Blob comparison utility
blob-compare
is a small library designed to provide some useful methods to compare two blobs in browser with various methods :
- Comparison on size of two blobs
- Comparison on types of two blobs
- Comparison on data types based on magic numbers of two blobs
- Comparison byte to byte on full data or sliced subsets
- A configurable combination of any of above to evaluate blobs or parts of blobs equality
Tool rely on native browsers buffer implementations and will likely work on any modern browser. It have been tested and benchmarked on Chrome, Firefox and Edge.
To provide better performance, blob-compare
automatically relies on web workers if available when performing operations on blobs.
Installation
You can install it from npm
or yarn
:
npm install blob-compare
yarn add blob-compare
Then, simply require/import it to use it :
const blobCompare = require('blob-compare').default;
// or
import blobCompare from 'blob-compare';
For browser direct usage, blob-compare
can be required as a script from any CDN mirroring NPM or Github, for instance :
<script src="https://cdn.jsdelivr.net/npm/blob-compare@latest"></script>
<script src="https://unpkg.com/blob-compare@latest"></script>
<script<EMAIL_ADDRESS>A global blobCompare
will be automatically set after script was downloaded.
Quick reference
See documentation for a full reference.
Conversion tools
All conversions are run asynchronously.
| Method | Description |
|---|---|
blobCompare::toArrayBuffer |
Converts a blob to an ArrayBuffer. it can be optionnally chunked and assigned to a web worker. Conversion is run asynchronously. |
blobCompare::toBinaryString |
Converts a blob to a BinaryString. it can be optionnally chunked and assigned to a web worker. Conversion is run asynchronously. |
Comparison tools
| Method | Description | Sync/Async |
|---|---|---|
blobCompare::sizeEqual |
Compares size of two blobs | sync |
blobCompare::typeEqual |
Compares types of two blobs. Types are not really reliable as they can be tricked when creating a blob | sync |
blobCompare::magicNumbersEqual |
Compares magic numbers of two blobs. A quick comparison is done, therefore weird data types may not be compared with 100% accuracy. In that case, simply clone repo and override this function to fit your needs | async |
blobCompare::bytesEqualWithArrayBuffer |
Converts blobs or chunk blobs to ArrayBuffers and performs a byte to byte comparison | async |
blobCompare::bytesEqualWithBinaryString |
Converts blobs or chunk blobs to BinaryString and performs a byte to byte comparison | async |
blobCompare::isEqual |
The swiss army knife to bundle multiple comparison methods above in one single call | async |
Usage examples
// assuming img1 and img2 are two blobs vars
/**
* Fully compare two blobs with default methods configuration
*/
blobCompare.isEqual(img1, img2).then(res...)
/**
* Comparing only file types
*/
blobCompare.isEqual(img1, img2, {methods: ['magic']}).then(res...)
// or
blobCompare.magicNumbersEqual(img1, img2).then(res => ...)
/**
* Compare file types AND the last 100 bytes of blobs
* Never find a use case ^^
*/
blobCompare.isEqual(img1, img2, {
methods: ['bytes'],
sizes: [-100]
}).then(res => ...)
/**
* Compare file types OR the last 100 bytes of blobs
*/
blobCompare.isEqual(img1, img2, {
methods: ['bytes'],
sizes: [-100],
partial: true
}).then(res => ...)
To speed up things, isEqual
with its default configuration checks first if sizes are equal, then types, then magic numbers and finally performs a byte to byte comparison to ensure blobs equality.
All methods working on bytes comparison are asynchronous, use web workers by default if available and works very well with async/await
syntax.
About performance
Trying to compare blobs can be tricky though the only real pitfall is most likely to run out of memory on the VM. There's not much to do with it except working only on smaller data chunks and use device storage like IndexedDb to buffer the unprocessed chunks.
Web workers
Another caveat is likely to consume device CPU to perform operations on blobs. Web workers can be very helpful in this case. blob-compare
is enabling web workers by default for two major reasons :
- A worker is constructed each time a blob needs to be converted to raw binary data or array buffer. On a multi-threaded system, it allows efficient concurrency
- Huge blobs operations won't freeze the main thread
The cons is that processing will be slower due to the copy operation. A workaround could be to use directly ArrayBuffers
and the blobCompare.compareBuffers
method that take advantage of the transferable interface of an ArrayBuffer.
Disabling web workers can also help prevent memory issues in some cases.
Benchmarking
Repo is quite heavy due to fixtures. I've tried to implement some automated bechnmarks around karma and benchmark.js but I'm quickly hitting some troubles with larger blobs, event with small blobs on Edge.
I'm not sure that I'm doing right with my benchmarks _Oo_
If I find some time, I may try on jsPerf.
Anyway, after cloning and installing this repository, you can play with fixtures and benchmarks (they are removed from npm version).
Just bash npm run bench:all
to run them into Chrome, Firefox and Edge. You can also make ChromeHeadless accessible and use npm run bench
Latest results for Chrome, Firefox and Edge are stored in results.json
Documentation
Methods are fully documented and docs are available on github pages.
Issues and PRs
Any bugs and issues can be filed on the github repository.
You are free and very welcome to fork the project and submit any PR to fix or improve blob-compare
.
Changelog
- 1.1.0 : Add magic numbers in default comparison methods as type value can be falsy.
blobCompare.isEqual
now returns immediately when a falsy value is encountered or at first successful comparison if partial option is set totrue
- 1.0.1 : Fix package content


{% extends 'shared/project_detail.html' %} {% load utils %} {% load static %} {% block menu %} {% with selected='services' %} {{ block.super }} {% endwith %} {% endblock %} {% block navtitle %} {% with project_delete_view='service_delete' icon='cogs' to_url='/services' header=object.name|add:' service' subheader='owned by '|add:object.owner %} {{ block.super }} {% endwith %} {% endblock %} {% block content %} {% with project_type='services' project_type_singular='service' project_checklist_view='service_checklist' project_update_view='service_update' %} {{ block.super }} {% endwith %} {% endblock %} {% block more_details %} {% if object.pagerduty_service_id %}
The classification of the issues is based on the number of days the event has been reported during a specific time window.
| stale | seen 14 out of last 14 days |
| decaying | seen 7 out of last 14 days |
| spoiled | seen 4 out of last 7 days |
| {{ sentry_issue.instance.short_id }} |
{{ sentry_issue.instance.title }}
|
{{ sentry_issue.instance.category }}
|
{{ sentry_issue.instance.events|short_int_word }} events
{{ sentry_issue.instance.users|short_int_word }} users
|
{% with height=32 width=5 results=sentry_issue.histogram %} {% include 'shared/histogram.html' %} {% endwith %} |


