Research Interests
My research focus is on the control and motion planning of mobile robots to make them more reactive to obstacles and capable of making safe and agile movements in cluttered and dynamic environments. I’m also interested in robot simulation and human-robot collaboration on challenging tasks.
Publications
Journal
-
“Human-in-the-Loop Control of a Humanoid Robot for Disaster Response: A Report from the DARPA Robotics Challenge Trials,”, DeDonato, M., Dimitrov, V., Du, R., Giovacchini, R., Knoedler, K., Long, X., Polido, F., Gennert, M., Padir, T., Feng, S., Moriguchi, H., Whitman, E., Xinjilefu, X., Atkeson, C., Journal of Field Robotics, Volume 32, Pages 275-292, 2015
-
“Team WPI-CMU: Achieving Reliable Humanoid Behavior in the DARPA Robotics Challenge”, DeDonato, M., Polido, F., Knoedler, K., Babu, B. P. W., Banerjee, N., Bove, C. P., Cui, X., Du, R., Franklin, P., Graff, J. P., He, P., Jaeger, A., Li, L., Berenson, D., Gennert, M. A., Feng, S., Liu, C., Xinjilefu, X., Kim, J., Atkeson, C. G., Long, X. and Padır, T, Journal of Field Robotics, doi:10.1002/rob.21685, 2017
Conference:
In Proceedings
-
“Human-Supervised Control of the ATLAS Humanoid Robot for Traversing Doors,”, Nandan Banerjee, Xianchao Long, Ruixiang Du, Felipe Polido, Siyuan Feng, Christopher Atkeson, Michael Gennert, Taskin Padir, IEEE Humanoids 2015
-
“NO FALLS, NO RESETS: Reliable Humanoid Behavior in the DARPA Robotics Challenge,”, Christopher Atkeson, Nandan Banerjee, Dmitry Berenson, Mathew DeDonato, Ruixiang Du, Siyuan Feng, Joohyung Kim, Kevin Knoedler, Chenggang Liu, Xianchao Long, Felipe Polido, Michael Gennert, Taskin Padir, X Xinjilefu, Benzun Pious Wisely Babu, Christopher Bove, Joshua Graff, Peng He, Aaron Jaeger, Lening Li, IEEE Humanoids 2015
-
“Image Stitching Techniques for An Intelligent Portable Aerial Surveillance System,”, Ruixiang Du, Taskin Padir, IEEE TePRA 2014 (pdf)
Workshop:
- “Robotics Enabled In-Home Environment Screening for Fall Risks,”, Ruixiang Du, Vinayak Jagtap, Yanren Long, Oke Onwuka, Taskin Padir, ACM MobiSys 2014 - Proceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems, Pages 9-12 (pdf)
Master Thesis:
- “An Intelligent Portable Aerial Surveillance System:Modeling and Image Stitching,”, Ruixiang Du, Worcester Polytechnic Institute, 2013 (pdf)
Similar Research
- Ron Alterovitz, The Computational Robotics Group
- Jur van den Berg, Algorithmic Robotics Lab, Google Scholar Page
- Lydia Kavraki, Kavraki Lab, Google Scholar Page
- Nicholas Roy, Robust Robotics Group - CSAIL, Google Scholar Page
- Sertac Karaman, MIT, Google Scholar Page
- Erion Plaku, CUA, Google Scholar Page
- Vijay Kumar, UPenn, Google Scholar


