29 March 2016 (TSR- Daily Trust) – Nigeria has become the largest producer of cassava in the world with an annual output of 45 million metric tons. The Minister of Agriculture and Rural Development, Mr Audu Ogbeh, has said.
Ogbe who was represented by the Minister of State of Agriculture and Rural Development, Heineken Lokpobiri made the remark at the sideline of the official launch of the State Partnership for Agriculture, organized by Synergos in Nigeria in collaboration with the Ministry and Bill and Melinda Foundation (BMGF), in Abuja, yesterday. He said that more than 35 improved varieties of cassava had been developed and registered in Nigeria.
"These varieties had increased the production of cassava to make Nigeria the largest producer of cassava in the world with an annual output of 45 million metric tons", he said.
According to him, flour mills are being encouraged to substitute at least 10per cent high quality cassava flour for wheat flour. He applauded Master Bakers for their positive response to Government's policy towards this direction.
He said that Federal Government would sustain its demand for inclusion of high quality cassava flour in bread in the country.


Sometimes you learn the best lessons at the least likely places. I received a valuable insight about Pathfinders from my neighborhood athletic shoe store.
The background: I teach tai chi, so I need shoes that enable me to be on my feet a lot. And tai chi shoes must give the wearer a sense of contact with the ground, and work in any condition (wet grass, inside a conference room, etc.). I like ones with a minimal "offset" (or drop), say under 4 cc, to mimic the barefoot feel. Finally, I like to wear socks, so some of the current runner's "minimal" shoes aren't an option.
I combed through Zappos, trying to find something that worked. I love Zappos, and I don't mind sitting on my computer pouring over my options. And I usually find what I'm looking for. But this time, I came up empty. It was too hard to determine which shoes had the right offset, and I gave up (sorry Zappos: you're still in my heart).
So, given the widening holes in my current shoes, I tried a different approach: a visit to my neighborhood runner's store. It was a little intimidating, given that I was surrounded by younger, fitter athletic men and women who were buying shoes for their upcoming marathons and triathlons. And the young man who stepped up to help me was probably still in college. So I prepared for a short visit, figuring this would be another failure. But an hour later I was the thrilled purchaser of not one, but two pairs of reasonably priced, good-looking, comfortable tai chi shoes. I've been in walking/tai chi heaven ever since.
So what does this have to do with Pathfinders, or with patient advocacy??? It's as close as I've come to finding the perfect metaphor for why our clients need us, and pick us us over their online or mobile options. It's not that the available technology options aren't awesome (many of them are). It's just that sometimes you need to spend time with an experienced expert who can help you look at the many available options so that you can tailor them accordingly to your own unique situation. Just like my need for shoes, clients have a variety of situations that open them up to a plethora of options. But there is simply no technological solution that can replace that one great expert who can walk you through the tricky course of identifying those options and choosing the one that is exactly right for you.
We can't help you buy athletic shoes, but we're the best at making sure you get the healthcare you want, and deserve. No, we're not the Zappos of healthcare, but that's OK with us.


85% polyester-15% Spandex. Excellent elasticity with enhanced range of motion.
Non abrasion fabric material with excellent elasticity and durability.
Safeguards your skin from harmful UV rays by more than 99% (UPF 50+).
Suitable for running, hiking, camping, shooting, foot basket sports, cycling, sports trends, fitness, sports equipment.


text:
Saint-Grégoire-le-Grand est une ancienne municipalité de paroisse du Québec, située dans le comté de Nicolet dans la province de Québec au Canada. Elle a existé de 1845 à 1847, puis de 1855 à 1965, et son territoire est aujourd'hui inclus dans le secteur Saint-Grégoire de la ville de Bécancour.

Histoire
La paroisse de Saint-Grégoire-le-Grand a été érigée canoniquement en 1802, afin de desservir des familles acadiennes originaires de Beaubassin qui avaient été expulsées lors de la déportation de 1755. Elles s'étaient établies près de Bécancour à partir de 1758, fondant notamment le village de Sainte-Marguerite. En raison de la proximité des paroisses de Nicolet et de Bécancour, la création d'une paroisse pour les habitants de Sainte-Marguerite fut reportée jusqu'en 1802, et prit le nom du pape Grégoire le Grand.

Le territoire de la paroisse constitua une municipalité lors de la première création des municipalités locales en 1845 jusqu'à leur dissolution en 1847, puis de nouveau en 1855.

La municipalité de Saint-Grégoire-le-Grand subit des pertes de territoire à quelques reprises. En 1863, le cœur du village devint la municipalité de village de Larochelle, en 1870 une partie du territoire fut détaché lors de la création de Sainte-Angèle-de-Laval, puis en 1904 une autre partie servit à la création de Très-Précieux-Sang-de-Notre-Seigneur.

Le , la municipalité de paroisse a été fusionnée avec d'autres municipalités pour former la ville de Bécancour qui résultait ainsi de la fusion des villages de Bécancour, de Gentilly, de Larochelle, de Laval et de Villers ainsi que des municipalités de paroisse de Bécancour, de Sainte-Angèle-de-Laval, de Saint-Édouard-de-Gentilly, de Sainte-Gertrude, de Saint-Grégoire-le-Grand et de Très-Précieux-Sang-de-Notre-Seigneur.

Référence

Voir aussi

Liens externes
 Ville de Bécancour
 Évolution démographique

Bécancour
Ancienne municipalité au Québec

redpajama_set_name: RedPajamaWikipedia


Hays - Fort Hays - Smithsonian Magazine Museum Day Live!
Fort Hays State Historic Site in Hay is participating in Smithsonian Magazine Museum Day Live! by offering two free admissions per household, per address. To participate, visit smithsonianmag.com/museumday and print the free admission tickets. Printed ticket from the Smithsonian website must be presented at time of purchase to receive free admission. Maximum two per household, per address.


text:
All Children's Buckles Down on Car Seat Safety
By Tessa Wiseman • Jul 7, 2015
All Children's Hospital is offering car seat safety classes to parents this month.
According to the U.S. Center for Disease Control and Prevention, car crashes are one of the top three causes of death among children nationally -- in 2011, they killed 650.
But health experts say that many fatalities can be prevented with the correct use of car seats. That's why All Children's Hospital is buckling down on car seat safety.
This month, All Children's is offering car seat classes to help parents learn the ins and outs of the straps, clips and latches to protect their precious cargo.
Petra Vybiralova is the Safe Kids Coordinator and car seat expert at All Children's. She said the back middle seat is the safest place for a car seat, but about 90 percent of parents in Florida make a mistake when installing it. Overconfidence, she said, can get in the way.
According to Vybiralova, using the correct anchor when installing the car seat is crucial, but many cars do not have car seat anchors in the back middle seat.
"The car seat itself is really a minimal portion of the whole picture," Vybiralova said. "And I would like everybody who has a car seat or is expecting a child, or has a child, to have it inspected to make sure they are using it correctly."
Vybiralova said three criteria are non-negotiable when shopping for car seats.
"One that fits your child, one that fits your car, and one that you can use correctly every time," Vybiralova said.
During the class, parents watch an instructional video, followed by hands-on practice outside. For parents unable to pay full boat for a car seat, discounted seats will also be offered.
To register, check out the Car Seat Safety webpage.
New Car Seat Law Starts Jan. 1
By Mary Shedden • Dec 30, 2014
Florida is about to increase the age requirement for children riding in car seats.
As of Thursday, the law requires five-point restraints until a child's sixth birthday. That's two years more that kids need to wait before moving into a booster seat.
Petra Vybiralova is a program supervisor at the Suncoast Safe Kids Coalition. She says the law may help lower the number of deaths and injuries.
FL Leads in Hot Car Deaths this Year
By Lottie Watts • Jun 28, 2013
So far this year, 15 children have died in the U.S. after they were accidentally left behind in hot cars. Florida has had four of those deaths, more than any other state.
"We're at a high point for this time of year as far as how many deaths have happened," said Amber Rollins, director of KidsandCars.org, a national advocacy group focused on preventing non-traffic fatalities. "It has so far been a very bad year."
According to Rollins, 2010 was the worst year, with a total of 49 children who died from heatstroke inside vehicles.

redpajama_set_name: RedPajamaCommonCrawl


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パッケージ org.apache.mina.filter.codec.demux の使用 (Apache MINA Core 2.0.0-RC1 API)
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org.apache.mina.filter.codec.demux の使用

org.apache.mina.filter.codec.demux を使用しているパッケージ

org.apache.mina.filter.codec.demux
Protocol codecs that helps you to implement even more complex protocols by
splitting a codec into multiple sub-codecs.

org.apache.mina.filter.codec.demux により使用される org.apache.mina.filter.codec.demux のクラス

MessageDecoder

          Decodes a certain type of messages.

MessageDecoderFactory

          A factory that creates a new instance of MessageDecoder.

MessageDecoderResult

          Represents results from MessageDecoder.

MessageEncoder

          Encodes a certain type of messages.

MessageEncoderFactory

          A factory that creates a new instance of MessageEncoder.

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redpajama_set_name: RedPajamaGithub


Binance is launching a new feature to help satisfy a sharp rise in demand from institutional investors.
The leading crypto exchange is allowing organizations to create multiple trading accounts under one profile, along with improved managerial control and asset audit tools.
Binance CFO Wei Zhou told The Block the update is designed to satisfy an influx of demand from institutional clients, giving them a higher level of control over their accounts.
The update includes a new dashboard panel with comprehensive balance reporting, account management, asset management, order management, transfer history, and login history features.
The new sub-account feature is up and running for corporate users and anyone with VIP 3 tier (or higher) accounts.