Getting Started with the 3D Balance Ball Environment
This tutorial walks through the end-to-end process of opening an ML-Agents example environment in Unity, building the Unity executable, training an agent in it, and finally embedding the trained model into the Unity environment.
ML-Agents includes a number of example environments which you can examine to help understand the different ways in which ML-Agents can be used. These environments can also serve as templates for new environments or as ways to test new ML algorithms. After reading this tutorial, you should be able to explore and build the example environments.
This walk-through uses the 3D Balance Ball environment. 3D Balance Ball contains a number of platforms and balls (which are all copies of each other). Each platform tries to keep its ball from falling by rotating either horizontally or vertically. In this environment, a platform is an agent that receives a reward for every step that it balances the ball. An agent is also penalized with a negative reward for dropping the ball. The goal of the training process is to have the platforms learn to never drop the ball.
Let's get started!
Installation
In order to install and set up ML-Agents, the Python dependencies and Unity, see the installation instructions.
Understanding a Unity Environment (3D Balance Ball)
An agent is an autonomous actor that observes and interacts with an environment. In the context of Unity, an environment is a scene containing an Academy and one or more Brain and Agent objects, and, of course, the other entities that an agent interacts with.
Note: In Unity, the base object of everything in a scene is the GameObject. The GameObject is essentially a container for everything else, including behaviors, graphics, physics, etc. To see the components that make up a GameObject, select the GameObject in the Scene window, and open the Inspector window. The Inspector shows every component on a GameObject.
The first thing you may notice after opening the 3D Balance Ball scene is that it contains not one, but several platforms. Each platform in the scene is an independent agent, but they all share the same brain. 3D Balance Ball does this to speed up training since all twelve agents contribute to training in parallel.
Academy
The Academy object for the scene is placed on the Ball3DAcademy GameObject.
When you look at an Academy component in the inspector, you can see several
properties that control how the environment works. For example, the
Training and Inference Configuration properties set the graphics and
timescale properties for the Unity application. The Academy uses the
Training Configuration during training and the Inference Configuration
when not training. (Inference means that the agent is using a trained model
or heuristics or direct control — in other words, whenever not training.)
Typically, you set low graphics quality and a high time scale for the
Training configuration and a high graphics quality and the timescale to
1.0
for the Inference Configuration .
Note: if you want to observe the environment during training, you can adjust the Inference Configuration settings to use a larger window and a timescale closer to 1:1. Be sure to set these parameters back when training in earnest; otherwise, training can take a very long time.
Another aspect of an environment to look at is the Academy implementation. Since the base Academy class is abstract, you must always define a subclass. There are three functions you can implement, though they are all optional:
- Academy.InitializeAcademy() — Called once when the environment is launched.
- Academy.AcademyStep() — Called at every simulation step before Agent.AgentAction() (and after the agents collect their observations).
- Academy.AcademyReset() — Called when the Academy starts or restarts the simulation (including the first time).
The 3D Balance Ball environment does not use these functions — each agent resets itself when needed — but many environments do use these functions to control the environment around the agents.
Brain
The Ball3DBrain GameObject in the scene, which contains a Brain component, is a child of the Academy object. (All Brain objects in a scene must be children of the Academy.) All the agents in the 3D Balance Ball environment use the same Brain instance. A Brain doesn't store any information about an agent, it just routes the agent's collected observations to the decision making process and returns the chosen action to the agent. Thus, all agents can share the same brain, but act independently. The Brain settings tell you quite a bit about how an agent works.
The Brain Type determines how an agent makes its decisions. The External and Internal types work together — use External when training your agents; use Internal when using the trained model. The Heuristic brain allows you to hand-code the agent's logic by extending the Decision class.  If none of these types of brains do what you need, you can implement your own CoreBrain to create your own type.
In this tutorial, you will set the Brain Type to External for training; when you embed the trained model in the Unity application, you will change the Brain Type to Internal.
Vector Observation Space
Before making a decision, an agent collects its observation about its state in the world. ML-Agents classifies vector observations into two types: Continuous and Discrete. The Continuous vector observation space collects observations in a vector of floating point numbers. The Discrete vector observation space is an index into a table of states. Most of the example environments use a continuous vector observation space.
The Brain instance used in the 3D Balance Ball example uses the Continuous
vector observation space with a State Size of 8. This means that the
feature vector containing the agent's observations contains eight elements:
the x
and z
components of the platform's rotation and the x
, y
, and z
components of the ball's relative position and velocity. (The observation
values are defined in the agent's CollectObservations()
function.)
Vector Action Space
An agent is given instructions from the brain in the form of actions. Like
states, ML-Agents classifies actions into two types: the Continuous
vector action space is a vector of numbers that can vary continuously. What
each element of the vector means is defined by the agent logic (the PPO
training process just learns what values are better given particular state
observations based on the rewards received when it tries different values).
For example, an element might represent a force or torque applied to a
RigidBody
in the agent. The Discrete action vector space defines its
actions as a table. A specific action given to the agent is an index into
this table.
The 3D Balance Ball example is programmed to use both types of vector action
space.
You can try training with both settings to observe whether there is a
difference. (Set the Vector Action Space Size
to 4 when using the discrete
action space and 2 when using continuous.)
Agent
The Agent is the actor that observes and takes actions in the environment. In the 3D Balance Ball environment, the Agent components are placed on the twelve Platform GameObjects. The base Agent object has a few properties that affect its behavior:
- Brain — Every agent must have a Brain. The brain determines how an agent makes decisions. All the agents in the 3D Balance Ball scene share the same brain.
- Visual Observations — Defines any Camera objects used by the agent to observe its environment. 3D Balance Ball does not use camera observations.
- Max Step — Defines how many simulation steps can occur before the agent decides it is done. In 3D Balance Ball, an agent restarts after 5000 steps.
- Reset On Done — Defines whether an agent starts over when it is finished. 3D Balance Ball sets this true so that the agent restarts after reaching the Max Step count or after dropping the ball.
Perhaps the more interesting aspect of an agent is the Agent subclass implementation. When you create an agent, you must extend the base Agent class. The Ball3DAgent subclass defines the following methods:
- Agent.AgentReset() — Called when the Agent resets, including at the beginning of a session. The Ball3DAgent class uses the reset function to reset the platform and ball. The function randomizes the reset values so that the training generalizes to more than a specific starting position and platform attitude.
- Agent.CollectObservations() — Called every simulation step. Responsible for
collecting the agent's observations of the environment. Since the Brain
instance assigned to the agent is set to the continuous vector observation
space with a state size of 8, the
CollectObservations()
must callAddVectorObs
8 times. - Agent.AgentAction() — Called every simulation step. Receives the action chosen
by the brain. The Ball3DAgent example handles both the continuous and the
discrete action space types. There isn't actually much difference between the
two state types in this environment — both vector action spaces result in a
small change in platform rotation at each step. The
AgentAction()
function assigns a reward to the agent; in this example, an agent receives a small positive reward for each step it keeps the ball on the platform and a larger, negative reward for dropping the ball. An agent is also marked as done when it drops the ball so that it will reset with a new ball for the next simulation step.
Building the Environment
The first step is to open the Unity scene containing the 3D Balance Ball environment:
- Launch Unity.
- On the Projects dialog, choose the Open option at the top of the window.
- Using the file dialog that opens, locate the
unity-environment
folder within the ML-Agents project and click Open. - In the
Project
window, navigate to the folderAssets/ML-Agents/Examples/3DBall/
. - Double-click the
Scene
file to load the scene containing the Balance Ball environment.
Since we are going to build this environment to conduct training, we need to set the brain used by the agents to External. This allows the agents to communicate with the external training process when making their decisions.
- In the Scene window, click the triangle icon next to the Ball3DAcademy object.
- Select its child object
Ball3DBrain
. - In the Inspector window, set Brain Type to
External
.
Next, we want the set up scene to play correctly when the training process launches our environment executable. This means:
- The environment application runs in the background
- No dialogs require interaction
- The correct scene loads automatically
- Open Player Settings (menu: Edit > Project Settings > Player).
- Under Resolution and Presentation:
- Ensure that Run in Background is Checked.
- Ensure that Display Resolution Dialog is set to Disabled.
- Open the Build Settings window (menu:File > Build Settings).
- Choose your target platform.
- (optional) Select “Development Build” to log debug messages.
- If any scenes are shown in the Scenes in Build list, make sure that the 3DBall Scene is the only one checked. (If the list is empty, than only the current scene is included in the build).
- Click Build:
a. In the File dialog, navigate to the
python
folder in your ML-Agents directory. b. Assign a file name and click Save.
Training the Brain with Reinforcement Learning
Now that we have a Unity executable containing the simulation environment, we
can perform the training. To first ensure that your environment and the Python
API work as expected, you can use the python/Basics
Jupyter notebook.
This notebook contains a simple walkthrough of the functionality of the API.
Within Basics
, be sure to set env_name
to the name of the environment file
you built earlier.
Training with PPO
In order to train an agent to correctly balance the ball, we will use a Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). This is a method that has been shown to be safe, efficient, and more general purpose than many other RL algorithms, as such we have chosen it as the example algorithm for use with ML-Agents. For more information on PPO, OpenAI has a recent blog post explaining it.
To train the agents within the Ball Balance environment, we will be using the python
package. We have provided a convenient Python wrapper script called learn.py
which accepts arguments used to configure both training and inference phases.
We will pass to this script the path of the environment executable that we just built. (Optionally) We can
use run_id
to identify the experiment and create a folder where the model and summary statistics are stored. When
using TensorBoard to observe the training statistics, it helps to set this to a sequential value
for each training run. In other words, "BalanceBall1" for the first run,
"BalanceBall2" or the second, and so on. If you don't, the summaries for
every training run are saved to the same directory and will all be included
on the same graph.
To summarize, go to your command line, enter the ml-agents
directory and type:
python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train
Note: If you're using Anaconda, don't forget to activate the ml-agents environment first.
The --train
flag tells ML-Agents to run in training mode. env_file_path
should be the path to the Unity executable that was just created.
Observing Training Progress
Once you start training using learn.py
in the way described in the previous section, the ml-agents
folder will
contain a summaries
directory. In order to observe the training process
in more detail, you can use TensorBoard. From the command line run:
tensorboard --logdir=summaries
Then navigate to localhost:6006
.
From TensorBoard, you will see the summary statistics:
- Lesson - only interesting when performing curriculum training. This is not used in the 3D Balance Ball environment.
- Cumulative Reward - The mean cumulative episode reward over all agents. Should increase during a successful training session.
- Entropy - How random the decisions of the model are. Should slowly decrease
during a successful training process. If it decreases too quickly, the
beta
hyperparameter should be increased. - Episode Length - The mean length of each episode in the environment for all agents.
- Learning Rate - How large a step the training algorithm takes as it searches for the optimal policy. Should decrease over time.
- Policy Loss - The mean loss of the policy function update. Correlates to how much the policy (process for deciding actions) is changing. The magnitude of this should decrease during a successful training session.
- Value Estimate - The mean value estimate for all states visited by the agent. Should increase during a successful training session.
- Value Loss - The mean loss of the value function update. Correlates to how well the model is able to predict the value of each state. This should decrease during a successful training session.
Embedding the Trained Brain into the Unity Environment (Experimental)
Once the training process completes, and the training process saves the model
(denoted by the Saved Model
message) you can add it to the Unity project and
use it with agents having an Internal brain type.
Note: Do not just close the Unity Window once the Saved Model
message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you simply close the window manually, the .bytes file containing the trained model is not exported into the ml-agents folder.
Setting up TensorFlowSharp Support
Because TensorFlowSharp support is still experimental, it is disabled by
default. In order to enable it, you must follow these steps. Please note that
the Internal
Brain mode will only be available once completing these steps.
- Make sure the TensorFlowSharp plugin is in your
Assets
folder. A Plugins folder which includes TF# can be downloaded here. Double click and import it once downloaded. You can see if this was successfully installed by checking the TensorFlow files in the Project tab underAssets
->ML-Agents
->Plugins
->Computer
- Go to
Edit
->Project Settings
->Player
- For each of the platforms you target
(
PC, Mac and Linux Standalone
,iOS
orAndroid
):- Go into
Other Settings
. - Select
Scripting Runtime Version
toExperimental (.NET 4.6 Equivalent)
- In
Scripting Defined Symbols
, add the flagENABLE_TENSORFLOW
. After typing in, press Enter.
- Go into
- Go to
File
->Save Project
- Restart the Unity Editor.
Embedding the trained model into Unity
- The trained model is stored in
models/<run-identifier>
in theml-agents
folder. Once the training is complete, there will be a<env_name>.bytes
file in that location where<env_name>
is the name of the executable used during training.- Move
<env_name>.bytes
frompython/models/ppo/
intounity-environment/Assets/ML-Agents/Examples/3DBall/TFModels/
.
- Move
- Open the Unity Editor, and select the
3DBall
scene as described above. - Select the
Ball3DBrain
object from the Scene hierarchy. - Change the
Type of Brain
toInternal
. - Drag the
<env_name>.bytes
file from the Project window of the Editor to theGraph Model
placeholder in the3DBallBrain
inspector window. - Press the Play button at the top of the editor.
If you followed these steps correctly, you should now see the trained model being used to control the behavior of the balance ball within the Editor itself. From here you can re-build the Unity binary, and run it standalone with your agent's new learned behavior built right in.


