text:
The Man Booker International Prize longlist is dominated by women authors from around the world
Moya Crockett
Expand your literary horizons with novels set in Oman, Chile, Japan, Poland, China and more.
It's tempting, when buying books, to stick to authors you know you love. It's like choosing what to watch at the cinema: you're probably more likely to plump for a movie helmed by one of your favourite directors than to take a chance on an obscure film you've never heard of.
But while staying in your cultural comfort zone is understandable, it's always worth branching out. Reading books by authors from countries that aren't your own, in particular, is a wonderful way of widening your horizons and giving you a new perspective on the world. Whether we realise it or not, we're all influenced by the Western literary canon, the selection of books widely deemed 'great' or 'classic' fiction by academics, educators and publishers – think Pride and Prejudice, The Great Gatsby and 1984. And the problem with the canon is that it has long prioritised books by white authors – particularly male authors – from the UK and US.
Astonishing work by writers from other countries, in contrast, tends to be sidelined. This isn't just hyperbole: while sales of translated fiction in the UK grew by 5.5% in 2018, new and classic translated works still account for less than 6% of all the novels sold in this country. The translated fiction that does get sold in the UK still tends to be overwhelmingly European in origin – meaning that we're missing out on an entire world of books by authors from countries in Asia, South America and Africa.
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If you'd like to read more books by authors from outside the UK and US, you could do worse than starting with this year's Man Booker International Prize longlist. The prestigious literary award recognises novels that have been translated into English from another language, and is open to authors from any country in the world (the Booker Prize, on the other hand, can only be won by novels originally written in English).
This year's Man Booker International Prize longlist is dominated by female authors and translators: out of the 13 nominated novels, eight have been written and translated by women. And while not all continents are represented – there is a conspicuous lack of books by African authors on the list – it still makes for a solid introduction to 21st century literature from around the world.
Below, we break down what each novel is about. Prepare to add several new titles to your reading list…
Celestial Bodies by Jokha Alharthi, translated from Arabic by Marilyn Booth
Set in the village of al-Awafi in Oman, Celestial Bodies tells the story of three sisters and their differing reasons for getting married – or not. Mayya married her husband after having her heart broken; Asma married out of a sense of duty; and Khawla refuses to marry anyone after the man she loves emigrates to Canada. While it is tightly focused on one family, the novel also provides a rich picture of how Oman is changing in the 21st century.
£8.99, Sandstone Press
Love in the New Millennium by Can Xue, translated from Chinese by Annelise Finegan Wasmoen
Experimental author Deng Xiaohua, who often writes under the pen name Can Xue, is widely considered to be one of China's most significant contemporary authors. In this dark, dystopian but very funny novel, a group of women live under constant surveillance, battling paranoia and occasionally trying to flee.
It's not what you'd call tightly plotted, but that's not the point. Instead, Xue explores different kinds of love and sex against a vivid, fantastical backdrop that's a mishmash of East and West.
£18.97 ($25), Yale University Press
The Years by Annie Ernaux, translated from French by Alison Strayer
A bona fide literary superstar in France, Annie Ernaux is still relatively little-known on these shores. Her lyrical, unconventional memoir The Years was originally published in France in 2008, but was only released in the UK in 2018 following its translation by Alison Strayer.
The book spans Ernaux's life from her birth in 1940 up until 2006, using memories, photographs, songs, diary entries, advertising and newspaper headlines (among other things). The result is a patchwork portrait of an entire generation, as well as the intimate story of one woman.
£12.99, Fitzcarraldo Editions
The Pine Islands by Marion Poschmann, translated from German by Jen Calleja
What would do you if you dreamed your partner had cheated on you? For Gilbert Silvester, the protagonist of The Pine Islands, there's only one answer to that question: you run away to Japan (obviously).
In Tokyo, Gilbert is yanked out of his mid-life crisis when he discovers the travel writings of the Japanese poet Basho, which inspire him to set out on a pilgrimage to the islands of Matsushima. Strikingly soothing and very funny, The Pine Islands is an international bestseller and won the Berlin Prize for Literature in 2017.
£12.99, Serpent's Tail (out 11 April)
Mouthful of Birds by Samanta Schweblin, translated from Spanish by Megan McDowell
This is the second time Argentine Spanish author Samanta Schweblin has been nominated for the Man Booker International Prize: her first novel Fever Dream appeared on the longlist in 2017.
Her most recent short story collection Mouthful of Birds features women and men in various states of emotional and psychological tension. New brides scream with rage; children transform; families try to resist falling apart. Melancholy, hypnotic and dreamlike.
£12.99, Oneworld
The Faculty of Dreams by Sara Stridsberg, translated from Swedish by Deborah Bragan-Turner
The writer and radical feminist Valerie Solanas was found dead in a San Francisco hotel room in April 1988, two decades after she achieved international notoriety by trying to assassinate Andy Warhol. In The Faculty of Dreams, Swedish writer Sara Stridsberg visits Solanas' childhood home in Georgia, the courtroom where she was tried and convicted, the psychiatric hospital where she was interred and the room where she died.
But this isn't a factual work: instead, Stridsberg weaves together imagined conversations and monologues to create a fictionalised impression of Solanas, a woman who seemed unknowable to so many.
£13.99, MacLehose Press (out 18 April)
Drive Your Plow Over the Bones of the Dead by Olga Tokarczuk, translated from Polish by Antonia Lloyd-Jones
Polish author, activist and intellectual Olga Tokarczuk is a prominent figure in her native Poland, where she has been criticised by nationalists and right-wing groups for her outspoken feminist views and criticisms of xenophobia and racism. Her 2007 novel Flights won the International Man Booker just last year after being translated into English – and now it's the turn of Drive Your Plow Over the Bones of the Dead.
A darkly feminist murder mystery, it follows retired schoolteacher Janina Duszejko as she tries to uncover why men are being murdered in a tiny Polish village.
The Remainder by Alia Trabucco Zerán, translated from Spanish by Sophie Hughes
The Remainder, the debut novel by 35-year-old Chilean author Alia Trabucco Zerán, received critical acclaim in South America when it was published in Spanish in 2015.
Exploring the legacy of Augusto Pinochet, the dictator who ruled Chile until 1990, it follows three children of ex-militants as they attempt to come to terms with their parents' past – and make sense of the trauma they've experienced themselves. A sensitive and moving exploration of how the effects of violence can ripple through generations.
£10, And Other Stories
Images: Courtesy of publishers
Man Booker International Prize
Moya is a freelance journalist and writer from London, and a former editor at Stylist.
Recommended by Moya Crockett
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Richard Gowan is a World Politics Review analyst and a fellow at the European Council on Foreign Relations (ECFR). He talks about rising NATO Russia tensions. His focus areas are the UN, European Security and Defence Policy, Africa, and Western Balkans. Dr. Gowan is also a nonresident fellow at the New York University's (NYU) Center on International Cooperation, where he was previously research director. He also teaches at Columbia University and writes as a contributor to E!Sharp, The Globalist, and other international affairs magazines. Foreign Policy Concepts spoke with Dr. Gowan about the growing differences between NATO and Russia and the rising tensions in the Baltic Sea.
The United States has decided to substantially increase its military presence in Central and Eastern Europe. It also plans to launch Anakonda military exercises next month in Poland with its European allies. What do you make of these moves by the U.S and its NATO allies? Some see these moves as a deliberate provocation of Russia, and some as an attempt at deterring Moscow from making aggressive moves in Central and Eastern Europe.
If the U.S. is ramping up its military presence in Europe, it is a matter of necessity not part of a conspiracy.
The Obama administration was originally keen to limit U.S. military commitments in Europe. Its overall strategic priority has been the pivot to Asia to balance China. It has also had to keep fighting wars in the Middle East, even though Obama has tried to limit American commitments there. The last thing that the U.S. needs is to spend more of its defense budget on another theater. But Russia's increasing military activism has forced the administration to rethink and invest more in European security. The idea that Washington is out to provoke Russia militarily ultimately doesn't make sense – it is reacting to Moscow's moves. There is, however, a big risk of a cycle of maneuvers by both sides that will ratchet up tensions.
Can you elaborate on the strategic rationale for the U.S defense site in Romania? Are east European states more concerned about a Russian threat than they are about an Iranian one?
The original U.S. reasoning for this defense site was to provide some defense against missiles from Iran. The anti-missile system would not be of much use against an all-out Russian attack. But for Eastern European states the mere fact that the U.S. is positioning more defense sites in the region is of some symbolic value, a reassurance against Russian maneuvers.
How prepared, in your opinion, are European members of NATO for a potential Russian military move against Eastern or Central Europe? Are European members of NATO a force to be taken seriously without the presence and involvement of the United States?
It is an open secret that the Baltic countries in particular are extremely vulnerable to any Russian attack.
The real question for many policy-makers in Eastern Europe is whether the U.S. and "old" Western members of NATO would come to their aid in a crisis with Russia, especially if there was a real danger of nuclear weapons being used. America's "return" to Europe offers some reassurance, but there is still a sense that Washington and many Western European countries would ultimately duck a fight over the Baltics – Russia could move so quickly that it would be impossible for NATO to react effectively in time.
Virtually all NATO countries spend less on defense than the U.S. argues that they should. Nonetheless, Germany has signaled that it will expand its military in the years ahead, which is a positive sign for NATO.
Do you think Russia's naval presence in the Black Sea through its naval bases in Sevastopol, Crimea enables Moscow to leverage more pressure on NATO and particularly on Turkey, a NATO member with a significant army and tense relations with Russia?
The combination of Russia bases in Crimea and its presence in Syria definitely does create strategic challenges for Turkey. The danger is less that Russia will launch an all-out attack on the Turks, who have a hefty military of their own, but Russia will stir up the Kurds in Syria and Turkey to challenge Ankara. Nonetheless we should keep in mind that NATO and Russia ultimately do have some common interests in the Middle East, above all confronting the long term challenge of Islamist terrorism.
Do you agree with the assertion that some of the actions by the Russian President Vladimir Putin are indicative of an urge to be treated on an equal basis by the West?
A lot of people have tried to look into Putin's soul. He clearly does want to reassert Russia's power on the world stage. Equally, he has used the Ukrainian crisis and Syrian war as opportunities to boost his domestic popularity – offsetting Russia's economic problems. Some analysts fear that he may yet face major domestic unrest in the near future if the economy doesn't get better, however: It is arguable that this would make him even more dangerous abroad. Overall, I see Putin as a gambler who takes the opportunities he can – such as the chance to grab Crimea – and keep other powers off balance.
In July of this year Western sanctions against Russia are expected to be extended. Do you expect to see a softening and or removal of some of those sanctions?
Unless there is real progress on consolidating peace in Ukraine, then the sanctions need to stay on. But it is crucial that the U.S. and EU indicate that they don't want to punish Russia just for the hell of it – the sanctions need to support continued diplomacy over Ukraine, Syria and other sources of tension. The longer these conflicts go on unresolved, the harder it will be to rebuild relations with Moscow.
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First, configure the environment. I have written a Getting Started Tutorial for VI Java API. You can easily follow this to set up the environment. It shouldn't take more than 5 minutes for you to get these done. The last two steps there actually shows you how to create a new Java project and download a sample from VI Java API code repository, and then run the code. You can skip those two parts if you want.
Secondly, try the code I listed as follows. I have all the comments and hints for you to add new code. You can import this file into your project either new or an existing one as you worked earlier.
Note: If you need help, you can take a look at the finished code listed in the end. You still need to change the IP address, username, and password to run the finished code though.
Have a question, leave a comment or ask in the VI Java API forum.
Copyright (c) 2008 VMware, Inc. All Rights Reserved.
Note: don't forget the s in https and the /sdk suffix.
Get the root folder object from the service instance.
Hint: type si and then ., you will see all available methods.
Hint: type rootFolder and ., and you can pick the right method.
Hint: type vm and ., pick the right ones.
Hint: type . after vminfo and vmc and pick the right methods.
For boolean type of data, the method name starts with is*().


