text:
\section{Introduction}
Spiking Neural Network (SNN) has been increasingly used in the energy-aware real-time applications on the embedded systems platforms \cite{akusok2019spiking, skatchkovsky2020federated, guan2020unsupervised}. Due to its event-driven nature, \ac{SNN} has the potential to be more energy-efficient than the \ac{ANN}. However, the \ac{SNN} architectures that achieve the high accuracy are usually deep and large, consisting of multiple layers and thousands of neurons and connections \cite{diehl2015fast, kheradpisheh2018stdp, rathi2020enabling, guan2020unsupervised}. For example, the \ac{SNN} architecture proposed in \cite{diehl2015fast} consists of 6 layers with more than 14,500 neurons and 29,000 connections, and achieved an accuracy of 99.14\% on the MNIST dataset. The authors of \cite{rathi2020enabling} implemented an \ac{SNN} based on the VGG-16 architecture \cite{simonyan15}, which consists of more than 138 million connections, and achieved an accuracy of 65.19\% on the ImageNet dataset. The large-scale \acp{SNN} require long computation time and large amount of hardware resources, which limits their potential to be realized on the embedded systems platforms \cite{sen2017approximate, wang2018fpga}. Therefore, we are motivated to explore the techniques to compress the \ac{SNN} to optimize the computation time, hardware resources, and energy consumption on the neuromorphic hardware. Our work aims to improve both the learning and the inference time of the \ac{SNN} while minimizing the loss in the accuracy.
\\\hspace*{1em}There are two common approaches to convert the continuous inputs to the spike events: rate-based coding and temporal coding. In the rate-based coding, the input value is encoded into the spike frequency. An input that has a large value is converted to a series of spike events that occur frequently. On the other hand, in the temporal coding, the input value is encoded into the spike timing, and each neuron spikes at most once for every input. An input that has a large value is converted to a spike event that occurs early. As compared to the rate-based coding, the temporal coding consumes less energy as a fewer number of spikes are generated \cite{rullen2001rate}. In the temporal coding, the input information can be encoded into the relative order (rank order coding) or the latency (\ac{TTFS} coding) of the spikes. The \ac{TTFS} coding has been used in many recent works, such as the ones in \cite{kheradpisheh2018stdp, kheradpisheh2020, mozafari2018first}, to solve challenging real-world problems. Therefore, our work focuses on developing a connection pruning algorithm for the \ac{SNN} with the \ac{TTFS} coding, which is more energy-efficient than the rate-based coding.
\\\hspace*{1em}Connection pruning is a compression technique that has been applied to \ac{ANN} \cite{lecun1990optimal, hassibi1993second} and \ac{SNN} \cite{iglesias2005dynamics, sen2017approximate, rathi2018stdp, chen2018fast, shi2018neuroinspired, shi2019soft, tang2019spike, deng2019comprehensive, kundu2021spike} to reduce the network complexity and energy consumption. It has been shown that more than half of the connections in a well-performing neural network can be removed with minimal impact on the classification accuracy \cite{lecun1990optimal, iglesias2005dynamics}. The connection pruning can be performed either on a pre-trained network \cite{sen2017approximate, chen2018fast} or during the network learning \cite{rathi2018stdp, shi2018neuroinspired, shi2019soft, tang2019spike}. The authors of \cite{sen2017approximate} have proposed a heuristic connection pruning algorithm for the pre-trained \ac{SNN}, in which the connection weights are obtained by converting from those of an \ac{ANN}. The connection pruning is triggered periodically during the inference stage, based on the neuron parameters such as spike rate, membrane potential, and connectivity. Although this approach has efficiently improved the number of operations and hardware energy during the inference stage, it cannot be applied to the applications that require the on-chip learning capability on hardware \cite{sahoo2018online, akusok2019spiking, skatchkovsky2020federated}. In addition, the work in \cite{sen2017approximate} focuses on the \ac{SNN} with the rate-based coding, in which a neuron spikes multiple times during the feed-forward computation of an input image. The spike rate indicates the activity of the neurons and varies greatly among the neurons in the network. In this case, it can be used as a pruning parameter to remove a higher percentage of connections from the less active neurons. However, in the \ac{TTFS} coding, each neuron spikes at most once in the feed-forward computation. Consequently, the spike rate does not vary as much as in the rate-based coding and cannot be used as a pruning parameter. For this reason, the works in \cite{sen2017approximate} and \cite{shi2018neuroinspired, shi2019soft, tang2019spike}, which depend on the spike rate of the neurons to prune the connections, are not applicable to the \ac{SNN} with the energy-efficient \ac{TTFS} coding. Therefore, we proposed a connection pruning approach that is applicable to the energy-efficient \ac{TTFS} coding. In addition, our approach can be applied during the \ac{STDP}-based learning to support the applications that require the on-chip learning capability.
\\\hspace*{1em}Previously, the authors of \cite{rathi2018stdp} have proposed an algorithm that skips the membrane potential update when the connection weight is below a threshold during the \ac{STDP}-based learning. The connection is not eliminated from the network; it can still participate in the \ac{STDP}-based learning and has a chance to be strengthened in the future. Completely eliminating the connections may lead to pre-mature pruning (as these connections may be strengthened later during the \ac{STDP}-based learning) and cause accuracy degradation. However, it helps to reduce not only the number of membrane potential updates but also the number of weight updates during the \ac{STDP}-based learning. Therefore, we are motivated to develop a connection pruning approach that is capable of eliminating the connections completely during the \ac{STDP}-based learning while minimizing the accuracy loss.
\\\hspace*{1em}In this work, we propose a novel connection pruning approach to prune the \ac{SNN} with the \ac{TTFS} coding during the on-chip \ac{STDP}-based learning. As will be discussed in Section \ref{sec:results}, our approach can improve the learning time by 2.1x, reduce the network connectivity by 92.83\%, and save 64\% energy consumption during the on-chip learning without causing accuracy degradation. As compared to our baseline (without pruning), we achieved 2.83x speed-up and 78.24\% reduction in inference time and energy consumption, respectively. Furthermore, our hardware implementation for the connection pruning incurs as little as 0.56\% overhead in the power consumption as compared to our baseline hardware implementation with the pruning units removed. Crucially, our proposed connection pruning approach can be applied during the on-chip \ac{STDP}-based learning for deep \ac{SNN} with the \ac{TTFS} coding. Note that it is more challenging to apply the connection pruning on a deep \ac{SNN} than on a one-layer \ac{SNN} as the learning errors may propagate through the network layers, causing more spuriousness in the deep layers. It is also more challenging to compress the \ac{SNN} with the \ac{TTFS} coding as compared to the rate-based coding. In the \ac{TTFS} coding, there are few spikes emitted, as one neuron emits at most one spike. Removing the connections, which causes the number of spikes to decrease, may lead to insufficiency of spikes to activate the spike activities in the deep layers.
\section{Preliminaries} \label{sec:model}
\begin{figure}[ht]
	\centering
	\includegraphics[width=.8\linewidth]{snn}
	\caption{An example of the potential accumulation in the postsynaptic neuron ${m}$ connected to three presynaptic neurons $n_{1}$, $n_{2}$, and $n_{3}$. The connection weights are $w_{1}$, $w_{2}$, and $w_{3}$ respectively. When there is a spike from one of the presynaptic neurons, the membrane potential $V_{m}$ of neuron $m$ is increased by the corresponding weight. When $V_{m}$ exceeds the firing threshold $V_{th}$, neuron $m$ emits a postsynaptic spike.}
	\Description{An example of the spike activities of three presynaptic neurons $n_1$, $n_2$, $n_3$ and the membrane potential update in the postsynaptic neuron $m$. When the spikes arrive at the presynaptic neuron, the membrane potential $V_m$ of the postsynaptic neuron is increased by the corresponding connection weight. When $V_m$ exceedes a pre-defined firing threshold, the postsynaptic neuron $m$ emits a spike.}
	\label{fig:snn}
\end{figure}
In \ac{SNN}, the inputs are discrete spike events. When a spike event arrives at a neuron, the corresponding synaptic weight is accumulated in the membrane potential of the neuron. In our work, the potential accumulation follows the \ac{IF} model \cite{ermentrout1996type}, formulated in the following equation:
\begin{equation}
v_{i}(t) = v_{i}(t-1) + \sum_{j}w_{j,i}s_{j}(t-1)
\end{equation}
where
\begin{equation}
s_{i}(t) =
\begin{cases}
1 &\mbox{if neuron $i$ spikes at time $t$}\\
0 & \mbox{otherwise}
\end{cases}
\end{equation}
$v_{i}(t)$ and $s_{i}(t)$ are the membrane potential and postsynaptic spike of neuron $i$ at time $t$, $w_{j,i}$ is the synaptic weight between neuron $i$ and neuron $j$. The overview of this membrane potential accumulation is illustrated in Figure \ref{fig:snn}. As soon as the membrane potential exceeds the firing threshold, the neuron fires a postsynaptic spike and inhibits the other neurons that are close to it in the network.
\\\hspace*{1em}The \ac{STDP}-based learning algorithm in \ac{SNN} is performed based on the correlation between the presynaptic spike and postsynaptic spike of a connection. If the postsynaptic spike occurs within a small time window from the presynaptic spike, the two spikes are considered correlated and the connection is strengthen. This process is called \ac{LTP}. Otherwise, the two spikes are considered uncorrelated and the connection is weakened. This process is called \ac{LTD} \cite{song2000competitive}. In our work, we applied a hardware-friendly \ac{STDP}-based learning algorithm, as proposed in the work in \cite{kheradpisheh2018stdp}. In this algorithm, the synaptic weights are updated based on the following Equation:
\begin{equation}