You must be loading this file over an http server for this example to work
First Name:
Below there should be a message that a load has failed.


Tag any section of youtube video.
Not only you can repeat any section of youtube video, you can also add note for it.
Tag Area
Right now this is a testing area, and I will add account machanism for user to add their own tags.
It's a free AB repeater for language learning...
Copy and paste the youtube video URL link into this website, and then you can repeat any section of this video.
Previous
Next
This is a AB repeater, and you can follow the instruction as below:
A: Tag start position
B: Tag end position
C: Clear start and end position


| Class | Description |
|---|---|
| AlternatingLeastSquare |
Sample Alternating Least Square application.
|
| AlternatingLeastSquare.CalculateNextMatrix |
DoFn for calculating next matrix at each iteration.
|
| AlternatingLeastSquare.CreateInitialMatrix |
A DoFn that creates an initial matrix.
|
| AlternatingLeastSquare.ParseLine |
Method for parsing the input line.
|
| AlternatingLeastSquare.TrainingDataCombiner |
Combiner for the training data.
|
| AlternatingLeastSquare.UngroupSingleVectorList |
A DoFn that relays a single vector list.
|
| AlternatingLeastSquare.UpdateUserAndItemMatrix |
Composite transform that wraps the transforms inside the loop.
|
| AlternatingLeastSquareInefficient |
Sample Alternating Least Square application.
|
| AlternatingLeastSquareInefficient.UpdateUserAndItemMatrix |
Composite transform that wraps the transforms inside the loop.
|
| Broadcast |
Sample Broadcast application.
|
| MultinomialLogisticRegression |
Sample Multinomial Logistic Regression application.
|
| MultinomialLogisticRegression.ApplyGradient |
DoFn class that applies the gradient to the model.
|
| MultinomialLogisticRegression.CalculateGradient |
Calculate Gradient.
|
| MultinomialLogisticRegression.CombineFunction |
Combine Function for two Double arrays.
|
| MultinomialLogisticRegression.CombineFunctionForIterable |
Combine Function for Iterable of gradients.
|
| MultinomialLogisticRegression.UpdateModel |
+ Composite transform that wraps the transforms inside the loop.
|
| NetworkTraceAnalysis |
An app that analyzes data flow from network trace.
|
| PartitionWordsByLength |
Partition words by length example.
|
| PerKeyMedian |
IP stat analysis example used in skew experiment.
|
| PerPercentileAverage |
Per percentile statistics application.
|
| SimpleSumSQL |
A simple SQL application.
|
| WindowedWordCount |
A Windowed WordCount application.
|
| WordCount |
WordCount application.
|
| WriteOneFilePerWindow |
This class is brought from beam/examples/common/WriteOneFilePerWindow.java.
|
| WriteOneFilePerWindow.PerWindowFiles |
A
FileBasedSink.FilenamePolicy produces a base file name for a write based on metadata about the data
being written. |
Copyright © 2019 The Apache Software Foundation. All rights reserved.


public final class
SystemPermission
extends java.security.BasicPermission
| Permission | Description | Risk |
|---|---|---|
| "jmx" "control" | Controls the ability of JMX clients to control Derby and view security sensitive attributes through Derby's MBeans. | JMX clients may be able to change the state of the running system |
| "jmx" "monitor" |
Controls the ability of JMX clients to
monitor Derby through Derby's MBeans, such as viewing number of current connections and
configuration settings.
Note: security related settings require
control
action on
jmx
|
JMX clients can see information about a runing system including software versions. |
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String
|
CONTROL
Action (
"control"
) to perform control actions through JMX
on engine, server or jmx.
|
static java.lang.String
|
ENGINE
Permission target name (
"engine"
) for actions applicable
to the core database engine.
|
static java.lang.String
|
JMX
Permission target name (
"jmx"
) for actions applicable
to management of Derby's JMX MBeans.
|
static java.lang.String
|
MONITOR
Action (
"monitor"
) to perform monitoring actions through JMX
on engine and server.
|
static java.lang.String
|
SERVER
Permission target name (
"server"
) for actions applicable
to the network server.
|
static java.lang.String
|
SHUTDOWN
The server and engine shutdown action (
"shutdown"
).
|
| Constructor and Description |
|---|
SystemPermission
(java.lang.String name,
java.lang.String actions)
Creates a new SystemPermission with the specified name.
|
| Modifier and Type | Method and Description |
|---|---|
boolean
|
equals
(java.lang.Object other)
Does this permission equal another object.
|
java.lang.String
|
getActions
()
Return the permission's actions in a canonical form.
|
boolean
|
implies
(java.security.Permission permission)
Does this permission imply another.
|
public static final java.lang.String SERVER
"server"
) for actions applicable
to the network server.
public static final java.lang.String ENGINE
"engine"
) for actions applicable
to the core database engine.
public static final java.lang.String JMX
"jmx"
) for actions applicable
to management of Derby's JMX MBeans.
public static final java.lang.String SHUTDOWN
"shutdown"
).
public static final java.lang.String CONTROL
"control"
) to perform control actions through JMX
on engine, server or jmx.
For JMX control permission is required to get attributes that are deemed sensiive from a security aspect, such as the network server's port number, security mechanisms and any information about the file system.
public static final java.lang.String MONITOR
"monitor"
) to perform monitoring actions through JMX
on engine and server.
public SystemPermission(java.lang.String name, java.lang.String actions)
name
- the name of the SystemPermission
java.lang.NullPointerException
- if name is null
java.lang.IllegalArgumentException
- if name is empty or not a legal SystemPermission
BasicPermission.BasicPermission(String)
public java.lang.String getActions()
getActions
in class
java.security.BasicPermission
public boolean equals(java.lang.Object other)
equals
in class
java.security.BasicPermission
public boolean implies(java.security.Permission permission)
implies
in class
java.security.BasicPermission
Apache Derby 10.9 API Documentation - Copyright © 2004,2012 The Apache Software Foundation. All Rights Reserved.


MSNBC's Wallace: 'Gobsmacked' By Patriotism of Taylor's 'Bombshell' Testimony | Breitbart
by Pam KeyDuring Wednesday’s broadcast of MSNBC’s impeachment inquiry coverage, network anchor Nicolle Wallace said she was “gobsmacked” by the patriotism of former U.S. ambassador to Ukraine, William Taylor’s testimony.
She referenced his “bombshell” claim one of his staffer’s overheard the U.S. ambassador to the European Union, Gordon Sondland, on a phone conversation with President Donald Trump.
Talyor said the staffer heard Trump saying he cared more about an investigation into former Vice President Joe Biden’s son Hunter Biden than he did about Ukraine.
Wallace said, “I’m gobsmacked by the contrast, the patriotism, the stoicism, the fact pattern, hysteria, alarm, public handwringing, but that they very calmly, meticulously took notes of the transgressions. They went through the chain of command at the State Department. I mean, I think we have to get right away to the bombshell. There’s a bombshell revelation in this testimony, another direct line drawn directly to Donald Trump in his extortion of the Ukrainians and holding up military aid in exchange for investigations, a brand-new set of facts and evidence tying him straight to that.”
Follow Pam Key on Twitter @pamkeyNEN


-
Bernoulli
({p: …})¶ - ps: probabilities (vector [0, 1])
Distribution over a vector of independent Bernoulli variables. Each element of the vector takes on a value in
{0, 1}
.
-
Beta
({a: …, b: …})¶ - a: shape (real (0, Infinity))
- b: shape (real (0, Infinity))
Distribution over
[0, 1]
-
Binomial
({p: …, n: …})¶ - p: success probability (real [0, 1])
- n: number of trials (int (>=1))
Distribution over the number of successes for
n
independentBernoulli({p: p})
trials.
-
Categorical
({ps: …, vs: …})¶ - ps: probabilities (can be unnormalized) (vector or real array [0, Infinity))
- vs: support (any numpy array, Variable, or list)
- one_hot: if
True
,sample()
returns a one_hot sample.True
by default.
Discrete distribution over elements of
vs
withP(vs[i])
proportional tops[i]
. Ifone_hot=True
,sample
returns a one-hot vector. Else,sample
returns the category selected.
-
Delta
({v: …})¶ - v: support element (any)
Discrete distribution that assigns probability one to the single element in its support. This is only useful in special circumstances as sampling from
Delta({v: val})
can be replaced withval
itself. Furthermore, aDelta
distribution parameterized by a random choice should not be used with MCMC based inference, as doing so produces incorrect results.
-
DiagNormal
({mu: …, sigma: …})¶ - mu: mean (tensor)
- sigma: standard deviations (tensor (0, Infinity))
A distribution over tensors in which each element is independent and Gaussian distributed, with its own mean and standard deviation. i.e. A multivariate Gaussian distribution with diagonal covariance matrix. The distribution is over tensors that have the same shape as the parameters
mu
andsigma
, which in turn must have the same shape as each other.
-
Exponential
({a: …})¶ - a: rate (real (0, Infinity))
Distribution over
[0, Infinity]
-
Gamma
({shape: …, scale: …})¶ - shape: (real (0, Infinity))
- scale: (real (0, Infinity))
Distribution over positive reals.
-
Normal
({mu: …, sigma: …})¶ - mu: mean (real)
- sigma: standard deviation (real (0, Infinity))
- dims: dimension of tensor (int (>=1) array)
Distribution over a tensor of independent Gaussian variables.
-
NormalChol
(mu: …, sigma: …, L: …})¶ - mu: mean (real)
- sigma: standard deviation (real (0, Infinity))
- L: Cholesky decomposition
A multi-variate normal distribution with arbitrary covariance sigma parameterized by its mean and its cholesky decomposition
L
. Parameters must have dimensions <= 2.
-
LogNormal
({mu: …, sigma: …})¶ - mu: mean (vector)
- sigma: standard deviations (vector (0, Infinity))
A distribution over probability vectors obtained by exp-transforming a random variable drawn from
Normal({mu: mu, sigma: sigma})
.
-
Multinomial
({ps: …, n: …})¶ - ps: probabilities (real array with elements that sum to one)
- n: number of trials (int (>=1))
Distribution over counts for
n
independentDiscrete({ps: ps})
trials.
-
Poisson
({mu: …})¶ - mu: mean (real (0, Infinity))
Distribution over integers.
-
Uniform
({a: …, b: …})¶ - a: lower bound (real)
- b: upper bound (>a) (real)
Continuous uniform distribution over
[a, b]


{% extends "base.html" %} {% block content %} {% if not user.is_authenticated %}
Stop trying to remember where you spent your money every day and instead just use ReceiptBudgets OCR to scan your receipts.
Can't make sense of long tables of your expense data? ReceiptBudget has a friendly and intuitive dashboard to help you understand your shopping patterns!.


