With Couchbase Server 4.5, a new full-text search service has been introduced. Check out how make it work for you including how to scale it out.
Couchbase Server 4.5 includes a new service, full text search (FTS) . In this blog, I'll talk about how FTS scales out across nodes, how to replicate indexes, and how it behaves in a rebalance.
The most notable new search feature in the 4.5 beta is the ability to run the FTS service across multiple nodes. You can try it out today with the beta, and what you read here will still apply when Couchbase Server 4.5 goes GA.
Ok, let's get to the good stuff. You can use the search service to index and search text in your Couchbase documents without relying on a third party search package. The new search service joins the data, index, and query services and can be managed like other services for the purposes of multi-dimensional scaling (MDS). Note that unlike N1QL, the search service does both full text query and indexing in a single service.
For the most part, distributed search indexes "just work": the Couchbase Full Text Search service takes advantage of new hardware as you add nodes, and full text indexes are failed over and rebalanced along with the data service. This section talks about the mechanisms that enable this, which you usually don't need to know as a user but you will sometimes encounter, as in partial search results (touched on later).
From the start, full text search was designed to distribute text indexes across nodes, in very much the same way that the data service distributes data in buckets. If you understand Couchbase Server's buckets and vBuckets, you've got a good mental model for understanding this. The bucket is a logical unit of data containment that is easy to work with, and the vBucket is a physical portion of the data that's in a bucket, that lives on a specific node on a cluster. When you turn on replication on a bucket, Couchbase Server creates copies of all the necessary vBuckets that make up that bucket. Couchbase Server also makes sure that the layout of those vBuckets is optimal, which is itself a complex topic but for now, you can just think of it as balancing the locations of the vBuckets so that they are spread evenly across nodes.
FTS works similarly. Full text indexes are automatically divided into fragments called pindexes, which is short for "partitioned indexes" or "physical indexes", depending on who you ask. Like a bucket, a full text index is a logical concept. The pindex is the physical implementation of the index, just like the vBucket is the physical implementation of the bucket.
Pindexes are physically distributed across the Couchbase nodes that run the data service, however many that might be. In the developer preview, it was a single node but that restriction has been lifted in the Beta. In the examples below, we'll use just two nodes to keep it simple.
Time to get hands on. If you have Couchbase Server 4.5 Beta or newer, you're all ready to go. You're going to need to set up more than one node, so get a VM ready. For this example, I'm going to use a couple of Windows Server 2012 VMs.
Let's go ahead and start with a single node running the search service. We'll create a simple index on the travel-sample bucket's hotel documents. If you don't have the travel sample installed, you can get it by clicking Settings > Sample Buckets and then checking the box.
For the purposes of this demo, any full text index will do. I created an index on type="hotel" (don't forget to disable the default type mapping) with two fields, "name" and "description", store = true, and "index only specified fields" just so it will build fast.
When you're done, you can search for a common word like "Inn" to make sure things are working.
When you take a look at your Couchbase data directory, you'll see an @fts directory. Open it and you will see a bunch of directories containing the pindexes.
Go ahead and add a second node to the cluster, like you normally would. Don't forget to tick the box to enable the search service.
You will see a screen that looks like this when you get done, showing a server pending rebalance.
In this case, I see a fail over warning because the travel sample data has no replicas. Go in and hit the rebalance button. Documents begin to copy to the new node, replicas are created, and the travel sample's views show as "building" because they are a form of local index, so the data rebalance means views also have to be rebuilt as the active data moves. (If you want to save yourself some time for the purposes of this demo, you can delete the views). Your pindexes are also rebalanced over the available nodes.
Once the process finishes, you will now have one-half of the pindexes on each server.
Full text indexes can be replicated, just like you can have active and replica documents in Couchbase. Like document replicas, text index replicas get automatically laid out in the cluster in a balanced distribution depending on what hardware is available. Replicating full text indexes is mainly to speed up failover. Unlike documents, full text indexes are not at risk of data loss because they can always be recreated by reindexing using the index definition.
When you create or update a full text index, you have the option to specify one or two replicas. To do this, navigate to your index definition, click Edit, and then check Show advanced settings.
In Plan Params, change numReplicas to 1 and then Update Index.
As soon as you save your definition, Couchbase Server will begin creating the active and replica text indexes. This will give you another copy of the index. Again, you can verify this by looking at one of the data\@fts directories and counting the pindexes. Each node should now have 1/2 of the active pindexes and 1/2 of the replica pindexes.
You can also see this by clicking on the Server Nodes tab, clicking on one of the servers and then expanding "Full Text Search Stats" for the index you created. You will see stats for pindexes actual (how many exist) and pindexes target (how many should exist, given the number of replicas you've requested).
Now failover one node. If you search now, you will get partial results—that is, you will get search results from the remaining pindexes. This is by design since some applications may elect to continue with partial results rather than throw an error. After all, with a large enough data set, users may not even notice missing documents.
Now, rebalance the node. Rebalance with replicas is quite fast. (If you didn't delete the travel sample views earlier, the failover takes significantly longer).
When you hit rebalance, the other node takes over very quickly by activating replicas - both document replicas and pindex replicas. This promotion is instantaneous. If indexing is ongoing, either because the index isn't fully caught up or document mutations are going on, the full text indexes may take more time to build. As vBuckets move, nodes running the FTS service will reconnect their DCP streams to wherever the vBuckets ended up in the rebalance.
You might be wondering what would happen if you were to do the above experiment without having created any full text index replicas first. That works too, and it's worth doing at least once to see what happens. You won't have primaries for some of your pindexes—exactly half in this case. That's OK—as I mentioned above, you don't have data loss because the missing pindexes can be recreated by reindexing the documents using the original index definitions. You will get partial results, and you will get them for a longer period of time, too. This is because when you do a rebalance, the missing pindexes have to start from scratch and will not be as caught up as they would be if there were pindex replicas available.
Distributed full text indexes are one of my favorite features of this release. They definitely take longer to explain than they take to use! We invite you to try them out and let us know what you think. Happy searching!
Published at DZone with permission of Will Gardella , DZone MVB. See the original article here.


Novell has released its first service pack for Suse Linux Enterprise Server 10, with capabilities for new quad-core processors.
The company also said it plans to release Suse Linux Enterprise Virtual Machine Driver Pack in July.
Service Pack 1 supports quad-core processors in the Intel Xeon and AMD Opteron product lines. Also featured are improvements in interoperability and system management, Novell said.
"What we further improved was the interoperability with Active Directory for both the server and the desktop," said Holger Dyroff, Novell vice president of Suse Linux Enterprise product management.
Novell also detailed Suse Linux Enterprise Virtual Machine Driver Pack. This package includes drivers to enable unmodified Windows and Linux guest operating systems to run with near-native performance in virtual environments created with Xen in Suse Linux.
Featured are para-virtualisation and support for a range of fully virtualised workloads. Customers can run legacy applications on newer hardware. Para-virtualisation bolsters communication with the hypervisor when a guest operating system is running on top of a host OS, thus improving performance.
Featured in Virtual Machine Driver Pack are drivers for Windows XP, Windows 2000, and Windows 2003. Virtual Machine Driver Pack is due for general availability in July. Drivers for Red Hat Enterprise Linux 4 and 5 will be released later this summer.
A one-year subscription to Driver Pack costs $299 per physical server for as many as four virtual machines, or $699 per physical server for an unlimited number of virtual machines.
Xen drivers for Suse Linux Enterprise Server are available now as part of Suse Linux Enterprise distribution.


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text:
Q: Show that the sequence $\left\{b_n\right\}:b_n=\frac{5n^2+2n-1}{3n^2-14}$ converges Show that the sequence $\left\{b_n\right\}:b_n=\dfrac{5n^2+2n-1}{3n^2-14}$ converges (using Weierstrass theorem).
So we are supposed to show that $\{b_n\}$ is 1) monotonic and 2) bounded. It's usually easier to start by proving that the sequence is monotonic. The $(n+1)$th term is $$b_{n+1}=\dfrac{5(n+1)^2+2(n+1)-1}{3(n+1)^2-14}=\dfrac{5n^2+12n+6}{3n^2+6n-11}$$ Neither $b_{n+1}-b_n$, nor $\dfrac{b_{n+1}}{b_n}$ looks easy. Can you give me a hint?

A: Just use brute force:
$$b_{n+1}-b_n=\frac{5n^2+12n+6}{3n^2+6n-11}-\frac{5n^2+2n-1}{3n^2-14}=-\frac{6 n^2 + 140 n + 95}{(3 n^2 - 14) (3 n^2 + 6 n - 11)}$$
So it easy to see that $b_{n+1}-b_n<0$ for $n\ge 3$
Also $b_n>0$ for the same $n\ge 3$, so $b_n$ is bounded as well, thus Weierstraß theorem applies.

