum resolution value.
- Returns:
- Returns U32
Implements IFXResManager. |
| U32 CIFXResManager::GetResolution |
( |
|
) |
[inline, virtual] |
|
|
|
This method gets the resolution range.The resolution range is from 0 to the number of vertices in the original IMESH.
- Returns:
- Returns U32
Implements IFXResManager. |
| IFXINLINE IFXUpdates * CIFXResManager::GetUpdates |
( |
|
) |
[virtual] |
|
| void CIFXResManager::IncreaseResolution |
( |
U32 |
delta |
) |
[virtual] |
|
|
|
This method increases the resolution.
- Parameters:
-
Implements IFXResManager. |
| IFXINLINE IFXRESULT CIFXResManager::Initialize |
( |
IFXCLODManager * |
pInCLODMgr, |
|
|
U32 |
meshIndex |
|
) |
[virtual] |
|
|
|
- Warning:
- Initialize must be called before any other method is called.
The input mesh is assumed to be at full resolution.
- Todo:
- Consistency checking, resolution option.
Implements IFXResManager. |
| IFXINLINE IFXRESULT CIFXResManager::Initialize_ResolutionZero |
( |
IFXCLODManager * |
pInCLODMgr, |
|
|
U32 |
meshIndex |
|
) |
[virtual] |
|
|
|
This method sets the resolution to zero.
- Parameters:
-
- Returns:
- Returns an IFXResult
Implements IFXResManager. |
| U32 CIFXResManager::SetResolution |
( |
U32 |
r |
) |
[virtual] |
|
|
|
The resolution range is from 0 to the number of vertices in the original IMESH.
Implements IFXResManager. |
| IFXINLINE IFXRESULT CIFXResManager::UpdateMesh |
( |
IFXMesh * |
pIn, |
|
|
IFXMesh ** |
ppOut |
|
) |
[virtual] |
|
Member Data Documentation
|
|
Pointer to the parent multi-res mgr:.
|
|
|
number of resolution change records currently applied.
|
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
|
|


Module pl.class
Provides a reuseable and convenient framework for creating classes in Lua
Two possible notations:
B = class(A)
or class.B(A)
.
The latter form creates a named class.
See the Guide for further discussionFunctions
| class (base, c_arg, c) | create a new class, derived from a given base class |


--- layout: chart ---
Figure 23. Share of Older Adults (65+) Households that are Owner Occupied by Head of Householder Race/Ethnicity in Cook County, 2012 and 2019


| Interface | Description |
|---|---|
| ClientSession |
Represents a session between the server and a client.
|
| ComponentSession |
Represents a session between the server and an external component.
|
| ComponentSession.ExternalComponent |
The ExternalComponent acts as a proxy of the remote connected component.
|
| ConnectionMultiplexerSession |
Represents a session between the server and a connection manager.
|
| IncomingServerSession |
Server-to-server communication is done using two TCP connections between the servers.
|
| OutgoingServerSession |
Server-to-server communication is done using two TCP connections between the servers.
|
| RemoteSessionLocator |
Locator of sessions that are being hosted by other cluster nodes.
|
| ServerSession | |
| Session |
The session represents a connection between the server and a client (c2s) or
another server (s2s) as well as a connection with a component.
|
| Class | Description |
|---|---|
| ClientSessionInfo |
Client session information to be used when running in a cluster.
|
| ConnectionSettings | |
| ConnectionSettings.Client | |
| ConnectionSettings.Component | |
| ConnectionSettings.Multiplex | |
| ConnectionSettings.Server | |
| GetSessionsCountTask |
Task that will be executed in cluster nodes to get the number of sessions hosted by the
cluster node.
|
| LocalClientSession |
Represents a session between the server and a client.
|
| LocalComponentSession |
Represents a session between the server and a component.
|
| LocalComponentSession.LocalExternalComponent |
The ExternalComponent acts as a proxy of the remote connected component.
|
| LocalConnectionMultiplexerSession |
Represents a session between the server and a connection manager.
|
| LocalIncomingServerSession |
Server-to-server communication is done using two TCP connections between the servers.
|
| LocalOutgoingServerSession |
Server-to-server communication is done using two TCP connections between the servers.
|
| LocalServerSession | |
| LocalSession |
The session represents a connection between the server and a client (c2s) or
another server (s2s) as well as a connection with a component.
|
Copyright © 2003-2008 Jive Software.


