text:
\section{Introduction}

Deep reinforcement learning (RL) is a framework for solving temporally extended tasks that has resulted in a number of breakthroughs in autonomous control including mastery of the game of Go~\citep{silver2018general,silver2017mastering}, learning to play video games~\cite{mnih2015human,OpenAI_dota,alphastar}, and learning basic robotic control~\citep{openai_rubik_cube,kalashnikov2018qt}.
However, today's RL systems require substantial manual human effort to engineer rewards for each task which comes with two fundamental drawbacks. The human effort required to design rewards is impractical to scale across numerous and diverse task categories and the engineered rewards can often be exploited by the RL agent to produce unintended and potentially unsafe control policies~\citep{hadfield2017inverse, amodei2016concrete,reward-side-effects}.  Moreover, it becomes increasingly difficult to design reward functions for the kinds of complex tasks with compositional structure often encountered real-world settings. In this work, we are interested in the following research question - {\it how can we learn robotic control policies that are aligned with human intent and capable of solving complex real-world tasks?}

Human-in-the-loop RL~\cite{preference_drl,ibarz2018reward,lee2021pebble} has emerged as a promising approach to better align RL with human intent that proposes an alternate approach to traditional RL algorithm design. Rather than manually engineering a reward function and then training the RL agent, human-in-the-loop RL proposes for humans to provide feedback interactively to the agent as it is training. This paradigm shift sidesteps reward exploitation by providing the RL algorithm immediate feedback to align it best with human intent and, if efficient in terms of human labels required, has the potential to scale RL training across a diverse variety of tasks more reliably than reward engineering.

So far human-in-the-loop RL systems have been used to play Atari games~\cite{ibarz2018reward}, solve simulated locomotion and manipulation tasks~\cite{preference_drl,lee2021pebble}, and better align the output of language models~\cite{language_human_prefs}. While these initial results have been promising, human-in-the-loop methods are still out of reach for the kinds of long-horizon compositional tasks that are desired for real-world robotics. The primary reason is that current methods do not scale efficiently with respect to human labels for more challenging tasks. As task complexity increases, the number of human feedback interactions required to attain a suitable policy becomes impractical.

To address the ability of RL algorithms to scale to more complex long-horizon tasks, a number of recent works~\cite{pertsch2020spirl,singh2021parrot,ajay2021opal} have proposed data-driven extraction of behavioral priors, which we refer to as {\it skills}. In these methods, a behavioral prior is fit to an offline dataset of demonstrations and is then used to guide the RL policy to solve downstream tasks by regularizing it to stay near the behavioral distribution. Such methods have been shown to successfully solve tasks such as diverse object manipulation~\cite{singh2021parrot} and operating a kitchen with a robotic arm~\cite{pertsch2020spirl}. However, they still require  engineered rewards for the downstream tasks and, more importantly, assume access to a clean offline dataset of expert demonstrations that are specifically relevant to the downstream tasks. In real-world scenarios, such clean datasets are highly unlikely to exist. We desire skill extraction methods that are robust to noisy datasets, collected by a range of policies, with highly multi-modal structure.

In this work, we introduce Skill Preferences (SkiP), an algorithm that integrates human-in-the-loop RL with data-driven skill extraction. Our main insight is that human feedback can be incorporated not only for downstream RL, as is done in prior work, but also for extracting human-aligned skills. SkiP learns a human preference function and uses it to weigh the likelihood of trajectories in the offline dataset based on their degree of alignment with human intent. By incorporating human feedback during skill extraction, SkiP is able to extract structured human-preferred skills from noisy offline data and addresses the core limitation of prior skill extraction approaches - the dependence on curated expert datasets. SkiP is both capable of efficiently extracting skills and solving different downstream tasks with respect to human labels. Similar to how prior work in human-in-the-loop RL suggested replacing manually engineered reward functions with human feedback, our work suggests to replace the manual effort needed to curate clean offline datasets with human feedback. We summarize our main contributions below:
\begin{enumerate}
    \item We introduce Skill Preferences (SkiP), an algorithm that incorporates human feedback to extract skills from offline data and utilize those skills to solve downstream tasks.
    \item We show that, unlike prior leading methods for data-driven skill extraction, SkiP is able to extract structured skills from noisy offline datasets.
    \item We show that SkiP is able to solve complex multi-step manipulation tasks in robotic kitchen environment substantially more efficiently than prior leading human-in-the-loop and skill extraction baselines.
\end{enumerate}

\begin{figure*} [t] \centering
\includegraphics[width=.99\textwidth]{figures/framework.jpg}
\caption{Our method - Skill Preferences (SkiP) - consists of two phases. During the skill extractions phase, human feedback is used to learn skills. During the skill execution phase, human feedback is used to finetune the skills to solve various downstream tasks. First, skills are extracted from a noisy offline dataset with human feedback to denoise behavioral prior. Second, skills are executed with RL in the environment with task-specific human feedback.  }
\label{fig:framework}
\vspace{-0.1in}
\end{figure*}

\section{Background}

 {\bf Reinforcement Learning:} As is common with RL methods, we assume that the control process is a Markov Decision Process (MDP) with discounted returns. Such MDPs are defined by the tuple $\mathcal{M} = (\mathcal{S},\mathcal{A}, R, \rho_0, \gamma)$ consisting of states $s \in \mathcal{S}$, actions $a \in \mathcal{A}$, rewards $R = R(s,a)$, an initial state distribution $s_0 \sim \rho_0(\cdot)$, and a discount factor $\gamma \in [0, 1)$. A control policy maps states to actions within the MDP and usually takes the form of a probability distribution -- $a \sim \pi (\cdot | s)$. The value function $V^\pi(s)$ and action-value function $Q^\pi(s,a)$ describe the value with respect to future expected returns with respect to an initial state or state-action pair.
\[
    V^\pi(s) := \mathbb{E}_{\mathcal{M}, \pi} \left[ \sum_{t=0}^\infty \gamma^t R(s_t,a_t) \mid s_0=s \right], \hspace*{5pt}
    Q^\pi(s,a) := R(s,a) + \gamma \mathbb{E}_{s' \sim T(\cdot|s,a)} \left[ V^\pi(s') \right],
\]
where the first expectation $\mathbb{E}_{\mathcal{M}, \pi}$ denotes actions are sampled according to $\pi$ and future states are sampled according to the MDP dynamics. The goal in RL is the learn the optimal policy:
\[
\pi^* \in \arg \max_\pi \ J(\pi, \mathcal{M}) := \mathbb{E}_{s \sim \rho_0} \left[ V^\pi(s) \right].
\]
In addition to the standard MDP setting, our method will also learn skills $z \in \mathcal Z$ which consist of an encoder that maps state-action sequences to a skill $q^{(e)}(z \vert s_t, a_t, \dots, s_{t+H-1}, a_{t+H-1})$ and a decoder that maps state-skill pairs to atomic actions $q^{(d)}(a_1, a_2, ..., a_H \vert s, z)$.

\section{Method}

The two primary contributions of SkiP are (i) introducing human feedback during the skill extraction process to learn structured skills from noisy data and (ii) utilizing human preferences over skills for downstream RL training.
Our approach shown schematically in Fig.~\ref{fig:framework} and detailed in full in Algo.~\ref{algo:skip}. Due to utilizing human feedback to learn the behavioral prior, unlike prior approaches of skill extraction from offline data~\cite{ajay2021opal,singh2021parrot,pertsch2020spirl}, our method is robust to suboptimal or noisy data.

{\bf The SkiP Algorithm:}
We first summarize the algorithm and then proceed with its derivation. Shown in Algo.~\ref{algo:skip}, SkiP consists of two phases - (i) \emph{skill extraction} and
(ii) \emph{skill execution}. A human teacher provides feedback during {\it both} phases. During skill extraction, a human teacher labels whether a trajectory is preferred or not (for details see Sec.~\ref{sec:exp_setup}) to train a preference classifier. A behavioral prior is then fit to the offline data with a weighted human preference function. During skill execution, the learned skills are rolled out by an RL agent - a Soft Actor-Critic (SAC)~\cite{haarnoja2018soft} - that is trained with task-specific human preferences. As such, human feedback is used during both phases of the algorithm. We proceed to define notation and provide a derivation.

{\bf Preliminaries and Notation:} Our method is composed of two phases - (i) the skill extraction phase and (ii) the skill execution phase. During the skill extraction phase, we are given an offline dataset $\mathcal D$ which consists of task-agnostic, multi-modal, and potentially noisy demonstrations. We denote trajectory sequences as $\tau_t = (s_t,a_t,\dots,s_{t+H-1},a_{t+H-1})$, action sequences as ${\bf a}_t = (a_t,\dots,a_{t+H-1})$, and skills which decode into action sequences as $z \in \mathcal Z$.

\begin{algorithm}
\caption{SkiP: Skill Preferences}
\label{algo:skip}
\begin{algorithmic}
    \small
  \STATE ==== {\bf Skill Extraction Phase} ====
  \STATE INPUT: offline dataset $\Tilde{\mathcal{B}}$
  \STATE Initialize prior $p$, skill encoder $q_{\phi_2}$ and skill decoder $p_{\phi_1}$. Initialize learned preference classifier $ P_{\psi}$
  \STATE A human provides labels $(y_1, y_2, ...)$ for 10\% of the trajectories in $\Tilde{\mathcal{B}}$ and stores them in a new buffer $\Tilde{\mathcal{D}}$
  \FOR{each iteration}{

    \STATE Update $\psi$ by maximizing $\mathbb{E}_{(y,\tau)\sim\Tilde{\mathcal{D}}}[y\cdot log P_{\psi}(\tau)+(1-y)\cdot log(1-P_{\psi}(\tau))]$
  }
  \ENDFOR
  \FOR{each iteration}{
    \STATE Update $p$,  $q_{\phi_2}$, $p_{\phi_1}$ by optimizing $\mathcal{L}^{prior}$  (\ref{eq:extraction_obj})  \hfill\COMMENT{Update preference weighted behavioral prior}
  }
  \ENDFOR