Part of the fun of throwing a shower is enjoying a delicious cake! (At least for me - ssh, don't tell anyone!) You may be sitting there right now asking you're self what type of cake to serve. If you are we've put together a selection of different bridal shower cakes to peak your apatite. Enjoy!
Are you throwing a bridal shower in the warm months? Depending on where you live you might want to consider something fresh and this zesty like a lemon cake. Learn more about planning a summer bridal shower.
I was thinking. . . Pastels?
Do you have a certain color theme in mind for your shower? If you don't forget to color coordinate your desert. This beautiful treat to the left is almost too good to eat. But it shows just how creative you can be when it comes to throwing a shower the girls will be talking about for years to come..
It's a celebration, that means you get to treat yourself and your guests. Why not do that with a cake that you won't find at any old party. One such cake is the crowd pleasing nougat.
Looking for an easy and elegant gift for the future bride? Nougat also make a wonderful gift. It is more exotic than chocolate and it goes over great at showers. My personal favorites are Walters Assorted Honey Nougat Gift Box and La Florentine Torrone.
Going formal? If you are this is one of the easiest cakes to choose, since they have a very distinctive elegant look. You will definitely know a formal bridal shower cake when you see it. These are also one of the easiest cake types to server since these are usually purchased from a baker. If you want to bake one yourself I would suggest a book like "Bake It Like You Mean It" from Gesine Bullock-Prado. Gesine, a Vermont native, makes the impossible not only seam possible but also achievable with these easy to follow recipes.
One of my absolute favorite types of shower theme is the country bridal shower. Another excellent part of these showers is the delicious cake. I strongly recommend serving the cherry cake. They're easy to bake and most importantly they are mouthwatering delicious. These recipes will give you a head start.
Chocolate cake. . . Do I need to say more? It is easily one of the most bake cakes the world over. It's delicious, moist and most importantly a hit for even the pickiest of eaters. It also fits well if you choose to throw a chocolate themed bridal shower. Who would have guessed?
You can decorate them for any theme, they're easy to handle and you can quickly create a wide variety with little effort. So when you're planning your shower don't forget the lovable little cup cake.
Fall is a beautiful time for weddings not to mention showers. Planning a cake around this time of the year is not only fun but it affords you a lot of creative possibilities. Just think about the color options and falvors you're able to combine. Yum!
Sometimes it doesn't matter what type of cake it is as long as it is cute and fun. This cake is tempting regardless of the flavor. Besides that they are very fun to bake and decorate. Again the only thing holding you back is your imagination.
Why not go all out and serve a cake that matches the occasion. This trendy cake will not only impress your guests, but it will also feed a small army of: sisters, mothers (current and future), cousins, aunts and friends! Do you need help planning your bridal shower? We have tips and information that will make the day fun and relaxing, just the way it should be.


text:
\section{Introduction}

The threat of accidental or deliberate release of toxic (bio or
chemical) aerosols into the atmosphere, has been well documented
\cite{kendal}.
Wind, as the dominant transport mechanism in
the atmosphere,
can generate strong turbulent motion, causing the released aerosol
to disperse as a plume whose spread increases with the downwind
distance \cite{Arya1998}. For the sake of public safety, it is of
utmost importance to rapidly detect and localise a source of toxic
release so that the mitigation actions can be carried out promptly.
Ideally, the environment should be monitored continuously by a
network of spatially distributed sensors to measure the
concentration of toxic aerosols at various locations. This paper is
devoted to the problem of source localisation using the
concentration measurements collected by such a sensor network.

Two major difficulties are encountered in the described context of
source localisation. The first is the choice of the most suitable
dispersion model; the second is the lack of a precise and accurate
probabilistic description of concentration measurements. Both
difficulties are elaborated below.

A dispersion model describes, via mathematical equations, the
physical processes that govern the atmospheric dispersion of the
released material within the plume. The primary purpose of a
dispersion model is to calculate the mean concentration of emitted
material at given sensor locations. A plethora of dispersion models
are in use today \cite{holmes_morawska_06} to account for specific
weather conditions, terrain, source hight, etc. The problem of
selecting the most suitable dispersion model using statistical
signal processing techniques \cite{makalic_14} is largely neglected
by the atmospheric research community. The only somewhat relevant
reference is \cite{yee_model_sel}, which applies a single dispersion
(Lagrange stochastic) model under multiple source assumptions in
order to estimate the number of active sources of a toxic release.

Many references have been published on the topic of biochemical
source localisation, under an adopted suitable dispersion model. The
standard solutions are based on optimisation techniques, such as the
nonlinear least squares \cite{matthes_05},  that are known to fail
due to local minima or poor convergence. A version of the
least-squares is used in \cite{thomson_07} with minimisation carried
out via simulated annealing. The alternatives are Bayesian
techniques. Keats {et al.} \cite{keats_07} solved the chemical
source localisation problem for the case of a transient release in
the Bayesian framework using Markov chain Monte Carlo (MCMC) and
assuming Gaussian likelihood function of measurements. A similar
approach was adopted in \cite{humphries_12}. Bayesian framework with
Gaussian likelihood of measurements  was also applied in
\cite{ortner} to localise a biochemical source in urban
environments. A version of the MCMC was proposed in
\cite{senocak_08}, but assuming the log-normal likelihood function
of measurements.

The Bayesian approaches, such as
\cite{keats_07,humphries_12,ortner,senocak_08,yee_model_sel}, are
preferred than the optimisation techniques, because they result in
the posterior density function of the source location, thereby
providing an uncertainty measure to any point estimate derived from
it. However, the Bayesian approaches referenced above, require a
precise specification of the probabilistic model of the likelihood
function (e.g. Gaussian, log-normal, with their parameters).  Note
that errors in the measurements are not only due to sensor noise but
also due to the modeling inaccuracies, both of which in practice are
very difficult to specify precisely. A large part of the difficulty
arises from the fact that most approaches to source estimation have
involved the use of ensemble mean concentration models of
atmospheric tracer dispersion, but in reality, sensors are exposed
to widely fluctuating concentration  fields in both space and time,
that are also stochastically non-stationary.
This brings us to the second difficulty mentioned above: the lack of
precise and accurate probabilistic descriptions of concentration
measurements.

In this paper we develop a framework which overcomes both of the
aforementioned difficulties: a likelihood-free approximate Bayesian
computation (ABC) method for the localisation of a biochemical
source in the combination with the selection of the most suitable
model from a set of candidate dispersion models. This estimation
framework is implemented via the Monte Carlo method and tested using
two experimental datasets.

The paper is organised as follows. Section \ref{s:2} describes the
problem in a formal manner and also introduces a solution framework
in the form of the multiple-model ABC rejection sampler. Section
\ref{s:3} presents the adopted dispersion models for source
localisation. Section \ref{s:4} describes the proposed adaptive
iterative multi-model ABC sampler. Section \ref{s:num_ana} presents
the numerical results, obtained using the experimental datasets.
Finally, the main findings of this study are summarised in Section
\ref{s:6}.

\section{Background}
\label{s:2}

\subsection{Problem formulation}
\label{s:2.1}

Let us assume that the system (the atmospheric tracer dispersion)
obeys one of a finite number of models. A discrete random variable
$m$ denotes the model, whose domain $\mathcal{M}\subset \mathbb{N}$
has cardinality $M = |\mathcal{M}|\geq 1$. Each model
$m\in\mathcal{M}$ is parameterised by a vector $\thB_m \in\ThB_m$,
where $\ThB_m\subseteq\mathbb{R}^m$ is the corresponding
parameter space. The models, in general, are not nested
\cite{makalic_14}, although the intersection of  parameter spaces
$\ThB = \ThB_1\cap\dots\cap\ThB_M $ is non-empty; $\ThB$ represents
the space of the core parameters which includes the coordinates of
source location.

Let a concentration measurement from sensor $s=1,\dots,S$ (sensor
locations are known) be denoted $\zeta_s$. All sensor measurements
are stacked into a vector $\zb = [\zeta_1,\dots,\zeta_S]^\intercal$.

The problem is cast in the Bayesian framework. Let
$\pi_{\thBs_m}(\thB_m|m)$ denote the prior distribution over the
parameter space $\ThB_m$, with $m=1,\dots,M$. The posterior
distribution of the parameter vector $\thB_m$ follows from the Bayes
theorem:
\begin{equation}
p(\thB_m|m,\zb) =
\frac{\ell(\zb|\thB_m,m)\pi_{\thBs_m}(\thB_m|m)}{p(\zb|m)}
\label{e:post}
\end{equation}
where $\ell(\zb|\thB_m,m)$ is the likelihood function and
\begin{equation} p(\zb|m) = \int_{\ThB_m}
p(\zb|\thB_m,m)\pi_{\thBs_m}(\thB_m|m)d\thB_m
\end{equation}
is the marginal probability of measurement $\zb$ given model $m$.

Let $\pi_m(m)$ denote the prior distribution over the candidate
models $\mathcal{M}$. Then the posterior distribution over the
models is also obtained using the Bayes theorem as:
\begin{equation}
p(m|\zb) =
\frac{p(\zb|m)\pi_m(m)}{\sum_{m\in\mathcal{M}}p(\zb|m)\pi_m(m)}.
\end{equation}
Our ultimate goal is the posterior of the core parameter vector
$\thB\in\ThB$, which can be obtained via  model averaging
\cite{hoeting_99},
\begin{equation}
p(\thB|\zb) = \sum_{m\in\mathcal{M}}p(\thB_m|m,\zb)p(m|\zb),
\label{e:core1}
\end{equation}
where $p(\thB|m,\zb)$ is the posterior (\ref{e:post}) over the
subspace $\ThB\subseteq\ThB_m$.