The Parish Nurse Program is designed to meet the emotional, physical, and spiritual needs of parishioners through a holistic approach. As a member of the parish staff, the Parish Nurse brings healthcare and related services to the Church family, using a variety of programs and activities. The role of the Parish Nurse is to be a health counselor and resource, an educator and support facilitator, and an interpreter of the relationship between faith and health. The primary objective of the Parish Nurse is to help parishioners take responsibility for their own health and help them understand the many causes of illness.


text:
\section{Introduction}

Receiving feedback is an important requirement for learning a task. In real life, a student receives this feedback from an expert teacher in the form of a score. While this is a common strategy for teaching a new skill to a novice user, human instructors are often biased~\citep{boysen2009bias}. Furthermore, as the complexity of the task increases, the scores predicted are noisy and have a high variance for users with similar skills. For teaching a novice student a challenging task of excavator automation requires frequent feedback in the form of a score, this is rarely possible with a human expert where the performance is evaluated mostly with a final pass/fail feedback. Similar to a novice student, efficient training of a reinforcement learning (RL) policy,  requires a feedback mechanism such that the agent's actions are evaluated at every time step in the form of a reward from the environment. This form of per-step reward also well known as dense reward requires manually modelling the reward function taking into account the machine dynamics and the safety criteria which is significantly challenging and leads to sub-optimal policies~\citep{zhu2020learning}. Other forms of feedback include a binary sparse reward where similar to human instructor feedback is provided as success or failure at the end of an episode which can lead to slow convergence~\citep{rengarajan2022reinforcement} as the delayed reward leads to a credit-assignment problem. The process of reward formulation thus plays a critical role in the optimization of the policy to reach the best performance~\citep{dayal2022reward}. Hence, automating the reward prediction using inverse reinforcement learning~\citep{Lim2020PredictionOR},\citep{Rosbach2019DrivingWS},\citep{ng2000algorithms} with expert demonstration can overcome the limitations of the above approach. In this work, we explore this direction of learning a score prediction model using demonstration from expert operators, which acts as feedback for teaching a student the task of excavator maneuvering. Since finding new students and setting up the whole system is time-consuming, we validate our feedback model by using it as a reward mechanism for the RL policy (considering the fact that any error in the reward design will lead to incorrect RL policy and unsuccessful task completion) to learn the task of maneuvering an excavator.

\begin{figure}[t]
  \centering
  \includegraphics[scale=0.39]{images/Trajectory.pdf}
  \caption{\textbf{Overview of the Vortex environment used for Excavator Maneuvering} The given environment from the Vortex Simulator is used for collecting data from experts, training new users as well as training the RL policy using the proposed automatic score predictive model. In the given environment, the final policy/user needs to reach a fixed goal (based on distance from the start), with precise coordination of multiple actuators that is Bucket ($a_{1}$), Stick ($a_{2}$), Boom ($a_{3}$) and Swing ($a_{4}$), to avoid any infractions while taking into account the dynamic constraints.}
  \label{figurelabel2}
\end{figure}

Excavators have been widely used both in urban as well as rural areas to perform different construction tasks like excavation, loading-unloading and soil levelling. These tasks require careful coordinated control of actuators while taking into account the safety criteria of the surrounding environment. Considering the long working hours with the requirement of precise focus throughout, it impacts the physical and mental well-being of the operators~\citep{la2019we}, impacting the overall performance. Excavator automation is therefore required for rectifying these issues and at the same time improve productivity. Attempts to automate the excavator by manually modelling the controllers \citep{koivumaki2015high}, \citep{zavodni2009actual} for each actuator have been proposed, but are not scalable. Instead, a learning-based approach that considers feedback based on environmental interactions can significantly improve performance. Taking into account this challenging application and in general the sensitivity of an RL policy for a given reward function, we use this application to validate our reward prediction model to guide new trainees.

In this work, we learn a score prediction model for evaluating trainees learning to operate heavy construction vehicles, specifically an excavator. We further validate our model by training an RL policy, as shown in Fig.~\ref{figurelabel}, to solve the task of excavator automation. Our proposed reward function is learned following a probabilistic approach using expert data. We show that a distribution taking into account the excavator dynamics and the number of infractions (e.g., unsafe behaviour resulting in collisions with surrounding objects) can act as an ideal source for reward prediction since it efficiently differentiates between actions based on the performance quality.

Our approach starts by collecting a wide range of diverse user data performing excavator maneuvering in a simulated environment. This dataset consists of excavator sensor readings and per-step infraction values for each user. Taking into account the temporal distribution of these sensor readings, we formulate an objective for learning the dynamics of an expert. Given a sequence of readings as input, the objective is to predict future states using the encoded dynamics. Since an expert and a novice user will have different action dynamics, by learning to predict the next state, the representation learned can efficiently capture these dynamics \citep{epstein2021learning}, further predicting the required reward. To take into account the safety criteria, a different distribution is learned to predict the number of infractions for each time step given the environment information. Thereafter, these reward functions are used to optimize the RL policy for the task of excavator automation in a simulated environment. The main contributions include:
\begin{itemize}[leftmargin=0.5cm]

\item An automated score prediction framework learned using the action dynamics and the number of infraction values of an expert user, which acts as a real-time feedback mechanism for both an external user and the reinforcement learning policy while learning the main task.
\item Demonstration of excavator automation in a simulated environment closely replicating the real world for the task of path maneuvering using the proposed reward function.
\end{itemize}
\begin{figure}[t]
  \centering
  \includegraphics[scale=0.5]{images/proposal.pdf}
  \caption{\textbf{Automatic Reward Prediction for Excavator Automation} Overview of the proposed approach for automating the reward prediction for a reinforcement learning algorithm. The approach involves two stages \textbf{a)} Representation learning where Encoder (\textbf{E}) and Decoder (\textbf{D}) learn a distribution (\textbf{L}), for the input (\textbf{I}), depending on the objective (\textbf{T}). \textbf{b)} A reinforcement learning algorithm uses the learned reward function \textbf{R} to solve the task of excavator automation in a simulated environment (Vortex).}
  \label{figurelabel}
\end{figure}
\section{Related Work}
\textbf{Reward Shaping}. Inspired by human learning, reward shaping is a well-studied problem that involves optimizing an RL policy using an external signal as elaborated in \citep{ng1999policy},\citep{Wiewiora2010}. The current trend mostly involves using a task-based sparse reward \citep{charlesworth2020plangan},\citep{wang2020deep} or manually designing a reward function for optimizing the policy \citep{grzes2017reward}. Sparse reward requires the agent to explore novel strategies \citep{riedmiller2018learning} for solving a task and can lead to slow (or no) convergence \citep{rengarajan2022reinforcement}. A dense (or per step) reward reduces the training time by providing feedback for every action \citep{luo2020balance}, it is usually not straightforward to manually design dense reward functions as the resulting optimal policy is sometimes hard to predict. Considering the simplicity of sparse reward most of the current work uses it over dense reward. \citep{agarwal2021goal} proposes a goal-based sparse reward with a curriculum along with manually designed constraints for the task of autonomous driving. Other strategies include using an intrinsic reward which is a function of state visited as used in \citep{pathak2017curiosity} as additional supervision along with the original reward. These approaches successfully demonstrate novel strategies for different complex tasks. However, none of these works includes subject- and safety constraints based on expert demonstrations of the original reward function leading to unsafe policies. By integrating a score predictive model learned using expert data which is also used as a feedback mechanism for external users, we demonstrate excavator automation for the task of maneuvering in a challenging environment.\\ \\
\textbf{Excavator Automation using Reinforcement Learning}. Excavator Automation is a challenging task that requires precisely coordinated control of multiple actuators in an unstructured environment. Considering the complexity and limited availability of accurate simulators this problem has been rarely explored. \citep{Jud2019AutonomousFT},\citep{Koivo1996ModelingAC} used classical PD and PID controllers for controlling the excavators. Model-based solutions \citep{Dolgov2010PathPF} were proposed which require domain expertise and hence are time-consuming and do not transfer well to new environments. Taking these issues into account the current research is moving towards a learning-based approach. In particular reinforcement learning seems an ideal solution considering its success in solving a wide range of complex robotics manipulation tasks \citep{Chen2021ASF},\citep{Mudigonda2018InvestigatingDR},\citep{Andrychowicz2020LearningDI}. Some of the recent work for excavator automation using RL include \citep{Egli2021AGA},\citep{Egli2020TowardsRH} which learned trajectory tracking for controlling excavator arms by penalizing the policy based on the difference between the expected and the actual trajectory. \citep{andersson2021reinforcementt} used RL for operating forestry cranes for the task of loading-unloading logs using multiple rewards for optimizing grasp success rate, energy, and distance in a curriculum setting. Given the simplicity of the task, each approach relied on manually modelling the reward, limiting its scope to other complex tasks like excavation or maneuvering in a complex environment. In contrast, we focus on optimizing the policy using an expert's demonstration which forces the RL agent to learn the required task while at the same time optimizing the actions to match the expert's dynamic distribution and infractions leading to safer policies following the required constraints.\\ \\
\textbf{Learning Distributions for Reward Prediction.}
To model an expert's action distribution, we learn two independent representations for different features, taking into account the dynamics and the infraction values. This representation is learned following a Variational Autoencoder \citep{kingma2019introduction} like model learning a distribution without any external annotations. Prior works have proposed different strategies for modelling this distribution using auxiliary tasks. \citep{lee2020making} use action-conditioned optical flow prediction of the input RGB images and multiple binary classification losses. This forces the representation to take into account the action dynamics and their impact on the given image which is an important factor while training a reinforcement learning agent. \citep{lesort2018state} describes several representation learning strategies like learning a forward model, using feature adversarial learning or using rewards as objectives. In the current work, we follow a similar strategy as \citep{hu2020probabilistic}, given expert trajectories with multiple sensor states we learn a common representation by predicting future states. This dynamic representation of expert trajectories encodes different excavator states. Using this as a reward function, we optimize the RL policy to complete a goal with a constraint such that the policy's state distribution follows the expert (discussed in detail in Section 4). Further, an independent distribution taking into account the infraction value is also learned to model the safety criteria where the distribution is learned using the objective of predicting the total infraction values given the environment state.

\section{Problem Statement}
This work aims to model a score-predictive model for real-time feedback to excavator operators. To validate this model we use it as a reward function for learning a policy for excavator automation for maneuvering in a complex environment in the presence of multiple external objects as shown in Fig. \ref{figurelabel2}. We study the impact of using different objectives for modelling the distribution of expert trajectories further using it as a feedback mechanism for both an external user and the RL policy. We then evaluate the importance of integrating these rewards as a form of constraint for learning the final policy.