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Agents
An agent is an actor that can observe its environment and decide on the best course of action using those observations. Create agents in Unity by extending the Agent class. The most important aspects of creating agents that can successfully learn are the observations the agent collects and, for reinforcement learning, the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks.
An agent passes its observations to its brain. The brain, then, makes a decision and passes the chosen action back to the agent. Your agent code must execute the action, for example, move the agent in one direction or another. In order to train an agent using reinforcement learning, your agent must calculate a reward value at each action. The reward is used to discover the optimal decision-making policy. (A reward is not used by already trained agents or for imitation learning.)
The Brain class abstracts out the decision making logic from the agent itself so that you can use the same brain in multiple agents. How a brain makes its decisions depends on the type of brain it is. An External brain simply passes the observations from its agents to an external process and then passes the decisions made externally back to the agents. An Internal brain uses the trained policy parameters to make decisions (and no longer adjusts the parameters in search of a better decision). The other types of brains do not directly involve training, but you might find them useful as part of a training project. See Brains.
Decisions
The observation-decision-action-reward cycle repeats after a configurable number of simulation steps (the frequency defaults to once-per-step). You can also set up an agent to request decisions on demand. Making decisions at regular step intervals is generally most appropriate for physics-based simulations. Making decisions on demand is generally appropriate for situations where agents only respond to specific events or take actions of variable duration. For example, an agent in a robotic simulator that must provide fine-control of joint torques should make its decisions every step of the simulation. On the other hand, an agent that only needs to make decisions when certain game or simulation events occur, should use on-demand decision making.
To control the frequency of step-based decision making, set the Decision Frequency value for the Agent object in the Unity Inspector window. Agents using the same Brain instance can use a different frequency. During simulation steps in which no decision is requested, the agent receives the same action chosen by the previous decision.
On Demand Decision Making
On demand decision making allows agents to request decisions from their brains only when needed instead of receiving decisions at a fixed frequency. This is useful when the agents commit to an action for a variable number of steps or when the agents cannot make decisions at the same time. This typically the case for turn based games, games where agents must react to events or games where agents can take actions of variable duration.
When you turn on On Demand Decisions for an agent, your agent code must call the Agent.RequestDecision()
function. This function call starts one iteration of the observation-decision-action-reward cycle. The Brain invokes the agent's CollectObservations()
method, makes a decision and returns it by calling the AgentAction()
method. The Brain waits for the agent to request the next decision before starting another iteration.
Observations
To make decisions, an agent must observe its environment to determine its current state. A state observation can take the following forms:
- Continuous Vector — a feature vector consisting of an array of numbers.
- Discrete Vector — an index into a state table (typically only useful for the simplest of environments).
- Visual Observations — one or more camera images.
When you use the Continuous or Discrete vector observation space for an agent, implement the Agent.CollectObservations()
method to create the feature vector or state index. When you use Visual Observations, you only need to identify which Unity Camera objects will provide images and the base Agent class handles the rest. You do not need to implement the CollectObservations()
method when your agent uses visual observations (unless it also uses vector observations).
Continuous Vector Observation Space: Feature Vectors
For agents using a continuous state space, you create a feature vector to represent the agent's observation at each step of the simulation. The Brain class calls the CollectObservations()
method of each of its agents. Your implementation of this function must call AddVectorObs
to add vector observations.
The observation must include all the information an agent needs to accomplish its task. Without sufficient and relevant information, an agent may learn poorly or may not learn at all. A reasonable approach for determining what information should be included is to consider what you would need to calculate an analytical solution to the problem.
For examples of various state observation functions, you can look at the example environments included in the ML-Agents SDK. For instance, the 3DBall example uses the rotation of the platform, the relative position of the ball, and the velocity of the ball as its state observation. As an experiment, you can remove the velocity components from the observation and retrain the 3DBall agent. While it will learn to balance the ball reasonably well, the performance of the agent without using velocity is noticeably worse.
public GameObject ball;
private List<float> state = new List<float>();
public override void CollectObservations()
{
AddVectorObs(gameObject.transform.rotation.z);
AddVectorObs(gameObject.transform.rotation.x);
AddVectorObs((ball.transform.position.x - gameObject.transform.position.x));
AddVectorObs((ball.transform.position.y - gameObject.transform.position.y));
AddVectorObs((ball.transform.position.z - gameObject.transform.position.z));
AddVectorObs(ball.transform.GetComponent<Rigidbody>().velocity.x);
AddVectorObs(ball.transform.GetComponent<Rigidbody>().velocity.y);
AddVectorObs(ball.transform.GetComponent<Rigidbody>().velocity.z);
}
The feature vector must always contain the same number of elements and observations must always be in the same position within the list. If the number of observed entities in an environment can vary you can pad the feature vector with zeros for any missing entities in a specific observation or you can limit an agent's observations to a fixed subset. For example, instead of observing every enemy agent in an environment, you could only observe the closest five.
When you set up an Agent's brain in the Unity Editor, set the following properties to use a continuous vector observation:
Space Size — The state size must match the length of your feature vector. Space Type — Set to Continuous. Brain Type — Set to External during training; set to Internal to use the trained model.
The observation feature vector is a list of floating point numbers, which means you must convert any other data types to a float or a list of floats.
Integers can be be added directly to the observation vector. You must explicitly convert Boolean values to a number:
AddVectorObs(isTrueOrFalse ? 1 : 0);
For entities like positions and rotations, you can add their components to the feature list individually. For example:
Vector3 speed = ball.transform.GetComponent<Rigidbody>().velocity;
AddVectorObs(speed.x);
AddVectorObs(speed.y);
AddVectorObs(speed.z);
Type enumerations should be encoded in the one-hot style. That is, add an element to the feature vector for each element of enumeration, setting the element representing the observed member to one and set the rest to zero. For example, if your enumeration contains [Sword, Shield, Bow] and the agent observes that the current item is a Bow, you would add the elements: 0, 0, 1 to the feature vector. The following code example illustrates how to add
enum CarriedItems { Sword, Shield, Bow, LastItem }
private List<float> state = new List<float>();
public override void CollectObservations()
{
for (int ci = 0; ci < (int)CarriedItems.LastItem; ci++)
{
AddVectorObs((int)currentItem == ci ? 1.0f : 0.0f);
}
}
Normalization
For the best results when training, you should normalize the components of your feature vector to the range [-1, +1] or [0, 1]. When you normalize the values, the PPO neural network can often converge to a solution faster. Note that it isn't always necessary to normalize to these recommended ranges, but it is considered a best practice when using neural networks. The greater the variation in ranges between the components of your observation, the more likely that training will be affected.
To normalize a value to [0, 1], you can use the following formula:
normalizedValue = (currentValue - minValue)/(maxValue - minValue)
Rotations and angles should also be normalized. For angles between 0 and 360 degrees, you can use the following formulas:
Quaternion rotation = transform.rotation;
Vector3 normalized = rotation.eulerAngles / 180.0f - Vector3.one; // [-1,1]
Vector3 normalized = rotation.eulerAngles / 360.0f; // [0,1]
For angles that can be outside the range [0,360], you can either reduce the angle, or, if the number of turns is significant, increase the maximum value used in your normalization formula.
Multiple Visual Observations
Camera observations use rendered textures from one or more cameras in a scene. The brain vectorizes the textures and feeds them into a neural network. You can use camera observations and either continuous feature vector or discrete state observations at the same time.
Agents using camera images can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all.
To add a visual observation to an agent, click on the Add Camera
button in the Agent inspector. Then drag the camera you want to add to the Camera
field. You can have more than one camera attached to an agent.
In addition, make sure that the Agent's Brain expects a visual observation. In the Brain inspector, under Brain Parameters
-> Visual Observations
, specify the number of Cameras the agent is using for its visual observations. For each visual observation, set the width and height of the image (in pixels) and whether or not the observation is color or grayscale (when Black And White
is checked).
Discrete Vector Observation Space: Table Lookup
You can use the discrete vector observation space when an agent only has a limited number of possible states and those states can be enumerated by a single number. For instance, the Basic example environment in ML-Agents defines an agent with a discrete vector observation space. The states of this agent are the integer steps between two linear goals. In the Basic example, the agent learns to move to the goal that provides the greatest reward.
More generally, the discrete vector observation identifier could be an index into a table of the possible states. However, tables quickly become unwieldy as the environment becomes more complex. For example, even a simple game like tic-tac-toe has 765 possible states (far more if you don't reduce the number of observations by combining those that are rotations or reflections of each other).
To implement a discrete state observation, implement the CollectObservations()
method of your Agent subclass and return a List
containing a single number representing the state:
public override void CollectObservations()
{
AddVectorObs(stateIndex); // stateIndex is the state identifier
}
Vector Actions
An action is an instruction from the brain that the agent carries out. The action is passed to the agent as a parameter when the Academy invokes the agent's AgentAction()
function. When you specify that the vector action space is Continuous, the action parameter passed to the agent is an array of control signals with length equal to the Vector Action Space Size
property. When you specify a Discrete vector action space type, the action parameter is an array containing only a single value, which is an index into your list or table of commands. In the Discrete vector action space type, the Vector Action Space Size
is the number of elements in your action table. Set the Vector Action Space Size
and Vector Action Space Type
properties on the Brain object assigned to the agent (using the Unity Editor Inspector window).
Neither the Brain nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an agent is in the AgentAction()
function. You simply specify the type of vector action space, and, for the continuous vector action space, the number of values, and then apply the received values appropriately (and consistently) in ActionAct()
.
For example, if you designed an agent to move in two dimensions, you could use either continuous or the discrete vector actions. In the continuous case, you would set the vector action size to two (one for each dimension), and the agent's brain would create an action with two floating point values. In the discrete case, you would set the vector action size to four (one for each direction), and the brain would create an action array containing a single element with a value ranging from zero to four.
Note that when you are programming actions for an agent, it is often helpful to test your action logic using a Player brain, which lets you map keyboard commands to actions. See Brains.
The 3DBall and Area example environments are set up to use either the continuous or the discrete vector action spaces.
Continuous Action Space
When an agent uses a brain set to the Continuous vector action space, the action parameter passed to the agent's AgentAction()
function is an array with length equal to the Brain object's Vector Action Space Size
property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an agent, for example, the training process learns to control the speed of the agent though this parameter.
The Reacher example defines a continuous action space with four control values.
These control values are applied as torques to the bodies making up the arm :
public override void AgentAction(float[] act)
{
float torque_x = Mathf.Clamp(act[0], -1, 1) * 100f;
float torque_z = Mathf.Clamp(act[1], -1, 1) * 100f;
rbA.AddTorque(new Vector3(torque_x, 0f, torque_z));
torque_x = Mathf.Clamp(act[2], -1, 1) * 100f;
torque_z = Mathf.Clamp(act[3], -1, 1) * 100f;
rbB.AddTorque(new Vector3(torque_x, 0f, torque_z));
}
You should clamp continuous action values to a reasonable value (typically [-1,1]) to avoid introducing instability while training the agent with the PPO algorithm. As shown above, you can scale the control values as needed after clamping them.
Discrete Action Space
When an agent uses a brain set to the Discrete vector action space, the action parameter passed to the agent's AgentAction()
function is an array containing a single element. The value is the index of the action to in your table or list of actions. With the discrete vector action space, Vector Action Space Size
represents the number of actions in your action table.
The Area example defines five actions for the discrete vector action space: a jump action and one action for each cardinal direction:
// Get the action index
int movement = Mathf.FloorToInt(act[0]);
// Look up the index in the action list:
if (movement == 1) { directionX = -1; }
if (movement == 2) { directionX = 1; }
if (movement == 3) { directionZ = -1; }
if (movement == 4) { directionZ = 1; }
if (movement == 5 && GetComponent<Rigidbody>().velocity.y <= 0) { directionY = 1; }
// Apply the action results to move the agent
gameObject.GetComponent<Rigidbody>().AddForce(
new Vector3(
directionX * 40f, directionY * 300f, directionZ * 40f));
Note that the above code example is a simplified extract from the AreaAgent class, which provides alternate implementations for both the discrete and the continuous action spaces.
Rewards
In reinforcement learning, the reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn.
Note: Rewards are not used during inference by a brain using an already trained policy and is also not used during imitation learning.
Perhaps the best advice is to start simple and only add complexity as needed. In general, you should reward results rather than actions you think will lead to the desired results. To help develop your rewards, you can use the Monitor class to display the cumulative reward received by an agent. You can even use a Player brain to control the agent while watching how it accumulates rewards.
Allocate rewards to an agent by calling the AddReward()
method in the AgentAction()
function. The reward assigned in any step should be in the range [-1,1]. Values outside this range can lead to unstable training. The reward
value is reset to zero at every step.
Examples
You can examine the AgentAction()
functions defined in the example environments to see how those projects allocate rewards.
The GridAgent
class in the GridWorld example uses a very simple reward system:
Collider[] hitObjects = Physics.OverlapBox(trueAgent.transform.position,
new Vector3(0.3f, 0.3f, 0.3f));
if (hitObjects.Where(col => col.gameObject.tag == "goal").ToArray().Length == 1)
{
AddReward(1.0f);
Done();
}
if (hitObjects.Where(col => col.gameObject.tag == "pit").ToArray().Length == 1)
{
AddReward(-1f);
Done();
}
The agent receives a positive reward when it reaches the goal and a negative reward when it falls into the pit. Otherwise, it gets no rewards. This is an example of a sparse reward system. The agent must explore a lot to find the infrequent reward.
In contrast, the AreaAgent
in the Area example gets a small negative reward every step. In order to get the maximum reward, the agent must finish its task of reaching the goal square as quickly as possible:
AddReward( -0.005f);
MoveAgent(act);
if (gameObject.transform.position.y < 0.0f ||
Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f ||
Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8)
{
Done();
AddReward(-1f);
}
The agent also gets a larger negative penalty if it falls off the playing surface.
The Ball3DAgent
in the 3DBall takes a similar approach, but allocates a small positive reward as long as the agent balances the ball. The agent can maximize its rewards by keeping the ball on the platform:
if (IsDone() == false)
{
SetReward(0.1f);
}
// When ball falls mark agent as done and give a negative penalty
if ((ball.transform.position.y - gameObject.transform.position.y) < -2f ||
Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f ||
Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f)
{
Done();
SetReward(-1f);
}
The Ball3DAgent
also assigns a negative penalty when the ball falls off the platform.
Agent Properties
Brain
- The brain to register this agent to. Can be dragged into the inspector using the Editor.Visual Observations
- A list ofCameras
which will be used to generate observations.Max Step
- The per-agent maximum number of steps. Once this number is reached, the agent will be reset ifReset On Done
is checked.Reset On Done
- Whether the agent'sAgentReset()
function should be called when the agent reaches itsMax Step
count or is marked as done in code.On Demand Decision
- Whether the agent requests decisions at a fixed step interval or explicitly requests decisions by callingRequestDecision()
.- If not checked, the Agent will request a new
decision every
Decision Frequency
steps and perform an action every step. In the example above,CollectObservations()
will be called every 5 steps andAgentAction()
will be called at every step. This means that the Agent will reuse the decision the Brain has given it. - If checked, the Agent controls when to receive
decisions, and take actions. To do so, the Agent may leverage one or two methods:
RequestDecision()
Signals that the Agent is requesting a decision. This causes the Agent to collect its observations and ask the Brain for a decision at the next step of the simulation. Note that when an Agent requests a decision, it also request an action. This is to ensure that all decisions lead to an action during training.RequestAction()
Signals that the Agent is requesting an action. The action provided to the Agent in this case is the same action that was provided the last time it requested a decision.
- If not checked, the Agent will request a new
decision every
Decision Frequency
- The number of steps between decision requests. Not used ifOn Demand Decision
, is true.
Monitoring Agents
We created a helpful Monitor
class that enables visualizing variables within
a Unity environment. While this was built for monitoring an Agent's value
function throughout the training process, we imagine it can be more broadly
useful. You can learn more here.
Instantiating an Agent at Runtime
To add an Agent to an environment at runtime, use the Unity GameObject.Instantiate()
function. It is typically easiest to instantiate an agent from a Prefab (otherwise, you have to instantiate every GameObject and Component that make up your agent individually). In addition, you must assign a Brain instance to the new Agent and initialize it by calling its AgentReset()
method. For example, the following function creates a new agent given a Prefab, Brain instance, location, and orientation:
private void CreateAgent(GameObject agentPrefab, Brain brain, Vector3 position, Quaternion orientation)
{
GameObject agentObj = Instantiate(agentPrefab, position, orientation);
Agent agent = agentObj.GetComponent<Agent>();
agent.GiveBrain(brain);
agent.AgentReset();
}
Destroying an Agent
Before destroying an Agent GameObject, you must mark it as done (and wait for the next step in the simulation) so that the Brain knows that this agent is no longer active. Thus, the best place to destroy an agent is in the Agent.AgentOnDone()
function:
public override void AgentOnDone()
{
Destroy(gameObject);
}
Note that in order for AgentOnDone()
to be called, the agent's ResetOnDone
property must be false. You can set ResetOnDone
on the agent's Inspector or in code.


