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Vancouver, Canada, September 27, 2013: Alpha Minerals Inc. (TSX-V: AMW), (the "Company" or "Alpha"), and its 50% Joint Venture partner Fission Uranium Corp (TSX-V FCU) are pleased to announce results for an additional eight holes drilled on the R390E zone. With a new strike length of approximately 255m, the R390E zone has more than quadrupled in length since the winter 2013 program. Holes PLS13-095 and PLS13-102 are of particular note with 61.5m (within 61.5m to 158.5m depth interval) and 59.0m (within 93.0m to 186m depth interval) total composite mineralization respectively, including narrow intervals of "off-scale" radioactivity.
Holes PLS13-087A, PLS13-088, PLS13-091, PLS13-093, PLS13-094, PLS13-095, PLS13-100 and PLS13-102 tested lines 225E, 300E, 450E and 480E of the R390E zone. All holes intersected zones of mineralization with varying degrees of radioactivity.
Drill hole PLS13-091 intersected mineralization approximately 20m south of the PL-3B ground EM conductor axis. This is the first mineralized intersection south of the ground conductor axis, and increases the possibility of extending the zone laterally to the south along the entire length of the corridor as it becomes further delineated.
Line 225E: Two vertically collared holes were drilled on and close to line 225E.
· Drillhole PLS13-091 was collared as a vertical hole and drilled to a depth of 373.0m. The collar is located approximately 105m grid west and 30m grid south of hole PLS13-075. The hole was collared to test a coincident radon in water and sediment anomaly. Bedrock was encountered at 53.5m depth. A locally pegmatite rich quartzitic gneiss was encountered from 53.5m to 234.3m. From 234.3m to the end of hole depth of 373.0m the lithology transitions to a dominantly pelitic gneiss with local mylonites. Two narrow intervals of weakly anomalous radioactivity were intersected between 258.0m to 272.5m (1.5m and 4.0m wide respectively) occurring within the pelitic gneiss. It is interpreted that this hole intersected the quartzitic gneiss hanging wall and pelitic gneiss contact too deep to hit the stronger mineralization generally seen associated with the PL-3B corridor.
· Drillhole PLS13-093 was collared as a vertical hole and drilled to a depth of 278.0m. Similar to hole -091, this drill hole was designed to test a coincident radon in water and sediment anomaly, but testing further up-dip to the north within the prospective pelitic corridor. Pelitic gneiss was encountered at the top of the basement at 50.4m, continuing to 171.1m, then transitioning to a semipelitic gneiss to the end of hole depth of 278.0m. Four narrow zones of anomalous moderate radioactive mineralization were intersected in widths ranging from 0.50m to 5.0m, from 117.0m to 148.5m.
Drill hole PLS13-091 is located close to line 225E and was drilled further south on the prospective corridor than previously tested in the R390E zone. This drill hole intersected anomalous radioactive mineralization about 20m south of the PL-3B ground EM conductor.
Line 300E: Two vertically collared holes were drilled on line 300E.
· Drillhole PLS13-095 is located approximately 30m grid west from hole PLS13-075 (see news release July 29, 2013). Devonian sandstone was encountered from 47.6m to 51.7m, which was underlain with a basement sequence comprising an intercalated mixture of mylonite, semipelitic gneiss, pelitic gneiss, and quartzitic gneiss to 68.1m. From 68.1m to 137.6m the lithology is dominantly a pelitic gneiss with alternating sequences of mylonites and cataclasites. From 137.6m to the end of drill hole depth of 275.0m the lithology is a semipelitic gneiss. Anomalous radioactivity begins at 61.5m coincident with the first appearance of pelitic gneiss. A total composite of 61.5m of mineralization within a 97.0m section (61.5m to 158.5m) occurs in several variably radioactive mineralized intervals ranging in width from 1.5m to 33.0m. Two narrow intervals of off-scale radioactivity were intersected (0.49m and 0.19m respectively).
· Drillhole PLS13-102 was collared 10m south of hole -095. Devonian sandstone was encountered from 58.3m to 58.8m, which is underlain by quartzitic gneiss to 63.1m.From 63.1m to 218.7m the lithology is dominantly a pelitic gneiss with alternating sequences of mylonites and cataclasites. From 218.7m to the end of drill hole depth of 275.0m the lithology is a semipelitic gneiss. A broad zone of anomalous radioactive mineralization comprised of several intervals ranging in width of from 0.5m to 29.0m for a total composite interval of 65.0m was intersected over a 93.0m wide section (93.0m to 186.0m). The intervals are separated by barren sections in intervals from 2.0m to 6.0m wide. Two narrow intervals of off-scale radioactivity were intersected (0.24m and 0.1m wide respectively).
A vertical offset of 7.1m from the top of basement rocks appears to exist between PLS13-095 and -102, which are located 10m apart. This is the first time at PLS that a vertical offset of this magnitude has been observed in basement rocks across such a short horizontal distance, and, if this offset is a true measurement, it may represent an important structural feature related to uranium mineralization.
· Drillhole PLS13-087A was collared as a vertical hole and drilled to a depth of 227.0m. The collar is located approximately 15m grid west of PLS13-075. Basement was intersected at 50.9m, immediately below a narrow 0.9m wide veneer of Devonian sandstone. Lithology consists of a pelitic gneiss from 50.9m to 87.5m, and a semipelitic gneiss from 87.5m to the end of hole depth of 227.0m. Mineralization starts at the top of the basement and extends 0.4m up into the overlying Devonian sandstone unit. A total composite of 44.5m of mineralization within a 68.5m section (50.5m to 119.0m) occurs in several variably radioactive mineralized intervals ranging in width from 1.0m to 17.0m, separated by barren intervals ranging from 2.5m to 9.5m wide. A narrow occurrence of off-scale radioactivity (0.25m wide) was intersected at 74.08m depth.
Line 450E: Two vertically collared holes were drilled on line 450E.
· Drillhole PLS13-094 is located approximately 15m grid east from hole PLS13-073 (see news release Sept 25, 2013) and drilled to a depth of 272.3m. Devonian sandstone was encountered from 50.7m to 53.4m. Basement was intersected at 53.4m, with a quartzitic gneiss from 53.4m to 103.6m. From 103.6m to the end of hole depth of 272.3m the basement is comprised dominantly of a pelitic gneiss, with localized mylonites and pegmatite-rich sections. A total composite of 24.0m of mineralization within a 55.5m section (105.5m to 161.0m) occurs in several variably radioactive mineralized intervals ranging in width from 0.5m to 12.0m. A narrow (0.15m wide) interval of off-scale radioactivity was encountered at 131.35m.
· Drillhole PLS13-100 was collared 10m grid north of hole PLS13-094. Devonian sandstone was encountered from 53.0m to 53.3m. Pelitic gneiss was intersected from 53.3m to 59.2m, which is underlain by semipelitic gneiss to 74.0m. From 74.0m to 219.0m the basement is comprised dominantly of alternating pelitic gneiss and mylonite with localized cataclasites. Semipelitic gneiss was encountered from 219.0m to the end of hole depth of 263.0m. A broad zone of anomalous radioactive mineralization comprised of several mineralized intervals ranging in width of from 2.0m to 20.0m for a total composite of 57.5m of mineralization intersected over a 104.0m wide section (53.0m to 157.0m). The mineralized intervals are separated by barren sections that range from 3.0m to 26.0m wide. Two narrow intervals of off-scale radioactivity were intersected (0.2m and 0.34m wide respectively).
· Drillhole PLS13-088 was collared as a vertical hole and completed to a depth of 296.0m. The collar is located approximately 30m grid east of PLS13-094. Devonian sandstone was intersected from 53.0m to 54.3m. Basement rocks consist of pelitic gneiss from 53.4m to 210.0m with several undifferentiated mylonite sequences throughout. The rock transitions into a semipelitic gneiss from 210.0m to the end of hole depth of 296.0m. A broad 160.0m section (54.0m to 214.0m) hosts alternating mineralized and barren intervals with mineralized zones ranging from 0.5m to 23.5m separated by barren intervals ranging from 2.5m to 40.0m wide. A narrow interval (0.12m) of off-scale radioactivity was intersected at 148.77m.


text:
\section{Introduction}
\iffalse
\begin{figure}
     \centering
    \includegraphics[scale=0.3]{img/figure-overview-whole.pdf}

    \caption{The overview of a vanilla DNN and our proposed two attention models, the red line connects a inner block and its previous block with the highest probability in its own attention vector.}
    \label{fig:overview}
\end{figure}
\fi

Recent progress in deep neural networks (DNNs)
has significantly benefited
many if not all tasks in artificial intelligence,
ranging from computer vision to
natural language processing.
The encouraging results, however,
come with an enormous cost:
state-of-the-art DNNs have been scaled up to hundreds and even thousands of
layers with millions of parameters,
making it demanding to
train and deploy even on GPU clusters, let alone
on edge devices like mobile phones.

Many research efforts have thus
been made to craft lightweight DNNs
applicable to in-the-wild scenarios.
Representative schemes include
weight pruning~\cite{Han2015LearningBW},
model quantization~\cite{Jacob2018QuantizationAT},
and knowledge distillation~(KD)~\cite{Hinton2015DistillingTK},
among which KD has recently emerged as one
of the most flourishing topics in
the field.
The goal of KD is to extract
knowledge from a well-behaved but
cumbersome teacher model,
often known as \emph{dark knowledge},
and to learn a compact student model
capable to handle the task of the teacher
but with fewer parameters.
Since the pioneering work of \cite{Hinton2015DistillingTK},
a variety of dark knowledge has been explored,
including \emph{hint}~\cite{Romero2015FitNetsHF},
\emph{attention}~\cite{Zagoruyko2017AT}, and
\emph{instance relationship}~\cite{Liu2019KnowledgeDV}.

Despite the above forms of dark knowledge showcase promising results,
the naive \emph{soft target}~\cite{Hinton2015DistillingTK}
is found still among the most competitive ones~\cite{tian2019crd}.
Nevertheless, few attempts have been dedicated to
explaining the rationale of soft targets on the student learning.
A common belief is that soft labels reveal richer
information like category similarities than the widely-used one-hot vector,
so that the student obtains more supervision signals for learning.
Recently, the work~\cite{Yuan_2020_CVPR} argues that the soft target is intrinsically a type of
learned label smoothing~\cite{Szegedy2016RethinkingTI}
regularization. ~\cite{Furlanello2018BornAN} conjectures
that soft targets resemble importance-weighting,
where weights correspond to confidences
of the teachers in the correct prediction.
~\cite{Tang2020UnderstandingAI},
on the other hand,
dissects the effects of soft targets into three main factors:
label smoothing, importance weighting, and category similarities.
Albeit the inspiring insights provided by these works,
the underlying mechanism of how category similarities
regularize the student model learning
has remained largely under-studied to date.