StrategyExtended
Additional APIs for algorithms that need to be distribution-aware.
Inherits From: StrategyExtended
Note: For most usage of tf.distribute.Strategy
, there should be no need to
call these methods, since TensorFlow libraries (such as optimizers) already
call these methods when needed on your behalf.
Lower-level concepts:
fn
on multiple
replicas, like experimental_run_v2(fn, args=[w])
with an
argument w
that is a wrapped value. This means w
will have a map taking
replica id 0
to w0
, replica id 11
to w1
, etc.
experimental_run_v2()
unwraps w
before calling fn
, so
it calls fn(w0)
on d0
, fn(w1)
on d1
, etc. It then merges the return
values from fn()
, which can possibly result in wrapped values. For
example, let's say fn()
returns a tuple with three components: (x, a,
v0)
from replica 0, (x, b, v1)
on replica 1, etc. If the first component
is the same object x
from every replica, then the first component of the
merged result will also be x
. If the second component is different (a
,
b
, ...) from each replica, then the merged value will have a wrapped map
from replica device to the different values. If the third component is the
members of a mirrored variable (v
maps d0
to v0
, d1
to v1
, etc.),
then the merged result will be that mirrored variable (v
).Replica context vs. Cross-replica context
replica context is when we are in some function that is being called once
for each replica. Otherwise we are in cross-replica context, which is
useful for calling tf.distribute.Strategy
methods which operate across the
replicas (like reduce_to()
). By default you start in a replica context
(the "default single replica context") and then some methods can switch you
back and forth. There is a third mode you can be in called update context
used when updating variables.
tf.distribute.Strategy.scope
: enters cross-replica context when
no other strategy is in scope.tf.distribute.Strategy.experimental_run_v2
: calls a function in
replica context.tf.distribute.ReplicaContext.merge_call
: transitions from replica
context to cross-replica context.tf.distribute.StrategyExtended.update
: calls a function in an update
context from a cross-replica context.In a replica context, you may freely read the values of variables, but
you may only update their value if they specify a way to aggregate the
update using the aggregation
parameter in the variable's constructor.
In a cross-replica context, you may read or write variables (writes may
need to be broadcast to all copies of the variable if it is mirrored).
Sync on read variables
In some cases, such as a metric, we want to accumulate a bunch of updates on
each replica independently and only aggregate when reading. This can be a big
performance win when the value is read only rarely (maybe the value is only
read at the end of an epoch or when checkpointing). These are variables
created by passing synchronization=ON_READ
to the variable's constructor
(and some value for aggregation
).
The strategy may choose to put the variable on multiple devices, like mirrored
variables, but unlike mirrored variables we don't synchronize the updates to
them to make sure they have the same value. Instead, the synchronization is
performed when reading in cross-replica context. In a replica context, reads
and writes are performed on the local copy (we allow reads so you can write
code like v = 0.9*v + 0.1*update
). We don't allow operations like
v.assign_add
in a cross-replica context for sync on read variables; right
now we don't have a use case for such updates and depending on the aggregation
mode such updates may not be sensible.
Locality
Depending on how a value is produced, it will have a type that will determine how it may be used.
"Per-replica" values exist on the worker devices, with a different value for
each replica. They are produced by iterating through a "distributed Dataset
"
returned by tf.distribute.Strategy.experimental_distribute_dataset
and
tf.distribute.Strategy.experimental_distribute_datasets_from_function
. They
are also the typical result returned by
tf.distribute.Strategy.experimental_run_v2
. You typically can't use a
per-replica value directly in a cross-replica context, without first resolving
how to aggregate the values across replicas, for instance by using
tf.distribute.Strategy.reduce
.
"Mirrored" values are like per-replica values, except we know that the value
on all replicas are the same. We can safely read a mirrored value in a
cross-replica context by using the value on any replica. You can convert
a per-replica value into a mirrored value by using
tf.distribute.ReplicaContext.all_reduce
.
Values can also have the same locality as a variable, which is a mirrored
value but residing on the same devices as the variable (as opposed to the
compute devices). Such values may be passed to a call to
tf.distribute.StrategyExtended.update
to update the value of a variable.
You may use tf.distribute.StrategyExtended.colocate_vars_with
to give a
variable the same locality as another variable. This is useful, for example,
for "slot" variables used by an optimizer for keeping track of statistics
used to update a primary/model variable. You may convert a per-replica
value to a variable's locality by using
tf.distribute.StrategyExtended.reduce_to
or
tf.distribute.StrategyExtended.batch_reduce_to
.
In addition to slot variables which should be colocated with their primary
variables, optimizers also define non-slot variables. These can be things like
"number of step updates performed" or "beta1^t" and "beta2^t". Each strategy
has some policy for which devices those variables should be copied too, called
the "non-slot devices" (some subset of the parameter devices). We require that
all non-slot variables are allocated on the same device, or mirrored across
the same set of devices. You can use
tf.distribute.StrategyExtended.non_slot_devices
to pick a consistent set of
devices to pass to both tf.distribute.StrategyExtended.colocate_vars_with
and tf.distribute.StrategyExtended.update_non_slot
.
How to update a variable
The standard pattern for updating variables is to:
tf.distribute.Strategy.experimental_run_v2
,
compute a list of (update, variable) pairs. For example, the update might
be a the gradient of the loss with respect to the variable.tf.distribute.get_replica_context().merge_call()
with the updates and
variables as arguments.tf.distribute.StrategyExtended.reduce_to(VariableAggregation.SUM, t, v)
(for one variable) or tf.distribute.StrategyExtended.batch_reduce_to
(for a list of variables) to sum the updates.
and broadcast the result to the variable's devices.tf.distribute.StrategyExtended.update(v)
for each variable to update
its value.Steps 2 through 4 are done automatically by class
tf.keras.optimizers.Optimizer
if you call its
tf.keras.optimizers.Optimizer.apply_gradients
method in a replica context.
They are also done automatically if you call an assign*
method on a (non
sync-on-read) variable that was constructed with an aggregation method (which
is used to determine the reduction used in step 3).
Distribute-aware layers
Layers are generally called in a replica context, except when defining a
functional model. tf.distribute.in_cross_replica_context
will let you
determine which case you are in. If in a replica context,
the tf.distribute.get_replica_context
function will return a
tf.distribute.ReplicaContext
object. The ReplicaContext
object has an
all_reduce
method for aggregating across all replicas. Alternatively, you
can update variables following steps 2-4 above.
Note: For new tf.distribute.Strategy
implementations, please put all logic
in a subclass of tf.distribute.StrategyExtended
. The only code needed for
the tf.distribute.Strategy
subclass is for instantiating your subclass of
tf.distribute.StrategyExtended
in the __init__
method.
__init__
__init__(container_strategy)
Initialize self. See help(type(self)) for accurate signature.
experimental_between_graph
Whether the strategy uses between-graph replication or not.
This is expected to return a constant value that will not be changed throughout its life cycle.
experimental_require_static_shapes
Returns True
if static shape is required; False
otherwise.
experimental_should_init
Whether initialization is needed.
parameter_devices
Returns the tuple of all devices used to place variables.
should_checkpoint
Whether checkpointing is needed.
should_save_summary
Whether saving summaries is needed.
worker_devices
Returns the tuple of all devices used to for compute replica execution.
batch_reduce_to
batch_reduce_to( reduce_op, value_destination_pairs )
Combine multiple reduce_to
calls into one for faster execution.
reduce_op
: Reduction type, an instance of tf.distribute.ReduceOp
enum.value_destination_pairs
: A sequence of (value, destinations)
pairs. See reduce_to()
for a description.A list of mirrored values, one per pair in value_destination_pairs
.
broadcast_to
broadcast_to( tensor, destinations )
Mirror a tensor on one device to all worker devices.
tensor
: A Tensor value to broadcast.destinations
: A mirrored variable or device string specifying the
destination devices to copy tensor
to.A value mirrored to destinations
devices.
call_for_each_replica
call_for_each_replica( fn, args=(), kwargs=None )
Run fn
once per replica.
fn
may call tf.get_replica_context()
to access methods such as
replica_id_in_sync_group
and merge_call()
.
merge_call()
is used to communicate between the replicas and
re-enter the cross-replica context. All replicas pause their execution
having encountered a merge_call()
call. After that the
merge_fn
-function is executed. Its results are then unwrapped and
given back to each replica call. After that execution resumes until
fn
is complete or encounters another merge_call()
. Example:
# Called once in "cross-replica" context. def merge_fn(distribution, three_plus_replica_id): # sum the values across replicas return sum(distribution.experimental_local_results(three_plus_replica_id)) # Called once per replica in `distribution`, in a "replica" context. def fn(three): replica_ctx = tf.get_replica_context() v = three + replica_ctx.replica_id_in_sync_group # Computes the sum of the `v` values across all replicas. s = replica_ctx.merge_call(merge_fn, args=(v,)) return s + v with distribution.scope(): # in "cross-replica" context ... merged_results = distribution.experimental_run_v2(fn, args=[3]) # merged_results has the values from every replica execution of `fn`. # This statement prints a list: print(distribution.experimental_local_results(merged_results))
fn
: function to run (will be run once per replica).args
: Tuple or list with positional arguments for fn
.kwargs
: Dict with keyword arguments for fn
.Merged return value of fn
across all replicas.
colocate_vars_with
colocate_vars_with(colocate_with_variable)
Scope that controls which devices variables will be created on.
No operations should be added to the graph inside this scope, it should only be used when creating variables (some implementations work by changing variable creation, others work by using a tf.compat.v1.colocate_with() scope).
This may only be used inside self.scope()
.
with strategy.scope(): var1 = tf.Variable(...) with strategy.extended.colocate_vars_with(var1): # var2 and var3 will be created on the same device(s) as var1 var2 = tf.Variable(...) var3 = tf.Variable(...) def fn(v1, v2, v3): # operates on v1 from var1, v2 from var2, and v3 from var3 # `fn` runs on every device `var1` is on, `var2` and `var3` will be there # too. strategy.extended.update(var1, fn, args=(var2, var3))
colocate_with_variable
: A variable created in this strategy's scope()
.
Variables created while in the returned context manager will be on the
same set of devices as colocate_with_variable
.A context manager.
experimental_make_numpy_dataset
experimental_make_numpy_dataset( numpy_input, session=None )
Makes a dataset for input provided via a numpy array.
This avoids adding numpy_input
as a large constant in the graph,
and copies the data to the machine or machines that will be processing
the input.
numpy_input
: A nest of NumPy input arrays that will be distributed evenly
across all replicas. Note that lists of Numpy arrays are stacked, as
that is normal tf.data.Dataset
behavior.session
: (TensorFlow v1.x graph execution only) A session used for
initialization.A tf.data.Dataset
representing numpy_input
.
experimental_run_steps_on_iterator
experimental_run_steps_on_iterator( fn, iterator, iterations=1, initial_loop_values=None )
DEPRECATED: please use experimental_run_v2
instead.
Run fn
with input from iterator
for iterations
times.
This method can be used to run a step function for training a number of times using input from a dataset.
fn
: function to run using this distribution strategy. The function must
have the following signature: def fn(context, inputs)
. context
is an
instance of MultiStepContext
that will be passed when fn
is run.
context
can be used to specify the outputs to be returned from fn
by calling context.set_last_step_output
. It can also be used to
capture non tensor outputs by context.set_non_tensor_output
. See
MultiStepContext
documentation for more information. inputs
will
have same type/structure as iterator.get_next()
. Typically, fn
will use call_for_each_replica
method of the strategy to distribute
the computation over multiple replicas.iterator
: Iterator of a dataset that represents the input for fn
. The
caller is responsible for initializing the iterator as needed.iterations
: (Optional) Number of iterations that fn
should be run.
Defaults to 1.initial_loop_values
: (Optional) Initial values to be passed into the
loop that runs fn
. Defaults to None
. # TODO(priyag): Remove
initial_loop_values argument when we have a mechanism to infer the
outputs of fn
.Returns the MultiStepContext
object which has the following properties,
among other things:
- run_op: An op that runs fn
iterations
times.
- last_step_outputs: A dictionary containing tensors set using
context.set_last_step_output
. Evaluating this returns the value of
the tensors after the last iteration.
- non_tensor_outputs: A dictionatry containing anything that was set by
fn
by calling context.set_non_tensor_output
.
non_slot_devices
non_slot_devices(var_list)
Device(s) for non-slot variables.
Create variables on these devices in a
with colocate_vars_with(non_slot_devices(...)):
block.
Update those using update_non_slot()
.
var_list
: The list of variables being optimized, needed with the
default tf.distribute.Strategy
.A sequence of devices for non-slot variables.
read_var
read_var(v)
Reads the value of a variable.
Returns the aggregate value of a replica-local variable, or the (read-only) value of any other variable.
v
: A variable allocated within the scope of this tf.distribute.Strategy
.A tensor representing the value of v
, aggregated across replicas if
necessary.
reduce_to
reduce_to( reduce_op, value, destinations )
Combine (via e.g. sum or mean) values across replicas.
reduce_op
: Reduction type, an instance of tf.distribute.ReduceOp
enum.value
: A per-replica value with one value per replica.destinations
: A mirrored variable, a per-replica tensor, or a device
string. The return value will be copied to all destination devices (or
all the devices where the destinations
value resides). To perform an
all-reduction, pass value
to destinations
.A tensor or value mirrored to destinations
.
update
update( var, fn, args=(), kwargs=None, group=True )
Run fn
to update var
using inputs mirrored to the same devices.
If var
is mirrored across multiple devices, then this implements
logic like:
results = {} for device, v in var: with tf.device(device): # args and kwargs will be unwrapped if they are mirrored. results[device] = fn(v, *args, **kwargs) return merged(results)
Otherwise this returns fn(var, *args, **kwargs)
colocated with var
.
Neither args
nor kwargs
may contain per-replica values.
If they contain mirrored values, they will be unwrapped before
calling fn
.
var
: Variable, possibly mirrored to multiple devices, to operate on.fn
: Function to call. Should take the variable as the first argument.args
: Tuple or list. Additional positional arguments to pass to fn()
.kwargs
: Dict with keyword arguments to pass to fn()
.group
: Boolean. Defaults to True. If False, the return value will be
unwrapped.By default, the merged return value of fn
across all replicas. The
merged result has dependencies to make sure that if it is evaluated at
all, the side effects (updates) will happen on every replica. If instead
"group=False" is specified, this function will return a nest of lists
where each list has an element per replica, and the caller is responsible
for ensuring all elements are executed.
update_non_slot
update_non_slot( colocate_with, fn, args=(), kwargs=None, group=True )
Runs fn(*args, **kwargs)
on colocate_with
devices.
colocate_with
: The return value of non_slot_devices()
.fn
: Function to execute.args
: Tuple or list. Positional arguments to pass to fn()
.kwargs
: Dict with keyword arguments to pass to fn()
.group
: Boolean. Defaults to True. If False, the return value will be
unwrapped.Return value of fn
, possibly merged across devices.
value_container
value_container(value)
Returns the container that this per-replica value
belongs to.
value
: A value returned by experimental_run_v2()
or a variable
created in scope()
.A container that value
belongs to.
If value does not belong to any container (including the case of
container having been destroyed), returns the value itself.
value in experimental_local_results(value_container(value))
will
always be true.
variable_created_in_scope
variable_created_in_scope(v)
Tests whether v
was created while this strategy scope was active.
Variables created inside the strategy scope are "owned" by it:
>>> with strategy.scope(): ... v = tf.Variable(1.) >>> strategy.variable_created_in_scope(v) True
Variables created outside the strategy are not owned by it:
>>> v = tf.Variable(1.) >>> strategy.variable_created_in_scope(v) False
v
: A tf.Variable
instance.True if v
was created inside the scope, False if not.