\subsection{Approximate Bayesian computation}

ABC constitutes a class of Bayesian-type algorithms developed for
the estimation of a parameter vector in situations where the
likelihood function is intractable or unknown \cite{marin_12}. For
the case we consider, with multiple candidate models,  the
expressions for likelihoods $\ell(\zb|\thB_m,m)$, which feature in
(\ref{e:post}), are unknown.  ABC methods replace the unknown
likelihood $\ell(\zb|\thB_m,m)$ with the comparison between the
observed measurement and the measurement synthesized using the model
$m\in\mathcal{M}$. The simplest ABC algorithm is the ABC rejection
sampler \cite{pritchard_99}. In the context of parameter estimation
with multiple models, the ABC rejection sampler draws $N$ samples
from an approximation of the joint posterior
$p(\thB_m,m|\zb)=p(\thB_m|m,\zb)p(m|\zb)$. The pseudo-code of the
ABC rejection sampler for multiple models is presented in
Alg.\ref{a:1}.
\begin{algorithm}[tbhp]
 \caption{Multiple-model ABC rejection sampler} {
\begin{algorithmic}[1]
\State {\textbf{Input}}: $\zb$; $\epsilon$; $N$ \State Initialise:
$\mathcal{X}_1=\cdots=\mathcal{X}_M = \emptyset$ \Repeat \State Draw
$m^*$ from $\pi_m(m)$ \State Draw $\thB_m^*$ from
$\pi_{\thBs_{m^*}}(\thB_m)$ \State Simulate measurement $\zb^*$
using model $m^*$ and parameter  $\thB_m^*$ \State Compute distance
$d^* = D(\zb,\zb^*)$ \If {$d^* \leq \epsilon}$ \State
$\mathcal{X}_{m^*} = \mathcal{X}_{m^*} \cup \{\thB_m^*\}$ \EndIf
\Until {$\sum_{m=1}^M |\mathcal{X}_m| = N$}
 \State {\textbf{Output}}:  $\mathcal{X}_1,\cdots,\mathcal{X}_M$
\end{algorithmic}
} \label{a:1}
\end{algorithm}

Distance $d=D(\zb,\zb^*)$ between the measurement vector and the
synthesized data using model $m^*$ is compared to the tolerance
$\epsilon>0$ in line 8 of Alg.\ref{a:1}. The output of Alg.\ref{a:1}
is a posterior $p(\thB_m,m|d(\zb,\zb^*)\leq \epsilon)$ approximated
by $M$ sets of random samples:
\begin{equation}
\mathcal{X}_m = \{\thB_m^{(i)}\}_{1\leq i \leq
L_m},\hspace{.5cm}(m=1,\dots,M)  \label{e:five}
\end{equation}
such that $\sum_{m=1}^M L_m = N$. Using (\ref{e:five}) one can
approximate the joint posterior
$p(\thB_m,m|\zb)=p(\thB_m|m,\zb)p(m|\zb)$ with:
\begin{eqnarray}
p(m|\zb) & \approx & \frac{L_m}{N} \label{e:bb1}\\
p(\thB_m|m,\zb) & \approx & \frac{1}{L_m}\sum_{i=1}^{L_m}
\delta(\thB_m - \thB_m^{(i)}) \label{e:bb2}
\end{eqnarray}
where $\delta(x)$ is the Dirac delta function. The accuracy of
approximation improves with larger $N$ and smaller $\epsilon$.

\section{Dispersion models}
\label{s:3}

This section describes $M=3$  candidate dispersion models to be used
in source localisation. The first two models are based on the
Gaussian plume model \cite{kendal,Arya1998, macdonald_03} while the third
model is referred to as the stretch exponential model \cite{Huang1999, Kumar2010, skvortsov_yee_11}.

Gaussian plume models adopt a Gaussian distribution of the plume in
the vertical and horizontal directions under steady state
conditions. These models are a solution of the equation of tracer
transport with constant wind velocity (advection-diffusion
equation).
By convention, the wind velocity vector coincides with the $x$ axis,
while the spread of the plume in $y$ and $z$ directions is
determined by the respective standard deviations $\sigma_y$ and
$\sigma_z$, commonly referred to as the Pasquill-Gifford sigmas
\cite{Arya1998, macdonald_03}.

Consider a biochemical source of the release rate $Q_0$ located at
coordinates $(x_0,y_0,z_0)$.  According to the Gaussian plume model,
the mean concentration of the released material at the location of
the $s$th sensor $(x_s,y_s,z_s)$ is given by \cite{Arya1998, macdonald_03}
\begin{equation}
C_{s} = \frac{Q_0}{2\pi\sigma_{y_s}\sigma_{z_s} U}\;
e^{-\frac{(y_s-y_0)^2}{2\sigma_{y_s}^2}}\left[
e^{-\frac{(z_s-z_0)^2}{2\sigma_{z_s}^2}}+e^{-\frac{(z_s+z_0)^2}{2\sigma_{z_s}^2}}\right]\label{e:disper}
\end{equation}
if $x_s>x_0$ and zero otherwise. Notation $U$ stands for the mean
wind speed. Note that the Pasquill-Gifford sigmas, $\sigma_{y_s}$
and $\sigma_{z_s}$, in (\ref{e:disper}) are assigned the sensor
index $s$, because they are computed at coordinate $x_s$. The
simplest model for $\sigma_{y_s}$ and $\sigma_{z_s}$ is based on the
linear relationship with the downwind distance, i.e.
\begin{eqnarray}
\sigma_{y_s} & = & \sigma_0 + \frac{\sigma_v}{U}(x_s-x_0), \label{e:lin} \\
\sigma_{z_s} & = & \sigma_0 + \frac{\sigma_w}{U}(x_s-x_0).
\label{e:linz}
\end{eqnarray}
Explanation of the terms that feature in
(\ref{e:lin})-(\ref{e:linz}): $\sigma_0$ is the size of the source
(in units of length); $\sigma_v$ and $\sigma_w$ are environmental
parameters which account for the fluctuations in transverse and
vertical velocities, respectively (in units of velocity). The first
model  we adopt for source localisation (referred to as $m=1$) is
the Gaussian plume model (\ref{e:disper}) with the spreads given by
(\ref{e:lin}) and (\ref{e:linz}). The parameter vector for model
$m=1$ consist of $7$ parameters, that is:
\begin{equation}
\thB_1 = \left[\begin{matrix} x_0 & y_0 & z_0 & \sigma_0 & B &
\alpha & \beta\end{matrix}\right]^\intercal , \label{e:th1}
\end{equation}
where $B=Q_0/U$, $\alpha = \sigma_v/U$ and $\beta = \sigma_w/U$.

While the first dispersion model is very simple,  it is unable to
handle different canopy properties (average height, roughness,
porosity). Model $m=2$ is adopted to overcome this shortcoming. It
is also based on (\ref{e:disper}) and (\ref{e:linz}), but the spread
in $y$ direction, $\sigma_{y_s}$, is a nonlinear function of the
downwind distance from the source:
\begin{equation}
\sigma_{y_s} = \sigma_0 + \frac{\sigma_v}{U}\rho
\left(\frac{x_s-x_0}{\rho}\right)^\gamma.  \label{e:sig_y_nonl}
\end{equation}
The exponent $\gamma$ and the scale $\rho$ (usually referred to as
effective roughness) in (\ref{e:sig_y_nonl}) explicitly capture the
canopy characteristics. The exponent $\gamma$ may also change with
meteorological conditions; its theoretical value for turbulent
dispersion is $\gamma=1/2$. The parameter vector for model $m=2$
thus consists of $9$ parameters:
\begin{equation}
\thB_2 = \left[\begin{matrix} x_0 & y_0 & z_0 & \sigma_0 & B &
\alpha & \beta & \rho & \gamma\end{matrix}\right]^\intercal.
\label{e:th2}
\end{equation}

The Gaussian plume dispersion models have two well-known
deficiencies \cite{Arya1998}. The assumption of constant wind speed
does not even hold approximately for ground releases (i.e. $z_0=0$),
and so vertical wind speed profiles are usually employed, such as
the logarithmic wall-law from turbulent similarity theory, or the
commonly used power-law profile \cite{Csanady1973, Stockie2011}. Use
of these more physically realistic terms in the advection-diffusion
equation imply fundamentally different solutions that are not of a
Gaussian nature. More accurate analysis implies an ``effective''
plume convection velocity \cite{skvortsov_yee_11} that should be a
function of downstream distance $x_s$, but this increases
significantly the dimension of the parameter space. Also, under
different meteorological conditions the functional form given by
(\ref{e:disper}) can vary in the manner that expression for
$\sigma_{y_s}$ in (\ref{e:sig_y_nonl}) cannot fully capture.

The third dispersion model adopted for source localisation is the
stretch exponential (SE) model. This model is included because it is
capable, at least in theory, to overcome the aforementioned
limitations of the Gaussian plume model. The SE model is a solution
of the equation for tracer transport with a power-law wind velocity
and turbulent diffusivity profiles, and is therefore more general
that the Gaussian plume model. It enables an explicit, and rather
simple, parameterisation of various meteorological condition and
canopy characteristics.
The pure SE model is only strictly applicable for ground-level sources.
For $z > 0$, modified Bessel function solutions apply, which become
difficult to handle for inverse source modelling applications.

For the current paper, we take a leading asymptotic term from
these solutions, so that the mean concentration at the location of
the $s$th sensor is given by \cite{Huang1999, Kumar2010, skvortsov_yee_11}
\begin{equation}
C_{s} = \frac{B}{2\rho^2}\;\left(\frac{\rho}{x_s-x_0}\right)^{\tau}
\left[\exp\left\{-\frac{z_s^r-z_0^r}{\sigma_z^r}
\right\}+\exp\left\{-\frac{z_s^r+z_0^r}{\sigma_z^r} \right\}
\right]\; e^{-\frac{(y_s-y_0)^2}{2\sigma_{y_s}^2}} \label{e:disper1}
\end{equation}
where
\begin{eqnarray}
\sigma_{y_s} & = & \sigma_0 +  \alpha\rho
\left(\frac{x_s-x_0}{\rho}\right)^{1/2}\\
\sigma_{z_s} & = & \sigma_0 + \phi\rho
r^{2/r}\left(\frac{x_s-x_0}{\rho}\right)^{1/r}
\end{eqnarray}
with $r=1+2\mu$ and $\tau = 1/2+(1+\mu)/(1+2\mu)$ such that $0\leq
\mu\leq 1$. Parameters $B$,  $\alpha$ and $\rho$ have already been
defined. Parameter $\phi$ is similar to parameter $\beta$ in
(\ref{e:th1}) and (\ref{e:th2}). Parameter $r$ is introduced to
capture the variability of meteorological conditions. It defines the
functional form of the vertical concentration profile: for $r=1$,
this profile is exponential, while for $r=2$ it is Gaussian.
Parameter $\tau$ describes the mean concentration decay along the
plume centreline and captures the variability of meteorological
conditions (but is also affected by the type of canopy). According
to the SE model, $r$ and $\tau$ are related (both depend on $\mu$).
However, this relationship is valid only for an idealized flow with
a power-law profile over the flat underlying surface (in this case
$\mu$ is the exponent in the wind velocity profile). In order to
make the model more flexible, we adopt a relationship $\tau = \nu
+(1+\mu)/(1+2\mu)$, where $\nu$ is a free parameter whose prior
probability has the mean value equal to $1/2$. Equation
(\ref{e:disper1}) is the first term of an expansion of the exact
solution for the mean concentration at downwind distance, much
greater than the plume spread.