\Delta w =
\begin{cases}
a^{+}w_{ij}(1-w_{ij}) &\mbox{if } t_{j}-t_{i} \leq 0\\
a^{-}w_{ij}(1-w_{ij}) &\mbox{otherwise}
\end{cases}
\end{equation}
where $a^{+}$ and $a^{-}$ are the learning rates, $t_{j}$ and $t_{i}$ are the time of the presynaptic and postsynaptic spikes, respectively. The values of the connection weights are in the range [0, 1]. The \ac{STDP}-based learning is performed layer by layer: a layer starts its learning only after the preceding layer finishes its learning. In the next section, we will describe our connection pruning approach applied to this \ac{SNN} model and \ac{STDP}-based learning algorithm. Note that our work focuses on the network compression technique rather than the neuron model and the learning algorithm, which were adopted from the work in \cite{kheradpisheh2018stdp}.
\section{Proposed Connection Pruning Approach} \label{sec:method}
\begin{figure*}[t]
	\centering
	\includegraphics[width=.9\linewidth]{pruning_overview}
	\caption{Overview of our two-stage connection pruning approach.}
	\Description{A diagram that explains our two-stage connection pruning approach. During the \ac{STDP}-based learning, every connection is associated with a prune parameter $P$, as described in Equation \ref{eq:p}. After every $k$ iteration, the connections that have $P$ greater than a threshold $\alpha$ are eliminated (i.e. set to zero value). This process is refered to as dynamic pruning. In addition, when the learning in each layer has finished, the connections that have the weights less than a threshold $\beta$ are eliminated. This process is refered to as post-learning pruning.}
	\label{fig:method_overview}
\end{figure*}
In order to compress the \ac{SNN} and reduce the on-chip \ac{STDP}-based learning time, we propose a connection pruning approach consisting of two stages: (i) dynamic pruning during the on-chip \ac{STDP}-based learning in every layer and (ii) post-learning pruning after each layer has learned, as shown in Figure \ref{fig:method_overview}. Note that both of these stages are performed during the on-chip \ac{STDP}-based learning.
\subsection{Dynamic Pruning During The On-chip STDP-Based Learning}
In the dynamic pruning stage, the connection pruning is performed after every $k$ iterations during the on-chip \ac{STDP}-based learning in each layer, based on two parameters: (i) weight update history, $h$, of the synaptic connection and (ii) time of postsynaptic spike, $t_{post}$. In the following, we will explain these parameters and present the details of our dynamic pruning approach.
\subsubsection{Weight Update History}
\begin{figure}[t]
	\centering
	\begin{subfigure}{.49\linewidth}
		\includegraphics[width=\linewidth]{weights_noprune}
		\caption{}
		\Description{Weight values in the range [0, 1] throughout the learning iterations, obtained from a run on our hardware implementation with the connection pruning turned off. There are three connection weights $w_1$, $w_2$, and $w_3$. $w_1$ and $w_2$ converge to 0 while $w_3$ converge to 1 after the learning is finished.}
		\label{fig:method_weights_noprune}
	\end{subfigure}
	\begin{subfigure}{.49\linewidth}
		\includegraphics[width=\linewidth]{weights_pruned}
		\caption{}
		\Description{Weight values in the range [0, 1] throughout the learning iterations, obtained from a run on our hardware implementation with the connection pruning turned on. The connection weights $w_1$ and $w_2$ decrease steeply and reach a low value at iteration 2000 and 2500, respectively. These connections are eliminated (i.e. set to zero value) at iteration 2000 and 2500.}
		\label{fig:method_weights_pruned}
	\end{subfigure}
	\caption{Weight values of selected connections throughout the iterations of the on-chip \ac{STDP}-based learning performed on our hardware implementation (a) without connection pruning (b) with connection pruning.}
\end{figure}
The weight update history, $h$, is defined in the following equation:
\begin{equation}\label{eq:h}
h = \frac{d}{w}
\end{equation}
where $d$ is the number of times the \ac{LTD} is performed on the connection, or the number of times the connection weight is decreased, in $k$ learning iterations. $w$ is the weight value at the time of pruning. The goal is to prune the connections having weights that are (i) small and (ii) decreasing steeply throughout a number of learning iterations. Note that the connection weights of the \ac{SNN} emulated in our work are in the range [0, 1]. The intuition is that if a connection weight, which is in the range [0, 1], has been decreasing steeply in a time period and has reached a small value, it will likely continue to decrease until it approaches zero. Therefore, we propose to prune these connections early to reduce the number of membrane potential updates and learning computations in the future iterations. For example, in Figure \ref{fig:method_weights_noprune}, which shows the learning progress of selected weights during the on-chip \ac{STDP}-based learning performed on our hardware implementation, weights $w_2$ and $w_3$ decrease steeply to a small value (less than 0.1) from iteration 2000 to 2500 and 1500 to 2000, respectively. In the later iterations, these weights contribute little to the spiking activities and will approach zero eventually. Consequently, $w_2$ can be pruned at iteration 2000 and $w_3$ can be pruned at iteration 2500, as shown in Figure \ref{fig:method_weights_pruned}. Note that having the weight value in the denominator helps to reduce the chance of pre-mature pruning, in which the connection is pruned when it still contributes non-negligibly to the spiking activities.
\subsubsection{Time of Postsynaptic Spike}
\begin{figure}[t]
	\centering
	\begin{subfigure}{.49\linewidth}
		\centering
		\includegraphics[width=.9\linewidth]{ttps_early}
		\caption{}
		\Description{An example of the spikes at four presynaptic neurons $s_{1}, s_{2}, s_{3}, s_{4}$ and a postsynaptic neuron $s_{post}$. $s_1$ spikes before $s_{post}$, $s_3$ spikes at the same time as $s_{post}$, and $s_{2}$ and $s_{4}$ spike after $s_{post}$.}
		\label{fig:method_spike_early}
	\end{subfigure}
	\begin{subfigure}{.49\linewidth}
		\centering
		\includegraphics[width=.9\linewidth]{ttps_late}
		\caption{}
		\Description{An example that shows similar spike activities as Figure \ref{fig:method_spike_early} except $s_{post}$ spikes at the same time as $s_2$.}
		\label{fig:method_spike_late}
	\end{subfigure}
	\caption{Examples of the spiking activities of four presynaptic neurons $s_{1}, s_{2}, s_{3}, s_{4}$ and a postsynaptic neuron $s_{post}$. (a) $s_{post}$ spikes earlier, more connection weights are decreased (b) $s_{post}$ spikes later, fewer connection weights are decreased.}
	\label{fig:method_spike_activity}
\end{figure}
In addition to the weight update history, our proposed pruning approach considers the synaptic competition during the on-chip \ac{STDP}-based learning to formulate the pruning decision. The connection weights that are decreased during a strong synaptic competition should be less likely to be pruned than those that are decreased during a synaptic competition that is not as strong. The strength of the synaptic competition can be estimated using the timing of the postsynaptic spikes. We will explain this relationship in the following.
\\\hspace*{1em}In our implementation of the on-chip \ac{STDP}-based learning, the connection weights are increased if the presynaptic neurons spike before the postsynaptic neurons; otherwise, the connection weights are decreased, as described in Section \ref{sec:model}. If there are many large connection weights, few spike events are needed to cause the membrane potential of the postsynaptic neuron to exceed the firing threshold. Hence, only the most competitive connections that have the largest weight values and carry the earliest presynaptic spikes will be able to contribute to the spiking of the postsynaptic neuron. Figure \ref{fig:method_spike_early} shows an example to illustrate this scenario. When the postsynaptic spike, $s_{post}$, is emitted early, it is only the presynaptic spikes $s_{1}$ and $s_{3}$ that were early enough to contribute to $s_{post}$ and their connection weights will be increased. Contrarily, although $s_{2}$ is relatively early as compared to $s_{4}$ and its connection weight may be large, it was not able to contribute to $s_{post}$. Consequently, the connection weight associated with $s_{2}$ will be decreased. On the other hand, if $s_{post}$ is emitted later, as shown in Figure \ref{fig:method_spike_late}, $s_{2}$ will be able to contribute to $s_{post}$ and its connection weight will be increased. Therefore, the weight decrements in the former scenario (Figure \ref{fig:method_spike_early}) should be weighted less than the weight decrements in the latter scenario (Figure \ref{fig:method_spike_late}) in the connection pruning decision.
\\\hspace*{1em}The strong synaptic competition, as explained above, may happen in the early iterations in the on-chip \ac{STDP}-based learning on our hardware, as shown in Figure \ref{fig:method_weights_noprune}. We initialized the connection weights to follow the normal distribution in the range of (0, 1) with the mean being skewed at 0.8, following the work in \cite{sdnnpython}. This is to encourage the spiking activities and to emphasize the most dominant presynaptic spikes and connections in the early iterations of the learning. The connections that have smaller weights or carry less dominant presynaptic spikes will have their weights decreased. However, these connections still have a chance to have their weights increased in a later learning iteration, when many of the other connections are weakened and the postsynaptic neurons do not spike as early as before, as shown in weight $w_1$ in Figure \ref{fig:method_weights_noprune}. Therefore, the time of postsynaptic spikes, $t_{post}$, helps to avoid pruning these connections in the early learning iterations when the synaptic competition is strong.
\subsubsection{The Dynamic Pruning Approach}
The proposed dynamic pruning approach combines $h$ and $t_{post}$ into a pruning parameter $P$:
\begin{equation}
P = h * t_{post} \label{eq:hc}
\end{equation}
Combining equations \eqref{eq:h} and \eqref{eq:hc}, we have:
\begin{equation}
P = \frac{d}{w} * t_{post} \label{eq:p}
\end{equation}
$P$ is evaluated for every connection in every $k$ iterations of the on-chip \ac{STDP}-based learning. If $P$ is greater than a pre-defined threshold $\alpha$, the connection is pruned. This dynamic pruning stage is performed during the on-chip \ac{STDP}-based learning in each layer. After the learning is finished, we proceed to the post-learning pruning stage, which we will describe next.
