Monitor, chart, & trend your web or mobile apps from tens of thousands of real PCs around the globe, for the most accurate measurements.
Our analytic engine automatically processes, correlates and compares thousands of data points in seconds.
We constantly monitor all host and third party services, and alert on outages or poor performance.
With hundreds of devices, browsers, connections, and location options available, you can clone virtually any customer profile.
Capture User Experience for Web 2.0 Applications that is undetectable with traditional monitoring tools
Constantly updated performance benchmarks provide an accurate view of how users perceive you versus your competitors.
Combine with Application Monitoring for deep code-level exposure of application performance bottlenecks.
Simulate your next big event months beforehand. High volume, real-world load tests offer peace of mind when you need it most.
Combine with User Experience Management to get deep insight into real user behavior and performance.
Test from where your customers really are, by emulating real user behavior from your key geographies around the world.
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Ensure your key web, mobile, cloud and streaming pages and transactions perform properly from all user locations, all the time.
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Seamlessly combine Synthetic Monitoring with Application Monitoring on a single platform to enable our patented PurePath Technology®. Drill-down into granular detail for instant root-cause isolation of performance bottlenecks. Capture timing and code-level context for all user-clicks, across all tiers, from app-to-backend.
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Simulate your next big event months beforehand. High volume, real-world load tests offer peace of mind when you need it most.
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Dynatrace offers the most complete solution for your user experience-driven needs. In addition to proactively monitoring your web and mobile applications from across the globe, you can get deep insight into real user behavior and application performance, all in the same context. Quantify the impact of failing transactions with Real User Insight.
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Throughout the year, we measure and rank more than 8,000 sites across 26 countries.
We make that data available to you, so that you can compare and optimize your performance against peers, competitors or industry leaders.
With more than 20 million measurements per month, our benchmarks are the most comprehensive source of industry based mobile and web performance information.
Our Mobile Client for Android and iPhone provides a simple and quick way to navigate a wealth of synthetic performance monitoring data in the palm of your hand. In a meeting, at lunch, or in the ‘war room’, with a couple of taps and a few swipes or receiving a proactive notification, you can quickly check the health of your mission critical application in real-time, isolate the fault domain of an application problem, and hand it off to the appropriate team to isolate the root cause. Connecting remotely to our Central Analysis Server extends the information gathered by dynaTrace to your smartphone or tablet.


ExplorerTreeGadget()
语法
Result = ExplorerTreeGadget(#Gadget, x, y, Width, Height, Directory$, [, Flags])概要
Creates a tree listing of the directory tree just as Explorer does. It lets the user navigate through his file-system, and choose a file or folder.
参数
#Gadget A number to identify the new gadget. #PB_Any 可以用来自动生成这个编码. x, y, Width, Height The position and dimensions of the new gadget. Directory$ The directory that will be initially selected. It can include one or multiple patterns, like "C:\*.pb;*.pbi". If no pattern is included, the directory must end with a '\'. Including no directory will display the root containing the drives. Including no pattern defaults to '*.*'. So a Directory$ of "" will display the root and set '*.*' as pattern. Flags (可选) Flags to modify the gadget behavior. 可以是下列值的组合︰ #PB_Explorer_BorderLess : Create Gadget without borders. #PB_Explorer_AlwaysShowSelection : The selection is still visible, even when the gadget is not activated. #PB_Explorer_NoLines : Hide the little lines between each node. #PB_Explorer_NoButtons : Hide the '+' node buttons. #PB_Explorer_NoFiles : No files will be displayed. #PB_Explorer_NoDriveRequester : There will be no 'please insert drive X:' displayed. #PB_Explorer_NoMyDocuments : The 'My Documents' Folder will not be displayed as a separate item. #PB_Explorer_AutoSort : The content will be sorted automatically by name.
返回值
Returns nonzero on success and zero on failure. If #PB_Any was used as the #Gadget parameter then the return-value is the auto-generated gadget number on success.
备注
A 'mini help' can be added to this gadget using GadgetToolTip().
The following functions can be used to control the gadget:
- GetGadgetText(): Get the full path of the currently selected directory/file.
- SetGadgetText(): Set the currently selected file/directory.
- GetGadgetState(): Check if the selected item is a file or a directory.
This gadget supports the SetGadgetColor() and GetGadgetColor() functions with the following values as 'ColorType':#PB_Gadget_FrontColor: Textcolor #PB_Gadget_BackColor : Backgroundcolor #PB_Gadget_LineColor : Color for the lines and buttons if they are displayedExplorerTreeGadget() supports the following Events reported by EventType():#PB_EventType_Change : The selection has changed. #PB_EventType_LeftClick : User clicked on an item with the left mouse button. #PB_EventType_RightClick : User clicked on an item with the right mouse button. #PB_EventType_LeftDoubleClick : User double-clicked on an item with the left mouse button. #PB_EventType_RightDoubleClick : User double-clicked on an item with the right mouse button. #PB_EventType_DragStart : User tried to start a Drag & Drop operation.After a #PB_EventType_DragStart event, the Drag & Drop library can be used to start a Drag & Drop operation.
示例
If OpenWindow(0, 0, 0, 300, 300, "ExplorerTreeGadget", #PB_Window_SystemMenu | #PB_Window_ScreenCentered) ExplorerTreeGadget(0, 10, 10, 280, 280, "*.pb;*.pbi") Repeat : Until WaitWindowEvent() = #PB_Event_CloseWindow EndIf
参阅
GetGadgetText(), SetGadgetText(), GetGadgetState(), GetGadgetColor(), SetGadgetColor(), ExplorerComboGadget(), ExplorerListGadget(), TreeGadget()
已支持操作系统
所有


Pygme
Booxing book - TFG
Sometimes reality is so fast that we barely see it, but that doesn't mean is not there. The concept behind this book is try to show what our eyes can't see in the boxing world. Pygme is my final degree project, it combines two passions that I have, boxing and books.
**The very first page of the book where I use the same style for text and images. Here you can see how they combine**
**Inside view of the book with a mix of text and images**
**Inside view of the book in the photography section**


0.9.5
- include method visiblity in show-doc and stat output
- tempfile should end in .rb (for edit -t)
- ls output should not be in array format
- exceptions should allow access to previous items in the backtrace
- input should allow multiple objects which are switched to automatically when EOF is reached on the preceding one
- pry -r should happen in pry
- more plugin-related commands in pry - see installed ones, see activated ones, see available on rubygems
- should also allow plugins be explicitly activated as a command line option
- should not raise if plugin activation fails (should show warning instead)
- more documentation on CommandContext, etc and also command helpers
- fix history saving (should not save all of Readline::HISTORY, but only what changed)
- prevent blank lines going to Readline::HISTORY
- ensure that cat —ex emulates the `whereami` format - includes line numbers and formatted the same, etc
- rename inp and out to inp and out otherwise than can overwrite locals by those names when debugging (not good)
- add source file to stat command
- make plugins use hash instead of array
- ensure edit -t has ‘edit’ alias (no parameters) and dumps eval_string into buffer
- whitelist exceptions
- hooks system
- jruby shell command support
0.9.3
- hist command now excludes last line of input (the command invocation itself)
- hist now has `history` alias
- `pry -r` requires now happen after plugin loading (so as not to interfere with
- new Pry.config.disable_auto_reload option, for turning off auto reloading by edit-method and related
- add better error messages for `cd` command
- add command_prefix
- change some command names to include hyphen, e.g version => pry-version, install => install-command
- do cat —ex and edit —ex
- add reload-method
- fixed exotic object regression - BasicObject.new etc now return “=> unknown“
- converted: import => import-set, version => pry-version, install => install-command
- fix show-doc bug for ruby 1.8 and Kernel.fork
- edit -t (opens a temporary file and evals it in current context when closed)
0.9.0 Major features
- Restructure command system and helpers (almost complete)
- Delete unnecessary commands, add a couple of new ones (e.g amend-line)
- Readline history
- Plugin support
- Support Rubinius core methods
- in[] and out[] arrays
- Improve test coverage (test some commands, etc)
Minor changes
- improve edit-method support for various editors
- ensure all commands have appropriate error handing and informative error messages
- show-doc should include signature of method
Optional
- multi-line readline support
0.8.0
- allow #{} interpolation of all commands
- update documentation! new commands and features and change in behaviour of `run`
- add ; at end of line to suppress return value output
- Remove message spam (before/after hooks)
- stop commands returning a value
- use `redo` in the r() method when encounter a command
- shell functionality should just use system(), but redirect in and
out to Pry.input and Pry.output by reassining $stdin and $stdout for duration of block.
- basicobject and no to_s/inspect support
- fix documentation, support rdoc and yard properly
- only load Ripper if 1.9 AND MRI (support jruby 1.9, using
RubyParser)
- shell commands invokable file .
- supercharge cat-file so it can syntax highlight sourcecode files
0.7.0
- add pry-doc support with syntax highlighting for docs
- add ‘mj’ option to ls (restrict to singleton methods)
- add ex local to hold last exception raised in an exception
0.6.8
- add whereami command, a la the `ir_b` gem
- make .pryrc be loaded by run-time pry sessions
0.6.7
- color support
- —simple-prompt for pry commandline
- -I mode for pry commandline
- —color mode for pry commandline
- clean up requires (put them all in one place)
- simple-prompt command and toggle-color commandd.
0.6.1
- !@ command alias for exit_all
- `cd /` for breaking out to pry top level (jump-to 0)
- made `-e` option work in a more effective way for `pry` command line invocation
- exit and exit-all commands now accept a parameter, this parameter becomes the return value of repl()
- `command` method from CommandBase now accepts a :keep_retval arg that determines if command value is returned to pry session or just `nil` (`nil` was old behaviour)
- tests for new :keep_retval and exit-all/exit behaviour; :keep_retval will remain undocumented.
0.5.0 release:
- !!!! UPDATE DOCUMENTATION !!!!
- Use clipped version of Pry.view() for large objects
- Exit Pry session on ^d
- Use Shellwords for breaking up parameters to pry commands
- Use OptionParser to parse options for default pry commands
- Add version command
- Refactor ‘status’ command: add current method info
- Add meth_name_from_binding utility lambda to commands.rb
- Add -M, -m, -v(erbose), -a(ll), -s(uper), -l(ocals), -i(ivars), -k(klass vars) options to ls
- add -i(nstance) option to show-method
- add —help option to most commands
- Get rid of ls_method and ls_imethods (subsumed by more powerful ls)
- Get rid of show_idoc and show_imethod
- Add special eval-file command that evals target file in current context


