text:
\section{Introduction}

The brain evolved over millions of years for the sole purpose of controlling the body in a goal-directed fashion.
Computations are performed relying on neural dynamics and asynchronous communication.
Spiking neural network models base their computations on these computational principles.
Biologically plausible synaptic plasticity rules for functional learning in spiking neural networks are regularly proposed (\cite{zenke2018superspike,kaiser2018synaptic,Neftci17_stocsyna,Pfister_etal06_optispik,Urbanczik_Senn14_learby}).
In general, these rules are derived to minimize a distance (referred to as error) between the output of the network and a target.
Therefore, the evaluation of these rules is usually carried out on open-loop pattern classification tasks.
By neglecting the embodiment, this type of evaluation disregards the closed-loop dynamics the brain has to handle with the environment.
Indeed, the decisions taken by the brain have an impact on the environment, and this change is sensed back by the brain.
To get a deeper understanding of the plausibility of these rules, an embodied evaluation is necessary.
This evaluation is technically complicated since spiking neurons are dynamical systems that must be synchronized with the environment.
Additionally, as in biological bodies, sensory information and motor commands need to be encoded and decoded respectively.

In this paper, we bring the fields of computational neuroscience and robotics closer together by integrating open-source software components from these two fields.
The resulting framework is capable of learning online the control of simulated and real robots with a spiking network in a modular fashion.
This framework is demonstrated in the evaluation of the promising neural reward-learning rule \ac{SPORE}~(\cite{Kappel2018,Kappel2015,Kappel2014,Yu2016a}) on two closed-loop robotic tasks.
\ac{SPORE} is an instantiation of the synaptic sampling scheme introduced in \cite{Kappel2018,Kappel2015}.
It incorporates a policy sampling method which models the growth of dendritic spines with respect to dopamine influx.
Unlike current state-of-the-art reinforcement learning methods implemented with conventional neural networks (\cite{mnih2015human,mnih2016asynchronous,lillicrap2015continuous}),
\ac{SPORE} learns online from precise spike-time and is entirely implemented with spiking neurons.
We evaluate this learning rule in a closed-loop reaching and a lane following~(\cite{bing2018end,kaiser2016towards}) setup.
In both tasks, an end-to-end visuomotor policy is learned, mapping visual input to motor commands.
In the last years, important progress have been made on learning control from visual input with deep learning.
However, deep learning approaches are computationally expensive and rely on biologically implausible mechanisms such as dense synchronous communication and batch learning.
For networks of spiking neurons learning visuomotor tasks online with synaptic plasticity rules remains challenging.
In this paper, visual input is encoded in Address Event Representation with a Dynamic Vision Sensor (DVS) simulation~(\cite{Lichtsteiner2008,kaiser2016towards}).
This representation drastically reduces the redundancy of the visual input as only motion is sensed, allowing more efficient learning.
It agrees with the two pathways hypothesis which states that motion is processed separately than color and shape in the visual cortex (\cite{Kruger2013}).

The main contribution of this paper is the embodiment of \ac{SPORE} and its evaluation on two neurorobotic tasks using a combination of open-source software components.
This embodiment allowed us to identify crucial techniques to regulate \ac{SPORE} learning dynamics,
not discussed in previous works where this learning rule was only evaluated on simple proof-of-concept learning problems~(\cite{Kappel2018,Kappel2015,Kappel2014,Yu2016a}).
Our results suggest that an external mechanism such as learning rate annealing is beneficial to retain a performing policy on advanced lane following task.

This paper is structured as follows.
We provide a review of the related work in \cref{sec:related}.
In \cref{sec:method}, we give a brief overview of \ac{SPORE} and discuss the contributed techniques required for its embodiment.
The implementation and evaluation on the two chosen neurorobotic tasks is carried out in \cref{sec:eval}.
Finally, we discuss in \cref{sec:conclusion} how the method could be improved.

\section{Related Work}
\label{sec:related}

The year 2015 marked a significant breakthrough in deep reinforcement learning.
Artificial neural networks of analog neurons are now capable of solving a variety of tasks ranging from  playing video games (\cite{mnih2015human}), to controlling multi-joints robots (\cite{Schulman2017,lillicrap2015continuous}) and lane following (\cite{Wolf2017}).
Most recent methods (\cite{Schulman2017,schulman2015trust,lillicrap2015continuous,mnih2016asynchronous}) are based on policy-gradients.
Specifically, policy parameters are updated by performing ascending gradient steps with backpropagation to maximize the probability of taking rewarding actions.
While functional, these methods are not based on biologically plausible processes.
First, a large part of neural dynamics are ignored.
Importantly, unlike \ac{SPORE}, these methods do not learn online -- weight updates are performed with respect to entire trajectories stored in rollout memory.
Second, learning is based on backpropagation which is not biologically plausible learning mechanism, as stated in \cite{bengio2015towards}.

Spiking network models inspired by deep reinforcement learning techniques were introduced in \cite{tieck2018learning} and \cite{Bellec2018}.
In both papers, the spiking networks are implemented with deep learning frameworks (PyTorch and TensorFlow, respectively) and rely on automatic differentiation.
Their policy-gradient approach is based on \ac{PPO} (\cite{Schulman2017}).
As the learning mechanism consists of backpropagating the \ac{PPO} loss (through-time in the case of \cite{Bellec2018}), most biological constraints stated in \cite{bengio2015towards} are still violated.
Indeed, the computations are based on spikes (4), but the backpropagation is purely linear (1), the feedback paths require precise knowledge of the derivatives (2) and weights (3) of the corresponding feedforward paths, and the feedforward and feedback phases alternate synchronously (5) (the enumeration refers to \cite{bengio2015towards}).

Only a small body of work focused on reinforcement learning with spiking neural networks, while addressing the previous points.
Groundwork of reinforcement learning with spiking networks was presented in \cite{izhikevich2007solving,florian2007reinforcement,legenstein2008learning}.
In these works, a mathematical formalization is introduced characterizing how dopamine modulated spike-timing-dependent plasticity (DA-STDP) solves the distal reward problem with eligibility traces.
Specifically, since the reward is received only after a rewarding action is performed, the brain needs a form of memory to reinforce previously chosen actions.
This problem is solved with the introduction eligibility traces, which assign credit to recently active synapses.
This concept has been observed in the brain (\cite{frey1997synaptic,pan2005dopamine}), and \ac{SPORE} also relies on eligibility traces.
Fewer works evaluated DA-STDP in an embodiment for reward maximization -- a recent survey encompassing this topic is available in \cite{bing2018survey}.

The closest previous work related to this paper are \cite{kaiser2016towards,bing2018end} and \cite{dauce2009model}.
In \cite{kaiser2016towards}, a neurorobotic lane following task is presented, where a simulated vehicle is controlled end-to-end from event-based vision to motor command.
The task is solved with an hard-coded spiking network of 16 neurons implementing a simple Braitenberg vehicle.
The performance is evaluated with respect to distance and orientation differences to the middle of the lane.
In this paper, these performance metrics are combined into a reward signal which the spiking network maximizes with the \ac{SPORE} learning rule.

In \cite{bing2018end}, the authors evaluate DA-STDP (referred to as R-STDP for reward-modulated STDP) in a similar lane following environment.
Their approach outperforms the hard-coded Braitenberg vehicle presented in \cite{kaiser2016towards}.
The two motor neurons controlling the steering receive different (mirrored) reward signals whether the vehicle is on the left or on the right of the lane.
This way, the reward provides the information of what motor command should be taken, similar to a supervised learning setup.
Conversely, the approach presented in this paper is more generic since a global reward is distributed to all synapses and does not indicate which action the agent should take.

A similar plasticity rule implenting a policy-gradient approach is derived in \cite{dauce2009model}.
Also relying on eligibility traces, this reward-learning rule uses a ``slow'' noise term to drive the exploration.
This rule is demonstrated on a target reaching task comparable to the one discussed in \cref{sec:reaching} and achieves impressive learning times (in the order of 100s) with proper tuning of the noise term.

In \cite{nakano2015spiking}, a spiking version of the free-energy-based reinforcement learning framework proposed in \cite{otsuka2010free} is introduced.
In this framework, a spiking \ac{RBM} is trained with a reward-modulated plasticity rule which decreases the free-energy of rewarding state-action pairs.
The approach is evaluated on discrete-actions tasks where the observations consist of MNIST digits processed by a pre-trained feature extractor.
However, some characteristics of \ac{RBM} are biologically implausible and make their implementation cumbersome: symmetric synapses and clocked network activity.
With our approach, network activity does not have to be manually synchronized into observation and action phases of arbitrary duration for learning to take place.

In \cite{gilra2017predicting}, a supervised synaptic learning rule named Feedback-based Online Local Learning Of Weights (FOLLOW) is introduced.
This rule is used to learn the inverse dynamics of a two-link arm -- the model predicts control commands (torques) for a given arm trajectory.
The loop is closed in \cite{gilra2017nonlinear} by feeding the predicted torques as control commands.
In contrast, \ac{SPORE} learns from a reward signal and can solve a variety of tasks.

\section{Method}
\label{sec:method}

In this section, we give a brief overview of the reward-based learning rule \ac{SPORE}.
We then discuss how \ac{SPORE} was embodied in closed-loop, along with our modifications to increase the robustness of the learned policy.

\subsection{Synaptic Plasticity with Online Reinforcement Learning (\ac{SPORE})}
\label{sec:spore}

Throughout our experiments we use an implementation of the reward-based online learning rule for spiking neural networks, named \textit{synaptic sampling}, that was introduced in \cite{Kappel2018}. The learning rule employs synaptic updates that are modulated by a global reward signal to maximize the expected reward. More precisely, the learning rule does not converge to a local maximum $\ve \theta^*$ of the synaptic parameter vector $\ve \theta$, but it continuously samples different solutions $\ve \theta \sim p^*(\ve \theta)$ from a target distribution that peaks at parameter vectors that likely yield high reward. A temperature parameter $T$ allows to make the distribution $p^*(\ve \theta)$ flatter (high exploration) or more peaked (high exploitation).