\subsection{Post-Learning Pruning}
Our post-learning pruning approach is performed after the learning in one layer has finished, before proceeding to the next layer, as shown in Figure \ref{fig:method_overview}. In the post-learning pruning stage, all the connections that have the weights less than a threshold, $\beta$, are eliminated. Note that although this stage is performed after the learning in a layer, it is still within the on-chip learning process of the \ac{SNN}. It differs from the existing pruning approaches on the pre-trained networks as it can affect the learning in the next layer. Overly aggressive pruning of the preceding layer will lead to learning errors in the next, causing losses in the classification accuracy.
\section{Evaluation Hardware Architecture} \label{sec:architecture}
\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{architecture}
	\caption{Our evaluation hardware architecture, which incorporates our connection pruning approach to reduce the on-chip learning time and the network connectivity for \ac{SNN}.}
	\Description{Our evaluation hardware architecture. The spike events are received by the Controller, which is connected to the Weight Memory, the STDP Unit, the Prune Unit, the Decrement Track Memory, the Spike Memory, and an array of PEs. Each PE is connected to a Potential Memory. The STDP Unit and the Prune Unit are connected to the Decrement Track Memory.}
	\label{fig:architecture}
\end{figure}
We designed an event-driven hardware architecture that incorporates our proposed connection pruning approach to reduce the on-chip learning time and network connectivity for \ac{SNN}, as shown in Figure \ref{fig:architecture}. The overall flow of our hardware architecture is as follows. In every time step, the Controller receives the spike events and forwards them to the \acp{PE}. The \acp{PE} compute the membrane potentials and update the Potential Memory. At the end of the time step, the \acp{PE} compares each membrane potential in the Potential Memory with a pre-defined firing threshold. If the membrane potential of a neuron exceeds the firing threshold, the \ac{PE} writes the corresponding neuron ID to the Spike Memory. Finally, the Controller activates the STDP unit to perform the weight updates based on the \ac{STDP}-based learning algorithm that was described in Section \ref{sec:model}. During the learning, the STDP unit records the decrements of the weight values of each connection in the Decrement Track Memory.
\\\hspace*{1em}The connection pruning is performed in the Prune Unit in every $k$ iterations. After the \ac{STDP} unit finishes updating the connection weights for the current time step, the Prune unit evaluates the pruning criteria for each connection weight (described in Section \ref{sec:method}). If the pruning criteria is met, the corresponding connection weight is set to zero in the Weight Memory. The pruning components incur little overheads in the resources and power consumption in our hardware implementation which we will discuss in the following sections.
\section{Experimental Setup} \label{sec:expsetup}
This section describes our experimental setup to evaluate our proposed connection pruning approach and hardware architecture. We will first present the hardware platform on which our hardware architecture was implemented, followed by the dataset and the \ac{SNN} network parameters used in our experiments.
\subsection{Hardware Platform}
Our hardware architecture was implemented on the Zynq-7000 Zedboard (XC7Z020) using Verilog. The hardware resources and power consumption were estimated by Xilinx Vivado 2018.3 \cite{feist2012vivado}. Our hardware implementation of the connection pruning incurs less than 3.52\% overheads in the LUTs and FFs and approximately 8.7\% overheads in the BRAM consumption as compared to our baseline hardware implementation (with the Prune Unit and the Decrement Track Memory removed). In addition, 11 DSP slices were used in the implementation of the connection pruning, which is 5\% of the DSP slices available on the board. The power consumption was increased by 0.56\% as compared to our baseline hardware implementation (no pruning). These overheads are reported based on the post-implementation estimation generated by Xilinx Vivado 2018.3. Our hardware implementation was run at the clock frequency 100 MHz.
\subsection{Dataset and Network Configuration} \label{sec:dataset}
Our connection pruning approach was evaluated on the Caltech 101 dataset \cite{fei2004learning}. The training set consists of 400 images of the two categories: (i) human face and (ii) motorbike in 160x250 pixels. The test set consists of 396 images. Our hardware implementation of the \ac{SNN} was based on \cite{kheradpisheh2018stdp} and \cite{sdnnpython}. The \ac{SNN} implemented on our hardware consists of three convolutional-pooling layers. The kernels of the three convolutional layers are of size 5x5x4, 17x17x20, and 5x5x20 (width x height x depth). In between the convolutional layers, the downsampling was performed with window sizes 7x7 and 5x5, respectively. Our connection pruning approach was applied to the convolutional layers. Following to the work in \cite{kheradpisheh2018stdp}, the images were pre-processed using a \ac{DoG} filter, followed by the \ac{SNN} emulated on our hardware implementation, and classified using a \ac{SVM}. Note that our work does not focus on improving this model but developing a connection pruning algorithm to reduce the network complexity of the \ac{SNN} architecture. The effects of our connection pruning approach will be discussed in the following section.
\section{Results and Discussion} \label{sec:results}
The performance of our connection pruning approach was evaluated based on the following four metrics: (i) the number of connections reduced in the network, (ii) the time speed-up, (iii) the energy saved, and (iv) the classification accuracy. In this section, we will discuss the trade-offs between these metrics.  In addition, the individual effects achieved by each of the stages: (i) dynamic pruning and (ii) post-learning pruning in our two-staged connection pruning approach will be analysed. Finally, we will discuss the impacts of connection pruning on the network behaviours in the inference stage. We will demonstrate that our connection pruning approach successfully eliminates a significant number of connections during the on-chip \ac{STDP}-based learning, which helps to reduce the time and energy in both the learning and the inference stages, without incurring any accuracy loss.
\subsection{Connectivity Reduction vs. Accuracy}
\begin{figure}[t]
	\centering
	\includegraphics[width=.9\linewidth]{connectivity_accuracy}
	\caption{Connectivity reduction and accuracy trade-offs. The connectivity reduction was computed as $n_{pruned}$/$n_{total}$, where $n_{pruned}$ is the number of connections being pruned and $n_{total}$ is the total number of connections in the network.}
	\Description{Connectivity reduction for different trade-offs in accuracy. The connectivity is reduced by 92.83\%, 92.9\%, 93.35\%, and 96.2\% when the accuracy loss is allowed to be up to 0\%, 1,2\%, 1.8\%, and 2\%, respectively.}
	\label{fig:conn_acc}
\end{figure}
\begin{table*}[t]
	\centering
	\caption{Classification accuracy achieved on the Caltech-101 (Face/Motorbike) dataset.}
	\label{tab:comparison_accuracy}
	\begin{tabular}{|c|c|c|c|c|c|c|c|}
		\hline
		& \textbf{\cite{lee2018deep}} & \textbf{\cite{mozafari2018first}} & \textbf{\cite{kheradpisheh2018stdp}} & \textbf{\cite{kheradpisheh2020}} & \textbf{\cite{zhang2020rectified}} & \textbf{Our Work} \\\hline
		SNN Architecture & CNN & CNN & CNN & Fully Connected & CNN & CNN \\\hline
		Learning Algorithm & \ac{STDP} & Reward-modulated \ac{STDP} & \ac{STDP} & Back-propagation & Back-propagation & \ac{STDP} \\\hline
		Platform & GPU & GPU & GPU & GPU & CMOS & FPGA \\\hline
		\# Learnable Parameters & 25,488 & 23,120 & 25,480 & 160,008 & >110 M & \textbf{2,383} \\\hline
		\shortstack{On-Chip Learning\\on Embedded Hardware} & No & No & No & No & No & \textbf{Yes} \\\hline
		Accuracy & 97.6\% & 98.9\% & 99.1\% & 99.2\% & \textbf{99.5\%} & 95.7\% \\\hline
	\end{tabular}
\end{table*}
Our proposed connection pruning approach helps to eliminate 92.83\% connections in the network without incurring any loss in the classification accuracy, as presented in Figure \ref{fig:conn_acc}. This is consistent with the result in \cite{iglesias2005dynamics} that more than 90\% of the connections can be eliminated after one million iterations of the \ac{STDP}-based learning, regardless of the network size. However, in the work in \cite{iglesias2005dynamics}, the connections are eliminated after a large number of iterations (one million iterations). Our connection pruning approach eliminates the connections early to save the on-chip learning time while preserving the accuracy. Different trade-offs between the number of connections eliminated and the accuracy loss are presented in Figure \ref{fig:conn_acc}. The number of connections in the network can be reduced by 92.83\% without incurring any loss in the accuracy. Moreover, when the accuracy is allowed to fall within 1.2\%, the network can be compressed by 92.9\%. When up to 1.8\% loss in the accuracy is allowed, the network connectivity can be reduced by 93.35\%. In addition, when the accuracy loss is maintained within 2\%, the connectivity can be reduced by up to 95.07\%. The proposed connection pruning algorithm, which does not cause any loss in the classification accuracy, helps to reduce the number of connections to 9.7 times-55 million times fewer than other \ac{SNN} implementations in the existing works, as shown in Table \ref{tab:comparison_accuracy}. Note our work does not aim to achieve the highest accuracy but a balance between the accuracy and the network complexity, which has significant impacts on the response time and the energy consumption on embedded systems platforms. In the case our connection pruning algorithm is performed off-chip (no on-chip learning is needed), it can help to reduce the number of entries in the Weight Memory by 92.83\% as compared to our baseline (the \ac{SNN} described in Section \ref{sec:dataset}).
\subsection{Improvements in Learning Time and Energy Consumption}
\begin{figure}[t]
	\centering
	\includegraphics[width=.9\linewidth]{speedup_accuracy}