In this paper, we take a closer look at the role of the soft label
through the lens of a novel attention model,  which we term as KDExplainer. KDExplainer is an interpretation-friendly
student model, which distinguishes itself from conventional student DNNs
from two main aspects, as illustrated in  Figure~\ref{fig:overview}.
First,  KDExplainer takes the form of
a Hierarchical Mixture of Experts (HME), where each expert
is expected to specialize in specific subtasks and
learn adaptive features.
Second, KDExplainer casts the multi-class classification task as a multi-task problem,
in which each task is formulated as a binary classification problem.
Such a design explicitly
shapes KDExplainer as a
neural tree,
for which the inherent working mechanism
is well understood,
in the aim to interpret KD.

We then carry out KD from a free-form pre-trained DNN to the dedicated
KDExplainer, through which process the rationale
of KD is highlighted thanks to the
interpretable essence of the HME architecture.
Interestingly, we find that the KD objective
promotes lower entropy of the attention distribution,
indicating that soft labels, in reality,
encourage specialization of different experts
and hence play a role in modulating the knowledge
conflicts for solving different subtasks.
To further understand
the connection and difference between KD and
label smoothing,
we train a KDExplainer using label smoothing,
and discover that the derived
attention distribution
exhibits no significant differences with those by vanilla training without KD.
This phenomenon marks that soft labels indeed have more to offer,
including feature specialization,
than label smoothing.

Inspired by these observations,
we further introduce a portable component,
termed as virtual attention module (VAM),
to enhance the performance of conventional DNNs under KD.
The key idea of VAM is to coordinate
the knowledge conflicts for discriminating different categories,
achieved via lowering the entropy of the attention distribution.
VAM can be readily integrated with existing DNNs
while bringing negligible additional computation cost.
Moreover, since VAM is naturally orthogonal to KD,
it can be seamlessly combined with various KD schemes,
rendering it a handy module
to facilitate KD.

Our contributions are therefore summarized as follows.
\begin{itemize}
    \item We propose a novel attention model of
    interpretable nature, KDExplainer,
    to understand the role of soft labels
    in training the student.
    \item Through KDExplainer, we observe that soft labels
    implicitly modulate the knowledge
    conflicts between different subtasks by promoting feature specialization,
    and offer more regularization
    than only label smoothing.
    \item Understanding the KD rationale via KDExplainer further motivates us to design  a portable and compact module,
    VAM, readily applicable to various DNNs and KDs.
\end{itemize}
Extensive experimental results across benchmarks
demonstrate that VAM not only consistently improves
the performance of vanilla KD under various experimental settings,
but also can be readily integrated with
other state-of-the-art KD schemes to further promote their results.

\section{Related Work}
\paragraph{Knowledge Distillation}
Knowledge distillation has attracted increasing attention thanks to its important role in deploying deep networks to low-capacity edge devices. The main idea is leveraging the \textit{dark knowledge} encoded in a bulky teacher to craft a lightweight student model with performance on par with the teacher. Over the last several years, most works devote themselves to the exploration of different forms of the dark knowledge, including soft targets~\cite{Hinton2015DistillingTK}, features~\cite{Romero2015FitNetsHF}, attention~\cite{Zagoruyko2017AT}, factors~\cite{NIPS2018_7541}, activation boundary~\cite{ABdistill}, and instance relationship~\cite{Liu2019KnowledgeDV,Park2019RelationalKD,tung2019similarity}. By imitating the teacher to behave in a similar way, the student achieves comparable performance even with much fewer parameters.

\paragraph{Attention Mechanism}
Inspired by human cognition, attention mechanisms  focus on relevant regions of input data to solve the desired task rather than ingesting the entire input. Attention-based neural networks have been broadly adopted in natural language models for machine translation~\cite{Bahdanau2015NeuralMT}, image caption generation~\cite{Xu2015ShowAA}, and unsupervised representation learning~\cite{Devlin2019BERTPO}. Attention mechanisms also achieve great success in vision models~\cite{Mnih2014RecurrentMO,Bello_2019_ICCV}. Except the performance boost, attention mechanism also provides an important way to explain the workings of neural models~\cite{Li2016UnderstandingNN,Wiegreffe2019AttentionIN,Xu2015ShowAA}.
Unlike most prior works,
our focus here is to utilize an
attention mechanism to interpret KD, which has been largely overlooked in previous literature.

\section{KDExplainer}

\begin{figure*}
     \centering
    \includegraphics[scale=0.65]{img/overview3.pdf}
    \vspace{-0.5em}
    \caption{A conceptual illustration. \textbf{Top}: a conventional student network for knowledge distillation. \textbf{Bottom}: the proposed KDExplainer. To explain the effects of soft targets as the distillation objective, we use the KDExplainer as the student model for knowledge distillation. }
    \label{fig:overview}
    \vspace{-1em}
\end{figure*}

\subsection{Knowledge Distillation with Soft Targets}
Vanilla KD~\cite{Hinton2015DistillingTK} distills the ``dark knowledge'' from the teacher via aligning the soft targets \begin{equation}\label{eq:vanilla_distill}
\mathcal{O}_{\text{KD}} =\alpha \mathcal{L}_{\text{CE}}(p(\mathbi{z}^s), \mathbi{y}) + (1-\alpha) \mathcal{D}_{\text{KL}}\left(p(\mathbi{z}^t; {\tau}), p(\mathbi{z}^s; {\tau})\right),
\end{equation}
where $\mathbi{z}^s$ and $\mathbi{z}^t$ are respectively the logits from the student and the teacher models.  $\mathbi{y}$ is its associated one-hot label vector, $p$ denotes the softmax function that produces the category probabilities given the logits, and
$\tau$ is a non-negative temperature hyperparameter  used to smooth the distributions. As for $p_i(\mathbi{z};\tau)$, we have
\begin{equation}\label{eq:soft_target}
  p_i(\mathbi{z};\tau)=\frac{\exp({z}_i/\tau)}
  {\sum\nolimits_{j}\exp({z}_j/\tau)}.
\end{equation}
Also, $\mathcal{L}_{\text{CE}}$ is the conventional cross-entropy loss, and $\mathcal{D}_{\text{KL}}$ is the Kullback-Leibler divergence between the categorial distributions predicted from the teacher and the student models. $\alpha$ is a hyperparameter to trade off the two objective terms.

\subsection{The Proposed KDExplainer}

We propose the KDExplainer, a task-oriented attention model, as the student model to uncover the rationale
underlying KD.
KDExplainer makes two main modifications based on exiting popular DNNs: (1) dissecting the effects of class similarity in soft targets, KDExplainer reformulates the multi-class classification problem as an ensemble of multiple binary classification problems; (2) KDExplainer remodels the student model as a Hierarchical Mixture of Experts (HME) and introduces a task-oriented attention mechanism as the gating function to make the student model more friendly for human interpretation. The overview of the proposed KDExplainer is shown in Figure~\ref{fig:overview}. KDExplainer is designed based on existing widely-used networks such as ResNet~\cite{he2016deep}, Wide Residual Network~\cite{zagoruyko2016wide}, and VGG~\cite{simonyan2014very}. These classic DNNs have some common characteristics: these models are usually composed by several blocks, each of which is a stack of convolutional layers, batch normalization layers~\cite{ioffe2015batch}, and nonlinear activation layers. The number of filters usually keeps fixed in the same block and changes across different blocks.

Formally, we use $B^i$ to denote the $i$-th block, and
then a standard DNN can be formulated as $F_{DNN} = B^C\circ B^{L}\circ\cdot\cdot\cdot \circ B^2\circ B^1$, where symbol $\circ$ denotes the function composition operation. $B^C$ is the classification block that consists of the fully connected layer and the softmax layer. KDExplainer roughly follows the design of the existing DNN, but divides each block $B^i$ (except the first block $B^1$) into $N_i$ equal-sized sub-blocks, \textit{i.e.}, $\hat{B}^i=\{B^i_1, B^i_2, ..., B^i_{N_i}\}$ for any $i>1$. We view each sub-block $B^i_j$ as an expert, and introduce a task-oriented attention module before each expert as the gating function to select a combination of the outputs from previous experts. Therefore, the whole model can be viewed as an HME model.

\subsubsection{Task-oriented Attention}
The proposed attention is ``task-oriented'' as the parameters of the attention module are trained on the whole dataset, which is largely different from the attention mechanism in existing literature~\cite{Bahdanau2015NeuralMT,Bello_2019_ICCV} where the attention weights are determined by instances. For each sub-block $B^i_j$, we use a trainable parameter vector $\mathbi{v}^i_j=[v^i_{j,1}, v^i_{j,2}, ..., v^i_{j,N_{i-1}}]$ to learn the attention distribution over previous experts.
Let output feature maps from previous blocks be $\left\{\mathbi{F}^{i-1}_1,...,\mathbi{F}^{i-1}_{N_{i-1}}\right\}$. At training phase, the input of sub-block $B^i_j$ is computed by
\begin{equation}
    \widetilde{\mathbi{F}^i_{j}} = \sum\nolimits^{N_{i-1}}_{k=1}a^i_{j,k}\cdot\mathbi{F}^{i-1}_{k},
\end{equation}
where $a^i_{j,k}$ is the attention weight of the $k$-th expert, $a^i_{j,k} = \frac{\exp({v^i_{j,k}}/T)}{\sum_m \exp({v^i_{j,m}/T})}$. $T$ is a temperature hyper-parameter shared by all attention modules. Note that the classification block $B^C$ is divided into $K$ blocks, where $K$ equals the number of classes in the classification problem. The multi-class classification problem thus turns into an ensemble of binary classification problems in KDExplainer, as shown in Figure~\ref{fig:overview}.