Class or ID?
Understanding the Basics
1/19/2015
When writing HTML, we often find ourselves using the same elements over and over again. This is great because later on, we can take all of the h1 content and make it bold with one swift CSS call. What happens when we don't want the content in every paragraph tag formatted the exact same way? Maybe you want to distinguish quoted paragraphs from your own using italics. Luckily, there's an easy way to do that!
Classes and ids are useful ways to describe content that we can target later for styling in CSS. Instead of being limited to just formatting an element uniformly, you can add an id or a class to style individual elements.
Classes and ids are great but knowing when to use each one is even MORE great. The thing to remember is that ids are unique! Each element can only have one id and each page can only have one element with that id. So if you want to style a specific paragraph on a page and you know that you want that paragraph to have a unique styling -- different from all of the others, use an id! Classes on the other hand, are not unique. You can use the same class on multiple elements and you can use that class multiple times on the same page. For example, if you have several quoted paragraphs on a page and you'd like to style them uniformly but differentiate them from the rest of your paragraphs, you'd use a class hook!


CSVExport
CSVGraphExport
CSVGraphImport
CSVImport
Exporter
FormatFactory
FormatFactoryImpl
GraphImporter
GraphMLExport
GraphMLImport
IvtkObviousGraphReader
IvtkObviousTableReader
IvtkObviousTableWriter
ObviousTableModel
ObviousxException
ObviousxRuntimeException
PrefuseObviousGraphReader
PrefuseObviousGraphWriter
PrefuseObviousTableReader
PrefuseObviousTableWriter
TableImporter
TypedFormat
|


The tarot is a pack of playing cards, used from at least the mid-15th century in various parts of Europe originally to play games such as Italian tarocchini, French tarot and Austrian Königrufen, many of which are still played today.
In the late 18th century, some tarot decks began to be used for divination via Tarot card reading and cartomancy leading to custom decks developed for such occult purposes.
In this app we are using just the 22 major arcana in the deck, but a full deck contais another 56 cards, called the Minor Arcana. It is possible to do readings using only Major Arcana, and also this practice it is recommended for someone who's beginning to study Tarot.


§48-7 Provisions of chapter controlling. Insofar as this chapter is inconsistent with the provisions of any law or charter, this chapter shall be controlling. The powers conferred by this chapter shall be in addition and supplemental to the powers conferred by any other law or charter. [L 1964, c 58, pt of §2; Supp, §140A-8; HRS §48-7]


PMG - A generator password manager
If we add data from various reputable sites whose identities have been exposed obtain more than 161 million. It is an alarming number, so use the same password on different sites is not a good idea.
To generate strong passwords there are several methods. We can use for example the "deThi4-go" method. We may also use encrypted databases as KeepPass. Or we can also use PMG, another method for generating strong passwords. Now we will explain belown its characteristics, how it works, and how to use it.
How PMG works?
PMG use the name of your account, a security code and a master password to generate a hash to use as the password for this account. May be this is not understood very well, but using the application is best way to understand how PMG works.
How to use PMG?
PMG is quite simple. The first screen asks you to enter the account name (for example: twitter, gmail, etc.) and the user name (for example: chemaalonso, hackathonlovers, etc.). So you can have differents users on the same service.
The account name and the user name are stored in the Navigator Local Storage, so in the future an autocomplete menu appears with the data and we have to write less and we can go faster.
After filling the data, we click on the "Calculate Coordinate" button and then we have another screen where you have to enter a code from a codes card and a master password.
The coordinate (in this case the coordinate 39) will always be the same for the tuple account name and user name. Indeed the coordinate is calculated normalizing the text, adding the numerical value of each character and dividing by the number of coordinates of the card, as shown in the following sample code:
The code of the codes card is not required to put it, although it advisable to increase security. A little below explain how to create our own codes card.
Both, the code from your codes card and your master password can be entered using a virtual keyboard to avoid keyloggers and further increase security.
After entering all the data, the generated password is displayed. We can't see it at first, so you have to select it to view and to copy it.
The generated password will always be the same for the same: account name, user name, code of the codes card and master password. It is the hash of the data set. It can be seen in the following sample code:
How to create your own codes card?
As we said before, although not mandatory, to increase security, PMG ask us a code from a codes card. If we want to use this feature, we must to have a codes card before starting using PMG. Fortunately PMG lets you create your own. To do this we click on "Generate Your Card Codes" and shows a screen where we have to fill some data.
These data include: your place of birth, the name of your best childhood friend, the name of your primary school. All these date can be obtained through social engineering, so we have to introduce also one more field that is the name of your codes card, which should be completely secret. Once the fields are filled, if we click on the "Generate Codes Card" button and get a codes card like this, that we can print:
For the same input data, we always obtain the same codes card, because it's the hash of the data, as can be seen in the following code sample:
PMG features
- PMG generates robust and different passwords for each of your accounts.
- PMG only forces you to remember one master password.
- PMG can increase your security by using a codes card.
- PMG does not store any data in the cloud, everything is generated locally.
- PMG is fully developed in HTML, CSS and JavaScript.
- PMG can be used on both PC and mobile, all you need is a browser.
- PMG has a free software license.
How was PMG born?
As good hackathon lovers, Carlos Azaustre (@carlosazaustre), Jorge Arévalo (@jorgeas80) and Adolfo Sanz De Diego (@asanzdiego) developed PMG during the first t3chfest hackathon


ansible.playbook package¶
Subpackages¶
Submodules¶
ansible.playbook.attribute module¶
-
class
ansible.playbook.attribute.
Attribute
(isa=None, private=False, default=None, required=False, listof=None, priority=0, class_type=None, always_post_validate=False)[source]¶ Bases:
object
ansible.playbook.base module¶
-
class
ansible.playbook.base.
Base
[source]¶ Bases:
object
-
DEPRECATED_ATTRIBUTES
= ['sudo', 'sudo_user', 'sudo_pass', 'sudo_exe', 'sudo_flags', 'su', 'su_user', 'su_pass', 'su_exe', 'su_flags']¶
-
load_data
(ds, variable_manager=None, loader=None)[source]¶ walk the input datastructure and assign any values
-
post_validate
(templar)[source]¶ we can’t tell that everything is of the right type until we have all the variables. Run basic types (from isa) as well as any _post_validate_<foo> functions.
-
ansible.playbook.become module¶
ansible.playbook.block module¶
-
class
ansible.playbook.block.
Block
(play=None, parent_block=None, role=None, task_include=None, use_handlers=False, implicit=False)[source]¶ Bases:
ansible.playbook.base.Base
,ansible.playbook.become.Become
,ansible.playbook.conditional.Conditional
,ansible.playbook.taggable.Taggable
-
get_vars
()[source]¶ Blocks do not store variables directly, however they may be a member of a role or task include which does, so return those if present.
-
static
load
(data, play=None, parent_block=None, role=None, task_include=None, use_handlers=False, variable_manager=None, loader=None)[source]¶
-
preprocess_data
(ds)[source]¶ If a simple task is given, an implicit block for that single task is created, which goes in the main portion of the block
-
serialize
()[source]¶ Override of the default serialize method, since when we’re serializing a task we don’t want to include the attribute list of tasks.
-
deserialize
(data)[source]¶ Override of the default deserialize method, to match the above overridden serialize method
-
ansible.playbook.conditional module¶
ansible.playbook.handler module¶
-
class
ansible.playbook.handler.
Handler
(block=None, role=None, task_include=None)[source]¶ Bases:
ansible.playbook.task.Task
ansible.playbook.handler_task_include module¶
-
class
ansible.playbook.handler_task_include.
HandlerTaskInclude
(block=None, role=None, task_include=None)[source]¶ Bases:
ansible.playbook.handler.Handler
,ansible.playbook.task_include.TaskInclude
ansible.playbook.helpers module¶
-
ansible.playbook.helpers.
load_list_of_blocks
(ds, play, parent_block=None, role=None, task_include=None, use_handlers=False, variable_manager=None, loader=None)[source]¶ Given a list of mixed task/block data (parsed from YAML), return a list of Block() objects, where implicit blocks are created for each bare Task.
ansible.playbook.included_file module¶
ansible.playbook.loop_control module¶
-
class
ansible.playbook.loop_control.
LoopControl
[source]¶ Bases:
ansible.playbook.base.Base
ansible.playbook.play module¶
-
class
ansible.playbook.play.
Play
[source]¶ Bases:
ansible.playbook.base.Base
,ansible.playbook.taggable.Taggable
,ansible.playbook.become.Become
A play is a language feature that represents a list of roles and/or task/handler blocks to execute on a given set of hosts.
Usage:
Play.load(datastructure) -> Play Play.something(...)-
compile_roles_handlers
()[source]¶ Handles the role handler compilation step, returning a flat list of Handlers This is done for all roles in the Play.
-
ansible.playbook.play_context module¶
-
class
ansible.playbook.play_context.
PlayContext
(play=None, options=None, passwords=None, connection_lockfd=None)[source]¶ Bases:
ansible.playbook.base.Base
This class is used to consolidate the connection information for hosts in a play and child tasks, where the task may override some connection/authentication information.
-
set_play
(play)[source]¶ Configures this connection information instance with data from the play class.
-
set_options
(options)[source]¶ Configures this connection information instance with data from options specified by the user on the command line. These have a lower precedence than those set on the play or host.
-
set_task_and_variable_override
(task, variables, templar)[source]¶ Sets attributes from the task if they are set, which will override those from the play.
-
ansible.playbook.playbook_include module¶
-
class
ansible.playbook.playbook_include.
PlaybookInclude
[source]¶ Bases:
ansible.playbook.base.Base
,ansible.playbook.conditional.Conditional
,ansible.playbook.taggable.Taggable
ansible.playbook.taggable module¶
ansible.playbook.task module¶
-
class
ansible.playbook.task.
Task
(block=None, role=None, task_include=None)[source]¶ Bases:
ansible.playbook.base.Base
,ansible.playbook.conditional.Conditional
,ansible.playbook.taggable.Taggable
,ansible.playbook.become.Become
A task is a language feature that represents a call to a module, with given arguments and other parameters. A handler is a subclass of a task.
Usage:
Task.load(datastructure) -> Task Task.something(...)-
static
load
(data, block=None, role=None, task_include=None, variable_manager=None, loader=None)[source]¶
-
preprocess_data
(ds)[source]¶ tasks are especially complex arguments so need pre-processing. keep it short.
-
static
ansible.playbook.task_include module¶
-
class
ansible.playbook.task_include.
TaskInclude
(block=None, role=None, task_include=None)[source]¶ Bases:
ansible.playbook.task.Task
A task include is derived from a regular task to handle the special circumstances related to the - include: ... task.


