text:
\section{Dueling bandit feedback}
\label{sec:dueling}

After an initial exploration phase, \FSDTS{} selects an arm according
to how likely its sample realization will dominate those of other
arms. This suggests that we are mostly interested in the stochastic
dominance probability, rather than the joint reward distribution.
Recognizing this, we now move to the {\em dueling bandits}
framework~\cite{yue2012k}, which examines pairwise stochastic dominance.

In a
dueling bandit setting, at each time step $t$, the decision maker
chooses two arms $a_t(1),a_t(2)$ to ``duel'' with each other. The
decision maker doesn't observe the actual rewards of $r_{a_t(1)}(t),
r_{a_t(2)}(t)$, but rather the outcome $\mathbbm{1}(r_{a_t(1)}(t) >
r_{a_t(2)}(t)).$
In this
section, we  extend our fairness results to the dueling
bandits setting.

\subsection{A Plackett-Luce model}

Consider the following model.  Denote the probability of arm $i$'s
reward being greater than arm $j$'s reward by:
\begin{align}
  p_{i,j}:=\Pr(i \succ j) := P(r_i > r_j),
\end{align}
where we assume a stationary reward distribution over time $t$.  To be
concrete, we adopt the {\em Plackett-Luce} (PL) model
\cite{guiver2009bayesian,cheng2010label}, where every arm $i$
is parameterized by a {\em quality parameter},
$\nu_i \in \mathbb R_{+}$, such that
\begin{align}
  p_{i,j} = \frac{\nu_i}{\nu_i + \nu_j}. \label{pl:pair}
\end{align}
Furthermore, let $\mathcal M = [p_{i,j}]$ denote the matrix of
pairwise probabilities $p_{i,j}$. This is a standard setting to
consider in the dueling bandit literature
\cite{yue2012k,szorenyi2015online}.

With knowledge of $\mathcal M$, we can efficiently simulate the best
arm realization. In particular, for the rank over arms
$\texttt{rank} \sim \mathcal M$ generated according to the PL model
(by selecting pairwise comparisons one by one, each time selecting one
from the remaining set with probability proportional to $\nu_i$), we
have~\cite{guiver2009bayesian}:
\[
P(\texttt{rank}|\nu) = \prod_{i=1}^k \frac{\nu_{o_i}}{\sum_{j=i}^k \nu_{o_j}},
\]
where $\mathbf{o} = \texttt{rank}^{-1}$.
In particular, the marginal probability
in the PL model that an arm is rank 1 (and the best arm) is  just:
\[
P(\texttt{rank}(1) = i) = \frac{\nu_i}{\sum_j \nu_j} = \frac{1}{1+\sum_{j \neq i} \nu_j/\nu_i}.
\]
Finally, knowledge of $\mathcal{M}$ allows us to directly qcalculate each arm's quality from \eqref{pl:pair}.
\[
\nu_j/\nu_i := \frac{p_{j,i}}{1-p_{j,i}}.
\]
Thus, with estimates of the quality parameters ($\mathcal M$) we can estimate $P(\texttt{rank}(1) = i) $ and  directly sample from the best arm distribution and simulate
stochastic-dominance Thompson sampling.

We will use dueling bandit feedback to estimate pairwise probabilities, denoted
$\tilde{p}_{i,j}$, along with
the corresponding comparison matrix
denoted by $\tilde{\mathcal M}$. In particular, let
$n_{i,j}(t)$  denote the number of times arms $i$ and $j$ are selected up to time $t$. Then
we estimate the pairwise probabilities as:
\begin{align}
  \tilde{p}_{i,j}(t) = \frac{\sum_{n=1}^{n_{i,j(t)}}\mathbbm{1}(r_{i}(n) > r_{j}(n))}{n_{i,j}(t)},~n_{i,j}(t) \geq 1. \label{est:p}
\end{align}

With accurate estimation of the pairwise probabilities, we are able to
accurately approximate the probability that each arm will be rank
1. Denote $\widetilde{\texttt{rank}} \sim \tilde{M}$ as the rank
generated according to the PL model that corresponds with matrix
$\tilde{M}$.  We estimate the ratio of quality parameters
$\widetilde{(\frac{\nu_i}{\nu_j})}$ using $\tilde{p}_{i,j}$, as
\[
\widetilde{(\frac{\nu_i}{\nu_j})} = \frac{\tilde{p}_{i,j} }{1-\tilde{p}_{i,j} }.
\]

Given this, we can then  estimate the probability than arm $i$ has the best
reward realization:
\begin{align}
  P(\widetilde{\texttt{rank}}(1) = i) = \frac{1}{1+\sum_{j \neq i} \widetilde{(\frac{\nu_j}{\nu_i}})}.\label{est:rank}
\end{align}

\begin{lemma}
  When $|\tilde{p}_{i,j} - p_{i,j}| \leq \epsilon$, and $\epsilon$ is small enough, we have that in the Plackett-Luce model,
  $
  |P(\widetilde{\texttt{rank}}(1) = i)  - P(\texttt{rank}(1) = i) | \leq O(k \epsilon).
  $\label{rand:perturb}
\end{lemma}

This lemma can be established by establishing a concentration bound on
$\widetilde{(\frac{\nu_i}{\nu_j})}$.  We defer the details to a long
version of this paper.

\begin{algorithm}[!ht]
  \caption{(\texttt{Fair\_SD\_DTS})
  \begin{algorithmic}
    \State At any $t$, select two arms $a_1(t), a_2(t)$, and receive a realization of the following comparison: $\mathbbm{1}(r_{a_t(1)}(t) > r_{a_t(2)}(t))$.
    \State Check the following set:
    \[
    \mathcal O(t) = \{i: n_{i,j}(t) \leq C(\epsilon_2,\delta)\},
    \]
    where $C(\epsilon_2,\delta)$  depends on $\epsilon_2$ and $\delta$.
    \begin{itemize}
    \item If $\mathcal O(t) = \emptyset$, follow  (\texttt{SD\_TS}), using the collected statistics. (\emph{exploitation})
    \item If $\mathcal O(t) \neq \emptyset$, select all pairs of arms equally likely. (\emph{exploration})

    \end{itemize}
   \State Update $\tilde{p}_{a_t(1),a_t(2)}$, for the pair of selected arms (Eqn. (\ref{est:p})).
   \State Update $P(\texttt{rank}(1) = i)$ using $\tilde{M}$ (Eqn. (\ref{est:rank})).
  \end{algorithmic}
  \label{ts:dueling}
\end{algorithm}

Given this, we can derive an algorithm similar to \FSDTS{} that can
achieve calibrated fairness in this setting, by appropriately setting
the length of the exploration phase, and by simulating the probability
that a given arm has the highest reward realization.  This dueling
version of \FSDTS{} is the algorithm \texttt{Fair\_SD\_DTS}, and
detailed in Algorithm~\ref{ts:dueling}.

Due to the need to explore all pairs of arms, a larger number of
exploration rounds $C(\epsilon_2,\delta)$ is needed, and thus the
fairness regret scales as $R_{f}(T) \leq \tilde{O}(k^{4/3}
T^{2/3})$:
\begin{theorem}
  For any $\epsilon_2,\delta>0$, setting
  $$
  C(\epsilon_2,\delta) \defn O(\frac{(2\max D(r_i||r_j)+1)^2k^2}{2\epsilon^2_2}\log \frac{2}{\delta}),
  $$
  we have that (\texttt{Fair\_SD\_DTS}) is $(2,2\epsilon_2,\delta)$-fair w.r.t. total variation; and further it has fairness regret bounded as
  $R_{f,T} \leq \tilde{O}(k^{4/3}T^{2/3})$.
\end{theorem}

This proof is similar to the fairness regret proof for Theorem \ref{thm:main}, once we established Lemma \ref{rand:perturb}.
We defer the details to the full version of the paper.

\section{Introduction}
\label{sec:introduction}
Consider a sequential decision making problem where, at each
time-step, a decision maker needs to select one candidate
to hire from a set
of $k$ groups (these may be a different ethnic groups, culture, and
so forth),
whose true qualities are unknown {\em a priori}. The
decision maker would like to make fair decisions with respect to each
group's underlying quality distribution and to learn such a rule
through interactions. This naturally leads to a stochastic \emph{multi-armed
  bandit} framework, where each arm corresponds to a group, and
quality corresponds to reward.

Earlier studies of fairness in bandit problems have emphasized the
need, over all rounds $t$, and for any pair of arms, to weakly favor
an arm that is weakly better in expectation~\cite{joseph2016fairness}.
This notion of meritocratic fairness has provided interesting results,
for example a separation between the dependence on the number of arms
in the regret bound between fair and standard non-fair learning.  But
this is a somewhat weak requirement in that it (i) it allows a group
that is slightly better than all other groups to be selected all the
time and even if any single sample from the group may be worse than
any single sample from another group, and (ii) it allows a random
choice to be made even in the case when one group is much better than
another group.\footnote{\citet{joseph2016rawlsian} also extend the
  results to contextual bandits and infinite bandits.  Here, there is
  additional context associated with an arm in a given time period,
  this context providing information about a specific individual.
  Weak meritocratic fairness requires, for any pair of arms, to weakly
  favor an arm that is weakly better in expectation conditioned on
  context.  When this context removes all uncertainty about quality,
then
  this extension addresses critique~(i).
But
in the more general case
we think it remains interesting for future work to generalize
our definitions to the case of contextual bandits.}

In this work, we adopt the framework of ``treating similar individuals
similarly'' of~\citet{dwork2012fairness}.  In the current context, it
is arms that are the objects about which decisions are made, and thus
the `individual' in Dwork et al.~corresponds to an `arm'. We study the
classic stochastic bandit problem, and insist that over all rounds
$t$, and for any pair of arms, that if the two arms have a similar
quality distribution then the probability with each arm is selected
should be similar.  This {\em smooth fairness} requirement addresses
concern~(i), in that if one group is best in expectation by only a
small margin, but has a similar distribution of rewards to other
groups, then it cannot be selected all the time.

