A young singer who posted a crying selfie on Instagram has been praised by parents for showing "real life" on social media.
Bessie Turner, 25, said that the image most project on social media is edited and fake.
She said that parents had got in touch to say she was a positive influence on their children.
Ms Turner told the BBC: "Lots of people sent me messages saying they were thinking of me and asking if I was OK," she said.
"I had a message from a dad who said it was good for his daughter to see, which I was really moved by.
"I exert this happy go lucky, smiley happy person - which I definitely am - but it's important to show the other side of it as well."
She said that the pressure to perform on social media means it is even harder to be a teenager than it was a decade ago.
"It's this involuntary pressure we put on ourselves," she said.
"People feel like they're missing out if they're not on social media, people feel that they're not good enough or as good as other people if they are on social media.
"It's hard just keeping up with the pace of life. You feel like you should be this good version of yourself."
The singer posted her selfie on World Mental Health Day, and wrote: "'Why do you have a selfie of you crying?' -Because it's just as relevant as all the others of me smiling and singing and feeling like a hot mama that rules the world.
"I took this photograph a few weeks back -the day I started taking antidepressants again- as a marker for me to look back on with pride or sadness or whatever comes to mind.
"Like so many others I've had my battles and some days they just wipe me right out. I feel crazed and unwell and like I need my brain to be drained and it's so 100% totally okay to have those feelings."
Many comments posted praise her for posting an insight into a bad day, in an edited feed showing her smiling and singing.
One Instagram user wrote: "Such encouraging words to so many people suffering with mental health issues, social media can be so plastic and false at times."
Another said: "Bless you Bessie- your words are so true and will be an inspiration and comfort to all who struggle ."


text:
Viral App FaceApp Now Owns Access To More Than 150 Million People's Faces And Names
July 19, 2019 Fraser Valley Website DesignBy admin
Everyone's seen them: friends posting pictures of themselves now, and years in the future.
Viral app FaceApp has been giving people the power to change their facial expressions, looks, and now age for several years. But at the same time, people have been giving FaceApp the power to use their pictures — and names — for any purpose it wishes, for as long as it desires.
Composite image of a diverse group of people
And we thought we learned a lesson from Cambridge Analytica.
More than 100 million people have downloaded the app from Google Play. And FaceApp is now the top-ranked app on the iOS App Store in 121 countries, according to App Annie.
(NEW: Apptopia now earning $90,000/day)
While according to FaceApp's terms of service people still own their own "user content" (read: face), the company owns a never-ending and irrevocable royalty-free license to do anything they want with it … in front of whoever they wish:
You grant FaceApp a perpetual, irrevocable, nonexclusive, royalty-free, worldwide, fully-paid, transferable sub-licensable license to use, reproduce, modify, adapt, publish, translate, create derivative works from, distribute, publicly perform and display your User Content and any name, username or likeness provided in connection with your User Content in all media formats and channels now known or later developed, without compensation to you. When you post or otherwise share User Content on or through our Services, you understand that your User Content and any associated information (such as your [username], location or profile photo) will be visible to the public.
FaceApp terms of use
That may not be dangerous and your likeness may stay on Amazon servers in America, as Forbes has determined, but they still own a license to do whatever they want with it. That doesn't mean the app's Russian parent company, Wireless Labs, will offer your face to the FSB, but it does have consequences, as PhoneArena's Peter Kostadinov says:
You might end up on a billboard somewhere in Moscow, but your face will most likely end up training some AI facial-recognition algorithm.
Peter Kostadinov
Whether that matters to you or not is your decision.
But what we have learned in the past few years about viral Facebook apps is that the data they collect is not always used for the purposes that we might assume. And, that the data collected is not always stored securely, safely, privately.
Once something is uploaded to the cloud, you've lost control whether or not you've given away legal license to your content. That's one reason why privacy-sensitive Apple is doing most of its AI work on-device.
And it's a good reason to be wary when any app wants access and a license to your digital content and/or identity.
As former Rackspace manager Rob La Gesse mentioned today:
To make FaceApp actually work, you have to give it permissions to access your photos – ALL of them. But it also gains access to Siri and Search …. Oh, and it has access to refreshing in the background – so even when you are not using it, it is using you.
Rob La Gesse
The app doesn't have to be doing anything nefarious today to make you cautious about giving it that much access to your most personal computing device.

redpajama_set_name: RedPajamaCommonCrawl


Macy's does a significant amount of business during the holiday season so they try to stay open or extend hours on some of the busiest shopping days of the year. Below you will find the holiday schedule for Macy's. Please note that local stores reserve the right to change hours as they deem necessary.
Macy's will have regular business hours on New Year's Eve and New Year's Day.


Gookooko'oog - Why can't we be France?
Hutton the saw-whet owl really looks unreal - lyosha said that a lot of times people look at/past him and think he's a stuffed bird - I understand why!
After spending some more time at Jungle Cat World, we headed to the other side of Toronto to Mountsberg to visit the raptor centre there - and the awesome Amy ( lyosha) who had invited us to meet the avian residents she manages.
If I had to pick one favorite bird at the centre it would be Tedd - this disabled barred owl (I am quite partial to barred owls...). Her story, like many of the birds is quite sad - due to major head trauma she has brain damage and a blind/destroyed eye. It is what makes her so placid and 'pettable' - raptors as a group are (in general) not birds to form the sort of bond with a keeper as say a parrot would - they are not 'cuddly' birds and internalize stress massively. Teddy tolerates people because of brain damage - not because she loves/is bonded to them. I know with most of my LJ here I'm preaching to the choir but I didn't want to make this look like a glamourous 'pet owl' Harry Potter experience. These are birds damaged due to human impact (car accidents, hunting, imprinting, etc.) and being given a permanent place to live out their lives and become educational ambassadors at the park.
Great horned owls are so gorgeous!
One of the resident bald eagles.
Fish beak! Pagwa, a juvenile female bald eagle.
Amy was awesome at getting the barred owl to hoot back at her - she make a really convincing mimic of the call. That's something I need to practice at, I need to learn more bird sounds.
Look at that face!! I have so much love for barred owls.
mbala managed to capture the translucency of the feathers on this barn owl. Their white feathers are so ghostly when they're in the right lighting.
Behind the scenes - Bernice is handling Otis and I've got Hutton.
I found Mountsberg Raptor Centre to be a wonderfully run place - I was very impressed. Major thanks to Amy for the memorable, thoroughly enjoyable trip!
After that, Bernice kindly hosted us at her place and showed us around her home (she has one of the most amazing collections of art and figures I've ever seen!) and we were sad to have to say goodbye so soon. I wish Toronto was closer and am definitely looking forward to going back to visit again (and hopefully spend more time!).
Oh my god that little saw-whet owl is adorable. They are the tiniest little predators, hee. Also, the shot of that barn owl on the fence is pretty neat!
Great pics! Do you mind if I snag a few for reference?
All the thanks should go to mbala - he took these (most of the time photos I post here are courtesy of him!).
I didn't even realize that was the same weekend until someone mentioned it right before you - I'm so out of touch with the world! I will drop you a line if we go one other crazy ventures in the future, anything taking us east takes us pretty much right through your neck of the woods and the weekend trips always end up such a blast!
I'm kind of bummed out I couldn't make it out there...I'll have to pay a visit sometime.
We are hoping to go back sometime during a summer so you'll have to join us then (or go out anytime and say hi to Amy since its relatively close for you :) - we miss Toronto (well, more you guys!) so much already!
I have seen socially bonded raptors in the Real World, but they are exceedingly rare. (That isn't counting vultures. I've had a vulture beg me for headscratches before!).
I have to wonder if the Barred Owl enjoyed the pets, even if half her instincts were turned off by the brain damage.
And yes, Saw-Whet Owls are unbearably cute for raptors. I've seen them in the field -- almost no bird can stand still and blend in quite like a Saw-Whet!
I've known a few bonded raptors as well, but they are always the exception to the rule. A friend of mine flew a redtail (wild caught amazingly!) that was absolute butter in her hands, just loved to be touched. It actually made it difficult for her to fly him because he'd much rather be getting attention than off working for a meal!
It was hard to tell if Teddy enjoyed my petting at all - she didn't react like a bird who was enjoying it (but she wasn't showing any signs of being bothered by it) - she didn't react much at all. Lyosha said it very well by saying that the raptors can learn to tolerate people - but tolerating, in general, is what it is.
I'm really happy there are places like Mountsberg, though, doing such great work by these birds and helping to make up a little for the impact that humanity/society has on them as a whole.
I'll have to tell you my Rough-Legged Hawk story sometime.
And yes, Mountsberg and raptor rehab centers in general are doing the work of the angels. They not only are helping birds which otherwise would have been simply smashed under humanity's steamroller, they're also teaching people about them -- I think they're a large part of why raptors went from being pests at best to being as widely admired as they seem to be now.
So cool- and the animated gif of Amy and Pagwa is just hilarious.
that gif!! really enjoying all these photos- you go on such fun trips!
The first time I saw a saw-whet owl in a zoo, I about died of the cute. It looks like it should live on marshmallows and cute-on-a-stick.
Otis thinks, "I'mma gonna devour all of you! My victims!"
sad story about that poor owl, they're gorgeous!


text:
Wino owocowe – napój winiarski wytwarzany na bazie owoców innych niż winogrona. W krajach, w których klimat nie jest dostatecznie sprzyjający do uprawy winorośli, na przykład w północnych i wschodnich rejonach Polski i dalej na północ oraz wschód, do wyrobu wina stosowane są owoce takie jak jabłka (zobacz wino jabłkowe), śliwki, agrest, maliny, jeżyny, wiśnie, truskawki, porzeczki (zobacz wino porzeczkowe), owoce róży dzikiej (zobacz wino z dzikiej róży), dziki bez i tym podobne. Wina owocowe zaliczane są do grupy przetworów owocowych.