\ac{SPORE} (\cite{spore}) is an implementation of the reward-based synaptic sampling rule \cite{Kappel2018}, that uses the NEST neural simulator (\cite{nest}). \ac{SPORE} is optimized for closed-loop applications to form an online policy-gradient approach. We briefly review here the main features of the synaptic sampling algorithm.

We consider the goal of reinforcement learning to maximize the expected future discounted reward $\mathcal{V}(\ve \theta)$ given by
\begin{equation}
\mathcal{V}(\ve \theta) \;=\; \expect[p(\ve r | \ve \theta)]{ \int_{0}^\infty e^{-\frac{\tau}{\tau_e}} \,r(\tau) \; d \tau } \;,
\label{eqn:reward-prob-factorized}
\end{equation}
where $r(\tau)$ denotes the reward at time $\tau$ and $\tau_e$ is a time constant that discounts remote rewards. We consider non-negative reward $r(\tau) \geq 0$ at any time such that $\mathcal{V}(\ve \theta) \geq 0$ for all $\ve \theta$. The distribution $p(\ve r | \ve \theta)$ denotes the probability of observing the sequence of reward $\ve r$ under a given parameter vector $\ve \theta$. Note that computing this expectation involves averaging over a number of experimental trials and network responses.

As proposed in \cite{Kappel2018} we replace the standard goal of reinforcement learning to maximize the objective function in \cref{eqn:reward-prob-factorized} by a probabilistic framework that generates samples from the parameter vector $\ve \theta$ according to some target distribution $\ve \theta \sim p^*(\ve \theta)$.
We will focus on sampling from the target distribution $p^{*}(\ve \theta)$ of the form
\begin{equation}
p^*(\ve \theta) \;\propto\; \ps{\ve \theta} \, \times \, \mathcal{V}(\ve \theta)  \; ,
\label{eq:bayes}
\end{equation}
where $\ps{\ve \theta}$ is a prior distribution over the network parameters that allows us, for example, to introduce constraints on the sparsity of the network parameters.
It has been shown in \cite{Kappel2018} that the learning goal in \cref{eq:bayes} is achieved, if all synaptic parameters $\theta_i$ obey the stochastic differential equation
\begin{equation}
d \theta_i \;=\; \beta \, \left( \frac{\partial}{\partial \theta_i} \,\log \ps{\ve \theta}  \, + \, \frac{\partial}{\partial \theta_i} \log \mathcal{V}(\ve \theta)  \right)  dt  \;+ \; \sqrt{2 \beta T} \, d \mathcal{W}_{i} \;,
\label{eq:sde}
\end{equation}
where $\beta$ is a scaling parameter that functions as a learning rate, $d \mathcal{W}_{i}$ are the stochastic increments and decrements of a Wiener process and $T$ is the temperature parameter.
$\frac{\partial}{\partial \theta_i}$ denotes the partial derivative with respect to the synaptic parameter $\theta_{i}$.
The stochastic process in \cref{eq:sde} generates samples of $\ve \theta$ that are with high probability close to the local optima of the target distribution $p^*(\ve \theta)$.

It has been further shown in \cite{Kappel2018} that \cref{eq:sde} can be implemented using a synapse model with local update rules. The state of each synapse $i$ consists of the dynamic variables $y_i(t)$, $e_i(t)$, $g_i(t)$, $\theta_i(t)$ and $w_i(t)$.
The variable $y_i(t)$ is the pre-synaptic spike train filtered with a postsynaptic-potential kernel.
$e_i(t)$ is the eligibility trace that maintains a brief history of pre-/post neural activity.
$g_i(t)$ is a variable to estimate the reward gradient, i.e. the gradient of the objective function in \cref{eqn:reward-prob-factorized} with respect to the synaptic parameter $\theta_i(t)$. $w_i(t)$ denotes the weight of synapse $i$ at time $t$.
In addition each synapse has access to the global reward signal $r(t)$.
The variables $e_i(t)$, $g_i(t)$ and $\theta_i(t)$ are updated by solving the differential equations:
\begin{align}
\frac{d e_i(t)}{dt} \;&=\; -\frac{1}{\tau_e} e_i(t) \,+\, w_i(t)\,y_i(t)\,(z_{post_i}(t) - \rho_{post_i}(t))  \label[equation]{eqn:synapse-sdes-e} \\
\frac{d g_i(t)}{dt} \;&=\; -\frac{1}{\tau_g} g_i(t) \,+\, r(t)\,e_i(t) \label[equation]{eqn:reward-gradient} \\
d \theta_i(t) \;&=\; \beta\,\bigg( c_p (\mu - \theta_i(t)) + c_g\,g_i(t) \bigg) dt \,+\, \sqrt{ 2 T_\theta \beta } \, \mathcal{W}_{i} \;, \label[equation]{eqn:synapse-sdes-th}
\end{align}
where $z_{post_i}(t)$ is a sum of Dirac delta pulses placed at the firing times of the post-synaptic neuron, $\mu$ is the prior mean of synaptic parameters ($\ps{\ve \theta}$ in Eq.~\eqref{eq:bayes}) and $\rho_{post_i}(t)$ is the instantaneous firing rate of the post-synaptic neuron at time $t$.
The constants $c_p$ and $c_g$ are tuning parameters of the algorithm that scale the influence of the prior distribution $\ps{\ve \theta}$ against the influence of the reward-modulated term.
Setting $c_p=0$ corresponds to a non-informative (flat) prior. In
general, the prior distribution is modeled as a Gaussian centered around $\mu$: $\ps{\ve \theta}=\normal{\mu,\frac{1}{c_p}}\,$. We used $\mu=0$ in our simulations.
The variance of the reward gradient estimation (\cref{eqn:reward-gradient}) could be reduced by subtracting a baseline to the reward as introduced in \cite{williams1992simple}, although this was not investigated in this paper.

Finally the synaptic weights are given by the projection
\begin{equation}
w_i(t) \;=\; \begin{cases}
               w_0 \, \exp ( \theta_i(t) - \theta_0 ) & \qquad \text{if }\theta_i(t)>0 \\
               0 & \qquad \text{otherwise}
             \end{cases}\;,
\label{eqn:synapse-weight-map}
\end{equation}
which scaling and offset parameters $w_0$ and $\theta_0$, respectively.

In \ac{SPORE} the differential equations \crefrange{eqn:synapse-sdes-e}{eqn:synapse-sdes-th} are solved using the Euler method with a time step of 1~ms.
The dynamics of the postsynaptic term $y_i(t)$, the eligibility trace $e_i(t)$ and the reward gradient $g_i(t)$ are updated at each time step.
The dynamics of $\theta_i(t)$ and $w_i(t)$ are updated on a coarser time grid with step width 100~ms for the sake of simulation speed.
The synaptic weights remain constant between two updates.
Synaptic parameters are clipped at $\theta_{min}$ and $\theta_{max}$.
Parameter gradients $g_i(t)$ are clipped at $\pm \Delta\theta_{max}$.
The parameters used in our evaluation are stated in \crefrange{table:nest}{table:music}.

\subsection{Closed-Loop Embodiment Implementation}

Usually, synaptic learning rules are solely evaluated on open-loop pattern classification tasks \cite{zenke2018superspike,Neftci17_stocsyna,Pfister_etal06_optispik,Urbanczik_Senn14_learby}.
An embodied evaluation is technically more involved and requires a closed-loop environment simulation.
A core contribution of this paper is the implementation of a framework allowing to evaluate the validity of bio-plausibe plasticity models in closed-loop robotics environments.
We rely on this framework to evaluate the synaptic sampling rule \ac{SPORE} (\cite{spore}), as depicted in \cref{fig:implementation}.
n
This framework is tailored for evaluating spiking network learning rules in an embodiment.
Visual sensory input is sensed, encoded as spikes, processed by the network, and output spikes are converted to motor commands.
The motor commands are executed by the agent, which modifies the environment.
This modification of the environment is sensed by the agent.
Additionally, a continuous reward signal is emitted from the environment.
\ac{SPORE} tries to maximize this reward signal online by steering the ongoing synaptic plasticity processes of the network towards configurations which are expected to yield more overall reward.
Unlike classical reinforcement learning setup, the spiking network is treated as a dynamical system continuously receiving input and outputting motor commands.
This allows us to report learning progress with respect to (biological) simulated time, unlike classical reinforcement learning which reports learning progress in number of iterations.
Similarly, we reset the agent only when the task is completed (in the reaching task) or when the agent goes off-track (in the lane following task).
We do not enforce finite-time episodes and neither the agent nor \ac{SPORE} are notified of the reset.

This framework relies on many open-source software components:
As neural simulator we use NEST (\cite{nest}) combined with the open-source implementation of \ac{SPORE} (\cite{Kappel2018}\footnote{\url{https://github.com/IGITUGraz/spore-nest-module}}).
The robotic simulation is managed by Gazebo (\cite{gazebo}) and ROS (\cite{ros})
and visual perception is realized using the open-source DVS plugin for Gazebo (\cite{kaiser2016towards}\footnote{\url{https://github.com/HBPNeurorobotics/gazebo_dvs_plugin}}).
This plugin emits polarized address events when variations in pixel intensity cross a threshold.
The robotic simulator and the neural network run in different processes.
We rely on MUSIC (\cite{Djurfeldt2010,Ekeberg2008}) to communicate and transform the spikes and we employ the ROS-MUSIC tool-chain by \cite{Weidel2016} to bridge between the two communication frameworks.
The latter also synchronizes ROS time with spiking network time.
Most of these components are also integrated in the Neurorobotics Platform (NRP) \cite{falotico2017connecting}, except for MUSIC and the ROS-MUSIC tool-chain.
Therefore, the NRP does not support streaming a reward signal to all synapses, required in our experiments.