By itself we don't consider smooth fairness to be enough because it
does not also provide a notion of meritocratic fairness--- it does
not constrain a decision maker in the case that one group is much
stronger than another (in particular, a decision maker could choose
the weaker group). For this reason, we also care about
{\em calibrated fairness}
and
introduce the concept of
{\em fairness regret}, which corresponds to the degree to which an
algorithm is not calibrated. Perfect calibration requires that
the probability of selecting a group is equal to the probability that
a group has the best quality realization.  Informally, this is a
strengthening of ``treating similar individuals similarly'' because it
further requires that dissimilar individuals be treated dissimilarly
(and in the right direction.)
In the motivating setting of making decisions about who to hire,
groups correspond to divisions within society and each activation of
an arm to a particular candidate. An algorithm with low
fairness regret will give individuals a chance proportionally to their
probability of being the best candidate rather than
protect an entire group based on a higher average quality.

\subsection{Our Results}

In regard to smooth fairness, we say that a   bandit algorithm is
  {\em $(\epsilon_1, \epsilon_2,
\delta)$-fair} with respect to a divergence function $D$ (for
$\epsilon_1, \epsilon_2\geq 0$, and $0\leq \delta\leq 1$) if,
with  probability $1-\delta$,
in every round $t$
and for every pair of arms $i$ and $j$,
$$
D(\pi_t(i)||\pi_t(j))\leq \epsilon_1 D(r_i||r_j)+\epsilon_2,
$$ where $D(\pi_t(i)||\pi_t(j))$ denotes the divergence between the
Bernoulli distributions corresponding to activating arms $i$ and $j$, and $D(r_i||r_j)$ denotes the divergence between the
reward distributions of arms $i$ and $j$.

The  {\em fairness regret} $\cregret_{f,T}$
of a bandit algorithm over $T$ rounds is the total deviation from calibrated fairness:
\begin{equation}
\cregret_{f,T}=\sum_{t=1}^T \E\biggl[ \sum_{ i = 1}^k \max( \PB{i} - \pi_t(i), 0)\biggr]
\end{equation}
where $\PB{i}$ is the probability that the realized quality of arm $i$
is highest and $\pi_t(i)$ is the probability that arm $i$ is activated
by the algorithm in round $t$.

Our main result is stated for the case of Bernoulli bandits.  We show
that a Thompson-sampling based algorithm, modified to include an
initial uniform exploration phase, satisfies:
\begin{enumerate}
\item $(2,\epsilon_2,\delta)$-fair with regard to total variation
   distance for any $\epsilon_2>0$, $\delta>0$,
  where the amount of initial exploration
  on each arm scales as $1/\epsilon_2^2$
  and $\log(1/\delta)$, and
  %
\item  fairness regret that is bounded by $\tilde{O}((kT)^{2/3})$,
  where $k$ is the number of arms and $T$ the number of rounds.
\end{enumerate}

We also show that a simpler version of Thompson sampling can
immediately satisfy a {\em subjective version} of smooth fairness.
Here, the relevant reward distributions are defined
with respect to the posterior reward distribution
under the belief of a Bayesian decision maker, this decision
maker having an initially uninformed prior.
In addition, we draw a connection between calibrated fairness and
proper scoring functions: there exists a loss function on reward whose
maximization in expectation would result in a calibrated-fair policy.
In Section~\ref{sec:dueling} we also extend our results to the {\em
  dueling bandit} setting in which the decision maker receives only
pairwise comparisons between arms.

\subsection{Related work}
\label{sec:related-work}

\citet{joseph2016fairness} were the first to introduce fairness
concepts in the bandits setting. These authors adopt the notion of
weak meritocratic fairness, and study it within the classic and
contextual bandit setting. Their main results establish a separation
between the regret for a fair and an un-fair learning algorithm, and
an asymptotically regret-optimal, fair algorithm that uses an approach
of chained confidence intervals.
While their definition promotes meritocracy in regard to expected
quality, this present paper emphasizes instead the distribution on
rewards, and in this way connects with the smoothness definitions and
``similar people be treated similarly'' of~\citet{dwork2012fairness}.

\citet{joseph2016rawlsian} study a more general problem in which there
is no group structure; rather, a number of individuals are available
to select in each period, each with individual context (they also
consider an infinite bandits setting.) \citet{jabbari:fair-mdp} also extend
the notion of weakly meritocratic fairness to Markovian environments,
whereby fairness requires the algorithm to be more likely to play
actions that have a higher utility under the optimal policy.

In the context of fair statistical classification, a number of papers
have asked what does it mean for a method of scoring individuals
(e.g., for the purpose of car insurance, or release on bail) to be
fair. In this setting it is useful to think about each individual as
having a latent outcome, either positive or negative (no car accident,
car accident.) One suggestion is that of {\em statistical parity},
which requires the average score of all members of each group be
equal. For bandits we might interpret the activation probability as
the score, and thus statistical parity would relate to always
selecting each arm with equal probability. Another suggestion is {\em
  calibration within groups}~\cite{kleinberg2016inherent}, which
requires for any score $s\in[0,1]$ and any group, the approximate
fraction of individuals with a positive outcome should be $s$; see
also~\citet{chouldechova2016fair} for a related property.  Considering
also that there is competition between arms in our setting, this
relates to our notion of calibrated fairness, where an arm is
activated according to the probability that its realized reward is
highest. Other definitions first condition on the latent truth; e.g.,
{\em balance}~\cite{kleinberg2016inherent} requires that the expected
score for an individual should be independent of group when
conditioned on a positive outcome; see
also~\citet{DBLP:conf/nips/HardtPNS16} for a related property. These
concepts are harder to interpret in the present context of bandits
problems. Interestingly, these different notions of fair
classification are inherently in conflict with each
other~\cite{kleinberg2016inherent,chouldechova2016fair}.

This statistical learning framework has also been extended to decision
problems by \citet{corbett2017algorithmic}, who analyze the tradeoff
between utility maximization and the satisfaction of fairness
constraints. Another direction is to consider {\em subjective
  fairness}, where the beliefs of the decision maker or external
observer are also taken into
account~\cite{dimitrakakis2017:subjective-fairness}.  The present
paper also briefly considers a specific notion of subjective fairness
for bandits, where the similarity of arms is defined with respect to
their marginal reward distribution.

\section{The Setting}
\label{sec:setting}

We consider the stochastic bandits problem, in which at each time
step, a decision maker chooses one of $k$ possible arms (possibly in a
randomized fashion), upon which the decision maker receives a
reward. We are interested in decision rules that
are fair in regard to the decisions made
about which arms to activate while achieving high
total reward.

At each time step $t$, the decision maker chooses a distribution
$\pi_t$ over the available arms, which we refer to as the \textit{decision
  rule}. Then nature draws an action $a_t \sim \pi_t$, and draws
rewards:
\begin{align*}
r_i(t) | a_t\!=\!i\ \sim P(r_i|\theta_i),
\end{align*}
where $\theta_i$ is the unknown parameter of the selected arm $a_t = i$,
and where we denote the realized reward for arm $i$ at time $t$ by $r_i(t)$.

We denote the reward distribution $P(r_i|\theta_i)$ of arm $i$ under
some parameter $\theta_i$ as $r_i(\theta_i)$, with $r_i$ denote the
true reward distribution.  Denote the vector form as
$\mathbf r = (r_1, ..., r_k)$, while $\mathbf r_{-i,j}$ removes $r_i$
and $r_j$ from $\mathbf
r$.
If the decision maker has prior knowledge of the parameters
$\boldsymbol{\theta} = (\theta_1, ..., \theta_k)$, we denote this by
$\beta(\boldsymbol{\theta})$.

\subsection{Smooth Fairness}

For divergence function $D$, let $D(\pi_t(i)\|\pi_t(j))$ to denote the divergence between
the Bernoulli distributions with parameters $\pi_t(i)$ and $\pi_t(j)$,
and use $D(r_i\|r_j)$ as a short-hand for the divergence between the
reward distributions of arm $i$ and $j$ with true parameters
$\theta_i$ and $\theta_j$.

We  define $(\epsilon_1,\epsilon_2,\delta)$-fair w.r.t. a divergence function $D$ for an algorithm with
an associated sequence of decision rules $\{\pi_t\}_t$ as:
\begin{definition}[Smooth fairness]
\label{obj:fair}
A bandit process is $(\epsilon_1,\epsilon_2,\delta)$-fair w.r.t. divergence function $D$, and $\epsilon_1\geq 0,\epsilon_2\geq 0$, $0\leq \delta \leq 1$,
if with probability at least $1-\delta$, in every round $t$, and for every pair of arms $i$ and $j$:
\begin{align}
D(\pi_t(i)\|\pi_t(j)) \leq \epsilon_1 D(r_i\|r_j) + \epsilon_2.
\end{align}
\end{definition}

\medskip

\paragraph{Interpretation.}  This adapts the concept of ``treating
similar individuals similarly''~\cite{dwork2012fairness} to the
banditrs setting.  If two arms have a similar reward distribution,
then we can only be fair by ensuring that our decision rule has
similar probabilities. The choice of $D$ is crucial. For the KL
divergence, if $r_i, r_j$ do not have common support, our action
distributions may be arbitrarily different. A Wasserstein distance,
requires to treat two arms with a very close mean but different
support similarly to each other. Most of the technical development
will assume the total variation divergence.

As a preliminary, we also consider a variation on smooth fairness
where we would like to be fair with regard to a posterior belief of
the decision maker about the distribution on rewards associated with
each arm.