Virus Spread Simulation
Configuration
Population
Immunity (Days)
Buildings
Movement Speed
0.48
Transmission Rate
Incubation Period (Days)
Lethality Rate
Disease Duration (Days)
0.12
0.06
Examination
Load Configuration
For a detailed explanation, please visit the
readme
.


Bachelor of Science Student in Pure Math at the University of Nairobi.
Currently taken an interest in Software Development which has led me to taking a Prep Course at Moringa School.
I love hiking and partake in running activities.
I love reading novels with Anne Rice being my favorite author. Baking is also
a part-time activity for me during some weekends.
I have been able to acquire a few skills using Python which I learnt at a Data Science Bootcamp.
For the past week, I have been able to learn HTML,CSS and Git at Moringa School


Quick Start: Quantum Gate decomposition¶
One can open this tutorial in Google Colab (is recommended)
In the given short tutorial, we show the basic steps of working with QGOpt. It is known that an arbitrary two-qubit unitary gate can be decomposed into a sequence of CNOT gates and one qubit gates as it is shown on the tensor diagram below (if the diagram is not displayed here, please open the notebook in Google Colab).
Local unitary gates are elements of the complex Stiefel manifold; thus, the decomposition can be found by minimizing Frobenius distance between a given two qubits unitary gate and its decomposition. In the beginning, let us import some libraries.
First, one needs to import all necessary libraries.
[ ]:
import tensorflow as tf # tf 2.x
import matplotlib.pyplot as plt
import math
try:
import QGOpt as qgo
except ImportError:
!pip install git+https://github.com/LuchnikovI/QGOpt
import QGOpt as qgo
Before considering the main part of the code that solves the problem of gate decomposition, we need to introduce a function that calculates the Kronecker product of two matrices:
[2]:
def kron(A, B):
"""
Returns Kronecker product of two square matrices.
Args:
A: complex valued tf tensor of shape (dim1, dim1)
B: complex valued tf tensor of shape (dim2, dim2)
Returns:
complex valued tf tensor of shape (dim1 * dim2, dim1 * dim2),
kronecker product of two matrices
"""
dim1 = A.shape[-1]
dim2 = B.shape[-1]
AB = tf.transpose(tf.tensordot(A, B, axes=0), (0, 2, 1, 3))
return tf.reshape(AB, (dim1 * dim2, dim1 * dim2))
Then we define an example of the complex Stiefel manifold:
[3]:
m = qgo.manifolds.StiefelManifold()
As a target gate that we want to decompose, we use a randomly generated one:
[4]:
U = m.random((4, 4), dtype=tf.complex128)
We initialize the initial set of local unitary gates \(\{u_{ij}\}_{i,j=1}^{4, 2}\) randomly as a 4th rank tensor:
[5]:
u = m.random((4, 2, 2, 2), dtype=tf.complex128)
The first two indices of this tensor enumerate a particular one-qubit gate, the last two indices are matrix indices of a gate. We turn this tensor into its real representation in order to make it suitable for an optimizer and wrap it into the TF variable:
[6]:
u = qgo.manifolds.complex_to_real(u)
u = tf.Variable(u)
We initialize the CNOT gate as follows:
[7]:
cnot = tf.constant([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]], dtype=tf.complex128)
As a next step we initialize Riemannian Adam optimizer:
[9]:
lr = 0.2 # optimization step size
# we also pass an example of manifold
# to the optimizer in order to give information
# about constraints to the optimizer
opt = qgo.optimizers.RAdam(m, lr)
Finally, we ran part of code that calculate forward pass, gradients, and optimization step several times until convergence is reached:
[10]:
# this list will be filled by value of
# error per iteration
err_vs_iter = []
# optimization loop
for _ in range(500):
with tf.GradientTape() as tape:
# turning u back into its complex representation
uc = qgo.manifolds.real_to_complex(u)
# decomposition
D = kron(uc[0, 0], uc[0, 1])
D = cnot @ D
D = kron(uc[1, 0], uc[1, 1])@ D
D = cnot @ D
D = kron(uc[2, 0], uc[2, 1])@ D
D = cnot @ D
D = kron(uc[3, 0], uc[3, 1]) @ D
# loss function
L = tf.linalg.norm(D - U) ** 2
L = tf.math.real(L)
# filling list with history of error
err_vs_iter.append(tf.math.sqrt(L))
# gradient from tape
grad = tape.gradient(L, u)
# optimization step
opt.apply_gradients(zip([grad], [u]))
Finally, we plot how error decreases with time
[11]:
print('[0, 0] element of the trained gate {}'.format(D[0, 0].numpy()))
print('[0, 0] element of the true gate {}'.format(U[0, 0].numpy()))
plt.plot(err_vs_iter)
plt.yscale('log')
plt.xlabel('iter')
plt.ylabel('err')
[0, 0] element of the trained gate (-0.034378823704696526-0.46822585286096785j)
[0, 0] element of the true gate (-0.03437882370484857-0.4682258528614082j)
[11]:
Text(0, 0.5, 'err')


No Documentation available as of now. Please check back after sometime.Contact me if you are willing to help with the documentation. I'll be happy to put your name here.


{% if ec2_images|length == 0 and openstack_images|length == 0 %}
You don't have any cloud images registered for this application.
{% else %}
| Image type | Image ID | Endpoint | Server/Client Image |
{% if ec2_images|length > 0 %}
{% for ec2_image in ec2_images %}
| Amazon EC2 | {{ ec2_image.image_id }} | {{ ec2_image.zone_url }} | {% if ec2_image.image_type == 'C' %}Client Image{% else %}Server Image{% endif %} |
|
{% endfor %}
{% endif %}
{% if openstack_images|length > 0 %}
{% for os_image in openstack_images %}
| Openstack | {{ os_image.image_id }} | {{ os_image.zone_url }} | {% if os_image.image_type == 'C' %}Client Image{% else %}Server Image{% endif %} |
|
{% endfor %}
{% endif %}
{% endif %}


Shipping
GENERAL INFORMATION
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- To CANCEL or MODIFY your order, please contact us at<EMAIL_ADDRESS>We will do our best to accommodate your needs, but we cannot cancel or modify orders that have already shipped.
- In cases of errors or delays caused by the shipping carrier, we will help resolve the dispute to the best of our abilities, but we will not be held liable or responsible for carrier delays or errors.
- Please note all orders over $200.00 will require a signature.
SHIPPING IN U.S.A.
- Free Ground Shipping (3-7 business days)
- $20 Expedited Shipping available on U.S. orders
SHIPPING INTERNATIONALLY
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DUTIES & TAXES
Duties, customs tariffs and VAT are set by the local government and determined based on a combination of the country of origin or manufacturing of the goods being purchased, and the classification of that merchandise in accordance with a harmonized system adopted and used by the local government. The amount of duty, tariffs and taxes will vary and will be due to your country’s customs authority upon delivery.
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International shipping is calculated based on a number of factors including the number of items, the weight and the destination country/region. International shipping will be calculated and displayed during checkout based on the items you are purchasing and your shipping destination.
We proudly ship to:
Country Rate Australia $35.00 Austria $15.00 Belgium $10.00 Canada $5.00 Czech Republic $15.00 Denmark $15.00 Finland $20.00 France $15.00 Germany $10.00 Greece $15.00 Hong Kong $25.00 Ireland $15.00 Israel $25.00 Italy $15.00 Luxembourg $10.00 Netherlands $10.00 New Zealand $15.00 Portugal $15.00 Singapore $25.00 South Korea $15.00 Spain $20.00 Sweden $15.00 Switzerland $20.00 United Kingdom $5.00
DOMESTIC HOLIDAY SHIPPING SCHEDULE 2017
To help ensure delivery by 12/24 please refer to the guidelines below
- USPS Parcel Select - December 13th
- UPS Ground - December 15th
- UPS 2 Day - December 20th
INTERNATIONAL HOLIDAY SHIPPING SCHEDULE 2017
- RRD Priority Parcel Canada - December 7th
- RRD International Mail - November 28th
*For consumer protection, all orders are subject to review. On occasion, this may cause a delay in processing your order. Weather issues may also cause a delay in delivery.