  \STATE ==== {\bf Skill Execution Phase} ====
  \STATE Initialize parameters of actor $\pi_{\theta1}$, critics $Q_{\theta2}$ and $Q_{\bar{\theta2}}$ and reward model $\widehat{\reward}_\eta$
  \STATE Initialize a dataset of preference $\mathcal{D}\leftarrow\emptyset$ and a dataset of transitions $\mathcal{B}\leftarrow\emptyset$
  \FOR{Each iteration}{
    \FOR{Each environment step}{
        \STATE $z_t\sim\pi(z_t|s_t)$,  $s_{t+H}\sim p(s_{t+H}|s_t, z_t)$,  $\mathcal{B}\leftarrow \mathcal{B}\cup(s_t, z_t, \widehat{\reward}_\eta(s_t, z_t), s_{t+H})$
    }
    \ENDFOR
    \IF{iteration $\%$ $K$ == 0}{
        \FOR {step t = 1...M}{
            \STATE$(\tau^{(z)}_0, \tau^{(z)}_1)\sim  \mathcal{B}$,  query human for label $y$,   $\mathcal{D}\leftarrow\mathcal{D}\cup(\tau^{(z)}_0, \tau^{(z)}_1, y)$ \hfill\COMMENT{Get preference labels}
        }
        \ENDFOR
        \FOR{each gradient step of $\widehat{\reward}_\eta$}{
            \STATE Sample $(\tau^{(z)}_0, \tau^{(z)}_1, y) \sim \mathcal{D}$, update $\widehat{\reward}_\eta$ with $\min \mathcal{L}^{reward}$ (\ref{eq:reward-bce})
            \hfill\COMMENT{Update preferences}

        }
        \ENDFOR
        \STATE Relabel entire replay buffer $\mathcal{B}$ with $\widehat{\reward}_\eta$
    }
    \ENDIF
    \FOR{each gradient step of agent}{
        \STATE Sample $(s, a, s', R) \sim \mathcal{B}$, update $\pi_{\theta1}$ by optimizing $\mathcal{L}^{SAC}_{actor}$ (Appendix A) \hfill\COMMENT{Update agent}
        \STATE Update $Q_{\theta2}$ and $Q_{\bar{\theta2}}$ by optimizing $\mathcal{L}^{SAC}_{critic}$  (Appendix A)
    }
    \ENDFOR
  }
  \ENDFOR
\end{algorithmic}
\end{algorithm}

{\bf Learning Behavioral Priors with Human Feedback (Skill Extraction):} Our main insight is to use human preferences in order to fit a weighted behavioral prior over an offline dataset of (potentially noisy) demonstrations. Our method builds on prior work for behavioral extraction from offline data via expected maximum likelihood latent variable models~\cite{ajay2021opal,singh2021parrot,pertsch2020spirl}.

Specifically, prior work~\cite{ajay2021opal,singh2021parrot,pertsch2020spirl} considers a parameterized generative model  $p_\alpha ({\bf a}_t  | s_t)$  over action sequences where ${\bf a}_t = (a_t,\dots,a_{t+H-1})$ that represents a behavioral prior and is trained to replicate the transition statistics in the offline dataset:
\begin{equation}
    \label{eq:unweighted_prior}
    p_{\alpha} \  \in \ \arg \max_{\alpha} \ \mathbb{E}_{\tau \sim \mathcal{D}} \left[ \sum_{t=0} \log \Big( p_\alpha ({\bf a}_t | s_t) \Big) \right].
\end{equation}

In our approach, we consider an adaptive behavioral prior that is biased towards trajectories that achieve higher rewards according to the human preference function. This can be particularly useful in diverse datasets collected with suboptimal or noisy policies or multiple policies of varying expertise. For example, one could imagine multiple humans collecting demonstrations or multiple robots exploring their environment.  Similar to \citet{Siegel2020KeepDW}, we seek a behavioral prior that is biased towards the high reward trajectories in the dataset while also staying close to the average statistics in the dataset. However, unlike prior work on weighted behavioral priors~\cite{Siegel2020KeepDW,awr2019peng,brac2019wu} the weight is determined through the human preference function and we aim to maximize action-sequence likelihood as opposed to single-timestep actions.

We formulate this as:
\begin{equation}
    p_{\alpha} \  \in \ \arg \max_{\alpha} \ \mathbb{E}_{\tau \sim \mathcal{D}} \left[ \sum_{t=0}^{|\tau|} \omega(\tau_t) \cdot p_\alpha({\bf a}_t | s_t) \right] \text{such that } \ \mathbb{E}_{\tau \sim \mathcal{D}} \left[ D_{KL} \left( p_\alpha \| \bar{p} \right) \right] \leq \delta,
\end{equation}
where $\bar{p}$ denotes the empirical behavioral policy and $\omega(s_t, a_t)$ is the weighting function. The non-parametric solution to the above optimization is given by:
\[
p_{\alpha} ({\bf a}_t | s_t) \propto \bar{p}({\bf a}_t | s_t) \cdot \exp \left( \omega(\tau_t) / T \right),
\]
where we have used $\propto$ to avoid specification of the normalization factor, and $T$ represents a temperature parameter that is related to the constraint level $\delta$. The above non-parametric policy can be projected into the space of parametric neural network policies as~\cite{awr2019peng, Siegel2020KeepDW}:
\begin{equation}
    \label{eq:adaptive_prior}
    p_{\alpha} \in \ \arg \max_{\alpha} \ \mathbb{E}_{\tau \sim \mathcal{D}} \left[ \sum_{t=0}^{|\tau|} \exp \left( \omega(\tau_t) / T \right) \cdot \log \Big( p_\alpha ({\bf a}_t | s_t) \Big) \right].
\end{equation}
For the choice of the weighting function, we use the learned preference classifier $P_{\psi}(y|\tau)$ which inputs a trajectory and outputs the likelihood of this trajectory being human-preferred with $y \in [0,1]$. $P_{\psi}(y|\tau)$ is learned by sampling a small subset of the offline dataset and soliciting human feedback to label preferred versus not preferred trajectory: $\omega(\tau_t) := \log P_{\psi} (\tau_t)$.

In this process, we treat the temperature $T$ as the hyper-parameter choice. This implicitly defines the constraint threshold $\delta$, and makes the problem specification and optimization more straightforward. For our practical implementation, we fit a variational autoencoder similar to ~\cite{ajay2021opal,pertsch2020spirl} but softly weighted to maximize the likelihood of human-preferred transitions. We introduce a latent variable $z$ with a Guassian prior such that the ELBO loss is given by:
\begin{equation}
	 \log{p ({\bf a}_t \vert s_t)} \geq \mathbb{E}_{\tau \sim \mathcal{D}, z \sim q_{\phi_2}(z \vert \tau)} [\underbrace{\log p_{\phi_1}({\bf a}_t \vert s_t, z)}_{\mathcal{L}_{\text{rec}}} + \beta \underbrace{(\log {p(z)} - \log q_{\phi_2}(z \vert \tau)}_{\mathcal{L}_{\text{reg}}} ].
\end{equation}

This is the standard $\beta$-VAE loss applied to action sequence modeling where $\beta$ is a scalar controlling the regularization strength and $\phi_1,\phi_2$ are neural network parameters that are optimized during training. Note that $q_{\phi_2}$ encodes trajectories into a latent vecotr and $p_{\phi_1}$ decodes  latent vectors and the starting state back into action sequences. Our training objective weighs this loss with the preference function. Thus, our overall skill extraction objective is to maximize:
\begin{align}
 \mathcal L =  \arg \max_{\phi_1,\phi_2} {E}_{\tau \sim \mathcal{D}, z \sim q_\phi(z \vert \tau)} \left [ P_{\psi} (\tau) (\mathcal{L}_{\text{rec}} + \mathcal{L}_{\text{reg}})\right ].
\end{align} \label{eq:extraction_obj}

{\bf Reward learning and human preferences over skills (Skill Execution):}
Unlike traditional RL where the hand-engineered rewards are available,
we consider the preference-based RL framework~\citep{preference_drl,ibarz2018reward,lee2021pebble,leike2018scalable}:
a (human) teacher provides preferences between the agent's behaviors and the agent uses this feedback to perform the task.
In order to incorporate human preferences into deep RL, \citet{preference_drl} proposed a framework that learns a reward function $\widehat{R}_\eta$ from preferences. In this work, we modify the preference framework to operate not over atomic state-action transitions but rather state-skill transitions that have substantially longer time spans.

Formally, we assume access to an offline dataset (the agent's replay buffer) $\mathcal B$ of state-action transitions
and sample state-skill sequence pairs $\tau^{(z)}_1,\tau^{(z)}_2$  for which a human provides a binary label $y \in \{0,1\}$, where $\tau^{(z)} = (s_t,z_t,s_{t+H},z_{t+H},\dots,s_{(t+M)H},z_{(t+M)H})$ where $H$ is the length of actions the skill decodes to and $M$ is the total number of state-skill transitions. Note how such trajectories are $H$ times longer than if we were to sample state-action trajectories  of length $M$.

The reward function $\widehat R$ therefore fits a Bernoulli distribution across sequences. In this work, we learn a parameterized reward function $\widehat{R}_\eta$ as in ~\citep{lee2021pebble} utilizing a Bradley-Terry model~\cite{bradley1952rank} in the following manner:
\begin{align}
  P_\eta[\tau^{(z)}_1\succ\tau^{(z)}_0] = \frac{\exp\sum_{t} \widehat{\reward}_\eta(s_{t}^1, z_{t}^1)}{\sum_{i\in \{0,1\}} \exp\sum_{t} \widehat{\reward}_\eta(s_{t}^i, z_{t}^i)}.\label{eq:pref_model}
\end{align}
Here, the operator $A\succ B$ means that $A$ is preferred to $B$. $\widehat{\reward}_\eta$ can therefore be interpreted as a binary preference classifier where labels are provided through human feedback. The parameters $\eta$ of the neural network are updated by optimizing a binary cross-entropy loss:
\begin{align}
  \mathcal{L}^{\tt Reward} =  -\mathbb{E}_{(\tau^0,\tau^1,y)\sim \mathcal{D}} \Big[ & y(0)\log P_\eta[\tau^{(z)}_0 \succ\tau^{(z)}_1] + y(1) \log P_\eta[\tau^{(z)}_1\succ\tau^{(z)}_0]\Big]. \label{eq:reward-bce}
\end{align}

\section{Experimental Setup}
\label{sec:exp_setup}
{\bf Environments:} For our experiments,
we use the robot kitchen environment and offline dataset from the D4RL suite~\cite{fu2020d4rl}. This environment requires a 7-DOF (6-DOF arm and 1-DOF gripper) robotic arm to solve complex multi-step tasks in a kitchen. Due to the 7-DOF control and compositional long-horizon nature of the tasks, this environment cannot be solved by standard  methods such as SAC or behavior cloning~\cite{pertsch2020spirl}.