For this, let the {\em posterior distribution on the parameter}
$\theta_i$ of arm $i$ be $\beta(\theta_i|h^t)$, where
$h^t= (a_1, r_{a_1}(1), \ldots, a_t, r_{a(t)}(t))$, is the history of
observations until time $t$.  The {\em marginal reward distribution
  under the posterior belief} is
\[
r_i(h^t) \defn \int_\Theta P(r_i \mid \theta_i) \, d\beta(\theta_i \mid h^t).
\]
\begin{definition}[Subjective smooth fairness]
A bandit process is $(\epsilon_1,\epsilon_2,\delta)$-subjective fair w.r.t. divergence function $D$,
and
$\epsilon_1\geq 0, \epsilon_2\geq 0$, and $0\leq \delta\leq 1$,
if,  with probability at least $1-\delta$,
for every period $t$, and every pair of arms $i$ and $j$,
\begin{align}
D(\pi_t(i)\|\pi_t(j)) \leq \epsilon_1 D(r_i(h^t)\|r_j(h^t)) + \epsilon_2,
\end{align}
\label{sub:fair}
where the initial belief of the decision maker is an uninformed
prior for each arm.
\end{definition}
\subsection{Calibrated Fairness}

Smooth fairness by itself does not seem strong
enough for fair bandits algorithms. In particular, it does not require
meritocracy: if two arms have quite different reward distributions
then the weaker arm can be selected with higher probability than the
stronger arm. This seems unfair to individuals in the group associated
with the stronger arm.

For this reason we also care about {\em calibrated fairness}: an
algorithm should sample each arm with probability equal to its reward
being the greatest. This would ensure that even very weak arms will be
pulled sometimes, and that better arms will be pulled significantly
more often.
\begin{definition}[Calibrated fair policy]
A policy $\pol_t$ is \emph{calibrated-fair} when it selects actions $a$ with probability
\begin{align}
\pol_t(a) &= \PB{a},
&
\PB{a}
&\defn P(a = \argmax_{j \in [k]} \{r_j\}),
\label{eq:calibration}
\end{align}
equal to the probability that the reward realization of arm $a$ is the highest,
and we break ties at random in the case that two arms have the same
realized reward.
\end{definition}

\medskip

Unlike smooth fairness, which can always be achieved exactly (e.g.,
through selecting each arm with equal probability), this notion of
calibrated fairness is not possible to achieve exactly in a bandits
setting while the algorithm is learning the quality of each arm.
For this reason, we  define the cumulative violation of calibration across
all rounds $T$:
\begin{definition}[Fairness regret]
\label{def:fair_regret}
The fairness regret $\regret_f$ of a policy $\pi$ at time $t$ is:
\begin{align*}
\regret_f(t) \defn \E \biggl [ \sum_{ i = 1}^k \max( \Pr^*(i) - \pi_t(i), 0)   ~\bigg|~ \boldsymbol{\theta} \biggr].
\end{align*}\
The cumulative fairness regret is defined as $\cregret_{f,T} \defn  \sum_{t = 1}^T \regret_f(t)$.
\end{definition}
\medskip

\begin{example}
  Consider a bandits problem with two arms, whose respective reward
  functions are random variables with realization probabilities:
  \begin{itemize}
  \item $P(r_1 = 1) = 1.0$;
  \item $P(r_2 = 0) = 0.6$ and
    $P(r_2= 2) = 0.4$.
  \end{itemize}

Since $\E(r_1) = 1.0$ and $\E(r_2) = 0.8$, a decision maker who
optimizes  expected payoff (and knows the distributions)
would prefer to always select arm $1$ over arm $2$. Indeed,
this satisfies weakly meritocratic fairness~\cite{joseph2016fairness}.

In contrast, calibrated fairness requires that arm $1$ be selected
60\% of the time and arm $2$ 40\% of the time, since this matches the
frequency with which arm $2$ has the higher realized reward.  In a
learning context, we would not expect an algorithm to be calibrated in
every period. Fairness regret measures the cumulative amount by which
an algorithm is miscalibrated across rounds.

Smooth fairness by itself does not require calibration. Rather, smooth
fairness requires, in every round, that the probability of selecting
arm 1 be close to that of arm 2, where ``close'' depends on the
particular divergence function. In particular, smooth fairness would
not insist on arm 1 being selected with higher probability than arm 2,
without an additional constraint such as maximising expected reward.
\end{example}

\medskip

In Section~\ref{sec:subjective-fairness}, we introduce a simple
Thompson-sampling based algorithm, and show that it satisfies
smooth-subjective fairness. This algorithm provides a building block
towards our main result, which is developed in
Section~\ref{sec:objective}, and provides smooth fairness and low
fairness regret.  Section~\ref{sec:dueling} extends this algorithm to
the dueling bandits setting.

\input{subjective}
\input{objective}
\input{dueling}

\section{Conclusion}
\label{sec:conclusion}

In this paper we adapt the notion of ``treating similar individuals
similarly''\cite{dwork2012fairness} to the bandits problem, with
similarity based on the distribtion on rewards, and this property of
smooth fairness required to hold along with (approximate) calibrated
fairness.  Calibrated fairness requires that arms that are worse in
expectation still be played if they have a chance of being the best,
and that better arms be played significantly more often than weaker
arms.

We analyzed Thompson-sampling based algorithms, and showed that a
variation with an initial uniform exploration phase can achieve a low
regret bound with regard to calibration as well as smooth fairness.
We further discussed how to adopt this algorithm to a dueling bandit
setting together with Plackett-Luce.

In future work, it will be interesting to consider contextual bandits
(in the case in which the context still leaves residual uncertainty
about quality), to establish lower bounds for fairness regret, to
consider ways to achieve good calibrated fairness uniformly across
rounds, and to study the utility of fair bandits algorithms (e.g.,
with respect to standard notions of regret) and while allowing for a
tradeoff against smooth fairness for different divergence functions
and fairness regret.  In addition, it will be interesting to explore
the connection between strictly-proper scoring rules and calibrated
fairness, as well as to extend Lemma \ref{rand:perturb} to more
general ranking models.

\paragraph{Acknowledgements.}
The research has received funding from: the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement 608743, the Future of Life Institute, and SNSF Early Postdoc.Mobility fellowship.
\bibliographystyle{plainnat}

\section{Objective fairness}
\label{sec:objective}

In this section, we introduce a variant of \SDTS{}, which includes an
initial phase of uniform exploration. We then prove the modified
algorithm satisfies (objective) smooth fairness.

Many phased reinforcement learning algorithms~\cite{kearns2002near},
such as those based on successive
elimination~\cite{jmlr:Even-Dar:ActionElimination}, explicitly
separate time into exploration and exploitation phases. In the
exploration phase, arms are prioritized that haven't been selected
enough times.  In the exploitation phase, arms are selected in order
to target the chosen objective as best as possible given the available
information.  The algorithm maintains statistics on the arms, so that
$\mathcal{O}(t)$ is the set which we have not selected sufficiently to
determine their value. Following the structure of the deterministic
exploration algorithm~\cite{vakili2011deterministic}, we exploit
whenever this set is empty, and uniformly choosing among all arms
otherwise.\footnote{However, in our case, the actual drawing of the arms is stochastic to ensure fairness.}

\begin{algorithm}[!h]
  \caption{\FSDTS{}
  \begin{algorithmic}
    \State At any $t$, denote by $n_i(t)$ the number of times arm $i$ is selected up to time $t$. Check the following set:
    \[
    \mathcal O(t) = \{i: n_i(t) \leq C(\epsilon_2,\delta)\},
    \]
    where $C(\epsilon_2,\delta)$ depends on $\epsilon_2$ and $\delta$.
    \begin{itemize}
    \item If $\mathcal O(t) = \emptyset$, follow  (\SDTS{}), using the collected statistics. (\emph{exploitation})
    \item If $\mathcal O(t) \neq \emptyset$, select all arms equally likely. (\emph{exploration})
    \end{itemize}
  \end{algorithmic}
  \label{ts:det}
\end{algorithm}

\begin{theorem}\label{thm:main}
  For any $\epsilon_2,\delta>0$, setting
  $$
  C(\epsilon_2,\delta) := \frac{(2\max D(r_i||r_j)+1)^2}{2\epsilon^2_2}\log \frac{2}{\delta},
  $$
  we have that (\texttt{Fair\_SD\_TS}) is $(2,2\epsilon_2,\delta)$-fair w.r.t. total variation; and further it has fairness regret bounded as
  $R_{f,T} \leq \tilde{O}((kT)^{2/3})$.
\end{theorem}

The proof of Theorem~\ref{thm:main} is given in the following sketch.

\begin{proof} (sketch) We begin by proving the first part of Theorem \ref{thm:main}: that for any $\epsilon_2,\delta>0$,
  and  setting
  $C(\epsilon_2,\delta)$ appropriately, we will have that \texttt{Fair\_SD\_TS} is $(2,2\epsilon_2,\delta)$-fair w.r.t. total variation divergence.

  In the exploration phase,
  $D(\pi_t(i)||\pi_t(j)) = 0$, so the fairness definition is satisfied. For other steps, using Chernoff bounds we have that with probability at least $1-\delta$
  \[
  |\tilde{\theta}_i - \theta_i| \leq \frac{\epsilon_2}{2\max D(r_i||r_j)+1},\forall i
  \]

  Let the error term for $\theta_i$ be $\epsilon(i)$.

  Note that for a Bernoulli random variable, we have the following for the mixture distribution:
  \[
  r_i(\tilde{\theta}_i) = (1-\epsilon(i)/2) r(\theta_i) + \epsilon(i)/2 r(1-\theta_i)
  \]
  with $\epsilon(i) \leq \frac{\epsilon_2}{2\max D(r_i||r_j)+1}$. Furthermore, using the convexity of $D$ we can show that:
  \begin{align}
    D(r_i(\tilde{\theta}_i)||r_j(\tilde{\theta}_j)) \leq D(r_i||r_j) + \epsilon_2
  \end{align}

  Following the proof for Theorem~\ref{thm:sdts}, we
  then obtain that
  $$D(\pi_t(i)||\pi_t(j)) \leq 2D(r_i(\tilde{\theta}_i)||r_j(\tilde{\theta}_j)),$$ which proves our statement.