As part of this work, we contributed to the Gazebo DVS plugin by integrating it to ROS-MUSIC, and to the \ac{SPORE} module by integrating it with MUSIC.
These contributions enable researchers to design new ROS-MUSIC experiments using event-based vision to evaluate \ac{SPORE} or their own biologically-plausible learning rules.
A clear advantage of this framework is that the robotic simulation can be substituted for a real robot seamlessly.
However, the necessary human supervision in real robotics coupled with the many hours needed by \ac{SPORE} to learn a performing policy is currently prohibitive.
The simulation of the whole framework was conducted on a Quad core Intel Core i7-4790K with 16GB RAM in real-time.

\begin{figure}
  \centering
  \includegraphics[width=0.49\textwidth]{figures/nest_gazebo_loop}~
  \includegraphics[width=0.49\textwidth]{figures/dvs_feature_reduction_combined.png}
  \caption{Implementation of the embodied closed-loop evaluation of the reward-based learning rule \ac{SPORE}.
    Left: our asynchronous framework based on open-source software components.
    The spiking network is implemented with the NEST neural simulator (\cite{nest}), which communicates spikes with MUSIC (\cite{Djurfeldt2010,Ekeberg2008}).
    The reward is streamed to all synapses in the spiking network learning with SPORE (\cite{spore}).
    Spikes are encoded from address events and decoded to motor commands with ROS-MUSIC tool-chain adapters (\cite{Weidel2016}).
    Address events are emitted by the DVS plugin (\cite{kaiser2016towards}) within the simulated robotic environment Gazebo (\cite{gazebo}), which communicates with ROS (\cite{ros}).
    Right: Encoding visual information to spikes for the lane following experiment, see \cref{sec:lane-follow} for more information.
    Address events (red and blue pixels on the rendered image) are downscaled and fed to visual neurons as spikes.
  }
  \label{fig:implementation}
\end{figure}

\subsection{Learning Rate Annealing}
\label{sec:annealing}
In the original work presenting \ac{SPORE} (\cite{Kappel2018,Kappel2015,Kappel2014,Yu2016a}), the learning rate $\beta$ and the temperature $T$ were kept constant throughout the learning process.
Note that in deep learning, learning rates are often regulated by the optimization processes (\cite{Kingma_Ba14_adammeth}).
We found that the learning rate $\beta$ of \ac{SPORE} plays an important role in learning and benefit from an annealing mechanism.
This regulation allows the synaptic weights to converge to a stable configuration and prevents the network to forget previous policy improvements.
For the lane following experiment presented in this paper, the learning rate $\beta$ is decreased over time, which also reduces the temperature (random exploration), see \cref{eq:sde}.
Specifically, we decay the learning rate $\beta$ exponentially with respect to time:
\begin{equation}
  \frac{d \beta(t)}{dt} = - \lambda \beta(t).
\end{equation}
\noindent The learning rate is updated following this equation every 10 minutes.
Independently decaying the temperature term $T$ was not investigated, however we expect a minor impact on the performance because of the high variance of the reward gradient estimation, intrinsically leading the agent to explore.

\section{Evaluation}\label{sec:eval}

We evaluate our approach on two neurorobotic tasks: a reaching task and the lane following task presented in \cite{kaiser2016towards,bing2018end}.
In the following sections, we describe these tasks and the ability of \ac{SPORE} to solve them.
Additionally, we analyze the performance and stability of the learned policies with respect to the prior distribution $\ps{\ve \theta}$ and learning rate $\beta$, see \cref{eq:sde}.

\subsection{Experimental Setup}

\begin{figure}
    \centering
    \includegraphics[width=.48\textwidth,trim={0 0 0 9cm},clip=true]{figures/target_reaching/side.png}
    \includegraphics[width=.48\textwidth]{figures/lane_following/lane_following_scenario.png}
    \caption{
      Visualization of the setup for the two experiments.
      Left: reaching experiment.
      The goal of the task is to control the ball to the center of the plane.
      Visual input is provided by a DVS simulation above the plane looking downward.
      The ball is controlled with Cartesian velocity vectors.
      Right: Lane following experiment.
      The goal of the task is to keep the vehicle on the right lane of the road.
      Visual input is provided by a DVS simulation attached to the vehicle looking forward to the road.
      The vehicle is controlled with steering angles.
    }
    \label{fig:setup}
\end{figure}

The tasks used for our evaluation are depicted in \cref{fig:setup}.
In both tasks, a feed-forward all-to-all two-layers network of spiking neurons is trained with \ac{SPORE} to maximize a task-specific reward.
Previous work has shown that this architecture was sufficient for the task complexity considered \cite{kaiser2016towards,bing2018end,dauce2009model}.
The network is end-to-end and maps the address events of a simulated DVS to motor commands.
The parameters used for the evaluation are presented in \crefrange{table:nest}{table:music}.
In the next paragraphs, we describe the tasks together with their decoding schemes and reward functions.

\subsubsection{Reaching Task}\label{sec:reaching}

The reaching task is a natural extension of the open-loop blind reaching task on which \ac{SPORE} was evaluated in \cite{Yu2016a}.
A similar visual tracking task was presented in \cite{dauce2009model}, with a different visual input encoding.
In our setup, the agent controls a ball of 2m radius which has to move towards the 2m radius center of a 20mx20m plane enclosed with walls.
Sensory input is provided by a simulated DVS with a resolution of 16x16 pixels located above the center which perceives the ball and the entire plane.
There is one visual neuron corresponding to each DVS pixel -- we make no distinctions between ON and OFF events.
We additionally enhance the input space with an axis feature neuron for each row and each column.
These neurons fire for each spikes in the respective row or column of neurons they cover.
Both 16x16 visual neurons and 2x16 axis feature neurons are connected to all 8 motor neurons with 10 plastic \ac{SPORE} synapses, resulting in 23040 learnable parameters.
The network controls the ball with instantaneous velocity vectors through the Gazebo Planar Move Plugin.
Velocity vectors are decoded from output spikes with the linear decoder:
\begin{equation}
  \begin{split}
    v &=
    \begin{bmatrix}
      \dot{x}\\
      \dot{y}\\
    \end{bmatrix}
    =
    \begin{bmatrix}
      cos(\beta_1) & cos(\beta_2) & \hdots & cos(\beta_N)\\
      sin(\beta_1) & sin(\beta_2) & \hdots & sin(\beta_N)
    \end{bmatrix}
    \begin{bmatrix}
      a_1\\
      a_2\\
      \vdots\\
      a_N
    \end{bmatrix}\\
    \beta_k &= \frac{2k\pi}{N} ,
    \end{split}
\end{equation}
\noindent with $a_k$ the activity of motor neuron $k$ obtained by applying a low-pass filter on the spikes with time constant $\tau$.
This decoding scheme consists of equally distributing $N$ motor neurons on a circle representing their contribution to the displacement vector.
For our experiment, we set $N=8$ motor neurons.
We add an additional exploration neuron to the network which excites the motor neurons and is inhibited by the visual neurons.
This neuron prevents long periods of immobility.
Indeed, when the agent decides to stay motionless, it does not receive any sensory input as the DVS simulation only senses change.
Since the network is feedforward, the absence of sensory input causes the neural activity to drop, leading to more immobility.

The ball is reset to a random position on the plane if it has reached the center.
This reset is not signaled to the network -- aside from the abrupt change in visual input -- and does not mark the end of an episode.
Let $\beta_\text{err}$ denote the absolute value of the angle between the straight line to the goal and the direction taken by the ball.
The agent is rewarded if the ball moves in the direction towards the goal $\beta_\text{err} < \beta_\text{lim}$ at a sufficient velocity $v > v_\text{lim}$.
Specifically, the reward $r(t)$ is computed as:
\begin{equation}
  \begin{split}
    r(t) &= 35  \sqrt{r_v} (r_\beta + 1)^5\\
    r_\beta &=
    \begin{cases}
      1 - \frac{\beta_\text{err}}{\beta_\text{lim}}, & \text{if}\ \beta_\text{err} < \beta_\text{lim} \\
      0, & \text{otherwise}
    \end{cases}\\
    r_v &=
    \begin{cases}
      |v|, & \text{if}\ |v| > v_\text{lim}\\
      0, & \text{otherwise}
    \end{cases}
    .
  \end{split}
\end{equation}
\noindent This signal is smoothed with an exponential filter before being streamed to the agent.
This formulation provides a continuous feedback to the agent, unlike delivering a discrete terminal reward upon reaching the goal state.
In our experiments, discrete terminal rewards did not suffice for the agent to learn performing policies in a reasonable amount of time.
On the other hand, distal rewards are supported by \ac{SPORE} through eligibility traces, as was demonstrated in \cite{Kappel2018,Yu2016a} for open-loop tasks with clearly delimited episodes.
This suggests that additional mechanisms or hyperparameter tuning would be required for \ac{SPORE} to learn from distal rewards online.

\subsubsection{Lane following Task}\label{sec:lane-follow}

The lane following task was already used to demonstrate spiking neural controllers in \cite{kaiser2016towards} and \cite{bing2018end}.
The goal of the task is to steer a vehicle to stay on the right lane of a track.
Sensory input is provided by a simulated DVS with a resolution of 128x32 pixels mounted on top of the vehicle showing the track in front.
There are 16x4 visual neurons covering the pixels, each neuron responsible for a 8x8 pixel window.
Each visual neuron spikes at a rate correlated to the amount of events in its window, see \cref{fig:implementation}.
The vehicle starts driving on a fixed starting point with a constant velocity on the right lane of the track.
As soon as the vehicle leaves the track, it is reset to the starting point.
As in the reaching task, this reset is not explicitly signaled to the network and does not mark the end of a learning episode.