In summary, the SE model consists of $10$ parameters; its parameter
vector is specified as follows:
\begin{equation}
\thB_3 = \left[\begin{matrix} x_0 & y_0 & z_0 & \sigma_0 & B &
\alpha & \phi & \rho & \mu & \nu\end{matrix}\right]^\intercal.
\label{e:th3}
\end{equation}

As it was mentioned in Sec.\ref{s:2.1}, the parameter spaces of the
three models are not nested.

\section{Adaptive iterative multiple-model ABC sampler}
\label{s:4}

The ABC rejection sampler described in Alg.\ref{a:1} is very
inefficient due to its low acceptance rate. Several improvements of
the ABC rejection have been proposed in the single model case, such
as the ABC MCMC sampler \cite{marjoram_03} and a few versions of the
ABC sequential Monte Carlo (SMC) sampler \cite{sisson_07, toni_09,
delmoral_12}. Following \cite{toni_09}, we propose an iterative
Monte Carlo  multiple-model ABC sampler, whose basic steps are
described by Alg. \ref{a:2}. The key feature of this algorithm is
that it performs the ABC rejection scheme using a monotonically
decreasing sequence of tolerance levels $\epsilon_1>
\epsilon_2>\dots \epsilon_{\text{\tiny T}}\geq 0$, until the final
tolerance
 is reached. The sequence of tolerances $\epsilon_t$,
$t=1,2\dots,T$ is computed by the algorithm, from the measurement
vector $\zb$, hence the number of iterations $T$ is not know in
advance. For a given model $m$, sampling is initially carried out
from the prior $\pi_{\thBs_m}(\thB_m)$, followed by sampling from a
sequence of intermediate distributions $p(\thB_m|m,d(\zb,\zb^*)\leq
\epsilon_t)$,  $t=1,2,\dots$, which gradually approach the target
distribution $p(\thB_m|m,d(\zb,\zb^*)\leq \epsilon_{\text{\tiny
T}})$. The theoretical justification of the proposed iterative
scheme, presented in \cite{toni_09}, is based on the sequential
importance sampling (SIS) paradigm.

There are two differences between our proposed Alg. \ref{a:2} and
the algorithm reported in \cite{toni_09}, both of which reflect the
adaptive nature of the former: (i) our algorithm does not require
the sequence of tolerances
$\epsilon_1,\epsilon_2,\cdots,\epsilon_{\text{\tiny T}}$ to be
specified as an input (it works it out from the data $\zb$); (ii)
The proposal distribution for each sample and at each iteration is
computed adaptively, in a manner similar to population Monte Carlo
techniques \cite{cappe_pMC}.

\begin{algorithm}[tbhp]
 \caption{:\hspace{3mm} Adaptive iterative multiple-model ABC rejection sampler} {
\begin{algorithmic}[1]
\State {\textbf{Input}}: $\zb$; $\Delta$; $N$; \State
$[\{\mathcal{X}^0_m\}_{1\leq m\leq M},\epsilon_1]$ =
Init-Iter$(\zb,N)$ \State $t=0$; $\epsilon_0 \leftarrow \infty$
\While {$(\epsilon_{t}- \epsilon_{t+1}) > \Delta$} \State $t = t+1$
\State $[\{\mathcal{X}^{t}_m\}_{1\leq m \leq M},\epsilon_{t+1}]$ =
Repeated-Iter$(\{\mathcal{X}^{t-1}_m\}_{1\leq m \leq
M},\epsilon_t,\zb,N)$ \EndWhile
 \State {\textbf{Output}}:  $\{\mathcal{X}^{t}_m\}_{1\leq m \leq M}$
\end{algorithmic}}
\label{a:2}
\end{algorithm}

 The initial iteration of  Alg.
\ref{a:2} (line 2) creates $M$ initial sample sets
$\{\mathcal{X}^0_m\}_{1\leq m\leq M}$ from the priors $\pi_m$,
$\pi_{\thBs_m}$, $m=1,\dots,M$, and computes the first tolerance
level $\epsilon_1$. The repeated iteration, line 6, performs the
rejection sampling at a given tolerance and also computes the
tolerance level for the next iteration. The ``while loop'' (lines
4-7) is terminated when the difference between the two consecutive
tolerances is below a certain threshold $\Delta$.

Since the algorithm is based on SIS, both the initial and the
subsequent samples $\{\mathcal{X}^t_m\}_{1\leq m\leq M}$, $t=0,1,..$
are {\em weighted}. Hence (\ref{e:five}) now takes the form:
\begin{equation}
\mathcal{X}^t_m =
\left\{\left(w_m^{(i,t)},\thB_m^{(i,t)}\right)\right\}_{1\leq i \leq
L^t_m},\hspace{.5cm}(m=1,\dots,M; t=0,1,\dots)  \label{e:nn}
\end{equation}
where importance weights $w_m^{(i,t)}$ are normalised, that is
$\sum_{i=1}^{L_m^t} w_m^{(i,t)} = 1$.

The steps of the initial iteration are given by Alg.\ref{a:3}. The
tolerance $\epsilon_1$ is computed in line 10 as an order statistic
$\varphi$ of sample distances $d_1,\cdots,d_N$.

\begin{algorithm}[tbhp]
 \caption{:\hspace{3mm} $[\{\mathcal{X}^0_m\}_{1\leq m\leq M},\epsilon_1]$ =
 Init-Iter$(\zb,N)$}
  {
\begin{algorithmic}[1]
\State {\textbf{Input}}: $\zb$;  $N$ \State $\mathcal{X}^0_1 =
\cdots = \mathcal{X}^0_M = \emptyset$ \For {$i=1,\dots,N$} \State
Draw $ m \sim \pi_{m}$ \State Draw $\thB^* \sim \pi_{\thBs_{m}}$
\State Simulate measurement $\zb^*$ using model $m$ with parameter
$\thB^*$ \State Compute distance $d_i = D(\zb,\zb^*)$ \State
$\mathcal{X}^0_{m} = \mathcal{X}^0_{m} \cup \{(w=1,\thB^*)\}$
\EndFor \State $\epsilon_1 = \varphi(d_1,\dots,d_N)$ \For
{$j=1,\dots,M$} \State Normalise weights in $\mathcal{X}^0_j$
\EndFor
 \State {\textbf{Output}}:  $\mathcal{X}^0_1, \cdots,
\mathcal{X}^0_M $, $\epsilon_1$
\end{algorithmic}}
\label{a:3}
\end{algorithm}

Finally, the pseudo-code of the repeated iteration  is given in
Alg.\ref{a:4}. The while-loop between lines 4 to 17 carries out
rejection sampling. The model $m$ is drawn from the prior in line 5,
followed by the selection of the sample index $k$ in line 7. This
selection is based on the weights in $\mathcal{X}^{t-1}_m$.  A
candidate sample $\thB^*$ is drawn from the proposal distribution
$q_m(\theta_m|\thB^{(k,t-1)}_m)$ in line 8. The proposal is adopted
as a normal distribution, whose mean is $\thB^{(k,t-1)}_m$ and the
covariance matrix is related to the sample covariance of the sample
$\mathcal{X}^{t-1}_m$. The practical implementation of line 8 is as
follows: $\thB^* = \thB^{(k,t-1)}_m + \lambda h \Sigma \varepsilon$,
where $0<\lambda<1$ is a parameter of the algorithm, $h$ is the
optimal bandwidth of the multivariate Gaussian kernel
\cite[Eq.(12.2.7)]{musso_et_al_00}, $\Sigma$ is the square-root of
the empirical covariance matrix of $\mathcal{X}^{t-1}_m$ and
$\varepsilon$ is a sample from the standard normal distribution. The
computation of an unnormalised weight for an accepted sample in line
12, is carried out in the manner of \cite{toni_09}. The output
tolerance $\epsilon_{t+1}$ is again computed as an order statistic
$\varphi$ of the sample $d_1,\dots,d_N$.

\begin{algorithm}[tbhp]
 \caption{:$[\{\mathcal{X}^{t}_m\}_{1\leq m \leq M},\epsilon_{t+1}]$ =
Repeated-Iter$(\{\mathcal{X}^{t-1}_m\}_{1\leq m \leq
M},\epsilon_t,\zb,N)$} {
\begin{algorithmic}[1]
\State {\textbf{Input}}: $\{\mathcal{X}^{t-1}_m\}_{1\leq m \leq M}$,
$\epsilon_t$, $\zb$, $N$ \State $\mathcal{X}^t_1 = \cdots =
\mathcal{X}^t_M = \emptyset$
 \State
$n=0$ \While {$n<N$}  \State Draw $m\sim \pi_{m}$ \State $L_m =
|\mathcal{X}^{t-1}_m|$ \State Select index $k\in\{1,\dots,L_m\}$
with $\mathbb{P}(k=j)=w^{(j,t-1)}_m$ \State Draw $\thB^* \sim
q_m(\cdot|\thB^{(k,t-1)}_m)$ \State Simulate measurement $\zb^*$
using model $m$ with $\thB^*$ \State Compute distance $d^* =
D(\zb,\zb^*)$ \If  {$d^* \leq \epsilon_t$} \State Weight
$\tilde{w}^* = \frac{\pi_{\thBs_m}(\thB^*)}{\sum_{i=1}^{L_m}
w_m^{i,t-1}\,q_{m}(\thB^*|\thB^{(i,t-1)}_m)}$ \State Accept:
$\mathcal{X}_m = \mathcal{X}_m \cup \{(\tilde{w}^*,\thB^*)\}$ \State
$n=n+1$ \State $d_n = d^*$ \EndIf \EndWhile \State $\epsilon_{t+1} =
\varphi(d_1,\cdots,d_N)$ \For {$m=1,\dots,M$} \State Normalise
weights in $\mathcal{X}^{t}_m$ \EndFor
 \State {\textbf{Output}}:  $\{\mathcal{X}^{t}_m\}_{1\leq m \leq M}$, $\epsilon_{t+1}$
\end{algorithmic}}
\label{a:4}
\end{algorithm}

