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Iterated Amplification
AlphaGo Zero and capability amplification
by Paul Christiano2 min read9th Jan 201916 comments
DeepMindIterated Amplification
How AlphaGo Zero works
Iterated capability amplification
The significance
AlphaGo Zero is an impressive demonstration of AI capabilities. It also happens to be a nice proof-of-concept of a promising alignment strategy.
AlphaGo Zero learns two functions (which take as input the current board):
A prior over moves p is trained to predict what AlphaGo will eventually decide to do.
A value function v is trained to predict which player will win (if AlphaGo plays both sides)
Both are trained with supervised learning. Once we have these two functions, AlphaGo actually picks it moves by using 1600 steps of Monte Carlo tree search (MCTS), using p and v to guide the search. It trains p to bypass this expensive search process and directly pick good moves. As p improves, the expensive search becomes more powerful, and p chases this moving target.
In the simplest form of iterated capability amplification, we train one function:
A "weak" policy A, which is trained to predict what the agent will eventually decide to do in a given situation.
Just like AlphaGo doesn't use the prior p directly to pick moves, we don't use the weak policy A directly to pick actions. Instead, we use a capability amplification scheme: we call A many times in order to produce more intelligent judgments. We train A to bypass this expensive amplification process and directly make intelligent decisions. As A improves, the amplified policy becomes more powerful, and A chases this moving target.
In the case of AlphaGo Zero, A is the prior over moves, and the amplification scheme is MCTS. (More precisely: A is the pair (p, v), and the amplification scheme is MCTS + using a rollout to see who wins.)
Outside of Go, A might be a question-answering system, which can be applied several times in order to first break a question down into pieces and then separately answer each component. Or it might be a policy that updates a cognitive workspace, which can be applied many times in order to "think longer" about an issue.
Reinforcement learners take a reward function and optimize it; unfortunately, it's not clear where to get a reward function that faithfully tracks what we care about. That's a key source of safety concerns.
By contrast, AlphaGo Zero takes a policy-improvement-operator (like MCTS) and converges towards a fixed point of that operator. If we can find a way to improve a policy while preserving its alignment, then we can apply the same algorithm in order to get very powerful but aligned strategies.
Using MCTS to achieve a simple goal in the real world wouldn't preserve alignment, so it doesn't fit the bill. But "think longer" might. As long as we start with a policy that is close enough to being aligned — a policy that "wants" to be aligned, in some sense — allowing it to think longer may make it both smarter and more aligned.
I think designing alignment-preserving policy amplification is a tractable problem today, which can be studied either in the context of existing ML or human coordination. So I think it's an exciting direction in AI alignment. A candidate solution could be incorporated directly into the AlphaGo Zero architecture, so we can already get empirical feedback on what works. If by good fortune powerful AI systems look like AlphaGo Zero, then that might get us much of the way to an aligned AI.
This was originally posted here on 19th October 2017.
Tomorrow's AI Alignment Forum sequences will continue with a pair of posts, 'What is narrow value learning' by Rohin Shah and 'Ambitious vs. narrow value learning' by Paul Christiano, from the sequence on Value Learning.
The next post in this sequence will be 'Directions for AI Alignment' by Paul Christiano on Thursday.
DeepMind5Iterated Amplification 4
Supervising strong learners by amplifying weak experts
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Directions and desiderata for AI alignment
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7Alignment Newsletter #41
[-]Ofer4y
Using MCTS to achieve a simple goal in the real world wouldn't preserve alignment, so it doesn't fit the bill.
Also, an arbitrary supervised learning step that updates p and v is not safe. Generally, making that Distill step safe seems to me like the hardest challenge of the iterated capability amplification approach. Are there already research directions for tackling that challenge? (if I understand correctly, your recent paper did not focus on it).
[-]Wei Dai4y
I think Techniques for optimizing worst-case performance may be what you're looking for.
I see how the directions proposed there (adversarial training, verification, transparency) can be useful for creating aligned systems. But if we use a Distill step that can be trusted to be safe via one or more of those approaches, I find it implausible that Amplification would yield systems that are competitive relative to the most powerful ones created by other actors around the same time (i.e. actors that create AI systems without any safety-motivated restrictions on the model space and search algorithm).
Paul's position in that post was:
All of these approaches feel very difficult, but I don't think we've run into convincing deal-breakers.
I think this is meant to include the difficulty of making them competitive with unaligned ML, since that has been his stated goal. If you can argue that we should be even more pessimistic than this, I'm sure a lot of people would find that interesting.
In this 2017 post about Amplification (linked from OP) Paul wrote: "I think there is a very good chance, perhaps as high as 50%, that this basic strategy can eventually be used to train benign state-of-the-art model-free RL agents."
The post you linked to is more recent, so either the quote in your comment reflects an update or Paul has other insights/estimates about safe Distill steps.
BTW, I think Amplification might currently be the most promising approach for creating aligned and powerful systems; what I argue is that in order to save the world it will probably need to be complemented with governance solutions.
How uncompetitive do you think aligned IDA agents will be relative to unaligned agents, and what kinds of governance solutions do you think that would call for? Also, I should have made this clearer last time, but I'd be interested to hear more about why you think Distill probably can't be made both safe and competitive, regardless of whether you're more or less optimistic than Paul.
How uncompetitive do you think aligned IDA agents will be relative to unaligned agents
For the sake of this estimate I'm using a definition of IDA that is probably narrower than what Paul has in mind: in the definition I use here, the Distill steps are carried out by nothing other than supervised learning + what it takes to make that supervised learning safe (but the implementation of the Distill steps may be improved during the Amplify steps).
This narrow definition might not include the most promising future directions of IDA (e.g. maybe the Distill steps should be carried out by some other process that involves humans). Without this simplifying assumption, one might define IDA as broadly as: "iteratively create stronger and stronger safe AI systems by using all the resources and tools that you currently have". Carrying out that Broad IDA approach might include efforts like asking AI alignment researchers to get into a room with a whiteboard and come up with ideas for new approaches.
Therefor this estimate uses my narrow definition of IDA. If you like, I can also answer the general question: "How uncompetitive do you think aligned agents will be relative to unaligned agents?".
My estimate:
Suppose it is the case that if OpenAI decided to create an AGI agent as soon as they could, it would have taken them X years (assuming an annual budget of $10M and that the world around them stays the same, and OpenAI doesn't do neuroscience, and no unintentional disasters happen).
Now suppose that OpenAI decided to create an aligned IDA agent with AGI capabilities as soon as they could (same conditions). How much time would it take them? My estimate follows; each entry is in the format:
[years]: [my credence that it would take them at most that many years]
(consider writing down your own credences before looking at mine)
1.0X:
10X:
100X:
Generally, I don't see why we should expect that the most capable systems that can be created with supervised learning (e.g. by using RL to search over an arbitrary space of NN architectures) would perform similarly to the most capable systems that can be created, at around the same time, using some restricted supervised learning that humans must trust to be safe. My prior is that the former is very likely to outperform by a lot, and I'm not aware of strong evidence pointing one way or another.
So for example, I expect that an aligned IDA agent will be outperformed by an agent that was created by that same IDA framework when replacing the most capable safe supervised learning in the Distill steps with the most capable unrestricted supervised learning available at around the same time.
I think they will probably be uncompetitive enough to make some complementary governance solutions necessary (this line replaced an attempt for a quantitative answer which turned out long; let me know if you want it).
what kinds of governance solutions do you think that would call for?
I'm very uncertain. It might be the case that our world must stop being a place in which anyone with $10M can purchase millions of GPU hours. I'm aware that most people in the AI safety community are extremely skeptical about governments carrying out "stabilization" efforts etcetera. I suspect this common view fails to account for likely pivotal events (e.g. some advances in narrow AI that might suddenly allow anyone with sufficient computation power to carry out large scale terror attacks). I think Allan Dafoe's research agenda for AI Governance is an extremely important and neglected landscape that we (the AI safety community) should be looking at to improve our predictions and strategies.
Generally, I don't see why we should expect that the most capable systems that can be created with supervised learning (e.g. by using RL to search over an arbitrary space of NN architectures) would perform similarly to the most capable systems that can be created, at around the same time, using some restricted supervised learning that humans must trust to be safe. My prior is that the former is very likely to outperform by a lot, and I'm not aware of strong evidence pointing one way or another.
This seems similar to my view, which is that if you try to optimize for just one thing (efficiency) you're probably going to end up with more of that thing than if you try to optimize for two things at the same time (efficiency and safety) or if you try to optimize for that thing under a heavy constraint (i.e., safety).
But there are people (like Paul) who seem to be more optimistic than this based on more detailed inside-view intuitions, which makes me wonder if I should defer to them. If the answer is no, there's also the question of how do we make policy makers take this problem seriously (i.e., that safe AI probably won't be as efficient as unsafe AI) given the existence of more optimistic AI safety researchers, so that they'd be willing to undertake costly preparations for governance solutions ahead of time. By the time we get conclusive evidence one way or another, it may be too late to make such preparations.
If the answer is no, there's also the question of how do we make policy makers take this problem seriously (i.e., that safe AI probably won't be as efficient as unsafe AI) given the existence of more optimistic AI safety researchers (so that they'd be willing to undertake costly preparations for governance solutions ahead of time).
I'm not aware of any AI safety researchers that are extremely optimistic about solving alignment competitively. I think most of them are just skeptical about the feasibility of governance solutions, or think governance related interventions might be necessary but shouldn't be carried out yet.
In this 80,000 Hours podcast episode, Paul said the following:
In terms of the actual value of working on AI safety, I think the biggest concern is this, "Is this an easy problem that will get solved anyway?" Maybe the second biggest concern is, "Is this a problem that's so difficult that one shouldn't bother working on it or one should be assuming that we need some other approach?" You could imagine, the technical problem is hard enough that almost all the bang is going to come from policy solutions rather than from technical solutions.
And you could imagine, those two concerns maybe sound contradictory, but aren't necessarily contradictory, because you could say, "We have some uncertainty about this parameter of how hard this problem is." Either it's going to be easy enough that it's solved anyway, or it's going to be hard enough that working on it now isn't going to help that much and so what mostly matters is getting our policy response in order. I think I don't find that compelling, in part because one, I think the significant probability on the range … like the place in between those, and two, I just think working on this problem earlier will tell us what's going on. If we're in the world where you need a really drastic policy response to cope with this problem, then you want to know that as soon as possible.
It's not a good move to be like, "We're not going to work on this problem because if it's serious, we're going to have a dramatic policy response." Because you want to work on it earlier, discover that it seems really hard and then have significantly more motivation for trying the kind of coordination you'd need to get around it.
I'm not aware of any AI safety researchers that are extremely optimistic about solving alignment competitively.
I'm not sure what you'd consider "extremely" optimistic, but I gathered some quantitative estimates of AI risk here, and they all seem overly optimistic to me. Did you see that?
Paul: I just think working on this problem earlier will tell us what's going on. If we're in the world where you need a really drastic policy response to cope with this problem, then you want to know that as soon as possible.
I agree with this motivation to do early work, but in a world where we do need drastic policy responses, I think it's pretty likely that the early work won't actually produce conclusive enough results to show that. For example, if a safety approach fails to make much progress, there's not really a good way to tell if it's because safe and competitive AI really is just too hard (and therefore we need a drastic policy response), or because the approach is wrong, or the people working on it aren't smart enough, or they're trying to do the work too early. People who are inclined to be optimistic will probably remain so until it's too late.
[-]Charlie Steiner4y
MCTS works as amplification because you can evaluate future board positions to get a convergent estimate of how well you're doing - and then eventually someone actually wins the game, which keeps p from departing reality entirely. Importantly, the single thing you're learning can play the role of the environment, too, by picking the opponents' moves.
In trying to train A to predict human actions given access to A, you're almost doing something similar. You have a prediction that's also supposed to be a prediction of the environment (the human), so you can use it for both sides of a tree search. But A isn't actually searching through an interesting tree - it's searching for cycles of length 1 in its own model of the environment, with no particular guarantee that any cycles of length 1 exist or are a good idea. "Tree search" in this context (I think) means spraying out a bunch of outputs and hoping at least one falls into a fixed point upon iteration.
EDIT: Big oops, I didn't actually understand what was being talked about here.
[-]Paul Christiano4y
I agree there is a real sense in which AGZ is "better-grounded" (and more likely to be stable) than iterated amplification in general. (This was some of the motivation for the experiments here.)
Oh, I've just realized that the "tree" was always intended to be something like task decomposition. Sorry about that - that makes the analogy a lot tighter.
[-]Gurkenglas4y
How do you know MCTS doesn't preserve alignment?
As I understand it - MCTS is used to maximize a given computable utility function, and so it is non alignment-preserving in the general sense that a sufficiently strong optimization of a non-perfect utility function is non alignment-preserving.