Running time: {{ RunningTime['from'].strftime("%Y-%m-%d %I:%M %p") }} to {{ RunningTime['to'].strftime("%Y-%m-%d %I:%M %p") }} (All times in {{ RunningTime['TimeZone'] }})
Needless to say: no bruteforcing (you'll never guess, anyway)
Automated vulnerability scanners will get you nowhere(we know 'cause we made the box)
Attacking this CTF infrastructure website is unnecessary. There are no hints.
This website is for the sole purpose of registration, hash submission and management.
We have tested the machine multiple, I repeat multiple times so if you are receiving a status code of 404 or 301. Figure it out yourself!


And Dr Dre said...


Welcome to Pig Dice
Player 1
Total Score:
Round Score:
Player 2
Total Score:
Round Score:
Roll Score:
Roll
Hold


Downscale images with an area-average downsampling and faster pixel manipulation with Typed Arrays.
Pick your local images (multiselection works)
Enable canvas smoothing (for newest browsers)


Binary Trees
A binary tree is a data structure is which each node has at most two children nodes, usually defined as left
node and
right node. In a binary tree, the degree of each node can be at most two. Binary trees are
used to implement binary search trees and binary heaps, and are used for efficient searching and sorting. A
binary tree is a special case of a K-ary tree, where k is 2.


| 1 |
research |
viruses |
antibodies |
detection |
vaccines |
nanoparticles |
models |
synthetic biology |
drug |
biosafety |
microarrays |
rnai |
coronavirus |
veterinary |
bioaerosol |
| 2 |
review |
viral |
production |
diagnostic |
vaccine |
applications |
animal |
network |
structural |
patent |
microarray |
gene |
respiratory |
species |
meeting |
| 3 |
diseases |
host |
recombinant |
diagnostics |
immune |
nanotechnology |
animal models |
data |
membrane |
policy |
protein |
RNA |
virus |
veterinary medicine |
held |
| 4 |
recent |
virus |
plant |
biosensors |
vaccination |
nanomaterials |
model |
dynamics |
protein |
bioterrorism |
proteomics |
delivery |
SARS |
common |
university |
| 5 |
development |
sequencing |
plants |
assays |
antigens |
materials |
mice |
social |
discovery |
united |
protein microarrays |
therapy |
hepatitis |
cat |
award |
| 6 |
advances |
genome |
antibody |
techniques |
DNA |
aptamers |
mouse |
epidemiology |
drug discovery |
public |
biomarkers |
rnas |
syndrome |
practice |
conference |
| 7 |
health |
genomes |
proteins |
water |
responses |
chemical |
lung |
mathematical |
structures |
countermeasures |
mass |
interference |
mers-cov |
medicine |
ICAR |
| 8 |
current |
interactions |
monoclonal |
devices |
vlps |
peptides |
human |
systems |
complexes |
concerns |
proteomic |
vector |
HCV |
intestinal |
6 |
| 9 |
provide |
molecular |
mabs |
microbiology |
dna vaccines |
carbohydrates |
stem |
evolutionary |
NMR |
patents |
MS |
rna interference |
HIV |
sepsis |
society |
| 10 |
infectious |
genetic |
monoclonal antibodies |
sensitivity |
vaccinology |
lectins |
cell |
networks |
screening |
forensics |
spectrometry |
sirna |
sars-cov |
- |
antiviral research |
| 11 |
years |
genomics |
expression |
tests |
immunogenicity |
properties |
tissue |
biology |
structure |
ethical |
technology |
cells |
ebola |
animals |
presentations |
| 12 |
future |
infections |
transgenic |
point-of-care |
adjuvants |
chemistry |
lines |
synthetic |
proteins |
biodefense |
profiling |
gene therapy |
severe |
asthma |
journal |
| 13 |
potential |
evolution |
biotechnology |
acid |
antigen |
carbohydrate |
mouse models |
computational |
inhibitors |
product |
markers |
silencing |
acute |
interferons |
EM |
| 14 |
clinical |
virology |
purification |
nucleic |
immune responses |
compounds |
animal model |
modeling |
targets |
safety |
parallel |
vectors |
coronaviruses |
livestock |
keynote |
| 15 |
important |
pathogen |
produce |
amplification |
VLP |
gold |
pig |
ecological |
crystallography |
hand |
mass spectrometry |
expression |
MERS |
reproductive |
american |
| 16 |
tools |
genetics |
engineering |
rapid |
dna vaccine |
peptide |
nonhuman |
phylogenetic |
multiprotein |
hygiene |
fluid |
gene expression |
middle |
periodontal |
featured |
| 17 |
studies |
understanding |
recombinant proteins |
sensitive |
virus-like |
medicinal |
cells |
landscape |
molecule |
united states |
biomarker |
vivo |
WNV |
pharmacy |
lectures |
| 18 |
progress |
sequence |
display |
molecular |
live |
biomedical applications |
disease |
biological systems |
macromolecular |
countermeasure |
proteins |
in vivo |
dengue |
wastewater |
china |
| 19 |
challenges |
viral infections |
products |
microfluidic |
antigenic |
surface |
humanized |
modelling |
structure-based |
dual |
analysis |
antisense |
fever |
dogs |
lecture |
| 20 |
human |
infection |
phage |
PCR |
immunity |
nanoscale |
primates |
theory |
fluorescent |
anthrax |
functional |
therapeutic |
east |
care |
air |
| 21 |
including |
pathogenesis |
farming |
microfluidics |
development |
chitosan |
hamster |
methods |
ligand |
convergence |
proteome |
interfering |
outbreak |
animal |
symposium |
| 22 |
field |
influenza |
molecular farming |
technologies |
virus-like particles |
antimicrobial |
recapitulate |
inference |
virtual |
countries |
SCI |
oligonucleotides |
pathogenesis |
microbiota |
workshop |
| 23 |
approaches |
antiviral |
affinity |
monitoring |
immunology |
activity |
vitro |
computing |
microscopy |
licensing |
omics |
immunotherapy |
antiviral |
authorized |
invited |
| 24 |
disease |
pathogens |
produced |
real-time |
humoral |
synthetic |
biomedical research |
evolutionary biology |
glycans |
industry |
quantitative |
tumor |
ebola virus |
online version |
annual |
| 25 |
emerging |
replication |
insect |
diagnosis |
protective |
biological |
cardiac |
analyse |
target |
intellectual |
high-throughput |
cpps |
RSV |
SUPPLEMENTARY |
june |
| 26 |
major |
metagenomics |
® |
imaging |
subunit |
medical applications |
ferret |
wild |
drug discovery process |
legal |
microarray technology |
genes |
ZIKV |
milk |
international |
| 27 |
present |
genomic |
protein |
sensors |
particles |
adsorption |
brain |
epidemic |
multiprotein complexes |
guidelines |
technologies |
baculoviruses |
acute respiratory syndrome |
farm |
special |
| 28 |
medicine |
NGS |
expression systems |
POC |
attenuated |
class |
humans |
novo |
x-ray |
terminologies |
arrays |
rnai-based |
PEDV |
pediatric |
prize |
| 29 |
key |
RNA |
therapeutic |
molecular diagnostics |
immunization |
synthesis |
vivo |
spatial |
multibac |
pools |
ARDS |
sirnas |
antivirals |
users |
interactive |
| 30 |
strategies |
pathogenic |
plant-made |
detect |
prediction |
molecules |
primate |
mathematical models |
dubs |
hand hygiene |
dna microarrays |
mirnas |
enteric |
version |
heparanase |
| 31 |
discuss |
interaction |
heterologous |
sample |
immunological |
structures |
in vitro |
bioinformatics |
cryo‐em |
dual use |
metabolomics |
baculovirus |
cov |
companion |
david |
| 32 |
past |
fungal |
biopharmaceuticals |
biosensor |
delivery |
DNA |
organ |
problems |
HTS |
business |
omics technologies |
non-viral |
antiviral drugs |
OA |
situ |
| 33 |
methods |
next-generation |
genetic engineering |
LAMP |
design |
cnts |
death |
big |
enzymes |
infrastructure |
protein microarray technology |
gene silencing |
reverse genetic systems |
NDV |
nobel |
| 34 |
basic |
complete |
therapeutic proteins |
identification |
mucosal |
marine |
tissues |
disease |
proteases |
sciences |
neutrophil |
nucleic |
respiratory syncytial virus |
management |
virology |
| 35 |
overview |
viral pathogenesis |
transgenic plants |
environmental |
efficacy |
toxicity |
organs |
synthesis |
membrane proteins |
scientific |
functional protein microarrays |
viral vectors |
sars‐cov |
article |
topics |
| 36 |
understanding |
biology |
mab |
testing |
HRV |
fabrication |
ferrets |
students |
roles |
medical |
protein-protein |
evs |
hepatitis c virus |
lesions |
in situ |
| 37 |
infectious diseases |
variation |
advantages |
clinical microbiology |
effective |
bioactive |
pigs |
promises |
crystal |
access |
MALDI |
autophagy |
middle east respiratory syndrome |
rats |
canadian |
| 38 |
focus |
respiratory viruses |
plant-based |
electrochemical |
immunomics |
metal |
b cells |
promote |
drug candidates |
societal |
applications |
translation |
norovirus |
cats |
otitis |
| 39 |
applied |
molecular epidemiology |
plastid |
diagnostic tests |
response |
chapter |
organoids |
transmission |
structural studies |
textiles |
characterization |
controllers |
sars-cov-2 |
periodontal diseases |
physics |
| 40 |
associated with |
host-pathogen |
recombinant protein |
pathogens |
inactivated |
graphene |
animals |
questions |
biological |
biological agents |
expression |
mirna |
dans |
hrps |
ISAR |
| 41 |
technologies |
virome |
plant-derived |
isothermal |
preparations |
functionalized |
immune |
models |
virtual screening |
technological opportunities |
tear |
therapeutics |
outbreaks |
pet |
skills |
| 42 |
researchers |
disease |
crops |
automation |
licensed |
analogues |
human-specific |
analysis |
x-ray crystallography |
journey |
liquid |
transfection |
middle east respiratory syndrome coronavirus |
laboratory |
otitis media |
| 43 |
prevention |
recombination |
system |
portable |
veterinary |
biomaterials |
humanized mice |
synthetic genomics |
ubiquitin |
seeks |
nanosized |
rna molecules |
nile |
piglets |
PH |
| 44 |
improve |
escape |
bsabs |
methods |
activation |
scaffold |
memory |
experimental data |
cellular processes |
patent pools |
rhinitis |
extracellular |
biologie |
farm animals |
anniversary |
| 45 |
types |
virus-host |
filamentous |
foodborne |
immunogen |
biomedical |
inbred |
behavioral |
process |
plan |
seldi-tof ms |
short interfering rna |
parechovirus |
rabbit |
issue |
| 46 |
remain |
virus discovery |
bioreactors |
QC |
dna immunization |
sialic |
lymphoid |
wildlife |
function |
HFE |
SELDI-TOF |
therapeutic approaches |
noroviruses |
ataxia |
research |
| 47 |
needed |
rna virus |
BEVS |
naats |
dna vaccination |
biocompatibility |
rat |
picture |
drug design |
regulations |
maldi ms |
viral vector |
envelope |
INFORMATION |
bioaerosols |
| 48 |
identifying |
genome sequencing |
hybridoma |
advanced techniques |
adenoviruses |
deals |
cardiotoxicity |
research synthesis |
glycan |
UV |
gateway(tm |
muscle |
microbiologie |
probiotics |
world |
| 49 |
growing |
molecular characterization |
systems |
CMM |
cellular immune responses |
organic |
NHP |
evolution |
DUB |
pool |
serum/plasma |
non-coding |
zika virus |
nutrition |
instructional |
| 50 |
community |
arenaviruses |
transgenic animals |
specific |
systems |
objects |
non-human |
phylogenetic analysis |
super-resolution |
emergency |
LMW |
transduce |
ebola outbreak |
pathology |
portugal |