{\bf Offline dataset:} We desire our method to work on suboptimal offline data and, unlike prior skill extraction approaches~\cite{pertsch2020spirl,singh2021parrot,ajay2021opal} do not assume that the offline dataset consists solely of expert demonstrations. We simulate a noisy offline dataset by combining 601 expert trajectories and 601 noisy trajectories generated by random policy. The expert trajectories involve various structured kitchen interactions such as opening the microwave and operating the stove. We solicit human feedback on 10\% of the total trajectories or equivalently 120 human labels .

{\bf Downstream tasks:} We use 6 different downstream tasks shown in Fig.~\ref{fig:six_env} that vary in difficulty to evaluate our approach. The task suite consists of tasks that require one, two, or three subtasks to be completed in a row in order to achieve the overall goal. We note that even the tasks with one subtask is challenging for RL methods that operate over atomic actions and do not leverage skills, as is shown in the experimental results.

{\bf Simulated human:} Similar to prior work \citep {preference_drl,lee2021pebble}, we obtain feedback from simulated human teachers instead of real humans. During skill extraction, human provides labels whether a trajectory is noisy or structured.\footnote{Here, we remark that limited number of human labels (10\% of the total trajectories) is utilized in our experiments for skill extraction.} During skill execution, the simulated human assigns positive labels to trajectory segments that have made more progress toward completing the desired task. Progress is calculated by computing $||s_{M\cdot H} - \bar{s}||_2 - ||s_1 - \bar{s}||_2$, where $\bar{s}$ is the state when the target task is completed.

\begin{figure*} [h] \centering
\includegraphics[width=.99\textwidth]{figures/six_envs.pdf}
\caption{We evaluate in the robot kitchen environment from D4RL~\cite{fu2020d4rl}, which requires a 7-DOF robotic arm to operate a kitchen. Within this environment, we consider a variety of manipulation tasks of varying difficulty. The simplest tasks involve one subtask - opening a microwave or moving the kettle - while more challenging tasks require the agent to compose multiple subtasks. Overall, we consider 6 evaluation tasks that require chaining one, two, or three subtasks. }
\label{fig:six_env}
\end{figure*}

\begin{figure*} [h] \centering
\includegraphics[width=.93\textwidth]{figures/skill_extraction.pdf}
\caption{An illustration of the skill extraction procedure within the robot kitchen environment. Starting with a noisy offline dataset, which consists of both expert and random actions, our method fits a behavioral prior to the offline data using human feedback to identify human-preferred motions which results in a set of diverse skills that can then be finetuned to downstream tasks.}
\label{fig:env}
\vspace{-0.1in}
\end{figure*}

{\bf Baselines:} In addition to our method, we compare to  {\it Atomic Preferences} which we based on PEBBLE~\cite{lee2021pebble}: a state-of-the-art human preference RL method. it pretrains the SAC agent with behavior cloning over the optimal offline dataset and trains the online SAC agent with human preferences over atomic transitions instead of high-level skill transitions. We also compare to {\it Flat Prior} which learns a single-step action prior on the atomic action space over the optimal dataset and trains an online SAC agent regularized with the action prior over ground-truth reward. The Oracle we compare to is {\it SPiRL}, a leading skill extraction with access to the ground truth (expert demonstrations and ground truth reward) in Fig.~\ref{fig:main_result}.

\section{Experimental Results}

For the experimental evaluation of our approach, we investigate the following questions: (a) Can SkiP solve challenging long-horizon tasks and how does our method compare to prior leading approaches? (b) How do SkiP compare to an oracle baseline that extracts skills from perfect expert demonstrations and has access to the ground truth reward? (c) Is it necessary to provide human feedback during skill extraction or is it sufficient to fit an unweighted behavioral prior over the offline data? (d) How should we incorporate human feedback during the skill execution phase?

{\bf Main Results:} We evaluate SkiP and related baselines on the 6 tasks shown in Fig.~\ref{fig:six_env} and display the learning curves in Fig.~\ref{fig:main_result}. We observe that SkiP is the only method (except for the Oracle) that is capable of solving the majority of tasks in the robot kitchen task suite and outperforms the baselines on all environments. On {\bf 5} out of {\bf 6} tasks, SkiP is able to match the oracle baseline asymptotically which means that it arrives at the optimal solution.

SkiP is also human-label efficient. During skill extraction, only 120 labels are required to train the preference classifier. During skill execution, 300-1K labels are required to solve most tasks depending on the task's complexity. We hypothesize that human label efficiency is better during the skill extraction phase because classifying structured and noisy skills from a static offline dataset is easier than classifying task-specific preferences from an evolving replay buffer. Further human label efficiency improvements pose interesting research directions for future work.

\begin{figure*} [t] \centering
\includegraphics[width=.99\textwidth]{figures/main_result.jpg}
\caption
SkiP and baselines (Sec.~\ref{sec:exp_setup}) evaluated over six tasks in the robot kitchen environment shown in Fig.~\ref{fig:six_env}. SkiP outperforms both baselines across the majority of the tasks and is the only method that is capable of matching the Oracle on most tasks. We also compare SkiP to SkiP with 3x more human labels and find comparable performance between the two versions. SkiP solves most tasks given 300-1000 human labels depending on the complexity of the task.}
\label{fig:main_result}
\vspace{-0.1in}
\end{figure*}

{\bf Ablation Studies:} To further understand the properties of the SkiP algorithm, we investigate whether human feedback is necessary during skill extraction as well as how the human preference reward function compares to alternate approaches to human feedback during skill execution.

{\it Is it necessary to provide human feedback during skill extraction or is it sufficient to fit an unweighted behavioral prior over the offline data?} The offline dataset used throughout this paper consists of suboptimal data that is a mixture of expert and random actions. We compare fitting a human-feedback weighted behavioral prior as opposed to an unweighted behavioral prior that maximizes the likelihood of all action sequences equally. For the skill execution phase, both methods have access to the same human preference reward function. The results shown in Fig.~\ref{fig:ablation1} indicate that the method, which extracts skills without human feedback, is unable to solve any of the tasks suggesting that human feedback is essential for skill extraction from suboptimal offline data.

\begin{figure*} [t] \centering
\includegraphics[width=.99\textwidth]{figures/need_human.jpg}
\caption{SkiP with human feedback vs SkiP without human feedback during skill extraction. learning curve with shaded region representing standard error across three seeds.  Both algorithms learns prior from the suboptimal dataset and were evaluated with online RL. SkiP with human feedback outperforms SkiP without human feedback on all 6 environments}
\label{fig:ablation1}
\vspace{-0.1in}
\end{figure*}

{\it  How should we incorporate human feedback during the skill execution phase?} Instead of preferences, a simpler approach to learning from human feedback is to provide binary feedback if a task (or subtask) has been solved and learning a reward classifier to guide the RL agent. We implement this by providing a positive reward of 1 for a high-level transition $(s_t, z, s_{t+H})$ when a subtask has been completed and 0 otherwise. Using the same number of human queries for both approaches, we compare learning with preferences as opposed to learning from sparse rewards. For both approaches, we use human feedback for skill extraction. As shown in Fig.~\ref{fig:ablation2}, RL with a reward classifier for subtask completion is able to solve some tasks but generally performs much worse than RL with human preferences.

\begin{figure*} [t] \centering
\includegraphics[width=.99\textwidth]{figures/reward.jpg}
\caption{SkiP with preferences vs SkiP with learned sparse reward. Learning curve with shaded region representing standard error across three seeds. both algorithms use the same prior. SkiP with preferences outperforms SkiP with learned sparse reward on 5 out of 6 environments.}
\label{fig:ablation2}
\vspace{-0.1in}
\end{figure*}

\vspace{-3mm}
\section{Related Work}
\label{sec:related_work}
\vspace{-2mm}

{\bf Human-in-the-loop Reinforcement Learning:} Several works have successfully utilized feedback from real humans to train RL agents~\citep{deepcoach,preference_drl,ibarz2018reward,tamer,lee2021pebble,coach,deeptamer}.
One of major directions is directly utilizing the human feedback as a learning signal~\citep{pilarski2011online,coach,deepcoach} but assumed unlimited access to human labels which limited their practicality for more challenging tasks.
To address this limitation, a number of works proposed learning reward model from human feedback~\citep{tamer,deeptamer,pinto2016supersizing, levine2018learning, fu2018variational, xie2018few}.
Recently, several works have successfully combined human preferences with deep RL algorithms to learn basic locomotion skills as well as playing video games from pixels using human ~\cite{preference_drl,ibarz2018reward,cao2020weak,lee2021pebble}. However, these methods are limited to short-horizon or cyclic tasks and do not scale to more challenging compositional multi-step tasks. In this work, we investigate how to scale human preferences to such challenging tasks by specifying preferences over skills.

{\bf Data-driven Extraction of Behavioral Priors:} Behavioral prior or skill extraction refers to fitting a distribution over an offline dataset of demonstrations and biasing the agent's policy towards the most likely actions from that distribution. Commonly used for offline RL~\cite{brac2019wu,Siegel2020KeepDW,awr2019peng}, behavioral priors learned through maximum likelihood latent variable models can also been used as skills for structured exploration in RL \cite{singh2021parrot}, to solve complex long-horizon tasks from sparse rewards \cite{pertsch2020spirl, ajay2021opal}, and regularize offline RL policies \cite{brac2019wu, awr2019peng, nair2020awac}. A limitation of these skill extraction methods is that the quality of the behavioral prior is highly dependent on the demonstrations in the offline dataset. Since a behavioral prior models maximum likelihood transitions in the offline dataset, suboptimal, noisy, or irrelevant transitions can degrade downstream policy learning. In this work, we introduce human feedback into the skill extractions phase to learn a human preferred behavioral prior which enables skill extraction methods to be robust to suboptimal offline data.
\vspace{-3mm}
\section{Conclusion}
\vspace{-3mm}

We presented Skill Preferences (SkiP) an algorithm that uses human feedback for both skill extraction as well as execution, and showed that SkiP enables robotic agents to solve long-horizon compositional manipulation tasks. We hope that this work excites other researchers about the potential of learning with skills and human feedback.