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Check point policing to resume in Gambia
By Kebba Jeffang
Landing Kinteh
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redpajama_set_name: RedPajamaCommonCrawl


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This got me thinking. How does this rank among others who have won a World title at wrestling's greatest stage? Specifically, I looked at those who won in the main event match. The results, as expected, were surprising.
Randy Savage – Wins title at Wrestlemania IV, in main event at V.
It's weird to think that the World title didn't change hands at a 'Mania until this one, isn't it? Of course, these were peak Hulkamania years, so I suppose its not that unusual. Savage held onto the belt until the following year, dropping it to. . .
Hulk Hogan – Wins title at Wrestlemania V, main event of VI.
See above – Hogan holds the belt until his match with. . .
Ultimate Warrior – Wins title at Wrestlemania VI, in retirement match with Savage at VII.
This is the first time the victorious champion "takes a step back", as it were. Again, knowing what we know now about Warrior's career, this makes sense. Still, being the one to (supposedly) retire Macho Man isn't anything to sneeze about.
Hulk Hogan – Wins title at Wrestlemania VII, wrestles Sid in main event of VIII.
While Hogan continued his main event run, his match with Sid was not for the title, which when you add this to the fact that the World title wasn't even DEFENDED at the initial Wrestlemania, proves that certain superstars, in the mind of Vince McMahon, eclipse the championship. Remember that when Cena vs. Rusev or HHH vs. Sting is the final match in 27 or so days.
Side note here: Randy Savage wins another World title at WM VIII, defeating Ric Flair. The following year? He's on commentary in Las Vegas. Mr. Macho is going to appear in tomorrow's "What If Wednesday" as well, so stay tuned.
Yokozuna – Wins title at Wrestlemania IX, wrestles Luger and Bret for the belt at X.
Hulk Hogan – Wins title at Wrestlemania IX, is gone from the WWF months afterwards.
Most Daniel Bryan fans should take solace in this fact, though those who enjoy ROH, Japan or are excited for the prospects of GFW might be wishing the American Dragon was there instead of in the mid-card of WWE.
Bret Hart – Wins title at X, in an "I Quit" match with Bob Backlund at XI.
Many, I imagine, have forgotten (if they were aware at all) how big this feud was back in the mid-90's. Bob Backlund doesn't get enough credit for completely rejuvenating his career by. . . Well, by being insane.
Shawn Michaels – Wins title at XII, is on commentary for main event of 13.
I don't recall specifics, but I imagine injury was involved here, so HBK gets a pass. Again, Bryan fans, be grateful DB isn't on the sidelines.
Undertaker – Wins title at 13, faces Kane at XIV.
This was the first showdown between the two brothers, thus essentially a second main event.
Steve Austin – Wins title at XIV AND at XV – doesn't appear at Mania 2000.
Stone Cold was rehabbing from neck surgery, so like Michaels, he gets a pass. Winning the title at 2 consecutive Manias is quite the feat, though, only duplicated once. Sorry, folks, you knew we were going to have to talk about Cena at some point, right?
Steve Austin – Wins title at X-7, wrestles Scott Hall at X8.
This is definitely a big step back, though to be fair, its partially Austin's fault. Refusing to lose to Hogan (Hogan would also refuse to lose to Austin, it should be mentioned), Austin was booked with Hall instead. Let's call this a self-imposed step back, with some chemical assistance.
Triple H – Wins title at X8, wrestles Booker T for a title at XIX.
Note the use of the indefinite article "a" here, for XIX is the first time there were 2 "top titles". HHH and Booker T was for the World Heavyweight Championship and was not the final match of the evening. That honor was shared with Kurt Angle and. . .
Brock Lesnar – Wins title at XIX, wrestles Goldberg at XX.
The name value for the Brock and Bill showdown was huge. . . As for the match, well. . . You can ask Jason Moltov about that. . .
Chris Benoit – wins title at XX, is part of the inaugural MITB match at XXI.
This looks familiar, doesn't it? Keep that in mind, we have a little longer to go yet. I'll try to speed things up a bit, since we're entering a more modern, Cena-filled era.
Batista – Wins title at XXI, is injured for 22.
Undertaker – wins title at 24, wrestles HBK at 25.
Having just seen this match from 25 recently, I can tell you that actually Taker had a step up in this instance.
John Cena – wins title at 29, wrestles Bray at 30.
We are lucky that most of Cena's Wrestlemania title wins didn't take place in show-ending matches. In fact, I think that tells us quite a bit, don't you?
Daniel Bryan – Wins title at 30, presumed to be in IC ladder match at 31.
So what can we learn here? Well, aside from the fact that there's been a whole LOT of title changes to end Wrestlemanias (this is where the Roman Empire squeals with glee), Daniel Bryan's professional wrestling career. . . SOLELY IN TERMS OF HIS "WRESTLEMANIA MOMENT". . . seems to be most mirroring the Canadian Crippler, Chris Benoit.
Note: While I don't feel like I really need this disclaimer, just to be safe, I am only referring to Benoit the wrestling talent. I am able to distinguish between the performer and the man. . . As we all should.
Benoit had one of the more emotional and personally satisfying Mania moments in history.
Benoit overcame a series of obstacles and his fairly diminutive size to achieve wrestling glory.
Benoit's title run was relatively short.
Following that, Benoit was back to winning mid-card titles.
This, my friends, like it or not, is what Daniel Bryan is. He had a momentous achievement in his career. . . And he'll almost certainly never reach that pinnacle again. He might win 4-5 Intercontinental and US titles, he'll likely have another couple Match of the Year candidates, and that's going to have to satisfy us.
Let me close here by drawing yet another comparison between the American Dragon and the Rabid Wolverine. If Twitter had existed back in 2003/2004, Chris Benoit (among others) would have been the IWC's darling. We'd all have been wondering when the hell this guy. . .This supremely talented guy. . . was going to get his day in the sun. We'd have worshipped the ground he walked on while also lambasting the "chosen one" with the family history who may or may not have taken his spot. (In this case, Randy Orton, who beat Benoit for the title 4-5 months after he won it.)
When WM 21 rolled around, we'd have howled at the audacity of WWE for putting our hero in such a "low-tier" spot. . . Until the match happened, when we'd have loved every second of it.
Daniel Bryan can still be our hero, even if he is never again our champion. He doesn't need a belt to be a phenomenal wrestler, and frankly, isn't that what we want?
Plus, if the career path (and ONLY the career path) continues like this, we've got a Best of Seven series coming up with Bryan and Dolph Ziggler. Admit it – you just got chills thinking about that possibility.
Tagged on: Bret Hart Chris Benoit Daniel Bryan Hulk Hogan WWE
DC Matthews March 20, 2016 WWE No Comments
← From the NAI Archive – February 26, 2015 – The NXT Day: Storytelling 101
From the NAI Archive – March 4, 2015 – What if Wednesday – What if Randy Savage had stayed in WWE? →


