text:
Solange reveals battle with autonomic nerve disorder
TheGrio Staff |
On Wednesday, Solange took to Instagram to reveal that she has been quietly battling an autonomic nerve disorder.
Because of her health concerns, then, she will not be able to go on with her Afropunk performance for New Year's Eve in South Africa.
"Wrote, deleted and re wrote this like 5 times…Still not sure what exactly or how much I want to share…However it's so important to me for the people in South Africa, a place that has tremendous meaning to me and that has given me SO SO MUCH, to know why I won't be performing at Afro Punk this NYE. The past five months I have been quietly treating, and working through an Autonomic Disorder," she explained in her post.
"It been a journey that hasn't been easy on me," she continued." Sometimes I feel cool, and other times not so cool at all. It's a complicated diagnoses , and I'm still learning so much myself."
— T.I. and Kandi Burruss give back in major way to single moms and families —
A cancellation that broke her heart
The Afropunk performance, she went on to say, would simply not be possible.
"Right now, my doctors are not clearing me for such an extended lengthy flight, and doing a rigorous show right after," she said.
Solange had visited South Africa in August and promised to return. So to be unable to do the show was simply crushing for her.
She did, however, promise that she would make it up to her fans.
"I can't put into words how saddened and sorry I am that I am unable to perform for you guys this NYE, there is simply no other place I wanted to be than there with my family to bring in 2018 with you…….but I give you my ABSOLUTE WORD I will come back with AfroPunk and deliver this performance…..as it is so extremely important to me to connect with the people who have so closely inspired me in so many ways," she swore.
https://www.instagram.com/p/BdNmg26BVcI/


text:
15/01/2013- Review for developing a single comprehensive Emergency Services Act.
DFES to conduct a review for developing a single comprehensive Emergency Services Act.
The Association has sent a letter and email to all SES Units today on the subject above. DFES have advised the Association that there has been a review initiated into the various Acts that they are involved in, with a view to developing a single comprehensive Emergency Services Act.
It is DFES's intention to gather data early in 2013 to enable them to put together what they believe should be in a single Emergency Services Act. The State Emergency Service has a long history of working in and with the communities and their culture has been built up over many years. SES Volunteers will need to think long and hard what they want changed and what they want left as it is.
The Association does not know what DFES intend putting into a single Act however, from past experience and history we will all need to be very cautious about what is placed into a new act. The Association, to assist SES Volunteers in being well informed, will be conducting a special meeting to discuss any issues that the Volunteers may have in relation to developing a single Emergency Services Act
In a letter and email sent to all SES Units the Association is inviting Unit Managers, Deputy Managers, Training Managers and other interested Volunteers to a meeting on this subject. Please see the email or letter and provide comment and feedback to your Unit Manager and Association Representatives on this very important matter. Please be aware that notes and feedback from this meeting will be placed on the web soon after the meeting.
Please feel free to contact your area representative, any committee member or the undersigned to discuss further.
PrevPrevious11/01/2013 – News from Gosnells SES
Next16/01/2013 – The following alert has been issued by the Department of Fire and Emergency Services (DFES).Next

redpajama_set_name: RedPajamaCommonCrawl


DORTMUND, Germany -- Borussia Dortmund jumped to the top of the table by beating Eintracht Frankfurt 3-1 on Friday, opening the third round of the Bundesliga. It marked the first of seven matches in 22 days for the Black and Yellows.
Centre-back Abdou Diallo almost looked too ashamed to celebrate after stumbling the ball over the line after a corner kick to hand his team the 1-0 lead in the 36th minute. Diallo also did not feel like celebrating in the 68th minute when his botched clearance bounced off Marcel Schmelzer and just moments later into his own net as Sebastien Haller poked home for the guests.
But the hosts responded well. Dortmund's latest addition Paco Alcacer directed a ball into open space for Jadon Sancho. The 18-year-old Englishman let two defenders slide the wrong direction with a shimmy and crossed the ball to Marius Wolf, who volleyed home against his former club -- also refusing to celebrate.
One who did celebrate emphatically in the 88th minute was Alcacer. The Spaniard put the dagger into Frankfurt's hearts and twisted it with a thumping strike from the right edge of the box. The curler took a slight deflection on its way to the top left corner but was unstoppable for Eagles keeper Kevin Trapp.
Usually, this is the place where one would praise the defensive stability or the promising debut of Alcacer, but that has to take a back seat to Borussia Dortmund's very important statement against racism on Friday.
Football is a fabric deeply woven into Germany's culture, and it's important that top-flight clubs consistently take a stand against all kinds of discrimination. BVB are leading by example, and that deserves praise -- and hopefully inspires more clubs, players and official to take a stand as well.
As mentioned above, Dortmund will play seven matches in three weeks. However, one might argue that their looming schedule might come too soon for them as the Ruhr side still looks like a team in preseason. Lucien Favre's men are behind in their progress: too many passes go astray, misunderstandings happen left, right and centre. It just happened to work out against the Eagles because Adi Hutter's men are in a similar spot.
6 -- In the end, substitutes Sancho and Alcacer put the Black and Yellows on the winning path.
GK Roman Burki, 5 -- The Swiss goalkeeper had a few interceptions to make but otherwise was hardly called into action as Frankfurt scored with their only shot on target.
DF Lukasz Piszczek, 4 -- Dortmund had designed a play in which the right-back was supposed to receive the ball in space by making an overlapping run. The routine broke down thrice as his teammates did not account for the 34-year-old's declining pace.
DF Manuel Akanji, 6 -- Both centre-backs arguably showed the most playmaking acumen on the field, feeding their midfielders with passes into half spaces. While that can be helpful, there should be more facets in BVB's buildup.
DF Abdou Diallo, 6 -- Scored a goal and caused a goal. A balanced night for the young centre-back.
DF Marcel Schmelzer, 6 -- "Gegenpressing" describes an aggressive running at opponents by a swarm of players. In the first half, the left-back exercised a one-man counter-pressing, being highly successful at that.
MF Thomas Delaney, 5 -- The Danish international indicated for his defenders where to play the ball, a nice service by the holding midfielder. On the flip side, that meant he was rarely demanding the ball for himself and when he did, his passes rarely went forward.
MF Mahmoud Dahoud, 6 -- Dortmund's only true playmaker on the pitch over- and undercooked a lot of balls.
MF Marius Wolf, 5 -- The former Frankfurt winger unlocked the achievement "worker bee" -- a species unable to pass opponents via take-ons. He did redeem a so-so performance with a thunderous strike to restore the lead.
MF Marco Reus, 4 -- Reus is fit for long enough to have a crisis of form for once. The Dortmund captain has struggled visibly in his past three games, including the ones on international duty for Germany. Many enticing chances died with his poor touch or wayward pass.
MF Jacob Bruun Larsen, 4 -- The 19-year-old earned his first Bundesliga start aqfter scoring four goals against third-tier side VfL Osnabruck a week ago. While making a lot of important defensive runs, his lack of routine was evident, but his corner kick yielded an assist. After 57 minutes, the young Dane was substituted due to hamstring issues.
FW Maximilian Philipp, 3 -- Philipp was a victim of BVB's lack of creativity in buildup, rendering him a passive member for most of the time until his substitution in the 67th minute.
MF Jadon Sancho, 7 -- The substitute skilfully and inadvertently assisted his team's third goal of the night as his attempted dribble bounced off a defender into the path of Alcacer.
FW Paco Alcacer, 7 -- One second assist and one stunning goal meant a promising 23 minutes on his Dortmund debut for the former Barcelona man.
MF Axel Witsel, 6 -- Always cool as a cucumber in his 20-minute run-out.


text:
Q: How can I add a namespace to Symfony 2.1? I have a 3rd party lib using namespaces I would like to add to the vendor directory. For certain reasons I can't use composer for this lib. Adding it using the add method of ClassLoader does not work for me ("class not found"). In Detail:
I am using Symfony 2.1.7.
// app/autoload.php
use Doctrine\Common\Annotations\AnnotationRegistry;
$loader = require __DIR__.'/../vendor/autoload.php';
$loader->add('Example', realpath(__DIR__.'/../vendor/example/src'));
AnnotationRegistry::registerLoader(array($loader, 'loadClass'));
return $loader;

Directory structure in the vendor directory:

//vendor/example/src/Foo.php
namespace Example;
class Foo {

}

Using it in my controller:
 $bar = new \Example\Foo();

Result:

Class 'Example\Foo' not found

Where is my mistake? And/or: What's the best way to debug this issue in Symfony 2.1?

A: The directory structure is wrong. Both UniversalClassLoader (used in Symfony < 2.1) and Composer's ClassLoader (used in Symfony 2.1) implement the PSR-0 autoloader standard.  This standard requires that the files with the namespaces cannot be in the root directory, and must be created under a minimum of one directory.
This worked for me:
Directory structure

// in autoload.php
// Symfony 2.1 using Composer's ClassLoader
$loader->add('Example', realpath(__DIR__.'/../vendor/example/example/src'));

A: You have different options:
Register the namespace in your actual autoloader.
Register your namespace in app/autoload.php. You can see an example of that here https://github.com/symfony/symfony-standard/blob/2.0/app/autoload.php#L9
Add the library to the PHP include_path
Add the namespace to composer (you can add custom namespaces in your composer.json even if the library doesn't have composer.json)

A: In the following file you will see some namespaces listed. You can add yours in the same manner:
// approot/vendor/composer/autoload_namespaces.php

return array(
    'Twig_Extensions_' => $vendorDir . '/twig/extensions/lib/',
    ....
    ....
    '' => $baseDir . '/src/',
);

redpajama_set_name: RedPajamaStackExchange


The John Jorgenson Bluegrass Band plays at the CAC on Nov. 15.
Don't forget that well-known Reno poet, Gailmarie Pahmeier, will give a poetry reading Saturday night at the Art Center.
Pahmeier teaches literature and creative writing at UNR and she will be reading from hew new collection "The Rural Lives of Nice Girls" which was recently published by the Black Rock Press.
The event is from 5 to 7 p.m. in the galleries at the Art Center. There will be a meet the author reception from 5 to 6 p.m. and the reading will begin at 6 p.m. The reception and reading are free and open to the public.
Music fans are in for a real treat when J2B2 (the John Jorgenson Bluegrass Band featuring Herb Pedersen, Jon Randall and Mark Fain) roll into town for a show at the Barkley Theatre on November 15.
Jorgenson is one of the most accomplished string musicians playing today, truly a world class musician who has performed with the likes of a host of talent including Bonnie Raitt, Luciano Pavarotti, Bob Dylan, Elton John, Earl Scruggs and many, many others.
In 2008, Jorgenson received a Grammy for Best Country Instrumental with Brad Paisley and was also, nominated for another for Best Bluegrass Album with Earl Scruggs.
The bluegrass ensemble brings together the talents of these four legendary musicians: Jorgenson on guitar, mandolin and vocals; Pedersen on bajo, guitar and vocals; Randall on guitar and vocals; and Mark Fain on Bass.
Jorgenson and Pedersen are founding members of the seminal country-rock group, The Desert Rose Band. Pedersen was also a member of the Dillards and the well-known bluegrass ensemble Old & In the Way. Jon Randall was, for many years, the guitarist for Emmylou Harris' band and has toured with Lyle Lovett and Linda Ronstadt. Mark Fain toured for more than 13 years with Ricky Skaggs and Kentucky Thunder and has recorded with Dwight Yoakam.
So, it's going to be a rousing good time at the Art Center. And, the band will hold a free and open to the public conversation on their musical and other influences in the Art Center's Bar at 3 p.m. on the day of the performance.
Tickets for the evening show are $17 for CAC members, $20 for non-members and are available at Jeff's Copy Express, ITT @ NAS Fallon or by calling Churchill Arts at 775-423-1440.
Kirk Robertson covers the arts scene. He may be reached at news@lahontanvalleynews.com.