Recall that out goal is to localise the source. Denote by vector
$\lb = [x_0\;\;y_0]^\intercal \in\mathbb{L}$ the coordinates of the
source. The space of source coordinates is clearly a subspace of the
core parameter space. From the output of Alg.\ref{a:2}, expressed by
(\ref{e:nn}) at the last iteration $t=T$, we can extract  $M$ sets
of random samples over the subspace $\mathbb{L}$:
\begin{equation}
\mathcal{L}^t_m =
\left\{\left(w_m^{(i,t)},\lb_m^{(i,t)}\right)\right\}_{1\leq i \leq
L^t_m},\hspace{.5cm}(m=1,\dots,M; t=T).  \label{e:nnl}
\end{equation}
According to (\ref{e:core1}), the posterior density of source
location is then approximated by $\mathcal{L}^t_m$ as follows:
\begin{equation}
p(\lb|\zb) \approx \sum_{m=1}^M \frac{L_m}{N} \sum_{i=1}^{L_m}
w_m^{(i,t)}\,\delta(\lb - \lb_m^{(i,t)})  \label{e:post_l}
\end{equation}

\section{Numerical analysis}
\label{s:num_ana}

\subsection{Experimental datasets}

Algorithm evaluation was carried out using two experimental datasets
collected by COANDA Research \& Development Corporation. The
experiments were carried out  using their large recirculating water
channel, specially designed for dispersion modelling. The water
channel is 10 m long, 1.5 m wide and 0.9 m deep. The floor of the
water channel was covered with a metal mesh of height 4 mm to give
surface roughness.

The source was releasing fluorescein dye, at a constant rate,  from
a narrow vertical tube, placed $z_0=4$ mm above the bottom of the
channel at coordinates $x_0=-373.5$ mm, $y_0=0$ mm. Concentration
data were collected at several downstream positions using 1D laser
induced fluorescence linescan system, at the rate of 300 lines per
second, for a total sampling time of 1000 seconds~\cite{yee06}.

Two experimental datasets are used for algorithm evaluation (both
are available as supplementary material of this submission). Dataset
1 was collected in the absence of any obstacles (mimicking an open
terrain scenario). Dataset 2 was collected in the presence of 10 mm
high obstacles, placed on a regular grid, thus mimicking an urban
scenario. Both datasets were  extracted from the full recordings
averaged over 100 seconds, and consist of $S=48$ sensor measurements
(four rows of 12 sensors) at downstream positions. The top-down view
of the experimental setup for both cases is shown in
Fig.\ref{f:setup1}. The source location at  coordinates $(-373.5,0)$
is marked by a red asterisk. The position of sensors is indicated by
blue circles whose radius is proportional (on the log-scale) to the
corresponding concentration measurement. The height of all sensors
in both setups was $z_s=9.3$ mm, for $s=1,\dots,48$.

\begin{figure}[htbp]
\centerline{\includegraphics[height=5.2cm]{setup1.eps}
\includegraphics[height=5.2cm]{setup2.eps}}
\centerline{(a)\hspace{6.5cm}(b)}
 \caption{Top-down view of the experimental setup: (a) dataset 1,  (b) dataset 2.  Location of the source
 is marked by a red asterisk at
 $(-373.5,0)$. Sensor locations indicated by blue
 circles, whose radius is proportional (on the log-scale) to the corresponding concentration measurement. The rectangles in (b)
 indicate the contours of the obstacles}
 \label{f:setup1}
\end{figure}

\subsection{Priors and parameters}

We adopted the prior over $M=3$ models to be uniform, that is
$\pi_m(1)=\pi_m(2) = \pi_m(3) = 1/3$.

The priors for various parameters included in vectors $\thB_1$,
$\thB_2$ and $\thB_3$, specified by (\ref{e:th1}), (\ref{e:th2}) and
(\ref{e:th3}), respectively, were adopted as follows (all units of
length are {\em millimeters}):
\begin{equation*}
\begin{aligned}
 \pi(x_0)  & = \mathcal{U}(-1000, 0), \\
 \pi(z_0)  & =  \mathcal{G}(1.333, 3), \\
\pi(B) & =  \mathcal{G}(2, 2.5), \\
\pi(\beta)  & =  \mathcal{G}(1.667,0.15),\\
\pi(\phi)  & =  \mathcal{G}(1.667,0.15),\\
\pi(\mu)  & =  \mathcal{B}(1.5, 3),
\end{aligned}\hspace{.5cm}
\begin{aligned}\pi(y_0)  & =   \mathcal{U}(-500, 500),\\
 \pi(\sigma_0) & =  \mathcal{G}(15.5,0.03),\\
 \pi(\alpha)  & =  \mathcal{G}(3, 0.5), \\
\pi(\rho) & = \mathcal{G}(6,1), \\
\pi(\gamma)  & =  \mathcal{B}(3,3),\\
 \pi(\nu) & = \mathcal{B}(6,6).
\end{aligned}
\end{equation*}
Here  $\mathcal{U}(a, b)$ is the uniform distribution, with limits
$a$ and $b$,  $\mathcal{G}(k, \eta)$ is the Gamma distribution with
shape $k$ and scale $\eta$, and $\mathcal{B}(p,q)$ is the Beta
distribution with parameters $p$ and $q$.

The proposed  ABC sampler described by Alg.\ref{a:2}, was executed
using $N=1000$ samples. The distance $D$ between the actual
measurement $\zb = [\zeta_1,\dots,\zeta_S]^\intercal$ and the
synthesised ``measurement'', using model $m$, denoted $\zb^* =
[C_1(\thB_m),\dots,C_S(\thB_m)]^\intercal$, was adopted as:
\begin{equation}
d(\zb,\zb^*)=\sum_{s=1}^S (\zeta_s - C_s(\thB_m))^2.
\end{equation}
Statistics $\varphi$, which computes the tolerance level for the
next iteration, was adopted to be the $128$th smallest value of
samples $d_1,\dots,d_{N=1000}$. The parameter $\lambda$, used in the
proposal $q_m$, was set to $\lambda=0.4$. The termination threshold
was adopted as $\Delta = 2\cdot 10^{-10}$.

\subsection{Results}

The results for dataset 1 are presented first.  The tolerance levels
computed by the proposed ABC sampler are shown in Fig.\ref{f:2}.(a).
It took $t=10$ iterations to reach the final tolerance level of
$\epsilon_{\text{\tiny T}} = 4.04\cdot 10^{-9}$.  The average
acceptance rate, over all iterations, was $2.8\%$. The model
probabilities, $p(m|\zb)$ are shown at each iteration in
Fig.\ref{f:2}.(b). After the initial iteration  ($t=0$), the
probabilities of all three  models are approximately $1/3$.
Subsequently, while the tolerance levels were high, the three
probabilities fluctuate until the iteration $t=7$. After that, the
probability of model $m=1$ drops to zero, while the probability of
models $m=2$ grows to about 0.85. The probability of model $m=3$
drops, but never goes to zero.

\begin{figure}[htbp]
\centerline{\includegraphics[height=5.cm]{tol1.eps}
\includegraphics[height=5.cm]{mod_prob1.eps}} \centerline{(a)\hspace{6cm}(b)}
 \caption{Tolerance levels and model probabilities over iterations (dataset 1): (a) $\epsilon_t$; (b)
 $p(m|\zb)$. }
 \label{f:2}
\end{figure}

Fig.\ref{f:3} shows the scatter plot of random samples
$\mathcal{L}^t_m$ which approximate the posterior density
$p(\lb|\zb)$ of (\ref{e:post_l}),  at iterations (a) $t=1$, (b)
$t=4$, (c) $t=7$ and (d) $t=10$. The true source location is marked
by a red asterisk. Localisation of the source clearly improves with
iterations, as the tolerance levels get smaller.

\begin{figure}[htb]
\centerline{\includegraphics[height=8cm]{scatter_plot_1.eps}}
 \caption{Scatter plots of random samples $\mathcal{L}^t_m$ in the $(x,y)$ plane (dataset 1). The samples approximate the posterior density
$p(\lb|\zb)$. Scatter plots shown after iteration: (a) $t=1$, (b)
$t=4$, (c) $t=7$, (d) $t=10$. The true source location is marked
with a red asterisk. }
 \label{f:3}
\end{figure}

Finally, Figs.\ref{f:4}.(a) and (b) display the final posterior
distribution after the iteration $t=10$, marginalised to $x$, and
$y$ axes, respectively. The density estimates from the random sample
are obtained using the kernel density estimation (KDE)
\cite{silverman_86}. The true source coordinates are marked by the
solid vertical lines.

\begin{figure}[htbp]
\centerline{\includegraphics[height=4.5cm]{poster_x1.eps}
\includegraphics[height=4.5cm]{poster_y1.eps}} \centerline{(a)\hspace{6cm}(b)}
 \caption{Estimated posterior density of the source location (dataset 1): (a) $x$ axis, (b) $y$ axis.
 The true values are indicated by the vertical solid lines. }
 \label{f:4}
\end{figure}

The first observation is that the estimated marginal posteriors are
remarkably accurate (considering that the prior was a uniform
density with the span of 1000 mm) and include the true source
location. The second observation is that the estimation is
significantly better for the coordinate $y_0$, than for $x_0$, which
is in accordance with the theoretical analysis carried out using the
Carmer-Rao bound \cite{ssp14_crb}.

The dataset 2 was collected in a more challenging setup with
obstacles. The results for dataset 2 are shown in Figs. \ref{f:5},
\ref{f:6} and \ref{f:7}. Fig.\ref{f:5}.(a) shows the computed
tolerance levels over all iterations: note that it took $t=12$
iterations to reach the final tolerance level of
$\epsilon_{\text{\tiny T}} = 2.75\cdot 10^{-9}$.  The average
acceptance rate, for all iterations, was $2.9\%$. The model
probabilities, $p(m|\zb)$ are plotted in Fig.\ref{f:5}.(b). We can
observe that after iterations 7,8 and 9, $m=1$ appears to be the
preferred model. However, at iteration 10,  its probability drops to
zero. As in the case of dataset 1, the probabilities of models $m=2$
and $m=3$ remain non-zero at the lowest tolerance levels. It has
already been noted that stretched exponential solutions may not be
the most appropriate model for these datasets, even though the model
has some physical motivation \cite{yee06}. The analysis presented
here reinforces these findings, and highlights the fact that more
complex, higher-dimensional models are not necessarily better in
describing inherently stochastic phenomena.