  We now establish the fairness regret.  The regret incurred during the
  exploration phase can be bounded as
  $O(k^2 C(\epsilon_2,\delta))$.\footnote{This is different from
    standard deterministic bandit algorithms, where the exploration
    regret is often at the order of $k C(\epsilon_2,\delta)$. The
    additional $k$ factor is due to the uniform selection in the
    exploration phase, while in standard deterministic explorations, the
    arm with the least number of selections will be selected.} For the
  exploitation phase, the regret is bounded by
  $O((\epsilon_2+\delta) T)$.

  Setting
  \[
  O((\epsilon_2+\delta) T)=O(k^2 C(\epsilon_2,\delta))
  \]

  we have the optimal $\epsilon$ is $\epsilon := k^{2/3}T^{-1/3}$. Further setting $\delta = O(T^{-1/2})$, we
  can show the regret is at the order of $ \tilde{O}((k T)^{2/3})$.
\end{proof}

It is possible to modify the sampling of the exploitation phase, alternating
between sampling according to \SDTS{} and sampling uniformly
randomly. This can be used  to bring the factor 2 down to any $\epsilon_1 > 0$, at the expense of reduced utility.

\subsection{Connection with proper scoring rules}

There is a connection between calibrated fairness and proper scoring
rules.  Suppose we define a {\em fairness loss function}
$\mathcal L_f$ for decision policy $\mathbf \pi$, such that
$L_{f}(\mathbf \pi) = \mathcal L(\mathbf \pi, a_{t, best})$, where arm
$a_{t, best}$ is the arm with the highest realized reward at time $t$.
The expected loss for policy $\pi$ is
\[
\E (L_f(\mathbf \pi)) = \sum_{i=1}^k \Pr^*(i) \cdot \mathcal L(\mathbf
\pi, i).
\]
If $\mathcal L$ is strictly proper \cite{gneiting2007strictly}, then
the optimal decision rule $\mathbf \pi$ in terms of $L_f$ is
calibrated.
\begin{proposition}
  Consider a fairness loss function $L_f$ defined as:
  \begin{align*}
    L_f(\mathbf \pi) = \mathcal L(\mathbf \pi, a_{t, best}),
  \end{align*}
  where $\mathcal L$ is a strictly proper loss function.
  Then a decision rule $\bar{\mathbf \pi}$ that minimizes expected loss is
  calibrated fair.

\end{proposition}
\begin{proof}
  We have:
  \begin{align*}
    \bar{\mathbf \pi} \in \argmin_{\mathbf \pi} \E (L_f(\mathbf \pi)) = \argmin_{\mathbf \pi} \sum_{i=1}^k \Pr^*(i) \cdot
    \mathcal L(\mathbf \pi, i) = \{ \Pr^*(i) \},
  \end{align*}

  where the last equality comes from the strict properness of $\mathcal L$.

\end{proof}

This connection between calibration and proper scoring rules
suggests an approach to the design of bandits algorithms with low
fairness regret, by considering different proper scoring rules along
with online algorithms to minimize loss.

\section{Subjective fairness}
\label{sec:subjective-fairness}

Subjective fairness is a conceptual departure from current approaches
to fair bandits algorithms, which empasize fairness in every period
$t$ with respect to the true reward distributions for each
arm. Rather, subjective fairness adopts the {\em interim} perspective
of a Bayesian decision maker, who is fair with respect to his or her
current beliefs.
Subjective smooth fairness is useful as a building block towards our
main result, which reverts to smooth fairness with regard to the true,
objective reward distribution for each arm.

\subsection{Stochastic-Dominance Thompson sampling}

In \emph{Thompson sampling} (TS), the probability of selecting an arm
is equal to its probability of being the best arm under the subjective
belief (posterior). This draws an immediate parallel with the Rawlsian
notion of equality of opportunity, while taking into account
informational constraints.

In this section we adopt a simple, multi-level sampling variation,
which we refer to as \emph{stochastic-dominance Thompson sampling},
\SDTS{}.  This first samples parameters $\theta$ from the posterior,
and then samples rewards for each arm, picking the arm with the
highest reward realization.

The version of this algorithm for Bernoulli bandits with a Beta prior,
where each arm's reward is generated according to a Bernoulli random
variable, is detailed in Algorithm \ref{ts:standard}, which considers
the marginal probability of an individual arm's reward realization
being the greatest, and immediately provides subjective smooth
fairness.

\begin{algorithm}[!h]
\caption{(\SDTS{}): Stoch.-Dom.~Thompson sampling
\begin{algorithmic}
\State For each action $a \in \{1,2,...,k\}$, set $S_a=F_a=1/2$ (parameters for priors of Beta distributions).
\For{$t =1,2,...,$}
	\State For each action, sample $\theta_a(t)$ from $\text{Beta}(S_a,F_a)$.
	\State Draw $\tilde{r}_a(t) \sim \text{Bernoulli}(\theta_a(t))$, $\forall a$.
	\State Play arm $a_t :=\text{argmax}_a \tilde{r}_{a}(t)$ (with random tie-breaking).
	\State Observe the true $r_{a_t}(t)$:
	\begin{itemize}
	\item If $r_{a_t}(t)=1$, $S_{a_t}:=S_{a_t}+1$;
	\item else $F_{a_t} :=F_{a_t}+1$.
	\end{itemize}
\EndFor
\end{algorithmic}
\label{ts:standard}
\end{algorithm}

\begin{theorem}
With (\SDTS{}), we can achieve $(2,0,0)$-subjective fairness under the total variation distance.
 \label{thm:sdts}
\end{theorem}
\begin{proof}
Define:
\begin{align*}
X_{j}(r_i) &= \begin{cases}
1 &\mbox{ if } r_i(h^t)  > \max\{r_j', \mathbf r_{-i,j}'\}\\
0 &\mbox{ if } r_j' > \max\{r_i(h^t) , \mathbf r_{-i,j}'\}\\
Bin(1, \frac{1}{2}) &\mbox{ otherwise }
\end{cases}
\end{align*}
where $r_j' \sim r_j(h^t) $ (similarly for $\mathbf r_{-i,j}'$) and $Bin$ is a binomial random variable.
First, we have for Thompson sampling:
\begin{align*}
&D(r_i(h^t)\|r_j(h^t))= \frac{1}{2} \cdot D(r_i(h^t)\|r_j(h^t))  + \frac{1}{2} \cdot D(r_i(h^t)\|r_j(h^t)) \\
 &\overset{(a)}{\ge}   \frac{1}{2} \cdot D(X_{i}(r_i(h_t)) \| X_{i}(r_j(h_t)) ) + \frac{1}{2} \cdot D( X_{j}(r_i(h_t)) \| X_{j}(r_j(h_t)))\\
 &= \frac{1}{2} \cdot D (\frac{1}{2} \| \pi_t(j) + \frac{1}{2}\cdot \pi_t(l \ne i,j)) +  \frac{1}{2} \cdot D (\pi_t(i) + \frac{1}{2}\cdot \pi_t(l \ne i,j) \| \frac{1}{2}) \\
&\text{($l$ denotes an arbitrary other agent than $i,j$.)}
\\
 &\overset{(b)}{\ge}  D(\frac{1}{2} \cdot \frac{1}{2} + \frac{1}{2} \cdot \pi_t(i) + \frac{1}{2} \cdot \frac{1}{2}\cdot \pi_t(l \ne i,j)
 \\
 &\qquad\| \frac{1}{2} \cdot \frac{1}{2} + \frac{1}{2} \cdot \pi_t(j) + \frac{1}{2} \cdot \frac{1}{2}\cdot \pi_t(l \ne i,j) )
\\
&= \frac{1}{2} |\pi_t(i) - \pi_t(j)| = \frac{1}{2} \cdot D(\pi_t(i)\|\pi_t(j))
\end{align*}

where step (a) is by monotonicty and step~(b) is by convexity of
divergence function $D$.
Therefore, $\epsilon_1$ is equal to $2$, and $\epsilon_2 = \delta=0$.
\end{proof}

To further reduce  the value of $\epsilon_1$, we can randomize between the selection of the arms in the following manner:
\begin{itemize}
\item With probability $\epsilon/2$ select an arm selected by (\SDTS{});
\item Otherwise select uniformly at random another arm.
\end{itemize}

In that case, we have:
\begin{align*}
 &D(\pi_t(i)\|\pi_t(j)) = D(\frac{\epsilon}{2}\pi_{t, ts}(i) + \frac{1-\epsilon}{2} \frac{1}{2}\|  \frac{\epsilon}{2} \pi_{t, ts}(j)  + \frac{1-\epsilon}{2} \frac{1}{2}) \\
 &\overset{\text{monotonicity}}{\le} \frac{\epsilon}{2} D(\pi_{t, ts}(i) \| \pi_{t, ts}(j) ) + \frac{1-\epsilon}{2} D(\frac{1}{2} \| \frac{1}{2}) \\
  &\le \epsilon D(r_i(h^t)\|r_j(h^t)).
\end{align*}

Also see~\citet{sason2016f} for how to to bound $D(r_i(h_t)||r_j(h_t))$ using another $f$-divergence (e.g. through Pinsker's inequality).