Wino owocowe ma w Polsce tradycję sięgającą XIII wieku. W XX wieku było towarem eksportowanym do Stanów Zjednoczonych, Japonii i Wielkiej Brytanii. W latach 60. działał Państwowy Rejestr Win Markowych czuwający nad odpowiednią jakością krajowych win i miodów pitnych. Utraciło swoją renomę w latach 70., kiedy to nakazowo stosowano wysoką alkoholizację doprowadzającą do obniżenia jakości. Na rynku oprócz "tanich win" można znaleźć coraz więcej produktów wysokiej jakości. Wysokogatunkowe wina owocowe oceniane są w Ogólnopolskim Konkursie Win, Miodów Pitnych i Napojów Winiarskich organizowanym przez Krajową Radę Winiarstwa i Miodosytnictwa.

Technologia wyrobu win owocowych zasadniczo nie różni się od technologii wyrobu wina tradycyjnego, to znaczy wyciśnięty moszcz owocowy zazwyczaj jest poddawany dodatkowym zabiegom przed przekazaniem go do fermentacji alkoholowej, przeprowadzanej najczęściej przy użyciu szlachetnych drożdży winiarskich. Dodatek drożdży jest ważniejszy w przypadku win owocowych niż ma to miejsce w przypadku win gronowych. W przypadku obydwu rodzajów win fermentacja może być przeprowadzona bez dodatku drożdży, gdyż do wyciśniętego moszczu przedostają się drożdże z powierzchni owoców. Jednak ze względu na to, że na powierzchni owoców innych niż winogrona znajduje się ich zdecydowanie mniej w stosunku do szkodliwych bakterii, brak dodatku drożdży może negatywnie wpłynąć na proces fermentacji.

Wyciśnięty moszcz z winogron ma odpowiednią zawartość cukrów i kwasów by można było z niego wytworzyć wino. W przypadku pozostałych owoców, zazwyczaj konieczne staje się regulowanie zawartości cukrów przez dosłodzenie i/lub kwasów poprzez dodatek wody, moszczu z mało kwaśnych owoców, lub w przypadku moszczów mało kwaśnych jak na przykład jabłek stosuje się dodatek kwasu cytrynowego, lub analogicznie moszczu z owoców kwaśnych jak na przykład porzeczek. Kolejnym dodatkiem do win owocowych, zwłaszcza mocnych produkowanych z silnie rozcieńczonych moszczów, jest tak zwana pożywka dla drożdży, która zawiera sole azotowo-fosforowe.

Po przeprowadzeniu fermentacji w butli (gąsiorze), beczce lub kadzi wino klaruje się, poddaje leżakowaniu i butelkowaniu. Czasem, by zapobiec zmętnieniu lub ponownej fermentacji, wino poddaje się procesowi utrwalania.

Znane metody utrwalania to:
 siarkowanie – dodaje się disiarczynu potasu lub inny związek, który w kontakcie z kwasami zawartymi w winie uwalnia SO2. Dwutlenek siarki ma działanie bakteriobójcze. Jest bardzo często stosowany w winiarstwie, zarówno jako konserwant, jak i do odkażania naczyń
 zwiększenie zawartości alkoholu – 16-procentowe stężenie działa konserwująco
 pasteryzacja

Niekiedy w procesie wyrobu win owocowych do moszczu dodaje się – zamiast cukru – miodu co zbliża smak napoju do smaku miodów pitnych, trunku zastępującego w Europie północno-wschodniej wina gronowe. Powstały tak napój bywa nazywany winem miodowym. Stosuje się też czasem dodatki w postaci owoców jarzębiny, głogu i innych, dodających aromatu.

Nazwa "wino owocowe"
W lipcu 2007 Komisja Europejska rozstrzygała możliwość zastrzeżenia nazwy "wino" jedynie dla trunków produkowanych z winogron. Zwolennikiem takiego rozwiązania były kraje południowej i zachodniej Europy. Jednak interwencja państw-producentów "win owocowych" (Polski, Niemiec, Szwecji i innych państw Europy środkowo-wschodniej i północnej) spowodowała, że ostatecznie nie zabroniono używania terminu "wino" na etykietach produktów z różnych owoców. Etykietę z napisem "wino owocowe" nakleja się niekiedy na wina alkoholizowane albo na tanie mieszanki soków owocowych ze spirytusem (roztworem etanolu); najczęściej wyroby te z winem (prócz procentowej zawartości alkoholu) nie mają nic wspólnego. Wyroby takie popularnie nazywane są "jabolami".