[1]
in contrast the breakfast is though more problematic.
[2]
prices are n't reflecting the compact sizes of.


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Sweet Free Stuff
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Free Comforts for Baby Diper Bag
Posted: 03 Feb 2015 11:18 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Each participant who completes an entry will receive two (2) coupon
booklets and one (1) Comforts for Baby diaper bag. While supplies last.
There will be one grand prize winner for a one-year supply of Comforts for
Baby health hygiene products (including baby wash, baby shampoos, baby
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The post Free Comforts for Baby Diper Bag appeared first on
SweetFreeStuff.com.
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Free Doughnut at Krispy Kreme on 2/24
Posted: 03 Feb 2015 02:20 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
On Feb. 24, it will open its 1,000th store in Kansas City, Kansas, and in
turn, will be offering free doughnuts at participating locations around the
country. If youre one of the first 1,000 people in line on that day, youll
get an original glazed doughnut. Free Doughnut at Krispy Kreme on 2/24
The post Free Doughnut at Krispy Kreme on 2/24 appeared first on
SweetFreeStuff.com.
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Free Coloring Book from Shenandoah Podiatry
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Free Warm Cookie at RaceTrac Stores
Posted: 03 Feb 2015 01:20 AM PST
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Free Warm Cookie at RaceTrac Stores Expires 2/28.
The post Free Warm Cookie at RaceTrac Stores appeared first on
SweetFreeStuff.com.
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Free Diabetic Recipe Book
Posted: 03 Feb 2015 12:00 AM PST
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Free Diabetic Recipe Book
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CommentsObviously there is no need to comment on here when you can
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Free Meta Health Bar Sample
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CommentsI would like to try a sample of your wonderful products. If
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Free Buca Small Fettuccine Alfredo at Buca di Beppo
Posted: 02 Feb 2015 10:23 PM PST
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on SweetFreeStuff.com.
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Free Coffee at Chick-fil-A In February
Posted: 02 Feb 2015 09:12 PM PST
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Free hot and iced coffee nationwide throughout the month of February at
Chick-fil- A. This offer includes 12 ounce (small) hot or 16 ounce (medium)
iced sizes and is available while supplies last. The offer is available
anytime during regular restaurant hours and is limited to one cup of coffee
per customer, per visit. No []
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SweetFreeStuff.com.
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Free Winter Samples
Posted: 02 Feb 2015 09:10 PM PST
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Posted: 02 Feb 2015 06:32 PM PST
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Free Star Recipe Booklet Olive Oil Coupon
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CommentsNo longer available by Snarf


You can also use the inline script syntax in attributes. To do this you simply nest your inline script syntax into two curly brackets {{ ... }}.

 Random color

Now whenever the render function is getting called, the attributes will get updates as well.
Keep in mind that syntaxes like

{{ item }}

may not work simply because this could get converted to junk:

{{ item }}

Let's say we want to generate a nav based on a variable called navigation. The variable looks like this:
const navigation = [ 'Home', 'About', 'Pricing', 'Contact' ];
We also have the variable currentPage which contains the current page we're currently on:
let currentPage = 'About';
Let's loop through our array and return another array filled with a-tags. But our goal is to highlight the current page. We can define a variable called className to whether 'current' or nothing and set this to our element:
{{ navigation.map(item => { const className = item === currentPage ? 'current' : ''; return ({ item }); }) }}


Welcome to {{project}}, an application generated by the Pyramid web application development framework.