\medskip

While, \SDTS{} algorithm is defined in a subjective setting, we can develop a minor variant of it in the objective setting. Even though the original algorithm already uses an uninformative prior,~\footnote{The use of Beta parameters equal to 1/2, corresponds to a Jeffrey's prior for Bernoulli distributions.} to ensure that the algorithm output is more data than prior-driven, in the following section we describe an algorithm, based on \SDTS{},  which can achieve fairness with respect to the actual reward distribution of the arms.

redpajama_set_name: RedPajamaArXiv


I am excited to install android studio on my chromebook and have the Samsung XE500C13 and I am not able to execute the shell command in the terminal. My first question is will my chromebook support it? I have Linux penguin 4.19.
I am a novice to linux and android studio and looking to learn.
Second, can you point me to directions on how I can install to my chromebook.
I see directions on the need to do a mount to an SFTP drive, but now idea how to get access to a linux server.
Thanks for helping me get started.
Try this article - it's fairly recent.
How is the battery life on the Pixel 3?
How to unblock "android is blocking this category of notifications from appearing on this"


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text:
Q: Unable to update React.useState("") after joining all elements in array to single string I am trying to update a React.useState("") using setDisplay in a function that takes an array of strings and joined them into a single string before setDisplay.
Without the setDisplay, I'm able to console.log() and get the joined array.
const App = () => {
  const [display, setDisplay] = React.useState("");

  const values = [];
  const onClick = (value) => {
    values.push(value);
    console.log(values) // w/o setDisplay output: ["1","2","3","4"]; w/ setDisplay output: ["1"]; ["2"]; ["3"]; ["4"];
    setDisplay(values.join(""))
  };

  return (
    <div className="p-5">
      {display}
      <NumPads data={keypress} onClick={onClick} />
      <OperatorPads data={keypress} onClick={onClick} />
    </div>
  );
};

A: setDisplay((values) => ([...values, value].join("")]))

A:   const onClick = (value) => setDisplay((dispay) => display.concat('', value));

A: The reason the console.log seems to work when you don't call setDisplay is simply because the state of the component did not change.
When you call setDisplay, you change the state, so the entire component re-renders, which causes values to reset back to an empty array.
Solution(s)

*

*One way to solve this is already shown in a previous answer.

*Another way is to wrap your values array with useRef:
const values = useRef([]);

Then access the array using values.current

*Yet another way is to eliminate the use of values all together and change the callback to:
const onClick = (value) => {
  setDisplay(display + value);
};

redpajama_set_name: RedPajamaStackExchange


installations, and mixed media 2-D art focus on material, transformation, and perception. Arnold describes"The Crystallized Book Series addresses the materiality versus the text or content of a book. The crystals remove the text and solidify the books into aesthetic, non-functional objects. The books, frozen with crystal growth, have become artifacts or geologic specimens imbued with the history of time, use, and memory".
You can see more on her website and Instagram.


When 19-year-old Boyan Slat told the world he had developed an ocean clean up array, the news went absolutely viral. He claimed that more than 7 tons of plastic could be cleaned from the world's oceans in a few years.
This idea from the CEO of The Ocean Clean Up is certainly thought-provoking, but it isn't without its detractors.
Considering that no one knows exactly how dire the plastic problem truly is, we can trust in an underwhelming estimate that roughly 15-40 percent of all plastics we make end up in our oceans. Scientists still don't know where more than 99% of ocean plastic debris ends up either, but it is killing ocean life and choking our ecosystems in ways that are mind-numbing.
An ocean gyre is a rotating current that circulates within one of the world's oceans. Non-profits like 5gyres.org state that, "These five massive, slow rotating whirlpools accumulate plastic, and because petroleum plastics are designed to last, the plastic trash will remain in the ocean for decades or longer. In the ocean, sunlight and waves cause floating plastics to break into increasingly smaller particles, but they never completely disappear or biodegrade. Plastic particles act as sponges for waterborne contaminants such as pesticides." Furthermore, some estimate that 98 percent of all plastics in our oceans are less than 5mm in size. Slat's device does not take into account the gyre aspect of ocean plastics, and though he claims his invention can retrieve plastics from the ocean, which can then be recycled, it does not account for the tiny, sometimes microscopic bits that end up in coral reefs, and all throughout the ocean biome.
The solution should start on land, not at sea. Considering how much of our trash is plastic, it would make sense that we would curb its creation from the start. Instead of using plastic cups, bags, utensils, etc., we should be turning to much more sustainable and environmentally friendly products that biodegrade (and would not then end up in our oceans – ever). Fifty percent of all the oil we import makes plastics, but there are other, cleaner alternatives, such as hemp plastics, and other plant-based materials. If we take plastics out of our oceans without addressing them on land, we have nothing but an endless cycle of clean-up. This is because once marine plastics are collected and brought back onto land, they are very likely to end up right back in the ocean. Plastics exist in something called "deep time," geologic timescales that far outstrip the timescales of species like humans. Rather than degrade into their constituent molecules, plastics fragment into smaller and smaller pieces of plastic, and according to laboratory studies, this "degradation of cm-size plastics results in microscopic particles that remain in Earth's environment indefinitely." So, no matter how great the device to take plastics out of the ocean, until we simply reduce petroleum derived plastics altogether, we haven't really solved the problem, but just bandaged a gushing wound.
Cleanup of plastic waste is impossible. Since even the tiniest ocean creatures have been contaminated with plastic bits and pieces, we cannot truly eradicate plastic from the oceans.
Though the Clean Up Array is a wonderful idea for a wicked problem, it is likely insufficient for addressing the issue at its core. Until we get our consumerist, wasteful, petroleum habit in check, the world's oceans wont be safe – even with genius technology like Boyan Slat's.


The 29th annual Huckleberry Days Arts Festival features over 120 art and food vendors. Huckleberry Days showcases handcrafted items with many vendors returning year after year. Local musicians play throughout the weekend to entertain the large crowd of festival attendees. A highlight of the weekend is the Huckleberry bake-off, which takes place Saturday afternoon.


text:
Department of French Studies
French Studies (Theses)
L'Homme Social selon Emile Zola: une Sociologie par la Littérature.
Ledent, D.(2012) L'Homme Social selon Emile Zola une Sociologie par la Littérature.(PhD Thesis).pdf (1.240Mb)
Ledent, David
Emile Zola (1840-1902), a nineteenth-century French writer whose name is synonymous with the school of literary naturalism, assembled a substantial corpus of fictional works in which a strong sociological perspective is immediately apparent to many readers. Before he wrote each novel, Zola used to carry out extensive preparatory studies which consisted of both documentary work and fieldwork observations. By taking these studies seriously, Zola aimed to construct his novels on the basis of accurate data, which is why he is often considered as a pioneer of the ethnographic approach. Indeed, Zola defined his own work as a practical sociology since he wanted his literary creation to contribute to the development of modern science. This thesis sets out to analyse the sociology which implicitly informs the Rougon-Macquart series of novels (1871-1893). Not only did Zola develop an ethnographic approach, but he also realized that the growth of modernity could be analysed as a sociological issue and was aware of the stakes involved for society. He displayed an acute sense of the réel and was among the first to formulate certain sociological problems which anticipated the key premises of French sociological thought as later developed by Emile Durkheim (1858-1917). Thus we can demonstrate that Zola was instrumental in an intellectual evolution which starts with the French philosopher Auguste Comte and leads via Hippolyte Taine to Emile Durkheim. Zola brought to literature what Durkheim brought to sociology, namely specific methods and concepts for analysing the social world. Working from a perspective of sociological analysis through literary representation, the present study seeks to elucidate fully the implicit sociology discernible in the Rougon-Macquart novels.
Literary naturalism
Mary Immaculate College, University of Limerick

redpajama_set_name: RedPajamaCommonCrawl


Keep your eyes on the road and turn safely using our bicycle indicator kit. The control unit mounts on to your handlebar and is within easy reach while the indicator mounts on the base of the seat for clearer visibility. Audible beeps confirm your choice of direction; one beep for a right turn and two beeps for a left turn. It even has a hazard light function for road-side emergencies.


Kurt had to take a bus from the airport in Paris to the central train station in Paris and get all these kids and their luggage on board. This is their luggage stacked up between their seats on the train.
This is Chateaugiron, we stayed here with a friend from church, Martine. We were hoping to live in this town because it is just so cute! The kids first nights stay in France.
This is the Castle at Chateaugiron. Isn't it so beautiful!
It's like Disneyland, just too cute! We are walking to the bakery in town.
First order of business in France is to go the Boulangerie!
We walked around town later and visited the castle. We did not get to go in for a tour, they are renovating the castle. I was so glad to be back together with my family. I left for France 2 weeks before they did to try to find a house and a car. I was completely unsuccessful in finding a house, actually, I did find a house but I wasn't able to convince anyone to rent a house to me. Apparently, it is difficult for the French to rent as well, so I didn't feel so bad. I was able to buy a car that seats 7 for 7,000 €. Harriet came out with me and we had a great time looking at houses, shopping, and trying different chocolate everyday!
2 comments to The Clement's first day in France!
I am madly in love with this!