Zobacz też

 wino domowe
 wino kwiatowe

Przypisy

redpajama_set_name: RedPajamaWikipedia


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Last week we met the El Niño-Southern Oscillation, or ENSO. I like to explain things as I learn about them. So, often I look back and find my explanations naive. But this time it took less than a week!
• J. D. Neelin, D. S. Battisti, A. C. Hirst et al., ENSO theory, J. Geophys. Res. 103 (1998), 14261-14290.
Nonetheless I found it fascinating — so, I'll pick off one small idea and explain it now.
As I'm sure you've heard, climate science involves some extremely complicated models: some of the most complex known to science. But it also involves models of lesser complexity, like the "box model" explained by Nathan Urban in "week304". And it also involves some extremely simple models that are designed to isolate some interesting phenomena and display them in their Platonic ideal form, stripped of all distractions.
Because of their simplicity, these models are great for mathematicians to think about: we can even prove theorems about them! And simplicity goes along with generality, so the simplest models of all tend to be applicable — in a rough way — not just to the Earth's climate, but to a vast number of systems. They are, one might say, general possibilities of behavior.
Of course, we can't expect simple models to describe complicated real-world situations very accurately. That's not what they're good for. So, even calling them "models" could be a bit misleading. It might be better to call them "patterns": patterns that can help organize our thinking about complex systems.
There's a nice mathematical theory of these patterns… indeed, several such theories. But instead of taking a top-down approach, which gets a bit abstract, I'd rather tell you about some examples, which I can illustrate using pictures. But I didn't make these pictures. They were created by Tim van Beek as part of the Azimuth Code Project. The Azimuth Code Project is a way for programmers to help save the planet. More about that later, at the end of this article.
As we saw last time, the ENSO cycle relies crucially on interactions between the ocean and atmosphere. In some models, we can artificially adjust the strength of these interactions, and we find something interesting. If we set the interaction strength to less than a certain amount, the Pacific Ocean will settle down to a stable equilibrium state. But when we turn it up past that point, we instead see periodic oscillations! Instead of a stable equilibrium state where nothing happens, we have a stable cycle.
The angle goes around at a constant rate while the radius does something more interesting. When , you can see that any solution spirals in towards the origin! Or, if it starts at the origin, it stays there. So, we call the origin a "stable equilibrium".
This has two solutions, and . Since is a solution, the origin is still an equilibrium. But now it's not stable: if is between and , we'll have , so our solution will spiral out, away from the origin and towards the circle . So, we say the origin is an "unstable equilibrium". On the other hand, if starts out bigger than , our solution will spiral in towards that circle.
The red solution starts near the origin and spirals out towards the circle . The green solution starts outside this circle and spirals in towards it, soon becoming indistinguishable from the circle itself. So, this equation describes a system where and quickly settle down to a periodic oscillating behavior.
Since solutions that start anywhere near the circle will keep going round and round getting closer to this circle, it's called a "stable limit cycle".
This is what the Hopf bifurcation is all about! We've got a dynamical system that depends on a parameter, and as we change this parameter, a stable fixed point become unstable, and a stable limit cycle forms around it.
• Yuri A. Kuznetsov, Andronov-Hopf bifurcation, Scholarpedia, 2006.
The time between El Niños varies between 3 and 7 years, averaging around 4 years. There can also be two El Niños without an intervening La Niña, or vice versa. One can try to explain this in various ways.
• Richard Kleeman, Stochastic theories for the irregularity of ENSO, in Stochastic Physics and Climate Modelling, eds. Tim Palmer and Paul Williams, Cambridge U. Press, Cambridge, 2010, pp. 248-265.
Perhaps the relevant slow modes interact with each other in a chaotic way.
Perhaps the relevant slow modes interact with each other in a non-chaotic way, but also interact with chaotic fast modes, which inject noise into what would otherwise be simple periodic behavior.
Perhaps the relevant slow modes interact with each other in a chaotic way, and also interact in a significant way with chaotic fast modes.
Kleeman reviews work on the first option but focuses on the second. The third option is the most complicated, so the pessimist in me suspects that's what's really going on. Still, it's good to start by studying simple models!
• H. A. Dijkstra, L. M. Frankcombe and A.S von der Heydt, The Atlantic Multidecadal Oscillation: a stochastic dynamical systems view, in Stochastic Physics and Climate Modelling, eds. Tim Palmer and Paul Williams, Cambridge U. Press, Cambridge, 2010, pp. 287-306.
They're the same as before, but with some new extra terms at the end: that's the noise.
This could easily get a bit technical, but I don't want it to. So, I'll just say some buzzwords and let you click on the links if you want more detail. and are two independent Wiener processes, so they describe Brownian motion in the and coordinates. When we differentiate a Wiener process we get white noise. So, we're adding some amount of white noise to the equations we had before, and the number says precisely how much. That means that and are no longer specific functions of time: they're random functions, also known as stochastic processes.
If this were a math course, I'd feel obliged to precisely define all the terms I just dropped on you. But it's not, so I'll just show you some pictures!
They look similar to the solutions we saw before for , but now they have some random wiggles added on.
In these examples, , so we would have a limit cycle if there weren't any noise — and you can see that even with noise, the solutions approximately tend towards the limit cycle. So, we can use an equation of this sort to describe systems that oscillate, but in a somewhat random way.
We get irregular oscillations even though there's no limit cycle! Roughly speaking, the noise keeps knocking the solution away from the stable fixed point at , so it keeps going round and round, but in an irregular way. It may seem to be spiralling in, but if we waited a bit longer it would get kicked out again.
That's nice. Of course it doesn't prove that a model based on a Hopf bifurcation plus noise is "right" — indeed, we don't really have a model until we've chosen variables for both and . But it suggests that a model of this sort could be worth studying.
If you want to see how the Hopf bifurcation plus noise is applied to climate cycles, I suggest starting with the paper by Dijkstra, Frankcombe and von der Heydt. If you want to see it applied to the El Niño-Southern Oscillation, start with Section 6.3 of the ENSO theory paper, and then dig into the many references. Here it seems a model with may work best. If so, noise is not required to keep the ENSO cycle going, but it makes the cycle irregular.
To a mathematician like me, what's really interesting is how the addition of noise "smooths out" the Hopf bifurcation. When there's no noise, the qualitative behavior of solutions jumps drastically at . For we have a stable equilibrium, while for we have a stable limit cycle. But in the presence of noise, we get irregular cycles not only for but also . This is not really surprising, but it suggests a bunch of questions. Such as: what are some quantities we can use to describe the behavior of "irregular cycles", and how do these quantities change as a function of and ?
You'll see some answers to this question in Dijkstra, Frankcombe and von der Heydt's paper. However, if you're a mathematician, you'll instantly think of dozens more questions — like, how can I prove what these guys are saying?
• John Guckenheimer and Philip Holmes, Nonlinear Oscillations, Dynamical Systems and Bifurcations of Vector Fields, Springer, Berlin, 1983.
• Zdzisław Brzeźniak and Tomasz Zastawniak, Basic Stochastic Processes: A Course Through Exercises, Springer, Berlin, 1999.
• Bernt Øksendal, Stochastic Differential Equations: An Introduction with Applications, 6th edition, Springer, Berlin, 2003.
• to have an online free access repository and make all software open source, to enable anyone to use the software, for example to reproduce the results on the Azimuth Project. Also to show by example that this can and should be done for every scientific publication.
• to implement the software with an eye to software engineering principles.
This less important because the world of numerical high performance computing differs significantly from the rest of the software industry: it has special requirements and it is not clear at all which paradigms that are useful for the rest will turn out to be useful here. Nevertheless I'm confident that parts of the scientific community will profit from a closer interaction with software engineering.
So, if you like programming, I hope you'll chat with us and consider joining in! Our next projects involve limit cycles in predator-prey models, stochastic resonance in some theories of the ice ages, and delay differential equations in ENSO models.
And in case you're wondering, the code used for the pictures above is a simple implementation in Java of the Euler scheme, using random number generating algorithms from Numerical Recipes. Pictures were generated with gnuplot.
This entry was posted on Friday, December 24th, 2010 at 2:32 am and is filed under azimuth, climate, mathematics, this week's finds.	You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.
By the way, people reading this blog might prefer the version of This Week's Finds on my website. You can see "week308" here, and latest edition can always be found here.
The version on my website doesn't suffer from the "skinny column" format of the blog version — in particular, some of the pictures are bigger than 450 pixels across!
The equations also look better — at least to me, now that I've started using jsMath to render TeX on my website. If the math symbols on "week308" look smaller than the other letters, let me know — and let me know what browswer you're using. To me, using Firefox on Windows, they look just right, but apparently that varies from browser to browser. They're easy to adjust at my end, but I may not be able to make everyone happy.
On my website, you may see an annoying red box at the top of "week308" saying "no jsMath TeX fonts found". This should not impede your ability to see the math symbols! The great thing about jsMath is that you don't need to download any math symbol fonts. But the math symbols will show up faster, and maybe better, if you download the fonts. You can do this by clicking the little grey box saying "jsMath Control Panel" and then doing some (supposedly) obvious things.
Great post and a very worthy software project. Best of luck with it. BTW, this blog's graphics look fine on Safari.
needs your home directory, not just "home"!
Tim van Beek notes that Terry Tao has a blog entry that explains Brownian motion in a rigorous way. This is a good way for mathematicians to get started on stochastic differential equations.
I find the idea of using random noise to make a model of something quite absurd. A model should capture causal relations of the modeled phenomenon. A "model" which produces a similar output for completely different reasons then the ones governing the actual phenomenon is a failure.
If one thinks that there is some noise which for some reason is impossible to model then it would make much more sense to filter this noise out and then compare such filtered data to a model without any noise.
I'm not sure your precept is valid in general. If you believe your model has to have exogenous variables and you are trying to fit data where those exogenous variables arrive in an irregular and uncertain way, then you neither want to filter them or try to explain them (which would threaten an infinite regress to another set of exogenous variables).
It is true that introducing noise without a clear understanding of where it's coming from (no specification of the exogenous variables) is less convincing, especially if it plays a major role in generating the structure of interest, as in some of the pictures above. But in that less-satisfying case, perhaps the results can be interpreted as a candidate pattern that any causal variable would have to match.
If a model increases our probability of making successful predictions, it's a useful tool, regardless of its nature.
Scientists are in love with knowledge, and engaged in an all-out war against ignorance. All is fair in love and war.
Models that incorporate randomness are widely used in physics and engineering. For a brief history, try this.