The network controls the angle of the vehicle by steering it, while its linear velocity is constant.
The output layer is separated into two neural populations.
The steering commands sent to the agent consist of the difference of activity between these two populations.
Specifically, steering commands are decoded from output spikes as a ratio between the following linear decoders:
\begin{equation}
  \begin{split}
  a_L &= \sum_{i=1}^{N/2} a_i, \\
  a_R &= \sum_{i=N/2}^{N} a_i, \\
  r &= \frac{a_L - a_R}{a_L + a_R}.
  \end{split}
\end{equation}
\noindent The first $N/2$ neurons pull the steering on one side, while the remaining $N/2$ neurons pull steering to the other side.
We set $N=8$ so that there are $4$ left motor neurons and $4$ right motor neurons.
The steering command is obtained by discretizing the ratio $r$ into five possible commands: hard left (-30\degree), left (-15\degree), straight (0\degree), right (15\degree) and hard right (30\degree).
The decision boundaries between these steering angles are $r=\{-10, -2.5, 2.5, 10\}$ respectively.
This discretization is similar than the one used in \cite{Wolf2017}.
It yielded better performance than directly using $r$ (multiplied with a scaling constant $k$) as a continuous-space steering command as in \cite{kaiser2016towards}.

The reward signal delivered to the vehicle is equivalent to the performance metrics used in \cite{kaiser2016towards} to evaluate the policy.
As in the reaching task, the reward depends on two terms -- the angular error $\beta_\text{err}$ and the distance error $d_\text{err}$.
The angular error $\beta_\text{err}$ is the absolute value of the angle between the right lane and the vehicle.
The distance error $d_\text{err}$ is the distance between the vehicle and the center of the right lane.
The reward $r(t)$ is computed as:
\begin{equation}
  r(t) = e^{\displaystyle -0.03~\beta_\text{err}^2} \times e^{\displaystyle -70~d_\text{err}^2}.
\end{equation}
\noindent The constants are chosen so that the score is halved every 0.1m distance error or 5\degree angular error.
Note that this reward function is comprised between $[0, 1]$ and is less informative than the error used in \cite{bing2018end}.
In our case, the same reward is delivered to all synapses, and a particular reward value does not indicate whether the vehicle is on the left or on the right of the lane.
The decay of the learning rate is $\lambda=\num{8.5e-5}$, see \cref{table:spore}.

\subsection{Results}

Our results show that \ac{SPORE} is capable of learning policies online for moderately difficult embodied tasks within some simulated hours.
We first discuss the results on the reaching task, where we evaluated the impact of the prior distribution.
We then present the results on the lane following task, where the impact of the learning rate was evaluated.

\subsubsection{Impact of Prior Distribution}
For the reaching task, a flat prior $c_p=0$ yielded the policy with highest performance, see \cref{fig:results_target_reaching}.
In this case, the performance improves rapidly within a few hours of simulated time, and the ball reaches the center about 90 times every \SI{250}{\second}.
Conversely, a strong prior ($c_p=1$) forcing the synaptic weights close to $0$ prevented performing policies to emerge.
In this case, after 13h of learning, the ball reaches the center only about 10 times on average every 250s, a performance comparable to the random policy.
Less constraining priors also affected the performance of the learned policies compared to the unconstrained case, but allowed learning to happen.
With $c_p=0.25$, the ball reaches the center about 60 times on average every \SI{250}{\second}.
Additionally, the number of retracting synapses increases over time -- even in the flat prior case -- reducing the computational overhead, important for a neuromorphic hardware implementation (\cite{bellec2017deep}).
Indeed, for $c_p=0$, the number of weak synaptic weights (below $0.07$) increased from 3329 to 7557 after 1h of learning to 14753 after 5h of learning (out of 23040 synapses in total).
In other words, only 36\% of all synapses are active.
The weight distribution for $c_p=0.25$ is similar to the no-prior case $c_p=0$.
The strong prior $c_p=1$ prevented strong weights to form, trading-off performance.
The same trend is observed for the lane following task, where only 33\% of all synapses are active after 4h of learning, see \cref{fig:results_lane_following}.

\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{figures/target_reaching/prior_comparison.pdf}
    \includegraphics[width=0.48\textwidth]{figures/target_reaching/weight_dev_strong_no_stacked.pdf}
    \caption{Results for the reaching task.
      Left: comparing the effect of different prior configurations on the overall learning performance.
      The results were averaged over 8 trials.
      The performance is measured with the rate at which the target is reached (the ball moves to the center and is reset at a random position).
      Right: Development of the synaptic weights over the course of learning for two trials: no prior ($c_p = 0$, top) and strong prior ($c_p=1$, bottom).
      In both cases, the number of weak synaptic weights (below 0.07) increases significantly over time.
    }
    \label{fig:results_target_reaching}
\end{figure}

The analysis of a single trial with $c_p=0.25$ is depicted in \cref{fig:trial_target_reaching}.
The performance does not converge and rather rise and drop while the network is sampling configurations.
On initialization (b), the policy employs weak actions with random directions.

After over \SI{4750}{\second} of learning (c), the first local maximum is reached.
Vector directions have largely turned towards the grid center (see inner pixel colors).
Additionally, the overall magnitude of the weights has largely increased, as could be expected from the weight histogram in \cref{fig:results_target_reaching}.
In particular, patterns of single rows and columns emerge, due to the 2x16 axis feature neurons described in \cref{sec:reaching}.
One drawback of the axis feature neurons can be seen in the center column of pixel.
The axis feature neuron responsible for this column learned to push the ball down, since the ball mostly visited the upper part of the grid.
However, at the center, the correct direction to push the ball towards the center is flipped.

At \SI{7500}{\second} (d), the performance has further increased.
The policy, as shown in the second peak has grown even stronger for many pixels which also point in the right direction.
The pixels pointing in the wrong direction mostly have a low vector strength.

After \SI{9250}{\second} (e), the performance drops to half its previous performance.
As we can see from the policy, the weights grew even stronger.
Some strong pixels vectors pointing towards each other have emerged, which can lead to the ball constantly moving up and down, without receiving any reward.

After this valley, the performance rises slowly again and at \SI{20000}{\second} of simulation time (f) the policy has reached the maximum performance of this trial.
Around the whole grid, strong motion vectors push the ball towards the center, and the ball reaches the center around $140$ times every \SI{250}{\second}.

Just before the end of the trial, the performance drops again (g).
Most vectors still point towards the right direction, however, the overall strength has largely decreased.

\begin{figure}
  \centering
  \includegraphics[width=0.8\textwidth]{figures/target_reaching/policy_evaluation_perf}
  \includegraphics[width=0.8\textwidth]{figures/target_reaching/policy_evaluation_weight}
  \caption[Reaching -- policy development for single trial]{%
    Policy development for selected points in time in a single trial.
    On the top, the performance over time for a single, well-performing trial is depicted.
    The red lines indicate certain points in time, for which the policies are shown in the bottom $6$ figures.
    Each policy plot consists of a 2d-grid representing the DVS pixels.
    Hereby, every pixel contains a vector, which indicates the motion corresponding
    to the contribution of an event emitted by this pixel.
    The magnitude of the contribution (vector strength) is indicated by the outer pixel area.
    The inner circle color represents the assessment of the vector direction (angular correctness).
    \label{fig:trial_target_reaching}
  }
\end{figure}

\subsubsection{Impact of Learning Rate}
For the lane following experiment, we show that the learning rate $\beta$ plays an important role for retaining policy improvements.
Specifically, when the learning rate $\beta$ remains constant over the course of learning, the policy does not improve compared to random, see \cref{fig:results_lane_following}.
In the random case, the vehicle remains about 10 seconds on the right lane until triggering a reset.
After about 3h of learning, the learning rate $\beta$ decreased to 40\% of its initial value and the policy starts to improve.
After 5h of learning, the learning rate $\beta$ approaches 20\% of its initial value and the performance improvements are retained.
Indeed, while the weights are not frozen, the amplitude of subsequent synaptic updates are drastically reduced.
In this case, the policy is significantly better than random and the vehicle remains on the right lane about 60s on average.

\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{figures/lane_following/annealing_comparison.pdf}
    \includegraphics[width=0.48\textwidth]{figures/lane_following/weight_dev.pdf}
    \caption{Results for the lane following task with a medium prior ($c_p=0.25$).
      Left: comparing the effect of annealing on the overall learning performance.
      The results were averaged over 6 trials.
      Without annealing, performance improvements are not retained and the network does not learn to perform the task.
      With annealing, the learning rate $\beta$ decreases over time and performance improvements are retained.
      Right: Development of the synaptic weights over the course of learning for a medium prior of $c_p=0.25$ with annealing.
      The number of weak synaptic weights (below $0.07$) increases from 41 to 231 after 1h of learning to 342 after 4h of learning (out of 512 synapses in total).
    }
    \label{fig:results_lane_following}
\end{figure}

\section{Conclusion}\label{sec:conclusion}
The endeavor to understand the brain spans over multiple research fields.
Collaborations allowing synaptic learning rules derived by theoretical neuroscientists to be evaluated in closed-loop embodiment are an important milestone of this endeavor.
In this paper, we successfully implemented a framework allowing this evaluation by relying on open-source software components for spiking network simulation \cite{nest,spore}, synchronization and communication \cite{Djurfeldt2010,Ekeberg2008,Weidel2016,ros} and robotic simulation \cite{gazebo,kaiser2016towards}.
The resulting framework is capable of learning online the control of simulated and real robots with a spiking network in a modular fashion.
This framework is used to evaluate the reward-learning rule \ac{SPORE}~(\cite{Kappel2018,Kappel2015,Kappel2014,Yu2016a}) on two closed-loop visuomotor tasks.
Overall, we have shown that \ac{SPORE} was capable of learning shallow feedforward policies online for moderately difficult embodied tasks within some simulated hours.
This evaluation allowed us to characterize the influence of the prior distribution on the learned policy.
Specifically, constraining priors deteriorate the performance of the learned policy but prevent strong synaptic weights to emerge, see \cref{fig:results_target_reaching}.
Additionally, for the lane following experiment, we have shown how learning rate regulation enabled policy improvements to be retained.
Inspired by simulated annealing, we presented a simple method decreasing the learning rate over time.
This method does not model a particular biological mechanism, but seems to work better in practice.
On the other hand, novelty is known to modulate plasticity through a number of mechanisms (\cite{rangel2016neurotransmitters,hamid2016mesolimbic}).
Therefore, a decrease in learning rate after familiarization with the task is reasonable.