This is a
select
with the
multiple
attribute enabled.
Melany
Ed
Joane
Quintin
Burl
Nida
Shayla
Armando
Gregory
Bernardo
Duane
Iva
Kevin
Juliet
Vicente
Malissa
Hsiu
Neva
Winfred
Leota
Hallie
Wilbur
Don
Brynn
Lisbeth
Leanne
Meryl
Gayle
Rocco
Risa


@()
The name Pokémon is the romanized contraction of the Japanese brand Pocket Monsters. The term Pokémon collectively refers to the numerous fictional species that have made appearances in Pokémon media
Every Pokémon is defined by a unique name. Each Pokémon can be of a type such as Fire, Water, Grass etc. and some may have 2 types. Each type has its own weaknesses and strengths, this helps in deciding which Pokémon could be stronger than the other. Each pokemon also has an ability which can give it an edge. Some Pokémon can evolve into stronger forms
A trainer is one who befriends Pokémon by capturing them. A trainer can have a team of upto 6 Pokémon.


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DH_INSTALLMANPAGES(1) Debhelper DH_INSTALLMANPAGES(1) NAME dh_installmanpages - old-style man page installer (deprecated) SYNOPSIS dh_installmanpages [debhelperÂ options] [fileÂ ...] DESCRIPTION dh_installmanpages is a debhelper program that is responsible for automatically installing man pages into usr/share/man/ in package build directories. This is a DWIM-style program, with an interface unlike the rest of debhelper. It is deprecated, and you are encouraged to use dh_installman(1) instead. dh_installmanpages scans the current directory and all subdirectories for filenames that look like man pages. (Note that only real files are looked at; symlinks are ignored.) It uses file(1) to verify that the files are in the correct format. Then, based on the files' extensions, it installs them into the correct man directory. All filenames specified as parameters will be skipped by dh_installmanpages. This is useful if by default it installs some man pages that you do not want to be installed. After the man page installation step, dh_installmanpages will check to see if any of the man pages are .so links. If so, it changes them to symlinks. OPTIONS file ... Do not install these files as man pages, even if they look like valid man pages. BUGS dh_installmanpages will install the man pages it finds into all packages you tell it to act on, since it can't tell what package the man pages belong in. This is almost never what you really want (use -p to work around this, or use the much better dh_installman(1) program instead). Files ending in .man will be ignored. Files specified as parameters that contain spaces in their filenames will not be processed properly. SEE ALSO debhelper(7) This program is a part of debhelper. AUTHOR Joey Hess<EMAIL_ADDRESS>9.20120909 2011-09-12 DH_INSTALLMANPAGES(1)


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 Blessed
is he who, in the name of charity and good will, shepherds the weak through the valley of darkness, for he is truly his brother’s keeper and the finder of lost children.  And you will know My name is the Lord when I
The path of the righteous man is beset on all sides by the iniquities of the selfish and the tyranny of evil men. Blessed
is he who, in the name of charity and good will, shepherds the weak through the valley of darkness, for he is truly his brother’s keeper and the finder of lost children.  And you will know My name is the Lord when I
The path of the righteous man is beset on all sides by the iniquities of the selfish and the tyranny of evil men. Blessed
is he who, in the name of charity and good will, shepherds the weak through the valley of darkness, for he is truly his brother’s keeper and the finder of lost children.  And you will know My name is the Lord when I
Sed varius suspendisse dictum leo ipsum amet pellentesque
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Supreme comfort
Immersive experince
complete focus
Convenient control
Convenient control
Why wow
Comfortable stap holder
Tracking with controler
Foam cushing
Step in wow
An immersive vertual reality helmet.


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{{scratch.Set "active" "false"}}
The following services are available from the HA Consul + Vault + Vault UI project.
| Link to service | Description |
|---|---|
| Consul UI | Consul UI frontend for browsing services discovered and other data. |
| Vault UI | The Vault UI which is built into Vault. If this link is dead, then you likely need to initialize Vault |
This section lists services which are registered with consul as being available within the web portal. To qualify as a portal service, a registered consul service must have a tag portal and set metadata title and description. Portal services are reverse proxied by this nginx server as a sub-path in the URL. Within this container the following files are updated via consul-template.
The following table is dynamically generated based on discovered services.
| Link to service | Description |
|---|---|
| {{.title}} | {{.description}} | {{end}}{{end}}{{end}}{{end}}{{if eq ( scratch.Get "active" ) "false"}}
| N/A | No portal services registered. |


????? ????? ???
on 2012-04-23 14:48:44
I agree it is a very informative article and I actually enjoy reading good stuff unlike all the crap out there on the internet
Kevin van Zonneveld
on 2010-04-23 11:43:52
@ Rafał Kukawski: It is, thanks!
http://github.com/kvz/phpjs/commit/0341548f49543d24a4f33f3261f5d844c3e12bbb
Rafa? Kukawski
on 2010-04-23 11:35:31
I forgot to cast the input value to string, please change
str.replace(/[a-z]/gi
to (str + '').replace(/[a-z]/gi
I think it's important.
Kevin van Zonneveld
on 2010-04-23 11:24:37
@ Rafał Kukawski: Excellent work! Tests pass, we fixes jslint issue, and and function is tighter. Thanks a lot!
http://github.com/kvz/phpjs/commit/f4fcf895c16ecce83550efc9c190c82ed118c06c
Kevin van Zonneveld
on 2010-04-23 11:24:21
@ Rafał Kukawski: Excellent work! Tests pass, we fixes jslint issue, and and function is tighter. Thanks a lot!
http://github.com/kvz/phpjs/commit/f4fcf895c16ecce83550efc9c190c82ed118c06c
Rafa? Kukawski
on 2010-04-23 09:18:06
My proposition (uses the conditional operator)
function rot13(x){
return x.replace(/[a-z]/gi,function(s){
return String.fromCharCode(s.charCodeAt(0)+(s.toLowerCase()<'n'?13:-13))})
}
Kevin van Zonneveld
on 2009-08-04 12:32:33
@ Eric & Brett Zamir: Both sites (php.js & my blog) have a pagerank of 5 currently. I guess it's just a matter of time for PHP.JS to receive more backlinks from other sites & get a higher ranking for people who want to find our javascript snippets.
Brett Zamir
on 2009-07-30 13:58:04
Not sure why it didn't make it, though at least str_rot13 with or without JavaScript made it to the first page. Thanks to your prompting, I went ahead and did a little further SEO investigation of the site and sent the recommendations to Kevin (though he's already done a fine job of bringing his site to sometimes even rival the PHP site when you search for PHP functions)
Eric
on 2009-07-30 10:14:00
Great!, only like 3 lines of code, I like this alot and its just what I'm looking for. Why on earth is this page not listed on Google for #1 spot of "rot13 javascript"?, on Google all I could find where old rot13 implementations that has like 40 lines of code written years ago using schemes for javascript 1.0. Glad I found this website! Thanks alot.
Kevin van Zonneveld
on 2008-01-22 21:58:53
@ Ates Goral: I agree, it's purdy ;)
Ates Goral
on 2008-01-22 21:11:54
Alternative implementation that doesn't use conditionals:
function str_rot13(str) {
// * example 1: str_rot13("Hello World!");
// * returns 1: "Uryyb Jbeyq!"
// * example 2: str_rot13(str_rot13("Hello World!"));
// * returns 2: "Hello World!"
return str.replace(/[A-Za-z]/g, function (c) { return String.fromCharCode((((c = c.charCodeAt(0)) & 223) - 52) % 26 + (c & 32) + 65); });
}


---
layout: post
title: 4/24 progress -- 55 days left in CO
date: '2014-04-24T22:26:00.002-06:00'
author: Jacob .
tags:
- progress-reports
modified_time: '2014-04-24T22:26:18.402-06:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-9004067071406172820
blogger_orig_url: http://www.svlark.com/2014/04/424-progress-55-days-left-in-co.html
---
I put a good dent in packing tools and going through unopened boxes from the last move.
The big puzzle of the moment, besides where to go, is what to do about internet access which is especially important in my current job. I need 200G per month at a bare minimum. 400 would be much more comfortable. If we can find somewhere with Comcast I'm golden. If need be I can use my Verizon Jetpack as a stopgap which provides excellent speeds on 4g LTE, but costs $5/G, which is obviously not sustainable.
More good stuff from both Fluenta and Hotspur today.


The Rock Hawk effigy and hiking trails are managed and maintained by the Georgia Power Company. Information is provided from the Georgia Power Company website. The rules and regulations for the recreational areas are as follows:
- Follow all posted rules.
- Be considerate of other visitors and the environment.
- No alcoholic beverages or other intoxicants are allowed.
- Hours: 7am – 9pm, Sunday – Thursday; 7am – 10pm Friday – Saturday.
Campground Rules
- Please register before setting up.
- At the time of registration, please pay for the total number of nights that you will be staying.
- Your overnight parking fee includes use of day-use facilities during regular hours. (Tallulah Gorge charges separate day-use fee.)
- Campsite occupancy is limited to 8 people per site.
- Please place tents, truck campers, motor homes, trailers, etc. on prepared gravel camp pads or driveways to avoid damaging grassed areas.
- Camping between designated sites is prohibited.
- Please attach your receipt to the clip on the numbered campsite post at your campsite.
- Length of stay on the same site is limited to 14 days during a 30-day period except with permission from the park manager.
- Lantern hangers should be used for lanterns. Please do not hang lanterns on trees.
- Fires should be confined to grills or fire rings and attended at all times. Dead trees or limbs on ground may be collected for firewood.
- Do not wash dishes or clothes at drinking faucets or in comfort station sinks.
- Washers and dryers are provided at some parks.
- Your RV’s holding tank should be used for sewage and grey water. A sanitary disposal station is provided in the park for your use.

- Please keep buildings and grounds clean and neat, and report any unsatisfactory conditions to the park attendant.
- 7:00 a.m. to 9:00 p.m. Sunday through Thursday and 7:00 a.m. to 10:00 p.m. Friday and Saturday.
- Tallulah Gorge campground hours are 7:00 a.m. to 10:00 p.m. Monday through Thursday and 7:00 a.m. to 11:00 p.m. Friday and Saturday.
- Check-out time is 1:00 p.m.
- Quiet Hours are 10:00 p.m. to 7:00 a.m.
- Park gates are locked when the park closes each night.
- Campers returning to the park after the gate is locked may park outside the gate until the park opens the next morning but should not block the road.
- Possession or use of firearms or fireworks is prohibited.
- Campers under 18 years of age should be accompanied by an adult.
- All vehicles, including mini-bikes, are subject to safety requirements and regulations of the Georgia Department of Safety.
- Visitors of registered campers should register with the Park Attendant to obtain a Day Use Pass and pay a parking fee. Visitors remaining overnight will be charged an additional fee.
- Pets are welcome. Please keep them on a leash, no longer than six feet, at all times, and please clean up after them.
- No swimming or fishing at boat ramps or from boat docks.
- may create a safety hazard or nuisance to persons using the campground
- may be offensive to others
- may be inappropriate for viewing by minors
- unnecessarily restrict a camper’s view of the lake
- interferes with a camper’s enjoyment of the lake and campground Visitors who engage in conduct the Company deems inappropriate, offensive, or unsafe may be asked to leave the property.