text:
class DebatersController < ApplicationController
  def show
    @debater = Debater.find(params[:id])
    video_ids = Video.with_debater(@debater)
    @videos = Video.where(id: video_ids)
  end
end

redpajama_set_name: RedPajamaGithub


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Wunong Net Technology Completes First Days of Trading on the Nasdaq Capital Market
Wunong Net Technology Completes First Days of Trading on the Nasdaq Capital Market with WNW Shares Closing Up
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Boustead's Dan McClory Discusses Nasdaq Restrictions On Chinese Listings with Channel News Asia
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Nasdaq's new restrictions on IPOs and accounting transparency, may impact Chinese companies looking to debut on its stock exchange
In May 2020 the Senate approved legislation that could lead to Chinese companies being barred from listing on U.S. stock exchanges amid increasingly tense relations between Washington D.C. and Beijing.
"The IPO's that were seeing in the first half of this year got started months ago," says Dan McClory." Adding that if Chinese sentiment about listing in the US is going to be affected, "then we may see that in the second half of this year or the first half of next year."
Dan McClory
Boustead's Dan McClory Discusses "Recent Developments in the Chinese Companies' IPOs in the US"
Boustead Securities' Dan McClory, Head of China and Head of Equity Capital Markets, lead the Kontaktsource virtual program, "Current State of Chinese Companies' IPOs in the United States."
Dan discusses the recent IPO market and geopolitical developments impacting emerging markets and especially Chinese companies listing in the US or seeking Nasdaq, NYSE and London Stock Exchange IPOs including: risk factors, new requirements Nasdaq has proposed to the SEC, key takeaways from the passing of U.S. Senate Bill S.945, the London-Shanghai Connect, STAR's first international listing, and more.
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text:
The Liberation Government was formed on 23 November 1944, when the government in exile came to Luxembourg from London and felt forced to include members of the Unio'n vun den Fraiheetsorgansatiounen, the umbrella group of the Luxembourgish Resistance which had been maintaining order since the liberation by American troops on 10 September 1944, in order to tame its critics.

On 23 February 1945 Robert Als and the aide-de-camp to the Grand-Duchess, Guillaume Konsbruck were added, as well as on 21 April 1945 Nicolas Margue, who was returning from resettlement.

One problem was that out of 55 pre-war Deputies, only 25 remained. The rest had been killed, resettled, or were suspected of collaboration with the Nazis. The government only wanted to organise new elections to the Chamber of Deputies when the war was over and people had returned from deportation. It therefore took decisions based on the laws of 1938 and 1939, which gave it increased powers in times of crisis. This provoked heavy criticism, so the government established a Consultative Assembly, which apart from the remaining Deputies also included members of the resistance.

21 October 1945 saw the first elections since the liberation, which provided the National Union Government on 14 November.

Return
On 23 September 1944, only two weeks after the liberation of the capital by the US Army, the Luxembourgish government returned from exile. It was confronted with grave material and moral difficulties. The immediate priorities were the population's food supply, the provision of coke for heavy industry, purges, the repatriation of deported Luxembourgers and reconstruction.

Before the liberation of the country's territory, the Allies had recognised the Luxembourgish government in London as the sole legal representative of the Grand Duchy. However, after their return from exile, the four ministers found their authority greatly limited, even contested. On the one hand, politicians had to bow to the military interests of the Allies as long as the war against Nazi Germany was still ongoing. The Allies sent a mission of Supreme Headquarters Allied Expeditionary Force, composed of American, British, Canadian and Luxembourgish officers. After the Liberation, this commission was de facto the supreme military and civil authority in Luxembourg. On the other hand, the Resistance movements, who sought moral legitimacy, contested the government's powers. To remedy the authorities' lack of power, the Unio'n's militia maintained order and arrested collaborators, which gave rise to abuses. The Resistance movements which were gathered in the Unio'n saw themselves as existing outside and above the traditional parties. In an appeal to the Grand Duchess, they demanded a new government. They accused the "London men" of being sluggish and unenthusiastic in their efforts to repatriate deported countrymen, and to punish collaborators. In response, Pierre Dupong decided to enlarge his government by including men who had lived in the country during the occupation. On 23 November 1944, Pierre Frieden joined the government to replace the minister for education, Nicolas Margue, who was still abroad after having been deported in the war. On 23 February 1945, the public prosecutor Robert Als was appointed minister for the interior and took over from Victor Bodson the difficult portfolio of purges. Guillaume Konsbruck, an officer and aide de camp to the Grand Duchess, became minister for agriculture. After his return from deportation, on 21 April 1945, Nicolas Margue rejoined the government, taking over the departments of agriculture and repatriation.

In the immediate aftermath of the war, the government worked without a parliament. When the Chamber of Deputies had its first session after the war on 6 December 1944, only 25 Deputies out of 55 were present. The rest were either still imprisoned in Germany, deceased, or suspected collaborators. However, the government wanted to await the end of the war and the return of the deported and political prisoners before calling elections. In the meantime, it relied on the laws of 28 September 1938 and 29 August 1939 to legislate. These expanded the government's powers to create new laws if the legislature was unable to carry out its functions. To give itself greater legitimacy, and to provide a forum for public discontent, the government decided to create a Consultative Assembly. This sat for 18 sessions between 20 March and 16 August 1945. Composed of former Deputies and representatives of the Resistance, the Assembly became the principal tribune for opposition to the government. On 31 May, Dupong defused the conflict by announcing legislative elections for 21 October 1945.

Foreign policy
By choosing exile and actively taking the Allied side, the Luxembourgish government had broken with its traditional security policy based on neutrality. After Liberation, it followed through with a reorientation of its foreign policy. On 26 June 1945, the Grand Duchy signed the United Nations Charter in San Francisco, becoming a founding member of the new organisation. This was a highly symbolic act, as it reflected the government's wish to participate in international politics. It was also this desire to assume international responsibilities which made the government introduce obligatory military service by decree of 30 November 1944. The gradual creation of an army with the help of the British would allow Luxembourg to participate in the occupation of Germany, and to be a party to the military agreements of the post-war period. In 1944, in the euphoria of the Liberation, the introduction of conscription encountered wide support among the population. is this even true?