\section{Acknowledgements}

We would like to thank Berkeley DeepDrive, Tencent, ONR Pecase N000141612723, and NSF NRI 2024675 for supporting this research.

redpajama_set_name: RedPajamaArXiv


While debugging an issue in a new StorPool deployment we re-discovered a kernel bug that exists in some of the latest Linux kernels and causes silent data corruption. The setup is a virtualized environment where the kernel containing the bug was running on the host. The guests were KVM virtual machines with io=threads (the default in standard libvirt/qemu packages) and virtual disk devices with cache=none. The bug has been fixed upstream in Linux kernel 4.18 in July 2018, but the fix has not yet propagated to commonly used affected kernels.
We have opened a bug with Ubuntu at https://bugs.launchpad.net/bugs/1796542 to track the issue.
The bug was introduced in Linux kernel 4.10 and fixed in Linux kernel 4.18.
– Rebuild the kernel with the relevant fixes described below.
The first workaround (using io=native instead of io=threads) currently seems to be the easiest to deploy. The remaining workarounds carry particular risks and limitations so we cannot recommend them.
XFS (vda1): Corruption warning: Metadata has LSN (35:14053) ahead of current LSN (35:11489). Please unmount and run xfs_repair (>= v4.3) to resolve.
A simpler test program to reproduce the issue was devised by Jan Kara from SUSE. It is linked from this post in the linux-block mainling list: https://www.spinics.net/lists/linux-block/msg28507.html . This test program can trigger the bug when running in the host OS, so it does not require the more complex virtualization setup.
The issue was introduced in 4.10-rc1 with the following commit 72ecad22d9f198aafee64218512e02ffa7818671 (https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?h=v4.19-rc6&id=72ecad22d9f198aafee64218512e02ffa7818671). As of this writing, it's included in kernel/distribution combinations described above.
It affects writes to block devices performed with O_DIRECT (qemu/libvirt cache=none). The silent data corruption in Linux can happen only if the writes are not page-aligned (but are otherwise correctly 512-byte aligned for O_DIRECT). This is the reason for the issue not to be reproducible with ext4 in the VM, as all of its writes are page-aligned.
The issue was first reported to SUSE, who developed and upstreamed the fixes for the issue in the patches below. We ran tests, first with added logging at the appropriate places to confirm the affected code path and then with the patches applied to verify that the problem was not present any more.
These commits appear first in 4.18-rc7.
We recommend that immediate steps should be taken by all affected to mitigate the issue.


Below is the equation of Hooke's law: *where F = the force applied, here is the weight (N), k = the spring constant (N/m), e = the size of displacement (m) Historical background: Hooke was never a person who did one thing at a time; indeed he seemed at his best when his mind was jumping from one idea to another. At the same time that he was working on the air pump he was also thinking about clocks and how they could be used in determining the longitude at sea.
o The period of a simple pendulum is independent of the amplitude of oscillation, provided it is small. o The period of a simple pendulum is directly proportional to the square root of length of the pendulum. o The period of a simple pendulum is inversely proportional to the square root of the acceleration due to gravity.  *where T = time for 1 cycle (s), l = length of pendulum (m), g = acceleration due to gravity (m/s2) This study invests the relationship between the period and the length of a simple pendulum to determine the approximate value of the acceleration due to gravity g on planet Earth and planet X.
Most Optical and electrical properties of semiconductors can be well explained by the band gap model. As metals, semiconductors are arranged in periodic structures called crystals. Let us explore shortly the origin of the band gap starting from the free electron model. The free electron model of metals has been used to explain the photo-electric effect. This model assumes that electrons are free to move within the metal but are confined to the metal by potential barriers as illustrated by Figure 2.1. The minimum energy needed to extract an electron from the metal equals q?M, where ?M is the work function.
Fermi Surface. The properties of a material is guided by the arrangement of atoms within the material, which reflects the shape of a Fermi surface, thus the Fermi surface has a direct correlation with properties of materials.
In order to create a visual representation of this, we imagine and theoretical three-dimensional "momentum space" where the usual Cartesian coordinates become components of momentum. Taking all of this into consideration, the Fermi surface can be defined as the surface of a given volume in momentum space which separates occupied electron states from empty ones. The properties of a material is guided by the arrangement of atoms within the material, which reflects the shape of a Fermi surface, thus the Fermi surface has a direct correlation with properties of materials.
The fall time was recorded in seconds in a table and was repeated for twenty trials. The average time of the twenty trials was calculated using the following equation where xN is the fall time and N is the number of trials. Then , the standard deviation was determined using the equation , where x is fall time. The experimental value of the free-fall time of the heavy metal ball was, which is the best estimate � uncertainty. Another table was made to determine the number of trials that occurred for each value of time.
Torque Physics Lab Report. The purpose of this experiment was to help understand torque by not only measuring it but also by manipulating and adjusting the weights experimentally.
The numbers were recorded and the weights of down and upward forces were measured as well as the clockwise and counter clockwise torques. For the last part of the experiment six clamps were arranged on the bar( with weights on them ) so that one was at 10cm and one at 90cm and the rest were spread in between , one end was supported by the knife edge and the other by the spring scale. The forced shown by the scale was recorded, the ends were than switched and the force was once again recorded.
The objective of this laboratory was to measure the speed at which sound was traveling through the air, using the resonance of longitudinal waves.
: V speed of sound = 331.4 + 0.6Tc m/s V speed of sound = 331.4 + 0.6(24) m/s V speed of sound = 345.8 m/s I found the % error using the following equation: % error = experimental value - theoretical value X 100 theoretical value % error = 344.064 m/s - 345.8 m/s X 100 345.8 m/s % error = +- 0.502 % Error Analysis The error analysis for this laboratory was a percent difference calculation between the theoretical value of the speed of sound traveling through air and the experimental value.
Once the ball was released, the time it took to fall the measured distance was collected and recorded. The height h and the time t of the freely falling object are related by the following formula: h = 1/2 gt2 (2) By using the relationship between height and time, as stated above, and plotting the data on a graph, we were able to determine the acceleration of the gravitational field strength g. Equipment error was also found and taken into consideration. Analysis And Results Once the ball was released, the time it took to fall the measured distance was collected and recorded in the table that follows.
Is there really an energy crisis?
Studies have predicted the world peak oil supply to fall within a common range of between 2006 and 2015. The world devours over 80 million barrels of oil a day, and near 30 billion barrels a year. This figure is rising fast and will keep doing so for years to come. The International Energy Agency, an advisory organization on energy set up by industrialized countries, forecasts 121 million barrels a day by 20303. It will be difficult for supply to meet this demand.
Is Human Activity Responsible For Increased Global Warming?
Describe in 3-4 sentences what you observed. Feel free to use diagrams or pictures to illustrate what you observed. Ball 1 was released and traveled down the ramp to hit the stationary ball (Ball 2) placed in the middle of the ramp. After the collision: Ball 1 ceased moving forward briefly but continued to spin, then continued up the ramp following the path of the second ball; Ball 2, once hit, moved up the ramp. Both balls collided again on their way back down the ramp before finally coming to a stop in the middle.
cuando pasamos de j�venes a adultos, o cuando una hoja cambia de color en Oto�o; Seg�n la cantidad la tiza se desgasta con el uso, el ni�o crece; y el Lugar como cuando nos trasladamos en Metro de un lugar a otro. Seg�n la ontolog�a aristot�lica todas las cosas que podemos percibir, todas las cosas sensibles (tanto las naturales como las artificiales) est�n compuestas con la estructura acto y potencia y, dado que el movimiento es el paso de la potencia al acto, todas las cosas sensibles tienen el movimiento como uno de sus rasgos m�s caracter�sticos y definitorios.
Este defecto o exceso de energ�a es el que se pone en juego en la reacci�n. La energ�a desprendida o absorbida puede ser en forma de energ�a luminosa, el�ctrica, mec�nica, etc... pero habitualmente se manifiesta en forma de calor. El calor intercambiado en una reacci�n qu�mica se llama calor de reacci�n y tiene un valor caracter�stico para cada reacci�n. Las reacciones pueden entonces clasificarse en exot�rmicas o endot�rmicas, seg�n que haya desprendimiento o absorci�n de calor. Energ�a At�mica y/o Nuclear: Es aquella que se libera como resultado de cualquier reacci�n nuclear. Puede obtenerse bien por fisi�n o por fusi�n. En las reacciones nucleares se libera mayor cantidad de energ�a que en las producidas en explosiones convencionales.
Therefore, the range the water bottle covered will be less than the theoretical value. In addition to air resistance, there is a presence of wind in reality. As no sophisticated equipment that can track the direction and velocity of the wind can be obtained, it is difficult to calculate the effects of wind on the course of the projectile motion. Therefore, the result of this experiment will be deviated from its theoretical prediction. 2.0 Objective * To determine the launching velocity and angle of the projectile.
Due to these fluctuations in the Earth's gravity, the local value for g is very useful for various reasons: from predicting the underlying geology of the rocks in the Earth's crust , to the oscillations of a pendulum. Therefore we decided to measure the value of g in Bristol as accurately as possible. There have been a number of different methods used previously to calculate the value for gravity, some of which are as follows. You can measure gravity by timing how long it takes for a body to drop a certain distance, as the distance a body falls is proportional to the time it takes squared, and the constant of proportionality is the gravitational acceleration .
Michael Jordan appeared in 11 all-star games in his 14 season in the national basketball league (N.B.A) and had won 4 all-star games most valuable player (M.V.P). His most memorable game was in Las Vegas in February 1999 were he scored 41 points and was just brilliant. These examples show how much Jordan was a great player. Jordan led the bulls to 6 N.B.A championships in the years of 1991, 1992, 1993, 1996, 1997, 1998. The reason why Jordan won those Simon 2 many championships because he was such a great player and it was difficult to stop his force on the court.
Figure 1 When the angles to the fringes are small, then it can be assumed that: By using trigonometry, it can be shown that: where y is the distance between the mth bright fringe and the central maximum, and D is the distance from the slits to the screen center point. By using the three previous equations, the following formula can be achieved: The spreading of light after passing through an aperture is known as diffraction. In addition to the interference caused by the superposition of the laser beams coming from two slit, a diffraction happens at each of the two slits which results in the envelope shown in the next page.
Procedure and Observations: Required Apparatus: 1. Current balance with calibrated weights 2. Telescope with support stand 3. Caliper 4. Power supply 5. Scale 6. Measuring tape Figure 2: The experimental setup The current balance was set up on the laboratory bench with the current carrying bars extending in a north-south direction to eliminate the effect of the Earth's magnetic field. The distance from the mirror to the scale was approximately 2 meters. The beam lift mechanism was designed to raise the beam, which caused the knife edges to come clear of their supports and then to return them to a certain position.
producing gradient value equal to 4T2/k. The value of K can be calculated from Slop = 4T2/k. By plotting the above equation g is already involved from which can be calculated. The value of g can then be calculated from Hooks Laws equation for the spiral balance. The equation g = Kb/m Where: K = spring constant (k) m = mass of the bob (kg) b = length (m) of the spiral spring with mass (l) less the length of the spiral spring without the mass (lo). b = (l-lo) m. Results Table 1: Time (s) taken for 20 swings of spiral spring balance The value of each swing was obtained by dividing the average values of 20 swings by 20.
This camera has large crystal detectors that detects the radiation signal and which then converts to faint light. This is then converted to electric signal and been reformed as an image by a computer (The images come sin grey scale). Production of x-ray The x-ray machine consists of an x-ray tube that contains two essential electrodes: the anode (target) made out of tungsten or any heavy metal and the cathode, which is used for the emission of electrons. The typical potential difference (kilo electrons kV) needed is the force that makes the electrons to accelerate. When the electrons are emitted from a heated filament, it accelerates at a high voltage.
How does changing the force applied on the elastic band affect the distance travelled by the margarine tub?
If no weights had been used, we would expect the tub to change direction and possibly jump due to the force applied and therefore this result would be deemed anomalous. Background knowledge- The forces on the tub at the start are equal and the margarine tub is therefore stationary. The elastic band is pulled and released, and as a result, when it comes into contact with the tub, the tub is pushed forwards by the force and accelerates. It then stops a distance away from the elastic band.
"The supreme task of the physicist is to arrive at those universal elementary laws from which the cosmos can be built up by pure deduction."
If you feel pulled in every direction by your interest in everything from black holes to computers, then a university degree in physics could be your ticket to universality. Physicists begin by learning about the physical laws that govern everything in the universe, and eventually specialise in anything under or over the sun. Physicists are at the centre of some of the world's most exciting research too, from climate change modelling to quantum computing.
Without lab reports and scientific papers, physics research would languish in obscurity. Bring your work into the light with Marked by Teachers' collection of physical sciences essays. Whether you're writing on anti-matter or semiconductors, our teacher-marked reports and papers will give you all the guidance you need to write consistently excellent papers.
Students of physics can go on to higher study and research careers; make the hop into related subjects like engineering and chemistry; or apply their analytical skills to the broader jobmarket, taking careers in business and finance.