\subsubsection{Explaining KD by KDExplainer}
Recall that KDExplainer is a substitute of the conventional student model, using KDExplainer to understand the effects of KD is straightforward: analyzing differences between the experimental results of KDExplainer trained with and without KD.
As KDExplainer reformulates the multi-class classification as multiple binary classification tasks, each binary classification task encounters the imbalanced data problem. Let $\mathbi{p}^s_k=[p^s_{k,0}, p^s_{k, 1}]$ be the probability predictions of KDExplainer for the $k$-th classification task, and $\mathbi{y}_k=[y_{k,0}, y_{k, 1}]$ be the ground truth in the form of a one-hot vector. When the proposed KDExplainer is trained with KD, the objective function is
\begin{equation}
\label{eq:KDEobj}
\begin{aligned}
\mathcal{O}_{\text{KD}} =\sum\nolimits_{k=1}^K \Big \{
\alpha \mathcal{L}_{\text{WCE}}\left(\mathbi{p}^s_k, \mathbi{y}_k\right) +
(1-\alpha)  \mathcal{D}_{\text{KL}}\left(\hat{\mathbi{q}}^t_k, \hat{\mathbi{p}}^s_k\right)
\Big \},
\end{aligned}
\end{equation}
where $\mathcal{L}_{\text{WCE}}$ is the weighted cross-entropy loss
\begin{equation}
\mathcal{L}_{\text{WCE}}=-w_0 y_{k,0}\log p^s_{k,0}-w_1y_{k,1}\log p^s_{k,1}.
\end{equation}
$w_0$ and $w_1$ are the weights balancing the cost incurred by the positive and the negative samples, which alleviates the negative effects caused by imbalanced data. $\hat{\mathbi{q}}^t_k$ and $\hat{\mathbi{p}}^s_k$ are the softened category probabilities from the teacher and the student models, respectively. Note that all teacher models involved in this paper are still conventional DNNs for multi-class classification, so we convert the $K$-class probability prediction $\mathbi{p}^t=[p^t_0,p^t_1, ..., p^t_K]$ from the teacher to $K$ two-dimensional probability vectors as follows
\begin{equation}
\hat{\mathbi{q}}^t_k = [1-p^t_k, p^t_k], \  \text{for any}\ k \in \{1, 2, ..., K\}.
\end{equation}
If KDEplainer is trained without KD, the second term in Eqn.~\ref{eq:KDEobj} is removed.

KDExplainer enjoys an appealing property thanks to the elaborate design: after training, it becomes a tree-like multi-branch network (i.e., neural tree) if only the maximum in the attention weights is kept:
\begin{equation}
\label{eq:keep-maximum}
   a^i_{j,k}= \left\{
   \begin{aligned}
        &1, \ \text{if}\ k=\arg\max_m a^i_{j, m}; \\
        &0, \    \text{otherwise}.
    \end{aligned}
   \right.
\end{equation}
The derived neural trees provide us with stronger support for interpretation and analysis of knowledge distillation.

\section{Virtual Attention Mechanism}
\begin{figure}[t]
     \centering
    \includegraphics[scale=0.51]{img/vam.pdf}
    \caption{The proposed virtual attention mechanism. For simplicity, here we only depict the details of how $\mathbi{F}^{i+1}$ is convolved with the virtual filter block $\mathcal{K}^{i+1}_j$ to produce $\mathbi{F}^{i+2}_j$.}
    \label{fig:vam}
    \vspace{-1em}
\end{figure}
KDExplainer is tailored to understand the effects of soft targets in KD. However, it may suffer from slight accuracy sacrifice compared to conventional student DNNs due to the \textit{ad hoc} design that multi-class classification turns into multiple binary classification tasks. Here we propose a virtual attention mechanism that is easily integrated into existing student models with few architecture modifications, to retain the capacity of these student models for higher KD performance. As shown in Figure 2, VAM views all convolution filters in a layer as several ``virtual'' filter blocks, each filter block akin to the expert in the KDExplainer. Note that ``virtual'' means that VAM does not really split the convolution filters into any blocks. The only modification is that VAM slightly changes the conventional convolution operation by incorporating an attention mechanism.

Formally, we use $\mathcal{K}^i\in \mathbb{R}^{C^i_{out}C^i_{in}S S}$ to denote the convolution filters in the $i$-th layer, where
$C^i_{in}$ and $C^i_{out}$ denote the input and the output channels. Here for simplicity, we assume all filters are square, where both the width and the length are $S$. Assume the input tensor of the $i$-th convolution layer is ${\mathbi{F}^i}\in \mathbb{R}^{C^i_{in}HW}$, where $H$ and $W$ are the height and the width of the feature map. After the $i$-th layer and the $(i+1)$-th layer, the features turn into $\mathbi{F}^{i+1}\in \mathbb{R}^{C^i_{out}HW}$ and $\mathbi{F}^{i+2}\in \mathbb{R}^{C^{i+1}_{out}HW}$, respectively.
\begin{figure*}[ht]
     \centering
     \subfigure[Normal]{\includegraphics[scale=0.38]{img/tree3-1.pdf}}
     \subfigure[Label Smoothing]{\includegraphics[scale=0.38]{img/tree3-2.pdf}}
     \subfigure[KD]{\includegraphics[scale=0.38]{img/tree3-3.pdf}}

    \vspace{-1em}
    \caption{Visualization of the derived neural trees from the proposed KDExplainer, which is designed based on ResNet18 and trained on CIFAR-10. (a) KDExplainer trained with normal cross-entropy loss; (b) KDExplainer trained with label smoothing; (c) KDExplainer trained with KD. Each node denotes the retained block by removing the unused blocks according to Eqn~\ref{eq:keep-maximum}. The histogram in each node represents the attention distribution over its input tensors. The red and the blue bars denote the maximum and the minimum, respectively.}
    \label{fig:experiment-tree}
    \vspace{-1em}
\end{figure*}

To incorporate the attention module into the DNN, we view convolution filters in each layer as a set of filter blocks. Assume the filters in the $i$-th layer are divided into $M$ virtual groups, such that $\mathcal{K}^{i}=\{\mathcal{K}^{i}_1, \mathcal{K}^{i}_2, ..., \mathcal{K}^{i}_M\}, \mathcal{K}^{i}_j\in \mathbb{R}^{(C^i_{out}/M)C^i_{in}S S}$ for $1\le j\le M$. After the $i$-th layer, the feature maps produced by each block can be calculated as follows
\begin{equation}
   \mathbi{F}^{i+1}_j = \mathbi{F}^{i}\odot \mathcal{K}^i_j,  \  \text{for any}\ j \in \{1, 2, ..., M\},
\end{equation}
where $\odot$ denotes the convolution operation. The whole feature tensor can be denoted by
\begin{equation}
   \mathbi{F}^{i+1} = \coprod\nolimits^{M}_{m=1}\mathbi{F}^{i+1}_m,
\end{equation}
where $\coprod$ denotes feature concatenation.  Assume filters in the $(i+1)$-th layer are divided into $N$ virtual blocks, such that $\mathcal{K}^{i+1}=\{\mathcal{K}^{i+1}_{1},\mathcal{K}^{i+1}_{2},..., \mathcal{K}^{i+1}_{N}\}, \mathcal{K}^{i+1}_{j}\in\mathbb{R}^{(C^{i+1}_{out}/N)C^{i+1}_{in}S S}$ for $1\le j\le N$. To align each filter block in the $(i+1)$-th layer with the output from each block of the $i$-th layer, we further divide each block $\mathcal{K}^{i+1}_{j}$ into $M$ groups along the input channel, such that $\mathcal{K}^{i+1}_{j}=\{\mathcal{K}^{i+1}_{j,1}, \mathcal{K}^{i+1}_{j,2}, ..., \mathcal{K}^{i+1}_{j, M}\}, \mathcal{K}^{i+1}_{j,l}\in\mathbb{R}^{(C^{i+1}_{out}/N)(C^{i+1}_{in}/M)S S}$ for any $1\le l\le M$. Similar to KDExplainer, we introduce a task-oriented attention module $\mathbi{a}^{i+1}_j=\left[a^{i+1}_{j,1}, a^{i+1}_{j,2}, ..., a^{i+1}_{j,M}\right]$ before each filter block $\mathcal{K}^{i+1}_{j}$, thus the output of the block can be computed by
\begin{equation}
   \mathbi{F}^{i+2}_j = \sum\nolimits^{M}_{m=1}a^{i+1}_{j,m}\mathbi{F}^{i+1}_{m}\odot\mathcal{K}^{i+1}_{j, m}.
\end{equation}
The attention weights are computed by a softmax function over trainable parameters $\mathbi{v}=\left[v^{i+1}_{j,1},..., v^{i+1}_{j,M}\right]$, i.e.,
\begin{equation}
    a^{i+1}_{j,m} = \frac{\exp{(v^{i+1}_{j,m})}}{\sum\nolimits^{M}_{k=1}\exp{}(v^{i+1}_{j,k})}.
\end{equation}
As soft targets are found to encourage lower entropy of the attention distribution (seen in Section~\ref{subsec:experiment-KDE}), we introduce another regularization term based on the conventional KD objective in Eqn.~\ref{eq:vanilla_distill}
\begin{equation}\label{eq:vam_obj}
\begin{aligned}
\mathcal{O}_{\text{KD}} = & (1-\alpha) \mathcal{D}_{\text{KL}}\left(p(\mathbi{z}^t; {\tau}), p(\mathbi{z}^s; {\tau})\right) + \\
& \alpha \mathcal{L}_{\text{CE}}(p(\mathbi{z}^s), \mathbi{y}) + \gamma\mathcal{H}(A),
\end{aligned}
\end{equation}
where $A$ denotes all the involved attention distributions, and $\mathcal{H}$ is the sum of their entropy
\begin{equation}
\label{eq:entropy-minimize}
    \mathcal{H}(A)=\sum\nolimits_{i}\sum\nolimits_{j}\sum\nolimits_k-a^{i}_{j,k}\log{a^i_j,k}.
\end{equation}

\section{Experiments}

\subsection{Explaining KD with KDExplainer}
\label{subsec:experiment-KDE}

\subsubsection{Experimental settings} Experiments are conducted on CIFAR-10 and CIFAR-100~\cite{krizhevsky2009learning}. We implement the proposed
KDExplainers based on four DNN architectures: ResNet18~\cite{he2016deep}, VGG8~\cite{simonyan2014very}, WRN-16-2, and WRN-40-1~\cite{zagoruyko2016wide}. For all models, we use ResNet50 as their teacher model for KD. The initial learning rate is 0.1 and decayed every 30 epochs. The training ceases at 300 epochs. For more details, please refer to supplementary materials.