On a functional scale, deep learning methods still outperform biologically plausible learning rules such as \ac{SPORE}.
For future work, the performance gap between \ac{SPORE} and deep learning methods should be tackled by taking inspiration from deep learning methods.
Specifically, the online learning method inherent to \ac{SPORE} is impacted by the high variance of the policy evaluation.
This problem was alleviated in policy-gradient methods by introducing a critic trained to estimate the expected return of a given state.
This expected return is used as a baseline which reduces the variance of the policy evaluation.
Decreasing the variance could also be achieved by considering an action-space noise as in \cite{dauce2009model} instead of a parameter-space noise implemented by the Wiener process in \cref{eq:sde}.
Lastly, an automatic mechanism to regulate the learning rate $\beta$ is beneficial for more complex task.
Such a mechanism could be inspired by trust-region methods (\cite{schulman2015trust}), which constrains weight updates to alter the policy little by little.
These improvements should increase \ac{SPORE} performance so that more complex tasks such as multi-joint effector control and discrete terminal rewards -- supported by design by the proposed framework -- could be considered.

\section*{Conflict of Interest Statement}
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

\section*{Author Contributions}
All the authors participated in writing the paper.
JK, MH, AK, JCVT and DK conceived the experiments and analyzed the data.

\section*{Funding}
This research has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1) and No. 785907 (Human Brain Project SGA2), as well as a fellowship within the FITweltweit programme of the German Academic Exchange Service (DAAD) [MH].
In addition, this work was supported by the H2020-FETPROACT project Plan4Act (\#732266) [DK].

\section*{Acknowledgments}
The collaboration between the different institutes that led to the results reported in the present paper was carried out under CoDesign Project 5 (CDP5 -- Biological Deep Learning) of the Human Brain Project.

\section*{Data Availability Statement}
No datasets were generated for this study.

\begin{params}{NEST Parameters}\label{table:nest}
  time-step/resolution       & $\SI{1}{\ms}$ \\
  synapse update interval   & $\SI{100}{\ms}$ \\

  (reaching) exploration noise         & $\SI{35}{\hertz}$ \\
  (reaching) noise to exploration exc. & $750.0$ \\
  (reaching) visual to exploration inh.& $\normal{-500, 50}$ \\
  (reaching) exploration to motor exc. & $10.0$ \\

\end{params}

\begin{params}{SPORE Parameters}\label{table:spore}
  visual to motor exc.      & $\normal{0.8, 0.6}$ (clipped at $0$) \\
  visual to motor mul.      & $10$ \\
  temperature ($T$)               & $0.1$ \\
  initial learning rate ($\beta$)   & $\num{1e-7}$ \\
  learning rate decay ($\lambda$)   & $\num{8.5e-5}$ \\
  integration time          & \SI{50}{\second} \\
  max synaptic parameter $(\theta_{max}$)   & $5.0$ \\
  min synaptic parameter $(\theta_{min}$)    & $-2.0$ \\
  (reaching) episode length & \SI{1}{\second} \\
  (lane following) episode length & \SI{2}{\second} \\
\end{params}

\begin{params}{ROS-MUSIC Parameters}\label{table:music}
  MUSIC time-step         & $\SI{1}{\ms}\ldots\SI{3}{\ms}$ \\
  DVS adapter time-step   & $\SI{1}{\ms}$ \\
  decoder time constant
                            & $\SI{100}{\ms}$ \\
\end{params}

\bibliographystyle{plain}
\input{root.bbl}

\end{document}

redpajama_set_name: RedPajamaArXiv


Solid stainless steel classic round shape 5 oz. flask. This 5 ounce asset holds whisky, gin, vodka, rum and other preferred spirits. Give it to your wedding party, rehearsal dinner or reception attendants, relatives and more, and it's also the perfect present for the groom or bride! And it doesn't stop there. This memorable design has three lines of name, personal message personalization available. And even add an image if you like to the very top of the flask!


text:
Q: how or where can i remove add to wishlist from shopping cart? Using 1.9.0.1
Here is the situation:

*

*not logged in

*adding some product to cart (no problem)

*go to "view cart"

*fields: image, description, edit, unit price, qty, subtotal, remove
Situation 2:

*

*logged in now

*adding some product to cart (no problem)

*go to "view cart"

*fields: image, description, edit, add to wishlist!!!, unit price, qty, subtotal, remove
How do i get rid of the add to wishlist from this?
I know i can set display none using like this:
.cart-table th:nth-child(3), .cart-table td:nth-child(3), .cart-table th:nth-child(4), .cart-table td:nth-child(4) {
display: none;
}
BUT...
when i do this it works great for when NOT LOGGED IN, and when LOGGED IN, it added an extra th so it is messed up...
Anyone know where i can safely delete that add to wishlist function from cart view?
Thanks

A: You have 2 options here.
Option 1.
Edit all cart and cart item templates and remove the wishlist column.
For this you need to edit:

*

*checkout/cart.phtml

*checkout/cart/item/default.phtml

*downloadable/checkout/cart/item/default.phtml

Option 2
Override the Mage_Wishlist_Helper_Data::isAllowInCart method and make it return false always.
For this second option you need a new module. Let's call it Easylife_Wishlist. Create these files:
app/etc/modules/Easylife_Wishlist.xml - the declaration file:
<?xml version="1.0"?>
<config>
    <modules>
        <Easylife_Wishlist>
            <codePool>local</codePool>
            <active>true</active>
            <depends>
                <Mage_Wishlist />
            </depends>
        </Easylife_Wishlist>
    </modules>
</config>

app/code/local/Easylife/Wishlist/etc/config.xml - the configuration file
<?xml version="1.0"?>
<config>
    <modules>
        <Easylife_Wishlist>
            <version>1.0.0</version>
        </Easylife_Wishlist>
    </modules>
    <global>
        <helpers>
            <wishlist>
                <rewrite>
                    <data>Easylife_Wishlist_Helper_Data</data> <!-- tell magento to use your own helper instead of the default one -->
                </rewrite>
            </wishlist>
        </helpers>
    </global>
</config>

app/code/local/Easylife/Wishlist/Helper/Data.php - your new helper
<?php
class Easylife_Wishlist_Helper_Data extends Mage_Wishlist_Helper_Data
{
    public function isAllowInCart()
    {
        return false;
    }
}

Clear the cache, disable compilation if enabled and you are done.

redpajama_set_name: RedPajamaStackExchange


Preheat the oven to 350 degrees. Line the bottom of a 9-inch spring-form pan with parchment paper. In a medium size bowl the mix crushed cookies and melted butter. Press the mixture on the bottom and up the sides of the pan. Bake for 12 minutes, let cool. Start by blooming 1 package of the gelatin. Mix the packet with 3 tablespoons cold water and let sit. Next, soften the hazelnut spread slightly by microwaving for 30 seconds. Set aside. In a large bowl beat 1 ½ cup whipped cream until stiff peaks form. Microwave the gelatin for a few seconds to liquefy. Add ½ cup sour cream, 3 tablespoons powdered sugar, hazelnut spread, and gelatin. Continue beating until everything is incorporated and the mixture is fluffy. Pour over the cookie crust and place in the freezer while you make the next layer. Bloom the second package of gelatin in the remaining 3 tablespoons of cold water. Mix the white chocolate and ½ cup sour cream. Place over a pot of simmering water and heat to melt the chocolate. Stir in the gelatin and dissolve. Set aside to come to room temp. Next beat the remaining heavy cream until stiff peaks form. Add the sweetened condensed milk, evaporated milk, remaining sour cream, and cinnamon, whip again until all ingredients are incorporated. Next add white chocolate mixture and whip again until smooth. Spread the mousse layer evenly over the mousse and chill for 6 hours or overnight. Garnish as desired before serving.


text:
American Entertainment
Sip Forum
Home › Entertainment › Music › Turnpike Troubadours Frontman Evan Felker Discusses New Found Sobriety
Turnpike Troubadours Frontman Evan Felker Discusses New Found Sobriety
by Jennifer Shea December 4, 2021
written by Jennifer Shea December 4, 2021 1:39 pm
John Shearer/Getty Images for Essential Broadcast Media
The Turnpike Troubadours have been on hiatus since May of 2019. But now they're back and ready to play some rollicking Americana music. Next May 14, the band is performing at Denver's Red Rocks Amphitheatre. And they're looking to add more dates in the meantime.
Enabling the band's triumphant return is frontman Evan Felker's newfound sobriety. In a new interview with Rolling Stone, the band talked about what they've been up to since 2019, and Felker opened up about what his sobriety means to him.
"I found sobriety and recovery, and that was the greatest thing that ever happened to me," he told Rolling Stone. "I'm coming up on two years, completely sober and out of recovery. And a few days after that will be my daughter's first birthday. So it's all a big one. I've learned so much about how my life was not well-rounded. I mean, I knew that. I knew that it was just one thing. It started out just being music, and then eventually it was just drinking. And that was it for me."
Turnpike Troubadours Frontman Has Gone Cowboy
Unlike the other members of the Turnpike Troubadours, Felker has not been traveling or playing music much over the past two years. He says that was a much-needed correction after years of giving himself entirely to his music and "let[ting] everything else slide." He traveled to Oklahoma to visit his family and back again, but that was about it.
Interestingly, getting back to the land and learning the cowboy lifestyle has transformed Felker's existence. He had some time while he was stuck in rehab to reevaluate how he'd been spending his time, and he says the person he's become now more accurately reflects who he wants to be.
"There were a lot of things, when I was stuck in a room in a rehab facility, that I thought I really am going to put some time into," Felker explained. "I wanted to be a decent carpenter or just pie-in-the-sky things like training cattle horses and stuff. Now, I'm really able to do all that! I've learned more than ever, this cowboying stuff has changed my life drastically."
"I'm really, really happy with the things I get to spend my time on now," he added. "I love music still, but it was time to spend a year or two and catch back up because the person I presented myself to be — and who I thought I was — was lacking in some areas. Now, I feel like myself, and I'm proud of what I'm capable of doing. It's a nice feeling, it truly is."
The Band Is Grateful to Be Back
But don't get Felker wrong – he's missed the band. And they've missed him. The band agreed that the musical chemistry they'd built together was something to be preserved and reinforced, and the time away only drove that point home for them.
"There's a thing that happens when you've worked with someone for a long time that's just magic," Felker told Rolling Stone. "The chemistry that we have, and being able to play off of each other without speaking or anything like that, it's an amazing little phenomenon. And it happens for three or four minutes at a time, and I'm very appreciative of things like that now." "
Check out the band's website for updated info on their upcoming shows.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Uadzsetrenput ókori egyiptomi hivatalnok volt a XVIII. dinasztia idején; háznagy Hatsepszut uralkodása alatt.