Fig.\ref{f:6} displays the scatter plot of random samples
$\mathcal{L}^t_m$  at iterations: (a) $t=3$, (b) $t=6$, (c) $t=9$
and (d) $t=12$. The true source location is marked by a red
asterisk. Once again, localisation of the source  improves with
iterations. However, a close inspection of the final localisation
posterior $p(\lb|\zb)$, shown in Fig.\ref{f:6}.(d), reveals that
this posterior is bi-modal. This can be seen from Figs.\ref{f:7}
which displays the final posterior distribution marginalised to $x$,
and $y$ axes, respectively. The two modes,  clearly seen in
Fig.\ref{f:7}.(a), correspond to models $m=2$ and $m=3$, after
iteration $t=12$. They both appear to be biased: the stronger mode
 is due to model $m=3$ (the probability of this model is higher at $t=12$, see see Fig.\ref{f:5}.(b));
 it peaks  approximately at $\hat{x}_0\approx -350$.

\begin{figure}[htbp]
\centerline{\includegraphics[height=5.cm]{tol2.eps}
\includegraphics[height=5.cm]{mod_prob2.eps}} \centerline{(a)\hspace{6cm}(b)}
 \caption{Tolerance levels and model probabilities over iterations (dataset 2): (a) $\epsilon_t$; (b)
 $p(m|\zb)$. }
 \label{f:5}
\end{figure}

\begin{figure}[htb]
\centerline{\includegraphics[height=8cm]{scatter_plot_2.eps}}
 \caption{Scatter plots of random samples $\mathcal{L}^t_m$ in the $(x,y)$ plane (dataset 2).
 The samples approximate the posterior density
$p(\lb|\zb)$. Scatter plots shown after iteration: (a) $t=3$, (b)
$t=6$, (c) $t=9$, (d) $t=12$. The true source location is marked
with a red asterisk. }
 \label{f:6}
\end{figure}

\begin{figure}[htbp]
\centerline{\includegraphics[height=4.5cm]{poster_x2.eps}
\includegraphics[height=4.5cm]{poster_y2.eps}} \centerline{(a)\hspace{6cm}(b)}
 \caption{Estimated posterior density of source location (dataset 2): (a) $x$ axis, (b) $y$ axis.
 The true values are indicated by the vertical solid lines. }
 \label{f:7}
\end{figure}

We can make the following observations with respect to the results
obtained using dataset 2. First, none of the models seem to be
correct since the estimation of the $x_0$ coordinate appears to be
biased. Second, the use of multiple-models was beneficial, because
the support of the posterior contains the true source location
(albeit in the tail of one of the modes).

Finally, a remark on the proposed iterative multiple-model ABC
sampler: this algorithm falls into the category of {\em anytime
algorithms} \cite{Zaimag96}, because it returns a valid approximate
solution (i.e. approximate posterior) even if it is interrupted
before the termination criterion is reached.

\section{Summary}
\label{s:6}

The paper presents a robust method for localisation of a biochemical
source. In the absence of an accurate model of the measurement
likelihood, a likelihood-free approximate Bayesian computation (ABC)
approach  is adopted. Furthermore, since there is no universal
dispersion model applicable for all situations (terrain,
meteorological conditions), a multiple-model approach is proposed,
whereby all candidate models are active in parallel and assigned the
probability of being correct. The proposed method computes
adaptively the tolerance levels which are required for ABC sampling.
The method has been tested using two experimental datasets (one in
the open terrain, the other with obstacles) using three dispersion
models (two Gaussian plume models and the stretch exponential
model). Source localisation was very accurate using the open terrain
dataset. The dataset with obstacles presented a significant
challenge, resulting in a bi-modal posterior distribution of source
location. Future work will investigate other dispersion models and
the performance of source localisation using binary sensors.

\bibliographystyle{elsarticle-num}

\small

redpajama_set_name: RedPajamaArXiv


text:
Jacqueline Doughty
Curatorial Manager, Ian Potter Museum of Art
Email jacqueline.doughty@unimelb.edu.au
Building: Ian Potter Museum of Art
Jacqueline Doughty is Curatorial Manager at The Ian Potter Museum of Art, University of Melbourne. Her work in the contemporary art sector encompasses curatorial projects, research, writing, arts administration, organisational management and strategic planning in the United States, United Kingdom and Australia. Most recently the Deputy Director and Senior Curator of Gertrude Contemporary, where she worked for over 15 years, Doughty managed over 150 exhibitions during this time and delivered bilateral cultural exchange programs in Malaysia, Singapore, China and Indonesia, including 35 residencies and eight international exhibitions. She also introduced Gertrude's Visiting Curators program, which led to exhibition opportunities for local artists in New York, London, Tokyo, and throughout Europe and developed a three-year pilot Education program that encouraged the engagement of schools and community groups in Gertrude Contemporary and its programs. In addition to her curatorial projects at Gertrude, she has undertaken freelance projects for the Perth Institute of Contemporary Art and the Melbourne Art Foundation and in 2015 she undertook a residency with Performa in New York.
See all staff profiles

redpajama_set_name: RedPajamaCommonCrawl


5 Signs You're Working for a Great Manager – Helping you get salespeople to want to deliver outstanding results for you and your organization.-Author of the Best Selling Book , 'Cracking The Code To Success' & 'Be The Manager People Won't Leave'.
Most concepts, ideas, and approaches in our organizations have been around for many decades. Our world has changed so much in the past few years yet our organizations are still stuck in a time warp. I like to say that we live in 2017 but work in 1975. Truly great managers understand that sometimes starting fires is more valuable and important than trying to put them out. These are the managers who not only ask, "Why are things done this way?" but they also embrace experimentation and challenging the status quo to come up with something better. Whether it be getting rid of the annual review, implementing a new workplace practice, introducing a new technology, or redesigning a workplace, truly great managers believe in going against the grain!
Do you have something you would add to this list?


In this ritual, the Rephaim, which included a council of the Didanu, were invoked to accompany the recently deceased king of Ugarit, Niqmaddu III, to the underworld. They were also there to bless the new king, Ammurapi III, who—although he probably didn't know it—was the last king of Ugarit. His kingdom was about to be overrun by the so-called Sea Peoples sometime around 1200 B.C.
Now, buckle up, because we're going to work through some challenging stuff in the rest of this chapter. Most of what follows comes from a paper published in 1999 by scholar Amar Annus of the University of Tartu, Estonia,2 whose research into the Mesopotamian origins for some of the weirder themes in the Old Testament, like the Watchers and their sin, is truly groundbreaking.
Who were these meropes anthropoi? Homer named a few of them in The Iliad: Theseus, who killed the Minotaur on Crete; Aegeus, the mythical founder of Athens; Polyphemus, the cannibalistic giant son of Poseidon, one of the Cyclopes; Caneus, a nigh invulnerable warrior, transformed from a woman into a man by Poseidon; Dryas, leader of a tribe that fought a long war with the Centaurs; and so on.
One of the nephews of Nimrod was named Dedan, maybe not coincidentally. Could his name have been in honor of the Titans, the old gods who descended at Mount Hermon in the dim, distant past? Given the later links between the Amorites and the Rephaim, and the pre-flood knowledge brought back into the world by the Amorite kingdom of Babylon, this speculation isn't exactly coming out of thin air.
Does the phrase "the iniquity of the Amorites" begin to make more sense?


Spika Camo Wrap is ideal for hunting, bird watching and getting up close to nature without being spotted. It is great for guns, scopes, binoculars, torches, knives, you name it!
Ideal for guns, binoculars, knives, torches etc.


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It's no secret that winter is one of my favourite seasons. No, not because I love the cold, quite the opposite, actually. It's because there's nothing I love more than sitting inside my home on a winter's day and watching Netflix. And if you've been paying attention to the weather lately, there's been lots of opportunity to avoid the cold and warm up with great TV instead!
If you're already a Shaw BlueSky TV customer, you've no doubt noticed how much easier it is to binge your favourite Netflix show now that the two systems are integrated. You can now simply say "Watch Stranger Things" and within a matter of seconds, you're enjoying Wynona Ryder's delightfully quirky role in the show about another dimension and the kids who are fighting against the government and monsters.
If you're not a BlueSkyTV customer, I've been using it for almost a year now, and honestly, one of my favourite things about it is that it makes watching TV even more fun than it already is. Talking to the BlueSky TV device makes finding your favourite shows easier than ever too, so as the snow starts falling and the temperature continues to drop, you'll be worry-free as you settle in for a winter TV hibernation. And if you switch right now to BlueSky TV, Shaw will give you six months of free Netflix, so here are some shows that are definitely worth staying in for.
It's the show everyone is talking about, and with good reason. There's quite simply nothing like it on TV. Inspired by the story-telling of The Twilight Zone, each episode is like a mini-movie about the pros and cons of welcoming more and more technology into our lives. My tip? Don't read what the episodes are about. Half the fun is figuring out the twist.
Speaking of Black Mirror, Alex Lawther stars in one of the show's most jarring episodes and now he stars in The End of The F****** World, which based on the trailer sorta looks like Dexter: The Teen Years, which I'm sorta okay with. One of my favourite things about Netflix is how often it introduces us to great British shows and this one is definitely no exception!
Probably the most unique Canadian comedy to hit the airwaves in a while (although Kim's Convenience and Baroness von Sketch Show are pretty great too). The third season of Schitt's Creek, I think, is its best. The cast brings its A+ comedy game and the laughs come from some strange and unexpected places, exactly what Schitt's Creek excels at.
This Netflix original series isn't based on the sun setting at 5pm during the Canadian winter, instead it's about the disappearance of two kids in a small German town. I haven't seen this show yet, but based on the spooky trailer, I'll probably start as soon as I'm done this blog post.
If you're looking for something lighter, the new season of Frankie and Grace premiers in just a few weeks, so now is the perfect time to start this late-in-life comedy that happens to be my mom's favourite show. Seriously, she's not much of a TV person and she texts me every week asking when this show is coming back. Well mom, it's almost time!
In this world that just seems to be getting crazier and crazier, a voice that has been sorely missed has definitely been David Letterman's. But we're in luck, Netflix is bringing him back for a new kind of talk show that looks to focus more on great interviews instead of the game shows that most late-night talk shows have turned into. The list of his first guests are as impressive as they come, including former President Barack Obama, who'll give Dave his first interview since leaving the White House. Talk about must-see TV!
Sometimes there's nothing better than a really good scary movie and young Canadian actor Jacob Tremblay looks to be starring in a creeping one about a young boy whose dreams come true…for better or worse! I think we've all been a fan of Tremblay ever since Room and I'm always impressed with all the movies he seems to be starring in these days. Definitely doing Canada proud!
I have been waiting for this show to make its way to Netflix for a long time. Before now, it's been sorta tricky to track this show down, but now all nine seasons of this Jerry Seinfeld-led show are available to stream. The show combines Jerry's three favourite things: coffee, cars and comedians. The result is a hilariously honest show about some of the funniest people in the world. I have a feeling you'll pour through all nine seasons before you know it!
I've never been one to obsess too much over food, mostly because I love all food. You could serve me a $5 burger or a $300 steak and I'll think they are both as delicious as the other. But this food-inspired show starring the creator of Everybody Loves Raymond, Phil Rosenthal, looks like my kind of show. A funny series about amazing food, but mostly about a fish-out-of-water learning about the culture behind some of his favourite dishes. I'm going to devour this one.
In 2018, Netflix will be producing more original movies than ever before and The Polka King is just one of many that we should come to expect over the next little while. The film stars Jack Black, who after watching Jumanji 2 is back in my good graces, as well as Jenny Slate, who literally can do no wrong! This is a true story of the greatest scandal in polka history. What could be funnier?