If you suspect you've been a victim of identity fraud, follow these five steps immediately.
1. Report the fraudulent activity. If the activity is related to Bank5 Connect, please contact us directly. If it is related to another financial institution, your credit card company, or any other organization, contact them directly.
3. Close any accounts that you suspect might have been tampered with or opened fraudulently. Report the transgression to a security spokesperson at the relevant company. Ask them about any additional steps – they'll probably ask you to send relevant copies of the fraudulent activity. You can also use the https://www.identitytheft.gov/ website to report instances of identity theft.
4. File your complaint with the FTC. Use the online complaint form; or call the FTC's Identity Theft Hotline, toll-free: 1-877-ID-THEFT (438-4338); TTY: 1-866-653-4261; or write Identity Theft Clearinghouse, Federal Trade Commission, 600 Pennsylvania Avenue, NW, Washington, DC 20580. Sharing your identity theft complaint with the FTC will help law enforcement officials track down identity thieves and stop them.
5. Call or visit the local police or police in the community where the identity theft took place and file a report. Have a copy of your FTC ID Theft complaint form available to give them. Obtain a copy of the police report and the police report number.


Sculptor Brian Porter recently sent us images of the progress he is making on Company F Memorial's statue form. He is trying a new process whereby the miniature clay model (featured in Slide #1) is scanned using 3D technology, and data sent to a CNC machine to cut out thin plywood slices that are then glued together. The wood structure is merely a subframe that the clay will be on top of and covered completely at final execution.
"It was a really interesting process to get to this point and many people that viewed it on campus were excited about the look of this stage of the structure," Brian reported. The next step will be to begin carving and prepping the surface for clay modelling.
Below is a slideshow featuring several images of the CNC machined plywood slices that have been glued together in sections of about 10 pieces each. Brian anticipates that they will be easier to model and mold in this way. The first image is the miniature clay model which is about 20″ high. Notice the photo showing the full size form with Brian standing next to it which better demonstrates the size the statue will be. These sections will remain dry fit only for quite some time while he models the work in sections.
Approximately 20" high, this is the miniature clay model of the Company F Memorial statue, created by Brian Porter.


Maid of Gingerbread is run by Emily Garland, a food artist specialising in 3D biscuit building. She produces everything by hand in her gingerbread studio-kitchen in Hackney, East London.
Drawing on years of baking experience and a passion for woodwork and construction, she founded Maid of Gingerbread in 2010 and has since produced show-stopping edible centrepieces for a wide range of clients including Zizzi, D&AD, Harrods and Fortnum & Mason.
She has become known for her original, modern designs and intricate details, always incorporating a sense of fun and colour into her work and now runs workshops and produces baking kits so that others can learn her style of biscuit building.
In her workshops you will learn modern techniques for constructing and decorating 3D biscuit creations (of a variety of different flavours, not just gingerbread). Emily believes that experimenting with baking is fun for everyone, not just kids, and loves working with people to bring out their creativity in the kitchen.
If you have any questions, please use the 'contact host' button.


The Sunseeker 48 is a fast and agile motor yacht, designed for high-powered, open water cruising. It is an excellent yacht charter for memorable cruises off the French Coast with friends and family.
This boat is spacious, comfortable, and performs well. You will find ample space to relax and unwind, with plenty of seating in the cockpit aft and a wonderful sunbathing space on the bow.
The interiors are beautifully designed and make you feel at home. This yacht can accommodate up to 11 passengers on overnight voyages in Cannes or in Monaco. It is great for families on holiday or for groups of friends.


text:
\section{INTRODUCTION}

Nowadays, most of the robots used in the industry are pre-programmed and necessitate a well-defined and controlled environment. Reprogramming these robots is often an expensive process necessitating an expert. Enabling the robot to learn tasks by demonstrating them would streamline the robot installation and task reprogramming. That is why, Robot Learning from Demonstrations (RLfD) \cite{Atkeson1997RobotDemonstration} is one of the key research areas in the field of robotics. However, constructing a robot that is able to learn by observation is still a challenging problem. Although, prototype platforms for robot learning by demonstration have been around for more than a decade, many complications have restrained the robots to operate only in restricted laboratory environments. Some of the key challenges are perception, task recognition, task generalization, motion planning, and object manipulation.

RLfD refers to the technique of teaching skills to a robot by giving examples of the desired behavior through human demonstrations. It is similar to the way human beings learn a new skill from demonstrations performed by the teacher. One of the great advantage of this technique is that it eliminates the need of expert level technical knowledge to program a robot. Moreover, it captures key features from the demonstration provided by a teacher who is expert in the specific task, which a robotic programmer might not be able to program. Thus, this technique has great potential in industrial as well as home robotic applications.

Learning the mapping between world state and actions is called as a \textit{policy}. This allows a robot to choose an action based on its current state. In RLfD, a robot learns a new policy from demonstrations provided by the human teacher. A demonstration is defined as sequence of state–action pairs that are recorded throughout the performance of the required robot behavior by a human teacher. RLfD uses the dataset of recorded demonstrations to form policy to reproduce the demonstrated behavior.

We focus on learning, representing, and generalizing of trajectory based skills from demonstrations. We propose a novel RLfD framework by which a collaborative robot can learn trajectory based skills from kinesthetic demonstrations performed by a human teacher. The prosed RLfD framework has three fundamental phases: data collection from demonstrations, learning behavior by deriving a policy, and robot execution or reproduction of the demonstrated behavior. It extracts key information from multiple demonstrations to learn a state-action policy, which is used to produce a generalized trajectory based skill.

\begin{figure}
\begin{center}
\includegraphics[width=0.85\linewidth]{demo_cup.jpg}
\end{center}
   \caption{Human performing demonstration using kinesthetic teaching.}
\label{fig:demo}
\end{figure}

\section{BACKGROUND}

RLfD is a technique to enable a robot to perform new tasks autonomously. Instead of necessitating users to logically decompose and manually program a robot for a desired behavior, RLfD enables to derive robot controller by observing human's own performance. The goal is to easily extend and adapt robot capabilities to novel situations, even by users who lag programming ability. Robot learns a model of a task based on the demonstrations performed by the teacher. In the last two decades, several RLfD approaches have been developed \cite{Argall2009ADemonstration}. However, until date most of the robots are tediously hand programmed for each of the task they have to perform. RLfD tries to minimize this challenging step by allowing users to teach their robot to fit in their specific needs. RLfD techniques are user friendly and enable robots to be employed more widely in daily activities with no need of specialist humans. Moreover, by using expert knowledge of human teacher, in form of demonstrations, the overall learning process would be faster as compared to hand-engineered control policy and learning by trial-and-error.

One of the well-known works in RLfD is by Calinon \textit{et~al}.~ \cite{Calinon2007OnRobot} \cite{Calinon2010Learning-basedRedundancies} \cite{Calinon2015RobotModels}, in which a probabilistic representation of the demonstrations is built using Gaussian Mixture Model (GMM) and a smooth trajectory is reproduced using Gaussian Mixture Regression. In another work by Schneider \textit{et~al}.~ introduced a Gaussian process regression model that clusters the input space from performed demonstrations into smaller subsets and work individually on these subsets \cite{Schneider2010RobotRegression}. Both of these Gaussian techniques generalized over a set of demonstrated trajectories, but the GMM is computationally expensive.

Rozo \textit{et~al}.~ proposed an end-to-end RLfD framework for teaching force-based manipulation tasks \cite{Rozo2013ATasks}. The demonstrations are encoded using a Hidden Markov Model (HMM), and the reproduction is done using a modified version of Gaussian Mixture Regression. Asfour \textit{et~al}.~ used continuous HMM to generalize movements demonstrated to a robot multiple times. The experiments were performed using a kinematics model of the human upper body to simulate the reproduction of arm movements \cite{Asfour2008ImitationRobots}.

In a recent work, Ahmadzadeh \textit{et~al}.~ proposed an approach that generates a continuous representation of the demonstrated trajectory based on the concept of canal surfaces \cite{Ahmadzadeh2016TrajectoryApproach}. Their experimental results show that the proposed approach can reproduce a wide range of trajectories that achieve the goal of the task. Koskinopoulou \textit{et~al}.~ developed a framework for Human Robot Collaborative (HRC) task execution \cite{Koskinopoulou2016LearningExecution}. Akgun and Thomaz developed an algorithm to simultaneously learning actions and goals from naïve human teacher demonstrations \cite{Akgun2016SimultaneouslyDemonstration}.

Konidaris \textit{et~al}.~ presented an online algorithm for constructing skill trees from demonstration trajectories in a dynamic continuous domain \cite{Konidaris2012RobotTrees}. They evaluated their algorithm on the uBot-5 mobile manipulator and showed that it was able to learn skills from both expert demonstration and learned control
sequences. Other techniques like  linguistic transfer of an
assembly task from human to robot have also been developed \cite{Dantam2012LinguisticRobots}.

\section{PROBLEM STATEMENT}

We can consider RLfD as a subset of Supervised Learning. In supervised learning, the agent takes the labelled training data and the algorithm learns an approximate model to fit this data. Similarly, in RLfD, the training data is collected from demonstrations provided by the teacher. Figure \ref{fig:problem} (top) shows training data $D$ being acquired from demonstrations by teacher to derive a policy.