	\caption{Trade-offs between learning time, energy consumption, and classification accuracy of the \ac{SNN} emulated on our hardware implementation. The learning time speed-up and energy reduction are computed as $t_{noprune}/t_{pruned}$ and $(1-E_{pruned}/E_{noprune})*100$, respectively. $t_{pruned}$ and $t_{noprune}$ are the learning time with and without the connection pruning, $E_{pruned}$ and $E_{noprune}$ are the energy consumption in the learning with and without pruning.}
	\Description{Learning time, energy consumption, and accuracy trade-offs. The learning time is reduced by 2.1x, 2.14x, 2.26x, and 2.31x, and the energy consumption is reduced by 64\%, 65.82\%, 68.71\%, and 69.62 when the accuracy loss is allowed to be up to 0\%, 1.2\%, 1.8\%, and 2\%, respectively.}
	\label{fig:speedup_acc}
\end{figure}
The speed-up in the on-chip \ac{STDP}-based learning time depends on (i) the number of connections being pruned in the network and (ii) the earliness of the pruning during the on-chip learning. Our connection pruning approach helps to speed-up the on-chip learning time by 2.1x without incurring any loss in the classification accuracy, as shown in Figure \ref{fig:speedup_acc}. In addition, when the accuracy loss is up to 1.2\%, the learning time is improved by 2.14x. Furthermore, the learning time can be improved by up to 2.26x and 2.31x when the accuracy is allowed to fall within 1.8\% and 2\%, respectively. In addition to the learning time, the energy consumption during the \ac{STDP}-based learning is also improved as a result of the network compression. In our experiments, the energy saving is estimated based on the number of operations reduced, similar to the related work in \cite{rathi2018stdp}. As shown in Figure \ref{fig:speedup_acc}, the energy consumption is reduced by 64-69.62\% with 0-2\% loss in the accuracy. The improvements on the learning time and the energy consumption were attributed to the two stages in our connection pruning approach: dynamic pruning and post-learning pruning, which we will analyse in the following.
\subsection{Dynamic Pruning vs. Post-Learning Pruning}
\begin{table}[t]
	\centering
	\caption{Impacts of dynamic pruning and post-learning pruning when applied individually and when combined together in our two-staged connection pruning approach.}
	\label{tab:dynpos_compare}
	\resizebox{\linewidth}{!}
		\begin{tabular}{|c|c|c|c|c}
			\hline
			& \textbf{\shortstack{Dynamic\\Pruning}} & \textbf{\shortstack{Post-Learning\\Pruning}} & \textbf{\shortstack{Combined\\Approach}}\\\hline
			Connectivity Reduction & 89.95\% & \textbf{92.8\%} & \textbf{92.8\%} \\\hline
			\shortstack{Learning Time\\Speed-up} & \textbf{2x} & 1.78x & \textbf{2.1x} \\\hline
			Energy Reduction & \textbf{62\%} & 48\% & \textbf{64\%} \\\hline
	\end{tabular}}
\end{table}
Each of the two stages: (i) dynamic pruning and (ii) post-learning pruning in our connection pruning approach has its own effects on the network connectivity and the learning time. While the dynamic pruning is performed periodically during the learning in each layer, the post-learning pruning is performed after the learning in the layer has finished, as illustrated in Figure \ref{fig:method_overview}. Therefore, the dynamic pruning can eliminate the connections earlier than the post-learning pruning. When the two approaches are applied separately, the dynamic pruning achieves a higher learning time speed-up and energy reduction, as shown in Table \ref{tab:dynpos_compare}. However, the connectivity reduction achieved by the dynamic pruning is less than the post-learning pruning, as shown in Table \ref{tab:dynpos_compare}. The reason is that during the learning, the network needs to maintain sufficient connections to generate the spike events in order to stimulate the \ac{STDP}-based weight updates. On the other hand, after the learning in the layer has finished, the weights can be pruned without the concerns of the learning activities. Our connection pruning approach combines the dynamic pruning and the post-learning pruning to achieve the high learning time speed-up, high energy saving and significant connectivity reduction.
\subsection{Impact on Inference Stage}
\begin{figure*}[t]
	\centering
	\begin{subfigure}{.49\linewidth}
		\centering
		\includegraphics[width=.9\linewidth]{featuremaps_noprune}
		\caption{}
		\Description{Selected feature maps in the three convolutional layers with the connection pruning turned off.}
		\label{fig:maps_noprune}
	\end{subfigure}
	\begin{subfigure}{.49\linewidth}
		\centering
		\includegraphics[width=.9\linewidth]{featuremaps_pruned}
		\caption{}
		\Description{Selected feature maps in the three convolutional layers with the connection pruning turned on.}
		\label{fig:maps_pruned}
	\end{subfigure}
	\caption{Selected feature maps in different convolutional layers after the \ac{STDP}-learning (a) without connection pruning and (b) with connection pruning on our hardware implementation.}
	\label{fig:featuremaps}
\end{figure*}
\begin{figure}[t]
	\centering
	\includegraphics[width=.9\linewidth]{inference_speedup}
	\caption{Reduction in energy consumption and response time in the inference, computed as $E_{pruned}/E_{noprune}$ and $t_{pruned}/t_{noprune}$, respectively. $E_{pruned}$ and $E_{noprune}$ are the energy consumption in the inference with and without pruning, $t_{pruned}$ and $t_{noprune}$ are the inference time with and without pruning.}
	\Description{Time speed-up and energy reduction in the inference. The inference time speed-up is 2.825x, 2.832x, 2.89x, 3.011x, and 3.1x and the energy consumption is reduced by 78.24\%, 78.32\%, 78.87\%, 80.81\%, and 81.47\% when the number of connections are reduced by 92.83\%, 92.9\%, 93.35\%, 94.63\%, and 95.07\%, respectively.}
	\label{fig:inference_speedup}
\end{figure}
The network compression during the \ac{STDP}-based learning significantly reduces the response time and energy consumption in the inference stage. As shown in Figure \ref{fig:inference_speedup}, the inference time on our hardware implementation is speeded-up by 2.83-3.1x when 92.83-95\% of the connections in the network are eliminated. In addition, the energy consumption is reduced by 78.24-81.47\% as compared to our baseline without pruning. Meanwhile, the number of spikes generated in the inference is preserved at 95\% compared to the \ac{SNN} trained without the connection pruning. Figure \ref{fig:featuremaps} shows the feature maps of different images in the Caltech 101 dataset in different convolutional layers. The first convolutional layer extracts the edges in the images while the deeper layers observe more abstract features. The feature maps obtained in the \ac{SNN} trained with the connection pruning (Figure \ref{fig:maps_pruned}) are similar to the ones obtained in the \ac{SNN} trained without the connection pruning (Figure \ref{fig:maps_noprune}). This demonstrates that our connection pruning approach does not significantly affect the network behaviours in the inference. Therefore, the classification accuracy was preserved.
\subsection{Comparison with Existing Works}
Prior to our work, there have been approaches that eliminate the weak connections and dormant neurons in the well-trained spiking networks \cite{sen2017approximate, chen2018fast}. However, these works are not applicable during the on-chip learning, when the weight values have not converged. This limits the potential of these works to be applied to the applications that require frequent online learning \cite{sahoo2018online, akusok2019spiking, skatchkovsky2020federated}. On the other hand, the approaches in \cite{rathi2018stdp, shi2019soft, kundu2021spike} and our work eliminate the connections during the on-chip learning. While the works in \cite{shi2019soft, kundu2021spike} focus on the \ac{SNN} with the rate-based coding, our work proposes a connection pruning approach for the \ac{SNN} with the \ac{TTFS} coding, which is more energy-efficient \cite{rullen2001rate}. Note that it is more challenging to eliminate the connections for the \ac{SNN} with the \ac{TTFS} coding, as the spike activities are sparser than the rate-based coding. The connection pruning may cause the number of spikes to drop drastically, causing the shortage of spike activities to trigger the weight updates in the \ac{STDP}-based learning. In addition, our connection pruning approach was evaluated on a deep \ac{SNN}, consisting of three convolutional layers, while the works in \cite{rathi2018stdp, bogdan2018structural, shi2019soft} was applied to the \acp{SNN} consisting of one layer. It is more challenging to eliminate the connections during the \ac{STDP}-based learning in a deep \ac{SNN} as compared to an \ac{SNN} with a single layer. In the deep \ac{SNN}, the errors caused by the overly aggressive pruning in a layer will be carried to the following layers and may eventually increase accuracy loss.
\section{Conclusions} \label{sec:conclusions}
In this paper, we have proposed a novel connection pruning approach to be applied during the on-chip \ac{STDP}-based learning for \ac{SNN}, in which the spikes are encoded using \ac{TTFS}. Our main contributions are three-fold. First, we proposed a novel connection pruning approach that helps to compress the network and speed-up the on-chip learning time on embedded systems hardware. Our connection pruning approach was evaluated on a deep convolutional \ac{SNN} and achieved the compression of 92.83\% without incurring any loss in the classification accuracy. Second, evaluation on the \ac{FPGA} platform showed that the on-chip learning time was reduced by 2.1x and the inference time was reduced by 2.83x as compared to our baseline with the connection pruning turned off. Moreover, the energy consumption was reduced by 64\% in the on-chip learning and 78.24\% in the inference as compared to the baseline. The hardware implementation of the connection pruning incurs 0.56\% power overhead as compared to our hardware implementation with the pruning units removed. Third, while most of the existing works perform the compression on a well-trained network, our connection pruning approach can be applied during the on-chip \ac{STDP}-based learning. In addition, our approach is applicable to the \ac{SNN} with the \ac{TTFS} coding while most of the existing works focus on the \ac{SNN} with the rate-based coding, which consumes more energy. In the future, our approach can be combined with various device-level optimizations to further reduce the delay, hardware resources, and energy consumption of \ac{SNN} implementations on embedded systems platforms.