A: For fractions of 2 polynomials, the method is always the same: factor out the highest degree term in both numerator and denominator, and then apply standard limit results:$$\frac{5n^2+2n-1}{3n^2-14}=\frac{5n^2}{3n^2}\cdot\frac{1+\frac2{5n}-\frac1{5n^2}}{1-\frac{14}{3n^2}}=\frac53\cdot \frac{1+\frac2{5n}-\frac1{5n^2}}{1-\frac{14}{3n^2}}.$$
There shouldn't be any indeterminate form left in the second fraction. The same method applies to all fractions of two polynomials, and you do not need to study monotonicity/boundedness of the sequence.

A: $$ \left|\dfrac{5n^{2}+2n-1}{3n^{2}-14}-\frac{5}{3}\right|=\left|\frac{15n^{2}+6n-3-15n^{2}+70}{3\left(3n^{2}-14\right)}\right|\le\left|\frac{6n+70}{3n^{2}-14}\right|\le\left|\frac{76n}{3n^{2}-14n}\right|=\frac{76}{3n-14}\le\varepsilon\Rightarrow \\76\le3n\varepsilon-14\varepsilon\Rightarrow76+14\varepsilon\le3n\varepsilon\Rightarrow\frac{76+14\varepsilon}{3\varepsilon}\le n $$
This is all true assuming $ 3n^2-14 $ is positive so we can take $ N_0=\max\left\{ \frac{76+14\varepsilon}{3\varepsilon},10\right\}  $ and then we found the limit.

redpajama_set_name: RedPajamaStackExchange


1. For almost two years, we have been in a domestic housing slump. This is the aftermath of a long housing boom fueled in it last days by extreme levels of speculation–by individuals buying houses they couldn't afford (or intended to quickly sell to a "greater fool"), and by banks making mortgage loans to unqualified borrowers. There are signs that the worst may be over for single-family homes, but not for apartments.
2. Last year, the public, the regulators and the financial companies themselves discovered that the securities trading desks that the financials had established over the past ten years were not the immense profit centers that the company accounts had portrayed. Instead, these trading desks had lost so much money, with top management unaware of what was going on, that they had driven their firms–big institutions like Citigroup, AIG, Lehman or Merrill Lynch–into bankruptcy in all but name. Since many of the securities being traded were either backed by worthless mortgages (para. 1) or were IOUs from the now-defunct firms, even the "winning" side of these trades were money losers.
The realization, late last year, of the extent of the losses made financial companies effectively stop making new loans of any type, shutting off the life blood of economic growth. This is by far the largest problem we have. Since then, governments around the world have been taking all sorts of extraordinary measures to restart the normal process of credit creation.
confidence in US institutions has been damaged. This is not only the banks. Investors seem to regard Congress as dysfunctional and to worry that, as a result, the government will not be able to fix the banking problems. I've never seen this kind of lack of confidence before, so I find the phenomenon tough to evaluate.
3. The internet continues to do its work of creative destruction. The economic downturn has accelerated the pace of change. Newspapers and local TV stations are the current focus of rapid change. Book publishing may be next.
4. The Detroit-based "Big Three" car companies have been losing market share for thirty years. For almost that long, they have been standard studies for MBAs on how not to run a business. Ford appears to finally begun restructuring a few years ago. It appears restructuring will be forced on GM and Chrysler. I don;t think this is the significant stock market issue it might have been four or five months ago. But Michigan will likely find the economic going hard over the next few years.
The most important practical thing an investor can do at any time, but particularly during a period like this of great emotion in the markets, is to step back and try to take a longer view of events. We are highly unlikely ever to be able to out-execute a hedge fund that concentrates on short-term trading. On the other hand, that approach brings with it enormous pressure to focus on near-term results, leaving the much more fertile field of seeking long-term winning companies and economic trends for us to try to exploit.
In a series of posts under the title of "Shaping a Portfolio for 2010" I'm going to talk about what I think are the key factors to consider. The idea is to develop a set of conclusions about what the future will look like and use them as the basis for a stock market strategy. These conclusions should be regarded as working hypothesises to be monitored and tested. But articulating what they are and how to judge whether they are correct or not will give us a key to altering strategy as events develop.
The Overall Market–Up or Down?
Yes, I think it's true that successful investing is not about timing the market, that is, trying to sell at the top, hold cash for a while, and reenter the market at the bottom. But the strength of the overall economy has a profound effect on what kinds of stocks do well.
If the economy is expanding, for example, then profits are rising and the more economically-sensitive stocks, like those in consumer durables, technology, and industrial sectors, are likely to be outperforming. If, on the other hand, the economy is contracting, defensive areas like consumer staples, medical services or utilities are typically the market stars.
Not only that, but relative market shares within industries can shift considerably with the business cycle. In good times, the #1 supplier may not be able to meet all the demand from even its best customers. So, out of necessity, orders flow to second- and third-tier vendors. In bad times, #1 may have spare capacity. If so, orders will shift back to the perceived higher quality and service, possibly hurting lower-order vendors severely.
Also, companies with very high operational or financial gearing (therefore, very high fixed costs to cover before they can show a profit) can show huge profit swings in response to small changes in demand or pricing.
Where are we now? I think that in stock market terms we're just past the worst of a vicious downturn. (Global economic growth won't resume until late this year, at the earliest; some industries won't recover for much longer. Remember, though, the stock market is a futures market that discounts economic performance in advance.) This would mean the next significant move for the markets is up. The markets are in the earliest stages of preparing for this, but expressing guarded optimism in only the most generic way by selling defensive groups and buying more aggressive ones. I think this is more an unwinding of a fear-driven excessively defensive posture, and calculation that such a posture is unlikely to provide outperformance, rather than strong belief that resumption of earnings growth is close at hand.
I think we're entering an important time for the market. We've crawled, maybe "sprinted" is a better word, out of the hole the market made for itself in February and early March. Some of the bank stocks are starting to slow down. We've had a couple of yo-yo days, where stocks have been both up and down a lot. Yesterday, not only were smaller stocks strong outperformers, but small laggards did a lot of catch-up. In other words, the patterns that have shaped the market action during the rally seem to be changing, and at a point where a load of selling has occurred in the recent past.
Today may tell us a lot.
Typically, the first surge in a new up market is a mad rush to cover shorts and get money back into stocks. Buying is relatively indiscriminate, but with a significant tilt toward larger market cap stocks, for no other reason than they can accept large amounts of money. After that phase, however, buying becomes more focused and new leadership areas emerge.
1. are we at that stage now?
2. if we stall at this level of the market, do we have to reverse direction and "test" the early-March low?
More on these topics over the weekend.
I'm in Florida watching spring training, so this will be brief.
The market continues its strong rotation away from defensive stocks and toward economically sensitive stocks. This pattern is very evident in almost every day's trading.
Financials have been the stars, but one must ask when this will end. Why? Balance sheet losses show the historical growth rate of profits was actually much lower than the companies reported. Future growth may be slower still because of stricter regulation. Bailed-out banks will likely be forced to compete against each other in the home market, lowering margins. Also (not a really good reason), leadership in a new bull market rarely, if ever, includes the leadership of the prior bull market.
Why the furious rally in financials so far? Typically, in times of national economic stress, the market declines, fearing the worst economic outcome,, until the government announces it recognizes the problem and intends to deal with it. The market immediately rallies. Not this time. It's clear investors feared the government in the US lacked the competence to understand and address the banking crisis. So the market waited until many concrete plans were already under way–including the original plan to reomove toxic assets from bank balance sheets–before bidding the banks back up.
A new bull market requires credible belief about upcoming eranings growth. I don't think we're there yet. I read the current market action as investors slowly moving away from extreme defensive positioning. So I think the current market rotation and a gradual upward drift of the markets will continue.
At some point soon, this very general idea won't be enough to guide an investment strategy. The key questions, as I see them, are: how badly has the continuing demonstration of cluelessness by our legislators damaged investor pereceptions of the attractiveness of investing in the US? (it's bad, but is it life-threatening?), and where will the strongest earnings growth be in 2010.? More on these topics later.
It's not hard to understand the reason. Given a roughly 50% decline in world stock markets from the top, plus possible redemptions by mutual fund or institutional shareholders, management fee revenues for this month could easily have dropped to 40% of their level at the top two years ago. Profits could now be a quarter of their high water mark. The situation is doubtless worse at smaller firms. Companies less dedicated to doing their own research than Capital will likely lay off proportionally more analysts and portfolio managers.
The equity market downturn is only the latest in a number of developments that have tended to reduce the amount of sophisticated analysis in the hands of institutional investors.
*Many years ago the question of which brokerage house employee gets credit for generating commission revenue from clients–the research analyst or the trader–has been decided in favor of the latter. As a result of their diminishing share in company profits, seasoned analysts have been gradually leaving the brokers for a long time. This movement has accelerated over the past few years as hedge funds have sought this expertise for themselves.
*At the same time, it seems to me there has been a long-term tendency among institutional money managers to deemphasize in-house research. This has two short-term benefits. It makes the business simpler. It also means part of the cost of obtaining specialist investment information is no longer borne by the manager and paid for from fee income, but is paid for by the manager's client through "soft dollar" "research commissions" that the manager directs to brokers and third-party research services.
Why is any of this good? The breaking down of established institutional information gathering an analysis networks, long in train but accelerated by the market downturn, will probably make world stock markets less efficient. But this gives the serious amateur the best chance he's had in the past twenty years at finding out and acting on information before the market does.
Early in my career I remember hearing stories about a fellow portfolio manager who fancied himself an expert billiards player. He regularly trounced the salesmen from brokerage houses he dealt with when they met socially for dinner and a game or two. Then he changed jobs, to become an investment strategist for a broker, that is, a colleague of his billiards buddies rather than a client. As I heard it, he never won a game again.
I don't know very much about AIG other than what I've read in the news. But the sheer magnitude of the losses AIG's Business Products division rolled up implies operating ineptitude of heroic proportions. As my story above suggests, it's a standard strategy for a trading counterparty to downplay his own competence and inflate your ego. After all, you trade more often and less cautiously if you think you're outsmarting the other guy, rather than worrying that he knows more than you. But everyone on Wall Street should know this and try to apply a little objectivity in assessing business relationships and one's own job performance. Apparently AIG didn't.
If AIG was such a bad trader, why did counterparties overload it with losses and kill the goose that was laying golden eggs? It's possible they saw the big bonuses AIG traders were collecting and misread the extent of its losses. It's also possible they thought the AIG people were so bad as to be beyond saving, and that if they didn't take AIG's money someone else would.
1. The plan is called a "Retention" plan. Employees were to share 30% of Business Products' profits, with no cap set on the maximum paid, and with a minimum guarantee. That guarantee, which presumably was the amount paid out for 2008, was set somewhat below the amount paid out for 2007.
Any realized losses above $225 million are not applied against 2008 earnings. Instead, they're carried over to following years and reduce a given year's bonus pool by a maximum of 30% of that number, or $67.5 million. The minimum guaranteed bonus is also reduced by the overhang of past years' losses, again at a rate of $67.5 million per year. Given that this unit appears to have realized losses of, in round numbers, $200 billion in 2008, the guarantee shrinks to nothing in at most two or three years. The overhang of losses looks like it lives on, depressing bonuses, into the twenty-fourth century. All the plan really seems to do is to allow one more round of large bonuses, before encouraging anyone who can get another job to leave. So it is a "Retention" plan only in the sense that it "retains" a final year of undeserved incentive pay.
2. The plan is dated December 1, 2007, and replaces an earlier plan. The differences are not described. I would bet, however, the main change is that the earlier plan did not limit, as this one does, the extent to which realized losses could shrink the bonus pool.
3. Bonuses are based, not on economic profits, but on realized gains and losses, that is, on transactions that were closed out during the year. I find this very unusual. The bonus pool can easily be inflated by cashing out profitable trades and keeping losers on the books. The plan specifically states that unrealized losses are excluded from the bonus calculations.
4. It's hard to see why AIG's management would okay a plan like this, other than that 70% of the Business Products' so-called profits become earnings of the parent company, on which presumably top management's bonuses are calculated.
5. It's also hard to see how former Secretary of the Treasury Paulson, a veteran Wall Street manager, did not hear the alarm bells that this bonus plan sets off.
The losses are huge. The bonus plan is shameful. No one in government or new management caught on. Retroactively changing the tax code may be emotionally satisfying, but it doesn't seem to me that punishing Wells Fargo or JPMorgan employees for something AIG did helps the country a lot. A better direction of attack might be maintaining that in trying to "game" the system with this bonus plan, AIG violated its obligations to shareholders.
One more thought on the House action. Is the House really being as hysterical as its rhetoric and voting make it appear? Doesn't it realize it is putting at risk any future private-public cooperation in rebuilding the financial system? Or is it calculating that it can be as bombastic as it wants, because the retroactive tax increase doesn't have the votes to pass in the Senate? My guess is that our representatives are relatively unaware of the risks, but are actively betting that nothing they resolve will survive the Senate. The coming week will most likely tell.