The robot world comprises of states $S$ and actions $A$. Mapping between the states, is defined by a probabilistic transition function called state transition matrix given by:

\begin{equation}
T(s\prime \vert s,a): S \times A \times S \rightarrow [0,1]
\end{equation}

The set A contains high-level behaviors as well as low-level motions. We make an assumption that the states are not fully-observable or hidden. However, the learning algorithm has access to the observed state $Z$, with the mapping $M: S \rightarrow Z$. The policy $\pi: Z \rightarrow A$ selects the next action based on the current robot state. Figure \ref{fig:problem} (bottom) shows one cycle of policy execution.

\begin{figure}
\begin{center}
\includegraphics[width=0.7\linewidth]{problem.png}
\end{center}
   \caption{Control policy learning and execution.}
\label{fig:problem}
\end{figure}

\section{PRPOSED APPROACH}

The proposed RLfD system consists of three fundamental phases. Figure \ref{fig:approach} shows a block diagram of the proposed approach. First phase is the data collection phase, in which the data is acquired from the demonstrations performed by the teacher. Real-time joint angle values and gripper state are received by the system, which applies Forward Kinematics to find the position and orientation of the manipulator. This process is repeated for all the demonstrations and the time series of position and orientation are stored.

The second phase is the task learning phase, in which the stored data from the first phase is used to learn a model of the task or the behavior. From each demonstration, key-points of trajectories are extracted and then \textit{k}-means clustering is used to cluster the key-points from all demonstrations. Centers of each cluster is calculated and mapped to a unique symbol representing a state. Baum-Welch algorithm is used to learn a policy from the demonstrations to find the probability matrix of state-action pairs. Then, Viterbi algorithm is used to find the most probable sequence of states. Dynamic Time Warping (DTW) is used to align the time vector of the learned trajectory.

Third phase is the robot execution phase, in which the saved model is used to produce the learned behavior. Cubic smoothing-spline regression is used to determine a generalized trajectory from the most probable sequence of states. Inverse kinematics is applied to obtain a generalized trajectory suitable for the robot.

\begin{figure*}
\begin{center}
\includegraphics[width=1\linewidth]{block_diagram.png}
\end{center}
   \caption{Block diagram of the proposed RLfD system.}
\label{fig:approach}
\end{figure*}

\section{DATA COLLECTION}

Data collection is the first phase of the system, where the robot collects data from the physical demonstrations performed by human teacher. In the next three sub-sections, we discuss the main steps of this phase.

\subsection{Demonstrations}

In our work, we have used kinesthetic teaching to demonstrate the task or trajectories to the robot. Kinesthetic teaching is a way of teaching in which, the teacher physically holds the robot arm or manipulator, and demonstrates the task by moving the robotic arm. The main advantage of this technique is that there is no need to map the real world space to robot world space, as the performed demonstrations are with respect to robot world.

One major roadblock for this type of teaching is that it requires a robot that has a feedback system to record the joint position in real-time. However, we do not face this problem as we are using one of the most advanced research robot called Baxter, which has inbuilt feedback system to measure the joint angles from all fourteen joints and manipulator state of the dual-arm robot. This allows us to read these joint values from joint trajectory server in real time. To get more precision for fast movements, we have increased the default rate (100Hz) of reading current joint angles from the robot to 1000 Hz. These values are stored with a timestamp and sent to the forward kinematics function.

Figure \ref{fig:demo} shows an example demonstration being performed using kinesthetic teaching to demonstrate a pick and place task to Baxter. In this, the teacher holds the force sensor at the manipulator of each robotic arm and guides the robot through the task. To change the state of the gripper, physical buttons near the manipulator are used. One button closes the gripper and the other opens the gripper. The system records the gripper states along with the joint angles throughout the demonstration, and for each demonstration.

\subsection{Forward Kinematics}

'Forward kinematics refers to the use of the kinematic equations of a robot to compute the position of the end-effector from specified values for the joint parameters'. Kinematics equations are series of transformation matrices to characterize the relative movement at each joint with respect to the previous joint. Instead of going through the complex matrix multiplications for calculating the relative movements at each joint, we use Denavit-Hartenberg parameters or DH parameters.

Each individual homogeneous transformation $A_i$ is calculated as:

\begin{equation}
\begin{aligned}
A_i &= Rot_{z,\theta_i}Trans_{z,d_i}Trans_{x,a_i}Rot_{x,a_i} \\
&= \begin{bmatrix}C_{\theta_i}&-S_{\theta_i}C_{a_i}&S_{\theta_i}S_{a_i}&\alpha_iC_{\theta_i}\\S_{\theta_i}&C_{\theta_i}C_{a_i}&-C_{\theta_i}S_{a_i}&\alpha_iS_{\theta_i}\\0&S_{a_i}&C_{a_i}&0\\0&0&0&1\end{bmatrix}
\end{aligned}
\end{equation}

\noindent where $\theta_i, d_i, \alpha_i$ and $a_i$ are known as DH parameters associated with link $i$ and joint $i$.

We use Baxter as our robot for learning, thus we calculated the DH parameters for both the limbs of the robot to perform the forward kinematics. We follow the standard convention of placing the frames at each joint to calculate the DH table. Figure \ref{fig:arm_model} shows an example of placing the frames on first two revolute joints and the physical parameters used to mathematically modeling Baxter's arm. Table \ref{dh_table} shows the derived DH parameters for all the joints and links for Baxter. These parameters are used to calculate the individual transformation matrices for each of the link.

\begin{table}
\centering
\caption{DH Table for Baxter}
\label{dh_table}
\begin{tabular}{|l|l|l|l|l|}
\hline
Joint & $\theta$ & $d$      & $a$     & $\alpha$    \\
\hline
1     & $\theta_1$ & 0.2703 & 0.069 & -1.571 \\
2     & $\theta_2$ & 0      & 0     & 1.571  \\
3     & $\theta_3$ & 0.3644 & 0.069 & -1.571 \\
4     & $\theta_4$ & 0      & 0     & 1.571  \\
5     & $\theta_5$ & 0.3743 & 0.01  & -1.571 \\
6     & $\theta_6$ & 0      & 0     & 1.571  \\
7     & $\theta_7$ & 0.2295 & 0     & 0      \\
\hline
\end{tabular}
\end{table}

\begin{figure}
\begin{center}
\includegraphics[width=0.65\linewidth]{arm_model.png}
\end{center}
   \caption{Approach to mathematically model Baxter's arm}
\label{fig:arm_model}
\end{figure}

Now, we use the DH parameters to calculate $A_1, A_2, .... A_7$. The homogeneous transformation matrix that expresses the position and orientation of the manipulator is called a transformation matrix $T$, and is calculated as:

\begin{equation}
T = A_1 \times A_2 \times A_3 \times A_4 \times A_5 \times A_6 \times A_7
\end{equation}

For each measurement, the calculated position and orientation are stored into a data file, along with the time stamp and the gripper state. This marks the end of the first phase.

\section{TASK LEARNING}

Task learning is the second phase of the system. In this phase, the data stored during the first phase is used to learn a model of the task.

\subsection{Key-point Extraction}
\label{kp}
We detect the characteristic features of each demonstration, and we call it key-points. By doing this, we avoid having large number of hidden states to be trained by HMM. We represent every recorded movement during the demonstration by a set of time-discrete sequence. For each arm we have a sequence $P_{d,1},P_{d,2},...,P_{d,len(d)}$ that defines the positions of manipulator, and a sequence $O_{d,1},O_{d,2},...,O_{d,len(d)}$ that defines the orientations of manipulator over time, where $l(d)$ is defined as the length of demonstration $d$. $P_i$ is a three dimensional vector, whereas $O_i$ is a four dimensional vector. A point $P_{d,i}$ is selected as a key-point $K_{d,j}$ in the sequence of points if:

\begin{equation}
\begin{aligned}
& \angle ( \overrightarrow{P_{d,t}} - \overrightarrow{P_{d,t-1}}, \overrightarrow{P_{d,t}}, \overrightarrow{P_{d,t}} - \overrightarrow{P_{d,t+1}}) < 2\pi - \epsilon_1   \\
\vee~~ & \norm{\overrightarrow{P_{d,t}} - \overrightarrow{P_{d,t-1}}} < \epsilon_2, i - \tau_{d,j-1}>\epsilon_3, \\
& \norm{\overrightarrow{P_{d,t}} - \overrightarrow{P_{d,\tau_{d,j-1}}}} > \epsilon_4 \\
\vee~~ & \norm{\overrightarrow{P_{d,t}} - \overrightarrow{P_{d,\tau_{d,j-1}}}} \geq \epsilon_5, i - \tau_{d,j-1}>\epsilon_6, \\
& \norm{\overrightarrow{P_{d,n}} - \overrightarrow{P_{d,\tau_{d,j-1}}}} < \epsilon_7 ~ \forall ~ n \in  [\tau_{d,j-1}, i)
\end{aligned}
\end{equation}

\noindent where, $\tau_{d,j}$ denotes the time-stamp of demonstration $d$ and $j^{th}$ key-point. This means that if the angle between the vector that goes from point $P_i$ to its successor $P_{i+1}$ and the vector that goes from point $P_i$ to its predecessor $P_{i-1}$ is less than $2\pi - \epsilon_1$, then point $P_i$ is selected as a key-point. Figure \ref{fig:key_point} shows an example of finding angles between the two vectors in a two dimensional plane. To detect only sharp corners in the manipulator trajectory as key-points $\epsilon_1$ should be high.

\begin{figure}
\begin{center}
\includegraphics[width=0.5\linewidth]{key_point.png}
\end{center}
   \caption{Key-point selection criteria}
\label{fig:key_point}
\end{figure}

To detect the key-points $O_{d,j}$ of the orientation angles of the manipulator, similar selection criterion for key-points is used. A point $O_d$,i is selected as a key-point $K_{d,j}$ in the sequence of points $O_{d,1},O_{d,2},...,O_{d,len(d)}$ if:

\begin{equation}
\begin{aligned}
& \angle ( \overrightarrow{O_{d,t}} - \overrightarrow{O_{d,t-1}}, \overrightarrow{O_{d,t}}, \overrightarrow{O_{d,t}} - \overrightarrow{O_{d,t+1}}) < 2\pi - \epsilon_8   \\
\vee~~ & \norm{\overrightarrow{O_{d,t}} - \overrightarrow{O_{d,t-1}}} < \epsilon_9, i - \tau_{d,j-1}>\epsilon_{10}, \\
& \norm{\overrightarrow{O_{d,t}} - \overrightarrow{O_{d,\tau_{d,j-1}}}} > \epsilon_{11} \\
\vee~~ & \norm{\overrightarrow{O_{d,t}} - \overrightarrow{O_{d,\tau_{d,j-1}}}} \geq \epsilon_{12}, i - \tau_{d,j-1}>\epsilon_{13}, \\
& \norm{\overrightarrow{O_{d,n}} - \overrightarrow{O_{d,\tau_{d,j-1}}}} < \epsilon_{14} ~ \forall ~ n \in  [\tau_{d,j-1}, i)
\end{aligned}
\end{equation}

A major challenge in detection of the key-points using this approach is to tune the values of thresholds $\epsilon_1 - \epsilon_{14}$, which will decide the number of key-points extracted from the recorded task or trajectories. If the number of detected key-points is low, some relevant characteristics of the task or trajectories can be missed from the generalization step. Whereas, if the detection of key-points is high, over fitting of the recorded task or trajectories can take place.