To use the proposed reward model for policy learning, we first learn multiple independent distributions, a probabilistic temporal representation $\textbf{q}_D\textbf{(z)}$ of excavator states given the sampled expert trajectories from the original dataset for modelling the dynamics and $\textbf{q}_I\textbf{(z)}$ of the given environment states at each time step to model infractions. For the dynamic distribution $\textbf{q}_D\textbf{(z)}$ the sequence of \textbf{N} different expert trajectories $\textbf{T}:\{T_0,T_1,...,T_{N-1}\}$ are modelled as a window of $\textbf{x}$ time steps such that for a given time step $\textbf{t}$ the input data is equivalent to $\textbf{I}_t:\{X_t,X_{t+1},...,X_{t+x-1}\}$ and the corresponding label is given as $\textbf{O}_t:\{X_{t+1},...,X_{t+x}\}$. For a given input data $\textbf{I}_t$, our goal is to learn an efficient representation $\textbf{q}_D\textbf{(z)}$ to predict $\textbf{x}$ future states $\textbf{O}_t$ by minimizing the reconstruction loss as shown in Eqn~\ref{eqn1}. The additional KL divergence loss acts as a regularizer such that the learned distribution follows a Normal Distribution. This representation is used to predict the reward by calculating the KL distance between its distribution and the new action (described in detail in Section 4).
\begin{equation} \label{eqn1}
	L_{Dt} = \|X_t - \hat{X_t}\|^{2} + KL(q_D(z_{D})||p_D(z_{D})),
	\end{equation}
with \textbf{$p_D(z_{D})$} representing the prior distribution,
\begin{equation} \label{eqn2}
	p_D(z_{D}) \sim N(0,1)
	\end{equation}
To model the infraction distribution $\textbf{q}_I\textbf{(z)}$ a similar strategy is used with modification in the loss function. At a given time step \textbf{t} the environment states represented as $\textbf{K}_t:\{k^1_t,k^2_t,...,k^f_t\}$ that is the values of \textbf{f} different infractions are used. The distribution is optimized using an objective of predicting the sum of the total infractions $\textbf{S}_t$ and the KL divergence loss as shown in Eqn. \ref{eqn3}. Since the distribution is optimized for the total infractions, the final reward $\textbf{R}_S$ is calculated as the distance between encodings for the infraction values of an expert and the current policy.
\begin{equation} \label{eqn3}
	L_{It} = \|S_t - \hat{S_t}\|^{2} + KL(q_I(z_I)||p_I(z_I)),
	\end{equation}
with \textbf{$p_I(z_I)$} following the same normal distribution as \textbf{$p_D(z_D)$}.

With these reward functions, the dynamic reward $\textbf{R}_D$, the safety reward $\textbf{R}_S$ and the task reward $\textbf{R}_{T}$ (that is a function of the distance of bucket from the goal location), we aim to learn a policy $\pi$ for excavator automation in a complex workspace. Given the actuator state configuration $\textbf{s}$ (described in Section 4) and the action space $\textbf{a}$, policy $\pi$ parameterized as $\theta$ is used to sample the action,
\begin{equation} \label{eqn4}
	a = \pi_{\theta}(s)
	\end{equation}
such that it maximizes the expected discounted reward $\textbf{J}$,
\begin{equation} \label{eqn5}
	J(\pi) = E_{\pi_{\theta}}[\sum_{t=0}^{t=m-1}\gamma^{t} r(s_t,a_t)]
	\end{equation}

Here $\gamma \in (0,1]$ is the discount factor, $t$ is the current time step of the episode, $r$ is the total reward considering the different reward functions and $m$ is the total time steps in a given episode.

Both latent space $\textbf{z}$ and policy $\pi$ are modelled using a neural network. $\textbf{q}_D\textbf{(z)}$ uses a Long Short Term Memory (LSTM) \citep{hochreiter1997long} while $\textbf{q}_I\textbf{(z)}$ and policy $\pi$ uses a simple 2-layered deep multi-layer perceptron. The methodology and the results are further elaborated in Sections 4 and 5 respectively. Considering the limited work in this direction we compare the use of our proposed reward function for learning a policy by considering a policy trained using only a task-based reward function as a baseline.

\begin{figure}[!ht]
  \centering
  \includegraphics[scale=0.35]{images/LatentSpaceLearning.pdf}
  \caption{\textbf{Model for learning the dynamic distribution} The latent vector \textbf{z} encoding the expert's dynamic distribution is learned using LSTM-based encoder-decoder by optimizing for the task of future state prediction. During training, the decoder uses the original last state (teacher-forcing strategy) and predicts the future state using LSTM by initializing its hidden state and the cell state with the dynamics predicted by the encoder.}
  \label{figurelabel3}
\end{figure}
\section{Method}
For tasks like navigation and manipulation, current reinforcement learning algorithms mostly use goal-based rewards \citep{nair2018visual},\citep{nasiriany2019planning}, maximising the cumulative rewards over a given time. This strategy does not take into account the machine dynamics or the external environmental constraints limiting its use for real-life applications. In this work, we address this problem by proposing additional reward signals along with the original goal-based reward. These rewards are learned using the distribution of expert demonstrations. We limit this distribution to learning the representation of certain features only, which needs to be taken into consideration by the excavator while solving the main goal. Our final aim is to use these rewards to learn an RL policy to solve the challenging task of excavator maneuvering in a constrained environment.
Fig. \ref{figurelabel} summarizes our automatic reward prediction framework for the given task. \newline \\
\textbf{Learning Distributions of Expert's Demonstrations}. Our model learns two independent distributions: a temporal representation of different excavator features and a non-temporal distribution of environmental states (Table \ref{tab:1}) given demonstrations of multiple experts. The given temporal features are directly impacted by the user's actions, efficiently modelling the dynamic distribution, while the infraction values at every time step take into account the excavator's interaction with the environment, acting as an ideal source for modelling the safety distribution. Other observations like different actuator trajectories and their velocity are not included in the learned distribution forcing the reinforcement learning policy to discover its own novel strategies based on the required goal.

To learn the dynamic distribution, for a given time step $\textbf{t}$ we sample the last 32 readings for each feature in the form of 32 x 3 time-series data. This sequence of temporal data is encoded into a single 8-d feature vector using Long Short Term Memory Network (LSTM). This representation is then used to predict the mean ($\mu$) and variance ($\sigma$) for a given distribution using two independent Multi-Layer Perceptrons (MLPs). Further, to encode the dynamics of the given temporal features in the predicted distribution $\textbf{q}_D\textbf{(z)}$, the objective formulated requires the decoder to predict the feature values for the next time steps given the sampled vector from the distribution and the feature values of the last time step as input. This decoder consists of LSTM which takes as input the original feature values for the last time step and predicts the future values. To integrate user dynamics we initialise the hidden and cell state of LSTM with the sampled vector from the predicted distribution. The overall architecture as shown in Fig. \ref{figurelabel3} is trained using the reconstruction loss of the predicted future states. Along with this, an additional regulariser in the form of KL Divergence loss is added such that the predicted distribution is Normal Distributed with a mean of 0 and variance 1.

The safety distribution $\textbf{q}_I\textbf{(z)}$ is modelled following a similar strategy where a 5-d feature vector of infraction values is encoded into a 2-d encoding representing the distribution using a 2-layered (8 neurons) fully connected network as shown in Fig. \ref{fig:safnet}. This is optimized using the objective of predicting the sum of the total infractions given the input along with the KL divergence loss (no reconstruction loss). As shown in Fig \ref{fig:safdist} this approach effectively differentiates between infractions with widely different values.

These distributions of experts' actions are then used to model the reward function. Given a new user, for a certain action at a given time step $t$ the learned model (weights frozen) takes as input the values of the features as specified in Table \ref{tab:1} and maps its distribution. The reward for the action taken is then evaluated using the KL Distance between the predicted and the expert's distribution. This is further summed  with the original task-based reward for training an RL policy.

\begin{table}
\begin{center}
\begin{tabular}{c|c|c}
\multicolumn{1}{c|}{\textbf{Dynamic}}&\multicolumn{2}{c}{\textbf{Safety}}
 \\ \cline{2-3}
 \hline
 Average Engine Torque & Poles Touched & Environment Collision\\
 \hline
 Average Engine Power &  Poles Fell & Balls knocked\\
 \hline
 \multicolumn{1}{c|}{Average Fuel Consumption} & \multicolumn{2}{c}{Equipment Collision}\\
 \hline
\end{tabular}
\end{center}
\caption{\label{tab:1}Inputs for Modelling the Dynamic and Safety Distribution.}
\end{table}
\begin{figure}
    \centering
    \subfloat[Network Architecture]{\includegraphics[width=6cm,height=6cm,keepaspectratio]{images/safetynet.pdf}\label{fig:safnet}}
    \subfloat[Safety Distribution]{\includegraphics[width=5cm,height=5cm,keepaspectratio]{images/vae_safety.pdf}\label{fig:safdist}}
    \caption{\textbf{Model for learning the safety distribution} By learning a distribution using the objective of predicting the total infractions the encodings efficiently differentiate between two infractions hence acting as an optimal reward function to integrate safety into the given policy.}
    \label{fig:Infraction-distribution}
\end{figure}

\textbf{Policy Learning.} The final aim is to learn an RL policy for solving the task of maneuvering an excavator in a complex environment. This task is challenging to model using classical controllers as it requires precise coordination of multiple actuators. Also, it is difficult to adapt these controllers \citep{Jud2019AutonomousFT},\citep{Koivo1996ModelingAC} across different excavators considering the wide variations. Reinforcement learning is an ideal solution as it forces each actuator to coordinate its actions to discover optimal policies \citep{liu2021deep}. Moreover, these policies are robust \citep{almasi2020robust} to variations,  providing a scalable solution for real-life adoption. Considering its stability and robustness to different hyperparameters, we use a model-free Proximal Policy Optimization (PPO) \citep{schulman2017proximal} an on-policy algorithm. This policy is modelled using a 2-layered MLP where the input space includes a 19-d vector as shown in Table \ref{tab:3}. The output is a continuous 4-d vector within a range of [-1,1] which represents the setpoints for each actuator, such that values less than 0 represent the movement of the actuators in the negative direction, while values greater than 0 move the actuator in the positive direction. For the swing actuator, it is equivalent to rotation in the left or right direction. To optimise this policy for the excavator to complete the specified goal we use the proposed reward functions that is the dynamic reward ($R_D$) and the safety reward ($R_S$) along with the distance-based reward ($R_G$), where the distance is calculated between the bucket's spatial coordinates and the specified goal as shown in Eqn \ref{eqn6}. For both the dynamic reward and the safety reward, we use the pre-trained model to predict the distribution $p_{AD}$ and $p_{AI}$ for the given time step.