| Category | Samples (and Ant target) | Description |
|---|---|---|
| SAX | SAXParsingSample | The sample parses data/inv1a.finf as specified in the build.xml and handle SAX events to display corresponding XML content. |
| SAXSerializingSample | Directly using the FastInfoset SAX Serializer, the sample writes a small piece of XML infoset taken from inv1a.xml into a FastInfoset file called data/inv1c.finf. For details of the XML infoset, please refer to samples JavaDoc. | |
| StAX | StAXEventReadingSample | Demonstrate the use of FI StAX EventReader to read FI and XML files. The sample
reads data/inv1a.xml and data/inv1a.finf and displays event types as it goes through
the documents. To try the sample with other xml files, edit the build.xml and change the "simple-xml-file" and "simple-finf-file" properties to point to the files you may want to test. |
| StAXParsingSample | Demonstrate the use of FI StAX StreamReader to read FI files. The sample reads
data/inv1.finf and displays contents as it goes through the document. Similar to the above EventReader, if you want to run the sample on other files, you would need to change the "simple-xml-file" and "simple-finf-file" properties to point to the files you may want to test. The finf file specified will be generated automatically. |
|
| StAXSerializingSample | Directly using the FastInfoset StAX Serializer, the sample writes a small piece of XML infoset taken from inv1a.xml into a FastInfoset file called data/inv1c.finf. For details of the XML infoset, please refer to samples JavaDoc. | |
| Transform | XMLToFastInfosetSAXSerializer | The sample demonstrates how to use SAXDocumentSerializer as a SAX handler and JAXP transformer to convert an XML file into a FI document. As shown in the sample, transforming a DOM source to SAX Result involves very little coding. However, the process may not be efficient due to the construction of DOM source. |
| XMLToFastInfosetStAXSerializer | The sample demonstrates how to use StAXDocumentSerializer as a SAX handler and JAXP transformer to convert an XML file into a FI document. As shown in the sample, transforming a DOM source to SAX Result involves very little coding. However, the process may not be efficient due to the construction of DOM source. | |
| Encoding/Decoding typed data | PrimitiveTypesWithElementContentSample | The sample demonstrates how to use the SAX serializers/parsers to encode and decode type primitive data that is element content. The typed data, represented as Java primitive types, is encoded into the FI document in efficient binary form and may be decoded into the same Java primitive types, or into lexical values (which depending on the handlers that are registered with the SAX parser. |


The UIARoundtrip module defines events that allow
tooling to analyze the absolute time elapsed between
a start point event and a stop point event. It differs
from UIABenchmark in that UIABenchmark supports context-awareness
(i.e. exclusive time elapsed) whereas UIARoundtrip is always
inclusive (i.e. the time elapsed that DVT displays includes
time spent in all other threads / tasks).
The following configuration script demonstrates how the application might
control the logging of ANALYSIS events embedded in the Mod module at configuration
time. In this case, the configuration script arranges for the Log
statements within modules to always generate ANALYSIS events.
Without these configuration statements, no ANALYSIS events would be generated
by any modules.
| config UIARoundtrip_start // module-wide |
|
Roundtrip event used to log the start of an operation
VALUES
fmt
a constant string that provides format specifiers for up to 6 additional parameters
EXAMPLE
The following C code shows how to log a simple
roundtrip 'start' event along with a user-specified
format string describing the event.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Log_write1(UIARoundtrip_start, "My roundtrip event");
The following text will be displayed for the event:
Roundtrip_Start: My roundtrip event
| config UIARoundtrip_startInstance // module-wide |
|
Roundtrip event used to log the start of an operation instance
extern const Log_Event UIARoundtrip_startInstance;
VALUES
fmt
a constant string that provides format specifiers for up to 5 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel.
EXAMPLE
The following C code shows how to log a roundtrip
'startInstance' event along with a user-specified
instance identifier and a format string describing the event.
#include <xdc/runtime/Gate.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
static volatile int gMyGlobalInstanceId = 0;
...
IArg key;
int localInstanceId;
// protect pre-increment operation from race conditions
key = Gate_enterSystem();
localInstanceId = ++gMyGlobalInstanceId;
Gate_leaveSystem(key);
Log_write2(UIARoundtrip_startInstance, "My roundtrip event: instanceId=%d",localInstanceId);
...
Log_write2(UIARoundtrip_stopInstance, "My roundtrip event: instanceId=%d",localInstanceId);
The following text will be displayed for the event:
Roundtrip_StartInstance: My roundtrip event: instanceId=1
Roundtrip_StopInstance: My roundtrip event: instanceId=1
| config UIARoundtrip_startInstanceWithAdrs // module-wide |
|
Roundtrip event used to log the start of an operation instance
extern const Log_Event UIARoundtrip_startInstanceWithAdrs;
VALUES
fmt
a constant string that provides format specifiers for up to 5 additional parameters
instanceId
a unique instance ID that can be used to match benchmark start and stop events
functionAdrs
the address of a function that can differentiate this pair of start and stop events from others
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'startInstanceWithAdrs' event along with a task handle as the
instance identifier, the function address and a format string
describing the event.
#include <ti/sysbios/knl/Task.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void myFunction(){
Task_Handle hTsk = Task_selfMacro();
Log_write3(UIARoundtrip_startInstanceWithAdrs, "My roundtrip event: task=0x%x, fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
...
Log_write3(UIARoundtrip_stopInstanceWithAdrs, "My roundtrip event: task=0x%x", fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
Roundtrip_StopInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
| config UIARoundtrip_startInstanceWithStr // module-wide |
|
Roundtrip event used to log the start of an operation instance
extern const Log_Event UIARoundtrip_startInstanceWithStr;
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
str
a constant string reference
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'startInstanceWithStr' event along with a unique instance identifier
and a string reference used only by the pair of start / stop events.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void packetHdlr(Int packetId){
Log_write3(UIARoundtrip_startInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
...
Log_write3(UIARoundtrip_stopInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Roundtrip_StopInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
| config UIARoundtrip_stop // module-wide |
|
Roundtrip event used to log the end of an operation
VALUES
fmt
a constant string that provides format specifiers for up to 6 additional parameters
EXAMPLE
The following C code shows how to log a simple
roundtrip 'stop' event along with a user-specified
format string describing the event.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Log_write1(UIARoundtrip_stop, "My roundtrip event");
The following text will be displayed for the event:
Roundtrip_Stop: My roundtrip event
| config UIARoundtrip_stopInstance // module-wide |
|
Roundtrip event used to log the end of an operation instance
extern const Log_Event UIARoundtrip_stopInstance;
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstance' event along with a user-specified
instance identifier and a format string describing the event.
#include <xdc/runtime/Gate.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
static volatile int gMyGlobalInstanceId = 0;
...
IArg key;
int localInstanceId;
// protect pre-increment operation from race conditions
key = Gate_enterSystem();
localInstanceId = ++gMyGlobalInstanceId;
Gate_leaveSystem(key);
Log_write2(UIARoundtrip_startInstance, "My roundtrip event: instanceId=%d",localInstanceId);
...
Log_write2(UIARoundtrip_stopInstance, "My roundtrip event: instanceId=%d",localInstanceId);
The following text will be displayed for the event:
Roundtrip_StartInstance: My roundtrip event: instanceId=1
Roundtrip_StopInstance: My roundtrip event: instanceId=1
| config UIARoundtrip_stopInstanceWithAdrs // module-wide |
|
Roundtrip event used to log the end of an operation instance
extern const Log_Event UIARoundtrip_stopInstanceWithAdrs;
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
functionAdrs
the address of the function
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstanceWithAdrs' event along with a task handle as the
instance identifier, the function address and a format string
describing the event.
#include <ti/sysbios/knl/Task.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void myFunction(){
Task_Handle hTsk = Task_selfMacro();
Log_write3(UIARoundtrip_startInstanceWithAdrs, "My roundtrip event: task=0x%x, fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
...
Log_write3(UIARoundtrip_stopInstanceWithAdrs, "My roundtrip event: task=0x%x", fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
Roundtrip_StopInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
| config UIARoundtrip_stopInstanceWithStr // module-wide |
|
Roundtrip event used to log the end of an operation instance
extern const Log_Event UIARoundtrip_stopInstanceWithStr;
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
str
a constant string reference
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstanceWithStr' event along with a unique instance identifier
and a string reference used only by the pair of start / stop events.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void packetHdlr(Int packetId){
Log_write3(UIARoundtrip_startInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
...
Log_write3(UIARoundtrip_stopInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Roundtrip_StopInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
| Module-Wide Built-Ins |
|
// Get this module's unique id
Bool UIARoundtrip_Module_startupDone();
// Test if this module has completed startup
// The heap from which this module allocates memory
Bool UIARoundtrip_Module_hasMask();
// Test whether this module has a diagnostics mask
Bits16 UIARoundtrip_Module_getMask();
// Returns the diagnostics mask for this module
Void UIARoundtrip_Module_setMask(Bits16 mask);
// Set the diagnostics mask for this module
| config UIARoundtrip.start // module-wide |
|
Roundtrip event used to log the start of an operation
msg: "Roundtrip_Start: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 6 additional parameters
EXAMPLE
The following C code shows how to log a simple
roundtrip 'start' event along with a user-specified
format string describing the event.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Log_write1(UIARoundtrip_start, "My roundtrip event");
The following text will be displayed for the event:
Roundtrip_Start: My roundtrip event
C SYNOPSIS
| config UIARoundtrip.startInstance // module-wide |
|
Roundtrip event used to log the start of an operation instance
msg: "Roundtrip_StartInstance: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 5 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel.
EXAMPLE
The following C code shows how to log a roundtrip
'startInstance' event along with a user-specified
instance identifier and a format string describing the event.
#include <xdc/runtime/Gate.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
static volatile int gMyGlobalInstanceId = 0;
...
IArg key;
int localInstanceId;
// protect pre-increment operation from race conditions
key = Gate_enterSystem();
localInstanceId = ++gMyGlobalInstanceId;
Gate_leaveSystem(key);
Log_write2(UIARoundtrip_startInstance, "My roundtrip event: instanceId=%d",localInstanceId);
...
Log_write2(UIARoundtrip_stopInstance, "My roundtrip event: instanceId=%d",localInstanceId);
The following text will be displayed for the event:
Roundtrip_StartInstance: My roundtrip event: instanceId=1
Roundtrip_StopInstance: My roundtrip event: instanceId=1
C SYNOPSIS
| config UIARoundtrip.startInstanceWithAdrs // module-wide |
|
Roundtrip event used to log the start of an operation instance
msg: "Roundtrip_StartInstanceWithAdrs: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 5 additional parameters
instanceId
a unique instance ID that can be used to match benchmark start and stop events
functionAdrs
the address of a function that can differentiate this pair of start and stop events from others
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'startInstanceWithAdrs' event along with a task handle as the
instance identifier, the function address and a format string
describing the event.
#include <ti/sysbios/knl/Task.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void myFunction(){
Task_Handle hTsk = Task_selfMacro();
Log_write3(UIARoundtrip_startInstanceWithAdrs, "My roundtrip event: task=0x%x, fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
...
Log_write3(UIARoundtrip_stopInstanceWithAdrs, "My roundtrip event: task=0x%x", fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
Roundtrip_StopInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
C SYNOPSIS
| config UIARoundtrip.startInstanceWithStr // module-wide |
|
Roundtrip event used to log the start of an operation instance
msg: "Roundtrip_StartInstanceWithStr: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
str
a constant string reference
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'startInstanceWithStr' event along with a unique instance identifier
and a string reference used only by the pair of start / stop events.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void packetHdlr(Int packetId){
Log_write3(UIARoundtrip_startInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
...
Log_write3(UIARoundtrip_stopInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Roundtrip_StopInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
C SYNOPSIS
| config UIARoundtrip.stop // module-wide |
|
Roundtrip event used to log the end of an operation
msg: "Roundtrip_Stop: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 6 additional parameters
EXAMPLE
The following C code shows how to log a simple
roundtrip 'stop' event along with a user-specified
format string describing the event.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Log_write1(UIARoundtrip_stop, "My roundtrip event");
The following text will be displayed for the event:
Roundtrip_Stop: My roundtrip event
C SYNOPSIS
| config UIARoundtrip.stopInstance // module-wide |
|
Roundtrip event used to log the end of an operation instance
msg: "Roundtrip_StopInstance: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstance' event along with a user-specified
instance identifier and a format string describing the event.
#include <xdc/runtime/Gate.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
static volatile int gMyGlobalInstanceId = 0;
...
IArg key;
int localInstanceId;
// protect pre-increment operation from race conditions
key = Gate_enterSystem();
localInstanceId = ++gMyGlobalInstanceId;
Gate_leaveSystem(key);
Log_write2(UIARoundtrip_startInstance, "My roundtrip event: instanceId=%d",localInstanceId);
...
Log_write2(UIARoundtrip_stopInstance, "My roundtrip event: instanceId=%d",localInstanceId);
The following text will be displayed for the event:
Roundtrip_StartInstance: My roundtrip event: instanceId=1
Roundtrip_StopInstance: My roundtrip event: instanceId=1
C SYNOPSIS
| config UIARoundtrip.stopInstanceWithAdrs // module-wide |
|
Roundtrip event used to log the end of an operation instance
msg: "Roundtrip_StopInstanceWithAdrs: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
functionAdrs
the address of the function
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstanceWithAdrs' event along with a task handle as the
instance identifier, the function address and a format string
describing the event.
#include <ti/sysbios/knl/Task.h>
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void myFunction(){
Task_Handle hTsk = Task_selfMacro();
Log_write3(UIARoundtrip_startInstanceWithAdrs, "My roundtrip event: task=0x%x, fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
...
Log_write3(UIARoundtrip_stopInstanceWithAdrs, "My roundtrip event: task=0x%x", fnAdrs=0x%x",(IArg)hTsk,(IArg)&myFunc);
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
Roundtrip_StopInstanceWithAdrs: My roundtrip event: task=0x893230, fnAdrs=0x820060
C SYNOPSIS
| config UIARoundtrip.stopInstanceWithStr // module-wide |
|
Roundtrip event used to log the end of an operation instance
msg: "Roundtrip_StopInstanceWithStr: %$S"
};
VALUES
fmt
a constant string that provides format specifiers for up to 4 additional parameters
instanceId
a unique instance ID that can be used to match Roundtrip start and stop events
str
a constant string reference
DETAILS
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
EXAMPLE
The following C code shows how to log a roundtrip
'stopInstanceWithStr' event along with a unique instance identifier
and a string reference used only by the pair of start / stop events.
#include <xdc/runtime/Log.h>
#include <ti/uia/events/UIARoundtrip.h>
...
Void packetHdlr(Int packetId){
Log_write3(UIARoundtrip_startInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
...
Log_write3(UIARoundtrip_stopInstanceWithStr, "My roundtrip event: packetId=0x%x",packetId,(IArg)"(routing)");
}
The following text will be displayed for the event:
Roundtrip_StartInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Roundtrip_StopInstanceWithStr: My roundtrip event: packetId=0x3bc3 (routing)
Event parameter provides instance data to differentiate
between multiple instances that can run in parallel
C SYNOPSIS
| metaonly config UIARoundtrip.common$ // module-wide |
|
Common module configuration parameters
DETAILS
All modules have this configuration parameter. Its name
contains the '$' character to ensure it does not conflict with
configuration parameters declared by the module. This allows
new configuration parameters to be added in the future without
any chance of breaking existing modules.