I bought guarana from a Holland and Barrett on the emphasis of several psychoactive plant information web sites, stating that taken with alcohol, very desirable effects are to be had. They did not state however exactly what these effects would be. Anyway the night started out like many other of my Saturday nights…me, my cousin and a mate waiting for a taxi to take us up town. We took three high strength guarana tablets each, each tablet containing 1200 mg of guarana. Half an hour later we were a pub, had a pint each then came to the conclusion that it wasn’t a very good pub so we soon left.
Our second destination was Wetherspoon’s, a local bar. This was where the fun started, well for me anyway. After I had consumed 2 and a half pints of blackthorn cider, and around 7 bottles of wicked blue (to get the taste of the cider out of my mouth). I decided to have a ginseng capsule at this point to try and kick-start the guarana, it seemed to work as within a matter of minutes I was starting to feel very light headed. I would have usually of been feeling drunk by now but this was definitely a different feeling. Five minutes later I found that I was starting to laugh and the most ridiculous things, and I mean really laugh, at one point I genuinely couldn’t stop and laughed for roughly 10 minutes straight.
We decided to leave quickly because we were getting funny looks off the staff, so we eventually ended up at the vodka bar (a bar upstairs and club downstairs). By this point I was definitely drunk but I also had so much energy. We went downstairs to the club and I rushed onto the dance floor without a care in the world.
My cousin at this point who had the same amount of guarana as me, was suffering from a migraine (he does not think this was due to the guarana) and therefore had hardly any booze at all that night.
 In retrospect I am surprised that I was not thrown out of the place they must surely of thought that I had taken something rather more illegal than guarana.
When the place closed it was two o’clock in the morning but I could of carried on dancing (if that was what you would call it) for hours longer. We eventually arrived home at roughly three o’clock and I was surprised at how tired I had become by this point, I had read about the drug causing insomnia and was surprised this was not the case with me.
The next morning I woke up without a hangover but feeling very drained and somewhat nauseous when walking around. But after I had a cup of green tea I was feeling a lot better and had recovered some energy again.
Overall I would say guarana is a fairly safe drug to use within limits, I personally doubt I will ever take more than five tablets in one night. I don’t think it really has much of an effect without alcohol and I would say it is a very fun going-out or party drug and certainly livens up a night. Its effects I would imagine are very similar to speed or amphetamines without as much euphoria. A safe and easy to get drug. Mixing it with other caffeine products reportedly enhances the effects of guarana so I will certainly try that within the next few days. Thanks for reading, enjoy yourself!
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.


2 And Yahweh says this to the people of Edom:
"I will soon cause you to become the weakest and most despised nation on the earth.
3 Your capital city is high in the rocky cliffs,
and you are very proud;
you think that you are safe because no army can attack you,
but you have deceived yourselves.
4 And I tell you that even if you had wings and could fly higher than eagles fly,
and if you could make your homes among the stars,
I would bring you crashing down from there.
5 When thieves break into someone's house during the night,
they certainly steal only the things that they want.
And people who pick grapes always leave a few grapes on the vines.
But your country will be completely destroyed!
6 Everything that is valuable will be taken away.

Those with whom you have peace now will trick you and defeat you.

10 "You acted cruelly to your relatives who are descendants of Jacob, the twin brother of your ancestor Esau.
So now you will be disgraced, and you will be destroyed forever.
11 Foreigners entered the gates of Jerusalem
and cast lots to share out among themselves the valuable things they found;
but you were as bad as those foreigners,
because you just stood there and did not help the Israelites.
12 You should not have gloated about the disaster that the Israelites were experiencing.
You should not have been happy when their towns were ruined.
You should not have made fun of them when they were suffering.
13 They are my people,
so you should not have entered their city gates when they were experiencing those disasters
and you should not have laughed at them.
And you should not have taken away their valuable possessions.
14 You should not have stood at crossroads
to catch those who were trying to escape.
You should not have captured them and put them into the hands of their enemies
when they were experiencing those disasters."
15 "There will soon be the time when I, Yahweh, will judge and punish all the nations.
And you people of Edom will experience the same disasters that you caused others to experience.
The same evil things that you have done to others will happen to you.
16 It is as though you drank a cup of very bitter liquid on Zion, my sacred hill.
But I will punish all the other nations just as severely,
and cause them to disappear completely.
17 But some people in Jerusalem will escape,
and Jerusalem will become a very holy place.
Then the descendants of Jacob will conquer and possess again the land that truly belongs to them.
18 The descendants of Jacob and his son Joseph will be like a fire,
and the people of Edom will be like stubble that will be completely burned in that fire.
Not one person will remain alive.
That will surely happen because I, Yahweh, have said it."
19 The Israelites who live in the southern Judean wilderness are the ones who will capture Edom.
And those who live in the western foothills will capture the region of Phoenicia and the areas of Ephraim and Samaria.
And people of the tribe of Benjamin will conquer the region of Gilead.
20 Those who were exiled in Babylon will return to their land
and capture the region of Phoenicia as far north as Zarephath on the coast of the Mediterranean Sea.
People of Jerusalem who were captured and taken to Sardis, the capital city of the region of Lydia, will capture the towns in the southern Judean wilderness.
21 The army of Jerusalem will attack Edom and conquer it,
and Yahweh will be their king.


amazon-chime-sdk
/
com.amazonaws.services.chime.sdk.meetings.session
/
MeetingSessionConfiguration
/
urls
urls
val
urls
:
MeetingSessionURLs


Functional denoising and functional ANOVA through wavelet-domain Markov groves. Fore more details see: Ma L. and Soriano J. (2016) Efficient functional ANOVA through wavelet-domain Markov groves. <arXiv:1602.03990v2 [stat.ME]>.
| Version: | 1.1 |
| Imports: | methods, Rcpp (≥ 0.11.2), wavethresh |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2018-01-07 |
| Author: | Jacopo Soriano and Li Ma |
| Maintainer: | Jacopo Soriano <jsoriano.stat at gmail.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| CRAN checks: | grove results |
| Reference manual: | grove.pdf |
| Package source: | grove_1.1.tar.gz |
| Windows binaries: | r-devel: grove_1.1.zip, r-release: grove_1.1.zip, r-oldrel: grove_1.1.zip |
| OS X binaries: | r-release: grove_1.1.tgz, r-oldrel: grove_1.1.tgz |
| Old sources: | grove archive |
Please use the canonical form https://CRAN.R-project.org/package=grove to link to this page.


Jupyter notebooks¶
Friendly supports Jupyter notebooks and variants. Currently, this has been done more as a proof of concept than anything else: I am not aware of anyone else having made use of this feature. I also almost never test it; please feel free to report any issues.
JupyterLab notebook¶
By default, tracebacks in JupyterLab notebooks (or Juypter notebooks) displayed in a browser are given a pink background (1).
The “location” of the error (2) includes is of the form
<ipython-input-A-B>
where A
is the code block number
and B
is an internal value (“hash_digest”) of no particular use
to a end user. This hash_digest is removed by friendly
as shown below.
To use friendly, we use a special import statement.
Tracebacks shown by friendly do not have a pink background. Note how the “file” name has been shortened, compared with Jupyter’s default.
The formatter value is changed from the default to
'bw'
which is meant to be short for “black and white” meaning that no special colouring is done..This
'bw'
choice results in a plain output on pink background.A different syntax colouring (
'light'
) is also available.The colour scheme with this formatter is fairly similar to the default used for Jupyter notebooks.
After changing to the 'light'
, I displayed a more complete
content displayed with explain()
When using from friendly.jupyter import *
,
the default formatter is known as 'jupyter'
.
Dark background¶
JupyterLab also gives the choice of a dark background theme. Here’s how the previous example look with this dark theme.
As a last step, I changed the formatter to use the 'dark'
option,
which is the default for the friendly console when used in a terminal.
Finally, here is the result from changing back to the default
'jupyter'
formatter.
For the dark Jupyter theme, some elements, such as numbers and operators,
are very difficult to read.
For this reason, I much prefer using the 'dark'
friendly
formatter in this case.
About the hash_digest¶
The hash_digest is meant to uniquely identify the content of a code block. However, this does not appear the case when the same code is entered in different cells for sessions where one uses more than one notebook. I suggested what I believe is a better approach using the kernel number instead of the hash_digest: the combination of the code-block number and kernel number should be unique. However, IPython developers are certainly swamped with issues to consider and would be right not to consider this a high priority item to look at, although it might prevent some messages one occasionally gets about the need to restart a kernel.