Számos említése fennmaradt, de egyiken sem szerepel dátum, így nem lehet megmondani, pontosan mikor töltötte be hivatalát Hatsepszut huszonkét uralkodási éve alatt; egy Dejr el-Bahariban talált osztrakonja a jól ismert Szenenmutot is említi, innen tudni, hogy Hatsepszut idejében élt. Említi egy sziklafelirat Asszuán közelében; ezen III. Thotmesz és Hatsepszut említése is szerepel, ami bizonyítja, hogy ők küldték kőért a kőfejtéséről ismert régióba. Egyes említéseinél az "Ámon-templom munkálatainak felügyelője" címet is viseli.

Jegyzetek

Fordítás

Ókori egyiptomi háznagyok
XVIII. dinasztia

redpajama_set_name: RedPajamaWikipedia


text:
Regina "Jean" Branz Daly
(March 21, 1931 – Nov. 20, 2017)
WATERBURY – Regina (Jean) E. Daly, widow of Dr. Joseph E. Daly, died Monday, Nov. 20, 2017, at her Waterbury home. She had suffered from the effects of cancer and Parkinson's disease in recent years, but she remained active until the week prior to her death.
She was born March 21, 1931, in Freeland, Pa., to parents who had emigrated from what was then Val di Non, Tirol, Austria. Her parents [Henry Branz 1897-1971 and Erminia Genetti 1896-1971] instilled in her the principles of hard work and thrift, to which she added her characteristic sense of humor. After graduating from high school, she entered a nursing program at Hazelton Hospital in Hazelton, Pa., and graduated in 1951. She later moved to New York City to become head operating room nurse at New York Polyclinic Hospital, where she met her husband, then a surgical resident.
Jean and Joe married on May 23, 1959, and then moved to Waterbury, where he had lived his whole life. He had been widowed six years earlier, and Jean happily took on the job of raising his five children from his first marriage. They then had one daughter together and were able to live to see their six children grow up and give Jean and Joe 21 grandchildren and 25 great-grandchildren. The youngest three great-grandchildren were born this past year, and Jean was delighted by the continued growth of her large family.
Jean loved her grandchildren and great-grandchildren, and spent many happy years baby-sitting and traveling for baptisms, first communions, graduations and weddings. She enjoyed doting on all of the children and never forgot a birthday or anniversary. In addition to her love of spending time with her family, Jean enjoyed bridge, bowling and tennis with many dear friends. She volunteered extensively – at her beloved parish, St. Margaret, where she assisted with everything from altar linens to planting flowers, and at St. Margaret School, and for many charitable organizations, including Saint Mary's Hospital and the Heart Fund. She was a longtime member of St. Margaret Ladies Guild and the Theresians. Very few people could keep up with the pace that she set.
Jean leaves behind two sons, Joseph and his wife, Candace, of Peacedale, R.I., and Kevin and his wife, Cheryl, of Summerfield, Fla.; and two daughters, Catherine Canning and her husband, Ray, of Bennington, Vt., and Barbara Joliat and her husband, Christopher, of Waterbury. She was predeceased by two sons, Terrance and Charles.
Funeral and burial will be private. There are no calling hours. The Murphy Funeral Home, 115 Willow St., is in charge of arrangements.
The following photographs are courtesy of Jean's daughter Barbara Joliat as a tribute to the life of her mother.
The wedding of Catherine Branz Laporte (Jean's sister), May 4, 1946. Jean was her sister's bridesmaid.
Jean with husband, Dr. Joseph Daly, while dating, at the annual white tie Benefit Dinner for the Polyclinic Hospital at the Waldorf Astoria in New York City. About 1955.
Jean with her mother and father, Henry Branz and Erma Genetti Branz, Christmas 1955-1956, Freeland, PA. Jean's daughter: "My Noni was rarely caught without an apron!"
Jean and Joseph Daly taken at a family reunion in Pennsylvania around 2006.
Jean with her sister Catherine, in Freeland, PA, about 2009.
Barbara Joliat with her mother Jean at the wedding of Barbara's daughter Catherine, 2007.
Jean with her granddaughter, Jeanne, (Barbara's daughter) at her wedding in 2014. This was the last family wedding attended by Jean.
The entire Daly family at the wedding of Jean's granddaughter, Jeanne, 2014. Photo includes three great-grandchildren (Beatrice, Aurelia, and Edmund). L to R: Ian (Barbara's husband), Chris, Barbara, Jean, Jeanne (bride), Jon Leaman (groom), Luke, Cecelia, Catherine and Aurelia, Catherine's husband, Joe Feil, and Ned.
Webmaster Note: During the past four years Jean Branz Daly contributed many photographs and memories to the Genetti Family Genealogy Project. Follow the links below to read more of Jean's family stories and browse our extensive Photograph page that include numerous photos attributed to Jean.
Photograph Page: https://genettifamily.com/gallery/ancestors/
Family Memories by Regina "Jean" Branz Daly:
https://genettifamily.com/regina-jean-branz-daly/


text:
Contractor May Recover on Differing Site Condition Despite Not Following the Notice Requirements of Contract
by Kent Holland | Dec 19, 2016 | differing site conditions, Newsletter Article | 0 comments
After final payment was made under its contract, a utility construction contractor submitted a written request for additional payment to the project owner seeking payment for extra work it performed as well as increased costs due to delays and disruptions during the project. When the owner refused to pay, the contractor filed a breach of contract complaint alleging that the owner failed to provide plans showing the location of underground facilities and was obligated to pay for extra work caused by differing site conditions. The work in question resulted from 122 incidents where other utility lines or structures were damaged because they were unmarked on the construction plans. The trial court found against the contractor – concluding that the risk of liability with respect to underground utilities was allocated by contract to the contractor. Moreover, the contractor claim was barred because it was untimely made. This was reversed on appeal with the court holding that the contract allowed for differing site conditions claims and did not assign all risk to the contractor, and that the written notice requirement was not fatal since the owner had actual notice of the claims through its onsite representative who documented each event as it occurred. J. F. Allen Corporation v. The Sanitary Board of the City of Charleston, WV, 785 S.E..2d 627 (W.VA. 2016).
The court cited earlier case precedent for the proposition that contract provisions providing for timely written notice of changes or claims can be amended, waived or abrogated by the conduct of the parties. Here, the contractor had sufficient allegations in its complaint concerning actual knowledge and action by the owner, that the complaint should not have been dismissed. The court cited a history of actions by the owner on this project in which the owner did not insist on adherence to the formal contractual requirements and even instances where the owner, in contravention to the formal requirements of the contract, instructed the contractor to bill in a manner different than called for by the contract and to perform work not specifically called for by the contract.
With regard to the extra work that was not formally required until after contract completion, the court found that the city had immediate, actual notice of the extra work as it was being performed and even directed the contractor to perform with the attendant expectation that the contractor would receive additional compensation. Consequently, the court found that the allegations in the complaint were adequate to support a claim for damages. On remand to the trial court, the contractor will have an opportunity for a trial on the merits but that whether it met the requirements of the contract, and whether the owner breached its obligations under the contract and whether elements of the contract were waived or amended by the parties are all questions of fact that will have to be resolved by the trier of fact.
Comment: This result is somewhat surprising in that "final payment" had already been made before the claims in question were submitted. It is a fundamental principle of filing claims that they must be filed before final payment. There are numerous decisions in state and federal courts holding that what might otherwise appear to be a late claim may, depending upon the facts, be deemed timely provided it was at least filed before final payment under the contract. But once the final payment request has been submitted that normally terminates any right to submit any other claims. The contractor is fortunate that the court held as it did. My advice to contractors is please follow the terms and conditions of the contract when it comes to submitting change order requests and claims. And submit all potential changes and claims before the final payment is accepted.
About the author: Article written by J. Kent Holland, Jr., a construction lawyer located in Tysons Corner, Virginia, with a national practice (formerly with Wickwire Gavin, P.C. and now with Construction Risk Counsel, PLLC) representing design professionals, contractors and project owners. He is founder and president of a consulting firm, ConstructionRisk, LLC, providing consulting services to owners, design professionals, contractors and attorneys on construction projects. He is publisher of ConstructionRisk.com Report and may be reached at Kent@ConstructionRisk.com or by calling 703-623-1932. This article is published in ConstructionRisk.com Report, Vol. 18, No. 7 (December 2016).


Aalto Talk with Linus Torvalds, hosted by Aalto Center for Entrepreneurship (ACE) in Otaniemi on June 14, 2012.
This whole video is in Q&A form, which is quite enjoyable to watch. I strongly agree with Linus in the following thoughts: Passion, hard work and attention to details are extremely important. Whether you have vision or not is totally another story. People who easily get offended, should be offended.