SRI International Sarnoff introduces the TerraSight EX video exploitation system, a compact, lightweight expeditionary version of the platform, which has already been deployed by the U.S. Department of Defense.
TerraSight EX brings advanced video exploitation to forward operators through what the company describes as a "ruggedized Windows laptop." The system can process standard video feeds from an unmanned aerial system (UAS) and convert them into real-time, integrated tactical information that can improve situational awareness, SRI says.
With this new system, the information from the full-motion video is transformed into a 3D map in one integrated display, SRI says. The system generates targeting coordinates for objects of interest from video and enables ground teams to reach back while sharing critical information, the company adds.
The mosaic process stitches together individual frames of video into a broader view that provides greater context, and TerraSight's geo-registration places the video onto the 3D map by assigning latitude, longitude and elevation coordinates for each video pixel, avoiding common problems associated with raw metadata, according to the company.
Platform will serve the state's airborne emergency response systems and incident command and control stations.
openGear is an open-architecture, 2 RU modular frame system supported Ross and more than 20 other terminal equipment manufacturers.


Heartburn and frequent urination may appear again.
Your body prepares for the delivery of your baby and you may feel false irregular labor contractions (Braxton Hicks).
You probably have breast enlargement, backaches may increase in intensity, and fatigue usually appears until the 38th week.
In this gestational age, it is important to pay attention to frequency of baby's movements and to contact to the doctor if you notice that the fetal activity level slow down.
Seventh month: The baby's taste buds have developed.
Eighth month: The fetus is growing rapidly.
The major organs are well developed, except to the lungs.
Ninth month: The lungs are mature.
The baby kicks less toward this month because he has less room.
The internal organs are fully developed.
Dr. Marinakis and his team support every woman who wants to have a natural labour, fully respecting the miracle of birth. The woman is the primary decision - maker in her care and our team helps her to make the appropriate decisions. At the same time we are by woman's side in order to ensure the safety of herself and her baby.


It would be great to have a dedicated Resource Module.
* If you schedule a class or event in the Training Module in that Resource Location, the time would also show on the Resource Module Calendar.
* Specify a limit for the number of dogs allowed for each Service.
* Specify a limit for the number of clients that can register for each Service.
* Have a configuration option to show or hide the Service, Therapist and Location on the Public Page.


The default choice for many businesses that need to venture off-road from time to time is a 4x4 double-cab pick-up – but it is not the only option.
A small number of four-wheel drive vans of varying capacities and capabilities are also available and – depending on the model you select – may feel rather more like a car to drive than most pick-ups do.
Toyota has decided to venture into what is admittedly a niche sector of the light commercial vehicle market with a van version of the 4x4 Land Cruiser.
Produced in both short- and long-wheelbase guise, with three and five doors respectively, the Land Cruiser Utility Commercial was launched at last April's Commercial Vehicle Show. It is a potential alternative to the legendary Hilux depending on the nature of the work you are on.
It is a van with few direct rivals and they seem to be getting fewer by the day. Mitsubishi's classic Shogun Commercial is departing and – at the budget end of the market – Dacia's Duster Commercial has gone, which is a great shame.
Returning to the Utility Commercial, it comes with one engine choice – a 2.8-litre pumping out 175hp – and one level of specification. We took to the chilly rural highways of Herefordshire and Gloucestershire in the short-wheelbase variant.