text:
Climate Change Has Calif. Vintners Rethinking Grapes
KQED | By Lauren Sommer
Published November 2, 2011 at 1:30 PM PDT
Grape breeder Andy Walker of the University of California, Davis inspects grapes on the campus vineyard. Walker says some Spanish or Italian grapes would do better in warmer temperatures, but growing and marketing new varieties is a big investment.
Prime California wine country areas like the Napa Valley could soon be facing rising temperatures, according to climate change studies. So some wineries are thinking of switching to grapes that are better suited to a warmer climate. But when vineyards have staked their reputations on certain wines, adapting to climate change is a tough sell.
The specific type of grape, or varietal, is how most of us think about wine. At one recent meeting of the San Francisco Wine Lovers Group, for instance, members listed pinot noirs, sauvignon blancs or cabernets among their favorite kinds of wine. All are well-known varieties grown in the California region.
"That's the big problem," says Andy Walker, a grape breeder at the University of California, Davis. "We've spent the last 100 years emphasizing varieties, and we've really marketed those names very effectively."
The university's test vineyard grows hundreds of different wine grapes from around the world. The vast majority are unknown to consumers because most wineries focus on only a handful of French grapes that prefer cool climates.
Extreme heat can be the enemy of good wine; it destroys acidity, and changes color and aromatics, Walker explains.
Resistance To Grape Breeding
According to a recent study from Stanford University, about 2 degrees of warming could reduce California's premium wine-growing land by 30 to 50 percent. That could happen as soon as 2040. Water supply is also expected to be an issue.
"I think the interesting thing for me as a breeder is to take advantage of this and say, OK, let's actually re-adapt varieties to California," Walker says.
But Walker says grape breeding is frowned upon in the industry. Most vines are grown from a branch that's taken off an existing plant.
"This essentially means that grapes have not really been having sex very much," says Sean Myles, a geneticist at the Nova Scotia Agricultural College. Myles says breeding is big business for other crops like corn, but wine grapes miss that opportunity to develop adaptability.
"That means that we're not allowing the genetic material to be shuffled anymore. That genetic material is now standing still in time," he says.
You could cross-breed today's varieties to make them more heat tolerant or drought resistant. But Walker says there's a big problem with that: Once you breed your pinot noir with something else, you can't call it pinot noir anymore.
"The last decision is the hardest," Walker says. "Can we market this variety?"
Hurdles On The Marketing Side
Walker says there are wine grapes from Italy and Spain that would do well in a warmer California.
"We could produce Barbera instead, or Negroamaro or Nero d'Avola from southern Italy, and we'd be far better ahead," he says.
Nick Dokoozlian, a vice president at E&J Gallo Winery, the largest family-owned winery in the U.S., says in most cases the company is responding to a consumer demand for a certain cultivar. The company has been testing new wine varieties and has found some promising grapes, Dokoozlian says. But the problem is that they can't necessarily sell those varieties.
"Consumers aren't aware of them. Really, the hurdles on the marketing side are much, much more significant," he explains.
Since vines can produce for up to 30 years, Dokoozlian says switching varieties is a major financial gamble.
"The wine business is an extremely capital intensive business. The financial risk of planting the wrong variety in the wrong place is pretty significant," he says.
Still, given the temperature and water supply changes projected for California, Dokoozlian sees the market shifting — eventually. Andy Walker says it's up to the industry to make it happen.
"I don't think it's the consumer that's going to make the shift. They have to be directed," Walker says.
And that means it could be some time before we see world-class Nero d'Avola from California on store shelves.
Copyright 2011 KQED
Lauren Sommer
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The house has 2 bedrooms and a bathroom. One with capacity for a double bed and the other with capacity for a bed of 90 cm and the bedrooms are bright and have fitted wardrobes.
all the necessary services, cafeterias, restaurants, supermarkets and very close to the health center.
It is a house with many possibilities, and it is a good option to have it as a habitual residence or as a holiday residence.


Dr. Lawrence M. Spindel is the top dentist in New York today. A graduate from the New York University College of Dentistry in the year 1980, holding an honors degree, Dr. Spindel has been a dedicated dentist specializing in a variety of cosmetic dentistry services. His dental office is the leading name in the entire New York City, providing some of the most proficient and state-of-the-art treatment options to people. Dr. Spindel completed his general practice residence from the Manhattan Veteran's Hospital in the year 1981. He then started his private dental practice in the city of Manhattan. Post 1982, he has been maintaining his private dental practice in different parts of New York, which includes cosmetic dentistry, comprehensive dental care and preventive dentistry.
By Dr. Lawrence M. Spindel, DDS from Lawrence Spindel Clinic.
The top dentist in New York, Dr. Spindel offers porcelain laminates or veneers to patients. It is the terrific method of improving your smile and getting back that dazzling and white appeal. It allows you to correct mild to moderate dental complications, thereby giving you a natural appearance. This is also most suitable for people having worn or missing positioned of teeth in the front line.
Teeth whitening is one cosmetic dental procedure that has been very much in demand since the time it was discovered. Because having whiter teeth is an important concern, the teeth whitening options offered by the top dentist in New York, Dr. Spindel has become a frequent procedure. Dr. Spindel provides in-office teeth whitening and at-home teeth whitening. It is a matter of choice for the patients and also the condition of the oral health. Zoom teeth whitening is popular with New York cosmetic dentists. The top dentist in New York also uses the Nupro Gold Whitening system that can produce great results.
Invisalign is one of the best cosmetic solutions that have replaced the metal braces. You can now wear these transparent and fine removable aligners called Invisalign to straighten teeth. Invisalign is the most effective solutions that the top dentist in New York offers. At Dr. Spindel's private practice, you can get excellent Invisalign solutions at great rates.


text:
Gay son and his mother speak at Tiberias pride about LGBTQ Acceptance
Avichai Abarbanel, CEO of Havruta (Israel's Religious Org for Gay Men), and his mom Hadassa Abarbanel speak about love and acceptance at this year's Tiberias pride.
"I'm here because I believe my family is real, that Avichai's family and the one he will have is real. I'm here because I'm refusing to accept anyone cursing and harming my children, or children and grandchildren of others."
The translation is below: (video here: https://bit.ly/2ZTs2jX ):
Hadassah: Good evening. My name is Hadassa Abarbanel. I live in Kibbutz Lavi together with Itzik, my husband for nearly thirty years, where we raised our eight children, including Avichai, for whom I choose to stand here today.
This is not my natural environment; the stage, the audience, the event of pride and tolerance. I am here despite the difficulties, despite the challenges and questions that sometimes arise within me.
I'm here because eleven years ago, my eldest son, Avichai, approached to me and came out to me. He told me, "I'm gay."
I'm here because like back then, when I immediately replied that "you are my son and I will always love you," also today I love you and will always love you Avichai, and I will do everything I can to help you build a happy life.
I want to tell you that when Avichai came out, a little dream I had broke. For years I imagined the family that one day my son would have, his wife and children, their religious home who would certainly be on the kibbutz next to us. I had this kind of sweet pink dream, which its realness didn't bother me, I just found it pleasant to think about it.
When Avichai came out, I had to cope, among other things, with the understanding that the dream I had isn't going to happen, that there would not be a woman, nor children. he probably will not live on the kibbutz when he grows up and maybe, and perhaps most unfortunately of all – will not keep a religious life. The possible distancing from religion – that's what hurt me the most.
Fortunately enough, pretty quickly I accepted the fact that he would not have a wife but a husband, and over the years I also met back through Avichai the dream of grandchildren from him. Also, through his activity in Havruta, IGY's religious groups and the Religious LGBTQ Community, I've realized that religion also has and probably will have a central place in his life.
I'm here because in recent months various organizations and movements have been trying to hurt us, my children, our families, and communities. I'm here because I believe my family is real, that Avichai's family and the one he will have is real. I'm here because I'm refusing to accept anyone cursing and harming my children, or children and grandchildren of others.
I am here because I believe that there is a place in Judaism and in the religious world for Avichai, his friends and his community. I'm here to cry out loud that "Derech Eretz" is prior to the Torah, and also that as Rabbi Akiva said – 'Love your neighbor as yourself' is the great principle of the Torah. I'm here because I believe in love, tolerance, acceptance, and respect for others. I'm here for Avichai, for you, but also for myself and for all of us.
Avichai: Good evening.
My name is Avichai Abarbanel, I was born and raised in Kibbutz Lavi, not far from here. As a child in Lavi, Tiberias has always been the big city where going out to was considered special and exciting, the cultural center of "outside the kibbutz".
I am really excited, after a decade of parading and taking part in pride events throughout the country, to have the honor not only to participate in the first pride event in Tiberias but the privilege of doing so with my mother next to me – in the first time she accompanies me to a pride event.
I'm also privileged and proud to come here when I am no longer the frightened and shy boy in the closet I was, but as the chairperson of Havruta a gay and religious organization, and a leader of the Religious LGBTQ Community, to know that I have the honor to take a significant part in our struggle for an egalitarian, tolerant and just society, and even more so – for our right for the Judaism and religion on whose knees we were raised.
In recent months we have witnessed several attempts to offend our children and families. The extremist margins of our society decided to launch a campaign against anyone who's not like them. "Hazon" movement, "Liba" and "Bocharim Ba`Mishpachah" are engaged in a competition of hatred, of spreading evil, of disqualifying people from their identity, of exclusion of families.
Come on, let's be real.
It's below us. We are stronger than that, more confident, and most of all, we exist.
We have no argument with them, a father and a mother are a family. One type of family. A father and a father are too, or mother and mother, or just a father, or just a mother and of course it does not stop there. Everything exists, everything is there. It can not be erased or eliminated, and in any case – you can only object to a thing when it simply exists.
But in the Religious LGBTQ Community and Havruta, that doesn't concern us. We are engaged in building families, creating life, finding the path to integrate religion and pride. We in the Religious LGBTQ Community and Havruta are creating alternatives, building hope, finding life possibilities.
IGY's youth marched with a slogan few years ago, announcing that we are here and everywhere – and this is also becoming true for us in the Religious LGBTQ Community. This year, we acted in an unprecedented number of cities and authorities and met countless communities and religious figures. We have enjoyed a wide-ranging dialogue with rabbis and reached out to quite a few batei-midrash and yeshivas.
And on a personal note, Mom, I love you and appreciate you being here.
Thank you for choosing to be here with me, to demand, for me and us, the life that comes to me, to us.
Just like that.
In a discourse of love, listening and mutual respect, we can create here the land on which we dream, according to the vision of the prophets.
Keep being as perfect as you are,
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AWB Impact Grant Supports 'Our Faces'…
Avichai Abarbanel, Havruta, Pride, Tiberias