Domestic policy
In the first days of the Liberation, the Unio'n's militia, which had given itself police powers, proceeded to arrest hundreds of collaborators or suspected collaborators. To put these imprisonments on a legal footing and reduce arbitrary arrests, the government stipulated by decree of 12 October 1944 that only the minister for justice, advised by a special commission, could order internment. By July 1945, the number of political prisoners had reached over 5,000. These were put to work in reconstruction or demining. On 30 November 1944, the government started a massive investigation into the attitude and actions of the civil servants who remained in place during the occupation. This was later extended to other professional categories. The administrative purge led to 20,000 cases being opened, of which 80% were closed without any action.

On 16 December 1944, the country was surprised by the Ardennes Offensive. The Germans breached the American front, which had stabilised along the Our and the Sûre, and re-occupied the northern half of the country. It was not until 22 February that the last German soldier had left the Grand Duchy's territory. The battle of the Ardennes devastated the Oesling and the region around Echternach, displaced large numbers of the population and worsened the food supply situation. Confronted with a state of emergency, the government created several institutions to allow it to intervene in the economy: the Office of Prices, the Supply Commissariat, the General Commissariat for Reconstruction, the Office for War Damages. Yet it also kept several of the measures introduced by the occupier concerning taxation and supply. The decree of 26 October 1944 stipulated that the measures taken by the occupiers before 10 September 1944 with regards to taxes, fees and charges would continue to be applicable. The German tax system, initially conceived to sustain the Nazi war effort, could also be used to finance reconstruction and the compensation of war damages. In addition, a special wealth tax of 5% was introduced. The difficulties of supply obliged the government to introduce rationing measures and to restrict consumption.

Despite this austerity policy, strikes were rare. Through social reforms, the government mitigated the negative consequences of the post-war period. The decree of 30 December 1944 introduced a minimum wage. The creation of a National Labour Conference, which included representatives of business-owners, workers and the government, made it possible to resolve social conflicts by consensus. This institution replaced the National Labour Council, created in 1936. Unemployment remained under control. The workers of the mining and steel industries, in which production had ceased due to lack of fuel, were employed in reconstruction work. The government tried to avoid being too strict in monetary policy while still minimising inflation. The decree of 14 October 1944 regulated the exchange of Reichsmarks and Luxembourgish francs. The Reichsmark, which had been introduced by the German occupiers at the rate of 1:10, was now worth 5 francs, but the first batch of 100 Reichsmarks were converted at 10 francs. Belgium provided Luxembourg with the required notes for the operation. While the government ably dealt with the various socio-economic problems of the post-war period, it seemed unable to overcome one major worry: that of the repatriation of 30,000 Luxembourgers who had been deported in the war. The slow pace of their return caused bitter criticism.

Composition

23 November 1944 to 21 April 1945
 Pierre Dupong (CSV): Minister of State, head of government, Minister of Finance and the Army
 Joseph Bech (CSV): Foreign Minister, Minister for Wine-growing
 Pierre Krier (Workers party): Minister for Labor and Social Security
 Victor Bodson (Workers): Minister for Justice and Transport
 Pierre Frieden (CSV): Minister for Schools, Culture, Arts and Sciences
From 23 February 1945 also:
 Robert Als: Minister for the Interior
 Guillaume Konsbruck: Minister for Agriculture, Trade, Industry, Occupations, and Supply

21 April 1945 until 14 November 1945
 Pierre Dupong (CSV): Minister of State, head of government, Minister of Finance and the Army
 Joseph Bech (CSV): Foreign Minister, Minister for Wine-growing
 Pierre Krier (Workers): Minister for Labor and Social Security
 Nicolas Margue (CSV): Minister for Agriculture
 Victor Bodson (Workers): Minister for Justice and Transport
 Pierre Frieden (CSV): Minister for Schools, Culture, Arts and Sciences
 Robert Als: Minister for the Interior
 Guillaume Konsbruck: Minister for Supply and the Economy

Footnotes

References

Ministries of Luxembourg
Luxembourg in World War II

redpajama_set_name: RedPajamaWikipedia


EVERY client I see has them, knotted muscles. Trigger points are a "natural" part of muscle tissue. Just as almost everyone gets some spots, sooner or later almost everyone gets muscle knots — and you have pain with no other explanation. When you say that you have "muscle knots," you are talking about myofascial trigger points.
There are no actual knots involved, of course — it's just a metaphor. Although their true nature is uncertain, the main theory is that a trigger point is a small patch of tightly contracted muscle, an isolated spasm affecting just a small patch of muscle tissue. That small patch of knotted muscle cuts off its own blood supply, which irritates it even more.
The inside of our body is covered with soft tissue called fascia. For the meat eaters among you (and apologies to the vegetarians) you may have seen this tissue when cutting up chicken. Its a thin, sticky wrapping. This tissue acts like smooth cling film and covers everything including organs, muscles, nerves and blood vessels. The particular fascia that covers your muscles is call myofascia.
When our myofascia is stressed from overuse or trauma it can tear and glue itself together to make small patches of tightly contracted muscle. When trigger points are present, on the microscopic level, part of the muscle fibre is contracted into a small thickened area, and the rest of the fibre is stretched thin. Several of these muscle fibre contractions in the same area, beaming 'gunned up' are probably what we feel as a "knot" in the muscle.
None of this is conducive to smooth muscle function. These patches build up and become "trigger points" which can prevent the muscles from working well and will need some care and attention to get smoothed out.
When pressed, trigger points feel like "knots" or tight bands in the muscle, and are usually very tender. If you self massage an area that feels tense, you can often feel them yourself. Healthy muscles usually do not contain knots or tight bands, are not tender to pressure, and when relaxed, they feel soft and pliable to the touch, not hard and dense, even if you work-out.
Multiple movements in our lives can cause this build up, it's not just those who are super active. Consider the micro movement you make using your mouse at your computer, day in day out. This builds a very common trigger point I see in the shoulder that builds up and causes aches and pains, headaches and more.
Trigger points lead to an increase in muscle stiffness and tenderness and a decrease in range-of-motion. The discomfort from trigger points can radiate from the adhesion and cause referral pain leading to a wide range of issues – back pain, headaches, stiffness and more.
Some people believe that massage needs to hurt to be effective and you could argue that massaging your trigger points it's going to hurt. Trigger point massage therapy is specifically designed to alleviate the source of the pain through cycles of isolated pressure and release. I believe that if too much pressure is used, your body starts to tense as it anticipates the next move, expecting pain. In this type of massage for trigger point therapy, I will ask you to actively participate through deep breathing as well as identifying the exact location and intensity of the discomfort. Together we can build up the intensity of the session – do your 'gunked-up' patches require soft general persuasion to release or will they prefer a spot of one-to-one attention? I like to treat your trigger points with a little bit of care and attention, warming them up to soften them up before going in to get them. Which ever way you prefer, I will find your 'gunked-up' points that are the myofascia monsters!
Can I 'fix' them in one session?
As you can imagine, trigger points take time to build and we can go along way to easing them in a massage session but you will need to take care to reduce their build up. Receiving massage with trigger point therapy on a regular basis can help naturally manage pain and stress from chronic injuries. For you, the client, becoming more aware of how our muscles function and applying regular self care can go along way to reducing those aches and pains.
I will work through some helpful hints and tips with you at the end of your massage session using an array of ideas that you can build into your day to day life.