\begin{acks}
	This work is supported in part by the NUS Start-up Grant, in part by the Ministry of Education (Singapore) Academic Research Fund (Tier 1), and in part by the A*STAR Programmatic Research Grant (SpOT-LITE).
\end{acks}

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


text:
Adolf Ludvig Christiernin, född 28 februari 1765 på Ekolsunds slott, död 13 september 1842 i Åbo, var en svensk militär.

Christiernin blev 1778 furir vid Konungens blå och gula livgarde och deltog som kapten vid Änkedrottningens livregemente i Gustaf III:s ryska krig samt utnämndes 1795 till överstelöjtnant och 1803 till chef för Karelska lätta dragonregementet.

Under Finska kriget 1808–09 utmärkte sig "gamle Christiernin" i synnerhet vid Revolax, då han, trots att han ingick i rytteriet, satte sig i spetsen för en bataljon av Savolax infanteriregemente och drev ut ryssarna från predikantbostället. 1810 fick han avsked som överste i armén. Han dog i Åbo 1842.

Källor

Noter

Vidare läsning

Personer i finska kriget
Födda 1765
Avlidna 1842
Män

redpajama_set_name: RedPajamaWikipedia


text:
Kaine, Luria rally the vote on Virginia's Eastern Shore
Democratic candidates Sen. Tim Kaine and Elaine Luria held a rally Sunday in Accomack County.
Kaine, Luria rally the vote on Virginia's Eastern Shore Democratic candidates Sen. Tim Kaine and Elaine Luria held a rally Sunday in Accomack County. Check out this story on delmarvanow.com: https://www.delmarvanow.com/story/news/local/virginia/2018/10/28/kaine-luria-rally-vote-virginias-eastern-shore/1738017002/
Katy Harris, Special to Salisbury Daily Times Published 10:40 a.m. ET Oct. 28, 2018 | Updated 12:03 p.m. ET Oct. 29, 2018
Sen. Tim Kaine led a roundtable discussion on the Eastern Shore about offshore oil and gas drilling.
Sen. Tim Kaine speaks at a rally Oct. 21 at the Mary N. Smith Cultural Enrichment Center in Accomac.(Photo: Katy Harris photo)Buy Photo
Democrats U.S. Sen. Tim Kaine and Elaine Luria came to the Eastern Shore of Virginia Sunday for a Get Out the Vote Rally in Accomac.
They spoke to a packed crowd at the historic Mary N. Smith Cultural Enrichment Center.
Kaine faces Republican challenger Corey Stewart in the Nov. 6 mid-term election while Luria takes on incumbent GOP congressman Scott Taylor for the 2nd District seat that includes both Eastern Shore counties.
Congressman Bobby Scott, D-3rd District, state Sen. Lynwood Lewis, D-100, and Accomack County supervisor Reneta Major also spoke at the rally.
Kaine and Luria both coordinated their schedules to make the Eastern Shore one of the last stops of their campaigns before election day on Nov. 6.
"I had told my campaign manager, 'We are doing a rally on the Eastern Shore. You got to figure it out, when, but we're going to do one," said Kaine.
Luria said the energy and enthusiasm from Eastern Shore residents have been motivating.
"We have been up here seven times to the Northampton and the Accomack Democratic Committee meetings. This has been in the works for a long time and I think it turned out really good and a good crowd," said Luria.
The Mary N. Smith Cultural Enrichment Center gymnasium doors opened at 1:30 p.m. for a mix and mingle opportunity with the candidates.
Elaine Luria, candidate for Virginia's 2nd congressional district, speaks at a rally Oct. 21 at Mary N. Smith Cultural Enrichment Center in Accomac. (Photo: Katy Harris photo)
Eric Dodge, an Onancock resident, said he has been following Luria and Kaine since the primaries. He decided to attend the rally after receiving an email.
"They seem very committed and I just want to be there to support. I think they actually can use our energy to keep going 24-7," said Dodge.
Dodge said a key issue for him is sustainability.
"Environmental, energy and the government. The recent erosion of the many protections the government gives us needs to be reversed," he said.
Melfa resident Peggy Corbin said "it was a great opportunity to get some first-hand information."
"I felt like I needed to be here because I needed to set the pace for some of the things in my community," said Corbin.
Kaine said working on Eastern Shore aquaculture issues was constant and continuous for him.
"Sometimes it's environmental issues. Sometimes it's workforce issues. Sometimes it's research through VIMS. So making sure that the aquaculture industry is vigorous and then because there's the federal Fish and Wildlife Service at Chincoteague, I'm always working on those issues too, to make sure that we find that right balance between preserving that national asset and continuing to provide public access that is good for people and good for the community so, Chincoteague and beach issues and promoting aquaculture," said Kaine.
Kaine said his current focus was ensuring growth and expansion at NASA Wallops.
"I've been pushing for more investments in NASA Wallops. I've been working with the new administrator Jim Bridenstine on that and I've gotten very good assurances from him that they really see the value of NASA Wallops. I think you're going to see additional NASA investments too."
State Sen. Lewis introduced Luria by saying she is a candidate who is made for this district.
"Elaine, a United States Naval Academy graduate, 20 years in the United States Navy, a great model of a career. She goes out and she forms a small business, becomes a small business person, all the while raising a family within this district, so nothing makes her a better representative for us, for our values and for our interests," said Lewis.
A big crowd turned out Oct. 21 at Mary N. Smith Cultural Enrichment Center in Accomac for a rally by Democratic candidates ahead of the November election. (Photo: Katy Harris photo)
"Elaine is the kind of person that you would have expected to find in Congress 30 years ago. Now not so much, but she will bring back that sense of responsibility, that sense of duty, those values that we desperately need in these very terrible times that we find ourselves."
Luria is a former Navy commander and small business owner living in Norfolk.
"I spent 20 years in the Navy. I drove ships. I operated nuclear reactors on aircraft carriers. I commanded a combat unit of 400 sailors and I feel like I've been serving our community, helping keep our nation safe and I want to continue that work by representing you," Luria told the crowd.
MORE: Va. elections: Mark your calendar for these deadlines
MORE: Scott Taylor leads challenger Elaine Luria in Va. 2nd District: Poll
"As a leader in the Navy my job was to create an environment where those people could succeed. I think it's the same in our communities. It's the people. It's you and your families who make our communities go, and I think it's the job of the representative to create an environment where you will have the opportunity to succeed."
Luria said environmental issues such as "stopping offshore drilling, for example, the effects of sea level rise and recurrent flooding and then the health of the Chesapeake Bay are just really very, very important issues to the Shore and to the livelihood of people on the Shore."
Luria said rural broadband was a challenge for the Shore in bringing businesses here.
She also highlighted the ripple effects of President Trump's tariffs that she said she saw during a recent tour of small businesses in Cape Charles.
Sen. Tim Kaine, Elaine Luria hold Shore rally
Elaine Luria, candidate for Virginia's 2nd congressional district, speaks at a rally Oct. 21 at Mary N. Smith Cultural Enrichment Center in Accomac. Katy Harris photo
Democratic candidate Sen. Tim Kaine talks to residents at a Get Out the Vote rally he and Congressional candidate Elaine Luria held Oct. 21 at the Mary N. Smith Cultural Enrichment Center in Accomac. Katy Harris photo
Elaine Luria, Democratic candidate for Virginia's 2nd District, speaks to voters during a rally Oct. 21 in Accomac. Katy Harris photo
Eastern Shore residents attend a Get Out the Vote rally in Accomac on Oct. 21 for Democratic candidates in the No. 6 election. Katy Harris photo
Sen. Tim Kaine speaks to the gathering at the rally Oct. 21 at Mary N. Smith Cultural Enrichment Center in Accomac. Katy Harris photo
A big crowd turned out Oct. 21 at Mary N. Smith Cultural Enrichment Center in Accomac for a rally by Democratic candidates ahead of the November election. Katy Harris photo
A packed room at the Mary N. Smith Cultural Enrichment Center in Accomac listens to Democratic candidates at a rally on Oct. 21. Katy Harris photo
Congressional candidate Elaine Luria talks to a volunteer in Accomac. Katy Harris photo
Luria said she also plans to focus on protecting the rural healthcare systems.
"Eighty-percent of people on the Shore get their healthcare through the rural health care system and we need to make sure that all the mechanisms stay in place to allow that system to still be effective," says Luria.
After attending the rally, Dodge said he was struck by Kaine's 6-word philosophy: a Virginia that works for all.
"It was very powerful the way he presented those ideas very simply. I want things to happen for all." Dodge said.
"None of this stuff is easy. It's very complex. All sides have valid issues. But I'm tired of politicians focusing on promises for individual gain, like reducing your taxes, versus what's better in a more global sense, benefitting all. We shouldn't just think about our own pocketbooks."
MORE: NASA's Wallops lands private company's first US launch site
MORE: Cape Charles unveils brand, logo
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Nationally, April is recognized as child abuse prevention month.
Organizations and individuals from around the state met at the capitol to recognize the month and raise awareness.
The pinwheel represents the bright, happy future that every child deserves. Individuals, businesses, schools, etc. may purchase pinwheels to plant in their yard or display in a vase to show their support of the prevention of child abuse.
To purchase pinwheels and learn more about the many events Prevent Child Abuse Utah has happening throughout April visitpcautah.org.
·April 23 Pinwheel Luncheon at Texas Roadhouse from 11am to 2pm, 685 S Ring Road, Layton. Join us for our second annual Pinwheel Luncheon. Lunch is $10 and includes: Homemade bread & butter, salad with choice of dressing, pulled pork sandwich, homemade baked beans and a non-alcoholic beverage. All proceeds go to support the programs at Prevent Child Abuse Utah.
·April 30 Partners in Prevention Breakfast recognizing the 2015 Child Advocates of the Year at the Eccles Conference Center, 2415 Washington Blvd. Ogden, from 8 am to 9:30 am. Each year nominations are taken statewide to recognize outstanding individuals throughout our state. Please join us as we recognize several of these amazing people at this breakfast awards ceremony.


text:
Home » Nick Cannon
Nick Cannon Joins Meruelo Media's Power 106 Los Angeles
Nick Cannon Mornings on Power 106 Starts this Monday, June 17!
(KPWR 105.9 – Los Angeles) Meruelo Media is pleased to announce that TV/Radio host, comedian, actor, producer, rapper, writer, philanthropist, children's book author, and activist, Nick Cannon, will join Power 106, LA's #1 hip hop station, as the morning show's main host and producer. The show, "Nick
Cannon Mornings – Power 106 #1 for Hip Hop," launched Monday, June 17, and will air daily from 5AM — 10AM.
"Nick is the quintessential entertainer and content creator who will take our media game to the next level," said Otto Padron, President, and COO of Meruelo Media, "His tremendous success across television, film, and music is testimony of his unique talent, work ethic, and impeccable reputation." Otto concluded, "Nick is the absolute right talent to start our mornings on Power 106 – this is an incredible match and we're fortunate to have him on our family!"
Cannon said, "Power 106 is one of the most substantial fixtures in our Hip Hop culture that has proven time and time again that they are #1 and not going anywhere! Los Angeles is used to star players joining star franchises! I'm just honored for the opportunity to make our community and Culture proud. All I can say is it's Ncredible!"
Power 106 Program Director, E-Man said, "Nick Cannon is that fresh sound we were looking for on Power 106. Nick is an incredible entertainer who embodies Hip Hop, pop culture, comedy, and community all in one! We are very excited to have him on our team."
Cannon is the Host and Executive Producer of Fox's #1 hit TV show, "The Masked Singer", as well as the Creator, Host, and Executive Producer of MTV's "Wild 'N Out", which will begin production on its 15th season later this year. Wild 'N Out has been influential in developing the careers of Kevin Hart, Kat Williams, DC Young Fly, and several other successful comedians. Wild 'N Out's YouTube channel has amassed almost 5 million subscribers since its official launch approximately 6 months ago. In 2009, Cannon was named chairman of the TeenNick network, making him "the youngest TV chairman in history". Simultaneously, the entertainment mogul has collaborated with Will Smith, Future, Migos, Chris Brown, Ty Dolla $ign, and others on personal music projects. As a music business curator and label head of Ncredible Entertainment, Nick has helped discover and develop award-winning talent such as, H.E.R., Kehlani, Soulja Boy, and Justin Bieber. Under Ncredible Entertainment, Cannon continues to curate a roster of groundbreaking talent that will dominate the airwaves and music landscape through the highly anticipated Summer 2019 releases from the Ncredible Gang, Lil Dope Boy, Suave, Black Squad, and Justina Valentine.
Cannon is represented by Michael Goldman, Del Shaw, and CAA.
Meruelo Media owns Power 106, 93.5 KDAY, KWHY-22 TV, KBEH 63 TV, Actify Media, Meruelo Digital and Meruelo Studios. Meruelo Media is the process of purchasing 95.5 KLOS-FM and 93.9 KXOS-FM. Meruelo Media is owned and operated by The Meruelo Group, a privately held, diversified management company founded in 1986 by US Latino business executive Alex Meruelo. Meruelo Media is the largest minority-owned Media Company in Southern California.
10:31 pm WAKE UP Travis Scott


What is Himalayan Buckwheat ?
Buckwheat belongs to a collection of foods usually called pseudocereals.
Pseudocereals are seeds which are eaten up in the equal way as cereal grains, but do no longer develop on grasses. different common pseudocereals encompass quinoa and amaranth.
Regardless of its name, buckwheat isn't always related to wheat, and is therefore gluten-unfastened.
Buckwheat has come to be famous as a fitness food in many countries, because of its high quantities of minerals and diverse antioxidants. It has additionally been proven to offer fitness advantages, along with progressed blood sugar manipulate.The groats may be utilized in an awful lot the same manner as rice. they're the main factor in lots of traditional Gilgit Baltistan and Asian dishes.
Buckwheat is particularly grown inside the northern hemisphere, specifically in critical and Himalayan People and Europe, Russia, Kazakhstan and China.
The grains are usually brown and abnormal in form.
Buckwheat is best protein, containing all nine critical amino acids, together with lysine.
buckwheat consists very large amount of iron.
Very high in carbohydrates (eighty%).
Packed with many minerals and nutrients inclusive of zinc, copper, and niacin.
1. Buckwheat is easily digestible proteins.
This makes it an awesome meat replacement.
High protein buckwheat flour is being studied for possible use in ingredients to lessen plasma cholesterol, body fat, and ldl cholesterol gallstones.
2. buckwheat is useful in fat.
Buckwheat starch also can act as a fat alternative in processed ingredients.
3. The excessive level of rutin is extracted from the leaves for medicinal drug to treat high blood strain.
4. Buckwheat is fully Non allergenic.
Buckwheat hulls are used as pillow stuffing for the ones allergic to feathers, dust, and pollen.
5.Buckwheat is helpful in diabetes.
New evidence has determined that buckwheat can be beneficial within the management of diabetes consistent with Canadian researchers in the journal of Agricultural and food Chemistry.
With a glycemic index of fifty four, it lowers blood sugars greater slowly than rice or wheat products.
6. Exceptional for the digestion.
Buckwheat grows so quick that it does now not generally require loads of pesticides or different chemicals to develop nicely.
Skin issues are a chief motive of issue within the present situation. Proper supply of nutrients to the frame is crucial for healthy and radiant skin. The first-rate of pores and skin largely relies upon genetic factors, but it is also encouraged via environmental elements to a large volume consisting of publicity to pollutants, stage of strain, lifestyle and so on. Being a powerhouse of nutrients, buckwheat has an essential function to play in skin health.