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Snoop-Around meets fashion designers Guðrún and María and lawyer Gréta. They are the co-founders of AS WE GROW, a fashion label for children. It all began with a single sweater that has now travelled through Guðrún's family for nine years, acting as their muse for creating clothes that are eco-friendly and promote sustainability. They are currently preparing to move into their new studio, so we meet at María's home. Guðrún's youngest is with us, just two months old, and as we munch on chocolate and peanuts, we get to know the three and their brand new product.
María: It's been developing for about a year now. Gréta travelled to Peru a few years ago where she met our suppliers in Lima and learned all about the Alpaca fibre.
Gréta: Because the camels live 400 metres above sea level, they have developed wool that acts as a strong thermal insulator with fibres that are warmer and lighter than other natural fibres. It's warmer than normal wool when it's cold and lets more air through when it's hot.
Guðrún: Usually it's the designers that search for a producer, but in this case it happened the other way around with Gréta initiating this collaboration. She is definitely the driving force while we, the designers, are the dreamers.
Guðrún: María and I have always wanted to work together. We have similar ideas about how to design children's clothes, based on how we want to dress our own children. We think that clothes should be timeless and we stay away from logos and decorations that might be following a certain trend.
María: I never find anything nice enough for my twin boys, except for quite pricey things. So I started thinking that it made sense to have fewer clothes that could last up to a whole year even, despite the fact that they grow quickly.
How do your designs differ from what we are used to?
María: The cut is made specifically so that children can use the garment for a longer period of time. The armholes are bigger and the waist stretches wider. For a child that's a few months of age, the trousers will reach up close to the armpits and gradually lower towards the waist with time. The trouser legs and sleeves are long so you fold them up to begin with until they eventually fit as knee-high trousers and shorter sleeves. Today you will pay around 5-6,000 krónur for a sweater, which perhaps lasts a few months with the usual wear and tear. Here you will pay around 13,000 krónur for a piece of clothing that will last for years, and because of the quality and our timeless design you will want to give it to the next child in line too. You can therefore use it for decades, going through friends and family.
Gréta: We are the opposite of H&M, although H&M is great at times. We want to promote the endurance of the product and we think that today people's disposition towards sustainability is changing for the better.
Where and when will we be able to buy the clothes?
Gréta: We introduced our first line last February at the CHP Kids trade show in Copenhagen. We start selling in September in various places, Barnabúðin Laugavegur 27, Mýrin Kringlan shopping mall, Icelandair's Saga Boutique with Icelandair, Rammagerðin, which includes Hafnarstæti, Egilsstaðir, Keflavík Airport and, soon, Akureyri. We are also selling to Berlin, Copenhagen and we have been selling our summer line at an internet shop in New York. We plan on selling from our website as well. We are building it up nice and easy, selling 85% here in Iceland and 15% abroad.
María: Barnabúðin told us that tourists have been asking about Icelandic labels in children clothing, that it can't keep up with demand for the traditional wool sweater in baby sizes. We didn't realise that there was a demand for it; our motivation was different as we said.
Gréta: We'll also soon have 100 hand-knitted scarves from Peru that are made from leftover yarn and profits from those will go to a charity we haven't chosen yet. There are exciting times ahead.
Visit As We Grow at aswegrow.is.


Having a rich fauna and flora, Turkey is fortunate to own the grounds which have been the host for many civilizations, which contributed to the traditional knowledge on the use of herbs. Also different climatic conditions throughout the country have been a significant contribution to the varieties grown. Today, in Turkey, there exist around 500 types of herbs, which grow naturally. To benefit from these resources, one has to know the exact effects of the herbs to maximize from their benefits.