In "week306" I interviewed Tim Palmer, who is a big proponent of stochastic (i.e., probabilistic) methods in weather and climate modelling. He explained how thanks to the "real butterfly effect", it's impossible to make accurate deterministic weather predictions beyond a certain short threshold. Unmeasured wind velocities, temperatures and pressures at short distance scales are examples of the 'exogenous variables' srp mentioned. This umeasured information at short distance scales quickly percolates up to affect larger distance scales. But we can model this unmeasured information stochastically, by treating it as random noise.
In short: just because we can't know things for sure, doesn't mean we should throw in the towel. It can help a lot to know the probability it's going to rain in a week.
• Steve Easterbrook, AGU talk: Tim Palmer on Building Probabilistic Climate Models, Serendipity, 19 December 2010.
• Tim Palmer and Paul Williams, editors, Stochastic Physics and Climate Modelling, Cambridge U. Press, Cambridge, 2010.
• H. A. Dijkstra, L. M. Frankcombe and A. S. von der Heydt, The Atlantic Multidecadal Oscillation: a stochastic dynamical systems view, in Stochastic Physics and Climate Modelling, eds. Tim Palmer and Paul Williams, Cambridge U. Press, Cambridge, 2010, pp. 287-306.
Of course I only scratched the surface: the Hopf bifurcation plus noise is pretty, but far from the whole story. These papers are really fun to read, so I hope folks dig into them!
(Milankovitch cycles), via a phenomenon called stochastic resonance. Luckily for me, Tim van Beek has been studying stochastic resonance and writing programs to show how it works.
But before I get to that, I'll probably give another example of a Hopf bifurcation plus noise, developed by Graham Jones over on the Azimuth Project. I won't give the link because I want it to be a surprise!
If anyone reading this likes to program, I hope you join the fun: drop us a line on the Azimuth Forum!
Merry Christmas, everyone. My wife and I going to Vietnam in 2 hours. We'll spend seven days in Hanoi and five days in Huế, which was the imperial capital of the Nguyễn Dynasty until 1945. I'll be back on January 5th. Be good while I'm gone.
A model should capture causal relations of the modeled phenomenon. A "model" which produces a similar output for completely different reasons then the ones governing the actual phenomenon is a failure.
The actual phenomenon is governed by an interaction of a slowly varying, long memory phenomenon, namely oscillations of temperature and other variables of the Pacific Ocean, and very short term, no memory influences from the atmosphere. The noise models the latter.
3) as a null hypothesis for other, more sophisticaed models.
The model in week 308 cannot be used to make predictions: to predict the next ENSO cycle one needs more sophisticated models that are closer to a full model using Navier-Stokes equations with real world data as initial and boundary data. But I think it can be useful for 2) and 3).
But: Note that "noise" can be and has been used very successfully for models that make very accurate predictions, examples can be found in financial mathematics (Black-Scholes formula), but also in engineering, for example in control theory.
There is a lot of literature out there, simply look for the keyword "stochastic control theory".
Note that something subtle is going on: adding noise is using a model, it's just that it's a very weak model which where we've decided (due to either ignorance of the details, inability to measure inputs to the details, computational intractability of the details or some combination thereof) is that all the model tells us is the probabilistic distribution of possibilities. Using those to model small residuals after dominant contributions have been modeled more closely is typical of modeling, and use of models in science. Have you ever tried to come up with a measure of the coefficient of friction between two surfaces directly without measuring the atomic level details and computing all the interactions? That's the same thing, except in this case — as with most cases in typical simple physics — the probability distribution is very, very tightly peaked so you can neglect the probabilistic dimension. What's different here is that (analogies to) very complex, huge-number-of-component systems are being considered where the system behaves very non-linearly to small perturbations.
Right: and this is very easy to see by noticing that there are infinitely many different kinds of noise, so building a model involving noise requires that you make assumptions about the nature of the noise involved.
In the simple "Hopf bifurcation plus noise" model described in this blog entry, we're adding independent identically distributed white noise terms to the formulas for the time derivatives of both variables (x and y). This has the mathematical advantage of being as simple as possible, and not destroying the circular symmetry of the problem. But in any serious attempt to model a specific real-world phenomenon, one should try to make the noise realistic, not just mathematically elegant.
For example, in weather simulation, where the noise might model the effects of turbulence at length scales not included in the model, one would want to think hard about how turbulence actually works. Luckily, people have been thinking about this ever since Kolmogorov wrote his amazing paper back in 1941. So, a lot of the hard thinking has already been done.
Anyway, in almost all applications (such as finance – where are those finance guys when you want them?) you have to use past measured behavior to predict future behavior, especially to make realistic noise aspects. Because you almost never have a clear idea of where the noise is coming from or what it's properties should be, especially apart from those measurements.
What exactly do they use for the noise terms in weather forecasting? Are there trade secrets?
So far I have not discovered a weather model using stochastic elements. People include subgrid phenomena via so called "parameterizations", which are heuristic deterministic influences on each grid cell based on an estimation of concrete physical effects. They then use "ensemble runs" which means running the models multiple times with different parameterizations and different initial data, to find out which predictions of the models are stable and which are not.
This is significantly different from having a stochastic effect on every time step of the model, like one has in stochastic partial or ordinary differential equations.
Stochastic equations are used with great success in engineering, for example for controlling the flow of fluids through pipes, as an approximation to the unaccessible solution of Navier-Stokes equations. So, IMHO, this could turn out to be of use for weather and climate models, too, but if I'm correct there would be the need for a minor paradigm change in the weather modeling community.
without stochastic parameters. It seems blind to me.
To a mathematician like me, what's really interesting is how the addition of noise "smooths out" the Hopf bifurcation.
The topic of "qualitative analysis of differential equations" is now over 100 years old, it was started by Henri Poincaré, and a lot of concepts bear his name, like Poincaré map. Of course, Poincaré could not resort to computer approximations to get an idea how solutions to certain differential equations look like. But although we have powerful computers today, the topic itself is still very important to supplement and cross check numerical solutions.
Would it count for what you're looking for, as "interesting results or methods", to find a result that energy conservation implies that the stochastic equations of natural processes need to incorporate finite developmental periods displaying accumulative proportional change?
Somebody's going to get it some day, why not you guys??
I own a wonderful desk toy called the Randomly Oscillating Magnetic Pendulum (ROMP). It gives some intuitive feel for how a simple system can exhibit limit points and cycles and can abruptly switch from one to another "for no good reason." You can rearrange the magnets into any configuration on the plate below the pendulum, causing a new set of complex behaviors. Entertaining, enlightening, and slightly hypnotic.
The first line sets up the parameters, the second sets up the dynamical system (where v is the vector having x and y as components), the third solves the ode from 0 to 10 seconds, with an initial condition of [0 2], and the fourth line plots the results (use plot(t,v(:,1)) to plot the first component versus time).
The plots are smoother than the one shown above, perhaps because I have assumed that the variance of the white noise is 1, and maybe a larger one was assumed above. Multiplying lambda by 10 produces very similar plots.
a) what algorithm does it use to generate random numbers? Does it always use the same seed i.e. does it always reproduce the same graphs?
b) what algorithm does it use to solve the SDE?
c) do different versions of Matlab produce the same graphics? (I doubt that, to be honest).
The plots are smoother than the one shown above, perhaps because I have assumed that the variance of the white noise is 1, and maybe a larger one was assumed above.
No, the variance of white noise is not a free parameter in an Itô stochastic differential equation driven by Brownian motion, which makes me wonder why you have to specify it at all and to what effect.
The graphics above were produced with the explicit Euler scheme with a step size of 0.001. Therefore, in every timestep one has to add noise in the form of a random variable drawn from , because the increments of Brownian motion are Gaussian with mean zero and variance h.
in every timestep one has to add noise in the form of a random variable drawn from multiplied by .
Tim, are you sure about this ? If the variance is 0.001 and lambda is 0.1 then the noise should be pretty much non-existent, and the plots would be really smooth.
Maybe you meant that the variance should be 1/sqrt(0.001) ? (and in fact i get plots that are really similar to yours in that case).
In any case i have checked also with forward euler and results are essentially the same, so it comes down entirely to the noise level.
Let me know if you want me to send you the time histories for comparison.
If the variance is 0.001 and lambda is 0.1 then the noise should be pretty much non-existent, and the plots would be really smooth.
It is important to keep in mind that the Euler scheme is a discrete approximation with a fixed stepsize h, and when we simulate a sample path we have to add noise at every step with variance h. So if the stepsize is rather small (like 0.001 in our sample) the variance of the noise added at each step is also very small. But the effect adds up!
Tim, are you sure about this ?
No, this is of course an important question! I don't trust my intution to tell me what a certain noise level should look like – in fact I should have written some tests before the publication of week 308!
A good test for the noise level is this: It is possible to formulate the Fokker-Planck equation for our example in polar coordinates and find the stationary, rotationally invariant solution which describes the distribution of r, the radius, in the equillibrium. This can be calculated in closed form.
Then it is possible to create a histogram approximation of the distribution of r by sampling a very long run of the SDE solver, and compare this to the solution obtained from the Fokker-Planck equation.
I did this, and the results match! so, yes, know I'm pretty confident that the SDE solver for week 308 gets the noise right.
I will explain all of this in more detail on the Azimuth project once I have more time (maybe tomorrow).
Thanks for your help and for getting me to finally do these chross-checks!
Hi Tim, yes of course h multiplies everything on the right hand side if you use Euler approx. But i am talking about the variance of the noise in the equations given by John.
The first term is [-1 1], the second is [1 1], and the third is -[2 2]. Now, here is the important part, we are adding a noise here and I want to be sure about the variance of the noise that we are adding. If you really add a noise of variance 0.001 and multiply it by 0.1 here in this equation (and i don't think you meant to add it here), you are adding something that is 3 to 4 orders of magnitudes below the other terms so you will hardly see anything !
I'm sorry, but I don't understand you: Is [1 1] the starting point of the process and you calculate the derivatives?
…but I am talking about the variance of the noise in the equations given by John.
The only free parameters are and . As you can see on the page stochastic differential equation, the Euler scheme is a simple generalization of the classical deterministic Euler scheme, in every step the position of the process is changed according to the deterministic part + noise.
Once a step size h is fixed, the increment has a fixed distribution, namely $N(0, h)$. There is no further choice involved here.
But when we compare the influence of the deterministic path to the influence of additive noise in the Euler scheme, we have to keep in mind that the influence of the deterministic part gets multiplied by h, ergo it is not true that the noise term is negligible.