October 26, 2018, Santa Ana, Calif.
First American Financial Corporation (NYSE: FAF), a leading global provider of title insurance, settlement services and risk solutions for real estate transactions, today released the First American Loan Application Defect Index for September 2018, which estimates the frequency of defects, fraudulence and misrepresentation in the information submitted in mortgage loan applications. The Defect Index reflects estimated mortgage loan defect rates over time, by geography and loan type. It is available as an interactive tool that can be tailored to showcase trends by category, including amortization type, lien position, loan purpose, property and transaction types, and can provide state- and market-specific comparisons of mortgage loan defect levels.
Compared to September 2017, the Defect Index decreased by 6.0 percent.
The Defect Index is down 23.5 percent from the high point of risk in October 2013.
The Defect Index for refinance transactions increased by 1.4 percent compared with the previous month, and is the same as a year ago.
The Defect Index for purchase transactions increased by 1.3 percent compared with the previous month, and is down 11.1 percent compared with a year ago.
Beyond the devastating effect of hurricanes on the lives of those in their path and the damage on their homes, natural disasters also impact loan application defect risk. Hurricanes, especially the flooding associated with these natural disasters, create the potential and opportunity for significant misrepresentation of collateral condition and identity fraud in mortgage applications.
According to trend data in the Defect Index, we're seeing this potential for mortgage fraud risk become a reality. Since the beginning of the year, the Defect Index has steadily decreased nationally, falling 8.4 percent from January through July 2018. However, the last two months have seen a reversal in this trend, with overall defect risk increasing 2.6 percent from August 1 through September 30. In fact, September is the first month this year to experience an increase in the Defect Index for purchase transactions. While we have not yet seen the full impact of the hurricane season on defect risk trends, we already see preliminary defect risk spikes in states impacted by Hurricane Florence, North and South Carolina.
Recent estimates show that Hurricane Florence's flooding and wind destruction damaged approximately 50,000 residential units, with nearly 80 percent of these homes located in North Carolina. Worst-case projections estimate a total of $28.5 billion in flooding losses, plus an additional $1.5 billion in wind damage. North and South Carolina experienced nearly identical monthly increases in the Defect Index in September, 5.3 percent and 5.2 percent respectively. The rise in defect risk is more pronounced when comparing with three months ago, as North and South Carolina experienced 6.6 percent and 9.7 percent respective increases in defect risk.
Unfortunately, Hurricane Florence did not mark the end of hurricane season. Using data from DataTree by First American and the National Hurricane Center, we estimate that Hurricane Michael, the strongest hurricane on record to hit Florida, will impact $125 billion of residential real estate in the state.
Defect Index trend data from 2017 provides a glimpse at what we might expect in the months ahead. Before Hurricane Irma hit Florida in 2017, defect risk was decreasing. However, following the storm, the trend reversed course in September 2017, rising 10 percent through December. Since December 2017, defect risk has declined in Florida. Unfortunately, historical trends indicate that we should expect defect risk to increase in Florida over the next few months.
The good news is that defect risk spikes due to natural disasters tend to stabilize given time. In the case of Hurricane Irma, defect risk in Florida took approximately three months to stabilize, while defect risk in the New York metropolitan area took almost a full year before defect, fraud and misrepresentation risk returned to pre-Hurricane Sandy levels.
The five states with the greatest year-over-year decrease in defect frequency are: Vermont (-19.4 percent), Minnesota (-18.6 percent), Arkansas (-17.0 percent), Alabama (-16.7 percent), and North Dakota (-15.7 percent).
Among the largest 50 Core Based Statistical Areas (CBSAs), the five markets with the greatest year-over-year increase in defect frequency are: San Diego (+11.7 percent), Los Angeles (+11.1 percent), Virginia Beach, Va. (+9.6 percent), Richmond, Va. (+7.2 percent), and Orlando, Fla. (+5.9 percent).
Among the largest 50 Core Based Statistical Areas (CBSAs), the five markets with the largest year-over-year decrease in defect frequency are: Minneapolis (-21.4 percent), Birmingham, Ala. (-21.2 percent), Raleigh, N.C. (-18.8 percent), St. Louis (-16.9 percent), and Salt Lake City (-14.9 percent).
The next release of the First American Loan Application Defect Index will take place the week of November 23, 2018.


Today's episode was written by Yaakov Bar Am for the Artisan's Call blog. It's titled "Ode to my Plane" and was originally posted November 18, 2011.
To find more great posts like today's, visit the Artisan's Call blog at fbyaakov.wordpress.com/.


text:
Coriolopsis occidentalis är en svampart som först beskrevs av Johann Friedrich Klotzsch, och fick sitt nu gällande namn av William Alphonso Murrill 1905. Coriolopsis occidentalis ingår i släktet Coriolopsis och familjen Polyporaceae.   Inga underarter finns listade i Catalogue of Life.

Källor

Basidiesvampar
occidentalis

redpajama_set_name: RedPajamaWikipedia


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text:
Liev Schreiber, Allison Russell Summary
Series Title : The Late Show [Colbert]
Episode Number : Season 7 Episode 71
Watched by 108 People
Network : CBS
S07E71 Liev Schreiber, Allison Russell Summary
Stephen welcomes actor Liev Schreiber and a musical performance by Allison Russell.
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The Late Show [Colbert] Show Summary
Stephen Colbert, the host, writer and executive producer of the Emmy and Peabody Award-winning "The Colbert Report", succeeds David Letterman as the host of "The Late Show". Stephen has been given near-full control of the show, with little interference from CBS management in regard to format.
Colbert will be bringing most of his staff from "The Colbert Report" with him to "Late Show", as well as outsiders such as Brian Stack, who is best known for his work on Conan O'Brien's programs. Jonathan Batiste and the "Stay Human" band joins "The Late Show" as the show's house band.
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text:
Q: Javascript: Distribute values into two arrays Im new here and into programming. Im not native english so please apologize if you dont understand me very well :-)
I`ve started learning Javascript and still doing.
Now I want to achieve a simple project which gives me two Teams with equal skills.
Questions:

*

*It seems to work but I want to get random teams. This gives me always the same. How can I resolve this?

*I am able to print the values to see that its correct, but how can I print the player names (Variables like P1, P2, P3...)

Thank you

//Player skill values

let P1 = 2;
let P2 = 3;
let P3 = 1;
let P4 = 3;
let P5 = 4;
let P6 = 4;
let P7 = 5;
let P8 = 2;

//try to achieve two equal teams

function aufteilen(arr) {
    arr.sort().reverse()
    let a = [], b = [], sumA = 0, sumB = 0, i = 0

    while (i < arr.length) {
        if (!sumA && !sumB || sumA == sumB) {
            a.push(arr[i])
            sumA += arr[i]
        } else if (sumA < sumB) {
            a.push(arr[i])
            sumA += arr[i];
        } else if (sumB < sumA) {
            b.push(arr[i])
            sumB += arr[i];
        }
        i++
    }

    console.log(`Total: ${sumA} ${sumB}`)
    return [a, b]
}

console.log(aufteilen([P1, P2, P3, P4, P5, P6, P7, P8]))

A: 1 - you need a shuffle method, something like below or from a library:
function shuffle(array) {
  array.sort(() => Math.random() - 0.5);
}

2 - you need an object to display player names. your players are just numbers.
// your player
let P1 = 2;

// what you would need to display a name
let P1 = { id: 2, name: "Mario" }

// then u can display the name with
P1.name // "Mario"

A: Your array.sort().reverse() is the reason why you get this.
I would recommend to shuffle() before passing it to your function.
Here is my guess :
function shuffle(arr) {
    for (let i = arr.length - 1; i > 0; i--) {
        let j = Math.floor(Math.random() * (i + 1));
        [arr[i], arr[j]] = [arr[j], arr[i]];
    }
    return arr;
}

console.log(aufteilen(shuffle([P1, P2, P3, P4, P5, P6, P7, P8])));

You can then create an object Players, add all infos you want and do it like this :
let players = {
    P1: 2,
    P2: 3,
    P3: 1,
    P4: 3,
    P5: 4,
    P6: 4,
    P7: 5,
    P8: 2,
};

let playerSkills = Object.values(players);
console.log(aufteilen(shuffle(playerSkills)));

Hope it will help !

A: Creating a team selection based on skill and making it random using the OP's algorithm is not really possible.
It's also possible for the OP's code to make teams based on player count not equal, I would assume a team picker would try to make teams equal on players & skill.
So one approach is just a simple brute force one, basically randomly pick teams, and compare the skills.  Stop comparing after a set amount of time, just in case a perfectly equal skill cannot be met, or when skills are equal.
Below is a working example doing the above, it will also handle making the teams equal if we don't have an even number of players.

const players = [
  {name: 'P1', skill: 2},
  {name: 'P2', skill: 3},
  {name: 'P3', skill: 1},
  {name: 'P4', skill: 3},
  {name: 'P5', skill: 4},
  {name: 'P6', skill: 4},
  {name: 'P7', skill: 5},
  {name: 'P8', skill: 2},
];

const score = team =>
  team.reduce((a, v) => {
    a += v.skill;
    return a;
  }, 0);

function createFairTeam(players) {
  function createTeams() {
    const team = {
      team1: [],
      team2: [],
      skillTot1: 0,
      skillTot2: 0,
      score: 0
    }
    const p = [...players];
    const t1size = Math.trunc(players.length / 2);
    //fill team1
    for (let l = 0; l < t1size; l++) {
      const pos = Math.trunc(Math.random() * p.length);
      const f = p.splice(pos, 1)[0];
      team.skillTot1 += f.skill;
      team.team1.push(f);
    }
    //fill team2
    for (let l = 0; l < p.length;) {
      const pos = Math.trunc(Math.random() * p.length);
      const f = p.splice(pos, 1)[0];
      team.team2.push(f);
      team.skillTot2 += f.skill;
    }
    //calc score.
    team.score = Math.abs(
      score(team.team1) -
      score(team.team2)
    );
    return team;
  }
  const team = createTeams();
  let best = team;
  const start_time = Date.now();
  while (Date.now() - start_time < 200) {
    //if total skill is odd, the best score
    //would be 1, if even it would be 0
    //so lets check for less than equal to 1
    //for early termination.
    if (best.score <= 1) break;
    const newTeams = createTeams();
    if (newTeams.score < best.score)
      best = newTeams;
  }
  return best;
}

function doIt() {
  const teams = createFairTeam(players);
  const table = document.querySelector('table');
  const tbody = table.querySelector('tbody');
  tbody.innerHTML = '';
  for (let l = 0;
  l < Math.max(teams.team1.length, teams.team2.length); l ++) {
    const p1 = teams.team1[l];
    const p2 = teams.team2[l];
    const tr = document.createElement('tr');
    ////
    let td = document.createElement('td');
    td.innerText = p1 ? p1.name : '';
    tr.appendChild(td);
    ////
    td = document.createElement('td');
    td.innerText = p1 ? p1.skill : '';
    tr.appendChild(td);
    ////
    td = document.createElement('td');
    td.innerText = p2 ? p2.name : '';
    tr.appendChild(td);
    tbody.appendChild(tr);
    ////
    td = document.createElement('td');
    td.innerText = p2 ? p2.skill : '';
    tr.appendChild(td);
    tbody.appendChild(tr);
  }
  //// tots
  document.querySelector('#t1total').innerText =  teams.skillTot1;
  document.querySelector('#t2total').innerText =
 teams.skillTot2;
}

doIt();

document.querySelector('button').addEventListener('click', doIt);
table {
  width: 100%;
}
<button>Another Team</button>