Potential Effects of Income Redistribution on Economic Growth Constraints: Evidence from Taiwan and South Korea
By Dennis Leslie Chinn
A.B. (University of Washington) 1968 DISSERTATION
Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY
in Economics
in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY
Approved: (2. h ie iets]
UN ee ee
sees CA
wearee hee usters eoepoeceeeeereoveee
Ar . i ULE TOBE et cr
Beene eee TE ae ae ge cr etee Me
Committee in Charge
eenvrenreseeereoneneeerenseeneeeseeseeeeseteeseresae


Introduction
This document details how to configure your Openfire installation to use
an external directory such as Open LDAP or Active Directory. Integration with a directory
lets users authenticate using their directory username and password. Optionally, you can
configure Openfire to load user profile and group information from the directory. Any group in
Openfire can be designated as a shared group, which means that you can pre-populate user's
rosters using directory groups.
Background
LDAP (Lightweight Directory Access Protocol) has emerged as a dominant standard
for user authentication and for storage of user profile data. It serves as a
powerful tool for large organizations (or those organizations integrating many
applications) to simplify user management issues. Many LDAP servers are available,
such as Open LDAP,
Active Directory,
and Novell's eDirectory.
By default, Openfire stores all user data in its database and performs
authentication using database lookups. The LDAP module replaces that
functionality and allows Openfire to:
- Use a LDAP server to authenticate a user's identity.
- Load user profile information from a LDAP directory.
- Load group information from an LDAP directory.
Note: Openfire treats the LDAP directory as read-only.
This document will guide you through configuring LDAP support in Openfire. These
instructions assume that you're a competent LDAP user, and that you're familiar
with Openfire setup issues.
Configuration
The Openfire setup tool includes an easy to use LDAP setup wizard.
Choose the LDAP option on the Profile Settings page to configure directory integration.
The wizard along with in-line help will guide you through the rest of the process.
Specific tips for working with Active Directory are noted below.
If you have already completed the setup process but need to enable LDAP integration, you
can re-run the setup tool. To do so:
-
Stop Openfire.
- Edit conf/openfire.xml in your Openfire installation folder and set
<setup>true</setup> to <setup>false</setup>.
-
Restart Openfire and enter the setup tool.
Microsoft's Active Directory is a broadly deployed directory system that supports the
LDAP protocol. You'll be prompted for several LDAP fields when connecting to Active Directory
servers, some of which are detailed below:
- Base DN
The base DN describes where to load users and groups. If you're using a default
Active Directory setup, all user accounts and groups are located in the
"Users" folder under your domain. In LDAP form, that's cn=Users;dc=<Your Domain>.
To get more specific, say your domain is activedirectory.jivesoftware.com. In that case,
your base DN would be cn=Users;dc=activedirectory,dc=jivesoftware,dc=com. If
you've customized where users are stored, you'll just need to replicate that folder
structure using LDAP syntax.
- Administrator DN
By default, Active Directory does not allow anonymous LDAP connections. Therefore,
you'll need to enter the DN of a user that's allowed to connect to the server and read
all user and group data. Unless you've created a special user account for this
purpose, an easy choice is to use the built-in administrator account. By default,
the administrator DN is in the form cn=Administrator,dc=<Your Domain>.
Using our previous example,
cn=Administrator,cn=users,dc=activedirectory,dc=jivesoftware,dc=com.
Manually Editing the Config File
If you prefer to edit the configuration file to enable LDAP integration directly, use the following
instructions. Open the configuration file conf/openfire.xml from your Openfire
installation in your favorite
editor and add or change the following settings. Properties flagged with (
*)
must be set. Properties flagged with (**) must be set in order
to enable LDAP group
support, all other properties are optional:
Main Settings
- provider.user.className * -- set the value to
"org.jivesoftware.openfire.ldap.LdapUserProvider".
- provider.auth.className * -- set the value to
"org.jivesoftware.openfire.ldap.LdapAuthProvider".
- ldap.host * -- LDAP server host; e.g. localhost or
machine.example.com, etc. It is possible to use many LDAP servers but all of them
should share the same configuration (e.g. SSL, baseDN, admin account, etc).
To specify many LDAP servers use the comma or the white space character as delimiter.
- ldap.port -- LDAP server port number. If this property is not set, the default value is
389.
- ldap.connectionTimeout -- The value of this property is the string representation of an integer
representing the connection timeout in milliseconds. If the LDAP provider doesn't
connect within the specified period, it aborts the connection attempt. The integer should
be greater than zero. An integer less than or equal to zero means no connection timeout is specified in which
case the default 10 seconds timeout is used. Does not apply to SSL connections.
- ldap.readTimeout -- The value of this property is the string representation of an integer
representing the read timeout in milliseconds for LDAP operations. If the LDAP provider doesn't
get an LDAP response within the specified period, it aborts the read attempt. The integer should
be greater than zero. An integer less than or equal to zero means no read timeout is specified which
is equivalent to waiting for the response infinitely until it is received which defaults
to the original behavior. Requires Java 1.6 or later.
- ldap.baseDN * -- the starting DN that searches for users
will performed with. The entire subtree under the base DN will be searched for user accounts.
- ldap.alternateBaseDN -- a second DN in the directory can optionally be set. If set, the
alternate base DN will be used for authentication, loading single users and displaying a
list of users. Content in the base DN and the alternate DN will be treated as one.
- ldap.adminDN -- a directory administrator's DN. All directory operations will be
performed
with this account. The admin must be able to perform searches and load user records. The
user does
not need to be able to make changes to the directory, as Openfire treats the
directory as read-only.
If this property is not set, an anonymous login to the server will be attempted.
- ldap.adminPassword -- the password for the directory administrator.
- ldap.usernameField -- the field name that the username lookups will be performed on. If
this property is not set, the default value is uid. Active Directory users
should try the default value sAMAccountName.
- ldap.nameField -- the field name that holds the user's name. If this property is not
set, the default value is cn. Active Directory users should use the default value
displayName.
- ldap.emailField -- the field name that holds the user's email address. If this property
is not set, the default value is mail. Active Directory users should use the
the default value mail.
- ldap.searchFields -- the LDAP fields that will be used for user searches. If
this property is not set, the username, name, and email fields will be searched. An example
value for this field is "Username/uid,Name/cname". That searches the uid and cname fields
in the directory and labels them as "Username" and "Name" in the search UI. You can add
as many fields as you'd like using comma-delimited "DisplayName/Field" pairs. You should
ensure that any fields used for searching are properly indexed so that searches return
quickly.
- ldap.searchFilter -- an optional search filter to append to the default filter when
loading users. The default search filter is created using the attribute specified by
ldap.usernameField. For example, if the username field is "uid", then the default search
filter would be "(uid={0})" where {0} is dynamically replaced with the username being searched
for.
The most common usage of a search filter is to limit the entries that are users
based on objectClass. For example, a reasonable search filter for a default Active Directory
installation is "(objectClass=organizationalPerson)". When combined with the default
filter, the actual search executed would be
"(&(sAMAccountName={0})(objectClass=organizationalPerson))".
- ldap.subTreeSearch -- by default, Openfire will search the entire LDAP sub-tree (starting
at the base DN) when trying to load users. If this property is set to false, then
sub-tree searching is disabled and users will only be loaded directly from the base DN.
Disabling sub-tree can improve performance, but it will fail to find users if your directory
is setup to use sub-folders under the base DN.
Group Settings
- provider.group.className ** -- set the value to
"org.jivesoftware.openfire.ldap.LdapGroupProvider".
- ldap.groupNameField ** -- the field name that the groupname
lookups will be performed on. If this property is not set, the default value is cn.
- ldap.groupMemberField -- the field name that holds the members in a group. If this property
is not set, the default value is member.
- ldap.groupDescriptionField -- the field name that holds the description a group. If this
property is not set, the default value is description.
- ldap.posixMode ** -- a value of "true" means that users are
stored within the group by their user name alone. A value of "false" means that users are
stored by their entire DN within the group. If this property is not set, the default value
is false. The posix mode must be set correctly for your server in
order for group integration to work. Posix modes for common LDAP servers:
- ldap.groupSearchFilter -- an optional search filter to append to the default filter when
loading groups. The default group search filter is created using the attribute specified
by ldap.groupNameField. For example, if the group name field is "cn", then the default
group search filter would be "(cn={0})" where {0} is dynamically replaced with the group
name being searched for.
The most common usage of a search filter is to limit the entries that are groups
based on objectClass. For example, a reasonable search filter for a default Active Directory
installation is "(objectClass=group)". When combined with the default
filter, the actual search executed would be
"(&(cn={0})(objectClass=group))".
Connection Settings
- ldap.debugEnabled -- a value of "true" if debugging should be turned on. When on, trace
information about buffers sent and received by the LDAP provider is written to
System.out
- ldap.sslEnabled -- a value of "true" to enable SSL connections to your LDAP server. If
you
enable SSL connections, the LDAP server port number most likely should be changed to
636.
- ldap.startTlsEnabled -- a value of "true" to enable StartTLS connections to your LDAP server. If
you enable StartTLS connections, the LDAP server port number most likely should be changed to
389.
- ldap.initialContextFactory -- the name of the class that should be used as an initial
context
factory. if this value is not specified, "com.sun.jndi.ldap.LdapCtxFactory" will be used
instead.
Most users will not need to set this value.
- ldap.autoFollowReferrals -- a value of "true" indicates that LDAP referrals should be
automatically followed. If this property is not set or is set to "false", the referral policy used is left
up to to the provider. A referral is an entity that is used to redirect a client's request to
another server. A referral contains the names and locations of other objects. It is sent by the server to
indicate that the information that the client has requested can be found at another location (or
locations), possibly at another server or several servers.
- ldap.connectionPoolEnabled -- a value of "false" disables LDAP connection pooling. If this
property is not set, the default value is "true".
Below is a sample config file section:
<jive>
...
<ldap>
<host></host>
<port>389</port>
<usernameField>uid</usernameField>
<nameField>cn</nameField>
<emailField>mail</emailField>
<baseDN>ou=People;dc=example;dc=com</baseDN>
<adminDN>cn=Directory Administrator</adminDN>
<adminPassword></adminPassword>
</ldap>
<provider>
<user>
<className>org.jivesoftware.openfire.ldap.LdapUserProvider</className>
</user>
<auth>
<className>org.jivesoftware.openfire.ldap.LdapAuthProvider</className>
</auth>
<group>
<className>org.jivesoftware.openfire.ldap.LdapGroupProvider</className>
</group>
</provider>
...
</jive>
You'll most likely want to change which usernames are authorized to login to the
admin console. By default, only the user with username "admin" is allowed to login. However,
you may have different users in your LDAP directory that you'd like to be administrators. The
list of authorized usernames is controlled via the admin.authorizedUsernames
property. For example, to let the usersnames "joe" and "jane" login to the admin console:
<jive>
...
<admin>
...
<authorizedUsernames>joe, jane</authorizedUsernames>
</admin>
...
</jive>
Custom Search Filter
By default, Openfire will load all objects under the baseDN that
have the attribute specified by ldap.usernameField. In the
case that the username field is set to "uid", the search for all users
would be "(uid=*)". However, there are cases when this logic does
not work -- for example, when a directory contains other objects besides
users but all objects share "uid" as a unique identifier field. In that
case, you may need to specify a custom search filter using
ldap.searchFilter. As an example, a search filter for all users
with a "uid" and a "cn" value of "joe" would
be:
(&(uid={0})(cn=joe))
The "{0}" value in the filter above is a token that should be present in
all custom search filters. It will be dynamically replaced with "*" when
loading the list of all users or a username when loading a single user.
Some custom search filters may include reserved XML entities such as
"&". In that case, you must enter the search filter into the openfire.xml
file using CDATA:
<searchFilter><![CDATA[(&(sAMAccountName={0})(|(givenName=GEORGE)(givenName=admin)))]]></searchFilter>
Custom Inital Context Factory
Some LDAP servers or application servers may require that a different LDAP
initial context factory be used rather than the default (com.sun.jndi.ldap.LdapCtxFactory).
You can set a custom initial context factory by adding the following to openfire.xml:
<ldap>
... other ldap settings here
<initialContextFactory>com.foo.factoryClass</initialContextFactory>
</ldap>
Connection Pooling
The default LDAP provider (Sun's) support pooling of connections to the LDAP
server. Connection pooling can greatly improve performance, especially on
systems with high load. Connection pooling is enabled by default, but can
be disabled by setting the Jive property
ldap.connectionPoolEnabled
to
false:
<ldap>
... other ldap settings here
<connectionPoolEnabled>false</connectionPoolEnabled>
</ldap>
You should set several Java system properties to change default pool settings.
For more information, see the following pages:
Note that if you turn on LDAP debugging, connection pooling will not be enabled.
If SSL LDAP mode is enabled, you must set a system property to enable pooling of
SSL LDAP connections.
LDAP vCard Integration
The LDAP vCard provider will expose LDAP profile information as vCard data for XMPP
clients that support the XMPP vCard extension. First, enable the provider:
<provider>
...
<vcard>
<className>org.jivesoftware.openfire.ldap.LdapVCardProvider</className>
</vcard>
...
</provider>
Next, you must add mappings between LDAP fields and vCard fields in the openfire.xml file.
The vcard attributes are configured by adding an attrs="attr1,attr2" attribute to the vcard
elements. Arbitrary text can be used for the element values as well as MessageFormat style
placeholders for the ldap attributes. For example, if you wanted to map the LDAP attribute
displayName to the vcard element FN, the XML snippet would be:
<FN attrs="displayName">{0}</FN>
The vCard XML must be escaped in CDATA and must also be well formed. It is the exact
XML this provider will send to a client after after stripping attr attributes and populating
the placeholders with the data retrieved from LDAP. This system should be flexible enough to
handle any client's vCard format. An example mapping follows.
<ldap>
<vcard-mapping>
<![CDATA[
<vCard xmlns='vcard-temp'>
<FN>{displayName}</FN>
<NICKNAME>{uid}</NICKNAME>
<BDAY>{dob}</BDAY>
<ADR>
<HOME/>
<EXTADR>Ste 500</EXTADR>
<STREET>317 SW Alder St</STREET>
<LOCALITY>Portland</LOCALITY>
<REGION>Oregon</REGION>
<PCODE>97204</PCODE>
<CTRY>USA</CTRY>
</ADR>
<TEL>
<HOME/>
<VOICE/>
<NUMBER>{telephoneNumber}</NUMBER>
</TEL>
<EMAIL>
<HOME/>
<INTERNET/>
<PREF/>
<USERID>{mail}</USERID>
</EMAIL>
<TITLE>{title}</TITLE>
<ROLE></ROLE>
<ORG>
<ORGNAME>{o}</ORGNAME>
<ORGUNIT></ORGUNIT>
</ORG>
<URL>{labeledURI}</URL>
<DESC>uid: {uidNumber} home: {homeDirectory} shell: {loginShell}</DESC>
</vCard>
]]>
</vcard-mapping>
</ldap>
LDAP FAQ
Can I create new users through Openfire when using LDAP?
No, Openfire treats LDAP directories as read-only. Therefore, it's
not possible to create or edit users through the application.
Why is the list of usernames not sorted in the admin console when using LDAP?
Several popular LDAP servers such as OpenLDAP do not support server-side
sorting of search results. On those servers, users will appear out of order.
However, you can enable client-side sorting of search results by setting
ldap.clientSideSorting to true in the XML configuration file.
I switched to LDAP and now cannot login to the admin console. What happened?
If you can no longer login to the admin console after switching, one of two
things most likely happened:
- By default, only the username "admin" is allowed to login to the
admin console. Your directory may not contain a user with a username
of "admin". In that case, you should modify the list of usernames authorized
to login to the admin console (see above).
- You may have set the baseDN to an incorrect value. The LDAP module
recursively searches for users under the node in the directory specified
by the baseDN. When the baseDN is incorrect, no users will be found.
You can also enable debugging to get more information from the LDAP module. To
do this, add <log><debug><enabled>true</enabled></debug></log>
to your conf/openfire.xml file. Log statements will be written
to the logs/debug.log file.