So the same code works fine in octave, (you just have to download ode45.m which you can just google for, you'll find several open sourced versions, there is one at the university of Boston. There is also an ode78.m which implements Runge-Kutta with 7th order formulas).
(yes you can reset the seed to repeat the same sequence).
(also, i think there are specific SDE objects and solvers in the econometrics toolbox, but i have never used them).
I have tried a couple of versions before 2008 and the graphs are identical, with R2010a it is slightly different, i think something changed in the number generator around that time.
Tim, I also meant to say that if you are using a simple forward Euler integration it's already a big difference right there, obviously.
gets Matlab to add random numbers at every step that are distributed according to N(0, 1).
Now, with some luck ode45 will use a constant stepsize h, in this case you'll get sample paths that look similar to those of TWF if $\lambda*h*N(0,1)$ looks close enough to $\lambda*N(0, h)$. But this is more of an accident if the stepsize $h$ is much smaller than the resolution of the graphic. Although ode45 chose 1.0 as the default initial stepsize, I suspect that it will calculate a much smaller one and use this according to the default error bounds, but I don't know how to find out which one it actually uses.
Ok, got it! dW/dt is approximated by (W(t+h)-W(t))/h, so you have a noise with variance 0.001 that _is divided by h_.
Indeed using the line dv=@(t,v) [-v(2);v(1)]+beta*v-v'*v*v+lambda*sqrt(0.001)*randn(2,1)/0.001;gives very similar plots to yours, especially when you fix the step size to 0.001.
This also gives plots that are basically identical to yours, so problem solved.
There should be also several additional packages for Matlab that are not part of the core with SDE solvers, also with adaptive step size control – at least such algorithms exist. Of course the noise level has to be adapted to the chosen step size at each step.
Matlab fan here. I also constantly write numerical routines in Basic, Fortran, C, and IDL, but (except for OPC – Other People's Code) I always develop numerical analysis algorithms in Matlab first.
By the way, almost all Matlab packages are just someone's collections of ordinary Matlab routines, so you probably don't need them or even want to have to learn them unless you're going to be mostly focused on something specialized, like say Computational Biology.
I'm trying to learn a little bit more about climate models. I understand that Matlab is a good tool to quickly code a simple algorithm and play with it. Climate models need an effort of several man decades. I doubt that Matlab is suitable to handle models that are comprised of millions of lines of code and are developed by hundreds of people over several years. On the other hand, this is quite usual for applications using either the Java or the .NET tool stack.
This is the first bug report for the Azimuth Code Project, so I created an issue with the issue tracker over at google projects here.
Thanks to Giampiero: It's very important that we cross-check our results and fix all bugs before they have any impact.
(I'm pretty sure that the pictures give a qualitative correct impression of the behaviour of the solutions, even if it turns out that there is some scaling error in the noise level – so such an error would not be that bad now. It would be very bad if we used the code to calculate e.g. crossing times for pricing options :-).
Hi, Tim! I'm in Vietnam, which is a fascinating place, so I don't want to spend spend much time blogging, but if you could fix the the pictures for "week308", that would be great. That way I can put up fixed versions when I get back.
Every report of unusual behaviour should result in a bug report, or an issue (these are synonymous, especially if one uses the bug tracker of google project). This does not imply that there actually is a bug in the software.
In this case I became suspicious that I maybe did something wrong in the random number generator when I translated it from C++ to Java, so I wrote a chi-square-test for it – which it passed, so now I'm pretty sure that there isn't a bug.
Nevertheless I'll calculate the Fokker-Planck equation in polar coordinates and compare the stationary solution to the approximation I get from my SDE solver – this is another sanity check (I'll try to get this done before new year's eve, since I won't have any time for that afterwardts :-(.
But right now I'm more supicious about what Matlab actually does when fed with the lines that Giampiero posted and could some help with that, since I don't have and don't know Matlab.
• Fokker-Planck equation, look for "Hopf-Bifurcation Example of TWF 308".
The distribution of r is very sensitive to the noise level λ, so I find it hard to believe that a rather exact match of both calculated and simulated values as displayed in the graphic over there is an accident.
Hi, I'm back in Singapore. I fixed this typo — thanks for catching it.
This post had me thinking about catastrophes. Interestingly, there were 7 types discussed in Thom's investigations (including the "butterfly catastrophe"), which suggests to me 3-cat level complexity. If viewed with minimal structure & in a box model context, I believe this could be a fruitful direction of further work in this instance.
This post had me thinking about catastrophes.
Indeed, I was wanting to say a lot more about catastrophe theory, but I restrained myself because I thought it would be too distracting. I decided it would be better to see some examples of catastrophe theory in climate science before chatting about the mathematical subject in its abstract glory!
• Azimuth Project, Snowball Earth.
In all cases these catastrophes display some extra interesting behavior when we add noise.
And yes, I'm very interested in thinking about them in terms of box models, because box models are really just applied category theory.
I'll probably spend a year or two writing some papers that make the last sentence more precise. I'm eager to bring some category-theoretic tools into climate physics, systems ecology and the like. I don't want to get too sidetracked by this theoretical desire — I doubt it will 'help save the planet' very much — but I do need to keep publishing math papers, and this is one obvious way.
Incidentally, related to your aside, procedural generation of music is something that I'd like to learn how to do at some stage (and the implementation of procedural techniques in general).
But I suspect that that would be sidetracking this discussion just a bit!
Nice post! I never thought adding noise could have this effect.
Just for fun I create an interactive Sage version of this phenomenon: you can play with the model at http://www.sagenb.org/home/pub/2630.
It is open source (it suffices to add ?? to any method name to view the implementation) and actively maintained. It doesn't aim to reinvent the wheel but instead incorporates GAP, Maxima, scipy, … into one consistent interface.
It's written in Python, which is easy to learn and (thanks to cython) can be as fast as compiled C. For instance, the whole interactive demo is no more than 10 lines.
Code that goes into Sage is peer reviewed and unit tested, so you don't need to worry about stress-testing the code (though it is always good to be cautious).
Just for fun I created an interactive Sage version of this phenomenon: you can play with the model at http://www.sagenb.org/home/pub/2630.
For some reason that doesn't work for me. I see some sliders at the bottom, and I can slide them, but I don't see a plot of the solution.
Also, for some mysterious reason the jsmath doesn't work for me either.
Does this stuff work for anyone else reading this? Maybe it's just me.
I have the same problems as John (using Mac Firefox 3.6.1 or Safari 5.0.3).
Unfortunately it does not work for me, either, I have the same problems as John.
Over at the Azimuth forum Staffan Liljegren already pointed out Sage but I haven't taken a closer look at it yet. The motivation of the authors of Sage is very similar to the motivation of the Azimuth project, so I do hope that we can use and maybe extend Sage for our purposes.
Thanks for pointing this out! It seems that we have stumbled upon a limitation within Sage: apparently, to see the interactive elements in a worksheet one needs to be logged in. Rather than asking you all to create an account with Sage, I noticed that there are some patches lying around to enable this, and once I get back from this conference I'm attending, I'll try to set up my own server running this worksheet.
The Poincaré–Bendixson theorem states that any orbit that stays in a compact region of the state space of a 2-dimensional planar continuous dynamical system, all of whose fixed points are isolated, approaches its ω-limit set, which is either a fixed point, a periodic orbit, or is a connected set composed of a finite number of fixed points together with homoclinic and heteroclinic orbits connecting these. Thus chaotic behaviour can only arise in continuous dynamical systems whose phase space has three or more dimensions. However the theorem does not apply to discrete dynamical systems, where chaotic behaviour can arise in two or even one dimensional systems.
A weaker version of the theorem was originally conceived by Henri Poincaré, although he lacked a complete proof. Ivar Bendixson (1901) gave a rigorous proof of the full theorem.
I.e., this is a proof that discrete and continous systems are not-isomorphic thus care seems appropriate in the development of models etc.
Since 1/f noise is found in some types of meteorological data, has anybody thought about using 1/f noise to model ENSO with Hopf bifurcation and noise? Since 1/f noise is said to model "long term memory effect" could it also be relevant to the current discussion in the El Nino project, part 6, between Steve Wenner and HyperG about Bayesian and frequentist interpretations of probability?
Instead of a single null hypothesis I wonder if an algorithm based on multiple null hypotheses would be useful. Don't know if that's standard but here goes.
This is an idea about an optimal capital budgeting process based on software prediction of ENSOs (using an algorithm based on multiple null hypotheses).
Why this kind of approach to software specification? Because ENSOs have big economic costs and so we might be able to cost-justify developing some software for this kind of optimal budgeting process.
Banks should be interested because, on behalf of their shareholders, they would be able to bet their money on better ENSO predictions. Governments should be interested because, on behalf of the people under their governance, they would be able to make better expenditures e.g. on appropriate corn storage and so on.
The idea comes from looking at Steve's spreadsheet, specifically the table for "Training." And then, thinking of all those models from the "big guys," that John showed us, as actually, null hypotheses. For what follows I assume that each null hypothesis from the list either predicts or does not predict an upcoming ENSO each year.
To make it easier (for me anyway), imagine a cartoon about a bank with a group of investment bankers, in three acts. Each investment banker is given just one dollar per year from a finite amount of dollars to invest based on predictions of an ENSO. After all it's a just a cartoon.
I believe that Tim was using "null hypothesis" more as a shorthand for "base model", in the sense that whenever you create a model which you know will not be a full, correct model of the phenomenon (which philosophically might be the only thing science does, but that's a tangential debate) you generally want to see if it performs better than your base model. For example, in a binary detection classifier one can compare against the classifiers "always true" and "always false": if your model performs worse than that then it's clearly no use and should (in its current incarnation) be abandoned.
That's related to what I think Tim was getting at; I'll have to think some more about what you proposed as an idea in its own right.
I do agree. Not being a scientist but an engineer (ret.) I admit to bringing up something that is perhaps tangential. From an equation that explained to me a well-studied phenomenon in psychology called probability learning, I think I see a way to apply this equation for the lab animals' behavior to guide investors' or governments' spending if and when ENSOs get worse and worse. Actually, my application of the equation wouldn't care at all about the details of particular models. It doesn't answer which model explains historical data better. The idea is just to give investors' and governments' some guidance for their spending behavior in real time– as a future unfolds in which the probability of ENSOs might well change over comparatively short periods of time.
Although I do think it would be interesting to use the equation in order to compare a number of differently initialized Hopf bifurcation models with 1/f noise against what is going to happen in the future. The algorithm might pick one or the other of the differently initialized Hopf models as time goes on. And in a separate running, their performance as the future unfolds could be compared to the same application of the equation to the models of the "big guys." To me it looks like a competition between simple and complex models.