Keeping in mind the trade-off between generalization and over-fitting, reasonable values for $\epsilon_1 - \epsilon_{14}$ were experimentally determined. Figure \ref{fig:k_cluster} shows the result of key-point extraction algorithm on multiple demonstrations. It shows the key-points extracted from three demonstrations. The key-points are marked with circles on each original trajectory of the demonstration.

\begin{figure}
\begin{center}
\includegraphics[width=1\linewidth]{k_cluster.png}
\end{center}
   \caption{The small points are the extracted key-points from multiple demonstrations and the big points are the centroids of the k clusters}
\label{fig:k_cluster}
\end{figure}

\subsection{Clustering}
\label{clust}
In contrast to the common key point approach used by Asfour \textit{et~al}.~ \cite{Asfour2008ImitationRobots}, we cluster the key-points derived from all the demonstrations and calculated the centroids of each cluster. To solve this problem, we use \textit{k}-means clustering algorithm. The \textit{k}-means clustering algorithm takes the number of clusters as an input to generate \textit{k} clusters, from a set of observation vectors. It returns the centroids for each of the \textit{k} clusters formed. For our system, we set the number of clusters \textit{k} equal to average number of key-point in each demonstration.

A key-point vector is classified with a cluster if the centroid of the cluster is closest to it, i.e. closer to centroid than any other centroids. \textit{k}-means clustering algorithm attempts to minimize the distortion, which is the sum of the squared distances between each key-point vector and its dominating centroid. At each step, \textit{k}-means refines the choice of centroids and tries to reduce the distortion. When this distortion change gets below a threshold, the algorithm stops. Figure \ref{fig:k_cluster} shows the result of the implement \textit{k}-means clustering algorithm to cluster the key-points and find the centroids of the clusters. In the plot, each centroid is marked with big red circles. In the next section, we discuss how these centroids are used to learn a model of the task or trajectories using a HMM.

\subsection{Model Learning using HMM}

A HMM (Hidden Markov Model) is a directed graphical model that is a statistical Markov chain of a sequence of unobserved or hidden states and a corresponding sequence of observation variables. It has been widely applied in the field of handwriting recognition, speech recognition, DNA sequence analysis, etc.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=0.7\linewidth]{hmm.png}
\end{center}
   \caption{Graphical representation of HMM}
\label{fig:hmm}
\end{figure}

Figure \ref{fig:hmm} shows a graphical representation of HMM, where $S_t$ denotes the hidden states and $O_t$ denotes the observed variables at time instants $t \in \{1, 2, 3,.. , t-1, t, t+1, ... \}$. The probability of going to $j^{th}$ state at time $t+1$, given that the current is state $i$ at time $t$, is denoted by:

\begin{equation}
a_{ij} = P(S_{t+1} = j ~\vert~ S_t = i)
\end{equation}

Using these probabilities, we form the state transition matrix, given by:

\begin{equation}
A = \{a_{ij}\}, ~\forall~ i,j = \{1,2,3, ..., N_s\}
\end{equation}

\noindent where, $N_s$ denotes the number of hidden states in the HMM. In this work, the number of hidden states $N_s$ was set equal to the number of centroids derived from the demonstrations.

The initial state probabilities is defined as the probability of model being in state $i$ at time $t = 1$, and is given by:

\begin{equation}
\pi = \{\pi_i = P(S_1=i)\}, ~\forall~ i = \{1,2,3, ..., N_s\}
\end{equation}

The observation probability is defined as the probability of observing a symbol $q_k$ at time $t$ given the model is in state $i$, and is denoted as:

\begin{equation}
b_i(k) = P(q_k ~\vert~ S_t = i)
\end{equation}

Using these probabilities, we form the observation probability matrix, given by:

\begin{equation}
B = \{b_i(k)\}, ~\forall~ i = \{1,2,3, ..., N_s\} and k = \{1,2,3, ..., Q\}
\end{equation}

\noindent where, $Q$ is the number of observation symbols. A complete HMM is described as:

\begin{equation}
\lambda = \{\pi, A, B\}
\end{equation}

In our research, we used a discrete-HMM for learning a model of the demonstrated trajectories, which required the recorded continuous trajectories to be mapped to discrete values. The key-point extraction and clustering technique applied in section \ref{kp} Key-point Extraction and section \ref{clust} Clustering were for vector quantization. As it is required to use most of the k clusters in order to preserve most of the features in the continuous trajectory, we mapped each cluster into a discrete symbol $o_{n,m}$ to from a codebook of symbols $\{q_1, q_2, q_3, ...., q_k\}$, where k is the number of clusters formed. The sequences of these observations form the set, and is given by:

\begin{equation}
\textbf{O} = \{O_m = (o_{1,m}, o_{2,m}, o_{3,m}, ..., o_{N_m,m})\}, ~\forall~ m = \{1,2,3, ..., M\}
\end{equation}

\noindent where, M denotes the total number of demonstrations.

The efficiency of learning with HMM depends on the number of available observations. As in RLfD, it is preferred to keep the number of observations or demonstrations low, an appropriate initialization of the model parameters is imperative. Next, we discuss the initialization process of the HMM parameters.

We implemented the Bakis left–right topology \cite{Rabiner1989ARecognition} to model the demonstrated task. The forward-transition probabilities $(\alpha_{i,i+1}, \alpha_{i,i+2}, ...)$ and self-transition probabilities $(\alpha_{i,i})$ were initialized in the state transition matrix $A_{i,j}$ as:

\begin{equation}
\begin{aligned}
& \alpha_{i, i+1} = (\frac{1}{\tau_{i,\delta}})(\frac{1}{Z}), \\
& \alpha_{i, i+2} = (\frac{1}{4\tau_{i,\delta}})(\frac{1}{Z}), \\
and ~~& \alpha_{i, i} = (1-\frac{1}{\tau_{i,\delta}})(\frac{1}{Z})
\end{aligned}
\end{equation}

\noindent where, $\tau_{i,\delta}$ is the amount of time spent in state $i$, and
$Z$ is a normalizing constant to make sure $ \sum \alpha_(i,j)= 1$.

All other state transition probabilities were set as zero, to make sure that transition to those states is impossible. Output probabilities $b_i (k)$ were initialized as:

\begin{equation}
b_i (k) = n_{i,\delta} (q_k)/\tau_{i,\delta}
\end{equation}

\noindent where, $n_{i,\delta} (q_k)$ denotes the number of times $q_k$ is observed in state $i$ of $O_\delta$. The state probabilities $\pi$, were initialized as $\pi = [1~ 0~...~0]$. This ensures that the learning always starts from the starting point.

Once the model parameters were initialized, Baum–Welch algorithm \cite{Rabiner1989ARecognition} was used to train on all demonstrations or observations $O_1, O_2, ... , O_M$. Later, Viterbi algorithm \cite{Rabiner1989ARecognition} was used to determine the most probable sequence of hidden state. Due to the nature of the implemented Bakis left-right topology, some hidden states or centroid points were not present in all the observed trajectories. We called them as zero-points and were ignored in the generalized trajectory.

\subsection{Dynamic Time Warping}

Since the length and velocities of the demonstrated trajectories differ, the time frames of extracted key-points of each demonstration are different. To produce the learned trajectory, we need to align the set of key-points along a common time vector. To solve this problem, we use DTW \cite{Keogh2005ExactWarping}, in which the temporal alignment of the clusters made by key-points is done by aligning the entire trajectory with respect to a reference trajectory.

DTW sequence alignment technique forms a matrix that consists of distances between two time-series, which is then used to find an optimal path that minimizes the distance between the two time-series. For a given test sequence $t_1, t_2, ..., t_T$ of length $T$, and a reference sequence $r_1, r_2, ..., r_R$ of length $R$, the distance matrix is calculated as:

\begin{equation}
H(x,y)= \norm{r_x-t_y}^2, ~\forall~  x=1,2,..,R, ~and~ y=1,2,..,T
\end{equation}

In our research, to calculate the distance between two time-`series, Euclidean l2-norm is used. The optimal alignment path $g(x,y)$  is calculated as:

\begin{equation}
g(x,y) = H(x,y) + min \{g(x-1,y),g(x-1,y-1),g(x,y-1)\}
\end{equation}

For selection of the reference sequence, forward algorithm was used to find the demonstration that has the highest probability for the learned model. As our data received from demonstration was eight dimensional data, we implemented multidimensional DTW, and the distance matrix is calculated as:

\begin{equation}
H(x,y)= \sum_{d=1}^D\norm{r_x^d-t_y^d}^2 ,  \forall  x = 1,...,R,~and~ y = 1,...,T
\end{equation}

\noindent where, $D$ is the number of dimensions, and in our case, $D = 8$.

\subsection{Generalized Trajectory}

After we have a common time-line for the model learned for the sequence of centroids, we need to connect these centroids to generate a generalized trajectory. To solve this problem, we used cubic smoothing-spline regression to determine a generalized trajectory from the most probable sequence of centroids. This method is widely applied for fitting a smooth curve to large set of scattered data.  In our case, we want to generate a smooth trajectory from the set of scattered centroid points. The spline curve was interpolated at intervals equal to the period size of clusters, which produced a generalized trajectory suitable for Baxter.

\section{ROBOT EXECUTION}

Once the model of the task is saved, we use the stored files to perform the task. The files contains the centroids, which are the position and orientation of the manipulator. For the robot to reproduce the learned task, we need to convert these position and orientation values into joint angles values that are suitable for the robot. To solve this problem, we use Inverse Kinematics. This technique is just the opposite of Forward Kinematics. It provides the set of joint angle values for a given the Cartesian pose of the manipulator.

We use the Baxter's IK server to perform the inverse kinematics during the execution of the task. It is a build-in service from Baxter SDK. It takes the position and orientation values as the input and returns seven valid joint angles values to achieve it. These values are sent to the robot and it moves to that state using Joint Trajectory Action Server (JTAS).

\section{EXPERIMENTS}

To evaluate the approach, we performed the experiments on the research robot by Rethink Robotics called Baxter. It is a dual-arm 7-DoF robot. A modular approach was used to create the framework and all the algorithms were developed as separate packages in python. This made it easier to test and debug each part of the framework separately. In this section, we discuss experiments performed and the results of those experiments.

The key-point extraction algorithm and clustering algorithm plays a vital role in the efficiency of the model. The variables for tuning the key-point extraction algorithm were experimentally determined. To make sure this algorithm works well on wide variety of task and complex trajectories, we validated them on different tasks. In the next two sub-sections, we discuss the details of the two complex tasks that the robot learned through multiple demonstrations.