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Despite Bomb Threats, Metal Detectors are too Urban for Highland Park High
Jim Schutze
Jim Schutze | March 29, 2013 | 11:16am
UPDATE: HPISD sent a letter in response to this post, and because it's too long to publish in our comments, we've added to the end.
I have a column coming out in next week's paper about the bomb and gun threats at Highland Park High School. It was inspired by (ripped off from) a story in The Huffington Post last week by Park Cities freelance writer Pamela Kripke, who is also the mother of a HPHS student.
It's a pretty amazing piece, which I can say with some confidence now having followed behind her and found all of her reporting solid. She says that University Park Police Chief Gary Adams told her the only way he could guarantee the safety of students at the high school would be with the installation of metal detectors.
She says Adams also told her that he had proposed metal detectors to the school district, even offering to find some loaners for them until they could buy their own, but that the school had turned down the idea.
You know why. Too ghetto.
Well, they say metal detectors are too "urban." A school district spokesperson told me requiring park cities students to go through metal detectors had a "cultural element" and a "psychological part" that they couldn't allow.
They've had seven explicit written bomb threats since January by my count, two incidents of live ammo found in the school or on the person of a student, more text message threats than they can count, and the FBI has been unable so far to link any of it to anybody.
After Crime Stoppers offered a reward, the organization received a note in the mail warning them that the threats are "not a hoax" and that the danger is "building."
But they don't need metal detectors. Helen Williams, director of communications for the district, told me, "When you've got a situation in an urban district where you're dealing with gangs and things that come with that, it probably is a more major consideration."
But not in the Park Cities. Williams told me Kripke had been what she called "quite verbal" and that the metal detectors were "kind of her one-woman crusade to tell you the truth."
Since Kripke's Huff Post piece came out, Chief Adams has been smartly boxed about the ears, I'm sure. Williams told me Adams had not said what Kripke quoted him saying, which would make Kripke a liar and a fabricator of fact. When I spoke with Adams, he did his yeoman best to play down any conflict with the school district, insisting he had not issued any edicts and that the district had not defied any warnings from him.
But Adams is obviously a deep-down honest guy (whom I am probably getting fired), and in response to my questions he confirmed the gist of what Kripke had reported: "She asked me about metal detectors and if I thought they needed them. I said at this point in time after we found .22 bullets, I thought we did."
Her piece is a compelling combination of solid reporting and the cry of a mother's terrified heart. Here's an excerpt:
I call the Chief of Police. He assures me that within two days, metal detectors would be installed on campus. He sounds relieved. Without them, he says he can't know if a weapon is in the school. He "cannot guarantee the safety of anyone in the building."
Two days later, when detectors are not installed, I call him to find out where they are. He tells me that he provided school administrators with detailed information about how to purchase them. "I even tried to borrow them, meantime, from other districts, the airport," says Chief Gary Adams ...
"We should have them. If it's going overboard, we should go overboard." Chief Adams has worked as a police officer for more than 35 years, and as Chief, here and elsewhere, for 20.
UPDATE:This letter, sent by email last Thursday from the Highland Park Independent School District, reflects the HPISD position:
Dear Jim,
I thought I'd send you some additional information for your story. Here is the link to the Crime Stoppers announcement, which also includes some safety updates from Chief Adams and Dr. Orr: http://archive.constantcontact.com/fs142/1102070697430/archive/1112880814723.html
I also am including some information from another recent message that discusses the debate over permanently installing walk-through metal detectors on campus(es), along with other related topics. Just to clarify, we are looking at all our options and have not ruled out walk-through metal detectors. As we discussed on the phone, it would be a major change and expense for the district, and we would need to look at how many metal detectors would be needed, whether they should be installed on all campuses, and how to manage the logistics of screening the thousands of students, staff members and community members who are on our campuses every day. Walk-through metal detectors also require trained staff to monitor all those who would pass through them and to search people and bags as needed. There are some parents who are in favor of making the change and others who oppose it. As I mentioned, we are consulting with safety experts, and at the recommendation of safety experts (UP Police and FBI officials), we have purchased handheld metal detectors, which will permanently remain on campus.
Also, we have hired a safety firm (Defenbaugh & Associates, headed by retired FBI agent Danny Defenbaugh) to conduct thorough safety audits on all our campuses and to make recommendations regarding facility enhancements and staff training.
Here are some excerpts from the recent message from Dr. Orr and Chief Adams:
What daily safety measures are being taken on campus?
· We are checking student and faculty IDs, both at entrances and with random checks throughout the school day. We appreciate our students' cooperation, as this has been an adjustment for them. We appreciate our volunteers and visitors also wearing their badges while on campus.
· The University Park Police Department and Town of Highland Park are continuing to provide armed officers on campus.
· We have limited the student entrances to the two main entrances, and two other entrances on Douglas Avenue. All entry points are staffed by personnel. We have expanded the hours for security at these points, and custodians will not be opening the entrances until a staff member is present.
· The building has been searched by officers, dogs and bomb squads as needed.
· We have provided additional staff and have instructed supervising administrators, security guards and custodians with specific guidelines to prevent unnecessary access to areas of the building outside of school hours.
· Surveillance video cameras record 24/7, and we are working with UPPD and the FBI to review all relevant video footage, along with the review of forensic evidence. There are video cameras posted outside bathroom entrances.
Have you considered using metal detectors?
· We have carefully considered the use of metal detectors, which range from handheld detectors to walk-through machines.
· At this point, we have chosen to use handheld metal detectors as needed. The district has purchased handheld metal detectors, which will remain on campus permanently. In addition, UP police officers have their own handheld metal detectors.
· Members of the public have voiced strong opinions on this issue. While some suggest that we should install permanent airport-level security, others suggest that the installation of walk-through machines sends the wrong message to students.
What is the status of the investigation?
· In the interest of the integrity of the investigation and in the interest of student safety, we cannot share specific facts about what information has been obtained during questioning.
· Many people have been interviewed, and investigators have followed through on all leads.
· We will continue to keep you updated, and if an arrest is made, we will make a timely announcement.
· The FBI is continuing to partner with UP police on the investigation.
Have you searched students? What are the legal guidelines?
· Officials have conducted searches of backpacks, lockers, cars, cell phones, clothing and bodily searches when there is reasonable suspicion of prohibited conduct.
· Please note that HPISD must conduct its investigations within the boundaries of what is permissible under state and federal law. These laws include the Fourth Amendment's right against unreasonable searches by the government.
· Under the law, the district may only conduct a search when it has reasonable suspicion of prohibited conduct. Reasonable suspicion means specific and describable conduct exists that would lead a reasonable person to conclude that a particular student has engaged in prohibited conduct. The requisite reasonable suspicion must be present from the outset of the search. Reasonable suspicion must be more than a hunch or supposition. For example, it would be illegal for the district to seize every student's cell phone and review it for clues. It would also be illegal for the district to fingerprint all students.
· Privacy expectations can be minimized by telling students beforehand that certain locations, such as lockers or vehicles parked on school grounds, are subject to random search, which is something the district does on a regular basis.
How are parents and students being notified about threats?
· HPISD is communicating all emergencies through its text-messaging system, email and postings on the district website.
· Text messages allow the district to push instant notifications to users' cell phones. The software allows for a limited number of characters to be sent, so the texts will usually be brief and to the point. For more information, please check your email and the website.
· To sign up a parent or student for the text alerts, please refer to this FAQ. Faculty and staff can refer to this FAQ to sign up for text alerts.
How can parents help?
· We encourage our parents to talk to their children about the seriousness of the situation we are facing. In this case, a terroristic threat can result in felony charges and imprisonment. A parent's wisdom, understanding and guidance goes a long way toward helping a child understand the harmful effects that threats such as those made against our campus can have.
· Parents can also help by reporting any information and leads to the University Park Police Department at 214-987-5354, members of the HPHS Administration at 214-780-3700 and Crime Stoppers at 1-877-373-TIPS (8477). Tips may also be submitted on the web 24 hours a day.
· Anyone interested in contributing to the Crime Stoppers fund may do so by emailing HPISD's Assistant Superintendent for Business Services Tim Turner at turnert@hpisd.org.
· In an earlier update, we included a list of tips for helping keep our campuses safe.
We know that this has been a stressful time for our students, families, staff and community members. If your child has concerns, please encourage him or her to talk to a school counselor or a trusted teacher or other adult on campus. We appreciate your partnership, and we will continue to keep you informed.
Sincerely, HPISD Superintendent Dr. Dawson Orr UP Police Chief Gary Adams
Let me know if you need any additional information.
Helen Williams Director of Communications Highland Park ISD
Jim Schutze has been the city columnist for the Dallas Observer since 1998. He has been a recipient of the Association of Alternative Newsweeklies' national award for best commentary and Lincoln University's national Unity Award for writing on civil rights and racial issues. In 2011 he was admitted to the Texas Institute of Letters.


San Diego Unified School District (SDUSD) allows students to attend a school other than their neighborhood school through the Choice Program, on a space available basis, and transportation must be provided by parents/guardians. The School Choice Program allows students to apply to any school within the San Diego Unified School District, with the exception of Magnet Schools.
For detailed information, please visit the SDUSD Enrollment Options website at http://www.sandi.net/page/902.
To check if your address is within Ericson Elementary School boundary, please go to Boundary Lookup/School Finder at http://old.sandi.net/schoolfinder/.


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Home Science Education Striking Tips From Chuck Liddell
Striking Tips From Chuck Liddell
Chuck Liddell Overhand and Livershot
SciFighting News Desk
Chuck Liddell was one of the greatest champions in the UFC — a superstar that propelled the UFC to new heights when they needed one.
In this video, with Glover Teixeira, he demonstrates the livershot and overhand right. When Liddell throws the livershot, he doesn't like to fully commit to leaving his upperbody leaned onto the side. In his opinion, staying mobile is critical to staying safe. It's also important to note that it's more of an "upperhook" — combining uppercut and a hook to the liver.
Then he demonstrates his version of the overhand — akin to throwing a baseball. Another aspect to the overhand is that it's a one-way punch, "there's no stopping it." It's important not to keep the arm too straight to the point where it could hyperextend on impact.
In MMA, since the initial motions of the overhand right simulates a takedown attempt, it can be a great feint as a wrestler. This is also why many great wrestlers also have a great overhand (Chuck Liddell was a wrestler as well), examples include Cain Velasquez, Daniel Cormier, Urijah Faber, Chad Mendez, Tyron Woodley, Josh Koscheck.
Another way to feint the overhand is by going for a right straight body-shot, or a right-straight. Though this punch is looping and can be disrupted by a good jabber, it's a great weapon to have under the arsenal. In MMA, it's been shown time and again that it works even at the highest level.
In kickboxing, Zambidis repeatedly lands this shot on various opponents, and Andre Dida almost finished Buakaw with it. A trademark thing that Liddell would do is test his opponent with his straight right. If the opponent leans back (without the shoulder-roll), then he's going to throw the overhand. This is how Roy Nelson finished one of his opponents (possibly Cheick Kongo or more likely Dave Herman).
chuck liddell champion
chuck liddell ko
chuck liddell left hook
chuck liddell livershot
chuck liddell overhand
chuck liddell technique strategy and tactics
glover teixeira
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SciFighting's mission is to bring a higher standard of fitness to both the professional athlete and general population through concise reporting on the latest trends in the science of fighting. Our involvement in mixed martial arts spans both the professional and recreational realms of the sports. In addition to reporting on the news in the industry we function as a think tank, researching new facets of combat science and developing new tools and techniques to analyze theoretical and practical applications.