"Heck yeah! I'm a triceratops balanced on a buttercup with spinning wheels, baby. Lets do this!"
"See, that's what it'd be like if you had me for a face!"
"Hey, if I close my eyes it doesn't seem so dark!"
"Remember, licking doorknobs is illegal on other planets!"
"No, Gary. I like wearing my underwear like this!"
"Oh well, I guess I'm not wearing any pants today!"
"Or a football playing king in space... with a moustache!"
"I anything can't do right since because pickles!"
"The sky had a baby from my cereal box!"
"You know, if I were to die right now, in some sort of fiery explosion due to the carelessness of a friend...well, that would just be ok."


text:
Multilingualism and anxiety in people with Autism Spectrum Disorder (ASD)
Multilingualism and anxiety are core research and policy issues (DfE, 2018). Anxiety disorders, a feeling of unease, worry or fear, are the most prevalent mental disorders and are associated with immense healthcare costs. Autism affects communication and behaviour (DSM-V, 2013). 1/100 people (700,000 people in the UK) have ASD. 50% of them exhibit significant anxiety, disrupting their day-to-day function. Anxiety exacerbates social withdrawal and repetitive behaviours. Left untreated, anxiety leads to depression and self-injury -early recognition/treatment convey better prognosis.
39% of the UK population is multilingual (EC, 2012). Researchers, practitioners, and politicians assembled at the House of Commons called for comprehensive policies recognising the UK as a multilingual society, and ensuring provisions are developed to protect languages (May, 2019).
There are over 1.5million learners with English as an Additional Language (EAL) in the UK. 150,000 multilingual pupils are estimated to have ASD, ~75,000 presenting with severe anxiety.
Benefits of multilingualism in people with typical cognition have been well-established. These benefits appear early and persist across the lifespan. Multilinguals perform better in memory and attention, experience slower cognitive-ageing, develop dementia 3–6 years later, and recover better from stroke (Bak, 2016). Previous research concluded that multilinguals have less anxiety.
However, if being multilingual reduces anxiety also in autistic individuals, is yet to be examined.
Lead Academic at Lead University
Dr Ozge Ozturk University of Sheffield
Lead Academics at other Universities
Dr Liz Littlewood, University of York
Dr Paula Clarke, University of Leeds
Other Staff associated with this project
Prof Elizabeth Milne, University of Sheffield, Department of Psychology
Prof Patricia Cowell, Health Sciences School, Division of Human Communication Sciences
Emma Morgan, University of Sheffield, Department of Psychology,
Marta Borowka, University of Sheffield, Health Sciences School, Division of Human Communication Sciences,
Sarah Cobbe, University of Leeds, School of Education,
Dr Megan Freeth, University of Sheffield, Department of Psychology
Dr Meesha Warmington, University of Sheffield, The School of Education
Dr Judith Hebron, University of Leeds, School of Education
Dr David Marshall, University of York, Centre for Reviews and Dissemination
Dr Lisa Henderson, University of York, Department of Psychology
Prof Li Wei, University College London, UCL Institute of Education
Dr Napoleon Katsos, University of Cambridge, Theoretical and Applied Linguistics
Dr Thomas Bak, University of Edinburgh, Department of Psychology


text:
Hans Viktor Simonsson, född 3 augusti 1880 i Holms församling i Älvsborgs län, död 16 juli 1965, var en svensk sjöofficer (viceamiral) och 1942–1945 befälhavande amiral i Ostkustens marindistrikt.

Biografi
Hans Simonsson var son till trafikdirektör Viktor Simonsson och Hanna Forssman. Han blev underlöjtnant i flottan 1901, löjtnant 1903 och kapten 1910. År 1920 blev Simonsson kommendörkapten av andra graden och 1925 kommendörkapten av första graden. Han blev han kommendör 1931 och konteramiral 1936. Simonsson deltog 1913 i vissa ubåtsprov i La Spezia i Italien och förordnades till medlem av kommissionen för utredning av viss undervattensbåtstyp 1914. Åren 1919–1923 var Simonsson chef för torpeddepartementet i Karlskrona, och var 1923–1929 förordnad ledamot av Marinförvaltningen och chef för dess torpedavdelning. Åren 1928–1933 var han förordnad inspektör för undervattensvapnet och var 1932–1933 flaggkapten i chefens för kustflottans stab. År 1933 blev Simonsson chef för Sjöförsvarets kommandoexpedition och 1938–1942 befälhavande amiral i Sydkustens marindistrikt. År 1945 utnämndes Simonsson slutligen till viceamiral och placerades i reserven.

År 1916 utgav Simonsson skriften Vilka fordringar böra ställas på för flottan lämpliga undervattensbåtar?. Han var ledamot av Kungliga Örlogsmannasällskapet, från 1917 som vanlig ledamot och 1938 som hedersledamot, och ledamot av Kungliga Krigsvetenskapsakademien från 1931.

Simonsson gifte sig 1912 med Märta Margareta (Greta) Holm (1890–1971), dotter till slottsfogden kapten Carl Holm och Märta Brunau. Deras son var regeringsrådet Lars Simonsson. Makarna Simonsson är begravda på Galärvarvskyrkogården i Stockholm.

Utmärkelser
  Kommendör med stora korset av Svärdsorden, 15 november 1945.
  Kommendör av första klassen av Svärdsorden, 15 november 1937.
  Riddare av första klassen av Svärdsorden, 1922.
  Riddare av Nordstjärneorden, 1933.
  Riddare av första klassen av Vasaorden, 1925.
  Storkorset av Polska Polonia Restituta, tidigast 1945 och senast 1947.
  Kommendör av andra klass av Polska Polonia Restituta, tidigast 1931 och senast 1940.
  Tyska örnens orden (med grad som på svenska angivits som Storofficer), tidigast 1937 och senast 1940.
  Kommendör av andra graden av Danska Dannebrogorden, tidigast 1925 och senast 1931.
  Kommendör av andra klassen av Finlands Vita Ros' orden, tidigast 1931 och senast 1940.
  Riddare av Italienska Sankt Mauritius- och Lazarusorden, tidigast 1909 och senast 1915.

Källor

Noter

Män
Födda 1880
Avlidna 1965
Svenska militärer under 1900-talet
Svenska amiraler
Ledamöter av Kungliga Krigsvetenskapsakademien
Hedersledamöter av Örlogsmannasällskapet
Ledamöter av Kungliga Örlogsmannasällskapet
Kommendörer med stora korset av Svärdsorden
Riddare av Nordstjärneorden
Riddare av Vasaorden
Storkorset av Polonia Restituta
Mottagare av Tyska örnens orden
Kommendörer av Dannebrogorden
Kommendörer av Finlands Vita Ros' orden
Riddare av Sankt Mauritius- och Lazarusorden
Personer från Holms socken, Halland
Gravsatta på Galärvarvskyrkogården


Following the 80th anniversary of the creation of the work, the Musée national Picasso-Paris, in partnership with the National Museum Reina Sofía Art Center, presents an exhibition about Guernica, masterpiece of Pablo Picasso. The anti-Franco, anti-fascist and pacifist symbol, which is one of the most famous paintings in the world, was preserved in 1981 to Madrid where it is now permanently displayed. Thanks to an exceptional collection of sketches and archives, the exhibition proposes a lesson on the history of Guernica and clarifies questions about Picasso's political engagement.


text:
As a club we do not take the contribution of our key volunteers for granted. We aim always to support their development. We fund all coaches to achieve their Level 1 coaching badges and we expect all of our coaches to achieve this within the first 6 months of commencing coaching with us.
From the age of 16 players are able to enrol on the Level 1 badge. Our first ever young coach was our original Amazon and Vice Chair Beth Ward. Beth gained her badge at age 16 and began coaching her own team at age 17, so many players have benefited from her coaching and playing expertise. A number of our younger players are now following in her footsteps. As soon as her sister, Lydia Ward, reached the age of 16 she was determined to join her in coaching, and also gained her badge. We were delighted earlier this year when Olivia Coxon became our youngest ever qualified coach. Olivia not only plays for the Amazines but also coaches our Bombers u11s team. Our youngest volunteer is Charlotte Hall who at the age of 13 supports the training of our Mini-Zons and Stompers. Kate Clifford, Charlotte Roche and Kate Lockey, all players in our Amazines u16s team, also support coaching across our club and all will embark upon their coaching qualifications later this year.
A number of our Amazons Women's team players also coach, they are Kattie Whitfield, Sophie Hanson, Sarah Barrett and Beth Maddox and we were so happy that we managed to convince Emma Henderson to remain in the club through coaching when she stopped playing. Fabulous coaches who bring coaching and also playing expertise into their training sessions which I know is of enormous benefit to our girls.
Many of our teams are coached by dads of players. One of our players from the Stompers, is even lucky enough to have her brother, Harry Brown, coaching them. They all manage to balance being a coach with being parents (or brother) perfectly. We always welcome new coaches, so if you
are a parent and would like to get involved, please don't hesitate to talk to us.
We provide dedicated Goalkeeper Coaching, through the expertise of Phil Powell and Sarah Barrett. Our goalies absolutely love their specialist training and it is clear to see the impact of Phil and Sarah's coaching when our goalies are expertly throwing their bodies (minds and souls too) between the ball and the net in every match.
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I can't find the solution to this anywhere but I could use some help (I'm not brilliant with Unreal, however this is for a university project and I am still learning).
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How to get AI controller input axis values?