Growing up, my town's local shopping mall was a bustling hub of commerce, chock full of busy shops and pesky tweens. But today, half the place has been gutted — it looks like an empty morgue of stores now.
With the explosive growth of online shopping and a shift in consumer preferences for experiences over possessions, the retail apocalypse of 2017 was inevitable. Brick and mortar stores aren't essential for consumers anymore.
But contrary to popular belief, the retail apocalypse isn't the final nail in the coffin for retail brands. Consumer preferences might have changed, but recent technological advances have allowed retailers to adapt to them.
If brands truly want to rise from the ashes of the retail apocalypse, though, they must stay laser-focused on adapting to the evolution of this technology and their customers' preferences. More specifically, they need to embrace change and prepare for the upcoming trends that will transform their industry in 2019.
1. Brands will sell experiences at their stores, not just products.
Most people cherish experiences over material possessions. And while we can attach material items like our new iPhone or car to our identity, these objects eventually lose their allure, and we throw them away to replace them with the newest, trend-setting product. Material possessions can really never be apart of your true identity.
On the other hand, we're the sum of our experiences, so they're ingrained in our identity. Sharing experiences with people also has a special ability to forge close relationships. And even if experiences end like your relationships with material objects do, they'll always be a part of your story, allowing you to bond with other people who've shared similar experiences.
For instance, who do you think you'll connect with on a deeper level — someone who also studied abroad in Scotland during college or someone who wears the same Apple Watch as you?
By setting up special shops that only sell experiences, like Nordstrom or Sephora's new stores that only provide styling, makeovers, and sampling services, retailers can pull customers into delightful, memorable experiences that make much more of an emotional impact than buying a product.
These experiences also make for a compelling story that consumers will always remember and be more than happy to share with one another.
2. The Internet of Things will make shopping easier, cheaper, and more convenient.
Since The Internet of Things connects over 11 billion everyday products, like home speakers, TVs, and cars, to the internet, retailers can collect hoards of data that provides context about their customers' product usage. This allows them to send relevant marketing messages to their customers throughout every phase of the buyer journey.
For instance, if you run out of milk or it spoils, a smart refrigerator can recognize your need and display a message on its screen or your phone about the best deals on milk in town. You can even order a carton through one of these smart devices if the refrigerator company partners with a grocery store.
Amazon has also sold a similar device, called Dash Buttons, since 2015. Dash Buttons are physical pieces of hardware that you press to buy everyday products, like laundry detergent, dog food, and snacks. As of today, the ecommerce giant has partnered with over 200 brands to sell their products through Dash Buttons.
Macy's has leveraged The Internet of Things to help customers save money, too. Since 2014, they've used beacons, which are Bluetooth devices that send alerts to people's smartphones based on their proximity to one of their stores, to promote discounts to any customer who walks by one of their stores and has their app.
The app can also pinpoint your location in a store, so if you're in the perfume section, the app will send you deals on the products and brands you liked online.
It's clear that The Internet of Things makes shopping easier, cheaper, and more convenient. And since Gartner expects consumers to possess over twice as many smart devices in 2020 as they do today, it's also clear that the technology could finally move beyond hype and enter reality in 2019.
3. Consumers will message brands' Facebook bots more to vet and purchase their products.
With businesses and consumers exchanging over 8 billion Facebook messages with each other per month, and the retail industry exchanging the second highest number of those messages, Facebook Messenger is one of the most popular and convenient channels for consumers to research and purchase retail merchandise today.
53% of consumers also prefer to shop with businesses they can directly message on Facebook, so it's crucial for retailers to set up Messenger bots that can handle moderately sophisticated interactions, like welcome conversations and product discovery conversations.
To drive even more sales with Facebook Messenger, retailers should also build bots that can send abandoned cart messages and let consumers place orders directly in the app.
Some of the top retail brands are already using Facebook Messenger bots to accelerate their buying process. With H&M's bot, you can customize and buy your own outfits, and with 1-800-Flowers' Messenger bot, you can order and ship flowers in under five minutes.
4. Brands will leverage cognitive computing to provide better, faster customer service.
Brands who implement cognitive computing into their customer support technology are able to assemble two-pronged customer service teams that help provide better, faster customer service.
By setting up computers and robots in their stores that can actually understand natural language and accurately answer people's routine questions, a retailer's human employees can service more customers who have more pressing needs.
For instance, Hilton Hotels introduced their first ever concierge robot, Connie, in 2016, and she can help guests find the best attractions and restaurants in the area. Connie can even move and point her body to direct guests toward any spot in the hotel. All the guests have to do is ask her their question, and she can quickly help them out.
With Connie's assistance, Hilton Hotel's front desk employees can focus on more important tasks, like picking up phones and checking in guests.
5. Brands will use augmented reality more to market their products.
The best furniture brands understand their customers' decision to design a room is just like getting a tattoo. If they buy the wrong furniture, it could ruin the room's entire aesthetic. On top of that, they'd have to deal with the embarrassment of living in an eyesore unless they shell out more money on some new furniture.
Without being able to picture how a store's furniture would look in their own home, customers will either stall their decision to buy any furniture or get mad at the brand if they buy the wrong sofa.
To help customers avoid this situation, retailers like IKEA and Wayfair have developed augmented reality apps that can place fully rendered 3D models of their own furniture in their customers' home. All the digital furniture is also resizable to fit a room's dimensions and observable from any angle and in any light. Users can even save specific room designs for future reference.
By leveraging augmented reality, these retailers have made it a lot easier for customers to engage with their brand, pick the right furniture for their house, and, ultimately, buy more of their products.
The retail industry has evolved at a breakneck speed, but hopefully, preparing for these five trends will arm you with the insights and skills to grow your retail store in 2019 and beyond.


What Are The Hottest Yard Sale Items? What Sells Best?
First, I'll share with you what I, personally, have experienced in terms of what sells and what doesn't at yard sales.
Then, we'll see how my list compares with what "the experts" say.
I've found that guys tend to gravitate toward tools, movies, sports stuff, CDs & DVDs, office equipment, computer stuff, and yard items.
Some of the more unusual things that ladies will scoop up are: unused (or slightly used) health & beauty products, crafty stuff, kids books & toys.
Teenagers like old cell phones (I sold 5 of 'em for $1 to $3 apiece). And you may be able to get another 50 cents for each "attachment" (AC charger, headset, car charger, belt clip, etc.) but I usually just include those in the price.
Larger, higher-quality items definitely bring crowds to your yard sale. Especially sofas and other pieces of furniture, as well as used lawn care items like mowers, trimmers, and leftover home building & repair items (bricks, stonework, old chandeliers & light fixtures).
Items for kids, toddlers & babies are also big sellers! As are pregnancy-related items, new baby things, and wedding stuff.
Magazines (even for 25 cents apiece, even for recent issues) don't sell well at all. These are best donated to a nursing home, hospital, or doctor's office. Or, recycle 'em and pass them onto friends or Freecyclers!
Regional items (from other regions) don't usually do well at yard sales. For example, I've been searching for "that needle in the haystack" for years now — waiting patiently for an Indiana University grad to buy up all of my old IU alumni memorabilia. They're few & far between here in Tennessee! Same with some excellent Florida travel guides that are worth a pretty penny. Few people around here are planning trips to Florida in the coming months. Much like with other collectibles, it takes that one special person to find value in such an item.
Clothes… unless you're willing to sell items of clothing for as little as 50 cents apiece, don't even bother. Your clothes may be worth much more than that, but let's face it, if they don't sell, you're probably just going to donate 'em (for FREE) to a local charity, right? You can find clothes for a dime a dozen at yard sales, and sometimes in better shape at places like Goodwill & Salvation Army stores. Few people are actually looking for clothes at yard sales. The sooner you come to grips with that, the better. That's why your best bet, if you really want to move some clothes is to price all clothing at rock-bottom prices. Period.
The Hottest Yard Sale Items Are Actually Shade & Shelter!
Shade is a hot commodity at most yard sales — especially after Noon! Most of your visitors have been going from yard sale to yard sale, so anyone who arrives at your garage sale in the afternoon hours is likely to be hot, tired, and thirsty!
If there's any way to move some of your items into the shade, those items will be looked at longer. Just the same, know that people will tend to congregate inside your garage (if there's enough room) — just to get out of the hot sun for a few minutes!
Also… should it start to rain (especially if it's a sudden downpour!) during your yard sale, you can bet people will try to cram into your garage, seeking shelter from the rain. And if you have a porch, they'll hang out there too. And kids will even duck under your tables — just to avoid getting wet. This is yet another reason, why it's a good idea to have snacks & water for sale… many appreciate having a way to pass the time. And the "freebies" become very popular during this time, as well.


