ATHENS, Greece — Voters delivered a shocking rebuff to Europe's leaders Sunday, decisively rejecting a deal offered by this nation's creditors in a vote that could redefine the country's place in Europe and shake the continent's financial stability.
As people gathered to celebrate in Athens' central Syntagma Square, the Interior Ministry reported that with almost 90 percent of the vote tallied, 61 percent of the voters had said no to a deal that would have imposed greater austerity measures on the beleaguered country.
The no votes carried virtually every district in the country, handing a sweeping victory to Prime Minister Alexis Tsipras, a leftist who came to power in January vowing to reject new austerity measures, which he called an injustice and economically self-defeating.
Late last month he walked away from negotiations in frustration at the creditors' demands, called the referendum and urged Greeks to vote no as a way to give him more bargaining power.
Tsipras' victory settled little, since the creditors' offer is no longer on the table. There remains the possibility that they could walk away, leaving Greece facing default, financial collapse and expulsion from the eurozone and, in the worst case, from the European Union.
At stake, however, may be far more than Greece's place in Europe, as experts have offered wildly differing opinions about what the referendum could mean for the future of the euro and, indeed, the world's markets.
The euro fell sharply as spot trading resumed, the Associated Press reported. The European currency slumped to $1.0993 late Sunday from $1.1110 late Friday.
Some European leaders began making efforts to contain the potential damage before the voting was over. Chancellor Angela Merkel of Germany said she would travel to Paris on Monday to meet with the French president, Francois Hollande, for a "joint assessment of the situation after the Greek referendum." Later, the two leaders called for a European Union summit meeting on Tuesday, the AP reported.
To some, the vote was virtually a point of no return. Germany's economy minister, Sigmar Gabriel, who is also the leader of the Social Democrats, said it was now hard to see how talks could resume on a bailout deal.
Nonetheless, many voters, tired of more than five years of soaring unemployment and a collapsing economy, said they could not accept the terms of the European offer, which imposed yet more pension cuts and tax increases, without any hint of debt relief.
At a polling station in a middle-class Athens neighborhood, Baizar Tazerian, 76, said she was angered by what she believed had been European interference in the ballot and had just voted no.
"No means that we don't have to say yes to whatever they are saying," Tazerian said.
In Ilisia, a middle-class neighborhood, poet Titos Patrikios, 87, said he was voting yes but urged everyone to vote their own consciences.


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text:
Innovationmore from Pivot issue
The Ticker: Forging a New Future
By [email protected]July 10, 2020No Comments
Whatever their phased-reopening plans look like, the governments across the globe that are managing the restart of economic activity in the wake of COVID-19 have generally been in consensus on one point: The final phase, full resumption of economic and social activity, will not happen until a COVID-19 vaccine is widely accessible.
With that stage unlikely to be reached before mid-2021 at the earliest, businesses have had to figure out how to create comfortable experiences for customers and workers while public-health regulations on social distancing and mask-wearing are still in place. In the Netherlands, McDonald's planned to welcome guests back into its restaurants with table service—delivering orders to customers' tables via trolleys from which they could pick up their food. The company also announced plans for handwashing stations at restaurant entrances and a "Take Out Plus" contactless ordering experience for pedestrians and cyclists who cannot use the drive-thru but do not want to dine indoors. Employees are to be spaced apart in 1.5-meter intervals with plastic partitions between them as necessary.
In the first three weeks of April 2020, order-online, pick-up-at-store orders climbed 208% year-over-year at 80 leading retailers.
Source: Adobe Analytics
To give some peace of mind to flyers as air travel slowly resumes, carriers including Delta Air Lines and American Airlines announced moves to block off some middle seats on their planes. Air Canada and Frontier in May became the
first North American airlines to roll out preboarding temperature checks for passengers.
Starbucks, acutely aware of its position as a "third place"—a sought-out destination away from work and home for customers—reopened the vast majority of its U.S. stores in May, incorporating lessons learned from its reopened stores in China. The coffee giant expanded mobile ordering and contactless pickup and debuted curbside pickup in some locations. Indeed, the rise of order-online, pick-up-at-store options may be one of the biggest retail trends to emerge from the COVID-19 pandemic.
In the first three weeks of April, according to data from Adobe Analytics, these orders climbed 208% year-over-year at the 80 leading retailers the company tracks. For consumers, curbside pickup options are compelling on several fronts: no shipping fees, the convenience of being able to get items the same day they are ordered, peace of mind from minimized exposure to other shoppers and associates—and still an opportunity to get out of the house.
Coming out of isolation, customers "crave the connection and community that are fundamental to humanity," Starbucks CEO Kevin Johnson wrote in a letter to customers and partners. However, he noted, they also want retail experiences that feel safe, familiar and convenient. Responding to evolving customer expectations and developing new adaptations and routines alongside customers will help forge a new, robust future for the "third place," he indicated.
This article appeared in the Summer 2020 issue of IQ Insigniam Quarterly, with the headline "Forging a New Future." To begin receiving IQ, go here.
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redpajama_set_name: RedPajamaCommonCrawl


text:
The Department of Justice seal. (Samira Bouaou/The Epoch Times)
DOJ Charges 10 Former NFL Players With Defrauding Health Care Plan of Millions of Dollars
By Janita Kan
Ten former National Football League players have been charged with defrauding millions of dollars from the league's health care plan that's meant to benefit retired players and their families, according to Justice Department officials.
Former Washington Redskins running back Clinton Portis, 38, and former Redskins cornerback Carlos Rogers, 38, were among those who were charged with various fraud counts for their roles in the nationwide scheme, according to charging documents unsealed on Dec. 12.
The indictments allege that the players made more than $3.9 million in false and fraudulent claims to the Gene Upshaw NFL Player Health Reimbursement Account Plan for expensive medical equipment that was never purchased and never received.
Some of the equipment included hyperbaric oxygen chambers, ultrasound machines used by doctor's offices to conduct women's health exams, and electromagnetic therapy devices designed for use on horses, Assistant Attorney General Brian Benczkowski said during a press conference on Dec. 12.
The plan paid out more than $3.4 million on those claims between June 2017 and December 2018, the DOJ said.
The players' alleged actions of "defrauding the Plan and treating it like their own personal ATM" had wider implications for the plan, because it risked the plan's tax-exempt status and the ability of law-abiding retired players from receiving tax-free reimbursements for legitimate medical expenses for themselves or their families, Benczkowski said.
"This is an $800 million or so plan, intended to provide benefits to law-abiding former players and their families. And this conduct, if it had gone unchecked, threatened the tax-exempt status of the fund," he said, adding that it could have potentially created problems for law-abiding players from access to benefits.
He said that the ringleaders of the scheme recruited other eligible former players by offering to submit or assist in submitting fake claims to the plan, in exchange for kickbacks ranging from a few thousand dollars to $10,000 or more for each claim. The size of each claim typically ranged from $40,000 to $50,000.
"When a defendant received his reimbursement check, he then kicked back the agreed-upon amount to the ringleader or recruiter," Benczkowski said. "This process was repeated over and over again, placing the integrity of the plan at risk."
The alleged fraud was halted after the appointed benefits administrator of the plan, Cigna, began detecting some of the claims and refused to pay them.
"Those who perpetrate scams to steal taxpayer dollars from intended beneficiaries threaten the viability of critical health care programs. We are committed to rooting out these fraudsters," FBI Special Agent in Charge George Piro of the Miami Field Office said at the press conference.
Along with Portis and Rogers, former players Robert McCune, 40, John Eubanks, 36, Tamarick Vanover, 45, Ceandris Brown, 36, James Butler, 37, Fredrick Bennett, 35, Correll Buckhalter, 41, and Etric Pruitt, 38, also have been charged with conspiracy or fraud.
Officials said the investigation is continuing, and could lead to additional arrests and charges if further evidence is found.
The NFL Players Association told The Epoch Times that they "do not have comment on this issue at this time."
Updated with NFL Players Association's response.
Follow Janita on Twitter: @janitakan


The ESCO skills mapping pilot was a project to test the creation of mappings between national skill classification systems and ESCO.
The Commission conducted the project together with the public employment services of Austria and Sweden between January and April 2018. Belgium and Romania joined the pilot as observers.
level of interoperability among the national skill classifications during cross-border matching.


text:
\section{Introduction}
\hspace*{1em}
As a special case of image object segmentation, object co-segmentation refers to the task of jointly discovering and segmenting the objects shared in a group of images.
It has been widely used to support various computer vision applications, such as interactive image segmentation~\cite{kamranian2018iterative}, 3D reconstruction~\cite{mustafa2017semantically} and object co-localization~\cite{Wei2017Unsupervised,Han2018Robust}, to name a few.

Image features that characterize the co-objects in the image group are vital for a co-segmentation task.
Conventional approaches use the hand-crafted cues such as color histograms, Gabor filter outputs and SIFT descriptors as feature representations~\cite{yuan2014novel,dai2013cosegmentation,lee2015multiple}.
Those hand-crafted features cannot well handle the challenging cases in co-segmentation such as background clutter and large-scale appearance variations of the co-objects in images.
In recent years, deep-learning-based co-segmentation methods have attracted much attention. For example,~\cite{li2018deep,chen2018semantic} leverage a Siamese network architecture for object co-segmentation and an attention mechanism is used to enhance the co-object feature representations. These methods have shown superior performance compared to the traditional methods~\cite{yuan2014novel,dai2013cosegmentation,lee2015multiple}, which inspire us to explore a deep-learning-based solution to object co-segmentation.

\begin{figure}[t]
\centering
\includegraphics[width=0.9\columnwidth]{newnewfigure1.pdf}
\caption{Object co-segmentation examples by our approach. (a) Horse group; (b) Horse group co-segmentation maps.}
\label{colocosegexample}
\end{figure}
One critical property of object co-segmentation is that the co-objects in images belong to the same semantic category. Those co-objects usually occupy part of each image. One illustrative example is shown in Figure~\ref{colocosegexample}. It is desirable that the deep convolutional network layers, being used as a feature extractor, are targeted on modelling the co-objects. To this end, we propose a spatial-semantic modulated network structure to model this property. The two modulators are achieved by the designed group-wise mask learning branch and co-category classification branch, respectively. We summarize the technical contributions of this work as follows:
\begin{quote}
\begin{itemize}
\item
We propose a spatial-semantic modulated deep network for object co-segmentation. Image features extracted by a backbone network are used to learn a spatial modulator and a semantic modulator. The outputs of the modulators guide the image features up-sampling to generate the co-segmentation results. The network parameter learning is formulated into a multi-task learning task, and the whole network is trained in an end-to-end manner.
\item
For the spatial modulation branch, an unsupervised learning method is proposed to learn a mask for each image. With the fused multi-resolution image features as input, we formulate the mask learning as an integer programming problem. Its continuous relaxation has a closed-form solution. The learned parameter indicates whether the corresponding image pixel corresponds to foreground or background.
\item
In the semantic modulation branch, we design a hierarchical second-order pooling (HSP) operator to transform the convolutional features for object classification. Spatial pooling (SP) is shown to be able to capture the high-order feature statistical dependency~\cite{gao2019global}. The proposed HSP module has a stack of two SP layers. They are dedicated to capturing the long-range channel-wise dependency of the holistic feature representation. The output of the HSP layer is fed into a fully-connected layer for object classification and used as the semantic modulator.
\end{itemize}
\end{quote}

We conduct extensive evaluations on four object co-segmentation benchmark datasets~\cite{faktor2013co,rubinstein2013unsupervised}, including the sub-set of MSRC, Internet, the sub-set of iCoseg and PASCAL-VOC datasets. The proposed model achieves a significantly higher accuracy than state-of-the-art methods. Especially, on the most challenging PASCAL-VOC dataset, our method outperforms the second best-performing state-of-the-art approach~\cite{li2018deep} by $6\%$ in terms of average Jaccard index $\mathcal{J}$.