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Introduction
In the long history of research into artificial neural networks, autoencoders are an interesting topic. The autoencoder is trained to be an identity
They are commonly used to learn an encoding of the input. Most use a dimension bottleneck in the hidden layers to achieve a non-linear dimension reduction, but it is also sometimes useful to use a higher dimension, like in sparse autoencoders. During the resurgence of neural networks as deep learning, autoencoders were largely used to do pretraining. (missing reference) first interprets a special kind of autoencoder, called the denoising autoencoders, as a generative model. One connection to make is that in their work, an unstructured noise is injected to the data,
The introduction of VAEs (Kingma & Welling, 2013) interprets the autoencoder as a graphical model with the encoding being a latent variable to be inferred. While autoencoders on their own weren’t a probabilistic model before, the VAE framework offers an alternative look at them as such.
Because they were introduced as a generative model, one of the more common applications of VAEs is to generate images, i.e: sampling from a Gaussian distribution and pushing that through the decoder. Another application of VAEs are what autoencoders were used for before: learning non-linear representations of the input.
However, the framework VAEs introduce is capable of much more. In DeepMind’s paper on the topic, they refer to these models as Deep Latent Gaussian Models (DLGMs) (Rezende, Mohamed, & Wierstra, 2014). We think this may be more descriptive, although we need not limit ourselves to Gaussian distributions. Sure, you can use VAEs to autoencode, but using them for just that is missing the point (Chen et al., 2016).
What's next?
References
- Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. ArXiv Preprint ArXiv:1312.6114.
- Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. ArXiv Preprint ArXiv:1401.4082.
- Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., … Abbeel, P. (2016). Variational lossy autoencoder. ArXiv Preprint ArXiv:1611.02731.


Can you watch KSI vs Logan Paul for free? TV, YouTube and live stream information
Millions of people around the world will be tuning in to watch as KSI and Logan Paul settle their rivalry in the ring
by Aaron Flanagan, Martin Domin, https://www.mirror.co.uk/authors/aaron-flanagan/, https://www.facebook.com/mirrorfightingKSI and Logan Paul will tonight look to establish themselves as the best YouTube boxer in the world when they clash in their rematch.
The social media stars first met in the ring last August when they fought to a controversial draw at a sold-out Manchester Arena.
The sequel has been a long time coming but the pair finally agreed terms earlier this year and were granted professional licences by the California State Athletic Commission.
That means they will get rid of the head guards and pull on 10oz gloves for the second instalment at the Staples Center in Los Angeles.
And there is plenty of star power on the undercard with world champions Billy Joe Saunders and Devin Haney in action.
Here is everything you need to know about the fights...
When is the fight?
The fight takes place on Saturday night, with the action taking place in the early hours of Sunday morning.
What UK time is the fight likely to start?
For those watching in the UK, KSI and Logan Paul are expected to step into the ring between 4am and 5am.
That is a local time of between 9pm and 10pm in Los Angeles, where the fight is taking place.
What TV channel is the fight being shown on?
If you are in the UK, the only place to watch the fight is on Sky Sports Box Office.
The fight can be bought for £9.95.
Can you live stream the fight?
If you have bought the fight on Sky Sports Box Office, you can stream the fight on their website.
Will the fight be shown on YouTube?
No. Unlike their first meeting, the fight is only being shown on broadcasting channels.
Is there a way to watch the fight for free?
Unfortunately not. This is a completely pay-per-view event.
However, you can follow all of the action as it happens on the Mirror Sport live blog.
Betting odds
- KSI: 7/4
- Draw: 10/1
- Logan Paul: 8/15
Odds courtesy of Ladbrokes.