First published back in November of 1961, US author Joseph Heller's debut novel 'Catch-22' quickly become a mainstay as a modern literary classic.
It's 1943 and the Second World War has a stranglehold over much of Europe. On the picturesque Mediterranean island of Pianosa, US B-25 bombardier Captain John Yossarian is stationed with his Air Force squadron. Here, in this otherwise idyllic setting, Yossarian finds himself and his fellow soldiers undergoing perilous missions at the whim of his unsympathetically ambitious superiors.
Yossarian knows the reality of war. It's a nightmarish and unjustified oppression upon everyone's personal rights. It has no mercy and has no regrets. It exists to destroy. And in that, it poses a very real danger to Yossarian and every other solider caught up within the turmoil of war. Which is why Yossarian has come to the firm belief that everyone is out to kill him.
Knowing that each and every day could be his last, Yossarian struggles to find a way out of this madness that surrounds him. His superiors continue to send him and his squadron on increasingly dangerous missions; bombing hostile targets for the glorious photographs that they can bring back. Yossarian is very aware of the absolute insanity in such missions. And he wants out.
But as Yossarian soon learns, one of the only ways to get out of the endlessly spiralling madness is to plead insanity. If indeed a solider is found to be crazy, then all they need do is request leave due to insanity and it would be granted. However, in doing so, it would be deemed that the solider is aware of their own safety in the face of the dangerous combat missions, and as such, could only have a fully rational mind. Therefore they cannot be truly insane, and so they must carry on with their missions. The military rule is the ingenious self-contradictory 'Catch-22'.
Meanwhile, First Lieutenant Milo Minderbinder has been running a complex shipping syndicate outside of his military duties. Seemingly unbeknown to his superiors, Milo has been surreptitiously taking the military planes on excursions across Europe, shipping a variety of foods and goods to a number of ports. And his scheming produces a healthy profit, which unsurprisingly, remains predominantly with Milo.
Okay, so this is probably one of those reviews that you're wondering why on earth it was included on this website. Well, along with other such modern classics, there's a very deep-rooted cultural element to these novels that is very much an inherent part to much of the fellow books which you see sitting alongside them. The absurdness that forms a predominant theme within this particular novel is often seen (or indeed replicated) within a lot of the other work that can be found within the pages of DLS Reviews. I've found myself referencing the novel a number of times across the length and breadth of the website. And as such, I decided that there's very much a place for the novel on here, and so the novel was reviewed and included.
The first thing that should really be said about 'Catch'22' is how utterly hilarious it is at times. Heller deals in the absurd, the bureaucratic nonsense that plagues our modern-day existences. He dishes out the ingeniously elaborate and wonderfully nonsensical. At first there appears to be no rhyme or reason behind many of the characters' odd actions. Give it time. Let Heller baffle and amuse you. Because everything in the novel has its reasons in the end. And that's really the crux of the whole novel's structure. Heller purposefully intends to baffle the reader from the outset. You need to get to know the characters, understand their quirks, their mannerisms, their motivations and their individual charms. It all comes together in time, piece by piece by piece. But not necessarily in the order that you thought it would!
Aside from the comedy aspect (which I will reiterate is absolutely superb), the tale deals with some quite thought-provoking and deeply emotive subjects. Death is an ever-present figure, quietly looming over the progression of the tale. Yossarian is aware of how perilous the missions are, but he is helpless against abandoning them. Insanity and utter desperation co-exist across the entirety of the tale. Characters are placed in the most traumatic and hopeless of situations, that underneath the jocular tone of the novel, it still yanks hard at the reader's heartstrings.
The multiple layers and subtle depths to the tale is what really pushes it (and deservedly so) into 'modern classic' territory. There's just so much that one can take from the novel, if you dip just a little below the comic surface. It's certainly true that Heller takes advantage of certain emotionally heavy opportunities, breaking through the loose cover of light-hearted comedy and laying down particularly emotive scenes. These moments feel true to the flow of the tale, always unforced and somehow not at all out-of-place. In its own befuddled and delightfully confusing way, the tale always seems to flow very naturally. I'm the first to admit that this seems a strange comment to make when starting out on the book, but give it time, become accustomed to the too-and-fro of the structure. And soon enough you'll see how it all pulls together, seamlessly and (dare I say it again) very naturally.
For me (and many, many, many others) 'Catch-22' holds a very special place in my heart. Running through the plot, and skirting over the synopsis, brings back wistful memories of the many delightfully unique characters. There are indeed a hell of a lot of characters in the novel. But it's a character-rich tale that almost solely revolves around their interplay and interaction. Without the characters the novel is absolutely nothing. There is no strong plot pushing the storyline onwards. There is no mad adventure or evil antagonist to conquer. There's just the situation, the absurd obligations and the characters. That's it. And what Heller has created is nothing short of a contemporary masterpiece.
The tale was later adapted into the film 'Catch-22' (1970) which was directed by Mike Nichols and starred a multitude of well-known and highly talented stars. The novel was also followed on by the somewhat less well-known sequel 'Closing Time' (1994).


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Ariana Grande Best Mistake Coldplay My Universe Why Don't We 8 Letters The Beatles Real Love (John Le... Britney Spears Work Bitch
Start Over Resume
Leona Lewis - Footprints in the Sand Piano Tutorial
Leona Lewis recorded "Footprints in the Sand" for her debut studio album "Spirit", and it was released in March 2008 as her third single. A double A-side single version (which also included the song "Better in Time") reached #2 on the German and British Singles Chart!

redpajama_set_name: RedPajamaCommonCrawl


Learn how to use simple filmmaking and animation to bring learning to life, and aid cross-curricular attainment and assessment.
This free online course will help anyone working with young people aged 5-19 to engage them in learning through simple filmmaking and animation. You'll discover how film and animation can be used as powerful tools for encouraging active learning and enabling pupils to establish strong connections with any area of the curriculum.
and learn how to use this creative artform as an assessment tool.
The course has been developed by Into Film – one of the biggest educational charities in the UK – which works with schools to deliver free training, resources and after-school film clubs.
Filmmaking and Animation in the Classroom builds on Into Film's successful Teaching Literacy Through Film course, developed in collaboration with the British Film Institute (BFI).
Filmmaking and Animation in the Classroom is open to anyone who works with young people, aged 5 to 19, who would like to enrich their learning through the creation of films and animations.
As a primary school teacher in South Wales Simon taught right across the KS 1&2 spectrum. Now the Teacher Training Manager at Into Film Simon oversees the organisation's CPD programme.
Into Film is a UK-wide film and education charity, which puts film at the heart of children and young people's learning, contributing to their cultural, creative and personal development.


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burnsmcd.com
September 12, 2011 Aviation
Airspace Awareness Tool: Making Complicated Airspace Easier for Developers
by The Burns & McDonnell Team
November 19, 2019 Aviation
Preparing Construction Projects to Face Mother Nature
by Andy Loftus
Future Aviation Trends: Newer, Bigger, Better
by Pat Brown
November 6, 2019 Aviation
Wisconsin Aims to Establish Aggressive PFAS Standards
by Erik Ehrengren
Kansas may not be known as a hotbed for innovation, but a snazzy new tool developed by the Kansas Department of Transportation (KDOT) that aims to make it easier for developers charged with navigating protected airspace is getting lots of attention.
Designed to protect the state's airspace from potential development obstructions, the tool aids in the safe placement of tall structures like water towers, cell towers and wind turbines — all of which could pose harm to aircraft and interfere with flight paths. The Federal Aviation Administration (FAA) requires advance notice of any proposed developments, but in the past when developers submitted their plans, it wasn't always clear if their site selection was in protected airspace. That's because airspace —which can be thought of as imaginary surfaces extending into the air around airports and along air travel routes — is quite complex.
KDOT's recently-launched Kansas Airspace Awareness Tool (KAAT) is the first tool of its kind. Just a month after being released publicly, it's gone global. People from 46 other states and six continents have accessed the tool that is currently only available in Kansas.
The KAAT makes it easier to understand the complicated airspace that surrounds Kansas airports using the Google Earth plug-in to map out these imaginary surfaces in color. Users can lay out obstructions on the 3-D map to determine if their development plans interfere with protected airspace before submitting their plans to the FAA. The new system eliminates the guesswork and simplifies the FAA's evaluation process.
Burns & McDonnell worked with KDOT to develop the software with a grant from the FAA. Our team of developers used innovative technologies to come up with a solution specific for KDOT, but that can be applied across the board — not only to other state transportation agencies, but to other industries as well.
Of course we think it's cool, but we're not alone. The project was nominated for a NASAO State Innovation Award, which recognizes innovative aviation programs that begin at the state level and have the potential to spread to other states, or be adopted by the federal government. The winner will be announced later this year. And Google thinks it's cool enough to feature as an example on its Google Earth plug-in site.
The KAAT is available for public use on KDOT's aviation website, so check it out. The beauty of the tool is that it simplifies complex information so that anyone can understand it. Even if you're not a developer or in to aviation, we think you'll like it!
Subscribe to your source for timely news, happenings and perspective on issues across the AEC landscape.