FALLEN A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): ***
Sometimes it is in the casting phase of pre-production that a film's success is assured, and so it is with FALLEN, a detective story and supernatural thriller starring Denzel Washington as Det. John Hobbes.
With a smartly written script by Academy Award nominee Nicholas Kazan (REVERSAL OF FORTUNE) and deft direction by Gregory Hoblit, FALLEN tells a detective story with a different solution but classic police tracking. Hoblit, the director of PRIMAL FEAR, that underappreciated gem from last year, takes a concept that has schlock potential written all over it and fashions it into an intensely compelling motion picture.
"I want to tell you about the time I almost died," Det. Hobbes tells us in the story's opening line. "I never thought it would happen to me, not at this age." Washington's warm intonation of the prescient and insightful voice-overs sets the tone for all of the key events in the drama.
As the story starts, a convict on death row faces execution. Elias Koteas, last seen in CRASH and GATTACA, plays the creepy serial killer Edgar Reese. Det. Hobbes, who put Reese away, comes to his execution. Both are utterly confident, with a gum chewing Det. Hobbes relaxed and a prancing Reese hyperactive. As Reese begins to taunt Hobbes, Reese speaks in a variety of language, especially ancient Aramaic, and shoots out his hand to touch Hobbes. Once in the gas chamber, Reese bursts out singing, "Time is on my side; yes it is." This recurring song becomes one of filmdom's most effectively chilling use of a single song.
"Something is always happening," Det. Hobbes tells us. "But when it happens, people don't always see it or accept it." Soon after Reese's execution, a series of mysterious murders starts, and the story surrounds the investigation of these murders.
Det. Hobbes works in a realistically cluttered police station office. The film's excellent supporting cast includes John Goodman as his partner Jonesy, James Gandolfini as fellow office Lou and Donald Sutherland as his boss Lt. Stanton. Although the other actors get fresh roles, Sutherland plays his usual slightly sinister part. Lt. Stanton, who knows something he's not telling, tries to get Det. Hobbes to stop his involvement in the investigation of the new murders.
Director Hoblit's precise staging has it just right with none better than the touching scenes. With touch playing a central element in the story, he sets up a fast paced sequence on a crowded city street that gets your adrenaline pumping. And cinematographer Tom Sigel switches from person to person using techniques first popularized in SLACKER.
As the story advances, we know more than Det. Hobbes, but the fascination comes from watching Hobbes unravel the mystery. As Gretta Milano, Embeth Davidtz from SCHINDLER'S LIST plays a woman whose father was a policeman who committed suicide years earlier. Milano could help Det. Hobbes, but rightly fears getting involved.
The beauty of the script can perhaps best be seen in the minutia. The writer takes care in the fashioning of even the minor characters. Det. Hobbes, for example, lives with his brother and his brother's son. One morning at breakfast, his brother looks over and speaks a simple, "I love you." Imagine that, adult brothers who frankly admit their love without it being some fake plot contrivance.
Along the way, the clues mount as they do in any good mystery. Among others, they include an ACLU video of Reese just before the execution that contains a host of information to be uncovered and items seemingly as simple as fingerprints. When Det. Hobbes uncovers the solution to the crimes, he ridicules it at first as preposterous. As Lt. Stanton explains it, "People want the world to make sense."
In a nice surprise, the completely predictable ending isn't. With a few carefully chosen and completely satisfying twists, the movie's ending is one of its best parts.
FALLEN is rated R for some violence and profanity and would be fine for teenagers. A small part is in Aramaic without benefit of English subtitles.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Most networks use TCP/IP as the network protocol, or set of rules for communication between devices, and the rules of TCP/IP require information to be split into packets that contain both a segment of data to be transferred and the address where the data is to be sent.
The figure below describes a small internet consisting of three networks 128.8, 128.9.1 and 128.9.2. Strictly speaking, the internet consists of 128.8 network and 128.9.1 and 128.9.2 sub-networks (subnets). As we have seen in the previous section, 128.8 and 128.9 should have been classified as the network portion of a class B IP address. In this network 128.9 has been divided into two sub-networks (128.9.1 and 128.9.2) by using one of the bytes of the two byte host id as sub-network id.
Another way to look at this is that the first three bytes of IP addresses in 128.9.1 and 128.9.2 subnets are used for routing the packet. The other bits in the IP address are don't care from routing point of view. The specification of bits that should be used for routing is specified by associating a subnet mask with a routing entry. In this example, the subnet mask is <IP_ADDRESS> (0xFFFFFF00).
Networks in the internet are connected to each other via routers. Routers carry traffic from one network/subnet to another. Routers maintain a routing table to decide how to route the IP packets. Each routing entry consists of the destination address, subnet mask and "route to" field. When a message needs to be routed to an IP address, the following steps are followed:
1. The destination IP address is masked with the subnet mask and then compared with the destination field for all entries in the routing table.
2. This comparison may yield a match with more than one entry the entry with the longest subnet mask will be selected. E.g. , a packet destined for <IP_ADDRESS> reaching Host A would match the entries corresponding to <IP_ADDRESS> and 128.8.0. The entry corresponding to <IP_ADDRESS> will be selected, as it has a longer subnet mask.
3. Once an entry has been selected, the "route to" field is consulted and the action taken depends on the contents of this field:
Note: IP routing also supports a default entry. If the packet does not match any other entry, it is routed according to the default entry.
Another important aspect of internets is a node in the internet can have multiple IP addresses. There will be one IP address per interface. For example, the Router in the figure above has three IP addresses, viz. <IP_ADDRESS>, <IP_ADDRESS> and <IP_ADDRESS>.
Here we will trace the path taken by an IP packet sent from Host A to Host C. Routing related fields in the Ethernet MAC header and IP header are shown.
1.Application sends a message to Host C by sending it to <IP_ADDRESS> IP address (Host C's IP address).
2.This IP address matches the entry corresponding to <IP_ADDRESS>. The "route to" field for the selected entry contains another IP address - <IP_ADDRESS>. This is the IP address of the Router.
3.The IP routing table is accessed again for <IP_ADDRESS>.
4.The entry that matches <IP_ADDRESS> contains LAN 0 interface id. This specifies that the destination node is directly connected to the host.
5.This packet is passed to the device driver.
6.Device driver consults the ARP cache to identify the Ethernet MAC address corresponding to the <IP_ADDRESS>. (ARP is covered in another article).
7.Ethernet frame is sent to the MAC address found by ARP.
The packet sent on the 128.8 LAN is:
1.Router receives the Ethernet frame and passes it to the IP layer.
2.IP routing table is consulted and a matching entry is found corresponding to 128.9.2 subnet.
3.Packet is routed on the LAN 2 interface.
4.Host C's MAC address is found from the ARP cache.
5.Ethernet frame is addressed to Host C MAC Address.
The packet sent over the 128.9.2 LAN is:
1.Host C receives the Ethernet frame and passes it to the IP layer.
2.IP routing table is searched and a match is detected with <IP_ADDRESS> entry.
3.The "route to" field contains SELF, so the message is passed to the higher layer for delivery to the application


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Overview of jBallerina 1.1.4
Ballerina 1.1.4 is a patch release iteration of its previous 1.1.3 release. Please refer to the Github repository for
a list of bug fixes and
improvements.
You can use the inbuilt updating capability (introduced in jBallerina 1.1.0) to update to jBallerina 1.1.4 as follows.
For existing users:
If you are already using jBallerina version 1.1.0 or above, you can directly update your distribution to jBallerina
1.1.4 by executing the below command:
ballerina dist update
However, you need to execute the below commands if you have installed:
- jBallerina 1.1.0 or above but switched to use a previous version:
ballerina dist pull jballerina-1.1.4
- jBallerina 1.1.4 via the installers:
ballerina dist use jballerina-1.1.4
- a jBallerina version below 1.1.0: install via the installers
For new users:
If you have not installed jBallerina, then download the installers to install.
Standard Library
- Added path component to file:FileInfo. This will give the absolute path of the file.
- Handled the NullpointerException while generating the JWT key.
Deployment
- Support to generate knative service artifacts with @knative annotation.
DEV Tools
Language Server
- Improvements and bug fixes to Auto-completion engine
IntelliJ Plugin
- Add support to view language server trace logs.
- Improve handling ballerina source files inside non-ballerina projects.
- Add minor improvements to plugin settings and language server debug log view.


--- layout: post status: publish published: true title: UML Quagmire author: display_name: ian login: ian email<EMAIL_ADDRESS>url: http://iancwill.com/blog author_login: ian author_email<EMAIL_ADDRESS>author_url: http://iancwill.com/blog wordpress_id: 10 wordpress_url: http://iancwill.com/blog/?p=10 date: '2007-03-14 12:13:00 -0600' date_gmt: '2007-03-14 12:13:00 -0600' ---
As I spend more time with UML diagrams, I'm torn. On one hand, I think they're a great construct that allows designers to -think- about design in more concrete ways, and to record those thoughts in ways that can be communicated to other people. However, whenever I have to UML diagram for more than a day or two straight, I always have problems fleshing out the entire system. Initially there are pieces that confuse me, and I can get enthusiastic about figuring those out. But after the complex parts are sorted out, there's still much more diagramming to be done, but I just can't get into it.
So I end up staring at these sparse diagrams--with maybe two classes and an aggregation relationship--for hours, making no progress. I don't know what these less interesting parts of the system really need to do. Maybe I just haven't thought about them enough. I'll try thinking more seriously about them today. I felt the need to document this feeling of being set adrift in a sea of UML without direction, plan, or course of action.


PAL, PNB evacuated due to bomb threat
by Itchie G. CabayanOFFICIALS and employees of the Philippine Airlines (PAL) and the Philippine National Bank (PNB) were evacuated the other day due to a ‘bomb threat.’
In her social media post, PAL spokesperson Ma. Cielo Villaluna said that employees and officials evacuated their office building Tuesday afternoon after a personnel received a bomb scare sent by an unidentified suspect.
Villaluna said that at 2:10 p.m. of November 19, an unknown number sent a text message to a PNB security personnel, saying that ‘a bomb will explode anytime within the PNB compound.’ PAL holds office in the same compound, which is located on Macapagal Avenue in Pasay City.
As all PAL and PNB-based employees and officials evacuated the PNB financial center building, Villaluna said bomb experts from the Philippine National Police were called to conduct a bomb sweep.
After a check was made in the area from 3 p.m. to 4:30 p.m., the whole compound was declared safe and free of any bomb or any other explosive device.
‘Did you know that carrying out a bomb hoax is a criminal offense and punishable by law? For those who have nothing better to do, and are behind this, may the law catch up with you!,’ Villaluna thus ranted in her post.