I WOULD LOVE TO HEAR FROM YOU! FILL OUT THE FORM OR FEEL FREE TO GIVE ME A CALL/TEXT. I DO MY BEST TO RESPOND WITHIN 48 HOURS. IF YOU ARE INQUIRING ABOUT A WEDDING, PLEASE INCLUDE THE WEDDING DATE ALONG WITH THE VENUE. THANK YOU!


Free kids game - Big Match!
See how fast you match each button pair by remembering their locations. Click the Timer button below to start a new game whenever you want. Give the page a few seconds to load the necessary images.


text:
If you're a fan of the hit show "TikTok," then you might be wondering where your favorite moments from the show are. If you're not… Read More »Can I Find My Comments On TikTok ?
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How Do You Delete All Messages On TikTok All At Once?
If you're like most people, you probably try to delete all your messages on TikTok within a few hours of receiving them. But if you're… Read More »How Do You Delete All Messages On TikTok All At Once?
What Button Is L3 On Ps4 Controller?
ps4 controller, what button is l3 on? The left analog stick (L3) is the button that allows players to control their PlayStation 4 games with.… Read More »What Button Is L3 On Ps4 Controller?
How To Add Multiple Videos To One Instagram Story?
If you're like most people, you probably have a few Instagram Stories open at all times. But what if you want to add multiple videos… Read More »How To Add Multiple Videos To One Instagram Story?


Interview: Sharon Sloan on Racing Her First Nationals in Hartford - "This Is Better Than Being Dead"
Normally, if you line up for your first Cyclocross National Championships, crash in the first 30 seconds and finish last, you might fly under the radar during a week that's jam packed with so many inspiring and heart-breaking stories.
Nationals may have been over two months ago, but that doesn't mean all the compelling stories have been told.
Sharon Sloan lined up for her first Nationals in Hartford this past January, having just upgraded to Cat 3 two races prior, and crossed the line in 17th, the last finisher in the Masters 40-44 women's race.
After the finish, she was upset about her misfortune after getting caught up in a first-turn crash and immediately having to switch to her not-quite-ready B bike with clinchers pumped too high.
Just the fact that the ER doctor from Boston was disappointed by her bad luck during a National Championship race actually highlighted just how lucky she really is to be racing again, let alone be alive.
In the winter of 2014, while Sloan, a relatively new cyclist, was out riding her road bike, a driver ran a stop sign, smashed into her and forever changed her life while almost taking her life.
"My spine got crushed, my face got crushed in…I got med flighted to one of the hospitals in Boston," Sloan recalled. Being a doctor herself might typically be beneficial during a minor accident, but in such a severe one, while lying on the pavement, her knowledge only helped her understand how severe her condition was. "I knew I was dying, and I tried really hard not to die," she remembers.
As an ER doctor, Sloan knows many of the community's EMTs, including the one who was trying to save her life. Only her face was so damaged from the accident, the EMT didn't even recognize her, and her Road ID bracelet was the only clue that she was someone he had worked with.
Landing in the hospital, Sloan was just beginning her long journey to recovery. "[I] had a big surgery and then I coded after the surgery and spent a week in a coma." Sloan, a collegiate rower at Tufts University, had been trying cycling as part of her rehab from knee surgery, and had only entered a few cyclocross races before her accident. She wasn't quite hooked yet, and certainly didn't know many in the New England scene. Yet word of her predicament and condition spread quickly.
"I woke up from the coma with 300 emails from the New England 'cross community, and they've been so awesome," Sloan recalls. That support was one source of inspiration to literally get out of bed, as walking, let alone riding or remounting, was no longer a subconscious skill. "I was in a back brace, a full leg cast, and I had some very subtle spinal cord damage, and it took a while to [walk] again. I learned to walk again, and then I started to ride a bike on a trainer," Sloan revealed.
Of course, once a racer, always a racer. Sloan goaded other patients into wheelchair races, and tried to force her way into a wheelchair wheelie class, but when they learned of her back and head injuries, she was banned from such risky activity. She wasn't deterred, and set her sights on some larger barriers.
See our full interview with Sharon Sloan below.
Throwback Thursday: A Pre-Hartford Nats Chat with Todd Wells, Was It His Last?


Audizine Forums > Regional Forums > North West > North West: Local Classifieds > Wanted: Silver B5 trunk lid..
View Full Version : Wanted: Silver B5 trunk lid..
Looking for a silver B5 trunk lid. I'd take a 96 A4 "euro" lid (preferred) but would do the standard north american lid. I currently have a factory spoiler on my S4 and want a different look. Would also be willing to swap trunk lids with someone looking for the factory spoiler.


It appears things got out of hand last night in Torbay, Newfoundland, when NYC rapper Nas failed to show up for his headlining performance. According to reports, an audience of 700 at Jack Byrne Arena waited for over an hour for the MC to show but upon realizing that there would be no Nas, organizers were forced to call in police after many in the crowd apparently caused an uproar.
According to CBC reporter Zach Goudie, "The crowd [was] getting out of control... By 10:30, Nas still hadn't taken the stage [after opener Kardinal Offishall performed], and it started to become obvious he wasn't coming. There was a lot of beer can throwing... and very quickly the police were called in by security."
Only a trio of concertgoers were arrested by police on alcohol-related charges, but Nas's no-show reportedly led to a long line of angry hip-hop fans looking for refunds.
The promoter, ODC Productions, told local radio station VOCM that Nas was indeed in Newfoundland on the night of the show but made the decision not to perform because of financial dispute between the rapper and Halifax-based LUX Entertainment.
ODC Productions told the station that the promoters don't have any plans to offer any refunds at this point. Tickets were $59.99 plus tax, or $100 for VIP.
Well, refunds or not, at least this particular no-show didn't result in a full-on riot like the one that recently went down in Vancouver after Necro failed to perform.


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Deontay Wilder v Tyson Fury: Fight confirmed for 1 December
Thread starter soccer star
The fight between WBC world heavyweight champion Deontay Wilder and Tyson Fury will take place on Saturday, 1 December.
Contracts have been signed with the venue for the bout, expected to be in the United States, announced next week.
American Wilder, 32, and Briton Fury, 30, agreed a fight in August.
"The two best heavyweights competing against each other, the best fighting the best, giving the people what they want," Wilder said on social media.
The promotional tour for the fight will start on 1 October in London and will continue to New York and Los Angeles.
In August, former world champion Fury registered a unanimous points victory over Francesco Pianeta in his second fight since returning from a two-and-a-half-year spell out of the sport.


Question: When one talks about the importance of the society and friends, is it already a connection of the points in the heart?
Answer: The importance of the society and friends is necessary in order to simply know that it is your soul. One can even imagine it purely egotistically that there is nothing else beside the inclusion in them. Everything else is pure illusion. We do not understand this.
The soul is your inclusion in others. This is where you begin to truly live outside of your present self.
Question: Is this the connection of the points in the heart?
Answer: It is achieved through the connection between us.
Question: Should I come to lessons and gatherings of friends with this preparation?
Answer: Yes, during the whole time that you are not in connection with your friends, you should think about it, that this is actually the way you want to realize yourself among them.


The Village Office Supply furniture team will help you measure, design and plan your space so square footage is used efficiently. We work with you to create an environment that suits your style, your needs and your budget. Choose from an extensive selection from the best brands with fast delivery! If you are looking for something different-don't hesitate to let us know as we can special order additional product from a multitude of vendors. When it comes to delivery-you will not find a better team than at Village Office Supply. We will assemble and inspect all items prior to delivery and be courteous to the our clients daily functioning when delivering. We can even accommodate off hours delivery. Give us an opportunity to help you on your next furniture project and see the positive impact we can have on your operation.


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Q: configuring log4j2.xml from log4j.xml currently i am upgrading log4j version from 1.2.17 to 2.8.2 , to do that i need to configure log4j2.xml,  how to convert the below mentioned log4j.xml file to log4j2.xml?
<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE log4j:configuration SYSTEM "log4j.dtd">

<log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">
  <appender name="ConsoleAppender" class="org.apache.log4j.ConsoleAppender">
    <layout class="org.apache.log4j.SimpleLayout" />
  </appender>
  <appender name="RootFileAppender" class="org.apache.log4j.RollingFileAppender">
    <param name="File" value="logs/webuif.log" />
    <param name="MaxFileSize" value="10MB" />
    <param name="MaxBackupIndex" value="10" />
    <layout class="org.apache.log4j.PatternLayout">
      <param name="ConversionPattern" value="%p %c: %m%n" />
    </layout>
  </appender>
  <appender name="ApplicationFileAppender" class="org.apache.log4j.RollingFileAppender">
    <param name="File" value="logs/ab.log" />
    <param name="MaxFileSize" value="10MB" />
    <param name="MaxBackupIndex" value="10" />
    <layout class="org.apache.log4j.PatternLayout">
      <param name="ConversionPattern" value="%p %c: %m%n" />
    </layout>
  </appender>
  <category name="com.abc" additivity="false">
    <priority value="debug" />
    <appender-ref ref="ApplicationFileAppender" />
    <appender-ref ref="ConsoleAppender" />
  </category>
  <root>
    <priority value="warn" />
    <appender-ref ref="ConsoleAppender" />
    <appender-ref ref="RootFileAppender" />
  </root>
</log4j:configuration>

redpajama_set_name: RedPajamaStackExchange


In November 1951 the average high temperature in Northway, AK was 8.6°F, and this was 2.3°F warmer than the average of 6.3°F. The hottest day in November 1951 was 4 November when the temperature reached 28.0°F. Overnight the average temperature in November is -8.7°F and in 1951 the average overnight temperature was 2.9°F warmer at -5.8°F.
In November the average monthly rainfall in Northway, AK is 0.28 inches with rain usually falling on 0 days. In November 1951 there was a total of 0.00 inches of rain, that fell on 0 days.