\subsubsection{Experimental results}
\begin{table*}[t]
    \centering
    \resizebox{\textwidth}{!}{
    \begin{tabular}{c|cccccc|cccccc}
      \toprule
      \textbf{Data} &\textbf{Model}& \textbf{P-DNN} &\textbf{VAM} &$\mathcal{L}_{\text{CE}}$ & $\mathcal{D}_{\text{KL}}$ &$\mathcal{H}$ & \textbf{VGG8} & \textbf{WRN-16-2} & \textbf{WRN-40-1} & \textbf{resnet20} & \textbf{ResNet18} & \textbf{ShufNetV1} \\  \midrule
      \multirow{6}{*}{\rotatebox{90}{CIFAR-10}} &M1&$\checkmark$
      &&$\checkmark$&& & 91.41 & 93.71 & 93.34 & 92.83 & 95.26 & 92.56   \\
      &M2&&$\checkmark$&$\checkmark$&& & 91.69 & 93.46 & 93.47 & 92.56 & \blue{95.51} & 92.82  \\
      &M3&&$\checkmark$&$\checkmark$&& $\checkmark$& 91.82 & 93.90 & 93.95 & 92.86 & 94.99 & 92.83  \\
      &M4&$\checkmark$&&$\checkmark$&$\checkmark$& & 93.14 & 94.55 & 93.86 & 93.08 & 95.43 & 93.50 \\
      &M5&&$\checkmark$&$\checkmark$&$\checkmark$& & \blue{93.19} & \blue{94.67} & \blue{93.96} & \blue{93.39} & \blue{95.51} & \blue{93.70} \\
      &M6&&$\checkmark$&$\checkmark$&$\checkmark$&$\checkmark$ & \textbf{93.36}  & \textbf{94.85} & \textbf{94.32} & \textbf{93.50} & \textbf{95.59} & \textbf{93.79} \\

      \midrule
      \multirow{6}{*}{\rotatebox{90}{CIFAR-100}} &M1&$\checkmark$
      &&$\checkmark$&& & 70.37 & 73.15 & 71.36 & 69.84 & 77.18 & 71.45  \\
      &M2&&$\checkmark$&$\checkmark$&& & 70.54 & 73.56 & 71.15 & 69.58 & 76.62 & 71.70 \\
      &M3&&$\checkmark$&$\checkmark$&& $\checkmark$& 70.98 & 73.92 & 71.61 & 69.71 & 78.30 & 71.81 \\
      &M4&$\checkmark$&&$\checkmark$&$\checkmark$& & 73.53 & 75.01 & 73.44 & 70.05 & 79.54 & 75.41 \\
      &M5&&$\checkmark$&$\checkmark$&$\checkmark$& & \blue{73.78} & \blue{75.43} & \blue{73.87} & \blue{70.32} & \blue{79.63} & \blue{75.62} \\
      &M6&&$\checkmark$&$\checkmark$&$\checkmark$&$\checkmark$ & \textbf{74.17} & \textbf{75.63} & \textbf{73.92} & \textbf{70.43} & \textbf{79.77} & \textbf{76.10} \\

      \midrule
      \multirow{6}{*}{\rotatebox{90}{Tiny-ImageNet}} &M1&$\checkmark$
      &&$\checkmark$&& & 56.47 & 57.52 & 56.26 & 50.54 & 65.59 & 60.52 \\
      &M2&&$\checkmark$&$\checkmark$&& & 57.18 & \blue{58.16} & 56.02 & 50.53 & 65.79 & 61.24 \\
      &M3&&$\checkmark$&$\checkmark$&& $\checkmark$& 57.63 & 58.08 & 56.23 & 50.70 & 66.33 & 62.47 \\
      &M4&$\checkmark$&&$\checkmark$&$\checkmark$& & 60.41 & 57.91 & 56.39 & 52.78 & 69.37 & \blue{65.54} \\
      &M5&&$\checkmark$&$\checkmark$&$\checkmark$& & \blue{60.45} & \textbf{58.24} & \blue{56.80} & \blue{52.83} & \blue{69.63} & 65.32 \\
      &M6&&$\checkmark$&$\checkmark$&$\checkmark$& $\checkmark$& \textbf{60.57} & 58.01 & \textbf{56.99} & \textbf{53.54} & \textbf{69.90} & \textbf{65.65} \\
    \bottomrule
    \end{tabular}
    }
    \vspace{-0.5em}
    \caption{Top-1 classification accuracy in $\%$ of six model variants. ``P-DNN'' denotes the plain DNN. Bold font indicates the best performance, and blue font denotes the second best. Experiments are repeated three times and the average results are reported.}
    \label{tab:vam-over-vanilla-kd}
    \vspace{-1em}
\end{table*}

To understand the effects of vanilla KD, KDExplainer is trained with and without soft targets as the optimization objective. Furthermore, as label smoothing~\cite{Szegedy2016RethinkingTI}  is
recently also viewed as a type of KD~\cite{Yuan_2020_CVPR}, we also train the KDExplainer with label smoothing to understand its effects. In Figure~\ref{fig:experiment-tree}, we visualize the derived neural trees by keeping only the maximum attention weight in every attention module as Eqn.~\ref{eq:keep-maximum}. It can be seen that KD significantly encourages sparse connectivity between blocks, as its retained blocks are much fewer than normal training and label smoothing. It can be also verified by the attention distribution shown in the histograms in internal nodes, where KD produces sharper~(i.e., lower entropy) attention distribution than normal training and label smoothing. Furthermore, KD produces more human-interpretable branching architecture than normal training and label smoothing. For example, in the derived trees, man-made vehicle categories are attached to the same branch, while animal categories are attached to other branches. This is a reasonable organization as similar categories or tasks share similar decision patterns, and thus they can share the same network branch. Less related categories or tasks, on the other hand, depend on different patterns to make their decision, such that they should be solved separately. The results of normal training and label smoothing somewhat violate this principle, thus their derived trees are much larger than that of KD.
\begin{figure}[t]
     \centering
     \subfigure[CIFAR-10]{
    \includegraphics[scale=0.595]{img/Graph1-10.pdf}}
     \subfigure[CIFAR-100]{
    \includegraphics[scale=0.595]{img/Graph2-100.pdf}}
    \vspace{-1em}
    \caption{Accuracy~(\%) of KDExplainers trained with different objectives in different architectures. ``DNN with KD'' and ``LS'' denote the conventional DNN trained with KD and label smoothing.}
    \vspace{-1em}
    \label{fig:kde-10-100}
\end{figure}

In Figure~\ref{fig:kde-10-100}, we provide the accuracies of all the KDExplainers. Under all experimental settings, KD significantly outperforms label smoothing and vanilla training. Label smoothing marginally improves the performance in most cases compared to vanilla training, but it sometimes leads to performance degradation. For example, it causes performance drops by $0.51\%$ and $0.14\%$ of WRN-40-1 and WRN-16-2 on CIFAR-100.  These results prove that by properly organizing categories into different branches, KD helps modulate the knowledge conflicts between different tasks and thus achieves higher accuracy. Label smoothing, on the other hand, provides some regularization on the model learning, but it seems to have play no role  in addressing  conflicts between decision patterns of different categories. In the supplementary material, we provide more results and analyses to explain the differences between KD and label smoothing.

\subsection{Improving KD with VAM}
\label{subsec:experiment-VAM}
\subsubsection{Experimental settings}
Experiments are conducted on  CIFAR-10, CIFAR-100, and Tiny-ImageNet~\cite{le2015tiny}. We incorporate VAM into six widely-used DNNs as student models, including ResNet18, resnet20\footnote{Fowlloing~\cite{tian2019crd}, ResNet and resnet represent cifar- and ImageNet-style networks, respectively.}, VGG8, WRN-16-2, WRN-40-1, and ShuffleNetV1~\cite{zhang2018shufflenet}. For all involved student models, we adopt ResNet50 as their teacher model to provide the soft logits. During the training phase of the student model, the learning rate is initially $0.05$ (attention module $0.01$), gets decayed at epoch $150$, $180$, $210$ by a factor of $0.1$, and ceases at $240$. The temperature hyper-parameter is set to $1$ for all attention modules and $4$ for KD loss. The trading-off factor $\alpha$ is set to $0.9$. The number of channels in each virtual block is $8$ for VGG8, WRN-16-2, and WRN-40-1, $4$ for resnet20, $16$ for ResNet18, and $10$ for ShuffleNetV1. For more details, please refer to the supplementary material.

\subsubsection{Experimental Results}
\textbf{Improving vanilla KD.}~~As VAM is motivated by vanilla KD, we first validate its effectiveness upon vanilla KD. To give a more comprehensive view of VAM, we make comparisons between six model variants. Note that we simply use VAM to denote the DNN incorporated with the proposed virtual attention module. (M1) Plain+$\mathcal{L}_{\text{CE}}$: plain DNN trained with only $\mathcal{L}_{\text{CE}}$; (M2) VAM+$\mathcal{L}_{\text{CE}}$: VAM trained with $\mathcal{L}_{\text{CE}}$; (M3) VAM+$\mathcal{L}_{\text{CE}}$+$\mathcal{H}$: VAM trained with $\mathcal{L}_{\text{CE}}$ and $\mathcal{H}$ (Eqn.~\ref{eq:entropy-minimize}); (M4) Plain+$\mathcal{L}_{\text{CE}}$+$\mathcal{D}_{\text{KL}}$: plain DNN trained with $\mathcal{L}_{\text{CE}}$ and $\mathcal{D}_{\text{KL}}$; (M5) VAM+$\mathcal{L}_{\text{CE}}$+$\mathcal{D}_{\text{KL}}$: VAM trained with $\mathcal{L}_{\text{CE}}$ and $\mathcal{D}_{\text{KL}}$; (M6) VAM+$\mathcal{L}_{\text{CE}}$+$\mathcal{D}_{\text{KL}}$+$\mathcal{H}$: VAM trained with  $\mathcal{L}_{\text{CE}}$, $\mathcal{D}_{\text{KL}}$ and $\mathcal{H}$. Experimental results are shown in Table~\ref{tab:vam-over-vanilla-kd}. In general, DNN incorporated with  VAM yields consistently superior performance to the plain DNN without VAM. For example, M5 consistently outperforms M4 in almost all our experiments, which validates its effectiveness under KD. Furthermore, when optimized with low entropy constraint $\mathcal{H}$, VAM produces better performance (M3$>$M2, M6$>$M5) under almost all settings. As $\mathcal{H}$ is motivated by the results from KDExplainer, it indicates that the proposed KDExplainer indeed provides us with general and valuable insights into KD.
\noindent\textbf{Improving state-of-the-art KD.}
\begin{table}[t]
\centering
\resizebox{0.48\textwidth}{!}{
\begin{tabular}{cc|cc|cc}
  \toprule
   & & \multicolumn{2}{c|}{\textbf{CIFAR-10}} & \multicolumn{2}{c}{\textbf{CIFAR-100}}  \\
   \textbf{Method} &\textbf{Model} & resnet20 & WRN-16-2 & resnet20 & WRN-16-2  \\  \midrule
  \multirow{2}{*}{FitNet} & \small{P-DNN} & 92.49 & 93.99 & 69.09 & 72.98  \\
   & \small{VAM} & \textbf{92.95} & \textbf{94.08} & \textbf{70.11} &  \textbf{73.79}
  \\ \midrule
  \multirow{2}{*}{FT} & \small{P-DNN} & 92.39 & 93.74 & 69.47 & 72.78 \\
   & \small{VAM} & \textbf{93.01} & \textbf{93.78} & \textbf{69.67} & \textbf{73.43}
  \\ \midrule
  \multirow{2}{*}{SP} & \small{P-DNN} & 93.01 & 94.55 & 69.17 & 74.40
  \\
   & \small{VAM} & \textbf{93.51} & \textbf{94.61} & \textbf{70.35} & \textbf{74.91} \\ \midrule
   \multirow{2}{*}{CRD} & \small{P-DNN} & 91.83 & 93.36 & 70.64 & 74.97 \\
   & \small{VAM} & \textbf{92.16} & \textbf{93.53} & \textbf{70.85} & \textbf{75.15} \\
 \bottomrule
\end{tabular}
}
\vspace{-0.5em}
\caption{Results of combing VAM with other KD methods. ``P-DNN'' denotes the plain DNN.}
\vspace{-1em}
\label{tab:vam-sota}
\end{table}
~~Although VAM is motivated by vanilla KD, it is also straightforward to be combined with other KD methods. Here we evaluate VAM combined with other KD methods, including FitNets~\cite{romero2014fitnets}, FT~\cite{kim2018paraphrasing}, SP~\cite{tung2019similarity}, and CRD~\cite{tian2019contrastive}. Results are listed in Table~\ref{tab:vam-sota}. It can be seen that combined with other KD methods, VAM still yields  performances consistently superior to the plain DNN though it is motivated by only vanilla KD. For resnet20, VAM achieves $0.20\%\sim1.18\%$ performance boosts on CIFAR-100 in four KD methods. For WRN-16-2, VAM improves $0.18\%\sim0.81\%$ on CIFAR-100. Since VAM brings negligible additional overhead, it is an economical way to further improve the performance of existing KD methods. Please refer to the supplementary material for more experimental results, including how the block size and the hyper-parameter $\gamma$ affect the final performance.