An accounting of relationships
Consider first the fact that there are over 7 billion unique individual human persons in the world.
Each person has relationships with other individuals in the world who they know to varying degrees.
For example, the relationship between Person A and Person B can be called Relationship A-B, the relationship between
Person A and Person C can be called Relationship A-C, etc.
All of Person A's relationships combined can be called Relationship Set A.
For each member of the network, there is a unique set of relationships which accurately represents the network
identity
of that
member. In other words, in the entirety of this global social network, there is only a single individual, Person A,
who fits
into the
nexus of relationships matching Relationship Set A.
Crowd-sourced identity verification and accumulation of social network relationship data
Each person who enters the network can contribute to ODIN their knowledge of their personal relationship data.
For example Person A knows 10 people. Person A joins ODIN as an early adopter. Person A teaches ODIN who they are by
populating the ODIN database with their personal data (name, date of birth, etc.) and the data from each
relationship in
their set of 10
relationships (Relationship Set A).
Since Person A is an early user, many of the other people who they know are not yet using ODIN, therefore, ODIN is
learning about them for the first time.
Later, Person B joins the network and person B knows many people, including Person A as well as 5 people that Person
A
also knows. Person B
populates ODIN with their relationship data (Relationship Set B), and corroborates the information already provided
about the 5 other
people that Person A mutually knows.
As more people join the network, the information stored in the network becomes corroborated and verified many
times over.
Eventually, it will become virtually impossible to attempt to defraud the identity of somebody else on the
network. Every unique human person in the world will have exactly one ODIN public identifier.
By relying on our biological ability to know the identity of another person, we can confirm with the network that
the identity of this person that we know in real life matches the identity of this single particular account on
the network.
It's a perpetual key-signing party and everyone's invited.
Nodes
A decentralized network is composed of a collection of nodes. Each node of a decentralized network is
simultaneously a server and a client.
The node is also a secure storage vault which stores portions of, but not necessarily the entirety of the full
collection of social network relationship data.
Nodes communicate and share information with each other as needed.
Because the software is free and open-source, anyone can run one or more nodes.
With regards to software and hardware considerations, there is some ratio of nodes to people where the entire population could access the services hosted on ODIN by some lesser number of nodes. A ratio of 1000:1 users to nodes seems feasible.
Credential Management
Among other things, the decentralized network acts as a highly-available, highly-secure authentication and storage
network.
Stored across nodes in the network is a copy of the personal data vault associated with an ODIN public identifier.
In each user's data vault are copies of their private keys, which in essence represent the authorship of their
identity.
Similar to existing open-source decentralized network projects (e.g. bitcoin), there will eventually be an
assortment of
software and
apps available to access ODIN, including native Android and iOS apps, webapps, Windows desktop apps, etc.
The apps act as authenticators (credential management) and access points to the personal data vaults.
When a user wants to sign in to a new ODIN-accessing app for the first time, they must adequately prove their
identity
to ODIN through
a variety of appropriate verification methods.
For example, Person B is trying to sign in to a new ODIN mobile phone app. Person A, who is already on the network
and
is a trusted personal friend of Person B,
verifies to the network that Person B is the same person as the individual making the request to sign in.
After doing so, Person B's app receives the data from the network, and now has a local, unlocked copy of all of
Person
B's private
keys and other valuable personal data.
Person B now bears their ODIN credentials and can use them wherever ODIN credentials are used.
Open-source native services
In its simplest incarnation, ODIN could be described as simply a decentralized encrypted global address-book with
some
form of a
user-interface that can be used to input data and confirm the identities of others on the network.
The address book stores for each user a private key, a public identifier, and their personal data and their
relationship
data.
In the process of creating such a decentralized address-book, there would need to be nodes (computers) running node
software, and there would need to be adequate means of interface with the network, for example mobile phone apps and
webapps.
Given that these components necessarily precede the existence of the ideal ODIN, the infrastructure will already be
in
place to add additional decentralized open-source services to ODIN natively by simply updating the software of the
nodes, perhaps
by adding these services as
optional add-ons by the operator of the node.
Since we will have
already successfully produced an automatic human-authentication platform with ODIN's base functions, this naturally
allows for a multitude of natively integrated communication and messaging services that can simply piggyback onto
existing ODIN authentication infrastructure.
For example, the first and most fundamental service will be uncensorable encrypted private messages.
Other kinds of services that could natively be provided by ODIN include: personal data storage, group messaging,
calendar and schedule sharing, discussion boards (like Reddit, for example), personal profile (for example, such
as
one
might have on Facebook), various blog formats, classifieds, etc.
The list goes on. In fact, nearly any for-profit service which currently exists today could probably eventually be
converted into a
decentralized open-source service that utilizes ODIN instead of a database governed by a central authority.


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Lotus Leaf Cafe
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STARTERS:
PUMPKIN & ALMOND DIP 8.69
sweet potato chips & house made seed crackers
SWEET POTATO CHIPS 7.29
sriracha aioli with sriracha tofu crème
CRISPY FRIED BUFFALO CAULIFLOWER 8.99
fried cauliflower tossed in a sweet & spicy buffalo sauce, served with fresh celery & tzatziki sauce
garnished with fresh lime zest & cilantro
SALADS:
ORIGINAL KALE 9.49
rubbed kale, avocado, dried cranberry, raw pumpkin seeds & lemon vinaigrette
BLACK BETTY 9.99
black bean burger, spring mix, cucumber, red onion, tomato, avocado, fresh jalapeno & agave
lime margarita vinaigrette
THE WALDO 10.49
chicken waldorf salad: house made chicken salad, crisp apple slices, roasted walnuts & pecans,
grapes, red onion, spring mix & apple walnut vinaigrette
SYNERGY SALAD 9.99
spring mix, tomato, cucumber, red onion, avocado, dried cranberry, raw pumpkin seeds, almond
“cheese” & ginger soy dijon
VEGETARIAN ANTIPASTO 11.49
kale, spinach, spring mix, roasted eggplant, roasted red pepper, chickpeas, pepperoncini, tomato,
artichoke hearts, herbed goat cheese & lemon vinaigrette
DRESSING OPTIONS
apple walnut vinaigrette • balsamic vinaigrette • lemon vinaigrette
agave lime margarita • ginger/soy dijon
SANDWICHES:
ALL SANDWICHES SERVED WITH CHOICE OF SWEET POTATO CHIPS OR HOUSE SALAD
OR CHOOSE QUINOA TABBOULEH, ROASTED VEGETABLE OR CUP OF SOUP FOR 1.49
glutenfree bun available 1.99
GRILLED CHEESE 9.49
swiss, goat cheese, sundried tomato pesto & wheat berry bread
AVERAGE TOM 9.79
roasted turkey panini, caramelized onion, swiss, tomato, spinach, *sriracha aioli & wheat berry
bread
EGGPLANT SUB VG 10.49
dijon, tomato pesto, green pesto (basil and herb), roasted eggplant, portobello mushroom, roasted
red peppers, spinach, feta, red onion & wheat roll
THE BIG D 9.99
turkey, spicy cranberry goat cheese spread, toasted walnuts, spinach & wheat berry bread
THE LIZZY 9.29
house chicken salad, cucumber, red onion, tomato, spring mix, dijon & wheat berry bread
THE APHRODITE 9.99
hummus, tzatziki, cucumber, red onion, tomato, mixed greens, kalamata olives & whole wheat
pita
GREEN GODDESS 9.79
garlicky spinach hummus, green pesto, avocado, cucumber, spinach
BURGERS:
ALL BURGERS SERVED WITH CHOICE OF SWEET POTATO CHIPS OR HOUSE SALAD
OR CHOOSE QUINOA TABBOULEH, ROASTED VEGETABLE OR CUP OF SOUP FOR 1.49
glutenfree bun available 1.99 add cheese 1.29
TRIPLE B 9.99
black bean/quinoa burger, spring mix, tomato, red onion, sriracha aioli & wheat bun
Vegan sub sriracha tofu crème
TURKEY BURGER 10.49
spring mix, tomato, red onion, sriracha aioli & wheat bun
MEAN GREEN 11.29
avocado, pickled jalapeno, spring mix & cilantro/lime aioli
your choice: turkey burger or Vegan black bean burger Vegan sub sriracha tofu crème
ENTREES:
FISH & CHIPS 13.99
almond crusted cod, sweet potato chips, red pepper cabbage slaw & curry aioli
ZUCCHINI “PASTA” PRIMAVERA 9.99
zucchini noodles, red pepper, spinach, mushroom & garlic/herb/lemon sauce
FISH TACOS 12.99
seasoned cod in a quinoa & flax tortilla, red pepper, cabbage, jalapeno, red onion & *cilantro lime
aioli, served with house salad & sliced avocado. Ask for a glutenfree tortilla
Contact
Address: 251 N. Washington Ave, Wichita, KS 67213
Phone:<PHONE_NUMBER>
Hours of Operation:
Monday - Saturday 11AM–9PM
Sunday 11AM–4PM


Coimbatore Smiley Project is a registered NGO founded and run by students for the purpose of educating less fortunate children.
Coimbatore Smiley Project provides a platform for students from different institutions in Coimbatore to support and educate children.
We believe that the best gift for the children is education, as it gives them the ability and the freedom to stand up for themselves.
Coimbatore Smiley Project aims to connect all the students in Coimbatore for a collective social responsibility. The NGO follows the structure of a Public company, i.e. the NGO is not led by one particular person but by a ‘Head Committee’ led by a Chairperson. This Head Committee is composed of student representatives of different colleges in the city. All discussions and decisions are made by this committee led by the Chairperson. This ensures equal voice among the members, a channel for all volunteers from different colleges to contribute to the NGO, and represent the student community as a whole for positive impact.
For donation, additional information, or related queries, feel free to write to us!


glance-api¶
Server for the Glance Image Service API¶
| Author: |<EMAIL_ADDRESS>|
|---|---|
| Date: | 2014-01-16 |
| Copyright: | OpenStack LLC |
| Version: | 2014.1 |
| Manual section: | 1 |
| Manual group: | cloud computing |
SYNOPSIS¶
glance-api [options]
DESCRIPTION¶
glance-api is a server daemon that serves the Glance API
OPTIONS¶
General options
- -h, –help
- Show the help message and exit
- –version
- Print the version number and exit
- -v, –verbose
- Print more verbose output
- –noverbose
- Disable verbose output
- -d, –debug
- Print debugging output (set logging level to DEBUG instead of default WARNING level)
- –nodebug
- Disable debugging output
- –use-syslog
- Use syslog for logging
- –nouse-syslog
- Disable the use of syslog for logging
- –syslog-log-facility SYSLOG_LOG_FACILITY
- syslog facility to receive log lines
- –config-dir DIR
- Path to a config directory to pull *.conf files from. This file set is sorted, to provide a predictable parse order if individual options are over-ridden. The set is parsed after the file(s) specified via previous –config-file, arguments hence over-ridden options in the directory take precedence. This means that configuration from files in a specified config-dir will always take precedence over configuration from files specified by –config-file, regardless to argument order.
- –config-file PATH
- Path to a config file to use. Multiple config files can be specified by using this flag multiple times, for example, –config-file <file1> –config-file <file2>. Values in latter files take precedence.
–log-config-append PATH –log-config PATH
The name of logging configuration file. It does not disable existing loggers, but just appends specified logging configuration to any other existing logging options. Please see the Python logging module documentation for details on logging configuration files. The log-config name for this option is depcrecated.
- –log-format FORMAT
- A logging.Formatter log message format string which may use any of the available logging.LogRecord attributes. Default: None
- –log-date-format DATE_FORMAT
- Format string for %(asctime)s in log records. Default: None
- –log-file PATH, –logfile PATH
- (Optional) Name of log file to output to. If not set, logging will go to stdout.
- –log-dir LOG_DIR, –logdir LOG_DIR
- (Optional) The directory to keep log files in (will be prepended to –log-file)
FILES¶
- /etc/glance/glance-api.conf
- Default configuration file for Glance API
- Glance bugs are tracked in Launchpad so you can view current bugs at OpenStack Glance


--- author: Andre Charland categories: app date: '2009-05-01 00:14:16' link: http://blogs.nitobi.com/andre/index.php/2009/04/30/phonegaps-winning-pitch-at-web-20-expo/ permalink: /2009/05/01/phonegap%e2%80%99s-winning-pitch-at-web-2-0-expo/ slug: phonegap%e2%80%99s-winning-pitch-at-web-2-0-expo status: publish title: PhoneGap’s Winning Pitch at Web 2.0 Expo type: post format: html ---
Earlier this month we were selected to present at the Web 2.0 Expo's LaunchPad event, and even better we won the people's choice award!! I was stoked, the whole team was! It was a big honor. I think my presentation was OK, but it's really the team, the idea and the community behind PhoneGap that made it a winner!
Here's my 5 min talk:
Here's the questions from the judges:
The media, customer and investor interest we've had since has been nothing short of amazing.