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Tag: Broker
Adam closes out the best of 2022 discussion with his XXL list of the Top Films of 2022. As I'veContinue Reading
by Marie O'Sullivan January 1, 2023
Film Marie O'Sullivan's Film Reviews
Marie's Top 10 plus 5 of 2022
Marie kicks off 2023 for The Movie Isle with her Top Ten (plus a wee bit extra) Films of 2022.Continue Reading
by Marie O'Sullivan December 26, 2022
Film Review: Broker (2022)
If there's anyone you can trust with a story about marginalised people somehow forming their own unique family to getContinue Reading
by A.W. Kautzer December 26, 2022
Only Hirokazu Kore-eda could make a beautiful delicate humane comedy-drama about Baby Traffickers in South Korea. Broker is one of the director'sContinue Reading


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Become a Crucible Leader
Warwick Fairfax
Ruza Markovic: Strength and Purpose Born of War #52
By Warwick Fairfax | Podcast | No Comments
Ruza Markovic had dreamt of and studied for a career in journalism — when war intervened to change the trajectory of her life. While her family had emigrated to the U.S. from their native Yugoslavia when she was just 4, Markovic didn't think twice when she was asked to return to her homeland in the early '90s as a communications liaison to the teetering government while political unrest and then war-ravaged the Balkans.
Daniel Harkavy: Building a Champion Perspective #51
Leadership is not, in Daniel Harkavy's eyes, a complex equation. Its essential elements are the decisions you make and the influence you cultivate. But putting that into practice, especially after a crucible, can certainly be challenging.
Looking Back to Move Forward #50
In this special 50th episode of BEYOND THE CRUCIBLE, host Warwick Fairfax and co-host Gary Schneeberger take a look back at some of the most powerful guests who have shared the trials and triumphs of not just surviving their crucible experiences, but moving beyond them to lead lives of significance.
Chart Your Own Course to Shake Off the Holiday Blues #49
It's the most wonderful time of the year, right? Except it doesn't always feel that way. The holidays can bring on some serious emotional crucibles — from tensions among family members, sadness over those who are no longer with us, even struggling to overcome our disappointment in decisions made by loved ones.
Rudolph With Your Nose So Bright, Won't You Guide Us Through Our Crucibles Tonight?
By Warwick Fairfax | Crucibles, Life of Significance | No Comments
Maybe you think of your favorite Christmas TV specials as inspiring, celebratory fare that set the tone for the season. That's certainly true – but so is the fact that just about every Yuletide classic we tune in to this time of year features about as many crucible moments as it does presents under the tree.
Lisa Blair, Part 2: 'How Do I Survive Tonight?' #48
She set out on a quest to become the first woman to sail solo around Antartica. But 72 days in, when a horrific storm with waves as high as a two-story building caused her mast to topple and nearly sank her boat, Lisa Blair's journey changed from setting a record to staying alive.
Lisa Blair, Part 1: Reframing Failure #47
She's known internationally as the first woman to sail solo around Antartica, a celebrated explorer who fought through unimaginable trials en route to setting her world record. But before she ever stepped foot off dry land, Lisa Blair was a shy, reserved Australian girl bullied at school. She headed off to university determined to shed the mask she wore in high school, and later learned the importance of viewing failure not as falling short, but not trying at all. She found her life's calling when a sailing journey with a friend led her to fall in love with the sea … and the beauty and adventure it offers.
Jeff Kemp: Beating Life's Blitzes #46
In more than 20 years of playing football, as an amateur and in the NFL, Jeff Kemp was only once earmarked as his team's starting quarterback before the season began.
Dom Brightmon: Turning Adversity Into Advantage #45
Life, Dom Brightmon freely admits, can be crappy sometimes. But each of us has the power to flip the switch to "happy" — the key is to view our crucible experiences as crystal experiences.
Battling Crucible Fatigue: The Power of One Small Step #44
Moving beyond your crucible is hard work, even harder when the circumstances that knocked you off your feet seem to linger forever. How do you get going again when it feels like your crucible is a bottomless pit, a black hole?


In other fun news, I'm a Best-selling author! (Assuming, of course, that you want to use random Amazon rankings on rather insignificant stories as a measure.) Yesterday, for about twelve hours, my short story "Hope of Elantris" was the number one best-selling short story on Amazon.com.
This is amusing to me because there's been news, recently, on the net of people inflating their Amazon rank numbers for a short time just to call themselves best-selling authors. The thing is, it's all pretty meaningless. Amazon is a very small part of the market, and because they update their sales rankings so frequently, it's very easy to get yourself some quick numbers there. Only people who manage to have a low number on Amazon over a very long period can have any real bragging rights.
That all said, and knowing that it's pretty meaningless to be number one on Amazon shorts, I did email my editor, take a screen shot, and pat myself on the back a few times. By this afternoon, however, Kim Stanley Robinson (a Hugo-award winning novelist) had unseated me! As of this post, I'm down to number six. What a tragedy! How quickly we fall! It looks like my fifteen minutes of Amazon Short fame has come to a quick demise. Ah, well.


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Home » News » News by category » Community Partnerships » Walk Together 2013 – if we're all people, we're all equal
Walk Together 2013 – if we're all people, we're all equal
18 June 2013	By Welcome to Australia
This weekend on Saturday 22 June, in cities across Australia, people will Walk Together to show our welcome to this country, and that we recognise the equality and dignity of all people.
We believe Australians are a welcoming, generous and compassionate people. We believe an Australia is possible where prejudice is unpopular and cruelty hurts at the polls. An Australia that recognises the equality and dignity of all people - no matter who you are, where you're from or how you arrived here – and extends our values of fairness and mateship to everyone.
>>> Find details of events across Australia here & invite family & friends
We dream of a nation that celebrates diversity, made up of communities where people of our First Nations, asylum seekers, refugees, international students, skilled migrants and every other human being can experience the joy and security of belonging.
Walk Together takes place on the Saturday of Refugee Week. Join us at 1pm on Saturday 22nd June as we Walk Together in recognition that if we're all people, we're all equal.
In cities and regional centres all over Australia, you're invited to join this celebration of diversity and present a picture to our leaders and media of the Australia that is possible.
An Australia that recognises in its public debate, media conversation and legislation that "if we're all people, we're all equal". Equally deserving of freedom, fairness, opportunity to contribute, welcome and belonging.
If we're all people, we're all equal. Join us at 1pm Saturday 22 June in your city.
Visit the Welcome to Australia website to find out more about the network.


Statement of Interest: Broadly stated -- I am interested in how museums express their relationship (if any) to global issues, especially resource extraction and environmental change. I wonder whether exhibitions, programming and publications etc. that do address these issues can create some transparency or even effect change.
Geographical area: I have been working in the Middle Sepik River region of Papua New Guinea for the past 10 years where forestry and mining are ongoing challenges. See my publication "In the Footprint of the Crocodile Man: Contemporary Art of the Sepik River, Papua New Guinea. You have a copy in your library.
Statement of interest: I am an environmental anthropologist with additional training in geography and forestry & ecology. My research interests are in the local social impacts of global environmental problems, with my particular expertise in biodiversity conservation, ecosystem services, and climate change. Most of my work integrates household-level analysis of decision-making regarding resource use with an examination of global institutional practices and norms that influence environmental management, with an aim to identify why people conduct certain resource use practices in different ecosystems, particularly in the context of rapid environmental change, and the ways in which these global environmental changes may in turn render some communities more vulnerable or impoverished. I am the author of a recent book Forests are Gold: Trees, People and Environmental Rule in Vietnam (U. Washington, 2016) and coeditor of the book Gender and Sustainability (U of Arizona, 2012). I serve as the Lead of the Cultural Practices and Ecosystem Management Thematic Group for IUCN, and as a lead author of the Global Assessment of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES).
Geographical/topical area of interest: Southeast Asia, particularly Vietnam and Myanmar.


So, you've found "the one." Congrats! You're that much closer to waking up in your very first home. You can feel the energy of the crowd and hear the sounds of your walk up song. But wait..we still have a few more innings before the game is officially over!
If you haven't met me...spoiler alert: I'm a huge Cubs fan.
You're up to bat and you've just submitted your initial offer. 1 run scored.
The seller comes back with a counter offer. Base hit.
Your inspector is now up to bat. Through his inspection of the home, he's able to find some things that need attention but overall the place looks great--you've scored a run! Seller's side didn't do much this inning, so no runs scored. 2-1.
You've told your attorney what items you'd like fixed in the home. Double.
Now your pinch hitter, your attorney, is up and your inspector is out of the game. You score with a sacrifice fly. 1 run scored.
Team Seller gets a run through negotiating what they will and will not agree to regarding the inspection items. Both teams agree to a final attorney review letter. 3-2.
Your mortgage contingency is the amount of time it will take the lender to confirm you will get your loan, so this may be a tricky inning.
Your appraiser goes up to bat to confirm the home is worth the amount you've agreed to pay. The wind is in your favor and the home appraises out! 1 run scored.
With the home now appraised, your lender is able to confirm the loan commitment. 1 run scored. 5-2.
With nothing to do this inning but wait, Team Buyer got a no hitter this inning. 5-2.
A few days or weeks prior to the closing date, the lender will officially give the clear to close. Grand Slam.
Team Seller is officially going to sell their home. 1 run scored.
You've won the game & are now officially a homeowner!
Want to learn more about buying your first place? Join us at Sip & Learn: Home Buying Edition!


MASS Design Group and Frank Reese are early in the process of designing, programming, and building the Good Shepherd Institute. Your support is crucial to the continued development of the project through the design phases, through construction, and into its future operations.
Support us today with a tax-deductible donation that will go straight to MASS Design Group and be used exclusively for the realization of this urgent project.


This week we take a look at the Numenera Boxed Set, Fireteam Zero, The Dracula Dossier, and Nova Aetas.
Fireteam Zero by Emergent Games — Kickstarter (kickstarter.com).
The Dracula Dossier by Pelgrane Press — Kickstarter (kickstarter.com).
Nova Aetas: Dark Renaissance Tactical Game (Canceled) by Ludus Magnus Studio LLC — Kickstarter (kickstarter.com).