in transaction SOLUTION_MANAGER 'Configuration' tab pushbuttons are not active and those are grayed out. For example 'Insert Lines' pushbutton is not active and eg. a new 'IMG Object' can't be added.
Other tabs like Transactions and Development are working normally and data can be updated there.
How 'Configuration' tab can be activated, so data could be entered there?


text:
The Big, Huge Story on How Our Country Got So Fat
I'm going to apologize ahead of time for this big-as* long post, but people have questions – especially those people with big as*es, who are doing everything "right," according to following the US dietary guidelines and are frustrated as heck.
This isn't a short answer and the history has to be explained in order to understand why and how we have all been so misled, so hence the need for this big as* long post.
I hope to educate those who want to know why our country (and those countries who have followed our lead) have exploded in their obesity rates since the 1980s. Although obesity rates have risen, so have solutions to combatting unwanted fat, such as lipo laser machines (http://www.usalipolasers.com) which many are using with the goal of improving their health.
The controversy really heated up in 1972 when John Yudkin, a British professor of nutrition and the most prominent nutritionist in the UK, was the first to publicly sound the alarm on sugar back in 1972, and a book called "Pure, White, and deadly." The book sold well, but Yudkin was quickly attacked by mainstream nutritionists and the food industry, who teamed up to destroy his reputation. Because of their power and influence, they succeeded – starting even before that, which I will get to here.
This mainstream influence is responsible for making fat the villain over the last several decades. Ever since the low-fat diet has been espoused as the healthy way to eat, our country has gotten fatter and sicker.
In 1980, the mainstream convinced our government to issue its first dietary guidelines. Doctors mindlessly started promoting it and food companies began to develop their processed foods around it. Now the influence has spread worldwide, and every country who adopts these guidelines gets sicker and fatter.
The main recommendation was to cut back on saturated fats and cholesterol. Before this, the recommendations were to eat more of certain things ? not less of something. Regardless, the populace bought it hook, line and sinker and so did I. And it didn't work for them or for me. Just like everyone else, I replaced meat with pasta, bread and other starches. I chose margarine over butter. Low-fat milk replaced whole milk on my grocery list and fruit juice was added to that list.
If you look at the graph of obesity rates above, you will see that in the early 1980s, the line takes off like an airplane, accelerated with the popularity explosion of the new invention of High Fructose Corn Syrup (HFCS). Nixon was facing re-election, but the cost of food was soaring. Nixon needed a quick fix and appointed Earl Butz to find a solution. Butz got farmers and their lobby on Nixon's side by promising to help them scale up their production – especially with corn. Cattle started eating more of it and got fatter, which meant more money for those farmers too. Fried food in corn oil became more popular – and cheaper to buy. Corn became a main ingredient in many other processed foods too.
By the mid 70's there was a surplus of corn because Butz push and government surpluses had pushed it too far. Butz flew to Japan to follow a lead he discovered might be able to use up more corn. Scientists there had developed a process to mass produce high fructose corn syrup (HFCS). Also at this time, sugar prices were increasing, so a cheaper alternative like HFCS could solve that problem too. Butz brought it to the USA and it was soon pumped into numerous processed foods and beverages. Plus, it extended the shelf like of processed foods from days to years.
By the mid 80's, health experts were noticing that people were getting fatter and no one could figure it out. Even those who eat low fat and exercise are still gaining weight. Less than 10% of Americans were obese before World War II. 15% were obese in 1980, and that rate shot up after that, so now more then one third of the US is overweight or obese.
Something else was going on that was just as strange. The more HFCS we ate, the hungrier we became. Anthony Sclafani, a researcher at NY University, noticed that when his lab rats ate rat food, they put on weight normally. But when they ate processed food with HFCS, they ballooned up in a matter of days and their appetite was insatiable. The processed food industry knows this, but they don't mind because it means more profit for them.
In the UK, things lagged behind by about five years because that's how long it took for our mistakes and misinformation to spread to that country. Only 6% of the people in the UK were obese in 1980. By the year 2000, that figure more than tripled. Today, more than two thirds of Britains are overweight, with many of them being obese. Diabetes has also risen parallel to the obesity rate lines on that chart and the same is true for the US and all other countries poisoned by our low-fat recommendations.
Instead of admitting they were wrong, the mainstream has chosen to blame the individual instead, claiming they are simply eating more than they used to and exercising less. In other words, the reason for the pandemic explosion in obesity, diabetes and heart disease is due to the fact that everyone has suddenly become lazy gluttons since 1980. This, in spite of the fact that we are exercising and dieting more than ever.
Now let's take a look at how it all began. On September 23, 1955, US Presisdent Dwight Eisenhower had a heart attack. The next day, Dr. White, his chief physician, gave a press conference, which he turned into a lesson on how to avoid heart disease. He recommended everyone stop smoking and cut down on fat and cholesterol. Most Americans bought it, but several smart people questioned it, wondering how Dr. White was making recommendations based on personal assumptions gathered from one person, without any research or science to back it up.
Dr. White scrambled to bolster his credibility for his statements and scrounged up some "research" from a nutritionist at the University of Minnesota named Ancel Keys, who seemed to agree with Dr. White's claims. This was a boon for Keys and effectively put him in the spotlight. All this publicity created a public scare about heart disease and Keys (already in the spotlight and loving it) eagerly stepped up to the plate, providing his "diet ? heart hypothesis." Keys claimed that saturated fats were doomed to collect on the walls of arteries, causing them to harden and narrow, until the blood stops flowing, resulting in a heart attack.
Keys was intelligent, charismatic and aggressively attacked anyone who disagreed with his point of view. Even Eisenhower's death from heart disease in 1969 didn't phase Keys. He had the public limelight, the momentum and was on a roll that he was enjoying immensely. Fame, fortune and influence followed him and he took to it like a duck to water.
At this time, John Yudkin was still the UK?s leading nutritionist. When Yudkin looked at Keys recommendations and the data on heart disease, he saw no correlation with fat, but he did see an extremely strong correlation with sugar. He conducted numerous laboratory experiments on the subject and found without a doubt that sugar was indeed the culprit – not fat. Yudkin also made the common sense observation that humans have been consuming fat since the beginning of our existence. In fact, every human still consumes fat at the beginning of their existence, through their mother's milk, which incidentally, is high in saturated fat and cholesterol.
Carbohydrates on the other hand, have only been around for about 5000 years (in significant quantities among various populations), thanks to the the invention of mass agriculture. Sugar, a pure carbohydrate, with all fiber and nutrients stripped out, has only been a part of modern diets for about 300 years. Yudkin supposed that the new kid on the block ? sugar ? was more likely to be the cause of heart disease than saturated fat, which has been around as long as humans have.
Keys, the most prominent US nutritionist, was quite aware of Yudkin?s opposition because it threatened his growing empire and notoriety. If Yudkin published a paper, Keys would discredit it and aggressively attack Yudkin, calling his reports ?a mountain of nonsense," and accusing him of being in bed with the meat and dairy industries. Yudkin never participated in the mud slinging though, because he was a kind, gentle man, who had no desire for political combat.
Keys quickly won over the British Sugar Bureau, who of course was already motivated to align with Keys, since their profits would increase with Keys on their side. With this combined influence, Yudkin began to slide in his credibility. Within just 10 years, Yudkin?s reputation had been destroyed. He found himself uninvited from international conferences and research journals refused his papers. Eventually, he became a laughingstock and largely forgotten.
Throughout the 60s, Keys formed many more powerful alliances among the most influential organizations in healthcare and the food industry. From 1958 to 1964, Keys and his team of researchers gathered data from seven select countries to back up his hypothesis. What people didn't know then (because Keys and his minions kept it top-secret), was that his ?Seven Countries Study" had more holes in it than grandma's kitchen colander. It was the epitome of "junk science." Keys even went so far as to pick the seven countries that did support his hypothesis, while omitting all of the countries that showed opposite results to his hypothesis. Keys also based his hypothesis on correlation causing causation, which in the scientific community, is the first indicator of junk science.
Smart people complained about this correlation/causation, so Keys and his minions attempted to create laboratory trials to support his hypothesis, but every single one failed. Recently, one of these studies was discovered and hit the mainstream. It was found in the basement of one of his deceased colleagues. The conclusions were exactly opposite of keys hypothesis, so the study was censored and relegated to a file cabinet. http://well.blogs.nytimes.com/2016/04/13/a-decades-old-study-rediscovered-challenges-advice-on-saturated-fat/?_r=0
Years later, the Seven Countries study?s lead Italian researcher, Alessandro Menotti, went back to the data and found that the food which correlated most closely with deaths from heart disease was not saturated fat after all, but sugar. By then it was too late though. Key's bogus study had become the gospel truth and a congressional committee chaired by Sen. George McGovern had already created the national advice to eat low-fat and especially avoid saturated fat and cholesterol. Yudkin was called before a committee and asked point blank by McGovern if he was really suggesting that a high fat intake was not a problem and that cholesterol presented no danger. Yudkin of course replied yes to both and McGovern answered with, ?That is exactly the opposite of what my doctor told me." So based on the "knowledge" of his family doctor, McGovern disregarded Yudkin's claims, despite the fact that he was the most prominent nutritional scientist and researcher in the UK at the time, although on the slide, thanks to Keys – who was close buddies with McGovern. Many think that McGovern called him to the public eye specifically to finish him off and complete the process of destroying his credibility.
Now the truth is slowly coming out – that Yudkin was right and Keys was "dead" wrong. Sugar is indeed the villain and fat is actually the hero. Sugar is a refined carbohydrate completely devoid of all nutrients. In fact, sugar and all other carbohydrates aren't even an essential nutrient to humans. Without it, we can live just fine. Fat on the other hand, has numerous beneficial nutrients and is essential. Our liver can't make enough on its own to keep us alive, so without eating it, we die.
Keys is dead now, so I guess the quote from physicist Max Planck is true: "A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because it's opponents eventually die, and a new generation grows up that is familiar with it."
Keys theories seemed to made intuitive sense to many because after all, if fat and cholesterol killed Eisenhower, shouldn't we stay away from them? The fundamental error here was the confusion between what a person puts in their mouth and with what it becomes after it's swallowed. The human body transforms food into nutrients it can use now, or store for later. Cholesterol, for instance, is present in every single one of our cells and it's created by the liver. It's a known fact that the more cholesterol you eat, the less your liver has to produce. This is why eating high amounts of cholesterol doesn't significantly raise blood cholesterol levels.
This is one of the reasons why modern health authorities have started backing away from this colossal nutrition guidance blunder. They are doing it slowly though, because if they all came out at once, admitting they were wrong, they would basically have to say, "Sorry everyone, for making you fat, sick and killing millions of you.? Think of the public stampede from an admission like that, let alone the credibility rebellion, rendering the government's recommendations on anything, ever again, completely worthless.
Still today, the majority of US doctors continue to believe that dietary cholesterol raises blood cholesterol. Most doctors don't do any research of their own. They just repeat what they've been taught in medical school and what the mainstream is feeding them. Progressive doctors on the other hand, who have open minds and spend time researching things for themselves, (like Dr. Mercola for instance), are actually able to help people get healthier and leaner instead set of sicker and fatter.
Hundreds of studies over the last 50 years have shown sugar to be the villain, not fat. The evidence is simply overwhelming. Gary Taubes book, ?Good Calories, Bad Calories? is one of the best references to these studies. Taubes?s second book, ?Why We Get Fat? is much easier to read and understand than his first, more clinical book. Still, even today, most mainstream nutritionists (who don't do their research) refuse to accept the truth. It's quite hard for someone to admit they are completely wrong and that they should have been counseling their patients and clients in the opposite direction all along. After all, at first glance, it does make "intuitive" sense that fat would make you fat. Plus, fat has more than twice the calories of sugar per gram, that can seem to be confirmation of the misinformation.
The whole calorie in/calorie out theory is also fatally flawed and completely wrong. Obesity is really more of a hormonal disorder, triggered by the kinds of calories we eat instead of the total calorie count itself. Sugar sends those hormones completely out of whack, out of ratio, and out of range. Fat on the other hand, brings them back into the healthy range.
Sugar also destroys the good to bad ratio of gut bacteria, which in very recent studies has been shown to be arguably the largest cause of obesity because of their effect on metabolic hormones and organ function. Another great book that addresses this topic is "The Calorie Myth" by Johnathan Bailor. My favorite book though, is "Cracking Your Calorie Code," written by another voracious researcher, yours truly.
A recent best seller on the topic, "The Big Fat Surprise" by Nina Teicholz, has created enough of a stir that a Congressional review panel has been formed to revisit the topic of official dietary guideline recommendations. In September 2015, she wrote an article for the British Medical Journal that flared tempers among scientists who were unwilling to backtrack. They actually requested that BMJ retract and censor the article. Journal retraction is only reserved for cases involving fraudulent data though, so when the scientists who sent the letter of retraction were questioned on the supposed errors, none of them were able to list even one. They were quick to point out however, that Teicholz lacks credibility because she is not a scientist. Neither is Gary Taubes, but maybe that?s why Gary and Nina are so right and the close-minded ?scientists? and so very, very, wrong.
Some other great books on the subject are from actual doctors, like Dr Hyman who wrote "Eat Fat Get Thin," Dr. Lustig, who wrote "Fat Chance," and Dr. Ludwig, who wrote "Always Hungry?" These doctors are saying the same thing as the researchers and journalists listed above.
What's the bottom line? Eat LOTS of fat first, GOBS of green vegetables second, moderate protein third, low to no carbohydrates (in the form of starchy foods) and ZERO sugar. Easier said than done? Yep, but I've got some short cuts for you and you will find them in my personal blog at pjfit.com.
← Step Seven
New Xercise Record This Year? →