ESCAPE MY ROOM is an interactive game and museum in New Orleans, Louisiana. You and up to 6 guests are confined to a room for one hour. Your group's goal is to work together to solve puzzles centering around New Orleans' infamous family, the DeLaportes.
Can you escape?
You have one hour to break the chamber's codes and solve the family's mysteries. Prepare to be challenged! A great activity for friends, families, team building, or special events.
The DeLaporte Mansion was demolished several years ago to make room for a new hospital complex near the New Orleans Superdome.
Before the house was torn down, everything within it was removed. A silent business partner has decided to make the family artifacts and heirlooms publicly available for the first time.
In each escape room you will uncover mysteries involving the secretive DeLaporte family and their hidden role in shaping New Orleans history.
- Arrive 15 minutes early to have a chance to view the museum in the waiting room
- Wear comfortable clothes. You may need to get on your hands and knees in order to solve a puzzle.
- Bring nothing except a working mind. You won't need any tools, and we require you lock all personal belongings in a locker in the waiting room before entering the room.
- If there are minors (under the age of 18) in your group, please make sure you bring a release form with their parents or guardians signature. Others will be required to sign a release form before entering.
- Visibly intoxicated guests will not be permitted entry, and will not receive a refund. If you've had too much to drink, you may not have the ability to solve the puzzles.
- Upon entering the room, you have exactly one hour.
In the words of one Nobel Prize-winning novelist, however, "fiction reveals truth that reality obscures."
Escape My Room is located in the U.S. Department of State Building on Loyola Avenue, between Girod and Julia Streets, one block from the Superdome and the Loyola-UPT Streetcar line. Our building is within walking distance or a short cab ride from most major hotels. The precise address of our museum waiting room will be provided to you following booking confirmation.
Two hours of free parking are provided in the adjacent lot.
Escape My Room does not allow walk-ins. Everyone visiting must have a ticket or otherwise will not be provided entry into the building.
Reservations for Escape My Room are currently full for this month. To stay abreast of openings and updates, please subscribe to our newsletter.


{% extends 'base.html' %} {% block content %} PELICAN PLUGINS A collection of plugins for pelican
A collection of plugins for pelican
Plugins are a way to add features to Pelican without having to directly modify the Pelican core


Difficult to Believe but Optimized Oct 20
Day 1 of studying backbonejs, and I found this gem in the source:
// A difficult-to-believe, but optimized internal dispatch function for // triggering events. Tries to keep the usual cases speedy (most internal // Backbone events have 3 arguments). var triggerEvents = function(events, args) { var ev, i = -1, l = events.length, a1 = args[0], a2 = args[1], a3 = args[2]; switch (args.length) { case 0: while (++i < l) (ev = events[i]).callback.call(ev.ctx); return; case 1: while (++i < l) (ev = events[i]).callback.call(ev.ctx, a1); return; case 2: while (++i < l) (ev = events[i]).callback.call(ev.ctx, a1, a2); return; case 3: while (++i < l) (ev = events[i]).callback.call(ev.ctx, a1, a2, a3); return; default: while (++i < l) (ev = events[i]).callback.apply(ev.ctx, args); } };
(ev = events[i])
is a nifty trick to save a few characters (and make the code more clear).
Why does this function exist at all?
Looking carefully, it seems that the function attempts to use call
rather than apply
, only using apply
as a last resort.
I think it's because call
is faster than apply
?
Cool stuff, shows just how much real-world Javascript performance is a dark art.