text:
Tags: Communist Party of China
Vietnamese economy greatly open, full of vitality: Chinese ambassador
Chinese Ambassador to Vietnam Xiong Bo has highlighted the significance of the Lunar New Year (Tet) festival, the comprehensive strategic cooperative partnership between the two countries, and their cooperation prospects for 2023.
Vietnam gathering momentum for sustainable development: Party leader
Party General Secretary Nguyen Phu Trong has highlighted Vietnam's achievements in 2022 and their impacts on the country's growth in 2023. He made the remark in an interview to the Vietnam News Agency on the occasion of the Lunar New Year Festival 2023 and the 93rd anniversary of the Communist Party of Vietnam (CPV) (February 3).
Top 10 significant events that shape Vietnam in 2022
GDP growth faster than expected, border reopening, and fuel shortages in many localities are among the top 10 events that shaped Vietnam in 2022, as selected by Vietnam News Agency.
Vietnam gives top priority to developing ties with China: PM
Prime Minister Pham Minh Chinh has affirmed that the Vietnamese Party, Government and people always treasure and give top priority to developing the comprehensive strategic cooperative partnership with China.
Party leader's China visit a success in all aspects: Foreign minister
The freshly-concluded visit to China by Party General Secretary Nguyen Phu Trong has helped enhance sentiments, political trust and mutual understanding between the two countries, Minister of Foreign Affairs Bui Thanh Son has said.
Vietnam, China issue joint statement
Vietnam and China have issued a joint statement on continuing to promote and deepen the Vietnam-China comprehensive strategic cooperative partnership on the occasion of Party General Secretary Nguyen Phu Trong's official visit to China from October 31 - November 1.
Vietnam, China sign 13 cooperation documents
Vietnam and China on October 31 signed 13 documents on bilateral cooperation across fields as part of General Secretary of the Communist Party of Vietnam (CPV) Central Committee Nguyen Phu Trong's ongoing official visit to China.
Vietnam gives top priority to developing ties with China: Party leader
Vietnam always treasures and gives top priority to developing its comprehensive strategic cooperative partnership with China, General Secretary of the Communist Party of Vietnam (CPV) Central Committee Nguyen Phu Trong told General Secretary of the Communist Party of China (CPC) Central Committee and President of China Xi Jinping during their talks in Beijing on October 31, following the welcome ceremony for the Vietnamese leader.
Vietnam-China relations to get new push to grow further: Foreign Minister
Minister of Foreign Affairs Bui Thanh Son highlighted the comprehensive strategic cooperative partnership between Vietnam and China in his article published on the People's Daily, the mouthpiece of the Communist Party of China (CPC) Central Committee, on October 30.
Party leader arrives in Beijing, starting China visit
Party General Secretary Nguyen Phu Trong and his entourage arrived in Beijing on October 30 afternoon, starting his official visit to China at the invitation of Party General Secretary and President of China Xi Jinping.


text:
The Access App
A Framework for Increasing the Accessibility of Museum Content
Access App Glossary of Terms
#A11Y
A community-driven effort to improve web accessibility.
A set of guidelines that specify how to make content accessible, primarily for people with disabilities, but also for all user agents (including highly limited devices, such as mobile phones). The current version, WCAG 2.0, was published in December 2008 and became an ISO standard, ISO/IEC 40500:2012 in October 2012. (Wikipedia – https://en.wikipedia.org/wiki/Web_Content_Accessibility_Guidelines)
Amazon Mechanical Turk (MTurk, Mechanical Turk)
Amazon Mechanical Turk is a crowdsourcing Internet marketplace enabling individuals and businesses (known as "Requesters") to coordinate the use of human intelligence to perform tasks that computers are currently unable to do. It is one of the sites of Amazon Web Services and is owned by Amazon. Employers are able to post jobs known as "Human Intelligence Tasks" (HITs), such as choosing the best among several photographs of a storefront, writing product descriptions, or identifying performers on music CDs. Workers (called "Providers" in MTurk's Terms of Service, or, more colloquially, "Turkers") can then browse among existing jobs and complete them in exchange for a monetary payment set by the employer. (Wikipedia – https://en.wikipedia.org/wiki/Amazon_Mechanical_Turk)
API (application program interface)
An application program interface is a set of routines, protocols, and tools for building software applications. An API specifies how software components should interact and talk to one another.
Audio Description (also known as visual description)
Audio description, also referred to as visual description, is commentary and narration that makes visual images in theater, television, movies, and other art forms accessible to people who are blind, have low vision, or are otherwise visually impaired. It attempts to describe what the sighted person takes for granted. In theaters, museums, and accompanying television, film, and video presentations, audio description guides the listener with concise, objective descriptions of new scenes, settings, costumes, body language, and sight gags delivered between portions of dialogue or songs. (American Council of the Blind – http://www.acb.org/adp/ad.html)
In software development, a beta test is the second phase of software testing during which a sampling of the intended audience tries out an early version of the product.
(WhatIS.com – http://whatis.techtarget.com/definition/beta-test)
A CMS is a computer application that supports the creation and modification of digital content. It is often used to support multiple users working in a collaborative environment. CMS features vary widely. Most include web-based publishing, format management, history editing and version control, indexing, search, and retrieval. By their nature, content management systems support the separation of content and presentation. (Wikipedia – https://en.wikipedia.org/wiki/Content_management_system)
In software development, a codebase (or code base) refers to a whole collection of source code that is used to build a particular software system, application, or software component. (Wikipedia – https://en.wikipedia.org/wiki/Codebase)
Community of Practice (CoP)
A community of practice (CoP) is a group of people who share a craft and/or a profession. The concept was first proposed by cognitive anthropologist Jean Lave and educational theorist Etienne Wenger in their 1991 book Situated Learning (Lave & Wenger 1991). Wenger then significantly expanded on the concept in his 1998 book Communities of Practice (Wenger 1998).
A CoP can evolve naturally because of the members' common interest in a particular domain or area, or it can be created deliberately with the goal of gaining knowledge related to a specific field. It is through the process of sharing information and experiences with the group that members learn from each other and have an opportunity to develop personally and professionally (Lave & Wenger 1991). (Wikipedia – https://en.wikipedia.org/wiki/Community_of_practice)
Crowdsourcing is a specific sourcing model in which individuals or organizations use contributions from Internet users to obtain needed services or ideas. This mode of sourcing, which divides work between participants to achieve a cumulative result, was already successful prior to the digital age (i.e., "offline"). Crowdsourcing is distinguished from outsourcing in that the work can come from an undefined public (instead of being commissioned from a specific, named group) and includes a mix of bottom-up and top-down processes. (Wikipedia – https://en.wikipedia.org/wiki/Crowdsourcing)
Framework (software framework)
A software framework provides a standard way to build and deploy applications. A software framework is a universal, reusable software environment that provides particular functionality as part of a larger software platform to facilitate development of software applications, products and solutions. (Wikipedia – https://en.wikipedia.org/wiki/Software_framework)
GitHub is a web-based version control repository and Internet hosting service. It offers all of the distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features, including bug tracking, feature requests, task management, and wikis for every project. (Wikipedia – https://en.wikipedia.org/wiki/GitHub)
Graphical User Interface (GUI)
GUIs allow users to interact with electronic devices through graphical icons and visual indicators such as secondary notation, instead of text-based user interfaces, typed command labels, or text navigation. (Wikipedia – https://en.wikipedia.org/wiki/Graphical_user_interface)
Institute of Museum and Library Services (IMLS)
The IMLS is a federal funding agency that supports museums and libraries. The mission of IMLS is to inspire libraries and museums to advance innovation, lifelong learning, and cultural and civic engagement. It provides leadership through research, policy development, and grant making. (IMLS.gov – https://www.imls.gov/)
iOS (formerly iPhone OS) is a mobile operating system created and developed by Apple Inc. exclusively for its hardware. It is the operating system that presently powers many of the company's mobile devices, including the iPhone, iPad, and iPod Touch. (Wikipedia – https://en.wikipedia.org/wiki/IOS)
An institutional review board is a type of committee used in research in the United States that has been formally designated to approve, monitor, and review biomedical and behavioral research involving humans. They often conduct some form of risk-benefit analysis in an attempt to determine whether or not research should be completed. The purpose of the IRB is to assure that appropriate steps are taken to protect the rights and welfare of humans participating as subjects in a research study. (Wikipedia –https://en.wikipedia.org/wiki/Institutional_review_board)
Latency (Network latency)
A computing term that refers to the delay before a transfer of data begins following an instruction for its transfer. (Oxford Dictionaries – https://en.oxforddictionaries.com/definition/latency)
LEAD Conference (Kennedy Center's Leadership Exchange in Arts and Disability)
The LEAD Conference brings experienced and new professionals together to explore practical methods for implementing accessibility in cultural environments. Participants share resources and knowledge, develop best practices, and experience accessibility in action. (The Kennedy Center – http://education.kennedy-center.org/education/accessibility/lead/conference.html)
In computing and interface design, a module is any of a number of distinct but interrelated units or blocks from which a program may be built. Modules are similar to subsections of a page that can be moved around the page when designing an interface. (Oxford Dictionaries – https://en.oxforddictionaries.com/definition/module)
Minimal Viable Product (MVP)
In product development, the minimum viable product is a product with just enough features to satisfy early customers and to provide feedback for future development. (Wikipedia – https://en.wikipedia.org/wiki/Minimum_viable_product)
Open-source Software
Open-source software is software with source code that anyone can inspect, modify, and enhance. (Opensource.com – https://opensource.com/resources/what-open-source)
Question Prompts
Questions prompts get people to think critically, analyze, reflect, and relate to a subject. They are often used to elicit focused and detailed responses.
Roundware (RW)
Roundware is an open-source software platform developed by sound artist Halsey Burgund. It is an open, flexible, and distributed framework that collects, stores, organizes, and re-presents audio content. Roundware lets developers collect audio from anyone with a smartphone or web access, upload it to a central repository along with its metadata, and then filter it and play it back collectively in continuous audio streams. See the Roundware Technical Glossary for additional information. (http://www.roundware.org/)
Swift is a programming language developed by Apple Inc. for iOS, macOS, watchOS, tvOS, and Linux. (Wikipedia – https://en.wikipedia.org/wiki/Swift_(programming_language)
TAP is a collection of free and open-source tools that supports the creation and delivery of mobile tours. The tools also serve as examples of producing and consuming tour content using the TourML specification. Currently TAP consists of authoring tools built on top of the content management system Drupal, a native iOS mobile application, and a web-based mobile application built upon the jQuery Mobile library. (http://www.tapintomuseums.org/)
TourML
TourML is a specification for building, sharing, and preserving mobile tours that can be used by museums of all types and sizes to create and deploy mobile experiences. This project is meant to deliver mobile standards and open-source tools for the benefit of the entire cultural technology community. TourML was created to serve as a simple and portable representation for tour content. Having a common language for this content will enable content management systems, kiosks, and mobile applications to speak a common language. Additionally, users of TourML will be better positioned to share and sustain their content no matter how the technology used to deliver these tours changes. (http://www.tourml.org/)
Universal Design refers to broad-spectrum ideas meant to produce buildings, products, and environments that are inherently accessible to everyone, including seniors and people with disabilities. (Wikipedia – https://en.wikipedia.org/wiki/Universal_design)
User-generated Content (UGC)
User-generated content, alternatively known as user-created content (UCC), is any form of content created by users of a system or service and made available publicly on that system. UGC most often appears as a supplement to online platforms, such as social media websites, and may include content like blog posts, wikis, videos, or comments. (Wikipedia – https://en.wikipedia.org/wiki/User-generated_content)
Visual Cues
A visual cue is a signal and reminder of something. Aiming to be self–explanatory and pre-attentive, it brings to mind knowledge from previous experiences providing a framework for its own interpretation. (Sandra E. Moriarty, An Interpretive Study of Visual Cues in Advertising
http://spot.colorado.edu/~moriarts/viscueing.html)
Visual description
See "audio description."
The World Wide Web Consortium (W3C) is the main international standards organization for the World Wide Web (abbreviated WWW or W3). Founded and currently led by Tim Berners-Lee, the consortium is made up of member organizations that maintain full-time staff for the purpose of working together to develop standards for the World Wide Web. The W3C also engages in education and outreach, develops software, and serves as an open forum for discussion about the web. (Wikipedia – https://en.wikipedia.org/wiki/World_Wide_Web_Consortium)
Roundware Technical Glossary
The following terms are specific to Roundware.
An individual piece of media contributed by a user. Roundware currently handles audio, photo, and text assets and will soon handle video assets as well.
A linear assemblage of audio assets and silence ("dead air") that dynamically forms part of each audio stream by incorporating audio assets into the stream.
Any computer or device that is used by a user/participant to connect to the service, in this case, Roundware.
A collection of assets and tags submitted by a user/participant. Envelopes can contain multiple assets (several audio recordings, audio and photo, etc.) collected at the same time by the same user.
A record of each time a client pings the server with an API call. All events are tagged with an event type, such as "startsession," "startlisten," "uploadrecording," etc. Event data provides the core source for all RW system analysis.
The highest level of segmentation/grouping for all RW data. One RW instance can run many projects simultaneously, governed by CPU, bandwidth, and memory resources.
A client server connection established when the client is started and terminated when the client app is closed. Session_id is established by the server and is used to keep track of multiple simultaneous sessions.
A speaker is a geo-located object that broadcasts continuous audio over a certain geographic area.
A unique session-based audio stream generated by RW based on the evolving filters supplied by a particular client; streams consist of a summation of speaker audio and asset audio (per the audiotracks).
Metadata used to describe assets. Tags are arranged by tag category. For example, the tags within the "age" tag category could be "young" and "old". Tags are very flexible and allow for collecting many different types of metadata to be used for filtering the assets at a later time.
This project was made possible in part by the Institute of Museum and Library Services. Artboard 2