\iffalse
\begin{table}[htbp]
    \centering
    \begin{subtable}[Effects of different attention group parameters $g$]{
    \centering
    \scalebox{1.0}{
    \begin{tabular}{c|cc|cc}
      \toprule
       \multirow{2}{*}{$g$} & \multicolumn{2}{c}{\textbf{CIFAR-10}} & \multicolumn{2}{c}{\textbf{CIFAR-100}}  \\
       & resnet20 & WRN-16-2 & resnet20 & WRN-16-2  \\  \midrule
       8 & 93.48 & 94.85 & 70.37 & 75.63 \\
       4 & 93.50 & 94.92 & 70.43 & 75.35 \\
       2 & 93.14 & 94.78 & 70.68 & 75.67 \\
       1 & 93.21 & 93.91 & 70.27 & 73.14 \\     \bottomrule
    \end{tabular}}}
    \label{tab:t3-1}
    \end{subtable}

    \begin{subtable}[Effects of different minimum entropy coefficients $\gamma$]{
    \centering
    \scalebox{0.95}{
    \begin{tabular}{c|cc|cc}
      \toprule
       \multirow{2}{*}{$\gamma$} & \multicolumn{2}{c}{\textbf{CIFAR-10}} & \multicolumn{2}{c}{\textbf{CIFAR-100}}  \\
       & resnet20 & WRN-16-2 & resnet20 & WRN-16-2  \\  \midrule
       $1e^{-4}$ & 93.33 & 94.64 & 70.12 & 75.20 \\
       $1e^{-3}$ & 92.45 &  94.43 & 70.17 & 75.39 \\
       $1e^{-2}$ & 93.18 & 94.72 & 70.44 & 75.49 \\
       $1e^{-1}$ & 93.50 & 94.85 & 70.43 & 75.63 \\     \bottomrule
    \end{tabular}}}
    \label{tab:t3-2}
    \end{subtable}

    \begin{subtable}[Effects of VAM \& ME without KD]{
    \centering
    \scalebox{0.95}{
    \begin{tabular}{c|cc|cc}
      \toprule
        & \multicolumn{2}{c}{\textbf{CIFAR-10}} & \multicolumn{2}{c}{\textbf{Tiny-ImageNet}}  \\
       & resnet20 & WRN-16-2 & resnet20 & WRN-16-2  \\  \midrule
       original & 92.83 & 93.71 & 52.66 & 57.52 \\
       \small{VAM} & 92.56 & 93.46 & 52.73 & 58.31 \\
       \small{VAM-ME} & \textbf{92.86} & \textbf{93.90} & \textbf{53.06} & \textbf{58.48}  \\
     \bottomrule
    \end{tabular}}}
    \label{tab:t3-4}
    \end{subtable}

    \caption{Influence of different settings.}
    \label{tab:t3}
\end{table}

\fi

\section{Conclusion and Future Work}
In this paper, we propose KDExplainer to shed light on
the working mechanism underlying soft targets
during KD. We find that KD implicitly modulates
the knowledge conflicts between different subtasks,
and effectively brings about more benefits as compared to label smoothing.
Based on these observations, we propose a portable module,  VAM,
to further improve the results of
vanilla KD alongside other state-of-the-art ones.
Extensive experimental results exhibit that
the proposed VAM significantly enhances the KD performance
at a negligible additional cost. In our future work, we will extend the proposed VAM to other tasks and systematically evaluate its effectiveness beyond the scope of KD.

\paragraph{Acknowledgment}
This work is funded by the National Key R\&D Program of China (Grant No: 2018AAA0101503) and the Science and technology project of SGCC (State Grid Corporation of China): fundamental theory of human-in-the-loop hybrid-augmented intelligence for power grid dispatch and control.

{\small
\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


text:
Agricultural mediation program unveiled
Macomb Legal News
The Michigan Community Mediation Association (MCMA) has been awarded the Michigan Agricultural Mediation Program (MAMP) by the United States Department of Agriculture (USDA).
This program offers free mediation services to Michigan farmers to resolve their disputes outside of court.
Farmers' disputes covered by this grant can range from contract issues, estate and probate complications, adverse determinations by the USDA, bankruptcy, and any other conflict they may face concerning their farm.
"Our association is honored to have been awarded this grant to provide a vital alternative to resolving disputes for our farmers," said Gabriella Reihanian Havlicek, executive director of MCMA. "We will be working with the Michigan Department of Agriculture and Rural Development, industry stakeholders, and local leaders to ensure every farmer knows this program is available to them for free to resolve their conflicts."
MCMA is an advocacy, not-for-profit association for the 17 Community Dispute Resolution Program (CDRP) mediation centers across the state of Michigan that are partially funded by the Michigan Supreme Court State Court Administrative Office (SCAO). MCMA's mission is to help advocate for the 17 CDRP and educate Michigan residents on the importance of mediation and restorative practices.
"Michigan's farmers work to feed our communities and families 24/7, 365 days a year and mediation provides an avenue for them to be an integral part of the conflict resolution process. MDARD is proud to support MCMA," said Gary McDowell, MDARD Director. "I encourage farmers to look into mediation as a viable option for resolving conflict."
Mediation is a confidential process where disputing parties will discuss their issues with a neutral third party, the mediator, who will help them come to a resolution.
"Farmers already have heavy issues to navigate on a daily basis," said Kelly Turner, CEO of Potato Growers of Michigan. "Whether it's a supply chain shortage, finding workers, or navigating continually changing weather conditions. What they don't need is to have extra legal issues hanging over their head for years to come. Now they can contact MCMA and request a free mediation to resolve any dispute they may be facing."
Buddy Sebastian, president of Michigan Ground Water Association, echoed these sentiments by sharing, "We at Michigan Ground Water Association are excited to be partnering with MCMA and the 17 Community Dispute Resolution Program mediation centers. The services they provide to our residents and now our farmers allow Michiganders the opportunity to resolve disputes in a free and faster way."
The 17 CDRP mediation centers are local nonprofits that offer mediation and restorative practice services. Their volunteer mediators are trained in the use of the facilitative model. This will ensure that all participants' voices are heard and that the disputing parties are the ones making the agreement. Not the mediators.
CDRP mediators are required to complete 40 hours of SCAO-approved General Civil training or 48 hours of SCAO-approved Domestic training, practical experience supervised by seasoned mediators, and continuing education. To mediate agricultural cases the mediators will also be required to participate in 20 additional hours of advanced training every two years.
"Our mediators are highly skilled and trained on how to best serve their community members facing conflict in a respectful, professional manner," said Shannon Taylor, executive director of Upper Peninsula Commission for Area Progress (UPCAP) Conflict Resolution Program and MCMA's Training Committee Chairwoman. "I am certain they will bring this same level of expertise to the Michigan Agricultural Mediation Program and to our farmers."
Farmers that wish to request a mediation may contact MCMA at www.micommunitymediation.org, via email micommunitymediationassoc@gmail.com, or by calling 800-616-7863.
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text:
El gasoducte Bakú-Tbilissi-Erzurum (BTE) est un gasoducte que uneix en 692 km Bakú a l'Azerbaidjan amb Erzurum a Turquia, passant per Tbilissi a Geòrgia. És paral·lel a l'Oleoducte Bakú-Tbilisi-Ceyhan. Seria un proveïdor del projecte del Gasoducte Nabucco.