Somalia's Farmajo, PM face hurdles ahead of universal suffrage poll
by AGGREY MUTAMBOIn Summary
- Fighting Al-Shabaab, corruption and looking for financial stability, the call from many in the country in the past few days has been for the country to talk and remain united.
- The president is accused of seeking to weaken the federal system, whose fate will be determined once a new constitution is passed by June 2020.
- A law that bans clan parties has not discouraged local rivalry. There is conflict between the federal and state governments as Al-Shabaab profits from the chaos.
Somalia’s President Mohamed Farmajo and his Prime Minister Hassan Khaire spent most of their adult lives abroad; the former as a diplomat who later attained American citizenship (which he claims to be renouncing) and the latter as a refugee in Norway who became an executive at Soma Oil.
They now face probably the biggest challenge of their lives: to ensure universal suffrage in the 2020 election.
Fighting Al-Shabaab, corruption and looking for financial stability, the call from many in the country in the past few days has been for the country to talk and remain united.
The programmes listed in the transition programme, the stakeholders argued, are at risk as politicians bicker on civil liberties.
RIGHTS
It began 10 days ago when ex-President Sheikh Sharif Ahmed said he could expel the federal government from Mogadishu if it continues to restrict his rights.
Also Read
-
Kisumu County to support NMG's Kusi festival
-
Protesters torch DR Congo mayor's office after massacre
-
Death toll in Kenya landslides hits 52
-
Huge poll win for Hong Kong pro-democracy camp
Advertisement
Ahmed, a former leader of the Islamic Courts Union — deemed the precursor to Al-Shabaab — was President of Somalia from 2009 to 2012.
As leader of Himilo Qaran party, he was recently chosen the presumptive head of the Forum for National Parties (FNP), a loose coalition of six outfits opposed to Farmajo’s administration.
With their movement restricted by the federal government, they protested and succeeded in having a meeting with the president on Wednesday.
“We have agreed on holding elections on time so that there is no unnecessary extension of the administration,” Hassan Sheikh Mohamud, Farmajo’s predecessor and who is a key member of the FNP and leader of the Union for Peace and Development (UPD), said.
“We also agreed that there will be adherence to the constitution, protection of citizens’ rights and opening up of the political landscape. We do not want security to be politicised. We are united against Al-Shabaab.”
CLAN PARTIES
But disunity in Somalia transcends parties. A law that bans clan parties has not discouraged local rivalry. There is conflict between the federal and state governments as Al-Shabaab profits from the chaos.
“It’s good the leadership of the federal government and the Mogadishu-based opposition coalition agreed to tone down rhetoric,” Abdirashid Hashi, Director of Heritage Institute, a think-tank in Mogadishu, said.
“The government also needs to make peace with Puntland and Jubbaland. If it doesn’t do it now, it will in January.”
This week, the UN Secretary-General Special Representative to Somalia, James swan, was more categorical on the need for peace.
“Progress on the ambitious agenda for 2020 will require a high degree of political consensus,” Swan told the UN Security Council in New York on Thursday.
“This will entail dialogue and compromise between the central government and federal member states; between the executive and legislature; between current office holders and those out of power; and between elite leaders and elders, civil society organisations, women and youth groups who give voice to many Somalis.”
ACCUSATIONS
Unity calls, however, could mask the personal interests of Farmajo and Khaire.
The president is accused of seeking to weaken the federal system, whose fate will be determined once a new constitution is passed by June 2020.
He wants to seek re-election, becoming the first Somali President in four polls to retain the seat, if he does.
Khaire is accused of seeking to strengthen his business interests, particularly in oil and gas. As the supervisor of the government, he has been at the centre of Somalia’s nascent oil industry that is expected to auction oil blocks next month.
“They have failed and will be gone. States have problems with the federal government and al-Shabaab has resurged,” Abdalla Ibrahim, a Somali politician, said.
“They have a common goal. They want that debt relief to attract businesses because they know oil firms cannot do business with an indebted country. The companies will pay $100 million per block in advance for the 87 blocks. They certainly want to be there to get that cash,” he added referring to Khaire’s latest efforts to push for debt relief.
ROAD MAP
Khaire’s enthusiasts say he is the brains behind the rebuilding of Somalia.
“He is the cog that ties key institutions and improves priority areas,” Abdullahi Sh Nor, an academic, argued.
“There is a tight deadline, especially on the constitutional review, part of the transition programme, with specific outlines on roles of the central and the federal governments. This road map is under his management.”
The concern by FNP is that Farmajo has failed domestically, allowing Al-Shabaab to re-emerge, amid a worsening humanitarian situation and persistent road blocks in Mogadishu.


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Andela kigali making a difference
For the love of technology we are changing the world
Andela kigali making a difference
For the love of technology we are changing the world
Andela kigali making a difference
For the love of technology we are changing the world
Andela kigali making a difference
For the love of technology we are changing the world
Andela kigali making a difference
For the love of technology we are changing the world
Andela kigali making a difference
For the love of technology we are changing the world


I just got back from UDS-M.
Anyway, I got back and started mucking around on my Ubuntu 10.04 LTS laptop on my home network. Everything was great until – suddenly – the wireless connection drops out. I have two other devices (Android, OS X) that are connected just fine, so I know it’s probably not a network configuration issue.
I also have noticed this behaviour all the way through Karmic and since the Lucid beta.
I want to fix this bug. I want this to be my personal contribution to Maverick. I’m inspired, I’m pumped, but I don’t really know where to begin. I don’t even know how to file a good bug about this.
That’s where I need your help.
That bug points to Redhat bug 493018, which claims to be recently fixed.
http://static.mumak.net/syslog-from-disconnect.log
Dan, I'd feel uncomfortable trying out a new version of the kernel without having a way of reproducing the bug or any understanding of what is actually causing it.
Mary, at UDS I sat in rooms full of those people. They really are quite nice once you get to know them. I'm hoping that with this bug, I can understand it deeply enough that the calls to retest won't seem as arbitrary.
Let me know if you find a way to trigger it. Without a way to do that, I'm really not clued enough to guess where it might be getting stuck.
For me this only happends with Atheros Chipsets. Especially the AR5001X. Compiling madwifi and blacklisting ath5k/9k only helps a little. I also tried ditching NetworkManager and using /etc/network/interfaces for configuration. That didn't help either (wireless performance/stability still way below jaunty).
That leaves me with the conclusion that this a kernel issue. The exact same drivers (madwifi) work nicely in jaunty.
Will check in syslog next time it happens.
Thanks!
Anyways, first step is to identify the culprit: you could try looking through syslog to see if the driver's making a fuss about anything, if NetworkManager got fed up, dhclient not getting enough love, etc.
Does restarting NetworkManager help anything? (sudo restart network-manager)
Does reloading the driver help?


Mercenary Redfoot:
Shall we open the box? This lock should be no problem for me... There we go... That was too easy. Now, what do we have here?
Hmm? What's this powder...? Do I dare taste it? Ah, this must be Gludio Wheat Flour! It can be used for baking bread. You should be able to get a decent price at the market.
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Percent
public struct Percent : NumericUnit
A struct that represents a percentage
-
The value for
maximumFractionDigits
that will be used with the empty initialiserDeclaration
Swift
public let maximumFractionDigits: Int
-
The string that will be appended to the end of the string when the value does equal 1
Declaration
Swift
public let singularValueSuffix: String
-
The string that will be appended to the end of the string when the value does not equal 1
Declaration
Swift
public let pluralValueSuffix: String
-
Generates a human-friendly string for the given value.
This will use a
NumberFormatter
with anumberStyle
ofpercent
, meaning that values are multiflied by 100 before being formatter, e.g.0.79
is represented as79%
Throws
SourcePropertyUnitError.unsupportedType
if thevalue
cannot be cast to anNSNumber
Declaration
Swift
public func formattedString(for value: NSNumber) -> String
Parameters
value
The value to be formatted. Must be castable to
NSNumber
Return Value
The formatted string


{% extends "base.html" %} {% import 'bootstrap/wtf.html' as wtf %} {% block app_content %}
{{ _('Forgot Your Password?') }} {{ _('Click here to reset it') }}
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Background
After many years of travelling the world, Bronwyn decided to settle once more in the seaside city of Seattle. She returned to a previous career in marketing and spent several years herding cats (ahem, engineers) in the Architecture/Engingeering industry.
Career
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Interests
Bronwyn loves to read and sometimes wonders if it was such a good idea to have books available from the library on her phone. It makes it much harder to put the book away and get things done!
Fun Facts
- Named her daughter after her motorcycle
- Has been white water rafting in 4 countries and 2 states
- Hates chocolate


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2011 Abacus Graduates
| Adrienne Lee - UCSD | Alice Peng - University of the Pacific |
| Cynthia Wang - Duke University | Jennifer Soong - Carnegie Mellon University |
| Samantha Chen - University of Washington in St. Louis | Tammy Luan - UC Berkeley |
| Wesley Kuang - UC Irvine |