\subsection{Stacking cup task}

The first task is to place a cup on a stack of cups. In this task, the robot takes the cup from a human and puts it on a stack of cups. Figure \ref{fig:demo} shows robot learning the stacking cup task from a human teacher through demonstration. Three demonstrations were performed to teach this task. Table \ref{data_cup} shows the data statistics for this task.

\begin{table}
\centering
\caption{Data statistics for stacking cup task}
\label{data_cup}
\begin{tabular}{|c|c|c|}
\hline
Demonstration \# & \# of recorded values & \# of key-points extracted \\
\hline
Demonstration  1     & 18024 & 18  \\
Demonstration  2     & 19759 & 19   \\
Demonstration 3      & 18140 & 20 \\
\hline
\end{tabular}
\end{table}

\begin{figure}[h]
\begin{center}
\includegraphics[width=0.8\linewidth]{perform_drop.jpg}
\end{center}
   \caption{Robot performing learned stacking cup task}
\label{fig:demo_cup}
\end{figure}

\begin{figure}
\begin{center}
\includegraphics[width=0.9\linewidth]{cup_plot.png}
\end{center}
   \caption{Demonstrated and learned trajectory for stacking cup task}
\label{fig:cup_plot}
\end{figure}

The robot was able to reproduce the task successfully. This task involved learning of precise motion of the manipulator to precisely drop the cup on top of the stacked cups. Figure \ref{fig:cup_plot} shows the manipulator position for the demonstrated task and the learned trajectory.  The small triangles on the trajectories marks the key-points extracted from that demonstration, and the red circles show the centroids of the clusters formed. In total, 19 clusters were formed and they acted as states for the HMM. Numbers from 0-18 named these centroids. The cyan line shows the generalized trajectory. This plot only shows the position of the manipulator only. They complete learning process involved the learning of the orientation of the manipulator as well. Robot performing the learned stacking cup task can be seen in figure \ref{fig:demo_cup}.

\subsection{Pick and place task}

The second task is a pick and place task, in which the robot picks up a block of wood and places it at some other spot and then pick the block from that spot and places it back to the original spot. This is a much more complex task than the previous task. Table 4 shows the data statistics for this task.

It can observed in Figure \ref{fig:pp_plot} that the three demonstrations were a lot different from each other, though they were performing the same task. The small triangles on the trajectories marks the key-points extracted from that trajectory, and the red circles show the centroids of the clusters formed. In total, 21 clusters were formed and they acted as states for the HMM. Numbers from 0-20 named these centroids. The cyan line shows the generalized trajectory. Please note that the trajectory in the graph is not the actual trajectory the robot went through. The derived trajectory in the plot is just by connecting the states with linear lines to illustrate the flow of states. The actual trajectory of the robot used JTAS spline interpolation and was much smoother than the one in the plot.

\begin{table}[h]
\centering
\caption{Data statistics for pick and place task}
\label{data_pp}
\begin{tabular}{|c|c|c|}
\hline
Demonstration \# & \# of recorded values & \# of key-points extracted \\
\hline
Demonstration  1     & 31510 & 24  \\
Demonstration  2     & 28593 & 21   \\
Demonstration 3      & 27025 & 21 \\
\hline
\end{tabular}
\end{table}

\begin{figure}
\begin{center}
\includegraphics[width=0.9\linewidth]{plot_pp.png}
\end{center}
   \caption{Demonstrated and learned trajectory for pick and place task}
\label{fig:pp_plot}
\end{figure}

In both the experiments, the robot was able to learn the complete task and successfully reproduce the learned task.

\section{DISCUSSION}

In this paper, we presented a framework for RLfD. This framework has three phases: Data collection, Task Learning and Robot Execution. The data collection module collects and stores the eight dimensional time-series data from each demonstration. The Task Learning module then uses this data to learn a model of the task. As we can observe from the experiments, the key-points are extracted from each of the recorded demonstration. This was done to limit the number of observed states for the HMM. As we can observe from the data statistics of the two experiments, the number of extracted key-points is much less than the recorded values. This reduction in the number of states lead to a faster learning of the model. Initially, we tried to learn the model of the task based on the joint angles values, and it avoided the implementation of the forward and inverse kinematics. Then, we implemented FK and IK and used the position and orientation of the manipulator, which made it easier to visualize the trajectories of the task. In addition, the key-points extracted from both the methods were the same in most of the task. Thus, we decided to move forward with the second approach using the position and orientation, as it is easier to plot and visualize the position of the manipulator.

The model learning part of our approach is similar to \cite{Vakanski2012TrajectoryWarping}, but the key point extraction technique we implemented allowed us to precisely extract key-points from the demonstrations. By adjusting the threshold values $\epsilon_1 - \epsilon_{14}$, we were able to fine-tune the key point extraction algorithm to extract only sharp corners from the demonstrated trajectory. The \textit{k}-means clustering technique that we used for clustering the key points allowed us to include all the key points from all the demonstrations performed. Thus, we were able to preserve the key features of the task that were missing from some demonstrations.  Thus, we got more key features of the task as compared to Asfour \textit{et~al}.~ \cite{Asfour2008ImitationRobots}, where they used the common key points concept, i.e. they just use the key points that are found in all demonstrations. Therefore, the generalized trajectory only includes the features that are present in all demonstrations.

It was experimentally determined that three or four demonstrations for most of the task, tends to produce a generalized trajectory closest to the most probable demonstrated trajectory. The learning of the orientation played a crucial role as well. For many tasks like picking up the wooden block, the orientation of the gripper has to be perfectly aligned with the object to grab the object from the right place. The model learning also involved learning of the gripper states. The model learned should not only learn whether the gripper should be closed or open, but also the width it should open or close to grasp or release an object. This ensures that the robot does not break delicate objects by learning the grasping width of the gripper during the task.

\section{CONCLUSIONS}

In this paper, a novel RLfD framework was introduced, which used HMM to model the trajectory skill demonstrated to a collaborative robot. Our results show that the robot can collaborate with the human teacher and learn variety of trajectory based task in short time and reproduce the task while working with a human user.

In future, other collaborative robots can be trained to learn complex task using the developed RLfD framework. With the advancements in the field of image processing and visual servoing, RLfD can be made more robust by allowing the robot to learn the position and orientation of the task relevant objects in addition to the position and orientation of the manipulator. This way, the robot will be able to adapt to the changing environment, as it will learn the environment parameters like distance from task relevant objects. This will enable the robot to learn a task and then reproduce it in a dynamic environment.

\addtolength{\textheight}{-12cm}

{\small
\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


From Pole To Pole, a three-part documentary series marks the International Polar Year 2007-2008 by exploring some of the world's most remote and vulnerable regions.
Examining one of the drill cores.
Tim Naish examines one of the drill cores.


text:
Guy Newall (25 May 1885 – 25 February 1937) was a British actor, screenwriter and film director. He was born on the Isle of Wight on 25 May 1885. He began his film career by acting in the 1915 film The Heart of Sister Ann. In 1920 he directed his first film, and went on to direct a further ten including The Chinese Puzzle before his death in 1937. He established a production company with George Clark whom he had met during the First World War, and they raised finance to construct a new studios at Beaconsfield Studios. Newall was married twice, to actresses Ivy Duke and Dorothy Batley.

Partial filmography
Director
 The Bigamist (1921)
 Fox Farm (1922)
 Boy Woodburn (1922)
 A Maid of the Silver Sea (1922)
 The Starlit Garden (1923)
 What the Butler Saw (1924)
 Rodney Steps In (1931)
 The Rosary (1931)
 The Marriage Bond (1932)
 The Chinese Puzzle (1932)
 Chin Chin Chinaman (1932)
 The Admiral's Secret (1934)

Actor
 The Heart of Sister Ann (1915)
 Esther (1916)
 Driven (1916)
 Money for Nothing (1916)
 Mother Love (1916)
 Trouble for Nothing (1916)
 The Manxman (1916)
 Comradeship (1919)
 The Garden of Resurrection (1919)
 I Will (1919)
 Fancy Dress (1919)
 The Lure of Crooning Water (1920)
 Duke's Son (1920)
 The Bigamist (1921)
 Boy Woodburn (1922)
 A Maid of the Silver Sea (1922)
 The Starlit Garden (1923)
 Ghost Train (1927)
 Number 17 (1928)
 The Road to Fortune (1930)
 The Eternal Feminine (1931)
 Potiphar's Wife (1931)
 The Marriage Bond (1932)
 Grand Finale (1936)
 Merry Comes to Town (1937)

References

External links

1885 births
1937 deaths
English male film actors
English male silent film actors
English film directors
Actors from the Isle of Wight
20th-century English male actors

redpajama_set_name: RedPajamaWikipedia


For companies that require the work to be completed within their own facilities and lack the work force needed to complete their contracts, CMSE provides an onsite work crew program. The mobile work crew is a small group of 2-8 individuals with disabilities who are employed in an industrial setting and managed by a specially trained supervisor. This program is a direct result of a federal initiative to provide integrated work opportunities to persons with severe disabilities.
Work is contracted based on a piece rate amount, which is agreed upon by both parties. The employees continue to be on CMSE's payroll and are covered by the sheltered workshop's own worker's compensation insurance. Training and supervision for the employees are provided through funding from the Department of Mental Health. This staff support is long-term and will always be provided as long as the employees remain at the job site and the employer feels it necessary. The number of employees at each site varies depending on the particular needs of that company.
Persons with disabilities work alongside others doing the same work, although limited work abilities and behavioral needs may require that workers be situated in proximity of each other to enhance training and supervision. Workers with disabilities receive the same benefits as others in the company with respect to such procedures as working hours, lunch and break times.
A job may start with only one or two employees and one staff member. Once these employees become acclimated to the new work environment and new job tasks then other employees with be added as they are needed. The workshop also has the capability of bringing in additional workers as needed and even shipping extra work back to the workshop if the need arises. Each site will have no more than eight employees, which are supervised by any one staff member.
Within the enclave, payment for work performed is commensurate with pay to others within the host company doing the same type and amount of work. Since the capability of each worker varies considerably, these employees are paid on a piece rate system that allows them to earn commensurate wages based on their actual work performance.


text:
/* Magic Mirror
 * Default Modules List
 *
 * By Michael Teeuw http://michaelteeuw.nl
 * MIT Licensed.
 */

// Modules listed below can be loaded without the 'default/' prefix. Omitting the default folder name.

var defaultModules = [
	"alert",
	"calendar",
	"clock",
	"compliments",
	"currentweather",
	"helloworld",
	"newsfeed",
	"weatherforecast",
	"updatenotification"
];

/*************** DO NOT EDIT THE LINE BELOW ***************/
if (typeof module !== "undefined") {module.exports = defaultModules;}

redpajama_set_name: RedPajamaGithub


I love this list! Linda Veganosity - April 13, Great list! Category Commons Cookbook Food portal, Health and fitness portal. Both meal programs provide foods that are easy to prepare and do not take a long time to cook. Sarah Elizabeth Alexander - July 20, Hello, Thank you for providing these wonderful blogs for my reference.