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text:
TONI BIRD JONES
BOOKS / EVENTS
CONTACT / MEDIA KIT
"Life is not always a matter of holding good cards, but sometimes, playing a poor hand well." — Jack London
"… a tension-filled tale of Ella, a savvy sailor who risks crewing on a drug run to fund her dream. Set in the islands of the Caribbean and South Florida, it's a high-stakes adventure of crime, love and deception that puts her moral code to the test against a cast of dangerous characters."
– Nina Schuyler, author, The Translator, The Painting
THE MEASURE OF ELLA
The worst kind of trouble, Ella Morgan thought, is the kind you didn't see coming. On a moonless night in the Straits of Florida due west of the Great Bahama Bank, she smelled rain in the freshening wind. She couldn't see a squall line, but stars to the west had begun to disappear, darkening the night and deepening her unease. When lightning branched across the sky, illuminating a tower of boiling clouds, it confirmed her sense that the night could get ugly.
The wind built and the waves began to crest, their tops streaming away in veins. The straining canvas lifted Grace's ninety-eight tons of wood and iron in a slow, heavy rise, and the hundred-foot pilot schooner plowed through the mounting seas like a great whale. Her sails hardened and her rigging groaned under the strain. Breakers slammed broad on her port bow, spewing plumes of spray across her deck.
Braced against the forward mast, Ella crouched and counted the seconds between each strike and the rumble of thunder that followed, trying to gauge the velocity and direction of the squall. Another flash, this one closer, brought her to her feet. She looked back. In the dim light thrown from the cabin, she made out the lone shape of the captain at the helm. She darted aft, moving in rhythm with the rise and fall of the boat, dodging sheets of stinging spray that whipped across the deck. Down below, the contents of the ship shifted and something crashed.
Ella ducked her head into the companionway. "Need help, Carlos?" she shouted to the first mate.
"I've got it — wine broke lose."
"The owner's?"
"No, our cheap stuff."
She pulled her watch cap further down. "Looks like this squall's got some teeth," she yelled to the captain.
Captain Rich Wells pressed into the wheel with one hand gripping a spoke and the other shoved deep into his pocket. "Carlos," he called out, squatting to see into the cabin, "what's the radar show?"
"It's moving easterly."
Rich turned back to Ella. "Take the helm. Keep her heading at a hundred-twenty-two degrees — as best you can."
Ella grabbed the wheel, shifting from leeward to windward and back again, trying to catch sight of the storm in the flashes of lightning. "Think it'll pass in front of us?"
"Possibly." Rich tugged the bill of his cap around back. "Kill the cabin lights, Carlos. I want to see what the lightning can show us."
The lights blinked out, plunging them into darkness. Phosphorescent flashes of breaking crests tumbled out of the black toward the boat. Lightning struck again, and Ella looked high off the water to see the ragged peaks of the squall line.
Carlos stuck his head out of the companionway, gripping the hatch rails to keep his balance. "It's building in the northeast — moving east near the six-mile mark."
"Looks closer than six miles," said Ella.
"It's coming right at us!" Rich shouted over the wind that now began to howl through the rigging. "Carlos, wake up Davis, we're shortening sail. God damn, we're in for a fight." He rubbed his hands together, smiling. "Make ready to reef the main!"
"Davis is up," said Carlos. "You want Jake too?"
"No." Rich ducked a sheet of spray. "Ella, flip on the spreader lights." Rich turned back to Carlos. "Alright, get the kid on deck. Just keep him out of the way. His father wanted him toughened up; this should do the trick."
Ella flipped on the lights just as Davis was coming up the companionway, zipping his foul weather jacket, heavy with sleep. His raven curls, caught by the wind, beat about a craggy face made handsome by dark eyes that flickered with a touch of wild. He threw Ella a smile.
"Welcome to the party," hollered Ella.
"You want me to take the helm?" Davis motioned to the wheel.
"No way!" Ella shouted through the wind. "Let you have all the fun?"
Over the year they'd been together, Ella's admiration for Davis had grown, and she'd opened her heart to him, a man unlike most, who lived life by the fistful. He was twenty years older than she was, an ex-Guardsman, ex-Merchant Marine and gale-tested captain with years of experience on the water delivering boats; a man who had crafted his life in the way that suited him, she thought, a trait she had respected in her father.
A distant bolt of lightning ripped open the night sky, affording her a glimpse of the storm's magnitude, its towering thunderhead dumping a wall of water into the building seas. With anxious excitement she steadied against the gunwale and gripped the wheel like a fighter going into battle, bending her knees in rhythm with the boat as it lumbered over the swells.
"She's sailing smartly," Davis yelled to Rich. "Let's wait to shorten sail."
Rich held onto the backstay, taking the measure of the sails in the flash of lighted sky. "And risk shredding them?"
"We can sail at the storm's edge. Still keep close to our course. We don't need to reef."
"Not tonight, Sinbad," said Rich. "Ella!" he shouted, pointing into the wind. "Prepare to head her up. We're shortening sail."
"Roger that." Ella smiled, her long braid buffeting against her back. She lifted her face to take a read on the wind that was kicking up the seas into four-foot whitecaps and shearing off their tops.
Rich held his eyes half-shut against the driving rain and turned to Carlos. "We're reefing deep!" He shot a look at Davis, and then took account of his crew. "Where the hell's the kid?"
"Here." Jake emerged from below. Wincing at the abrupt slap of wind, he tucked his head in and put his back to it. "I was asleep. What time is it?"
"No time for comfort," Rich shouted. "It's going to get nasty out here, so pay attention!"
"Going to?" Jake gripped the safety of the companionway and huddled against a blast of sea spray.
Ella cringed at the sight of the well-mannered boy, tall and thin as parchment. A strong gust could blow him away. He didn't belong here, on this stormy night sea.
"Get in position." Rich turned to take in the kid, then reached in the locker for a tether line, clipped one end to Jake's life vest and the other to the lifeline.
"What the hell, you're putting me on a leash? I'll trip over it."
"Then watch yourself!" Rich muttered.
Carlos held a tight grip on the mast. "Do you want to leave the jib up?"
"There's no time to hank on a storm jib. Reef the main deep, second reefing line. We'll drop the jib," said Rich. "It's a squall. It'll be over in an hour."
Ella watched Rich and Carlos work together, admiring their competence. She had sailed with enough captains and crews whose bungling ways led to dicey consequences. They're a good team, she thought, remembering Rich saying that Carlos had been his preferred first mate for the past five years, and Rich thought of him as a brother.
"Head up!" Rich looked back at Jake. "Kid, snap that tether onto the lifeline. You take it off one more time, I'll throw you overboard myself." He shot his arm up at the mainsail. "Grab hold of the sail and pull it down inside the lazy jacks. Tie off at the second reef." Rich turned to Carlos. "Keep an eye on him."
Davis had been right; they caught the edge of the squall line. But still, the wind was a stiff thirty knots with gusts to forty, screaming through the rigging, pounding against the sails and battering them with sheets of pelting rain. The sails snapped and strained and the great hull heeled with the driving wind hitting her on the beam.
Ella pulled the wheel hand over hand, bringing Grace's nose into the wind. The hood of her foul weather jacket billowed and flapped against her face, blinding her. She shoved it back and held the lumbering hull pointing up into the wind, every muscle from her clenched jaw down to her white-knuckled hands straining against the force of the pounding waves.
Carlos eased the halyard and the men yanked the massive sail down to the second reefing point. The wind caught the sail, snapping the thick canvas out of their hands and sending it flailing in great billowing folds. Without a sail to steady her, Grace moaned and heaved as she mounted the waves, throwing the men off balance. They leaned into the boom, using their bodies to flatten the sail, and tied off the reefs. When the job was done they headed to the bow.
Jake stood clinging to the boom, watching the men bring down the jib.
"Jake!" Ella hollered, signaling the boy. "Come back to the cockpit."
Heedless of wind or wave, Jake let go of the boom. Ella watched with dread as he stretched to reach the stay, then the boat took a sudden jolt and the boy tumbled off the cabin top.
"Jake!"
In an instant, the sea opened beneath them and the ship plunged into what seemed an endless drop as Ella sensed a terrifying mass rising in the darkness.
"Incoming! Port bow!" she screamed. "Davis, grab Jake!" But her words were lost to the wind.
Illuminated by the spreader lights, the towering rogue wave reared up like a fist coiling back for a punch and smashed into the hull, a massive wall of green water exploding over the deck and crew. Ella pressed her body into the wheel, wrapping her arms around its spokes, and tried to hold their course with all her strength as seawater flooded the deck and swept over her head.
For a long moment, she was underwater and terror shot through her like a bullet. Were they going down? All she could feel was a tangle of ropes sweeping past. She fought her instinct to let go of the wheel, free herself from the boat and struggle to surface for air. If she did, she'd be swept away. Panic overtook her, driven by the involuntary urge to breathe. Choking on the saltwater, she lost her grip and was swept aft with the current until her body hit hard against the backstay and she grabbed on. After what seemed an eternity, the ship threw off the sea and shuddered to the surface, and Ella gulped the precious air. Then the spreader lights flickered for a moment and went out. She could hear Rich calling out for the crew to sound off, Carlos's reply and then Davis's.
"Ella!" Rich called out, racing back toward the cockpit. "Ella!"
She fought her way back to the wheel and sputtered, "Here! I'm here."
She swept the strands of wet hair from her eyes. "Pretty wild." Her shoulders shook uncontrollably. "I'm good." The wind pressed her wet clothes to her frame, making her look as small as a drenched cat.
Rich stood for a moment holding onto the shroud, looking at her. "Hang in there. One of us will relieve you when all's secured." Rich hollered over the wind. "Besides, it's the safest spot on deck."
"Not so sure about that." Ella stood. "Where's Jake?"
"Jake?" Rich spun around. "Jake. Jake!"
She grabbed the high-powered searchlight from the cockpit and thrust it at Rich.
"Carlos, Davis, where's Jake?" Rich made his way forward across the heaving deck, shining the light along the rail, looking for Jake's safety tether.
"Starboard," yelled Carlos.
"Was he tethered?" Rich ran the light along the topsides.
"Don't know."
"He's here!" Rich found the boy hanging over the side. "Jake, hold fast, we'll get you." Grace dropped into a trough and a wave plowed the boy under.
"Ella," Davis called out. "Man overboard. Tethered. Leeward side."
"Holy hell," Ella screamed. "Carlos, we're doing a short tack. Now!"
Carlos ran to the cockpit and manned the winches.
"He's underwater," Davis hollered.
"Oh god." Ella screamed, "Tacking!" She brought the bow through the wind, lifting the leeward side out of the water and Jake along with it. Carlos worked at a racer's speed pulling in the sheets.