The rest of this work is organized as follows: In~\S~\ref{sec:relatedwork}, we introduce the related works of
our study. \S~\ref{sec:proposedapproach} describes the proposed framework and its main components. Afterwards, comprehensive experimental evaluations are presented in~\S~\ref{sec:experiments}. Finally, we conclude this work in~\S~\ref{sec:conclusions}.
\section{Related Work}
\label{sec:relatedwork}
\subsection{Object Co-segmentation}
\hspace*{1em}
A more comprehensive literature review about image co-segmentation can be found in~\cite{zhu2016beyond}. Existing object co-segmentation methods can be roughly grouped into four categories including graph-based model, saliency-based model, joint processing model and deep learning model.
Conventional approaches such as \cite{yuan2014novel,collins2012random,lee2015multiple} assume the pixels or superpixels in the co-objects can be grouped together and then they formulate co-segmentation as a clustering task to search for the co-objects.
Saliency-detection-based methods assume regions of interest in the images are usually the co-objects to be segmented. They conduct image co-segmentation through detecting the regions that attract human attention most. Representative models include~\cite{tsai2018image,zhang2019co,lu2019survey}.
The work in~\cite{dai2013cosegmentation,jerripothula2017object} employs a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. The idea of joint processing can exploit the inherent interdependencies of two tasks to achieve better results jointly.
Recently, \cite{li2018deep,chen2018semantic} respectively propose an end-to-end deep-learning-based method for object co-segmentation using a Siamese encoder-decoder architecture and a semantic-aware attention mechanism.
\subsection{Network Modulation}
\hspace*{1em}
Modulation module has been proved to be an effective way to manipulate network parameter learning. The modulator can be modelled as parameters or output of the auxiliary branch that are used to guide the main branch parameter learning. In the segmentation method~\cite{dai2015convolutional}, an image mask is used as a modulator for background removal. In the Mask R-CNN model~\cite{he2017mask}, a classification branch is used to guide the segmentation branch learning. Feature-wise linear modulation is a widely-used scheme, which has been applied to object detection~\cite{Lin2017Feature} and graph neural networks learning~\cite{brockschmidt}. In visual reasoning problem, network modulation is used to encode the language information~\cite{de2017modulating,perez2018film}. The attention module in the image caption model~\cite{chen2017sca} can be viewed as a modulator. \cite{yang2018efficient} proposes to model the visual and spatial information by a modulator for video object segmentation. In~\cite{flores2019saliency}, a saliency detection branch is added to an existing CNN architecture as a modulator for fine-grained object recognition. A cross-modulation mechanism is proposed in~\cite{prol2018cross} for few-shot learning.
\begin{figure*}[t]
\centering
\includegraphics[width=1\textwidth]{network.pdf}
\caption{Overview of the proposed object co-segmentation framework. Firstly, a group of images are fed into the backbone network to yield a set of muti-resolution CFMs. Then, the CFMs are modulated by a group of spatial heatmaps and a feature channel selector vector. The former is generated by a clustering approach that can well capture the coarse localizations of the co-objects in the images. Under the supervision of co-category labels, the latter is obtained by learning a group-wise semantic representation that indicates the importance of the feature channels. Finally, the multi-resolution modulated CFMs are fused in a way similar to the feature pyramid network (FPN)~\cite{Lin2017Feature} to produce the co-segmentation maps. `\textbf{conv}', `\textbf{FC}', `\textbf{up}' and `\textbf{down}' are short for convolutional, fully-connected, upsampling and downsampling layers, respectively.}
\label{network}
\end{figure*}
\section{Proposed Approach}
\label{sec:proposedapproach}
\subsection{Problem Formulation}
\hspace*{1em}
Figure~\ref{network} presents an overview of our model. Given a group of $N$ images $\mathcal{I}=\{I^n\}_{n=1}^N$ containing co-objects of a specific category, our objective is to learn a feed-forward network $f$ that produces a set of object co-segmentation masks $\mathcal{M}=\{\textit{\textbf{M}}^n\}_{n=1}^N$:
\begin{equation}
\mathcal{M} = f(\mathcal{I};\bm{\theta}),
\end{equation}
where $\bm{\theta}$ denotes the network parameters to be optimized.
The network $f$ is composed of three sub-networks: spatial modulation sub-net $f_{spa}$, semantic modulation sub-net $f_{sem}$ and segmentation sub-net $f_{seg}$.
The renowned SPP-Net~\cite{he2015spatial} has shown that the convolutional feature maps (CFMs) for object recognition encode both spatial layouts of objects (by their positions) and the semantics (by strengths of their activations).
Inspired by this model, we design $f_{spa}$ and $f_{sem}$ to encode the spatial and semantic information of the co-objects in $\mathcal{I}$, respectively.
The two modulators guide the convolution layers learning in $f_{seg}$ to focus on the co-objects in the images.
Specifically, the sub-net $f_{spa}$ is to learn a mask for each image to coarsely localize the co-object in it.
Given the input CFMs $\{{\bm\varphi}(I^n)\}_{n=1}^N$ produced by fusing all the output CFMs of our backbone network, the sub-net $f_{spa}$ produces
a set of spatial masks $\mathcal{S}=\{\textit{\textbf{S}}^n\in \Re^{w\times h}\}_{n=1}^N$ with width $w$ and height $h$:
\begin{equation}
\label{eq:spa modulator}
\mathcal{S}=f_{spa}(\{{\bm\varphi}(I^n)\}_{n=1}^N;\bm{\theta}_{spa}),
\end{equation}
where $\bm{\theta}_{spa}$ denotes the corresponding network parameters to be optimized.
Although the coarse spatial layout information of the co-objects in all images can be embedded into $\mathcal{S}$ in (\ref{eq:spa modulator}), the useful high-level semantic information that are essential to differentiate co-objects from distractors fails to be transferred into $\mathcal{S}$.
To address this issue, we further propose $f_{sem}$ as a complement.
The sub-net $f_{sem}$ learns a channel selector vector $\bm{\gamma}\in \Re^d$ with $d$ channels. The entries of $\bm{\gamma}$ indicate the importance of feature channels, that is
\begin{equation}
\label{eq:semantic modulator}
\bm{\gamma}=f_{sem}(\{{\bm\phi}(I^n)\}_{n=1}^N;\bm{\theta}_{sem}),
\end{equation}
where $\bm{\phi}$ denotes the output CFMs with the lowest resolution generated by our backbone network, and $\bm{\theta}_{sem}$ is the corresponding sub-net parameters to be learned.
$\bm\gamma$ is optimized using the co-category labels as supervision.
Finally, we use the spatial and the semantic modulators as guidance to segment the co-object regions in each image $I^n$:
\begin{equation}
\label{eq:seg}
\textit{\textbf{M}}^n = f_{seg}(I^n,\textit{\textbf{S}}^n,\bm\gamma;\bm\theta_{seg}),
\end{equation}
where $\bm\theta_{seg}$ is the parameters of the segmentation sub-net.
To be specific, we transfer the spatial and semantic guidance $\{\mathcal{S},\bm\gamma\}$  into $f_{seg}$ using a simple shift-and-scale operation on the input CFMs of $f_{seg}$:
for each image $I^n\in \mathcal{I}$, its modulated feature maps are formulated as
\begin{equation}
\label{eq:modulator}
\textit{\textbf{Y}}^n_c = \gamma_c\textit{\textbf{X}}^n_c+\textit{\textbf{S}}^n, c=1,\ldots,d,
\end{equation}
where $\textit{\textbf{X}}^n_c$, $\textit{\textbf{Y}}^n_c\in \Re^{w\times h}$ are the input and output CFMs in the $c_{th}$ channel, $\gamma_c$ is the $c_{th}$ element of $\bm{\gamma}$.
\begin{figure}[t]
\centering
\includegraphics[width=0.4\textwidth]{clustering.pdf}
\caption{Schematic illustration of the proposed clustering objective for spatial modulator. The objective considers the mutual effects between any two samples, which pulls the samples of the same cluster together while pushing away the samples of different clusters. }
\label{fig:cluttering}
\end{figure}
\subsection{Spatial Modulator}
\hspace*{1em}
In the sub-net $f_{spa}$ (\ref{eq:spa modulator}),  the $i$-th channel feature $\textit{\textbf{x}}_i^n\in\Re^d$ of the input ${\bm\varphi}(I^n)\in\Re^{w\times h\times d}$ represents a corresponding local region in $I^n$.
For expression clarity, we represent all the channel feature representations of $\mathcal{I}$ as $\mathcal{X}=\{\textit{\textbf{x}}_i\in\Re^d\}_{i=1}^{whN}$.
The sub-net $f_{spa}$ aims at partitioning the data points in $\mathcal{X}$ into two classes $\mathcal{C}_f$, $\mathcal{C}_b$ of foreground and background.
However, if training $f_{spa}$ using a supervised learning method with a fixed set of categories, it cannot generalize well to unseen categories.
To this end, we propose a simple yet effective clustering approach to partitioning $\mathcal{X}$ into two clusters $\mathcal{C}_f$, $\mathcal{C}_b$ without knowing object semantic categories.
Our unsupervised method can highlight category-agnostic co-object regions in images and hence can better generalize to unseen categories.
As shown by Figure~\ref{fig:cluttering}, this can be achieved by maximizing all the distances between the foreground and the background
samples while minimizing all the distances between the foreground samples and between the background ones respectively. To this end, we define the clustering objective as follows:

\begin{equation}
\label{eq:spacluter}
\min\{\ell_{spa}=-2\sum_{i\in \mathcal{C}_f,j\in\mathcal{C}_b}d_{ij}+\sum_{i,j\in \mathcal{C}_f}d_{ij}+\sum_{i,j\in \mathcal{C}_b}d_{ij}\},
\end{equation}
where $d_{ij}=\|\textit{\textbf{x}}_i-\textit{\textbf{x}}_j\|_2^2$ is the squared Euclidean distance between samples $i$ and $j$.
Since we use normalized channel features satisfying $\|\textit{\textbf{x}}_i\|_2^2=1$, $d_{ij}$ can be reformulated as
\begin{equation}
\label{eq:dij}
d_{ij} = 2-2\textit{\textbf{x}}_i^\top \textit{\textbf{x}}_j.
\end{equation}