These release notes cover new developer and user-facing incompatibilities, important issues, features, and major improvements.
Hello,
In method open in JDBCInputFormat.java, while using dbConn.createStatement, the resultSetType & resultSetConcurrency are hardcoded. These two fields may vary with different JDBC drivers & hence it fails in a few cases like SAP HANA Jdbc driver. There are two variants of the method dbCon.createStatement, one with parameters & the other without parameters. Both should be supported.
Thanks & regards, Subhobrata
I was just trying to run a storm topology on flink using flink-storm. I got this exception -
{noformat} Caused by: java.lang.NoClassDefFoundError: org/mortbay/util/ajax/JSON at org.apache.hadoop.hdfs.web.WebHdfsFileSystem.jsonParse(WebHdfsFileSystem.java:325) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem$FsPathResponseRunner.getResponse(WebHdfsFileSystem.java:727) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem$AbstractRunner.runWithRetry(WebHdfsFileSystem.java:610) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem$AbstractRunner.access$100(WebHdfsFileSystem.java:458) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem$AbstractRunner$1.run(WebHdfsFileSystem.java:487) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem$AbstractRunner.run(WebHdfsFileSystem.java:483) at org.apache.hadoop.hdfs.web.WebHdfsFileSystem.listStatus(WebHdfsFileSystem.java:1277) {noformat}
My topology list some files on hdfs using webhdfs API. org.mortbay.util.ajax.JSON was included in the application uber jar. I noticed that flink loads the application jar in a child classloader. This is what most likely happened -
Ideally all the referenced classes should be available in the distribution jar so that these sort of issues may not occur.