Windows XP, SVN 6048 self build version(TDM GCC 4.4.1-2).
Does someone encounter the same problem?
I have the same problem. The old svn 5911 works well.
I noticed that during this seconds the C::B CPU load is very high (more than 50%).
If you open C::B the program hangs for 5-6 seconds with high CPU load before you can start working. SVN 5911 works well.
Yes, I have such a feeling. And my side of the CB users, are also reflected in very slow start-CB!
I think that the CB at boot time to load too many wizard, which in fact is unnecessary!
Now, CB boot speed is not much faster than the Eclipse CDT!
Even slower than the VS!
Does this happen just after starting blank C::B ?
Please start it with -d form commandline to get the debug-output.
You might have to start C::B, go to the Code::Blocks debug panel in the logger-pane, close C::B and open it again, to make sur that you can see the debug log while starting.
I hope it works, because on my XP, the content of the log is not visible without scrolling it a tleast one time.
To make sure it's a plugin, you can disable them all and reenable them one after the other, or disable them one after the other, to see whether one of the plugins slows down C::B.
By the way I have no such issue on my XP box.
I don't think it's a problem of too many plugins because I have only the default ones (no additional ones).
I tried, and the problem is in the "Code Completion" plugin.
If I enable only this one the problem happens. If I enable all the others, the problem doesn't happen.


MOUNT GILEAD — "Feeding those in need one bag at a time," is the Morrow County Food Pantry's motto.
At its Wednesday meeting, Morrow County Commissioners thanked County Food Pantry Board Members Brenda Harden and Russ Merrin, along with the county pantries that partner in giving to those in need.
Harden said people come to the county food pantry at the Community Services Building in Mount Gilead for many reasons. Some have just lost their job, some have health problems and there are many other emergency situations that cause local families and individuals to have an immediate need for food.
Harden said it requires three days after application until food stamp cards are approved. People in need can go to the food pantry and receive food for three meals for three days. Each family is allowed only one visit per month.
Food items are provided to Morrow County residents who qualify based on the Emergency Food Assistance Program income eligibility guidelines.
The Morrow County Food Pantry has served 250 families in 2016 and that number continues to grow.
Shipman said the food pantry is able to refurbish their food supply from the Mid-Ohio Food Bank. They also receive donations of food from Kroger, Drug Mart, churches and individuals. The Morrow County Hospital and other groups have had major food drives in the past year.
County Food Pantries that partner with Mid-Ohio Food Bank are: Trinity United Methodist Church Food Pantry and Meals, Edison Enterprise Baptist Pantry, No Limits Outreach Pantry/Safe Harbor in Edison, Cardington Food Pantry/First United Methodist Church, Morrow County Food Pantry, and Angel Food Pantry/Marengo United Methodist Church.
The Morrow County Food Pantry is at Entrance C in the Morrow County Community Services Building, 619 West Marion Road in Mount Gilead. The Food Pantry welcomes donations of canned goods, mac and cheese, pasta, rice and other non-perishable food items.
The Morrow County Food Pantry is open Monday and Thursday from 9 a.m. to noon.
In the Morrow County Food Pantry are, from left, Pantry board members Brenda Harden and Teresa Shipman, Ellen Wilson who is Pantry Coordinator from Experience Works and volunteer Ashley Hudgel.
https://www.morrowcountysentinel.com/wp-content/uploads/sites/29/2017/08/web1_DSCN1944-2-.jpgIn the Morrow County Food Pantry are, from left, Pantry board members Brenda Harden and Teresa Shipman, Ellen Wilson who is Pantry Coordinator from Experience Works and volunteer Ashley Hudgel.
Morrow Çounty Commissioners proclaim Hunger Action Month. From left, Commissioners Burgess Castle and Tom Whiston, Pantry Bboard members Brenda Harden and Russ Merrin and Commissioner Warren Davis.
https://www.morrowcountysentinel.com/wp-content/uploads/sites/29/2017/08/web1_DSCN1942-2-.jpgMorrow Çounty Commissioners proclaim Hunger Action Month. From left, Commissioners Burgess Castle and Tom Whiston, Pantry Bboard members Brenda Harden and Russ Merrin and Commissioner Warren Davis.


text:
Landmark textbook aims to improve clinician, consumer understanding of lesbian health
Initially conceived as a textbook for physicians and nurses, the book is written in consumer-friendly language and can be used as a reference for students or by lesbians interested in learning about their own health.
Published by the UCSF Nursing Press, Lesbian Health 101- A Clinician's Guide covers a range of health-related issues for lesbians along the lifecycle continuum, including health screenings, substance abuse, depression, reproduction, menopause, partner violence, disabilities, and hospice care.
"Many clinicians wrongly assume that lesbians face the same health issues as heterosexual women when in fact, this population is more at risk for cancer, heart disease and substance abuse. As the first evidence-based textbook focused on lesbian health for clinicians, Lesbian Health 101 is intended to help doctors and nurses identify and more sensitively manage the needs of lesbian patients," said Suzanne L. Dibble, DNSc, RN, the book's co-editor, professor at the Institute for Health and Aging in the UCSF School of Nursing, and co-founder and former co-director of the UCSF Lesbian Health & Research Center (LHRC).
The book comes one decade after the government released two significant reports recognizing the underserved health needs of lesbians. In 1999, the Institute of Medicine reported that the US health care system does not adequately serve the lesbian population. In 2000, the Department of Health and Human Services released "Healthy People 2010," its 10-year plan which identified lesbian and gay Americans as one of six groups affected by health disparities.
"Lesbians often face barriers in accessing quality health care due to lack of insurance, the non-legal status of their partners, homophobic attitudes among clinicians, and gaps in research on their specific health issues," said Patricia A. Robertson, MD, the book's other co-editor, professor in the UCSF Department of Obstetrics, Gynecology, and Reproductive Sciences, and former co-director of the UCSF LHRC. "I hope in another 10 years, as we gather more data on which to base clinical guidelines and as health policies become more enlightened, that the need for this book or its content will have changed dramatically."
Each chapter in the book begins with a scenario based on the author's clinical experiences with lesbian patients and provides in-depth information about management of the particular health issue. By putting a face on the issues, the scenarios may help clinicians become aware that some of their patients—whether they disclose it or not—are lesbians with unique health needs.
"At long last clinicians now have a primer that challenges the long held medical practice of 'don't ask don't tell.' The contemporary social and policy debate on costs and inclusiveness in our health care system gets a scientific boost from the authors and researchers who methodically document the importance of culturally competent care for lesbians and their families. Lesbian Health 101 shatters myths with evidence. Our health care system can no longer say we didn't know," said Sandra R. Hernández, MD, chief executive officer of The San Francisco Foundation, the community foundation of the Bay Area, and member of the National Advisory Council for the UCSF LHRC. Hernández previously was director of public health for the City and County of San Francisco.
"This is the first comprehensive textbook on lesbian health, reflecting the exponential increase in research on lesbian health over the past 10 years. I highly recommend this textbook to all clinicians and students who provide health care for women, and to inquisitive patients," said Susan Love, MD, president of the Susan Love Breast Cancer Foundation, in a statement on the book cover.
For more information, visit http://nurseweb.ucsf.edu/public/npress/ord-lh.htm .
About the Lesbian Health and Research Center at UCSF
The UCSF LHRC is dedicated to improving the health of lesbians, bisexual women, transgender people, and their families by creating comprehensive programs of research, education and trainings, public events and community collaborations. LHRC is a collaboration between the UCSF School of Medicine and the UCSF School of Nursing and is located within the Institute for Health & Aging in the nursing school. LHRC directly advances the Institute's goals of serving women across the lifespan and of providing research data for making public policy decisions, improving public education, and facilitating community interventions. LHRC is also affiliated with the UCSF National Center of Excellence in Women's Health and is a collaborative partner of the UCSF Center for Gender Equity.
UCSF is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care.
Karin Rush-Monroe
Karin.Rush-Monroe@ucsf.edu
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L'université Saint-Sébastien (en espagnol : Universidad San Sebastián) est une université privée chilienne située à Concepción, Santiago, Valdivia, Osorno et Puerto Montt.

Histoire
Fondée en 1989 comme établissement d'enseignement supérieur, l'université reçoit, en 2001, la reconnaissance de l'État.

Certaines Facultés
 Art et design
 Sciences de la Santé
 Sciences sociales
 Droit
 Médecine Vétérinaire
 Médecine et soins infirmiers
 Technologie dentaire et médecine
 Éducation

Notes et références

Voir aussi

Article connexe
 Liste des universités au Chili

Lien externe
  Site officiel

Saint-Sebastien
Enseignement à Santiago


text:
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<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" xmlns:treeView="http://schemas.android.com/apk/res/pl.polidea.treeview"
	android:orientation="vertical" android:layout_width="fill_parent" android:layout_height="fill_parent">
	<ListView android:layout_width="0dip" android:layout_height="0dip" android:scrollbars="vertical"
		android:visibility="gone"> <!-- Just to test some attributes in IDE -->
	</ListView>
	<pl.polidea.treeview.TreeViewList android:id="@+id/mainTreeView" android:layout_width="fill_parent"
		android:layout_height="fill_parent" treeView:indicator_gravity="right|center_vertical"
		android:scrollbars="vertical" android:smoothScrollbar="true"/>
</LinearLayout>


text:
Q: Error with tableview I'm new to Swift and IOS programming but I am  trying to get some data to load using a table view but I get an error that I have never seen before. Can someone please help me out. The error is showing up on the line where I am trying to pass the cell text label my array that I created. The error is "Could not find an overload for subscript that accepts the supplied arguments"

import UIKit

class ViewController: UIViewController, UITableViewDelegate {

    var cellContent = [1,2,3]

    override func viewDidLoad() {
        super.viewDidLoad()
        // Do any additional setup after loading the view, typically from a nib.
    }

    func tableView(tableView: UITableView, numberOfRowsInSection section: Int) -> Int {

        return cellContent.count
    }

    func tableView(tableView: UITableView, cellForRowAtIndexPath indexPath: NSIndexPath) -> UITableViewCell {

        var cell = UITableViewCell(style: UITableViewCellStyle.Default, reuseIdentifier: "Cell")

        cell.textLabel?.text  = cellContent[indexPath.row]
        return cell

    }

A: Looks like the error is a little misleading. The actual issue here seems to be that you are trying to set an Int as a String property. You can do that like this:
cell.textLabel?.text  = String(cellContent[indexPath.row])

With this change, your code works fine for me.