      Team 1
      Team 2

      Player
      Skill
      Player
      Skill

      Total
      Total


Global Innovation Conference was held in Osaka which I didn't go, but I thought I'd share its keynote video.
The speaker was Dave McLure, and he told the Japanese people to wake up, it's no longer the time for the Japanese to be sitting in the comfort zone, because this country is on fire. He tells people to become the nail that sticks out, move fast and break things, and don't be afraid to fail, because key factor to innovation is creativity, failure and iteration.
There is a famous saying in Japanese "七転び八起き" which means even if you fall 7 times, get up 8 times and move forward. But at the same time, the Japanese wants to do things well and organized, the citizens does not like failure, it's a country that fears shame.
"Failure is necessary, it is a process, not a problem." says Dave, and urges Osaka's mayor Hashimoto to dance without practicing, show everyone his failure in public, and urge the citizens to make failures- make them feel they are allowed to fail, practice, and become successful taking that failure as a process.
Project Hayano is a project to understand the risk of the children in Fukushima and surrounding areas to get thyroid cancer due to internal contamination of Iodine 131. The team mashed up simulation of Iodine plume emission from the nuclear power plant with traffic congestion map which shows where people actually were.
The purpose of this project, at the end of the day, is to work on a legislation so that when someone gets thyroid cancer in the future, the government will need to compensate the medical fee without the citizens to prove the causal relationship between the cancer and the nuclear power plant accident. Calculations done during Project311 will be used for the financial estimations related to that bill.
combined those with measurements from whole body counters (WBC). From those data, he learned that the internal contamination due to radioactive cesium in contaminated foods was extremely low. For example, the following diagram shows the results of whole body counter scans since April 1st within Fukushima Prefecture. 99.1% of scans were below the detection threshold. And for children, 100% of the scans were below the detection threshold.
According to data from 1964, Japanese person's body had about 10 becquerel of cesium per kilogram of body weight.
If we compare this to the data from Fukushima, we can see that most people in the prefecture are far below the level of data back in 1964.
thyroid cancer is for the children in Fukushima and the surrounding prefectures.
Professor Hayano started "Project Hayano" aiming to evaluate openly the risk of internal contamination by radioactive iodine. In order to do so, he had to know how many people inhaled how much iodine at what time at each location. We have to rely on simulations such as SPEEDI etc to guess the amount of iodine (since real data does not exist). The problem until now was to estimate when and how many people were present at each location. Thanks to the congestion data provided by ZENRIN DataCom at this workshop, they were able to solve that problem.
Risk evaluations are made by multiplying radiation dose times the number of people. For example, if 1 person had a thyroid radiation dose of 100 millisieverts, the risk would equal that for 10 people with 10 millisieverts each. This is the hypothesis they used.
Of course, the federal and local governments have also done population behavior studies, asking where the people were on paper surveys, and their survey results were used for evaluation of radiation doses. However, because this is private information, the data will not be open to public and third-party evaluation is impossible. Therefore, they used the data provided by ZENRIN DataCom and SPEEDI to conduct a risk evaluation that is verifiable by third-parties.
There are many kinds of simulations, he used 4 of them.
Professor Watanabe made a mashup map on Google Earth and overlaid the data with the congestion data from ZENRIN DataCom. For example, in the screenshot below, the green bars and circles underneath is data from ZENRIN, showing where people were at about 3 p.m. on March 15 -and you can move the sliders to change the time frame. The red bars represent the concentrations of iodine in the atmosphere close to the surface.
Before we move on, we need to understand how reliable the basic data we are using is.
Prof. Hayano plotted how many people were within radius of 5 kilometer intervals from the reactor along with the time. From the chart, on 3/10-12, we can see people actively entering and leaving the 5-kilometer zone, going to and from work, before and during the accident .
On 3/12-14 we can see fewer and fewer people within 5, 10, and 20 kilometers, due to the evacuation order with people fleeing from the reactor, and the population is decreasing. On 3/13-14 it looks like the population is decreasing, but this was due to the cell phone base stations going down, so we don't have accurate data during this time.
However between March 14th and 15the the base stations started working again and it looks like the number of people is increasing. The most problematic time of the reactors were from the early morning of March 15th to March 16th, when the concentration of iodine was at its highest. Fortunately the ZENRIN data for this time is from after the base stations restarted, so it appears to be useable.
Further investigation on ZENRIN's data: this population frequency distribution chart shows how many people were within this 250-meter grid, and we see peaks at 200, 400, 600, and 800.
What we can guess about this data is that here were certain number of people with automatic GPS features on their phones within the grid (like 1, 2, 3 people) , and we can see that ZENRIN is multiplying them by the proportion of contracted cell phone users, and guessing that there are 200, 400, or 600 people there. Then, the intervals are filled in.
Taking a closer look at the data for March 10, 3 p.m., as an example- people aren't moving much, and the peaks are sharp. But at 8 p.m., when people start changing locations the peaks become flatter. So we can imagine that the data has been derived using the fractions of people leaving and entering the grid. Due to this reason, prof. Hayano didn't use the 250-meter grid, instead made the space-time grid coarser.
Here, Professor Hayano evaluated 2 kinds of SPEEDI calculations (in red and blue), and overlaid them with the calculations from the National Institute for Environmental Studies (in yellow) to check the difference. Unfortunately, the data is extremely different. The source terms must have been pretty different. Therefore, they made the grid coarser here as well.
He published the population figures for a 10-kilometer grid openly, and asked many people to help check the calculations. As a result he was able to approximate the magnitude.
The purpose of this project, at the end of the day, is to work on a legislation so that when someone gets thyroid cancer in the future, the government will need to compensate the medical fee without the citizens to prove the causal relationship between the cancer and the nuclear power plant accident. Calculations done during Project3111 will be used for the financial estimations related to that bill.
Q (Professor Murai): What do you think the relationship between these data presented today should be - data published and analyzed by the authorities, and data that is publicly available and can be analyzed by anyone?
A: That is a difficult issue. Ever since March 11th, there were many questions surrounding the way authorities that are supposedly owning data- how they communicate or publish data, how those data are utilized or not utilized. It is important that our project published the data openly in a way anyone can see what was happening and verify.
You can see how cautious Professor Hayano is about the data itself. Getting reliable data is extremely important in analyzing data, because if the data is rubbish all of the analysis will be rubbish as well. Therefore, he takes a lot of efforts verifying the data, making the data available and asking many people to check and verify his calculations.
The other important aspect about this project is that it is goal oriented- Prof. Hayano has the aim to make a legislature to save the victims get compensated if they get cancer in the future, and he is very focused on making that happen. Data, research and analysis is tools for making bigger decisions, not the goal.
He was also very successful in involving other people help his project- many experts provided data outside Project311 itself, due to his credibility. He was also able to get many people to help verify his calculations.
We learned so much from professor Hayano through watching his projects.
He continues working on his projects, and the latest presentation slides can be seen as below, it is a comparison of thyroid radiation intake between Chernobyl and Fukushima.
It looks like Fukushima (red) is extremely lower than Chernobyl (blue) and the distribution differs greatly as well.
BTW, Project Hayano was actually the very first project that launched at Project311. Upon launching Project311, we weren't sure how many people would participate. We didn't know how many valuable projects will start from our data. On 9/19, we did an event called "office hour". We gathered participants and all of the data providers in our office, and made this opportunity for the participants to ask questions to the data providers. At the after party of this office hour, Professor Hayano started talking about what he wants to accomplish from this project- which was so concrete and so compelling. The moment I thought there definitely will be great findings coming out of the workshop was when I talked with Professor Hayano. Thanks again!