text:
PSD Publications - CY 2018
Group by: Item Type | Authors | Editors | No Grouping
Jump to: Article | Book Section | Monograph | Dataset
Number of items: 122.
Albergel C., S. Munier, A. Bocher, B. Bonan, Y. Zheng, C. Draper, D. J. Leroux and J.-C. Calvet (October 2018): LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables. Remote Sens., 10 (10), p. 1627. doi:10.3390/rs10101627
Albers J. R., J. Perlwitz, A. H. Butler, T. Birner, G. N. Kiladis, Z. D. Lawrence, G. L. Manney, A. O. Langford and J. Dias (January 2018): Mechanisms Governing Interannual Variability of Stratosphere to Troposphere Ozone Transport. J. Geophys. Res. Atmos., 123 (1), 234-260. doi:10.1002/2017JD026890
Alexander M. A., J. D. Scott, K. D. Friedland, K. E. Mills, J. A. Nye, A. J. Pershing and A. C. Thomas (January 2018): Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem. Sci Anth., 6 (1), p. 9. doi:10.1525/elementa.191
Asgarimehr M., V. U. Zavorotny, J. Wickert and S. Reich (November 2018): Can GNSS Reflectometry Detect Precipitation Over Oceans? Geophys. Res. Lett., 45 (22), 12585-12592. doi:10.1029/2018GL079708
Averyt K., J. D. Derner, L. Dilling, R. Guerrero, . . ., A. J. Ray, et al. (May 2018): Regional Climate Response Collaboratives: Multi-institutional Support for Climate Resilience. Bull. Amer. Meteor. Soc., 99, 891-898. doi:10.1175/BAMS-D-17-0183.1
Badger A. M., B. Livneh, M. P. Hoerling and J. K. Eischeid (October 2018): Understanding the 2011 Upper Missouri River Basin floods in the context of a changing climate. J. Hydrol., 19, 110-123. doi:10.1016/j.ejrh.2018.08.004
Banta R. M., Y. L. Pichugina, W. A. Brewer, E. P. James, . . ., L. Bianco, I. Djalalova, J. M. Wilczak, et al. (June 2018): Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way. Bull. Amer. Meteor. Soc., 99, 1155-1176. doi:10.1175/BAMS-D-16-0310.1
Bianucci M., A. Capotondi, R. Mannella and S. Merlino (November 2018): Linear or Nonlinear Modeling for ENSO Dynamics? Atmosphere, 9 (11), p. 435. doi:10.3390/atmos9110435
Bianucci M., A. Capotondi, S. Merlino and R. Mannella (October 2018): Estimate of the average timing for strong El Niño events using the recharge oscillator model with a multiplicative perturbation. Chaos, 28 (103118), doi:10.1063/1.5030413
Brönnimann S., Y. Brugnara, R. J. Allan, M. Brunet, G. P. Compo, R. I. Crouthamel, P. D. Jones, S. Jourdain, J. Luterbacher, P. Siegmund, M. A. Valente and C. W. Wilkinson (October 2018): A roadmap to climate data rescue services. Geosci. Data J., 5 (1), 28-39. doi:10.1002/gdj3.56
Buizza R., P. Poli, M. Rixen, M. Alonso-Balmaseda, M. G. Bosilovich, S. Brönnimann, G. P. Compo, et al. (August 2018): Advancing Global and Regional Reanalyses. Bull. Amer. Meteor. Soc., 99, ES139-ES144. doi:10.1175/BAMS-D-17-0312.1
Capotondi A. and P. D. Sardeshmukh (October 2018): The Nature of the Stochastic Wind Forcing of ENSO. J. Climate, 31, 8081-8099. doi:10.1175/JCLI-D-17-0842.1
Castro S. L., L. A. Monzon, G. A. Wick, R. D. Lewis and G. Beylkin (November 2018): Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique. Remote Sens. Environ., 217, 292-308. doi:10.1016/j.rse.2018.08.019
Cheng L., M. P. Hoerling, L. Smith and J. K. Eischeid (February 2018): Diagnosing Human-Induced Dynamic and Thermodynamic Drivers of Extreme Rainfall. J. Climate, 31, 1029-1051. doi:10.1175/JCLI-D-16-0919.1
Cifelli R., V. Chandrasekar and H. Chen (May 2018): High Resolution Radar Quantitative Precipitation Estimation in the San Francisco Bay Area: Rainfall Monitoring for the Urban Environment. J. Meteor. Soc. Japan, 96A, 141-155. doi:10.2151/jmsj.2018-016
Creamean J. M., R. M. Kirpes, K. A. Pratt, N. J. Spada, M. Maahn, G. deBoer, R. C. Schnell and S. China (December 2018): Marine and terrestrial influences on ice nucleating particles during continuous springtime measurements in an Arctic oilfield location. Atmos. Chem. Phys., 18, 18023-18042. doi:10.5194/acp-18-18023-2018
Creamean J. M., M. Maahn, G. deBoer, A. McComiskey, A. J. Sedlacek and Y. Feng (January 2018): The influence of local oil exploration and regional wildfires on summer 2015 aerosol over the North Slope of Alaska. Atmos. Chem. Phys., 18, 555-570. doi:10.5194/acp-18-555-2018
Dias J., M. Gehne, G. N. Kiladis, N. Sakaeda, P. Bechtold and T. Haiden (June 2018): Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems. Mon. Wea. Rev., 146, 1763-1784. doi:10.1175/MWR-D-17-0362.1
Ding H., M. Newman, M. A. Alexander and A. T. Wittenberg (July 2018): Skillful climate forecasts of the tropical Indo-Pacific Ocean using model-analogs. J. Climate, 31, 5437-5459. doi:10.1175/JCLI-D-17-0661.1
Dippe T., R. J. Greatbatch and H. Ding (July 2018): On the relationship between Atlantic Niño variability and ocean dynamics. Clim. Dyn., 51 (1-2), 597-612. doi:10.1007/s00382-017-3943-z
England M. H., L. M. Polvani and L. Sun (August 2018): Contrasting the Antarctic and Arctic Atmospheric Responses to Projected Sea Ice Loss in the Late Twenty-First Century. J. Climate, 31, 6353-6370. doi:10.1175/JCLI-D-17-0666.1
Fairall C. W., S. Y. Matrosov, C. R. Williams and E. J. Walsh (March 2018): Estimation of Rain Rate from Airborne Doppler W-band Radar in CALWATER2. J. Atmos. Oceanic Technol., 35, 593-608. doi:10.1175/JTECH-D-17-0025.1
Gallagher M. R., M. D. Shupe and N. B. Miller (November 2018): Impact of Atmospheric Circulation on Temperature, Clouds, and Radiation at Summit Station, Greenland, with Self-Organizing Maps. J. Climate, 31, 8895-8915. doi:10.1175/JCLI-D-17-0893.1
Gleason S., V. U. Zavorotny, D. M. Akos, S. Hrbek, I. PopStefanija, E. J. Walsh, D. Masters and M. S. Grant (June 2018): Study of Surface Wind and Mean Square Slope Correlation in Hurricane Ike With Multiple Sensors. IEEE J. Select. Topics Appl. Earth Obs. Remote Sens., 11 (6), 1975-1988. doi:10.1109/JSTARS.2018.2827045
Gou Y., Y. Ma, H. Chen and Y. Wen (May 2018): Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau. Atmos. Res., 203, 286-297. doi:10.1016/j.atmosres.2017.12.017
Gou Y., Y. Ma, H. Chen and J. Yin (December 2018): Utilization of a C-band Polarimetric Radar for Severe Rainfall Event Analysis in Complex Terrain over Eastern China. Remote Sens., 11 (1), p. 22. doi:10.3390/rs11010022
Grachev A. A., L. S. Leo, H. J. S. Fernando, C. W. Fairall, E. Creegan, B. W. Blomquist, A. J. Christman and C. M. Hocut (May 2018): Air-Sea/Land Interaction in the Coastal Zone. Boundary-Layer Meteor., 167 (2), 181-210. doi:10.1007/s10546-017-0326-2
Grachev A. A., P. O. G. Persson, T. Uttal, E. Akish, C. J. Cox, S. M. Morris, C. W. Fairall, R. S. Stone, G. Lesins, A. Makshtas and I. A. Repina (May 2018): Seasonal and latitudinal variations of surface fluxes at two Arctic terrestrial sites. Clim. Dyn., 167 (2), 181-210. doi:10.1007/s00382-017-3983-4
Grell E. D., J.-W. Bao, D. E. Kingsmill and S. A. Michelson (May 2018): On the Importance of a Consistent Treatment of Prognostic Moisture Variables between Convective and Microphysical Parameterizations. Mon Wea. Rev., 146, 1527-1548. doi:10.1175/MWR-D-17-0305.1
Guest P. S., P. O. G. Persson, S. Wang, M. Jordan, Y. Jin, B. Blomquist and C. W. Fairall (June 2018): Low‐Level Baroclinic Jets Over the New Arctic Ocean. J. Geophys. Res. Oceans, 123 (6), 4074-4091. doi:10.1002/2018JC013778