A: Swift is treating cellContent as an integer array. You need to either (1) use cell.textLabel?.text  = cellContent[indexPath.row] as String or change the array to var cellContent = ["1","2","3"]

A: You have to use strings for text labels. This will fix your error:
var cellContent = ["1","2","3"];


text:
Bare Knoll bezeichnet mehrere Erhebungen in den Vereinigten Staaten:

 Bare Knoll (Kalifornien)
 Bare Knoll (Massachusetts)


If you enjoy playing golf amongst stunning scenery - Callander Golf Club is a great place to visit. Situated just a short distance from the town centre there is ample car parking on site. Buggy hire, electric trolley and trolley hire are also available.
The course itself is challenging enough for all levels of golfers and the views of the foothills of the Scottish highlands form a stunning backdrop.
The 18-hole course has some really memorable holes to test your skills – the par 3, 15th is one of the most famous par 3s in Scotland.
Highly rated by many golf writers, Callander Golf Club appeared in a list of Scotland's top 10 courses to visit compiled by Golf Monthly.
The National Club Golfer magazine described Callander as "... a fantastically secluded location, diverse holes with great greens and stunning views of Ben Ledi and the Trossachs all combine to create a memorable experience".
Scotland's Golf Gems stated "The backdrop at Callander is as uplifting as the course is diverse and it is a pleasure to see your ball scorching towards its target against such amazing scenery"
There are many other golf courses to enjoy not too far away. Aberfoyle, Killin, Stirling and Balfron all have reasonable green fees and the world famous Gleneagles golf complex, with three superb championship courses is just a 25 minute drive from Callander.


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Remembering cyclists 100 years on from the start of World War One
Then and now: Cyclists gather at the War Memorial in Meriden
By Cycling UK
In May 2014 Cycling UK remembered the thousands of cyclists who gave their lives in the First World War and conflicts since
In May 2014 Cycling UK specifically honoured the Cyclist Battalions and other fallen cyclists at the annual memorial at Meriden, with a special service at the Cyclists' War Memorial.
Throughout World War One, the Cyclists' Touring Club (now Cycling UK) played an active role in the enrolment of CTC members into the battalions' ranks. The September 1914 issue of CTC's flagship magazine, The Gazette, contained a form for "members desirous of serving" to fill in so that they could be put on a waiting list for a possible outlet for "active patriotism". A few months later and CTC had made arrangements for two special CTC Companies to form part of the newly formed Cyclist Battalion of the Royal Sussex Regiment.
It may be hard to believe CTC's role in the call-up of cyclists to the front line, especially given the publication of the real dangers posed there for cyclist messengers. Mr G. Reddick of the Daily Chronicle reported seeing several messenger soldiers killed as they cycled. However, such acts reflected the opinion of the readership of the time that CTC could and should be doing something for the war effort.
Cycling UK has long been remembering fallen cyclists in the war. On 21 May 1921 the Meriden memorial, chosen as being closest to the centre of England, was unveiled and dedicated 'To the lasting memory of those cyclists who fell in the Great War, 1914-1919.'
The opening ceremony was fully reported in The Gazette, which described it as an "event unparalleled in the history of cycling…..never had there been such a concourse of cyclists from all posts of the United Kingdom assembled in the common cause of recognition of heroism of their comrades fallen in the war."
A fund of £1,000 had been raised for the memorial and Mr G. Herbert Stancer OBE, Secretary of CTC and Editor of The Gazette 1920–1944, helped to develop the scheme. Over 10,000 people attended the ceremony; such were the feelings of the cycling community.
At 6pm, Mr Gritten, the local MP, took to the stand and addressed the congregation:
My Lords, my ladies, ladies and gentlemen, fellow cyclists. We are met together to perform a solemn function. We are to pay a last tribute to our dead comrades by the unveiling and the dedication to them of this permanent memorial. This occasion is, indeed, unique in the history of cycling. Nothing like this has happened before; and we pray that there will never again be such a great war as to make it reoccurrence necessary.
MP W. G. Howard Gritten
It was with great sadness that later additions to the inscription would have to be made because of the Second World War.
The 2014 commemorations marked 100 years since the start of World War One and provided a national focus to remember those who were killed whilst cycling in the line of duty in all conflicts. Following the Saturday's traditional Audax and leisure rides, an extra special memorial service took place on Sunday 18 May 2014. An additional plaque was dedicated by the Bishop of Warwick at the service at 11am.
Thanks to CTC's Kay Lakin for compiling the archive information - there is an interview with John Bennett in the Heritage Podcast.
Dispatches from Meriden View
'Biking Bishop' attends 90th Coxwold Memorial Service View
How CTC supported injured soldiers during WW1 View
Cyclists pay tribute to the fallen at 95th Meriden service View
Hundreds attend Meriden centennial service commemorating sacrifice of WW1 cyclists View


text:
Breaking Bob Kennedy's AR: What it means, by Larry Eder
By Larry Eder on August 28, 2009 2:41 PM | 0 Comments
Bob Kennedy and Todd Wiliams, Running US champs, Indianapolis, 1997, photo by PhotoRun.net.
I thought I would take a shot at what Dathan Ritzenhein breaking Bob Kennedy's AR for 5,000 meters really means. So, here we go:
In the day, the 1990's, Bob Kennedy, Todd Williams and Mark Croghan were three of the loneliest guys in North America. There were a few others in that realm. Some would settle for running 13:45 over here and getting squashed in Europe. All three of the above realized, however, that to be the best in the US was not good enough. You can count the other guys who raced in Europe from the US on one hand.
Kennedy began training with the best runners in the world, many of them Kenyans and Ethiopians: all of them worked hard, and challenged themselves constantly. Todd Williams was our lone guy at 10,000 meters, Mark Croghan in the steeplechase and Bob Kennedy in the 3,000 to 5,000 meter range. His record, 12:58.21, was a big bulls eye in my mind. If you really wanted to be world class.
If you thought you could be among the best half dozen in the world, then you have to put it on the line, you have to train hard, race hard and find a way to get to that place where, as Frank Shorter once said, the knife slices into you and the pain just becomes a constant. You must embrace the pain. You use it as the barometer. Repeat miles at near four minutes? Fast 400 meters? This is not running for fun. This is running to see what one is made of, to see where the limits are. This is character building, and the effects last a lifetime. Try and see what you are truly made of means risking failure, and for many, that is a frightening place to be. Some are not brave enough to go there, some have to go there, some are drawn there.
Ritzenhein was in seventh place with two laps to go, and by the finish, he was in third, driving, driving to the finish, 12:56.27! On the broadcast, Ritz's facial expression was a combination of oxygen debt and real shock-what happen's when your dreams become reality?
Ritz's sense of racing is exciting to watch, and his focus and drive give us a sense of how far ahead Bob Kennedy was thirteen lonely years ago...
For more on our sport, please click http://www.runningnetwork.com
Larry Eder
Bio: Larry Eder has had a 44 year involvement in the sport of athletics. Larry has experienced the sport as an athlete, coach, magazine publisher and now, journalist and blogger. His first article, on Don Bowden, America's first sub 4 minute miler, was published in RW in 1983. Larry has published several magazines on athletics, from American Track & Field to the U.S. version of Spikes magazine. He currently manages the content and marketing development of the RunningNetwork, The Shoe Addicts and RunBlogRun. Of RunBlogRun, his daily pilgrimage with the sport, Larry says: "I have to admit, I love traveling to far away meets, writing about the sport I love, and the athletes I respect, for my readers at runblogrun.com, the most of anything I have ever done, except, maybe running itself."
Website: https://www.runblogrun.com
Twitter: @RunBlogRun
Instagram: @RunBlogRun
Facebook: http://www.facebook.com/RunBlogRun
LinkedIn: https://www.linkedin.com/in/larry-eder-5497253
Wake up to RunBlogRun's news in your inbox. Sign up for our newsletter and we'll keep you informed about the Sport you love.
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This blog is licensed under a Creative Commons License.


text:
THA LAST MEAL(2LP/EX
Decrease quantity for THA LAST MEAL(2LP/EX
Increase quantity for THA LAST MEAL(2LP/EX
Personnel includes: Snoop Dogg, Bad Azz, Kokane, Eve, Lil HD, Tha Eastsidaz, Suga Free, Butch Cassidy, Master P, Mr. Magic, Mac Minista, Nate Dogg, MC Ren, The Lady Of Rage, Ice Cube (vocals); Clarence Jimmy Roach (guitar, bass); La Tonya Holmes, Mica Fisher, Traci Nelson (background vocals).
Producers include: Dr. Dre, Swizz Beatz, Battlecat, Meech Wells, Timbaland.
Though Ice-T is credited with creating gangsta rap, and the original N.W.A. brought it to the forefront of the pop culture, it was Snoop Dogg that took the genre to the next level with his debut album DOGGYSTYLE. Snoop's decision to transfer from the Suge Knight-owned Death Row Records to the Master P-owned No Limit had a significant effect on the sound of his music.
However, his fifth album, THA LAST MEAL, returns Snoop to the funk-inspired sound that made his debut a classic. "Snoop Dogg (What's My Name Part. 2)," with its Timbaland-produced background, re-establishes Snoop as the G-funk king. The posse cut "Set it Off," which features Ice Cube, MC Ren, Nate Dogg, and the Lady of Rage, highlights Snoop's laid-back flow and skilled lyrical style. Check out "True Lies" for that bouncy old Snoop Dogg/Dr. Dre feel.
Released: 9/08/2017


Step-by-step, Rockies right-hander and former closer Adam Ottavino is coming back from last May's Tommy John surgery.
In his last bullpen session, he threw breaking balls off the mound for the first time. He'll do that again on Wednesday, and if all goes well, he'll head back to the club's spring training facility in Scottsdale, Ariz. next week to slowly expand his throwing program.
There is no timetable for Ottavino's return. It could be sometime around the All-Star Game in mid-July, but that is a very soft target date.
"I feel good, I'm making progress," Ottavino said Tuesday before the Rockies hosted the Giants.
** Right-handed starter Jon Gray will pitch another rehab assignment for High-A Modesto. He's scheduled to pitch on Friday, hiking his pitch count up to about 85 pitches, meaning he could pitch six innings.
Gray, recovering from a strained abdomen, made his first rehab start on Sunday for Modesto, throwing 64 pitches in 4 2/3 innings. He gave up two runs on four hits, struck out three and walked one.
Manager Walt Weiss said Gray has made excellent progress.
** Right-handed reliever Jason Motte, expected to be a big part of the back end of the bullpen, has yet to make his Rockies debut, but he's making some progress. He's in Scottsdale, still healing his sore shoulder.
** Lefty starter Tyler Anderson (strained oblique) threw a 25-pitch bullpen session in Scottsdale Tuesday, and is making progress toward a rehab start.
** Infielder Dan Descaslo (broken hand) remains a long way from returning to the field. He's scheduled to have his brace removed on April 25 or 26.
** Lefty starter Tyler Matzek (anxiety) is also in Scottsdale. He's worked his way up to 35-pitch bullpen sessions.
"We are still just kind of going a bullpen at a time," Dugger said.