Package net.sf.jasperreports.components.list
A list component is a report element that iterates on a set of records and renders a cell for each record.
The data for a list is obtained via a subdataset defined in the report. The list component
includes a <datasetRun>
JRXML element (equivalent to a
JRDatasetRun
at the API level), which contains
information required to instantiate a report subdataset. A data source object can be sent
to the subdataset, or, when the subdataset embeds a query, connection parameters and
query parameters can be provided. The subdataset is instantiated when the list
component is evaluated and it iterates through the records it produces.
The list cell consists of a report element which will be rendered for each record in the
dataset. This element can be a frame which nests several elements. The list cell is
evaluated in the context of the subdataset, therefore expressions used by the elements can
only refer to parameters, fields and variables which are defined in the subdataset. Also,
delayed evaluation times will not work for elements in the list cells because the elements
are not evaluated in the context of the main report dataset.
The cell width is the same as the defined width of the component element; the height is
given by an attribute of the list. The contents of the cell must fit within the width and
height of the cell.
When iterating on the dataset records, the list component renders cells vertically in a
single column if printOrder
is Vertical
, or it renders cells side by side, in a single
row, if the printOrder
attribute is set to Horizontal
. For horizontally filled lists, the
row breaking behavior is controlled by the ignoreWidth
attribute, which either forces a
row break when list width limit is reached, or lets the cells stretch beyond the list
declared width limit. The list cell height is used as the column's minimum height. If the
space remaining at the bottom of the page is less than the cell height, a column/page
overflow is triggered and the list continues rendering on a new column/page.
The height of the list component element itself can be greater than the list cell height.
The list element height is used as minimum height of the entire list, that is, the list will
not start rendering unless the space remaining on the page is at least the height of the
element.
-
Interface Summary Interface Description ListComponent List component interface.ListContents Container of report elements that are to be printed for each record in the list subdataset. -
Class Summary Class Description BaseFillList Base fill list component implementation.BaseFillList.AppendingPrintElementContainer BaseListContents ListContents
implementation used in compiled reports.DesignListContents ListContents
implementation used at report design time.FillDatasetRun Used to iterate on the list subdataset at fill time.FillListContents List contents fill element container.FillListDatasetFactory FillListFactory Factory oflist fill component
instances.HorizontalFillList Horizontal fill list component implementation.ListComponentCompiler Compile-time handler oflist component
instances.ListDesignConverter List preview converter.StandardListComponent StandardListComponent
implementation.UnusedSpaceImageRenderer Am image renderer used to mark the unused vertical space in a list component preview.VerticalFillList Vertical fill list component implementation.


1 write to ExperimentAction
System.Management.Automation (1)
engine\ExperimentalFeature\ExperimentalFeature.cs (1)
329
ExperimentAction
= experimentAction;
2 references to ExperimentAction
System.Management.Automation (2)
engine\CommandProcessorBase.cs (1)
46
string errorTemplate = expAttribute.
ExperimentAction
== ExperimentAction.Hide
engine\ExperimentalFeature\ExperimentalFeature.cs (1)
375
_effectiveAction = ExperimentalFeature.GetActionToTake(ExperimentName,
ExperimentAction
);


Personal Plans
Business Plans
Guide
Blog
Insurance list:
Company
Covering
Type
Metal level
DRugs
Primary Care Visits
Yearly Deductible
Price
Insurcom
Health
PPO
Platinum
$15
$75
$6000
$356/mon
Overview
BUY
Health Ins
All-in-one
HMO
Silver
$15
$75
$6000
$611/mon
Overview
BUY
Dentalife
Dental
EPO
Gold
$15
$75
$6000
$283/mon
Overview
BUY
Insurcom
Vision
POS
Bronze
$15
$75
$6000
$214/mon
Overview
BUY
Insurcom
Health
PPO
Platinum
$15
$75
$6000
$356/mon
Overview
BUY
Health Ins
All-in-one
HMO
Silver
$15
$75
$6000
$611/mon
Overview
BUY
Dentalife
Dental
EPO
Gold
$15
$75
$6000
$283/mon
Overview
BUY
Insurcom
Vision
POS
Bronze
$15
$75
$6000
$214/mon
Overview
BUY


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| 016:039 Khan | : |
In order that He may make manifest to them the truth of that wherein they differ, and that those who disbelieved (in Resurrection, and in the Oneness of Allah) may know that they were liars. |
| 016:039 Maulana | : |
So that He might make manifest to them that about which they differ, and that those who disbelieve might know that they were liars. |
| 016:039 Pickthal | : |
That He may explain unto them that wherein they differ, and that those who disbelieved may know that they were liars. |
| 016:039 Rashad | : |
He will then point out to everyone all the things they had disputed, and will let those who disbelieved know that they were liars. |
| 016:039 Sarwar | : |
(Through the resurrection) God wants to make a clear distinction between right and wrong and make the unbelievers know that they were liars. |
| 016:039 Shakir | : |
So that He might make manifest to them that about which they differ, and that those who disbelieve might know that they were liars. |
| 016:039 Sherali | : |
HE will surely raise them up that HE may make clear to them that wherein they differed, and that those who disbelieved may realize that they were liars. |
| 016:039 Yusufali | : |
(They must be raised up), in order that He may manifest to them the truth of that wherein they differ, and that the rejecters of Truth may realise that they had indeed (surrendered to) Falsehood. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > الوعد والوعيد Pillars of Islam> Islamic> Oneness of Allah> Promise and Threat أركان الإسلام > التوحيد > الملحدون المنكرون ليوم البعث Pillars of Islam> Islamic> The Apostates Who Denies the Resurrection الإنسان والعلاقات الاجتماعية > المجتمعات > اختلاف الناس Man and The Social Relations>The Societies>Different Kinds of People
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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WITNESS LIST
Economic & Small Business Development Committee
April 6, 2016 - 9:00 AM
Local economic development programs and incentives
For:
Blake, Leslyn (Sachse Economic Development Corp.)
Hutchenrider, Kenneth (Richardson Chamber of Commerce)
Stewart, Scott (Texas Chemical Council)
On:
Castillo, Korry (Texas Comptroller of Public Accounts)
Collins, Kris (Greater Waco Chamber & McLennan County Spaceport Dev. Corp.)
Craymer, Dale (Texas Taxpayers & Research Association)
Elliott, Verma (Texas State Auditor's Office)
Gallahan, Russell (Comptroller of Public Accounts)
Grabenhorst, Jim (City of Rowlett)
Hicks, Willie (State Auditor's Office)
Lehman, Jennifer (Texas State Auditor's Office)
Mayer, Paul (Garland Chamber of Commerce, Dallas County Manufacturers Assn.)
Schwab, Carlton (Texas Economic Development Council)
Simpson, Stephanie (TX Assn of Manufacturers)
Registering, but not testifying:
On:
LeBas, James (Texas Assoc of Manuf'ers & Chemical Council)
State's competitiveness with high-growth, high-tech industries
For:
Baffer, Barbara (Tech Titans)
Chen, Charlie W. (Self)
On:
Peterson, Larry (Texas Foundation for Innovative Communities)
1


Mario is a Sophmore in the Computer Science program. Currently he is working for the Living Scriptures College Program. Some of his personal projects are developing iOS applications and learning JavaScript. As the publicity coordinator for ACM, he is looking for individuals that want to get involved and make BYU ACM one of the best known clubs at BYU.
Mike is a Master's student studying Computer Science. He researches in the Neural Network and Machine Learning Lab - and plans to pursue a Ph.D. upon graduation. In his free time, Mike loves building web apps, composing music, and reading up on the latest technologies.
Connor is a Junior in the Computer Engineering program and is minoring in Computer Science and Mathematics. He currently works as a software engineer at Lucid Software. He has a passion for all things electrical, especially computer architecture and programming. As a Publicity Coordinator, he is responsible for reaching out to potential new members of ACM and keeping current members informed on upcoming events.
Keith is a senior in the Computer Science Program. He loves software projects. He has worked with the ME department to develop a multi-user CAD prototype for Boeing, and with a team of friends to create registerBYU.com. Last semester he taught a special topics, competitive programing class. As the sponsorship officer he is always looking for opportunities to provide students with exposure to great tech companies.
Sydney Shen is a junior/senior in computer science program. She is Taiwanese and is currently interning in Domo Inc. She is Treasurer for ACM.
Kevin is a Junior studying Computer Science with a minor in Business Management. He was in the BYU Mechanical Engineering program until he took his first programming class and decided computer software was his true passion. He works as a TA for CS 428 - Software Engineering, which teaches the importance of the non-technical aspects of software creation. After he graduates, Kevin will pursue a double Master's degree in Software Engineering and Business Administration. He hopes to eventually make a living as a Software Consultant.
Jeremy is a senior studying computer science. He loves programming and especially enjoys writing 3D computer graphics applications. He is also a pretty chill dude. You should hang out and play video games with him sometime.
I am currently a sophomore majoring in Computer Science. I am an officer in training for ACM. I am interested in becoming a better programmer and developing the skills that I need to make the latest and newest technological advances in the world.
Derek is a sophomore in the Computer Science program and currently works at Verisage doing custom mobile apps. He is currently focusing on iOS, Android, and Django Python development with a sprinkle of AngularJS for web apps. He loves attending Hackathons and is always looking for a travel buddy. He looks forward to serving the ACM club and bringing the BYU CS department to a new level.
Scott is a Senior in the Bioinformatics major and minoring in Computer Science. He currently works as Managing Director at World Tech Academy. His passions involve producing elegant code as well as enhancing user experience. As Web Master, Scott maintains the ACM site.


Our Website Uses an ineractive methology of teaching maths using the lastest web rechologies your learning experience with us guranteed to to be initutive and fun.You will have fun while learning.This is the main motive of our website.


Collection
Case studies from schools showing good practice in dealing with bullying, disciplining poor behaviour and encouraging good behaviour


xUnit Style Database Testing
Abstract
Enterprise databases may start out as a well organised bucket of data, but
right from the word go they often contain behaviour that can be tested in
isolation. With each new release more and more logic will find its way in as
the types of consumers diversify in their requirements and technology stacks
and performance problems force us into redesign. But how do we ensure the
functional aspects of the system remain intact with each new
iteration? The xUnit testing model is commonly used in the higher
software layers but there appears to be very little in the database community
to aid with unit testing those requirements often implemented with check constraints,
foreign keys, triggers, stored procedures etc. This presentation takes a look
at how you can apply modern automated unit testing practices (such as the xUnit
model) to the world of SQL databases.
Bio
Chris started out as a bedroom coder in the
80s, writing assembler on 8-bit micros. These days itís C++ and C# on Windows
in big plush corporate offices. He is also the commentator for the
Godmanchester Gala Day Duck Race and can be contacted via<EMAIL_ADDRESS>Chris Oldwood
06/12/2010


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