Hacker J., C. Draper and L. Madaus (March 2018): Challenges and Opportunities for Data Assimilation in Mountainous Environments. Atmosphere, 9 (4), p. 127. doi:10.3390/atmos9040127
Hamill T. M. and M. Scheuerer (December 2018): Probabilistic Precipitation Forecast Postprocessing Using Quantile Mapping and Rank-Weighted Best-Member Dressing. Mon. Wea. Rev., 146, 4079-4098. doi:10.1175/MWR-D-18-0147.1
Hartten L. M., C. J. Cox, P. E. Johnston, D. E. Wolfe, S. Abbott and H. A. McColl (June 2018): Central-Pacific surface meteorology from the 2016 El Niño Rapid Response (ENRR) field campaign. Earth Syst. Sci. Data, 10, 1139-1164. doi:10.5194/essd-10-1139-2018
Hartten L. M., C. J. Cox, P. E. Johnston, D. E. Wolfe, S. Abbott, H. A. McColl and X.-W. Quan (June 2018): Ship- and island-based soundings from the 2016 El Niño Rapid Response (ENRR) field campaign. Earth Syst. Sci. Data, 10, 1165-1183. doi:10.5194/essd-10-1165-2018
Hoell A. (June 2018): Cold Season Southwest Asia Precipitation Sensitivity to El Niño-Southern Oscillation Events. J. Climate, 31, 4463-4482. doi:10.1175/JCLI-D-17-0456.1
Hoell A. (November 2018): Middle East and Southwest Asia Daily Precipitation Characteristics Associated with the Madden–Julian Oscillation during Boreal Winter. J. Climate, 31, 8843-8860. doi:10.1175/JCLI-D-18-0059.1
Hoell A. and L. Cheng (May 2018): Austral summer Southern Africa precipitation extremes forced by the El Niño-Southern oscillation and the subtropical Indian Ocean dipole. Clim. Dyn., 50 (9-10), 3219-3236. doi:10.1007/s00382-017-3801-z
Jacox M. G., M. A. Alexander, N. Mantua, J. D. Scott, G. Hervieux, R. S. Webb and F. E. Werner (January 2018): Forcing of Multiyear Extreme Ocean Temperatures that Impacted California Current Living Marine Resources in 2016. Bull. Amer. Meteor. Soc., 99, S27-S33. doi:0.1175/BAMS-D-17-0119.1
Jana S., B. Rajagopalan, M. A. Alexander and A. J. Ray (May 2018): Understanding the Dominant Sources and Tracks of Moisture for Summer Rainfall in the Southwest United States. J. Geophys. Res. Atmos., 123 (10), 4850-4870. doi:10.1029/2017JD027652
Jeong S., S. Newman, J. Zhang, A. E. Andrews, L. Bianco, E. Dlugokencky, J. Bagley, X. Cui, C. Priest, M. Campos-Pineda and M. L. Fischer (May 2018): Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions. J. Geophys. Res. Atmos., 123 (9), 4758-4771. doi:10.1029/2017JD028166
Jones R. M. and A. J. Bedard (November 2018): Atmospheric gravity wave ray tracing: Ordinary and extraordinary waves. J. Atmos. Solar-Terrest. Phys., 179, 342-357. doi:10.1016/j.jastp.2018.08.014
Kikuchi K., G. N. Kiladis, J. Dias and T. Nasuno (June 2018): Convectively coupled equatorial waves within the MJO during CINDY/DYNAMO: slow Kelvin waves as building blocks. Clim. Dyn., 50 (11-12), 4211-4230. doi:10.1007/s00382-017-3869-5
Kim J., L. E. Johnson, R. Cifelli, J. Choi and V. Chandrasekar (June 2018): Derivation of Soil Moisture Recovery Relation Using Soil Conservation Service (SCS) Curve Number Method. Water, 10 (7), p. 833. doi:10.3390/w10070833
Lacour A., H. Chepfer, N. B. Miller, M. D. Shupe, V. Noel, X. Fettweis, H. Gallee, J. E. Kay, R. Guzman and J. Cole (November 2018): How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet. J. Climate, 31, 9293-9312. doi:10.1175/JCLI-D-18-0023.1
Le Bris A., K. E. Mills, R. A. Wahle, Y. Chen, M. A. Alexander, A. J. Allyn, J. G. Schuetz, J. D. Scott and A. J. Pershing (February 2018): Climate vulnerability and resilience in the most valuable North American fishery. Proceed. Natl. Acad. Sci., 115 (8), 1831-1836. doi:10.1073/pnas.1711122115
Li F., P. Newman, S. Pawson and J. Perlwitz (February 2018): Effects of Greenhouse Gas Increase and Stratospheric Ozone Depletion on Stratospheric Mean Age of Air in 1960–2010. J. Geophys. Res. Atmos., 123 (4), 2098-2110. doi:10.1002/2017JD027562
Lund B. , H. C. Graber, P. O. G. Persson, M. Smith, M. Doble, J. Thomson and P. Wadhams (June 2018): Arctic Sea Ice Drift Measured by Shipboard Marine Radar. J. Geophys. Res. Oceans, 123 (6), 4298-4321. doi:10.1029/2018JC013769
Ma Y., M. Lu, H. Chen, M. Pan and Y. Hong (September 2018): Atmospheric moisture transport versus precipitation across the Tibetan Plateau: A mini-review and current challenges. Atmos. Res., 209, 50-58. doi:10.1016/j.atmosres.2018.03.015
Mahoney K. M., D. Swales, M. J. Mueller, M. A. Alexander, M. Hughes and K. Malloy (August 2018): An Examination of an Inland-Penetrating Atmospheric River Flood Event under Potential Future Thermodynamic Conditions. J. Climate, 31, 6281-6297. doi:10.1175/JCLI-D-18-0118.1
Mahrt L., C. K. Thomas, A. A. Grachev and P. O. G. Persson (December 2018): Near-Surface Vertical Flux Divergence in the Stable Boundary Layer. Boundary-Layer Meteorol., 169 (3), 373-393. doi:10.1007/s10546-018-0379-x
Martinez-Villalobos C., D. J. Vimont, C. Penland, M. Newman and D. J. Neelin (February 2018): Calculating State-Dependent Noise in a Linear Inverse Model Framework. J. Atmos. Sci., 75, 479-496. doi:10.1175/JAS-D-17-0235.1
Marvel K., R. Pincus, G. A. Schmidt and R. L. Miller (February 2018): Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations. Geophys. Res. Lett., 45 (3), 1595-1601. doi:10.1002/2017GL076468
Matrosov S. Y. and D. D. Turner (May 2018): Retrieving mean temperature of atmospheric liquid water layers using microwave radiometer measurements. J. Atmos. Oceanic Technol., 35, 1091-1102. doi:10.1175/JTECH-D-17-0179.1
McNeeley S. M., C. Dewes, C. J. Stiles, T. J . Beeton, I. Rangwala, M. T. Hobbins and C. L. Knutson (March 2018): Anatomy of an interrupted irrigation season: Micro-drought at the Wind River Indian Reservation. Clim. Risk Mgt., 19, 61-82. doi:10.1016/j.crm.2017.09.004
Miller N. B., M. D. Shupe, J. T. M. Lenaerts, J. E. Kay, G. deBoer and R. Bennartz (May 2018): Process‐Based Model Evaluation Using Surface Energy Budget Observations in Central Greenland. J. Geophys. Res. Atmos., 123 (10), 4777-4796. doi:10.1029/2017JD027377
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Neiman P. J., D. J. Gottas and A. B. White (October 2018): A Real-Time Online Data Product that Automatically Detects Easterly Gap-Flow Events and Precipitation Type in the Columbia River Gorge. J. Atmos. Oceanic Technol., 35, 2037-2052. doi:10.1175/JTECH-D-18-0088.1
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Persson P. O. G., B. W. Blomquist, P. S. Guest, S. Stammerjohn, C. W. Fairall, L. Rainville, B. Lund, S. Ackley and J. Thomson (July 2018): Shipboard observations of the meteorology and near‐surface environment during autumn freeze‐up in the Beaufort/Chukchi Seas. J. Geophys. Res. Oceans, 123 (7), 4930-4969. doi:10.1029/2018JC013786
Pettersen C., R. Bennartz, A. J. Merrelli, M. D. Shupe, D. D. Turner and V. P. Walden (April 2018): Precipitation regimes over central Greenland inferred from 5 years of ICECAPS observations. Atmos. Chem. Phys., 18 (7), 4715-4735. doi:10.5194/acp-18-4715-2018
Pithan F., G. Svensson, R. Caballero, D. Chechin, T. W. Cronin, A. M. L. Ekman, R. Neggers, M. D. Shupe, A. Solomon, M. Tjernström and M. Wendisch (October 2018): Role of air-mass transformations in exchange between the Arctic and mid-latitudes. Nature Geosci., 11, 805-812. doi:10.1038/s41561-018-0234-1
Quan X.-W., M. P. Hoerling, J. Perlwitz and H. F. Diaz (September 2018): On the Time of Emergence of Tropical Width Change. J. Climate, 31, 7225-7236. doi:10.1175/JCLI-D-18-0068.1