"Is he conscious?" Ella asked.
"Not sure." Rich shone the light onto the boy, and then his tether: it was tangled among the lines and had somehow wrapped around a stanchion. "How in the hell?"
"Get me up!" Jake cried, gasping as he came up for air and banged hard against the rolling hull. "Stop it!" His hands, fiercely gripping the tether, were visible with every break in the waves, and then he was lost again — underwater.
"Hang on, Jake. We'll get you," Rich screamed. "Ella, once we grab him, heave-to."
"She knows." Davis grabbed hold of the lifeline and swung his legs over the side. "We've practiced this."
"Davis, hold the light."
Rich lay flat against the rail, hooking his foot on a stanchion and stretching for the boy. "We've got you." He grabbed Jake's life vest, but the tether was taut and tangled and he couldn't pull the boy up.
And then the storm let loose a downpour. Rain rattled on the deck so hard that it sounded like hail and the rigging banged against the mast, adding to the cacophony of the hardening wind and sea.
"Rich!" Jake cried, catching his breath. "Get me up!"
"Shit!" Rich grimaced at the pelting rain. "Can't get him in."
Davis tore at the wet lines, trying to free them from the tangled mess, but the tether was still caught up around the stanchion. "Easiest to unclip him and pull him in."
"You're right," said Rich. "It's getting hung up somewhere. I can't see."
"Jake!" Davis leaned over the rail and shouted to the boy. "We need you to unclip the tether so we can pull you in."
"No way! I can't."
"We'll have hold of you," Rich shouted. "Fastest way to get onboard."
"No!" Jake shot a look over his shoulder into the black void of night.
"Christ," muttered Rich. "We'll have to cut the tether."
Rich turned to the helm. Ella had somewhat stabilized the boat by heaving to and tying off the wheel while Carlos trimmed the sails to balance the ride.
"Carlos!" Rich signaled him over.
"Keep a good hold on him, Rich," said Davis. "I'll lower over and wrap my legs around him. Carlos, cut the tether."
Davis seized the lifeline with one hand and Jake's tether with the other. Grace rode up a wave, lifting Jake free from the water, and Davis quickly lowered himself over the side, snagging the boy with his legs to keep him from being swept under.
"Got him." Rich grabbed Jake's life vest with one hand and a handful of jeans with the other. "Cut the line, Carlos."
"No!" Jake screamed as Carlos severed the tether.
"We got you," said Rich.
The three men pulled the boy under the lifelines, heaving him onboard. When he hit the deck, he scrambled to the cockpit on his hands and knees and cowered against the coaming, looking too afraid to stand up, as though the wind might blow him off deck again and into the sea, in the middle of nowhere, in the middle of the black night. He sat in the shelter of the cockpit trembling, clutching the cabin's handrail.
"Jake, get below and stay there," said Rich.
"No problem," said Jake.
"And take off those wet clothes, or you'll get pneumonia." Rich watched him go down the companionway. "Hopeless," he muttered under his breath.
"Okay, men," Rich twisted around to smile at Ella. "Let's get her back on course."
Grace had sailed out of Port Everglades en route to Nassau, Bahamas, for a night crossing of the powerful Gulf Stream. Now, on the axis where the river of seawater flowed north with a steady force, the crew aboard Grace sailed along a major shipping lane where merchant ships and sailors took advantage of the prevailing winds, like migrating birds on a flyway, to all points of the Caribbean and beyond.
Ella brought up a sandwich and thermos of strong coffee and set it next to Rich at the helm. He had taken Jake's watch. Even though the squall had passed, it had left a confused sea and the kid was permanently relieved for anything but fair weather sailing. Davis and Carlos had coiled the tangled lines and checked the boat for damages, then they too went below to rest, leaving only Ella and Rich on deck.
"Want me to take the wheel while you change into dry clothes?"
"I'll wait," said Rich. "I want to be sure this squall is finished with us. Can you sit for a minute?"
"Sure. What's up?"
"You kept a cool head tonight."
"Not much choice, was there?"
"Good answer," he said, studying her. "Look, there's something coming up that needs a special crew. What I'm about to ask can't go any further, not to a soul — not even Davis. Will that be a problem?"
"He doesn't need to hear about everything I do." Ella kept her back to the wind, hugging herself against the cold, and sat down. "Besides, lately we're seldom in port at the same time. So, what's up?"
"Another passage. I need a capable crew I can trust, people like you. It'd be a commitment of about a week, the pay is phenomenal, but it involves some risk."
"What kind of risk?" Ella suspected what was coming — in this part of the world, secrecy and boats often meant drugs.
Rich looked hard at her before he spoke. "Sailing a load of weed into Florida."
Not Rich, too, she thought, checking over her shoulder to see if they were alone.
"Premium marijuana, no cocaine."
"That's unexpected, I never had the slightest …" Ella sat back, realizing she sounded disapproving. She had been asked before by people she wouldn't have trusted to organize a picnic. She remembered how upset Davis had been when she'd told him, and his warning against taking such risk: smuggling marijuana across a U.S. border was a Federal offense that carried a heavy jail sentence, even though it had been legalized in some states.
"I mean, you're the last person I would have suspected."
"That's good. I just run an occasional trip to help fund my research work."
"Your entomology studies?" It was easy to forget that Rich was a scientist; he seemed a man most suited for the sea, not bending over a microscope to study insects.
"Funding is drying up," said Rich.
"Aren't you worried about the risk? If you got caught, you'd be sent to prison."
"Sure." Rich wolfed down half of his sandwich, followed by a swig of hot coffee. "But with careful planning and known resources — it's an acceptable risk, price of being committed to what you love. Good coffee, Ella."
Rich stood to get a clear read on the compass, then sat back down behind the wheel. "We could use a strong woman and you're a damn good sailor — a natural, with good judgment. A lot of people would have panicked when Jake fell overboard."
"At least he was on a tether," Ella said, remembering her father bragging to his shipmates that she was a natural. "Why do you want a woman onboard?"
"Deflect suspicion. Sailing into port we'll look like a couple coming in from a day sail."
Ella looked away, out at the sea, feeling more disappointed in Rich than complimented by his trust. An invitation into a secret world with the lure of fast money: it was against all she had been taught by her father, who regarded people in the drug trade as criminals unwilling to perform an honest day's work for what they wanted.
"You know I'm planning on opening a restaurant? I put a deposit on a place in the Keys."
"I've heard that."
"Captain Morgan's Table, named for my dad. He's the one who taught me to sail." Ella leaned against the coaming and pulled her knees to her chest. "I'd never do a run, Rich. Curious though— how much does it pay?"
"Still being worked out, but around twenty, twenty-five thousand."
"A pile of money for one week's work," said Ella.
Enough money to buy the latest kitchen appliances instead of used ones as she had planned, Ella thought — but not enough to risk going to prison. And not enough to make a mockery of the sacrifices her father had made to send her to the culinary academy.
"I'm flattered you asked, Rich, that you trust me enough. But, I have to say no."
"I thought that would be your answer."
Ella looked up at the night sky that was now clearing, allowing a few stars to shine through, and wondered if crewing for him would now be over.
"Will we still work together?"
"Sure, of course."
"Good." Ella nodded, giving Rich a smile. "I'd love for you to see my restaurant: a beautiful old Florida house shaded by a grove of pine and Jacaranda trees, right on the water."
"Sounds nice," Rich's voice tapered off.
"It's a little rundown, but the frame is solid and the wood floors are in okay shape. And the kitchen…" Ella shook her head. "My god, it's huge, with plenty of room for commercial appliances and two walls of windows that open out. I can hardly wait to prepare my recipes in a real kitchen."
"When does this happen?"
"It'll take some work to bring it up to code. I'll have to gut the dining and living room, except for the fireplace, which is made out of coral and so massive you can practically walk into it. I still can't believe I found it. It even has a solid pier where I can dock my boat and for customers that come by water."
"Sounds like you're on a good course."
"I am." Ella straightened her back. "I know exactly what it'll look like: old Florida intrigue, seaside dining in the romantic tropics. It's really going to be something."
"Big plans take a lot of money. Couldn't use a little more?" Rich asked.
"I'll have enough," she said. "You remember my father passed away last year?"
"I remember. That's why you came down here," said Rich.
"Partly. Everything back home reminded me of him; he was all the family I had left. And partly because I've always wanted to live in the tropics." Ella rubbed her arms, trying to keep warm in the pressing wind. "I have a buyer for his tugboat. I'm hoping to have a signed sale agreement waiting when I return to Lauderdale."
"Depending on the shape it's in, that should bring you a nice chunk of change," said Rich.
"It's contingent on a survey, of course, but he took good care of things. That was like him. Always on the up and up."
"Sounds like someone I would have liked to know."
"Yeah," said Ella. "He would have liked you, too."
"I think the squall's through with us." Rich stood up, pulling the wet sweatshirt away from his chest. "Take the wheel while I go change."
At the helm, Ella shone a light on the sails and eased the mainsheet for a smoother ride. The boat was working too hard and everyone onboard was exhausted from the boy's rescue, the storm, and the late hour. She watched the eastern sky brighten a little, the sun still well below the horizon.
In the morning they would reach the Berry Islands where Davis would step off Grace and pick up a yacht waiting for delivery to Venezuela. She wanted to go along, but Davis had said it was out of his hands; a crew had been assigned. Now it could be a month before she saw him again.
Rich came on deck in a hooded sweatshirt and pants, carrying a mug of coffee. "There's a fresh pot," he said, raising his mug to Ella.
"I'm getting some sleep." Ella looked below and saw Davis enter her cabin. Maybe not, she thought. She wanted him tonight, even if it was only to say good-bye.
"Tell Davis we should be reaching the Berry Islands in about three hours," said Rich.
"You need something more to eat? More coffee?"
"No, I'm good. Carlos will relieve me soon."
Ella lowered her voice. "Please be careful."
"Always. If you change your mind… I can't do anything until I get back to the States."
"Thanks. And don't worry, I can keep a secret."
"I know," he smiled. "Good night, Ella."
She patted his shoulder as she stepped past him. She walked below, unwinding her braid as she went, shaking her hair loose before opening her cabin door.
© 2019 by Toni Bird Jones


An opportunity has arisen for a new apprenticeship at The Alexander Centre.
Thank you so much to all the staff for making sure our wedding day was as special as we had hoped - everyone went the extra mile to ensure it all ran smoothly on the day. It's a stunning venue and we are so pleased we chose it to celebrate our marriage.