She's doing the best she can, though. Honest.
And as if that wasn't headache enough for one Earth-girl, she also finds herself falling hopelessly in love with the five hottest, most powerful rulers in the galaxy, all of whom want to be her first—and then some.
Imagine stepping out of your front door and into an entirely alien world. Literally. That's what happens to Evainne Wagner on what was otherwise set to be a perfectly normal day. All she planned to do was go for a run, but instead, she finds herself in the middle of a firefight in an intergalactic maximum-security prison. Salvation arrives in the nick of time in the form of a mysterious leather-clad warrior, skidding in and whisking her away. Trusting her heart, she follows, hoping to find answers as well as a way home.
After the collapse of everything Earth once was, only a few are left. Ava and Lily Smith are two such survivors, and they make their living—if it can be called that—by scavenging the ravaged surface. As the eldest of the two, Ava does what she has to for survival.
Especially after she's kidnapped by space pirates.
If you caught my review of book one in this series, you know how much I adored it. You would also know that installment ended on a gnarly cliffhanger. The events of that book are going to be spoiled in this review, so if you haven't read Fortune's Pawn, stop now and check it out before you go any further.


Discreditably is a 13 letter long Word starting with D and ending with Y. Below are Total 1717 words made out of this word.
Note There are 4 vowel letters and 9 consonant letters in the word discreditably. D is 4th, I is 9th, S is 19th, C is 3rd, R is 18th, E is 5th, T is 20th, A is 1st, B is 2nd, L is 12th, Y is 25th, Letter of Alphabet series.


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Each 5ml of liquid contains Brompheniramine Maleate 2mg, Phenylephrine Hydrochloride 5mg and Dextromethorphan Hydrobromide 10mg. Also contains sodium benzoate, saccharin sodium, alcohol, sorbitol (32.76g/60mL). Sorbitol may have a laxative effect or cause diarrhoea.
Medication may cause drowsiness. If affected do not drive a vehicle or operate machinery and avoid alcohol. If you suffer from high blood pressure, asthma, heart or thyroid disease, diabetes, glaucoma, difficulty in urination due to enlargement of the prostate gland, hypersensitivity to any of the ingredients or are taking medication for high blood pressure, or are on antidepressants, sedatives or tranquillisers seek medical advice before taking this medication. Also consult your doctor if you have chronic cough as occurs with smoking, breathing problems or chronic lung disease such as asthma, chronic bronchitis - emphysema or have a cough with excessive phlegm. If symptoms persist for more than 48 hours or are accompanied by a high fever, a doctor or pharmacist should be consulted.


Nearby: Just over Tel Aviv's promenade (the Tayelet), which features good restaurants and all sorts of places to relax on. The Tayelet also leads to historical Jaffa in the south and to the old Port of Tel Aviv in the north. Located near the American embassy and the vibrant city center, Bugrashov and Ben Yehuda streets. This area is known for its vibrant night life, delicious eateries, 24/7 supermarkets, cool coffee shops, young and chic clothing for moderate prices and the most beautiful and attractive beaches of Tel Aviv: Banana Beach, Bugrashov Beach, Frishman Beach and the renowned Gordon Beach. It is also a walking distance from the charming quarter of Neve Tsedek with its village-like atmosphere and Tel Aviv's Old Railway Station (HaTachana complex), full of cafes, restaurants and fashion boutiques. Few minutes from the Yemenite Quarter (Kerm Hateimanim) where you will find tens of restaurants of traditional Yemenite dishes.


text:
<?php

namespace Store\Bundle\BackendBundle\FileHandle;

use Symfony\Component\DependencyInjection\ContainerInterface;
use Symfony\Component\Filesystem\Filesystem;

class SaveFileHandler
{
    protected $container;
    public function __construct( ContainerInterface $container)
    {
        $this->container = $container;
    }

    public function save( $fileObject , $path)
    {

        $dir = $this->container->get('kernel')->getRootDir() . '/../web/' . $path;

        $sub_path = md5( $fileObject->getClientOriginalName() . microtime() );

        $dir .= '/' . $sub_path . '/';
        $fs = new Filesystem();

        if( !$fs->exists( $dir ) )
        {
            try {
                $fs->mkdir( $dir );
            } catch (IOExceptionInterface $e) {
                echo "An error occurred while creating your directory at ".$e->getPath();
            }
        }

        $file = str_replace( 'image/' , mt_rand(1,99) .'.' , $fileObject->getMimeType() );

        $fileObject->move( $dir , $file );

        return '/'.$path.'/' . $sub_path . '/' . $file;
    }

    public function remove( $file)
    {

        if( !$file )
            return ;

        $fs = new Filesystem();
        $full_path =  $this->container->get('kernel')->getRootDir() . '/../' . $file ;

        $full_path = preg_replace('/\/\d+\.\w+$/' , '' , $full_path);

        if( $fs->exists( $full_path) )
        {

            try {
                $fs->remove( $full_path );
            } catch (IOExceptionInterface $e) {
                echo "An error occurred while creating your directory at ".$e->getPath();
            }
        }

    }
}

redpajama_set_name: RedPajamaGithub


Perhaps, you are wondering if you and your boyfriend will get back together, especially if he is ignoring you. Is there any way to tell? Well, there are really no hard and fast rules. But it will be useful to look at your current stage of break up. 1)	You have just broken up with your boyfriend If your boyfriend has just broken up with you and is ignoring you currently, it is best not to contact him at the moment.
Calling him more often is not likely to make him pick up the phone anyway since he has already decided to ignore you. However, do not despair. He may be ignoring you at the moment, for various reasons, but that does not mean he will ignore you forever. At this point of time, it is still too early to tell whether both of you will get back together. Wait at least 30 days before calling him again.
2)	You have already broken up with your boyfriend for quite a while Now, you are at the next stage of the break up. I am assuming that you have already spent the time healing your emotion and thinking about what you really want in the relationship. So, it is time to contact your boyfriend. Your chances of getting your boyfriend back can be affected by the things you do.
There are many common mistakes that women made when they try to get their boyfriend back. You will want to avoid making them if possible as making these mistakes can make it harder for you to get your boyfriend back. Here is one of the many mistakes that you will want to avoid. When you are calling your boyfriend, if he is still ignoring you, it is quite normal that you will feel a bit of panic. Therefore, you will automatically want to call him a few more times hoping that he will pick up the phone.
No! Don't do that. Why? Well, even though he didn't pick up the phone, he will know who is calling him. If he realizes that you are calling him so many times when he didn't pick up the phone, he will know that you are acting out of desperation. You do not want to be perceived as being desperate. This makes it harder for you to get your boyfriend back. What you should do is to just call him once.
If he didn't answer, wait at least a few days before calling him again.
Will me and my boyfriend get back together? Watch a video that shows you exactly what you must NEVER do, what you should do to get your ex back and why at Will Me And My Boyfriend Get Back Together Video You will also learn how to reverse the situation if you have already done those things that should NEVER be done.