Quan X.-W., M. P. Hoerling, L. Smith, J. Perlwitz, T. Zhang, A. Hoell, K. Wolter and J. K. Eischeid (January 2018): Extreme California Rains During Winter 2015-16: A Change in El Niño Teleconnection? In Special Report: Explaining Extreme Events of 2016 from a Climate Perspective, Bull. Amer. Meteor. Soc., 99 (1), S49-S53. doi:10.1175/BAMS-D-17-0118.1
Rohrer M., S. Brönnimann, O. Martius, C. C. Raible, M. Wild and G. P. Compo (April 2018): Representation of Extratropical Cyclones, Blocking Anticyclones, and Alpine Circulation Types in Multiple Reanalyses and Model Simulations. J. Climate, 31, 3009-3031. doi:10.1175/JCLI-D-17-0350.1
Sakaeda N., S. W. Powell, J. Dias and G. N. Kiladis (April 2018): The Diurnal Variability of Precipitating Cloud Populations during DYNAMO. J. Atmos. Sci., 75, 1307-1326. doi:10.1175/JAS-D-17-0312.1
Savelyev I., L. N. Thomas, G. B. Smith, Q. Wang, R. K. Shearman, T. Haack, B. W. Blomquist, M. Sletten and H. J. S. Fernando (January 2018): Aerial Observations of Symmetric Instability at the North Wall of the Gulf Stream. Geophys. Res. Lett., 45 (1), 236-244. doi:10.1002/2017GL075735
Scheuerer M. and T. M. Hamill (October 2018): Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields Based on GEFS Model Output. J. Hydrometeor., 19, 1651-1670. doi:10.1175/JHM-D-18-0067.1
Schmeisser L., J. Backman, J. A. Ogren, E. Andrews, E. Asmi, S. M. Starkweather, T. Uttal, M. Fiebig, S. Sharma, K. Eletheriadis, S. Vratolis, M. Bergin, P. Tunved and A. Jefferson (August 2018): Seasonality of aerosol optical properties in the Arctic. Atmos. Chem. Phys., 18, 11599-11622. doi:10.5194/acp-18-11599-2018
Screen J. A., C. Deser, D. M. Smith, X. Zhang, R. Blackport, P. J. Kushner, T. Oudar, K. E. McCusker and L. Sun (February 2018): Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nature Geosci., 11, 155-163. doi:10.1038/s41561-018-0059-y
Shields C. A., J. J. Rutz, L.-Y. Leung, F. M. Ralph, M. Wehner, . . ., K. M. Mahoney, . . ., G. A. Wick, et al. (June 2018): Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design. Geosci. Model Dev., 11, 2455-2474. doi:10.5194/gmd-11-2455-2018
Shlyaeva A. and J. S. Whitaker (June 2018): Using the Linearized Observation Operator to Calculate Observation Space Ensemble Perturbations in Ensemble Filters. J. Adv. Model. Earth Syst., ONLINE, doi:10.1029/2018MS001309
Slivinski L. C. (August 2018): Historical Reanalysis: What, How, and Why? J. Adv. Model. Earth Syst., 10 (8), 1736-1739. doi:10.1029/2018MS001434
Smith M., S. Stammerjohn, P. O. G. Persson, L. Rainville, G. Liu, W. Perrie, R. Robertson, J. Jackson and J. Thomson (May 2018): Episodic Reversal of Autumn Ice Advance Caused by Release of Ocean Heat in the Beaufort Sea. J. Geophys. Res. Oceans, 123 (5), 3164-3185. doi:10.1002/2018JC013764
Solomon A., G. deBoer, J. M. Creamean, A. McComiskey, M. D. Shupe, M. Maahn and C. J. Cox (December 2018): The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds. Atmos. Chem. Phys., 18, 17047-17059. doi:10.5194/acp-18-17047-2018
Stillwell R. A., R. R. Neely, J. P. Thayer, M. D. Shupe and D. D. Turner (February 2018): Improved cloud-phase determination of low-level liquid and mixed-phase clouds by enhanced polarimetric lidar. Atmos. Meas. Tech., 11, 835-859. doi:10.5194/amt-11-835-2018
Sun L., M. A. Alexander and C. Deser (October 2018): Evolution of the global coupled climate response to Arctic sea ice loss during 1990-2090 and its contribution to climate change. J. Climate, 31, 7823-7843. doi:10.1175/JCLI-D-18-0134.1
Sun L., D. Allured, M. P. Hoerling, L. Smith, J. Perlwitz, D. Murray and J. K. Eischeid (March 2018): Drivers of 2016 record Arctic warmth assessed using climate simulations subjected to Factual and Counterfactual forcing. Web. Clim. Extremes, 19, 1-9. doi:10.1016/j.wace.2017.11.001
Swales D., R. Pincus and A. Bodas-Salcedo (January 2018): The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2. Geosci. Model Dev., 11, 77-81. doi:10.5194/gmd-11-77-2018
Thomas A. C., A. J. Pershing, K. D. Friedland, J. A. Nye, K. E. Mills, M. A. Alexander, N. R. Record, R. Weatherbee and M. E. Henderson (August 2018): Seasonal trends and phenology shifts in sea surface temperature on the North American northeastern continental shelf. Elem. Sci. Anth., 5 (48), doi:10.1525/elementa.240
Thomas E. E., D. J. Vimont, M. Newman, C. Penland and C. Martinez-Villalobos (November 2018): The Role of Stochastic Forcing in Generating ENSO Diversity. J. Climate, 31, 9125-9150. doi:10.1175/JCLI-D-17-0582.1
Thomson J., S. Ackley, F. Girard-Ardhuin, F. Ardhuin, A. Babanin, G. Boutin, . . ., C. W. Fairall, . . ., P. O. G. Persson, et al. (December 2018): Overview of the Arctic Sea State and Boundary Layer Physics Program. J. Geophys. Res. Oceans, 123 (12), 8674-8687. doi:10.1002/2018JC013766
Timmermann A., S.-I. An, J.-S. Kug, F.-F. Jin, W. Cai, A. Capotondi, et al. (July 2018): El Niño–Southern Oscillation complexity. Nature, 559, 535-545. doi:10.1038/s41586-018-0252-6
Tsai C., M. Spolaor, S. F. Colosimo, O. Pikelnaya, R. Cheung, E. Williams, J. B. Gilman, B. M. Lerner, R. J. Zamora, et al. (February 2018): Nitrous acid formation in a snow-free wintertime polluted rural area. J. Atmos. Chem. Phys., 18 (3), 1977-1996. doi:10.5194/acp-18-1977-2018
Turi G., M. A. Alexander, N. S. Lovenduski, A. Capotondi, J. D. Scott, C. Stock, J. Dunne, J. John and M. G. Jacox (February 2018): Response of O2 and pH to ENSO in the California Current System in a high-resolution global climate model. Ocean Sci., 14, 69-86. doi:10.5194/os-14-69-2018
Turner D. D., M. D. Shupe and A. B. Zwink (April 2018): Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska. J. Appl. Meteor. Climatol., 57, 953-968. doi:10.1175/JAMC-D-17-0252.1
Vera C. S., P. L. M. Gonzalez, B. Liebmann and G. N. Kiladis (September 2018): Seasonal cycle of precipitation variability in South America on intraseasonal timescales. Clim. Dyn., 51 (5-6), 1991-2001. doi:10.1007/s00382-017-3994-1
Voronovich A. G. (December 2018): A Method of a Tangent Cylinder in the Theory of Wave Scattering by Convex Surfaces. IEEE Trans. Antennas Prop., 66 (12), 7515-7518. doi:10.1109/TAP.2018.2872739
Voronovich A. G. (September 2018): A complex scalar form of the incompressible Navier–Stokes equations. Waves Random Complex Media, ONLINE, doi:10.1080/17455030.2018.1524184
Wadhams P., G. Aulicino, F. Parmiggiani, P. O. G. Persson and B. Holt (January 2018): Pancake Ice Thickness Mapping in the Beaufort Sea From Wave Dispersion Observed in SAR Imagery. J. Geophys. Res. Oceans, 123 (3), 2213-2237. doi:10.1002/2017JC013003
Wang K., C. Deser, L. Sun and R. A. Tomas (April 2018): Fast Response of the Tropics to an Abrupt Loss of Arctic Sea Ice via Ocean Dynamics. Geophys. Res. Lett., 45 (9), 4264-4272. doi:10.1029/2018GL077325
Wang Q., D. P. Alappattu, S. Billingsley, B. W. Blomquist, . . ., A. A. Grachev, et al. (July 2018): CASPER: Coupled Air-Sea Processes and Electromagnetic (EM) ducting Research. Bull. Amer. Meteor. Soc., 99, 1449-1471. doi:10.1175/BAMS-D-16-0046.1
Webb R. S. and F. E. Werner (January 2018): Explaining Extreme Ocean Conditions Impacting Living Marine Resources. Bull. Amer. Meteor. Soc., 99, S7-S10. doi:10.1175/BAMS-D-17-0265.1