Using a cluster indictor vector $\textit{\textbf{s}}=[s_1,\ldots,s_{whN}]^\top$ subject to $\|\textit{\textbf{s}}\|_2^2=1$, where $s_i=1/\sqrt{whN}$ if $i\in \mathcal{C}_f$ and $s_i=-1/\sqrt{whN}$ if $i\in \mathcal{C}_b$, the loss function $\ell_{spa}$ in (\ref{eq:spacluter}) can be reformulated as
\begin{equation}
\label{eq:sparegularizor}
\ell_{spa}(\textit{\textbf{s}}) = whN\textit{\textbf{s}}^\top \textit{\textbf{D}}\textit{\textbf{s}},
\end{equation}
where the $(i,j)$-th entry of  $\textit{\textbf{D}}=d_{ij}$.
Putting (\ref{eq:dij}) into (\ref{eq:sparegularizor}) and removing the trivial constant $whN$, $\ell_{spa}$ can be reformulated as
\begin{equation}
\label{eq:spaloss}
\ell_{spa}(\textit{\textbf{s}}) = -\textit{\textbf{s}}^\top\textit{\textbf{G}}\textit{\textbf{s}},
\end{equation}
where $\textit{\textbf{G}}=\textit{\textbf{X}}^\top\textit{\textbf{X}}-\textit{\textbf{1}}$ with $\textit{\textbf{X}}=[\textit{\textbf{x}}_1,\ldots,\textit{\textbf{x}}_{whN}]$,
$\textit{\textbf{1}}$ denotes an all-ones matrix.
Relaxing the elements in $\textit{\textbf{s}}$ from binary indictor values to continuous values in $[-1,1]$ subject to $\|\textit{\textbf{s}}\|_2^2=1$, the solution $\widehat{\textit{\textbf{s}}}=\arg\min_{\textit{\textbf{s}}}\ell_{spa}(\textit{\textbf{s}})$ satisfies~\cite{Ding2004K}
\begin{equation}
\textit{\textbf{G}}\widehat{\textit{\textbf{s}}}=\lambda_{max}\widehat{\textit{\textbf{s}}},
\end{equation}
where $\lambda_{max}$ denotes the maximum eigenvalue of $\textit{\textbf{G}}$, and its corresponding eigenvector is $\widehat{\textit{\textbf{s}}}\in \Re^{whN}$.
The optimal solution $\widehat{\textit{\textbf{s}}}$ is then reshaped to a set of $N$ spatial masks $\{\widehat{\textit{\textbf{S}}}^n\in\Re^{w\times h}\}_{n=1}^N$ as the spatial guidance in (\ref{eq:modulator}).
\begin{figure}[t]
\centering
\includegraphics[width=0.88\columnwidth]{paperfigure4.pdf}
\caption{Illustration of the SP and the HSP. The sub-net $f_{sem}$ (\ref{eq:semantic modulator}) is composed of the HSP module.}
\label{fig:semanticmodulator}
\end{figure}
\subsection{Semantic Modulator}
\hspace*{1em}
Figure~\ref{fig:semanticmodulator} shows the diagram of the key modules in the sub-net $f_{sem}$ (\ref{eq:semantic modulator}), including the SP and the HSP.
The SP exploits the high-order statistics of the holistic representation to enhance the non-linear representative capability of the learned model~\cite{gao2019global}, while the HSP can capture the long-range dependency along channel dimension of the group-wise feature tensors, paying more attention to important channels for classification task under the supervision of co-category labels.

\textbf{SP:} Given input feature tensor $\bm\phi\in \Re^{w\times h\times d}$,  we firstly leverage a $1\times 1$ convolution to reduce the number of channels from $d$ to $c$ to reduce the computational cost for the following operations.
Then, we compute pairwise channel correlations of the $w\times h\times c$ tensor to yield a $c \times c$ covariance matrix.
Each entry in the $c \times c$ covariance matrix measures the relevance between the feature maps in two channels, which leverages a quadratic operator to model high-order statistics of the holistic representation, hence enabling to enhance non-linear modeling capability.
Afterwards, we use an FC layer to transform the $c\times c$ covariance matrix into a $1\times 1\times d$ tensor that indicates the feature channel importance.

\textbf{HSP:} For each image $I^n\in \mathcal{I}$, its feature tensor $\bm\phi(I^n)$ is fed into an SP layer, outputting a $1\times 1\times d$ indicator tensor.
Then, all the indictor tensors are concatenated vertically to yield a group-wise semantic representation, which is again fed into an SP layer to capture the long-range dependency along the channel dimension of the group-wise semantic representation, yielding an indictor vector $\bm\gamma$ that steers attention to the important channels that are essential for co-category classification.

\textbf{Loss:} The output $\bm\gamma$ of $f_{sem}$ is followed by an FC layer and a sigmoid layer, yielding a classifier response:
 %
 \begin{equation}
 \label{eq:cls}
 \widehat{\textit{\textbf{y}}}=sigmoid(\textit{\textbf{W}}\bm\gamma+\textit{\textbf{b}}),
 \end{equation}
 %
 where $\textit{\textbf{W}}\in\Re^{L\times d}$ and $\textit{\textbf{b}}\in\Re^{L}$ are the parameters of the FC layer, $L$ denotes the number of the co-category in the training set.

 %
 The widely used cross-entropy loss function for classification is adopted to learn the indictor $\bm\gamma$ in (\ref{eq:cls}):
 %
\begin{equation}
\label{eq:semloss}
\ell_{sem} = - \frac{1}{L} \sum\limits_{l=1}^{L} y_{l} \log \widehat{y}_{l} - (1 - y_{l}) \log (1 - \widehat{y}_{l}),
\end{equation}
where $\widehat{y}_{l}$ is the $l$-th entry of $\widehat{\textit{\textbf{y}}}$ that is the prediction value for the $l$-th co-category and $y_l\in\{0,1\}$ is the ground-truth label.

\subsection{Segmentation Sub-net}
\hspace*{1em}
Given the input group-wise CFMs $\{\textit{\textbf{X}}^n\}_{n=1}^N$ of the images $\mathcal{I}$, the sub-net $f_{seg}$ are modulated by the outputs $\{\mathcal{S},\bm\gamma\}$ of $f_{spa}$ and $f_{sem}$, yielding a group of modulated representations $\{\textit{\textbf{Y}}^n\}_{n=1}^N$ using (\ref{eq:modulator}).
Each $\textit{\textbf{Y}}^n$ is composed of a group of multi-resolution representations $\{\textit{\textbf{R}}_i^n\}_{i=1}^4$. Similar to the FPN~\cite{Lin2017Feature}, we fuse $\{\textit{\textbf{R}}_i^n\}_{i=1}^4$ from coarse to fine: with the coarser-resolution feature maps, we use a $1\times 1$ convolution layer to make the channel number equal to the corresponding top-down ones, following by an upsampling layer to make their spatial resolutions the same. Then, the upsampled maps are merged with the corresponding top-down ones via element-wise addition.
The process is repeated until the finest resolution maps are generated as $\textit{\textbf{R}}^n=\textit{\textbf{R}}_1^n\oplus\cdots\oplus\textit{\textbf{R}}_{4}^n$. Finally, the maps $\textit{\textbf{R}}^n$ are fed into a convolutional layer, following by a $1\times 1$ convolutional layer and an upsampling layer to generate the corresponding segmentation mask $\widehat{\textit{\textbf{M}}}^n$.

Denoting the ground-truth binary co-segmentation masks in the training image group as $\mathcal{M}_{gt} =\{\textit{\textbf{M}}_{gt}^n\}_{n=1}^{N}$, the loss function for the segmentation task is formulated as a weighted cross-entropy loss for pixel-wise classification:
\begin{align}
\label{eq:segloss}
\ell_{seg} =&- \frac{1}{NP} \sum_{n=1}^N\sum_{i=1}^{P}\{\delta^n \textit{\textbf{M}}_{gt}^{n}(i)\log \widehat{\textit{\textbf{M}}}^{n}(i)\notag\\&- (1-\delta^n)(1-\textit{\textbf{M}}_{gt}^{n}(i))\log (1-\widehat{\textit{\textbf{M}}}^{n}(i))\},
\end{align}
where $P$ is the number of the pixels in each training image, $i$ denotes the pixel index,
$\delta^n$ is the ratio between all positive pixels and all pixels in image $I^n$, which balances the positive and negative samples.
\subsection{Loss Function}
\hspace*{1em}
The three sub-nets $f_{spa}$, $f_{sem}$ and $f_{seg}$ are trained jointly via optimizing the following multi-task loss function
\begin{equation}
\label{eq:loss}
\ell = \ell_{spa}+\ell_{sem}+\ell_{seg},
\end{equation}
where $\ell_{spa}$, $\ell_{sem}$ and $\ell_{seg}$ are defined by (\ref{eq:spaloss}) , (\ref{eq:semloss}) and (\ref{eq:segloss}), respectively.
\section{Experiments}
\label{sec:experiments}
\subsection{Implementation Details}
\hspace*{1em}
We leverage the HRNet \cite{sun2019deep} pre-trained on ImageNet \cite{deng2009imagenet} as the backbone network to extract the multi-resolution semantic features.
Moreover, we also report the results of using the VGG16 backbone network~\cite{simonyan2014very}, which still demonstrate competing performance over state-of-the-art methods.
Except for using the pretrained backbone network parameters as initialization, all other parameters are trained from scratch.
We follow the same settings as  \cite{wei2017group,wang2019robust}: the input image group $\mathcal{I}$ consists of $N=5$ images that are randomly selected from a group of images with co-object category, and a mini-batch of $4\times \mathcal{I}$ is fed into the model simultaneously during training.
All images in $\mathcal{I}$ are resized to $224\times 224$ as input, and then the predicted co-segmentation maps are resized to the original image sizes as outputs.
We leverage the Adam algorithm \cite{kingma2014adam} to optimize the whole network in an end-to-end manner, among which the exponential decay rates for estimating the first and the second moments are set to $0.9$ and $0.999$, respectively.
The learning rate starts from 1e-4 and reduces by a half every $25,000$ steps until the model converges at about 200,000 steps.
Our model is implemented in PyTorch and a Nvidia RTX $2080$Ti GPU is adopted for acceleration.

We adopt the COCO-SEG dataset released by~\cite{wang2019robust} to train our model.
The dataset contains $200,000$ images belonging to $L=78$ groups, among which each image has a manually labeled binary mask with co-category label information.
The training process takes about $40$ hours.
\subsection{Datasets and Evaluation Metrics}

~~\textbf{Datasets:} We conduct extensive evaluations on four widely-used benchmark datasets~\cite{faktor2013co,rubinstein2013unsupervised} including sub-set of MSRC, Internet, sub-set of iCoseg, and PASCAL-VOC. Among them, the sub-set of MSRC includes $7$ classes: bird, car, cat, cow, dog, plane, sheep, and each class contains $10$ images.
The Internet has $3$ categories of airplane, car and horse. Each class has $100$ images including some images with noisy labels.
The sub-set of iCoseg contains $8$ categories, and each has a different number of images.
The PASCAL-VOC is the most challenging dataset with $1,037$ images of $20$ categories selected from the PASCAL-VOC 2010 dataset~\cite{Everingham10}.