Gas natural
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Infraestructures de transport a l'Azerbaidjan

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BY Mary Ellen Sanders
In Food & Ingredients, Gut Microbiota, Probiotics, Probiotics, Research & Practice
Many consumers and healthcare providers are aware that certain probiotics may be beneficial for health. But they sometimes have difficulty identifying high quality products in the marketplace. What is a high quality probiotic product? It contains microbial strains that are properly identified, characterized and quantified, it meets specifications for purity, it is safe for its intended use, and it is…
Mary Ellen Sanders
Mary Ellen Sanders is a consultant in the area of probiotic microbiology, with special expertise on paths to scientific substantiation of probiotic product label claims. Dr. Sanders served as the founding president of the International Scientific Association for Probiotics and Prebiotics (ISAPP) and is currently the organization's Director of Scientific Affairs/ Executive Officer. This international, non-profit association of academic and industrial scientists is dedicated to advancing the science of probiotics and prebiotics (www.isapp.net). Through numerous written, oral and video pieces, including a website, www.usprobiotics.org, she strives to provide objective, evidence-based information on probiotics for consumers and professionals. Key activities include: Panels to determine GRAS status of probiotic strains ; member of the American Gastroenterological Association Scientific Advisory Board for AGA Center for Gut Microbiome Research and Education ; World Gastroenterology Organisation Committee preparing practice guidelines for the use of probiotics and prebiotics for GI indications (2008, 2011, 2014) ; working group convened by the FAO/WHO that developed guidelines for probiotics (2002).
A new meta-analysis of controlled clinical trials shows the antidepressant and anxiolytic potential of probiotics
In Anxiety & Depression, Gut Brain Axis, Gut Microbiota, Gut Microbiota Composition, Probiotics, Research & Practice
Experimental research has previously shown how the gut microbiota is involved in regulating brain function through the gut-brain axis. Depression and anxiety are among the most prevalent mental health conditions in industrialized countries and there is a current need for novel psychopharmacological medications to be developed for both conditions. Although the gut microbiota has been hypothesized to be involved in…
Bifidobacterium longum 1714 may modulate brain function in response to social stress in healthy adults
BY Paul Enck
In Gut Brain Axis, Gut Microbiota, Probiotics, Research & Practice
Modulating the gut microbiota has emerged as a means of affecting the central nervous system function and, thus, human behavior, especially in the context of stress, mood and anxiety disorders and even neurocognitive disorders. Clinical studies with probiotics using neuroimaging methods have started exploring the benefits of probiotics in the human brain. Among them, the probiotic Bifidobacterium longum 1714 has…
Paul Enck
Prof. Dr. Paul Enck, Director of Research, Dept. of Psychosomatic Medicine and Psychotherapy, University Hospital Tübingen, Germany. His main interests are gut functions in health and disease, including functional and inflammatory bowel disorders, the role of the gut microbiota, regulation of eating and food intake and its disorders, of nausea, vomiting and motion sickness, and the psychophysiology and neurobiology of the placebo response, with specific emphasis on age and gender contributions. He has published more than 170 original data paper in scientific, peer-reviewed journals, and more than 250 book chapters and review articles. He is board member/treasurer of the European Society of Neurogastroenterology and Motility and of the German Society of Neurogastroenterology and Motility, and has served as reviewer for many international journals and grant agencies.
How to take care of your digestive health: a new study shows that regular fermented milk consumption can improve mild digestive symptoms
BY Allison Clark
In Diet, Digestive Health, Fermented Foods, Food & Ingredients, Gut Microbiota, Gut Microbiota Composition, IBS, Inflammation, Kefir, News Watch, Nutrition, Probiotics, Probiotics, Yogurt
Occasional gastrointestinal symptoms are common in the otherwise healthy population. Most symptoms respond to dietary changes presumably via changes in the gut microbiota. Evidence shows that diet has a major impact on the gut microbiota and overall gastrointestinal health, and dietary interventions, such as consuming probiotics, especially Bifidobacterium, and the low oligo-, di-, and monosaccharide and polyol (FODMAP) diet, are…
Allison Clark
Allison Clark has a master in nutrition and health from Open University in Barcelona and a master in journalism. She is a freelance writer and nutritionist and has written various peer review papers about the role the gut microbiota plays in health, disease and endurance exercise performance. Allison is passionate about the role diet and the gut microbiota play in health and disease. Follow her on Twitter @Heal_your_Gut
New infographic to meet Lactobacilli
In Digestive Health, Fibers, Gut Microbiota, Gut Microbiota Composition, Immune Health, Infographics, News Watch, Nutrition, Prebiotics, Probiotics
Have you ever wondered about the main microorganisms in our gut microbiota? With this second illustration of our series "Get to know your bacteria", we invite you to discover Lactobacilli. The Gut Microbiota for Health Publishing Team created this infographic to teach you the benefits of this bacteria and which foods contain it. Do not hesitate to share this infographic…
The gut microbiome, diet and antibiotic treatment all shape E. coli Nissle evolution in the mouse gut
In Antibiotics, Diet, Gut Microbiota, Gut Microbiota Composition, Infections, Nutrition, Probiotics, Research & Practice
Probiotics have different potential therapeutic uses, although their mechanisms of action and how they might change after administration are not fully studied. In contrast to traditional drugs, probiotics can evolve during treatment, but how this process works inside the gut remains largely elusive. A new study, led by Dr. Gautam Dantas from the Washington University School of Medicine in St.…
Empowering patients to improve their gut health: Advice from dietitians
In Diet, Digestive Health, Gut Microbiota, Gut Microbiota Composition, IBS, News Watch, Nutrition, nutrition, Prebiotics
It is clear that the gut plays an important role in health. Beyond digestion and absorption of nutrients, the intestine acts as a barrier and a filter, selecting for the good and eliminating the bad. Recent research has shown the importance of the gut microbiota for gut health, and diet remains one of the most powerful ways to influence this…
Gut Microbiota for Health has been created by the Gut Microbiota and Health Section of the European Society for Neurogastroenterology & Motility (ESNM), member of United European Gastroenterology (UEG)
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Do you have food bloggers constantly knocking on your door, offering to promote your products in exchange for samples or cash? If so, here are 5 top tips to help you choose the right ones for your biz.
If you want to get your fabulous food business into trade and consumer magazines, then here's a PR secret to help you plan ahead – go and grab their forward features list!


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Walking safaris, holidays & tours
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Discover the secrets of the bush from a unique perspective
Walking safaris, holidays & tours available in Botswana, Kenya, South Africa, Tanzania
Custom designed Walking safaris in Africa
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In the days before vehicles carved up the landscape, people were forced to walk from place to place, and in so doing, a powerful connection with their natural environment was formed. While game drives are the fastest way to see wildlife, nothing is quite like exploring the bush on foot. Not only will you be able to view animals such as elephant, plains game and giraffe from a different perspective, you'll also notice the little things; insects, small mammals, reptiles and uniquely adapted flora that are just waiting to be discovered.
Walking safaris are completely safe and only require a low fitness level (not if you're climbing a mountain or tracking mountain gorillas in the forest of course). You will be accompanied by a professional game ranger or field guide, who will be able to explain the sights and sounds that make this activity one of the highlights of an African safari.
Popular Walking safaris in Africa
Sun City and Madikwe Family Safari (6 days)
From Sun City's hedonistic pleasures to the majesty of nature at Madikwe Game Reserve, this journey combines the best of two very different worlds
Luxury South Africa to Zimbabwe safari (8 days)
Spot the Big Five in Thornybush Private Game Reserve and enjoy the majesty of Zimbabwe's roaring Victoria Falls
Zimbabwe Wildlife Expedition (7 days)
Spot huge herds of elephants in the Hwange National Park; and enjoy kayak trip down the Zambezi through the Mana Pools National Park
Grand Zambia Wildlife Safari (9 days)
Combine water and wildlife in a journey that takes you from the Victoria Falls to Kafue and South Luangwa national parks – Zambia at its best
Tanzania July to October Great Migration Safari with HerdTracker (9 days)
The wildebeest migration, is one of the "Seven New Wonder of the World" and also known as The World Cup of Wildlife. If there is a safari you should go on, this has to be it!
Enchanting Moremi Mobile Camping Experience (7 days)
Moremi Game Reserve in the beautiful Okavango Delta is well-known for self-drive camper vanning. Explore this exciting way of travel with us
Spectacular July to September Wildebeest River Crossings (10 days)
Experience one of Africa's greatest spectacles – smell the dust and hear the noise of thousands of animals crossing the Mara River
Experience Sossusvlei's red dunes glowing in the early morning light, find desert-adapted elephant in Damaraland and meet the Himba nomads in Namibia's far north-west
Enjoy a romantic honeymoon at the Ngorongoro Crater where you can spot the Big Five and experience the wildlife-rich Serengeti National Park
See the beauty of nature's greatest wildlife spectacle in two countries
View all of our tours
Let us help on a no obligation custom tour
More on Walking safaris in Africa
Many camps and lodges offer a choice of a walking safari as part of your safari itinerary. This will most likely be at camps and lodges in private reserves and concessions (most national parks do not often offer this safari activity). However, we have a few walking-only safaris that bring you that much closer to nature. A walking safari is typically more affordable than a traditional, game drive-based safari.
The best place to experience a walking safari
The ideal terrain for a walking safari is arguably the grasslands and savannahs of Africa. Not only will animals and birds be more visible, but you'll be able to truly appreciate the natural environment of your destination. However, during the wet season, the dry plains transform into a lush garden of grass and wildflowers, depending on where in Africa you'll be exploring. In areas where the vegetation is much thicker, walking safaris are usually not a viable safari activity.
We offer walking-only safaris in the Kruger National Park and Wild Coast of South Africa, as well as the Serengeti National Park in Tanzania. All of these countries are prime walking safari destinations, with plenty of wildlife, birds and naturally beautiful landscapes to enjoy.
Best time of year to go
Depending on the season in your chosen destination, we would recommend a dry season safari, simply because it is much easier to spot wildlife and birds when the vegetation has receeded after the rains.
What should I pack?
You will require comfortable clothes that you can walk in, closed shoes and socks, a hat, sun screen, camera and a pair of binoculars. An insect repellent will also help to keep the bugs away.
Frequently Asked Questions about Walking safaris in Africa
When is the best time to visit Shamwari Game Reserve?
Wildlife viewing in Shamwari Game Game Reserve is good throughout the year. However, the best wildlife viewing is during the driest winter months of June to September. The lack of water in the bush drives animals to open water sources -… go there.
How does the weather influence a safari?
While a safari holiday can be at any time of year, it is worth noting that seasonality will impact the type of experience you're likely to have as well as the cost of your safari. During the dry season, the wildlife tends to congregate… go there.
What is a typical day on safari like?
Life on safari has a rhythm of its own, largely dictated by the animal's movements. Generally, the most rewarding times for game viewing is in the cooler early mornings and also late in the afternoons when the animals are most active. A… go there.
Will I be able to use my smartphone during my safari?
Wi-Fi is the best option when planning on using your smartphone during your African safari. Using apps such as Skype for Android and iOS, Viber for Android and iOS as well as Google Hangouts for Android is the best way to stay in touch with… go there.
What happens if the weather affects my hot air balloon safari?
If the weather causes unforeseen problems on the day of your hot air balloon safari, we will try to move it to another day or you will be refunded. go there.
What research should one do when thinking of travelling through Africa?
Africa for centuries has captured the imagination of explorers hence there really is a lot of information out there available on the continent from guide books to novels. go there.
Where is the best place to go for wildlife viewing in Tanzania?
Northern Tanzania! Ngorongoro Crater, a World Heritage Site, has some of the greatest concentrations of wildlife in the world as well as the Serengeti, but don't forget about the southern circuit. go there.
What kinds of animals will I see on safari?
Whether you are in the Southern or East Africa you will generally be able to see the big five. The difference really comes in when considering antelope as they are based on your chosen location. go there.
What vaccinations should I get?
This is all dependant on the countries that you will be visitng and due to the status of certain vaccinations changing at times, please consult our experts before getting vaccines. go there.
Reviews on our Walking safaris in Africa
This was my first safari. I didn't expect to be so pampered. Everything was first class. We had everything provided for us before even asking. We had a dedicated team at our service. Megan was on call whenever we needed something and she…
from Nigeria
Tanya was an absolute delight to deal with. From the first phone call she was extremely polite, professional and very eager to facilitate my requests regarding our safari. Our 3 nights at the V & A Hotel were fabulous and Motswari Game…
Perfectly tailor-made tours that take all your wishes and requests into consideration. All my expectations were beyond fulfilled.
Henrik Wetterstrand
Living afar and trusting in an organisation with whom you have no previous knowledge can lead to feeling of mistrust and wondering whether what we have paid for actually exists. A little more proactive communication would have eased those…
Tony Russell
I just wanted to thank Discover Africa and Karl Svendsen - the safari was great - we saw many more animals than expected including cheetah, leopard, many lions and even a three-day old elephant calf. Additionally, we had our own hippo…
Margaret Dean
Planning the trip was managed extremely well especially since travel was at a busy time of year and not much notice was given. All communication was by email, and was responded to promptly and efficiently. Different options were provided…
Suzanne McKeen
Africa is the perfect post-COVID holiday destination
Sure we were all upset about being locked indoors with all travel plans coming to a complete halt earlier this year. At the same time allowing all parties involved to positively take stock, adapt and implement responsibility toward future… read more
A traveller's guide to Botswana's Moremi Game Reserve
Botswana's Moremi Game Reserve forms part of the Okavango Delta - the world's 1000th World Heritage Site. It's also the only official protected area in the Delta - and holds significant scientific, environmental and conservation… read more
Five romantic honeymoon accommodation options in Zambia
Welcome to the wild land of Zambia - where safari thrill, evening delight, five-star meets wonder and respect in a truly inspiring natural wonderland. Here you'll find beautiful private hideaways, secluded private dinners in the wilderness… read more
Exploring the Selinda Canoe Trail in Botswana
This part of the Okavango Delta was pretty much dried up for almost 30 decades until one fateful day in August of 2009 when heavy rains caused the area to flood once again, creating what is today known as the Selinda canoe trail. Whether… read more
Free safari planning advice from destination experts