Wen Y., A. Behrangi, H. Chen and B. Lambrigtsen (November 2018): How well were the early 2017 California Atmospheric River precipitation events captured by satellite products and ground‐based radars? Q.J.R. Meteor. Soc., 144 (S1), 344-359. doi:10.1002/qj.3253
Wick G. A., T. F. Hock, P. J. Neiman, H. Vömel, M. L. Black and J. R. Spackman (August 2018): The NCAR/NOAA Global Hawk Dropsonde System. J. Atmos. Oceanic Technol., 35, 1585-1604. doi:10.1175/JTECH-D-17-0225.1
Williams C. R. , M. Maahn, J. Hardin and G. deBoer (September 2018): Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra. Atmos. Meas. Tech., 11, 4963-4980. doi:10.5194/amt-11-4963-2018
Wolter K., M. P. Hoerling, J. K. Eischeid and D. Allured (January 2018): Was the January 2016 mid-Atlantic snowstorm "Jonas" symptomatic of climate change? In Special Report: Explaining Extreme Events of 2016 from a Climate Perspective, Bull. Amer. Meteor. Soc., 99 (1), S54-S59. doi:10.1175/BAMS-D-17-0130.1
Wood K. R., S. R. Jayne, C. W. Mordy, N. Bond, J. E. Overland, C. Ladd, P. J. Stabeno, A. K. Ekholm, P. E. Robbins, M.-B. Schreck, R. Heim and J. M. Intrieri (March 2018): Results of the First Arctic Heat Open Science Experiment. Bull. Amer. Meteor. Soc., 99, 513-520. doi:10.1175/BAMS-D-16-0323.1
Worsnop R. P., M. Scheuerer, T. M. Hamill and J. K. Lundquist (June 2018): Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. Wind Energy. Sci., 3, 371-393. doi:10.5194/wes-3-371-2018
Yano J.-I., M. Z. Ziemiański, M. Cullen, P. Termonia, J. Onvlee, L. Bengtsson, et al. (April 2018): Scientific challenges of convective-scale numerical weather prediction. Bull. Amer. Meteor. Soc., 99, 699-710. doi:10.1175/BAMS-D-17-0125.1
Zhang T., M. P. Hoerling, K. Wolter, J. K. Eischeid, L. Cheng, A. Hoell, J. Perlwitz, X.-W. Quan and J. J. Barsugli (January 2018): Predictability and Prediction of Southern California Rains during Strong El Niño Events: A Focus on the Failed 2016 Winter Rains. J. Climate, 31, 555-574. doi:10.1175/JCLI-D-17-0396.1
Zhang X., J.-W. Bao, B. Chen and E. D. Grell (July 2018): A Three-Dimensional Scale-Adaptive Turbulent Kinetic Energy Scheme in the WRF-ARW Model. Mon. Wea. Rev., 146, 2023-2045. doi:10.1175/MWR-D-17-0356.1
Zheng J., F. Wang, M. A. Alexander and M. Wang (March 2018): Impact of South Pacific Subtropical Dipole Mode on the Equatorial Pacific. J. Climate, 31, 2197-2216. doi:10.1175/JCLI-D-17-0256.1
de la Cámara A., J. R. Albers, T. Birner, R. R. Garcia, P. Hitchcock, D. E. Kinnison and A. K. Smith (September 2018): Sensitivity of Sudden Stratospheric Warmings to Previous Stratospheric Conditions. J. Atmos. Sci., 74, 2857-2877. doi:10.1175/JAS-D-17-0136.1
deBoer G., M. Ivey, B. Schmid, D. Lawrence, D. Dexheimer, F. Mei, J. Hubbe, A. Bendure, J. Hardesty, M. D. Shupe, A. McComiskey, H. Telg, C. Schmitt, S. Y. Matrosov, I. Brooks, J. M. Creamean, A. Solomon, D. D. Turner, C. R. Williams, M. Maahn, B. Argrow, S. Palo, C. N. Long and R. S. Gao (June 2018): A Bird's Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska. Bull. Amer. Meteor. Soc., 99, 1197-1212. doi:10.1175/BAMS-D-17-0156.1
Hamill T. M. ; Vannitsem S., D. Wilks and J. Messner (May 2018): Chapter 7. Application of Post-Processing to Weather Forecasts. In: Statistical Postprocessing of Ensemble Forecasts. Elsevier Press. ISBN 9780128123720
Webb R. S. ; Rosenzweig C., D Rind, A. Lacis and D. Manley (March 2018): CLIMATE LECTURE 9: Wisdom, Climate, and Water Resources. In: Our Warming Planet: Topics in Climate Dynamics. Lectures in Climate Change, 1 . World Scientific, 197-213. ISBN 978-981-3148-77-2
Herdman L., J. Kim, L. E. Johnson, T. Coleman, R. Cifelli, R. Martyr-Koller, J. Finzi-Hart, L. Erikson and P. Barnard (July 2018): San Francisco Bay Integrated Flood Forecasting Project - Summary Report. MONOGRAPH - Technical Report. NOAA Technical Memorandum OAR PSD-317, NOAA Printing Office, Silver Spring, MD. -- monograph
Mahoney K. M., J .J. Lukas and M. Mueller (November 2018): Colorado – New Mexico Regional Extreme Precipitation Study Summary Report, Volume VI: Considering Climate Change in the Estimation of Extreme Precipitation for Dam Safety. MONOGRAPH - Technical Report. CO Division of Water Resources and NM Office of the State Engineer. -- monograph
Wick G. A., J. P. Dunion and J. Walker (January 2018): Sensing Hazards with Operational Unmanned Technology : impact study of Global Hawk unmanned aircraft system observations for hurricane forecasting, Final Report. MONOGRAPH - Technical Report. NOAA Technical Memorandum OAR UAS; 002. -- monograph
Bariteau L., C. W. Fairall, B. W. Blomquist and S. Pezoa (January 2018): CAPRICORN 2016 Field campaign: surface meteorological data and turbulent fluxes collected from the RV Investigator by the National Oceanographic and Atmospheric Administration (NOAA) in the Indian and South Pacific Oceans from 2016-03-14 to 2016-04-15 (NCEI Accession 0170257). Version 1.1. doi:10.7289/V5Q81BBC
Shupe M. D. (September 2018): Ceilometer Cloud Base Height Measurements at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A2KP7TQ9P
Shupe M. D. (September 2018): Cloud and Aerosol Polarization and Backscatter LiDAR measurements taken at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A25T3FZ9D
Shupe M. D. (September 2018): Millimeter Cloud Radar measurements taken at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A2F47GT18
Shupe M. D. (September 2018): Precipitation Occurrence Sensor System measurements taken at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A20Z70W42
Shupe M. D. (September 2018): SOnic Detection And Ranging (SODAR) measurements taken at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A2QF8JJ3F
Walden V. P. and M. D. Shupe (September 2018): Radiosonde temperature and humidity profiles taken at Summit Station, Greenland, 2018. Arctic Data Center. doi:10.18739/A2RF5KF45
This list was generated on Thu Jul 18 06:00:14 2019 MDT.
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text:
Q: How to Unit test a method in java which has asynchronous calls in it using Mockito I have below class in which a method has CompletableFuture.supplyAsync call and returns CompleteableFuture
How can I unit test this method? Please check below for more details
public class ServiceA {

    private final ThreadPoolTaskExecutor taskExecutor;
    private final ClassA objA;
    private final ClassB objB;

    public CompletableFuture<ResponseEntity> methodA(Object obj,  int number) {

        validating(obj)

        someProcessing(obj, userId);

        CompletableFuture.supplyAsync(() ->
                                       objA.getData(num,obj), taskExecutor)
                        .thenApply(response -> convertToSomething(response,obj))
                        .thenAccept(response -> objB.doSomeThing(response,
                                                                          arg)))
                        .exceptionally(throwable -> {

                            return null;
                        })
                        .thenAccept(val -> ResponseEntity.ok().build())
                        .join();

        return CompletableFuture.completedFuture(ResponseEntity.ok()
                                                               .build());
    }
  }

I have to unit test methodA. Can someone help me out?
Thanks In Advance

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