text:
Q: Calculate sum of values I'm trying to get the sum of some values in jQuery. I do this with
var sum = 0;
$(".total").text(sum += parseInt($(".numbers").text(), 10));

but is is not making a sum. I have tried the following which is also not working?
var sum = 0;
$(".total").parseInt(sum += parseInt($(".numbers").text(), 10));

How can I simply calculate the sum here?
Demo: http://jsfiddle.net/M7ghD/

A: To sum values you need to iterate .numbers elements:
var sum = 0;
$('.numbers').each(function() {
    sum += parseInt($.text(this), 10);
});

$('.total').text(sum);

DEMO: http://jsfiddle.net/M7ghD/5/

A: You have to take each value of class=numbers and sum it.
Try:
var sum = 0;

$( ".numbers" ).each(function() {
  sum=sum+parseInt($(this).text(), 10)
})

$(".total").text(sum);

DEMO

A: Try this http://jsfiddle.net/M7ghD/1/:
It seems doing $(".numbers").text() will give you a concatenated string of all your span.numbers at the same time (= '421293'). You have to loop through the elements and sum them up:
var sum = 0;
$('.numbers').each(function(){
  sum += parseInt($(this).text(),10);
});
$(".total").text( sum );

A: You can use as also
  var sum = 0;
  $('.numbers').each(function() {
        sum += Number($.text(this));
  });

 $(".total").text(sum);

parseInt(string) will convert a string containing non-numeric characters to a number, as long as the string begins with numeric characters.
Number(string) will return NaN if the string contains any non-numeric characters
DEMO

redpajama_set_name: RedPajamaStackExchange


text:
2001 Trouble in Shangri-La
Stevie Nicks postpones more shows, plans December benefit concert
Stevie Nicks' ongoing bout with a respiratory problem has resulted in the postponement of two more California tour stops. The singer has now postponed a total of six concerts in recent weeks due to the illness.
Nicks' Tuesday and Wednesday night (8/21, 22) performances at Universal Amphitheatre near Los Angeles have been postponed. Make-up dates will be announced soon, and tickets for the original dates will be honored at those shows, according to Nicks' official website. Refunds are available at the point of purchase, according to the venue box office.
Last week, Nicks postponed her Aug. 14 and Aug. 15 California engagements. Those performances have since been rescheduled for Sept. 30 and Oct. 1, respectively, according to Nicks' website.
Last month, Nicks postponed her July 24 stop in Virginia and July 25 stop in North Carolina. Those dates will be made up on Sept. 4 and Sept. 5, respectively.
Nicks is expected to resume touring as scheduled on Friday night (8/24).
Despite the recent troubles, Nicks' tour in support of May's "Trouble in Shangri-La" (Reprise) has been one of the summer's most successful outings. Pollstar's latest breakdown of the top-grossing acts on the road right now places Nicks at No. 11, and attributes her last 12 shows with an average gross of about $470,000 and average ticket sales of 10,320 per night.
Nicks recently announced plans to give a Dec. 6 benefit concert at Phoenix's America West Arena, one that will also feature guest performers. Proceeds from the event will support the Arizona Heart Institute Foundation's cardiovascular disease research and education programs, according to a press release. A similar benefit that Nicks gave last year featured appearances by Sheryl Crow, Don Henley, Chris Issak and Nicks' former Fleetwood Mac bandmate Lindsey Buckingham.
Premium seats to the benefit, priced at $1,000, are on sale now; a premium ticket gives one access to a private, post-concert party that Nicks will attend. Regular tickets will hit the box office on Sept. 29. More details are available at Nicks' official website.
[Note: The following tour itinerary has been provided by artist and/or tour sources, who verify its accuracy as of 21-Aug-2001. Changes may occur before tickets go on sale. Check with official artist web sites, ticketing sources and venues for late updates.]
Check for Stevie Nicks tickets:
21, 22 – Universal City, CA – Universal Amphitheater (POSTPONED)
24 – Chula Vista, CA – Coors Amphitheater
25 – Las Vegas, NV – Aladdin Theater
28 – St. Louis, MO – Riverport Amphitheatre
29 – Bonner Springs, KS – Sandstone Amphitheatre
31 – Minneapolis, MN – Target Center
1 – Noblesville, IN – Verizon Wireless Music Center
4 – Virginia Beach, VA – Verizon Wireless Virginia Beach Amphitheater (make-up date for 7/24)
5 – Raleigh, NC – ALLTEL Pavilion at Walnut Creek (make-up date for 7/25)
7 – Charlotte, NC – Verizon Wireless Amphitheatre
8 – Reading, PA – Sovereign Center
10 – Toronto, Ontario – The Molson Amphitheatre
14 – New York, NY – Radio City Music Hall
15 – Atlantic City, NJ – Trump Taj Mahal
17 – Columbus, OH – Polaris Amphitheater
19 – Columbia, MD – Merriweather Post Pavilion
22 – Atlanta, GA – Chastain Park Amphitheatre
26 – West Palm Beach, FL – Mars Music Amphitheatre
30 – Mountain View, CA – Shoreline Amphitheatre (make-up date for 8/14)
1 – Concord, CA – Chronicle Pavilion (make-up date for 8/15)
6 – Phoenix, AZ – America West Arena (Stevie Nicks and Friends Arizona Heart Institute benefit)
Jon Zahlaway / LiveDaily / August 24, 2001
Trouble in Shangri-La Tour
The tao of Stevie
Stevie Nicks postpones more shows due to illness


text:
UTEP and the community
Black History Month events highlight Dailey's contributions to UTEP
Special to The Prospector
Maceo Crenshaw Dailey, Jr. Ph.D., the late director of the African American Studies Program.
Amanda Guillen , Editor-In-Chief
The university's African American Studies Program has put together a multitude of events throughout Black History Month, with the last event being held on March 1.
The program has put together the events without a familiar face, Maceo Crenshaw Dailey, Jr. Ph.D., the late director of the African American Studies Program.
Dailey died last year on Oct. 11 at the age of 72.
His death came as surprise to many since Dailey was still very active in his role as the director of the African American Studies Program and was still working on two big projects that he didn't see completed.
As of now, a replacement for Dailey's position has not been named and information as to when a new director will be appointed has not been disclosed.
Selfa Chew-Melendez Ph.D., history professor and interim African American Studies director, was Dailey's student and he was also her mentor and director for her dissertation. She said Dailey worked until the very end.
"He was so committed to his work that he didn't stop," Chew-Melendez said.
The projects Dailey worked on before his death were the series focusing on Race, Authority and Violence in 21st Century America and the Black History Month festivities held at UTEP.
Chew-Melendez said Dailey also expressed the importance of not only Black History Month, but the importance of the African American community.
"He always said that Black History Month is a period in which we open our home for people to see how far we have come and what we should be proud of and also what needs to be done in order to achieve equality as a community," Chew-Melendez said.
In his close to 10 years at UTEP and in El Paso, Dailey made a name for himself in both communities.
Dailey founded the African American Studies Program at UTEP and acted as its director until his death. As both his student and colleague, Chew-Melendez said he always made it a point to listen to students and was a genuine man who worked hard.
"He was very proud of the culture of African Americans and was very proud of all cultures and appreciated the collaboration between racial borders and racial lines to fight for social justice," Chew-Melendez said. "He was a very caring person and excellent human being and he also approached students and respected their personalities and always tried to assist them with their goals."
Director of the Student Engagement and Leadership Center, Corey Bailey, said that his connection with Dailey was instant. When he came to UTEP, someone made sure that they were in contact because of the similarities that they shared.
Both Bailey and Dailey are from Maryland and both are very familiar with New York, as Dailey taught at New York University before coming to El Paso.
"To describe impact and Dr. Dailey in one sentence is hard to measure impact that is so vast," Bailey said. "You can tell his reach was far beyond his office."
On Sunday, Feb. 21, the annual Gospel Explosion will be held in the Fox Fine Arts Recital Hall and will pay homage to Dailey.
"We tried to build him into the program," Bailey said.
Although Dailey passed before the committee could plan the event, Bailey said that he indirectly had an impact on the program.
The program is free and open to the public, and Bailey encouraged students and anyone in the El Paso community to come and celebrate.
"Regardless of spiritual faith, we welcome folks to come out and be around people who respect and love this man," Bailey said.
For more on the Gospel Explosion turn to page 12.
African American Studies Program
Dailey


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