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BUS205: Business Law
Course Terms of Use
Back to 'Course Introduction'
All Saylor Academy courses are licensed under a Creative Commons Attribution 3.0 Unported License. For licensing purposes, a "course" is defined as the structure, syllabus, learning outcomes, unit descriptions, and the framing text that accompanies resources curated from third parties. That excludes the third party resources themselves, including the following resources used in this course, which contain the individual licenses listed below:
The Legal and Ethical Environment of Business:
Provided without attribution as requested by the work's original creator or licensor.
Licensed under CC BY-NC-SA 3.0
Business Law and the Legal Environment:
U.S. Government Resources:
These materials are in the public domain.
Cornell University Resources:
Attributed to Boundless
Original versions can be found here: https://www.law.cornell.edu/
Wikipedia Resources:
Attributed to Wikipedia
Licensed under CC BY-SA 3.0
Original versions can be found here: https://www.wikipedia.org/
Wikibooks Resources:
Attributed to Wikibooks
Original versions can be found here: https://en.wikibooks.org/
New World Encyclopedia Resources:
Attributed to New World Encyclopedia
Original versions can be found here: http://www.newworldencyclopedia.org/
Alison Monahan's Resources:
Attributed to Alison Monahan
Original versions can be found here: http://www.triplepundit.com/2012/03/technology-be-driver-social-good/
Connexions Resources:
Attributed to William Frey and Jose A. Cruz-Cruz
Original versions can be found here: http://cnx.org/
All other material is under copyright and may not be reproduced without the explicit permission of the copyright holder.
Last modified: Tuesday, February 2, 2016, 4:37 PM
◄ Course Syllabus
Jump to... Jump to... Course Syllabus The Legal and Ethical Environment of Business: "Chapter 1: Introduction to Law" The Legal and Ethical Environment of Business: "Chapter 1, Section 1: What Is Law?" "What Is Law?" The Legal and Ethical Environment of Business: "Chapter 1, Section 4: Importance of the Rule of Law to Business" The Legal and Ethical Environment of Business: "Chapter 1, Section 5: How Law Affects Business Disciplines" The Legal and Ethical Environment of Business: "Chapter 1, Section 2: Sources of Law" C-SPAN: "How a Bill Becomes a Law" The Legal and Ethical Environment of Business: "Chapter 2: Introduction to the Court System" The Legal and Ethical Environment of Business: "Chapter 2, Section 1: The Third Branch" The Legal and Ethical Environment of Business: "Chapter 2, Section 3: Trial and Appellate Courts" Unit 1 Assessment The Legal and Ethical Environment of Business: "Chapter 3: Litigation" The Legal and Ethical Environment of Business: "Chapter 3, Section 1: The Parties Involved" The Legal and Ethical Environment of Business: "Chapter 3, Section 2: Standing and Personal Jurisdiction" The Legal and Ethical Environment of Business: "Chapter 3, Section 3: Pretrial Procedures" The Legal and Ethical Environment of Business: "Chapter 3, Section 4: The Trial and Appeal" The Legal and Ethical Environment of Business: "Chapter 4: Alternative Dispute Resolution" The Legal and Ethical Environment of Business: "Chapter 4, Section 1: Negotiation" The Legal and Ethical Environment of Business: "Chapter 4, Section 2: Mediation" The Legal and Ethical Environment of Business: "Chapter 4, Section 3: Arbitration" Unit 2 Assessment "Business Law" The Legal and Ethical Environment of Business: "Chapter 7: Torts" The Legal and Ethical Environment of Business: "Chapter 7, Section 2: Negligence" The Legal and Ethical Environment of Business: "Chapter 7, Section 1: Intentional Torts" The Legal and Ethical Environment of Business: "Chapter 7, Section 3: Strict Liability" Unit 3 Assessment The Legal and Ethical Environment of Business: "Chapter 6: Contracts" Cornell Law School, Legal Information Institute: "Contracts" Cornell Law School, Legal Information Institute: "Negotiable Instruments Law: An Overview" Business Law and the Legal Environment: "Chapter 10, Section 4: Capacity" "The Formalities of a Contract" The Legal and Ethical Environment of Business: "Chapter 6, Section 2: Performance and Discharge, Breach, Defenses, Equitable Remedies" "Breach of Contract" "Unenforceable Contracts" "Oral Contracts" Unit 4 Assessment The Legal and Ethical Environment of Business: "Chapter 8, Section 1: Personal Property" The Legal and Ethical Environment of Business: "Chapter 8, Section 2: Real Property" Unit 5 Assessment The Legal and Ethical Environment of Business: "Chapter 9: Intellectual Property" The Legal and Ethical Environment of Business: "Chapter 9, Section 4: Trademarks" The Legal and Ethical Environment of Business: "Chapter 9, Section 2: Patents" The Legal and Ethical Environment of Business: "Chapter 9, Section 5: Copyrights" The Legal and Ethical Environment of Business: "Chapter 9, Section 3: Trade Secrets" Wikipedia: "The Digital Millennium Copyright Act (DMCA)" Intellectual Property and the Internet: "Online Copyright Infringement Liability Limitation Act" Wikipedia: "PRO-IP Act" Unit 6 Assessment Business Law and the Legal Environment: "Chapter 50, Section 2: Employment at Will" United States Department of Labor: "Questions and Answers about the Minimum Wage" "Prevailing Wage Laws" The Legal and Ethical Environment of Business: "Chapter 12: Employment Discrimination" United States Department of Labor: "OSHA's 40th Anniversary" National Labor Relations Board: "Your Right to Form a Union" Cornell Law School, Legal Information Institute: "Collective Bargaining and Labor Arbitration" National Labor Relation Board: "Frequently Asked Questions" United States Department of Labor: "Whistleblower Protection Programs" Unit 7 Assessment The Legal and Ethical Environment of Business: "Chapter 10: Criminal Law" The Legal and Ethical Environment of Business: "Chapter 10, Section 1: The Nature of Criminal Law, Constitutional Rights, Defenses, and Punishment" The Legal and Ethical Environment of Business: "Chapter 10, Section 2: Crime" Unit 8 Assessment The Legal and Ethical Environment of Business: "Chapter 11: Business Organizations" Forms of Business Ownership The Legal and Ethical Environment of Business: "Chapter 11, Section 1: Sole Proprietorships" The Legal and Ethical Environment of Business: "Chapter 11, Section 2: Partnerships" The Legal and Ethical Environment of Business: "Chapter 11, Section 4: Limited Liability Entities" The Legal and Ethical Environment of Business: "Chapter 11, Section 3: Corporations" Business Law and the Legal Environment: "Chapter 47, Section 1: Corporate Expansion" United States Securities and Exchange Commission: "What Every Investor Should Know: Corporate Bankruptcy" The Legal and Ethical Environment of Business: "Chapter 11, Section 4: Limited Liability Entities" Unit 9 Assessment Federal Securities Regulation in the United States State Securities Regulation in the United States The Sarbanes-Oxley Act Business Law and the Legal Environment: "Chapter 48, Section 1: History and Basic Framework of Antitrust Laws in the United States" Administrative Law and Business Unit 10 Assessment Unit 1 Study Guide: The Nature and Sources of Law Unit 2 Study Guide: Litigation vs. Alternative Dispute Resolution Unit 3 Study Guide: Torts Unit 4 Study Guide: Contracts Unit 5 Study Guide: Property Law Unit 6 Study Guide: Intellectual Property Unit 7 Study Guide: Employment Law Unit 8 Study Guide: Criminal Law and Business Unit 9 Study Guide: Business Organizations Unit 10 Study Guide: Business Regulation Course Feedback Survey BUS205: Certificate Final Exam BUS205: Saylor Direct Credit Final Exam
The Legal and Ethical Environment of Business: "Chapter 1: Introduction to Law" ►
Unit 1: The Nature and Sources of Law
Unit 2: Litigation vs. Alternative Dispute Resolution
Unit 3: Torts
Unit 4: Contracts
Unit 5: Property Law
Unit 6: Intellectual Property
Unit 7: Employment Law
Unit 8: Criminal Law and Business
Unit 9: Business Organizations
Unit 10: Business Regulation


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The True Cost of Watching the Premier League (from Your Sofa)
The Premier League 2019/20 season's fixtures were announced on Thursday and soon after, Amazon announced that for the first time ever in the UK, all December midweek games and all Boxing Day games will be shown live on Prime. But with another new player in town set to rival Sky and BT, we wondered just how much does it cost a season to watch Premier League games at home? So, we crunched some numbers…
While we've no doubts whatsoever that Amazon have loftier ambitions, initially they've signed up to show 20 games next season in just two rounds of 10-game December fixtures. Currently, you can get Amazon Prime for £7.99 a month but you do get 30 days to trial the service free of charge. So if you sign up at the start of December and cancel your subscription before January 2020, in theory you can get the games for free.
As you're almost certainly aware, Sky Sports once again have the biggest package for the 2019/2020 season with 128 live games and first choice on games every weekend. Sky are currently offering a deal of £23 a month for the Sky Sports channels for 18 months. But given the Premier League season starts in August and finishes in May, we'll call it 10 months for a total of £230.
But that's not the end of things. In order to get the Sky Sports package, you'll also need to purchase the bog standard Sky package at £22 a month. So realistically, for 10 months of Premier League football on Sky Sports, you're looking at £450.
Now, BT Sport is where things get complicated. Believe it or not, the cheapest way to get all the BT Sport channels (if you want all the Sky Sports channels too) is to get it through Sky TV (or TalkTalkTV), as it doesn't require you to sign up to BT Broadband internet as well. As a side, currently BT TV customers can only buy Sky Sports Main Event as a bolt-on for £27.50 per month, and are not able to get the other Sky Sports channels.
The cheapest deal we found was for £29.99 a month on a rolling 1-month contract. This also requires a £35 upfront fee. So for 42 games per season, £334.90. In fairness, that does also get you Champions League (every game), Europa League (every game) and FA Cup (24 games per season) too.
So when you crunch the numbers, if you sign up for Premier League football for the 2019/2020 season today, you could be paying for a total of 190 live Premier League games for £792.89. That works out at £4.17 per game if you watch every single game, which is an impossibility with Amazon showing multiple games at the same time.
Of course, there are plenty of deals to be made when it comes to Premier League coverage and the figure also obviously doesn't take into account all the other benefits you get with BT, Sky and Amazon. But when you break it down, it's still a staggering amount of money if Premier League coverage is your primary reason for signing up to the channels.
So what do you think? Are we being taken for a ride by Sky and BT? Or is it worth the money? Let us know in the comments below or join the conversation over on Twitter.
by David Saunders
@dksaunds
9 Best (Recent) Documentaries on UK Netflix 2019
Samsung Launch a 292-Inch Luxury TV for Your Wall