One of the commentators for Project311 workshop was professor Jun Murai from Keio University, Dean of the Faculty of Environment and Information Studies.
He is called the "father of Japan's Internet", the founder of JUNET and president of WIDE Project. He also served in the past as the president (currently board of trustees member) of Japan Network Information Center (JPNIC), Advisory Member of Information Security Policy Council, Cabinet Secretariat Information Security Center, Cabinet Secretariat of Japan and Advisory Member of IT Strategy Headquarters at Cabinet Secretariat of Japan.
Professor Murai is currently tackling Open Government in Japan, he is the advisor of the Japanese government's "Open Data Promotion Consortium [ja]".
"This was an awesome project. The point is, how can we leverage this learning to the future." says Prof. Murai. "As you heard during Mr. Suzuki's comments, and as was mentioned in many of the presentations, I'd like to stress what we're doing here can save lives on the grounds in the future. It is important to think about how we link the people on the grounds and the information that was analyzed here."
"The presentations were great- lots of diversity, and this whole day was a great experience for me. I would like to thank and express my respect to the organizers for making this happen, all the data providers that released such data, and participants who did the analysis of those data. What we need to do now, is to connect this with the people on the grounds."
"What we learned today is that the role of public sectors, private sectors and individuals surrounding data is important. Individuals have shown great strength after the disaster, both good and bad. In today's workshop it was all about how to prepare for future disasters, but in a broader terms, how can we build a new global society where the public sectors, private sectors and individuals interacts surrounding data. We experienced a lot of experience on that during the disaster, and we were able to have a precious experience of analyzing and sharing that today. We recognized the importance of properly understanding the data and analyzing data, and then we need to move from theory to action."
"The data used today had time-limited conditions and it's been made available to you because you're academics. Personal info has been made available because of its potential to save lives. Otherwise you won't be able to keep using it. I think the most important thing is to work on making a society in which data is more available. There were many parties who were hesitant about releasing data and didn't provide them. There's a lot of data that we really could have utilized. We need to make an environment where we can use data properly. A society where those who have data can provide it without hesitation. We need to create a system, environment and society that allows to do this. If we can realize such society, people like you can continue and further develop your present research and connect it to concrete actions that will save human lives. All that depends on how data would be available to the public and private sectors and the individual. If we can realize that, we can make this country a new, data-driven advanced nation. Japan learned a lot from last year's disaster, and with such experience we can make contributions to the whole world. We should take action toward creating such an environment. To make productive use of all this work and research you did today, I want to help create a society where data can be used effectively and safely. What you all have done here today is extremely useful evidence of how people can utilize such open data. I would like to ask all of you here to help. It was a wonderful event today. Thank you everyone."
One of the commentators for Project311 workshop was Mr. Hidemitsu Suzuki from Kesennuma City. He was working at city of Kesennuma Crisis Management division, so when the earthquake happened he was in charge of coping with it. I visited Kesennuma last year and wrote a post about it here. You can see the video of Kesennuma right after the crisis here. The city was hit by earthquake, tsunami and was caught on fire.
You are able to watch his comments in English with the subtitle on this video.
After hearing all the presentations, he shows a video clip of tsunami attacking his city. He says he was impressed by the analysis, he thinks it is helpful to cope with future disasters.
While coping with the crisis, he felt the importance of information provided by government agencies, but in reality the government lacked people who can work on it. At the workshop, he felt encouraged by hearing people talk about the possibility of citizens to help the government publish data.
In Kesennuma, the power outage took two months to fix. It took three months to fully restore the water supply. Missing persons were announced with paper notices in the city hall lobby. That is reality. It wasn't digital.
Traffic jam happened, and the roads were stuck in all directions. Not only were people trying to escape to hills, some were trying to get back to the dangerous city worrying about their families. (According to the survey by the Ministry Internal Affairs and Communications, 27% of the people who got on the cars replied they got on the car in hope to rescue their families.) People were trying to move in all directions and the roads were stuck everywhere. If these people were able to get the information about their families' safety, maybe they wouldn't have gotten back to the city.
There were power outages. Maybe, researches presented in the workshop may not function with power outages. We should be prepared for that, too.
How should people respond to a tweet that simply reads "Help me"? Should you go look for that person?
Here is an example. The central public hall of Kesennuma was hit by tsunami, and citizens who evacuated there were surrounded by fire. There was a mother getting ready to suffocate her own child so that the child wouldn't burn to death. Fortunately they were rescued, thanks to the power of social media. The principal of a kindergarten sent an email via her mobile phone to her son in London. "I'm in the public hall, surrounded by fire. I may not make it, but I will do my best." He asked to spread that information on Twitter and Tokyo's vice governor Naoki Inose saw it. The next day, Inose sent helicopters and rescue units from Tokyo's fire department and rescued them. This example demonstrated the potential of social media during disaster.
Mr. Suzuki closed his comments hoping that the researchers here will be "intellectuals with wilderness who is able to cope with issues even with power outages".
Let me add some more details about the example described above. It is actually called "the miracle of Kesennuma".
Naoko Utsumi, principal of a school for disabled children was surrounded by tsunami water and fire. She writes an email to her son, Naohito, who live in London: "I'm in the public hall, surrounded by fire. I may not make it, but I will do my best." Her mobile phone was almost out of battery.
Please retweet: My mother is the principal of a school for disabled children and she is left on the 3rd floor of the central public hall of Kesennuma City, Miyagi prefecture with dozens of children. The surroundings and the lower floor of the building is drained in water due to tsunami and there is no way to get close to them from the ground. If there is any way to get close to them from the sky, please help at least the children there."
Information spread, and Shuichi Suzuki who lives in Tokyo tweeted it to Naoki Inose, vice governor of Tokyo. He of course did not know Inose in person.
Inose replied "I printed this out and gave it to the executives of Tokyo Disaster Control Center."
The fire department in Kesennuma itself was hit by the disaster and there was no request from the local fire department asking for help. "There never was a case like this, but I want you to go" told Inose, to the manager of Disaster Center. "During the crisis, it is important to clear up the flow of information, to connect the information between the people on the grounds and the people who needs those information. In a critical situation, leaders should tell the team that they will take responsibility and push people to take action." says Inose.
The next morning helicopter arrived in to the city hall. "Great news! The children are saved!" tweeted Inose.
Due to a single tweet from London, the lives of 446 people were saved- including an elderly over 90 years old, newly born baby and an expecting mother (in 10 days), and many children.
Why did this happen? There were lots of tweets, Twitter was flood of information, and Inose gets lots of replies- why was he able to distinguish this tweet? There were lots of false tweets like this. Why did this tweet get in to Inose's eyes, why did he trust it and why was he able to act upon it?
The point, according to Inose was that the tweet was very logical, calm and trustworthy. It had details, it had 5W1H, the sentence was structured well which made him believe that it was true.
In fact, "It took me 1 hour to write that 140 characters. One single sentence can become facts or lies" recalls Naohito.
It is also important to use tools in everyday life, since you just can't suddenly start using tools you're not used to when disaster hits. "I think they sent me this tweet because they knew I was using Twitter regularly" says Inose. If he wasn't using it regularly, there is no way people can expect Inose would take action based on a Tweet.
The other important thing is to write trustworthy posts regularly. When determining whether a tweet is true or false, people will read other tweets by that person, to determine the trustworthiness. Social media is not just about single post- it is accumulation of your posts, trustworthiness and behaviour.
Several articles on miracle in Kesennuma.