Care For Smiles Dental Clinic Fairfield. Your visit to the dentist.
We will confirm the date and time of your appointment the day before. If you are unable to keep your appointment please give us at least 24 hours notice so that we can offer the appointment to someone else. Please also refer to our Cancellation Policy.
Please arrive 10 minutes before your appointment time. It helps if your teeth are clean.
New patients will be required to fill in a New Patient Form. For your convenience, you can download and fill the New Patient Form prior to your appointment.
At your appointed time, the dentist will meet you in the waiting lounge and take you to the dental room. The support staff are introduced, your Medical and Dental History reviewed and the reasons for your visit discussed. We will explain the treatment options available to you and answer any questions you may have.
For new patients, a dental examination and clean is usually performed. The examination involves a tooth-by-tooth charting to identify and record the current condition of your teeth, and an assessment of the condition of your gums. Usually, digital x-rays and intraoral photos are also taken and advice about maintaining good oral health provided.
If additional treatment is required, you will be presented with a treatment plan and a quotation.
Care For Smiles offers Oral Sedation or Nitrous Oxide (laughing gas) sedation. Higher levels of sedation (including general anaesthesia or sleep dentistry) requires referral to a specialist (and therefore much higher costs).
Payment is required on the day of treatment. Cash, EFTPOS, American Express, Visa and Mastercard are accepted. There is no surcharge for credit card use.
Care For Smiles accepts all private health insurance funds and is a Preferred Provider for the HCF More For Teeth and the CBHS Choice Network programs. Health fund rebates are processed with your payment. You will only need to pay the gap between the Care For Smiles fee and the health fund rebate.
Care For Smiles provides discounts for holders of a valid Seniors or Carers card, full time university students or for those on a Disability Support Pension. For patients with private health insurance, the discount is applied to the gap amount.
Care For Smiles also bulk bills Medicare for eligible dental treatments under the Child Dental Benefits Schedule (CDBS) and for holders of DVA (Department of Veteran Affairs) Gold or White cards (conditions may apply).


text:
Alturi » News Items » Europe » Russia » Russia: New Anti-Gay Crackdown in Chechnya
Russia: New Anti-Gay Crackdown in Chechnya
05/08/2019 | Human Rights Watch
Police in Chechnya have carried out a new round of unlawful detentions, beatings, and humiliation of men they presume to be gay or bisexual, Human Rights Watch said today. The new abuses come against a backdrop of absolute impunity for the vicious large-scale anti-gay purge in spring 2017. The Russian authorities should carry out an effective investigation into the anti-gay abuses and hold those responsible to account. Human Rights Watch interviewed four men who were detained for between three and 20 days, between December 2018 and February 2019, at the Grozny Internal Affairs Department compound. Police officials there kicked them with booted feet, beat them sticks and polypropylene pipes, and tortured three of the four with electric shocks. One was raped with a stick. The men's accounts are consistent with a crime report filed on January 29 with Russia's chief investigative agency by the Russian LGBT (lesbian, gay, bisexual, and transgender) Network, a prominent LGBT rights group, which stated that in December and January, police in Grozny, Chechnya's capital, rounded up and abused 14 men. The report suggested that the true scope of detentions was broader.
ISSUES: Culture Religion and Politics, Discrimination and Equality, Health and Well Being, Human Rights, Protection and Safety
COUNTRIES: Russia
REGIONS: Europe


text:
/ Rob Beschizza / 1:24 pm Tue Nov 6, 2018
Delta forced passenger to sit in feces: "sit in your seat or you can be left behind."
During boarding, a Delta passenger noticed that he'd just sat in a seat covered in shit. The shit was all over his leg. Delta's stewards gave him a paper towel and a mini bottle of gin to clean himself with, and made it clear they wouldn't be cleaning the shit off the seat.
The gate agent called a manager, who [passenger Matthew] Meehan described as confrontational, while he was trying to remain calm and "not get kicked off the plane." "I tell her what happened and she said, 'If the cleaning crew didn't do their job, that's not my problem. What do you want me to do about it?'" Meehan alleges. "Very confrontational, like, so what? So I said, 'Can we get that cleaned up so I can sit down?' So she says, 'Sir, it's almost time for that plane to leave. You can sit in your seat or you can be left behind.'"
Meehan and the manager realized he wasn't the only passenger upset about this. "At that point, four or five other passengers had gotten up and out of their seats as well, standing at the flight attendant area in front in protest and wouldn't sit until it was cleaned," he says. To avoid causing a commotion, the manager had someone clean that area with paper towels. "To my knowledge, they did not use any kind of sanitizing solution, and I was supposed to be OK with that because she quote unquote, cleaned it."
A whole new meaning to the term "Business Class". More gin, please! The passenger, Matthew Meehan, took a photo of the shit that got on his shoe:
He asked again but was told in no uncertain terms what he had to do if he wanted to be on that plane: "fester in feces for two hours. ... We sat there during the entire flight, my row, the rows around me, it still smelled horrific. There was still feces caked into the carpet"
Here's more from CNBC:
In a statement to CNBC, the airline apologized for the incident and said, "Delta has issued a refund and additional compensation to the customer impacted by this incident. The safety and health of our customers and employees is our top priority, and we are conducting a full investigation while following up with the right teams to prevent this from happening again. Upon landing in Miami, the aircraft was taken out of service to be deep cleaned and further disinfected."
That apology is not enough for Meehan.
Introducing the Delta Diarrhea Medallion™ Traveler Program, exclusive to Delta Diamond Medallion™ passengers travelling on overbooked planes that are simply too revolting to clean.
airlines / biohazards
Video about woman who fell 33,000 feet and survived
On January 26, 1972, a suspected bomb exploded on board a Yugoslav Airlines DC-9 and the debris of the plane rained down on mountains in the former Czechoslovakia. Everyone died except flight attendant Vesna Vulovic. After a long but full recovery, she returned to work for the airlines until she was fired in 1990 for […]
American Airlines forces black doctor to wear blanket to cover up her "inappropriate" dress
Dr. Latisha Rowe, a family-medicine specialist, was on the way to Miami with her 8-year-old son. She says that an American Airlines flight crew publicly humiliated her by demanding she wrap herself in a blanket to conceal her dress, which they deemed "inappropriate". Here is what i was wearing when @AmericanAir asked me to deplane […]
Lufthansa sues a man because he missed his flight
When booking a flight, it's often cheaper to buy tickets for a connecting flight — one in which your destination is the first leg of the flight. You then get off the plane and ignore the second leg. This is a trick that some passengers do to save money, and apparently Lufthansa isn't pleased. The […]


text:
Thomas Greenwood (2004)
Thomas Greenwood
LIFETIME ACHIEVEMENT AWARD BOXING
Tommy, as he is affectionately known by all, was born on the 9th of May 1948 in Melbourne, Victoria. He is the eldest of 5 children. At the age of 12 he first discovered boxing by way of a demonstration in a church hall. It attracted him as he saw it as the answer to his problems, which largely consisted of being bullied at school and home. He was a diminutive boy who had low self-esteem.
He first trained at Billy Murphy's backyard boxing gym in Carlton for 2 or 3 years. He went to Sydney in 1967 as a confused 18 year old who was yet to find his place in the world. He found his way to another backyard boxing gym, run by a man named Johnny Mansell, who became one of the great mentor's in his life. Under Johnny he had his first crack at amateur boxing. The need for money motivated Tommy to turn professional in 1968. He fought in the clubs of Sydney and also fought in Melbourne. Never a champion, just a stumbling block, a fighter if you beat you could go somewhere if you didn't you wouldn't. He had a passion for boxing with the discipline, respect, mateship and physical strength he gained from it Tommy became a confident young bloke.
In 1975 he arrived in Perth Western Australia and sought out a boxing gym to train in. He found a Police and Citizens Youth Club in Maddington. It was operating in a condemned building and although boxing was on offer, the club had no coach. A small group of eager boys and a copper greeted him on his first visit. Tommy wasn't partial to the law and decided not to return. The Police constable discovered where he was staying and repeatedly implored him to teach the kids. Reluctantly he agreed to a 2-month stint prior to heading north for work.
Those 2 months somehow became 27 years!
In 1981 he became State Coach for the first time for the West Australian Boxing Association, travelling interstate with the team.
1982 he obtained his level 1 coaching accreditation and trained his first 2 National Champions.
1983 He trained his first Oceania Games Gold Medallist
In 1991 the West Australian Sports Federation nominated him for Coach of the year.
In 1992 he was again the State Coach.
In 1993 he was the first ever West Australian boxing coach to obtain level 2 accreditation.
In 1994 he travelled to the Oceania Games as assistant National Coach.
In 1996 he coached the Asian Pacific – Australian Cruiserweight Champion who was ranked number 6 by the World Boxing Association. He also coached professional fighter David Letizia. A boy who first came to Tommy when he was 15 years old and today remains a close and trusted friend.
From 1996 He completed his first marathon.
In 1999 He entered 7 boxers in the W.A. Junior Golden Gloves – had 7 winners.
Tommy has never focused on champions; in fact he has lost count of the number of State Title – Golden Gloves Champions he has trained. His boxing gym was always open to all. He never deemed one boy to be more worthy of his time than another. He considers it a real privilege and triumph to have trained a profoundly deaf boy to be Australian Champion and to have training alongside him a boy who was totally blind from birth.
His ambition has always been to help others to help themselves. He has true empathy for the disheartened. Those whom lack self-esteem – confidence find a place of belonging under Tommy's tutelage.
Boxing brought order – discipline into his own life, giving him self worth, Tommy is thrilled when he sees others become empowered to make positive changes in their lives. Always sharing his unique wisdom by equally unique means has endeared Tommy to many. He is proud to have been made a Paul Harris Fellow by Rotary, although he is a not a Rotarian and a Life Member of the West Australian Police – Citizens Youth Clubs.
Tommy has never asked more of anyone than he himself would be prepared to do. In 1998 Tommy was truly tested, he underwent quadruple bypass surgery on his heart. Setting goals and overcoming obstacles is what Tommy has always taught others to do. At this time he must do it for himself.
He set his goal, to run another marathon; his initial training session was just 48 hours post operative. He shuffled along the 100m-heart ward corridor at Royal Perth Hospital 30 times, 3km, that first day. He achieved his goal 1 year and 2 weeks later by running the Perth Marathon in 3hrs 59mins and 26 seconds.
He was back at the boxing gym just 3 weeks later, again training his boys.
In 1999 Tommy took his first vacation! Shortly after his return, he met Sharni, who is today his wife. Between them they have 5 children and an increasing number of adored grandchildren! They are passionate about service to others and travel.
In 2000 Tommy began reaching a wider audience through his inspirational speaking. He is an Accredited Speaking Professional with the National Speaker's Association of Australia. Something he refers to as 'Not bad for a kid who used to stutter until he found boxing.
In 2002 Tommy opened his own Private Boxing Gym in Victoria Park. Where he continues to train young boxers and inspire all manner of athletes to achieve their dreams.
He often greets people by saying "Welcome To My Dream"