\begin{equation} \label{eqn6}
	R = R_G + R_D + R_S
	\end{equation}
\quad $R_G = 1 - ||G - B_L||$;\quad
$R_D = 1 - KL(p_{AD}(z_D)||p_{D}(z_D))$; \quad
$R_S = 1 - KL(p_{AI}(z_I)||p_{I}(z_I))$
\begin{table}[htp]
\begin{center}
\begin{tabular}{c|c|c|c|c|}
\multicolumn{1}{c|}{Inputs}&\multicolumn{4}{c|}{Actuators}\\ \cline{2-5}
&Swing&Boom&Stick&Bucket\\ \hline
Linear Position&\CheckmarkBold&\CheckmarkBold&\CheckmarkBold&\CheckmarkBold\\ \hline
Angular Velocity&\CheckmarkBold&\CheckmarkBold&\CheckmarkBold&\CheckmarkBold\\ \hline
Linear Velocity&\textbf{--}&\CheckmarkBold&\CheckmarkBold&\CheckmarkBold\\ \hline
\end{tabular}
\end{center}
\caption{\label{tab:3}The observation space of the reinforcement learning policy.}
\end{table}

\section{Experiments}
Through our experiments, we aim to verify:
\begin{enumerate}[leftmargin=0.5cm]
    \item The effectiveness of using expert distribution for modelling the reward function.
    \item The impact of learning the RL policy for the task of excavator maneuvering using these additional rewards formulated as dynamics and infraction distribution in comparison to task-based reward.
\end{enumerate}

\textbf{Simulator Setup and Dataset Collection.} For collecting both the user data as well as for training the reinforcement learning policy Vortex simulator\footnote{https://www.cm-labs.com/vortex-studio/} is used. This simulator provides a range of customisable construction environments for different crane automation tasks. In our case, we use the scenario shown in Fig. \ref{figurelabel2}, where the task is to maneuver the crane to reach a pre-specified goal location. We fixed the goal and start location as shown in Table \ref{tab:4} in a way to provide a complex challenging task in the presence of all the infractions for the crane to complete for proper analysis.

For our dataset, we collect trajectories for a total of 40 users where each is ranked based on pre-defined heuristics. This heuristic is formulated such that a user is automatically penalised in case of any infractions. The final score ranges from 0 (maximum) to -100 (minimum) for each session. We consider a user to be an expert if the total score is greater than -25, while others are considered a novice. In the current dataset, 7 users have a score greater than -25 and are further used to learn the distributions. \newline
\\
\textbf{Implementation.} The model for learning the distributions is trained using the trajectories of 5 experts and the remaining trajectories are used for evaluating the model. Both the LSTM-based encoder-decoder setup and the 2 layered fully connected network is trained for a total of 1000 epochs with a batch size of 8 using an Adam Optimizer with a learning rate of 1e-4. Similarly, for policy training using an actor-critic architecture, the networks are trained for 500 episodes with a learning rate of 3e-4 for the actor and 1e-3 for the critic. For all the layers other than the output we used the ELU activation function
\begin{table}[!h]
\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
 Complexity & Start & Goal & Distance (in m) \\
 \hline
  1 &(-9.8,-144.1,2.4)&(-2, -150, 0.5) & 10.2\\
 \hline
\end{tabular}
\end{center}
\caption{\label{tab:4}Description of Task to learn.}
\end{table}
\cite{clevert2015fast} as it showed better performance and faster learning. For a given episode the RL policy  runs for a maximum of 280 steps and the episode terminates on reaching the goal. The networks are trained on an Nvidia GTX 1050 Ti with Intel Core i5-8300H CPU @ 2.30GHz × 8, which takes around 2 to 3 hours for learning the distributions and 4 to 5 hours for training the policy. \\
\newline
\textbf{Results.} We evaluate the impact of using our proposed reward model on the final policy by comparing different dynamic features and the infraction values as shown in Table \ref{tab:5}. Each policy is trained with and without the different reward functions along with the original task reward. The excavator in the absence of any constraints performs the worst when compared with the expert (see values of Task Reward in Table \ref{tab:5}). With the addition of constraints in the form of the proposed reward function the performance drastically improves to follow the expert's dynamics and infraction values. It is interesting to observe the performance of the policy when both the dynamic and safety rewards are included, both showing almost similar performance much better than the policy trained with only task reward. The safety reward optimises the policy, for the infractions (Table \ref{tab:5}), reducing the infractions to zero (a safe policy) while using it as the only reward does not take into account the dynamics. A similar trend is observed when only dynamic reward is used.

In Fig.\ref{fig:graphs_ablation}, the optimisation of policy for different rewards is compared. For the task reward, (Fig. \ref{fig:reward1}) which is the original goal of reaching the final specified position, the best performance is achieved with the addition of a dynamic reward, better than the task reward alone. This is because by adding a dynamic reward with the task reward, the excavator is constrained to the dynamic features of an expert, preventing it to explore unwanted actions and leading to faster convergence. Policy's dynamics, (Fig. \ref{fig:reward2}) experience a significant drop when trained without the inclusion of dynamic reward. A similar trend is observed for policy trained without any safety reward (Fig. \ref{fig:reward3}). Comparing each constraint, only using task rewards for learning, shows the worst performance while for the policies integrated with additional constraints, the performance closely follows that of an expert.
\begin{figure}
    \centering
    \subfloat[Task Reward - Final Distance of the bucket from Goal (less is better) ]{\includegraphics[width=5cm,height=5cm,keepaspectratio]{images/TaskReward.pdf}\label{fig:reward1}}
    \subfloat[Dynamic Reward]{\includegraphics[width=4.5cm,height=4.5cm,keepaspectratio]{images/DynamicReward.pdf}\label{fig:reward2}}
    \subfloat[Safety Reward]{\includegraphics[width=5cm,height=5cm,keepaspectratio]{images/SafetyReward.pdf}\label{fig:reward3}}
    \caption{Impact of training the policy using different reward functions.}
    \label{fig:graphs_ablation}
\end{figure}
\begin{table}[!h]
    \scriptsize
    \begin{center}
    \setlength{\tabcolsep}{0.033\linewidth}
    \begin{tabular}{cccccc}
    \toprule

          & \textbf{Expert} & Task & \textbf{Dynamic} & Safety & \textbf{Dynamic+Safety}\\\cline{2-6}\\
         Avg. Torque (in \%) & \textbf{24-34}& 51.73 & \textbf{45.61}& 51.28& \textbf{46.44}\\
         Avg. Power (in \%) & \textbf{10-20}& 26.77 & \textbf{22.63}& 26.84& \textbf{23.72}\\
         Avg. Fuel Consumption (in \%) & \textbf{3-5}& 5.77 & \textbf{5.01}& 5.71& \textbf{5.12}\\
         Total Infractions & \textbf{0}& 35 & 18& \textbf{0}& \textbf{1}\\
         \midrule
    \end{tabular}
    \end{center}
    \caption{\label{tab:5}The Dynamic and the Infraction values of the final policy for different reward functions.}
\end{table}
\section{Conclusion}
\textbf{Summary.} In this work, we propose a novel approach for modelling a feedback mechanism in the form of multiple independent distributions  using demonstrations of expert operators to teach new students the task of excavator maneuvering. These distributions that are the dynamic and the safety distribution are learned using the temporal states of the excavator and infraction values of the expert's trajectory. Once learned, the distributions are used as feedback for trainees by taking into account the KL divergence with the current action. This automatic score predictive model is further validated by using it as a reward mechanism for the RL policy  to learn the task in a simulated environment.  We show that the policy learned using this additional supervision completes the goal (using the task reward) while also optimising for the additional constraints. The final policy successfully maneuvers the excavator while closely emulating the dynamics and the infraction values of an expert operator.

\textbf{Limitations and Future Work.} For future work, we plan to explore additional constraints by learning a richer representation. The current strategy is computationally inefficient as it requires learning independent distributions for each additional constraint, instead, a single complex model may lead to significant improvements. Along with this, we plan to extend our work for a diverse range of challenging real-life applications for different construction tasks like loading-unloading, soil excavation and levelling.

\section{Acknowledgements}
We would like to thank Mitacs and CMLabs for funding the project. Further, we are grateful to Hsiu-Chin Lin for the valuable feedback during the initial phase. We are also thankful to the Digital Research Alliance of Canada for the computing resources and CIFAR for research funding.