\textbf{Evaluation Metrics:} We adopt two widely-used metrics to evaluate the co-segmentation results, including the \textit{precision} $\mathcal{P}$ and the \textit{Jaccard index} $\mathcal{J} $.
The precision $\mathcal{P}$ measures the percentage of the correctly segmented pixels for both foreground and background, while the Jaccard index $\mathcal{J}$ is defined as the intersection area of the predicted foreground objects and the ground truth divided by their union area.
\renewcommand\arraystretch{1.2}
\begin{table}[t]
\caption{Quantitative comparison results on the sub-set of MSRC. The bold numbers indicate the best results. }\smallskip
\centering
\resizebox{.95\columnwidth}{!}{
\smallskip\begin{tabular}{|l||c|c|}
\hline
\multicolumn{1}{|c||}{MSRC} & Ave. $\mathcal{P} ($\%$)$ &Ave. $\mathcal{J}$ ($\%$)\\
\hline
\cite{vicente2011object} & 90.2 & 70.6 \\
\cite{rubinstein2013unsupervised} & 92.2 & 74.7 \\
\cite{wang2013image} & 92.2 & - \\
\cite{faktor2013co} & 92.0 & 77.0 \\
\cite{mukherjee2018object} & 84.0 & 67.0 \\
\cite{li2018deep} & 92.4 & 79.9 \\
\cite{chen2018semantic} & \textbf{95.2} & 77.7\\
\hline\hline
Ours-VGG16 & 94.3 & 79.4 \\
Ours-HRNet & \textbf{95.2} & \textbf{81.9} \\
\hline
\end{tabular}
}
\label{MSRC}
\end{table}
\renewcommand\arraystretch{1.2}
\begin{table}[t]
\caption{Quantitative comparison results on the Internet. The bold numbers indicate the best results.  }\smallskip
\centering
\resizebox{1\columnwidth}{!}{
\smallskip\begin{tabular}{|l||cc|cc|cc|}
\hline
\multirow{2}{*}{~~~~~~~~~~~~~~~~~~~~~Internet}
 & \multicolumn{2}{|c|}{Airplane} & \multicolumn{2}{c|}{Car} & \multicolumn{2}{c|}{Horse} \\
 & Ave. $\mathcal{P}$ ($\%$) & Ave. $\mathcal{J}$ ($\%$) & Ave. $\mathcal{P}$ ($\%$) & Ave. $\mathcal{J}$ ($\%$) & Ave. $\mathcal{P}$ ($\%$) & Ave. $\mathcal{J}$ ($\%$) \\
\hline
\cite{joulin2012multi} & 47.5 & 11.7 & 59.2 & 35.2 & 64.2 & 29.5\\
\cite{rubinstein2013unsupervised} & 88.0 & 55.8 & 85.4 & 64.4 & 82.8 & 51.6\\
\cite{chen2014enriching} & 90.2 & 40.3 & 87.6 & 64.9 & 86.2 & 33.4\\
\cite{jerripothula2016image} & 90.5 & 61.0 & 88.0 & 71.0 & 88.3 & 60.0\\
\cite{quan2016object} & 91.0 & 56.3 & 88.5 & 66.8 & 89.3 & 58.1\\
\cite{sun2016learning} & 88.6 & 36.3 & 87.0 & 73.4 & 87.6 & 54.7\\
\cite{tao2017image} & 79.8 & 42.8 & 84.8 & 66.4 & 85.7 & 55.3\\
\cite{yuan2017deep} & 92.6 & 66.0 & 90.4 & 72.0 & 90.2 & 65.0\\
\cite{li2018deep} & 94.1 & 65.4 & 93.9 & \textbf{82.8} & 92.4 & 69.4\\
\cite{chen2018semantic} & - & 65.9 & - & 76.9 & - & 69.1\\
\cite{MaCoSNet} & 94.1 & 65.0 & \textbf{94.0} & 82.0 & 92.2 & 63.0\\
\hline\hline
Ours-VGG16 & 94.6 & 66.7 & 89.7 & 68.1 & 93.2 & 66.2\\
Ours-HRNet & \textbf{94.8} & \textbf{69.6} & 91.6 & 82.5 & \textbf{94.4} & \textbf{70.2}\\
\hline
\end{tabular}
}
\label{Internet}
\end{table}
\renewcommand\arraystretch{1.3}
\begin{table*}[t]
\caption{Quantitative comparison results on the sub-set of iCoseg. The bold numbers indicate the best results.}\smallskip
\centering
\resizebox{2.0\columnwidth}{!}{
\smallskip\begin{tabular}{|l||c|cccccccc|}
\hline
\multicolumn{1}{|c||}{iCoseg} & Ave. $\mathcal{J}$ ($\%$) & bear2 & brownbear & cheetah & elephant & helicopter & hotballoon & panda1 & panda2 \\
\hline
\cite{rubinstein2013unsupervised} & 70.2 & 65.3 & 73.6 & 69.7 & 68.8 & 80.3 & 65.7 & 75.9 & 62.5\\
\cite{jerripothula2014automatic} & 73.8 & 70.1 & 66.2 & 75.4 & 73.5 & 76.6 & 76.3 & 80.6 & 71.8\\
\cite{faktor2013co} & 78.2 & 72.0 & 92.0 & 67.0 & 67.0 & \textbf{82.0} & 88.0 & 70.0 & 55.0\\
\cite{jerripothula2016image} & 70.4 & 67.5 & 72.5 & 78.0 & 79.9 & 80.0 & 80.2 & 72.2 & 61.4\\
\cite{li2018deep} & 84.2 & 88.3 & \textbf{92.0} & 68.8 & 84.6 & 79.0 & 91.7 & 82.6 & 86.7\\
\cite{chen2018semantic} & 86.0 & 88.3 & 91.5 & 71.3 & 84.4 & 76.5 & 94.0 & \textbf{91.8} & \textbf{90.3}\\
\hline\hline
Ours-VGG16 & 88.0 & 87.4 & 90.3 & 84.9 & 90.6 & 76.6 & 94.1 & 90.6 & 87.5\\
Ours-HRNet & \textbf{89.2} & \textbf{91.1} & 89.6 & \textbf{88.6} & \textbf{90.9} & 76.4 & \textbf{94.2}& 90.4 & 87.5\\
\hline
\end{tabular}
}
\label{iCoseg}
\end{table*}
\renewcommand\arraystretch{1.3}
\begin{table*}[t]
\caption{Quantitative comparison results on the PASCAL-VOC. The bold numbers indicate the best results.}\smallskip
\centering
\resizebox{2.1\columnwidth}{!}{
\smallskip\begin{tabular}{|l||c|c|cccccccccccccccccccc|}
\hline
\multicolumn{1}{|c||}{PASCAL-VOC} & Ave. $\mathcal{P}$ ($\%$) & Ave. $\mathcal{J}$ ($\%$) & A.P. & Bike & Bird & Boat & Bottle & Bus & Car & Cat & Chair & Cow & D.T. & Dog & Horse & M.B. & P.S. & P.P. & Sheep & Sofa & Train & TV\\
\hline
\cite{faktor2013co} & 84.0 & 46 & 65 & 14 & 49 & 47 & 44 & 61 & 55 & 49 & 20 & 59 & 22 & 39 & 52 & 51 & 31 & 27 & 51 & 32 & 55 & 35\\
\cite{lee2015multiple} & 69.8 & 33 & 50 & 15 & 29 & 37 & 27 & 55 & 35 & 34 & 13 & 40 & 10 & 37 & 49 & 44 & 24 & 21 & 51 & 30 & 42 & 16\\
\cite{chang2015optimizing} &82.4 & 29 & 48 & 9 & 32 & 32 & 21 & 34 & 42 & 35 & 13 & 50 & 6 & 22 & 37 & 39 & 19 & 17 & 41 & 21 & 41 & 18\\
\cite{quan2016object} & 89.0 & 52 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
\cite{hati2016image} & 72.5 & 25 & 44 & 13 & 26 & 31 & 28 & 33 & 26 & 29 & 14 & 24 & 11 & 27 & 23 & 22 & 18 & 17 & 33 & 27 & 26 & 25\\
\cite{jerripothula2016image} & 85.2 & 45 & 64 & 20 & 54 & 48 & 42 & 64 & 55 & 57 & 21 & 61 & 19 & 49 & 57 & 50 & 34 & 28 & 53 & 39 & 56 & 38\\
\cite{jerripothula2017object} & 80.1 & 40 & 53 & 14 & 47 & 43 & 42 & 62 & 50 & 49 & 20 & 56 & 13 & 38 & 50 & 45 & 29 & 26 & 40 & 37 & 51 & 37\\
\cite{wang2017multiple} & 84.3 & 52 & 75 & 26 & 53 & 59 & 51 & 70 & 59 & 70 & 35 & 63 & 26 & 56 & 63 & 59 & 35 & 28 & 67 & 52 & 52 & 48\\
\cite{li2018deep} & 94.2 & 65 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\
\cite{hsu2018co} & 91.0 & 60 & 77 & 27 & 70 & 61 & 58 & 79 & 76 & 79 & 29 & 75 & \textbf{28} & 63 & 66 & 65 & 37 & 42 & 75 & 67 & 68 & 51 \\
\hline\hline
Ours-VGG16 & 93.7 & 66 & \textbf{83} & 35 & \textbf{75} & 69 & 58 & 87 & 77 & \textbf{80} & 26 & 86 & 7 & 74 & 79 & 71 & 45 & 39 & 81 & 68 & 83 & 59 \\
Ours-HRNet & \textbf{94.9} & \textbf{71} & 82 & \textbf{37} & 74 & \textbf{70} & \textbf{67}& \textbf{88} & \textbf{82} & 77 & \textbf{36} & \textbf{87} & 15 & \textbf{75} & \textbf{82} & \textbf{72} & \textbf{58} & \textbf{46} & \textbf{82} & \textbf{77} & \textbf{84} & \textbf{69}\\
\hline
\end{tabular}
}
\label{PASCALVOC}
\end{table*}
\subsection{Results}
\hspace*{1em}
We quantitatively and qualitatively compare our algorithm with several state-of-the-art co-segmentation methods on the four benchmark datasets.