Closures in Javascript
Closures is yet another topic in Javascript that often throws people off. The thing about closures is that it is used all the time in Javascript, but most don’t recognize it. But once you get it, it sticks. Now let’s carry forward from my other posts
and check this one off the To-Learn-Debt at the end of this post. Understanding closures requires a good understanding of variable scopes in Javascript. Although this post is not meant to teach that,
I’ll make a quick mention of some ground rules to keep in mind when it comes to scope: Functions can be nested forming variable access chains. The deepest function has access to all variables dec


Welcome to The ArtemisCloud documentation. This documentation is open source and can be found at here. All contributions are welcome
Welcome to The ArtemisCloud documentation. This documentation is open source and can be found at here. All contributions are welcome


Drop models here
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This site has one simple purpose - Help YOU find loans FAST!
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This is a tool created to deserialize a font file in order to obtain a valid JSON format that can be appended to the fonts database.


Jack Welch had Six Sigma. George Bush had his "vision thing". And an animated sponge has his "This is going to be the best day ever!". So forgive me for enunciating GChart's fundamental principles in this grandiose "GChart Manifest" (and no, I did not forget the "o").
I'd like to say these principles guided me in every step of GChart's design and implementation. In fact, after many attempts at something better, I ended up choosing the only strategy I was actually able to code, and then came up with the these principles to rationalize what I'd done (a statistical justification is planned for the next version).
Why leave the GWT-Java sandbox just to add a few charts?
Want to trace into your chart library in the Eclipse IDE and tweak a line that isn't working the way you want it to? GChart gives you this level of complete, Java-based, source-level, control.
And, because it's layered on GWT's standard library, your deployed application is fully optimized by GWT's compiler, and compatibility with all GWT-supported browsers is practically automatic.
OK, I admit it: our simple "just say it with rectangles" implementation strategy often leaves us no choice but to forget the flashy eye candy and embrace our inner squareness.
So, we aim for charts that clearly communicate the meaning of a data set in, say, an engineering or scientific application. We scorn 3-D pie charts with perfectly manicured edges. Instead we ask: "Why insist on solid fill pie slices when banded-fill uses a fraction of the HTML elements?"
Square is simple and fast and works even in IE6 quirks-mode. Or, in the words of SpongeBarSquareCharts (no relation), "This is going to be the boxiest chart ever!"
Sure, you can generate great charts from a server. Indeed, with the new Google Chart API, you can even generate them for free off of someone else's servers. But if you need off-line charting, or simply prefer that your application's performance be constrained by the client computer, not by the connection and server, you need a client-side GWT charting solution.
Flexible Apache licensing and zero out of pocket costs isn't enough.
You have to be able to easily understand the code, so you can fully exploit, and even change it, as needed.
The library also needs complete docs, a full set of examples, on-line demos, and a thorough test set so you don't spend more figuring out how to use it than you saved by not having to buy it.
Fortunately, GChart's straightforward implementation frees up a lot of time that can be devoted towards making the library reliable and easy to use.
These are our principles. We stand by them. Until, of course, we figure out a better way to implement the darn thing.


Russian military convoy filmed heading to Assyrian town in northeast Syria: video
by News DeskBEIRUT, LEBANON (5:30 P.M.) – A Russian military convoy was recently filmed heading to the Assyrian town of Tal Tamr in northeastern Syria.
According to the reports, the Russian military convoy traveled from the Al-Raqqa Governorate town of ‘Ayn ‘Issa to Tal Tamr in the Al-Hasakah Governorate.


Dalits take out protest march
by Special CorrespondentMembers of the Dr. B.R. Ambedkar Dalit Sangharsh Samiti took out a protest march in Hubballi on Monday seeking action against all those involved in uploading a handbook on a website that demeaned B.R. Ambedkar.
Beginning from the Ambedkar statue near the Head Post Office, the protestors marched through Lamington Road and raised slogans against those responsible for the act and also against the State government for failing to initiate strict action against them.
They said that it was a deliberate attempt to demean Ambedkar, architect of the Constitution, and squarely held Principal Secretary to Government Uma Shankar responsible for the whole development.
They demanded that the officer concerned, and also others who were involved in the issue, be dismissed from service.
On reaching the Mini Vidhana Soudha, they held a demonstration and submitted a memorandum, addressed to the Governor, to the officials of the taluk administration. In the memorandum, they have said that such acts by officials would create unrest in society and they should be dealt with seriously.


Laravel 5.8 - Client Side Form Validation using Parsleys.js
@CSRF
First Name
Last Name
Email
Password
Confirm Password
Website
I Accept the Terms & Conditions


Server side data binding allow users to bind large data sets that are not directly accessible as a full collection in client side due to various reasons. Server side data binding can improve performance drastically for large data sets but integration is fairly complex compared to Client Side Basic and Client Side Stream Data Binding techniques. This data binding technique can be used to fetch data via HTTP GET requests on demand.
Unlike in Client Side Basic Data Binding, Server Side data binding will request for data on table initialization and whenever user performs Filtering, Sorting, Pagination etc. operations. Ornamentum has provided an optional opinionated service to integrate external data sources. However, you can build a custom data provider depending on your requirement.
onDataBind: DataTableDataBindCallback<any>
Default Value: undefined
Target Component: <ng-data-table>
Server side data bind functionality can be integrated via onDataBind callback function input property. Data source associated logic is decoupled from Ornamentum core module to make it data source agnostic and easy to customize.
Import DataTableResourceModule along with DataTableModule.
Within the target component use DataTableHttpResourceFactoryService to construct an instance of DataTableHttpDataFetchService<T> class. Invoke onDataBind method with the API resource path to get the data bind callback function reference.
or else you can write your own custom data provider without using getResourceProvider in DataTableHttpResourceFactoryService service. Please refer Custom Data Provider documentation for more information.
Set the obtained data bind and filter value extract callback function references to onDataBind and onFilterValueExtract input properties of data table accordingly.
Data fetch HTTP request (Your backend REST API) should support following
Pagination query parameters
limit
- The limit parameter controls the maximum number of items that may be returned for a
single request. This parameter reflects the current page size. If limit is not specified, it will
default to 10.
offset
- Number of records you wish to skip before selecting records with specified limit.
For example, if offset is 10 and limit is 5 it should return 5 items starting from 10th index.
Note that offset is zero index based.
Usage scenarios and examples
Page 1 request when limit is 5:
https://yourapi/resource/path?limit=5&offset=0
Page 2 request when limit is 10:
https://yourapi/resource/path?limit=10&offset=10
Sorting and Filtering query parameters - Sortable and/or filterable columns field names will get appended as query parameters. The format of these field query parameters are as follows.
<field name>=
[<filter value>
][|<sort order>|<sort priority>
]
field name
- Sortable and/or filterable column field name. These fields are appended as query
parameters only when filtering and/or sorting is applied on target column which is sortable or filterable.
filter value
- Filter value is available only when filter is applied. Multi filter values are
passed as a comma separated value.
sort order
- Sort order is appended only when field sort order is 'asc'
or
'desc'
sort priority
- Sort priority is passed as an index incrementer starting from value 1. This
property is useful when multi column sorting is enabled to identify the sortable field order.
The target field is ignored if no filtering or sorting is applied on the target column to reduce the data fetch
request URL length by avoiding appendage of empty valued query param. The filter and sort field values are
separated by pipe |
character when both sorting and filtering is applied on the same field. The
sort order and sort priority properties are also separated by pipe character.
Usage scenarios and examples
filtering applied and sorting applied
- both filter and sort order values are appended to the
request URL via a query parameter with target field name.
i.e: data GET request URL generated when Product Line column is sorted in ascending order and filtered by 'equipment' keyword.
https://yourapi/resource/path?limit=10&offset=0&productLine=equipment|asc|1
filtering applied and sorting not applied
- filter value is appended to the
request URL via a query parameter with target field name.
i.e: data GET request URL generated when Product Line column is filtered by 'equipment' keyword.
https://yourapi/resource/path?limit=10&offset=0&productLine=equipment
filtering not applied and sorting applied
- sort order value is appended to the
request URL via a query parameter with target field name.
i.e: data GET request URL generated when Product Line column is sorted in ascending order.
https://yourapi/resource/path?limit=10&offset=0&productLine=|asc|1
filtering not applied and sorting not applied
- field name is not appended to the
request URL as a query parameter.
i.e: data GET request URL generated when Product Line column is not sorted or filtered.
https://yourapi/resource/path?limit=10&offset=0
Refer Sorting and Filtering documentation for more information and how to enable sorting and filtering.
You can inspect network traffic of this page with "/api/data
" filter in browser developer tools to
understand backend service requests and behaviors of above example.
Server side and client side data binding can be implemented via a custom data provider as Ornamentum data sources are fully decoupled from component logic. Please refer Custom Data Provider for more information.