layout: true
--- class: center, middle # Machine Learning reminders Guillaume Ligner - Côme Arvis --- # Outline of the class - Part 1: Machine Learning reminders - Part 2: The fundamentals of Neural Networks - Part 3: Convolutional Neural Networks - Part 4: Deep Learning for Natural Language Processing 1 - Part 5: Deep Learning for Natural Language Processing 2 - Part 6: Unsupervised Generative Neural Networks --- # How this course works ### Lecture ~ 35% of the course ### Coding sessions ~ 65% of the course ### Project: Deep Learning NLP challenge,released after part 4 - Work by pairs for labs - Evaluation on labs and project - Courses and labs on GitHub GitHub link: [https://github.com/Deep-Learning-courses/courses_labs](https://github.com/Deep-Learning-courses/courses_labs) Recommended reading: [deeplearningbook.org](http://www.deeplearningbook.org/) --- # What is Machine Learning?
###.center[“A computer program is said to learn from experience **$E$** with respect to some class of tasks **$T$** and performance measure **$P$**, if its performance at tasks in **$T$**, as measured by **$P$**, improves with experience **$E$**.”] > --
Tom Mitchell
--- # The Task $T$ - Usually tasks that are too difficult to solve with fixed programs written -- - Complex tasks examples: - Learning a robot how to walk - Autonomous cars -- - Most common task types: - Classification - Regression - Transcription - Machine translation - Denoising - ... --- # The Performance $P$ - Basically a quantitative measure of the performance of a machine learning algorithm -- - Usually, it is specific to a given task **$T$** and the desired behavior of a system -- - Examples: - Accuracy, Brier score, F1 score and AUC for classification tasks - MSE, MAE for regression tasks - ... -- - We frequently build custom metrics to suit design choices that depend on the application -- - Always evaluate these performance measures on a **test set** of data --- # The Experience $E$ - In general, machine learning algorithms are allowed to experience an entire **dataset** -- - Three main different experience types: - Unsupervised learning algorithms experience datasets containing **features** only -- - Supervised learning algorithms experience datasets containing features with a **label** or **target** -- - Reinforcement learning algorithms experience an **environment** -- - We usually describe a dataset with a **design matrix**, which contains a different multi-dimensional example in each row --- # Unsupervised learning - Usually the task is to find the "best" **representation** of the data - In many cases we are looking to preserve **as much information** from $X$ as possible while obtaining a **simpler** and more accessible representation of $X$ .center[
] .left-column50.small[ - t-SNE dimension reduction example ] .right-column50.small[ - Multivariate gaussian mixture model clustering example ] --- # Supervised learning: classification tasks .center[
] - A **classification task** aims to predict which class a data point is part of - In this case, the target value is **discrete** and belongs to a **finite number** of values - Some classification algorithms: logistic regression, perceptron, decision tree, KNN, SVM, **neural networks**, ... --- # Supervised learning: regression tasks .center[
] - A **regression task** aims to associate a real-valued prediction for each data point - Some regression algorithms: linear regression, decision tree, SVR, **neural networks**, ... --- # Machine learning: often a non-linear problem - Possible solution: find a **non-linear transformation** of the features and apply a **linear decision** in the new projected space .center[
] --- # Machine learning: often a non-linear problem - Other solution: design an algorithm that can intrinsically deal with non-linear interactions .center[
] .center[Decision tree algorithm as example] --- # Linear Regression as Maximum Likelihood - Let $\mathcal{D} = ${$ (\mathbf{x}_1, y_1), \dotsc, (\mathbf{x}_n, y_n)$} be a training set of i.i.d variables
with: $\mathbf{x}_i \in \mathbb{R}^p$ a feature sample and $y_i \in \mathbb{R}$ a target
- Our goal, in order to predict $Y$ given $\mathbf{X}$, is to estimate a conditional probability $P(Y | \mathbf{X} ; \mathbf{\theta})$ - Then, the conditional maximum likelihood estimator is: .center[$\hat{\mathbf{\theta}}_{ML} = \underset{\mathbf{\theta}}{\operatorname{argmax}} P(Y | \mathbf{X} ; \mathbf{\theta})$] --- # Linear Regression as Maximum Likelihood - Our linear model is assumed to take the following form: .center[$y_i = \mathbf{\theta}^\top \mathbf{x}_i + \sigma \epsilon_i$] .center[(we have volontary omitted the bias term here for simplicity)] - $\sigma \epsilon_i$ is the inherent noise in our data, unhandlable by our linear model, with some variance $\epsilon_i$ - We are going to make the asumption that the noise is gaussian and is normally distributed with mean 0: .center[$p(\epsilon_i) = \mathcal{N}(0, 1)$] - That implies: .center[$p(y_i | \mathbf{x}_i ; \mathbf{\theta}, \sigma) = \mathcal{N}(y_i ; \mathbf{\theta}^\top \mathbf{x}_i, \sigma^2) = \frac{1}{\sqrt{2 \pi \sigma^2}} \exp{(-\frac{(y_i - \langle {\mathbf{x_i}, \mathbf{\theta}} \rangle) ^2}{2 \sigma^2})}$] --- # Linear Regression as Maximum Likelihood - In addition, we have: $p(y_1, \dotsc, y_n | \mathbf{x}_1, \dotsc, \mathbf{x}_n ; \mathbf{\theta}, \sigma) = \prod_1^n p(y_i | \mathbf{x}_i ; \mathbf{\theta}, \sigma)$ - Maximize this conditional distribution is equivalent to minimizing the following negative log-likelihood: $$ \begin{equation} \- \sum_1^n \log p(y_i | \mathbf{x}_i ; \mathbf{\theta}, \sigma) \\\\ = - \sum_1^n \log \mathcal{N}(y_i ; \mathbf{\theta}^\top \mathbf{x}_i, \sigma^2) \\\\ = n \log \sigma + \frac{n}{2} \log(2 \pi) + \frac{1}{2} \sum_1^n \frac{(y_i - \langle {\mathbf{x}_i, \mathbf{\theta}} \rangle) ^2}{\sigma^2} \end{equation} $$ --- # Linear Regression as Maximum Likelihood - It naturally leads to the MSE minimization with respect to $\mathbf{\theta}$: .center[$\hat{\mathbf{\theta}} = \underset{\mathbf{\theta}}{\operatorname{argmin}} \frac{1}{2n} \sum_1^n (y_i - \langle {\mathbf{x}_i, \mathbf{\theta}} \rangle) ^2$] - Hence, it turns out that the maximum likelihood estimate for our linear model is the hyperplan which minimizes the sum of squared errors --- # Linear Regression: closed form Compact and closed forms: - We denote $\mathbf{X} \in \mathbb{R}^{n \times p}$ our training design matrix, and $\mathbf{y} \in \mathbb{R}^n$ our target vector - The minimization problem over $\mathbf{\theta}$ can be rewritten as: .center[find $\hat{\mathbf{\theta}} = \underset{\mathbf{\theta}}{\operatorname{argmin}} \frac{1}{2n} || \mathbf{y} - \mathbf{X} \mathbf{\theta} ||_2 ^2 = \underset{\mathbf{\theta}}{\operatorname{argmin}} J(\theta)$] with $J$ being our loss function we want to minimize - $\nabla J(\theta) = 0 \Leftrightarrow \frac{1}{n} \mathbf{X}^\top (\mathbf{X} \hat{\theta} - \mathbf{y}) = 0 \Leftrightarrow \hat{\theta} = (\mathbf{X}^\top \mathbf{X})^{-1} \mathbf{X}^\top \mathbf{y}$ The last equation is known as the **normal equation** - Problem: too expensive to compute if $n$ and $p$ are large - Usually we prefer an iterative approach: the **gradient descent** --- # Gradient descent - Iterative algorithm using the information from the gradient of the loss function to make a step - For step $t$, make a step forward the negative gradient:
.center[$\mathbf{\theta}^{t+1} := \mathbf{\theta}^{t} - \epsilon \nabla_\mathbf{\theta} J(\mathbf{\theta})$] .center[
]
.center.small[Unidimensional example of how the gradient descent uses the derivatives to reach to a minimum] --- # Gradient descent .center[
]
.center.small[Left : visualization of the loss function during the optimization of the parameter p] .center.small[Right : visualization of the linear predictions $x \cdot p$ fitting the target $t$] --- # Fitting the signal or fitting the noise - In the real world, machine learning is **not only** an optimization problem - Often, we collect noisy data:
measurement errors, outliers, extra randomness, ... - We want to capture the **underlying phenomenon** by being robust to the noise .center[
]
.center.small[We would like to retrieve the underlying phenomenon from the noisy data] --- # Overfitting and penalization - Modifying the loss to include a **penalization term**, here the penalization $L2$, or **ridge** penalization:
.center[$J(\mathbf{\theta}) = MSE_{train} + \lambda || \mathbf{\theta} ||^2_2$] .center[
] | Underfitting | Right fitting | Overfitting | | ------------- | ------------- | ------------- | | Excessive $\lambda$ | Appropriate $\lambda$ | $\lambda \to 0$ | | High bias | Optimal bias-variance tradeoff | High variance | | Model too simple | Optimal model capacity | Model too complex |
Left: underfitting case with an excessive $\lambda$ value. The learned function does not have the sufficient **capacity**
Center: underfitting case with an excessive $\lambda$ value. The learned function does not have the sufficient **capacity**
Right: overfitting case with $\lambda \to 0$ . The learned function does not have the sufficient **capacity** --- # Bias-variance tradeoff Bias-variance error decomposition:
.center[ $MSE\_{train} = \mathbb{E}[(\hat{\mathbf{\theta}}\_{train} - \mathbf{\theta})^2] = Bias(\hat{\mathbf{\theta}}\_{train})^2 + Var(\hat{\mathbf{\theta}}\_{train})$ $ = (\mathbb{E}[\hat{\mathbf{\theta}}\_{train}] - \mathbf{\theta})^2 + Var(\hat{\mathbf{\theta}}\_{train})$ ] - The bias measures the expected deviation from the true value of the parameter - The variance provides a measure of the deviation from the expected estimator value that any particular sampling of the data is likely to cause .center[
] --- # Cross-validation - **K-fold cross-validation** allows us to evaluate the **generalization error** of our model - Pick a **random partition** $I_1, \ldots, I_K$ of {$1, \ldots, n$}, with $n$ the total number of samples, where $|I_k| \approx \frac{n}{K}$ for any $k = 1, \ldots, K$ - For example, with $K=10$ : .center[
]
.center.small[We evaluate the model error $K$ times on the test sets, then we average them as our global generalization error] --- # Logistic Regression .center[
] - Traditional model in **binary classification**: - $prediction = \begin{cases} 1 & \quad \text{if } P(y=1|x) \geq 0.5\\\\ 0 & \quad \text{if } P(y=1|x) < 0.5 \end{cases}$ - The logistic regression is based on the **sigmoid function**: $\sigma(x)$ - $\sigma(x) : \mathbb{R} \mapsto ]0,1[$, thus used to modelize a probability distribution --- # Logistic Regression - We have: $\sigma(x) = \frac{1}{1 + \exp(-x)}$ and $\sigma'(x) =\sigma(x)(1 - \sigma(x))$ - More generally, $\mathbf{x}$ is usually a vector in $m$ dimensions - $P(y=1|\mathbf{x})$ modelized by $\sigma(\mathbf{\theta}^T\mathbf{x})$ $= \frac{1}{1 + \exp(-\mathbf{\theta}^T\mathbf{x})}$ with $\mathbf{x} \in \mathbb{R}^m$, the feature vector, $\mathbf{\theta} \in \mathbb{R}^m$ the parameter vector - Usually for binary classification we use the **binary cross-entropy** loss: $$J(\mathbf{\theta}) = -\frac{1}{n}\sum_{i=1}^{n}y_i\cdot\log(\sigma(\theta^T\mathbf{x_i})) + (1-y_i)\cdot\log(1-\sigma(\theta^T\mathbf{x_i}))$$ --- # Logistic Regression On the one hand, the logistic regression outputs non-linear predictions - When $\sigma(\theta^{T} x)$ is close to $0.5$, so $\theta^{T} x$ close to $0$, a **small increment** on $x$ leads to a **significant change in probability** - When $\sigma(\theta^T x)$ is already high or low, a much **bigger increment** on $x$ is needed to get the same amount of change in probability - Useful as it relates to real world phenomena, for instance: - physical performance with respect to training increments - happiness sensation with respect to wealth increments - ... --- # Logistic Regression On the other hand, the logistic regression is of **Generalized Linear Model** (GLM) type - The sigmoid function is not linear but the logistic regression model **boundary decision** function is **linear**: .center[ $prediction = \begin{cases} 1 \text{ if } \sigma\_{\theta}(\mathbf{x}) \geq 0.5\\\\ 0 \text{ if } \sigma\_{\theta}(\mathbf{x}) < 0.5 \end{cases}$ $\Leftrightarrow$ $ \begin{cases} 1 \text{ if } \mathbf{\theta}^T\mathbf{x} \geq 0\\\\ 0 \text{ if } \mathbf{\theta}^T\mathbf{x} < 0 \end{cases}$ ] .center[Separation between $1$s and $0$s predicted samples is the plane $\theta^T\mathbf{x} = 0$] --- # Logistic Regression - In weighted sum, $\text{feature} \times \text{weight} \le 0$ may be seen as an inhibitory neurotransmitter on neuron - Output signal is $\sigma(\beta_1\cdot x_1 + \dotsc + \beta_n\cdot x_n + bias) \mapsto \left]0, 1\right[$ .right-column50.small[
] .left-column50.small[
] --- # Deep Learning class outline - Understand Deep Learning - How/why it works - When to use it and when to prefer standard ML - Have a broad understanding of its applications --
- Practical matters - Implement neurals nets with different frameworks - Implement low level concepts such as backpropagation, regularization --- # Deep Learning - Can learn non-linear, hierarchical, abstract data representations - Differentiable functions allowing gradient based optimization - Complex models able to learn all sorts of input to output relations -- __Universal Approximation Theorem__: ###.center[“A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly”] > --
Ian Goodfellow
--- # Machine Learning weaknesses - Difficulty to engineer features from signals that are naturally processed by our brains, without abstraction .center[
] - Some examples : Tasks related to **audio** signals, computer **vision**, natural **language** processing --- # ML versus DL Machine Learning - A human worker thinks of smart features "relevant for the prediction" - Algorithm finds and then learns the relation from smart features to output .grey[Deep Learning] - .grey[ Combines and recombines the inputs from layer to layer ] - .grey[ Relation between final abstract features and output is linear ] .center[
] --- # ML versus DL .grey[Machine Learning] - .grey[A human worker thinks of smart features "relevant for the prediction"] - .grey[Algorithm finds and then learns the relation from smart features to output] Deep Learning - Combines and recombines the inputs from layer to layer - Relation between final abstract features and output is linear .center[
] --- # ML versus DL .grey[Machine Learning] - .grey[A human worker thinks of smart features "relevant for the prediction"] - .grey[Algorithm finds and then learns the relation from smart features to output] Deep Learning - Combines and recombines the inputs from layer to layer - Relation between final abstract features and output is linear .center[
] - classification: usually logistic regression comes last on final features - regression: usually linear regression comes last on final features --- # Libraries & Frameworks
.center[
] --- # Computation Graph - A Neural Networks is simply a math function - A very deeply nested function: - $f(x) = g[f_1(x), \dotsc, f_n(x)]$ - $f_1(x) = h_1[k_1(x), \dotsc, k_n(x)], \dotsc, f_n(x) = h_n[l_1(x), \dotsc, l_n(x)]$ - $k_1(x) = \dotsc $ and so on .right-column30.small[
] .left-column70[ - Goal is to unfold $f$ and into "elementary" operations: - activations functions: sigmoid, tanh, reLU... - matrix products - element-wise sums and products ] --- # Computation Graph - To vizualize the architecture of your network - To compute the gradient of the loss with respect to the network parameters - We know $\frac{\mathrm d output}{\mathrm d input}$ for any elementary operation in the graph - We use the __chain rule__ (see next course) to retrieve the derivative of the loss with respect to any parameter - To update the network parameters using the gradient --- # Computation Graph - A simple example
.center[
] --- # Computation Graph - A Deep Learning example .center[
]
.center.small[Computation graph of the loss of the Neural Net, with $l_2$ regularization]


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Discover the benefits of data analytics and make better decision regarding revenue, customer experince, and overall efficiency
Compnies
Templates
queries