\textbf{Quantitative Results:}
Tables \ref{MSRC}, \ref{Internet}, \ref{iCoseg}, \ref{PASCALVOC} list the comparison results of  our method with other state-of-the-arts on the sub-set of MSRC, Internet, sub-set of iCoseg and PASCAL-VOC.
For fair comparisons, the reported results of the compared methods are directly obtained from their publications.
We can observe that our algorithm outperforms the other state-of-the-arts in term of both metrics on most object categories in each dataset.
Especially on the PASCAL-VOC, which has more challenging scenarios, the proposed algorithm achieves the best average $\mathcal{P}$ and average $\mathcal{J}$  with a score of $94.9\%$ and $71\%$, respectively, significantly outperforming the others by a large margin.
Moreover, on the sub-set of MSRC and sub-set of iCoseg, the average $\mathcal{J}$ by our method has a score of $81.9\%$ and $89.2\%$, outperforming the others by about $3\%$.
Besides, on the Internet, our algorithm achieves the best performance on airplane and horse categories, as well as a competitive performance on car category in terms of both metrics average $\mathcal{P}$ and average $\mathcal{J}$.
\begin{figure*}[t]
\centering
\includegraphics[width=0.497\textwidth]{figseen.pdf}
\includegraphics[width=0.497\textwidth]{figunseen.pdf}
\caption{Some qualitative comparison results generated by the proposed method, SAAB~\cite{chen2018semantic} and DCOS~\cite{li2018deep} for co-segmenting objects associated to the training categories and unseen categories, respectively.}
\label{coseg}
\end{figure*}

\textbf{Qualitative Results:}
Figure \ref{coseg} shows some qualitative results by comparing our method with SAAB~\cite{chen2018semantic} and DCOS~\cite{li2018deep}.
Those images are chosen from all of the four datasets composed of co-objects with seen categories (inside the $78$ categories of the COCO-SEG) and unseen categories (outside the categories of the COCO-SEG).
For the seen categories shown by Figure \ref{coseg}(a), we can observe that SAAB and DCOS cannot discover the co-objects in the dog group accurately and two distractors (sheep) have been mis-classified as co-objects.
However, the proposed approach does not suffer from this issue since it uses co-category labels as supervision to learn an effective semantic modulator that can well capture high-level semantic category information.
Besides, as shown by Figure~\ref{coseg}(a), (b), the proposed approach can discover the whole co-objects of seen and unseen categories well because its spatial modulator is learned by an unsupervised method that can not only help to locate the co-object regions of seen categories well, but also generalize well to unseen categories.
\renewcommand\arraystretch{1.2}
\begin{table}[t]
\caption{Ablative experiments of the proposed model on the PASCAL-VOC. The bold numbers indicate the best results. The symbol `$-f$' denotes removing the module $f$.}\smallskip
\centering
\resizebox{.75\columnwidth}{!}{
\smallskip\begin{tabular}{|l||cc|}
\hline
\multicolumn{1}{|c||}{PASCAL-VOC} & Avg. $\mathcal{P}$ ($\%$) & Avg. $\mathcal{J}$ ($\%$)\\
\hline
$f_{spa}\&f_{sem}\&f_{seg}$ & \textbf{94.9} & \textbf{71} \\
$-$ $f_{spa}$  & 94.5 & 69 \\
$-$ $f_{sem}$ & 85.0 & 38  \\
$-$ ($f_{spa}\&f_{sem}$) & 82.0 & 27  \\
\hline
\end{tabular}
}
\label{fig:ablative}
\end{table}
\subsection{Ablative Study }
\hspace*{1em}
To further show our main contributions, we compare different variants of our model including those without spatial modulator ($-f_{spa}$), semantic modulator ($-f_{sem}$) and both modulators ($-(f_{spa}\&f_{sem})$), respectively.
Table~\ref{fig:ablative} lists the results of ablative experiments on the PASCAL-VOC.
We can observe that without $f_{spa}$, the average $\mathcal{P}$ score drops from $94.9\%$ to $94.5\%$ while the average $\mathcal{J}$ score reduces by $2\%$ from $71\%$ to $69\%$, which verifies the effectiveness of the proposed module $f_{spa}$.
Moreover, without $f_{sem}$, the performance suffers from a significant loss with a big drop of $9.9\%$ and $33\%$ for the  average $\mathcal{P}$ and $\mathcal{J}$ scores, respectively, indicating the critical role of the semantic modulator as a guidance to learn an effective segmentation network for accurate co-segmentation.
Besides, compared to that only removes $f_{sem}$, removing both modulators $f_{spa}$ and $f_{sem}$ further makes the performance of our model drop by $3\%$ and $11\%$ in terms of average $\mathcal{P}$ and average $\mathcal{J}$, respectively. These experiments confidently validate that both modulators have a positive effect to boost the performance of our model.
\section{Conclusions}
\label{sec:conclusions}
\hspace*{1em}
In this paper, we have presented a spatial-semantic modulated deep network framework for object co-segmentation. Our model is composed of a spatial modulator, a semantic modulator and a segmentation sub-net.
The spatial modulator is to learn a mask to coarsely localize the co-object regions in each image that captures the correlations of image feature descriptors with unsupervised learning.
The semantic modulator is to learn a channel importance indictor under the supervision of co-category labels. We have proposed the HSP module to transform the input image features of the semantic modulator for classification use.
The outputs of the two modulators manipulate the input feature maps of the segmentation sub-net by a simple shift-and-scale operation to adapt it to target on segmenting the co-object regions.
Both quantitative and qualitative evaluations on four image co-segmentation benchmark datasets have demonstrated superiority of the proposed method to the state-of-the-arts.
\section*{Acknowledgments}
This work is supported in part by National Major Project of China for New Generation of AI (No. 2018AAA0100400), in part by the Natural
Science Foundation of China under Grant nos. 61876088, 61825601, in part by the Natural Science Foundation of Jiangsu Province under Grant no. BK20170040.

redpajama_set_name: RedPajamaArXiv


text:
Finding Nemo Dat in the Land Title Act: A Comment on Gill v. Bucholtz
Douglas C. Harris, Allard School of Law at the University of British ColumbiaFollow
Karin Mickelson, Allard School of Law at the University of British ColumbiaFollow
University of British Columbia Law Review
British Columbia; Land Title Act; Property; Title Registration; Torrens system; Indefeasible title; Mortgages; Risk; Title Insurance
This comment reviews the decision of the British Columbia Court of Appeal in Gill v. Bucholtz and explores the implications for the allocation of risk in the province's title registration system. The case involved a rogue who forged the transfer of a fee simple and registered that transfer. The person on title, also a party to the fraud, then transferred two mortgages to bona fide purchasers. The purchasers of these charges held title insurance. The question before the court was whether the title registration system's assurance fund would compensate the original holder of the fee simple interest for the mortgages that, because of fraud, had been registered against the fee simple interest, or whether the title insurers would pay out the value of the mortgages to the mortgagees. The answer turned on whether the mortgagees held valid interests because they had dealt in good faith with the person registered on title, or whether they held invalid interests because the person on title was a party to the fraud. In interpreting British Columbia's Land Title Act, the court re-asserted the common law principle of nemo dat quod non habet in respect of charges: title holders can only give that which they validly hold. If they hold because of fraud, then they have nothing to give. As a result, the title insurers had to pay out for the invalid mortgages. More generally, the decision confirms that registering charges, defined in the Act as any interest less than the fee simple, does not cure defects in those interests. After Gill, even when the instrument creating the charge is apparently valid because it has been executed by the person registered as the holder of the fee simple interest, the charge will be invalid if the registered title holder is on title because of fraud. The comment concludes by exploring the implications of this decision for the allocation of risk in British Columbia's title registration system.
Douglas C Harris & Karin Mickelson, "Finding Nemo Dat in the Land Title Act: A Comment on Gill v. Bucholtz" (2012) 45:1 UBC L Rev 205.
Property Law and Real Estate Commons


text:
HomeCollege FootballPhil Jurkovec Shines in Red Bandana Game
November 6, 2021 Tanner Kern College Football
There were rumors circulating around the internet this week that Phil Jurkovec would be returning from injury to lead Boston College on Friday night. However, not everyone believed these speculations and it was unknown heading into warmups who would make the start.
Jurkovec went down in week two because of an injury to his throwing hand. The prognosis revealed that the junior would likely miss the remainder of the season. This was the plan according to medical personnel, but Jurkovec had other ideas.
The quarterback returned to the field in warmups for the Red Bandana Game, and at that point, there was no doubt he would be leading the Eagles against Virginia Tech. Jurkovec did just that, sparking his team to a 17-3 win at home.
Phil Jurkovec Finds a Way
Jurkovec underwent surgery following his week two injury. Most players would not be able to come back from this adversity, but Jurkovec's rehab went well and it gave him a second chance in 2021. He took advantage of the opportunity on Friday completing 7 of 13 passes, for 112 yards, and rushing for 65 with a touchdown.
The Eagles are now 5-4 after their first ACC victory against the Hokies. Coach Jeff Hafley said Jurkovec was cleared to play last Friday.
"I called him in and I would have said that's a 50-50 chance with the amount of reps he was going to take. I didn't want to do anything to jeopardize or get him hurt. We wanted to guide him, and he walked in and said: 'I'm playing."
The 6'5" quarterback took some shots on the first drive, and Hafley knew he had not missed a step since week two.
"We wouldn't have let him play if we didn't feel confident about him getting hit and getting hurt. Maybe part of me was concerned, but when I saw him bounce up and the look in his eye, we felt good about that."
Pat Garwo added 116 yards on 30 carries, including a three-yard touchdown score for the team.
Boston College Defense Shines
Virginia Tech looked like they stayed in Blacksburg on Friday. The Hokies were horrendous on offense, and they barely completed a pass throughout the game. Quarterback Braxton Burmeister left with an undisclosed injury in the first quarter which brought Knox Kadum into the game.
Virginia Tech head coach Justin Fuente was disappointed with his team's effort. He will likely be looking for a new job following the season.
"We made basically no plays in the passing game. You have to play better on the road in an environment like that to deserve to win the game."
Fuente was spot on with this analysis, especially on a night where Boston College was playing for more than a win. The team wore their red bandana uniforms to honor Eagles' alumni Welles Crowther.
Crowther died in the September 11 attacks on the World Trade Center in an attempt to rescue as many victims as possible. He was identified by survivors through his red bandana.
Crowther's team mentality on September 11 was evident throughout the Boston College sideline on Friday night. It all started with Jurkovec's dedication to his teammates coming off the injury.
Jurkovec said, "I knew if I was going to come back that I couldn't play tentatively. I said a few prayers and went out and did it. I ran out like I can. You can't run without confidence."
Boston College will travel to Georgia Tech next weekend and Virginia Tech will host Duke.
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LSU and Ed Orgeron are set to part ways at the end of the season. Who should be the favorite to land the LSU job? […]
Why Ohio State Will Win The National Championship
August 22, 2022 John Pearson NFL
The Ohio State Buckeyes are the best team in college football. Read this article to see why John Pearson will win this season's national championship.
NFL or NCAA Experience
August 16, 2022 wordpress@knupsolutions.com College Football
College football brings a more personal and exciting experience to the audience. It's more exciting than the NFL in many different ways. […]