Trips Organised -
{{trip.tName}}
{{trip.tDescription}}
{{trip.memberCount !== undefined ? trip.memberCount : 0 }} Member(s)
From : {{trip.tFromDate !== undefined ? trip.tFromDate : " Not Specified" }}
To : {{trip.tToDate !== undefined ? trip.tToDate : " Not Specified" }}


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InputStream
.
OutputStream
.
Socket
.
MessagePayload
NagiosSettings
NagiosSettings
with default values
NagiosSettings
from a properties file
NagiosSettings
from a stream containing properties
NagiosSettings
from a properties object
MessagePayload
Encryptor
used to encrypt the passive check
String
If the String
length is larger than the byte
array, the String will be truncated to the fixed size
NagiosSettings
, MessagePayload
using builders and sends
using NagiosPassiveCheckSender
MessagePayload
with hostname being the short
hostname of this machine, level unknown, service name "undefined" and
empty message
MessagePayload
MessagePayload
using a builder pattern e.g.NagiosException
with the cause
NagiosPassiveCheckSender
with the provided
NagiosSettings
NagiosSettings
instance using a builder pattern
e.g.NagiosSettings
from a Properties
object
via file, stream or an instance.Properties
instanceNagiosPassiveCheckSender
.NonBlockingNagiosPassiveCheckSender
with the
provided NagiosSettings
PassiveCheckSender
'sEncryption
to use to encrypt the passive check
Encryptor
to use to encrypt the passive check
String
The case of the
String
is ignored
Level
equivalent of the string level provided ignoring
case and leading or trailing whitespace
Level
equivalent of the int levelValue provided
NagiosSettings
created will use the supplied connection
timeout
NagiosSettings
created will use the specified
Encryption
constant
NagiosSettings
created will use the specified
Encryptor
NagiosSettings
created will have large message support enabled
see NagiosSettings.enableLargeMessageSupport()
NagiosSettings
created will use the supplied nagiosHost
NagiosSettings
created will have an empty string set as
the password
NagiosSettings
created will use the supplied password
NagiosSettings
created will use the supplied port
NagiosSettings
created will use the supplied response
timeout
|
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Tax
Revenue announces extension to 'Pay and File' deadline after site crash
Revenue
by David LynchRevenue have announced an extension of the 2018 Pay and File deadline until 6pm tomorrow, 13 November, due to what they describe as "very heavy usage volumes" which was causing the site to crash.
In a statement this afternoon, Revenue said:
"With taxpayers and agents working towards a deadline of midnight tonight, 12 November, the ROS system has been experiencing very heavy usage volumes, with some 3,000 – 4,000 returns filed per hour throughout the morning. These volumes caused intermittent downtime for some of those using the ROS system and while it was still possible to file returns, the volume of submissions were less than expected.
"Revenue’s technical team took time to fully refresh the system, which resulted in the ROS system being unavailable for a period of time this afternoon. The ROS system is now operational again and Revenue’s technical team will keep the system and its performance under constant review.
"Revenue acknowledges the difficulties these issues have caused for taxpayers and agents and has announced an extension to the deadline until 6pm tomorrow, 13 November 2019."


@Untainted
annotations, returned from annotated methods
or assigned to annotated fields, parameters or local variables. Kotlin `set` and `get` methods for fields are not supported as entry points.
Safe object is:
@Untainted
@Untainted
and are not assigned from non-safe objectsThe analysis is performed only inside the one file. To process dependencies from other classes, use options. The analysis extends to private or static methods, final fields and has limit of depth propagation. Example:
void doSmth(boolean b) {
String s = safe();
String s1 = "other";
if (b) s1 = s;
sink(s);
}
String sink(@Untainted String s) {}
Here we do not have non-safe string assignments to s
so warning is not produced. On the other hand:
void doSmth(boolean b) {
String s = safe();
String s1 = "other";
s1 = foo();
if (b) s = s1;
sink(s); // warning here
}
String foo();
String sink(@Untainted String s) {}
Here we have a warning since s1
has an unknown state after foo
call result assignment.
Use the Untainted annotations table to specify annotations, which will be used as @Untainted
annotations during the analysis.
The first annotation from this list will be used for propagation if it exists in classpath.
Use the Tainted annotations table to specify annotations, which will be used as @Tainted
annotations during the analysis
If the Consider external methods untainted if receivers and arguments are untainted option is enabled, then external methods outside the current class will be considered as safe if its receivers and arguments are safe. For some cases, it is not applicable, but it can be useful for stateless classes. If this option is disabled, then all external methods will be considered as unsafe.
Use the Safe classes option to specify classes, expression with this type will be considered as safe
New in 2021.2


Interface IBoundConfiguration<TRow>
Represents and Options and Type pair.
Used to create readers and writers.
Namespace: Cesil
Assembly: Cesil.dll
Syntax
public interface IBoundConfiguration<TRow>
Type Parameters
| Name | Description |
|---|---|
| TRow |
Properties
| Improve this Doc View SourceOptions
The Options used to create this configuration.
If any settings are used that defer decisions (like ReadHeader.Detect) this Options object will remain unchanged when a decision is made. In other words, this Options object will always match what was used to create the configuration - it is not updated.
Declaration
Options Options { get; }
Property Value
| Type | Description |
|---|---|
| Options |
Methods
| Improve this Doc View SourceCreateAsyncReader(PipeReader, Encoding, Object)
Create an asynchronous reader for the given reader.
The provided encoding is used to convert the bytes provided by the reader into characters for parsing.
Takes an optional context object which is made available during certain operations as a member on ReadContext.
Declaration
IAsyncReader<TRow> CreateAsyncReader(PipeReader reader, Encoding encoding, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| System.IO.Pipelines.PipeReader | reader | |
| Encoding | encoding | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IAsyncReader<TRow> |
CreateAsyncReader(TextReader, Object)
Create an asynchronous reader for the given reader.
Takes an optional context object which is made available during certain operations as a member on ReadContext.
Declaration
IAsyncReader<TRow> CreateAsyncReader(TextReader reader, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| TextReader | reader | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IAsyncReader<TRow> |
CreateAsyncWriter(PipeWriter, Encoding, Object)
Create an asynchronous writer for the given writer.
The provided encoding is used to convert characters into bytes.
Takes an optional context object which is made available during certain operations as a member on WriteContext.
Declaration
IAsyncWriter<TRow> CreateAsyncWriter(PipeWriter writer, Encoding encoding, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| System.IO.Pipelines.PipeWriter | writer | |
| Encoding | encoding | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IAsyncWriter<TRow> |
CreateAsyncWriter(TextWriter, Object)
Create an asynchronous writer for the given writer.
Takes an optional context object which is made available during certain operations as a member on WriteContext.
Declaration
IAsyncWriter<TRow> CreateAsyncWriter(TextWriter writer, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| TextWriter | writer | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IAsyncWriter<TRow> |
CreateReader(ReadOnlySequence<Byte>, Encoding, Object)
Create a synchronous reader for the given sequence, converting bytes to characters using the provided encoding.
Takes an optional context object which is made available during certain operations as a member on ReadContext.
Declaration
IReader<TRow> CreateReader(ReadOnlySequence<byte> sequence, Encoding encoding, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| ReadOnlySequence<System.Byte> | sequence | |
| Encoding | encoding | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IReader<TRow> |
CreateReader(ReadOnlySequence<Char>, Object)
Create a synchronous reader for the given sequence.
Takes an optional context object which is made available during certain operations as a member on ReadContext.
Declaration
IReader<TRow> CreateReader(ReadOnlySequence<char> sequence, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| ReadOnlySequence<System.Char> | sequence | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IReader<TRow> |
CreateReader(TextReader, Object)
Create a synchronous reader for the given reader.
Takes an optional context object which is made available during certain operations as a member on ReadContext.
Declaration
IReader<TRow> CreateReader(TextReader reader, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| TextReader | reader | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IReader<TRow> |
CreateWriter(IBufferWriter<Byte>, Encoding, Object)
Create a synchronous writer for the given writer, converting chars to bytes using the given encoding.
Takes an optional context object which is made available during certain operations as a member on WriteContext.
Declaration
IWriter<TRow> CreateWriter(IBufferWriter<byte> writer, Encoding encoding, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| IBufferWriter<System.Byte> | writer | |
| Encoding | encoding | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IWriter<TRow> |
CreateWriter(IBufferWriter<Char>, Object)
Create a synchronous writer for the given writer.
Takes an optional context object which is made available during certain operations as a member on WriteContext.
Declaration
IWriter<TRow> CreateWriter(IBufferWriter<char> writer, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| IBufferWriter<System.Char> | writer | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IWriter<TRow> |
CreateWriter(TextWriter, Object)
Create a synchronous writer for the given writer.
Takes an optional context object which is made available during certain operations as a member on WriteContext.
Declaration
IWriter<TRow> CreateWriter(TextWriter writer, object context = null)
Parameters
| Type | Name | Description |
|---|---|---|
| TextWriter | writer | |
| System.Object | context |
Returns
| Type | Description |
|---|---|
| IWriter<TRow> |


Her Favorite Things
These are a few of her favorite things
People
Here are some of her favorite people.
- Her brother Christopher
- her mom and dad
- Her friend Jessica
- Her friend Candy
Flavors of Ice Cream
- Pistachio
- Malted milk chocolate
- Black licorice (what, yuk!!!!)
Thanks for having us write about you!


Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI and nrrd file formats. RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and geometry-based statistics. Almost all calculations are done using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters only takes minutes on a single core of a conventional PC.
| Version: | 1.4.1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | oro.dicom (≥ 0.5.0), oro.nifti (≥ 0.9.1), nat (≥ 1.8.11) |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-06-24 |
| Author: | Marton Kolossvary [aut, cre] |
| Maintainer: | Marton Kolossvary <marton.kolossvary at cirg.hu> |
| License: | AGPL-3 |
| URL: | https://www.ncbi.nlm.nih.gov/pubmed/29233836 |
| NeedsCompilation: | no |
| Citation: | RIA citation info |
| Materials: | README |
| CRAN checks: | RIA results |
| Reference manual: | RIA.pdf |
| Vignettes: |
RIA package manual |
| Package source: | RIA_1.4.1.tar.gz |
| Windows binaries: | r-devel: RIA_1.4.1.zip, r-release: RIA_1.4.1.zip, r-oldrel: RIA_1.4.1.zip |
| OS X binaries: | r-release: RIA_1.4.1.tgz, r-oldrel: RIA_1.4.1.tgz |
| Old sources: | RIA archive |
Please use the canonical form https://CRAN.R-project.org/package=RIA to link to this page.


public class DateTimeUtils
extends java.lang.Object
| 构造器和说明 |
|---|
DateTimeUtils() |
| 限定符和类型 | 方法和说明 |
|---|---|
static java.lang.String |
currentTime() |
static java.util.Date |
days2Date(long days)
Get date of the giving days from which 1900-1-1.
|
static long |
getDays(int year,
int month,
int date)
Get how many days from 1900-1-1 to the specific year,month,date.
|
static long |
getDays(long timeInMilliSeconds)
get how many days from '1900-1-1' to time specified in timeInMillis.
|
static long |
getHours(int year,
int month,
int date,
int hour)
Get how many hours from 1900-1-1 00:00:00 to the specific
year,month,date,hour.
|
static long |
getHours(long timeInMilliSeconds)
get how many hours from '1900-1-1' to time specified in timeInMillis.
|
static long |
getMinutes(int year,
int month,
int date,
int hour,
int minute)
Get minutes from 1900-1-1 00:00:00 to the specified
year,month,date,hour,minute.
|
static long |
getMinutes(long timeInMilliSeconds)
get how many minutes from '1900-1-1' to time specified in
timeInMilliSeconds.
|
static int |
getMonths(int year,
int month)
Get how many months from 1900-1-1 to the specific year,month.
|
static int |
getMonths(long timeInMilliSeconds) |
static int |
getWeeks(int year,
int month,
int date)
Get weeks from 1900-1-1 to the specific year,month,date.
|
static int |
getWeeks(long timeInMilliSeconds) |
static java.util.Date |
hours2Date(long hours)
Get date of the giving hours from which 1900-1-1 00:00:00
|
static boolean |
isLeapYear(int year)
Check a year is a leap year or not.
|
static void |
main(java.lang.String[] args) |
static java.util.Date |
minutes2Date(long minutes) |
static java.util.Date |
months2Date(int months)
Get date of the giving months which from 1900-1.
|
static java.lang.String |
timeConsumed(long startTime) |
static java.util.Date |
weeks2Date(int weeks)
Get date of the giving weeks from which 1900-1-1.
|
public static long getMinutes(int year, int month, int date, int hour, int minute)
year
- (>=1900)month
- (1-12)date
- (1-31)hour
- (0-23)minute
- (0-59)public static long getMinutes(long timeInMilliSeconds)
timeInMilliSeconds
- the timeInMilliSeconds is from 1970-1-1 00:00:00public static long getHours(int year, int month, int date, int hour)
year
- (>=1900)month
- (1-12)date
- (1-31)hour
- (0-23)public static long getHours(long timeInMilliSeconds)
timeInMilliSeconds
- the timeInMilliSeconds is from 1970-1-1 00:00:00public static long getDays(int year, int month, int date)
year
- (>=1900)month
- (1-12)date
- (1-31)public static long getDays(long timeInMilliSeconds)
timeInMilliSeconds
- the timeInMilliSeconds is from 1970-1-1 00:00:00public static int getMonths(int year, int month)
year
- (>=1900)month
- (1-12)public static int getMonths(long timeInMilliSeconds)
public static int getWeeks(int year, int month, int date)
year
- (>=1900)month
- (1-12)date
- (1-31).public static int getWeeks(long timeInMilliSeconds)
public static java.util.Date minutes2Date(long minutes)
public static java.util.Date hours2Date(long hours)
hours
- (0,1,2,...) .public static java.util.Date days2Date(long days)
days
- .public static java.util.Date weeks2Date(int weeks)
weeks
- .public static java.util.Date months2Date(int months)
months
- .public static boolean isLeapYear(int year)
year
- .public static java.lang.String currentTime()
public static java.lang.String timeConsumed(long startTime)
public static void main(java.lang.String[] args)
args
-


The transition function is \(F(C_{i-1},C_i,C_{i+1})\) is defined by a look-up table of the form stated in table [wolframcodeGeneral], and an example of an instance of a function is given (rule 110, an important rule on which [@cook2004] proved universal computational power, as Wolfram had conjectured in 1985, and is arguably the simplest Turing complete system [@wolfram2002]) in table [wolframcodeGeneral].
l [wolframcodeGeneral]
\(F(1,1,1)=\{0,1\}\)
\(F(1,1,0)=\{0,1\}\)
\(F(1,0,1)=\{0,1\}\)
\(F(1,0,0)=\{0,1\}\)
\(F(0,1,1)=\{0,1\}\)
\(F(0,1,0)=\{0,1\}\)
\(F(0,0,1)=\{0,1\}\)
\(F(0,0,0)=\{0,1\}\)
\(\overset{instance}{\longrightarrow}\)
| [wolframcoderule] |
|---|
| \(F(1,1,1)=0\) |
| \(F(1,1,0)=1\) |
| \(F(1,0,1)=1\) |
| \(F(1,0,0)=0\) |
| \(F(0,1,1)=1\) |
| \(F(0,1,0)=1\) |
| \(F(0,0,1)=1\) |
| \(F(0,0,0)=0\) |
More generally Wolfram’s code [@wolfram1983; @wolfram2002] can be calculated Conventionally neighborhoods are sorted in non-decreasing order ,(111=7),(110=6),(101=5) etc., and the may be interpreted as a 8-digit number \[01101110=2^0\times0+2^1\times1+2^2\times1+2^\times1+2^4\times0+2^5\times1+2^6\times1+2^7\times0=110\]
List and sort in decreasing numerical (if interpreted as number) order all the possible configuration of the neighborhood of a given cell.
For each configuration, list the state which the given cell will have, according to this rule, on the next iteration.
Interprets the resulting list as binary number and convert it to decimal. That is the Wolfram’s code.
Note that is not possible to understand from that code which is the size or the shape of the neighborhood. Is tacit to suppose that those information are already known.


Preparation time
1-2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 4
For the pastry, pulse the flour, baking powder, salt, butter and egg yolk in a food processor until the mixture resembles breadcrumbs.
Gradually add the water, a tablespoon at a time, mixing continuously until the mixture just comes together as a dough. (You may not need to use all the water.) Roll the dough into a ball, then wrap it in cling film and chill in the fridge for an hour.
Meanwhile, for the filling, heat two tablespoons of the olive oil in a saucepan over a medium heat, add the chicken pieces and cook for 4-5 minutes, or until browned. Add the wine, water, tarragon and thyme, and season, to taste, with salt and freshly ground black pepper. Bring the mixture to the boil, reduce the heat, and simmer for 15-20 minutes, or until the volume of liquid has reduced by half and the chicken is cooked through.
Heat the remaining olive oil in a separate frying pan over a medium heat, add the mushrooms, and cook for 4-5 minutes, or until golden-brown, then season, to taste, with salt and freshly ground black pepper.
Remove the cooked chicken from the frying pan with a slotted spoon and place into a bowl, then add two spoonfuls of the residual juices from that pan. Add the mushrooms, then stir in the soured cream until well combined. Set aside to cool.
Preheat the oven to 180C/350F/Gas 4. Grease a deep 20cm/8in pie tin with butter. Add a small amount of flour, then turn the tin to coat the base and sides, shaking out any excess.
When the pastry has chilled, roll out two thirds of it onto a clean, floured work surface, until it is almost twice as wide as the diameter of the tin. Using the rolling pin, lift the pastry and lay it over the pie tin to line the base and sides. Gently press the pastry into the corners of the tin, trimming off any excess, then prick the base of the pastry case several times with a fork.
Cover the pastry with a sheet of greaseproof paper and fill it with rice or dried beans. Bake in the oven for 10-15 minutes, or until the pastry is pale golden-brown. Remove from the oven, discard the beans and greaseproof paper and set the pastry aside to cool. When cool, pour the pie filling into the pastry case.
Roll the remaining one third of the pastry out onto a clean, floured work surface until it is a little bit larger than the diameter of the pie tin. Brush the rim of the cooked pastry case with some of the beaten egg and place the pastry lid on top of the pie. Trim off any excess pastry. Seal the pastry lid to the pastry case by crimping the edges of the pastry lid with a fork.
With a knife, make two small holes in the centre of the pastry lid to allow steam to escape. Brush the pastry lid with the remaining beaten egg, then transfer to the oven and bake for 30-35 minutes, or until golden-brown.