PORSCHE is responding to the energy crisis with a Cayenne Hybrid, but that's still at least three years away. So the next most economical SUV it can offer is the Cayenne V6. Yep, not since the front-engined 924 of 1977-1982 has a Porsche featured a VW-sourced engine, but these days, with all the fine direct-injection units going around, is this really such a bad thing? And does the base Cayenne offer any sort of reasonable economy benefit, considering that it weighs almost 2200kg? We think you may be surprised with the outcome.


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Lenalidomide is indicated for the treatment of multiple myeloma in combination with dexamethasone. It is also indicated for the treatment of patients with transfusion-dependent anemia due to low- or intermediate- risk myelodysplastic syndromes (MDS) associated with a deletion 5q cytogenetic abnormality with or without additional cytogenetic abnormalities.
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Alice Covatta
Europan 14 New Ulm
Tokyo Playground
Parallel Implosion
Suncheon Art Platfrom
Yamanote-Sen
Errare
Tsukiji Strips
Nuclear Repository
Urban Cupboard
Flip Scraper
Castello Sforzesco square
Piccoli Aeroporti
Ready Made Tokyo
Flipscraper.
A Metabolist Utopia in the crisis of a Flooded World
Location: Tokyo Bay
Team: L.Zuccaro Marchi
Project type: Unbuilt Utopia
World Architecture Community Awards 26th Cycle
The proposal envisages the extreme future condition of a contemporary pressing issue: the flooding of the world due to climate change and environmental abuse. It is an Utopian reinterpretation of the Water Cities proposals designed by Metabolist Architect Kiyonori Kikutake.
Within this extreme "risk society" (Beck), in a flooded World or Waterworld, the Skyscraper frees itself from the ground, from its gravity support. Moreover it finally gets rid of any capitalist-childish, altitude anxiety of competition. Floatation is more unifying.
The skyscraper becomes a flexible element for a new form of human settlement. It is vertical and aggregated with other Flips when it needs protection, closeness, introversion, compactness. This is typical in frozen climates, or when facing the destructive force of a tsunami or war. It is horizontal when it navigates over a flooded Metropolis or when it enhances Aquafarming or Aquaculture in between each Flip.
Kiyonori Kikutake, Water Cities Project
RP FLIP (FLoating Instrument Platform)
The main reference of the technological flip structure is the RP FLIP (FLoating Instrument Platform), owned by the U.S. Office of Naval Research (ONR) and designed in the 1960s. This boat is able to flip 90 degrees, in order to make studies and carry out research in the ocean. Interestingly, the Metabolist Architect Kiyonori Kikutake adopted this boat, which is extremely stable when vertical, as his main reference for the design of his famous Water Cities proposals.
The Utopia of living in the water is now reinterpreted starting from Tokyo Bay flooding map as paradigmatic case and main reference for the Metabolism Movement , reactivated when faced with the pressing and compelling environmental issues that affect our contemporary society. Water flooding is becoming a dangerous condition but a new architectural opportunity as well.
Similarly to boats, the building presents an "upper-works" or "deadworks" which is always above the water, and "liveworks" which are that part of the ship that lays underwater.
This basic nautical distinction has been interpreted also in the FLIP-Scraper, where a flat, rigid hull is opposed to a fragmented, informal settlement as upper works.
Liveworks: It is almost empty in order to be filled with water or compressed air, depending on the balance of the Flip-scraper. When Vertical, most of the ballast for the platform is provided by water at depths below the influence of surface waves. However, the structure has a double internal skin in order to provide internal space for the production of food (algae, mushrooms, etc-), for research laboratories, and other functions which need to block out daylight.
Deadworks/Upper-works: The apartments are designed in order to function both horizontally and vertically. A defensive internal room, circular in section, is present in each apartment and rotates according to the slope of the building. Each apartment is linked to a technological and distributing core, which hosts a lift, stairs for vertical distribution, and a corridor for horizontal living.


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African American Social Justice and Civil Rights Questions
Chapter on "Color of Justice"
Directions: . Please answer the following questions for pages 109-139 in complete answers (paragraph format) written under each question. Please type the paper, double spaced, 12 inch Times New Roman font, and two to three pages
1. Compare and contrast the McClesky v. Kemp, Armstrong v. United States and the Purkett v. Elm cases. Explain the outcome of the cases, write three similarities and
three differences in the cases.
2. Explain why racial bias is most acute at the point of entry into the system? What does this mean for the African American and Hispanic youth?
3. American Apartheid by Douglas Massey and Nancy Denton explains why poor black communities are easy targets for the police. Why are they targets?
4. What is the significance of the term "The Occupation?"
5. Explain the results of the 2002 team of researchers at the University of Washington that tested the study of drug law enforcement in Seattle.
6. What are your thoughts on the "Supreme Court's ruling in the Lyons case on the police use of the deadly chokehold? How does this case compare to the Eric Garner's case?
(The New York Police Department has banned, for more than two decades, the use of chokeholds, which it defines broadly to include any police maneuver that puts "any pressure to the throat or windpipe, which may prevent or hinder breathing or reduce intake of air.")
7. In New Jersey, the data showed that only 15 percent of all drivers on the New Jersey Turnpike were racial minorities, yet 42 percent of all stops and 73 percent of all arrests were of black motorists. The Maryland studies produced similar results. Pedestrian stops have also been the subject of study and controversy. An example is the shooting of Amadou Diallo, an African immigrant. Do you believe that the disproportionate imprisonment of people of color was, in part, a product of racial profiling ? Why or why not?
8. What are your thoughts on the Supreme Court's decision to close the courthouse doors to claims of racial bias at every stage of the criminal justice process, from stops and searches to plea bargaining and sentencing? Give your reasons and evidence from the chapter supporting your answer.
Published September 4, 2020 By Jessica White
Immigration Face Of America
Political science assignment


According to leading figures in business, including former Google CEO Eric Schmidt, the top soft skills they want in employees include persistence, curiosity and an excellent ability to communicate.
Indeed, hard skills such as knowledge of IT systems, accountancy methods or marketing tactics are crucial to finding a new role. However, your intangible skills, which reflect your attitude and character traits, can often determine whether you're going to succeed in a role and fit in with the company culture.
Every employer is different with each CEO having their own opinion on what works for their organisation. The combination of soft skills required by the financial team at Sainsbury's is unlikely to match the qualities needed by a financial professional of a trendy tech firm.
Below are a few examples of the type of soft skills in demand in today's job market. Which ones do you possess yet haven't been promoting?
Being optimistic influences how we handle work-related challenges. Among other benefits, positivity drives us to find solutions, helps us to deal with stress and avoid conflict at work, as well as keeping our minds open to new opportunities. This personal quality shouldn't be undervalued, especially when businesses need to innovate in order to thrive.
"Genius is 1% inspiration and 99% perspiration," claimed Thomas Edison. Considering he's one of the most important inventors of the industrial revolution, we'd do well to listen to his advice. Smart employers appreciate that a job interviewee may be gifted, but without evidence of their tenacity, their longevity within a company may be questioned.
Having an instinct for engaging with and understanding other people is essential in most jobs. No longer is empathy seen as a weakness; it's become a valued quality as fewer businesses are based on the traditional hierarchical structures, and instead embrace collaboration thanks to digital technology.
Therefore, being able to relate to others underpins successful projects, and, ultimately, fuels business growth.
Simple politeness goes a long way in building business relationships. Demonstrating courteous conduct in your day-to-day dealings with people is likely to earn you respect and recognition from colleagues, employers and customers.
Taking ownership for our mistakes not only helps us to learn quicker, but it also earns admiration for our integrity. Turning a failure into a learning experience enables us to establish what went wrong and make changes to avoid a repeat incident.
It also creates opportunities to share your hard-earned knowledge with colleagues. Whatever you do, avoid blaming others – this behaviour can ruin team morale.
Having a creative streak is no longer considered to be a flaw in business. This skill, once confined to the Arts and advertising, is now highly valued across all industries as the corporate world sees the benefits of creativity to the bottom line.
It helps us look at life differently, leading to imaginative solutions to problems, increased resourcefulness, improved team building, unique customer and employee communications, among other outcomes.
Our advice to job seekers is this: research the most desired soft skills in your industry or preferred company and read job descriptions thoroughly. Then, during the application process, highlight your relevant soft skills and their value, and how you plan to develop any skills you may currently lack.
If you'd like to discuss the importance of soft skills, our consultants across the East of England are happy to help.


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Q: How to display only few pages of magazine ebook in turn.js I create magazine ebook with turn.js. Every pages of ebook is images. I create 52 pages.
When I host the ebook to my website. My ebook only can be seen after all pages have been loaded

My question is :
How to display only first page (ebook cover) or when user click next arrow, user see next pages in turn.js ?
Do you ask what I do ? I just put images of pages inside folder pages. I didn't do anything :

A: You have to enable either loading through Ajax requests dynamically if it properly supports that per-page or use an alternative that supports lazyloading.
In case of the latter you might have to resort to a paid alternative like this one which seemingly supports lazyloading.