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text:
China: The thirsty drowning dragon
In all articles these days about China's position in the world, one subject has remained underexposed: the country's great ecological vulnerability
By: EBR - Posted: Monday, October 5, 2020
With 20% of the world's population and 10% of the global agricultural area and only 7% of the world's freshwater reserves, China, in addition to increasing its own production, is forced to import food in a major way, certainly food that requires water in production, such as grains and meat.
by Wouter Veening*
In all articles these days about China's position in the world, one subject has remained underexposed: the country's great ecological vulnerability.
With 20% of the world's population and 10% of the global agricultural area and only 7% of the world's freshwater reserves, China, in addition to increasing its own production, is forced to import food in a major way, certainly food that requires water in production, such as grains and meat. In Northern China water availability is extremely low; the Yellow River suffers from severe water scarcity and there were farfetched plans to divert water from the Tsjing Po, the in Tibet located upper reaches of the Brahmaputra river, essential for the downstream countries India and Bangladesh, to the Yellow River.
Groundwater in China is also largely irreversibly polluted and the farmland erosion due to climate-related droughts is increasing seriously. During the past months, large parts of mainly Western and South-Western China has been ravaged by flooding as a result of persistent downpours also related to global warming atmosphere. Here the Chinese dragon, the largest greenhouse gas exhaler in the world (see International Energy Agency reports) bites in its own tail!
Very ominous are the floods upstream of the Three Gorges Dam, the largest hydroelectric dam in the world, in the Yangtze River. Concerns are increasing that the level of the reservoir behind the dam gets so high that it runs over the dam, because the flushing capacity is insufficient. There are speculations that the dam could even break and there is another speculation that the Chinese authorities, knowing that the dam in the end cannot hold, would take this up themselves in a period when the reservoir is not too full. See an animation that shows how it water then makes its way through the bed of the Yangtze along Wuhan to Shanghai on the coast.
There is an iron logic that the Chinese rulers may have an interest to dismantle the dam as early as possible, because, as the dam is getting older and the construction defects will become more pronounced, plus the fact that the pressure on the dam as a result of the on-going climate crisis will intensify, the risk of an un-controlled collapse is increasing day-by-day. If this happens it will have hardly foreseeable consequences for the population, agriculture and industry, the environment and nature, and an insurmountable loss of face for the regime. It is better for them to have the process in own hands, also because a break cannot be attributed to a foreign interference.
Can a controlled dismantling, however, be organised? The Yangtze, which also rises in Tibet, is the longest river in China with the economic metropolis Shanghai at its mouth and the catchment area is home to more than a third of the Chinese population, over 400 million people. According to a calculation published in Fortune 2019, the basin as such would be the third largest economy in the world in terms of GDP. For the vital exports from China, this area must therefore also remain intact in terms of the environment and hydrologically, but the question is whether at this stage of ecological disruption that is still possible. When considering the position and positioning of China in the world its environmental vulnerability must be taken into account.
Probably China's aggressive stance in the South China Sea towards Hong Kong and Taiwan, on the border with India, towards Japan across the Senkaku Islands, is to a large extent prompted to turn the attention off the ecological drama unfolding at home.
*President of the Institute for Environmental Security, The Hague


Bangladeshi women searching for answers to taboo subjects in their conservative society are getting help courtesy of a groundbreaking digital platform.
The Maya app is probably one of the biggest initiatives helping to create a virtual safe space for people from all walks of life in Bangladesh.
The digital platform offers a unique and crucial opportunity for users to seek information and foster discussions on a range of topics that society has for too long deemed taboo.
The app builds on the popularity of the open "Question and Answer" platform of the website Maya Apa Ki Bolen – Ask Sister Maya (maya.com.bd). The app has proved a blessing for many, especially women who often had questions and problems that no one was willing to discuss in this conservative society.
The idea behind the app is simple: users sign in, create an anonymous profile and post a question. The question is then answered within 48 hours. Legal, medical and numerous other professional experts address the questions relating to their field and find solutions or offer sound advice to the faceless individuals behind the usernames.
The community takes part, too. This way, constructive discussions occur on matters that people generally shy away from – not only in their living rooms but also their bedrooms.
"Maya has been working to empower women through technology and access to information on women's health, and anything of relevance to a woman's life, on a daily basis for the last three years," explains Sheepa Hafiza, director of Gender Justice and Diversity at the BRAC Migration Programme.
"We hope the information generated through this app will promote advocacy for the formulation of new policies or the revision of existing policies in favor of women's and girls' rights," says Hafiza.
"The Maya app is a unique output of BRAC and [website] Maya's partnership. Together, we intend to reach out to more women and girls in the quickest possible time and with an effective mode of interaction for sharing, learning and empowering," she adds.
For Shamima Parvin (not her real name), the Maya app provided a much-needed platform for her to talk about domestic violence and seek legal advice on how to get a divorce and retain custody of her child.
"No one in my family was willing to help me – they didn't want me to leave my abusive husband. They kept telling me, 'What will people say about a divorced daughter? Learn to adjust,'" recalls Shamima.
Given that Maya Apa receives hundreds of anonymous messages every day, it's not a simple task to ensure that each user receives the best possible service, free of judgment. In order to do so, each query is first screened and then passed onto a relevant expert.
Special attention is paid so that no hateful speech is generated during discussions and a sense of community is generated.
Maya Apa is the first-ever help service app for women in Bangladesh that has also been designed, developed and implemented by female engineers, doctors and entrepreneurs.
Shubramy Moutushy was the project's lead software engineer, aided by fellow software engineer Syeda Prima Rasnim and project manager Achia Khaleda Mila.
The app has enabled important data to be collected, which can be used to formulate better policies and strengthen women's moves for equality. It has also highlighted the harm caused by the creation of taboos, which frequently result in women or men refusing to seek the help they may urgently need.
The app can also be used as an important tool for patient management, with the entire history of the user stored; this may be critical to a doctor looking to provide effective treatment.
Maya Apa was conceived as a harbinger of social change. And if the statistics and discussions are anything to go by, it seems that a lot of real change has indeed taken place over the years. It demonstrates how to initiate so-called controversial dialogues without fear of societal reprisal, and enables us to challenge our own backdated beliefs.
This article first appeared in Bangladeshi newspaper The Daily Star.


Google is releasing a new 'channel gadget' that will aim shorten the path to purchase and translate video views to sales. The new technology will finally allow brands to sell products through their YouTube Channels.
The new 'shoppable' videos will allow brands to partner with third-party technology providers to allow viewers to click on a product and make a purchase.
Hair care brand, TREsemmé, is the first to use the technology and has already built a YouTube channel featuring a number of prominent YouTube personalities and other trend setters in the hair and beauty category.
Engagement technology company, Gloto, is powering the TREsemmé site and has partnered with YouTube for similar interactive projects in the past.
Viewers watching the channel can now click on the products used in the demo videos for more information and to buy from a selection of retailers, however the new function only appears on brand channel pages — not when videos are embedded on other sites.
The experience is being powered by Gloto, an "engagement technology company" that has partnered with YouTube in the past to produce interactive mobile apps.
Google says that the new gadget will be made available as a "premium offering" for its consumer good clients, and is asking advertisers to seek out their respective Google representatives to explore the option. Google does not take a cut of sales made through the gadget, a spokesperson for the company said.


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Replay: FOX31 News at 10 p.m. FOX31 & Channel 2 TV Schedule
Alcohol suspected in Thornton crash that leaves woman dead, 4 injured
Posted 8:17 am, August 25, 2019, by Chuck Hickey, Updated at 12:55PM, August 25, 2019
THORNTON, Colo. -- One person was killed and four others were injured in an early-morning, three-vehicle crash on Sunday, the Thornton Police Department said.
The crash happened about 1:40 a.m. at Highway 7 and Quebec Street, near Todd Creek Golf Club.
Police said a woman was pronounced dead at the scene and three men and one woman were taken to area hospitals in critical condition. One of the injured was airlifted to a hospital.
Highway 7 was closed between Quebec and Yosemite streets. It reopened just before 12:30 p.m.
The cause of the crash is under investigation, but police said alcohol is suspected.
6 killed in crashes on Colorado highways in 12-hour period
2 people, dog killed in Lakewood rollover crash
One dead, 5 injured in 2-vehicle crash in Lakewood
Suspect dies following officer-involved shooting near 92nd and Federal
One killed, 3 injured in 3-vehicle crash south of Fort Lupton
One dead in wrong-way crash in Commerce City
I-76 in Adams County reopens after 5-vehicle crash leaves one dead
Officer, suspect injured in shootout in Weld County, another suspect is dead
One dead, 2 seriously injured in wrong-way crash on I-25 in Northglenn
Man killed after being hit by at least 3 vehicles while running across I-225
One person seriously injured in suspected road-rage shooting in Thornton
One transported to hospital following three-vehicle crash in Erie


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Q: Google sheets track the value of a cell on a daily basis I'm trying to create a log/historical sheet that tracks the the value of a live cell C4 on a daily basis. I've attached an image of what I want the output to look like.
The sheet name is PRThistory and the cell which changes on a daily basis is C4. I've already tried running my own code however I haven't been successful.
function HistoryLog() {

  var ss = SpreadsheetApp.getActiveSpreadsheet();

  var sheet = ss.getSheetByName("PRThistory");

  var source = sheet.getRange("C4");

  var values = source.getValues();

  values[0][0] = new Date();

  sheet.appendRow(values[0]);

};

A: Replace
  var values = source.getValues();

  values[0][0] = new Date();

  sheet.appendRow(values[0]);

by
sheet.appendRow([new Date(), source.getValue()]);

Explanation
The problem with your code is that source.getValues() returns something like [[500]], then values[0][0] = new Date() replaces 500 by the a date-time value.

A: Sounds like you need to setup a trigger in the Google App Script environment. It can be done right from the same place where you are posting the code, but from another tab on the left hand side. Trigger will fire on a daily basis in a specified time, and will run your script of reading the cell. Then the same script will send the received result to your spreadsheet of choice. You can also read the current time and post it in a cell next to your daily value, so it will become a nicely looking log of things.

redpajama_set_name: RedPajamaStackExchange


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Holy Terror ist eine US-amerikanische Speed-Metal-Band aus North Hills (Kalifornien).

Geschichte
Im Sommer des Jahres 1985 verließ Kurt Colfelt Agent Steel mit der Absicht, eine eigene Band zu gründen. Recht schnell stand für ihn der Name fest: "Holy Terror" (engl. heiliger Terror) war eine umgangssprachliche Bezeichnung für ein Problem-Kind. Kurt mochte außerdem den religiösen Unterton des Namens.

Im Folgenden suchte er sich die Mitglieder für seine neue Band zusammen. Er besorgte sich die Telefonnummer des Schlagzeugers Jack Schwartz, der kurz zuvor aus Dark Angel hinausgeworfen wurde. Kurt Colfelt rief den Gitarristen Floyd Flanary an und lud ihn zu einer Jamsession ein. Jack Schwartz war ein Freund des Gitarristen Mike Alvord, der ebenfalls eingeladen wurde.

Nach kurzer Zeit zu viert entschloss sich die Band dazu, einen Sänger zu suchen. Eine Annonce wurde in der Zeitschrift The Recycler aufgegeben, auf welche sich unter anderem Keith Deen meldete. Kurt Colfelt musste diesen erst überreden, bei der Band als Sänger mitzumachen.

Nun komplett, machte sich die Urbesetzung daran, ein Demo-Tape aufzunehmen. Darauf enthalten waren vier Lieder, die Kurt Colfelt im Wesentlichen schon lange vorher fertiggestellt hatte. Diese Aufnahme schickten sie an viele Labels und Magazine und erhielten positives Feedback und sogar Fanbriefe.

Eines Tages war unter den Briefen auch einer des Musiklabels Music for Nations UK, in dem der Band ein Plattenvertrag angeboten wurde.
Jack Schwartz schloss ohne Absprache mit der restlichen Band sofort per Telefon den Vertrag ab. Daraufhin wollte Kurt ihn aus der Band werfen, Jack jedoch wollte nicht gehen. Die Situation eskalierte und Kurt schlug Jack auf die Nase, ein Schritt den er später bereute.

Auf der Suche nach einem neuen Schlagzeuger inserierten sie in dem Magazin Music Connection und erhielten Antwort von Joe Mitchell. Sechs Wochen später begannen die Aufnahmen zu Terror and Submission, welches ursprünglich Holy Terror heißen sollte, jedoch auf drängen der Plattenfirma umbenannt wurde.

Im Herbst des Jahres 1987 ging die Band zusammen mit D.R.I. auf Tour in Europa.