#include <GrMeshVB.h>
Public Member Functions | |
| GrMeshVB (unsigned int vertexCount, const SVec3 *positions, const SVec3 *normals, const SVec3 *biNormals, const SVec3 *tangents, const unsigned long *colors, const SVec2 *texCoords, unsigned int flags, unsigned int maxVertexCount=0, bool cacheData=true) | |
| class GrVertexBuffer | |
| GrMeshVB (unsigned int vertexCount, unsigned int usedComponents, unsigned int flags, bool cacheData=true) | |
| ~GrMeshVB () | |
| unsigned int | GetVertexCount () const |
| return the vertex count. | |
| unsigned int | GetMaxVertexCount () const |
| return the max vertex count. | |
| void | AdjustVertexCount (int growSize) |
| adjust the number of active vertices | |
| unsigned int | GetUsedComponents () const |
| returns used vertex components. | |
| unsigned int | GetFlags () const |
| returns creation flags. | |
| SVec3 * | GetVertices () const |
| SVec3 * | GetNormals () const |
| SVec3 * | GetBiNormals () const |
| SVec3 * | GetTangents () const |
| unsigned long * | GetColors () const |
| SVec2 * | GetTexCoords () const |
| void | MarkAsDirty (unsigned int components) |
| marks vertex data as dirty and causes the data to be sent to the graphics card. | |
| void | UploadUncached (unsigned int writeOffset, const void *source, unsigned int sourceSize) |
| void | Cache () |
| cache/evict mesh data. | |
| void | Evict () |
| void | Bind (unsigned int components) |
| bind the vertex buffer and it's components. | |
| void | PreContextReset () |
| context reset. | |
| void | PostContextReset () |
| void | PretransformVertices (UFastArray< SVec3 > &results, const MMat4x4 &transform) |
| returns a list of vertex positions that have been transformed by the meshinst's transform. | |
Static Public Attributes | |
| static const unsigned int | kDynamic = 1 |
Definition at line 25 of file GrMeshVB.h.
| GrMeshVB::GrMeshVB | ( | unsigned int | vertexCount, | |
| const SVec3 * | positions, | |||
| const SVec3 * | normals, | |||
| const SVec3 * | biNormals, | |||
| const SVec3 * | tangents, | |||
| const unsigned long * | colors, | |||
| const SVec2 * | texCoords, | |||
| unsigned int | flags, | |||
| unsigned int | maxVertexCount = 0 , |
|||
| bool | cacheData = true | |||
| ) |
class GrVertexBuffer
ctor & dtor
Max vertex size: 12 + 12 + 12 + 12 + 4 + 8 = 68.
allocate the space required to fit all of the vertex data.
Definition at line 37 of file GrMeshVB.cpp.
| GrMeshVB::GrMeshVB | ( | unsigned int | vertexCount, | |
| unsigned int | usedComponents, | |||
| unsigned int | flags, | |||
| bool | cacheData = true | |||
| ) |
Max vertex size: 12 + 12 + 12 + 12 + 4 + 8 = 68.
allocate the space required to fit all of the vertex data.
Definition at line 108 of file GrMeshVB.cpp.
| GrMeshVB::~GrMeshVB | ( | ) |
Definition at line 183 of file GrMeshVB.cpp.
| void GrMeshVB::AdjustVertexCount | ( | int | growSize | ) |
adjust the number of active vertices
public methods
calculate the new vertex count.
adjust the total vertex count.
force a rebuild the vertex buffer.
Definition at line 203 of file GrMeshVB.cpp.
| void GrMeshVB::Bind | ( | unsigned int | components | ) |
bind the vertex buffer and it's components.
if this hits, then the vertex buffer does not contain one or more of the required components!
cache the vertex buffer if necessary.
bind our vertex buffer.
if we need to be updated, then do so now.
bind components.
enable arrays.
Definition at line 263 of file GrMeshVB.cpp.
| void GrMeshVB::Cache | ( | ) |
cache/evict mesh data.
if we're managing data, then set the dirty flags.
build the vertex buffer.
Definition at line 235 of file GrMeshVB.cpp.
| void GrMeshVB::Evict | ( | ) |
Definition at line 252 of file GrMeshVB.cpp.
| SVec3* GrMeshVB::GetBiNormals | ( | ) | const [inline] |
Definition at line 55 of file GrMeshVB.h.
| unsigned long* GrMeshVB::GetColors | ( | ) | const [inline] |
Definition at line 57 of file GrMeshVB.h.
| unsigned int GrMeshVB::GetFlags | ( | ) | const [inline] |
| unsigned int GrMeshVB::GetMaxVertexCount | ( | ) | const [inline] |
| SVec3* GrMeshVB::GetNormals | ( | ) | const [inline] |
Definition at line 54 of file GrMeshVB.h.
| SVec3* GrMeshVB::GetTangents | ( | ) | const [inline] |
Definition at line 56 of file GrMeshVB.h.
| SVec2* GrMeshVB::GetTexCoords | ( | ) | const [inline] |
Definition at line 58 of file GrMeshVB.h.
| unsigned int GrMeshVB::GetUsedComponents | ( | ) | const [inline] |
| unsigned int GrMeshVB::GetVertexCount | ( | ) | const [inline] |
| SVec3* GrMeshVB::GetVertices | ( | ) | const [inline] |
gets vertex data. You must call MarkAsDirty() to cause data to be resent to the graphics hardware.
Definition at line 53 of file GrMeshVB.h.
| void GrMeshVB::MarkAsDirty | ( | unsigned int | components | ) |
marks vertex data as dirty and causes the data to be sent to the graphics card.
Definition at line 218 of file GrMeshVB.cpp.
| void GrMeshVB::PostContextReset | ( | ) |
| void GrMeshVB::PreContextReset | ( | ) |
| void GrMeshVB::PretransformVertices | ( | UFastArray< SVec3 > & | results, | |
| const MMat4x4 & | transform | |||
| ) |
returns a list of vertex positions that have been transformed by the meshinst's transform.
allocate enough space to contain the vertices.
transform each vertex by the meshinst's transform.
Definition at line 396 of file GrMeshVB.cpp.
| void GrMeshVB::UploadUncached | ( | unsigned int | writeOffset, | |
| const void * | source, | |||
| unsigned int | sourceSize | |||
| ) |
writes data directly to the vertex buffer instead of writing to the cache, and then uploading. This can be used to achieve maximum performance. However, data must be ordered properly. See vertex-flag bit positions for proper data ordering. Data types must match the types in the above Get* functions.
Definition at line 226 of file GrMeshVB.cpp.
const unsigned int GrMeshVB::kDynamic = 1 [static] |
Definition at line 28 of file GrMeshVB.h.


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Programmable cloud sync.
Hypergolix has automatic LAN failover, NAT traversal, and more. Monitor your home server from airplane WiFi with 30 minutes of coding.
See examplesWith Hypergolix, a peer-to-peer "Hello world" is always two lines of code, no matter what network infrastructure is in the way.
Learn moreHypergolix uses personal, client-side encryption and explicit data removal. Only you control data access and lifetimes.
Read the whitepaperHypergolix is a product of Muterra, Inc.
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About Pig Dice
Pig Dice is a two-person game of chance. During each turn, a player repeatedly rolls a die until either a 1 is rolled or the player decides to "hold". The other rules are:
- If the player rolls a 1, they score nothing and it becomes the next player's turn.
- If the player rolls any other number, it is added to their turn total and the player's turn continues.
- If a player chooses to "hold", their turn total is added to their score, and it becomes the next player's turn.
- The first player to score 100 or more points wins.
back home


In Video: First look of Kangana Ranaut in ‘Thalaivi’
by Raj BaddhanT-Series has today unveiled the first look video of Kangana Ranaut, who will be seen in and as ‘Thalaivi’.
Ranaut plays politician, J Jayalalithaa. The film went on the floors earlier this month.
Producer Vishnu Vardhan Induri, who is also producing ’83’, says, “I am excited to show this story of an actor turn politician, which is based on one of India’s most loved and followed public figures. Thalaivi is a very dear project to all of us as it is a story that needs to be told. And the way, Kangana has slipped into the character, is simply amazing. Can’t wait to present this film to the audience.”
The film releases on 26th June 2020.


{%extends 'base.html'%} {% block content %} {{block.super}}
{% include 'navbar.html' %}“When words become unclear, I shall focus with photographs. When images become inadequate, I shall be content with silence.” — Ansel Adams
“It is more important to click with people than to click the shutter.” “Taking pictures is like tiptoeing into the kitchen late at night and stealing Oreo cookies.” – Diane Arbus
“You don’t take a photograph, you make it.” – Ansel Adams “There are always two people in every picture: the photographer and the viewer.” – Ansel Adams
“What I like about photographs is that they capture a moment that’s gone forever, impossible to reproduce.” – Karl Lagerfeld


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I have used python for algorithms, pygame, web scraping, and backend web development.
I use html, css, bootstrap and materialize to create responsive web-designs.
I have experience collaberating with team members using GitHub on projects like CodeforEveryone and Django Girls Seoul.
It is of the first framework I used and instantly fell in love with it! Nowadays, I am developing three websites using Django including my personal portfolio site.
I am starting to go more indepth into front-end so I've been working on algorithms and projects with Javascript! Check out my progress!


Gate
A gate appears in Tokyo's Ginza district sometime in the 21st century. From the gate pours out monsters, knights from middle-age Europe, and other fantasy-like beings, and they kill many of the citizens of Tokyo. This event is known as the Ginza Incident. The government sends a small group of soldiers from the Japanese Self-Defense Forces to the alternate world beyond the gate. Led by otaku soldier Yōji, they find that the villages in the world are being attacked by a dragon. An elf girl who is a survivor from the dragon's rampage joins the group in their travels across the dangerous new world.