\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


text:
Three-Minute Oral Testimony
June 16, 2015 Hearing
I am in staunch opposition to the proposed development of Campus Advantage at 3407 Forbes Avenue and trust this commission will be the same. The true purpose of this project, as I have provided in additional testimony to this commission, is to provide student housing for the University of Pittsburgh. Campus Advantage is seeking a height variance from the Zoning Board of Adjustment. A decision has not yet been rendered. Should the variance be granted, appeals will be made until the decision is reversed.
Campus Advantage executives stated to the Zoning Board of Adjustment and at a community meeting that they could not proceed without a variance. They initiated this meeting, which is their prerogative, but the action should signal a red flag to this commission, the city and county justice departments, and to investigative reporters in our local free press.
You can dismiss the letters of support from city council members in the zoning board files, because the council members are too far removed from the proposed project. If the University of Pittsburgh wanted to destroy businesses and build student housing on any major business street where council members live, there would not be letters of support. The agenda of these members seems to be confining Pitt's cancerous spread to Oakland, and not allowing it into their own neighborhoods.
You can also dismiss the letter of support from Oakland Planning and Development Corporation. This project does not support the Oakland 2025 Master Plan, because Pitt refuses to slow its ever-increasing student enrollment. The recently built Nordenberg Hall houses 559 first-year students. Administrators constructed that building knowing they could count on the planning commission to give them whatever student housing they needed to satisfy their uncontrolled growth. South Bouquet Street went from 200 longtime residents and a dozen students decades ago, to three residents and 700 students today. What if that happened where you and your family live?
This project is about the insatiable greed of the University of Pittsburgh and its partners. It is not about human dignity. I asked an executive of Campus Advantage: "What are the benefits to the elderly longtime residents of Oakland?" His reply was, "I never thought about that question."
That is precisely the question and issue that should be examined by this commission. Oakland is an iconic and historical neighborhood. The residential community of Oakland existed long before there was a University of Pittsburgh there. It doesn't exist to make money for Pitt or its partners. You have promised to uphold the code that states "Pittsburgh's overall character is defined to a great degree by its diverse and unique residential areas," and that any development must "preserve the character of existing neighborhoods." The decimation of Oakland's residential community must end. Let the changes begin today.
Further information about Pitt's impact on Oakland is at www.OaklandDignity.com
Salus Populi Suprema Lex
Additional Hand-Delivered Testimony
I would like to provide additional testimony for City Planning Commission members regarding the proposal of Campus Advantage at 3407 Forbes Avenue.
This project, for all intents and purposes, is to provide student housing for the University of Pittsburgh. For Campus Advantage and its supporters to insist otherwise is to willfully deceive the City Planning Commission and the Oakland residential community.
Campus Advantage specializes in student housing management and operations, and claims to be the industry leader in student housing. Its website states that it has "markets featuring a university with more than 8,000 full-time undergraduate students enrolled" and "properties within three miles of a university campus." It should be of interest to commission members why Campus Advantage chose the Oakland community.
Here are a few examples detailing why the fundamental purpose of this project is to provide student housing for the University of Pittsburgh:
When the Oakland Planning and Development Corporation hosted a community meeting for this project, the community announcement subject heading was: Public Mtg On Plans For Proposed Student Rental Housing at 3407 Forbes, Tue 4/14 6pm.
Campus Advantage executives at that meeting stated that they have met with University of Pittsburgh administrators, and they have not approached any other universities. They referred to the University of Pittsburgh as their "Host University."
There are no letters of support from university administrators to the Zoning Board of Adjustment, other than from the University of Pittsburgh.
In his letter of support to the Zoning Board of Adjustment as Chair of the Oakland Task Force, Attorney Paul Supowitz, University of Pittsburgh Vice Chancellor for Community and Governmental Relations, referred to the project as a "student apartment project."
Campus Advantage submitted to the Zoning Board of Adjustment a letter of support from Councilman Dan Gillman which stated: "the Apartment Building will fill a gap for the high demand of student housing in Oakland." The submissions also contained a letter from Councilman R. Daniel Lavelle, which stated: "the project will provide 130 units of much needed, quality student housing in Oakland."
A letter of support from Hurley Associates stated: "it will offer much needed safe, modern student housing."
One cannot say that this project will provide "dormitory beds" for the University of Pittsburgh students because of the city's definition of dormitory. However, it should be abundantly clear from the above that the purpose of this project is to provide student housing for the University of Pittsburgh.
The Planning Commission members must ask probing questions that push past Campus Advantage's assertions that this project is intended as a multifamily development and an apartment building available to everyone. To permit the continuation of their deception would be a dereliction of duty of the planning commission members.
These questions must also address the potential devastating impact upon the lives of Oakland residents, especially the longtime residents who have suffered the most from the University of Pittsburgh's uncontrolled growth.
It is of paramount importance that an in-depth Impact Statement as to how this project will affect the quality of life for Oakland residents, especially the longtime residents, be in the possession of City Planning Commission members before any decision is made to approve this project. For members not to have that in-depth assessment would be a significant breach of trust with the Oakland community.
Other similar projects in the past have had a negative impact on Oakland's residential community. Decline in longtime residential population, the devastation of South Bouquet Street, binge drinking, litter, traffic and parking are just a few samples of this negative impact.
Commission members should be aware of the contents of the Oakland 2025 Master Plan. It's difficult to believe that commission members can, in good conscious, assert that the master plan specifically encourages the uncontrolled construction of student housing in Oakland.
There are already 30,000 University of Pittsburgh students in Oakland. The recently built Nordenberg Hall houses 559 first-year students only. Where are these new students going to be housed when they leave Nordenberg Hall? Are University of Pittsburgh administrators confident the City Planning Commission will green light any project they support to build student housing?
Are commission members absolutely certain that this project will lessen the demands for students to live in the residential neighborhood? Will it "relieve a great deal of pressure that has been placed on a densely populated residential neighborhood," as asserted by Council President Bruce Kraus in his letter of support to the Zoning Board? Constructing more student housing has not done so in the past.
What will be the impact to the binge drinking problem in the residential neighborhood of Oakland? How much of a further decline in Oakland's longtime residential community will there be? What are the benefits to the longtime residents of Oakland? Do commission members have definitive answers to these important questions?
The proposed project is not just about brick and mortar and the physical makeup of the building. It is about the civil liberties and quality of life for the residents of this historical neighborhood, especially the longtime residents.
Should the residential community of Oakland not be given full protection under the law, a class action lawsuit is a very real possibility. For commission members not to have full knowledge of how this project will negatively impact these residents would be morally irresponsible, as well as a serious injustice to the Oakland community.
Note: The Campus Advantage project is completed and now known as the "Bridge on Forbes". The University of Pittsburgh has leased nearly 300 beds for its students.


text:
Cruz Takes Down Planned Parenthood, DOJ, McConnell at Pro-Life Rally
Nick Arnold
Ted Cruz delivered an impassioned speech on the Capitol Hill steps today in which he called for the defunding and investigation of Planned Parenthood. The video of Cruz's full speech is below:
First, he addressed the turning point the videos represent in the debate over abortion:
These are videos of Planned Parenthood officials laughing, swilling chardonnay, and casually, callously, heartlessly bartering and selling body parts of unborn children. I would encourage every American to watch these videos. Regardless of your views on the right to life, even if you are a supporter of abortion, I would encourage you to watch these videos. And just ask a very simple question: Are these my values?
Cruz went on to call for a criminal investigation of Planned Parenthood, blasting the inaction by the Justice Department as unprecedented:
Today, the U.S. Department of Justice should open an investigation into all of those individuals. Today, the U.S. Department of Justice should open an investigation into whether Planned Parenthood nationally is a criminal enterprise breaking the law.
The Department of Justice has had a long tradition over the centuries of fairly and impartially applying criminal law and enforcing criminal law regardless of partisan allegiance. How sad is it that almost no media observer thinks there's any chance that the Department of Justice would investigate whether Planned Parenthood senior officials have violated federal law?
Today, the Obama Justice Department is operating as little more than a partisan arm of the Democratic National Committee. When you have multiple officials caught on tape apparently confessing to multiple felonies that carry with them long jail times, justice must be served. I call on the Department of Justice to enforce the criminal laws and investigate Planned Parenthood.
Cruz then took Senate Majority Leader Mitch McConnell (R-KY) to task for cancelling an amendment to defund Planned Parenthood on the highway bill, saying that the bill McConnell is fast tracking is a "show vote" that is less likely to succeed:
And let me be clear what we should demand. We should not demand an empty show vote, because one of the things leadership occasionally likes is to tee up a vote that is designed to fail, and then tap people on the head and say "you should be happy you got a vote." Leadership knows how to fight when they're fighting for corporate welfare. Leadership knows how to fight when they're fighting for cronyism. Leadership knows how to fight when they're fighting for more spending and debt and power in Washington. How about we see a fraction of the fight for the principles and values of the American people?
Nick Arnold is a researcher for American Principles in Action.
Nick Arnold is a researcher for the American Principles Project.
Ben Carson Excites Crowd at Pro-Life Rally in DC
Jon Schweppe
Rand Paul Promises Vote to Defund Planned Parenthood "Before We Go Home in August"