Nach ihrer Rückkehr arbeiteten sie weiter an Terror and Submission, das Album wurde neu abgemischt, klang aber immer noch nicht so, wie die Band es sich gewünscht hatte. Das Album wurde in Großbritannien von Music For Nations und in den USA von Roadracer Records vertrieben.

Der Nachfolger Mind Wars wurde in nur 17 Tagen aufgenommen. Aus Mangel an finanziellen Mitteln wurde am Tag 17 bis 20 Stunden daran gearbeitet, weil dies die Studiokosten verringerte.

Im Sommer 1988 ging die Band zusammen mit D.R.I. und Kreator auf Tour durch Nord-Amerika. Zu dieser Zeit hatte Kurt Colfelt ein großes Problem mit Drogen.

Nach dieser Tour kaufte sich die Band einen Bus um das Geld für Mietfahrzeuge zu sparen, doch schon beim ersten Einsatz ging der Bus kaputt. Das Geld zum reparieren fehlte, also blieb Kurt mit einem Roadie zurück um den Bus selbst wieder fahrbereit zu machen. Der Rest der Band reiste allein weiter und spielte die Auftritte ohne Kurt. Dieser versuchte nach gelungener Reparatur wieder zur Band aufzuschließen, der Bus jedoch machte immer mehr Probleme.

Nach dieser Tour durfte die Band vier Konzerte für Motörhead eröffnen. Im Winter 1988 gingen sie mit Exodus und Nuclear Assault erneut auf Europa-Tournee. Auf einem Konzert in Deutschland sagte der betrunkene Keith Deen in einem Interview, dass die Band einen Vertrag bei Roadrunner Records unterzeichnet hätte. Der Journalist rief bei Music for Nations an um diese Information bestätigt zu wissen. Da die Tour selbst von Music for Nations gesponsert wurde, schloss man die Band von den weiteren Konzerten aus. Kurt versuchte telefonisch das Problem zu lösen, nach dem letzten Konzert in Zürich jedoch fanden sie ihr Equipment auf der Straße wieder. Ein Rückflug war nicht möglich, da die Tickets erst drei Wochen später für einen britischen Flughafen gebucht waren.

Die Band wollte unbedingt wieder bei der Tour dabei sein, also mieteten sie einen Kleinbus und fuhren nach Augsburg, um sich dort Equipment von Kreator zu leihen. Als sie dann jedoch bei einer Show ankamen, sahen sie gerade die Ersatz-Band an ihrer Stelle spielen. Der Tourmanager ging auf Kurt los, welcher sich wehrte und diesen ins Gesicht schlug. Die Band flüchtete aus der Lokalität, kurz darauf sagte der Gitarrist Mike Alvord, dass er die Band verlassen würde, weil er gegen Gewalt sei.

Der Rest der Band verbrachte zwei Wochen in München in dem Kleinbus, bis Keith erkrankte und man sich entschloss, ein Hotel-Zimmer zu buchen. Nach dieser Zeit flog die Band auf Kosten von Kurts Freundin zurück nach Amerika.

Diskografie
 1986: 1986 Demo (Demo)
 1987: Terror and Submission (Music for Nations)
 1988: Mind Wars (Music for Nations)
 2006: El Revengo (Kompilation) (Blackend Records)

Weblinks
 Offizielle Website

Einzelnachweise

Speed-Metal-Band
Thrash-Metal-Band
US-amerikanische Band

redpajama_set_name: RedPajamaWikipedia


We are doing our part to help orphans and families living in poverty. Most of our employees came from these similar backgrounds with very low skill and education. Your indulgence towards our employee's insufficiency is your contribution for helping them. For any comment or complaint, please e-mail us. Your satifaction is our concern.
City River Hotel is beautifully located on the bank of Siem Reap River. Our superb location offers our guests the unique experience of peacefulness in a lively city. We're just a walking distance away from the ever popular Pub Street and Night Market.
Our Deluxe rooms are beautifully decorated with all standard amenities and a balcony overlooking the city. In addition, guests are able to pamper themselves with the traditional Khmer massage at our Massage Center after spending a long day at the wonderful temples.


WILL Keane waited a while to get the better of his twin brother – and, after doing exactly that last weekend, the Preston striker has grand plans to kick on.
Keane and twin brother Michael went head to head in terrible conditions at Turf Moor eight days ago and there was a clear winner, with the on-loan Lilywhites striker opening the scoring as high-flying Burnley were beaten 2-0.
The twins, who both graduated through the Manchester United academy, had faced one another in a competitive game just once before, in August 2014.
After their latest meeting, Will certainly holds the upper hand and the 22-year-old, who is on loan at Deepdale from Old Trafford, admits it was a meaningful afternoon's work.
"I told him before the game that we were going to win, so I've got the bragging rights," he said. "I think we were both looking when that game was scheduled straight away at the start of the season.
"He stormed straight off the pitch after the game and I had come off, so I didn't get a chance to see him, but I spoke to him later on. He was obviously disappointed with the result, but I think he was happy for me.
"We played against one another once before, when he was on loan at Blackburn and I was at QPR at the time. He played right-back, so we weren't actually against each other.
The outcome was not just a significant one for Keane, however, as Simon Grayson's Preston picked up a coveted three points on the road.
With that, the newly-promoted side moved five points clear of the drop zone and, such is the nature of a competitive Championship, Preston began this weekend just eight points off a play-off place. "To begin with, you want to make sure you remain in the league," said Keane.
"But there is a confidence and we know we have the quality to push up the table as well.
"We have had the foundation of a lot of clean sheets and we can build on that.
"I was desperate to get a goal. I know my job is to try and get goals so I was chuffed to do that against Burnley.
"The game was probably my first start for six or seven weeks. I felt like I had made a good impact a couple of times. I started the season well. I didn't really get among the goals, but I felt I was performing well.
"Then, we went through a bit of a dry spell as a team and all the strikers were suffering a bit, but I think we are also gelling as a team." Unlike Michael, who signed for Burnley on a permanent deal this summer, Will also has his future at Old Trafford to think about.
While his loan is season-long, there is a break clause next month – and, with the Red Devils desperately short of healthy striking options, the lure of Premier League football is undoubtedly strong.
"I am just eager to get games and be playing week in, week out," he added. "I will have to speak to both clubs in January and see what my position is.
"I keep in touch with the U21 manager Warren Joyce. He comes to watch quite a lot of games and, playing under him for the last few years, we have a good relationship.
"I am sure he will keep an eye on my progress and report back.
"In hindsight, if I had stayed at United I might have had a sniff at the team but you can't really think like that in football.
"You have got to stick with your decision.


Laser cutters are fantastic tools that allow for intricacy and precision. While I used the laser cutters at Techshop to start Moonish Goods, I now have access to my very own! I use a Boss model 2436. It has a 24x36 inch bed (which means i can work on projects up to that size).
Acrylic, wood, leather, some fabrics, paper, felt cardboard and more. The most common thickness of wood I cut is 1/8 inch thick, but I've cut up to 1/2 inch. The best way to see examples of laser cutting see my Etsy Shop. Also check out some past collaborations.
Any organic material can be engraved: wood, stone, glass. Anodized aluminum (available in various colors) can also be engraved as well as most acrylics. There is special laserable rubber with which I have created some pretty neat stamps.
Here are some swatches of the most common materials I use in both plywood and acrylic. Extra cost for fancier materials is on a project by project basis. We are not limited to these materials, so reach out if you have a question.
Have questions about laser cutting, engraving, or the materials available? Ask away!


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Naval Intelligence Warns: 'Stop Criticizing Biden'
August 30, 2021 admin News 0
(TeaParty.org Exclusive) – A brand new reminder has been issued to both active duty and retired members of the Office of Naval Intelligence last week that says they are barred by the Uniform Code of Military Justice and other Department of Defense regulations from being able to criticize either President Joe Biden or other folks in his Cabinet and the Pentagon.
So apparently, the First Amendment no longer applies if you work for the Office of Naval Intelligence. That doesn't seem to sit well with me.
According to a report straight from BizPacReview, the memo concerning this particular reminder was sent out through email and was originally reported on by the good people over at the Daily Wire. The message was sent out by the ONI's chief of staff the day after deadly suicide bombing attacks took place outside the international airport located in Kabul.
The bombings killed 11 Marines, two U.S. soldiers, a Navy corpsman, and 169 Afghan civilians.
Along with Biden, members were also instructed that they were not to publicly criticize or be contemptuous of Vice President Kamala Harris, Secretary of Defense Lloyd Austin, or other members of Congress.
"Given the heightened political and social atmosphere surrounding Afghanistan, it is important to remind our uniformed personnel (active duty and reservists on temporary active duty) and military retirees of their responsibilities and obligations under Article 88 of the Uniform Code of Military Justice and Department of Defense Directive 1344.10," the email said, as quoted by The Daily Wire.
"Given the heightened political and social atmosphere surrounding Afghanistan, it is important to remind our uniformed personnel (active duty and reservists on temporary active duty) and military retirees of their responsibilities and obligations under Article 88 of the Uniform Code of Military Justice and Department of Defense Directive 1344.10," the email read.
"While it is vital to protect the constitutional right of freedom of expression for these groups, consistent with mission accomplishment, national security, and good order and discipline, it's important to remember certain limitations," the email went on to explain. "Namely, uniformed personnel and military retirees are prohibited from disrespecting senior government leadership (e.g. the President, Vice President, Congress, Secretary of Defense, Service Secretaries, etc.)."
"The outlet reported that under the same UCMJ and DoD regulations, current and retired ONI staffers cannot take part in partisan political activities including the distribution of partisan literature and materials," the report said.
The report from the Daily Wire then cited a current ONI staffer who stated that the same policies were not nearly as strict when President Donald Trump was in office. The source then went on to recall that "retired officers condemning the former president."
Of course not. Because the left wanted and needed all of these folks to turn against Trump in order to help aid them in stealing the presidential election last year. They had to ensure that as many people as possible bought into the "orange man bad" narrative the mainstream media had been pumping up forever.
"Even for civilians, you are reminded of the danger that your public comments will/could be attributed to ONI or the Department of Defense," the memo continued. "While you are entitled to your opinion and to share your opinion amongst those you know and trust, being too vocal in criticism of, say, the President or members of the military and civilian leadership may reflect poorly on ONI.
"It is important to share your opinions, if you choose to do so, in a professional manner that does not call into question your employment, the Department of Defense, or the federal government as a whole," the email went on to add.
The memo then addressed that attack that happened in Kabul, Afghanistan last week.
"After Kabul fell to the Taliban last week, the scenes of chaos and desperation have been hard for all Americans to watch, especially for those of us in the national security establishment, as well as those who served selflessly in Afghanistan over the last 20 years," the ONI Chief of Staff stated. "Watching the Taliban sweep back into power after two decades of investment can be a bitter pill to swallow."
This memo just so happens to come out right after a video was posted by Marine Lt. Col. Stuart Scheller, a 17-year decorated combat veteran who took senior Pentagon leaders to the woodshed over the attack that happened in Kabul, openly challenging them to take responsibility for the incident.
Not long after the video hit the Internet, Scheller was essentially fired from his position as a battalion commander.
It's time for Joe Biden and the rest of his administration to take responsibility for what happened and resign. If they refuse, they should be impeached. Period. These lost lives could have been avoided had the troop pullout been done differently.
ALERT: Japan Makes Major Moderna Covid-19 Vaccine Announcement
BREAKING: Study Shows Natural Immunity To COVID Is Superior To Pfizer Vaccine