According to independent consumer surveys of satellite TV providers and cable companies in Pecos, TX, DISH TV has earned the title of best TV service provider in Pecos.
Independent consumer surveys show that among satellite TV and cable companies in Pecos, TX, the best TV service provider is DISH TV. When comparing cable companies and various television services available in your area, this data can help you determine which package will work best for your needs and budget.
At CableTV.com, you'll find the channel selection, package options, and prices offered by each of the highest rated providers available in Pecos, listed in an easy-to-navigate layout that allows you to compare your options and get the best provider for your needs. That's an important benefit if catching every Dallas Cowboys or game is a priority for you, or if you like to keep up on the hottest new shows and series. Generally, major networks are available everywhere, but TV packages in Pecos might have a different selection of smaller channels compared to similar packages in other cities. By comparing prices, packages, and features, you'll have no problem selecting the best cable company in Pecos, TX for your needs and budget.
When you choose a cable company in Pecos, TX that CableTV.com recommends, you can count on reliable service and better customer support. That's because dependability and quality customer service are both factored into our TV provider rankings, so you can easily evaluate your options and choose a provider that meets your needs.
There are a number of Internet service providers available in Pecos, TX, but data proves that ViaSat Satellite Internet is the top provider in the area.What do you look for in an Internet provider? For starters, you should make sure you're getting the speed and bandwidth you need, since a faster connection allows you to work more efficiently, stream videos smoothly, and easily connect with friends and family. Overall rankings of Internet companies include data about speed—but since speed can fluctuate depending on your area, it is important to check the listed speeds against the average speed in Texas: 33 Mbps. A quick check can help you make sure your Pecos, TX Internet provider offers a first-rate connection speed in your neighborhood.
How would you like to save $100 a year or more on your cable TV package and Internet service? With TV and Internet bundles, you can save a considerable amount. So check out the Internet and TV bundles available in Pecos to see which ones offer the most savings.
Whether you want to know what your TV provider options in Pecos, TX are or how they stack up to one another, you can turn to CableTV.com for any information related to TV providers. We'll give you professional advice based on packages, pricing, and product reviews so you can choose a TV provider that will suit your preferences. We can even help you find a reputable Internet provider in Pecos!
Sorry, no stores were found near zip code 79772 for the provider selected.


A woman needs a second pair of prescription eyeglasses to wear while she waits for the pair she is always losing to reappear.
She doesn't have a lot of money to spend, and her insurance won't cover backup glasses.
So she takes her prescription to an eyewear business that advertises bargain pricing. She picks out a pair of frames and plunks down the prescription and her credit card.
Later that day, she discovers that the "bargain priced" glasses will cost $75 more than the pair she bought several months earlier from her regular optician.
Can she cancel her order for the glasses and have her credit card credited for the payment? Probably not, although if the bargain eyewear business offered to meet or beat any other price, she may get the glasses for $75 less.
Maryland law does not provide a three-day cooling off period for buyers to change their minds and cancel contracts. Generally, there is no cooling off period in this state unless a contract falls under the Door-to-Door Sales Act.
A door-to-door sale under the law is pretty much what it sounds like. It doesn't have to be at your door, but the seller or his representative must have solicited the sale, and you agreed or offered to buy at a location other than his place of business. The sale must be more than $25.
If your contract meets those criteria, the written contract must state that you may cancel at any time, "up to midnight of the third business day after the date of this transaction." A "notice of cancellation" form must be attached.
There are a few non door-to-door exceptions where state law requires the seller to allow you to cancel within three days: contracts for health clubs, credit service centers, self-defense schools or weight loss centers. If you sign a contract for a timeshare or vacation club, state law gives you up to 10 days to cancel.
The eyeglass contract does not fall under the Door-to-Door Sales Act, because the seller did not solicit the sale and the buyer did not offer to buy at a location other than the eyewear seller's place of business.
Nor will our woman get much help from the Federal Trade Commission's Cooling-Off Rule. The rule is similar to the Maryland act, giving buyers until midnight of the third day after certain transactions to cancel without penalty. It applies to purchases made outside the seller's regular location, which can be door-to-door or at sites such as fairgrounds or convention centers.
The federal rule does not cover sales of cars at temporary locations if the seller has at least one permanent place of business; sales of real estate, insurance or securities; or sales of arts and crafts at locations such as fairs or schools. Other exemptions from the rule: telephone sales; sales of items not primarily for personal, family or household use; sales under $25.


sukekiyo returns with video and audio compilation "PASSIO"
sukekiyo starts the new year by announcing the release of their second video and audio compilation, PASSIO. The new material will be on sale exclusively at the band's Igyou no Ma (異形の間 弐) live to be held on February 18.
The set is comprised of a blu-ray disc with live footage and a CD. The video material features 12 songs from the live concert Naburi Jigoku (嬲り地獄) held last year on January 14, and 10 songs from Gyakushuu no Naburi Jigokuu -Shikkoku no Gi- (逆襲の嬲り地獄 -漆黒の儀-) on May 15. The second disc is a CD containing a demo version of the song Rongai na Iikimono Toshite (論外な生き物として).
sukekiyo brought ADORATIO to its next stage, and this compilation will allow you to get into the new world created by the band.
Last year we saw vocalist Kyo alongside DIR EN GREY release their long-awaited 10th studio album The Insulated World. The singer is now returning to activities with his side band sukekiyo. In late 2018, the band held a few performances and released the sold-out venue-limited single Kisses. A second press of the said single can be purchased through sukekiyo's official online shop. We were also surprised with a lyric video for 2017's Hakudaku (白濁) from their second album ADORATIO.


In October 2008 we had the classic Egyptian holiday with our good friend Jo -we cruised the Nile for a week then finished with four nights in Cairo. We flew into Luxor, then cruised to Aswan and back, stopping off at Edfu and Kom Ombo on the way. While at Aswan, we flew to Abu Simbel visiting its re-sited temples. In Cairo we stayed in Giza, near to the Pyramids and Sphinx. We also visited Sakkara to see the earliest Pyramids and the old capital of Memphis. In the city of Cairo itself, all we did was to visit the Archaeological Museum (twice!) and the Citadel.
Ahmed at MISR Travel organised our well planned trip. The guides and services were superb. Thanks again.
I have uploaded over 200 photos of our Egypt holiday. About half of them are of the fabulous ancient temples along the Nile. So if you just want to look at a few photos, the Egypt Highlights page has some of the best photos (IMHO).
We had a decadent time on board the MS Sherry Boat. The Nile has dramatic scenery with the desert looming just beyond the narrow fertile strip on both banks.
The West bank of the Nile opposite Luxor has more ancient historical sites per square yard than anywhere else in the world, let alone Egypt. In addition to the actual Valley of the Kings with its numerous Pharaonic tombs, we visited the Colossi of Memnon, Hatshepset's Mortuary Temple, the Valley of the Nobles and the Workmen's village of Deir el Medina. If we had more time, there are many other sites we would have loved to have seen here.
We visited six main temples along the Nile, each very different. They were built to impress on an epic scale. They didn't disappoint!
They are so monstrous that I felt very small walking by them. For some four thousand years they were the tallest man made structures on the planet. Truly awe-inspiring.
The Citadel has magnificent views over Cairo - air pollution permitting. The Mohammed Ali Mosque is its dominant feature.
Egypt Reflections is a collection of various photos taken while travelling round Egypt that do not fit into any of the above pages.


I love Kahlil Gibran. Love him! If you have never read anything of his before, please do yourself a favor and buy The Prophet immediately. It may be the only book you ever need to read about love, about parenting, about relationships, about life. Dude has it going on!