Acknowledge:
Nobody likes to wait so personally I would not like to hang on a call or chat, if possible, try to keep your response time as quick as possible. One good practice is to use and alert that allows you to know when the interaction started and try to keep an eye on the time that has elapsed. Also refresh your clients if you need more time to troubleshoot their issue.
Greeting:
Though we live in a new era and people have a tendency to be more casual. It is extremely impolite to forget a good “hello”, just to reassure the customer that their existence is recognized.
Probing Questions:
This one is difficult because you need to balance out the number of questions you can ask before the client gets upset, therefore, I would prefer to pose quality questions rather than random questions to our customers.
Empathy/Sympathy
These two go with each other, it's really subtle, and most of the times what people believe is sympathy its a masked empathy with a drop of pity, which in CS is a little different. My opinion is that empathy its to understand customers journey (“How does he/she is able to end up with this question/issue/concern) and when you are able to recognize the empathy, then you can ask yourself (“How can I relate this kind of experience that my customer it's having with my owns?), and voila! A deadly combo I might add…
Resolution
This relies on your knowledge and support team and also on the willingness to keep on learning as the interactions became more complicated. We will never know everything but that does not mean that we are not going to try with all the tools available at our disposal.
Expectations
Don't promise if you are not willing to deliver, part of a great experience is the honesty behind our words, just because we want to avoid a difficult situation or not knowing the process by giving misinformation and false expectations.
Closing
Finally, but also important «The closure», This part of many agents I've trained to think it's just saying goodbye. Which is true, but midway, because we can close and reopen. What I mean by this is as soon as we resolved the concern and customer it's happy and satisfied with the resolution he/she could also have a different issue/problem. So to seal the deal, I always encourage on offering additional assistance.
Tools/Data:
If you don't have the tools then create/purchase them and if you don't have a data knowledge hub, then create it! There is always a solution, all you have to do is have some vision.
Questions:
When in doubt, ask, everyone has the right to be wrong at some point even more than once so don't be frustrated over a doubt regarding a process, just be willing to ask questions in the correct manner and you will be ok.
Patience:
Don't rush yourself to find the quickest solution, take your time to check all the possibilities, just don't turn it into a research paper.
Double check everything:
It's not an OCD thing. I'm writing this part from experience. I prefer to be certain of what I am saying or doing before I misinform or confuse my customers.
Know your product or service:
This is a key point to really understand what type of client support you would like to provide. It's not just a question of external knowledge; it's a question of, “How does it work?” Is there a better way to deal with that? If so, how can we enhance our own tool? Such wandering gives you an incredible amount of teachings, which can then be used to figure out a solution.
Know the pro and cons of it:
I draw upon the earlier point. Part of an excellent tool is that you can make it better. We can do more and bring our solution to the next level of success. It is extremely important to know what you can highlight and what still needs to be tweaked to be portrayed as a feature.
Know your team and how it was developed:
Although I don't recommend stalking every one of your teammates. It is primordial to know a little bit about them, just so you can develop and strengthen ties with one another to increase a new level of trust in the workplace.
Know the company vision:
I learned this from my former team leader, who always said, "How can you do the best you can if you don't even know why you're here?" Then I realize, I still didn't know the path of my company with which I worked, it's really important to know so you can give the best of yourself.
Believe in your team:
Have faith in your teammates! Keep in mind that you have a support group and they are there to assist you. Never underestimate a fellow man/woman and his/her incredible minds, go forward and strive to succeed.
Get on the same page:
I learn this when I continually doubt the goal to be achieved for a particular action or task, “when in doubt, ask another question”.
Learn from everyone, not only within the confines of your team.
It's great working with your team, but also to try to extend yourself to the entire business. Learn about the people behind the ideas that shape the business's future.
Be receptive to constructive criticism:
Once I have received feedback, I always try to detach myself from my feelings to understand where the feedback comes from. This particular approach allows me to better understand their thinking and how it can help me improve.
If you find yourself comfortable, it's time to innovate:
You might think that innovation in the client support space is its impossible right?. Well, think again, as you grow and became and expert in the process you are going to start finding new ways to be more efficient and improve the quality upon the service. If you feel that your job is becoming a routine and you are unable to find any improvement, it means that you reach a wall. Therefore, you will have to start thinking of a new approach, does not matter what kind just gets those brain juices going.
Peer to peer reviews
As a matter of fact, I found this while I was at QA. I realized that not only is it good to get feedback from your leadership, but also from your peers. From my perspective, it's the most important thing, because we provide the same type of service. We will be able to understand one another and come up with specific opportunities for improvement.
Try new things:
Personally, it's a way for me to get away from the monotony and move creativity forward. When you learn something new and sound unrelated to your field of work, it causes something in your brain. Your brain starts thinking differently, no matter what you've learned, it's important to break out of the routine.
Try to live in the current and aim for the future:
That's part of my core values, just because I have a bad day doesn't mean I'll always have a bad day. Moving forward seems simple, but it may be difficult to take action and make it happen.
Mistakes are weapons, use them properly:
There is a correlation between the number of errors and the number of achievements you are able to obtain.


Guardian.Hooks behaviour
This module helps to hook into the lifecycle of authentication.
Callbacks
Specs:
- after_mint(resource :: term, type :: atom, claims :: Map, token :: String.t) :: term
Specs:
- after_sign_in(conn :: Plug.Conn.t, location :: atom | nil) :: term
Specs:
- before_mint(resource :: term, type :: atom, claims :: Map) :: term
Specs:
- before_sign_out(conn :: Plug.Conn.t, location :: atom | nil) :: term


This project involved creating a fully-functional photo sharing/tagging website.
One of the highlights included a javascript-powered photo lookup feature that immediately displayed the photos that matched the search terms.
Users could create accounts and login; if there was an error, a message would be displayed accordingly.


USER DETAILS
Customer ID
Age
Contact
-
Education Status
Select
Basic 4th Year
Basic 6th Year
High School
Illiterate
Professional Course
Bachelors
Masters
Last Contacted Before
Marital Status
Select
Married
Single
Divorced/Widowed
Personal Loan
Select
Yes
No
Job
Select
Management
Technician
Enterpreneur
Blue-Collar
Retired
Admin
Service
Self Employed
Unemployee
House Maid
Student
Housing Loan
Select
Yes
No
Credit Default
Select
Yes
No
Previous Attempts :
Balance
Previous Outcome :
---Select---
Failure
Other
Sucess
Output :
Scored Probability :
Output :
Scored Probability :
Output :
Scored Probability :
Output :
Scored Probability :
Output :
Scored Probability :
Output :
Scored Probability :


What is the size of Rhombus?
Choose color for Even position
Blue
Red
Orange
Choose color for Odd position
Pink
Green
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Choose symbol for Rhombus
*
$
@
Display Rhombus


Leading and validating Industrial Project
Reduce your wastes and improve the projects efficiency
Visualize forecasts and predict the next step
Automate repetitive business processes to let your workforce focuse in what it matters


Gecode::Iter::Ranges::ComplVal< I > Class Template Reference
[Range iterators]
Inherits Gecode::Iter::Ranges::MinMax.
Detailed Description
template<class I>
class Gecode::Iter::Ranges::ComplVal< I >
Range iterator for computing the complement (described by values).
The complement is computed with respect to a given universe set provided as arguments upon initialization. The universe must always be a superset!
Definition at line 89 of file ranges-compl.icc.
Constructors and initialization | |
| ComplVal (void) | |
| Default constructor. | |
| ComplVal (int umin, int umax, I &i) | |
| Initialize with iterator i. | |
| void | init (int umin, int umax, I &i) |
| Initialize with iterator i. | |
Iteration control | |
| void | operator++ (void) |
| Move iterator to next range (if possible). | |
Protected Member Functions | |
| void | start (void) |
| Initialize. | |
Protected Attributes | |
| int | UMIN |
| Values describing the universe set. | |
| int | UMAX |
| I | i |
| Iterator to compute complement for. |
Constructor & Destructor Documentation
template<class I>
| Gecode::Iter::Ranges::ComplVal< I >::ComplVal | ( | void | ) | [inline] |
template<class I>
| Gecode::Iter::Ranges::ComplVal< I >::ComplVal | ( | int | umin, | |
| int | umax, | |||
| I & | i | |||
| ) | [inline] |
Member Function Documentation
template<class I>
| void Gecode::Iter::Ranges::ComplVal< I >::start | ( | void | ) | [inline, protected] |
template<class I>
| void Gecode::Iter::Ranges::ComplVal< I >::init | ( | int | umin, | |
| int | umax, | |||
| I & | i | |||
| ) | [inline] |
template<class I>
| void Gecode::Iter::Ranges::ComplVal< I >::operator++ | ( | void | ) | [inline] |
Member Data Documentation
template<class I>
int Gecode::Iter::Ranges::ComplVal< I >::UMIN [protected] |
template<class I>
int Gecode::Iter::Ranges::ComplVal< I >::UMAX [protected] |
Definition at line 92 of file ranges-compl.icc.
template<class I>
I Gecode::Iter::Ranges::ComplVal< I >::i [protected] |
The documentation for this class was generated from the following file:
- gecode/iter/ranges-compl.icc (Revision: 4973)


--- title: Action Recognition keywords: fastai sidebar: home_sidebar summary: "Implementation of various architectures to solve the UCF 101 actions dataset" description: "Implementation of various architectures to solve the UCF 101 actions dataset" nb_path: "nbs/index.ipynb" ---
It is based on the implementations found on Action Recognition.
I try to keep with updated architectures that come out. Right now transformers are all we need... Follow @lucidrains to get the next attention based model ASAP.
First install fastai
:
$ pip install fastcore fastai
Results are computed on a random splut 80%/20%. Using fastai2
built-in fit_one_cycle
training.
This package also provides function to download nad process the video dataset into multiple frames.


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