text:
Samuel L. Jackson Stands with SU2C and St. Baldricks
New Campaign to Raise Awareness in the Fight Against Childhood CancersJAN. 9, 2014 (LOS ANGELES / NEW YORK) –St. Baldrick's Foundation and Stand Up To Cancer (SU2C) will unveil a new print and broadcast public service announcement featuring actor Samuel L. Jackson. The PSA, entitled "In Play," is designed to increase awareness about childhood cancers and the critical importance for infants and children, and in particular teens and young adults with childhood cancers, to be treated by pediatric oncologists. Survival for teens and young adults is far greater when treated on pediatric cancer protocols.
The campaign will also highlight the need to support the best pediatric cancer research that will deliver cures and better treatments with fewer harsh side-effects and long-term complications for survivors. Each year, more than 175,000 children are diagnosed with cancer worldwide, and in the U.S., cancer is the number one disease killer of children. The St. Baldrick's Foundation and SU2C are working to change those statistics.
"When I was a child in the 1950s, most kids who got diagnosed with cancer had little chance of survival. Now, there is hope," said the Academy Award-nominated Jackson. "I am honored to join Stand Up To Cancer and the St. Baldrick's Foundation to help educate people about childhood cancer and the importance of seeing a pediatric oncologist." Samuel L. Jackson is an award-winning actor and film producer. He has appeared in more than 100 movies and is the highest grossing film actor in history. His movie credits include such blockbusters as Pulp Fiction, The Avengers, Die Hard with a Vengeance, The Incredibles and the Star Wars prequel trilogy, among others.
"Samuel L. Jackson is a powerful actor and has played some seriously tough guys," said Kathleen Ruddy, chief executive officer of the St. Baldrick's Foundation. "It seems very fitting that we have someone with his level of intensity and power speaking out against childhood cancers, which are seriously tough diseases. We are grateful to him for getting the message out that childhood cancer patients – even those who are teens and young adults – must be seen by pediatric cancer specialists for the best chance at survival."
Jackson is joined in the PSA by four St. Baldrick's Honored Kids, all of whom have had – or are still fighting—cancer. Along with their families, they serve as advocates to raise awareness for childhood cancer and the critical need for research funding, bringing hope and inspiration to countless St. Baldrick's volunteers and supporters. The Honored Kids who appear in the PSA are Avery, 10, who's stable; Aiden, 11, who's currently cancer-free; William, 14; and Julia, 19, who are both in remission.
SU2C President and CEO, Sung Poblete added: "We are grateful to Samuel L. Jackson for lending his support to this extremely important campaign. While great progress has been made in the battle against childhood cancer, the sad fact remains that one in five children diagnosed in the U.S. will not survive. Through the creation of the St. Baldrick's-Stand Up to Cancer Dream Team, we are making progress in the fight against childhood cancer research."
Earlier this year, SU2C and the St. Baldrick's Foundation announced the formation of a Dream Team dedicated to childhood cancer research. The SU2C-St. Baldrick's Pediatric Dream Team Translational Cancer Research Grant will provide $14.5 million in funding over four years for this innovative project that is uniting researchers in two highly productive disciplines of translational pediatric cancer research that historically functioned independently – genomics and immunotherapeutics.
The Dream Team is led by John M. Maris, M.D., director of the Childhood Cancer Research at The Children's Hospital of Philadelphia; and Crystal L. Mackall, M.D., chief of the Pediatric Oncology Branch of the National Cancer Institute (NCI) in Bethesda, MD.
Researchers on this Dream Team represent seven institutions: The Children's Hospital of Philadelphia, The National Cancer Institute (NCI), the University of British Columbia, Baylor College of Medicine, The Hospital for Sick Children in Toronto, Seattle's Children's Hospital and the University of Wisconsin. While researchers at the NCI, part of the federal government's National Institutes of Health, will be participating fully as members of the Dream Team, in accordance with policy, no funds from the grant are going to the NCI.
The joint venture between the St. Baldrick's Foundation, the leading non-governmental provider of childhood cancer research grants, and SU2C, formed in 2008 to accelerate innovative cancer research, has brought together two of the world's leading cancer research fundraising groups. Formation of the team, the first SU2C Dream Team focused solely on pediatric cancer research, will help fulfill the mission of both organizations to fund the most promising childhood cancer research that will ultimately enable survivors to lead long and healthy lives.
To learn more, visit http://www.standup2cancer.org/pediatrics or www.stbaldricks.org/inplay. To join the conversation like us on Facebook at @StandUpToCancer or @StBaldricksFoundation or follow us on Twitter at @SU2C or @StBaldricks.
Stand Up To Cancer – an initiative of the Entertainment Industry Foundation, a non-profit organization – began in the USA in 2008 with a landmark telecast on the ABC, CBS and NBC networks. The third U.S.-based telecast was broadcast on ABC, CBS, NBC, FOX and 22 cable networks on Sept. 7, 2012. Internationally, in addition to the Netherlands, Stand Up To Cancer launched in the United Kingdom with a four-hour live televised fundraiser on Channel 4 in collaboration with Cancer Research U.K. on Oct. 17, 2012.
Stand Up To Cancer raises funds to hasten the pace of groundbreaking translational research that can get new therapies to patients quickly and save lives. SU2C marshals the resources of the media and entertainment industries in the fight against this disease. Current members of the SU2C Council of Founders and Advisors (CFA) include Talk Show Host, Journalist and well-known Cancer Advocate Katie Couric; Sherry Lansing, Chairperson of the Entertainment Industry Foundation's Board of Directors and Founder of the Sherry Lansing Foundation; EIF President and CEO Lisa Paulsen; EIF Senior Vice President Kathleen Lobb; Rusty Robertson and Sue Schwartz of the Robertson Schwartz Agency; Pamela Oas Williams, President of Laura Ziskin Productions and Executive Producer of Stand Up To Cancer's In-house Production Team, and Nonprofit Executive Ellen Ziffren. All current members of the CFA were co-producers of the 2012 televised special. The late co-founder Laura Ziskin executive produced both the Sept. 5, 2008, and Sept. 10, 2010, broadcasts. SU2C was formally launched on May 27, 2008. Sung Poblete, Ph.D., R.N., has served as SU2C's president and CEO since 2011.
SU2C's "Dream Team" approach to funding translational cancer research enables scientists from different disciplines at research centers across the country and internationally to collaborate on projects geared toward getting new, less toxic treatments to patients as quickly as possible. Monies also support innovative cancer research projects that are often deemed "too risky" by conventional funding sources. One hundred and one institutions are currently involved. As SU2C's scientific collaborator, the American Association for Cancer Research, led by a prestigious SU2C Scientific Advisory Committee, provides scientific oversight, expert review of the research projects and grants administration. For more information, visit standup2cancer.org.
About St. Baldrick's Foundation
The St. Baldrick's Foundation is a volunteer-driven charity committed to funding the most promising research to find cures for childhood cancers and give survivors long and healthy lives. St. Baldrick's coordinates its signature head-shaving events worldwide where participants collect pledges to shave their heads in solidarity with kids with cancer, raising money to fund research. Since 2005, St. Baldrick's has awarded more than $127 million to support lifesaving research, making the Foundation the largest private funder of childhood cancer research grants. St. Baldrick's funds are granted to some of the most brilliant childhood cancer research experts in the world and to younger professionals who will be the experts of tomorrow. Funds awarded also enable hundreds of local institutions to participate in national pediatric cancer clinical trials, and the new International Scholar grants train researchers to work in developing countries. For more information about the St. Baldrick's Foundation please call 1.888.899.BALD or visit http://www.StBaldricks.org.
Rubenstein Public Relations
Adam Miller 212.843.8032 amiller@rubenstein.com
IDPR
Sheri Goldberg 646.723.3800 sgoldberg@id-pr.com
St. Baldrick's Foundation
Marc McCarthy 626.792.8247 x 40 marc@stbaldricks.org


Epithelioid hemangioendothelioma (EH) is a very rare vascular tumor of intermediate biological behavior. It can arise from various sites including bone, liver, spleen, pleura, lung, skin, or soft tissue. We report a case of a 24-year-old man who presented with a history of acute urinary retention and a palpable lump in the lower abdomen. Imaging showed a large pelvic mass displaying heterogeneous texture with solid and cystic components compressing and displacing adjacent structures. The patient underwent exploratory laparotomy with excision of the pelvic mass. Histopathological examination confirmed the diagnosis of EH. Pelvic soft tissue origin of EH is extremely rare. The diagnosis of EH may be suggested by characteristic magnetic resonance imaging (MRI) findings.


Freshman pitcher Kevin Smith (1-0) tossed seven innings of one-run ball to lead Georgia (2-1) to a 5-1 victory and a series win over Georgia Southern (1-2) on Sunday at Foley Field.
The Bulldogs struck first in the fourth inning on an RBI fielder's choice for freshman LJ Talley, his first career RBI. The Eagles evened the score at 1-all in the top of the fifth on an error, but Georgia responded in a big way in the bottom half. The Bulldogs sent eight batters to the plate and scored four runs, all with two outs, to open up a 5-1 lead. Sophomore Keegan McGovern launched a double off the center field wall to score two runs, while the other two runs came via a misjudged fly ball to right field off the bat of freshman Michael Curry.
Smith's impressive debut came at the expense of the Georgia Southern lineup, which struck out seven times and managed only two hits on the lefty in seven innings pitched.
The Bulldogs' Skyler Weber and McGovern accounted for five of Georgia's seven hits hitting out of the two- and three-hole, respectively. Weber and McGovern also lead the team in batting average after the first series.
Georgia Southern's Hunter Phillips (0-1), one of six pitchers utilized by the Eagles, was charged with the loss after surrendering two earned runs on one hit in two-thirds of an inning.
– Kevin Smith provided Georgia with a big start in his collegiate debut. The freshman went 7.0 innings while allowing only one run (unearned) on two hits, two walks and seven strikeouts.
– Keegan McGovern finished Sunday 3-4 with two doubles, two RBIs and two runs scored, capping an impressive opening weekend. The sophomore finished the series 5-12 at the plate with a home run, two doubles, and four RBIs.
– In the Bulldogs' big fifth inning, Nick King started a streak of four straight Georgia batters (Weber, McGovern, Nichols) to come around to score.
– Georgia starting pitchers (Tyler, Holder and Smith) combined to throw for 18.2 innings on the weekend. The Bulldog starters only allowed three earned runs and seven hits in three games.
– Georgia freshmen combined to pitch the entire game for the Bulldogs, each in his respective debut. After Smith went seven innings, Shane Tucker worked 1.1 innings of scoreless ball without allowing a hit. Adam Goodman earned the final two outs in the ninth without allowing a baserunner.
Georgia will host the Bulldog Invitational next weekend. The Bulldogs will face South Alabama at 5 p.m. on Friday, Cincinnati at 4 p.m. on Saturday and Western Kentucky at 3 p.m. on Sunday at Foley Field.
For all the latest on Georgia baseball, follow the team on Twitter (@baseballuga), Instagram (@baseballuga) and on Facebook (Georgia Baseball).


text:
Q: css position absolute making the textbox go down
*

*I build a prototype using chips material-UI.

*

*When I click the Test IPA the textbox should show immediately below the text Test IPA.

*In the prototype, it shows immediately below but whereas when I include with other functionalities, it's showing at the bottom.

*I debugged the CSS, its due to position absolute for each scenario it's showing different values and its coming from material UI.

*Can you tell me how to fix it with respect to other components?

*Providing my code snippet, sandbox and bug screenshot below.

Problem with other components
https://codesandbox.io/s/qqqk23x3q

Working properly individually
 https://codesandbox.io/s/1y2mvo0ol3
JSX:
<td>
    <ChipsTextbox chipName="test IPA" />
</td>

<Menu id="simple-menu" open={open} onClose={this.handleClose}>
    <TextField onChange={this.onChange} onKeyDown={this.handleKeyPress} />
</Menu>

CSS:
.MuiPopover-paper-1706 {
    outline: none;
    position: absolute;
    min-width: 16px;
    max-width: calc(100% - 32px);
    overflow-y: auto;
    overflow-x: hidden;
    min-height: 16px;
    max-height: calc(100% - 32px);
}

A: The parent element of the .MuiPopover-paper-1706 should have position: relative CSS attribute. If it doesn't then the element with position: absolute will be positioned relatively to the nearest parent with relative positioning.

redpajama_set_name: RedPajamaStackExchange


text:
A Faithful Man () is a 2018 French romantic comedy-drama film directed by Louis Garrel, from a screenplay by Garrel and Jean-Claude Carrière. It stars Laetitia Casta, Lily-Rose Depp, Joseph Engel and Garrel.

The film had its world premiere at the 2018 Toronto International Film Festival on 9 September 2018. It was released in France on 26 December 2018.

Plot
Abel (Garrel) learns that his girlfriend, Marianne (Casta), is leaving him for his best friend Paul, who is the father of her child. Seven or eight years later, Paul dies and Abel and Marianne resolve to restart their relationship as she and her son Joseph (Engel) move in. Joseph claims that his father died because Marianne poisoned him. Abel is also sought after by Ève (Depp), Paul's younger sister who's been infatuated with Abel since she was 14 years old. When Ève directly tells Marianne to leave Abel or "it's war", Marianne prompts the confused Abel to sleep with Ève. Now caught in a difficult situation between two women and a young boy with an eerie fixation on murder, will Abel keep his relationship with Marianne intact?

Cast
 Louis Garrel as Abel
 Laetitia Casta as Marianne
 Lily-Rose Depp as Ève
 Joseph Engel as Joseph

Production
In February 2018, it was announced Louis Garrel would direct the film, from a screenplay he wrote with Jean-Claude Carrière, and star in the film, alongside Laetitia Casta and Lily-Rose Depp. Why Not Productions produced the film.

Release
The film had its world premiere at the 2018 Toronto International Film Festival on 9 September 2018, followed by its US premiere at the New York Film Festival. Shortly after, Kino Lorber acquired US distribution rights to the film. It was released in France on 26 December 2018. In the United States, it received a limited release on 19 July 2019.

Critical reception
A Faithful Man holds  approval rating on review aggregator website Rotten Tomatoes, based on  reviews, with an average of . The site's critical consensus reads, "A Faithful Man's lack of tonal commitment may frustrate, but the end results should still prove entertaining for viewers in the mood for a French romantic farce." On Metacritic, the film holds a rating of 67 out of 100, based on 15 critics, indicating "generally favorable reviews".