text:
[01] Greek Team Meets President
[02] Foreign Policy Discussed in Cabinet
[03] Hear Their Thoughts
[04] Baby's Day Out!
[05] Dimitris Sioufas in the USA
[06] Ano Liossia Dump Closed
[07] Athens-Corinth in an Hour
[08] Guilty of abuse in Abu Ghraib
[09] Al Qaeda's No 2 In Iraq Killed
[10] Fulfillment of Pledges Paramount
[11] Oil Prices Remain High
[12] Greek Economy Minister at the NYSE
[13] Atromitos Wins in Kalamaria
[01] Meetings with the Greek President and PM Greek Team Meets President
27 Sep 2005 09:49:00 (Last updated: 27 Sep 2005 12:58:43)
By Mary Lou Tzempelikou
The players of the Greek National Basketball team were glorified yesterday by the thousands of people who gathered at the airport to welcome them back from Belgrade. At 12:30 today, the squad arrived at the Presidential Mansion, where it met with the President of the Hellenic Republic, Karolos Papoulias, while at 13:00 they will visit Maximos Mansion, as they have been invited by the Greek Prime Minister, Kostas Karamanlis. The entire delegation will meet at Hotel Divani Caravel, where they will board on a bus for the Presidential Mansion.
Champions Arrive in Greece
27 Sep 2005 11:09:00
Todays Cabinet meeting, which commenced at 11:00am and is presided over by the Greek Prime Minister Kostas Karamanlis, will focus on the issues with Cyprus and FYROM, as well as the latest developments in the Greek-Turkish relations, in view of the commencement of accession talks between Ankara and the European Union.
[03] Reading the Thoughts of People with ALS Hear Their Thoughts
By Annita Paschalinou
Until recently, people suffering from amyotrophic lateral sclerosis (ALS) were doomed in silence and immobility. They could neither talk nor blink, but they did think. Today, these people can communicate thanks to a state-of-the-art device manufactured by electronic and medical equipment company Hitachi, in collaboration with Excel of the Mechantronix group. The device reads the "yes" and "no" answers of patients within 36 seconds and the success percentage rises to 80%. It will be released in the Japanese market by the end of 2005 and its price is estimated at 3,500 euros.
Inside Their Minds
The device is named Kokoro Gatari (meaning speaking through the mind).
The device reads the mind of people with ALS, recording their yes and no answer to certain questions.
But how can that be?
Researchers developed a type of sensor placed around the patients head, which measures the blood volume change in the cortex using near-infrared light.
When the patient wants to answer yes to a question, researchers ask him to imagine gripping his hands, for example, and then his brain function is activated and blood volume in the cortex increases. When the answer is no, researchers ask him to stay still. In this case the blood volume change is small.
Translated by Sofia Soulioti
[04] He Sent his Granddad, Mom and Sister to Hospital Baby's Day Out!
The next time you decide to leave your child alone in the car, make sure you take the keys from the ignition. Even if the child is under two. This is a very useful piece of advice, considering the adventure a family from Poland went through when they witnessed the youngest member of their family achieving the impossible. At the tender age of 18 months, the baby started the car and literally all hell broke loose. Surely, though, his mother is at fault, since she left the keys in the ignition. The car began reversing, running over the mother and her four-year-old daughter, while the grandfather was pinned against the wall of the barn. All three were rushed to hospital, while the little girl suffered the most serious injuries.
[05] Meetings with Officials and Expatriate Greeks Dimitris Sioufas in the USA
By Athina Saloustrou
Greek Development Minister Dimitris Sioufas will meet with government and financial officials, as well as members of the Greek community. The Minister is arriving in the USA today, heading the Greek delegation that will participate in the "Technology & Investment in Hellas" symposium, organised by the World Council of Hellenes Abroad (SAE) in Chicago. The Development Minister will make a first stop in the American capital, where, tomorrow, he will hold a series of meetings with officials of the Bush administration, members of Congress and businessmen. Tomorrow evening he will depart for Chicago, where he will be welcomed by SAEs President Andrew Athens and Metropolitan Bishop Iakovos, while on Thursday he will participate in the SAE Symposium. Mr Sioufas will make the keynote address, entitled "Emerging Opportunities in Greece", while many notable university professors will also speak at the event. Furthermore, he will meet with Greek scientists and businessmen, as well as representatives from American companies. On Friday, he will visit New York, where he will meet with members of the American-Hellenic Chamber of Commerce, while in the evening he will attend a reception with businessmen.
Meetings with Expatriate Greeks
On Saturday, the Development Minister will continue his contacts in New York with officials and members of the Greek community. He is scheduled to meet with Archbishop Demetrios of America, at the offices of the Greek Orthodox Archdiocese in Manhattan, while after that he will meet with the board members of the Federation of Greek Societies of New York.
Mr Sioufas is accompanied by the General Secretary of Development, Nikos Stefanou, the General Secretary of Research and Technology, Professor Giannis Tsoukalas, Professor and academic Dimitris Nanopoulos, the President of the National Council of Research and Technology, Professor Vasilis Makios, as well as officials from the Development Ministry. The Minister is scheduled to arrive back in Greece on Sunday.
[06] Employees on Strike Ano Liossia Dump Closed
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The work stoppages by employees at the Ano Liossia refuse dump are expected to cause major problems in Athens as of today. The employees, who are backed by the Panhellenic Federation of Workers Associations of the Local Government, are asking that the Psittalia sewage sludge is transferred somewhere else, as the stench is unbearable. Mayor of Athens Dora Bakoyannis launched an appeal to the citizens of the capital not to take their rubbish out for the next couple of days. Speaking on radio station NET 105.8, the president of the refuse dump workers, Giorgios Chardas, declared that the sewage sludge has created a major problem for the workers, describing their working environment as extremely adverse, while he noted that they have submitted alternative solution to the Ministry for the Environment, Physical Planning and Public Works.
Refuse Dump under Lock
[07] Suburban Railway Inaugurated Athens-Corinth in an Hour
By Jenny Charalampidou
The first official route of the suburban railway arrived in Corinth a little after 11am, with Greek Transport Minister Michalis Liapis onboard. The suburban railway is a great project, with fast state-of-the-art trains, which guarantee quick trips, stressed Mr Liapis. He also declared that passengers would be able to travel for free as of Tuesday, and for one week, so that the public can get to know this modern, comfortable, quick and safe mode of transport. The aim, as he noted, is to complete the entire network, from Chalkida to Kiato, within the next two years. Furthermore, the Transport Minister pointed out that, apart from offering improved services, the suburban railway would also contribute towards the areas economic and tourist growth. The new Athens-Corinth route is 88km long and the trains can reach speeds of up to 200 kmph. The project cost 475 million euros, while the train takes one hour to reach its final destination.
Schedules and Tickets
From Corinth, the first train departs for Athens at 8:13 and for Eleftherios Venizelos Airport at 7:08. The last train departs for Athens at 20:13 and for Eleftherios Venizelos Airport at 17:08.
From Athens, the first train departs for Corinth at 6:06am and the last at 18:06
From Eleftherios Venizelos Airport, the first train departs for Corinth at 7:05 and the last at 19:05.
Tickets for the Athens-Corinth route cost six euros, while for the Airport-Corinth route eight euros.
At the moment, the interim stops of the new railway line are Nea Peramos, Megara, Kineta and Agioi Theodoroi, while the stations Ano Liossia, Thriasio, Magoula and Zefyri will be ready by the end of 2006.
It should be noted that passengers taking the suburban railway to and from Corinth can connect to the electric railway at Nerantztiotissa Station and to the Metro at Doukissis Plakentias Station.
US soldier Lynndie England, aged 22, may be facing up to 10 years imprisonment. Photos of England, who was stationed in Iraq, were published around the world, shocking the international public opinion, as she was shown holding a naked Iraqi prisoner by a leash, and pointing to a naked inmates genitals. The Ford Hood military court in Texas found her guilty on six counts of maltreating detainees, as they rejected the defence argument that she had been led astray by her colleagues, in particular her then boyfriend, Specialist Charles Graner, who too has been convicted of prisoner abuse, and is serving 10 years in jail.
A military prosecutor argued that England had humiliated prisoners, because she enjoyed it, had a sick sense of humour and wanted to entertain the other soldiers.
England is the last reservist, out of a total of nine, to be convicted of abusing prisoners. The rest have already been convicted and are serving sentences ranging between six months and 10 years.
Englands original guilty plea was rejected at a military tribunal earlier this year. The military judge then was not convinced that she knew that what she had been doing was wrong.
[09] Killed in Joint US-Iraqi Operation Al Qaeda's No 2 In Iraq Killed
The second-in-command of al Qaeda in Iraq, Abu Azzam, was shot and killed in a joint US-Iraqi operation in Baghdad on Monday. Iraq's national security advisor Muwaffaq Rubaie told AFP, "We managed to kill the number two of Al Qaeda in Iraq," while he added that the operation was a "major coup", which resulted from a tip-off from a local Iraqi. Abu Azzam, who was believed to be Palestinian, was shot dead Monday during a raid on a house in the capital, stressed Rubaie. In the meantime, 10 people were killed and another 26 injured during a suicide attack in Baquba, 65km from Baghdad. A suicide bomber blew himself up in a crowd waiting outside a police recruitment centre. According to the police, the death toll is expected to rise.
[10] Political Declaration Draft Ready Fulfillment of Pledges Paramount
The EUs Political Declaration draft, which the British presidency will be presenting to the Turkish Government on 3 October, emphasises that Turkeys progress in the negotiations will depend in fulfilling its commitments and implementing the Customs Union Protocol. According to diplomatic sources, the draft, which is expected to be approved by the EU Committee of Permanent Representatives (COREPER) this week, together with the negotiating framework, refers to the differences between Greece and Turkey, thus satisfying all of Athens aims.
Implementation of Protocol and Commitments
Paragraph 5 of the draft political declaration, which will be presented by British Foreign Secretary Jack Straw to his Turkish counterpart Abdullah Gul notes:
"The monitoring of your commitments will become a key element for taking, in the Council context, the necessary decisions on the conduct of negotiations with you.
Particular attention will be paid to the implementation of the Protocol regarding the adaptation of the Ankara Agreement, on which the EUs position was set out in its Declaration of 20 September 2005."
Paragraph 4 of the draft satisfies Cyprus position that it must be made clear to Ankara that "The negotiations being inaugurated today (3 October) are based on article 49 of the Treaty for the European Union. Let us remind you that the negotiations are taking place in an Intergovernmental Conference with the participation of all Member States, as well as your country."
The same paragraph mentions the reforms in Turkey and Greek-Turkish relations.
The draft underlines the need for Turkey towards "full implementation of the pre-accession strategy and reforms, in particular as regards strengthening the independence and functioning of the judiciary, the overall framework for the exercise of fundamental freedoms (association, expression and religion), cultural rights, the further alignment of civil-military relations with European practice and the situation in the Southeast of the country."
Furthermore, it notes, "in conformity with Turkeys commitment to peace and stability, and to the development of good neighbourly relations, including solutions to pending bilateral issues, Turkey should actively promote regional cooperation. This should contribute to regional stability."
The draft also mentions the development of negotiations and notes that the Member-States will decide when the time comes, whether conditions allow completing the negotiations, bearing in mind the developments regarding the community acquits from the time of the start of accession negotiations.
With regard to the negotiating framework, the draft satisfies yet another demand by Nicosia, whereby it asked that Turkey does not block the Cypriot Republics accession in various international Organisations.
Paragraph 5 of the draft states, "In the period up to accession, Turkey will be required to progressively align its policies towards third countries and its positions within international organisations [including its stance in relation to the participation of all the Member-States in those organisation] with the policies and positions adopted by the Union and its Member States."
Paragraph 4 of the negotiating framework draft stresses, "The advancement of the negotiations will be guided by Turkeys progress in preparing for accession, within a framework of economic and social convergence."
Progress Measured Against Following Requirements
The Copenhagen criteria Turkeys unequivocal commitment to good neighbourly relations and its undertaking to resolve any outstanding border disputes in conformity with the principle of peaceful settlement of disputes in accordance with the United Nations Charter, including if necessary compulsory jurisdiction of the International Court of Justice. Turkeys continued support for efforts to achieve a comprehensive settlement of the Cyprus problem within the UN framework and in line with the principles on which the Union is founded, including steps to contribute to a favourable climate for a comprehensive settlement, and progress in the normalisation of bilateral relations between Turkey and all EU Member States, including the Republic of Cyprus. The fulfilment of Turkeys obligations under the Association Agreement and its Additional Protocol extending the Association Agreement to all new EU Member States, in particular those pertaining to the EU-Turkey customs union, as well as the implementation of the Accession Partnership, as regularly revised. Finally, the draft also mentioned, "The shared objective of the negotiations is accession. These negotiations are an open-ended process, the outcome of which cannot be guaranteed beforehand."
[11] Fears over its Distillation Oil Prices Remain High
The price of oil in the Asian markets went marginally over $66 per barrel, due to fears over the US refineries ability to continue its distillation, as per the analysts. The price of crude oil for November delivery contracts reached $66.17 per barrel during trading at the Singapore Stock Exchange, while it later dropped to $65.95. In New York, the price of black gold closed at $65.82 per barrel. US President George Bushs statements over the possibility of releasing supplies from the nations strategic reserves did not seem to put the fears to rest. Oppenheimer & Co oil analyst Fadel Gheit said that when President Bush speaks of strategic reserves, then we understand that not enough oil is being produced. He further noted that the problem does not lie in the production, but in the distillation of oil.
By Betty Savourdou
Sources: ANA
The CEO of the NY Stock Exchange, John Thias, warmly welcomed the Greek Economy Minister on Monday, during his visit to Wall Street. In his opening remarks, Mr Thain voiced the hope that more Greek companies will start trading on the NYSE. On his part, Minister Giorgos Alogoskoufis referred to the successful privatisation of the National Bank of Greece and OTE, two enterprises that are already trading in Wall Street. Meanwhile, he appeared confident that even more Greek companies would follow suit. A little later, Mr Alogoskoufis attended a working luncheon, hosted by NASDAQs President, Robert Greifeld.
Meetings with Institutional Investors
Initially, the Economy Minister had visited Washington, where he participated in the annual summit of the International Monetary Fund and the World Bank. On the sidelines of the summit, he met with officers from International Organisations, bankers, members of Congress and notables from the Greek-American community.
During his visit to New York, he will hold a series of meetings with institutional investors, while on Tuesday, he will talk about the outcomes of his visit to the USA, during a Press conference at the Greek Press Centre in Manhattan.
On the same day, he will give an interview to the US TV network Blumberg, while in the afternoon, he will speak at the Greek Studies Program at Yale University.
Greek Economy Minister in New York
By Vasilis Kalpinos
Atromitos achieved a significant championship victory, as far as the points are concerned, defeating Apollon Kalamarai with a score of 2-1, during the closing match for the 4th round. Following this victory, Giorgos Paraschos squad reached eight points overall and climbed to the fourth place in the rankings, while Apollon remained close to the bottom with four points. The guests kicked their first goal in the 8 with Skarmoutsos, while they doubled their score in the 61 with Doe. Apollon just managed to close the gap during injury time, after a successful penalty kick by Cleyton.
Atromitos demonstrated its power right from the start of the game, managing an early goal. In the 8, Doe entered the box, avoiding all the defenders, and took a shot. But the ball bounced off the goalposts ending up in Skarmoutso path, who sent it to the nets.
The guests then came close to scoring a second goal in the 15, but Geladaris sent the ball wide.
From that point onwards, neither of the teams managed to threaten the others nets, with Paraschos team finishing the first half in the lead.
Atromitos essentially sealed its victory in the 61. Geledaris crossed a header to Doe, who had an easy time scoring his teams second goal.
The only thing that changed until the end was the final score, as Apollon managed to close the gap during injury time, after a successful penalty kick by Cleyton, who had been previously knocked over by Michalidis.
Referee: Kakos (Corfu)
Yellow Cards: Doe, Karadimos, Chalaris, Michalidis
APOLLON KALAMARIA: Kovac, Wellington, N.A. Papadopoulos, Amponsah, Petkakis, Karamanlis, Ioannou, N.H. Papadopoulos, Cleyton, Mouzaoui (46 Christoforidis), Pappas (73 El Omari)
ATROMITOS: Michailidis, Sotirchos, Mikes, Chalaris, Katemis, Koutsis, Dimos, Karadimos (83 Pappas), Skarmoutsos (57 Dagas), Doe (77 Manikas), Geladaris.
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                        {{innerrow.nome}} {{innerrow.cognome}}

                            <input  id="gd{{row.id}}_{{column}}" name = "gd{{row.id}}_{{column}}" type="number" ng-init="get(row.id,column)=0" [ngModel]="get(row.id,j)" (ngModelChange)="set(row.id,innerrow.id,j,$event)">

    <button class="btnsubmit" type="submit">AVANTI</button>
</form>

that returns this:

as you can see that's not what i need, is there somw way to fix it up? thanks

A: Here you'll have the blueprint of your table. You can fill it with every variable you want:

        topic
        argument
        month1
        month2
        month3
        month4

            topic 1

                        argument 1 of topic 1

                        argument 2 of topic 1

                        ...

                        month 1 of topic 1

                        month 1 of topic 1

                        ...

            topic 2

                        argument 1 of topic 2

                        argument 2 of topic 2

                        ...

                        month 1 of topic 2

                        month 1 of topic 2

                        ...