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
For the curry gravy, heat the groundnut oil in a wok or large pot and add the onions, garlic and chilli and fry for 1-2 minutes, or until softened. Add the tomatoes, green peppers, ginger, cardamom pods, cinnamon stick, nigella seeds, ground coriander seeds and ground cumin. Season with a pinch of salt and cover with the water. Bring to the boil, then reduce the heat and simmer for 4-5 minutes.
Cover the wok or pot with a lid and simmer for 30-40 minutes over a low heat. Remove the cinnamon stick then, using a stick blender, blend the mixture until smooth. Set aside.
For the pilau rice, heat the ghee in a pan and fry the onion and garlic for 4-5 minutes, or until softened and beginning to colour.
Add the half cinnamon stick and the cardamom pods, then stir in the basmati rice and pour over the hot vegetable stock. Stir well, then cover the pan with a lid and simmer gently for 20-25 minutes, or until all of the stock has been absorbed and the rice is tender.
For the beef Madras, mix together the spices, ginger, garlic and chillies in a large bowl. Add the beef slices and stir well to coat in the mixture.
Heat the ghee in a pan and fry the beef for 2-3 minutes, then add the chopped aubergine, okra and tomato purée. Season, to taste, with a pinch of salt, then cook for a further 3-4 minutes, or until the beef is cooked through.
Pour the curry gravy over the beef and cook for 1-2 minutes, to warm through.
To serve, spoon the pilau rice onto serving plates and spoon the beef madras alongside. Sprinkle the garam masala over the beef and garnish with the fresh coriander leaves.


Welcome to Appoint Website
Discover the best doctors, clinic & hospital and make an appointment quick and easy
Clinic and Specialities
Urology
Neurology
Orthopedic
Cardiologist
Dentist
Book Our Doctor
The aim of Appoint Website is to help users (patients) to ask for an appointment to a doctor, consult and give emergency help for the ones who want it. . The main objective of this study is to design and implement a computerized appointment system that will facilitate patients to book an appointment with the doctor online and even make consultation without moving in order to achieve time management and reduce long queues on hospitals.
Read More..Aimee Paci
MDS - Periodontology and Oral Implantology, BDS
- Kigali, Rwanda
- Available Mon-Fri
- $500 - $1500
Diane Umuhoza
BDS, MDS - Oral & Maxillofacial Surgery
- Rubavu, Rwanda
- Available Weekends
- $500 - $1000
David Nkaka
MBBS, MD - General Medicine, DNB - Cardiology
- Musanze, Rwanda
- Available on Mon-Wed
- $100 - $400
Sofia Mukamana
MBBS, MS - General Surgery, MCh - Urology
- Rwamagana, Rwanda
- Available on Fri-Sun
- $550 - $1250
Calvin Mugisha
MBBS, MD - Ophthalmology, DNB - Ophthalmology
- Huye, Rwanda
- Available on Fri
- $150 - $700
Joel Ruti
MS - Orthopaedics, MBBS, M.Ch - Orthopaedics
- Karongi, Rwanda
- Available on Thu-Sat
- $100 - $500
Jason Kabayiza
MBBS, MD - General Medicine, DM - Neurology
- Gasabo, Rwanda
- Available on Tue-Thu
- $100 - $800
Paul Rutikanga
MBBS, MD - Dermatology , Venereology & Lepros
- Ngoma, Rwanda
- Available on Wed
- $100 - $400
Availabe Features in Our Clinic
Patient Ward
Test Room
ICU
Laboratory
Operation
Medical


The most common way to do arithmetic calculations in Prolog is to use the built-in predicate is/2.
is/2
is
Term must not contain any uninstantiated variables. Do not confuse is/2 with =/2.
=/2


About Me
Misaki Koonce loves web development. She enjoys building applications with JavaScript, CSS. Wait.
Certifications
React:
React Nanodegree
Frontend Developer:
Front-End Web Developer Nanodegree
jQueryt:
Certified jQuery Developer
JavaScript:
Certified JavaScript Developer
CSS:
Certified HTML Developer
HTML:
Certified HTML Developer
Other links
misakikoonce.com
LinkedIn
Twitter
GitHub


Flink Community Update - August'20
September 4, 2020 - Marta Paes (@morsapaes)Ah, so much for a quiet August month. This time around, we bring you some new Flink Improvement Proposals (FLIPs), a preview of the upcoming Flink Stateful Functions 2.2 release and a look into how far Flink has come in comparison to 2019.
The Past Month in Flink #
Flink Releases #
Getting Ready for Flink Stateful Functions 2.2 #
The details of the next release of Stateful Functions are under discussion in this @dev mailing list thread, and the feature freeze is set for September 10th — so, you can expect Stateful Functions 2.2 to be released soon after! Some of the most relevant features in the upcoming release are:
-
DataStream API interoperability, allowing users to embed Stateful Functions pipelines in regular DataStream API programs with
DataStream
ingress/egress. -
Fine-grained control over state for remote functions, including the ability to configure different state expiration modes for each individual function.
As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration. If you’d like to get involved, we’re always looking for new contributors!
Flink 1.10.2 #
The community has announced the second patch version to cover some outstanding issues in Flink 1.10. You can find a detailed list with all the improvements and bugfixes that went into Flink 1.10.2 in the announcement blogpost.
New Flink Improvement Proposals (FLIPs) #
The number of FLIPs being created and discussed in the @dev mailing list is growing week over week, as the Flink 1.12 release takes form and some longer-term efforts are kicked-off. Below are some of the new FLIPs to keep an eye out for!
| FLIP-131 |
The community proposes to deprecate the DataSet API in favor of the Table API/SQL and the DataStream API, in the long run. For this to be feasible, both APIs first need to be adapted and expanded to support the additional use cases currently covered by the DataSet API. The first discussion to branch out of this "umbrella" FLIP is around support for a batch execution mode in the DataStream API (FLIP-134). |
| FLIP-135 |
To better accommodate recovery scenarios where a certain amount of data loss is tolerable, but a full pipeline restart is not desirable, the community plans to introduce a new failover strategy that allows to restart only the failed task(s). Approximate task-local recovery will allow users to trade consistency for fast failure recovery, which is handy for use cases like online training. |
| FLIP-136 |
The Table API has seen a great deal of refactoring and new features in recent releases, but the interfaces to and from the DataStream API haven't been updated accordingly. The work in this FLIP will cover multiple known gaps to improve interoperability and expose important functionality also to the DataStream API (e.g. changelog handling). |
| FLIP-139 |
Python UDFs have been supported in PyFlink since 1.10, but were so far limited to stateless functions. The community is now looking to introduce stateful aggregate functions (UDAFs) in the Python Table API. Note: Pandas UDAFs are covered in a separate proposal (FLIP-137). |
For a complete overview of the development threads coming up in the project, check the Flink 1.12 Release Wiki and follow the feature discussions in the @dev mailing list.
New Committers and PMC Members #
The Apache Flink community has welcomed 1 new PMC Member and 1 new Committer since the last update. Congratulations!
New PMC Members #
New Committers #
The Bigger Picture #
Flink in 2019: the Aftermath #
Roughly a year ago, we did a roundup of community stats to understand how far Flink (and the Flink community) had come in 2019. Where does Flink stand now? What changed?
Perhaps the most impressive result this time around is the surge in activity in the @user-zh mailing list. What started as an effort to better support the chinese-speaking users early in 2019 is now even exceeding the level of activity of the (already very active) main @user mailing list. Also @dev1 registered the highest ever peaks in activity in the months leading to the release of Flink 1.11!
For what it’s worth, the Flink GitHub repository is now headed to 15k stars, after reaching the 10k milestone last year. If you consider some other numbers we gathered previously on repository activity and releases over time, 2020 is looking like one for the books in the Flink community.
1. Excluding messages from<EMAIL_ADDRESS>To put these numbers into perspective, the report for the financial year of 2020 from the Apache Software Foundation (ASF) features Flink as one of the most active open source projects, with mentions for:
- Most Active Sources: Visits (#2)
For more details on where Flink and other open source projects stand in the bigger ASF picture, check out the full report.
Google Season of Docs 2020 Results #
In a previous update, we announced that Flink had been selected for Google Season of Docs (GSoD) 2020, an initiative to pair technical writers with mentors to work on documentation for open source projects. Today, we’d like to welcome the two technical writers that will be working with the Flink community to improve the Table API/SQL documentation: Kartik Khare and Muhammad Haseeb Asif!
-
Kartik is a software engineer at Walmart Labs and a regular contributor to multiple Apache projects. He is also a prolific writer on Medium and has previously published on the Flink blog. Last year, he contributed to Apache Airflow as part of GSoD and he’s currently revamping the Apache Pinot documentation.
-
Muhammad is a dual degree master student at KTH and TU Berlin, with a focus on distributed systems and data intensive processing (in particular, performance optimization of state backends). He writes frequently about Flink on Medium and you can catch him at Flink Forward later this year!
We’re looking forward to the next 3 months of collaboration, and would like to thank again all the applicants that invested time into their applications for GSoD with Flink.
Upcoming Events (and More!) #
With conference season in full swing, we’re glad to see some great Flink content coming up in September! Here, we highlight some of the Flink talks happening soon in virtual events.
As usual, we also leave you with some resources to read and explore.
If you’d like to keep a closer eye on what’s happening in the community, subscribe to the Flink @community mailing list to get fine-grained weekly updates, upcoming event announcements and more.