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= 4"> {{ section.title }} See all Featured Sales Agents See all Don't miss anythingon Archipel Market Check out Upcoming Screenings Featured films See all More Details


org.clapper.util.logging - Logging Classes.
This package contains logging-related classes. The Logger class provides a front-end to the Jakarta Commons Logging API, insulating calling applications from the specifics of the underlying logging layer, while providing a simpler, thinner interface than Commons Logging. Other classes, such as JavaUtilLoggingTextFormatter, provide useful formatters for the java.util.logging framework.
WARNING: If your application installs its own class loader (e.g., as the thread context class loader), you may have problems with both Jakarta Commons Logging and java.util.logging (which Jakarta Commons Logging uses as its default logging implementation).
java -Dorg.apache.commons.logging.Log=org.apache.commons.logging.impl.Log4JLogger -Dlog4j.configuration=file:/path/to/log4j-debug.properties


Brussels launches legal action against UK over commissioner row
by Sandrine AmielBrussels launched an infringement proceeding against the UK, the European Commission said in a statement on Thursday, accusing the country of "breaching its EU Treaty obligations by not suggesting a candidate for the post of EU Commissioner."
Prime Minister Boris Johnson had refused to appoint a candidate for the post, arguing that he was not in a position to do so in view of the upcoming general election on December 12.
In July, Johnson had promised that he would “under no circumstances” nominate a new European commissioner.
“So we will not nominate a UK Commissioner for the new Commission taking office on 1st December – under no circumstances – though clearly this is not intended to stop the EU appointing a new Commission”, Johnson told the House of Commons in his first speech to parliament as prime minister.
READ MORE:
Ursula von der Leyen sends reminder to Johnson to nominate EU commissioner
Brexit delay: Will the UK be obliged to name a new EU commissioner?
According to EU treaties, each member state has an obligation to nominate a commission member.
While the UK is in the process of leaving the EU following the 2016 Brexit referendum, it remains a full EU member until the official exit date, which has recently been extended to January 31, 2020.
So what's next in this legal case?
"The UK authorities have until Friday 22 November at the latest to provide their views. This short time period is justified by the fact that the next Commission must enter into office as soon as possible," the Commission said.
"After examining these observations, or if no observations are submitted within this time-limit, the Commission may, if appropriate, issue a Reasoned Opinion," the EU Commission said.
According to analysts, incoming EU chief Ursula von der Leyen is wary that Britain's failure to nominate a Commissioner will result in further delays for her tenure as Commission president.
MEPs blocked three picks for von Der Leyen's team, deeming them unsuitable to become commissioners. That has pushed back the date she hoped her Commission would take over, from November 1 to December 1 at the earliest.
The infringement proceeding is, therefore, a way for von Der Leyen to minimize the risks of a legal challenge against her new Commission, as MEPs are in the process of approving her replacement picks for commissioner posts.
READ MORE:
EU Parliament approves France's Commissioner-designate Thierry Breton


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vignettes/EmpiricalCiCalibrationVignette.Rmd
EmpiricalCiCalibrationVignette.Rmd
Observational studies are prone to bias, but unfortunately this bias is often brushed aside with a single comment in the discussion of a paper and is never quantified. Elsewhere we have proposed using negative controls (exposure-outcome pairs where the true relative risk is believed to be one) to produce an empirical bias distribution, and subsequently calibrate p-values. Here we extend this approach to calibrated confidence intervals (CIs). However, this requires us to specify how systematic error changes with true effect size. We currently support two approaches:
The first is for the researcher to specify an assumption, for example to specify that systematic error doesn’t change as a function of true effect size. Although this is likely to be correct in many instances, if an estimation method is for example biased towards the null the assumption will be violated, and the calibrated confidence intervals will have a lower than nominal coverage.
The second approach is to us positive controls to empirically derive how systematic error changes with true effect size. Since real positive controls are problematic, we typically generate synthetic positive controls by injection additional (simulated) outcomes on top of negative controls.
In this document we use an example study to illustrate how CIs can be calibrated using the EmpiricalCalibration
R package. In the example, we have replicated a previously published new-user cohort study comparing dabigatran to warfarin for the risk of GI bleeding. The study does not apply any adjustment for confounding.
The results from this study are available in the package, and can be loaded using the data()
command:
data(southworthReplication)
The estimate for the outcome of interest can in this dataset be identified because trueLogRr
is NA:
outcomeOfInterest <- southworthReplication[is.na(southworthReplication$trueLogRr), ]
outcomeOfInterest
## outcomeName trueLogRr logRr seLogRr
## 1 GiBleed NA -0.3575234 0.06668903
computeTraditionalCi(outcomeOfInterest$logRr, outcomeOfInterest$seLogRr)
## rr lb ub
## 1 0.6994063 0.6137108 0.797068
Here we see that the effect estimate for GI bleed for dabigatran versus warfarin is 0.7, with a very tight CI.
Negative controls are drug-outcome pairs where we believe the drug does not cause (or prevent) the outcome. In other words, we believe the true effect size to be a relative risk of 1. We would prefer our negative controls to have some resemblance with out hypothesis of interest (in our example dabigatran vs warfarin and GI bleed), and we therefore typically pick negative controls where the outcome is the same (exposure controls), or the exposure is the same (outcome controls). In this example, we have opted for outcome controls, and have identified a set of outcomes not believed to be caused by either dabigatran or warfarin. We have executed exactly the same analysis for these outcomes, resulting in a set of effect size estimates, one per negative control:
negatives <- southworthReplication[southworthReplication$trueLogRr == 0 &
!is.na(southworthReplication$trueLogRr), ]
head(negatives)
## outcomeName trueLogRr logRr seLogRr
## 4 Neck pain 0 -0.10015508 0.05005106
## 6 Curvature of spine 0 0.01854604 0.08024194
## 10 Dislocation of joint 0 -0.14348342 0.11084576
## 14 Peripheral vertigo 0 -0.03627483 0.08998388
## 21 Effusion of joint 0 -0.20650211 0.06302084
## 25 Urinary tract infectious disease 0 -0.25308826 0.03858693
Positive controls are drug-outcome pairs where we believe the drug does cause (or prevent) the outcome. In this case we have generated synthetic positive controls with various true effect sizes. Similar to the negative controls we have executed exactly the same analysis for these outcomes, resulting in a set of effect size estimates, one per positive control:
data(southworthReplication)
positives <- southworthReplication[southworthReplication$trueLogRr != 0 &
!is.na(southworthReplication$trueLogRr), ]
head(positives)
## outcomeName trueLogRr logRr seLogRr
## 2 Neck pain 1.3862944 1.1466961 0.03324708
## 3 Neck pain 0.6931472 0.5539283 0.03959980
## 5 Neck pain 0.4054651 0.2888025 0.04344616
## 7 Curvature of spine 1.3862944 1.3519970 0.05327228
## 8 Curvature of spine 0.6931472 0.6918903 0.06357177
## 9 Curvature of spine 0.4054651 0.4272736 0.06932738
We can start by creating a forest plot of our controls:
controls <- southworthReplication[!is.na(southworthReplication$trueLogRr), ]
plotTrueAndObserved(controls$logRr, controls$seLogRr, controls$trueLogRr)
The thick black lines indicate the true effect size, and the blue and orange dots and lines represent the effect size estimate and 95% confidence intervals of the controls. We see that many controls have a confidence interval that does not include the true effect size (orange lines), certainly more than the expected 5%. This indicates the analysis has systematic error.
If we only have negative controls and no positive controls, we might still want to calibrate CIs. This requires us to make an assumption how systematic error changes as a function of the true effect size. The most obvious (and therefore default) assumption is that the systematic error does not change as a function of true effect size. We can fit the empirical null distribution, and convert it to a systematic error model:
null <- fitNull(negatives$logRr, negatives$seLogRr)
model <- convertNullToErrorModel(null)
See ?convertNullToErrorModel
for how to use a different assumption.
If we do have positive controls, like in this example, we do not have to make an assumption, but instead can estimate the full systematic error model using the estimates for both negative and positive controls:
model <- fitSystematicErrorModel(controls$logRr, controls$seLogRr, controls$trueLogRr)
model
## meanIntercept meanSlope sdIntercept sdSlope
## -0.066550795 0.933292141 0.187468405 0.002979502
## attr(,"class")
## [1] "systematicErrorModel"
We see that the intercept of the mean (meanIntercept
) is slightly lower than 0, indicating the analysis is negatively biased when the null is true. Also note that the slope for the mean (meanSlope
) is slightly lower than 1, meaning that the method appears to be somewhat biased towards the null; the negative bias tends to become larger as the true effect size increases.
We can also visualize the model:
plotErrorModel(controls$logRr, controls$seLogRr, controls$trueLogRr)
Here we see how systematic error changes as a function of the true effect size. As visible, the model assumes the mean and standard deviation (SD) of the error distribution are linearly related to the true effect size. For this plot, the mean and SD are also estimated at each true effect size independently, allowing us to evaluate whether the linearity assumption is far off.
There is also another way to plot the error model, and its subsequent calibration:
plotCiCalibrationEffect(controls$logRr, controls$seLogRr, controls$trueLogRr)
To evaluate whether our estimation of the systematic error distribution is a good one, we can test whether the calibrated CI is truly calibrated, meaning the fraction of controls where truth is inside the calibrated CI is approximately the specified width of the CI. We also evaluate ‘centeredness’, whether truth outside of the CI is equally distributed above and below the CI:
plotCiCoverage(controls$logRr,
controls$seLogRr,
controls$trueLogRr,
crossValidationGroup = controls$outcomeName)
## Fitting error models within leave-one-out cross-validation
The data shown here is produced using a leave-one-out cross-validation. It is important to remember that for this dataset positive controls were generated based on negative controls and will therefore be correlated. To perform an unbiased evaluation the ‘one’ in the leave-one-out should therefore be the group of a negative control and its derived positive controls, and this is achieved by specifying the crossValidationGroup
argument. Thus, in this evaluation for each negative control and the positive controls derived from that negative control, we fit systematic error models using all other controls, and compute calibrated CIs for the left-out controls across a wide range of widths.
In the graph the dashed lines indicate a perfectly calibrated study. We see that the calibrated CIs are much closer to the ideal than the uncalibrated CIs.
We can now use the estimated systematic error model to compute the calibrated CI for our outcome of interest:
ci <- calibrateConfidenceInterval(outcomeOfInterest$logRr, outcomeOfInterest$seLogRr, model)
ci
## logRr logLb95Rr logUb95Rr seLogRr
## 1 -0.3117702 -0.733957 0.1067054 0.2144587
exp(ci[,1:3])
## logRr logLb95Rr logUb95Rr
## 1 0.7321498 0.4800058 1.112606
In this case, even though the point estimate has not changed much, the calibrated CI is much wider than the uncalibrated one to take in to account the residual error due to lack of any adjustment for confounding.
citation("EmpiricalCalibration")
##
## To cite EmpiricalCalibration in publications use:
##
## Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D (2013).
## "Interpreting observational studies: why empirical calibration is
## needed to correct p-values." _Statistics in Medicine_, *33*(2),
## 209-218. <URL: http://dx.doi.org/10.1002/sim.5925>.
##
## Schuemie MJ, Hripcsak G, Ryan PB, Madigan D, Suchard MA (2018).
## "Empirical confidence interval calibration for population-level effect
## estimation studies in observational healthcare data." _Proc. Natl.
## Acad. Sci. U.S.A._, *115*(11), 2571-2577. <URL:
## https://doi.org/10.1073/pnas.1708282114>.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.