When the Great East Japan Earthquake with Magnitude 9.0 struck Japan on 3/11 2011, an enormous amount of information was dispersed through social networking sites and the mass media. While much of it was accurate, there were also many falsehoods and rumors, emphasizing just how important information can be. How was misleading information conveyed, exactly? And what were the causes behind these mixups?
It was impossible to do such data verification during and right after the earthquake, with all the chaotic situation. But almost 2 years have passed, and it is time to review the data from the time of the disaster and digest the learning from it, and figure out what we can do to prepare for the next disaster. If we don't do it now, we will start forgetting about what happened.
That is why we - Google and Twitter - hosted "The Great East Japan Earthquake Big Data Workshop: Project 311" back in 9/12-10/28 2012. We provided participants data that was produced during the week following the earthquake. They reexamined the data, discussed what can be done to prepare for future disasters, and apply it to service brainstorming.
Honda's Internavi roads data is based on the data they had from car navigations, which was extremely useful when aggregated as a drivable map, since many of the roads were damaged by tsunami, and was not drivable. You can still see it at Google's Crisis Response page.
Some videos to understand more about Honda's data.
Zenrin DataCom's traffic congestion statistics data comes from mobile phones. This data is extremely useful when aggregated to understand where people were and their moves. In the following video you can see data from Tokyo on 3/11/2011. People are moving quickly commuting in the morning, the city of Tokyo getting really congested, and the motions suddenly slows down on and after 14:45 when the earthquake happened, since many of the public transportation stopped. They slowly move out of Tokyo, and the speed accelerates towards the night as the public transportation recovers.
Many of the data provided for this workshop was not available till the workshop. It was a good opportunity not only for the participants, but also for the data providers as well, to reconsider their policy, and prepare what they can do with their data in the future. They learned the value of opening their data and having the citizens use that data for more valuable findings. Some companies are already getting ready for their actions. True, we would've loved to have more data providers- but I did hear comment from a participant that "companies that can't provide data for workshops won't be able to provide data during crisis anyways."
We announced the event on 9/12, had office hour on 9/19 where all the participants can come to Google Japan office, and all the data providers will answer all of their questions. We held mid-term report event on 10/13 and final report event on 10/28. There were many discussions about this schedule. Some say it was too short for deep analysis. Some say it was good that it was short, they were able to concentrate and come up with results in short period of time. During the next crisis, we won't be able to analyze for 1.5 months, we need to move quickly to provide valuable analysis, so I think we did the right thing. I may be wrong.
The format was 5 minute presentation and 2 minute QA/comments, with the exception of top 5 teams that were voted to speak 2 minutes extra. At the mid-term report event, I was unable to stop some people going over time- the presentations were extremely interesting and was hard to stop them, but I did get some complaints in terms of fairness. At the final report event, we used a gong and stopped everyone however interesting they are. The event went smoothly on schedule. Although 5 minutes were very short, the presentations became crispy and did not get bored even after listening to 50 presentations in one day so I think it was good.
After the mid-term report event, one of the participants- Professor Ryugo Hayano told me we should fix the format. People were just reporting what they learned from the analysis, which is not enough. Therefore, for the final report event, we made a rule for the presenters to finish the last slide with visions on how their analysis will benefit the victims from Tohoku disaster, or future disasters. Some actionable proposals came out from their last slides.
Communication tools among the participants were the offline events, mailing list, Twitter, Google+ etc and some people leveraged from that, some didn't. An example of a good collaboration was "geolocation info tweet list project". Twitter data was just a list of tweets with geo-id and tweet-id, and each participant had to use Twitter API to get the location information. So one of the projects that came up was making a JSON format dataset of tweets with geolocation data. Some people announced to start working on it so that it will be valuable for others to use, and many who thought it is valuable started helping out. They have never met each other, but started collaborating online. "Twitter data cleaning project" was another one- data that was provided by Twitter had line breaks and was difficult to use. Therefore, some of the participants started a project to clean the data and provide a script in a matter of hours or couple of days- some wrote a Python script some with Ruby, some wrote a script to extract posts with specific words, some made a tool to put into charts. This thread started with "Houston, I have a problem with Twitter data. I think this is applicable to all of you" and others just jumped in to help and was able to avoid duplicate works in the very early stage and fixing the issue - so I think it worked more on the tools side. The problem was analysis side- since many teams were working on rumor analysis from Twitter data, it was hard for them to throw away their own analysis and join other groups which were doing similar analysis. So we had some duplicate efforts there, which could have been solved by more collaboration.
I really think it is important that we remember and reflect and learn from the past experiences so that we don't make the same mistake again. Also, it is important to share what we learned. That is exactly why I'm writing this post.
It is easy for the governments and data owners to resist opening the data- they have a bunch of reasons not to. Maybe citizens will not use the data even if they make it open (after a lot of efforts). Maybe some bad things happen. This project is antithesis to that. People WILL come and use the data and come up with valuable analysis if we open the data. I am glad we were able to prove that.
I think it is easy for the analysts, researchers and developers to just complain that the data is not open, it doesn't exists, the format is bad, etc. This project is antithesis to that as well. The dataset was not complete- it had some restrictions. We didn't have some data that we would've wanted to have. But there will never be a single day that EVERYTHING is open. It shouldn't be- some data should be open, some should be closed, like personal data. Complaining is easy- but I believe if we can show the value of opening the data and making use of it, more governments, more companies, more entities will start opening their data. I think Project311 was evidence of such move.
Also, the data was too big for some people to handle- their tools didn't work. But there will never be a single day that a perfect tool will be ready for you- we'll just need to collaborate and work around and tackle the data.
Oh and by the way, it doesn't even have to be "big data". It should be "valuable data", but rubbish data for some people may be valuable to others, so don't discount that. "Open data in a standardized format". No PDF, please.
Lastly, open data is not just about crisis. The Japanese government, companies and the society needs to get used to opening various data all the time. Also, "opening data" does not merely mean making data available- it includes the responsibility of the citizens, researchers, developers etc to make use of those data, so information literacy and data literacy is a big issue here.
Professor Jun Murai from Keio University (left), the father of Internet in Japan, and Mr. Hidemitsu Suzuki (middle), who was in charge of coping with the disaster in the city of Kesennuma Crisis Management division stayed the full 9 hours of the presentations and commented on each and every 50 presentations that took place. In fact, they were still discussing about the presentations during the breaks. On the right is Professor Fumihiko Imamura from Tsunami Engineering Laboratory Disaster Control Research Center or Tohoku University.
University of Tokyo kindly offered to provide us their classroom for the presentation.
We livestreamed the presentations on Hangout On Air so that those who could not come to the venue can watch. We also had NPO in Sendai working on crisis response and local city government official from Ishinomaki city join the hangout to provide comments to the presentations. I think it is important to involve people on the grounds on those researches, so that we are not doing those activities for self satisfaction- but to work on something that is actually useful for those in need, via direct discussion.
I have seen too many projects after the earthquake that are self satisfactory for engineers in Tokyo that is not useful on the grounds. It hurts to hear those honest opinion, but we need to listen to those voices and refocus on what is actually useful, than using valuable resources on projects that are not going to be used.
The walls of the room were used for poster sessions.
This is not the end- in fact, it is just the beginning. We should do more.
NHK Project "3 Maps for the Evolution of Disaster Information Transmission"
Shibuya project : Crowd management in urban disaster by real-time Information analysis.
Is There a Connection between the Mass Media and the Spread of Information on Twitter?