References

External links

2018 films
2018 romantic comedy-drama films
Films directed by Louis Garrel
Films set in Paris
Films shot in Paris
French romantic comedy-drama films
2010s French-language films
2010s French films


Letting you all know that we will be closed September 26 and 27 but will be open Saturday and Sunday at regular hours. We will be participating in the fall edition of Puces Pop in Montreal Quebec. Thank you for your understanding and we will see you soon!


Designed to fit comfortably through any incision down to 1.8mm. Precise interlocking serrated tips with a sharp point enable the surgeon to initiate the capsule tear then securely grasp the capsule to perform the capsulorhexis. Designed specifically for corneal placed incisions, tip to pivot length reduced to 8.5mm. Keeping the pivot in the incision reduces the leakage from the anterior chamber, it also enables the tips to operate fully at the smaller incision sizes without stretching the incision and causing corneal deformation.


text:
Obama Declares "Our Democracy Is at Greater Risk than Ever"
On January 14, 2022 January 12, 2022 By AZ ConservativeIn Uncategorized
By: John Semmens
In honor of the January 6 incursion into the Capitol, Former President Barack Obama released a statement asserting that "one year ago a violent attack on our Capitol made it clear just how fragile the American experiment in democracy really is. In the November 2020 election more than 81 million votes were cast for Joe Biden. Rather than accepting this ringing endorsement for the person voters wanted to rule them for the next four years, Republicans are still working to torpedo his agenda."
"As the last year has demonstrated, the average American has supported the President by obeying his request that they get vaccinated for the good of the nation," Obama pointed out. "They have joined in the effort to shame those who stubbornly resist being unified into the collective whole. They have supported proposals to sanction nonconformists by firing them from their jobs, barring them from public places, and confiscating their property. Despite this evidence of success, President Biden has been frightened into declaring that 'there is no federal solution to the covid pandemic.' This is the position former President Trump took during 2020 and a significant reason for his defeat at the polls."
"Now is not the time to back away from destiny," Obama advised. "Backing down now will only encourage further resistance from the enemies of progress. President Biden is the Commander-in-Chief of America. He has vast forces awaiting his order to punish those who promote disunity. The fact that there has been no significant protest against holding dozens of January 6th arrestees without bail and denying them the speedy trials promised in the 6th Amendment shows that the vast majority of Americans are on his side. So, I say to my old friend Joe, don't let the bastards up. Tighten your choke hold on the country until everyone gets in line with your agenda and the world will thank you for saving democracy from its enemies."
Contrary to Obama's interpretation, a recent CBS News poll showed that 76% of respondents rejected the "insurrection" narrative explanation for the January 6 Capitol incursion and saw it as "a protest that became a riot." The fact that not a single person arrested for the January 6 riot has been charged with insurrection seems a pretty clear indication that there are no grounds for such a charge no matter how ardently Democrats may wish for it.
In related news, Gov. Jay Inslee (D-Wash) has proposed legislation that would "make it illegal to challenge an election's results once the media has declared a winner. Peaceful acceptance of election results is what differentiates democracy from authoritarian regimes. If we don't put a damper on post-election criticism of the winners democracy can't survive. I understand some being disappointed in the outcome, but for unity's sake it is crucial that this disappointment not be made public. Punishing those who can't bring themselves to congratulate and support the winners is the best way I know for quelling negative thoughts."
Supreme Court Ponders Constitutionality of Vaccine Mandate
Biden Spends 27% of His Time Vacationing


text:
package org.onosproject.net.pi.runtime;

import com.google.common.annotations.Beta;
import com.google.common.base.Objects;
import org.onosproject.net.pi.model.PiCounterId;
import org.onosproject.net.pi.model.PiCounterType;

import static com.google.common.base.Preconditions.checkArgument;
import static com.google.common.base.Preconditions.checkNotNull;

/**
 * Identifier of a counter cell in a protocol-independent pipeline.
 */
@Beta
public final class PiCounterCellId {

    private final PiCounterId counterId;
    private final PiCounterType counterType;
    private final long index;
    private final PiTableEntry tableEntry;

    private PiCounterCellId(PiCounterId counterId, PiCounterType counterType,
                            long index, PiTableEntry tableEntry) {
        this.counterId = counterId;
        this.counterType = counterType;
        this.index = index;
        this.tableEntry = tableEntry;
    }

    /**
     * Returns the identifier of the counter instance where this cell is
     * contained. Meaningful only if the counter is of type {@link
     * PiCounterType#INDIRECT}.
     *
     * @return counter identifier
     */
    public PiCounterId counterId() {
        return counterId;
    }

    /**
     * Returns the type of the counter identified.
     *
     * @return counter type
     */
    public PiCounterType counterType() {
        return counterType;
    }

    /**
     * Returns the counter index to which this cell ID is associated. Meaningful
     * only if the counter is of type {@link PiCounterType#INDIRECT}.
     *
     * @return counter index
     */
    public long index() {
        return index;
    }

    /**
     * Returns the table entry to which this cell ID is associated. Meaningful
     * only if the counter is of type {@link PiCounterType#DIRECT}, otherwise
     * returns null.
     *
     * @return PI table entry or null
     */
    public PiTableEntry tableEntry() {
        return tableEntry;
    }

    /**
     * Return a direct counter cell ID for the given counter ID and table
     * entry.
     *
     * @param tableEntry table entry
     * @return counter cell ID
     */
    public static PiCounterCellId ofDirect(PiTableEntry tableEntry) {
        checkNotNull(tableEntry);
        return new PiCounterCellId(null, PiCounterType.DIRECT, -1, tableEntry);
    }

    /**
     * Return an indirect counter cell ID for the given counter ID and index.
     *
     * @param counterId counter ID
     * @param index     index
     * @return counter cell ID
     */
    public static PiCounterCellId ofIndirect(PiCounterId counterId, long index) {
        checkNotNull(counterId);
        checkArgument(index >= 0, "Index must be a positive number");
        return new PiCounterCellId(counterId, PiCounterType.INDIRECT, index, null);
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj) {
            return true;
        }
        if (obj == null || getClass() != obj.getClass()) {
            return false;
        }
        final PiCounterCellId other = (PiCounterCellId) obj;
        return Objects.equal(this.counterId, other.counterId)
                && Objects.equal(this.counterType, other.counterType)
                && Objects.equal(this.index, other.index)
                && Objects.equal(this.tableEntry, other.tableEntry);
    }

    @Override
    public int hashCode() {
        return Objects.hashCode(counterId, counterType, index, tableEntry);
    }

    @Override
    public String toString() {
        return counterType == PiCounterType.DIRECT
                ? tableEntry.toString()
                : counterId.toString() + ':' + String.valueOf(index);
    }
}


A typical home in the Houston-The Woodlands-Sugar Land metro area costs $168,300, higher than in Texas as a whole, where the typical home is worth $152,000. Houston's median home value is also the fourth highest of any metro area in the state.
The price of a typical Houston home is, however, less than the national median home value of $194,500.
Residents of areas with less expensive real estate also tend to have relatively low incomes. In Houston, the typical household earns $61,465 annually, higher than both the $55,653 median household income statewide and $55,775 national figure. Houston has the fifth highest median household income of any Texas metro area.
Home values tend to be higher in dense, urban areas, where space is limited and land is more expensive as a result. In the Houston-The Woodlands-Sugar Land metro area, however, there are 717 people per square mile, far greater than the average urban population density of 283 Americans per square mile across all metro areas nationwide. Houston has the highest population density of any Texas metro area.
Home values in the metro area may also be hurt by the condition of the local job market. People often relocate for occupational reasons, and an area with higher unemployment is more likely to have less expensive real estate. The Houston-The Woodlands-Sugar Land metro area's unemployment rate was 5.5% as of August 2016, higher than the 4.9% jobless rate nationwide.
Despite the high unemployment rate today, improved unemployment among the Houston-The Woodlands-Sugar Land workforce over the last five years — like most of the country — may have helped increase home values as a result. Since August 2011, the Houston unemployment rate has fallen by 2.4 percentage points. Over the same period, home values in the metro area increased by 20.2%. Nationwide, the unemployment rate improved by 4.1 percentage points as the median home value increased by 12.0%.
Areas with low home values are often fairly well-educated. In addition to the lower-paying jobs that educated residents often hold, the lack of good schools and universities are likely to decrease demand for nearby homes. In Houston, 31.5% of adults have at least a bachelor's degree, a share roughly equivalent to the 30.6% of Americans with similar education nationwide.