How to fulfill your legal duty to someone's estate.
Have you been named executor of someone's estate?
Though that may seem like an honor, it's usually a thankless assignment — and often a difficult one too.
As executor you have the legal responsibility to pay the deceased's taxes, debts and creditors — in the proper order — from estate assets and then to distribute the remainder according to the decedent's wishes as expressed in a will or trust documents.
That typically involves a great deal of paperwork and deadlines — and all the while you might have to walk a tightrope to avoid getting embroiled in family squabbles. Worse, you could even be sued if you don't handle everything correctly.
So if you're an executor (some states use the term personal representative), you have my sympathy.
Some 25 percent of wealthy families who responded to a 2014 U.S. Trust survey said they have had family disagreements about money. Disputes over asset distribution are among the top five risks to family wealth, the survey concluded.
The core responsibilities of executors haven't changed much over the past century, but meeting them has grown more complex. In the past, an executor would typically find estate documents in the deceased's filing cabinet or safe deposit box. But today, such items are increasingly stored digitally. Even if you know the digital locations, do you have or can you get usernames and passwords to access the documents?
Laws in many states are unclear as to an executor's ability to gain access to a deceased person's digital property. Email providers often have terms that make it difficult for friends or family members — with or without executor authority — to open email accounts. Some email providers, once they find out that an account holder has died, will delete the account.
Don't assume that you (as executor) must pay the deceased's bills, such as credit card and utility statements, as soon as they arrive. Other obligations — including federal and state taxes and funeral expenses — take priority. Paying routine debts before others can be a breach of fiduciary duty for which an executor can be held liable.
For example, an executor might not know that the estate owes federal income taxes. If he or she pays other expenses, there might not be enough money left to pay the taxes, and the executor could be required to pay them personally.
So before paying any creditors, consult with an estate attorney to learn the priority of payments, and get an estimate of all tax liabilities from the estate's accountant. Even after you've set aside enough assets to satisfy the highest-priority creditors, consider paying lesser-priority debts only after the entire estate administration is complete, tax returns are filed and taxes are paid.
Depending on the estate's size and complexity, the process could take many months. Although heirs are likely to pressure executors to rush the process so they can get their inheritances, don't succumb to them. Explain that your job as executor involves much more than merely distributing money and other items. Tell them you're working expeditiously and will get to them in the proper order. And never pay one heir sooner than other heirs. You must treat them all equally.
Executors are charged with keeping all estate assets safe prior to distribution. In the days immediately following death, visitors come and go, and household items can disappear. The executor needs to secure all property and monitor its safekeeping. It's often wise to hire a security firm to protect the home and its contents during the settlement process, experts say.
be living in it. If there's an agreement to sell, the executor must decide the listing price and the amount of sales commission the estate will pay. If a real estate agent recommends making improvements to a property to help sell it, the executor needs to reach an accord with heirs before spending money on improvements. A financial advisor and/or estate attorney can help an executor resolve such issues amicably; otherwise the executor might need to seek assistance from probate court.
Executors should maintain the homeowners insurance on the deceased person's house until it's sold, but be aware that insurance companies don't like to cover vacant properties for extended periods because of the risk of fires, vandalism and other damage.
Tempted to invest the decedent's assets in the stock market until you're able to distribute the money?
accounts, contact a financial advisor for help.
Before you begin serving as executor, you should understand that most major legal and financial tasks don't have to be handled immediately. Step back. Dealing with such matters at a time when you might be coping with a personal loss is difficult. In the days following death, you could be involved in helping arrange the funeral, collecting information for an obituary, contacting clergy and other urgent tasks.
When those tasks are completed, take time to get a clear understanding of the estate. And realize that you don't have to do everything by yourself. But don't ask other family members or friends to help. Instead, enlist the aid of three specialists: an estate attorney, your financial advisor and the deceased's accountant. (Contact us if you need a referral to an estate attorney.) Some states let you appoint co-executors who have special expertise to serve alongside you.
Gather all the deceased's important documents — starting with the will, if any. Ideally, the decedent would have told you where to find all the legal, tax and financial documents you need, as well as usernames and passwords for digital material. Don't assume the decedent told you everything — look for bank safe deposit boxes, insurance policies and other items that can lead to unknown assets. Interview family members to help your search.
File the will with the probate court. Make yourself a few copies first.
Get several certified copies of the death certificate, typically signed by the physician who verified the death and issued by the county of residence. You'll need one every time you must provide proof of death (although you might find that a few institutions now accept copies).
Notify the post office, utility companies, credit card companies, banks and other businesses of the death so that recurring charges are stopped. Have the deceased's mail forwarded to you.
Notify the Social Security Administration, unions and any other organizations that had been providing benefits to the deceased. Payments made after the date of death will have to be returned.
Inventory all assets and have valuable items appraised. The inventory is needed for probate court. It will also show you how assets are titled, making distributions easier, and help you determine whether estate taxes are due.
Determine whether probate is necessary by totaling the value of property subject to probate, seeing how title is held and checking your state's rules on what estates qualify for simplified procedures. An estate attorney can help you conduct a probate court proceeding if one is needed.
If there's a living trust, work with the trustee(s) to coordinate many tasks. Executors are not always the trustees, so know where your responsibilities (and rights) stop.
Notify the heirs and beneficiaries. The estate attorney and probate court can help you. Stay in touch with them. See if the deceased's state requires that they receive a copy of the will or trust.
Safeguard estate assets until you distribute them. Never commingle estate assets with your own.
Get the consent of beneficiaries before taking major steps. Otherwise they may suspect wrongdoing. Even during a lull in activity, let them know the status. Don't startle them with surprise announcements.
Pay the estate's bills in proper order. You are not obligated to pay any of the deceased's debts personally. If you think there won't be enough money to pay all debts, stop paying bills and ask your attorney or the court for guidance.
File the decedent's final income tax returns. Consider hiring the deceased's tax preparer to help.
If the estate exceeds $5.49 million in value, file an estate tax return. Smaller estates may owe state estate taxes. Check with your attorney.
Distribute the assets. After all creditors are paid in full, pay remaining funds to the heirs and beneficiaries, including distribution of such items as furnishings, jewelry and other valuables.
Congratulate yourself and breathe a sigh of relief. And decide who your biggest enemy is — and name that person to serve as your executor.


Halflings rarely ally themselves with others. Normally, a group of halflings will ally only with those cities that have provided historical or long-standing protection to a particular clan, or with people who share their nomadic lifestyle.
Even so, the halflings recognize that in order to travel the lands, one must remain neutral to many other clans and people. This philosophy is deeply ingrained in the halfling mindset; while they tend to skirt any region that is known for darker deeds, preferring to avoid contact, they will remain neutral when an encounter is unavoidable.
Halflings openly oppose people who impede their ability to wander freely. Their worst enemies are militant expansionists who place taxes, tolls or restrictions on travelers, or anyone who completely blocks their travel. Other than that, halflings make few enemies. Though they will defend their clans when necessary, they try to avoid becoming involved in politics.


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roxanne van gemert
E-Estonia
How did Estonia become the most advanced digital society in the world?
Photo by Franki Chamaki on Unsplash
Estonia is a northern European country nestled on the eastern shore of the Baltic sea, across from the Gulf of Finland. Its population is just 1.3 million — the same as the state of New Hampshire — making it one of the least populous member states within the European Union.
Last year, Wired Magazine named Estonia the most advanced digital society in the world. E-Estonia is a term used to describe the rise of Estonia as one of the most innovative, forward-thinking information societies globally. The advancements that Estonia has contributed to the movement toward a digital government is admirable and should be used as a framework for what can happen when safe and easy-to-use e-services ecosystems are used as a platform to streamline public services, connect with systems, and create innovative e-solutions for governments and businesses at a municipal, federal, and international level.
Here are some public services that Estonia has digitized for citizens:
Through E-Identity, 98% of Estonian citizens can create and have access to their own digital identity. With this asset, a citizen can digitally sign documents and verify their identity across all public services.
Through X-Road, the public and private sectors can share information and collaborate with one another daily. Data that a citizen has requested is linked to their identity and can be accessed if needed in the future, like filling out a form at your bank or doctor's office.
Through E-File, the public service can facilitate the exchange of information between the various information systems that may exist within a court case: policy, courts, legal offices, the legal aid system, tax boards, and more.
Through the E-Patient Portal, doctors can have access to a centralized version of a citizen's records from one electronic file. In an emergency situation, a doctor can use a patient's ID code to access the entirety of that individual's medical history, including recent treatments and medications.
Through I-Voting, voters have the ability to cast their ballots from a device connected to the internet, anywhere in the world. The voters' identification information is removed from the ballot before it reaches the electoral commission.
It is estimated that I-Voting saves over 11,000 working days per election.
Through 'border queue management', those travelling in a vehicle through a border checkpoint are able to reserve a time slot at the checkpoint of their choice. Border wait times are now only 30 minutes long.
Furthermore, 90% of parking in Estonia is now done through a downloadable mobile parking application.
Through E-Banking, over 99% of all banking transactions in Estonia are completed online. Opening an account can also be carried out online, with an e-ID, facial recognition technology, and an interview recording.
Through E-School, teachers and parents can communicate, collaborate, and record information related to students. Teachers can enter grades, attendance, and post homework, while students and parents can keep up to date on evaluations, assessments, and attendance.
85% of schools are currently using e-school.
What does the future hold for the most advanced digital society in the world?
Estonia is in the spotlight again with E-Residency. Through E-Residency, anyone in the world can apply to become a resident of Estonia, so that entrepreneurs around the world can benefit from an advanced digital infrastructure filled with opportunity.
"In the future, it is believed that countries will compete for e-residents based on the quality of their public e-services and their business environment."
The key to the success of E-Estonia? Continuous experimentation. Claiming the title of the most advanced digital society in the world is a result of making mistakes, learning from them, and evolving their e-services into every day interfaces that citizens actively rely upon each day.
co-founder @ disco innovation studio. writing thoughtful dialogue on the design of everyday interfaces + experiences.
Public Sector Innovation
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LOOK before, at, and after the word.
REASON - connect what you know with what the author has written.
RESOLVE or REDO - Decide if you know enough, should try again, or consult an expert or reference.
Introduce this strategy through teacher modeling, group work, and discussion. To prepare, select a passage and target a word or words for discussion. Prepare a transparency by typing a passage, or photocopying the passage and putting masking tape over the target word.
After students become familiar with using a contextual process, student teams can lead the lesson. Working with the teacher, they can choose two words a week they think would be unfamiliar to the group and model the process with the first word and lead the discussion on the second.