THE PACKAGE A film review by Mark R. Leeper Copyright 1989 Mark R. Leeper
Capsule review: Fast-paced if not entirely satisfying political thriller has Gene Hackman on the run from the Army and the police, trying to avert the sabotage of a nuclear disarmament treaty. Very reminiscent of other thrillers, especially SEVEN DAYS IN MAY, but with a few new wrinkles of its own. Rating: low +2.
 The story is reminiscent of some of the better political thrillers from the early 1960s though most of all, and perhaps too closely, it parallels the plot of SEVEN DAYS IN MAY. THE PACKAGE is, however, not as cerebral a thriller as is SEVEN DAYS IN MAY, having less discussion of the political reasons for what is going on and substituting instead action and car chases. That choice makes the film more entertaining on one level but less involving on a deeper level. While in SEVEN DAYS IN MAY we are led to conclude that the real enemy is an age, in THE PACKAGE there is little doubt that the real enemy is a group of "bad guys" whose motives are all too quickly glossed over. That is just not as satisfying.
[Minor spoilers follow.]
The story begins at a disarmament summit meeting in East Berlin at which United States and Soviet diplomats agree to disarm and cooperate with each other. However, a dissenting group of high-ranking United States and Soviet military people decide they do not want to cooperate with each other, so they team up to sink the treaty so they can go back to distrusting each other. (Now that I think about it, that does seem a bit ironic.) Into this situation is dropped Johnny Gallagher (played by Gene Hackman), Gallagher is a career military man who becomes a cat's-paw for the conspirators. Also on hand is Tommy Lee Jones as a brawling soldier whom Gallagher must "escort" back to the United States and who clearly is not quite what he seems to be. It is not long before Gallagher is on the run from the army, the police, and the conspirators. He enlists the aid of his ex-wife Eileen Gallagher (played by Johanna Cassidy), also a career army officer. It is extremely refreshing, incidentally, to see an intelligent action character played by a woman over 40.
As political thrillers go, THE PACKAGE has a complex plot involving a wide spectrum of characters from the intelligence community to Communists to neo-Nazis. While the ultimate goal of the conspirators is not hard to guess, many of the details of their plot are unexpected enough to keep the viewer off-balance and guessing. I give it a low +2 on the -4 to +4 scale.
Mark R. Leeper att!mtgzx!leeper<EMAIL_ADDRESS>.
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Hello!
Welcome to my first Phase 0 web page!
I'll use this space to introduce myself.
My name is Andrew Koines
I studied economics at the University of Maryland.
Our mascot is
Testudo the Terrapin!
Web Development Resources
Below is a list of a few websites related to web development that I've found helpful
Below is a list of four common waveforms
- Sine wave
- Square Wave
- Triangle Wave
- Saw Wave
Contact Me
Feel free to e-mail me!


Moi's Assistant Minister Dies at KNH
by Derrick OkubasuAn assistant minister who worked for former President Daniel Moi on Friday, November 22, passed on while receiving treatment at Kenyatta National Hospital.
A report carried by Daily Nation indicated that former Galole Member of Parliament Tola Kofa died after spending a week at the hospital's private wing.
Galole's son Patrick Mgawa disclosed that he passed on aged 75 after an illness.
Tofa served as a member of the National Assembly for 15 years and as the assistant minister for water between 1998 and 2002 and Moi's regime.
In 2010, the former lawmaker shocked constituency fund officials in Hola when he turned up among students who were seeking bursary funds. He claimed that he had not finished his masters.
At the time, he explained that he needed the money urgently in order to settle fee balanced he owed the University of Nairobi's Political and Social Sciences faculty.
When the officials wondered why he was seeking the finances yet he owned a fleet of public service vehicles, the former lawmaker explained that there was a political reason behind it and that he was not at liberty to reveal it.
In 2003, the former minister was also at loggerheads with the police after they found a water pump in his home.
They raided his house at the time and went away with the pump valued at Ksh700,000 at the time which they claimed was stolen from the Ministry of Water Development.
Tana River police chief Danson Mutun, at the time, explained that the pump was given to the former leader to take to Hola constituency but he instead took it to his home.


Poll: 90% of Evangelicals Say Impeachment Inquiry Is a ‘Witch Hunt'
A Charisma News poll found that as of November 15 almost 90 percent of evangelical Christians think the Democrat’s impeachment inquiry into President Donald Trump is a “witch hunt” rather than a process to hold a president accountable for alleged high crimes and misdemeanors.
by Penny StarrThe inquiry was launched after a “whistleblower” raised alarms about Trump’s phone call with Ukrainian President Volodymyr Zelensky. Trump released the transcript of that call, which included the president’s inquiry about corruption concerns linked to former Vice President Joe Biden’s son Hunter Biden and his dealings with a Ukrainian oil company.
In a survey of 1,246 Christians connected via email to the conservative news outlet, only five percent of respondents believe that the impeachment inquiry is a “valid course of action,” and only five percent said that it is being done to “hold Trump accountable.”
A smaller number of respondents — 28.3 percent — said the impeachment inquiry is “a waste of time and money.”
The impeachment hearings continue on Tuesday with two officials set to testify before the House Intelligence Committee: Jennifer Williams, Special Advisor for Europe and Russia, Office of the Vice President; and Lt. Col. Alexander Vindman, Director for European Affairs, National Security Council.
On Wednesday three officials will testify: Ambassador Gordon Sondland, U.S. Ambassador to the European Union; Laura Cooper, Deputy Assistant Secretary of Defense for Russian, Ukrainian, and Eurasian Affairs; and David Hale, Under Secretary of State for Political Affairs.
On Thursday Fiona Hill, Former Senior Director for Europe and Russia, National Security Council, will testify.
The survey was conducted by Charisma Media on Nov. 13 and 14.
Follow Penny Starr on Twitter


Android-x86
Run Android on your PC
This project could not have been a reality without these:
Developers
- Chih-Wei Huang (cwhuang in Taiwan) and Yi Sun (beyounn in U.S.) who founded the project. Current project maintainer is Chih-Wei Huang.
- Mauro Rossi who maintained the most of mesa and kernel porting.
- Michael Goffioul who improved stagefright-plugins including hardware codecs support.
- Paulo Sergio who contributed a lot of bugfixes.
- Jaap Jan Meijer who contributed the LineageOS porting.
- 0xlab, a group of engineers enthusiastic in open source software development , contributed many fixes and improvements to the Android frameworks, included the OpenGL ES hardware acceleration for Android-x86.
- Michael Chen who developed the stagefright-plugins to improve the multimedia support.
- Dima Zavin, the original guy ported the Android to EeePC 701
- Geunsik Lim, wrote original "HOWTO Build Android-X86 Full Source" and "How to use x86 android platform on virtualbox"
- Chen Yang for the instructions on the how to make boot-able image and how to build Android for VMware, and investigations on almost all of the build breaks and run time issues
- Arya Bhushan and Aditya Sood for developing and maintaining this website.
- Lewi Hussey for navigation bar design.
- Jackie Wu for the fix in alarm.c
- Niels Keeman for the investigation on the path issue in the battery service jni code, v4l2 camera support (android-m912 project)
- Luca Belluccini for the first patch to fix the path issue in the battery service jni code
- Kevin Jeng for adding the mouse wheel support
- Andrew Hughes for the works on iwlwifi4965 and Thinkpad x61 testing
- Al Sutton for testing and work on the ViewSonic ViewPad 10
Of course, there are other people who gave helps. But there are too many e-mails and it is very hard for me to go through each e-mail to find the related person to list here. So, if you think we missed your name here, please kindly drop us an e-mail.