text:
European Research Council (ERC)
Marie Sklodowska Curie – individual fellowships (MSCA-IF)
The European Cooperation in Science and Technology (COST) Actions
Our Motivation
Smooth transition of ERC to Horizon Europe
by SCIOTHECA
26 Pro 2020
SCIOTHECA News
26. 12. 2020 SCIOTHECA
European Research Council (ERC) is considered the flagship of European research funding. To date, ERC has supported almost 10 000 breakthrough scientific projects and ERC grantees have won prestigious prizes, including 7 Nobel Prizes, 4 Fields Medals and 5 Wolf Prizes. It is great news that the key features to which the ERC owes its uniqueness will remain unchanged even during forthcoming framework program Horizon Europe. These features are:
(1) OPENNESS: ERC focuses on frontier research across all fields and supports talented scientists of any nationality. ERC uses investigator-driven bottom-up approach, with no predefined thematic priorities.
(2) SINGLE EVALUATION CRITERION: The sole evaluation criterion is scientific excellence, which is independent of any political, geographic or economic considerations. ERC is a rare example of funding body where granting decision resists the lobbying efforts to base decision on other evaluation criteria apart of peer-reviewed excellence.
(3) IMPACT ON SCIENCE: ERC funds creative individuals who identify high-risk, high-reward challenges and demonstrate a great potential to revolutionise science with new significant advances of knowledge.
The approval of Horizon Europe's final budget, and hence the ERC budget, was accompanied by a number of turbulent changes. Finally, the Horizon Europe budget of 95.5 billion EUR has been agreed this week. Since ERC was established by European Commission in 2007 its budget rose from 7,5 billion EUR for period 2007-2013 under FP7 to current 13 billion EUR for period 2014-2020 under H2020. As for the forthcoming period 2021-2027 the available reports speculate that in Horizon Europe, the ERC receives about 16 billion Euro in current prices. It means that the ERC's budget will increase compared to last years, unfortunately not to the level initially expected (at least 19 billion EUR).
The organizational structure of the ERC will remain. The ERC Scientific Council, composed of 22 distinguished scientists and scholars representing the European scientific community, is the independent governing body of the ERC. Its main role is setting the ERC strategy and selecting the peer review evaluators. In 2021, five new members will join the Scientific Council. Prof. Jean-Pierre Bourguignon, who is the fourth and at the same time the sixth President of the Scientific Council in the history of ERC temporarily returned to this post when his successor Prof. Mauro Ferrari resigned in April 2020. The ERC Executive Agency (ERCEA) will continue to implement the ERC strategy and be in charge of day-to-day grant administration.
Timing of opening and closing of the forthcoming calls is changed. The deadline for submission of ERC Starting is foreseen to be on 9th March 2021 and the deadline for submission of ERC Consolidator grant is expected to be on 20th April 2021. However, possible delays for these early calls has been announced recently due to the late adoption of the EU Budget 2021-2027 and the Horizon Europe programme. Any shifts in the timetable of these calls should not affect the eligibility periods of candidates applying for ERC Starting and Consolidator Grants. These eligibility periods are based on the time elapsed between the date of the first PhD of the applicant and the 1st of January of the budget year to which the Work Programme refers. The call for ERC Advanced grants is expected to be open and closed as usual with deadline for proposal submission on 31st August 2021. ERC Synergy and Proof of Concept grants will not be open in 2021 due to the delay during transition to Horizon Europe. Both of these grant schemes should reappear in the work program in 2022.
Early this year, ERC Scientific Council revised the evaluation panel structure to reflect evolving nature of scientific disciplines, to maintain the balance between panels in terms of the number of proposals assessed in each call, and to maintain the disciplinary coherence within and across panels. As a result, several changes have been introduced to the structure of the ERC evaluation panels within Horizon Europe. Final list of the evaluation panels for ERC calls 2021 and 2022 is already available here. One additional panel has been included in the domain Physical Sciences and Engineering (PE11 – Materials Engineering) and in the domain Social Sciences and Humanities (SH7 – Human Mobility, Environment and Space). Other changes include an update of panel subtitles and descriptors in both above mention domains and redefinition of former panels and enrichment of descriptors in the Life Sciences domain. Grant evaluation experts for the first Horizon Europe calls will be same as in the Horizon 2020. The new Horizon Europe expert contracts are expected by the end of the year 2021.
Newly, applicants for ERC Advanced grant who succeed in Step 1 of the evaluation will be invited for an interview in Step 2. In this way, the Scientific Council aims to give applicants chance to defend their projects personally in front of the evaluation panel and to align the ERC Advanced Grant evaluation process with the processes used for the Starting and Consolidator Grant.
In Sciotheca we offer full support with preparation of competitive ERC grants. Our consultants have scientific backgrounds and experiences with grant writing, reviewing and management. If you have any questions do not hesitate to contact us, we will be honoured to help you with your ERC proposal.
SCIOTHECA
View all posts by SCIOTHECA
Veronika Mikitova, PhD.
Hvozdec 122
Hvozdec 664 71
info@sciotheca.eu
Company registration number (IN): 09709631
Please don't hesitate in contacting us with any question you may have. We will get back to you as soon as possible.


text:
using System.IO;

namespace DrawShip.Common
{
    public class StreamRenderResult : IRenderResult
    {
        private readonly Stream _output;

        public StreamRenderResult(Stream output)
        {
            _output = output;
        }

        public void WriteResult(Stream output)
        {
            _output.CopyTo(output);
        }
    }
}


Coral bleaching refers to a natural stress response of corals that causes them to expel their symbiotic zooxanthellae in large numbers, lose their natural colour and expose the underlying white calcium carbonate skeleton. The symbiotic zooxanthellae in coral tissue provide their host with 90% of their food source. In return, the corals provide shelter for the microscopic algae. By losing their primary source of food, bleached corals become more susceptible to disease and death.
Small scale local bleaching events can be caused by a variety of factors such as pollution or sedimentation, variations in salinity, excessive amounts of ultraviolet light, and toxins (such as pesticides). However, climate change and the increase of sea water temperature are the main reasons for large-scale mass coral bleaching.
Bleached corals are still alive. However, prolonged bleaching periods can cause the death of corals. Bleached corals, however, are dangerously weakened as they have lost their symbiotic zooxanthellae, which once provided up to 90% of their food. Bleached coral therefore rely 100% on active feeding to nourish themselves.
Corals that survive a bleaching event involving the loss of zooxanthellae will eventually regain normal density of zooxanthellae (recoloured) when environmental conditions improve and stresses are eliminated.
After the mass bleaching event of 2010, RCM developed a Coral Bleaching Response plan, which was adopted by the Department of Marine Parks Malaysia (DMPM) in 2011. The plan ensures effective communications and coordination of management efforts to protect coral reefs during future bleaching events.
By combining satellite data with a community-based monitoring network, bleaching will be reported to various authorities when it occurs. This enables the prediction and identification of possible bleaching events, which will provide information for communication to stakeholders, government agencies and the media.
This is done by assessing and measuring the level and impact of bleaching by setting up a bleaching task force to carry out monitoring and investigation. Once data is gathered, a brief report of the preliminary results can be prepared.
It is important to let all stakeholders know how they can adapt to bleaching problems, and also how human activities can be managed to reduce further damage to bleached reefs.
In order to give coral reefs the best chance of survival, relevant authorities will take appropriate steps to remove and reduce human stresses to the reef.
If you come across any coral bleaching in Malaysia, report the location to us. Click to download the Bleaching Watch Survey Report Form.


Did you hear about the Restaurant on the 13th Moon? The food was OK, but it had no atmosphere.
Nationally Certified and Licensed massage therapist Sara J. Gibbs-Cook has practiced therapeutic bodywork since 1994.
She is an active member of the American Massage Therapy Association.
Sara created Twelve Moons Products to complement her bodywork practice, Peace Thru Touch Massage Therapy.
allows her to cater sessions to every client's need.
Sara's specialties are Prenatal Massage and Energy Balancing.
pressure point techniques, and holistic crystal therapy upon request.
Her work stimulates circulation and energy flow, leaving her clients deeply relaxed, peaceful, and vibrant.
Take time for yourself - experience Peace Thru Touch.


text:
<!DOCTYPE html>
<html>
<body>
<script src="../resources/runner.js"></script>
<script>
var value = PerfTestRunner.loadFile("resources/chrome_code_search.txt");
PerfTestRunner.measureTime({
  description: "This benchmark tests Sanitizer sanitize function for a large DOM tree",
  run: function() {
    s = new Sanitizer();
    fragment = s.sanitizeFor("template", value);
  },
  iterationCount: 5
});
</script>
</body>
</html>


text:
Jean Moulin (1899-1943), member of the French Resistance and préfet in Eure-et-Loir, was one of Général de Gaulle's assistants. Discharged from his duties by the Pétain government, he was arrested in 1943 by the Gestapo and died in the train which was taking him to Germany to be interrogated.
Jean Moulin (1899-1943), member of the French Resistance and préfet in Eure-et-Loir, was close to Général de Gaulle. His first act of resistance took place in Chartres, where he refused to accuse a group of Senagalese Tirailleurs of committing crimes, when he knew they were victims of the German bombings. Discharged from his duties by the Pétain government, he met De Gaulle in London who put him in charge of creating the National Council of Resistance in France. Under the codename 'Max', he was arrested in Caluire-Cuire on 21st June 1943 by the head of the Gestapo, Klaus Barbie, following an accusation. He was tortured before being sent by train to Germany to be subjected to further interrogations. He died in the train on 8th July 1943. A memorial can be seen in La Taye.
Monday : open
Thuesday : open
Wednesday : open
Thursday : open
Friday : open
Saturday : open
Sunday : open
28190 SAINT-GEORGES-SUR-EURE


We are experiencing a weird responsive bug on iPhone 5s, but not on any other IOS device. I tried searching the forums and looking online, but it can't seem to find a similar case. If someone could point me into the right direction.
You've set max-width as 290px and your icons are 65px wide which add up to 325px plus you've got spacer cells.
So either set your width to more or make your icons smaller.
Personally I would set a media query at 325px and make each td in the icon list a width:20%;, hide the spacer cells then set the icons as width:95%; height:auto; to give a little space between them.
This is the second time you are helping us out, much appreciated!!


Dr. Martin is the medical director of Sacred Heart Medical Group's Weight Management & Nutrition program. He and his staff develop individualized weight-loss plans to help each of his patients reap the physical and emotional benefits that result from weight loss.
Dr. Martin believes weight loss is a key to preventing many other health difficulties, such as joint pain, back problems, heart disease, cancer and more. He also knows that successful weight loss and weight management are the result of a partnership between physician and patient.
Dr. Martin also works closely with the Sacred Heart Surgical Weight Loss Center to help patients prepare for bariatric surgery. He is very familiar with the weight-loss trials required by most insurance carriers. He helps patients set realistic goals of diet, exercise and lifestyle changes, and then documents patients' success or failure to lose weight by these traditional methods.
Dr. Martin is a valuable resource both before and after bariatric surgery to help patients learn how to adjust their diet to meet their weight-loss goals while fulfilling their nutritional needs.
To learn more about Dr. Martin or to schedule an appointment, please call 850-416-4960 or visit his online profile.
Dr. Martin's office is located in the Gulf Coast Medical Arts Center at 4501 North Davis Hwy., Suite C, in Pensacola. Download map.


Mallard Ridge is a dog and cat friendly community. We allow up to two pets per apartment, with no weight limit. Dogs and cats must be spayed or neutered and shots up to date. Vet records may be requested. Other small or caged animals allowed, please call for details.
Breed restrictions (mixed breed or full) include Akita, Belgian Sheepdog, Belgian Tervuren, Bouvier Tervuren, Bouvier Des Flandres, Belgian Malinois, Cane Corso, Chow Chow, Dalmatian, Doberman Pinscher, German Shepherd, Giant Schnauzer, Irish Water Spaniel, Komondor, Pit Bull (or variation including or aka American Pit Bull Terrier, American Staffordshire Terrier, Staffordshire Bull Terrier), Presa Canario, Rottweiler. We reserve the right to refuse any dog based on temperament, behavior, or appearance, regardless of breed.
Just moved here a couple months ago. I love my apartment. It's very pretty and big. My neighbors are very nice and the staff here is helpful and very friendly.