text:
BBC – Travel – Austria's tiny village with 10,000 day-trippers
Selena Taylor makes her living road-tripping across continents and flying to disparate locations, but each place she visits must have a certain aesthetic. As a travel blogger and photographer, Taylor hunts out the world's most charming destinations and shares her exploits with her 175,000 Instagram followers.
"Knowing your audience is key," said Taylor on what makes a post popular.
In February 2020, Taylor visited the village of Hallstatt, located in Austria's Salzkammergut region, that's beloved by Instagrammers and influencers for its scenic beauty. She was drawn to the "charming homes and cafes, beautiful backdrops and prime location on the water," she said. "Hallstatt has grown popular in recent years and it checks a lot of boxes that make it appealing."
The world had stopped turning. It was frightening, quiet and calm
Taylor spent several days wandering the streets of this fairy-tale village, drinking coffee amongst the mountain landscape and watching the sun set across the lake. But a month after Taylor ticked Hallstatt off her bucket list, this tiny village went from one of the most Instagrammed places on Earth, according to Harper's Bazaar, to experiencing something it hasn't for years.
At this time of year, the village of near 800 people would ordinarily open its doors to almost 10,000 daily visitors. The sound of reversing coaches and conversation in countless languages would fill the air, and the sky would be illuminated by flashes from digital cameras.
On 16 March, though, lockdown was implemented across Austria, changing everything for the locals who call Hallstatt home. "It was like living in a ghost village," said Hallstatt resident Sonja Katharina. "The world had stopped turning. It was frightening, quiet and calm. There were no cars, no buses and no tourists. We could even hear the swans swimming."
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As the initial shock made way for the warmer spring months, Katharina has been enjoying the benefits of solitude. She can drive through the streets without risk of oncoming pedestrians and spends her days cycling the Seestraße, or "Lake Street", Hallstatt's most photographed road that would usually be packed with around 4,000 tourists per day.
"The positives were that we had time to speak to each other – from a distance of course," Katharina laughed. "We don't have to hurry, and you learn to think about what is really important in life."
Like Katharina, others are cherishing the change in pace. "The drop in tourism happened more or less completely unexpectedly," said local musician Gerhard Hallstatt. "All of a sudden, Hallstatt has gone back to its roots."
A decade ago, the village received around 100 visitors a day. Now, pre-Covid-19, reports more than 1 million overnight stays per year. In the summer months, many travel from across Asia, the US and UK; while in autumn and winter, tourists from nearby countries like Poland, Hungary, Germany and Czech Republic are enticed by the many mountain biking, climbing and hiking trails open at points throughout the year.
Why is Hallstatt so popular? Some reasons are easily explained. Hallstatt and the surrounding region was awarded in 1997 for its magnificent Alpine landscape and ancient salt mining tradition. Its salt mine – one of the world's oldest – welcomed 19,700 visitors from January until 15 March this year, all keen to learn the history of these 7,000-year-old tunnels or stare out at the mesmerising views of the salt lake.
Since lockdown hit, it's been zero. And while locals may be reconnecting with their roots, this also brings uncertainty.
"Hallstatt has always been a tourist hot spot, so a lot of people have lost their work or have reduced working hours," said Kurt Reiger, CEO of the Hallstatt salt mine. "It doesn't feel like having an old town back because our old town was always filled with tourists, a situation we love."
All of a sudden, Hallstatt has gone back to its roots
Other reasons for Hallstatt's allure are more unexpected. In 2006, episodes of the popular South Korean soap opera Spring Waltz were filmed in and around Hallstatt, which introduced the village to millions in Asia.
And Chinese tourists flock here too, partly thanks to the Chinese mining company Minmetals, which in 2012 unveiled a life-size replica of Hallstatt in the Chinese province of Guangdong. Since then, tourists have travelled thousands of miles to experience the real thing. The build reportedly cost more than 6.5bn yuan (£738m) and acts as a high-end property development for the wealthy, rather than the "pearl of Austria" as the original is known.
Travellers also descend on Hallstatt thanks to the hugely popular animated film Frozen. Many people believe that Hallstatt's spires and frosted mountaintops are the inspiration behind much of Frozen's fictional fantasy land, the kingdom of Arendelle. There is just one problem to this claim. It isn't true.
"When it comes to Frozen, that's just rumours," said Gregor Gritzky, CEO of the local tourist board Dachstein Salzkammergut. "We've had international press ask about it, but it's all rumour." In reality, the true inspiration behind Frozen is found in several locations across Norway, from Oslo's Akershus Fortress to the old merchant quarter of Bryggen, Bergen.
What can be accurately believed, however, is that the village's reputation as Austria's most beautiful location has perhaps made Hallstatt too Instagram-friendly. Searching #Hallstatt on Instagram brings up more than 600,000 perfectly framed holiday snaps from tourists in choreographed poses.
"Tourism has two sides of the coin. There have been challenges with the number of guests, but it's good for the municipality to have tourists," Gritzky said. "We want people to do more than just take a picture, not get off the bus and go to the next place."
The people of Hallstatt are glad to share the beauty of the village with friends and guests from all over the world
While bringing obvious financial benefits, this level of tourism, has come with side effects. Some locals have complained of tourists turning up unannounced in their gardens, and drones are a common grievance. In January, Hallstatt's tourism chiefs took steps to limit tourist buses to 50 per day.
But, while more recent travel restrictions due to Covid-19 have given locals some well-deserved rest, many residents are beginning to miss the visitors.
"It's a beautiful feeling to have some empty lanes for 'ourselves', but the people of Hallstatt are glad to share the beauty of the village with friends and guests from all over the world," Gerhard Hallstatt said. "The villagers – like all those who live in touristy areas – have a certain love-hate relationship to tourism, but a huge amount of the population depends on it."
There is good news for the village's recovery, as local tourists are beginning to return as lockdown measures ease across Austria. In mid-May, Simon König and his partner, who live around an hour's drive from Hallstatt, took the golden opportunity to "visit this beautiful place without the mass tourism".
"It's never been so beautiful," König remembered. "When we arrived, there were literally no people around except the locals working on their properties. Around midday, some people came from the surrounding communities for the same reason we did."
In Hallstatt, some hotels have just reopened, and so too have the famous salt mines. There are still more challenges to come: "I think tourism will take time [to return], but one of the biggest questions is who will be able to come and in what timespan?" said Gritzky.
But for Taylor, Hallstatt hasn't lost what makes it special, even if it remains off limits "Hallstatt is particularly unique," she said. "It is one of the few 'trendy' destinations that really exceeds expectations in person."
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www.bbc.com 2020-06-04 21:05:21
AustriaAustriasBBCCOVID-19culture-identitydaytrippersEuropeimage
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just for being out there in a big way.
and full descriptions of the books.
...at least as far as books are concerned. We've put together our lists of the 60 most interesting titles to watch out for this spring, along with our top 10 picks, in each of 18 categories.


As a reflection of our shared a commitment to excellence and initiative in photojournalism, FoteEvidence and World Press Photo will work together on the FotoEvidence Book Award. The award will now be known as the "FotoEvidence Book Award with World Press Photo." The winner of the book award and two other selected finalists will be exhibited during the World Press Photo Exhibition each year in Amsterdam, in conjunction with the launch of the winner's book. The book will also accompany the World Press Photo Exhibition to many of the 100 cities worldwide to which it travels each year.
This collaboration will help FotoEvidence reach a worldwide audience and strengthen our mission to support photographers who demonstrate courage and commitment in the pursuit of human rights.
By David Stuart — Svetlana Bachevanova announces the jury for the 2018 FotoEvidence Book Award with World Press Photo. It includes: Daniella Zalcman , ..


text:
Ералка — река в России, протекает в Ашинском районе Челябинской области. Правый приток реки Сим.

География
Река Ералка берёт начало у села Ерал. Течёт на запад вдоль железной дороги. Устье реки находится у станции Симская в 170 км по правому берегу реки Сим. Длина реки составляет 23 км.

Данные водного реестра
По данным государственного водного реестра России относится к Камскому бассейновому округу, водохозяйственный участок реки — Сим от истока и до устья, речной подбассейн реки — Белая. Речной бассейн реки — Кама.

Код объекта в государственном водном реестре — 10010200612111100019065.

Примечания

Притоки Сима
Реки Ашинского района


Sudan's Information Minister, Ahmed Bilal Osman, has announced that the coming period will witness expanding of press freedoms, pointing out that the Press and Publications Law will forward to the National Council for its endorsement.
In press statements on Sunday, the minister said the next few days will witness the lifting of suspension on the four newspapers that has been suspended during the last period, indicating that the current year will see progress of the media.
NISS confiscated the print-runs of 10 daily newspapers on 25 May against what the International Press Institute (IPI) calls "the backdrop of an ongoing brutal security campaign against the freedoms of speech and the press".
NISS also suspended the publication of four of the newspapers - Al-Intibaha, AkhirLahzah, Al-Jareeda and Al-Khartoum - indefinitely. The other six were Al-Sudani, Al-Akhbar, Alwan, Al-Youm al-Tali, Al-Rai Ala'am and Al-Tayyar.
The mass confiscation repeated a similar action by the NISS on 16 February when the print runs of 14 daily papers were seized.
IPI, the global press freedom organization, argues that the two confiscations stand out as the most egregious press freedom violations in Sudan's modern history.
It says that NISS exercises rigorous censorship on newspapers and other media outlets by sending messages to editors instructing them not to discuss issues it deems to be beyond "red-lines" related to "national security".


Celebrate the summer with fellow Dickinsonians at The DL's rooftop bar. Enjoy great company on a beautiful rooftop during our first event of the summer!
Light hors d'oeuvres and a cash bar will be available. Please register by Friday, June 15.


Flint is a Seattle-based creative studio dedicated to building compelling brands. On a mission to design out their beautiful new space, they outfitted their office with IdeaPaint dry erase and PULL, our magnetic wallcovering. Check out how Flint pulled it all together (pun intended) and watch as their team puts their now-high-functioning space to good use.
Flint is a creative studio dedicated to helping their clients fulfill their brand aspirations. They craft everything from brand identity development, to web experiences, interior design, and original content.


Which Punisher will resonate most with fans (and me)?
The Marvel Comics and Netflix deal has (to my surprise) turned out to be successful. Originally conceived as set of four 13-episode series starring Daredevil, Jessica Jones, Luke Cage, and Iron Fist (plus a fifth series-The Defenders-which sees all four characters form a superhero team), the popularity of Daredevil led to the announcement of a second season for the show (debuting March 18). Likewise, in the wake of the well-received Jessica Jones series, many fans speculated that the character would also receive a season 2 (the speculation is over, season 2 is coming). Season 2 of Daredevil is set to feature additional Marvel Comics character, including Elektra (former lover of DD turned ninja/assassin/enemy) and the vigilante hero, the Punisher.


Models should be good looking, smart & handsome with perfect physique preferably freshers. Height should be 5.9" or above. X-Factor among the models (singer/ stand-up/ any other unique skill) would be an added bonus. Shoot in July :10 weeks.	* By applying to the job I agree to the terms and conditions for the show.