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Australian survivor 2016 season 3
Rob cesternino and stephen fishbach october 30, 2016. Australian survivor will be returning to our screens in august and to get us hyped up network ten have started announcing contestants who will be competing on the brand new season. Nigel wright photography, endemolshine australia 2016. Smartphones have really opened the door for reading in a variety of positions. This edition features 20 legendary winners of survivor who will return to compete against each other. Season 3, episode 9 is the ninth episode of australian survivor 2016 which aired september 11th, 2016. The first four people eliminated from the challenge will be exiled on exile beach. Nines local version lasted just one season in 2002, while seven tried a. Aug 21, 2016 season 3, episode 1 is the premiere episode of australian survivor 2016 which aired august 21, 2016. The season was filmed in samoa from may to july 2016 and premiered on 21 august 2016 on network ten. In a worldfirst, australian survivor has pitted 12 giants of australian.
First, we got a look at the rumored buffs and tribe names, and then network ten released a sneak peek trailer of the series. Best endurance challenges season 3 australian survivor. Australian survivor 2016 episode 18 survivor wiki fandom. With a mixed bunch of brave souls, we can look forward to many. Australian survivor season 7 episode 22 spectroworld.
Australian survivor 2016 final 3 winner poll popsugar. Host jonathan lapaglia was the perfect man for the job, there were shocks and. Survivor international versions rob has a podcast wiki. The network ten tribe has spoken, and these are the 24 contestants wholl be taking part in the revamped australian survivor. The 20th anniversary edition of the groundbreaking series, unites the most memorable, heroic and celebrated champions from the past two decades in the biggest battle in the shows history, as the series best. This is the season finale of australian survivor 2016. Russell hantz to appear on australian survivor season 3. Australian survivor season 3 australian survivor wikia fandom. This is the first australian survivor season to have three starting tribes. Ive heard its really good and ive already seen every american season.
Australian survivor has 1,355 posts on their instagram profile. Australian survivor tv show season 3 episodes list. Australian survivor all stars episode 3 recap duration. Dec 24, 2019 australian survivor 2016 gives users a new cast size option and some twists that are missing from survivor 3338. Season 4 of survivor australia, champions vs contenders has already aired. Shannon gaitz then took over coverage starting in 2019. Season 3, episode 3 is the third episode of australian survivor 2016 which aired august 28, 2016.
The season opens to shots of the samoan coast as jonathan lapaglia introduces twentyfour new castaways as they compete over fiftyfive days until one person is left standing. This new season gives australian survivor the rare distinction of being one of the few australian programs to have aired across all three commercial networks in. What i notice about survivor 3 is a grittiness and a sense of danger that i dont remember as much as in this season. Australian survivor 2016 episode 26 survivor wiki fandom. Season 3, episode 1 is the premiere episode of australian survivor 2016 which aired august 21, 2016. This season, announced by ten in november 2015, is the third australian edition of the program, the second to feature noncelebrity contestants and the first to air on network ten. A spokesperson for the network told the daily telegraph on. Once the winner is decided, those back at camp plan a. My name is mike bloom, and i am thrilled to join the staff here at true dork times to hopefully write many, many words about australian survivor over the next couple of months.
Australian survivor season 3 episode 21 australian survivor sees 24 tough and tenacious people marooned on a tropical island with little more than the clothes on their backs and the drive to be the sole survivor. At the end of each day, one person will be voted out. But today comes the biggest news so far the reveal of the brand new. Season 3, episode 8 season 3, episode 7 is the seventh episode of australian survivor 2016 which aired september 5th, 2016. Watch exclusive behind the scenes content from the all stars meet. Season 3 2018 mat, moana, lydia, sharn, shane, shonee and zach. It has evolved over time and it has managed to keep its essence throughout all of its changes and twists. Australian survivor 2016 winner revealed part 3 youtube. The castaway who keeps the ball spinning for the longest wins individual immunity. There is no such thing as absolute certainty in the game of survivor, and there is no better proof of this concept than the gripping conclusion to australian survivor much of the postmerge game through the last few weeks has felt like a disappointing march towards a predictable ending. The fallout from one of the most dramatic tribal councils this season sends shockwaves through one tribe as castaways scramble to repair broken bonds. The skies the limit with australian survivor 2017 and has top 3 potential although still a ton to go. Season 3, episode 1 australian survivor wikia fandom.
One of the worst winners in any sort of survivor season ever. Australian survivor 3 edgic by drmonkeyman on deviantart. New season of australian survivor is postponed indefinitely. Shannon guss is joined by rhap stalwart and survivor journalist mike bloom to discuss week 5 of australian survivor all stars and take stock of. Season 3 of australian survivor saw some of the most physically gruelling tests of the series. Lee was an entitled and arrogant hypocrite who got his comeuppance. Australian survivor season 3 episode 2 episode 2 watch. The survivors enter one of the biggest tribal councils of the season as an unexpected move turns the game on its head.
This is the eighteenth episode of australian survivor 2016. Aug 21, 2016 when available, episode names will be translated into your preferred language. Aug 21, 2016 australian survivor 1 is the first season of the network ten incarnation of the competitive reality tv show australian survivor. Mar 30, 2020 the final episode of survivor all stars saw david, sharn, and moana face off to be the first australian all star sole survivor on 10 and 10 play. Aus where do you rank australian survivor among the. Australian survivor is an australian reality game show based on the popular international survivor format. Later, at a massive tribal council, one players confidence just might cost them the game. Austin smith recaps the epic season finale of australian survivor. The emmy awardwinning series returns for its 40th season, themed winners at war. Following the exciting winner reveal to crown australian survivor champion for 2017, 24 castaways reunite for an explosive. The third season of australian survivor is a television series based on the international reality game show franchise survivor.
This is the first australian survivor season to use hidden immunity idols. Australian survivor is a game show where 24 australians are divided into twothree tribes on an island with little more than the clothes on their backs and must survive for over 50 days. I own none of this and do not profit from this video in any way, this is purely for entertainment purposes. At the reward challenge jonathan announces that the winning team will each receive a letter from home. Create fun and exciting simulations with the brantsteele survivor simulator.
Australian survivor was revived in 2016 by channel ten and has so far aired two seasons. Love 2017 as a top 4 season though but only becos im so stuck on top 3 and love 2016 as number 12 or so but still enjoyed a lot of it. By 2016, channel ten had claimed ownership and it was the beginning of the survivor we know and love today. Tengaged big brother game survivor online game fashion game online hunger games. The winner reveal of the 3rd season of australian survivor. A new leader steps forward to call the shots in the champions camp, and its a case of boys against girls in the contenders camp. This is the third episode of australian survivor 2016. Otherwise they will be shown using the series origin language.
Australian survivor 2016, 2017, 2018, survivor new zealand s2, survivor south africa s6. Mar 19, 2020 production on the upcoming season of channel tens australian survivor has been postponed indefinitely amid the coronavirus pandemic. The cast of 2016 arrive on the island australian survivor duration. Australian survivor 2016 is my favorite season post cagayan but only comes in at about 16 because of the rough run from merge til final 8 or so. Unhook the chook castways take on a threeround elimination course featuring a doityourself balance beam, rope ladder and basketball snooker shootout. The new season premieres wednesday 1 august on ten. Join the facebook group the tribe has spoken and search australian survivor episode 1 when you are accepted. Unlike the previous seasons this instalment features twists introduced from the american franchise. Follow their account to see all their photos and videos.
Should network ten renew australian survivor for season 8. Australian survivor 2016 episode 3 survivor wiki fandom. The fourth season of australian survivor is a television series based on the international reality game show franchise survivor. Three trucks that carry eight castaways each drive through the local samoan towns and villages. This new season gives australian survivor the distinction of being one of the few australian programs to have aired across all three major commercial television networks in australia. Christmas update australian survivor 2016 brantsteele blog. This season, announced by ten in november 2015, is the third australian edition of the program, the second to feature noncelebrity contestants and the first to air on. Australian survivor season 3 australian survivor wikia. Australian survivor 1 is the first season of the network ten incarnation of the competitive reality tv show australian survivor. Survivor australia 2017 is easily a top 3 season ever. Jul 05, 2016 the network ten tribe has spoken, and these are the 24 contestants wholl be taking part in the revamped australian survivor. At the beginning of the show, the 16 are split into two tribes kucha and ogakor.
Season 3 network ten s1 english when available, episode names. This is the sixteenth episode of australian survivor 2016. Season 3 guide for australian survivor tv series see the episodes list with schedule and episode summary. Australian survivor 2016 is the third season overall of the australian franchise of survivor. Australian survivor 2016 is the first season after the reboot. Nick iadanza chat with his australian survivor season 1 friendturned enemyturned friend, jennahlouise salkeld about week 3 of season 2.
Hand on an idol the finalists must hold onto an idol while standing on uneven poles. Centrifuge the castaways must spin a ball around a circular track using centripetal force, using momentum to keep the ball spinning. Jul 28, 2018 inside survivor previously revealed that former threetime survivor player russell hantz will be competing on this season of australian survivor. Season 3, episode 9 australian survivor wikia fandom. I own none of this and do not profit from this video in any way, this is purely for. Australian survivor tv show season 3 episodes list next. Australian survivor season 1 intro turn back time and rewatch the intro from season 1 of australian survivor. Season 3, episode 7 australian survivor wikia fandom. These people compete in challenges for rewards and, more importantly, immunity. Home series australian survivor official order season 3.
Australian survivor is a reality game show that is a part of the international survivor hit series. For australia, rob cesternino and stephen fishbach originally did the coverage, but that got passed off to former australian survivor contestant nick iadanza. Close up and muddy racing out in pairs across a slippery pad to a mud pit, the tribes must cover themselves in as much mud as possible before racing back and placing the mud in a. Australian survivor 2016 has a lot of australian survivor firsts. Australian survivor season 3 premiere week duration. We announce the winner of australian survivor for 2018, plus a reunion special hosted by. Were itching to see phoebe pull some major blindsides in the upcoming season.
Evan my tribemates who were originally an alliance that i was working with got incredibly scared because i. The show, which filmed its first two seasons in samoa, has upped sticks to fiji, a location very familiar to fans of the u. Oct 03, 2016 this is the eighteenth episode of australian survivor 2016. Australian survivor 2016 episode 16 survivor wiki fandom. Aug 21, 2016 australian survivor s03e01 first reward challenge. With one tribe member on the bottom of the group, they know the only way to secure their place in the game is to win individual immunity. In november 2015, network ten revealed at its upfront event that it would air a new season featuring regular contestants to air in the last quarter of 2016.
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Highlights from MiFriendly Cities
Adult education (16+), Uncategorized
We are pleased to share the following news stories from the partnership work of Birmingham City Council with the MiFriendly Cities initiative and Central Law England (as part of a MiFC project).
MiFriendly Cities is a West Midlands initiative funded by Urban Innovation Action (and part of the European Regeneration Development Fund) that aims to create migrant friendly cities. A partnership of 11 organisations including Birmingham, Wolverhampton and Coventry.
The project – now in its final year of delivery – has created the a Newsletter for anyone keen to know more, supporters and participants. Understandably, they are keen to keep you up-to-date with their progress – and share their activities.
See the latest newsletter which highlights some of the brilliant work participants have been doing – and showcases the really positive and inspiring stories coming out of our communities. So, please take a few minutes to take a look at this wonderful snapshot of the breadth and depth of programmes and support MiFriendly Cities provides – including the story of Khaled, a resettled Syrian refugee, who came to the UK in 2017 – and has now received the accolade of Student of the Year!
MiFriendly Cities celebrating Black History Month HM published Faye's inspirational story here – read about how Faye overcame a variety of obstacles in her journey seeking sanctuary and her success on joining the MiFriendly Social Innovation programme, establishing 'Ona Creations' – an initiative selling beautifully crafted leather work made into decorative shoes/ handbags. Faye also became a Local Health Champion.
Faye said: "Birmingham is my home, people in Birmingham are wonderful"
Central England Law Centre & Birmingham City Council held a free Webinar- Pathways to Citizenship for Young People
The target audience was made up of school staff, social workers and people who work with migrant children in Birmingham. The webinar commenced with a presentation introducing the City of Sanctuary movement, particularly the Birmingham CoS group, and their work with schools. The session continued with an informative and enlightening presentation from Balvinder Hayre, (CELC) which raised awareness of the issues for children without regular migration status, and how children can apply for citizenship. The Webinar was very well received and hopefully will be repeated in the future as a solution to the restrictions imposed by Covid-19.


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